In this paper, we explore the differences in item non-response that result from different modes of interview and find that mode makes a difference. The data are from an experiment in which we randomly assigned an adult population to an in-person or self-completed survey after subjects agreed to participate in a short poll. For nearly every topic and format of question, we find less item non-response in the self-complete mode. Furthermore, we find the difference across modes in non-response is exacerbated for respondents with low levels of cognitive abilities.

The number of people having to commute to work (usual place of work or no fixed workplace address) has risen considerably over the past five years from 13 450 900 in 2001 to 14 714 300 in 2006 or 9.4%. While the car is still the most frequently used mode of transportation for getting to work, there was a decrease in the proportion of drivers in the past five years, from 73.8% of workers in 2001 to 72.3% in 2006. In 2006, 11% of Canadian workers used public transit to get to work, compared to 10.5% in 2001 and 10.1% in 1996.

Now that people on mobile devices can easily choose their mode of communication (e.g., voice, text, video) survey designers can allow respondents to answer questions in whatever mode they find momentarily convenient given their circumstances or that they chronically prefer. Investigators conducted an experiment to explore how mode choice affects response quality, participation, and satisfaction in smartphone interviews. Respondents were interviewed on their iPhone in one of four modes: Human Voice, Human Text, Automated Voice, and Automated Text. Respondents were either assigned the mode of their interview (Assigned Mode), in which case the contact and interviewing mode were the same, or they were required to choose the mode of their interview (Mode Choice) after being contacted in one of the four modes. 634 respondents completed the interview and a post-interview online debriefing questionnaire in the Assigned Mode group and 626 respondents completed the interview and online debriefing in the Assigned Mode group. This dataset contains 2691 cases, the 1,260 respondents who completed the interview and debriefing, as well as 1,431 cases that were invited to participate but ended their participation somewhere shy of the last debriefing question (either they did not choose a mode, did not answer the first question, started but did not finish the interview, or finished the interview but did not complete the debriefing). All respondents (who completed the interview) answered 32 questions from US social surveys. 13 interviewers from the University of Michigan Survey Research Center administered voice and text interviews (five administered interviews in both experimental conditions, three conducted only Assigned Mode interviews, and five conducted interviews in just the Mode Choice condition). Automated systems launched parallel text and voice interviews at the same time as the human interviews. Respondents who chose their interview modes provided more conscientious (fewer rounded and non-differentiated) answers, and they reported greater satisfaction with the interview. Although fewer respondents started the interview when given a choice of mode, a higher percentage of Mode Choice respondents who started the interview completed it. For certain mode transitions (e.g., from automated interview modes) there was no reduction in participation. The results demonstrate clear benefits and relatively few drawbacks resulting from mode choice, at least among these modes and with this sample of iPhone users, suggesting that further exploration of mode choice and the logistics of its implementation is warranted. Demographic variables include participants' gender, race, education level, and household income.

This dataset contains the simulation data of the combinatorial metamaterial as used for the paper 'Machine Learning of Combinatorial Rules in Mechanical Metamaterials', as published in XXX.

In this paper, the data is used to classify each \(k \times k\) unit cell design into one of two classes (C or I) based on the scaling (linear or constant) of the number of zero modes \(M_k(n)\) for metamaterials consisting of an \(n\times n\) tiling of the corresponding unit cell. Additionally, a random walk through the design space starting from class C unit cells was performed to characterize the boundary between class C and I in design space. A more detailed description of the contents of the dataset follows below.

Modescaling_raw_data.zip

This file contains uniformly sampled unit cell designs and \(M_k(n)\) for \(1\leq n\leq 4\), which was used to classify the unit cell designs for the data set. There is a small subset of designs for \(k=\{3, 4, 5\}\) that do not neatly fall into the class C and I classification, and instead require additional simulation for \(4 \leq n \leq 6\) before either saturating to a constant number of zero modes (class I) or linearly increasing (class C). This file contains the simulation data of size \(3 \leq k \leq 8\) unit cells. The data is organized as follows.

