esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Demonstration of a Multichannel Elastic Wave Filter in a Phononic Crystal Slab
## 1 Introduction
These theoretical results have led to many fundamental concepts and opens opportunities for potential applications in the area of elastic and acoustic metamaterials. However, experimental studies verifying these fundamental concepts are limited to a few. This paper analyses, designs and implements a demultiplexing operation across three distinct linear waveguides fabricated on the same planar structure of a pillar-based phononic crystal slab. The three waveguides have been realized by removing one line of pillars. According to the waveguides width variation, the proposed system shows the capability of filtering three frequency regions in the band gap with high level of rejection. The rest of the paper is organized as follows. Section 2, describes the model geometry, the material parameters and the methods of fabrication. The experimental setup and band gap characterization are also introduced in Section 2. Section 3 examines numerically and experimentally the demultiplexing behavior. Conclusions and remarks are given in Section 4.
## 2 Motivation and Related Works
### Energy-Efficient Transmissions  
[PERSON] _et al._[17] proposed a cooperative communication protocol for energy-efficient transmission, which considers a cluster of sensor devices cooperating together in WSNs. [PERSON] _et al._[18] presented an energy-efficient transmission strategy for WSN deployments over large geographical areas. The performance is enhanced by cooperative multiple-input multiple-output (MIMO). However, [17,18] did not consider the role of the HAP. By considering a single HAP architecture, [PERSON] _et al._[19] discussed the design and evaluation of an adaptive cooperative scheme intended to extend the survivability of the battery-operated aerial-terrestrial communication links. He emphasized that the cooperation between mobile terrestrial terminals on the ground could improve energy efficiency in the uplink. By considering a satellite architecture, [PERSON] _et al._[20] presented and discussed networking from the perspective of energy efficiency. Satellite communications can become drastically more energy efficient if the constituent systems can exploit their inherent energy-efficiency-related advantages. However, [19,20] did not consider more practical multiple HAP/satellite scenarios. [PERSON] _et al._[21] and [PERSON] _et al._[22] developed optimal and approximately optimal energy-efficient transmission strategies with the objective of either minimizing the outage probability or maximizing the ergodic rate in a Rayleigh fading environment. However, in our work, the channels of the HAPs to the terminals and the channels of the satellite to the terminals are Rician fading rather than Rayleigh fading [23,24].
## 3 Results
### Applications in Coherent Imaging
As the \(\theta\) value is farther away from \(0.440\pi\), the corresponding \(E_{I}\) should be larger. From the above results, it can be determined that, in this experiment, the retrieval can tolerate an error of \(\theta\) less than \(0.006\pi\). From our characterization (see in the inset of Figure 3d), \(\theta\) is \(0.437\pi\) when the gray scale of the image loaded on the SLM is \(109\), and \(0.447\pi\) when it is \(111\). They both cannot lead to a successful retrieval. This indicates that by direct use of the phase value in the Fourier domain, the phase modulation of the SLM can be precisely characterized in the practice. Figure 5: (**a**) A photograph of the specimen and the lighttight blue taps around it. (**b**) a typical grid-like mask sent to the SLM; (**c**) a diffractogram received by the camera; (**d**) the retrieved modulus of the specimen. The scale bar here is \(1\) mm. Figure 6: Results of the retrieved modulus of the specimen for different \(\theta\) values. (**a**) \(\theta=0.439\pi\); (**b**) \(\theta=0.437\pi\); (**c**) \(\theta=0.443\pi\); (**d**) the relationship between \(E_{I}\) and \(\theta\). The scale bar is \(1\) mm.
## 5 Numerical Results
### Radar Detection Performance Analysis
#### 5.1.6 Detection Performance  
To demonstrate the robustness of the proposed algorithm, we verify the detection probability of the algorithm using Monte Carlo experiments (10,000 times) with a false alarm rate \(P_{fa}=10^{-4}\). The target parameters are consistent with those described in the preceding section. Given the rarity of integration algorithms in OFDM signals, we compare MTD and HI with the proposed method. The results are illustrated in Figure 12.  
Figure 11: Communication BER results. (**a**) OFDM IRCS BER with AWGN, (**b**) BER with different modulation methods, (**c**) BER with different demodulation methods.  
Figure 12: Detection probabilities of different methods.  
