esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Solid Phase
#### 3.1.1 Sediment Geochemistry and Enrichment Factor (EF) of Metal(loid)
Among them, the industrial site of Torviscosa represents the main source of concern [11], being the primary chemical stress for the lagoon environment. In addition to Hg, which was discharged from 1949 to 1984 into the Aussa-Corno River system from the decommissioned chlor-alkali plant of Torviscosa, the occurrence of other metal(loid)s (Cd, Zn, As, Pb and Cu) could be related to several industrial activities that took place in this site [11]. Moreover, hot-spot contamination sources due to industrial activities, as well as waste disposal, have been identified at the industrial area of Porto Nogaro harbour [11]. Anthropogenic metal(loid)s successively accumulated in the central-western sector of the lagoon, included in this fish farm, before the construction of the embarkments. Moreover, we cannot discard that these inputs could be partially associated with the terrigenous supplies of the Isonzo River flowing into the Gulf of Trieste. Here, the main anticlockwise circulation system and the longshore currents are responsible for the transport of fluvial sediments to the eastern sector of the lagoon through the tidal \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & **Level** & **Al** & **Fe** & **As** & **Cd** & **Cr** & **Cu** & **Hg** & **Mn** & **Ni** & **Pb** & **V** & **Zn** \\ \cline{2-13}  & **(cm)** & **\%** & **\%** & **\#g**/g** & **\#g**/g** & **\#g**/g** & **\#g**/g** & **\#g**/g** & **\#g**/g** & **\#g**/g** & **\#g**/g** & **\#g**/g** \\ \hline \multirow{4}{*}{**VN1**} & 0–1 & 7.03 & 5.71 & 7.62 & 1.64 & 84.8 & 25.9 & 5.10 & 404 & 55.7 & 32.6 & 78.3 & 85.0 \\  & 1–2 & 8.88 & 6.78 & 7.73 & 1.94 & 83.7 & 27.4 & 4.19 & 359 & 57.0 & 37.4 & 81.1 & 87.0 \\  & 2–3.5 & 7.26 & 6.19 & 6.44 & 1.89 & 78.8 & 23.6 & 3.62 & 340 & 55.2 & 36.8 & 77.7 & 100 \\  & 3.5–5 & 8.74 & 6.93 & 5.93 & 2.13 & 90.3 & 30.7 & 3.20 & 313 & 64.9 & 41.2 & 93.4 & 121 \\  & 5–7 & 6.76 & 4.99 & 6.47 & 1.90 & 74.0 & 19.0 & 2.15 & 396 & 47.6 & 32.9 & 67.2 & 72.2 \\ \hline \multirow{4}{*}{**VN3**} & 0–1 & 5.34 & 7.18 & 8.29 & 2.22 & 86.5 & 32.8 & 4.20 & 366 & 60.2 & 43.2 & 87.1 & 95.4 \\  & 1–2 & 6.18 & 6.55 & 11.2 & 2.29 & 85.3 & 29.9 & 5.11 & 444 & 58.4 & 43.1 & 86.3 & 93.4 \\ \cline{1-1}  & 2–3.5 & 6.86 & 6.82 & 12.3 & 2.26 & 83.1 & 25.4 & 6.87 & 497 & 54.8 & 40.9 & 80.0 & 85.9 \\ \cline{1-1}  & 3.5–5 & 7.86 & 5.97 & 11.5 & 2.39 & 85.7 & 24.3 & 5.98 & 512 & 54.2 & 39.8 & 78.5 & 80.7 \\ \cline{1-1}  & 5–7 & 6.87 & 6.09 & 10.7 & 2.00 & 84.8 & 24.6 & 5.23 & 529 & 57.4 & 37.1 & 83.5 & 86.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Metal(loid) concentrations in each level of the VN1 and VN3 sediment cores. Figure 2: Score and loading plots resuming the results of principal component analysis (PCA) performed on total and labile metal(loid) concentrations is sediments at sites VN1 and VN3. inlets [39]. No enrichment (EF \(\approx\) 1) for As, Cr, Cu, Mn, Ni and V was observed at either site (Supplementary Materials Table S1).
## 3 Circuit Design
### Stability Analysis and Design  
The stability analysis is an essential step in this MMIC PA design. To ensure the stability of the developed MMIC PA, stability circuits were designed for each HEMT. Figure 3 shows the block diagram of the designed stability circuits. A parallel RC network, which is composed of the parallel connection of a resistance and a capacitance, is inserted  
Figure 1: The characteristics of 0.25 \(\upmu\)m GaN HEMT: (**a**) I-V curve of 0.25 \(\upmu\)m GaN HEMT; (**b**) transfer curve of 0.25 \(\upmu\)m GaN HEMT.  
