esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.3 Comparisons Between the Combined Groups A and B and Group C
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Feature Selection Mode** & **Feature Subsets** & **AUC** & **Accuracy** & **Precision** & **Recall** & **Specificity** \\ \hline \hline \multirow{9}{*}{**Dataset**} & Full dataset & All 25 cytokines & 0.717 & 0.660 & 0.777 & 0.660 & 0.757 \\ \cline{3-8}  & & CXCL9, IFN-\(\gamma\), VEGF, IL-2R, IL-1 RN, CCL5, IL-1B, & 0.727 & 0.640 & 0.768 & 0.640 & 0.743 \\ \cline{3-8}  & & IL-1B, PARK7, IL-15 & 0.713 & 0.600 & 0.714 & 0.600 & 0.700 \\ \cline{3-8}  
Interestingly, the model predicting mortality based solely on lymphocytes and RBCs demonstrated the best performance (AUC = 0.887, R = 0.820; Sp = 0.847), exhibiting slightly better results than the hybrid model that also incorporated CXCL9 (AUC = 0.875, R = 0.780; Sp = 0.837), though the difference was not statistically significant (\(p\) = 0.716). Just like when comparing Groups B and C, lymphocyte and RBC counts once again had a significant impact on model performance. This aligns with the univariate analysis, which had already identified these variables as significant predictors. As a result, models based solely on lymphocytes and RBCs achieved solid predictive performance (AUCs of 0.743 and 0.813, respectively). Furthermore, hybrid models (\(p\) < 0.001) and those based solely on routine blood analyses (\(p\) = 0.029) significantly outperformed models based solely on cytokines (Table 9). The poorer performance of models relying solely on cytokines likely resulted from the high variability in cytokine levels within the mixed group (A + B), which included patients in different clinical statuses, thereby reducing their reliability in predicting patient outcomes. On the other hand, lymphocyte and RBC counts, being more stable and reflective of broader physiological states [64], were less affected by this variability, leading to better predictive accuracy, even when included in hybrid models. These findings highlight once
## 4 Design and Implementation of the Optimization Framework  
### Architecture of the Framework (iMASKO)  
In this section, a generic GA-based optimization framework called iMASKO (iNL@MATLAB GA-based **S**ensor **N**etwork**K** **O**ptimizer) is proposed. It is able to quickly and efficiently explore simulation-based results or the theoretical model-based results. Note that in this context, iMASKO is applied to automatically fine-tune the results from our house-built SystemC-based simulation [32, 33, 34]. In practical use, the optimization of related parameters in the design space can help find the application-specific solution to achieve the most optimal network performance for the given scenario before real deployment. By using iMASKO, only two things are needed: (i) a set of input parameters in the design space, and (ii) a user defined fitness function. As long as the system/model under evaluation can return the required data metrics to the fitness function, the detailed implementation of the system/model (simulators/emulators/mathematical models) can be ignored, which is of special interest for non-experts in wireless sensor network design. Finally, the configuration of the GA optimization process is very simple in iMASKO for users, since the interfaces of the corresponding algorithm parameters are provided and the configurations can be done via command lines and a MATLAB-based GUI. Firstly, a brief workflow of iMASKO is presented in Figure 2 and the detailed optimization process of iMASKO is given in Table 2. Before starting the optimization, relevant parameters are fed to the simulation environment and to the GA evaluation engine respectively. Once the corresponding metrics have been generated from simulations and processed within the fitness function, the GA evaluation engine will decide whether the final result can satisfy the criteria of the given task. If so, the solution can be considered and applied in real applications. Otherwise, another cycle of the parameter tuning will start until the qualified solution for the task is found. In Table 2, the typical steps of optimization are given by easily identified syntax: generate, evaluate and compare. The parameter space \(X\) could be a set of communication strategies/protocols, different configurations of a given protocol, or different application scenarios as long as the parameters that under the investigation can be represented as the qualified individuals in GAs. For the performance space \(P\), it contains the metrics that can reflect network performance presented in Section 4.2. The metrics are returned by the fitness function in two ways: one is to combine all related metrics into a weighted sum (Section 4.3), while another is to return separated and independent metrics for multiobjective optimization problems (Section 4.4).
## 2 Fundamentals and State of the Art
### NVH Optimization in the Digital Design Phase  
In the early design phase, NVH optimization relies on simulation, as no hardware is available yet [5]. Nowadays, evaluations are based on three different simulation model origins: CAD models of recent generations, predecessor FE models or new simulation models from scratch [3] (p. 2), [6]  
Figure 1: Structure of this paper.  
