esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 2 Materials and Methods
### ExerPlayer Module
#### 2.2.2 ExerPlayer Task Mode
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task Motion** & **Principal Joint** & **Joint Pairs** & **Radius** & **Minimum Range** & **Maximum Range** \\ \hline R shoulder abduction & 5 & 5-1/6-5 & 7-5 & 0\({}^{\circ}\) & 180\({}^{\circ}\) \\ L shoulder abduction & 2 & 2-1/3-2 & 4-2 & 0\({}^{\circ}\) & 180\({}^{\circ}\) \\ R shoulder flexion & 5 & 5-1/6-5 & 7-5 & 0\({}^{\circ}\) & 180\({}^{\circ}\) \\ L shoulder flexion & 2 & 2-1/3-2 & 4-2 & 0\({}^{\circ}\) & 180\({}^{\circ}\) \\ R hip abduction & 11 & 11-1/12-11 & 11-13 & 0\({}^{\circ}\) & 45\({}^{\circ}\) \\ L hip abduction & 8 & 8-1/9-8 & 8-10 & 0\({}^{\circ}\) & 45\({}^{\circ}\) \\ R hip flexion & 11 & 11-1/12-11 & 11-13 & 0\({}^{\circ}\) & 80\({}^{\circ}\) \\ L hip flexion & 8 & 8-1/9-8 & 8-10 & 0\({}^{\circ}\) & 80\({}^{\circ}\) \\ R knee flexion & 12 & 12-11/13-12 & 12-13 & 0\({}^{\circ}\) & 140\({}^{\circ}\) \\ L knee flexion & 9 & 9-8/10-9 & 9-10 & 0\({}^{\circ}\) & 140\({}^{\circ}\) \\ R elbow flexion & 6 & 6-5/7-6 & 6-7 & 0\({}^{\circ}\) & 170\({}^{\circ}\) \\ L elbow flexion & 3 & 3-2/4-3 & 3-4 & 0\({}^{\circ}\) & 170\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of task motion and joint movements. Figure 4: Example of shoulder abduction task. (**a**) Placement of the targets and elements to help the correct positioning of the patient. (**b**) Patient performing shoulder abduction. (**c**) Estimating ROM. the coordinates of the centers of the targets are obtained in an incremental manner The calculation of two consecutive center points is shown in the following Equation (4). \[ \begin{array}{l}\text{x1}=r\;\cos\theta\\ \text{y2}=r\;\sin(\theta+d\theta)\end{array} \qquad\qquad \begin{array}{l}\text{x2 }=r\;\cos(\theta+d\theta)\\ \text{y2}=r\;\sin(\theta+d\theta)\end{array}  \tag{4}\]  
Once the task is completed, the application generates a JSON file with all the data obtained (patient data, task data, time spent, targets reached, coordinates of the joints involved in the task). This file is sent via the REST API to the DB ExerCam Database and is used later by the evaluation algorithm to show the results of the tasks (ROM calculation. In Section 2.3.1, the evaluation algorithm is explained with the results from the ROM calculation) to the therapist.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 2 Materials and Methods
### Proposed Load Forecasting Methodology  
The proposed forecasting model integrates the GAF, CNN, SENet, and GRU methods and is thus abbreviated as GCSG (GAF-CNN-SENet-GRU). The overall architecture of the GCSG load forecasting model for the Logical Range is shown in Figure 3.  
The processing pipeline of the GCSG model comprises three stages: (1) spatial feature extraction via GAF and CNN-SENet; (2) temporal feature extraction using GRU; (3) attention-based feature fusion.
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 2 The Biology of _Ulva_ sp.
### Sporulation Inhibitors  
In the various works carried out by scientists on different species of _Ulva_, two sporulation inhibitors were identified (inhibitor-1a (SI-1a) and inhibitor-2 (SI-2)) and a compound as a swarming inhibitor (SWI), acting on the joint in the process of inducing reproduction and releasing gametes or zoospores, regardless of photoperiod control.  
