esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Anttimicrobial Activity
#### 3.4.2 Grapefruit Polyphenol-Rich Extracts (GF-CE and GF-PE)  
In addition to GF-EOs, extracts rich in phenolic compounds, another group of secondary metabolites in plants, are important BCs due to their bioactive properties. They demonstrate remarkable effectiveness as inhibitors against numerous foodborne pathogenic and spoilage bacteria. Moreover, they are excellent natural alternatives (2019). The results of the antimicrobial activity measured by the disc diffusion method of the polyphenol-rich extracts (GF-CE and GF-PE) are shown in Figure 5. The results show that the GF peel extract has a good activity against both Gm\(+\) and Gm\(-\) and is also active against _Candida albicans_. However, the GF-PE shows a better activity than the GF-CE against _P. aeruginosa_ (7 mm) and _L. monocytogenes_ (7 mm). Although extracts rich in phenolic compounds can effectively inhibit pathogens, they are less potent than EOs.  
Figure 4: Antimicrobial activities of the GF-EO and the standards (amoxicillin and miconazole) determined by the disc diffusion assay. Values represent the mean \(\pm\) standard deviation. Different letters mean significant differences between the GF-EO and the standard (\(p<0.05\)).  
To the best of our knowledge, there is limited literature available on the antimicrobial activity of GF peel extracts rich in polyphenols. Generally, GF polyphenol-rich extracts are obtained from seeds. A recent study from [PERSON] et al. [98] demonstrated that the antibacterial activity of a hydroethanolic extract (80%, \(v/v\)) was dose-dependent, and a higher inhibition diameter was observed for the Gm+ bacteria \(S\). _aureus_ (13 mm) and the Gm\(-\) bacteria \(E\). _coli_ (11 mm). Overall, the results are in line with those observed in our study. To the best of our knowledge, the antifungal activity of the polyphenol-rich extracts obtained from GF peels against _Candida albicans_ is reported for the first time in this study. However, a study from [PERSON] et al. [99] showed that a treatment with mixtures of polyphenol-rich extracts and EO from GF peels (25:75%) for 8 h had an effective antifungal activity against \(C\). _albicans_. Therefore, EOs are generally considered to be particularly effective in this context. The research indicates that phenolic compounds, such as rutin, quercetin, and naringenin, interact with bacterial cell membranes, increasing permeability, reducing ATP production, binding to metabolic enzymes, and disrupting membrane integrity, ultimately resulting in bacterial cell membrane destruction [100]. Additionally, flavonoids, a large group of phenolic compounds found in GF, demonstrate the ability to inhibit bacterial metabolism and the synthesis of DNA and RNA in bacteria [101; 102].
## 7 Results of Experiments
### Effectiveness of Individual Model  
The classification effectiveness for individual tests is shown collectively in Table 3. The testing data are normalized by subtracting the mean of the training set and dividing by the standard deviation of the training set for each feature. The results at this stage of the research show the best recognition obtained using different the types of kernel in the SVM method and different distances in the k-nn method (described in Section 6). Almost all classification systems produced results which are significant, but the phonation and articulation system seems to be the best choice. The best recognition result (accuracy 87.8%) is achieved when we use a system based on the speech articulation model with 1 nn classification. However, the highest sensitivity result is achieved by the speech phonation model. Using the SVM classifier, the system achieved 100% recognition. In order to obtain the parameters that are of the most significance, task feature selection is used.
## 2 Methods  
### Experiment and Data  
The method was evaluated using a reference data set, previously acquired for the purpose of assessing new methods. Data collection was performed using a consumer smartwatch (Microsoft Band 2, Microsoft Corporation, Redmond, Washington, USA), a smartphone (Samsung Galaxy S7, Samsung, Schwalbach, Germany) and a custom app. The inertial measurement unit of the smartwatch was used to measure its acceleration, given in \(g\) unit and sampled at 30 Hz. The purpose of the app was to collect the data from the smartwatch via Bluetooth transfer and to store them locally. Thirteen healthy and voluntary subjects were recruited to participate in the experiment. The four females and nine males (aged between 24 and 33) were asked to wear the smartwatch and to perform specific activities: rest on a chair, work at a desk, walk and run (both in a park). Each activity was asked to last for approximately 20 min. To help with later labelling of the signals, they were asked to perform a succession of 3 brief strikes with their wrist. The produced peaks were then selected as reference boundaries for each activity segment.
