esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Subgrade Defect Intelligent Identification
### Recognition Effect Evaluation  
After obtaining the intelligent recognition results, we evaluated the algorithm's matching performance against the target using five indicators: _Precision_, _Recall_, \(F\)1, average precision (_AP_), and mean average precision (_mAP_). As shown in Equations (10)-(14), the precision is the ratio of true-positive predictions to the total predictions generated; the recall is the ratio of true-positive predictions to the total actual positives in the dataset; the \(F\)1 is  
Figure 19: YOLO v3 target detection algorithm: roadbed defect target prediction results in GPR files.  
Figure 18: SSD target detection algorithm: roadbed defect target prediction results in GPR files.  
the harmonic mean of the precision and recall; the \(AP\) is the area under the precision-recall curve; and the \(mAP\) is the average of the \(AP\) values for each class [25; 26].  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{12}\]  
\[Ap=\int_{0}^{1}P(R)d(R) \tag{13}\]  
\[mAP=\frac{1}{N}{\sum_{i=1}^{N}}\int_{0}^{1}P(R)d(R) \tag{14}\]  
In the equations, \(P(R)\) represents the curve expression in the \(P\)-\(R\) curve; \(d(R)\) is the differential form along the R axis; \(N\) is the number of classes in the image dataset; \(TP\) (true positive) is the number of actual positive samples that are correctly predicted; \(FP\) (false positive) is the number of actual negative samples that are incorrectly predicted as positive; \(TN\) (true negative) is the number of actual negative samples that are correctly predicted; and \(FN\) (false negative) is the number of actual positive samples that are incorrectly predicted as negative.  
The \(AP\) results for the SSD and YOLO v3 algorithms and for different subgrade defects are shown in Figure 20. The larger the area covered by the blue shading in the P-R curve, the better the detection and recognition performance of the object detection algorithm for the dataset. The SSD demonstrated balanced recognition performance across the three target categories, with \(AP\) values for all three subgrade defects above 71%. YOLO v3 achieved an \(AP\) value of 78.95% for cavity defects, while the SSD achieved only 64.22% for the same category.  
Figure 20: AP trend charts for different roadbed defects.  
As shown in Table 5, based on the \(F1\) obtained from the combined precision and recall metrics, YOLO v3 outperforms the SSD in detecting pipeline, cavity, and loose subgrade defects, with the highest \(F1\) reaching 0.82. Regarding the AP, YOLO v3 performs better for void and cavity targets, whereas the SSD performs better for loose subgrade defects. Overall, on the GPR subgrade defect image dataset constructed in this study, YOLO v3 demonstrates superior detection performance.
## 1 Introduction
Additionally, the theory of sparse representation shows that the linear combination of a \"few\" atoms from an over-complete dictionary can effectively describe the salient features of objects [21; 22; 23]. Thus, it has been widely used for image processing. [PERSON] et al. [24] introduced sparse representation into image fusion. With their method, source images were divided into small patches and transformed into vectors via lexicographic ordering. They then realized sparse representation using an over-complete dictionary. [PERSON] et al. [25] combined multi-scale transformation and sparse representation to constitute a general image fusion framework, which presented more details in the fused images. However, these works mainly focused on multi-focus and multi-modal fusion, and because they did not consider the characteristics of multi-exposure source images, they are still not appropriate for multi-exposure image fusion. In this paper, we propose a halo-free multi-exposure fusion method based on sparse representation of gradient features. In our method, the local gradient sparse descriptor, obtained from a gradient map through sparse representation, is used for both local detail extraction and halo artifact removal. The main contributions of this paper can be summarized in the following three points:  
1. By analyzing characteristics of the luminance gradient in multi-exposure source images, the cause of halo artifacts in the fused image is discussed. Because the local brightness changes in HDR scenes are violent, they can far exceed the dynamic range of a single photograph. Thus, the halo artifacts in the fused image are generated by the invalid large-amplitude gradients in the source images. 2. By calculating the \(l_{1}\)-norm of the sparse coefficients in the luminance gradient map, we obtain the local gradient sparse descriptor as the feature of the images. This descriptor can effectively extract local details of source images. A new multi-exposure fusion method based on the local gradient sparse descriptor is proposed, which can achieve a halo-free fused image by restraining the invalid large-amplitude gradients. Meanwhile, the fused image of the proposed method is not recovered by the over-complete dictionary, which effectively improves the fusion efficiency. The remainder of this paper is organized as follows: In Section 2, we analyze the cause of halo artifacts in the multi-exposure fused images; Section 3 presents the proposed halo-free fusion method; the experimental results and discussions are described in Section 4; and finally, Section 5 concludes the paper.
