esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis
## 2 Data-Driven Bearing Fault Detection
### Image Transformation of Vibration Signals  
NSP [25] is a data wrangling method that uses image transformation of correlated time series data for multi-variate correlation analysis and machine learning. Multi-channel signals are represented in fixed-size images that are generated through three steps: compression in nested clusters, imaging, and accumulating (Figure 2). The first step was compression in the nested clusters, in which the values of time-series data with a given range were mapped into a cluster, and each cluster held a count of mapped values. In the imaging step, a scatter plot was drawn for the multi-channel nested clusters with given colors. To sustain the fixed image size, the sizes of the clusters for each channel signal were controlled. To represent the intensity of each cluster in the time-series data, the count of mapped values was translated into pixel intensity. In the accumulating step, multiple sets of correlated signals were concatenated as a single RGB image.  
Figure 1: Test bench and bearings. (**a**) test bench (model: EMOD FKFIE2100 LA, Pmech: 3 kW, nnom: 1440 / min, Vnom: 400 V, Inom: 6.4 A, cos\(\phi\): 0.78), (**b**) sensors mounted on the motor, (**c**) bearing with different faulty conditions.  
Figure 2: Bearing fault-nested scatter plot (BF-NSP) process.  
In [11], signal processing techniques such as the Hilbert-Huang transformation (HHT) and wavelet transform were employed for vibration signal decomposition to detect bearing faults. We also exploited NSP representation of vibration signals for bearing fault detection (denoted as BF-NSP), but using a simpler decomposition method. Specifically, we extracted signals in different bandwidths from the vibration data and represented them with different colors in a single image.  
Selecting the bandwidths of decomposition is important. In [29], faulty condition data showed high spectral density in a high-frequency band (30 to 40 kHz), known as the resonance band. Our analysis also showed that the spectral density for 25 to 40 kHz of faulty condition data was greater than that of normal condition data (Figure 3). In this study, BF-NSP used three bandpass filters: 10 to 30 kHz, 30 to 50 kHz, and 0 to 250 kHz.
# Multiresponsive Hybrid Microparticles for Stimuli-Responsive Delivery of Bioactive Compounds
## 3 Results and Discussions
### Passive and US-Triggered DOX Release Kinetics
The synergic effect of pH and US is observable considering the amount of DOX released at the same US power and exposure time but at different pH levels. When increasing the pH, the amount of released drug increases simultaneously. An exception is noted after application of US at 20 W/cm\({}^{2}\) for 30 s where, independently from the pH, the whole amount of the loaded drug is released. The effect of pH can be mostly ascribed to the ionization state of the functional groups on the surface of the particles (ZV) and on those displaced along the polymer (core-shell) affecting the electrostatic interactions and formation of H-bonds between particles and drug. Conversely, the ultrasound tends to physically alter the structure, with a consequent increase, or burst, in the release.
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 3 Experiment and Results
### Ablation Studies  
We designed ablation studies to verify the optimization effect of each improved module: model A used the k-means clustering algorithm to recalculate the parameters of new anchors to match our dataset, and then replaced the initial anchor with them. We also disabled the autoanchor function of the YOLOv5 algorithm. In model B an ECA layer was added at the end of the backbone network. In model C the PANet structure of the neck network was improved by replacing it with BiFPN modules. YOLOv5s-KEB is the model proposed in this paper, which uses the improved methods from models A, B and C simultaneously. Comparisons of the parameters of each model and the results of the ablation studies are shown in Tables 3 and 4, respectively, where the inference time refers to the average time required for the model to infer a single image, and FPS (detection speed) is calculated based on the total time taken by the model to recognize an image, consisting of the sum of the pre-process time, inference time and non-maximal suppression (NMS) time. Weight represents the volume of the model.  
