esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### DSPE Process  
In this method, the sorbent was dispersed in the sample solution, adsorbing impurities or targets, and then using a suitable solvent to desorb the targets. Figure 1 shows the detailed steps of the DSPE procedure from sample to extraction and then to qualitative and quantitative analysis of the targets. The time for a single sample preparation was around 15-20 min. First, the honey product was diluted into solution according to \"2.6 Sample Collection\" before the DSPE procedure. A sample solution (40 mL) was added into the 50 mL centrifuge tube, and then 50 mg of zeolite H-Beta was put into the tube. To adsorb the target analytes completely, the tubes were vortexed at 2500 rpm for 7 min. Subsequently, the zeolite H-Beta was divided from the aqueous phase by centrifugation at 3200\(\times\)\(g\) for 3 min. The sorbent remained for desorption. The desorption of neonicotinoid insecticides from the sorbent was conducted with 2 mL of acetonitrile (0.5% ammonium hydroxide) under a vortex for 1 min and ultrasonication for 1 min. The eluent was separated from zeolite H-Beta by centrifugation for 3 min at 3200\(\times\)\(g\). The supernatant was passed through a 0.22 \(\upmu\)m membrane filter before transferring to the vial for UHPLC-MS/MS analysis.
## 3 Results and Discussion
### Energy Parameters in Different Strategies
The results related to drying efficiency showed that the highest drying efficiency was 12.71%, obtained in the HA70+IR500 method, and the lowest efficiency was 4.19% in the HA40-IR250 method. The drying efficiency in the third strategy (HA+IR) was 10.02% on average, which was 18.51, 67.80, and 33.99% higher than the first, second, and fourth strategies, respectively. The highest value of drying efficiency in the combined methods (HA70+IR500) was about 55.2% higher than the highest value of drying efficiency in the hot air method (HA70). In other research, the amount of drying efficiency for the convective drying of apples [31] was in the range of 3.42-12.29%, and, for convective drying of kiwi [32], it was 4.11-12.15%. Figure 6: Energy efficiency and specific moisture extraction rate.
# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 2 Wind Turbine and Wind Speed Modelling and Wind Turbine Control
### Wind Speed  
The point wind speeds (as measured by an anemometer) across the cluster of five turbines, which consider the flow dynamics across the turbines, the interactions between the turbines and the flow fields, including the wakes and turbulence, are obtained using Bladed, a high-fidelity aeroelastic model by DNV. (Rotor) effective wind speed, defined as the spatially-averaged wind speeds across the rotor plane [19] experienced by the wind turbine, is obtained by filtering the point wind speed through the wind speed model introduced in [20]. The resulting effective wind speeds used to simulate the wind turbine in this paper are shown in Figure 2. Note that effective wind speeds are only shown for a mean wind speed of 14 m/s because they exhibit very close trends at other mean wind speeds.
## 2 Setting the Stage: Fundamental Mathematics and Knowledge
### Fundamentals of BEC  
The work in [22] presented the BEC model introducing the mathematical formulations for BEC within a prime field. Consequently, the BEC model within a prime field can be represented by the following equation:  
\[x^{2}+y^{2}=1+dx^{2}y^{2}. \tag{1}\]  
Equation (1) defines the curve with variables \(x,y,\) and \(d\). Here, variables \(x\) and \(y\) are the starting points whereas variable \(d\) indicates a curve parameter. It is important to note that the handling of curves with relatively larger prime fields is a challenging task. Addressing this concern, the work in [23] presented a binary variant of Edwards curves. Consequently, the binary variant of Edwards curves can be shown by the following equation:  
\[E_{B,d_{1},d_{2}}:d_{1}(x+y)+d_{2}(x^{2}+y^{2})=xy+xy(x+y)+x^{2}y^{2}. \tag{2}\]  
Equation (2) is the mathematical form of BEC, incorporating initial points \(x\) and \(y\), along with curve parameters \(d_{1}\) and \(d_{2}\). Notably, Equation (2) holds true only when \(d_{1}\) is a number other than zero and \(d_{2}\) is not equal to \(d_{1}^{2}+d_{1}\). In addition to the mathematical formulations, working with BEC requires taking into account the following fundamental notions. First, the binary aspect of digital systems is reflected in binary fields, which use bitwise XOR and AND operations, whereas prime fields use conventional arithmetic operations with modulo computations. Second, curve parameters, represented by variables like \(d_{1}\) and \(d_{2}\), are important because they have a major impact on how the elliptic curve behaves. In addition to defining the curve, these parameters affect the computing efficiency and security in cryptographic operations. Appropriate parameter selection is essential since it directly affects how security and performance are balanced in BEC applications.
