esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 1 Introduction  
Long Term Evolution (LTE) is 4 th generation mobile communication technology and can provide a better data transmission service than 3G dose in the cyber world that is essential for constructing a future mobile environment [1; 2; 3]. Since network traffics have exponentially increased as people have started to use more than just one mobile device, they has become more important [4; 5; 6]. Release 12 is currently underway in LTE standards, and LTE networks where part of Release 10 technologies are applied based on Release 9 can be classified as a LTE advanced technology introduction country [7]. Even though LTE standards that have been drafted since 2004 solved various security vulnerabilities found in LTE, some vulnerabilities have still not been security-updated, but they are specified as vulnerabilities in the standards [8; 9; 10]. The most issued vulnerability among various vulnerabilities is the one where the identification parameter values in user equipment (UE) are exposed as plain texts [11; 12].  
Therefore, a method is proposed in this paper for transmitting identification parameters securely and for hiding key parameter based on mutual authentication between UE, eNB, and MME in 4 initial attach cases to make a secure cyber world.  
Table 1 displays the definitions and terms used in this paper.  
This paper consists of six sections. Section 2 analyzes LTE. In Section 3, four kinds of initial attach security schemes in each case are to be proposed so that identification parameters can be securely transmitted. Section 4 compares a security analysis of the proposed scheme, and Section 5 evaluates the performances between the proposed scheme and the existing process. Section 6 concludes the discussion.
## 2 Materials and Methods
### Feature Selection
#### 2.3.2 Maximum Relevance-Minimum Redundancy (mRMR)  
Maximum relevance-minimum redundancy (mRMR) works in several rounds. In mRMR, we want to pick the feature that has the most relevance to the target variable and the least redundancy to the features that were already chosen in earlier rounds. Assume there are \(m\) features in total and that, for each feature, \(X_{i}\) (\(i\in\{1,2,3,\,\dots,\,m\}\)), based on the mRMR criteria, its feature importance may be represented as [37]:  
\[f^{mRMR}(X_{i})=I(Y,\,\,X_{i})-\frac{1}{|S|}\sum_{X_{s}\in S}I(X_{s},\,\,X_{i}) \tag{2}\]  
where \(Y\) denotes the target variable (patient diagnosis), \(S\) denotes the collection of features that was chosen in earlier rounds, \(|S|\) is the number of features in the feature set, \(X_{s}\in S\) is one of the features in the feature set \(S\) and \(X_{i}\
otin S\) signifies a feature that is not chosen. The function \(I(.,\,.)\) is the mutual information that is calculated as follows:  
\[I(Y,\,\,X)=\int_{\Omega Y}\int_{\Omega X}p(x,y)\text{log}(\frac{p(x,y)}{p(x)p( y)})dxdy \tag{3}\]  
where \(\Omega Y\) and \(\Omega X\) are the sample spaces for \(Y\) and \(X\), respectively; \(p(x,y)\) is the joint probability density; and \(p()\) is the marginal density function. For the discrete variables \(Y\) and \(X\), the mutual information formula takes the following form:  
\[I(Y,\,\,X)=\sum_{y\in\Omega Y}\sum_{x\in\Omega X}p(x,y)\text{log}(\frac{p(x,y) }{p(x)p(y)}) \tag{4}\]  
In the mRMR, at each round of the feature selection procedure, the feature with the highest feature significance score is chosen, and \(max_{X_{i}\
otin S}f^{mRMR}(X_{i})\) will be added to the selected feature set \(S\).  
After applying mRMR to our dataset, the ranking of the features is as follows: (1-C\({}_{4}\)H\({}_{8}\)O, 2-C\({}_{4}\)H\({}_{8}\)O\({}_{2}\), 3-C\({}_{13}\)H\({}_{22}\)O, 4-C\({}_{5}\)H\({}_{10}\)O, 5-C\({}_{2}\)H\({}_{4}\)O, 6-C\({}_{2}\)H\({}_{4}\)O\({}_{2}\), 7-C\({}_{6}\)H\({}_{12}\)O, 8-C\({}_{8}\)H\({}_{16}\)O, 9-C\({}_{9}\)H\({}_{16}\)O\({}_{2}\), 10-C\({}_{7}\)H\({}_{6}\)O, 11-C\({}_{3}\)H\({}_{4}\)O\({}_{2}\), 12-C\({}_{6}\)H\({}_{12}\)O\({}_{2}\), 13-C\({}_{10}\)H\({}_{20}\), 14-C\({}_{4}\)H\({}_{6}\)O, 15-C\({}_{7}\)H\({}_{14}\)O, 16-C\({}_{15}\)H\({}_{10}\)O, 17-C\({}_{4}\)H\({}_{4}\)O\({}_{2}\), 18-C\({}_{11}\)H\({}_{22}\)O, 19-C\({}_{9}\)H\({}_{18}\)O, 20-C\({}_{12}\)H\({}_{24}\)O, 21-C\({}_{5}\)H\({}_{8}\)O, 22-C\({}_{3}\)H\({}_{4}\)O, 24-C\({}_{7}\)H\({}_{11}\)O, 25-C\({}_{3}\)H\({}_{6}\)O, 26-C\({}_{4}\)H\({}_{6}\)O\({}_{2}\) and, finally, 27-C\({}_{13}\)H\({}_{26}\)O).
