esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Blockchain-Based Federated Learning
## 4 Applications
### Internet of Vehicles  
BFL solutions have been widely applied to the IoV to facilitate data sharing and autonomous driving [81; 118; 132]. [PERSON] et al. [81] proposed a fully decentralized BFL framework. This framework achieves end-to-end trustworthy communication within the IoV, and the communication latency remains within an acceptable range, thus promoting effective communication for automated vehicles. They use BFL to verify model updates in on-vehicle machine learning (oVML), enhancing the performance and privacy security of automated vehicles. [PERSON] et al. [87] introduced a BFL framework composed of a primary permissioned blockchain maintained by roadside units and a local DAG run by vehicles, aiming for efficient data sharing in the IoV. Additionally, [PERSON] et al. also proposed an asynchronous FL scheme based on edge data. By using the Delegated Proof of Stake (DPoS), it selects optimized participating nodes, thereby improving the efficiency of FL. In [133], a blockchain-based hierarchical FL algorithm is introduced which reduces storage consumption and improves training accuracy. The proposed knowledge-sharing method based on BFL enhances the reliability and security of in-vehicle networks. Using the proof of learning (PoL) consensus mechanism, a lightweight blockchain was realized, preventing the wastage of computational power.  
Additionally, BFL is gradually being expanded to various domains. In the field of content caching, [PERSON] et al. [134] presented a new algorithm called the blockchain-assisted compressed algorithm of FL, applied for content caching (CREAT). This blockchain-assisted FL algorithm aims to predict cache files and enhance the cache hit rate. In the domain of location prediction, the scheme proposed in [135] utilized BFL for local training on users' mobile devices. This approach safeguards user privacy while making better use of the data for more accurate location predictions. In the realm of mobile crowd sensing, [PERSON] et al. [136] introduced the secure FL for an unmanned aerial vehicle (UAV)-assisted crowdsensing (SFAC) framework. This is a secure FL architecture for UAV-assisted mobile crowd sensing (MCS), employing local differential privacy to protect the privacy of data providers. Moreover, BFL has been applied to disaster response. The study in [137] proposed a blockchain-authorized BFL framework that will implement a disaster response system using wireless mobile modules on UAVs using future 6G networks. Additionally, BFL has also been adopted in the news recommendation field. [PERSON] et al. [138] presented a cloud-edge collaborative filtering recommendation system based on FL. This system incorporates noise into the training model using differential privacy technology, further preventing data privacy exposure.
## 4 Results and Discussion
### Compressive Strength of Stone Bodies  
The compressive strength is a pivotal metric for assessing the material properties of alkali-activated GGBFS-zeolite powder slurry. As shown in Figure 6, the compressive strength of the slurry is influenced by various factors. When the ratio of zeolite powder to GGBFS was 50:50, the 28 d compressive strength of the stone bodies reached a minimum value of 12.49 MPa. The incorporation of zeolite powder significantly reduced the total pore volume of the slurry. Furthermore, the internal structure and composition of the pores were optimized, and the stone bodies were densified. However, due to the zeolite powder's high water absorption and storage capacity [36], a consequent high level of water absorption occurred during the chemical reaction, which hindered the hydration reaction. It was observed that when CL was dosed at 5%, the 28 d compressive strength of the slurry stone bodies reached a maximum of 17.19 MPa, 26.30% higher than the lowest value. CL was hypothesized to exert an air-entraining effect [37], which was theorized to promote the hydration reaction, increase the product, decrease porosity, and improve the compressive strength of the slurry stone bodies.  
As the NaOH dosage, increased, the compressive strength of the slurry stone bodies first increased, then decreased. A maximum value of the 28 d compressive strength of 17.59 MPa was attained at a dosage of 5%, and the maximum increase in the 28 d  
Figure 6: Effects of different factors on the compressive strength of grouting slurry stone: (**a**) zeolite powder–GGBFS; (**b**) CL dosage; (**c**) NaOH dosage; (**d**) water–binder ratio.  
compressive strength was 52.49% at a dosage of 3%. Thus, the correct amount of alkali activator promoted the binding of active molecules in the chemical reaction [38]. As the NaOH dosage exceeded 5%, the elevated OH\({}^{-}\) concentration resulted in a rapid reaction rate, leading to the generation of numerous hydration products in a short period. These products accumulated rapidly on the surface of the active particles, impeding the ensuing reaction and consequently diminishing the strength of the stone bodies. This observation is consistent with the results reported by [PERSON] et al. [39] and [PERSON] et al. [40].  
