esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Aca-Leach  
The ACA-LEACH algorithm proposed in [66] is an improved version of LEACH which performs the Ant Colony Algorithm (ACA) into inter-cluster routing mechanism to reduce the energy consumption of CHs and finally prolong the lifetime of networks. ACA-LEACH also considers the node residual energy and the distance between the nodes and CHs for the selection of CHs. When the distance between two CHs is less than the threshold distance, ACA-LEACH compares the energy value between the two, and then chooses the CH with more energy as the new CH but with less as the member node. Due to the random choice of CHs, the phenomenon of relative concentration of CHs may exist. \(k\) ants are placed on each CH and set a matrix which is used to store and record the generated path, ACA selects the optimal path between each CH and the sink helps by the value of pheromones. Afterwards, the \(k\) ants in each CH are transferred to the next accessible CH with a possibility \(p\) of each adjacent CHs being selected as the next hop of adjacent clusters. The \(k\) ants in each CH should select the shortest path with the lowest energy consumption. In order to evaluate ACA-LEACH, the authors have used 200 nodes randomly deployed in a squared sensor field. ACA-LEACH is compared to traditional LEACH and results have shown that the proposed algorithm has a lower average energy consumption than LEACH and its number of alive nodes is better than in the other algorithm.
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
###### Abstract  
Aeromagnetic compensation is the main method for eliminating magnetic interference on flight platforms. With the improved sensitivity of aeromagnetic sensors, the generalization and compensation accuracy of existing aeromagnetic compensation methods have become insufficient to meet the needs of current aeromagnetic survey applications. In this article, we propose an aeromagnetic compensation method based on temporal convolutional networks to improve both generalization and compensation accuracy. In the proposed method, a neural network based on separable convolution with a residual connection is employed to improve the compensation accuracy and convergence stability, and a gradient correction loss function based on the Tolles-Lawson model is used to improve the generalization ability. We conducted experiments based on both simulated and real datasets and compared typical neural network compensation methods proposed by previous researchers. The results indicate that the method proposed in this article can achieve a lower standard deviation of residual magnetic interference than other neural network methods, demonstrating a better compensation performance and generalization ability.  
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Footnote †: journal: Applied Sciences
## 3 Size Optimization of Coal Pillar in Double-Roadway Excavation
### Theoretical Calculation of Coal Pillar Width  
The dimension \(L\) of the coal pillar separating roadways is expressed as \(L=L_{A}+L_{B}+L_{C}\). In the formula, \(L_{A}\) represents the length of the crushed zone in the inter-entry coal pillar; \(L_{B}\) denotes the length of the relatively stable zone in the inter-panel coal pillar; and \(L_{C}\) signifies the length of the bolt support zone, as shown in Figure 2.  
Figure 1: (**a**) Geographical location map of Yulin, Shaanxi; (**b**) working face layout diagram; (**c**) engineering position of drilling columnar structure.  
Once the primary mining influence stabilizes, the boundary of the coal pillar between areas A and B reaches a limit equilibrium state, satisfying the following equation:  
\[\left\{\begin{array}{c}\frac{\partial\tau_{x}}{\partial_{x}}+\frac{\partial_{x }}{\partial_{y}}+f_{x}=0\\ \frac{\partial\sigma_{y}}{\partial_{y}}+\frac{\partial\tau_{xy}}{\partial_{x}}+ f_{y}=0\\ \tau_{xy}=-\left(\sigma_{y}\text{tan}\;\varphi+C\right)\end{array}\right. \tag{1}\]  
In the equation, \(\sigma_{x}\) and \(\sigma_{y}\) represent the principal stresses in the \(x\) and \(y\) directions. \(\tau\) is the shear stress; \(f_{x}\) and \(f_{y}\) are the friction coefficients in the \(x\) and \(y\) directions; \(\varphi\) is the coal seam interface's internal friction angle; \(C\) is the cohesion of the coal seam interface.  
