esa-sceva/satcom-chunk-collection huggingface dataset:
# Antifungal Effects of Fermented _Sophora flavescens_ and _Eleutherococcus sessiliflorus_ Extract
## 3 Results and Discussion
### Antifungal Effects of the Fermented and Unfermented Plant Extracts  
_S. flavescens_ and _E. sessiliforus_ extracts were inoculated with the isolated microbial strain to prepare the fermented plant extracts. The fermented and unfermented _S. flavescens_ and _E. sessiliforus_ extracts were tested for their antifungal effects against four plant pathogens including _Plytoplthora cactorum_, _Botrytis cinerea_, _Colletotrichum fructicola_ Sau-3, and _Colletotrichum glooseporioides_ according to incubation times of 24, 48, 72, 96, and 120 h, respectively. _P. cactorum_ is a soilborne pathogen that can infect many plant species. It causes various types of damage, including leaf spots, shoot blight, root rots, and crown rots [29,46]. _B. cinerea_ is a destructive fungal pathogen that affects numerous plant hosts. It causes fuzzy  
Figure 1: Classification and 16S rRNA sequences of bacteria isolated from humus soil. gray-brown mold and soft decay of plant tissues [47; 48]. _C. fructicola_ is known to cause anthracnose, bitter rot, and leaf spotting diseases on over 90 cultivated and non-cultivated woody or herbaceous plant species. The pathogen has been reported from all five continents and affects a wide range of crops, including apple, pear, strawberry, mango, avocado, and coffee [36; 49]. _C. gloeosporoides_ is a fungal pathogen that causes anthracnose and fruit rotting diseases in a wide range of host plants [34]. As shown in Figure 2 and Table 1, the fermented _S. flavescens_ and _E. sessiliflorus_ extracts showed more active antifungal activities against four fungi than the unfermented extracts. The growth inhibitory potential of _P. cactorum_ was slightly exhibited by the effects of fermented _S. flavescens_ extract (FSE) and fermented _E. sessiliflorus_ extract (FEE). FSE and FEE exhibited the same inhibition patterns against _P. cactorum_, as follows: no inhibition at 24 h, mycelial growth inhibition with less than 2 mm at 48 h, and more than 2 mm from 96 h. Similarly, _B. cinerea_ growth was inhibited by less than 2 mm up to 72 h and more than 2 mm thereafter by FSE and FEE treatments. The most inhibition of FSE and FEE was observed against the _Colletotrichum_ genus (_C. fructicola_ Sau-3 and _C. gloeosporoides_). For the inhibition of _C. fructicola_ Sau-3, we observed that mycelial growth was hindered at a level of more than 2 mm by 96 h; moreover, it was inhibited by more than 3 mm when cultured for up to 120 h. The growth of _C. gloeosporoides_ was inhibited by approximately 2 mm for up to 72 h, but FSE inhibited its growth by over 3 mm. The effects of FEE confirmed that _C. gloeosporoides_ was suppressed at the 2 mm level throughout the entire culture period. These findings suggested that the fermented extracts of _S. flavescens_ and _E. sessiliflorus_ have potential as antifungal agents against these plant pathogenic fungi.
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Design of the Status-Byte-Assisted RDMA Transmission Mechanism
#### 3.2.3 Data-Filling Module  
The data-filling module on the sender end is responsible for filling the data into the sending memory blocks. First, it checks the status byte stored in the first status byte memory block to determine whether there are available sending memory blocks. If available, the data-filling process is executed.  
Figure 5 shows the flowchart of the data-filling process. Specifically, the steps of the data-filling process are as follows:  
1. Check the status of the sending memory blocks: The filling thread first checks the first status byte memory block to determine the status of the \(i\)-th sending memory block. If the \(i\)-th memory block already contains data (status is 1), it needs to wait. If the \(i\)-th memory block has no data (status is 0), the filling thread proceeds with the data-filling operation;
2. Perform data-filling operation: During this operation, the filling thread writes the device number and packet number into predefined positions within the block;
3. Update the status byte: Finally, after completing the data-filling operation, the filling thread updates the status byte of the sending memory block in the first status byte memory block to indicate that it contains data.  
\begin{table}
\begin{tabular}{c c c} \hline device number (one byte) & packet number (two bytes) & valid data (fixed size) \\ \hline \end{tabular}
\end{table}
Table 2: Structure of sending and receiving memory blocks.
