esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Propagation and Formulations  
From the model depicted in Figure 1, we designed a new propagation diagram, as shown in Figure 2. The coupled relays feed back data to the BS about their energy capacities. The BS decides which relay has more energy to forward the superimposed signal, while the remaining relay has less energy to maintain EH. Two main phases take place: EH and data transmission (DT). To illustrate, Figure 2 depicts two transmission blocks: an odd transmission block \(T^{(odd)}\) and an even transmission block \(T^{(even)}\). Each transmission block \(T^{(\theta)}\) for \(\theta=\left\{odd,even\right\}\) is separated into two equal time slots. The first time slot \(T_{1}^{(odd)}=T^{(odd)}\Big{/}2\) in odd transmission block \(T^{(odd)}\) is used by the BS to transmit wireless energy and superimposed information to coupled relays \(\mathcal{R}=\left\{R_{1},R_{2}\right\}\). In terms of the SWIPT technique with a PS protocol, during the first time slot \(T_{1}^{(odd)}\), a fraction \(\lambda\) of the power domain \(P_{S}\) is used for EH while the remaining fraction \((1-\lambda)\) of the power domain \(P_{S}\) is used to superimpose data from the BS. The second time slot \(T_{2}^{(odd)}=T^{(odd)}\Big{/}2\) in odd transmission block \(T^{(odd)}\) is used by the best powered relay to forward the superimposed signal to devices, while the worse powered relay applies EH from the BS, where the PS factor \(\lambda=1\). The present study assumes that relay \(R_{1}\) is selected in the odd transmission block because relay \(R_{1}\) has more energy than relay \(R_{2}\). Therefore, relay \(R_{2}\) maintains EH from the BS. Similarly, the even transmission block \(T^{(even)}\) is also separated into two equal time slots. The first time slot \(T_{1}^{(even)}=T^{(even)}\Big{/}2\) in even transmission block \(T^{(even)}\) is also used by the BS to simultaneously transmit wireless energy and the superimposed signal to the coupled relay \(\mathcal{R}=\left\{R_{1},R_{2}\right\}\). However, in the second time slot \(T_{2}^{(even)}=T^{(even)}\Big{/}2\) in even transmission block \(T^{(even)}\), the relay \(R_{2}\) is selected to forward the superimposed signal to devices instead of relay \(R_{1}\) because relay \(R_{2}\) harvested energy from the BS, where \(\lambda=1\) during \(T_{2}^{(odd)}\), and then relay \(R_{2}\) contains more energy than relay \(R_{1}\). As a result, relay \(R_{1}\) maintains EH from the BS with PS factor \(\lambda=1\) during \(T_{2}^{(even)}\). Although the network model shown in Figure 1 is complex, it requires two time slots for signals to propagate through the network as in studies [28; 29; 31]. Specifically, the present study examines how multiple devices are served simultaneously. We therefore adopt the emerging NOMA technique. The BS also superimposes all the information of devices in the same signal by sharing the spectrum. The devices may be thus served simultaneously. As a result, the massive MIMO-NOMA network in the present study are low latency.  
Figure 1: Coupled relays in a cooperative MIMO-NOMA network with the application of TAS and SWIPT.
