esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WSN Platforms and Energy Harvesting Techniques Investigations
### Energy Harvesting Techniques
#### 4.2.5 Thermal Energy Harvesting Systems  
In this case, the ambient energy is extracted using thermal gradients. For this purpose, thermoelectric generators (TEGs) are utilized. They transform thermal gradients into electricity. During the transformation process, the Seebeck effect is used. When the Tegs are compared with the vibration based EH systems, it can be noticed that the former have no kinetic components [83]. Table 15 presents an overview of the thermal energy harvesting techniques. The second column identifies the output voltage. The maximum differential temperature is expressed in the third column. The fourth column presents the performance  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Voltage (V)**} & \multirow{2}{*}{**Wind Turbine**} & \multicolumn{2}{c}{**Performance**} & \multirow{2}{*}{**Rectification**} & \multirow{2}{*}{**Integration**} \\ \cline{4-4} \cline{6-7}  & & & & **Max. Power (mW)** & & & \\ \hline
[38] & 5 & 0.063 & 10 & 85 & DC-DC converter & 50 F supercapacitors \\ \hline
[69] & 29 & 1.14 & 22,000.4 & - & Boost-up converter & Battery \\ \hline
[70] & 64 m & - & 0.00809 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 13: Wind energy harvesting systems.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Voltage (V)**} & \multirow{2}{*}{**Sensitivity (dBm)**} & \multicolumn{2}{c}{**Performance**} & \multirow{2}{*}{**Rectification**} & \multirow{2}{*}{**Integration**} \\ \cline{4-4} \cline{6-7}  & & & **Max. Power** & & & & \\ \hline
[72] & 2.5 & \(-\)39 & 13 \(\mu\)J & 60 &
\begin{tabular}{c} AC-DC converter \\ and boost converter \\ \end{tabular} & Capacitor (100 \(\mu\)F) \\ \hline \hline \end{tabular}
\end{table}
Table 14: RF energy harvesting systems.  
of the conversion process in terms of maximum output power and conversion efficiency. The fifth and the sixth columns express the rectification and the integration processes, respectively. There is only one selected study in this SLR that deployed the thermal energy harvesting technique. It shows that this technique is not widely deployed. This is due to the relatively low performance of TEGs for minor variations in temperature.
# Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
## 2 Dynamic Model  
### Equations of Motion of an AUV in Three Degrees of Freedom  
Figure 1 shows an AUV attempting to dock onto a platform. For simplicity, the problem is constrained to the vertical plane, with motions limited to surge, heave and pitch. Furthermore, the effects from currents are neglected. Let us define the vector of generalised coordinates in the inertial reference frame as  
\[\mathbf{\eta}=\begin{bmatrix}x&z&\theta\end{bmatrix}^{T}, \tag{1}\]  
where \(x\) is the displacement in surge, \(z\) in heave and \(\theta\) the pitch angle, and the vector of generalised coordinate rates in the body-fixed frame as  
\[\mathbf{v}=\begin{bmatrix}u&w&q\end{bmatrix}^{T}, \tag{2}\]where \(u\) and \(w\) are the components of the absolute velocity of the centre of buoyancy in the body-fixed frame in surge and heave, respectively, and \(q\) is the pitch rate (identical in the inertial and body-fixed frames, since the analysis is planar). Note that, as shown in Figure 1, a right-hand reference system is employed, with the \(z\)-axis being positive downwards, as is standard practice [33]. It is possible to show that the velocity of the AUV in the inertial reference frame is given by the following relation [33]:  
\[\dot{\mathbf{\eta}}=\mathbf{J}(\mathbf{\eta})\mathbf{v}, \tag{3}\]  
where the transformation matrix is  
\[\mathbf{J}(\mathbf{\eta})=\begin{bmatrix}\cos\theta&\sin\theta&0\\ -\sin\theta&\cos\theta&0\\ 0&0&1\end{bmatrix}. \tag{4}\]  
Here, the AUV is assumed to be torpedo-shaped, so that is actuated by a propeller and a sternplane. Additionally, the centres of gravity and buoyancy are assumed to be vertically in line to avoid trim angles, but the centre of gravity is lower to provide hydrostatic stability. Therefore, it is possible to show that the velocity vector of the AUV in the body-fixed frame can be expressed with the following system of nonlinear equations [24; 33]:  
\[\dot{\mathbf{v}}=\mathbf{M}^{-1}\mathbf{f}(\mathbf{\eta},\mathbf{u}), \tag{5}\]  
where the input vector is  
