dataesr/scientific-paragraphs-categorization huggingface dataset:
)] or negative (-; negative correlation). It reflects the current state of scientific knowledge for each variable, according to previous studies and experts. Abbreviation shows the name used in the figures  (Win, winter; Spr, spring; Sum, summer). Source details the origin from which the data used to build these variables were collected. Bourget, and Lake Annecy, it was obtained from the Haute-Savoie and Savoie Direction Départementale du Territoire (DDT) and from each respective fisheries services of the Swiss cantons (Genève, Vaud, Neuchâtel, and Fribourg) bordering the Lakes Geneva and Neuchâtel. The stocking data were expressed as equivalent yolk-stage fry (Tab. 1; Appendix A;  Gerdeaux, 2004). In Lake Annecy, whitefish stocking has not been practiced since the end of the 1990s. For Lake Aiguebelette, stocking data was unreliable due to interannual variations of life stages at which they were stocked and unreported sharing information between the lakes stocked with the Lake Aiguebelette hatchery.
To assess the temporal fluctuation of whitefish abundance, two complementary fishery indicators were used. The first proxy was based on the total annual landings declared by fishermen (Ctot) (Fig. 3a ). However, since both fishing effort and stock abundance can affect total landings, the second indicator was based on the Catch Per Unit of Effort (CPUE) (Fig. 3b ), which is obtained by dividing the annual landings by the annual fishing effort. The CPUE is considered a better proxy for fish abundance in fisheries sciences  (Ricker, 1940). Both indicator trends were compared and confirmed using data from selected dependable fishermen. The accuracy and reliability of the fishing effort variable varied depending on the nature of the available data for each lake. For instance, the number of drifting nets was the most accurate measure of fishing effort for Lake Geneva, Annecy, and Bourget (Fig.
Similarity analysis The R package distantia  (Benito and Birks, 2020)  was used to explore similarities and dissimilarities among lakes characterized by the set of eight environmental variables and the fishing effort time series (Tab. 2; Fig. 2 ). The package computes a measure of dissimilarity Ψ (eq. 1) between datasets (in our study, each lake is considered as a multivariate dataset composed of the different environmental time series) as the pairwise Euclidean distance between each pair of multivariate ecological time series (i. e. here, each pair of lake). The distance between two lakes l 1 and l 2 is denoted AB between (l 1 ,l 2 ) (eq. 2) and is the sum of the distance between each environmental variable for each year: (eq. 1) (eq. 2) where X l1,j (y) is the value of one of the J environmental variables j (e.
g. , winter temperature, spring temperature, etc. ) measured at lake l 1 in year y. Note that in the original distantia method, AB between is standardized by a metric AB within that aims to normalize AB between in cases where the datasets have different number of years. However, since our datasets have the exact same years, and AB within tends to overdrive the dissimilarity values for variables that show high interannual variations, we used only the AB between values to measure distances between lakes. We examined the inter-lake Euclidean distances between each lake pair using adjacency networks and then identified the variables that contributed the most to these distance values. It was done by calculating the drop in distance values between two lakes when removing a variable from the dataset: an important drop in the distance when removing a variable means that it greatly contributes to the multivariate distance between the two lakes, indicating different temporal trends for this variable between these two lakes.
The package uses restricted permutation tests to estimate the significance of distance values and calculates the probability of randomly obtaining a distance value lower than or equal to the one first calculated. Throughout this analysis, we used a 5 % level of significance. This approach requires that each dataset has the same set of variables to measure AB between. However, there was no zooplankton monitoring data for Lake Aiguebelette. Thus, two different runs were carried out: [5lakes_noZOO], which includes the five studied lakes , which includes all nine variables in four lakes, i. e. without Lake Aiguebelette. Moreover, two different scaling methods were adopted to compare both the absolute values and trends of each variable across lakes. The INTER approach scaled the values of the variables on the overall dataset, standardizing each variable by subtracting the mean over all lakes and dividing by the standard deviation over all lakes.