# Validation of Drifting Buoy Data for Ocean Wave Observation
## 4 Discussion
### Comparison with the Moored GPS Buoy Data
The correlation coefficient between the moored and drifting buoy periods was approximately 0.82 in Reference [8], while the correlation in Figure 2b was 0.80. The wave period for the comparison in Reference [8] was \(T_{1}=M_{0}M_{1}^{-1}\). The wave periods \(T_{g}\) and \(T_{b}\) were \(T_{1/3}\), which was close to \(T_{0}=M_{-1}M_{0}^{-1}\). The effect of the low-frequency noise on \(T_{0}\) was larger than that on \(T_{1}\). The accuracy of \(T_{1/3}\) was lower than that of \(T_{1}\). The RMSD, correlation, and SI of the wave periods showed that the accuracy of the drifting buoy-observed wave periods was better than that of the ERA5 wave periods. However, its statistical significance is lower than in the case of wave height. The agreement of wave periods was not robust compared with the wave height case.
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 4 Experiments and Results
### Experimental Results
#### 4.2.2 The Effect of Fusion Techniques  
Further, we performed experiments to show the effectiveness of the proposed fusion method. The rightmost two bars in Figure 8a,b compare the ensemble classifiers using two different voting methods: max voting and entropy voting. The max voting shows less enhancement in performance for the ensemble classifier; the reason is that it does not consider how much the model is confident about its decision. The superior performance of entropy voting is due to the fact that it takes into account the certainty of a model in its classification decision.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline ResNet152 & 94.7\% \\ DensNet201 & 94.5\% \\ EfficientNetB0 & 94.36\% \\ Ensemble classifier 1 + Max voting & 95.27\% \\ Ensemble classifier 1 + Entropy voting & 95.63\% \\ \hline ResNet50\_CA & 93.63\% \\ ResNet50\_SE & 93.45\% \\ ResNet50\_NL & 94\% \\ Ensemble classifier 2 + Max voting & 97.27\% \\ Ensemble classifier 2 + Entropy voting & 98.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results summary for the ensemble learners (ELs).  
Figure 9: Venn diagram of base learners’ errors. (**a**) Error analysis of base learners of ensemble classifier 1. (**b**) Error analysis of base learners of ensemble classifier 2.
## 3 Research and Results  
This section evaluates and compares the performances of wave kinematics-based DSPEB (W-DSPEB), color-based DSPEB (C-DSPEB), and S2 Shores. In the following, we analyze our results based on two different criteria. First, Section 3.2 compares the performance of the models in reconstructing bathymetry using a single Sentinel-2 image (single date) as input. Section 3.3 then compares the different models based on aggregate (composite) estimates, which are created by calculating the point-wise mean over all six dates.  
The metrics used to evaluate the predictions of each of the models are the root mean squared error (RMSE), the Pearson correlation coefficient (r), the concordance correlation coefficient (CCC) [48], and the slope of the predictions compared to the target depths.
# Research on Grooved Concrete pavement Based on the Durability of Its Anti-Skid Performance
## 2 Materials and Methods
### LT Method  
The anti-skid force applied to vehicles mainly relates to the macro structure of the cement concrete pavement [22]. It can be increased by grooves, thus resulting in higher skid resistance in grooved compared to non-grooved pavement [23]. Grooved concrete pavements are commonly applied in two forms, L and T, and can effectively improve the skid resistance. In accordance with the aforementioned research and engineering practice, the T form is superior to the L form in terms of anti-skid performance, durability, driving comfort, direction control, and incidence of accidents [24]. Additionally, the L form is one constituent of the LT form. Thus, LT concrete pavement is proposed to provide greater braking performance to T, and to develop the advantages of both T and L pavement. Figure 1 shows a sketch of the LT form.