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Processing
#### 2.1.7 Beer Brewing  
The Birmingham Centre for Energy Storage, at the University of Birmingham (UK), and the company Estrelta de Levante (Spain), work on the development of a heat storage [20], which aims to reduce the use of fossil fuels in the brewing industry. The storage is for waste heat recovery, by increasing the storage capacity of the production plant, for efficient management of energy resources. The storage is constructed with a heat exchanger concept, filled with the PCM. The base of the PCM is Mg(NO\({}_{3}\))\({}_{2}\) \(\cdot\) 6H\({}_{2}\)O, and it melts around 88 \({}^{\circ}\)C. The storage was tested successfully in a lab scale system. A larger storage has been constructed and integrated into the brewing plant for further testing.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 7 Conclusions  
In this letter, joint user-slice association and user-slice pairing algorithms for eMBB and uRLLC UEs in HetNet are suggested based on game theory. The suggested algorithms are designed as an optimization problem to maximize the overall DL throughput while assuring the slice's QoS, minimizing the latency of uRLLC traffic, and achieving intra and inter-isolation using flexible rate isolation. To improve its tractability, we have divided the problem between (1) dynamic association matching of BSs and UEs and (2) superimposing of uRLLC and eMBB UEs. Matching game algorithms for U-S. A and U-S. P are proposed to solve these subproblems. Moreover, we propose a dynamic eMBB and uRLLC clustering technique called H-NOMA in HetNet systems to balance system performance. The computation complexity for the proposed algorithms is analyzed. Simulation results have confirmed that the submitted algorithm achieved throughput significantly above the compared algorithms and defeated uRLLC UE latency deterioration. The proposed algorithm had the highest throughput and the lowest latency compared with DAA-OMA, DAA-PUNC, EAA-NOMA, GA-NOMA, GA-OMA, MAX-SINR-NOMA, and MAX-SINR-OMA.  
Conceptualization, M.A.R.; Data curation, M.A.R.; Formal analysis, M.A.R. and A.M.A.E.-H.; Methodology, M.A.R.; Validation, M.A.R., O.E.-G. and A.M.A.E.-H.; Writing--original draft, A.M.A.E.-H.; Writing--review & editing, M.A.R. and A.M.A.E.-H. All authors have read and agreed to the published version of the manuscript.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
# Method of Feature Reduction in Short Text Classification Based on Feature Clustering
## 6 Patents  
The method of this paper involves a patent under review named \"an optimized short text classification method\".  
