esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Manually Selected Features  
To demonstrate how much better the proposed method would perform than the handcrafted features method, a typical approach of handcrafted features was carried out for comparison.  
In this approach, original signals were firstly processed by Fourier transform and wavelet transform to extract features manually in spectral domain and time-frequency domain, respectively. All extracted features were then used as input data to train a neural network. An eighteen-dimensional feature vector for each signal, \(\overrightarrow{v}\), could be obtained as follows:  
(1) Maximum amplitude in time domain: \(Max_{T}=\max(T_{i})\)  
(2) Minimum amplitude in time domain: \(Min_{T}=\min(T_{i})\)  
(3) Maximum difference in time domain:  
Figure 4: The structure of the proposed CNN.  
\begin{tabular}{l l} (4) & Standard deviation in time domain: \(Std_{T}=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(T_{i}-\overline{T}\right)^{2}}\) \\ (5) & Envelope area in time domain: \(S_{T}=\sum_{i=1}^{n}T_{i}\) \\ (6) & Maximum amplitude in spectral domain: \(Max_{F}=\max(F_{i})\) \\ (7) & Minimum amplitude in spectral domain: \(Min_{F}=\min(F_{i})\) \\ (8) & Maximum difference in spectral domain: \(\Delta_{F}=Max_{F}-Min_{F}\) \\ (9) & Standard deviation in spectral domain: \(Std_{F}=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(F_{i}-\overline{F}\right)^{2}}\) \\ (10) & Envelope area in spectral domain: \(S_{T}=\sum_{i=1}^{n}F_{i}\) \\ \end{tabular} where \(T_{i}\) and \(F_{i}\) are, respectively, amplitude values corresponding to \(i\) sampling point in time domain and spectral domain and \(\overline{T}\) and \(\overline{F}\) are, respectively, mean amplitude values of signals in time domain and spectral domain. The other eight features are ratios of the energy of each frequency band to the total energy after wavelet decomposition of signals in the third layer.
## 3 Results and Discussion
### Performance of Blob-Level Classification
#### 3.4.3 Results on Test Set with Small FMs  
The overall performance of the Fusion model on the test set with small 2 \(\times\) 2 mm\({}^{2}\) FMs was better than the performance of the VNIR and SWIR models. The F1-score (94%) and JAC (88%) of the developed Fusion model on the test set was higher compared with both the VNIR (F1 score: 76% and JAC: 62%) and SWIR (F1 score: 89% and JAC: 79%) models (Table 4). The detection rates of the fusion algorithm for each group on the test set were 81% (metal), 95% (wood), and 95% (polymer), respectively, which were better than the VNIR and SWIR models. The average detection rate (90.4%) of the Fusion model for all polymer, wood, and metal samples was higher than the VNIR and SWIR models by 38% and 5%, respectively. In summary, the Fusion model outperformed the VNIR and SWIR models in detecting small FMs in the test set. An example of the classification images obtained from the three FM blob detection models on the test set is shown in Figure 8.  
Figure 8: Classification images with (**a**) VNIR model only, (**b**) SWIR model only (**c**) developed fusion algorithm on the test set: Fusion + VNIR + SWIR. Green color represents chicken meat and red color represents FMs.  
Figure 7: Classification images with (**a**) VNIR model only, (**b**) SWIR model only (**c**) developed fusion algorithm on the training set: Fusion + VNIR + SWIR. Green color represents chicken meat and red color represents FMs.
# Groundwater Detection Using the Pseudo-3D Resistivity Method: A History of Case Studies
###### Abstract  
With the rapid growth of the economy and population, the desire to use groundwater supplies is increasing. In order to detect groundwater, conducting a geophysics survey is a common way to map the subsurface. This method can describe the subsurface's physical properties. For example, the direct current (DC) resistivity method provides a resistivity map of the subsurface, in which the groundwater is normally located in low resistivity anomaly zones. However, the DC method needs to overcome the challenges of the urban environment, with its infrastructure and background noise. In this paper, we propose a pseudo-3D resistivity surveying method to solve these problems, and it is applied to situations of groundwater detection within the city environment, dam leakage, and drinking water. From the pseudo-3D resistivity surveying, we detected the low-resistivity anomaly in the area, which is interpreted as groundwater through borehole verification.  
