esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Practical Field Trials on the Efficacy of EF Fumigation in Farmers' Vinyl Houses  
Based on the promising results from the laboratory and scale-up vinyl house evaluations on insecticidal activity and phytotoxicity of EF fumigation, more practical assessmentson the EF fumigation were conducted in farmers' vinyl houses in triplicate. The first field trial was conducted in the vinyl house (580 m\({}^{3}\), 55 m \(\times\) 3.5 m \(\times\) 3.0 m) in Seongju county, South Korea, where the harvest of yellow melon fruits was completed with SPW occurring naturally on the leaves. EF fumigation was conducted for 2 h with a dose of 2.0 g m\({}^{-3}\) using a portable vaporizer from 19:00 p.m. (21-32 \({}^{\circ}\)C, 80 \(\pm\) 10% RH). EF sample lines were placed at 12 spots (4 spots each at 3.0, 1.5, and 0.8 m above the soil surface) to check the uniformity of EF concentrations in the vinyl house. Air collections from the sampling spots were made at 0.5, 2.0, 4.0, and 12.0 h after exposure. The EF concentrations in the vinyl house were determined by analyzing the air with GC-FID. The GC conditions used in this experiment were mentioned in the \"Measurement of EF concentration and determination of CT (concentration \(\times\) time) products\" above. The effectiveness of EF fumigation to SPW adults was evaluated in two ways: (1) by comparing the number of SPW occurring naturally on leaves of the YM plant before and after treatments, and (2) by checking the mortality of SPW inoculated onto tobacco leaf disks in 9 breeding dishes. After completing the 12 h fumigation, an assessment of phytotoxicity of the yellow melon plants was made in the same way mentioned above. Again, all measurements were repeated.  
The second field trial was carried out in the same vinyl house described in the first trial, except for the EF dose, fumigation time, and exposure time. EF was fumigated with a dose of 1.5 g m\({}^{-3}\) for 4 h from 19:00 p.m. (27-30 \({}^{\circ}\)C, 90 \(\pm\) 10% RH). After vaporization, the EF concentration was determined at 0.5, 2.0, and 4.0 h.  
The third field trial was conducted in the larger vinyl house (1140 m\({}^{3}\), 100 m \(\times\) 3.8 m \(\times\) 3.0 m) than the first and second trials at Chilgok-gun county, South Korea, in which the harvest of yellow melon fruits was completed, and where SPW was occurring naturally on yellow melon leaves as in the cases of the prior trials. EF was fumigated for 2 h with the dose of 2.0 g m\({}^{-3}\) by vaporizing the EF using a portable vaporizer from 19:00 p.m. (29-31 \({}^{\circ}\)C, 85 \(\pm\) 15% RH. EF concentrations inside the vinyl house were determined by the same methods previously indicated at trials 0.5, 1.0, and 2.0 h after vaporization. In addition, the mortality of SPW adults and phytotoxicity to YM plants were assessed by the same methods in the prior trials.
## 3 Results and Discussions
### Crystallinity Analysis from XRD  
The XRD analysis results in Figure 3, mainly show the crystalline structures of calcites, which is in accordance with the FTIR spectra in which the peaks assigned to CO3\({}^{2-}\) and the chelate structure are noticed, including the strong peaks at 1429 cm\({}^{-1}\) (sample P1) and 1438 cm\({}^{-1}\) (sample P2) specific to CaCO3 in the crystalline state. While the P2 sample presented peaks that correspond to only the calcite phase, CaCO3 hexagonal (reference card ICSD 98-001-8166), the P1 sample presented two additional diffraction lines that were associated with anhydride, CaSO4 orthorhombic (reference card ICSD 98-000-1956) and moganite, SiO2 monoclinic (reference card ICSD 98-006-7669).  
Calcium carbonate (CaCO3) in a crystalline state was identified in the Murray meteorite, but was identified as argonites (CaCO3 orthorhombic) by [PERSON] et al. [6].
