esa-sceva/satcom-chunk-collection huggingface dataset:
# A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves
## 6 Conclusions  
During long-term operation, the degradation of the iron cores in saturable reactors may become more and more severe, especially in terms of the looseness degree. This will have a negative impact on the healthy operation of the converter valve. Therefore, how to accurately identify the saturable reactor core looseness degree under complex operating conditions is of great significance for achieving condition-based maintenance of the converter valve. To solve this problem, this paper proposes a vibration signal recognition model for iron core looseness based on synchrosqueezed wavelet transform and a convolutional neural network. The advantages of this model are as follows: (1) It can cope with the challenges of complex variable operating conditions. (2) The method of time-frequency analysis plus a CNN can effectively identify the looseness degree of the iron core. (3) Compared with some other methods, this method has better diagnostic ability.  
Conceptualization, L.Z. and X.W.; methodology, T.S. and X.Z.; software, L.Z.; validation, T.S. and X.Z.; resources, X.W. and L.Z.; data curation, X.Z.; writing--original draft preparation, T.S.; writing--review and editing, T.S. and X.Z.; visualization, T.S.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the State Key Laboratory of Advanced Power Transmission Technology (Grant No. GEIRI-SKL-2020-003) and Beijing Natural Science Foundation (Grant No. 3212039).  
Not applicable.  
Figure 10: Comparison of Confusion Matrix for Looseness Degree Identification.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to intellectual property protection.  
We would like to thank [PERSON] from North China Electric Power University for his help and contribution to this study.  
All Authors are employed by the company State Grid Smart Grid Research Institute Co., Ltd. Author [PERSON] is also employed by the Beijing Institute of Smart Energy. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Mice Immunization  
Specific pathogen-free (SPF) 6- to 8-week-old male BALB/c mice (obtained from the animal facility of the Veterinary School, University of La Plata, Argentina) were immunized with 10\({}^{9}\) or 10\({}^{10}\) viral particles (vp) of Ad.C (empty vector), CoroVaxG.5 or CoroVaxG.3 in 30 \(\mu\)L PBS via an intramuscular injection in the hind leg. Serum samples for intermediate time points were obtained by submandibular bleeds for humoral immune response analyses. Final serum samples were obtained via the cardiac puncture of anesthetized mice. The collected whole blood was allowed to clot at 37 \({}^{\circ}\)C for 1 h before spinning down at 500\(\times\)\(g\) for 10 min. The clarified sera were stored at \(-20\)\({}^{\circ}\)C. Sera from naive animals were assayed for the presence of neutralizing 5 anti-adenovirus antibodies as described previously [27]. Briefly, sera dilutions (1:20-1:4860) were added to 96-well plates and mixed with 2.5 \(\times\) 10\({}^{7}\) vp hAd5 expressing Firefly luciferase and incubated for 1 h at 37 \({}^{\circ}\)C 5\(\times\) CO\({}_{2}\). Then, 5 \(\times\) 10\({}^{4}\) T84 cells per well were added and mixed fully with the medium. The results were read after 48 h of incubation. All pre-immune sera showed titers below the limit of detection of the assay (<1:20).  
For RBD inoculations, 8-week-old male BALB/c mice were immunized with 7.5 ug of the receptor binding domain of the Spike protein (kindly gifted by [PERSON], Argentina) in 75 \(\mu\)L Complete Freund's Adjuvant (CFA, Sigma, St. Louis, MO, USA) via subcutaneous injection and boosted 2 weeks later with 5 ug of RBD in 100 \(\mu\)L Incomplete Freund's Adjuvant (IFA, Sigma, St. Louis, MO, USA). The mice were bled 14 days after the boost and were maintained under specific pathogen-free conditions at the Institute Leloir animal facility.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 3 Materials and Methods
### Energy Model
If multiple routes are available, the system selects the optimal path by prioritizing minimal hop count, reduced energy cost, and the lowest retransmission rate. This step enhances network efficiency by mitigating excessive retransmissions and ensuring energy-aware data forwarding. Finally, the algorithm integrates a low-power mode activation mechanism. If a node's energy level drops below the energy threshold \(E_{thr}\), it transitions into a low-power state to extend its operational lifespan. This strategy helps prolong the overall lifetime of the network by preventing energy depletion in critical nodes. Throughout its execution, the algorithm continuously logs network adjustments and protocol switching decisions, providing a record of network behavior and adaptation strategies. By integrating adaptive protocol switching, intelligent routing, and energy-aware transmission policies, the proposed methodology ensures robust, energy-efficient, and resilient WSN operation, even in the presence of jamming attacks.
