esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data
### Datasets for Evaluating Smoke Detection
#### 2.6.3 Manually-Delineated Smoke Plumes  
We also manually delineated smoke plumes, based on VIIRS true-color images, to create smoke reference data for validation of the SSDA smoke detection results. To obtain high-quality smoke reference data, image subsets, instead of whole VIIRS granules, were used to select smoke plumes with relatively clear boundaries that could be clearly distinguished from clouds, bright surfaces, and dust. This subset process also confined our attention to small areas containing smoke plumes (subset areas) without considering large areas devoid of smoke (outside of the image subsets) because smoke area takes up only an extremely small portion of global area. The month of August was selected as the validation period because fire activity generally peaks in August across most of the selected fire-prone regions [47; 48], which provides more opportunities than other periods to choose smoke samples for evaluating the new SSDA algorithm. A total of 109 qualified image subsets, distributed uniformly over the fire-prone regions (Figure 2), were selected during August 2018. The delineated smoke plume extents were assigned as the smoke class while other areas inside the image subsets were assigned as the non-smoke class. Since the spatial pattern of the smoke plume shows a discontinuity in some areas due to interference from clouds or bare lands inside the plume boundary, we conservatively refined the smoke reference data by grouping these cloud or bare land pixels into the non-smoke class. For quantitative evaluation, we did not select validation samples from southeastern North America (Figure 1b) because August was not part of the typical fire season (i.e., November-April) in this region and we were not able to select enough samples. Instead, we selected two evaluation samples from this region in March, accompanied by another two big fire events in Australia and California, to qualitatively evaluate the performance of the new algorithm (see Section 4.1).  
Figure 2: Locations of the manually-delineated fire smoke plumes (red polygon) within subset regions (blue polygon) used in the reference dataset. The numbers of fire smoke plume reference data are 22, 12, 9, 29, 17, and 20 in the fire-prone regions (gray box) of Africa, Australia, Indonesia, North America, South America, and Siberia, respectively.
## 3 Experimental Results
### 2 \(\times\) 2 Array Performance
#### 3.2.2 Radiation Parameters  
To carry out the circular polarization radiation parameters measurements, we connect and program the beamformer chip to feed the patches with the same amplitude RF signal, and different phase. Figure 10 shows the normalized radiation patterns in the \(\varphi=0^{\circ}\) cut at 28 and 30 GHz for the RHCP and LHCP.  
Co-polar results present a beam-width in the range of \(53.4^{\circ}\) (27.5 GHz) to \(42.1^{\circ}\) (31 GHz) with directivity values in boresight that varies from 10.7 dBi to 12.9 dBi, respectively. Good cross-polar discrimination levels below \(-15\) dB are also achieved.  
Lastly, Figure 11 shows the axial ratio in boresight as a function of frequency for both polarizations, RHCP and LHCP. In the whole band of interest, values below 3 dB are achieved, ensuring a good circular polarization purity. These data improve when frequency increases.  
Figure 8: \(2\times 2\) antenna array configuration (**left**); the manufactured prototype (right).  
Figure 9: Mutual coupling results.  
Figure 11: Axial ratio measurements for the 2 \(\times\) 2 array normalized radiation patterns at \(\varphi\) = 0\({}^{\circ}\) for RHCP and LHCP.  
Figure 10: 2 \(\times\) 2 array normalized radiation patterns at \(\varphi\) = 0\({}^{\circ}\) for RHCP (**above**) and LHCP (**below**).
