esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parametric Methods
### Multivariate Distribution Functions  
A scheme that detects sinkhole and Denial of Service (DoS) attacks is proposed by the authors in [34]. This scheme detects global long term anomalies that cannot be detected by conventional point-based techniques. As with most IWSN applications, a decentralised approach is followed in order to decrease the communication overhead. The scheme uses a Kullback-Leibler divergence-based algorithm that employs segment-based recursive kernel density estimation. It was found to have generally favourable results although dimensionality is still a major issue.  
The problem of data loss/modification was addressed by the authors in [35] who developed a scheme that can both detect and recover lost data. Both detection and recovery use a partial least squares and principal component-based multivariate technique but the latter additionally utilises trimmed scores regression. Both models had favourable results with the latter performing best when there is a strong correlation between the data of the compromised node and its neighbours. In both cases, the selected routing algorithm affected both the anomaly detection and the effect the compromised data has on the system.  
The aforementioned problem can be alleviated by using a dynamic multi-hop routing strategy [36]. In static routing, data always travels via the same route to the sink while in dynamic routing the next hop is chosen using a random selection process. In this way, the effect of the compromised data is spread across the neighbourhood making it easier to manage. While this technique improves the data recovery, a major drawback is that it introduced a traffic imbalance which can affect the energy consumption of the nodes. In addition to this, it becomes more difficult to find the source of the attack because its effects have been spread over a larger area.
## 3 Results
### Corrosion Rate Measurement
#### 3.5.1 Potentiodynamic Polarization  
Figure 5 shows typical potentiodynamic polarization (PDP) curves for samples of the technically pure iron and Fe-Mn-5 Si alloy. The curves presented in Figure 5a show the result of single PDP measurement. The curve was chosen so that the corresponding corrosion potential was closest to the mean \(E_{corr}\) for each state. The open circuit potential (\(E_{oc}\)) and corrosion potential (\(E_{corr}\)) are almost the same. For the ECAP-treated state, the mean \(E_{oc}\) is \(-\)802.72 \(\pm\) 6.9 mV and \(E_{corr}\) is \(-\)801.00 \(\pm\) 6 mV (Table 4). The results of the potentiodynamic analysis of the Fe-Mn-5 Si alloy in both the annealed state and after ECAP, as well as of the commercially pure iron, indicate a significantly lower corrosion resistance of the studied alloys compared to pure iron (Figure 5a,b). A significant shift in the corrosion potential (\(E_{corr}\)) occurs from \(-\)539 \(\pm\) 4 mV for Fe to \(-\)778 \(\pm\) 2 mV and \(-\)801 \(\pm\) 6 mV for the alloy under study in the annealed state and after ECAP, respectively (Figure 5b).  
The trend towards a decrease in the corrosion potential in the alloy under study can be explained by the different (by a factor of 100) dislocation density after annealing and after ECAP. However, despite the reduction in the corrosion potential, which indicates  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Processing** & **\(E_{corr}\)1, mV** & **\(i_{corr}\)2, uA/cm2** & **\(C_{R}\)3, mm/year** \\ \hline Fe (for comparison) & Annealing & \(-\)539 \(\pm\) 4 & 22.9 \(\pm\) 2.4 & 0.24 \(\pm\) 0.03 \\ \hline \multirow{2}{*}{Fe-Mn-5 Si} & Annealing at 1100 \({}^{\circ}\)C (12 h) & \(-\)778 \(\pm\) 2 & 21.5 \(\pm\) 3.7 & 0.21 \(\pm\) 0.05 \\ \cline{2-5}  & ECAP at 400 \({}^{\circ}\)C & \(-\)801 \(\pm\) 6 & 21.4 \(\pm\) 1.7 & 0.25 \(\pm\) 0.02 \\ \hline \end{tabular}  
* corrosion potential; 2 corrosion current density; 3 corrosion rates.  
\end{table}
Table 4: Potentiodynamic polarization parameters of the Fe-Mn-5 Si alloy in physiological saline solution.  