The results showed that, compared to the original YOLOv5s model, the inference time of model A was reduced by 20.7%, mAP was increased by 0.8% and FPS was improved by 4.5%, which indicates that the anchors regenerated by the k-means algorithm reduced the inference time and also improved the detection accuracy and speed of the model. For model B, the mAP increased by 0.3% compared to the original YOLOv5s model, thus verifying the effectiveness of introducing the attention mechanism, and hardly affected the model size and inference time. This indicates that the ECA mechanism improved the generalization ability and detection accuracy for some types of defects with only a few parameters added.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **K-Means** & **ECA** & **BiFPN** & **Weight** & **Inference Time 1 ** \\ \hline YOLOv5s & \(\times\) 2  & \(\times\) & \(\times\) & 14.4 MB & 2.9 ms \\ A & \(\surd\) & \(\times\) & \(\times\) & 14.4 MB & 2.3 ms \\ B & \(\times\) & \(\surd\) & \(\times\) & 14.4 MB & 2.9 ms \\ C & \(\times\) & \(\times\) & \(\surd\) & 16.5 MB & 2.8 ms \\ YOLOv5s-KEB & \(\surd\) & \(\surd\) & \(\surd\) & 16.5 MB & 2.6 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of parameters of each model.  
The BiFPN module was used in model C, which increased in volume by 14.6% and resulted in a 3.4% reduction in detection speed. However, the mAP increased by 0.4%, indicating that the BiFPN module integrated the defect features of different scales more fully.  
From the comparison of the YOLOv5s-KEB model proposed in this paper and the original YOLOv5s model, it can be seen that the volume increased by 14.6% and the detection speed was reduced by 3.1%, but the inference time was reduced by 10.3% and the mAP was improved by 1.0%. In particular, the detection accuracies for crack and scratch defects were respectively improved by 2.0% and 1.1%, which was a significant enhancement.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 2 Materials and Methods
### Image Processing  
The color of the wheat image acquired in the natural scene of the field will be different from the light intensity of visual perception due to changes, which will impact the effectiveness of CNN for disease feature extraction. In this study, we use the single-scale Retinex [23] algorithm to enhance the images. Its purpose is to separate the illumination and reflection from the image, and then process the reflection to improve the contrast and color restoration of the image. The algorithm is as follows:  
Take the logarithm of the image:  
\[\log I(x,y)=\log L(x,y)+\log R(x,y) \tag{1}\]  
where \(I(x,y)\) is the brightness of the image, \(L(x,y)\) is the illuminance, and \(R(x,y)\) is the reflection. Illuminance represents the intensity and direction of the light source, and reflection represents the inherent properties of the object.  
Perform Gaussian filtering on the image to obtain an approximation of the illuminance:  
\[L(x,y)\approx I(x,y)*G(x,y) \tag{2}\]  
Among them, \(G(x,y)\) is a Gaussian kernel function and \(*\) represents a convolution operation.  
Subtract an approximation of the illuminance from the image to obtain the logarithm of the reflection:  
\[\log R(x,y)\approx\log I(x,y)-\log L(x,y) \tag{3}\]  
Gray-scale stretching is performed on the logarithm of the reflection to obtain an enhanced reflection:  
\[R^{\prime}(x,y)=\frac{\log R(x,y)-\min(\log R(x,y))}{\max(\log R(x,y))-\min( \log R(x,y))}\times 255 \tag{4}\]  
Multiply the enhanced reflection with the original illuminance to obtain the enhanced image:  
\[I^{\prime}(x,y)=L(x,y)R^{\prime}(x,y) \tag{5}\]  
Figure 2a is the original image, and Figure 2b is the enhanced image.  
Figure 2: Image enhancement. (**a**) Original image; (**b**) enhanced image.
# A 7.5-9 GHz GaAs Two-Channel Multi-Function Chip
## 2 Circuit Design
### Attenuator Design  
The attenuator is comprised of six attenuation bits which contain 64 states. The maximum attenuation is 31.5 dB, in 0.5 dB steps. Figure 3 shows the schematic of the attenuator; the order of 6-bit attenuation is ranked by the performance of input and output return loss. The on-state (0 V) and off-state (\(-\)3 V) of each FET is controlled by the gate bias voltage that is provided by the serial-to-parallel converter.  
