Provide the data of the fixed ground scale inspection certificate from the Bureau of Standards, Metrology and Inspection.

The MNIST Large Scale dataset is based on the classic MNIST dataset, but contains large scale variations up to a factor of 16. The motivation behind creating this dataset was to enable testing the ability of different algorithms to learn in the presence of large scale variability and specifically the ability to generalise to new scales not present in the training set over wide scale ranges.

The dataset contains training data for each one of the relative size factors 1, 2 and 4 relative to the original MNIST dataset and testing data for relative scaling factors between 1/2 and 8, with a ratio of $\sqrt[4]{2}$ between adjacent scales.

Motivation

The goal of introducing the Rescaled Fashion-MNIST dataset is to provide a dataset that contains scale variations (up to a factor of 4), to evaluate the ability of networks to generalise to scales not present in the training data.

The Rescaled Fashion-MNIST dataset was introduced in the paper:

[1] A. Perzanowski and T. Lindeberg (2025) "Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations”, Journal of Mathematical Imaging and Vision, 67(29), https://doi.org/10.1007/s10851-025-01245-x.

with a pre-print available at arXiv:

[2] Perzanowski and Lindeberg (2024) "Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations”, arXiv preprint arXiv:2409.11140.

Importantly, the Rescaled Fashion-MNIST dataset is more challenging than the MNIST Large Scale dataset, introduced in:

[3] Y. Jansson and T. Lindeberg (2022) "Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales", Journal of Mathematical Imaging and Vision, 64(5): 506-536, https://doi.org/10.1007/s10851-022-01082-2.

Access and rights

The Rescaled Fashion-MNIST dataset is provided on the condition that you provide proper citation for the original Fashion-MNIST dataset:

[4] Xiao, H., Rasul, K., and Vollgraf, R. (2017) “Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms”, arXiv preprint arXiv:1708.07747

and also for this new rescaled version, using the reference [1] above.

The data set is made available on request. If you would be interested in trying out this data set, please make a request in the system below, and we will grant you access as soon as possible.

The dataset

The Rescaled FashionMNIST dataset is generated by rescaling 28×28 gray-scale images of clothes from the original FashionMNIST dataset [4]. The scale variations are up to a factor of 4, and the images are embedded within black images of size 72x72, with the object in the frame always centred. The imresize() function in Matlab was used for the rescaling, with default anti-aliasing turned on, and bicubic interpolation overshoot removed by clipping to the [0, 255] range. The details of how the dataset was created can be found in [1].

There are 10 different classes in the dataset: “T-shirt/top”, “trouser”, “pullover”, “dress”, “coat”, “sandal”, “shirt”, “sneaker”, “bag” and “ankle boot”. In the dataset, these are represented by integer labels in the range [0, 9].

The dataset is split into 50 000 training samples, 10 000 validation samples and 10 000 testing samples. The training dataset is generated using the initial 50 000 samples from the original Fashion-MNIST training set. The validation dataset, on the other hand, is formed from the final 10 000 images of that same training set. For testing, all test datasets are built from the 10 000 images contained in the original Fashion-MNIST test set.

The h5 files containing the dataset

The training dataset file (~2.9 GB) for scale 1, which also contains the corresponding validation and test data for the same scale, is:

fashionmnist_with_scale_variations_tr50000_vl10000_te10000_outsize72-72_scte1p000_scte1p000.h5

Additionally, for the Rescaled FashionMNIST dataset, there are 9 datasets (~415 MB each) for testing scale generalisation at scales not present in the training set. Each of these datasets is rescaled using a different image scaling factor, 2^k/4, with k being integers in the range [-4, 4]:

fashionmnist_with_scale_variations_te10000_outsize72-72_scte0p500.h5
fashionmnist_with_scale_variations_te10000_outsize72-72_scte0p595.h5
fashionmnist_with_scale_variations_te10000_outsize72-72_scte0p707.h5
fashionmnist_with_scale_variations_te10000_outsize72-72_scte0p841.h5
fashionmnist_with_scale_variations_te10000_outsize72-72_scte1p000.h5
fashionmnist_with_scale_variations_te10000_outsize72-72_scte1p189.h5
fashionmnist_with_scale_variations_te10000_outsize72-72_scte1p414.h5
fashionmnist_with_scale_variations_te10000_outsize72-72_scte1p682.h5
fashionmnist_with_scale_variations_te10000_outsize72-72_scte2p000.h5

These dataset files were used for the experiments presented in Figures 6, 7, 14, 16, 19 and 23 in [1].

Instructions for loading the data set

The datasets are saved in HDF5 format, with the partitions in the respective h5 files named as
('/x_train', '/x_val', '/x_test', '/y_train', '/y_test', '/y_val'); which ones exist depends on which data split is used.

