esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement
### Airspace Allocation  
It is assumed that a flight area is composed of cubic airspaces in which several UASs are flying through their determined trajectories. Figure 2 shows the flight area divided in cubic airspaces \(C_{(x,y,z)}\), where \(u,v\), and \(w\) denote their number of cubes along the \(x,y,z\) axes, respectively.  
The flight area 3D size is defined by the product \(u\times v\times w\), where \(u,v\), and \(w\) are positive integers. UASs are located in cubes according to their GPS coordinates (i.e., latitude, longitude, and altitude), and they fly through these cubes following desired trajectories. To avoid conflicts, UASs should never be located in the same cube. Therefore, the supposed ASM must assign a safe cube \(C_{(x,y,z)}\) coordinates to each UAS. In our case study, each cube size was considered 100 m \(\times\) 100 m \(\times\) 100 m in the cubic airspace. The  
Figure 1: The ATM ecosystem.  
Figure 2: Flight area divided into cubic airspaces [43].  
cubic airspace was constructed by a 16 km\({}^{2}\) squared area and 6 km height (regarding the UAS-S4 operating range).  
Relying on the latitude and longitudes of the Earth, the airspaces can be mapped to cubes, and then the cube's coordinates are normalized. We first convert the spherical coordinates (latitude \(\alpha\) and longitude \(\beta\)) to Cartesian coordinates for a unit sphere:  
\[\begin{cases}x=\cos\alpha.\cos\beta\\ y=\cos\alpha.\sin\beta\\ z=\sin\alpha\end{cases} \tag{1}\]  
In order to project these coordinates onto a cube, our approach is a direct mapping wherein you multiply the coordinates based on the furthest point from the origin on the unit sphere to the cube's sides. The method described will project points from the sphere directly outwards to the enclosing cube. Given a point (\(\dot{x},\dot{y},\dot{z}\)) on the unit sphere, the corresponding point on the enclosing cube can be found by  
1. Find the axis with the maximum absolute coordinate value: \[m=\max\,(|\dot{x}|,|\dot{y}|,|\dot{z}|)\] (2)
2. Scale the point by the inverse of this value to project it onto the cube: \[\begin{cases}x=\dot{x}/m\\ y=\dot{y}/m\\ z=\dot{z}/m\end{cases}\] (3)  
Cubic airspace considers population and obstacle information that are stored on the blockchain. The verification logic is based on the consensus protocol explained as Algorithm 1, which relies on the conflict probability analyses explained as the conflict solution.  
```
1:Input: Application for propagation of blocks to all UASs
2:Output: Application is accepted/refused
3:Confirmation process of consensus algorithm (0)
4:\(h=0\)
5: waiting time \(\geq 2*T\)
6:while\(h\leq h_{max}\)do
7: Send agreement to neighbor UASs
8:if confirmed by more than 51% nodes then
9: execute the new block on the chain
10: break
11:else
12:\(h=h+1\)
13: Choose \(R\in\left[0,\ 2^{h}-1\right]\) randomly and wait for \(R*T\)
14:endif
15:endwhile
16:if\(h>h_{max}\)then
17: airspace rejection, and block execution is refused
18:endif
19:end process
```  
**Algorithm 1** Consensus procedure
## 3 Results and Discussion
### Influence of the (Li,Na)F/AlF\({}_{3}\) Molar Ratio on the Migration Number of Y(III) Ions  
In this section, AlF\({}_{3}\)-(Li,Na)F-3 wt.%Y\({}_{2}\)O\({}_{3}\) molten salt is studied at a temperature of 950 \({}^{\circ}\)C, current of 2 A, cathode current density of 1.25 A/cm\({}^{2}\), electrolysis time of 1 h, and (Li,Na)F/AlF\({}_{3}\) molar ratios of 2.0-3.0. The active Y(III) ion mobility is plotted as a function of the (Li,Na)F/AlF\({}_{3}\) molar ratio in Figure 4. When the (Li,Na)F/AlF\({}_{3}\) molar ratio varies between 2.0 and 2.5, the migration number of Y(III) ions is relatively stable, and its average value is equal to approximately 0.75. When the (Li,Na)F/AlF\({}_{3}\) molar ratio exceeds 2.5, the migration number of Y(III) ions decreases from 0.75 to 0.45, suggesting that the ion composition of the studied system is strongly affected by the (Li,Na)F/AlF\({}_{3}\) molar  
Figure 3: Variation of the Y(III) ion migration number with temperature.  
