esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Results
#### 4.2.2 Decoding Stage  
We use the beam search algorithm to look for the best solution in the inference stage. It expands the solution space relative to the greedy search and reduces the complexity relative to the exhaustive search. The beam size is an important parameter of the beam search algorithm, affecting performance and efficiency. We compare the test BLEU and the test time based on different beam sizes (from 1 to 6) in Figure 8 and Table 4. The results indicate that the translation performance and time consumption will increase as the beam size increases. However, when the value of the beam size exceeds three, the performance improvement is negligible, while the time consumption increases steeply. To conclude, the best beam size is 3 on the Titan RTX.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GPU** & **Best Dev BLEU** & **Test Loss** & **Test BLEU** \\ \hline RTX 2080 Ti (bs = 8, step = 4) & 26.86 & 4.15 & 26.86 \\ Titan RTX (bs = 16, step = 2) & 26.92 & 4.16 & 26.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The best Dev BLEU and the Test Loss and BLEU on two GPUs.  
Figure 7: The training process on RTX 2080 Ti (**a**) and Titan RTX (**b**).
## 2 Proposed Method
### Transient Behavior of the First-Order Linear Circuit  
One of the parameters used to describe the transient behavior of the first-order linear circuit is the time constant. The value of the time constant depends on the values of the circuit elements. In most cases, the circuit is designed (choosing the values for the circuit elements) based on the desired time constant value. The time at which the transient response of the circuit reaches 63% of its steady-state value is defined as the time constant. The transient response reaches 95% of its steady-state value at 3 \(\tau\) seconds and 99% of its steady-state at 5 \(\tau\) seconds [36].  
A first-order linear circuit contains one energy storing element (capacitor or inductor). Consider a first-order linear circuit connected to a step source (A) at time \(t=0\). The differential equation of the circuit can be written as [37].  
\[\tau\frac{dy}{dt}+y=A \tag{3}\]  
where \(y\) is the response across the energy storing element (\(y\) is the voltage response if the storage element is a capacitor and \(y\) is the current response if the storage element is an inductor) and \(\tau\) represents the time-constant of the circuit.  
By solving differential Equation (3), the output response of the circuit is:  
\[y(t)=A\Big{(}1-e^{-t/\tau}\Big{)} \tag{4}\]  
Suppose at \(t=t_{o}\) the step source is disconnected from the circuit. Now the circuit becomes a source free circuit and the differential equation for the output response can be written as  
\[\tau\frac{dy}{dt}+y=0 \tag{5}\]  
By solving differential Equation (5), the output response of the circuit is:  
\[y=y(t_{o})e^{-(t-t_{o})/\tau} \tag{6}\]  
where \(y(t_{o})\) is the output response at \(t=t_{o}\) and \(y(t_{o})=y(t_{o}^{-})=y\Big{(}t_{o}^{+}\Big{)}\).  
The following is the generalized mathematical expression based on the initial value \((y(0))\) and the final value \((y(\infty))\) of the output response:  
\[y=y(\infty)+(y(0)-y(\infty))e^{-(t-0)/\tau} \tag{7}\]  
By using Equation (7), the output response of the circuit at \(t=t_{1}\) can be expressed as  
\[y=y(t_{1})+(y(t_{o})-y(t_{1}))e^{-(t-t_{o})/\tau},\quad t_{o}<t_{1}<\infty \tag{8}\]  
where \(y(t_{o})\) is the response of the circuit at the initial value \(t_{o}\) and is equal to \(A\) and \(y(t_{1})\) is the final response of the circuit and \(y(t_{1})=0\) as \(t_{1}\rightarrow\infty\).  
Equations (6) and (8) are used in the proposed method for approximating the grayscale histogram to the transient response of the first-order linear circuit.
