esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Study
### Effect of Retaining Wall Slope Angle \(\theta\)  
Figure 11 shows the \(K_{\rm a}\), \(\alpha_{\rm a}\), and normalized dynamic active earth pressure distribution for variations in \(\theta\), \(c/\gamma H\), and \(k_{\rm h}\) with \(f_{\rm a}=1.4\), \(\varphi=30^{\circ}\), \(\delta=\varphi/2\), \(i=8^{\circ}\), and \(k_{\rm v}=0\). In Figure 11a, \(K_{\rm a}\) increases significantly as \(\theta\) increases from \(-20^{\circ}\) to \(20^{\circ}\). The magnitude of the increase remains nearly constant with increasing \(k_{\rm h}\). When \(k_{\rm h}=0.2\) and \(c/\gamma H=0\), \(K_{\rm a}\) increases from 0.401 to 0.857 as \(\theta\) changes from \(-20^{\circ}\) to \(20^{\circ}\). Comparing the cases of \(c/\gamma H=0\) and \(c/\gamma H=0.05\) in Figure 11b, it is found that the existence of cohesion (\(c/\gamma H=0.05\)) reduces \(K_{\rm a}\) significantly.  
In Figure 11c, the failure wedge angle \(\alpha_{\rm a}\) in the active condition is increased as \(\theta\) changes from \(-20^{\circ}\) to \(20^{\circ}\) when \(k_{\rm h}\) is less than 0.25. When \(k_{\rm h}\) is larger than 0.25, the \(\alpha_{\rm a}\) values obtained with different \(\theta\) values are almost the same. The failure wedge angle increases when \(c/\gamma H\) changes from 0 to 0.05, and the difference amplitude rises as \(k_{\rm h}\) increases.  
Figure 11d shows the typical normalized dynamic active earth pressure distribution for different values of \(\theta\) with \(k_{\rm h}=0.2\) and \(k_{\rm v}=0\). When \(\theta\) increases from \(-20^{\circ}\) to \(20^{\circ}\) in the case of \(c/\gamma H=0\), the dynamic active earth pressure at the bottom of the wall changes from 0.373 to 0.810. When \(\theta\) is \(-20^{\circ}\), \(0^{\circ}\) and \(20^{\circ}\), in the case of \(c/\gamma H=0.05\), the corresponding normalized critical depth \(z_{\rm c}/H\) is 0.274, 0.174, and 0.116, respectively. It can be found that \(\theta\) has a significant impact on the normalized critical depth.
# BER Aided Energy and Spectral Efficiency Estimation in a Heterogeneous Network
###### Abstract  
In this work, we adopt the analysis of a heterogeneous cellular network by means of stochastic geometry, to estimate energy and spectral network efficiency. More specifically, it has been the widely spread experience that practical field assessment of the Signal-to-Noise and Interference Ratio (SINR), being the key physical-layer performance indicator, involves quite sophisticated test instrumentation that is not always available outside the lab environment. So, in this regard, we present here a simpler test model coming out of the much easier-to-measure Bit Error Rate (BER), as the latter can deteriorate due to various impairments regarded here as equivalent with additive white Gaussian noise (AWGN) abstracting (in terms of equal BER degradation) any actual non-AWGN impairment. We validated the derived analytical model for heterogeneous two-tier networks by means of an ns3 simulator, as it provided the test results that fit well to the analytically estimated corresponding ones, both indicating that small cells enable better energy and spectral efficiencies than the larger-cell networks.  
heterogeneous network; BER; energy efficiency; spectral efficiency +
Footnote †: journal: 2022, 10, 162. [[https://doi.org/10.3390/computation10090162](https://doi.org/10.3390/computation10090162)]([https://doi.org/10.3390/computation10090162](https://doi.org/10.3390/computation10090162))
# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 4 Scrambled Linear Block Code Identification Based on Correlation Features Extraction Network  
### Correlation Feature Extraction Network Model  
In this paper, the received scrambling sequence features are divided into two lines: one line contains the original symbol sequence of the cross-correlation feature, and the other is the normalized partial autocorrelation function of the sequence; and the two form the 2 \(\times\)\(\beta\)-dimensional matrix feature as the input of the network.  
