esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Empirical Setup
### Investment and Operational Costs  
The success and financial gain of expanding energy capabilities rely on the costs of operation and the initial investment required for each project, as outlined in Tables 3 and 4, which detail the investment expenses per megawatt (MW) as well as the operational costs in various policy and market scenarios.
## 5 Results and Discussion
### Computational Efficiency Analysis
#### 5.7.3 Comparative Efficiency  
The comparison underscores the advantages of 2D models:  
* **2D Models**:
* Highly efficient with low ERT and RAM usage;
* Performance remains stable across different segmentations, making them ideal for rapid and large-scale simulations;
* **3D Models**:
* Substantially higher computational demands, limiting their practicality for extensive or iterative studies;
* The complexity increases sharply with segmentation and length, posing significant challenges for simulating large-scale or twisted conductor configurations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Segment** & **Parallel** & **CPU Time** & **ERT** & **RAM Usage** \\  & **Cores** & **(Hours)** & **(h-c)** & **(GB)** \\ \hline
1 Segment (2D) & 4 & 0.7 & 2.8 & 6.7 \\
3 Segment (2D) & 4 & 0.9 & 3.6 & 7.1 \\
5 Segment (2D) & 4 & 0.9 & 3.6 & 8.2 \\ \hline
1 Segment (3D) & 24 & 3.1 & 74.4 & 154.1 \\
3 Segment (3D) & 24 & 3.0 & 72.0 & 373.2 \\
5 Segment (3D) & 64 & 2.5 & 160.0 & 1072.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Computational resource utilization.
## 4 Simulation Results and Discussion
### Influence on Fault Indicator Configuration Scheme by Environment
#### 4.3.2 Weather State  
[PERSON] et al. studies the influence of temperature on LoRa communication performance in [31]. [PERSON] et al. [32] found that extreme weather conditions will affect the service life of LoRa and NB-IoT. Moreover, [PERSON] et al. have applied LoRa to time-aware monitoring of overhead transmission line sag and temperature and verified its working state in [33]. [PERSON] et al. [34] have presented a low-cost LoRa-based network, which is able to evaluate the level of fire risk and the presence of a forest fire. The system has been tested in a real environment and the results show that it works well. Similarly, [PERSON] et al. [35] has adopted the LoRa and NB-IoT in the agriculture industry. The experimental verification shows that the weather state such as temperature has a certain influence on the communication performance of LoRa and NB-IoT, but it does not affect its normal work.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Junction Nodes** & **Installation Number of Acquisition Unit** & **Installation Position of Acquisition Unit** & **Installation Number of Collection Unit** & **Installation Position of Collection Unit** & **Total Cost (USD)** \\ \hline \hline \multirow{4}{*}{Original: 23} & \multirow{4}{*}{14} & 3, 5, 6, 8, 9, 10, & \multirow{4}{*}{1} & \multirow{4}{*}{7} & \multirow{4}{*}{228.48} \\  & & 11, 12, 13, 15, & & & \\  & & 18, 19, 22, 23 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal placement of fault indicators under different junction nodes of the power line.  
Influence on Fault Indicator Configuration Scheme by Changing the Relationship between Probability of Error and Distance  
In order to prove the fitness of the simplified linear relationship between probability of error and distance, we studied the impact of the relationship between probability of error and distance on the optimal configuration scheme by changing the Equation (7) to  
\[p=\left[\sqrt{(x_{i}-x_{j})^{2}+\left(y_{i}-y_{j}\right)^{2}}\ /d\right]^{2} \tag{21}\]  
By solving the model, the result is shown in Table 6.  
As shown in Table 6, the installation number and position of the acquisition unit and collection unit have no change under different relationships between probability of error and distance. Hence, the simplified linear relationship between probability of error and distance is appropriate to be used in the optimal configuration scheme of fault indicators. It is worth mentioning that it would be interesting to further study the relationship between probability of error and distance in the proposed model. We set this aside for our future work.
