esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Incorporation of Wearable Sensors to Measure OA Risks
### Electromyography (EMG)  
One of the most compelling developments in EMG applications for motor control dysfunction is feedforward sensing of neurotransmitters, allowing intended muscle actions and associated limb movements to be predicted. Neurotransmitters have already been used to control exoskeleton actuators [57; 58] and incorporation into an active knee orthosis for OA is a promising research direction. Intention to contract muscles generates an action potential (e.g., 30-40 millivolts) [59] that can be recorded using a non-invasive system of surface electrodes attached to the muscle belly (surface-EMG). Analysis of amplified EMG signals can show which muscles are active in producing the targeted movement [60].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Factor** & **Problem** & **How to Manage by Active** \\  & & & **Exoskeletons** \\ \hline
1 & Knee adduction moment & Excessive & Provide counteracting moment \\
2 & Knee extension moment & Insufficient & Provide additional moment \\
3 & Knee power & Insufficient & Same as 2 \\
4 & Knee eccentric work & Insufficient & Provide knee power for a \\
5 & Maximum knee flexion & Insufficient & prolonged time \\
6 & Knee compression force & Excessive & Provide counteracting force, \\
7 & Knee abduction angle & Excessive & Regulate excessive joint motion \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of knee biomechanical parameters and potential application of active knee orthosis [18; 22; 23; 24; 25; 26; 27; 28].  
Despite controversy surrounding EMG noise-reduction filtering techniques, on/off is the most reliable EMG information, because the intensity of muscle contraction is difficult to assess without specialised techniques such as relative amplitude methods [61]. Wearable EMG systems, designed primarily for research, are commercially available [62], but future developments in gait monitoring systems for knee OA may be able to incorporate wearable EMG devices to record activation patterns associated with power absorption by the quadriceps, dorsiflexors and plantar flexors as described in Table 1[63].
## 5 Conclusions  
For radiologists, detecting brain tumors is a time-consuming and difficult process. In addition, since it is based on expertise, the probability of false detection is high. Accurate and fast detection and classification of tumors is of great importance with regard to determining the appropriate treatment and early diagnosis. In this study, a CNN model was developed and trained with MRI images and strong features were extracted. Using the extracted features, the images were classified with the optimized XGBoost. The proposed model classified four different brain tumors with 99.77% accuracy. When the performance of XGBoost was compared with other machine learning methods, the decision tree had the closest results with 99.16% accuracy rate. However, compared with other methods, the highest success rate was obtained by combining the CNN model with XGBoost. In addition, when the proposed model was compared with pre-trained EfficientNetB5, DenseNet121, ResNet50, VGG19, Xception, InceptionV3 and MobileNetV3, the accuracy rates were 97.49%, 98.33%, 96.36%, 92.79%, 98.18%, 98.51% and 96.13%, respectively. The closest performance belongs to the InceptionV3 model. Overall, the proposed model outperformed the four pre-trained models in terms of performance. The results were visualized using the Grad-CAM approach to show which regions the model takes into account and to increase the model's reliability. With its high accuracy rate, usage of XAI,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Class Type** & **Model** & **XAI** & **Accuracy (\%)** \\ \hline
[36] & Multi & SVM & No & 96.67 \\
[34] & Multi & InceptionV3 & No & 97.12 \\
[37] & Binary & DenseNet121 & No & 97.86 \\
[38] & Binary & CNN & No & 98.82 \\
[39] & Binary & CNN & No & 98.50 \\
[40] & Binary & VGG16 & LRP & 97.33 \\
[41] & Multi & NeroNet19 & LIME & 99.30 \\
[42] & Binary & CNN & No & 98.66 \\
[43] & Multi & EfficientNetB0 & Grad-CAM & 98.72 \\
**Proposed model** & **Multi** & **CNN + XGBoost** & **Grad-CAM** & **99.77** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison with state-of-the-art methods.  
and multiple classification, the proposed model outperforms the state-of-the-art methods. As a result, this study has presented a useful approach to classifying brain tumors based on performance and explainability.  
This work was supported by the Scientific Research Projects Coordination Unit of Firat University under Grant No: MF24.119.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the author.  
The author declares no conflicts of interest.
