esa-sceva/satcom-chunk-collection huggingface dataset:
# A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra
## 3 Results and Discussion
### Establishment of Regression Models
In Equation (2), the concentrations of acetamiprid and deltamethrin were all from 100 mg\(\cdot\)kg\({}^{-1}\) to 10\({}^{-3}\) mg\(\cdot\)kg\({}^{-1}\). \[Z=1.235-0.02271\text{A}+0.01886\text{D}+0.0007016\text{A}^{2}-0.0001841\text{AD }+1.296\times 10^{-5}\text{D}^{2} \tag{2}\]  
where Z is the correction coefficient, A is the estimated concentration value of acetamiprid, D is the concentration value of deltamethrin. The fitting surface of characteristic peak at 735 cm\({}^{-1}\) was shown in Figure 5. And for the characteristic peaks of deltamethrin at 735 cm\({}^{-1}\), the correction coefficient increased with the increase of acetaminophen concentration and the decrease of deltamethrin concentration. With the increase of acetamiprid content and decrease of deltamethrin content, the correction coefficient could be up to 1.32. The sum squared residual (SSE) of the fitting model was 0.0438. Root Mean Square Error (RMSE) and coefficient of correlation were 0.0382 and 0.9266 respectively. The polynomial regression Equation (Equation (3)) of characteristic peaks at 735 cm\({}^{-1}\) was acquired and shown below. In Equation (3), the concentrations of acetamiprid and deltamethrin were all from 100 mg\(\cdot\)kg\({}^{-1}\) to 10\({}^{-3}\) mg\(\cdot\)kg\({}^{-1}\). \[Z=1.167+0.02066\mathrm{A}-0.002236\mathrm{D}-0.0006892\mathrm{A}^{2}-8.851 \times 10^{-6}\mathrm{AD}-2.88\times 10^{-6}\mathrm{D}^{2} \tag{3}\]  
where Z is the correction coefficient, A is the concentration value of acetamiprid, D is the concentration value of deltamethrin. Figure 4: The fitting surface of characteristic peaks change rule at 634 cm\({}^{-1}\) (**a**) and 1114 cm\({}^{-1}\) (**b**); between pesticide concentration and correction coefficient.
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 3 Background
### Threat Description  
Although encryption algorithms have been relied on to protect sensitive data, the statistical patterns of underlying ciphers that successfully reveal encryption keys have become common [30], meaning that billions of terabytes of data transmitted daily over computer networks face many internet-based threats. In addition to the difficulty in ensuring a secure network environment, multi-agent systems are vulnerable to complex distributed attacks from multiple adversaries.  
The traditional information security model characteristics, i.e., confidentiality, integrity, and availability, inspire our threat classification scope of understanding threats that vary from passive hidden communication lines that attack confidentiality or active adversaries that affect real-time data exchange. Both attacks aim at confidentiality, integrity, or availability categories. Our threat model considers both active and passive attackers who obtain unauthorized network connectivity. Our primary focus is on the availability of a computing resource flagged as unavailable when the device is under attack. Notably, although availability is the primary focus of our defense system, an attack connection could translate to the loss of confidentiality and integrity as well. Our focus is mainly on exposing the consequences of cyberattacks on the technical space, i.e., on computing resources [8].  
We describe the following malware mechanisms that have severe high impacts.
## 4 Localized Query Loss Discovery with Distributed Query Resolution
### Scalability of Loss Discovery  
The scope of loss discovery in the rebroadcast-based approach is the whole network with \(N\) nodes. On the other hand, the proposed approach requires only \(n\) one-hop neighbor nodes for query loss discovery and recovery. Therefore, the ratio of query loss discovery in the conventional approach to the one with local detectors is as shown in Equation (2).  
\[R_{scope}=\frac{n}{N} \tag{2}\]  
Equation (2) can be interpreted as follows. For every query loss in the standard flooding method, all \(N\) nodes need to be involved in the rebroadcast process. On the other hand, when using local detectors, the scope of rebroadcasts is limited to only the \(n\) neighboring nodes at the node where the query was lost. Since we usually have \(N\gg n\), our approach can significantly limit the traffic overhead caused by rebroadcast messages and it scales linearly with the number of neighbors per node.  
Figure 5: (**a**) The probability of successful query loss detection for a detector cluster (\(S_{2}\)) and (**b**) \(R_{success}\).
# Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots
## 4 Performance Evaluation
An arrow identifies the case of \(p_{s,new}=p_{m,new}=0.0\) (the cutoff priority scheme of [7]) and another arrow identifies the case of \(p_{s,new}=p_{m,new}=1.0\) (the nonpriority scheme of [7]). Figure 3 presents the analytical together with the simulation results of the total CBP of new calls for the five different sets of \(p_{s,new}\) and \(p_{m,new}\). Figure 4 presents the corresponding results of the CBP of handover calls (based on (15) and (24)). Finally, Figure 5 presents the analytical and simulation results of the system's utilization. All graphs show that the analytical results are similar to the corresponding simulation results. Regarding Figure 3, we observe that: (i) the increase of \(\lambda_{new}\) increases the total CBP of new calls; (ii) the choice of \(p_{s,new}\) and \(p_{m,new}\) affects the CBP, leading to high CBP values (when \(p_{s,new}=p_{m,new}=0.0\)) and low CBP values (when \(p_{s,new}=p_{m,new}=1.0\)); (iii) the CBP results obtained via [7] cannot capture the behavior of the proposed models, a fact that is expected since the models of [7] do not consider the case of the probabilistic BR policy. Regarding Figure 4, we observe that: (i) the increase of \(\lambda_{new}\) increases the CBP of handover calls (in the stop phase); (ii) the choice of \(p_{s,new}\) and \(p_{m,new}\) affects the CBP of handover calls leading to high CBP values (when \(p_{s,new}=p_{m,new}=1.0\)) and low CBP values (when \(p_{s,new}=p_{m,new}=0.0\)) (i.e., the increase of \(p_{s,new}\) and \(p_{m,new}\) has the opposite impact on the CBP of new and handover calls); (iii) the CBP results obtained via [7] cannot capture the behavior of the proposed models. Finally, regarding Figure 5, we observe that: (i) the increase of \(\lambda_{new}\) increases the system's utilization and (ii) a slightly higher utilization appears when \(p_{s,new}=p_{m,new}=1.0\) (i.e., when the BR policy is not applied in favor of handover calls). In Figures 6-8, we present the total CBP of new calls (Figure 6), the CBP of handover calls in the stop phase (Figure 7) and the system's utilization (Figure 8) for the different values of \(\theta_{s}^{-1}\). Our conclusions are similar to those obtained in Figures 3-5. In addition, we observe that an increase of \(\theta_{s}^{-1}\) results in the CBP increase of both types of calls. This is expected, since the increase of the stop phase duration results in increased new and handover calls competing for acceptance in the system. Figure 3: Total CBP of new calls for different values of \(\lambda_{new}\). Figure 4: CBP of handover calls (in the stop phase) for different values of \(\lambda_{\text{new}}\). Figure 5: Utilization for different values of \(\lambda_{\text{new}}\). Figure 6: Total CBP of new calls for different values of \(\theta_{\rm s}^{-1}\)  
Figure 7: CBP of handover calls (in the stop phase) for different values of \(\theta_{s}^{-1}\). Figure 8: Utilization for different values of \(\theta_{s}^{-1}\).
## 3 Method and Data Presentation
These professionals were also invited to explain the results which would be discussed in the following sections. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Characteristics** & **Categorization** & **N** & **\%** & **Characteristics** & **Categorization** & **N** & **\%** \\ \hline \multicolumn{6}{c}{_Respondents_} & \multicolumn{6}{c}{_Organizations_} \\ \hline \multirow{6}{*}{Discipline} & Government agent & 2 & 2.3 & \multirow{6}{*}{Main business} & Architect firm & 18 & 20.7 \\ \cline{2-5}  & Developer & 5 & 5.7 & & Structural engineering firm & 6 & 6.9 \\ \cline{2-5}  & Architect & 21 & 24.1 & & MEP engineering firm & 13 & 14.9 \\ \cline{2-5}  & Structural designer & 9 & 10.3 & & General construction firm & 30 & 34.5 \\ \cline{2-5}  & MEP designer & 9 & 10.3 & & Trade construction firm & 3 & 3.4 \\ \cline{2-5}  & General contractor & 28 & 32.2 & & Facility management firm & 3 & 3.4 \\ \cline{2-5}  & Subcontractor & 6 & 6.9 & & Others & 14 & 16.1 \\ \cline{2-5}  & Supplier/Manufacturer & 2 & 2.3 & & & & \\ \cline{2-5}  & Facility manager & 5 & 5.7 & & & & \\ \hline \multirow{6}{*}{Work experience} & 5–10 years & 39 & 44.8 & & 0 & 9 & 10.3 \\ \cline{2-5}  & 11–15 years & 10 & 11.5 & & 1–3 & 41 & 47.1 \\ \cline{2-5}  & 16–20 years & 8 & 9.2 & & 4–5 & 22 & 25.3 \\ \cline{2-5}  & 21–25 years & 9 & 10.3 & & 6–10 & 13 & 14.9 \\ \cline{2-5}  & \multicolumn{2}{c}{\(>\)25 years} & 21 & 24.1 & & \(>\)10 & 2 & 2.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Profile of respondents and their organizations.
