esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Propagation Modes Analysis  
The self-made data processing program was used to process the velocity signals of each pressure bar in Figure 9 (the signal sampling rate was 10 MS/s). Time-frequency joint spectrums were then obtained by using FFT. The FFT length of STFT was set as 8192 sampling points, the window length was 301 sampling points, and the moving step size was 1 sampling point. Figure 10 shows typical time-frequency joint spectrums of the free surface velocity of bars 01.  
The time of elastic wave propagation to the free end of the bar 01 can be obtained by the elementary theory as in Equation (20), where the length of the bar is 1500 mm and the corresponding elastic wave velocity given in Table 3 is 5.2595 km/s.  
\[0.5T_{0}=z/C_{0}=1500/5.2595=285.2\ \upmu\text{s} \tag{20}\]  
The diameter and radius of the pressure bar are 6 mm and 3 mm respectively, substituting those values into Equation (6):  
\[\frac{fa}{C_{0}}=\frac{3\ \text{mm}}{5.2595\ \text{km}/\text{s}}f=\frac{f}{1.753 \ \text{MHz}} \tag{21}\]  
The abscissa and ordinate in Figure 5 are converted into time and frequency by Equations (20) and (21), then the time-frequency joint spectrum is obtained after a 3.666 ms-shift on the time axis as shown in Figure 11. It can be seen that the time-frequency components in the measured signal are in agreement with the first-order propagation mode given by theory, but with a lower frequency upper limit (less than 1 MHz). In addition, the frequency components between 3.8 ms and 3.9 ms are different from the longitudinal stress wave given by the theory, which is determined as a bending wave and will not be discussed in this paper. The results demonstrated that the axial velocity of the center of the pressure bar 01 is dispersed according to the first-order propagation mode.  
Figure 11: The first order mode included in the time-frequency joint spectrum of bar 01.  
Figure 10: Time-frequency joint spectrum of the free surface velocity of bar 01.  
As for the other sets of pressure bar sensors except bar 04, the elastic stress waves are similar to that of bar 01 and follow the first-order propagation modes, while the spectrum of bar 04 represents a more complex pattern as illustrated in Figure 12.  
A similar analysis method as that used on bar 01 was applied on data from bar 04. Values of size and elastic wave velocity of bar 04 were used in the corresponding formulas, meanwhile, the theoretical propagation mode curves of corresponding orders were drawn in the time-frequency diagram. As shown in Figure 13, the first three propagation modes are excited and in good agreement with the theoretical ones. Table 4 shows the frequency range of the first three propagation modes.
Sensors_**2015**, _15_, 24026-24053; doi:10.3390/s150924026  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling  
**[PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu Natal,  
Durban 4041, South Africa; E-Mail: [EMAIL_ADDRESS]  
2 School of Electrical and Information Engineering, University of Witwatersrand,  
Johannesburg 2000, South Africa; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +266-5330-5421.  
Academic Editor: [PERSON]  
_Received: 11 March 2015 / Accepted: 21 July 2015 / Published: 18 September 2015_  
This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols.  
medium access control (MAC); scheduling; diversity; channel quality indicator; intra-cluster; inter-cluster
# _Sci_
## 5 Conclusions  
Laser crosslinks offer several benefits to current and near-future civil, commercial, and security sectors; the most obvious being highly secure, low error, high throughput data communications. Due to the protracted pace of laser crosslink development, melded with the lengthy lead-times for satcom development, compounded by the prolonged government procurement process, makes the time right for inclusive action now. Laser crosslinks technology is sufficiently mature to place payloads of small size, weight, and power on to-be-launched vehicles and follow in short order with the terrestrial legs--whether fixed, at sea, or in the air. The demand for data--raw facts--and its successful evolution to information--arranged in context--is growing, arguably at an exponential rate. The U.S. Department of Defense's demand for data, information, and intelligence is exceeding its satellite communications capacity. Transitioning to laser crosslink is becoming necessary. Advantages and disadvantages from Section 4 are summarized in Table 3, while the key performance parameters are summarized in Table 4.  
