esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Sensor Locations during the Recording Campaign  
During the recording campaign, the sensors were placed at the portals of the highway in the suburban area, which is the exact position they will be installed for the WASN thanks to the collaboration of the DYNAMAP's ANAS S.p.A partner. However, they were located at more than 2 m from the buildings in the urban scenario, which can differ from the positions where the WASN low-cost sensors will be placed when deploying the pilot system of the DYNAMAP project in Milan (e.g., fixed at the buildings facade). Moreover, also different elevation angles were considered in the sensors' orientation during the recording campaign. At this point, an interesting question arises: To what extent do the position and orientation of the sensors influence aspects like acoustic salience of sound events and spectral content of sound or road traffic noise? More work needs to be done to answer these questions, e.g., by performing contrast analyses with the gathered measurements obtained with different sensors' positions and orientations, beyond the preliminary comparisons already conducted.
## 3 Proposed Model
### Proposed Mobile-Aware Cluster Head-Election Scored (MACHES) Algorithm
#### 3.3.1 Initialization Phase  
In the initialization phase of the proposed protocol, the node positions are randomly assigned within a specified area \(M\). Each node is then predefined with a predefined energy level, \(E_{init}\). To carry out efficient data transmission and energy utilization, the protocol undertakes an initial clustering process using the K-means algorithm. This step is crucial, as it determines the optimal number of clusters, \(K_{opt}\), based on the spatial distribution of the nodes. The clusters are formed around calculated center points, which are adjusted dynamically in response to changes in node availability and energy levels. In the initial phase, the cluster configuration is re-evaluated at the first operational round and any subsequent rounds following the complete depletion of energy by any node. This re-evaluation helps in maintaining optimal clustering structure, thus enhancing the network's overall performance and longevity. The re-calculation and re-location of the center points continues until a stable cluster configuration is confirmed.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 3 Numerical Analysis and Results
### Comparison of LoS Rate between the HAPS and UAVs
#### 3.1.1 Simulation Model  
Simulations were performed using a pre-existing MATLAB 3D model environment that mimics the town of Chicago, shown in Figure 7.  
An overall view of the simulation is shown in Figure 8. The simulation was conducted assuming that the HAPS was 2000 m above the ground and the UAV was 200 m above the ground.  
Figure 8: Simulation overview of LoS ratio calculation.  
Figure 7: 3D model of the evaluation environment.  
For simplicity, we will use the simulation of the LoS rate over the town to illustrate the simulation. First, random radio sources are generated in the town, as shown in Figure 9. Here, the height of each antenna was set to 10 m.  
Next, as shown in Figure 10, a receiving antenna (in this case, a UAV) is generated over the town, the number of direct waves is calculated using a ray tracing simulation, and the LoS rate is calculated by calculating the ratio of the number of direct waves to the number of radio wave sources. In this simulation, the number of radio wave sources is set to 1000. The same simulation is performed for the HAPS.  
Figure 10: An example of ray tracing simulation in progress.  
Figure 9: Randomly generated radio wave sources.  
When the simulation is performed away from the center, receivers are generated in several directions, as shown in Figure 11, and the average value of the LoS ratio obtained from each receiver is used as the LoS ratio.
## 6 Experimental Evaluation and Results
### Comparative Analysis of Defense Strategies
It should be noted that the current simulation does not consider scenarios where executors may be subjected to common-mode attacks, which could potentially affect multiple executors simultaneously. Such attack scenarios and their implications for system resilience will be further evaluated and analyzed in Section 6.4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Failures** & \
\begin{tabular}{c} **MTTR** \\ **(min)** \\ \end{tabular} &
\begin{tabular}{c} **MTBF** \\ **(min)** \\ \end{tabular} & **Availability (\%)** \\ \hline Static redundancy & 2 & 44.92 & 75.08 & 62.57 \\ Moving target defense & 2 & 16.17 & 103.83 & 86.53 \\ Proposed method & 0 & 0.00 & 240.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of reliability metrics among different defense strategies under attack scenarios. Figure 3: Pipeline pressure variations under different defense strategies.
## 6 Conclusions  
This paper proposes the SBPVEM method for the distributed cooperative localization of sparse anchor targets. A simplified belief propagation method is proposed as the overall reasoning framework by modeling the cooperative localization problem as a graph model. The high-aggregation sampling and variation expectation-maximization algorithm are applied to sample and fit the complicated distribution.  
SBPVEM can obtain accurate node localization in a challenging environment while reducing bandwidth requirements. Our work solves the self-localization problem of distributed nodes under the condition of a minimal number of anchor nodes, makes cooperative localization with sparse anchor nodes in wireless sensor networks a reality, and lowers the cost in large-scale applications.  
