esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial Metaverse: A Comprehensive Review, Environmental Impact, and Challenges
## 3 Industrial Metavere's Architecture, Roadmap, and Core Technologies Overview
### Three-Dimensional Modeling/Scanning  
Three-dimensional laser scanning in the Industrial Metaverse comprises a shared, digital representation of the physical world that incorporates data from various sources to create an interactive, three-dimensional laser scanning services model [36]. The goal is to provide a platform for users to visualize and interact with data naturally and intuitively to make decisions faster. Three-dimensional laser scanning in the Industrial Metaverse is probably the best option to simulate real-world conditions accurately. A digital twin may be a better choice if one requires a less expensive or more accurate alternative. Digital twins are digitized representations of an object or system that do not include human interaction. However, the Metaverse of 3D scanning services enables dynamic human interaction in environments composed of digital images, such as furniture, cars, and product pictures, without allowing users to alter the physical state of those objects. This convergence offers new possibilities of how humans can interact with digital models and opens up new potential applications for twins and the 3D laser scanning in the Metaverse. For example, humans could use twins to test different scenarios before interacting with their real-life counterparts in the Metaverse. The major companies in 3D scanning technology include Faro, Artec, Hexagon, and Leica Geosystems:  
* [noitemsep,topsep=0 pt]
* Faro Technologies: Faro is a leading 3D scanning company. It provides many software tools, laser scanners, and 3D measuring, imaging, and realization technology.
* Artec 3D: Artec 3D, which is well-known for its portable, handheld 3D scanners, offers solutions for businesses in the automotive, aerospace, and entertainment sectors, as well as for independent producers.
* Hexagon: Hexagon provides high-precision 3D scanning and metrology solutions for sectors including automotive, aerospace, and manufacturing, with a strong emphasis on industrial applications.
* Leica Geosystems: Leica, a member of the Hexagon group, is well known for its precise, high-quality 3D scanning solutions for a range of markets, including building, surveying, and mapping sectors. They have cutting-edge laser scanning technology, such as the Leica BLK series, which makes it possible to seamlessly incorporate places and items from the real world into the Metaverse.
## 4 Case Study: Eastern Regions of South Korea
Additionally, in order to maintain the system balance and stability, sufficient load is restored during the restoration, and the total load restored in the proposed restoration strategy of the eastern regions is 2728.80 MW. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Gen**} & \multicolumn{3}{c}{**STEP 3**} & \multicolumn{3}{c}{**STEP 4**} & \multicolumn{3}{c}{**STEP 5**} & \multicolumn{3}{c}{**STEP 6**} \\ \cline{2-13}  & \(\boldsymbol{ST_{\text{GEN}}}\) & \(\boldsymbol{ETL}\) & **Total** & \(\boldsymbol{ST_{\text{GEN}}}\) & \(\boldsymbol{ETL}\) & **Total** & \(\boldsymbol{ST_{\text{GEN}}}\) & \(\boldsymbol{ETL}\) & **Total** & \(\boldsymbol{ST_{\text{GEN}}}\) & \(\boldsymbol{ETL}\) & **Total** \\ \hline G1 & 0.55 & 0.17 & 0.72 & 0.55 & 0.17 & 0.72 & 0.55 & 0.17 & 0.72 & 0.18 & 0.17 &  
Table 4 shows the comparison of the generator start-up sequence between the proposed method and the existing method using mixed integer programming (MIP). The GSS is determined through the MIP. The existing method only considers the restoration of the maximum system capacity. The case studies from both methods are shown in the table below. As the proposed method aims to restore the system within the shortest amount of time, the generators that can be activated in a very short time are restored preferentially. On the other hand, the existing method intends to restore the system with the maximum capacity restoration. The time to commit all generators is 36 min for the proposed method and 55 min for the existing method. Also, the total time is 657 min using the proposed method and 643 min using the existing method when all generators reach the maximum power-generating outputs. In the proposed method, the recovery times of transmission lines and transformers are included in the restoration time; on the other hand, the time using the existing method only takes into account the start-up time of the generators.
## 5 Conclusions
### Challenges and Future Research Directions
Third, empirical evidence and mathematical proofs have by now extensively established the prevalence of inherent trade-offs between the constraints imposed with the notions of fairness and performance accuracy of algorithms [63; 114]. This has practical implications as the designer of the system and the user need to decide on level of performance accuracy that they are willing to forgo in order to ensure fairness constraints. Design of algorithms that aim at handling such trade-offs in a systematic way could be beneficial and further explored. Fourth, our review has also discovered that fairness in machine learning and recommender systems is excessively focused on supervised learning. Though there has been some progress in unsupervised learning such as word embedding and clustering [5; 115; 116; 117], it is limited as compared to supervised setting. Future work should further advance fair decision-making with respect to unsupervised learning algorithms.