Simulation data for \(3 \leq k \leq 5\) and \(1 \leq n \leq 4\) is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4.npy", and contain a [Nsim, 1+k*k+4] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:

• col 0: label number to keep track
• col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form.
• col k*k+1 - k*k+5: number of zero modes \(M_k(n)\) in ascending order of \(n\), so: \(\{M_k(1), M_k(2), M_k(3), M_k(4)\}\).

Note: the unit cell design uses the numbers \(\{0, 1, 2, 3\}\) to refer to each building block orientation. The building block orientations can be characterized through the orientation of the missing diagonal bar (see Fig. 2 in the paper), which can be Left Up (LU), Left Down (LD), Right Up (RU), or Right Down (RD). The numbers correspond to the building block orientation \(\{0, 1, 2, 3\} = \{\mathrm{LU, RU, RD, LD}\}\).

Simulation data for \(3 \leq k \leq 5\) and \(1 \leq n \leq 6\) for unit cells that cannot be classified as class C or I for \(1 \leq n \leq 4\) is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4_classX_extend.npy", and contain a [Nsim, 1+k*k+6] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:

• col 0: label number to keep track
• col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form.
• col k*k+1 - k*k+5: number of zero modes \(M_k(n)\) in ascending order of \(n\), so: \(\{M_k(1), M_k(2), M_k(3), M_k(4), M_k(5), M_k(6)\}\).

Simulation data for \(6 \leq k \leq 8\) unit cells are stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. Note that the number of modes is now calculated for \(n_x \times n_y\) metamaterials, where we calculate \((n_x, n_y) = \{(1,1), (2, 2), (3, 2), (4,2), (2, 3), (2, 4)\}\) rather than \(n_x=n_y=n\) to save computation time. These files are named "data_new_rrQR_i_n_Mx_My_n4_kxk(_extended).npy", and contain a [Nsim, 1+k*k+8] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:

• col 0: label number to keep track
• col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form.
• col k*k+1 - k*k+9: number of zero modes \(M_k(n_x, n_y)\) in order: \(\{M_k(1, 1), M_k(2, 2), M_k(3, 2), M_k(4, 2), M_k(1, 1), M_k(2, 2), M_k(2, 3), M_k(2, 4)\}\).

Modescaling_classification_results.zip

This file contains the classification, slope, and offset of the scaling of the number of zero modes \(M_k(n)\) for the unit cells in Modescaling_raw_data.zip. The data is organized as follows.

The results for \(3 \leq k \leq 5\) based on the \(1 \leq n \leq 4\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:

col 0: label number to keep track

col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n \leq 4\))

col 2: slope from \(n \geq 2\) onward (undefined for class X)

col 3: the offset is defined as \(M_k(2) - 2 \cdot \mathrm{slope}\)

col 4: \(M_k(1)\)

The results for \(3 \leq k \leq 5\) based on the extended \(1 \leq n \leq 6\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4_classC_extend.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:

col 0: label number to keep track

col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n \leq 6\))

col 2: slope from \(n \geq 2\) onward (undefined for class X)

col 3: the offset is defined as \(M_k(2) - 2 \cdot \mathrm{slope}\)

col 4: \(M_k(1)\)

The results for \(6 \leq k \leq 8\) based on the \(1 \leq n \leq 4\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scenx_Sceny_slopex_slopey_offsetx_offsety_M1k_kxk(_extended).txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:

col 0: label number to keep track

col 1: the class_x based on \(M_k(n_x, 2)\), where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n_x \leq 4\))

col 2: the class_y based on \(M_k(2, n_y)\), where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n_y \leq 4\))

col 3: slope_x from \(n_x \geq 2\) onward (undefined for class X)

col 4: slope_y from \(n_y \geq 2\) onward (undefined for class X)

col 5: the offset_x is defined as \(M_k(2, 2) - 2 \cdot \mathrm{slope_x}\)

col 6: the offset_x is defined as \(M_k(2, 2) - 2 \cdot \mathrm{slope_y}\)

col 7: \(M_k(1, 1)\)

Random Walks Data

This file contains the random walks for \(3 \leq k \leq 8\) unit cells. The random walk starts from a class C unit cell design, for each step \(s\) a randomly picked unit cell is changed to a random new orientation for a total of \(s=k^2\) steps. The data is organized as follows.

