esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models
## 4 Case Study
### Experimental Setup  
In this subsection, we present the setup of the experimental application regarding the evaluation of the proposed ML-based framework for estimating energy savings of EE renovation actions. The structure of the experimental application is summarized in Figure 4. As can be seen, the input of the methodology consists of the six features presented in Section 4.1. Some of these features are used without being further processed. However, the categorical variables, namely the sector, country, investment category, and investment type, have been categorically encoded with the label encoder function. For example, for the sector feature, the category \"Building\" has been encoded with the value of 0, while the category \"Industry\" has the value of 1. Following this, the final data set is split into training and test sets with random sampling, keeping 80% of the data set for the training, and the remaining 20% for the evaluation of the proposed framework.  
The core of the ML framework proposed in this study involve the tree-based algorithms presented in Section 3. These algorithms are based on decision trees and exploit some of the most successful ensembling techniques like bagging and boosting. Thus, we have employed the RF, the XGBoost, and the LightGBM models to predict the energy savings of EE renovation actions. Moreover, we have applied an extra ensembling level in order to blend the predictions of the three tree-based models with the objective to mitigate model and parameter uncertainty [58]. In this respect, after the three models generate their predictions, their values are averaged using equal weights to obtain the final forecast, i.e., energy savings for the examined EE renovation actions. Effectively, this extra level of ensembling reduces the spread or dispersion of the predictions and further improves forecasting performance (for an encyclopedic review on the benefits of ensembling in forecasting, please refer to section 2.6 of [55]).  
The final step of the experimental setup involves the determination of the hyperparameters for the selected regression models. For each of the models, the optimal hyperparameter values were determined using part (20%) of the data available for training as a  
Figure 4: Structure of the experimental application.  
validation set. In order to make sure that the ML models will be robust for all investment categories, the validation set consisted of samples that represented different categories, measures, and countries of the original data set. The mean absolute and squared errors were used to identify the optimal hyper-parameter values for each model. The optimization of the hyper-parameter values was conducted using grid search. The results of the hyper-parameter fine-tuning process for each model are summarized in Table 2 along with the respective search space and selected values.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 4 Impedance Controller  
To actively remove space debris, a servicer CubeSat will have to perform the final approach, deploy the dedicated mechanism, and then perform the capturing phase of the ADR mission. Having a hybrid-compliant system implies that both _passive_ and _active_ compliance are involved. Since the passive compliance has fixed stiffness and damping coefficients, it is required to analyse and model the adequate controller to get the active compliance's right coefficients. The CubeSat and the debris are specific in mass, but the capturing mechanism's compliance can be modified for the optimal response of the ADR system regarding the contact time with the debris. In this section, the aim was to study the behaviour of the systems during contact and then regulate the relationship between the ADR system's tip and contact force, employing an impedance controller. A single-axis analysis was undertaken (central impact), as is common in the literature [45].
## 4 Results and Discussion
To validate the prediction model that we built in the first experiment, we applied this prediction model and conducted the second experiment by controlling parameters explored in the first experiment, without any human interference from experienced farmers. Figure 10 shows the hatching rate rose from 47% in the first experiment to 62% in the second experiment, and this result validates the effectiveness of our prediction model. In addition, we furthermore evaluated the effects of O\({}_{2}\) and CO\({}_{2}\) values inside the incubator and hatcher toward the survivability of the black Muscovy duck egg between two experiments. Figure 10 illustrates the four types of air quality values inside the incubator. The data from the first and second experiments are displayed using blue and red lines, respectively. The first experiment shows a 47% hatchability, and the second experiment demonstrates a 62% hatchability. The different 'hatchability' between the two experiments is highly related to the difference in CO\({}_{2}\) values in the two experiments. This means that more eggs survived in the second experiment than the first experiment; therefore, the oxygen consumption and CO\({}_{2}\) value of the second experiment were higher than that of the first experiment. However, the CO\({}_{2}\) and O\({}_{2}\) values are the results of the survivability of the black Muscovy duck egg rather than the cause of the survivability of the black Muscovy duck egg. Therefore, our model automatically excludes the CO\({}_{2}\) and O\({}_{2}\) values as being the key characteristics that predict survivability. Figure 9: Egg tray of the second experiment: the fertilized egg status on E31.
