esa-sceva/satcom-chunk-collection huggingface dataset:
# Interferences of Electromagnetic Pulses on Microcontroller Units
## 2 Experimental Environment  
### Mcu  
MCU is a sophisticated semiconductor IC that includes a processor unit, memory modules, communication interfaces, and peripherals. It achieves chip-level computing by consolidating the memory, counter, USB, A/D conversion, UART, PLC, DMA, and LCD drive circuits onto a single chip while reducing the frequency and specifications of the central processing unit (CPU). This versatile device can be utilized in various control applications. In this study, we conducted EMI tests to assess the performance of a specific chip against EMPs. The MCU considered in this study was an enhanced 1T 8051 Flash MCU, which has a max 48 MHz peripheral operation, a 24 MHz core operation, an operating voltage range of 2.1-5.5 V, and a general-purpose input/output (GPIO) of up to 46. It contains two-way analog comparators, two-way operational amplifiers, and provides six-channel complementary pulse-width modulation (PWM) pulse output with dead zone control. Moreover, it supports up to 23 channels of 12-bit ADC, 4 UARTs, 1 SPI, and 1 I2C. It is built to industrial standards, works at temperatures from \(-40\)\({}^{\circ}\)C to \(105\)\({}^{\circ}\)C, and provides LQFP32, LQFP44, and LQFP48 packages. The IC package of the MCU in this study was 44 pins.  
According to IEC 62132-1, the input and output pins of analog signals were configured as 10 k\(\Omega\) resistance grounding, the input pins of digital signals were configured as direct grounding, and the output pins of digital signals were configured as 14 pF capacitor grounding. According to the recommendations of IEC-61967-1, IEC-61967-2, IEC-62132-1, and IEC-62132-2 regarding the standard test plate and the size of the TEM cell, the circuit used in this study was 104 mm \(\times\) 104 mm (Figure 1).
# Extended SSH Model in Non-Hermitian Waveguides with Alternating Real and Imaginary Couplings
## 3 Bloch Mode
The results are accordant with the prediction by the coupled mode theory. In Figure 3e,f, the relative coupling strength satisfies \(c_{1}>c_{2}\). In this condition, the real part of two energy bands is separated, and the imaginary parts of two energy bands cross each other. The cross points always emerge when the Bloch momentum is \(\varphi=\pm\,\pi/2\). This can be understood form Equation (1) as the term \(2 ic_{1}c_{2}\)cos\(\varphi\) vanishes at these two points.
## 2 Related Work  
### Architecture Framework  
AFs serve different purposes and are defined using metamodels. Widely used AFs include Zachman [23], the Open Group Architecture Framework (TOGAF) [24], and the Department of Defense Architecture Framework (DoDAF) [8]. Although the Zachmanframework does not explicitly propose the concept of \"enterprise architecture\", it is a pioneering multi-perspective framework emphasizing the hierarchical and multi-perspective nature of complex systems' cognition. TOGAF defines an enterprise as \"a collection of organizations with a common goal\". Therefore, an SoS can also be considered a type of enterprise, and AFs are an efficient way to handle SoSs. While the Zachman framework and TOGAF define concepts with ambiguity, DoDAF takes data as the core and defines DoDAF Metamodel (DM2) for different situations. However, DoDAF is tailored by the Department of Defense to its military doctrine and may not apply directly to other countries or civilian domains. Governments and organizations such as the UK and NATO have proposed architecture frameworks, including the Ministry of Defense Architecture Framework (MODAF) [25] and NATO Architecture Framework (NAF) [26], respectively, for their military domains. Matching metamodels and defining concepts that effectively communicate between AFs is challenging due to their differing contents [27]. The Unified Architecture Framework (UAF) inherits and develops DM2, is compatible with existing AFs, and provides a more standardized approach to architecture description. UAF extends the application from the military domain to the general enterprise domain [28]. However, the UAF's metamodel comprises over 80 views with complex concepts and relationships, which challenges selecting appropriate views and organizing them in proper order. Thus, metamodel should be tailored for modeling SoS views in practice.
