esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Results Analysis
### Stiffness and Strength Degradation  
Secant stiffness (\(K\)) was used to evaluate the stiffness degradation under different displacement and twist levels, while strength degradation coefficient (\(\lambda\)) was used to evaluate the degree in different cycles under the same displacement and twist level. To better observe the trend of stiffness degeneration, the torsion stiffness and bending stiffness degradation curves are shown in Figure 12. The degradation curves of the torsion strength and bending strength are shown in Figure 13.  
(1) Due to the formation of new cracks and yielding of reinforcements, the initial torsion stiffness degradation rate was high. Specifically, the torsion stiffness degraded by 50% when the displacement reached 3\(\Delta\)y. After that, the rate of torsion stiffness degradation slowed down. Comparing with the RCL-1, RCL-5, and RCL-6, the initial stiffness and stiffness degradation decreased with the increase of T/M ratio. Due to the existence of torsion, the diagonal cracks extended to the whole specimen, which led to smaller torsion stiffness.  
(2) The degradation of bending stiffness was more stable than torsion stiffness. The higher n ratio, the larger the bending stiffness, while the higher the T/M ratio, the less the bending stiffness.  
(3) As shown in Figure 13, with the increase of axial compression ratio, the torsion strength degeneration coefficient increased due to the high compression, which could prevent the development of cracks.  
Figure 11: Bending and torsion energy dissipation capacity of specimens.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen No.** & **Energy Dissipation** & **1\(\Delta_{y}\)** & **2\(\Delta_{y}\)** & **3\(\Delta_{y}\)** & **4\(\Delta_{y}\)** & **5\(\Delta_{y}\)** & **6\(\Delta_{y}\)** \\ \hline \multirow{2}{*}{RCL-1} & Bending & 0.104 & 0.113 & 0.141 & 0.157 & 0.220 & 0.283 \\  & Torsion & 0.132 & 0.137 & 0.155 & 0.144 & 0.142 & 0.170 \\ \hline \multirow{2}{*}{RCL-2} & Bending & 0.080 & 0.120 & 0.145 & 0.166 & 0.205 & 0.282 \\  & Torsion & – & – & – & – & – & – \\ \hline \multirow{2}{*}{RCL-3} & Bending & 0.101 & 0.121 & 0.129 & 0.155 & 0.222 & – \\  & Torsion & 0.151 & 0.142 & 0.149 & 0.169 & 0.165 & – \\ \hline \multirow{2}{*}{RCL-4} & Bending & 0.100 & 0.142 & 0.116 & 0.141 & 0.182 & 0.228 \\  & Torsion & – & – & – & – & – & – \\ \hline \multirow{2}{*}{RCL-5} & Bending & 0.116 & 0.115 & 0.117 & 0.126 & 0.163 & 0.196 \\  & Torsion & 0.144 & 0.147 & 0.152 & 0.148 & 0.152 & 0.202 \\ \hline \multirow{2}{*}{RCL-6} & Bending & 0.083 & 0.129 & 0.140 & 0.172 & 0.193 & 0.218 \\  & Torsion & 0.168 & 0.210 & 0.173 & 0.202 & 0.206 & 0.228 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Equivalent damping ratio of specimens.