pseudo-3D; resistivity; groundwater; dam leakage Article
## 4 Results and Discussion
### The Grid-Connected Photovoltaic Systems (On Grid-PV) Design  
#### 4.3.1 Selection of Solar Panels  
The energy requirements are determined as a target of 22,000 watts, so 56 panels are required to obtain the 22 kW; therefore, it is determined to use the monocrystalline panel  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & **Air** & **Relative** & **Deily-** & **Horiamal** & **Atmosphere** & **Wind** & **Soil** \\  & **Temperature** & **Humidity** & **Social Radiation** & **Pressure** & **Speed** & **Temperature** \\ \hline Units & & & & & & & \\ \hline  & \({}^{\circ}\)C & \% & mm & kWh/m\({}^{2}\)/d & kPa & m/s & \({}^{\circ}\)C \\ January & 21.6 & 80.2 & 38.44 & 3.65 & 100.7 & 5.5 & 21.8 \\ February & 22.3 & 79.8 & 26.60 & 4.23 & 100.6 & 5.4 & 22.9 \\ March & 24.3 & 78.4 & 23.87 & 4.86 & 100.4 & 5.2 & 25.1 \\ April & 26.2 & 77.4 & 36.0 & 5.35 & 100.2 & 5.1 & 27.6 \\ May & 28 & 77.8 & 74.40 & 5.46 & 100.1 & 4.4 & 29.1 \\ June & 28.3 & 80.2 & 231.20 & 5.07 & 100.1 & 4.2 & 28.4 \\ July & 27.7 & 81.9 & 285.20 & 5.27 & 100.3 & 3.7 & 27.2 \\ August & 27.6 & 82.4 & 282.72 & 5.05 & 100.3 & 3.5 & 27.2 \\ September & 27.4 & 81.9 & 307.50 & 4.46 & 100.2 & 4.0 & 26.9 \\ October & 26.2 & 80.2 & 161.20 & 4.29 & 100.3 & 4.6 & 25.6 \\ November & 24.2 & 80.0 & 84.30 & 3.95 & 100.6 & 5.1 & 23.8 \\ December & 22.3 & 79.8 & 38.13 & 3.55 & 100.7 & 5.1 & 22.2 \\ \hline Annual mean & 25.5 & 80.0 & 1586.66 & 4.60 & 100.4 & 4.6 & 25.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Meteorological data and solar radiation at the study site, obtained with Retscreen®.  
JAM/2S09-390/PR. The technical specifications of the panels are shown in Table 4. The following data are required for sizing the inverter.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Mimo
#### 3.4.2 Mimo-Mbnn  
MIMO-MBNN is featured in a hybrid structure that combines both two linear and one nonlinear equalizer to improve the received signal quality. It further removes the nonlinear loss using a NN network, which enables this equalizer to work within high nonlinearity. Although DNN could provide a powerful fitting function, its training consumes considerable computation compared with LMS and Volterra. Previous work [73] has shown that MIMO-MBNN outperforms SISO-DNN and SISO-LMS in operation range (2.33 times the area) and refreshed the record (2.1 Gbps within 7% FEC) of communication rate in SR-MIMO (single receiver MIMO) VLC, demonstrating a '1 + 1 \(>\) 2' effect.  
Using the SR-MIMO system in [73] as an example: the system faces both ISI and ICI; therefore, the equalizer has to remove them both. Firstly, the MIMO signal is arranged in fixed length to train the equalizer. The two linear branches deal with the linear ISI within the corresponding single channel. In the meantime, a nonlinear branch imports the training vector from both channels and uses an NN to fit the nonlinear function including the ISI and ICI. The NN outputs an R^2 vector; each element is specified for a single channel. Next, the output from a linear branch is mixed with the corresponding output from the nonlinear branch. The mixed result is the final output. As a supervised learning process, the output result is compared with the label. An Adam optimizer updates the weight in both linear and nonlinear branches. This combined structure successfully utilizes the strength of the linear equalizer and nonlinear NN network and avoids their weakness. Underwater VLC or long-haul optical communication systems could be benefited from this algorithm that improves their robustness against nonlinear loss and power jittering.
# Linguistic Patterns and Linguistic Styles for Requirements Specification: Focus on Data Entities
## 4 Data Entities' Linguistic Patterns and Linguistic Styles
### Data Entity
#### 4.1.2 Linguistic Styles  
The following statements define concrete representations for the DataEntity pattern according to the CNL-A, CNL-B, and RSL languages.  
Style According to CNL-A (Compact):  
<id> {<subType>}? <type> DataEntity [, extends <isA>]?  ls1-cnl-a  [, with attributes: <DataAttribute>*]? [, <DataEntityConstraint>]?.  