###### Abstract  
This paper presents a compact, high-performance metasurface-based leaky-wave MIMO antenna with dimensions of 40 \(\times\) 30 mm\({}^{2}\), achieving a gain of 12.5 dBi and a radiation efficiency of 85%. The antenna enables precise control of electromagnetic waves, featuring a flower-like metasurface (MTS) with coffee bean-shaped arrays on substrates of varying permittivity, separated by a cavity layer to enhance coupling. Its dual-port MIMO design boosts data throughput operating in three bands (3.75-5.25 GHz, 6.4-15.4 GHz, and 22.5-30 GHz), while the leaky-wave mechanism supports frequency- or phase-dependent beamsteering without mechanical parts. Ideal for CubeSat communications, its compact size meets CubeSat constraints, and its high gain and efficiency ensure reliable long-distance communication with low power consumption, which is crucial for low Earth orbit operations. Circular polarization (CP) maintains signal integrity despite orientation changes, and MIMO capability supports high data rates for applications such as Earth observations or inter-satellite links. The beamsteering feature allows for dynamic tracking of ground stations or satellites, enhancing mission flexibility and reducing interference. This lightweight, efficient antenna addresses modern CubeSat challenges, providing a robust solution for advanced space communication systems with significant potential to enhance satellite connectivity and data transmission in complex space environments.  
CubeSat, LWA; metasurface; multi-band MIMO antenna; beamsteering; LEO
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 2 Design Space  
This study organizes the design space into two components: geometric design space and flow conditions design space.  
* The geometric design space defines the shape of the wing, including planform parameters and airfoil shapes, which dictate the aerodynamic performance of UAVs.
* The flow conditions design space considers environmental and operational parameters such as velocity and altitude, which influence aerodynamic performance under different mission scenarios.  
Specifically, the wing geometries and flow conditions incorporated into the framework are derived from a diverse set of UAVs and their respective operational conditions. The design space includes UAVs ranging from the Micro class (<2 kg MTOW) to MALE and HALE-Strike categories (>600 kg MTOW), as classified by NATO and detailed in [8]. Table 1 summarizes these UAV categories along with their associated operating conditions.  
The following sections elaborate on the geometric and flow conditions design space, detailing how they are parametrized and integrated into the framework to accommodate this wide range of UAV designs. Although this framework also covers conventional planforms, particular attention is given to tailless and Blended Wing Body (BWB) configurations, which, despite not being universally optimal for all missions, remain an important research focus in modern aviation. This is evidenced by multiple global initiatives--such as NASA's X-48 and X-56 prototypes [23; 24]--and the recent EU-funded EXAELIA project [25], whose flight demonstrators prominently feature BWB architectures. Moreover, BWBs pose complex challenges in simultaneously achieving lift generation and flight stability, making them valuable for testing and refining the framework's capabilities. Thus, they serve as exemplary case studies even if they are not invariably the most efficient choice for every UAV application.
# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
###### Abstract  
Retinex theory represents the human visual system by showing the relative reflectance of an object under various illumination conditions. A feature of this human visual system is color constancy, and the Retinex theory is designed in consideration of this feature. The Retinex algorithms have been popularly used to effectively decompose the illumination and reflectance of an object. The main aim of this paper is to study image enhancement using convolution sparse coding and sparse representations of the reflectance component in the Retinex model over a learned dictionary. To realize this, we use the convolutional sparse coding model to represent the reflectance component in detail. In addition, we propose that the reflectance component can be reconstructed using a trained general dictionary by using convolutional sparse coding from a large dataset. We use singular value decomposition in limited memory to construct a best reflectance dictionary. This allows the reflectance component to provide improved visual quality over conventional methods, as shown in the experimental results. Consequently, we can reduce the difference in perception between humans and machines through the proposed Retinex-based image enhancement.  