This way, the absolute values of each lake's variables are compared to the global mean of these variables across all lakes, highlighting extremely high or low values with a high dissimilarity. The INTRA approach scaled each lake dataset separately, standardizing each variable of each lake centered around its own mean and scaled with its own standard deviation. This way, the temporal trends are compared to the global trends across all lakes, highlighting lakes with trends as opposed to the global trend with high dissimilarity. Therefore, two lakes with similar temporal trends in environmental conditions (e. g. , temperature) but with different mean values (e. g. , one warm and one cold lake) will have a large distance with INTER scaling but a small distance with INTRA scaling. Due to missing values, the distantia analysis was applied to 13 years for the [5lakes_noZOO] run and 14 years for the [4lakes_withZOO] run.
Linking fisheries data and environmental variables Linear models were used to explore the relationships between proxies of whitefish abundance and environmental variables. Each lake was analyzed separately by fitting models with Ctot and CPUE as response variables and the eight environmental factors (Tab. 2) as explanatory variables. In addition, fishing effort and stocking were included as explanatory variables; however, the former was only added when modeling total landings since it is already included in the CPUE calculation. All models with one variable at a time were tested, and in the case of multiple significant variables, additive effects were tested. The models were compared based on several criteria, including R² (proportion of the response variable that is explained by the explanatory variable), BIC (Bayesian Information Criterion, used for model selection based on the maximum of likelihood, the number of parameters, and the number of data), F value (taken from the F-test, which tests the null hypothesis that the coefficients are null), and residual analysis.
The contribution of each environmental variable to the multivariate distance between two lakes is evaluated by measuring the drop in distance values between two lakes when removing the variable from the dataset (Fig. 5 ). Results are consistent between the two scaling methods, with few differences observed with the INTER scaling method (Fig. C2 ). Wind intensity plays a greater role in distances between lakes with the INTER scaling method, indicating similar trends but dissimilar ranges of values. However, zooplankton variables have a higher impact on the multivariate distance in most pairwise comparisons when using the INTRA scaling method than with the INTER scaling method (Fig. 5a ). Results obtained with the INTRA scaling method suggest that zooplankton abundance greatly contributes to dissimilarity in temporal fluctuations among lakes.
However, only linear relationships were found to be statistically significant, possibly due to the relatively small dataset. Therefore, we switched to linear models. The relationships were not consistent across all lakes. For Lakes Geneva and Bourget, the recent decline in landings could be attributed to lower Daphnia abundances in spring, which may be limiting the production capacity of total larvae and early juveniles. This food resource has already been identified as a potentially important driver of European whitefish abundance in Lake Geneva  (Anneville et al. , 2017). Lake Geneva and Lake Bourget exhibited similar smoothed trends of spring Daphnia abundance over the period. There was a peak around 2010 (Fig. 2 ) that corresponds to the high whitefish landings, followed by a substantial decrease.
Years with high Daphnia concentrations in spring (>2000 ind. m -3 ) may significantly impact the growth and survival of whitefish larvae in Lake Geneva and Lake Bourget. Conversely, Lake Annecy and Lake Neuchâtel had relatively low and stable Daphnia abundance over the period (863 ± 455 ind. m -3 for Lake Neuchâtel; 756 ± 377 ind. m -3 for Lake Annecy), which could be compensated by other food sources such as small copepods and Bosminidae  (Ponton and Müller, 1989; Anneville et al. , 2007). This hy- o n l y pothesis could not be verified for Lake Aiguebelette, as zooplankton was not monitored. Developing a regular monitoring program similar to those of the other four lakes to extend the dataset and strengthen future inter-lake comparisons would be highly valuable. Zooplankton abundance was based on long-term surveys conducted only twice a month, so rapid variations in abundance may have been missed.