## 3 Results
### Von Mises Stress Distribution on PDL  
Since the root geometry of our FE model was linearly symmetrical with the YZ plane and all loads that applied in this study were along to YZ plane, a similar stress distribution was observed in two buccal roots. In all hook positions, the calculated Pearson's correlation coefficient indicated a high negative relationship between the counter load and the stress  
Figure 5: Comparison between no counter load (left side; (i)) and optimal load condition of 1.22 N in Mi load (right side; (ii)). (**A**) Direction of molar intrusion in three times display, (**B**) von Mises stress distribution on the molar, (**C**) von Mises stress distribution on the cortical bone, and (**D**) von Mises stress distribution on the PDL. With no counter load, notable palatal tipping of the crown (**A**(**i**)) and excessive stress concentrations were observed on the root surface (**B**(**i**)), the cortical bone around the palatal root (**C**(**i**)), and the PDL around the root apical regions (**D**(**i**)). With optimal counter load, intrusion avoiding tipping movement was observed (**A**(**ii**)), and stress concentrations were dispersed (**B**(**ii**),**C**(**ii**),**D**(**ii**)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Regression Lines**} \\ \multicolumn{4}{c}{**Horizontal Displacement**} & \multicolumn{4}{c}{**Vertical Displacement**} \\ \hline
**Hook Position** & **Crown** & **Root** & **Crown** & **Root** \\ \hline _Co_ & \(y(x)=-1.21\ x+1.58\) & \(y(x)=4.43\times 10^{-1}\ x\) & \(y(x)=-3.76\times 10^{-3}\ x\) & \(y(x)=9.78\times 10^{-2}\ x\) \\  & & \(-7.22\times 10^{-1}\) & \(+4.78\times 10^{-1}\) * & \(+2.70\times 10^{-1}\) * \\ _Mi_ & \(y(x)=-1.21\ x+1.48\) & \(y(x)=4.45\times 10^{-1}\ x\) & \(y(x)=-3.69\times 10^{-3}\ x\) & \(y(x)=1.16\times 10^{-1}\ x\) \\  & & \(-7.35\times 10^{-1}\) & \(+6.68\times 10^{-1}\) * & \(+4.41\times 10^{-1}\) * \\ _Ap_ & \(y(x)=-1.21\ x+1.38\) & \(y(x)=4.47\times 10^{-1}\)x & \(y(x)=-3.69\times 10^{-3}\ x\) & \(y(x)=1.15\times 10^{-1}\ x\) \\  & & \(-7.43\times 10^{-1}\) & \(+6.97\times 10^{-1}\) * & \(+4.79\times 10^{-1}\) * \\ \hline \hline \end{tabular}  
* Hook positions indicate as follows: _Co_; coronal, _Mi_; middle, _Ap_; apical. The asterisk represents significant reference among groups (\(p<0.05\)).  
\end{table}
Table 3: Regression lines and results of statistical analysis in horizontal and vertical displacements.  
value on PDL-P and PDL-B (r = \(-\)0.9190 and \(p\)-value = 0 for \(Co\), \(-\)0.7817 and \(p\)-value = 0.003 for \(Mi\), and \(-\)0.7551 and \(p\)-value = 0.0049 in PDL-B; r = \(-\)0.9986 and \(p\)-value = 0 for \(Co\), \(-\)0.9961 and \(p\)-value = 0 for \(Mi\), and \(-\)0.9962 and \(p\)-value = 0 in for \(Ap\) in PDL-P), but the stress on PDL-P decreased drawing a curve, and that of PDL-B showed a parabolic-like change regardless of the hook positions (Figure 6). In both PDL-P and PDL-B, \(Co\) showed lower stress value than the other hook position.  
In all analyses, no error messages were reported in output files.
## 3 Results  
In this study, 40 participants were divided into two groups: case group (\(n\) = 20, ALS patients) and control group (\(n\) = 20, healthy subjects. The mean ages for the case and control groups were 52.7 and 50.6 years, respectively. Among ALS patients, 45% were female, and 30% had bulbar onset ALS. Additionally, the average disease duration was 44 (20-55) months, and the ALSFRS-R score was 22.6 (8.8). In comparison to the control group, the case group exhibited significantly lower values in BMI (\(p\) = 0.0002), FFM, TBW, PhA, and R/H (all \(p\) < 0.0001), and Xc/H (\(p\) = 0.0017) (Table 1).  
The evaluation of tolerance ellipses shows that the vectors of ALS patients are shifted to the right, and all patients are placed out of ellipses at 95% for both men and women, which means lower PhA and cell mass (Figure 1). The mean impedance vector was statistically different between cases and controls (\(p\) < 0.0001), with case vectors shifting significatively to the right (Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **Cases (\(n\) = 20)** & **Controls (\(n\) = 20)** & \(p\) \\ \hline Sex, Male/Female & 11/9 & 11/9 & - \\ Age, years & 52.7 (11.6) & 50.3 (10.0) & - \\ Body Mass Index, kg/m\({}^{2}\) & 21.9 (2.9) & 27.2 (5.1) & **0.0002** \\ Fat Mass, Kg & 19.2 (5.8) & 22.1 (9.6) & 0.2616 \\ Fat Free Mass, Kg & 37.9 (6.0) & 51.7 (11.0) & **<0.0001** \\ Total Body Water, Kg & 27.1 (4.3) & 37.4 (8.3) & **<0.0001** \\ Phase Angle, \({}^{\circ}\) & 3.4 (0.9) & 6.2 (1.0) & **<0.0001** \\ R/H, Ohm/m & 455.2 (76.4) & 322.4 (73.6) & **<0.0001** \\ Xc/H, Ohm/m & 26.7 (6.6) & 34.2 (7.3) & **0.0017** \\ \hline \end{tabular} Values expressed as mean (standard deviation). R/H, Resistance/Height. Xc/H, Reactance/Height.  
