esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To meet the demand for modern communication technology, the development of satellite communications has been consistently investigated. In this article, a rectangle-type SRR is attached to circular-type SRR for obtaining two frequencies in X-band operation. The designed structure exhibits negative metamaterial properties (Epsilon, mu and refractive index are negative) and the design was fabricated on a polyimide dielectric material with a 10 \(\times\) 10 mm\({}^{2}\) size. The polyimide dielectric material is chosen with a thickness of 0.1 mm and a dielectric constant of 0.0027. The proposed unit cell is designed and simulated by using one of the numerical simulation tools, CSTMW studio, in which the frequency limit is chosen from 7 to 12 GHz. From the results, we can observe that the proposed design resonates at two X-band frequencies at 9.84 GHz and 11.46 GHz and the measurement results of the proposed design resonate at 9.81 GHz and 11.61 GHz. It is worth noting that the simulation and measurement findings both obtain the same X-band frequencies, with only a minor difference in the frequency values. Thus, the recommended design is very much useful for X-band applications.  
SRR; metamaterial; numerical simulation; satellite communication; modern communication  
2023 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/))).
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 4 Methodology
### Feature Selection  
Although the initial dataset consisted of 23 columns, only 10 columns were selected for the final dataset. All columns of factual future values, such as factual temperature, have been removed, leaving predicted parameters and thus simulating a realistic scenario with no factual future observations. This has helped to reduce the complexity of the model by identifying and preserving only the most relevant features.  
Given the structure of the dataset, which contains both categorical and quantitative features, the importance of features was assessed in two different ways, using the Random Forest ML algorithm and correlation analysis. Random Forest-based feature importance evaluation effectively captures non-linear relationships and handles categorical features, while correlation analysis provides insights into the linear relationships between numerical features and target variables.
# Metaheuristic with Cooperative Processes for the University Course Timetabling Problem
## 4 Computational Results
### Landscape and Efficacy of SACP
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multicolumn{6}{c}{**SACP**} \\ \cline{2-9}  & **Min** & **Max** & **Mean** & **\(\mathbf{t}\) s** & \(\mathbf{\sigma}\) & **Mode** & **Median** \\ \hline Easy01 & 0 & 0 & 0 & 6 & 0 & 0 & 0 \\ \hline Easy02 & 0 & 0 & 0 & 8 & 0 & 0 & 0 \\ \hline Easy03 & 0 & 0 & 0 & 5 & 0 & 0 & 0 \\ \hline Easy04 & 0 & 0 & 0 & 6 & 0 & 0 & 0 \\ \hline Easy05 & 0 & 0 & 0 & 5 & 0 & 0 & 0 \\ \hline Medium01 & 44 & 60 & 50.69 & 4049.36 & 4.80 & 50 & 50 \\ \hline Medium02 & 48 & 56 & 51.5 & 4004.90 & 3.06 & 54 & 51 \\ \hline Medium03 & 57 & 77 & 57 & 3967.19 & 8.54 & 57 & 55 \\ \hline Medium04 & 47 & 62 & 51.7 & 3979.48 & 5.54 & 52 & 52 \\ \hline Medium05 & 7 & 20 & 11.8 & 3651.59 & 3.77 & 12 & 11.5 \\ \hline Large01 & 321 & 411 & 377.7 & 8354.97 & 32.51 & 407 & 379 \\ \hline Large 02 & 285 & 390 & 346.5 & 8656.48 & 32.93 & 390 & 358 \\ \hline \multirow{2}{*}{**Problem**} & \multicolumn{6}{c}{**SAP**} \\ \cline{2-9}  & **Min** & **Max** & **Mean** & \(\mathbf{t}\) s & \(\mathbf{\sigma}\) & **Mode** & **Median** \\ \hline Easy01 & 0 & 0 & 0 & 7 & 0 & 0 & 0 \\ \hline Easy02 & 0 & 0 & 0 & 8 & 0 & 0 & 0 \\ \hline Easy03 & 0 & 0 & 0 & 7 & 0 & 0 & 0 \\ \hline Easy04 & 0 & 0 & 0 & 5 & 0 & 0 & 0 \\ \hline Easy05 & 0 & 0 & 0 & 5 & 0 & 0 & 0 \\ \hline Medium01 & 53 & 60 & 55.8 & 3899.92 & 2.44 & 57 & 55.5 \\ \hline Medium02 & 48 & 62 & 52 & 3954.85 & 7.29 & 46 & 52.5 \\ \hline Medium03 & 58 & 65 & 60.9 & 3885.50 & 4.07 & 65 & 61 \\ \hline Medium04 & 48 & 59 & 53.6 & 3901.92 & 3.66 & 54 & 54 \\ \hline Medium05 & 16 & 25 & 21.8 & 3560.87 & 3.43 & 22 & 22 \\ \hline Large 01 & 410 & 504 & 478.22 & 8339.55 & 22.98 & 488 & 484 \\ \hline Large 02 & 450 & 528 & 500.33 & 8617.84 & 22.79 & 507 & 507 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of simulated annealing metaheuristic with cooperation of processes (SACP) and simulated annealing metaheuristic with processes (SAP) but without cooperation.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 2 Basic Principles of FL over Wireless Networks
