esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Works  
Herein, one approach to trace associated CAPEC-IDs directly from CVE-IDs is proposed and different patterns are evaluated. The conventional method follows the links between each cybersecurity database, which requires manual linking. This leads to accuracy issues. By contrast, the tracing approach in this study uses a similarity algorithm. Different patterns (three similarity measures and three algorithms) using the proposed approach were experimentally evaluated. Although each algorithm has its own merits, TF-IDF is the most suitable overall.  
In the future, employing ensemble learning [46] may improve accuracy. Ensemble learning merges multiple models that have been trained individually. Specifically, it combines the predictions of various similarity algorithms in a process, for example, \"taking an average\".  
This study has limitations in tracing perfectly the best attack pattern. However, the impact of the traced attack patterns on the actual vulnerability management is evaluated. This study used CAPEC attack pattern information, but attack information is available elsewhere. Examples are the Pattern Language [47] and ATT&CK. In the future, the candidates of the tracing attack information should be expanded. Additionally, studies should focus on expanding the proposed approach into a comprehensive and proactive cyber threat intelligence (CTI) [48; 49] by collecting and analyzing data from many cyber security databases.  
Conceptualization, methodology, and writing--original draft preparation, K.K.; funding acquisition, H.W.; formal analysis and writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the SCAT Research Grant, the MEXT enPiT-Pro Smart SE: Smart Systems and Services innovative professional Education program, and the JST-Mirai Program grant number JPMJM2088.  
Not applicable.  
The data are available upon request.  
The authors declare no conflict of interest.
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 1 Introduction
However, the switchless call is essentially designed for asynchronous threading models, which makes applying it to legacy codes not always efficient. To this end, we propose an optimization strategy of leveraging switchless calls on legacy SGX-ported applications. Specifically, we develop a heuristic method to derive a metric, switchless efficiency, based on comprehensive analysis over switchless calls. To demonstrate the proposed strategy satisfies our design goal, we perform a case study to optimize a performance-critical network application, an SGX-enabled network middlebox [11]. The evaluation shows that our optimization successfully improves the performance of the SGX-enabled middlebox while a naive adoption of switchless calls degrades its performance. The remainder of this paper is organized as follows. Related studies are reviewed in Sections 2 and 3 explains the technical background of our paper. In Section 4, we first describe the motivation, challenges, and the problem scope of our work. We then demonstrate how we build our optimization strategy. Section 5 presents our evaluation with micro-benchmarks and macro-benchmarks and Section 6 includes further discussion, respectively. Finally, we conclude this chapter in Section 7.
# Rotation Invariant Graph Neural Network for 3D Point Clouds
## 2 Related Work
### Rotation Invariant Classification and Part Segmentation Networks
Multiplying the input point cloud with its corresponding rotation normalization matrix \(P^{\prime}\) places it in a canonical orientation regardless of other rotations applied on the point cloud. A paper of interest is [32], which computes features that display rotation invariance. The features are computed in the neighborhoods of sampled points from the point clouds. PCA algorithm is used on each neighborhood to determine a canonical position. They solved the problem of ambiguity of PCA in each neighborhood by choosing a predefined anchor point. As described in [3], PCA has inherent ambiguities in the order of the eigenvectors and the sign. Ref. [8] creates four views of the point cloud by using the PCA rotation matrix and iteratively changing the sign of one eigenvector. They apply a shared DGCNN [4] network on each view and aggregate the information using a self-attention module and average pooling. We tested a similar architecture for classification, but we used the RGCNN module [5]. The big downside of using four views is that the network needs much more memory than one view. For embedded devices with limited memory, this option can become problematic. [PERSON] et al. [3] also switched the order of the eigenvectors leading to 24 different views. They used a simpler 1D convolution network to aggregate the information from each of the 24 rotated point clouds to eliminate ambiguity. Ref. [31] created 120 poses from the 4 obtained by changing the sign of the eigenvectors. A pose selector module chooses the best pose used in the rest of the network. For local features, it uses hand-crafted rotation invariant features obtained in knn neighborhoods, similar to [33]. Unlike the PCA rotation normalization [3; 8] methods described above, our method offers additional information that describes the object. The oriented bounding box has an approximate width, length, and height of the shape sampled by the point cloud, along with a position and an approximate orientation given by the orientation of the bounding box. Using the oriented bounding box method, a robot has a first-order approximation of the volume of an object along with its orientation. In PCA rotation normalization, only the orientation is used. A visual comparison between the Bounding Box normalized rotation and the Eigenvector normalized rotation is shown in Figure 2. The original point cloud is rotated in different positions. Both methods obtain a canonical rotation but the oriented bounding box also offers volumetric information, which can be used in robotic settings. The points obtained from the convex hull of the point cloud are used for the oriented bounding box computation, which leads to an approximation of the minimum volume bounding box.
