esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design with Simulation and Experimental Results
### UV Channel Modeling Analysis and Simulation
#### 3.1.2 NLOS Single Scattering Model
In computer simulations with parameters configured as \(\gamma=1.815\times 10^{-2}\), \(g=0.72\), \(f=0.5\); the relationships between path loss and distance are presented in Figures 4 and 5. Figure 3:3 Transmission elevation angles and path loss. Figure 4 illustrates the path loss versus launch elevation angle. As the distance increases, the path loss tends to increase for all transmission elevation angles. Additionally, at the same distance, the smaller the transmission elevation angle is, the lower the path loss is. For instance, the path loss at a 10\({}^{\circ}\) elevation angle remains lower than those at 20\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\) across all distance segments. This is attributed to the reduced propagation distance of ultraviolet light through the atmosphere at smaller elevation angles. The shorter propagation distance directly results in a corresponding reduction in path loss. Conversely, increasing the elevation angle enhances forward scattering effects according to Mie scattering theory. As the angle deviated from the transmission direction increases, scattered energy decreases significantly, leading to a degradation in SNR. Figure 5 illustrates the effect of different receiver elevation angles on the path. At shorter distances (0-500 m), the path loss increases significantly with distance, and notable discrepancies exist among curves corresponding to different elevation angles. Specifically, the 10\({}^{\circ}\) elevation angle exhibits relatively higher path loss compared to the 90\({}^{\circ}\) and 120\({}^{\circ}\) configurations. At longer distance (4500-4600 m), while path loss continues to increase with propagation distance, the growth rate decelerates appreciably. Moreover, the path loss curves for different elevation angles converge to parallelism, indicating that the influence of receiver elevation becomes less pronounced under these conditions. Notably, the 45\({}^{\circ}\) and 30\({}^{\circ}\) elevation angles demonstrate relatively lower path loss values in this regime. These results suggest that in practical communication systems, optimizing receiver elevation angles can effectively mitigate path loss in short-range scenarios. However, for long-distance transmissions, adjusting receiver elevation provides limited improvement, necessitating the consideration of alternative strategies to enhance communication quality.
#### Inverse Kinematics with ANNs  
During the last decade, robotics had an outstanding development in the industry, particularly in aerospace, military, and medical areas, among others, especially in manipulators with a large number of degrees of freedom (DOF), due to their high flexibility and control to perform complex tasks [17; 29; 30].  
Modern manipulators, usually kinematically redundant, allow complex tasks to be solved with high precision in the results. These types of manipulators have at least six DOF, allowing greater flexibility and mobility to perform complex tasks. The complexity in manipulator control design based on an inverse kinematic solution approach can be computationally complex, due to the nonlinear differential equation systems that are usually present. Traditional methods with geometric, iterative, and algebraic approaches have certain disadvantages and can often be generically inappropriate or computationally expensive [16; 31].  
The ANNs present major advantages related to nonlinearity, parallel distribution, high learning capacity, and great generalization capacity, and they can maintain a high calculation speed, thus fulfilling the real-time control requirements. Consequently, various approaches have been proposed by the scientific community in the use of intelligent algorithms applied to the control of robotic manipulators such as the use of ANNs [19; 20; 32; 33], genetic algorithms [31; 33; 34; 35; 36; 37; 38], recurrent neural networks (RNNs) [37; 38], optimization algorithms [18; 23; 39; 40], and the use of neural networks and optimization methods for parallel robots [24; 41].  
The organization of this work is as follows: In Section 2.1, the kinematic model of the Quetzal manipulator is established. Section 2.2 describes the procedure for generating the training and testing dataset. Section 2.3 describes the implementation of the RDANN methodology for the optimization of structural parameters in the BPNN. In Section 3, the results obtained are subjected to a reliability test stage through the use of a cross-validation method to verify that the dataset is statistically consistent. The results of training in the optimized BPNN show a significant improvement in the accuracy of the results obtained compared with the use of conventional procedures based on trial-and-error tests.