## 5 Result and Discussion
### Evaluation Result
#### 5.3.1 Comparative Analysis of YOLOv4 Model's Inference Performance in the COCO Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Framework** & \multirow{2}{*}{**Precision**} & **Average** & **Average** & **Average** & **Average** (ms)** & **Average** \\ (YOLOv4-Tiny in the COCO Dataset) & & **CPU** & **GPU** & **Accuracy (\%)** & **Latency (ms)** & **Power (W)** \\ \hline TensorFlow & Float32 & 27.99 & 52.19 & Person: 33.33 & & \\  & & & Car: 60.73 & & \\ \hline TF-Lite & & 20.32 & 0.13 & Person: 33.96 & & \\  & & & Car: 60.38 & & \\ TF-TRT & Float32 & 28.13 & 51.08 & Person: 33.95 & & \\  & & & Car: 60.46 & & \\  & & & Person: 34.70 & & \\  & & & Car: 60.31 & & \\ \hline TF-Lite & & 20.57 & 0.13 & Person: 33.76 & & \\  & & & Car: 60.10 & & \\ TF-TRT & Float16 & 31.81 & 38.62 & Person: 33.76 & & \\  & & & Car: 60.93 & & \\  & & & Person: 34.02 & & \\  & & & Car: 61.01 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of YOLOv4-Tiny.weights’s inference performance in the COCO Dataset. For TF-TRT in the COCO dataset, both YOLOv4-Native and YOLOv4-Tiny use the GPU efficiently with more than 50% of GPU usage. In YOLOv4-Native, the power consumption of TF-TRT is measured as 15.87 and 11.79 W, which are slightly higher than the 9.67 and 9.40 W of TF-Lite. In the case of YOLOv4-Tiny, the values are slightly different, but the results are similar. Moreover, when compared to TensorFlow, the accuracy of objects is almost unchanged, and both result in lower latency and lower power consumption. For TRT in the COCO dataset, it has the highest CPU utilization among the four deep learning frameworks, and GPU utilization is appropriately used from at least 42.20 to 54.75%. In the case of TRT (float32, float16) of YOLOv4-Tiny, the average power consumption is low at about 12 W, but in the case of TRT (float32, float16) of YOLOv4-Native, the power consumption is measured at about 22 W. When compared with the basic TensorFlow of 28.24 W, as a low number is measured, the object accuracy of people and cars is almost unchanged, and it shows low latency.
## 1 Introduction
Several publications have proposed different approaches and methods related to the adoption, ideal design and control of energy consumption for new buildings, the ideal renovation or retrofit of existing buildings, and the ideal dimensioning of different RES, which are objectives to decrease the demand for energy and increase their efficiency [5; 18; 19; 20]. This paper analyses and evaluates the energetic suitability of wind turbines to be installed on the rooftop of a building. It is important to numerically evaluate the influence on the air stream from buildings and rooftops and to model wind flows over buildings and their roofs to better design and install WTs based on local wind meteorological data and local urban terrain characteristics for assessing and improving local urban wind power production. The main objective of this research work is to assess the impact that integrated wind production in high-rise buildings can have on the economic sustainability of the consumption inherent in the management of the building itself. To this end, it is important to know the load diagrams involved in this process, the study of low-power wind technologies already on the market for application in this type of building, and which solutions best fit your needs. To minimize the consumption of energy to the grid, a linear programming (LP) optimization model was developed to optimize the self-consumption system, minimizing the total costs that include the costs of installing the turbines and batteries, as well as the cost of energy purchased from the distributor. Several case studies are considered, where the economic feasibility study will be investigated, and simulations will be carried out with the proposed optimization model. This paper is organized as follows. Section 2 characterizes the study area and the adopted methodology; Section 2.1 addresses and characterizes the studied scenarios and refers to the data processing; Section 2.2 describes the scenarios for the CFD simulations and briefly how they were built; Section 2.3 presents a mixed-integer linear programming model to optimize the wind energy production for a self-consumption system to minimize the total cost of energy purchased from the grid, maximizing the return on investment; Section 3 presents the results of the case studies about two roof formats; and finally, Section 4 draws the main conclusions.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 3 Ground Theory
### Multidimensional Projection
Following this example, each item \(x_{i}\) of a multidimensional dataset \(X\) can be normalized with:  
\[\hat{x}_{ij}=\frac{x_{ij}}{\|x_{i}\|}, \tag{2}\]  
being \(j\) less than \(m\) (the number of data features), and \(\|\cdot\|\) the Euclidean norm. The importance and effects of normalization were extensively documented in papers such as [PERSON] and [PERSON] [33]. Another example of transformation is the \(z\)-score standardization applied to each \(j\)-column of an \(X\) dataset, making columns' mean equal to zero and standard deviation equal to one. The process consists in subtracting all values of a \(j\)-column of its mean and dividing the result by the standard column deviation as in  