# Investigation of the Fatigue Life of Bottom-Up Cracking in Asphalt Concrete Pavements
## 4 Prediction of Fatigue Crack Life of Asphalt Pavements
### Fatigue Cracking Life  
Various fatigue cracking models of asphalt pavements have been mentioned. However, to keep consistent with the design specifications in China, the fatigue model recommended specifications for the Design of Highway Asphalt Pavement [22] was adopted in this paper, as shown in Equation (4).  
\[N_{i}=280\delta^{-4.5} \tag{4}\]  
where \(\delta\) is tensile stress, and \(N_{i}\) is fatigue cracking life.  
The tensile stresses at the bottom of a semirigid base of an asphalt concrete pavement and the flexible base of an asphalt concrete pavement are substituted into the fatigue equation to calculate the fatigue cracking life of each layer of the asphalt layer.  
When the temperature is 25 \({}^{\circ}\)C, 0 \({}^{\circ}\)C, and \(-10\)\({}^{\circ}\)C, the fatigue cracking life of each structural layer of the flexible base of the asphalt concrete pavement is shown in Table 8, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{2}{c}{**AC-13**} & \multicolumn{2}{c}{**AC-20**} \\ \cline{2-5}  & \(A\) & \(n\) & \(A\) & \(n\) \\ \hline
25 \({}^{\circ}\)C & \(1\times 10^{-6}\) & 2.572 & \(2\times 10^{-6}\) & 2.428 \\
0 \({}^{\circ}\)C & \(2\times 10^{-6}\) & 2.128 & \(1\times 10^{-5}\) & 1.906 \\ \(-10\)\({}^{\circ}\)C & \(6\times 10^{-6}\) & 2.515 & \(5\times 10^{-5}\) & 2.620 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fracture parameter calculation.  
Figure 11: The relationship between crack change rate and SIF.  
When the temperature is 25 \({}^{\circ}\)C, 0 \({}^{\circ}\)C, and \(-10\)\({}^{\circ}\)C, the fatigue cracking life of each structural layer of the semirigigid base in asphalt concrete pavement is shown in Table 9, respectively.  
It can be seen from Tables 5, 6, 8, and 9 that the fatigue cracking life increased with the increase in asphalt surface thickness and increased sharply with the decrease in tensile stress. The fatigue life increased from \(1\times 10^{7}\) to \(2.4\times 10^{8}\) when the tensile stress decreased from 0.16 to 0.08 (these tensile stresses at the bottom have been obtained in Figures 5 and 6). Therefore, it can be concluded that the tensile stress at the bottom of the asphalt layer decreased with the increase in surface thickness. The fatigue life of asphalt pavements can be improved by increasing the surface thickness, but this method will increase the construction cost. Therefore, the pavement structure design needs a reasonable thickness value.
# U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images
## 2 Related Work
### Object Detection Methods and U-Net
The main network structure of SSD is VGG16 [35], which replaces two full-connection layers with the convolution layer and adds four convolution layer network structures. Regarding the feature maps output by the five different convolution layers, the convolution layers of two 3 \(\times\) 3 convolution kernels are used to process them separately. One of the convolution layers outputs the confidence for classification, and each default box generates an N + 1 confidence value. The other convolutional layer outputs regression localization. Thus, SSD is highly accurate in real-time object detection and highly flexible in a network architecture. Therefore, this paper chose this architecture as the core, replaced VGG16 with U-Net, and the edge information was merged and retained through the autoencoder feature. The image segmentation network based on the encoder-decoder structure was proposed by [PERSON] et al. [28] in 2015, which is mainly applied in medical imaging. As the network structure is similar to U, it is called U-Net. Its network structure is a classical full convolutional network, which is divided into two parts. The first part is the Encoder, which performs feature extraction through down-sampling of the contracting path to obtain feature maps of different sizes, and a 32 \(\times\) 32 feature map is output. The second part is the Decoder. After up-sampling and feature extraction of the expansive path, the overlap-tile strategy is used to merge with corresponding sizes to overcome the problem of feature loss during feature transmission. This paper introduced this architecture to enhance the ability of feature retention.
