esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 2 Literature Review of PLS Techniques for IoT
### PLS Schemes for IoT: State of the Art
#### 2.2.2 Compressive Sensing  
Compressive sensing (CS) can compress sparse signals with a much lower rate compared with the Nyquist sampling rate. Recently, the CS technique is utilized to realize physical layer security [26]. In CS, a linear transformation is applied to the sparse information-bearing signal by multiplying it with a measurement matrix. Transmission secrecy can be guaranteed if the measurement matrix is unknown to the eavesdropper. To achieve this goal, [PERSON]. [36] proposed a scheme that utilizes an m sequence to construct the measurement matrix. The random seed used to generate the m sequence is distilled from the RSSI values of packets exchanged between the legitimate user pairs. Because the channel coefficient of the legitimate link is uncorrelated with that of the eavesdropping link, the eavesdropper cannot compute the same measurement matrix as the legitimate nodes and thus information secrecy can be guaranteed. In [37], a CS-based encryption scheme was developed for multi-carrier systems. In order to lower the probability of correct recovery of the measurement matrix by the adversary, the authors proposed transmitting artificial noise together with a sparse message. Furthermore, the channel state information (CSI) was exploited to selectively transmit an artificial noise such that its detrimental effect upon the legitimate receiver can be minimized. In [38], the security performance of the CS-based cryptosystem was studied. The analysis therein shows that the CS-based cryptosystem with circulant matrices over wireless channels can be computationally secure in terms of the indistinguishability, as long as the channel gains and the plaintext-to-noise ratio of the adversary are kept to be low for a long keystream and a short ciphertext. CS technique is also used to achieve physical layer security in multi-node cooperative systems, where the channel matrix between multiple sources and multiple relays is considered as the CS measurement matrix [39]. As is shown by [40], by adopting this method, the eavesdropper's probability of signal recovery is zero. It should be noted that [39; 40] only focused on the security transmission in dual-hop systems with CS. Different from these papers, [PERSON]. [41] utilized the multi-hops to implement the projection process of CS, thus achieving keyless secure communication for multi-hop networks.
###### Abstract  
To reduce environmental contamination following the release of various radionuclides during the Fukushima Daiichi Nuclear Power Plant accident, the Japanese government has continued decontamination work and decided to use interim storage facilities (ISFs) in the towns of Okuma and Futaba to house large quantities of contaminated soil and waste until their final disposal. This study aims to clarify whether there are differences in the acceptance of removed soil for recycling between people who provided land for ISFs and those who do not. Furthermore, we analyzed the risk perception of the radiation effects on offspring, trust in information from public authorities, and intention to return to their hometown. A questionnaire survey was conducted at Okuma and Futaba town homes in the ISF area. Compared with those who did not provide land to the ISFs (n = 538, 68.0%), people who provided land (n = 141, 77.9%) responded that they were accepting the building of the ISFs (\(p\) = 0.018). Meanwhile, approximately half of the respondents were \"unable to decide\" concerning the recycling of removed soil in their current region of residence regardless of whether the land provided or not. Similarly, there were no significant differences between the groups regarding radiation risk perceptions of the offspring effects on residents and intention to return, or whether to provide land to the ISFs. The study suggests that it is important to continue radiation risk communication to determine whether people have provided land to the ISFs.  
Fukushima Daiichi Nuclear Power Plant accident; interim storage facilities; removed soil +
Footnote †: journal: Applied sciences
## 3 System Model and Problem Formulations
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Notations** & **Descriptions** & **Notations** & **Descriptions** \\ \hline UMa & Urban macro cells & UMi & Urban micro cells \\ \hline \(R_{RRU_{M}}\) & Cell radius of macro-RRU & \(R_{RRU_{m}}\) & Cell radius of micro-RRU \\ \hline \(MAPL_{RRU_{M}}\) & MAPL for macro-RRU & \(MAPL_{RRU_{m}}\) & MAPL for micro-RRU \\ \hline \(N_{\text{Mcor}}\) & Number of RRU\({}_{\text{M}}\) for coverage & \(N_{mcov}\) & Number of RRU\({}_{\text{m}}\) for coverage \\ \hline \(N_{Mcap}\) & Number of RRU\({}_{\text{M}}\) for capacity & \(N_{mcap}\) & Number of RRU\({}_{\text{m}}\) for capacity \\ \hline \(PL_{\text{LIMa}}\) & Path loss model for UMa & \(PL_{\text{LIMi}}\) & Path loss model for UMi \\ \hline \(N_{RRU_{M}}\) & Number of users served by macro-RRU & \(N_{RRU_{m}}\) & Number of users served by micro-RRU \\ \hline \(NUM_{RRU_{M}}\) & Total number of set of macro-RRU & \(NUM_{RRU_{m}}\) & Total number of set of micro-RRU \\ \hline \(RRU_{M}(x^{\prime},y^{\prime})\) & Location of \(i^{th}\) macro-RRU & \(RRU_{m}(x^{\prime},y^{\prime})\) & Location of \(i^{th}\) micro-RRU \\ \hline UE(x^{\prime},y^{\prime})\) & Location of \(i^{th}\) UE & \(NU_{total}\) & Total number of users to be served \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter notations and description. Figure 1: 5G- mixed cell structure.