Through analysis and calculation, the width of Area A within the coal pillar is determined to be:  
\[L_{A}=\frac{m\lambda}{2\text{tan}\;\varphi}\text{ln}\left(\frac{K\rho gH+\frac {C}{\text{tan}\;\varphi}}{\frac{C}{\text{tan}\;\varphi}+\frac{P}{\lambda}}\right) \tag{2}\]  
In the formula, the extraction elevation of the coal seam is denoted by \(m=5.8\); \(\lambda\), the lateral pressure coefficient, from on-site monitoring, is \(\lambda=0.316\); \(K\), the stress concentration factor, is \(K=2\); \(\rho\), the average density of the overlying strata, is \(\rho=2.5\) t/m\({}^{3}\); H, the burial depth of the roadway, is 263 m; \(C\), the cohesion of the coal seam interface, is \(C=2.82\) MPa; \(\varphi\), the internal friction angle of the coal seam interface, is \(\varphi=37.64^{\circ}\); and \(P_{x}\), the lateral pressure of the support on the roadway, is 0.15 MPa.  
Substituting each parameter into the equation yields L\({}_{\text{A}}\) = 7.12 m.  
The bolt support length of the coal roadway is 2.0 m, and the width of the relatively stable central region can be calculated using Equation (3):  
\[\text{L}_{\text{B}}=\xi(\text{L}_{\text{A}}+\text{L}_{\text{C}}) \tag{3}\]  
In the formula, \(\xi\) is the stability coefficient, set to 0.7, and L\({}_{\text{B}}\) = 6.384 m.  
Substituting each parameter, we get L = 15.504 m, that is, through a theoretical analysis, the suitable width for the section of the coal pillar is 16.00 m.
## 2 Related Work
### Cbam  
Images of drain discharge contain numerous amounts of unimportant feature information, such as natural scenes, which may adversely impact the robustness of the detection model. To enhance the model's focus on relevant features within the image, an approach was adopted to weight the input feature image, thereby amplifying the significance of the useful regions. Consequently, the Convolutional Block Attention Module (CBAM) [34] was integrated into the YOLOv5 model to prompt the network to prioritize key sewage-related information, thereby facilitating the training of the network model to achieve optimal outcomes. CBAM, comprising a channel attention module (CAM) and a spatial attention module (SAM), is a straightforward yet efficient attention module that can be seamlessly incorporated into the CNN architecture in a plug-and-play manner, allowing for end-to-end training. The module operates as follows:  
(1) The Channel Attention Module (CAM) primarily focuses on extracting features from channels. The input feature map F (H \(\times\) W \(\times\) C) undergoes Global MaxPool (GMP) and Global AveragePool (GAP) operations based on width and height, respectively, and is processed through a Multilayer Perceptron (MLP) within the neural network. Subsequently, an element-wise summation operation is performed on the MLP output features to derive the final channel-specific feature Mc after applying a sigmoid activation function. Mc is then element-wise multiplied by the input feature map F to generate the required input feature F\({}^{\prime}\) for the spatial attention module. The mathematical representation of CAM is presented in Equation (1).  
\[Mc(F)=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F)))=\sigma\Big{(}W_{1}\Big{(}W_{0} \Big{(}F^{s}_{avg}\Big{)}\Big{)}+W_{1}(W_{0}F^{s}_{max})\Big{)} \tag{1}\]  
(2) The Spatial Attention Module (SAM) primarily conducts feature extraction in the spatial domain. Initially, channel-based Global Max Pooling (GMP) and Global Average Pooling (GAP) operations are applied to the input feature map F to produce two distinct feature maps, which are subsequently concatenated in the \"Concat\" module. Subsequently, a feature mapping (H \(\times\) W \(\times\) 1) is derived through a (7 \(\times\) 7) sub-convolution operation, integrating the channel dimension while preserving spatial information to enhance the capture of target location and spatial relationships. Following the S-shaped activation  
Figure 3: Comparison of BiFPN with FPN and PANet.  
function, the final spatial attention feature, denoted as Ms, is generated. Ms is then multiplied by the input feature map F' of the module to yield the ultimate generated feature map. The formulation of SAM is presented in Equation (2), while the complete Channel Attention and Spatial Attention Module (CBAM) mechanism is illustrated in Figure 4.  
\[M\!s(F)=\sigma\Big{(}f^{i\times i}([A\!v\!g\!Pool(F));Max\!Pool(F)]\Big{)}= \sigma(f^{i\times i}(\Big{[}F^{s}_{avg};F^{s}_{max}\Big{)}) \tag{2}\]
## 5 Analysis of Experimental Results
### The Defect Is Located in the Heated Area  
To evaluate the ability of the LPM method to detect defects in the HTZ, this paper conducts the third heating experiment. Initially, the echo signal of the pipeline in a healthy state is collected. After a period of time, an iron block is attached at a position 4.4 m from one end of the pipeline as indicated in Defect 2 in Figure 6. The process and settings for attaching the iron block are identical to those in the second experiment. The signals collected after the attachment, which include defects, are shown in Figure 18.  
