esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 4 Tasking Offloading Algorithm Based on DRL
### The MDP Framework  
#### 4.1.1 State Space  
The state space variables usually depend on the environmental settings and thus influence the rewards gained by the chosen actions [31]. We define the state space at the current decision epoch \(t\in\mathcal{T}\) as a union of the task size, \(\lambda_{m}(t)\); the waiting time of computation queue, \(\varphi_{m}^{loc}(t)\); the waiting time of the transmission queue, \(\varphi_{m,n}^{trans}(t)\); the queue length, \(d_{m,n}^{deg}(t)\); and the load level history of the edge queue, \(H(t)\), which is denoted as \(s(t)=\{\lambda_{m}(t),\varphi_{m}^{loc}(t),\)\(\varphi_{m,n}^{trans}(t),d_{m,n}^{deg}(t),H(t)\}\). When MD, \(m\), has a task processing request, the five-tuple state variables, \(s(t)\), will be passed to the improved DQN input layer and the network will output the best action to be taken.
# Multimodal Classification of Anxiety Based on Physiological Signals
## 5 Discussion
### Feature Selection  
The main challenges faced were concerned with the physiological characterization through the different signals, arising either from the combination of several features extracted from them or from the potential of each signal to contribute more to a better description. This required feature importance and selection to be carefully attained.  
Figure 7: Classifiers’ execution times (in seconds) without data balancing techniques.  
The frequencies of the features chosen at least three times by RFECV, as depicted in Figure 4, allow for identifying which features were more relevant when classifying anxiety. We can say that the top 10 most frequently selected features are predominantly ECG-extracted features, while Med EMG was the only EMG-extracted feature. However, Med EMG was the only feature present in the feature sets for all classifiers, reinforcing its ability to distinguish between anxiety classes. It is also worth noting that no features extracted from the EDA signal are in the top 10. This might suggest that the ECG and EMG features are more important when combined with other physiological signals, while the EDA features do not add relevant information.  
It is also important to notice that five features were not chosen for any of the classifiers. Three of them were extracted from the EDA signal--Range SCR, Mean, and Median Rise Time--and the other two were extracted from the EMG signal--Range Envelope and Mean EMG. Again, this further confirms the lower importance of the EDA signal on anxiety classification when compared to the ECG signal.
## 3 Results and Discussion
### Rheological Characterization  
Figure 4 shows the master curves obtained for the asphalt mixtures evaluation. In general, the positioning of the curves indicates higher stiffness for the modified asphalt mixtures. An exception, however, is the mixture SBS 60/85 which, at high load frequencies (equivalent to low temperatures), shows a very similar rheological behavior to the conventional mixture. In comparison with the other studied mixtures, the mixture 3% NC + 2% SBS showed notably high values for the complex modulus. This characteristic can be considered as an indication that the mixture will have greater resistance to permanent deformation but can be hampered in terms of fatigue resistance.  
In addition, an analysis of the master curves also reveals the lower frequency susceptibility of the modified mixtures based on the slopes of the curves. In the field, this would mean that the stiffness of these mixtures should be less sensitive to variations in the traffic speed, suggesting a high potential for their use along segments with a steep slope and slow traffic. In this regard, it is possible to establish a behavior hierarchy, where the mixture modified with both modifiers is at the top, followed by SBS 60/85, 3% NC, and the conventional mixture.  
It also can be noted in Figure 4 that the gains related to the frequency susceptibility of the modified mixtures are more significant at lower load frequencies (equivalent to higher temperatures). This may be considered a positive aspect in relation to the permanent deformation phenomenon in regions of tropical climate and on highways submitted to slow loads or in mountainous regions.  
Figure 4: Master curves (reference temperature = 20 \({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Asphalt Mixture** & **Plate** & **Void Volume (\%)** \\ \hline \multirow{2}{*}{CAP 50/70} & P1 & 6.13 \\  & P2 & 4.50 \\  & P1 & 6.55 \\
3\% NC & P2 & 6.68 \\  & P1 & 5.87 \\  & P2 & 5.57 \\
3\% NC + 2\% SBS & P1 & 5.37 \\  & P2 & 5.69 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Volumetric characterization of plates used in permanent deformation tests.  
Figure 5 shows the black spaces for the different asphalt mixtures.  
