esa-sceva/satcom-chunk-collection huggingface dataset:
This study aims to apply image processing techniques and a fuzzy inference system to analyze and classify lightweight concrete containing EVA and piassava fibers. The proposed methodology encompasses three key steps: (1) image segmentation; (2) feature extraction; and (3) development of a fuzzy system to classify the internal objects within the samples. One of the primary challenges in this task is the absence of precise reference values for assessing the accuracy of the estimates related to different composition features. This is due to the spatial heterogeneity of specimens, stemming from the variable morphology and granularity of the concrete components. Although there is a concrete homogenization process during production, the volumetric density of each component within a specimen is not constant. Consequently, the composition of an extracted core may vary depending on its extraction location and have significant lower or higher volumetric fractions than the average expected from the concrete mix. Therefore, quantitative evaluation of estimation errors is not feasible; we can only assess weather estimated values are \"similar\" to the theoretical values expected based on the mix. The primary contribution of this work is the presentation of a classification model based on the Takagi-Sugeno-Kang (TSK) inference system, wherein the inputs are fuzzy and the output is crisp. Additionally, this approach's application in material characterization, specifically in lightweight concrete, is unprecedented. The structure of this article is as follows: Section 2 describes the materials used to carry out the research, along with the segmentation, feature extraction, and fuzzy inference system steps. Section 3 presents and discusses the obtained results, and Section 4 provides the conclusions of this research stage and offers suggestions for future studies.
# Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure
###### Abstract  
To enhance the load-bearing mechanical properties and broadband electromagnetic characteristics of the conformal antenna, a broadband microstrip antenna array with a conformal load-bearing structure is proposed in this paper, which consists of three flexible substrate layers and two honeycomb core layers stacked on each other. By combining the antenna and honeycomb core layer in a structural perspective, the antenna array is implemented in the composition function of surface conformability and load-bearing. Additionally, the sidelobe level of the antenna is suppressed based on the reflection surface loaded. Meanwhile, an equivalent model of a honeycomb core layer has been established and applied in the design of a conformal antenna with a load-bearing structure. The presented model increases the accuracy of simulated results and reduces the memory consumption and time of the simulation. The overall size of the proposed antenna array is 32.84 \(\times\) 36.65 \(\times\) 4.9 mm (1.36 \(\lambda_{0}\)\(\times\) 1.52 \(\lambda_{0}\)\(\times\) 0.2 \(\lambda_{0}\), \(\lambda_{0}\) is the wavelength at 12.5 GHz). The proposed antenna element and array have been fabricated and measured in the flat state and under other various bending states. Experiment results show the operating relative bandwidth of the antenna array is 20.68% (11.67-13.76 GHz and 14.33-14.83 GHz) in the flat state. Under different bending conditions, the proposed antenna array covers 24.16% (11.08-14.1 GHz), 23.82% (10.63-13.5 GHz), and 23.12% with 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) in the _xz_ plane (11.55-14.33 GHz). In terms of mechanical load bearing, the structure has better performance than the traditional single-layer honeycomb core load-bearing structure antenna.  
conformal load-bearing structure; microstrip antenna array; broadband; mechanical test Article
## 3 Materials and Methods
### Adversarial Attacks  
In the realm of FL, adversarial attacks were broadly categorized into insiders and outskers. While Section 1 delved into outsider attacks (refer to Figure 2), the subsequent discussion focused specifically on insider attacks, as depicted in Figure 7. This paper concentrated on white-box attacks, wherein the attacker possessed comprehensive knowledge of the model's architecture. Notably, in the FL scenario, the global model was shared among all clients, potentially granting attackers access to the model architecture upon joining as clients.  
The adversarial samples in this study were generated through the utilization of the fast gradient sign method (FGSM) and projected gradient descent (PGD). These methods had been widely adopted in prior research for both attacking and training models [31; 57; 58; 59; 60]. The proposed approaches were subsequently assessed through adversarial sample attack scenarios and federated adversarial training scenarios. The FGSM, initially suggested by [PERSON] et al. [61], was employed, and the formula governing the sample generation was articulated as follows:  
\[x_{\text{adv}}=x+\epsilon\cdot\text{sign}(\
abla_{x}J(\theta,x,y)) \tag{1}\]  
where \(x_{\text{adv}}\) is the adversarial example, \(x\) is the original input, \(y\) is true labile of input image, \(\epsilon\) is the perturbation strength, \(J\) is the loss function, \(\
abla_{x}J\) is the gradient of the loss with respect to the input, \(\theta\) is our model, sign is the sign function.  
