esa-sceva/satcom-chunk-collection huggingface dataset:
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 2 Establishment of Mathematical Model
When \(x_{k,i,j}=1\), it means that the \(k\)th UAV flies from the task point \(i\) to the task point \(j\); otherwise, it does not fly from the task point \(i\) to the task point \(j\); \(t_{k,i}\) represents the time spent by the \(k\)th UAV inspecting the task point \(i\); \(t_{max}\) is the maximum flight time that the UAV can operate under full charge. \(t_{use}\) is the time that has been flown, \(n_{k}\) is the number of task points assigned to the \(k\)th UAV, \(m_{k}\) is the total number of flight sections required by the \(k\)th UAV during the execution of the task, \(F\) is the value of the objective function, indicating the maximum time consumed by each UAV to complete the task, \(F_{k}\) is the time consumed by the \(k\)th UAV to complete the task. \(S_{i,j}\) represents the distance from mission point \(i\) to mission point \(j\), and \(v\) represents the flying speed of the drone. Equation (2) indicates the constraint that the UAV needs to return to the parking point after completing the task or at low power. The reference battery level is set at 15% based on the fact that the power consumption for a single task point will not exceed 10%. This threshold ensures both the maximization of task efficiency and the safe return of the UAV. Equations (3) and (4) ensure that only one UAV is required to perform the inspection task for each task point \(i\) or \(j\). Equation (5) ensures that all task points have been inspected. Equation (6) shows how the value of the objective function is calculated.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 4 Benchmark Execution and Evaluation
### Data Transmission Time Analysis  
In this paper, the data transmission time of the image calibration experiment and AES encryption experiment results of the Jetson AGX Xavier and Loongson platforms were analyzed. The GPU of the experimental platform is NVIDIA Volta GV10B and Veiglo BI-V100. Figure 9a shows the data transmission time comparison of the image calibration experiment, and Figure 9b shows the data transmission time comparison of the AES encryption experiment. D2H (device to host) represents the transfer of data from the device side to the host side, and H2D (host to device) represents the transfer of data from the host side to the device side. When the size of the transferred data is small, the data transmission time of the two platforms is similar. However, as the transferred data increase, the data transmission time of the Jetson AGX Xavier platform is much shorter than that of the Loongson platform. NVIDIA provides the interface of GPUDirect RDMA for professional-grade GPUs [29], which allows direct access to the bus address of GPU  
Figure 8: A comparison of throughput computation results in Mbytes/s for the AES encryption algorithm benchmark executed on three experimental platforms, where (**a**) is the OpenMP framework and (**b**) is the CUDA framework, which is computed by averaging the entire processing pipeline over 20 iterations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Data Length** \\ **(Bytes)** \\ \end{tabular} } & \multicolumn{4}{c}{**Power Consumption**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Jetson AGX Xavier**} & \multicolumn{2}{c}{**ASUS FL8000**} & \multicolumn{2}{c}{**Loongson Platform**} \\ \cline{2-7}  & **CPU (Omp)** & **GPU (Cuda)** & **CPU (Omp)** & **GPU (Cuda)** & **CPU (Omp)** & **GPU (Cuda)** \\ \hline
1,048,576 & 5.15 & 5.2 & 9.8 & 16.9 & 77.6 & 79.5 \\
4,194,304 & 6.65 & 6.8 & 28.7 & 20.7 & 82.9 & 81.2 \\
16,777,216 & 8.8 & 9 & 31.2 & 27.2 & 84.3 & 84.6 \\
67,108,864 & 14.5 & 14.9 & 36.8 & & 84 & 99.1 \\
104,857,600 & 15.6 & 17.94 & 39.9 & & 84.7 & 106.8 \\ \hline \hline \end{tabular} Note: For input data of 67,108,864 bytes and 104,857,600 bytes, it exceeded the maximum computational capacity of the ASUS FL8000 notebook CUDA framework, so no statistical results were obtained.  
\end{table}
Table 8: Power Consumption of each experimental platform under AES Encryption Algorithm Benchmark (unit W).  
memory. When performing large-scale data transmission and computation, the Jetson AGX Xavier platform has advantages over the Loongson platform.
