esa-sceva/satcom-chunk-collection huggingface dataset:
# Recommendation System Using Autoencoders
## 3 Methodology  
Machine learning and Data Mining (DM) are becoming increasingly important areas of engineering and computer science and have been successfully applied to a wide range of problems in science and engineering [34; 35]. Machine Learning is the scientific field dealing with the construction of computer systems that have the ability to adapt, learn and improve their performance in a given domain through experience. On the other hand, DM is a multidisciplinary area that incorporates mathematical functions, Machine Learning techniques, and statistical analysis to uncover hidden patterns or rules and extract previously unknown and potentially meaningful knowledge [36]. DM techniques include descriptive algorithms, for finding interesting patterns in the data, like associations, clusters,and subgroups [37], and predictive algorithms, that perform induction to make predictions of a specific attribute, which results in models that can be used for regression and classification [36; 38].  
There are three main categories of DM strategies reported in the literature: supervised, unsupervised, and semi-supervised learning. In supervised learning, a set of input variables named training set is used to learn model parameters and predict a target or dependent variable. In unsupervised learning, the target variable does not exist and no training set is used. In turn, DM techniques are used to discover patterns, clusters, or relationships in the dataset. In semi-supervised learning, the target variable exists but the value is only provided for a small amount of examples and DM techniques are used to predict the values of missing target values or extract patterns, clusters, or relationships in the dataset [39; 40]. In this study we will use a neural network named autoencoder, an unsupervised learning technique, based on a collaborative filtering method to create a product recommendation system.  
TensorFlow 2.0.0 [41] was used for the creation and training of the model. TensorFlow supports both large-scale training and inference. In addition, it is flexible enough to support experimentation and research into new Machine Learning models and system-level optimizations [42].  
The DM process will follow the CRISP-DM Methodology (Cross Industry Standard Process for Data Mining), one of the most popular methodologies used in DM projects worldwide. CRISP-DM is a cyclic process, which is divided into six phases, namely, _Business Understanding_, _Data Understanding_, _Data Preparation_, _Modeling_, _Evaluation_ and _Deployment_. Figure 2 depicts the main stages of the CRISP-DM lifecycle.  
The following subsections contain a detailed description of each stage of the CRISP-DM lifecycle.
# Evacuation Management System for Major Disasters
## 2 Mathematical Model
### Evacuation Routing
The set of routes for a given evacuation \(e_{j}\) can be defined as \(R_{i}=\{r_{1},r_{2},\ldots,\;r_{n}\}\) where a particular route is defined by the geographical coordinates  
Figure 2: Uniform spatial distribution of shelters avoiding damaged assets and alternative evacuations. Risk areas to avoid are represented by red polygons, areas to evacuate in blue and green areas are assumed safe. \(r_{i}=\{(\phi_{1},\,\theta_{1}),(\phi_{2},\,\theta_{2}),\ldots,(\phi_{l},\,\theta_{ l})\}\) where the first and last coordinates correspond to both the assembly point and the destination shelter.
# A Low-Profile HF Meandered Dipole Antenna with a Ferrite-Loaded Artificial Magnetic Conductor
## 1 Introduction
A meandered dipole antenna optimized by a binary genetic algorithm (GA) is designed and combined with the designed AMC. Compared to previous studies [22], the proposed antenna shows improved VSWR (voltage standing wave ratio) characteristics in the 20-30 MHz band, i.e., the HF band, and has higher gain characteristics than previous antenna within the aforementioned operating bandwidth. Therefore, it can be applied to applications such as ground vehicles, where the antenna can be installed in a relatively large space and that require high gain characteristics on the HF band. The proposed antenna with the AMC can operate on the HF band due to the link between the meandered dipole antenna and the AMC. ANSYS high-frequency structure simulator (HFSS) software is employed to conduct all simulations in this paper. The designs of the AMC and the antenna are described in Section 2. In Section 3, the simulated and measured results are introduced and compared. Finally, Section 4 concludes the paper.
