esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 4 IoT in 5G Performance Requirements
### Spectrum Efficiency  
**Spectrum efficiency** is defined as the data throughput per unit of spectrum resource per cell or per unit area (\(\text{bps}/\text{Hz}/\text{cellar~{}bps}/\text{Hz}/\text{km}^{2}\)) [216]. In order to achieve network sustainability, required for 5G networks [216], spectrum efficiency needs to be improved 3-5 times [20; 32; 216]. Minimumpeak spectrum efficiency is 30 bps/Hz for downlink and 15 bps/Hz for uplink [32]. This is mostly important for the xMBB services class [212; 214]. Since this performance requirement is not relevant for activity-based IoT application classes which are associated with the mMTC and uMTC service class, it has not been further considered herein.
# Speed Optimization in DEVS-Based Simulations: A Memoization Approach
## 2 DEVS Methodology
### Atomic Model  
The atomic model is always the base model of the hierarchy in DEVS and represents the behavior of the system. The mathematical expression of the atomic model M is as follows:  
\[\begin{array}{c}\text{M}=\textless X,\textless Y,\textless\text{int},\textless \text{ext},\textgreater{}\text{,}\lambda,\text{ta}\textgreater{}\\ \text{X}\textless\text{is the set of input values.}\\ \text{S}\textless\text{is a set of states.}\\ \text{Y}\textless\text{is the set of output values.}\\ \text{$\textless$int$: $S\to S$ is the internal transition function.}\\ \text{$\textless$ext$: Q}\times\text{X}\text{b}\to\text{S}\textless\text{is the external transition function.}\\ \text{Q}\in\{(\textless,e)\textless\text{is}\in S,0\leq\text{e}\leq\text{ta(s)}\} \textless\text{is the total state set, e is the time elapsed since}\\ \text{last transition, and Xb denotes the collection of bags over X.}\\ \text{$\lambda$: $S\to Yb$ is the output function.}\\ \text{ta: $S\to R+0$, is the time advance function}\end{array} \tag{1}\]  
The atomic model expresses the state of the target system as a set S. In general, if there is no external input X during the time advance inside the model, the \(\lambda\) function is executed and the output Y is sent. Afterwards, the internal state transition function \(\delta\)int is executed to change the state of the current model, and the time advance for when the next output will occur is determined through the ta function. The output Y acts as input X to another atomic model, and if input X occurs during a time advance of a fixed state, the external state transition function \(\delta\)ext is executed to change the state and perform a predetermined process.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 4 Results and Experimentation
### Probability of Delivery Success  
The possibility of a packet being successfully delivered to the BS while going through intermediate nodes is defined as the probability of success. The success of sending data is primarily dependent on the average number of hops taken by each packet throughout the delivery process. According to Figure 10, an increase in the number of UAVs also improves the density of the network and the likelihood of success. The probability of delivery success improves as the number of unmanned aerial vehicles (UAVs) grows, but the packet loss ratio decreases.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Number of Rounds** & **LEACH** & **LEACH-C** & **PSO-HSA** & **SEED** & **EE-SS** \\ \hline First node dead & 457 & 515 & 1267 & 1542 & 2456 \\ Half node dead & 549 & 567 & 2555 & 3601 & 4498 \\ Last node dead & 634 & 754 & 3145 & 4242 & 5164 \\ Average & 548 & 612 & 2322 & 3128 & 4039.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANA for all algorithms.  
Figure 9: Clustering building time.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 4 Results
### Images with Real (Non-Simulated) Motion  
As a final validation step, we applied the MC-Net algorithm to human brain images with various degrees of motion artifacts (not simulated). Some representative results are shown in Figure 9, with the apparent degree of motion corruption increasing from Figure 9A-C. As before, the image with minor motion artifacts Figure 9A was well preserved by MC-Net, whereas the two scans with moderate and severe artifacts, Figure 9B,C, showed substantial improvements in quality after being processed by MC-Net. While it is obvious that no reference images are available for quantifying improvements in image quality, the visual image scores provided by our blinded readers improved significantly between the original and corrected scans (\(p\) = 0.04).  
