esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This configuration allows for a smoother transition over steps and ledges, outperforming standard wheel systems in unstructured or multi-level environments. This paper focuses on the locomotion of a mobile robot on flat surfaces, aiming to evaluate and validate its basic navigation capabilities, including movement stability, manual control responsiveness, and obstacle detection under planar conditions. The remainder of this article is structured as follows: Section 2 introduces the system architecture, detailing the simulation environment in which the robotic components are developed. This section also outlines the integration and communication mechanisms with the Kubernetes cluster, as well as the process of exporting and generating the URDF model from the CAD environment and embedding it into the simulation framework. Section 3 focuses on the architecture of the simulation subsystem, describing the communication configuration and required ROS2 packages. Additionally, it presents the validation of the URDF model, including aspects related to visual representation, collision geometry, mass distribution, and inertia properties of the robot's components. Section 4 presents the simulation of the mobile robot on a flat surface within a customized environment. The robot is equipped with four differential drive controllers for motion control. This section also includes results related to SLAM-based map scanning and the success rate achieved using a single mobile robot. Section 5 proposes the extension of the system by introducing a second mobile robot, leading to a collaborative mapping strategy that achieved a 100% scan success rate on the first attempt. The final section, Section 6, presents the conclusions and outlines future research directions, including the secure integration of the simulation environment with other network components, the development of autonomous navigation strategies based on SLAM outputs using Nav2, and the exploration of complex emergency scenarios. These scenarios envision a collaborative response involving two ground robots and one aerial unit operating in a coordinated search and rescue mission. This paper, which is built upon and is thoroughly documented in the third research report of the doctoral thesis [19], played a central role in shaping the development of the robotic platform. It contributed significantly to both the mechanical design choices and the formulation of the robot's kinematic strategy. By grounding the simulation approach in a well-defined theoretical and experimental framework, this paper ensures that the current simulation work is not only technically consistent but also aligned with validated design principles. Furthermore, it establishes a direct link between the foundational design studies and the present implementation in simulated environments, thereby reinforcing the coherence and scientific rigor of the overall research.
###### Abstract  
As a key technology in wireless sensor networks (WSNs), target tracking plays an essential role in many applications. To improve energy efficiency, clustering is widely used in tracking to organize the network to achieve data fusion and reduce communication costs. Many existing studies make dynamic adjustments based on static clusters to track moving targets. However, the additional overhead caused by frequent cluster reconstruction and redundant data transmission is rarely considered. To address this issue, we propose a tracking-anchor-based clustering method (TACM) in this paper, in which tracking anchors are introduced to provide activation indications for sensors according to the target position. We use the rough fuzzy C-means (RFCM) algorithm to locate the anchors and use the membership table to activate sensors to form a cluster. Since there are no sending, receiving, and fusing data tasks for anchors, they are lightly burdened and can significantly reduce the frequency of being rotated. Moreover, the state of cluster members (CMs) is scheduled using the linear 0-1 programming to reduce redundant transmissions. The simulation results demonstrate that, compared with some existing clustering methods, the proposed TACM effectively reduces the energy consumption when tracking a moving target, thus prolonging the network lifetime.  
wireless sensor networks; target tracking; clustering method; energy efficiency
## 3 Numerical Methodology
### Application of the ATC  
In the RVE model shown in Figure 2, the thermal conductivity of the matrix is isotropic, but the thermal conductivity of the fiber bundles is anisotropic, and the thermal conductivity in the axial direction is bigger than that in the radial direction. To represent the ATC of the fiber bundles, a local Cartesian coordinate system (\(\zeta\), \(\eta\), \(\
u\)) was introduced, with the \(\zeta\) axis along the yarn's axial direction. The \(\zeta\) axis was considered as the principal direction of the thermal conductivity (PDTC) because the thermal conductivity was largest in this direction.  
The axial yarn's direction was along the \(Y\) axis of the global Cartesian coordinate system (\(X\), \(Y\), \(Z\)), as shown in Figure 4, so the ATC of axial yams could be represented by three thermal conductivities on the \(X\), \(Y\), and \(Z\) axes, respectively. However, the directions of the braided yams were changed in different parts, as shown in Figure 4. There was an inclined angle between the local Cartesian coordinate (\(\zeta\), \(\eta\), \(\
u\)) and the global Cartesian coordinate (\(X\), \(Y\), \(Z\)), so the ATC applied for (\(X\), \(Y\), \(Z\)) needed to be transferred, and the transformation method was described as follows.  
