esa-sceva/satcom-chunk-collection huggingface dataset:
# Sliding Mode Integral Separation PID Control for Hydrogen Fuel Cell Systems
## 3 Sliding Mode Integral Separation PID Control (SMC\(-\)Is\(-\)Pid)
### Four\(-\)Stage Integral Separation PID Control (IS\(-\)PID)  
The integral module in the traditional PID control is used to eliminate the static error in the control process, but the accumulation of the integral also increases the overshoot of the system, leading to system oscillation and affecting the control accuracy [22]. Therefore, in order to decrease the quantity of overshooting and increase control precision, IS\(-\)PID control was invented. When the error is significant it cancels the integral action, and adds it when the error is minor. The IS\(-\)PID control equation [23] is as follows:  
\[u_{\text{IS$-$\text{pid}}}=k_{\text{p}}\cdot s+\beta\cdot k_{\text{i}}\int sdt +k_{\text{d}}\frac{ds}{dt}, \tag{11}\]  
where \(\beta\) is the switching coefficient of the integral term.  
The switching coefficients are set for only two cases in general integral separation PID control. A deviation threshold \(\varepsilon\) is set, and when the deviation \(\,|e(k)|>\varepsilon\), the integral action is switched off (\(\beta=0\)) and only proportional and differential control is used. When \(\,|e(k)|\,\leq\varepsilon\), the integral action is introduced (\(\beta=1\)) and PID control is used.  
In this paper, four\(-\)segment integral separation PID control is used to select three error thresholds, divide the interval into four segments, refine the switching coefficients, improve the state coefficients, and enhance the control effect [23].  
\[\beta=\left\{\begin{array}{l}1,\quad\quad\quad|e(k)|\leq\varepsilon_{1}\\ 0.2,\varepsilon_{1}<|e(k)|\leq\varepsilon_{2}\\ 0.8,\varepsilon_{2}<|e(k)|\leq\varepsilon_{3}\\ 0,\quad\varepsilon_{3}\leq|e(k)|\end{array}\right., \tag{12}\]  
As shown in (12), the state coefficients of 0, 0.2, 0.8, and 1 are selected as the weights of integral control. Additionally, the error threshold value varies since the system needs to  
Figure 2: Sliding mode integral separation PID switching control block diagram.  
regulate the oxygen excess ratio, pressure, and temperature differently, and the threshold value is taken as follows:  
\[\varepsilon_{OER}=\left[\begin{array}{c}0.3\\ 1\\ 2\end{array}\right]\quad\varepsilon_{T}=\left[\begin{array}{c}3\\ 8\\ 20\end{array}\right]\quad\varepsilon_{Pa}=\left[\begin{array}{c}500\\ 1000\\ 1500\end{array}\right], \tag{13}\]
# A Study of 5G Edge-Central Core Network Split Options
## 3 Edge-Central Split Models  
In this section, we present our edge-central split models for the 5G core network, and we make an initial assessment of the benefits provided by each of them for the different use cases. As illustrated in Figure 3, we developed three conceptual models of splitting the network each with multiple sub-options depending on the role transferred from the center to the edge of the network.
## 5 Data Evaluation
### Accuracy Results
#### 5.3.4 Estimating Five-Node Motif Frequencies  
Figure 10 shows the NRMSEs of \(\hat{n}_{i}^{(5)}\), the estimates of five-node undirected motif frequencies for \(p=0.1\), \(p=0.2\), and \(p=0.3\), respectively. The experiment was conducted on the graphs com-Amazon, com-DBLP, p2p-Gnutella08, ca-GrQc, ca-CondMat, and ca-HepTh. We can see that most five-node undirected motifs of all graphs except ca-GrQc have NRMSEs smaller than 1 and 0.1 for \(p=0.05\) and \(p=0.1\), respectively. For instance, the largest three graphs, com-Amazon, com-DBLP, and ca-CondMat, exhibit smaller errors than the other graphs, while the smallest graph, ca-GrQc, has a larger NRMSE.
## 6 Conclusions  
A sparse Bayesian learning method, VG-SBL, based on grid adaptive split refinement for high-resolution identification of sound sources is proposed. This method adopts a significantly advantageous sparse Bayesian learning framework, which uses discrete grid iterative adjustment to retain the computing grids with high weights, performs fission updates near them, and removes the effects of noise during the iteration process by utilizing a low-rank sparse decomposition. It continuously approaches the actual location of the sound source at any location dynamically until the grid parameter variation is less than the threshold, and finally obtains the estimation of the sound source location and strength.  
