esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Suitability of ML Models in SM Estimation
Their findings indicated that the combined models significantly outperformed the individual models in SM estimation. Similarly, [PERSON] et al. [70] conducted a study in Iran and found that a hybrid model integrating convolutional neural network and long short-term memory (CNN-LSTM) yielded superior results compared to individual models. Numerous studies across various regions have  
Figure 5: RF: Random Forest, SVR: Support Vector Regressor, XGBoost: Extreme Gradient Boosting, GBM: Gradient Boosting Machine, ANN: Artificial Neural Network, ELM: Extreme Learning Machine, CNN: Convolutional Neural Network, GA-BPNN: Genetic Algorithm–Backpropagation Neural Network; other classical ML includes Multiple Linear Regression (MLR), M5 Prime (MSP), Relevance Vector Machine (RVM), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), Extra Trees Regressor (ERT), Boosted Regression Trees (BRT), Gaussian Process Regression (GPR), Stacking Ensemble (Stacking), Ensemble Learning, Cubist Model, k-Nearest Neighbors (KNN), Classification and Regression Tree (CART), Extra Trees, Copula Quantile Regression, Ridge Regression, Elastic Net Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Statistical Boosting Classifier (SBC), Principal Component Analysis (PCA); other NNs include Deep Learning: Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Belief Network (DBN), General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN), Backpropagation (BP), Convolutional Long Short-Term Memory (ConvLSTM), Deep Forest, Transfer Learning; other neuro-fuzzy models include Adaptive Neuro-Fuzzy Inference System (ANFIS), Hybrid Fuzzy Inference System (HyFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS). In some studies, multiple models performed equally well, leading to ties. In such cases, all tied models were counted as best-performing models. consistently demonstrated that combining models substantially improves SM estimation accuracy [71; 72; 73; 74; 75; 76]. Hence, future research should prioritize the implementation of hybrid models to enhance SM estimation accuracy rather than relying solely on individual models.
## 4 Experiments
### UniformerV2 Model Experiments  
The experimental parameter settings for the UniformerV2 model are shown in Table 3. The checkpoint used was pre-trained on the Kinetics-400 dataset. We set the training duration to 80 epochs and utilized the AdamW optimizer with a base learning rate of \(5\times 10^{-6}\) and a weight decay coefficient of 0.05. To improve model accuracy, multiple learning rate scheduling strategies were adopted. During the first 20 epochs, a linear warm-up strategy was applied, gradually increasing the learning rate from 50% of the base value to the set value. For the remaining 60 epochs, the cosine annealing strategy was employed, with a learning rate adjustment cycle set to 4 epochs.  
Figure 6a shows the trend of the loss function during the model training process. It can be observed that, with the increase in training epochs, the loss consistently decreases, particularly in the early epochs, where the rate of decline is more rapid. This indicates that the model's training process is gradually converging, and during the initial stages, the adjustment of model parameters is more significant, allowing the model to effectively capture the key spatiotemporal dependencies within the video data. In the later stages, the rate of loss reduction slows down, suggesting that the model's convergence is tapering off, but optimization continues. Ultimately, the loss value approaches 0.6, indicating that the model has successfully learned the features of the training data and possesses a certain degree of generalization capability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Pre-trained Data & Kinetics-400 \\ Epoch & 80 \\ Optimizer & AdamW \\ Base Learning Rate & \(5\times 10^{-6}\) \\ Weight Decay & 0.05 \\ \hline \hline \multicolumn{3}{c}{Phase 1: LinearLR} \\ \hline Learning Rate Multiplier & 0.5 \\ Begin & 0 \\ End & 20 \\ \hline \multicolumn{3}{c}{Phase 2: CosineAnnealingLR} \\ \hline Learning Rate Adjustment Cycle & 4 \\ Begin & 20 \\ End & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter Settings.  
Figure 6: Curves of the model training process. (**a**) Training loss curve; (**b**) validation accuracy curve.  