, 2022). Fishery statistics were obtained from professional fisheries in all lakes except in Lake Aiguebelette, where they came from recreational fishing. Consistent CPUE series between professional and recreational fisheries were reported in Lake Annecy  (Gerdeaux and Janjua, 2009)  and Lake Bourget (unpublished data), indicating that data from both sources may be used in line with the observed synchronized landings between Lake Aiguebelette and the other three lakes. Fishing effort was not significant in all models except for those of Lake Annecy and Lake Geneva. In Lake Annecy, significant variations in fishing efforts during the survey appeared to be the primary factor contributing to the different trends, which was the only significant relationship found for this lake. The negative correlation between total landings and the fishing effort variable suggests high sensitivity towards fishing effort for this whitefish population.
ACKNOWLEDGMENTS This research was carried out as part of a PhD funded by the  ANRT  ( Association Nationale de la Recherche et de la Technologie ), three Swiss cantons (the  Geneva Canton , the  Vaud Canton , the  Valais Canton) , the  OFEV (Office Fédéral de l'Environnement) , and the  DDT (Direction Départementale des Territoires) of Savoie and Haute-Savoie. The authors acknowledge all the organizations who gave access to the data and people who contributed to the collection and analysis of these data. Special thanks to  Alice Theurel  and  Lilia Guillet , who markedly took part in data collection and the first explanatory approach of this study.
Energy consumption assessment of storage stages The energy consumption related to the cooling and temperature maintenance of one strawberry punnet (primary packaging) was eval- uated for three cold storage stages (S1 packing station, S2 distribution platform, S3 retail cold room) using the method developed in our previous work  (Duret et al. , 2020)  and data from the field study. For each stage (Si), the energy consumption was calculated as follows: E punnet,Si = Q f , Si η Si × COP Si [J] (1) Where E punnet,Si (J) the energy (electrical) consumption of one punnet in stage Si; Q f, Si (J) the cooling demand for one punnet; η Si the global performance coefficient of the cooling unit; COP Si the Carnot Coefficient Of Performance of the refrigeration unit. The cooling demand was calculated using Eq.
Life cycle inventorydata collection This section presents the collected data for LCA on packaging and refrigerated transport. The modeling of the supply chain was performed with information and data from the field measurement and interviews (Section 3. 1) and the energy assessment result (Section 3. 2). It is acknowledged that the packaging materials may affect the quality of the strawberry and hence the loss rate of the strawberry fruits due to the cushioning or other functional protection performance. However, this information was not available in the present study and hence was not considered. 2. 5. 1. 1. Packaging. Five types of packaging materials: cardboard, molded pulp, recycles paper, PET and RPET (Fig. 3 ) were selected for the environmental assessment. These packaging materials were representative of the primary packaging of strawberry, according to the interviews.
Data on the manufacture of punnets were presented in Table  1. These data were calculated for a 250 g punnet of strawberries (about 15 medium-sized strawberries). The dimensions of the punnets were (length x width x height): 13. 5 ± 0. 5 cm x 9. 0 ± 0. 5 cm x 5. 5 ± 0. 5 cm. Although the type of primary packaging (punnets) may be different, the field study showed that the macro-perforated polypropylene films covering the trays, secondary and tertiary packaging were of the same type and did not depend on the primary packaging. The cover film, the secondary and the tertiary packaging were not taken into account in this work as a first approach. The data in Table  1  referred to one type of punnets from one manufacturer. The variability of these data as a function of the punnet mass was investigated in the sensitivity analysis (Section 2.
5. 3). For molded pulp, cardboard and recycled paper, these data were obtained by exchanges with manufacturers. The production of a cardboard punnet uses only kraft paper and adhesion. However, energy and water are needed for the production of kraft paper and adhesion. The production of PET and RPET punnets are done by thermoforming process, a stretching technique of manufacture of three-dimensional mouldings from flat plastic preforms such as films or sheets, under the influence of heat and pressure or vacuum. The data for different processes (kraft paper, adhesive, sawdust, PET and RPET granulate, thermoforming, energy and water consumption) are from EcoInvent database  (ecoinvent, 2022). The transport of the packaging could impact on the present analysis. For instance, if the punnets were produced far away from strawberry fields, it would increase the environmental impact of the packaging itself.
