esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid EDA Solution for the GC-CRSN Problem
### EDA for GC-CRSN Problem
algorithm has a population, which comprises individuals with feasible relay and source power levels and the associated assignment variables \(\varepsilon\). Step 4: If the convergence criterion (e.g., number of iterations) is satisfied, then terminate; else, continue. Step 5: Select the best \(|\eta_{t-1}|=\rho_{s}|\Delta_{t-1}|\) candidate solutions (individuals) from the current population, \(\Delta_{t-1}\). This selected population is used to compute the mean and standard deviation of the selected individuals. Step 6: Determine the mean,'m', and standard deviation, \(\sigma\). Based on these estimates of'm' and \(\sigma\), update the search window bounds, \(W_{Low}\) and \(W_{High}\), as \(W_{Low}=\) m-\(\sigma\) and \(W_{High}=\) m+\(\sigma\). Step 7: Generate new \(|\eta_{t-1}|\) individuals on the basis of this new estimated \(W_{Low}\) and \(W_{High}\) using Equation (10). Combine these newly generated \(|\eta_{t-1}|\) individuals with members of \(\eta_{t-1}\) to form a new population, \(\Delta_{l}\). Step 8: Go to step 1 and repeat the steps. From a practical point of view, the following observations and suggestions can help in implementing the proposed EDA for relay assignment and power allocation in a green cooperative cognitive radio sensor network. A good sensing mechanism is inevitable for a cognitive radio that will add robustness to the system. We also need optimal parameter settings of EDA for different geographical regions. A good estimate of the initial population of EDA can also increase its convergence rate towards good solutions. This increase in convergence rate eventually decreases the computational complexity of the central controller. Now, we will explain iterative greedy schemes that jointly assign relays and ensure the feasibility of the solution.
# Mail Inspection Based on Terahertz Time-Domain Spectroscopy
## 2 System Design
### Evaluation Methods  
For evaluation of spectra with respect to identifying known substances, a plethora of methods is conceivable. In the case of a restricted identification problem, where the expected spectra are completely covered by the database and not covered by unknown influences, a principal component analysis is suitable [29; 30; 31; 32]. The PCA method reduces the dimensions of the spectra by a coordinate transformation into a lower dimension, taking the spectral components into account, which contain the most significant information to describe all expected spectra. Then, in dependence of the location of a spectrum under  
Figure 8: Schematic of the air-drying circuit according to the setup shown in Figure 5. A closed loop containing a zeolite reservoir for air drying is enclosing most of the terahertz beam path. A humidity regulated pump is used to circulate the air and ensuring the interaction of the humid air with the zeolite granulate.  
Figure 7: Simulation of the effect of water vapor in the terahertz beam path of 80 cm in comparison to 8 cm using the HITRAN database [28]. By purging 90% of the terahertz beam path in the system, the spectral quality can be significantly enhanced.  
evaluation in this new coordinate system, it is classified by the location of known spectra taken into account. The decision can be made by suitable classifiers, which are e.g., based on k-nearest neighbors algorithm (k-NN), linear discriminant analysis (LDA), or support vector machines (SVM). Since most classifiers are not parameter free, at this point sensitivity and specificity are assessed by analyzing the influence of the parameters on the decision. Receiver operator characteristic curves (ROC) are used to design the best possible true positive rates while limiting false positive rates [33; 34; 35].  
In case of a non-restricted identification problem, where also unknown spectra have to be considered, this PCA might not be suited best. As unknown spectra possibly carry new, significant information differing from the expected spectra--describing the difference--the PCA might drop this significant difference (as its transformation only bases on the known, expected spectra) and lead to a false classification.  
In the case of paper-packed envelopes, multiple reflections have to be considered that can lead to extinction variation throughout the whole spectrum. This can lead to unfavorable false-positive detections as well as to false-negative results. As will be shown, in the following, this can be overcome by making use of reflection properties.
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 1 Introduction
Improving water use efficiency is an important strategy to alleviate the water crisis and achieve sustainable crop production. The analysis of sustainable water use and water use efficiency provides a scientific basis for the efficient use and sustainable management of water resources in the North China Plain. As mentioned above, this study selected different ecological zones in the North China Plain to study the spatial, temporal, and driving factors of WUE in different ecoregions in the North China Plain.
