esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dye Synthesis  
The synthesis conducted was a modification of a method reported previously [18].  
_4-(4'-Methyl-benzyloxy)-phthalonitrile_ (**3**), 4-Nitrophthalanonitrile (3 g, 17.34 mmol) and 4-methylbenzyl alcohol (2.12 g, 17.34 mmol) were dissolved in 20 mL of dry DMF and stirred under N\({}_{2}\). After 5 min, K\({}_{2}\)CO\({}_{3}\) (7.19 g, 52.01 mmol) was added and the reaction was stirred at 85 \({}^{\circ}\)C for 7 h. After cooling, the reaction mixture was poured into cold water. The crude product (1.75 g) was purified by column chromatography using silica gel (230-400 Mesh) and dichloromethane as the eluent, to give compound **3** (1.3 g, 30%) having R\({}_{\rm f}\) = 0.82 and mp =132-134 \({}^{\circ}\)C. FTIR(cm\({}^{-1}\)): 3044.9 (Ar-CH), 2949.5 (CH), 2235.3 (CN), 1600.3, 1560.0, 15175.5, 1487.3, 1470.0, 1428.9, 1382.5, 1355.4, 1301.6, 1279.9, 1251.7, 1210.9, 1200.4, 1183.9, 1172.5, 1091.1, 997.3, 965.2, 950.9, 920.2, 879.1, 859.0, 834.0, 819.2, 775.7. \({}^{1}\)H-NMR (400 MHz, DMSO-d\({}_{6}\)): 8.02 (dd, \(J\) = 8.8 Hz, \(J\) = 2.1 Hz, 1H), 7.81 (t, \(J\) = 2.3 Hz, 1H), 7.48 (dt, \(J\) = 8.8 Hz, \(J\) = 2.5 Hz, 1H), 7.35-7.27 (m, 2H), 7.18 (d, \(J\) = 6.6 Hz, 2H), 5.19 (s, 2H), 2.28 (s, 3H). ESI-MS (Elemental composition C\({}_{16}\)H\({}_{12}\)N\({}_{2}\)O): Theoretical 271.08418 [M+Na]\({}^{+}\); Experimental 271.08381 [M+Na]\({}^{+}\).  
_2(3), 9(10), 16(17), 23(24)-Tetrakis-(4'-methyl-benzyloxy) phthalocyanine zinc(II)_ (**4**), Compound **3** (174 mg, 0.7 mmol) was dissolved in 3 mL of dry DMF and 10 drops of DBU were added. While stirring the reaction under N\({}_{2}\), Zn(OAc)\({}_{2}\) (38.4 mg, 0.175 mmol) was added and the mixture was heated to 150 \({}^{\circ}\)C and stirred for 18 h. After cooling, the solution was slowly added to cold water (50 mL). The solid was collected and washed with methanol until the solvent was colorless. The product was dried under vacuum at room temperature to give 149 mg, m.p. \(>\) 200 \({}^{\circ}\)C. FTIR (cm\({}^{-1}\)): 3044.9 (Ar-CH), 2922.9 (CH), 10607.6, 1516.9, 1487.7, 1455.8, 1397.2, 1377.1, 1338.1, 1277.1, 1223.7, 1180.2, 1119.2, 1093.4, 1050.1, 1019.4, 944.8, 802.4, 744.9, 724.9, 728.7. \({}^{1}\)H-NMR (400 MHz, DMSO): 8 ppm = 8.78 (dt, \(J\) = 26.5 Hz, \(J\) = 8.0 Hz, 2H), 8.52-8.31 (m, 2H), 7.73-7.47 (m, 12H), 7.46-7.17 (m, 12H), 5.47 (d, \(J\) = 9.8 Hz, 8H), 2.41-2.27 (m, 12H). R\({}_{\rm f}\) = 0.87 in 1:1 DCM/THF. HESI (C\({}_{64}\)H\({}_{48}\)N\({}_{8}\)O\({}_{4}\)Zn): Theoretical 1057.31628 [M+H]\({}^{+}\), Experimental [M+H]\({}^{+}\) 1057.31828. MS (MALDI): m/z 1057.31828.