(p. 1653), [7] (p. 4180). The first method requires a parametric CAD model to allow fast modifications [4] (p. 27). However, each design change still has to be meshed in order to receive a new FE model [8] (p. 170). This slows down the development process [3]. The second method, using old FE models, requires a method to alter the existing FE model to resemble the new concept under consideration. One of these methods is morphing, where an algorithm distorts the existing FE mesh to approximate the desired design. As this process is done in the digital design phase, the main target of these generated simulation models is to roughly approach NVH demands in the design process, not to predict the behavior as accurately as possible [4] (p. 30), [9] (p. 28). In this paper, we use this approach to receive modified FE models. The third method, creating new simulation models from scratch, is time-consuming and requires manual activity for each design variation [3].
## 6 Conclusions  
This study comprehensively evaluates three holographic interferometry algorithms through theoretical analysis, MATLAB simulations, and experimental validation. Compared to the single-exposure method--which suffers from real-time limitations due to acquisition frequency constraints and only resolves deformation amplitudes--and the continuous-exposure method--prone to instability from intensity sensitivity and noise interference--the double-exposure method demonstrates superior performance. By constructing a noise-suppressed fringe field characterized by cosine modulation, the double-exposure approach enables active control of interference patterns, overcoming the environmental dependency inherent in conventional techniques.  
Key innovations include the following: (1) Quantitative mapping model: A fringe displacement-to-deformation mapping framework was established by using the double-exposure method, enhancing amplitude detection accuracy. (2) Vector resolution algorithm: A fringe gradient field-based algorithm was proposed to determine deformation directions, achieving simultaneous improvements in computational efficiency and measurement precision. (3) The tripartite verification framework integrating theory, simulation, and experiment provides a robust methodology for validating holographic measurement systems.  
This work advances precision optical metrology by offering (1) enhanced adaptability to dynamic deformation scenarios; (2) improved noise immunity through active fringe modulation; and (3) a systematic pathway for algorithm development and optimization.  
These findings establish the double-exposure method as a versatile solution for high-accuracy deformation analysis with real-time capability in engineering applications.  
Y.L.: writing--original draft preparation, writing--review and editing; Y.Z.: formal analysis; D.J.: methodology; S.G.: validation; M.Z.: investigation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 1 Introduction
In contrast, BOTDA is capable of achieving a spatial resolution of 2 cm by using a 2 km sensing optical fiber, and it exhibits a sensing range of up to 120 km [14; 15]. Rayleigh scattering OFDR is capable of achieving spatial resolution at the millimeter scale [16]. Among all of the aforementioned DOFSs, BOTDA is currently the most extensively researched and utilized in the field of structural health monitoring owing to its relatively high spatial resolution at the centimeter scale [17]. This technology has been successfully applied in large-scale civil and structural engineering. The long sensing range of the DOFS has been fully exploited for bridge structure monitoring [18; 19; 20], the high spatial resolution of the DOFS has been utilized for pile foundation monitoring [21; 22; 23; 24], and the DOFS has been applied to pavement structures with satisfactory results [25; 26]. In addition to structural engineering, DOFSs are widely used in geotechnical engineering. For slope and mining engineering, the optical fiber is usually directly buried to ensure minimal deformation in the soil and to facilitate early disaster warning [27; 28; 29]. Direct burial installation is also adopted for monitoring large ground deformation [30; 31; 32], where optical fibers are attached to pipes, anchors, and other structures, and the strain measured in the optical fiber is converted to a displacement deformation commonly used in engineering [33; 34; 35]. Although the DOFS has been widely used in geotechnical engineering monitoring, ensuring deformation coupling between an optical fiber or affiliated structures and the soil constitutes a technical bottleneck. Excessive soil deformations may cause slippage between the optical fiber and the surrounding soil [36; 37]. That is, if the optical fiber is buried directly into the soil, the DOFS is not suitable for measuring large soil deformations [38; 39]. A distributed and reliable method for monitoring settlement is required to evaluate the safety of a subgrade or foundation. Therefore, ensuring coupling between an optical fiber and the soil to realize continuous monitoring of internal settlement is very important. A novel monitoring method based on an optical-fiber-embedded beam (OFEB) was developed in this study. First, the principle of differential pulse-width pair BOTDA (DPP- BOTDA) was introduced, followed by a testing mechanism and a measurement method based on the OFEB. Furthermore, the performance of the OFEB was evaluated using a simple calibration test and a test on a similarity model. Finally, numerical simulation was used to optimize the material and size parameters of the OFEB, and a method to improve the coupling between the OFEB and the soil was developed to increase the OFEB measurement range. The results show that the OFEB is suitable for subgrade distributed settlement monitoring and that the measurement result of the OFEB is basically consistent with the calibrated sensor test results, with the exception of large slippage between the subgrade soil and the OFEB.