ratio. The compositions of anions and cations in the system are mainly determined by Equations (14) and (15). When the (Li,Na)F/AlF\({}_{3}\) molar ratio increases, reaction (14) shifts to the right, and the (Li,Na)\({}_{3}\)AlF\({}_{6}\) content increases. Meanwhile, the number of AlF\({}_{6}^{3-}\) ions in the system increases, and reaction (15) shifts to the right, increasing the number of free fluoride ions. (When the molar ratio of system (Li,Na)F/AlF\({}_{3}\) is lower than 2.5, with an increase in the system (Li,Na)F ratio, the free fluorine ions in the system mainly fluorinate with Y\({}_{2}\)O\({}_{3}\) in the system, the number of active Y(III) ions in the system increases, and the share of conductivity increases. At the same time, the number of Li\({}^{+}\), Na\({}^{+}\), and F\({}^{-}\) ions in the system also increases, and the share of conductivity also increases. The comprehensive effect is that the electric transfer number of Y(III) ions does not change significantly. When the molar ratio of system (Li,Na)F/AlF3 is higher than 2.5, with a further increase in the system (Li,Na)F ratio, the number of active Y(III) ions in the system reaches saturation; Li\({}^{+}\), Na\({}^{+}\), and F\({}^{-}\) ions in the system become the main carrier of current; the proportion of active Y(III) ions participating in conductivity decreases; and the number of electric transfer decreases approximately linearly.  
The obtained experimental data also show that when the (Li,Na)F/AlF\({}_{3}\) ratio exceeds 2.5, the mobility number of Y(III) ion decreases sharply.  
\[\text{AlF}_{3}+3\text{(Li,Na)F}\rightarrow\text{(Li,Na)}_{3}\text{AlF}_{6} \tag{14}\]  
\[\text{AlF}_{6}^{3-}\rightarrow\text{AlF}_{4}{}^{-}+2\text{F}^{-} \tag{15}\]
## 5 Construction of a Predictive Model for Accident Losses in Coal Mining Equipment Safety Investment
### Improvement of Particle Swarm Optimization Algorithm  
The BP neural network is capable of predicting accident losses following the implementation of safety investment strategies, but it often faces challenges such as local optima and overfitting during prediction. Traditional particle swarm optimization (PSO) [45] algorithms are praised for their simplicity, minimal parameter settings, and practical utility, yet they may suffer from over-reliance on parameter settings and premature convergence, leading to reduced search precision. Therefore, this study focuses on enhancing the PSO algorithm by optimizing critical parameters such as inertia weight (\(\omega\)) and learning factor (\(c\)) to improve its performance and stability [46; 47]. By fine-tuning these parameters, the enhanced PSO algorithm effectively explores solution spaces during BP neural network training, preventing it from becoming trapped in local optima and thereby enhancing the accuracy and reliability of the predictive model.  
(1) Improving Inertia Weight in PSO Algorithm  
In the PSO algorithm, the inertia weights balance the global exploration and local convergence by regulating the key parameter of the particle velocity updating process, so we choose to introduce the weight control factor based on the cotangent function, the value of which dynamically adjusts the size of the control factor with the change in the number of iterations, so that we can balance the global exploration and local convergence more efficiently, and thus improve the convergence speed and optimization effect of the algorithm.  
\[\omega_{i}=\omega_{max}-(\omega_{max}-\omega_{min})|\text{cot}(3\pi/4t/Maxt)| \tag{6}\]  
Here, \(\omega_{max}\) and \(\omega_{min}\) represent the maximum and minimum values of the inertia weight, respectively, while \(t\) denotes the current iteration count, and \(Maxt\) indicates the maximum number of iterations.  
