esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Kinematic GNSS Measurements
#### 2.2.1 Crossing Point Comparison  
For this purpose, the data are converted into the appropriate format so that they can be read and processed by the Linux-based program Generic Mapping Tool (GMT). The crossing points are determined as a linear interpolation between two different years. It should be noted that these are calculated values and not measured values. However, the advantage is that the position coordinates match exactly, and local unevenness has less influence. Since there are no large jumps on the Greenland ice sheet, the method is well suited. The x2 sys package included in GMT is used for the calculation. The obtained crossing points are transformed to UTM coordinates in order not to neglect the curvature of the Earth. Then, the crossing points are assigned to the continuous track of the 2015 expedition by using the closest data point in each case. This method is sufficiently accurate over the entire track of nearly 700 km. In each case, the distances within a UTM zone are searched. The altitude differences previously calculated with GMT can now be visualized and analyzed.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 6 Performance Evaluations
### Simulation Results
#### 6.3.4 Average End-to-End Delay Evaluation  
A further series of experiments are carried out in this part with the purpose of evaluating the suggested technique in terms of end-to-end latency. Comparisons are made  
Figure 11: Influence of increasing average traffic rate on deadline miss ratio under the second scenario.  
between the proposed method and SEA [18], EGTDA [19], and ACO-based techniques in [22], and EARP [24] for the situations indicated above at varying traffic rates.  
1. Average end-to-end delay evaluation under the first scenario  
The primary purpose of this simulation experiment is to analyze average end-to-end latency with average traffic rate in the first scenario. This experiment varied traffic from three to nine packets per second. Figure 12 depicts end-to-end delay changes with traffic volume. The previous studies [18; 19; 22; 24], using the suggested routing algorithm, had the lowest end-to-end latency compared to [28]'s PSO-based routing technique for urgent data collecting. It picks forwarding nodes that can transmit the packet on a more stable connection, reducing packet loss and retransmission, which reduces delivery latency. In addition, it picks the cluster heads that can transmit the urgent data packet on time. The relaying delay is used to pick the next forwarder from these cluster heads, resulting in quick packet delivery and reduced delivery latency. In the scenario described in [28], using the PSO-based routing technique, packets are unable to bypass the lossy links, which results in an increase in the end-to-end latency as a result of the retransmission of lost packets. Because it does not enable real-time communications, the transmission of data will be delayed as a result.  
As the suggested route design takes zone priority into account, the proposed method has the shortest end-to-end latency. As was just said, the delivery time may be reduced by increasing the average distance that separates the mobile sink from the cluster heads that have urgent data messages and increasing the movement delay. In addition, the suggested method for selecting cluster heads takes into account the average distance travelled inside the cluster as well as the quality of the links between clusters. This helps reduce the end-to-end latency, as was discussed  
Average end-to-end delay evaluation under the second scenario  
This simulation experiment studies how average end-to-end latency varies with traffic rate in the second scenario. This simulation experiment varied from three to nine packets per second. Figure 13 demonstrates how end-to-end latency varies with traffic volume. This figure shows that the recommended strategy reduces end-to-end latency. The suggested solution outperformed other algorithms in the first scenario for the reasons indicated above. In addition, the proposed approach attempts to construct data routes less likely to be cut off due to environmental reasons, leading to delivery of data packets on more reliable links and thus less delivery delay.
