esa-sceva/satcom-chunk-collection huggingface dataset:
# Spin Hall Effect before and after the Focus of a High-Order Cylindrical Vector Beam
## 4 Numerical Simulation  
To confirm the theoretic findings, we performed a numerical simulation. For this purpose, we computed the electric and magnetic fields near the sharp focus by using the expressions (2) and verified them by computing the field directly using the Debye double integrals.  
Figure 3 illustrates the distributions of intensity (Figure 3a,e), radial component of the Poynting vector (Figure 3b,f), longitudinal components of the SAM (Figure 3c,g), and OAM (Figure 3d,h) vectors of a sharply focused Gaussian beam with 3 rd-order (\(n=3\)) cylindrical polarization (1) in two transverse planes, one before the focus (Figure 3a-d) and one beyond the focus (Figure 3e-h). For computation, we used the following parameters: wavelength \(\lambda=532\) nm, focal length \(f=10\) um, numerical aperture \(\theta_{0}=0.49\pi\) (NA = 0.999), calculation area is \(4\times 4\) um, the longitudinal coordinate (relative to the focal plane) is \(z=-\lambda\) (Figure 3a-d) and \(z=+\lambda\) (Figure 3e-h). To describe the Gaussian shape of the input field, radial distribution of the complex amplitude was equal to \(A(\theta)=\exp[-(\sin\ \theta/\sin\ \alpha_{0})^{2}]\) with \(\alpha_{0}=\pi/3\). Figure 4 depicts the same distributions and the same parameters as in Figure 3, but for another polarization order \(n=4\).  
Figure 3: Distributions of intensity (**a,e**), radial component of the Poynting vector (**b,f**), normalized-to-maximum longitudinal component of the SAM vector (**c,g**), and normalized-to-maximum longitudinal component of the OAM vector (**d,h**) of a sharply focused Gaussian beam with 3 rd-order cylindrical polarization before the focus (**a–d**) and beyond the focus (**e–h**). In all the figures, light and black colors mean, respectively, maximum and minimum. Scale marks (in the bottom left corner) denote 1 μm.  
Despite the visual similarity of SAM and OAM distributions (columns 3 and 4 in Figures 3 and 4), they were computed quite differently: SAM distributions were computed by Equation (5), whereas the OAM distributions were computed by Equation (17) with numerical differentiation by the angular coordinate \(\varphi\). Nevertheless, the obtained patterns confirm that the OAM \(L_{2}\) and the SAM \(S_{z}\) compensate each other, and that after passing the focal plane, the rotation of the polarization vector changes its direction (i.e., longitudinal SAM component changes its sign).  
Figures 3 and 4 also confirm that, near the focus, there are 4(\(n\!-\!1\)) areas with a different rotation direction of the polarization vector. Figure 3 contains 4(\(n\!-\!1\)) = 8 local subwavelength areas with elliptic polarization (Figure 3c,g), including 4 dark areas with left elliptic polarization and 4 light areas with right elliptic polarization. In Figure 4, there are 4(\(n\!-\!1\)) = 12 such subwavelength areas. Thus, the simulation confirms that the areas with a different \"spin\" are spatially separated near the focus, which means that the Hall effect occurs.
## 3 Methodology
### SHAP (SHapley Additive exPlanation)  
SHAP (SHapley Additive exPlanation) is a model-agnostic explanation method based on cooperative game theory. It attributes the output of a machine learning model to individual features by using Shapley values, ensuring both consistency and local accuracy [44]. SHAP provides both global and local explanations for model interpretation in LSP. Global explanations offer consistent and accurate attribution values for each feature (e.g., environmental or geological factors), illustrating how they influence the model's overall predictions. Local explanations demonstrate how specific features contribute to the prediction for an individual landslide event by analyzing specific input data [45; 46; 47].  
