dataesr/scientific-paragraphs-categorization huggingface dataset:
Before this, it needs to be noted that in the same way a tag can go undetected despite being within detection range, false-positive detections can also happen. These occur when a receiver detects a signal with a tag ID code that was generated from the collision of two or more tags and their frequency, among other reasons, depends on the number of tags simultaneously within range of the receiver  [180]. A false detection can produce a code tag ID that is either different or identical to a code tag ID in the study. Cases, where there is no match with a deployed transmitter, are straightforward to detect and exclude, yet these might correspond to a tag of a different study. False code tag IDs identical to one of the tags deployed for the study are harder to detect. False detections do not occur frequently  [180] , however, ways of filtering them out exist.
Land is grey and water is dark blue 30-min COAs obtained from the Dasyatis pastinaca sample data set that will be used in the paper. Hedger et al. [96]  evaluated the local polynomial regression or Freidman's SuperSmoother  [81]  as a nonparametric alternative to the weighted mean COA. An advanced method of calculating COA has been developed by  Winton et al. [205]  using the Bayesian spatial point process (SPP) model that also allows accounting for the variation of detection probability over time, yet the authors acknowledge that the computational expense of the method may make it inaccessible to many users. Detections of a tagged animal are viewed as samples of an underlying spatial process that depends on (therefore is biased by) the observation process, which is composed of the position and detection range of the receivers used to detect the tags.
Receiver-and time-related variations in detection probability can be included in the SPP model, by integrating data from stationary test tags in the study area. Including this information improves the model, as it can reveal otherwise overlooked fine-scale movements. These characteristics make it a more computationally demanding process than the mean weighted COA, however, it is comparatively less biased, especially when including data on variation in detection probability. Detections that are only recorded by peripheral receivers in the array (i. e. , from individuals present in the area but not within the array) are also used, and lower errors are obtained for position estimations in these areas. COAs can be estimated with the SPP model using the R package TelemetrySpace  [205]. An important difference between this method of estimating pseudo-positions and the triangulation-based methods in the following section is the area where a calculated pseudo-position/position can be found.
However, it must be noted that frequencies above 100 kHz are greatly attenuated by salt water  [3] , which affects transmitter efficiency. Vemco/ Innovasea developed the software Vemco user environment (VUE), 1 which centralized many tasks. Among these, it is used to collect, organize, and visualize the data, also allowing to run some initial analyses. VUE is also used for tasks like receiver clock synchronization and memory clearing. From here, data sets can be exported to be used in other programs.
Error sensitivity in the calculation of positions (horizontal dilution of precision, HDOP) is estimated by Thelma (as Innovasea's HPE). This is done by placing a grid of points around the receiver array, for each of which the signal travel time to reach each receiver is calculated. Then a random time error is added to each position, yielding simulated travel times which are replicated several times with a new random error for every grid point. The deviation between these calculated positions and the known position is then referred to as HDOP. Higher HDOP values indicate higher error sensitivity  [140]. Thelma also developed the software ComPort 3  to upload the data, visualize it in several ways, and do preliminary explorations. This software can also be used to configure receivers and manage the data in an SQLite database, allowing to clean and filter it, for example, to remove false detections before analysing it with software such as R.
Open-source positioning systems Lotek code division multiple access The Lotek MAP acoustic telemetry system is based on the code division multiple access (CDMA) technology, used to enhance GPS precision and to provide many users simultaneous use of a cellular network also used in acoustic telemetry  [43]. Central receivers are used to monitor, clock-synchronize, and store the information of additional hydrophones. CDMA systems also achieve higher sampling rates (faster pulse bursts), better code discrimination in cases of high noise environments, multipath signals (detections that did not travel in a straight line between tag and receiver) or overlapping signals, and lower signal-to-noise ratio threshold, meaning signal power does not need to be much stronger than noise power. Moreover, signal output appears to be less affected by distance compared to other tags  [17].