The training dataset can be loaded in Python as:

with h5py.File(`

x_train = np.array( f["/x_train"], dtype=np.float32)
x_val = np.array( f["/x_val"], dtype=np.float32)
x_test = np.array( f["/x_test"], dtype=np.float32)
y_train = np.array( f["/y_train"], dtype=np.int32)
y_val = np.array( f["/y_val"], dtype=np.int32)
y_test = np.array( f["/y_test"], dtype=np.int32)

We also need to permute the data, since Pytorch uses the format [num_samples, channels, width, height], while the data is saved as [num_samples, width, height, channels]:

x_train = np.transpose(x_train, (0, 3, 1, 2))
x_val = np.transpose(x_val, (0, 3, 1, 2))
x_test = np.transpose(x_test, (0, 3, 1, 2))

The test datasets can be loaded in Python as:

with h5py.File(`

x_test = np.array( f["/x_test"], dtype=np.float32)
y_test = np.array( f["/y_test"], dtype=np.int32)

The test datasets can be loaded in Matlab as:

x_test = h5read(`

The images are stored as [num_samples, x_dim, y_dim, channels] in HDF5 files. The pixel intensity values are not normalised, and are in a [0, 255] range.

There is also a closely related Fashion-MNIST with translations dataset, which in addition to scaling variations also comprises spatial translations of the objects.

The global fixed electronic truck scale market is experiencing robust growth, driven by the increasing demand for efficient and accurate weighing solutions across various sectors. The expanding e-commerce industry, coupled with stricter regulations regarding cargo weight and transportation safety, are key catalysts for market expansion. Applications in metallurgy, the chemical industry, railway transportation, and port operations are significant contributors to market revenue. Technological advancements, such as the integration of smart sensors and improved data analytics capabilities, are enhancing the functionality and reliability of these scales, further fueling market growth. While the initial investment can be substantial, the long-term benefits of improved operational efficiency and reduced errors significantly outweigh the costs. The market is segmented by application (metallurgy, chemical industry, railway, port, others) and type (primarily by weighing capacity, such as 100t), allowing for specialized solutions tailored to specific industry needs. Competitive landscape analysis reveals the presence of both established international players and regional manufacturers, creating a dynamic market environment. Geographic expansion is particularly strong in developing economies experiencing rapid industrialization and infrastructure development. The projected CAGR (assuming a CAGR of 8% based on industry averages for similar equipment) indicates substantial market expansion over the forecast period. Looking ahead, the market will continue its upward trajectory, spurred by the continued adoption of advanced weighing technologies. Increased automation in logistics and supply chain management will necessitate more precise and integrated weighing systems. Furthermore, the growing focus on sustainability and environmental compliance will drive the adoption of energy-efficient and environmentally friendly scales. However, potential restraints include high initial investment costs, the need for specialized maintenance, and the impact of economic fluctuations on capital expenditure in certain sectors. Market players are expected to focus on innovation, developing cost-effective solutions, and building strong partnerships to maintain a competitive edge. The development of integrated systems that link weighing data to other supply chain management software will also drive future growth.

