esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Multiplicative Fault Detection  
#### 3.1.1 Input-Output Signals Under Multiplicative Faults  
To ensure accurate estimation of sudden multiplicative faults, the input signal must be persistently exciting, i.e., rich enough to guarantee a unique and identifiable set of parameter estimates [49]. The Pseudo-Random Binary Sequence (PRBS), which is readily available in most software libraries, is, therefore, widely adopted for system identification.  
A PRBS signal with values between 2 V and 3 V, as shown in Figure 14, was generated using LabVIEW and applied as the input to the electronic amplifier. Figures 15 and 16 illustrate the amplifier and motor output voltages over a 20 s period, during which a sudden multiplicative fault is introduced at the 10 th s. This fault corresponds to an abrupt change in a selected amplifier or motor parameter from its nominal value to a predefined fault value. For the amplifier, faults were introduced in the following components: \(R_{1}\) (from the nominal value of 1 \(\Omega\) to fault values 10 \(\Omega\), 56 \(\Omega\), and 100 \(\Omega\)), Figure 15a; \(R_{9}\) (from the nominal value of 10 k\(\Omega\) to the faults values 33 k\(\Omega\), 56 k\(\Omega\) and 100 k\(\Omega\)), Figure 15b, \(C_{8}\) (from the nominal value of 47 pF to the fault values 1 \(\mu\)F, 10 \(\mu\)F, 47 \(\mu\)F), Figure 15c; \(R_{12}\) (from the nominal value of 10 k\(\Omega\) to the fault values 2 k\(\Omega\), 3.3 k\(\Omega\) and 5.6 k\(\Omega\)), Figure 15d. For the motor, faults were introduced in the parameters: armature resistance \(R_{\text{a}}\) (from the nominal value 7.41 \(\Omega\) to the fault values 0.741 \(\Omega\) and 74.1 \(\Omega\)), Figure 16a,b, viscous friction coefficient \(B\) (from the nominal value 1.8\(\cdot\)10\({}^{-6}\) Nm/rad/s to the fault values 1.8\(\cdot\)10\({}^{-4}\) Nm/rad/s and 1.8\(\cdot\)10\({}^{-8}\) Nm/rad/s), Figure 16c, moment of inertia J (from the nominal value 1.43\(\cdot\)10\({}^{-6}\) kgm\({}^{2}\) to the fault values 1.43\(\cdot\)10\({}^{-4}\) kgm\({}^{2}\) and 1.43\(\cdot\)10\({}^{-8}\) kgm\({}^{2}\)), Figure 16d.  
Figure 14: Amplifier input voltage \(U_{\text{c}}\)(t)—PRBS excitation signal with a magnified view of the waveform.  
Figure 13: Multisim model of the DC motor.
## 3 Methodology
### POD-MCNN Model
#### 3.3.3 Training of Multi-Scale Convolutional Neural Networks  
For such a deep model, all operational layers can be considered as the complex function \(G\). The network output is:  
\[\hat{Y}=G(C,\theta) \tag{8}\]  
where \(\theta\) are the parameters to be learned in the network. The training of the model is an iterative process driven by model attrition. The parameters of the model are updated by minimizing the difference between the network output and the true value and propagating the error back to each layer of the network. The loss function of our model consists of data loss and a regularizing term. The mean square error is used for the data loss, and the L2-norm is used for the regularizing term. The purpose of including the regularizing term is to prevent the model from overfitting, which leads to the degradation of the generalization performance:  
\[\text{Loss}= \frac{1}{N}\sum_{n=1}^{N}\left(\hat{Y}-Y\right)^{2}+\lambda\sum_{i }^{M}\theta_{i}^{2} \tag{9}\]  
where \(\hat{Y}\) is the network output; \(Y\) is the ground truth; \(\lambda\) is the regularization coefficient, which is used to control the strength of regularization; \(\theta_{i}\in\theta\) is the parameter to be learned in the network.  