The compressive strength of the stone bodies decreased with the water-binder ratio. The 28 d compressive strength decreased most significantly at a water-binder ratio of 0.5, reaching 31.80%. As the water-binder ratio increased, the free water molecules in the slurry also increased. In addition, the excess free water molecules diluted the alkaline material in the system and reduced the OH\({}^{-}\) concentration. This reduction slowed the rate of the hydration reaction, limiting the formation of hydration products and their ability to adequately fill the pores, further reducing the compressive strength of the stone bodies [41; 42].
# Supporting Immersive Video Streaming via V2X Communication
## 4 Conclusions
Given the extensive data exchange between vehicles and infrastructure in V2X communication, including vehicle location, speed, navigation information, and passenger data, the confidentiality, integrity, and availability of these data are crucial. Blockchain has emerged as a popular research focus in recent years in the field of information security, due to its decentralization, transparency, and resistance to data tampering [61]. Combining blockchain with other emerging technologies such as edge computing and AI can enhance real-time data processing and decision-making capabilities in V2X networks. By addressing these challenges, future research can significantly contribute to the development of secure, efficient, and sustainable 6G vehicular networks, thereby advancing the field of intelligent transportation systems and the realization of smart cities. **Author Contributions:** Conceptualization and methodology, C.-J.H. ; software, K.-W.H. ; writing and editing M.-E.J. ; writing and editing Y.-H.L. ; writing and editing, H.-W.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Science and Technology Council, Taiwan, for financially supporting this research under Contract Number NSTC 112-2221-E-259-006. **Data Availability Statement:** Research data are available upon individual requests to the corresponding author and are intended for use by collaborators engaged in research with the corresponding author's research team. **Conflicts of Interest:** Author [PERSON] was employed by the company Lookout, Inc. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 2 Single Antenna ADS-B Signal Model  
The ADS-B 1090 MHz Extended Squitter (1090 ES) baseband signal is composed of an 8 bit fixed preamble and 112 bit data block, with a duration of 120 \(\upmu\)s. The 8 bit preamble contains four fixed position pulses at 0, 1, 3.5 and 4.5 \(\upmu\)s, respectively, while the remaining 112 bit data block vary with the content of the message. ADS-B signal is modulated by PPM technique, and each bit data is composed of two pulses with a duration of 0.5 \(\upmu\)s. When the previous pulse is 0 and the next pulse is 1, it represents 0, and 1 otherwise. Therefore, every ADS-B signal has 240 pulses. The structure of an ADS-B baseband signal is shown in Figure 1.  
ADS-B baseband signal \(\mathbf{b}(t)\) at initial time (0 \(\upmu\)s) is expressed as  
\[\mathbf{b}(t)=\sum_{\text{k=1}}^{240}b_{k}\mathbf{g}(t-kT_{b}) \tag{1}\]  
where \(k\) is number of ADS-B signal pulse, \(k=1,2,\cdots,240\). \(b_{k}\) is the value of the \(k\)th impulse and can be 0 or 1. \(T_{b}\) is the unit pulse width of ADS-B baseband signals, \(T_{b}\) = 0.5 \(\upmu\)s. \(\mathbf{g}(t)\) is unit rectangular pulse with the \(T_{b}\) pulse width at \(-0.5\)\(\upmu\)s starting position.  
The ADS-B baseband signal is modulated to the carrier frequency using the Amplitude Shift Keying (ASK) modulation. ICAO allows the carrier frequency of ADS-B 1090 ES signal to be (1090 \(\pm\) 1) MHz. Therefore, the modulated ADS-B signal in the time-domain can be expressed as  
\[\mathbf{s}(t)=A\mathbf{b}(t)\cos(2\pi ft) \tag{2}\]  
where \(A\) is the signal amplitude. \(\mathbf{b}(t)\) is the baseband signal and \(f\) is the carrier frequency.  
In the case of single antenna, an ADS-B overlapping signal is linearly superimposed by \(d\) independent signals, and the ADS-B overlapped signal can be expressed as:  
\[\mathbf{x}(t)=\sum_{i=1}^{d}\mathbf{s}_{i}+\mathbf{n}(t) \tag{3}\]  
The Formula (2) can be further obtained by substituting Formula (3):  
\[\mathbf{x}(t)=\sum_{i=1}^{d}A_{i}\mathbf{b}_{i}(t-t_{i})\cos(2\pi f_{i}t)+ \mathbf{n}(t) \tag{4}\]  
where \(\mathbf{x}(t)\) is single antenna ADS-B overlapped signal. \(d\) is the number of overlapping signals. \(A_{i}\), \(\mathbf{b}_{i}(t)\), \(t_{i}\), and \(f_{i}\) are the amplitude, baseband signal, time of arrival, and carrier frequency of \(i\)th signal, respectively. \(\mathbf{n}(t)\) is the received Gaussian white noise.  