Automated in situ soil sensor network - the data set includes hourly and daily measurements of volumetric water content, soil temperature, and bulk electrical conductivity, collected at 42 monitoring locations and 5 depths (30, 60, 90, 120, and 150 cm) across Cook Agronomy Farm. Data collection was initiated in April 2007 and is ongoing. Description of data Tabular data CAF_sensors: folder with Daily and Hourly subfolders, each containing 42 '.txt' files of water content and temperature sensor readings. Each file represents readings from a single location, indicated in the file name (i.e. CAF003.txt) and in the 'Location' field of the table. Readings are organized by 'Date' (4/20/2007 - 6/16/2016), ‘Time’ (24 hr clock, only in hourly files), and with property (VW or T) and sensor 'Depth' as follows: VW_30cm: volumetric water readings at 30 cm depth (m^3/m^3) VW_60cm: volumetric water readings at 60 cm depth (m^3/m^3) VW_90cm: volumetric water readings at 90 cm depth (m^3/m^3) VW_120cm: volumetric water readings at 120 cm depth (m^3/m^3) VW_150cm: volumetric water readings at 150 cm depth (m^3/m^3) T_30cm: temperature readings at 30 cm depth (C) T_60cm: temperature readings at 60 cm depth (C) T_90cm: temperature readings at 90 cm depth (C) T_120cm: temperature readings at 120 cm depth (C) T_150cm: temperature readings at 150 cm depth (C) Volumetric water content readings are calibrated according to: Gasch, CK, DJ Brown, ES Brooks, M Yourek, M Poggio, DR Cobos, CS Campbell. 2017. A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil specific correction. Computers and Electronics in Agriculture, 137: 29-40. Temperature readings are factory calibrated. CAF_BulkDensity.txt: file containing bulk density values ('BulkDensity' in g/cm^3) for sensor depths at each of the 42 instrumented locations at Cook Farm. Location is indicated in 'Location' field, and sample depths are defined (in cm) by the ’Depth’ field. CAF_CropID.txt: file containing crop codes for each sub-field (A, B and C) and strip (1-6 for A and B, 1-8 for C) at Cook Farm for 2007-2016. This is also part of the attribute table for 'CAF_strips.shp' CAF_CropCodes.txt: file containing crop code names and crop identities, used in 'CAF_CropID.txt' and 'CAF_strips.shp' CAF_ParticleSize.txt: file containing particle size fractions ('Sand', 'Silt', and 'Clay' as percent) for each 'Location' at sensor depths ('Depth', in cm). Spatial data All spatial data have spatial reference NAD83, UTM11N CAF_sensors.shp: file containing locations of each of the 42 monitoring locations, the 'Location' field contains the location name, which coincides with locations in tabular files. CAF_strips.shp: file containing areal extents of each sub-field, stip, and crop identities for 2007-2016. Crop identity codes are listed in 'CAF_CropCodes.txt' CAF_DEM.tif: file containing a 10 x 10 m elevation (in m) grid for Cook Farm. CAF_Spring_ECa.tif, CAF_Fall_ECa.tif: files containing 10 x 10 m apparent electrical conductivity (dS/m) grids to 1.5 m depth for spring and fall at Cook Farm. CAF_Bt_30cm.tif, CAF_Bt_60cm.tif, CAF_Bt_90cm.tif, CAF_Bt_120cm.tif, CAF_Bt_150cm.tif: files containing 10 x 10 m predictive surfaces for probability (0-1) of Bt horizon at the five sensor depths. Quality Control The Flags folder consists of the files containing the quality control flags for the Cook Farm Sensor Dataset. The nomenclature for the files indicates flags for either temperature (T) or water content (VW) and sensor depths. For example: T_30 is for the temperature data at 30cm. depth VW_120 is for the Volumetric water content at 120 cm. depth Files starting with “missing” contain flags (“M”) for locations and dates (mm/dd/yyyy) with missing data (NA in original dataset). Files starting with “range” contain flags for locations and dates (mm/dd/yyyy) with values outside acceptable ranges: Soil moisture (0-0.6 m^3/m^3) flagged as “C” Soil temperature (<0 deg. C) flagged as “D” Files starting with the name “flats” contain flags (“D”) for locations, dates (mm/dd/yyyy), and times (hh:mm) with constant values (within 1%) for a 24 hour period, as in Dorigo et al. 2013. Files starting with the name “spikes” contain flags (“D”) for locations, dates (mm/dd/yyy), and times (hh:mm) with sudden spikes in VWC readings. Files starting with the name “breaks” contain flags (“D”) for locations, dates (mm/dd/yyy), and times (hh:mm) with sudden breaks (jumps or drops) in VWC readings. Code (implemented in R) for the screening and flagging is included in “Code Snippet.txt” A list of the sensor versions as of 06/16/16 at each location and depth. Resources in this dataset:Resource Title: Data package for automated in situ soil sensor network. File Name: CAF_Sensor_Dataset.zipResource Description: Data file descriptions for Cook Farm sensor network data set (CAF_Sensor_Dataset). Data set compiled by Caley Gasch, under supervision of David Brown, Department of Crop and Soil Sciences, Washington State University, Pullman, WA. Updated: 04/01/2017 Tabular data: CAF_sensors: folder with Daily and Hourly subfolders, each containing 42 '.txt' files of water content and temperature sensor readings. Each file represents readings from a single location, indicated in the file name (i.e. CAF003.txt) and in the 'Location' field of the table. Readings are organized by 'Date' (4/20/2007 - 6/16/2016), ‘Time’ (24 hr clock, only in hourly files), and with property (VW or T) and sensor 'Depth' as follows: VW_30cm: volumetric water readings at 30 cm depth (m^3/m^3) VW_60cm: volumetric water readings at 60 cm depth (m^3/m^3) VW_90cm: volumetric water readings at 90 cm depth (m^3/m^3) VW_120cm: volumetric water readings at 120 cm depth (m^3/m^3) VW_150cm: volumetric water readings at 150 cm depth (m^3/m^3) T_30cm: temperature readings at 30 cm depth (C) T_60cm: temperature readings at 60 cm depth (C) T_90cm: temperature readings at 90 cm depth (C) T_120cm: temperature readings at 120 cm depth (C) T_150cm: temperature readings at 150 cm depth (C) Volumetric water content readings are calibrated according to: Gasch, CK, DJ Brown, ES Brooks, M Yourek, M Poggio, DR Cobos, CS Campbell. 2017. A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil specific correction. Computers and Electronics in Agriculture, 137: 29-40. Temperature readings are factory calibrated. CAF_BulkDensity.txt: file containing bulk density values ('BulkDensity' in g/cm^3) for sensor depths at each of the 42 instrumented locations at Cook Farm. Location is indicated in 'Location' field, and sample depths are defined (in cm) by the ’Depth’ field. CAF_CropID.txt: file containing crop codes for each sub-field (A, B and C) and strip (1-6 for A and B, 1-8 for C) at Cook Farm for 2007-2016. This is also part of the attribute table for 'CAF_strips.shp' CAF_CropCodes.txt: file containing crop code names and crop identities, used in 'CAF_CropID.txt' and 'CAF_strips.shp' CAF_ParticleSize.txt: file containing particle size fractions ('Sand', 'Silt', and 'Clay' as percent) for each 'Location' at sensor depths ('Depth', in cm). Spatial data: all spatial data have spatial reference NAD83, UTM11N CAF_sensors.shp: file containing locations of each of the 42 monitoring locations, the 'Location' field contains the location name, which coincides with locations in tabular files. CAF_strips.shp: file containing areal extents of each sub-field, stip, and crop identities for 2007-2016. Crop identity codes are listed in 'CAF_CropCodes.txt' CAF_DEM.tif: file containing a 10 x 10 m elevation (in m) grid for Cook Farm. CAF_Spring_ECa.tif, CAF_Fall_ECa.tif: files containing 10 x 10 m apparent electrical conductivity (dS/m) grids to 1.5 m depth for spring and fall at Cook Farm. CAF_Bt_30cm.tif, CAF_Bt_60cm.tif, CAF_Bt_90cm.tif, CAF_Bt_120cm.tif, CAF_Bt_150cm.tif: files containing 10 x 10 m predictive surfaces for probability (0-1) of Bt horizon at the five sensor depths. (Dataset updated on 10/23/2017 to include QC information.)

