esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures
###### Abstract
Additionally, research on AC and the impulse breakdown of polluted PCB surfaces has found that high humidity and salt contamination lower the breakdown voltage. This effect is particularly pronounced when the distance along the surface is close to the insulation thickness. The influence of background electrodes on the breakdown voltage is less noticeable under these conditions, highlighting the role of environmental factors in PCB insulation performance under both AC and impulse voltage stresses [23, 24, 25, 26]. Further studies by [PERSON] et al. [27] on PCB breakdown under rectangular pulse injection have suggested that dielectric failure typically occurs as a solid-gas composite breakdown. The key factors influencing this include trace spacing, pulse width, and pulse-repetition frequency (PRF). A larger spacing and wider pulses tend to increase the breakdown voltage, whereas a higher PRF and longer pulse durations generally lower it. These findings are important for improving the electromagnetic compatibility and the insulation strength of high-voltage applications. However, there are limited data on PCB behavior under AC sinusoidal conditions, which more closely simulates real-world operational stresses. This study aims to analyze PDIV and breakdown behavior under different electrode geometries using sinusoidal AC stress, which more accurately reflects the challenges faced by PCBs in real-world applications. This study focuses on the breakdown test procedure applied to five distinct electrode configurations fabricated using fiberglass circuit boards. The configurations investigatedinclude parallel electrode (PE), triangular electrode (TE), right-angled electrode (RE), and two floating electrode (FE) designs with gaps of 0.3 and 0.5 mm. The selected insulation gaps represent typical high-voltage PCB design constraints, where the 0.3 mm gap allows for analyzing elevated dielectric stress and early breakdown behavior, while the 0.5 mm gap highlights the benefits of increased spacing for enhanced breakdown resilience. By employing rigorous breakdown testing protocols, this study aims to evaluate the influence of variations in the electrode geometry and insulation gap on the PD inception voltage (PDIV) and final breakdown voltage. The key findings presented in this study not only deepen the understanding of insulation performance under varied electrical stress conditions, but also emphasize the importance of precise design parameters in mitigating PD risks. By informing design strategies that enhance dielectric resilience, these findings contribute to the development of more-reliable PCB systems, especially for high-stress AC applications. The remainder of this paper is organized as follows. Section 2 details the experimental setup and the production of the PCB-designed samples. The PD breakdown test procedure is described in Section 3. Section 4 presents the experimental results of the PD breakdown tests. Finally, Section 5 details the key conclusions drawn from this study and suggestions for future research.
# Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
## 2 Related Work
### Anomaly Detection in Industrial Control Systems  
Anomaly detection is an important research topic, especially in cybersecurity in the ICS. The ICS is intimately involved in our daily lives and cyberattacks on these systems can cause significant damage [17]. Therefore, technologies that can quickly detect anomalies in the ICS, and thereby prevent potential cyber-attacks, are crucial. AI technologies are becoming an important tool for this, with machine learning and deep learning techniques, in particular, showing promise in detecting anomalies. They can learn complex patterns in data and detect anomalies based on them. They are also effective at detecting anomalies in time series data, considering changes over time [18]. [PERSON] et al. [19] applied unsupervised machine learning techniques to detect anomalies in a water treatment system. [PERSON] et al. [20] proposed to apply a deep learning method using gated recurrent units (GRUs) to an intrusion detection system for IoT networks. The method showed a higher detection accuracy than traditional methods. They also proposed a lightweight and multi-layered design to enhance the security of IoT networks. [PERSON] et al. [21] proposed an unsupervised machine learning-based detection model based on LSTM-AE and GANs, which can learn complex patterns in time series data to detect anomalies more accurately. In addition, [PERSON] et al. [22] introduced an unsupervised learning approach using RNNs to learn the changes in data patterns over time and use them to detect anomalies. In addition, [PERSON] et al. [23] used random forests to detect anomalous activity in industrial control systems. They showed that this method outperformed other classifier algorithms, which can significantly improve the detection of cyberattacks. [PERSON], [PERSON], [PERSON], [PERSON] et al. applied various algorithms for anomaly detection in water treatment, IoT networks, and time series data, and [PERSON] et al. utilized random forests to effectively detect anomalous activity in industrial control systems. These techniques are proving to be highly effective in anomaly detection by learning complex data patterns and considering changes over time.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 4 Implementation and Validation
### Illustrative Example  
Figure 3 represents an illustrative example of the transaction flow of the proposed model. We consider the case of a patient who has a set of smart devices that help to adhere to a healthcare protocol. The user is connected to many IoT devices, i.e., insulin pumps, cardiac pacemakers, deep brain neurostimulator smart phones, and several other devices. Each device can share, store, and update its transaction data, and must be registered and authenticated through registration smart contracts by the Membership Service Provider.  