Figure 5: PDP curves in physiological saline solution at scan rate of 1 mV/s (**a**) and E vs. C\({}_{R}\) (**b**) of the Fe–Mn–5 Si alloy after annealing and ECAP in physiological solution with the pH = 7.4 at room temperature (commercially pure iron for comparison).  
a lower corrosion resistance of the alloy after ECAP, the corrosion rate measured by the potentiodynamic method remains virtually unchanged even compared to commercially pure iron. As mentioned above, the twin boundaries in the austenitic Fe-based alloys are the low-energy special boundary that improves corrosion resistance. The presence of twin boundaries in the structure of the Fe-Mn-5 Si alloy both after annealing and after ECAP eliminates not only the imperfection of the deformed structure, but even the presence of a high manganese content in the alloy compared to commercially pure iron.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
###### Abstract  
Model predictive control (MPC) is commonly used to compensate for modeling inaccuracies and measurement noise in voltage control problems. The length of the prediction horizon and control horizon of a MPC-based method has significant impact on the control performances. In existing relevant works, those horizon parameters are determined off-line based on experience or enumeration, and keeps constant during the entire receding-horizon optimization process. This paper presents a system voltage correction scheme based on adaptive horizon model predictive control (AH-MPC). The reactive power compensation and voltage regulation devices are coordinated to maintain the system voltages within a desired range. An evaluation index is proposed to determine the horizon parameters, which reflects the maximum voltage regulation ability with the current parameter configuration. Within each sampling interval, the horizon parameters are updated according to the evaluation index and real-time measurements periodically, which comprehensively considers the system uncertainties and voltage recovery speed, and the computational effort is remarkably reduced. The validation and effectiveness of the proposed method is verified by the simulation analysis on the test system.  
voltage control; adaptive horizon; model predictive control (MPC); power system; real-time control +
Footnote †: journal: Applied Physics Letters
# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
## 2 Related Works
### Equipment Condition Detection  
Numerous scholars have focused on detecting the health conditions of industrial facilities. [PERSON] et al. [3] proposed a progressive fault diagnosis method for rotors using sparse autoencoder reconstruction errors for initial detection and support vector machines for fault confirmation. Tested on rotor test bench data, this method analyzed normal, contact friction, unbalance, and misalignment conditions. [PERSON] et al. [4] developed the GSC-multichannel deep ResNet network, merging 2D and 1D signal features and introducing involution for enhanced feature extraction. The convolutional block attention module optimizes channel fusion by assigning weights to each feature. [PERSON] et al. [5] used convolutional neural networks (CNNs) to diagnose structural damage in rotating systems. They created a six-layer 1D-CNN model for each sensor based on measured impedance signatures from PZT sensors. [PERSON] et al. [6] presented a sensorless method for detecting misalignment by analyzing motor current. They transformed motor current time series data into images and used a ResNet-based CNN, applying hyper-parameter optimization and transfer learning. [PERSON] et al. [7] proposed a fault diagnosis method for wind turbines (WTs) using long short-term memory (LSTM) and feature optimization strategies to reduce maintenance costs. Redundant features are removed using Pearson correlation, and wavelet transform optimizes the fault data. The LSTM model is trained with selected features and validated with real data, effectively improving wind farm reliability and economic benefits. [PERSON] et al. [8] proposed an information fusion method using One vs. Rest (OvR) evidence classifiers. This method reduces imprecision by integrating evidence from all FFSs through a two-level fusion framework. Experiments on rotating machinery show that the proposed method outperforms traditional fault diagnosis models like SVM, BPNN, KNN, FCNN, RF, and Bayes in classification accuracy. [PERSON] et al. [13] proposed a rolling bearing fault diagnosis technique based on recurrence quantification analysis (RQA) and a Bayesian-optimized support vector machine (SVM). The method extracts nonlinear features using RQA, constructs a feature matrix, and optimizes SVM parameters with Bayesian optimization.  
