esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 3 Preliminaries  
### Notations  
* \(f\): pseudorandom function.
* \(f^{-1}\): reverse function of \(f\), in other words, decryption function of \(f\).
* i or j: each participant such as H or E.
* \(k_{i}\): secret key between participant i and CC.
* \(f_{k_{i}}\): encryption using pseudorandom function \(f\) with a secret key \(k_{i}\).
* \(x_{i}\): private key of participant I.
* \(y_{i}=g^{x_{i}}\): public key of participant I.
* \(m\): a message.
* \(C=~{}(C_{1},C_{2})\): ElGamal-style ciphertext of \(m\).
* \(D\): decryption of \(m\).
* \(RE=~{}(R_{1},R_{2})\): re-encryption of \(m\).
* \(r,~{}\alpha,~{}\gamma\): randomly generated numbers every time.
* \(O_{i}\): an order from CC to participant i.
* \(R_{i}\): report of participant i.
* \(S_{1},~{}S_{2}/S_{3},S_{4}\): re-encryption/decryption keys.
* \(\mathfrak{D}\): oracle.
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 1 Introduction  
Cloud computing offers several benefits, including cost reduction, sharing and configuring computing resources, on-demand service, and high flexibility and scalability [1, 2]. In addition, cloud computing provides services on the internet that have become widely used technology. The cloud computing model evolved from virtualization technology, distributed computing, utility computing, and other computer technologies to provide availability and scalability features for large enterprise-level applications. It also offers services to virtual machines assigned through a sizable physical resource pool. The five vital features of cloud computing include on-demand self-service, extensive access to the network, resource pooling, high-speed resiliency, and measurable services [3]. These benefits encourage large companies to dump their IT infrastructure into the cloud environment. For reliable services, cloud computing needs to be secured for users' data. Cloud computing faces several typical cloud risks, such as data abuse, malicious insider, insecure interfaces, access points, shared technological problems, loss of data, and hijacking. Therefore, an accurate understanding of cloud security is a fundamental requirement for the successful deployment of cloud computing [4].  
Different types of attacks make it difficult for cloud providers and administrators to deploy possible solutions needed by customers [5]. This is because various attacks areassociated with different threats, where the importance of risks varies depending on the security needs of other customers using cloud services. Thus, security administrators will evaluate and implement security mechanisms to meet essential security requirements as service providers. However, the security can be further improved as it is almost impossible to configure a fully secure system in practice [6]. Consequently, it is necessary to find security threats followed by their associated solutions, such as accountability, authentication, and privacy preservation [5; 7; 8].  
This work discusses security problems and issues caused by the unique characteristics of the cloud. Thus, we offer a complete sketch of the diverse issues and proposed solutions to prevent potential attacks. We studied various strategies and mechanisms to explain the security management methods, thereby reducing risks and vulnerabilities and enhancing people's confidence in cloud services. We also provide a complete analysis of security issues, classified security challenges, and countermeasures to address the issues.
## 2 Experimental Details
### Experimental Device  
A schematic diagram of the LCLD system is shown in Figure 2. A near-infrared (NIR) fiber laser (SP-20P-HS, SPI corporation, Southampton, UK) having a wavelength of 1064 nm was used as the irradiating source. This laser can be operated in either the continuous wave (CW) mode or pulse mode (PM) with a pulse width of 200 ns. The maximum output power was 20 W. A galvanometer scanner (6220H, Cambridge Technology, Bedford, MA, USA) was used to control the scanning path of the laser beam using the EzCad software (EzCad2.1.4, JCZ, Beijing, China). The scanning speed range of the laser beam was 1-1000 mm/s. An F-theta lens with a focal length of 163 mm was used to scan and focus the laser beam. Therefore, the beam spot diameter was approximately 55 \(\upmu\)m at the optimized setting. The specifications of the laser system are given in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reagents** & **Contents** \\ \hline CuSO\({}_{4}\) & 15 g/L \\ EDTA & 35 g/L \\ HCHO & 10 g/L \\ NaOH & pH 12.6 \\
2.2 dipyridyl & 10 mg/L \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of LCLD solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Typical** & **Unit** \\ \hline Working material & Nd:YAG & \\ Mode of operation & Pulsed/Continuous wave & \\ Wavelength & 1064 & nm \\ Maximum output power & 20 & W \\ Pulse width & 200 & ns \\ Pulse repetition rate & 1–500 & kHz \\ Beam quality, M\({}^{2}\) & 2 & \\ Focused beam diameter & 55 & \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main specifications of the Nd:YAG fiber laser.  