Figure 6: Location results of connection scenario 3. (**a**) exhibits the setting of scenario 2. Anchors are represented by cyan nodes, whereas yellow nodes represent targets. Blue dotted lines link two nodes with communication. PVSPA, SPAWN, NBP, and SBPVEM iterative positioning results and corresponding positioning errors are shown in (**b**–**e**). (**f**) comparing the prediction errors of various algorithms in the iterative process.  
**Author Contributions:** Conceptualization, M.W. and J.C.; software, X.W.; validation, X.W.; formal analysis, X.W. and Y.G.; investigation, X.W.; resources, Y.G.; data curation, X.W.; writing--original draft preparation, X.W., C.L.; writing--review and editing, Y.G. and Z.S.; visualization, X.W. and Z.S.; supervision, Y.G. and M.W.; project administration, M.W. and J.C.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (No. 62103424) and Junior faculty Innovation Research Project, College of Intelligence Science and Engineering, National University of Defense Technology (No. ZN2019-16).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BP & Belief Propagation \\ LBP & Loopy Belief Propagation \\ NBP & Nonparametric Belief Propagation \\ SPAWN & Sum-Product Algorithm over a Wireless Network \\ PVSPA & Parametric Variational Sum-Product Algorithm \\ SBPVEM & Simplify Belief Propagation and Variation Expectation Maximization \\ \end{tabular}
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 3 Topology Inference and Estimation of Link Parameters  
In this section, we present in detail the nearest neighbor (NN) chain-extended agglomerative clustering algorithm and the modifications/extensions we incorporate to fulfill our objectives.
# Application of Tucker Decomposition in Temperature Distribution Reconstruction
## 2 Fundamental Measurement Principles  
### Tucker Decomposition Algorithm Principle  
By Tucker decomposition, the N-order high-dimensional tensor can be transformed into an n-order low-dimensional core tensor and n decomposition factor matrices (\(N>n\)). The Tucker decomposition of N-order tensors \(\chi\in\mathbb{O}^{I\times J\times K}\) can be written as [31]  
\[\chi=f\Big{(}U^{(1)},U^{(2)},\ldots,U^{(N)}\Big{)}+\varepsilon \tag{1}\]  
where \(U^{(n)},n=1,\ldots,N\) is the decomposition factor, and \(\varepsilon\) is N-order error tensor.  
Therefore, the factor matrix can be obtained by the following optimization problems:  
\[\Big{(}\hat{U}^{(1)},\ldots,\hat{U}^{(N)}\Big{)}=\arg\min_{U^{(1)},\ldots,U^{( N)}}\|\chi-f\Big{(}U^{(1)},\ldots,U^{(N)}\Big{)}\|_{2}^{2}, \tag{2}\]Considering that the Tucker decomposition of the N-order tensor is a higher-order singular value decomposition (HOSVD),  
\[\min_{U^{(1)},\ldots,U^{(N)},G_{(n)}}\|X_{(n)}-U^{(n)}G_{(n)}U^{(n)}_{\otimes}\| _{2}^{2}, \tag{3}\]  
In higher-order SVD, the factor matrix \(U^{(n)}\in\mathbb{C}^{I_{n}\times I_{n}}\) satisfies the semi orthogonal condition \(U^{(n)T}U^{(n)}=I_{j}\):  
\[U^{(n)}_{\otimes}U^{(n)T}_{\otimes}=I_{I_{1},\ldots,I_{n-1},I_{n+1},\ldots,I_{ N},} \tag{4}\]  
Then, to solve equation \(X_{(n)}\approx U^{(n)}G_{(n)}U^{(n)}_{\otimes}\), make SVD \(X_{(n)}=U^{(n)}S^{(n)}V^{(n)T}\):  
\[U^{(n)}S^{(n)}V^{(n)T}=U^{(n)}G_{(n)}U^{(n)}_{\otimes}, \tag{5}\]  
Then, both sides multiply \(U^{(n)T}\) left and \(U^{(n)T}_{\otimes}\) right, respectively, and the following can be obtained from Equation (5):  
\[G_{(n)}=S^{(n)}V^{(n)T}U^{(n)T}_{\otimes}, \tag{6}\]  
Therefore, the mathematical model of the n-order tensor \(\mathbf{\chi}\in\mathbb{C}^{I_{1}\times I_{2}\times I_{3}\times\ldots I_{N}}\) decomposition process is expressed as follows:  
\[\mathbf{\chi}=\mathbf{\zeta}\times_{1}U^{(1)}\times_{2}U^{(2)}\ldots\times_{N}U^{(N)}, \tag{7}\]  
where \(\mathbf{\zeta}\)--tensor \(\mathbf{\chi}\) Tucker decomposition core tensor, \(\mathbf{\zeta}\in\mathbb{C}^{I_{1}\times I_{2}\times I_{3}\times\ldots\times I_{N}}\), \(J_{N}<I_{N}\); \(\mathbf{\times}_{N}\)--tensor \(\mathbf{\chi}\) N-mode product and \(\mathbf{U}^{(N)}\)--tensor \(\mathbf{\chi}\) decomposition factor matrix in the N-module direction, \(\mathbf{U}^{(N)}\in\mathbb{C}^{I_{n}\times I_{n}}\).