# Robust Full-Screen Wavelength Calibration Algorithm
## 2 Spectrometer Construction and Related Principles
### Theoretical Background  
Based on the optical theory, we set out to establish the wavelength calibration model for the central pixels in the linear-array CCD [10]. Theoretically, the grating diffraction equation is  
\[m\lambda=d(\sin\omega_{1}-\sin\omega_{2}), \tag{1}\]  
where \(d\) is the grating line spacing, \(\omega_{1}\), and \(\omega_{2}\) are the incidence angle and diffraction angle of the relative grating normal, respectively. \(m\) is the order of the diffraction spectrum, and \(\lambda\) is the wavelength of the diffraction light. In addition, the diffraction order is set as level 1.  
In order to simplify the calibration process, we designed a grating sine mechanism for the spectrometer. By applying the differential product transformation of trigonometric functions to Equation (1), and setting \((\omega_{1}+\omega_{2})/2=\tau_{1}\) and \((\omega_{1}-\omega_{2})/2=\tau_{2}\), the dispersion equation can be expressed as  
\[\lambda=2d{\cos\tau_{1}}{\sin\tau_{2}}. \tag{2}\]  
In the test instrument, \(\tau_{1}\) is a fixed value. \(\tau_{2}\) represents the rotation angle of the grating; that is, the rotation angle when the grating scans to a certain diffraction light wavelength, starting from the alignment of zero-order light.  
Assuming that the length of the sinusoidal bar is \(L\), the translation distance of the slider is \(x\), i.e., the feedback value, and the angle of the sinusoidal bar is \(\tau_{2}\), then the relationship [11] should be  
\[x=L{\sin\tau_{2}}. \tag{3}\]  
As the installation position of the slider is variable, the wavelength and the feedback value of the sine mechanism are related through the formula  
\[\lambda=2d{\cos\tau_{1}}\frac{x}{L}+a. \tag{4}\]Equation (4) shows that the linear displacement distance of the sinusoidal machine has a linear relationship with the diffraction wavelength of the grating. This configuration can greatly simplify the central wavelength calibration. However, in the experiment (Section 4.1), we found that linear fitting is not as effective as sinusoidal fitting.
# Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
## 2 Related Work  
### Methods for Respiratory Parameter Analysis  
Methods for analyzing respiratory parameters are generally classified into two categories: contact-based and contactless approaches. Contact-based methods involve direct physical sensors attached to the body, such as respiratory inductance plethysmography (RIP) and pulse oximeters. In contrast, contactless methods employ non-invasive techniques, such as cameras or radar, which offer greater comfort and are particularly suitable for newborns. These methods have garnered increasing interest due to their potential for improved functionality and integration with advancing technologies.  