## 3 Performance Implication of NVM Based Storage
### Effects of Concurrent Accesses  
In modern computing systems, various applications access storage concurrently increasing the randomness of access patterns and aggravating contention for storage. For example, popular social media web servers service a large number of user requests simultaneously, possibly incurring severe contention due to large random accesses generated from concurrent requests. In virtualized systems with a multiple number of guest machines, I/O requests from each guest machine eventually get mixed up generating a series of random storage accesses [24]. Mobile systems like smartphones also face such situations as the mixture of metadata logging by Ext4 and data journaling by SQLite generates random accesses [25]. Database and distributed systems also have similar problems [26].  
Thus, supporting concurrent accesses to storage must be an important function of modern storage systems. To explore the effectiveness of NVM under such concurrent accesses, we measure the throughput of HDD, RAMDisk, and PCMdisk as the number of IOzone threads increases. Figure 11 shows the throughput of each storage as the number of threads changes from 1 to 8. As shown in the figure, NVM performs well even though the number of concurrent threads increases exponentially. Specifically, performance improves until the number of threads becomes 8 for most cases. Note that the large number of threads increase access randomness and storage contention. However, we observe that this only has a minor effect on the performance of NVM storage. In contrast, the throughput of HDD degrades as the number of concurrent threads increases.  
In summary, NVM can constitute a contention-tolerable storage system for applications with highly concurrent storage accesses in future computer systems.
## 3 Experimental Results
### Macroscopic Weathering Indicators
#### 3.1.3 Surface Hardness  
Figure 8 illustrates the variation curves of surface hardness and loss rate for the two types of rocks. It is evident that as the number of cycles increases, the surface hardness values of both rock types consistently decrease, albeit with notable disparities in the magnitude of change and decay rate [27]. The hardness value of limestone demonstrates an exponential decline (this function has the highest fitting coefficient), starting at 674 HL and decreasing to 632 HL, resulting in a cumulative decrease of 42 HL. The decay of the sandstone hardness value is very special and can be divided into several stages according to the number of salt weathering cycles.  
Figure 8: Variation curves of surface hardness and hardness loss for sandstone and limestone. (**a**) variation curves of surface hardness for the two types of rocks; (**b**) comparison chart of hardness loss for the two types of rocks.  
Figure 7: The cumulative damage in two kinds of rock mass and its representation in change rate curves. (**a**) Cumulative mass loss curve; (**b**) variation curves of mass loss in the two types of rocks.  
During cycles 1 to 5, the hardness of the rock samples decreased by 110 HL, going from 630 HL to 520 HL. This decrease represents approximately 1/6 of the hardness value of fresh sandstone. Consequently, the surface microstructure of the sandstone was severely damaged, resulting in significant softening.  
Between cycles 6 and 10, the hardness value of the sandstone decreases from 524 HL to 278 HL, and the surface layer of the sandstone sample begins to peel off.  
From cycles 11 to 25, the surface structure of the sandstone sample undergoes complete damage. Through comprehensive testing of the sample from various regions and angles, the hardness value stabilizes at around 250 HL [28].  
Throughout the entirety of the experimental process, the hardness value of the sandstone sample experienced a cumulative decrease of 380 HL. The decay rate followed a pattern of change during the \"fast-rapid-slow-stable\" cycles, which is in accordance with the inherent characteristics of the Boltzmann distribution function:  
\[R=a+\frac{b-a}{1+e^{(n-c)/d}} \tag{4}\]  
where \(R\) represents the surface hardness value, \(n\) represents the number of cycles, and \(a\), \(b\), \(c\), and \(d\) are constants.
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 3 Joint Optimization
### Power and Path Loss Optimization Subproblem  
Given height variable \(h\) and elevation variable \(\theta\), Equation (13) can be expressed as follows:  
\[\min_{\mathbf{P},\mathbf{P}}\ P_{e}, \tag{15}\]  
\[s.t.\ \ 0\leq P_{\text{G}}\leq P_{\max},\ 0\leq P_{\text{U}}\leq P_{\max}, \tag{15a}\] \[PL_{\text{n}}\leq PL_{\max} \tag{15b}\]  
The BER function in Equation (15) is convex with respect to \(P_{\text{G}}\) and \(P_{\text{U}}\), which can be solved using standard convex optimization techniques. Therefore, the optimal power allocation is realized.
