esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 4 Methodology  
### Data Acquisition and Processing  
The data set acquired from each volunteer is divided into 4 measurement sets for each of the \(i=\{1,2,3,4\}\) arm poses: resting position (\(i=1\)), extended position (\(i=2\)), free motion (\(i=3\)) and motion with object (\(i=4\)). Every measurement set consists of 4 acquired subsets \(j=\{1,T,M,F\}\), one for every accelerometer positioned on the volunteer index (\(j=I\)), thumb (\(j=T\)), metacarpal (\(j=M\)) and forearm (\(j=F\)), as summarized in Table 4 for each arm pose \(i\). Each subset contains 3 acceleration signals, one for each axis \(k=\{x,y,z\}\) as defined in Figure 1. Therefore, the data set acquired from each volunteer consists of a total 48 acquired acceleration signals defined as \(a_{i,j,k}\) for each measurement set \(i\), accelerometer position \(j\) and accelerometer axis \(k\).  
All measurements acquisitions were performed with respect to the Nyquist criterion to avoid undersampling. The maximum sampling rate achieved for each accelerometer was 62.5 Hz, enough to safely measure the maximum tremor frequencies of PT and ET. Each measurement consisted of approximately 1800 samples, taking into account the duration of each recording. The data were then post-processed, while for each accelerometer and measuring position the frequency spectrum was calculated for extracting the dominant tremor frequency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position** & **Index** & **Thumb** & **Metacarpal** & **Forearm** \\ \hline Rest & \(I_{1}\) & \(T_{1}\) & \(M_{1}\) & \(F_{1}\) \\ Postural & \(I_{2}\) & \(T_{2}\) & \(M_{2}\) & \(F_{2}\) \\ Free motion & \(I_{3}\) & \(T_{3}\) & \(M_{3}\) & \(F_{3}\) \\ Motion with object & \(I_{4}\) & \(T_{4}\) & \(M_{4}\) & \(F_{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement points for selected arm poses.  
Figure 3: The selected arm poses: (**a**) resting position, (**b**) extended position, (**c**) free motion, (**d**) motion while holding an object.
## 3 RUL Prediction Methodology
### The Envelope Spectral Indicator  
Accurate and reliable health indicators play a vital role in uncovering the health state of rolling bearings for reliable life prediction. Since creating a direct indicator specifically for assessing the bearing life may be challenging, commonly used HIs rely on physical or statistical quantities to evaluate the machine condition, such as the impulsiveness measure and the spectral energy of vibration signals. However, given the intricate nature of rotor-bearing systems, traditional HIs may contain extraneous components that are unrelated to bearing faults, which could compromise their reliability in RUL prediction. For example, synchronous speed vibration with rotor imbalance and misalignment due to manufacturing and assembly errors introduces extra components to the bearing vibration. Furthermore,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Number of Balls** & **Inner Diameter** & **Outer Diameter** & **Contact Angle** \\ \hline
**Dimension** & \(8\) & 29.30 mm & 39.80 mm & \(0^{\circ}\) \\ \hline
**FCF** & **FTF (Hz)** & **BSF (Hz)** & **BPFO (Hz)** & **BPFI (Hz)** \\ \hline
**Condition 1** & 13.49 & 72.33 & 107.91 & 172.09 \\ \hline
**Condition 2** & 14.45 & 77.50 & 115.62 & 184.38 \\ \hline
**Condition 3** & 15.42 & 82.66 & 123.32 & 196.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of test bearings and FCFs.  
Figure 2: A typical example of the envelope spectrum with multiple bearing FCFs.  
as the bearing deteriorates, the vibration response can be more complex when compound defects develop on the connected structure.  
