esa-sceva/satcom-chunk-collection huggingface dataset:
# MAC Address Anonymization for Crowd Counting
## 1 Introduction
### A Short Description of the Monitoring System Architecture  
Figure 1 depicts the experimentally validated counting scheme in [3; 4]. Sensors (three in Figure 1) monitor an area and, because their effective detection range is not known precisely (it depends on the propagation environment and decreases as the density ofpeople increases because of body-induced attenuation), they are usually installed densely enough to make detection ranges overlap. Data transfers between sensors and the central server are secured using hypertext transfer protocol secure (HTTPS) connections (with transport layer security (TLS)) so that the traffic is encrypted and the identity of the central server is verified--the latter preventing man-in-the-middle attacks. Sensors synchronize their clocks using network time protocol (NTP) servers.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
### Mean Packet Delay
The RDBA prioritizes higher-priority traffic, which can lead to increased delays for AF and BE packets under fault conditions. The simulation results highlight the importance of having a robust DBA mechanism that incorporates resilient AI-enhanced fault detection and recovery to effectively manage delay, particularly for high-priority traffic such as EF and TI packets.
## 4 Results and Discussion
### Influence of PV System Forms on Power Generation Output  
This section investigates the power generation, generation loss, and self-shading ratio of rooftop PV systems under three tilt angles (0\({}^{\circ}\), 5\({}^{\circ}\), and 10\({}^{\circ}\)) and two orientations (south-facing and north-facing). The analysis is based on simulation results from the PVsyst software, with detailed outputs provided in Appendix A.  
As shown in Figure 9a, the horizontally mounted system, which is free from inter-panel self-shading, achieves the highest power output of 120.4 MWh and is used as the reference for evaluating generation losses in other configurations. For south-facing single  
Figure 8: Airflow velocity in the vent area. The solid and dashed legends indicate the relationship between the main transformer room and wind direction. Case 3 (Net: 1.51 m/s, C: 0.30 m/s) and Case 4 (Net: 0.60 m/s, C: 0.11 m/s), both with horizontally mounted PV systems, are used as reference cases for comparison.  
slope systems with 5\({}^{\circ}\) and 10\({}^{\circ}\) tilt angles, the power outputs are 118.3 MWh and 115.7 MWh, corresponding to generation losses of 1.74% and 3.90%, respectively. These systems outperform the south-facing symmetrical pitched roof configurations, which generate power outputs of 106.1 MWh and 103.7 MWh, with respective losses of 11.88% and 13.87%.  
For north-facing single-slope systems with 5\({}^{\circ}\) and 10\({}^{\circ}\) tilt angles, the influence of tilt on self-shading becomes particularly significant. At a 10\({}^{\circ}\) tilt, severe shading leads to the lowest power output of 96.3 MWh and a loss rate of 20.02%, making it the least favorable option for PV generation.  
As shown in Figure 9b, symmetrical pitched roofs exhibit the highest self-shading ratio. However, within this roof type, the tilt angle has a limited impact on power output; the 10\({}^{\circ}\) configuration generates 2.26% less electricity than the 5\({}^{\circ}\) configuration.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
###### Abstract  
Visual speech recognition (VSR) is a challenging task that aims to interpret speech based solely on lip movements. However, although remarkable results have recently been reached in the field, this task remains an open research problem due to different challenges, such as visual ambiguities, the intra-personal variability among speakers, and the complex modeling of silence. Nonetheless, these challenges can be alleviated when the task is approached from a speaker-dependent perspective. Our work focuses on the adaptation of end-to-end VSR systems to a specific speaker. Hence, we propose two different adaptation methods based on the conventional fine-tuning technique, the so-called Adapters. We conduct a comparative study in terms of performance while considering different deployment aspects such as training time and storage cost. Results on the Spanish LIP-RTVE database show that both methods are able to obtain recognition rates comparable to the state of the art, even when only a limited amount of training data is available. Although it incurs a deterioration in performance, the Adapters-based method presents a more scalable and efficient solution, significantly reducing the training time and storage cost by up to 80%.  
