esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot
## 3 Dynamic Models
### The Dynamic Model of AMBSS
Therefore, the relationship of the linear actuator between the motor speed and the load output force can be expressed by the following Equation [36]:  
\[w=-K_{f}f_{pm}+b_{s}, \tag{12}\]  
where \(K_{f}=\frac{R_{a}l_{0}}{2\pi k_{a}k_{T}}\) is the relationship coefficient between motor speed and output force, \(f_{pm}\) is the motor output force, and \(b_{s}=\frac{V_{a}}{k_{a}}\) is the no-load speed. Substituting Equations (10)-(12) into Equation (9) and defining \(\textbf{x}_{2}=\big{[}x_{21},\quad x_{22}\big{]}^{T}=\Big{[}f_{pm},\quad\dot{f }_{pm}\Big{]}^{T}\in R^{2}\) as the state vector, the state-space form of the AMBSS as a 2 nd-order general nonlinear system can be derived as:  
\[\textbf{\Psi}_{AMBSS}:\left\{ \begin{array}{l}\dot{x}_{21}=x_{22}\\ \dot{x}_{22}=f_{2}(\textbf{x}_{2})+g_{2}(\textbf{x}_{2})u_{2}+d_{2}\\ y_{2}=x_{21}\end{array} \right., \tag{13}\]where \(u_{2}\) and \(y_{2}\), respectively, denote the input and output of the AMBSS, \(f_{2}(\mathbf{x}_{2})\) and \(g_{2}(\mathbf{x}_{2})\) are nonlinear functions, \(d_{2}\) is the lumped effect from unmodeled dynamics, model uncertainties and unknown external disturbances.
In WSNs, multi-hop cluster head communication is more energy efficient, and the CHs collaborate with each other to forward their data to the BS [25]. Thus, data forwarding on a multi-hop path through intermediate CHs is a more realistic solution [25]. Cluster head communication facilitates energy efficient and safe routing of data in WSNs. A cluster based trust-aware routing protocol allows cluster member nodes to forward reports through the trusted CHs towards the BS [26]. The trust-aware routing protocol allows for re-election of a new CH to maintain safe routing in the network. Multi-hop cluster head-based communication is scalable and provides energy efficiency in WSNs [27; 28]. Therefore, cluster-based model becomes a natural option due to its suitability as a filtering mechanism. Cluster head communication saves energy in multi-hop communication based WSNs [7; 26]. In [11], we proposed the Fuzzy Adaptive Selection of Intermediate verification Nodes (referred to as FASIN hereafter), which adaptively selects verification nodes based on the attack situation, the energy levels and the distance of the nodes. FASIN surpasses the PVFS in terms of energy saving and extended network life in the sensor network, and it provides equivalent security against FRIA and FVIA at the same time. We propose a fuzzy rule-based route selection coupled FASIN that uses dynamic authentication key dissemination technique to achieve earlier detection of fabricated reports during the filtering process and enhance the energy efficiency of the filtering-based WSNs. The proposed method considers the following three important factors in the sensor network for selecting the fittest routing path:  
1. The number of verification nodes on the path within \(h\) hops from the e-CH where \(h=L\) (the number of nodes in a cluster). 2. The hop-count distance ratio of the routing path being considered and the longest path between the e-CH and the BS. 3. The remaining energy status of the intermediate nodes on the path. By considering the above three input parameters, fuzzy inferencing carried out at the e-CH helps choose the most appropriate routing path for the current situation. FASIN is used in conjunction with the path selection to improve the performance of the less fit routes in an effort to increase their chance of being selected and avoid the emergence of a favored path anomaly. The remainder of this paper is organized as follows. In Section 2, we present an overview of the PVFS and FASIN algorithms and provide a rationale for the proposed scheme. The proposed method is presented and explained in Section 3. Simulation results are presented and discussed in Section 4, followed by a study of related work in Section 5. Finally, Section 6 presents the conclusion.
# Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
## 2 Methodology
### Feature Extraction Network
#### 2.3.1 Transfer Learning  
Given that sufficient public datasets for the diagnosis of DR do not exist, it is difficult to obtain a satisfactory result based on deep learning technology. To solve this problem, transfer learning technology is adopted in our model, and its steps of implementation are as follows: (1) Pre-training the network. To obtain the pre-trained network, the EfficientNet is pre-trained on ImageNet, which is currently the largest image recognition dataset in the world, with 1.2 million images of 1000 categories. Then the diabetes data is loaded into the pre-trained EfficientNet. (2) Fine-tuning. The final step of the feature extraction backbone is a fine-tuning stage, at which we build a new fully connected layer and use Adam as optimizer with 0.001 learning rate.
# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 4 Experimental Results and Analysis
### Ablation Studies
#### 4.3.2 Effectiveness of the CMF Module  
To verify the effectiveness of the CMF module, we first removed it, allowing the modality-specific network results to be directly connected and passed to the next layer, resulting in \"B1\". Notably, since the DA and RA sub-modules in the CMF module are responsible for cross-enhancing and refining module features, we deleted these sub-modules while keeping the CMF module intact, resulting in \"B2\". Additionally, because the CMF module effectively aggregates the complementary properties of the two modes and transfers this complementary information to subsequent layers, we deleted the connections between CMF modules so the output of one CMF would not pass to the next CMF, resulting in \"B3\". The results of \"B1\", \"B2\", and \"B3\" are shown in Table 4. The comparison indicates  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**NJU2K**} & \multicolumn{2}{c}{**NLPR**} & \multicolumn{2}{c}{**SIP**} & \multicolumn{2}{c}{**STERE**} \\  & \(S_{A}\uparrow\) & \(M\downarrow\) & \(S_{A}\uparrow\) & \(M\downarrow\) & \(S_{A}\uparrow\) & \(M\downarrow\) & \(S_{A}\uparrow\) & \(M\downarrow\) \\ \hline Ours & 0.925 & 0.028 & 0.933 & 0.019 & 0.892 & 0.043 & 0.907 & 0.036 \\ A & 0.924 & 0.028 & 0.929 & 0.020 & 0.883 & 0.049 & 0.902 & 0.040 \\ B1 & 0.924 & 0.030 & 0.933 & 0.020 & 0.890 & 0.043 & 0.906 & 0.036 \\ B2 & 0.924 & 0.029 & 0.930 & 0.022 & 0.892 & 0.044 & 0.905 & 0.039 \\ B3 & 0.920 & 0.029 & 0.926 & 0.021 & 0.889 & 0.045 & 0.902 & 0.040 \\ C1 & 0.925 & 0.029 & 0.931 & 0.020 & 0.896 & 0.042 & 0.908 & 0.036 \\ C2 & 0.923 & 0.030 & 0.931 & 0.020 & 0.891 & 0.045 & 0.908 & 0.037 \\ C3 & 0.922 & 0.030 & 0.930 & 0.021 & 0.953 & 0.030 & 0.906 & 0.037 \\ \hline \hline \end{tabular}
\end{table}
Table 4: This table presents the quantitative evaluation of the ablation studies.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Ours** & **DMRA22** & **DSU22** & **CFIDNet22** & **DAL23** & **SPNet23** & **EGANet23** \\ \hline
**Model Size (MB)** & 145 & 187 & 96 & 55 & 6 & 174 & 170 \\
**Inference Time (ms)** & 31 & 50 & 48 & 46 & 38 & 84 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of model size and inference time of different methods.  
that saliency detection performance declines in these cases, confirming the effectiveness of the DA and RA sub-modules and the importance of connections between CMF modules.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.8. Resistance to Stolen Verifier Attacks  
The \(CS\) stores \(\{UID_{i},UA_{i}\}\) for the user and \(\{SID_{j},r_{k}\}\) for the sensor in its database. We assume that the data is stolen by adversary \(\mathcal{A}\). Then, \(\mathcal{A}\) can attempt to calculate a session key \(SK=h(UID_{i}||SI_{j})\oplus h(n_{i1}||n_{i2}||n_{i3})\). However, \(\mathcal{A}\) cannot obtain the secret value \(SI_{j}\) and random numbers \(n_{i1}\), \(n_{i2}\), and \(n_{i3}\) without master key \(S_{k}\) from the \(CS\). Thus, our scheme can prevent stolen verifier attacks.
# Cm-Level Photonic-Crystal-Like Subwavelength Waveguide Platform with High Integration Density
## 1 Introduction
### A Square Lattice of Circular Air Holes
#### 2.2.2 Reflective Performance of the Waveguides  
As shown in Figure 8, one can see that the waveguide with 0.5-column air holes (or with 1-column air holes) has a stop-band (near-zero transmittance) around 1.65 \(\upmu\)m (or 1.95 \(\upmu\)m). According to the simulation result, in terms of wavelength filtering, it can be applied by utilizing its high reflective character. Therefore, we choose the waveguide with one column of air holes to do some studies.  
In Figure 10, one can find that the band gap increases linearly by the increasing of the period of air holes. One can fine tune the peak reflecting wavelength by changing the period. This feature will provide a new pathway for reflection-type wavelength filters.  
Figure 8: Spectra of the square lattice waveguides with different columns of air holes.  
