esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures and Intervention  
#### 2.2.1 Kinesiotherapy  
Patients treated with kinesotherapy aimed to improve motor and sensory functions by the application of exercises supervised by a physiotherapist. The kinesotherapy program was performed at the Neurorehabilitation Center for Treatment of Spinal Cord Injuries (AKSON, Wroclaw, Poland) with a similar, uniform methodology. Patients were treated 4-5 h per day, five days a week, under physiotherapists' supervision. Daily training included a range of motion and stretching exercises with loadings for 1 h (the minimal increase was 100 g), adjusted individually for certain groups of partially paralyzed muscles  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**K + E Group (N = 42)**} & \multicolumn{2}{c}{**K Group (N = 25)**} \\ \hline
**Variable** & **Mean \(\pm\) SD** & **Min–Max** & **Mean \(\pm\) SD** & **Min–Max** \\ \hline Age & 36.6 \(\pm\) 5.3 & 26–49 & 37.2 \(\pm\) 4.1 & 25–48 \\ Height (cm) & 174.0 \(\pm\) 4.7 & 162–183 & 175.5 \(\pm\) 4.9 & 158–181 \\ Weight (kg) & 74.9 \(\pm\) 8.7 & 53–81 & 72.3 \(\pm\) 6.5 & 52–84 \\ Spine injury & \multicolumn{2}{c}{C4–Th12} & \multicolumn{2}{c}{C2-Th12} \\ Time from spine injury (months) & 7.2 \(\pm\) 2.7 & 4–12 & 7.5 \(\pm\) 1.3 & 5–13 \\ Observation time (months) & 10.3 \(\pm\) 1.6 & 6–14 & 11.2 \(\pm\) 1.1 & 6–17 \\ Expected stimulation (hours) & 153.6 \(\pm\) 25.5 & 60–210 & NA & NA \\ Detected stimulation (hours) & 144.2 \(\pm\) 25.4 & 67–198 & NA & NA \\ Train stimulation frequency (Hz) & 39.0 \(\pm\) 11.0 & 20–70 & NA & NA \\ Single stimulus duration (ms) & 17.5 \(\pm\) 2.59 & 11–22 & NA & NA \\ Train duration (s) & 2.6 \(\pm\) 0.5 & 2–3 & NA & NA \\ Interval between trains (s) & 2.5 \(\pm\) 0.5 & 2–3 & NA & NA \\ Session duration (minutes) & 17.0 \(\pm\) 2.3 & 15–20 & NA & NA \\ Applied stimulus strength (mA) R & 28.9 \(\pm\) 6.3 & 18–45 & NA & NA \\ Applied stimulus strength (mA) L & 28.8 \(\pm\) 6.2 & 18–42 & NA & NA \\ \hline \hline \end{tabular}  
* E—electotherapy, K—kinesiotherapy, R—right side, L—left side, NA—not applicable.  
\end{table}
Table 1: Characteristics of the subjects studied in two groups.  
showing the activation improvement. A verticalator was used depending on the spasticity or hypotonia. Minor neurological symptoms, such as tingling or a decrease in muscle strength or function of the autonomic nervous system, determined the verticalization bed usage. Locomotor training on a treadmill with a handrail, and later without any support, lasted approximately 3 h a week. Sensory training of posture and balance on a specially designed vibration platform was performed for 1 h. The number of repetitions in one cycle ranged from 6 to 15, depending on the patient's condition, five days per week. The stretching exercises were performed in two sessions lasting 3 h each. Spasticity or hypertonia level determined the magnitude of loading exercises. The principles of uniform kinesotherapy treatment have been described elsewhere [18].
# Dynamic Characterization of Cohesive Material Based on Wave Velocity Measurements
## 3 Results and Discussion
### Comparison of Results from Different Methods  
The input signals employed in all of the tests were single sinusoidal pulses with different frequencies, obtained using peak-to-peak and start-to-start methods. The amplitude of input signals in both cases was fixed at \(\pm 15\) V. The tip-to-tip distances between the transducers were used to calculate the shear wave velocity of the samples (Equation (5)). All of the test traces were examined in the time domain, in order to obtain the travel time of the wave through the specimens.  
In Figure 16, S-wave velocities determined by means of the two analyzed time domain techniques are portrayed. It is clearly visible that travel time derived from the start-to-start method must be longer due to the presence of the greatest values of the shear wave velocities. The difference in the obtained values of \(\upsilon_{S}\) between these two methods remains at the average level of 21.25 m\(\cdot\)s\({}^{-1}\), in favor of the start-to-start method (Figure 17a). Naturally, these spotted differences vary, depending on the mean effective stress.  