## 2 Materials and Methods  
### Data  
In this study, three ESMs that performed well in historical GPP reproduction [10] were chosen to project future GPP in China: (1) the Beijing Climate Center Climate System Model (BCC-CSM2-MR) [11], (2) the Euro-Mediterranean Centre on Climate Change coupled climate model (CMCC-CM2-SR5) [12], and (3) the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI-ESM1.2-HR) [13]. Specifically, BCC-CSM2-MR and MPI-ESM1-2-HR, out of seven ESMs, gave the best estimation of climatological GPP in China from 1980 to 2013. MPI-ESM1-2-HR performed best in characterizing the spatial structure. BCC-CSM2-MR and CMCC-CM2-SR5 best captured the response of the ecosystem to the climate [10]. The land surface models used for the three ESMs were BCC-AVIM2.0, CLM4.5, and JSBACH3.2. Major improvements or parameterizations have been made to these models in comparison with their predecessors; they make use of new scientific understanding to better simulate vegetation phenology [12; 13; 14]. These ESMs could provide not only the monthly GPP, but also the monthly surface air temperature and precipitation. In CMIP6, new SSPs were employed for climate modelling. The SSPs included five narratives describing alternative socio-economic developments, such as sustainable development, fossil-fueled development, etc. [8]. The middle of the road development--i.e., the SSP2 scenario--featured a continuation of the current fossil fuel-dominated energy mixed with intermediate challenges for both mitigation and adaptation, which resembled the historical pattern most [8]. SSP245, as the sole scenario of SSP2 implemented in CMIP6, was thus chosen to represent the most possible future world. Historical data (1980-2013) were applied to determine the ecosystem-climate relationship--i.e., correlation and multiple linear regression. Data from the BCC-CSM2-MR, CMCC-CM2-SR5, and MPI-ESM1.2-HR ESMs were output as 1.125\({}^{\circ}\)\(\times\) 1.125\({}^{\circ}\), 0.9375\({}^{\circ}\)\(\times\) 1.25\({}^{\circ}\), and 0.9375\({}^{\circ}\)\(\times\) 0.9375\({}^{\circ}\) grids, respectively. Because the grids were not uniform, they were first transformed to a 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) grid through bilinear interpolation for comparative purposes.  
A climate division map of China was applied for regional analysis. It divided the country into four climate zones--i.e., temperate continental, temperate monsoonal, subtropical-tropical monsoonal, and high-cold Tibetan Plateau, as in [PERSON] et al. (2015) and [PERSON] et al. (2016).
## 3 Adaptive Background Calibration Method
### Calibration Structure  
The error signal \(e[n]\) can be ideally reconstructed exploiting the MHT structure if the input \(x[n]\) is available and the nonlinear coefficients are precisely estimated. However, we cannot achieve perfect reconstruction even if the nonlinear parameters are known because only the \(M\)-TIADC output \(y[n]\) is available. One solution is approximating \(x[n]\) by \(y[n]\). This is reasonable in a well-designed TIADC, which is a weakly nonlinear system, because the energy of error signal \(e[n]\) is much smaller than the energy of the input signal. Thus, the reconstructed error signal is expressed as:  
\[\hat{e}[n]=\sum_{l=2}^{L}y^{l}[n]\sum_{k=0}^{M-1}T_{k}[n]\hat{c}_{k,l}[n]. \tag{16}\]  
By subtracting the reconstructed error \(\hat{e}[n]\) from the \(M\)-TIADC output \(y[n]\), the calibrated TIADC output signal is written as:  
\[y_{c}[n]=x[n]+\vec{T}_{n}^{T}(\mathbf{C}-\mathbf{\hat{C}}[n])\vec{P}_{y[n]}+\vec{T}_{ n}^{T}\mathbf{C}\vec{R}_{e[n]}, \tag{17}\]  
where \(\vec{T}_{n}=[T_{0}[n]\ T_{1}[n]\ \cdots\ T_{M-1}[n]]^{T}\), \(\mathbf{C}=[\vec{c}_{2}\ \vec{c}_{3}\ \cdots\ \vec{c}_{L}]\), \(\vec{R}_{e[n]}=\vec{P}_{x[n]}-\vec{P}_{y[n]}\) and \(\hat{\mathbf{C}}[n]\) is the estimated version of \(\mathbf{C}\). As the power of \(\vec{R}_{e[n]}\) is far less than the power of \(\vec{P}_{x[n]}\) in a weakly nonlinear system, the power of the residual error signal \(e_{r}[n]=\vec{T}_{n}^{T}\mathbf{C}[n]\vec{R}_{e[n]}\) is far less than the power of error \(e[n]\) when the estimated coefficients \(\hat{\mathbf{C}}[n]\) converge to the true nonlinear parameters \(\mathbf{C}\). More specifically, the magnitude of the error signal is suppressed from the order of \(O(c_{k,l})\) to the order of \(O((c_{k,l})^{2})\) exploiting the MHT calibration structure. Thus, the power of the error signal decreases from the order of \(O((c_{k,l})^{2})\) to the order of \(O((c_{k,l})^{4})\).  