\end{table}
Table 1: Anthroometric data, body composition, and bioelectrical parameters of participants.  
Figure 1: Bivariate vector positioning and tolerance ellipses of ALS patients (cases) and healthy subjects (controls). Impedance vectors plotted in the tolerance ellipses of 50%, 75%, and 95%, according to the reference population. R/H, Resistance/Height. Xc/H, Reactance/Height.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Multi-Access Edge Computing (MAEC)
#### 2.1.4 Connected Vehicles  
Intelligent connected vehicles can communicate with each other on the road through the network. Leveraging the MAEC environment along the road can integrate the intelligent vehicles and enable two-way communication between them. One vehicle can share information with surrounding vehicles. Additionally, MAEC can enable scalable, reliable, and distributed environments synchronized with local sensors [26].
## 4 Semantic Understanding of Object Layer
### Spatial Structure Semantics
#### 4.3.3 Rule-Partition-Based Method  
The rule-partition-based method is similar to grid division. Firstly, the image is divided into regular (generally square) image blocks. Then, each image block is used as the processing unit to describe the spatial structure features of each image block [86]. This method is especially suitable for the detection of the spatial structure semantics of complex objects such as residential areas and aircraft. It does not focus on the detailed structure of objects in the image block but only on the statistical properties of the overall structure [87, 88]. The deficiency of this method lies in how to determine the suitable partition of image blocks, especially when it cannot locate the object boundary accurately [89].
## 3 Results
### Omission Estimate for the RF25/AO25 Change Map  
All polygons with change probabilities of 25%-50% switched from no-change polygons to candidate change polygons in the RF25 map. This altered the population of no-change polygons from which the omission sample was drawn. Of the original 5346 polygons in the RF50 omission sample, only 96 had a reassignment from no-change to change in the RF25 analysis. While the change threshold was cut in half, only 1.8% of the polygons from the original omission sample were relabeled. The distribution of polygons by change probability was heavily skewed towards low probabilities with 74.5% of all polygons having lower than a 5% probability of being change (Figure 7) and, thus, most of the random sample to estimate omission came from these low probability bins. However, the 1.8% that did switch accounted for 39% of the previously identified omission errors and was remapped as change in the RF25 and subsequent AO25 maps. The remaining 58 omission polygons previously assessed as change had an area of 8.2 ha which included 5.6 ha (68%) of development, 1.5 ha (18%) of forestry and 1.1 ha (14%) of natural change.  
The size distribution of the missed change polygons was different from the overall distribution of polygon sizes. The average omitted change polygon was 0.14 ha compared to the overall average polygon checked for omission that was 0.58 ha. Therefore the estimated missed change was the ratio of observed change area in the Omission sample divided by the overall area of the Omission sample (Equation (2)). Only 8.2 ha of omitted change were found among 3046 ha of observations lowering the omission rate from 0.45% to 0.27%. This rate was multiplied by the remaining area of predicted non-change for a total omission estimate of 701 ha. This also raised the total estimated change to 5222 ha with a mapped change area of 4521 ha. The final APA was 86.6% (4521 ha / (4,521 ha + 701 ha)). A simple areal accuracy rate for the final change map was 99.73% which assumes only the predicted 701 ha of omission error was incorrectly mapped. That also yielded a final annual change rate of 0.55% or a cumulative change of 1.66% for the three year period.
