esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
###### Abstract  
Aiming at the phenomenon of partial discharge caused by the insulation fault of switchegears, an ultra-high frequency (UHF) partial discharge online monitoring system based on the Internet of Thing (IoT) is designed. The hardware of the system mainly consists of UHF sensors, signal conditioning circuits, and data acquisition circuits to realize the monitoring of the discharge signal. The monitoring data is uploaded to the cloud platform through the 4G DTU module, and the host computer software based on B/S architecture is designed on the server side, which can monitor the operation status of the switchgear anytime and anywhere. The experimental results show that the detection system is stable and reliable and can meet practical needs.  
partial discharge; ultra-high frequency signal; logarithmic detection circuit; online monitoring +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: School of Electronic Engineering, Xi’an University of Post and Telecommunications, Xi’an 710121, China; [EMAIL_ADDRESS] (X.Y.); [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (L.B.)  
* Correspondence: [EMAIL_ADDRESS]
# _remote sensing_
## 4 Discussion
This phenomenon indicates that there is an optimal time for disease monitoring. In a disease control process, the appropriate time should be selected for disease monitoring based on the physiological characteristics of the disease. In terms of the sensitive bands of the disease, there is consistency at the leaf scale and canopy scale, and the disease monitoring model developed based on the sensitive bands has good performance in the monitoring of yellow rust when using Sentinel-2 images. These results indicate that the bands more sensitive to the disease, identified at the leaf scale on the ground, can be transferred to large scale disease monitoring. However, since the pixels of satellite images contain complex information, regional-scale disease monitoring should combine spectral information with meteorological and environmental information to develop a more comprehensive monitoring model.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 2 Related Works
### Adversarial Defense
#### 2.3.2 Defensive Distillation
In defensive distillation, the gradient amplification concept derived from knowledge distillation is used defensively to hinder the attackers' access to the gradient. Unlike knowledge distillation, defensive distillation employs the same structure for both the teacher and student models. Defensive distillation escalates the softmax output of the supporting network during the learning process via temperature modulation and transfers it to the target network, thereby reinforcing adversarial resilience. After training, the temperature returned to 1, which amplified the certainty in the target class. This makes it difficult for attackers to decipher the gradient while simultaneously augmenting the classification process of the model. However, defensive distillation did not enhance the robustness of the model. Rather, it prevents attackers from understanding the gradient, thereby aligning it with the characteristics of the obfuscated gradients. These characteristics suggest limitations when applied alone, as they do not conform to fundamental defense strategies. In accordance with the technique proposed by [PERSON] et al. [26], it was ascertained that defensive distillation lacks robustness when utilized independently. Furthermore, studies investigating the use of knowledge distillation in a defensive context have confirmed that a model employing both knowledge and defensive distillation techniques has security vulnerabilities in the commercialization stage [42; 43].
## 2 Materials and Methods
### Performance Assessment  
Evaluation of the proposed method was based on accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). Accuracy was defined as the ratio of correctly diagnosed subjects to all subjects. Sensitivity was defined as the ability to detect true positives (early PD) as positives, and specificity was defined as detecting true negatives (HC) as negatives. The receiver operating characteristic curve was defined as the plot of the classification ability, with the \(x\)-axis representing the false-positive rate (1-specificity) and the \(y\)-axis representing the true positive rate (sensitivity). Theoretically, AUC would be in the range of 0.5 to 1, with an AUC of 1 representing the perfect classification result.  
To evaluate the performance of SVM and LR, we applied two methods for classification models. The first method was to use all datasets to obtain average performance from 10-fold cross-validation. Because data distribution in folds for training and validation affects the performance of models due to the small number of datasets, data distribution was randomized 10 times and divided differently into folds. Thus, each cross-validation has different training and validation datasets. The average performance of models was calculated. The second method was to use 80% of datasets for training and 20% of datasets for the test. The training was performed to determine optimal features; datasets validated one time and training model with the optimal features. For NMSS classification models, data were not divided into training and test sets because the number of datasets is only 48. Instead, the first method was performed to evaluate the average performance of the NMSS models.  
For SVM, the nested cross-validation was performed to measure the optimal performance. The nested cross-validation is used to search hyperparameters of the SVM model during cross-validation. The nested cross-validation consists of two folds: inner and outer folds. The inner fold determines the optimal hyperparameters of a classification model, changing the hyperparameters of the model iteratively. In the outer fold, the same dataset is applied to the classification model tuned with optimal hyperparameters to estimate generalization error [24].  