## 3 Results
### Correlation between the Expression of _SATB1_, _Smad3_, _Ezrin_ and _CTNNB1_ in TCGA Cohort
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Univariate Analysis of TCGA Cohort**} \\ \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**HR**} & \multicolumn{2}{c}{**95\% CI**} & \multirow{2}{*}{\(p\)**Value**} \\ \cline{3-3} \cline{5-5}  & & **Lower** & & **Upper** \\ \hline SATB1 & 0.73 & 0.48 & 1.09 & 0.12 \\ \hline SMAD3 & 2.26 & 1.45 & 3.54 & **<0.0001** \\ \hline EZR & 4.65 & 2.02 & 10.69 & **<0.0001** \\ \hline CTNNB1 & 2.21 & 1.34 & 3.65 & **0.002** \\ \hline age & 1.03 & 1.01 & 1.05 & **0.01** \\ \hline gender & 0.81 & 0.54 & 1.23 & 0.33 \\ \hline grading & 2.18 & 1.15 & 4.13 & **0.02** \\ \hline pN & 2.10 & 1.25 & 3.52 & **0.01** \\ \hline pT & 2.21 & 1.14 & 4.28 & **0.02** \\ \hline TNM stage & 0.74 & 0.23 & 2.34 & 0.60 \\ \hline \hline \end{tabular}  
* Significant \(p\) values (\(p<0.05\)) are marked in bold. Abbreviations: CI, confidence interval; HR, hazard ratios; TCGA—The Cancer Genome Atlas.  
\end{table} 
Table 5: Univariate analysis of prognostic factors by Cox proportional hazard model in TCGA cohort. Figure 5: Kaplan–Meier survival curves stratified by SATB1 (**A**), SMAD3 (**B**), EZR (**C**) and CTNNB1 (**D**) mRNA levels in TCGA cohort.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 4 Diagnosability Design Based on Structural Analysis  
Diagnosability design mainly includes inherent diagnosability design and actual diagnosability design. Inherent diagnosability primarily refers to the fault diagnosis performance reflected by the hardware design characteristics. Inherent diagnosability is in the system design stage. The designer goes through the system hierarchy, functional structure, physical architecture, electrical, and other divisions to give the system specific diagnosability capabilities. Inherent diagnosability is the meeting of the diagnosability requirements as a prerequisite to configure the diagnosis resources to maximize the diagnosis capability rationally. Actual diagnosability is the fault diagnosis, fault detection, and fault isolation capability determined by the inherent diagnosability. It mainly includes fault diagnosis methods, fault diagnosis strategies, fault diagnosis metrics, etc.
# Fusion Classification Method Based on Audiovisual Information Processing
## 3 Proposed Method  
### Audio-Visual Fusion Network Model  
Figure 1 illustrates the architecture of M-AVFA. To address the issue of cross-modal fusion between audio and visual data, we explored a novel approach. First, since audio signals are one-dimensional temporal signals, we leveraged the relationship between the time domain and the frequency domain. By applying the Fast Fourier Transform (FFT), we converted the temporal information into frequency information. Subsequently, we extracted audio features through a series of operations, including triangular filters and discrete cosine transforms, and transformed them into corresponding audio feature spectrograms.  