**Author Contributions:** Conceptualization, M.D.; methodology, M.D.; formal analysis, M.D.; investigation, M.D.; supervision, M.C.; validation, M.C.; resources, T.S.; data curation, M.D.; writing--original draft preparation, M.D.; writing--review and editing, T.S.; funding acquisition, T.S. Please turn to the CRediT taxonomy for the term explanation. Authorship has been limited to those who have contributed substantially to the work reported. [PERSON] is not a tenured faculty member of Columbia University; rather he is the Associate Dean of the Naval Postgraduate School's Graduate School of Engineering and Applied Sciences. Upon the advice of Naval legal counsel, in order to avoid legal jeopardy, [PERSON] publishes government-funded research under his association with the Naval Postgraduate School, while publishing non-government funded research under his continuing associations with Stanford and Columbia Universities. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This research was funded by the U.S. Air Force nuclear enterprise's distance learning education program [139] in response to an increased need for critical thinking in the nuclear enterprise [140] in a period of global uncertainty [141; 142; 143; 144; 145; 146; 147]. The APC was funded by program publications manager, [PERSON]: [EMAIL_ADDRESS].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Background
### WebAssembly  
**Overview**. WebAssembly is a technology that aims to address performance, compatibility, and security issues that have plagued previous approaches. It was developed by a consortium of tech companies, including Mozilla, Microsoft, Apple, and Google, and was released in 2017 [25]. WebAssembly has since gained widespread adoption and is currently supported by 96% of all browser instances [5]. Additionally, it is an official World WideWeb Consortium (W3C) standard [26], and is natively supported on the web. An overview of WebAssembly is given in Figure 1.  
WebAssembly is a low-level bytecode language that runs on a stack-based Virtual Machine (VM). More specifically, instructions push and pop operands to the evaluation stack. This architecture does not use registers; instead, values are stored in global variables that are accessible throughout the entire module or in local variables that are scoped to the current function. The VM manages the evaluation stack, global variables, and local variables.  
**Host Environment.** WebAssembly modules run within a host environment, which provides the necessary functionality for the module to perform actions such as I/O or network access. In a browser, the host environment is provided by the JavaScript engine, such as V8 or SpiderMonkey. WebAssembly exports can be wrapped in JavaScript functions using the WebAssembly JavaScript API [27], allowing them to be called from JavaScript code. Similarly, WebAssembly code can import and call JavaScript functions. Other host environments for WebAssembly include server-side environments like Node.js [28] and stand-alone VMs with accompanying APIs. For instance, the WebAssembly System Interface (WASI) [29] allows WebAssembly modules to access the file system.  
**Module.** WebAssembly modules serve as the fundamental building blocks for deployment, loading, and compilation. A module contains definitions for types, functions, tables, memories, and globals. In addition, a module can declare imports and exports, as well as provide initialization through data and element segments or a start function.  
**Compilation.** Languages like C, C++, and Rust can be compiled into WebAssembly since it is designed as a compilation target. Toolchains like Enscripten [22] or wasmpack [30] can be used to compile these languages to WebAssembly. The resulting binary is in the wasn binary format, but can also be represented in the equivalent human-readable text format called wat. A module corresponds to one file. The WebAssembly Binary Toolkit (WABT) [31] provides tools for converting between wasn and wat representations, as well as for the de-compilation and validation of WebAssembly binaries.  
**Use Cases.** WebAssembly has been adopted for various applications on the web due to its near-native execution performance, such as data compression, game engines, and natural language processing. However, the usage of WebAssembly is not only limited to the web. It is also being extended to desktop applications [6], mobile devices [7], cloud computing [8], IoT [12; 13], and embedded devices [14].
## 2 Modeling of Hypersonic Target Motion Characteristics
### Measurement Model  
The measurement of the single-station radar includes the pitch angle \(a\), azimuth angle \(e\), and relative distance \(r\). According to the geometric relationship shown in Figure 2, the following systematic observation equations can be established:  
\[\begin{split}\mathbf{Z}_{k}=\left[\begin{array}{c}a\\ e\\ r\end{array}\right]=\left[\begin{array}{c}\arctan(\frac{z}{\sqrt{x^{2}+y^{2} }})\\ \arctan(\frac{y}{x})\\ \sqrt{x^{2}+y^{2}+z^{2}}\end{array}\right]+\mathbf{v}_{k}\end{split} \tag{6}\]  
where \(\mathbf{v}_{k}\) is the measurement noise, \(\mathbf{v}_{k}\sim N(0,\mathbf{R}_{k})\), and \(\mathbf{R}_{k}\) is the noise covariance matrix.  