A paired \(t\)-test was used to compare SVM and LR models' results with the same number of features and compare the best SVM and LR models. Paired t-tests were used to assess the accuracy, sensitivity, specificity, and AUC of the models, and a \(p\)-value \(<\) 0.05 was considered significant.
## 3 Quasi-Static Force Analysis
### Tooth Contact Analysis  
Hertzian contact theory is the theory of contact mechanics applicable to the analysis of non-adhesive contact; there are several assumptions in Hertzian contact theory:  
1. The strain is very small and within the elastic range of the contacting object.
2. The contact surface of the contacting object is much smaller than the radius of the contacting object.
3. The contacting surface is continuous.
4. There is no friction on the contacting surface.  
The contact between two cylindrical surfaces with parallel center axes is depicted in Figure 2. According to the assumption of [PERSON]'s contact theory, it can be deduced that when two cylindrical surfaces with parallel central axes are in contact, the force is linearly proportional to the depth of the indentation, as shown in Equation (9):\[F=\frac{\pi}{4}E^{*}Ld \tag{9}\]  
where \(d\) is the depth of indentation at the point of contact, \(L\) denotes the cylinder's length, and \(E^{*}\) corresponds to the equivalent elastic modulus. This modulus can be derived from Equation (10), where \(E_{1}\) and \(E_{2}\) are the elastic moduli of the materials for the two contact bodies, with \(\
u_{1}\) and \(\
u_{2}\) symbolizing their respective Poisson ratios.  
\[E^{*}=\left(\frac{1-v_{1}^{2}}{E_{1}}+\frac{1-v_{2}^{2}}{E_{2}}\right)^{-1} \tag{10}\]  
Considering the continuous contact surfaces of both the roller and the cycloid gear, their interaction is conceptualized as a parallel cylindrical contact along the central axes in accordance with Hertzian theory, which provides the framework for force analysis between these components.
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
## 3 Experimental Test of Output Torque for a PMSM
### Error Analysis and Compensation  
The rotor of the PMSM is heavy, which causes great pressure on the contact surface of the support structure. Therefore, it inevitably brings error for measuring output torque during the start-up process of the rotor. When the rotor moves, the error caused by the friction will reduce the accuracy of torque measurement. This type of error can be estimated by repetitive experiments and the targeted error compensation can be conducted to improve the accuracy of the measurement.  
In order to study the influence of friction torque on the measurement error of the output torque, the maximum static friction in the initial position of rotor is measured by a tension meter. Figure 16 shows the measurement of the maximum static friction. The distance from the top of the rotor output shaft to the center of the rotor is 0.1 m, that is, the force arm of the maximum static friction force is 0.1 m. The tension meter and the top of the output shaft are connected by a thin nylon wire. A spirit level is used to ensure that the tension is perpendicular to the output shaft. Then, the tension meter is slowly pulled along the negative direction of the OX axis and the data displayed in the tension meter are recorded at the moment of rotor startup. The measurement for static friction is repeatedly conducted by 20 times and the measured results are listed in Table 4.  
The average value of the static friction measured by the above 20 groups of experiments is used as the estimated maximum static friction for the compensation. The average is 69.7 mN-m. The output torques are compensated and are shown in Figure 17.  
Tables 5 and 6 show the measurement errors after the compensation, and it can be seen that the measurement errors decrease to 0.39-23.47%.  
Figure 16: Measurement of the maximum static friction.  
After the compensation of the maximum static friction, the remaining error can be analyzed in two ways. Firstly, since the initial position of the rotor is manually adjusted, the motor stator and rotor in the coordinate system OXYZ cannot be perfectly aligned, which will change the experimental motion conditions and affect the measurement of the output torque. Secondly, only the maximum static friction is considered in this paper for compensation because of the limitation of the experimental method. In fact, other complex friction which is not estimated will affect the effectiveness of the compensation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Maximum Static Friction (mN-m)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Measurements of maximum static friction.  
Figure 17: Measured torques after compensation: (**a**) Measured torques at different currents; (**b**) Measured torques at different angles.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 4 Results
### Variation in Runoff in the GRB  
#### 4.4.1 Validation of Runoff Simulation Accuracy  
Figure 5 shows the scatter plots of the simulated and measured monthly runoff data at Waizhou station during the parameter calibration period, with an overall good fitting accuracy and an R\({}^{2}\) of 0.88 between the simulated and measured values.  