To fully exploit the semantic correlations between auditory and visual modalities, we designed a dual-stream network architecture. The audio spectrogram and target image are independently processed through a 1D CNN and 2D CNN, respectively, to extract modality-specific features (denoted as audio feature \(X_{audio}\) and visual feature \(X_{img}\)). These unimodal features are then fused through cross-modal interaction to generate a joint semantic representation \(\widetilde{X}_{cat}\). To preserve modality-specific discriminability while enhancing complementary information, both the original features (\(X_{img}\) and \(X_{audio}\)) and the fused representation \(\widetilde{X}_{cat}\) are adaptively integrated via a self-attention-based feature fusion module, producing the final unified semantic embedding \(X_{cat}\). This hierarchical fusion mechanism ensures the retention of critical unimodal patterns while capturing cross-modal dependencies. The resulting embedding \(X_{cat}\) is subsequently fed into a Softmax classifier to accomplish unified target classification.
## 4 Survey Results and Analysis
### Exploratory Factor Analysis  
The data analysis method used in this study is an exploratory factor analysis which aims to rank the importance of key influencing indicators. Using Principal Component Analysis reduces the number of variables used initially (to increase the speed of data processing) by considering a reduced number of representative variables. Strategic directions are also determined by identifying hidden patterns in the relationships between data.
## 2 Design and Analysis
### Single Antenna Element
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline W\({}_{\text{p}}\) & 9.70 & W\({}_{\text{f}}\) & 0.70 \\ L\({}_{\text{p}}\) & 9.70 & L\({}_{\text{f}}\) & 10.0 \\ W\({}_{\text{slot1}}\) & 0.50 & W\({}_{\text{inset}}\) & 0.50 \\ L\({}_{\text{slot1}}\) & 2.00 & L\({}_{\text{inset}}\) & 2.50 \\ R\({}_{\text{slot2}}\) & 0.60 & W\({}_{\text{sub}}\)\({}_{\text{Lsub}}\) & 25.0 \\ W\({}_{\text{slot3}}\) & 0.25 & h & 0.787 \\ L\({}_{\text{slot3}}\) & 0.76 & W\({}_{\text{g}}\)\({}_{\text{Lg}}\) & 25.0 \\ S & 0.93 & W\({}_{\text{gslot}}\) & 0.50 \\ \hline \end{tabular}
\end{table} 
Table 1: Single-element antenna parameters. Figure 2: Parametric sweep of the antenna design illustrating the impact of L\({}_{\text{inset}}\) on reflection coefficient versus frequency. At 28 GHz
* E-plane: 9 dBi gain, main lobe at \(\theta=4^{\circ}\) (beamwidth = 50.8\({}^{\circ}\)). * H-plane: 9.3 dBi gain, main lobe at \(\theta=0^{\circ}\) (beamwidth = 27.3\({}^{\circ}\)). * At 35 GHz
* E-plane: 5.9 dBi gain, main lobe at \(\theta=42^{\circ}\) (beamwidth = 36.9\({}^{\circ}\)). * H-plane: 5.7 dBi gain, main lobe at \(\theta=29^{\circ}\) (beamwidth = 26.2\({}^{\circ}\)). * At 38 GHz
* E-plane: Weaker gain pattern. * H-plane: 11.2 dBi gain, main lobe at \(\theta=24^{\circ}\) (beamwidth = 21.4\({}^{\circ}\)). Figure 3: Radiation patterns of single-element antenna: (**a**) E-plane, (**b**) H-plane at 28 GHz, (**c**) E-plane, (**d**) H-plane at 35 GHz, (**e**) E-plane, and (**f**) H-plane at 38 GHz. This comprehensive analysis of radiation patterns across frequencies provides essential insights for antenna design optimization, ensuring efficient radiation characteristics and the desired performance across operational bands.
## 2 Numerical Calculation Model
### Turbulence Model  
In recent years, the dynamic hybrid RANS-LES method (HRL), a hybrid simulation method, has been widely used. The concept of this method is that the whole flow field is partitioned into several regions, and then the turbulence boundary layer flow is solved in near wall regions using the Reynolds averaged Navier-Stokes (RANS) turbulence model method, and the large-scale turbulence structure is solved in the far field using the large eddy simulation (LES) method with a higher accuracy. The HRL method, combining the respective advantages of RANS and LES, reduces calculational complexity significantly and improves calculational efficiency while ensuring accuracy.  