### Energy Consumption Model  
#### 2.3.1 Computation Energy Consumption  
The number of CPU cycles required by the \(k\)-th client to process a single data sample is denoted as \(N_{c,k}\). This parameter is typically determined offline by analyzing the execution time of local computation in FL, along with the CPU clock speed and the number of input bits [37]. For a dataset of size \(D_{k}\) at the \(k\)-th client, the total CPU cycles required for a single local computation round are \(N_{c,k}D_{k}\), leading to the computation time:  
\[temp_{k}=\frac{N_{c,k}D_{k}}{f_{k}}, \tag{6}\]  
where \(f_{k}\in[f_{\text{min}},f_{\text{max}}]\) is the CPU frequency of the \(k\)-th client, adjustable to meet specific performance objectives. According to [38], the power consumption of the mobile CPU processor at the \(k\)-th client is modeled as \(Pcmp_{k}=\zeta_{k}f_{k}^{3}\) (Joule/s), where \(\zeta_{k}\) is the effective capacitance coefficient dependent on the processor architecture. Thus, the total computation energy consumption (in Joule) at the \(k\)-th client during each local round is calculated as  
\[Ecmp_{k}=Pcmp_{k}temp_{k}=\zeta_{k}N_{c,k}D_{k}f_{k}^{2}. \tag{7}\]
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 1 Introduction
Our system continuously updates the location of the destination so that an accurate route can be set. GFG is performed in a greedy manner, forwarding packets to neighbors closer to the destination's physical location; it can be repeated at intermediate nodes and overload the network [14]. GLAN enables optimized routing by learning the optimal path through simulation according to the given environment. Compared to conventional mapping technologies, our proposed system has several advantages, as described below. Figure 1: Forwarding system in GLAN. Routing algorithm considering UAV characteristics. Previous approaches require the maintenance of routing tables to pass data, which can waste memory and power and can lead to network bottlenecks. This algorithm can deliver data in any environment, without a routing table, because all UAVs have IP settings related to geographic location information in consideration of mobility, which is a UAV characteristic. * End-to-end principle preserved. Previously, when forwarding data, passing nodes needed to check whether they were the final destination of the data. The proposed system does not pass datagrams with the support of the application layer while changing the source and destination information. This means that our correspondence does not violate the end-to-end principle. * Accurate delivery. The routing method used to deliver UAV data so far has been unable to solve communication problems that occur in the middle. However, since this system primarily delivers data through broadcasting, the probability of data being delivered to the destination is high; moreover, since it divides the forwarded area, data can be delivered accurately with less power. The rest of this paper is organized as follows. In Section 2, we introduce the related research and show differences and similarities with our system. Then, we introduce the routing protocol design of our system in Section 3 and the RL approach on GLAN in Section 4. We describe our system's performance and compare it with other algorithms in Section 5. Finally, Section 6 concludes the paper.
# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 4 Conclusions  
The use of reflectance data obtained by spectroscopy allows for the quantification of nutrient content; however, the degree of precision of the models varies according to the phenological stage of the crop and the quantity of data utilised, being more precise with a greater quantity of information and with the utilisation of data from all stages studied. The partial least squares regression associated to the spectroscopy shows the great potential for the development of predictive models of nutrients in _Vigna unguiculata_ (L.) Walp.  
The bands located in the visible and near-infrared regions seemed promising for estimating nutrient content in the leaves of _Vigna unguiculata_ (L.) Walp. The estimate of zinc content in the leaves did not generate satisfactory results and presented low predictive potential.  
The technique for predicting nutrient content in leaves provides faster results for monitoring the nutritional needs of the crop since it does not require laboratory analyses, generation of residues and does not require removal of a large quantity of plant material, hence, it allows the nutritional correction of the crop quickly and easily, reducing the possible harm that can come from nutritional imbalance.  
