esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Deep Reinforcement Learning
### Sensor Fusion for Enhanced Path Planning in DRL  
Sensor fusion has become a crucial component in reinforcement learning-based robot navigation, allowing autonomous agents to integrate multiple sensory inputs to improve decision-making in complex environments. Combining LiDAR, camera images, and IMU data enhances depth perception, spatial awareness, and obstacle avoidance, leading to improved policy robustness in deep reinforcement learning (DRL).  
Recent studies have demonstrated the effectiveness of LiDAR-camera fusion in DRL-based navigation. [PERSON] et al. [17] introduced a sensor fusion framework that integrates LiDAR and camera data to establish a more reliable environmental representation. Their approach enables mobile robots to navigate unfamiliar environments more efficiently than traditional single-sensor setups. Additionally, [PERSON] [18] designed a lightweight multi-modal data fusion network that combines LiDAR and image-based semantic segmentation, bridging the gap between simulated and real-world navigation.  
Incorporating IMU data into sensor fusion frameworks further enhances DRL-based path planning by providing motion stability and aiding localization. [PERSON] and [PERSON] [19] proposed a short-term visual-IMU fusion memory agent for drone motion planning, significantly reducing neural network complexity while maintaining robust obstacle avoidance. Similarly, [PERSON] et al. [20] developed a sensor fusion system that integrates IMU with LiDAR-based SLAM to improve 3D color reconstruction in large-scale environments.  
Several DRL frameworks now explicitly incorporate sensor fusion to improve real-time navigation. [PERSON] et al. [21] introduced a deep deterministic policy gradient (DDPG)-based approach that fuses laser and vision sensor data, achieving a 10% improvement in navigation success rates in dynamic environments. Furthermore, [PERSON] et al. [22] developed a multi-sensor fusion framework that preprocesses RGB and depth images from cameras before integrating them with LiDAR data, significantly improving obstacle avoidance in real-world scenarios.  
These advancements highlight the growing importance of sensor fusion in DRL-based robotics. Future research should focus on optimizing real-time data processing pipelines and developing lightweight fusion architectures to enhance computational efficiency.
## 3 Results  
### FTIR Investigations  
IR maps of dropcast and MAPLE thin films are presented in Figure 1. Surface mapping exhibited the chemical properties and the distribution of functional groups across the sample region.  
The mapping intensities were recorded based on the IR absorbance intensity values of the examined peaks [28]. Typical FTIR spectra of the MAPLE-deposited samples are shown in Figure 2.  
Figure 1: Infrared spectroscopy (IR) mapping data. Intensity distribution of dropcast surfaces: (**1**) polyvinylpyrrolidone–quercetin dihydrate–ambhotericin B and (**2**) polyvinylpyrrolidone–resveratrol–voriconazole. Intensity distribution of thin films deposited by matrix-assisted pulsed laser evaporation (MAPLE): (**3**) polyvinylpyrrolidone–quercetin dihydrate–ambhotericin B, (**4**) polyvinylpyrrolidone–resveratrol–ambhotericin B, (**5**) polyvinylpyrrolidone–quercetin dihydrate–voriconazole, and (**6**) polyvinylpyrrolidone–resveratrol–voriconazole, where monitored peaks: (**a**1**–**a**6): 2957 cm\({}^{-1}\); (**b**1**–**b**6): 1672 cm\({}^{-1}\); (**c**1**–**c**6): 1461 cm\({}^{-1}\); (**d**1**–**d**6): 1291 cm\({}^{-1}\); (**e**1** and **e**3): 1620 cm\({}^{-1}\); (**f**1** and **f**3): 3310 cm\({}^{-1}\); (**e**2**,**e**4**,**e**5, and **e**6): 1270 cm\({}^{-1}\); (**f**2**,**f**4**,**f**5, and **f**6): 1644 cm\({}^{-1}\).  
Figure 2: Typical Fourier-transform infrared spectroscopy (FTIR) spectra of MAPLE-deposited thin films: (**a**) polyvinylpyrrolidone–quercetin dihydrate–ambhotericin B, (**b**) polyvinylpyrrolidone–resveratrol–ambhotericin B, (**c**) polyvinylpyrrolidone–quercetin dihydrate–voriconazole, and (**d**) polyvinylpyrrolidone–resveratrol–voriconazole.
