esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Soil Moisture Index Estimation and Validation
However, both of the models achieve relatively low R\({}^{2}\) values at site 4 (Xingzi) and site 5 (Fengcheng), since the two sites are located near buildings or ponds, and might be less representative than other sites. Figure 3: Time series of **(a)** daily mean temperature and **(b)** daily accumulated precipitation and measured soil moisture index over Nanchang site (115.9\({}^{\circ}\)E, 28.6\({}^{\circ}\)N) for the period from April 2015 to December 2016. Figure 5 shows the overall accuracies of soil moisture indices predicted by the linear mixed and fixed effects models. The overall R\({}^{2}\) and cross-validated R\({}^{2}\) of the linear fixed effects model are 0.56 (Figure 5b) and 0.45 (Figure 5d), respectively. In contrast, both the values increase considerably in the LME model, which are 0.68 (Figure 5a) and 0.64 (Figure 5c), respectively. Both models have a larger prediction error for the lower soil moisture index, which might be partly due to the shallower  
Figure 4: Measured (black solid lines) and predicted soil moisture indices of 15 observation sites, with the blue dash lines predicted by the linear fixed effects model and the red dash lines predicted by the LME model. Number in lower left corner for each subplot denotes site ID. observation depth of C-band microwaves than ground measurements. Since the upper layer of soil is more subjected to rapid drying and rewetting, soil moisture variations in this layer are more pronounced [56], especially under the drier conditions. When compared with that of the fixed effects model, the cross-validated R\({}^{2}\) of the LME model is only slightly lower than its overall R\({}^{2}\), indicating a more robust performance of the LME model. Moreover, the LME model increases the slopes of the linear regressions between the predicted and measured soil moisture indices, making the regression lines closer to the 1:1 lines when compared with those of the fixed effects model, which further demonstrates the better prediction ability of the LME model.
# Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species
## 2 Post-Harvest Classification
### Classification Algorithms
#### 2.2.1 Convolutional Neural Network  
Among the many machine learning algorithms used in image classification, the CNN is one of the most recurrent and remarkably good-performing ones; it implements deep learning to facilitate image processing [9, 16, 17]. It adds another layer of complexity and hierarchical data representation that was not achievable with traditional machine learning. This progress has been partly made possible by the development of hardware and information processing technology, which allows the utilization of massive amounts of data [9].  
In this study, we emphasize preprocessing techniques owing to the complexity of the data and noise included. However, these algorithms are well built and consider several classification factors. Specifically, we compared and evaluated multiple algorithms and tested over 50 possible combinations of CNN architectures to identify the most suitable architecture for our case study. The most important aspects of the CNN architecture that we focused on are as follows:  
1. Diversity within the training data (shuffling, data augmentation, dropout);
2. Performance metrics (accuracy);
3. Runtime environments;
4. Others (learning rate, early stopping).  
Small datasets, such as the one used in this study, are susceptible to overfitting. To eliminate this possibility, we combined a set of preprocessing and training techniques. First, we shuffled the data by increasing their size from 2000 to 6332, adding 1000 at every iteration. This shuffling ensured that the dataset remained diverse and unbiased. Moreover, it allowed us to analyze the correlation between the training set size and accuracy. During shuffling, we ensured that the training set was randomly selected from the data pool in every loop. As a second measure to avoid overfitting, a data-augmentation layer was added to the training layers. The principle of data augmentation using the Keras library involves generating new images based on the current training images. Data augmentation can be performed using various parameters. In this study, two parameters were selected: Random-Zoom and RandomRotation. Using these parameters, the newly generated images were rotated and zoomed versions of the input images. Therefore, there was greater diversity within the training dataset. Finally, a dropout layer was added to the CNN architecture.  
Figure 2 shows the architecture the models used in the experiments. Constructing CNNs in Python offers several possibilities. To enhance the performance of our model, we utilized certain functionalities offered by Keras, such as the learning rate for modulating learning and the early stopping of monitored metrics.
## 2 Data and Method
### Data
#### 2.2.2 Microwave Brightness Temperature Datasets  
In this study, the brightness temperature datasets measured by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) were introduced. TRMM is a joint-mission conducted by America and Japan to monitor tropical rainfall and to estimate its associated latent heating between 38\({}^{\circ}\)S and 38\({}^{\circ}\)N. This satellite carries three rainfall measuring sensors, including the Visible and Infrared Scanner (VIRS), Precipitation Radar (PR) and TMI. The TMI is designed to observe the microwave radiation of Earth at 10.7, 19.4, 21.3, 37.0 and 85.5 GHz. Except for 21.3 GHz, all frequencies are double-polarized (e.g., one vertically-polarized channel and one horizontally-polarized channel), making it possible to acquire the polarization difference between the land surface and precipitation. The brightness temperatures (termed TBs, hereafter) of these nine channels are provided by the TRMM Goddard Earth Science Data and Information Services Center (GESDISC), with the name TRMM 1B11.  