## 3 Results and Discussion  
### Total RNA Isolation and mRNA Enrichment  
Total RNA was successfully obtained from all samples. Actively feeding adults resulted in the highest yield of mRNA at 1.88 \(\mathrm{ng}\)/\(\mathrm{\SIUnitSymbolMicro m}\)L with an A\({}_{260}\)/A\({}_{280}\) ratio of 2.1. mRNA from each of the life stages was used as a template to generate the RACE-ready cDNA.
## 3 Results and Discussions
### XRF Soil Analysis
All concentrations were found to be above the legislated normal value of 2 mg kg\({}^{-1}\) but remained below the sensitive alert threshold of 10 mg kg\({}^{-1}\)[52] (Figure A7, Appendix A). Cobalt (Co) was found at an average concentration of 2.91 mg kg\({}^{-1}\), with levels ranging from 2.20 to 3.90 mg kg\({}^{-1}\). All the results were below the legislated normal value of 15 mg kg\({}^{-1}\)[52]. Manganese (Mn) concentration values ranged from 640 mg kg\({}^{-1}\) to 837 mg kg\({}^{-1}\), all being below the legislated normal value of 900 mg kg\({}^{-1}\), but comparable to the European average of 810 mg kg\({}^{-1}\). Vanadium (V) concentrations ranged between 61 and 106 mg kg\({}^{-1}\), with an average of 84.15 mg kg\({}^{-1}\), comparable to the average value reported by [55] for urban sites, which was 86.34 mg kg\({}^{-1}\). At all locations, the V concentrations exceeded the legislated normal value of 50 mg kg\({}^{-1}\)[52] (Figure A8, Appendix A). Additionally, at locations 4 PS and 4 ES, concentrations were above the sensitive alert threshold of 100 mg kg\({}^{-1}\)[52]. For cadmium (Cd), the concentration values ranged between 0.07 and 0.25 mg kg\({}^{-1}\), with an average of 0.14 mg kg\({}^{-1}\). All values were below the legislated normal value of 1 mg kg\({}^{-1}\)[52], and comparable to the European soil average and findings from an agricultural soil study by [33]. Barium (Ba) concentrations ranged between 316 and 521 mg kg\({}^{-1}\), with an average of 413 mg kg\({}^{-1}\). All Ba levels exceeded the normal value of 200 mg kg\({}^{-1}\)[52] (Figure A9, Appendix A). At locations 1 ES, 2 PS, 4 PS, 4 ES, 5 PS, 6 ES, 7 PS, 7 ES, 8 PS and 10 ES, the values also exceeded the sensitive alert threshold of 400 mg kg\({}^{-1}\). The average value obtained in this study is comparable to the European average of 400 mg kg\({}^{-1}\) and to the findings of [34] for industrial soils, which reported an average of 355.6 mg kg\({}^{-1}\). Rubidium (Rb) was found in park soils with an average concentration of 74.88 mg kg\({}^{-1}\) and a maximum value of 88 mg kg\({}^{-1}\), comparable to the European mean of 86.8 mg kg\({}^{-1}\) and the industrial area average of 82.53 mg kg\({}^{-1}\) reported by [34]. Results of Pollution Indices of Soil Contamination and Potential Human Health Risk Assessment 3.2.1. Coefficients of Variation  
The values of the coefficient of variation (CV) of the heavy metal concentrations, presented in Tables 5 and 6, were found to be within (0.09-0.29), usually with higher values obtained for the edge of the parks. A high CV value suggests an anthropogenic input [29; 46]. In particularly, for ESs, the values of CV for Ni (0.65), Cu (0.50), Zn (0.86) and Pb (0.73) indicate a supplementary contribution due to secondary traffic effects. Based on the CV values, the soil can be divided into soils that are affected by anthropogenic activity and soils that are not affected by anthropogenic activity.