**Author Contributions:** Conceptualisation, J.B.C.A., F.B.L. and A.C.M.d.M.; methodology, J.B.C.A., F.B.L., A.C.M.d.M., C.A.K.T., A.d.S.T., T.R.G.Q. and E.M.d.A.; software, J.B.C.A. and F.B.L.; validation, J.B.C.A. and F.B.L.; formal analysis, J.B.C.A., F.B.L., S.K., I.C.d.S.A. and G.N.; investigation, J.B.C.A., F.B.L. and A.C.M.d.M.; resources, F.B.L. and A.C.M.d.M.; data curation, J.B.C.A., F.B.L. and A.C.M.d.M.; writing--original draft preparation, J.B.C.A. and F.B.L.; writing--review and editing, J.B.C.A., F.B.L., A.C.M.d.M., S.K., C.A.K.T., A.G.T., C.F.d.L., T.R.G.Q., E.M.d.A., I.C.d.S.A. and G.N.; visualisation, J.B.C.A. and F.B.L.; supervision, F.B.L.; project administration, F.B.L.; funding acquisition, F.B.L., A.d.S.T., C.F.d.L., C.A.K.T. and E.M.d.A. All authors have read and agreed to the published version of the manuscript.  
The research received no external funding.  
Not applicable.  
The authors would like to thank the Universidade Federal do Ceara (UFC) and the Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) Agroindustria Tropical for providing the necessary infrastructure to carry out the laboratory analyses. The authors also wish to thank the Agencia de Desenvolvimento do Estado do Ceara ADECE, process no 09/2017 and the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico CNPq, process no 439.304/2018-0. Further thanks are due to the Instituto Nacional de Ciencia e Tecnologia em Salinidade INCTSal, and to the anonymous reviewers for their reviews and suggestions for improvements to the article.  
The authors declare there to be no conflict of interest.
# Coupling Denoising to Detection for SAR Imagery
## 5 Conclusions  
In this study, we develop a novel object detection framework, where an unsupervised denoising network is combined with a two-stage detection network and two sets of region proposals extracted from a real noisy SAR image and a synthetically denoised SAR image are complementarily merged. The coupling structure of denoising network with detection network together intends to replace a cumbersome preprocessing step for denoising with our denoising network and at the same time, the integrated denoising network performs denoising to support the subsequent object detection. To remedy a potential risk due to fine information loss after denoising, we keep raw information from input SAR image within detection network while only utilize a set of region proposals inferred from the synthetically denoised SAR image. The extensive qualitative and quantitative experiments on our own datasets with TerraSAR-X and COSMO-SkyMed satellite images suggest that the proposed object detection framework involves the adaptive denoising for directly influencing detection performance. Our method shows significant improvements over several detection baselines on the datasets constructed from TerraSAR-X and COSMO-SkyMed satellite images.  
Conceptualization, all authors; methodology, S.S.; software, S.S.; validation, Y.K., I.H. and J.K.; formal analysis, J.K.; investigation, I.H.; resources, S.S.; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S.; visualization, S.S. and Y.K.; supervision, S.K.; project administration, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research was supported by the Defense Challengeable Future Technology Program of Agency for Defense Development, Republic of Korea.  
The authors declare no conflict of interest.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 4 Results
### Selection of Basic Sentinel-1 Features  
Selection between the intensity variables, average, median and quantiles were done using the SVM classifier and the Sentinel-1 scenes starting on 4 January 2017 and reaching until 20 August, in total 19 image layers (see Table 4). The date of 20 August was selected close enough after the damage of 12 August. In total, three images after the damage were available. The averages of the intensities, more precisely, \(\sigma^{0}\) (1), gave the largest OA in the validation data, 0.771 (Table 6). The averages also worked well in the cases of UA and PA. Only the results based on the averages are therefore reported here as the main results.