## 2 Materials and Methods
### Mathematical Theory of Transfer
These parameters can be respectively obtained through linear or logarithmic extrapolation of Equation (9) and the tangent of the kinetic curve of Equation (10). \[F=F_{0}=\frac{1}{S}\times lim_{t\to 0}\frac{dM}{dt} \tag{10}\]  
To obtain a deeper insight into the behavior and diffusion process of EOPH in PG material, two intermediate steps were conducted: a kinetic study and a simulation. The kinetic study involved analyzing the rate of desorption of the EOPH from the PG material over time. This allowed for a better understanding of the diffusion process and helped to determine the diffusion coefficient (\(D\)) and the evaporation flux (\(F\)) parameters. In addition, concentration profiles within the cylinder were simulated to further understand the behavior of EOPH in the PG material. These intermediate steps provided a more detailed understanding of the diffusion process and aided in the precise determination of important parameters. Various kinetic models have been mentioned in previous literature to examine the desorption mechanism, and in this study, the Fickian model of diffusion was used [24; 25; 26]. The research methodology included two intermediate steps to analyze the behavior and diffusion process of EOPH in PG material, namely, the investigation of desorption kinetics and the simulation of concentration profiles within the cylinder. These steps aided in understanding the mechanism involved in the diffusion process and accurately determining parameters such as the diffusion coefficient (\(D\)) and the flux (\(F\)).
## 5 5G FWA Field Trials
### 5G FWA Trial Results  
In an effort to investigate the potentials of the 5G network in practice, the selected customers in the aforementioned FWA trial were inspired to concurrently use different applications such as cloud gaming and 4K videos, which demand substantial resources. The obtained result by the operator was promising as the better performance was observed compared with the theoretical simulation. For instance, the coverage of more than 1 km from the AP was realized. Besides, NLOS service was also delivered by exploiting the reflective paths in the medium. For example, few users were able to receive an average aggregated DL throughput of 3 Gbps concurrently. Note that this can reach 6 Gbps with the same spectrum. Likewise, a number of users were able to take part in multiplayer online games and 4K internet videos, which were successfully streamed. Furthermore, a supermarket and a city hall both in Floresti employed the offered reliable internet connectivity for their entire services. This helped in validating the effectiveness of the 5G technology solution for public sector or business customers. In addition, Orange made further real live measurements at a demo tent (the cell edge) which is 1.2 km beyond the lower tower. The throughput of 1 Gbps was realized at that point in real-world conditions. The 5G FWA advantages were established by the chosen customers who valued the offered benefits such as stable connection, installation simplicity, and enhanced performance. They experienced the wireless connection to be comparable or better than their previously employed system. Consequently, the measurements showed that the 5G FWA is an imperative and state-of-the-art step towards commercial 5G services provisioning. Furthermore it is capable of offering residential customers an experience comparable to the one obtainable with fiber solution [329, 347, 348].  
Meanwhile, there was research that demonstrated that optimal implementation of 5G FWA solution might require hybrid terminal antenna placement. As depicted in Figure 19, the placement could be on the rooftop, wall-mounted, and indoor. The subscribers who are at a considerable distance from the BS need rooftop antennas to be deployed. On the other hand, those in close proximity to the small cell BS can employ either indoor or outdoor wall-mounted options. The results of the antenna placement research showed that, for an ISD of 350 m, 78% of the households realized good throughput with indoor antennas implementation. Out of the other households, 17% needed outdoor wall-mounted antennas whereas the remaining 5% required rooftop-mounted antennas at the same ISDto achieve sufficient signal quality [5; 11; 27]. In essence, the terminal antenna placement depends on factors such as the employed frequency band, cell size, operating environment (density and height of the obstacle), and the associated losses [8; 27; 266; 267; 270].
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
###### Abstract  
Change detection (CD) is in demand in satellite imagery processing. Inspired by the recent success of the combined transformer-CNN (convolutional neural network) model, TransCNN, originally designed for image recognition, in this paper, we present STDecoder-CD for change detection applications, which is a combination of the Siamese network (\"S\"), the TransCNN backbone (\"T\"), and three types of decoders (\"Decoder\"). The Type I model uses a UNet-like decoder, and the Type II decoder is defined by a combination of three modules: the difference detector, FPN (feature pyramid network), and FCN (fully convolutional network). The Type III model updates the change feature map by introducing a transformer decoder. The effectiveness and advantages of the proposed methods over the state-of-the-art alternatives were demonstrated on several CD datasets, and experimental results indicate that: (1) STDecoder-CD has excellent generalization ability and has strong robustness to pseudo-changes and noise. (2) An end-to-end CD network architecture cannot be completely free from the influence of the decoding strategy. In our case, the Type I decoder often obtained finer details than Types II and III due to its multi-scale design. (3) Using the ablation or replacing strategy to modify the three proposed decoder architectures had a limited impact on the CD performance of STDecoder-CD. To the best of our knowledge, we are the first to investigate the effect of different decoding strategies on CD tasks.  
