esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods of Atmospheric Density Inversion for Low Earth  
Atmospheric density inversion is classified into three categories based on the data source used to calculate the drag force [23]. The first method involves obtaining the atmospheric density by detecting temperature, humidity, and pressure in the atmosphere using sensors carried by aerospace instruments. Notable examples include China's Shenzhou series and Tiangong series spacecraft. In addition to the direct detection of atmospheric density with physical detectors, we can also use the changes in relevant parameters caused by atmospheric drag force during the operation of LEO satellites to calculate the atmo spheric density. The second method entails calculating the attenuation of the half-length axis of the orbit or the change in the overall energy of the satellite based on satellite orbital data, thereby indirectly calculating the atmospheric drag force on the satellite and inverting the atmospheric density. The third method involves calculating the atmospheric density directly after calibrating the acquired onboard accelerometer data. Either method necessitates the calculation of the atmospheric drag force, the atmospheric damping coefficient, and the effective cross-sectional area.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 3 Risk Analysis
### Semi-Quantitative Risk Assessment (Sqra)
#### 3.3.2 Event Tree Analysis  
An event tree analysis (ETA) is performed following the bottom-up approach. It identifies all potential event sequences which may result from the initial event. Event trees were applied in many cases to analyze risks for chemical processes [59]. This analysis consists of two parts: analyzing the causes of an event (failure mode) using DFTA and identifying the sequence of events using ETA. The combination of DFTA and ETA forms a Bow-Tie Analysis (BTA).
# Development of a Power and Communication Bus Using HIL and Computational Intelligence
###### Abstract  
This paper deals with the development of a power and communication bus named DLN (Device Lightweight Network) that can be seen as a new interface with auto-addressing functionality to transfer power and data by means of two wires in modern cars. The main research goal of this paper is to elaborate a new method based on a hardware in the loop technique aided by computational intelligence algorithms in order to search for the optimal structure of the communication modules, as well as optimal features of hardware parts and the values of software parameters. The desired properties of communication modules, which have a strong influence on the performance of the bus, cannot be found using a classical engineering approach due to the large number of possible combinations of configuration of the hardware and software parts of the whole system. Therefore, an HIL-based optimization method for bus prototyping is proposed, in which the optimization task is formulated as a multi-criteria optimization problem. Several criterion functions are proposed, corresponding to the automotive objectives and requirements. Different soft computing optimization algorithms, such as a single-objective/multi-objectives evolutionary algorithm and a particle swarm optimization algorithm, are applied to searching for the optimal solution. The verification study was carried out in order to show the merits and limitations of the proposed approach. Attention was also paid to the problem of the selection of the behavioural parameters of the heuristic algorithms. The overall results proved the high practical potential of the DLN, which was developed using the proposed optimization method.  
caputational intelligence; soft computing optimization; automotive bus; wire harness; hardware-in-the-loop; multi-objectives optimization
## 3 Experimental Setup  
The actuator-beam system developed for this experiment was made up of two actuators and a thin long, narrow aluminum beam, as seen in Figure 3a. The beam's dimensions are 305 \(\times\) 0.68 \(\times\) 29.9 mm, with each actuator approximately 10 mm from a beam end. A hole was drilled in each side of the beam to allow the actuators to be bolted to it. Each actuator was secured to the beam identically using an M4 socket head cap screw, a washer, the M4 hex nut on the actuator, and an additional hex nut for spacing. This system was used to demonstrate how the vibrotactile signals produced by the actuators interact in a case comparable to one-dimensional. A vibrotactile signal is produced by each actuator, creating a vibration response in the beam. If both actuators are being excited, a mixed vibration response is created, depending on the actuation parameters of each actuator. Using this system, multiple experiments were conducted to better understand how the actuators' input parameters relate to the beam's output vibration response. To measure the vibration output transmitted to the aluminum beam, measurement points were marked on the beam, and an accelerometer was mounted on a measurement point to measure the vibration output. This acceleration measurement was performed using a validated accelerometer (Provider: PCB Piezotronics Inc., Model num: 352C66, Mass: 2 g, Type: ceramic shear ICP(r)accel., 100 mV/g, 0.5 to 10,000 Hz). The mass from the accelerometer likely affects the beam's response, meaning all acceleration measurements reported in this study represent the response with a small amount of additional mass at the measurement location. This is a potential limitation of this study, and the effect will be further evaluated in future testing. To control the actuation parameters of two ERAs simultaneously, a microprocessor-based controller and a tablet PC-based system equipped with haptic authoring GUI were prepared. As shown in Figure 3b, a haptic authoring GUI was developed to control the frequency, magnitude, phase delay, and duration of each of the two actuators.  