## 3 Experiment and Analysis
### Experimental Parameter Setting  
The neural network's internal parameters are derived from iterative model training, while certain hyperparameters require manual configuration before training. In our experiments, the training settings included a batch size of 12, the Adam optimizer, an initial learning rate of 0.0001, and a weight decay of 0.00001. We use the parameters trained on ResNext-50 in ImageNet as the initial weights to improve the stability and generalization ability of the model [30]. The number of iterations was set to 30. As depicted in Figure 5, the model's loss value exhibited a decline from the initial 0.924 to 0.165 after 10 epochs of training. Subsequently, during the course of 30 training epochs, the model displayed a propensity to stabilize and converge.
## 2 Related Work
### Development of Feature Optimization  
Overall, high dimensionality has a substantial impact on classification accuracy and efficiency. To ensure higher precision and shorter calculation time, the number of feature dimensions should be reduced. In reference [46], three-dimensionality reduction methods--LDA, PCA, and PCA + LDA--were used to reduce the number of dimensions of the features. In reference [34], the sequential floating forward selection algorithm, which is an iterative method for finding features that are close to the optimal features, was proposed. In reference [47], a feature selection method, particle swarm optimization-assisted biogeography-based optimization, was proposed. In reference [48], an optimal discriminative dimensionality-reduction matrix was used in the QPSO algorithm to reduce the number of dimensions.  
In recent years, in deep learning, the SAE has also been applied to research feature dimensions. The multilevel structure of the SAE is suitable for the fusion and compression of information in input data and can extract necessary features. The SSAE, which consists of several SAE units, is used to extract the key information of the features. The following results prove the feasibility of using the SSAE to optimize the features. In reference [49], to identify distinguishing features of nuclei, an SSAE was used to learn high-level features from pixel intensities. In reference [50], an SSAE was used to extract high-level features from fast and noncell samples in the training set. In reference [51], a method of feature representation for multicell Pap smear images, using an SSAE was proposed. Reference [52] used a trained SSAE to extract high-level features from image patches. Therefore, an SSAE can capture advanced features by learning from low-level features. Advanced high-level features can differentiate between various emotions.  
To combine the advantages of the CNN and SSAE to extract deep optimized features, a CNN-SSAE neural network method is proposed in this paper.
## 2 Materials and Methods
### Defects of WTBs and Detection Methods
#### 2.1.2 Methods for Safety and Quality Assurance  
During the manufacture of the WTBs, it is very important to ensure quality control and avoid various internal defects. Such flaws can be revealed quickly, reliably, and effectively using the Ultrasonic NDT [11; 41; 42]. The Ultrasonic Testing (UT) is named one of the most versatile techniques directly related to mechanical properties of the object, sensitive to any changes, enabling extraction of detailed information (e.g., the determination of the depth of the defect) and leading to certification [43; 44; 45]. Moreover, it is important to identify the structural or mechanical problems in real time to avoid any blade failures and to ensure the safety of on-site operating WTs, extend the service life, and reduce the number of temporary suspensions of WTs during the inspection by NDT personnel [11]. For that purpose, the SHM technique with permanently installed transducers is being effectively exploited to perform continuous monitoring and data acquisition, informing a remotely located operator in case of defect appearance [11; 46]. The mentioned advantage of SHM significantly reduces the risk of the overall turbine failure, which could occur between the regular intervals of conventional NDT and undetected evolving critical defects. The schematic diagram of the safety and quality assurance of WTBs during manufacturing (application of NDT) and on-site operation (application of SHM) is presented in Figure 3a. In the case of SHM application, the transmitting ultrasonic transducer TR\({}_{1}\) and three receiving ultrasonic transducers RC\({}_{1}\), RC\({}_{2}\), RC\({}_{3}\) are presented in Figure 3b. All transducers are permanently fixed on the surface of the sample. In the case of NDT application, the attached and mechanically scanned pair of transmitting (TR) and receiving (RC) ultrasonic transducers are presented in Figure 3c.  