Motivation

The goal of introducing the Rescaled Fashion-MNIST with translations dataset is to provide a dataset that contains scale variations (up to a factor of 4), to evaluate the ability of networks to generalise to scales not present in the training data, and to additionally provide a way to test network object detection and object localisation abilities on image data where the objects are not centred.

The Rescaled Fashion-MNIST with translations dataset was introduced in the paper:

[1] A. Perzanowski and T. Lindeberg (2025) "Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations”, Journal of Mathematical Imaging and Vision, 67(29), https://doi.org/10.1007/s10851-025-01245-x.

with a pre-print available at arXiv:

[2] Perzanowski and Lindeberg (2024) "Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations”, arXiv preprint arXiv:2409.11140.

Importantly, the Rescaled Fashion-MNIST with translations dataset is more challenging than the MNIST Large Scale dataset, introduced in:

[3] Y. Jansson and T. Lindeberg (2022) "Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales", Journal of Mathematical Imaging and Vision, 64(5): 506-536, https://doi.org/10.1007/s10851-022-01082-2.

Access and rights

The Rescaled Fashion-MNIST with translations dataset is provided on the condition that you provide proper citation for the original Fashion-MNIST dataset:

[4] Xiao, H., Rasul, K., and Vollgraf, R. (2017) “Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms”, arXiv preprint arXiv:1708.07747

and also for this new rescaled version, using the reference [1] above.

The data set is made available on request. If you would be interested in trying out this data set, please make a request in the system below, and we will grant you access as soon as possible.

The dataset

The Rescaled FashionMNIST with translations dataset is generated by rescaling 28×28 gray-scale images of clothes from the original FashionMNIST dataset [4]. The scale variations are up to a factor of 4, and the images are embedded within black images of size 72x72. The objects within the images have also been randomly shifted in the spatial domain, with the object always at least 4 pixels away from the image boundary. The imresize() function in Matlab was used for the rescaling, with default anti-aliasing turned on, and bicubic interpolation overshoot removed by clipping to the [0, 255] range. The details of how the dataset was created can be found in [1].

There are 10 different classes in the dataset: “T-shirt/top”, “trouser”, “pullover”, “dress”, “coat”, “sandal”, “shirt”, “sneaker”, “bag” and “ankle boot”. In the dataset, these are represented by integer labels in the range [0, 9].

The dataset is split into 50 000 training samples, 10 000 validation samples and 10 000 testing samples. The training dataset is generated using the initial 50 000 samples from the original Fashion-MNIST training set. The validation dataset, on the other hand, is formed from the final 10 000 images of that same training set. For testing, all test datasets are built from the 10 000 images contained in the original Fashion-MNIST test set.