Figure 6 depicts the flow duration curves (FDCs) of the SWAT model at monthly time scales. The variability in the runoff is displayed by the FDC at the Waizhou station for the calibration and validation periods. We can find that the SWAT model simulates the pattern of low, medium, and high runoff satisfactorily at the monthly scale both in the calibration and validation period.  
Figure 5: Scatter plot of simulated and measured monthly runoff of Waizhou station during calibration period. Note: the cumec represents the unit of runoff in cubic meters per second.  
The measured runoff data of Fenkeng station, Hanlinqiao station, Xiashan station, and Junmening station in 2012 and 2015 were used to further verify the accuracy of the simulated runoff, as shown in Figure 7. The results show that R\({}^{2}\) between the modeled and measured runoff ranges from 0.66 to 0.85.
# Permeability and Stiffness Assessment of Paved and Unpaved Roads with Geocomposite Drainage Layers
## 3 Test Methods
### Air Permeameter Test (APT)  
The APT device was developed for rapid in situ saturated hydraulic conductivity determination for granular materials (Figure 5a). The APT device consists of a contact ring, differential pressure gauges, precision orifices, and a programmable digital display (Figure 5b).  
Figure 4: Photos of (**a**) core-hole permeameter (CHP) device; (**b**) CHP test conducted on the paved test sections and (**c**) GC-1 material after the CHP test.  
The APT device measures the gas pressure on the inlet and outlet sides of the precision orifice and calculates the gas flow rate. From these measurements and the material parameters, the gas permeability of the material being tested can be derived and converted to water saturated hydraulic conductivity (K\({}_{\text{sat}}\)) by taking into account the effect of partial saturation (Equation (4)).  
\[\text{K}_{\text{sat}}=\left[\frac{2\mu_{gas}QP_{2a}}{rG_{0}(P_{2a}{}^{2}-P_{atm} {}^{2})}\right]\times\frac{\rho g}{\mu_{water}(1-S_{e})^{2}\big{(}1-S_{e}(^{(2+ \lambda)/\lambda)}\big{)}} \tag{4}\]  
where K\({}_{\text{sat}}\) is the saturated hydraulic conductivity (cm/s), \(\mu_{gas}\) is the kinematic viscosity of the gas (Pa-s), \(Q\) is the flow rate (cm\({}^{3}\)/s), \(P_{2a}\) is the absolute gas pressure on the surface (Pa), \(r\) is the radius at the outlet (cm), \(G_{0}\) is the geometric factor determined from test layer thickness, \(P_{atm}\) is the atmospheric pressure (Pa), \(\rho\) is the density of water (g/cm\({}^{3}\)), \(g\) is the acceleration due to gravity (cm/s\({}^{2}\)), \(\mu_{water}\) is the kinematic viscosity of water, \(S_{e}\) is the effective saturation, and \(\lambda\) is the Brooks-Corey pore size distribution index.  
The derivation of Equation (4) involves an expansion of Darcy's Law considering the compressibility and viscosity of the gas, the gas flow under partially saturated conditions, and the Brooks-Corey pore size distribution index. The development of the testing method, description of the device, and derivations of the theoretical relationship to calculate the hydraulic conductivity from the  
Figure 5: (**a**) Air permeameter test (APT) test conducted on unpaved test section and (**b**) schematic showing the major components of the APT device [13].  
gas flow are detailed by [PERSON] et al. [19]. The repeatability of the APT measurements, expressed as a coefficient of variation (COV), is \(\leq\)1% [20]. In this study, the APT test was conducted to measure the in situ saturated hydraulic conductivity as a function of depth for the unpaved road test sections.
# Improving Safety in Food Allergen-Specific Oral Immunotherapy
## 3 Food Allergen Immunotherapy
### Adjuants in Oral Immunotherapy
#### 3.4.4 Vitamin D  
The immunomodulatory effect of vitamin D has gained a lot of attention in allergology recently. Vitamin D is responsible for the downregulation of proinflammatory signaling pathways and affects the Th1/Th2 balance. Vitamin D deficiency can be responsible for the overproduction of IgE and IL-4 in allergen responses [86; 87]. Additionally, vitamin D possibly plays a role in the stability and integrity of the mucosal epithelial barrier, and its insufficiency leads to inappropriate exposure to antigens and food allergies [88]. Cross-sectional studies in infants found that CMA was related to lower vitamin D levels, and infants with deficiency had a few-fold increased risk of egg, peanut, and multiple food allergies [89; 90]. While recent observations confirmed the positive effects of vitamin D adjuvant therapy in allergic rhinitis, such data for food allergy OIT are missing [91].