Figure 7: Square-lattice waveguide structures with e \(0.5,1.0,1.5,2.0\) air-hole columns, respectively, at each side of waveguide cores (\(Period=0.43\,\upmu\)m)  
The study presented in Section 2 provides systematical investigation of the characters of the waveguides as well as a theoretical basis for subsequent control and application of the waveguides.
# A New Method for 2D-Adapted Wavelet Construction:
## 2 Materials and Methods
### Detection of the 2D Pattern with the Proposed Strategy  
The signal \(f[\cdot,\cdot]\) is decomposed, obtaining DST-II 2D\((f[\cdot,\cdot])\) using the algorithm based on tensor product. The detection is based on the normalized similarity measure \(\mathbb{S}\), which emphasizes the presence of zeros in the _shapelet_ coefficients. In particular, we will onlyapply this measure on the diagonal detail coefficients of a 2D DST-II since the 2D _shaplet_ is designed to detect the pattern in these coefficients.  
Let the image \(f[\cdot,\cdot]\) of dimension \(L_{1}\times L_{2}\) be analyzed with the 2D DWT and the corresponding matrix of diagonal coefficients \(cD\) of dimension \(M_{1}\times M_{2}\). Let a pixel be at position \((i,j)\) of \(cD\). Let the normalized similarity measure be \(\mathbb{S}=e^{-(|\text{DST-II}(f[\cdot])|)^{\alpha}}\), \(0<\alpha\leq 1\)[22]. If \(\mathbb{S}(\alpha=0.1)[cD_{ij}]>0.7\), then the 2D pattern occurs inside a rectangle whose upper right corner has the following coordinates:  
\[x = (2\cdot M_{2}-N_{2}+L_{2})\mod(L_{2}),\] \[y = (2\cdot M_{1}-N_{1}+L_{1})\mod(L_{1}),\]  
with a width of \(N_{2}\) pixels and a length of \(N_{1}\) pixels. If there are no coefficients that meet this condition, then the pattern is not found in the image.  
Figure 1 depicts a flowchart of the strategy designed in this section.
# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
## 2 Materials and Methods
### Literature Research
#### 2.3.2 Selection of an Appropriate Maturity Model  
In contrast to the initial DLP focus, our intention was not to compile an exhaustive theoretical overview of potential frameworks. Instead, the aim was to glean a comprehensive survey of well-established frameworks within the domain. Adhering to the methodology proposed by [PERSON] et al. [46], a sequential search for representative literature using a keyword-based approach was conducted. This strategy enabled the identification of frameworks that have been recognized for their applicability and effectiveness in organizational cybersecurity contexts.  
A multitude of papers have been identified comparing various maturity models, such as [47, 48, 49]. Systematic literature reviews in the field were also examined, including the work of [PERSON] et al. [15].  
The outcome of the literature research reveals that, while frameworks such as the NIST CSF 2.0 and the ISO/IEC 27002 standard [10] can be employed as maturity frameworks, they are not purpose-specific to maturity assessments. Frameworks with substantial practical application in industry, including ISO 21827 [50], C2M2, CMMI, and CMMC, have been recognized for their utility and adoption in the field.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Web of Science** & **IEEE Xplore** & **Science Direct** & **Wiley** \\ \hline \multirow{4}{*}{**Search Term**} & “Data Leakage & “Data Leakage & (“Data Leakage & “Data Leakage \\  & Prevention” OR “Data & Prevention” OR “Data & Prevention”) OR (“Data & Prevention”, “Data \\  & Loss Prevention” OR & Loss Prevention” OR & Loss Prevention”) OR & Loss Prevention”, \\  & “Information Leakage & “Information Leakage & (“Information Leakage & “Information Leakage \\  & Prevention” OR “Data & Prevention” OR “Data & Prevention”) OR (“Data & Prevention”) OR (“Data \\  & Leakage Detection” & Leakage Detection” & Leakage Detection”) & Leakage Detection” \\ \hline \multirow{4}{*}{**Search Fields/Topics**} & Topic Information & Information Leakage, Information Security, Data Leakage & Title, Abstract or Author-Specified Keywords & Title or Abstract \\  & Awareness & & & Keywords \\ \hline \multirow{4}{*}{**Additional Requirements**} & Articles, Proceeding & \multirow{4}{*}{Conferences, Journals} & Research Articles, Review Articles & \multirow{4}{*}{-} \\  & Papers, Review & & & \\ \cline{1-1}  & Articles, Conference & & & \\ \cline{1-1}  & Papers & & & \\ \hline
**Hits** & 164 & 107 & 27 & 1 \\ \hline
**Hits with \textgreater{}5 citations** & 44 & 31 & 16 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Knowledge databases queried.
