esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 4 Design of Network Coding Routing Protocol
### Network Coding Design
#### 4.3.1 SLT Encoder  
Suppose \(X_{1},\cdots,X_{K}\) are data packets in a block. Then the the encoded packet \(Y_{i}\) can be obtained in Algorithm 3.  
```
1:procedure\(SLT\_Encoder()\)
2:\(d_{n}\gets GetDegree(\rho(d))\);
3:\(S_{i}\leftarrow SelectPackets(d_{n})\);
4:\(Y_{i}\gets XOR(S_{i})\);
5: Return \(Y_{i}\);
6:endprocedure
```  
**Algorithm 3** SLT Encoder  
Here the function of \(GetDegree()\) is to randomly select a degree \(d_{n}\) from degree probability distribution \(\rho(d)\). \(SelectPackets()\) means to randomly select \(d_{n}\) data packets from \(X_{1},\cdots,X_{K}\) and the output encoded packet \(Y_{i}\) is the XOR of the \(K\) packets. Here \(d_{n}\) is the number of data packets participated in an output packet. The key problem is the design of degree probability distribution \(\rho(d)\). [PERSON] et al. proposed the robust soliton distribution which is used for a large number of data packets. This distribution is not appropriate for UWSNs because the number of sent packets is small and the bit length of code vector is relatively long. To solve the above problems, we design a simplified degree distribution \(\rho(d)\) based on the simple variant of tornado (SVT) codes proposed by [PERSON] et al. [31]. In our designed \(\rho(d)\), we set the maximum degree \(d_{max}\) and the probability of \(d_{n}>d_{max}\) equals zero. So each code vector can be expressed as the combination of data packet IDs and the degree \(d_{n}\). This operation can reduce the length of code vector significantly. Meanwhile, we need to consider the tradeoff between decoding successful probability and the irrelevance of different packets. An example of degree distribution is presented in Section 5.1 and several degree distributions are also tested for analysis and comparison.
###### Abstract  
Urbanization has led to rapid growth in energy consumption and CO2 emissions in the building sector. Building operation emissions (BCEs) are a major part of emissions in the building life cycle. Existing studies have attempted to estimate fine-scale BCEs using remote sensing data. However, there is still a lack of research on estimating long-term BCEs by integrating multi-source remote sensing data and applications in different regions. We selected the Beijing-Tianjin-Hebei (BTH) urban agglomeration and the National Capital Region of Japan (NCRJ) as research areas for this study. We also built multiple linear regression (MLR) models between prefecture-level BCEs and multi-source remote sensing data. The prefecture-level BCEs were downscaled to grid scale at a 1 km\({}^{2}\) resolution. The estimation results verify the method's difference and accuracy at different development stages. The multi-scale BCEs showed a continuous growth trend in the BTH urban agglomeration and a significant downward trend in the NCRJ. The decrease in energy intensity and population density were the main factors contributing to the negative growth of BCEs, whereas GDP per capita and urban expansion significantly promoted it. Through our methods and analyses, we contribute to the study of estimating greenhouse gas emissions with remote sensing and exploring the environmental impact of urban growth.  
building operation; carbon emissions; urban growth; multi-scale; comparative study Article
## 4 Results & Discussion
### Figure of Merits  
Performance metrics are availed to ascertain the performance of a given image quality assessment IQA method. The research in the sphere of IQA mainly deploys three benchmark metrics, i.e., Spearman rank order correlation coefficient (SROCC), Kendall rank order correlation coefficient (KROCC) and Pearson linear correlation coefficient (PLCC). PLCC mainly accounts for the linear relationship which exists among variables that are continuous in nature [34]. It can be expressed as:  
\[PLCC=\frac{\sum_{i=1}^{I}(n_{i}-\hat{n})(m_{i}-\hat{m})}{\sqrt{\sum_{i=1}^{I}(n _{i}-\hat{n})}\sqrt{\sum_{i=1}^{I}(m_{i}-\hat{m})}}, \tag{4}\]  
where \(n\) and \(m\) are sampling points gathered from the predicted and ideal score values, \(I\) is the total number of images and \(\hat{n}\) and \(\hat{m}\) are utilized as the means of two samples.  