## 3 Results
### Vapor Pressure Differences between COSMIC and Radiosonde Data  
The vertically averaged COSMIC vapor pressure biases compared to radiosonde observations in China are presented in Figure 6. The COSMIC vapor pressure exhibits large negative biases in southern and south-eastern China, while the maximum positive bias occurs at a station in north-western China. The vapor pressure biases are insignificant in the remainder of China. Among the 120 stations, 79 stations (66%) have biases with a magnitude of less than 0.03 hPa, and 27 stations (23%) with a magnitude of between 0.03 hPa and 0.06 hPa. The vapor pressure bias varies from \(-\)0.09 hPa to 0.09 hPa, and the average value is \(-\)0.01 hPa. Like the pressure bias, a negative correlation is found between the absolute vapor pressure bias and station latitude, with a correlation coefficient of \(-\)0.4895 at a significance level of greater than 99%.This indicates that station latitude is also one of impact factors on the vapor pressure bias between COSMIC and radiosonde.  
Figure 7 presents the vapor pressure RMSEs between COSMIC and radiosonde data in China. Except for the maximum RMSE at a station in north-western China, large RMSEs mainly occur in southern China. There are 99 stations (82%) with an RMSE of less than 0.9 hPa, and 20 stations (17%) with an RMSE of between 0.9 hPa and 1.2 hPa. The minimum RMSE of 0.20 hPa occurs at a station in central QTP, while the maximum value of 1.64 hPa occurs at the westest station in China, and the average vapor pressure RMSE is 0.64 hPa. Additionally, a negative correlation exists between the vapor pressure RMSE and station altitude, with a correlation coefficient of \(-\)0.5449 at a significance level of greater than 99%. This implies that the vapor pressure RMSE between COSMIC and radiosonde is impacted by station al  
Figure 5: The pressure RMSEs between COSMIC and radiosonde data in China. The number of radiosonde stations for each RMSE class is listed in parentheses.
## 3 Simulated Results and Discussions
### Shielding Effectiveness (SE)  
The shielding effectiveness (SE) of an FSS shield refers to its ability to attenuate/block incident EM waves, as expressed in (1).  
\[SE\left(\mathrm{dB}\right)=-20\log_{10}\left|\frac{E_{t}}{E_{t}}\right|, \tag{1}\]  
Figure 2: Stepwise design evolution process of the FSS shield and its performance at normal incidence.  
Figure 3 depicts the SE of the FSS obtained from the EM simulations and ADS. The FSS offers an effective shielding not less than 33 dB at the operating frequency.
## 4 Implementation and Discussion of Results
### Selecting the CNN Parameters
#### 4.4.1 Building, Training, and Testing the CNN Model  
By fitting the training dataset, the CNN method is used to predict the number of new confirmed, recovered, and mortality cases on the test dataset. This section describes the CNN model's forecasting findings for future COVID-19 cases. The dataset is split into training and testing, and the test dataset's forecasted and actual values are reported. With the forecasted confirmed, dead, and recovered cases of COVID-19 using the CNN model, good forecasting efficiency and precision are attained. We summarize the different values of the error metrics in Table 3. The MAPE values of the confirmed, death, and recovered cases for the CNN model were found to be 10.03%, 17.62%, and 38.86%, respectively. By further looking at the six error measures of the CNN model, it can be observed that CNN has excellent performance in predicting confirmed cases. In addition, CNN has a very good performance in predicting the death cases, while it has a good performance on the recovered cases. For example, the CNN's nRMSE and \(R^{2}\) values, for predicting the number of confirmed and death cases, respectively, are 4.74% and 0.95, and 8.48% and 0.90. In the recovered cases, the two metrics are 9.05% and 0.67. The scatter plots that compare the actual and the predicted values of the three cases on the testing sets can be seen in Figures 14-16.  
CNN's prediction in the mortality cases is very good, comparable to that in the confirmed cases. The model shows somewhat superior performance even for the higher number of mortality cases.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 3 The Proposed Method of Trust-Aware Reactive Ad Hoc Routing
### Trust Distribution  
In order to successfully deploy the suggested solution, each node needs to know the estimated _PDRs_ of its neighbors. In sink-based networks, the sink node always communicates with all other nodes. Thus, it can collect all necessary statistical data, estimate the trust of the nodes using a NN, and distribute trust estimations to all nodes in the network. We assume that the topology of the network does not change, or changes slowly in time, and each route found by the AODV protocol is used periodically or very often, therefore it does not expire.  
In this article, we do not consider scenarios in which routes are rediscovered and the sink node recomputes the impacted trust values as a reaction to network changes. We will simply assume that all nodes receive the trust estimation, and we will focus on how the TARA method performs compared to the standard AODV protocol.