\[\mathbf{u}=\begin{bmatrix}Q_{\text{m}}&\delta_{\text{s}}\end{bmatrix}^{T}, \tag{6}\]  
By merging Equations (3), (5), (9a) and (9b), it is possible to express the dynamic model of the AUV through a system of nonlinear, ordinary differential equations:  
\[\dot{\mathbf{x}}=\mathbf{g}(\mathbf{x},\mathbf{u}), \tag{10}\]  
where  
\[\mathbf{x}=\begin{bmatrix}x&z&\theta&u&w&q&n&u_{\rm P}\end{bmatrix}^{T}. \tag{11}\]  
A standard integration scheme can then be employed to solve the equations in time, with a fixed-step, fourth-order-accurate Runge-Kutta scheme being used in this study [34].
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 2 The Equivalent Layers of Coal Seam Gas Emission
### Equivalent Lithology  
Equivalent lithology pertains to the diverse lithologies of the roof and floor that are encompassed within the influence sphere of coal seams and their respective mining operations. These diverse lithologies are deemed equivalent to a single lithological type based on a predetermined set of rules. This determination is founded on the comprehensive analysis of rock core data derived from exploration boreholes that are specifically targeted at coal-bearing strata. The equivalence is established longitudinally along the borehole's trajectory.  
The primary purpose of equivalent lithology is to ascertain the varying degrees of influence that the roof and floor lithologies exert on gas emissions. Typically, the classification system for equivalent lithology encompasses six distinct categories: fine sandstone, medium-fine sandstone, medium sandstone, gritstone, clay rock, and thick mudstone. It is noteworthy that the extent of the roof and floor that is impacted by mining and excavation activities varies, and as a result, the types of equivalent lithology that are encountered also differ.
## 1 Introduction  
### Research Background and Necessity  
Recent advancements in virtual environment technology have led to rapid progress in reconstructing real-world environments. The concept of a digital twin has emerged as a crucial technology for efficiently integrating, processing, and visualizing large-scale 3D spatial data, and various industrial applications utilizing this technology continue to emerge [1]. Three-dimensional terrain models are essential components of digital twin technology, presenting challenges in the rapid and precise processing of extensive datasets. In particular, the optimization of 3D object generation speed, enhancement of texture mapping quality, and reduction in memory consumption have become primary focal points of research [2]. Modeling complex urban structures as 3D objects serves as an essential component in various domains including smart cities, urban planning, and disaster management, with CityGML being widely utilized as a standardized data format for these applications. The recently established CityGML 3.0 standard [3] has emerged as a pivotal framework for urban modeling through enhanced data management and interoperability [4]. However, the high-resolution texture mapping process remains technically complex and necessitates optimized data processing techniques [5]. A 3D model with meticulously applied textures enhances visual realism, improves the accuracy of spatial analysis, and facilitates intuitive interpretation in decision-making processes [6].
###### Abstract  
Orbital angular momentum beams have been extensively researched due to their ability to enhance the channel capacity of microwave systems. Metasurface near-field calculations of different sizes have been completed. Near-field calculations with Gaussian noise for metasurfaces of different sizes were also completed. The presence of noise suggests that the vortex electric field generated by the small metasurface of the vortex wave may experience disturbance and be overwhelmed by strong noise. On the other hand, the large vortex wave metasurface exhibits superior anti-noise capability. Its anti-interference characteristic was verified by conducting full-wave simulations on metasurfaces of \(1=-3\) and \(1=-5\). Based on the OAM spectral analysis, the mode purity of the generated vortex waves was calculated in detail. Simulation results indicated that a large-scale metasurface exhibits stronger anti-interference capability, which may inspire the design and research of vortex wave metasurfaces in the future.  