Figure 3: Schematic diagrams of the full-size model.  
According to the study in Reference [27], the anisotropic thermal conductivity \(k_{ij}\) can be expressed as the matrix  
\[k_{ij}=\left[\begin{array}{cc}k_{XX}&k_{XY}&k_{ZZ}\\ k_{YX}&k_{YY}&k_{YZ}\\ k_{ZX}&k_{ZY}&k_{ZZ}\end{array}\right] \tag{2}\]  
Equation (2) can be rewritten as shown below:  
\[\left[\begin{array}{cc}k_{XX}&k_{XY}&k_{ZZ}\\ k_{YX}&k_{YY}&k_{YZ}\\ k_{ZX}&k_{ZY}&k_{ZZ}\end{array}\right]=\left[\begin{array}{cc}l_{1}&m_{1}&n_{ 1}\\ l_{2}&m_{2}&n_{2}\\ l_{3}&m_{3}&n_{3}\end{array}\right]\left[\begin{array}{cc}k_{\zeta}&0&0\\ 0&k_{\eta}&0\\ 0&0&k_{\
u}\end{array}\right]\left[\begin{array}{cc}l_{1}&l_{2}&l_{3}\\ m_{1}&m_{2}&m_{3}\\ n_{1}&n_{2}&n_{3}\end{array}\right] \tag{3}\]  
where  
\[\left[\begin{array}{cc}l_{1}&m_{1}&n_{1}\\ l_{2}&m_{2}&n_{2}\\ l_{3}&m_{3}&n_{3}\end{array}\right]=\left[\begin{array}{cc}\cos\gamma\cos \beta&-\cos\alpha\sin\gamma+\cos\gamma\sin\beta\sin\alpha&\sin\gamma\sin\alpha+ \cos\gamma\sin\beta\cos\alpha\\ \sin\gamma\cos\alpha&\cos\gamma\cos\alpha+\sin\gamma\sin\beta\sin\alpha&-\cos \gamma\sin\alpha+\sin\gamma\sin\beta\cos\alpha\\ -\sin\beta&\cos\beta\sin\alpha&\cos\beta\cos\alpha\end{array}\right] \tag{4}\]  
Equation (4) shows the transform matrix between the local coordinate system (principal direction of the thermal conductivity) and the global Cartesian coordinate system, with the inclined PDTC angles \(\alpha\), \(\beta\) and \(\gamma\).
## 5 Experiments  
In this section, we first utilize the proposed method to determine the optimal number of AGVs under different shop-floor layouts. Then, we compare its performance with compound scheduling rules and other DRL frameworks. Specifically, Section 5.1 describes the benchmark datasets used in the experiments, the layout configurations, and the hyperparameter settings; Section 5.2 applies the proposed CRGPPO-TKL approach to determine the optimal number of AGVs for each layout; Section 5.3 compares the scheduling performance of various methods, assuming the optimal AGV configuration for each layout is fixed.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Notation** & **Description** \\ \hline \(\pi_{\theta}(a_{l}|s_{t})\) & Probability distribution of actions output by the current policy network \\ \(a_{l}^{(i)}\) & \(i\)-th candidate action \\ \(A_{\max}\) & Maximum number of candidate actions \\ \(\pi_{\theta_{\text{add}}}\) & Old policy network (pre-update policy) \\ \(r_{\text{action}}\) & Probability ratio between current and old policies \\ \(r_{t}^{(i)}(\theta)\) & Candidate action probability ratio \\ \(\overline{r}_{\text{candidates}}\) & Average probability ratio of candidate actions \\ \(I_{\text{action}}\) & Out-of-bound indicator function for action probability ratio \\ \(I_{\text{candidates}}\) & Out-of-bound indicator function for candidate average ratio \\ \(\delta_{\text{KL}}\) & KL divergence deviation \\ \(\epsilon_{\text{new}}\) & Updated clipping range \\ \(\epsilon_{\min},\epsilon_{\max}\) & Minimum/Maximum clipping range \\ \(\delta_{t}\) & Temporal Difference (TD) error \\ \(A_{t}^{GAE}\) & Generalized Advantage Estimation (GAE) \\ \(\gamma\) & Discount factor \\ \(\lambda\) & GAE smoothing coefficient \\ \(\eta\) & Adjustment rate \\ \(L^{\text{CLIF}}(\theta)\) & Clipped policy objective \\ \(H(\pi_{\theta})\) & Policy entropy \\ \hline \hline \end{tabular}
\end{table}
Table 3: Notations used in CRGPPO-TKL.  