## 3 Results  
In this study, the effect of cold physical plasma-derived ROS on metastatic skin melanoma punch biopsies was investigated _ex vivo_ (Figure 1a). Tissues were sectioned, stained with DAPI (staining for nuclei), and assessed for apoptosis induction using TUNEL. Qualitative analysis showed an increase in apoptotic cells after exposure to physical plasma (green cells in Figure 1b). Quantitative image analysis over dozens of fields of view was performed. Elaborating the percentage of apoptotic cells, a marked increase was observed in the plasma treatment group compared to untreated control tissue (Figure 1c). The mean increase in apoptotic cells after plasma treatment was 10%. To validate these results with a second quantitative image analysis strategy, the mean fluorescence intensity (MFI) was identified in the perinuclear regions of tissues. This analysis also revealed a marked increase in TUNEL signal in the plasma-treated samples compared to untreated tissue (Figure 1d). These results suggest an increase of apoptosis in cutaneous melanoma samples exposed to cold physical plasma _ex vivo_. As not only apoptosis but also ROS engage in the modulation of inflammation, we performed a chemokine/cytokine measurement spanning 24 target molecules secreted from human cutaneous melanoma tissue sample into the culture medium after 24 h. These secretory patterns in untreated samples alone are a valuable resource for future research to investigate inflammatory responses after melanoma treatment _ex vivo_. Specifically, all metastatic skin melanoma tissue samples showed high secretion levels of CCL2, G-CSF, IL1\(\beta\), IL6, IL8, PDGF, and VEGF (Figure 2). By contrast, secretion of other target molecules was mostly (in \(>\) 66% of the samples) absent, including sFAS, sFASL, granzyme B, IL2, IL4, IFN\(\gamma\), perforin, and TNF\(\alpha\). In physical plasma-treated samples, secretory changes were observed for some of the 24 targets. However, an overall similar and distinguishable pattern could not be identified for the treated melanoma tissue compared to untreated tissue over the six patients investigated. With regard to similar changes in equal or more than one-third of the patients' tissue, a marked secretory decrease after plasma treatment was observed for CCL2, IL10, and VEGF while the secretion of CCL5, EGF, and granulysin was increased after plasma treatment. For all other chemokines/cytokines, there was no change of expression after plasma treatment, at least in samples of at least two different patients.  
Figure 2: Chemokine/cytokine secretory profile (in pg/mL) of 24 targets measured in culture medium from cutaneous melanoma punch biopsy cultures 24 h after exposure to cold physical plasma (dark bars) or left untreated (grey bars). LOD= lower limit of detection, except for IL8 (upper limit of detection); red asterisks remark significant difference (\(p<0.05\)) between plasma and control samples for each patient (1–6); given are mean and the standard error of three technical replicates per patient and sample.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 3 Dataset Creation  
In this paper, we adopt a generative adversarial network (GAN)-based approach to perform image fusion between infrared and visible-light modalities. The goal is to exploit the complementary information contained in both modalities and generate fused images that are both perceptually informative and suitable for downstream tasks such as object detection.
## 2 Scale Factor Calibrated Using Different AG Values
(2) The optimal SFs were obtained when all the AG stations participated in the calibration, while when few AG values are available in a measurement survey campaign, no fewer than two AG stations are needed for SF calibration. (3) With increase of the gravity interval among the AG values, the ABIC value decreases, and SFs that are more accurate can be obtained. Therefore, the gravity interval of the AGs participated in the adjustment should be more larger to cover the maximum of the GD in the survey area. (4) When the number of AG stations involved in calibration in the same campaign remains the same, the ABIC value can be considered the criterion on which to judge the calibration results. (5) Irrespective of how the number of AG values changes in a survey campaign, the calibrated SF changes almost synchronously, and the ratio between the two instruments remains broadly the same. (6) The difference between the two AG stations involved in the calibration process should cover no less than half that of the maximum GD in the network, and the difference of the SFs are no more than 100 ppm.
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 4 Method Design
### Data Collection  
In the process of gesture data collection, we adopt the method of one transmission and three receptions, that is, one transmitting antenna and three receiving antennas, and each antenna can receive 30 subcarrier channel values. We designed six gestures, as shown in Figure 3. They are: \"OK\", \"left arm slide\", \"right arm slide\", \"push\", \"rotate\" and \"raise your hand\".