Style According to CNL-B (Verbose):  
DataEntity <id> {<name>}? Is a [ < 9 subtype>]? <type> [ and a <isA>]?  [<DataAttribute>*]? [, <DataEntityConstraint>]?  ls1-cnl-b  [, described as <description>]?.  
\begin{table}
\begin{tabular}{l p{142.3 pt}} \hline \hline
**Type** & **Description** \\ \hline \multirow{4}{*}{Parameter} & Data entity used to define functional or behavioral parameters, for instance, required to set up a deployment or a module for a specific customer; usually, data that contain only one item, where the attributes are values for settings. Common examples of this type are company configuration or application-specific parameters. \\ \multirow{4}{*}{Reference} & Data entity is used to define simple reference data of small quantities, which is required to operate a business process. Common examples of this type are units, dimensions, postal codes, tax codes. \\ \multirow{4}{*}{Master} & Data entity is used to define data assets of the business; in general, these are the “nouns” of the business, which typically fall into categories such as people, places, and concepts. Common examples of this type are customers, vendors, projects, products. \\ \multirow{4}{*}{Document} & Data entity is used to define the operational data of the business, like documents that have complex structures, such as several line items for each header record, and data that is converted into transactions later. \\ \multirow{4}{*}{Transaction} & Common examples of this type are invoice orders, purchase orders, open balances, blogs, and journals. \\ \multirow{4}{*}{4.1.1} & Data entity used to define operational transaction data of the business, like posted transactions. Common examples of this type are closed invoices, closed orders, historical data. \\ \hline \hline \end{tabular}
\end{table}
Table 1: DataEntity and DataEntityCluster types (adapted from [36]).  
Style According to RSL:  
'DataEntity' name=ID (nameAlias=STRING)? ':' type=DataEntityType  
{':' subType=DataEntitySubType}? ('{' {'isA' super=[DataEntity | QualifiedName] }}? ls1-rsl (attributes+=DataAttribute)* (constraint=DataEntityConstraint)? {'description' description=STRING)? '}'}?
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 4 Results
### Performance of Crop Classification Models in Artificially Generated Cloudy Conditions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**f1-Score (\%)**} & \multicolumn{3}{c}{**PA (\%)**} & \multicolumn{3}{c}{**UA (\%)**} \\ \hline
**Crop** & **S2\({}_{\text{c}}\)** & **S1/S2\({}_{\text{c}}\)** & **GA\({}_{\text{c,15}}\)** & **S2\({}_{\text{c}}\)** & **S1/S2\({}_{\text{c}}\)** & **GA\({}_{\text{c,15}}\)** & **S2\({}_{\text{c}}\)** & **S1/S2\({}_{\text{c}}\)** & **GA\({}_{\text{c,15}}\)** \\ \hline wheat & 88.38 & 90.91 & 92.16 & 91.08 & 93.65 & 94.22 & 85.84 & 88.34 & 90.19 \\ maize & 90.60 & 94.02 & 93.88 & 89.23 & 93.40 & 92.88 & 92.16 & 94.72 & 94.93 \\ barley & 85.83 & 90.51 & 91.51 & 85.08 & 90.04 & 91.02 & 86.60 & 90.99 & 92.01 \\ oats & 76.57 & 81.30 & 84.70 & 71.98 & 75.09 & 80.23 & 81.83 & 88.65 & 89.72 \\ sunflower & 87.31 & 91.67 & 92.50 & 86.50 & 88.75 & 89.45 & 88.22 & 94.85 & 95.78 \\ rapeseed & 84.83 & 91.40 & 92.84 & 81.75 & 89.21 & 92.24 & 88.17 & 93.73 & 93.48 \\ broad bean & 78.97 & 82.23 & 86.98 & 72.11 & 76.26 & 82.11 & 87.81 & 89.36 & 92.55 \\ shrub grass & 72.55 & 73.83 & 76.36 & 68.68 & 70.79 & 72.68 & 77.00 & 77.28 & 80.50 \\ vineyards & 77.99 & 80.34 & 83.33 & 79.11 & 84.52 & 85.62 & 85.62 & 76.71 & 81.23 \\ cherry trees & 76.32 & 78.40 & 83.78 & 75.96 & 71.91 & 82.02 & 76.99 & 86.45 & 85.72 \\ \hline macro & 81.94 & 85.46 & 87.80 & 80.15 & 83.36 & 86.25 & 84.19 & 88.11 & 89.61 \\ weighted & 85.44 & 89.08 & 90.60 & 85.56 & 89.18 & 90.66 & 85.55 & 89.23 & 90.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: UA, PA, and f1-score of the SVM model when trained (i) only on the artificially-clouded Sentinel-2 time-series (S2\({}_{\text{c}}\) model), (ii) both on the artificially-clouded Sentinel-2 time-series and the Sentinel-1 PolSAR time-series (S1/S2\({}_{\text{c}}\) model), and (iii) on the 120 features derived when used 15 as threshold (GA\({}_{\text{c,15}}\)).