# MIMO Over-the-Air Computation for Distributed Estimation
## 4 Distributed Estimation using MIMO-AirComp
Thus, the proposed framework computes the linear estimation \(\hat{\mathbf{x}}\) over the MIMO channel as  
\[\hat{\mathbf{x}}=\underbrace{\mathbf{P}}_{\hat{\mathbf{\psi}}}\mathbf{\eta}^{-1}\left( \mathbf{\upsilon}^{H}\left(\sum_{k=1}^{K}\mathbf{H}_{k}\mathbf{\mu}_{k}\mathbf{\eta}( \underbrace{\mathbf{C}_{k}^{\top}\mathbf{R}_{v,k}^{-1}\mathbf{y}_{k}}_{\hat{ \mathbf{\phi}}_{k}})+\mathbf{n}\right)\right), \tag{14}\]  
where \(\mathbf{d}_{k}=\mathbf{\eta}(\mathbf{C}_{k}^{\top}\mathbf{R}_{v,k}^{-1}\mathbf{y}_ {k})\in\mathbb{R}^{L_{x}}\) is the input multimodal symbol vector after the local preprocessing, and \(\mathbf{\psi}=\mathbf{P}\) is the application-dependent postprocessing function. The proposed distributed estimator using the MIMO-AirComp consists of a three-step procedure:  
1. First, all nodes generate the input multimodal symbol vector \(\mathbf{d}_{k}\) through the local preprocessing function \(\mathbf{\eta}(\mathbf{C}_{k}^{\top}\mathbf{R}_{v,k}^{-1}\mathbf{y}_{k})\). 2. Then, the MIMO-AirComp applies the appropriate transmit beamforming matrix \(\mathbf{\mu}_{k}\) of node \(k\) and the receive beamforming matrix \(\mathbf{\upsilon}\) of the FC to compute \(\hat{\mathbf{d}}\) while compensating for the channel fading and noise. 3. Finally, the FC estimates \(\hat{\mathbf{x}}\) after the postprocessing operation \(\mathbf{P}\mathbf{\eta}^{-1}(\cdot)\) to the aggregated received vector \(\hat{\mathbf{d}}\). The proposed MIMO-AirComp framework reduces the computation complexity of the FC by delegating some computational load to the nodes. Each node runs the matrix multiplication \(\mathbf{W}_{k}\mathbf{y}_{k}\), where \(\mathbf{W}_{k}=\mathbf{C}_{k}^{\top}\mathbf{R}_{v,k}^{-1}\in\mathbb{R}^{L_{x} \times L_{y}}\) is the precomputed weight matrix. Then, the source encoder applies the element-wise division to \(\mathbf{s}_{k}\in\mathbb{R}^{L_{d}}\). Since each node only needs to compute a linear combination of its sensing vector as the local preprocessing function, this low-complexity algorithm can be implemented on typical wireless embedded systems with low energy consumption [25]. Furthermore, the wireless channel enables the automatic matrix summations involved in the global estimation due to its superposition property. In addition, the communication delay is also very small, as all nodes simultaneously transmit their scaled state measurements. The result with the star topology can be generalized for the arbitrary graphs with the lines of the average consensus algorithms (see, e.g., [26]).
# Skin-Compatible Amorphous Oxide Thin-Film-Transistors with a Stress-Released Elastic Architecture
###### Abstract  
A highly reliable reverse-trapezoid-structured polydimethylsiloxane (PDMS) is demonstrated to achieve mechanically enhanced amorphous indium-gallium-zinc oxide (\(a\)-IGZO) thin-film-transistors (TFTs) for skin-compatible electronics. Finite element analysis (FEA) simulation reveals that the stress within \(a\)-IGZO TFTs can be efficiently reduced compared to conventional substrates. Based on the results, a conventional photolithography process was employed to implement the reverse-trapezoid homogeneous structures using a negative photoresist (NPR). Simply accessible photolithography using NPR enabled high-resolution patterning and thus large-area scalable device architectures could be obtained. The \(a\)-IGZO TFTs on the reverse-trapezoid-structured PDMS exhibited a maximum saturation mobility of 6.06 cm\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\) under a drain bias voltage of 10 V with minimal strain stress. As a result, the proposed \(a\)-IGZO TFTs, including stress-released architecture, exhibited highly enhanced mechanical properties, showing saturation mobility variation within 12% under a strain of 15%, whereas conventional planar \(a\)-IGZO TFTs on PDMS showed mobility variation over 10% even under a 1% strain and failed to operate beyond a 2% strain.  
amorphous oxide semiconductors; \(a\)-IGZO TFTs; stretchable electronics; finite-element analysis; skin compatible electronics; conformable robotics +
Footnote †: journal: Applied Sciences
# Wind Turbines Optimal Operation at Time Variable Wind Speeds
###### Abstract  
The wind turbine's operation is affected by the wind speed variations, which cannot be followed by the wind turbine due to the large moment of the power plant's inertia. The method proposed in this paper belongs to the wind turbine power curves (WTPC) approach, which expresses the power curve of the permanent magnet synchronous generator (PMSG) by a set of mathematical equations. The WTPC research papers published before now have not taken into consideration the total power plant inertia at time-variable wind speeds, when the wind turbine's optimal operation is very difficult to be reached, and its efficiency is thus threatened. The study is based on a wind turbine having a large moment of total inertia, and demonstrates, through extensive simulation results, that the optimal values of the PMSG's power can be determined based on the kinetic motion equation. This PMSG's optimal power represents an ideal time-varying curve, and the wind turbine should be controlled so as to closely follow it. For this purpose, proportional integral (PI) and proportional integral derivative (PID) type-based control methods were implemented and analyzed, so that the PMSG's power oscillations could be reduced, and the PMSG's angular speed value made comparable to the optimal one, meaning that the wind turbine operates within the optimal operation area, and is efficient. The simulations are actually the numerical solutions obtained by using the Scientific Workplace simulation environment, and they are based on the wind speed measurements collected from a wind farm located in Dobrogea, Romania.  