## 3 Approximate CrowbMATCH Based on Matching Theory
```
1:Input:\(\{I_{n},a_{n},P_{n}\}_{\forall n\in\mathcal{N}}\), \(\mathcal{N}\), \(\mathcal{M}\), \(\{N^{max}_{m},\gamma_{m},\delta_{m},P_{m}\}_{\forall m\in\mathcal{M}}\)
2:Output: Matching Results \(\Lambda\)
3:Initialization:
4: Initialize the unmatched set as \(\mathcal{N}^{*}\leftarrow\mathcal{N}\) and the alternative crowdsourcers of crowdworkers as \(\mathcal{M}_{n}\leftarrow\{m\mid m\in\mathcal{M}\}\) for all \(n\in\mathcal{N}\)
5:while\(\mathcal{N}^{*}\
eq\varnothing\) and \(\exists n\in\mathcal{N}^{*}:\mathcal{M}_{n}\
eq\varnothing\)do
6:for\(n\in\mathcal{N}^{*}\)do
7: Crowdworker \(n\) selects their preferred crowdsourcers and sends an invitation based on Equation (5). 8:endfor
9:for\(m\in\mathcal{M}\)do
10:if\(|\Lambda(m)|\leq N^{max}_{m}\wedge\) (\(m\) received invitation) then
11: Crowdsourcer \(m\) selects the favorite crowdworkers to pair from the ones who sent a pair invitation based on Equation (6). 12: Delete \(m\) from the alternative crowdsourcers of the crowdworkers that sent an invitation but were not accepted. 13:endif
14:endfor
15:endwhile
```  
**Algorithm 1** Approximate CROWDMATCH Algorithm  
The Approximate CROWDMATCH algorithm is based on a set of fundamental principles. Initially, all the crowdworkers participate in the competition for crowdsourcing tasks independently, without being paired with specific crowdsourcers. This approach provides flexibility, allowing the crowdworkers to be matched with any available crowdsourcer offering incentives. Unmatched crowdworkers take the initiative by extending invitations to pair with their preferred crowdsourcers. When a crowdsourcer possesses available rewards to accommodate crowdworkers, it consistently selects preferred crowdworkers for pairing from those who extended invitations. In instances where a crowdworker's invitation to a crowdsourcer is not accepted, it signals that the crowdsourcer should consider a more favored crowdworker to maximize its utility, leading to the rejection of the initial pairing. Consequently, the rejected crowdworkers from a crowdsourcer strategically refrain from extending further invitations to disinterested crowdsourcers, preventing the wastage of time. The convergence of the Approximate CROWDMATCH algorithm occurs when either all crowdsourcers have recruited the maximum number of crowdworkers based on the available rewards they offer or when all the crowdworkers have been successfully paired.
## 6 Performance Evaluations
### Strategy of Jumping over Failed Nodes by Extending the Transmission Range  
In order to still be able to transmit its DRN data successfully despite the lack of connectivity to its immediate neighbor, the DRN can increase its transmission power and double its range in order to reach the DRN that follows the current one. If multiple consecutive links are lost, then the DRN can increase its transmission range appropriately in order to bypass the broken links. This process can happen until the transmission power is maximal. If even with maximal transmission power the broken links cannot be bypassed, then the message is dropped. In the protocol, this maximal DRN transmission power is represented by a network variable named MAX_JUMP_FACTOR which holds the maximum number of broken links or \"disabled nodes\" that a DRN transmission can bypass.
# Optimal Model-Free Mean-Square Consensus for Multi-Agents with Markov Switching Topology
## 6 Discussion  
The existing research mainly focuses on the analysis of tracking errors. For example, in Ref. [29], when the topology changes at the time \(k+1\) and the agent cannot obtain the information of the neighbor, the \(e_{i}(k)\) of the previous time was used to estimate; that is, \(e_{i}(k+1)=e_{i}(k)\). In order to construct a time-invariant control object, Ref. [30] defined an augmented LNTE. The joint communication graph served as the foundation for the time-invariant augmented LNTE. In this paper, the randomness of \(e_{i}(k)\) is directly analyzed. Since the analysis of random variables is difficult, we introduce the concept of mean-square consensus and prove that it is convergent in mean-square. The consensus error in Ref. [31] is bounded by the tracking error, and we prove a new relation that, when the mean-square of the tracking error converges, the consensus error also converges. According to the approach, the expected value function form is employed here to deal with the situation that the variable topology will make the neighbor change, which is different from Refs. [14; 15; 16; 17; 18; 19; 20].
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 3 The IACO-Based Spectrum Allocation Method
### Differential Evolution Process in IACO  
DE generate new individuals by cooperation and competition among populations to guide the direction for optimization searching. It is operated by three stochastic steps: mutation and crossover as well as selection. The DE process in IACO with \(l\)-dimensional space, and \(N\) is the number of individuals. In IACO, we use DE to generate a new individual to optimize the convergence speed. Specifically, we use the following steps to get the new individual \(m_{i}\) of generation \(i\). Firstly, mutate the population to get the variant \(v_{i}\):  
\[v_{i,j}=x_{r1,j}+K\times(x_{r2,j}-x_{r3,j}) \tag{5}\]  
where \(r1,r2,r3\in\{1,2,\ldots,N\}\) are three individuals randomly selected from current population, the mutation factor \(K\) is a real parameter in \((0,1]\). We choose \(DE/rand/1/bin\) to mutation the population instead of \(DE/best/1/bin\)[19]. Next, crossover is applied to generate vector \(u_{i}\) by crossing element \(x_{i}\) and variant \(v_{i}\) with a certain probability, which is used to increase the diversity of the trial vector \(u_{i}\), as shown:  
\[u_{i,j}=\begin{cases}v_{i,j},&rand(j)\leq CR\text{ or }j=rnbr(i)\\ x_{i,j},&\text{otherwise}\end{cases} \tag{6}\]  
where \(rand(i)\) is a uniform random number in \([0,1]\), \(CR\) means crossover probability in \([0,1]\) and \(rnbr(i)\) is a random quantity in \(1,2,\ldots,D\). Finally, the new individual \(m_{i}\) is selected from \(u_{i}\) and \(x_{i}\) based on the greedy thought, and the selection operation can be expressed as:  
\[m_{i}=\begin{cases}u_{i},&f(u_{i})<f(x_{i})\\ x_{i},&\text{otherwise}\end{cases} \tag{7}\]  
Figure 2: Transform Process.