The calibration structure is shown in Figure 4. The reconstructed error signal \(\hat{e}[n]\) is generated utilizing the MHT schemes with input \(y[n]\).  
Figure 4: Calibration structure for nonlinear mismatches in \(M\)-TIADCs. The HT block is the same as Figure 3b, where the parameters \(c_{k,l}\) are replaced by the estimated parameters \(\hat{c}_{k,l}[n]\).
## 4 Statistical Analysis of Surrounding Rock Pressure  
### Variance Analysis  
To study the influence of cover depth on surrounding rock pressure, one-way ANOVA was used. For different positions of lining structure, the difference of influence of cover depth on surrounding rock pressure was analyzed. Following the method of reference [30], the data were grouped and analyzed. The results are listed in Tables 2-4. F was the value of F-Distribution, F-crit was critical value of F-Distribution, and P-value was the probability of F-Distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position** & **Cover Depth Levels** & **F** & **F-Crit** & \(p\)**-Value** & **Testing** \\ \hline A & 10; 35; 44 & 1.015 & 4.459 & 0.405 & Not Sign. \\ B & 10; 35; 44 & 0.593 & 3.443 & 0.561 & Not Sign. \\ D & 10; 35; 44 & 1.403 & 3.467 & 0.268 & Not Sign. \\ E & 10; 35; 44 & 3.165 & 3.422 & 0.061 & Not Sign. \\ F & 10; 35; 44 & 0.416 & 3.634 & 0.667 & Not Sign. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation analysis between surrounding rock pressure and cover depth (10 m, 35 m, 44 m) at 12.6 m span.  
Figure 6: Statistical analysis approach.  
Tables 2 and 3 show that when the cover depths were 30 m, 35 m, and 44 m, and the spans were 12.6 m and 15.2 m, the influence of the tunnel depths on the surrounding rock pressure was not significant. However, Table 4 shows that when the cover depths were 44 m, 62 m and 95 m, and the span was 15.2 m, the influence of the tunnel depths on the surrounding rock pressure was significant. It was concluded that the influence of tunnel depth on surrounding rock pressure was not significant in shallow tunnels, but significant in deep tunnels.
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 6 Conclusions and Future Work  
In this study, we addressed the sleep control problem pertaining to MECS in a multi-access edge computing system to reduce energy consumption while maintaining task service delay at a reasonable level. To reduce the complexity of the optimization-based algorithms, we adopted the Delta-Notch inter-cell signaling model as a lightweight distributed approach. We evaluated the performance of the proposed approach and analyzed the impact of parameters, such as the range of the neighborhood, workload rate, and threshold to determine the activation of sleep control. The experimental results revealed that compared with a conventional method, the proposed method reduces energy consumption in a MEC system by more than 13% while providing a comparable service delay. In addition, our method reduces the variations in the service delay by more than 35%.  
As our future works, we will investigate the optimal wake-up policy that determines the set of MECSs to be woken up and the number of tasks to be transferred to the woken-up MECSs. In addition, we will scrutinize the impact of heterogeneous types of tasks with different QoS requirements on our sleep control mechanism.  
Figure 11: Length of the queue per server for 10 MECSs with workload rate = 1.0. (**a**) The neighborhood range is 4 (\(q_{th}\) = 0.5 or 0.7); (**b**) The neighborhood range is 6 (\(q_{th}\) = 0.5 or 0.7).  
Figure 12: Length of the queue per server for 10 MECSs with workload rate = 1.5. (**a**) The neighborhood range is 4 (\(q_{th}\) = 0.5 or 0.7); (**b**) The neighborhood range is 6 (\(q_{th}\) = 0.5 or 0.7).  