For example, [PERSON] et al. [22] used two color cameras to capture visual information and applied dense optical flow analysis to track motion for respiratory parameter estimation. Similarly, [PERSON] et al. [17] utilized two 3D cameras to capture temporal point-cloud data, applying surface reconstruction techniques to accurately model the thoracoabdominal surface. They then calculated the volume for each frame and measured the respiratory rate through a volume-time graph. In another study, [PERSON] et al. [18] proposed a method to assess thoracic-abdominal asynchronous motion using a single RGB-D camera, which calculates the 3D scene flow between consecutive frames to analyze motion. Additionally, [PERSON] et al. [20] developed a contactless method utilizing depth cameras to monitor infants' breathing patterns and thoracoabdominal asynchronous movements. [PERSON] et al. [21] employed an RGB camera to capture the visual temporal information of patients, which was subsequently analyzed using 3D convolutional neural networks (CNNs). This approach aimed to non-invasively identify respiratory distress conditions by recognizing subtle visual cues associated with thoracoabdominal movements. It is important to note that only a limited number of studies have explored the ARD detection task through either contact-based or contactless methods.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} Acronym & Full Meaning \\
3D & Three Dimensional \\
5G & Fifth Generation \\
6G & Sixth Generation \\ ADMM & Alternating Direction Method of Multipliers \\ AI & Artificial Intelligence \\ ANN & Artificial Neural Networks \\ AP & Access Point \\ ARIMA & Auto-Regressive Integrated Moving Average \\ AtG & Air-to-Ground \\ BCA & Block Coordinate Ascent \\ BCA & Block Coordinate Descent \\ BS & Base Station \\ CA & Conventional Approaches \\ CAPEX & Capital Expenditure \\ CCP & Concave Convex Procedure \\ CNN & Convolution Neural Networks \\ C-RAN & Centralized Radio Access Network \\ CS & Cucker-Smale \\ D2D & Device-to-Device \\ DBS & Data Base Station \\ DC & Direct Current \\ DDPG & Deep Deterministic Policy Gradient \\ DRL & Deep Reinforcement Learning \\ D-RRH & Drone-Mounted Remote Radio Head \\ DSC & Drone Small Cell \\ EE & Energy Efficiency \\ GA & Genetic Algorithm \\ GMM & Gaussian Mixture Model \\ GPS & Global Positioning System \\ HIL & Hardware in Loop \\ FL & Federated Learning \\ FSO & Free Space Optics \\ kNN & k-Nearest Neighbour \\ IoT & Internet of Things \\ IRS & Intelligent Reflective Surface \\ LiPo & Lithium Polymer \\ LoS & Line of Sight \\ LSTM & Long and Short Term Memory \\ MBS & Macro Base Station \\ MDP & Markov Decision Process \\ MEC & Mobile Edge Computing \\ MIMO & Multiple Input Multiple Output \\ ML & Machine Learning \\ mmWave & Millimeter Wave \\ MNIST & Modified National Institute of Standards and Technology \\ NFV & Network Function Virtualization \\ NLP & Natural Language Processing \\ NR & New Radio \\ NOMA & Non-Orthogonal Multiple Access \\ OEM & Original Equipment Manufacturer \\ OPEX & Operating Expense \\ PSO & Particle Swarm Optimization \\ PV & Photo voltaic \\ \end{tabular}
# Micro-LEDs, a Manufacturing Perspective
## 3 Assembly Technologies
### Monolithic Fabrication of Micro-Displays
This approach comprises fabrication of III-nitride based micro-LED arrays, manufacturing of Si-based CMOS driver matrix, and then their hybrid integration in order to allow control of the LED pixels individually. The hybridization process is generally based on the flip-chip bonding technology, which has been widely developed in III-V/Si photonics for high-performance switching and computing [50]. For example, by using 6 \(\upmu\)m-sized indium metal bumps deposited on micro-LED pixels as bonders, [PERSON] et al. have demonstrated fabrication of a 160 \(\times\) 120 VGA micro-display with a pixel pitch of 15 \(\upmu\)m [51]. To further shrink the pixel pitch down to below 10 \(\upmu\)m for higher resolution, CEA-Leti has developed a micro-tube technology to perform the integration process [52; 53]. The key step to the interconnection is the insertion of gold capped micro-tubes on the pads of Si circuit into the soft indium pads on the micro-LED array, with the micro-tubes created by using conformal metal deposition and \"gap fill\" type processes. Blue and green active-matrix LED micro-displays with a 10 \(\upmu\)m pixel pitch and a resolution of 873 \(\times\) 500 using 6 \(\upmu\)m-sized micro-LEDs have been demonstrated via this technology. In addition to the abovementioned monolithic fabrication approaches, it has also been demonstrated that micro-LEDs displays can be built by using GaN-based nanowire LEDs, without the need of mass transfer. Prototypes including a 1.5\" 264 PPI RGB wearable display using 20 \(\upmu\)m-sized micro-LEDs on a LTPS glass backplane, and a 0.7\" 1000 PPI RGB display using 10 \(\upmu\)m-sized micro-LEDs on a CMOS backplane have been unveiled by glo-USA, in which direct wafer-to-wafer transfer technology was employed.