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 2 Principle of System  
The gyroscope principle is applied to convert the undulating wave energy into the gyroscope precession torque. The reference frames are used to analyze the movement of the system in Figure 1. The moving coordinate system \(x_{1}y_{1}z_{1}\) is obtained by the fixed coordinate system XYZ through two rotating angular velocities \(\dot{\delta}\) and \(\dot{\epsilon}\). In the initial position without wave excitation, the moving coordinate system coincides with the fixed coordinate system, where \(\dot{\delta}=0\) and \(\dot{\epsilon}=0\), and the flywheel rotates at a constant velocity \(\dot{\varphi}\) along the \(Z\)-axis. If the torque applied by the external waves is along the \(X\)-axis, the gyroscope's rotating angular velocity direction and the wave torque acting direction are perpendicular to each other. The flywheel is affected by two angular velocities \(\dot{\varphi}\) and \(\dot{\delta}\), and the gyroscopic effect produces a torque on the direction y1, which is perpendicular to the two velocities. By driving the gear to increase the angular velocity of precession, and driving the rotating PTO to generate energy, then the energy is converted from the wave to electric energy. But if the flywheel rotates in the direction of the wave (\(X\)-axis), the two angular velocities coincide and no power will be generated.  
The functional layout of the gyroscope wave energy converter (GWEC) is presented in Figure 2. The gyroscope device is sealed in the float, and the monitor must be connected to the system to obtain real-time power and efficiency. In this case, the gyroscope is considered to be a motor on the damper, and the system extracts the energy of the damper for power generation in the PTO. The damper can be directly connected to the generator on the \(Y\)-axis. In this paper, the damping is connected to the monitor to verify the prototype performance, and the prototype will produce gyroscope torque, regardless of whether it contains external damping.
## 5 Results
### Robustness Analysis  
#### 5.2.1 Method Applicability  
The true velocity model (Figure 11a) exhibited more complex structural features than the initial velocity model. Specifically, there is a significant high-velocity anomaly in the left region of the model located outside the effective inversion area of the vertical seismic profile (VSP), which poses a challenge to the inversion process. Additionally, another high-velocity anomaly exists near the wellbore, where the vertical velocity gradient is large, forming significant velocity discontinuities that further increase the inversion difficulty. Figure 11b shows the inversion results obtained by FAFWI, demonstrating that the method accurately reconstructed the velocity structure from shallow to deep depths and precisely recovered the location and structure of both high-velocity anomalies. Similarly, we extracted velocity profiles at 75 m left and 100 m right of the wellbore (Figure 12). At a depth of 90 m, the velocity value abruptly jumped from 1700 to 4300 m/s and then transitioned back to 2300 m/s at a depth of 150 m (Figure 12a). Despite these significant velocity discontinuities, the FAFWI method accurately inverted the structural information of velocity variations. For regions with more gradual velocity structures (Figure 11b), the FAFWI inversion results almost perfectly matched the true model, indicating a high inversion accuracy. The R\({}^{2}\) score, structural similarity index (SSIM), and normalized correlation coefficient were calculated for the inversion results within this range and were compared with the true model (Table 2). The results are similar to those of previous studies; the inverted model showed strong linear relationships and structural similarity with the true model. The results demonstrated that the FAFWI method is applicable to different velocity models and is not limited to specific velocity structures.  
Figure 11: Comparison of FAFWI inversion results using a constant velocity model of 1900 m/s for the BP TTI model. (**a**) True model. (**b**) FAFWI inversion results.  
Figure 10: (**a–c**) Evolution curves of loss function, total phase difference \(\tau\), and number of selected traces as a function of Epoch.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 2 Influential Factors on the Aging and Reliability of EMD Components
### Non-Electromagnetic Stress
#### 2.2.1 Thermal Stress  
Thermal degradation of components in EMDs is the most common cause of failure. Even under normal operating conditions, heat dissipation and heating of EMD components occur, leading to the degradation of the components themselves. There are various methods to estimate the lifetime of components due to temperature-related stress, but the most commonly used in the literature [41; 42; 43; 44] is the Coffin-Manson Equation (3):  
\[AF=(\frac{\Delta T_{test}}{\Delta T_{usc}})^{m} \tag{3}\]  
where \(AF\) is acceleration factor, \(\Delta T_{test}\) is test temperature difference, \(\Delta T_{usc}\) is use temperature difference, and \(m\) represents fatigue or Coffin-Manson exponent. Equation (3) is used to calculate \(AF\) in order to conduct accelerated stress test (AST) under specified conditions with temperature cycling. To obtain the lifetime of component using accelerated test conditions, the number of testing cycles must be known.  
As stated in [45], thermal stress is the most common cause of failure in semiconductor switches used in adjustable-speed drives (ASDs). The most widely used semiconductor switches in ASDs are MOSFETs and insulated-gate bipolar transistors (IGBTs). These switches consist of different material layers, such as silicon (semiconductor), metal contact surfaces, oxide insulation layers, and soldered joints. Each of these layers of material has its own coefficient of thermal expansion (CTE). When temperature cycling occurs between layers, these materials expand, but because of their different CTE values, they expand at varying rates and amplitudes. This mismatch leads to contact failures after a certain number of cycles, which ultimately results in the failure of the device.  