Figure 2: Target measurement in the radar observation coordinate system. The origin \(O\) is the radar coordinate, the azimuth angle is \(e\), the elevation angle is \(a\), and the relative distance is \(r\).  
The radar measurement equation is established in the ENU coordinate system, while the HTV-2 ballistic reference coordinate system in the previous text is the LLA coordinate system. Therefore, coordinate transformation is required for the hypersonic target state information, as shown in Figure 3.  
Assuming that the coordinates of the hypersonic target are \((\mu,\lambda,h)\) in the LLA coordinate system and \((X,Y,Z)\) in the ECEF coordinate system, the conversion relationship is as follows:  
\[\begin{cases}X=(N+h)\cos(\mu)\cos(\lambda)\\ Y=(N+h)\cos(\mu)\sin(\lambda)\\ Z=(N(1-e^{2})+h)\sin(\mu)\\ \end{cases} \tag{7}\]  
\[\begin{cases}e^{2}=\frac{a^{2}-b^{2}}{a^{2}}\\ N=\frac{a}{\sqrt{1-e^{2}\sin^{2}(\mu)}}\\ \end{cases} \tag{8}\]  
where \(N\) is the radius of the reference ellipsoid, \(e\) is the eccentricity of the ellipsoid, \(a\) is the long half-axis of the Earth, and \(b\) is the short half-axis of the Earth.  
Similarly, the LLA coordinates \((\mu_{0},\lambda_{0},h_{0})\) of the radar can be converted into ECEF coordinates \((X_{0},Y_{0},Z_{0})\) by Equations (6) and (7). Therefore, the position information of the hypersonic target in the ENU coordinate system can be obtained:  
\[\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=\left[\begin{array}{c}e\\ n\\ u\end{array}\right]=\mathbf{T}_{\text{ECEF}}^{\text{ENU}}\left[\begin{array}{c}X-X _{0}\\ Y-Y_{0}\\ Z-Z_{0}\end{array}\right] \tag{9}\]  
\[\mathbf{T}_{\text{ECEF}}^{\text{ENU}}=\begin{bmatrix}-\sin(\lambda_{0})&\cos( \lambda_{0})&0\\ -\sin(\mu_{0})\cos(\lambda_{0})&-\sin(\mu_{0})\sin(\lambda_{0})&\sin(\mu_{0})\\ \cos(\mu_{0})\cos(\lambda_{0})&\cos(\mu_{0})\sin(\lambda_{0})&\sin(\mu_{0}) \end{bmatrix} \tag{10}\]
# Treatment of Water Contaminated with Diesel Using Carbon Nanotubes
## 4 Conclusions  
The results obtained allow the following conclusions to be drawn:  
Carbon nanotubes have shown a high diesel adsorption capacity. The high molecular weights of the hydrocarbon molecules present in the diesel did not create resistance and were adsorbed by the carbon nanotubes without obvious problems.  
The adsorption rates were quite high, and maximum adsorption was reached within a few minutes. In particular, in the system that used 10 mL of diesel/40 mL of water and 0.7 g of carbon nanotubes, within 2.5 min, almost 100% of diesel was adsorbed. The quantities of carbon nanotubes to be used proved to be an important parameter to consider and evaluate. Exceeding the optimal amount of carbon nanotubes to use for a system does not result in improved adsorption. This can be attributed to carbon nanotube agglomeration phenomena, which become increasingly significant as the quantities of the latter increase, making it difficult for the large molecules present in diesel to reach the carbon nanotube adsorption sites.  
Both gasoline and diesel contain hundreds of different hydrocarbon molecules. As is known, however, the organic molecules constituting diesel are, on average, larger than those present in gasoline [68]. It is possible to hypothesize that adsorption mechanisms, both of diesel and gasoline, are similar, mainly due to physical adsorption, \(\pi\)-\(\pi\) and electrostatic interactions. The different dimensions of the molecules do not lead to substantial variations in the adsorption processes. However, a more evident difference is observed in the agglomeration phenomena, which are more evident in the case of diesel. Probably, this can be justified with a higher density of diesel (830-950 kg/m\({}^{3}\)) than of gasoline (700-800 kg/m\({}^{3}\)).  
