dataesr/scientific-paragraphs-categorization huggingface dataset:
For the joining of non-removable plastic parts, methods such as friction welding, friction-stir welding, ultra-sonic welding, chemical joining, and hot-plate welding are used. Rotary friction welding is generally used for joining the filter parts of water treatment devices. After the rotating friction welding of the filter parts, semi-melted agglomerations are formed in the inner parts. To prevent semi-melted accumulation, a weld joint profile design was developed in the ABAQUS software in a previous study. In this study, injection moulds had been produced according to weld joint profile design. The taken plastic presses were welded with the friction welding machine and the joint was controlled and compared. It is seen that the semi-melted agglomeration was successfully confined. By performing Taguchi experimental method and ANOVA analysis on friction welding machine parameters, the optimum pressure conditions were determined in order to carry out static and dynamic tests according to NSF standards and field tests by producing 1500 pilot products, and it has been put into use.
The weld joint profile structure was developed using the ABAQUS program to contain semimelt accumulation. After the welding joint profile structure was developed, the plastic volume mould was revised, and prints were taken on the injection machine. Static and dynamic tests were carried out in accordance with National Sanitation Foundation (NSF) standards to measure the strength of the welded joint profile structure. Then, the Taguchi experimental method was applied to the parameters of the welding machine, and the optimum parameter values were determined. The field test was carried out by making pilot production according to the optimum parameter values determined.
Fig. 3c  shows the semi-melt agglomeration result of the ABAQUS analysis. It is seen that the semi-melted accumulation state indicated by the red arrow in Fig. 3  is similar to the structure formed on the piece and the structure made after the analysis. In the ABAQUS software, three different weld designs were made to contain the semi-melted accumulation, and the weld joint profile design was developed by making simulations of these weld joint profile designs. Fig. 4a  shows the vectorial drawing of design no. 3 weld joint profile design. Fig. 4b  shows the design developed weld joint profile design no. 3 and the semi-melted accumulation simulation made in the ABAQUS program  [10]. As a result of the analysis of the semi-melted accumulation, it is seen that the melt accumulation is successfully confined to the region indicated by the red arrow in Fig.
Determination of Design Parameters The effects of FW machine values such as rotation speed, friction welding pressure, friction welding time and waiting time on joint quality will be analysed. Minitab 15  [19]  statistical software has been used to explain the effects of welding parameters and Taguchi experimental method analyses processes. The Taguchi method finds a solution by combining the three tools orthogonal experimental design, rate of signal/noise (S/N), and analysis of variance (ANOVA) to analyse and evaluate the numeric data  [20]  and  [21]. In Table  2 , the FW parameters and levels are shown. From these factors, the rotation speed level values were taken to be close to each other. If the speed is selected higher, it will cause the material to burn due to excessive friction in the weld area or the material structure will turn into an amorphous structure, causing an increase in brittleness.
S N n y i n i / l og. § © ¨• ¹ ¸ ¦ 10 1 1 1 2 (1) In Eq. (  1 ), y i is the observed data at the i th experiment and n is the number of experiments. For each test determined in accordance with Taguchi experimental method, which is in Table  4 , three FW processes and for each part the static pressure test has been made at arbor press. For each test number, the average pressure test result made before has been acquired and the S/N value is calculated according to the average value. In Table  4 , the strength test results and the S/N values calculated for Minitab 15 software are shown. In Fig. 9 , the kit of arbor press where the static pressure test has been tested, the maximum value of 14/1 numbered combination's durability and the breaking point of the kit after the pressure test has been shown.
RESULT & DISCUSSION In inline filters used from water purification devices, semi-melted accumulations occur in the inner and outer parts after friction welding. The semimelted accumulations formed in the interior cause clogging, a decrease in flow rate and clogging after the narrowing of the surface area in the activated carbon felt. Schmicker et al. [9]  performed friction welding simulations of metal parts by rotating them in the software program they developed. To prevent metal semi-melted accumulation in the interior, a joint profile design has been developed and simulations have been carried out. To prevent metal semi-melted accumulation in the interior, a joint design has been developed and simulations have been practiced. A finite element analysis method (ABAQUS) of the weld joint profile design has been developed to solve this problem and confine the semi-melted agglomeration in the interior.
Innovation: We developed R functions to detect the effect of these sources of variability in the distribution of animal groups when inferred from individual tracking data. These procedures can be adapted for their use in most tracking datasets and tracking techniques. We demonstrated our procedures with simulated datasets and showed their applicability on a real-world dataset containing 1346 year-round migratory trips from 805 individuals of three closely related seabird species breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years. We detected an effect of ISF in one of the colonies, but no effect of the environmental stochasticity on the distribution of birds for any of the species. We also identified among-colony variability in nonbreeding space use for one species, with significant effects of population size and longitude.