## 2 Materials and Methods
### Satellite Data and Image Processing
#### 2.4.1 Retrieval of Leaf Chlorophyll Content (LCC)  
As demonstrated in studies [42; 43; 44], SPAD readings were directly correlated with remotely sensed data, and feasible for the retrieval of LCC over carrot fields. A scatter plot was drawn between SPAD measurements and VIs derived from Sentinel-2 (S2) selected vegetation indices (VIs) and the performed regression analysis for the retrieval of LCC over carrot fields. We obtained imperial model for the simulation of LCC and expressed as Equation (2). The best-fit models were identified with coefficients of determination (R2) and root mean square error (RMSE), and, subsequently, based on the statistical analysis best or suitable vegetation index for upscaling of field-measured SPAD readings to simulated LCC layers. Thereafter, the simulated LCC layers were utilized in crop health assessment and the development of yield prediction models.  
\[Mi=aVI(x)+b \tag{2}\]  
where \(a\) and \(b\) are the model parameters, \(i\) represents for number of models, and \(x\) indicates the studied vegetation index.
# Unit Commitment with Ancillary Services in a Day-Ahead Power Market
## 2 Problem Formulation
### DPSO-SQP Implementation Procedure  
The procedure of using DPSO-SQP to compute unit commitment with ancillary services is described as follows.  
1. Input the load demand, as well as the AGC, RSR, and SR demands on ancillary services.
2. Input the operating limits of the units in ancillary services and the costs of electric energy, AGC, RSR, and SR.
3. Set the maximum population size \(j_{max}\) and maximum number of iterations \(t_{max}\) of DPSO.
4. Import the equality and inequality constraints into the SQP program.
5. Initialize the search area, and set the initial position with a local random search.
6. Copy the initial position to the generated optimum particle position.
7. Use DPSO to solve the states of units in a state matrix.
8. Use SQP to compute the generation output of various units, and calculate the total cost.
9. Update the optimal solutions of \(Xbest_{i}^{l}\) and \(Gbest^{l}\).
10. Go to (7) for the next generation.
11. If the preset maximum number of iterations is reached, iteration terminates, otherwise go to (5).
12. Export the minimum cost and the assigned generation output of various units, including electric energy, AGC, RSR, and SR.  
The flow chart of the DPSO-SQP problem is shown in Figure 1.  
Figure 1: Flow chart of DPSO-SQP applied to unit commitment with ancillary services.
## 3 Results and Discussions  
In this section, numerical simulations and hardware experiments are presented to investigate the performance of the proposed PWC-based \(m\)CAP-DM scheme with 2D chaotic encryption and compare it with other benchmark schemes. In our simulations and experiments, we considered the 4 CAP case in which the overall bandwidth is divided into four subbands, i.e., \(m=4\). Moreover, each subblock was assumed to have two subbands, where one subband was selected to adopt constellation mode 1 while the remaining one adopted constellation mode 2 at each time slot, i.e., \(N=2\) and \(k=1\). The corresponding simulation parameters are shown in Table 2. To make a fair comparison, both 4 CAP-DM and 4 CAP-IM achieved the same SE of 2.5 bits/s/Hz. Specifically, the square 16-QAM constellation was used in 4 CAP-IM, while the circular (7,1)-QAM constellation was adopted in 4 CAP-DM. In our previous work [18], our simulation and experimental results showed that the circular (7,1)-QAM constellation achieved better BER performance than conventional square 8-QAM or 8-ary phase-shift keying (8-PSK) constellations in orthogonal frequency division multiplexing with dual-mode index modulation (OFDM-DM) based VLC systems. Therefore, the circular (7,1)-QAM constellation was adopted in 4 CAP-DM in this work. Moreover, the interleaving-based constellation partitioning approach was considered to obtain two constellation modes from the circular (7,1)-QAM constellation. Please refer to our previous work [18] for more details. Since the basic 4 CAP with general \(M\)-ary constellations cannot achieve a SE of 2.5 bits/s/Hz, it was not considered in our following simulations and experiments.
# Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
## 1 Introduction  
Human cutaneous melanoma is the deadliest type of skin cancer, characterized by a swiftly rising incidence rate [1]. It is among the most aggressive neoplasms, rapidly metastasizing to distant organs. When it progresses to a metastatic stage, it establishes powerful mechanisms to resist current medical therapies (such as chemo- and radiotherapy). However, when detected early, in almost all cases, it is possible to treat melanoma with a simple surgical excision [2].  