Refrigerated transport. Generic data and inventories on refrigerated transport were presented in Table  2. Distances and vehicle types were obtained from the field study: small truck (<7. 5 ton) was used for the first transport T1 from the grower to the packing station, while larger refrigerated lorries were used for second and third transports T2 and T3. The refrigerated transport processes in ecoinvent database was used (ecoinvent, 2022). These data considered both the transport fuel and the refrigeration unit operation for the transportation of refrigerated goods. The vehicle operates with diesel and uses R134a as refrigerant. The lorry has a life span of 15 years and its emission standard is classified as EURO3.
Energy generation mix. The environmental impacts of the energy generation system (the electricity mix in this study) can be obtained Fig. 3. Different types of primary packaging. by combining of the impacts from different parts of the system: the generation plants of different energy sources, the transmission networks and the distribution lines. Each part can have environmental impacts at different stages of their life cycle: construction, use, decommissioning and disposal. In this study, the environmental impacts of the energy generation system were obtained from the electricity consumption (kWh) using the ecoinvent database  (ecoinvent, 2022). The data in this database were collected mainly from national statistics and the International Energy Agency (IEA).
Assessment of the energy consumption of a strawberry punnet in the storage stages Table  4  shows the results of the energy consumption assessment (Section 2. 2) of a strawberry punnet in the storage stages S1, S2 and S3. The evaluation was performed using data from the field measurement and interviews (Table  3 ). Although data from the interviews may be subjective and inaccurate, the data used in this assessment were most frequently encountered values (mode columns in Table  3 ) and can be considered representative of the commercial supply chain. The assessment was carried out for one strawberry punnet so that it could be used as input data for LCA. Two ambient temperatures (T ext ) of 20 and 40 • C were considered to represent moderate and high temperatures. The energy consumption for one punnet (250 g) in stage Si (E punnet,Si ) was calculated from Eq.
Conclusion A strawberry supply chain (transport from growers to retail) was investigated. First, a field study was carried out to explore the organization of the strawberries logistic circuit. Field measurements were performed in order to study the evolution of product and air temperatures through various stages of the circuit. Significant product temperature heterogeneity was observed. A difference of 3 • C between the coldest and warmest products in the second refrigerated transport was measured. Interviews with stakeholders at different stages of the strawberry supply chain were also conducted to gather information on the conditions of transport and storage of the strawberries. The information from the field measurement and interviews constituted an essential database for energy and environmental assessment.
The baseline assessment was used to set the goals of the program and to establish the kind of exercises to be included in the training program. A self-education handbook, co-designed with the patient and the therapists, included the selected exercises. The posture of the exercises was selected according to the patient's abilities and preferences (standing, sitting, lying on the bed or on the floor). For patients with low back pain, exercises focused on muscle strengthening, stretching, relaxation and reducing kinesiophobia. For patients with neurological disorders, the exercises were taskoriented, balance improvement, strengthening and stretching. At each session, the E-Kermed videoconferencing software (a specific videoconferencing platform for the health sector developed for the Brittany region) sent the patient a connection link by email.
Analysis of the open-ended text 91 participants responded to the open-text question amounting to a total of 5,168 words. The open-ended text was analyzed using Rstudio, Text Mining Package V 0. 7. 9 and Wordcloud V 2. 6. The analysis of word occurrences listed the 17 most frequently used words (Table  5 ), represented as a word cloud. Under the term "therapist", we grouped together the different names of therapists, sometimes by first name, last name, or simply named as therapists (physiotherapist, occupational therapist, etc. ).
Currently, up to 64 states (6 bits) have been demonstrated  [3]. As new devices with higher bit counts become available, it is intuitive to expect better precision from them. However, in this paper we show that amplitude-oPCM actually degrades in accuracy as more bits are available in the presence of small perturbations. Besides, this approach further requires: i) precise adjustments of input intensity, ii) precise writing of values to the multilevel memory, iii) multi-wavelength light sources for MAC implementation. In this context, we propose stochastic-oPCM, This work was funded by Agence Nationale de la Recherche (ANR) under grant no. ANR-20-CE24-0019 (OCTANE). a single-wavelength approach that requires only 1-bit modulators, and thus does not depend on the precise adjustment of either the light pulses or the memory state.