Retinex; image enhancement; convolutional sparse coding; human visual system +
Footnote †: journal: Applied Sciences
###### Abstract  
This paper presents a power line interference (PLI) reduction technique with a current-reused current-feedback instrumentation amplifier (CFIA) for electrocardiogram (ECG) recording. In a portable two-electrode ECG monitoring application, the presence of undesired PLI may severely corrupt the quality of ECG recording. Since PLI can be over a few volts, the input signal including the ECG signal can exceed the supply or ground level by an electrostatic discharge (ESD) diode in input/output (I/O) pad. To prevent this problem, this paper presents a continuous-time input common-mode current feedback loop that can limit displacement current from a capacitive coupling between the human body and a power line. The continuous-time input common-mode current feedback loop can clamp an input common-mode voltage to the saturation region of the input transistor of the current-reused CFIA. After the clamping procedure, the clamped input signal is amplified by the current-reused CFIA. The proposed circuit was designed using a 0.18-\(\upmu\)m bipolar-complementary metal semiconductor-double-diffused metal oxide semiconductor (BCDMOS) process with an active area of 1.8 mm\({}^{2}\). The total power consumption is 18 \(\upmu\)W with 1.8 V. The input-referred noise and noise efficiency factor (NEF) of the current-reused CFIA is 2.68 \(\upmu\)V\({}_{\text{RMS}}\) and 4.28 with 107 Hz, respectively.  
ECG monitoring; power line interference; continuous-time input common-mode current feedback loop; current-reused current-feedback instrumentation amplifier +
Footnote †: journal: Applied Sciences
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 3 Results of the Review
### Target M2M Communication in 6G Networks
\begin{table}
\begin{tabular}{c c c} \hline
**Generation** & **Network Services** & **Security Solutions** \\ \hline
1G & Voice services & Unencrypted telephone services \\ \hline
2G & Voice services and short messages & One-way authentication, unauthorized access \\ \hline
3G & High-speed Internet, web browsing & Internet protocol (IP) privacy, wireless interface threats \\ \hline
4G & Improved spectrum efficiency, reduced latency & Media access control (MAC) layer attacks, threats from new devices \\ \hline
5G & High-speed Internet, more secure systems & Non-terrestrial networks (NTN), software-defined networks (SDN), cloud threats \\ \hline
6G & Ultra-low latency, variety of applications, extremely reliable and low-latency communication (ERLLC), Internet of Everything (IoE) & AI/ML threats, system attacks \\ \hline \end{tabular}
\end{table} 
Table 4: The security evolution of mobile communications from 1G to 6G [4]. Figure 2: Non-terrestrial networks [7]. It has also been necessary to develop 6G-oriented waveguide and microstrip linear filters operating in the frequency range from 2 GHz to 65 GHz with increased resistance  
Figure 4: Challenges in NTN-based IoRT systems [7]. Figure 5: Planned 3 GPP standard releases on NTN [7]. Figure 3: Generic IoRT system model [7]. to design imperfections [8]. IRS controls the propagation of electromagnetic waves by changing their properties [9].
# Progress in Construction Robot Path-Planning Algorithms: Review
## 4 Local Path-Planning Algorithms
### Deep Reinforcement Learning Algorithm
#### 4.4.2 Monte Carlo Methods  
The birth of the Monte Carlo method is closely related to the Second World War. In the early 1940s, while working at the Los Alamos National Laboratory, [PERSON] and [PERSON] began to study how to make decisions under uncertain conditions. [PERSON]'s initial inspiration came from gambling, and he proposed an idea to solve complex problems using random sampling, thus naming the method \"Monte Carlo\" in honor of the famous casino in Monaco. The core idea of the Monte Carlo method is to use random number generators to estimate a mathematical function or simulate the behavior of complex systems through a large number of random samples. [PERSON] proposed a new algorithm based on the Monte Carlo Tree Search (MCTS) algorithm for robot path planning, which increases the probability of the integrity of finding the best path and the convergence speed in complex environments [97]. [PERSON] proposed an improved Ant Colony Optimization (MC-IACO) based on Monte Carlo to address the issue of slow convergence at the initial stage of robots [98]. [PERSON] introduced a novel approach that extends the principles of Partially Observable Monte Carlo Planning (POMCP) to address the challenge of regulating robot speed in environments reminiscent of industrial settings. These environments often present uncertain motion difficulties that can complicate the operational efficiency of robotic systems. By employing this method, it becomes possible to better navigate the complexities associated with varying levels of uncertainty in motion, ultimately leading to enhanced performance and reliability for robots functioning in such contexts [99]. A novel approach known as the sliding MCTS method was introduced by [PERSON] that builds upon the principles of the Monte Carlo Tree Search (MCTS) algorithm. In this innovative framework, [PERSON] adeptly achieved an effective equilibrium between the optimization and search functionalities inherent in the algorithm. This was accomplished through the implementation of a dynamic moving root node, which allows for more adaptable search strategies. Additionally, the method incorporates a mechanism for regulating the sampling duration, thereby enhancing the overall efficiency and effectiveness of the search process [100]. [PERSON] deployed a particle filter using the Monte Carlo localization method to resolve a Hidden Markov Process based on Recursive Bayesian Estimation using information entropy to identify outliers and probabilistic methods to remove detected outliers. Additionally, a new mutation process was added to the localization algorithm to actively detect high-likelihood areas using the posterior probability density function. The improved Monte Carlo localization algorithm was applied to mobile robots, and experiments demonstrated an improvement in the accuracy of path planning [101].