Figure 1: The topography of the study area and the spatial distribution of the rain gauge network in this study.  
In this dataset, the TBs at nine channels, the time of each scan and the geo-locations of all pixels (e.g., location of the center of the instantaneous field of view (IFOV) at the altitude of the Earth ellipsoid) are stored in the Hierarchical Data Format (HDF). The horizontal footprint spatial resolution of TRMM 1B11 at 85.5 GHz is about 5 km and about 10 km at other four frequencies. Compared with another space-borne microwave sensor, the Special Sensor Microwave/Imager (SSM/I), the TMI could provide higher spatial resolution microwave observations, making it more suitable to monitor the precipitation at the meso- and micro-scale.
_Article_  
Theoretical Study of Nanoporous Graphene  
Membranes for Natural Gas Purification  
[PERSON]  
[PERSON]  
[PERSON]  
Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; [EMAIL_ADDRESS] (G.T.); [EMAIL_ADDRESS] (G.C.)  
Correspondence: [EMAIL_ADDRESS], Tel.: +39-0110904333  
Received: 31 July 2018; Accepted: 31 August 2018; Published: 3 September 2018  
Gas filtration by means of membranes is becoming increasingly important for industrial processes due to its low cost. In particular, membranes can be applied to separate methane in natural gas from pollutants such as hydrogen sulfide and carbon dioxide. The recent advent of nanoporous graphene as material for membranes helped to overcome the current problems of polymeric membranes, namely the permeability/selectivity tradeoff. However, the factors that determine gas filtration through nanoporous graphene are not completely clear yet. In this work, we show that pore size, shape and functionalization severely affect the selectivity of the membrane toward CO\({}_{2}\) and H\({}_{2}\)S with respect to CH\({}_{4}\). We identified that the critical diameter of circular pore for the separation of contaminants from methane with graphene membranes is 5.90 A. An elliptical pore is discovered to select gas species having similar sizes on the basis of their shape. The more elongated CO\({}_{2}\) is allowed to pass though the pore while the more spherical H\({}_{2}\)S and CH\({}_{4}\) are rejected. Finally, the gas-membrane interactions are found to decisively affect the filtration performances. Functionalization with hydroxyl groups led to a higher permeability of the gas species with polar bonds while keeping an excellent selectivity.  
gas purification; molecular dynamics; membrane; graphene
## 2 Soil-Bentonite Vertical Cut-Off Wall Service Performance and Improvement Measures  
### SB Vertical Separation Wall Construction Methods and Technical Characteristics  
Vertical cut-off walls primarily utilize the low permeability of the wall material to impede the horizontal transport and diffusion of pollutants from contaminated sites [18; 19]. Soil-bentonite (SB) vertical cut-off walls can be categorized into three types based on construction methods: slurry walls, growing walls, and deep soil mixing [20]. The common construction methods and technical characteristics of soil-bentonite (SB) vertical cut-off walls are summarized in Table 3[21].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Slurry Walls** & **Grouting Walls** & **Deep Soil Mixing** \\ \hline Depth of construction & Less than or equal to 60 m [22; 23] & Depth 45–60 m, influenced by soil type [24; 25] & Larger than 15 m [26; 27] \\ \hline Construction methods & Excavate and backkill the slurry mixing & Jet routing: cementhousing routing:chemical & Mechanical in situ mixing of water, soil and cement or other mineral additives [32] \\  & area [28; 29] & growing & etc. [30; 31] & \\ \hline Construction defects & Improper construction or substandard slurry ratios may create high permeability zones [33] & Differences in ground permeability affect the & Damage to the soil, construction easily \\  & slurry ratios may create high & growing effect, which in turn affects & deflected [35] \\  & permeability zones [33] & consolidation and waterproofing [34] & \\ \hline Major limitations & The main limitation is in the materials, which are relatively costly [36] & Unsuitable for substrates with high water content, ground disturbance due to footing & Strength may be adversely affected by factors such as organic matter, high soil moisture \\  & & which are relatively costly [37; 38] & present and uplift [39; 40] \\ \hline Technical Benefits & Proven construction techniques, early research, effective cut-off of contaminants [41] & Grouting slurry is injected into the soil and rock in a special way to maintain cut-off [42] & Enhanced soil strength and stiffness, reduced settlement, low cost, high efficiency, thin and deep walls, structural flexibility [43; 44] \\ \hline \hline \end{tabular}
\end{table}
Table 3: SB vertical separation wall construction methods and technical characteristics [21].  