In this context, in order to ensure that the chosen HI accurately represents the deteriorating condition of rolling bearings, the envelope spectral indicator (ESI) is developed based on the harmonics of the bearing FCFs in the average envelope spectrum. As a result, the rotor dynamics are eliminated, leaving only the components related to bearing faults. Consequently, the subsequent prediction of RUL may become more realistic since the presence of gear or rotor faults is not taken into account. Specifically, as a discrete time stochastic process, the ESI at time step \(t\) is given by  
\[ESI(t)=\sum_{m=1}^{N}\Bigl{[}S_{e}^{(t)}(nf_{FTF})+S_{e}^{(t)}(nf_{BSF})+S_{e} ^{(t)}(nf_{BPFO})+S_{e}^{(t)}(nf_{BPFI})\Bigr{]}, \tag{4}\]  
where \(n\) represents the order of the FCF harmonics and \(S_{e}^{(t)}(f)\) denotes the acceleration amplitude of the average envelope spectrum of time \(t\) at the specific frequency location. Typically, the diagnostic informative components of the envelope spectrum concentrate within the low-frequency range. Also, it is observable (from Table 2) that for the analyzed test bearings, the third multiple of BSF is close to the second harmonic of BPFO. In this work, to ensure the reliability of the bearing health indicator, the FCF harmonics are utilized up to the third multiple, i.e., \(N=3\).
# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 3 Energy-Throughput Efficient Cross-Layer Solution Using MPNMRA for WUSNs
### Statistical QoS Guarantees  
In MI-USNs, ensuring statistical QoS is essential for reliable end-to-end data traffic [24]. The analysis of statistical QoS parameters helps to assess the performance and reliability of communication in underground sensing applications, with a specific emphasis on minimizing packet loss [20].  
Packet loss can significantly impact the reliability of transmissions in MI-USNs. Maintaining a high level of reliability is crucial for various underground sensing applications, as it ensures that the transmitted data reach the intended destination without loss or corruption. In addition to reliability, bounded delay is another important aspect of QoS in MI-USNs. Timing constraints and real-time monitoring scenarios require the transmission of data within specific time limits to ensure timely and accurate decision-making. By keeping the packet delay within acceptable bounds, the system can meet the application's timing requirements.  
To evaluate statistical QoS in MI-USNs, the communications link between nodes \(i\) and \(j\) is considered. Parameters such as packet delay and link reliability are analyzed to assess the distributed functionality between sensors. This analysis helps to determine the effectiveness of the communication link and identify any potential bottlenecks or areas for improvement. By focusing on statistical QoS parameters, MI-USNs can enhance the reliability and timeliness of data transmissions, ensuring that the system meets the requirements of underground sensing applications.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Authentication and Key Agreement
#### 3.2.4 Biometrics  
For WBAN, biometric authentication introduces a novel dimension of security by ensuring the alignment of the right user with the correct device [24]. This method employs unique biological, physiological, or behavioral characteristics like fingerprints, voice patterns, retinal scans, or more advanced technologies like the use of gait, ECG, or EEG [25]. It transcends the limitations of traditional device authentication, which focuses solely on the device's trustworthiness to a more holistic view. The integration of biometric authentication is particularly vital in healthcare applications, where the security and accuracy of medical data are paramount. Biometrics offer a robust layer of security, as these personal traits are inherently difficult to replicate or forge, compared to conventional passwords or PINs [26]. However, this enhanced security comes with its own set of challenges and privacy concerns. The collection and management of biometric data necessitate stringent protection measures and compliance with privacy regulations to prevent misuse or unauthorized access. Implementing biometric systems in WBANs also involves balancing the complexity and computational demands of these systems against the energy efficiency of wearable devices. Innovations such as lightweight biometric algorithms and energy-efficient data transmission methods are essential to addressing these concerns. As technology evolves, it is expected that biometrics will become increasingly integral to enhancing the security and functionality of WBAN systems, especially in the sensitive domain of healthcare [27].
## 4 Seismic Damage Assessment based on Cloud Model
### Assessment Process  
The traditional seismic analysis for pipeline takes a section of pipeline as the research object and obtains the stress response of the pipeline. The stress value determined by the section corresponds to the yield limit stress of the material as an evaluation of the damage. Finally, the damage of the pipeline is evaluated by comparing the stress values determined by the section with the yield limit stress of the material. However, when a buried pipeline is subjected to earthquake, the degree of damage to the pipeline and its uncertainty cause randomness and fuzziness in the evaluation process. Thus, it is necessary to establish a method for determining the seismic damage to pipelines, which is mainly to solve the evaluation indicators of fuzziness and randomness in seismic damage assessment of pipelines.  