## 6 Proposed Method
### System Model
#### 6.1.1 Energy Model  
In our model two types of power loss is used which are free space power loss (d\({}^{2}\)) and multipath fading power loss (d\({}^{4}\)) and according to the transmission distance between the source and the sink the channel model is chosen. In case if this distance is less than its threshold value (d\({}_{\text{th}}\)), then it employs the free space model as a channel model or else the multipath fading model is chosen as a channel model. The energy utilization of the data based on the distance factor is mathematically given as follows [55]:  
\[E_{TX}(l,d)=\left\{\begin{array}{l}l\times E_{energy}+\ l\times E_{tm}\times d ^{2},\ if\ d\leq d_{th}\\ l\times E_{energy}+l\times E_{am}\times d^{4},\ if\ d>d_{th}\end{array}\right. \tag{10}\]  
where E\({}_{\text{energy}}\) is a total dissipated energy of the circuit per bit, E\({}_{\text{tm}}\) and E\({}_{\text{am}}\) are the transmitter and amplifier model of the network, and d\({}_{\text{th}}\) is the threshold distance of the network and it is given as below in (8)  
\[d_{th}=\ \sqrt{\frac{E_{tm}}{E_{am}}} \tag{11}\]  
The energy utilization of the sink is given in (9).  
\[\text{E}_{\text{RX}}\left(l\right)=l\ ^{*}\ \text{E}_{\text{energy}} \tag{12}\]
## Materials and Methods
### Diffusion of Sodium Fluorescein across the Hydrogels  
To investigate the physical barrier function of the hydrogel samples, diffusion studies were performed through a mixed cellulose ester membrane (pore size of 0.2 \(\upmu\)m, diffusion area of 1.76 cm\({}^{2}\), Whatman, GE Healthcare, Chalfont St Giles, United Kingdom) in Franz diffusion cells (Gauer Glas, Puttligen, Germany). The acceptor chamber was filled with 12 mL of PBS. After 1 h of equilibration of the membrane in phosphate-buffered saline, it was placed in the acceptor chamber and the donor chamber was attached. Then 300 \(\upmu\)L of a hydrogel sample (prepared in artificial saliva) was applied to the membrane, which resulted in a layer thickness of 0.17 cm. The control diffusion cell was filled with artificial saliva. A 0.01% (w/v) sodium fluorescein solution in phosphate-buffered saline was used to visualize the diffusion through the sample. For this purpose, 200 \(\upmu\)L of the sodium fluorescein solution (20 \(\upmu\)g) was placed onto the sample. Throughout the experiment, the temperature of the diffusion cells was kept at 33 \({}^{\circ}\)C and the PBS in the acceptor chamber was stirred with a magnetic stirrer at 500 rpm. Samples of the acceptor medium were taken at certain points in time (20 min, 40 min, 1 h, 1.5 h, 2 h, 2.5 h, 3 h, 4 h, 5 h, and 6 h) to determine the amount of sodium fluorescein present.  
Figure 1: Overview of the different plaque assays performed in this work.  
Each time, the withdrawn acceptor medium was replaced with fresh phosphate-buffered saline. It was determined in triplicate for each sample.  
To quantify the sodium fluorescein, the fluorescence signal was measured using a Synergy(tm) HTX Multi-Mode Microplate Reader with the software Gen5(tm) 2.07 (BioTek Instruments, Winooski, VT, USA), (absorbance: 485/20 nm, emission: 528/20 nm, gain: 35). The concentration of sodium fluorescein was then calculated with four-parameter logistic regression using the software MyAssays Desktop Version R3.2 1.0.995.570 (MyAssays, Brighton, UK).