Figure 16: The reflected signal of Defect 1 in pipeline at 24 °C.  
Figure 17: Variation curves of damage index for the BSS method and LPM method over time with the defect located in the unheated area.  
Figure 18 shows the reflected signal after the iron block is attached in the HTZ at 24 \({}^{\circ}\)C. At 4.4 m, a clear echo can be observed, confirming the effectiveness of the iron block in simulating defect. Subsequently, one end of the pipeline is heated to create an NTF. After 90 min of heating, the temperature at that end reaches a maximum of 80 \({}^{\circ}\)C. The DI curves over time for both methods are calculated and presented in Figure 19.  
Figure 19 shows the DI curves over time for the BSS and LPM methods. During the first 23 min of the experiment, the damage indexes for both algorithms remain stable within a small range. At the 23 th minute, the iron block is attached, resulting in a noticeable step change in both indexes, which persists for 15 min until heating commences. When the experiment reaches 38 min, the DI of the BSS method sharply increases with the rising temperature. In contrast, the DI of the LPM method remains relatively stable after heating, effectively mitigating the impact of the NTF.  
The third heating experiment demonstrates that the LPM method can successfully detect defects in the high-temperature area of a pipeline under an NTF.
## 3 Theory and Discussions
The non-degenerate biphotons--composed by signal photons and idler photons with different wavelength--are collinear with the pump and diffracted by the single slit. The distribution of the diffraction pattern manifests the de Broglie wavelength of the non-degenerate biphotons. However, people used to directly measure the de Broglie wavelength of a particle by making the particle interfere with itself [1]. Therefore, we design the second experiment to study the interference of a non-degenerate biphoton with itself in a [PERSON]'s interferometer by replacing the single slit with a double slit. The measurements are similar to those in the first experiment. We first measure two different non-degenerate ghost interferences: in the first one, the detector \(D_{2}\) is fixed, and detector \(D_{1}\) is scanned, shown in Figure 2d; in the second one, the detector \(D_{1}\) is fixed, and detector \(D_{2}\) is scanned, shown in Figure 2e. In the third measurement, both detectors are scanned in their planes synchronously, shown in Figure 2f. In three measurements, the distances between the maximums of interference patterns are measured, and the ratio between three distances is 2.34:1.88:1. The ratio between wavelengths of the idler (788 nm), signal (633 nm) and pump (351 nm) is 2.25:1.80:1. According to the Abbe theory of image formation, with the object illuminated by non-degenerate entangled photon pairs, the resolution enhancement is 2.25-times compared to the one with classical 788-nm light; the resolution enhancement is 1.80-times compared to the one with classical 633-nm light. The experiment result shown in Figure 2e is the direct measurement of the photonic de Broglie wavelength of non-degenerate biphotons. The double slit in the setup functions as a [PERSON]'s double slit interferometer. It is known that biphotons are strongly correlated both in momentum and position due to the entanglement. After generation, they could be any place on the output surface of the crystal. However, once a signal/idler is detected at one specific position on the output surface, its twin photon must be at the same position with 100% certainty [16]. Since the double slit is very close to the output surface of the crystal, the generated signal-idler pairs can pass through either the upper slit or the lower slit together, and the chance the they pass through different slits is negligible. In the experiment, the photon counting rate is very low (\(\sim\)10\({}^{3}\) Hz), and it is reasonable to assume that there is only one pair of biphotons in the experimental scheme in each coincidence measurement. The interference-diffraction pattern measured when two detectors are scanned synchronously is the result of the interference between a biphoton and itself. In other words, it is the interference between two two-photon probability amplitudes, \(P_{cc}=|A_{u}+A_{l}|^{2}\) where \(A_{u}\) and \(A_{l}\) represent two different ways to trigger a two-photon coincident counting event, respectively.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 7 Discussion  
Instead of using a signed distance field as an intermediate representation, we directly generate each voxel cell's representative vertices and surface normal using well-understood point cloud voxelization and local surface estimation [50; 66]. With this decision, we effectively only reconstruct data ultimately used in meshing. However, since dual contouring on an SDF does not consider diagonal neighborhoods in the voxel grid, we must extend the recursive traversal to include all 32 diagonal quad face cases (Section 4.1). Furthermore, to utilize simplification, we require an exhaustive condition for the preservation of the connectivity (Section 5.1) and a nonmanifold edge detection (Section 5.2) to preserve the original silhouette.  