The acral lentiginous melanoma (ALM) is a kind of lentiginous skin melanoma located mainly on soles and palms (Figure 1). Although ALM is rare among people with lighter skin types, in non-Caucasian populations, nearly half of melanomas are located in acral volar areas [3]. ALM has four patterns considering pigmentation: parallel-furrow, lattice-like, fibrillar, and parallel-ridge pattern [4]. Among these patterns, ALM lesions with the furrow and ridge patterns occurs most frequently [3]. In particular, the parallel ridge pattern shows 86% sensitivity and 99% specificity as a characteristic feature of melanoma [5]. The examination of pigmentation patterns is possible through a digital epiluminescence microscopy (ELM, also dermoscopy or dermatoscopy) which is a non-invasive, in vivo technique that makes subsurface structures of the skin accessible for examination in anoptic magnification between 10 to 40 times [2]. Nevertheless, the accuracy of expert dermatologists in diagnosing melanoma is estimated to be 75-84%, which is partly due to the subjectivity of the diagnostic judgments [6].  
To overcome this problem, some methods for automated diagnostics of melanoma using dermoscopic images of suspected lesions were already developed [7]. However, they are aimed at detecting non-acral melanomas. Since the appearance of ALM differs significantly from that of melanoma in other sites of the body, it is difficult to apply those methods to ALM [8].  
In this study, the authors propose an automatic segmentation algorithm designed for acral lesions. It is expected that the proposed algorithm will help localize dermoscopic patterns in ALM more easily, thus contributing to the more effective early diagnosis of ALM.
## 2 Gaussian Color Mapping Image Segmentation Algorithms
### Otsu Image Threshold Segmentation  
Otsu is an adaptive threshold segmentation algorithm [22; 23; 24]. It separates the background and the target by calculating the maximum inter-class variance of the image. The idea of [PERSON]'s algorithm is that given an image, it is assumed that its gray level is divided into m levels. When the gray level is \(\theta\) and the number of pixels is \(n\), then the total number of pixels \(N\) and the pixel probability \(P_{\theta}\) can be expressed as:  
\[N=\sum_{\theta=1}^{m}n \tag{4}\]  
\[P_{\theta}=\frac{n}{N} \tag{5}\]  
Introducing the initial threshold \(\delta\), the pixels of the image can be divided into two parts according to whether the pixel value is greater than \(\delta\), and the expressions for the probabilities \(\omega_{1}\) and \(\omega_{2}\) are as follows:  
\[\omega_{1}=\sum_{\theta=\delta+1}^{\delta}P_{\theta}=\omega \tag{6}\]  
\[\omega_{2}=\sum_{\theta=\delta+1}^{m}P=1-\omega(\delta) \tag{7}\]  
In the formula, \(\mu\) is the average gray value, so the expression of \(\mu\) with respect to \(\mu_{1}\) and \(\mu_{2}\) is as follows:  
\[\mu=\omega_{1}\mu_{1}+\omega_{2}\mu_{2} \tag{8}\]  
The variance of the pixel values between the two regions is as follows:  
\[\sigma^{2}=\omega 1(\mu_{1}-\mu)^{2}+\omega^{2}\Big{(}\mu^{2}-\mu\Big{)}^{2}= \frac{\left[\mu\cdot\omega(\delta)-\mu(\delta)\right]^{2}}{\omega(\delta)[1- \omega(\delta)]} \tag{9}\]  
Figure 6: Image Grayscale contrast curve.  
Figure 7: Image contrast curve.  