Figure 2: High-voltage controller for two ERAs: (**a**) Schematic of a circuit diagram capable of generating and controlling two channels of high-voltage signals; (**b**) fabricated microprocessor-based high-voltage controller and two connected ERAs.  
Figure 3: Experimental setup showing: (**a**) Actuator-beam system and high-voltage controller; (**b**) haptic authoring GUI used to control actuator frequency, magnitude, phase delay, and duration.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 3 Materials and Methods
### Data Acquisition  
All experiments were conducted using a notebook computer with an Intel Core i7-9750 processor (2.60 GHz), 16 GB RAM, and an NVIDIA GeForce RTX 2080 GPU, using the Windows 10 operating system. The image was acquired at 640 \(\times\) 480 resolution using a Logitech C920 Pro webcam, as shown in Figure 8a. To prevent loss due to video compression, all captured images were stored as RGB raw data and photographed at a speed of 20 fps. The Vernier Go Direct Respiration Belt was used as reference equipment for breathing signals, as shown in Figure 8b, and data were acquired at 20 fps, according to the speed of the camera. All implementations required for the experiment were executed in the Python programming language (version 3.8), and we used libraries such as OpenCV, Pytorch, and ONNX Runtime to process images and to train and evaluate the models.  
Figure 8: Data acquisition equipment for remote respiration measurement: (**a**) Logitech C920 pro, (**b**) Go Direct Respiration Belt.  
Figure 7: Examples of trended raw signals and detrending results: (**a**) raw signals, (**b**) detrending results.  
The respiration dataset was obtained from 14 participants. The subjects breathed comfortably approximately 1 m from the camera, and the front of their upper body was photographed by the camera while they were guided to breathe according to a provided breathing guideline signal. The guidelines were used to induce three types of respiratory patterns to include various respiratory patterns in the data, and images and respiratory signals were photographed for 260 s per the guidelines. Guideline #1 induced breathing at 40, 35, 30, 25, 20, 15, 10, 15, 20, 25, 30, 35, and 40 ppm for 20 s, and Guideline #2 induced breathing in the form of rapid change in bpm speed to reflect rapid changes in the data. Guideline #3 induced breathing that included pauses during breathing to reflect a stationary state in the learning data. To include movement information other than breathing in each image, moving objects were included in the background, in addition to the subjects, as shown in Figure 9. For each subset, 15,700 image frames were photographed, 15,600, 5200 for each guideline, and a total of 218,400 image frames and respiratory signal values corresponding to those frames were obtained for all subjects.  
We obtained testing data to evaluate the performance of the model for 13 people in a spontaneous breathing situation without any separate control related to the subjects' breathing patterns. The subjects were photographed at a close distance of about 0.5 m and a long distance of about 1.5 m and were also photographed during thoroughly controlled movement and a situation in which movement noise was generated in RoI. For the situation with noise, the subjects' RoI was photographed for an environment in which various objects, such their hands and clothes, were also intermittently moving.
###### Abstract  
The rise in internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal intrusions to guarantee data integrity and user privacy. In this situation, machine learning techniques used in data mining have proven to be effective tools for constructing intrusion detection systems (IDS) and improving their precision. We use the well-known AWID3 dataset, a comprehensive collection of wireless network traffic, to investigate the effectiveness of machine learning in enhancing network security. Our work primarily concentrates on Krack and Kr00k attacks, which target the most recent and dangerous flaws in IEEE 802.11 protocols. Through diligent implementation, we were able to successfully identify these threats using an IDS model that is based on machine learning. Notably, the resilience of our method was demonstrated by our ensemble classifier's astounding 99% success rate in detecting the Krack attack. The effectiveness of our suggested remedy was further demonstrated by the high accuracy rate of 96.7% displayed by our neural network-based model in recognizing instances of the Kr00k attack. Our research shows the potential for considerably boosting network security in the face of new threats by leveraging the capabilities of machine learning and a diversified dataset. Our findings open the door for stronger, more proactive security measures to protect IEEE. 802.11 networks' integrity, resulting in a safer online environment for all users.  