## 3 Results  
### Accuracy and Repeatability: pXRF vs. Certified Reference Materials (CRMs)  
Accuracy refers to the degree to which a measured value for a sample agrees with a reference of true value for a CRM. An acceptable range of 80-120% of recovery with respect to the certified value was used to evaluate accuracy and two CRMs (PACS-3 and MESS-4) were analysed via pXRF for this purpose (Table 2). Except for Hg and Sb, which were not detected by pXRF, acceptable recoveries were obtained for Cr, Fe, Mn, Pb, and Zn. In the case of Cu, accuracy was acceptable only in the PACS-3, which has higher concentrations of Cu, and As was not acceptable in either, with the better recoveries on the MESS-4 than in the PACS-3 most likely due to interferences related to the relatively higher concentration of Pb in PACS-3. Repeatability expressed as RSD% was <10% for  
\begin{table}
\begin{tabular}{c c} \hline
**Data Quality Level** & **Statistical Parameter** \\ \hline \multirow{2}{*}{Definitive} & r\({}^{2}\) = 0.85 to 1.0. The repeatability (RSD%) must be less than or equal to 10%, and inferential statistics indicate the two data sets are statistically similar. \\  & r\({}^{2}\) = 0.70 to 1.0. The RSD% must be less than 20%, but the inferential statistics indicate that the data sets are statistically different. \\  & r\({}^{2}\) = less than 0.70. The RSD% is greater than 20%. The data must have less than a 10 percent false-negative rate. \\ \hline \end{tabular}
\end{table}
Table 1: US EPA Criteria for establishing data quality [11].  
all the analytes, with the lone exception of Pb in the MESS-4, which showed a slightly higher value (13%). In the case of As, the accuracy was lower in PACS-3, which has a higher Pb content (As mean recovery = 164% with Pb concentration = 188 mg/kg) than in MESS-4, which, conversely, has a concentration of Pb one order of magnitude lower (As mean recovery = 121% and Pb = 21.5 mg/kg) because the As-K and Pb-L X-ray overlaps can be so severe that the resolution may not be enough to discriminate between the two elements [7], and the identification of As may be compromised. In fact, when the Pb:As ratio exceeds 10:1, As is not efficiently detected by means of pXRF [31].
## 4 Results  
Python programming language was implemented for developing these regressions and classification-based algorithms. Metric features such as Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R\({}^{2}\)) are used for measuring the performance of the employed supervised machine learning algorithms. Figure 2a shows the plot of loss function of Artificial Neural Network model with increasing number of epochs. While evaluating the performance of classification-based algorithms, the F1-Score and area under curve (AUC) scores of each algorithm are evaluated. Equation (2) is used for the calculation of the F1-Score value.  
\[F1\text{-}Score=2\times\frac{precision\times recall}{precision+recall} \tag{2}\]  
Tables 3 and 4 shows the obtained results for Supervised Machine learning based regression and classification algorithms.  
Figure 2: _Cont._  
In the Decision Tree classification-based algorithm, entropy is used as a criterion which is calculated by Equation (3). Entropy is a unit of measurement for information that depicts the unpredictability of the target's features. The feature with the lowest entropy selects the optimal split, just like the Gini Index does. A node is pure when the entropy has its lowest value, which is zero, and it reaches its largest value when the probabilities of the two classes are equal. Figure 2b represents the obtained Decision Tree plot of the present work.  
\[Entropy=-\sum_{j}p_{j}\cdot log_{2}\cdot p_{j} \tag{3}\]  
where \(p_{j}\) stands for class \(j\) probability.
# Explainable Post-Occupancy Evaluation Using a Humanoid Robot
## 4 Case Study
### Survey Session and Results
Figure 10: The robot Nao and the Choreographe environment. From the user interview, it can be noted that two situations needed a correction. Hence, the robot recommended the user to decrease the temperature up to the min value of the standard, and it also suggested to verify if she was heavily dressed. Moreover, it suggested using a table lamp with warm lights, as she preferred, and to verify if it was a more pleasant situation. The user, according to the robot suggestions, tried to follow them to improve her comfort opportunely. In particular, she placed a table lamp with warm light (i.e., 3000 K) on her work-plane, as is possible to see from the real figure of the room in Figure 4. As concerns her perception of the temperature, she tried to set a lower operating point temperature of the fan-cooler. Thus, a new POE questionnaire was performed with the user after a suitable time interval to evaluate if the corrections effectively improved the situation. The robot detected the following new situations. Old results are reported near the most recent (old result -> new result). * True negative -> true positive. The environment complied with the standard of visual comfort. The user perceived a better visual situation. * False negative -> false negative. The environment conformed to the standard of thermal comfort. The current temperature was in the range of the value recommended by the standard during the winter months. Nevertheless, the user continued to feel warm. * True positive. Finally, the environment complied with the standard of acoustic comfort. The user perceived a quiet environment. The introduction of a table lamp effectively improved the visual comfort of the user, although this situation was obtained only close to the user's working area. However, a report recommending an improvement of the artificial lighting system was sent to the maintenance manager. Conversely, the situation of the thermal comfort remained unsolved. A further investigation put in evidence that the thermostatic control was out of operation.