Figure 8: Results for two T2-weighted (FLAIR) images, obtained using simulated motion, from Dataset 3. The left set (**A**) was corrupted with relatively minor motion, and the right set (**B**) was corrupted with more-severe motion. Note the appearance of false anatomical “features” (yellow arrows). Within each set, images in each column are original images, corrupted images, and outputs from MC-Net (left to right). The second row shows a magnification of the region of interest (ROI) within the red rectangle. The third row shows the error maps between reference (i.e., clean image) and corrupted image and motion correction results. The difference between each pixel shown in the error maps was multiplied by a factor of five.
## 3 Experimental Investigation
### Test Setup and Instruments  
After the assembly process, all the specimens were subjected to a controlled atmosphere, so as to achieve different degrees of moisture in wood (at the same temperature of 20 \({}^{\circ}\)C). The experiments reported herein comprised three artificial climates being selected as extreme examples of operational conditions for service class 1, 2 and 3 (Table 1). All test series were in fact performed at a constant temperature of 20 \({}^{\circ}\)C, with moisture content in wood in the order of 9%, 18% and 27% respectively.  
The stiffness and strength characteristics in short-term loading were thus investigated according to EN 15274:2015 recommendations [28]. Further, all tests were carried out based on the EN1382:2016 provisions [29], on a Zwick Roell 50 kN capacity machine, with data recording frequency of 10 Hz. The reference pull-out setup is shown in Figure 3. Each specimen was fixed to the machine with a steel clamping plate. Possible relative displacements of wood logs were restrained by four M8 anchoring bolts. The single rod was hence clamped to the pull machine. To this aim, the nominal cross-head displacement rate was set to ensure a reference value of 0.5-2.0 mm/min (depending on specimen type). The latter was then calibrated (test by test) in order to reproduce a short-term failure mechanism for all the specimens. The axial load \(F\) applied to each specimen was recorded and compared with the average relative displacement of the rod with respect to the wood log. In support of these experimental investigations, contactless optical measurements of strain (based on digital image correlation (DIC) techniques), were also implemented to provide full-field strain maps of specimens under load, until failure. The experiments of 24 specimens were further supported by a Canon 700D camera with macro lens and photo recording frequency of 0.5 Hz (Figure 3). All specimens had preprepared surface adapted for recording. The whole postprocessing stage of acquired images was carried out by VIC-2D (Correlated Solutions, University Santa Barbara, Santa Barbara, CA, USA). This approach was used to reveal local aspects of load transfer mechanisms from the rod to the adhesive and wood that could be of importance for further analysis using theoretical or even numerical models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**KGK EPOCON ’88**} & \multicolumn{2}{c}{**LOCTITE**} & \multirow{2}{*}{**Wood**} \\ \cline{3-4}  & & & **PUREBOND CR 821** & \\ \hline Compressive strength & (MPa) & 91.5 & 79.9 & 61.5 \\ Tensile strength & (MPa) & 32.5 & 27.5 & 120.5 \\ Bending strength & (MPa) & 60.9 & - & 97.8 \\ Modulus of elasticity & (MPa) & 29.5 & 29.5 & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nominal mechanical properties for KGK EPOCON ’88 (two-component epoxy), LOCTITE PUREBOND CR 821 (two-component polyurethane) and Siberian larch wood (_Larix sibirica_).
## 3 Results and Discussions
### Sensory Analysis  
Finally, a sensory analysis was carried out to evaluate the quality attributes of the wines, including flavor, aroma and color, which have effects on consumer acceptance (Figure 5). Samples S-Ew, H-Ew, F-Ew, S/F-Ew, H/F-Ew, S/F-Ew and S/H/F-Ew had higher scores in floral (7.00, 7.11-8.22), fruity (7.22, 7.76-8.50), taste (6.00, 6.00-8.67) and acceptance (6.17, 6.33-8.50) but lower scores in appearance (9.17, 8.33-8.83) compared to SCw. [PERSON] et al. (2017) reported that crude \(\beta\)-glucosidase from _P. fermentatus_ could promote the release of varietal aroma compounds and fortified fruity and floral traits ([PERSON], 2017). Taste qualities in wine mainly contained sweetness, sourness and bitterness, contributed by sugars, organic acids and ethanol, respectively. Acetic acid is mostly responsible for the sour and vinegarly smell and taste in wines ([PERSON], 2017; [PERSON] et al., 2017). We analyzed the volatile acid of wines, which mainly contained acetic acid. Therefore, adding SLY-4E, F2-24E and HX-13E  
Figure 4: Hierarchical cluster analysis of aroma compounds in wines. All the detected fermentative aroma compounds were clustered into four classes and designated as class I, II, III, and IV.  