## 3 Laser Tracker as a Measurement System
### Retroreflector  
A retroreflector is an item or surface that reflects light back towards the source regardless of the angle of incidence. This behavior is not equivalent to a mirror since it only reflects beams of light in the direction of incidence.  
The most commonly used retroreflectors are the SMR and cat's eye. A traditional corner cube retroreflector SMR of 1.5\" has an incidence angle of \(\pm 30^{\circ}\), while a cat's eye has an approximate angle of \(\pm 90^{\circ}\). As in MT verification the LT replaces the part and the retroreflector is placed on the tool, the relative movement between LT and retroreflector causes its angle of incidence to exceed these values. As shown by [28], the incidence angle between beam and retroreflector affects the measurement coordinates and workspace to be verified.  
Since 2010, a new motorized target retroreflector (Active Target) developed by API allows any API LT to automatically position itself, so it will never lose the beam. The Active Target reflector works in a similar way to any SMR. The difference is that the Active Target features motorization technology that automatically positions the reflector adjusting the beam so it is perpendicular to the reflector.
## 3 Analysis of Abutment Pressure Evolution Law Based on Microseismic Monitoring
### Analysis of the Mining Influence Range in the Working Face  
Taking the microseismic monitoring data from 21 May 2015 to 20 June 2015 as an example, during this period, the working face advanced a total of 87.2 m. Figure 4 shows the plane projection of microseismic events in this time period. The scatter points with different colors reflect the analysis results of a single microseismic event. The corresponding is the energy magnitude of a single microseismic event, which is taken as lg(E). E represents the magnitude of the seismic event energy, measured in Joules. The advance rupture envelope and lag rupture envelope of the coal and rock mass are shown in the envelope area of the red and green dashed lines, among which the advance severe impact area is delineated by the red solid line, indicating a higher frequency of microseismic events, and the lag severe impact area is located in the green solid line area behind the working face.  
As can be seen from Figure 4, microseismic events are widely distributed, indicating a large mining influence range in the working face, including 430 m in front and 320 m behind the face. There are also a large number of microseismic events outside the auxiliary transportation roadway behind the working face and inside the 80 m coal pillar. This area contains two large faults, Yf1 and Yf2. It is speculated that after the working face passes through the fault, the influence of mining lag induces fault activation again.  
Figure 5 shows the profile projection of microseismic events during the mining process of the working face, including the projection along the strike and the dip direction. The red solid line represents the position of the working face, the black solid line represents the unmined coal seam, and the gray solid line represents the completed mining of the coal seam.  
Figure 4: Analysis of the mining influence range of the fully mechanized caving face (microseismic plane projection).  
The evolution of the spatial morphology of the roof and floor of the solid working face in the mining process can be seen in the figure. As can be seen from Figure 5a, the roof rupture height is about 38 m on average and the floor is about 20 m, and microseismic events are concentrated in the coal pillar area of 80 m. In the 1301 working face, there are concentrated events at the belt transport down chute, indicating that there is a high stress difference near this area. The microseismic activity of the 1300 auxiliary track trough is more intense than that of the belt track trough, which is closely related to the spatial layout of the track trough and the nearby fault activity. Figure 5b further confirms that the mining influence range is about 320 m behind the working face and 430 m in front of it.
