esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Efficiency and Computational Complexity Discussion
Figure 7: Comparison of the mean time and standard deviation of the computational complexity for each transmission round in the deployment scenarios of the 100 m \(\times\) 100 m square region and the coordinate (50, 50) of the BS: (**a**) mean time for diverse numbers of sensor nodes, and (**b**) standard deviation of the computational complexity for diverse numbers of sensor nodes. In Sections 5.2-5.4, many different strategies were adopted to compare the performance of the DHCO algorithm with that of the LEACH, HEED, GASONeC, and DCFR algorithms. All experimental results indicated that the DHCO algorithm has superior performance over state-of-the-art WSN routing algorithms. These state-of-the-art WSN routing algorithms employ the clustering mechanism to improve WSN longevity, but fail to solve the issue of unevenly distributed CHs well, which has a poor impact on their performance. Differently to state-of-the-art WSN routing algorithms, the DHCO algorithm utilizes all sensor nodes and the BS to construct the hierarchical network structure, and employs the combinatorial optimization theory to establish the feasible routing set for each sensor node, taking advantage of the maximum-minimum criterion to obtain their optimal routes based on the node residual energy; it significantly reduces the effect of randomness and ensures each sensor node has an equal opportunity to participate in the network routing operation, which contributes to a balanced energy consumption of the sensor nodes, improves WSN longevity, and reduces the computational complexity of the algorithm.
# Optimization Study on Longitudinal Joints in Quasi-Rectangular Shield Tunnels
## 4 Bearing Capacity of the Joint with DIJPs
### Connecting Reinforcement Strains  
The connecting reinforcements are used to anchor the DJIPs in the concrete, and a reliable connection is important for the joint safety. As an example, the strains from the Type-A joint under positive bending moments at the measurement points indicated in Figure 9c are shown in Figure 18. In Figure 18a, the strains along the connecting reinforcement at the inner layer, which is the most stressed reinforcement in this test case, are shown. The closer to the joint section, the larger the strains are. For the strains of the connecting reinforcements at different layers, it is obvious that the inner layer resists most of the tension force, as can be seen in Figure 18b. When the bending moment is small, the middle and outer layers are under compression. As the joint is rotating, the connecting reinforcements are gradually submitted to tension forces. The trends of the strain curves obtained from the joint model and from the tests are generally in good agreement. For the bending moment equal to 300 kN-m/m, the strains of all connecting reinforcements are smaller than 200 \(\upmu\epsilon\). Figure 19 shows a joint section after testing. Both DJIPs are still in good condition, and few cracks have appeared around them. From this, it can be concluded that the DJIPs, as well as their connections to the concrete, have proved to be reliable.  
Figure 17: Evolution of the bolt strains with increasing bending moments: (**a**) Type-BPos joint in positive cases; (**b**) Type-BNeg joint in negative cases.
## 4 Results  
Figure 4 shows the cost of thermal energy delivered to DEN using M-TES transportation modes and various system level energy storage densities compared to the levelized cost of other energy sources in $/MWh. Biomass boiler uses fuel from the biofuel facility with levelized cost of $70/MWh. To compete with biomass boiler, a diesel truck M-TES system, will need to have at least 0.4 MJ/kg ESD. However, an electric truck or RNG truck will need a system-level ESD of at least 0.3 MJ/kg. Similarly, Figure 5 shows the cost of the avoided emissions ($/tCO\({}_{\text{2e}}\) Avoided) for various transportation modes and ESDs. Both Figures 4 and 5 are for a transport distance of 15 km between the IWH location and the DEN of the City of Surrey. The cut-off system level ESD to make M-TES competitive is 0.3 MJ/kg. However, the thermal energy demand of the DEN should be considered in the analysis. The cut-off system-level ESD for M-TES technology to meet the target 7% of the total demand with a cost competitive with other low-carbon sources, in terms of both the levelized cost and the cost of avoided emissions, is estimated at 0.7 MJ/kg.  