Main conclusions: This work provides a useful, much-needed tool for researchers using animal tracking data to model species distributions or establish conservation measures. This methodology may be applied in studies using individual tracking data to accurately infer the distribution of a population or species and support the delineation of important areas for conservation based on tracking data. This step, designed to precede any analysis, has become increasingly relevant with the proliferation of studies using large tracking datasets that has accompanied the globalization process in science driving collaborations and tracking data sharing initiatives.
In this work, we aim to provide a set of tools to understand the effects that (a) ISF, (b) environmental stochasticity over time and (c) the geographic extent of sampling effort, have on the distributions of mobile species tracked using animal-borne devices, regardless of tracking method, or habitat and characteristics of the study species, and to demonstrate their applicability to a real set of tracking data. To do so, we first tested the performance of the method with artificial datasets simulating different degrees of ISF, as well as different sampling regimes and efforts, and then demonstrated its applicability to real data by collating, for the first time, a dataset of 1346 year-round trips from 805 individuals of three Calonectris shearwater species breeding in 34 colonies. This constitutes a robust and diverse dataset and provides a relevant example to demonstrate the applicability of the method.
| ME THODS We describe here the procedures developed to detect instances in which the methodology used to obtain population-or species-level space-use measures from individual tracking data can introduce biases. In particular, we aim to test the effects of ISF and temporal and 14724642, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/ddi. 13642 by Readcube (Labtiva Inc. ), Wiley Online Library on  [21/11/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License spatial variation in a tracking dataset, applicable to most sets of animal tracking data, as long as it can be split into discrete bouts such as foraging trips, migratory cycles, or even days, weeks or years (bouts, hereafter).
We first demonstrate the efficiency of the procedures in simulated datasets and then illustrate the use of these methods using the nonbreeding distributions of Calonectris shearwaters tracked with Global Location Sensors (GLS,  Wilson et al. , 1992 ). In our worked example, the bouts were clearly defined as a migratory trip. In other types of data and species, bouts might be less evident and need to be carefully decided based on the research question and spatial ecology of the study species.
| Testing for ISF To detect the bias caused by individuals with larger sample sizes preferring certain areas, we propose a method that consists of obtaining an estimate of space use for each bout and then comparing the similarities of bouts from the same individual to those of bouts from different individuals. If ISF exists within the sample being evaluated, space use of bouts from the same individual should be, on average, more similar among them than that of bouts of different individuals of the same sample. We estimate the space use of each individual with the autocorrelated kernel density estimate method (AKDE;  Fleming & Calabrese, 2017)  provided by the package "ctmm"  (Fleming & Calabrese, 2021). This method models the movement of the animal, from the relocations obtained with the tracking device, as a continuous-time stochastic process, which separates the discrete observation process (a relocation every few minutes or hours depending on the device) from the continuous-time process which is the movement of the animal (the animal has continued to move between relocations, even if our observation process does not have information on where the animal has been in that time).
From these models, an unbiased measure of space use can be estimated that considers the existing autocorrelation in tracking data with high spatiotemporal resolution  (Fleming et al. , 2015). We developed a function, indEffectTest, in the R programming language, that computes, for every possible pair of bouts regardless of the individual that performs them, the similarity (i. e. spatial overlap) between their utilization distributions (UD). To assess this similarity, we used the Bhattacharyya affinity (BA), which is based on the comparison of two UD (p 1 and p 2 , evaluated at locations determined by x and y) to their joint distribution, and ranges from 0 (completely disjunct UD) to 1 (identical UD): In the BA version provided in the overlap function from the "ctmm" package, the calculation of the BA is corrected for small sample bias  (Winner et al.
, 2018)   To demonstrate the function's ability to detect the effect of ISF, we performed practical examples by generating datasets that simulated 10 different degrees of fidelity (one dataset for each scenario). To simulate ISF, we generated the bouts over a resistance surface with a "hole" of null resistance inside. We set high resistance outside the "hole" to simulate maximum levels of fidelity and to simulate sequentially decreasing levels of fidelity, we sequentially decreased the value of resistance. BA = ∫ +∞ -∞ ∫ +∞ -∞ √ p 1 (x, We performed additional tests on simulated datasets to analyse the performance of the function under different sample sizes for each individual, as well as for different sampling rates. The code for simulating the trips and running the different tests can be found at the GitHub repository specified in Appendix S1.
