esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Contrasting Performance of MODIS and BESS GPPs in Southwest China  
Our results showed that seasonal BESS GPP demonstrated consistent seasonal trajectory with flux observation data and a low RMSE of approximately 1.30 g C m\({}^{-2}\) d\({}^{-1}\). The single hump-shaped trend (Figure 6B) of seasonal BESS GPP is consistent with the growing trajectory of normal vegetation and similar to the results of previous studies [28; 33]. By comparison, MODIS GPP exhibited an unusually lower value in growing seasons and higher value in other seasons (Figure 6B); this result was also reported in a previous study [33]. The possible reason for this finding was that the algorithms of the MODIS models disregarded the differences in C\({}_{3}\)/C\({}_{4}\) plants [29]. Consequently, MODIS overestimated GPP during the periods from January to March and from October to December and underestimated GPP from March to September. In addition, BESS GPP also exhibited consistent interannual variability with flux data from 2000 to 2010 (Figure 11A). This finding is likely due to the improvement in the process-based model of BESS with an adequately modelled spring phenology and soil thawing that largely contributed to the consistent trajectory [28]. Both BESS and MODIS GPPs can spatially capture total interannual variations and presented increasing trends (Figure 7). However, the two GPP products failed to capture the large GPP decrease (Figure 11B) caused by an extreme drought during the period of 2009-2010 [52; 53]. Overall, BESS GPP performed better than MODIS GPP and field observation data.  
Figure 10: Original land cover types converted into forest and their area percentage in relation to the total afirorestation area from 2001 to 2015.
## 4 Experimental Results and Analysis
### Load-Strain Behavior  
Figure 11 delineates the strain distribution by the tensile reinforcing bar within the RC beam, and Figure 11b illustrates the strain exhibited by the Z-shaped side plate in specimens retrofitted with modularized steel plates at maximum load. The analysis of the load-strain relationship for the tensile reinforcing bar illustrated in Figure 11a reveals that all specimens reached a yield strain threshold of 0.002 just prior to peak load, indicating the initiation of yielding. Following this, the specimens transitioned into the plastic region, culminating in failure. In the comparative assessment of strain within the tensile reinforcing bar at identical loads, the non-retrofitted concrete beam manifested the most pronounced strain. In beams retrofitted with modularized steel plates, the strain within the tensile reinforcing bar was decreased slightly. This can be attributed to the resistance to tensile forces by not solely the tensile reinforcing bar of the existing RC beam but also the vertical grid and bottom plate of the added section. This observation accentuates the efficacy of the modularized steel plate in counteracting tensile forces.  
Figure 11: (**a**) Strain of the tensile reinforcing bar and (**b**) strain of the steel plate.  
Figure 11b provides an in-depth depiction of the deformation behavior exhibited by the Z-shaped side plate, which is laterally attached to the existing beam. The S1 gauge located within the beam's compression zone recorded minimal deformation across all specimens, a result of the steel plate's inability to resist tensile forces in that region. A pattern was observed wherein the strain in the steel plate progressively increased as it neared the tension zone of the beam. The added beam is positioned 350 mm from the compressive surface of the existing RC beam, and the S4 and S5 gauges are situated within this region. Importantly, in the MSP-C specimen, which is distinguished by its interconnected steel plates promoting integrated behavior, the strains registered at the S4 and S5 locations were notably higher compared to that at S3, reaching a yield strain of 0.001375. This suggests that interconnected steel plates allow synergistic function between the pre-existing RC beam and the newly incorporated section. These results are evidence of the substantial tensile resistance provided by a modularized steel plate when effectively bonded to the beam.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 5 Further Analysis
### Different Numbers of Actual Samples  
Another important factor is the number of actual signal samples. In this experiment, we randomly selected 360, 720, 1080, 1440, and 1800 actual samples to fine-tune the model and freeze the first convolutional layer. Because a small number of actual signal samples are used to fine tune the model during transfer learning, the selection of a small number of samples has a large impact on the model performance. Therefore, we repeat the experiment five times with different numbers of randomly selected samples, and the average of the five sets of experimental results is taken as the final experimental result. The performance of the model on the test dataset \(D\) on four evaluation metrics is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Freeze Method** & **Evaluation Metrics** & **MAE (\({}^{\circ}\))** & **RMSE (\({}^{\circ}\))** & **MAXE (\({}^{\circ}\))** & **Ratio-1** \\ \hline Freez\_0 & 0.716 & 0.912 & **3.775** & 0.737 \\ Freez\_1 & **0.678** & **0.860** & 4.020 & **0.766** \\ Freez\_2 & 0.791 & 1.050 & 17.415 & 0.699 \\ Freez\_3 & 0.780 & 1.186 & 13.830 & 0.707 \\ Freez\_4 & 0.908 & 1.186 & 8.713 & 0.643 \\ Freez\_5 & 3.992 & 6.807 & 179.662 & 0.168 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of freezing different network layers.  