In the 0.5 dB and 1 dB attenuation bits, a simplified T-type network with series-FETs topology is used; the topology introduces a low insertion loss. Two dB and 4 dB bits adopt the bridge-T-type structure, which has great terminal matching characteristics. For the large attenuation of the 8 dB and 16 dB bits, parasitic parameters of FETs would bring more additive phase shift and deteriorate the flatness of the attenuation characteristic. Therefore, switch-path-type topology is employed. The required attenuation is obtained by switching the signal between a long micro-strip and a pi-type resistor network. By adjusting the length of the micro-strip between the two paths, the additive phase shift can be decreased. Meanwhile, a good flatness of attenuation is achieved by using two single pole double throw (SPDT) switches, which provide high isolation between the reference and attenuation states.  
The parameters of attenuator design are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline FET1 & 4 \(\times\) 45 \(\upmu\)m & FET6 & 8 \(\times\) 80 \(\upmu\)m \\ FET2 & 6 \(\times\) 45 \(\upmu\)m & \(C\) & 2.59 pF \\ FET3 & 4 \(\times\) 80 \(\upmu\)m & \(L1\) & 632 pH \\ FET4 & 6 \(\times\) 45 \(\upmu\)m & \(L2\) & 605 pH \\ FET5 & 6 \(\times\) 65 \(\upmu\)m & \(L3\) & 571 pH \\ \hline \end{tabular}
\end{table}
Table 1: The parameters of SPST switch design.  
Figure 3: The schematic of the 6-bit digital attenuator.  
Figure 2: The structure of the absorptive single pole single throw (SPST) switch.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 5 Proposed Scheme
### RTT Estimation  
As shown in Figure 3, successful delivery without retransmission may be rare depending on the network condition. In such a case, it is useful to employ weak RTT estimation, which measures RTT using retransmitted segments. As mentioned in Section 3, CoCoA measures RTT between sending the first transmission and receiving the ACK, and it ignores RTT samples obtained after the third retransmission in order to avoid taking a much larger RTT estimate than the actual RTT. However, the results of our preliminary test with ContikiMAC indicated that we need to take a different approach to measuring weak RTT and setting RTO. We found that RTT sometimes got very large even when we took an RTT sample using the latest retransmission. In this case, measuring RTT between sending the first transmission and receiving the ACK would have rendered an RTT sample much larger than the actual RTT. Thus, we always measure RTT between the latest (re)transmission and the ACK regardless of how many retransmissions are performed. Once an RTT sample is taken, we calculate RTO without differentiating weak RTT from strong RTT.
## 5 Discussion
### Limitation and Future Works
#### 5.3.3. Expandability of Tactics Model  
The current tactics model relies on a short-term dynamic optimization approach, where each state is determined solely based on the previous state. As a result, optimizations that consider the long-term motion state or more refined tactics capable of controlling the system within a single step were not addressed. Additional tactics could be incorporated to enhance further the model's expressiveness, such as time-varying tactics to account for fatigue or an energy-focused strategy to optimize the running economy.  
The movements of professional runners are optimized through training, making their motion tactics distinct. By constructing a motion tactics model based on such representative motion data, it becomes possible to investigate the tactics required to maximize a given objective. Based on the findings, we can analyze how tactics change over time and identify the necessary strategies for energy optimization, enabling the rational addition of new tactics. Furthermore, in the automation of gain tuning, we propose constructing a feasible parameter space based on typical movements. By subsequently applying optimization algorithms or machine learning techniques, the tuning process can be automated effectively.  
It is, however, impractical to include all aspects of human body control in the model; rather, it is essential to design the model appropriately based on the specific kinematic elements that are to be reproduced. Designing a tactics model tailored to the specific motion to be reproduced enables simulating motion generation according to the desired objectives.