The adsorption and desorption cycles showed that carbon nanotubes can allow for the recovery of adsorbed diesel and their reuse with losses of low-adsorption-efficiency percentages. In particular, after the second cycle, there was a loss of 5% and 8% in the third, compared to the amount of diesel adsorbed in the first cycle.  
The present research represents only a preliminary study to test the possibility of using carbon nanotubes as adsorbent materials in the purification of diesel-contaminated water. Surely, other studies and insights are needed, but the results obtained, being encouraging, will hopefully motivate other researchers.  
Conceptualization, P.D.L. and A.M.; methodology, P.D.L. and C.S.; validation, P.D.L., C.S. and A.M.; formal analysis, P.D.L. and C.S.; investigation, P.D.L. and C.S.; writing--review and editing, P.D.L.; supervision, P.D.L., A.M. and J.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data are contained within the article.  
We dedicate a particular memory to Ing. [PERSON], who contributed to this research but whose life was cut short.  
The authors declare no conflict of interest.
## 20 mmWave Four-Element MIMO Antenna for Future 5G Systems  
In this paper [19], an S-shape four-port Multiple Input Multiple Output (MIMO) wideband mmWave antenna that operates at 25 GHz to 39 GHz is proposed. The dimensions of single element are 10 \(\times\) 12 mm\({}^{2}\), and the proposed S-shape four-port MIMO is 24 \(\times\) 24 mm\({}^{2}\). A decoupling network is applied to further compress mutual coupling among elements. The proposed MIMO antenna is fabricated, and measured results agree well with the simulated results. Through the results obtained, the authors claim that the proposed MIMO antenna can be applied for future mmWave devices.  
A Novel Single-Fed Dual-Band Dual-Circularly Polarized Dielectric Resonator Antenna for 5G Sub-6 GHz Applications  
In this paper [20], a single-fed dual-band circular polarized (CP) dielectric resonator antenna (DRA) for dual-function communication is proposed. To obtain CP fields, the cross-shape metal strip is applied to operate in the fundamental and the high-order mode in the two frequency bands. A metal strip is designed on the top of the rectangular DRA to improve the impedance matching and widen the axial ratio (AR) bandwidth. This step led to a 2.73% and 6.5% improvement on the lower band and the upper band, respectively. The AR bandwidth is also significantly improved in both frequency bands. Through these results, the authors claim that the proposed antenna can be applicable to Global Positioning System (GPS) and Wireless Local Area Network (WLAN).  
This research received no external funding.  
I would appreciate the opportunity to organize this Special Issue. Thanks to all authors and peer reviewers, this Special Issue was able to be completed successfully. I also want to give special thanks to editorial staffs of _Applied Sciences_ for their best support on the publication of this issue.  
The author declares no conflict of interest.
# Improving Adversarial Robustness of CNNs via Maximum Margin
## 2 Related Work
### Adversarial Attacks  
With the proposal of adversarial examples, the earliest method used to generate adversarial examples is the fast gradient sign method (FGSM) [1]. FGSM generates adversarial examples with a single normalized gradient step. Since then, a number of gradient-based and multi-step iterative attacks have been proposed such as projected gradient descent (PGD) [16], C&W attack [24], and DeepFool [25]. We review the PGD attack which is the most popular and widely used attack.  
PGD aims to find an adversarial example \(\hat{\mathbf{x}}\) for an input \(\mathbf{x}\) that satisfies a given boundary \(||\hat{\mathbf{x}}-\mathbf{x}||_{p}<\epsilon\). Let \(\mathbf{B}\) denotes the \(l_{p}\)-ball of radius \(\epsilon\) centered at \(x\). The attack initializes the adversarial example with a random point \(x_{0}\in\mathbf{B}\), and iteratively updates it with the gradient, as shown in Equation (1).  
