esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 5 Security and Performance Analysis
### Security Analysis
#### 5.1.1 Analysis of Using a Packet with a Random Value to Calculate the MSR Token  
In this paper, the random value included in the data packet is used to calculate the MSR token. This packet, which contains a random value, will be shown to have all three of the required properties of a secure MSR token. The MSRLV operates on the assumption that there will be at least one packet exchanged between sensors within a certain time period. The rationale for this is that sensors in wireless sensor networks occasionally transmit the sensing data, such as temperature, pressure or wind speed, to the sink.  
First, these packets have mutual symmetry since they can be observed by both the claimant and verifier in the model presented in this paper. Other sensors that do not locate at the right position are not able to listen to all of the necessary packets to calculate the MSR token. Even though malicious sensors are equipped with powerful devices, they can extend their transmission ranges only, not their listening ranges.  
Second, the token derived from packets can be independently calculated by both the claimant and the verifier. Based on the assumption that the claimant and the verifier select the same packet to be used in the verification calculations, they do not need to exchange any other packets. Note that it is safe to assume that timing is synchronized between sensors because the data flowing through the sensor networks usually include the time, which the sensors use to calibrate themselves [24].  
Finally, attackers are not able to deduce the token without knowing the data on which the calculation is based. The mathematical process to calculate the token is known to every sensor, but the data to calculate the token are available only to both sensors, since they come from the MSR between them. The physical constraint that the claimant must be at the correct location to collect data necessary to calculate the token makes it impossible for an attacker to know all of the packets generated from within the MSR. As shown in the Figure 5, there is an area (i.e., gray shaded in the figure) where  
Figure 6: Verification failure example.  
the attackers cannot access. In addition, the fact that the piece of input data is a random value adds another layer of security against attackers from calculating the token.
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 5 Conclusions  
In this paper, we propose a traffic prediction model named Time-Evolving Graph Convolutional Recurrent Network. Our method uses a tensor-composing method to generate adaptive time-evolving graphs, then integrates the graph convolution module with these time-evolving graphs into the RNN structure to a form general prediction framework. Experiments on two real-world traffic datasets demonstrate the superiority of TEGCRN over multiple competitive baseline models especially in short-term prediction. We verify the effectiveness of time-evolving graph convolution in capturing more comprehensive inter-node dependency than the models with static graph convolution. The proposed model  
Figure 6: Parameters study on METR-LA. (**a**) Performance with different embedding dimensions. (**b**) Performance with different hidden sizes.  
Figure 7: Parameters study on PEMS-BAY. (**a**) Performance with different embedding dimensions. (**b**) Performance with different hidden sizes.  
can be employed as a support block in traffic-related systems and provide more accurate predictions for multiple applications. In the future, we plan to work on the heterogeneous relationships in traffic networks and build a predicting model with more explainability.  
Conceptualization, W.M. and J.C.; methodology, W.M.; software, W.M.; validation, [PERSON], J.C. and X.C.; formal analysis, [PERSON] and X.C.; investigation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], J.C. and X.C.; visualization, [PERSON]; supervision, X.C.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (No. 2019 YFE0114000), Guangdong R&D Project in Key Areas under Grant (2019B010158001), Industry-University-Research Cooperation Project in Zhuhai (No. ZH22017001200072 PWC).  
Not applicable.  
The data used to support the findings of this study are included in the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RNN & Recurrent Neural Network \\ CNN & Convolutional Neural Network \\ GCN & Graph Convolutional Network \\ POI & Point of Interest \\ TEGCRN & Time-Evolving Graph Convolution Recurrent Network \\ ARIMA & Auto-Regressive Integrated Moving Average \\ VAR & Vector Auto-Regression \\ SVR & Support Vector Regression \\ LSTM & Long Short-Term Memory \\ GRU & Gated Recurrent Unit \\ \end{tabular}
## 2 Materials and Methods
### Urban Environmental Parameters
#### 2.3.1 Normalized Difference Vegetation Index  
The NDVI is used to quantify the presence of vegetation and its level of greenness, so that the vegetation density and changes in plant health can be determined from the index value. In this work, Sentinel-2 Bottom of Atmosphere (_BOA_) reflectance data is used, with bands 4 (_Red_ band) and 8 (near infrared band), both at a spatial resolution of 10 m. This index is calculated using Equation (1):  
\[NDVI=\frac{R_{BOA}NIR-R_{BOA}Red}{R_{BOA}NIR+R_{BOA}Red} \tag{1}\]  
The average summer _NDVI_ at 10 m resolution is computed using Google Earth Engine (r) [34], providing insights into vegetation cover. The average _NDVI_ value within the same 20 m buffer as described in Section 2.2.2 is then assigned to each 50 m street/road segment.