It can be noted in Figure 5 that the modified mixtures present an aspect of graphic shortening (in relation to the phase angle) when compared with the conventional mixture. This shortening illustrates the obtainment of smaller phase angles and reflects the more elastic behavior of these mixtures. Of these, it can be observed that the 3% NC + 2% SBS mixture has a higher concentration of low phase angles, followed by the SBS 60/85 mixture. The elastic behavior of them, both modified with SBS, reflects the influence of the presence of an elastomeric polymer in the binders. Considered in isolation, this would suggest a better performance of these mixtures in relation to their resistance to permanent deformation and to fatigue cracking.
## 4 Results and Discussion
### Network Lifetime  
The evaluation of network lifetime is shown in Figure 8. In comparison to earlier CEELBRP [24] and EECRP-PSO [25], the lifetime of the proposed ECTSA has increased. Additional sensor nodes start guiding packets randomly as the network's node count increases, and there is a good possibility that the node dies at some point. In the proposed ECTSA technique, only the best node is selected to transport packets, providing longer battery life and network lifetime. Table 8 shows the results for the comparison of network lifespan performance.  
Figure 8: Analysis of network lifetime.  
Figure 7: Analysis of routing overhead.  
Figure 8 shows the performance analysis of network lifetime. As a consequence, the suggested ECTSA has a longer network lifetime than traditional protocols. The lifespan of the proposed ECTSA has been extended because of the increasing amount of data packets delivered to the destination point.
# Artificial Intelligence in Cybersecurity: A Review and a Case Study
## 6 Enhancing Security in Low-Rate Wireless Networks  
The ubiquity of wireless networks requires a variety of protocols that meet specific needs. IEEE 802.11 is a commonly used protocol in many wireless applications. For low power consumption and low-rate communication, the IEEE 802.15.4 protocol is a preferred choice in battery-powered devices in home appliances and industrial settings demanding robust operation amidst noise and interference [28].  
However, the encryption mechanisms such as WEP, TKIP, and CCMP used in IEEE 802.11 are not compatible with IEEE 802.15.4 due to their high processing power and energy requirements. Since IEEE 802.15.4 protocols require low-power compatibility, lightweight encryption approaches are necessary. Therefore, low-cost encryption algorithms should be used in these networks.  
In this section, we propose a novel encryption methodology specifically designed for IEEE 802.15.4 communications. In this methodology, we utilize a GA to generate a symbol-to-chip sequence table, effectively ciphering data. Unlike the standard IEEE 802.15.4 protocol, where this table is publicly available, our anonymous solution makes data symbols undercptable for listeners passively monitoring the channel.  
Traditionally, security in IEEE 802.15.4 networks is addressed through higher-layer protocols, introducing additional overhead. Here, our innovation lies in integrating encryption seamlessly within existing protocol operations, eliminating the need for extra processing demands. This enables secure communication within the inherent low-power and low-overhead constraints of IEEE 802.15.4 networks, enhancing their overall performance and security posture.  
Traditionally, security in IEEE 802.15.4 networks is provided through higher-layer protocols. Such protocols introduce additional overhead because adding higher-layer algorithm operations. Our innovation here is to integrate encryption into existing protocol operations in IEEE 802.15.4 existing PHY-MAC (Physical-Media Access Control) layer, eliminating extra processing demands. This enables secure communication within the low power and low load constraints inherent in IEEE 802.15.4 networks, improving their overall performance and security posture.
## 3 Mathematical Modeling and Design Methodology
### Modeling of a PV Array Output Power Generation
#### 3.1.4 Solar Array Model  
A single PV panel does not produce enough power to satisfy common applications. A configuration of cells is called a module. In other words, a panel consists of one or more PV cells assembled and pre-wired for field installation [37]. A similar series-parallel configuration of PV panels composes the array or a solar PV plant. The wattage rating of a PV array is usually the maximum DC power output under STC. Figure 7 represents the circuit model of TD for the solar PV array.  
A PV array is composed of a number of strings connected in parallel (\(N_{Par}\)). The number of PV modules connected in series per string is (\(N_{ser}\)). Note that (\(N_{Par}\times N_{ser}\)) is the number of PV modules (\(N_{PV}\)) of the PV array, which is one of the design variables to be optimized by using the proposed methodologies and optimization algorithms in Sections 3.4.3-3.4.5, respectively. The optical and saturation currents for a PV array can be expressed as in [16].  
