esa-sceva/satcom-chunk-collection huggingface dataset:
# A NavCom Signal Authentication Scheme Based on Twice Two-Way Satellite Time Transfer
## 4 Conclusions  
In this paper, a novel secure authentication NavCom authentication scheme is proposed for LEO navigation-communication integration systems, and the meaningful works to emerge from this study are as follows: (1) This paper analyzes the advantages of integrating NavCom signals in LEO systems, as well as the limitations of traditional security authentication methods, which can be served as performance guidelines for designing a reliable NavCom authentication scheme for LEO satellite systems. (2) For those guidelines,  
Figure 11: Authentication time at different satellite orbital altitudes.  
a novel NavCom signal authentication scheme based on twice TWSTT is proposed, where the user terminal initiates twice TWSTT to the same satellite and realizes security authentication by comparing the twice clock difference measurement. Owing to the relative position of the satellite and the user terminal changing rapidly, it is impossible to ensure that the two clock difference measurements are equal when the repeater spoofing interference signal is present, so the existence of spoofing can be determined easily. (3) Overall, the analysis shows that the proposed authentication scheme can achieve highly reliable authentication with low signal CNR. Moreover, it can effectively address repeater spoofing interference, greatly improve the security of signals, and make full use of the communication and measurement functions of the NavCom signal at a low cost.  
Methodology, X.T.; validation, S.W., J.L. and F.W.; writing--original draft preparation, X.T. and S.W.; writing--review and editing, J.L. and S.W.; supervision, F.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. U20A0193 and No. 62003354).  
The data presented in this study are available on request from the corresponding author. The codes presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Electrical Properties
In high-speed communication, a lower value of \(\delta\) is critically important as transmission depends directly on the cumulative dielectric loss factors [1; 30].
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 2 Problem Formulation and Proposed Algorithm Description
### Improved Square Sine Wave Interpolation Algorithm
#### 2.3.2 Improved SSWI Function  
The previous section analyzed the spectral characteristics of the SSWI function. The PRI jitter will affect the spectrum of the SSWI function, resulting in spectrum broadening and spectral peak reduction. According to the analysis, the smaller the variance of the difference interval of the discrete signal, the more accurate the spectral estimation. A clustering-based SSWI method is proposed to solve this problem in this study. The difference interval is estimated by the clustering method and the SSWI function is generated according to the estimated difference interval.  
For mixed pulse sequences, the first-grade SSWI of discrete signals may not be able to obtain the desired results due to the mutual interference of different pulse sequences. Therefore, it is necessary to calculate the multi-grade SSWI function. This paper defines the difference grade as \(c\), where \(c\) represents the pulse interval when calculating the difference interval and initializes \(c=1\). The steps of the \(c\)-th grade improved SSWI method are as follows.  
1. Let \(C_{i}\) be the \(i\)-th subclass after clustering, \(c_{i}\) the center value of the \(i\)-th subclass, \(m_{i}\) the elements number of the \(i\)-th subclass, and \(P\) the subclasses number after clustering;
2. Initialization: Pulse interval sequence \(\mathbf{\Delta x}\) indicates the \(c\)-th grade difference sequence of the discrete signal \(x[n]\), \(\mathbf{\Delta x}=x[1+c:N]-x[1:N-c]\); \(C_{1}=\{\Delta x(1)\}\), \(c_{1}=\Delta x(1)\), and \(j=2\) indicates the difference interval number;
3. For \(p=1:P\), calculate the relative error between the difference interval and each cluster center \[R_{\epsilon}(p)=\frac{\left|c_{p}-\Delta x(j)\right|}{c_{p}};\] (9)
4. Find the minimum of relative error vector \(\mathbf{R_{\epsilon}}\). \([\,\ p_{c}]=min(\mathbf{R_{\epsilon}})\), and \(p_{c}\) is the position corresponding to the minimum value of \(\mathbf{R_{\epsilon}}\). If \(R_{\epsilon}(p_{c})<l_{c}\), go to Step (v); otherwise, go to Step (vi). \(J_{c}\) indicates the error threshold, determined by the PRI jitter upper limit;
5. Update subclass \(C_{p_{c}}\) \[\begin{cases}c_{p_{c}}=\frac{c_{p_{c}}\times m_{p_{c}}+\Delta x(j)}{m_{p_{c}}+ 1}\\ m_{p_{c}}=m_{p_{c}}+1\\ C_{p_{c}}=\{C_{p_{c}},\Delta x(j)\}\end{cases}\] (10) \(j=j+1\), update the pulse interval number, and go to Step (vii);
6. Generate a new subclass and update the number of subclasses \[\begin{cases}C_{P+1}=\{\Delta x(j)\},c_{P+1}=\Delta x(j)\\ m_{P+1}=1,P=P+1\end{cases}\] (11)\(j=j+1\), update the pulse interval number, and go to Step (vii);
* If \(j\leq N-c\), go to Step (iii), proceed to the next clustering; otherwise, the clustering algorithm ends, so go to Step (viii);
* Correct the difference interval sequence according to the clustering result, and generate an improved SSWI function from the corrected difference sequence.