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## Appendix A  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Forecast** & **Forecast** & **Seenality** & **Lags** & **nMAE** & **nRMSE** & **nMSE** & **sMAPE** \\ \hline \multicolumn{10}{c}{**Statistical Time Series Methods**} \\ \hline
**sARIMA**[32] & 24 h & 24 h & yes & yes, real & 0.0596 & - & 0.081 & - \\
**sARIMA**[32] & 24 h & 24 h & yes & yes, real & 0.0619 & - & 0.0677 & - \\
**ARIMA**[33] & 1 h & 1 h & no & yes, real & 0.11 & 0.15 & - & 0.3771 \\
**sARIMA**[34] & 24 h & 1 h & yes & yes, real & - & 0.146 & - & - \\
**sARIMA**[35] & 24 h & 1 h & yes & yes, real & - & 0.1222 & - & - \\
**Linear regression**[36] & 1 h & 1 h & no & no & 0.0433 & 0.1088 & - & - \\
**Markov chain model**[37] & 24 h & 1 h & yes & no & 0.062 & 0.714 & - & - \\ \hline \multicolumn{10}{c}{**Machine Learning Methods**} \\ \hline
**LSTM-SMI**[33] & 1 h & 1 h & no & yes, real & 0.02 & 0.02 & - & 0.0913 \\
**XGBoost**[33] & 1 h & 1 h & no & yes, real & - & - & - & 0.1406 \\
**RNN**[33] & 1 h & 1 h & no & yes, real & - & - & - & 0.2727 \\
**GRU-MSI Seasonal** & 1 h & 1 h & no & yes, real & - & - & - & 0.0768 \\
**Mean Imputation**[33] & 1 h & 1 h & no & no & - & 0.0821 & - & - \\
**SVR (support** & & & & & & & \\
**vector regression**[38] & 24 h & 1 h & no & no & - & 0.0899 & - & - \\
**ELM (extreme** & & & & & & & & \\
**learning machines**[39] & 24 h & 1 h & no & no & - & 0.0899 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature results analysis.
## 2 The Proposed Methods
### 1D-MobileNet  
MobileNet uses Depthwise Separable Convolution to replace the standard convolution operations found in traditional networks. This approach not only significantly reduces the number of model parameters but also decreases the network's reliance on hardware resources, all while maintaining strong feature extraction capabilities.  
Since vibration signals are one-dimensional, they cannot be directly applied to the original MobileNet architecture. To address this, a 1D-MobileNet network is constructed using Depthwise Separable Convolutions as its modules. Depthwise Separable Convolution consists of two key components: Depthwise Convolution and Pointwise Convolution [36], as illustrated in Figure 3. In Depthwise Convolution, each convolutional kernel focuses on a single channel of the input signal, allowing the feature information within each channel to be processed independently. However, this operation does not integrate information across channels. To overcome this limitation, Pointwise Convolution is applied after Depthwise Convolution. Pointwise Convolution uses a \(1\times 1\) convolutional kernel to integrate feature  
Figure 2: The structure of 1D-SENet network.  
information across all channels. Specifically, it weights the features from all channels produced by Depthwise Convolution to create a new feature vector. In doing so, Pointwise Convolution achieves dimensionality transformation with fewer parameters. Together, Depthwise and Pointwise Convolutions form Depthwise Separable Convolution, equipping MobileNet with efficient and lightweight feature extraction capabilities.  
The shape of the input one-dimensional vibration signal is \(L\times C\), where \(L\) is the length of the signal and \(C\) is the number of channels, the calculation method for Depthwise Convolution is as follows:  
\[Y_{i,c}=\sum_{m=1}^{M}X_{i+m-1,c}\cdot K_{m,c} \tag{12}\]  
where \(Y_{i,c}\) is the output value of the Depthwise Convolution at position \(i\) and channel \(c\), \(X_{i+m-1,c}\) is the input signal value at position \(i\) and channel \(c\), \(m\) is the index of the Depthwise Convolution kernel, and \(K_{m,c}\) is the Depthwise Convolution kernel of channel \(c\) with a size of \(1\times M\).  
The calculation method for Pointwise Convolution is:  
\[Z_{i,j}=\sum_{c=1}^{C}Y_{i,c}\cdot W_{c,j} \tag{13}\]  
where \(Z_{i,j}\) is the output value of the Pointwise Convolution at position \(i\) and channel \(j\), \(Y_{i,c}\) is the output value of the Depthwise Convolution at position \(i\) and channel \(c\), and \(W_{c,j}\) is the weight of the Pointwise Convolution kernel that connects the \(c\)-th input channel to the \(j\)-th output channel. Equation (13) indicates that the Pointwise Convolution linearly combines the Depthwise Convolution results from all channels to obtain the output signal.
# Determination of the Fracture Locus of a Cor-Ten Steel at Low and High Triaxiality Ranges
## 2 Materials and Methods
### Determination of the Fracture Locus  
The experimental points obtained from the tensile testing of the four specimens were integrated with the data obtained by [PERSON] and [PERSON] for Cor-Ten steel [6]. As shown in Figure 6, the experimental points obtained in the present study correlate well with the parabolic trend proposed by [PERSON] and [PERSON]. Particularly, the experimental points belonging to the fracture onsets for SPEC60, SPEC90L, and SPEC90S suggest a strong gradient of the fracture locus approaching the 0.4 value of triaxiality, where the transition between fracture mechanisms [5] would be expected, and thus the fracture locus shows a local maximum. By including the point related to the SPECT specimen, the fracture locus can be determined in the high stress triaxiality range by means of an exponentially decreasing function [2; 7; 8].  