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
## 5 Results
### Experiment on the TDOA Measure Error  
The following simulation experiments are used to compare the performance of the three methods over the TDOA measurement error. The location of the interference source is 125\({}^{\circ}\) E, 30\({}^{\circ}\) N, and the multi-moment location uses the TDOA measured at five consecutive times to remove mirrored points. The time interval is 4 min, and the root mean square of the initial location error of the iteration is 1\({}^{\circ}\). The number of Monte Carlo is 500. It can be found in Figure 6 that, when the TDOA measurement error increases, the location error of the three methods increases. Among them, the single-moment location is mainly affected by the initial location error of the iteration when the TDOA measurement error is small, and it is not significantly affected by the TDOA measurement error. The multi-moment localization method is greatly affected by the time difference measurement error due to the method of solving the equations. The single-moment positioning method based on multi-moment measurement has good robustness when the time difference measurement error increases.
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 3 Classification of Attacks in VANETs
### Classification Based on VANET Layers  
Vehicular networks are made up of different layers just like the OSI model [68]. All these layers may also become vulnerable under some attacks. * _Application Layer_[69]: This layer is responsible for receiving the inputs from the user and forwarding it to further layers. All the application-based attacks try to modify the basic functionalities of this layer. The heterogeneity of VANET modules make it a greater concern to create a security baseline for this layer. * _Transport layer_[70]: This layer ensures process to process delivery of messages. It also ensures that these messages are sent in proper order without any alteration. Replaying, tunneling, session hijacking, or message sequence tampering are some of the attack examples of this category. * _Network layer_[71]: It propagates data packets from one node to another node. In VANETs security, concerns are not the same as in the case of other networks. Because it has features like topology, mobility, and network size, attacks launched on this network are also different. Location revealing and routing attacks are some of the examples in this layer. * _LLC Layer and MAC layer_[72]: It helps in congestion control using various algorithms. Congestion control may be proactive, reactive, or it may be a hybrid. This layer performs the tasks of scheduling and contention window adjustment. The jamming and identity impersonation are some of the examples of vulnerabilities in this layer. * _Physical layer_[72]: DSRC uses 802.11p OFDM that works in the frequency spectrum of 5.9 GHz band (5.885-5.905) with a 10 MHz wide channel. This type of communication data rate is generally 3 Mbps with a 6 Mbps default data rate. Eavesdropping, signal loss, and jamming are some of the attack examples in this layer. Analysis of such frequencies, even with speech signal in a compromised environment, is also very easy [73]. A list of layer-wise attacks in VANETs is shown in Table 2. We also provide a short and summarized table for attacks in Table 3. It shows that the application layer and network layer of VANETs are more vulnerable. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Attacked Layer** & **Types of Attack** & **Reference(s)** \\ \hline \multirow{6}{*}{Application layer} & DoS and DDoS & [PERSON] et al. (2014) [52], [PERSON] et al. (2012) [53], [PERSON] et al. (2014) [74] \\ \cline{2-3}  & Message tampering & [PERSON] and [PERSON] (2019) [50] \\ \cline{2-3}  & Impersonation attack & [PERSON] et al. (2014) [75] \\ \cline{2-3}  & Repudiation attack & [PERSON] et al. (2014) [65] \\ \cline{2-3}  & Replay attack & [PERSON] et al. (2018) [48], [PERSON] et al. (2019) [49] \\ \cline{2-3}  & Illusion attacks & [PERSON] and [PERSON] (2007) [51] \\ \cline{2-3}  & False position attacks & [PERSON] et al. (2020) [41] \\ \cline{2-3}  & Sybil attack & [PERSON] et al. (2015) [61], [PERSON]. (2002) [62] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Attack classification based on VANET layers.
## 2 The Information Bottleneck Method and Coarsely Quantized Information Bottleneck Signal Processing  
This section gives an overview of the information bottleneck method and explains its connection to the fundamental design purpose of communication receivers (i.e., extracting relevant information on the transmitted data from the received signal). From that, the idea of information bottleneck signal processing is derived and explained using several examples.
