esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Design Procedure and Methods
### Operating Principle and Simulation of the GWs  
A GW is typically implemented by placing a metallic plate parallel to a high-impedance textured surface acting as an artificial magnetic conductor (AMC). When the spacing between the two parallel plates is smaller than a quarter of a wavelength, an electromagnetic band gap (EBG) is formed that forbids the propagation of all parallel-plate modes inside the region between the two plates. The textured surface usually consists of periodic metal pins to create a high-impedance boundary to realize the AMC condition over the operating frequency band [26]. If a certain area of the textured surface is replaced by a ridge or a groove, which is then surrounded by the metallic pins, wave propagation is restored and confined in the region above that modified area. This effectively creates a track for guiding the wave with minimum lateral leakage while providing the unique advantage of a contactless structure.  
To design the GW components with the desired operating frequency ranges, we used the eigenmode solver of CST Studio, commercial full-wave simulation software, to obtain the dispersion diagrams for unit cells of the EBG structures. It can be seen from Figure 2 that the stopbands emerged in the Ka-band and K-band for the two respective parallel-plate structures. Optimized dimensions of these GW units were found to be pa1 = pa2 = 0.9 mm, ph1 = 2.4 mm, ph2 = 3.1 mm, ag1 = ag2 = 0.1 mm, and d = 1.9 mm.  
Figure 1: Illustration of the 3D structure of the proposed 4 \(\times\) 4 dual-band, dual-CP antenna array.  
Figure 2: Simulated dispersion diagrams of the two GW unit cells. (**a**) Ka-band layer. (**b**) K-band layer.
## 4 Conclusions  
A cold-chain insulated container integrated with PCM has been developed for a temperature-controlled transportation in a range of 2-8 \({}^{\circ}\)C. A 72-h transportation under various temperature conditions has been achieved with the high insulation performance of the container and the double-layer PCM panels. Different preconditioning methods have been determined to take full advantage of the phase transition of the PCM.  
The container performance depends largely on the phase transition temperature range, the melting/freezing enthalpy, and the mass of that is PCM used. The container presented a subcooling effect in a range of \(-1\)-2 \({}^{\circ}\)C for 72 h under extremely high temperature and alternating temperature conditions by applying water as PCM. For the utilization at extremely low temperature, the holding time was only 10 h. However, the temperature-controlled time was extended from 1 h to more than 80 h by using a PCM with a melting/freezing point at 5 \({}^{\circ}\)C. Furthermore, the air temperature in the container was maintained at a steady at 4-5 \({}^{\circ}\)C for most of the time under extremely high, extremely low, and alternating temperature conditions, which would contribute to extending the shelf-life of drugs or food. The experimental values of the temperature-controlled time agreed well with the theoretical values. The results showed that the insulated containers with PCM could be an attractive alternative to refrigerated trucks or containers having a compressor for cold-chain systems.  
This work was developed within the framework of the research project 2017C34002 \"Development of isothermal packaging technology based on phase change materials (PCMs) and intelligent monitoring system\" funded by Science Technology Department of Zhejiang Province of China.  
[PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] contributed materials and wrote the paper.  
There are no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ambient Temperature (\({}^{\circ}\)C)**} & \begin{tabular}{c} **Maximum or Minimum** \\ **Temperature Allowed in** \\ **the Container (\({}^{\circ}\)C)** \\ \end{tabular} & \
\begin{tabular}{c} **Temperature-Controlled Time (h)** \\ **Experimental** \\ \end{tabular} &
\begin{tabular}{c} **Deviation (\%)** \\ **Theoretical** \\ \end{tabular} \\ \hline
35 & 8 & 81 & 88 & \(-\)7.95 \\ \(-20\) & 2 & 102 & 108 & \(-\)5.56 \\ \(35\)\({}^{\circ}\)C for the first 48 h and & 8 \({}^{\circ}\)C for the first 48 h and & 100 & 98 & \(+\)2.04 \\ \(-20\)\({}^{\circ}\)C for rest time & 2 \({}^{\circ}\)C for the rest time & 100 & 98 & \(+\)2.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The experimental and theoretical values of the temperature-controlled time of the container with OP5E as PCM under different temperature conditions.  
Figure 13: The ambient temperature and air temperature inside the container with OP5E as PCM under alternating temperature condition.
