esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Performance Variation Across Agents and Environments  
As detailed in Table 1, the five PPO agents trained independently in each of the eight environments achieved markedly different final scores. The agents are consistently listed from #1 (highest score) to #5 (lowest score). The performance gap between the best and worst agents varied dramatically across environments.  
Environments such as Freeway and Pong showed high consistency. In Freeway, scores were tightly clustered between 21.27 and 22.03, a minimal difference. In contrast, other environments displayed extreme variability. In KFM, the top agent scored 37,453, while the lowest-scoring agent achieved only 3600. Breakout also showed a massive spread, from 343.6 down to 41.2. Boxing and Qbert demonstrated significant, though less extreme, variance. This wide range of performance disparities provides a strong basis for investigating the correlation with observation space divergence. Table 2 shows the mean episode lengths, which often correlate inversely with performance (e.g., in Boxing, higher-scoring agents have shorter episodes).
# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 2 Materials and Methods
### Feature Extraction Networks  
#### 2.2.1 SNP Feature Extraction  
In this study, the extraction of SNP features was performed using a Transformer-based network. First, the SNP data underwent pre-screening using the GWAS approach, retaining SNP loci with \(p\)-values smaller than \(1\times 10^{-6}\). The selected SNP data were then converted into 4D one-hot encoded vectors with a format of \((B\times 434\times 4)\), where \(B\) represents the batch size, 434 corresponds to the number of filtered SNP loci, and 4 indicates the four possible allele states at each locus. The input SNP features were subsequently processed through a one-dimensional convolutional layer, generating embedded representations (embeddings). This convolutional layer, utilizing a sliding window approach, processed the input data, enabling the network to capture local patterns within the SNP features. The output from the convolutional layer was then fed into a positional encoding and multi-head attention mechanism module for further processing. The process is shown in Formula (1).  
\[\mathbf{E}=\text{ReLU}(\text{Conv}1\text{D}(\mathbf{X}))\in\mathbb{R}^{B\times 4 34\times 32} \tag{1}\]  
Positional encoding is employed to encode the positional information of SNP loci, enabling the model to capture the relative positional relationships between SNPs. This section utilizes an attention mechanism with a dimension of \(d_{k}=32\) and two attention heads. By using multi-head attention, the model is able to model subspaces of different features, thereby enhancing its ability to perceive interactions between SNP loci. As shown in Formula (2), this mechanism enables the network to capture complex dependencies among the SNPs.  
\[\text{Attention}(Q,K,V)=\text{softmax}\Big{(}\frac{QK^{T}}{\sqrt{d_{k}}}\Big{)}V \tag{2}\]  
After processing through the multi-head attention layer, the model applies a Global Average Pooling operation to generate the final extracted SNP features. The entire feature extraction process is illustrated in Figure 1.
## 2 Materials and Methods
### KAP and CAK Accuracy  
The current fluoroscopy equipment must display both the kerma area product (KAP) and the cumulative air kerma (CAK) values at the console. These dose parameters are measured using radiation detectors integrated into the fluoroscopic unit head [13; 15; 16]. Thus, it is vital to calibrate the console displayed KAP and CAK dosemeters and compare their performance with the recommended practice.  
Measurements were carried out with the fluoroscopic unit mounted in its vertical configuration, and air kerma (kair) free in the air was measured using the calibrated dose rate meter at the center of the radiation beam field. KAP was determined from the measured (kair) according to [17; 18; 19]:  
\[\text{KAP}=\text{k}_{\text{air}}.\text{A}\,\left(\text{Gy}.\text{cm}^{2}\right) \tag{1}\]  
where kair is the air kerma measured free in air and A is the beam area. CAK was derived from the measured kair using the inverse square law as shown in [17; 18]:  
\[\text{CAK}=\text{k}_{\text{air}}.\left(\frac{\text{FDD}}{\text{d}_{\text{ref} }}\right)^{2}\,\left(\text{Gy}\right) \tag{2}\]  
where FDD is the focus-to-detector distance used for kair measurements and dref is the reference point where CAK is defined (15 cm back from the isocentre towards the focal spot) [17; 18; 19]. Measured KAP and CAK values were compared with the indicated fluoroscopic values.
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 3 Results and Discussions
### Evaluation of Model Estimation Results
Vector data of buildings are crucial for the fine-scale population estimation model proposed in this study. Thus, the lack of correct building data is the main explanation for the underestimation of the population numbers in some subdistricts.