to wines decreased the content of volatile acids and improved wine taste. In addition, there were other compounds that imparted better taste to S/H/F-Ew and this should be investigated in the future. The lower appearances score in wines with added SLY-4E, F2-24E and HX-13E was probably due to the decreased anthocyanin content [43]. S/F-Ew and S/H/F-Ew had higher appearance scores and anthocyanin content than other adding strategies, which demonstrated that S/F-E and S/H/F-E were more favorable for wine color development. Wines' quality attributes include flavor, aroma and color, which impact consumer acceptance. Results showed that adding SLY-4E, F2-24E and HX-13E could improve wine sensory qualities.
## 3 Results and Discussion
### High-Temperature Performance
#### 3.1.2 Putting Test  
In comparison with the Marshall test, the rutting test can better simulate the wheel loads and is closer to the actual service environment. Therefore, the high-temperature performance of the four mixtures was further evaluated by the rutting test, and the main reference indexes included the test parameters' dynamic stability (_DS_) and rutting depth (_RD_). The _DS_ selected in this study was determined by the deformation of the specimen and calculated as follows:  
\[DS=\frac{(t_{2}-t_{1})\times N}{d_{2}-d_{1}}\times C_{1}\times C_{2} \tag{5}\]  
where \(DS\) stands for dynamic stability, times/mm; \(d_{1}\) represents the deformation (mm) at \(t_{1}\) = 45 min; \(d_{2}\) represents the deformation (mm) at \(t_{2}\) = 60 min; \(C_{1}\) refers to the type coefficient of the testing machine, which was 1.0; and \(C_{2}\) indicates the size coefficient of the specimen, which was 1.0.  
As seen from Figure 8, the _DS_ values of the three WER-EA mixtures were much larger than those of the pure EA mixtures. \(DS_{\text{E20-T}}\), \(DS_{\text{E20-T}}\), and \(DS_{\text{E20-T}}\) were 446%, 414%, and 407% higher than \(DS_{\text{EA}}\), respectively. At the same time, \(DS_{\text{E20-T}}\) was significantly larger than the other two and was 433 times/mm higher than the minimum \(DS\) value of 5577 times/mm. Meanwhile, the minimum value of \(DS_{\text{E20-T}}\) was 1026 times/mm, which is greater than the minimum value of 1000 times/mm required by the specification. That is, these four types of mixtures all met the specification requirements in terms of _DS_ value.  
Figure 7: Marshall modulus results of asphalt mixtures.  
The \(RD\) values of the three WER-EA mixtures were reduced by 71-78% compared with those of the EA mixture, which showed that the WER system could significantly improve the high-temperature rutting resistance of the emulsified asphalt mixtures. The \(RD_{\text{E20-T}}\) ratio was 18.6 and 31.1% lower than the \(RD_{\text{E44-T}}\) and \(RD_{\text{E51-T}}\) ratios, respectively, indicating that WER with a low epoxy value is more conducive to improving the high-temperature performance of WER-EA mixtures.  
In summary, the three WER systems can significantly improve the high-temperature performance of emulsified asphalt mixtures. This is because the WER system generates a dense three-dimensional skeleton structure in the mixture after curing, and the skeleton structure can tightly wrap the asphalt so that its fluidity will not change much even at high temperatures, thus forming a high-performance whole with the mixture. In addition, the E20-T mixture was better than the other two WER-EA mixtures in all indexes. This observation is consistent with the published investigation on asphalt performance analysis [19], which indicates that WER with a low epoxy value is more conducive to improving the high-temperature deformation resistance of WER-EA mixtures.