# Absolute Distance Measurement Based on Self-Mixing Interferometry Using Compressed Sensing
## 4 Conclusions  
An approach to distance measurement based on SMI using compressed sensing is proposed in this work. Compared with traditional methods, compressed sensing requires far fewer sampling points. Acceleration based on the symmetry of the Fourier spectrum was added to the recovery algorithm for faster convergence. In addition, considering the need for interpolated correction, atoms at specific positions were selected to guarantee the robustness of the algorithm. Distance measurements were carried out for assessing the feasibility of the proposed method. The results showed that the differences in distance between the traditional method with 1800 data points and the CS method with 300 data points were within 0.15 \(\upmu\)m, which is minor when compared to the system measurement error of 60 \(\upmu\)m. Further experiments will be carried out to verify the practical application of using the CS method on satellites. To sum up, the proposed approach provides a promising idea for SMI distance measurement using limited resources.  
Conceptualization, Y.D.; project administration, Y.Z. ([PERSON]) and X.L.; writing--original draft preparation, L.L.; software and visualization, Y.Z. ([PERSON]); writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Strategic Priority Research Program of Chinese Academy of Sciences [grant number: XDA15020400], the Shanghai Municipal Science and Technology Major Project [grant number: 2019 SHZDZX01], and the Science and Technology Innovation 2030--Quantum Communication and Quantum Computer [grant number: 2021 ZD0300100].  
Not applicable.  
The data presented in this study are available upon request from the first author for noncommercial use.  
The authors declare no conflict of interest.
## 2 Problem Background
### Existing OppNet Routing Protocols for Sensed Data Collection
#### 2.2.2 OppNet Routing Protocols in Sensed Data Collection Scenarios
The energy consumption on the central nodes in Bubble Rap is about five, six and seven times that of the other nodes under low, medium, and high locality, respectively. This results in declining energy distribution as spatial locality increases. The throughput (i.e., the fraction of messages generated at source nodes that were delivered) under low, medium and high degrees of spatial locality is recorded in Figure 5. All the routing protocols experience a significant drop in throughput when the degree of spatial locality increases. Increasing the degree of spatial locality from low to medium reduces throughput by at least 52% and further increasing the degree of spatial locality from medium to high reduces throughput by at least 71%. The maximum throughput under low, medium, and high degree of locality is less than 45%, 19% and 6%, respectively. The encounter-based protocols (i.e., SnF and PRoPHET) perform slightly better under the different degrees of spatial locality. Figure 5: Throughput under low, medium and high degrees of spatial locality.
## 3 Results
### Bifacial Gain
The 3-D RT model also matches well, typically within 0.5%, with the bifacial gain measurements on the fixed-tilt system. However, 2-D VF models, such as the one integrated in SAM, compared equally well to field measurements. Indeed, the measured bifacial gain shown in Figure 12 is influenced by the value of \(\mathit{Bif}_{loss}\). The static \(\mathit{Bif}_{loss}\) values used here, in actuality, change dynamically over the day with the prevailing conditions [19; 57; 58]. All the commercial software tested here has the capability to use only a single value. This simplification offers room to improve the accuracy of the bifacial PV performance simulation tools used in industry today.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 3 Methods
### Validation  
To quantitatively represent the results of the comparison between CEMS-derived maps and AUTOWADE-derived open floodwater/permanent water maps, a confusion matrix  
Figure 11: Flowchart of the algorithm developed to map flooded vegetation implemented in AUTOWADE. Dark gray rectangles represent operations; parallelograms represent data (light gray: input/output data, dark gray: intermediate data).  
has been computed for each case study listed in Table 2, assuming the CEMS-derived maps as reference data. A confusion matrix summarizes the performance of a classification algorithm; three thematic classes have been considered: (1) non-water, (2) open floodwater, (3) permanent water. From the confusion matrices, the following accuracy metrics have been derived: overall accuracy (OA), kappa coefficient (\(\kappa\)), F1-score (computed as the arithmetic mean of the per-class F1-scores [49]), errors of commission (\(\varepsilon_{\text{comm}}\)) and omission (\(\varepsilon_{\text{om}}\)) for the classes of open floodwater and permanent water.  