Figure 6 illustrates the cost ($/MWh) for the considered truck modes with variable distances between IWH source and DEN. Since diesel has the highest $/MWh, it was further compared with other low-carbon sources in Figure 7. The cost for other low-carbon resources is based on the Surrey DEN analysis.  
Similarly, Figure 8 shows the cost of avoided emissions ($/tCO\({}_{\text{2e}}\) Avoided) for the considered truck modes with variable distances between the IWH source and the DEN. Since diesel has the highest $/tCO\({}_{\text{2e}}\), it was further compared with other low carbon sources in Figure 9.  
Figure 4: M-TES $/MWh with different ESD and transportation modes with a 15 km distance between the industrial waste heat and district energy network.  
Figure 5: The avoided $/tCO2e with different System ESD and transportation modes with 15 km distance between the IWS and DEN.  
Figure 6: The M-TES levelized cost with different IWH locations and truck modes.  
Figure 8: The cost of avoided CO\({}_{2}\)e with different IWH locations and truck modes.  
Figure 7: The levelized cost of diesel truck M-TES ($/MWh) varies with a distance and system ESD of 0.7 MJ/kg and other Survey DEN low-carbon sources cost estimation.  
Figure 9: The avoided GHG Cost ($/tCO\({}_{2}\)e avoided) for diesel truck M-TES varies with the distance. Results are shown for the levelized cost estimation of M-TES with a system level ESD of 0.7 MJ/kg and other Surrey DEN low-carbon sources.
###### Abstract  
Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes' high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1\({}^{\circ}\) of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information.  
laser scanning data; normal position vector; segmentation; planar patches; attribute homogeneity +
Footnote †: journal: Sensors
## 3 Results
### Test Phase  
The exoskeleton was controlled by the BMI decoded commands and the BMI classifiers were trained with training trials. Tables 3-5 summarize the results from closed-loop trials. TPR is 100% in the majority of trials, which means that the exoskeleton was activated at least once during the MI event. The number of false positive activations during idle state ranged from 0 to 2. Regarding %Commands, it improved by 13% from the first to the last session of S2, although their performance in each session was not always superior to the previous one. In the last session, the average %Commands for both subjects was 64.50 \(\pm\) 10.66%.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & & **Trial 1** & **Trial 2** & **Trial 3** & **Trial 4** & **Trial 5** & **Avg.** \\ \hline \multirow{8}{*}{**Session 1**} & \%MI & 50 & 44.57 & 43.48 & 42.39 & 43.48 & 44.78 \\  & \%Att & 47.83 & 56.52 & 53.26 & 53.26 & 50 & 52.17 \\  & \%Commands & 59.00 & 54.00 & 53.00 & 53.00 & 53.00 & 54.40 \\  & Acc. commands & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\  & TPR & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\  & FP & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\  & FP/min & 2.31 & 2.31 & 2.31 & 2.31 & 2.31 & 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results, first two sessions of subject S2. Trials in close-loop control.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & & **Trial 1** & **Trial 2** & **Trial 3** & **Trial 4** & **Trial 5** & **Avg.** \\ \hline \multirow{8}{*}{**Session 1**} & \%MI & 64.13 & 50.00 & 48.91 & 61.96 & 56.52 & 56.30 \\  & \%Att & 51.09 & 58.70 & 53.26 & 47.83 & 55.43 & 53.26 \\  & \%Commands & 63.00 & 78.00 & 62.00 & 60.00 & 83.00 & 69.20 \\  & Acc. commands & 50.00 & 50.00 & 50.00 & 0.00 & 50.00 & 40.00 \\  & TPR & 100.00 & 100.00 & 100.00 & 0.00 & 100.00 & 80.00 \\  & FP & 1.00 & 0.00 & 1.00 & 0.00 & 0.00 & 0.40 \\  & FP/min & 2.31 & 0.00 & 2.31 & 0.00 & 0.00 & 0.92 \\ \hline \hline \multirow{8}{*}{**Session 2**} & \%MI & 61.96 & 52.17 & 50.00 & 60.87 & 57.61 & 56.52 \\  & \%Att & 64.13 & 46.74 & 61.96 & 60.87 & 57.61 & 58.26 \\  & \%Commands & 60.00 & 57.00 & 63.00 & 57.00 & 69.00 & 61.20 \\  & Acc. commands & 75.00 & 75.00 & 75.00 & 50.00 & 66.67 & 68.33 \\  & TPR & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\  & FP & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\  & FP/min & 2.31 & 2.31 & 2.31 & 2.31 & 2.31 & 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results, subject S1. Trials in close-loop control.