(2) Improvement of Learning Factors in PSO Algorithm  
In the PSO algorithm, the acceleration coefficients (\(c_{1}\)) and (\(c_{2}\)) are critical parameters that govern the update of particle velocities, influencing the proportion of movement towards local and global optimum positions, respectively. By dynamically adjusting the  
Figure 3: Safety investment accident loss prediction rule model based on BP neural network.  
values of the acceleration coefficients (\(c_{1}\)) and (\(c_{2}\)), a balance between global exploration and local exploitation can be achieved as the number of iterations increases.  
\[c_{1}=(c_{start}-c_{end})(Maxt-t)/Maxt-c_{end}0<c_{end}<c_{start}<4.5 \tag{7}\]  
\[c_{2}=4-c_{1} \tag{8}\]  
Here, \(t\) denotes the current iteration count, \(Maxt\) indicates the maximum number of iterations, and \(c_{start}\) and \(c_{end}\) represent the initial and final values of \(c_{1}\), respectively.
# Effects of Diverging Nozzle Downstream on Flow Field Parameters of Rotating Detonation Combuster
## 3 Numerical Results and Discussion
### Effect of Diverging Nozzle on the Formation and Propagation Process of Detonation Wave
In order to further confirm that, a series of diverging angles were selected to explore the formation and propagation process of detonation waves, whose \(\theta\) were 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\), respectively. Here the detonation wave height is introduced as a key parameter to reflect the propagation characteristics of detonation wave in RDCs [52; 53]. Figure 10 demonstrates the evolution of detonation wave height over time near the inner and outer wall of RDC with \(\theta\) = 0\({}^{\circ}\). Obviously, in the stable self-sustaining propagation stage of the detonation wave, detonation wave height shows a significant periodic change characteristic. Considering the convergence and divergence effect of combustor walls, the detonation wave height near the inner wall (\(h_{1}\)) is smaller than that of the outer wall (\(h_{2}\)) [54]. The average detonation wave height (\(h_{d}\)) is defined here by:  
\[h_{d}\ =\ (h_{1}+h_{2})/2. \tag{11}\]  
Table 3 shows the values of \(h_{d}\) for RDCs with specific angles. The results indicate that with increasing \(\theta\), the \(h_{d}\) changes slightly. For example, when \(\theta\) is changed from 0\({}^{\circ}\) to 45\({}^{\circ}\), the \(h_{d}\) is only reduced by 1.56%. The detonation wave height is reflected by the heat release rate of the positive shock wave [52]. Thus, this calculated result of \(h_{d}\) means that the positive shock wave has not been significantly affected under the diverging nozzle. Combined with the analysis of Figures 6-9, it can be concluded that the formation and propagation processes of detonation waves are not affected by the diverging nozzle. Figure 10: _Cont._  
Figure 9: Pressure contour in the RDC with \(\theta\) = 30\({}^{\circ}\).
## 3 Nanocellulose  
In recent years, aside from the production of biofuel and other chemicals, the global research focus has switched to the synthesis of nanomaterials such as nanocellulose (NC), utilizing cellulose from LB. NCs are cellulose fibrils or crystallites that have at least one dimension falling within the region of the nanoscale. Generally, the aspect ratio of NC is high, with a length of up to several microns and lateral dimensions of lower than 100 nm [35]. NC made from different sources of cellulose may differ in the amount of cellulose present, degree of crystallinity, and most notably, the size of the crystalline domains [31]. NC possesses excellent properties, such as special morphology and geometrical dimensions, crystallinity, mechanical strength, high specific surface area, thermal stability, rheological properties, stiffness, alignment and orientation, barrier properties, light weight, non-toxicity,  
Figure 1: Chemical structure of cellulose from lignocellulosic biomass.  