# Long-Range Wireless Radio Technologies: A Survey
## 3 Discussion  
### On the Energy Consumption  
As the energy consumption of each technology entirely depends on the hardware and the transmitting power, it is not very relevant to present an energy consumption rate alongside each technology. Rather, here we propose a ranking of which technology should consume more or less energy compared to the others, based on the modulation and the frequency used, as well as the data rate offered by each protocol. We also present informative energy rate reported from the data-sheet of popular wireless chipsets. Also note that some low-energy technologies feature low data rates and thus, require more time to send the same amount of data, leading to equivalent energy consumption thanmore energy greedy but faster technologies. UNB technologies should be the ones that use less energy, thanks to the modulation used and the very narrow bandwidth (which in return also means a very low data rate). Among all the aforementioned technologies, Sigfox and Telensa are UNB technologies. As an example, this Sigfox chipset [41] consumes 37 mA in transmission mode. Nwave, which is based on Weightless-N, is also a UNB technology, and thus should have a similarly low energy consumption. Qowisio should offer a similar consumption although the specification is not yet known. Then, the LoRa technology have a similar or higher consumption than UNB technologies, depending on the parameters used (e.g., [42] consumes between 18 and 125 mA in transmission mode). Weightless-P consume an amount of energy that is close to LoRa's (e.g., [43] 49 mA at 15 dBm in transmission). Mobile technologies come after as they consume much more power (e.g., [44] 235 mA LTE-M and 190 mA NB-IoT in transmission mode). This is mainly due to the modulation, the data rate and the synchronization to the base stations which is required for mobile communications. We can note that due to a high data rate and a fast frequency, Ingenu RPMA should consume more energy than the previously cited technologies, although they claim the opposite.  
While the absolute energy rates are important, another aspect that must be taken into account is the on-air duration of the communications [45]. Indeed, if a technology provides a better data rate, the transmission time will be shorter. Thus, a technology consuming a higher amount of energy could, in the end, consume less energy than a more restrained technology. This is because, for the same amount of information, the duration of the transmission would be shorter.
## 3 Data Fusion
### State-of-the-Art Research on Data Fusion
#### 3.1.4 Data Compression  
After preliminarily treatment of signals, sensor nodes generally do compression and encryption rather than sending it to a base station or sink node directly. Studies show that data compression can effectively reduce the amount of data transmission. Since the memory module and data sending module consume lots of energy [26], data compression can lower power consumption effectively on BSN sensors, by reducing information stored in memory and transmitted by the transceiver.  
In BSNs, data compression can be achieved by not only classical data compression algorithms, such as source encoding, differential encoding, and Huffman encoding, but also some novel methods proposed by several researchers. Some collections and transmissions of physiological signals have instantaneity requirements, so minimizing the amount of data transmission is necessary. For example, Reference [62] proposes a compression algorithm based on interception and differential encoding techniques, because the temporal and spatial of data collected by inertial sensors have correlations. Experiments prove that the algorithm can effectively reduce the amount of data transmission and give better real-time performance. Reference [79] adopts a algorithm called joint orthogonal matching pursuit (JOMP) based on distributed compressive sensing (DCS) theory, combining it with the synchronous mechanism, which can control interval times and efficiently reduce the number of samples, the amount of data processing, transmission and storage. In Reference [80], researchers evaluate the effectiveness of a wireless neural recording system using compressed sensing for transmission. They propose a union of support techniques for compressive sensing. Compared to conventional basis pursuit reconstruction, the proposed technique provides an average signal-to-noise and distortion ratio (SNDR) improvement of 6 dB and a maximum SNDR improvement of 9.5 dB. It also provides a 2-fold reduction of the output data rate. Reference [81] adopts the DFT method, and solves compression and decompression problems by discrete sinc interpolation. In the ECG monitoring application, Reference [82] proposes a compression algorithm based on WT, which achieves low transmission delay by reducing the size of frames. Similarly, in Reference [83], researchers also study the data compression of ECG, and propose a low-cost quadratic compression algorithm which can increase encoding speed and reduce energy consumption effectively, while maintaining high signal quality. For the sake of minimizing the energy consumption of a multi-hop wireless network based on joint routing and compressed aggregation, [PERSON] _et al._[84] analyzed the complexity of this optimization problem and find that it is NP-complete. They further propose a mixed-integer programming formulation (MIP) to obtain the optimal aggregation trees for small scale problems, along with a greedy heuristic algorithm to obtain the near-optimal aggregation trees for large scale problems. Finally, the energy efficiency is improved.