[PERSON] proposed the idea, [PERSON] designed and performed the experiments and analyzed the results, [PERSON] and [PERSON] drafted the initial manuscript, and [PERSON], [PERSON], [PERSON], and [PERSON] revised the final manuscript.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**SIMIARITY** & **SVM (F-Score)** & **NB (F-Score)** & **LR (F-Score)** & **Feature Reduction** \\ \hline
**TF-IDF+TSFC** & **China Mobile SMS** & & & \\
**Baseline** & 85.12\% & 83.55\% & 80.45\% & - \\
**40\%** & 85.22\% & 84.78\% & 82.11\% & 22.9\% \\
**45\%** & 87.70\% & 84.59\% & 82.35\% & 20.7\% \\
**50\%** & 88.16\% & 85.60\% & 81.72\% & 17.0\% \\
**55\%** & 85.49\% & 84.35\% & 81.09\% & 12.5\% \\
**60\%** & 85.45\% & 81.43\% & 80.21\% & 7.1\% \\
**TF-IDF+TSFC** & **Fudan News** & & & \\
**Baseline** & 93.54\% & 91.02\% & 87.87\% & - \\
40\% & 93.89\% & 91.27\% & 85.20\% & 27.54\% \\
**45\%** & 95.54\% & 95.74\% & 87.21\% & 23.11\% \\
**50\%** & 96.69\% & 93.58\% & 87.87\% & 17.98\% \\
**55\%** & 96.18\% & 92.11\% & 88.01\% & 15.25\% \\
**60\%** & 93.82\% & 91.94\% & 87.49\% & 10.80\% \\
**Word2 vec+TSFC** & **China Mobile SMS** & & & \\
**Baseline** & 83.23\% & 83.49\% & 81.13\% & - \\
**40\%** & 79.50\% & 80.04\% & 79.48\% & - \\
**45\%** & 83.31\% & 84.56\% & 81.87\% & - \\
**50\%** & 89.93\% & 86.20\% & 84.99\% & - \\
**55\%** & 86.17\% & 84.35\% & 83.19\% & - \\
**60\%** & 82.21\% & 83.48\% & 82.25\% & - \\
**Word2 vec+TSFC** & **Fudan News Data** & & & \\
**Baseline** & 91.67\% & 90.00\% & 86.86\% & - \\
**40\%** & 87.81\% & 87.26\% & 86.19\% & - \\
**45\%** & 91.74\% & 89.52\% & 87.51\% & - \\
**50\%** & 93.19\% & 92.47\% & 88.92\% & - \\
**55\%** & 93.05\% & 90.46\% & 87.28\% & - \\
**60\%** & 91.82\% & 89.84\% & 86.95\% & - \\ \hline \end{tabular}
\end{table}
Table 4: Result of classification methods.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant Nos. 61401519, 61872390), the Natural Science Foundation of Hunan Province (Grant Nos. 2016J)4119, 2017J]3415), and the Postdoctoral Science Foundation of China (Grant No. 2016M592450).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models\textbackslash\{}Metrics** & **RMSE** & **MAE** & **MAPE** \\ \hline PointNet [30] & 189.80 & 139.61 & None \\ PointNet++ [30] & 188.85 & 142.80 & None \\ DGCNN [30] & 161.61 & 129.66 & None \\ GS-Net [30] & 180.85 & 145.63 & None \\
3 DVI [30] & 151.79 & 115.15 & None \\ BioNet [30] & 99.33 & 71.23 & None \\ PointMixer & 113.41 & 93.26 & 0.194 \\ Point Transformer & 155.49 & 128.74 & 0.230 \\ PointNeXt & 146.96 & 116.76 & 0.215 \\ BMP\_Net & **84.48** & **67.31** & **0.131** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of different models on the SGCBP dataset (data for PointNet to BioNet is from paper [30], where the original paper’s authors did not use the MAPE metric).
# Assessment of Novel Modal Testing Methods for Structures Rotating in Water
## 4 Results  
### Results in Air  
#### 4.1.1 Natural Frequencies of the Non-Rotating Disk in Air  
Figure 8 presents the natural frequencies and corresponding mode shapes of the non-rotating disk in air, calculated through a numerical modal analysis. The 1 ND presents the lowest natural frequency, with a value of 37.5 Hz. Subsequently, there are the 0 NC, 2 ND, 3 ND, 4 ND and 1 NC, with values of 57.3 Hz, 67.3 Hz, 140.8 Hz, 246.2 Hz and 329.3 Hz, respectively. The combined mode 1 NC+1 ND presents the highest natural frequency in the investigated range of frequencies, with a value of 355.1 Hz. The same mode shapes and natural frequencies were also obtained numerically with a harmonic analysis in which the system was excited with a chirp signal. Figure 9 shows a frequency response and some mode shapes calculated using the harmonic analysis.  
Figure 8: Numerical modes of vibration of the non-rotating disk in air: (**a**) 1 ND; (**b**) 0 NC; (**c**) 2 ND; (**d**) 3 ND; (**e**) 4 ND; (**f**) 1 NC; (**g**) 1 NC+1 ND.  
Figure 7: (**a**) Numerical model of the test rig when the disk was submerged inside the tank of water. (**b**) Close-up around the piezoelectric patch model, with its active and soft layers indicated and the disk flipped to see the PZT in detail.  
