esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In order to manage the reliability and performance of the non-intrusive continuous sensor, a clustering model algorithm was proposed and implemented using the historical failure mode data from the sensor. The K-mean clustering model algorithm proposed in this paper was validated using the following models: the mini-batch K-means clustering model, the spectral clustering model, the Gaussian mixture clustering model, and the birch clustering model algorithm. The study can therefore conclude that the clustering method proposed (the K-mean clustering model algorithm) is an excellent model.  
Although the research study's goal and objectives were met and successfully presented in the thesis, there are still many suggestions and questions that need to be addressed in future research work, including:  
* What is the algorithm's performance on each of the features and datasets? The algorithm's performance for data other than the data used in this investigation could be drastically different. It would be fascinating to examine how alternative data, such as data from other fault modes in the sensor and the heat exchanger system, compared to those utilized in this study.
* The determination of a faster and more reliable method for estimating the number of clusters used in the implementation of the model algorithms.
* The need to design a self-learning diagnosis system that employs the proposed technique updates the model for each new failure and re-estimates the number of clusters as new data are accumulated. It would be interesting to see if the previous model and earlier estimates of the number of clusters might be used to speed up the calculation and maybe increase performance when updating the model as new data are added.  
Conceptualization, D.O.A.; methodology, D.O.A.; software, D.O.A.; validation, D.O.A., D.E.I. and S.S.; formal analysis, D.O.A.; investigation, D.O.A.; resources, D.O.A.; data curation, D.E.I.; writing--original draft preparation, D.O.A.; writing--review and editing, D.O.A.; visualization, D.O.A.; supervision, D.E.I. and S.S.; project administration, D.O.A.; funding acquisition, S.S. and D.E.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are not available to the public.  
The authors declare no conflict of interest.
## 3 Results
### Visual Analogue Scale (Primary Outcome)  
The VAS scores during the course of time revealed a significant time effect in the EG (\(p<0.0001\)) but not in the CG (\(p=0.07912\)). The group \(\times\) time interaction was not statistically significant (\(p=0.0574\)). The different scores at each time point are reported in table 2, while Figure 3 shows the median VAS scores over time in the two treatment groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**Control Group (CG)**} & \multicolumn{2}{c}{**Experimental Group (EG)**} & \multirow{2}{*}{**Median (95% CI)**} \\ \cline{3-4} \cline{6-7}  & & \multicolumn{1}{c}{_Mean \(\pm\) SD_} & \multicolumn{1}{c}{_Median (Min-Max)_} & \multirow{2}{*}{_Mean \(\pm\) SD_} & \multicolumn{1}{c}{_Median (Min-Max)_} & \multicolumn{1}{c}{**(95% CI)**} \\ \hline \multirow{4}{*}{**Visual**} & **T0** & 52.5 \(\pm\) 27.7 & 54.5 (3.0–98.0) & 50.0 \(\pm\) 21.9 & 52.0 (12.0–98.0) & \(-\)2 (\(-\)20;11
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 2 System Description
### System Model
#### 2.3.2 Relay  
The relay received signal can be expressed as:  
\[\mathbf{r}=R_{s}\sum_{k=1}^{K}h_{k}\mathbf{x}_{k}+\mathbf{w}, \tag{15}\]  
where \(R_{s}\) is the responsivity of the detector, \(\mathbf{x}_{k}\) is the optical signal intensity and \(h_{k}\) is the channel state. The \(\mathbf{w}\) term is modelled as additive white Gaussian noise with zero mean and variance \(\sigma^{2}=N_{0}/2\), where \(N_{0}/2\) is the two-sided noise power spectral density. The relay performs PNC demapping/mapping on the received \(\mathbf{r}\) signal. However, in PNC, the relay node does not need to know the individual bits of the four unknowns within QPSK. Instead, it only needs to derive the demapping form \(\mathbf{b}_{R}\) on the received symbols, which consists of two bits to produce the PNC mapping \(\mathbf{s}_{R}\). The common approach for the relay demapping and then mapping of the synchronization algorithms embodies the application of a maximum likelihood (ML) criterion [25].