# Measurement of the Resonant Magneto-Optical Kerr Effect Using a Free Electron Laser
## 7 Conclusions and Outlook  
In summary, we reviewed experimental measurements of the polar resonant magneto-optical Kerr effect (P-RMOKE) using soft X-rays and the extension of the method to time-resolved measurements using a free electron laser (FEL). We showed that the method has various advantages, including: (1) element specificity, (2) large Kerr rotation angle, (3) probing of the out-of-plane magnetization with various experimental setup at \(M\)-edge range for \(3d\) transition metals, (4) use of the linearly-polarized light and (5) femtosecond-scale time resolution. Because the experiments were performed using the photon-in and photon-out configuration, we expect that the measurements could also be taken under magnetic and electric fields. This type of operando experimental method will be a promising tool for research into femtomagnetism and for the development of ultrafast spintronics.  
This work was partly supported by the MEXT program \"X-ray Free Electron Laser Priority Strategy Program\", by the Hyogo Science and Technology Association and by the Japan Society for the Promotion of Science with a grant-in-aid for Scientific Research (C) (Grant No. 26400328). [PERSON] was supported by a Grant-in-Aid for JSPS Fellows and by the Program for Leading Graduate Schools (Materials Education program for the future leaders in Research, Industry, and Technology, MERIT). The supporting experiments were performed using the facilities of the Synchrotron Radiation Research Organization of the University of Tokyo, Nos. 2014A7401, 2014B7401, 2014B7403, 2015A7401 and 2015B7401. The authors are grateful to [PERSON]. Iwata and [PERSON] of Nagoya University for synthesizing the GdFeCo sample and measuring the visible MOKE. [PERSON] and [PERSON] supported the static RMOKE measurement experiments at Ultraviolet Synchrotron Orbital Radiation (UVSOR). [PERSON], [PERSON], [PERSON] and [PERSON] supported the TR-RMOKE experiments at FERMI@Elettra. [PERSON], [PERSON] and [PERSON] conducted the TR-RMOKE experiments together.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AFMRAntiferromagnetic resonance  
BLSBrillouin light scattering  
EUVExtreme ultraviolet  
FELFree electron laser  
FMRFerromagnetic resonance  
FTHFourier transform holography  
Figure 12: Time evolution of normalized Kerr rotation angle obtained from TR-RMOKE measurements. The sample used was a thin film structure composed of Ta (2 nm)/Gd\({}_{21}\)(Fe\({}_{90}\)Co\({}_{10}\))\({}_{79}\) (20 nm)/Ta (10 nm)/SiO\({}_{2}\). The vertical axis represents the Kerr rotation angles divided by the average of the Kerr angles for negative delay times of \(-\)500 fs and \(-\)100 fs.
## 2 Materials and Methods
### Validations  
The daily GPP estimates by the four AI models were validated using the leave-one-year-out (a.k.a. jackknife) method. The calibration was carried out from a 10 year dataset except for one year, and the validation was conducted for the excluded one year. Such cross-validation processes were repeated ten times for each year from 2006 to 2015. Validation statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), and CC were used.  
To compare the performance of the AI predictions with that of the currently operative GPP products, we collected the MODIS and the FluxCom GPP during 2006-2015. MODIS Terra and Aqua products (MOD17A2 HGF and MYD17A2 HGF) comprise a gap-filled dataset that has less uncertainty than the previous version (MOD17A2 and MYD17A2). The gap-filling process was conducted at the end of each year using a reliable dataset for the fraction of photosynthetically active radiation (FPAR) and LAI [47]. Because these products have a spatial resolution of 500 m and a temporal resolution of 8 days, our daily GPP estimates were aggregated into an 8 day composite for the comparison of performance. For another comparison, we obtained the AI-based FluxCom GPP products with a spatial resolution of 10 km and a temporal resolution of 8 days. However, all the pixels on the Gwangneung site had missing values for the entire period, thus the performance comparison with the FluxCom GPP could not be carried out.  
Figure 3: The parameter optimization process for a deep neural network (DNN) model in this study, which was adopted from [46]. L1—regularization uses the Manhattan norm; L2—regularization uses the Euclidean norm, for the adjustment of weights.