# Dynamic Response and Geogrid Strain Analysis of GRS Retaining Wall
## 2 Model Tests
### Test Scheme  
The loading equipment used in the test was the electro-hydraulic servo loading system of MTS Company in the United States, which can respond to the output pressure load and loading frequency in real time, and can achieve static or dynamic application of load by regulating the relevant parameters. In the dynamic test, the vertical dynamic stress generated by the moving vehicle in the road bed is used as the main test object, and the half-sine wave is applied through the loading plate (60 cm \(\times\) 20 cm) to simulate the traffic load generated by the moving vehicle [39; 40; 41]. The value of loading value \(P\) for half-sinusoidal function is as follows:  
\[P=P_{0}+P_{\mathrm{A}}sin(2\pi ft), \tag{1}\]  
where \(P_{0}\) is the average load (kPa); \(P_{A}\) is the load amplitude (kPa); \(f\) is the frequency of load (Hz).  
The loading method of this test load considered the effect of peak value, average value, amplitude value and frequency of dynamic load on the deformation damage of the retaining wall, and further analyzed the sensitivity of different loading factors on the mechanical response of the GRS retaining wall. A series of orthogonal tests with different peak, average, amplitude and frequency of dynamic load, resulting in two different loadings, were conducted. One is the same frequency loading method, keeping the loading frequency of 2 Hz unchanged, the dynamic load of each stage lasts for 10 min and the loading plate is gradually loaded until the inclined instability occurs, which is regarded as the failure of the GRS retaining wall. Another one is the same amplitude loading method, starting from 10 \(\pm\) 10 kN, with each load corresponding to four different frequencies of 2 Hz, 4 Hz, 6 Hz and 8 Hz. Under each load frequency, the loading lasts for 10 min, and the average value of the next load increases by 20 kN, thus forming a gradual growth relationship of 0-20 kN, 20-40 kN, 40-60 kN, etc. until destruction. The specific output mode of dynamic load is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Longitudinal ultimate tensile strength (kN/m) & 31.4 \\ Transverse ultimate tensile strength (kN/m) & 32.5 \\ Longitudinal yield elongation (\%) & 13.4 \\ Transverse yield elongation (\%) & 13.6 \\ Tensile strength at 2\% elongation in the longitudinal direction (kN/m) & 13.7 \\ Tensile strength at 2\% elongation in the transverse direction (kN/m) & 14.0 \\ Tensile strength at 5\% elongation in the longitudinal direction (kN/m) & 24.3 \\ Tensile strength at 5\% elongation in the transverse direction (kN/m) & 24.7 \\ Aperture size (mm \(\times\) mm) & 36 \(\times\) 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific parameters of geogrid.  
Figure 5: Soil grading curve.
## 1 Introduction
### Wire and Arc Additive Manufacturing  
Wire and arc additive manufacturing (WAAM) is a fast emerging additive manufacturing technique and is utilized for a wide range of materials [11; 12; 13; 14]. The main material focus in research is currently steel, aluminum, and titanium. Wire and arc additive manufacturing is part of the direct energy deposition (DED) processes and summarizes the different welding processes, such as TIG- and GMA-welding. In this work, the focus lies on the GMA-welding-based additive manufacturing. This process allows a large variety in geometries to be manufactured, including overhangs [15; 16]. Furthermore, it can be used for a wide range of materials and also multi-material and functionally graded approaches [17; 18; 19; 20; 21; 22; 23; 24; 25]. In this case, the manufacturing process could have a significant impact on the properties of the materials that are summarized and shown in [13; 26; 27] and need to be considered for the prediction of the material properties. The WAAM-process needs in every case a substrate material for the start of the manufacturing process. These materials are not always of the same chemical composition of the additively manufactured part. Such mixed material joints are commonly known in welding and possible to achieve, e.g., in explosion welding [28; 29] or for cladding [14].  
One limiting factor is the lack of knowledge about the welding or additive manufacturing and the influence of these processes on the properties of the CCAs [30]. Another limiting factor of this additive manufacturing process is the availability of the used wire-shaped filler materials. Classically, these are manufactured as solid wires. For these solid wires, special melts are prepared and the feedstock is cast for subsequent forging and wire drawing processes. This limits the range of possible alloy systems, especially if they have unknown processing properties. In addition to solid wires, cored wires have also become well established. These have a formed sheet metal strip outer sheath filled with powder. This allows a wider range of possible alloy compositions with relatively small quantities of filler. As a relatively new variant of additives, cable-like wound filler wires have become established in research. Here, too, the degrees of freedom in composition are significantly greater for small quantities of filler compared to solid wires. These cable-like fillers have been realized for complex concentrated alloys; demonstrated in [31; 32; 33]. Here, the limiting factor is the available diameter of the different alloying elements.  