We can observe from Table 5 that MAE, RMSE, and MAXE keep decreasing overall and Ratio-1 keeps increasing as the actual sample number increases. The maximum angular error MAXE refers to the error of the one with the largest angular error among all the test samples, which is further influenced by some individual samples. Therefore, it is reasonable for MAXE to have some volatility. When all 1800 samples in the training set are used, the performance of the model is relatively the greatest, with an MAE of 0.678\({}^{\circ}\), RMSE of 0.86\({}^{\circ}\), MAXE of 4.02\({}^{\circ}\), and ratio-1 of 0.766. It can be inferred that the larger the sample size in the target domain, the more knowledge the model learns, and the better the performance of the model in the target task.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 2 Notation and SRAM-PUF Statistical Model
### Sram-Puf Statistical Model
#### 2.2.4 \(\Theta\)-Distribution  
Until now, we have assumed that \(\lambda_{2}=0\) and thus the SRAM-PUF is unbiased. This assumption is sufficient for the security and achievability proofs in Section 4. However, to predict the performance of our scheme in a practical setting, we also need to set a value for \(\lambda_{1}\). In correspondence with previous works [7; 11] we choose \(\lambda_{1}=0.51\) for our simulations, which corresponds to average error probability regarding dominant (most likely) value of a cell  
\[\overline{\psi}\stackrel{{\Delta}}{{=}}\int_{0}^{1}\min(\theta, (1-\theta))p_{\Theta}(\theta)\,d\theta\approx 0.15. \tag{10}\]  
In Figure 2, we plot the \(\Theta\)-distribution that is used for the calculations later in the paper. Please note that stable cells (one-probability close to 0 or 1) are more likely than unstable cells. We will see later that knowledge about the stability (error probability) of specific cells can improve the performance of helper data schemes.  
Figure 2: Distribution of the one-probability \(\Theta\) as used for the simulations in this work. Generated according to the statistical model for SRAM-PUF [7] with \(\lambda_{1}=0.51\), and \(\lambda_{2}=0\).  
Finally, note that temperature and voltage ramp-up time of the power up may influence the behavior of the SRAM PUFs [21]. Here, we do not model this behavior and only assume a worst-case average error probability of 0.15 (see (10)). However, it is worthwhile mentioning that the model (4) can be extended to also model the temperature dependence, see [22]. Knowledge about the temperature behavior can be exploited to improve the performance of the helper data schemes, especially when the temperature is known [23]. Therefore, in the future, it may be beneficial to extend the current work to also consider temperature dependent behavior of the SRAM PUFs.
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
## 3 The Proposed C-DL-NOMA System Model  
Having introduced the DL power-domain NOMA communication system, we want to support the security of data transmission through it via encryption of this data (Figure 2) using different chaotic maps. In the area of CBSC, chaotic signals are frequently employed for various modulation techniques, including chaos on-off keying (COOK), CSK, chaos-parameter modulation, and CM. In this study, we applied CM modulation to DL power-domain NOMA using hybrid chaotic maps. In this technique, plain data are added to the chaotic signal sequence, after which the produced data are transmitted through the NOMA channel. The plain data can be restored through subtraction of the regenerated chaotic signal sequence from the received data. We now explain the chaotic maps used in our proposed C-DL-NOMA system and how we employ them to encrypt the data. Chaotic maps have attractive characteristics, such as easy generation that utilizes low-cost circuits, a noise-like spectrum, unpredictable behavior, sensitivity to initial conditions, and rapid iteration. Therefore, chaos-based data encryption techniques are suitable for real-time applications. Some existing chaotic maps are discussed below.
