The Electrically Engaged UnduLation (EEL) system is a buoyancy-driven submersible device for powering oceanographic instruments. Physically, EEL is a slender body whose flexible spine is made up of energy units interconnected by uniaxial hinges. Each unit consists of a pair of piezoelectric elements that converts the bending stress into electrical current to a battery charging circuit. An outer plastic skin forms a seal against water and allows for flexibility at hinge locations. At the top is a bluff body with electronics that holds a ballast for buoyancy adjustment. The bluff body is also responsible for creating fluid instabilities in its wake. When gliding through the water (mode 2), the spine will flex in response to the alternating vortices that shed from the head. This "lock-in" phenomenon occurs when the frequency at which vortices shed resonates with the EEL natural frequency, during which the efficient gaits were found in species of sea snake, eels, and fish. For active propulsion, a single motor can be placed at the first segment and provide the oscillatory input for propulsion similar to a dolphin's kick. Such an efficient swimming is both efficient and nearly silent compared to a spinning propeller. Ultimately, mimicking bio-locomotion provides a viable path to a drag-reduced, self-propelled energy harvesting system for ocean monitoring. Project is part of the TEAMER RFTS 1 (request for technical support) program.

This is an acoustic telemetry dataset published by the Research Institute for Nature and Forest (INBO). It contains animal (fish) tracking data collected by the Permanent Belgian Acoustic Receiver Network (https://lifewatch.be/en/fish-acoustic-receiver-network) for the project/study 2012_leopoldkanaal, using VEMCO tags (V7, V13) and receivers (VR2AR, VR2W). In total 104 female individuals of European eel (Anguilla anguilla) were captured, tagged and released in 2011 and 2012, to study their movement behaviour during the yellow eel stage and migration behaviour during the silver eel stage for 4 years in Meetjesland, a polder area in Flanders, Belgium. Polder areas are lowland systems below sea level that are drained for agricultural and urbanization purposes. Hence, they are characterised by water regulating structures such as dykes, water pumping stations and weirs. They are characterised by a network of canals, connected ponds and ditches, resulting in a high habitat diversity and thus many potential growth areas. When the water level rises beyond a certain threshold during precipitation events, water is pumped from the polder area into the sea to maintain a specific water level. Not only does this result in irregular water flows, water pumping stations have already shown to negatively affect fish passing through them by various injuries and mortalities. In this tracking study, we investigated the movement and ranging behaviour of resident, yellow eels to understand their spatio-temporal habitat use in the polder. Second, we analysed what routes migrating silver eels take, what environmental factors influence this migration and to what extent they are delayed by the migration barriers. This dataset was collected using infrastructure provided by VLIZ and INBO funded by the Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch. The study was commissioned by the Agency of Nature and Forest (ANB). Data have been standardized to Darwin Core using the etn package and are downsampled to the first detection per hour. The original data are managed in the European Tracking Network data platform (https://lifewatch.be/etn/) and are available in Verhelst et al. (2020, https://doi.org/10.14284/428).

This is an acoustic telemetry dataset published by Ghent University. It contains animal (fish) tracking data collected by the Permanent Belgian Acoustic Receiver Network (https://lifewatch.be/en/fish-acoustic-receiver-network) for the project/study 2015_phd_verhelst_eel, using VEMCO tags (V13) and receivers (VR2AR, VR2Tx, VR2W). In total 136 female individuals of European eel (Anguilla anguilla) were captured, tagged and released between 2015 and 2018 in the Scheldt estuary, to study their migration behaviour - especially their use of selective tidal stream transport - in a tidal system without migration barriers. Large estuaries with a complete salinity gradient from a tidal freshwater zone to marine have become rare due to water regulating constructions such as sluices, shipping locks, water pumping stations and dams. However, the Scheldt estuary in Belgium still has an extensive estuary of ca 160 km. Diadromous fish have to overcome substantial distances which come with a high energetic cost. Due to the high energetic cost of migration and the low adult survival, some of these species have developed semelparity. Consequently, a bioenergetic trade-off between migration and reproduction may exist for semelparous fish species, especially since many will stop feeding during migration: the smaller the energy expenditure during migration, the larger the amount of energy that may remain available for gonad maturation. An example where migration can have important bioenergetic repercussions is migration through strong tidal systems. To reduce energy loss in such systems, fish may perform selective tidal stream transport (STST): an animal ascends into the water column with the appropriate tide and rests on or in the bottom during the opposite tide. However, the use of STST by silver European eels is still controversial. In this study, we found strong evidence that silver European eels apply STST. The results illustrate that eels can distinguish between ebb and flood and suggest that tides play a role in orientation, either directly or indirectly. The general migration speed was higher in the downstream part of the estuary compared to the upstream part, while tidal migration speed was equal in both parts, indicating that eels migrated more consistently in the downstream part. The results of this study give insight in how a diadromous species migrates through an estuary and underline the importance of the tides. This dataset was collected using infrastructure provided by VLIZ and INBO funded by the Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch. Data have been standardized to Darwin Core using the etn package and are downsampled to the first detection per hour. The original data are managed in the European Tracking Network data platform (https://lifewatch.be/etn/) and are available in Verhelst et al. (2020, https://doi.org/10.14284/434).