## 3 Computational Analysis and Modeling
### Meshing, Boundary Condition and Loading  
For a good robust numerical convergence of the FEM analysis results in Abaqus/Standard and to promote useful comparisons with the IGA method with T-Splines results, we carried out a mesh refinement analysis for the various proposed mesh sizes. For an efficient processing time, the 1 mm mesh size was employed throughout the entire LPG cylinder, as shown in Figure 8, for the following results in this present research.  
In the present research, the aim is to employ IGA with the T-Splines method to evaluate industrial mechanical performance issues, in particular, to numerically study the storage capacity of liquefied gas (LPG) cylinders in the case of empty pressure, as well as to evaluate their mechanical performance. These studies are part of a global approach to optimizing storage space, considering geometric constraints, material properties, and safety requirements. This loading is modeled by an applied load (\(F\)) (Figure 9). The loading F of the empty weight of the LPG cylinder is given by  
\[F=mg\]  
Figure 8: Mesh of LPG cylinder in Abaqus/Standard for FEM analysis. Total number of nodes: 847. Total number of elements: 774.  
Figure 7: Stress–strain–curve for aluminum alloy 6061-T6 [12].  
where m is the mass of the empty LPG cylinder; in our case, we take the mass 15 kg for an LPG cylinder of 13 kg of gas (one LPG cylinder is modeled with a loading of 147 Newton) and g = 9.81 m/s\({}^{2}\) is the gravitational acceleration.  
In consideration of geometry symmetry and for a good processing time ratio, it takes half an LPG cylinder to model the storage capacity in the case of empty pressure for the two numerical methods of investigation, the finite element method (FEM) according to Abaqus/Standard and the IGA method with T-Splines.  
To obtain good numerical convergence of the FEM analysis results in Abaqus/Standard, we used a linear element shell of type S4R with reduced integration to model the LPG cylinder case (Figure 8). The S4R element is efficient in terms of good computational time savings, is less prone to negative Jacobian errors than 3D solid elements and is simpler to mesh.
# A General Solution for the Errors in Variables (EIV) Model with Equality and Inequality Constraints
## 2 EIV Model with Equality and Inequality Constraints  
The errors in variables model (EIV) can be defined as the following [6]:  
\[\mathbf{L}+\mathbf{V_{L}}=(\mathbf{A}+\mathbf{E_{A}})\mathbf{X} \tag{1}\]  
where \(\mathbf{L}\) and \(\mathbf{V_{L}}\) are the \(\mathbf{m}\times\mathbf{1}\) observation vector and its random error vector, respectively. \(\mathbf{A}\) and \(\mathbf{E_{A}}\) are the \(\mathbf{m}\times\mathbf{n}\) coefficient matrix and the corresponding error matrix, respectively. \(\mathbf{X}\) is the \(\mathbf{n}\times\mathbf{1}\) unknown parameter matrix.  
The corresponding error vector and the stochastic model are expressed as [19]  
\[\mathbf{V}=\begin{bmatrix}\mathbf{V_{L}}\\ \mathbf{V_{A}}\end{bmatrix}=\begin{bmatrix}\mathbf{V_{L}}\\ \mathbf{vec}(\mathbf{E_{A}})\end{bmatrix}\sim\left(\begin{bmatrix}0\\ 0\end{bmatrix},\,\mathbf{\sigma}_{0}^{2}\mathbf{Q}=\mathbf{\sigma}_{0}^{2}\begin{bmatrix} \mathbf{Q_{L}}&\mathbf{Q_{LA}}\\ \mathbf{Q_{AL}}&\mathbf{Q_{A}}\end{bmatrix}\right) \tag{2}\]  
where \(\mathbf{V_{A}}=\mathbf{vec}(\mathbf{E_{A}})\) is the \(\mathbf{m}\times\mathbf{1}\) order error vector and \(\mathbf{vec}\) denotes the operator of the vectorization resulting a column vector by stacking the columns of a matrix on top of one another. \(\mathbf{\sigma}_{0}^{2}\) is the variance factor, \(\mathbf{Q_{L}}\) and \(\mathbf{Q_{A}}\) are the cofactor matrices for \(\mathbf{V_{L}}\) and \(\mathbf{V_{A}}\), and \(\mathbf{Q_{LA}}\) is the cofactor matrix, which refers to the correlations of \(\mathbf{V_{L}}\) and \(\mathbf{V_{A}}(\mathbf{Q_{LA}}=\mathbf{Q_{AL}^{T}})\).  
