esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Temperature Annealing Effects on Atomically Thin Tungsten Diselenide Field-Effect Transistor
## 2 Materials and Methods  
Figure 1a,c shows the schematic drawing and optical microscope image of the WSe\({}_{2}\) device. In order to fabricate this device, thin WSe\({}_{2}\) flakes were produced from the bulk WSe\({}_{2}\) crystal by the standard exfoliation method using scotch tape. The flakes were then transferred on a silicon substrate capped with 300 nm SiO\({}_{2}\). An optical microscope was used to scan the substrate and identify suitable flakes, followed by Raman spectroscopy and atomic force microscopy (AFM). The back gated devices were prepared by electron beam lithography followed by metal deposition in an electron beam evaporation chamber. Electron beam lithography was used to make contact patterns on the selected WSe\({}_{2}\) flake. Titanium/gold (T\(\mathrm{\SIUnitSymbolAngstrom}\)/Au) with a thickness of 10/40 nm were chosen as contact materials as T\(\mathrm{\SIUnitSymbolAngstrom}\) forms Ohmic-like contacts with WSe\({}_{2}\). The metal deposition was carried out in an electron beam evaporation chamber, followed by the lift-off in acetone.  
Figure 1: (**a**) Schematic of the WSe\({}_{2}\) device. (**b**) Raman spectrum of the WSe\({}_{2}\) before and after annealing. (**c**) Optical microscope image of the device. (**d**) AFM image and height profile of the WSe\({}_{2}\) layer.
## 4 Analysis of Plane to Points Histogram (P2 PH)
### Discussion  
The calculated five histograms were averaged in each sample. However, the number of points is not the same among the averaged histograms of each sample, and the class interval of C2M distance of each histogram is also different. The histogram areas calculated by summation of the C2M distance class multiplied by the points are different among the different samples and hence it is not possible to compare the histograms of the three samples directly. In this paper, each histogram was normalized by the summation of the number of points at each C2M distance multiplied by each C2M distance class, which means the area of the histogram. Normalized histograms can be compared with each other because they have the same area (see Figure 9). In the grouting sample, the most points are distributed at a short C2M distance which is less than 1.8 mm (see 'Section A' in Figure 9). At a C2M distance of 1.8 mm, the histograms of the grouting sample and the sand sample intersected each other, and the sand sample is dominant between 1.8 and 3.3 mm. It is affected by the sand grain size of about 2 mm and local roughness (see 'Section B' in Figure 9). The intersection between the sand sample and the gravel sample is located at a C2M distance of 3.3 mm. The points of the gravel sample are dominantly distributed larger than 3.3 mm due to the size of gravel (see 'Section C' in Figure 9). In the sand and the gravel samples, the roughness or local failure of the points on the failure surface mostly occurs less than a C2M distance of 3.3 mm so that the distribution of points is reduced dramatically above a C2M distance of 3.3 mm. The proposed P2 PH method can be distinguished by dividing C2M distance sections in which points of three samples are predominantly distributed. Therefore, a criterion for determining the roughness of the three-dimensional point cloud can be established by these sections.  
Figure 7: Fitting planes and distribution of points within the fitting plane for each sample.
## 4 Discussion
### Trade-Offs and Synergies in ESs
Furthermore, the distribution of trade-offs and synergies is influenced by a combination of factors. Therefore, in managing and optimizing ESs, the interplay among geographical, climatic, and socioeconomic factors should be considered to promote the coordinated development of services.
## 2 Materials and Methods
### Windthrow and Landslide Detection
#### 2.3.3 Support Vector Machine Method  
The SVM is a classifier that separates different classes by a hyperplane in an n-dimensional space. Support vectors are data points near the hyperplane that influence the position and orientation of the hyperplane, trying to maximize the margin of the classifier [32].  
The SVM model can handle outliers by creating a soft margin on the classifier; in other words, the SVM allows a certain number of misclassifications to keep the margin as wide as possible so that other points can be classified correctly.  
In this study, the SVM method was applied using the Google Earth Engine platform [23]. The stacked and masked composite mosaics were uploaded to the platform, and classification was performed using the SVM classifier with the linear kernel type due to its relatively simple implementation and fast performance [33].  
Training samples were created by digitizing polygons in the damaged area, and the cost parameter, which defines the margin of the hyperplane, was set to the default value of 1 to deliver the best results in both study areas. We applied the value after confirming that low values, such as \(10^{-5}\), reduced the overall accuracy of the final result.  
For _Setana North_, two training samples for windthrow areas (1.0234 ha and 0.4005 ha), two training samples for nonwindthrow areas (2.6990 ha and 0.9608 ha), and one training sample for water (5.1028 ha) were selected. The water class was posteriorly merged with the nonwindthrow class. In _Setana South_, we selected two training samples for landslides (0.01843 ha and 0.03507 ha) and two training samples for nonlandslide areas (9.9478 ha and 26.4685 ha).  