## 3 Reviews of UPC System and Three Applications
### OpenPose  
Next, we review _OpenPose_. It has been developed by researchers at Carnegie Mellon University and is an popular open-source software for real-time human pose estimation [3].  
Figure 1: Overview of UPC system.  
It extracts the feature points, called _keypoints_, of the human body in the given image using _Convolutional Neural Network_ (_CNN_). The _keypoints_ represent the important joints in a human body, the contours of eyes, lips in the face, fingertips, and joints in the hands and feet. Using the _keypoints_, the shapes of a body, face, hands, and feet can be described. Since it has been developed based on CNN, the CPU time is very long when computed on a conventional PC.  
_OpenPose_ is used in our group for developing the _exercise and performance learning assistant system_ (_EPLAS_) to assist practicing exercises or learning performances by themselves at home [19]. _EPLAS_ offers video content of _Yoga_ poses by instructors whose performances should be followed by users. During the practice, it automatically takes photos of important scenes of the user. Then, it extracts the keypoints of the human body using _OpenPose_ to rate the poses in the photos by comparing the coordinates of them between the user and the instructor.
## 4 Simulation Experiments and Optimization
### Simulation Experiments
Table 8 shows the prediction results of forward kinematics, reflecting the terminal pose obtained by giving six joint angles, which is represented as \([\theta_{1},\theta_{2},\theta_{3},\theta_{4},\theta_{5},\theta_{6}]\to[X,Y,Z,A,B,C]\). Table 9 shows the prediction results of inverse kinematics, reflecting the six joint angles obtained by the given position and orientation, which is represented as \([X,Y,Z,A,B,C]\to[\theta_{1},\theta_{2},\theta_{3},\theta_{4},\theta_{5}, \theta_{6}]\), where \([X,Y,Z]\) represents position (first three columns) and \([A,B,C]\) represents orientation (last three columns). The Nesterov momentum included in the Nadam optimizer is capable of producing a better gradient update than classical momentum (Adam). Take advantage of this and train the model with RMSProp (Root Mean Square Prop algorithm, which can adjust the running distance of the model in this direction according to each parameter to accommodate different depths.) containing Nesterov momentum. It can produce a significant effect of consistent dimensions in the training process along the gradient direction, the next accuracy is predicted in advance and accelerating gradient descent learning. As can be seen from the results in Figures 17 and 18, Tables 8 and 9, the Nadam optimizer is used to optimize the processing results of DNN, and more accurate solutions can be obtained. It can be shown that this method can accelerate the convergence rate of trajectory prediction error, improve the accuracy of trajectory prediction, and realize the process of deep learning. DNN can be used to enhance robots' ability to carry out human commands, it is particularly good at acquiring representations of linguistic expressions, often requiring training them on large data sets that include robot movements, verbal descriptions, and information about different environments. Therefore, efficient prediction and real-time motion control of robot kinematics can be realized.
## 5 Results and Discussion
### Image Enhancement Results
#### 5.1.5. False Color Composite (FCC)  
As dimensionality reduction and image-enhancement techniques, PCA and ICA transformations generate information-dense bands ideal for identifying the spectral features of minerals or rock units within the study area. Although principal component analysis (PCA) or minimum noise fraction (MNF) can compress and separate overlapping information, the lithological information we focus on often concentrates on the later components after performing PCA or MNF transformation. Therefore, practical applications, by means of combining different components obtained by diverse methods, can yield better results than using a single transformation method. The combined ICA-PCA technique demonstrates higher noise suppression capabilities compared to using ICA or PCA separately. Using PCA4 and ICA2 in FCC PCA4-PCA4-ICA2 in RGB (Figure 10a) provides excellent identification of the mantle peridotite, where it separates the boundaries of mineralized  
Figure 8: Lithological discrimination using OLI principal component analysis of **(a)** PCA5-PCA4-PCA2; **(b)** PCA6-PCA4-PCA2; **(c)** PCA2-PCA4-PCA5; and **(d)** ICA1-ICA7-ICA6 in RGB.  
lithological units in blue. Additionally, the OLI RGB combination of ICA2-MNF5-PCA4 demonstrated mantle peridotite in purple-red in color (Figure 10b).