Figure 2: The circuit structure block diagram of the 7–13 GHz MMIC PA design.  
at the gate of the GaN HEMT. Figure 4a,b give the simulation results of the maximum gain and stability factor, respectively. It can be seen that the simulated stability factors are higher than 1.03 throughout a frequency from 0 to 20 GHz, which confirms that the designed stability circuit can effectively improve the stability.
# Synchronous Generator Stability Characterization for Gas Power Plants Using Load Rejection Tests
## 2 Theoretical Models
### AVR Model: SEXS  
The model chosen to represent the behaviour of the power plants AVR is SEXS. Figure 5 shows the voltage regulation control diagram of the AVR model. In the AVR-SEXS model, the input voltage to the excitation system is denoted by \(V_{S}\). This voltage is obtained by adding two signals--VPSS, which represents the voltage feedback from the power system stabilizer, and VOEL, which is the difference between the reference voltage setpoint and the generator terminal voltage. Thus, the value of \(V_{S}\) is the summation of these two signals and serves as the input to the excitation system. Additionally, the model uses two other inputs: \(V_{ref}\), which is the reference voltage setpoint, and \(V_{C}\), which is the compensated terminal voltage, both expressed in per-unit values.  
Figure 4: Case example: calculating SG inertia on an actual generator.  
Figure 5: Simplified excitation system (SEXS) model.  
Table 2 displays the parameters for the AVR model, which are determined through simulation fitting based on tests. These tests include an individual test of the AVR controller that varies the voltage reference without a load, as well as a load rejection test that enables the fitting of the transient response. The transient response allows for the fitting of the time constant T\({}_{E}\), as well as the first block parameters T\({}_{A}\) and T\({}_{B}\). The remaining parameters can be fitted using the steady-state varying points observed during no-load conditions.
# Sensitivity Analysis of P-Wave Polarization Direction and Velocity Gradient Inversion
## 4 Inversion Method Based on Polarization Direction Sensitivity
Then, we determine the maximum partial derivative value for each source-receiver pair sequentially, producing \(I\) maximum values. We then calculate the multiples of these maximum values with each source-receiver pair's partial derivative values, resulting in \(II\times J\) values. Subsequently, we sequentially compute the average of the multiples from the first column to the \(J\)th column for all source-receiver pairs, generating \(JJ\) average values. Finally, these \(JJ\) average values are sequentially assigned to each column. We refer to this factor as the adjustment factor, denoted as  
\[\gamma_{c}=\frac{\sum_{l=1}^{num_{sr}}\left|\frac{\left(\rho_{S,R}\right)_{max} }{\rho_{S,R,L}}\right|}{l},(\rho\
eq 0), \tag{22}\]  
where the subscript \(S\) is the numbering of the sources, \(R\) is the numbering of the receivers, \(l\) is the numbering of the rows, and \(c\) is the numbering of the columns. \(num_{sr}\) represents the total number of source-receiver pairs. \(\rho\) represent the partial derivative of polarization direction with respect to the relative velocity gradient of nodes. Combining Equation (22), we transform Equation (21) into the form shown below:  
\[G^{k+1}(j)=G^{k}(j)+\lambda_{k}\frac{\mathbf{n}_{e}(i)-\sum_{j=1}^{N}\left(\gamma _{c}\cdot\mathbf{W}_{i,j}\right)G^{k}}{\sum_{j=1}^{N}\left(\gamma_{c}\cdot\mathbf{W}_{ i,j}\right)^{2}}\left(\gamma_{c}\cdot\mathbf{W}_{i,j}\right). \tag{23}\]  
Equation (23) is about the correction rule applied to the relative velocity gradient in our method. By implementing the correction rule outlined in Equation (23), the inversion results of the relative velocity gradient at each position can be more accurate.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 2 Related Works  
To design effective defenses against cyberattacks, understanding the attacker's intent is critical. In the context of GPS spoofing, an attacker may try to force a UAV to collide with other UAVs or obstacles, disregarding the risk of detection. Alternatively, the attacker may aim to capture the UAV while carefully avoiding actions that trigger detection systems. To achieve this, the attacker can either collect GPS signals from one location and re-broadcast them in another or generate GPS signals that mimic authentic satellites. As described in [20], GPS spoofing attacks are distinguished by their strategy and impact on UAV navigation:  
1. Simplistic attacks: The basic form of a GPS spoofing attack involves launching GPS signals that carry false position information at higher power than authentic satellite signals. This forces the target GPS receiver to lose lock and perform the re-acquisition process, raising the concern of an ongoing attack. If, by any chance, the attacker successfully prevents the loss of lock, the attack still lacks time synchronization with previous authentic signals. Moreover, it disregards the UAV's motion, making the spoofed signal appear clearly abnormal to the target receiver. Nevertheless, the basic GPS spoofing attack can still function as a jamming technique, preventing the receiver from acquiring authentic signals.