[PERSON] et al. [15] outlined that vegetative thalli secrete a high molecular mass cell wall glycoprotein (SI-1) into the surrounding medium while simultaneously harboring a second low molecular weight inhibitor or non-protein molecule (SI-2) within the space between the two cellular layers of the thallus. The transition of a lamina cell into a gametagium only occurs when SI-1 levels diminish and the continually present SI-2 is no longer detected by the algae. SWI serves as a mechanism to coordinate gamete release during the swarming phase, with a decline in concentration observed on the third day post-induction, further amplified by an additional light stimulus, thereby enhancing the likelihood of mating [13; 15; 38]. Cells are no longer susceptible to the action of sporulation inhibitors after 23-26 h (when cells enter the determination phase) [13; 39].  
Unmated gametes can develop parthenogenetically into haploid clonal gametophytes, ideal for genetic manipulation and reproducible standardized experiments. It was reported according to [PERSON] [40] and [PERSON] et al. [15] that the generation time of _Ulva compressa_ (as _U. mutabilis_) is short: only 3 to 5 weeks of growth are required between the potential inducibility of gametogenesis.  
[PERSON] et al. [15], utilizing _Ulva compressa_ (formerly known as _U. mutabilis_), along with [PERSON] and [PERSON] [38] and [PERSON] et al. [41], who studied _U. lactuca, U. linza_, and _U. rigida_, observed that _U. linza_ generates similar self-inhibitors (SI) as _U. compressa_ during gametogenesis. Both types of SI from _U. compressa_ (SIM1 and SIM2) demonstrated the ability to impede gamete production in _U. linza_ and _U. compressa_, albeit with a somewhat lesser effect (particularly for medium-derived SIM1 and SIM2 between cell layers) in _U. linza_[15; 38; 41]. On the other hand, _U. linza_ SI (SIL1 and SIL2) were able to inhibit gamete formation in both _Ulva_ species tested. They concluded that these SI use analogous signals regulate the induction of reproduction in different species and that life cycle regulators are not specific to the species but rather to the clade in _Ulva_. Thus, to increase the potential of _Ulva_ sp. as a model organism, gametogenesis can be artificially induced by removing both IS through cutting the thallus into single-layer fragments and subsequent washing, as exemplified in the studies above [4; 38; 41].
## 2 Frost-Heave Model and Thermosiphon Model
### TM Model: Unfrozen Water Content of Soil  
The unfrozen water content represents the pore water inside the soil that does not freeze even if the soil freezes. The tendency of the unfrozen water content is to decrease as the temperature drops, and its residual amount varies depending on the type of soil. These aspects are illustrated in Figure 1[26].  
The aforementioned tendency of the unfrozen water content is manifested as the frost heave characteristic of the soil. Sand having a particle size as shown in Figure 1a has large pores and low capillary pressure, so the pore water cannot be continuously supplied after freezing begins. Therefore, as shown in Figure 1b, the unfrozen water is rapidly decreased and converged, and the formation of ice lenses is severely restricted. Conversely, for silt or silty clay with relatively high capillary pressures, the tendency of the unfrozen water content to decrease is moderate due to the sufficient movement of pore water; therefore, the ice lens develops adequately. Soils that allow ice lenses to develop easily are called frost-susceptible soils.  
Studies have been conducted to fit the trend of unfrozen water content in frost-susceptible soils into an exponential-trend curve through laboratory experiment data [27], as formulated in Equation (1). As a foundational equation, the unfrozen water content \(\omega\) can be calculated as  
\[\omega=\ \alpha^{*}\theta^{\beta^{*}}, \tag{1}\]  
where \(\alpha^{*}\) and \(\beta^{*}\) represent the trend coefficients of the curve according to the type of soil and \(\theta\) denotes the temperature.  
As Equation (1) possesses an infinite value at the freezing point (0 \({}^{\circ}\)C), the problem of convergence is encountered in the numerical analysis. To address this problem, [PERSON] [7] proposed a trend-based equation characterizing the unfrozen water content, expressed in Equation (2) as  
\[\omega=\omega^{*}+(\overline{\omega}-\omega^{*})e^{\alpha^{*}(T-T_{0})},\ [T<T_{0}], \tag{2}\]  
where \(\omega^{*}\) depicts the unfrozen water content at such a low temperature that the trend of the residual unfrozen water content finally converges, \(\overline{\omega}\) denotes the pore water content at a temperature above 0 \({}^{\circ}\)C, \(\alpha\) represents the trend coefficient corresponding to the unfrozen water content according to the type of soil, \(T\) represents the average temperature, and \(T_{0}\) symbolizes the freezing-point temperature (0 \({}^{\circ}\)C).  