\[\hat{x}_{ij}=\frac{(x_{ij}-\tilde{x}_{j})}{\sigma_{j}}. \tag{3}\]  
And another common transformation is the _min-max_ normalization, which converts the \(j\)-column range \([min_{x_{j}},max_{x_{j}}]\) to a new range \([min_{new},max_{new}]\) with a simple linear transformation:  
\[\hat{x}_{ij}=(x_{ij}-min_{x_{j}})\times\left(\frac{max_{new}-min_{new}}{max_{ x_{j}}-min_{x_{j}}}\right)+min_{new}. \tag{4}\]  
the new range is frequently \([0,1]\), and variations with other ranges and application of logarithm and square root functions can be performed [32]. This section presented elementary approaches focused on multidimensional data pre-processing real numerical values. However, other transformations may be more appropriate depending on the data domain (categorical, textual, images, and others) and the problem to be solved. There are several other ways to transform the data and measure its dissimilarities according to the data context, and a detailed review on the subject can be found in [PERSON] et al. [30].
# Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops
###### Abstract  
Leaf inclination angle distribution is an important canopy structure characteristic which directly impacts the fraction of the intercepted solar radiation. Together with the leaf area index (LAI) it determines the structure and fractional cover of a homogeneous crop canopy. Unfortunately, this key canopy parameter has usually been ignored when applying common multispectral vegetation indices to the mapping of LAI, although its impact is known from model simulations. Therefore, we measured leaf angles and determined their distribution (quantified using the leaf mean tilt angle, MTA) for six crop species with different structures growing on 162 plots with a broad range of LAI (1.1-5.0) and leaf chlorophyll content (26-94 \(\mu\)g cm\({}^{-2}\)). Next, we calculated six vegetation indices widely used for LAI monitoring--the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the two band enhanced vegetation index (EVI2), the modified triangular vegetation index (MTVI2), the optimized soil adjusted vegetation index (OSAVI) and the wide dynamic range vegetation index (WDRVI)--from airborne imaging spectroscopy data. We calculated the [PERSON]'s correlation coefficient \(R_{\mathrm{s}}\), a non-parametric statistic chosen because of the non-normal distribution of canopy parameters. All studied indices depended on the LAI (\(0.50\leq R_{\mathrm{s}}\leq 0.71\)), but the dependence on the MTA was of similar magnitude (\(-0.83\leq R_{\mathrm{s}}\leq-0.53\)) with EVI, EVI2, OSAVI and MTVI2 depending more strongly on MTA than on LAI. All studied indices were good proxies (\(0.78\leq R_{\mathrm{s}}\leq 0.88\)) for vegetation fractional cover (Fcover) which, for homogeneous crop canopies, is a nonlinear function of LAI and MTA. EVI2 and MTVI2 were the most strongly correlated with Fcover, although the difference to the other studied indices was small. This first study involving a large range of crop structures confirms the results from canopy reflectance simulations and emphasizes the necessity of leaf angle information for the successful mapping of LAI with Earth observation data.  
c 2017994603390/rs9100994
# Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models +
## 2 Explainability Techniques
### Explainability by Feature Relevance  
An important technique used to determine the relevance of the input features is using Shapley values [19]. These values are calculated as the average marginal contribution across all possible input combinations. The marginal contribution is the difference between the prediction with and without the feature. Shapley values have desirable properties such as efficiency (the sum of the Shapley values is equal to the difference between the actual and average prediction) and symmetry. A major drawback of this technique is the algorithmic complexity, which is exponential in the number of input variables. However, it is possible to use approximation algorithms. Finally, Shapley scores assume that the features are independent. In real datasets, features can often be correlated, which can affect the calculation and interpretation of Shapley values.  
LIME (local interpretable model-agnostic explanations) creates a local surrogate model around the prediction to be explained [9]. This local model is a simpler, interpretable model, such as a linear model or a decision tree. LIME generates a new dataset by perturbing the instance to be explained. It then uses the original classifier to predict the outcomes for these perturbed samples. Each perturbed instance is assigned a weight according to its proximity to the original instance. The closer the perturbed instance is to the original instance, the higher its weight. The local surrogate model is fitted to the perturbed dataset and is then used to explain the prediction of the original classifier. At the end, a local surrogate model is created for each prediction to be explained.
