esa-sceva/satcom-chunk-collection huggingface dataset:
# On Hybrid Energy Utilization in Wireless Sensor Networks
## 2 Problem Formulation and Markov Decision Process
### System States Transition Probabilities  
Since the events of packet arrival and solar harvested energy arrival are independent of each other, the transition probability from the current state \(\left(S_{Q},S_{B}\right)=(i,j)\) to the next state \(\left(S_{Q},S_{B}\right)=(i^{\prime},j^{\prime})\) with respect to the action \(A=(a_{C},a_{H})\) can be represented by the following equation:  
\[\begin{split} P_{A}((S_{Q},S_{B})=(i^{\prime},j^{\prime})|(S_{Q}, S_{B})=(i,j))=\\ P_{A}((S_{Q}=i^{\prime})|(S_{B}=i))\cdot P_{A}((S_{B}=j^{\prime})|(S_{Q} =j))\end{split} \tag{1}\]  
A two-state Markov chain model is considered here for describing the packet arrival and the solar energy arrival [10]. If an action \((a_{C},a_{H})\) is taken, the battery and packet state transition probabilities \(P_{A}\left(\left(S_{Q}=i^{\prime}\right)|\left(S_{Q}=i\right)\right)\) and \(P_{A}\left(\left(S_{B}=j^{\prime}\right)|\left(S_{B}=j\right)\right)\) can be specified as follows:  
\[P_{A}((S_{Q}=i^{\prime})|(S_{Q}=i))=\begin{cases}\eta_{P},&i^{\prime}=i-(a_{ C}+a_{H})+1;\\ 1-\eta_{P},&i^{\prime}=i-(a_{C}+a_{H});\\ 0,&Otherwise.\end{cases} \tag{2}\]  
\[P_{A}((S_{B}=j^{\prime})|(S_{B}=j))=\begin{cases}\eta_{S},&j^{\prime}=j-a_{H} +1;\\ 1-\eta_{S},&j^{\prime}=j-a_{H};\\ 0,&Otherwise.\end{cases} \tag{3}\]  
where \(\eta_{S}\) and \(\eta_{P}\) are the probabilities of one solar energy unit arrival and one packet arrival, respectively.  
dropping a packet is given by \(\left(1-\alpha\right)\eta_{p}\). On the contrary, the cost of using a constant energy source is \(\alpha\cdot a_{C}\) when packets in the buffer are transmitted. Accordingly, the PDP is emphasized if \(\alpha\) approaches zero. On the other hand, if \(\alpha\) is set approaching to one, it reflects the importance of saving the constant energy source.
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 5 Trust Management
### Trust Initialisation  
Trust initialisation happens when a node starts sending packets to its neighbouring node. In this step, a node in MANET has no previous interaction with its neighbour and has no background about its behaviour. This is a risky situation and most of the direct trust management algorithms proposed consider the default trust value to be neutral. For example, if the range of the trust value is between 0 and 1, the default value for the unknown node is 0.5.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Cluster Maintenance  
In the FANETs, due to the high-speed movement of UAVs, UAVs often join or leave the cluster. Violent node movement sometimes leads to the updating of the CH and the reconfiguration of network structure, which introduces large computation and communication overhead. Therefore, a reasonable cluster maintenance mechanism is needed to minimize the overhead and maintain the stability of the cluster structure.
# Modeling and Simulation of Sky Survey
## 3 Proposed Methods
### Constraints  
In order to more accurately reflect the real conditions of a survey telescope in space while taking into account the complexity of the model, this section describes the use of several important constraints on the model in this paper.  
Light Angle: The stray light constraints considered in this paper are divided into two kinds, sunlight and moonlight. Stray light from the Sun and Moon into the hood will increase the noise and seriously affect the quality of imaging. It is necessary to make the angle between the sunlight, moonlight, and the field of view meet a certain angle for imaging.  
\[C_{1}:\alpha=\theta(Sun_{i},Eph_{i})\geq\omega_{1},\beta=\theta( Moon_{i},Eph_{i})\geq\omega_{2},\hskip 28.452756 pt\forall t_{i}\in T. \tag{7}\]  
Singularity Constraint: It is necessary to constrain any time window to observe no more than one sky zone. Thus, we have the constraint \(C_{1}\):  
\[C_{2}:\sum_{j=1}^{M}x_{ijk}\leq 1,\hskip 28.452756 pt\forall t_{i}\in T,\forall \tau_{k}\in R \tag{8}\]  
CMG Constraint: As the CMG rotation process needs to consume capacity, the larger the rotation angle, the more energy consumed. The number of maneuvers of different angles of the monorail rotation needs to be limited to ensure the normal operation of the CMG. Let \(C=\{c_{p},p=1,2,\ldots,8\}\) be a CMG rotation times set.  