# Estimating Specific Patient Organ Dose for Chest CT Examinations with Monte Carlo Method
## 3 Results  
### Voxelized Chest Models of Patients  
In this study, a total of 18 chest models for the patients were voxelized. Their \(WED\), organ volumes, and displayed \(CTDI_{vol}\) undergoing chest CT examinations are listed in Table 1. As shown in Table 1, the \(WED\), \(CTDI_{vol}\), lung volume, heart volume, and breast volume (glandular breast tissue for both breasts) of females vary significantly, especially for the breast volume. As expected, the \(CTDI_{vol}\) generally increases with the \(WED\). Through  
Figure 1: An example within the chest CT image sequences used to create voxelized patient model, where different colors represent the heart wall, muscle, bone, fat, and skin segmented by the 3D Slicer software: (**a,b**) are the transverse and sagittal axial image of the patient, respectively.  
the linear regression analysis and Pearson correlation analysis, it was found that there was a strong correlation (R\({}^{2}\) = 0.88, \(p\) < 0.001) between the \(CTDI_{vol}\) and the \(WED\); however, the correlations between the lung, heart, or breast volume and the \(WED\) were all weak (R\({}^{2}\)\(\leq\) 0.15, \(p\) > 0.05). The results indicate that it is hard to estimate the volumes of lung, heart, or breast simply by the \(WED\) for each person, and it implies that, for a more accurate estimation of the organ volume for each person, the voxelized model is necessary.
## 4 Finite Element Numerical Simulation of Casing Damage Well
### Finite Element Simulation of Casing Damage Well under Non-Uniform In-Situ Stress
#### 4.1.2 Von Mises Stress Analysis of Casing  
In order to study the effect of different loads on the casing damage, it is taken as \(\sigma_{\rm h}=30\) MPa, \(\sigma_{\rm H}=20\)-40 MPa. For the yield failure of materials, the Von Mises yield criterion is generally used. Therefore, when analyzing casing failure, the Von Mises stress of the inner wall is extracted for research. Figures 7-9 show the Von Mises stress cloud and curve of the inner wall of the casing simulated at 20 MPa, 25 MPa, 30 MPa, 35 MPa, and 40 MPa for the X1 and X2 wells, with \(0^{\circ}\) representing the A0 point of the mechanical model and \(90^{\circ}\) representing the B0 point.  
When \(\sigma_{\rm H}\) = 20-30 MPa, the maximum Von mises stress is at point A0; the larger \(\sigma_{\rm H}\) is, the smaller is the maximum Von Mises stress. When \(\sigma_{\rm H}\approx\sigma_{\rm h}\), the maximum Von mises stress is the smallest, and the Von Mises stress at each point on the inner wall of the casing is equal; When \(\sigma_{\rm H}\) = 30-40 MPa, the maximum Von mises stress is at point B0 and the larger \(\sigma_{\rm H}\) is, the greater is the maximum Von Mises stress.  
For the X1 well, when \(\sigma_{\rm H}\) is 20 MPa and 40 MPa, the maximum Von Mises stress of the casing will exceeds its yield limit and deforms, which is consistent with the casing damage situation in this area. For the X2 well, when \(\sigma_{\rm H}\) is between 20 and 40 MPa, the Von Mises stress on the inner wall of the casing will not exceed its yield limit, meaning there will be no casing damage. The actual casing damage occurred in the well, indicating that the well is affected by other additional load factors.
