esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Threats
### Implementation
#### 4.4.4 Control and Management Time Period Violations  
Despite their differences, the majority of spectrum sharing approaches, either centralized or distributed, have a common important requirement. They impose specific time periods during which no node is allowed to exploit the spectrum for transmitting data packets.  
To begin with, no node should ever access a special spectrum portion while a negotiation procedure is being carried out over it. Similarly, often no packet transmission is allowed while an activity like sensing or monitoring is in progress. To clarify the latter statement, we should point out that many modern protocols, including IEEE 802.22 [56], define special time periods during which a subset, or even all, wireless nodes have to sense the available spectrum and report their findings back to either a central entity (when a centralized mechanism is used for spectrum assignment), or the other neighbor nodes (in case of distributed mechanisms). Obviously, any \"illegal\" transmission during such a procedure will not allow it to capture the real view of the underlying network, in terms of channel occupancy, interference, and noise conditions, and consequently it will affect the overall spectrum sharing procedure [60].  
Another misbehavior scenario which could arise here is that of a malicious node transmitting during the so called guard-bands [61]. Perfect synchronization as well as the implementation of global clocks cannot be easily achieved. All methods proposed in the time division domain allow for the existence of a (small) synchronization error. A way to deal with such synchronization errors is the definition of special idle periods between consecutive slot allocations (preventing packets sent by the previous and current slot owners to overlap and destroy each other). For instance, each time a sharing mechanism re-allocates the available channels to nodes, it introduces idle periods between these two successive allocation instances for allowing all in-flight packets to reach their destination, as well as providing enough time to the loosely synchronized network nodes to adapt their operation to any possible rule changes. What is obvious here is that each node of such networks should abide by the rules and avoid transmitting anything during these periods. Again, an adversary may ignore the defined guard-bands and attempt transmissions over them aiming at maximizing his own profit and/or degrading both the spectrum utilization level characterizing the DSA network and the QoS received by other legitimate nodes.
## 4 Experimental Settings and Results
### Rationality Validation of MAF-NBC  
A second experiment was carried out to evaluate the rationality of MAF-NBC in terms of whether the designed ANN can transform the one-hot encoded attributes corresponding to the original mixed attributes into independent encoded attributes. On the representative _Page_small_ data set, an ANN with 50 hidden-layer nodes was constructed with random weights in the [-1,1] interval, a balance factor \(\lambda=0.50\), and learning rates \(\xi=\zeta=0.01\). Ten representative encoded attributes were selected from the hidden-layer  
Figure 3: Convergence of the ANN’s objective function. (**a**) Encoding error. (**b**) Attribute dependence.  
Figure 2: Convergence of the ANN’s weights. (**a**) Input-layer weights. (**b**) Output-layer weights.  
outputs of ANN. The dependence between the two encoded attributes was measured with the mutual information, which is calculated with _sklearn.metrics.mutual_info_score_ ([[https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html)), accessed on 14 October 2022) package of the _scikit-learn_ machine learning library.  
Figure 4 displays a series of heatmaps corresponding to iterations #1, #50, #100, and #150. These heatmaps show the change in attribute dependence as the iteration number increases during the ANN's training. It can be clearly seen that the dependence between encoded attribute gradually decreases as ANN weights are updated. It indicates that independent attributes can be obtained by transforming the original mixed attributes into the encoded attributes. The experimental results shown in Figure 4 are consistent with the experimental results shown in Figure 3, where the attribute dependence gradually decreases with the increase in iteration number. This experiment demonstrated that the ANN is able to transform the original mixed attributes into independent encoded attributes for NBC construction.  
Figure 4: Dependence change of the encoded continuous attributes.