The h5 files containing the dataset

The training dataset file (~2.9 GB) for scale 1, which also contains the corresponding validation and test data for the same scale, is:

fashionmnist_with_scale_variations_and_translations_tr50000_vl10000_te10000_outsize72-72_scte1p000_scte1p000.h5

Additionally, for the Rescaled FashionMNIST with translations dataset, there are 9 datasets (~415 MB each) for testing scale generalisation at scales not present in the training set. Each of these datasets is rescaled using a different image scaling factor, 2^k/4, with k being integers in the range [-4, 4]:

fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte0p500.h5
fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte0p595.h5
fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte0p707.h5
fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte0p841.h5
fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte1p000.h5
fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte1p189.h5
fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte1p414.h5
fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte1p682.h5
fashionmnist_with_scale_variations_and_translations_te10000_outsize72-72_scte2p000.h5

These dataset files were used for the experiments presented in Figure 8 in [1].

Instructions for loading the data set

The datasets are saved in HDF5 format, with the partitions in the respective h5 files named as
('/x_train', '/x_val', '/x_test', '/y_train', '/y_test', '/y_val'); which ones exist depends on which data split is used.

The training dataset can be loaded in Python as:

with h5py.File(`

x_train = np.array( f["/x_train"], dtype=np.float32)
x_val = np.array( f["/x_val"], dtype=np.float32)
x_test = np.array( f["/x_test"], dtype=np.float32)
y_train = np.array( f["/y_train"], dtype=np.int32)
y_val = np.array( f["/y_val"], dtype=np.int32)
y_test = np.array( f["/y_test"], dtype=np.int32)

We also need to permute the data, since Pytorch uses the format [num_samples, channels, width, height], while the data is saved as [num_samples, width, height, channels]:

x_train = np.transpose(x_train, (0, 3, 1, 2))
x_val = np.transpose(x_val, (0, 3, 1, 2))
x_test = np.transpose(x_test, (0, 3, 1, 2))

The test datasets can be loaded in Python as:

with h5py.File(`

x_test = np.array( f["/x_test"], dtype=np.float32)
y_test = np.array( f["/y_test"], dtype=np.int32)

The test datasets can be loaded in Matlab as:

x_test = h5read(`

The images are stored as [num_samples, x_dim, y_dim, channels] in HDF5 files. The pixel intensity values are not normalised, and are in a [0, 255] range.

There is also a closely related Fashion-MNIST dataset, which in addition to scaling variations keeps the objects in the frame centred, meaning no spatial translations are used.

The global fixed truck scale market is a significant sector within the broader weighing equipment industry, experiencing steady growth driven by increasing demands from logistics, transportation, and agricultural sectors. The market's expansion is fueled by the rising need for precise weight measurement in various applications, including inbound and outbound freight management, compliance with transportation regulations, and inventory control. Technological advancements, such as the integration of digital technologies and automation, are further propelling market growth. The increasing adoption of smart scales with data connectivity and remote monitoring capabilities enhances operational efficiency and reduces manual intervention, which is a key driver. While the precise market size for 2025 is unavailable, based on industry reports and observed CAGR trends in similar sectors, a reasonable estimate would place the market value at approximately $2.5 billion. This figure reflects a consistent, albeit moderate, growth trajectory, influenced by factors like global economic conditions and investment in infrastructure projects. However, certain factors may restrain market growth. High initial investment costs associated with installing and maintaining fixed truck scales can be a barrier for smaller businesses. Furthermore, stringent regulatory compliance and the need for periodic calibration add to the overall operational expenses. Despite these challenges, the long-term outlook for the fixed truck scale market remains positive, driven by ongoing infrastructure development, increasing e-commerce activities leading to higher freight volumes, and the continuous evolution of weighing technology towards greater accuracy, reliability, and connectivity. The market segmentation likely includes different scale types (e.g., single-axle, multiple-axle), weighing capacities, and functionalities (e.g., data logging, integration with transportation management systems). This leads to various niche markets catered to specific industry needs.

In our work, we have designed and implemented a novel workflow with several heuristic methods to combine state-of-the-art methods related to CVE fix commits gathering. As a consequence of our improvements, we have been able to gather the largest programming language-independent real-world dataset of CVE vulnerabilities with the associated fix commits. Our dataset containing 29,203 unique CVEs coming from 7,238 unique GitHub projects is, to the best of our knowledge, by far the biggest CVE vulnerability dataset with fix commits available today. These CVEs are associated with 35,276 unique commits as sql and 39,931 patch commit files that fixed those vulnerabilities(some patch files can't be saved as sql due to several techincal reasons) Our larger dataset thus substantially improves over the current real-world vulnerability datasets and enables further progress in research on vulnerability detection and software security. We used NVD(nvd.nist.gov) and Github Secuirty advisory Database as the main sources of our pipeline.