Bring in different values of \(\delta\) until \(\sigma^{2}(\delta)\) reaches the maximum and then stop the iteration. Otsu determines the threshold by calculating the maximum between-class variance. This method can adaptively determine the threshold and has a good segmentation effect on the bimodal histogram. In order to reduce the degree of the segmentation algorithm, the color-transformed RGB image is converted into a grayscale image, and the spot area is segmented using the Otsu algorithm. There may be a few noise points in the transformed image. The redundant noise points can be removed by morphological filtering [25; 26]. The result is shown in Figure 8:
## 1 Introduction
Each driver's contribution to the trend in WUE change includes not only the influence of the relationship but also the direction and degree of the change trend of the driver. Based on the sensitivity of WUE to these variables, using the changing trends of the variables themselves to assess contributions can prevent underestimating the contributions of weakly sensitive factors. The dominant factors that influence WUE dynamics can be identified by comparing the contribution of each driver to WUE dynamics. At the same time, the multiple driving factors are interlinked and often jointly regulate the WUE of an ecosystem; therefore, analyzing the control mechanisms of WUE is often difficult. The multiple regression method is commonly utilized to enumerate the contributions of environmental drivers. Although multiple regression is often impacted by the challenge of multicollinearity, ridge regression can effectively eliminate factor collinearity [7; 8]. Moreover, apart from its direct effects, environmental variables may also exert indirect effects on WUE through mediating variables. For example, vapor pressure deficit (VPD) directly impacts the characteristics of plant physiology (such as stomatal conductance) as a transient change in GPP and ET, but can also show a delayed impact on WUE by modulating plant phenology and canopy structure; in this process, LAI is the mediating variable [19]. Path analysis has proven to be very effective in assessing both the indirect and direct impacts of assumed causality [1; 28]. Approximately 25% of China's land area contains subtropical forests typified by elevated biodiversity, special types of vegetation, and unique ecological functions; this forest area plays a key function in maintaining the region's ecological balance [29]. Understanding the water-carbon coupling relationship of vegetation and its driving mechanisms is crucial. However, previous studies have mainly focused on the temporal and spatial variation characteristics of WUE and its relationship with precipitation and temperature [30]. The response of WUE to climate change, vegetation growth, and elevated atmospheric CO\({}_{2}\) concentration remains unclear, especially in the subtropical region of China (STC). Therefore, the goals of this study were as follows: (1) to assess interannual variations in the WUE of the subtropical region of China from 1982 to 2018; (2) to analyze the direct and indirect effects of climate change, vegetation growth, and elevated atmospheric CO\({}_{2}\) concentration on WUE trends; and (3) to estimate relative and actual contributions of drivers to WUE change. It was hypothesized that the interannual variation in WUE was mostly controlled by vegetation growth (as represented by LAI). By studying changes in WUE within different landscape types and the underlying mechanisms, the interaction between ecosystems and climate change will be better understood. As such, the results of this study offer a scientific reference for ecological management and restoration of the STC under climate change.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### Model Training and Backpropagation
```
Input: Medical image dataset \(D\) (COVID, Non-COVID) ;  CNN-GNN feature extractor \(\text{FE}_{\theta}\) ;  Transformer classifier \(ViT_{\phi}\) Output: Trained ViTGNN model
1 Step 1: Preprocessing ;
2 Apply CLAHE, Gaussian blur, normalization, and resizing on \(D\);
3 Augment data (crop, flip, rotation, color jitter);
4 Step 2: Dataset Splitting ;
5 Split \(D\) into training (80%), validation (10%), and test (10%);
6 Initialize data loaders;
7 Step 3: CNN Pretraining ;
8 Train a baseline CNN classifier on \(D_{\text{train}}\);
9 Save trained CNN weights;
10 Step 4: GNN Pretraining ;
11 Freeze CNN weights;
12 Add GNN layers and train on CNN feature maps;
13 Save CNN-GNN combined weights;
14 Step 5: ViT Integration ;
15 Load pretrained CNN-GNN as \(\text{FE}_{\theta}\);
16 Adjust channel dimensions and upsample to \(224\times 224\);
17 Initialize \(ViT_{\phi}\) with 50% trainable parameters;
18 Replace ViT classifier head;
19 Step 6: Model Training ;
20 Train ViTGNN model (\(\text{FE}_{\theta}\to ViT_{\phi}\)) with AdamW;
21 Apply early stopping based on validation loss;
22 Step 7: Evaluation ;
23 Evaluate model on \(D_{\text{test}}\); accuracy, precision, recall, F1-score, and AUC;
```  
**Algorithm 1****ViTGNN Model for SARS-CoV-2 Detection**
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 5 Policy Gradient for MA-Stream
### Model-Free Multi-Agent Policy Gradient Algorithm  
In [15], a novel model-free algorithm is proposed in order to solve the nonlinear (in time) optimization problems in a finite time horizon scenario. Like the traditional deep policy gradient RL algorithm, the MAPG-finite algorithm utilizes the observed system state \(s(t)\) as the input of a neural network that is parameterized by \(\theta\), then takes the output of the neural network to be the decision policy \(\pi_{\theta}\), which indicates the action probabilities. Finally, according to the policy \(\pi\), an action is randomly chosen to interact with the environment.  