* A transaction proposal (smart contracts), as seen in Figure 3, must be created by the application. Users send transaction proposals to one or more peers concurrently in order to compile endorsement responses and meet endorsement requirements. At the application level, the smart contract process operates.
* The transaction is then processed by the orderer, who includes it in a block and sends it to all peers for validation and commitment.
* When a peer proposes and commits a transaction, the other orderer is notified of the new transaction. The user sends the transactions to the ordering service if they are endorsed. If this is not the case, the transaction will be canceled.  
Figure 3: Illustrative model of healthcare.  
* The ordering service, also referred to as the orderer, approves blocks into the ledger. It communicates with peers and endorses peers. It provides a shared communication channel to patients, hospitals, and peers over which the transaction can store and check records.  
Blockchain technology has significant advantages for the healthcare sector and can be used to keep medical records securely. We suggest using blockchain-based smart contracts to manage patient and medical device information and to preserve personal device-generated information. There are several benefits to implementing blockchain in healthcare IoT, including decentralization, record security, safe data management, privacy protection of sensitive data, and dependable remote patient monitoring.
# PSI Analysis of Adversarial-Attacked DCNN Models
###### Abstract
To test our hypothesis, we applied the same DCNN models as in the study by [16], with the exception that we used adversarial examples instead of benign examples. We employed three different adversarial attacks to generate adversarial examples for the experiments: FGSM attack, PGD attack, and CW attack [19]. In our experiment, we analyzed the distribution of the PSI at each layer in DCNNs for adversarial examples. Also, we compared the experimental results in [16] with our findings on the changes in the PSI according to the adversarial examples. In particular, we systematically assessed the layer-by-layer sparsity in the featured objects. Subsequently, we delineated the operational aspects of sparsity by investigating how sparsity correlates with performance at each layer. Lastly, we scrutinized the factors influencing the encoding scheme. This paper is organized as follows: Section 2 is divided into four subsections related to the experimental setup. Section 2.1 provides descriptions of the visual image datasets used in the experiments, namely the ImageNet dataset and the Caltech256 dataset. Section 2.2 explains the architectures of the two DCNN models utilized in the experiments, namely the AlexNet and VGG11 models. In Section 2.3, we elucidate the formulation and significance of the PSI, a method employed to interpret the internal structure of DCNN models. Section 2.4 describes the attack techniques used to generate the three types of adversarial examples employed in the experiments. In Section 3, we conduct PSI analysis on the two DCNN models on a per-layer basis and analyze the implications of the findings. Lastly, we conclude the study, outline its limitations, and propose future research directions in Section 4.
## 2 Related Work
### Spectrum Sharing Techniques in Licensed Band IoT Systems
One of the approaches used to minimize secondary interference is the use of Multiple Inputs Multiple Output (MIMO) strategies [24]. However, this approach is not very feasible in the most context of NB-IoT nodes designs because of their limited energy, computational and memory resources. Other approaches even go further by using a dynamical game approach to study the spectrum sharing among a primary user and multiple secondary users [25]. Unfortunately, unlike cognitive radios, in the NB-IoT networks, the primary transmitters are normally the NB-IoT nodes and BSs. Besides, although the cognitive radio like techniques may work for few connected NB-IoT nodes (10 to 20 nodes per cell), they become completely non-feasible for massive NB-IoT systems as the probability of interference rises significantly to the extent of almost nullifying their benefit. Therefore, how Device-to-Device (D2D) networks such as the NB-IoT systems should access the spectrum remains an open problem with its specific challenges as discussed in Section 2.2.