## 1 Introduction  
Multi-criteria decision analysis (MCDA) is used in a wide variety of decision support applications. In keeping with the theme of this Special Issue, the hypothesis examined in this paper is that such methods are underutilized in the disciplines normally associated with civil engineering, i.e., those relating to structures, transportation, geotechnics, environment, water, and waste. This may be because information about the potential of these methods and about the tools available to implement them is not as widely known amongst practicing engineers, suggesting a communications lacuna. In this regard, the published technical literature is an important communications channel and here we investigate whether its use in disseminating information on MCDA can be improved. The methodology is to undertake a series of searches and reviews of the published literature relating to the use of MCDA in these disciplines and to address the hypothesis by analyzing the number and patterns of documents emerging from the different searches. The paper begins with (i) a short concise introduction setting out the background to MCDA, and (ii) a summary of previous surveys or comparisons of some MCDA methods related to civil engineering. Then, the main contribution of this paper follows in three further sections; (iii) a description of the methodology used in the literature review followed by (iv) a detailed analysis and discussion of the results, (v) a short description of MCDA software and (vi) a summary of the conclusions arising from this analysis.  
This analysis is motivated, in part, by the realization that, not only should the engineer make good and appropriate design choices, but that she or he should be able to explain and justify those choices to (i) other professionals, (ii) project funders and increasingly to (iii) the legal profession when there are court challenges to the choices made, and (iv) to the general public, particularly where environmental, health and safety matters are concerned. In situations where many factors, both quantitative and qualitative, must be considered, systematic MCDA methods that are well regarded and algorithmically sound are becoming essential for this purpose. Thus, it is essential that civil engineers are familiar with the key methods and tools and this is assessed here based on technical publications on this topic.
## 2 Materials and Methods
### Methodology
#### 2.3.3 Ridge Regression  
Ridge regression analysis is a refined least squares estimation method [41] and biased estimation regression method that can eliminate the collinearity of independent variables. Therefore, in this paper, we used ridge regression to explore the sensitivity of WUE to seasonal drought.  
The multiple linear regression models can be expressed as follows:  
\[Y=X\cdot\beta+\varepsilon\]  
where \(Y\) is the dependent variable; \(X\) is the independent variable (in the form of a multivariate matrix); \(\beta\) is the regression coefficient; and \(\varepsilon\) is the error, which obeys the normal distribution.  
If the regression coefficient, \(\beta\), is estimated according to the least square method, then:  
\[\beta_{1}=\left(X^{\mathrm{T}}\cdot X\right)^{-1}\cdot X^{\mathrm{T}}\cdot Y\]  
However, if there are multiple collinearities between the data of the independent variable, \(X\), it may cause instability in the obtained coefficients and a lack of explanatory and physical meaning. Therefore, an improved least square estimation method, ridge regression, is proposed [33]. Its general form is:  
\[\beta_{2}=\left(X^{\mathrm{T}}\cdot X+k\cdot I\right)^{-1}\cdot X^{\mathrm{T}}\cdot Y\]  
where \(k\) is the ridge parameter (usually, \(k\geq 0\); when \(k=0\), it becomes the least squares estimator), and \(I\) is the unit matrix. The greater the \(k\) value, the better the effect of eliminating the effect of collinearity, whereas the smaller the \(k\) value, the smaller the fitting variance and the greater the fitting accuracy but the poorer the effect of eliminating the effect of collinearity [33].  
In this paper, WUE is the dependent variable; and spring drought (March to May), summer drought (June to August), fall drought (September to November), and winter drought (December to February of the following year) are independent variables. All variables are detrended linearly before ridge regression, and the calculated regression coefficient is the sensitivity coefficient of each variable to the dependent variable, represented by \(\gamma\)[33]. Ridge regression was performed in the R language (Ridge package), with significance level of 0.05.
## 5 Discussion
### Complexity Analysis
#### 5.6.2 Prediction Time  
To delve deeper into the models' resource usage, we compared their prediction speeds. We measured the average time each model took to process a set of samples from the test data. This average time represents the mean prediction time.  
