esa-sceva/satcom-chunk-collection huggingface dataset:
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 3 Methods  
A common feature-engineering approach was applied to the date variable of the InSAR metadata before ingesting into the EE-DL and RF approaches [42]. Instead of representing the date as a string such as \"2023-05-22\", it was expanded with other representative columns to enrich and assist the machine learning model. The date column was expanded into 13 other columns, such as the day, year, day of the week, and number of days elapsed since 1970. These were then calculated for each date of the InSAR measurement and were used as the input into the model.  
Figure 2: Indicative across-TSF temporal plot of InSAR Line-Of-Sight (LOS) deformation: **(a)** NTSF slump area and **(b)** stable south TSF. Downstream indicates the location of the dam toe, and upstream indicates the direction towards the deposited tailings.  
The redundancy and importance of each of these InSAR metadata features were evaluated using a simple RF model. Figure 3 plots the relative importance of each of the features predicting deformation using all data points on the full temporal domain. For example, the top-three variables were the location of the measurement as described by the latitude (\"x_lat\"), longitude (\"x_lon\"), and the \"elapsed\" variable, which summarises each date as the number of days elapsed since 1970. The RF hyper-parameters are specified in Table 1. The most-important features were then used as guidance for choosing the variables to be taken forward into the EE-DL approach: latitude, longitude, elapsed, coherence, year, month, and day. The performance of the various approaches was evaluated using the Root-Mean-Squared Error (RMSE), which is described by Equation (1), where \(x_{i}\) and \(\hat{x}_{i}\) are the measured and predicted deformations, respectively, for variable \(i\), where N is the number of measurements.  
\[RMSE=\sqrt{\Sigma_{i=1}^{N}\frac{(x_{i}-\hat{x}_{i})^{2}}{N}} \tag{1}\]
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 3 Template Matching Based on PCA-LMN
### Learnable Matching Network
#### 3.2.1 Correlation Layer  
Suppose the feature map of a base image is \(f_{B}\in R^{w_{b}\times h_{b}\times 2}\), and the feature map of a template is \(f_{T}\in R^{w_{b}\times h_{b}\times 2}\), the correlation layer between them is shown in Figure 6.  
The similarity between the feature map of the base image and the feature map of the template is calculated with the following equation,  
\[S_{BT}(i,j,k)=\mathrm{f_{B}}(i,j)^{T}\mathrm{f_{T}}(i_{k},j_{k}) \tag{6}\]  
where \((i,j)\) indicates the individual feature position in the feature map of the base image, and \((i_{k},j_{k})\) indicates the individual feature position in the feature map of the image. The correlation layer output \(S_{BT}\) contains all pairs of similarities between individual features of \(\mathrm{f_{B}}\in f_{B}\) and \(\mathrm{f_{T}}\in f_{T}\).
## 4 Results and Discussion
### Model Appraisal  
The root-mean-square error [42] (RMSE) and mean absolute percentage error [43] (MAPE) are utilized in this study for performance appraisal of the FNN-FDS hybrid model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Well No.**} & **Annual** & **Cumulative Gas** & **Cumulative Water** & **Permeability** & **Well Depth** \\  & **Output (m\({}^{3}\))** & **Storage (10\({}^{4}\) m\({}^{3}\))** & **Storage (m\({}^{3}\))** & **(mD)** & **(m)** \\ \hline LG01 & 6231 & 186 & 7361 & 11000 & 6800 \\ LG02 & 2281 & 150 & 526 & 5000 & 6200 \\ LG03 & 4062 &  
RMSE is one of the most commonly used indicators to validate the accuracy of the predicted data, which takes the form of  
\[\text{RMSE}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\bigl{(}x_{i,p}-x_{i,e}\bigr{)}^{2}} \tag{11}\]  
MAPE is used to evaluate the overall accuracy of the predicted values, described as  
\[\text{MAPE}=\frac{1}{m}\sum_{i=1}^{m}\left|\frac{x_{i,e}-x_{i,p}}{x_{i,e}} \right|\times 100(\%) \tag{12}\]  
where \(x_{i,e}\) is the actual value and \(x_{i,p}\) is the predicted value.
# A Compact Planar Wi-Fi Antenna with Optimized Radiation Patterns for Small UAV Applications
## 2 Antenna Design and Analysis
### Antenna Structure and Main Design Steps  
Figure 2 shows the structure of the proposed antenna with geometrical parameters. At 5.8 GHz, the four corners of its outer conductor are the primary radiation sources, forming end-fire radiation. At 2.4 GHz, the two sides of the outer conductor are the main effective radiation sources, forming quasi-omnidirectional radiation.  