## 5 Conclusions  
We propose a reliable data delivery mechanism for power quality monitoring system, by modeling an optimal data delivery function. There are several contributions in our method: we have designed the collection subsystem to deliver power quality data from pole transformers to the substations. In order to deliver the power quality data reliably, we developed a reliable link cost function using the neural network concept. For the development, we applied the input type of input feature in connecting them in the neural network, which is an important factor for improving the performance. Also, we showed the feasibility of our method from comparison of our method with the FCNN and the conventional method. From the comparison, we can conclude that the performance of our method is better than those of the conventional methods with respect to PTSR.  
There are two approaches for efficient data delivery; one is power control and the other is the link cost function. In this paper, we focused on the development of link cost function to solve the data delivery problem. Since the power control approach can be one of solutions to solve the problem, we will consider the approach as another research direction. Also, we need to extract more input features to meet the requirements and characteristics of applications and systems such as ITS (Intelligent Transportation System) and wireless mesh network.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 5 Parametric Study  
As shown in Table 3, a total of 33 simulations were performed to study the further influences of connector dimension and material properties on the pull-out resistance of notched perfobond shear connectors. The parameters included the hole diameter \(d_{p}\), the cut width \(c_{w}\), the perfobond thickness \(t_{p}\), the concrete strength \(f_{cut}\), the rebar diameter \(d_{r}\), the yield strength \(f_{ry}\) of the rebar, and the yield strength \(f_{sy}\) of the structural steel.  
Figure 13: Tested and analyzed load–slip curves. (**a**) Conventional perfobond shear connector; (**b**) notched perfobond shear connector.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & \(d_{p}\) **(mm)** & \(c_{w}\) **(mm)** & \(t_{p}\) **(mm)** & \(f_{cut}\) **(MPa)** & \(d_{r}\) **(mm)**
# Assessment of BeiDou-3 and Multi-GNSS Precise Point Positioning Performance
## 5 Data Tests and Results Analysis  
This section performs GPS-only, BDS-2-only, BDS-2/BDS-3, GLONASS-only, Galileo-only, and multi-GNSS combined PPP tests in order to assess the global performance of GPS, BDS, GLONASS, Galileo, and its contribution to multi-GNSS. The capacity of the multi-GNSS PPP is also verified in terms of its positioning accuracy, convergence time, and ZTD accuracy.
## 2 Materials and Methods
### Metal Interaction Experiments
#### 2.2.6 Erythrocyte Lysis Assay  
This assay was performed according to previous studies with some modifications [31; 32]. Blood samples were obtained from adult rats ([PERSON], Velaz, s.r.o., Czech Republic) by exsanguination into heparinized tubes. The exsanguination was performed by a trained researcher in accordance with The Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (8 th edition, revised 2011, ISBN-13: 978-0-309-15400-0). The blood was used as a by-product from rats after isolation of aorta, aimed at testing the vasodilatory effect (approval by the Czech Ministry of the Health No. MSMT-34121_2017-2). Afterwards, the blood was centrifuged at 3000g and plasma was removed. The erythrocyte fraction was purified by adding saline and further centrifugation. Afterwards, additional heparin (final concentration of 10 IU/mL in erythrocyte suspension) was added, and this mixture was diluted 10 times with 1 mM glucose solution in saline. Erythrocyte suspension obtained was then used for erythrocyte toxicity evaluation.  
940 \(\upmu\)L of this suspension was incubated with 10 \(\upmu\)L of compound No. **9** dissolved in DMSO (different concentrations, final concentration of DMSO was 1%) and 50 \(\upmu\)L of a metal solution (cupric or ferrous sulfate dissolved in saline, at final concentrations of 500 and 2500 \(\upmu\)M, respectively) for 4 h at 37 \({}^{\circ}\)C. The sample was then centrifuged at 7000 \(g\) for 10 min and 250 \(\upmu\)L of supernatant was used for the determination of lactate dehydrogenase (LDH) activity, the marker related to metal-induced erythrocyte lysis. The remaining liquid was discarded and a lysis buffer (2 mM EDTA, 1 mM dithioteitol, 1% Triton X, 0.1 M phosphate buffer of pH 7.8) was added to the sediment in the same quantity as the removed supernatant. After 20 min of incubation at room temperature, the samples were treated similarly as the previous supernatant and determined LDH activity was considered to be the marker of the remaining, non-lysed erythrocytes. The protocol used for LDH evaluation was adapted from [PERSON] et al. [32] with minor modifications, erythrocyte suspension was used instead of cell culture and \(\upbeta\)-NAD conversion was used to quantify the enzymatic activity. Results were calculated as a percentage of erythrocytes lysed and compared with the positive control, where the solvent DMSO was used instead of the tested compound. Negative control was not treated with the metal, but otherwise elaborated as other samples.
# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 4 Algorithm Simulation and Analysis
### Energy Efficiency Analysis  
While Section 4.1 demonstrates LEAD-RP's network lifetime extension, energy minimization efficiency can be further characterized by  
\[\eta_{\text{eff}}=\frac{E_{\text{saved}}}{E_{\text{LEACH}}}=1-\frac{\sum_{\tau=1 }^{R_{\text{LEAD-RP}}}E_{\text{LEAD-RP}}(r)}{\sum_{\tau=1}^{R_{\text{LEACH}}}E_{ \text{LEACH}}(r)} \tag{28}\]  
where \(R\) is the total rounds until 95% node depletion. Our simulations yield \(\eta_{\text{eff}}=0.39\) for \(N=100\) nodes, indicating 39% cumulative energy savings. This aligns with the 42% reduction in long-range transmissions (Figure 6), as energy waste predominantly occurs during CH-to-BS communication.  
Key Insights:  
* Cluster radius adaptation (Equation (10)) reduces average transmission distance by 28%.
* Dynamic CH selection avoids energy \"hotspots\" (Figure 7's lower std. dev.).
* Energy savings are nonlinear with the network size due to multi-hop effects.
# Compositional Analysis and Aroma Evaluation of Feijoa Essential Oils from New Zealand
## 2 Results and Discussion
### Aroma Profile of Feijoa Essential Oil  
Based on the identified aroma active compounds, five aroma attributes were further selected to establish the aroma profile of feijoa essential oil. They are 'fruity and honey-like', 'herbal and woody','sweet and floral','metallic and pungent', and 'grassy and minty' notes.  
As shown in Figure 6, essential oils extracted from the four different feijoa cultivars varied in the strength of each aroma attribute. The Apollo and Wiki Tu essential oils had stronger 'fruity and honey-like' and'metallic and pungent' notes than the Unique and Opal Star essential oils, and the Opal Star essential oil was more 'herbal and woody' than the others. Regarding the 'grassy and minty' note, the Opal Star essential oil had the highest score, followed by the Wiki Tu essential oil and then the Apollo and Unique essential oils. However, no difference was observed in the strength of the'sweet and floral' note among the four essential oils. Furthermore, the Opal Star essential oil was found to be more balanced in the aroma distribution of 'fruity and honey-like', 'herbal and woody','sweet and floral', and 'grassy and minty' notes, and was detected with less'metallic and pungent' note, than the other essential oils. The Wiki Tu essential oil, which had the highest production yield (Figure 2), was dominated by the 'fruity and honey-like' note. In addition, the essential oil from the Unique cultivar was the least aroma intensive, as the enclosed area by the five attributes (Figure 6) was significantly smaller than the other three cultivars.  
The 'fruity and honey-like' note was the leading attribute, with a higher score than the other attributes (Figure 6) in the aroma profile of feijoa essential oils. The typical 'fruity and honey-like' note was mostly formed by ester compounds including ethyl hexanoate, \((Z)\)-3-hexenyl acetate, methyl benzoate, ethyl benzoate, and \((Z)\)-3-hexenyl hexanoate, and the alcohol compound \((E)\)-geraniol. To the contrary, the unpleasant'metallic and pungent' note, bestowed by \(\beta\)-myrcene and the unknown compound (RI of 1153), was the weakest attribute in the aroma profile of feijoa essential oil. Therefore, the aroma property of feijoa essential oil predominantly possessed pleasant aromas and could be friendly to the flavor and fragrance industry to develop feijoa aroma-based products.