## 3 Experiments and Results
### Performance Comparison Results  
Figure 3 illustrate the performance comparison of SOTA FL algorithms on the MNIST dataset, generated using a Dirichlet distribution with \(\alpha=0.05\), for MLR and MLP models with 20 and 50 clients. Table 1 summarizes the average test accuracy after 600 training rounds across two non-IID settings of the MNIST dataset with fine-tuned hyperparameters. In terms of prediction accuracy in MNIST dataset for the MLR model, under high heterogeneity (\(\alpha=0.05\)), pFedKD-WCL outperforms all methods. For \(N=20\), it improves upon FedAvg, FedProx, PerFedAvg, pFedMe, and FedGKD by 20.6%, 19.8%, 2.6%, 1.7%, and 23.1%, respectively. For \(N=50\), the gains remain substantial: 14.8%, 14.3%, 3.1%, 1.6%, and 15.4%. Under more extreme heterogeneity (\(\alpha=0.5\) with top two classes), pFedKD-WCL yields improvements of 10.1%, 9.9%, 1.9%, 2.3% and 17.4% over FedAvg, FedProx, PerFedAvg, pFedMe, and FedGKD, respectively, when \(N=20\). In the most challenging setting \(N=50\), the gains are even more pronounced: 24.1%, 22.6%, 2.8%, 3.4%, and 25.3% over the same baselines, indicating that pFedKD-WCL scales well with larger numbers of clients. For the MLP model, with \(\alpha=0.05\) and \(N=20\), pFedKD-WCL maintains performance gains of 2.8%, 3.0%, 3.7%, 2.9%, and 1.9%. Under \(\alpha=0.05\) and \(N=50\), pFedKD-WCL outperforms FedAvg, FedProx, PerFedAvg, and pFedMe by 1.2%, 1.3%, 3.5%, and 2.8%, but is slightly outperformed by FedGKD by 0.1%. Under \(\alpha=0.5\) and \(N=20\), pFedKD-WCL achieves improvements of 8.3%, 9.9%, 1.8%, 2.0%, and 1.3% over the five baselines. With \(N=50\), the margins narrow but remain positive: 3.1%, 3.2%, 3.9%, 3.3%, and 0.3%. Table 2 depicts the performance of algorithms on the synthetic dataset using MLR and MLP models, respectively. With the MLR model, pFedKD-WCL outperforms FedAvg, FedProx, PerFedAvg, and pFedMe by 32.0%, 30.0%, 7.6%, and 6.7%, respectively. Similarly, with the MLP model, the improvements are 23.4%, 21.7%, 12.6%, and 11.6% compared to the same baseline methods. However, FedGKD did not perform well with the synthetic data. The two-class per client setting exacerbates client drift, and FedGKD's historical ensemble fails to provide effective guidance. Figure 2: Effect of KD weight \(\gamma\) on the two-layer multilayer perceptron (MLP) model using the MNIST dataset generated by Dirichlet distribution (\(\alpha=0.5\)) with top 2 classes over 800 global rounds: (**a**) Average test accuracy (%). (**b**) Average training loss.
# Charging Platform of Chess-Pad Configuration for Unmanned Aerial Vehicle (UAV)
## 6 Conclusions  
For this study, a charging platform with a chess-pad configuration was applied to a landing UAV. All onboard components were exceptionally light and compact. By its design, the proposed of a small diode bridge configuration with only six pieces and four spring contact pins was installed in a UAV to directly touch its landing station. Using direct link configuration, it showed 86% efficiency of the battery recharging system compared to wireless charging methods. A simulation for the dimensions of the platform regarding the design of the plates could be configured to the required shape and size of any UAV. The onboard part of the charging system and the configuration of the six diode bridge rectifiers enabled the battery to charge even if just two contact pins successfully touched the platform.  
Future work should be done to address the following problems. The landing pad design configuration could mitigate moisture issues to resistively sense changes in load between adjacent plates. Additionally, the current flow could be restricted in the ground platform to help avoid short circuiting, and a heating component could be included after precipitation to evaporate moisture. It is also conceivable for indoor and outdoor ground stations to use independent power sources. The development of this platform offers a practical and scalable UAV charging system.  