## 3 Case Analysis and Discussion
### Data Preprocessing
#### 3.2.3 Data-Preprocessing Results  
1. Velocity mean and standard deviation  
The average and standard deviation of the speed can reflect the quality of a driver's driving behavior during driving. The higher the average speed of the vehicle is, the greater the possibility of overspeed during driving will be. A large standard deviation of speed indicates that the speed varies greatly. Through the analysis of the data in the dataset, the mean and standard deviation of the vehicle speed were obtained (as shown in Figure 2).  
Figure 2 shows the mean and standard deviation of the vehicles' speed according to the sample data. The variation range of the average vehicle speed was 4-14 m/s, and that of the standard deviation was 1-3. Data analysis showed that vehicles with a higher mean speed also had higher standard deviations, which also suggests a poorer driver behavior.  
2. Acceleration mean and standard deviation  
The acceleration can reflect the amplitude of the driving speed adjustment during driving, further reflecting the driving stability of vehicles. Therefore, an overall analysis was conducted on the acceleration data in the dataset in this study, selecting the mean and standard deviation of the acceleration for analysis, as shown in Figure 3.  
Figure 2: Mean and standard deviation of vehicles’ speed.  
Figure 3 shows that the values of acceleration averages were mainly within the range of (\(-0.5\), \(0.5\)). Excessive acceleration means that the vehicle accelerates or decelerates rapidly. In the sample data with vehicle ID greater than \(400\), the standard deviation of acceleration largely fluctuated, and the possibility of speed instability was high.
# OPC-UA Agent for Legacy Programmable Logic Controllers +
## 4 Performance Evaluation
### OPC-UA Client Function Blocks for IEC 61131-3
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Function Block**}} & \multicolumn{3}{c}{**Function Response Time (ms)**} \\  & \multicolumn{2}{c}{**(Average, Std. Dev.)**} \\  & \multicolumn{2}{c}{**\(R_{func}\)**} & \multicolumn{2}{c}{\(R_{client}\)} \\ \hline \multirow{10}{*}{**Function block without the number of nodes argument**} & UA\_Connect & 79.09, 1.23 & 26.93, 8.09 \\  & UA\_Disconnect & 79.30, 0.47 & 3.90, 0.63 \\  & UA\_TranslatePathList & 78.25, 2.72 & 8.10, 1.75 \\  & UA\_NodeGetInformation & 77.13, 5.44 & 14.57, 5.94 \\  & UA\_SubscriptionCreate & 75.50, 7.97 & 1.70, 0.56 \\  & UA\_SubscriptionDelete & 75.05, 8.74 & 14.15, 1.08 \\  & UA\_SubscriptionModify & 77.28, 6.49 & 2.57, 0.46 \\  & UA\_SubscriptionProcessed & 74.68, 9.14 & 8.69, 3.59 \\  & UA\_Browse & 77.82, 6.69 & 5.85, 0.47 \\  & UA\_ConnectGetStatus & 77.41, 6.76 & 3.67, 1.77 \\  & UA\_NamespaceGetIndexList & 77.06, 6.41 & 4.20, 2.06 \\  & UA\_ServerGetUnByIndex & 77.00, 6.16 & 2.65, 0.09 \\  & UA\_ServerGetIndexByUnList & 75.70, 6.02 & 4.54, 0.40 \\ \hline \multirow{10}{*}{**Function block having the number of nodes argument**} & UA\_NodeGetHandleList & 77.05, 6.19 & 3.76, 0.28 \\  & UA\_NodeReleaseHandleList & 76.20, 7.15 & 2.59, 0.26 \\  & UA\_MonitoredItemAddList & 77.29, 6.09 & 19.95, 0.89 \\  & UA\_MonitoredItemRemoveList & 77.47, 6.35 & 17.26, 6.35 \\  & UA\_MonitoredItemModifyList & 77.50, 6.56 & 27.00, 2.31 \\  & UA\_MonitoredItemOperateList & 76.80, 6.10 & 7.35, 0.33 \\  & UA\_ReadList & 76.00, 7.33 & 10.51, 1.87 \\  & UA\_WriteList & 78.05, 4.70 & 12.31, 0.48 \\  & UA\_MethodGetHandleList & 77.95, 4.53 & 0.97, 0.26 \\  & UA\_MethodReleaseHandleList & 78.61, 4.67 & 0.05, 0.02 \\  & UA\_MethodCall & 77.31, 5.52 & 8.08, 2.25 \\  & UA\_EventItemAdd & 77.67, 5.89 & 1.59, 0.35 \\  & UA\_EventItemOperateList & 76.24, 7.92 & 0.06, 0.01 \\  & UA\_EventItemRemoveList & 76.00, 7.14 & 1.81, 0.16 \\  & UA\_HistoryUpdate & 76.00, 7.33 & 11.38, 2.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average response time of OPC-UA client functions.