Table 1 displays the values of geometrical parameters for the proposed antenna. The thickness of the metal layer (M\({}_{\rm t}\)) is 0.035 mm, of which 0.031 mm is copper, 0.003 mm is nickel, and 0.001 mm is gold. It was printed on the FR4 substrate with a relative permittivity of 4.4, a loss tangent of 0.02, a thickness of 1.6 mm (h), and planar dimensions of 16.5 mm (L) by 30.3 mm (W). At the fed port, the width of the central conducting track (W\({}_{\rm f}\)) was 2.5 mm, and a pair of outer conducting tracks were separated from the central conducting track by a small gap (g) of 0.3 mm to achieve a characteristic impedance of 50 \(\Omega\). The antenna was symmetrical on both sides of the x-z plane, so the radiation generated by the current components in the +y and \(-\)y directions would cancel out. Therefore, the currents in the +z and \(-\)z directions are the main radiation sources, providing the antenna with good vertically polarized radiation.  
Figure 3 shows the main design steps of the proposed antenna. Steps 3 to 6 are the main design steps for key performances. The design theory is explained in detail in Sections 2.1 and 2.2.  
Figure 2: The structure of the proposed antenna with geometrical parameters.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 8 Discussion
### Research Challenges
#### 8.2.1 Unavailability of Recent Dataset  
The study highlighted the lack of recent datasets that represent new attacks that are used against our networks. Most of the solutions studied were not able to detect zero-day attacks because the models were not sufficiently trained with enough diverse attacks. To have a powerful and efficient IDS, it is needed to train it with a large dataset that includes the most recent attacks. The dataset needs to have both old and new attacks so that the IDS would be able to learn the important features of each attack to detect them. Therefore, one of the challenges for researchers is to have an up-to-date dataset that would contain enough samples of all types of attacks. In addition, the dataset should be frequently updated with new intrusions samples from the real-world environment.
## 3 Results and Discussion
### Effect on Post-Treatment Storage
#### 3.5.2 pH and Color  
The pH and color of ADCP treated CBCs during storage are presented (Table 5). Depending on the ADCP treatment, the pH of CBCs stored at 4 \({}^{\circ}\)C and 24 \({}^{\circ}\)C, remained within the 6.5-6.6 range, results did not have significant differences among themselves (\(p>\) 0.05), indicating that ADCP treatment had not influenced the pH of CBCs. Reactive species generated during cold plasma treatment may react with food surface moisture to produce acidic compounds, which may cause changes in the pH [42]. As seen in the previous investigation of nitrate and nitrite contents, nitrogen species generated during cold plasma treatment may induce the formation of nitric acid leading to a consequent decrease in pH [37]. However, it is likely that the number of nitrogen species generated in this study were insufficient, as several studies have reported that cold plasma treatment did not alter the pH of meat [43; 44]. The CBCs used in this study had been given the WPI coating, and based on the characteristically short penetration depth of cold plasma (e.g., \(\leq\)10 nm, 1 \(\upmu\)m) [45; 46], it is thought that the reaction was with the WPI coating on the surface of CBCs and was not substantial to change the bulk pH. Figure 1: Effects of atmospheric dielectric barrier discharge cold plasma treatment (ADCP) treatment on the growth of total indigenous mesophilic aerobic microorganism on chicken breast cubes (CBCs) during storage at 4, 14, and 24 \({}^{\circ}\)C (**A**) and _Salmonella_ strains on CBCs during storage at 4 and 10 \({}^{\circ}\)C (**B**).
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 4 Results
### Validation of Radiometric Cross-Calibration
#### 4.2.3 Validation of Cross-Calibration Coefficients Accuracy  
In order to validate the radiometric cross-calibration coefficients accuracy of the proposed method in this paper, the calibration coefficients with BRDF correction, calibration coefficients without BRDF correction, and the site calibration coefficients are compared, and the relative errors between them are calculated. The relative errors are defined as \"1-cross-calibration coefficients/site calibration coefficients\" and listed in Table 5.  