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 5 Performance Analysis and Evaluation
### Performance Evaluation Parameters  
We have evaluated the capability of DBSCAN and LOF in detecting outliers based on the classification parameters _precision_ and _recall_. We have also analysed the overall model accuracy.  
**Accuracy** provides a measure of the overall performance of the algorithm. It corresponds to all true classifications (true positives, true negatives) over all classified values (positive and negative).  
By definition, **precision** corresponds to the number of items correctly labelled as true positives, divided by the total number of elements belonging to the positive class. Precision, defined in Equation (11), provides a measure of how well an algorithm can detect only outliers. Precision is provided as a percentage of the number of outliers in the dataset. The precision is as close to one as the false positives are close to zero.  
**Recall**, defined in Equation (12), provides a measure of how well all outliers are identified. The recall is defined as the number of true positives divided by the total number of elements that actually belong to the positive class (i.e., the sum of true positives and false negatives, which are items which were not labelled as belonging to the positive class but should have been). A model that produces no false negatives has a recall of 1.  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
where TP--true positives; FP--false positives; TN--true negatives; FN--false negatives.
# A Survey of Vehicle to Everything (V2X) Testing
## 5 V2X Testing Methods
### Vehicle Gateway Testing  
Vehicle gateway testing is a mean to ensure that the vehicle gateway is running correctly, so it can meet the needs of V2X network security. The test architecture of the vehicle gateway is shown in Figure 7. The test architecture consists of two parts: the system under test and the test system. The system under test is composed of vehicle gateways and other systems or devices (including in-vehicle devices, networks, and service platforms), as shown by the dashed boxes in Figure 7. The test system is connected with the A-side and B-side of the system under test in Figure 7. According to test cases, the test system inputs the test data to the system under test, which generates the corresponding response according to the input of the test system. The test system conducts tests by analyzing the differences of the expected results and the actual results obtained from the system under test. The test system supports inputting the test data and obtaining the results from the A-side and B-side in Figure 7 simultaneously.
## 8 Conclusions and Outlook
Two methods were applied in preparing such composites: one is a direct synthesis method in which a thiol (-SH) group was oxidized _in situ_ to a sulfonic acid (-SO\({}_{3}\)H) by hydrogen peroxide; the other is a two-step route in which periodic mesoporous organosilicas containing different fractions of 1,4-diethylene-benzene groups in the framework were synthesized first, and then the sulfonic acid sites were generated by framework sulfonation of 1,4-diethylenebenzene with chlorosulfonic acid. It has been found that these sulfonic acid-functionalized mesoporous organosilicas show high catalytic activity and stability in the liquid-phase condensation of phenol with acetone to form bisphenol A and esterification of ethanol with acetic or other aliphatic acids. * As swelling of polymeric ion exchange resins has been an obstacle to their application as catalysts in the catalytic hydration of ethylene oxide, we prepared resin/CNT composite catalysts to overcome this problem. The addition of CNT into a resin can greatly enhance its thermostability and anti-swelling capability during the reaction of hydration of ethylene oxide. The remarkable stability observed should be ascribed to the incorporation of MWNTs in the cross-linking network and the ability to keep the active site loss at low level. Zeolite catalysts have been industrialized for more than 50 years. Along with the pressing requirements for energy saving and environmental protection, these catalysts are still under intense study. As can be seen, the most recent advances concerning composite catalysts made from porous materials mainly involve combining the advantages of composite components based on a good understanding of the chemical reactions, so that composite catalysts are good candidates for achieving high performances features including high activity, remarkable selectivity and good resistance to deactivation. It is, and will still be, a challenging subject to design composite catalysts based on porous materials rationally and successfully apply them in the chemical industry.
## 3 Results
### Spatiotemporal Variation Characteristics of Vegetation in Xinjiang
#### 3.2.1 Spatiotemporal Variation Analysis of Vegetation NDVI and SIF
The bare soil regions (NDVI < 0.1) constituted 52.69% of the total area of Xinjiang. These figures indicate that, besides vast bare soil areas, most vegetation in Xinjiang remains in a low-coverage state over the long term. The spatial distribution of SIF was generally similar to that of NDVI. Low SIF areas (0.00-0.05) were the most widespread, present across all ecological regions. Slightly lower SIF areas (0.05-0.10) were concentrated in northern Xinjiang. Similar to the moderate SIF areas (0.10-0.15), their distribution was relatively scattered, though the former was more concentrated in the R1. Higher SIF areas (0.15-0.20) and very high SIF areas (>0.20) were the least represented, mainly located in R3. This distribution pattern reflects functional differences in vegetation across ecological regions, where R1 and R3 exhibited higher vegetation productivity, as indicated by elevated NDVI and SIF values, whereas R4 and R5 were characterized by sparse vegetation with lower productivity and photosynthetic activity. These findings suggest that the northern ecological regions of Xinjiang are more favorable for vegetation growth and photosynthesis, whereas the southern regions are dominated by deserts and low-coverage vegetation with weaker ecological functionality.