## 6 Experimental Results
### Performance Evaluation
#### 6.3.6 Woo Lam Pi Protocol  
We have established the following delay values for the WLP protocol (Figure 8): \(D_{1}=2\), \(D_{2}=3\), \(D_{3}=4\). Our analysis revealed two paths indicating potential Man-in-the-Middle behaviour, with locations 71 or 131 being reachable. These two paths represent honest executions, with locations 5 or 11 being reachable, while ten other paths represent executions with an intruder, where locations 17, 29, 35, 41, 53, 77, 83, 89, 101 or 113 are reachable. For this protocol, the values \(L_{1}=L_{2}=8\) are the minimum values allowing both honest executions. Furthermore, the minimum values that allow Man-in-the-Middle behaviour to appear (locations 71 and 131 are reachable) are \(L_{1}=L_{2}=10\).  
Figure 7: NSPKT Lowe’s modification.
## 3 Results and Discussion
### Water Quality in the Miranda River Watershed  
A multitemporal and spatial analysis of water quality was conducted in the 17 Areas of Influence for the parameters DO, pH, BOD, thermotolerant coliform, TN, TP, TS, Tu, and WQI. The quality parameters were analyzed following the classification metrics for Class 2 (Figure 4), according to CONAMA Resolution No. 375/05 [27] and IMASUL's CECA-MS Deliberation N\({}^{\circ}\). 36/12 [18].  
The results showed that compliance with water quality parameters according to Brazilian legislation in the Miranda Basin in the period ranging from 2005 to 2018 is similar, both for wetland areas and for the Cerrado. It is observed that most samples collected for thermotolerant coliforms and TP do not comply with the legislation, both for wetland areas and for the Cerrado, with a compliance rate below 32.14% for TP. However, an improvement is observed for thermotolerant coliforms compliance with the legislation when wetland areas are compared to the Cerrado. On the other hand, it is observed that pH has the best compliance rate, reaching 100% in wetland areas.  
For DO, a reduction in compliance with the legislation is observed; in the Cerrado, 88.1% of samples were within the allowed limit, while in wetland areas, there was a reduction to 76.47%. The reduction in DO in Pantanal wetland areas is related to the natural phenomenon known as \"dequada\" or \"decoada\", where floodwaters promote the decomposition of submerged organic matter, reducing the availability of DO in the water column [28]. Wetland areas have large amounts of stored organic matter, about 50% of which is carbon. Carbon associated with other nutrients such as phosphorus and nitrogen can reduce the amount of DO due to its demand in biochemical reactions involving these elements and also by decomposition of the stored organic matter [29].
## 3 The Proposed Solution
### Detailed Protocol Description
#### 3.3.3 V2I Communication
To verify the signature \((l,s,b),(m,m^{\prime})\), send by \(V\) to an RSU, the receiver must compute again \(e=H(l||m^{\prime}||\overline{m})\) and check if \(e=H(g^{s}v^{\prime}||m^{\prime}||H(m||b))\). Figure 3: Secure Communication. \(V\) forwards to \(RSU\)\([(l,s,b),(m,m^{\prime})]\). The RSU will verify the signature and the current time \(t_{cur}\) included in \(m^{\prime}\) to accept a message. Notice that even if SA is corrupted it can gain no additional information if the RSU is honest. In addition, a corrupted RSU will learn nothing of a vehicle's identity since it only checks the validity of SA's signatures.