t 17 June 2022  
Accepted: 4 August 2022  
Published: 6 August 2022  
**Published:** **Notic**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright**: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
**Keyword:** transformer; neural network; remote sensing; change detection
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 2 Analysis of Robot Structure and Steering Radius
### Analysis of Tracked Robot Steering Radius  
In the process of steering, owing to the influence of track skid and slip, the track with higher speed will be affected by sliding resistance, and its speed will be reduced. On the contrary, the track with lower speed will be affected by the sliding traction force, and its speed will increase. \(v\) is the speed of the robot. \(v_{R}^{\prime}\) and \(v_{L}^{\prime}\) are the theoretical speeds of the right and left tracks, respectively, and \(v_{R}\) and \(v_{L}\) are the actual speeds of the right and left tracks, respectively. \(v_{R}=\omega_{R}r\) and \(v_{L}=\omega_{L}r\), where \(\omega_{R}\) and \(\omega_{L}\) are the rotational speeds of the right and left driving wheels, respectively. The theoretical Instantaneous Center of Rotation (ICR) is inconsistent with the actual Instantaneous Center of Rotation (ICR), as shown in Figure 3. The turning radius of a tracked robot can be described by the following formula:  
\[\left\{\begin{array}{l}R=\frac{v_{\rm R}+v_{\rm L}}{v_{\rm R}-v_{\rm L}}= \frac{\omega_{\rm R}+\omega_{\rm L}}{\omega_{\rm R}-\omega_{\rm L}}\\ R^{\prime}=\frac{\omega_{\rm R}+\omega_{\rm L}}{\omega_{\rm R}-v^{\prime}_{\rm L }}=\frac{\omega^{\prime}_{\rm R}+\omega^{\prime}_{\rm L}}{\omega^{\prime}_{\rm R }-\omega^{\prime}_{\rm L}}\\ \rho=R/B\\ \rho^{\prime}=R^{\prime}/B\end{array}\right. \tag{6}\]  
where \(R\) is the actual turning radius, and \(R^{\prime}\) is the theoretical turning radius, \(\rho\) is the actual relative steering radius, and \(\rho^{\prime}\) is the theoretical relative steering radius. \(B\) is the width of the robot body.  
Considering the slip and skid of two tracks, the relationship between the actual speed \(v\) and relative steering radius \(\rho\) of the tracked robot can be obtained as follows:  
\[\left\{\begin{array}{l}v=\frac{1}{2}[w_{R}r(1-i_{R})+w_{L}r(1-i_{L})]\\ \rho=\frac{w_{R}(1-i_{R})+w_{L}(1-i_{L})}{w_{R}(1-i_{R})-w_{L}(1-i_{L})}\end{array}\right. \tag{7}\]  
where \(w_{R}\) is the angular velocity of the right track and \(w_{L}\) is the angular velocity of the left track. \(i_{R}\) and \(i_{L}\) are the
## 4 Evaluation Metrics
### Mobility Models' Metrics
#### 4.1.2 Average Number of Components  
A component is a set of nodes that are all reachable to one another through a path of edges. This metric tells us whether the network is partitioned or not. For example, a network that has two components is partitioned into two; three components means that the network is partitioned into three, _etc._ The less the network is partitioned, the better the routing and flooding of packets will perform.
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 3 Antenna/Detector Array Design
### Unit Antenna/Detector Module  
A perspective view of the unit antenna/detector module is shown in Figure 4a. The unit antenna/detector module consists of an elliptical mini-lens, a dual-dipole antenna with a Schottky diode, an antenna balun, an LNA, and a unit frame, as shown in Figure 4b. The elliptical mini-lens, made of high-density polyethylene (HDPE) with dielectric constant of 2.27, is placed at the bottom side of the dual dipole antenna. The dual dipole antenna with an antenna balun is printed on the Rogers 4003 substrate with 12 mil thickness. At the center location of the dual dipole radiators, a Schottky diode (W-Band single anode diode from Virginia Diodes) is mounted. An LNA (HMC7/2 LC4 of Analog Devices) with a gain of 15 dB and a noise figure of 1.8 dB is placed at the end of the antenna balun. A bias connector, to supply bias voltages to the LNA and the Schottky diode, is mounted at the upper section on the bottom side of the antenna substrate. The unit frame is manufactured of aluminum. The dimension of the unit antenna/detector module is 27.7 \(\times\) 95.0 \(\times\) 32.2 mm.