# A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems
## 1 Introduction
The channel hardening property of the mMIMO channel can be exploited to relatively relieve the computational complexity by replacing the matrix inversion challenge by matrix-vector multiplications. The truncated Neumann series expansion (NSE) approach is an example of such replacement where it achieves a significant performance enhancement when the BUAR is large. The convergence rate of the NSE method is slow. Therefore, another method, namely, the Newton iteration (NI), is proposed and it has faster convergence rate than the NSE method. Performance of the NSE and NI methods decays significantly when the BUAR is small, i.e., BUAR\(<5\)[13]. In other words, the NSE and NI methods are limited to the mMIMO system when the number of user terminals is comparatively small. Therefore, iterative methods have been proposed to avoid the matrix inversion by representing and solving the detection problem as a set of linear equations, and then refinements are performed iteratively over the solutions. It is well known that iterative methods, such as the successive over relaxation (SOR), the Gauss-Seidel (GS), the Jacobi (JA), the Richardson (RI), and etc., approach the MMSE performance with low complexity, i.e., \(\mathcal{O}\left(K^{2}\right)\). The performance and the complexity of RI and SOR methods depend on a relaxation parameter (\(\omega\)). If \(\omega=1\), we obtain the GS method from the SOR method. The GS method achieves the MMSE performance when the BUAR is small. The JA method can be implemented in a parallel pattern. However, the performance-complexity profile and the convergence rate of iterative methods are highly dependent on the initial solution. Therefore, initial solution has to be selected carefully. In the literature, the initial solution is usually selected based on the diagonal property of the equalization matrix. In recent literature, a stair matrix have been utilized to initiate the initial solution which shows a considerable performance enhancement as well as a fast convergence rate [14; 15; 16]. In this paper, a robust low complexity joint detection algorithm is proposed based on the JA and GS methods to achieve the MMSE performance where the initial solution is selected based on properties of the stair matrix. The convergence rate of proposed algorithm is fast even when the BUAR is small. This paper is organized as follows. Section 2 presents the fundamentals and the background of proposed detection algorithm. Section 3 illustrates the iterative methods, whereas Section 3.6 presents the proposed hybrid low complexity detection algorithm. Section 4 presents the complexity analysis of the proposed algorithm. Section 5 illustrates the performance and complexity profiles of the proposed algorithm as well as a comparison with up-to-date methods is presented. Finally, Section 6 concludes the paper.
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
## 2 Materials and Methods
### Data  
According to the availability of data and the economic development of the research area over the past 30 years, the data used in this study include (1) Three Landsat 5-TM images of Yantai from 1990 to 2010, with a spatial resolution of 30 m, were collected at 10-year intervals. Landsat 5-TM images with good quality covering the study area were obtained through geospatial data cloud ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))). Then, the tide level data of Yantai city at the corresponding time of each scene image were inquired through the Yantai tide gauge station, from which the relatively high tide level image was selected as the data source for this study. (2) The data from 2015 to 2018 are mainly based on GaoFen-2 (It was successfully launched by China on August 19, 2014) images with an accuracy of 4 m. In the key monitoring area (The key monitoring area refers to the area that can verify the correctness of the extracted information results and the area that is hard to reach by human beings), tilt photography of five-lens unmanned aerial vehicle (DJI M600 UAV) was used as the data source with an accuracy of 0.1 m. (3) The Basic Geographical Information Monitoring Data in Shandong Province in 2018. (4) Topographic maps and administrative boundary maps of the study area and other relevant information.  
Seven remote sensing images of Yantai from 1990 to 2018 and UAV images were calibrated and registered in this study. Firstly, the GF-2 images from 2015 to 2018 were geometrically corrected. Then, the image data of other years were corrected according to the GF-2 images in Yantai in 2015. Six typical objects were selected as control points, and the relative geometric accuracy of the images from 1990 to 2010 was corrected and registered respectively to ensure that the error was within one pixel. The key step of UAV image registration was the selection of ground control points. The number  
Figure 1: Figure in the study area.  
of control points depended on the size of monitoring area. In this study, five control points were generally selected for correction. The images taken by UAV were set up in a unified coordinate system, and then the images were corrected and processed according to the space coordinates of different ground control points.