Figure 16: Comparison of \(\upsilon_{S}\) obtained using various methods of travel time determination.  
Figure 15: Elastic moduli from the start-to-start method under different stress levels.  
In Figure 17b, the evolution of the shear stiffness modulus based on travel time deduced by means of the peak-to-peak and start-to-start techniques is summarized. In this comparison, the second of the aforementioned methods comes off as the better one. The average difference in stiffness of the examined cohesive material falls in the range of about 33.44 MPa. For the \(E_{\text{max}}\) (Figure 17c), the discrepancy between the results of two different interpretation methods achieves a maximum percentage of about 16% (89.2 MPa).  
In conclusion, it is worth noting that in the case of the start-to-start method, the findings of \(\upsilon_{S}\) and \(G_{\text{max}}\) lie on a straight line, but of a different gradient. This observation is not reflected in the outcomes regarding \(E_{\text{max}}\). The results obtained with the use of the peak-to-peak method appear to be more scattered. On the basis of Figure 17, it can be noted that the S-S method (start-to-start method) of identification results in a relatively smaller variation, when compared to the P-P technique (peak-to-peak method). The latter method yields slightly smaller values of the dynamic parameters.
# Novel MIMO Antenna System for Ultra Wideband Applications
## 4 Diversity Performance Parameters
### Diversity Gain (DG)  
Another important feature that needs to be assessed is DG. The DG of the MIMO antennas should be high (\(\approx\)10 dB) in the operating bandwidth. It is expressed in terms of ECC as [28]:  
\[\text{DG}=10\sqrt{1-ECC^{2}} \tag{2}\]  
Figure 15 depicts the DG of the proposed MIMO antenna, and it is observed that it fluctuates around a 10 dB scale for the entire operating bandwidth.  
Figure 14: ECC of the proposed UWB MIMO antenna.  
Figure 15: Diversity gain of the proposed UWB MIMO antenna.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 3 Physiological Roles
### Plasmid Maintenance and Copy Number  
The first TA system was discovered as an \"addiction module\" to kill plasmid-free cells [91; 97]. While cells carrying TA modules continue to transcribe both TA genes to ensure sequestration of toxins from cellular targets by antitoxins, daughter cells devoid of plasmids cannot replenish the pool of antitoxins, which are preferentially degraded by proteases. The cells without plasmids will thus be removed from the population by the actions of free toxins.  
The _phd-doc_ module of plasmid-prophage P1 is one of the most studied modules responsible for postsegregational killing [98]. Although the exact mechanism of how the toxin Doc induces cell death of the plasmid-free cell remains unknown, inhibition of protein synthesis in plasmid-less cells has been observed [93]. Moreover, postsegregational killing by the _phd-doc_ module of plasmid prophage P1 requires the presence of chromosomal _mazEF_, suggesting that the inhibition of translation by MazF and possibly other nucleases is essential for killing plasmid-free cells [93].  
The direct involvement of TA ribonuclease in postsegregational killing is observed with Kid, a MazF homologue, from plasmid R1 [44]. When the copy number of R1 plasmids becomes low, Kid cleaves both host and plasmid-derived mRNAs at UUACU. One of the transcripts digested by Kid is CopA, antisense RNA that impedes the translation of the R1 replication protein, RepA. Kid activation allows for the increase in RepA, allowing for the restoration of the plasmid copy number. Simultaneously, the digestion of cellular mRNA inhibits bacterial growth until the plasmid copy number has increased.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 5 Experiment and Analysis
### Results and Analysis
#### 5.2.1 Comparison Methods
DeepLog and LogAnomaly demonstrate the strengths of deep learning, while LogBERT further enhances performance through self-supervised learning. Ultimately, LogEDL consistently achieves the best overall performance across the three datasets, proving its efficiency and reliability in log anomaly detection tasks. In real-time log anomaly detection, computational efficiency is crucial. In Table 4, we analyzed computational efficiency using the HDFS dataset. The results show that PCA and iForest have the shortest computation times, followed by LogCluster, DeepLog, LogBERT, and LogEDL, with OCSVM being the slowest. DeepLog and LogAnomaly have relatively low computation and parameter counts with moderate computation times, making them efficient methods. Although LogEDL has the highest computation and parameter counts, its computation time is similar to LogBERT. For practical online deployment, our method's inference time remains long, necessitating future research on model acceleration and quantization.