# A Dosimetric Analysis of Reduction Cardiac Dose with Lead Shielding in Breast Cancer Radiotherapy
###### Abstract  
Adjuvant radiotherapy is an important treatment modality after breast-conserving surgery. Due to its proximity, radiation therapy for the left breast can often lead to an escalated heart dose that can result in heart diseases. The purpose of this study was to evaluate the heart dose reduction by using lead shields surrounding the left breast. The doses of a 3D conformal radiotherapy (3D-CRT) plan, an intensity-modulated radiotherapy (IMRT) plan, and volumetric-modulated arc therapy (VMAT) to the left breast tumor in a CIRS ATOM anthropomorphic female adult phantom were measured by optically stimulated luminescence dosimeters (OSLDs). To protect critical organs, the skin around the target area was covered by lead shields of two different thicknesses (0.125 mm and 0.25 mm). The results showed that compared to IMRT and 3D-CRT, VMAT provided better planning target volume (PTV) coverage, a better conformity index (CI), and homogeneity index (HI). With the use of lead shields, the thyroid dose was reduced by 5.12-27.5% and 20.51-30%, respectively; the heart dose was reduced by 49.41-50.12% and 56.38-57.42%, respectively; and the lung dose was reduced by 1.23-45.22% and 0.98-57.83%, respectively. Although the clinical application of lead shields was rare, this study verified that it could effectively decrease the heart dose from 4.31 \(\pm\) 0.09 Gy to 1.88-2.18 Gy, thereby potentially reducing the risk of associated heart diseases by 14.8%. Further works to implement this method into clinical practice are needed.  
breast cancer; lead shield; heart dose; VMAT +
Footnote †: journal: Medical Imaging and Radiation Oncology  
H.-W.-L.-H.-T.-C.-Y. A Dosimetric Analysis of Reduction Cardiac Dose with Lead Shielding in Breast Cancer Radiotherapy  
10 September 2021  
Accepted: 14 October 2021  
Published: 17 October 2021  
Published: 18 October 2021
# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 2 Materials and Methodology Applied to the Forecast and Estimation Model
### B-Splines: Data Interpolation  
The frequency in data sets obtained by satellites measures the appearance of missing observations due to climatological circumstances. Unobservable measures can be derived from cloudy atmospheric conditions when the satellite captures images on a given day or in a certain area. Unfortunately, time series methods for estimating purposes require full data bases, with no missing observations. Interpolation techniques allow us to construct new data points within the range of a discrete set of known data points. The simplest methods include linear interpolation, i.e., considering the two nearest available points and the linear expression between them to estimate missing observations, and polynomial interpolation, which is a generalization of the previous method considering a higher degree polynomial data. These methods have the advantage of being computationally fast, but the error is proportional to the square of the distance between the data points in the case of linear interpolation, and polynomial interpolations are computationally expensive and may exhibit oscillatory artifacts, especially at the end points (Runge's phenomenon) [36]. B-splines is generally used in this case, like global regression interpolation considering polynomial interpolation locally. The advantage of this method is the availability to generate maximum and minimum values, which is not possible when obtained by weighted averages. These methods also provide smoother interpolants. The best results for the present study were obtained considering cubic B-splines when compared to Lagrange and Newton polynomials, avoiding Runge's phenomenon.