The configurations for each step are stored in the files named "configlist_test_i.npy", where i is a number and corresponds to a different starting unit cell. The stored array has the shape [k*k+1, 2*k+2, 2*k+2]. The first dimension denotes the step \(s\), where \(s=0\) is the initial configuration. The second and third dimension denote the unit cell configuration in the pixel representation (see paper) padded with a single pixel wide layer using periodic boundary conditions.

The class for each configuration are stored in "lmlist_test_i.npy", where i corresponds to the same number as for the configurations in the "configlist_test_i.npy" file. The stored array has

The number of people having to commute to work (usual place of work or no fixed workplace address) has risen considerably over the past five years from 13 450 900 in 2001 to 14 714 300 in 2006 or 9.4%. While the car is still the most frequently used mode of transportation for getting to work, there was a decrease in the proportion of drivers in the past five years, from 73.8% of workers in 2001 to 72.3% in 2006. In 2006, 11% of Canadian workers used public transit to get to work, compared to 10.5% in 2001 and 10.1% in 1996.

Attached are the data sets in forms of python pickle files from the following submission https://arxiv.org/abs/2108.07235

Abstract:

Majorana zero modes were predicted to exist as edge states of a physical system called the Kitaev chain. Such zero modes should host particles that are their own antiparticles and could be used as a basis for a qubit that is to large extent immune to noise - the topological qubit. However, all attempts to prove their existence gave inconclusive results. Here, I experimentally show that Majorana zero modes do in fact exist on a Kitaev chain composed out of 3 noisy qubits on a publicly available quantum computer. The signature of Majorana zero modes is a degeneracy with the ground state which is not lifted by noise of the quantum computer. I also confirm that Majorana zero modes have a number of theoretically predicted features: a well-defined parity with switches at specific points and a non-conserved particle number. Furthermore, I show that Majorana zero modes favour long-range Majorana pairing at low chemical potential and short-range pairing at large values of the chemical potential. The results presented here are a most comprehensive set of validations ever conducted towards confirming the existence of Majorana zero modes in nature. I foresee that the findings presented here would allow any user with an internet connection to perform experiments with Majorana zero modes. Furthermore, the noisy intermediate scale quantum computing community can start building topological processors composed out of contemporary noisy qubits.

This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for over 69 percent of all work trips in 2023. This is the same rate as 2019, and the first increase since 2017, both years being before the COVID-19 pandemic began.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. The percentage of people carpooling to work in 2023 was lower than every year except 2016 since this data first started being tracked in 2005. The percentage of people walking to work increased from 2022 to 2023, but this increase is not statistically significant.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure is still about 2.5 times higher than 2019, even with the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

The ITER electron cyclotron heating transmission lines will consist of 63.5 mm diameter corrugated waveguides, each carrying 1 MW of 170 GHz microwaves. These transmission lines must include expansion units to accommodate expansion and contraction along the path from the gyrotron microwave sources to the tokamak. A numerical mode matching code has been developed to calculate power losses due to mode conversion of the operating mode, HE11, to higher order modes as a result of the radial discontinuities in a sliding joint. Two expansion unit designs were evaluated, a simple gap expansion unit and a more complex tapered expansion unit. The gap expansion unit demonstrated loss that oscillated rapidly with expansion length, due to trapped modes within the unit. The tapered expansion unit has been shown to effectively suppress these trapped modes at the expense of increased fabrication complexity. In a gap expansion unit, for a waveguide step size of 2.5 mm, loss can be kept below 0.1% to a maximum expansion length of 17 mm. Expansion units without corrugation on interior walls were also evaluated. Expansion units that lack corrugations are found to increase mode trapping within the units, though not beyond useful application. The mode matching code developed in this paper was also used to estimate mode conversion loss in vacuum pumpouts for the ECH lines; the estimated loss was found to be negligibly small.