wireless; IDS; machine learning; Krack; Kr00k; IEEE8021.11
# Stimuli-Responsive Photonic Crystals
## 1 Introduction  
Photonic crystals (PhCs) represent versatile building blocks in optics, although they are mostly used as passive optical elements. In these systems, the periodic arrangement of materials with different refractive indices gives rise to the so-called photonic band gap (PBG) and, thus, to structural coloration [1, 2]. In this context, however, anything that can interfere with either the periodicity or the refractive index contrast can be translated into an active modulation of the PBG. This in fact enables application of PhCs as optical active elements. From conceptualization to fabrication, the simplest photonic crystal is represented by a multilayer that alternates materials with different refractive indices [3, 4]. From an application point of view, multilayer photonic crystals, also known as distributed Bragg reflectors (DBRs) or Bragg stacks (BSs), have been used as resonators for distributed feedback lasers [5, 6], smart dielectric layers for light-emitting transistors [7] and light-induced tunable filters [8], among others.  
In this paper, we will review the latest advancements in the field of responsive 1D PhCs, with particular attention to electro-responsive systems. In particular, this paper will discuss both the theoretical methods that are commonly used to predict the optical properties of 1D PhCs, as well as the most employed fabrication techniques. In addition, we will describe the different approaches utilized to achieve active tuning of the photonic band gap.
# Estimation of Aircraft-Dependent Bumpiness Severity in Turbulent Flight
## 2 Methodology
### Vertical Acceleration Response by UVLM
At each time step \(t_{k}\), total aerodynamic force \(\mathbf{F}=[F_{x},F_{z}]^{T}\) is the force sum of all vortex rings on the wing and horizontal tail. Besides, the pitching moment around the gravity center is obtained by  
\[M_{y}(t_{k})=\sum_{i=1}^{N}(x_{cg}-x_{i})F_{z,i}(t_{k})+\sum_{i=1}^{N}(z_{i}- z_{cg})F_{x,i}(t_{k}) \tag{13}\]  
where \(\big{(}x_{cg},z_{cg}\big{)}\) represents the location of gravity center. According to the flight dynamics model, the differential equations of vertical acceleration and pitching moment induced by turbulence are  
\[\left\{ \begin{array}{c}a_{z}(t_{k})=q(t_{k})V_{Gx}^{b}(t_{k})+g\cos\theta\; (t_{k})+\frac{F_{z}(t_{k})}{m}\\ I_{y}\hat{q}(t_{k})=M_{y}(t_{k})\end{array} \right. \tag{14}\]  
where \(\theta(t_{k})\) is pitching angle at \(t_{k}\), \(m\) is the mass of aircraft, and \(I_{y}\) stands for the inertia moment around pitching axis. In order to separate aircraft maneuvers effects from the acceleration response, Equation (14) also needs to be computed twice. Firstly, total aerodynamic force \(\mathbf{F}(t_{k})\) is used in Equation (14) to get a total acceleration response \(a_{z}(t_{k})\), induced by aircraft maneuvers and external turbulence. After that, the aerodynamic force only with aircraft maneuvers, \(\mathbf{F}^{0}(t_{k})\) acts as the input to get the acceleration \(a_{z}^{0}(t_{k})\). As a result, the incremental acceleration value only induced by turbulence is obtained by \(a_{z}(t_{k})-a_{z}^{0}(t_{k})\). Figure 6: The circulation of neighboring rings. There inevitably exists a gap of longitudinal aerodynamic performance between the whole aircraft and the wing-tail assembly. However, this paper makes a compromise between the computation efficiency and algorithm accuracy. Compared with the fuselage, aircraft wing and horizontal tail play a major role in pitching and plunge motion, while the integration of fuselage will greatly increase the algorithm complexity. Furthermore, only the acceleration increment caused by turbulence needs to be considered for aircraft bumpiness estimation. Thus, the error accumulation during the computation process can be reduced. To summarize, with the time series of turbulence components as the input, the time series of vertical acceleration is obtained by UVLM. The next step is to get the estimation of bumpiness severity with the computed acceleration data.
