esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Simulation of Lightning Current Discharge on Rocks
## 2 Materials and Methods
### Electrical Measurements and Calculations  
Voltage and current waveforms were measured on the primary (440 V) side with a sampling frequency of \(f_{s}\) = 250 kHz. With the ideal transformer assumption, the measured current \(I_{p}\) as referred to the secondary (rock side) of the transformer is \(I_{R}=I_{s}=a^{-1}I_{p}\) where \(a\) = 341 was the turns ratio of the transformer. The measured voltage \(V\) referred to the secondary side is \(V_{s}=a\ V_{p}\), and the voltage across the rock is \(V_{R}=V_{s}-RI_{s}\) where \(R\) = 233.1 k\(\Omega\) was the series resistance placed to limit fault current.  
Due to equipment limitations, some current measurements were clipped. Cubic spline interpolation was used to reconstruct the clipped portion of these signals (Figure 3).  
From the voltage and current waveforms, power and energy estimates were performed as follows:  
\[p_{R}[k]=v_{R}[k]i_{R}[k] \tag{1}\]  
Figure 3: Example voltage and current waveforms for dry and let experiments: (**a**) data from sample A1 (dry), and (**b**) data from sample A3 (wet). The peaks of the current waveforms were reconstructed using cubic spline interpolation.  
Figure 2: Close-up of voltage and current waveforms around fault onset, referred to the secondary (150 kV) side of the transformer, for sample A2, trial 5. The voltage ripple introduced non-60-Hz oscillations that were used to identify fault onset using a wavelet transformation.  
\[e[n]=T_{s}\sum_{k=k_{0}}^{n}p_{R}[k] \tag{2}\]  
where \(p_{R}[k]\), \(v_{R}[k]\), and \(i_{R}[k]\) are the power, voltage, and current at the rock specimen on sample \(k\), \(k_{0}\) is the estimated sample on which the strike occurs, \(T_{s}=f_{s}^{-1}\) is the sampling period, and \(e[n]\) is the total energy of the fault up to sample \(n\).  
To compute the total energy dissipation for a given arc discharge event, it was necessary to use the data to estimate the instants of fault onset and of fault extinction. Fault onset occurs when the magnitude of the voltage across the electrodes is large enough for an electrical discharge to occur through a path connecting the electrodes. The physical discharge path is three-dimensional and random (i.e., current may not flow in a straight line connecting the electrodes). Fault extinction occurs when the circuit breaker opens. The procedures for estimating fault onset and extinction times from the recorded current and voltage waveforms are explained next.
## 1 Introduction
The output current of TENGs is extremely small, requiring consideration of the discontinuous conduction mode (DCM), where the current flowing through the inductor of the DC-DC converter becomes discontinuous [18]. Furthermore, DC-DC converters operating in DCM exhibit nonlinear characteristics, making precise control difficult. A buck converter, as used in this research, is a type of DC-DC converter that exhibits linearity in continuous conduction mode (CCM) but demonstrates nonlinearity in DCM. Typically, when constructing control systems for nonlinear control objects, the control object is linearized to apply linear control theory. However, this linearization can potentially degrade the performance of the control system in terms of response speed, tracking accuracy, and sensitivity to characteristic variations [19]. Therefore, research on the robust design of control objects exhibiting nonlinearity is being conducted. While H-infinity control and Sliding Mode control are widely recognized as nonlinear control theories [20; 21], in this paper, the control system is constructed using operator theory, which is a type of nonlinear control theory [22; 23]. Sliding Mode control has the drawback of chattering behavior and the challenge of designing an appropriate sliding surface, which is attributed to the difficulty in finding a suitable Lyapunov function for nonlinear systems [24; 25]. However, a control system based on operator theory has the advantage of not having these drawbacks. In the proposed control system, target value tracking is enabled by combining right coprime factorization based on operator theory with a low-pass filter [26]. By controlling a buck converter, which outputs a voltage lower than the input voltage in the designed control system, the output voltage of the triboelectric nanogenerator is adjusted to obtain the desired voltage. The structure of this paper is as follows: In Section 2, the operating principle of the Vertical Contact-Separation Mode TENG used in this study is discussed. In Section 3,a mathematical model of a buck converter compatible with DCM for controlling the output voltage of the TENG is presented. In Section 4, a control system based on operator theory is designed. In Section 5, the results of simulations and real experiments are presented. Finally, Section 6 concludes the paper.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 2 Materials and Methods  