Yet another positioning solver Many manufacturers obtain estimate positions and their associated errors using methods that are commonly unknown to the researchers, resulting in lower control over the analysis process and hindered comparability across studies  [10, 11]. Yet another positioning solver, or YAPS  [11] , is one of the newest fine-scale positioning estimators and uses time of arrival calculations to position tangs in a receiver grid. The implementation seeks to maximize the utilization of data in a transparent and open source way that applies to all acoustic telemetry brands and indeed to any acoustic signal that can be multilateralized among stations  [10, 11]. An associated R package, yaps  [10] , is available on CRAN and in ongoing development on GitHub. 4 The workhorse of YAPS is the synchronization model, which relies on the user to create a list of data frames including the receiver locations with corresponding sync tags (sync tags are assumed to be attached to each receiver) and sync tag detection data.
The synchronization tag detections within the network will determine the suitability of the design for triangulating animal positions in the next step. The sync model residuals can be evaluated by the user and tuned to optimize performance. The synchronization model is then applied to the remainder of the data such that receivers are operating on the same clock and times of arrival at receivers are exact. Once the receivers are synchronized, the analyst can generate time of arrival estimates and fit the yaps function to the data, typically in chunks separated into one individual per day. Instead of using time-differences of arrival (TDOA) to obtain position estimates as other position estimators (e. g. , VPS), YAPS directly uses each location's time of arrival (TOA) at each receiver, which allows taking fuller advantage of the data.
Characteristic hull polygons The characteristic hull polygons (CHPs) method  [52]  is a hull construction approximation based on the characteristic shapes algorithm  [57]. CHPs are obtained by creating triangles by Delaunay triangulation of neighbouring points, which favours the construction of more regularly shaped triangles. Home range estimates are obtained by removing the triangles with the largest perimeters and retraining the 95% of the smallest triangles. Other features like area can be used as a sorting criterion, but perimeter allows to eliminate the slenderer triangles with exceptionally acute angles that normally form at the boundaries of the used area. An MCP equivalent is obtained if no triangles are removed  [52]. Point distribution of the data is better represented using Delauney triangles than with MCPs, as the former method can build non-convex hulls and more complex shapes  [52, 57]  (Fig.
3b. ). CHPs can also have "holes" and be composed of disjoint polygons, which can accommodate distribution patterns of animals that avoid certain areas. CHP are fairly robust to sample size variations and to inhomogeneous point distributions  [51]  and do not overestimate areas of use like MCP  [52, 148]. However, they perform relatively worse when home ranges have a concave, or convex shape compared to linear, disjoint or perforated, but this could be related to the process of removing triangles  [52]. Despite its advantages, CHP are not as studied and frequently used as MCP  [52, 165]. The Delaunay triangulation required for producing CHP can be implemented in many GIS softwares  [52, 57] , such as standard functions in ArcGIS 5 (ESRI, Inc. , requires subscription purchase) e. g. ,  [51, 148]  and also QGIS 6 (free and open-source).
This way the importance of the areas an individual visits is appropriately levelled by upweighting under-sampled areas and downweighting over-sampled areas  [77]. Additionally, similar to Fleming and Calabrese,  [72] , this approximation improves home range estimations from evenly sampled but small effective sample size data  [77]. AKDE is included in the R package continuous-time movement modeling (ctmm)  [29]  and in the point-and-click graphical interface ctmmweb  [30]. Guidelines for the use of these estimators and the correction of the biases here described are compiled into a document that includes an R script  [178]. 7   State-space models Essentially, a state-space model (SSM) analyses movement data and integrates error correction, the calculation of metrics, and statistical analysis by combining (1) an observational model to statistically describe the sampling process and (2) a process or movement model, which relates to the description of the dynamics of movement in space and time.
As a function of distance, at the time of successful detection, the likelihood of an animal's position is higher closer to the receiver that detected it. The detection function also incorporates positional information on undetected individuals, assuming they are more likely to be found in areas farther from the receiver  [156]. Changes in detection probability over time produced by environmental interference, for example, day/night cycle, seasons, or temperature, can also be integrated into 7 https:// ecois ilva. github. io/ AKDE_ minir eview/ code/ AKDE_R-tutor ial. html. the model by collecting reference data at the study site. Not accounting for this can lead to misinterpretation of results (for example unaccounted diel change in detection range can be wrongly interpreted as the animal leaving during what is a period of reduced detection capability), however, this is not straightforward to incorporate  [156].