Figure 2: Schematic diagram of LCLD system.  
A digital multi-meter (FLUKE 115, FLUKE, Everett, WA, USA) was used to identify the resistance of the specimens. A jig was used to maintain the probe spacing at 10 mm for a stable measure of resistance. A potentiostat/galvanostat (SP-50, BioLogics, Seyssinet-Pariset, France) was used to measure resistance and draw the current-voltage curve accurately. The potentiostat was operated using a scan rate of 0.02 mV/s with a resolution of 5 \(\upmu\)V. A surface profile measuring instrument (LT-9010M, Keyence, Osaka, Japan) was used to measure the deposited and the bare surface profile. To observe the microstructure and elemental composition of the specimens, scanning electron microscopy (SEM, VEGA3 LM, TESCAN, Brno-Kohoutovice, Czech Republic) was used.
# Application of the MiL and HiL Simulation Techniques in Stewart Platform Control Development
## 3 Model-in-the-Loop (MiL) Simulation of the Stewart Platform
(**a**) Desired (\(\varphi_{w}\), \(\theta_{w}\), \(\Psi_{w}\)) and actual (\(\varphi_{y}\)) position of the moving plate in the \(X_{b}\) axis; (**c**) Desired (\(y_{w}\)) and actual (\(y_{y}\)) position of the moving plate in the \(Y_{b}\) axis; (**d**) Desired (\(z_{w}\)) and actual (\(z_{y}\)) position of the moving plate in the \(Z_{b}\) axis. Figure 7: Feedback control loop for a single linear actuator. permanent control deviation that resulted in the error in position and orientation of the moving plate, as we can see in Figure 8.  
In Figure 10, the acting forces \(F_{a}\) that are equivalent to the action input \(u\), can be seen over time. It is visible that when the desired value changes the piston sharply accelerates and then decelerates until it reaches the steady state. Since bodies in the multibody simulation are subjected to gravitational forces, the steady state force of the linear actuators is non-null. It should also be noted that the applied force reaches both positive and negative values, which means that linear actuators are subjected to compressive and tensile loads. In this section, we have described the simulation model of the Stewart platform and the MiL scenario. Section 4 describes the Hardware-in-the-Loop experiment and compare its results to the results obtained in the MiL scenario. Figure 4:0 Acting forces of the Stewart platform linear actuators (MiL). Figure 4:1 Actual and desired piston rod extensions of the Stewart platform linear actuators (MiL).
## 4 Simulation Analysis  
### Simulation Model in Adams  
To further validate the static performance of the QVIP, the simulation experiment is conducted by using Adams 2023 software. In the simulation, all rods of the QVIP were assumed to be rigid. The bottom plane of the QVIP was fixed, while a translational joint was applied to the upper plane to impose a vertically downward displacement.  
The simulation model in Adams software is shown in Figure 7a, and the motion pairs and constraints are shown in Figure 7b. To validate the QZS characteristics of the QVIP, simulation experiments were conducted using the parameters in previous parametric analysis that can generate QZS. With different stiffness ratios \(\mu\) and \(\
u\), the simulation results and the theoretically derived equivalent nonlinear stiffness force curves are compared in the Figure 7c,d (parameters are set as: \(L=0.1\), \(\varepsilon=1\), \(\lambda=1\), \(\xi=1\), \(\theta=\pi/6\), \(k=2000\)). The simulation results are consistent with the theoretical results, and the range of quasi-zero stiffness and the variation law of force amplitude are consistent, which verifies the accuracy of both of the theoretical model and simulation model of the QVIP.  
Figure 6: The influence initial angle \(\theta\) on static stiffness force (parameters are set as \(L=0.1\), \(\varepsilon=1\), \(\lambda=1\), \(\mu=0.68\), \(\
u=1\), \(\xi=1\), \(k=2000\)).  