### Sample Collection, Preparation, and Storage  
A total of 90 white cheese samples were collected between June and August 2024 from various local retail shops across Thessaly, Greece. The samples included 50 traditional Feta cheese samples and 40 non-Feta white brined cheese samples. A slightly higher number of Feta samples (\(n=50\)) were collected to account for possible exclusions following ELISA-based adulteration screening. The non-Feta white brined cheeses were 100% cow milk-based, commercially available in local supermarkets, and verified via label inspection to lack any vegetable fat or milk blends. All samples were transported to the laboratory in a cooled container with ice packs to maintain refrigeration conditions. Upon arrival, each sample was subjected to visual and olfactory inspection to ensure suitability for analysis.  
Approximately 30-50 g of each cheese sample was homogenized manually using a sterile knife and glass rod to achieve a uniform texture. Following homogenization, the sample was divided into two equal portions.  
One portion was analyzed raw, while the other underwent freeze-drying using a BIOBASE Freeze Dryer (Model: BK-FD10P, BIOBASE Group, Jinan, China). The samples were first frozen at \(-50\)\({}^{\circ}\)C for 2 h. Then, they were dried under vacuum at a pressure of 50 Pa for about 48 h, with the temperature gradually increasing from \(-50\)\({}^{\circ}\)C to \(-20\)\({}^{\circ}\)C. A final drying step was carried out at \(+25\)\({}^{\circ}\)C for 12 h to remove remaining moisture. Althoughmoisture was not measured after drying, the freeze-drying conditions used are commonly known to reduce water content below 2% in cheese, ensuring stable and reproducible FTIR spectra [24; 25; 26; 27]. All samples (raw and freeze-dried) were stored at \(-18\)\({}^{\circ}\)C until further analysis. Before FTIR scanning, frozen samples were thawed slowly in the fridge for 24 h. Freeze-dried samples were checked visually to confirm complete drying.
## 3 Results
### Segmentation Assessment  
Following the training of YOLO with both datasets, the loss functions were evaluated on the segmentation task to ensure that the number of epochs and patience settings were appropriate.  
Figure 6 shows the trend in the segmentation loss function in both training sessions. In the case of Dataset A (Figure 6a), training was stopped at epoch 101 because no improvement in validation metrics had been observed in the last 30 epochs. For Dataset B (Figure 6b), the training continued until epoch 150, as there were slight improvements up to that threshold. However, the number of training epochs set to 150 was appropriate, as the loss function reached a plateau beyond which no further decrease was likely to occur.  
After training and tuning the network, the segmentation performance was evaluated by running inference on the Test Set with the weights obtained from both training sessions. Figure 7 shows an example of the output results from the trained network, given as input the example RGB image (Figure 7a). The object of interest was detected through a bounding box reporting the confidence level (Figure 7b). Subsequently, each pixel within the bounding box was classified as either belonging or not belonging to the object of interest, resulting in the segmentation of the detected lesion. From the YOLO network's inference output, a binary mask was obtained for further analysis, as shown in Figure 7c.  
Figure 6: (**a**) YOLO segmentation loss function on the Validation Set of dataset A. (**b**) YOLO segmentation loss function on the Validation Set of dataset B. In the case of Dataset A, early stopping occurred at epoch 101. For Dataset B, the training continued until epoch 150, with slight improvements still being observed.  
Figure 7: (**a**) The RGB image provided as input to the network to perform inference. (**b**) Network output. (**c**) Binary mask obtained from the automatic segmentation returned by the network. These figures show an example of YOLO network inference for an RGB image of the Test Set.  
