esa-sceva/satcom-chunk-collection huggingface dataset:
# Varroa Control by Means of a Hyperthermic Device
## 2 Materials and Methods  
### The Bee Ethic System  
The Bee Ethic system is a technological hive consisting of a set of heated frames and an electronic control unit integrated in the frame. Bee Ethic has been recognized and financed through Reg 1308/2013-OCM Miele as a valid biotechnology against _Varroa_ by several Italian regions.  
Each Bee Ethic hive kit consists of two stainless steel spacers and eight frames with a thermal foundation (Figure 1a). One of the frames (probe frame) includes a microchip (Figure 1b) and must be placed in the hive in a central position (Figure 1c). The foundations are supplied with wax or are to be waxed using a specially designed and manufactured mold (Figure 1d). Broad combs are readily built by honey bees on such foundations (Figure 1e).  
Thanks to the microchip integrated in the probe frame, the Bee Ethic hive automatically carries out heat treatment every 25 days, bringing the brood temperature to 42 \({}^{\circ}\)C for 90 min. Moreover, various sensors and the BEE-SMART control unit allow the beekeeper to remotely check the main parameters of each hive--brood and air temperature inside the hive, as well as humidity and sound frequency--on top of ambient temperature and humidity (Figure 1f).  
Figure 1: The Bee Ethic system: (**a**) hive kit: two stainless steel spacers and eight thermal frames; (**b**) the microchip inserted in the probe frame; (**c**) the thermal frames in the hive with the one with the microchip in central position; (**d**) mold for waxing thermal foundations; (**e**) the microchip and sensors integrated in the probe frame; (**f**) the main parameters of a hive as shown by BEE-SMART control unit; (**g**) 24-volt AC/DC transformer to connect the Bee Ethic system to a socket; (**h**) photovoltaic panel; (**i**) apiary not served by the electric network.  
The Bee Ethic system operates on 24-Volt DC and can be powered either by an electrical outlet with a 24-Volt AC/DC transformer (Figure 1g), or by a photovoltaic panel (Figure 1h) if the apiary is located in a remote place and not served by the electricity network (Figure 1h).
## 3 Current Signal Processing and Feature Extraction
### Characteristic Processing of High-Frequency Current Signal
#### 3.1.1 Maximum Rate of Current Rise  
During an arc fault, the amplitude of the current undergoes a sudden change, leading to a sudden change in the maximum rate of increase of the current at zero crossing. The difference between the current signal sampling values of two adjacent discrete points within a cycle is selected, and the maximum absolute value represents the maximum rate of increase of the current. The calculation formula is as follows:  
\[V_{\text{max}}=(|I_{2}-I_{1}|,|I_{3}-I_{2}|,\ldots,|I_{N}-I_{N-1}|)_{\text{ max}} \tag{1}\]  
In the formula, \(I\) is the instantaneous value of current, and \(N\) is the number of sampling points.  
Figure 5: Normal and arc current waveform of hair dryer + kettle load. (**a**) Normal current. (**b**) Arcing current.
# Kohonen Mapping of the Space of Vibration Parameters of an Intact and Damaged Wheel Rim Structure
###### Abstract  
The research presented in this paper takes another step towards developing methods for automatic condition verification to detect structural damage to vehicle wheel rims. This study presents the utilisation of vibration spectra via Fast Fourier Transform (FFT) and a neural network's learning capabilities for evaluating structural damage. Amplitude and time cycles of acceleration were analyzed as the structural response. These cycles underwent FFT analysis, leading to the identification of four diagnostic symptoms described by 20 features of the diagnostic signal, which in turn defined a condition vector. In the subsequent stage, the amplitude and frequency cycles served as input data for the neural network, and based on them, self-organizing maps (SOM) were generated. From these maps, a condition vector was defined for each of the four positions of the rim. Therefore, the technical condition of the wheel rim was determined based on the variance in condition parameter features, using reference frequencies of vibration spectra and SOM visualisations. The outcome of this work is a unique synergetic diagnostic system with innovative features, identifying the condition of a wheel rim through vibration and acoustic analysis along with neural network techniques in the form of Kohonen maps.  