## 5 Recent Technologies Used in Ammonia Production
### New Ammonia Generation Routes  
5.3.1. Electrochemical Ammonia Synthesis  
Electrochemical ammonia production is an example of a new way to produce ammonia without the hydrogen production plant but using water as a source of hydrogen. This reaction involves oxidizing water at the anode, which releases protons that move to the cathode side via a solid or liquid electrolyte chosen based on the type of electrochemical cell. This electrolyte is a conductive solution that facilitates the migration of ions between the electrodes when electricity is applied. Under mild conditions, nitrogen interacts with the protons in the cathode to form ammonia. Table 10 highlights several experimental efforts using electrochemical electrolyte ammonia production. Figure 21 displays the different electrolytes employed in the synthesis of ammonia [99].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Electrochemical** & & & & & \\
**Synthesis** & **Reaction** & **T (\({}^{\circ}\)C)** & **Electrolyte** & **Cathode** & **Ammonia Yield** \\
**Electrolyte** & & & & & \\ \hline High Temperature & & 550 & BaZr\({}_{0.7}\) Ce\({}_{0.2}\) Y \({}_{0.1}\)O\({}_{3}\) & Ni-BZCY72 & 286 \(\times\) 10\({}^{-11}\) \\ \hline \multirow{3}{*}{Molten State} & In electrolytes with pH \(<\) 6: & & & & \\ \cline{2-5}  & N\({}_{\text{2(g)}}\) + 6H\({}^{+}\) + 6e\({}^{-}\)\(\rightarrow\)2 NH\({}_{\text{3(g)}}\) & 400 & LiCl, KCl, CsCl + Li3N & Al & 367 \(\times\) 10\({}^{-10}\) \\ \hline \multirow{3}{*}{Low Temperature} & In electrolytes with pH \(>\) 8: & Room T & Nafion & Fe/Fe\({}_{3}\)O4 & 111 \(\times\) 10\({}^{-11}\) \\  & N\({}_{\text{2(g)}}\) + 6H\({}_{2}\)O + 6e\({}^{-}\)\(\rightarrow\)2 NH\({}_{\text{3(g)}}\) & & & & \\ \cline{1-1}  & 2H\({}_{2}\)O + 2e\({}^{-}\)\(\rightarrow\)H\({}_{\text{2(g)}}\) + 2 OH\({}^{-}\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Electrochemical Electrolyte ammonia production [99].  
Figure 20: Green ammonia production technologies [99].
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 2 Related Work
### Gaps in Existing Research  
The literature review reveals that current DMS research predominantly focuses on system architectures and functionality exploration. A significant gap exists in comprehensive security analysis, with [PERSON] et al. [13] representing the primary contribution in this area. Most existing studies lack a thorough examination of security vulnerabilities, particularly regarding information flow through the system. The impact of different communication protocols on the security of DMSs remains unexplored, despite their critical role in system security.  
User experience and system acceptance in continuous driver monitoring represent another understudied area. While this aspect warrants deeper investigation, the present work addresses the technical gaps through a detailed analysis of a generic DMS architecture, with particular attention paid to information flow paths.  
While recent research has explored the design and performance of Driver Monitoring Systems (DMSs), the cybersecurity dimension has often been addressed only at a high level, without systematic threat modeling or alignment with established standards such as ISO/SAE 21434. Some studies focus primarily on hardware-based monitoring techniques (e.g., eye tracking, facial recognition, or heart-rate sensors), emphasizing functionality and usability but overlooking potential vulnerabilities in data processing and communication layers.  
In contrast, this study contributes a detailed Threat Analysis and Risk Assessment (TARA) tailored to a representative DMS architecture, integrating the STRIDE threat modeling framework to systematically categorize and prioritize risks. Unlike prior works that either discuss security conceptually or focus on isolated threats, our approach provides a structured methodology for identifying and mitigating realistic attack scenarios across system components and dataflows. Moreover, we highlight privacy concerns associated with the handling of biometric driver data, an aspect often under-represented in DMS research.  