Conception and design of study, M.R.A.-O., W.Z.W.H., M.A.M. and N.A.; Analysis and/or interpretation of data, M.R.A.-O., W.Z.W.H.; Drafting the manuscript and revising the manuscript critically for important intellectual content, M.R.A.-O. and W.Z.W.H.; Approval of the version of the manuscript to be published, M.R.A.-O., W.Z.W.H., M.A.M. and N.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank the University Putra Malaysia and Ministry of Education for supporting and funding this study under Putra Grant scheme of Universiti Putra Malaysia and Ministry of Higher Education Malaysia with the grant number is GP-IPB/2020/967801.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Details of Design** & **Number of Plates** & **Charging Probability Percentage (\%)** \\ \hline \(10\times 10\) cm & 36 & 57 \\ \(12\times 12\) cm & 25 & 55 \\ \(14\times 14\) cm & 16 & 69 \\ \(16\times 16\) cm & 16 & 76 \\ \(18\times 18\) cm & 9 & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Charging probability with different plate sizes.
## 2 Background Information
### Review on the Effect of Wind Turbines on Microclimate  
There are some references about the effect of wind turbines on the microclimate [6; 7; 8; 9; 10; 11; 12; 13]. At the European level, despite the rapid development of wind energy, there are few observations or records related to the effect of wind turbines on the microclimate. On the other side of the Atlantic, there are several publications on this topic, especially on the impact of wind farms on surface air temperatures. Temperature data from a huge wind farm (3218 operational units) at San Gorgonio, California, were collected during 18 June-9 August 1989 and has shown that large scale wind farms may affect near-surface air temperatures [6; 7]. According to the collected data from the field campaign, near-surface air temperatures downwind of the wind farm were slightly different from the upwind regions. More specifically, a warming effect of the wind farm was observed during the night and a cooling effect during the day. These effects result from enhanced vertical mixing due to turbulence generated by wind turbine rotors. Recent modeling studies agree that wind farms can significantly affect local-scale meteorology [8; 9]. More specifically according to simulations for a wind farm consisted of 21 wind turbines of 2 MW in flat terrain, the hydrometeorology of an area up to 18-23 km downwind could be affected, while the effects depend on the size of wind farms and on the stability of the atmosphere [6]. According to [PERSON] [6], the impacts of wind farms on local weather can be minimized by changing rotor design or by siting wind farms in regions with high natural turbulence. Although these effects have a scientific and research interest, in practice they are very small (1-3 \({}^{\circ}\)C for the extremely large wind farm of San Gorgonio, California with flat terrain) and therefore without any real impact on the flora and fauna of the area.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 2 Motivation
This is still a major open issue, which this work seeks to address. In addition, experimental studies [17; 18] have shown the importance of considering the highly unreliable nature of wireless links when considering higher layer protocols. Although this work also considers the issue of instant channel quality assessment as a fundamental component, the details regarding this aspect will only be discussed where necessary. Thus, in order to keep this paper concise, we refer readers to our previous work _reliable geographical forwarding in cognitive radio sensor networks using virtual clusters_(ROR) [13] for the detailed fundamentals of implementing instant channel quality assessment for CRSNs. In line with the above-mentioned issues, implementing mobile routing can be more challenging because of the unique resource-constrained nature of CRSNs. In [13], we noted that two specific reasons make geographic forwarding schemes the best choice for routing in CRSN: (i) the minimal stored state; this is because, in order to forward packets, the transmitting node needs to only store location information of its immediate neighbors; (ii) high energy and bandwidth conservation; this is because the discovery floods and state propagation are not required beyond a single hop. In [13], we proposed a novel geographical forwarding technique that does not restrict the choice for next hop selection to the nodes in the previously-selected route. ROR is characterized by the seamless formation of virtual clusters and the utilization of instantaneous link quality as the criteria for the next hop selection. Furthermore, ROR ensures network stability by utilizing receiver contention prioritization and idle listening to avoid the occurrence of routing hot spots and to minimize network energy. When compared to the usual CRHANrouting strategy of joint channel and next hop selection, it was proven that ROR makes more per-hop advancement to the sink, while ensuring channel quality. However, the design of ROR did not consider the high route instability and mobile estimation of channel availability, which reflects the fluctuating spectrum opportunities that are induced by SU mobility. Thus, in this work, we propose another version of ROR, which is specifically modeled considering high route instability and mobile estimation of channel availability. This version of ROR, which is called MROR, builds on the strengths of ROR to properly address routing in mobile CRSNs.