Figure 1: The diagram of ADS-B baseband signal.
## 5 Automatic Signal Recognition
### Extended Validation for Mixed Signals
#### 5.3.3 Result Analysis  
The mixed RSSI traces are combined with the existing ones to verify the extended features and algorithm. A similar three-fold cross-validation process is applied, and the new confusion matrix is displayed in Table 3. As expected, the true positive rates of LTE and DVB-T are identical as shown in Table 2, since neither the threshold learning procedure, nor the algorithm changed for these signal classes. The true positive rate of the mixed signal is 98.15%, which shows that the extended algorithm and feature space are well capable of identifying the mixed signals from the rest of the signal classes. Interestingly, the true positive rates of Wi-Fi obtained by the new algorithm are actually higher than before. This can be better understood when looking at the false positives: Wi-Fi signals are no longer recognized as LTE or noise, thanks to the relaxed thresholds \(\theta_{n}\) and \(\theta_{p}\); instead, all of the Wi-Fi traces that failed to be recognized are now identified as the mixed signal, and 1.85% of mixed traces are falsely determined to be LTE.
## 5 Conclusions  
Within the limitations of this study, the image-assisted digital shade guides constructed using images obtained from a smartphone camera (SC-DSG) demonstrated significantly higher accuracy than those obtained from a mirrorless camera (MC-DSG), with an acceptable mean color difference of \(\Delta\)E\({}_{00}\) = 1.8 and closer agreement with the VITA Easyshade V, suggesting SC-DSG has potential as an accessible and effective tool for dental shade matching. While MC-DSG, SC-DSG, and VITA Easyshade V demonstrated excellent overall reliability, the VITA Easyshade V showed superior reliability, particularly for a* and b* values.  
Conceptualization, S.T.C. and I.M.T.; methodology, S.T.C.; software, S.T.C.; validation, S.T.C.; formal analysis, S.T.C.; investigation, S.T.C.; resources, S.T.C.; data curation, S.T.C.; writing--original draft preparation, S.T.C.; writing--review and editing, S.T.C., I.M.T., S.Y.S., M.Z.K. and K.Y.L.; visualization, S.T.C. and I.M.T.; supervision, I.M.T., S.Y.S., M.Z.K. and K.Y.L.; project administration, S.T.C.; funding acquisition, S.T.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data presented in this study are available on request from the corresponding authors.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
MC-DSG Mirrorless Camera Digital Shade Guide  
SC-DSG Smartphone Camera Digital Shade Guide  
CII Commision International de l'Eclairage
# New Orthogonal Transforms for Signal and Image Processing
## 2 Orthogonal Generalized Transform Matrix  
Let us consider an elementary square matrix \(N\times N,N=2\) consisting of four elements. Assuming the first row of this matrix has different elements \(a_{1},a_{2}\) and the matrix is symmetric and orthogonal, we have:  
\[A(1)=\begin{bmatrix}a_{1}&a_{2}\\ a_{2}&-a_{1}\end{bmatrix} \tag{1}\]  
where \(a_{1},a_{2}\in(-\infty,\infty)\), \(a_{1},a_{2}\)--any real numbers, \(a_{1},a_{2}\
eq 0\).  