1. SUDOANG PROJECT

The SUDOANG project has provided common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). One of the goals of the project was to develop an eel abundance and distribution atlas in the three countries, based on the results of the implementation of Eel Density Analysis (EDA). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat.

1. SUDOANG DATABASE

Electrofishing data for Spain and Portugal were imported in the SUDOANG database (deliverable 1.2.1), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated.

The data providers (mainly SUDOE water managers, SUDOANG researchers and pilot basins from GT6: Task Group on eel stock monitoring transnational network) are listed below:

    Data Source
    Region
    Country
    Notes




    Ministry for ecological transition and the demographic challenge (MITECO)
    Spain
    Spain



    Spanish Fsih Chart (SIBIC)
    Spain
    Spain
    Data from different sources (indicated in the data) 


    Basque Water Agency (URA)
    Basque Country
    Spain



    Gipuzkoa Council
    Gipuzkoa (B.C.)
    Spain



    Navarra Council (GAN-NIK)
    Navarra
    Spain



    Asturias Council (DGPM)
    Asturias
    Spain



    Galicia Council
    Galicia
    Spain



    University of Córdoba (UCO)
    Andalucía
    Spain



    Valencian Regional Hunting and Fishing Service (GVA)
    Valencia
    Spain



    Catalan Water Agency (ACA)
    Catalonia
    Spain

ACUERDO GOV/139/2013, de 15 de octubre, por el que se aprueba el Programa de seguimiento y control del Distrito de cuenca fluvial de Catalunya para el período 2013-2018

    University of Porto (UP), CIIMAR

    Portugal





    Portugal

1. DATA DESCRIPTION

Electrofishing data is mainly based on fishing stations, operations and eel biometry.

The station level corresponds to a location, identified by coordinates (Spatial Reference System 4326). The attributes associated with stations are:

op_id: identifier [data type: UUID]

institution: data provider [data type: character]

ref_article: reference to the article from which the data originated (only for SIBIC data source) [data type: character]

articletitle: reference linked with data (only for SIBIC data source) [data type: character]

op_placename: station name [data type: character]

x_espg_4326: longitude (EPSG: 4326) [data type: numeric]

y_espg_4326: latitude (EPSG: 4326) [data type: numeric]

country: country [data type: character]

The operation level corresponds to an event occurring at a specific date. At this level, a few more details such as the method or the material used, the wetted area, and electrofished length and width are added. The total number of eels, the numbers collected at each pass, and the number of measured eels are also included. The type of sampling used could not be specified but it is mostly single or several pass surveys. Therefore, the type was set to an unknown type of fishing. All electrofishing reporting eels in the second pass were considered as full electrofishing. The attributes associated with operation are:

id: identifier [data type: UUID]

op_id: station identifier [data type: UUID]

op_gis_layername: data provider [data type: character]

data_provider: data provider [data type: character]

op_placename: station name [data type: character]

ob_id: observation identifier [data type: UUID]

ob_starting_date: period starting date

ef_wetted_area: wetted area of the station, in m2 [data type: numeric]

ef_nbpas: the number of electrofishing pass during the observation [data type: numeric]

ef_fished_length: the electrofished river length, in m [data type: numeric]