For the evaluation of the detection and segmentation performance, the IoU and counting error values were obtained for each image. The results obtained with the training on Dataset A are shown in Figure 8a, with an average IoU of \(0.56\pm 0.23\), a median value of \(0.62\), and five images where the network failed to detect any lesions.  
Using Dataset B instead, the results shown in Figure 8b were obtained, with an average IoU of \(0.66\pm 0.25\), a median value of \(0.77\), and only two images where no detection occurred. The counting errors on individual images were also lower, with 18 images having no error in the number of detected lesions, compared to 11 with the initial dataset. Overall, the increase in the number of training and tuning examples, even with wound images acquired using devices with different characteristics, led to an 18% improvement in segmentation performance in terms of average IoU.
###### Abstract  
With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor's processing time in key block generations, non-repeating checks, and data block transfers, respectively.  
hardware acceleration; information security; Internet of Things; one-time password; one-time pad; zero-trust network +
Footnote †: journal: Sensors  
1
Footnote 1: email: w.-C.  
Field-Programmable Gate Array-Based Implementation of Zero-Trust Stream Data Encryption for Enabling 6G-Narrowband Internet of Things Massive Device Access.
## 4 Integration of DL-Based NOMA with Emerging Technologies
### NOMA-Assisted SWIPT  
By simultaneously gathering energy and information, SWIPT [176] aims to eliminate energy waste and increase the battery life of communication terminals. However, the current research on the SWIPT system tends to optimize them individually since the transmission rate and the gathered energy are incompatible [177]. As a result, it is important to investigate how these two goals trade-off. A lot of studies have been conducted on the use of SWIPT technology in various schemes, including heterogeneous cellular networks [178], two-way cooperative networks [179], multiple-input single-output (MISO) systems [180], multiple-input multiple-output (MIMO) systems [181], and OFDMA systems. SWIPT is also seen as a potential energy-efficient solution for 5G and beyond wireless networks.  
Figure 11: A MIMO-NOMA system with multiple clusters [166].  
Numerous studies have investigated how to minimize terminal power consumption by combining SWIPT technology with the NOMA network. For instance, investigations on SC-NOMA systems based on SWIPT have been examined in [182; 183]. In particular, the work in [182] explored the SWIPT in cooperative MISO SC-NOMA systems. It proposed a method of simultaneously optimizing the PS ratio and the beamforming vectors to optimize the data rate of the \"strong user\" while fulfilling the QoS criteria of the \"weaker user.\" Sum secrecy rate (SSR) maximization was examined in [184] in a SWIPT-based SC-NOMA system, and the numerical findings showed that performance improvements in terms of SSR over both traditional OMA and SWIPT-based OMA systems could be made. The NOMA-based SWIPT system is presented in Figure 12.