Positions are estimated by considering the previous position and the following movement parameters: position of the home range center at a given time (r H ), exploration rate of the home range in min -1 (k), and size of the circular home range (radius, r). The Bayesian SSM is computationally more demanding than frequentist SSMs, however, the R package Template Model Builder  [4] , fits SSM models to movement data and can compensate for this  [5]. Additionally, the supporting information of the manuscript includes code that can be used in R to estimate movement parameters (home range behaviour) and positions with acoustic tracking data  [5]. Brownian bridge movement model Brownian bridge movement model (BBMM, Fig. 3f. ) interprets data as a collection of consecutive, known locations and models a time-structured path to reconstruct the expected track the animal traversed  [24, 102].
e. , of size Tmax). Therefore, the longer the time between an interpolated and a recorded position, the higher h or uncertainty of the position. Habitat-specific diffusion coefficients (D H ) can be identified by calculating the diffusion coefficient of all track segments in the same habitat type. This can be included in the MKDE by assigning habitat-specific h max and allowing to differentiate, for example, preferred habitat types from others the tracked animal traverses faster. The MKDE also addresses the boundary biases typically found in traditional kernel density estimators, by modeling boundaries as contiguous straight segments and correcting them via boundary-based coordinate transformations  [15]. BRB is available in the R package adehabitatHR  [31]  (Fig. 3g ).
Despite the applicability of NA to the study of space use using acoustic telemetry, not only in ecology but also in management and conservation  [82, 105, 106] , its use has been scarce compared to traditional home range estimators  [121]. Network analysis can be conducted in the R environment with the R packages sna  [27] , igraph  [45] , and tnet  [149]. Similarly, advances to facilitate the inclusion of time in NA have been proposed, by providing guidelines to analyse the temporal succession of network motif patterns  [152]  or by applying a moving window approach  [21]  using the bespoke R package netTS. 8  As mentioned before, neighbouring receivers with overlapping detection ranges are often used to calculate positions/pseudo-positions for space use estimation. However, for spatial NA generally nonoverlapping detection ranges are used, e.
g. , as in  [105, 121]. Simultaneous (i. e. , "duplicate") detections at adjacent receivers are avoided with this configuration, as this can impact the centrality metrics. This presents a challenge when the goal is to both study space use and use NA using the same acoustic array. Two ways to work around this can be followed: either to modify the data to eliminate the overlap, or to create occupation pixels based on calculated positions/pseudo-positions before NA  ([13, 152. To eliminate the overlap, the 8 netTS: https:// github. com/ tbonne/ netTS. receivers connected by overlapping detection ranges can be combined into one node, e. g. , as Stehfest et al. [185]  did when creating receiver "curtains" to cover a passage. Alternatively, a subset of receivers can be selected, e. g. , as done by Papastamatiou et al.
The most common method of estimating space use volume is a three-dimensional version of KUD (3D-KUD) available in the R-package ks  [58]. To build a 3D-KUD, first, a three-dimensional cube is created and gridded. This results in a cube in space that contains a user-defined number of threedimensional pixels known as voxels. A density estimate is then calculated for each voxel, for which a bandwidth value is selected. Utilization distributions can then be visualized at different isopleths, like 50% 3D-KUD and 95% 3D-KUD. As in two-dimensional KUD estimation, under-or oversmoothing are consequences that can arise from inappropriate bandwidth selection  [41]. Sample code to estimate 50% and 95% 3D-KUDs is available in the Appendix of  [181]  and  [41]. Considering that marine species live and move in three dimensions, this approach can be a better fit which offers more ways to explore and analyse the data  [122, 193].