It can be seen that, obviously, the relative error decreases significantly after BRDF correction. Particularly, when MODIS sensor images are used as reference, the relative errors of each band become lower from 14.06%, 10.99%, 10.85%, and 4.59% without BRDF correction to \(-\)2.47%, \(-\)4.43%, \(-\)2.26%, and \(-\)4.76% with BRDF correction over the Dunhuang test site, while at the Golmud test site, from \(-\)12.02%, \(-\)7.29%, \(-\)0.69%, and \(-\)7.84% without BRDF correction to 0.36%, 3.04%, 3.64%, and \(-\)3.83% with BRDF correction. This is because the MODIS images of calibration test sites have large sensor zenith angles--49.68\({}^{\circ}\) of the Dunhuang test site and 53.12\({}^{\circ}\) of the Golmud test site.  
On the whole, the absolute value of relative errors of cross-calibration results obtained at both Dunhuang and Golmud test sites are all smaller than 5%, whether Landsat 8 OLI or BRDF-corrected MODIS images are used as a reference. Furthermore, after BRDF correction, the relative error of the Golmud test site is smaller than that of the Dunhuang test site for most bands, except for the NIR band (using Landsat 8 OLI as reference sensor) and the red band (using MODIS as reference sensor). In general, the relative errors illustrate that the cross-calibration method proposed in this paper has high radiometric calibration accuracy.
## 4 Fuzzy Fatigue Reliability Assessment of Welded A-Type Frame
### Experimental Results for Material Properties  
The finite element analysis was needed to calculate response values for constructing fuzzy fatigue reliability performance function. Therefore, the material properties had to be determined first. The welded joints were designed to be flat dog-bone shaped specimens, as shown in Figure 4.  
The monotonic tensile test was conducted on this specimen under velocity control at the rate of 0.025 mm/s. The gauge length of the extensometer used for measuring the strain was 12.5 mm. Figure 5 shows both the engineering and true stress/strain curve. Afterwards, the Young modulus \(E\), tensile strength \(\sigma_{b}\), and yield strength \(\sigma_{y}\) could be obtained from this figure, and Table 3 lists their values.  
Figure 4: Dimension of specimen (the purple region was weld metal, length in mm, roughness in micro meters).  
Figure 3: Flow chart for fatigue reliability analysis and optimization of welded A-type frame.  
In addition, the cyclic material properties were needed to calculate equivalent stress. Therefore, the fatigue tests were conducted on welded specimens under strain control. The cyclic elastic and plastic strains and the corresponding stresses were obtained from the cyclic tests by measuring strains. Subsequently, the Ramerg-Osgood equation was employed to gain the cyclic stress and stain curve by fitting the experimental data, as shown in Figure 6. The cyclic strength coefficient \(K^{\prime}\) and cyclic strain hardening exponent \(n^{\prime}\) were both ascertained, as listed in Table 3.
## 3 Results and Discussion
### Electrochemical Studies
However, SSC-connected devices utilized the excess power for SSC charging and facilitated the high power for the system initialization by discharging in burst mode. Figure 7: Design and characterization of wireless powered BLE/PPG module. Illustration (**a**), PCB design (**b**), and real image of a wireless powered device with supercapacitor for temporary power storage (**c**), current flow chart of system, (**d**) system block diagram of device, (**e**) current consumption characterization of BLE/PPG module with or without supercapacitor, (**f**) measured pulse signals from the device placed on the index finger with (**g**) red LED and (**h**) IR LED. Such results shows that SSC provided energy effectively to the BLE/PPG module, and it is one of the best options for a wireless powered system to stabilize power consumption. Figure 7g,h show measured pulse signals from the oximeter placed on the index finger with red and IR LEDs. Each of the data from the two wavelengths have a different amplitude due to the dependency of the molar extinction coefficient spectra of the HbO2 and Hb solutions [74; 75], (Figure S8) but shows the same heart rate (77 bpm). In this sense, the SSC can be a better option than the battery for a wireless powered system. All capacitive and practical performances indicate that the novel electrode materials that were prepared have tremendous potential in future energy storage applications.
## 5 Conclusions  
The 3D laser scanning technology of RPC under fatigue load conditions can clearly and effectively reveal the point cloud data features during the damage process. This work can provide a basis for the health monitoring of the RPC structure. Some of the conclusions drawn are as follows:  
1. The production principle and process of the inventive 3D laser scanning system was described. Using the system, the damage process of RPC under fatigue load was studied. In the process, the scanned 3D point cloud data simulates the RPC damage process.