The proposed method is compared with five conventional methods: the on-grid compressive beamforming methods SBL, OMP, FISTA, and RPCA-SBL, and the block-sparse off-grid compressive beamforming method off-grid-GL\({}_{1}\). The simulation results  
Figure 16: The error box plots. (**a**) RMSEL. (**b**) RMSEM. To facilitate distinction, different colors are used to represent different methods.  
show that compared with the conventional fixed-grid and off-grid methods, the source identification accuracy of the VG-SBL is significantly improved over a wide range of SNRs and source frequencies.  
Tests were carried out in a wind tunnel using speakers on a standard aircraft model as the sound source. The results showed that the traditional methods had different degrees of failure within a flow velocity of 40 m/s, wherein the mean values of RMSEL and RMSEM of the best-performing RPCA-SBL were 0.1 m and 5.37 dB, respectively. The mean values of RMSEL and RMSEM of VG-SBL were able to be reduced to 0.07 m and 4.76 dB, proving that VG-SBL can effectively solve the base mismatch error and improve the sound source identification accuracy. In addition, the amplitude differences between the reconstructed sources of the VG-SBL method are closest to the true values, indicating that it has the ability to rank the acoustic energy contributions.  
The proposed VG-SBL method can be extended to more complex actual device sound source recognition scenarios in future work.  
Conceptualization, W.P., M.L. and Y.C.; methodology, W.P., M.L. and D.F.; software, W.P., Y.S. and D.F.; validation, W.P., D.F., Y.S. and Y.C.; formal analysis, Y.C. and Y.S.; investigation, W.P., D.F. and M.L.; resources, M.L.; data curation, M.L. and W.P.; writing--original draft preparation, W.P. and M.L.; writing--review and editing, W.P. and M.L.; visualization, W.P., D.F. and Y.C.; supervision, M.L.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China, grant 2020 YFA0405700.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Results and Analysis
### Loss Function and Evaluation Metrics  
During network training, in order to obtain optimal Raman spectral prediction results, there are two stages. In the first stage, only the backbone noise signal component feature and spectral temporal dependency capture branches are trained. In the second stage, the overall network structure is trained based on the weights from the first stage training. The Adam optimization algorithm [26] is utilized to find the global optimum during training. The loss function of the neural network prediction model is defined as:  
\[L_{\gamma}(y,y^{P})=\sum_{ijy_{i}<y_{i}^{P}}(1-\gamma)\Big{|}y_{i}-y_{i}^{P} \Big{|}+\sum_{ijy_{i}>y_{i}^{P}}\gamma\Big{|}y_{i}-y_{i}^{P}\Big{|} \tag{14}\]  
In Equation (14): \(\gamma\) is the desired quantile, between 0 and 1, \(y\) represents the original signal value of the model, referring to the signal before the addition of noise, \(y_{i}^{P}\) is the predicted value of the model. By minimizing the loss function, the parameters of the neural network model are iteratively updated.  
The mean squared error (MSE) [27] and signal-to-noise ratio (SNR) [28] are utilized, as two common objective evaluation metrics, to comparatively analyze the performance of different denoising algorithms.  
The mean squared error can measure the deviation between the denoised signal and the original signal, effectively reflecting the denoising accuracy. It is defined as:  
\[MSE=\sum_{i=1}^{L}\frac{\left(X(i)-Y(i)\right)^{2}}{L} \tag{15}\]  
where: \(X(i)\) represents the original signal, which refers to the signal before the addition of noise, \(Y(i)\) represents the denoised signal.  
The signal-to-noise ratio is the ratio of signal power to noise power, which can objectively indicate the presence of noise in the signal. It is defined as:  
\[SNR=10\log_{10}(\frac{E_{signal}}{E_{noise}}) \tag{16}\]  
\[E_{signal}=\sum_{n=1}^{L}\left(S_{ori}(n)-\bar{S_{ori}}\right)^{2} \tag{17}\]  
\[E_{noise}=\sum_{n=1}^{L}\left(S_{ori}(n)-S^{\prime}(n)\right)^{2} \tag{18}\]  
where: \(E_{signal}\) represents the signal energy, \(E_{noise}\) represents the noise energy, \(S_{ori}\) is the ideal spectrum, \(S^{\prime}\) is the denoised Raman spectral signal, \(\bar{S_{ori}}\) is the mean of the original spectral signal, \(n\) is the Raman shift, \(L\) is the length of the Raman spectrum.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Intrinsic Mode Functions** & **IMF1** & **IMF2** & **IMF3** & **IMF4** & **IMF5** \\ \hline \(Z_{0}\) & 0 & 0.0080 & 0.0268 & 0.065 & 0.498 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Zero-crossing rate of different modal components.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### Comparative Analysis with State-of-the-Art Wildfire Detection Systems  
To thoroughly benchmark the proposed architecture versus contemporary approaches, Table 3 compares key performance metrics across eight state-of-the-art WSN-based wildfire detection systems. The evaluation spans total power consumption, per-node efficiency, PLR, node density, and coverage area.  