In the case of the matrix \(A(n)\), where \(n=\log_{2}N,N=4\) the first row consists of four different elements \(a_{1},\ a_{2},\ a_{3},\ a_{4}\in(-\infty,\infty),\ a_{i}\
eq 0\). Such a sequence is referred to as  
For instance, to create the basis sequence for the orthogonal matrix \(A(3)\) we select the first three elements: \(a_{1},\ a_{2},\ a_{3}\), e.g., \(1,\ \frac{1}{2},2\). Then, the next elements are obtained using (7): \(a_{4}=\frac{a_{2}\cdot a_{3}}{a_{1}}=1\), \(a_{5}=\frac{a_{2}\cdot a_{4}}{a_{2}}=4\), etc. Finally, we obtain the basis sequence: \(1,\ \frac{1}{2},2,1,4,2,8\), \(4\) and the matrix \(A(3)\) is:  
\[A(3)=\left[\begin{array}{cccccccc}1&\frac{1}{2}&2&1&4&2&8&4\\ \frac{1}{2}&-1&1&-2&2&-4&4&-8\\ 2&1&-1&-\frac{1}{2}&8&4&-4&-2\\ 1&-2&-\frac{1}{2}&1&4&-8&-2&4\\ 4&2&8&4&-1&-\frac{1}{2}&-2&-1\\ 2&-4&4&-8&-\frac{1}{2}&1&-1&2\\ 8&4&-4&-2&-1&2&-1&1\\ 4&-8&-2&4&-1&2&\frac{1}{2}&-1\end{array}\right] \tag{8}\]  
The above matrix is symmetric and orthogonal and Equation (5) holds true. It should be emphasized that changing the order of basis sequence elements causes at least one of Equation (5) to be not fulfilled. For instance, by changing the elements \(1,\ \frac{1}{2}\ \text{to}\ \frac{1}{2},\ 1\), we obtain the basis sequence for which the matrix \(A(3)\) is not orthogonal. An example of another sequence matching Equation (5) is the sequence \(3,\ 9,\ 2,\ 6,\ \frac{4}{3},\ 4,\ \frac{8}{3},\ \frac{8}{3}\) or \(-2,\ 1,\ 4,\ -2,\ -8,\ 4,\ 16,\ -8\). For each subsequent group of four elements of the basis sequence:  
\[a_{1},a_{2},a_{3},a_{4}|a_{5},a_{6},a_{7},a_{8}|a_{9},a_{10},a_{11},a_{12}|a_{ 13},a_{14},a_{15},a_{16}|\ldots\]  
there is a relationship:  
\[\frac{a_{i+4}}{a_{i}}=\beta=const\qquad\text{for}\ \ i=1,\ 2,\ 3,\ 4,\ \ldots,N-4 \tag{9}\]  
The coefficient \(\beta\) may be chosen arbitrarily as any positive number or by Equation (5). Using this equation we find:  
\[\beta=(\frac{a_{3}}{a_{1}})^{2}>0 \tag{10}\]  
This means that each subsequent group of four elements of the basis sequence is obtained by multiplying the previous group by \(\beta\) under condition (3).  
In the general case, the basis sequence for the creation of a symmetric and orthogonal matrix has the form:  
\[a_{1},a_{2},a_{3},a_{4},\beta a_{1},\beta a_{2},\beta a_{3},\beta a_{4},\beta ^{2}a_{1},\beta^{2}a_{2},\beta^{2}a_{3},\beta^{2}a_{4},\ldots,\beta^{i}a_{1}, \beta^{i}a_{2},\beta^{i}a_{3},\beta^{i}a_{4} \tag{11}\]  
where  
\(\beta\)--positive real number.  
\(j=\frac{N}{4}-1\), \(N=2^{n}\), \(n=2,\ 3,\ 4,\ldots\)
## 6 Discussion
The distance-from-road network is considered to have an influence on the occurrence of groundwater springs since its presence can cause local hydrological and erosion issues while affecting indirectly the groundwater table [85]. Also, the presence of a road may influence the amount of soil moisture but also the infiltration rate as a result of the removal of geological formations and the disturbance of the surface during of the construction phase [43; 85]. Concerning the validation and comparison of the three models (KLR, RF, and ADTree), the RF model appears to provide slightly higher AUC values, lower SE values, and shorter CI intervals than the other two methods. Several studies have indicated that RF models have higher accuracy, compared to other models. According to [PERSON] et al. [12], who applied support vector machine (SVM), RF, and genetic algorithm optimized RF (RFGA) methods to assess groundwater potential by spring locations, RF and optimized RF models outperformed SVM models. According to [PERSON] et al. (2018) [86], this could be attributed to the methodological approach they followed, which involves aggregating the outcomes of many decision trees in order to limit overfitting effects as well as to limit error due to bias and error due to variance, thus producing more accurate predictions. However, other studies report that the performance of RF models could be influenced by the presence of datasets with noisy data and by the presence of data that includes categorical variables with different numbers of levels where, in such a case, RF models are biased in favor of those variables that appear with more levels [36]. In the present study, KLR gave more accurate results than those from the ADTree model. In similar studies concerning landslide susceptibility assessments, which implemented KLR and ADTree methods, it was found that KLR produced more balanced results for the training and validation datasets in terms of the statistical index, while the ADTree models showed significant variance [74]. Finally, although the presented models appear to have satisfactory predictive performance, it must be kept in mind that their results are influenced by the quality and quantity of the available input,and also the identification of nonspring areas. Concerning future work, the presented approach could be applied to an area with different geo-environmental settings or include in the analysis dynamic variables, such as precipitation and temperature, that may vary over short timeframes, so as to estimate the efficiency of the proposed models.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 3 A New Risk Measurement Method Considering Multiple Driving Behaviors
### Validation of the Effectiveness of \(R_{total}\)
#### 3.3.1 Extraction of Car-Following and Lane-Changing Behaviors  
The \(R_{\text{total}}\) model integrates both longitudinal and lateral risks. The proposed indicators are related to the parameters of the target vehicle, as well as those of the preceding and following vehicles. Therefore, we extracted both car-following and lane-changing platoons, each consisting of the target vehicle, the leading vehicle, and the following vehicle. In line with existing literature [22], these platoons must meet several conditions listed in Table 2.  