## 2 Materials and Methods
### MuSC-EV Isolation, Labeling, and Uptake  
EVs were isolated from MuSC culture media using ExoQuick TC (System Bioscience, Palo Alto, USA), following the manufacturer's instructions and resuspended in 1\(\times\) phosphate-buffered saline (PBS) for long term storage at \(-80\)\({}^{\circ}\)C. A total of \(1.8\times 10^{11}\) isolated MuSC-EVs were labeled using 10 \(\upmu\)M carboxyfluorescein succinimidyl ester (CFSE) dye in PBS for 2 h at 37 \({}^{\circ}\)C. Following the incubation period, ExoQuick TC was used via manufacturer's instructions to re-pellet and isolate the labeled MuSC-EVs. The labeled MuSC-EVs were then resuspended in 100 \(\upmu\)L PBS and 50 \(\upmu\)g labeled MuSC-EVs were added to the media of C2C12 myotubes in an eight-chamber cover glass slide and incubated at 37 \({}^{\circ}\)C for 24 h. Following the 24 h incubation period, the myotubes were washed with PHEM buffer (36.28 g PIPES, 11.92 g HEPES, 7.6 g EGTA, 1.97 g MgSO\({}_{4}\cdot 7\)H\({}_{2}\)O) to remove any labeled protein that was not incorporated into the myotubes and fixed in 4% paraformaldehyde(PFA) for 10 min at room temperature. Imaging of the MuSC-EV protein uptake was performed on the Leica LSM-880 confocal microscope system using the 488 nm laser line and 63\(\times\) objective lens (Zeiss, Oberkochen, Germany) to obtain a representative image of labeled MuSC-EV protein uptake in the myotubes.  
To quantify the uptake of MuSC-EVs, C2C12 myotubes were cultured in a 96-well micro-clear plate (Greiner Bio-One, Monroe, LA, USA). Separately, EVs were isolated from MuSC culture media and labeled via incubation with 10 \(\upmu\)M CFSE at 37 \({}^{\circ}\)C for 2 h. Excess dye was removed by re-isolating the labeled-EVs using ExoQuick TC. \(1.00\times 10^{9}\) labeled EVs were then incubated in each well of the 96-well plate for 5 min, 30 min, 1 h, 2 h, 6 h, 24 h, and 48 h. Preliminary data from our lab found \(1.00\times 10^{9}\) fluorescently labeled EVs added per well of differentiated myotubes in a 96-well plate (i.e. \(3.13\times 10^{9}\) EVs per cm\({}^{2}\) well surface area) resulted in significant uptake of EVs by the myotubes as measured by fluorescent intensity in the myotubes (data not shown). Further, significant levels of fluorescence were detected at various times points and thus \(3.13\times 10^{9}\) EVs per cm\({}^{2}\) plate surface area was chosen for the in vitro uptake experiments. Following the incubation period, the myotubes were washed with PHEM buffer to remove any labeled protein that was not incorporated into the myotubes and fixed in 4% PFA for 10 min at room temperature. The average spot intensity measurement on the CellInsight CX7 (Thermo Fisher Scientific, Waltham, MA, USA) was used to analyze the uptake of CFSE fluorescently labeled MuSC-EV protein delivered to C2C12 myotubes. The 488 nm laser line and 20\(\times\) objective lens on the Leica LSM-880 confocal microscope system were used to obtain representative images of CFSE-labeled MuSC-EV protein in the myotubes at each timepoint.
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
## 4 Results and Discussion
A comparison between the features of the proposed antenna with similar antennas is illustrated in Figure 11:0 From the table, it can be found that the proposed structure has the advantage of compact size, multiband operation, directive radiation pattern, and a simple and planar structure over its counterparts. Figure 11:1 Variation of gain vs. frequency for the bandwidth of operation for a resonant frequency of (**a**) 22.7 and (**b**) 34.9 GHz, and variation of radiation efficiency vs. frequency for the bandwidth of operation for a resonant frequency of (**c**) 22.7 and (**d**) 34.9 GHz. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Antenna Type** & \
\begin{tabular}{c} **No of** \\ **Elements** \\ \end{tabular} &
\begin{tabular}{c} **Resonating** \\ **Frequency (GHz)** \\ \end{tabular} & **Bandwidth (\%)** & **Gain (dBi)** & **Size (mn2)** & **Advantage** \\ \hline
[12] & \
\begin{tabular}{c} Collinear Microstrip \\ Patch Antenna \\ \end{tabular} & 3 & 0.869 & 2.80 & 12.4 & \(298\times 680\) &
\begin{tabular}{c} It has a directional \\ pattern. \\ \end{tabular} \\ \hline
[13] & \
\begin{tabular}{c} Collinear Microstrip \\ Patch Antenna \\ \end{tabular} & 3 & 2.4 & 7.10 & 15.8 & \(298\times 680\) &
\begin{tabular}{c} Simple structure \\ without the need \\ for any feeding \\ network. \\ \end{tabular} \\ \hline
[14] & \
\begin{tabular}{c} Modified Microstrip \\ Franklin Array \\ Antenna \\ \end{tabular} & 1, 3, and 5 & 24 & 1.04 & 6, 8, and 11 & \(90\times 25\) &
\begin{tabular}{c} High absolute gain, \\ good directivity, \\ and large-range \\ coverage. \\ \end{tabular} \\ \hline
[15] & \
\begin{tabular}{c} Franklin Array \\ Antenna \\ \end{tabular} & 32 & 2.4 & 8.33 & 12 & \(520\times 520\) &
\begin{tabular}{c} High gain, \\ enlarged antenna \\ aperture, increase \\ in available power. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of various antenna performance indicators. Figure 17: The radiation pattern for the proposed antenna at frequencies of (**a**) 22.7 and (**b**) 34.9 GHz.