During the last decade, the WTBs geometry and materials were changed; the blade shapes are significantly increasing in size, and the complex design from fibre composites with sandwich structures joined by adhesive bonding is used for their manufacturing. These changes create new challenges for the WPIs quality control. Regarding that, new testing, inspecting, and monitoring methods are essential in all manufacturing, installation, and service stages [11].  
Thus, ultrasonic Lamb waves are identified as one of the promising tools to address the quality control tasks of WTB by NDT, and SHM as the potential prevention tool.
## 1 Introduction
With the advancement of artificial intelligence, machine learning and deep learning methods are increasingly becoming popular for segmenting medical images. Unlike traditional manual segmentation approaches, which often require the creation of complex rules, data-driven artificial intelligence models demonstrate superior accuracy and generalization capabilities [21,22]. These models reduce user dependency, providing faster, more repeatable, and consistent results. It has been reported that AI-based segmentation can be performed successfully in pediatric patients in the period of deciduous and mixed dentition as well as individuals in the permanent dentition [23]. AI-assisted CBCT file (DICOM) segmentation reduces user dependency, enabling faster, more repeatable, and consistent results. These systems can process large datasets in a short time, offering a significant advantage in analyzing anatomical structures such as airway volume and condylar morphology with greater precision than manual methods. Additionally, they minimize potential human errors, thereby enhancing measurement accuracy. Studies have demonstrated that AI-assisted segmentation methods achieve successful results in evaluating airway volume in CBCT images and provide more objective analyses compared to traditional techniques [18; 22]. AI-performed CBCT DICOM segmentations enable the analysis of relationships between craniofacial structures, the measurement of these structures, and the creation of patient-specific appliances for treatment processes. Therefore, using an AI-assisted segmentation method provides significant advantages for our study. While previous studies [7; 12; 24] have examined airway dimensions and vertical growth patterns, few have investigated their impact on condylar volume. Traditional segmentation methods introduce operator-dependent variability, which can compromise measurement accuracy. To address these limitations, AI-assisted segmentation ensures precise and reproducible volume assessments. The key novelty of this study lies in utilizing AI-assisted segmentation to evaluate relationships between craniofacial structure volumes, reducing human-related segmentation errors for a more objective analysis. **H\({}_{0}\):**: _There is no statistically significant correlation between airway volume and condylar volume segmented using AI-assisted methods in skeletal Class I malocclusion patients with different vertical growth patterns._
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 4 Data Processing
### Data Transmission and Storage  
In case of the LoRa geosensor network the encrypted data packets sent from the nodes are relayed through the LoRa gateways on site to an off-site LoRa Network Server server using a redundant internet connection via DSL, Cellular network and/or microwave radio (Figure 8). The LoRa server manages the LoRa communication and relays the data to the LoRa application server, which then analyses and decodes the encrypted data packets and writes the data into the Inform@Risk cloud database. The LoRa server is also responsible for sending control commands to the LoRa nodes via the most appropriate gateway. For the inform@risk project the open-source LoRa network server \"Chirpstack\" (www.chirpstack.io) (accessed on 1 March 2021) is used.  
As the CSM measurements produce a large amount of data (up to about 350 kB uncompressed data per measurement), LoRa can not be used to transmit the CSM data. Therefore, the system is designed so that, whenever possible, the CSM measurement devices are located at the LoRa-gateways. This allows the CSM devices to directly utilize the internet connection of the gateways to transfer the data to the Inform@Risk cloud. Whenever this is not possible, local wireless LAN connections with directional antennas are used to relay the data to the gateways. All gateways are equipped with backup batteries (200 Ah) which allow them to operate for two weeks without external power in case of an emergency. An open source MariaDB database is used as central data storage. In  
Figure 7: Exemplary inclination data taken from an accelerometer on top of a subsurface node. While the measurements do not yet show deformations, daily cycles as well as sensor noise are visible. The sensor noise in this real-world application is higher than that measured in the laboratory.  