## 2 Key Concepts and Enablers for 5G Private Networks
### Network Slicing  
The term network slice refers to a composition of adequately configured network functions, network applications, and the underlying cloud infrastructure (physical, virtual, or even emulated resources, RAN resources etc.), that are bundled together to meet the requirements of a specific use case (or service level agreement (SLA)), e.g., bandwidth, latency, processing, and resiliency, coupled with a business purpose [33; 34]. In 5G and beyond networks, network slicing emerges as a unique approach for segmenting the physical infrastructure into multiple isolated logical networks (i.e., slices), each one specialized to satisfy a particular service (and its respective SLA). Moreover, as analyzed in the previous subsection, different MNOs may partially or holistically share the same 5G infrastructure via the instantiation of different dedicated slices. Since the network resources for a specific slice depend highly on the time-varying user demands, a slice management entity is required to dynamically deal with the slice resource allocation.  
The slicing solution specified in our proposed approach will be in position to provide E2E slices in the computation, network, and access network domains and perform slice resource partitioning at the infrastructure, network slice, and vertical service levels. Notably, the solution will include an AI component responsible for processing data analytics information provided by the system in order to predict possible scarcities in slice resources that can have an impact on the performance of the running services.
# REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks
## 2 Related Work
#### Research Gaps  
There are various research gaps that researchers have not yet explored:  
**Stability [6; 7; 8]:** By expanding the number of IoT nodes in the various advanced zones, the stability period increases to 65%, which can provide higher efficiency and the maximum lifetime of IoT nodes that is considered in the proposed approach, but the number of transmission rounds for data is not taken into account, which leads to achieving lower efficiency and minimum lifetime.  
**Scalability [9; 10; 11]:** If the multicast routing and integration of OSR with RPL are not considered when using OSR with WSN for downward multicast routing, which can improve scalability and for massive amounts and limited resources, the coding of the proposed bloom filter path will have a higher false positive rate, which will cause the network to be slower and less scalable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
**Reliability [12; 13; 14; 15]:** This protocol could be expanded with a mac layer created with spread power-management plans and flow-adaptable fluid disagreement doors to have a cross-layer layout, but the absence of survival and connection results in less dependability. By making the network more resilient while preserving connectivity by selecting more resilient paths as routing options.  
**Network lifetime [16; 17; 18; 19]:** For WSN/DIoT, this technique can produce superior outcomes in simultaneous and immediate information transfer on a wide scale. Because of this, the effectiveness and longevity of networks are minimized while using the proposed method in a variety of actual-time applications and systems that may be created for the production and analysis of huge data.  
**Performance [20; 21; 22; 23; 24]:** The suggested ETRPL protocol can be beneficial for IoT networks with comparatively small regions, increasing the power usage of the entire network. By employing the routing approach, IoT nodes may be deployed in larger areas, minimizing the use of energy.  
To revive the network, the sensor nodes are frequently implemented with the same characteristics and initial energy levels (homogeneous networks); a few nodes with additional energy are incorporated into the network, presenting a source of heterogeneity and providing the chance of longer network longevity. Furthermore, the multi-hop region-based routing protocol is developed, in which the CH is chosen depending on the sensor nodes' remaining energy, as different nodes might get deployed in different regions (Figure 1). This will reduce the dissipation of energy amongst nodes, and in this way, nodes with higher initial energies are much more likely than nodes with lower energies to be selected as cluster heads.
## 3 Results
### Quantification of Phenolic Compounds Using HPLC-RP in the Extracts from Lysimachia nummularia L.  
The seven phenolic compounds, gallic acid, chlorogenic acid, 3-O-methylgallic, caffeic p-coumaric, ferulic acid and _trans_-resveratrol, were separated, identified and quantitatively determined in the three extracts based on the calibration curves, the limit of detection (LOD) values, the limit of quantification (LOQ) and the retention times (Table 1).  