Generally, the AUC is the most commonly used metric for evaluating model performance [48], but relying solely on the AUC may not sufficiently capture the spatial  
Figure 3: Research workflow.  
characteristics of the model's predictions [49]. The AUC is based on the relationship between observed and predicted values, but high prediction accuracy does not necessarily imply good interpretability [50]. For instance, [PERSON] demonstrated that models with similar or identical AUC values can produce markedly different spatial prediction patterns [51]. To address these limitations, this study incorporates the SHAP method, which provides advantages [44].  
Improved interpretability: SHAP offers clear insights into feature contributions, helping to understand the decision-making process within \"black-box\" models. By quantifying the contribution of each feature to the model's predictions, SHAP enhances both interpretability and trust in the model.  
Enhanced transparency: By calculating the Shapley values associated with each input feature, SHAP explains how individual features influence LSP results, improving the model's performance and interpretability.  
SHAP quantifies the contribution of each feature to the model's output, representing the model prediction as the sum of the Shapley values for each input feature:  
\[g\big{(}x^{\prime}\big{)}=\varphi_{0}+\sum_{M}^{j=1}\varphi_{j} \tag{3}\]  
\(g(x^{\prime})\) denotes the model output, \(\varphi_{0}\) is a constant explaining the model, and \(\varphi_{j}\) denotes the Shapley value assigned to each feature.
## 4 Semantic Understanding of Object Layer
### Local Area Semantics  
Remote sensing images can also be divided according to specific rules. By extracting the local image descriptor of each sub-block, the correspondence between the local descriptor and the local semantic concept is established, and the object layer semantics are extracted. Due to the differences of descriptors, feature extraction methods of the local area can be divided into three categories: visual dictionary, feature mapping and topic model.
## 3 Results and Discussion
### Evolution of Soluble and Polymeric Fractions
Figure 1b,d showed that the degradation of organic matter was clearly detected through the evolution of the soluble fraction. [PERSON] et al. [36] also previously described this behavior. The results of the fiber fractions require long periods of active biodegradation to be effectively mineralized. The recalcitrance of these polymeric structures causes a challenge to microbial degradation [37]. Despite the above, during composting it is common to find quantifiable degradative processes in all fractions of organic matter. That is precisely what was intended to be determined in this study. For this, the degradation percentages reached by the different  
Figure 1: (**a**) Fiber fractions in raw materials; (**b**) soluble fractions in raw materials; (**c**) fiber fractions in compost; (**d**) soluble fractions in compost. Abbreviations: cellulose (Cel), hemicellulose (Hemicel), lignin (Lig), reducing sugars (RS), total proteins (TP), soluble organic carbon (SOC), N-NH4\({}^{+}\) (NH4), N-NO3\({}^{-}\) (NO3). fractions were evaluated and are shown in Figure 2. The data were highly revealing to analyze the performance of the facilities. Degradation of the holocellulose fraction (hemicellulose + cellulose) was detected in up to seven of the nine facilities studied, with situations where this fraction was consumed by up to 50% (SS1 and SS2). In fact, these results differed greatly depending on the raw material to be composted. Thus, in MSW there was hardly a significant degradation of holocellulose while in SS it was intense. On the other hand, in neither of these two residual materials a consumption of the most recalcitrant fraction of the fibers (lignin) was reported. Regarding the soluble fractions, a considerable depletion of nutrients was detected in both residues. In fact, it was higher than that detected in green waste facilities. Although the use of this fraction by the microbiota produced variable results depending on the composted bio-materials. In this way, it was possible to corroborate a greater decrease in SOC levels in MSW, compared with that registered in SS. Even, this decrease far exceeded 70% in two MSW facilities (MSW1 and MSW2). On the contrary, according to the results of the SS facilities, in this bio-material, the consumption of the soluble fraction was mainly attributed to the reduction in the nitrogen fraction of the organic matter. The reduction in this nitrogen fraction, as a whole, is widely described in composting [38].