However, extending the neighborhood also causes unwanted, accidental interior faces constrained by the voxel size (Figure 8(b)). A manual fix-up of poorly aligned voxel layers is also necessary (Figure 8(b)), which would not be an issue with implicit surfaces encoded as a signed distance field.  
While our method is not intended to compete with general-purpose surface reconstruction methods due to limited hole filling, which requires dense input point clouds to generate a voxelization without holes, our performance results, room size, and number of cameras are on par with the FusionMLS [15] stateless reconstruction system while also performing the simplification of flat surfaces. The efficiency of the task-based CPU parallelization (Figure 16(a)) of our current prototype can be further improved using better, state-of-the-art lightweight task scheduling systems [76].
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.2 Result on the AG's News Dataset  
We also conduct comparative experiments with baseline pre-trained models on the AG's News dataset, and the results are shown in Table 4. Considering the length of the dataset text, the tokenizer's expansion length is set to 256 here to avoid numerous zero elements in the word vectors. When changing the language of the topic recognition task, our method still achieves an accuracy of 94.8%. ELECTRA improves the performance of the discriminator during iterative \"replacement\" by recovering words from the full-scale scale during training for discrimination, enabling it to identify correct semantics and achieve excellent performance. This approach is similar to the \"repeated questioning\" mechanism in our method on the parameter level, proving the importance of key semantic weight information in improving the accuracy of long-text classification. Our method fully learns key semantic weight in multi-layer residual stacking, demonstrating the effectiveness of this approach. In comparison with BERT series models, our method achieves a minimum performance improvement of 1.2% in accuracy and a 2.5% improvement over BERT, demonstrating that our two parallel learning modules significantly optimize pre-training weights for this task, effectively learning and extracting weight features in the bidirectional attention mechanism, and demonstrating higher performance in long-term dependency semantic correlation. Overall, RQ-OSPTrans performs exceptionally well among all methods listed in Table 4, demonstrating that our model can further learn implicit semantic information and subtle discriminative representations in long texts based on pre-trained models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & \(M_{padding}\) & **Test** & **Dev** \\ \hline HAN [13] & & 88.7 & 90.1 \\ BERT [1] & & 97.6 & 97.4 \\ ALBERT [25] & & 97.6 & 97.9 \\ RoBERTa [24] & & 97.5 & 97.9 \\ RBT6 [26] & & 97.3 & 98.1 \\ ERNIE [52] & & 96.8 & 98.0 \\ GPT-2 [54] & & 97.4 & 97.5 \\ ELECTRA [53] & & 97.5 & 97.9 \\ MacBERT [26] & & 97.5 & 98.0 \\ XLNet [23] & & 97.0 & 97.4 \\ KFE-CNN [50] & & 97.8 & 97.8 \\ BTCDC [51] & & 95.2 & 95.2 \\ \hline
**RQ-OSPTrans (ours)** & & **97.7** & **98.5** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of RQ-OSPTrans and state-of-the-art pre-trained models on THUC-News dataset.
## 6 Results and Discussions
### Techno-Economic Assessment of Each Case's Optimum Results  
In terms of economic evaluation, Tables 9 and 10 demonstrate specific technical and economic parameters of each case based on the optimal indicators exhibited in Table 8 above.  
Concerning the economic evaluation of the different cases in Table 9 above, a single stage has the best result in terms of LCOE, CO\({}_{2}\) avoided, and captured cost. Increasing the number of membrane stages to three raised the LCOE by around 23% due to the larger membrane surfaces used and the energy consumption required. Utilizing three stages instead of two to obtain 99% purity of CO\({}_{2}\) helped to reduce most of the economic parameters due to the lower electric energy needed to capture 90% of the carbon dioxide content.  