# Design Studies of Re-Entrant Square Cavities
## 2 Design and Operation of RRSC and RF Section
### Design of R.F. Section
#### 2.2.1 Distance between Adjacent Cavities (\(d\))  
It depends on the DC electron velocity \(v_{o}\). This can be obtained by multiplying the number of electron transit cycles in the drift space (\(N\)) with the transmitted beam wavelength (\(\lambda_{e}\)) given by  
\[d=N\lambda_{e}, \tag{2}\]  
where  
\[\lambda_{e}=\frac{2\pi}{\beta_{e}}=\frac{v_{o}}{f}=\frac{\sqrt{2\eta V_{o}}}{f}. \tag{3}\]  
Here, \(f\) is the operation frequency, \(\beta_{e}\) is the beam propagation constant, \(\eta\) is the electron charge to mass ratio, and \(V_{o}\) is the beam voltage. Equation (2) can be simplified as  
\[d=N\left(\frac{\sqrt{2\eta V_{o}}}{f}\right), \tag{4}\]  
and therefore  
\[d=N\left(5.93\times 10^{5}\sqrt{V_{o}}\right)/f. \tag{5}\]  
Here, \(f\) is in Hz, \(V_{o}\) is in volts and \(d\) is in meters. It is obvious that the distance between adjacent cavities (\(d\)) is directly proportional to input electron beam voltage (\(V_{o}\)).
## 3 D2 WaVe: Design Rationale and System Architecture
### System Settings and Adversary Model  
The proposed D2 WaVe framework assumes permissioned network environments where trustworthy oracles maintain the registration of all participants. Each node (e.g., vehicle, camera, edge server, and user) uses its public key and privacy key for mutual authentication among peers. In addition, video data generated from source devices (e.g., vehicle camera or networked camera) are sent to edge nodes (RSU or edge server), which act as trust nodes to perform digital watermarking functions and publish data.  
Regarding the system model, we assume (i) a secure system environment for video data generation at the source side and processing at the edge side and (ii) security communication channels between the source and edge. Thus, an adversary cannot tamper with and modify video frames and watermarks by compromising an in-vehicle network, a networked camera, and their associated edge computing platforms. Moreover, we assume an adversary cannot break cryptographic hardness, such as computing conflict hash values and compromising digital signature and encryption schemes. We assume the attackers aim to manipulate the video streams in storage and transmission. Here are several possible security attacks on video data in the IoV network.  
_Forgery attacks:_ An adversary can use powerful image editing tools and software to modify original video frames and replace tampered data with real data. Due to advancements in generative AI technology, attacks can leverage deepfake tools to create fake video recording content to disturb normal surveillance system functions [36]. The genuineness of video data is very important in vehicle forensics and accident investigation. Forgery attacks change the meaning of evidence (video frames) without leaving any detectable clues, making it challenging to verify the authenticity of video data generated in IoV networks during post-investigation.  
_Integrity attacks:_ malicious participants in the IoV network can tamper the content of data stored by service providers or modify data transmitted between source and destination.  
For example, attackers can insert fake frames, replace them with tampered frames, and even delete frames in original video recordings generated by their owners. Therefore, an adversary not only destroys the integrity of each frame but also breaks the time-sequence properties of video recordings that follow a continuous process.  