The code for classification is provided in Google Earth Engine (Supplementary information S1 and S2).
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Power Allocation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Technique** & **Model** & **System Description** & **Model Limitations** & **Research Contribution** \\ \hline
[35] & Reinforcement & \begin{tabular}{c} Deep neural \\ networks \\ \end{tabular} & \begin{tabular}{c} Physical layer \\ framework, BS with \\ multiple antennas \\ and multiple \\ single-antenna users \\ \end{tabular} & \
\begin{tabular}{c} Complicated DNN, \\ the data set \\ acquisition and \\ model selection \\ problems \\ \end{tabular} &
\begin{tabular}{c} Introduces an efficient, \\ cutting DL-assisted 5G \\ and beyond \\ communication. \\ \end{tabular} \\ \hline
[48] & Reinforcement & \begin{tabular}{c} Artificial Neural \\ Networks \\ \end{tabular} & \begin{tabular}{c} Attention-based \\ neural network \\ exploiting an \\ encoder-decoder \\ structure, \\ \end{tabular} & \
\begin{tabular}{c} The key problem is \\ how to allocate \\ limited resources to \\ multiple users \\ \end{tabular} &
\begin{tabular}{c} Introduces a method to \\ optimally allocate \\ transmission resources \\ through an \\ attention-assisted neural \\ network for channel \\ assignment. \\ \end{tabular} \\ \hline
[50] & Unsupervised & K-mean & \begin{tabular}{c} K-means-based \\ online user \\ clustering algorithm \\ \end{tabular} & \
\begin{tabular}{c} It does not consider \\ more complicated \\ clustering \\ algorithms that are \\ robust to noises and \\ outliers \\ \end{tabular} &
\begin{tabular}{c} Satisfy the total \\ transmission power \\ sum-rate maximization \\ issue until the QoS \\ demands of the users. \\ \end{tabular} \\ \hline
[52] & Reinforcement & Q-learning & \begin{tabular}{c} A single base station \\ in a NOMA system \\ equipped with \\ multiple antennas \\ contending with a \\ smart jammer \\ \end{tabular} & \
\begin{tabular}{c} It considers a \\ simplified jamming \\ scenario. Theoretical \\ analysis of more \\ practical scenarios is \\ still needed. \\ \end{tabular} &
\begin{tabular}{c} It considers a \\ simplified jamming \\ carries out our execution \\ of power distribution. \\ The process is developed \\ as a game with a \\ zero-sum outcome. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of studies on DL-based power allocation types in NOMA. Figure 3: Power allocation and channel assignment of DL-based NOMA system.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Aerial Mobile Sink-Based Frameworks  
This category is the newest among the investigated sink types. UAVs are being used and tested for different application scenarios, one of which is the collection of WSN data. To collect WSN data, both multirotor and fixed-wing UAVs have been proposed in the literature. Multirotor drones have the ability to fine-tune data-gathering positions, and these drones can hover in a steady position. However, compared to multirotor drones, fixed-wing UAVs are faster and consume less energy. Therefore, for precise data collection, multirotor UAVs are used, and for fast data collection, fixed-wing UAVs may be employed.
## 1 Introduction
Recent studies have demonstrated that SLM components exhibit higher crack growth rates and lower fracture toughness compared to their conventionally processed counterparts, primarily due to the presence of large grains and \(\alpha^{\prime}\) martensite plates that promote crack initiation and propagation [3; 4]. Comparative investigations of SLM-produced and drawn Ti-6 Al-7 Nb report that while ultimate tensile strength and hardness can be similar across production technologies, fatigue life and fracture resistance are significantly impacted by internal defects and grain structure, with wrought alloys displaying more predictable and superior crack propagation resistance, as well as more stable mechanical performance [2; 3; 4]. This shows the importance of obtaining comparable fracture mechanics data, including threshold stress intensity factors, growth curves, and resistance curves, for both production processes. Developments in AM have enabled the understanding of fracture mechanics in metallic materials regarding the role of microstructural features in crack propagation behavior [16], demonstrating that high-strength submicron precipitates in additively manufactured aluminum alloys can significantly influence fracture performance through their obstructive effect on crack propagation. That study revealed that submicron Al7 Cu4 Ni precipitates with high bonding strength can withstand concentrated stress while maintaining structural integrity during alloy fracture, leading to a transformation from intergranular to intragranular fracture modes. This provides the insight that manufacturing process parameters should be optimized to achieve desired precipitate structures that will enhance the balance between strength and ductility. Despite recent progress, there remains a lack of experimentally obtained data to support the design of load-bearing implants, particularly regarding the prediction of service life under cyclic loading, which is critical for any application. This study addresses this gap by experimentally determining and comparing key fracture mechanics parameters for Ti-6 Al-7 Nb fabricated by drawing and SLM, thereby providing essential data to guide material selection and engineering design of the biomedical implants and supporting the development of computational models to predict cycles-to-failure [2; 3; 4].