The proposed hybrid star/circular topology achieves the lowest total power (0.0718 W) and per-node consumption (0.003 W), outperforming Mesh 2D [6], Hierarchical Tree [11], and Dynamic Clusters [64] by over 50\(\times\). With only 24 nodes deployed over 0.2826 km\({}^{2}\) (node density: 85 nodes/km\({}^{2}\)), it maintains a low PLR/node (2.08%), rivaling optimized star topologies [25] while avoiding the scalability limitations of dense deployments (e.g., 300-500 nodes [43]). This balance of minimal hardware, energy sustainability, and reliable data delivery validates the architectural improvements.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 3 Simulation Analysis of Factors Affecting Detection Effect
### Thermographic Process Parameters
On the other hand, when the speed of laser scanning is too high, the thermal resistance phenomenon is cut down, and resolution for detecting crack will be lower. For the detection of iron-based alloy and nickel-based alloy repair layer, the scanning speed should not be too large. It is recommended to select 10-20 mm/s in the experiment, and it should still be properly adjusted in combination with the specimen size. Figure 10 shows the crack shape and geometric distribution characteristics under different thermal image acquisition frequencies, which is reflected by the temperature gradient field. Based on this, it can be seen that at the laser scanning speed of 10 mm/s, when the acquisition frequency is 20 Hz, there is almost no information missing along the scanning path. At this time, two frames of thermal images are collected within the laser moving distance of 1 mm. The laser radius is 1 mm, reflecting the size of the heat-affected area, so the minimum thermal image acquisition frequency should be \(2v/a\), where \(v\) is the laser scanning speed and \(a\) is the laser spot radius. When the thermal image acquisition frequency is lower than \(2v/a\), the detection information is easy to be lost. Consequently, a better characterization of the crack characteristics, i.e., a better detection quality can be obtained by increasing the acquisition frequency. Considering the performance of infrared camera and laser scanning speed commonly used at present, the acquisition frequency of thermal image is generally set in the range of 20-50 Hz. If the laser scanning speed is higher, a higher-speed infrared camera is needed. For example, the maximum acquisition frequency of Vari oCAM(r) HD series camera developed by InfraTec in Germany can reach 240 Hz, and the number of pixels in thermal images reaches 2048 \(\times\) 1536 pixels. However, its price is relatively expensive, so its application in the field of nondestructive testing is limited. Therefore, in order to improve the detection quality, we mainly focus on the more controllable parameters, such as laser scanning speed and laser power. Figure 9: Crack detection at different laser scanning speed (\(P\) = 1800 mW, \(f\) = 50 Hz): (**a**) temperature field of laser scanning through the crack; (**b**) superposition results of \(\Delta T\); (**c**) temperature gradient field (\(T_{G}\geq\ 0\) ).
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 5 Experimental Performance Verification
### Performance Verification of the Robust and Accurate Tracking Algorithm Based on the PRS  
After synchronizing the PRS to chip accuracy, fine tracking is required to obtain ranging information with intra chip accuracy. This section verifies the tracking performance of the PRS through MATLAB 2023 environment simulation, and the resource allocation for 5G is shown in the following Table 2:  
To improve time-domain resolution, a larger subcarrier spacing and number of fast Fourier transform points are required. Therefore, the subcarrier spacing SCS is set to 60 kHz, and the number of fast Fourier transform points \(N_{\text{FFT}}\) is set to 4096. Currently, the length corresponding to each chip is \(\frac{c}{\text{SCS}\times N_{\text{FFT}}}=\frac{3\times 10^{8}}{60\times 10^{ 5}\times 4096}\approx 1.22\,\text{m}\). To improve tracking performance, it is necessary to increase the number of symbols of the PRS in one time slot as much as possible. In this section, the maximum value is taken to make \(L_{\text{PRS}}=12\).  