## 3 Method and Model
### Emotional Voice Synthesis Using LLM  
#### 3.2.1 English Spoken Language Emotion Synthesis Method and Fine-Tuning of LLM  
To ensure sufficient augmentation of the dataset and to provide an accurate reference standard for the subsequent student emotion evaluation network, this study employed an LLM based on the transformer architecture with a masked self-attention mechanism. This model successfully generated 2892 emotionally rich speech data entries, further enhancing the diversity of the dataset. The self-attention mechanism played a central role in this process, enabling the model to more accurately capture and process emotional information.  
The overall structure, as shown in Figure 3, illustrates that the model consists of a series of encoders and decoders. The encoders are responsible for transforming the input text into feature representations, while the decoders utilize these feature representations to generate the corresponding speech output. This design allows the model to flexibly adjust its focus on different parts of the text, thereby better capturing and expressing emotional information.  
The LLM based on the Transformer architecture not only demonstrated its capability in handling emotional text but also highlighted its crucial role in emotional voice synthesis technology. The audio generated is not only suitable for assessing students' spoken language skills but can also serve as standardized audio materials, providing learning guidance for students.  
Figure 2: Heatmap of student spoken language emotion evaluation scores.
## 4 Discussion
### Data Pre-Processing  
Table 3 showed that 27.91% of the studies did not specify the EEG cleaning process, 23.26% carried out little pre-processing in the data (which is the application of filters without eliminating artifacts), and 48.84% of the articles used artifact-free EEG signals. Inthe case of those studies that did not specify the EEG cleaning, it was assumed that they did not perform any pre-processing of the signals. When the results of these three types of cleaning for the same model and the same objective problem were compared, it was observed that the articles [38; 49; 65] which corresponded to artifact-free signals, signals with unspecified cleaning, and signals with little preprocessing, respectively, used CNN in the classification problem of patients with PD vs. healthy controls, and obtained accuracy values of 100%, 99.62%, and 99.22%, and sensitivity values of 100%, 99.17% and 98.98% for the articles mentioned, respectively. It can be noted that the values obtained in both metrics are independent of the EEG cleaning procedure. This also occurs in the articles [51; 66], which considered artifact-free signals and unspecified cleanliness, respectively, and used MLP in the same classification problem, obtaining sensitivity values of 100% and 96.7%, and specificity values of 94% and 100%, respectively. These results reinforce the conclusions that were obtained in [28], where it was indicated that the EEG cleaning protocol did not influence the results of the ML models.  
Regarding the extracted characteristics, we found a great heterogeneity, although the use of spectral characteristics predominates in 88.37% of the articles. This may be due to the fact that, especially in PD, no visual alterations are observed in the EEG signals of PD patients, whereas a spectral analysis provides information on variations in the EEG bands, which are related to alterations in the patients' condition and are therefore applicable in the clinical setting.
## 2 Data and Methods
### Experimental Process  
The experimental process involved three stages, namely, data acquisition, model design, and model testing, as shown in Figure 4. In the data acquisition stage, we collected images of locusts of various types and instars outdoors, performed image processing (including locust frame, type and instar information labeling, cropping, image graying, and histogram equalization enhancement), and constructed a locust classification sample dataset (total number of samples: 30,000). The model design stage involved designing the network structure, selecting the model parameters, and developing the model. In the model test stage, we entered the training dataset, implemented the model training process, constructed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **CNN Model** & **Accuracy** & **No.** & **CNN Model** & **Accuracy** \\ \hline
1 & AlexNet & 73.68\% & 4 & ResNet50 & 80.84\% \\
2 & GoogLeNet & 69.12\% & 5 & VggNet & 80.70\% \\
3 & ResNet18 & 67.60\% & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the classification accuracy of several mainstream CNN models of locusts.