It can be seen from the analysis in Section 2 that the scrambling sequence has timing-related characteristics. As a special recurrent neural network, LSTM has achieved good results in processing timing-related information [37; 38]. In the present study, CNN and LSTM are combined. The structure of the network, which is mainly composed of two convolutional layers, i.e., one LSTM layer and one fully connected layer, is shown in Figure 6. The activation function of the convolutional layer adopts RELU, the activation function of the LSTM layer adopts tanh, the final fully connected layer uses the normalized exponential function (SoftMax) for classification, and the output is a dimensional one-hot encoding form, corresponding to linear block codes, linear block code self-synchronous scrambling, and linear block code synchronous scrambling.  
To prevent overfitting, we first introduce a Dropout layer after each layer of the network to randomly drop some neurons in order to reduce the overfitting phenomenon of the network. Moreover, the EarlyStopping function is used to detect the loss value and to stop training when it is not decreasing.  
Figure 5: Normalized partial autocorrelation function diagram when SNR = 6 dB.  
Figure 6: Correlation feature extraction network model diagram.
## 4 Results and Discussion
### Data Generation Performance Testing
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**50 Epochs**} & \multicolumn{2}{c}{**60 Epochs**} & \multicolumn{2}{c}{**70 Epochs**} & \multicolumn{2}{c}{**80 Epochs**} & \multicolumn{2}{c}{**90 Epochs**} & \multicolumn{2}{c}{**100 Epochs**} \\ \cline{2-11}  & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline Decision Tree & 0.39 & 0.39 & 0.54 & 0.54 & 0.88 & 0.88 & 0.74 & 0.74 & 0.60 & 0.60 & 0.43 & 0.43 \\ Naive Bayes & 0.75 & 0.75 & 0.75 & 0.75 & 0.72 & 0.72 & 0.77 & 0.77 & 0.77 & 0.75 & 0.75 \\ RF & 0.93 & 0.93 & 0.97 & 0.97 & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 & 0.96 & 0.96 \\ MLP & 0.80 & 0.80 & 0.76 & 0.76 & 0.81 & 0.81 & 0.81 & 0.81 & 0.80 & 0.80 & 0.86 & 0.86 \\ Gradient & & & & & & & & & & & \\ Boost & 0.41 & 0.41 & 0.54 & 0.54 & 0.87 & 0.87 & 0.74 & 0.74 & 0.60 & 0.60 & 0.43 & 0.43 \\ XGBoost & 0.79 & 0.79 & 0.73 & 0.73 & 0.88 & 0.88 & 0.92 & 0.92 & 0.82 & 0.82 & 0.69 & 0.69 \\ LightGBM & 0.42 & 0.42 & 0.55 & 0.55 & 0.88 & 0.88 & 0.74 & 0.74 & 0.60 & 0.60 & 0.44 & 0.44 \\ Average & 0.64 & 0.64 & 0.69 & 0.69 & 0.85 & 0.85 & 0.81 & 0.81 & 0.74 & 0.74 & 0.65 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The accuracy and F1 score of training the classifiers using the generated dataset and testing using the real dataset. Figure 5: The plots show the cumulative sums per feature for both real and generated datasets. Each plot displays the data points of the respective feature.
## 3 Experiments
Figure 5 shows the processes of the three systems in the field operation. Both aerial seeding operations were conducted under the first operation mode (see Section 2.1), in which the field was lightly filled by the rotary tiller on the ground planter without seeding. For all three seeding operations, subsequent investigations were conducted including counting seed germination rate on 10 November 2019 (30 days after seeding), monitoring seedling growing status in later months, and calculating yields and counting stubbles after harvesting on 10 May 2020. Figure 6 shows pictures taken during subsequent investigation processes. The yield and the stubble number for an area of 1 m\({}^{2}\) were calculated for comparison with the ground planter and the other aerial seeding system. Figure 5: Field seeding experiments using three systems: (**a**) An aerial seeding system based on a Lockheed 3 WDM6-10 UAV with an air-assisted centralized metering device; (**b**) An aerial seeding system based on an XAG P20 UAV with a centrifugal seed metering device and a seed guiding tube; and (**c**) A ground rapeseed planter (Weiye 2 BFG-4) based on an air-assisted seed metering device. Figure 6: Subsequent investigations processes. (**a**) Monitoring seedling status; (**b**) Counting seed germination rate; (**c**) Harvesting by a combine harvester; (**d**) Counting stubbles after harvesting.