The net present value was reduced by integrating three stages instead of one stage by about 30%, where two stages have the lowest value of NPV. Regarding the profitability factor, three stages of membrane have more profitability than two stages, around 11%, because of the lower energy required for Case 3. However, one stage of membrane was the most profitable case compared to the others.  
Figure 12: CO\({}_{2}\) purity impact on power consumption at different CO\({}_{2}\) permeabilities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cases** & **-** & **Case 1** & **Case 2** & **Case 3** \\ \hline First membrane surface & m\({}^{2}\) & 400,000 & 800,000 & 800,000 \\ First and second compressor pressure & bar & 4.4; n.a. & 7.3; 2 & 7; 4 \\ CO\({}_{2}\) capture rate & \% & 90.3 & 90.0 & 90.7 \\ Power needed & MW & 238.5 & 340.6 & 298.3 \\ CO\({}_{2}\) purity & \% & 73.3 & 99.1 & 99.7 \\ CO\({}_{2}\) recovered/membrane surface & kmol/(h\(\cdot\)m\({}^{2}\)) & 0.0239 & 0.0157 & 0.0120 \\ \hline \hline \end{tabular}
\end{table}
Table 8: A comparison of the optimum three cases assumed results.  
Figure 13 below shows the accumulated cash flow of different membrane schemes integrated into the coal power plant throughout its duration (25 years). After almost 8 years, the investment cost will be recovered in the case of a single stage. Integrating three stages decreased the period of investment recovery by approximately 13% compared with using two membrane stages.  
Figure 14 below demonstrates the effect of CAPEX, fuel, and other various indicators' price on the levelized cost of electricity at CFPP with the integration of the third case of the membrane scheme. The impact of the CAPEX and plant capacity factors is distinguished  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicator** & **Unit** & **Case 1** & **Case 2** & **Case 3** \\ \hline NPV & million EUR & 2197.7 & 1251.9 & 1630.8 \\ IRR & \% & 19.92 & 14.85 & 16.86 \\ DPP & year & 8.09 & 11.0 & 9.6 \\ PI & - & 2.17 & 1.64 & 1.84 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The specific economic indicators of CFPP with different membrane configurations.  
Figure 13: Discount payback period regarding the three cases of accumulated cash flow.  
on the LCOE, where LCOE differed from almost 124 to 143 EUR/MWh by modification of the CAPEX price +/ -10%.
(ii) Anti-Tyrp1 opsonization of established wild-type (WT) B16F10 melanoma metastases does not significantly affect lung tumor burden on day 14 after tail-vein inoculation with \(2\times 10^{5}\) B16F10 cells. Tumors are allowed to establish, and mice are then tail-vein injected with anti-Tyrp1 (250 \(\upmu\)g dosing) on days 4, 5, 7, 9, 11, and 13. Mice are sacrificed and organs are surveyed for B16F10 metastases. Results are illustrative of three independent experiments (\(n=5\) per group, mean \(\pm\) s.e.m., n.s. not significant for \(p>0.05\), unpaired t-test). (**B**) CD47-deleted lung metastases require anti-Tyrp1 for significant suppression. (i) Mice are tail-vein injected with \(2\times 10^{5}\) CD47 KO B16F10 cells on day 0 and later treated by tail-vein injections of anti-Tyrp1 (75 \(\upmu\)g dosing). Mice are sacrificed and organs are surveyed for metastases, but only the lung shows nodules. Representative images from untreated controls or anti-Tyrp1 treatment. (ii) Tumor area on both sides of lung lobes were quantified, showing significant suppression by anti-Tyrp1 (\(n=5\) per group, mean \(\pm\) s.e.m., \({}^{*}p<0.05\), \({}^{**}p<0.01\), \({}^{***}p<0.0001\), one-way ANOVA, uncorrected Fisher’s LSD). (iii) Exponential-type growth (as power law: Area = A\({}_{0}\) t\({}^{\text{B}}\): A\({}_{0,\text{tctrl}}=0.0019\), A\({}_{0,\text{to-t}}=0.0087\), B\({}_{\text{tctrl}}=4.1\), B\({}_{\text{Rx}}=3.0\)). (iv) CD47 deletion does not affect tumor growth relative to WT (Area = A\({}_{0}\) e\({}^{\text{k}\text{t}}\), where A\({}_{0}=0.05\), k = 0.45).