The formula that describes the relationship between the output current (I) and voltage (V) of a PV array is derived as in [6; 37; 39]. The output power from a PV array can be obtained as shown in Equation (4). Moreover, the values of the series and parallel resistances of the PV array are given by the equations described in [16]:  
\[P=VI=N_{Par}I_{Ph}V-N_{Par}V\left(\sum_{n=1}^{2}I_{on}\left(e^{(\frac{1}{\alpha _{n}V_{T}})(\frac{V}{N_{ser}}+\frac{IR_{s}}{N_{par}})}-1\right)\right)-\frac{N_ {Par}}{R_{p}}\ V\left(\frac{V}{N_{ser}}+\frac{IR_{s}}{N_{Par}}\right) \tag{4}\]
## 5 Conclusions  
In this study, the model of an ICE-ORC system was built in the software GT-POWER. Then, the optimal operation conditions of the ORC system were determined based on various engine conditions. The control strategy of the ORC system was designed using a feedforward method in MATLAB/Simulink. Finally, the operation performance of the ICE-ORC combined system was analyzed under a transient driving cycle. The main conclusions are summarized as follows:  
1. The influence of the expander inlet pressure on the net power output and the thermal efficiency of the ORC system was greater than the expander inlet temperature when the engine was operating on a fixed condition. Therefore, the mass flow rate of the working fluid should be adjusted to improve the evaporation pressure as high as possible while maintaining the working fluid slightly superheat at the inlet of the expander. The expander efficiency also had a great influence on the performance of the ORC. The measured efficiency of the prototype single-screw expander was relatively small. If its efficiency is improved to a level that industrial products can achieve, the net power output and the thermal efficiency of the ORC can be increased by more than 20%.
2. There exists an optimal operation condition of the ORC system defined by the pump speed and the expander speed corresponding to each operation point of the engine. For the ICE-ORC  
Figure 11: Results for the transient simulation of the ICE-ORC combined system: (**a**) The vehicle speed, (**b**) the engine speed, (**c**) the engine torque, and (**d**) the expander power output.  
combined system, this pattern can be programed as a feedforward control method to coordinate the operation of the ORC with the engine under transient operation conditions. When the engine operates under the ECE-EUDC driving cycle, the designed control strategy can adjust the operation point of the ORC system accordingly. The exhaust energy must be greater than a minimum value to ensure the ORC system operating properly. The ORC system operates in a pulsating way when the vehicle is running under the urban conditions because of a frequent start and stop and a low exhaust energy. However, the designed ORC system can operate for most of the time during the high-way conditions except during the braking process. The net power output of the expander essentially follows the power output of the engine.  
Conceptualization, E.W. and T.L.; methodology, M.F. and T.L.; software, T.L. and R.Z.; validation, E.W., T.L. and M.F.; formal analysis, T.L.; investigation, T.L. and M.F.; resources, F.Z. and H.Z.; data curation, T.L. and R.Z.; writing--original draft preparation, T.L.; writing--review and editing, E.W.; supervision, C.Z.  
This research was funded by the National Natural Science Foundation of China, grant number 51876009.  
The authors declare no conflict of interest.
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 3 Performance Analysis  
In this section, we detail and derive the expressions of \(STP\) and \(AT\) for the considered V2V communication system model in LoS and NLoS scenarios.  
Figure 2: Illustration of V2V LoS communication scenarios.
## 1 Introduction  
Global environmental pollution and destruction are issues of increasing concern in today's society, thus necessitating effective catalysts to degrade harmful pollutants. In recent years, the utilization of semiconductors has garnered widespread interest, as a means to induce and improve photocatalytic reactions. A prime example would be metal oxide semiconductor powders, which have exhibited activity when irradiated with ultraviolet (UV) light, and have been widely used as photocatalysts for the degradation of pollutants [1; 2]. Titania (TiO\({}_{2}\)) has been widely used as a catalyst, due to its many advantageous qualities, namely optical and electronic properties, low cost, high level of photocatalytic activity, chemical stability and non-toxicity [3; 4; 5].  