\[C_{3}:\sum_{i\in I_{0}}\omega_{ip}\leq c_{p},\hskip 28.452756 ptp=1,2,\ldots,8, \tag{9}\]  
where \(t_{0}\in\left[\left\lfloor t_{i}^{i}/T\right\rfloor\times T,t_{i}\right]\), \(\left\lfloor*\right\rfloor\) stands for rounding down to the nearest integer for \(*\); \(t_{i}\) is the current time; and \(\omega_{ip}\) represents the \(p\)_-th_ CMG rotation angle at the \(i\)_-th_ time window.
## 3 Results and Discussion
### Reactive Azo Dye Oxidation
#### 3.2.6 Catalyst Stability and Reusability  
To investigate the adequacy of the catalyst activity, its cyclic use was examined. To assist this investigation, the catalyst was collected after each use, washed with distilled water, and then subjected to oven drying for 1 h (105 \({}^{\circ}\)C). The catalyst loss was calculated, and it did not reach more than 1%. Then, the recovered catalyst was used for dye treatment at the optimal reagent doses of 818 mg/L, 1.02 mg/L, and 3.0 for H\({}_{2}\)O\({}_{2}\), the Fuller's earth catalyst, and pH. The catalyst was repeatedly used, and its efficiency for treatment was examined. The catalyst reactivity reduced, achieving only 73% dye removal in the sixth cycle compared to the 99% achieved with the use of the fresh catalyst, as shown in Figure 12. However, it is worth mentioning that the catalyst was still efficient in removing dye from the aqueous solution. This reduction in the catalyst efficiency could be associated with the dye molecules occupying the active sites of the catalyst surface. Thus, its efficiency in producing hydroxyl radicals is reduced, and they are the horsepower of the oxidation reaction. Such a reduction in the catalyst efficacy after multiple uses has previously been reported in the literature [6] investigating the magnetized biomass catalyst recyclability in treating polluted wastewater.  
It is worth mentioning that various researchers [6; 11; 33] have suggested that using a solvent for dye desorption might result in the regeneration of the catalyst. Moreover, eliminating the dye molecules occupying the catalyst surface through temperature elevation might also regenerate the catalyst. Additionally, changing the pH of the medium to achieve dye desorption could facilitate catalyst regeneration [34]. Moreover, introducing supercritical CO\({}_{2}\) to substitute the organic solvents for the desorption ability is a promising technique since it overcomes the environmental concerns regarding hazardous solvents. These suggested methods might elaborate the stability and recyclability of [PERSON]'s earth. Not only can these methods regenerate the Fuller's earth material, but they can also recover and enable the collection of the dye adsorbate rather than destroying it, which is suggested to be an ideal sustainable solution. Therefore, these techniques are recommended for improving catalyst regeneration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{4}{c}{**Temperature (\({}^{\circ}\)C)**} \\ \cline{2-5}
**Thermodynamic Parameters** & **25** & **40** & **50** & **60** \\ \hline \(\Delta\)G\({}^{\prime}\) (kJ/mol) & 80,720.78 & 81,487.94 & 82,314.45 & 84,839.69 \\ \(\Delta\)H\({}^{\prime}\) (kJ/mol) & \(-\)2426.76 & \(-\)2551.47 & \(-\)2634.61 & \(-\)2717.75 \\ \(\Delta\)S\({}^{\prime}\) (J/mol) & \(-\)279.02 & \(-\)268.49 & \(-\)263.00 & \(-\)262.93 \\ \(\text{Ea}\) (kJ/mol) & & & 50.8 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermodynamic properties of organics removal using modified Fuller’s earth Fenton oxidation.
## 2 Fundamental Principles of Quasi-Zero Stiffness  
### Basic Principles of QZS  
Static stiffness refers to the system's resistance to static loads, while dynamic stiffness is the system's resistance to dynamic loads such as vibrations. QZS vibration isolators are crafted via the parallel integration of positive and negative stiffness mechanisms, culminating in a total stiffness approaching zero. The load-carrying capacity of the isolator is governed by the positive stiffness mechanism, while the negative stiffness mechanism regulates the dynamic stiffness of the system. Collaboration between these components results in a reduction in the intrinsic frequency while maintaining the bearing capacity, thereby expanding the frequency band for vibration isolation. Within a specified range, the QZS vibration isolation system achieves near-zero total stiffness through the incorporation of a negative stiffness mechanism, dimensions, and system parameters, and by employing positive and negative stiffness mechanisms in parallel to offset some of the positive stiffness within the equilibrium range. This system not only showcases the outstanding load-bearing capability of the positive stiffness mechanism but also demonstrates reduced dynamic stiffness.  