ef_fished_width: the electrofished river width, in m [data type: numeric]

ob_origin: origin of the observation, raw data [data type: character]

ob_type: type of observation, electro-fishing [data type: character]

ob_period: time step used for observation period, daily [data type: character]

ef_fishingmethod: type of method used during the scientific sampling [data type: character]

ef_electrofishing_mean: mean used to realize the scientific sampling, by foot [data type: character]

density: density of eels collected, in nb/m2 [data type: numeric]

totalnumber: total number of eels collected [data type: numeric]

nbp1: number of eels collected during the 1st pass [data type: numeric]

nbp2: number of eels collected during the 2nd pass [data type: numeric]

nbp3: number of eels collected during the 3rd pass [data type: numeric]

nb_size_measured: number of measured eels [data type: numeric]

The individual level corresponds to the biological characteristics (length and weight) of the measured eels. The attributes associated with indivial are:

dp_name: name of data provider [data type: character]

ob_id: observation identifier [data type: UUID]

bc_id: batch (eels sampled during the observation) identifier [data type: UUID]

bc_ba_id: sub-batch identifier [data type: UUID]

size: total length of eel, in mm [data type: numeric]

fish_id: individual identifier [data type: UUID]

weight: body weight of eel, in g [data type: numeric]

The three tables can be related to each other through the identifiers, meaning that the eels measured in the individual table can be identified with the operations through the ob_id identifier, and these operations can be linked to the stations through the op_id identifier, allowing for a comprehensive view of the electrofishing sampling collected for Spain and Portugal.

• Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).

1. VERSIONS

   Versions

     1.0.0
     2023-06-06
     initial upload
     Closed access
     10.5281/zenodo.8009823
   
     1.0.1
     2023-08-02
     fixed data provider in station and operation tables.
     Closed access
     10.5281/zenodo.8009823
   
     1.0.2
   
     Open access
   

2. READ MORE

Cumulated dam impact in France, Spain and Portugal (SUDOANG project) (10.5281/zenodo.7825552)

Eel data (Anguilla anguilla) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (10.5281/zenodo.7964967)

Atlas of European Eel Distribution (Anguilla anguilla) in Portugal, Spain and France (10.5281/zenodo.7546419)

Customs records of are available for EEL TEC SYSTEMS PRIVATE LIMITED. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Background: The two North Atlantic eel species, the European and the American eel, represent an ideal system in which to study parallel selection patterns due to their sister species status and the presence of ongoing gene flow. A panel of 80 coding-gene SNPs previously analyzed in American eel was used to genotype European eel individuals (glass eels) from 8 sampling locations across the species distribution. We tested for single-generation signatures of spatially varying selection in European eel by searching for elevated genetic differentiation using FST-based outlier tests and by testing for significant associations between allele frequencies and environmental variables. Results: We found signatures of possible selection at a total of 11 coding-gene SNPs. Candidate genes for local selection constituted mainly genes with a major role in metabolism as well as defense genes. Contrary to what has been found for American eel, only 2 SNPs in our study correlated with differences in temperature, which suggests that other explanatory variables may play a role. None of the genes found to be associated with explanatory variables in European eel showed any correlations with environmental factors in the previous study in American eel. Conclusions: The different signatures of selection between species could be due to distinct selective pressures associated with the much longer larval migration for European eel relative to American eel. The lack of parallel selection in North Atlantic eels could also be due to most phenotypic traits being polygenic, thus reducing the likelihood of selection acting on the same genes in both species.