Figure 6b presents the Top-1 and Top-5 accuracy on the validation set across epochs. The Top-5 accuracy increases rapidly after the initial few epochs, eventually stabilizing around 99%. This indicates that the model can accurately identify the top five possible categories of the target under a relatively lenient standard. Meanwhile, the Top-1 accuracy rises sharply during the first 20 epochs and then grows more slowly, eventually stabilizing around 76%. This suggests that the model exhibits strong classification capability under stricter evaluation criteria, where the first predicted category must be correct. The sustained improvement in validation accuracy, especially the significant rise in Top-1 accuracy, indicates the model's robustness against overfitting. Furthermore, the final stability of the accuracy validates that the model's overall predictive performance has reached an optimal state.
## 4 Simulation Analysis
### Simulation Results in the Static Sink Scenario
Along with those performance gains, finally, we simulated the packet delivery ratio or \(\text{PDR}=\left(\frac{\text{Successfully received data at }D}{\text{total generated data by }5}\right)\). PDR is one of the fundamental performance measures of every routing strategy is also analyzed. Figures 17 and 18 show the PDR comparison of the proposed and the AHH-VBF scheme for different network size and transmission range, respectively. Figures show that the proposed scheme has almost similar PDR as the AHH-VBF as the network size or \(T_{r}\) is very large. On the other hand, the ESEVBF has slightly less PDR because ESEVBF selects the potential forwarders in the in PFZ that have closeness to the center of the cylinder and have larger residual energy. However, due to small number of nodes in the PFZ due to network sparseness, it is quite difficult to find suitable forwarders. Hence, the PDR of the ESEVBF is lower in those network scenarios. Conversely, in the dense network scenario, the chances of finding the suitable forwarder from the PFZ becomes very high that increases the PDR. The above results show that the proposed scheme achieves energy efficiency,  
Figure 16: Average no. of hops data messages traversed versus transmission range. Figure 15: Average No. of Hops data messages traversed versus the network size. broadcast less number of Data packets, and has lower end-to-end delay at the cost of slightly lower or almost identical PDR is different network scenarios. In the next subsection, we briefly analyze and contrast the impact of Sink mobility on the performance gain of both ESEVBF and the AHH-VBF schemes.
###### Abstract  
Light use efficiency (LUE) is a key indicator of vegetation photosynthesis, which provides important insights into how vegetation productivity responds to environmental conditions. The photochemical reflectance index (PRI) is based on reflectance at 531 and 570 nm, which reflects the xanthophyll cycle process of plants under different radiation conditions, and makes LUE related to plant optical characteristics. In this study, tower-based PRI and eddy covariance (EC) based LUEs were used to explore the ability of PRI to track LUE variations in a subtropical, evergreen mixed forest in South China. The results indicate that there is a stronger relationship between PRI and LUE, corrected by the bidirectional reflectance distribution function (BRDF), where R\({}^{2}\) = 0.46 before correction and R\({}^{2}\) = 0.60 after correction. Generally, PRI is able to capture diurnal and seasonal changes in LUE. Simultaneously, this study highlights a significant correlation between LUE and PRI, but there is also a large seasonal difference in its correlation. The correlation in winter was significantly stronger than summer. The strongest correlation is found in November (R\({}^{2}\) = 0.91) and the weakest is in July (R\({}^{2}\) = 0.34). Photosynthetically active radiation (PAR) had a strong influence on the LUE-PRI relationship, while vapor pressure deficit (VPD) and air temperature (Ta) had negative influences on the relationship between LUE and PRI. Terrestrial laser scanning is used to retrieve the vertical structure of forest crown. Our results show that the vegetation canopy structure (i.e., effective leaf area index, LAI\({}_{e}\)), extracted from terrestrial laser scanning (TLS) point data in subtropical mixed forests, had a weak influence on LUE. Our research suggests that environmental factors and vegetation canopy structures should be considered when using PRI to accurately estimate LUE.  