During the training process, the purpose of the network is to find a set of optimal parameters \(\theta\) to minimize the loss function, denoted as:  
\[\theta=\underset{\theta}{argmin}\left(\frac{1}{N}\sum_{n=1}^{N}\left(\hat{Y}- Y\right)^{2}+\lambda\sum_{i}^{M}\theta_{i}^{2}\right) \tag{10}\]  
To efficiently train the model, we adopted a mini-batch-based learning strategy, and the batch size was set to 64. The mini-batch training has been shown to effectively improve the generalization performance and significantly reduce the memory cost [41], using the Adam optimization algorithm as the optimization algorithm for the model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer Name** & **Output Shape** & **Kernel Size** & **Numbers of Kernel** & **Stride** & **Padding** \\ \hline Fully connected 1 & 64 & - & - & - & - \\ Multi-scale conv 1-1 & 3 \(\times\) 4 & 1 & 3 & 1 & 0 \\ Multi-scale conv 1-2 & 3 \(\times\) 64 & 3 & 3 & 1 & 1 \\ Multi-scale conv 1-3 & 2 \(\times\) 64 & 5 & 2 & 1 & 2 \\ Concat & 9 \(\times\) 64 &
## 3 Results and Analysis
### Correlation of Fractal Dimension of Particle Size Distribution with Dry Density  
It is observed that there is a linear correlation between the fractal dimension of particle size distribution and dry density, which can be fitted using the following expression:  
\[D=0.6617\rho_{d}+1.0044\ (R=0.9438) \tag{7}\]  
As demonstrated by the fitting results, there must be a certain internal relationship between the fractal dimension \(D\) and dry density \(\rho_{d}\) of graded crushed stone. The larger the fractal dimension of the graded crushed stone, the greater the dry density it can achieve. The fractal dimension of graded crushed stone can well reflect the compaction effect.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Repeated Compaction Times** & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) & \(\mathbf{R^{2}}\) \\ \hline
1 & \(-\)0.0062 & 0.0690 & 2.3053 & 0.9774 \\
2 & \(-\)0.0064 & 0.0701 & 2.3287 & 0.9771 \\
3 & \(-\)0.0072 & 0.0785 & 2.3231 & 0.9897 \\
4 & \(-\)0.0081 & 0.0891 & 2.3065 & 0.9900 \\
5 & \(-\)0.0100 & 0.1071 & 2.2880 & 0.9849 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fitting results of fractal dimension and water content.  
Figure 9: Distribution of the fractal dimension against the water content.  
Figure 10 shows the obtained results for 25 groups of different size distributions and dry densities.
## 3 Results
### X-Ray CT Visualisation  
After one WD cycle (top row, Figure 10), the untreated soil (left) exhibits a heterogeneous microstructure with moderate pore connectivity and visible interparticle spaces, indicating loose particle arrangement. The cement-treated sample (middle) presents a denser and more homogeneous structure, characterised by bright grayscale regions corresponding to cement hydration products and reduced visible pore spaces. Although some macropores are present at the periphery, the guard gum-treated sample (right) also shows improved densification compared to the untreated sample, with relatively uniform grayscale intensity and fewer macropores in the middle, suggesting an early-stage binding effect of guard gum on soil aggregates.  
After 10 WD cycles (bottom row), significant structural degradation is observed in the untreated sample, with prominent crack networks and large voids (highlighted by red arrows), especially near the periphery. The cement-treated sample shows minor cracking (red arrows) and limited structural deterioration, maintaining overall integrity due to cementitious bonding. The guard gum-treated specimen exhibits minimal cracking  
Figure 8: Deviator stress is plotted as a function of axial strain (average for three samples) measured at saturation for (**a**) untreated soil, (**b**) cement-treated soil, and (**c**) guard gum-treated soil for 1, 5, 10, and 15 wetting–drying cycles.  
Figure 9: Variation in (**a**) undrained shear strength, (**b**) modulus of elasticity, and (**c**) modulus of toughness measured at saturation for untreated soil and soil treated with cement and guard gum, subjected to 1, 5, 10, and 15 wetting–drying cycles.  
(red arrow) and preserves a relatively intact microstructure, indicating the biopolymer's resilience against moisture-induced cyclic stresses (Figure 10).  
These CT images collectively highlight the superior resistance of cement and guar gum treatments in preserving the microstructural stability of expansive soils under repeated environmental loading.