biodegradability, low density (around 1.6 g/cm\({}^{3}\)), biocompatibility and surface chemical reactivity. In particular, it has a higher stiffness than Kevlar fiber, with an elastic modulus of up to 220 GPa. Owing to these remarkable attributes, NC demonstrates a significant potential for a wide range of applications, providing new opportunities for the production of novel value-added materials, including food packaging, polymer and paper reinforcement, additives to composites emulsions, barrier films, water treatment and within the health care sector [8; 19; 35]. However, it has been reported in some studies that nanocellulose possesses low thermal stability, which restricts their application and the conditions in which their nanocomposites can be produced at high temperatures. This problem is especially serious for cellulose nanocrystals that have been hydrolyzed in sulfuric acid, since it has much less heat stability than the original starting material or other types of nanocellulose [38].  
The presence of a large number of hydroxyl groups aids in the functionalization of NC and the tuning of its various surface properties. This characteristic of NC paves the way for their application in different areas. The source of cellulose, its isolation process, as well as pre- and post-treatment greatly impacts the shape, architecture, structure, size and other properties of NC. Additionally, it is feasible to model the properties of the NC for a particular application based on the source from which the cellulose is derived, the pre-treatment and the conversion methods selected, bringing up a wide range of opportunities [19; 35]. The three major types of NC, i.e., cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs) and bacterial nanocellulose (BNC), are discussed in the below sections. Despite having identical chemical compositions, the various types differ from one another in terms of shape, particle size, crystallinity and other characteristics, as a result of variation in the method of isolation and source of cellulose.
# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 3 Experimental Results
### Comparison with Offline Learning Algorithms
#### 3.2.1 VOC2005 Multi-Class Classification  
In this section, we compare offline learning algorithms that use a single feature. The database is VOC 2005 [29], which includes bikes, vehicles, motorcycles, and pedestrians-four classes for classification. There are a total of 63 bikes, 159 vehicles, 109 motorcycles, and 81 pedestrians to use for positive samples in the training data. Negative samples of one class are the positive samples of the other classes that do not belong to this class. For example, the negative samples of bikes are the 159 vehicles, 109 motorcycles, and 81 pedestrian images. VOC 2005 provides two testing data, the simple one-test1 and the difficult one-test2. This paper adopts the difficult one-test2 dataset. It is difficult because the variability in samples is large. For example, there are side views, front views, and fallen down vehicles. There are different pedestrian poses, and sometimes there is occlusion. These testing data included 342 bikes, 295 vehicles, 202 motorcycles, and 874 pedestrians. The negative samples were chosen in the same way as the training data were selected.  
We performed the experiment eight times and reported the results in this section. Each experiment used 40% of the training data for initializing, and then 10% of the other 60% of data was used as a unit for updating the model. The final result used EER for evaluation, as shown in Table 3. We can see that the standard deviation of the average was only 0.7%. This proves that the stability of the proposed method is very good. A comparison with other work is shown in Table 4. In Table 4, the main comparison method, ICBS [31], uses Saliency  
Figure 24: Equal Error Rate.  
Driven Nonlinear Diffusion and Multi-scale Information Fusion to blur the background and make the goal object have a better effect during feature extraction. Finally, it uses single feature SIFT [32] combined with SVM [10] to perform training and classification. From Table 4, it is found that although we used online learning for training, it could still achieve good performance by making the feature pool more adaptive to the pattern pool because of dynamically allocation of multiple features. The proposed method outperformed other single-feature offline learning algorithms by at least 3.1% in both average and best results. This proves that the proposed method can obtain a good result in multi-class classification.
