esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Results on The Energy Efficiency  
Figure 3 shows the numerical results of the energy consumption _versus_ the transmission range with different node density \(\rho\) and different covering radius \(a\). From all these results, it can be seen that with the increase of the transmission range \(r\), the energy consumption decreases first but later increases after reaching a certain point. Such a pattern can be explained as follows. As the transmission range \(r\) increases, the probability of finding relay nodes closer to the sink would be higher, leading to a larger distance progress. Note, however, that the minimum transmission power is fixed at 8 W for small values of \(r\). As a result, a larger \(r\) (\(<\)\(3,000\) m) renders a lower energy consumption. On the other hand, as can be seen from Equations (1) and (4), the transmission power becomes very large for large values of \(r\), which increases exponentially with the increase of \(r\). Despite the fact that a larger transmission range would induce a larger distance progress, the energy consumption per unit distance (e.g., per meter) still increases as the transmission range \(r\) becomes large. That is why we see a minimum point in the curve \(\epsilon(r)\) which represents an optimal transmission range in energy consumption.  
Figure 3: The numerical results of the average energy consumption _versus_ the transmission range. (**a**) \(a=5,000\) m; (**b**) \(a=10,000\) m.  
Figure 5(a) shows the optimal transmission range (\(r_{opt}\)) _versus_ the node density for both \(a=5,000\) m and 10,000 m. Again, the close match between the simulation and analytic results is demonstrated. In addition, two observations can be made from this figure. First, the optimal transmission range decreases as the increasing of \(\rho\). This is due to the increase in relative one-hop progress with respect to the transmission range. A smaller transmission range achieves better energy efficiency when \(\rho\) is larger. Second, a larger network radius \(a\) introduces a larger optimal transmission range in the case of the same \(\rho\). This is because larger area corresponds to larger number of hops, which introduces more overhead. Figure 5(b) gives the corresponding average energy consumption results with the optimal transmission ranges.
## 4 Discussion
### Influence of Vertical Loading  
In view of the fact that the vertical load is an essential factor which can influence the lateral response of the pile shaft, such influences are investigated by varying the ratio of vertical load and passive load for the pile shaft \(N/q\) as 0, 5, 10, 20 and 40. Figure 15 compares the relationships between lateral deformation and depth with varied \(N/q\) when the passive load \(q\) is equal to 400 kN. It is revealed that both the horizontal deformation and bending moment of the pile shaft are increasing as the vertical load is increasing. There is an apparent amplified action for the lateral response of the pile shaft resulting from the \(P\)-\(\Delta\) effect of the vertical load.  
Figure 15: Influence of vertical load on lateral behavior of pile shaft. (**a**) Lateral displacement/mm; (**b**) bending moment/kN \(\times\) m.  
In order to further explore the influence on the lateral response of the pile shaft caused by the vertical load, the relationship between passive load \(q\) and the lateral response of the pile shaft under different \(N/q\) are obtained and illustrated in Figure 16. It is noteworthy that, in Figure 16, when \(q\) is larger than 300 kN, as the value of \(q\) is increasing, the differences among maximum horizontal deformations and bending moments under varied \(N/q\) reveal an increasing trend, while the amplified action caused by the \(P\)-\(\Delta\) effect of the vertical load on the lateral response of the pile shaft is significant. However, when \(q\) is less than 300 kN, such influence can be ignored. Moreover, it is also found that, when \(N/q\) is less than 10, as the value of \(q\) increases, the maximum horizontal deformation and the bending moment of the pile shaft increases linearly, while a non-linear growth trend can be observed when \(N/q\) is greater than 10.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 6 Conclusions  
In this work, an attempt was made to test the applicability and effectiveness of adaptive DE on Lp-norm gravity inversion. As far as this work is concerned, this is the first attempt at applying DE for physical property inversion using gravity data. In the application of this stochastic search algorithm, to further improve the exploration ability, the evolved direction of the DE population in this paper is guided adaptively. Besides, in order to maintain the physical property to distribute in a continuous space, the perturbation direction in the mutation operation is smoothed by using the weighted moving average smooth technique. In addition, adaptive adjustment of the regularization factor is proposed to balance the effect of data misfit and Lp-norm regularization term. In the present algorithm, synthetic data experiments are performed using noise free and noisy data sets calculated from simple-shaped causative bodies. From the example of synthetic models, the adaptive DE algorithm with improved mechanisms shows favorable results and is capable of achieving a density distribution with sharp boundary by setting a proper \(p\) value. In addition, the proposed algorithm was also tested on a field data from the Shihe iron deposit; the predicted density distribution was verified by drill-hole information. Finally, in this work, we discussed the application of an improved JADE version in Lp-norm gravity inversion. Some recent DE variants demonstrated excellent performance for single objective optimization problems. Their application in geophysical inversion will be done in future research.  