In the simulation process, assuming that the distance between the base station and the terminal is 27 m, with a length of approximately 22.12 chips, to verify the tracking performance of the tracking loop on the PRS, a time delay error of decimal times the sampling interval is added to the OFDM symbols that have not yet been sent by the base station, which can be expressed as:  
\[X_{i,n}=X_{i,n}e^{-2\pi(n-1)\text{Fra}/N_{\text{FFT}}} \tag{68}\]  
Among them, \(Fra=0.1184\) represents the delay error of decimal times the sampling interval, and the tracking result is shown in the following Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Carrier frequency & 3.5 GHz & Fast fourier transform point number \(N_{\text{FFT}}\) & 4096 \\ Subcarrier spacing & 60 kHz & Number of resource blocks & 272 \\ PRS resource set period \(T_{\text{per}}^{\text{PRS}}\) & 4 & PRS resource repetition cycle \(T_{\text{rep}}^{\text{PRS}}\) & 4 \\ PRS symbol number \(L_{\text{PRS}}\) & 12 & Cyclic prefix mode & normal \\ Comb count \(K_{\text{comb}}^{\text{PRS}}\) & 2 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting based on PRS ranging.  
Figure 9: Sector ID detection.  
After a certain number of iterations, the loop can track up to 0.13 chips, and the ranging accuracy is better than 0.1 m.
## 3 Results
Although the relationship between drought events and midday depression was the weakest among the climatic events, it still reached a substantial R\({}^{2}\) value of 0.68 (Figure 5b). The compound drought and heat events significantly increased the occurrence of midday depression (R\({}^{2}\) = 0.79, \(p\) = 0.024; Figure 5c). Figure 6 shows the probability of midday depression being concurrently triggered by these climatic events. Among them, compound drought and heat events were most likely to induce midday depression, with a probability of 74.3%, followed by heat events at 66.0%, and drought events at 47.5%. Figure 4: The number of midday depression occurrences in different years (**a**), different months (**b**), and different seasons (**c**) at the Huitong fir forest site from 2016 to 2022. Figure 5: Regression between the number of midday depression occurrences and the number of climatic event occurrences from 2016 to 2022. (**a**) Regression between the number of midday depression occurrences and the number of heat event occurrences. (**b**) Regression between the number of midday depression occurrences and the number of drought event occurrences. (**c**) Regression between the number of midday depression occurrences and the number of compositevents. (**d**) Mean annual number of climatic event occurrences from 2016 to 2022. The C event, D event, and HT event represent composite events (compound drought and heat), drought events, and heat events, respectively.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 6 H\({}_{2}\) Generation in DORs
### Reservoir Engineering Aspects
#### 6.2.2 Economics  
With some assumptions using values from related laboratory data (Tables 3 and 4) and with the application of ISC literature, the following economic evaluation can be made.  
In Table 4, a maximum H\({}_{2}\) generation rate of 50 g H\({}_{2}\)/kg oil is provided as the result of ISC experiments, a value significantly higher than that in any other study. Assuming this yield, a numerical assessment of the total H\({}_{2}\) generation can be made, similar to the above-given cases, for 1 m\({}^{3}\). With a porosity of 0.2 and an oil saturation of 0.7, an oil volume of 0.14 m\({}^{3}\) is stored in the pore volume. Considering a density of 960 kg/m\({}^{3}\) for the heavy oil, about 135 kg oil yields 7 kg of H\({}_{2}\) with the assumption of perfect oxidation in situ. Various references discussing the economic features of ISC provide an approximate value of 150-200 USD/m\({}^{3}\) oil, being slightly lower than SF costs [53]. This results in a cost of H\({}_{2}\) production in the range of 3-4 USD/kg. Two crucial aspects of this simplified assessment should be emphasized. The first is that all assumed numbers including the H\({}_{2}\) yield are highly optimistic. The second aspect is the cost of processing H\({}_{2}\) after production, which is not included in this simplistic calculation. Produced H\({}_{2}\) should be separated, captured from the other gas components, and conditioned for transport at the surface, thus significantly increasing both the CAPEX as well as the OPEX. Taking these cost components into account, H\({}_{2}\) production from DOR using ISC technology can be estimated at no less than USD 5/kg. The economic value of the process can be higher if both the incremental oil as well as the potential use of the reservoir for CO\({}_{2}\) storage are considered as opportunities. It is often mentioned that ISC is the most powerful EOR method for heavy oil. Therefore,  
Figure 6: Main components of surface installations of an ISC in DOR for oil and H\({}_{2}\) production.  
additional oil recovery can bring the H2 generation cost to a competitive level. In this case, however, the release of additional carbon emissions and their elimination should be parts of the economic sensitivities. An optimal solution would be a combination of technologies using ISC to produce both H2 and additional oil, separating the H2 from other fluids in the wellbore, capturing the CO2 produced in this process at the surface, and injecting this amount of CO2 into the same or a neighboring reservoir. Another alternative is to ensure the successive use of reservoir heat for geothermal energy, a case that has been highlighted in recent publications [92]. These scenarios are highly unrealistic given the state of the art.  