# Influence of Different Fibers on Performance of Bitumen Binders and Thin-Overlay Bitumen Mixtures
## 2 Materials and Methods  
### Materials  
Road petroleum bitumen adopts finished SBS-modified bitumen. Both coarse and fine aggregates are diabase, produced in Baise, Guangxi. The ore powder is made of ground limestone as filler. The bitumen-aggregate ratio of the SMA-10 mixture is 5.98%, and its gradation range requirements are shown in Table 1.  
Three kinds of fiber bundles were selected as the research objects, namely basalt fiber, polypropylene fiber, and glass fiber. In prior research and engineering applications, fibers with diameters of 3 mm, 6 mm, and 9 mm have been utilized quite often. However, when it comes to enhancing the toughness of the composite, the 3 mm fibers have proven to be somewhat lacking. The 9 mm fibers are quite difficult to disperse due to their bulkier size, which ultimately prevents the establishment of a robust spatial network within the bitumen mortar. Therefore, 6 mm was selected as the test size [33; 34; 35]. According to DB45/T 2531-2022 [36], the content of polymer fiber should be 0.2-0.4% of the quality of the bitumen mixture, which is adjusted according to the technical performance of the mixture. When the bitumen-aggregate ratio is 5.98%, the fiber content accounts for 3.5-7% of the bitumen. Due to the high content of bundled fiber, it is more likely to agglomerate and result in the loss of low-temperature performance of the mixture [37; 38; 39; 40; 41]. Therefore, this paper used 0%, 2%, and 4% three dosage tests. (In the following, the samples with different fiber contents of 2% and 4% are abbreviated as basalt fiber: B-2, B-4; polypropylene fiber: P-2, P-4; glass fiber: G-2, G-4). The details of fiber indicators are shown in Table 2.
## 3 Methods
### Time Series-Based Methods
#### 3.3.3 Convolutional Long Short-Term Memory  
ConvLSTM processes data by utilizing convolutional operations to update the states of the network. Let the input at time step \(t\) be denoted as \(\mathbf{X}_{t}\in\mathbb{R}^{h\times w\times c}\), where \(h\) and \(w\) are the height and width of the input, and \(c\) is the number of channels. The hidden state and cell state at time step \(t\) are represented as \(\mathbf{H}_{t}\) and \(\mathbf{C}_{t}\), respectively, each with dimensions \(h\times w\times d\), where \(d\) is the number of filters (or hidden units). The ConvLSTM equations for the input gate, forget gate, cell state, and output gate are defined as follows [31]:  
\[\begin{split}\mathbf{i}_{t}=\sigma(\mathbf{W}_{xi}*\mathbf{X}_{t }+\mathbf{W}_{hi}*\mathbf{H}_{t-1}+\mathbf{W}_{ci}\circ\mathbf{C}_{t-1}+ \mathbf{b}_{i})\\ \mathbf{f}_{t}=\sigma\Big{(}\mathbf{W}_{xf}*\mathbf{X}_{t}+ \mathbf{W}_{hf}*\mathbf{H}_{t-1}+\mathbf{W}_{cf}\circ\mathbf{C}_{t-1}+ \mathbf{b}_{f}\Big{)}\\ \mathbf{C}_{t}=\mathbf{f}_{t}\circ\mathbf{C}_{t-1}+\mathbf{i}_{t }\circ\tanh(\mathbf{W}_{xc}*\mathbf{X}_{t}+\mathbf{W}_{hc}*\mathbf{H}_{t-1}+ \mathbf{b}_{c})\\ \mathbf{o}_{t}=\sigma(\mathbf{W}_{xo}*\mathbf{X}_{t}+\mathbf{W}_{ho }*\mathbf{H}_{t-1}+\mathbf{W}_{co}\circ\mathbf{C}_{t}+\mathbf{b}_{o})\\ \mathbf{H}_{t}=\mathbf{o}_{t}\circ\tanh(\mathbf{C}_{t})\end{split} \tag{14}\]  
In Equation (14), \(\mathbf{W}_{xi}\), \(\mathbf{W}_{xf}\), \(\mathbf{W}_{xc}\), and \(\mathbf{W}_{xo}\) are the convolutional kernels for the input-to-state connections, while \(\mathbf{W}_{hi}\), \(\mathbf{W}_{hf}\), \(\mathbf{W}_{hc}\), and \(\mathbf{W}_{ho}\) are the convolutional kernels for the state-to-state connections. The weight matrices \(\mathbf{W}_{ci}\), \(\mathbf{W}_{cf}\), and \(\mathbf{W}_{co}\) are associated with the peephole connections, and the terms \(\mathbf{b}_{i}\), \(\mathbf{b}_{f}\), \(\mathbf{b}_{c}\), and \(\mathbf{b}_{o}\) are the bias terms. The convolution operation is represented by \(*\), while \(\circ\) denotes the Hadamard (element-wise) product. Equation (14) shows how ConvLSTM incorporates spatial information through convolutional operations rather than matrix multiplications. The input gate (\(\mathbf{i}_{t}\)) controls how much of the current input information is passed to the cell state, while the forget gate (\(\mathbf{f}_{t}\)) determines which information from the previous cell state (\(\mathbf{C}_{t-1}\)) should be retained or forgotten. The cell state update equation combines the effects of the input and forget gates, allowing the network to maintain long-term dependencies. The output gate (\(\mathbf{o}_{t}\)) determines how much of the updated cell state contributes to the hidden state (\(\mathbf{H}_{t}\)), which is passed on to the next time step.