Discussion Technological advances and the increased complexity and refinement of analytical methods have provided unprecedented insight into the study of animal movement in aquatic environments using acoustic telemetry  [103]. However, quantifying a complex and continuous phenomenon is a challenging task. In the study of animal movement, a common objective is to estimate space use, for which several methods exist, some of which have been described here. These methods are commonly analysed using the R software  [166] , a free, open-source software in which research can be conducted in a reproducible way. It runs on all common operating systems and has a growing community constantly developing and updating bespoke code packages (Table  1 ). Further reading can be found in the review by Joo et al.
The performance of estimators can be compared, for example, using the standardised workflow in the amt R package by Signer & Fieberg  [175]  that compares some of the most commonly used home range estimators (MCP, LoCoH, KDE and AKDE). The most traditional estimators such as MCP and KUD are commonly favoured  [120]  over newer, likely bettersuited ones for a variety of reasons  [175]. The higher familiarity and more frequent use of the former might convey a greater sense of confidence and preclude from considering others. For most researchers, these reasons might at least presently exceed the analytical advantages offered by newer methods, which might appear too complex and of higher operational costs. For example, the wide application of SSMs in ecology had been impeded by computational limitations until some time ago  [153] , while three-dimensional methods, despite offering a more realistic estimation of the movement of animals  [181] , have their more extensive application hindered by the higher implementation costs and required analytical power.
However, several of the newer, more complex methods offer guidelines and even R code to analyse data, for example for AKDEs  [178] , B-SSM  [5] , dBBMM 9,   10   , and 3D-KUDs  [41, 181]. One of the most important factors affecting an estimator's efficiency is its compatibility with the question(s) driving the study and deciding on a method should be done with this in consideration. A table of key steps and considerations was provided by Fieberg & Börger,  [69] , as the properties of an estimation method can help highlight the aspects of the data that are of interest. For example, SSMs  [153]  and the Brownian bridge-based methods BRB and dBBMM are useful to explore behavioural changes in movement  [176, 177]. Brownian bridge-based methods allow for studying changes in movement patterns and space use over different timescales (diel, seasons) or across different habitats (to forage, travel, rest)  [90, 109, 136].
In such scenarios, the use of occurrence estimators (i. e. , to answer the question "where has the animal been during the tracking period?") might better fit the data  [76]. If the extent of the animal's home range is effectively covered, whether the estimator operates under the assumption of IID or autocorrelation is important to consider  [120, 146, 173]. Independent data points are obtained by sampling at intervals that are large enough to ensure they are not autocorrelated, which is habitually over timescales greater than the time an individual needs to cross its linear home range. The number of such locations in a data set is referred to as effective sample size. Larger effective sample sizes are preferred, so the study duration should ideally span for much longer than the home range crossing time  [72, 146].
This differs from (and is sometimes confounded with) absolute sample size, which is the total number of locations in a data set regardless of the sampling frequency  [72, 146]. The time of statistical independence (time interval after which two subsequent locations are statistically independent)  [187]  can be tested, for example, with the R package rhr  [173]. However, autocorrelation is an intrinsic property of animal movement  [72, 146]  and most studies do not include it  [120]. Moreover, the assumption of IID is incompatible with (most) movement data sets, because autocorrelation intensifies with an increasing sampling frequency  [48, 187] , which is especially relevant when working with acoustic telemetry data  [103]. Modern acoustic telemetry techniques allow the collection of data at very high frequencies, so the number of locations corresponds to the absolute sample size  [72, 146].