In this section, the presented experimental natural frequencies of the non-rotating disk in air were obtained using both the rowing hammer technique and a chirp applied with the PZT patch. In the current study, both experimental methodologies resulted in the same natural frequencies as in previous investigations [26,27]. Subsequently, these results were compared with the numerical predictions. Figure 10 shows the numerical mode shapes of the 2 ND and 3 ND modes (plotted in the left column) and the corresponding experimental results (plotted in the right column). This figure shows that the number of nodal diameters and their positions (see the green lines) were captured numerically. Moreover, the highest relative displacements were usually found on the external perimeter of the disk in both the numerical and experimental results. In general, the numerical mode shapes were in close agreement with the experimental mode shapes.  
Figure 10: Numerical (left) and experimental (right) mode shapes of the non-rotating disk in air: (a) 2 ND and (b) 3 ND.  
Figure 9: Numerical frequency response obtained from a harmonic analysis when the disk was not rotating in air.  
Table 1 shows a comparison of the numerical and experimental natural frequencies. The absolute percentage of the deviation between the results was lower than 7% for the diametral modes, 1 ND, 2 ND, 3 ND and 4 ND, whereas the discrepancy was higher for the circular modes, 0 NC and 1 NC, and the combined mode, 1 NC+1 ND, where the deviation increased up to 28%. This higher discrepancy may have been associated with the uncertainties in the bearings' properties, which influence the NC modes more than the ND modes.
## 2 Related Work
### Subject-Independent Modeling Approach  
In contrast to the subject-specific modeling approach, subject-independent models mitigate performance degradation in inter-patient schemes by expanding the model's generalization ability.  
[PERSON] et al. [21] developed a subject-independent model using a symbolization approach to minimize patient variations. They employ 1D ECG signals with RR intervals as inputs to the model, converting the symbolized ECG signals and RR intervals into embedded matrices, which are then concatenated to form the input to the classifier. Toobtain multi-perspective representations of distinct heartbeats, a multi-perspective CNN was used for classification. [PERSON] et al. [22] introduced a subject-independent model that integrates a denoising autoencoder and a CNN with a lightweight transformer. They enhanced the performance of minority classes in the inter-patient scheme by extracting local features through the proposed seq2 seq network and global features from adjacent heartbeats through a lightweight transformer encoder. [PERSON] et al. [23] proposed a CNN-based subject-independent model combining multi-scale convolution and frequency convolution block attention. They exhibited an improved ability to learn comprehensive features by leveraging convolution modules of varying sizes in the early layers. [PERSON] et al. [24] proposed a hybrid model that combines two architectures. This model employs densely connected convolutional neural networks, gated recurrent unit networks, and attention mechanisms to capture the morphology and temporal differences of ECG beats to address inter-patient issues.  
Several recent studies address inter-patient variability in ECG signals using domain adaptation techniques [25; 26; 27; 28]. [PERSON] et al. [25] propose a multi-level unsupervised domain adaptation framework that utilizes atrous spatial pyramid pooling residuals and a graph convolutional network model to extract the spatio-temporal structure and data-structure features, respectively. In the domain adaptation process, three types of alignment, namely domain alignment, semantic alignment, and structure alignment, are applied to reduce the gap between the source and target domains. [PERSON] and [PERSON] [26] propose a source-free domain adaptation method for arrhythmia classification. This approach applies domain adaptation without requiring access to the source dataset by utilizing the local structure clustering method, which aligns target ECG features with similar neighbors. By not accessing the source dataset, this approach resolves data-privacy issues regarding patient information.  
These subject-independent models offer the advantage of not requiring a separate fine-tuning process for each individual. However, the absence of a fine-tuning process may lead to the performance not being as high as subject-specific models. Despite the potential performance trade-off, using subject-independent models appears to be a more reasonable approach in real-world settings.
### Motivation  
Despite the number of research works devoted to the length of the anchoring chain, anchorage circle radius, safety distance between anchoring ships, anchorage planning, utilization of anchorage, and so on which could play a supporting role in the detection research of mooring area, there are some research gaps in the existing studies. The extensive literature search shows that there are few studies on the mooring area detection. The existing anchorage circle radius model is relatively primitive, because it only takes the length of the anchoring chain and water depth into consideration and lacks consideration of the ship type, loading condition and the safety of the anchoring ships and the ships passing through the anchorage. It is necessary to conduct further research on the anchorage circle radius and the safety distance model between the anchor positions. More importantly, there are few intelligent detection methods for the mooring area. Meanwhile, the existing research cannot meet the needs of the development of ship industry, and the navigation practice needs the support of scientific detection method of mooring area.  
In order to improve the detection capacity and accuracy of the mooring area, the following aspects were investigated in this study:  
1. The anchorage circle radius model was improved by considering parameters such as ship type, ship width, ship length, distance from anchor chain hole to bow and stern line, trim angle and water depth and so on. The improved model reflects engineering practice more accurately, ensuring the authenticity of the results.