## 5 Performance Evaluation
### Result Analysis and Discussion
#### 5.2.2 End-to-end Delay Analysis  
The latency or end-to-end delay analysis discusses a faster data delivery of the network, which is one of the prime factors in monitoring the applications. The faster the information is revealed at the sink node, the more easily it is to prevent and execute the necessary action be taken at the target area. Keeping in mind that we analyzed the end-to-end delivery as the data holding time at each level of the nodes. As shown in Figure 12a, b, the delay factor indicates the time a packet of data takes to reach the sink node from the target region. As the network size increases, the propagation delay of the nodes also increases, since it searches for the sink node every time the data needs to be delivered, which increases the delay in LBDD, MSRP and Mob M-LEACH, as given in Figure 12a. However, the ring and the modified ring are based on the sink advertisement prior to the data delivery, due to the kinds of data held for a shorter amount of time in the respective nodes, thereby improving the data delivery time. Similarly, as shown in Figure 12b, the static sink increases the delay, which is improved as and when the sink node moves towards the desired point and collects the data at a much faster rate. Again, the frequent sink advertisement by the ring node and the sink mobility makes the CH receive the data delivery more quickly, thereby justifying that our proposed ring routing is a better choice.  
Figure 11: Throughput analysis; (**a**) variation in the network density; (**b**) variation in the sink speed.
## 4 Discussion of Prediction Results
### Comparison with the Method Based on Ground Motion Intensity Measures  
Ground motion intensity measures are often adopted to reflect the destructive power of earthquakes in machine learning-based seismic damage assessment methods. For example, [PERSON] et al. (2016) used \(Sa(T_{1})\) as the ground motion intensity measure and combined it with structural parameters to predict the seismic damage of bridges. Therefore, in this section, a comparative analysis was conducted to investigate the performance of the MI-CNN model (adopting the ground motion sequence as the input) and the method based on ground motion intensity measures.  
For the method based on ground motion intensity measures, the PGA, peak ground velocity (PGV), maximum spectral acceleration (\(Sa_{\text{max}}\)), and spectral acceleration of fundamental period \(Sa(T_{1})\) were calculated for the collected ground motions. The calculated four ground motion intensity measures were combined with the building attribute data (building fundamental period and fortification level) to form each sample for model training. The division of the training, validation, and test sets is consistent with the previous section.  
As for the selection of machine learning models for the comparative analysis, the LightGBM and XGBoost models have been widely used in classification problems with multiple feature inputs ([PERSON] et al., 2016; [PERSON] et al., 2017). These two models have the advantages of good training efficiency, low memory consumption, and high accuracy compared to other machine learning models. Therefore, the LightGBM and XGBoost models were adopted as the machine learning model for the comparative analysis. During the training process, the Multiclass_Logloss multi-category logarithmic loss function was adopted, and the maximum epoch was set to 50. The early stopping function was also used in this section.  
The confusion matrices for the method based on ground motion intensity measures are shown in Figure 14. The prediction accuracies of the test set using the LightGBM and XGBoost models are 73.7% and 67.4%, respectively. These accuracies are lower than those of the MI-CNN (prediction accuracy = 79.7% in Figure 8). The above results show that the proposed MI-CNN model can better predict the seismic damage of buildings compared to the method based on ground motion intensity measures.  
Figure 13: Confusion matrices for buildings with different fortification levels: (**a**) fortification level of 6 (prediction accuracy = 79.9%); (**b**) fortification level of 7 (prediction accuracy = 79.6%); (**c**) fortification level of 8 (prediction accuracy = 78.4%); (**d**) fortification level of 9 (prediction accuracy = 80.8%).