Referring to the classical division of damage to pipelines, the grades of damage can be expressed as:  
\[U=\left[{U_{1},U_{2},U_{3},U_{4},U_{5}}\right] \tag{34}\]  
where \(U_{1}\) represents essential integrity; \(U_{2}\) represents minor damage; \(U_{3}\) represents medium damage; \(U_{4}\) represents severe damage; and \(U_{5}\) represents destruction.  
Figure 10: Normal backward cloud generator.  
According to the three states of failure criteria and interval theory, this paper establishes a new evaluation norm, as shown as Table 1. By taking cloud model into consideration, the result of assessment is a bilateral constraint, \([a,b]\), and the cloud parameter computation equations are given by [22]:  
\[E_{x}=\frac{a+b}{2} \tag{35}\]  
\[E_{n}=\frac{a+b}{6} \tag{36}\]  
\[H_{e}=k \tag{37}\]  
where \(a\) is the lower boundary of the interval; \(b\) is the upper boundary of the interval; and \(k\) is a constant adapting to fuzzy comments.  
To assess the safety of pipelines, the procedure of the assessment method is proposed as shown in Figure 11. First, the response of interval through non-random process can be obtained. Second, the assessment system of pipelines depends on the material of the pipeline being established. Third, the evaluation chart can be established based on cloud model by combining the calculated results with cloud algorithm. Last, the integrated cloud inference results can be shown intuitively by adding the practical response of the pipeline.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
### Paper Outline  
We have reviewed several strategies that pursue new connectivity standards by expanding network coverage, especially for developing countries, compared with developed countries such as European countries. These approaches aim to list the possible technologies that will improve connectivity in rural zones after studying the researched options in the alternative deployment of networks for optimizing those regions.  
After stating the motivations and the principal purposes roughly, we outlined the article as follows: Section 2 presents a perspective of the network environment (outdoor and indoor), highlighting the solutions that engage emergent services such as the Internet of Things (IoT). Section 3 sketches out the possible researched technologies in order to enhance coverage in rural zones and achieve high Quality of Service (QoS) and the network's throughput. Sections 4 and 5 set forth the discussion and conclusions about the assessed solutions in prior sections, in addition to bearing in mind new research opportunities in this field. To this end, Figure 2 shows the overall organization of the cited references in this review article.  
Figure 1: Correlation between population and current mobile network coverage in both study cases.  
Figure 2: Some Used references in our survey.
## 4 Conclusions  
The impact of HPP on the quality of cauliflower was investigated. During storage, pressures at 600 MPa for 2 min resulted in minor changes in texture, color, drip loss, dry matter, and nutritional value compared to the control. Regarding bacterial counts, 600 MPa  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**DPPH Antioxidant Activity (\%,)**} & \multicolumn{2}{c}{**TPC as mg Gallic Acid**} & \multicolumn{2}{c}{**AA (mg/100g DW)**} \\ \hline  & **Control** & **HPP** & **Control** & **HPP** & **Control** & **HPP** \\ \cline{2-7}
**Storage (Days)** & **0.1 MPa** & **600 MPa** & **0.1 MPa** & **600 MPa** & **0.1 MPa** & **600 MPa** \\ \hline  & 0 min & 2 min & 0 min & 2 min & 0 min & 2 min \\ \hline
0 & 72.1 \(\pm\) 2.5 \({}^{\text{A}}\) & 52.4 \(\pm\) 6.9 \({}^{\text{C,b}}\) & 579.6 \(\pm\) 19.4 \({}^{\text{A}}\) & 506.6 \(\pm\) 55.6 \({}^{\text{B, a}}\) & 890.9 \(\pm\) 53.6 \({}^{\text{A, B}}\) & 862.1 \(\pm\) 45.6 \({}^{\text{B, a}}\) \\
14 & nm \({}^{**}\) & 46.5 \(\pm\) 4.6 \({}^{\text{c}}\) & nm \({}^{**}\) & 477.8 \(\pm\) 47.9 \({}^{\text{a}}\) & Nm \({}^{**}\) & 730.7 \(\pm\) 22.2 \({}^{\text{b}}\) \\
28 & nm & 68.3 \(\pm\) 4.3 \({}^{\text{a}}\) & nm & 510.8 \(\pm\) 24.6 \({}^{\text{a}}\) & nm & 790.4 \(\pm\) 95.0 \({}^{\text{a, b}}\) \\ \hline  & & & & & & \\ \end{tabular}
\end{table}
Table 5: DPPH antioxidant activity (%,), total phenolic content (TPC) expressed as mg gallic acid equivalents (GAE)/100 g DW and content of ascorbic acid (AA) (mg/100g DW) of cauliflower measured before (control) and after HPP treatment.  