## 6 SAR Payload Performance Analysis
### Impulse Response Function  
The SAR image performances are characterized by an impulse response function to a point target, in terms of the range and azimuth resolutions, peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR). The single-look impulse response function can be expressed as  
\[h_{r}(y)=\parallel\frac{1}{\tau_{p}}\int_{t_{1}}^{t_{2}}F_{r}\big{(}t_{f}(y),t_ {s}\big{)}\ dt_{s}\parallel^{2}, \tag{6}\]  
where the integrand function in range \(y\) can be written as  
\[F_{r}\big{(}t_{f},t_{s}\big{)}=w_{r}\big{(}t_{f}\big{)}\cdot A(t_{s})\exp\biggl{\{} j\biggl{[}\theta\big{(}t_{f}\big{)}+\theta\big{(}t_{s}\big{)}+\pi\frac{B_{t,n}}{ \tau_{p,n}}tf_{s}\biggr{]}\biggr{\}}, \tag{7}\]  
where \(w_{r}\big{(}t\big{)}\) is a window function in range, \(t_{f}\) is the fast time, \(t_{s}\) is the slow time, and, in azimuth \(x\) as  
\[h_{a}(x)=\parallel\frac{1}{T_{sa}}\int_{-\frac{T_{sa}}{2}}^{\frac{T_{sa}}{2}}F _{a}\big{(}t_{f},t_{s}(x)\big{)}\ dt_{f}\parallel^{2}, \tag{8}\]  
where \(T_{sa}\) is the synthetic aperture time, the integrand function in azimuth is  
\[F_{a}\big{(}t_{f},t_{s}\big{)}=w_{a}(t_{s})\cdot A\big{(}t_{f}\big{)}\exp\biggl{\{} j\biggl{[}\theta\big{(}t_{f}\big{)}+\pi\frac{B_{d}}{T_{sa}}tf_{s}\biggr{]} \biggr{\}}, \tag{9}\]  
where \(w_{a}(t_{s})\) is a window function in azimuth.  
Furthermore, the expected performances of the SAR system were confirmed to satisfy the requirements in both the high-resolution (stripmap) and wide-swath (ScanSAR) modes with 5% HW error, as depicted in Figure 25a,b for the high-resolution mode and in Figure 26a,b for the wide-swath mode.  
Figure 24: NESZ of the S-STEP SAR payload in the (**a**) high-resolution and (**b**) wide-swath modes. NESZ is lower than the required level of \(-14\) and \(-16\) dB at an incidence angle of \(25^{\circ}\) for the high-resolution and wide-swath modes, respectively. Each color shows the corresponding swath.
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
###### Abstract  
In this paper, the performance, advantages and challenges of polymer microwave fibers (PMF) for sub-THz links are evaluated first. Then, a simple and elegant transceiver-integrated circuit (IC) and PMF wireless coupling scheme is presented. The proposed solution utilizes an advanced IC packaging technology to implement a Vivaldi antenna-in-package (AiP). The antenna is designed to provide lateral radiation and excellent directivity, so the proposed solution is very simple, compact, robust and cost-efficient: the IC readily connects to the PMF and the coupling is merely achieved by the packaged IC, without the need for any extra interface. The system operates at around 140 GHz, achieving a coupling loss of just 3.5 dB.  
advanced IC packaging; antenna-in-package (AiP); high data rate; polymer microwave fiber (PMF); Vivaldi antenna; embedded wafer level ball grid array (eWLB) Article
## 1 Introduction
### Main Contribution  
This study strives to improve the results obtained in [24; 32] by embodying the concept of the anisotropic Gaussian filter (AGF) and Haar transform as preprocessing operations. The studies in [24; 32] presented a wavelet-Gaussian filter and a deep-CNN-based model to eliminate Gaussian and speckle noise. This study adopts an augmented methodology by implementing an anisotropic Gaussian filter, Haar wavelet transform, and DnCNN. The study contributes to the following aspects:  
1. An ensemble approach is proposed using DnCNN, the anisotropic Gaussian filter (AGF), and Haar wavelet transform. The AGF and Haar transform are applied as preprocessing operations. The choice of AGF was primarily due to its adaptability to edge orientation, adaptive filtering, and directional information, effectively handling edges based on gradient magnitude and preventing blurring along edges commonly encountered with standard filters.
2. The ensemble approach demonstrates better results when compared to CNN-based methods and other standard spatial filtering techniques in reducing the blurring effect and improving image quality and restoration.