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 4 Detectability of GDSS Signals
### Detection Using Modulation Stripping  
In this section, a simple modulation stripping method is used to evaluate the detection performance of the GDSS signals. A non-linearity can be applied to the signal to produce discrete spectral spurs to aid detection. As an example, in the case of QPSK modulation, a fourth power produces a discrete spur at four times the carrier frequency offset. Figure 9 shows the distribution plots obtained for both GDSS and DSSS signals at different SNRs. The distribution plot generated from the detector for a set of Gaussian distributed complex values (Gaussian noise) is presented as a reference in Figure 9a. It should be noted that the developed GDSS scheme in Figure 9c,e also appear random with no significant phase distribution convergence. However, Figures 9b,d show a bell shaped distribution for the DSSS. This means that the phase distribution converges to a particular point, which gives evidence for a set constellation scheme being used, as opposed to the GDSS scheme and Gaussian noise.  
Figure 8: Average deviation of the estimated 20 th-order moment from theory for \(N=256\).  
Figure 9: Phase angle modulation stripping detector with QPSK, normalised intensity versus phase, \(N=256\).
## 4 Experiments and Results
### Evaluation Metrics  
In this paper, we use the mean average precision [41] (mAP) as an evaluation metric. The mAP is an important metric for evaluating object detection performance. We divide the samples into true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) cases to calculate the precision (P) and recall (R) as shown in Equation (4).  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}},\text{R}=\frac{\text{TP}}{\text{ TP}+\text{FN}} \tag{4}\]  
Figure 9: Images in the DIOR datasets. (**a**) airplane; (**b**) airport; (**c**) baseball field; (**d**) basketball court; (**e**) bridge; (**f**) chimney; (**g**) dam; (**h**) harbor; (**i**) expressway toll station; (**j**) expressway service area; (**k**) ground track field; (**l**) golf field; (**m**) overpass; (**n**) ship; (**o**) stadium; (**p**) storage tank; (**q**) tennis court; (**r**) train station; (**s**) vehicle; and (**t**) windmill.  
The precision and recall are two mutually constrained and balanced metrics. To measure these two metrics, we introduce the mAP, which is defined as the area under the average PR curve of each category at different confidence levels as shown in Equation (5).  
\[\text{mAP}=\frac{1}{\text{N}_{\text{C}}}\sum_{i=1}^{\text{N}_{\text{C}}}\int_{0} ^{1}\text{P}_{i}(\text{R}_{i})\text{dR}_{i} \tag{5}\]  
where N\({}_{\text{C}}\) represents the number of categories in the datasets.
## 3 Systems
### Query-by-Example Spoken Term Detection
#### 3.2.3 End-to-End with Z-Score Normalization System (E2E + ZNORM)  
This system is the same as the E2E system except that, in this case, a \(z\)-score normalization approach was included. To do so, each detection score is normalised according to Equation (12):  
\[z=\frac{x-\mu}{\sigma}, \tag{12}\]  
where \(x\) represents the original detection score, \(\mu\) is the mean of all the detection scores, and \(\sigma\) is the standard deviation of all the detection scores.
# Ultrasound for Meat Processing: Effects of Salt Reduction and Storage on Meat Quality Parameters
## 3 Results and Discussion  
### Sodium Uptake and Moisture  
The sodium content of pork primed in 15% NaCl (NS) or 15% NaCl/KCl (RS) with or without ultrasound processing is presented in Table 1. As expected from the brine formulations, significant differences (\(p\) < 0.05) in the amount of sodium (Na) were observed between the samples. The sodium content of the RS meat, was significantly lower (\(p\) < 0.05) than the one printed in NaCl, containing 0.62 g and 0.90 g Na/100 g meat, respectively. No differences (\(p\) > 0.05) were observed between the level of sodium uptake in the control and sonicated samples using the optimized processing times of 1 h and 4 h, respectively, for ultrasound or immersion curing.  