## 2 Formation and Influence of Tunnel Geotemperature  
### Development Characteristics of High Geotemperature in Tunnels  
The formation of high geotemperature in tunnels is mainly determined by heat source and heat conduction channel. Primarily, heat sources can be divided into four categories [16; 17; 18]: (1) Hot spots are formed in the upper dome of mantle asthenosphere, i.e., deep crustal thermal anomaly; (2) Heat dissipation during cooling of underground magma chambers or  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tunnels** & **Depth/m** & **Length/km** & **Temperature/\({}^{\circ}\)C** \\ \hline Wuzhishan Tunnel & - & 4.45 & 37 \\ Daiyunshan Tunnel & 400 & 1.562 & 38.8 \\ Qinling Tunnel of Xikang Railway & 1645 & 18.448 & 40 \\ Fudang Tunnel & 90 & 7.517 & 44 \\ Luogu hydropower Station diversion tunnel & - & 1.779 & 47 \\ SaGa tunnel & - & 4.941 & 51.6 \\ Old Village Tunnel of Yummeng Railway & 150 & 4.46 & 52 \\ Baishui hydropower station diversion & - & 2.314 & 62 \\ tunnel & 700 & 7.21 & 64 \\ Dagar Mountain Tunnel & 104 & 3.974 & 65.2 \\ Givosiga Tunnel & 380 & 7.215 & 76 \\ Luquan lead factory diversion tunnel & 33 & 2.865 & 76.4 \\ Padang Hill Tunnel & 33 & 2.865 & 76.4 \\ Niangyong hydropower station diversion & 640 & 0.295 & 78 \\ Hunnel & 300 & 17.8 & 105 \\ Qir Khattar hydropower station diversion & & & \\ tunnel & 1720 & 15.64 & 110 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The other tunnels with high geotemperature in China.  
high-temperature intrusions in the crust or surface ejecta, i.e., waste heat of magmatic rock; (3) Frictional heat from fracture activity in the Earth crust; (4) Rock radioactive elements decay to generate heat.  
At present, the heat source of high geotemperature in-tunnel engineering mainly comes from the thermal anomalies in the deep crust and the decay of radioactive elements in the rock. And the main heat conduction channels of the deep circulation of the deep crustal thermal anomaly include faults and fractures. According to the length, scale, and influence scope of the channel, it can be divided into a main channel and a secondary channel. Wherein, the main channels are generally deep and large faults, while the secondary channels are contact zone, secondary fault, structural fracture, dissolution fracture, karst pipeline, etc.
## 3 Preliminaries
### DIRECT Algorithm
#### 3.1.2 Division and Sampling Strategy  
Sampling starts with points \(\mathbf{c}\pm\delta\mathbf{e}_{j},\,j=1,\ldots,m\), where \(\mathbf{c}\) is the center of the hypercube, \(\delta\) is one-third of the hypercube's side length, and \(\mathbf{e}_{j}\) is the \(j\)th unit vector. The strategy in [28] is employed for hypercube partitioning, which prioritizes larger rectangles containing better function values, thereby enhancing the attractiveness of exploring areas with favorable function values.  
Specifically, we use the following rule for subdividing a hypercube. Let  
\[\omega_{j}=\min\{f_{i}\big{(}A^{-1}(\mathbf{c}+\delta\mathbf{e}_{j})+\mathbf{x}^{\text{low }}\big{)},f_{i}\big{(}A^{-1}(\mathbf{c}-\delta\mathbf{e}_{j})+\mathbf{x}^{\text{low}}\big{)}\}\]  
denote the best function value sampled along dimension \(j\). We start by splitting along the dimension with the smallest \(\omega\) value. Once this is completed, the rectangle containing \(\mathbf{c}\) is divided into thirds along the dimension with the next smallest \(\omega\) value. This process continues until all long dimensions have been split. In order to guarantee that rectangles shrink in all dimensions, we divide them only along the long dimensions. Algorithm 1 presents this rectangle division procedure.  