Figure 5: Comparison between the specimens’ deformed shapes as predicted by the FEM model and the experimental results obtained through tensile testing: (**a**) equivalent plastic strain for SPECCT and (**b**) broken SPECT specimens, (**c**) equivalent plastic strain for SPEC60 and (**d**) broken SPEC60 specimens, (**e**) equivalent plastic strain for SPEC90L and (**f**) broken SPEC90L specimens, and (**g**) equivalent plastic strain for SPEC90S and (**h**) broken SPEC90S specimens.  
The fracture locus of the Cor-Ten steel can be expressed mathematically after proper interpolation of the experimental points according to Equation (8):  
\[\epsilon_{ps,eq}(\eta)=\left\{\begin{matrix}42.871\eta^{2}-23.181\eta+3.648&\text {if }-0.33\leq\eta\leq 0.39\\ 286.737e^{-14.339\eta}&\text{if }\eta>0.39\end{matrix}\right. \tag{8}\]  
The curve obtained by interpolation is in good agreement with the literature concerning the fracture loci of metals, in which a local maximum is expected in the range of \(\eta=0.3\)-\(0.5\)[9; 10; 11] near the conditions under which a uniaxial stress state is generated.
## 4 Experimental Set Up and Result Analysis
### Experimental Results
Topically, all existing attacks based on guest login accounts have applied malicious activities on the transmission control protocol (TCP). On the other hand, the number of detected anomalous activities by existing attacks based on the personal login account in the internet control message protocol (ICMP) is higher than other protocols. Also, the number of detected anomalous activities by existing attacks based on the personal login account for the TCP is higher than the number of predicted anomalous activities for the user datagram protocol (UDP). Figure 8 shows a prediction analysis of anomalous activities identified with a red color based on server error rates in the existing protocols. When the server error rate is increased in TCP, the number of anomalous activities is increased. In other words, the TCP provides a safe status to protect against attacks and malicious activities in the intrusion detection system. On the other hand, when the server error rate of TCP is decreased to zero, the proposed intrusion detection model based on the GA-RF algorithm correctly finds normal and anomalous activities. When the server error rate of UDP is increased up to one, the proposed intrusion detection model based on the GA-RF algorithm correctly finds some anomalous activities in the IoMT. Finally, in ICMP, most of the detected anomalous activities using the GA-RF algorithm occurred in server error rate of zero. Figure 9 shows a technical analysis of anomaly detection activities based on five main protocols in the UNSW_2018_IoT_Botnet data set. It is observed that DoS and DDoS attacks were applied on two main UDP and TCP protocols. Also, we can observe that the IPv6 protocol is a safe protocol with existing cyber-attacks in the UNSW_2018_IoT_Botnet data set. Finally, to show efficiency of the proposed GA-RF algorithm to detect anomalous behaviors in the IoMT, we compared our simulation results with other case studies that have investigated their prediction approaches using NSL-KDD and BoTNet_IoT data sets. Table 4 illustrates the performance of the GA-RF algorithm in comparison with particle swarm optimization-recurrent neural network (PSO-RNN) algorithm, PSO-random forest (PSO  
Figure 6: Comparative analysis of error rates among all models in UNSW_2018_IoT_Botnet data set. RF) algorithm, PSO-k-nearest neighbors (PSO-KNN) algorithm, RF-synthetic minority, oversampling technique (RF-SMOTE), enhanced genetic algorithm-PSO (EGA-PSO) and a hybrid convolutional neural network-Cuda deep neural network long short-term memory (CNN-cuDNNLSTM) algorithm. Figure 8: Anomaly detection activities based on the server error rate in NSL-KDD data set. Figure 7: Anomaly detection activities based on the existing protocols as guest login status in NSL-KDD data set.
## 3 Results and Discussion
### Complementary Tests
unknown materials. The XRD results (Figure 6) show that both compositions (C and P.NMS) present similar gypsum crystalline phases [21; 24; 38]. Furthermore, the P.NMS results indicate the existence of an amorphous phase between 15\({}^{\circ}\) and 45\({}^{\circ}\) 2 theta, which is a characteristic of EP [39; 40]. Adding perlite and vermiculite in their expanded forms decreases the weight loss of gypsum-based mortar due to their insulating properties, as previously mentioned [39; 40; 41; 42; 43]. However, this situation could only be seen in P.NMS, as the weight loss of C is similar to the weight loss of pure gypsum, according to the literature. In conclusion, in both cases, the first peak is due to the gypsum's dehydration, and the second peak is due to the decomposition of calcium carbonate and the evaporation of sulphate phases. Furthermore, the difference in weight loss is due to the percentage of the content of insulating aggregates, EP and/or EV, in the composition. SEM images of the mortars are shown in Figure 7.  