# A Branch-and-Price Algorithm for the Online Scheduling of Valet Drivers
## 4 Column Generation-Based Method
### Set Partition Formulation  
Let \(\Omega\) be the set of all feasible routes of the valets. Each route \(r\in\Omega\) represents a route that starts from the source node and ends at the sink node, and its cost is denoted by \(c_{r}\). The variable \(b^{r}_{ij}\) indicates whether route \(r\) traverses through arc \((i,j)\): \(b^{r}_{ij}=1\) means route \(r\) traverses thought arc \((i,j)\), otherwise \(b^{r}_{ij}=0\). Variable \(a_{ir}\) represents whether node \(i\) is visited in route \(r\): \(a_{ir}=1\) means node \(i\) is visited in the route \(r\), otherwise \(a_{ir}=0\). Then, the following relationship \(c_{r}=\sum_{(i,j)\in A}b^{r}_{ij}c_{ij}\), \(a_{ir}=\sum_{[i\in V](i,j)\in A}b^{r}_{ij}\) is obtained. There is only a binary decision variable \(\theta_{r}\) in the set partitioning model; \(\theta_{r}\) equals 1 if route \(r\) is selected in the solution, and 0 otherwise. There is also a binary parameter \(a_{ir}\), which equals 1 if node \(i\) is visited in route \(r\), and 0 otherwise.  
The set partitioning model is presented as follows:  
\[Min\quad\sum_{r\in\Omega}c_{r}\theta_{r} \tag{17}\]  
s.t.  
\[\sum_{r\in\Omega}a_{ir}\theta_{r}=1\text{,}\forall i\in O\cup P \tag{18}\] \[\sum_{r\in\Omega}\theta_{r}\leq|K| \tag{19}\]  
The objective Function (17) is to minimize the total cost of all the selected routes. Constraint (18) ensures that each order can only be served once and every valet can only start once. Constraint (19) means that the number of all selected routes is less than or equal to the number of valets.
## 3 Results and Dissscussion  
### Xrd  
The X-ray diffraction patterns of the Cu-coated SiC particles, for two different durations of the coating stage, are presented in Figure 2.  
At a first look at the XRD patterns depicted in Figure 2 one can observe that the Cu phase appears at multiple angular positions after the 60 min coating time when compared to the 30 min coating time pattern. This can suggest that together with the growth of the coating time, the Cu quantity also increases, as expected. This aspect can be translated into a better wettability of the SiC particles in the aluminum alloy.  
Although it is expected that part of the metallic coating will be detached from the ceramic particles, according to the research results shown in Figure 3 it is found that the Cu film remains attached to the SiC particle, its role being to help integrate more of the SiC particle in the aluminum metal mass. SEM analyzes were performed on the presented sample, which also demonstrates the presence of copper on the surface of the SiC particle--Figure 3 and Table 3. The presence of pores and microcracks at the Al alloy-SiC interface is a result of the surface preparation process of the metallographic sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element Line** & **Net Counts** & **Weight, \%** & **Atom, \%** \\ \hline \(C\)\(K\) & 292 & 10.01 & 18.82 \\ \(O\)\(K\) & 1017 & 12.69 & 17.90 \\ \(Al\)\(K\) & 9203 & 50.56 & 42.31 \\ \(Si\)\(K\) & 3337 & 25.56 & 20.55 \\ \(Cu\)\(L\) & 95 & 1.18 & 0.42 \\ \hline _Total_ & & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of EDS analysis on the studied material.  
Figure 2: Composite’s structural arrangement. (**a**) 30 min coating time, (**b**) 60 min coating time.
## 2 Related Works  
### Performance Forecasting  
Various approaches are used in research to predict the performance of TBM tunneling, including empirical models, machine learning (ML) and artificial intelligence (AI) algorithms, and simulation models. [PERSON] et al. (2016) developed an empirical model to estimate TBM performance [6]. [PERSON] and [PERSON] (2009) discussed crucial soil mechan  
Figure 1: Design of a slurry shield machine.  
ics parameters and key performance indicators to estimate TBM performance including the excavation, ringbuild, and stoppage duration for EPB shield TBMs [7]. Another area of focus is on the relationship between intact rock properties and machine parameters like penetration and cutter load in soft sedimentary rock in the empirical model to predict TBM performance by [PERSON] et al. (2021) [8]. ML algorithms and AI methods can be used on the time series data received by sensors in TBM tunneling. [PERSON] et al. (2021) compared different ML models with single and multiple outputs to forecast three TBM load parameters: the torque, advance rate, and thrust [9]. In addition to shield operational parameters, [PERSON] et al. (2021) utilized ML algorithms to predict tunneling-induced settlement [10]. A variety of AI algorithms are used, e.g., by [PERSON] et al. (2024) [11] to predict the penetration rate and by various authors to predict TBM performance [12; 13; 14; 15; 16].  