Floating platforms can be categorized into four main types: barge (30-50 m), tension leg (>50 m), semi-submersible (>50 m), and spar (>100 m) as shown in Figure 8a. Among these four types, the spar platform exhibits significant vibration in the vertical direction, which is suitable for the utilization of the proposed QVIP. According to research by the Xiangshui Yangtze Wind Power Co., Ltd. in Yancheng City of China [39], the spar platform exhibits significant vertical oscillations. Under conditions with 5 m/s wind speed, 0.26 m wave height, and 2.3 s wave period, the vertical excitation amplitude \(A_{0}\) reaches 0.009 m with an excitation angular frequency \(\omega=2.73\) rad/s. The spar platform model was imported into Adams software. Figure 8b illustrates the operational schematic of the QVIP under working conditions.
## Materials and Methods
### Hyperspectral Imaging Data Processing  
The raw hyperspectral data was calibrated to relative reflectance (R) via an in-field calibration process, where the dark current and white references were averaged to two horizontal reference lines. Each image was calibrated with its own dark current and white reference signals. Then, the relative reflectance values were transformed to absorbance (A), with the equation A = log\({}_{10}\)\(1/\)R. Then, these absorbance spectral images were used for subsequent data analysis. The hyperspectral data processing was done with custom Matlab scripts. The wavelength range of the calibrated data was cropped to 1100-2500 nm. The white reference image in the top of each image was removed and the bottom edge of each sample was also removed, which was automatically done with an image-processing algorithm based on edge detection and image segmentation. Intensity thresholding was used for the image segmentation of an individual sample. Then, a region of interest (ROI) with a rectangular shape was automatically detected inside each segmentation image of an individual sample. The ROIs were automatically detected by an image-processing algorithm in Matlab. More specifically, note that a measured hyperspectral image consisted of three sections, i.e., dark current, white target, and sample sections, from top to bottom. A waveband at 1789 nm in the approximately middle of the measured spectral range was chosen to find three horizontal line edges dividing three sections via a y-profile analysis technique. A y-profile was obtained by projecting the 2-D waveband image onto the y-axis and averaging all intensity values along each y point. Then, the first derivative was applied to the y profile and the peaks were searched to find y edges of the dark current, white target, and sample. After the sample's y edges (top and bottom edges) were detected, its left and right edges were detected similarly by a x-profile analysis technique such that the sample became segmented and enclosed by a bounding box obtained from the coordinates of the detected four edges. An ROI was detected inside this bounding box by shrinking the bounding box of the sample by 8% and 5% in the y and x directions, respectively. These ROIs were used to extract spectral data for analysis.
# High Vacuum Packaging of MEMS Devices Containing Heterogeneous Discrete Components
###### Abstract  
Vacuum packaging of Micro-electro-mechanical system (MEMS) devices is a hot topic for its advantages of improving performance and reducing power consumption. In this paper, the physics package of a chip scale atomic clock (CSAC), as a typical kind of MEMS device, is performed by vacuum packaging based on a systematic method proposed by us. The whole process, including low outgassing and thermal stable materials selection, prebaking for desorption, getter firing for absorption and solder reflow for vacuum sealing is introduced thoroughly. The thermogravimetric analysis or thermal gravimetric analysis (TGA) is used to analyze the thermal stability and desorption of materials. The leak rate of physics packages is measured to be less than 4 \(\times\) 10\({}^{-10}\) Pa-m\({}^{3}\)/s by helium leak detection. The residual gas pressure and composition in physics packages are analyzed after vacuum packaging. The results show a high vacuum \(\sim\)0.1 Pa in the physics package. The frequency stability is improved from 4.68 \(\times\) 10\({}^{-11}\) to 1.07 \(\times\) 10\({}^{-11}\) @40,000 s. The presented method for high vacuum packaging is also applicable to other MEMS devices.  