Background Living anguilliform eels represent a distinct clade of elongated teleostean fishes inhabiting a wide range of habitats. Locomotion of these fishes is highly influenced by the elongated body shape, the anatomy of the vertebral column, and the corresponding soft tissues represented by the musculotendinous system. Up to now, the evolution of axial elongation in eels has been inferred from living taxa only, whereas the reconstruction of evolutionary patterns and functional ecology in extinct eels still is scarce. Rare but excellently preserved fossil eels from the Late Cretaceous and Cenozoic were investigated here to gain a better understanding of locomotory system evolution in anguilliforms and, consequently, their habitat occupations in deep time. Results The number of vertebrae in correlation with the body length separates extinct and extant anguilliforms. Even if the phylogenetic signal cannot entirely be excluded, the analyses performed here reveal a continuous shortening of the vertebral column with a simultaneous increase in vertebral numbers in conjunction with short lateral tendons throughout the order. These anatomical changes contradict previous hypotheses based on extant eels solely. Conclusions The body curvatures of extant anguilliforms are highly flexible and can be clearly distinguished from extinct species. Anatomical changes of the vertebral column and musculotendinous system through time and between extinct and extant anguilliforms correlate with changes of the body plan and swimming performance and reveal significant shifts in habitat adaptation and thus behaviour. Evolutionary changes in the skeletal system of eels established here also imply that environmental shifts were triggered by abiotic rather than biotic factors (e.g., K/P boundary mass extinction event).

This TEAMER RFTS 1 (Request for Technical Support) project supported the flume tank testing of a long range, high endurance unmanned underwater vehicle (UUV) to monitor maritime space. Today, battery-powered remotely operated vehicles (ROVs) lack the duration to make persistent, wide-area data collection possible.The proposed solution, an Electrically Engaged UnduLation (EEL) drone, can sustain missions for longer duration through hydrodynamic energy harvesting. Power is provisioned via the piezoelectric effect, a material-led phenomenon that converts applied stress into electricity. The EEL subsystems include power, propulsion, navigation, ballast, telemetry, and instrumentation. By mimicking the gait of aquatic eels, EEL can counter currents during maneuvering and level-flight. The identified opportunity is in the future capability of extreme endurance UUVs in swarms. The specific goal for the EEL development is to expand the spatio-temporal coverage of the existing ocean observation mission by overcoming significant challenges of autonomous robotics. Some of the challenges presented include novel compliant mechanism for robust actuation, bio-inspired design to emulate efficient locomotion, smart material-based energy harvesting for sustained power, and swarming architecture through enabled autonomy.

Timeseries data from 'EEL R A FERNBRIDGE CA (USGS 11479560)' (gov_usgs_nwis_11479560) _NCProperties=version=2,netcdf=4.8.1,hdf5=1.12.2 cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3 defaultDataQuery=water_surface_height_above_reference_datum_above_localstationdatum_qc_agg,water_surface_height_above_reference_datum_above_localstationdatum,water_surface_height_above_reference_datum_above_navd88_qc_agg,z,time,water_surface_height_above_reference_datum_above_navd88&time>=max(time)-3days Easternmost_Easting=-124.202833 featureType=TimeSeries geospatial_lat_max=40.615685 geospatial_lat_min=40.615685 geospatial_lat_units=degrees_north geospatial_lon_max=-124.202833 geospatial_lon_min=-124.202833 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from USGS National Water Information System (NWIS) at id=132141 infoUrl=https://sensors.ioos.us/#metadata/132141/station institution=USGS National Water Information System (NWIS) naming_authority=com.axiomdatascience Northernmost_Northing=40.615685 platform=fixed platform_name=EEL R A FERNBRIDGE CA (USGS 11479560) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://waterdata.usgs.gov/monitoring-location/11479560,, sourceUrl=https://waterdata.usgs.gov/monitoring-location/11479560 Southernmost_Northing=40.615685 standard_name_vocabulary=CF Standard Name Table v72 station_id=132141 time_coverage_end=2025-06-29T04:15:00Z time_coverage_start=2023-12-15T18:30:00Z Westernmost_Easting=-124.202833

Samples and data from the eel aggregation were collected opportunistically during three surveys that aimed primarily to map the seabed and associated benthic biodiversity on the continental slope off southern Tasmania in an area containing a cluster of small volcanic seamounts. Details of the three surveys are respectively at https://www.marine.csiro.au/data/trawler/survey_details.cfm?survey=SS200702 https://www.marine.csiro.au/data/trawler/survey_details.cfm?survey=IN2015_E02 and https://www.marine.csiro.au/data/trawler/survey_details.cfm?survey=IN2018_V06 If this data has been used in any products, please acknowledge with the following: We acknowledge the use of the CSIRO Marine National Facility (https://ror.org/01mae9353) in undertaking this research.