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 4 Result and Discussion
### Overlap Pooling Experiment  
To verify the effect of overlap pooling, the pooling kernels in the VGG16 were changed to \(2\times 2\), \(3\times 3\), \(4\times 4\), and \(5\times 5\).  
In Figure 11, it can be seen that the \(3\times 3\) overlap pooling kernel has the highest accuracy on the two datasets. The main reason is that the \(3\times 3\) overlap pooling kernel has a greater ability to eliminate the redundancy of image feature information than the \(2\times 2\) pooling kernel while retaining useful information. At the same time, a larger kernel may reduce information redundancy while causing more useful information to be lost in the pooling process, thereby affecting the overall performance of the network.
## 2 Materials and Methods
### Characterization Techniques  
The microstructure of the MS/CID/ZrO\({}_{2}\) samples was observed using a scanning electron microscope (SEM, Regulus 8100, Hitachi, Tokyo, Japan). The functional groups of MS/CID/ZrO\({}_{2}\) were analyzed using a Fourier transform infrared spectrometer (FT-IR, Nicolet Si 50, Nicolet Instrument, Madison, WI, USA). The samples before and after adsorption of U were analyzed by X-ray photoelectron spectroscopy (XPS) to study its adsorption mechanism. X-ray diffraction (XRD) analysis of MS/CID/ZrO\({}_{2}\) was carried out using an X-ray diffractometer. The XRD patterns were identified by power XRD using a D/max 2500 PC (Rigaku, Tokyo, Japan) with Cu k\(\alpha\) radiation (\(\lambda\) = 0.15406 nm).
## 4 Proposed Joint Active/Reactive Power Dispatch Model
### Constraints  
The objective function (26) should be minimized subjected to the following equality and inequality constraints, besides the operation constraints of DGs discussed in Section 2:  
Power flow constraints:  
\[P_{G,n}^{h}-P_{D,n}^{h}=|V_{n}^{h}|\sum\limits_{m=1}^{NBus}|V_{m}^{h}||Y_{nm}| cos\ (\Theta_{n}^{h}-\Theta_{m}^{h}-\varphi_{nm}) \tag{32}\]  
\[Q_{G,n}^{h}-Q_{D,m}^{h}=|V_{n}^{h}|\sum\limits_{m=1}^{NBus}|V_{m}^{h}||Y_{nm}| sin(\Theta_{n}^{h}-\Theta_{m}^{h}-\varphi_{nm}) \tag{33}\]  
Bus Voltage magnitude:  
\[V_{min}\leqslant|V_{n}^{h}|\leqslant V_{max} \tag{34}\]  
Limits of generation capacity of DGs and Disco:  
\[P_{Disco}^{min}\leqslant P_{Disco}^{h}\leqslant P_{Disco}^{max} \tag{35}\]\[P_{DC,i}^{min}\leqslant P_{DC,i}^{h}\leqslant P_{DC,i}^{max} \tag{36}\]  
Limit of reactive power provision by Disco:  
\[Q_{Disco}^{h,min}\leqslant Q_{Disco}^{h}\leqslant Q_{Disco}^{h,max} \tag{37}\]  
Limits of the reduction in the initial scheduled active power of DCs:  
\[|\Delta P_{DC,i}^{Adj,h}|\leqslant x_{DC,i}^{max}.P_{DC,i}^{ini,h} \tag{38}\]  
Limit of transformer tap:  
\[Tap_{min}\leqslant Tap^{h}\leqslant Tap_{max} \tag{39}\]  
Thus, the voltage of substation is calculated as:  
\[V_{sub}=1+Tap^{h}*Tap\ ratio \tag{40}\]  
Limit of steps of capacitors:  
\[CStep_{i}^{min}\leqslant CStep_{i}^{h}\leqslant CStep_{i}^{max} \tag{41}\]  
The proposed model for joint active/reactive power market settlement in distribution systems is basically a non-convex MINLP optimization problem, due to the presence of the binary variables and the discrete nature of OLTCs and Sh.Cs, as well as the nonlinear power flow constraints. The solution of non-convex MINLP problems is very challenging, because they contain both the integer variables and non-convex functions in the objective or the constraints. Therefore, even when the integer variables are relaxed to be continuous ones, the feasible area is usually non-convex, resulting in many local optimums. DICOPT is a program for solving MINLP problems. This program is based on the extensions of the outer-approximation algorithm for the equality relaxation strategy. The MINLP algorithm inside DICOPT solves a sequence of nonlinear programming (NLP) and Mixed-Integer Programming (MIP) sub-problems.  