## 3 Results and Discussion
### Vegetation Indices  
#### 3.3.1 Derived VIs Models  
The NDVI model (Figure 12a) qualitatively separates areas under vegetation but shows shortcomings in differentiating types of canopies. The NDRE VI model (Figure 12b) distinguishes vegetation and shows olive tree canopies with high reliability. The GNDVI index model (Figure 12c) was chosen due to its ability to distinguish types of vegetation within the study area, while the MCARI2 index model (Figure 12d) reliably distinguishes vegetation surfaces. The last derived model of the RDVI2 index (Figure 12e) was chosen due to its ability to recognize olive trees in relation to other vegetation.
# Drag and Attitude Control for the Next Generation Gravity Mission
## 3 Drag and Attitude Control Role in the Measurement of the Non-Gravitational Accelerations
### Drag Environment on NGGM Reference Orbits
Figure 15: Drag acceleration components over 2 orbits at 396 km and 492 km, computed at the epoch of maximum solar activity with 95 th percentile on F\({}_{10.7}\) and A\({}_{\rm p}\) prediction. From the above Figure 16, it is apparent how external perturbations have a direct influence on the residual linear accelerations that the SC is subject to at the different altitudes. In order to respect the requirements on the residual linear accelerations described in Table 4, it is necessary to exercise linear drag-free control to compensate for the external drag as well as for dominant thruster effects. Indeed, independently from the thruster layout and thruster usage (impulsive or proportional thrusters), the accelerometer will always measure the thruster effects due to the non-ideal thrust caused by thruster misalignments, noise, etc. The latter effect is particularly evident on the cross-tack and radial axes, while along the in-flight direction, the dominant source is external drag. Consequently, the in-flight direction performance worsens when flying at a lower altitude.
## 1 Introduction  
Satisfying the needs of employees is crucial for building managers as it has a major impact on well-being and productivity [1]. Although information and communication technologies (ICT) have enabled greater control and customization of building systems, they may not yet fully meet users' expectations. Recent research has aimed to help managers understand the activities, preferences and conditions of employees in their working environment by collecting and integrating data generated by occupants and buildings themselves [2]. However, challenges remain in representing domain knowledge and integrating heterogeneous data.  
In the last decade, the Architecture, Engineering, Construction and Operations (AECO) sector has shown a significantly increased interest in data-driven management of built environments and the development of the Digital Twin (DT) concept [3]. Semantic Web technologies and the Linked Building Data (LBD) approach have facilitated the integration of information from different sources, overcoming the limitations of BIM. This has enabled a better understanding of the interactions between buildings and occupants, initiating a shift towards building management approaches centered on occupants [4].  
Buildings are responsible for about 40% of total energy consumption in industrialized countries, and therefore offer great opportunities and potential for improving energy efficiency through the introduction of advanced Smart Building-oriented technological solutions.  
Advances in building intelligence involve the use of connected devices and automation tools, such as sensors and actuators, which enable efficient energy management while maintaining occupant comfort. However, internal responses to control changes can be slow, resulting in periods of temporary discomfort [5]. Traditionally, efforts have been made to maintain stable internal conditions through constant reference points, but this approach is energy inefficient. With the increase of renewable energy sources, buildings need to offer more energy flexibility, such as Demand Response programs [6,7]. Model Predictive Control (MPC) is well suited to these new requirements.  
To address the practical challenges of applying MPC, predictive models need to be developed that take into account the physical properties of the building, weather forecasts, occupancy forecasts and thus, consumption [8]. The developed framework creates a virtual replica of the building's physical environment and helps building managers to optimize energy use and monitor building performance while ensuring adequate comfort levels for users.