The total least squares objective function can be defined as follows [3]:  
\[\mathbf{V^{T}}\mathbf{Q^{-1}}\mathbf{V}=\mathbf{\min} \tag{3}\]  
The functional and stochastic models of Equations (1) and (2) are equivalent to  
\[\hat{\mathbf{V}}=\hat{\mathbf{A}}\mathbf{X}-\hat{\mathbf{L}}\qquad\hat{\mathbf{V}}\sim(0,\,\mathbf{ \sigma}_{0}^{2}\hat{\mathbf{Q}}\Big{)} \tag{4}\]where \(\hat{Q}=(I_{m}-X^{T}\otimes I_{m})Q(I_{m}-X\otimes I_{m})\), \(\hat{V}=V_{L}-E_{A}X,\hat{A}=A+E_{A},\hat{L}=L+E_{A}X\) and \(\otimes\) stands for the Kronecker-Zehfuss product. The corresponding objective function can be expressed as follows:  
\[\hat{V}^{T}\hat{Q}^{-1}\hat{V}=\min \tag{5}\]  
When equality and inequality constraints are available simultaneously, the corresponding EIV model (EIC-EIV model) can be defined as the following:  
\[\left.\begin{array}{l}\vspace{0.2 cm}L+V_{L}=(A+E_{A})X\\ G_{1}X=W_{1}\\ G_{2}X\leq W_{2}\end{array}\right\} \tag{6}\]  
where \(G_{1}\) is the \(s_{1}\times n\) coefficient matrix of equality constraints, \(G_{2}\) is the \(s_{2}\times n\) coefficient matrix of inequality constraints, \(W_{1}\) is the \(s_{1}\times 1\) constant vector, and \(W_{2}\) is the \(s_{2}\times 1\) constant vector.  
Combining Equations (5) and (6), the corresponding objective function of EIC-EIV model is stated as follows:  
\[\left.\begin{array}{l}\vspace{0.2 cm}\hat{V}^{T}\hat{Q}^{-1}\hat{V}=\min\\ G_{1}X=W_{1}\\ G_{2}X\leq W_{2}\end{array}\right\} \tag{7}\]
## 3 Results and Discussion  
Caffeic acid, quinic acid, rosmarinic acid, quercetin, and rutin (Figure 1) were selected because of the recent great interest in them for dietary polyphenols and their supplementation, as they are known to exhibit several biological activities, mainly antioxidant and anti-inflammatory properties, and for their direct effect on the intestinal environment modulation. These compounds are widely distributed in vegetable foods and beverages. In particular caffeic and quinic acids are the most abundant phenolic acids in coffee beans, while rosmarinic acid is present in different aromatic herbs (_Lamiaceae_ family), and many fruits (e.g., grapes, apricots, apples, cherries, and blackberries) are rich in quercetin and rutin [33; 34; 35; 36]. Despite the abundance in many different foods, their intestinal absorption is low with important implications for their bioactivities.  
Therefore, Caco-2 cells seeded on a permeable PET membrane in the dynamic bioreactor were used to better investigate the absorption mechanism of these compounds possessing similar chemical features, and in order to verify the results reported in the literature on static Caco-2 cell models. Cells were plated in millifluidic chambers and treated with the compounds described above at a concentration of 0.1 mg/mL for 6 and 24 h, under a medium flux of 150 \(\upmu\)L/min. During the incubation, withdrawals were made at increasing contact times (1, 2, 4, 6 and 24 h) and the samples were analyzed by an RP-HPLC-DAD method, in which mobile phase composition was modified for each compound, to test the quantity and trend of absorption. In addition, cell viability under dynamic conditions was evaluated after 6 and 24 h contact by the Alamar Blue method and no toxicity was observed.  
The tested concentration (0.1 mg/mL) was fixed after cell viability experiments, considering the potential daily intake of each compound.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 2 Background and Related Works
### Background
#### 2.1.5 Searchable Encryption  
Encryption or cryptography is the process of securing a message over an insecure network. Traditional search methods are ineffective when dealing with encrypted data. Using Searchable Encryption (SE) is an appropriate solution [5] for this purpose. Searchable encryption is regarded as a key technology for achieving data confidentiality and retrieval functions which is an encryption technique that encrypts data in such a way that it can be searched using keywords [23]. Searchable Encryption (SE) stands for \"searching without decryption\" on encrypted data stored on an untrusted server or cloud [5].  