# Galileo: The Added Value for Integrity in
## 5 Experimental Results  
In this section, the experimental results are presented, and the performance of the configuration considered is analyzed in terms of accuracy and availability. Three parameters are used for the evaluation of the accuracy; specifically, SD, maximum and mean errors. The metrics are computed for horizontal and vertical components of the position and velocity solutions. The continuity of the navigation solution is assessed using two different parameters; specifically, the SA and reliable availability (RA) depending on the application or not of RAIM techniques. The first one is the time percentage when the solution is computed: this parameter is used for the configuration without RAIM; whereas the RA is the percentage of time when the solution is computed and declared reliable: this parameter is used when the RAIM algorithms are performed.
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 3 Coupling Mechanism Design and Array Switching Control Strategy
### Transmitting Array Switching Control Strategy Based on Receiving Coil Position Estimation  
During the operation of the array UWPT system, due to the influence of water flow disturbance, the position of the underwater electromechanical equipment is not fixed;  
Figure 8: Coupled mechanism model.  
that is, the receiving coil is prone to offset. Therefore, it is necessary to select the optimal transmitting coil power supply according to the position of the receiving coil to improve the transmission efficiency of the system. In this paper, a transmitting array switching control strategy based on the receiving coil position estimation is proposed. According to the real-time detection of the receiving coil position, the optimal transmitting coil switching control is completed.
## 2 Materials and Methods
### Microdevices Characterization  
The microdevice cross sections were characterized with a field emission scanning electron microscopy (Zeiss Supra 40 FE-SEM) to check the quality of microchannels. The PDMS microdevices were cut perpendicular to the pattern and then metalized with Chromium to allow the FE-SEM characterization.  
The chemical characterization was carried out by X-ray photoelectron spectroscopy (XPS). XPS measurements were performed using a Kratos Axis Ultra DLD instrument (Kratos Analytical, Manchester, UK) equipped with a hemispherical analyzer and a monochromatic Al K\(\alpha\) (1486.6 eV) X-ray source, in spectroscopy mode. PDMS planar surface and microdevices were analyzed with a take-off angle between the analyzer axis and the sample surface of 0\({}^{\circ}\), corresponding to a sampling depth of approximately 10 nm. For each microdevice sample, a survey (in the 1300, \(-\)5 eV energy range) was recorded inside and outside channels in order to check for the presence of impurities derived from the fabrication process. Charge compensation was achieved using a charge neutralizer located at the bottom of the electrostatic input lens system. Survey spectra were aligned setting C 1s core level peak at 284.38 eV, the PDMS value in the literature [24]. The quantification, reported as relative elemental percentage, was carried out extracting core levels from the survey spectra. All XPS data were analyzed using the software described in [PERSON] and Canteri [25].
## 3 Bearing State Space Model Establishment and Fault Identification Process
### Fault Detection and Identification Process of Bearing
According to the generalized resonance principle, when there is a weak fault in the bearing, the fault will have a generalized resonance phenomenon with the fiber Bragg grating resonant sensor. Bearing fault detection can be realized by time-domain analysis and power spectrum analysis. Figure 4 shows the process. The bearing fault identification process is as follows: improved autocorrelation envelope spectrum analysis is performed on the signal after noise reduction, the fault frequency is extracted, and the fault frequency is compared with the fault characteristic frequency of the bearing to determine the location of the bearing fault. The bearing fault identification process is shown in Figure 5.  
Figure 3: Noise reduction process. Figure 2: The weak fault picking-up process based on the principle of generalized resonance.