Figure 11 illustrates the average time in milliseconds each model took to process a single data window across the three datasets (Opportunity, PAMAP2, and REALWORLD16). The BiGRU models were the slowest, requiring 0.32-0.78 ms per window for all datasets. This is likely due to their sequential processing nature, where each step relies on the previous output. In contrast, convolutional layers, like those used in Att-ResBiGRU, can operate in parallel, leading to faster processing. Consequently, Att-ResBiGRU achieved faster prediction times, averaging 0.54 ms, 0.58 ms, and 0.27 ms for Opportunity, PAMAP2, and REALWORLD16, respectively.
## 4 Results
### Tests with Seven Classification Classes
#### 4.2.1 Analysis of the Radial Stray Flux Signal  
With the radial stray flux, the accuracy of the network also remained very high with seven classification classes. The network reached convergence quickly, as can be seen in Figure 9; it reached convergence in about 4 epochs, with a validation accuracy of 100%. The confusion matrix is reported in Figure 10, showing that every class was recognized correctly.  
Figure 8: Example of scalograms from current signal: (**a**) decimated sampling frequency and (**b**) non-decimated sampling frequency.
## 4 Performance Evaluation
### Evaluation Parameters and Assumptions  
Default parameters and assumptions used for the system-level evaluation are given in Table 2. To comply with the recommendations of standards bodies [18], omnidirectional path loss models with arbitrary antenna patterns are considered for small cells operating in only mmWave spectra. Further, no small-scale fading is considered at mmWave frequencies due to insignificant small-scale fading variation (e.g., \(\pm 3\) dB) in the mmWave received signals [19]. Furthermore, even though the CIF large-scale model at the 28 GHz band is available for both line-of-sight (LOS and non-line-of-sight (NLOS) signal propagations [18], because there is less multi-path fading at mmWave frequencies in indoor environments, we consider the CIF large-scale LOS model. However, since the path loss exponent in NLOS propagation of mmWave signals increases considerably from that in LOS (e.g., from  
Figure 2: The optimal minimum distance versus normalized co-channel interference constraint in the intra-floor level.  
2.1 in LOS to 3.4 in NLOS propagation of the 28 GHz signal [18]), the size of an optimal 3D cluster of small cells in a building, in general, would also decrease, particularly at the intra-floor level. Hence, considering NLOS instead of LOS would help improve the overall system-level performance due to reusing the same mmWave spectrum more for NLOS than LOS for each building. In other words, the performance analysis using the LOS 28 GHz mmWave spectrum could be considered as a worst case scenario analysis in this band, which we carry out in this paper. Additionally, since the mmWave path loss indoors is frequency-dependent, which typically increases with an increase in frequency, we consider the lower 28 GHz mmWave path loss model to estimate the optimal 3D cluster size of small cells within a multistory building, which is applicable to both 28 and 60 GHz spectrum bands.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Parameters and Assumptions**} & \multicolumn{1}{c}{**Value**} \\ \hline Evolved Universal Terrestrial Radio Access (E-UTRA) & & 3 rd Generation Partnership Project (QGTP) case 3 \\ simulation case 1 [FOOTNOTE:1]Footnote 1: The 28 GHz frequency is 1 GHz frequency, 2
# Feasibility Investigation of SIW Cavity-Backed Patch Antenna Array for Ku Band Applications
## 4 Antenna Fabrication and Characterization
Figure 6:6 Prototype of the 16 \(\times\) 16, dual layer, SIW cavity backed antenna array: (**a**) top view; (**b**) bottom view. Figure 6:7 Simulated (solid curve) and measured (dashed curve) \(|\)S\({}_{11}\)\(|\) scattering parameters (**a**) and gain G (**b**) versus the frequency for the 16 \(\times\) 16 antenna array. By increasing the number of the array elements, the insertion losses due to the larger feeding network and the coupling among the feeding lines and the radiating elements cannot be neglected. In fact, by increasing the number of array elements, the antenna radiation efficiency decreases. In particular, the measured efficiencies for the 2 \(\times\) 2, 4 \(\times\) 4, 8 \(\times\) 8, and 16 \(\times\) 16 arrays were 93%, 89%, 69%, and 52%, respectively, at a frequency of 11.7 GHz where the maximum gain was obtained. Even if these values are larger with respect to those reported in [26], the efficiency behavior was confirmed. Figure 6:8 Simulation of the 3D radiated gain at a frequency of f = 11.8 GHz obtained with CST Microwave Studio®. The maximum gain was G = 30.59 dBi. Figure 6:9 Radiated gain measurement of the 16 \(\times\) 16 array antenna made with StarLab Antenna Measurement Systems-Satimo®. Figure 7:0 Radiation pattern of the 16 \(\times\) 16 antenna array gain at f = 11.8 GHz in the y–z plane (**a**) and the x–z plane (**b**). By considering the beam shape, the negligible main lobe squint and the other measured characteristics, these planar antenna arrays seem very promising and are feasible for applications in the field of satellite communication.