For the generation of FGSM adversarial samples, we adopted epsilon (\(\epsilon\)) = 0.01, requiring only one iteration to produce a sample image. Similarly, the generation of adversarial samples for PGD also utilized epsilon (\(\epsilon\)) = 0.01. Unlike FGSM, PGD perturbation is executed iteratively, and for this paper, we employed 20 iterations (\(t\)) with a step size (\(\alpha\)) of 0.005. The PGD method was introduced by [PERSON] et al. [59], and the formula governing the sample generation is expressed as follows:  
\[x_{\text{adv}}^{(0)}=x\quad\text{initialization} \tag{2}\]  
\[x_{\text{adv}}^{(t+1)}=\text{clip}_{x,\epsilon}\Big{(}x_{\text{adv}}^{(t)}+ \alpha\cdot\text{sign}(\
abla_{x}J(\theta,x_{\text{adv}}^{(t)},y)),x\pm \epsilon\Big{)} \tag{3}\]  
where \(x_{\text{adv}}^{(t)}\) is the adversarial example at iteration \(t\), \(x\) is the original input, \(\epsilon\) is the perturbation strength, \(\alpha\) is the step size, \(J\) is the loss function, \(\
abla_{x}J(\theta,x_{\text{adv}}^{(t)},y)\) is the gradient of the loss of model \(\theta\) with respect to the input for the target label \(y\), sign is the sign function, \(\text{clip}_{x,\epsilon}\) is the function that clips values to ensure they stay within the range \(x\pm\epsilon\).
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 2 Localization Analysis of Single Array  
### Localization Algorithm  
The LPV used for the current study travelled on level ground such that its height remained constant relative to the array sensors. The height of the vehicle was about 2.0 m, which was approximately 1.5 m higher than the arrays themselves. Compared to the range of about 100 m or more, the constant height difference between the vehicle and the arrays had little influence on the localization performance and accuracy. Therefore, the localization was operated in the \(x\)-\(y\) coordinate system while height difference was ignored. The five-element cross array was taken as a basic array pattern, as illustrated in Figure 1.  
The coordinates are defined within the plane of the array. The center acoustic sensor is located at \(O\) (0, 0), while the remaining four were \(M_{2}\) (D, 0), \(M_{3}\)(0, D), \(M_{4}\) (\(-\)D, 0), and \(M_{5}\) (0, \(-\)D), where D represents the distance from \(M_{i}\) to \(O\). The noise source is assumed to be located at \(T\) (\(x,y\)), with an angle \(\varphi\) between \(OT\) and \(x\) axis as indicated in Figure 1. The time delays between the arrival time of noise at the center sensor and the other four sensors are referred to as \(\tau_{1i}\). Similarly, \(d_{1i}\) (\(i\) = 2, 3, 4, 5) represents the distance between the center sensor and the neighboring sensors, such that \(d_{1i}\) = c \(\times\)\(\tau_{1i}\) (\(c\) is current sound velocity). R is the distance from \(O\) to \(T\).  
Figure 1: Model of single array.  
From the simple geometry in Figure 1, the distances can be expressed.  
\[\left\{\begin{array}{l}x^{2}+y^{2}=R^{2}\\ (x-D)^{2}+y^{2}=(R+d_{12})^{2}\\ x^{2}+(y+D)^{2}=(R+d_{13})^{2}\\ (x+D)^{2}+y^{2}=(R+d_{14})^{2}\\ x^{2}+(y-D)^{2}=(R+d_{15})^{2}\end{array}\right. \tag{1}\]  
The solution of Equation (1) is  
\[\left\{\begin{array}{l}x=\frac{2R(d_{14}-d_{12})+d_{14}^{2}-d_{12}^{2}}{2D} \\ y=\frac{2R(d_{13}-d_{15})+d_{13}^{2}-d_{15}^{2}}{2D}\end{array}\right. \tag{2}\]  
\[\left\{\begin{array}{l}\tan\varphi=\frac{y}{x}=\frac{(\tau_{15}-\tau_{13})|2R -c(\tau_{15}-\tau_{13})|}{(\tau_{14}-\tau_{12})|2R-c(\tau_{14}-\tau_{12})|}\\ R=\sqrt{x^{2}+y^{2}}=\frac{4D^{2}-d_{12}^{2}-d_{13}^{2}-d_{14}^{2}-d_{15}^{2}} {2(d_{12}+d_{13}+d_{14}+d_{15})}\end{array}\right. \tag{3}\]  
when \(R>>c\times\tau_{1i}\),  
\[tan\varphi\approx\frac{(\tau_{15}-\tau_{13})}{(\tau_{14}-\tau_{12})} \tag{4}\]  
then Equation (3) can be simplified:  
\[\left\{\begin{array}{l}\varphi=\arctan\frac{(\tau_{15}-\tau_{13})}{(\tau_{14 }-\tau_{12})}\\ R=(4D^{2}-c^{2}\sum\limits_{i=2}^{5}\tau_{1i}{}^{2})/2c\sum\limits_{i=2}^{5} \tau_{1i}\end{array}\right. \tag{5}\]  
The location of the noise source is given by Equations (2) and (5) and, with reference to their derivation, it is evident that the localization algorithm is based on the time delays between the arrival times of noise at the sensors in the array.