This work does not aim to propose a novel monitoring modality or sensing solution. Instead, it focuses on enhancing the resilience of existing architectures through context-aware cybersecurity analysis, offering practical guidance aligned with industry standards for safe DMS deployment in automotive environments. Building on the gaps identified in current DMS research--particularly regarding systematic threat modeling and protocol-specific vulnerabilities--the next section introduces the methodological framework adopted to carry out a comprehensive security assessment aligned with ISO/SAE 21434.
# Exploring Structural Changes in Ge-Te Amorphous Films Through Small-Angle Neutron Scattering
## 2 Materials and Methods
### Small-Angle Neutron Scattering  
The SANS experiments were performed using the small momentum transfer diffractometer D16 at the Institut Laue-Langevin (Grenoble, France) using a wavelength of \(\lambda=4.47\) A. This provides a high \(\Delta Q/Q\) resolution of 1%, suitable for detailed structural analysis. Six detector positions (SANS and WANS configurations) were used to achieve a broad \(Q\)-range from \(2\times 10^{-2}\) A\({}^{-1}\) to 2.2 A\({}^{-1}\) (\(Q=4\pi/\lambda\sin\theta\), where \(2\theta\) is the scattering angle).  
The monochromatic neutron beam dimensions at the sample position were defined by slits of \(25\times 5\) mm\({}^{2}\) and centred on a cylindrical vanadium sample holder with a 4 mm inner diameter and 66 mm length. This setup ensured the uniform illumination of the sample cell by the neutron beam.  
Amorphous Ge\({}_{x}\)Te\({}_{1-x}\) films were placed in the unsealed cylindrical vanadium containers, and the room-temperature diffraction patterns were collected by moving the neutron detector around the sample at a fixed distance. The sample cells were mounted in a rotative sample changer. In addition to the samples, the necessary ancillary measurements were performed, including a vanadium rod (5 mm in diameter), empty cell, and cadmium, all with dimensions closely matching those of the sample.  
The resulting 2D data were radially averaged to obtain the scattered intensity \(I(Q)\) as a function of \(Q\). Data were corrected by taking into account the empty cell, environmental background, sample transmission, and the thickness of the sample. Additionally, the data were then normalised using a vanadium standard to correct for the non-uniform detector response. This data reduction was carried out using the ILL's LAMP software ([[https://www.ill.eu/users/support-labs-infrastructure/software-scientific-tools/lamp](https://www.ill.eu/users/support-labs-infrastructure/software-scientific-tools/lamp)]([https://www.ill.eu/users/support-labs-infrastructure/software-scientific-tools/lamp](https://www.ill.eu/users/support-labs-infrastructure/software-scientific-tools/lamp)), accessed on 9 December 2024) [32] and specific routines for SANS experiments.
# A Cooperative Control Method for Excavation Support Robot with Desired Position/Posture
## 5 Experimental Verification and Analysis
Parameters such as the posture angles and the distances from the key position point to the side wall of the space edge in the verification process are adjusted by the system to the desired state and close to the minimum deviation area of the expected value. It is pointed out here that the maximum error of the posture angle is controlled to be no more than 0.5\({}^{\circ}\), and the \begin{table}
\begin{tabular}{c c c c} \hline
**Left/Right Edge Side** & **Number of Groups** & **Initial Data** & **Simulation Data** \\ \hline \multirow{4}{*}{Left edge side \(d_{mL}(\mathrm{m})\)} & 1 & 0.696 & 0.986 \\  & 2 & 1.236 & 1.035 \\  & 3 & 0.787 & 0.982 \\  & 4 & 1.102 & 1.042 \\  & 5 & 0.993 & 1.031 \\ \hline \multirow{4}{*}{Right edge side \(d_{mR}(\mathrm{m})\)} & 1 & 0.832 & 0.973 \\  & 2 & 0.879 & 0.998 \\ \cline{1-1}  & 3 & 1.187 & 1.023 \\ \cline{1-1}  & 4 & 1.236 & 1.039 \\ \cline{1-1}  & 5 & 0.819 & 0.989 \\ \hline \end{tabular}
\end{table} 
Table 4: Experimental data of distance. \begin{table}
\begin{tabular}{c c c c} \hline