Overall, currently, the metal-cored fillers offer the widest range in possible composition, and they will be the focus in this work.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 5 Results and Discussion  
From 27 December 2018, BDS-3 has completed the construction of the basic constellation and started the initial open service (OS). The integrity risk requirements design and verification work completed by BDS-3 in the latest draft of ICAO SARPs is based on the actual operation of the system in 2019. Therefore, we correspondingly use the system data of 2019 for testing and analysis in this paper.
## 3 Methodology  
This study aims at detecting small scale forest fire burned areas with various combinations of input variables and machine learning models to compare their performance. To this end, classification of forest fire burned areas was conducted by using nine bands of Sentinel 2A/B satellite images before and after occurring forest fires, together with four vegetation indices and their differences (NBR, NDVI, dNBR, dNDVI) as input variables. Three different machine learning models, LightGBM, RF, and U-Net, were applied.  
Of the three types of machine learning models used in this study, the LightGBM and the RF are ensemble learning-based machine learning models that operate on a pixel basis, and the U-Net is a deep learning-based machine learning model that operates based on image patches. The operation process of the pixel-based machine learning models (i.e., Light GBM and RF) and the image patch-based machine learning model (i.e., U-Net) used in this study is shown in Figure 2.  
The flow chart of this study is presented in Figure 3. After conducting the preprocessing to prepare the input variables, three Schemes were developed according to the combination of input variables for performing the machine learning-based burned area detection approaches. More specifically, Scheme 1 used post forest fire image only and two indices, which are NDVI and NBR. Scheme 2 exploited pre and post forest fire images without vegetation index. Finally, using the pre and post forest fire images, four vegetation indices (i.e., two indices for each image) and two difference images of the indices were  
Figure 2: Input data characteristics according to machine learning models.  
selected as input variables for Scheme 3. The dataset of the three divided cases was trained in machine learning models, LightGBM, RF, and U-Net. Then, 60% of the dataset was utilized for training, and 40% of the remaining data was used for validation. Models' performance was tested using the label image of post forest fire. As a result, the quantitative and qualitative forest fire burned area detection performances were analyzed using nine difference cases (i.e., three Schemes with three models for each Site A and Site B). The detailed explanation of the machine learning approaches with the tuned hyperparameter values and of the classification Scheme design was given in the following subsections.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 5 E-Health Sustainability Model
### E-health Sustainability Model
#### 5.2.2 Organization  
The organization dimension addresses changes introduced to the organization because of the adoption of the e-health solution. The organization dimension is important for the sustainability of the e-health solution because it ensures the availability of plans, policies, and practices that streamline the workflow integration of the e-health solutions and acceptance of users. In the results of the SLR (refer to Table 8), some studies (i.e., PS1, PS2, PS5, PS6, PS13, PS17, PS19) represented the political/legal as aspects while in other studies they were mentioned as dimensions. As mentioned in Section 5.1, because the definition of political/legal overlaps partially with the organization's definition, we included political/legal as part of Planning and Policies aspect under the organization dimension. The aspects of this dimension are support and commitment, organizational structures and strategy, planning and policies, stakeholders, the culture of electronic information use, outcomes identification, and demand and need.  
The support and commitment of the top management and their awareness of the importance of adopting the e-health solution are essential to its sustainability. The importance of this aspect is that the top management always has the final decision on whether to continue running and funding the e-health or not, and whether to enforce the changes associated with the e-solution or not. The absence of their support may result in an unplanned suspension of the e-solution; hence, the money and resources that were allocated to it are wasted. Similarly, the support and commitment of the e-health solutions' vendors to continue providing the services impact the sustainability of the e-health solution. Therefore, it is necessary to consider this when assessing sustainability.  
Furthermore, having well-established plans and policies that deal with the e-health regulations provides a solid foundation to adopt and sustain any e-health solution. In  
Figure 5: E-health sustainability model.  
addition, it is the responsibility of the healthcare organization to identify the outcomes of adopting and using e-health and to have an evaluation plan to assess these outcomes regularly. Moreover, determining the need and demand on e-health plays a key role in its sustainability because low demand or unfulfilled needs will be a waste of the organization's resources, leading to stopping the e-health solution.
## 4 Experiments  
### Experimental Setup  
All experiments were conducted on a workstation running Ubuntu 21.04, equipped with an NVIDIA RTX 3090 GPU and 32 GB of RAM. The implementation was based on PyTorch 1.11.0 and Python 3.10.  
The dataset used in this study consists of ten manually collected sign gestures, captured from a frontal view. Raw hand landmark data was extracted using MediaPipe, and subsequently converted into structured CSV files. Each sample in the dataset is represented as a sequence of 100 frames, with 21 hand keypoints per frame, and each keypoint described by its 3D spatial coordinates (x, y, z). Thus, the final input format for all models is standardized as a tensor of shape [100; 21; 3].  