# Phased System Reliability Modeling and Assessment for Construction of Lunar Scientific Base
## 1 Introduction
The Bayesian method combines pre-test reliability with field-test information, which is advantageous for small sample data [27; 28]. However, the subjective selection of the prior distribution limits its application [29]. The Bootstrap method assumes that the empirical distribution fits the sample distributions well and extends the sample by resampling [30; 31]. This method is intuitive and convenient, but the estimates are usually approximate. Inspired by the realistic problems encountered in the reliability assessment of LSB construction, this study makes two contributions: a phased reliability model for LSB construction and a building block assessment (BBA) method for phased system reliability. The phased reliability model ensures the independence of the reliability confidence limits of each stage and meets the prerequisite for solving the system reliability confidence limits using the BBA method. The BBA method enables a bottom-up assessment of phased system reliability with high confidence using lower-level reliability information and realizes the small-sample reliability assessment of LSB construction. The rest of the paper is organized as follows. Section 2 describes in detail the three main processes of LSB construction and compares the proposed phased reliability model with the conventional model. In Sections 3 and 4, the reliability assessment methods for the main processes and the overall LSB construction are proposed, all including reliability models and confidence limit calculation formulas. Numerical examples are provided in Section 5 to illustrate the applicability and effectiveness of the proposed method. Finally, Section 6 summarizes the paper.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 5 Classification of Efforts for Evolving the Control Plane
### Efforts to Evolve the Control Plane of Inter-Domain Routing  
The efforts to evolve the inter-domain routing control plane were classified as follows: _Brand new design; Incremental improvement;_ and _Inter-domain communication_.
## 5 Conclusions  
In this paper, in order to evaluate the performance of robust methods in continuous-discrete tracking systems, four robust square-root continuous-discrete cubature Kalman filter algorithms are proposed. From the results, we can draw the following conclusions:  
1. The effectiveness of the algorithms in this paper is evaluated through a remote distance passive target tracking scenario, and the simulation results demonstrate the better environmental adaptability of the proposed algorithms in solving continuous-discrete robust problems. What's more, this paper aims to solve the slow tracking problem in remote distance passive target tracking, and the proposed algorithms can also be used in other navigation domains.
2. As a common tool for data processing, Huber's estimation has a wide range of applications. The RSRCD-MCCKF algorithm and the VBSRCD-CKF algorithm proposed in this paper are better than the Huber's estimator, which provides an alternative means for the field of robust estimation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & \
\begin{tabular}{c} **Computation Time** \\ **(Scenario 4.1)** \\ \end{tabular} &
\begin{tabular}{c} **Computation Time** \\ **(Scenario 4.3)** \\ \end{tabular} \\ \hline SRCD-CKF & 1 & 1 \\ HRSRCD-CKF & 1.029 & 1.034 \\ MRSRCD-CKF & 1.005 & 1.018 \\ RSRCD-MCCKF & 1.076 & 1.080 \\ VBSRCD-CKF & 1.068 & 1.071 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The relative computation time of each algorithm.  
* In the simulation results, we can see that the filtering performance of the algorithm without robustness is very poor. Therefore, when the system's data are contaminated by outliers, the relevant robust estimation method is indispensable.  
Conceptualization, R.H. and S.C.; methodology, H.H.; software, H.H.; validation, H.W. and H.H.; formal analysis, H.W.; investigation, H.H. and S.C.; resources, R.H.; data curation, H.H. and R.H.; writing--original draft preparation, H.H.; writing--review and editing, H.W.; visualization, H.H.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. 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 [62073337, 61703420], the National Postdoctoral Program for Innovative Talent grant number [BX20190260], the China Postdoctoral Science Foundation [2019M663998], the Natural Science Foundation of Shannxi Province grant number [2020 JQ-479], and the Young Elite Scientists Sponsorship Program by SNAST [20190109].  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Analysis of the Aroma Chemical Composition of Commonly Planted Kiwifruit Cultivars in China
###### Abstract  
The aroma chemical composition of commonly planted kiwifruit cultivars in China was analyzed. The combination of 2-octanone with 3-octanone was the most suitable dual internal standard for quantitative analysis in GC-MS. A total of 172 aroma components in 23 kiwifruit cultivars were detected, and ethyl butanoate, (E)-2-hexen-1-ol, and (E)-2-hexenal could be considered the core aroma components in kiwifruit, but still need further confirmation using Sensomics. E-nose could effectively distinguish different cultivars of kiwifruit. Clustering based on GC-MS and E-nose results tends to be consistent and demonstrate a certain degree of similarity. Kiwifruit cultivars with different flesh colors cannot be effectively distinguished by their aroma chemical compositions. Different species of kiwifruit can be distinguished to some extent by their aroma chemical compositions, but the effect was not satisfactory. These results could prove valuable in the breeding, planting, and marketing of kiwifruits.  