The thermal conditions of an electric motor play a crucial role in its performance, longevity, and overall reliability. According to [46], changes in the temperature of electric  
Figure 3: Classification of non-electromagnetic stress.  
motors cause the degradation of insulation materials, leading to reduced electrical resistance and a decrease in overall efficiency. Elevated temperatures also affect the performance of the motor's permanent magnets, potentially weakening and demagnetizing them. Furthermore, temperature swings cause thermal expansion of different materials within the motor, resulting in mechanical stress and potential damage to components such as windings, bearings, and shafts. Eddy currents, inrush currents, higher-order current harmonics, and mutual induction voltages lead to the heating of the electric motor, resulting in a reduction of its lifespan and reliability.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 4 A Closer Look at the Time-Based Functions
### Near-Real-Time Function Loop  
Near-real-time functions include sensor-data fusion, residual-stress estimation, 3D Optical coherence tomography (OCT) model reconstruction, defect detection, process-parameter selection, and part-quality prediction. Today's AM machines are instrumented with a myriad of sensors often from third party vendors. Those sensors collect and communicate a large variety of high-dimensional data over extremely short periods of time. Typically, tens of thousands of images are collected using high-speed cameras. Large, field-of-view, staring cameras produce several high-definition, global images of each layer. All this sensor data must be fused before the other functions can be executed. Sensor-data fusion is still a major area of research [24; 25]. Fused images, machine-control commands and other sensor measurements are necessary for this control loop. Consequently, the computational requirements to perform near-real-time functions are very high.  
One of those requirements includes making an accurate, residual stress estimation, which is used to obtain the maximum dimensional accuracy and to avoid early fatigue failure [26]. Many process parameters affect the residual stress in AM [27]. Estimation results are used as inputs to optimize the scan-path and process parameters for the next build layer.  
Another requirement is defect detection, which is based on both 3D Optical coherence tomography (OCT) and dimension analysis at all layers of the part. Due to its high resolution and nondestructive nature, OCT is also useful for surface-void detection, loose-powder detection, and subsurface-feature detection. OCT is also used to find cracks and areas of un-melted powder [28; 29].  
Finding defects on layer-wise images is critical so that we can choose more appropriate process parameters for the next build layer. Many existing methods can be used to find defects such as ANN, Bayesian classifier, support vector machines (SVM), and Convolutional Neural Networks (CNN). CNNs got attention due to its accuracy and fast execution time as compared to other approaches. The execution speed of a CNN is improved by using high-performance computing resources such as an advanced GPU. The productivity of these techniques can be used for (layer-wise) process control, supplementary process decisions, or remedial actions [30; 31; 32]. Some investigators use an acoustic signal for in situ quality estimation in AM using deep learning [33; 32].  
Automatic virtual metrology (AVM) is a relatively new technique in which the quality of the manufactured part can be estimated by using earlier acquired in-process measurements. For AM quality estimation, AVM is based on in situ data detected during previous manufacturing [6; 34].
## 3 Results
### Depicting the Spatial Autocorrelation between Variation Patterns  
As a way to reveal the possible agreement in the distribution forms between the spatially distributed LST and built-up areas (represented by NDBI), bivariate LISA and [PERSON]'s \(I\) analysis approaches were employed. For [PERSON]'s \(I\), the vertical axis designated the neighboring grid values of LST, whereas the horizontal axis specified the average NDBI values. With respect to the study period (30 years), this analysis intended to examine the status of the spatial variability between the specified variables through considering pilot stages in terms of the year 1990, which was given as the starting period, and the year 2020, to represent the period's end. For the year 1990, the resultant BiLISA clustering map, along with its specific significance for the spatially autocorrelated LST and NDBI and accompanied by [PERSON]'s \(I\), are presented in Figure 8a-c. Likewise, Figure 9a-c presents the spatial autocorrelation status represented by BiLISA clustering, as well as significance maps and [PERSON]'s \(I\) for the year 2020.  
The BiLISA spatial autocorrelation for the year 1990 revealed significant consistency between LST and NDBI (Figure 8a), wherein the proportion of high LST areas was mostly correlated with the high NDBI areas (high-high) and was dominant over the non-vegetated areas. In contrast, areas with low LST were observed to correlate with the lowest NDBI (low-low), and were centered over the oasis farm areas, with a significance (\(p\)-value) ranging between 0.05 to 0.001. However, some sporadic areas were occupied by a holistic surface cover type (farms, impervious surfaces, bare soils, etc.), resulting in insignificantly  
Figure 8: The resultant spatial autocorrelation between LST and NDBI, in which (**a**) represents BiLISA cluster, (**b**) significance maps, and (**c**) [PERSON]’s \(I\), for the year 1990.  
correlating grids (outliers) due to the mismatch between the vegetation, soil, and built-up surface covers within the 1 km\({}^{2}\) grid.  