A key question in transportation research is whether drivers show behavioral adaptation, that is, slower or faster driving, when new technology is introduced into the vehicle. This study investigates behavioral adaptation in response to the sport mode, a technology that alters the vehicle’s auditory, throttle-mapping, power-steering, and chassis settings. Based on the literature, it can be hypothesized that the sport mode increases perceived sportiness and encourages faster driving. Oppositely, the sport mode may increase drivers’ perceived danger, homeostatically causing them to drive more slowly. These hypotheses were tested using an instrumented vehicle on a test track. Thirty-one drivers were asked to drive as they normally would with different sport mode settings: Baseline, Modified Throttle Mapping (MTM), Artificial Engine Sound enhancement (AESe), MTM and AESe combined (MTM-AESe), and MTM, AESe combined with four-wheel steering, increased damping, and decreased power steering (MTM-AESe-4WS). Post-trial questionnaires showed increased perceived sportiness but no differences in perceived danger for the three MTM conditions compared to Baseline. Furthermore, compared to Baseline, MTM led to higher vehicle accelerations and, with a smaller effect size, a higher time-percentage of driving above the 110 km/h speed limit, but not higher cornering speeds. The AESe condition did not significantly affect perceived sportiness, perceived danger, and driving speed compared to Baseline. These findings suggest that behavioral adaptation is a functional and opportunistic phenomenon rather than mediated by perceived sportiness or perceived danger.

Motivated by the recent experimental discovery of strongly surface-plane-dependent superconductivity at surfaces of KTaO3 single crystals, we calculate the electron-phonon coupling strength, λ, of doped KTaO3 along the reciprocal-space high-symmetry directions. Using the Wannier-function approach implemented in the EPW package, we calculate λ across the experimentally covered doping range and compare its mode-resolved distribution along the [001], [110] and [111] reciprocal-space directions. We find that the electron-phonon coupling is strongest in the optical modes around the Γ point, with some distribution to higher k values in the [001] direction. The electron-phonon coupling strength as a function of doping has a dome-like shape in all three directions and its integrated total is largest in the [001] direction and smallest in the [111] direction, in contrast to the experimentally measured trends in critical temperatures. This disagreement points to a non-BCS character of the superconductivity. Instead, the strong localization of λ in the soft optical modes around Γ suggests an importance of ferroelectric soft-mode fluctuations, which is supported by our findings that the mode-resolved λ values are strongly enhanced in polar structures. The inclusion of spin-orbit coupling has negligible influence on our calculated mode-resolved λ values.

There are several ongoing challenges in non-contacting blade vibration and stress measurement systems that can address closely spaced modes and blade-to-blade variations (mistuning). Traditional NSMS systems are applicable but have limitations due to the undersampling that is inherent in time-of-arrival data processing and the uncertainty that is introduced by inferring, as opposed to calculating, the mode of vibration.

Based on Navy SBIR research, MSI is developing a radar-based blade vibration measurement system with the following capabilities: • Provides a continuous time series of blade displacement data over a portion of a revolution (solving the undersampling problem). • Includes data reduction algorithms to directly calculate the blade vibration frequency, modal displacement, and vibratory stress (solving the mode inference problem). • Uses a single sensor per stage to monitor all of the blades on the stage.

The goals for the proposed project are to design and construct an innovative blade vibration measurement system with resolution capable of characterizing mistuning parameters and closely spaced modes of vibration. Development and demonstration of such a system will provide substantially superior capabilities to current blade vibration technology. Phase I demonstration testing will be conducted in MSI's laboratory with an existing instrumented compressor rig.