## 4 The Proposed Method and Principle
### The Improved Coherent Noise Suppression (ICNS) Method
\begin{array}{l}y_{1}(n)=s(n)+e_{1}(n)\\ y_{2}(n)=s(n)+e_{2}(n)\end{array} . \tag{16}\]  
By taking into account the coherence of the vibration interference field, the residual interference fields \(e_{1}(n)\) and \(e_{2}(n)\) can be expressed by Equation (17), where \(h(n)\) is the transfer function. The coherent noise suppression method regards the detector as a 2 \(\times\) 2 linear time-invariant system to suppress the residual interference field from the helicopter. \[e_{2}(n)=h(n)\times e_{1}(n). \tag{17}\]  
By applying a discrete Fourier transform (DFT) on both sides of Equation (18), they can be represented in the frequency domain as follows:  
\[ \begin{array}{l}Y_{1}(\omega)=S(\omega)+E_{1}(\omega)\\ Y_{2}(\omega)=S(\omega)+E_{2}(\omega)=S(\omega)+H(\omega)\cdot E_{1}(\omega) \end{array} . \tag{18}\]  
Hence, the auto-spectra and cross-spectra of \(Y_{1}(\omega)\) and \(Y_{2}(\omega)\) are shown as Equation (19), where \(P_{SS}(\omega)\) and \(P_{EE}(\omega)\) are the auto-spectra of the signal and interference field, and \(H(\omega)\) is the discrete Fourier transform of \(h(n)\). \[ \begin{array}{l}P_{Y_{1}Y_{1}}(\omega)=P_{SS}(\omega)+P_{EE}(\omega)\\ P_{Y_{1}Y_{2}}(\omega)=P_{SS}(\omega)+H^{*}(\omega)P_{EE}(\omega)\\ P_{Y_{2}Y_{1}}(\omega)=P_{SS}(\omega)+H(\omega)P_{EE}(\omega)\\ P_{Y_{2}Y_{2}}(\omega)=P_{SS}(\omega)+H(\omega)P_{EE}(\omega)\end{array} . \tag{19}\]The transfer function \(H(\omega)\) can be expressed as Equation (20). Then, the signal \(s(n)\) can be written again in a time domain by using the inverse Fourier transform, \(F^{-1}\), as shown in Equation (21). \[H(\omega)=\frac{P_{\gamma_{2}\gamma_{2}}(\omega)-P_{\gamma_{2}\gamma_{1}}(\omega )}{P_{\gamma_{1}\gamma_{2}}(\omega)-P_{\gamma_{1}\gamma_{1}}(\omega)}. \tag{20}\]  
\[s(n)=F^{-1}\bigg{(}y_{1}(\omega)-\frac{y_{2}(\omega)-y_{1}(\omega)}{H(\omega) -1}\bigg{)}. \tag{21}\]  
Finally, the structure of the aeromagnetic compensation and the improved coherent noise suppression algorithm is shown in Figure 7, including the compensation process and the interference suppression process.
## 2 Background
### Attention Mechanism  
Attention mechanism is widely utilized in CNNs to solve different computer vision tasks, like video classification [37] and object detection [38]. [PERSON] et al. [37] applied the attention mechanism to non-local filter operation, which is effective in performing classification tasks in videos through computing the feature relationships in different positions. [PERSON] [38] exploited the attention mechanism to learn the spatial relationships between salient features, which shows promising results for object detection and segmentation tasks. The above works are to investigate to model spatial correlations. In contrast, another method, squeeze-and-excitation network (SENet) [39], is to model the correlative dependence between channels of CNN features to perform feature recalibration. It utilizes global information to learn layout or emphasis of the scene and to obtain the context information which guides visual processing to task-related image regions. Inspired by SENet [39], we find that we can recalibrate the features in the decoder aiming at guiding the detector to pay more attention to pedestrian locations. Specifically, we propose an attention module that adopts SE blocks to learn attention vectors from the feature maps in the encoder. Then the learned attention vectors are utilized to scale the feature maps from the decoder to enhance informative features and to suppress less useful features. The attention module indicates the probability that each pixel belongs to a pedestrian region. It has a high discriminative capability to recognize a human target, because background interference is reduced, and pedestrians are highlighted.  