2. When the damage degree of the RPC structure is slight, the displacement and deflection of the damage source area are small. However, as the degree of damage continues to develop, the location and elevation information of the damaged area is constantly changing. The nonlinear variation of the damage region is described by the GLCM. And the damage characteristic parameter is extracted.
3. The sensitive characteristic index is selected based on DFS. The reliability of indicators is verified by the texture characteristics and the random test sample.
4. Damage point cloud data acquired from 3D laser scanning technology contains a considerable amount of information to the damage. Damage monitoring method based on 3D laser scanning performs an important role in condition monitoring and life prediction of RPC. In addition, the combination of texture analysis technologies and statistical theory can further demonstrate the advantages of 3D laser scanning technology.  
The system can be popularized and applied to the detection and diagnosis of the health status of other composite materials. The damage mechanism is analyzed by the extracted damage index. However, the scanning system is limited to the detection of damaged areas inside the three-dimensional space that can be scanned by laser. Therefore, it is necessary to use methods combined with other  
Figure 10: Fitting relationship between damage index and crack development trend.  
non-destructive testing new technologies to try to explore the indicators that are more in line with modern structural damage monitoring.  
Conceptualization, K.L.; Methodology, K.L. and J.W.; Software, K.L. and J.W.; Validation, K.L.; Formal analysis, K.L.; Investigation, K.L.; Resources, K.L.; Data curation, K.L.; Writing--original draft preparation, K.L.; Writing--review and editing, K.L. and D.Q.; Visualization, K.L.; Supervision, K.L., J.W. and D.Q.; Project administration, K.L. Funding acquisition, K.L.  
This work was founded by the Heilongjiang Natural Science Fund Project(ZD2019E001), the National Science Foundation of China (Grant No. 51605459), funded by the Fundamental Research Fund for the Central University (No.2572018 AB31) and Heilongjiang Provincial Postdoctoral Scientific Research Development Fund (Project No.LBH-Q15011).  
The authors declare no conflict of interest.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 8 Discussion and Limitations
### Abuse of Semantic Error Analysis  
Although ErrorExplainer is designed to improve Web3 usability, detailed error diagnostics inevitably have a dual-use aspect. By observing which branch reverts under forged inputs, an attacker can reverse-engineer contract logic and find vulnerabilities.  
Prior work confirms that revert data leak valuable information. SmartState [25] demonstrates that \"state-reverting\" patterns can be searched to locate profitable and denial-of-service faults. ContractFuzzer [26] uses the presence or absence of specific revert selectors as guidance for deeper fuzzing.  
In practice, however, abusing semantic error analysis, as in ErrorExplainer, is limited by a mismatch of objectives. Attack tools must explore a vast input space to _trigger_ reverts, but ErrorExplainer delivers richer information only _after_ a revert has occurred. Therefore, we position ErrorExplainer as a usability enhancement rather than a new attack oracle.
# A Defect Detection Method Based on YOLOv7 for Automated Remanufacturing
###### Abstract  
Remanufacturing of mechanical parts has recently gained much attention due to the rapid development of green technologies and sustainability. Recent efforts to automate the inspection step in the remanufacturing process using artificial intelligence are noticeable. In this step, a visual inspection of the end-of-life (EOL) parts is carried out to detect defective regions for restoration. This operation relates to the object detection process, a typical computer vision task. Many researchers have adopted well-known deep-learning models for the detection of damage. A common technique in the object detection field is transfer learning, where general object detectors are adopted for specific tasks such as metal surface defect detection. One open-sourced model, YOLOv7, is known for real-time object detection, high accuracy, and optimal scaling. In this work, an investigation into the YOLOv7 behavior on various public metal surface defect datasets, including NEU-DET, NPSD, and KolektorSDD2, is conducted. A case study validation is also included to demonstrate the model's application in an industrial setting. The tiny variant of the YOLOv7 model showed the best performance on the NEU-DET dataset with a 73.9% mAP (mean average precision) and 103 FPS (frames per second) in inference. For the NPSD dataset, the model's base variant resulted in 88.5% for object detection and semantic segmentation inferences. In addition, the model achieved 65% accuracy when testing on the KolektorSDD2 dataset. Further, the results are studied and compared with some of the existing defect detection models. Moreover, the segmentation performance of the model was also reported.  
YOLOv7; metal defect; defect detection; defect segmentation +
Footnote †: journal: Medical Engineering  
0000-0001-8891-5000]G.R.R.S.