## 3 Support Vector Machine for Malicious Primary User Emulation Detection
### Energy Calculation  
An additive white Gaussian noise (AWGN) channel is assumed, and we use a time-based energy detection method as in [26]. \(N\) is the total number of samples of the energy and \(Y(n)\) is added during one \(S_{i}\) detection interval, as shown in Equation (6).  
\[Z(Y_{n})=\frac{1}{N}{\sum_{n=1}^{N}\left|Y(n)\right|^{2}} \tag{6}\]  
The energy detector compares \(Z\) to a threshold \(\lambda\) and makes a decision regarding the presence or absence of signals. The probability of detection and the probability of false alarm for energy detection is defined in Equation (7) [26].  
\[P_{d}=p(Z\geq\lambda|H1), \tag{7}\]  
\[P_{fa}=p(Z\geq\lambda|H0).\]  
\(Z\) is a Gaussian random variable with a mean of zero and variance of \(\sigma m2\), and \(\gamma\) is the signal-to noise-ratio (SNR). The probability of false alarm (\(P_{fa}\)) and the probability of detection (\(P_{d}\)) are defined in Equation (8) [26].  
\[P_{d}=Q\bigg{(}\frac{\lambda-N-N\times\gamma}{\sqrt{2\times N+4\times N\times \gamma}}\bigg{)} \tag{8}\]In our proposed solution, we do not define a hypothesis or a threshold; we take the energy samples and filter, average, and use them as an input for the entropy calculation.
# The Influence of the Load Term of the Strain Energy Sensitivity in the Shell Shape Optimization
## 7 Conclusions  
The role of the load term on strain energy sensitivity was investigated by analysis of the components of strain energy sensitivity, including the load term and the comparison of various schemes, to study the effect of strain energy sensitive load term on structural evolution.  
The results of the numerical examples showed that the load term of strain energy sensitivity accounts for a small proportion of the whole strain energy sensitivity, but it still has a cumulative impact on the structural evolution. The shell thickness had an obvious influence on the role of the load term of the strain energy sensitivity. When the thickness was thin, the load term showed its role in the optimization clearly. Conversely, when the thickness was thick, the role of the load term could be ignored. The load term could influence the increment rate of the structural weight and the strain energy and had an impact on the shape change to some extent. In addition, the numerical examples showed that self-weight had the same role with the external vertical load. The optimization using the strain energy sensitivity method without the load term would terminate early compared to that with the load term when the convergence condition was the same.  
At last, for general construction projects, the shape optimization only by the stiffness term of strain energy sensitivity can meet the requirements of engineering, and the load term of strain energy sensitivity can be ignored. However, if the shell thickness is very thin or the higher performance of the structure is required, the strain energy sensitivity with the load term should be considered. This study can extend the results to the other shell elements optimized by the strain energy sensitivity method. The reason was the shell element bearing the force by the membrane stress and the bending stress.  
Conceptualization, B.J.; methodology, C.C. and B.J.; writing--original draft preparation, B.J.; writing--review and editing, B.J., C.C. and J.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 51968018 and 51578185; and the Science and Technology Innovation Project of the Hainan Science and Technology Association for the Young Elite, grant number 201505.  
The authors declare no conflict of interest.  
Figure 16: The vector length of the load term, the stiffness term and the whole value of the structural strain energy in Scheme A.
## 5 Engineering Application Examples
### Design Scheme 1  
The linear cumulative damage theory with the sequence of first a high load first and then a low load can be calculated according to Equation (9), while the nonlinear cumulative damage theory can be calculated according to Equations (15) and (17). For three and four loads with different load spectra, the relevant calculated data are shown in Tables 2 and 3. The damage values given in tables are the cumulative damage values corresponding to all applied loads after the action of each corresponding load.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Level \(i\)** & \(\sigma_{i}\)**/MPa** & \(n_{i}\)**/\(\times\)10\({}^{7}\)** & \(N_{i}\)**/\(\times\)10\({}^{7}\)** & \(D_{i}\)**(M)** & \((n_{i-1}\sigma_{i-1}^{m}\)**/\(C\))\({}^{a_{i}}\) & \((n_{i-1}\sigma_{i-1}^{m}\)**/\(C\))\({}^{a_{i}^{*}}\) \\ \hline
1 & 0.85 & 0.3 & 0.51429 & 0.2267 & 0.0812 & 0.0804 \\
2 & 0.70 & 0.3 & 0.65687 & 0.3886 & 0.1698 & 0.1521 \\
3 & 0.55 & 0.4 & 3.27326 & 0.4512 & 0.3967 & 0.3484 \\ Total & - & 1.0 & - & 0.4512 & 0.3967 & 0.3484 \\ Miner model & \(\Delta\sigma_{\text{ref}}=0.6237\,\text{MPa}\) & \(\Delta\sigma_{\text{eq}}=0.6026\,\text{MPa}\) & \(s=0.9661\) \\ M–H model & \(\Delta\sigma_{\text{ref}}=0.6237\,\text{MPa}\) & \(\Delta\sigma_{\text{eq}}=0.8023\,\text{MPa}\) & \(s=1.2863\,\text{MPa}\) & \(s=1.1974\) \\ Modified M–H model & \(\Delta\sigma_{\text{ref}}=0.6237\,\text{MPa}\) & \(\Delta\sigma_{\text{eq}}=0.7468\,\text{MPa}\) & \(s=1.1974\) \\ \hline \hline \end{tabular} Note: \(D_{i}(M)\) represents the damage accumulation calculated by the Miner model; \((n_{i-1}\sigma_{i-1}^{m}/C)^{a_{i}^{*}}\) represents the damage accumulation calculated by the M–H model; \((n_{i-1}\sigma_{i-1}^{m}/C)^{a_{i}^{*}}\) represents the damage accumulation calculated by the modified M–H model, similar to other tables.  