Figure 1: Comparison of different soils characteristics: (**a**) grain size of different soils; (**b**) unfrozen water contents of different soils.
## 3 Dynamic Routing Algorithm for Inter-Satellite Network Based on Q-Learning
### Q-Learning Routing System Model
#### 3.2.1 Network Model  
The network data plane in SDN is represented as an undirected graph of n nodes, each node represents a satellite, and n nodes are connected by undirected links. The network topology is represented by a graph G = (V, E), where V represents a set of nodes with \(|\)V\(|\) = n, and E represents a set of links connecting nodes in the network. Assuming that G is a connected graph without any isolated nodes, the link bandwidth capacity, transmission delay and packet loss rate are expressed as \(b_{ij},d_{ij}\) and \(l_{ij}\).
## 4 Experimental Results
### Few-Shot Transfer  
In this experiment, we evaluate the models trained on our dataset in a common transfer learning scenario. This largely mirrors the experimental setup in Ref. [13]: we replace the classification head with a newly initialized classification head for each benchmark dataset and fine-tune the models on a sample of the data. The amount of training data used from each target dataset is 50, 100, 150, and 200 examples, and sampling was stratified. Evaluation is conducted on the remaining data in the benchmark dataset with results shown in Table 14. The baseline is the same model architecture (see Section 2.6) fine-tuned on the few-shot examples. The comparison model was trained on the GoEmotions dataset (the same model from Section 4.3). All fine-tuning in these experiments used a batch size of 8 over 8 epochs with a learning rate of \(2\times 10^{-5}\) (summarized in Table 15). The results are an average of three runs.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Tales** & **ISEAR** & **EMOINT** & **GoEmotions26** \\ \hline Self & 0.83 & 0.76 & 0.82 & 0.52 1 \\ \hline Transfer & & & \\ \hline GoEmotions & 0.74 (89\%) & 0.52 (68\%) & 0.50 (61\%) & NA 2 \\ Emolit (Hard Labels) & 0.74 (89\%) & 0.53 (70\%) & 0.57 (70\%) & 0.28 (54\%) \\ EmoLit (Soft Labels) & 0.77 (93\%) & 0.56 (74\%) & 0.60 (73\%) & 0.28 (54\%) \\ \hline \hline \end{tabular} 1  
All models trained on emotions significantly outperform the baseline at a low number of examples. As the number of examples increases, the difference between fine-tuning a model pretrained for emotion classification and just the base model pretrained on the self-supervised language modelling objective decreases. At 200 examples, they are either identical or near-identical. The actual results on each benchmark are very similar for all models pretrained on emotion datasets--ours or GoEmotions. We can conclude that our dataset performs equally well in fine-tuning based few-shot transfer learning. Zero-shot results (Table 13 are significantly better for Tales and ISEAR, indicating that adaptation based on not discarding the classification head would likely be preferable. Zero-shot would also be preferable for both datasets if less than 200 examples were available for Tales, less than 100 for ISEAR, and around 50 or less were available for EMOINT.
## 3 Experiments and Results
### Evaluation Metrics
Classification + Localization Error (Cls + Loc): Both the class prediction and spatial alignment are incorrect, though the confidence score exceeds the background exclusion threshold (\(t_{b}\leq IoU^{\text{max}}\leq t_{f}\)). 4. Duplicate Detection Error (Dupe): Multiple detection boxes correspond to a single GT (\(IoU^{\text{max}}\geq t_{f}\)) instance; only the highest-confidence box is valid, while the others are redundant. 5. Background Error (Bkg): the background is detected as a target, and the predicted bounding box with GT satisfies \(IoU^{\text{max}}\leq t_{b}\). 6. Missed Detection Error (Miss): A GT instance is not matched by any detection box and is not attributable to the preceding error categories.
## 4 Experiments
### Data Collection  
CDAR 2011 SigComp dataset is used for signature verification competition and contains both online and offline signature samples. The offline section of this dataset has different sample sizes of skilled forgeries for each genuine author, see Table 2.  