On the other hand, SROCC describes the monotonic relationship between two variables and it is expressed in mathematical terms as:  
\[SROCC=1-\frac{6\sum_{i=1}^{I}(a_{i}-q_{i})^{2}}{I(I^{2}-1)}. \tag{5}\]  
\(I\) is the total number of images, while \(q_{i}\) and \(a_{i}\) are the predicted and actual quality values.  
KROCC is utilized to find out the ordinal relationship among two variables and it is illustrated as:  
\[KROCC=\frac{U-Y}{\sqrt{(s_{[0]}-s_{1})(s_{0}-s_{2})}} \tag{6}\]  
\[s_{0}=\frac{s(s-1)}{2} \tag{7}\]  
\[s_{1}=\sum_{x}\frac{v_{x}(v_{x}-1)}{2} \tag{8}\]  
\[s_{2}=\sum_{l}\frac{h_{l}(h_{l}-1)}{2}. \tag{9}\]  
In the above equations, \(U\) and \(Y\) illustrates the number of concordant and discordant pairs, respectively. \(v_{x}\) represents the samples from the \(x\) group and \(h_{l}\) represents the samples which combine with the second quantity from the \(l\) group.
## 3 Results and Discussion
### GFET Based Flexible Receiver
#### 3.5.1 Graphite Flexible Antenna  
We designed and fabricated a graphite based microstrip patch antenna at 2.4 GHz on flexible polyimide substrate (Figure 6a). The performance of this antenna is similar but relatively more lossy than copper based patch antenna. This antenna is designed towards the goal of achieving an all graphene/carbon flexible RF receiver system. The antenna was designed using a graphite sheet since it is more conductive than monolayer graphene at microwave frequencies [20]. Graphite sheets offer superior flexibility and thermal conductivity compared to copper, albeit lower electrical conductivity. It is expected that flexible planar antennas based on graphite sheets are compatible with roll to roll  
Figure 5: Schematic of a Flexible graphene based RF system.  
manufacturing for large-area or high volume applications. In addition, the DC conductivity of intercalated graphite antennas can be tuned for the design of sensors and reconfigurable RF systems [21]. The antenna simulation results from a 3D electromagnetic solver, (CST) and experimental measurements are shown in Figure 6b. The antenna dimensions are width \(W=39.11\) mm and length \(L=30.95\) mm. The far field radiation pattern is shown in Figure 6c.  
To investigate the flexibility of the antenna, convex bending tests were carried out as shown in Figure 7a. The antenna was placed in the middle of a test vice fixture and mechanically bent (see Figure 7b). The S\({}_{11}\) was measured _in situ_ for each bending radius (see Figure 7c). The resonant frequency changes are relatively small down to \(\sim\)30 mm bending radius. This result offers new opportunity for carbon/graphene RF electronics that can be integrated with flexible substrates, including bendable GHz electronics and antennas.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 5 Evaluation
### Evaluation of Buffer Hardware
#### 5.2.2 Timing and Power Evaluation  
In addition to the hardware resource usage, timing performance and power consumption were also evaluated. The results of the post place-and-route design were obtained from the vendor tools (Xilinx Vivado) and presented in Figure 12, where (a) is the \(f_{max}\) with different configurations in Table 3 and (b) is the power consumption. Compared with SWIM, the D-SWIM design slightly decreases the \(f_{max}\). This is because the multi-stage logics for dynamic controlling lengthen the critical path. We selected the proper pipeline stage as described in Section 4.2, while trading the \(f_{max}\) and resource overhead in further pipelining. We observed that \(N_{blk}\) is a significant factor to the \(f_{max}\). In Configurations 1-6, the worst \(f_{max}\) is 329.5 MHz with Configuration 4 in which \(N_{blk}\) is 32.  