### Factors Influencing Water Production
#### 4.2.4 Groundwater Sealing Coefficient  
Based on the groundwater sealing coefficient \(F\) proposed by previous researchers, the groundwater dynamic conditions in the study area were confirmed. The expression for the sealing coefficient is as follows:  
\[F=\frac{\rho\left(\text{K}^{+}+\text{Na}^{+}+\text{HCO}_{3}^{-}+\text{CO}_{3}^{ 2-}+\text{CI}^{-}\right)}{\rho\left(\text{Ca}^{2+}+\text{Mg}^{2+}+\text{SO}_{4 }^{2-}\right)} \tag{7}\]  
Each unit of ion concentration is given in mg/L. The sealing coefficient is constant. The larger the sealing coefficient, the better the sealing of the groundwater, which is more favourable for the enrichment and preservation of coalbed methane [36].  
The groundwater sealing coefficient varies between 58.1 and 344.0, with an average of 194.6 in the study area. Approximately 65% of the coalbed methane wells exhibit a sealing coefficient exceeding 100, indicating that the groundwater in the study area has good sealing performance, poor hydrodynamic conditions, and strong groundwater retention. The average water production decreases with the increase of the sealing coefficient (Table 2; Figure 8).  
Figure 7: Relationship between average water production and groundwater stability index.
# A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Turkije)
## 3 Materials and Methods
### Statistical Analysis
#### 3.3.4 Index of Entropy (IOE)  
Entropy in thermodynamics represents the thermal energy of a system that cannot be converted to mechanical work. It is often defined as the randomness and disorder (chaos) in a system. Entropy shows the extent of instability, disorder and uncertainty in a system [111]. The entropy level of a system has a one-to-one relevancy with the degree of disorder. This relevancy, called the Boltzmann principle, is used to describe the thermodynamic state of a system [111]. An entropy model for information theory was established using the Boltzmann principle by [112]. The IOE method is widely used to determine the weight index of natural hazards [113; 114; 115].  
As noted by [PERSON] [116], a \"landslide is a complex system for material and energy exchange with the environment and therefore a landslide can be measured and defined using the information entropy method\". The entropy of a landslide refers to the degree to which different factors affect the progress of a landslide [117]. Some important factors, such as slope, lithology and curvature, produce additional entropy to the index system. Finally, the level of entropy can be used to determine the objective weights of the index system [117; 118; 119]. The following steps are used to compute the weight values of geo-environmental factors and to produce the LSM using these weighted values.  
The following equations (Equations (6) and (7)) are used to calculate the information coefficient \"\(W_{j}\)\", corresponding to the weight value of the parameter as a whole [119; 120; 121]:  
\[P_{ij}=\frac{a}{b} \tag{6}\]  
\[(P_{ij})=\frac{P_{ij}}{\sum_{j=1}^{S_{j}}P_{ij}} \tag{7}\]  
where \"\(a\)\" and \"\(b\)\" are percentages of landslide-free area and landslide-occurred area, respectively. \"\(S_{j}\)\" is the number of classes and (\(P_{ij}\)) is the probability density. \"\(H_{j}\)\" and \"\(H_{jmax}\)\", indicated by Equations (8) and (9) below, represent the entropy values.  
\[H_{j}=\sum_{i=1}^{S_{j}}\left(P_{ij}\right)\log_{2}\left(P_{ij}\right),\ J=1,2, \ldots,n \tag{8}\]  
\[H_{jmax}=\log_{2}S_{j} \tag{9}\]\"\(I_{j}\)\" is the information coefficient calculated by Equation (10) and \"\(W_{j}\)\", calculated by Equation (11), represents the weight of the parameter as a whole.  