While methods such as self-encoders, convolutional neural networks, and clustering algorithms effectively assess the health status of devices, they may have limitations in feature extraction, often leading to incomplete outcomes. These methods typically overlook the intrinsic relationships within the data, resulting in less comprehensive insights.
## 2 Descriptive Analysis of the Maritime Communication Network on Medium Frequency
The connection between AVISOMEF and the Communication Network is established by the different Listening/Calling Channels (channel L/C). The new receivers will not only allow for the new communication process that is being presented (implemented in Figure 3 within the upper grid), by means of listening only to the channels of the Maritime Communication Network on Medium Frequency, making the information flow in the appropriate direction, but they will also be in charge of monitoring the listening/calling channel (channel L/C), responsible for initiating the communication process and creating the link between AVISOMEF and the Maritime Communication Network on Medium Frequency. The communication that is established will be a duplex communication. Both receivers (E and F) will listen to the transmitting channels corresponding to the cells before and after the cell in which the ship is located within the Maritime Communication Network on Medium Frequency, helping communication flow in one direction or the other. Multiplexing technologies can be used to reduce the cost of the reception process of the new network. Therefore, the total number of receivers involved can be reduced. However, this hardware architecture model has been chosen to avoid diminishing the commitment of the receivers involved in AVISOMEF, as the process of remote identification of the different vessels in the area where the Communications Network on Medium Frequency is intended to be established is considered essential.
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 5 Materials and Methods
### Temporal Convolutional Network  
Previous works have used Temporal Convolutional Networks (TCNs) extensively in SOTA models for temporal feature extraction [37; 38; 39]. According to [29], this module has two main limitations. First, it has problems in detecting temporal dependencies. Second, the applied convolutions can create discontinuous temporal features with respect to the smooth motion representation in the skeleton data. Therefore, taking inspiration from [29], we decided to use a system that combines 3D spatial feature extraction with a 1D temporal convolution module for feature representation derived from skeleton-based joint data. Its structure is depicted in Figure 5.  
A TCN is introduced as a novel 1D temporal convolution module which smoothly models short-term dependencies across different scales. The TCN consists of four branches \(MaxPool\) and a \(1\times 1\) convolution branch. The first two such branches halve the number of channels through \(1\times 1\) convolutions to reduce the computational load. Depth-wise convolutions are then applied to extract multi-scale temporal features. In the third layer, the number of channels is halved again and a \(MaxPool\) layer is added following the \(1\times 1\) convolution in the third column. Finally, all four branches are concatenated to match the original input-output specifications. Crucially, the applied TCN does not increase the overall complexity.  
Figure 5: The overall structure of the temporal convolution network.
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 3 Methodology and Problem Definition
### Genetic Algorithm
#### 3.3.1 Population Initialization  
The initial population of chromosomes is generated randomly, with each chromosome representing a possible route through selected charging stations along the route. The number of charging stations in each chromosome can vary, ensuring diversity in the initial population. This diversity is crucial for the GA to explore various possible solutions within the search space [38].  
In the initialize-population function, we generate an initial set of possible routes, including the shortest and longest routes. In order to prevent premature convergence on suboptimal solutions, genetic diversity is essential. The initial population includes the following:  
* Shortest path: A chromosome that represents the shortest valid path with at least one intermediate point;
* Longest path: A chromosome that represents the longest path through all charging stations;
* Random paths: Each of the other chromosomes represents a random path with a different number of stops between the origin and destination.  
By combining various initial solutions, the genetic algorithm is able to explore the solution space effectively and avoid premature convergence. Algorithm 2 describes the pseudocode for generating the longest and shortest routes inserted into the population.  
```
1 Input: Initial control points
2 Output: Feasible path from
3 num_points = len(points_with_ids)
4 Add a chromosome with a single randomly chosen charging station (shortest valid path)
5 ifnum_points > 0
6 shortest_path_chromosome = [random.choice(range(num_points))]
7 population.append(shortest_path_chromosome)
8 Add a chromosome with a path covering all charging stations (longest path)
9 longest_path_chromosome = list(range(num_points))
10 population.append(longest_path_chromosome)
11 end
```  
**Algorithm 2**Adding the longest and the shortest route to the population.  
Figure 3: Chromosome representation.
