esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dataset  
### Data Acquisition and Analysis  
The quasi-distributed fiber optic sensing system designed in this paper for abnormal vibration detection in metro tunnels is shown in Figure 2. To detect the vibration signals generated by the construction of mechanical equipment inside or above the tunnel, multiple highly sensitive fiber optic acceleration sensors are installed at different locations in the uplink and downlink of the metro tunnel with equal intervals. The transmission cable is connected to the monitoring host through the optical fiber distribution frame (ODF) and transmits the signal to the system host, which processes and analyzes the abnormal vibration information and provides alarms, thus achieving the all-weather, all-time, real-time detection of abnormal vibration in metro tunnels.  
To explore robust external intrusion detection methods in construction environments, we conducted preliminary experiments using percussive tap (PT) to simulate external anomaly intrusion. Figure 3 illustrates the geometry of the metro tunnel, the sensor installation locations, and the percussion test setup. The figure shows two adjacent metro tunnel segments, separated by a 10 m ventilation shaft. Each  
Figure 2: Quasi-distributed fiber optic sensing-based external intrusion detection system for metro tunnels.  
diameter and 20 m long, and is located 21 m below ground level. The sensor locations are labeled A, B, and C, and the four percussion tap points are labeled PT1 to PT4. We performed several percussive taps at different locations and acquired these abnormal signals with multiple optic fiber acceleration sensors based on a Michelson interferometer.  
The sensors are installed in the following three different orientations to collect data: aligned with the axial direction of the pipe, mounted perpendicular to the pipe wall, and positioned tangentially to the pipe wall, as shown in Figure 4. By capturing vibrations in these different directions, the data collection ensures that the training dataset contains multiple vibration modes and directions, which is critical for accurate external intrusion detection in real-world applications. Figure 5 gives an example of a collected intrusion signal; it can be observed that the signal collected at point A can clearly distinguish each percussive tap, whereas the signals at points B and C are almost indistinguishable, probably due to signal attenuation [35] over a long distance. In addition, as the experiment is conducted in a construction environment, there were various sources of noise in the surrounding area, such as the operation of tunnel-boring machines and fans, manual work, metro running in the distance, etc. Therefore, there is an urgent need to develop external intrusion detection models that are robust in complex noise environments.  
Figure 4: Three ways to mount the optical fiber acceleration sensor.  
Figure 3: Geometry of metro tunnels and sensor mounting points and percussion tap settings.
## 6 Theoretical and Security Analysis
### Theoretical Analysis  
#### 6.1.1 Decentralization of the NexoNet  
Throughout the operation of the NexoNet, all users joining the system can participate in its various functions through sending transactions. Unlike a traditional centralized OSM, users can engage not only in content publication, consumption, and commenting processes but also accumulate PV to partake in the consensus mechanism, including voting for maintainers and applying to become maintainers themselves. The operational parameters of the system and settings are subject to change through proposals by maintainers. Users have complete control over their personal information, conducting various actions in the system through private key signatures, without interference from any individual or organization in their network transactions, thereby establishing a truly user-autonomous decentralized social media.