Timeseries data from 'MF EEL R NR DOS RIOS CA' (urn:ioos:station:gov.usgs.waterdata:11473900) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.0 defaultDataQuery=river_discharge,z,time,height_geoid_local_station_datum,water_surface_height_above_reference_datum_geoid_localstationdatum&time>=max(time)-3days Easternmost_Easting=-123.3252913 featureType=TimeSeries geospatial_lat_max=39.70626687 geospatial_lat_min=39.70626687 geospatial_lat_units=degrees_north geospatial_lon_max=-123.3252913 geospatial_lon_min=-123.3252913 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from USGS National Water Information System (NWIS) at id=17196 infoUrl=https://sensors.ioos.us/#metadata/17196/station institution=USGS National Water Information System (NWIS) naming_authority=com.axiomdatascience Northernmost_Northing=39.70626687 platform=fixed platform_name=MF EEL R NR DOS RIOS CA platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://waterdata.usgs.gov/usa/nwis/uv?site_no=11473900,, sourceUrl=https://sensors.axds.co/api/ Southernmost_Northing=39.70626687 standard_name_vocabulary=CF Standard Name Table v72 time_coverage_end=2022-04-30T09:00:00Z time_coverage_start=2015-05-05T11:30:00Z Westernmost_Easting=-123.3252913

Cell by gene matrix of the EEL mouse 440 gene experiment. Single cell data generated by EEL FISH on a saggital mouse brain section. The dataset is available in 3 file formats:

.loom Loom file see: http://loompy.org/ .h5ad AnnData file see: https://anndata.readthedocs.io/en/latest/index.html .tab Tab-delimited file.

Metadata Age - Age of animal. Codebook - Name of EEL codebook. ColorDict - Cell cluster color dictionary. Cycles - Number of barcoding cycles. Expansion - Pixels the nuceli were expanded (pixelsize = 0.27um). Expansion_um - Expansion of nuclei in micrometer. Experiment - Experiment ID. ExperimentDate - Date of experiment. FOVoverlapPercentage - Overlap between field of view. GenerationDate - Loom file generation date. LOOM_SPEC_VERSION - Loompy version. MaxHammingDist - maximum allowed Hamming distance. Operator - Experiment operator. Orientation - Cutting orientation. Probes - Probe sequences file. Protocol - Protocol used. Quality - Manual evaluation of quality. RNAfile - RNA file used Removal - Method of removal of overlapping RNA in overlapping fields of view. Sample - Sample ID Segmentation - Segmentation algorithm. Species - Species of sample. Stitching - Field of view stitching method. StitchingChannel - Between cycle alignment channel. Strain - Strain of animal. System - Microscope system used. Tissue - Tissue in experiment. TotalMolecules - Total number of molecules assigned to cells.

Column metadata X - X coordinate in pixels of 0.27um (multiply by 0.27 to get micrometer). X_um - X coordinate in micrometer. Y - Y coordinate in pixels of 0.27um (multiply by 0.27 to get micrometer). Y_um - Y coordinate in micrometer. tSNE_X - tSNE component 1. tSNE_Y - tSNE component 2. Clusters - Cluster label of each cell. TotalMolecules - Total molecules per cell. Cluster colors are saved as individual RGB values: R - Red. G - Green. B - Blue. (In the AnnData file, the tSNE and RGB values are availabe as a single array under "obsm" with keys "tSNE" and "RGB" respectively. )

Row metadata Gene - Gene name. GeneTotal - Total number of detected molecules per gene.