## 3 GNSS-Free IoT Localization Methods
### Signal-Based Localization Methods
* Time Difference of Arrival (TDoA): unlike ToA, TDoA needs three or more GWs with only precise time synchronization between GWs for end-node localization. When an end node in location (\(x\),\(y\)) transmits a message, the message is received by gateways with different timestamps \((t_{1},t_{2},\ldots,t_{n})\). Then, differences in signals propagation times \((t_{i,j}=t_{i}-t_{j})\) is measured at the receiver for physical distance computation by Equation (2). * Angle of Arrival (AoA): In the AoA method, an array of antennas is implemented at the receiver side to estimate the angle between transceiver and receiver. For doing this, the time difference of transmitted signal at individual elements of the antennas array is computed. The AoA method is not dependent on signal strength for location estimation. Therefore, channel fading and environment features have minimal effect on location estimation accuracy. In addition, AoA can decompose the received signals into direct and reflected paths, thus, the multipath effect can be minimized [46]. AoA has a high deployment cost, and it needs careful calibration. A low error in angle computation can lead to huge error in location estimation. \[t_{i,j}.c=\sqrt{(x_{i}-x)^{2}+(y_{i}-y)^{2}+(z_{i}-z)^{2}}-\sqrt{(x_{j}-x)^{2} +(y_{j}-y)^{2}+(z_{j}-z)^{2}}\] (2) As TDoA work with propagation time, it is dependent on CR, SF, and BW of the system same as ToA.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 3 Proposed Architecture
### Entities of Proposed Scheme  
Before introducing design architecture, we give definitions of related terms used in this paper.  
* **Vehicle**: The vehicle is the primary entity of VECNets that contains OBUs, which are used to communicate with RSU or other vehicles. An OBU is equipped with a memory unit, a communication module, and a sensor device to make simple computation and communication, as well as collect detailed information. Each vehicle has public and private keys to share their data using V2V and V2I communication. Here, those keys are used for encrypting the information message that is generated by vehicles in VECNets.
* **Information message**: There are two categories of information message that are distinguished based on the message generated by the sending and receiving vehicle. The sending vehicle generates and broadcasts message information to the neighboring vehicle and nearest RSUs. This message contains report events, such as accident information, safety warnings, traffic jams, weather conditions, and snow reports on the road. Meanwhile, the receiving vehicle (neighboring vehicle) generates and uploads the information message to the RSUs that contain the trust value rating of the message information received from the sending vehicle.  
Figure 1: Overview of design architecture.  
* **Road side unit (RSU)**: RSUs represent the traffic handler that provides wireless communication from roadside infrastructure to the vehicles. RSUs are distributed along the road and are designed to manage a group of OBUs in a specific distance range. RSUs have higher computing power and storage compared to the OBUs. Therefore, RSUs have preselected nodes in consortium blockchain that participate as miners in the consensus mechanism process.
* **Local storage and record pool**: In this paper, smart contracts are exploited to achieve efficiency, reliability, and secure data storage and sharing in VECNets. Here, two smart contracts, i.e., local storage smart contracts (LSSC) and record pool smart contracts (RPSC), are deployed on distributed edge nodes (RSUs) to collect the trust value rating and conduct the consensus mechanism.
* **Trusted authority**: Trusted authority (TA) is responsible for managing the key, generating public parameters, and managing the users' identities.