## 3 Result and Discussion
### Ejection Experiment
Details of the orientation of the surfaces represented by each marker are shown in Figure 13. Each figure shows the orientation of the ball panel as seen from the receiver's side. Six different panel faces were defined, taking into account the front and back of the air valve placement. The position of the air valve can also be divided into four types in each panel: up, down, left and right; thus a total of 24 ejection patterns were performed. Figure 14 shows the average landing point for each panel. The graph for the initial speed of 65 km/h shows that the ball landed on the court in 22 different ball directions. Figure 11: Ideal receiver position and standard deviation. Figure 12: Landing points by different ejection speed on V200W with different panel orientation. Figure 5:0 Relationship between ball orientation and ball valve at the time of ejection. Figure 15 shows the difference between the case where the ball is ejected from the machine and the case where a person hits the ball. The triangle marks are the case of hitting the ball with panel C. The white marks are the case of hitting the ball randomly. The distance tends to be greater when the ball is hit by a person than when it is hit by a machine, but in both cases, the ball falls toward the front when it is hit with panel C facing it. Thus, with the floater serve, it is possible to control the falling position of the ball by hitting it while paying attention to the direction of the panel surface. Figure 5:1 Average landing point for each panel in Figure 5:2  
Figure 5:3 Difference in the drop position of the ball between the ejection machine and a human hitter. The triangles represent the case of serving with panel C facing, and the white circles represent the case of serving with a random panel orientation.
## 2 Materials and Methods
### Stacking Strategies to Improve Signal-to-Noise Ratio  
Stacking is a traditional method to improve the signal-to-noise ratio in many geophysical techniques. It consists of summing traces from different records to decrease the random noise. In GPR surveys, stacking is generally performed in two ways. The first is hardware stacking, performed by the instrument during data collection, and is generally invisible to the user. The second is post-processing stacking and is usually performed along adjacent traces acquired in the profile direction, in this text called in-line stacking, in which in-line adjacent traces, often collected a few centimeters apart from each other, are stacked with the aim to increase the signal-to-noise ratio (see Figure 2).  
We here tested a third way of stacking that we can call off-line stacking. We stacked traces recorded in the same in-line position but by adjacent channels (see Figure 2). This stacking is also aimed to increase the signal-to-noise ratio but at the expense of the lateral spatial resolution. As an example, given the lateral resolution of the VV dipoles of 4 cm, an off-line stacking of 5 adjacent traces would result in a 16 cm lateral resolution of the stacked radargrams. We performed this stacking using the \"merge files--stack profiles\" option of ReflexW. This option allows for merging different files resulting from profiles of adjacent channels in the off-line direction by stacking them.  
It must be recalled that the in-line stacking approach also results in a loss in resolution in the in-line direction, depending on the number of stacked traces. Therefore, the two stacking approaches can be considered sort of similar with respect to the potential loss of resolution issue. The stacking direction, however, can be relevant and provide different results depending on the orientation of the investigated structures (i.e., parallel or perpendicular to the acquisition direction). Also, a reduced resolution in the in-line direction generally worsens the quality of the radargram (shown later) and may compromise the imaging potential.  
Figure 2: Schematic diagram of the different stacking approaches attempted on the VV dipoles acquired with the adopted GPR Stream C system: each blue dot represents the in-line and off-line positions of the GPR traces acquired considering the indicated survey direction.  
We compared the data after the processing but without any stacking to the data after the processing and with in-line stacking, off-line stacking, and both in- and off-line stackings. These comparisons were performed at three different levels of data visualization: we compared traces, radargrams, and depth-slices.
## 3 Results and Discussion
### Plywood Test Results
#### 3.4.3 Mechanical Properties of Plywood Panels  
Plywood Panels Evaluation According to the European Standards  
The shear strength and wood failure performance of panels was evaluated according to the European standards EN314.1 and EN 314.2 as described in Section 2.5.4. The results are shown in the following Table 6.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline
**Formulation:** & **1 (Control 1)** & **2 (Control 2)** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline \hline Resin: & & & & & & & & & & & \\ \hline Biomaterial: & - & WF & EOP & EOP & ROS & ROS & OS & OS & OL & OL & HS \\ \hline Type & - & & NE & E & NE & E & NE & E & NE & E & E \\ \hline EN314.1 & & & & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 6: Properties of plywood panels made with pMDI/SF/various OPW and HS.  