2. Intermediate attacks: Intermediate-level attacks include using a portable receiver-spoofer device. It is typically built with low-cost commonly available components and runs an open-source GPS signal simulator. An attacker places this device in proximity to the UAV target to estimate its position, velocity, and time. The purpose is to synchronize the spoofed GPS signals with authentic satellite signals, then gradually increase their amplitude, and subtly alter the reported position to divert the target from its intended path. This attack overcomes the limitations of the basic spoofing attack and is capable of deceiving most traditional detection techniques. However, angle-of-arrival discrimination remains the most effective countermeasure against this attack form as the signals seem to originate from the same direction.
3. Sophisticated attacks: In more sophisticated attacks, adversaries broadcast synchronized signals from multiple angles using a series of receiver-spoofer devices that emulate satellite constellations. The complexity of this attack type makes it extremely challenging to detect. Even advanced detection techniques based on angle of arrival are deceived and may incorrectly classify multipath signals as spoofing attacks.  
To address this, numerous studies have proposed various techniques to detect and mitigate the risks posed by these attacks. As described in [8], the existing countermeasures can be classified into five main categories:
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 2 Traffic Adaptive Dynamic Meshed Backhaul Construction For Outdoor Dynamic Crowd
### Traffic Adaptive Mesh Construction  
This paper considers a dynamic construction of mmWave meshed backhaul network taking into account both energy consumption and traffic delivery successful rate. For the first purpose, it attempts to deactivate as many as possible unnecessary mmWave SC-BSs (which are also called Access Points (APs) or a node in this paper). For the second purpose, the remaining activated SC-BSs should efficiently construct a traffic delivery meshed network to fulfill UEs' traffic demands. For such objective function, this paper employs a similar heuristic approach as [25], which involves the following three steps to avoid solving an NP-hard problem which requires the optimization of both ON/OFF status of all mmWave interfaces and multi-hop backhaul path for load balancing.
# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 5 Experiments and Analysis
### Experimental Setting Up and Dataset Description
The distance between human I and the anchor behind it was maintained at 0.1 m, and human I remained in the same position during each batch of data collection (measuring tag and anchor positions at the five locations shown in the figure). After each batch, human I moved to the next position (backward by 2 m). In the office corridor scenario shown in Figure 8b, data were collected for LoS II, wooden door II (0.06 m), human II (1.68 m, 53 kg), glass IV (0.02 m), and concrete wall II for NLoS obstructions. The distance between human II and the anchor behind it was maintained at 0.5 m. Human II moved 2m for each batch of data collection, following the same measurement method as described for Figure 8a. Data processing and model training were performed on a system with a Ryzen 3600 processor (3.59 GHz), GTX 1660S graphics card, and 16 GB RAM. The following metrics are used to evaluate NLoS identification accuracy [17]:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{23}\]  
\[\text{Recall}=\frac{1}{n}\sum_{i<1}^{n}\frac{\text{TP}_{ic}}{\text{TP}_{ic}+ \text{FN}_{ic}} \tag{24}\]  
\[\text{Precision}=\frac{1}{n}\sum_{i<1}^{n}\frac{\text{TP}_{ic}}{\text{TP}_{ic} +\text{FP}_{ic}} \tag{25}\]  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{26}\]  
where \(ic=1,\ 2,\ 3,\ 4,\ 5\) is the five types of CIR signal propagation channel, true positive (TP) is the correctly classified positive samples, true negative (TN) is the correctly classified negative samples, false positive (FP) is the misclassified positive samples, and false negative (FN) is the misclassified negative samples.
# Temporal Convolutional Network-Based Axle Load Estimation from pavement Vibration Data
## 2 Methods
### Data Preprocessing  
[PERSON] et al. (2018) developed a pavement vibration sensing system using distributed optical vibration sensing technology. A fiber cable was looped in a vibration sensing system, providing a positioning accuracy of 0.3 m and a sampling frequency of 2500 Hz. In this study, a DOVS-based vibration sensing system was utilized to measure traffic-induced vibration by embedding it into a pavement structure.  