To minimize the impact of lane-changing maneuvers, the analysis is limited to the inner lane (lane 2). Based on the conditions in Table 2, a total of 742 car-following trajectories and 509 lane-changing trajectories were extracted. These datasets form the basis for the validation of the proposed indicators.
## 3 Experiment and Results Analysis
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Model** & **Precision (P)** & **Recall (R)** & **F1 Score** \\ \hline
1 & IDCNN+CRF & 81.14 & 79.90 & 80.57 \\
2 & BERT+IDCNN+CRF & 85.29 & 85.17 & 85.22 \\
3 & BERT-BiGRU-Att-CRF & 86.25 & 86.77 & 86.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of overall recognition performance with mainstream models (%). Figure 8: Comparison of overall model recognition performance (%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Model** & **Precision (P)** & **Recall (R)** & **F1 Score** \\ \hline
1 & CRF & 75.23 & 73.65 & 74.44 \\
2 & BiLSTM+CRF & 81.03 & 82.05 & 81.62 \\
3 & BiGRU+CRF & 81.56 & 82.33 & 81.94 \\
4 & BERT+BiLSTM+CRF & 83.95 & 83.81 & 83.88 \\
5 & BERT+BiGRU+CRF & 84.14 & 84.09 & 84.11 \\
6 & BERT-BiLSTM-Att-CRF & 85.96 & 85.68 & 86.06 \\
7 & BERT-BiGRU-Att-CRF & 86.25 & 86.77 & 86.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of overall model recognition performance (%). these four components--BERT, BiGRU, the attention mechanism, and CRF--enables the model to more precisely identify and classify named entities in texts.
## 2 Numerical Calculation Model
### One-Way Fluid-Solid Coupling Numerical Simulation Method
In the cooling system of the DCM, the deformation of the solid structure is minimal and insufficient to affect the motion characteristics of the fluid significantly. Simultaneously, the movement of the fluid has a relatively small impact on the shape of the structural domain. Therefore, during the simulation, significant changes in the meshes of both domains are not expected. A numerical simulation method using one-way fluid-solid coupling is chosen to improve computational efficiency. This better aligns with the actual conditions of the DCM cooling system and facilitates simulation analyses under various operating conditions.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 6 Experiment
### Evaluation Metric  
Five kinds of metrics were used in our work: _Precision_, _Recall_, _F1-Score_, _AUC_, and _PRAUC_, to evaluate the anomaly detection performance. _Precision_ describes the proportion of positive examples predicted by the classifier that are real positive examples. _Recall_ describes the proportion of real positive examples in the test set that have been selected by the classifier. The _Precision_ and _Recall_ can provide a diagnostic tool for binary classification models. Moreover, the _ROC_ curve (_AUC_) and the _Precision-Recall_ curve (_PRAUC_) are used to evaluate the performance of anomaly detection. _AUC_ and _PRAUC_ can be optimistic about severely imbalanced classification problems with few samples  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Type** & **Number** & **Features** & **Number of Abnormalities** & **Abnormal Ratio** \\ \hline \multirow{2}{*}{SWaT} & Train & 496,800 & 51 & 0 & 0.00\% \\  & Test & 449,919 & 51 & 54,621 & 12.14\% \\ \hline \multirow{2}{*}{WADI} & Train & 784,571 & 123 & 0 & 0.00\% \\  & Test & 172,801 & 123 & 9977 & 5.77\% \\ \hline \multirow{2}{*}{KDDCUP99} & Train & 562,387 & 34 & 0 & 0.00\% \\  & Test & 494,021 & 34 & 97,311 & 19.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the three datasets used in experiments.  
of the minority class, i.e., anomaly classification. The _AUC_ and _PRAUC_ are obtained by varying the hyperparameters. _Precision_, _Recall_, and _F1-Score_ are the model performance under the best set of parameters selected.