# Study on Water Inrush Characteristics of Hard Rock Tunnel Crossing Heterogeneous Faults
## 5 Results
### Evolution Law of Velocity  
#### 5.2.1 Maximum Water Velocity Change Rule with Advance Length  
Transient seepage calculations were conducted in ABAQUS to analyze the temporal variation of water inrush velocity. The calculations were carried out for a duration of 500,000 s. Figure 6 showcases the cloud maps of water inrush velocity at selected characteristic sections, specifically at x = 70 m, x = 80 m, x = 85 m, and x = 90 m, at different characteristic time points (T = 100 s, T = 100,000 s, T = 500,000 s). The lateral changes in Figure 6 reflects the change rule of water inrush velocity with advance length under the same time step. The maximum water inrush velocity \(\left(v_{\max}\right)\) of the tunnel face can reflect the severity of the water-surge accident. It can be seen from Figure 6 that the maximum value of water inrush velocity appears in the initial moment of calculation. Take T = 100 s as an example: when x = 80 m, \(v_{max}=2.47\) m/s. Conversely, at x = 90 m, where the tunnel face intersects with the fault core, \(v_{max}\) drops to 0.51 m/s, marking a decrease of 79.3% from the preceding value.  
Figure 5: Numerical calculations of the seepage pressure field in the fault zone: (**a**) contour plot of the seepage pressure distribution within the fault in the undisturbed stage; (**b**) cloud plot of the seepage pressure distribution and pressure drop equivalent curve \(L_{l}^{DZ}\) when x = 70 m; (**c**) cloud plot of the seepage pressure distribution and pressure drop equivalent curve \(L_{l}^{PC}\) when x = 90 m.  
To visually represent the dynamics of \(v_{max}\), the extremal values from the computational results were extracted, culminating in Figure 7a. This figure illustrates that the maximum water intrusion velocity exhibits an initial increase, then decrease, and then increase pattern as the tunnel crosses the heterogeneous fault zone. According to Equation (4), the water intrusion velocity is directly proportional to the pore pressure gradient. Insights from Section 5.1 reveal that the pore pressure gradient in the vicinity of the tunnel within the fault core is notably lower compared to that within the damage zone. This discrepancy results in a diminished water inflow rate at the fault core.  
Figure 7b,c showcases photographic evidence of the water inrush event. Figure 7b depicts the damage zone, where several water inrush points can be identified, manifesting as streams and jets with rapid flow rates. By contrast, Figure 7c, captured at the fault core, reveals a higher number of water inrush points, albeit with considerably slower velocities, indicative of a low-speed seepage phenomenon. These visual observations from the field corroborate the numerical simulation outcomes, thereby enhancing the credibility of the research findings.
## 4 Discussion
### Calculation on Temperature Rise to Assess Fire Risk  
The above non-contact method is also helpful in anticipating how much local loss in specific solar cells under given operating conditions leads to a local temperature rise, which is responsible for fires. It is also helpful to detect symptoms of infant degradation, potentially leading to fires caused by partial shading (leading to hot spots) and whether this local temperature rise exceeds the firing point. The magnitude of the local heat source is calculated using the following equation:  
\[\text{(local heat source)}=-\text{(current at the string)}\times\text{(surface potential voltage)}\]  
Note that the surface potential varies according to the operating conditions, such as the degree of partial shading and solar irradiance. The standard surface potential value is positive, typically \(0.6\) V for Si solar cells. However, the cell creates a hot spot, resulting in a negative and much more significant absolute surface potential value.  
It is also helpful for non-destructively evaluating the performance variation of solar cells in a PV module. Given uniform illumination by an artificial light source and measurement of the surface potential in each solar cell, the surface potential variation corresponds to the variation in the output current potential (short-circuit current of the solar cell when independently measured). Specifically, the detected reverse voltage indicates that the solar cell has a lower short-circuit current than the cells of the other modules.  
Since it detects electrical fields from individual cells, it can only be applied to the modules with explicitly separate cells and not used like amorphas modules. Although the principle of the fire risk is the same given the temperature rise of the solar cells induced by the reverse-bias voltage, the impact of the hot-spot creation leading to the fire hazards varies by the type of modules [36; 40; 41; 42; 43], including even the difference of the cell structure in single-crystalline solar cell family [36]. This work investigated the non-contact and non-destruction inspection of the reverse current voltage of each cell in the module, and it is common to every type. However, the criteria may vary among types of solar cells and modules. For example, that of the Perovskite solar cells may be \(-9\) V [43]. The impact of efficiency difference also affects the criterion through the term of the operating temperature of the solar cell under the worst-case solar irradiance.