resulted in better preservation compared to 400 MPa, with moderate bacterial counts until day 28 of storage. From day 28 to day 42, high bacterial counts were observed (up to 7.5 log cfu/g) for 600 MPa. Low bacterial counts for both aerobic and anaerobic spores were detected for all treatments. Since the holding time of pressure treatment did not significantly impact the physical quality, a processing time of 2 min can be advised.  
To obtain better preservation and quality, a combination of HPP with other treatments, such as heat, should be investigated. As heat treatment influences sensory parameters, analysis of taste, texture, and color of the processed cauliflower should be carried out. In addition, it would be interesting to investigate the processed cauliflower after reheating, as it would in an RTE meal.  
The microbial flora also needs to be further investigated, where different target pathogens could be inoculated in the cauliflower. Since nutritional analysis was only performed for the 600 MPa-2 min samples, it would have been of interest to perform nutritional analysis to evaluate and compare with other pressure and time combinations.  
M.A.K.--conceptualization, project administration, supervision, writing, C.F.--investigation, draft preparation, visualization, D.S.--conceptualization, methodology, validation, and T.M.R.--conceptualization, methodology, validation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## Appendix C Phase Aberration Correction (PAC) Methods
### PAC II: Tilt Correction (ACF)  
An autocorrelation function (ACF) analysis was used to correct for phase distortions caused by surfaces inclination. The ACF analysis was performed in the Fourier domain using the Wiener-Khinchine theorem implemented in the _'autocorr2d.m'_ function [51]:  
\[\left|V_{ACF}\right|\ =\ \left|F_{d}{}^{-1}\left(F_{d}(V_{gb})conj\left(F_{d}(V_{gb })\right)\right)\right|, \tag{8}\]  
where \(V_{ACF}\) is the ACF signal, \(\left|V_{ACF}\right|\) the magnitude of \(V_{ACF}\), and \(F_{d}()\) and \(F_{d}{}^{-1}()\) are the discrete Fourier and inverse Fourier transforms, respectively, of the gated backside signals \(V_{gb}\) using Hanning-window. For each receiving channel, the backside echoes were gated after adding the beamforming delay shift, PAC I correction, and before summation (Figure A3b). From all received backside signals, the maximum signal from all received signals was used to define a threshold value for ACF correction. The threshold was defined at 40% of the maximum signal. All backside signals above the threshold were used to fit an ellipsoid on \(\left|V_{ACF}\right|\) using the _'regionprops.m'_ function of the Matlab Image Processing Toolbox (Figure A3c). The inclination angle of the ellipsoid to the major semi-axis \(\alpha_{\mathrm{ACF}}\) (Figure A3c) was used to apply a linear time shift correction \(\Delta t_{ACF}\) to remove the tilt such that \(\Delta t_{\mathrm{ACF}}\) at the channel with the highest backside amplitude was zero. The proper correction of the wavefront tilt was verified by repeating the ACF analysis after PAC II correction (Figure A3d).
## 5 Denial-of-Service Attack on Node to Node Key Agreement Protocol  
Although AVISPA could not find any attack on the models we have developed, by analyzing the node to node key agreement protocol, a potential denial-of-service (DoS) attack might be found. A DoS attack is always considered a threat for any kind of wireless network where jamming a network with a strong signal is always possible. Especially, this might be critical in sensor networks due to its use in life-critical applications [16]. Figure 14 shows a possible DoS attack on the node to node key agreement protocol, where the intruder is masquerading as the role of the node A. Pretending to be in the role of A, the intruder triggers the key agreement protocol and, thus, forces node B and server S to generate and exchange the rest of the three messages. Hence, the intruder may engage the server S and other nodes in the network to generate and exchange numerous messages. This is clearly a threat for a potential DoS attack, where the server is being engaged in useless message generation and exchange. Thus, the server denies legitimate nodes from receiving services that they have requested, _i.e._, key generation and distribution.  
\[I(A)\to B:N_{A},A\] \[B\to S:N_{A},N_{B},A,B,MAC(K^{\prime}_{BS},N_{A}||N_{B}||A||B)\] \[S\to A:\{SK_{AB}\}_{K_{SA}},MAC(K^{\prime}_{SA},N_{A}||B|| \{SK_{AB}\}_{K_{SA}})\] \[S\to B:\{SK_{AB}\}_{K_{SB}},MAC(K^{\prime}_{SB},N_{B}||B|| \{SK_{AB}\}_{K_{SB}})\]  
Figure 14: DoS attack on the node to node key agreement protocol.  