## 4 Results and Discussion
### End-to-End Delay  
EED is the average time required to perform successful data transmission over the MANET; EED is expressed in Equation (19).  
\[EED=\frac{Sum\;of\;time\;taken\;to\;broadcast\;packet\;in\;receiver}{No.\;of\;packet\; received\;by\;receiver} \tag{19}\]  
Table 5 shows the performance analysis of EED. Figure 6 shows the comparison of EED for the CEELBRP [24], EECRP-PSO [25], and ECTSA. The EED of the ECTSA is varied from 3.1 ms to 5.5 ms, which is less than the CEELBRP [24] and EECRP-PSO [25]. The developed ECTSA achieves less EED because of its transmission path with less distance and fewer control packet requirements during path generation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Node Count**} & \multicolumn{4}{c}{**Packet Delivery Ratio**} \\  & **CEELBRP [24]** & **EECRP-PSO [25]** & **HEFF-GSO [28]** & **Proposed ECTSA** \\ \hline
50 & 0.61 & 0.63 & 0.93 & 0.94 \\
75 & 0.70 & 0.74 & 0.94 & 0.95 \\
100 & 0.76 & 0.89 & 0.96 & 0.97 \\
125 & 0.83 & 0.90 & 0.98 & 0.99 \\
150 & 0.91 & 0.93 & - & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of PDR.  
Figure 5: Analysis of PDR.
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 1 Introduction
The quantity of data collected by smart city sensors is variable, and the computational overhead per unit of data (a single frame) is stable, so the load balance of the devices can be achieved by balancing the quantity of data or the calculation cost. That is, the DNN computation tasks on each frame are divided according to the computational power of the devices, and the data are assigned to each device based on the model division results. Therefore, we propose a DNN collaborative inference framework as shown in Figure 1, which can divide a single frame into multiple parts to achieve finer-grained data volume balancing. Furthermore, we perform an intralayer division of the model, enabling the partitioning of DNN computation tasks and parallel inference of the model, effectively realizing accelerated inference of the model with device load balancing under the edge-end collaboration. Compared with existing edge-end collaborative inference methods, we have the following novel contributions:  
* We propose a distributed DNN inference framework (ADDP), which can dynamically and unevenly divide the computational tasks of DNN models according to the end-device computational capacity and network communication conditions, achieve parallel inference on multiple ends, and ensure the consistency of inference time across IoT devices as much as possible, in order to maximize the inference acceleration for DNNs and computational resource utilization for devices. * We consider the continuous arrival of smart application data on the device and take the total inference time of the task as the optimization target. Instead of optimizing only the single DNN inference process, which is more in line with the actual application scenario, we share the edge's computation task with the end device's computation, effectively reducing the computation burden of the edge. * We evaluate the ADDP to confirm its superiority by collaboratively reasoning for widely adopted DNN models on multiple devices in a real network. ADDP reduces single-frame response latency by 10-25% compared to the pure on-device processing. The rest of the paper is organized as follows. Section 2 provides a summary of the literature related to our work. Section 3 proposes a regression model for predicting the computation time of DNN layers and models the problem of minimizing DNN inference latency. Section 4 describes the ADDP framework and the modules in it, and designs the methods for solving the collaborative inference strategy of DNN models in the smart city. Section 5 evaluates the effectiveness and convergence of the algorithm through experiments, and simulation results and analysis are presented. Section 6 provides a full-text summary.
# Designing of the Tracks and Modeling of the Carrier Arrangement in Square Rotary Braiding Machine
## 3 Analysis of the Carrier Arrangement
### Numerical Results and Analysis of the Carrier Arrangement in 3D Square Rotary Braiding Machine  
In the logical relationship between carrier positions established in Section 3.1, it is possible to judge whether there will be a collision between carriers, in order to avoid such collision. This will ensure that the carriers can achieve a full arrangement.  
Then, the specific azimuth data of two intersecting tracks are obtained, according to the position and direction of the tracks.  
\[S=\left[\begin{array}{cccccc}s(x,2,2,\phi^{+})&s(x,4,4,\phi^{+})&s(z,6,4,\phi ^{+})&s(x,8,4,\phi^{+})&s(x,10,6,\phi^{+})&s(x,12,8,\phi^{+})&s(z,14,8,\phi^{+ })\\ s(z,2,2,\phi^{-})&s(x,4,2,\phi^{-})&s(x,6,4,\phi^{-})&s(x,8,6,\phi^{-})&s(z,10,6, \phi^{-})&s(x,12,6,\phi^{-})&s(x,14,8,\phi^{-})\end{array}\right] \tag{24}\]  
\[S^{\prime}=\left[\begin{array}{cccccc}s(z,2,8,\phi^{+})&s(z,4,6,\phi^{+})&s( x,6,6,\phi^{+})&s(z,8,6,\phi^{+})&s(z,8,6,\phi^{+})&s(z,10,4,\phi^{+})&s(z,12,2, \phi^{+})&s(x,14,2,\phi^{+})\\ s(x,2,8,\phi^{-})&s(z,4,8,\phi^{-})&s(z,6,6,\phi^{-})&s(z,8,4,\phi^{-})&s(x,10,4, \phi^{-})&s(z,12,4,\phi^{-})&s(z,14,2,\phi^{-})\end{array}\right] \tag{25}\]  
where the parameters S and S' are the matrices of two intersection tracks.  