large-scale metasurface; near-field calculation; OAM spectrum; vortex wave Article
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 4 Mobile Radiation Detection Systems
### Combination of Neutron and Gamma Detection Systems
's [217] study is related to RPMs, it is worth mentioning that, among several detectors analyzed (BF3, boron lithium-loaded glass detector, and coated plastic fiber detector), the best candidate to replace \({}^{3}\)He technology is the boron-lined detector since it has good neutron detection efficiency; gamma ray insensitivity; unlike BF3, is not a hazardous material; and has a reasonable operating voltage. With the same goal, a European project called SCINTILLA [218; 219] developed and tested some \({}^{3}\)He alternatives for the detection of nuclear sources using RPMs: the EJ200 plastic scintillator, Gd-lined plastic scintillator, and a LiZnS neutron sensor. For the test and benchmark of detection systems, it was used a facility which can reproduce many scenarios as moving sources (at fixed portals) or search with moving detectors. This international platform allows testing of the response of different detection systems to several sources available: neutron and gamma response (e.g., using different shielding), the influence of gamma radiation on neutron response, the radionuclide categorization/identification (SNM, NORM, medical and industrial isotopes), and by using different masking scenarios (e.g., SNM with NORM or medical isotopes). SCINTILLA also considered spectrometric personal radiation monitor technologies, like the scintillators and CZT detectors (e.g., CZT gamma cameras), for the radionuclide identification and masking scenarios.
# Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction
## 3 Proposed Method
### Input Features  
The RUL prediction problem typically deals with time series that, without loss of generality, we can assume as sequences of real values. Given that SN P systems only handle integers, we employ a discretization technique, namely the Symbolic Fourier Approximation (SFA) [35], to convert floating-point numbers to integers. SFA was originally proposed for improving the similarity search in time-series data that typically lie in high-dimensional spaces. In this method, a time series on high dimension is mapped into a lower dimensional space by means of the Fourier approximation, and it is then discretized to symbols that are, in turn, represented as integers. As such, the process of SFA consists of two parts: the Discrete Fourier Transform (DFT) approximation and the Multiple Coefficient Binning (MCB) discretization. This process has three parameters:  
* \(n\): number of real values in the input time series (i.e., the size of the input vector);
* \(w\): size of the integer vector resulting from the SFA (i.e., number of selected Fourier coefficients);
* \(s\): number of values to discretize each Fourier coefficient to (i.e., number of symbols).  
In the SFA process, an input time series of length \(n\) is decomposed into a sum of orthogonal basis functions that have the form of sinusoidal waves by using the DFT. Then,  
Figure 1: Flow chart of a data-driven RUL prediction task.  
each wave is associated to the corresponding Fourier coefficient. The sequence of the first \(w\) coefficients are selected as an outcome of the DFT approximation.  
As shown in Figure 2, each selected coefficient is then discretized to a symbol independently, based on discretization intervals. To define these intervals, a technique called MCB is used, which enables minimizing the loss of information induced by discretization. Specifically, we apply the DFT approximation to all the time series of length \(n\) in the dataset and arrange all the \(i\)-th coefficients into a group. Then, a histogram is built for each group. For each histogram, the interval of the bins are determined by equi-depth binning, which enforces an equal frequency for all the bins by adjusting the interval of the bins; the frequency of each bin should be \(N/s\) where \(N\) denotes the total number of given time series of length \(n\). This process, determining the fixed discretization intervals, is considered as a pre-processing phase. After we get a simple lookup by pre-processing, a time series of length \(n\) can be transformed into a vector of \(s\) symbols (\(s<n\)). Then, each output symbol by the SFA is mapped into an integer, which finally gives the input representation that can be used for our SN P system.