It must be considered that the classes of water pixels are often underrepresented compared to the non-water class. This implies that very high values of OA can be easily obtained. To tackle this problem at least partially, limited geographic areas have been considered to derive the confusion matrices. The boundaries of the geographic areas have been determined using the \"area of interest\" shapefile included in the CEMS data (see [34] for more details), whose size is reported in Table 2 (6 th column).  
To further evaluate the quality of the AUTOWADE-derived maps, for each test case, the total extent (in ha) of the area classified as open floodwater has been calculated to be compared with the total flooded surface estimated by CEMS (Table 2, last column). The percentage of the flooded surface with respect to the size of the area of interest (AOI) has been computed too.
# Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5
## 5 Results  
### Model Performance Comparison  
To determine the impact of each optimization structure on network performance, this study conducted ablation experiments by adding different improvements to the YOLOv5-s model. The results, including the model's parameters and test performance after the improvements, are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Biformer** & **C3-Faster** & **NWD** & **CARAFE** & **Parameters** & **GFLOPS** & **P/\%** & **R/\%** & **AP50/\%** & **Weight/KB** & **FPS/s** \\ \hline \multirow{3}{*}{\(\surd\)} & & & & 7,030,417 & 16 & 81.1 & 42.2 & 52.3 & 14,126 & 131.58 \\  & & & & 7,296,145 & 26.3 & 66.9 & 61.5 & 59.5 & 14,649 & 97.09 \\  & \(\surd\) & & & 5,799,889 & 12.8 & 51.6 & 60.7 & 51.6 & 11,717 & 117.65 \\  & & \(\surd\) & & 7,030,417 & 16 & 68.4 & 58.4 & 56.1 & 14,126 & 128.21 \\  & & & \(\surd\) & 7,030,417 & 16 & 67.3 & 50.6 & 55.4 & 14,126 & 129.87 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 6,206,049 & 23.5 & 53.3 & 64.5 & 62.9 & 12,525 & 101.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Various types of improved model performance data.  
Figure 6: Improved model loss curves.  
By analyzing the experimental results in the above table, it can be observed that adding the Biformer attention mechanism to the network increases the model size and consumes more computational resources. Compared to the original YOLOv5 algorithm, the number of training parameters increases by 3.78%, floating-point operations increase by 64%, and weight volume increases by 3.7%. Thanks to these improvements, Biformer is able to better handle global and local relationships in images, as well as capture both the overall structural features and fine-grained details. This enables better discrimination between defect targets and backgrounds, resulting in a 7.2% improvement in AP50 and significant optimization in detection accuracy.  
On the other hand, replacing the C3 module with C3-Faster, using a novel convolutional approach similar to PConv, results in a notable decrease in training parameters and computational complexity. Specifically, the number of training parameters decreases by 17.5%, floating-point operations decrease by 20%, and weight volume decreases by 17.05%. However, the AP50 only decreases by 0.7%. This lightweight algorithm model effectively ensures detection accuracy while achieving significant model simplification.  
The introduction of NWD and CARAFE does not affect the network's parameter count, floating-point operations, or model weight volume. Both improvements are reflected in the improvement of detection accuracy. NWD benefits from using Gaussian Wasserstein distance as a similarity metric, which allows for more accurate discrimination between small objects and backgrounds. Compared to the original YOLOv5 model, AP50 is improved by 3.8% and 3.1%, respectively. CARAFE, on the other hand, utilizes local information to reassemble features while preserving details and expanding the receptive field. This results in a 3.1% improvement in AP50.