wheel rim; vibrations; Kohonen self-organising map; failure identification +
Footnote †: journal: applied sciences  
Article  
Kohonen Mapping of the Space of Vibration Parameters of an Intact and Damaged Wheel Rim Structure
## 6 High Frequency Characterization and Modelling
### TCAD and Monte Carlo Simulations
Subsequently, in Figure 22c, a lower peak\(f_{\text{MAX}}\) was observed (even though the new generation exhibits a slightly higher \(f_{MAX}\) compared to what had been predicted in [34]), in comparison with the values reported in [33] for the 0.13 \(\times\) 2 \(\upmu\)m\({}^{2}\) state of the art InP HBTs. In fact, the technologies under study have a balanced \(f_{T}-f_{\text{MAX}}\) performance, and extrapolation to smaller geometries maintains this feature. Despite the improvement in overall \(f_{MAX}\) for the new generations, due to higher significance of peak\(f_{\text{MAX}}\) for circuit applications, further efforts are required to achieve enhanced performances, in order to attain the targeted terahertz operations. These efforts mainly require the modification of the epitaxial base, either at the epitaxial level to induce a higher quasi-electric field [33] or base emitter spacer optimization at the process level [1]. In terms of thermal performances of the DHBT technology, we obtain very similar thermal resistances compared to the preceding generation [34], as depicted in Figure 22d. Similar to our predictions for the previous technology generation, the 0.13 \(\times\) 2 \(\upmu\)m\({}^{2}\) HBT shows significantly elevated values of \(R_{TH}\). Despite appearing as a crucial drawback at such scaled dimensions, we previously observed that the increase in the internal junction temperature (\(\Delta T_{j}=R_{\text{TH}}\times\ V_{\text{CE}}I_{C}|f_{T,\ peak}\)) at peak-\(f_{T}\) (Figure 22a) (plotted with solid symbols in Figure 22d, right \(y\) axis) exhibited a slightly downward trend with scaling. The prediction for the current technology generation (G1) demonstrates that there is an even stronger reduction in the internal device temperatures in the scaled version of this technology. This is due to the fact that the scaling of the DHBTs reduces the \(V_{\text{CE}}I_{C}\) product predominantly, which in turn results in the drastic reduction in the junction temperature, even though a high thermal resistance was predicted. This is promising from the scaling point-of-view indicating an already improved thermal response has been achieved between subsequent process runs. Approaches such as transferring epitaxial structures on a substrate of higher thermal conductivity [35; 36] without modifying the epitaxial structure [36], substrate thinning or even using a quaternary graded base alloy gallium-indium-arsenic-antimonite (GaInAsSb) [37] could be followed in future process runs that can further enhance the figures of merit. Guidelines from the predictive scalable compact model simulations will continue to accompany the future technology generations through further process optimization at the epitaxial level along the InP HBT scaling roadmap.
## 4 Experimental Results
### Results
#### 4.3.2 Domain Adaptation
Besides, the accuracies for the female patient are \(97.34\%\) and \(96.55\%\) for \"2D skeleton plus depth\" and \"3D skeleton\", respectively. For the input of 2D skeleton plus depth, the accuracy is higher than the highest accuracy (i.e., \(96.92\%\)) achievable in Table 4, where only volunteer samples are used. This reflects that the proposed technique is able to overcome the domain-diversities situation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Setting 1**} & \multicolumn{3}{c}{**Setting 1**} & \multicolumn{3}{c}{**Setting 6**} \\  & **(2D Skeleton Plus Depth)** & & \multicolumn{3}{c}{**(3D Skeleton)**} & \multicolumn{3}{c}{**Setting 7**} \\ \hline  & \multicolumn{3}{c}{Pred.} & \multicolumn{3}{c}{Pred.} & \multicolumn{3}{c}{Pred.} & \multicolumn{3}{c}{Pred.