\[I_{j}=\frac{H_{jmax}-H_{j}}{H_{jmax}}\ I=(0,1),\ J=1,2,\ldots,n \tag{10}\]  
\[W_{j}=I_{j}\times P_{j} \tag{11}\]
# Ballast Flow Characteristics of Discharging Pipeline in Shield Slurry System
## 4 Ballast-Carrying Performance and Distribution State in Slurry System
### Influence Law of Ballast Distribution State in Each Pipe Section  
We found that the ballast at I\({}_{2}\) of the inclined pipe is mainly located in the middle and upper part, the ballast at H\({}_{2}\) of the horizontal pipe is located in the bottom part, the ballast at V\({}_{1}\) of the vertical pipe is located on the flushing side, and the ballast at V\({}_{2}\) of the vertical pipe is in the center of the pipeline while using the statistics of the ballast distribution state at positions I\({}_{2}\), H\({}_{2}\), V\({}_{1}\), and V\({}_{2}\) under different working conditions. When the \(d_{b}\) was 17 mm and the \(C_{v}\) was 3%, the ballast distribution state under different slurry flow rates was as shown in Figure 13a. When the \(Q\) exceeded 1145 m\({}^{3}\)/h, the ballast at the I\({}_{2}\) of the inclined pipeline changed from bottom movement to suspension motion. However, it did not change the ballast motion state of the horizontal pipe at H\({}_{2}\), but the ballast distribution area reduced from regions 1-3 and 14-16 to regions 1-2 and 15-16. The ballast diffusion area at the V\({}_{2}\) of the vertical pipeline decreased from 60% to 24%. When the \(d_{b}\) was 17 mm and the \(Q\) was 891 m\({}^{3}\)/h, the ballast distribution state under different \(C_{v}\) was as shown in Figure 13b. When it increased from 1.5% to 6%, the ballast motion states in each pipeline were basically unchanged, but the ballast diffusion area in each pipeline increased. The ballast diffusion area increased along the left and right pipe sides at H\({}_{2}\) of the horizontal pipeline, and the accumulation area expanded from regions 1 and 16 to the regions 1-3 and 14-16. The ballast diffusion area extended along the slurry flushing side at the V\({}_{1}\) of the vertical pipe. The ballast diffusion degree at I\({}_{2}\) and V\({}_{2}\) increased obviously, the area proportion at I\({}_{2}\) increased from 36% to 59%, and the area proportion at V\({}_{2}\) increased from 17% to 60%. When the \(Q\) was 891 m\({}^{3}\)/h and the \(C_{v}\) was 1.5%, and the ballast distribution state under different ballast sizes was as shown in Figure 13c. When the ballast size was larger than 22 mm, the ballast at I\({}_{2}\) changed from suspension motion to bottom movement, and the ballast was mainly distributed in the bottom 1 and 16 areas. At the horizontal section H\({}_{2}\) and vertical sections V\({}_{1}\) and V\({}_{2}\), the diffusion degree and ballast motion state were basically unchanged.  
Figure 12: Diagram of the influence law diagram of ballast-detained mass percentage. The effect of (**a**) ballast volumetric concentration, (**b**) slurry flow rate, and (**c**) ballast size.
## 4 Results and Discussion
### ANFIS in Predicting MRD  
Obviously, the prediction model developed in this study contained three input variables and one output variable, according to the characteristics of the input parameters and output parameters of the model, making it a standard multiple-input, single-output (MISO) system. The system structure of ANFIS is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Machining Parameter** & **Degrees of Freedom** & **Sum of Squares** & **Mean Square** & **F Ratio** & **F\({}_{\text{0.05}}\)(4, 12)** \\ \hline Speed & 4 & 2.998 & 0.749 & 3.410 & 3.259 \\ Force & 4 & 47.303 & 11.825 & 53.880 & 3.259 \\ Mesh & 4 & 231.250 & 57.812 & 263.410 & 3.259 \\ Error & 12 & 2.634 & 0.219 & - & 3.259 \\ Total & 24 & 284.185 & - & - & 3.259 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of variance results from MRD.  
Figure 8: Two-way interaction between speed, force and mesh and their influences on MRD at different initial parameter levels.  
Figure 9: Input and output display of ANFIS model for abrasive belt grinding.
## 3 Optimal Beamwidth Maximizing Uplink Coverage Probability Scheme
### Beamwidth Optimization  
Controlling the beamwidth through the number of active antenna elements, also known as the equivalent antenna aperture, is crucial for optimizing coverage in NTNs. Increasing the number of active antenna elements results in a narrower beam with higher antenna gain and vice versa. Although a narrow beam provides higher antenna gain, leading to improved signal strength and quality for users within the beam, it also results in a smaller footprint. This limited coverage area may allow users to move out of the serving area, potentially causing service interruptions or handover issues. To address this challenge, the optimal equivalent antenna aperture at different times (elevation angles) and service demands (target SINRs) can be determined using the uplink coverage probability function as follows:  
\[m_{t}^{*}=\underset{m}{\text{argmax}}\;p(t,m,\gamma) \tag{27}\]  
\[\text{s.t.}\;(\ref{eq:17}),(\ref{eq:18}),(\ref{eq:26}). \tag{28}\]  
Using the optimization function, satellites can dynamically adjust the number of active antenna elements during the beam-fixed time to achieve optimal performance with the service demand.  