_Availability attacks:_ An adversary can launch Denial of Service (DoS) attacks by sending fake requests to nodes that provide IoV data service. As a result, these service nodes cannot allocate enough resources to serve genuine requests from honest users. In Distributed Denial of Service (DDoS) attacks, an adversary can use bot (zombie) networks to flood network traffic and paralyze a target cloud server. Thus, the participants will not obtain the required data to support ITS functions.
# Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations
## 4 Experimental Data  
ABA data acquired during a measurement campaign in Austria were analyzed to investigate the performance of the cepstrum algorithm at normal operating conditions.
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 2 Algorithm
### Reprocessing  
In order to obtain smooth heart rate results, the reprocessing of the estimated heart rate consists of two main parts: removal of abnormal values and smoothing. Sometimes, the motion artifacts are too large and cause abrupt changes in the heart rate values. A small sliding window of length 9 is first used to traverse all the heart rate estimates, starting from the sixth window, each of which contains five historical values, one current value, and three future values. By calculating the variance HR_std for each window, if the absolute value of the difference between the current value and the mean heart rate value HR_mean for this sliding window is greater compared to HR_std, then the estimate at this point can be determined to be an abnormal value, and HR_mean is used to replace the current estimate. Finally, one-dimensional convolutional smoothing with a window size of 3 is performed on all heart rates.
## 2 Review of Phase-Locked Loops
### Discrete Model
#### 2.2.1 Hold Equivalent  
The hold equivalence method is to design a discrete system with an input consisting of samples of input signals, which has an output that approximates the output of the continuous-time transfer function whose input is the input signals in the continuous-time domain. This design is accomplished by acquiring a sequence of samples and extrapolating or holding them to produce a continuous signal. Depending on the order of the holding approximation, there are several variations in this method, such as zero-order holding and first-order holding. However, this method is only used to model a continuous system, because it relies on the time response of the system being unable to faithfully reproduce the frequency response, whereas filter design of the loop filter used in this work is based on their frequency response characteristics.  
Figure 2: Linearized digital PLL (DPLL) model in the discrete-time domain (assuming unity gain; \(AK=1\)).
# Performance Analysis of Pile Group Installation in Saturated Clay
Therefore, the installation of pile groups not only induces deformation and internal forces in adjacent piles but also affects their bearing performance. This study utilized the FLAC\({}^{\text{3D}}\) (version, 7.0) finite difference program to simulate the entire process of jacked pile installation. Subsequently, the radial displacement and EPP induced by single pile installation, as well as soil consolidation after pile installation, were analyzed and validated. Based on these analyses, a separated auxiliary tube method was proposed to simulate the installation of pile groups. Additionally, the effects of pile center distance (\(S\)) and length-to-diameter ratio (\(L/D_{\text{e}}\)) on the displacement, internal forces, and vertical bearing capacity of adjacent piles were investigated.
###### Abstract
As presented by [PERSON] et al., in [20], a 3 dB axial ratio bandwidth of 5.5% was attained by using two annular vias (holes) in a rectangular DR excited by a rectangular slot coupled microstrip line feed. As reported by [PERSON] et al., in [21], the CP bandwidth of 2.29% is achieved using rectangular DR excited by a triangular ring-shaped aperture with a parasitic patch. A cross-slot coupled with a microstrip line to attain CP bandwidth of 2.2% using rectangular-shaped DR was reported in [22]. In all discussed literature a sufficient bandwidth of impedance matching was achieved to cover the attained AR bandwidth. However, it is clarified that the generation of circular polarization using a single feeding mechanism with simple design geometry is quite challenging and a solution to this problem is reported in this article. In this research paper, a new feeding mechanism, using a uniquely shaped conformal metal strip, is introduced to excite the rectangular DRA for CP response. The proposed feeding technique excites the degenerate higher-order mode pair to generate the circularly polarized wave. A prototype was built to study the experimental results. Antenna parameters, such as impedance matching, axial ratio, beamwidth, and gain, were studied theoretically and experimentally. A reasonable similarity was observed in all antenna parameters. The proposed antenna could be used for 5G Sub-6 GHz communications around 4.4-4.8 GHz, such as n79 [2]. In Section 2, antenna design geometry is explained. In Section 3, antenna design, optimization, and CP generation mechanism are discussed in detail. In Section 4 comparison between experiment and theory is demonstrated and explained. Section 5 features the conclusion to the article.
# Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route
## 2 Model and Problem Definitions  
### Problem Definition  
Given a driver \(d\), supposing the passenger capacity of \(d\) is \(CAP_{d}\) and the current number of onboard riders is \(OBR_{d}\), we define that \(d\) could be at one of three possible situations: idle, full, and not-full, which represent \(OBR_{d}=0\), \(OBR_{d}=CAP_{d}\), and \(0<OBR_{d}<CAP_{d}\), respectively.  
The problem of our concern is, for the driver \(d\) and the riders set \(\mathcal{R}\), to find the best match between the driver and \(v\) riders along with the optimal route to pick up and drop off all riders while protecting users' privacy, in which \(v\leqslant CAP_{d}-OBR_{d}\). The additional travel time (\(ATT\)) of the matching result should be minimal, and the schedule will miss no deadlines of the riders.
## 5 Research Progress on Key Technologies of IPPCS
### Research Progress on Manufacturing Technology of Pressurized Capsule Structure  
#### 5.4.1 Analysis of the Spinning Process  
Spinning forming is an advanced plastic forming process with less or no cutting (process principle is shown in Figure 16). This process has been widely used in aerospace and aviation fields because of its advantages of good metal deformation conditions, high material utilization rate and improving product performance [46,47].  
The majority of large-sized spin-formed components are thin-walled parts with thicknesses ranging from 10 mm to 20 mm. The contour dimensions are predominantly in the range of 1000 mm to 2000 mm in diameter. However, there is a lack of literature discussing the spin forming of large-sized (diameter exceeding 3000 mm) and ultra-thick (thickness greater than 50 mm) aluminum alloy components.
# Review of sEMG for Robot Control: Techniques and Applications
###### Abstract  
Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human-machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.  
sEMG; signal processing; pattern recognition; robot control; rehabilitation +
Footnote †: journal: Applied Sciences
# An Energy Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks
###### Abstract  
Wireless NanoSensor Networks (WNSNs) are a new type of network that combines nanotechnology and sensor networks. Because WNSNs have great application prospects in intra-body health monitoring, biomedicine and damage detection, intra-body Wireless NanoSensor Networks (iWNSNs) have become a new research hotspot. An energy balance clustering routing protocol (EBCR) is proposed for the intra-body nanosensor nodes with low computing and processing capabilities, short communication range and limited energy storage. The protocol reduces the communication load of nano-nodes by adopting a new hierarchical clustering method. The nano-nodes in the cluster can transmit data directly to the cluster head nodes by one-hop, and the cluster head nodes can transmit data to the nano control node by multi-hop routing among themselves. Furthermore, there is a tradeoff between distance and channel capacity when choosing the next hop node in order to reduce energy consumption while ensuring successful data packet transmission. The simulation results show that the protocol has great advantages in balancing energy consumption, prolonging network lifetime and ensuring data packet transmission success rate. It can be seen that EBCR protocol can be used as an effective routing scheme for iWNSNs.  
intra-body wireless nanosensor networks; terahertz communication; clustering; routing protocol +
Footnote †: journal: Sensors
## Appendix B Case Study DBN Formation
### Overview  
Figure 11 is the illustration of the DBN model nodes and directed relationships within their respective information regions. This network structure was determined by a general understanding of the nature of the scramming mechanism and the two described failure modes in the study by [PERSON], et al. [24]. Table 11 lists the nodes constructed in the model, the information region they are located in, and the number and the value of node states.  