# Observing Material Properties in Composite Structures from Actual Rotations
## 4 Discussion  
The applicability of the proposed methodology is illustrated by several academic examples. In addition, to illustrate the applicability of the proposed method on actual structures, a simplified model of an intermediate construction stage of a cantilever composite bridge in China is studied. It is important to highlight that all the measurements are generated numerically and the bridge structure is simplified. The example results clearly demonstrate that when actual rotation measurements on-site are employed in the COM, the COM is not able to properly observe any material properties even in very simple structures such as in examples 1 and 2; the observed values with the COM either lack physical meaning or contain significant errors. Conversely, the 2 COM procedure, proposed by the authors in this paper, consistently exhibits its power by accurately capturing the inertia and shear areas (shear and flexural stiffness) in the same scenarios. In the context of the bridge example, the COM method once again falters in observing the shear and flexural stiffness, and its observation lacks physical meaning. However, despite the increased complexity of this example, the 2 COM method, while not achieving pinpoint accuracy in observing the shear area, demonstrates a commendable ability to accurately observe the inertia. It is important to highlight that, to the best of the authors' knowledge, no other SSI method based on SMM in the existing literature is able to include the effects of shear rotations in the inverse simulation. The result shows the robustness and power of the 2 COM method when it works with the actual rotation measurement, while other SSI models are not able to observe any properties. Future research aims to transition from numerical simulations to practical implementation with real structures. This transition will involve addressing the challenges associated with acquiring and working with on-site measurements, as well as accounting for potential modeling errors. By doing so, we intend to enhance the applicability and relevance of our method to real-world engineering scenarios. A deeper analysis of the shear and flexural rotations is presented by [PERSON] et al. [24]. This work also includes a parametric analysis of these deformations for different beam geometries.
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 5 Conclusions and Future Work  
### Summary and Comparison of WCET and H\({}_{\text{rtp}}\)  
The current mainstream methods for evaluating real-time systems primarily focus on Worst-Case Execution Time (WCET) analysis. The WCET and H\({}_{\text{rtp}}\) analysis methods differ in their analytical objectives and scope. WCET analysis focuses solely on \"speed\" (the smaller the WCET, the faster the execution), whereas H\({}_{\text{rtp}}\) analyzes both \"speed\" and \"stability\". Therefore, it is not feasible to conduct a relatively fair quantitative comparison between the two. From a qualitative perspective, each method has its unique advantages. Advantages of H\({}_{\text{rtp}}\) Analysis:  
* H\({}_{\text{rtp}}\) analysis applies specifically to hardware components such as processors and interface control hardware, enabling a focused real-time evaluation of hardware alone (only needing to meet predefined requirements). This facilitates the independent development and testing of hardware during the real-time system development process. In contrast, WCET analysis requires comprehensive information about the entire real-time system, including both software and hardware aspects.
* H\({}_{\text{rtp}}\) extends and refines WCET assessment specifically for hardware evaluation. The calculation of H\({}_{\text{rtp}}\) utilizes the results of WCET analysis (i.e., T\({}_{\text{wcet}}\)), making it not only an indicator of whether hardware meets system real-time requirements but also reflecting the degree of \"real-time\" performance quality when these requirements are met. Advantages of WCET Analysis:
* WCET analysis has a longer history and covers nearly all types of real-time systems.
* Based on WCET analysis methods, several mature commercial software tools have been developed, offering reliable and convenient solutions.
* Research into WCET analysis algorithms and related topics has developed rapidly.  
Undoubtedly, the H\({}_{\text{rtp}}\) parameter analysis method proposed in this paper falls short of WCET in both application scope and development history.  
However, with the large-scale application and development of real-time systems in areas such as the Internet of Things (IoT) and unmanned aerial vehicle (UAV) systems, there is a growing need to evaluate the \"goodness\" and \"badness\" of processors and real-time systems. Therefore, we believe that the application of the H\({}_{\text{rtp}}\) parameter analysis method will gradually increase in future developments.