Figure 12 presents the power consumption of buffer modules with SWIM and D-SWIM design methods. Each power bar is composed of two portions that represent static power (lighter part) and dynamic power (darker part). Apparently, SWIM and D-SWIM have identical static power with all configurations, but D-SWIM performs better in the dynamic power aspect. This is mainly due to fewer LBs (\(N_{LB}\)) used in the D-SWIM case, which leads to less BRAM usage. For example, with Configuration 5, SWIM consumes 2.1\(\times\) BRAMs of that in D-SWIM; thus, the dynamic power  
\begin{table}
\begin{tabular}{c|c c c|c c|c c c|c c|c c c} \hline \multirow{2}{*}{**Config.**} & \multicolumn{2}{c|}{**Parameters**} & \multicolumn{4}{c|}{**D-SWIM**} & \multicolumn{4}{c|}{**SWIM**} & \multicolumn{4}{c}{**SWIM-2 pass**} \\ \cline{2-13}  & \(N_{line}\) & \(H\) & \(N_{blk}\) & \(N_{LB}\) & **LUT** & **REG** & **BRAM** & \(N_{LB}\) & **LUT** & **REG** & **BRAM** & \(N_{LB}\) & **LUT** & **REG** & **BRAM** \\ \hline
1 & 630 & 3 & 8 & 3 & 1950 (1.45) & 2140 (1.35) & 6 (0.75) & 4 & 1346 & 1589 & 8 & 4 & 1346 & 1589 & 8 \\
2 & 630 & 3 & 16 & 3 & 3427 (1.33) & 3553 (1.29) & 9 (0.56) & 8 & 3895 & 5127 & 32 & 4 & 2650 & 26  
is increased proportionally. Compared with SWIM, the D-SWIM buffer saves up to 45.7% power consumption in the case of Configuration 6.
## 2 Related Works and Problem Formulation
### Single Image Super-Resolution and Channel Estimation  
Single image super-resolution is an image processing algorithm in the field of computer vision which aims to upgrade a low-resolution image to a high-resolution image, the result of single image super-resolution is shown in Figure 3. The mathematical model of single image super-resolution can be seen as a matrix imputation problem, using known data point value to infer surrounding unknown data point value.  
As shown in Figure 4, in channel estimation assisted by pilots, the CFR at the pilot position is pre-estimated, which is the known data point value in single image super resolution situation. However, the CFR at the data position is unknown and needs to be inferred using the CFR obtained from pilot positions, which is the surrounding unknown data point value in single image super resolution situation. The time-frequency grid of the SISO communication link channel response is modeled as an image known only at the pilot locations. Consider the channel response in the pilot positions as a low-resolution image and the estimated channel response as the proposed high-resolution image. In the process of promoting resolution, the whole time-frequency response channel is recovered by the transmitted pilot. Given that pilot-aided channel estimation has a similar problem formulation with image super-resolution, it is reasonable to introduce computer vision tools to solve the problem of underwater acoustic channel estimation.  
Figure 3: Upgrade of low-resolution image to high-resolution image.  
Figure 2: Structure of OCDM system in UWA channel.  
The image super-resolution mathematical expression is shown by Equation (9):  
\[\mathbf{L_{h}}=f(\mathbf{L_{i}};\theta) \tag{9}\]  
where \(\mathbf{L_{i}}\) is the blurry low-resolution image, \(\mathbf{L_{h}}\) is the restored high-resolution image, \(f\) is the super-resolution model, and its parameter is denoted as \(\theta\).  
Similarly, the relationship between estimated CFR and genius CFR can be concluded as Equation (10):  
\[\widehat{\mathbf{H}}=f\Big{(}\widehat{\mathbf{H_{p}}};\theta\Big{)} \tag{10}\]  
where \(\widehat{\mathbf{H_{p}}}\) represents the CFR at the pilot within a transmission frame and \(\widehat{\mathbf{H}}\) represents the complete CFR of the transmission frame.
