esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Catalytic C-C Coupling  
Heck reaction was carried out as follows. CNT-NM (p-MWCNT-Pd or np-MWCNT-Pd) (3 mg, 0.01 mol %), iodobenzene (496.8 uL, 4.44 mmol), styrene (765.1 uL, 6.69 mmol), tributylamine (TBA) (2.014 mL, 8.92 mmol) and 0.5 mg PPh\({}_{3}\) were mixed in 5 mL of dry solvent in a two necked round bottom flask. The resulting suspension was placed in a preheated rota-mantle at 140 \({}^{\circ}\)C with magnetic stirring. The course of the reaction was followed by periodically withdrawing aliquots (50 uL) from the reaction mixture and analyzing by GC. The reaction was carried out with triphenylphosphine (PPh\({}_{3}\)) in dimethylformamide (DMF) and toluene. The same reaction was carried out with commercial Pd/C (24 mg, 0.01 mol %) and also with PdCl\({}_{2}\) (2 mg, 0.01 mol %) to compare the catalytic activity of the CNT-NMs. The reaction is shown in Scheme 1.  
After 1 h reflux, the mixture was cooled for 15 min and extracted with diethyl ether. This was followed by washing with 2 N HCl and water to remove any base present. The ether layer was then dried over CaCl\({}_{2}\). Finally, the solution was filtered and dried in vacuum desicator to constant weight. The obtained product was recrystallized from absolute alcohol and its composition was confirmed by comparison with pure stilbene. The final product did not show the presence of any byproducts by thin layer chromatography (TLC) analysis. The melting point of the recrystallized product and UV-\(\lambda_{\text{max}}\) were found to be 122.5 \({}^{\circ}\)C and 296 nm which was in line with what was expected for pure stilbene [44]. GC analysis of the product also showed only the presence of stilbene.  
The aforementioned reactions were also carried out in microwave conditions at 420 W [45]. The amount of reactant, catalyst, and base (but no solvent) were maintained the same as done under reflux conditions. But, here the vessel was kept in microwave oven for 17 min where reaction went to completion with no reactant observed in the TLC analysis.
## 3 Results and Discussion  
### Characterization of BT-3  
The properties of BT-3 are listed in Table 1. The average particle size is about 0.5-1 mm. Furthermore, the bulk density and true density are 1.56 and 2.38 g cm\({}^{-1}\), respectively, which indicates that it is easy to separate from aqueous solutions. The BET specific surface area and pore volume of BT-3 are 174 m\({}^{2}\)/g-solid and 0.14 m\({}^{2}\)/g-solid, respectively, which reveals the high surface area of BT-3 adsorbent. Figure 2 displays four different magnifications of the morphology of BT-3. An oval shape  
Figure 1: Fluidized bed reactor.  
with irregular surface morphology reveals its high surface area. Figure 3 shows the XRD patterns of BT-3. According to the diffraction files of the Joint Committee on Powder Diffraction Standards (JCPDS), the main diffraction peaks of BT-3 adsorbent at 2\(\theta\) = 21.5\({}^{\circ}\), 36.7\({}^{\circ}\)and 53.3\({}^{\circ}\) adsorbent was identified as the \(\alpha\)-FeOOH phase.  
\begin{table}
\begin{tabular}{l r} \hline
**Properties** & **BT-3** \\ \hline Color & Black \\ Material & Iron oxide \\ Bulk density (g:cm\({}^{-1}\)) & 1.56 \\ True density (g:cm\({}^{-1}\)) & 2.38 \\ Total iron content (g:Kg\({}^{-1}\)) & 649 \\ Specific surface area (BET) (m\({}^{2}\)/g-solid) & 174 \\ Pore vol. (cm\({}^{3}\)/g) & 0.14 \\ Average grain size (mm) & 0.5–1 \\ \hline \end{tabular}
\end{table}
Table 1: Physical properties of BT-3.  
Figure 2: SEM-EDS images of **(a)** BT-3, **(b)** BT-3 covered by MnO\({}_{2}\).