efficiency; inertia; kinetic energy; wind energy; time-varying wind speeds +
Footnote †: journal: Applied Sciences
# Application of the Random Decrement Technique in Damage Detection under Moving Load
## 4 Results and Discussion  
### Preliminary Data of Simply Supported Beam  
The beam was loaded using a 27.5 kg bogie at different speeds (0.3, 0.4, and 0.5 m/s) to simulate the traffic moving load. All acceleration data were recorded at a sampling frequency of 500 Hz. The node 1 (according to Figure 4) was considered as the location of the main signal. Auto-RDS for node 1 and cross-RDSs for Nodes 2-9 were calculated using level-crossing triggering condition. As mentioned previously, to use the level-crossing triggering condition, the optimum value was assumed to be a \(=\sqrt{2}\times\) standard deviation [24]. In this work, this optimum value was computed for all signals, and the minimum of these values was chosen as triggering condition of the main signal. This step was made by more than 2000 segments (\(N>1000\)) for each RDS. Figure 10 shows three signals of the intact beam, namely, the acceleration signal recorded in node 1, the auto-RDS of node 1, and the cross-RDS of node 5 (middle of the beam). The length of RDS in the whole of this work is 5 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **1** & **2** \\ \hline section area loss in twisted cable & 43\%, 70\%, and 100\% & 43\% and 70\% \\ Location & Node 2 & Node 4 \\  & Second node from left side & (middle of bridge) \\ Name & N2D3-7, N2D5-7, and N2D7-7 & N4D3-7 to N4D5-7 \\ \hline \hline \end{tabular} Note: To simplify the naming process of different scenarios, each condition was named as N (place of damage) D (number of small cables removed from the twisted cable). For example, N4D1-7 means 14% damage at node 4 (1/7 \(\cong\) 14%). The no-damage condition is shown by WD (without damage).  
\end{table}
Table 3: Five damage scenarios considered in a laboratory model of the bridge.  
Figure 9: The twisted cable composed of 7 small sub-cables.
## 5 Application Results and Analysis
### Simulation PD Models and Denoising Performance Evaluation
#### 5.1.2 Denoising Performance Evaluation  
In order to quantitatively evaluate denoising performance, three statistical evaluation indexes are used, where \(x(i)\) is the noised signal, \(y(i)\) is the denoised signal, \(N\) is their length.  
(1) Mean Square Error (MSE) [8; 15; 25]  
\[MSE=\sum\limits_{i}{{{(x(i)-y(i))}^{2}}/N} \tag{19}\]  
(2) Normalised Correlation Coefficient (NCC) [15; 19]  
\[NCC=\frac{\sum\limits_{i}{{x(i)y(i)}}}{{\sqrt{\sum\limits_{i}{{x(i)}^{2}}{\sum \limits_{i}{{y(i)}^{2}}}}}} \tag{20}\]  
Figure 4: Waveforms of simulation PD models (**a**) DEP, (**b**) DOP.  
(3) Maximum Amplitude Error (MAE) [17]  
\[MAE=\left|\frac{\underset{i}{\max}(x(i))-\underset{i}{\max}(y(i))}{\underset{i}{ \max}(x(i))}\right| \tag{21}\]
# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 2 Trends in Close-Range RS Approaches for Assessing FH
### Close-Range RS Approaches--Towers

Table 3: Example studies illustrating the use of spectral laboratory, plant phenomics and controlled environmental facilities in common FH applications. as spectrometers or soil sensors. Such combinations allow continuous measurements of vegetation productivity and environmental variables such as fluxes, soil moisture, and evapotranspiration, which can be assessed in relation to FH attributes. Permanently installed towers acquire individual point information but are of particular importance in terms of long-term measurements for the calibration and validation of airborne and space-borne RS data. Linking flux tower systems in an international network (FLUXNET, [50]) supports a better understanding of ecological processes and changes in FH using RS [74; 75].