With a proper training process, the neural network is improved by the reward feedback. Expectedly, the neural network parameter \(\theta\) should evolve to stage \(\theta^{*}\), which maximizes the objective function \(f\):  
\[\theta^{*}=\arg\max_{\theta}f\Big{(}(1-\gamma)J_{\pi_{\theta}}^{1},...,(1-\gamma )J_{\pi_{\theta}}^{K}\Big{)}, \tag{29}\]  
where \(J_{k}^{\pi_{\theta}}\) denotes the long term reward obtained by agent \(k\) running policy \(\pi_{\theta}\):  
\[J_{\pi}^{k}=\mathbb{E}_{s_{0},a_{0},s_{1},a_{1},...}\Bigg{[}\lim_ {L\rightarrow\infty}\sum_{l=0}^{L}\gamma^{l}f(T_{s}(l,T_{l},k))\Bigg{]},\] (30) \[s_{0}\sim\rho_{0}(s_{0}),a_{l}\sim\pi(a_{l}|s_{l}),s_{l+1}\sim P (s_{l+1}|s_{
## 4 Experiments
### Generalization Ability Study  
An excellent quality evaluation algorithm should not only achieve good performance in the single dataset experiment but also perform well in the face of unknown distortion, which is the premise for its successful application. To this end, we tested the generalization capabilities through cross-database experiments on internal databases that were either authentic distortions or synthetic distortions. The results of the cross-database tests of the synthetic distortion are shown in Table 6, and the results of the cross-database tests of the authentic distortion are shown in Table 7.  
For the synthetic cross-database tests, we selected seven methods for comparison, namely DIIVINE [61], BRISQUE [22], BLIINES-II [29], HOSA [28], WaDIaM-NR [24], PQR [35], and DBCNN [39]. The proposed HFNet achieved the best performance with 0.531, when the CSIQ was the training set, and 0.823, when the TID2013 was the training set. The other compared algorithms were obviously inferior to the proposed HFNet. With the same synthetic distortion, the performance of these two cases was quite different. One of the main reasons is that the TID2013 contained more distortion types and categories, so that it was difficult for the models trained on CSIQ to obtain high performances. For the authentic distortion, the three most competitive methods, the PQR [35], the DBCNN [39], and HyperNet [25], were selected for comparison. In six authentic cross-database tests, the proposed HFNet achieved the best performance five times; in particular, the SRCC, when using the LIVEC as the training set and the BID as the testing set, was 0.045 higher than that of the second metric. From these results, it is indicated that the proposed HFNet has strong generalization ability, which is desired in real-world applications.
## 4 Results and Discussion
### Eddy Covariance Gross Primary Productivity and MODIS-Derived Data
It is reasonable to assume that the dry season signal contains information about land memory effects [81,82]. However, it is unlikely that such effects would be passed on through soil moisture since upper layer dries out within two months of the start of the dry season [69]. Thus, such a memory could be retained in the dormant seeds, roots and foliage [68,83]. The spectral range of MODIS NBAR Band 7 is most sensitive to surface soil water content in general, and the rate of evaporation during the dry season in particular [35]. This is discernible in the relatively rapid increase of the surface reflectance of MODIS NBAR Band 7 compared to the decrease in EVI in response to the decline of the vegetative cover during the dry season (Figure 7). On the other hand, LST is linked to VPD due to the feedback between land (vegetated or bare) and near-surface air [49]. This is clearly shown in the relationship between EVI and LST during both the greening and browning phases in Figure 5. There have been other recent attempts to model GPP in water limited regions using EVI combined with other variables. For example, in a tropical xeric savanna ecosystem, [PERSON] et al. [84] were able to explain 88% of the variability in EC GPP by parameterizing ecosystem light use efficiency using MODIS EVI and top-of-atmosphere photosynthetically active radiation. In another study, [PERSON] et al. [21] found that the combination of MODIS EVI with flux tower-derived evaporative fraction, as a proxy of water availability, and photosynthetically active radiation significantly improved the modeling of EC GPP for seven African sites, including Demokeya and Wankama Fallow, with up to 73% of the variance explained. Using only Earth observation data, our model is able to explain 89% of the variance in EC GPP across the three Sahelian sites. Figure 9: Cross-site comparison of 8-day EC GPP, EVI, the MLR GPP model, and MOD17 GPP for (**a**) Demokeya, (**b**) Wankama Fallow and (**c**) Dahra. The shading indicates the growing season (July–October). The large underestimation displayed by MOD17 GPP is due to the low value assigned to the maximum light use efficiency in the MOD17 algorithm [30].