# Mdpi
## 1 Introduction
By iteratively mitigating part of IN samples with stronger amplitude, the quality of channel estimation could be improved gradually. Thus, the quality of the estimation of residual IN samples was gradually improved as well, i.e., the channel estimation and IN mitigation are mutually beneficial. In Reference [23], the authors used a linear precoder at the transmitter and applied overlapped frequency-domain equalizers to mitigate IN for wavelet-OFDM-based PLC systems. The linear precoder, which is implemented by using a Walsh-Hadamard transform, improves the efficiency of the frequency domain equalizer. However, adding the linear precoder to the transmitter does not comply with IEEE 1901 specifications. Moreover, the frequency domain equalizer requires extra FFT and inverse FFT operations. Although many IN mitigation approaches have been proposed on FFT-based OFDM systems, these existing approaches cannot directly apply to the wavelet-based OFDM. To the best of our knowledge, researchers have largely overlooked the issue of IN mitigation for wavelet-OFDM-based PLC systems. Inspired by the iterative receiver for the FFT-OFDM-based PLC system [22], this paper presents a robust receiver architecture for wavelet-OFDM-based PLC systems to fill this research gap. The receiver comprises a pre-IN mitigation block, an adaptive DWT block, and an iterative IN reconstruction block. The pre-IN mitigation block copes with the stronger portion of the IN to facilitate the training of equalizers and channel estimator. Adaptive per-subchannel equalizers are applied to the conventional DWT block to flatten out the passband of each subchannel, and the IN reconstruction block iteratively estimates the residual IN. Note that to alleviate the impact of IN samples on the training algorithms, we applied a variable step-size adaptive algorithm [24]. Comparing with our previous work [22], the new contributions of this paper are summarized as follows. First, we proposed an adaptive analysis filter bank, comprised of the DWT block and adaptive per-subchannel equalizers, such that the channel is equalized in the time domain for each subchannel. In this way, no additional FFT operation is required to perform equalization in the frequency domain. Note that a wavelet-based OFDM system does not implement guard interval and causes performance degradation in the multipath PLC channels. Thus, the realization of equalizers form wavelet-based OFDM systems is an important issue that needed to be addressed. Second, we decoupled the channel estimation from the per-subchannel channel equalization, such that the inter-dependency between these two functions could be reduced. Third, we exploited a variable step-size adaptive algorithm to estimate the channel impulse response in the time domain, such that the impact of the IN on the channel estimation could be reduced. The remainder of this paper is organized as follows. Section 2 describes the system models used in this paper. Section 3 details the proposed IN mitigation scheme. Section 4 presents results obtained using a simulation model based on IEEE 1901 specifications. Conclusions are drawn in Section 5.
## 5 Model Formulation and Description
### Model Description  
#### 5.2.1 Step One: Searching for Interest  
For the purpose of simplifying the calculation and minimizing the processing time, we seek the interest that should collect every sensor only within a cluster where the majority of the points are within the sensing range of the sensor. This cluster is a square, with L as its length L \(\geq\) 2 \(\times\) R\({}_{\mathrm{S}}\), and the sensor position as its center.  
We use the triangular grid deployment technique, because based on [10], when R\({}_{\mathrm{T}}\) = 2 \(\times\) R\({}_{\mathrm{S}}\) the triangular lattice is the best deployment pattern.  
Firstly, we began by putting sensor nodes line by line, d\({}_{\mathrm{min}}\) is the distance between nodes. Assuming that for the initial deployment d\({}_{\mathrm{min}}\) = d\({}_{0}\), the initial number of nodes to deploy is N\({}_{0}\) = \(\frac{\mathrm{A}}{\mathrm{d}_{0}}\times\frac{\mathrm{B}}{\mathrm{d}_{0}}\). When d\({}_{\mathrm{min}}\) increases, the number of sensor nodes N to deploy decreases. Figure 5(a,b) show this process at two different time steps. For each deployed sensor, we calculate the collected interest. In Figure 7, nodes in gray gathering no interest are removed, then the selected nodes collecting information are chosen to be saved (Figure 8).