Although the most widely used system, TiO\({}_{2}\), is usually in nanopowdered-form, as it provides high surface area, filtration is required at the end of a reaction, which complicates the whole process. The other popular form of TiO\({}_{2}\), supported TiO\({}_{2}\) thin films, on the other hand, mitigates the need for any further post-treatment filtering of the decontaminated fluid, in spite of the limited surface area compared to the dispersed nanoparticle form [6]. Film coatings induce the catalytic properties of TiO\({}_{2}\) while taking advantage of the substrate's mechanical properties. There are a number of ways by which TiO\({}_{2}\) films are fabricated onto supporting materials (substrates), such as the sol gel method, sputtering technique, electrochemical process, chemical vapour deposition (CVD) and spray pyrolysis [7].  
Spray-ILGAR is an extension of the ion layer gas reaction (ILGAR) technique, which was developed and patented by the group of Professor Dr. [PERSON] at the Helmholtz-Zentrum Berlin for Materials and Energy, in Berlin, Germany [8], and which is equivalent to the aerosol assisted chemical vapour deposition (AACVD) method [9]. ILGAR is a non-vacuum, thin-film deposition technique, involving a sequential and cyclic process that enables the deposition of semiconductor thin films, especially chalcopyrite absorber layers and buffer layers. This chemical process permits deposition of layers in a homogenous, adherent and mechanically stable form with the absence of vacuum or high temperatures. This whole process is able to be automated and scaled up. For the case of metal oxides, it usually involves the deposition of an intermediate precursor film (alkoxide), followed by the addition of energy (heating) to chemically convert the precursor to the final product (metal oxide).  
In the present work, titania on stainless steel with different concentrations, thicknesses and preparation temperatures was obtained by the aforementioned Spray-ILGAR technique. The photocatalytic activity of the prepared samples was measured in the photocatalytic degradation of gaseous acetaldehyde under UV light irradiation.
## 2 Instrument Design
### Mobile Unit
#### 2.4.1 Telescopic Robotic Arm  
This mobile unit consists of a telescopic robotic arm able to handle the radiometer and to manage its positioning, mounted on a NISSAN ATLEON truck that allows to transport the entire system. In order to position the radiometer, the arm is capable to rise it up to 8 m from the ground, and then to change its pointing direction using two different rotors. One controls the azimuth of MERITXELL. Azimuth can be set from 45\({}^{\circ}\) to 315\({}^{\circ}\), where 0\({}^{\circ}\) is pointing to the cabin of the truck, and the angle increase in counterclockwise direction. The other rotor controls the elevation movement, defined as the zenith angle, where it can be modified from 0\({}^{\circ}\) (zenith) to 155\({}^{\circ}\) (incidence angle of 25\({}^{\circ}\)). Both angle restrictions are set for safety reasons. Moreover, the telescopic arm has three states: up or measuring, down or parked, and calibration or pointing to a microwave absorber, which is used to perform the hot load calibration. All the structure is mounted inside a fiber-glass housing for transportation and storage purposes. Figure 7 shows MERITXELL assembled to the telescopic arm above the housing structure and the truck.  
Figure 7: MERITXELL assembled to the telescopic robotic arm above the housing structure and the truck.  
Moreover, the mobile unit has four stabilization legs manually controlled covering the maximum surface allowing to work with an instrument at eight meters high withstanding winds of up to 100 km/h. Both the telescopic robotic arm and the stabilization legs work with an hydraulic unit.
# Interaction of Machining Parameters on MRR of Sintered NdFeB Processed by EDM-Milling
## 2 Experimental Conditions  
### Experimental Setup and Materials  
The experimental setup is based on the DM71 EDM machine tool from Taizhou Changde Mechanical Manufacture Co., Ltd. (Taizhou, China). An electrode rotating device and a sliding table were added to build the experiment platform for EDM-milling, as shown in Figure 1a. Figure 1b shows the diagram of workpiece milling. The workpiece material is sintered NdFeB from Jiujiu Gaoke Magnetic Material Co., Ltd. (Beijing, China), the size of which is 10 mm \(\times\) 10 mm \(\times\) 10 mm. The tool electrode is columnar copper, with a diameter is \(\varphi\)6 mm. The dielectric is oil.
## 5 Back-Reaction
### Dynamics of the Number of Atoms in the Condensate  
From Equation (90) we observe that the dynamics of the perturbation of the condensate affects not only the position of the horizon, but also the global properties of the system, such as the number of atoms in the condensate state.  