# Modification of Nonlinear Controller for Asymmetric Marine Vehicles Moving in Horizontal Plane
## Appendix A  
The dynamic equation of a vehicle, instead of (2), may be expressed in the form:  
\[M\dot{\
u}_{r}+C(\
u_{r})\
u_{r}+D\
u_{r}=Bf, \tag{1}\]  
where:  
\[B=\left[\begin{array}{cc}b_{11}&0\\ 0&b_{22}\\ -Y_{13}b_{11}&b_{32}\end{array}\right], \tag{2}\]  
is the actuator configuration matrix and \(f=[T_{u},T_{r}]^{T}\), where \(T_{u}\) is the thruster force and \(T_{r}\) is the rudder angle. The vector \(Bf\) can also represent the force and moment resulting from the configuration of the motors. Taking into account the velocity transformation and its time derivative:  
\[\
u_{r}=Y_{1}\kappa_{r},\ \ \kappa_{r}=Y_{1}^{-1}\
u_{r},\ \ \kappa_{r}=[u_{r}-Y_{13}r, \
u_{r},r]^{T},\ \ \dot{\kappa}_{r}=Y_{1}^{-1}\dot{\
u}_{r}. \tag{3}\]  
Instead of (4), the kinematic Equations (\(u_{r}=\kappa_{1r}+Y_{13}r\)) have the form:  
\[\dot{x} =(\kappa_{1r}+Y_{13}r)\cos\psi-v_{r}\sin\psi+V_{x}, \tag{4}\] \[\dot{y} =(\kappa_{1r}+Y_{13}r)\sin\psi+v_{r}\cos\psi+V_{y},\] (5) \[\dot{\psi} =r. \tag{6}\]Inserting \(\mathrm{Y}_{1}\) from (30) into (A1), and multiplying by \(\mathrm{Y}_{1}^{T}\), as a result, the equation of motion dynamics is as follows:  
\[M\dot{v}_{r}+C(v_{r})v_{r}+Dv_{r}=Bf,\] (A7) \[\mathrm{Y}_{1}^{T}M\mathrm{Y}_{1}\dot{\kappa}_{r}+\mathrm{Y}_{1}^{ T}C(v_{r})v_{r}+\mathrm{Y}_{1}^{T}Dv_{r}=\mathrm{Y}_{1}^{T}Bf,\] (A8) \[P\dot{\kappa}_{r}+\mathrm{Y}_{1}^{T}C(v_{r})v_{r}+\mathrm{Y}_{1}^ {T}Dv_{r}=\mathrm{Y}_{1}^{T}Bf.\] (A9)  
Denoting \(B^{*}=\mathrm{Y}_{1}^{T}B\), it can be written:  
\[\dot{\kappa}_{r}=-P^{-1}H+P^{-1}B^{*}f,\] (A10)  
where \(H=\mathrm{Y}_{1}^{T}C(v_{r})v_{r}+\mathrm{Y}_{1}^{T}Dv_{  
it should be assumed \(W=a_{1}\delta_{3 max}r+a_{1}\delta_{3 max}v_{r}\), where \(a_{1}\) is a constant guaranteeing that \(W\geq\delta_{3 max}\). Taking into account the results obtained and denoting that \(X_{1}=\beta_{22}m_{11}+\beta_{23}m_{23}\), \(Y_{1}=\beta_{23}m_{22}-\beta_{23}m_{11}\) as well as \(X_{2}=\delta_{1 max}-\beta_{22}d_{23}-\beta_{23}d_{312}\) (or \(X_{2}=a_{1}\delta_{3 max}-\beta_{22}d_{23}-\beta_{23}d_{312}\)), \(Y_{2}=\beta_{22}d_{22}+\beta_{23}d_{32}-\delta_{2 max}\) (or \(Y_{2}=\beta_{22}d_{22}+\beta_{23}d_{32}-a_{1}\delta_{3 max}\)) Equation (118) can be rewritten as:  
\[\hat{v}_{r}=X(\kappa_{1r})r+Y(\kappa_{1r})v_{r}, \tag{121}\]  
where: \(X(\kappa_{1r})=-X_{1}\kappa_{1r}+X_{2}\) and \(Y(\kappa_{1r})=-Y_{1}\kappa_{1r}-Y_{2}\).  
Equations (121)-(122) are analogous to the dynamic equations in [38], but include the quasi-velocity \(\kappa_{1r}\) resulting from the partial decomposition of the inertia matrix \(M\). A similar analog can be found in Equation (121).
# Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites
## 2 Detection Process of Surface Defects in SiCp/Al Composites
### Training and Evaluation of Defect Detection Model  
#### 2.3.1 Environment Configuration and Evaluation Criterion  
The environment configuration used for the implementation of the algorithm is as follows.  
Operating system: Microsoft Windows 10; development language: Python 3.6; GPU accelerated library: CUDA 9.2 And CUDNN 7.6.2; CPU: Intel Xeon W-2255; GPU: Nvidia GeForce RTX 2080 Ti 11G; RAM: 64G.  