Figure 1 illustrates the characteristic curve of the QZS vibration isolator. The mass of the isolated object is denoted by \(m\) and \(x_{e}\) represents the equilibrium position of the system. At this juncture, the total stiffness of the system equals the summation of the positive and negative stiffness components. Through adjustments to the positive stiffness mechanism, the positive stiffness consistently surpasses the absolute value of the negative stiffness mechanism's stiffness. Consequently, the system's total stiffness approaches zero, thereby diminishing the intrinsic frequency of the isolation system and enhancing its low-frequency isolation capacity. The QZS isolation system distinguishes itself from high static and low dynamic stiffness isolation systems by virtue of the proximity of its total stiffness to zero near the static equilibrium position. Figure 1 represents positive stiffness, where the restoring force increases linearly with the displacement. This is the typical behavior of a conventional spring. Figure 1 shows negative stiffness, where the restoring force decreases with the displacement. The point \(x_{e}\) in this figure denotes the static equilibrium position at which the system achieves quasi-zero stiffness. At \(x_{e}\), the negative stiffness counteracts the positive stiffness, resulting in a total stiffness that is near zero. Figure 1 depicts the quasi-zero stiffness (QZS) condition, where the combination of positive and negative stiffness  
elements creates a system that has very low stiffness around the equilibrium position \(x_{e}\). This configuration allows for effective vibration isolation by minimizing the system's natural frequency. The high static and low dynamic stiffness isolation system lacks the capability of structural self-adjustment, thus culminating in a system possessing a total stiffness nearing zero near the static equilibrium position.
## 1 Introduction
NMR is applied in the microstructure detection of porous media because of its rapidity, efficiency, and non-destructiveness [14]. Previous tests show that after adding superhydrophobic nano-SiO\({}_{2}\), the shrinkage and expansion rate of expansive soil decrease, and the UCS of expansive soil increases. Moreover, superhydrophobic nano-SiO\({}_{2}\) can inhibit the cracking of expansive soil during shrinkage [15]. Expansive soil modified by superhydrophobic nano-SiO\({}_{2}\) may be used as replacement soil to inhibit soil cracking, reduce the permeability of shallow soil, and improve the mechanical properties and stability of expansive soil slope. The present study aimed to investigate the effect of different contents of superhydrophobic nano-SiO\({}_{2}\) on the hydraulic conductivity and PSD of expansive soil, as well as analyse the mechanism of superhydrophobic nano-SiO\({}_{2}\) and expansive soil minerals that changes the hydraulic conductivity of expansive soil from a microscopic view. The influence of superhydrophobic nano-SiO\({}_{2}\) on the hydraulic conductivity of expansive soil was obtained via hydraulic-conductivity tests. The pore distribution of samples after the hydraulic-conductivity test was calculated using the \(T_{2}\) spectrums obtained from the NMR test. Then, the effect of superhydrophobic nano-SiO\({}_{2}\) on the PSD of expansive soil was further analysed. Fourier transform mid-infrared (FTIR) spectroscopy was used to determine whether the soil reacted after adding superhydrophobic nano-SiO\({}_{2}\). The hydrophilic change of the soil sample after adding superhydrophobic nano-SiO\({}_{2}\) was obtained via the contact-angle test. Thermogravimetric analysis (TGA) provided the thermal properties of the soil samples and the mass loss of the sample was calculated with respect to the increase in temperature. The effect of the pore distribution on the hydraulic conductivity of the expansive soil was also analysed through the test results and the Young-Laplaceequation. This work could be a basis of the application of nanomodified expansive soil in civil-engineering structures and geoenvironmental projects.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 3 System Model and Problem Formulation
### Problem Description  
According to the above scenario, the equivalent complex baseband signal sent by the ambient RF source is denoted as \(s(n)\). The signals reach the tag by the reflected RF source-RIS-tag path and the direct RF source-tag path, which is denoted by the symbol \(x(n)\), and can be obtained by the following formula:  
\[x(n)=\Big{(}h_{0}+\mathbf{h}_{sr}^{H}\mathbf{\Phi}\mathbf{h}_{rt}\Big{)}s(n) \tag{1}\]  
where \(\mathbf{\Phi}=\text{diag}\big{(}e^{i\theta_{1}},e^{i\theta_{2}},\cdots,e^{i\theta_ {N}}\big{)}\in\mathbb{C}^{N\times N}\) denotes the phase shift matrix of the RIS, and \(\theta_{n}\in[0,2\pi)\) for \(n=1,2,\cdots,N\) is the phase shift induced by the \(n\)-th reflect element. The tag can be switched between reflection and non-reflection by adjusting the impedance, so that two kinds of information \"0\" and \"1\" can be carried, which is also called on-off modulation. The tag is a passive device that cannot achieve the complete reflection due to the material limitations, and hence there will be a certain attenuation during reflection. Therefore, after being modulated by backscatter, the transmitted signal of the tag can be expressed as  
\[t(n)=\eta d(l)x(n),\ d(l)=0\ or\ 1 \tag{2}\]  
Figure 1: RIS-assisted AmBC system.  