Furthermore, works in [37; 38; 39; 40] resolve the problem in a double-scale way, significantly reducing the RIS reconfiguration rate. Fewer works consider multi-user tracking problems. Recently, [PERSON] et al. [41] studied a multi-user localization problem and introduced two semi-passive RISs to estimate the AoA on the RIS; the consequent hardware overhead would be inconsistent with the low-cost intention of introducing RIS. [PERSON] et al.proposed a multi-user tracking algorithm based on Bayesian theory, carrying out positioning by estimating AoA and optimizing passive beamforming of RIS [42]. However, the model adopted is only valid for the far-field scenario, and a two-step approach starting from intermediate parameters is suboptimal to direct means, as it demands an appropriate characterization of the measurement statistics. Methods and types of the most relevant RIS-assisted positioning literature are listed in Table 1. With carrier frequencies moving toward mmWaves and higher sub-THz spectrum, large antenna arrays are widely used in tracking scenarios, where the targets often move in near-field regions. However, the signal model adopted in existing works is only valid for the far-field scenario. Among existing works involving RIS-assisted localization, most consider snapshot localization without exploiting the temporal correlations of user locations. In dynamic scenarios, the localization performance of these methods can be severely limited. Further, few works investigate the multi-user tracking problems in RIS-assisted systems. Enlightened by the correlated works and considering their limitations, we study an uplink multi-user tracking problem aided by multiple passive RISs under the near-field circumstance, where the BS and users are equipped with multiple antennas and a single antenna, respectively. As indicated in Figure 1, the LoS paths between the BS and users are presumed to be blocked during the tracking process. The core insights offered by this research include:  
* Considering the temporal correlations between each user's coordinates changes, a multi-user tracking system is designed based on the extended Kalman filter (EKF), where the non-linear relationships between the signals received at the BS antennas and the coordinates of the moving users are directly utilized. * Based on the near-field model accounting for the incident spherical wavefront, its geometric properties are further exploited. Additional measurement dimensions can be provided by the RISs to suffice high-precision multi-user tracking with a single BS, thus avoiding the reliance on the LoS paths. * The Bayesian Cramer-Rao bound (BCRB) is derived for the multi-user tracking system in a pattern consistent with the EKF process, providing a theoretical lower bound of mean square error (MSE) for the tracking problem. * An optimizing scheme of passive phase shift design at the RISs is devised. The issue is formatted as a problem of minimizing the derived BCRB of tracking errors and solved by leveraging the Gradient Descent (GD) method. Numerical results indicate that the accuracy of the proposed tracking scheme can approach the derived BCRB.
## 2 System and Channel Models
### Atmospheric Attenuation  
Atmospheric attenuation refers to the absorption and scattering of light waves by gas molecules and aerosol particles in the atmosphere, which causes the attenuation of light energy. With the help of the Beers-Lambert law [26], we can express \(I_{al}\) as  
\[I_{al}=\exp(-L\psi), \tag{3}\]  
where \(L\) represents the communication distance, and \(\psi\) is an attenuation coefficient that varies with visibility.
## 4 Analysis and Comparison
### Measure Metrics
#### 4.1.1 Threshold Setting  
When the threshold is too high, most decision variables \(m_{n}\) are lower than it, so  
Double sliding window. For this algorithm, when the lengths of two windows are the same, the peak point of \(m_{n}\) can be used as a reference for threshold setting. At \(m_{peak}\), the value of \(a_{peak}\) contains the sum of signal S and noise N, and the \(b_{peak}\) only contains noise N, thus \(m_{peak}\) can be estimated by the received SNR as Equation (11). When knowing the SNR in the environment, the threshold setting has a reference range, which is smaller than SNR+1.  
\[m_{peak}=\frac{a_{peak}}{b_{peak}}=\frac{S+N}{N}=SNR+1 \tag{11}\]  
Auto-correlation. Similarly, the results of the auto-correlation algorithm can also be denoted by SNR. When the noise signal is additive white Gaussian noise (AWGN), the auto-correlation result of Gaussian noise signal is 0. Thus, there is only the signal power in the numerator after auto-correlation calculation, and the relationship between \(m_{peak}\) and SNR is shown as Equation (12).  
\[m_{peak}=E[(\frac{c_{peak}}{p_{peak}})^{2}]=(\frac{S}{S+N})^{2}=(\frac{SNR}{1+ SNR})^{2} \tag{12}\]  
Then, a moderate value in this range is chosen or a fixed scale factor is set, such as 0.75, multiplied by \(m_{peak}\) as the threshold. The thresholds for experiments in this paper were chosen properly to balance \(P_{f}\) and \(P_{m}\), and each algorithm achieves a high \(P_{d}\).
# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 3 Enhanced Labeling Workflow
### Clustering Algorithm  
The next step of this proposed workflow consists of applying the clustering algorithm to the principal components output in order to get the cluster that represents the geological features. We choose K-means clustering as the clustering algorithm. K-means clustering is an unsupervised method designed to split unlabeled data distribution into a fixed number K of clusters which share common characteristics for each group [37].  