## 2 Materials and Methods
### Instrumentation  
Each specimen was instrumented to provide data needed to understand its response under high sustained loads. Two load cells with 89 kN (20 kip) capacity were placed between the plate and washer at the top of the beam specimen to measure the applied load. In addition, strain gauges were attached to each rod connected to the lab floor to provide an additional measurement of the applied load. The readings from the two systems were consistent. Two linear variable differential transformers (LVDTs) were utilized to determine the vertical deflections of the beam at the loading points. The LVDTs were placed underneath the beam as near as possible to the through-hole and threaded rod. The LVDTs (BDI LVDT-01-005) have a range of \(\pm\)12.7 mm (0.5 in.) and an accuracy of \(\pm\)0.025 mm (0.001 in.). Each beam had two strain gauges (one on a tension bar and the other one on a compression bar) attached at a distance of 76 mm (3 in.) longitudinally from beam center. In beam series II, the tension side gauges were attached to the center bar. The strain gauges (Vishay C4A-13-125 SL-350-39P) have an accuracy of \(\pm\)10\({}^{-6}\) strain.  
Table 1 summarizes the specimens' characteristics, including loading age, sustained load duration, and sustained load intensity. Temperatures and relative humidity were also recorded and are shown in Table 1, which also shows the designation of the test specimens applied with sustained loads. The testing lab had no cooling in the summer so there was a greater variation in the daily temperature for the beams tested in the summer. Furthermore, the temperature and humidity variations more closely replicated the actual conditions of beams in service. Differences in the average temperatures for each test may have significant effects. Higher average temperatures will accelerate creep, while lower temperatures will decelerate creep [1,2]. Temperature variations of 11 \({}^{\circ}\)C, as reported during testing, will result in about 5% increase in the temperature correction coefficient used in the CEB-MC90 creep model [1]. Because the tests are short and the specimens have been pre-dried for some time, humidity is unlikely to have a significant effect.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.4 Machine-to-Machine (M2M) Communication  
M2M communication technology is necessary to convey small quantities of time-sensitive data. Intelligent machines may generate, process, and transfer data automatically when they are linked together via M2M connections [3]. The provision of communication services via autonomous sensors, devices, and machines that require little human intervention is called \"M2M communication\". The capacity of such networks must be sufficient to sustain several concurrent connections over a wide coverage region. With M2M communication, data offloading and aggregation can also be implemented to improve 5G communication and energy efficiency.
# A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems
## 3 Methods  
### System Model  
We consider an uncoded mMIMO system where the BS is furnished with \(N\) antennas to serve \(K\) single antenna users simultaneously in a single cell where \(K\ll N\). The vector \(\mathbf{x}\) presents the transmitted data by all users and the symbol vector \(\mathbf{y}\) presents received data at the BS where \(\mathbf{x}\) and \(\mathbf{y}\) are \(K\times 1\) and \(N\times 1\), respectively. The received vector \(\mathbf{y}\) is usually corrupted by channel effects and the noise (\(\mathbf{w}\)). The channel matrix (\(\mathbf{H}\)) entries are independent and identically distributed (i.i.d) with zero mean and unit variance. The mMIMO model is given as  
\[\mathbf{y}=\mathbf{H}\mathbf{x}+\mathbf{w}. \tag{1}\]  
In the MMSE algorithm, the equalization matrix (\(\mathbf{A}\)) plays a role in estimating the signal as  
\[\mathbf{\hat{x}}=\mathbf{A}^{-1}\mathbf{y}_{MF}, \tag{2}\]  
and  
\[\mathbf{A}=\mathbf{G}+\sigma^{2}\mathbf{I}_{K}, \tag{3}\]  
and  
\[\mathbf{y}_{MF}=\mathbf{H}^{H}\mathbf{y}, \tag{4}\]  
where \(\sigma^{2}\) is the noise variance and \(\mathbf{I}_{K}\) is the \(K\times K\) identity matrix. The _Gram matrix_ (\(\mathbf{G}\)) is \(\mathbf{H}^{H}\mathbf{H}\). However, a direct computation of \(\mathbf{A}^{-1}\) requires \(\mathcal{O}\left(K^{3}\right)\). However, literature is rich with methods to avoid the direct and exact matrix inversion which relieves a burden of high computational complexity.