We release to the community a 16GB PostgreSQL database that contains information on CVEs up to 2024-09-26, CWEs of each CVE, files and methods changed by each commit, and repository metadata. Additionally, patch files related to the fix commits are available as a separate package. Furthermore, we make our dataset collection tool also available to the community.

cvedataset-patches.zip file contains fix patches, and postgrescvedumper.sql.zip contains a postgtesql dump of fixes, together with several other fields such as CVEs, CWEs, repository meta-data, commit data, file changes, method changed, etc.

MoreFixes data-storage strategy is based on CVEFixes to store CVE commits fixes from open-source repositories, and uses a modified version of Porspector(part of ProjectKB from SAP) as a module to detect commit fixes of a CVE. Our full methodology is presented in the paper, with the title of "MoreFixes: A Large-Scale Dataset of CVE Fix Commits Mined through Enhanced Repository Discovery", which will be published in the Promise conference (2024).

For more information about usage and sample queries, visit the Github repository: https://github.com/JafarAkhondali/Morefixes

If you are using this dataset, please be aware that the repositories that we mined contain different licenses and you are responsible to handle any licesnsing issues. This is also the similar case with CVEFixes.

This product uses the NVD API but is not endorsed or certified by the NVD.

This research was partially supported by the Dutch Research Council (NWO) under the project NWA.1215.18.008 Cyber Security by Integrated Design (C-SIDe).

To restore the dataset, you can use the docker-compose file available at the gitub repository. Dataset default credentials after restoring dump:

POSTGRES_USER=postgrescvedumper POSTGRES_DB=postgrescvedumper POSTGRES_PASSWORD=a42a18537d74c3b7e584c769152c3d

Please use this for citation:

 title={MoreFixes: A large-scale dataset of CVE fix commits mined through enhanced repository discovery},
 author={Akhoundali, Jafar and Nouri, Sajad Rahim and Rietveld, Kristian and Gadyatskaya, Olga},
 booktitle={Proceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering},
 pages={42--51},
 year={2024}
}

The global axle load scale market is experiencing robust growth, driven by increasing demand for efficient transportation and infrastructure development. Stringent regulations regarding vehicle weight and load capacity are compelling transportation companies and construction firms to adopt axle load scales for ensuring compliance and preventing overloading. Furthermore, the rising adoption of advanced technologies like digital weighing systems and data analytics is enhancing the accuracy and efficiency of these scales, contributing to market expansion. This report estimates the market size in 2025 to be $2.5 Billion, with a Compound Annual Growth Rate (CAGR) of 6% projected from 2025 to 2033. This growth trajectory is expected to be fueled by continuous infrastructure projects globally, particularly in developing economies, along with a growing focus on optimizing logistics and reducing transportation costs. The market segmentation reveals a strong demand for both fixed and portable axle load scales across various sectors. The transportation sector dominates, followed by construction and industrial applications. Key players like MacWeigh System Co., Ltd, SAUTER GmbH, and Fairbanks Scales, Inc. are driving innovation and competition through the introduction of technologically advanced products and expanding their geographical reach. The regional analysis indicates a strong presence in North America and Europe, with significant growth opportunities emerging in the Asia-Pacific region, particularly in China and India, due to rapid industrialization and infrastructure development. Despite the overall positive growth outlook, factors like the high initial investment cost of sophisticated axle load scales and potential fluctuations in raw material prices could act as restraints on market expansion in the coming years. This report provides a detailed analysis of the global axle load scale market, projected to reach $2.5 billion by 2028. It examines market dynamics, key players, and emerging trends, offering valuable insights for businesses operating in or seeking to enter this sector. The report leverages extensive research encompassing market size, segmentation, growth drivers, challenges, and competitive landscapes, with a focus on actionable strategic recommendations.

This workbook includes all necessary data to run the bifacial PV simulations performed in:

"Validation of Bifacial Photovoltaic Simulation Software Against Monitoring Data from Large-Scale Single-Axis Trackers and Fixed Tilt Systems in Denmark"

Appl. Sci. 2020, 10(23), 8487; https://doi.org/10.3390/app10238487

Revisions/Corrigendum: Version 2: We added the PlantPredict .ppp file used to perform the HSAT bifacial simulation.

Version 3: A ~5min lead was discovered in the data acquisition system responible for GPOA, DC Power, and TMOD measurements. The timestamps and hourly aggregation is now adjusted accordingly. The result is improved model to measurement agreement particularly in the morning and afternoon.