## 3 Results and Discussion
### Effect of Liquid Nitrogen Pulsation on DCM Stability
#### 3.2.2 Amplitude Influence  
In the experiment, this research investigated the influence of different amplitude pulsating liquid nitrogen at 200 Hz on the vibration of the DCM. The relevant results are presented in Figure 12. As the flow pulsation amplitude continuously increases, the phase angle and frequency of the DCM response remain consistent, with the only variation being a gradual enhancement of the response amplitude. According to Equations (15)-(17), when the excitation frequency is constant, the harmonic vibration response is only related to the excitation amplitude. Since the DCM's flow velocity amplitude is positively correlated with the pressure pulsation amplitude, changes in the pulsation amplitude will not alter the trend of the DCM vibration response. They will only affect the amplitude size of the vibration.  
In Figure 13, it is found that there is a linear relationship between the amplitude of DCM vibration and the amplitude of flow velocity pulsation. The intercept of the linear equation is 9.544, indicating that when the excitation amplitude is 0, the DCM's vibration amplitude will attenuate to a few nanoradians. The non-zero intercept is due to errors in the simulation analysis. From Equations (8) and (15), it can be observed that when the amplitude changes of flow velocity fluctuation are small (\(\Delta v^{2}\to 0\)), the pressure changes have a linear relationship with \(\Delta v\). This linear relationship is crucial for engineering practice because it validates the correctness of our linearized analysis of the nonlinear problem and provides a method for controlling and predicting system responses. In practical applications, a flow rate pulsation attenuator can be designed to reduce the vibration amplitude of the DCM.
## 3 Multi-Objective Optimization  
### Factorial Design  
Based on the optimal model in [9], the general dimensions of the single-phase, 8-slot 8-pole machine are presented in Figure 6. This machine was optimized for the design targets of maximum and average power of 180 and 75 W, maximum and average power density of 0.180 and 0.075 W/cm\({}^{3}\), respectively, and the electromagnetic force was minimized simultaneously.  
As shown in Figure 7, the geometries of the proposed machine can be defined by several factors, but in this case, three factors were selected, the tooth width \(\mathrm{w_{t}}\), axial PM width \(\mathrm{w_{am}}\), and radial PM thickness \(\mathrm{t_{m}}\). Because pole pitch \(\mathrm{\tau_{p}}\) and slot pitch \(\mathrm{\tau_{s}}\) are constant (23 mm), when one factor (tooth width or axial PM width) varies, other variables (slot width, number of turns per slot, resistance per phase, radial PM width) are varied too. In the first step, using Minitab software, the fractional factorial designs were applied to clarify the sensitivity of each variable.  
Table 3 shows the combination of different variables and the performance of the machine, including maximum and average power (\(\mathrm{P_{m}}\) and \(\mathrm{P_{av}}\)), maximum and average power density (\(\mathrm{P_{d\_m}}\) and \(\mathrm{P_{d\_av}}\)), and the maximum value of the electromagnetic force (\(\mathrm{F_{m}}\)). As mentioned in [9], the iron teeth have to be beveled for the winding path, hence, the investigated machine has an equivalent outer air gap of 0.25 mm.  
Figure 6: General dimensions of the 8-slot 8-pole machine (mm) [10].  
Figure 7: Factors for optimization (mm) [10].  
Figure 8 is the Pareto charts, which show the magnitude and importance of the three factors to the machine's performance. Not only the major factors but their interactions are investigated in the fractional factorial designs. The reference line corresponding to alpha is 0.05 and the confidence interval is 95%. Although none of the three factors are significantly sensitive to the output power and power density, the tooth width \(\mathrm{w_{t}}\), and axial PM width \(\mathrm{w_{am}}\) are the factors most sensitive to the electromagnetic force. Therefore, tooth width \(\mathrm{w_{t}}\) and axial PM width \(\mathrm{w_{am}}\) were selected for the next optimization step using RSM. The total thickness of the spacer and radial PM (or axial PM thickness) is 9.05 mm, and the thickness of the radial PM \(\mathrm{t_{tm}}\) was randomly chosen to be 6.5 mm (the same as the machine in [9]), so the thickness of the spacer tsp is 2.55 mm.