**Author Contributions:** Conceptualization, J.P.; Software, Y.L.; Writing--original draft, J.P.; Writing--review & editing, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. 2022R1F1A1065371). The present research was conducted using a research grant from Kwangwoon University in 2022.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
We proposed an ANN-based model to re-rank documents retrieved using a conventional IR model, BM25, to improve the performance of MRC models. The proposed model was composed of five subnetworks: query encoder, passage encoder, phrase modeling layer, attention layer, and similarity network. By calculating the mutual information of the phrase unit for queries and passages, the passage scores for queries were effectively reflected. In the experiments with the MS-MARCO dataset, the proposed model demonstrated better MRRs, 0.8%p-13.2%p, than the previous models, except for the BERT-based models. Although the proposed model demonstrated lower MRRs than the BERT-based models, it demonstrated significantly more efficient (approximately 8 times less memory usage and approximately 3.7 times faster response time) than the latter in terms of memory usage and response time. We conclude that these efficiencies are very important engineering factors in the development of a practical MRC system for massive concurrent users.  
Conceptualization and methodology, H.K.; software, validation, formal analysis, investigation, resources, data curation, and writing--original draft preparation, Y.J.; writing--review and editing, visualization, supervision, project administration, and funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Konkuk University in 2020.  
We are grateful for the technical support of the members of the NLP laboratory in Konkuk University.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Memory Usage (MB)** & **Training Parameters (MB)** &
\begin{tabular}{c} **Response Time** \\ **(ms)** \\ \end{tabular} \\ \hline BERT-Base [25] & 1289 & 110 & 1100 \\ Proposed model & 161 & 3.5 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of memory usage.
## 2 State of the Art
### Implementation of a SDN Approach over Legacy GPON Equipment
In addition, since a maximum bandwidth is assigned to each service as an important QoS requirement (Internet, Video, VoIP) to control, for example, the guaranteed bandwidth, this parameter is reflected in the OpenFlow standard using meters [28] which are directly associated with the flows of the said service. Therefore, a meter measures the rate of packets and controls the maximum rate of its associated flow, so it controls the maximum bandwidth associated with the corresponding service of one residential user.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 2 Defining Quantum Money and Quantum Wallets
### Classical Money vs. Quantum Money  
Traditional forms of money, whether physical or digital, rely on cryptographic or physical measures to prevent duplication and forgery. Quantum money, by contrast, uses intrinsic quantum properties to secure itself. The no-cloning theorem provides a level of security unattainable by classical systems, making quantum money potentially the most secure form of money. Furthermore, quantum money could be stored and transferred within secure quantum wallets, offering a fundamentally new structure for monetary systems.  
Furthermore, unlike digital money, which represents only a claim to funds stored in a bank account, quantum money allows for direct possession of value in the form of unique quantum states stored within a quantum wallet. This setup gives users the comfort of actually holding their money, without relying on a bank for storage. With quantum money, ownership is tied directly to the individual's control over these quantum states, adding a new dimension of personal security and privacy while preserving the advantages of digital currency.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 8 Performance Analysis
### Energy Consumption Analysis  
As drones have limited computational capability and resource capacity, the energy efficiency of the proposed protocol is crucial. We can compute the energy consumption (\(E\)) as \(E=E_{cp}+E_{cm}\)[39]. \(E_{cp}\) represents the computational energy consumption, and \(E_{cm}\) represents the communication energy consumption. According to [39], the energy consumption for the \"ECC point multiplication\" (\(E_{em}\)), \"HECC multiplication\" (\(E_{hm}\)), \"hash function\" (\(E_{hf}\)), \"fuzzy extractor\" (\(E_{fe}\)),\"symmetric encryption/decryption\" (\(E_{sd}\)), and \"physical unclonable function\" (\(E_{puf}\)) are calculated as follows: \(E_{epm}=3.1164\) mJ (\(3.5\ V\times 0.4\text{A}\times 2.226\) ms), \(E_{hem}=1.5582\) mJ (\(3.5\ V\times 0.4\text{A}\times 1.113\) ms), \(E_{hf}=0.00322\) mJ (\(3.5\ V\times 0.4\text{A}\times 0.0023\) ms), \(E_{fe}=3.1164\) mJ (\(3.5\ V\times 0.4\text{A}\times 2.226\) ms), \(E_{sd}=0.00644\) mJ (\(3.5\ V\times 0.4\text{A}\times 0.0046\) ms), and \(E_{puf}=0.0756\) mJ (\(3.5\ V\times 0.4\text{A}\times 0.054\) ms), respectively. Furthermore, we can compute the communication energy consumption as \(E_{cm}=n_{s}E_{s}+n_{r}E_{r}\), where \(n_{s}\) represents the number of bytes sent by the drone and \(n_{r}\) represents the number of bytes received by the drone. We denote the energy consumption for sending and receiving messages as \(E_{s}\approx 5.9\) mJ and \(E_{r}\approx 4.7\) mJ, respectively. Therefore, we can compute the computational and communication energy consumption of the proposed protocol as \(E_{cp}=10E_{hf}+1E_{puf}=0.1078\) mJ and \(E_{cm}=68E_{s}+104E_{r}=0.89\) mJ. Thus, the energy consumption of the proposed protocol is 0.9978 mJ. Table 8 presents the results of the energy consumption analysis of relevant protocols.