## 2 Seismically Base-Isolated Structures  
A typical seismically base-isolated structure is composed by two substructures, as shown in Figure 1: the _superstructure_, that is designed by assuming a linear elastic behavior, and the _base isolation system_, that is designed in order to allow the superstructure to deform within its linear elastic range.  
The base isolation system consists of special devices, called seismic isolators, and a full diaphragm, above the seismic isolation bearings, which is generally introduced to distribute the transverse loads uniformly among them [26].  
There exist two main types of seismic isolation devices: _elastomeric_ and _sliding bearings_. Both of them are characterized by a complex hysteretic behavior which allows the designers to employ one of the two existing seismic isolation strategies, denominated _period elongation strategy_ and _force limitation strategy_, respectively.  
In the former, the isolation bearings are employed to increase the fundamental period of the structure thus making its value conveniently different from the dominant period of the earthquake excitation; conversely, in the latter, the seismic isolators are adopted to control the maximum value of the transverse force acting at the base of each superstructure column.  
In the next two subsections, we briefly describe the above-mentioned types of seismic isolators and we illustrate the typical force-displacement hysteresis loops characterizing their transverse response.
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 1 Introduction
* Demonstrate the efficacy of the proposed strategy even in challenging conditions, enabling reliable information transmission during significant events. * Compare the number of transferred messages between traditional and proposed architectures in a greenhouse environment. * Conduct an extensive evaluation of topic categorization and pub-sub systems (scalable and non-scalable MQTT) regarding message processing time and subscription throughput. This research discusses four MQTT brokers as low-resource-consuming messaging applications: Mosquitto 1.4.15, Bevywise MQTT Route 2.0 (known for scalability), ActiveMQ 5.15.8, and EMQ X 4.0.8 (known for non-scalability). These brokers are evaluated based on their suitability for the pub-sub communication framework, considering the reliability specifications of the client's pub-sub structure. The diagram depicted in Figure 1 demonstrates the three-tier architecture adopted for messaging communication in an IoT greenhouse. The paper is organized as follows. Section 2 discusses related works on the existing solutions of various messaging protocols. Section 3 is about the research methodology, in which Section 3.1 describes our proposed protocol architecture. Then, we introduce in detail the overall system of our proposed message standardization and topic configuration in Section 3.2. In Section 3.3, the pub-sub system for MQTT is proposed. Sections 4 and 5 discuss the performance evaluation, summarize, and conclude the paper.
# Circularly Polarized Vivaldi Antennas Integrated with Septum-like Polarizer
## 3 Experimental Verification  
For verification of the CP Vivaldi antenna designed in full-wave simulation, fabrication and measurements were performed on the structure as shown in Figure 11. Because the \(-10\) dB reflection coefficient and \(3\) dB AR bandwidth of the LHCP design are more stable than the RHCP design, the LHCP Vivaldi antenna was fabricated as a prototype for experimental verification. Anritsu's MS46522B vector network analyzer and an anechoic chamber containing Keysight's E8362B network analyzer was used to measure the reflection coefficient and radiation patterns. The receiving antenna was positioned \(7\) m away from the fabricated prototype. The fabricated LHCP Vivaldi antenna was mounted on a Styrofoam jig for orthogonal coupling.  