The stress-blended eddy simulation model (SBES), the most advanced hybrid simulation calculation model to date, can implement a fast and automatic switch between RANS and LES by refining the length and scale of definition grids compared with the traditional detached-eddy simulation (DES) model and delayed detached-eddy simulation (DDES) model. This method has greater advantages but no apparent grid dependence in dealing with the turbulence problem with a larger geometric curvature of the boundary wall, and is more applicable to the capture of the flow details of a propeller tip vortex [48; 49; 50]. The SBES model is the reconstruction of the hybrid function based on DES and a divergence term is added into the equation. This model implements a fast and automatic switch from RANS to LES by combining the stresses in RANS and LES using the fSBES screen function, as depicted in Equation (3):  
\[\tau_{ij}{}^{SBES}=f_{SBES}\tau_{ij}{}^{RANS}+(1-f_{SBES})\tau_{ij}{}^{LES} \tag{3}\]  
where \(\tau_{ij}{}^{SBES}\), \(\tau_{ij}{}^{RANS}\) and \(\tau_{ij}{}^{LES}\) are the stress tensors of SBES, RANS and LES, respectively.
# Stress Analysis and Spalling Failure Simulation on Surrounding Rock of Deep Arch Tunnel
## 4 Discussion
### Spalling Process  
By comparison, it was found that the failure process of the sidewalls of the three specimens was similar. Under the initial loading, there is a calm period. This means the period during which the pores within the specimens were compacted and internal microcracks initiated, as shown in Figure 19a. Then, particle ejection occurred at the foot or shoulder of sidewalls, accompanied by a further expansion of internal microcracks (Figure 19b). This is consistent with the stress analysis in Section 4.2. In other words, the maximum tangential stress of the three specimens reached the maximum at the foot of the arch, followed by that at the shoulder. This results in the stress concentration and the release of the accumulated energy, as shown in Figures 7a, 8 and 9a. As the vertical stress increased, macro fractures were created on the straight wall under the action of internal microfractures. Rock fragments were created due to the penetration of macro fractures. As shown in Figure 19c, under the tangential stress, the surrounding rock bent and opened until toppled onto the floor the test results corresponding to this period are shown in Figure 7c-f, Figure 8b-e and Figure 9b-e. The rock fragments produced on the sidewall kept bulging and breaking from the middle straight wall towards the axial ends of the hole, gradually flexing and opening towards the free surface. Spalling occurred after the rock fragments opened to a certain extent. With the development of the sidewall failure, a wide range of spalling areas was produced on both sidewalls and eventually the V-shaped or curved grooves were produced on the sidewalls, as shown in Figure 19d.  
Overall, the failure process hole sidewalls can be divided into the following four periods: the calm period of internal crack expansion, the ejection period of fine particles at the foot or shoulder of the arch, the macroscopic crack penetration and spalling period, and the V- or arc-shaped groove formation period. The spalling process of specimen A12-42.22-27.12 is consistent with the results in the literature [48; 49; 50] (specimens containing D-shaped holes of the same size were tested), the spalling finally results in a V-shaped groove. Unlike the semi-circular tunnel, for specimens A13-22.2-22.2 and A14-17.3-28.9, finally, arc-shaped grooves were produced due to the changes in arch height and arch structure in the three-centered arch holes. The results of the true triaxial compression test on specimens containing 40 mm rectangular holes also suggested that the spalling zone gradually developed horizontally towards the deeper hole walls and finally produced a symmetrical arc-shaped groove along the axial direction of the hole [72]. The maximum tangential stress of the rectangular hole was reached at the four corners of the hole. The final distribution of tangential stress tended to be similar to that of the rectangular hole as the semi-circular arch hole changed to the three-centered arch hole with lower arch heights.