Conceptualization, T.S. and X.H.; methodology, T.S. and L.C.; software, L.C. and T.X.; Formal analysis, T.S., X.H., and W.D.; Resources, Q.L.; writing--original draft preparation, T.S.; writing--review and editing, X.H. and W.D.; visualization, T.X.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the Discipline and Master's Site Construction Project of Guiyang University by Guiyang City Financial Support Guiyang University, grant number SX-2020; Natural Science Research Program of Guizhou Provincial Department of Education under grant number KY[2013]110; Youth Science and Technology Talent Growth Project from Guizhou Provincial Department of Education, grant number QJHKYZ[2020]075.  
Not applicable.  
Figure 9: The Lp-norm inversion results of abnormal gravity field in the Shihe area. (**a**,**c**,**e**,**g**) are the results while \(p=1\), \(p=1.1\), \(p=1.5\) and \(p=2\), respectively; the line denotes the drill-hole, the cyan block denotes the ore body, and (**b**,**d**,**f**,**h**) are the corresponding data fit lines.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 8 Underwater SLAM Systems Evaluation and Comparison
### Comparison of Underwater SLAM Methods  
Underwater SLAM systems have undergone significant advancements, leading to a variety of traditional and deep learning-based approaches. These methods are evaluated against key metrics such as trajectory error and robustness across different datasets and environments. This section presents a comparative analysis of traditional and deep learning-based SLAM techniques, highlighting their performance, strengths, and limitations in underwater conditions.  
While trajectory error and robustness assess accuracy, computational efficiency (FPS, memory usage) and power consumption determine the feasibility of SLAM methods in underwater deployments. FPS reflects real-time performance, critical for dynamic environments; memory usage impacts hardware selection on resource-constrained robots; and power consumption affects mission duration due to limited battery capacity. Unfortunately, many cited studies do not report these metrics, as indicated by \"NR\" (not reported) in Tables 1 and 2. This gap highlights a need for standardized reporting in the field. Future research should include these metrics to enable comprehensive comparisons and optimize SLAM systems for underwater operations.  
Traditional underwater SLAM methods often rely on classical filtering and optimization techniques adapted to work with data from sensors like sonar, cameras, and IMUs. Table 9 summarizes the performance of these methods across different datasets. However, the reliance on handcrafted features and the inability to adapt dynamically to challenging underwater environments limit these methods. For example, McConnell et al. (2022) [162] and Mu et al. (2022) [163] achieved relatively high RMSE values due to the noisy nature of acoustic and visual data, respectively, in underwater SLAM systems.  
Deep learning-based approaches have emerged as a powerful alternative, leveraging neural networks to enhance feature extraction, pose estimation, and mapping. Table 10 provides a detailed comparison of these methods evaluated across diverse underwater datasets. Despite these advancements, the performance of deep learning methods varies significantly across datasets. This variation reflects the influence of environmental fac  
\begin{table}
\begin{tabular}{c c c} \hline \hline  
tors such as turbidity, lighting, and feature richness. Methods specifically designed for underwater conditions, such as RU-SLAM, outperform general-purpose SLAM techniques.  
The localization error for various underwater SLAM methods using the Aqualoc dataset is shown in Figure 15. Traditional methods are highlighted in blue, while DL-based methods are highlighted in orange. This comparison provides an evaluation of their performance in the same dataset.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 3 Signal Processing and Design
### Pilot Component  
For the design process of the pilot component of a PNT system, we refer the reader to the study logic followed in [11]. This study looks for the shortest chirp period possible in order to minimize complexity. Unfortunately, we must fix a chirp period large enough to satisfy different requirements in terms of different KPIs. In particular, the KPIs that drive the design of the pilot component are the sensitivity and the accuracy. Since the target design is the complexity, we should look for the minimum chirp period (\(T_{\text{c}}\)) that provides the target sensitivity. Then, with this fixed chirp period, the ranging accuracy can be evaluated. Recall that the pilot component uses a BOK-chirp signal, so the acquisition and estimation blocks will deal with 4 de-chirp processes as the one in Figure 1[11].  
Figure 1: De-chirp process to receive a CSS signal: data demodulation block for FSK-CSS when synchronized.  