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 2 Topologies of Electric Drives
### Other Electric Drive Topologies
#### 2.4.2 Dual Stator Drives  
Dual stator winding induction machines (DSIMs) have two three-phase windings with different numbers of poles and a standard squirrel-cage rotor (Figure 12). Although various pole number combinations are possible, optimum utilization of the magnetic material is achieved with a pole number ratio of 1.3. Each stator winding is supplied by a separate inverter with a common DC link [111]. Since the windings are not magnetically coupled, DSIM behaves like two independent IMs that are mechanically coupled via the shaft. The drive operates in two different modes: the synchronous mode, in which the stator frequencies are in the same ratio as the number of poles, and the asynchronous mode, which is used in the low-speed range and maintains a constant frequency for the low-pole winding [112].  
Figure 11: Schematic diagram of an open-end winding three-phase electric drive with two separate three-phase two-level converters.  
Figure 12: A schematic of a dual stator drive with different number of stator pole pairs, supplied by separate three-phase two-level converters. Red letters a/b/c represent a three-phase 2-pole winding, while green letters a/b/c represent a 6-pole winding.  
The drive offers several advantages, including improved ability to operate at low speeds and at standstill, improved speed sensorless operation [113], increased reliability, and full utilization of the over-speed torque range [114]. DSIMs can be used in automotive DC power generation as two sets of independently controlled but not coupled windings produce two different voltage levels [115].
# Seatbelt Detection Algorithm Improved with Lightweight Approach and Attention Mechanism
## 4 Experimental Result
### Dataset and Evaluation Metrics  
A dataset is a principal condition for implementing experiments and evaluating the performance of algorithms in seatbelt detection tasks. Hence, this paper gathered pictures from a traffic department. The pictures were taken by cameras positioned in different parts of a road, encompassing various lighting conditions. The dataset contains various types of motor vehicles. The quantity of samples utilized in various instances is illustrated in Table 1. Specifically, the training and validation experiments for windshield positioning involve 4000 and 363 samples, with 366 samples employed for the test. Moreover, 3333, 400, and 250 images were utilized in the training, validation, and test steps of the seatbelt classification algorithm, respectively. Additionally, Figure 8 showcases several samples used in the experiment.  
To quantitatively judge the behavior of different approaches and ensure the fairness of the comparative experiment, the evaluation metrics, such as Accuracy, Precision, Recall, F1 score, and Mean Average Precision (mAP), were introduced.  
\[\text{Accuracy}= \frac{TP+TN}{TP+TN+FP+FN} \tag{9}\] \[Precision=\frac{TP}{TP+FP}\] (10) \[Recall=\frac{TP}{TP+FN}\] (11) \[F1 score=\frac{2 TP}{2 TP+FP+FN} \tag{12}\]  
where \(TP\) indicates a true positive, \(TN\) represents a true negative, \(FP\) denotes a false positive, and \(FN\) is a false negative.  
\[AP=\int\limits_{0}^{1}PRdr \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Different Scenarios** & **Training Set** & **Validation Set** & **Validation Set** \\ \hline Windshield & 4000 & 363 & 366 \\ Seatbelt & 3333 & 400 & 250 \\ \hline \end{tabular}
\end{table}
Table 1: The quantity of examples for various instances.  
Figure 8: Sample pictures of the experimental dataset. (**a**) Daytime images; (**b**) nighttime images; (**c**) driver seatbelt images.  
\[mAP=\frac{1}{N}\sum_{i=0}^{n}AP_{i} \tag{14}\]  
where \(P\) indicates _Precision_, \(R\) represents _Recall_, \(N\) denotes the total amount of detection categories, and \(AP_{i}\) is the average accuracy of class \(i\). By calculating the area below the P-R curve for each category, the corresponding \(AP\) can be determined, and then the mAP can be determined by averaging the \(AP\) values for each category.  
Moreover, Params, GFLOPs, and Size are opted to judge the computational complexity of model. Params stands for the quantity of parameters within a model and the spatial complexity is denoted in millions (M). GFLOPs refer to a network with a billion floating-point operations per second, indicating the temporary complexity in Gigabytes (G).
# Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery
## 2 Blister Defect Detection for PLB Based on CNN  
### Data Capture  
Visual inspection is employed to detect blister of sheet net of PLB. PLB sheet is controlled by the manipulator and images of both sides are captured. The field image acquisition in the automated production line is shown in Figure 1. The damages and scratches of PLB sheet are easy to be detected using traditional image processing methods. Due to the inconsistent background color, shape and size, blister defects cannot be well detected using the usual image processing method. The image of the PLB sheet is divided into multiple patch images, and each patch image is detected. Some images of blister defects are shown in Figure 2.