Compared to multi-frame integration, the proposed method achieves an approximately 4.5 dB higher SNR improvement for a detection probability of 0.9. This enhancement arises from the proposed method's hybrid coherent and noncoherent integration across subfragments, which yields superior SNR gains despite the similar intra-frame and inter-frame operations shared with multi-frame integration. In contrast to MTD, which employs only intra-frame coherent integration and integration across entire frames, the proposed method achieves an approximately 7.5 dB higher SNR improvement for a detection probability of 0.9. This improvement is attributed to the proposed method's ability to mitigate range inconsistencies across the entire integration.
# AEMB: An Automated Exploit Mitigation Bypassing Solution
## 3 Exploit Mitigations Bypassing
### Bypassing Payload Generation
#### 3.2.1 Program Analysis  
To begin, the _AEMB_ must examine the target application and its memory layout in the operating environment. It can produce some basic information that is critical for either an automated or manual exploit. The following information is specifically required for _bypassing payload generation_.  
* **The memory with known address**. This part is mainly aimed at the Address Space Layout Randomization (ASLR) exploit mitigation, which is inextricably linked to the whole exploit process, and therefore the system sees it as a feature of the operating system. The _AEMB_ will determine whether the address of each memory page is known. The known address is obtained through two methods: 1. The address of the target memory page is fixed. 2. The address of target memory pages was leaked via the PoC provided.
* **The memory affected by the PoC**. This part mainly judges which memories may be affected by the PoC. It is required for the exploit since the PoC is converted into the EXP by creating the memory affected by the PoC. Taint analysis is used to acquire this kind of memory. The PoC is marked as the source of taint, and the location where the PoC triggers the vulnerability is marked as the sink of taint, as shown in Figure 4.
## 1 Introduction
This results in a less responsive system that may not fully leverage the fast dynamic response that FCS-MPC controllers are designed to provide. This paper presents a significant contribution to the field of power electronics by presenting a novel application of FCS-MPC for DC-DC boost converters. It addresses the non-minimum phase issue, a common challenge in the control of boost converters, by incorporating a PI regulator to adapt the reference current. The proposed method distinguishes itself by employing the Ideal Reference Calculator (IRC) block, which generates the primary reference current for the system based on ideal conditions. This allows the PI controller to focus solely on making small corrections to the reference current in steady-state situations, rather than managing fast dynamic changes. By decoupling the PI controller from fast dynamics, the FCS-MPC, combined with the IRC block, takes full responsibility for real-time adjustments and load changes, ensuring a fast dynamic response. This dual approach leverages the strengths of both controllers--steady-state precision from the PI controller and rapid adaptation from the FCS-MPC combined with the IRC block. The effectiveness of the proposed method is validated through both simulation and experimental results by hardware-in-the-loop (HIL) tests, demonstrating its potential for real-time applications requiring optimal performance and rapid response. The rest of the paper is organized as follows: In Section 2, the DC-DC boost converter and its discrete model are introduced, establishing the foundation for the control strategy. The FCS-MPC strategy is detailed in Section 3, including its implementation, the choice of prediction horizon, and the formulation of the cost function. Section 4 focuses on the development of the proposed dual control approach, in which a PI controller is integrated to dynamically adjust the reference current, addressing the non-minimum phase issue. In Section 5, the simulation and hardware-in-the-loop (HIL) test results are presented, vali-dating the proposed method, and demonstrating significant improvements in response time and stability compared to traditional control methods. Finally, Section 6 provides the conclusions, summarizing the findings.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 2 System Model and Problem Formulation
### SINR and Transmission Rate  
Denote the distance between the \(m\)-th BS and the \(n\)-th device by \(d_{nm}\), and \(h_{nm}\) the channel fading gain, the received SINR received at the \(n\)-th device from the \(m\)-th BS can be expressed as [23]  
\[\Gamma_{nm}=\frac{P_{m}A_{0}d_{nm}^{-\alpha}h_{nm}}{\sum_{m^{\prime}\in\mathcal{ M},m^{\prime}\
eq m}P_{m^{\prime}}A_{0}d_{nm^{\prime}}^{-\alpha}h_{nm^{\prime}} +\sigma^{2}} \tag{1}\]  
Figure 1: The system model of network slicing. (**a**) Example of wireless IIoT; (**b**) the architecture of network slices.  