Figure 5: FPA micro-ATR-FTIR analysis or the bonding line of the plywood manufactured using the adhesive mixture with non-extracted residual olive skin (ROS-NE), as representative of the plywood panels. (**a**) Cross-section image (350 \(\upmu\)m \(\times\) 350 \(\upmu\)m) in visible region. The area of interest is indicated with the red rectangle. (**b**) FPA chemical image representing the spatial distribution of the band at \(\sim\)1710 cm\({}^{-1}\) (70 \(\upmu\)m \(\times\) 70 \(\upmu\)m). (**c**) ATR-FTIR spectra collected from single 1.1 \(\upmu\)m \(\times\) 1.1 \(\upmu\)m pixels of the wood veneer (blue area in (**b**)) and the glue mixture (red area in (**b**)).  
With some exceptions, most plywood panels with olive and hemp residue binding systems exhibited similar mechanical behavior compared to the reference panel, indicating that the novel biobased adhesives under study could potentially replace the conventional ones in terms of mechanical behavior. The panels with pMDI/SF/ROS-NE resin exhibited the best mechanical performance among all other panels, meaning it exhibited the highest endurance to bending deformation. Specifically, it could withstand maximum flexural stress (flexural strength) and flexural modulus close to 17 MPa and 650 MPa, respectively. [PERSON] et al., 2024 [44], investigated starch-based gels as green alternatives adhesives for plywood applications providing flexural data comparable to the present bio-based binders.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 4 Results and Comparison with Existing Models
### Model Comparison  
To validate the effectiveness of the proposed CNN-NLP model, a comparison was made with benchmark models in the field of steganalysis, including Xu-Net, Yedroudi-Net, and SRNet. These models have been specifically designed for image steganography detection and have been trained on databases such as BOSSbase. Xu-Net, for example, includes an initial convolution layer with fixed filters; Yedroudi-Net extends the architecture with additional layers and early batch normalization; and SRNet proposes a deeper network without pooling convolutions to preserve low-frequency information. The comparison focuses on three key aspects: accuracy, F1-score, and semantic validation capability.  
As shown in Table 2, the proposed CNN-NLP model outperforms the traditional architectures in all performance metrics. In terms of accuracy, Xu-Net reaches 87.75%, Yedroudi-Net reaches 90.10%, and SRNet reaches 92.60%. In contrast, the CNN-NLP model achieves an accuracy of 99.96%, which represents an improvement of 7.36% compared to the best of the compared models (SRNet). This implies that most of the cases that it detects as carrying steganography effectively are carrying steganography, which significantly reduces false positives and strengthens the reliability of the system, a key aspect in forensic analysis.  
The differences are also notable in terms of accuracy: the proposed model achieves 99.96%, representing an improvement of more than six percentage points compared to the best performer (93.80% with SRNet). This indicates that the proposed model not only accurately identifies altered images but also avoids misclassifying authentic images as manipulated, thus improving the overall accuracy of the system.  
These results are due not only to the architectural design and the residual preprocessing applied, but also to the inclusion of semantic validation, which allows verification of whether the hidden message is consistent in Spanish. With additional validation and the absence of traditional models, false positives can be ruled out. Although they exhibit  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Database** & **Accuracy (\%)** & **Precision (\%)** & **Semantic Validation** \\ \hline Xu-Net [11] & Bossbase & 88.30 & 87.75 & No \\ \hline Yedroudi-Net [27] & Bossbase & 90.90 & 90.10 & No \\ \hline SRNet [29] & Bossbase & 93.80 & 92.60 & No \\ \hline CNN-NLP & PNG(25K) & 99.96 & 99.96 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the proposed CNN-NLP model and reference models in LSB steganography detection.  