## 3 The Lossless Adaptive Data Compression Method Based on the Binomial Number System (BNS)
### Methods for Converting Binary Sequences to Binomial Code and Vice Versa
However, the overall compression effect for all binomial numbers in Table 3 will be higher since the total number of bits for all binary numbers of 56 will be less than the number of all bits of binomial numbers 64 by 8 bits. As the length \(n\) increases, this difference will only increase. Accordingly, the proposed binomial compression method can be represented by the following three key steps:  
1. In a compressible binary sequence of code length \(n\), the number of units \(k\) is counted. Thus, there is a transition from the binary sequence to the code combination \(k\)-equilibrium code. Accordingly, the number of zeros in it is \(n-k\). 2. In the \(k\)-equilibrium code combination, the LSB zeros to the first unit or the LSB units to the first zero are removed. As a result, the corresponding \(k\)-binomial number is obtained, compressing the equilibrium code combination. 3. The obtained \(k\) is a binomial number using the binomial numbering function converted into the number of the binary number system. End procedure. Meanwhile, the binary sequence is restored in reverse order in two following key steps:  
1. By the known value \(k\) and the binary number \(l\), the transition to the corresponding binomial number \(m\) occurs. 2. The transition to the original binary sequence from the binomial code \(m\) by adding to LSB ending in 1, \(n-k\) zeros or ending in 0, \(k\) ones to \(n\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**N**} & \multirow{2}{*}{**Binomial Code**} & \multicolumn{2}{c}{**Equilibrium**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Binomial Code**} & \multirow{2}{*}{**Equilibrium**} \\  & & & **Combinations** & & **Combinations** \\ \hline
0 & 00 & 001111 & 8 & 10111 & 101110 \\
1 & 010 & 010111 & 9 & 1100 & 110011 \\
2 & 0110 & 011011 & 10 & 11010 & 110101 \\
3 & 01110 & 011101 & 11 & 11011 & 110110 \\
4 & 01111 & 011110 & 12 & 11100 & 111001 \\
5 & 100 & 100111 & 13 & 11101 & 111010 \\
6 & 1010 & 101011 & 14 & 1111 & 111100 \\
7 & 10110 & 101101 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Conversion of binary code to binomial code. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\textbf{N}\) & **Binomial Code** & **Binary Code** & **N** & **Binomial Code** & **Binary Code** \\ \hline
0 & 00 & 0000 & 8 & 10111 & 1000 \\
1 & 010 & 0001 & 9 & 1100 & 1001 \\
2 & 0110 & 0010 & 10 & 11010 & 1010 \\
3 & 01110 & 0011 & 11 & 11011 & 1011 \\
4 & 01111 & 0100 & 12 & 11100 & 1100 \\
5 & 100 & 0101 & 13 & 11101 & 1101 \\
6 & 1010 & 0110 & 14 & 1111 & 1110 \\
7 & 10110 & 0111 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Converting binomial code to binary code.
# A 1D-2D Coupled Lattice Boltzmann Model for Shallow Water Flows in Large Scale River-Lake Systems
## 1 Introduction
However, each iteration must go through an overall calculation, and one time step may need several iterations, resulting in a great calculation amount and a low computational efficiency. The coupling strategies listed above are tailored for traditional numerical methods. But for the LB method, the coupling strategy can be implemented at the mesoscopic level since it solves hyperbolic equations at the mesoscopic level. Currently, the works addressing the coupling procedure of interconnected subdomains for the LB-based shallow water simulations are rare. We found only one similar work that tried to couple a 2D shallow water submodel with 2D free-surface submodel [29], but the coupling strategy was still implemented at the macroscopic level, and the numerical stability was affected by both relaxation time and the size of the overlapping region. Here, we presented a rather simple and efficient 1D-2D coupled shallow water model, with the coupling strategy at the mesoscopic level. The coupling interface was regarded as an interior lattice node of the 1D domain with only three unknown distribution functions to be solved, and the numerical stability was only affected by the relaxation time. Moreover, the proposed model retained all the merits of the LB method [31; 32], especially its intrinsic parallelism, convenient processing complex boundaries, and easy programming, making it quite suitable for hydrodynamic simulations on a large scale river-lake systems. The paper is organized as follows. The 1D-2D coupled model is described in Section 2. Numerical tests are carried out to verify the accuracy and stability of the proposed model in Section 3. An application to simulating the surge wave propagation and reflection processes in a practical run-of-river hydropower station is presented in Section 4. Finally, a brief conclusion is made to end the article.