general, the raw data received from the sensors is stored and kept unchanged in the database. Any further processing (see below) of the data is stored at different locations in the database, making it possible to change calibration and analysis parameters afterwards if deemed necessary.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Part 1: Raw Data Preprocessing
#### 2.3.2 Data Segmentation  
The raw data consisted of time-series recordings lasting several minutes. To prepare the data for modeling, data segmentation was performed on the cleaned vibration signal using a sliding window technique with no overlap. During this process, the entire recording was divided into continuous 1 s data samples, with a window shift equal to the Segmentation Step (Sstep). In this context, continuous means that if any portion of a 1 s sample was removed due to data cleaning criteria, the sample was considered incomplete and excluded from further processing. A 1 s window was selected to ensure sufficient temporal resolution for capturing unbalance-related vibration features across varying speeds. This duration provides enough data to capture several full shaft revolutions even at low speeds (300 RPM), enabling effective frequency-domain analysis. This choice is also consistent with prior vibration-based fault diagnosis studies, where 1 s segments are commonly used.  
All the data samples generated using the same Sstep from the development recordings (0-4D) were combined into a single dataset. Samples from the balanced case (0D) were labeled as Class 0 (no unbalance), and samples from all unbalance levels (1-4D) were labeled as Class 1 (all unbalance levels), forming the preprocessed dataset D (Figure 3). The same process was applied to the evaluation recordings (0-4E), resulting in preprocessed dataset E.  
The Sstep determines the amount of data extracted. Different Sstep were used across various scenarios, and details are provided in Section 2.5.
## 5 Hybrid Seismic Analyses of Studied Building Models  
### Results and Discussion for Building 1  
Seismic analyses of Building 1 subjected to GMs 2, 3, 4, and 5 are performed using both the conventional NTHA (OpS method) taking as a reference and the new proposed hybrid analysis (MLM1 simulating the isolation layer: NRB, LRB, and Oil Damper). The superstructure response is evaluated by its analytical models in both cases. Although the hybrid analysis computes the full response of the building (acceleration, velocity, and drift at each degree of freedom), only hysteresis loops of the isolation layer are shown in Figure 12 for ease of presentation (black color for the hybrid analysis and red color for the reference analysis). Since both drift and shear are computed in both analyses and at each time-step, the drift RMSE is also evaluated as follows,  
\[\text{Drift RMSE }(\text{cm})=\sqrt{\frac{\Sigma_{\text{time}}\Big{(}\text{ drift}_{OpS,i}-\text{drift}_{\text{Hybrid},i}\Big{)}^{2}}{N_{\text{time steps}}}} \tag{3}\]  
Hysteresis loops of the isolation layer are not perfectly bilinear because of the effect of the Oil Damper. Hybrid seismic analyses results fit very well reference curves for all the GMs considered in this study, despite the long nonlinearities (drift up to 13.5 cm for the case of GM2). The maximum drift RMSE (respectively shear RMSE) is 0.098 cm for GM5 (respectively 32.4 kN for GM4). All RMSEs are of the same order and remain within an acceptable range. Maximum deformations at each floor are given in Figure 13 for each GM (4 colors) and each seismic analysis (solid lines for hybrid analysis and dashed lines  
Figure 12: Nonlinear cyclic hystereses of isolation level of Building 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training** & **Sequence Length** & **Epochs** & **Batch Size** & **Number of Training** & **Time (h)** \\ \hline
1 st screening & 1251 & 500 & 32 &
\begin{tabular}{c} LSTM: 19 \(\times\) 10 = 190 \\ GRU: 19 \(\times\) 10 = 190 \\ \end{tabular} & 23.93 \\
2 nd screening & 1251 & 500 & 32 & \
\begin{tabular}{c} 5 \(\times\) 20 = 100 \\ \end{tabular} & 09.82 \\ Model reliability & 1251 & 500 & 32 &
\begin{tabular}{c} 10 + 20 + 30 + 40 + 50 = 150 \\ \end{tabular} & 14.80 \\ Sensitivity analysis & 3751 & 500 & 16–128 & 20 & 10.27 \\ MLM1 & 3751 & 1500 & 32 & 5 & 08.49 \\ MLM2 & 3751 & 1500 & 32 & 1 & 01.65 \\ MLM3 & 3751 & 1500 & 32 & 1 & 01.66 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computation time for developing the MLMs.  