Keywords:visual speech recognition; speaker adaptation; fine-tuning; Adapters; Spanish language; end-to-end architectures +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Description of the System
### Data Management  
Considering that the system stores the vehicle's position every second, from the viewpoint of data management, the system represents a case of a mobile system for massive data management. To guarantee the integrity of the data, the system uses the principles of data management in ubiquitous environments. According to [PERSON] [17], this type of system is characterized by four properties. The first one is the capacity to operate in environments in which the number of data spaces and applications that access those data spaces is dynamic. The second property is the capacity to operate with different catalogues and data schemes. The third property is necessary due to the high risk of data inconsistency that exists in ubiquitous environments, caused by the spontaneity of connections and disconnections of the applications that can access them, and consists of providing mechanisms that resolve these possible inconsistencies. The fourth property is that collaboration mechanisms must exist between the applications to facilitate access to the data.  
In our case, these challenges are resolved through a collaborative model of agents that obtain the data provided by the different devices of the previously mentioned systems [18]. The conceptual model and its implementation in the form of a database are described in Garc \(\hat{\texttt{a}}\)[19]. Primarily, all relevant events that occur in the vehicle are represented by a set of data produced by devices (sensors, driver console, card readers, _etc._). The amount of data and the structure of this set of data vary and depend on the type of event represented. Ontology is used to guarantee the interoperability of the data and is implemented through a catalog and data scheme based on the conceptual model for public transportation TRANSMODEL [20].
## 4 Case Studies
### Case 2: Double Fault on Bus 2-3 and Bus 10-11  
Two faults on buses 2-3 and buses 10-11 are assumed in case 2 to consider two branches out at the same time resulting in severe outages on most of the loads in the system. Table 6 indicates the simulation result for case 2, and loads of 842.89 kW, which are 86.0% of the total capacity of DGs are restored by the best SR plan. Three controllable loads at buses 7, 18, and 63 are disconnected to restore more important loads. It can be seen that the same amount of class 1 loads is restored by the SR algorithm compared to case 1 with the help of the self-sufficient capability of DGs in the distribution system despite more open branches.  
Figure 11: Voltage profiles of each island for case 1: (**a**) island 1; (**b**) island 2; (**c**) island 3; (**d**) island 4; (**e**) island 5.  
Figure 10: Island partitioning result for case 1.  
It can be concluded from the simulation results that the proposed intentional islanding scheme for SR can derive a near-optimal solution to restore outage loads in a distribution system by utilizing an optimization-based approach using a meta-heuristic algorithm. Furthermore, simple control and operation are achieved since the derived SR plan guarantees the anti-parallel operation of DGs. In addition, the time required for obtaining the best SR plan can be significantly reduced by applying the anti-parallel operation constraint to the searching process.
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 3 Methodology
### Model Training  
We train Autoencoder models with different neural network architectures to learn the normal state of the observed time series data. Autoencoder models are a type of artificial neural network architecture designed to learn an efficiently compressed representation (encoding) of input data by training them to reconstruct the input as closely as possible at the output layer after compression (decoding). Autoencoders consist of two primary components as seen in Figure 2:  
* Encoder: Compresses the \(R\) dimensional input time series data \(X(s)\in\mathbb{R}^{R}\), where \(s\in\mathcal{S}\subseteq\mathbf{R}\) and \(\mathcal{S}\) is a compact time interval, into a latent, lower-dimensional representation \(Z(s^{{}^{\prime}})\) in the subsequent layers, where \(Z(s^{{}^{\prime}})\in\mathbb{R}^{R^{{}^{\prime}}}\), where \(R^{{}^{\prime}}<R\), and where \(s^{{}^{\prime}}\in\mathcal{S}^{{}^{\prime}}\subseteq\mathbf{S}\). This process aims to retain the most relevant features of the data.
* Decoder: Reconstructs the original input as \(\widehat{X(s)}\in\mathbb{R}^{R}\) from the latent representation \(Z(s^{{}^{\prime}})\).  
This bottleneck architecture forces the network to learn a compressed yet informative representation of the input time series, making Autoencoders valuable for dimensionality reduction, anomaly detection, and feature learning.  
We explore three different Autoencoder architectures by replacing the red neurons in Figure 2 with traditional fully connected neurons, LSTM cells, or functional neurons, as described below.