# The Spatial Structure of Passively Simulated Atmospheric Boundary Layer Turbulence
## 5 Conclusions  
In this study, based on the analysis of turbulence theory, the lateral and vertical spatial structures of atmospheric turbulence passively simulated by a wind tunnel were discussed. The following conclusions can be drawn from this study:  
1. The passively simulated atmospheric turbulence can be approximately regarded as conforming to the assumption of horizontal average isotropic turbulence, but the vertical turbulence component is more significantly disturbed by the nonisotropic component and cannot be ignored.
2. Isotropic turbulence theory sufficiently describes the horizontal and lateral spatial structures of the along-wind turbulence component, but the vertical component deviates slightly. The modified theoretical coherence model can better describe the distribution of the lateral coherence of the vertical component due to improvements to the theoretical coherence model.
3. The vertical spatial structure that passively simulated atmospheric turbulence was discussed. Due to the influence of turbulent friction, isotropic turbulence theory cannot accurately describe the vertical spatial structure of atmospheric boundary layer turbulence simulated by wind tunnels. Through the improvement of the theoretical model, a modified vertical coherence model of different turbulence velocity components was obtained.  
Conceptualization, methodology, software, and writing, J.Z.; investigation and validation, J.Z. and Z.L.; resources, supervision, and revision, M.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hainan Provincial Department of Science and Technology Fund Project, grant numbers 520 RC520, 520 CXTD015, and 519 QN191, and the National Natural Science Foundation of China, grant numbers 52068020 and 51938012.  
Not applicable.  
The authors gratefully acknowledge the support of the Hainan Provincial Department of Science and Technology Fund Project (Grant Nos. 520 RC520, 520 CXTD015, and 519 QN191), and the authors are also indebted to the National Natural Science Foundation of China for research projects granted in recent years (Grant Nos. 52068020 and 51938012). We thank the reviewers and the editors for the valuable comments and suggestions that helped us improve the manuscript.  
The authors declare no conflict of interest.
## 3 Frequency Design Formula and Approximate Analytical Solution for NTMD
### Validation of the Complex Variable Averaging Method  
To verify the correctness and accuracy of the approximate analytical solutions obtained by the complex variable averaging method, Equations (6) and (7) are calculated using the conventional numerical method, and the modulation-demodulation Equations (26)-(29) obtained by the complex variable averaging method are calculated. A two-degree-of-freedom system consisting of a single-degree-of-freedom primary structure and an NTMD with cubic nonlinear stiffness is used as the study object, and the structure of the study object is shown in Figure 1, where the parameters of the primary structure and the NTMD are set as follows:  
\[\begin{gathered} m_{1}=1\text{ kg; }c_{1}=0.1\text{ N-s/m; }k_{1}=1\text{ N/m}\\ m_{2}=0.02\text{ kg; }c_{2}=0.0031\text{ N-s/m; }k_{2}=0.0161\text{ N/m} \end{gathered}\]  
The corresponding dimensionless parameters can also be obtained as  
\[\lambda_{1}=5;\;\lambda_{2}=0.155;\;f=15\]  
The set external excitation frequency is  
\[\omega=1\text{ rad/s}\]It is worth mentioning that the analysis of the study focuses on the 1:1 resonance case, so in the subsequent analysis, the analysis is performed by default based on \(\omega=1\ rad/s\), except for the specially stated cases and the graphs with changing excitation frequencies.  
Based on the above parameters, a frequency amplitude diagram related to the NTMD frequency can be obtained.  
Figure 2 shows the frequency-amplitude diagram of the structure. The horizontal coordinates are the frequencies of the NTMD, and the vertical coordinates are the amplitudes of the primary structure and the NTMD. The blue dashed line is the result obtained by the numerical method, and the gray solid line is the result obtained by the complex variable averaging method. It can be seen from the figure that the results obtained by the numerical method and the complex variable averaging method are in good agreement with almost no error. This proves the correctness of the modulation-demodulation equation obtained by the complex variable averaging method, and it also proves that the modulation-demodulation equation is highly accurate and meets the requirements of the subsequent analysis.