Thus, a large WBR indicates that the overlap values within temporal units are similar to the overlap values between temporal units, which suggests a lack of effect of the time in the spatial distribution at a population level. In the case of individuals tracked repeatedly each year, and then for consecutive years, the ISF effect and year effect can be confounded: in case there is an effect of the year (i. e. distributions shift from year to year), testing the effect of ISF using the dataset containing all years of tracking might cause an underestimation of within-individual overlap, thus underestimating in turn the WBR for the individual effect test. In this case, we suggest testing first for the temporal effect and, if the test shows no differences among years, calculating the effect of ISF for the entire dataset.
Empirical tracking data collected at different colonies have biases related to different sampling efforts and population sizes in all sampled colonies (i. e. the larger colonies will not necessarily be the ones with a larger sample). To correct these biases, we first simulate distributions for each colony by pooling all trips of each colony together and calculating colony-level space use  (Figure 1a, b). In this instance, we are going to estimate space use using independent and identically distributed (IID) kernel density estimation (KDE) provided by the functions in package "ade-habitatHR"  (Calenge, 2006) , since the AKDE method uses individual movement models that are not applicable to population-level spaceuse estimates. The user should be aware that, for tracking data with high temporal resolution and for small sample sizes, this method, which does not consider autocorrelation of tracking data, can underestimate space use  (Fleming et al.
The drawback of this process is that it requires a good knowledge of the population distribution to generate a simulated location dataset against which researchers can compare their real location data. In our worked example, we have used our own data, since the dataset includes a good representation of the entire breeding distribution of the species. If such a comprehensive dataset is not available, users can skip the simulateDistribution step, and simply this model, we calculate the value for the Inclusion mean at which continuing to increase sample size will not increase inclusion further (i. e. the asymptote; Figure  2g ). We regard this value as the maximum inclusion value (MIV) possible for that colony. Because MIV is calculated based on 50% UD, they range between 0 and 0. 5 (i. e. even if a sample represents the entire species perfectly, its 50% UD will only include around 50% of the simulated locations).
| Empirical application To demonstrate the use of these functions, we put together a dataset containing year-round trips from Cory's (Calonectris borealis), Scopoli's (C. diomedea) and Cape Verde (C. edwardsii) shearwaters obtained between the summers of 2007 and 2016, from 34 breeding colonies (Table  1 ). Locations were obtained from GLS  (Wilson et al. , 1992) , which register ambient light and derive longitude from the time of twilight and latitude from the length of the light period, obtaining low spatial accuracy positions with a temporal resolution of one or two positions per day  (Halpin et al. , 2021). The dataset contains individuals tracked for up to eight consecutive years, which allows us to also test the effect of ISF. Details of GLS deployment for each of the colonies can be found in Appendix S2: Table  S2.
| Data preparation The twilight events were calculated from the raw light measurements (obtained from the GLS) and visually inspected and adjusted when necessary. The locations were obtained using either Intiproc® (Migrate Technology, 2012) or Biotrack® (BiotrackLtd) software, or the "GeoLight" package in R  (Lisovski & Hahn, 2012). We discarded position data from 20 days before and after each equinox, as latitudes cannot be correctly inferred from day length during these periods  (Ekstrom, 2004) , and applied a quadratic speed filter following  McConnell et al. (1992)  to remove other highly inaccurate locations. Phenological states (migrating, breeding, wintering or staging) were assigned using custom-made R routines and confirmed by visual inspection: the first two consecutive days of directional commuting movement were assigned as the beginning of migration, and the first two consecutive days without a fixed direction were considered as the beginning of a stationary period (either breeding, wintering or staging).
| Temporal differences Since in our dataset each of the trips corresponded to a year, and to avoid confounding interannual differences with ISF, we used a subset of the original data containing only one trip per individual, randomly selected but ensuring a similar sample size for each year (through a stepwise custom-made R function that randomly selected trips per year, without selecting two trips from the same individual, but weighted the selection at every step to ensure all years had the same number of trips, whenever possible). With the resulting dataset (Appendix S2: Table  S4 ), we ran the indEffectTest test for each colony, using year as the grouping variable.
| Examples of applicability of the developed methods To further demonstrate the applicability of these methods, we used them in two different types of analyses, which exemplify possible uses. First, we calculated the nonbreeding distribution of each species, and to ensure these distributions were unbiased by different population and sample sizes from each colony, and due to different colony sizes, we used the following procedure: we first selected the colonies with a sample size larger than five individuals and a local representativeness >80%. Then, for each of those colonies, we selected the full dataset (if ISF has not been detected) or one track per individual (if ISF has been detected), and with the resulting dataset we followed the workflow of the package "track2kba"  (Beal et al. , 2020)  to obtain rasterized abundance maps proportional to the colony sizes.