he file “Eel-CurrentBiology-Data.xlsx” contains data used to support the findings in Naisbett-Jones et al. (2017). The file contains two worksheets, “Orientation” and “Simulation.”The “Orientation” worksheet provides information on the magnetic displacement experiments performed with European glass eels. Experiments were performed using a coil system that could precisely control the magnetic field experienced by eels, recreating conditions that exist in specific regions along the oceanic migration route of the eels. 16 orientation arenas were placed on a platform at the center of the coil system. Upon removal of a plastic settling cylinder, eels could escape in 1 of 12 directions, spacing of 30°. Column A indicates the arena number (1-16), Column B indicates the region of the magnetic field (NW Atlantic, Mid Atlantic, Sargasso Sea, and Ambient (Test Site)), Column C indicates the escape direction of the eel (0°-330°, 0°=north, 90°=east, 180°=south, 270°=west), Column D and E indicate the date (ddmmyy) and time (h:m) that the trials begun. The eels we tested were captured in the Severn Estuary, UK. Column F and G indicate the phase of tide (ebb, flood, or slack) and height above Mean Sea Level (in meters) in Severn Estuary at the time eels were tested in Brecon, Wales. Tidal data were obtained from https://www.worldtides.info/. Tests conducted within +/- 15 minutes of high or low water marks were designated as "slack" tide.The “Simulation’ worksheet provides information on the virtual particle tracking simulations performed within the Global Hybrid Coordinate Ocean Model. Particles were released within the region of the model that corresponded to the location of the test fields. Particles were released in different years and at different depths and were programmed to either drift passively with the modelled ocean currents or to swim in the median escape direction that the eels adopted in corresponding test field. If eel orientation could not be distinguished from random, swimming was not simulated. 15000 particles were released during the month of May for each year, region, depth and behaviour and were allowed to drift for 180 days. From each region, we assessed the percentage of particles entering the Gulf Stream within this time period. For the Sargasso Sea simulations, particles were counted as entering the Gulf Stream if they crossed north of 25°N and west of 77°W. For the NW Atlantic region, particles were counted as entering the Gulf Stream if they crossed north of 40°N and east of 53°W. For the Mid Atlantic simulations, particles were already released within the Gulf Stream. Thus, values presented indicate the percentage of particles that had net eastward movement after 180 days. Column A indicates the region of particle release (Sargasso Sea, NW Atlantic, Mid Atlantic), Column B indicates the year of release (2000, 2005, 2010), Column C indicates the depth of release (30, 150, 300 m), Column D indicates the percentage of passively drifting particles that entered the Gulf Stream within 180 days, and Column E indicates the percentage of swimming particles that entered the Gulf Stream within 180 days (if applicable).Naisbett-Jones et al., A Magnetic Map Leads Juvenile European Eels to the Gulf Stream, Current Biology (2017), http://dx.doi.org/10.1016/j.cub.2017.03.015

Edge Emitting Laser (EEL) Module Market Outlook

The Edge Emitting Laser (EEL) Module market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 3.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.8% during the forecast period. The robust growth of this market can be attributed to factors such as the increasing demand for high-speed data transmission, advancements in telecommunications infrastructure, and the expanding applications of EEL modules in various industries such as healthcare and defense.

One of the significant growth factors for the EEL module market is the rising demand for high-speed internet and the ongoing evolution of 5G technology. As telecommunications companies worldwide strive to offer faster and more reliable internet services, the need for efficient and high-performance EEL modules has surged. These modules are critical in ensuring seamless data transmission over long distances, thereby bolstering the telecommunications sector's infrastructure. The integration of EEL modules into fiber optic communication systems enhances bandwidth capabilities, contributing to the overall growth of the market.

Another driving factor is the burgeoning data center industry. With the exponential growth of cloud computing, artificial intelligence, and data analytics, data centers are under tremendous pressure to increase their capacity and efficiency. EEL modules play a pivotal role in data centers by facilitating high-speed data transfer and minimizing latency. The rising investments in data center expansion and the adoption of advanced technologies are expected to fuel the demand for EEL modules. Additionally, the focus on energy-efficient solutions in data centers further propels the market growth, as EEL modules are known for their high efficiency and low power consumption.

The healthcare sector also presents significant opportunities for the EEL module market. The adoption of EEL modules in medical devices and equipment, such as surgical lasers and diagnostic tools, is on the rise. These modules offer precise and controlled laser emissions, making them ideal for various medical applications. The growing emphasis on minimally invasive surgeries and advanced diagnostic techniques drives the demand for EEL modules in the healthcare industry. Furthermore, the increasing prevalence of chronic diseases and the aging population contribute to the market's growth as healthcare providers seek innovative solutions to improve patient care.

Regionally, Asia Pacific is expected to dominate the EEL module market during the forecast period. The region's rapid industrialization, expanding telecommunications sector, and significant investments in infrastructure development are key factors driving market growth. North America and Europe are also anticipated to witness substantial growth, driven by advancements in technology, increased adoption in the healthcare and defense sectors, and the presence of major market players. The Middle East & Africa and Latin America are projected to experience moderate growth, supported by ongoing developments in telecommunications and industrial sectors.