# Shape Modelling and Volume Optimisation of Salt Caverns for Energy Storage
## 2 Three-Dimensional Numerical Model and Assessment Criteria
### Constitutive Models and Mechanical Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Shape**} & **Initial Maximum** & **Initial Minimum** & **Height** & **Initial H/D\({}_{\text{max}}\)** & **“Smoothing”** & **Cavern Volume** \\  & **Diameter D\({}_{\text{max}}\) (m)** & **Diameter D\({}_{\text{min}}\) (m)** & **H (m)** & **Ratio** & **Radius (m)** & **(m\({}^{3}\))** \\ \hline \multirow{4}{*}{Cylindrical} & \multirow{4}{*}{60} & \multirow{4}{*}{60} & \multirow{4}{*}{120} & \multirow{4}{*}{2/1} & 0 & 338,324 \\ \cline{6-6}  & & & & & 10 & 330,199 \\ \cline{6-6}  & & & & & 20 & 309,994 \\ \cline{6-6}  & & & & & 30 & 281,517 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Enlarged \\ top/bottom \\ \end{tabular} } & \multirow{4}{*}{75} & \multirow{4}{*}{60} & \multirow{4}{*}{120} & \multirow{4}{*}{8/5} & 0 & 430,444 \\ \cline{6-6}  & & & & & 10 & 420,915 \\ \cline{6-6}  & & & & & 20 & 397,020 \\ \cline{6-6}  & & & & & 30 & 362,430 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Enlarged \\ top/bottom \\ \end{tabular} } & \multirow{4}{*}{90} & \multirow{4}{*}{60} & \multirow{4}{*}{120} & \multirow{4}{*}{4/3} & 0 & 536,427 \\ \cline{6-6}  & & & & & 10 & 525,090 \\ \cline{6-6}  & & & & & 20 & 496,673 \\ \cline{6-6}  & & & & & 30 & 454,459 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Enlarged \\ top/bottom \\ \end{tabular} } & \multirow{4}{*}{120} & \multirow{4}{*}{60} & \multirow{4}{*}{120} & \multirow{4}{*}{1/1} & 0 & 790,878 \\ \cline{6-6}  & & & & & 10 & 774,674 \\ \cline{6-6}  & & & & & 20 & 732,414 \\ \cline{6-6}  & & & & & 30 & 669,064 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Shapes and dimensions of simulated salt caverns. addition, creep parameters for rock salt (determined in laboratory tests and calculated based on the Norton Power law) were n = 5.0 and A = 1.08\(\cdot\)10\({}^{-45}\) Pa\({}^{-5.0}\) s\({}^{-1}\).
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 4 System Model
For\(E_{b}/N_{0}=min\ E_{b}/N_{0}\): step \(E_{b}/N_{0}\): \(max\ E_{b}/N_{0}\)
2. For\(j=1:N_{f}\)
3. Get\(K_{info}\) information bits for frame \(j\)
4. if code type = Polar
5. Concatenate CRC bits generated using \(g_{CRC}\) to the information bits
6. Construct polar code using GA technique with optimum design value of the coefficient of variation C\({}_{p}\) using Equation (16), where \(K=K_{info}+CRC\) bits, and max \(N=2^{n}<N^{\prime}\)
7. Encode using Polar code for (\(K_{info}+CRC\)) bits and generate \(N\) encoded bits
8. if\(N^{\prime}\
eq N\)
9. Use a puncturing pattern
10. end if
11. elseif code type = LDPC
12. Encode using LDPC code for \(K_{info}\) bits and generate \(N\)/ encoded bits
13. end if
14. Modulate the frame size \(N^{\prime}\) bits for the transmission signal form
15. Pass the modulated data through the channel
16. Demodulate the received signal to the frame size \(N^{\prime}\) bits
17. if code type = Polar
18. if\(N^{\prime}\
eq N\)
19. Remove the puncturing bits
20. end if
21. Decode using FA-SCL decoder with max. list size \(L_{max}\) for \(N\) bits to get \(K\) bits
22. Remove\(CRC\) bits to get \(K_{info}\) decoded bits
23. elseif code type = LDPC
24. Decode using LDPC decoder with max. iteration \(i_{max}\) for \(N\) bits to get \(K_{info}\) bits
25. end if
26. end For
27. Calculate the performance metrics like BER, encoding/decoding latency and overall throughput
28. end For
```  
**Algorithm 1** Polar / LDPC-based simulated transceiver system  
It is worth mentioning that since Release 16 (released in 2017) Third Generation Partnership Project (3 GPP) has been interested in using satellites in the development of 5G networks by considering and integrating SatCom technology in standardization [41]. However, 3 GPP uses other non-3 GPP standards for the communications between the ground station and satellites. Such non-3 GPP standards are the DVB-S2, DVB-S2X which were considered in this paper. This makes our work a potential candidate to be considered in the satellite communication aspect of 3 GPP future standards.