where \(A_{0}\) denotes the path losses at the reference distance \(d_{nm}=1\) and \(\alpha\) denotes the path-loss exponent; \(\sigma^{2}\) denotes the noise power. Hence, the transmission rate that the \(n\)-th device can achieve when associated with the \(m\)-BS can be calculated as  
\[R_{nm}=b_{nm}\times\log_{2}(1+\Gamma_{nm}). \tag{2}\]  
For the \(n\)-th device, the transmission rate achieved is given by  
\[R_{n}=\sum_{m\in\mathcal{M}}x_{nm}\times R_{nm}. \tag{3}\]
## 3 Results and Discussions
### Acoustic Emission Analysis
Figure 8 (amplitude) and Figure 9 (counts) show the trends of the detected cumulative amplitude values, as a function of the radial and longitudinal positions. It was noted, as in the graphs, that the values along the longitudinal position remained constant whereas those along the radial position were more variable due to the direction of the fracture. In Figure 10, on the other hand, the energy values have a wider variability, ranging from the minimum of sampling S081 (4.72 \(\times\) 10\({}^{6}\) eu) up to the maximum of sampling S101 (2.94 \(\times\) 10\({}^{9}\) eu) for Group 1. Therefore, energy is a parameter that seems to depend on other factors rather than the positioning of the sample inside the wooden beam, which is representative of specific EMC values. These factors are, for example, the sensor location and the presence of defects. In the next section, the same parameters will be plotted as a function of moisture content at equilibrium, to calibrate AE with the EMC model. All the samplings studied using 3D graphs indicate that the radial and longitudinal positioning influence the elongation of the fracture, and the radial section has more variability with respect to the longitudinal section. Consequently, both \(\mathrm{X_{R}}\) and \(\mathrm{X_{L}}\) can influence acoustic emission events detected during the brittle phenomenon of fracture.
# Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer
## 2 Preliminaries
### Generative Adversarial Networks  
In [22], the ground-breaking method of learning the true data distribution in a non-parametric way was proposed in the form of Generative Adversarial Networks (GANs). In that constellation, the discriminator network \(D\) is trying to discriminate between the synthetic samples artificially generated by the generator network \(G\) and the ground truth observations available in the training data. The generator \(G\) models the true data distribution by learning to confuse, i.e., deceive the discriminator \(D\) by providing \(D\) with synthetic examples that are hardly distinguishable from the real ones. Thus, the two structures, the discriminator \(D\) and the generator \(G\), are mutually competing in order to reach the Nash equilibrium expressed by the minimax loss of the training procedure, where the optimization problem is given by:  
\[\min_{G}\;\max_{D}\;\;\mathbb{E}_{x\sim p(x)}\;\big{[}\;\ln\big{(}D(x)\big{)} \;\big{]}\;+\;\mathbb{E}_{z\sim p(z)}\;\big{[}\;\ln\big{(}\;1-D\big{(}\,G(z) \big{)}\;\big{)}\;\big{]}, \tag{1}\]  
where \(p(x)\) represents the true data distribution over domain \(\mathcal{X}\), while the latent variable \(z\) is sampled by the distribution \(p(z)\).  
The first term in Equation (1), In \(\big{(}D(x)\big{)}\), corresponds to the cross-entropy between true distribution \([\;1\;\;0\;]^{T}\) and distribution \([\;D(x)\;\;1-D(x)\;]^{T}\) corresponding to discriminator's probabilistic output. Similarly, the second term: \(\ln\big{(}\;1-D\big{(}G(z)\big{)}\;\big{)}\), is the cross-entropy between \([\;1\;\;0\;]^{T}\) and \([\;D\big{(}G(z)\big{)}\;\;1-D\big{(}G(z)\big{)}\;]^{T}\).  
In Figure 1, generators in both directions are assumed to be in form of GANs, and are denoted by mappings \(F\) and \(G\), while the corresponding discriminators over domains \(\mathcal{X}\) and \(\mathcal{Y}\) are denoted by \(D_{X}\) and \(D_{Y}\).