Keywords:photochemical reflectance index; light use efficiency; BRDF; leaf area index +
Footnote †: journal: Remote Sens. 2020, 12, 550; doi:10.3390/rs12030550
## 3 Load Forecasting
LSTM training was the fastest, but its performance declined with longer input sequences [63]. STLF, with its range of minutes to days, plays a central role in power utility operations and planning. It guides load control, dispatch, and secure power system operations, ensuring efficient use of different forms of generation. Accurate STLF is critical for economic dispatch and ensuring power system reliability. Accurate forecasting is critical to avoid power supply problems or inefficient resource allocation due to underestimation or overestimation of load demand. In the last decade, significant progress has been made in load accuracy [64]. This paper focuses on STLF, and Section 5 provides a comprehensive review of STLF approaches, which are categorized into statistical, intelligent-computing-based, and hybrid methodologies, which are further subdivided into subcategories. VSTLF specializes in short-term load forecasting, ranging from minutes to an hour. It differs from other methods in that it relies on the most recent load patterns rather than multi-variable relationships. The VSTLF model can be derived from the STLF model by including loads from a few previous hours as inputs. The conversion processes between STLF, LTLF, MTLF, and VSTLF are shown in Figure 5.  
Figure 5: Classifying electrical load forecasting categories based on various time periods and the conversion process between STLF and LTLF, MTLF, and VSTLF.
# Modernising Receiver Operating Characteristic (ROC) Curves +
## 4 Results and Discussions
### Measurement Uncertainties  
Logistic regressions (Section 4.1) with the Rasch formula (Equation (1)) also furnish estimates of the expanded (Coverage factor, \(k=2\)) measurement uncertainties (details of how these uncertainties are derived can be found in any Rasch program documentation, such as the WINSTEPS(r) manual), \(U(\theta)\), in the abilities, \(\theta\), of each device type (Table 3) and \(U(\delta)\), in the level of difficulty, \(\delta\), of each task. Those measurement uncertainties can be used to add uncertainty intervals to traditional ROC curves (example shown for device type 1 in Figure 8) by calculating for each device type an upper and lower curve where the combined, expanded uncertainty:  
\[U(z)=\sqrt{{U(\theta)}^{2}+{U(\delta)}^{2}}\]  
Figure 7: _AUC_ values compared to (**a**) experimental Linacre \(c\) intercepts, (**b**) device ability \(\theta_{TPR}\), (**c**) device ability \(\theta_{FPR}\), and (**d**) new \(z_{ROC}\) plot intercepts \(C\) for the five device types (1,... 5) used for pregnancy testing.  
is, respectively, added or subtracted to each logistic curve:  
\[P_{success}^{\pm}=\frac{e^{z\pm U(z)}}{1+e^{z\pm U(z)}}\]  
Comparing the scatter between the upper and lower ROC curves (shown in traditional form for device type 1 in Figure 8a) with the measurement uncertainties for the different device types shown in Table 3 gives an indication of how reliably one can distinguish true differences in device type performance from apparent scatter due to uncertainty and limited measurement quality. As pointed out above, such comparisons are particularly challenging for traditional ROC curves (Figure 1) since not only are ROC curves more difficult technically to interpret than straight lines [12], but they are also affected by counted fraction ordinality (Appendix A.1).  
In Figure 8b, a corresponding plot with measurement uncertainties for the \(\mathbf{z}_{ROC}\) curves based on the cumulative rates (Figure 5a) is shown for device type 1. When comparing Figure 8a,b, measurement uncertainties are suppressed in Figure 8a at either extreme of the scale when not compensating for counted fraction ordinality while, as shown more clearly in Figure 8b, measurement uncertainties at the upper and lower end of the scale actually increase due to less information at the scale extremes regarding the performance, leading, in turn, to less accuracy in determining device type ability. Secondly, the response probabilities of traditional ROC curves in general confound the level of difficulty of the classification task with the ability of the classifier to make a correct classification. The latter point--concerning what [PERSON] referred to as specific objectivity--is examined in detail in the next and final part of the Results section.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 6 Privacy-Preserving Architectures for Scalable IoT-Based Healthcare Systems
### Hybrid Cloud Architectures for Flexibility  
Hybrid cloud architectures offer a balance between utilizing cloud resources for computational power and keeping sensitive healthcare data on-premises for enhanced privacy. Healthcare institutions can maintain control over their data while benefiting from cloud scalability. Ensuring secure data transmission between on-premises infrastructure and the cloud and managing data synchronization pose challenges in this approach [13].