## 5 System Overview
### Client Applications  
Client applications are remote applications that receive filing service from KUSDFS system. The applications are platform independent because they make all requests on TCP/IP. It is enough that applications needing filing service must have a static IP. Client applications can easily use this  
Figure 1: Windows service routines and JSON packages.  
system commonly as a 'write once read many' (WORM) system for filing and archiving purposes. An application that wants to receive filing service from KUSDFS should install the Dynamic Link Library on its system. In this system, GetAvailableDatanodelp, ChangePassword, SaveFile, ReadFile, DeleteFile, GetFileFromReplica methods are available and detailed further on next sections. The client application communicates with the KUSDFS data node, which is reserved for itself and manages all its operations via this node. Authentication for secure logon needing username and password is done by using symmetric cryptography methods. However, static client IP is also registered on the server to strength connection security. As there is no limit to the number of client applications, these applications can use more than one data node defined on KUSDFS when needed. Here is one of the key differences of our system that others do not have. The general structure of our system is shown in Figure 2.
## 5 Results
### Development of the Transformer-Attention RUL Prediction Model  
The proposed transformer-attention model demonstrated good performance in predicting the RUL of bearings compared to other benchmark models. It achieved the lowest RMSE of 0.989 and a corresponding loss function value of 0.173; it showed the highest accuracy in capturing the complex temporal dependencies and degradation patterns present in the vibration signals. The CNN-LSTM model showed a higher RMSE of 1.356 and a loss value of 0.276, which was less accurate in extraction capabilities. The transformer model without the attention mechanism yielded an RMSE of 2.111; the importance of attention mechanisms is validated in the proposed approach. TCN and MLP showed higher RMSE values of 1.477 and 1.283, respectively, and the transformer-attention model was the most accurate in predicting bearing RUL. Figure 6 and Table 1 show the training accuracy of different models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **RMSE** & **Loss Function** \\ \hline Transformer-Attention & 0.989 & 0.173 \\ CNN-LSTM & 1.356 & 0.276 \\ Transformer & 2.111 & 0.268 \\ TCN & 1.477 & 0.481 \\
2 layer MLP & 1.283 & 0.399 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different models.  
Figure 5: The preprocessed bearing vibration data collection and preprocessing. (**a**) Bearing raw data. (**b**) Pre-processed data. (**c**) Partitioned data sample 1. (**d**) Partitioned data sample 2.
###### Abstract
In terms of mechanisms, eccentric actions (such as the countermovement of a vertical jump), with an immediate concentric contraction (such as the take-off phase) results in a stretch reflex, potentiating power output during the concentric contraction [21]. Muscle spindles are not sensitive to the loading of muscle but are sensitive to the rate and magnitude of stretch [22; 23]. For this reason, one method to augment glyometric training adaptation is to use additional loads in addition to body mass of individuals [13; 24; 25; 26; 27]. Furthermore, short sprints with change-of-direction exercises have been also used as a training method of increasing explosive performance of athletes [28; 29]. In addition, a common trend in the training programs indicates that a combination of training methods is most effective to improve athletic performance than standalone approaches [30]. To our knowledge, the only study that investigated the effects of combined loaded pyjometric and short sprints training on athletic performance of male young soccer players is that of [PERSON] et al. [10]. They reported significant improvement in sprint performance, jumping, change of direction ability, and balance performance. Despite the efficacious nature of glyometric, sprint training, and agility training to enhance jumping, sprinting, and agility, scarce research has concerned effects of loaded pyjometric with short sprints and change-of-direction training on jumping, sprinting, agility, and balance in elite youth soccer players (U17). The aim of this investigation was therefore to explore effects of loaded pyjometric with short sprints and change-of-direction training in elite male junior soccer players (U17). We hypothesized a priori that replacing part of the regular in-season training by 8 weeks of biweekly loaded pyjometric with short sprints and change-of-direction training would improve both horizontal and vertical jump performance, sprint performance, agility, and balance when compared with a control group, who continued with their usual training during the season. Specifically, we examined vertical and horizontal jump performance (i.e., squat jump [S]), countermovement jump with arm swing [CMJA], and the 5 jump test [5 JT]), sprint performance (i.e., 10 m and 30 m sprint), change-of-direction ability (S90\({}^{\circ}\)), repeated change of direction ability (RCOD), and dynamic balance performance (Y balance test).