conventional one). The superstructure deforms linearly, and both analysis responses fit very well. The maximum drift RMSE is 0.00528 cm (GM 2, roof), and the maximum shear RMSE is 27.1 kN (GM4, first floor).
## 3 Results
### Orbit and Clock Accuracy of SRP Modeling  
Orbit and clock offset accuracy can be used as an indirect measure of the accuracy of the estimated parameters. Figure 5 illustrates the SLR residuals for G1 and J1 based on the ABW model. Compared with the results of the G1, the ISL observation reduces the dispersion of the residuals. At \(\varepsilon<45^{\circ}\), the upper bound of the SLR residuals for SECM is up to about 20 cm, while the residuals are less than 10 cm for J1, which indicates that the ISL observation can improve the orbit at a low \(\varepsilon\) angle. Table 4 compares the statistics of the SLR residuals. The inclusion of ISL observations reduces the deviation of SLR residuals by about 3.3 cm for SECM satellites and improves the standard deviation by about 1.3 cm. The root mean squares (RMS) for CAST satellites and SECM satellites are reduced by 10.7% and 58.1%, respectively. The trend of J1 residuals is basically eliminated. The evaluation period revealed a significantly greater improvement in the SECM satellites compared to the CAST satellites. This may be attributed to the larger difference in the X-panel and Z-panel areas of the SECM satellites, leading to a more pronounced systematic trend in the residuals and consequently a more noticeable enhancement.  
Clock errors are concentrated in the satellite station direction and have a large correlation with the radial errors of the satellite orbit. The stability of the estimated clock offset arcs can be used indirectly to assess the quality of the radial direction of the orbit. We measured the stability of the clock offsets at 9000 s per 1-day orbit arc using OADEV due to the absence of SLR data for other satellites during the evaluation period. The variation in OADEV for 9000 s integration with the solar elevation angle (\(\beta\) angle) was evaluated to assess different modeling strategies. Figures 6 and 7 depict the ADEV values relative to the \(\beta\) angle. The satellites C20, C30, C33, and C43 were plotted as examples to represent four types of MEO satellites. J1/J2 have lower OADEV values and a lesser scatter compared to G1/G2. CAST-A, CAST-B, and SECM-A demonstrate an almost linear \(\beta\) dependence in ADEV values. However, for satellites equipped with a SAR antenna, the enhancement is not significant when considering the SAR antenna. As for the SECM-B satellite, only C43 and C44 satellites are in orbit. C43 shows a clear \(\beta\)-dependency in the J1 strategy (see Figure 7). The orbits of C43 and C44 spanned only 82 days due to the absence of precise WUM orbits as initial orbits. Therefore, no further analyses are performed in this paper.  
Figure 4: Optical parameter estimated by strategy G1 (blue), G2 (purple), J1 (red), and J2 (green).  
Figure 5: SLR residual dependent on \(\varepsilon\) angle for satellite without SAR antenna. The first row represents G1 estimates, and the second row represents J1 estimates.  
Figure 6: OADEV at 9000 s of CAST-A (C20) and SECM-A (C30) with respect to the \(\beta\) angle.  
Figure 7: OADEV at 9000 s of CAST-B (C33) and SECM-B (C43) with respect to the \(\beta\) angle.