## 3 Methodology
### Time Series Forecasting Models
#### 3.1.3 Temporal Convolutional Network  
Temporal Convolutional Network (TCN) is a new architecture of CNN but more specialized in temporal series. This architecture is inspired by _Wavenet_ autoregressive model [28], which was designed for audio generation problems.  
TCN architecture is an adapted CNN with these special characteristics:  
1. Convolutions are causal to prevent information loss.
2. The architecture can process a sequence of any length and map into an output of the same length.  
TCN architecture uses dilated causal convolutions to learn the long-term dependencies in the time series. These convolutions increase the network's receptive field without losing resolution because the pooling operation is not needed. Additionally, residual connections are employed to increase the network's depth to deal effectively with an extensive history size.  
TCN has low memory requirements for training due to shared convolutional filters, long input sequences can be processed with parallel convolutions, and are more stable training schemes.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 5 Evaluation
### Performance Evaluation on a Wireless Sensor Testbed
#### 5.2.3 Response Time  
Another key indicator of the performance of any group communication solution is its response time. Figure 12 shows that the average response time when using our group communication solution increases with the increase of the Wi-Fi interference in the WSN. When there is no loss, the response time for unicast is just above the sum of the delays between the requests that the EM added, i.e., 1, 2 and 3 s for the groups of 10, 20 and 30 members, respectively. For multicast, the response time is determined by the leisure (5 s in our case). It is always lower than 5 s, since the nodes choose a value between 0 and 5 s before sending their replies. The value that is shown here is the value until the last response was received at the EM. When the interference increases, the response times for multicasts remain almost unchanged, since either the packet is delivered on time or it is just dropped. This way, the multicast solution is capable of maintaining lower response times at the expense of a decreased reliability. In the case of our solution, when a packet is dropped, CoAP attempts to retransmit it, leading to an increased overall response time.  
Figure 11: Entity reliability for both unicast and multicast group communication and varying group sizes in the presence of Wi-Fi interference. The reliability of the complete group is less than the reliability of individual members (Figure 10). Again, the reliability of the complete group is much better when using entity-based group communication.  
Figure 12: Entity response time for both unicast and multicast group communication and varying group sizes in the presence of Wi-Fi interference.  
These experimental results are also in line with the simulated results in [9]. Again, a direct comparison with the simulated results is not possible, as explained at the end of Section 5.2.2.
## 3 Results and Discussion
### Compatibility of MPNTS
Combined with the common functions of all kinds of surfactants, we infer that MPNTS has good compatibility with cationic surfactants and has a good application prospect in printing aids, fungicides, hair conditioners, and other products. Then, it has good compatibility with anionic surfactant and has a good application prospect in detergents, such as detergent and shampoo. Finally, it has good compatibility with nonionic surfactants and has a good application prospect in dispersants, solubilizers, and other products.
## 3 Research Model and Hypothesis Development
### SNS Fatigue, Living Disorder, and Reduced SNS Use Intention  
This study examined three variables (SNS fatigue, living disorder, and reduced SNS use intention) that occur as negative consequences of SNS use. First, SNS fatigue is defined as the extent to which people experience psychological discomfort or stress while using SNSs. Living disorder refers to the extent to which users believe that SNS fatigue causes discomfort in their everyday lives, such as at work and in learning. Finally, reduced SNS use intention is defined as users' degree of willingness to reduce and limit SNS use as much as possible. [PERSON] et al. (2019) argued that social overload is an important antecedent of behavioral responses. In other words, negative perceptions of SNSs, such as social overload, reduce or eliminate the use of SNSs. [PERSON] et al. (2019) also claimed that SNS-related stressor factors decrease SNS utilization. The psychological inconsistency of SNS users may have a negative impact on their work and lives.  