## 3 DC-SoC Consortium Chain Network Model
### Initial Clustering
```
Set of ActiveNodes, Set of InactiveNodes, \(eps\), \(min\)Pts Output: Updated Clusters for each node \(i\in\) ActiveNodes do if\(i\) is a CoreNode then  Maintain current status as ClusterHead elseif\(i\) has newly joined the network then  Assign \(i\) to the nearest cluster \(C\) such that: \(C=\arg\min(\text{distance}(i,\text{CoreNode\_C}))\) subject to: distance \(\leq\)\(eps\), neighborhood size \(\geq\)\(min\)Pts endif endfor for each node \(j\in\) InactiveNodes do if\(j\) is a CoreNode then  Elect a new CoreNode from cluster \(C\): \(\text{CoreNode\_C}=\arg\max(\text{Trustworthiness}(j))\) elseif\(j\) has rejoined the network then  Reassign \(j\) to its previous cluster \(C_{\text{prev}}\) or to a new cluster \(C_{\text{new}}\) based on proximity and trust: \(C_{\text{new}}=\arg\min(\text{distance}(j,\text{CoreNode\_C}))\) subject to: distance \(\leq\)\(eps\), neighborhood size \(\geq\)\(min\)Pts endif endfor for each cluster \(C\)do if cluster \(C\) has insufficient members or is fragmented then  Merge \(C\) with a neighboring cluster \(C_{\text{neighbor}}\): \(C_{\text{neighbor}}=\arg\min(\text{distance}(C,C_{\text{neighbor}}))\) endif endfor
```  
**Algorithm 1** Dynamic Clustering Maintenance  
The DBSCAN++ algorithm is employed to identify clusters in the network. The primary variables that govern this clustering process are \(eps\), which defines the radius for neighborhood detection, and \(min\)Pts, the minimum number of nodes required to form a core object. These core objects form the basis of clusters, which are created around nodes that meet these density criteria. The number of clusters is determined dynamically based on the density of nodes and their network latency values. The clustering process adapts in real-time, adjusting to changes in node availability and network topology. As the number of peers or the network's geographic distribution changes, DBSCAN++ dynamically re-calculates the number of clusters. Clusters are formed when a node's local neighborhood, defined by \(eps\), contains at least \(min\)Pts nodes. Nodes that do not meet this criterion are treated as outliers but are still included in the network's propagation structure to ensure fairness and robustness.
## 3 Results and Discussion
Feature extraction from the lumbar vertebrae CT scans was conducted using [PERSON]'s method is an effective thresholding technique that separates the foreground (vertebrae) from the background by maximizing the between-class variance, as shown in Figure 7. The process involved computing the histogram of pixel intensities from the CT images determining the probabilities of the background and foreground classes, computing cumulative sums and means calculating the between-class variance for each possible threshold selecting, the threshold that maximizes the between-class variance and applying the optimal threshold to create a binary mask segmenting the vertebrae from the surrounding tissue. This feature extraction process ensures that the chosen threshold maximizes the distinction between the two classes enhancing the accuracy of segmentation. The final results of segmentation are shown in Figure 8. The 3D figure surface has been smoothened by using the 3D editor tool Autodesk for fine finish. Overall, the Res U-Net model results combined with the feature extraction algorithm ([PERSON]'s method) demonstrate robust performance in vertebrae segmentation with high DSC sensitivity and specificity values. Further research could explore optimizing the model to achieve even higher accuracy and robustness in clinical applications. The findings from this study hold the potential to enhance clinical practices, influence public health policy and shape personalized treatment strategies in spinal fusion operation, especially during pre-operative planning. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **Test Sample** & **DSC** \\  & **(CT Scans)** & **(in \%)** \\ \hline Res U-Net (Proposed) & 214 + 25 & 87.10 \(\pm\) 3.72 \\ \hline CNN [29] & 15 & 85 \\ \hline U-Net [14] & 14 & 90.40 \\ \hline PaDBN [15] & 3 & 86.1 \\ \hline PCCN [16] & 17 & 65.7 \(\pm\) 15.4 \\ \hline MLPNN [16] & 17 & 91.1 \(\pm\) 2.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dice Coefficient (DSC) comparison with related work. Figure 8: Final segmented 3D image of the vertebrae. Figure 7: Vertebra feature extraction using [PERSON]’s method. (**a**) CT scan image (**b**). Segmented binary mask. (**c**) CT image with segmented contour.
# Numerical Calculation and Experimental Study of the Axial Force of Aero Fuel Centrifugal Pumps
## 3 Axial Force Measurement Test
### Analysis of Test Results  
In order to obtain axial force data at different speeds, the outlet throttling state and inlet pressure of the centrifugal pump were kept unchanged during the test and the speed was continuously increased from zero until it reached the maximum speed. The flow, fuel outlet pressure, and axial force values under different speed conditions were measured and the numerical simulation values were compared with the test values, as shown in Figure 13. As can be seen from the figure, the variation trend of the simulation value and the test value is roughly the same. Under the condition of low rotating speed, the simulation value is slightly larger than the test value, while under the condition of high rotating speed, the simulation value is smaller than the test value. The maximum error of 9.7% is allowed in the calculation of axial force.