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 2 Vehicle Dynamics Modeling  
### Vehicle Model  
Accurately acquiring dynamic state parameters during vehicle operation is fundamental for controlling vehicle dynamics. Nevertheless, while a vehicle is in motion, it behaves as a highly complex system that is not linear. The conventional two-degrees-of-freedom model (2-DOF), which assumes linearity, cannot adequately represent the vehicle's nonlinear state. This study proposes a nonlinear 3-DOF vehicle model. Figure 1 is the 3-DOF model and makes the following assumptions:  
1. The vehicle's center of gravity is located at the origin of the coordinate system and possesses structural symmetry;
2. The degrees of freedom in pitch, roll, and vertical directions are disregarded, along with the influences of air resistance and slope resistance;
3. The steering system's impact is disregarded, with the front wheel angle selected as the system's input;
4. The body is rigidly connected to the chassis, with the role of the vehicle suspension neglected;
5. The vehicle's mass distribution is assumed to be uniform and constant without considering mass transfer during acceleration or deceleration.  
Below are the equations for the 3-DOF vehicle dynamics:  
\[m(\dot{v}_{x}-v_{y}r) = (F_{x11}+F_{x12})\cos\delta-(F_{y11}+F_{y12})\sin\delta+F_{x21}+F_{x22} \tag{1}\] \[m(\dot{v}_{y}+v_{x}r) = (F_{x11}+F_{x12})\sin\delta+(F_{y11}+F_{y12})\cos\delta+F_{y21}+F_{y22}\] (2) \[I_{z}r=[(F_{x11}+F_{x12})\sin\delta+(F_{y11}+F_{y12})\cos\delta]I _{f}\] (3) \[+[(F_{x11}+F_{x12})\cos\delta+(F_{y11}-F_{y12})\sin\delta]\frac{ \mathrm{I_{st}}}{2}\] \[+(F_{x21}+F_{x22})\frac{\mathrm{I_{st}}}{2}-(F_{y21}+F_{y22})I_{r}\]  
where \(F_{xij}\) and \(F_{yij}\) indicate the longitudinal and lateral forces the tires exert, respectively (\(ij=11,12,21,22\)). \(t_{w1}\), and \(t_{w2}\) represent the track width of the front and rear wheels, respectively.\(V_{x}\), \(V_{y}\) represent the longitudinal and lateral speeds at the center of mass of the vehicle, respectively. \(m\) denotes the vehicle's curb weight. \(l_{f}\) and \(l_{r}\) denote the front and rear axle distances to the center of mass, respectively. \(\delta\) denotes the steering angle of the front wheels.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 4 HAP Beam Pointing for Extended Coverage
### HAP Beam Geometry  
The proposed algorithm described in this section considers the geometry of a HAP beam footprint on the ground as shown in Figure 6, which is dependent on the HAP antenna profile and varies in size with the pointing angle. Beams pointing away from the SPP broaden in size due to the limitations of beam forming at lower elevation angles. As discussed in Section 3, we assume that the beam footprint is elliptical in shape. Figure 6 highlights the geometry of the cells with three tessellated cells: one pointed at the SPP and the others at distances away.  
Figure 6: The HAP cell geometry. Dotted arrow lines show the initial cell boresight when neighbouring cells only touch each other. Solid arrow lines show the new boresight after adjusting the initial pointing angle to add overlap between neighbouring cells. The angle between the solid and dashed lines for each cell highlight the angle \(\rho\) between the boresight and cell edge, which is constant.
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 3 Methods
### Secondary Pruning of Feature Extraction Network Based on Alternative Convolutional Kernels
\begin{split}& kernel_{\Delta i}=\mathcal{F}^{-1}\left(\frac{ \mathcal{F}(F_{GAP}^{i})}{\mathcal{F}\left(\sum_{j=1}^{PP\text{CNN}_{i-1}}F_{(i-1 ),j}\right)}\right)\end{split}  \tag{11}\]  
where \"\(\mathcal{F}\)\" denotes the Fourier transform and \"\(\mathcal{F}^{-1}\)\" denotes the inverse Fourier transform. After the secondary pruning operation and introducing the alternative kernel \(kernel_{\Delta i}\) derived from Equations (10) and (11) into the pre-pruned feature network, the deep-pruned feature network with alternative kernels is obtained. The deep-pruned feature network with alternative kernels still has \(CL\) convolutional layers, but each layer has \(KN_{i}\) important convolutional kernels and one alternative kernel \(kernel_{\Delta i}\). By using the deep-pruned feature network with alternative kernels as the feature network in the tracking system, on the one hand, the alternative kernel \(kernel_{\Delta i}\) can effectively replace the multiple convolutional kernels removed in the secondary pruning process and compensate for the accuracy loss of the feature network caused by pruning; on the other hand, compared with the pre-pruned feature network, it further reduces the number of channels, decreases the computation, and improves the real-time performance of the tracking model.