To enhance the quality of the data, several steps were taken, including filtering, normalization, down-sampling, and window selection. According to [PERSON] et al. (2019), surface waves induced by passing vehicles have concentrated energy in the frequency band of 0.5 Hz to 200 Hz. Low-frequency energy corresponds to quasi-static deformation caused by the weight of vehicles. Thus, 0.5 Hz to 200 Hz bandpass Butterworth filters were applied for noise reduction. Random windows of length 3500 were selected, and the signal was down-sampled by a factor of 5 to reduce network parameters. Figure 1 shows the details of the data preprocessing process. These steps aimed to expand the data set, improve the signal-to-noise ratio, and accelerate network convergence.
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 3 Gibbs Sampling for MIMO Detection  
### Conventional Gibbs Sampling  
When applying MIMO detection via GS, a reversible Markov chain is assumed, so that asymptotic convergence to the optimal solution is guaranteed. In other words, if the detector is run for a sufficiently long time, there is a certain positive probability that the optimal solution is visited.  
Upon receiving \(\mathbf{y}\), the BS starts the detection procedure. Under the assumption that the various transmitted symbols are uncorrelated, retrieving \(\mathbf{x}\) from (1) can be regarded as a closest vector problem (CVP) in a lattice where the received symbol is one of the lattice points perturbed by a Gaussian distribution [11]. At the initial time instant \(t=0\), an \(n\)-dimensional starting candidate \(\mathbf{x}^{(0)}\) is given to the MCMC detector, which then performs an \"ingenious\" walk over the real alphabet \(\mathcal{A}^{n}\) in search of the optimal solution to the following problem:  
\[\mathbf{\hat{x}}=\arg\min_{\mathbf{x}\in\mathcal{A}^{n}}||\mathbf{y}-g \mathbf{H}\mathbf{x}||. \tag{3}\]In particular, the \(n\)-dimensional lattice formed by \(\overline{\mathbf{H}}=g\mathbf{H}\) (known as the lattice basis) is \(\Lambda=\{\overline{\mathbf{H}}\mathbf{x}:\mathbf{x}\in\mathbb{Z}^{n}\}\). Then, one can define the Gaussian function centered at \(\mathbf{y}\in\mathbb{R}^{n}\) for standard deviation \(\sigma_{n}^{2}=1>0\) as  
\[\rho(\overline{\mathbf{H}}\mathbf{x})=\exp\bigg{(}-\frac{||\mathbf{y}- \overline{\mathbf{H}}\mathbf{x}||^{2}}{2}\bigg{)}, \tag{4}\]  
for all \(\overline{\mathbf{H}}\mathbf{x}\in\mathbb{R}^{n}\). Hence, the previous expression suggests that the joint probability of interest satisfies  
\[p(x_{1},x_{2},...,x_{n}|\overline{\mathbf{H}},\mathbf{y})\ \propto\ \exp\bigg{(}-|| \mathbf{y}-\overline{\mathbf{H}}\mathbf{x}||^{2}\bigg{)}. \tag{5}\]  
From the above expression, the transition rule between consecutive states can be computed. Particularly, it can be regarded as a discrete Gaussian distribution over \(\Lambda\); hence, sampling techniques such as Gibbs algorithm are applicable. Assume that at time index \(t\), the current state is given by \(\mathbf{x}^{(t)}\in\mathcal{A}^{n}\). Then, at the next time index, the method uniformly picks one random position \(j\) out of \(\{1,...,n\}\) and computes the conditional probability of transitioning to each of the possible constellation points. With the remaining \((n-1)\) positions fixed, the \(j\)th index is updated according to a stationary distribution to be detailed in the next subsection.
## 5 Conclusions  
This paper proposes a novel protection algorithm for PV-connected transmission lines based on two-dimensional spatial current trajectory images and a data-enhanced convolutional neural network. The proposed method addresses the unique protection challenges introduced by low fault current levels, short fault durations, and CT saturation effects in renewable-integrated systems. Based on extensive simulation validation, the main conclusions are summarized as follows:  
(1) The proposed method effectively maps PV-side and grid-side current waveforms into spatial trajectory images, allowing the extraction of dynamic fault features. This eliminates reliance on preset thresholds and fault models.  
(2) The A-CNN model demonstrates excellent performance across a wide range of fault conditions, including different fault types, locations, fault resistances, and severe CT saturation. It achieves 100% identification accuracy under all tested scenarios.  