Figure 5: The training process of CRGPPO-TKL.  
To compare optimization performance across different methods, we adopt the Optimality Deviation Ratio (ODR), which is calculated as follows:  
\[\text{ODR}(s_{i})=\left(\frac{s_{i}-s_{\text{best}}}{s_{\text{best}}}\right)\times 1 00\% \tag{44}\]  
where \(s_{i}\) represents the objective value obtained by algorithm \(i\) on a given instance, and \(s_{\text{best}}\) is the minimum objective value obtained among all competing methods for that instance. A lower ODR indicates better performance and is closer to the optimal result.  
In addition to the ODR metric, this study also employs the independent two-sample \(t\)-test to evaluate the statistical significance of performance differences between the proposed CRGPPO-TKL method and baseline approaches, including composite dispatching rules and other DRL-based methods. For each test instance, the ODR values obtained from multiple independent runs are used as input data for the \(t\)-test. A significance level of 0.05 is set to determine whether the performance differences are statistically significant.
## 2 Materials and Methods
### Evaluation of the Created Models and a Standard Line for Brain MRI  
The predicted bounding boxes were incorporated into the MATLAB software to show only one box representing the area with the highest confidence for each anatomy. The detection of different anatomies was evaluated using the average precision (AP) and the mean average precision (mAP) [23]. The ROIs measuring 64 \(\times\) 64 pixels and 32 \(\times\) 32 pixels, respectively, were evaluated separately. The higher mAP among the two ROI sizes was selected to evaluate for comparison with the standard lines of the brain imaging, that is, the OM-line and the AC-PC line. The delineation of a standard line for brain MRI was calculated using the MATLAB software (Figure 4). The standard lines were calculated by using the central coordinate of the predicted bounding boxes. If there was no detection of the bounding boxes, which is a necessary requirement to draw the standard line, the line was not represented in the software. The software also had a function of calculating the angle of the predicted line that was formed at an angle relative to the horizontal direction of the image. The number of the predicted lines was calculated as a line-detection rate. The accuracy rates of the delineation for the standard line were calculated to obtain the difference between the original and prediction angles among the detected lines. The processing speeds by calculating the computation time per image were also measured. The angles were evaluated from 0.5 to 10.0 degrees of the absolute value of the angles. All the results were represented as means and SDs according to the number of six-fold datasets.  
Figure 3: The procedure for data augmentation of the training datasets. The original images and supervised bounding boxes were rotated from \(-\)30 to 30 degrees in 3-degree steps.
## 3 Results and Discussion
As an alternative, laser techniques can be used in combination with the enrichment of the surface layer with additional strengthening factors, such as Cr- and/or Ni-phase particles in this case, which did not undergo solution because of their low solubility as well as the short time of laser remelting. It is difficult to find publications in the scientific literature discussing the impact of Cr and Ni additions on the structure and properties of laser-melted silver. Publications most  
Figure 14: Cross-section of the laser-treated surface layer, presenting the schematic zone arrangement in the Ag samples: the remelting zone (RZ) and heat-affected zone (HAZ), as well as the matrix material. Figure 13: Surfaces of the remelted samples: (**a**) 2.0 kW, (**b**) 2.5 kW, (**c**) 3.0 kW laser power, with visible roughness differences. often discuss the impact of laser melting on the structure and properties of pure silver and 925 silver [1; 30; 31] and investigations concerning silver alloys with the addition of Cu, C, and W [32], or the alloy Ag-Cu [2; 33]. Investigations concerning the alloy Ag-Cu-Ge have also been published [34]. It was found that the processing of Ag-Cu alloys using SLM (selective laser melting) inhibits the growth of columnar grains, which results in the refinement of the alloy [2]. In [30], after the laser alloying of pure silver powder, the presence of pores and gas trapped in the metal was found in the microstructure produced. What was particularly visible was the use of the smallest hatch distance (0.14) and the lowest laser scanning speed (650 mm/s). The presence of irregular pores and non-melted particles was also confirmed in [34]. Moreover, it was found that, in treated pure silver and 925 silver (with the addition of Cu), the presence of pores and the grain size are the main reasons for the 400% reduction in thermal conductivity compared to silver as a cast. As shown in [33], the density of the resulting alloy was determined according to the size of the melting pool and the thermal shrinkage. As the laser energy density increased, the density of the Ag alloy decreased due to the pores and microcracks formed, as well as thermal shrinkage. In turn, the hardness of the obtained material depended primarily on the grain size of the microstructure, as well as the obtained density of the material; therefore, obtaining a high specific density is a prerequisite for obtaining an alloy with higher hardness. Moreover, the SLM process caused grain refinement and an increase in the residual stresses of the Ag alloy, which in turn increased the Vickers hardness by three times compared with the Vickers hardness obtained during the casting process.