# An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
## 3 Proposed Two-Stage Cooperative Routing
### Two-Stage Link Cost
It can be seen from Equations (7), (9) and (19) that \(\mathcal{C}_{i,j}^{\text{MTE}}\) and \(\mathcal{C}_{i,j}^{\text{FA}}\) are the special cases of \(\mathcal{C}_{i,j}^{\text{TSC}}\) when \(u_{i,j}^{*}=i\), therefore, the link cost of the proposed is smaller or equal than that of MTE and FA. On the other hand, by comparing Equation (18) with (8) and (10), we can see that cost \(\mathcal{C}_{\mathcal{T}\left(u\right),j}\) is also smaller than the corresponding cost of PC-\(l\) and FACR. This is because that the denominators of Equations (18), (8) and (10) will decrease if we limit the transmitter set \(\mathcal{T}\) to its subset. In our scheme, all nodes which have received the data from \(i\) and are reachable to \(j\) can participate in the cooperative beamforming, while in PC-\(l\) or FACR, only the nodes in the current non-cooperative path are allowed to join.
## 4 Discussion
### Spatial and Temporal Distribution Characteristics of EVI and GPP
These findings align with the hypothesis of this study that the spatial distribution and clustering characteristics of EVI and GPP can reflect the ecological environment and vegetation growth conditions across different regions. However, the low-value aggregation of GPP in the Yangtze River Deltaregion is inconsistent with the expected high-productivity areas, suggesting the presence of unique environmental pressures or human-induced impacts in this region.
# Special Issue on Small Satellites Missions and Applications
## 3 Small Satellite Subsystems  
Due to the constraints they have to deal with, the design of small satellite subsystems often presents state-of-the-art solutions. It is therefore important to examine the contributions on small satellite subsystems in this Special Issue.  
One of the most investigated fields is that of guidance, navigation, and control (GNC). Modern small satellite missions heavily rely on autonomy to achieve their objectives. One of the new frontiers in this sense is the autonomous execution of close-proximity operations, which would open the way to a new market based on in-orbit services. Ref. [10] describes the design of the guidance and navigation algorithms necessary to perform a rendezvous between two small satellites. The guidance is based on an iterative algorithm so that the errors of the navigation are corrected at every firing step. Innovative algorithms rely on novel sensors, including image sensors. An artificial intelligence-based algorithm for brightest object segmentation in night sky images' field of view is presented in [11]. This algorithm has a dual use for both attitude determination and control operations and space surveillance and tracking. Other algorithms are dedicated to sensor calibration, as in the case of [12]. This paper focuses on the calibration of a magnetometer through genetic algorithms using data observed by BIRDS-3 CubeSats. The design of the electrical interface bus for the same nanosatellites is presented in [13]. This design allows for accommodating multiple missions and different payload requirements. An interface like this allows one to maximize the productivity of the system from a manufacturing point of view. Ref. [14] expands this concept to the structural design, introducing flexible 3U and 1U CubeSat platforms based on the slot concept, instead of the conventional stacking design method.  
Some sensors place stringent requirements on the spacecraft to guarantee their functioning. The temperature operational range is a classic example of the challenges involved in sensors' design and integration. The design of isostatic bipod mounts for the LIDAR primary mirror of the HERA mission is presented in [15]. Passive thermal control methods for imagers on micro and nanosatellites are investigated in [16]. The advantage of passive methods like the ones proposed in [15] and [16] is that they meet the specific design re quirements of the spacecraft and save energy on board, which is limited by the reduced dimensions of small satellites. The production of power through solar energy is an essential task and the power system needs to be reliable and robust to failures. Ref. [17] proposes using machine learning methods to evaluate faults on the solar panels.