###### Abstract  
This study aimed to investigate the effects of the size and weight content of waste rubber particles on the relevant performances of rubberized concrete. First, the fine aggregates were partially replaced by rubber particles of different sizes to produce rubberized concrete. Secondly, the mechanical and thermal insulation properties of rubberized concrete were investigated. Finally, microstructural analyses of rubberized concrete including scanning electronic microscope (SEM) and energy distribution spectroscopy (EDS) were examined. Experimental results indicated that uniaxial compressive strength of rubberized concrete was reduced, while the peak strain was gradually increased and thermal insulation properties were improved with the increase of rubber content or decrease of rubber particle size. In addition, rubber particles affected the failure modes, endowing concrete with weak brittleness and strong cracking resistance. Additionally, it was observed that the interfacial adhesion between the matrix rubber and the aggregates was weak under SEM, which seemed to be a key factor that reduced the strength of rubberized concrete.  
Keywords:rubberized concrete; compressive strength; failure mode; thermal insulation; SEM +
Footnote †: journal: Applied Sciences
# State-Sensitive Black-Box Web Application Scanning for Cross-Site Scripting Vulnerability Detection
## 2 Background
### Challenges  
The state-of-the-art black-box web scanner, Black Widow, fully models the dynamic characteristics of web programs to a certain extent. The strategy taken by Black Widow is to build a navigation model for the web application, a transformation graph between client-side states. State refers to the content of the client page. It models the client-side state to further map the server state. The transformation between the state mainly includes the GET requests, form submissions, iframes and JavaScript events.  
**Challenge 1:** There are numerous repetitive visits to similar states in the scanning process, especially for the state of HTML forms. Based on Black Widow's excellent modeling of web applications, there are certain flaws in its use of the model. Especially due to insufficient restrictions on similar states, the scanner spends a lot of time on repetitive visits to similar states. For example, every product page [[http://example.com/product.php?id=X](http://example.com/product.php?id=X)]([http://example.com/product.php?id=X](http://example.com/product.php?id=X)) contains a form [[http://example.com/product.php?id=X&action=edit](http://example.com/product.php?id=X&action=edit)]([http://example.com/product.php?id=X&action=edit](http://example.com/product.php?id=X&action=edit)) for modifying information. X represents the number of the product. Ideally, a form for editing the product only needs to appear once in the navigation model. However, insufficient restrictions may cause undesirable repetition. It will significantly reduce the efficiency of the scanner. This inspired us to design a filtering method for similar states.  
**Challenge 2:** Current black-box web scanners lack an appropriate method for judging the importance of HTML forms to guide the fuzzing order of them. In Black Widow and other black-box scanners [7; 19], there is only a limited connection between HTML forms and XSS vulnerabilities. The fuzzing order of the form in current black-box scanners refers to the sequence in which the scanners discover the forms. They lack an appropriate method for judging the importance of HTML forms. This also leads to low efficiency of detecting XSS vulnerabilities, further resulting in missing some XSS vulnerabilities within a limited scanning time. It is inspired by previous research [31; 32; 33] on fuzzing of other types of applications. We attempt to explore information in the state that is closely related to XSS vulnerabilities. Based on this information, a ranking method is designed to provide priority to fuzzing specific HTML forms.
## 3 Investigation of Scalability and Impact of Virtualization on Performance of the System
### Observations and Conclusions  
We can conclude with two observations. Firstly, using more physical cores as well as HyperThreading gives a noticeable increase in server processing performance. Typical gains from HyperThreading [40] are observed in the range of 0+ up to 40%, with examples from the high performance computing field such as 20-25% for numerical integration, 10-11% for image recognition applications [41]. In our case we observe an increase of about 11% for results shown in Figure 3 which is in line with expectations. Secondly, even  
Figure 6: Difference of successfully processed requests—bare-metal and container.  
Figure 7: Difference in processing times on server side \(difference=\frac{t_{physical}-t_{side}}{t_{physical}}\).  
though we see small differences in favor of bare-metal, considering times from the client perspective with network delays, Docker appears as a viable alternative.