## 3 Loss Calculation of IM Driven FESS  
There are different losses in a FESS including power converter losses, stator losses and rotor losses all of which fall into the category of electrical losses and contribute to the conversion efficiency. The mechanical losses of the system are mainly due to the bearing losses and aerodynamic or windage losses. The power balance Equation for squirrel cage induction machine FESS (SCIM-FESS) is given by Equation (6).  
\[P_{out}=P_{ag}-P_{rel}-P_{rfe}-P_{mch} \tag{6}\]where:  
\[P_{ag}=P_{in}-P_{scl}-P_{sfe} \tag{7}\]  
\[P_{out}=P_{in}-P_{scl}-P_{rcl}-P_{rfe}-P_{sfe}-P_{mech} \tag{8}\]  
\[P_{in}=P_{out}+P_{scl}+P_{rcl}+P_{rfe}+P_{sfe}+P_{barring}+P_{windage} \tag{9}\]  
where \(P_{ag}\) is the power transferred from stator to rotor of IM across the air-gap, \(P_{in}\) is the input power, \(P_{out}\) is total output power, \(P_{scl}\) is stator copper loss of IM, \(P_{rcl}\) is rotor copper loss of IM, \(P_{rfe}\) and \(P_{sfe}\) are core losses in the rotor and stator, \(P_{barring}\) and \(P_{windage}\) are bearing and windage losses of FESS, respectively.
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 3 System Model and Problem Formulation
### System Model and Proposed BSs Switching On/Off Mechanism
#### 3.1.2 Low Traffic Load (0 < \(\lambda\)\(\leq\) 0.4)
_Pre-processing Status_: In this phase, it is the duty of the LTE BSs to continuously monitor the generated traffic load and make a decision on whether the 5G BSs should be switched on or off. If the current load is more than the stated threshold of 0.4 and stays over the threshold for a given duration, it is plausible to invoke the 5G BSs switching-on decision. This decision is solely dependent on LTE BSs signal information. 2. _Decision Status_: Having analyzed the instantaneous traffic load, the LTE BSs initiate a unicast control signal to be sent to the switching-off/on module, requesting that the 5G BSs be switched on. After the traffic load has been analyzed and the switching-off/on module deems it necessary, multicast signals are then transmitted to wake up the 5G BSs' control signals. Next, the 5G BSs begin to gradually increase their transmission power and, subsequently, propagate a unicast signal to the switching-off/on module containing the response to the switch-on request. Using the signal strength paths metrics, the UEs serviced by the LTE BSs are handed over to the active 5G BSs (small cells). This transfer is solely determined by the signal strength path of the UE_BS. Meanwhile, LTE BSs remain active to support the 5G BSs while guaranteeing coverage and radio services. 3. _Post-processing Status_: In this status, there is a role reversal in which the post-processing phase of the switching-on algorithm assumes the role and duties of the pre-processing state of the switching-off algorithm already discussed. In this phase, the 5G BSs become active and continue the task of monitoring the UE load traffic.
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 12 Comparison Codec Selection  
The AMR-NB/WB codec is a workhorse in digital cellular and VoIP applications. It has outstanding reliability and consistent performance across many platforms and networks. The newer Enhanced Voice Services (EVS) codec was standardized in 2014 with the goal of not only improving performance for narrowband and wideband applications but also to develop a codec for superwideband applications and to improve performance for music and mixed inputs in conversational speech and to add stereo audio, plus improved VoIP performance through jitter buffer management, better packet loss robustness, and additional input and output processing [11]. All this improved functionality came at the price of greatly increased complexity and longer encoding/decoding delay. The EVS codec is at least twice as complex as AMR-WB and 5 times more complex than the AMR-NB codec used in the comparisons here [39].  