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 3 Switchable Receiver Front-End Architecture: HARRF LNA and SPDT Integration
### Impact on Noise Figure and Gain  
Impedance mismatch can significantly degrade the overall noise figure and gain performance of the receiver front-end. Using Friis' formula, the total noise figure incorporating mismatch losses can be represented as:  
\[NF_{total}=NF_{Switch}+\frac{NF_{LNA}-1}{G_{Switch}}+10\log_{10}(1-|\Gamma_{ LNA}|^{2}), \tag{24}\]  
where the last term accounts for the reflection loss due to impedance mismatch.  
Similarly, the cascaded gain of the receiver front-end considering mismatch reflections is given by:  
\[G_{total}=G_{Switch}\times G_{LNA}\times(1-|\Gamma_{LNA}|^{2})\times\left(1- \frac{|S_{11}|^{2}}{|S_{21}|^{2}}\right), \tag{25}\]  
where \(S_{11}\) and \(S_{21}\) represent the scattering parameters of the SPDT switch.  
Simulated results confirm that the designed matching network minimizes impedance discontinuities, ensuring optimal signal power transfer between the SPDT switch and LNA.
# Ultrawideband Cross-Polarization Converter Using Anisotropic Reflective Metasurface
## 5 Working Mechanism
Figure 5 shows the simulation of surface current distribution on the unit cell's top and bottom metallic layers for two distinct resonance frequencies. At 13.8 GHz, the resonance frequency current flows on the top surface toward the corner aligned with the positive \(u-\)axis, and the bottom layer current flows toward theupper right corner, which is antiparallel to the top surface current. This reveals that the resonance at 13.8 GHz is magnetic in nature. The top and bottom currents intensify the magnetic field in the substrate of the metasurface. This leads to an increase in the surface impedance, and the HIS condition is satisfied. The current impedance imbalance in the \(y-\)direction causes phase reflection and changes the current flow in the \(x-\)direction. Finally, the cross\(-\)polarization phenomenon occurs, allowing for the conversion of a \(y-\)polarized wave to an \(x-\)polarized wave and vice versa.
## 2 Materials and Methods
### Spectral Profile Extraction and Data Calibration  
To eliminate the impacts of uneven illumination and dark current noise, the raw hyperspectral imagery was calibrated by standard white and dark reference images according to the formula [37]  
\[R_{c}=\frac{R_{0}-B}{W-B} \tag{1}\]  
where \(R_{0}\) indicates the raw hyperspectral image, \(R_{c}\) represents the calibrated hyperspectral image, \(W\) represents the standard white reference image obtained using a rectangular Teflon plate, and \(B\) denotes the standard black reference image obtained by covering the lens completely with an opaque black cover [38].  
For each hyperspectral image, a region of interest (ROI) was used to measure the mean VNIR spectral reflectance. The ROI (a circle with a diameter of about 150 pixels) was positioned in the middle of the sample image, and close to Petri dish (\(90\times 17\) mm) edge [20; 39] (Figure 2). The spectral bands for this study are 519, 560, 564, 576, 697, 700, 703, 706, and 749 nm. The standard deviation of the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**TAs** & **No.** & **Maximum** & **Minimum** & **Mean** & **Std.** & **Skewness** & **Kurtosis** & **Per\%** \\ \hline Total data set & 59 & 16.41 & 7.04 & 9.6527 & 1.4699 & 1.74 & 5.71 & 100 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical descriptions for the arsenic content (\(\mu\)g/g) and the soil sample percentages.  
Figure 1: (a) Hyperspectral imaging setup. (b) Acquired imagery (R: 640 nm, G: 548 nm, B: 470 nm).  
averaged spectral band of each sample is between 30-50. The standard deviation of the 697 nm band is even lower than 20.
# Hybrid Zoning Algorithm to Optimize Overhead in Smart Mobile Communication
###### Abstract  
With the increase of mobile terminals, routing protocols in wireless communications must provide better quality of service to meet bandwidth and reliability requirements. In networks without infrastructure, such as ad hoc and sensor networks, where a device performs as both a terminal and a router to forward data of other nodes, maintaining the network topology consumes considerable resources in terms of energy and bandwidth. These parameters need to be considered when designing routing protocols for wireless networks. To reduce the cost of the protocol overhead, some algorithms act on the forwarders, while others act on the transmission of messages. Finally, the hybrid ones are a combination of both. In this paper, we propose a new algorithm with two zoning strategies to enhance the performance of mobile network. The first strategy helps to select dispersive forwarders in order to reduce the collision in radio channel. The second strategy aims to reduce the transmission of redundant messages. Both strategies are based on the location information of nodes. We implemented our algorithm in the optimized link state routing protocol, the most used protocol in mobile ad hoc networks. We showed by simulations that our solution reduces drastically the cost of the overhead with no hindrance to the network topology.  