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 1 Introduction
This is beneficial to the problem of reducing maintenance time. However, smarter autonomous threat prevention and improved self-checking and self-healing capabilities of smart grids should also be considered. Deep reinforcement learning (DRL) combines the awareness capabilities of deep learning and the decision-making capabilities of reinforcement learning, allowing control directly from the information provided by the environment. It is an AI approach closer to the human way of thinking. On top of providing detection capabilities, DRL is also able to autonomously move the system's safety action strategy toward the greatest long-term rewards by learning from the awareness of the environment. In industrial IoT, DRL is increasingly considered to handle high-complexity optimization problems [18,19] and as able to provide efficient SSA solutions in a timely manner by supporting computational resources located at the edge. In this paper, a multiagent deep deterministic policy gradient (MADDPG) algorithm for smart grids' SSA under edge computing is proposed. The framework integrates edge computing and deep reinforcement learning (DRL) to direct efficient SSA deployment in smart grids, and enable the edge computing-based grid system to make long-term expectation-maximizing actions against security threats through the setting of reward values to achieve security situational awareness. Some of the main contributions in this paper are summarized as follows. 1. By implementing an edge computing paradigm in smart grids' SSA architecture, issues of massive heterogeneous connections to power terminals and low latency of SSA strategies are solved. 2. The multiagent-based deep reinforcement learning MADDPG algorithm contributes to handling the continuous and dynamic situation space in smart grids. 3. The proposed algorithm achieves the minimum processing cost of SSA with the premise of minimum detection error rate. 4. This paper is the first work to incorporate the edge computing paradigm and DRL in SSA for smart grids. The remainder of this paper is organized as follows. In Section 2, we present the related work on SSA and AI combined with edge computing. Models of SSA, edge computing, and DRL are introduced in Section 3. In Section 4, we formulate the optimization problem of security situational awareness under the smart grids. The MADDPG-based SSA algorithm is proposed in Section 5. In Section 6, we perform the performance evaluation. Conclusions are given in Section 7.
# Special Issue on Wastewater Treatment and Reuse Technologies
## 3 Sludge Management and Disinfection  
Conventional water and wastewater treatment processes rely on extensive chemical disinfection to comply with stringent microbiological safety standards for drinking water production and wastewater reuse, respectively [6]. The traditional disposal methods of sludge generated from wastewater treatment plants on one hand constitute a major cost and on the other hand pose potential environmental risks that could result from various pollutants, such as pathogenic microorganisms, organic pollutants, and heavy metals [7]. Recently, hydrothermal carbonization has been suggested as a cost-effective and eco-friendly solution for this sewage sludge management challenge. In their article, \"_Removal of Escherichia coli by Intermittent Operation of Saturated Sand Columns Supplemented with Hydrochar Derived from Sewage_ Sludge\", [PERSON] et al. [8] evaluated hydrochloric derived from sewage sludge as an additive adsorbent in sand filtration setups for _Escherichia coli_ removal during water treatment. Supplementation of activated hydrochar in a sand filtration unit significantly improved _E. coli_ removal efficiency. Given that pathogen removal based on the use of hydrochar is a new concept, [PERSON] et al. [8] recommended follow up investigations on the optimization of the process parameters and the durability of the filtration unit for practical implementation in developing countries.