## 2 System Model
### Recursive Least-Squares  
The matrix inversion operation in (6) requires significant computational complexity when \(M\) is large. We derive an RLS adaptive algorithm for efficient computation of the MVDR beamformer. The inverse covariance matrix \(\mathbf{R}^{-1}\) can be obtained by solving the standard least-squares (LS) problem; the LS cost function with an exponential window is given by:  
\[\mathcal{J}_{k}[i]=\sum_{\tau=1}^{i}\alpha^{i-\tau}\Big{|}\mathbf{\omega}_{k}^{H}[ i]\mathbf{x}_{k}[\tau]\Big{|}^{2} \tag{8}\]  
where \(0\ll\alpha<1\) is the forgetting factor. By replacing the upper equation of (5) with (8), the Lagrangian is obtained as:  
\[\mathscr{L}_{\text{LS}}(\mathbf{\omega}_{k}[i])=\sum_{\tau=1}^{i}\alpha^{i-\tau} \Big{|}\mathbf{\omega}_{k}^{H}[i]\mathbf{x}_{k}[\tau]\Big{|}^{2}+2\Re[\lambda(\mathbf{ \omega}^{H}\mathbf{a}(\theta_{k})-1)] \tag{9}\]  
Taking the gradient of (9) with respect to \(\mathbf{\omega}[i]\), equating the terms to a zero vector and solving for \(\lambda\), we obtain the beamformer as:  
\[\mathbf{\omega}_{k}[i]=\frac{\mathbf{R}[i]^{-1}\mathbf{a}_{k}}{\mathbf{a}_{k}^{H}\mathbf{R}[i]^{-1 }\mathbf{a}_{k}} \tag{10}\]  
where the estimated covariance matrix is:  
\[\hat{\mathbf{R}}[i]=\sum_{\tau=1}^{i}\alpha^{i-\tau}\mathbf{x}[\tau]\mathbf{x}^{H}[\tau] \tag{11}\]  
By comparing Equations (6) and (10), we can see that the MVDR beamformer can be implemented in an iterative manner, and the complexity can be significantly reduced. The filter \(\mathbf{\omega}_{k}[i]\) can be estimated efficiently via the RLS algorithm. However, the laws that govern their convergence and tracking behaviours imply that they depend on the number of sensor elements \(M\) and on the eigenvalue spread of the covariance matrix \(\mathbf{R}\). In order to estimate \(\mathbf{R}^{-1}\) without matrix inversion, we use the matrix inversion lemma [2], the gain vector and the Riccati equation for the RLS algorithm given as:  
\[\mathbf{k}[i]=\frac{\alpha^{-1}\mathbf{R}^{-1}[i-1]\mathbf{x}[i]}{1+\alpha^{-1}\mathbf{x}^{H} [i]\mathbf{R}^{-1}[i-1]\mathbf{x}[i-1]} \tag{12}\]  
\[\mathbf{R}^{-1}[i]=\alpha^{-1}\mathbf{R}^{-1}[i-1]-\alpha^{-1}\mathbf{k}[i]\mathbf{x}^{H}[i] \mathbf{R}^{-1}[i-1] \tag{13}\]  
The inverse correlation matrix \(\mathbf{R}^{-1}\) is obtained at each step by the recursive processes for reduced computational complexity. Equation (13) is initialized by using an identity matrix \(\mathbf{R}^{-1}=\delta\mathbf{I}\) where \(\delta\) is a positive constant. The above-mentioned full-rank beamformers usually suffer from high complexity and low convergence speed. In the following section, we focus on the design of the proposed low-complexity reduced-dimension beamforming algorithms.
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 3 Results and Discussion
### Sensor Structure Design
#### 3.2.2 The Hardware Design  
The hardware module of this rapid-detection sensor can be divided into three main parts: system control, signal acquisition and functional circuits. More specifically, the system control part includes the central processing unit (CPU) main control circuit, Ethernet circuit, time-sharing signal acquisition control circuit, human-machine interface circuit and LED constant current source drive circuit. The signal acquisition part includes temperature and humidity digital acquisition circuits, a photoelectric signal acquisition circuit, and an Analog Signal to Digital Signal (AD) signal acquisition circuit. In addition, the functional circuits include a power circuit and a flash data storage circuit.  