In Table 2, we can see there are very limited forgery samples in the dataset. However, author ID No. 14 and No. 16 contain the largest number of forged reference signatures (each author has 16 skilled forgeries from four authors). In this paper, we need a certain number of forged signature samples, which are not only used for training the network, but also for further evaluating the performance. Therefore, we use the No. 14 and No. 16 author's signatures as experimental samples. That is, our experimental database includes two genuine authors (each genuine author has eight signature samples) and eight forgery (each forger has four signature samples). Note that in the original SigComp dataset, the number of genuine signature samples far exceeds the needs of our experiment (there are 24 signature samples for each genuine author), so we just take the first eight samples based on the ascending order of signature's ID number. For the simplicity sake, we denote them as No. 1-No. 8.  
Figure 4: **(a)** Sub-datasets used for experiment; **(b)** genuine signatures appearance in training stage; **(c)** forged signatures appearance in testing stage.  
No. 1 as the training data, Nos. 2\(-\)4 as the validation data, and Nos. 5\(-\)8 as the test data, as shown in Figure 3(a).  
It must be noted that samples of author ID: 16 have the high intra-class variability. The genuine signatures used for training and testing processes have very different styles, as shown in \"ID: 16 (No. 1)\" and \"ID: 16 (No. 5)\" in Figure 3(b). Obviously, it is difficult to verify if these signatures are from the same person, even with human visual comparison. This will also affect the performance of the network. We will discuss later.  
In Figure 3(a), each sub-dataset contains eight genuine signatures from the single genuine author, and 16 forged signatures from four different authors. We use the last author (No. 4) for performance evaluation (not used in the training process). The appearances of the signatures are shown in Figures 3(b) and 3(c).
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE  
### WMSN Architecture and Objectives  
We consider a WMSN as depicted in Figure 1 including:  
1. one multimedia end device equipped with a camera, called source, generating either low rate data or bulk data (image)
2. router devices forwarding this traffic inside the network
3. gateway device collecting data generated by the sources  
One example of targeted application is recording images from the monitored area for future usage (post-analysis of data in case of accident for instance [1]). All devices are battery powered, except for the gateway which has a permanent energy power. The gateway has two interfaces: one with the network devices enabling it to collect data, and the other is linked to the end-user to allow the remote management, the monitoring, etc.  
Figure 1: WMSNs architecture.  
ODYSSE is responsible for routing data from the source to the gateway while optimizing the lifetime of the network. In addition, ODYSSE aims at keeping low data transmission delays and overhead since it deals with small capacity devices.
## 5 Conclusions  
This study utilizes continuous long-term land cover data from 1991 to 2019 to comprehensively assess the impact of various cropland use transitions on GPP, revealing their spatiotemporal patterns and regional differences. Integrating population and socio-economic data, it employs spatial mismatch analysis to explore the intrinsic relationships among population, economic development, and food production.  
Results indicate that the total area of cropland in Guangdong Province has decreased overall, largely opposite to trends in the cropland use transition rate. Specifically, the total cropland area decreased by 9.85 thousand km\({}^{2}\). Non-agriculturalization and abandoned cropland accounted for annual average losses of 2.76 thousand km\({}^{2}\) and 3.09 thousand km\({}^{2}\), respectively, while the annual average new cropland area was 2.94 thousand km\({}^{2}\). From the perspective of centroid migration, the cropland centroid has shifted northwestward, whereas the population centroid has moved southeastward, indicating a diverging trend in their spatial distribution, exacerbating the decoupling phenomenon between cropland spatial distribution and population.  
Furthermore, from 1991 to 2020, Guangdong Province lost 55.6 million tons of GPP while only gaining 40 million tons, resulting in a deficit of 15.5 million tons annually, which roughly translates to meeting the food needs of about 4.6 million people per year. Under China's policy of the requisition-compensation balance of cropland, the cropland area and food production in Guangdong Province have achieved a dynamic equilibrium, but considering the impact of cropland abandonment on GPP, the newly added GPP has not fully compensated for the lost GPP.  
The study also found that the overall spatial mismatch index in Guangdong Province showed a rapid upward trend, indicating an increase in the spatial mismatch between food production, population size, and the level of economic development. In conclusion, government departments should formulate targeted policies based on different land use types to ensure food security.  