The phenolic acids were clearly noticeable at a wavelength of 216 nm for gallic acid, 3-O-methylgallic acid, 310 nm for p-coumaric acid and _trans_-resveratrol and 324 nm for chlorogenic, caffeic and ferulic acids. Both the retention time and UV spectrum were compared with the reference compound to identify the compounds. Results are expressed in mg/100 g of dry sample (Figures 4 and 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analyte** & **Calibration Relationship** & **R\({}^{2}\)** & **RSD (t\_)** & **RSD (Area)** & \
\begin{tabular}{c} **LOD** \\ **(mg/mL)** \\ \end{tabular} &
\begin{tabular}{c} **LOQ** \\ **(mg/mL)** \\ \end{tabular} \\ \hline Gallic acid & y = 31.175x \(-\) 10.371 & 0.9994 & 0.182 & 2.182 & 1.457 & 4.415 \\ \hline
3-O-Methylgallic acid & y = 22.558x + 17.797 & 0.9992 & 0.228 & 2.945 & 1.778 & 5.387 \\ \hline Chlorogenic acid & y = 8.231x \(-\) 7.331 & 0.9997 & 0.167 & 1.829 & 1.346 & 4.078 \\ \hline Caffeic acid & y = 12.006x \(-\) 4.727 & 0.9996 & 0.225 & 3.058 & 1.174 & 3.558 \\ \hline p-Coumaric acid & y = 30.371x + 3.228 & 0.9998 & 0.099 & 2.073 & 1.634 & 4.951 \\ \hline Ferulic acid & y = 6.001x + 0.991 & 0.9996 & 0.082 & 2.977 & 1.402 & 4.248 \\ \hline _Trans_-resveratrol & y = 0.993x \(-\) 0.877 & 0.9993 & 0.332 & 2.541 & 1.060 & 3.211 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calibration curves, LOD and LOQ for the determination of phenolic compounds by HPLC-DAD.  
Figure 4: HPLC-DAD chromatograms using the developed method of mixed standards at 216 nm. Red line corresponds to the ethanolic solution of _Lysimachia nummularia herba_ samples with standard solution, blue line corresponds to the ethanolic extracts without standard solution.  
Table 2 displays the experimental results obtained for the identification of the different phenolic compounds from the three extracts obtained from different parts of the _Lysimachia nummularia_ L. plant (_radix_, _herba_ and _flores_) and the graphic representations are presented in Figure 6. The results are expressed in mg/100 g of dry sample.
## 4 Experiments and Analysis
### Adaptive Parameter Behavioral Analysis  
To further substantiate the efficacy of adaptive parameters grounded in a normal distribution, a side-by-side analysis is undertaken between the algorithm that employs adaptive parameters and its counterpart without such modulation. This evaluative process spans four test functions, holding the parameters at D = 10 and D = 20, and imposing an evaluation ceiling of 12D. The comparison charts are illustrated in Figures 8-11. It is worth noting that within these figures, ASAPSO-WGP denotes the version of the proposed algorithm that forgoes the use of adaptive parameters. From Figures 8-11, it can be further substantiated that the proposed algorithm exhibits faster convergence on almost all functions compared to other compared algorithms.  
Through an analytical catenation encompassing the proffered algorithm, a pair of classical algorithms (namely PSO and GPEME), and an avant-garde algorithm (CALSAPSO), discernible augmentations in performance are manifested by the proposed algorithm, especially when \(D=10\) is under scrutiny. An in-depth perusal of the convergence trajectories unveils that our proposed algorithm not only attains a more rapid initial convergence but also preserves a modicum of local exploratory prowess during the advanced phases. When extended to \(D=20\), its efficacy transcends that of both PSO and GPEME, marginally eclipses CALSAPSO, and underscores its dominance over algorithms devoid of an adaptive framework. In a further series of trials, bifurcated by the presence and absence of adaptive parameters, it is evident that ASAPSO, when armored with the adaptive parameter regimen, registers superior outcomes across a multitude of functions at \(D=10\), albeit with nuances that are less conspicuous. It merits particular emphasis that, at \(D=20\), ASAPSO elucidates pronounced refinements in trio functions, namely the Elliptic function, Griewank function, and Rastrigin function. While the apex fitness value optimized for the Rosenbrock function does not particularly dazzle when juxtaposed with its parameter-absent counterpart, a deeper dive into the convergence trajectory illuminates that ASAPSO, when synergized with adaptive parameters, indeed fares better in the Rosenbrock function, evincing more pronounced oscillatory patterns. This revelation accentuates the algorithm's amplified exploratory finesse, thereby confirming the efficacy of the strategy proposed in this paper.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 2 Methodology  
The formal problem for designing an optimal AoO can be described as follows. An undirected, connected graph \(G=(V,E)\) is given that represents the entire planning area. Each vertex \(i\in V\) represents a subdivision of the planning area, and edges indicate geographically adjacent subdivisions. Figure 1a shows an example where subdivision (white) are represented by vertices (blue). We are further given some revenues \(p_{ij}\) (scaledmobility demands) for each pair of vertices \(i,j\in V\), costs \(c_{i}\) for each vertex \(i\in V\) (costs for expanding the car sharing business to the area), and a budget \(B\).  