Consistent with the principle of moisture content determination using NIR spectroscopy, the core portion of hardware was designed as follows. The linear predictive coding (LPC) 1768 chip equipped with the Advanced RISC Machines (ARM) Cortex M3 (NXP Semiconductors, Eindhoven, Netherlands) was selected as the core CPU, which contains rich on-chip peripherals and up to 512 KB Read Only Memory (ROM) and 64 k Synchronous Dynamic Random Access Memory (SDRAM). On this basis, it can store the detected data with flash memory chip or external expansion flash storage chip. Moreover, the voltage signal, collected by sensor prove and dealt with signal conditioning, was conducted with AD conversion by the 16-bit AD conversion chip AD7705. The converted data is related to the corresponding moisture content of rice grain samples through the calibration model and displayed through the DOG12864 LCD. In addition to displaying the moisture content of rice grain, this sensor is capable of model selection, data viewing, data transmission and other functions. Furthermore, the hardware platform also includes a signal conditioning circuit and an LED loaded constant current source driver circuit. The specific hardware block diagram is shown in Figure 4, while the sensor probe and internal hardware of this sensor is shown in Figure 5.  
Figure 3: The schematic diagram and components of the sensor probe.
## 3 Results and Discussion
### Evaluation of Fire Heat Release Rate Prediction Performance
In contrast, as shown in Figure 14b, the HRR values predicted by the models with the AFH closely followed the experimental results, and the HRR fluctuations were significantly reduced, resulting in an MAE of 4.7 kW for the YOLO-YCbCr model and an MAE of 4.0 kW for the YOLO-seg model. Thus, it was inferred that HRR prediction models incorporating the AFH calculation are more suitable for real-time HRR prediction in real fire videos that provide a sufficient number of images. Figure 13: HRR prediction results during the peak HRR period: (**a**) one box, (**b**) one wood crib, and (**c**) corn oil in aluminum pan. frames. Future studies should focus on improving HRR prediction models and validating them using high-frame-rate fire videos to further improve their applicability. In addition, since the proposed approach lacks structural consideration of temporal dynamics, future research incorporating temporal modeling techniques will be conducted to better predict transient fire HRR behavior. To quantitatively evaluate the real-time applicability of the proposed models, key indicators such as frames per second (FPS), number of model parameters, and floating-point operations per second (FLOPs) were measured. The proposed models were operated using a 13 th Generation Intel(r) CoreTM i9-13900 KS [EMAIL_ADDRESS] GHz (24 cores), NVIDIA GeForce RTX 4090 GPU (24 GB VRAM), 128 GB DDR5 memory, and the Windows 11 operating system. The YOLO-YCbCr model achieved an inference speed of 107.9 FPS, while the YOLO-seg model recorded 100.2 FPS, indicating that the YOLO-YCbCr model is slightly faster. Both models are suitable for real-time HRR prediction, as the commonly accepted threshold for real-time performance is above 30 FPS [40]. In terms of model complexity, the YOLO-YCbCr model contains 3.0 million parameters, whereas the YOLO-seg model has 3.3 million. Furthermore, the computational cost, measured in floating-point operations, was lower for the YOLO-YCbCr model (8.2 GFLOPs) than for the YOLO-seg model (12.1 GFLOPs). These results demonstrate that the YOLO-YCbCr-based HRR prediction model provides more efficient operation for real-time HRR prediction.
# Design of Supply Chain System Based on Blockchain Technology
## 3 Blockchain-Based Supply Chain System  
### Overall System Architecture  
The supply chain system has high requirements for the real-time nature of the trading process, while information sharing is mainly concerned with availability, reliability, and security. Combining the characteristics of the supply chain system and blockchain technology, we divided the system into a trade chain and information chain platforms, as shown in Figure 3.  
(a) The trade chain platform is responsible for the trading functions of the supply chain system. Its main participant is the blockchain-based trading alliance chain, where trade smart contracts are deployed. The trade chain platform has two main roles. First, it achieves an efficient and convenient trading process. It controls the trading process between supply chain enterprises, during which a trade smart contract will verify a transaction's legitimacy, completion, and other information, reducing the steps in manual verification, improving the  
Figure 3: Overall framework of supply chain system.  
Figure 2: Smart contract model.  
efficiency and security of supply chain transactions, and reducing supply chain costs. Second, it makes transaction information transparent and traceable. When the trade chain platform executes a transaction, it records the initiation time, parties to the transaction, completion status, and other information, and makes it available to supply chain enterprises.  
(b) The information chain platform is responsible for information management. Its main participant is the information alliance chain based on the blockchain, where information smart contracts are deployed. The information chain platform stores relevant data of supply chain products. Enterprises can share their product information here, and can obtain information about their supply chain. The information chain platform can also be used as a product cycle management system. Through recorded product information, the full life cycle of a product can be traced. If a product has a problem, then consumers and the core enterprise can trace through the information chain platform to locate the step where the problem occurred.