The workbook tabs include: -Site Data -Shade Scene -Structural Dimensions -PV Module Specifications -Inverter Specifications -Albedo Data -Meteorological Data -Validation (monitoring) Data

If you use these data/resources in a published work, please cite the publication and the dataset.

The global fixed truck scale market is experiencing robust growth, driven by the increasing demand for precise and efficient weighing solutions in various industries. The market size in 2025 is estimated at $1.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033. This growth is fueled by several factors, including the expanding logistics and transportation sectors, stringent regulations concerning weight control and safety, and the rising adoption of advanced technologies like IoT-enabled scales for real-time data monitoring and improved operational efficiency. Furthermore, the increasing need for accurate inventory management and supply chain optimization across diverse industries like agriculture, manufacturing, and waste management is significantly contributing to market expansion. Companies are also increasingly investing in upgrading their existing weighing infrastructure to meet evolving industry standards and enhance operational effectiveness. The market's growth trajectory is projected to remain positive throughout the forecast period (2025-2033). However, certain restraints, such as high initial investment costs associated with installing and maintaining fixed truck scales, and the potential impact of economic fluctuations on capital expenditure, might influence the market's growth rate. Nevertheless, the long-term benefits of improved operational efficiency, reduced errors, and enhanced safety measures outweigh these challenges, sustaining the overall upward trend. Key market segments include different weighing capacities (e.g., low-capacity, medium-capacity, high-capacity), types (e.g., single-axle, multiple-axle), and end-user industries. The competitive landscape is populated by major players like Rice Lake Weighing Systems, Mettler Toledo, and Avery Weigh-Tronix, who are continually innovating to offer advanced features and superior weighing solutions.

The portable truck scale market is experiencing robust growth, driven by increasing demand across diverse sectors like agriculture, construction, and logistics. The market's expansion is fueled by the need for efficient and accurate weighing solutions in various applications, particularly where fixed scales are impractical or uneconomical. The rise of e-commerce and the associated surge in last-mile delivery further bolster the demand for portable scales, ensuring accurate weight verification and efficient freight management. Technological advancements, including the integration of electronic and digital weighing systems, are enhancing precision and data management capabilities, improving overall operational efficiency and reducing errors. While the initial investment cost might be a restraint for some businesses, the long-term cost savings from improved accuracy and reduced operational inefficiencies make it a worthwhile investment. Considering a current market size of approximately $1.5 billion (a reasonable estimate given typical scale market sizes and CAGR) and a Compound Annual Growth Rate (CAGR) of 6%, the market is projected to reach roughly $2.2 billion by 2033. This growth is anticipated across all segments, with electronic truck scales leading the market due to their superior accuracy and data integration capabilities. Regionally, North America and Europe currently dominate the market, but emerging economies in Asia-Pacific are showcasing significant growth potential, presenting substantial opportunities for market expansion in the coming years. The segmentation of the portable truck scale market reveals strong growth across various applications. The agricultural sector benefits from precise weighing for yield monitoring and efficient input management. Construction and mining rely on accurate weight measurements for materials handling and transportation optimization. The logistics sector leverages these scales to streamline freight operations, ensuring accurate billing and efficient inventory management. The continuous advancements in sensor technology, data analytics, and connectivity are likely to drive further innovation in the portable truck scale market, resulting in more sophisticated and integrated weighing solutions. Improved durability, portability, and user-friendly interfaces are key features influencing purchasing decisions. The market will witness increasing competition amongst established and emerging players, leading to further price optimization and innovative product offerings.

Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation. This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine. The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too. Please cite: Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108. Dataset is provided as a single zip file. The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input. The archive also contains the file: "ReadMeMetadataDataSetPrecipitationScaling" which explains the data structure.

This dataset contains key characteristics about the data described in the Data Descriptor A global-scale data set of mining areas. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

China Securities & Futures Operating Institutions Private Asset Mgt: New Registered: Scale: Fixed Income data was reported at 30.953 RMB bn in Dec 2024. This records an increase from the previous number of 24.755 RMB bn for Nov 2024. China Securities & Futures Operating Institutions Private Asset Mgt: New Registered: Scale: Fixed Income data is updated monthly, averaging 40.453 RMB bn from Nov 2018 (Median) to Dec 2024, with 74 observations. The data reached an all-time high of 122.114 RMB bn in Dec 2020 and a record low of 14.151 RMB bn in Apr 2022. China Securities & Futures Operating Institutions Private Asset Mgt: New Registered: Scale: Fixed Income data remains active status in CEIC and is reported by Asset Management Association of China. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Securities & Futures Operating Institutions Private Asset Mgt: Scale.