2. The safety distance model between the anchor positions was also improved. The improved model not only considered the parameters of the anchorage circle radius model, but also took into account the safety impact caused by ships passing through the anchorage. The improved model presents a better reflection of the engineering practice, making the distance safer and more reasonable.
3. An intelligent detection algorithm combining the anchorage area detection model and the Monte-Carlo stochastic simulation method was established, and a large amount of random numbers were used to perform the detection operation and simulation, so as to quickly obtain the distribution of the anchor position of ships waiting to anchor. Under the support of the anchor position conversion model, and the dropping anchor position conversion model, the draping anchor position or the ship position of the dropping anchor were transferred from the anchor position.
## 6 High Frequency Characterization and Modelling
### High-Frequency Characterization of UTC Photodiodes
#### 6.2.3 Optical Frequency Response  
The optical frequency response of the photodiodes was measured using a heterodyne bench measurement setup. All UTC-PDs were biased at \(-2\) V under a photocurrent of 1 mA. In addition, 3-dB bandwidths of above 35 GHz for the largest geometry, 7 \(\times\) 25 \(\upmu\)m\({}^{2}\)  
Figure 18: Measured (symbol) and simulated (line) \(S_{11}\) parameter for three geometries 5 \(\times\) 15, 5 \(\times\) 30 and 7 \(\times\) 25 \(\upmu\)m\({}^{2}\): (**a**) magnitude, (**b**) phase.  
and about 50 GHz for the smallest UTC-PD geometry, 5 \(\times\) 15 \(\upmu\)m\({}^{2}\), can be observed in Figure 19. The model simulation shows good agreement with the measured data validating the accuracy of Equation (4) implemented in the model.  
Co-Simulation for Design of Future Electronic-Photonic Integrated Circuits and Performance Prediction  
While the previous sections presented detailed characterization and compact modeling approaches of the individual electronic and photonic modules, in order to develop a predictive, multiphysics simulation framework for the OEICs, in-depth understanding of underlying transport mechanisms is crucial. With that in mind, this section provides insights into these aspects through a complementary TCAD simulation framework, performance evaluation using compact model extrapolation to scaled dimensions and ultimately a unified reliability-aware design methodology for the conception of the OEICs.
## 2 Materials and Methods
### Method's Validation  
The analytical method was verified against the assumptions outlined in document SANTE/11312/2021v2. The evaluated parameters included the linearity of the calibration curve based on the matrix samples. The linearity of the calibration curve was determined by calculating the coefficient of determination (R2) and BCC \(\leq\)\(\pm\)20% at 5 concentration levels performed for each compound in the linearity range of 0.002 mg/mL-0.5 mg/mL. The matrix effect was examined in comparison with the calibration curve for chromatographic standards in the solvent. The matrix effect verification aimed to quantify the analytes' suppression or enhancement of the response by the detection system. The mean percentage recovery (RE%) was set at 70-120%, with an RSD \(\leq\) 20%. In exceptional cases, an average recovery in the range of 70-120% may be accepted if the analytes are consistent (RSD \(\leq\) 20%), but the average absolute recovery should not be below 30% or above 140%. According to the assumptions of the document, the lowest concentration should be \(\geq\)LOQ, while the next validation level should fall within the range of 2-10 times the LOQ value. In the examined validation experiment, the second enrichment level was set at 5 \(\times\) LOQ. The  
Figure 1: Analytical sample extraction steps.  
method's precision should be within \(\leq\)20%, and the expanded uncertainty of the method should be within \(\leq\)50%. Quality parameters of the chromatographic resolution should be \(\pm\)30% (relative value) of the average ratio of ions of calibration standards in the same sequence, and the retention time should be \(\pm\)0.1 min. Additionally, limits of detection (LODs) and limits of quantification (LOQs) were investigated, where the LOQ value should be \(\leq\)MRL. The values of LOQ and LOD were calculated using the signal-to-noise (S/N) ratio comparison method. The determination of LOQs and LODs involved comparing the concentration of the chromatographic peak with the lowest signal to the chromatographic signal obtained from the measurement of a pesticide-free sample. The LOD value represents a signal-to-noise ratio of 3, while the LOQ is set at 10 compared to the noise level of the pesticide-free sample signal. The test material subjected to examination consisted of olive oil samples devoid of pesticide residues. The oil matrix was organic olive oil purchased for validation purposes.