The former methods correct biases that are frequently encountered when working with autocorrelated movement data, like failing to consider autocorrelation, small effective sample sizes, and missing or irregularly sampled data, producing comparatively unbiased home range estimates relative to traditional methods  [146]. Since restrictions to movement are not explicitly included in the estimation process, AKDE can still present drawbacks such as positive bias from spilling over boundaries, but corrections can be made to address this  [146, 178]. AKDEs have also been improved in userfriendliness, as they were compiled into one publication with guidelines and an R script to facilitate their use  [178]. Furthermore, in addition to the R package ctmm  [29] , a point-and-click graphical interface is available and can be installed using the R console or used directly through a website  [30].
a. GitHub (https:// rdrr. io/ github/ aspil laga/ fisht rack3d/) b. GitLab (https:// gitlab. ocean track. org/ Great Lakes/ glato s/-/ wikis/ insta llati on-instr uctio ns) c. Removed from the CRAN repository and archived: https:// cran. r-proje ct. org/ src/ contr ib/ Archi ve/ mkde/ d. GitHub (https:// github. com/ tbonne/ netTS) e. R-Forge (https:// rdrr. io/ rforge/ rhr/) f. GitHub (https:// rdrr. io/ github/ YuriN iella/ RSP/) g. R-Forge (https:// rdrr. io/ rforge/ tlocoh/) h. GitHub (development version: https:// github. com/ RossD wyer/ VTrack).
Vignettes in https:// CRAN. R-proje ct. org/ packa ge= survi val 10. https:// vinay udyaw er. github. io/ ATT/ docs/ ATT_ Vigne tte. html i. GitHub (https:// github. com/ jedal ong/ wildl ifeTG) 1. https:// cran. r-proje ct. org/ web/ packa ges/ adeha bitat HR/ vigne ttes/ adeha bitat HR. pdf 2. typing "browseVignettes("actel")" in R / https:// CRAN. R-proje ct. org/ packa ge= actel 3. Signer & Fieberg [175] 4. https:// github. com/ aspil laga/ fisht rack3d/ blob/ master/ vigne ttes/ model ling_ ud3d. Rmd 5. https:// ecois ilva. github. io/ AKDE_ minir eview/ code/ AKDE_R-tutor ial. html 6. https:// cran. r-proje ct. org/ web/ packa ges/ move/ vigne ttes/ move. html 7. http:// www. spamw ell. net/ index. php 8. typing "browseVignettes("RSP")" in R/http:// 127. 0. 0. 1: 24834/ sessi on/ Rvig.
Italian System Implementation When discussing integration flows in the context of the Pharaon project we are referring to flows of data between technologies from different partners that were used for the implementation of certain scenarios. The socialization scenario was realized end-to-end by using technology from a single technology provider: Sentab. Sentab (Figure  4a ) uses interoperable Native Client Applications across Android apps (targeting both the Android TV interface on the Android TV box, but also native Android apps for tablet and phone usage scenarios), iOS, and web applications. The backend solution is based on Enterprise Java on Jetty, open source RabbitMQ, and Redis dockers. Information is stored in.
Italian System Implementation When discussing integration flows in the context of the Pharaon project we are referring to flows of data between technologies from different partners that were used for the implementation of certain scenarios. The socialization scenario was realized end-to-end by using technology from a single technology provider: Sentab. Sentab (Figure  4a ) uses interoperable Native Client Applications across Android apps (targeting both the Android TV interface on the Android TV box, but also native Android apps for tablet and phone usage scenarios), iOS, and web applications. The backend solution is based on Enterprise Java on Jetty, open source RabbitMQ, and Redis dockers. Information is stored in a MYSQL database on Ubuntu servers. For WebRTC video calling, the solution uses Open-Source C Language Project in the TURN server, NodeJS for the signaling server, and Nginx for the reverse proxy server.
The Content Delivery Network (CDN) is built on Amazon CloudFront and S3. The public services can be accessed via REST (REpresentational State Transfer) APIs using JSON data interchange formats. APIs are secured by JWT (JSON Web Tokens) and TLS (Transport Layer Security). The monitoring scenario, on the other hand, was realized by the integration of technologies from several different technology providers; namely IoTool, SmartHabits Platform, and Discovery. The high-level sensor data flow between different technologies in the monitoring scenario is presented in Figure  5. The monitoring scenario, on the other hand, was realized by the integration of technologies from several different technology providers; namely IoTool, SmartHabits Platform, and Discovery. The high-level sensor data flow between different technologies in the monitoring scenario is presented in Figure  5.