## Appendix A  
The baseline deformation mentioned in Section 3 is defined considering that the deformed mean line can be represented by a polynomial curve. Deformation is considered only in the vertical direction (x-coordinates of the airfoil and thickness distribution do not change). The polynomial curve of m degree has the general form:  
\[\eta(\xi)=\alpha\xi^{\mathrm{m}}(\xi+\beta) \tag{1}\]  
where \(\xi\) denotes the non-dimensional distance in the x-direction, \(\eta\) denotes the deflection in the y-direction and \(\alpha\), \(\beta\) are constants to be determined. Polynomial curve should be continuous at \(\xi=0\) (beginning of deformation) and smooth at \(\xi=1\) (end of deformation), therefore the following conditions must be fulfilled:  
* At \(\xi=0\), the value and the first derivative should be zero (applies).  
The above conditions suggest that \(\beta=-3\) and that \(\alpha=\eta_{\mathrm{TE}}\) or \(\alpha=\eta_{\mathrm{LE}}\), where \(\eta_{\mathrm{TE}}\) and \(\eta_{\mathrm{LE}}\) are the (maximum) deflections at TE and LE respectively. Assuming polynomial of second degree, m = 2, the relationships providing the dimensionless \(\xi\), coordinate along with the maximum deflection and the deflection at each x-position are summarized below:  
* In the TE region, x \(>\) x\({}_{0}\), x\({}_{0}\) = 0.8: \[\begin{array}{l}\xi=(\mathrm{x}-\mathrm{x}_{0})/(\mathrm{x}_{\mathrm{TE}}- \mathrm{x}_{0})\\ \eta_{\mathrm{TE}}=(\mathrm{x}_{\mathrm{TE}}-\mathrm{x}_{0})\tan\delta\\ \eta=\eta_{\mathrm{TE}}\times\xi^{2}(3-\xi)/2\end{array}\] (2)
* In the LE region, x \(<\) x\({}_{0}\), x\({}_{0}\) = 0.25: \[\begin{array}{l}\xi=(\mathrm{x}_{0}-\mathrm{x})/(\mathrm{x}_{0}-\mathrm{x}_ {\mathrm{LE}})\\ \eta_{\mathrm{LE}}=\mathrm{x}_{0}\tan\delta\\ \eta=\eta_{\mathrm{LE}}\times\xi^{2}(3-\xi)/2\end{array}\] (3) where \(\delta\) is the deployment angle which defines the maximum deflection. The deformed mean line and the relative notations are depicted in Figure 11. Finally, the new y-coordinates, y\({}_{\mathrm{def}}\) of the deformed geometry are obtained by adding the deflection, \(\eta\), to the y-coordinates of the original geometry, y\({}_{\mathrm{org}}\): \[\mathrm{y}_{\mathrm{def}}=\mathrm{y}_{\mathrm{org}}+\eta\] (4)
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 4 Online Static Security Assessment Method
### Selection of Observations Considering Adjustable Devices  
The MSA-Lasso algorithm is adopted to prediction, and the observations are divided into two parts: one part is the states of all of the lines in the power system comprising running lines, which are represented by 1, and outage lines, which are represented by 0; the other is controlled variables among the variables in an AC power system, and the controlled variables are generated randomly in their ranges.  
In order to achieve better regulation and control of the power system, the impacts of the transformer and compensation equipment are taken into account in this paper. Owing to the controlled variables that vary in the process of load flow computation, they are selected as one part of the observations of the MSA-Lasso algorithm. The controlled variables and their ranges are listed as follows:  
\[\begin{array}{ll}P_{G;\min}\leq P_{G_{i}}\leq P_{G;\max},&i=1,\cdots,N_{G}\\ Q_{G;\min}\leq Q_{G_{i}}\leq Q_{G_{i};\max},&i=1,\cdots,N_{G}\\ U_{\min}\leq U_{i}\leq U_{\max},&i=1,\cdots,N_{ac}\\ T_{\min}\leq T_{i}\leq T_{\max},&i=1,\cdots,N_{T}\\ Q_{C;\min}\leq Q_{C_{i}}\leq Q_{C;\max},&i=1,\cdots,N_{C}\end{array} \tag{13}\]  
where \(P_{G_{i}}\) and \(Q_{G_{i}}\) are respectively the active and reactive power outputs of generator \(i\), \(U_{i}\) is the magnitude of voltage in bus \(i\), \(T_{i}\) is the tap of transformer \(i\), \(Q_{C_{i}}\) is the switching capacity of reactive power compensation capacitor \(i\), and \(N_{G}\), \(N_{ac}\), \(N_{T}\) and \(N_{C}\) are respectively the numbers of generators, buses, adjustable transformer taps, and reactive power compensation capacitor banks in the AC power system; the upper and lower limits of each variable are distinguished by the subscripts \(\min\) and \(\max\)  
Figure 2: Overall structure of the online static security assessment method.