## 4 Model Description  
### Computational Model  
As mentioned before, E-STR is a product modified from the existing E-GasTM gasifier design. The model in this study adopts the major geometry and dimension of the previously developed E-Gas model [2; 4], of which the height (\(H\)) and length (\(L\)) is 12 m and 8 m, respectively. For the E-Gas model, 80% of coal/slurry and all oxidant are fed on both sides of the 1 st stage, while the remaining 20% of coal/slurry is fed at the 2 nd stage. In the E-STR model, the length is shortened from 8 m to 2 m by literally removing the horizontal cylindrical section, as the schematic diagram shown in Figure 1. The fuel feeding configuration is also modified by feeding all coal/slurry at the 2 nd stage and all oxidant at the 1 st stage. The unreacted char leaves the reactor and recycles back to the 1 st stage fed port. A reasonable recycling rate will be deduced in the following section.  
The grid sensitivity study was conducted with three grids (94,683, 186,200, and 379,573). The difference of the species between the cases of 94,683 and 186,200 is about 3.5%, and it reduces to 1.9% at the case of 379,573. The difference of outlet temperature between the two cases is less than 1%. To save computational time, the grid containing 186,200 tetrahedral meshes is used.  
\begin{table}
\begin{tabular}{c c c} \hline
**Reaction** & \(A_{r}\) & \(E_{r}\) (J/kmol) \\ \hline C\({}_{(\rm{s})}+1/2\)O\({}_{2}\rightarrow\) CO & 0.052 & 6.1E+07 \\ C\({}_{(\rm{s})}+\) CO\({}_{2}\rightarrow\) 2 CO & 0.0732 & 1.125E+08 \\ C\({}_{(\rm{s})}+\) H\({}_{2}\)O \(\rightarrow\) CO + H\({}_{2}\) & 0.0782 & 1.15E+08 \\ CO + 1/2O\({}_{2}\rightarrow\) CO\({}_{2}\) & 2.2E+12 & 1.67E+08 \\ H\({}_{2}+1/2\)O\({}_{2}\rightarrow\) H\({}_{2}\)O & 6.8E+15 & 1.68E+08 \\ \hline CH\({}_{2.761}\)O\({}_{0.264}\rightarrow\) 0.479H\({}_{2}+\) 0.264 CO + 0.356 CH\({}_{4}\) + & & \\ 0.19C\({}_{2}\)H\({}_{2}\) & & \\ C\({}_{2}\)H\({}_{2}+\) O\({}_{2}\rightarrow\) 2 CO + H\({}_{2}\) & & \\ CH\({}_{4}+1/2\)O\({}_{2}\rightarrow\) CO + 2H\({}_{2}\) & & \\ \hline CO + H\({}_{2}\)O \(\rightarrow\) CO\({}_{2}+\) H\({}_{2}\) & 2.75E+02 & 8.38E+07 \\ \hline \end{tabular}
\end{table}
Table 1: Global reaction rate constants in this study.  
Two operating pressures (28 and 70 atm) are considered. The wall is assumed to be adiabatic. The feedstock and operating conditions of the base case are listed in Table 4. In the base case, no unreacted char is recycled, and the operating pressure 28 atm is considered. The properties of the employed coal particles are listed in Table 5.
## 4 Network Layer: The Main Building Block of Fixed Networks
### F1G--Optical Access Networks  
A completely new era of access networks is the optical access network [7]. Their installation should be justified economically, considering the high cost of installing an optical fiber for the user [8]. To this end, the following solutions were adopted: (a) Fiber to the home (FTTH) when the replacement of the existing network seemed necessary, (b) Fiber to the curb (FTTC), or (c) Fiber to the building (FTTB), according to which extent the copper cables should be maintained. Three optical access network architectures were developed [9]: (i) _Point-to-point architecture_, where the users are connected to the central office (CO) via a dedicated fiber. It has the advantage of a high bandwidth for the user, while the disadvantages are the high cost of installing many FTTH systems and the user's equipment for the electro-optical signal conversion. Two transceivers per connection are needed (point-to-point). (ii) _Active star (point-to-multipoint) architecture_, where users are connected to nearby active nodes (hubs) by either copper or fiber cables, or even wirelessly, while the active hub is connected to the CO (and through the CO to the backbone network) by a single optical fiber. The hub comprises one transceiver and is called active just because it needs a power supply. Each user also requires one transceiver. (iii) _Passive Optical Network (PON) architecture_, like the active star architecture, but the active node is replaced by a passive device, which is a splitter/combiner (depending on the traffic flow direction) or an arrayed waveguide grating (AWG). This is the most popular architecture since the passive device reduces the cost of the entire access network. In PON, the transmission takes place between an optical line terminal (OLT) and the optical network units or terminals (ONUs or ONTs) via the passive device, covering a maximum distance of 50 km with a speed in the order of Gb/s. The OLT belongs to the CO, while the ONU/ONT are located in the user area (ONU at the edge of FTTC or FTTB, while ONT at the edge of FTTH). Communication between PON users is possible only via the OLT.