\[\begin{array}{rl}T_{ji}&=\prod\limits_{k=1}^{P}\prod\limits_{l=1}^{N}(\tau_{ ji}\tau_{kl})\\ &=\ (\tau_{11}\tau_{21})(\tau_{21}\tau_{11})(\tau_{13}\tau_{23})(\tau_{23}\tau_{13 })(\tau_{15}\tau_{25})(\tau_{25}\tau_{15})(\tau_{17}\tau_{27})(\tau_{27}\tau_{17}) \end{array} \tag{26}\]  
with the condition  
\[S_{12}^{C}=S_{14}^{C}  
According to Figure 6 and Equations (3), (4), and (28), the state matrices of each slot in the track with a rectangular-shaped (Figure 8) and T-shaped (Figure 9) cross-section, formed by ten and three intersected 484 cross-break tracks, respectively, were obtained. The fabrics with two cross-sections were obtained on the square rotary braiding machine (Figure 7), with 64 horn gears mounted on the track disc, thus verifying the correctness of the mathematical model.  
\[S=\left[\begin{array}{c}S_{1}\\ S_{2}\\ \vdots\\ S_{17}\end{array}\right]=\left[\begin{array}{cccc}s_{11}&s_{12}&\cdots&s_{ 1(15)}\\ s_{21}&s_{22}&\cdots&s_{2(15)}\\ \vdots&\vdots&\ddots&\vdots\\ s_{(17)1}&s_{(17)2}&\cdots&s_{(17)(15)}\end{array}\right]\]  
Figure 8: Arrangement of carriers for the rectangular shape by 484 cross-break track and its fabric.  
Figure 9: Three-track combination of 484 cross-break track and its fabric.
## 2 Instrument Design
### Monitoring and Control Systems
#### 2.3.1. Enclosure  
Thermal stabilization is an essential need of a microwave radiometer, since variations of the physical temperature will lead to variations in the measurements. The origin of these variations is the dependence on the physical temperature of the gain and noise figure of all the elements of the radio-frequency chain. Consequently, the radiometer must be thermally insulated. This is achieved using an enclosure designed to minimize the heat exchange between inside and outside the radiometer. The walls of MERITXELL are 20 mm dual-side metallized foam boards (both for electromagnetic interference and thermal insulation), except for the front-part. The antennas are covered with a 5.5 mm thick Depron(r) radome (a kind of white polystyrene foam) showing losses of approximately 0.25 dB at W-band. The metallic part of the walls is composed of two sheets of 1 mm thickness aluminum. Both the metallic enclosure, and the white radome can be seen in Figure 6, with approximate dimensions of 180 cm \(\times\) 90 cm \(\times\) 90 cm.
## 3 Constraint-Handling Techniques in Metaheuristic Algorithms
### Metaheuristic Algorithms
#### 3.1.3 Genetic Algorithm  
The GA was proposed by [PERSON] [49]. This algorithm is inspired by genetic-molecular evolution. The essential operation of GA is shown in Algorithm 3.  
```
1: Generate a initial population \(X_{0}\) with \(NP\) chromosomes.
2: Evaluate \(X_{0}\)
3:\(G\gets 0\)
4:while\(G\leq G_{max}\)do
5:for all\(x_{i}\in X_{G}\)do
6: Obtain \(x_{r_{1}}\) and \(x_{r_{2}}\) in \(X_{G}\) by tournament.
7: Generate a child \(v_{i}\) by (16)
8: Generate a mutant \(u_{i}\) by (17)
9: Evaluate \(u_{i}\)
10:endfor
11: Replace the population \(X_{G}\) to \(X_{G+1}\) considering \(X_{G}\) and \(U_{G}\) in the CHT. \(X_{G+1}\gets fncCHT(X_{G},U_{G},\sim)\) (This function is associated to the CHT.)