# Biocompatibility of Membranes Based on a Mixture of Chitosan and _Lythri herba_ Aqueous Extract
## 2 Materials and Methods
### Membrane Evaluation
#### 2.3.3 FT-IR Spectroscopy Analysis of CS-Ly-Based Membranes  
The Fourier-transform infrared (FT-IR) spectroscopy analysis of chitosan (CS) membranes with _Lythri herba_ aqueous extracts (Ly) was carried out with a Perkin-Elmer Spectrum BX FT-IR spectrophotometer equipped with ATR. IR spectra of solid samples (membranes) were recorded in the 4000 cm\({}^{-1}\) and 900 cm\({}^{-1}\) ranges, with a resolution of 8 cm\({}^{-1}\) and accumulation of 64 scans, at room temperature and low humidity.
## 5 Results and Discussion  
### Development and Validation of the Numerical Models  
In the first step, validation was performed with both models. However, for geometrical investigation cases, the momentum source model was solely used. The main difference between the two models lies with the fact that simulations with sliding mesh require a longer processing time than the imposed momentum source model. One reason for this difference is related to the moving interface between the sliding and fixed region. Moreover, it is necessary to simulate the whole computational domain. In contrast, the momentum source model allows the mesh to be well refined since the domain can be simulated by one quarter of the tank and the geometry is simpler than the sliding mesh model.  
Figure 10: Independent mesh chosen from the GCI method. (**a**) Side view of the tank. (**b**) Top view of the tank.  
There is a difference in agreement between the sliding mesh model and the momentum source model compared to the data presented in the literature. The momentum source model was able to more efficiently predict velocity and turbulence kinetic energy profiles. One possible reason for the deviations found for the sliding mesh model concerns the difficulty of generating refined meshes, which is more complicated for the sliding mesh since the entire domain is simulated and the physical surfaces of the impeller must be built in the domain. In this sense, the computational cost for a more refined mesh in sliding model would be much higher, which would become prohibitive for future geometrical investigation. The results also demonstrate that the momentum source model presented a good agreement with the numerical and experimental solutions reported in the literature. Therefore, the model is considered valid.  
Once the momentum source model was able to reproduce with some efficiency the flow in the tank at a much lower computational cost, it was used to test different geometrical configurations in the present work.  
Figure 15a shows the velocity magnitude field in the vertical plane (between the baffles), and Figure 15b depicts the horizontal plane (at impeller height). The flow behavior shown in Figure 15a is similar to the literature [29], showing the formation of two vortices when the fluid reaches the region of the tank wall. Part of the fluid goes to the top of the tank, forming a vortex above the impeller, and the other part of the fluid goes to the bottom of the tank, forming another vortex below the impeller. Afterwards, the fluid tends to return to the impeller region to repeat this behavior.  
Figure 12: Results for tangential velocity profiles obtained with the present code and those predicted by [PERSON] et al. [11].  
Figure 13: Results for axial velocity profiles obtained with the present code and those predicted by [PERSON] et al. [11].
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 1 Introduction
The CU control plane of the RSU should take decisions quickly, in the  
Figure 1: Types of V2X communications. order of seconds, to determine the appropriate assignments and activation, which lead to minimizing the energy consumption while satisfying the QoS requirements in terms of ultra-low latency and available radio resource, i.e., resource blocks (RBs). To this end, in this study, we investigate the problem of how to allocate the resources efficiently in MAEC to optimize resource management and save energy while meeting the QoS requirements for 6G V2X Open RAN. In particular, given the number of RSUs with available RBs and the number of vehicles with required RBs for sending tasks, we need to find an optimal vehicle-to-RSU association and a state of each RSU to minimize the number of active RSUs and energy consumption while satisfying the constraints on the number of required RBs, data rates, allowed latency, and available resources. The main contributions of this work are summarized as follows:  
* We formulate the resource allocation problem as an integer linear programming (ILP) optimization problem to minimize energy consumption and the number of active RSUs by determining the best vehicle-to-RSU association and whether to turn the RSU on or off. * We reduce the number of RSUs that are active and minimize energy consumption while satisfying the constraints on the number of RBs, data rates, SINR, and latency. * We use a CPLEX implementation [12] to find the optimal solutions to our optimization problem and evaluate our proposed model. * We transform the aforementioned problem into an equivalent DRL environment and subsequently propose a proximal policy optimization (PPO) algorithm to solve it. * We perform a comprehensive comparison between optimal and approximate solutions to assess the effectiveness of the DRL environment and the performance of our proposed model. * We examine the performance of our proposed model in 6G V2X Open RANs for different scenarios with various vehicle densities and RSUs. The remainder of this paper is organized as follows: Section 2 reviews previous works on energy efficiency for MAEC networks. Section 3 describes the system model and the formulation of the optimization problem, including the DRL environment and the proposed PPO algorithm. Section 4 presents the performance of the model and the numerical results. Section 5 concludes the article and points out future work.
# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 3 BEVCorner
### Depth Fusion Module  
This module focuses on ways to fuse two pieces of depth information. We have the following depths:  
* Depth map (\(D_{BEV}\) from multi-view LiDAR supervision of BEVDepth): A base layer that provides a soft depth distribution over each pixel, or the overall depth estimate for the entire scene.
* Object Depth (\(D_{mono}\) from monocular image of MonoFlex): Provides accurate, object-specific depth estimates.  
Initially in vanilla BEVDepth, only \(D_{BEV}\) is used, but we argue that this depth map distribution is a soft probabilistic estimate and lacks context. It only assumes that each image pixel is centered on one depth value, through LiDAR supervision. Occlusion problems thus cannot be handled.  
With this, we propose the fusion from those depths to obtain a more contextual depth map. There are four different methods used here (see Figure 2):  
**Direct Replacement**  
For each object \(i\) detected by the monocular pipeline, the object-specific depth \(D_{\text{mono}}(i)\) directly replaces the depth map distribution \(D_{\text{BEV}}(i)\) for regions corresponding to the object in the BEV grid. The depth map is retained in areas outside the detected objects.  
For each 3D pixel \((x,y,z)\), we combine the depth map and object-specific depth predictions as follows:  
\[\mathbb{E}[D_{\text{fused}}(x,y,z)]=\begin{cases}\mathbb{E}[D_{\text{mono}}(i )],&\text{if }(x,y)\in\text{object }i,\,z\in D_{\text{mono}}(i)\\ \mathbb{E}[D_{\text{BEV}}(x,y)],&\text{otherwise}\end{cases} \tag{4}\]Here, \(D_{\text{mono}}(i)\) represents the object-level depth from the monocular object detection pipeline for object \(i\), while \(\mathbb{E}[D_{\text{BEV}}(x,y)]\) is the expected depth from the BEVDepth softmax distribution for pixel \((x,y)\). The value of \(\mathbb{E}[D_{\text{mono}}(i)]\) can either be a constant (e.g., 1), the loss of 2D bounding box regression \(L_{\text{dim}}\), or a Gaussian kernel-fused value to ensure smooth transitions at object boundaries.  
This approach is straightforward: it replaces depth values within object regions while leaving the rest of the grid unchanged. However, it has limitations. Since the object-specific depth from monocular detection cannot always be fully trusted, this method only works well when the monocular depth is highly accurate and should take precedence in detected object regions.