Basically, the K-means algorithm's objective is to reduce the error between each data point and \(K\) centroid, using the following equation:  
\[\mathrm{J}=\sum_{j=1}^{k}\sum_{i=1}^{n}\parallel x_{i}{}^{(j)}-c_{j}\parallel^ {2} \tag{1}\]  
where the \(x_{i}{}^{(j)}\) is the distance indicator of the n dataset points and \(c_{j}\) is the representation of K centroids. Usually, the distance is measured with the Euclidean distance [38]. The K-means algorithm composed with the following four steps [39]:  
1. Place K centroid in randomly in space, which represent the item coordinates that are being clustered;
2. Group each item in the group that have the same similarity to it;
3. Recompute the K centroid and change their position, respectively;
4. Iterate step (2) and (3) until the position of the K centroid does not change with respect to the distances between all the elements of their group.  
After receiving the optimum result of K-means clustering, we assigned those clustered result as the label for our geological features that we wanted to detect. This result will be the ground truth or the true output that will be used on the training model.
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 5 Conclusions  
In this paper, a comprehensive analysis of AoI is carried out for the real-time \"multi-scale explanation\" information fusion in the satellite-UAV synergy remote sensing system. The closed-form expression of mean AoI for the fused status information in the system is derived. Based on the closed-form expression, AoI performance, energy advantage, and spectrum advantage of FFR strategy over the FDMA strategy are analyzed. The analysis shows that the FFR strategy yields improved AoI-related performance, especially under spectrum-constrained conditions and within some commonly used transmit power ranges. Moreover, the analysis results emphasize the requirement for adaptive power control strate  
Figure 9: AoI-related spectrum advantage of FFR strategy over FDMA strategy.  
gies, spectrum management strategies, and interference management strategies in SAGIN. The proposed analytical methodology holds significant value for the examination and optimization of AoI for the real-time remote sensing system in SAGIN. It provides a robust framework for assessing the impact of various queuing disciplines and communication strategies on the freshness of information, thereby facilitating the development of more efficient and effective remote sensing systems.  
Given the promising findings of this study, it is essential to address the practical limitations and challenges that arise in real-world applications. The future work should focus on addressing practical challenges, such as the limitations of UAVs in terms of battery management and susceptibility to weather conditions, as well as the constraints of LEO satellite systems, including limited communication time. To enhance the applicability of the proposed methodology in real-time systems, the future works will explore the development of adaptive energy and spectrum management strategies, robust UAV path planning algorithms that account for environmental variability, and optimized rate transition and sampling time strategies. These efforts will ensure the proposed methods can be effectively applied in dynamic and unpredictable real-world environments.  
Conceptualization, L.W., X.Z., K.Q. and Z.W.; methodology, L.W.; software, L.W.; validation, L.W., J.Z. and D.S.; formal analysis, L.W.; investigation, L.W.; resources, K.Q.; data curation, L.W., J.Z. and D.S.; writing--original draft preparation, L.W., X.Z. and Z.W.; writing--review and editing, L.W., Z.W. and X.Z.; visualization, L.W.; supervision, K.Q.; funding acquisition, K.Q. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Sichuan Natural Science Foundation (General Program) under Grant 2024 NSFSC0176, in part by the National Natural Science Foundation of China under Grant 62371014, and in part by the Beijing Natural Science Foundation under Grant 4222002.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Eye State Identification Based on Discrete Wavelet Transforms
## 3 Proposed System
### Feature Extraction and Classification  
Once the brain signals of the user have been captured and digitized, they are analyzed and decomposed with the DWT for extracting the features. Thanks to the dual core nature of the ESP32, complex processing tasks such as DWT and its subsequent classification can be performed while the signal is sampled.  
As previously described, the coefficients extracted by the DWT at different levels can reveal the inner characteristics of the signal. Thus, both the selection of a proper mother wavelet and the number of decomposition levels are of primary importance for the analysis of the brain signals [37]. The number of decomposition levels is based on the dominant frequency component of the signal. Therefore, the levels are chosen such that those parts of the signal that correlate well with the frequencies needed for signal classification are retained in the wavelet coefficients [37]. In our system, in order to decompose the signal according to the main EEG rhythms, the number of levels of decomposition is 4. Hence, the signal is decomposed into four detail levels, D1-D4, and one final approximation level, A4. Table 1 shows the wavelet coefficients and their EEG rhythm equivalence.  