# ILC with Initial State Learning for Fractional Order Linear Distributed Parameter Systems
## 2 Preliminaries and Problem Statement  
In this section, some basic definitions and properties (for more details, see [25]) are first introduced, which will be used in the following sections. Definition 1 ([25]): The definition of the Liouville fractional integral is described by:  
\[{}_{t_{0}}I_{t}^{\alpha}f(t)=\frac{1}{\Gamma(\alpha)}\int_{t_{0}}^{t}(t-\tau) ^{\alpha-1}f(\tau)d\tau,\ \ \alpha>0,\]  
where \(\Gamma(\cdot)\) is the well-known Gamma function, which is defined by \(\Gamma(z)=\int_{0}^{\infty}e^{-z}t^{z-1}dt\). Definition 2 ([25]): The Caputo derivative is defined by:  
\[{}_{t_{0}}^{C}\mathcal{D}_{t}^{\alpha}f(t)=\frac{1}{\Gamma(n-\alpha)}\int_{t_{ 0}}^{t}(t-\tau)^{-\alpha+n-1}f^{(n)}(\tau)d\tau,\]  
where \(n\) is the first integer, which is not less than \(\alpha\), i.e., \(\alpha\in[n-1,n)\). In the following, for simplicity, we denote the fractional integral \({}_{t_{0}}I_{t}^{\alpha}\) by \(I^{\alpha}\) and the Caputo derivative \({}_{t_{0}}^{C}\mathcal{D}_{t}^{\alpha}\) by \(\mathcal{D}_{t}^{\alpha}\), respectively. **Definition 3** ([25]). : _The two-parameter Mittag-Leffler function is defined by:_  
\[E_{\alpha,\beta}(z)=\sum_{k=0}^{\infty}\frac{z^{k}}{\Gamma(\alpha k+\beta)},\ \ \ \alpha>0,\ \beta>0.\]  
_For \(\beta=1\), \(E_{\alpha,\beta}(z)=E_{\alpha}(z)\). Especially, \(E_{1,1}(z)=e^{z}\)._  
To proceed with the discussion of the main results, the following lemmas are given. **Lemma 1** ([25]). : _From the definition of the Caputo derivative, when \(0<\alpha\leq 1\), we have:_  
\[I_{t}^{\alpha}(\mathcal{D}_{t}^{\alpha}(x(t))=x(t)-x(t_{0}).\]  
**Lemma 2** ([35]). : _Let \(x(t)\in\mathbb{R}^{n}\) be a vector of a differentiable function. Then, for any time instant \(t\geq t_{0}\), the following relationship holds:_  
\[\frac{1}{2}\mathcal{D}_{t}^{\alpha}\left(x^{T}(t)Px(t)\right)\leq x^{T}(t)PD_ {t}^{\alpha}x(t),\ \forall\ \alpha\in(0,1],\]  
_where \(P\in\mathbb{R}^{n\times n}\) is a constant, square, symmetric and positive definite matrix._  
**Lemma 3** ([25]). : _The fractional order differentiation or integral of the Mittag-Leffler function is:_  
\[{}_{t_{0}}\mathcal{D}_{t}^{\rho}[t^{\beta-1}E_{\alpha,\beta}(\lambda t^{ \alpha})]=t^{\beta-\rho-1}E_{\alpha,\beta-\rho}(\lambda t^{\alpha}),\]  
_where \(\rho<\beta\)._  
**Lemma 4** (Generalized Gronwall inequality [36]). : _Suppose \(\alpha>0\), \(a(t)\) is a nonnegative, nondecreasing function locally integrable on \(0\leq t\leq T\)(some \(T\leq+\infty\)) and \(g(t)\) is a nonnegative, nondecreasing continuous function defined on \(0\leq t\leq T\), \(g(t)\leq M\)(constant), and suppose \(u(t)\) is nonnegative and locally integrable on \(0\leq t\leq T\) with:_  
\[u(t)\leq a(t)+g(t)\int_{0}^{t}(t-s)^{\alpha-1}u(s)ds,\]  
_on the interval. Then:_  
\[u(t)\leq a(t)E_{\alpha}(g(t)\Gamma(\alpha)t^{\alpha}).\]  
In this paper,  
**Remark 1**.
# Instability of High Liquid Limit Soil Slope for the Expressway Induced by Rainfall
## 3 Numerical Simulations
### Analysis of Numerical Simulation Results
#### 3.3.3 Stress Variation Characteristics  
As can be seen in Figures 9 and 10, the changing trend of surface and deep, the Mises stress of slope in Group A and Group B is different with the duration of rainfall. On the surface of the slope, the matrix suction decreases, and the pore pressure decreases due to rainfall. In the deep layer of the slope, there is mainly saturated soil, and the Mises stress is mainly affected by the overlying load. Therefore, with the passage of rainfall time, the Mises stress in the deep layer of the slope increases. The increase of rainfall intensity accelerates the decrease of surface Mises stress, and compared with the surface minimum Mises stress of Group B for 96 h and Group C for 48 h (6.304 kPa and 6.294 kPa respectively), it can be found that the effect of doubling of rainfall intensity on the surface minimum Mises stress of high liquid limit soil slope is greater than that of rainfall time.  