(3) Compared to traditional FDP and other mainstream deep learning models, the proposed scheme exhibits stronger robustness and faster detection, with a total fault identification delay of 8 ms, meeting the speed requirement of relay protection systems.  
(4) The method satisfies the four fundamental criteria of relay protection--selectivity, sensitivity, speed, and reliability--and is therefore well-suited for modern power systems with high penetration of inverter-based resources.  
Conceptualization and methodology, J.L; validation, P.J. formal analysis, W.S.; investigation, X.Z.; data curation, Y.Z.; writing--original draft preparation and visualization, J.L.; supervision, P.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
Not applicable.  
Conflicts of Interest: Authors [PERSON], [PERSON] and [PERSON] were employed by the company Economic and Technical Research Institute of Gansu Electric Power Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 4 Algorithm Verification
### Real Vehicle Verification
#### 4.2.2 Mass Estimator Experiment  
To validate the stability and accuracy of the proposed mass estimator in actual vehicles, a public straight road was chosen. The vehicle traveled at a constant speed of 20 km/h, carrying a full load of 1000 kg. Signals from sensors were collected through the vehicle's CAN bus, including the motor's torque, acceleration, and speed. Test results are shown in Figure 11.  
The figure displays the testing effects of RSL and MFFRSL. It can be observed that the estimation result of RSL did not converge and exhibited jitters under the influence of sensor noise. It also presented a significant steady-state error. In contrast, the curve of MFFRSL smoothly and stably converged to 4071 kg. It demonstrated strong robustness against noise interference, capable of stable operation in complex environments with an error of 1.01%. Generally, errors below 5% are considered accurate, indicating that the designed mass estimator meets the demands of actual working conditions.
## 3 Materials and Methods
### Traffic Scenario Map Construction
#### 3.1.2 Monocular to Bird's-Eye View  
After obtaining the stabilized trajectories in the image space, these trajectories are converted to a bird's-eye view to capture the relative positions of different objects better. The geometric relation formulation described in GitNet [39] is used for object localization to achieve this conversion. Each object is assigned a reference point, namely the centroid of the lane marker for lanes and the point adjacent to the road for vehicles and other landmarks, as shown in Figure 2d. Let \(b=(x,y,1)\) represent the reference point in homogeneous coordinates in the image. These homogeneous coordinates \(b\) are then converted to coordinates in the bird's-eye view as follows:  
\[B=(Bx,By,Bz)=-\frac{hK^{-1}b}{\eta^{T}K^{-1}b} \tag{1}\]  
where \(K\) is the camera internal reference matrix, \(\eta\) is the surface normal, \(h\) is the height of the camera to the ground plane, and \(B\) is the 3D coordinates of point \(b\) in the bird's eye view.  
Figure 3: Traffic scenario map construction.  
The specific process is as follows: A perspective transformation is performed using the intrinsic matrix \(K\) and the extrinsic parameters of the camera. Assume a point in the camera's coordinate system is \((X,Y,Z)\). The ground space is represented as \((X,0,Z)\) by setting the \(y\) -coordinate of the camera's coordinate system to 0. The camera's coordinate system is simplified by removing the \(y\)-dimension, resulting in \((X,Z)\). Consequently, the coordinates in the bird's-eye view also remove the \(y\)-dimension and are represented as \((X,Z)\). The one-to-one correspondence between image coordinates and ground coordinates can be expressed by the following equation:  
\[\begin{pmatrix}u\\ v\\ 1\end{pmatrix}=K\begin{pmatrix}X\\ Y\\ Z\end{pmatrix} \tag{2}\]  
The inverse transformation from image coordinates to ground coordinates is given by the following:  
\[\begin{pmatrix}X\\ Y\\ Z\end{pmatrix}=K^{-1}\begin{pmatrix}u\\ v\\ 1\end{pmatrix} \tag{3}\]  
After obtaining the trajectories of these objects in image space, they are relocated in the bird's-eye view (top view) by projecting the image coordinates to 3D coordinates. This repositioning helps determine the spatial relationships between different entities. Each object is assigned a reference point to account for height differences. For lane markers, the reference point is at the center, while for vehicles, it is a point close to the road. Using the geometric relations described above, the transformation from perspective space to a bird's-eye view can be achieved, allowing for the recovery of approximate ground coordinates from the image coordinates and the camera height [40]. However, in a real monocular perception system, the camera height is not directly available. This issue is addressed by learning the camera height parameters through the network. By combining optical flow information with instance features and semantic segmentation, each object in the scene can be accurately tracked in a bird's-eye view, providing a more comprehensive understanding of the environment.