# Optical Chirality Determined from Mueller Matrices
## Appendix A Conventions
### Mueller Matrix of a Circular Diattenuator  
The Mueller matrix in Equation (3) can be derived from the circular Jones matrix of a circular diattenuator given by  
\[\mathbf{J}_{\mathrm{D}}^{\mathrm{ir}}=\left[\begin{array}{cc}e^{-\frac{2 \pi d}{\lambda}k_{1}}&0\\ 0&e^{-\frac{2\pi d}{\lambda}k_{\tau}}\end{array}\right], \tag{10}\]  
where the superscript \(\mathrm{ir}\) indicates the use of circular Jones basis vectors. Conversion to Cartesian (\(xy\)) Jones basis vectors are performed according to [14]  
\[\mathbf{J}_{\mathrm{D}}^{\mathrm{xy}}=\mathbf{F}\mathbf{j}^{tr} \mathbf{F}^{-1}=\frac{1}{2}\left[\begin{array}{cc}1&1\\ -\mathrm{i}&\mathrm{i}\end{array}\right]\left[\begin{array}{cc}e^{-\frac{2 \pi d}{\lambda}k_{1}}&0\\ 0&e^{-\frac{2\pi d}{\lambda}k_{\tau}}\end{array}\right]\left[\begin{array}{ cc}1&\mathrm{i}\\ 1&-\mathrm{i}\end{array}\right]=\\ \hskip 113.811024 pte^{-\frac{2\pi d}{\lambda}\frac{(k_{1}+k_{2})}{2} \left[\begin{array}{cc}\cosh C_{\mathrm{D}}/2&-\mathrm{i}\sinh C_{\mathrm{D}}/ 2\\ \mathrm{i}\sinh C_{\mathrm{D}}/2&\cosh C_{\mathrm{D}}/2\end{array}\right]}. \tag{11}\]Finally, \(\mathbf{M}_{\text{D}}\) in Equation (3) is obtained by the Jones to Mueller conversion \(\mathbf{M}=\mathbf{A}(\mathbf{J}\otimes\mathbf{J}^{*})\mathbf{A}^{-1}\) where \(\otimes\) is the Kronecker product and [14]  
\[\mathbf{A}=\left[\begin{array}{cccc}1&0&0&1\\ 1&0&0&-1\\ 0&1&1&0\\ 0&i&-i&0\end{array}\right]\quad\mathbf{A}^{-1}=\frac{1}{2}\left[\begin{array}[ ]{cccc}1&1&0&0\\ 0&0&1&-\mathrm{i}\\ 0&0&1&\mathrm{i}\\ 1&-1&0&0\end{array}\right]. \tag{10}\]  
Equation (10) is independent of conventions for time dependence (\(e^{-\mathrm{i}\omega t}\) or \(e^{+\mathrm{i}\omega t}\)), which holds also for Equation (3).