## 4 Time Delayed Acceleration Feedback Control  
In this section, we study the nonlinear response of the beam under time-delayed acceleration feedback, i.e., \(u(t)=p\ddot{q}(t-\tau)\). Similar to the derivation process in Section 3, the frequency response equation of primary resonance response are obtained as follows:  
\[\begin{split}\frac{f^{2}}{4\omega_{0}^{2}}=\frac{1}{4}(\mu-p \omega_{0}\sin\omega_{0}\tau)^{2}a^{2}+[(\sigma+\frac{p\omega_{0}}{2}\cos \omega_{0}\tau)-\frac{3\alpha a^{2}}{8\omega_{0}}]^{2}a^{2},\\ \tan\gamma=\frac{4\omega_{0}(\mu-p\omega_{0}\sin\omega_{0}\tau)}{ 8\omega_{0}(\sigma+\frac{p\omega_{0}}{2}\cos\omega_{0}\tau)+3\alpha a^{2}}. \end{split} \tag{32}\]  
The frequency-response curves of the first mode amplitude \(a\) as a function of the detuning parameter \(\sigma\) for the primary resonances response are shown in Figure 12. Compared with time-delayed velocity feedback, the same control parameters are selected here. As can be seen from the Figure 12, when the control gain is \(p=-0.05\) and different time delay values are selected, the response amplitude of the system is suppressed to a certain extent when it is uncontrolled (\(p=0\)). When \(\tau=\pi\), the control effect is not obvious, and when \(\tau=\pi/2\), the amplitude is reduced by about 20% compared with the uncontrolled one. Meantime, Figure 13 shows the variation of the amplitude of the response with the amplitude of the excitation for several values of \(\sigma\) and \(\tau\). These curves were obtained directly from  
Figure 11: Subharmonic response for the nonlinear beam.  
Figure 10: Regions where subharmonic responses exist.  
Equation (32). We note that, depending on the value of \(\sigma\), some curves are multivalued while others are single-valued.  
Figure 14 shows that the locus of the SN bifurcation points in the \(f-\sigma\) plane for \(p=-0.05\) and different values of \(\tau\). It is worth noting that, unlike the speed feedback control, as the time delay increases, the amplitude of the minimum excitation also increases.  
Figure 14: Loci of saddle-node bifurcation points, for \(p=-0.05\) and different values of \(\tau\) with acceleration feedback.  
Figure 12: Frequency response curves showing the primary resonances response of the nonlinear beam with time delayed acceleration feedback.  
Figure 13: Amplitude of the response as a function of amplitude of the excitation for several detunings.  
Similar to the derivation of Equation (25), the the frequency response equation of \(1/3\) subharmonic resonance response are obtained as follows:  
\[\frac{1}{4}(\mu+p\sin\frac{1}{3}\omega\tau)^{2}a^{2}+\left(\sigma-\frac{p}{2 \omega}\cos\frac{1}{3}\omega\tau-\frac{729}{256\omega}\alpha F^{2}-\frac{9 \alpha}{2\omega}a^{2}\right)^{2}a^{2}-\left(\frac{81}{32}\alpha F\right)^{2}a^ {4}=0, \tag{33}\]  
Figure 15 shown that the frequency-response curves of \(1/3\) subharmonic resonance response with time delayed acceleration feedback. When the time delay increases, the response amplitude increases with the time delay.
# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 2 Design of Seismic Signal Denoising Model Based on DnCNN
### Principle of Residual Learning  
Deep learning advancements have led to increasingly deeper CNNs, enabling the extraction of more nuanced features, which is advantageous for training models. Yet, enhancing the depth of traditional CNNs can trigger gradient vanishing or explosion issues. To address these challenges, [PERSON] and his collaborators introduced ResNet, a network architecture predicated on residual learning units [15]. This architecture, as depicted in Figure 1, leverages skip connections to streamline the network's structure and employs identity mapping to sidestep layers with nonlinear transformations.  
ResNet's architecture addresses depth-related training difficulties by introducing residual blocks, which simplify the training of very deep networks. These blocks allow the network to learn residuals, or the difference between the input and output, rather than the output itself. This approach alleviates the vanishing gradient issue by providing an alternative path for gradients, ensuring that signals can pass through the network without diminishing. As a result, ResNet facilitates the training of deeper networks by maintaining the flow of gradients and preventing the typical issues associated with network depth, as shown in Figure 1.  
If the input is set to \(x\), \(H(x)\) represents the mapping of the backbone neural network, \(F(x)\) represents the mapping of multiple convolutional layers, and \(F(x)\) can be expressed by Equation (2):  
\[F(x)=H(x)-x \tag{2}\]  
The mapping of the original neural network is expressed by Formula (3):  
\[H(x)=F(x)+x \tag{3}\]  
The optimization difficulty of the function \(F(x)+x\) is less than that of \(H(x)\), so the problem of gradient vanishing or exploding can be alleviated by using the residual learning unit.
## 2 A Generic Multi-Path and Multi-Channel Proposal for HWMP
### Multipath Proposal and Implementation
#### 2.1.1 Route Management  
To allow the existence of more than one path between each pair of nodes, it is first necessary to modify the acceptance criteria for both PREQ and PREP packets. Table 1 summarizes all the functions and variables needed for the new PREQ-PREP mechanism. As shown in Algorithm 1, first of all, the most relevant fields are extracted. Then, the metric value for every path is updated. Next, the algorithm has to validate the message, that is verify if the current message has more recent information (the sequence number of the current message is greater than the previous one, \(SN_{C_{OA}}>SN_{P_{OA}}\)) or if there is a better metric when the sequence number is the same (\(SN_{C_{OA}}=SN_{P_{OA}}\)). By default, HWMP updates the route to the originator address and replaces the previous route when the sequence numbers of multiple received PREQ messages are equal, but one of them has a better metric. For instance, in the example shown in Figure 2a, the source node S generates a PREQ message to find a path to the destination node D. Two instances of this PREQ (first \(PREQ_{1}\) and then \(PREQ_{2}\)) are received by \(N_{3}\) from two different nodes (paths), and only the one with the best metric will be retransmitted to D (\(PREQ_{1}\) in the figure). In our proposal, the \(PREQ_{2}\) message is also retransmitted (Figure 2b), because we need to compare not only the sequence numbers and the metrics in order to validate a PREQ message, but also take into account the previous (retransmitter) node in the path (\(f_{r}\) in Table 1 and Algorithm 1). The objective is to maintain multiple paths to the originator address (OA) through different \(f_{r}\) nodes. After validating the message, the routing table is updated (specifically, the table entries related to the neighbor and to the source nodes). In addition, if there were queued packets for the new or updated route, they would be immediately transmitted. In the case that the PREQ destination address is the one of the receiving node, it means that a route has been found. Therefore, this node transmits directly a unicast PREP message towards the S node. Figure 1: General view of the multi-path and multi-channel modules inclusion in HWMP. In the same way, with the default algorithm, the destination node responds with a PREP message to the source node if and only if the PREQ received has a better metric than the previous ones (Figure 3a). However, in our implementation, a PREP message is sent, although the received PREQ has a worse metric. This action allows propagating not only the best path to the source, but also having several paths with different metric values (Figure 3b).
## 3 Experimental Setup
Subsequently, the dual DNN decomposes the complex QAM architecture into independent I and Q paths and performs deep equalization on each. Figure 4c illustrates the iterative pruning process for the neural network. Pruning the I and Q networks of the dual DNN architecture to varying degrees can eliminate redundancy as much as possible while compensating for the imbalance between the I/Q paths to some extent.