The global fixed electronic truck scale market is experiencing robust growth, driven by increasing demand for efficient and accurate weighing solutions across diverse sectors. The market's expansion is fueled by the rising adoption of advanced technologies like IoT integration and improved data analytics capabilities within the weighing systems. This allows for real-time monitoring of weight data, enhancing operational efficiency and reducing manual labor. Key industries such as metallurgy, chemical manufacturing, and logistics (including railways and ports) are significant contributors to this growth, as precise weighing is crucial for inventory management, billing accuracy, and safety compliance. Furthermore, stringent regulations regarding weight limits and transportation safety are compelling businesses to upgrade their weighing infrastructure to meet compliance standards. The market is segmented by application (metallurgy, chemical industry, railway, port, others) and type (weighing capacity, typically categorized by ranges like 100t and above). While precise market sizing data is unavailable, considering the industry trends and the presence of numerous established players like Rice Lake Weighing Systems and Mettler Toledo, the market is projected to be substantial, with a compound annual growth rate (CAGR) likely within the range of 5-7% over the forecast period (2025-2033). This growth is expected to be relatively consistent across regions, although North America and Asia Pacific are likely to maintain the largest market shares due to their substantial industrial bases and infrastructure development initiatives. Competition within the fixed electronic truck scale market is intense, with both large multinational corporations and specialized regional players vying for market share. The competitive landscape is characterized by ongoing product innovation, focusing on factors such as improved accuracy, durability, and integration with other logistical and operational systems. Pricing strategies also play a crucial role in determining market share, with some vendors focusing on premium, high-precision solutions while others offer more cost-effective options to cater to various budgetary constraints. Growth in emerging economies is expected to contribute significantly to market expansion, with the increasing industrialization and infrastructural development in regions like Asia Pacific creating lucrative opportunities for vendors. However, factors like high initial investment costs and the potential need for specialized maintenance could present challenges to market growth in some regions.

This data set consists of image files (Tif files with tfw files of the same name) of the Stadtkartenwerk Köln optimized for the scale 1:20,000 in the color variants orange, light and gray. They enable the integration of the city map into GIS systems, so that a background map with a fixed scale is available there. In contrast to WMS services, in which the labeling is automated, scale-based and, if necessary, selective, the map labeling is complete in the present raster files. The dataset can be downloaded via Open Data Cologne (see DOWNLOAD LINKS).

The global flat plate road scale market is experiencing steady growth, projected to reach a value of $4.27 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 5.4% from 2019 to 2033. This growth is fueled by several key factors. The increasing demand for efficient and accurate weighing solutions across diverse sectors like transportation, logistics, and warehousing is a primary driver. Stringent regulations regarding cargo weight and safety compliance in many countries are further propelling market expansion. Advancements in technology, including the integration of smart sensors, data analytics, and improved software for weight management, are also contributing significantly to market growth. The trend towards automation and digitalization in supply chains is pushing businesses to adopt advanced weighing systems like flat plate road scales to streamline their operations and improve efficiency. While the market faces certain challenges, such as high initial investment costs and the need for regular maintenance, these are being offset by the long-term benefits of increased operational efficiency and reduced errors associated with these scales. The market's segmentation across mobile and fixed scales, and its application across various sectors, provides further opportunities for growth. The market is expected to see increased adoption in emerging economies as infrastructure development progresses and businesses prioritize supply chain optimization. The competitive landscape is characterized by a mix of established players like Caterpillar and Mettler Toledo alongside smaller, specialized manufacturers. This indicates a market with diverse offerings catering to varying customer needs and budget constraints. The presence of several manufacturers in regions like Asia Pacific reflects the increasing adoption of these scales in rapidly developing economies. Further growth is anticipated through strategic partnerships, product innovation, and expansion into new geographic regions. The forecast period of 2025-2033 promises further market expansion driven by the ongoing technological advancements and the global trend towards efficient and reliable supply chain management. The continued adoption of these scales across industries will be crucial for the sustained growth of this market.

China Fund Co Private Asset Mgt: New Registered: Scale: Fixed Income data was reported at 2.069 RMB bn in Feb 2025. This records a decrease from the previous number of 10.448 RMB bn for Jan 2025. China Fund Co Private Asset Mgt: New Registered: Scale: Fixed Income data is updated monthly, averaging 9.435 RMB bn from Oct 2022 (Median) to Feb 2025, with 28 observations. The data reached an all-time high of 51.606 RMB bn in Dec 2022 and a record low of 1.888 RMB bn in Nov 2024. China Fund Co Private Asset Mgt: New Registered: Scale: Fixed Income data remains active status in CEIC and is reported by Asset Management Association of China. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Securities & Futures Operating Institutions Private Asset Mgt: Scale.