## 2 RBFNN Approximation Method
### Theoretical Background of RBFNN and SPP
The value of SSP is estimated generally through iterative evaluation to minimize the maximum deviation while satisfying the accuracy of a given RBFNN meta-model in the approximate design optimization. The overall performance was evaluated using two standard accuracy measures--root mean square error (RMSE) and maximum error (MAX). RMSE provides an overall error measure over the entire solution space and is defined as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(f(x_{i})-f^{\prime}(x_{i}))^{2}} \tag{6}\]  
The MAX is indicative of local deviations and also expressed as follows:  
\[\text{MAX}=\underset{i}{max}\big{|}f(x_{i})-f^{\prime}(x_{i})\big{|} \tag{7}\]  
where \(f(x_{i})\) denotes the exact function value for the test point \(x_{i}\), \(f^{\prime}(x_{i})\) is the corresponding predicted value using the meta-model, and \(n\) is the number of test points chosen for calculating the error measure [29; 30]. Ideally, it would be favorable that both of error measures are to be as small as possible for higher accuracy. The RMSE is the accuracy measure commonly used in case of dealing with the difference between the predicted value of the meta-model and the actual response value and is suitable for expressing the overall accuracy of the meta-model. The RMSE is used for the loss function in the training process. On the other hand, because the MAX calculates the maximum error among the predicted values of the meta-model, local accuracy can be confirmed. Since the RBFNN can be applied not only to linear design space but also to approximation of highly nonlinear design space, both the overall accuracy and the local accuracy should be reviewed simultaneously. Therefore, it is reasonable to evaluate the accuracy of the RBFNN meta-model by applying both the RMSE and the MAX. For the rotor blade design problem, statistical analysis was also performed to evaluate the main effects of design variables on the output responses of design performance functions. Since RBFNN is the interpolant meta-model, results such as RSME and MAX are feature values for the meta-model accuracy, and the feature values depend on of the learning parameters such as the SSP value, the number of training data, and the number of hidden layers. Therefore, in case that new variables are to be applied in the design process, the learning parameters should be redefined. Hyperparameters used for the RBFNN modeling were summarized in Table 1.  
Figure 1: Neural network learning process of RBFNN. In Table 1, the learning rate determines how fast it moves in the direction of the gradient. The number of training iterations was used to determine the early stopping of learning and applied to prevent overfitting.
# Special Issue Cognitive Buildings
## 7 An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management  
[PERSON] et al. [6] present the digital transformation era in the Architecture, Engineering, and Construction (AEC) industry, where Cognitive Digital Twins (CDT) are introduced as part of the next level of process automation and control towards Construction 4.0. CDT incorporates cognitive abilities in order to detect complex and unpredictable actions, and reasons about dynamic process optimization strategies to support decision-making in Building Lifecycle Management (BLM) are provided. Nevertheless, there is a lack of understanding of the real impact of CDT integration, Machine Learning (ML), Cyber-Physical Systems (CPS), Big Data, Artificial Intelligence (AI), and Internet of Things (IoT), all connected to self-learning hybrid models with proactive cognitive capabilities for different phases of the building asset lifecycle. This study investigates the applicability, interoperability, and integrability of an adapted model of CDT for BLM to identify and close this gap. Surveys of industry experts were performed, focusing on life cycle-centric applicability, interoperability, and the CDT model's integration in practice, besides decision support capabilities and AEC industry insights. The evaluation of the adapted model of the CDT model supports approaching the development of CDT for process optimization and decision-making purposes, as well as integrability enablers confirm a progression towards Construction 4.0.  
The Implementation of Visual Comfort Evaluation in the Evidence-Based Design Process Using Lighting Simulation  
[PERSON] et al. [7] work on a validation of the EBD-SIM (evidence-based design simulation) framework, a conceptual framework developed to integrate the use of a lighting simulation in the EBD process, which suggested that EBD's Post-Occupancy Evaluation (POE) should be conducted more frequently. A follow-up field study was designed for a subjective-objective results implementation in the EBD process using lighting simulation tools. In this real-time case study, the visual comfort of the occupants was evaluated. The visual comfort analysis data were collected via simulations and questionnaires for subjective visual comfort perceptions. The follow-up study, conducted in June, confirmed the results of the original study, conducted in October, but additionally found correlations with annual performance metrics. This study shows that, at least for the variables related to daylight, a POE needs to be conducted at different times of the year to obtain a more comprehensive insight into the users' perception of the lit environment.