} \\ \cline{2-11} GT & left & supine & right & GT & left & supine & right & GT & left & supine & right \\ left & 95.82 & 3.99 & 0.19 & left & 95.63 & 4.27 & 0.09 & left & 88.52 & 11.48 & 0 \\ supine & 1.52 & 94.78 & 3.70 & supine & 1.90 & 94.97 & 3.13 & supine & 0.83 & 90.19 & 8.98 \\ right & 0 & 4.39 & 95.61 & right & 1.0 & 3.63 & 96.27 & right & 0.09 & 16.67 & 83.24 \\  & \multicolumn{3}{c}{Accuracy: 95.40} & \multicolumn{3}{c}{Accuracy: 95.62} & \multicolumn{3}{c}{Accuracy: 87.31} \\ \hline  & \multicolumn{3}{c}{**Setting 1**} & \multicolumn{3}{c}{**Setting 1**} & \multicolumn{3}{c}{**Setting 1**} \\  & **(2D skeleton plus depth,** & \multicolumn{3}{c}{**(3D skeleton,** & \multicolumn{3}{c}{**Setting 7**} \\  & **nose-centered joints)**} & \multicolumn{3}{c}{**nose-centered joints)**} & \multicolumn{3}{c}{Pred.} \\ \hline  & \multicolumn{3}{c}{Pred.} & \multicolumn{3}{c}{Pred.} & \multicolumn{3}{c}{Pred.} \\ \cline{2-11} GT & left & supine & right & GT & left & supine & right & GT & left & supine & right \\ left & 95.73 & 4.18 & 0.09 & left & 96.39 & 3.51 & 0.10 & left & 86.48 & 13.52 & 0 \\ supine & 2.09 & 95.63 & 2.28 & supine & 1.71 & 96.30 & 1.99 & supine & 5.28 & 84.44 & 10.28 \\ right & 0.09 & 2.30 & 97.61 & right & 0 & 1.91 & 98.09 & right & 0 & 21.94 & 78.06 \\  & \multicolumn{3}{c}{Accuracy: 96.32} & \multicolumn{3}{c}{Accuracy: 96.92} & \multicolumn{3}{c}{Accuracy: 82.99} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy and confusion matrix (all numbers in terms of %) for different settings. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{_diff_} & \multicolumn{2}{c}{**Left-Sided**} & \multicolumn{2}{c}{**Supine**} & \multicolumn{2}{c}{**Right-Sided**} \\ \hline Volunteeer & \(-131.16\) & \(-7.21\) & \(118.82\) \\ Male patient & \(-16.92\) & \(11.134\) & \(29.90\) \\ Female patient & \(-86.17\) & X & \(115.40\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The depth difference between the right shoulder and the left shoulder (in mm).
# Nighttime Image Dehazing Based on Point Light Sources
## 2 Related Work
To reduce the halo effect and retain details, [PERSON] et al. [33] were inspired by the dark channel algorithm to assign higher weights to high-contrast image blocks, and finally used the Laplace pyramid decomposition of the input multi-scale \begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Year** & **Title** & **Author** & **Methods** \\ \hline
2011 & Single Image Haze Removal Using Dark Channel Prior & [PERSON] et al. [14] & The dehazing model is simplified by the dark channel prior, and the image dehazing problem is transformed into a matting problem. \\ \hline
2015 & Nighttime Haze Removal with Slow and Multiple Light Colors & [PERSON] et al. [25] & A night-time halo term is added to the atmospheric lighting model to eliminate the halo effect. \\ \hline
2016 & Night-time dehazing by fusion & [PERSON] et al. [33] & Atmospheric light terms for image blocks are estimated using a Laplace pyramid decomposition of the input multiscale image block and a weight map. \\ \hline
2017 & Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior. & [PERSON] et al. [32] & This article proposes the maximum reflection prior (MRP) method to estimate atmospheric light maps. \\ \hline
2018 & Nighttime Image Dehazing Based on Low-Pass Filtering and Joint Optimization of Multi-Feature & [PERSON] et al. [34] & The article uses low-pass filtering to estimate ambient light and use ambient light to predict nighttime transmittance. \\ \hline
2020 & Nighttime Dehazing with a Synthetic Benchmark & [PERSON] et al. [35] & The article proposes an optimal scale based maximum reflectance prioritization algorithm (OSFD) on the basis of MRP. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work of dehazing algorithms. image blocks and weight maps to estimate the atmospheric light terms of the image blocks. A new model for imaging nighttime fog images containing artificial light sources was developed by [PERSON] et al. [34] by deeply analyzing the imaging pattern of nighttime fog images. The ambient light is estimated using low-pass filtering, followed by the prediction of the night scene transmittance using ambient light, and, finally, the histogram matching method is used to color correct the result.