Let us consider the dynamics of \(\left\langle\delta\rho\right\rangle\). The integral of this quantity in space gives the rate at which the atoms leave the condensate and move to the excited part. It is a global process which can already be described at the level of the Bogoliubov-de Gennes equations, considering the derivative with respect to time of the total number of atoms in the condensate 1-particle state, and assuming the stationarity of the unperturbed condensate wavefunction:  
\[\partial_{t}N=\partial_{t}N_{\mbox{TOT}}-\partial_{t}\int\mathrm{d}x\,\left \langle\delta\phi^{\dagger}\delta\phi\right\rangle\,. \tag{118}\]  
The same derivative can be expressed as the derivative of the integral of the number density, i.e., of the squared norm of the condensate wavefunction. Since the unperturbed configuration is assumed stationary, the only contribution is made by the classical perturbation, i.e., by the back-reaction of the analogue quantum field. From Equation (90) we get  
\[\partial_{t}N = \partial_{t}\int\mathrm{d}x\,\left\langle\delta\rho\right\rangle= \tag{119}\] \[= -\int\mathrm{d}x\,\left\langle\rho_{0}\right\rangle\left(\frac{ \
abla\left\langle\theta_{0}\right\rangle}{m}\right)\
abla\left\langle\theta_ {1}\theta_{1}\right\rangle-\frac{1}{m}\underbrace{\int\mathrm{d}x\,\
abla \left(\left\langle\delta\rho\right\rangle\Sigma\left\langle\theta_{0}\right \rangle\mp\left\langle\overline{\rho_{0}}\right\rangle\
abla\left\langle \delta\theta\right\rangle\right)}_{=}\] (120) \[= 4\pi c_{s}r_{H}^{2}\left\langle\rho_{0}\right\rangle\int_{0}^{ \infty}\mathrm{d}r\partial_{r}\left\langle\theta_{1}\theta_{1}\right\rangle=\] (121) \[= -3N_{BH}T\left[\left\langle\theta_{1}\theta_{1}\right\rangle \right]_{0}. \tag{122}\]  
We observe that the time derivative of the number of atoms in the condensate--in the entire occupied volume--depends on three factors: the number of condensate atoms in the region within the horizon \(N_{BH}\) ; the Hawking temperature of the black hole, as defined in Equation (78), that goes with the inverse of the horizon radius (plus subleading corrections describing the imperfection in the adiabaticity of the evolution); the value that the vacuum polarization \(\langle\theta_{1}\theta_{1}\rangle\) assumes at the origin, which is effectively a dimensionless structure factor depending on the velocity profile. The vacuum polarization requires a proper renormalization, and it is generally difficult to extend it down to the singularity in the origin, not only when studying the canonical analogue black hole but also, e.g., for the Schwarzschild black hole [52].  
Anyhow, in the integral form as in Equation (121) it is clear that it is the radial derivative of the vacuum polarization, on both sides of the horizon that determines locally the contribution to the depletion.
# Adaptive Prosthetic Trajectory Estimation Based on Key Points Constraints
## 5 Discussion  
### The Advantages of the Kinematic Estimation Method  
Upon theoretical analysis and experimental verification, this study's proposed kinematic estimation method based on the key points has the following benefits:  
(1) This method is designed to obtain a global continuous kinematic model with limited gait database data. Human experimental data are limited, making it impossible to obtain gait data for arbitrary continuous speeds and slopes. This method builds a kinematic model using the limited data and estimates the unknown gait between experimental data based on the regular differences caused by speeds and slopes. Gait kinematics are divided into two parts: the invariant basis function and task function. Fitting and estimation are carried out, respectively, to reduce the complexity of the model and the difficulty of the calculation.  
(2) A new method has been proposed to estimate the key points of the gait model. The improved iterative D-P algorithm automatically locates these key points and introduces the constraint of the key points into the basis function solution. The use of the key points in the basis function has several advantages. Firstly, it ensures that the key points, which determine gait characteristics, are retained to ensure that the same action is performed with the reference gait in the key gait phases. This helps to improve walking comfort and stability. Secondly, individualization is usually a difficult problem to solve in prosthetic control. However, the gait differences of different people can be reflected by the key points, which makes it an effective and concise method to adjust the basis function through the key points.  
(3) The RBF neural network is used to estimate the task function, adaptively adjust the network model, and build an adaptive estimation method for speeds and slopes with high estimation accuracy.