However, the majority of empirical models and ML and AI methods are limited to predicting TBM performance based on sensor data; the logistical aspects influencing the TBM performance are missing. Process simulation models serve as a valuable instrument for investigating intricate process interactions and identifying performance-limiting elements while considering uncertainties. Due to their repetitive process chains, mechanized tunneling projects are particularly suitable for process simulation studies. For tunneling, several simulation approaches exist, of which most are limited to the planning phase [4; 17; 18; 19; 20; 21; 22].
# GPP of a Chinese Savanna Ecosystem during Different
## 2 Materials and Methods
### MODIS Land Surface Reflectance and GPP Product Data  
The MODIS land science team provides a suite of eight-day composite products, including an eight-day SR product (MOD09A1) and an eight-day GPP product (MOD17A2). The MOD09A1 Collection 6 product provides 500 m spatial resolution and 8-day estimates of the SR for seven spectral bands that are used for vegetation and land surface measurements. A detailed description of MOD09A1 is given at [[https://lpdaac.usgs.gov/products/mod09a1v006/](https://lpdaac.usgs.gov/products/mod09a1v006/)]([https://lpdaac.usgs.gov/products/mod09a1v006/](https://lpdaac.usgs.gov/products/mod09a1v006/)), accessed on 3 April 2024. After data quality control of MOD09A1, the land SR could be obtained, and the NDVI, EVI, and LSWI (Equations (2)-(4)) were calculated in the GEE.  
GPPMOD17A2 was included in this study for comparison with other remote sensing data sources. Over the study period, which was centered on the EC flux site, MOD17A2H products were selected and accessed through the GEE. The GPP of MOD17A2H corresponded to the sum of the GPP values during an 8-day period, so the original MOD17A2 GPP values were transformed from kg C m\({}^{-2}\) 8-day\({}^{-1}\) to g C m\({}^{-2}\) day\({}^{-1}\) to enable pairwise comparisons with the daily flux tower measurements.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Spectral Bands** & **From OLI to MSI** & **From ETM+ to MSI** \\ \hline Blue & \(\mathrm{MSI}=-0.0107\) + \(1.0946\times\mathrm{OLI}\) & \(\mathrm{MSI}=-0.0139\) + \(1.1060\times\mathrm{ETM}+\) \\ Green & \(\mathrm{MSI}=0.0026\) + \(1.0043\times\mathrm{OLI}\) & \(\mathrm{MSI}=0.0041\) + \(0.9909\times\mathrm{ETM}+\) \\ Red & \(\mathrm{MSI}=-0.0015\) + \(1.0524\times\mathrm{OLI}\) & \(\mathrm{MSI}=-0.0024\) + \(1.0568\times\mathrm{ETM}+\) \\ Near-infrared (NIR) & \(\mathrm{MSI}=-0.0021\) + \(1.0283\times\mathrm{OLI}\) & \(\mathrm{MSI}=-0.0140\) + \(1.1515\times\mathrm{ETM}+\) \\ Shortwave infrared (SWIR1) & \(\mathrm{MSI}=0.0065\) + \(1.0049\times\mathrm{OLI}\) & \(\mathrm{MSI}=0.0041\) + \(1.0361\times\mathrm{ETM}+\) \\ SWIR2 & \(\mathrm{MSI}=0.0046\) + \(1.0002\times\mathrm{OLI}\) & \(\mathrm{MSI}=0.0086\) + \(1.0401\times\mathrm{ETM}+\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross-sensor transformation functions for the MSI, ETM+, and OLI TOA data used in this study.