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 4 Experiments
### Ablation Study  
We tested the training results of different combinations of deep sampling at different stages of the algorithm. As shown in Figures 13 and 14, this is the convergence of the accuracy indicators on our validation set. The results show that: (1) under the same number of deep samplings, using our method in the large-scale stage, the ability to capture local details is superior to that of the baseline method, which only uses uniformly sampled depth assumptions, with the same network structure and tuning; (2) we increased the number of neighborhood depth samplings from 0 to 8 in the second stage and the 2 mm difference on the validation set was lower. This indicates that our method is closer to the ground truth point cloud within a 2 mm difference error than other methods.  
We further calculated the accuracy and completeness of the above model on the DTU test dataset and the results are shown in Table 4, which further demonstrate the superiority of our proposed method.  
Figure 12: Some reconstruction results of the Tanks and Temples dataset are displayed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Family** & **Francis** & **Horse** & **Lighthouse** & **M60** & **Panther** & **Playground** & **Train** & **Mean** \\ \hline MVSNet [13] & 55.99 & 28.55 & 25.07 & 50.79 & 53.96 & 50.86 & 47.90 & 34.69 & 43.48 \\ R-MVSNet [21] & 69.96 & 46.65 & 32.59 & 42.95 & 51.88 & 48.80 & 52.00 & 42.38 & 48.40 \\ CVP-MVSNet [14] & 76.50 & 47.74 & 36.34 & 55.12 & 57.28 & 54.28 & 57.43 & 47.54 & 54.03 \\ CasMVSNet [15] & 76.37 & 58.45 & 46.26 & 55.81 & 56.11 & 54.06 & 58.18 & 49.51 & 56.84 \\ D2 HC-RMVSNet [34] & 74.69 & 56.04 & 49.42 & 60.08 & 59.81 & 59.61 & 60.04 & 53.92 & 59.20 \\ ours & 76.15 & 52.35 & 36.94 & 55.15 & 57.58 (2 nd) & 53.55 & 58.64 (2 nd) & 50.05 (2 nd) & 55.05 (3 rd) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of F score with other methods on the Tanks and Temples dataset.
## 5 Result and Analysis
### Task Completion Time  
Figure 6 shows the average interaction time for each interaction method. In terms of task completion time, the data indicated significant differences between interaction methods (\(F_{885,26}^{4}=54.807\), \(p<0.001\), \(\eta^{2}=0.276\)). No significant differences were found in interaction time between the Touch-Slider and Pinch-Slider. However, both the Touch-Slider and Pinch-Slider methods showed significant differences in task completion time compared to other interaction methods (\(p<0.001\)), with both slider-based methods exhibiting significantly longer interaction times compared to the keyboard and the study's proposed multimodal method, suggesting issues with the slider input method's efficiency in data input and highlighting the superior efficiency of this study's proposed methods.  
No significant differences in efficiency were observed among Bimanual Scaling, Gesture and Voice, and Keyboard; yet, significant differences existed compared to the remaining methods (\(p<0.001\)). This suggests that while this study's proposed methods offer advantages over the slider method in terms of efficiency, they do not show a significant difference in efficiency compared to the numeric keyboard.