Type Analysis

The EEL module market is segmented by type into single-mode and multi-mode modules. Single-mode EEL modules are renowned for their ability to transmit data over long distances with minimal signal loss. This makes them ideal for applications requiring high precision and reliability, such as telecommunications and data centers. The demand for single-mode EEL modules is expected to rise significantly, driven by the ongoing expansion of fiber optic networks and the increasing adoption of 5G technology. These modules offer superior performance and efficiency, making them a preferred choice for high-speed data transmission.

On the other hand, multi-mode EEL modules are designed for shorter distance data transmission and are commonly used in applications where data integrity over vast distances is not a primary concern. These modules find extensive use in local area networks (LANs) and intra-building communications. As organizations continue to upgrade their internal communication systems to support higher data rates and enhanced connectivity, the demand for multi-mode EEL modules is expected to grow. Additionally, the cost-effectiveness and ease of installation of multi-mode EEL modules further contribute to their widespread adoption in various industries.

In the industrial sector, both single-mode and multi-mode EEL modules are utilized for various application

Timeseries data from 'EEL RIVER AT RT 3A NEAR PLYMOUTH, MA (USGS 01105876)' (gov_usgs_nwis_01105876) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=water_surface_height_above_reference_datum_above_localstationdatum_qc_agg,water_surface_height_above_reference_datum_above_localstationdatum,z,time&time>=max(time)-3days Easternmost_Easting=-70.622534 featureType=TimeSeries geospatial_lat_max=41.94177 geospatial_lat_min=41.94177 geospatial_lat_units=degrees_north geospatial_lon_max=-70.622534 geospatial_lon_min=-70.622534 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from USGS National Water Information System (NWIS) at id=132690 infoUrl=https://sensors.ioos.us/#metadata/132690/station institution=USGS National Water Information System (NWIS) naming_authority=com.axiomdatascience Northernmost_Northing=41.94177 platform=fixed platform_name=EEL RIVER AT RT 3A NEAR PLYMOUTH, MA (USGS 01105876) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://waterdata.usgs.gov/monitoring-location/01105876,, sourceUrl=https://waterdata.usgs.gov/monitoring-location/01105876 Southernmost_Northing=41.94177 standard_name_vocabulary=CF Standard Name Table v72 station_id=132690 time_coverage_end=2025-06-26T12:00:00Z time_coverage_start=2023-12-15T03:00:00Z Westernmost_Easting=-70.622534

This data set was acquired with a ship-based Navigation system during R/V Wecoma expedition W9605B conducted in 1996 (Chief Scientist: Dr. Craig Fulthorpe). These data files are of ASCII MGDS:Nav format and include Primary Navigation data and were processed after data collection.

This data set was acquired with the LDEO Portable HiRes Multi-Channel Seismic system during R/V Wecoma expedition W9605B conducted in 1996 (Chief Scientist: Dr. Craig Fulthorpe; Investigator: Dr. Gregory Mountain). These data files are of ASCII format and include Seismic Shot Point Navigation data that provide the primary seismic navigation source for this cruise.

This dataset maps fishing locations of American eels on the east coast of Canada, derived from logbook records, interviews with fisheries officers, and data collected by government agencies. Fishing locations indicate presence of eels because fishers would not fish at these locations if eels were absence. However, absence of records does not indicate absence of eels. There may be no eel fishery in a given area for several reasons, including regulatory prohibitions, lack of markets, and lack of a local eel fishing tradition.