Public key encryption with keyword (PEKS) enables anybody to encrypt the data with the public key of the data owner, but searches can be performed only by the private key holder [23]. Different cryptographic algorithms are used for confidential data exchange over a public network. There are several public-key cryptography algorithms available, each with its own set of strengths and weaknesses. RSA and ECC are the most extensively used public-key cryptosystems. Today, several elliptic curve protocols are in use, including ECDSA, ECIES, and ECDH. The researchers in [24] provide a comparative analysis between RSA and ECC and all comparing parameters show that our elliptic curve-based implementations outperform the RSA algorithm.
## 4 Design Results  
The proposed method is configured with the following requirements and parameters for cost calculation:  
* Design frequency \(f=11\) GHz and \(\theta_{sat}=-5^{\circ}\);
* Azimuth 3 dB beamwidth: \(10^{\circ}\). Elevation 3 dB beamwidth: \(6^{\circ}\);
* Azimuth 10 dB beamwidth: \(16^{\circ}\). Elevation 10 dB beamwidth: \(9^{\circ}\);
* SLL: \(-20\) dB;
* \(K_{1}=0.1\); \(K_{2}=2\); \(K_{1}=10\).  
The method described in Section 2 gives the best cost result (\(C=9.1\)) for beam positions \(\varphi_{b1}=0.0509\) and \(\varphi_{b2}=0.0671\), zero-beam pondering as half (\(A_{b0}=0.5\)) and central element phase \(\varphi_{cent}=48.3^{\circ}\) (ring element of \(w_{ring}=2.8\) mm). Azimuth and elevation cuts of the resulting RP are shown together with \(mask_{inf}\) in Figure 6(a). Figure 6(b) shows the 3D RP of the achieved design and the \(mask_{2D}\) employed for SLL minimization.  
The phase distribution obtained in the optimization process was employed to create a reflectarray in ANSYS HFSS with elements described in Section 3.3. It has been simulated by employing the Finite Element-Boundary Integral (FE-BI) method, because the design is electrically large, and being fed with the _Potter_ horn previously optimized to approximate the real one (Figure 7(a)). _Foam_ spacers that will allow to have a thick air substrate between the _FR4_ and the ground plane are also included in the simulation.  
Simulation results are shown in Figure 7(b). A 3 dB azimuth beamwidth of \(9.4^{\circ}\) is achieved as well as \(5.2^{\circ}\) elevation beamwidth, with a maximum directivity of \(19.13\) dBi achieved at \((\varphi,\theta)=(1.8^{\circ},-4.8^{\circ})\) from the broadside. High radiation levels appear in the elevation cut at angles between \(-50^{\circ}\) and \(-20^{\circ}\) due to the specular radiation from the feed. In [14], it was proposed to take advantage of this specular radiation by steering the main beam towards it, but in this case it is not useful for the radar application to have the main beam steered to \(37^{\circ}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element Type** & **Phases** & **Losses** \\ \hline Circle plus triangle & \((-180;-125.5]\cup[152;180)\) & \(|S_{11}|>-0.6\) dB \\ Ring \(w_{ring}=1.8\) mm & \((-125.5;-20]\) & \(|S_{11}|>-3.0\) dB \\ Ring \(w_{ring}=2.8\) mm & \((-20;152]\) & \(|S_{11}|>-2.1\) dB \\ \hline \hline \end{tabular} Mean values (\(n=3\) repetitions) preceded by one common letter (a, b) were not significantly different (\(p<5\%\)).  
\end{table}
Table 1: Range of phases covered by each element and its associated losses.  
Figure 7: Results of the proposed optimization method launched in Matlab for reflectarray design at 11 GHz.
## 1 Introduction
In this paper, a new modeling approach based on Holm electrical contact theory was proposed, with the purpose being to investigate the dependance between the electric potential and the temperature of the electrical contact via numerical methods using the COMSOL software and FEM. Several simulations were conducted for different values of the electric potential difference to obtain the corresponding contact temperature. The results show that the temperature increase has a parabolic shape, and that for values of the voltage drop that are higher than 4 mV, the contact temperature increases by 79.25 degrees and more over the ambient temperature. These values are close (10-12%) to those calculated analytically, and are also in line with research addressed in the literature review. These values of temperature can compromise the integrity and performance of contact insulation, leading to potentially unwanted effects. The paper is structured as follows: Section 1 introduces the purpose of the paper and presents a comprehensive literature review related to the studied themes. Section 2 presents the theoretical aspects regarding electric contacts. Section 3 shows the initial geometric model developed using SolidWorks software, followed by the step-by-step creation of the COMSOL model and the simulation studies run in the same software, and Section 4 discusses the results obtained.