The effect of ultrasound processing on the meat surface is shown in Figure 2. The SEM pictures clearly show the formation of micro-channels on the meat surface, when processed using ultrasound in comparison to untreated meat [10]. The visible formation of micro-channels on the meat surface allow for a faster and more homogenous distribution of the brine on the meat; this physical disruption at the cellular level in response to acoustic cavitation, is the main reason for the shorter processing time achieved by US in comparison to standard methods. At the measured sodium levels, no differences in expressible moisture content, water activity and pH of the meat were observed, Table 1. Similarly, a recent work from [PERSON] et al. [23] has shown that no significant effects on moisture and pH were found in pork sausage patties when salt was replaced with potassium chloride. In regard to the effects of using US for meat processing, different studies have looked at its effect on pH in beef muscles, reporting no or small differences when ultrasound was applied with a frequency between 24 and 45 kHz [24; 25; 26].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Brine** & \multicolumn{2}{c}{**NS**} & \multicolumn{2}{c}{**RS**} \\ \hline
**Processing** & **Control** & **US** & **Control** & **US** \\ \hline Time (h) & 4 & 1 & 4 & 1 \\ Sodium & 0.90 \(\pm\) 0.04 a & 0.88 \(\pm\) 0.08 a & 0.65 \(\pm\) 0.03 b & 0.62 \(\pm\) 0.02 b \\ Moisture (\%) & 72.02 \(\pm\) 1.06 a & 71.47 \(\pm\) 0.68 a & 71.53 \(\pm\) 0.12 a & 72.87 \(\pm\) 0.02 a \\ A\({}_{\text{w}}\) & 0.934 \(\pm\) 0.001 a & 0.933 \(\pm\) 0.001 a & 0.936 \(\pm\) 0.001 a & 0.932 \(\pm\) 0.001 a \\ pH & 6.01 \(\pm\) 0.10 a & 6.00 \(\pm\) 0.20 a & 6.10 \(\pm\) 0.21 a & 6.10 \(\pm\) 0.11 a \\ \hline \hline \end{tabular} Values followed by different letters in the same row are significantly different at \(p\) < 0.05.  
\end{table}
Table 1: Sodium content, moisture, water activity, and pH of pork meat burned with or without ultrasound. Sodium content is express as g of sodium/100 g of meat. Comparisons are between the processing conditions (static burning and ultrasonic (US)) and the brines used for curing NaCl (normal sodium content, NS) and NaCl/KCl (reduced sodium, RS).
###### Abstract
[17] combined Unet with a conditional variational autoencoder (CVAE), giving Unet the ability to quantify and predict uncertainty. [PERSON] et al. [18] presented the Unet 3+ network, which uses full hop connection and deep monitoring on the basis of the Unet++ network. There are also various other networks developed based on Unet-based frameworks: DAR-CNN [19], DoDnet [20], Swin Net [21] (Unet-like Transformer), CaraNet [22], and ParNet [23]. Additionally, [PERSON] et al. [24] proposed the Unet network, which, compared with Unet, introduces a residual mechanism, and uses the element method for residual connection. A profound question arises: how can we improve the design of urban household waste landfills using aerial image segmentation integrated with ecological restoration theory? Our solution is to use an enhanced SEVnet model to accurately identify the landfill, and then combine it with ecological restoration theory to make the landfill landscape design more scientific and reasonable. In this article, an improved 2 dVnet was developed by combining a sequence lightweight network and a stimulation network (SENet) [25] to address the issue of aerial image recognition for potential urban garbage dumps. The Vnet of two-dimensional images was studied, combined with the SE model, and optimized to achieve efficient image processing in multi-channel environments. This article utilizes the Saihanba landfill site in China as a case study. Initially, aerial image data were collected, which was followed by GIS-assisted analysis and image segmentation. Then, by incorporating ecological restoration theory and conducting land-use analysis, landscape application and design of garbage landfills were explored from a landscape ecology perspective. Finally, a comprehensive planning and landscape restoration design method combining SWOT analysis from four perspectives, including strengths, weaknesses, opportunities, and threats, was proposed for the Saihanba landfill site in Hebei, China, aiming to improve the sustainable development ecological chain. The framework of this article is shown in Figure 1.  
The subsequent sections of this article proceed as follows: Section 2 presents the background technologies; Section 3 introduces the innovative methods proposed in this article; and Section 4 presents the data experiments and SWOT analysis, and eventually provides a summary and explanation.