Figures 12 and 13 show the measurement results of the fabricated prototype. The measured results were confirmed to have a bandwidth of reflection coefficient S\({}_{11}\) of \(1.29\) GHz (=9.7%), \(3\) dB AR of \(1.57\) GHz (=13.7%), peak gain of \(6\) dB, and \(-1\) dB gain bandwidth of \(0.68\) GHz (=6.2%), respectively. Compared to the results of the simulation, the bandwidths of S11 and peak gain were narrowed by \(1.7\) dB and \(1\) dB, the \(3\) dB AR was widened by \(1.4\) dB, and the peak gain was reduced by \(1\) dB, respectively. Also, it is confirmed that the center frequencies are upshifted by approximately \(0.2\) GHz compared to the simulated results. The differences between the simulated and measured results is mainly due to fabrication errors in the width of the dielectric slot and microstrip lines, imperfect hand soldering for assembly, and bending of the substrate during the fabrication process, etc. Nevertheless, the simulated and measured results were found to have similar trends.  
Figure 11: Prototype of LHCP Vivaldi antenna.  
Figure 12: Performance of the proposed CP Vivaldi antenna: (**a**) reflection coefficient, (**b**) axial ratio, (**c**) boresight gain for RHCP design.
## 1 Introduction
of these ammonium salts is reported in Figure 2. With respect to the \(N\)-Alkyl-\(N\),\(N\)-dimethyl-\(N\)-[(methacryloyloxy)ethylammonium iodides (Figure 1), the polarizable monomer of the AATEABs is directly bonded to the main aliphatic chain, while three ethyl groups are attached to the quaternary nitrogen. Moreover, the authors found a correlation between the number of carbons in the main aliphatic chain and the antibacterial activity of these compounds [19]. In the \(N\)-alkyl-\(N\),\(N\)-dimethyl-\(N\)-[(methacryloyloxy)ethylammonium iodides [18], the polymerizable group is bonded to a chain with only two carbon atoms, in the AATEABs, this aliphatic chain was stretched to 12 atoms, in order to also obtain good surfactants. In fact, thanks to its structure, the AATEAB, with n equal to 11 (acryloyloxyundecyltriethylamine-monium bromide AUTEAB), has been used as a surfactant for surface modification [20], with a significant increase of the membrane bio-fouling resistance. Moreover, an analogous of the AUTEAB was also used to study the exchange of bromide ions with antibacterial Keggin polyoxometalates [21]. The antibacterial activity of the synthetized AATEABs was also measured in previous works [19], and a correlation between their activity and carbons number of the aliphatic chain was found. In particular, the biocide activity of these surfactants increases along with the carbons number of the chain. Thus, in this paper, some molecular properties, such as the charge distributions, maximum molecular length, and aspect ratio, correlated to molecular antibacterial activity, were calculated using _ab initio_-based molecular modeling, with the aim to investigate the correlations between the AATEABs' structure and target properties. The purpose of the work is to evaluate if the folding of the aliphatic chain, correlated to the carbons in the chain, affects the charges of the head group and the maximum molecular length, through hydrogen bonds or noncovalent interactions between carbonyl and ammonium. Water molecules surrounding the surfactants, as well as hydrophobic interactions are necessary in order to evaluate the folding effect. Indeed, the water molecules could act as a bridge between the carbonyl and ammonium groups, and this bridge could reduce the maximum surfactant length or act as thickness (i.e., shock absorber), avoiding an excessive chain reduction (i.e., winding), mostly for the surfactants with a high number of carbons. Thus, the influence of the water molecules and hydrophobic interaction is not evident. Moreover, it is interesting to evaluate if, for short aliphatic chains, a direct interaction between the carbonyl and the positive ammonium can occur in presence of the water molecules. Although this _ab initio_ modeling was applied to study the structure-property correlations of these particular surfactants, this procedure is quite general, as it does not use any adjustable parameters; thus it can be applied to predict analogous properties of new structures for anti-bifouling functionalization of membranes.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 3 Proposed Low-Complexity Multibeam Wideband Transmit Beamformer
### Two-Dimensional Sparse FIR Trapezoidal Filter  
The 2-D sparse FIR trapezoidal filter \(H(z_{x},z_{ct})\) is designed by using the FIR filter design method presented in [52]. To this end, the required passband \(\mathcal{R}_{PB}\) of the 2D filter \(H(z_{x},z_{ct})\) having \(K\) double trapezoidal passbands inside the principal Nyquist square \(\mathcal{N}\) can be derived as  
\[\mathcal{R}_{PB}=\bigcup_{k=1}^{K}\mathcal{R}_{k} \tag{8}\]  
where \(\mathcal{R}_{k}\) is the trapezoidal passband corresponding to the \(k\)th bandpass 2-D PW, which is given by  
\[\mathcal{R}_{k} =\Big{\{}(w_{x},w_{ct})\in\mathcal{N}\mid\Big{[}\Omega_{ct}^{ \mathrm{c}k}-\frac{B_{ct}^{k}}{2}\geq|w_{ct}|\geq\Omega_{ct}^{\mathrm{c}k}+ \frac{B_{ct}^{k}}{2}\Big{]}\] \[\cap\Big{[}(\omega_{x}\leq\tan(\alpha_{k}+\delta_{k})\omega_{ct} \cap\omega_{x}\geq\tan(\alpha_{k}-\delta_{k})\omega_{ct})\] \[\cup(\omega_{x}\geq\tan(\alpha_{k}+\delta_{k})\omega_{ct}\cap \omega_{x}\leq\tan(\alpha_{k}-\delta_{k})\omega_{ct})\Big{]}\Big{\}}, \tag{9}\]  
where \(B_{ct}^{k}\) is the temporal bandwidth of the \(k^{\text{th}}\) PW, \(\Omega_{ct}^{\mathrm{c}k}\) is the center frequency of the \(k^{\text{th}}\) PW, and \(\delta_{k}\) is the half-angle of the double trapezoidal-shaped \(k^{\text{th}}\) passband. The value of \(\delta_{k}\) affects the beamwidth of the multibeams produced by the transmit beamformer.  