## 3 Furrowing Depth Stability Control--Design of Profiling Adjustment Device
### Passive Profiling Adjusting Device
#### 3.1.2 Mechanical Spring-Type Downforce Adjustment Device  
Mechanical spring-type downforce adjustment devices usually have a mechanical spring mounted on a parallel four-bar mechanism and utilize a depth-limiting wheel to limit the depth, reducing the vibration of the planter monobloc and maintaining the furrowing depth's stability [37]. Monosem, USA, has recently launched a fast planter, NG Plus M, which adopts Monosem's Monoshox technology, installing monoblock shock absorbers and double mechanical springs, which can reduce the vibration amplitude when the planter travels at high speeds, reduce the degree range of the four-linkage, achieving smoother imitation. The stability of the furrowing depth is improved. Monoshox technology can increase the seeding speed of the planter to 13 km per hour and maintain a high degree of seeding stability [30]. [PERSON] et al. designed a Panax notoginseng pressure wheel profiling furrowing device, which can realize transverse and longitudinal profiling, and determined through theoretical analysis that the best operation effect is achieved when the spring pretension reaches 211.90 N. The test results showed that the depth stability of the furrowing device after parameter optimization was 89.41% [44]. [PERSON] et al. designed a potato seeding monolithic sowing depth regulating device, and the test results showed that when the operating speed was 1 m/s, the initial traction angle was 0. The spring stiffness was 10 N/mm, the operating effect was better, the potato seeding furrowing depth qualification rate reached 96.6%, and the coefficient of variation reached 8.9% [45]. The Australian John Shearer planter, with two spring bodies mounted on the furrow opener, can provide more than 1 kN of downforce, allowing the planter to achieve a good profiling furrow opening operation (John Shearer). [PERSON] et al. designed a sowing depth profiling adjustment device for a twin-ridge cross-belt whole-film ridging corn planter, which can be freely rotated and adjusted to a sowing depth of up to 30-50 mm using a sowing depth profiling adjustment lever [46].  
The passive profiling adjustment device is mainly a mechanical element with a simple structure and low cost. The nature of the soil varies considerably in different regions, fields, and surface undulation. In order to keep the profiling wheel in close contact with the ground at all times, the appropriate pressure on the ground is achieved by increasing or decreasing the number of springs or adjusting the spring preload before operation. Adjusting the height difference between the depth-limiting wheel and the furrow opener is also necessary. The machine is checked for proper operation using trial sowing [4]. However, this will significantly affect the sowing efficiency and increase labor costs. Passive adjusting devices cannot match the soil conditions very well; there are profiling advances or imitation lag and other problems, and it is difficult to ensure the consistency of the depth of the open furrow.
# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 3 Results and Discussion  
In the feature selection process, each random shuffling would produce a round of optimized combinations, statistics of the optimized combinations of 100 times the results. The number of times a feature appeared in an optimized combination was recorded as the  
Figure 4: The flowchart of machine learning. RFE was used to rank the importance of features. The classification algorithms use SVM (poly, rbf, linear, sigmoid), LR and GaussianNB. This method was randomly repeated 100 times, with 80% of the data used for training and 20% for testing.  
frequency, and the ranking of features was counted and computationally analyzed based on all the results.  
In the classification training stage, the evaluation metrics used to assess the performance of the CAD system-based melanoma lesion identification method were accuracy, sensitivity, and specificity. Also, the ROC-AUC [37] curve was drawn. These metrics are commonly used in medical image analysis to measure the effectiveness of classification models. The formulas for accuracy, sensitivity, and specificity [38] are as shown in (13)-(15):  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{13}\]  
\[\text{Sensitivity}= \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{14}\]  
\[\text{Specificity}= \frac{\text{TN}}{\text{TN}+\text{FP}} \tag{15}\]  
where 'TP' is the number of correctly identified melanoma lesions as melanoma, 'TN' is the number of correctly identified benign lesions as non-melanoma, 'FP' is the number of benign lesions incorrectly identified as melanoma, and 'FN' is the number of melanoma lesions incorrectly identified as benign lesions.  
The ISIC dataset is widely recognized as the largest public database for research in dermoscopic image analysis. In the experiment, a total of 200 dermoscopic images of melanoma and 200 dermoscopic images of benign nevus were selected from the ISIC 2019 database. This dataset consists of 400 images in total, with an equal representation of both melanoma and benign nevus cases. The balanced dataset allows for the fair and reliable evaluation of the CAD system's performance in distinguishing between melanoma and benign lesions.  
The PH\({}^{2}\) dataset, introduced and detailed by [PERSON] et al., comprises 200 dermoscopic images. This dataset categorically segregates images into melanomas and benign nevi.  
Melanoma has a relatively similar presentation with benign nevi [39], and the classification between melanoma and benign nevi is mainly designed to minimize misclassification.