It is well established that the increased complexity does yield better performance than AMR-NB/WB [40], so it is a given that the EVS codec outperforms both AMR-NB/WB and our MMT-WP codec in terms of speech quality at the same bitrate. However, many applications that focus on NB speech, such as we do here, do not need all the functionality in the EVS codec and the increased complexity and latency is not worth the improved speech-coding performance over AMR-NB/WB, which performs very well. Since AMR-NB/WB is widely installed and used, and has much reduced complexity and slightly reduced latency than EVS, AMR-NB/WB is the codec we compare to since our first priorities are much reduced complexity and latency.
# Federated Learning for Secure In-Vehicle Communication
## 4 Experimental Setup
### Data Augmentation  
In IID settings, local models converge toward the global optimum, leading to stable aggregation. However, in non-IID scenarios, local model gradients diverge, causing drift and reducing global model accuracy [45]. This results in biased updates, slower convergence, and inefficient communication. The Hellinger distance is a metric used to quantify the similarity between two probability distributions [46]. It can assess the degree of non-IID data among clients. A higher Hellinger distance indicates a greater disparity between local datasets, which can adversely affect the performance of the FedAvg algorithm [46].  
The global Hellinger distance formula [40] is defined in Equation (2).  
\[H(G,S)=\frac{1}{\sqrt{2}}\sum_{i=1}^{C}\Bigl{(}\sqrt{G_{i}}-\sqrt{S_{i}} \Bigr{)}^{2} \tag{2}\]  
where \(G\) represents the local label distribution, and \(S\) is the reference balanced distribution.  
To compare the similarity of class distribution across the dataset, we apply the Hellinger distance measure between the three sub-datasets (i.e., Hyundai, Chevrolet and Kia).  
The distance is equal to 0.0671 between Hyundai (Dataset 1) and Chevrolet (Dataset 2) datasets. This distance is equal to 0.0494 for Hyundai (Dataset 1) and Kia (Dataset 3), and to 0.1009 for Chevrolet (Dataset 2) and Kia (Dataset 3). Note that these values indicate the degree of divergence between class distributions of the datasets. The higher the Hellinger distance, the more different the label distributions are, meaning the datasets are more non-IID. Chevrolet and Kia have the greatest difference among the pairs (0.1009), indicating a more varied data distribution than the other dataset pairs. In contrast, Hyundai and Kia have the smallest Hellinger distance (0.0494), indicating that their class distributions are more similar. This decomposition facilitates the measurement of the quantity of non-IID data, which can significantly impact FL model performance, particularly in algorithms like FedAvg, where differences in local distributions can lead to gradient divergence and the slowdown in convergence.
## 2 Methods of Atmospheric Density Inversion for Low Earth
### Calculation of Atmospheric Drag Force  
In the Low Earth Orbit (LEO) operating region, the interaction of the spacecraft with the atmosphere is predominantly characterized by free molecular flow [24]. This interaction results in the spacecraft being subjected to a drag force; the formula is provided in the following Equation (1). Based on the following equation, the drag force is calculated and then combined with the mass of the spacecraft and the atmospheric damping coefficient, \(C_{D}\), which is estimated based on parameters such as its morphology, attitude, and panel material. This information can be obtained from official data center. Synthesizing the above information, this allows the atmospheric density to be calculated.  
\[a_{drag}=-\frac{1}{2}*\frac{C_{D}*A}{M_{sat}}\rho v_{r}^{2} \tag{1}\]  
The graphene \(A\) serves to represent the corresponding cross-sectional area of the orbital trajectory of the spacecraft. While the letter \(M_{sat}\) denotes the mass of the satellite, \(C_{D}\) represents the atmospheric damping coefficient, and \(C_{D}*A\) is the ballistic coefficient, which represents the characteristics of the satellite's surface. \(v_{r}\) denotes the velocity of the spacecraft relative to the atmosphere, and \(\rho\) denotes the atmospheric mass density in the thermosphere near the spacecraft [25; 26; 27].