# An Integrated Testbed for Power System Cyber-Physical Operations Training
## 3 CPS Testbed Architecture
### Hardware Layer  
The HIL concept uses the real-time capability of the OPAL-RT simulator to both send the model's measurements and receive signals from external monitoring, control, and protection devices [16]. In an electrical power systems environment, this information exchange can emulate the performance of a power substation, where RTUs are responsible for aggregating analog signals measured by CTs and PTs, sending them to the SCADA system through a communication protocol. In parallel, protective relays also receive analog signals from CT and PT measurements to identify fault situations and control the simulated switch models. The simulator can create virtual IP addresses for each RTU using the information from the simulation model. These virtual IEDs can exchange data through the most common communication protocols, such as DNP3, Modbus, C37.118, IEC 61850, etc. At the hardware layer, the present testbed has two protection relays (SEL-351S) and one real-time automation controller (RTAC/SEL-3530) as a controller and data concentrator. The two SEL-351 Ss are connected to OPAL-RT through analog (voltage and current measurements)and digital (breaker status and open/close commands) wires. The relays are configured based on a protection study for the power network being tested and may act under fault conditions. In addition, the relays are configured to send measurements to the RTAC and receive open/close commands from RTAC through DNP3.
# Design of a Power Converter for Solar Energy Storage System
## 3 Design and Experimental Results
### Specifications and Parameter Design
In this experiment, a 330-\(\mu\)F capacitor was selected. \[C_{bat}\geq\frac{D}{R(\Delta V_{bat}\Big{/}V_{bat})\cdot f_{s}} \tag{18}\]  
(3) Design for the turn ratio of the transformer  
The turn ratio can be determined as follows:  
\[\text{n}=\frac{{N_{S}}}{{N_{P}}}=\frac{{V_{DC}}\;{\it Grid}}{{2\cdot D_{max} \cdot V_{bat,min}}}\approx 1.678 \tag{19}\]  
(4) Design of the leakage inductor  
The converter operates when the DC grid charges the battery. By utilizing the continuity of the inductor, the switches turn off during the dead time. Both \(L_{\text{Ik}}\) and \(C_{\text{OSS}}\) assist in the resonance process for ZVS. However, to easily achieve ZVS, \(L_{\text{Ik}}\) must have a sufficiently high value. Therefore, the minimum value of \(L_{\text{Ik}}\) can be calculated as follows:  
\[L_{Ik}\geq\frac{{2\cdot\left(\frac{1}{n}\right)}^{2}\cdot{V_{DC}}\;{\it Grid }^{2}\cdot C_{OSS}}{{I_{bat}}} \tag{20}\]  
The MOSFET used in the converter is manufactured by Cree (model number C2M0160120D). According to the datasheet, the parasitic capacitor \(C_{\text{OSS}}\) is approximately 50 pF. Therefore, the leakage inductor \(L_{\text{Ik}}\geq 1.5\)\(\mu\)H. When the switches turn off, the parasitic capacitors and leakage inductor of the transformer begin to resonate. To facilitate ZVS in the charger, a higher leakage inductor is required. For the purposes of this experiment, \(L_{\text{Ik}}=16\)\(\mu\)H.  
(5) Design of \(C_{\text{Grid}}\)  
Equation (18) shows the relationship between \(C_{\text{Grid}}\), \(\Delta v_{\text{CGrid}}\), and \(\Delta i_{\text{Llk}}\). In this equation, \(65\times 10^{-6}\) is the equivalent series resistance (ESR) of the electrolyte capacitor. After the converter parameters are substituted into Equation (21), \(C_{\text{Grid}}\) can be calculated using Equation (22), where \(\Delta v_{\text{CGrid}}\) is set to 0.05% \(V_{\text{DC}}\). Thus, \(C_{\text{Grid}}\) is approximately 532.4 \(\mu\)F. In this experiment, 810 \(\mu\)F/500V was used. \[C_{Grid}=\frac{\Delta i_{L\text{Ik}}}{\Delta v_{\text{CGrid}}}\times\left( \frac{1}{16\times f_{s}}+65\times 10^{-6}\right) \tag{21}\]  
\[C_{Grid}=\frac{4}{0.2}\times\left(\frac{1}{16\times 100 kHz}+65\times 10^{-6} \right)\approx 532.4\mu F \tag{22}\]  
Table 2 lists the device parameters selected for practical implementation. To achieve predetermined phase-shift PWM control for the adjustable duty cycle, a TMS320F28335 control chip from Texas Instruments (Dallas, TX, USA) was used. To drive the power switches, the gating signals were directly created using software.