In this research, a novel attention-guided encoder-decoder convolutional neural network (AED-CNN) is developed to detect pedestrians in IR images. To better describe pedestrians in low-resolution and noisy IR images, the encoder-decoder module is adopted, in which a skip connection block is integrated to effectively combine the features from the encoder and decoder for increasing additional context information. Furthermore, the attention module is proposed to highlight pedestrians while eliminating background interference, which can help to detect pedestrians under different weather conditions.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 2 Materials and Methods
### Experimental Programs  
Unconfined compressive strength tests and triaxial tests are methods used to determine the mechanical properties of materials such as soil and rock, primarily for evaluating the natural strength of soil and its sensitivity index. This study aimed to investigate the effects of stabilizers and recycled aggregate on the mechanical properties, strength characteristics, and water stability of soft soil under standard curing and immersion curing conditions. The moisture content was set at the optimal value of 20% for red clay.  
Mixing Ratios: When the recycled aggregate content was 20%, stabilizer contents were set at 10%, 15%, and 20%. When the stabilizer content was 20%, recycled aggregate contents were set at 40% and 60%. Standard curing was conducted for 28 days, with water immersion curing times of 1, 5, 20, and 40 days. Parameter settings were adjusted based on related research and preliminary results. The selected mixing ratios of RFA (20%, 40%, 60%) and stabilizer (10%, 15%, 20%) were based on optimal ranges reported in previous studies. Stabilizer content typically does not exceed 20% to avoid diminishing strength improvement, while RFA content is limited to 60% to prevent excessive voids. Preliminary experiments validated that these ratios achieve a balance between strength enhancement and material stability, confirming their suitability for this study [33; 34].  
Scheme Description: The contents of the stabilizer, recycled aggregate, and water were expressed as mass ratios to red clay. The experimental scheme notation used M (immersion curing age), C (stabilizer content), and R (recycled aggregate content). For example, \"R20C20-M40\" denotes a recycled aggregate content of 20%, a stabilizer content of 20%, and an immersion curing age of 40 days. Specific details are shown in Table 2.
# Analysis and Prediction of Universities' Buildings' Renovation Costs Using a Regression Model
## 3 Materials and Methods
### Research Analysis
The coefficient indicates the proportion between the dependent variable (\(y\)) and the independent variables for the given set of \(n\) data points, for \(i\) = 1, 2, 3,..., \(n\). Generally, the higher the \(R^{2}\) variable is, the better. The coefficient of determination has been calculated according to the formula:  
\[R^{2}=\mathbf{1}-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i} -\overline{y})^{2}} \tag{2}\]  
where \(R^{2}\) is the coefficient of determination, \(\mathbf{y}_{i}\) is the value of the variable \(y\), \(\hat{\mathbf{y}}_{i}\) is the fitted value for observation \(i\), \(\overline{y}\) is the arithmetic average of the y values from the sample, and \(n\) is the number of observations. To predict future cost expenditure, a cumulative year-on-year bivariate linear regression was calculated, and the Statistica [45] software has been used. Firstly, four variables have been chosen (year of construction, last renovation, usable area, and planned renovation costs), the correlation has been checked, and the regression analysis has been carried out. After the regression analysis, the statistical significance of the year of construction attribute was much bigger than 5%, and that is why it has been erased from the database. Other attributes have been used in further calculations. Table 3 shows the statistical calculations for the dependent and the independent variables taken from the database containing 55 buildings for the years 2017-2021. The average renovation cost per 1 m\({}^{2}\) according to this database was 53.52 EUR, and the median was 33.50 EUR. The average usable area was 3864.45 m\({}^{2}\) and the median was 2468.81 m\({}^{2}\). The last modernization period was in the range 1966-2020, while the average was 2008 and the median was 2012. The chart shown in Figure 4 shows the renovation costs per 1 m\({}^{2}\) for the analyzed database in the analyzed period (years 2017-2021). The renovation cost range varied between 0.77 and 2 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Min.** & **Max.** & **Average** & **Median** \\ \hline Last modernization (year) & 1966 & 2020 & 2008 & 2012 \\ Usable area (1 m\({}^{2}\)) & 65.00 & 19,344.00 & 3864.45 & 2468.81 \\ Renovation costs (in EUR) per 1 m\({}^{2}\) & 0.77 € & 277.63 € & 53.52 € & 33.50 € \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistical calculations for the dependent and the independent variables.