# Analysis of Road Sign-Related Factors Affecting Driving Safety with Respect to City Size
## 4 Results
### Analysis of Road Sign-Related Factors Affecting Driving Safety
#### 4.4.2 Analysis with Respect to City Size
In comparison, in other cities and counties, the influence of road signs on the driving characteristics of those with driving experience of 21 years or more was 53.1% lower than the reference figure (significant at the 1% level). These observations are in contrast with the insignificant results found in Seoul and Gyeonggi Province, which are part of the SCA. In the context of road sign satisfaction, daytime recognition was observed to be significant in all city size categories (with influences varying between 37.8 and 73.5%). In particular, the greatest influence of daytime recognition was observed in Seoul, with an odds ratio of 1.735 (significant at the 0.1% level). For reference, in Seoul, all variables except daytime recognition were insignificant. Nighttime recognition was significant only in the case of other cities and counties, with an odds ratio of 0.823 (significant at the 5% level)--its increase was observed to decrease driving safety by 17.7%. Satisfaction with guide information sufficiency was observed to be significant in cities of all sizes except Seoul--the largest influence was exhibited in the Gyeonggi Province, with an odds ratio of 1.768 (significant at the 0.1% level). The route accuracy variable was significant in the case of metropolitan cities (odds ratio = 1.364, significant at the 1% level) and other cities and counties (odds ratio = 1.747, significant at the 0.1% level). Thus, route accuracy safety outside the SCA influenced driving safety, and the influence was greater in the provinces at 74.7%. The variables of sign size adequacy, location/frequency adequacy, and esthetic/safety management were found to be insignificant. The road sign improvement requirements were also classified and analyzed in terms of present and future items and facility maintenance. In terms of the improvement of current road sign information, customized guidance based on location was observed to be significant only in metropolitan cities (odds ratio = 1.302, significant at the 5% level). Customized guidance for various forms of mobility was significant only in Seoul (odds ratio = 1.363, significant at the 1% level). In the context of future road sign development requirements, real-time guidance according to the situation was observed to be significant in metropolitan cities (odds ratio = 1.335, significant at the 1% level) and the Gyeonggi Province (odds ratio = 1.350, significant at the 1% level). The variable of preparation for future roads was not significant. In the context of facility maintenance measures, the variable of integrated installation of road facilities was significant only in the provinces (other cities and counties) (odds ratio = 1.340, significant at the 1% level). Facility safety management was significant in Seoul (odds ratio = 1.399, significant at the 5% level) and the Gyeonggi Province (odds ratio = 1.536, significant at the 0.1% level).
# Development of an Automatic Low-Cost Air Quality Control System: A Radon Application
## 2 Materials and Methods
### The System  
The system is composed of two main parts: the devices and the server (backend). Figure 1 shows the architecture of the system.  
Figure 1: The architecture of the air quality control system.  
There are two types of devices: monitoring devices and control devices.  
The monitoring devices measure several variables related to air quality and send them to the server.  
The server gathers all the data from the monitoring devices and stores it in a database. The system uses the data to predict the air quality, using an algorithm developed in this work. With each prediction, the system decides whether to activate/deactivate the airflow system associated with the monitoring device to control the air quality.  
The ability to (de)activate the airflow system is provided by the control devices, which can be attached to an existing airflow control mechanism such as a ventilation system or a motorized window and controlled via messages from the backend.  
The control devices also inform the server of its associated airflow system state, which the server also stores in the database. Figure 2 depicts the structure of the system's database.  
There are some situations where there is not an airflow system installed. The air quality control system can send alerts to the configured user via different channels: it can send emails, MQTT messages to the MQTT topic configured for the device, and send messages to a Telegram bot associated with the device. The system can inform the users configured for a given monitoring device via any of those channels. Once notified, the users can decide to open a window or any other measure to improve the air quality.  
The system also provides a web frontend where we can visualize the system devices state (both the monitoring devices measures and the control devices state) and the collected data for different time ranges.  
The server functionality was implemented using Node-RED, a programming tool for wiring together hardware devices and Application Programming Interfaces (APIs) [40]. Node-RED provides a browser-based editor that makes it easy to wire together processing flows using predefined nodes that can be easily deployed to its runtime. It also provides nodes to connect to databases and nodes to design and deploy web frontends.