## 3 Results
### Biomarkers of Inflammation and Cartilage Degradation  
As shown in Figures 2 and 3, plasma levels of IL-1\(\upbeta\) and MMP-3 decreased significantly in patients assigned to supplementation with cucumber extract when final levels were compared with baseline (\(p\) = 0.05 and \(p\) = 0.001, respectively). Changes in the placebo group were not statistically significant. In both IL-1\(\upbeta\) and MMP-3 biomarkers, within-group differences were significant in favor of the experimental group. However, changes in plasma levels of COMP were not observed (Table 7).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Variables** & **Baseline** & \begin{tabular}{c} **End of** \\ **Study** \\ \end{tabular} & \
\begin{tabular}{c} **Within-Group** \\ **\(p\) Value** \\ \end{tabular} &
\begin{tabular}{c} **Between-Group** \\ **\(p\) Value** \\ \end{tabular} \\ \hline Weight, kg & & & & \\ \hline Cucumber extract (n = 29) & 76.4 \(\pm\) 13.7 & 76.0 \(\pm\) 13.4 & 0.200 & 0.753 \\ \hline Placebo (n = 26) & 75.7 \(\pm\) 13.4 & 75.1 \(\pm\) 12.7 & 0.102 & 0.753 \\ \hline Fat mass, kg & & & & \\ \hline Cucumber extract (n = 29) & 23.4 \(\pm\) 7.2 & 22.7 \(\pm\) 7.5 & 0.083 & 0.929 \\ \hline Placebo (n = 26) & 21.6 \(\pm\) 7.9 & 20.8 \(\pm\) 7.6 & 0.078 & 0.929 \\ \hline Fat mass, \% & & & & \\ \hline Cucumber extract (n = 29) & 30.7 \(\pm\) 8.0 & 29.8 \(\pm\) 8.4 & 0.107 & 0.835 \\ \hline Placebo (n = 26) & 28.4 \(\pm\) 8.3 & 27.7 \(\pm\) 8.5 & 0.212 & 0.835 \\ \hline Muscle mass, kg & & & & \\ \hline Cucumber extract (n = 29) & 50.3 \(\pm\) 11.3 & 50.6 \(\pm\) 10.8 & 0.529 & 0.215 \\ \hline Placebo (n = 26) & 51.5 \(\pm\) 10.3 & 51.0 \(\pm\) 10.7 & 0.264 & 0.215 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Changes in body composition after supplementation with cucumber extract or placebo for 8 weeks.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Variables** & **Baseline** & \begin{tabular}{c} **End of** \\ **Study** \\ \end{tabular} & \
\begin{tabular}{c} **Within-Group** \\ **\(p\) Value** \\ \end{tabular} &
\begin{tabular}{c} **Between-Group** \\ **\(p\) Value** \\ \end{tabular} \\ \hline IL-1\(\upbeta\), pg/mL & & & & \\ \hline Cucumber extract (n = 29) & 1.8 \(\pm\) 0.9 & 1.3 \(\pm\) 0.6 & 0.050 & 0.044 \\ \hline Placebo (n = 26) & 2.1 \(\pm\) 2.3 & 2.4 \(\pm\) 2.3 & 0.305 & 0.04 \\ \hline MMP-3, ng/mL & & & & \\ \hline Cucumber extract (n = 29) & 80.6 \(\pm\) 25.3 & 70.1 \(\pm\) 25.0 & 0.001 & 0.018 \\ \hline Placebo (n = 26) & 77.2 \(\pm\) 22.5 & 75.5 \(\pm\) 20.8 & 0.532 & 0.018 \\ \hline COMP, ng/mL & & & & \\ \hline Cucumber extract (n = 29) & 28.2 \(\pm\) 17.9 & 31.0 \(\pm\) 18.7 & 0.493 & 0.896 \\ \hline Placebo (n = 26) & 27.6 \(\pm\) 12.7 & 29.6 \(\pm\) 20.0 & 0.638 & 0.896 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Changes in plasma biomarkers after 8 weeks of supplementation with cucumber extract or placebo.
# Non-Contact Dermatoscope with Ultra-Bright Light Source and Liquid Lens-Based Autofocus Function
## 3 Results
### Camera Parameters
#### 3.1.4 Image Scale  
In order to be able to determine the size of interesting skin features for images taken at different distances to the imaging system, a method for reliable scaling of the images was developed. The image scale was calibrated by taking pictures of a scale bar from various distances. These distances where correlated to the output voltage of the distance sensor. In this way the already mentioned look-up table was realized. The image size at different distances to the patient can be seen in Table 1. The scale allows comparing images taken at different examinations and measuring changes in size of the lesions with time. This is for example important for the widely known ABCDE-rule in melanoma screening or for follow-up examinations in the therapy of inflammatory skin diseases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance to the Patient in cm** & **Imaged Area of the Skin (Horizontal \(\times\) Vertical) in mm** \\ \hline
41 & 15.4 & 11.6 \\
43 & 16.2 & 12.2 \\
45 & 17 & 12.8 \\
47 & 17.8 & 13.4 \\
49 & 18.2 & 13.7 \\
51 & 19.1 & 14.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image sizes for different distances to the target.  