## Abbreviations  
A Ascending  
ALOS Advanced Land Observing Satellite  
ANX Ascending Node Crossing  
API Application Programming Interface  
ARIA Advanced Rapid Imaging and Analysis  
ASF Alaska Satellite Facility  
ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer  
AWS Amazon Web Services  
CEDA Centre for Environmental Data Analysis  
CEOS Committee on Earth Observation Satellites  
ComCat Comprehensive Earthquake Catalog  
COMET Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics  
D Descending  
DAAC Distributed Active Archive Centers  
DEM Digital elevation model  
DIAS Data and Information Access Services  
DLR Deutsches Zentrum fur Luft  
E East  
ECMWF European Centre for Medium-Range Weather Forecasts  
EIDP Earthquake InSAR Data Provider  
EO Earth Observation  
EODC Earth Observation Data Center  
EPOS European Plate Observing System  
EPT Estimated processing time  
ESA European Space Agency  
GACOS Generic Atmospheric Correction Online Service  
GEE Google Earth Engine  
GeoTIFF georeferenced Tagged Image File Format  
GEP Geohazard Exploitation Platform  
G-POD Grid Processing on Demand  
HPC High-performance computing  
InSAR Synthetic Aperture Radar Interferometry  
IFF Instrument Processing Facility  
IWS Interferometric Wide Swath  
JAXA Japan Aerospace Exploration Agency  
JPL Jet Propulsion Laboratory  
KMZ Keyhole Markup language Zipped  
LiCS Looking Inside the Continents from Space  
LiCSAR Looking Into Continents from Space with Synthetic Aperture Radar  
LOS Line-of-sight
# A Comparative Analysis of International Standards on Curved Surface Isolators for Buildings
## 3 Design Procedures
### US Standard Approach  
The American standard ASCE/SEI 7-22 [58] adopts the ELF procedure, a method used to estimate seismic forces acting on a structure based on its fundamental period and site-specific seismic parameters. This approach follows a similar procedural sequence to that of the European standard (EC8) [57], ensuring consistency in methodology. The fundamental relationships outlined in Equations (2)-(9) remain applicable in this context. The procedure begins with an updated formulation in Equation (10), which accounts for the specific spectral acceleration, damping adjustments, and effective period of the isolated structure as prescribed by ASCE/SEI 7-22:  
\[A=\frac{C}{B_{M}}=0.895\frac{\mathrm{m}}{\mathrm{sec}^{2}}\approx 0.091\; \mathrm{g} \tag{13}\]  
\[D_{\mathrm{max}}=\left(\frac{T_{\mathrm{eff}}}{2\pi}\right)^{2}=229.17\; \mathrm{mm} \tag{14}\]  
\[F_{\mathrm{max}}=W\left(\frac{D}{R}+\mu\right)=73206.57\;\mathrm{kN} \tag{15}\]  
where the damping factor \(B_{M}=0.982\) derived from ASCE/SEI 7-22 [58].Since this is an iterative procedure, the calculated displacement must converge with the initially assumed displacement, as mentioned at the beginning of this section. After several iterations, convergence is achieved, and the resulting values are presented and compared across different codes in Table 2.
# Wood Defect Detection Based on Depth Extreme Learning Machine
## 6 Conclusions  
The depth extreme learning machine proposed in this paper has reasonable dimensions, effectively manages heterogeneous data, and works within an acceptable run time. Our results suggest that it is a promising new solution to problems such as obtaining marking samples, constructing features, and training. It has excellent feature extraction ability and fast training time. Based on the method of machine learning, The NSST transform is used to preprocess the original image (i.e., reduce its complexity and dimensionality while minimizing the down-sampling process in CNN), then SLIC is applied to optimize the CNN model training process. This method effectively reduces the redundancy of local image information and extracts relatively accurate supplementary feature contours. The optimized CNN is then used to extract wood image features and secure corresponding image features. The feature is input to the ELM classifier and the parameters of the related neural network are optimized. The GA is used to select the initial weight threshold of ELM to improve the prediction accuracy and stability of the network model. Finally, the image data to be tested is input to the well-trained network model and final test results are obtained.  
We also compared the stability of D-ELM and ELM network models. The standard deviation of D-ELM was only 0.0967 and the accuracy of D-ELM improved by about 3% compared to ELM; the stability of the D-ELM network was also found to be higher and less affected by test quantity than ELM. We also found that D-ELM has an accuracy of up to 96.72% and a shorter test time than ELM or SVM at only 187 ms. The D-ELM network model is capable of highly accurate wood defect recognition within a very short training and detection time.  