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 3 Azimuth Bias  
Azimuth bias can be modelled as the superposition of several terms that can be separated in three different groups. The first one is the erroneous orientation of the antenna boresight due to misalignments between the antenna and the rotation axis. The second one is the non-orthogonality between the rotation axis and the Earth surface. And the last one is the calibration error of the dispositive of angle determination (azimuth encoder). Some of these terms will have the same effect in the measures and they cannot be distinguished in the estimation process using measures from radar. For example, the azimuth offset of the antenna, the azimuth offset of the rotation axis and the azimuth offset of the encoder will have equivalent effects and only one of them will be considered. In this case, the model adds all the effects in a single parameter.  
In other cases, the superposition of these terms will depend on aircraft position. Antenna skew and rotation axis skew will be distinguishable if the aircraft are distributed in azimuth. For these two bias terms both the elevation and azimuth diversity of the aircraft are needed in order to estimate them. Generally, the aircraft will be distributed in the airspace (position and altitude) but sometimes there will be cases where all the aircraft will be concentrated in a small rank of azimuth, altitude or range. In these cases, the bias estimation algorithms will not have the capability to separate the different azimuth and bias components (there would be an observability problem [15]).
# Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents
## 4 Experiment
### Sensitivity Analysis  
To assess the robustness of our model, we conduct a sensitivity analysis on key hyperparameters during fine-tuning, including the learning rate, batch size, and number of training epochs. Most other hyperparameters remain consistent with pre-trained defaults.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Values** \\ \hline Training Steps & 10,000 \\ Warmup Ratio & 0.1 \\ Learning Rate & \(2\times 10^{-5}\) \\ Dropout Rate & 0.1 \\ Epoch & 2 \\ Batch size & 4 \\ Context Window Size & 250 \\ Max Span Length & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter setting.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**RAMS**} & \multicolumn{2}{c}{**WikiEvents**} \\ \cline{2-5}  & **Arg-I** & **Arg-C** & **Arg-I** & **Arg-C** \\ \hline BART-Gen & 48.64 & 51.2 & 67.62 & 61.17 \\ PAIE & 53.2 & 48.0 & 69.3 & 63.4 \\ TSRA & 53.01 & 48.06 & 67.52 & 60.11 \\ TARA & 52.34 & 48.06 & 68.76 & 62.18 \\ TabEAE & 56.4 & 51.5 & 70.0 & 65.6 \\ \hline
**SPARE** & **57.3** & **52.9** & **73.8** & **69.1** \\ _Gain over TabEAE_ & +0.9 & +1.4 & +3.8 & +3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall performance of the SPARE model versus the benchmark model on the RAMS and WikiEvents datasets for argument recognition and argument classification. The highest scores are in bold and the next highest scores are underlined.  
This approach aligns with best practices in prior work [12], where only a small set of critical parameters are varied while others are kept fixed.  
The following ranges are explored:  
* Learning Rate (Adam): \(5\times 10^{-5}\), \(3\times 10^{-5}\), \(2\times 10^{-5}\)
* Batch Size: 4, 8
* Number of Epochs: 2, 3, 4
* Dropout Rate: 0.05, 0.1, 0.2  
Performance is evaluated on the development sets of both RAMS and WikiEvents. Across all hyperparameter combinations, the F1 score varies within \(\pm\)1%, indicating that the model is relatively insensitive to moderate changes in these settings. The optimal performance is observed when the learning rate is set to \(2\times 10^{-5}\), batch size to 4, the number of epochs to 2, and dropout rate to 0.1, demonstrating the effectiveness of this configuration.  
These results confirm that the proposed method maintains stable performance under typical hyperparameter fluctuations, supporting its robustness and applicability across domains.