Figure 4: Image of the depth of field target (**left**) and the intensity profile over the left stripe pattern of the image (**right**).  
Figure 3: Images of a 1951 USAF resolution test chart at a distance of 45 cm with (**a**) and without (**b**) the liquid lens in front of the camera lens.
# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 4 Simulation Results
The higher alpha value will make the system value the more recent information, thus resulting in a faster Q-value change. It can be seen from Figure 7b that the higher alpha value achieved better results utilizing the new information. Although the PRR difference was slim, the higher alpha value showed more promising results. Figure 7a shows that the proposed RL methods achieved a stable performance around 40,000 events, each event representing one packet transmission. Assuming a uniformly random distribution packet generation, the convergence time shown in Figure 7a was around 20 s. The estimated convergence time achieved by  
\[T_{converge}=\frac{converged\ event\ number}{total\ number\ of\ transmitted\ packets}\times T, \tag{10}\]  
where \(T=60\) s is the simulation time, total number of transmitted packets = 120,000 packets, and the converged event number = 40,000. Figure 8 shows the rewards evolution against the number of events. Similar to Figure 7, the rewards evolution results were taken from two different epsilon values, \(\varepsilon=0.1\) and \(\varepsilon=0.9\). As we can see from Figure 8b, the results of \(\varepsilon=0.9\) show more scattered points compared to the results of \(\varepsilon=0.1\). The lower epsilon will result in the RL not exploring the possible new actions; in other words, the RL will tend to exploit the already-known knowledge. The exploring behavior can be seen in the more scattered points of the results of \(\varepsilon=0.9\). As the RL explored every possible action provided, it resulted in various performance states. These various states caused more rewards to be produced in the learning episode, resulting in more scattered points. Figure 6: Overall PRR performance comparison for \(n_{V}=200\) vehicles scenario.
## 3 Results and Discussion
### Adsorption Performances
At around 35 \({}^{\circ}\)C, there is a notable sudden rise in water uptake. The peak water absorption occurs at the lowest temperature, especially evident at 20 bar, where a maximum vapor uptake of 47.5 wt.% was recorded, demonstrating the optimal sorption capacity of these sorbent materials. Equilibrium isobars were also measured during desorption, gradually increasing the temperature up to 70 \({}^{\circ}\)C, showing no notable hysteresis effect. This finding proves crucial for assessing the energy efficiency of adsorption cycles. Table 6 shows a comparative overview of the maximum water vapor uptake capacities of various porous materials and those of the MIL-100(Fe) synthesized in this study. The obtained MIL-100(Fe) shows an absorption of 47.5%, which is consistent and exceeds the lower end of the range reported for MIL-100(Fe) in the literature [52; 53]. This suggests that the synthesized material exhibits competitive sorption performance. Compared to other materials, MIL-100(Fe) (both from this study and others in the literature) demonstrates higher water vapor uptake than MIL-53(Al) (34.0%), SAPO-34 zeolite (31.7%), and silica gel (32.5%). This highlights the material's promise for sorption applications (i.e., moisture capture, adsorption-driven cooling, and humidity control) where high water uptake is crucial. This study successfully synthesized a high-purity MIL-100(Fe) MOF through a carefully controlled iron precursor ratio and washing procedure. The resulting material show-cased promising gas uptake for hydrogen, CO\({}_{2}\), and water vapor. This optimized MIL-100(Fe) demonstrated the achievement of high gas uptake and water adsorption. These promising results pave the way for further exploration of its potential in clean energy technologies. Future research will delve deeper into optimizing pore size and functionality for targeted gas capture and energy storage processes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sorbent** & **Water Vapor Uptake (\%)** & **Ref.** \\ \hline MIL-100(Fe) & 47.5 & Our study \\ MIL-100(Fe) & 42–78 & [53; 54] \\ MIL-53(Al) & 34.0 & [55] \\ SAPO-34 zeolite & 31.7 & [56] \\ Silica gel & 32.5 & [57] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Maximum water vapor uptake of obtained MIL-100(Fe) and other porous materials. Figure 10: Water vapor sorption isobars (8.3 mbar, 12.3 mbar, 17 mbar, 20 mbar) for MF-S1-W2 sample; adsorption (dashed line) and desorption (solid line).