\[\begin{split}&\mathbf{x}_{i+1}=\text{Proj}_{\mathbf{B}}(x_{i}+\alpha \cdot g)\\ &\text{s.t.}\quad\ g=\underset{\|\mathbf{v}\|_{p}\leq 1}{\arg\max}\quad v ^{\top}\
abla_{\mathbf{x}_{i}}L(f(\mathbf{x}_{i}),y)\end{split} \tag{1}\]  
where \(L(.,.)\) is a suitable loss-function (e.g., cross-entropy), \(f\) is the objective model, \(\alpha\) is a step size, \(\text{Proj}_{\mathbf{B}}\) projects an input onto the \(l_{p}\)-ball \(\mathbf{B}\), and \(g\) is the direction of gradient that loss ascents fastest for a given \(l_{p}\)-norm. For the \(l_{\infty}\)-norm, \(\text{Proj}_{\mathbf{B}}\) is a clipping operator and \(g=\text{sign}(\
abla_{x_{i}}L(x_{i},y))\).
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 2 Proposed Methods  
The proposed method, CSVM, is implemented from spatial and spectral dimension, as shown in Figure 1. The processing in spatial dimension contributes to handle local-scope spectral variability, while the processing in spectral dimension aims to deal with large-scope spectral variability and makes the final decision on endmember extraction. CSVM will be introduced in four steps, which are spatial clustering, spectral purity analysis, spectral clustering and simplex volume maximization.  
Figure 1: The flowchart of CSVM algorithm.
###### Abstract  
High-speed rail (HSR) environments present unique challenges due to their high mobility and dense passenger traffic, resulting in dynamic and unpredictable task generation patterns. Mobile Edge Computing (MEC) has emerged as a transformative paradigm to address these challenges by deploying computation resources closer to end-users. However, the limited resources of MEC servers necessitate efficient task partitioning, wherein a single task is divided into multiple sub-tasks for parallel processing across MEC servers. In the context of HSR environments, the task partitioning ratio is pivotal in ensuring quality of service (QoS) and optimizing resource utilization, particularly under dynamic and high-demand conditions. This paper proposes a deep reinforcement learning (DRL)-based task partitioning mechanism using Twin Delayed Deep Deterministic Policy Gradient (TD3) for HSR environments with MEC servers (MECSs). The proposed method dynamically adjusts task partitioning ratios by leveraging real-time information about task characteristics and server load conditions. The experimental results show that when the task arrival rate is 20, the delay is improved by about 5% compared to _random_ and about 13% compared to _no_partition_. When it is 50, there is no significant difference from _random_ and about 2% improvement compared to _no_partition_. The task throughput is almost the same when it is 20. However, when it is 50, _random_ is much better. We also looked at the performance change according to the number of serving MECSs. In this process, we can also note the research direction of finding an appropriate number of serving MECSs \(K\). The results highlight the efficacy of DRL-based approaches in dynamically adapting to the unique characteristics of HSR environments, achieving optimal resource allocation and maintaining high QoS. This paper contributes to advancing task partitioning strategies for HSR systems and lays the groundwork for future research in MEC-based HSR systems.  
task partitioning; Deep Reinforcement Learning (DRL); High-Speed Railway (HSR); Mobile Edge Computing (MEC); Twin Delayed Deep Deterministic policy gradient (TD3) 2020 IEEE/RSJ International Conference on Intelligent Transportation Systems (IMS), pp. 1-10. IEEE.
## 4 Software/Hardware Overview
### Encoders  
The proposed SEA are designed for passive-compliance, as described in Section 3. If the spring stiffness \(k\) would be constant, the displacement would be linear with respect to the external forces exerted to the load. Thus, the forces acting on a joint could be estimated by applying _Hooke's law_. This can be highlighted in simulation by monitoring the motor- and load-positions and showing that they can be divergent due to external forces acting upon the load, as illustrated in Figure 9. However, in a real-world application, this is not realistic. The spring stiffens can be nonlinear. This is the reason each joint module of _Serpens_ is fitted with a rotary incremental encoder that is connected to the _OpenCM 9.04_. The encoder is vital to the control of each SEA, as it provides feedback for the absolute position of the load. In particular, a _RoLin_ encoder system is adopted [33] for _Serpens_. The _RoLin_ component level encoder system consists of a read-head and a magnetised ring. The actuator is a periodically magnetised ring with a pole length of 2 mm. Axial reading of the ring is adopted for _Serpens_.