## 3 Background
### Overview of Target Algorithms
#### Cham  
CHAM is a lightweight block cipher algorithm based on the Feistel structure [2]. CHAM is divided into three types according to the length of the plaintext and the length of the key, CHAM-64/128, CHAM-128/128, and CHAM-128/256. In CHAM-\(n/m\), \(n\) means the length of the plaintext and \(m\) means the key length. The length of the plaintext can be 64 and 128 bits, and the key length can be 128 and 256 bits. The first suggested number of rounds per block and key length of CHAM was 80, 80, and 96 rounds, but, to prevent the threat of related-key differential attacks, in the revised version of CHAM presented in 2019 [2], the number of rounds per block and key length was revised to 88, 112, and 120 rounds. Unlike AES, CHAM does not use a table, and it encrypts plaintext using only XOR, ADD, and ROTATE operations. One plaintext block consists of four words, each word size being 16 bits when the plaintext length is 64 bits and 32 bits when the plain text length is 128 bits. The encryption process of CHAM is shown in Figure 1. The block divided into four words, and words are rotated for each round. At this time, the number of bits in the bit rotation differs depending on round i is even or odd.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Rounds** & **Key Length (bit)** \\ \hline AES-128 & 10 & 128 \\ AES-196 & 12 & 196 \\ AES-256 & 14 & 256 \\ \hline CHAM-64/128 & 88 & 128 \\ CHAM-128/128 & 112 & 128 \\ CHAM-128/256 & 120 & 256 \\ \hline LEA-128 & 24 & 128 \\ LEA-196 & 28 & 196 \\ LEA-256 & 32 & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of rounds according to key length.
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 4 Online Static Security Assessment Method
### Selection of Responses Considering Bus Voltages and Power Flows  
After NRLF analysis, the corresponding security index \(PI_{c}\) is chosen as the response of the MSA-Lasso algorithm. The index can assess the security of the power system under \(N-1\) contingencies [16], and \(PI_{c}\), which considers bus voltages and power flows, is defined as:  
\[PI_{c} =\left[\sum_{i}\Big{(}q_{UI_{i}}^{\max}\Big{)}^{2n}+\sum_{i} \Big{(}q_{UI_{i}}^{\min}\Big{)}^{2n}+\sum_{j}\Big{(}q_{P_{j}}\Big{)}^{2n} \right]^{\frac{1}{2n}} \tag{14}\] \[s.t.\ q_{UI_{i}}^{\max}=\left\{\begin{array}{l}\frac{UI_{i}-H_{ i}^{\max}}{A_{i}^{\min}-H_{i}^{\min}}\,\ if\ U_{i}>H_{i}^{\max}\\ 0\,\ otherwise\end{array}\right.\] \[q_{UI_{i}}^{\min}=\left\{\begin{array}{l}\frac{H_{i}^{\min}-U_ {i}}{H_{i}^{\min}-A_{i}^{\min}}\,\ if\ U_{i}<H_{i}^{\min}\\ 0\,\ otherwise\end{array}\right.\] \[q_{P_{j}}=\left\{\begin{array}{l}\frac{|P_{i}|-P_{H_{j}}}{P_{ H_{j}}-P_{A_{j}}},\ if\ |P_{j}|>P_{H_{j}};\\ 0\,\ otherwise\end{array}\right.\]  
where \(n\) is the exponent, and \(n=2\) in this paper; \(A_{i}^{\max}\) and \(A_{i}^{\min}\) are respectively the upper and lower alarm limits of \(U_{i}\); \(H_{i}^{\max}\) and \(H_{i}^{\min}\) are respectively the upper and lower security limits of \(U_{i}\); \(P_{A_{j}}\) and \(P_{H_{j}}\) are the upper alarm limit and security limit of the \(j\)th line active power flow \(P_{j}\). In this equation, \(q_{UI_{i}}^{\max}\) and \(q_{UI_{i}}^{\min}\) are employed for evaluating the voltage limit violations, while \(q_{P_{j}}\) can determine the extent of line overloading. In the power flow calculation, there are no limits of power flows; thus, the value of \(PI_{c}\) can be very large, and the security index can better evaluate the severity of contingency caused by overloading.  
By adopting the security index \(PI_{c}\) as the response, the operating states are distinguished into three classes as following:  
1. \(PI_{c}=0\), the system is in the secure state,
2. \(0<PI_{c}\leq 1\), the system is in the alarmed state,
3. \(PI_{c}>1\), the system is in the insecure state.  
The contingencies can be identified as one of the three states, and the severity can be sorted by the values of security index \(PI_{c}\), where a higher value of \(PI_{c}\) means a more serious contingency.