Furthermore, the 2020 applied BiLISA analysis (Figure 9a) also showed a significant positive (Figure 9b) correspondence (\(p\)-value between 0.05 and 0.001), indicated by high-high and low-low observed values over the urban and farm areas, respectively. Grids that had contributions of multi-surface cover formed a non-significant status. [PERSON]'s \(I\), which was utilized to assess the autocorrelation strength, had values of 0.41 and 0.45 for 1990 and 2020, respectively, with a permutations process of 999 plus 0.001 pseudo \(p\)-value for the two periods. It worth highlighting that the recently (2020) propagated (low-low) BiLISA zone, observed in the northern areas, was completely consistent with the change in the UHIs (Figure 5d) during the year 2020, particularly over the same geographical extent.
# Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks
## 3 Proposed Method
### Network  
This section details the experiment for selecting the network used in the classifier and explains the network structure in detail. The network was based on a convolutional neural network (CNN). The network was selected through experiments by changing the number of convolutional layers, fully connected layers, output channels, and the presence or absence of pooling layers. Approximately 80% of the data was used for training, and the remaining 20% for testing. The cross-entropy function and Adam optimizer were used for  
Figure 6: Examples of the binarized spectrograms for action (**a–k**).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Threshold** & 0.1 & 0.125 & 0.15 & 0.175 & 0.2 \\ \hline
**Accuracy (\%)** & 91.8 & 92.0 & 93.1 & 92.4 & 92.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of threshold selection experiment.  
training. The epoch and batch sizes were set to 200 and 32, respectively. The learning rates were set to 0.005, 0.001, 0.0005, 0.0001, 0.00005, and 0.00001 when the epochs were 0, 60, 100, 120, 140, and 160, respectively. NVIDIA GeForce RTX 2080 Ti GPU was used for training, requiring 1.5 s per epoch. Table 4 shows the detailed structure of the network used in the experiments, as well as the number of parameters and the accuracy of each network.  
Figure 7 shows the number of network parameters and accuracy trend. The experimental results showed that as the number of parameters increased, the accuracy increased and then converged. Network 5 performs much better than Networks 1-4 and slightly worse than Networks 7-9. However, Network 5 has much fewer parameters compared to the accuracy difference with networks 7-9. Therefore, this network is best suited for lightweight applications. The detailed structure of the proposed network is shown in Figure 8. The number of channels in the three convolution layers were 32, 64, and 128, and the kernel sizes were 5, 3, and 3. Batch normalization and max-pooling layers were applied after all convolution layers. A hard hyperbolic tangent was used as the activation function.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{6}{c}{**No. of Output Channels**} & \multirow{2}{*}{**No. of Par 1**} & \multirow{2}{*}{**Acc 2 (\%)**} \\ \cline{2-2} \cline{5-8}  & **CL1** & & & & & & \\ \hline
1 & 32 & 64 & - & - & 2 & - & 21,472 & 84.1 \\
2 & 64 & 64 & - & - & 16 & 2 & 55,168 & 88.9 \\
3 & 64 & 64 & 64 & - & 16 & 2 & 76,800 & 91.6 \\
4 & 32 & 64 & 128 & - & 2 & - & 93,664 & 92.4 \\
5 & 32 & 64 & 128 & - & 32 & 2 & 97,632 & 93.1 \\
6 & 64 & 128 & 128 & - & 2 & - & 223,680 & 92.2 \\
7 & 128 & 128 & 128 & - & 2 & - & 299,136 & 93.1 \\
8 & 64 & 128 & 256 & - & 64 & 2 & 387,776 & 93.8 \\
9 & 32 & 64 & 128 & 256 & 64 & 2 & 454,624 & 93.8 \\ \hline \hline \end{tabular} \({}^{1}\) Parameters: 2 Accuracy.  
\end{table}
Table 4: Results of network configuration experiments.  
Figure 7: Number of parameters and accuracy of networks.
## 2 Materials and Methods
### Signal Processing and Feature Extraction  
The recorded signals were processed using the Fast Fourier Transform (FFT) to convert them into frequency-domain representations. All audio signals were sampled at 48,000 Hz and converted to mono by selecting a single channel from stereo inputs, ensuring consistency across files. A 1 s segment (48,000 samples) was extracted from the beginning of each recording and multiplied by a Hamming window to minimize spectral leakage. The FFT was then computed using real-valued FFT, yielding only the positive-frequency components. The magnitude spectrum was calculated from the complex output and paired with the corresponding frequency bins to form a set of frequency-amplitude features. These features were stored in CSV format for each audio file, with one row per frequency component. The entire pipeline was implemented in Python using NumPy (1.26.4), SciPy (1.14.1), and other standard libraries. Custom scripts were developed to automate the batch processing of all recordings.