Our goal is to select a connected vertex subset \(S\subseteq V\) (the area of operation) that maximizes the overall revenue between all pairs of selected vertices while ensuring that the set of selected vertices does not contain \"holes\" (i.e., it does not enclose any non-selected vertices) and the total costs of \(S\) do not exceed budget \(B\).
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 5 Performance Evaluation  
In this section, we present an evaluation of our proposed protocol based on the simulation results obtained using MATLAB (version R2015a) [42] and compare it with recent authentication-based schemes for VANETs [18; 19; 20; 21]. We analyze the following performance metrics: communication cost, storage cost, execution time, and RSU's overhead. In the end, we discuss and summarize the obtained results.
## 3 Theory Analysis
### MPA Optimized MVMD
(2) Population optimization iteration consists of three iterative stages: initial, middle and final stages to update the population's moving step size and position. (3) The effect of eddy current and artificial fish-gathering device: By simulating the dual effects of ocean eddy current and an artificial fish collection device on the distribution and behavior of marine biological populations in a real marine environment, MPA greatly reduces the probability of a population falling into local optimum by integrating the secondary population renewal mechanism. (4) Marine memory update: The fitness function value of the prey matrix is calculated, and the elite matrix is updated accordingly. If the convergence requirement is met, the iteration ends, and vice versa. The specific process of MPA optimizing MVMD parameters is shown in Figure 3:  
Envelope entropy is a reflection of the sparseness of a signal. Smaller envelope entropy indicates stronger signal sparsity, higher PD signal certainty and less noise. The average envelope entropy represents the mean of the envelope entropy of all IMF components decomposed by MVMD. The IMFs set with the smallest AEE have the highest degree of determination, which can improve the quality of the noise reduction of the PD signal effectively. Therefore, the MAEE is used as the fitness function to optimize \(k\) and \(\alpha\) of MVMD, so as to realize the effective decomposition of the PD contaminated signal. The fitness function is defined as follows:  
\[\langle\hat{K},\hat{\alpha}\rangle=\underset{(K,\alpha)}{\text{argmin}}\Bigg{\{} \frac{1}{\hat{K}}\sum_{i=1}^{k}H_{\text{en}}(i)\Bigg{\}} \tag{16}\]  
In (16), \(\hat{K}\) and \(\hat{\alpha}\) are the optimal parameters and \(H_{\text{en}}(i)\) is envelope entropy of each modal component \(u_{k}\), which is calculated by (17) and (18). \[H_{\text{en}}(j)=-\sum_{j=1}^{N}p_{j}\log_{2}(p_{j}) \tag{17}\]  
Figure 3: MPA algorithm optimization of MVMD flow chart. \[p_{j}=a(j)/\sum\limits_{j=1}^{N}a(j) \tag{18}\]  
In (17) and (18), \(N\) is the number of sampling points, \(p_{j}\) is the normalized form of \(a(j)\) and \(a(j)\) is the envelope signal of each modal component after Hilbert transform. In order to verify the effectiveness of AEE as the fitness function, WGN with a step size of 1 dB and from \(-\)10 dB to 20 dB is superimposed, respectively, on the pure PD signal in Figure 1a. The input parameter \(k\) of MVMD is set to 6, and the \(\alpha\) is set to 3000. The value of AEE at each step size is obtained as shown in Figure 4.  
Figure 4 shows AEE decreases as PD signal SNR increases, indicating more certain discharge signals have smaller AEE values.
# Experimental Study on the Impact of CO2 Treatment on Different Lithofacies in Shale Oil Reservoirs
## 2 Materials and Methods
### Experimental Apparatus and Methods  
#### 2.2.1 Experimental Apparatus  
The element distribution in a large area of each shale sample is measured through the XRF analysis using an M4 TORNADO X-ray fluorescence spectrometric instrument from Bruker, Germany. In this experiment, the area scan method is adopted and the vacuum degree, voltage and current are kept at 20 mbar, 50 kV and 200 \(\mu\)A, respectively.  