# Prediction of MAG Welding Formation Based on Fuzzy Interpolation Reasoning
## 3 The Fuzzy Interpolation Methods
### Experimental Result  
In this part, several examples are given for the proposed interpolation method and compared with KH methods and HS methods. The examples involve interpolation between two adjacent rules.  
Example 1: This example demonstrates the use of the proposed method involving a single antecedent with triangular fuzzy sets. All the conditions and the fuzzy interpolative reasoning consequences for the different methods are shown in Table 1 and Figure 3.  
First, according to (24) to (30), \(A^{\prime}\) and \(B^{\prime}\) can be calculated as \(A^{\prime}\) (5.7, 9.15, 10.15) and \(B^{\prime}\) (5.19, 6.67, 8.67), respectively. Then, according to the width similarity relationship between the observed \(A^{*}\) and antecedent of the intermediate rule \(A^{\prime}\), the solutions \(B_{1}^{*}\) and \(B_{2}^{*}\) of Equation (33) can be obtained as \(B_{1}^{*}\) (5.84, 6.27, 8.27) and \(B_{2}^{*}\) (\(-\)3.744, \(-\)3.314, \(-\)1.314), respectively. The similarity of \(SS(B_{1}^{*},B^{\prime})\) and \(SS(B_{2}^{*},B^{\prime})\) can be calculated as \(SS(B_{1}^{*},B^{\prime})\) = 0.6675 and \(SS(B_{2}^{*},B^{\prime})\) = 0.0939, according to (35), respectively. Finally, \(B_{1}^{*}\) is selected as the final inference consequence as the consequence \(B^{*}\) (5.84,6.27,8.27). From Table 1 and Figure 3, it can be seen that the KH method resulted in a nonconvex con  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute Values** & **Method** & **Results** \\ \hline \(A_{1}\) (0,5,6) & KH Method & (6.36,5.38,7.38) \\ \(A_{2}\) (11,13,14) & HS Method & (5.83,6.26,7.38) \\ \(B_{1}\) (0,2,4) & Proposed Method & (5.84,6.27,8.27) \\ \(B_{2}\) (10,11,13) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reasoning consequences of Example 1.  
Figure 3: A comparison of the fuzzy interpolative reasoning consequences of Example 1 for different methods.  
sequent fuzzy set, and the reasoning consequence of this method and HS method are both reasonable.  
Example 2: This example demonstrates the use of the proposed method involving multiple antecedent variables with trapezoidal fuzzy sets. All the conditions and the fuzzy interpolative reasoning consequences for the different methods are shown in Table 2 and Figure 3.  
First, according to (37) to (38), \(A_{1}^{\prime}\), \(A_{2}^{\prime}\) and \(B^{\prime}\) can be calculated as \(A_{1}^{\prime}\) (5.77, 8.19, 9.19, 10.19), \(A_{2}^{\prime}\) (7.17, 8.73, 9.73, 10.73) and \(B^{\prime}\) (5.43, 6.88, 7.88, 8.88), respectively. Then, according to the width similarity relationship between the observation and antecedent of the intermediate rule, the solutions \(B_{1}^{*}\) and \(B_{2}^{*}\) of Equation (43) can be obtained as \(B_{1}^{*}\) (4.79, 6.03, 8.03, 10.03) and \(B_{2}^{*}\) (\(-\)5.14 9, \(-\)3.915, \(-\)1.