```
Step 1 Determine the set \(I\) of dimensions with the longest side lengths. Set \(\delta\) to one-third of this longest side length. Step 2 Evaluate the function at points \(\mathbf{c}\pm\delta\mathbf{e}_{i}\) for all \(i\in I\), where \(\mathbf{e}_{i}\) is the \(i\)th unit vector, and \(\mathbf{c}\) represents the center of the rectangle. Step 3 Divide the rectangle containing \(\mathbf{c}\) into thirds along the dimensions in \(I\), beginning with the dimension having the lowest value of \(\omega_{i}=\min\{f\big{(}A^{-1}(\mathbf{c}+\delta\mathbf{e}_{i})+x^{\text{low}}\big{)},f \big{(}A^{-1}(\mathbf{c}-\delta\mathbf{e}_{i})+x^{\text{low}}\big{)}\}\), and proceeding to the dimension with the next smallest \(\omega_{i}\) until all dimensions \(i\in I\) have been split.
```  
**Algorithm 1** Procedure for dividing rectangles.
# Maintaining the Sense of Agency in Semi-Autonomous Robot Conferencing
## 7 Discussion  
### Visual Guidance and Intention  
The experiment indicated the probability that the semi-autonomous robot conferencing system can guide the subjects' eye movement without making them notice the visual guidance. In the auto- and semi-conditions, the degree of improvement of the gaze rate from that seen in the tele condition means the degree of success in the visual guidance. The subjects looked at the face of the experimenters without visual guidance when they began to speak, because looking at the face of a person while they are speaking is a natural social behavior. The effect of visual guidance was remarkable with regard to the backchannel response. The gaze rate was greater than 70% in both the auto- and semi-conditions, which is a 40% increase from the tele condition. In the auto condition, although the subjects' eye movement was guided to the green circle when there was visual guidance, it was clear for the subjects that the head movement of the robot was autonomous. In contrast, in the semi condition, one half of the subjects did not notice the visual guidance and the autonomous head movement of the robot despite being informed that the robot will change its line of view autonomously. Furthermore, the other half of the subjects noticed the visual guidance only when the robot moved based on Rule 4, and they noticed it only one or two times, even though it was conducted seven times in the conversation. Thus, no subject noticed the visual guidance when the robot moved based on Rules 1-3.  
The opinions of the subjects appear to support these results. According to the interview regarding the intention items, almost all of the subjects thought that the following were natural social behaviors: looking at a questioner when the subjects answer (Rule 1), looking at a partner who is speaking (Rule 2), and taking a glance at a partner who gives a backchannel response to the subjects' speech (Rule 3). However, several subjects said that taking a glance at a partner who gives a backchannel response after another answerer spoke (Rule 4) is not necessarily a natural social behavior. Therefore, it is considered that when the behavior is acceptable for the operator, the teleoperation of an operator is easily guided to the autonomous behavior of a robot, unconsciously.  
In the semi condition, most subjects felt that the head movement was their intentions, even after comparing the recorded videos of the tele- and semi-conditions. As described above, visual guidance increased the gaze rate by the same degree in the auto- and semi-conditions. However, in the auto condition, the subjects felt that the head movement of the robot was different from their intentions. Thus, it is considered that mixing the visual guidance corresponding to the autonomous behavior into teleoperation makes the operator postdict that the behavior is their intention.
## 3 Methods
### Disentangled Representation  
#### 3.2.1 Content Encoder  
The content encoder is implemented as an improved four-layer U-Net, consisting of an encoder and a decoder. The specific structure is depicted in Figure 2a. Each layer in the encoder comprises two consecutive \(3\times 3\) convolution layers for feature extraction, alongside a batch normalization (BN) layer and a rectified linear unit (ReLU) function. The layers are linked by a \(2\times 2\) max-pooling layer. The decoder adopts a symmetrical structure to the encoder, with each layer employing the same structure as the encoder. The distinction lies in the use of an upsampling layer to restore the image resolution. The skip connection in the decoder stage facilitates the fusion of low-level features and high-level features at various resolutions. To extract multi-scale information, we designed an MSFE at the skip connection, the specific implementation of which is described in a later section. Following the \(1\times 1\) convolution layer, softmax activation function, and differentiable rounding operator, the content encoder produces an 8-dimensional vector.