Because the instability failure of the slope often occurs at the foot and top of the slope, the Mises stress of the top and foot of the high liquid limit soil slope is analyzed with time. Figure 11 shows that after 96 h of continuous rainfall, the surface Mises stress at the top and bottom of the slope gradually decreases with time. The cumulative changes of Mises stress in Group B and Group C are \(-2.19\) kPa and \(-2.91\) kPa, respectively, and the cumulative decreases were \(-7.22\%\) and \(-9.60\%\), respectively. The cumulative changes of Mises stress in Group B and Group C were \(-2.05\) kPa and \(-4.32\) kPa, respectively, and the cumulative decreases were \(-4.50\%\) and \(-9.48\%\), respectively. In addition, it can be seen in Figure 11a,b that the effect of rainfall intensity on the Mises stress at the top of the slope is less than the  
Figure 8: Variations of slope safety factors of Groups A, B, and C with suffering rainfall infiltration time: (**a**) Group A; (**b**) Group B; and (**c**) Group C.  
Figure 12 illustrates the shear stress with rainfall time at different depths from the top of the slope. At the position above 28.8 m, the shear stress increases gradually with the depth, mainly because the overlying load weight of the upper soil can be ignored. When the total load is constant, the pore pressure increases, and the Mises stress decreases. It is saturated at 28.8 m before the rainfall, which mainly reflects that the increase of overlying load leads to the increase of Mises stress. With the increase of rainfall time, the shear stress in the surface layer decreases gradually and approaches 0, which is easily causes a landslide. With the infiltration of rainwater, the shear stress of each layer decreases except for that of the deep layer.
## 5 Discussion and Future Perspectives
### Environmental Challenges  
For biogas upgrading technologies, ensuring environmental sustainability often relies on various factors, such as resource efficiency, emission types, associated risks, and more. For example, thermal decomposition is well on track to recover the used solvent. The utilization of waste streams is presented as an environmentally friendly alternative to recovering a solvent via a chemical reaction. The development of new solvents is mainly focused on improving energy efficiency [175]. The innovative separation of CO2 from biogas through adsorption and desorption using polymeric resins has the potential to reduce the energy demand; however, it needs to be tested over the long term in a biogas plant before implementation. It has been proven that many of the less-expensive technologies for biogas upgrading are quite inefficient [176].  
Sustainable waste management, including waste prevention and reduction, has become a priority to reduce pollution and greenhouse gas emissions [177]. The application of biochar presents certain advantages, such as not requiring recovery after its usage; furthermore, biochar production helps enhance waste management [178]. It improves the quality of digestate and contributes to soil fertility improvement [179]. However, there are still no studies that comprehensively explain the role of biochar in biogas generation. Most studies describe the production of biochar from different sources. Ash utilization is an example of waste valorization, resource efficiency, and circular economy.  
The utilization of bacterial and algal fermentation to remove CO2 is the most environmentally friendly process, but biological processes are complex. The technique using microalgae can be employed simultaneously for wastewater treatment, as well as biogas upgrading, as an environmentally friendly process [106].  
Other studies emphasize the environmental benefits of methanation in freight transport, highlighting the direct methanation of biogas as a more sustainable way to utilize biogenic CO2 as a carbon source for biomethane synthesis. However, other environmental impact categories should also be considered [180].
## 1 Introduction
Preservation of disk image integrity: This method embeds watermarks into JPEGs within a disk image without altering the disk structure. It preserves forensic validity and introduces traceability. 2. Effective watermarking of stand-alone JPEGs: The technique applies to both colour and greyscale JPEGs with high imperceptibility and covers the full range of image evidence types encountered in forensic analysis. 3 Chain of custody adherence: The approach preserves an auditable chain of custody for both watermarked and unmodified JPEGs, avoiding procedural overhead. It integrates seamlessly into established protocols where precise custody records determine evidentiary admissibility. 4 Forensic tool compatibility: Watermarked disk images function without errors in standard forensic analysis tools. This avoids workflow disruption and eliminates the need for retraining or toolchain modification. As such, this approach offers a practical and immediate solution to maintain the integrity and traceability of evidence within current forensic workflows. It functions as an interim safeguard that addresses pressing operational needs until comprehensive cloud-based infrastructures are fully deployed. This work fills a critical gap in the field of forensic watermarking by introducing a solution that preserves the integrity of forensic disk images and protects the security of shared JPEGs without complicating the chain of custody. The system embeds traceable digital fingerprints in distributed images to ensure accountability and defend against unauthorised redistribution of evidential material.