## Part B: Network Design for Energy Optimization
### Related Work and Comparison
#### Coverage  
Sensor deployment strategies play a very important role in providing better QoS, which relates to the issue of how well each point in the sensing field is covered. However, due to severe resource constraints and hostile environmental conditions, it is nontrivial to design an efficient deployment strategy that would minimize cost, reduce computation, minimize node-to-node communication, and provide a high degree of area coverage.  
Several deployment strategies have been studied for achieving an optimal sensor network architecture. [PERSON] _et al._ propose a grid coverage algorithm that ensures that every grid point is covered with a minimum confidence level [13]. They consider a minimalistic view of a sensor network by deploying a minimum number of sensors on a grid that would transmit a minimum amount of data. Their algorithm is iterative and uses a greedy heuristic to determine the best placement of one sensor at a time. It terminates when either a preset upper limit on the number of sensors is reached or sufficient coverage of the grid points is achieved. However, the algorithm assumes line of sight of the target and the sensor. Also, since a complete knowledge of the terrain is assumed, the algorithm is not very applicable in cluttered environments, such as interior of buildings, because modeling obstacles becomes extremely difficult in those scenarios. Finally, a main difference of this approach (and in fact the other coverage methods as well) with ours, is that we explicitly take time into account.  
In contrast to static sensor networks, nodes in mobile sensor networks are capable of moving in the sensing field. Such networks are capable of self-deployment starting from an initial configuration. The nodes would spread out such that coverage in the sensing field is maximized while maintaining network connectivity. A potential field-based deployment approach using mobile autonomous robots has been proposed to maximize the area coverage [40]. Clearly, the assumptions of this method (and the one described below) are different to ours, since we do not use sensor mobility as an algorithmic design element (although we handle mobility when it appears).  
Similar to the potential field approach, a sensor deployment algorithm in the presence of mobility based on virtual forces has been proposed in [53] to increase the coverage after an initial random deployment. A sensor is subjected to forces, which are either attractive or repulsive in nature. In this approach, obstacles exert repulsive forces, while areas of preferential coverage (sensitive areas where a high degree of coverage is required) exert attractive forces, and other sensors exert attractive or repulsive forces. A hard threshold distance is defined between two sensors to control how close they can approach each other.  
Other interesting network coverage approaches are discussed in the book chapter by [PERSON] and [PERSON] in [43].
# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 4 Welding Position Recognition
### Use of Convolution Neural Network  
An algorithm that automatically recognizes the classified welding position is necessary. Considering that position recognition is a classification domain that distinguishes different postures, the SVM [54]), which is the most used model in classification problems, was first considered. SVM is a model that maximizes the distance of the data closest to the decision boundary. To recognize a position with the SVM, body features such as the coordinates or angles of the joints are used as learning data. However, accurate coordinate values from the depth image were not available in this study, hence jeopardizing SVM use.  
Another machine learning model considered is the CNN. CNN is a deep learning model with multiple convolutional layers and is most often used in classification problems, especially for image classification. Since CNN learns image features using image data, there is no need for a separate feature extraction process. Therefore, an approach to recognizing welding positions with a CNN model is appropriate.  
We used the GoogLeNet network [62] to train the depth image data. This network becomes deep by introducing the Inception network structure but has the advantage that the amount of computation does not increase significantly because the number of parameters is reduced. It is widely used in image classification problems and is divided into several versions, such as Inception v1, v2, v3, v4, and ResNet. In this study, the welding position is recognized using the pre-trained Inception v3 model. This model uses the Inception module, as shown in Figure 18, to construct the network structure. Unlike the previous versions, the amount of computation is reduced by the asymmetric convolutional decomposition method.  
Figure 17: Results of classified welding positions: (**a**) flat, and (**b**) horizontal.  
Figure 18: Inception module implemented in Inception v3.