Other studies e.g., ([PERSON] et al., 2019) have shown that SNS fatigue is a negative emotion experienced by individuals in the course of using SNSs. These studies suggested that this fatigue reduces the need for SNSs and causes problems in individuals' daily lives. Furthermore, [PERSON] (2019) contended that once SNS users feel fatigue because of excessive use, they are often unable to focus on their tasks, resulting in low job performance. In addition, that study explained that problems such as stress and psychological inconsistency affect the discontinuation of SNS use. Users who experience SNS fatigue often experience a loss of balance in their daily lives because they invest more time and effort in other activities to alleviate the factors that cause irritation or fatigue to nearby people ([PERSON] et al., 2019). Furthermore, [PERSON] et al. (2019) claimed that excessive SNS usage increases users' fatigue, which often leads to both living and job disorders. Consequently, the negative perception of and experience with SNSs can lead to reductions in SNS use. Taking into account these previous findings, we proposed the following two hypotheses regarding decreased SNS use intention-related variables.  
**Hypothesis 5**.: _SNS fatigue is positively associated with living disorder._  
**Hypothesis 6**.: _Living disorder is positively associated with reduced SNS use intention._
# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 4 Experimental Setup
### Evaluation  
The effectiveness of the proposed ArSL classification was evaluated based on four major outcomes [32]: true positives (_tp_), false positives (_fp_), true negatives (_tn_), and false negatives (_fn_). The accuracy determines the ability to differentiate ArSL signs correctly, which was computed by the following formula:  
\[Accuracy=\frac{tp+tn}{tp+tn+fp+fn}\]  
In deep learning, the loss metric is a measure of how well the model is performing in terms of its ability to make accurate predictions. It represents the discrepancy between the predicted output of the model and the actual output or target value. The loss metric is commonly used to quantify the error or cost associated with the model's predictions. The goal is to minimize this error by adjusting the model's parameters during the training process. The loss measure in this study was computed by the following formula:  
\[Loss\ function=\frac{1}{N}\ \sum\Bigl{(}y_{pred}-y_{true}\Bigr{)}^{2}\]  
where \(y_{pred}\) is the predicted output of the model, and \(y_{true}\) the ground-truth output or target value. We also used AUC, precision, recall, and F1-score to evaluate our models. The AUC (area under the receiver operating characteristic curve) measures the overall performance of a binary classification model across various classification thresholds. It quantifies the model's ability to distinguish between positive and negative samples.  
The AUC represents the area under the curve of the receiver operating characteristic (ROC) curve, which plots the true positive rate (recall) against the false positive rate (\(1-\) specificity) at various classification thresholds. Precision measures the proportion of true positive predictions out of all positive predictions made by the model. It indicates how well the model correctly identifies positive samples.  
\[Precision=tp\ /\ (tp+fp)\]  
Recall, also known as sensitivity or true positive rate, measures the proportion of true positive predictions out of all actual positive samples. It indicates how well the model captures the positive samples.  
\[Recall=tp\ /\ (tp+fn)\]  
The F1 score is the harmonic mean of precision and recall. It provides a balanced measure of the model's performance by considering both precision and recall simultaneously.  
\[F1\ Score=2\times(Precision\times Recall)\ /\ (Precision+Recall)\]The F1 score ranges between 0 and 1, with 1 indicating perfect precision and recall, and 0 indicating poor performance.
# Feature Extraction and Recognition of Chinese Mitten Crab Carapace Based on Improved MobileNetV2
## 3 Experiment and Analysis
### Ablation Experiment  
To assess the specific impact of different components on model performance, this study conducted a series of ablation experiments. These experiments aimed to verify the contributions of the coordinate attention mechanism and the choice of loss function to the recognition accuracy of Chinese mitten crabs; the results are shown in Table 2.  
In the baseline model MobileNetV2, which only outputs feature vectors, we observed a recognition accuracy of 69.96%, setting the benchmark for subsequent experiments. After introducing the coordinate attention mechanism, the model's accuracy improved to 75.72%. This significant increase demonstrates the effectiveness of the CA mechanism in enhancing the model's ability to recognize the morphological features of river crabs. This suggests that the CA mechanism, by finely allocating attention across spatial dimensions, enables the model to focus more on the key morphological features of the crab carapace, thus improving recognition precision.  