# Automatic Test Data Generation Using the Activity Diagram and Search-Based Technique
## 2 Related Work
### Evolutionary Approaches Using Metaheuristic Algorithms  
The search-based technique using metaheuristic optimization algorithms for test case generation is common in the existing literature [19; 33; 34]. A recent study [35] used metaheuristic algorithms to adapt a model checker for integration testing of systems specified by graph transformation and generate a test suite that satisfies all def-use coverage criteria. In addition, several studies suggested model-based test-case generation and prioritization using ADs and GAs. For instance, [36] applied a GA to prioritize test case scenarios generated from UML ADs by identifying the critical path clusters. Other test-case prioritization techniques in the context of model-based testing using ADs and GAs were proposed in [37] and [38]. A UML AD-based approach using an evolutionary algorithm for transition sequence exploration is presented in [39]. It generates test cases that include both test scenarios and test data. Lately, a so-called bio-inspired methodology using the ant colony algorithm applied on concurrent subpart of ADs to identify different feasible test scenarios [13]. The study is inspired by the food search pattern of an ant for exploring paths to reduce duplicity in the test scenarios.  
In conclusion, a large body of literature uses the ADs of the SUT for automatic test-case generation with different strategies, including search-based metaheuristic algorithms. Currently, AD-based testing methods focus on test automation mainly from the HLBP, analyzing control flow information that is missing important aspects (data flow). Previously, we performed an experimental investigation of data flow annotated activity diagram (DFAAD)-based testing [24], in which the flow of data is explicitly annotated in the ADs of the SUT manually to investigate the feasibility of using ADs to meet the data flow coverage criterion. Regarding the application of the search-based technique, our study differs from the existing studies because we follow a different approach using a different fitness function that enforces maximum coverage of def-use pair paths.
## 5 Conclusions  
Predicting _EGFR_ mutation status by CT imaging can improve the determination of the most appropriate treatment for each lung cancer patient and is a less invasive alternative compared to the traditional biopsy. This study proposed a comprehensive approach for the classification of _EGFR_ mutation status using only radiomic features extracted from a ROI containing the entire lung where the nodule is located, changing the direction of traditional approaches, which until now have been mainly focused on the nodule. The results obtained with this novel and holistic approach showed that information from more extensive regions of the lung containing the nodule allows for a more complete lung cancer characterization. Our work suggested that Linear SVM, Elastic Net, and Logistic Regression are the most robust models for _EGFR_ mutation status prediction and supports their use by others in future radiogenomics studies. Furthermore, we recommend the application of methods that reduce the number of features prior to model training, in particular, PCA methods, as they seem to improve predictive performance. We also encourage the study and comparison of various features and modeling approaches for predicting _EGFR_ mutation status since improvements in prediction are often achieved when different combinations are utilized.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)]([https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)) 7/11/7/3273/s1.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conceived the study; [PERSON] and [PERSON] provided the pneumology insights; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] gave the radiology insights; and [PERSON] gave the molecular biology insights. [PERSON] and [PERSON] drafted the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is financed by the ERDF--European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation--COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT--Fundacao para a Ciencia e a Tecnologia within project POCI-01-0145-FEDER-030263.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data was obtained from the open-access NSCLC-Radiogenomics dataset publicly available at The Cancer Imaging Archive (TCIA) database [23; 52]. Imaging and clinical data have been de-identified by TCIA and approved by the Institutional Review Board of the TCIA hosting institution. Ethical approval was reviewed and approved by the Washington University Institutional Review Board protocols. Informed consent was obtained from all individual participants included in the study.  