## 2 Materials and Methods
### Soil Sampling and Analysis  
Soil samples were collected from an area of \(\sim\)54,000 m\({}^{2}\) during July 2013. Each sampling location covered a grid area of 20 m \(\times\) 20 m, with each separated by 20 m. In total, 111 sampling locations were chosen (Figure 1). At each location, sub-samples of soil (\(\sim\)7 cm\({}^{3}\) each) were collected using a soil auger (3 cm in diameter) at a depth of 0-10 cm at three sampling points which were approximately 100 cm apart. Manual measurements of soil temperature and moisture were taken at the same time as soil sampling using a WET sensor (Delta-T Devices Ltd., Cambridge, UK). The three sub-samples obtained at each location were subsequently mixed to produce one sample representing that location. After the soil samples were taken back to the laboratory they were air-dried and ground (<0.25 mm) prior to further physical and chemical analysis. The soil pH was determined in a water extract (soil: water, 1:2.5) using a pH meter (Thermo Fisher Scientific Inc., Waltham, MI, USA). Soils were extracted with 0.01 M CaCl\({}_{2}\) at a ratio of 25 mL:10 g (volume to oven dry mass equivalent), with this extraction procedure lasting approximately 30 min. The extracts were shaken for 10 min using an end-to-end shaker and then filtered through Whatman#42 filter paper into plastic tubes [11]. The extracts were stored at \(-\)20 \({}^{\circ}\)C prior to dissolved organic carbon (DOC) and dissolved total nitrogen (DTN) measurements using a TIC-TOC analyser (TOC-V Shimadzu Corporation, Tokyo, Japan).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil Properties** & \(n\) & **Mean** & **Standard Deviation** \\ \hline Bulk density (g cm\({}^{-3}\)) & 6 & 0.83 & 0.10 \\ pH & 6 & 4.28 & 0.29 \\ Total C (\%) & 6 & 4.84 & 1.42 \\ Total N (\%) & 6 & 0.27 & 0.02 \\ Sand (\%) & 6 & 27 & 5.63 \\ Silt (\%) & 6 & 25 & 4.65 \\ Clay (\%) & 6 & 48 & 6.86 \\ Ammonium (mg N kg\({}^{-1}\) soil) & 6 & 6.52 & 2.03 \\ Nitrate (mg N kg\({}^{-1}\) soil) & 6 & 1.67 & 2.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bulk soil properties at the study site.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 2 Literature Review
### AI Assistance with Deep Learning in Retail and E-Commerce  
The integration of deep learning and artificial intelligence (AI) in the retail and e-commerce industries has resulted in significant transformations, enhancing consumer experiences, optimizing operations, and increasing profits. This section explores the application of deep learning models in several industries, emphasizing their impact on different business processes and results.  
Deep learning models have played a vital role in the advancement of complex recommendation systems, predictive analytics, and automated customer care solutions. [PERSON] Long Short-Term Memory (LSTM) networks to predict retail sales, showcasing its efficacy in predicting demand and managing inventories [24]. [PERSON] highlighted the significance of artificial intelligence (AI) in the field of electronic commerce (e-commerce). In this context, deep learning models are utilized to forecast user behavior and improve consumer involvement by providing tailored recommendations [25].  
Deep learning models have been utilized in e-commerce to enhance digital sales forecasts and optimize consumer pleasure. [PERSON] suggested implementing a system that utilizes Deep Belief Networks and Convolutional Neural Networks (CNNs) to predict sales, thereby assisting companies in making informed choices [26]. [PERSON] demonstrated the versatility of deep learning algorithms by employing them in the fields of e-commerce, stock market analysis, and retail promotions [27].  
AI-driven inventory management systems have revolutionized the way e-commerce enterprises handle logistics and supply chain operations. [PERSON] [28] investigated the application of artificial intelligence (AI) methods to forecast consumer buying behavior and optimize inventory control, with the goal of minimizing expenses and increasing operational effectiveness. In addition, [PERSON] [29] created an artificial intelligence (AI) system that uses deep learning models to forecast the behavior of repeat buyers. This system assists e-commerce companies in recognizing and maintaining their loyal clients.  