# The Helical Resonator: A Scheme for Radio Frequency Plasma Generation
## 1 Introduction
Despite being a concept developed at the end of the XIX century, the literature on the helical resonator working principle is somehow scattered and also includes empirical formulas not derived from first principles [2]. This is also due to the fact that the same topic has been studied from several angles. Indeed, the concept is interesting for the following fields:  
* Plasma generation [3; 4; 5; 6];
* Ion trap antennas [7];
* Self-capacitance of coils [8; 9];
* RF filters [10; 11];
* Tesla coil construction [12];
* Precision measurements and metamaterial research [13; 14; 15; 16; 17]. In each of these fields, different authors treated the problem with different taste and emphasis on some issues, and collecting all these contributions in a unified view has proven to be not trivial. We thus would like to give a summary of the most important results on this subject, in the hope that this can prove useful to future scientists and practitioners who wish to properly understand this concept. In particular, we derive formulas that allow to predict the performance of a resonator from its basic properties, and illustrate a possible use for designing resonators, satisfying the given requirements. The whole discussion surrounds the resonator without plasma: the effects of a plasma formed inside the device will be the object of a forthcoming publication. The paper is organized as follows: in Section 2, we introduce the geometry of the device we wish to describe, and the different flavors that it can take; in Section 3, we describe the propagation of electromagnetic perturbations in the resonator, deriving the main characteristics of the device, which are characteristic impedance and the velocity factor, as a function of its geometry. Armed with these results, we then move to Section 4 to model the resonator as a transmission line, looking for the resonance condition, which is the frequency that maximizes the voltage amplification at the open end, and for the input impedance, which should be adapted so as to match the output impedance of the generator. In Section 5, the special case of a resonator with the shield immediately near the helical conductor is treated, as this is both the most promising for plasma production and also the one that allows a fully analytical treatment. In Section 6, the effect of a capacitive load connected to the resonator (including parasitic capacitance) is discussed. The results found up to this point are then checked against the experimental data in Section 7. Finally, in Section 8, the conclusions are drawn.
## 2 Materials and Methods
### Gap Filling  
One objective was to test the utility of optical data as a proxy of EC flux measurements, thus allowing gap-filling of the flux dataset. Non-gap-filled flux data were augmented by both filtered proxy NDVI data (providing the most continuous time-series of all three NDVIs used in this study, but not necessarily the most spatially representative NDVI) and MODIS data (providing a less continuous, but more spatially representative NDVI). The relationships between these different NDVIs and fluxes were then used to estimate missing flux data by applying several parameterizations of the LUE model (Equation (1)), each assuming an invariant efficiency (\(\epsilon\)) but using variable NDVI inputs to obtain APAR\({}_{\rm green}\). According to the LUE model, we used and compared different model terms: parameterizations ranged from simple NDVI (proxy NDVI and MODIS NDVI) to APAR\({}_{\rm green}\).  
Figure 4: Sample diurnal course of proxy NDVI (from phenology station, circles) and net CO\({}_{2}\) flux (from eddy covariance, triangles), showing average values (open symbols) calculated for the 5-h midday period (arrow between two vertical lines). See Methods and Supplemental Materials for further details on averaging (aggregation). Data from site E3, 7 June 2012.  
When comparing proxy NDVI to CO\({}_{2}\) fluxes, both datasets were averaged for a 5-h interval around solar noon (11 am-4 pm; Figure 4) and these values were used as the daily value for flux and proxy NDVI, respectively. These midday average values were then used to model and fill data gaps in the flux data from optical measurements.  
When using MODIS NDVI values, NDVI for each day over the entire season was calculated through a linear interpolation of actual date values from MODIS NDVI products (MOD13Q1 from Terra and MYD13Q1 from Aqua). The relationships between daily interpolated MODIS NDVI and net CO\({}_{2}\) fluxes were then used to estimate flux data throughout 2012 for comparison with the proxy NDVI method.  
Lastly, the midday average APAR\({}_{\text{green}}\) was calibrated against midday average net CO\({}_{2}\) flux (Figure 3). The relationships between APAR\({}_{\text{green}}\) and net CO\({}_{2}\) flux were used to estimate midday flux data for 2012, and to gap-fill missing flux data. Finally, measured fluxes were compared with the gap-filled, modeled fluxes based on proxy and MODIS NDVI (Figure 3).
## 2 The Method for Self-Healing Efficiency Estimation
### Self-Healing Efficiency Estimation  
After damage maps are created for every measurement carried out during self-healing process monitoring, they are stacked, forming matrix \(E(x,y,T)\), where \(T\) stands for time of the self-healing process.