In 2012 LIVES and FORS designed an experiment to provide evidence about which survey designs work best in the Swiss context, to maximise the quality of future quantitative research. As well as, to find the best combination of modes regarding response rates, biases, sample, budget and timing.
Survey-based data collection makes a fundamental contribution to social science research in Switzerland. Because different features of the design of a survey can have implications for the quality of the data collected, optimising the survey design is key to ensuring the accuracy of the conclusions drawn from analyses of the data, and hence for the validity of both theoretical and policy developments derived from these. Today it is especially difficult to reach the households without a registered landline and it is increasingly difficult to convince people to participate. A multi-modal approach in terms of information gathering is thus increasingly necessary. This allows on the one hand to reach people through some specific method and not another and on the other hand people might be convinced to participate by offering the mode that suits them best (for some telephone, for others face-to-face or other). In this experiment, single mode surveys (paper, CATI and web) and sequential mixed mode surveys (CATI plus paper, and web plus paper plus CATI/CAPI) are compared with respect to response rates and the representativeness of the responding sample.
The questionnaire was designed by selecting questions from various previous LIVES surveys and surveys executed by FORS, especially the European Social Survey (ESS). The selected questions related to the well-being and unease theme, but are also questions that seems particularly sensible to different modes, as under-representativeness of certain groups, different responses in different mode, etc.
The results lend support to the conclusion that mixing modes sequentially can help to increase response rates and improve sample representativeness, though differences were observed as a function of the availability of telephone numbers for sample members.

The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this.

Data regarding the Mie Fano Comb scattering of an evaporating, optically levitated droplet.Two .cvs data files and one video are included. The video shows the far field interference pattern of the levitated droplet collected by the lens. Together with Eq.(4) this can be used to calculate the radius of the droplet. The distance between the stripes, S, is calculated from the video using that the diameter of the circle of stripes in the video is 25 mm, the distance, d, is 52 mm, and the wavelength is 532 nm."DropletRadius-30sps.cvs" contains the calculated radius for each frame (30 frames per second). The data stops when the lines cannot be used to calculate the size anymore."DirectionalMieSpectrumTimeSeriesAndRadiusFit-10000sps.cvs" contains the scattering intensity and the radius of the droplet at 10 000 samples per second. The size is calculated using a fit of the data above. Plotting Intensity and radius results in Fig. 2a.

In coal mining environments with complex topographic and geological conditions, the presence of primary cracks in the rock strata of the upper mining airspace is critical to mine safety, especially when roof slabs are broken. Cracked roof slabs not only increase risks during mining but also make mining operations more challenging. Therefore, studying the initial damage state of the roof rock formation is great significance. In this study, the effects of different prefabricated crack sizes and inclination angles on the mechanical properties of the coal-rock composite containing cracks were analyzed through the uniaxial compression experiments and PFC2D numerical simulations. The results show that the peak strength and elastic modulus of the coal-rock composites fall between those of pure coal and sandstone, while the macroscopic mechanical parameters of crack-containing composites are significantly lower than those of non-crack-containing composites. Coal-rock composites with different crack characteristics exhibited different mechanical properties, with their damage modes were caused by the combined effects of tensile and shear damage. The increase in crack inclination altered the crack extension path, and the final damage of the specimen manifested first in the upper part, then the middle part, and ultimately in the lower part of the coal body, with tension-induced bulk damage being the dominant failure mode. Analysis of the radial cumulative map revealed that cracks primarily extended along 90° and 270° directions, indicating a strong tendency for crack propagation under axial pressure. The damage evolution curves indicate a nonlinear relationship between the damage factor and strain. While increased crack inclination enhances the compressive performance of coal-rock composites, it simultaneously accelerates structural destabilization. These findings offer theoretical insights into the damage mechanisms of coal-rock composites with cracks, serving as valuable references for coal mining safety.