The above-performed assessment does not include the costs of the risks, particularly considering that the ISC is a highly risky EOR operation compared to other EOR methods.
## 1 Introduction
We also tested the effect of oxygen-enriched air using a CME flame with 35% and 50% O\({}_{2}\) to form C-layers and determined the effect that increased oxygen content has on C-layer structure and hydrophobic properties. Data collected include the mass of the C-layer, degree of hydrophobicity of the C-layer (via measuring the CA of a water droplet placed on the C-layer), identifying flame regions that resulted in the formation of a C-layer (recorded as Height Above Burner (HAB)), and the effect of time over which the C-layer was formed (recorded as substrate residence time); these results are discussed in Section 3. Our findings allowed us to determine that the flame method for creating hydrophobic surfaces is effective and allows for the creation of C-layers with customizable properties based on fuel type, oxygen content, residence time, and deposition location within the flame. The research presented in this paper provides a general guide of how to create hydrophobic C-layers and how to alter properties of the deposited layer by controlling deposition parameters. By utilizing the hierarchical nature of deposited soot, we can create hydrophobic films on stainless steel substrates. These flame-formed hydrophobic C-layersare a promising technology for industrial application to create effective hydrophobic surfaces quickly and at a low cost.
## Background
### Protocol for Proving the Equality of Discrete Logarithms  
We review the generalized interactive protocol for proving the equality of discrete logarithms [17; 18; 19]. Let \(\mathbb{G}_{1},\ldots,\mathbb{G}_{n}\) be groups of prime order \(p\), and let \(g_{i}\in\mathbb{G}_{i}\) be a generator for \(i\in[n]\). Now, we consider the NP-language  
\[\mathcal{L}=\{(g_{1},\ldots,g_{n},A_{1},\ldots,A_{n}):\exists\ x\in\mathbb{Z} _{p}\ \text{s.t.}\ A_{i}=g_{i}^{x}\ \text{for}\ i\in[n]\}.\]  
The discrete logarithm \(\mathsf{wit}=x\) is a witness for a statement: \(\mathsf{stmt}=(g_{1},\ldots,g_{n},A_{1},\ldots,A_{n})\). Then, the interactive protocol \((\mathcal{P},\mathcal{V})\) for proving the above language is as follows.  
1. For a statement \(\mathsf{stmt}=(g_{1},\ldots,g_{n},A_{1},\ldots,A_{n})\), the prover \(\mathcal{P}\) selects a random exponent \(r\in\mathbb{Z}_{p}\) and computes a commitment \(\mathsf{com}\) as follows. \[\mathsf{com}=(B_{1},\ldots,B_{n})=(g_{1}^{r},\ldots,g_{n}^{r}).\]
2. The verifier \(\mathcal{V}\) randomly selects a random challenge \(\mathsf{chal}=c\in\mathbb{Z}_{p}\).
3. \(\mathcal{P}\) computes a response \(\mathsf{resp}=s=r+cd\in\mathbb{Z}_{p}\).
4. Given a proof \(\pi=(\mathsf{com},\mathsf{chal},\mathsf{resp})\), \(\mathcal{V}\) checks that \[B_{1}=g_{1}^{s}A_{1}{}^{-c},\ \ldots,\ B_{n}=g_{n}^{s}A_{n}{}^{-c}.\]  
It is well known that this protocol is a non-trivial three-round public coin interactive proof system satisfying completeness, HVZK, (statistical) soundness, and _unique response_ properties.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 3 Methodology
### Establishment of AR Exhibition-Viewing System
#### 3.1.3. Uploading User Behavior Data to the Cloud  
The app asked users for their consent to collect their behavior data. The coordinates of the visitors' smartphones in the 3D space were collected every three seconds. This information, along with the time, smartphone model, and operating system, was used to calculate the distance between two consecutive three-dimensional coordinates, which, when summed, represented the total distance traveled. The sum of all the timestamps represents the total time spent. These data were synchronized and automatically uploaded to Google Sheets. The system also automatically converted the path coordinates into a 3D path diagram for each visitor to facilitate data interpretation.