SEM analysis shows a common type of structure between the compositions due to the similarity of materials used and the visualisation of different porosities in the compositions: C presents a higher percentage of voids. The gypsum is formed mainly by prismatic shaped crystal structures, with an average length of 4 \(\upmu\)m to 6 \(\upmu\)m, in accordance with the literature [37; 43]. EP and EV show a lamellar shape with different lengths, but are always longer than the gypsum structures. The structure formation and distribution follow several previous studies [20; 33; 43; 44]. Due to the reduced quantity of NMS, SEM analysis did not show its particles.
# Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition
## 2 Methods
### Machine Learning for Pattern Recognition  
The pattern matrix \(\mathbf{P}_{R,Q}\) formed an input for the two-layer neural network presented in Figure 1b having \(R\) inputs, \(S1\) neurons in the first layer and \(S2\) neurons in the second layer. The outputs of individual layers included the values  
\[\mathbf{A1}_{S1,Q}=F1(\mathbf{N1}),\quad\quad\mathbf{A2}_{S2,Q}=F2(\mathbf{N2}) \tag{2}\]  
where  
\[\mathbf{N1}=\mathbf{W1}_{S1,R}\ \mathbf{P}_{R,Q}+\mathbf{b1}_{S1,1}\ ones(1,Q), \quad\quad\mathbf{N2}=\mathbf{W2}_{S2,S1}\ \mathbf{A1}_{S1,Q}+\mathbf{b2}_{S2,1}\ ones(1,S1) \tag{3}\]  
define the arguments of the transfer functions \(F1,F2\).  
An associated matrix of target values \(\mathbf{T}_{S2,Q}\) with \(S2=5\) rows was defined by the corresponding sleep stages specified by a neurologist in the learning stage (as presented in Figure 1b). For each column vector in the pattern matrix, the corresponding target vector has one unit element in the row pointing to the correct target value. The network coefficients included elements of the matrices \(\mathbf{W1}_{S1,R}\), \(\mathbf{W2}_{S2,S1}\) and associated vectors \(\mathbf{b1}_{S1,1}\), \(\mathbf{b2}_{S2,1}\). The proposed model used the sigmoidal transfer function \(F1\) in the first layer and the probabilistic softmax transfer function \(F2\) in the second layer. The values of the output layer, based on the [PERSON] theorem [43], using the function  
\[F2(\mathbf{N2})=\frac{exp(\mathbf{N2})}{sum(exp(\mathbf{N2}))} \tag{4}\]  
provided the probabilities of each class. Figure 1c,d present an example of estimated and target probabilities for selected sleep pattern vectors. Each column of the output matrix presents class membership to individual classes.  
The training of the neural network was performed in the MATLAB (R2017b, The MathWorks, Inc., Natick, MA, United States, 2017) environment using 70% of pattern vectors for training, 15% for validation, 15% for testing, and random initial conditions. Model coefficients were updated for each new sleep record that decreased (with the upper 3% limit) the performance value calculated as the average squared error between the network outputs and target values. The optimization process was done by a single PC with the i7-6500U CPU (2.5 GHz), 16 GB RAM and the 64-bit operating system (Windows 10 Pro).  
The evaluation of the classification results was performed by the analysis of the multi-class Receiver Operating Characteristic (ROC) [44; 45] to illustrate the performance of the classifier system, and by a confusion matrix presented in Table 2 for classification into 5 classes. The associated common performance metrics are summarized in Table 3. The ROC analysis was performed on the basis of a pairwise comparison of one class against all other classes [46].
## 4 Methodology of System Design
### Problem Formulation
#### 4.1.1 Needs Definition  
This step begins the design process when the stakeholders introduce to the designers the concept and define the general problem. In the proposed DM, this stage should be performed together with the future users in order to include their desires. With such an approach, both stakeholders and future users can express their needs and expectations of the outcome of the working system. In this step, participants should not focus on detailed requirements, but rather general goals of the system, so that the designers would be able to preliminarily assess whether the problem is solvable with their resources.