## 5 Analysis for Torsional Stiffness Properties of Leaf Spring Torsional Vibration Damper
### Influence of Clamping Groove Arc Radius on Torsional Stiffness Properties  
The clamping groove, crucial in the interaction between the leaf spring group and the hub, necessitates an investigation into its design parameters for the damper's torsional stiffness properties. Altering solely the arc radius of the clamping groove while maintaining other parameters reveals its impact on the damper's torsional stiffness (Figures 10 and 11). The figures illustrate a notable variance among the damper's torsional stiffness curves at varying radii, with this variance amplifying as the radius expands. Notably, a smaller arc radius renders a less obvious nonlinear property in the torsional stiffness curve, almost remaining linear at 60 mm. However, with increasing radii, the nonlinear characteristics become more conspicuous. Around 160 mm, this nonlinearity peaks; further increments to 180 mm produce marginal changes in the curve. In Figure 10, the torsional stiffness curve under varied clamping groove arc radii starts displaying nonlinear changes at approximately 0.6\({}^{\circ}\) of torsion angle. This illustrates the considerable influence of the clamping groove arc radius on both the torsional stiffness and its nonlinear traits. Extremely small radii result in nearly linear stiffness curves, losing the benefit of nonlinear effects. Conversely, beyond a certain arc radius, the damper's torsional stiffness nearly plateaus. Determining an optimal clamping groove arc radius based on torsional stiffness requirements is pivotal for future leaf spring damper design.  
Figure 9: Comparison of hub torque results between mechanical model and finite element analysis model.
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 2 Related Work
### The Communication Requirements of VPP
Large-scale network congestion will cause the interaction between the VPP and the power system to fail, which may lead to serious power system security risks. [PERSON] et al. evaluated the performance of a communication system in which a VPP participates in auxiliary services based on traffic data captured during real-time VPP operation [17]. [PERSON] et al. analyzed the effects of communication latency, link noise, and topology changes on VPP system operation and economic benefits [18]. [PERSON] et al. proposed a method to minimize the probability of communication failures by monitoring the communication QoS parameters (latency, packet loss, bandwidth, etc.) during VPP operation [19]. However, the communication requirements of different VPP services vary greatly, and the types of VPP service terminals (smart sensors, actuators, meters, power line communication devices, etc.) and access methods (230 MHz wireless, power line communication, Bluetooth, B5G/6G, etc.) also differ greatly. It is necessary to accurately monitor the network status to ensure the effective development of various auxiliary services.