The authors declare no conflict of interest.
# An Adaptive UI Based on User-Satisfaction Prediction in Mixed Reality
## 3 Proposed Method  
In this section, we propose a method to provide an adaptive UI based on interaction-satisfaction prediction. In general, in order to examine user satisfaction in MR, it should be asked after the user performs an interaction. However, in the above method, it is difficult to provide an adaptive UI in real time when a user uses an application because satisfaction can be examined only after performing the interaction. Therefore, in the proposed method, we try to use a user-satisfaction-prediction method before the interaction is completed. Once user satisfaction is predicted, the time to obtain user satisfaction for providing adaptive UI can be shortened as shown in Figure 2 (highlighted in blue color).  
Another method to examine user satisfaction is to attach a sensor such as an EEG and analyze the sensed data. The above method has the advantage of not requiring a response from the user but has a disadvantage that it can affect usability because an additional sensor is attached. Therefore, we try to predict user satisfaction based on information that  
Figure 2: Example of saved time when applying prediction: (**a**) interaction without prediction, (**b**) interaction with prediction.  
can be measured in real time when performing an interaction and that does not require an additional device other than an MR device. Based on the conventional studies introduced in Section 2.1, the interaction information selected as the input in this study is shown in Figure 3.  
Among them, gaze, hand, and head information are the ones based on user movement in the real world, so they are difficult to control in an MR environment. However, since object information is of the virtual world, it is easier to control in an MR environment than the other information. Therefore, the correction for providing an adaptive UI is performed based on object information. The procedure for providing the proposed adaptive UI described above is shown in Figure 4, and the detailed description is as follows.  
1. Interaction information input: raw data related to gaze, head, hand and object information are obtained when user performs an interaction.
2. User-satisfaction prediction: feature vector is extracted from the obtained raw data, and user satisfaction is predicted form the extracted feature vector.
3. UI-position correction: predicted results are compared with threshold value, and UI position is corrected based on the comparison result.
4. Adaptive UI display: Adaptive UI is displayed to the user according to the result of UI-position-correction step. If the predicted user satisfaction is lower than the threshold value, UI position is corrected to improve user satisfaction. If the predicted user satisfaction is higher than the threshold value, UI position is not corrected.  
Figure 4: Procedure to provide adaptive UI by the proposed method.  
Figure 3: Interaction information used as input for predicting user satisfaction.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 3 Composite Kernels for SVM
### Composite Kernels  
Composite kernels we considered are based on the concept of composite kernels [19]. In SVM, the information contained in the features is encoded within the kernel matrix, whose elements are the value of the kernel function between feature vector pairs.  
Some popular kernels are as follows:  
* Linear kernel: \(\kappa\bigl{(}X_{i},X_{j}\bigr{)}=\bigl{\langle}X_{i},X_{j}\bigr{\rangle}\).
* Polynomial kernel: \(\kappa\bigl{(}X_{i},X_{j}\bigr{)}=\bigl{(}\bigl{\langle}X_{i},X_{j}\bigr{\rangle} +1\bigr{)}^{d},d\in^{+}\).  