Figure 13: Validation output of OFMC for node to node key agreement with strong freshness.
## 1 Introduction
Machine learning has been employed to predict the properties of mono or hybrid nanofluids [29; 30; 31; 32; 33] as well as the performance of thermal systems with nanofluids, including the Nusselt number, pressure drop, heat transfer coefficient of heat exchangers [34; 35], and the outlet temperature, collector efficiency of solar energy systems [36; 37]. It is worth noting that all these studies of thermal system prediction achieve high accuracy with R\({}^{2}\) > 0.99. Moreover, compared to conventional methods [38; 39; 40], estimation time is largely saved. Recently, several authors have investigated deep convolutional neural networks (CNNs) to predict the physical field of heat transfer systems due to their great capacity for accurate and quick image processing. [PERSON] et al. [41; 42] successively developed two deep CNN-based models to predict heat conduction and then steady-state heat convection around objects. They directly used the signed distance function image to present problem geometry, and set it as the input of the neural network model. The results show that the computational speed is one and two orders faster, respectively, than the numerical simulations, and the accuracies of the temperature field prediction are both higher than 98.75%. Furthermore, the same research group also applied deep CNN to predict the velocity field [43; 44; 45] around airfoils or different objects. When it comes to nanofluid field reconstruction using deep learning, not many articles are available. [PERSON] et al. proposed a GAN network [46] and then a CNN network [47] to predict the global flow field in a two-dimensional nanofluid-filled microchannel; the results also showed the accuracy and efficiency of the proposed model. In this study, we apply deep CNN to build two ROMs to predict the nanofluid heat transfer in a PTC absorber tube with rectangle fins of various heights, widths, and relative positions. The first ROM consists of the convolutional and deconvolutional layers, and the second ROM consists of the convolutional and fully connected layers. We set the SDF-presented cross-sectional geometry of the absorber tube directly into the convolutional neural network, and then the temperature field and outlet heat flux are predicted. The CFD method is used to generate the training, validation and testing dataset, and is validated with experimental data in literature. According to our literature review, this is the first study to estimate the temperature field and heat flux of the outlet cross section in 3D nanofluid-filled finned absorber tubes using a deep convolutional neural network. The rest of the paper presents the detailed method, including the dataset preparation, the network architecture, the operations in neural networks, and the training algorithm. Then, we show the performance of the developed ROMs through the temperature field and outlet heat flux prediction of the absorber tube with random fin heights, widths, and relative positions.
## 4 Finite Element Analysis
### The Result Analysis of Test and Finite Element  
As shown in Figure 16, the hysteresis curve of the finite element analysis is more full with a \"shuttle\" shape and the plastic deformation ability is better than the experimental results, but the overall fit is good. As shown in the figure above, when the beam section begins to yield under cyclic load, the hysteretic curve bends, the area of hysteretic loop increases, and the stiffness of the structure slightly decreases. With the increasing load, the residual deformation of energy-dissipating link continuously increases, the hysteresis loop tends to be full and stable, and the maximum hysteresis loop area is large without pinched, which indicates that the replaceable energy-dissipating link has strong plastic deformation capacity and good energy-dissipating performance.  
Figure 15: Finite element model (**a**); Mesh division diagram (**b**); and, Finite element model constraint diagram (**c**).  
It can be seen from Table 9 that the test results and finite element simulation results have a good degree of fitting, which can achieve mutual verification. Although there surely exists a difference in the bearing capacity, and the maximum difference even reaches 30.44% (this is because the specimen RSL-8 has some defects due to processing). On the other hand, the minimum difference is 1.00%, and when removed the accidental factors in the test, the difference between the test and the finite element bearing capacity is within 10%, which can meet the research requirements.