# The Potential for the Use of Hydrogen Storage in Energy Cooperatives
## 3 Materials and Methods  
### Sampling Energy Cooperatives for Simulation Purposes  
The first stage of the analyses and simulations was to conduct an optimal selection of generating sources so to filter out the customers characterized above to obtain prosumers with full energy independence after the first quarter. Depending on the capacity of the source, these prosumers are subject to a discount factor of 0.8 or 0.7. The simulations allowed us to obtain optimal levels of generating source capacity and storage capacity for each prosumer. These prosumers were then grouped into energy cooperatives and presented in Table 2. The average energy demand for the entire cooperative EC1, EC2, EC3 and EC4 is, respectively: 33.305 MWh/year, 39.535 MWh/year, 38.496 MWh/year and 39.227 MWh/year. In Figure 1 below the Table 2, the aggregated data for EC_1, EC_2, EC_3, EC_4 are visualized according to the RES sources adopted.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{P} & \multirow{2}{*}{MYD} & \multicolumn{3}{c}{MIX} & \multicolumn{3}{c}{PV} & \multicolumn{3}{c}{WIND} \\ \cline{3-10}  & & PV & WIND & MSC & PV & WIND & MSC & PV & WIND & MSC \\ \cline{2-10}  & MWh & kW & kW & MWh & kW & kW & MWh & kW & kW & MWh \\ \hline
1 & 11.565 & 3.357 & 9.659 & 1.856 & 15.528 & 0 & 4.176 & 0 & 13.656 & 2.095 \\ \hline
2 & 12.674 & 4.737 & 9.468 & 1.942 & 16.643 & 0 & 4.239 & 0 & 15.166 & 2.293 \\ \hline
3 & 9.067 & 2.176 & 7.967 & 1.444 & 12.137 & 0 & 3.389 & 0 & 10.626 & 1.666 \\ \hline \multicolumn{10}{c}{**EC2**} \\ \multirow{2}{*}{P} & \multirow{2}{*}{MYD} & \multicolumn{3}{c}{MIX} & \multicolumn{3}{c}{PV} & \multicolumn{3}{c}{WIND} \\ \cline{3-10}  & & PV & WIND & MSC & PV & WIND & MSC & PV & WIND & MSC \\ \cline{2-10}  & MWh & kW & kW & MWh & kW & kW & MWh & kW & kW & MWh \\ \hline
1 & 7.278 & 2.113 & 6.079 & 1.168 & 9.772 & 0 & 2.628 & 0 & 8.594 & 1.318 \\ \hline
2 & 17.370 & 7.868 & 11.924 & 2.505 & 22.950 & 0 & 5.133 & 0 & 21.117 & 3.467 \\ \hline
3 & 14.887 & 5.831 & 11.167 & 2.812 & 19.364 & 0 & 4.665 & 0 & 18.291 & 3.589 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario-based characteristics of the selection of the type and capacity of generating sources and the storage capacity in EC1–EC4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Designation of the Cooperative**} & \multicolumn{1}{c}{**\#**} & \multicolumn{1}{c}{**Tariff Group**} & \multicolumn{1}{c}{**Energy Consumption [MWh/Y]**} \\ \hline \multirow{4}{*}{EC1} & 1 & C11 & 11.565 \\  & 2 & C12a & 12.674 \\  & 3 & C12b & 9.067 \\ \hline \multirow{4}{*}{EC2} & 1 & C11 & 7.278 \\  & 2 & C12a & 17.370 \\  & 3 & C12b & 14.887 \\ \hline \multirow{4}{*}{EC3} & 1 & C11 & 9.883 \\  & 2 & C12a & 1  
Figure 1: Aggregated data visualization for EC_1, EC_2, EC_3, EC_4, depending on the RES sources adopted. Based on the data from Table 2.
# Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
## 3 Trajectory Planning of the Par4 Parallel Robot
### Space-Path Coordinate Transform
#### 3.3.2 Piecewise Motion Planning Based on the Lame Curve
\right]^{\!\!T}\text{, Equation (\ref{eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq: eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq  
Since \(s^{\prime}(t)=s^{\prime}(\varphi)\varphi^{\prime}(t)\), and the motion of the Lame curve shown in Figure 3 is in the rising state, i.e., \(s^{\prime}(\varphi)>0\), then \(\varphi^{\prime}(t)\) can be calculated as Equation (25):  
\[\varphi^{\prime}(t)=\frac{s^{\prime}(t)}{s^{\prime}(\varphi)}=\frac{s^{\prime }(t)}{\sqrt{\left[u^{\prime}(\varphi)\right]^{2}+\left[w^{\prime}(\varphi) \right]^{2}}}. \tag{25}\]  
Thus, \(\varphi^{\prime\prime}(t)\) can be calculated by performing the derivation of Equation (25) with respect to the variable t.  