## 5 Conclusions and Future Work  
In the paper, we propose a task assignment algorithm based on the RNN techniques that can efficiently dispatch rescuers to aid victims to decrease the fatalities in emergency situations considering the uncertainty of dynamic hazard environments. The simulations show that the algorithm can provide an approximate real-time allocation of rescuers to victims. Furthermore, the allocation solutions are close to the optimal ones, where if the number of injured civilians is less than the total number of people that the rescuers can save, all injured civilians could be rescued by the rescuers, and if not, the number of saved civilians could reach the overall maximum capacity of the rescuers.  
In future work, the task assignment algorithm can be evaluated with more constraints, such as the cost of each assignment, \(C(r,v)\), and the fail rescue probability, \(L(r,v)\), can be generated, relying on the situations of both the rescuers and victims. Our simulations take these two parameters to be independent from the assigned victim and initialize them randomly, but it may be better if we generate them considering the associated victim. For example, for each rescuer-victim pair, the distance between them can be computed with the help of the DBES platform, and the hazard intensity around the victim also can be estimated. Then, the cost of assignment and the failed execution probability can be evaluated taking these two factors into account. In this way, we can obtain a more precise prediction of the expense of the allocation and make a more optimal assignment. Furthermore, we will explain more details about distributed control and information flows, for instance, how to get information for failed execution and how to re-assign tasks in the case of a failed execution. Additionally, when there are fewer rescuers than injured civilians, an efficient clustering scheme [25] can be applied to group victims according to their locations. Thus, the rescuer can provide support to as many civilians as possible, such as delivering protective devices, though they cannot carry all of them out of the building. What is more, the congestion problem caused by the rescuers should be alleviated. The navigation approach with the CPN can be applied in the rescue process, which is able to provide a faster optimal path finding and cause less congestion compared with [PERSON]'s algorithm [14].
## 3 Results and Discussion
### Evaluation of Basic Characteristics of Tight Reservoir
#### 3.2.3. Maturity Characteristics of Reservoir Sand Body  
The results of the identification of core thin sections from different regions and types within the basin indicate that feldspar sandstone and lithic feldspar sandstone are the primary components of Chang 9 sandstone. These results are presented in Figure 5. The average content of quartz is low, at less than 31%, while the average content of feldspar is high, at more than 34%. The average content of rock debris is between 10% and 20%. Therefore, the compositional maturity (quartz/feldspar + rock debris) of the Chang 9 sand body is generally low.  
The structural maturity of a sand body is primarily determined by particle roundness, sorting, impurity content, cementation type and other parameters. Observation of thin core sections reveals that the particle support structure of the Chang 9 sand body is pronounced, with the majority of debris particles exhibiting sub-angular or sub-circular shapes and predominantly linear contacts. These observations are presented in Figure 6.
# Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition
## 4 Experiments
### Ablation Studies
#### 4.3.2 Ablation Study of MSTI Module  
The multi-scale spatial-temporal feature interaction (MSTI) module employs multiple \(3\times 1\) convolutional layers with different receptive fields to capture long-term motion correlations. Additionally, it incorporates a learnable vector to improve spatial information interaction among joints. As shown in Table 1, the ablation study was employed by varying the number of branches and exploring different weight fusion strategies. On the NTU RGB+D dataset, the MSTC-4, i.e., the multi-scale temporal convolution with 4 branches, achieves 90.2% (X-Sub) and 94.2% (X-View). Compared to MSTC-4, MSTC-6, the multi-scale temporal convolution with 6 branches further improves accuracy by 0.3% (X-Sub) and 0.7% (X-View).  