Conceptualization, Y.Y. and Y.L.; methodology, Y.Y.; software, X.Z.; validation, Y.Y., Z.H. and F.D.; resources, X.Z. and Z.H.; writing--original draft preparation, Y.Y.; writing--review and editing, Y.L. and X.Z.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2019 Jiangsu Province Key Research and Development Plan by the Jiangsu Province Science and Technology under grant BE2019112, and was funded by the Jiangsu Province International Science and Technology Cooperation Project under grant BZ2016028, and was supported from the 948 Import Program on the Internationally Advanced Forestry Science and Technology by the State Forestry Bureau under grant 2014-4-48.  
Figure 11: System interface diagram based on depth learning.
# Mathematical Modeling to Estimate Photosynthesis: A State of the Art
## 2 Techniques
In this review, a series of papers related to the topic of mathematical modeling were analyzed for inclusion. During data extraction, some studies were excluded because they did not present the information required in the criteria established for selection. These excluded models have the following issues:  
* They did not estimate gross photosynthesis, net photosynthesis, photosynthetic rate, biomass, or carbon assimilation; instead, they modeled chlorophyll, fluorescence, or stomatal conductance, among others. * The information was related to the measurement methodology [40; 41] and not to the mathematical modeling. * Some articles did not present the mathematical formulation for photosynthesis [60; 61]. * Some models estimated crop growth [62; 63; 64; 65], which is not the focus of this review. * Some articles use previously developed models to carry out experiments different from those originally proposed [66; 67; 68; 69], but they do not propose any new model. * Some authors present a mathematical formulation of the model; however, they do not present validation or results obtained [70]. * Some authors expose models to estimate photosynthesis only for the Calvin cycle [71; 72], which is only one section of all the photosynthetic processes. * Some authors used repeated or similar models to those presented in this manuscript [73; 74; 75; 76]. * Some models are developed based on soft computing [77; 78]. These models were excluded because they are not analytical models, that is, they are not models that present explicit algebraic expressions. Finally, a total of 39 reported papers were selected and reviewed, being essential for conducting this review. In addition to these criteria, the type of cultivation, the mathematical formulation, as well as the validation of the modeling were considered. The information retrieved was analyzed to determine their corresponding classification, according to the previously described types of mathematical modeling: white box, black box, or gray box.
## 2 Theoretical Background
### Tools and Methods in Deception Detection
#### 2.3.2 Machine Learning  
Machine learning (ML) is a field of artificial intelligence that deals with the study of methods that independently learn from data and use it for the purpose of improving performance when solving given problems. The models are built on the training data and used to make predictions. Today, machine learning is used across all fields, such as medicine, computer vision, text classification/grouping, speech recognition, etc. The base steps of machine learning are data collection, data preprocessing, model selection, model training and model evaluation, parameter tuning, and prediction. The process of data collection and preprocessing is of great importance given that the models rely on the given data when making decisions. Due to the general lack of labeled data, these are also the most difficult tasks.  
Machine learning models are one of the methods that can be used in deception detection. Prior to the actual model training, the data is filtered, cleaned, and analyzedusing natural language processing methods, and then, it is transformed into a form that is acceptable to a certain machine learning model. The models used in this are based on logistic regression, naive Bayes, SVM (Support Vector Machine), and Random forest:  
* Logistic regression is a statistical model of machine learning that is used for classification and belongs to supervised machine learning techniques. It outputs a probabilistic value between 0 and 1.
* SVM is a machine learning model that is often used for classification, but it is also used for regression. It belongs to supervised machine learning techniques. Its task is to separate N-dimensional data into classes by selecting the best decision boundary (discriminant function).
* Naive Bayes also belongs to supervised learning techniques. It is based on Bayes theorem, which naively assumes that the value of a certain variable/feature is independent of other variables/features.
* The Random Forest model is used for the classification and regression problem. It is based on the construction of a large number of decision trees, each of which makes decisions on the outcome of the prediction. In the case of classification, the prediction with the majority of votes is selected while in the case of regression, the average value of all predictions is taken as the output of the model.  
K-fold cross validation is used as a method of evaluating the obtained machine learning models. The parameter k determines the number of groups into which a given data set is divided to separate the training from the testing data. Specifically, one set is taken to test the model while the other k-1 datasets are used to train the model. The accuracy of the model is calculated by taking the average value of the model's prediction through k iterations. This method provides a less optimistic but less biased assessment of model performance than other methods.