\end{table}
Table 2: Three-level loads are designed in the high-and-then-low loading sequence.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Level \(i\)** & \(\sigma_{i}\)**/MPa** & \(n_{i}\)**/\(\times\)10\({}^{7}\)** & \(N_{i}\)**/\(\times\)10\({}^{7}\)** & \(D_{i}\)**(M)** & \((n_{i-1}\sigma_{i-1}^{m}\)**/\(C\))\({}^{a_{i}}\) & \((n_{i-1}\sigma_{i-1}^{m}\)**/\(C\))\({}^{a_{i}^{*}}\) \\ \hline
1 & 0.85 & 0.2 & 0.51429 & 0.15472 & 0.05423 & 0.05017 \\
2 & 0.70 & 0.2 & 0.65687 & 0.12801 & 0.12136 & 0.11845 \\
3 & 0.65 & 0.2 & 1.74563 & 0.30117 & 0.19652 & 0.17143 \\
4 & 0.55 & 0.4 & 3.27326 & 0.36542 & 0.30147 & 0.26406 \\ Total & - & 1.0 & - & 0.36542 & 0.30147 & 0.26406 \\ Miner model & \(\Delta\sigma_{\text{ref}}=0.6237\,\text{MPa}\) & \(\Delta\sigma_{\text{eq}}=0.5618\,\text{MPa}\) & \(s=0.9008\) \\ M–H model & \(\Delta\sigma_{\text{ref}}=0.6237\,\text{MPa}\) & \(\Delta\sigma_{\text{eq}}=0.8277\,\text{MPa}\) & \(s=1.3271\) \\ Modified M–H model & \(\Delta\sigma_{\text{ref}}=0.6237\,\text{MPa}\) & \(\Delta\sigma_{\text{eq}}=0.7143\,\text{MPa}\) & \(s=1.1453\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Four-level loads designed in the high-and-then-low loading sequence.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 2 Materials and Methods
### Data Acquisition
#### 2.2.4 Experiment Three: Individual Vines in Field Conditions  
The same RGB and RGB-D sensors were used to capture in-situ images at plant-level (31 individual vines imaged for both FC and LR treatments):  
1. _RGB imagery:_ Imagery was captured at 12H00, under natural solar illumination. The camera was positioned 2 m from the vine, capturing an image for each side of the canopy. This protocol was repeated for both canopy treatments (FC: Figure 5a; LR: Figure 5b).
2. _RGB-D (Kinect) imagery:_ The Kinect sensor captured point clouds--instead of meshes--of the 31 vines, due to the scale difference. Point clouds consisted of thousands of individual points to create 3-D models; for both the FC and LR treatments (Figure 5c,d). The Real-Time Appearance-Based Mapping (RTAB-Map) software [26] was used for point-cloud modelling and regeneration. In RTAB-Map [26], the default filtering parameter and a 3-D cloud decimation ('thinning' of the point cloud) value of '2' were used during exportation of the point clouds in _.ply_ format. The point cloud was captured per individual row, repeated for both sides of the canopy. The Kinect sensor was hand-held approximately 2 m from the vines at a perpendicular angle and was moved along the row in a north to south direction. The imagery was collected at 19H00, immediately before sunset, using the last natural illumination of the day.  
Figure 4: Data acquisition of individual bunches in the field. (**a**) RGB image with full canopy (FC); (**b**) RGB image with leaf removal (LR); (**c**) RGB-D (Kinect mesh) with FC; and (**d**) RGB-D (Kinect mesh) with LR.