e +
Footnote †: journal: Applied Sciences
## 4 Craniomy Trajectories by the Spherical Parallel Mechanism
### Torque Minimization Strategy  
It is planned that while the robotic manipulator performs the trajectory for craniotomy, its reconfigurability will be used to improve its behavior. One of the major aspects in the robotization of craniotomy is to ensure that the manipulator can generate enough force to drill the skull in Section 2. In the present study, the skull reaction force is assumed to remain tangential to the skull surface and in the direction of the cutting. Its maximum magnitude was identified through the experiments on the human cadaver presented in Section 3.2. Based on the kinematic and static models presented in Section 4.1, the required input joint coordinates and torques to perform these trajectories can be determined. Both models are implemented on a MATLAB program that uses as input the magnitude of the skull reaction force and the angular motion of the surgical drill. The direction of the reaction force is determined by the trajectory of the drill. As an example, the input joint coordinates \(\theta_{k}\) and torques \(\tau_{k}\) obtained for a recorded trajectory are displayed in Figure 10. In this example, the magnitude of the reaction force is set to 1 N, and the spinning angle \(\varphi\) of the end effector is purposely maintained to zero in order to illustrate the performance of the mechanism when its reconfigurability is not used.  
The present strategy is to determine the end-effector spinning angle that will minimize the torques for a given position (\(\psi\), \(\theta\)). For each point of a recorded trajectory, the measured end-effector position and the force are extracted. Then, the corresponding input joint coordinates and torque are calculated for an end-effector spinning angle varying in an interval between preset values [\(\varphi_{min}\), \(\varphi_{max}\)]. For each spinning angle, the average of the three torque \(\tau_{AV}\) is calculated. Then, the minimum value \(\tau_{OPT}\) is saved, and the corresponding spinning angle \(\varphi_{OPT}\) is collected. This strategy is implemented on a new MATLAB program and is tested on the same recorded trajectory used in Figure 10. As a result, it can be seen in Figure 11a that on the same trajectory, the input coordinates are different. This means that the mechanism will modify its configuration as it searches to minimize its torque average. As displayed in Figure 11b, the torque average was reduced compared to the classical trajectory where the spinning angle is maintained to zero.  
Figure 10: (**a**) Input joint coordinates calculated for a trajectory with a fixed spinning angle. (**b**) Torques calculated for the same trajectory.
## 1 Introduction
As a result, the structure, electronic properties and gas adsorption behavior of Pt decorated or doped graphene has been systematically studied using quantum chemistry methods. The decoration of Pt adatom on graphene surface can apparently elevate the adsorption energy and electron transfer with not only inorganic small molecules (such as SO\({}_{2}\), O\({}_{3}\), NO, H\({}_{2}\)S), but also organic molecule (such as C\({}_{2}\)H\({}_{2}\), C\({}_{2}\)H\({}_{4}\), CH\({}_{3}\)OH and C\({}_{2}\)H\({}_{5}\)OH) [18; 19; 20; 21; 22; 23]. Because of the excellent adsorption and gas sensing properties of Pt-graphene, in this work, we discussed the adsorption behavior of several kinds of SF\({}_{6}\) decomposition products on the surface of Pt doped graphene, we carried out the ab initial study with the density functional theory calculation including the adsorption energy, adsorption distance, electron transfer and electronic properties. Considering the probable appearance of several kinds of gas molecules on the surface, we further discussed the co-adsorption of two gas molecules with the same type or different type. The study can provide a guiding effect for Pt doped graphene based gas sensor used in field of detecting SF\({}_{6}\) decomposition products in electrical engineering.