In addition to a network structure of nodes and directed arcs, a DBN model requires associated conditional probability tables, as well as an additional initial distribution table for the dynamically changing nodes. Therefore, the following is the list of CPT tables needed for the model designed for the SFR scenario:  
* **Static Conditional Probability Tables**
* 1. \(P(Scram\ State)\);
* 2. \(P(Power-to-Flow|Scram\ State)\);
* 3. \(P(Pressure|Scram\ State)\);
* 4. \(P(Inlet\ Temperature|Scram\ State)\);
* 5. \(P(Outlet\ Temperature|Scram\ State)\);
* 6. \(P(Failure:Clad\ Melting[Scram\ State,Power-to-Flow)\);
* 7. \(P(Failure:Fuel\ Relocation|ScramState)\);
* 8. \(P(System\ Diagnostic|Failure:Clad\ Melting,Failure:Fuel\ Relocation)\).  
* **Dynamic Conditional Probability Tables**
* 1. \(P(Power-to-Flow|ScramState,Power-to-Flow(t-1))\);
* 2. \(P(Pressure|Scram\ State,Pressure(t-1))\);
* 3. \(P(Pressure|Scram\ State,Pressure(t-1))\);
* 4. \(P(Pressure|Scram\ State,Pressure(t-1))\);
* 5. \(P(Pressure|Scram\ State,Pressure(t-1))\);  
Figure 11: DBN node structure and relationship graph for the SFR TOP case study. The dashed boxes represent the different node regions for the diagnostics and prognostics model for CES. The arrows are reflective on the current time step, with the exception of the dynamic relationship in the nodes in the “System Information/Sensor Data” box.  
\(P(\mathit{Inlet\ Temperature}|\mathit{Scram\ State},\mathit{Inlet\ Temperature}(t-1))\);
4. \(P(\mathit{Outlet\ Temperature}|\mathit{Scram\ State},\mathit{Outlet\ Temperature}(t-1))\).  
To quantify these tables, either prior expert knowledge or operational data are required. For this case study, a hybrid approach was used to complete the CPTs. Expert-based opinions were determined from either source documents (i.e., the preliminary safety information document (PSID)) [34], or were mentioned in the study by [PERSON] et al. [24]. The operational data used were received from Sandia National Laboratories.
# Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength
## 2 Methodology  
As Figure 1 illustrates, the implementation of this study comprises three major steps:  
1. As is known, providing a proper dataset is an essential task in the utilization of computational intelligence tools. Hence, data provision and preprocessing is the first stage. This process is broadly explained in the following section.
2. After determining the appropriate structure of the basic model (i.e., the multi-layer perceptron (MLP) neural network), the optimization algorithms of DA, WOA, and IWO are synthesized with it to design the DA-ANN, WOA-ANN, and IWO-ANN hybrid ensembles. Next, an extensive sensitivity analysis is applied to the ensembles in order to find the best-fitted structure of them.  
Lastly, the results are evaluated using three well-known accuracy criteria, namely root mean square error (\(RMSE\)), coefficient of determination (R\({}^{2}\)), and mean absolute error (\(MAE\)). The formulation of these indices is expressed by the following equations:  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{\left[{\left({Y_{i_{observed}}-Y_{i_{ predicted}}}\right)}\right]}^{2}}} \tag{1}\]  
\[MAE=\frac{1}{N}{\sum\limits_{I=1}^{N}{\left|{Y_{i_{observed}}-Y_{i_{predicted}}} \right|}} \tag{2}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{N}{\left({Y_{i_{predicted}}-Y_{i_{observed}}} \right)^{2}}}{\sum\limits_{i=1}^{N}{\left({Y_{i_{observed}}-\overline{Y}_{ observed}}\right)^{2}}} \tag{3}\]  
where \(Y_{i\_{predicted}}\), \(Y_{i\_{observed}}\), and \(\overline{Y}_{observed}\) symbolize the predicted, observed SSSs, and the average of the observed SSSs, respectively. Also, \(N\) denotes the number of data.  
The considered intelligent model, as well as hybrid optimization techniques, are described in the following.  
Figure 1: The steps taken to predict the shear strength in this study.