We obtained 21 raster layers, seven for Cory's shearwater colonies, 12 for Scopoli's shearwater colonies and two for Cape Verde shearwater colonies. Finally, for each species, we added the colony-level raster layers to produce a more accurate, unbiased representation of the distribution of the species. Second, we used generalized linear models to model the obtained colony inclusiveness value, that is, how well they represent species nonbreeding distribution in relation to several variables that could be of interest to disentangle ecological from methodological drivers of differences in inclusiveness: geographic location, centrality in the breeding distribution and colony and sample size. We ran these models for Cory's and Scopoli's shearwaters (data from Cape Verde were excluded since we had data from only two colonies).
, 2013)  values of the predictor variables and diagnostic plots to sequentially remove variables in order to obtain the most parsimonious model. To compensate for the small sample size in these models, particularly for Cory's shearwaters, we used a robust method to estimate standard errors, provided by the package "jtools"  (Long, 2019) , which is more stable to the effects of highly leveraged points than the ordinary least squares (OLS) method provided by the lm function  (Cribari-Neto et al. , 2007). All analyses were performed in R (R Core Team, 2021) versions from 3. 4. 4 to 4. 0. 2. All the developed functions and their arguments are defined and explained in the supplementary material (Appendix S2: Table  S1 ). The code for all the functions and performance tests can be found at https://github.
| Results of ISF test on the simulated dataset We ran the ISF test in data simulating a situation where the tracked animals were not colonial (e. g. a residential species where all trips from the same individual had a common origin but trips from different individuals had different origins). Within that paradigm, we tested the function in 10 simulated datasets of decreasing site fidelity (Figure  3 ). At null resistance (i. e. simulating the absence of ISF), WBR value was 4. 8%, increasing up to 85. 8% at maximum resistance (Table  3 ).
| Spatial differences in species representativeness Before the analyses, we excluded three of the 34 sampled colonies, which had less than five trips. From the 31 remaining colonies, we selected one trip per for Flifla (Scopoli's shearwater) since a WBR higher than 50% suggested an effect of ISF. With the resulting dataset, we obtained values of local representativeness >80% in 22 of them (Table  2 ). With these, we proceeded to test their species inclusiveness. F I G U R E 3 Simulated datasets created to test the ISF function. The datasets consist of 10 trips of 100 positions each for each of the 10 animals simulated (in different colours). From left to right and top to bottom, ISF has been increased by increasing the resistance of the surface outside the allowed area (see Section 2) from 0 to 1.
We simulated nonbreeding distributions from each of those breeding colonies using the simulateDistribution function and obtained a dataset that contained more than 78,000 locations for Cory's shearwaters, more than 250,000 for Scopoli's shearwaters and more than 10,000 for Cape Verde shearwaters (Table  2 ). Values of species inclusiveness for Cory's shearwater colonies ranged between 27. 4% (Timanfaya) and 52. 2% (Vila); for Scopoli's shearwater colonies from 17. 6% (Strofades) to 53. 4% (Zembra); and for Cape Verde shearwaters from 49. 4% (Curral Velho) to 50. 8% (Raso; Table  2 ). For Cory's shearwaters, after running the full model we found none of the predictors had a significant effect on the species inclusiveness value. We performed stepwise selection on the full model and found that none of the models improved the intercept-only model, with all effects being nonsignificant despite an R 2 of.
| DISCUSS ION In this study, we provide a workflow to test the effect of three major sources of variability in spatial studies at the species level (ISF, temporal variability and spatial variability), potentially applicable to a wide variety of tracking datasets. In a context of increasingly sophisticated, miniaturized, and inexpensive animal tracking devices, studies using individual tracking data are proliferating, with ever larger extents both spatially and temporally. This emphasizes the need to account for the biases described here when studying space use at a population or species level, that is, before putting all tracks together towards generating a single measure of space use that does not consider potential differences in individual behaviour. The ISF test is useful for datasets with uneven amounts of data The test for interannual differences gives a measure of how spatial distribution varies among years at the population level.
We would expect this test to find a significant effect of temporal variability in adaptable species that live in dynamic but predictable environments. On the contrary, species less able to track environmental conditions, or those that live in relatively constant environments, would have the same distributions year after year, resulting in nonsignificant effects of temporal variability on the distributions. Practically, what to do with a dataset that exhibits temporal variability will depend on the type of analysis planned. As an example, if the goal is to model species distributions based on environmental variables (a species distribution model), the effect of year should be considered by either modelling the tracks of each year separately and with the corresponding values for the environmental variables, or using a spatiotemporal model that will account for temporal as well as spatial autocorrelation (e.