## 2 Data Preparation  
Due to the particularity of the detection targets, there is presently no publicly accessible dataset for foreign object detection in underground coal mines. Therefore, the dataset utilized in the experiment was acquired from video images captured by intelligent inspection robots in coal mines during the belt conveyor operation in the Mining Fluid Control Engineering Laboratory of Shanxi Province. The experimental environment and hardware setup are illustrated in Figure 1.  
The equipment and image parameters are as follows:  
* The conveyor belt operates at a speed of 4 m/s.
* The mining inspection robot captures frames at a rate of 40 frames/s.
* The image resolution is 1920 \(\times\) 1080.  
However, transmitting data at this resolution and under these testing conditions demands a substantial amount of memory and bandwidth. High hardware computing capability is needed when implementing this in an industrial setting, especially in edge computing. To reduce the computational costs and enhance network performance, this study resizes the images to 224 \(\times\) 224 using Python (version: 3.7.0) batch processing. The images are annotated using Labelme software (version: 5.1.1) and stored in the VOC2007 format as the dataset.  
Considering the adverse underground environment in coal mines and the impact of vibrations caused by the movement of inspection robots on the network detection performance, all images in the dataset are subjected to processing techniques such as motion blur, dust and fog effects, and reduced brightness. After data augmentation, the dataset comprises 17,483 samples of foreign object images with 44,480 corresponding data labels. Throughout the training procedure, the entire collection of image samples was divided into a training set (comprising 12,238 images), a validation set (comprising 3496 images), and a test set (comprising 1749 images) in a ratio of 7:2:1. The dataset includes various types of foreign objects, such as anchor rods, angle irons, trays, gangue, nuts, and screws. Some sample images are illustrated in Figure 2.  
Figure 1: Experimental environment. (**a**) Belt conveyor used in the experiment; (**b**) mine inspection robot.
# 3D Reconstruction Based on Iterative Optimization of Moving Least-Squares Function
## 3 Methods  
Our method presents an end-to-end 3D surface-generation algorithm. As depicted in Figure 1, the algorithm involves the following steps:  
1. Adaptive Octree Voxel Partitioning: The input point cloud undergoes adaptive octree voxel partitioning based on cosine similarity, as detailed in Section 3.1;
2. Contextual Information Encoding: A convolutional neural network (CNN) is employed to extract local features within the smallest octant voxel. Subsequently, a GRU transmits contextual information across different scales, as described in Section 3.2;
3. Moving Least Squares Surface Generation: Following MLP processing, the accumulated contextual information from voxels of various scales is utilized to generate a moving least squares point set. This enables the approximation of locally smooth and coherent surface geometry on a large scale, as elaborated in Section 3.3;
4. Model Optimization: The network parameters are optimized by minimizing the loss function defined in Section 3.4.
# Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
## 2 Related Work
### Semantic Segmentation in Remote Sensing  
In the field of remote sensing, semantic segmentation is also widely used [17; 18], including urban planning [19], vehicle detection [20], climate prediction [21] and ocean vortex tracking [22]. For different remote sensing scenes and their characteristics such as large changes in object scale, dense arrangement of objects, and too small target objects, researchers also modified the generic semantic segmentation network accordingly to better fit the characteristics of the remote sensing images. For example, SSCDnet [23] uses semi-supervised learning of domain adaptive features to solve the data distribution drift problem of remote sensing images. [PERSON] et al. [24] added spatial relations and channel information to convolutional networks to improve the competitiveness of the network. FarSeg [25] correlates target objects with background features through geospatial scenes and enhances the recognition of foreground objects with contextual information, which greatly reduces the false positives due to excessive similarity between target objects and background. Factseg [26] enhances the feature recognition of small targets by designing foreground activation drives. The framework consists of a double branch, partly used to suppress the large-scale background and partly used to activate the features of small objects. Some recent methods [5; 27; 28] try to incorporate modules that are effective in the field of general segmentation into remote sensing image segmentation networks, such as the well-known transformer or attention mechanisms, which are effective in improving the accuracy of the networks to some extent. However, these methods mainly target special application scenarios and are not effective in solving problems of semantic segmentation for high-resolution remote sensing images, such as multi-scale variation of objects and loss of foreground details.