All models were trained exclusively on these structured 3D pose keypoints extracted from video using the MediaPipe Hands framework. No image frames, rendered videos, or skeletal animations were used during training. This ensures that the input data is purely geometric and fully standardized across all experiments.  
This setup enables a consistent pipeline for training and evaluating various gesture recognition models under controlled conditions.  
All models were trained using specific hyperparameters tuned via the Optuna optimization framework. Each model architecture CNN3D, TCN, Transformer, Informer, and Sparse Transformer was independently optimized using cross-validated search over learning rate, batch size, dropout rate, hidden dimensions, and weight decay. The optimization process was conducted within a fixed computational budget to ensure fairness. The final hyperparameter configurations, selected based on best validation accuracy, are summarized in Table 1. These values were held constant across all training runs involving raw data, rotation-augmented data, and IK-AUG data to ensure a controlled and unbiased comparison across augmentation strategies.
## 5 Massive MIMO
### Open Challenges of mMIMO
Thirdly, The researcher will be unable to justify techniques and algorithms due to insufficient channel models for mMIMO as to cope with the huge amount of data, tremendously fast algorithms will be needed. Furthermore, enhancing the antenna elements creates consequential challenges not only to equipment but also to operators and manufacturers.
## 3 Results
### Resolution of Superimposed OAMs  
The resolution of multiple superimposed OAM modes was further investigated here. As shown in Figure 4a, the overlap area of two adjacent focusing spots decreases with the increase in \(\Delta l\), their mutual interference gradually decreases, and their resolution gradually increases. When the topological charge interval is seven, the intensity curve of two-phase OAMs reduces to 38.2%, and the two focal spots can be distinguished clearly. Based on the interval \(\Delta l\) = 7, 180 different OAM modes ((200 \(\times\) 2\(\pi\))/7 + 1 = 180) can be detected simultaneously on the circle, and the large mode detection capability is demonstrated. In addition, decreasing the size of the focusing spot or increasing the radius of the focusing ring is expected to improve the angular resolution, such as adding a super-oscillatory phase on the original metasurface or designing a helical focusing mode.  
To verify the validity of the OSM detection on the whole circumference, in this paper, FDTD was used to simulate the single topological charge, and then all the single simulation results were superimposed together to obtain all the focusing modes, as shown in Figure 4b.  
To receive a more uniform focusing light intensity, the theoretical focal length (\(f=\frac{k_{0}\sigma_{0}^{2}}{l_{0}}\)) is increased by 50 \(\upmu\)m as a new detection focal length. In addition, the intensity of the single OAM is normalized to compare two adjacent focusing spots more conveniently. As is shown in Figure 4c, for OAM with different topological charges, the azimuth Angle difference between adjacent normalized intensity peaks is about 0.05 rad, and adjacent peaks are separated obviously. Then, it can be concluded that 1256 topological charges can be detected by the OSM when measuring a single topological charge. Moreover, the OAMs of 180 different topological charges can be detected simultaneously in multiple topological detections. This improvement in the detection efficiency indicates that the OSM can detect large-mode OAMs efficiently and quickly, suggesting the application of the OSM on integrated optical systems that require high-capacity communications.
## 3 Operating Principle of the RSOA-FCL  
The operating principle of the RSOA-FCL as an US transmitter in the MFH is depicted in Figure 3a. First, for the RSOA-FCL to be used as a transmitter, the RSOA performs three functionalities simultaneously:  
1. It is used as a modulator to encode the US data onto the optical field;
2. It acts as a gain section of the Fabry-Perot fiber cavity and thus has to compensate for the cavity losses in order to achieve lasing;
3. It nonlinearly suppresses the residual reflected modulation on the incoming reflected light.  
In the following we are going to explain the nonlinear suppression of the incoming signal in more detail.  
We start the discussion with Figure 3b,c, which show plots of the output power and the gain, respectively, of a typical RSOA for varying input power. The shaded regions emphasize the linear and nonlinear operation regions based on the input power. In the linear region, the RSOA provides a relatively constant gain such that the input power and output power have a linear dependence. In the nonlinear region, the gain is decreasing for a larger input power. Figure 3b also shows how an incoming signal is either linearly amplified if its input power is weak, or nonlinearly suppressed if its input power is strong [28]. Thus, for the RSOA-FCL to suppress an input signal, the reflected incoming signal needs to be operated in the nonlinear region of the RSOA.  
Figure 2: Arrangement of the self-seeded RSOA-FCL in a WDM-PON-based mobile fronthaul (MFH) network as an upstream transmitter. CO: central office, BBU: base band unit, US: upstream, DS: downstream, ONU: optical network unit, RRH: remote radio head.