where \(\eta\) represents a plural number indicating the attenuation of the tag, \(d(l)\) denotes a 1-bit effective information that the tag needs to send to the reader. The tag information's symbol rate is often lower than that of the ambient RF source, due to the limitations in tag equipment and energy consumption. Therefore, assume that \(L\) RF sources symbols are spanned in 1 backscattered tag symbol. Thus, \(L\) satisfies the following relationship as:  
\[L=\frac{T_{tag}}{T_{source}} \tag{3}\]  
where \(T_{source}\) and \(T_{tag}\) are the duration of the RF source and tag symbol, respectively.
employs edge resources near the user equipment (UE) to reduce latency while enhancing reliability and stability [7; 8]. The offloading task sends computational tasks/data to the MEC server for processing [9; 10]. Once the MEC executes the received task, it is responsible for sending the results back to the appropriate user. Edge computing has several advantages, such as decreased end-to-end latency, increased multimedia bandwidth, computation-rich resources, and enhanced flexibility. The drawback with most UEs is resource limitation due to computation requirements, even though new and improved hardware is available. However, MEC could solve this issue by hosting the computational tasks.  
This study proposes two algorithms for choosing the best parameters in the optimisation function. Online algorithms are used to enhance the performance evaluation. The handover (HO) time cost is also considered in the formulation of the optimisation function while making optimum offloading decisions. The two algorithms presented in this work assume that the upcoming future tasks are unknown, and online task offloading (TO) decisions are therefore considered. The proposed methods are not model-based and can be implemented in any mobility scenario. In the first algorithm, called the online UE-base station (BS) algorithm, the users have both the user-side and BS-side information. In the second algorithm, BS-learning, users only have user-side information. The overall simulations are conducted using the MATLAB tool, and are verified in the case where the BS-side information is known. Our proposed method exhibits near to optimum performance. For the scenario based on observation and learning, the results reveal a decrease in performance due to additional costs, such as the HO time cost. The energy performance is also compared with other related papers, proving that in terms of the energy budget, our two proposed methods exhibit near to the optimum solution.  
The remainder of this paper is organised as follows: A comprehensive literature review with the motivation of this work is provided in Section 2. Section 3 provides the system model analysis. Section 4 presents the problem formulation and the optimisation problem. Section 5 discusses the proposed online task offloading decision algorithms, problem formulation, and proposed online algorithms. Section 6 displays the simulation setting and numerical results. Finally, Section 7 includes the conclusion of this paper and additionally future tasks that can be employed for improving this manuscript.
## 4 Simulation and Analysis  
To validate the effectiveness of the proposed integrated collision avoidance control system, a joint simulation model is built through CarSim and Matlab/Simulink for the double lane change emergency collision avoidance, as shown in Figure 6. A high-fidelity C-class vehicle model provided in CarSim is used for simulation. The specific model parameters and controller parameters are listed in Table 2. The model parameters include vehicle mass, the position of center of mass, and tire lateral stiffness, etc. The controller parameters include controller sample time, size of the prediction horizon and the control horizon etc.  