## 6 Limitations and Challenges in Passkeys
### Challenges
#### 6.1.2. Usability Challenges  
Passkeys need to be usable from a user's perspective to be viable and a feasible alternative or supplement to existing authentication methods [45]. Passkeys must, therefore, be accessible and adaptable (learning curve) [17]. Hence, to achieve satisfaction, user psychology must be considered.  
From current academic research, we see the following key usability challenges:  
* Lack of user awareness [6, 42, 69, 71, 82];
* Setup complexity [11, 17, 27, 28, 24, 85];
* Lack of portability and platform dependency concerns [6, 69, 87, 88];
* Account delegation [42, 88] ;
* Account recovery [6, 88, 84, 79, 86, 13, 6, 13, 78, 6];
* Account revocation [83];
* Adoption in enterprise environments [69, 83, 85].
# Rainfall Similarity Search Based on Deep Learning by Using Precipitation Images
## 3 Rainfall Similarity Search Based on NDCG-IPSO
### Feature Extraction
#### 3.1.3 Precipitation Center  
Flood processes are largely influenced by the precipitation center. When the precipitation center is located upstream of the watershed, the long distance to the watershed section leads to a long lag time for the flood peak and presents a short and plump flood process. Meanwhile, when the precipitation center is located downstream, the short distance to the watershed section will make it a short lag time for the flood peak and form a sharp and thin flood hydrograph. Hence, it is important to take the precipitation center as a major feature in the precipitation image similarity research. Combined with the actual precipitation situation and image similarity retrieval requirements, take the block with the maximum precipitation in the precipitation image after being divided into blocks as the precipitation center.  
Let \(P_{(i,j)}\) be the maximum precipitation of the blocks in the precipitation image, then the precipitation center \(C_{(i,j)}\) could be the block at the \(i^{\text{th}}\) row and the \(j^{\text{th}}\) column. Let \(C_{(i_{1},j_{1})}\) and \(C_{(i_{2},j_{2})}\) be the precipitation centers of two precipitation images. The Euclidean distance \(D_{c}\) is defined as the difference between the two precipitation centers, which can be calculated as follows:  
\[D_{C}=\sqrt{(i_{1}-i_{2})^{2}+(j_{1}-j_{2})^{2}} \tag{8}\]
## 3 Methods
### Data Collection and Preprocessing  
#### 3.1.1 Data Collection  
The core of this study lies in exploring the relationship between power production and carbon emissions in coal-fired power generation enterprises. For this purpose, we meticulously selected 25 representative coal-fired power generation enterprises, and collected and compiled their production and carbon emission data, forming a dataset with 372 detailed data records. Each data record targets an individual power generation unit, ensuring the precision and detail of the research. In terms of data feature selection, this study comprehensively considered variables directly related to power production, including unit type, installed capacity, cooling method, and product type, while focusing on collecting key data such as power generation, power supply, and heating supply. Additionally, the model's predicted output variable, the unit's carbon emission, was included. These selected features not only cover the main aspects of power production, but also show significant differences from the characteristics required by the commonly used carbon emission estimation method in China--the emission factor method. Our goal is to simplify the carbon emission estimation process by constructing an electricity-carbon relationship model for power generation enterprises, directly using power-related data to estimate carbon emissions, thereby effectively reducing the cumbersome calculations and extensive manpower and material resources required by the traditional emission factor method, making subsequent verification and validation work more efficient.  
Through such a data collection and feature selection strategy, this study aims to break through the limitations of traditional carbon emission estimation methods and provide a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Optimal Value** \\ \hline learning\_rate & 0.2678385997976869 \\ max\_depth & 5 \\ n\_estimators & 223 \\ subsample & 0.9106501334447481 \\ \hline \hline \end{tabular}
\end{table}
Table 4: XGBoost optimal hyperparameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Optimal Value** \\ \hline learning\_rate & 0.27153172823922805 \\ max\_depth & 2 \\ n\_estimators & 994 \\ num\_leaves & 14 \\ min\_child\_samples & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: LightGBM optimal hyperparameters.  
new, more direct, and efficient approach to carbon emission estimation. Furthermore, the application potential of this method is not limited to coal-fired power generation enterprises; it can be extended to a wider range of energy production and industrial production fields in the future, contributing to low-carbon development and environmental protection in China and globally.