kiwifruit; chemical compositions; E-nose; GC-MS; flesh color; species Article
## 1 Introduction
In another study the effect of different cannula positions on interventricular haemodynamics was measured using PIV in idealized transparent models of the left ventricle. The models were fabricated from platinum-cured silicone rubber with a 0.7 mm wall thickness. However, the authors did not report the manufacturing process, or the wall thickness tolerance [33]. Early work by [PERSON] et al. [34] manufactured a compliant model of the artery for haemodynamic measurement. A segment of the artery obtained from autopsy was pressurized and used as a mould. Silastic E RTV elastomer was injected into the mould and cured. The mould was removed by cutting the artery. However, the authors did not report the wall thickness precision. [PERSON] et al. [35] directly fabricated compliant phantoms using Polyjet 3D printers and TangoPlus FullCure(r) (Objet Ltd., Rehovot, Israel) material without the need for casting. Despite the high accuracy of the phantom, this material is semi-transparent and was not useful for PIV studies. However, advancements in polymers and resins may allow direct printing of high accuracy phantoms in the future. Another method which has been used for compliant phantom fabrication is a lost-core casting method. In this method, a male mould is placed centrally in a female mould. Then, the gap in between is filled with silicone and once cured the male mould is dissolved or mechanically removed. [PERSON] et al. [24] fabricated a compliant model of the carotid artery using the lost-core casting method. The comparison of the phantom with the initial stereolithographic (STL) file showed 3%-5% cross-sectional area error. Similarly, [PERSON] et al. [36] fabricated a one-to-one compliant model of the aortic arch. Comparison with the original STL file for the internal dimension of the phantom showed less than 0.52 mm variance in geometry at four positions on the male mould. They reported a 2 mm uniform wall thickness but no declaration of wall thickness variation. However, this wall thickness is also potentially larger than appropriate [37]. [PERSON] et al. [38] used 3D printed moulds to fabricate an aortic compliant phantom. A drawback of the method was that the hollow inner mould was removed mechanically which damaged the coronary artery. The wall thickness deviated about 1 mm from the nominal value at the lower part of the aortic arch. The deviation was reported to be due to mould misalignment. In addition, the wall thickness at the ascending aorta was 4.5 mm which is larger than appropriate [39]. This study presents a new manufacturing method for fabrication of compliant silicone arterial phantoms. The geometry selected includes multiple complex shapes experienced in the arterial tree, including both arterial curvature and arterial branching. Therefore, the techniques developed here are applicable to a vast array of arterial geometries.
## 3 Sliding Mode Control Theory and Terminal Sliding Mode Controller Design
### Terminal Sliding Mode Control  
Terminal sliding mode control (TSMC) is an improved method of sliding mode control aimed at further enhancing the dynamic performance and steady-state accuracy of a system. While traditional sliding mode control offers good robustness and response speed, it may exhibit slow convergence to the terminal state in some cases. Terminal sliding mode control addresses this issue by designing a non-linear sliding surface that ensures the system state converges to the equilibrium point within a finite time, overcoming the drawback of slow terminal state convergence [10].
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 2 The NAND Memory Array
### Array Operation  
As mentioned above, digital information is stored as charge onto the floating gate of a NAND cell. While the first devices relied on two discrete charge levels to store a single bit (the so-called single-level cells, SLCs), it was soon realized that the linear relation between floating-gate charge and cell \(V_{T}\) could be exploited to store more than one bit per cell [35; 36]. Nowadays, TLCs are in production and used in SSDs [6], while quadruple-level cells (QLCs) are under active development [37]. Figure 2 shows the \(V_{T}\) distribution of a TLC NAND array: eight different levels are necessary to encode three bits. In the NAND architecture, all but one \(V_{T}\) level are positive (shown in different shades of blue) and are referred to as the _programmed_ levels. The negative (red) \(V_{T}\) level is instead the _erased_ one.  
Figure 1: **(Left)** Schematic view of a NAND Flash array. Vertical strings of series-connected devices are attached to the bitlines and the sourceline via select transistors driven by a drain select line (DSL) and a source select line (SSL); (**Right**) schematic cross-sections of the array along the WL direction (**a**) and the string direction (**b**) (green = silicon, red = floating gate, magenta = WL, white = silicon oxide). Inset (**c**) shows the layout of the array with the elementary cell (dashed square).