In this section, we show the results of the sensitivity analysis useful to design the chirp period and further evaluate the performance of the data component. For the analysis of the accuracy performance, we refer to [11]. For the sensitivity, we define a sensitivity performance given by the probabilities of false alarm (PFA) and detection (PD) of the acquisition module, e.g., \(\text{PD}\geq 0.9\) when \(\text{PFA}=10^{-5}\). Then, the main target of a sensitivity analysis is to find the minimum chirp period that provides the target sensitivity performance for different values of carrier-to-noise ratio (CN0), constellation size (\(N_{\text{sat}}\)), and visible satellites (\(N_{\text{vis}}\)). For the results shown in this paper, we will consider \(\{N_{\text{sat}},N_{\text{vis}}\}=\{50,5\}\) satellites. An important analysis for the design of the CSS signal is the trend of \(T_{\text{c}}\) as a function of CN0. For instance, Figure 2 shows this analysis in the range of \(\text{CN0}=[35,50]\) dB-Hz and different bandwidths in the range of 100 kHz to 10 MHz. We take these results as a reference for the analysis of the data component. More details are given in [11].
## 2 Design of Components and Fabrication
### Experimental Setup
#### 2.1.4 Impact Energy and Velocity  
The comprehensive control over these critical parameters ensures the ability to achieve desired impact velocities and energies. For instance, impact velocities can be varied (e.g., from 0.1 m/s to 32 m/s) to study the response of materials and structures under different loading rates. Correspondingly, impact energies can be adjusted (e.g., from 0.005 J up to 130,000 J) to investigate the material's deformation behavior and structural integrity under varying impact energies.
## 1 Introduction
For single base station, RSS fingerprint is vulnerable to non-line of sight (NLOS) and multipath propagation. The features of different positions may be similar and results in poor accuracy. Therefore, multiple measurements fingerprint is necessary. The reference signal reception power (RSRP) is combined with the reference signal reception quality (RSRQ) in the 4G cellular network. This physical layer information of the signal is used to build a fingerprinting database to improve the positioning accuracy [16]. [PERSON]. [17] presents a localization method employing a Hybrid Wireless fingerprint (HW-fingerprint) based on CNN in Wi-Fi systems. [PERSON]. [18] proposed random forest variable selection (RFVS) to sort variable importance and combinations for establishing multivariable fingerprinting database in 5G cellular network to improve the robustness of the positioning system. Indoor positioning methods include trilateration and fingerprinting. Trilateration obtains the positioning result by calculating the intersection between the geometry, such as circle or hyperbola. RSS is commonly used to fit the radio propagation path-loss model [19; 20]. It is suitable for large open scenes because of sufficient line of sight (LOS) path information. Fingerprinting uses the features of the scene to estimate the target position. The position of the target device is usually determined as the reference point with the most similar features, such as RSS, delay or channel delay extension [21]. Fingerprints take the advantages of multipath propagation. Therefore, a combination of localization algorithms is implemented to improve the overall performance. Adaptive Enhanced Cell-ID (AECID) adjusts the similarity of signal power fingerprinting according to roundtrip time (RTT), and uses weighted KNN (WKNN) algorithm to calculate the final position [22]. Ref. [23] combines the fingerprinting system with CSI model under LOS environment to improve the robustness and accuracy of multidimensional scaling (MDS)-KNN system. In this paper, we study the indoor positioning problem based on fingerprint in 5G systems. The fingerprint is established by using four measurements in secondary synchronization signal (SSS). We use CNN to transform the indoor positioning problem into an image classification problem. At the same time, the path-loss model is trained to improve the overall positioning accuracy. The method in this paper only uses a single base station, but can also be extended to multiple base stations. The main work of this paper is as follows:  
1. We combine four measurements in SSS to construct 5G physical layer multivariable fingerprints (MVF), and use MVF to train a CNN location classification model for indoor positioning. 2. We use MVF to train the path-loss model, which indicates the relationship between distance and radio signal measurement. A hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning results. 3. We conduct experiments in the actual indoor environment to verify the effectiveness of the proposed method.
# Characterization of Natural Gypsum Materials and Their Composites for Building Applications
## 1 Introduction
### Thermal and Structural Characteristics: State of Art  
Thermal behavior of Moroccan gypsum material can depend on its whole rock geochemistry, but also on other features, such as morphology of the crystals, microstructure and mechanical properties. Several researchers have focused on natural gypsum for building applications. Among them, Tunisian researchers [2; 6; 7] studied Tunisian gypsum, discovering that it presents an interesting behavior in terms of thermal insulation. The thermal conductivity of the Tunisian gypsum powder and plaster, measured with an experimental apparatus based on the radial flux meter, was \(0.10\pm 0.04\) W/mK and \(0.22\pm 0.03\) W/mK, respectively [6]. However, the thermal performance of the plaster constituted of water and powder was strongly influenced by the mixing ratio: a negative effect of the presence of water was demonstrated on the plaster thermal insulation performance.  