## 3 System Layout
### Battery Bank
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference Case**} \\ \hline  & \multicolumn{2}{c}{**Working Hours**} & \multicolumn{2}{c}{**Trail/Pressure Value**} & \multicolumn{2}{c}{**Energy**} & \multicolumn{2}{c}{**Total Flow**} & \multicolumn{2}{c}{**Total Conductivity**} & \multicolumn{2}{c}{**E\({}_{\text{Btd}}\) kWh/m3**} & \multicolumn{2}{c}{**Batt\({}_{\text{num}}\) pu of the Reference**} \\ \hline
**from** & **To** & **Total** & **(bar)** & **(\(\mathbf{0}\)**\(\mathbf{k}\)**Wh/m3)** & **Rate (\(\mathbf{/}\)**\(\mathbf{day}\))** & **Total Conductivity** & **E\({}_{\text{Btd}}\) kWh/m3** & \multicolumn{2}{c}{**Batt\({}_{\text{num}}\) pu of the Reference**} \\
**0** & **24** & **24** & **Ref. 24 h/48** & **4.23** & **223.2** & **1.4** & **9.0448** & **1.00** \\ \hline \multicolumn{8}{c}{Scenario 1} \\ \hline
0 & 10 & & & & & & & \\
14 & 24 & 20 & Low irr/40 & 4.0774 & 135.6 & 1.7 & 8.7183 & 0.5856 \\
10 & 14 & 4 & Peak irr/58 & 5.17 & 41.28 & 1.1 & 10.7267 & 0.2192 \\  & & **Average** & **4.2967** & **176.88** & **1.4** & **9.187** & **0.8049** \\ \hline \multicolumn{8}{c}{Scenario 2} \\ \hline
0 & 8 & 16 & Low irr/40 & 4.0772 & 108.48 & 1.7 & 8.7177 & 0.44685 \\
16 & 24 & 16 & 8 & Peak irr/58 & 5.017 & 82.56 & 1.1 & 10.728 & 0.4387 \\ \hline \multicolumn{8}{c}{Average} & \multicolumn{2}{c}{**4.4834**} & **191.04** & **1.4** & **9.5865** & **0.9072** \\ \hline \multicolumn{8}{c}{Scenario 3} \\ \hline
0 & 8 & 14 & Low irr/40 & 4.0771 & 94.92 & 1.7 & 8.7179 & 0.4099 \\
18 & 24 & 10 & & & & & \\
8 & 10 & 6 & Medium irr/44 & 4.2282 & 47.16 & 1.5 & 9.0416 & 0.2112 \\
14 & 18 & 4 & Peak irr/58 & 5.017 & 41.28 & 1.1 & 10.7267 & 0.2194 \\ \hline \multicolumn{8}{c}{Average} & \multicolumn{2}{c}{**4.3276**} & **183.36** & **1.43** & **9.2534** & **0.8405** \\ \hline \multicolumn{8}{c}{Scenario 4} \\ \hline
0 & 8 & 14 & Low irr/40 & 4.0771 & 94.92 & 1.7 & 8.7179 & 0.4088 \\
18 & 24 & 11 & & & & & \\
8 & 11 & 8 & Medium irr/44 & 4.2287 & 62.88 & 1.5 & 9.041 & 0.2816 \\
13 & 18 & 2 & Peak irr/58 & 5.0194 & 20.64 & 1.1 & 10.7267 & 0.1097 \\ \hline \multicolumn{8}{c}{Average} & \multicolumn{2}{c}{**4.2395**} & **178.44** & **1.43** & **9.0641** & **0.8012** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Battery sizing for different scenarios.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 5 Conclusions  
This paper has proposed an efficient time-classification algorithm using the sky region detection approach based on a single image. The sky region of an image contains a change in brightness over time. For sky detection, we trained the Mask R-CNN model and made reference color histograms of all time periods (day, dawn, and night). We employed two measures for the proposed windowed-color histogram. Additionally, we designed the weighting factor of each channel to reflect the characteristics of each color and made more efficient classifications. Based on the proposed algorithm, we have achieved 91% of the verification accuracy with our hierarchical classification structure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compared Methods** & **Accuracy (\%)** \\ \hline