1. Long-term perspectives are critical to understand contemporary ecological systems. However, historical data on the distribution of biodiversity have only rarely been used in applied environmental sciences. 2. Here, we use historical sources to reconstruct the historical range of the European eel, a critically endangered species. We then use this baseline range to set range targets for the recovery of the European eel, as opposed to the abundance-based targets established by the European Union, which are constrained by the poor information on pre-collapse stocks. 3. We collected over 10 000 historical freshwater fish records from Spain in the 19th and 16th centuries, as well as over 25 000 records from the global biodiversity information facility (GBIF) to characterize historical and current European eel distribution in the Iberian Peninsula. We converted fish records into an eel presence–absence data set using subcatchment as spatial unit of analysis and modelled eel distribution in the different historical periods. 4. The eel was historically widespread throughout the Iberian Peninsula, but it has lost over 80% of its original range, mainly due to river fragmentation by dams. Distribution models applied to 16th- and 19th-century data showed a high agreement, supporting the use of the 19th-century estimated distribution as a baseline range. We identified the number and identity of dams that should be made passable for accomplishing specific range recovery targets, for example showing that acting upon 20 dams would make available 60% of the baseline eel range. 5. Synthesis and applications. This work exemplifies how insights gained from historical ecology can support and guide present-day management of migratory fishes. Similar approaches could be developed throughout Europe to plan the recovery of the eel, since there are large amounts of historical eel records. Historical baseline ranges for the eel range should be incorporated into the European Union legal mandates aimed at the recovery of the species.

Edge Emitting Lasers (EEL) Market Outlook

The global market size for Edge Emitting Lasers (EEL) was valued at approximately USD 2.1 billion in 2023, and it is projected to reach USD 5.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. The growth of this market is driven by increasing demand for high-performance lasers in various applications such as telecommunications, industrial, medical, defense, and consumer electronics.

The rising adoption of EELs in telecommunications is one of the primary growth factors for this market. With the rapid development and deployment of 5G technology, the demand for efficient and reliable laser components has surged. EELs play a crucial role in the optical communication systems used in 5G networks. Their ability to offer high-speed data transmission and low-cost implementation makes them indispensable in modern telecommunications infrastructure. Furthermore, advancements in fiber optics technology have underscored the need for sophisticated EELs, further bolstering market growth.

The industrial sector is another major driver of the EEL market. Edge emitting lasers are extensively used in material processing applications such as cutting, welding, and engraving. The shift towards automation and precision manufacturing has amplified the need for reliable laser systems. EELs, with their high efficiency and superior beam quality, are being increasingly adopted in various industrial processes. The growing emphasis on enhancing production efficiency and reducing operational costs has led to widespread adoption of EELs in industries across the globe.

The medical field also presents significant opportunities for the EEL market. Edge emitting lasers are utilized in various medical applications, including surgical procedures, diagnostics, and therapeutic treatments. The precision and controlled output of EELs make them suitable for delicate medical operations. The increasing prevalence of chronic diseases and the growing demand for minimally invasive procedures have propelled the adoption of laser technologies in healthcare. Additionally, technological advancements in laser medicine and the increasing investment in healthcare infrastructure globally are expected to drive market growth.

Regionally, North America and Europe are anticipated to be significant contributors to the EEL market growth. These regions have well-established telecommunications and industrial sectors, coupled with advanced healthcare infrastructure. The presence of key market players and continuous technological innovations further augment the market in these regions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid industrialization, burgeoning telecommunications sector, and increasing healthcare investments in countries like China, Japan, and India are key factors driving the market in this region.

Type Analysis

Edge Emitting Lasers (EEL) can be broadly classified into two types: single-mode and multi-mode. Single-mode EELs are known for their ability to emit light in a single spatial mode, which allows for high beam quality and coherence. These lasers are predominantly used in applications where precision and beam quality are crucial, such as in high-speed communication systems and certain medical procedures. The demand for single-mode EELs is expected to grow significantly due to their superior performance characteristics, especially in telecommunications where they support high bandwidth and long-distance data transmission.

Multi-mode EELs, on the other hand, emit light in multiple spatial modes, resulting in higher output power compared to single-mode lasers. These lasers are widely utilized in industrial applications such as material processing, including cutting, welding, and marking. The robustness and high power output of multi-mode EELs make them ideal for heavy-duty industrial tasks. With the ongoing industrial automation and the need for efficient manufacturing processes, the market for multi-mode EELs is anticipated to experience substantial growth. The versatility offered by these lasers makes them suitable for a broad range of applications, thereby enhancing their market demand.

The technological advancements in EEL designs have also opened new avenues for their application. Innovations such as wavelength stabilization and beam shaping have significantly improved the performance and reliability of both single-mode and multi-mode EELs. These advancements are particularly beneficial in precision-d