Lightweight gypsum composites containing Polymeric-SiO\({}_{2}\)PCMs (phase change materials up to 15 wt%) were studied in [8]. As expected, the thermal insulation of the gypsum was improved (+18.4%) by the thermoregulating effect of the PCM. Nevertheless, there was no variation in the crystal size and structure of the gypsum, as demonstrated by SEM analysis, with constant porosity despite the density decreasing. On the other hand, the addition of Polymeric-SiO\({}_{2}\)PCMs produced a reduction of mechanical properties (maximum flexural strength was reduced of about 45% with the 15 wt%): however, they satisfied the European Regulation EN 13279-2 [9] standard.  
[PERSON] and [PERSON] [10] investigated the thermal and mechanical properties of gypsum plaster (calcium sulfate dehydrate) mixed with expanded polystyrene (EPS) (20%, 40%, 60%, and 80% percentages in weight) and tragacanth resin with adhesive characteristics (0.5%, 1%, and 1.5% by weight of the mixture). The thermal conductivity decreased up to 78.85% (with 80% of EPS) depending on the EPS ratio. The lowest thermal conductivity (0.047 W/mK) was measured for 80% EPS and 1.5% resin content, which decreased in density due to the artificial pores that occurred later.
## 2 Correlation Method
### Gramian Angular Field  
Given a time series \(X=\{x_{1},x_{2},\cdots,x_{n}\}\) with \(n\) value, normalizing the time series between \([0,\ 1]\), it can be expressed as \(\widetilde{X}=\{\widetilde{x_{1}},\widetilde{x_{2}},\cdots,\widetilde{x_{n}}\}\), where \(\widetilde{x_{i}}\) is value of the normalized time series. The calculation is shown in Formula (4):  
\[\widetilde{x_{i}}=\frac{x_{i}-\min(X)}{\max(X)-\min(X)}. \tag{4}\]  
By solving the inverse cosine function of the normalized time series, the time series can be converted to the angle in polar coordinates, and the time stamp can be encoded as the radius, which can be represented in polar coordinates. Since the cosine function is monotonic at \(\left[0,\ \frac{\pi}{2}\right]\), the angle of mapping between time series on polar coordinates is unique. At the same time, radii of different lengths are used to represent time values in polar coordinates to maintain time dependence.  
\[\left\{\begin{aligned} \varphi=\arccos(\widetilde{x_{i}}),& 0 \leq\widetilde{x_{i}}\leq 1,\widetilde{x_{i}}\in\widetilde{X},\\ r_{i}=\frac{t_{i}}{N},& t_{i}\in N\end{aligned}\right. \tag{5}\]  
where \(t_{i}\) is the timestamp and \(N\) is the length of the time series. By calculating the cosine value of the sum of angles between each point or the sine value of the difference of angles, the Gramian angular sum field (GASF) and Gramian angular difference field (GADF) are formed, respectively, to reflect the time correlation of data at different time intervals.  
\[GASF=\left[\cos(\varphi_{i}+\varphi_{j})\right]=\widetilde{X}^{T}\cdot \widetilde{X}-\sqrt{I-\widetilde{X}^{2}}^{T}\cdot\sqrt{I-\widetilde{X}^{2}}, \tag{6}\]  
\[GADF=\left[\sin(\varphi_{i}-\varphi_{j})\right]=\sqrt{I-\widetilde{X}^{2}}^{T }\cdot\widetilde{X}-\widetilde{X}^{T}\cdot\sqrt{I-\widetilde{X}^{2}}, \tag{7}\]  
where \(I\) is the unit row vector.  
Each value in the matrix is then mapped to the grayscale level to visualize it as a GAF plot. In the GAF plot, each data point corresponds to one pixel of the image. The horizontal and vertical axes of the image represent the index of the data points, and can also represent the order of time. The gray level in an image reflects the similarity or correlation between data points.
# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 4 Methodology
### Training Protocol  
The hardware used for training was a single NVIDIA GTX1080 graphics processing unit (GPU) with 8 GB of memory. The operating system was Ubuntu 16.04, and the models were implemented on the PyTorch framework. The training process was organized in two stages: initial training and deep transfer learning. During the initial training stage, the model was pre-trained on the available dataset COCO, which consists of over 200,000 images with over 500,000 annotated object instances from 80 categories. For convenience, the trained model weights derived from YOLOv5's model export were downloaded. Then we employed transfer learning by fine-tuning the entire model on the simulated dataset. The fine-tuning process used the following settings: SGD optimizer with initial learning rate = 0.01, weight decay = 0.0005, number of epochs = 200, batch size = 16, single-class training mode = True, and the real dataset for evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Camera**} & \multicolumn{2}{c}{**Laser**} \\ \hline No. of camera pixels & 1280 \(\times\) 1024 & Wavelength & 532 nm \\ Pixel size & 4.8 μm & Power & 0–10 w \\ Frame rate & 210 & Divergence angle & 10 mrad \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameters.  
Figure 5: Photograph of experimental devices (**a**) and experimental scene (**b**).