## 3 Results
### Ablation Experiments of BMP_Net
#### 3.4.2 PSCDR Algorithm Parameter Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mu\) & \(\lambda\) & **RMSE** & **MAE** & **MAPE** \\ \hline  & 0.001 & 77.48 & 66.15 & 0.132 \\  & 0.003 & 82.36 & 71.69 & 0.138 \\
0.01 & 0.005 & 104.52 & 86.83 & 0.170 \\  & 0.007 & 122.49 & 107.46 & 0.203 \\  & 0.009 & 188.16 & 155.32 & 0.352 \\  & 0.001 & 79.31 & 65.67 & 0.119 \\  & 0.003 & **75.92** & **63.19** & **0.115** \\
0.05 & 0.005 & 81.68 & 69.38 & 0.125 \\  & 0.007 & 86.53 & 73.54 & 0.149 \\  & 0.009 & 154.63 & 124.79 & 0.213 \\  & 0.001 & 99.43 & 82.45 & 0.159 \\  & 0.003 & 110.82 & 90.07 & 0.164 \\
0.1 & 0.005 & 80.55 & 66.31 & 0.125 \\  & 0.007 & 82.67 & 70.76 & 0.149 \\  & 0.009 & 191.36 & 152.13 & 0.292 \\  & 0.001 & 129.39 & 104.48 & 0.208 \\  & 0.003 & 91.76 & 76.12 & 0.151 \\
0.5 & 0.005 & 84.72 & 71.56 & 0.143 \\  & 0.007 & 126.83 & 99.73 & 0.196 \\  & 0.009 & 100.45 & 82.21 & 0.174 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment on the weighting coefficients \(\mu\) and \(\lambda\) or similarity calculation in PSCDR. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\tau\) & **RMSE** & **MAE** & **MAPE** \\ \hline
0.03 & 88.92 & 72.93 & 0.159 \\
0.05 & 132.38 & 107.49 & 0.205 \\
0.1 & 116.12 & 90.28 & 0.194 \\
0.2 & 102.54 & 81.36 & 0.162 \\
0.5 & **75.92** & **63.19** & **0.115** \\
0.7 & 92.66 & 78.21 & 0.164 \\
0.9 & 99.13 & 80.22 & 0.167 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment on the parameter \(\tau\) in PSCDR. causes the output distribution of \(\Lambda_{PSCDR}\) to become excessively smooth, which blurs the differences between samples, diminishes the model's discriminative ability, and may lead to overfitting. Conversely, the ablation experiments revealed that when \(\tau\leq 0.01\), the values of all three evaluation metrics become NaN. This indicates that an excessively low temperature parameter results in an overly sharp output distribution of \(\Lambda_{\text{PSCDR}}\), increasing numerical instability in computations. Such instability may cause numerical overflow or underflow in the loss function, potentially rendering optimization infeasible. Therefore, selecting an appropriate temperature parameter is crucial for the performance of the PSCDR algorithm. While \(\tau=0.5\) was optimal in our experiments on the SGCBP dataset, this parameter generally acts as a hyperparameter that may require tuning (e.g., via a validation set) when applying PSCDR to new datasets or different model architectures, typically by exploring values within a range such as [0.05, 1.0].
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 3 Sensor Operating Modes and Energy Consumption Measurements
### Consumption Profiles in Active and Idle Mode
#### 3.3.6 Sleep Mode  
Among all the events, both the BLE radio and the MCU wait in either idle or sleep mode, which are both power-saving modes. According to the official documentation, the nRF8001 BLE radio consumes 2 \(\upmu\)A in idle mode between connection or advertising events [31]. The MSP430F2274 MCU consumes only around 1 \(\upmu\)A in low-power mode 3 (LPM3), from where very quick transitions to an active mode are possible (fewer than 1 \(\upmu\)s) [32]. The measured ECG sensor's sleep current consumption in active mode \(I_{sleep\_active}\) is 716 \(\upmu\)A, which translates to \(P_{sleep\_active}=2.957\) mW at \(V_{batt}=4.13\) V battery voltage. During the ECG sensor's idle mode, the sleep current consumption \(I_{sleep\_idle}\) is 265 \(\upmu\)A, which translates to \(P_{sleep\_idle}=1.095\) mW at \(V_{batt}=4.13\) V battery voltage. This measurement includes the current consumption of the entire sensor board with all analog circuits included.