Automated checkout systems at retail outlets have also been improved by developments in deep learning. [PERSON] proposed a technique that employs YOLOv5 and DeepSORT algorithms to automate the process of product counting, thereby improving the efficiency of checkouts and increasing consumer happiness [30].  
Deep learning algorithms have enhanced the study of e-commerce sales, enabling organizations to acquire a more profound comprehension of consumer preferences and behavior. [PERSON] employed computer vision techniques and deep learning models to examine e-commerce photos, yielding useful insights into the determinants that impact consumer buying choices [31].  
In general, the incorporation of deep learning in the retail and e-commerce sectors has not only enhanced operational effectiveness but also enhanced consumer experiences by providing customized and fast services.
## 5 Conclusions  
This study demonstrated the potential of integrating remote sensing products with the crop model by considering LAI as the principal component to estimate the yields at the village level. Optimization of ground data points represented the entire rice growing areas of the district. Mapping of rice growing areas showed an accuracy of 93.04%, comparable to the government statistics. Input parameters in the DSSAT model were collected during the field visits, and an overall correlation of 0.80, D-Index of 0.88, and NSE of 0.70 were observed, indicating that the model reliably produced results reflective of the field data. NDVI is a remote sensing product that correlated well with LAI (R\({}^{2}\) = 82%). Subsequently, a spatial LAI map was generated, which was used to create a spatial yield map. It can be concluded that integrating the remote sensing satellite data with crop models can effectively predict crop yields, which can benefit policy- and decision-makers in implementing insurance schemes.
# Design of OCC Indoor Positioning System Based on Flat Panel Light and Angle Sensor Assistance
## 5 Experimental Results and Analysis  
As shown in Figure 12, the simulation scenario in Figure 9 is built in this paper. Due to the limited laboratory environment, the whole space size is 180 \(\times\) 180 \(\times\) 200 cm\({}^{3}\) and is surrounded by a black, thick, blackout cloth to cover the light. The modified flat panel light was taped to the ceiling and kept level with the floor. The mobile terminal was fixed on a tripod to control the height h and pitch angle a of the terminal, and 16 test points were selected on the ground, which were consistent with the test points of the light experiment. Table 2 lists the parameter information of the equipment used in this experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Equipment**} & \multicolumn{1}{c}{**Parameters**} \\ \hline Transmitting terminal & Flat panel light & 30 \(\times\) 60 cm \\  & Graphic code & 25 \(\times\) 50 cm \\  & Cell phone model & Honor v300 XF-AN00 \\ Receiving terminal & Tripod model & QZ-WORLD \\  & Pitch angle measurement tool & Angle sensors \\ Test points & 16 & 60 \(\times\) 60 cm \\ Algorithm implementation & Software & Matlab2016b \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for test experiments.  
Figure 11: Schematic diagram of the coordinate filtering algorithm.
# Noise Reduction Mechanisms of an Airfoil with Trailing Edge Serrations at Low Mach Number
## 3 Computational Setup and Test Case
To investigate the dependence of the grid resolution, we changed the resolution in the spanwise direction to 20, 60 and 100 under the same other conditions of the first calculation. Figure 2: Computational domain and boundary conditions. To elucidate the relationship between the variation in the hydrodynamic field due to trailing edge serrations and the underlying noise reduction mechanism, NACA0012 airfoil with serrated and straight trailing edges at zero angle of attack was computed. The condition of \(0^{\circ}\) angle of attack was selected since the effect of the serration loading on the hydrodynamic flow and the radiated noise can be isolated. In Table 1, \(N_{\alpha}\) and \(\Delta_{\alpha}^{+}\) denote the number of grid points and grid spacing in the \(\alpha\) direction. The superscript \(+\) means the wall unit, that is,  
\[\Delta_{\alpha}^{+}=\frac{u_{\tau\Delta_{\alpha}}}{\
u},\quad u_{\tau}=\sqrt{ \frac{\tau_{up}}{\rho}}, \tag{30}\]  
where \(u_{\tau}\) is the averaged local wall friction velocity and \(\tau_{up}\) denotes the wall stress. In Table 1, the grid width in wall unit was obtained on the suction side at \(X/C=0.5\).