broadcasting; manets; sensor networks; location information +
Footnote †: journal: Sensor and Actuator Networks
## Results and Discussion
### Classification of Different Solvent Extracts by Principal Component Analysis (PCA)  
Assignment of metabolites in the \({}^{1}\)H NMR and 2D NMR spectra of acetone, chloroform, hexane, methanol and 70% ethanol extracts revealed significant variations in metabolite constituents. Consequently, multivariate data analysis was used to analyze the differences in metabolites present in the solvent extracts. It was evident from the PCA model that the 30 samples were clustered into five groups with good fitness (R2X cum) and high predictability (Q2 cum) values of 0.98 and 0.97, respectively (Figure 2A). In addition, no outliers were seen in the score plot. The first principal component (PC1) accounted for 69.8% of the variance, while the second principal component (PC2) counted for 24.6% of the variance, cumulatively explaining the total variance of 94.4%. Thus, the metabolite profiles of the five solvent extracts were clearly different from each other. The acetone, chloroform and hexane extracts were well separated from the rest of the extracts by first principal component (PC1), while the hexane, methanol and 70% ethanol extracts were separated from the acetone and chloroform extracts by second principal component (PC2). Interestingly, the spectral signals for methanol and 70% ethanol extracts were in the same cluster, indicating that they have the same chemical profiles.  
The loading plot highlighted the potential marker compounds responsible for the grouping. The loading plot with PC1 and PC2 for the five solvent extractions is presented in Figure 2B. Further detailed examination of the loading plot showed that the acetone and chloroform extracts contained significantly higher amounts of carotenoids and chlorophyll pigments compared to the other extracts. The carotenoids detected were fucoxanthin, canthaxanthin, astaxanthin, violaxanthin, zeaxanthin and lutein, while the chlorophyll detected were Chl \(a\) and Chl \(c_{1}\). Signals for the carotenoids were resonated in the spectral region between 6.10-6.80 ppm, which were contributed by the olefinic and methyl proton. The significantly high amounts of carotenoids and chlorophyll pigments indicated their potential as antioxidants. Fatty acid, particularly \(\alpha\)-linolenic acid, identified from the characteristic signal at 1.30 ppm, was shown to be present in relatively higher amounts in the hexane extract in comparison to the other extracts. Meanwhile, the discriminating factors for the 70% ethanol and methanol extracts from the other extracts were mainly due to amino acids. These compounds are primary metabolites in lower plants and have been reported to contribute to numerous bioactivities.  
Figure 2: _Cont._
## 3 Data Collection and Preprocessing  
### Description of Data Sources  
The dataset for this experiment was obtained from a PV power plant with a 1 MW installed capacity located in Canakkale, Turkey. Canakkale is situated in the northwest of Turkey, and most of its land lies within the borders of the Marmara Region. It is between 25\({}^{\circ}\)40\({}^{\prime}\)-27\({}^{\circ}\)30\({}^{\prime}\) east longitudes and 39\({}^{\circ}\)27\({}^{\prime}\)-40\({}^{\circ}\)45\({}^{\prime}\) north latitudes. The weather station integrated into the power plant provides real and reliable meteorological data. Many PV power generation systems have been installed in the region. Among the PV systems, the system located between 26\({}^{\circ}\)41\({}^{\prime}\) longitude and 40\({}^{\circ}\)12\({}^{\prime}\) latitude was selected as the one that best meets the ideal standards for data collection. The collected data, which contains records between 1 August 2022 and 3 August 2024 in 15 min intervals, is published for the first time in the literature and is publicly available in Supplementary Materials. Besides the active power data of the PV panels, the dataset includes meteorological data on total radiation, wind speed, PV module temperature, and ambient temperature, obtained from the meteorological measurement station of the power plant where the panels are located. Other data that could not be measured at the PV power plant meteorological station (such as relative humidity, wind direction, and dew point) were obtained from source [79] for the same dates and within the same time range. The satellite image of the PV power plant from which the data were taken is given in Figure 1[80].