* Radial basis function: \(\kappa\big{(}X_{i},X_{j}\big{)}=\exp\Bigl{(}-\|X_{i}-X_{j}\|^{2}/2\sigma^{2}\Bigr{)},\sigma\in R^{+}\) The parameter of a kernel can be set by a cross-validation step [35]. In mathematics, a real-valued function \(\kappa(x,y)\) is said to fulfill Mercer's condition if all square integrate functions \(g(x)\) have \[\iint g(x)\kappa(x,y)g(y)dxdy\geq 0.\] (6) Based on the Hilbert-Schmidt theory, \(\kappa(x,y)\) can be a form of dot production if it fulfills Mercer's condition. The linear kernel, polynomial kernel and radial basis function fulfill Mercer's condition [38]. Some properties of Mercer's kernels are as follows: Let \(\kappa_{1}\) and \(x_{2}\) are two Mercer's kernels, and \(\eta>0\). Thus, \[\begin{array}{l}\kappa\big{(}X_{i},X_{j}\big{)}=\kappa_{1}\big{(}X_{i},X_{j }\big{)}+\kappa_{2}\big{(}X_{i},X_{j}\big{)}\\ \kappa\big{(}X_{i},X_{j}\big{)}=\eta\kappa_{1}\big{(}X_{i},X_{j}\big{)}\end{array}\] (7) are valid Mercer's kernels. Therefore, it is possible to encode the information contained in polarimetric and spatial features into two kernel matrixes if we use Mercer's kernel. [39]. A composite kernel called weighted summation kernel that balances the polarimetric and spatial weight can be created as follows: \[\kappa(X_{i},X_{j})=\eta\kappa^{P}(X_{i}^{P},X_{j}^{P})+(1-\eta)\kappa^{S}(X _{i}^{S},X_{j}^{S})\] (8) where \(X_{i},X_{j}\) are two pixels, \(\kappa^{P}\) is the kernel function for polarimetric features, \(\kappa^{S}\) is the kernel function for spatial features, and \(\eta\in[0,1]\) is tuned in the training process and constitutes a tradeoff between the polarimetric and spatial information to classify a given pixel. The composite kernel allows us to extract some information from the best tuned \(\eta\) parameter [30]. Based on practical situation, we can set value of the parameter flexibly. In addition, the composite kernel method can make fusion more naturally and effectively, regardless of the weight between different features, bringing better performance. Once all kernel functions are evaluated and combined, the obtained kernel matrix is fed into a standard SVM for PolSAR image classification.
## 1 Introduction
ments between institutions ultimately hinder uptake of the technology and slow scientific progress. The purpose of this short communication is to provide a concise technical review of Planet's PS smallsat sensors alongside a summary of their capabilities and practical considerations to maximize their utility and impact. The communication is designed to serve as a resource for the remote sensing community to locate important geometric and radiometric details of the various generations while also providing a guide to the state-of-the-art processing algorithms being developed by the user community to pre-process PS imagery. This work first reviews the PS sensors, documentation, and data streams to help clarify differences in sensor specifications and performance. Next, several important radiometric and geometric issues are discussed that should be considered when using the data for scientific objectives. While visual interpretation of PS images is suitable in some studies, we focus explicitly here on processing for analytical workflows. The existing literature employing PS imagery is reviewed and current developments for radiometric and geometric processing of the data are reported.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
###### Abstract  
This paper examines the mechanical performance of fibre-reinforced composite sandwich structures (FRPSSs) for maritime applications, focusing on the impact bending and damage sequence after seawater exposure. Glass-fibre/epoxy facesheets with various PVC foam core configurations underwent low-velocity single and multiple impacts. An in situ moisture-uptake methodology monitored moisture ingress until saturation. Results showed moisture uptake reduced impact bending capacity and bending stiffness to varying degrees. While energy-absorption performance remained largely unchanged under single impacts, significant differences were noted for multiple impacts. Failure analysis confirmed the reductions in some damage modes such as facesheet fracture, indentation, and core shear failures, while core shearing, delamination, core/facesheet debonding, and fibre breakage were identified as the main failure modes. These insights enhance understanding and optimisation of FRPSSs for improved out-of-plane damage resistance in marine applications.  