# AG1478 Elicits a Novel Anti-Influenza Function via an EGFR-Independent, GBF1-Dependent Pathway
## 2 Results
### The Target of AG1478 Is GBF1  
Off-target effects of AG1478 were demonstrated previously [16; 17; 18]. GBF1, a potential AG1478 cellular target [16], was also identified as one of the 91 host factors that affect IAV production [19]. Thus, we tested the hypothesis that the antiviral effect of AG1478 was mediated by GBF1. Indeed, compared with the overexpression of an irrelevant protein-GFP (control), GBF1 overexpression (Figure 4A) significantly enhanced IAV production (Figure 4B). Reciprocally, GBF1 knockdown by a siRNA against GBF1 (siGBF1) (Figure 4C) repressed IAV production (Figure 4D). Thus, GBF1 was required for IAV production.  
GBF1 is the guanine nucleotide exchange factor (GEF) that catalyzes GDP/GTP exchange of ARFs from their inactive GDP-bound (ARF1-GDP) to active GTP-bound (ARF1-GTP) form [20; 21; 22]. ARF1 is one of the downstream substrates of GBF1, and the amount of ARF1-GTP complex was a direct indicator of GBF1 activity. In our study, AG1478 treatment significantly reduced the amount of ARF1-GTP complex (Figure 4E), confirming the notion that AG1478 repressed GEF activity of GBF1. Additionally, the inhibitory kinetics of AG1478 were different from two established GBF1 inhibitors (i.e., GCA and BFA) [23]. GEF activity of GBF1 was mostly restored within 24 h under the treatment of AG1478, but not GCA or BFA (Figure 4E). GBF1 has also been shown to shuffle between cytoplasm and Golgi [22]. We found that AG1478 treatment concentrated GBF1 in the Golgi at the expense of its cytoplasmic pool (Figure 4F). There was very little cytoplasmic GBF1 after 24 h treatment of AG1478. Although this effect lasted longer than the inhibition of GEF activity, it was reversible as AG1478 washout quickly restored cytoplasmic GBF1 within 1 h (Figure 4F). Nonetheless, daily dosing of AG1478 demonstrated persistent antiviral activity as shown in Supplemental Figure S1.  
Figure 3: Anti-influenza activity of AG1478 was IFN independent. Beas2b cells were treated with 2 uM AG1478 and infected with A/WSN/1933, and gene expressions of (**A**) IFN \(\beta\) and (**B**) IFN \(\lambda\)1 were quantified by using real-time PCR. (**C**) IRF3 was knocked out by CRISPR. IRF3 protein expression was detected in wild-type cells (control), but not in the knockout cells (IRF3-KO). ACTIN was the loading control. This is a representative image from three independent repeats. In the wild-type or IRF3-KO cells, (**D**) IFN \(\beta\), (**E**) IFN \(\lambda\)1, (**F**) MX1, and (**G**) ISG15 gene expressions were measured by using real-time PCR. (**H**) Wild-type and IRF3-KO cells were infected with A/WSN/33 influenza viruses for 24 h, and viral titers were quantified by plaque assay. (**I**) IRF3-KO cells were treated with 2 uM AG1478 and infected with A/WSN/33 influenza virus (MOI 0.1), and viral titers were quantified by plaque assay. Data shown are mean \(\pm\) SEM. \(n=3\), \({}^{*}\) and \(\#\): \(p<0.05\). NS: not significant.