## 3 Experimental Verification
For example, for benchmark _RSA_v2_ (49 places, 58 transitions) the proposed algorithm returns the results within 6.34 ms in contrast to 22.05 ms (almost 250% higher run-time) for the reference algorithm (note that PIPE was not able to compute the results). The advantage increases for the systems such as _manufacturing_AM_deadlock_ and _decision_making_system_v2_, where the proposed algorithm is over 73 times faster than the traditional Martinez-Silva method. Finally, the highest profit can be noticed for the systems where the reference algorithm was not able to give a result within an hour, for instance for _photovoltaics_single_module_ (a real-life photovoltaic system, described in [16]). For this benchmark, both reference techniques (the Martinez-Silva algorithm and the PIPE tool) were not able to compute the results, while the proposed algorithm returned the results in just 1.93 ms. Another interesting example is _crossroadSM_FPGA_ (a description of a real-life traffic light controller [19]). This system contains a very large number of reachable states, although it is of relatively average size (32 places, 12 transitions). In this system, the reference method (as well as the proposed one) was able to compute the results, but PIPE failed. Furthermore, the benchmark _airplane_flight_procedure_ (with just 28 places, 17 transitions) caused problems for both the Martinez-Silva algorithm and PIPE. Neither of them was able complete the analysis within an hour, while the proposed method was able to compute the results in less than one millisecond. This may be highly surprising; however, let us underline that the computational complexity of the proposed algorithm is strongly related to the number of places and transitions (which was proved in the previous subsection), while this system consists of just 28 places and 17 transitions. In conclusion, let us point out that the performed experiments confirmed theoretical assumptions, since the proposed method computed results for all 386 benchmarks. In contrast, the reference Martinez-Silva algorithm, as well as PIPE, was not able to finish the computation for all the tested cases due to the exponential computational complexity of those methods. Moreover, the proposed algorithm obtained the proper results for 384 (out of 386) tested cases, which, in addition, indicates its very high effectiveness. Let us now explain the functionality of the algorithm in a real-life example of a concurrent system.
# Challenges for the Implantation of Symbiotic Nanostructured Medical Devices
## 2 Is it Sufficient to Rely Only on Technological Advances?  
Here, we consider the example of an enzymatic biofuel cell as a symbiotic implanted medical device. The present challenges in the field of implanted enzymatic biofuel cells are to improve the performance and life-time of the biofuel cells operating inside the body. The energy density can be increased with the utilization of enzyme cascades to ensure the complete oxidation of fuels [5]. In addition to the challenge of increasing the energy density in enzymatic biofuel cells, [PERSON] et al. [6] summarized the other main technological challenges to be overcome in order to improve the performance of the enzymatic fuel  
Figure 1: Examples of symbiotic devices (symbio-bots) (**A**)–(**D**) that can be developed using bioinspiration from existing natural symbiotic systems (**E**). Each device is separated with a smart porous packaging that allows for duplex communication. Therapeutic cells (**A**) and (**B**) need a porous encapsulation that avoids an immune reaction and allows for protection from both sides. They may be human mesenchymal stem cells (MSC), specialized cells (such as \(\beta\)-cell from Langerhans islets) (**A**), other eukaryotic or prokaryotic cells (**B**), an implantable biofuel cell (IBFC) linked to an electronic medical device (**C**), or a generic device delivering a therapeutic molecule (**D**). Existing symbiosis (e.g., microbiota or pregnancy) is a source of bio-inspiration to establish duplex communication between the body and its implants (**E**). Regenerative medicine should embrace this concept of bioinspiration for the better design and integration of implants, especially for future symbiotic-bots. (Figure reproduced with permission from [4]).  
cells as the needs to increase power density, operational stability, and voltage output. To increase power density, [PERSON] et al. [6] proposed to increase enzyme activities, to facilitate electron transfers, to use nanomaterials, to design more efficient enzyme-electrode interfaces, and to combine enzymatic fuel cells with (super) capacitors. To improve stability, they proposed the use of different enzyme immobilization approaches, the tuning of enzyme properties, the designing of protective matrixes, and the use of microbial surface displaying enzymes. To improve voltage output, they proposed optimizing mediators, serial connection, and external boost converter electronic circuits [6]. However, the implantation of devices brings the dimension of biocompatibility as perhaps the key challenge that needs to be overcome to increase the life-time of implanted enzymatic biofuel cells. Our experience from implanting enzymatic biofuel cells in mammals has shown that producing a biocompatible system is the major issue to solve for enhancing the performance and life-time of implanted enzymatic biofuel cells [7].
# Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU
## 3 GPU-Accelerated Parallel Algorithm for 3D Laplacian Smoothing
### Details and Key Issues of the Parallel Algorithm
#### 3.2.2 Determining Boundary Nodes  
Constraint points are used to guarantee the overall topological characteristics of the mesh in the smoothing process. In this implementation, we use boundary points as constraint points.  
In a planar mesh, a point can be judged as a boundary point by checking its neighbors. Similarly, the boundary points in three dimensional space can be judged by the relationships of the mesh structure, except that the basic elements are faces rather than edges. The basic idea is that if a face is used twice in the spatial mesh, it must be an internal surface and the point on it must be an internal point, while boundary points are the points on surfaces that are used only once.  
Figure 4: Simple illustration of finding neighbor. (**a**) Original edge data; (**b**) Keys represent edge sets after sorted, value is an auxiliary array; (**c**) Array after unique. And keys represent nodes, value is the pointer of neighbors for each node; (**d**) Keys represent edge sets after sorted, value is an auxiliary array; (**e**) Array after reduce. Keys represent nodes, value is the number of neighbors for each node.  
The process of determining boundary nodes is as follows:  
1. Loop over all elements and store node indices representing their four faces from small to large;
2. Sort the surface array so that any surface can be compared with its adjacent surface to determine whether it has been used only once (i.e., boundary face);
3. Run a CUDA kernel to record points on the boundary surfaces and set them as constraints.
# Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation
## 4 Result  
### Single-Stage Examples  
For single-stage examples, we focus on the feasibility of the network and training strategy. The overall quantitative metrics are listed in Table 1. As indicated in Equation (8), the hybrid dataset here denotes a hybrid strategy involving both synthetic and augmented data. It shows that the hybrid training strategy outperforms the other two training strategies for almost all metrics in the velocity inversion task under the same conditions.  
The predicted one-dimensional velocity profiles of the Unet model using the three training strategies are shown in Figure 4. The displayed velocity values correspond to two arbitrary events and are averaged along the horizontal direction. We can find that augmented data and the hybrid training strategy yield better fittings to the reference velocity model. Figure 5 shows the two-dimensional profiles corresponding to Figure 4b by the hybrid training strategy. Training with the synthetic data involves first learning the low-frequency information in the data, and then it can provide an initial velocity model (Figure 5c,d). The model obtained by training the synthetic data (low frequency) may also predict high-frequency velocity components with the real data (with high frequency), but the results have a large error since the model did not learn these high-frequency features. After training with the augmented data containing high-frequency information, the model improves the precision of the predicted velocity models (Figure 5e,f).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Dataset** & **Phase** & **PSNR** & **SSIM** & **MAE** \\ \hline \multirow{2}{*}{Synthetic} & P & 19.88 & 0.7097 & 272.243 \\  & S & 19.92 & 0.8139 & 133.958 \\ \hline \multirow{2}{*}{Augmented} & P & 27.90 & 0.8644 & 113.514 \\  & S & 27.71 & 0.8912 & 57.431 \\ \hline \multirow{2}{*}{Hybrid} & P & 30.04 & 0.8591 & 93.143 \\  & S & 29.60 & 0.8911 & 48.308 \\ \hline \end{tabular}
\end{table}
Table 1: The mean values of quantitative metrics for single-stage examples.  
Figure 4: One-dimensional profiles of the reference and predicted velocity values of two arbitrary events from the first stage. (**a**) Velocity curves of one sample event. The red solid and dashed lines indicate the reference velocity for P- and S-wave, respectively, and the blue, magenta, and black dashed lines indicate the results of training with synthetic, augmented, and hybrid dataset. (**b**) Velocity curves of another sample event. The meanings of the symbols and colors are the same with (**a**).