## 2 Research on Anti-Skid Mechanisms of Asphalt Pavement
### Mechanical Mechanism Between Types and Road Surfaces
#### 2.1.2 Adhesion Between Tyre and Road Surface  
In the context of tyre and pavement material research, rubber, as a viscoelastic material with a low modulus of elasticity, demonstrates an aptitude for recuperating deformation due to the intrinsic friction of the rubber material. This enables the tyre to adhere to the road surface, with the contact area exhibiting a nonlinear relationship. The adhesion characteristics of asphalt pavement, which is also a viscoelastic material sensitive to temperature, become evident when the tyre is in close contact with the road surface, resulting in intermolecular adhesion between the two. Asphalt pavement is also a temperature-sensitive viscoelastic material. Consequently, intermolecular adhesion occurs between the tyre and the pavement when the two are in close contact [15]. The friction test between the tyre and the road surface revealed the presence of minute particles of road material on the surface of the tyre, as well as the observation of similar particles of tyre rubber material on the surface of the road. This evidence indicates that adhesion does exist between the tyre and the road surface. During the test, when the tyre slips and slides over the aggregate surface, the tyre undergoes an adhesion deformation process. This process consists of a compression phase on the left side of the rubber particles and a recovery phase on the right side of the rubber particles. It is not possible to transfer equal amounts of energy during the compression and recovery phases. Consequently, the dissipated energy is released in the form of heat generation [16], as illustrated in Figure 2. For this part of energy dissipation, [PERSON] found the equation for the energy dissipation of the cell due to the adhesion between tyre and road surface as well as due to heat generation [17], while [PERSON] et al. [18] developed a model for the physical quantities of the adhesion and hysteresis forces between tyres and the road surface.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 2 Materials and Methods
### Machine Learning Models
#### 2.3.1 Multiple Linear Regression (MLR)  
Multiple Linear Regression (MLR) is a basic yet powerful regression model that describes the relationship between two or more features and a response by fitting a linear equation to observed data. MLR was used as a baseline model for predicting water-quality indicators from the collected data. The equation of the MLR model is represented as:  
\[\text{Y}=\text{a}_{1}\text{X}_{1}+\text{a}_{2}\text{X}_{2}+\ldots+\text{a}_{n} \text{X}_{n}+\text{b},\]  
where:  
Y is the dependent variable (water quality indicator), \(\text{X}_{1}\) through \(\text{X}_{n}\) are different independent variables, and \(\text{a}_{1}\) through \(\text{a}_{n}\) are the regression coefficients.  
Despite its simplicity, MLR offers robust interpretability and ease of implementation. It provides a clear understanding of how each independent variable impacts the dependent variable. However, its limitation lies in its assumption of linearity and independence between variables, which is not always the case in real-world datasets [33; 34; 35].
## 1 Introduction  
Coherent mid-infrared (MIR) radiation is important for applications in many fields, such as gas sensing, ranging, spectroscopy, biochemistry, atmospheric science, and security [1; 2; 3]. Direct MIR generation can be achieved using quantum cascade lasers, but the output power, and thus the sensing and interaction distance, is limited [4; 5]. On the other hand, high power MIR radiation can be generated from nonlinear optical frequency conversion in the form of optical parametric oscillators (OPOs) [6; 7; 8; 9], and an output power up to 10 W has been reported [10]. However, most OPOs are built on optical tables with bulky optics and thus are not compatible for portable applications, such as moving vehicles based on the air, land or water [11; 12; 13].  
In this article, we demonstrate an OPO module, which is embedded in a monolithic metal frame, with dimensions and a weight of 220 \(\times\) 60 \(\times\) 55 mm\({}^{3}\) and 2.5 kg, respectively. Such a module is compatible, and can be easily aligned, with any continuous-wave laser centered around 1064 nm for the MIR generation, and an yterbium-doped fiber laser pump was used for our test. The MIR tuning ranges were 2416.17-2932.25 nm and 3142.18-3452.15 nm, using two channels of a periodically poled lithium niobate (PPLN) crystal with poling periods of 31.59 and 30.49 \(\upmu\)m channels, respectively. At a maximum pump power of 50 W, the output power exceeded 10.4 and 9.1 W at 2.7 and 3.3 \(\upmu\)m, respectively. The 10 h power stability was measured to be 2.13% root meat square (RMS) in a room without any special temperature stabilization.
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 2 Materials and Methods
### Evaluation Metrics  
Different tasks require different evaluation indexes to measure the model effect. The main role of evaluation indexes for the regression task is to quantify the degree of fit and error between the model prediction results and the real values, which helps assess and compare the performance of different models. In this study, the coefficient of determination (\(R^{2}\)) and Root Mean Square Error (RMSE) were used to evaluate the TOC content prediction model, as shown in Equations (12) and (13). A value of \(R^{2}\) closer to 1 and a smaller RMSE value indicate the better fitting effect of the established TOC content prediction model.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}} \tag{12}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{13}\]  
where \(y_{i}\) is the actual value of TOC content; \(\overline{y}\) is the actual mean value of TOC content; \(\hat{y}_{i}\) is the predicted value of TOC content; \(n\) is the number of samples.  
Figure 1: XGBoost model structure.