## 3 Results
### Optical Properties
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Band Gap (eV)** \\ \hline TiO\({}_{2}\) & 3.12 \\ \hline Cr\({}_{\rm Ti}\):TiO\({}_{2}\) & 3.04 \\ \hline Cr\({}_{\rm Ti}\):TiO\({}_{2}\) & 3.09 \\ \hline Mo\({}_{\rm Ti}\):TiO\({}_{2}\) & 2.87 \\ \hline Mo\({}_{\rm Ti}\):TiO\({}_{2}\) & 2.77 \\ \hline W\({}_{\rm i}\):TiO\({}_{2}\) & 2.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The calculated band gap values for every interstitial and substitutional doping case. We present the absorption coefficient a(\(\omega\)) in Figure 5. The major peak ranges from 2.5 to 12.3 eV for the undoped TiO\({}_{2}\), which is in good agreement with other works [57]. Pure TiO\({}_{2}\) only shows absorption in the UV region, created from the excitations from the O-2p states of the valence band to the Ti-3d states to the conduction band. It is seen that after doping, Mo gives the most substantial peak at 0.3 eV (Figure 5b). In the visible region, it is seen that Cr provides the highest absorption. All these findings suggest that Cr, Mo, and W doped TiO\({}_{2}\) exhibit better electrical and optical characteristics compared to the undoped case and should also be investigated for other energy applications, beyond photovoltaics and photocatalysis [64]. Figure 4: The real \(\varepsilon_{1}\)(\(\omega\)) and imaginary part \(\varepsilon_{2}\)(\(\omega\)) of the complex dielectric function for (**a**) Cr doped TiO\({}_{2}\), (**b**) Mo doped TiO\({}_{2}\), (**c**) W doped TiO\({}_{2}\), (**d**) undoped TiO\({}_{2}\). Figure 5: _Cont._
## 4 Research Methods
### Hardware Method  
The Vitis AI [43] is a convenient and efficient tool which can deploy and accelerate AI inference on Xilinx hardware platforms, including edge devices and Alevo accelerator cards. In this research, we deploy the pruned YOLOv3 model on the ZCU102 evaluation board using the Vitis AI. Specifically, as shown in Figure 4, the whole process can be approximately divided into the following steps:  
Figure 4: Flow of deploying the YOLOv3 model on the ZCU102 evaluation board.  
Model conversion. With the weights file and the corresponding cfg file, the YOLOv3 model could be converted into a keras.h5 model that will be used for subsequent operations.
2. Model quantization. Typically, when training a CNN, we use 32-bit floating point weights and activations on CPU or GPU platforms. The Vitis AI quantizer can convert 32-bit floating point weights and activations to INT8 format, so we can use it to reduce computational complexity without loss of detection accuracy. In addition, in this research, 100 unlabeled images are used for quantitative calibration. After this process, we can obtain a.pb file converted from the.h5 file and then use the.pb file for next operation.
3. Model compilation. After model quantization, we can use the Vitis AI compiler to convert the.pb file to a.xmodel file, which can run on the FPGA board.
4. Model deployment. In this process, we need to copy the compiled.xmodel file and.protototr configuration file to the SD card that has been prepared; then, we can use the YOLOv3 model to implement object detection of dishes on FPGA. In addition, we need to adjust some parameters in the.protototr file to obtain better results. Generally speaking, the biases in the.protototr file are the anchors in the original cfg file, and they should be arranged in the order of the anchors. However, in fact, in the experiment, we find that, if the biases are set in the order of the original anchors, there will be many boxes that are obviously not the correct detection results, so the biases must be modified. After trying, by swapping the first 6 numbers and the last 6 numbers of the biases, the test results returned to normal.  
On the FPGA platform, there are two ways to verify the accuracy of the model. The first way is to perform object detection on specific pictures, as shown in Figure 5. The second way is to infer all 68 test images in batches to obtain a result file similar to the original label file, including the type and coordinates of each dish in the test image, and the confidence of each test result. Then, we need to compare the result file with the original label file to determine accuracy of the model. It should be noted that, unlike the relative coordinates in the label file, the coordinates in the result file are absolute coordinates, so accurate test results can only be obtained after some transformation. In addition to accuracy, we also recorded the time from running the YOLOv3 model on the ZCU102 evaluation board to obtain the result file, as an evaluation of performance of the model.