Figure 2: Comprehensive flowchart illustrating the systematic evaluation methodology and key research themes for the service performance of soil–bentonite vertical cut-off walls at heavy metal contaminated sites.
## 4 Experiments
### System Offsets  
The position of the unit for navigation purposes is taken from the navigation GPS placed on the top of the mast located in the geometric center of the USV. The GPS antenna location is determined by experience and typical recommendations for unmanned units; that is, the best place for navigations system is the geometric center of the unit and at the highest possible place to diminish interference with board electronics and ensure best satellite visibility. These actions were taken in the present study.  
For the hydrographic equipment, this consisted of a multi beam echosounder (MBES), a precise SGB IMU, and an RTK receiver. The best practice for that equipment localization is that the IMU sensors and the MBES antenna should be as close as possible, if not, all offsets should be entered into the system. In that case, the offsets represent coordinates in the local unit coordinate system of all hydrographic equipment including the GPS, the RTK antennas, and the IMU unit, where the IMU sensor is the coordinate system origin with the Y axis parallel to the unit long axis of symmetry (Figure 8). The hydrographic equipment is calibrated, and offsets are entered into the hydrographic system in accordance with values presented in Table 4. This means that the GPS RTK position was already reported with offsets by the hydrographic system at the origin.  
Figure 8: Measurement equipment location on the unit (**a**) and local unit coordinate system (**b**).  
Given that the system navigation GPS is working out of the hydrographic system and for proper evaluation a vector between the RTK and GPS readings has to be included. The corrected GPS position was calculated from already interpolated GPS positions according to the following formula:  
\[\hat{\mathbf{X}}_{G_{i}}^{\star}=\hat{\mathbf{X}}_{G_{i}}-\left(d_{\text{xG}}\ \cos(HDG_{i})-d_{\text{yG}}\sin(HDG_{i})\right) \tag{12}\]  
\[\hat{\mathbf{Y}}_{G_{i}}^{\star}=\hat{\mathbf{Y}}_{G_{i}}-\left(d_{\text{xG}} \sin(HDG_{i})+d_{\text{yG}}\ \cos(HDG_{i})\right. \tag{13}\]  
where \(\hat{\mathbf{X}}_{G_{i}}^{\star},\hat{\mathbf{Y}}_{G_{i}}^{\star}\) are the antenna coordinates of the GPS receiver in the national plane rectangular coordinate system, \(d_{\text{xG}}\), \(d_{\text{yG}}\) are the antenna offset values of the GPS antenna unit on the vehicle coordinate system, defined at the center of IMU(RTK) unit with the y axis being parallel to the unit symmetry axis, and the x axis being perpendicular to the y axis, and HDG is the vessel's actual course reported by the hydrographic system (Figure 9).
# Development of a Digital System for Monitoring Emergency Conditions in 20 kV Distribution Networks
## 1 Introduction
### Literature Review and Problem Statement
In the future, options such as the use of uncrewed aerial vehicles (UAVs) as relays for measurement signals [5], as well as the remote spectral analysis of the measured voltage to compare the amplitudes of harmonics at the fundamental frequency of 50 Hz, could be considered. These methods would enable more precise identification of ground fault locations. These options may be explored in other articles within this thematic Section 1.2.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 4 Fall Detection with WiFi Signal Based on LSTM Model
### CSI Data Preprocessing  
Data noise reduction is an important process in WiFi-based fall detection tasks because the original CSI signal data contain high-frequency ambient noise data, affecting the final detection accuracy. Moving average filtering can effectively reduce the influence of noise, retain the main features of signal data, and improve the detection accuracy.  
The first step is unpacking the collected data, and the result is shown in Table 1. The channel state information required is CSI of a \(2\times 3\times 30\) complex matrix. In the CSI matrix, \"2\" indicates the number of transmitting antennae, \"3\" indicates the number of receiving antennae, and \"30\" indicates the number of subcarriers contained in each antenna (the Intel 5300 network card used by the CSI tool determines the number of subcarriers per antenna to be 30). Therefore, it can be seen that the three-antennae links form 180 subcarriers, that is, each time stamp's data are composed of 180 subcarriers. This matrix is made up of complex numbers related to the characteristics of the wireless signal. Signals are generally represented as complex numbers in wireless communication, where the real part represents the amplitude (or intensity) and the imaginary part represents the phase (or angle).  