**Proposed algorithm** & **91.00** \\ ResNet152 [6] & 87.67 \\ ResNet101 [6] & 86.00 \\ NasNet [28] & 82.00 \\ EfficientNetB4 [29] & 77.00 \\ EfficientNetB3 [29] & 73.67 \\ EfficientNetB2 [29] & 75.67 \\ EfficientNetB1 [29] & 72.33 \\ EfficientNetB0 [29] & 69.67 \\ InceptionResNet [27] & 74.00 \\ XceptionNet [26] & 73.67 \\ DenseNet201 [25] & 69.67 \\ DenseNet169 [25] & 69.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of the proposed algorithm with the existing deep learning classification models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weight** & **Accuracy (\%)** \\ \hline
9:1:0 & 90.33 \\
9:0:1 & 89.33 \\
8:2:0 & 89.00 \\
8:1:1 & 90.33 \\
8:0:2 & 90.67 \\
7:0:3 & 90.33 \\
7:1:2 & 90.33 \\
7:2:1 & 89.67 \\
7:3:0 & 87.67 \\
6:0:4 & 89.67 \\
6:1:3 & 91.00 \\
6:2:2 & 90.67 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of classifying “dawn” or “dusk” and “day” time periods using the correlation method with the weighting approach.  
**Author Contributions:** Conceptualization, B.-G.K.; methodology, B.-G.K. and H.-J.P.; software, H.-J.P.; validation, J.-J.J. and H.-J.P.; formal analysis, B.-G.K. and H.-J.P.; investigation, J.-J. and H.-J.P.; writing--original draft preparation, J.-I.J. and H.-J.P.; writing--review and editing, B.-G.K.; supervision, B.-G.K.; project administration, B.-G.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received funding from the Ministry of Culture, Sports and Tourism (MCST) and from the Korea Copyright Commission in 2021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This research project was supported by Ministry of Culture, Sports, and Tourism (MCST) and from the Korea Copyright Commission in 2021.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 7 Performance Evaluation
### Environment for Performance Evaluation
\begin{array}{l}TO\left(x\right)=\frac{\text{Total amount of bytes to be transmitted}}{\text{Pavlload size (bytes)}}\\ =\frac{(\underline{hdr+x})/\text{PRR}}{x}=\frac{\underline{hdr+x}}{x\cdot(1- BER)^{8(\underline{hdr+x})}}\\ \text{Find }x_{opt}\text{ to minimize }TO(x)\ \ \rightarrow\ \ \frac{d}{dx}TO(x_{opt})=0\\ x_{opt}=\left\{\begin{array}{ll}\frac{-4\cdot\text{hdr}\cdot\ln(1-BER)- \sqrt{\left(4\cdot\text{hdr}\cdot\ln(1-BER)\right)^{2}-8\cdot\text{hdr}\cdot \ln(1-BER)}}{8\cdot\ln(1-BER)}&if\ x_{opt}\ <\ 110\\ 110&if\ x_{opt}\geqslant 110\end{array}\right.\end{array}  \tag{12}\]  
where \(x\) is the size of the data payload in Figure 11, and \(\underline{hdr}\) includes all headers in Figure 11 and all cryptographic overhead such as hashes and MACs. Since the total amount of the data payload, which is the size of the code image, is fixed in code dissemination, the total transmitted bytes decrease as TO becomes lower, thus reducing energy consumption. For the sake of simplicity, we assume that the optimal payload size, which minimizes \(TO\) in Equation (12), of each scheme in each link is predetermined by applying BER and \(\underline{hdr}\) into Equation (12) as shown in Table 2. Each packet is lost in each hop with the probability of \(PER=1-PRR=1-(1-BER)^{8L}\) where \(L\) is a packet size, including the data payload and the header, in bytes. The lost packet is retransmitted according to the procedure of Deluge as shown in Figure 1b. It is important to note that all kinds of Deluge messages such as the ADV, the REQ and DATAs are transmitted as a long frame in the active period in Figure 10 since the MPDU size of all Deluge messages is larger than 18 bytes. Other parameters for the performance evaluation are summarized in Table 1.