## 2 Materials and Methods
### Biomass Estimation  
From May to August 2012, vegetation samples were harvested from four biomass calibration points approximately every 20 days using a 30 cm diameter ring in both sites (E3 and E5) at a distance of 70 m from the tower in four directions (South-East, North-East, North-West, South-West) (Figure 2). The grass material within the ring was cut at ground level and placed into labeled paper bags. The following year, the sample size was increased, and samples from 12 points were collected from May to July about every 20 days (Figure 2). Note that each biomass sample was collected from a slightly different site within 2 m of each of the 12 locations indicated above (Figure 2) to avoid repeatedly harvesting the same locations. To develop calibrations between biomass, \(f\)APAR\({}_{\text{green}}\), and NDVI\({}_{680,800}\), biomass samples were collected at each of the harvest sampling points just after the collection of \(f\)APAR and reflectance measurements used to calculated NDVI\({}_{680,800}\). Each sample was manually sorted into green biomass and brown biomass, put into an oven at 60 \({}^{\circ}\)C for 24 h, and weighed. The selection was carried out considering the living tissue that was visibly green as green biomass and the visibly dead tissue as brown biomass. This selection of green and dead biomass allowed us to measure the current year's production, since the green tissue represented the current year's growth, and the dead tissue consisted of the previous year's growth. For each date, average green and total (green plus brown) above-ground biomass was calculated from the manually sorted and weighed biomass samples. For each sampling date, green biomass was calculated as the average of green biomass at all sampling locations (Figure 2), expressed as g: m\({}^{-2}\). By providing a direct metric of productivity, this measure of biomass provided an independent check on the validity of the optical (NDVI) measurements and the resulting LUE model.  
Figure 2: Optical and biomass sampling design around each eddy covariance (EC) and phenology station (PS) location. Canopy-level NDVI\({}_{680,800}\) was sampled at regular intervals within a one hectare region surrounding each flux tower using 10 m grid spacing (approx. 100 samples; sampling locations represented by black dots). Biomass sampling and \(f\)APAR\({}_{\text{green}}\) calibration occurred at the locations indicated by numbered circles (1–12), with the sampling years indicated by each circle.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 3 Methods
### Squeeze-and-Excitation Attention Module
The formula is as follows:  
\[\mathbf{s}=F_{ex}(\mathbf{z},\mathbf{W})=\sigma(g(\mathbf{z},\mathbf{W}))= \sigma(\mathbf{W}_{2}\delta(\mathbf{W}_{1}\mathbf{z}  
in both DeepLabv3 [45] and DeepLabv3+ [13]. However, DeepLabv3+ still leads to the problem of blurred segmentation boundaries. Inspired by [PERSON] et al. [19], we added a second ASPP module to the decoder structure to further capture the multiscale contextual information of low-level features. Moreover, inspired by separable convolution [54] and [PERSON] et al. [18], we similarly change ASPP to factorized ASPP. The feature map goes through a separable convolution with a kernel size of \(1\times 1\), then into a parallel dilated convolution structure with different rates. This separated structure can be more convenient for us to adjust the number of channels of the network and can reduce the amount of calculation. In our SISnet, instead of duplicating the ASPP module, we put the high-level and low-level features into two different ASPP modules, as shown in Figure 7 below. The ASPP module entered by the high-level features, after going through a \(1\times 1\) separate convolution, goes through a \(1\times 1\) standard convolution in parallel, three \(3\times 3\) dilated convolutions with dilation rates of [6; 12; 18], and a global average pooling layer. The module superimposes the parallel feature maps, then connects them by \(1\times 1\) convolution, and, finally, the feature maps are enlarged by four times bilinear interpolation up-sampling. In the ASPP module entered by the low-level features, the overall structure remains the same, and only changes are made in the parallel internal layers. It removes the \(1\times 1\) standard convolution and expands the dilation rate of the three-layer dilated convolution [12; 24; 36], which expands the receptive field of low-level features. Moreover, we  
Figure 6: The dimensional change graph of the original DRN (**left**) and the SE-DRN module (**right**). Figure 7: The schema of the parallel-ASPP structure. retain a low-level features branch that does not enter the ASPP module and use the feature maps from SEM to perform subsequent feature fusion directly. This branch is equivalent to not entering two standard convolutional layers, reducing unnecessary computation. Finally, the feature maps of these three branches are combined for feature fusion. The fused features are fine-tuned by 3 \(\times\) 3 convolution and then four times up-sampling using a bilinear interpolation method to predict the spacecraft mask. This asymmetric ASPP structure makes the segmentation boundary more complete and the semantic information clearer. These two ASPP modules form a parallel effect and fuse features together in the subsequent network, so we call it parallel-ASPP.