After dimensionality reduction and amplitude extraction of the CSI data, low-frequency filtering, Hampel filtering, and median filtering are, respectively, used to preprocess the CSI signals' amplitude. The amplitudes of original CSI signals and filtered amplitude CSI signals are shown in Figures 4-7. The original CSI signals contain many noisy signal components, causing the original CSI signals' amplitude to fluctuate greatly, as shown in Figure 4. From Figures 5-7, we can see that the CSI signals' amplitude is smoothed and the noisy signal components are filtered; compared with other filters, the Hampel filter can handle outliers well and can better constrain the noise reduction effect for the amplitude. Therefore, the Hampel filter was finally selected to process the CSI data in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Explanation** \\ \hline timestamp\_low & \(1.8345\times 10^{9}\) & Timestamp \\ Nrx & 3 & Number of receiving antennae \\ Ntx & 2 & Number of transmitting antennae \\ rssi\_a & 39 & The received signal strength indicator of the first receiving antenna \\ rssi\_b & 33 & The received signal strength indicator of the second receiving antenna \\ rssi\_c & 37 & The received signal strength indicator of the third receiving antenna \\ noise & \(-81\) & Noise power \\ csi & \(2\times 3\times 30\) & Channel state information \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main contents of the CSI packet.  
Figure 4: Original CSI signals.  
Figure 5: Filtered CSI signals with Hampel filter.  
Figure 6: Filtered CSI signals with low pass filter.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 8 Acoustic Emission Technique
### Steel Structures  
[PERSON] et al. [93] presented the application of AE signals for in situ monitoring of the technical condition of steel structures and particularly assessed destruction processes during construction work on the steel support structure of a cable car. Fatigue crack growth was investigated by [PERSON] et al. [94], who tested 40 CrMo steel beam specimens with a single edge notch. The work-related selected parameters of AE signals with the crack growth and fracture mechanics and concluded that there is a need for a better characterization of AE parameters, for the integration of new analysis methods and for developing an approach to link localized damage evolution with the global structural integrity. Improved characterisation of plastic deformation and crack growth was attempted by [PERSON] and [PERSON] [95], who processed the AE signals by wavelet technique and provided a correlation between AE measurements and fracture modes. To ensure good data quality, the data acquisition system was initially calibrated with a pencil lead break procedure. Moreover, the surface of the sensor was covered with silicon grease to provide efficient acoustic coupling between the material and the sensor, and an indicating channel was applied to avoid the effects of acoustic disturbance.
# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 3 Results
### Spatial and Temporal Patterns of Chlorophyll Fluorescence  
The estimated spatial changes in ChF from April to October 2022 are shown in Figure 8. The areas with non-wetland vegetation such as forests, arables, and meadows were excluded using classification from previous studies [79; 80]. Most of the greening areas are in the Biebrza Lower Basin, consistent with the previous studies [26; 27; 32]. In addition, the spatial estimations on wetland vegetation with high ChF also confirm the results on mapping gross primary productivity (GPP) with remote sensing data from 2022 [80].  
Spatio-temporal changes for five periods are presented in Figure 9. The periods of April-May and June-July exhibit a significant increase in vegetation cover in the Biebrza Valley, particularly in the wild regions of the Biebrza Lower Basin, whilst at the turn of the months July-August, the majority of the Biebrza Valley shows insignificant ChF decreases, with some noticeable browning. This is in agreement with the study of [PERSON], 1990 [29]. During the following month August until October, areas with significant ChF changes were dominated by browning trends under the temperate climate. This is also supported by other studies on Cepkeliai wetlands in Lithuania that are located 200 km from the Biebrza Valley [81]. Moreover, [PERSON] et al. 2019 [81] found that the summit of the growing season of herbaceous plants is noted in June, while vegetation index variations in July reflect environmental changes indicating browning. The findings suggest that there are significant fluctuations in the spatial distribution of ChF changes across different time periods. We observe an overall greening development stage at a maximum ChF of 0.84 and browning at 0.70.  
Figure 7: Chlorophyll fluorescence predictions from XGBoost algorithm on validation dataset (abbreviations: RMSE—Root Mean Square Error, RRMSE—Relative Root Mean Square Error, MBE—Mean Absolute Error).  
Figure 8: Spatial estimation of chlorophyll fluorescence based on Sentinel-2 June–July mosaic. Areas of interest mapped applying wetland vegetation classification elaborated in previous studies [79; 80].  
Figure 9: Overview of the predicted chlorophyll fluorescence expressed by \(\mathrm{Fv}/\mathrm{Fm}\) values during the growing season in 2022.