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 6 Conclusions and Future Works  
In this paper, we focus on vehicle networks in mixed coverage scenarios and propose an optimal task offloading strategy. By considering the mixed coverage of BSs and RSUs in traffic environments, we formulate a constrained optimization problem based on a communication and computing delay model with the objective of minimizing the task processing delay. To address this, we propose a two-stage optimization algorithm. The first stage utilizes LSTM to predict the load state of BSs, and based on the predictions, we optimize the task offloading ratios for vehicles using the PPO algorithm. The simulation results demonstrate that both the selection of offloading targets and the offloading ratios significantly affect system delay, leading to the efficient utilization of computing resources from nearby RSUs and BSs. LSTM effectively predicts the BS load state, and the PPO output strategy facilitates optimal offloading decisions, reducing task processing delay. The results show that the proposed two-stage optimization algorithm reduces task processing delay by approximately 5.6%. The proposed strategy enhances vehicular task processing speed and improves vehicle intelligence.  
Figure 8: The performance of the trained model under different conditions.  
Future work will focus on developing strategies suited to more realistic road conditions. We plan to further refine the proposed approach by considering more complex road environments and developing more nuanced strategies.  
Conceptualization, X.H. and C.Y.; methodology, X.H. and C.Y.; software, X.H. and Y.C.; validation, X.H. and Y.C.; formal analysis, X.H. and X.C.; investigation, X.H. and Y.L.; writing--original draft preparation, X.H. and Y.C.; writing--review and editing, Y.C., C.Y. and Y.L.; supervision, X.C. and C.Y.; project administration, X.C.; funding acquisition, C.Y. and X.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the 2024 Guangdong Province Science and Technology Innovation Strategic Special Fund with grant: pdjh2024a138 and in part by the Guangdong Basic and Applied Basic Research foundation with Grant 2024A1515012745.  
Not applicable.  
Data Availability Statement: Data are contained within article.  
The authors declare no conflicts of interest.
## 2 Methodology
### Objective Function
#### 2.3.2 Time Lapse Term  
Time-lapse inversion constrains the changes in models over time by introducing a time-lapse term to the objective function. The time-lapse term is defined as follows (Equation (8)):  
\[\varphi_{t}=\sum_{mt=1}^{\infty}\parallel m_{nt}-m_{nt-1}\parallel^{2}=M^{T}C ^{T}CM \tag{8}\]  
where \(\mathrm{m}_{\mathrm{nt}}\) is the model parameter at time step nt, \(\mathrm{C}\) is the coefficient matrix, and the expression is given in Equation (9), where nt is the total number of measured time steps. The time-lapse term establishes connections between spatial models at different time steps. Each iteration simultaneously inverts the observation data at nt time steps and obtains the underground electrical structure model at nt time steps. The mutual constraint of the observation data at each time step facilitates the realization of the time constraint effect.  
\[C=\begin{bmatrix}I_{1}&-I_{2}&0&0&0\\ 0&I_{2}&-I_{3}&0&0\\ 0&0&I_{3}&-I_{4}&0\\ 0&0&\ddots&\ddots&0\\ 0&0&0&I_{nt-1}&-I_{nt}\end{bmatrix} \tag{9}\]
## 3 Case Study
### Comparison of Effects Before and After Improvement  
To evaluate the performance of the prediction algorithm, metrics such as the mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R\({}^{2}\)) were utilized. Further comparisons were conducted to analyze the prediction accuracy and computational efficiency of the Informer algorithm before and after the improvement across different timescales.
# Evaluation of Shear Effect on Deflection of RC Beams
## 2 Experimental Study
### Test Setup and Loading Procedure  
The test setup of a typical specimen is illustrated in Figure 3. A static monotonic loading test was done on the specimen using a UTM with a capacity of 2000 kN. The experiment was controlled by the continuous deformation and terminated when the load dropped to less than 85% of the observed peak load. As shown in Figure 3, linear variable differential transducers (LVDTs) with 100 mm capacity were installed at the opposite side of the top loading points and the mid-span point of the specimens to measure deflections. In addition, as shown in Figure 3, strain gauges with 60 mm length and 2% measuring range for concrete were attached at positions 0, 20, and 40 mm away from the extreme compression fiber to measure the compressive strain of the specimens.
# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 2 An Overview of the Techniques Used for Underwater Concrete Crack Detection
### Deep-Learning-Based Methods: Image Analysis of Cracks Using Deep Learning
Finally, the paper [45] introduced the UIS-1 underwater inspection system, comprising a proprietary underwater robot and a unique quantitative analysis approach. The technology was tested in the field at the dam in Sichuan, China, and its efficacy was compared to that of other approaches. The suggested picture technique detected the coarse aggregate exposed automatically using SLIC super pixels and SVM machine learning, and the total exposure ratio was derived to assess the degree of abrasion.
# Ultrawideband Low-Profile and Miniaturized Spoof Plasmonic Vivaldi Antenna for Base Station
## 4 Results  
In order to validate the proposed antenna, the plasmonic AVA has been fabricated and tested. A 50-\(\Omega\) SMA connector was used to feed the antenna. The fabricated plasmonic AVA is shown in Figure 6a,b. The antennas were measured by using vector network analyzer (Keysight 8722 ES), and the anechoic chamber operates from 600 MHz to 40 GHz.  
The simulated and measured reflection coefficients are illustrated in Figure 7a. It can be observed that the lowest operating frequency (\(S_{11}\leq-10\) dB) of Structure A (typical AVA) is 2.4 GHz, while it is 1.8 GHz for Structure C (plasmonic AVA). The corresponding electrical sizes are 0.75\(\lambda_{0}\) and 0.56\(\lambda_{0}\), respectively, where \(\lambda_{0}\) is the wavelength in the air corresponding to the lowest operating frequency. Hence, it can be seen that the electrical size of the plasmonic AVA has been reduced by 25.3 percent, compared with that of the typical AVA. The measured \(S_{11}\) of the plasmonic AVA is also plotted in Figure 7a. It can be observed that the measured results agree well with the simulation results. The simulated and measured gains of the AVA and the plasmonic AVA are shown in Figure 7b. Compared to the gains of the AVA, it can be seen that the gain of the plasmonic AVA has been increased at the frequencies lower than 5.5 GHz and decreased at frequencies higher than 5.5 GHz. The measurement results agree well with the simulated results for the plasmonic AVA. The gain is larger than 5.5 dBi over the operating frequency from 1.8 GHz to 6 GHz. The measured peak gain is 9.12 dBi at 5.4 GHz. The measured average gain is 7.24 dBi over the operating frequency band, which covers 2G/3G/4G/5G bands.  
Figure 6: (**a**) The top view and (**b**) the bottom view of the fabricated plasmonic AVA.  
Figure 7: (**a**) The simulated and measured reflection coefficients of structures A and C (the typical AVA and the plasmonic AVA); (**b**) the simulated and measured gains of structures A and C.  
The simulated and measured radiation patterns of the plasmonic AVA at 1.8 GHz, 4 GHz, and 6 GHz are illustrated in Figure 8. The upper and lower panels are the E-plane and H-plane radiation patterns, respectively. The half-power beam width (HPBW) in E-plane of the AVA and plasmonic AVA is shown in the Table 1. It can be seen that the radiation patterns of the plasmonic AVA are stable, compared with the results of the typical AVA.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix A Literature Review
### PCA and Anomaly Detection  
In the literature, PCA is usually used in anomaly detection for its dimension reduction properties. Anomalies are identified on the latent space, either by applying some anomaly detection algorithm or by assuming a given distribution on the principal component and identifying the anomalies relative to a quantile [35]. Assuming that a normal subspace representation of the data set can be constructed with the first \(k\)-components [36], anomaly detection is achieved by looking at the observations that cannot be expressed in terms of the first \(k\)-components [37]. Note that the particular power-fullness of an auto-encoder, a nonlinear PCA, as a data compressor is not desirable herein since auto-encoders compress all patterns including abnormal ones. Although [38] claimed that the PCA-based models are stable with respect to their parameters, such as the number of principal components \(k\) spanning the subspace or the cutoff level, Ref. [36] found instead that PCA-based anomaly detection is sensitive to these parameters and to the amplitude of the anomalies. Indeed, the latter may undermine the construction of the normal subspace representation, in turn leading to the misidentification of anomalies. In light of this, we took extra care regarding these aspects, and appropriate tests were conducted to show that the proposed approach was not subject to these issues. In the end, with a relatively low number of features given by PCA, we were able to accurately describe the dynamics of the market risk factors represented by times series. The reason behind this is the high correlation structure displayed by the market risk factors.