Four different control systems as listed in Table 3 are compared on low, medium, and high friction roads, respectively. The controller consists of a pre-emptive braking controller for longitudinal motion control and a 2 WS controller for path tracking control. When the vehicle speed exceeds the safe speed, pre-emptive braking is applied to the autonomous vehicle. Additionally, the 4 WS and DYC systems are adopted to enhance stability during the emergency collision avoidance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter Description** & **Value** \\ \hline \(m/(\text{kg})\) & Vehicle mass & 1413 \\ \(l_{r}/m\) & Distance from center of mass to front axle & 1.895 \\ \(l_{r}/m\) & Distance from center of mass to rear axle & 1.015 \\ \(\text{I}_{x}/(\text{kg}\cdot\text{m}^{2})\) & Moment of inertia about the \(Z\) axis & 1536.7 \\ \(C_{f}/(\text{N}\cdot\text{m}^{-1})\) & Front axle lateral stiffness & 70,000 \\ \(C_{r}/(\text{N}\cdot\text{m}^{-1})\) & Rear axle lateral stiffness & 35,000 \\ \(N_{p}\) & Size of the prediction horizon & 20 \\ \(N_{c}\) & Size of the control horizon & 5 \\ \(T\) & Controller sample time & 0.05 \\ \(Q\) & State weighting matrix & Diag (24, 16.8, 1, 1) \\ \(R\) & Controls input weighting matrix & Diag (1, 1, 1) \\ \(\rho\) & Relaxation factor weight coefficient & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters and controller parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Control System** & **Description** \\ \hline \hline
1 & 2 WS \\
2 & Pre-emptive braking control + 2 WS \\
3 & Pre-emptive braking control + 4 WS \\
4 & Pre-emptive braking control + 4 WS + DYC \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different control systems.
## 4 Verification and Analysis of Solution  
### Verification of Solution  
By setting \(c_{1}=c_{2}\), the solution presented in this paper can be degenerated into a consolidation solution under the condition where the consolidation coefficients of the upper and lower layers are the same. It is then compared with the analytical solution by [PERSON] et al. [23] under this condition. If the curves of the solution in this paper coincide with those by [PERSON] et al. [23], it can be verified that the numerical solution in this paper is correct. The comparative results are as follows:  
[PERSON] et al. [23] derived an analytical solution for one-dimensional nonlinear consolidation of double-layered soil under the premise of a compression and penetration index of 1, based on the continuous drainage boundary condition. Figure 2 presents a comparative illustration of the consolidation degree derived from the difference method against [PERSON] et al. [23]. The congruence between the outcomes from both investigations substantiates the accuracy of the solution introduced in this study. Additionally, Figure 2 delineates that augmenting interface parameters corresponds to heightened soil consolidation degree and rate, ascribed to the elevated drainage capacity at the boundary stemming from such parameter increments. Furthermore, for large interface parameters, the soil consolidation rate is faster for small time factors and slower for large time factors, and vice versa.
## 4 Experiment  
### Dataset and Experimental Configuration  
Our investigation primarily leveraged two distinct datasets: the NEU-DET and GC10-DET datasets. The NEU-DET dataset serves as an authoritative benchmark within the steel surface defect detection, encompassing 1800 grayscale images, each originally \(200\times 200\) pixels in size. It comprehensively covers six typical categories of steel surface imperfections, as depicted in Figure 8[34, 35, 36], and features meticulous bounding box annotations, providing precise information on both the type and location of multiple defects present in an image. To maintain independence and ensure reliable model evaluation, we randomly split the NEU-DET dataset into training and testing sets using a 4:1 ratio, as illustrated in Figure 8.  
Complementing the NEU-DET, the GC10-DET dataset was employed for cross-dataset evaluation [37]. This dataset features high-resolution images (\(2048\times 1000\) pixels) and covers 10 representative steel surface defect categories, thus offering a broader spectrum of defect types. Employing GC10-DET allowed us to further substantiate our model's reliability and robustness.  
All experiments utilized images resized to 200 \(\times\) 200 pixels as the standardized input. The model optimization employed the Adam optimizer, starting with a learning rate of 0.01 and a weight decay parameter set at 0.0005. Training spanned 200 epochs, using batches of 16 samples. Computational operations were executed on a system outfitted with an NVIDIA GeForce RTX 3060 GPU, configured with CUDA 11.8 and PyTorch 2.0.0. Additionally, Mosaic augmentation was employed during training, which merges four images into one to enhance dataset diversity and improve model generalization.  
Similar to the NEU-DET dataset, as detailed in Table 1, the GC10-DET dataset was also randomly divided into training and testing sets at a 4:1 ratio (80% for training, 20% for testing), ensuring the consistency of sample proportions across each defect class during the division process.  
Figure 8: **Overview of the NEU-DET dataset partitioning, illustrating the separation into training and testing subsets. This collection features images representing six prevalent steel surface imperfections: crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches. Their respective abbreviations are CR, IN, PA, PS, RS, and SC. The dataset was randomly split into training and testing sets with a 4:1 ratio.**