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 2 Coupled CFD-DEM Mathematical Equations
### Adhesion Energy Parameter  
In this research, two types of particles, dust and fine sand, were used for modelling the deposition of particles. The physical parameters of dust particles are shown in Table 1. The cohesion energy parameter, \(K\), between a particle and the solar panel was difficult to determine, so a numerical experiment was designed for testing the above parameter. The numerical experiment used the free settlement of particles from a plane with 20 \(\times\) 20 cm dimensions above an inclined solar panel with a 30-degree angle. The kinetic deposition process of the particles included three modes of motion in the numerical experiment: complete rebound, partial adhesion and complete adhesion. Since the surface wind speed was approximately zero, the movement of dust was only affected by gravity in the numerical experiments. The deposition of particles with different cohesive forces was calculated, and the release rate of the particles was 2500 particles per second from the PV panels.  
Figure 3 shows the deposition process of 10 \(\upmu\)m particles, i.e., clay, with \(K=0.5\times 10^{6}\) J/m\({}^{3}\). From Figure 3a, it can be seen that the clay particles were almost all deposited on the solar panel because of the cohesiveness between the particles and the solar panel surface, and the results with a much higher value of \(K\) were consistent with the above results. The particle number of zero at the first time point (less than 0.075 s) in Figure 3b was because the clay particles were still falling and had not touched the solar panel yet. The deposition simulation results of 100 \(\upmu\)m sand particles, i.e., fine sand, on the solar panel were different to those of clay, which would be reflected or deposited with a different cohesiveness. Since the cohesiveness can be indicated by the cohesion energy parameter \(K\), we then calculated the deposition process of fine particles with \(K=0.5\times 10^{6}\) J/m\({}^{3}\), \(1.05\times 10^{6}\) J/m\({}^{3}\) and \(3.5\times 10^{6}\) J/m\({}^{3}\), and the results are shown in Figure 4. From Figure 4,  
Figure 2: Sketch of the numerical model’s geometry. (**a**)The simulation zone. (**b**) Modeling grids.  
we can see that the falling fine sand particles were difficult to deposit on the solar panel when \(K=0.5\times 10^{6}\) J/m\({}^{3}\), while the deposition amount increased as the parameter \(K\) increased. Therefore, it is of great significance to characterize the adhesion force between particles and PV panels accurately. Unfortunately, experimental results are rare.