# Wave Diffraction from a Bicone Conjoined with an Open-Ended Conical Cavity
## 3 Fields Representation
In order to satisfy the Meixner condition at the vertex of the cone, we consider the sets of the positive roots \(\{\mu_{n}\}_{n=1}^{\infty}\), \(\{v_{n}\}_{n=1}^{\infty}\), and \(\{z_{n}\}_{n=1}^{\infty}\) that are arranged in growing sequences; the set of the positive roots of the transcendental equation \(R_{v_{n}-1/2}(\cos\gamma_{2})=0\) starts from \(v_{1}=1/2\) and \(v_{n}\
eq n-1/2\) for \(n=2,3,4,\dots\). Without loss of generality, the bicone \(Q\) is excited by the TM-modes produced by the vertical dipole that is located at the axis of symmetry \(\{0\leq r<\infty;\,\,\theta=\pi\}\) in the conical region \(D_{1}\). For further convenience, we represent the incident field in the form of the total field of the vertical dipole in the conical region limited by the semi-infinite conical surface \(Q_{1}\)[22]:  
\[H_{\varphi}^{(i)}=\frac{i\omega e}{\sqrt{sr}}\sum\limits_{n=1}^{\infty}A_{n} \partial_{\theta}[P_{z_{n}-1/2}(-\cos\theta)]\bigg{\{} \begin{array}{l}I_{z_{ n}}(sr_{0})K_{z_{n}}(sr),\,\,r>r_{0},\\ K_{z_{n}}(sr_{0})L_{z_{n}}(sr),\,\,r<r_{0}.\end{array}  \tag{17}\]  
Here,  
\[A_{n}=\frac{2A^{(0)}{}_{2n}P_{z_{n}-1/2}(\cos\gamma_{1})/\sqrt{sr_{0}}}{\cos( \pi z_{n})\partial_{z}[P_{z_{n}-1/2}(-\cos\gamma_{1})]},\]  
\(A^{(0)}=\pi l_{r}^{s}hZ/r_{0}\), \(0<r_{0}<a_{1}\), \(I_{r}^{s}\) is the electric current, and \(h\) is the length of the dipole. Let us represent the total magnetic field in each of the sub-regions (8) as  
\[H_{\varphi}^{(i)}(r,\theta)=\frac{i\omega e}{\sqrt{sr}}\left\{ \begin{array}{ l}H_{\varphi}^{(i)}(r,\theta)\frac{\sqrt{sr}}{i\omega e}+\sum\limits_{n=1}^{ \infty}x_{n}^{(1)}\partial_{\theta}[P_{z_{n}-1/2}(-\cos\theta)]\frac{I_{z_{n} }(sr)}{I_{z_{n}}(sa_{1})},\,\,\,(r,\theta)\in D_{1},\\ \\ \sum\limits_{n=1}^{\infty}\partial_{\theta}[P_{\mu_{n}-1/2}(-\cos\theta)]\Big{[} x_{n}^{(21)}\frac{K_{\mu_{n}}(sr)}{K_{\mu_{n}}(sa_{1})}+x_{n}^{(22)}\frac{I_{\mu_{n}}( sr)}{I_{\mu_{n}}(sa_{1})}\Big{]},\,\,\,\,(r,\theta)\in D_{2},\\ \\ \sum\limits_{n=1}^{\infty}x_{n}^{(3)}\Psi_{v_{n}-1/2}(\cos\theta)\frac{K_{v_{n}} (sr)}{K_{v_{n}}(sa_{1})},\,\,\,\,\,\,(r,\theta)\in D_{3}.\end{array} \right. \tag{18}\]  
Here, \(x_{n}^{(1)}\), \(x_{n}^{(21)}\), \(x_{n}^{(22)}\), and \(x_{n}^{(3)}\) are unknown expansion coefficients. It should be noted that according to the relations (11), (13), and (16), the representation (18) satisfies the boundary conditions (4) as well as the radiation and energy limitation conditions. In order to satisfy the Meixner condition at the aperture's edge, we search the unknown expansion coefficients in the class of sequences \(x_{n}^{(1)},\ x_{n}^{(2,1)}=O(n^{-2})\), and \(x_{n}^{(3)}=O(n^{-3/2})\) for \(n\to\infty\). The asymptotic behaviour of the unknowns \(x_{n}^{(2,2)}\) is to be determined.