# Computing Fault-Containment Times of Self-Stabilizing Algorithms Using Lumped Markov Chains
## 3 System Model
The containment behavior of a self-stabilizing algorithm is characterized by the contamination radius and the containment time. Definition 2: Let \(\mathcal{A}\) be a self-stabilizing algorithm. 1. The containment time of \(\mathcal{A}\) denotes the worst-case number of rounds any execution of \(\mathcal{A}\) starting at a 1-faulty configuration needs to reach a legitimate configuration. 2. Let \(R_{v}\) be the subgraph induced by the nodes engaged in the recovery process from a 1-faulty configuration of \(\mathcal{A}\) triggered by a fault at \(v\). Then \(\max\{dist(v,w)\mid w\in R_{v}\}\) is called the contamination radius. The stabilization time \(ST_{\mathcal{A}}(R_{v})\) is an obvious upper bound for the containment time.
## 5 Curriculum Data Scheduling
### Marginal Utility of Data Samples  
Despite data valuation, the performance of a trained model also relies on the quantity of data used in DML. However, the test accuracy of a trained model does not increase linearly with the number of data samples [34]. In fact, as data quantity \(d_{n,r}\) grows larger, model test accuracy increases more slowly. In other words, the marginal utility of data samples diminishes with data quantity in terms of test accuracy. Specifically, the term marginal refers to one health data sample in this paper. In round \(r\), the utility of data samples, denoted by \(U\), is used to measure contribution \(C_{n,r}\) for improving the test accuracy of global model \(\mathbf{w}_{r}\). We define the marginal utility of data samples as  
\[\Delta U=\frac{\Delta C_{n,r}}{\Delta d_{n,r}}, \tag{17}\]  
where \(\Delta C_{n,r}\) represents the change of contribution by adding one data sample, and \(\Delta d_{n,r}=1\).  
To demonstrate the diminishing of the marginal utility in DML, we define the marginal utility of data samples by the reciprocal of \(d_{n,r}\) multiplied by a system parameter \(\lambda\):  
\[\Delta U=\frac{\lambda}{d_{n,r}}. \tag{18}\]  
Therefore, \(C_{n,r}\) is calculated by  
\[C_{n,r}=\int_{1}^{d_{n,r}}\Delta C_{n,r}=\int_{1}^{d_{n,r}}\frac{\lambda}{d_{n,r}}\Delta d_{n,r}=\lambda\ln(d_{n,r}), \tag{19}\]  
where \(d_{n,r}>1\), meaning at least two samples are required for the calculation of the marginal utility. For a finite set of data samples \(\mathbf{d}_{n,r}\), maximizing the marginal utility of each sample results in the highest possible value for \(C_{n,r}\). In this paper, we propose a novel solution to maximize data utility by optimizing the marginal value of each sample.
## 2 Related Work  
At present, there are many research works on cross-site scripting, which are mainly divided into cross-site scripting attack detection and cross-site scripting vulnerability discovery. These two main research directions have been developed in recent years with many new research results. Many researchers have developed efficient XSS detection models or XSS discoverers. When referring to their research, we also think about how to optimize them.  
In terms of cross-site scripting attack detection, [PERSON] et al. [8] proposed a method of detecting XSS attacks using machine learning algorithms, extracting the characteristics of URLs and JavaScript code and using three machine learning algorithms (naive Bayes, SVM, and J48 decision trees) to detect XSS. Similarly, [PERSON] et al. [9] proposed a method for XSS attack detection on a social networking services (SNS) website based on machine learning algorithms. The method extractsthree characteristics of the URL, web page, and SNS website and classifies the dataset into XSS and non-XSS by using 10 different machine learning classification algorithms. What is more, with the development of deep learning, we published the \"DeepXSS: Cross Site Scripting Detection Based on Deep Learning\" in the 2018 International Conference on Computing and Artificial Intelligence [10], which proposed to extract word vectors with semantic information based on Word2 Vec, and Long Short-Term Memory (LSTM) algorithm based deep learning technology was used to extract the deep features of cross-site scripting attacks automatically.  