# The Relationship between Process Capability and Quality of Measurement System
## 1 Introduction  
After setting up the CNC machine (lathe), a trial production of shafts (for surgical drills) was started. Dimension d\({}_{2}\) was measured by metrological appraiser A on the first 45 products. Each measurement was repeated three times. A digital screw micrometer Kinex with resolution d* = 0.001 mm was used as the measurement equipment. The measured values were compared with the value specifically required by the customer. This represented the target value T = 7.98 mm, LSL = 7.958 mm and USL = 7.98 mm. The calculated values of descriptive statistics (arithmetic mean, standard deviation, test for outliers and normality) and process capability indices are given in Table 1. The presented calculation assumed that the investigated parameter (shaft diameter) followed the normal distribution. Quantum XL software (Quantum XL 2016 v5.60, Orlando, FL, USA) was used for the calculation. The results are shown in Table 1. Outliers were determined by the Grubbs' test (at the selected significance level \(\alpha=0.05\)) using GraphPad software ([[https://www.graphpad.com/quickcals/index.cfm](https://www.graphpad.com/quickcals/index.cfm)]([https://www.graphpad.com/quickcals/index.cfm](https://www.graphpad.com/quickcals/index.cfm)), accessed on 26 January 2021). The test confirmed that the measurement was subject to gross errors and was not under statistical control. The Anderson-Darling test was used to test for normality. If the value in Table 1 was \(p>0.05\), the analyzed file had a normal distribution. The most common explanation of the C\({}_{\rm P}\) and P\({}_{\rm P}\) indices is this: C\({}_{\rm P}\) is the short-term capability of a process, and P\({}_{\rm P}\) is its long-term capability. The truth is that these statistical indices are much more than that, and it is important to understand what the process and capability statistics really mean. However, in order to reveal the true state of the process, obtained data must be accurately assessed and interpreted (B). Process capability studies are conducted for three reasons:
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 3 Link-Level Model
### Correlated Shadowing  
The correlated shadowing generation model is mainly based on the decaying exponent of distance (i.e., distance-only model), as described in [38]:  
\[P_{rx(i,j)}\big{(}d_{i,j}\big{)}=e^{-\frac{d_{i,j}}{\sigma_{0}}}\,, \tag{6}\]  
where \(P_{rx(i,j)}\) is the measured received power at \(ED_{j}\), when a packet is transmitted by \(ED_{i}\,d_{i,j}\) is the distance between two EDs (\(i\) and \(j\)); and \(d_{0}\) is a de-correlation distance parameter that is tunable and always greater than zero. The \(d_{0}\) was set to 110 m in [2; 39]. The shadowing values of EDs, which are not closely positioned on a vertex of the grid, are interpolated using an exponential covariance function described in [40]. In addition, the correlated shadowing in wireless communication is generally classified into two cases, and its implementation details are described in [5]:  
1. When an ED transmits a packet to a GW, it is anticipated that the shadowing experienced by the GW is correlated with another shadowing disturbance at any other ED, which is \"near\" to it. This phenomenon is dependent on \(d_{i,j}\) and has been demonstrated with an exponential function in [41].
2. If two EDs near each other transmit, it is expected that the shadowing values are correlated at the GW. This consequence is the site-to-site cross-correlation, as described in [42].  
The model shown in Equation (6) captures the first case accurately and indicates that a GW \"observes\" two correlated shadowings caused by adjacent EDs. This observed shadowing caused by nearby EDs is used in the grid for every similar point. It results from the fact that signals of two adjacent EDs are mutually affected. In addition, the amount of shadowing that the GW experiences will be the same for the signals of two nearby EDs; therefore, the signal losses are correlated [43].