**Left/Right Edge Side** & **Number of Groups** & **Initial Data** & **Simulation Data** \\ \hline \multirow{4}{*}{Left edge side \(\gamma_{L}(^{\circ})\)} & 1 & 3.83 & 0.33 \\  & 2 & \(-\)3.16 & \(-\)0.42 \\  & 3 & 2.37 & 0.23 \\  & 4 & 3.72 & 0.52 \\  & 5 & \(-\)3.34 & \(-\)0.49 \\ \hline \multirow{4}{*}{Right edge side \(\gamma_{R}(^{\circ})\)} & 1 & 2.43 & 0.42 \\  & 2 & 1.82 & \(-\)0.23 \\ \cline{1-1}  & 3 & \(-\)2.06 & 0.33 \\ \cline{1-1}  & 4 & \(-\)2.42 & \(-\)0.52 \\ \cline{1-1}  & 5 & 2.93 & 0.23 \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental data of scroll angle of ESR. maximum error of the distance between the key position point and the edge of the space domain is no more than 0.03 m, which meets the actual collaborative adjustment accuracy requirements. Compared with the traditional manual adjustment method, the adjustment accuracy is significantly improved, the synergistic effect is credible, and the promotion and application prospects are promising.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
###### Abstract  
Adaptive antenna arrays are widely used to protect Global Navigation Satellite System (GNSS) receivers from interference. However, conventional blind anti-jamming array processing methods can cause satellite signal attenuation, which reduces signal tracking precision and measurement accuracy. To alleviate this problem, this paper proposes the noncoherent channel combining method for GNSS signal tracking with an adaptive antenna array. A two-stage processing architecture is applied in the proposed method, where the first stage implements interference suppression, and the second stage achieves noncoherent channel combining. The proposed method has the advantage of blind characteristics that can be implemented without any other auxiliary information. It also reveals a unified processing architecture for anti-jamming array processing methods. Thus, a detailed comparison and analysis of the proposed method with several typical blind anti-jamming methods is performed. Simulation results demonstrate the effectiveness of the proposed method and its superiority over existing methods in terms of tracking performance.  
adaptive antenna; GNSS; interference suppression; channel combining; signal tracking +
Footnote †: journal: remote sensing
## 3 Brain Inspired Processing
### LSNN Input Coding  
In the previous section, we discussed possible encoding methods proposed in the literature for LSNNs. In this section, we describe the method we applied to SHM data in our brain-inspired processing pipeline. An example of the input signal is shown in Figure 2.  
In Figure 4 the entire data flow is shown. After collecting the data from the sensor, we extract only the z-axis values since they are more sensitive to the vehicles passages. These data are collected with a sampling frequency of 100 Hz and successively downsampled to 12.5 Hz. Subsequently, we get only the windows where vehicles passages have been detected. This was done by thresholding the signal.  
The FFT of these windows is computed. The number of input neurons has been selected as two possible values: 50 or 150. This choice was made because a larger number of inputs would have caused a matrix of input weights (\(W^{I}\)) too large to train for such a small network, while a smaller number of inputs risks not extracting enough features from the signal [31]. So each vehicle's window can be composed of 100 or 300 coefficients that, in time, is equal to 8 or 24 s.  
FFT coefficients are then given as input to the encoding stage. In the case of Current-Driven encoding, they are provided to the LSNN network one to each neuron for a constant time corresponding to \(t_{inf}\). On the other side, in the case of Event-Driven encoding, the ROC algorithm is applied.  
Each coefficient is thus encoded as a spike time interval. The larger the coefficient, the smaller the \"time-to-spike\". As a result, the spike time interval is inversely proportional to the value of the coefficients. As such, higher coefficients, which are more relevant for the damage detection because they have more energy, will fire first. Lower coefficients will fire later. Since \(t_{inf}\) is the inference time for each sample, all neurons that have not fired for \(t<t_{inf}\) will no longer be able to fire. This implies that the coefficients with low energy (no information) will not impact the network input. The applied ROC encoding algorithm (Algorithm 1) is shown below:  
```
input : A signal \(S\) of \(I\) coefficients and a inference time \(tinf\) output : A Time-to-Event \(T\) array of \(I\) spiking intervals \(m=min(S)\); \(M=max(S)\); for\(i\gets 0\)to\(I-1\)do \(t=(S[i]-m)/(M-m)\); \(tcheck=round(1/t)\); if\(tcheck\leq tinf\)then \(\text{T[i]}=\text{tcheck}\)  end if end for return\(T\);
```  
**Algorithm 1**Compute Time-to-Event  
The signal \(S\) is the sequence of FFT coefficients corresponding to the observed acceleration window made of \(I\) coefficients. The inference time \(t_{inf}\) is also an input of the algorithm as it is a network parameter.  
Figure 4: Data preprocessing.