The pore structures and micromorphologies of the shale samples are measured through SEM analysis, using an Apreo field-emission scanning electron microscopy from ThermoFisher Scientific, USA. The instrument's maximum resolution is 1 nm under 1 kV. In this experiment, the work distance, voltage and current are kept at 10 mm, 10 kV and 1.6 nA, respectively.  
The mineral composition of shale samples is measured through XRD analysis using D/max 2500 PC X-ray diffractometer from Rigaku Corporation, Japan. In this experiment, the average particle size of shale samples is 125-150 \(\mu\)m, and the scanning angle is 3-85\({}^{\circ}\) at a scanning speed of 1\({}^{\circ}\)/min. The detailed procedures can be found in SY/T5163-2010.  
The porosity and permeability of shale samples are measured with helium before and after CO\({}_{2}\) treatment, using an AP-608 Automated Permeameter-Porosimeter from Coretest systems, USA. The maximum confining pressure is 9000 psi and the measuring range of porosity and permeability are 0.1-40% and 0.001-10000 mD, respectively.
# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
## 5 Performance Analysis of CNN Based Robot Face Recognition Models
### Simulation Settings  
In the NVIDIA GEFORCE GTX 1050 TI variant, all tests were carried out utilizing the Windows platform with Intel core(R) i7-9700k CPU @ 3.60 GHz with 32 GB RAM in the case of robot environment and Intel(R) Xeon(R) Silver 4114 CPU @ 2.20 GHz with 32 GB RAM in the case of a cloud environment. The Python version 3.7.3 with TENSORFLOW  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **NPCR** & **CC** & **MSE** & **PSNR** & **UACI** \\ \hline DNA & 99.446 & **0.0615** & 6372.06 & 10.088 & 24.6696 \\ AES & 99.572 & 0.1188 & 7033.57 & 9.6590 & 26.5178 \\ GA & **34.297** & 0.3828 & **4293.95** & **11.802** & **9.7257** \\ Bit slicing & 86.403 & 0.4029 & 6483.72 & 10.0125 & 22.4563 \\ Chaos & 99.116 & 0.9529 & 7262.96 & 9.5196 & 26.2900 \\ RSA & 99.996 & 0.0678 & 7432.85 & 9.4192 & 29.8552 \\ RCP & 99.621 & 0.0285 & 7542.40 & 9.3557 & 29.4176 \\ Hill-Cypher & 99.631 & 0.0328 & 7541.06 & 9.3564 & 29.4601 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Security measures of the studied encryption algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & _Time_ & _Avg. time_ & _Std. Time_ \\ \hline DNA & 4.8877 & 4.9674 & 0.89898 \\ AES & 131.11 & 66.092 & 37.7128 \\ GA & 1.4454 & 1.4376 & 0.00937 \\ Bit slicing & 2.3361 & 2.2671 & 0.02760 \\ Chaos & **0.0478** & **0.0473** & **0.00097** \\ RSA & 1.4402 & 1.4357 & 0.03938 \\ RCP & 1.2341 & 1.3879 & 0.98765 \\ Hill-Cypher & 2.8552 & 1.4336 & 0.82310 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Encryption time, average, and standard deviation to encrypt 40 images with various encryption algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cloud** & **Robot** \\ \hline Processor & Intel(R) Xeon(R) Silver 4114 & Intel core(R) i7-9700k CPU @ 2.20 GHz & 3.60 GHz \\ RAM & 40 GB & 32 GB \\ OS & 64-bit Linux & 64-bit Windows \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of cloud and robot.  
version \(<1.15.0>=1.14.0\) is introduced to test the process and extract the features and for classification of images. As mentioned above, a pre-processing is needed before the formation process is started for the convolution neural network architectures. A re-scale is used for the resized images of data set in order to convert the images to 224 \(\times\) 224 as AlexNet input and to 224 \(\times\) 224 as ResNet input. Based on established Quality parameters, the efficiency of the trained neural network is assessed based on accuracy.