Finally, we summarize the proposed optimal beamwidth maximizing the uplink coverage probability scheme in Algorithm 1, which consists of two parts. In the first part, the satellite steers the beam to the specified position, where the departure angle follows \(\phi_{l}(t)\) and \(\theta_{l}(t)\) as calculated in (13) and (14). In the second part, the optimal equivalent antenna aperture \(m_{t}^{*}\) is calculated, and the antenna configuration is adjusted accordingly based on this optimal equivalent antenna aperture.  
Figure 3: The illustration of probability for receiving antenna gain.  
```
0: Beam position on Earth \(\mathbf{v}_{l}(0)\), satellite height \(h\), and time \(t\)
0: Beam boresight direction \(\phi_{l}(t)\), \(\theta_{l}(t)\), and optimal equivalent antenna aperture \(m_{t}^{*}\)
1:for all Beam \(l\in\mathcal{L}_{f}\)do
2: Calculate relative beam position \(\mathbf{v}_{l}(t)\) from (9) to (11)
3: Calculate beam boresight direction \(\phi_{l}(t)\) and \(\theta_{l}(t)\) from (13) and (14)
4: Satellite steers the beam \(l\) following the beam boresight direction \(\phi_{l}(t)\) and \(\theta_{l}(t)\)
5:endfor
6: Calculate the optimal equivalent antenna aperture \(m_{t}^{*}\) from (27) and (28)
7: Satellite tunes the beamwidth based on the optimal equivalent antenna aperture \(m_{t}^{*}\)
```  
**Algorithm 1** Optimal Beamwidth Maximizing Uplink Coverage Probability Scheme
## 1 Introduction
The large bandwidth in the frequency domain gives us a high resolution in the time domain of 0.2 ns. A long in-depth measurement campaign was carried out in a long pedestrian tunnel. The experimental results were compared to a mathematical model, validating the mathematical model.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 9 Challenges and Future Directions
### Open Problems
#### 9.1.3 Limited Range  
Various steps have already been taken to improve the communication range of BackCom, which includes using different coherent receivers for bistatic scatter [66], long-range (LoRa) BackCom [129], BackCom quantum tunneling [164; 167], power amplifiers [136], multi-antenna and MIMO systems [32; 74; 89; 91; 171; 174; 175], relaying [93], and D2D communication [181; 182]. Researchers are also focusing on the limited range problem of BackCom, to render it suitable for long-range communication, by deploying dense power beacons [170]. BackCom aims to replace battery-powered wireless communications with passive device communication, targeting a similar communication range. Hence, there is immense need for improvement in the communication range to enhance the coverage of backscattered signals.
## 3 Results and Discussion
We estimated the porosity of the separator in the aged cell from the electron density because the Compton scattering intensity is proportional to the electron density as shown in Equation (1). The electron density \(\rho_{\rm e}\) was calculated as:  
\[\rho_{\rm e}=\frac{N_{A}\cdot\rho}{M}, \tag{6}\]  
where \(N_{A}\) is the Avogadro constant. \(\rho\) is the density of the separator in the fresh cell, and the value is 0.6956 g/cm\({}^{3}\). \(\rho\) was obtained by measuring the weight and size of the separator using the electric balance after drying by pulling it out from the fresh cell. \(M\) is the atomic weight. The atomic weight of 42.078 in the polypropylene is used as the separator. The obtained \(\rho_{\rm e}\) of the separator is 9.9548 \(\times\) 10\({}^{21}\) cm\({}^{-3}\), and we assume the porosity at 100%. Then, the electron density of the separator, which includes 23% of the dimethoxyethane (the atomic weight is 90.118) electrolyte is calculated as the measured polypropylene density (0.6956 g/cm\({}^{3}\)) is small, approximately 77% compared with the theoretical polypropylene density (0.905 g/cm\({}^{3}\)). This value is 1.1298 \(\times\) 10\({}^{22}\) cm\({}^{-3}\), and we assume the porosity at 0%. The averaged Compton scattering intensities of the separator in the discharged fresh and aged cells are 0.3538 \(\pm\) 0.0001 and 0.3146 \(\pm\) 0.0001, respectively. When the Compton scattering intensity of the separator in the fresh cell is assumed with a porosity of 0%, the porosity of the separator in the aged cell is approximately 93.2%. As the Compton scattering intensity from vacancy is weak, this considers that the vacancy is introduced in the separator by the degradation of the cell. From the above, the degradation mechanism of the VL2020 cell is that first, the stable phase of the anode is formed by the overvoltage cycle, then the appearance of the microbubbles is induced by the decomposition of the electrolyte [35] as lithium reactions with the cycle are prevented by the formation of the stable phase of the anode. In this study, by combining Compton scattering intensity and S-parameter analysis, we non-destructively quantified the lithium composition and identified the degradation mechanism of the commercial VL2020 cell. These Compton scattering imaging features are expected to be useful as a novel non-destructive analysis of commercialized lithium rechargeable cells.