Conceptualization, X.L. ([PERSON]); data curation, Y.Q.; formal analysis, Y.L.; funding acquisition, Y.X. and X.L. ([PERSON]); methodology, Y.L. and J.L.; software, J.L.; supervision, L.L.; validation, J.H. and Z.Q.; writing--original draft, Y.L.; writing--review and editing, X.L. ([PERSON]) and Z.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the SASTIND, Major Special Project for High Resolution Earth Observation System (88-Y40G35-9001-18/20) and Guizhou Provincial Science and Technology Projects (Qiankehe-Basic [2021] 100, [2022] 276, and [2023] 231).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
## 3 Experiments and Analysis of Results
### Intelligent Setting of Indoor Temperature and Humidity
#### 3.3.2 Multi-Method Performance Comparison  
The reward values based on the DDPG algorithm, SARSA, Q-Learning, and DQN methods are shown in Figure 11. The results show that the DDPG controller can achieve faster convergence compared with other methods because DDPG does not require action space discretization and has a smaller number of network outputs to learn the air conditioning system temperature and humidity settings more effectively and obtain higher reward values. Q-Learning and SARSA use Q-table to store and update discrete state-action values, which is more effective when the state and action space is discrete and low-dimensional, but if the state and action space is high-dimensional continuous, there will be an exponential growth of computation as the number of dimensions increases. DQN uses an experience  
Figure 9: Regression analysis of prediction results.  
replay mechanism, which requires discrete action space and slower convergence speed. Compared with SARSA, Q-Learning, and DQN methods, DDPG can obtain the highest reward value and has more effective thermal control performance.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 5 Validation, Simulation and Comparison with Existing Algorithms
### Comparative Study  
In this section, we present the key features of \(MCCM\) and existing algorithms in the state of the art. We discuss the coverage gain, the displacement of nodes as well as the energy gain by the algorithms. Table 2 lists the following characteristics: The algorithm's architecture (Centralized or Distributed), the type of distribution (Homogeneous or Heterogeneous), the resource usage (low, high, medium) and the time complexity.  
The time complexity of \(MCCM\) is O(nlog(n)) similarly to \(CMHN\) and \(ABSDC\), where n is the number of devices. Other algorithms (such as \(OPODCC\) and \(HMRC\)) require high computational resources. They are either centralized or suitable for homogeneous environments only. For instance, \(DCHGA\) uses genetic code approach to calculate the final positions of the devices within the network. It requires high resources since the required memory allocation is \(n\times p\times g\) where \(n\) is the number of devices, \(p\) is the population size, and \(g\) is the number of generations.  
In the next section, we rely on the following indicators to measure the performance of our algorithm: (1) the average number of movements per device \(mov(\Omega)\) related to distance, (2) the coverage gain \(A_{gain}(\Omega)\), which is the probability that each point in the area is covered and (3) the energy depleted by the moving devices denoted \(E\).
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 2 Figures of Merit for Photodetectors
### Photosensitivity  
The parameter most commonly employed to evaluate the performance of photodetectors is photoresponsivity. Photresponsivity quantifies the ability of a photodetector to convert light signals into electrical signals and is defined as [50]:  
\[R_{I}=\frac{I_{ph}}{P}\text{ or }R_{V}=\frac{V_{ph}}{P} \tag{1}\]  
Here, \(I_{\text{ph}}\) (\(V_{\text{ph}}\)) represents the photocurrent (photovoltage), which is the difference between the currents (voltages) with and without light illumination. \(P\) denotes the light power incident on the device. \(R_{\text{I}}\) is referred to as the current photoresponsivity, while \(R_{\text{V}}\) is known as the voltage photoresponsivity. Typically, light intensity or illumination intensity, denoted as \(F\) with a unit of mW/cm\({}^{2}\), is utilized in these measurements. Assuming that the photosensitive area of the photodetector is \(A\), the light power incident on the device can be expressed as \(P=FA\). The photoresponsivity serves as an indicator of a photodetector's sensitivity to light illumination. It is important to note that photoresponsivity depends on both the wavelength and intensity of the incident light. In practical applications, a photoresponsivity that remains unaffected by light intensity is preferable.