According to these decomposition levels and their equivalent EEG rhythms, those detail and approximation coefficients are studied and employed for extracting the features and estimating the ocular state of the user. To this end, we propose two schemes based on different feature sets defined from data obtained in alpha and beta rhythms. It is important to note that alpha rhythms correspond to the detail coefficients of level 4 (D4), while beta rhythms correspond to the detail coefficients in level 3 (D3).  
Let \(PD_{3}\) be the average power of wavelet coefficients at D3, \(PD_{4}\) be the average power of wavelet coefficients at D4 and \(R=PD_{3}/PD_{4}\) as the ratio between these two average powers. Thus, the first scheme, termed as Scheme 1, will employ the ratio \(R=PD_{3}/PD_{4}\) as the only feature for eye state identification. Conversely, in the second scheme, termed as Scheme 2, two different features are extracted from the wavelet coefficients: the standard deviation of the coefficients of level D4 (\(SD_{4}\)) and \(R=PD_{3}/PD_{4}\). In both cases, the LDA classification algorithm is applied for the eye state identification.
## 3 Results
### Relationship between Site Index and FR  
Using the sigmoidal relationship to predict volume from SI, values for volume at SI values of 10.7 m and 30.5 m were calculated as 26.7 and 281.8 m\({}^{3}\) ha\({}^{-1}\) at age 11. Based on the assumption that these represent the minimum and the maximum SI for loblolly pine in the southeastern United States, the value of FR was set to 0 at 26.7 m\({}^{3}\) ha\({}^{-1}\) and the FR value was set to 1 at 281.8 m\({}^{3}\) ha\({}^{-1}\). Based on the observation that the relationship between plant productivity and soil nutrient concentration between the nutrient deficient stage and nutrient sufficient stage is linear [18; 38; 39; 40], the difference between 281.8 and 26.7 was distributed evenly between the FR values of 0 and 1 (Table 5) and the relationship between FR and SI was determined using a sigmoidal model (Figure 3):  
\[\mathrm{FR}=\frac{1.190}{1+e^{-(-5.899+0.245\mathrm{~{}SI})}} \tag{3}\]  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model Name** & **Model Form** & **RMSE** & **MAE** & **PRESS** \\ \hline Exponential & Volume = e\({}^{(2.855+0.094\mathrm{~{}SI})}\) & 20.60 & 15.51 & 13157.4 \\ Sigmoid & Volume = \(\frac{379.57}{1+\mathrm{e}^{(4.556-0.185\mathrm{~{}SI})}}\) & 19.70 & 16.36 & 12029.6 \\ Linear & Volume = -207.693 + 16.115 SI & 18.51 & 15.33 & 10622.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model fit statistics for exponential, sigmoid, and linear relationships between stand volume outside bark (m\({}^{3}\) ha\({}^{-1}\)) and site index (m) at base age 25 based on leave-one-out cross validation, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Predicted Residual Sums of Squares (PRESS).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Volume (m\({}^{3}\) ha\({}^{-1}\))** & **Site Index (m)** & **FR** \\ \hline
26.7 & 10.7 & 0 \\
52.0 & 14.7 & 0.1 \\
77.6 & 17.3 & 0.2 \\
103.0 & 19.4 & 0.3 \\
128.5 & 21.1 & 0.4 \\
154.1 & 22.7 & 0.5 \\
179.7 & 24.2 & 0.6 \\
205.6 & 25.6 & 0.7 \\
230.7 & 27.1 & 0.8 \\
256.2 & 28.7 & 0.9 \\
281.8 & 30.5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Value of volume (m\({}^{3}\) ha\({}^{-1}\)) that corresponded to the value of FR based on the assumed linear incremental relationship and the respective value of site index (m) based on the sigmoidal relationship between stand volume and site index.  
All parameters used to derive the relationship between site index and FR were highly significant (\(p<~{}0.0001\)).