# Model Updating of Bridges Using Measured Influence Lines
## 1 Introduction
In contrast [15], a non-linear optimisation approach,  
Figure 1: Correlation between bridge collapses [2] and world GDP [3] in the last 100 (**a**) and 40 years (**b**). drawing from a substantial number of random vehicles in traffic flow with unknown axle loads to define the shape of the influence line, has a notable advantage since the inherent dynamic responses of both the bridge and vehicles influence the IL shape less. This can skew results based solely on a limited number of pre-weighed vehicles. This paper follows this aspect of leveraging continuous advances in sensor technology, signal processing, and data-driven optimisation to enhance IL-based FEMU, ensuring more precise and robust methodologies for bridge health monitoring and structural safety assessment. This paper builds on previous work on using strain ILs obtained from B-WIM measurements. Strain ILs are essential for B-WIM algorithms to calculate accurate axle loads [15]. Algorithms have been developed to calculate strain ILs from all heavy gross vehicle crossings. Hundreds are statistically evaluated to obtain reliable information on bridge performance under moving loads [16]. In the past, these B-WIM-calculated strain ILs have already been used to calibrate the analytical models of simple short-span bridges. In this study, we first enhanced the strain IL calculation methodology to account for quantities better suited to bridge analyses. Then, we used the results to update the case study bridge and compared the FEMU results with more traditional frequency and mode shape-based FEMU results. For the FEMU, we replaced the traditional methods, such as Bayesian model updating and residual minimisation, with the EDMF methodology. Lastly, we validated the FE model response with measured displacements under calibration vehicle passages. The novelty of the proposed method lies in integrating measured strain ILs, derived from B-WIM data, into the FEMU process, representing an important advancement over traditional approaches that rely solely on modal parameters, like frequencies, MAC values, or on maximum strains under traffic load. It offers a cost-effective alternative to displacement-based methods, which were independently validated to yield comparable results in terms of response prediction. Methodologically, this study replaces more demanding Bayesian updating and residual minimisation with the error-domain model falsification (EDMF) approach. This falsification-based framework identifies plausible variable values of the FE model by systematically eliminating FE models that fail to match experimental data within defined uncertainty bounds. This makes it more robust to systematic uncertainties and model biases and increasingly recognised for its accuracy and practical effectiveness in structural identification. These methodological contributions not only simplify the FEMU process, but also enhance the role of B-WIM in structural identification and long-term SHM.
## 2 Materials and Methods
### Loss Function  
Two loss functions, which are the Mean Square Error (MSE) and Binary Cross Entropy (BCE), have been used. The MSE loss function computes the similarity between the produced and original specimens. At the same time, the MSE loss function learns to decrease the mistake by calculating the difference between the original and produced values using an Adam optimization technique. The MSE loss is defined as the squared L2-norm, often known as the least squares error (LSE). It reduces the sum of the squares of the variations between the genuine and synthetic specimens in meteorological data. The MSE loss function is defined in Equation (4) [40]:  
\[\text{Loss}_{\text{MSE}}=\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\left(\text{y }_{\text{i}}-\hat{\text{y}}_{\text{i}}\right)^{2} \tag{4}\]  
where y\({}_{\text{i}}\) and \(\hat{\text{y}}_{\text{i}}\) are real and augmented data. The MSE loss is used to pinpoint the Euclidean distance between the target vector and its assessment in the complex domain, which helps the model generate samples that are structurally reasonably similar to the training data.  
The BCE loss function is used to stabilize the training process and verify that the augmented specimens have the appropriate qualities in terms of their overall structure. Equation (5) [41] shows the BCE loss function.  
\[\text{Loss}_{\text{BCE}}=-\sum_{k=1}^{\text{c}}y_{\text{Klog}\left(\text{f} \left(\text{s}\right)_{k}\right)} \tag{5}\]  
where y\({}_{\text{k}}\) and s\({}_{\text{k}}\) are real data and augmented data for every feature (k) in solar radiation data (c).  
During the training process, the model's parameters are adjusted iteratively to minimize the value of the loss function until the convergence. This loss function convergence is a steady state, which means that the model has learned to minimize the loss and has reached a point where further updates to the parameters of the model do not significantly reduce the loss function value anymore.  
Multi-Objective loss: It is a loss function that incorporates multiple objectives by combining the individual components. It combines two loss functions, (MSE) and (BCE). The combined loss function can be defined as  
\[\text{Combined Loss}=\alpha\text{ * MSE}+\beta\text{ * BCE}\]  
where both \(\alpha\) and \(\beta\) are set to 0.5, and the combined loss function equally weighs the MSE and BCE, simultaneously optimizing both objectives.