The mean average precision (mAP) is usually chosen as the evaluation criterion for detection results. The mAP is the weighted average of the average accuracy (AP) for all detection categories. The AP value is the area under the P-R curve formed by connecting points consisting of precision and recall at different confidence levels. The values of precision, recall, AP, and mAP can be obtained from Equations (1)-(4).  
\[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\] \[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}}\] (2) \[\text{AP}=\int_{0}^{1}\text{P(R)\text{ d}R}\] (3) \[\text{mAP}=\frac{1}{N}\sum_{i=1}^{N}\text{AP}_{i} \tag{4}\]  
TP (True Positive) is the number of positive samples predicted to be positive, TN (True Negative) is the number of negative samples predicted to be negative, FP (False Positive) is the number of negative samples predicted to be positive, and FN (False Negative) is the number of positive samples predicted to be negative.
# Research on the Optimization Method of Bus Travel Safety Considering Drivers' Risk Characteristics
## 5 Results
### Driver Safety Assessment  
Based on historical bus warning data, a quantitative evaluation of bus driver safety under different adverse weather and traffic time periods was conducted. A safety assessment database for each driver in different driving environments was established, as shown in Table 5. It can be observed that the safety of driving behavior fluctuates for the same driver under different driving environments, indicating significant variations in individual drivers' sensitivity to different conditions.  
Comparing the weights of acceleration risk and deceleration risk, it is evident that the weight of acceleration risk for bus drivers is generally higher than the corresponding weight of deceleration risk. This indicates significant differences in acceleration risk among drivers, warranting closer attention. When comparing the weights of indicators for different weather conditions and time periods, it can be observed that the weights for each time period are relatively high and similar. Regarding the weights for different weather conditions, those for low temperature and low visibility are higher, suggesting that these two weather conditions may lead to sudden incidents and should be given more attention.
# Direct Selective Oxidation of Hydrogen Sulfide: Laboratory, Pilot and Industrial Tests
## 22 Facility for the Purification of Gases Caused by Blowing-off Sour Crude Oil
\begin{table}
\begin{tabular}{c c c c} \hline \hline \# & **Compound** & **Initial Feedstock Gas, \%Vol.** & **Purified Gas, \%Vol.** \\ \hline
1 & H\({}_{2}\)S & 1.50 & \textless{}50 ppmv \\
2 & Water & 0.69 & 2.030 \\
3 & He & 0.05 & 0.04 \\
4 & Hydrogen & 0.006 & 0.004 \\
5 & Oxygen & 0.04 & 0.92 \\
6 & CO\({}_{2}\) & 4.70 & 4.56 \\
7 & Nitrogen & 39.82 & 41.00 \\
8 & Ethane & 9.60 & 9.60 \\
9 & Methane & 25.60 & 24.22 \\
10 & Propane & 9.96 & 9.80 \\
11 & iso-Butane & 2.02 & 1.96 \\
12 & n-Butane & 3.45 & 3.34 \\
13 & neo-Pentane & 0.003 & 0.003 \\
14 & iso-Pentane & 1.23 & 1.19 \\
15 & n-Pentane & 0.85 & 0.81 \\
16 & Hexanes & 0.32 & 0.31 \\
17 & Heptanes & 0.07 & 0.07 \\
18 & Octanes & 0.10 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: GC analysis of initial and purified gas. bed according to fluctuations in the H\({}_{2}\)S content in the feed gas. With such technology, capital costs are significantly reduced thanks to the need of fewer parts in the process chain and the significantly lower metal weight. The operational costs are also reduced due to lower energy consumption and the reduced number of required service personnel, which ultimately leads to a decrease in the cost of the final product, i.e., elementary sulfur. The absence of a flame furnace increases the environmental friendliness of the process due to the absence of the formation of toxic side products which occur due to high-temperature interactions of H\({}_{2}\)S with CO\({}_{2}\)-carbonyl sulfide and carbon disulfide.