An external Raspberry Pi TM (R-Pi) microcomputer (Raspberry Pi TM Foundation, UK) interfaces with the MCU, collecting stored data and running processing algorithms to extract pressure dependent resonant frequency. A laptop connected to R-Pi permits user control through graphical user interface (GUI) and can upload data to the cloud for further processing and remote retrieval. Although this approach does not limit the number of LC sensors that can be incorporated within a PGM system, the manifestation described in this paper uses four LC sensors incorporated into a flow test setup, wirelessly interrogated by a readout printed circuit board (PCB), and an external control unit located outside the flow chamber. The PGM system was successfully used to measure flow-induced pressure gradients with a pressure resolution of <1 mbar, measuring ≈ 10 mbar pressure drop between adjacent sensors at a flow rate of 14 mL/min representing values for a typical core-flood experiment.
When the readout coil is excited with input voltage, VRO and input current, IRO, with a resulting complex input impedance, Zin (3), the pressure-dependent resonant frequency, f0, of the mutually coupled LC sensor, manifests as a peak in the real part of the input impedance, Re{Zin}, of the readout coil. The value of Re{Zin} is monitored by the readout circuitry, described below. To extract the pressure dependent f0 of the LC sensor, a set of discrete data points are collected, where each data point is the magnitude of Re{Zin} at the specific interrogated frequency. Two typical captured datasets of Re{Zin} are shown in Figure  2b ; a Gaussian curve, referred to as fitted frequency response waveform, FZin (8) is computed for each dataset. From this, f0 can be determined through interpolation.
This method allows frequency resolution to be achieved without requiring collection of an intractably large number of data points. Bandwidth, σ, is defined as f0/Q, and mean (μ) is defined as f0. In this work, σ is defined as the full width at half maximum, in the frequency span of the Re{Zin} dataset. Rin, defined in Equation (  9 ), is determined by first identifying the maximum value of Re{Zin}, The resonant frequency of the LC sensor, f 0 (2) is sensitive to pressure through the changes in sensor capacitance, C XDCR. When the readout coil with inductance, L RO , and LC sensor coil with inductance, L S , are in close proximity, they become coupled, resulting in mutual inductance, M (4). When the readout coil is excited with input voltage, V RO and input current, I RO , with a resulting complex input impedance, Z in (3), the pressuredependent resonant frequency, f 0 , of the mutually coupled LC sensor, manifests as a peak in the real part of the input impedance, Re{Z in }, of the readout coil.
The value of Re{Z in } is monitored by the readout circuitry, described below. To extract the pressure dependent f 0 of the LC sensor, a set of discrete data points are collected, where each data point is the magnitude of Re{Z in } at the specific interrogated frequency. Two typical captured datasets of Re{Z in } are shown in Figure  2b ; a Gaussian curve, referred to as fitted frequency response waveform, F Zin (8) is computed for each dataset. From this, f 0 can be determined through interpolation. This method allows frequency resolution to be achieved without requiring collection of an intractably large number of data points.
Simulated Pres. Resol. Resol Sim = C f 0 AR•Q• R in (14) Bandwidth, σ, is defined as f 0 /Q, and mean (µ) is defined as f 0. In this work, σ is defined as the full width at half maximum, in the frequency span of the Re{Z in } dataset. in , defined in Equation  (9) , is determined by first identifying the maximum value of Re{Z in }, found at approximately f 0 , and minimum value of Re{Z in }, found far away from f 0 (at approximately f 0 /10); it can also be estimated using M, Q, L RO , and L S. The quality factor, Q, defined in  (10) , is f 0 divided by σ; it can also be estimated using total effective resistance, R S (6), L S , and total capacitance, C S  (7). The shift in resonant frequency, ∆f 0 , with applied pressure, ∆P, (caused by the change in capacitance, ∆C, of the transducer over the full-scale pressure range, ∆P FS ) is defined as Absolute Response, AR,  (11).