## 3 Calibration Equipment and Tests  
### Calibration Equipment  
Due to the novel structure of the developed FBG sensor, it is inconvenient to calibrate it with traditional calibration test equipment. Therefore, a simple calibration device specifically for the FBG sensor was designed and manufactured (Figure 13). The device consists of support frame, guide, and load components. The support frame includes four adjustable bases, a bearing plate, and a sensor base. The guide component includes four guide bars that are fixed onto the bearing plate. The load component includes a loading plate, a loading bar, and some weights. The weights are placed onto the loading plate and the forces are transferred to the sensing unit via the loading bar.
## 2 Three-Dimensional Imaging Theory
### Measurement Principle
\right.. \tag{8}\]  
After \(M\) measurements, two sets of measurement values, A and B, are obtained from Equation (8):  
\[\left\{ \begin{array}{l}A=\left[a^{1},a^{2},a^{3},\cdots\cdots,a^{k},\cdots \cdots,a^{M}\right]^{T}\\ B=\left[b^{1},b^{2},b^{3},\cdots\cdots,b^{k},\cdots\cdots,b^{M}\right]^{T} \end{array} \right.. \tag{9}\]  
Equation (9) expresses the product of the sine and cosine of the phase difference of each pixel and the signal amplitude as two column vectors:  
\[\left\{ \begin{array}{l}x_{1}=\left[\begin{array}{l}M_{R_{11}}\sin\Delta \varphi_{11},M_{R_{12}}\sin\Delta\varphi_{12},\cdots\cdots,M_{R_{1n}}\sin \Delta\varphi_{1n},\cdots\\ \cdots,M_{R_{n1}}\sin\Delta\varphi_{n1},\cdots\cdots,M_{R_{nn}}\sin\Delta \varphi_{nn}\end{array}\right]^{T}\\ x_{2}=\left[\begin{array}{l}M_{R_{11}}\cos\Delta\varphi_{11},M_{R_{12}} \cos\Delta\varphi_{12},\cdots\cdots,M_{R_{1n}}\cos\Delta\varphi_{1 nt},\cdots \\ \cdots,M_{R_{n1}}\cos\Delta\varphi_{n1},\cdots\cdots,M_{R_{nn}}\cos\Delta \varphi_{nn}\end{array}\right]^{T}\end{array} \right.. \tag{10}\]  
According to Equations (8)-(10), and the measurement matrix \(\Phi\) corresponding to \(M\) measurements, we can obtain  
\[\left\{ \begin{array}{l}A=\Phi x_{1}\\ B=\Phi x_{2}\end{array} \right.. \tag{11}\]  
The two equations in Equation (11) are solved by the compressed sensing reconstruction algorithm, and the vectors \(x_{1}\) and \(x_{2}\) are reconstructed. We can eliminate the unknown parameter \(M_{Rij}\) in \(x_{1}\) and \(x_{2}\), and we then get the delay phase \(\Delta\varphi_{nn}\) of the target. The distance of each point is calculated according to the phase method ranging principle to complete the compressed sensing 3D imaging of the target.
###### Abstract  
In some geographically challenging areas (such as deserts, seas, and forests) where direct connectivity to a terrestrial network is difficult, space communication is the only option. In these remote locations, Internet of Space Things (IoT) applications can also be used successfully. In this paper, the proposed payload for IoT applications demonstrates how an Automatic Identification System (AIS) and a fire detection system can be used effectively. A space mission based on efficient and low-cost communication can use a constellation of nanosatellites to better meet this need. These two applications, which use a constellation of nanosatellites, can provide relevant university-level data in several countries as an effective policy for the transfer of space technology in an educational initiative project. To enhance educational participation and interest in space technology, this paper shares the lessons learned from the project feasibility study based on an in-depth design of a nanosatellite with several analyses (data budget, link budget, power budget, and lifetime estimation). Lastly, this paper highlights by experiments the development and application of a cost-effective sensor node for fire detection and the use of GPS to enable AIS capabilities in the IoST framework.  
nanosatellites; constellation; internet of space things; AIS; fire detection 2023233  
K.D.E.; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Applications of Nanosatellites in Constellation: Overview and Feasibility Study for a Space Mission Based on Internet of Space Things Applications Used for AIS and Fire Detection. _Sensors_**2023**, 23, 6232. [[https://doi.org/10.3290/s23136232](https://doi.org/10.3290/s23136232)]([https://doi.org/10.3290/s23136232](https://doi.org/10.3290/s23136232))