The ideal frequency response \(H_{d}(e^{i\omega_{x}},e^{i\omega_{ct}})\) of \(H(z_{x},z_{ct})\) inside \(\mathcal{N}\) is specified as  
\[H_{d}(e^{i\omega_{x}},e^{i\omega_{ct}})=\begin{cases}1,&(\omega_{x},\omega_{ct })\in\mathcal{R}_{PB}\\ 0,&\text{otherwise}.\end{cases} \tag{10}\]  
Note that \(H_{d}(e^{i\omega_{x}},e^{i\omega_{ct}})\) has a zero group delay with respect to both \(\omega_{x}\) and \(\omega_{ct}\).
## 3 Secure Rate Maximization Scheme  
In this section, we first use the K-means cluster pair algorithm to obtain the UAV's 2D coordinates \(\mathbf{Q}\) and the user's pairing mode. Then, given the UAV's 2D coordinates, we optimize the channel allocation from the UAVs to the HAP, the users' power, and the UAVs' height, respectively. Afterward, the BCD iterative algorithm is proposed to obtain the optimal secure rate.
# A Systematic Review of Radio Frequency Threats in IoMT
## 2 Methodology
### Data Collection and Visualization  
We then collected data from the relevant publications to conduct our analysis. We gathered basic information about the publications: title, authors, publication type, publication domain, and publication year. The basic information we collected led us to focus on two fields of interest--side-channel attacks and healthcare. Figure 3 visualizes the spectrum attack search between 2003 and 2022 through the Power BI Microsoft platform [14] and shows that computer science and medical informatics had the most spectrum attack publications, followed by the engineering and telecommunication domain.  
Figure 2: PRISMA flowchart. * The number of records identified from each database. ** Records excluded from the database based on search criteria.  
Figure 4 shows spectrum attack publications in the institute of electrical and electronics engineers (IEEE) access, sensors, wireless personal computing, journal of medical internet research (JMIR), and association of computer machinery (ACM) computing, followed by other journals between 2003 and 2022. However, few studies on sniffing and replay attacks in health care show a significant research gap (Table 2) in IoMT RF attacks.  
A full counting relevance study is shown in Figure 5. The content is visualized based on the number of occurrences of a term exceeding 10 (143 terms from 5219). The Each term is given a relevance score. Based on this score, the most relevant term is selected. The default choice is 60% of the most relevant term. The small size of the red bubble with replay attack and the light blue bubble with side-channel attack indicate significant research gaps in those areas [17].  
Figure 4: Physical layer attack search from Scopus categorized by research field.  
Figure 3: Physical layer attack search from PubMed and WoS categorized by research field.  
Figure 6 shows the binary counting relevance study. A VOS viewer [18] shows a visualization of the content based on the number of occurrences of a term greater than 10 (104 from 5219 terms). Each term is given a relevance score. Based on this score, the most relevant term is selected. The default choice is 60% of the most relevant term. Regarding the replay attack, the relevant keyword topics are well connected with other key terms highly prone to spectrum attacks [18]. The red bubble illustrates a method or attributes research gap in relation to replay attack, and the light blue and green bubbles indicate significant research gaps in the side-channel attack.  
Figure 5: Relevance and occurrence study-full counting.  
Figure 6: Relevance and occurrence study—binary counting.