In terms of cross-site scripting vulnerability discovery, the research on XSS vulnerability discovery focuses on how to generate XSS attack vectors. Due to improper data encoding, [PERSON] [11] proposed a grammar-based attack generator that automatically generates an XSS test case to evaluate cross-site scripting vulnerabilities in the target page. [PERSON] et al. [12] proposed a black-box fuzzy tester based on the genetic algorithm to generate malicious input detection XSS automatically, which was named KameleonFuzz. [PERSON] et al. [13] proposed a method for mining XSS vulnerabilities based on the optimized XSS attack vector library, which constructs the XSS attack vector grammar, and built the attack vector pattern library, resource library, and mutation rule library based on the attack vector grammar to generate the XSS attack vector library.  
Due to the complex and varied web application environment, it is difficult to exploit fully the vulnerability of XSS vulnerabilities based on attack vector generation and automated testing. More importantly, most of the current research focuses on attack detection or vulnerability discovery, but the research on the security of the XSS detection model itself is lacking. Therefore, how to optimize the XSS detection model's ability to defend against adversarial attacks through reinforcement learning will be the focus of this paper.
## 1 Introduction
### Background  
Data centers are dedicated facilities housing computer systems and associated components, such as telecommunications and storage systems. They serve as centralized locations for organizations to store, process, and disseminate high amounts of data, integral to the operations of businesses, government agencies, and cloud service providers [8; 9].  
Key components include servers, storage systems, networking equipment, cooling systems, power supplies, and security systems. Data centers vary in size and capacity, and are categorized as small, medium, or large.  
DCs are significant consumers of electrical energy, accounting for about 1-3% of worldwide electricity use. A typical large data center can consume as much power as a small town, with heat output ranging from tens of kilowatts to several megawatts [8; 10; 11]. The high-power computing activities in data centers generate considerable heat, primarily from servers and storage units, usually wasted due to the low quality [12; 13; 14; 15]. Cooling systems, crucial for maintaining optimal operating temperatures, can account for a substantial portion of energy use [12].  
With the growing demand for data processing and storage, the need for efficient, sustainable, and scalable solutions becomes increasingly pronounced [1; 16]. Accordingly, energy efficiency measures are fundamental for sustainability.
## 3 Interference Mitigation Schemes
### UWB Approach  
In a UWB-based WBASN, multiple access interference (MAI) occurs in the transmission between devices on a single person. In [44], a successive interference cancellation (SIC) algorithm is introduced to cancel the interference. Also, the UWB/MIMO (multiple input/multiple output) system model is defined. A zero correlation duration code (ZCD) is applied to the UWB/MIMO WBASN system to spread the transmitted UWB signals. SIC orders the relative signals between the users based on the strongest signal. SIC determines the strongest signal to cancel and repeat until it achieves the desired signal with low probability of error. In this scheme, a transmitted signal is re-estimated at the receiver by using the interference cancellation scheme. At the receiver, zero forcing algorithm and minimum mean-square error algorithm are used to counteract the interference.  
Another study focused on mitigating interference in a UWB body area network is conducted in [45]. A single hybrid hermit pulse is used to cancel the narrowband interference. The hermit pulse is shifted and modulated to create a hybrid hermit pulse. Because the hybrid pulses are designed to have notches at the desired bands, the narrowband interference is cancelled at the notches.
# A Survey of Vehicle to Everything (V2X) Testing
## 5 V2X Testing Methods
### Testing Tools  
Testing tools are the basis of V2X testing. Many companies, organizations and research institutes have designed and developed a variety of testing tools. Spinent developed the 802.11p-based V2X Emulator to test V2X functionality and performance, and to support conformance testing of the WAVE protocol stack. OmniAir in the U.S. have developed DSRC conformance testing specifications and have conducted testing and certification work. In Europe, the 5 GAA organizes cross-industry companies to establish a C-V2X test and evaluation system and has conducted corresponding large-scale field testing. Neusoft, Jilin University, etc. have developed cooperative perception sensing components and tools for testing in accordance with national standards. The V2X simulation runtime infrastructure (VSimRTI) was designed for the assessment of new solutions for Cooperative Intelligent Transportation Systems [106].  