Furthermore, simply integrating the ArcFace loss function into MobileNetV2 resulted in a further substantial increase in accuracy, reaching 97.71%. The ArcFace loss function optimizes the feature space by introducing angular margins, promoting intra-class compactness and inter-class distinguishability and significantly enhancing the model's discriminative ability. This result underscores the importance of angular margins in class discrimination and their contribution to enhancing feature differentiation.  
Ultimately, combining the strengths of the coordinate attention mechanism and the ArcFace loss function in one model achieved the highest accuracy, at 98.56%. This further proves the complementary nature of CA and ArcFace; their combination not only enhances feature extraction capabilities but also optimizes the separation between categories. This integrated approach provides an effective strategy for achieving the high-accuracy recognition of individual Chinese mitten crabs.
## 3 System Description
### Methodology
#### 3.1.4 Proposed Formulae and Algorithm Validation  
Instead of using the iterative approach (as described in Section 3.1.1), this stage involved the generation of optimized transmitter locations via the proposed Algorithm 1. Next, its validation was performed by comparing these positions to the best case acquired from Section 3.1.3. If the values were approximately same, the proposed technique was considered to be valid, and vice versa.  
Figure 2: An overview of the Methodology adopted in this work to achieve signal-to-noise ratio (SNR) and illuminance uniformities.  
Figure 3: LED lamps placement scenarios: Cases I to XII.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 3 System Model and the Novel Schedulers
### Recursive Maximum Expansion Modified
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(RB_{1}\) & \(RB_{2}\) & \(RB_{3}\) & \(RB_{4}\) & \(RB_{5}\) & \(RB_{6}\) \\ \hline \(UE_{1}\) & 0.52 & **1.8** & 0.2 & 1.7 & 1.8 & 1.6 \\ \(UE_{2}\) & 0.9 & 0.5 & **1** & 0.6 & 0.52 & 0.65 \\ \(UE_{3}\) & 0.71 & 0.4 & 0.8 & 0.23 & **1.2** & **0.55** \\ \hline \end{tabular}
\end{table} 
Table 2: Example with recursive maximum expansion. Figure 3: Flow chart of RME-M uplink scheduler for M2M devices. Figure 4 represents an example of resource blocks allocation using RME-M scheduler, if there is no user with critical delay. Figure 4a shows that \(UE_{0}\) has the highest metric value at \(RB_{14}\), so \(UE_{0}\) is selected to be scheduled. Then a search of other higher values for \(UE_{0}\) is done. In this example, \(UE_{0}\) has only one maximum that allows to send data with good channel quality. So \(RB_{14}\) is assigned to \(UE_{0}\). RBs allocation is expanded at the right and left hand side of \(RB_{14}\), until another UE with a better metric is found (\(UE_{2}\)). Consequently, RBs from \(RB_{9}\) to \(RB_{18}\) are allocated to \(UE_{0}\); then, the latter is put into an idle state. The search of the next maximum among the non-idled UEs (Figure 4b) shows that \(UE_{2}\) has two maximums at \(RB_{6}\) and \(RB_{22}\); however, the allocation of \(RB_{22}\) to \(UE_{2}\) allows for assigning more RBs with good channel quality than if \(RB_{6}\) is allocated to \(UE_{2}\). Consequently, resource blocks from \(RB_{19}\) to \(RB_{26}\) are assigned to \(UE_{2}\). The search of the maximum among non idled UEs is repeated (Figure 4c). When all UEs are idled and not all RBs have been allocated, remaining resources are allocated to the adjacent UE with highest metric (Figure 4d,e). A comparison between the final resource blocs allocation using RME-M and RME schedulers is shown in Figure 4e,f respectively. RME-M allocates the set of RBs with best CQI, contrarily to RME (Figure 4f which only searches for the combination \(UE_{i}-RB_{j}\) with the highest metric value and assigns \(RB_{j}\) to \(UE_{i}\) without considering metric value of adjacent RBs. Figure 4: Example of resource allocation by RME-M and comparison with RME.