The acceleration converted to the robot end space coordinate OXYZ is  
\[\left[ \begin{array}{cc}a_{xi}&a_{yi}&a_{zi}\end{array} \right]^{T}=\mathbf{Q}_{ 1}\!\left[ \begin{array}{cc}a_{ui}&a_{xi}\end{array} \right]^{T}=\mathbf{Q}_{1}\! \left[ \begin{array}{cc}u^{\prime\prime}(t_{i})&w^{\prime\prime}(t_{i})\end{array}  \right]^{T}. \tag{26}\]
3. CF Segment Motion Planning  
When the displacement is taken as \((H_{a}-f+l_{BC})\leq s(t)<(H_{a}-f+l_{BC}+B_{a}-2e)\), the motion of the CF segment is a plane horizontal movement, and in the \(\mathbf{\mathrm{U}O}\mathbf{\mathrm{W}}\) coordinate system, there are the following equations: \(\Delta u_{i}=\Delta l_{i}=s(t_{i})-s(t_{i-1})\), \(\Delta w_{i}=0\), \(v_{ui}=s^{\prime}(t_{i})\), \(v_{wi}=0\), \(a_{ui}=s^{\prime\prime}(t_{i})\), and \(a_{wi}=0\). Taking the coordinate of point C in the \(\mathbf{\mathrm{U}O}\mathbf{\mathrm{W}}\) coordinate system as \((u_{C},w_{C})\), then, \(u_{i}=u_{i-1}+\Delta u_{i}=u_{i-1}+\Delta l_{i}\), and \(w_{i}=w_{i-1}=w_{C}\). The planning converted to the robot end space OXYZ is  
\[\left\{ \begin{array}{ccc}\left[\begin{array}{ccc}x_{i}&y_{i}&z_{i}\end{array} \right]^{T}=\mathbf{Q}_{1}\!\left[\begin{array}{cc}u_{i}&w_{C}\end{array} \right]^{T}+\mathbf{Q}_{2}\\ \left[\begin{array}{ccc}v_{xi}&v_{yi}&v_{zi}\end{array}\right]^{T}=\mathbf{Q}_{1 }\!\left[\begin{array}{cc}s^{\prime}(t_{i})&0\end{array}\right]^{T}\\ \left[\begin{array}{ccc}a_{xi}&a_{yi}&a_{zi}\end{array}\right]^{T}=\mathbf{Q}_{1 }\!\left[\begin{array}{cc}s^{\prime\prime}(t_{i})&0\end{array}\right]^{T} \end{array} \right.. \tag{27}\]
4. Other Segment Motion Planning  
Due to the left-right symmetry of the gate-shaped trajectory, the FG segment's motion is identical to the BC segment's, and the GH segment's motion to the AB segment's, so they will be not elaborated here. According to the motion planning described above, the interpolation displacement, velocity, and acceleration of the gate-shaped trajectory at each moment can be obtained based on the Lame curve, and then the rotation angle of each driving unit can be obtained through the inverse kinematic equation analysis, so that the desired trajectory planning can be achieved.
## 3 ROR: Reliable Opportunistic Routing for CRSN
### VCG Formation
Any node that receives the VCG formation packet performs a VCG eligibility operation, which is similar to the one done during the route formation phase, as presented in Section 3.2 above; the only difference being that the first condition, \(d_{sk}^{lmr}\geq d_{sk}\geq d_{sk}^{snd}\), which ensures that eligible nodes that will return a one for the VCG-based eligibility operation have to be located in a position between the broadcasting node and the next downstream LMR node. This condition is considered alongside all other three criteria, as in Equation (11). Once a node gets a one for its VCG eligibility operation, the node automatically becomes a member of the virtual cluster of the LMR node's address indicated in the VCG payload, as illustrated in the Figure 4. All eligible nodes then read the corresponding channel  
Figure 4: VCG organization after the VCG formation stage. VCG, virtual contention group; PU, primary user. assignments and cache them in their routing tables, after which, the node will switch its transceiver to the channel, Cr0, in preparation for the data routing phase. \[El_{veg}=\left\{ \begin{aligned} & 1,\begin{cases}d_{sk}^{lmr}\geq d_{sk}\geq d_{sk}^{ snd}\\ & E_{th}\geq E_{th}^{min}\\ & clc=clc_{rreq}\\ & R(t)\geq R_{t}\\ \end{aligned} \right.\end{aligned} \tag{11}\]  
The next LMR node that receives the VCG formation packet inserts the necessary VCG formation information with respect to its location and broadcasts the VCG formation packet. After this operation, it also switches its transceiver to its Cr. In this manner, all the VCGs will be formed, until the VCG formation packet gets to the source node. It should be noted that due to the relatively high density deployment mode characteristic to WSN networks, there will also be a reasonably high correlation of channel quality among VCG proximity nodes [39]. The LMR node periodically geocasts a still-alive packet to its member nodes via its operating channel to indicate that the VCG region is still active. At the end of the initialization phase, the network will look as illustrated in Figure 5.