Figure 8: Test with high-complexity message. (**a**) Image with LSB message of 1398 characters. (**b**) Histogram of the image (case of correct classification).  
statistical patterns similar to those of a hidden message, they do not contain linguistically valid content. This enhances the utility of CNN-NLP, particularly in legal and forensic contexts where the content of the message is as crucial as its detection.
## 2 Materials and Methods  
La\({}_{1-x}\)Y\({}_{x}\)Fe\({}_{11.5}\)Si\({}_{1.5}\) (x = 0, 0.03, 0.07, 0.15, 0.2) ingots were prepared by arc melting of the stoichiometric mixtures of constituent elements under an argon atmosphere. The purity of starting materials was 99.4 wt % for La and Y (obtained from the Materials Preparation Center of Ames Laboratory), 99.9 wt % for Fe (purchased from Alfa Aesar Inc., Haverhill, MA, USA), and 99.999 wt % for Si (purchased from Alfa Aesar Inc.). The samples were melted four times to ensure homogeneity. The samples were wrapped in Ta foils, and sealed in helium-filled quartz tubesusing Mo K\(\alpha\) radiation at room temperature. The microstructural characterization was performed using JEOL 5910 scanning electron microscopy (SEM, JEOL USA, Inc., Ames, IA, USA) with an energy dispersive spectrometer (EDS).  
Magnetic measurements were performed using an MMPS XL-7 superconducting quantum interference device magnetometer manufactured by Quantum Design, Inc. Initial magnetization, \(M(T)\), was measured at varying temperatures at a constant magnetic field of 0.01 T in order to identify the Curie temperatures (\(T_{\rm C}\)s). The magnetization as a function of applied magnetic field, \(M(H)\), was measured isothermally near the corresponding \(T_{\rm C}\)s.  
The isothermal magnetic entropy change, \(\Delta S_{M}(T\), \(H)\), was estimated from isothermal magnetization curves using the following equation [17]:  
\[\Delta S_{M}(T_{\rm av})_{\Delta H}=\frac{\delta H}{2\delta T}\left(\delta M_{ 1}+2\sum_{k=2}^{n-1}\delta M_{k}+\delta M_{n}\right) \tag{1}\]  
Here, \(\delta T\) = \(T_{u}\) - \(T_{l}\) is the temperature difference between the two isothermal magnetization curves, \(n\) is the number of points measured for each of the two isotherms with the magnetic field changing from \(H_{1}\) = \(H_{l}\) to \(H_{n}\) = \(H_{\rm F}\) at \(\delta H\) = \(\Delta H/(n-1)\), and \(\delta M_{k}\) = \(M(T_{u})_{k}-M(T_{l})_{k}\)is the difference in the magnetization at \(T_{u}\) and \(T_{l}\) for each magnetic-field step from 1 to \(n\).
###### Abstract
Genuinely, group communication can improve the overall efficiency of the entire network environment [6]. There may exist a group leader in each group. A group leader will be in charge of the group resources' bookkeeping and coordination with the base station [7]. The presence of group leaders further reduces the loading of base stations. The hop-count in data transmission also affects the efficiency of group communication. Several alternative paths may exist from a source UE to a destination UE. A single-hop, long-distance transmission link may experience low bandwidth due to a low Signal-to-Noise Ratio (SNR). On the other hand, multi-hop, short-distance transmission links may suffer from longer delays [8]. Therefore, finding the best route between UEs in a group is a critical issue that deserves further investigation. This study contributes to the group routing problem, that is, finding the best path from a source UE to a destination UE with consideration of both lowering the delay and raising the throughput. More specifically, in this study  
* A utility function of D2D paths is defined considering both delay and throughput. * Finding the best path is formally formulated as an NP-hard problem. * A Bidirectional and InCerenmental A-star (BICA*) algorithm incorporating the concept of bidirectional search and lifelong planning is developed to tackle the NP-hard search problem. * A series of simulations are conducted to verify the feasibility and effectiveness of the proposed approach. The rest of this paper is organized as follows. Previous works on D2D communication and related technology are briefly reviewed in Section 2. We define the network model and formulate the route planning as an NP-hard optimization problem in Section 3. Then, our Bidirectional and InCerenmental A-star algorithm (BICA*) for group communication is presented in Section 4 with an in-depth explanation and discussion. A series of simulations validating our approach's feasibility and effectiveness is presented in Section 5. Finally, some conclusions are drawn in Section 6.