12:\(G\gets G+1\)
13:endwhile
```  
**Algorithm 3** Genetic algorithm pseudocode.  
GA initializes with a group \(X_{0}=\{x_{1},x_{2},...,x_{NP}\}\) of \(NP\) chromosomes. In the group, two parent chromosomes \(x_{r_{1}}\) and \(x_{r_{2}}\) are selected by tournament [50] and recombined to generate a child chromosome \(v_{i}\) as shown in (16). The recombination process uses a piece-wise multipoint crossover [5], where the crossover points are given by a crossover rate \(CR\in[0,1]\). The child chromosome \(v_{i}\) can mute using uniform mutation [51] as shown in (17), where \(MR\in[0,1]\) is a mutation rate, and \(x^{j}_{min}\), \(x^{j}_{max}\) are the minimum and maximum values of the design variables, respectively.  
\[v^{j}_{i}=\left\{\begin{array}{cc}x^{j}_{r_{1}},&\text{if }rand_{j}(0,1)<CR\\ x^{j}_{r_{2}},&\text{Otherwise}\end{array}\right. \tag{16}\]  
\[u^{j}_{i}=\left\{\begin{array}{cc}rand(x^{j}_{min},x^{j}_{max}),&\text{if }rand_{j}(0,1)<MR\\ v^{j}_{i^{\prime}},&\text{Otherwise}\end{array}\right. \tag{17}\]  
Between the mutant chromosomes \(U_{G}\) and the parent one \(X_{G}\), the best chromosomes are selected based on the replacement mechanism using the CHT defined in Section 3.2.
# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
## 2 Materials and Methods
### Bulbus Bow Deformation  
This study employed the FFD method to achieve deformation of the bulbous bow section of the DTMB5415 ship model by manipulating the control points of its NURBS surface. Initially, the ship model was created using Rhino modeling software, and the NURBS surface control points were extracted using the Grasshopper plug-in. The control points of the bulbous bow were selected for modification, and appropriate constraints were established based on actual conditions. The parameter samples were generated based on these constraints to manipulate the NURBS control points, which were then regenerated to finalize the bulbous bow deformation. The software generates a NURBS surface to facilitate the deformation of the bulbous bow, and the resultant deformed hull is recorded in IGES format, as illustrated in Figure 2.  
During the software extraction process, numerous control points are typically acquired, and the resultant surface may exhibit irregularities during the subsequent manipulation of these control points. Therefore, to ensure that the surface of the deformed bulbous bow is smooth and level, the control points must be uniformly distributed with adequate spacing between them. Figure 3 illustrates the selection of control points for the preliminary bulbous bow.  
In the parameter design, as illustrated in Figure 3, we manipulated the control points along the \(x\)-axes, \(y\)-axes, and \(z\)-axes to alter the dimensions of the bulbous bow. The \(y\) coordinate of the four sets of control points, designated as \(p_{1}\)\(\sim\)\(p_{4}\), is established as the characteristic parameter for width, whereas the \(x\) and \(z\) coordinates of \(p_{5}\) serve as the characteristic parameters for the length and height, respectively. To ensure that the configuration of the generated bulbous bow closely resembles the original ship shape, the design minimizes the number of parameters to reduce model complexity. The width feature parameter of the y coordinate utilizes proportional adjustments of the shifted control points. The limitations of the design variables are determined based on actual needs, as shown in Table 2.  
Figure 4 shows the process of deforming the bulbous bow. First, the blue NURBS control points on the initial ship bulbous bow are obtained from step a; then, the coordinates of the control points are shifted using FFD to generate the green control points in step b. Finally, the new NURBS surface is generated from step c. Compared with the direct deformation of the bulbous bow by embedding it into a cube, this method can realize more complex shape changes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variable** & **Lower Limit** & **Limit** \\ \hline \(x\) & 0 & 0.02 \\ \(y\) & 0.3 & 2 \\ \(z\) & \(-\)0.004 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range of values of design variables for a bulbous bow.  
Figure 3: Distribution of NURBS control points on a bulbous bow.  
Figure 2: Flow chart of the parametric geometric deformation of a bulbous bow.