The structure and time horizon of the proposed joint active and reactive power dispatch model for competitive electricity markets in distribution systems are illustrated in Figures 4 and 5 respectively.
## 4 Discussion  
In this article, we take the fracture as the main controlling factor of production productivity, and use the imaging logging data to evaluate the production productivity of the fractured gas reservoirs in metamorphic rocks. Since there are more fracture-related factors controlling the production productivity, this part focuses on the other factors of fracture on the production productivity control.  
(1) Angle between the natural fracture strike and the direction of the present maximum principal stresses  
According to previous research results, when the angle between the natural fracture strike and the direction of the present maximum principal stress is basically the same or the angle is smaller, the fracture effectiveness and permeability are better, and the production capacity is relatively higher. Combined with the actual data, the angles between the fracture strikes and the present-day maximum principal stress direction in the wells of high, medium, and low production areas were statistically analyzed and compared with the new method of production capacity prediction, and it was found that the correlation between the three was consistent (Figure 9).  
(2) The degree of fracture filling  
The effect of the degree of filling of a fracture occurs during or late in the formation of the fracture, when the generated sediment plugs the pore seams formed earlier, narrowing or blocking the fracture channel. The higher the degree of filling, the poorer the permeability of the fracture and the relatively low capacity. The filling degree of fractures is reflected according to the percentage of unfilled seams in the wall core data. Combined with the actual production data, by comparing with the new method of capacity prediction, it is found that the correlation between the three is consistent (Table 3).  
Figure 8: Production Capacity Evaluation and Processing Results of D-DST2 and G Wells.
# Toughening Behavior of Carbon/Epoxy Laminates Interleaved by PSF/PVDF Composite Nanofibers
## 3 Results
### Toughening Mechanisms  
Figure 5 shows the force-displacement curves for the reference and modified samples under DCB loadings, and Table 3 presents the results. As seen, the PSF and PVDF did not affect the slope of the linear loading phase before crack propagation. An interesting phenomenon is observed while the crack propagates. In the reference laminate, a high number of short rises and falls of the force is registered, unlike the modified laminates, especially the PSF- and PVDF/PSF-modified ones, where a lower number of variations is registered (see the orange ovals in the figures). In nanomodified samples, the force rises after a drop up to about 6N, which is 15% of the maximum load.  
Figure 7 presents the SEM micrographs of the fractured surfaces of the reference and nanomodified laminates. As seen in Figure 7a, the surface of fractured neat epoxy is smooth, a sign of a brittle type of fracture; instead, for the other samples, images show a different situation and various toughening mechanisms.  
Due to high porosity and specific surface area, PSF mats were easily impregnated by the epoxy. On the other hand, owing to PSF's high viscosity and fast curing of resin, the diffusion of PSF in the epoxy was more difficult. Subsequently, the nanofibers were dissolved in the resin. By continuing the curing process, PSF started a phase separation from the epoxy and changed to spherical particles (see Figure 7b). When the crack tip reached these particles, it was restrained and deflected from its path, requiring higher energy to propagate.