## 1 Introduction
Vehicle to Grid (V2G) is considered an innovative and disruptive solution that could strengthen the development and wide spreading of the energy management in micro-grids. The possibility for aggregated car owners to provide balance services to the grid will give a benefit (and revenue stream) that allows the gaps to be filled with conventional transport. The grid organization typically follows a scheme like in Figure 1, where there is no direct energy flow or communication among the RES, e.g., PV panels with relevant solar inverter mounted on the roof of the building, and the energy storage system installed on-board the vehicle. All communications and energy flows are typically implemented between the RES sub-system and the main grid (managed by a national electricity provider, e.g., ENEL in Italy) and between the main grid and the vehicle to be recharged. The energy flows in Figure 1 are mainly unidirectional: From PV source towards the grid node, due to a solar inverter (DC/AC energy conversion); and from the main grid to the energy storage (battery pack) on-board the vehicle for EV recharging, due to an AC/DC power conversion. This classic approach has some limits that have not yet been overcome. RESs are intermittent in nature. For example, the produced energy is limited at night-time, in case of cloudy days or during less sunny seasons, when using PV sources. Moreover, when a recharged car is parked (e.g., during office time), the energy stored on-board cannot be used as a source to fulfill the energy requirements of other loads. The lack of flexibility of the classic energy grid in Figure 1 can be overcome by a new energy grid concept, that is described in this survey paper in Section 4. By mixing DC and AC sources, the hybrid micro-grid in Section 4 proposes an alternative architecture where the use of bi-directional EV chargers may allow a micro-grid to be created by directly interconnecting with bi-directional energy flows all the nodes: The main grid node, RES node, energy storage nodes, both on-board the vehicle, and inside the micro-grid structure. This model is further sustained by the new market products, since new solar inverters are appearing [2], e.g., from ABB, where a local energy storage for the RES is foreseen, and hence, the power flow from/towards the RES becomes bi-directional. Hereafter, Section 2 reviews trends in RES, power converters and controllers. Section 3 analyzes trends in battery energy storage and the relevant issues in battery management and charging. Section 4 presents an alternative micro-grid architecture. Conclusions are finally drawn in Section 5.
## 5 Experimental Setup
### Performance Metrics
#### 5.1.2 Multi-Objective Metrics  
To evaluate the quality of CCUS solutions, three metrics considering the multi-objective aspect have been used: the Hyper-Volume (HV), the Inverted Generational Distance (IGD) and the spread (\(\Delta\)). For a set of solutions, let \(P=\left\{p_{i}\right\}_{i}\) be the subset of non-dominated ones, and \(P^{*}=\left\{p_{j}^{*}\right\}_{i}\) be the optimal Pareto front. These two sets are ordered according to one of their objective values. The Euclidean distance in the objective space between two points from both fronts is given by \(d:\{1,\cdots,|P^{*}|\}\times\{1,\cdots,|P|\}\rightarrow\mathbb{R}_{+}\).  
\[d(i,j)=\left\|p_{i}^{*}-p_{j}\right\|_{2}\]  
Hyper-Volume (HV)  
HV represents the volume in the objective space covered by the points in \(P\), relative to a reference point \(w\).  
\[HV=\texttt{volume}\left(\bigcup_{i=1}^{|P|}v_{i}\right)\]  
with \(v_{i}\) the hypercube in the objective space made by \(p_{i}\) and \(w\).  
Figure 7: Usage of both swarm metrics and MO metrics to assess the performance of a heuristic.  
Inverted Generational Distance (IGD)  
IGD measures the proximity of the front \(P\) to the optimal front \(P^{*}\).  
\[IGD=\frac{1}{\left|P^{*}\right|}\sqrt{\sum\limits_{i=1}^{\left|P^{*}\right|} \left(\min\limits_{1\leq j\leq\left|P\right|}d(i,j)\right)^{2}}\]  
Spread (\(\Delta\))  
\(\Delta\) indicates how well the non-dominated solution are spread on the front \(P\). It also takes into account the width of \(P\) compared to the optimal front \(P^{*}\).  
\[\Delta=\frac{d_{f}+d_{l}+\sum\limits_{i=1}^{\left|P\right|-1}\left|d_{i}-d \right|}{d_{f}+d_{l}+(\left|P\right|-1)\bar{d}}\]  
with \(d_{f}=d(1,1)\) and \(d_{l}=d(\left|P^{*}\right|,\left|P\right|)\) the distances in the objective space between the first edges of \(P\) and \(P^{*}\) and their last edge. Moreover, \(d_{i}=\left\|p_{i}-p_{i+1}\right\|_{2}\) is the distance between two adjacent points in \(P\), with \(\bar{d}=\frac{1}{\left|P\right|}\sum\limits_{i=1}^{\left|P\right|-1}d_{i}\) the average distance.