# System Design and Experimental Study of a Four-Roll Bending Machine
## 4 Control System Design
### Roll Bending Control System Module Division  
The roll bending control system primarily consists of HMI, multi-axis motion control unit, drive system module, execution system module, work rolls, feedback module, etc. The block diagram of the four-roller roll bending machine control system modules is shown in Figure 13. Based on the control system requirements, the multi-axis motion controller selected is the Googol GTS-800, equipped with a terminal board. The drive system employs YLK parallel servo-electric cylinders and stepper motors, with the servo-electric cylinders driven by Delta ASDA-B3 servo drives. Among which, the stepper motor employs a three-phase power supply configuration with a 5.5 kW power rating, operates within a frequency range of 50/60 Hz, and is coupled with a GV-50 series gear reducer unit. The execution system includes drive shafts and ball screws: the stepper motor drives the spindle rotation via the drive shaft, while other rollers extend/retract through ball screw transmission. The feedback module utilizes OMRON E6B2-CWZ1X encoders for monitoring the rotational displacement of the drive roller, and encoders integrated with the servo-electric cylinders enable HMI-based monitoring of speed and displacement for all rollers. The HMI status monitoring interface provides real-time limit status checks for each roller. Interactions and functions between module components are detailed in Table 5. The control architecture achieves full servo-driven operation with \(\pm\)0.01 mm positioning accuracy, outperforming conventional hydraulic/pneumatic systems. For real-time motion control, the GTS-800 motion controller handles motion planning and servo drive control, while the PC focuses on process logic and system status monitoring, forming a master-slave dual-core architecture with \(\leq\)0.05 mm synchronization error and \(\leq\)10 ms dynamic response time, ensuring system real-time performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Function** \\ \hline Googol GTS-800 Controller (1 channels) & Driving each axis \\ Servo Drive ASDA-B3 (3 channels) & Receiving and outputting signals \\ YLK Parallel Servo-Electric Cylinder (3 channels) & Implementing roller telescoping \\ Kinco FV20 Variable Frequency Drive (1 channels) & Controlling drive wheel rotational speed \\ OMRON E6B2-CWZ1X Incremental Rotary Encoder (1 channels) & Monitoring profile displacement \\ \hline \hline \end{tabular}
\end{table}
Table 5: Control Module Components and Functions.
# Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning
###### Abstract
This method is considered a drawback for dynamic systems, where constraints and objectives are continuously varying; it is important to redesign these heuristic models [5]. A zero-touch network can provide an effective placement of the VNF-FGs. Thus, in this work, we are considering Deep Reinforcement Learning (DRL) techniques for the placement of dynamically arriving VNF-FGs. In an environment where the network conditions change vigorously, learning from the past experiences is a beneficial factor for decision-making. In general, the RL approach has been proven to provide better solutions than supervised learning for NP-Hard problems [7]. Additionally, while solving the VNF-FGE problem, we are also examining the performance of the DRL under certain conditions like nodal outages due to disaster or vulnerability, variation in VNF-FGs complexity (i.e., number of VNFs in one forwarding graph), nodal capacity, core capacity and Network (topology) density. The rest of the paper is organized as follows: Section 2 provides a brief literature review. Followed by the problem formulation of VNF-FGE problem, which is modelled as  
Figure 2: VNF-FG embedding. a Markov Decision Process (MDP) given in Section 3. Section 4 provides an overview of DRL's functionality and an elaborate description on State Space, Action Space, Environment, and Rewards. Section 5 describes the architecture of Deep Neural Networks, and Section 6 will present a critical analysis of the DRL model. Results and discussions are presented in Section 7. Lastly, Section 8 concludes the paper and additionally provides information about our future plan.
## 4 Forecasting Methods
### Transformer-Based Models  
Transformer-based models, with their self-attention mechanisms, have become a cornerstone in a variety of sequence modeling tasks [12], including time series analysis. These models are particularly adept at capturing the inherent temporal dynamics and complex patterns in time series data.  
AnomalyBERT [36] utilizes a self-supervised method, which does not require labeled data, a common scenario in real-world situations. Its innovative data degradation scheme introduces synthetic outliers, enhancing the model's ability to detect various types of anomalies while effectively capturing temporal dependencies and contextual information using the Transformer architecture. However, the model may face challenges in training due to the complexity of the Transformer architecture and the need to balance the data degradation scheme. MAD [34] also adopts a self-supervised learning approach, eliminating the need for labeled data. However, MAD focuses on masked modeling, simplifying the training process by learning representations of normality, which reduces the complexity introduced by the integration of synthetic outliers. MAD improves upon the traditional Next Step Prediction (NSP) task by using masked inputs, allowing the model to learn from bidirectional contexts. Nevertheless, compared to NSP models, MAD is slower during the inference process, especially when masking all steps sequentially. To enhance inference speed while maintaining accuracy, CLformer [35] proposes a lightweight Transformer, enhancing local feature extraction through multi-scale causal convolution and reducing error accumulation. Additionally, the block recurrent prediction strategy decreases time and space complexity, leading to faster inference.