## 5 Discussion and Future Research Directions
### Resource and Service Management  
The first key to the efficient management of resources and services is dynamic and flexible modeling. Network services need to be efficiently modeled towards many parameters such as performance metrics, resource requirements, network configuration parameters, services management policies, etc. Flexible modeling will lead to an efficient abstraction of resources, architecture capabilities, and the providing services. In other words, it simplifies the understanding of network features by providing a generic way to represent resources and service. The second key is to optimize resource utilization while providing on-demanding services.
# Detection of Hidden Communities in Twitter Discussions of Varying Volumes
## 3 Models and Methods in SNA
### Description of Community Detection Methods
#### 3.2.1 The Directed Louvain Algorithm  
Directed Louvain [26] is an extension for directed graphs of the greedy Louvain [13] algorithm: starting from any set of vertices, the algorithm calculates the increase in modularity from moving vertices between communities. This increase is calculated using the following formula:  
\[\Delta_{Q_{d}}=\frac{d_{i}^{C}}{m}-\left[\frac{d_{i}^{out}*\Sigma_{tot}^{int}+d _{i}^{inv}\Sigma_{tot}^{out}}{m^{2}}\right],\]  
where \(d_{i}^{C}\)--the degree of node \(i\) in community \(C\); \(d_{i}^{in}\) (\(d_{i}^{out}\))--the indegree (outdegree) of node \(i\); \(\Sigma_{tot}^{in}\) (\(\Sigma_{tot}^{out}\))--the incoming (outcoming) edges of community \(C\).
## 5 Discussion
### Paleoredox Conditions  
The paleoredox conditions of the water directly affect the preservation of organic matter [27]. In an oxidizing environment, organic matter is oxidized and decomposed, while a stable reducing environment is conducive to the preservation of organic matter. Among trace elements, V (Vanadium), Zn (Zinc), Cu (Copper), and Ni (Nickel) are common redox-sensitive elements. These elements share similar chemical properties, as they tend to precipitate under reducing conditions and dissolve in water under oxidizing conditions. Once deposited, they are unlikely to migrate and, thus, serve as excellent indicators of redox environments.  
In this study, based on previous research methods, the V/(V + Ni) and Cu/Zn ratios are selected as discriminative parameters for determining the redox environment. When the V/(V + Ni) ratio is less than 0.46, it indicates an oxidizing environment; when the ratio is between 0.46 and 0.54, it indicates a weak oxidizing-weak reducing environment; and when the ratio is greater than 0.54, it indicates a reducing environment. The discriminative standard of Cu/Zn is more refined than that of V/(V + Ni). When the Cu/Zn ratio is less than 0.21, the sedimentary water body is considered to be anaerobic; between 0.21 and 0.38, it indicates a weak reducing environment; between 0.38 and 0.5, it indicates a weak oxidizing-weak reducing environment; between 0.5 and 0.63, it indicates a weak oxidizing environment; and when greater than 0.63, it indicates an oxygen-rich environment [28].  
Using the above indicators to analyze the paleoredox conditions of Ying IV in the study area, the V/(V + Ni) ratios of the selected samples range from 0.41 to 0.75, with an average value of 0.56. Most samples have ratios greater than 0.46, accounting for 86.7% of the total samples, and the remaining samples have ratios close to 0.46, indicating that the water body in the study area is predominantly in a weakly oxidizing to reducing environment. Among the selected samples, only one sample has a Cu/Zn ratio of 0.22, while the rest have ratios less than 0.21, reflecting that the water body environment in the study area is mainly reducing. A comprehensive judgment based on the relationship diagram of the two ratios (Figure 10) shows that the water body of Ying IV in the Xujiaweizi area is generally characterized by reducing conditions.