## 2 Materials and Methods
### Singular Vector Decomposition (SVD)
The fluorescence and reflectance are estimated using a numerical optimization technique by comparing the at-sensor radiance computed by Equation (3) and HyPlant observations until the best match is found between the two spectra. Neutral Atmosphere (NA)  
The NA retrieval algorithm is based on the implementation of different steps running in sequence, which are dedicated to (1) refining the instrumental spectral response function (ISRF) through the determination of the spectral shift and band-broadening; (2) correcting the acquired image from the atmospheric effects by applying a classical atmospheric correction scheme [57]; and (3) applying a fluorescence-retrieval strategy at TOC. In this particular case, the NA module includes the SFM [54], initialized by the peak-height method proposed by [58]. The atmospheric correction implemented in the NA strategy makes use of MODTRAN5 lookup tables previously computed. As with the SFM module described above, the model input parameters are derived from sun photometer measurements and the navigation data of a HyPlant image cube. The NA method models the at-sensor acquired radiance following Equation (4), which assumes a Lambertian behavior of the surface reflectance, an isotropic fluorescence emission and considers successive reflectance contributions until second order. \[L_{\Lambda}^{AIS}=\ \langle L_{0}\rangle+\frac{\langle(E_{dir}cos\theta_{il}+E_{ dif})(T_{dir}^{\uparrow}+T_{ dif}^{\uparrow})\rangle}{\pi}\langle\rho_{app}\rangle+\frac{\langle(E_{dir}cos \theta_{il}+E_{diff})(T_{dir}^{\uparrow}+T_{dif}^{\uparrow})S\rangle}{\pi} \langle\rho_{app}\rangle^{2} \tag{4}\]  
In Equation (4), \(L_{0}\) is the path radiance; \(E_{dir}\) and \(E_{dif}\) are the direct and diffuse terms of the solar irradiance at surface level; \(T_{dir}^{\uparrow}\) and \(T_{dif}^{\uparrow}\) are the upward direct and diffuse transmittance components; \(S\) is the spherical albedo; and \(\langle\rho_{app}\rangle\) is the surface reflectance, accounting for the added normalized contribution of the fluorescence emission. The series expansion is assumed here to avoid the division with the convolved spherical albedo term \(S\) (\(t_{3}\) in Equation (3) and \(\langle\rho_{dd}^{\lambda}\rangle\) in Equation (4). See more details of this approximation in [59]. After atmospheric correction, a novel retrieval approach based on the height of the peaks in the apparent surface reflectance (\(\rho_{app}\)) provides estimates of fluorescence without making any assumption about the shape of the true reflectance [58], being the retrieval limited to bands inside strong absorption features. However, fluorescence estimates from these bands are sufficient for initializing the SpecFit numerical optimization routine from a value closer to the best solution, thus reducing the computation time.
The key issues in the EMP interaction with plasma are the transformation of the waveform and the loss of the pulse energy. In order to model these phenomena, one has to, first, ensure propagation of an EMP in the form of a transverse TEM mode and, second, create a partially ionized medium with controlled parameters along the path of EMP propagation. The first successful attempts to experimentally study transformations of the EMP waveform during EMP propagation in plasma were made in the USA in the 1970s using pulses, which had durations of about 1 ns, and pulse rise fronts of about 250 ps [18; 19]. Within the framework of these studies, the concept of a double plasma-filled transmitting line, which maintained propagation of an EMP in the form of a TEM mode, was realized for the first time. A number of interesting effects, which were presumably connected with additional nonstationary ionization of gas in the EMP field, were observed in a line about 1 m long, that had a transverse size of about 10 cm. The first experiment, in which the dispersion transformation of the nanosecond-long UWB EMP wave form in plasma was observed, was staged on the KROT facility [20], using a nanosecond generator of high-voltage pulses about 1 ns long, which was loaded with a horn antenna that radiated into a wide (more than 1 m in diameter) and long (about 5 m) column of transparent isotropic plasma. In this paper, we describe a new instrument for studying the interaction of UWB EMPs with gas media in a large volume; specifically, the gigantic plasma-filled coaxial line for a large-scale plasma facility KROT at the Institute of Applied Physics of the Russian Academy of Sciences (IAP RAS, Nizhny Novgorod, Russia).
## 8 Routing Protocols of Multi-UAV Systems  
The increasing number and variety of mobile connected objects has led to numerous studies [22; 76] to develop routing protocols adapted to highly mobile environments. Each protocol is adapted according to a particular routing standard, which is largely influenced by the communication infrastructures. A protocol fits into the acceptance standards if it allows us to convey, ensure the integrity of, and secure packets (messages) sent between the nodes of its network. In the case of UAV networks, the communication protocols used are adapted for Flying Ad-Hoc Network (FANET) routing protocols [19]. In this paper, we investigate 17 FANET routing protocols. We distinguish three categories of routing protocols: topology [23; 75], geographic position [20; 77; 78], and hierarchical-based [79; 80] routing protocols; refer to Figure 15.