## 5 Shaking Table Test
### Test Results  
#### 5.4.1 Resonance Frequencies and Damage Modes  
The resonance frequencies were determined by calculating the transfer function (\(T_{ab}\)) of the response acceleration at each measurement position of the test specimen for the acceleration locations measured on the shaking table. The transfer function is given by Equation (6), where \(P_{aa}\) is the power spectral density (PSD) of the input signal, and \(P_{ba}\) is the cross-power spectral density (CSD) between the input and output signals:  
\[T_{ab}(f)=\frac{P_{ba}(f)}{P_{aa}(f)} \tag{6}\]  
The resonance frequencies for the horizontal directions (X, Y) were 14.25 Hz and 13.25 Hz, respectively. Since the test specimen is symmetric in the X and Y directions, the resonance frequencies should ideally be the same as the first and second mode results from the eigenvalue analysis mentioned in the analytical verification. However, due to various factors--such as manufacturing errors in the anchorage part and foundation concrete flatness as well as installation errors caused by bolt fastening--the actual boundary conditions may differ from those applied in the analytical model.  
According to the input seismic motion based on the common application criteria of the seismic design standards (as shown in Figure 16), the specimen was damaged during the shaking table test. The failure mode exhibited by the specimen is consistent with the  
\begin{table}
\begin{tabular}{c c} \hline
**Procedure** & **Method** \\ \hline Inspection & Visual inspection \\ \hline \multirow{4}{*}{Pre-resonance search test} & Random wave (amplitude: 0.05 g) \\  & Uniaxial motion (X, Y, Z, respectively) \\  & Time duration: 60 s \\  & Frequency range: 0.5–50.0 Hz \\ \hline \multirow{4}{*}{Seismic simulation test} & Cross-correlation function < 0.3 \\  & 3-axis simultaneous motion \\ \cline{1-1}  & Time duration: 30 s \\ \cline{1-1}  & Strong motion duration: 20 s \\ \cline{1-1}  & Frequency range: 0.5–50.0 Hz \\ \hline \multicolumn{2}{c}{Post-resonance search test} & Same as pre-resonance search test \\ \hline \multicolumn{2}{c}{Inspection} & Visual inspection \\ \hline \end{tabular}
\end{table}
Table 6: Test procedure and method.  
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Specifications** \\ \hline Size & 4 m \(\times\) 4 m \\ Maximum payload & 30 tons \\ Frequency range & 0.1–60 Hz \\ Maximum stroke & Horizontal: \(\pm\)300 mm, \(\pm\)200 mm Vertical: \(\pm\)150 mm \\ Maximum acceleration & \(\pm\)3 g (bare table condition) \\ \hline \end{tabular}
\end{table}
Table 5: Shaking table specifications.  
analysis results, indicating damage due to stress concentration in the settlement area caused by external forces and seismic loads, similar to cases of protrusion concrete damage and stress concentration at the boundary of protrusion concrete observed in the analysis. The concrete along the protrusion concrete area and the boundary with the foundation concrete was damaged, resulting in complete separation of the two structures. Due to this condition, further modal analysis through additional resonance search tests was not feasible.
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 6 Proposed Metaheuristic Algorithm
### Initialization  
In the first iteration \(k=0\), a population \(\mathbf{P}^{0}\) of \(N\) agents \(\{\mathbf{x}_{1}^{0},\ldots,\mathbf{x}_{N}^{0}\}\) is randomly produced considering to the following equation,  
\[x_{i,j}^{0}=rand\cdot\left(b_{j}^{high}-b_{j}^{low}\right)+b_{j}^{low}i=1,\ 2,\ \ldots,\ N;\ j=1,\ldots,d \tag{13}\]  
Figure 5: Flowchart of the proposed metaheuristic method based on the response of second-order systems.  
where \(b_{j}^{high}\) and \(b_{j}^{low}\) are the limits of the \(j\) decision variable and \(rand\) is a uniformly distributed random number between [0,1].  
To each individual \(\mathbf{x}_{i}\) from the population, it is assigned a vector \(\zeta_{i}=\{\zeta_{i,1},\ \ldots,\zeta_{i,d}\}\) whose elements \(\zeta_{i,j}\) determine the trajectory behavior of each \(j\)-th dimension. Initially, each element \(\zeta_{i,j}\) is set to a random value between [0,2]. Under this interval, all the second-order behavior are possible: Underdamped (\(0<\zeta<1\)), critically damped (\(\zeta=1\)), and overdamped (\(\zeta>1\)).