In addition, compared to MSTC, the MSTI module incorporates a learnable weight vector to enhance spatial information interaction among joints. As shown in Table 1, with fixed weights, MSTI-4 outperforms MSTC-4 by 0.5% (90.7% vs. 90.2%) and 1.2% (95.4% vs. 94.2%). Dynamic weighting further widens the gap to 0.6% (90.8% vs. 90.2%) while retaining the 1.2% advantage (95.4% vs. 94.2%). The MSTI module enhances joint-level interaction within frames. When expanded to six branches with dynamic weight, it achieves optimal performance (91.0% X-Sub, 95.8% X-View), surpassing MSTI-4 with dynamic weight by 0.2% and 0.4%, respectively. This highlights the significance of long-term motion modeling, as MSTI effectively captures long-term motion dependencies and facilitates joint motion information interaction.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis
### Forecast Error Analysis  
Table 5 lists the prediction performance errors of five models (PSO-SVR, BPTT, CNN-LSTM, GA-ACO-BP, and APSO-CNN-Bi-LSTM) proposed in this paper on the four datasets. From Table 5, it can be seen that, as far as RMSE and MAE were concerned, the models in this paper almost achieved the best results. The ablation experiments of the three models (APSO-Bi-LSTM, CNN-Bi-LSTM, and APSO-CNN-Bi-LSTM) also showed that the PSO algorithm based on the optimization of adaptive inertia weights and the two modules of CNN both contribute to the increase in the model prediction accuracy. Comparisons \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particle Number** & **Emae** & **Edmse** & **Training Time** \\ \hline
3 & 28.6326 & 50.2209 & **31.1 min** \\
4 & **28.5812** & **49.6534** & 32.3 min \\
5 & 29.0253 & 50.4226 & 33.6 min \\
6 & 28.7704 & 50.4704 & 34.7 min \\ \hline \hline \end{tabular} Note: the model performance evaluation indicators (E\({}_{\mathrm{MAE}}\), E\({}_{\mathrm{RMSE}}\), and Training time) represent the average values of the predictions for the next 1-6 h.  
\end{table} 
Table 4: Effect of Particle Number on Experimental Results in APSO-CNN-Bi-LSTM. with the APSO module optimized solely with adaptive inertia weights revealed that the APSO-CNN-Bi-LSTM model, featuring both modules, yielded improvements of 3.52%, 3.83%, 3.81%, and 0.9% across the four datasets. Furthermore, in contrast to the CNN-Bi-LSTM model with only a CNN module, the APSO-CNN-Bi-LSTM model exhibited enhancements of 7.32%, 2.51%, 0.23%, and 0.64% on the same datasets, underscoring the complementary roles of the CNN and APSO modules in boosting prediction performance. Moreover, the APSO-CNN-Bi-LSTM model proposed in this study showcased superior performance compared to several other prediction models, reaffirming its effectiveness in the air quality prediction tasks. As is widely recognized, predicting air quality becomes increasingly challenging as the prediction time step extends. To further evaluate the short-term AQI prediction capability of the APSO-CNN-Bi-LSTM model, we predicted the AQI for the next 1 to 12 h. The RMSE and MAE change curves, illustrated in Figure 9, provide a comparative analysis of the model's prediction performance over these intervals. The data presented in Figure 9 represent the average performance across the four datasets. As shown in Figure 9, the predictive ability of all models decreases as the prediction time step increases. It is evident that the APSO-CNN-Bi-LSTM model consistently outperforms other deep learning models based on the two performance evaluation indicators, E\({}_{\text{RMSE}}\) and E\({}_{\text{MAE}}\). For prediction time steps less than 6 h, the models' differences are insignificant. However, when the prediction time step exceeds 6 h, APSO-CNN-Bi-LSTM
# Influence of Material on the Density of a Point Cloud Created Using a Structured-Light 3D Scanner
## 6 Conclusions  
To investigate the influence of material properties on point cloud density, a total of 12 different materials were selected, whose samples were first scanned without any modifications. For some materials, additional scanning was carried out with a material surface treatment that could eliminate the influence of their negative properties on thedensity of the point cloud. The measured values of the point cloud density and the arrangement of the points, density changes in the sample, and noise were monitored.  
From our own experience working with the creation of digital models [2; 11; 32] from point clouds, we know that a very accurate 3D model can usually be semi-automatically created at a density of a point cloud of 2200 points/cm\({}^{2}\). For this reason, we have scanned each material to find out if the point cloud density reaches this limit. For those materials where scans have not reached the desired point cloud density, we have used a surface treatment to increase the point cloud density.  
RQ1: To What Extent Is the Density of the Point Cloud Predictable during Building Object Reconnaissance?  
In Figure 5, it is possible to see the results of scanning individual materials. On the basis of the properties of these materials, the materials were divided into the following four groups: well-scannable materials, materials with very fine elements, glossy materials, and transparent materials. Before scanning, each material was scored 1-10 points according to visible properties. The results show us that these visible properties according to the point scale are not always directly proportional to the density of the obtained point cloud. Clear textured glass has five points and polycarbonate has four points, which are very close values. However, their point clouds have completely different results, with raw surface values 24,033 and 44,346 points in area and 123,292 and 228,581 points in area after the first surface treatment, respectively. For other materials, the point evaluation corresponds to the obtained point cloud results. The results show that surface properties can significantly affect the point cloud density, but an estimation of the point cloud density based on the visible properties of the material only may be misleading in some cases.