## 2 Materials and Methods
### Isotherm and Kinetic Model Parameter Evaluation  
#### 2.5.1 Equilibrium Isotherm Models  
To understand the inherent mechanisms in the MB adsorption process, the equilibrium data obtained from the experiments were fitted to different isotherm models. The conventional solid-liquid adsorption isotherm models (single component) that were evaluated in this study are presented in Table S1 (Supplementary Materials). This table presents both linear and non-linear (conventional) forms of isotherm models. It is common practice by most researchers to linearize non-linear model expressions, as it is not straightforward to calculate constants in the non-linear models. Even though the linearized model equation adequately fits the equilibrium data, providing higher \(R^{2}\), the predicted values give inferior values, while successive constants are substituted in the non-linear model form. [PERSON] et al. [27; 28] in their study concluded that the inherent mechanisms of the adsorption process could be underestimated using the non-linear model expression. Thus, when using the non-linearity of the model and when estimating the parameters (constants) in the non-linear isotherm model, we need an appropriate optimization approach or technique, which estimates the optimal set of parameters that best represent the isotherm model based on an excellent statistical error function. Therefore, in this study, root-mean-squared error (RMSE) was used as the error function, as it is the most frequently used statistical measure. RMSE is an excellent error function for measuring the accuracy and aggregating the magnitude of errors; it is computed as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(q_{e,pred}^{i}-q_{e,exp}^{ i}\right)^{2}} \tag{1}\]  
where \(i\) is the number of samples (experimental runs), and \(q_{e,exp}\) (mg/g) and \(q_{e,pred}\) (mg/g) are the experimental and theoretically predicted equilibrium values, respectively. Another statistically significant metric is Pearson's chi-square (\(\chi^{2}\)) metric, which measures the goodness of fit between the model-predicted values and the experimental data. The mathematical expression is expressed as follows:  
\[\chi^{2}=\sum_{i=1}^{n}\frac{\left(q_{e,exp}^{i}-q_{e,pred}^{i}\right)^{2}}{q_ {e,pred}^{i}}. \tag{2}\]  
This metric gives a smaller value when the model-predicted values are in close agreement with the experimental values, whereas it produces a higher value if they vary distinctly.
# Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities +
## 2 Characteristics and Performance of Some European SST Radars
The system operates at 5.56 GHz and aims to provide information on objects in LEO with cross sections above 10 cm\({}^{2}\) at 1000 km [16].
# Research on the Reciprocating Friction and Installation Force of Seals in Deep-Sea Samplers
###### Abstract  
It is crucial to accurately characterize the static and dynamic properties of seals for the design of deep-sea equipment. This study investigated the movement resistance of O-rings under high pressure (up to 30 MPa) and low temperature (2 \({}^{\circ}\)C). Numerical simulation models were set up to investigate the effects of material properties and seal structure dimensions on the reciprocating friction and installation force of O-rings. Furthermore, a novel resistance testing rig was developed to facilitate the measurement of reciprocating friction and installation force of O-rings under high pressure and low temperature. The results of this study indicate that nitrile rubber (NBR) has a reduced sensitivity to temperature variations, and the hardening of the material due to the low temperatures encountered in deep-sea environments does not substantially increase its movement resistance. Conversely, fluororubber (FKM) exhibits superior static sealing reliability. We optimized the sealing structure of the pressure-maintaining trapping instrument (PMTI) and successfully conducted field tests at the South China Sea. The results of this study may serve as a valuable reference for the sealing design of deep-sea equipment.  
Keywords:sealing performance; O-ring; reciprocating friction; installation force; deep-sea equipment +
Footnote †: journal: Applied sciences