composite sandwich; impact bending; moisture uptake; acoustic emission; X-ray micro-CT +
Footnote †: journal: Applied sciences  
1
## 4 Road Sensing
### Monitoring Road Condition  
In addition to structural health monitoring of transportation infrastructures such as bridges, typically carried out by WSNs by means of accelerometers [68], WSNs can also be used to examine other aspects related to traffic safety. In this regard, the occurrence of adverse weather conditions is a major concern for traffic safety, accounting for 1.5 million accidents per year in the United States [69]. WSNs can play an important role in reducing the accident rate associated with weather by detecting and reporting in advance different situations affecting the vehicle maneuverability or drivers' visibility. In contrast to the detection of vehicles, the relatively slow variation in the road conditions facilitates low sampling rates, with the consequent reduction in power consumption.  
The sensors employed to this purpose include those typically intended for environmental conditions monitoring with WSNs: PIR and temperature sensors for ice and snow detection, humidity sensors for fog and rain detection, and ambient light sensors. The downside of these sensors in the case of detecting ice and snow is that they can only estimate a probability of occurrence according to other environmental factors. More accurate detection methods commonly imply higher power consumption. However, there are feasible alternatives such as measuring material's permittivity [70]. This work presents a small sensor which measures the permittivity of the material found between two electrodes at two different frequencies, distinguishing among water, ice and air.  
The presence of pedestrians and animals in the immediacy of roads also constitutes a risk. There are many methods for detecting their presence but, if power consumption is the priority, PIR-based detectors are the most appropriate solution. They recognize the presence of moving objects, with a detection range which depends on the size of the targets and whose sampling rates are determined by the motion speeds.
# Camera Calibration with Phase-Shifting Wedge Grating Array
## 2 Principle
### Feature Detection  
The emphasis of feature detection is how to detect the PWG centers. The detailed procedures of our method can be summarized, as follows:  
1. Let \(J_{1}(u,v)\), \(J_{2}(u,v)\), and \(J_{3}(u,v)\) respectively represent the three PWG array images that are captured at the same viewpoint. Adding up the three images, the phase-modulated area \(\Omega\) (Figure 3a) for PWG arrays can be obtained with a suitable gray threshold \(T\): \  
For each \((\hat{u}_{ij},\hat{v}_{i})\), we select its \(7\times 7\) neighborhood points \((\hat{u}_{ij},\hat{v}_{ij})\) with wrapped phase \(\phi(\hat{u}_{ij},\hat{v}_{ij})\). The \(\phi(\hat{u}_{ij},\hat{v}_{ij})\) with \(2\pi\)-phase jumps can be unwrapped as: \[\phi t(x_{ij},y_{ij})=\left\{\begin{array}{ll}\phi(x_{ij},y_{ij}),&\phi(x_{ ij},y_{ij})\geq\pi\\ \phi(x_{ij},y_{ij})+2\pi,&\text{\emph{otherwise}}\end{array}\right..\] (10) Let \(R_{ij}\) be the Euclidean distance between \(\left(\hat{u}_{ij},\hat{v}_{ij}\right)\) and its corresponding center \((u_{c},v_{c})\). Then, we can establish the function relations \(x=f_{x}(\Phi,R)\) and \(y=f_{y}(\Phi,R)\) by the windowed bicubic fitting algorithm. The optimized \(2\pi\)-phase points \((\widetilde{u},\widetilde{v})\) can be obtained through substituting \(\Phi=2\pi\) and \(R=\sqrt{\left(\hat{u}_{ij}-u_{c}\right)^{2}+\left(\hat{v}_{ij}-v_{c}\right)^{ 2}}\). Those \(2\pi\)-phase points with sub-pixel accuracy are utilized to locate the real center \((u_{0},v_{0})\).
4. When all of the \(2\pi\)-phase points are optimized, two \(2\pi\)-phase straight lines for each PWG can be obtained with linear fitting algorithm. The intersection point of the two straight lines can be treated as the PWG center, as well as the feature point. Using the one-to-one correspondences between the world and image coordinates of the PWG centers, the camera parameters can be estimated.