Science & Engineering Applications Datentechnik GmbH (S.E.A.) provides a complete V2X testing tool chain, including a series of V2X communication products and test systems, and provides customizing, support, and consulting services. Figure 9 shows the S.E.A. test tool set. The testing scope includes the application layer, network layer, access layer, and physical layer. The test architecture is similar to that discussed in Sections 5.1-5.3, but it is divided into two parts: open-loop test and closed-loop test. The open-loop test uses pre-designed scenarios to test the intrinsic behavior of the device under test. The closed-loop test adopts interactive driving scenarios to test platooning, dynamic controlling, and other interactive activities. NI also provides V2X test services, including PXI systems  
Figure 8: Parallel Testing Architecture.  
for testing RF, m3 systems for testing GNSS, HIL test systems, and wireless test systems. For the applications and traffic scenarios, simulators are the main technology. Whether a simulator is excellent depends on the accuracy between its simulating output and the real world. For the communication, simulators are used to test the protocol. But in the actual testing process, communication devices usually be black-box and the channel may be simulated by testing tools. As for field testing, the communication environment is hard to build, because it is very expensive to build an extreme scenario such as traffic jam. Test tools mainly collect data in the field testing by which testers analyze the performance and function of a real vehicle application or device.
# An Application of BOTDR to the Measurement of the Curing of a Bored Pile
## 3 Field Test
### Monitoring Scheme  
For the arrangement of optical fiber sensor, the symmetrical U-shape is used. In view of the construction technology and process of the bored pile, the optical fiber sensor can be laid on the main reinforcement of steel cage, and the optical fiber sensor and the bored pile can be connected as a whole through the pouring of concrete. The arrangement scheme is shown in Figure 3.  
The optical fiber sensor layout and construction of the bored pile are shown in Figure 4. It shows the installation procedure of the sensor; the optical fiber sensor is laid on the base of steel reinforcement cage with the shape of U before placing the reinforcement cage  
Figure 3: Arrangement scheme.  
Figure 2: BOTDR demodulator.  
(Figure 4a). The optical fiber sensor is fixed along the steel bar by fixing a point every 30-50 cm, and it can ensure that the optical fiber sensor and steel bar are bonded together (Figure 4b). Then, the steel reinforcement cage is placed down, and the redundant optical fiber sensor is drawn out for protection (Figure 4c). Finally, the concrete is poured, and the optical fiber sensor and the pile are connected as a whole (Figure 4d). In this way, under the action of concrete condensation, the optical fiber and concrete can be considered to coordinate deformation together.  
After the concrete curing is completed, the joint deformation of the main reinforcement of steel cage, concrete, and fiber can be achieved, and the optical fiber sensor is used to monitor the deformation of the pile during concrete curing. When the pile deforms under the influence of load or temperature, the reinforcement and concrete deform incoordination due to the bonding effect of concrete. BOTDR data demodulator is used to collect the Brillouin frequency shift of the optical fiber, and then Formula (1) is used to calculate the pile strain. The test aims to assess the deformation of the pile during the curing stage just after the completion of concrete casting; the initial data of optical fiber sensor is obtained by the first measurement after concrete casting. Then the data is recorded every 24 h to evaluate the deformation of the pile during concrete curing. The main parameters set by the data acquisition instrument during the test are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Project Name** & **Parameter Settings** & **Project Name** & **Parameter Settings** \\ \hline Range & 0.5 km & Pulse Width & 10 ns \\ Resolution Ratio & 0.05 m & Starting Frequency & 10.700 GHz \\ CS & 0.0500 MHz/\(\mu\epsilon\) & Stop Frequency & 11.200 GHz \\ fB\({}_{0}\) & 10.800 GHz & Frequency Space & 5 MHz \\ Refractive Index & 1.4650 & Data Points & 10,240 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of acquisition instrument.  
Figure 4: Sensor installed in the field.