esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Lipophilic Efficiency (LipE) or Ligand Lipophilic Efficiency (LEE)  
Lip E or LLE is an avenue to determine compound affinity with respect to its lipophilicity.  
Nowadays, the lipophilic efficiency (LipE) index (LEE), which includes lipophilicity and potency, is becoming more and more popular in drug design. It allows for the normalization of observed potency with changes in the lipophilicity, and it is considered an effective and practical tool for keeping lipophilicity under control to avoid any \"molecular obesity\".  
LipE or LLE is calculated as the difference between the potency and log P as illustrated in the following equation:  
\[\text{Lip E}=\text{pIC}50\text{ - cLog P}\]  
According to data revealed in Table 3, compound **5g** showed best value in comparison to other derivatives between both cell lines.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**R**} & \multirow{2}{*}{**NHA**} & \multirow{2}{*}{**cLog**} & \multicolumn{3}{c}{**Breast Cancer Cells**} & \multicolumn{3}{c}{**Colon Cancer Cells**} \\ \cline{4-9}  & & & **P** & **pIC\({}_{50}\)** & **LE** & **LipE** & **pIC\({}_{50}\)** & **LE** & **LipE** \\ \hline
5a & 35 & 5.02 & 5.22 & 0.2 & 0.20 & 5.34 & 0.2 & 0.32 \\
5b & 37 & 5.82 & 5.26 & 0.19 & -0.56 & 5.3 & 0.2 & -0.52 \\
5c & 37 & 6.44 & 5.52 & 0.26 & -0.92 & 5.53 & 0.26 & -0.91 \\
5e & 37 & 5.56 & 5.52 & 0.26 & -0.04 & 5.3 & 0.2 & -0.26 \\
5f & 37 & 6.72 & 5.6 & 0.2 & -1.12 & 5.66 & 0.21 & -1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of ligand efficiency scores for the target compounds.
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 3 Ore Rock Fragmentation Calculation Method
### SDM Calculation
#### 3.2.2 SDM Calculation  
The computation of the gradient map effectively suppresses backgrounds with subtle grayscale variations. However, there exists noise in the ore rock images. It struggles to accurately differentiate between edges and noise characterized by pronounced grayscale variations. To address this issue, this research employs the statistical characteristics of local gradient variations to compute the SDM. This approach enables reliable discrimination between edges and noise, enhancing the effectiveness of edge detection.  
On the gradient map, the gradient difference \(d_{ij}(m,n)\) between a pixel \(G(i,j)\) and any other pixel in its local neighborhood \(N(i,j)=\{G(i+m,j+n)|-k\leq m,n\leq k\}\) is determined as follows:  
\[d_{ij}(m,n)=|G(i+m,j+n)-G(i,j)|, \tag{8}\]  
The mean value \(M(i,j)\) is calculated as:  
\[M(i,j)=\sum\limits_{m=-k}^{k}\sum\limits_{n=-k}^{k}d_{ij}(m,n)/\left((2k+1)^{ 2}-1\right), \tag{9}\]  
The standard deviation _S(i,j)_ is calculated as follows:  
\[S(i,j)=\sqrt{\frac{\sum\limits_{m=-k}^{k}\sum\limits_{n=-k}^{k}\bigl{(}d_{ij} (m,n)-M(i,j)\bigr{)}^{2}}{\left(2k+1\right)^{2}-1}}, \tag{10}\]  
Figure 2 presents a comparison of the gradient map and SDM. From Figure 2a, it can be seen that the gradient map successfully distinguishes the background from the foreground. However, it struggles to effectively differentiate between edges and noise within the foreground, leading to a reduction in the accuracy of rock fragmentation segmentation. In contrast, Figure 2b demonstrates that the SDM significantly suppresses noise, minimizing its interference with the rock edges. This establishes a strong foundation for subsequent edge detection and rock block segmentation.  
Figure 2: Comparison results between the gradient map and the SDM: (**a**) Gradient map; (**b**) SDM.
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 5 Simulation Results
### Computation Complexity versus Sparseness  
Regarding to the computation complexity, a visual method is to compare the runtime of each estimator [11]. We performed simulations in MATLAB R2015b using a 3.0 GHz Inter Core i5-7400 CPU with 8 GB of memory and used 5000 Monte Carlo simulation trials to average the results. To ensure the fairness, we only treated the direct computational procedure as the runtime [11].  
In Figure 8, we can observe that the runtime of IR algorithm increases squarely with the sparseness, approximately. At the low sparseness range, calculating the gradient has no significant contribution to runtime but it is the main source when the sparseness increases. The runtime of CVX algorithm remains unchanged but much longer than others as the CVX is a common toolbox and its runtime is not affected by the sparseness. It is worth mentioning that the runtime of BCS algorithm decreases with the sparseness increasing because the algorithm will break out ahead when the accuracy is satisfied. On the other hand, it can been observed that the runtime of greedy iterative algorithms is shortest and  
Figure 7: Success probability of channel estimation versus the number of antennas (\(N_{T}\)) with received \(SNR=10\) dB, \(N_{R}=4\), \(N_{RF}^{R}=4,\gamma_{th}=-10\) dB, sparseness \(L=5\) and pilot symbol frames \(M=16\) and without hardware impairment.  
grows linearly with the sparseness increasing, since there are no complicated operations of the matrix. Among the greedy iterative algorithms, the runtime of OMP algorithm is longest as only one column is selected in each iteration.
## 3 Application of AIEgens in the Detection of Pesticide Residues in Fluorescence Sensors
### Detection of Pesticides by Supramolecular Assemblies Based on AIEgens
(Reproduced with permission from ref. [79]. copyright 2022 Elsevier). performance of A15 in paraquat detection offers a new perspective on the development of pesticide sensors and shows significant potential for future growth. [PERSON] et al. developed a simple and rapid fluorescent probe based on an anionic TPE derivative Su-TPE (A16) for detecting trypsin and methyl paraoxon (Figure 16) [82]. This probe operates through a supramolecular assembly formed between A16 and a polycationic protamine molecule (PrS). A16 does not fluoresce in isolation but emits intense fluorescence when aggregated. PrS, a cationic polyelectrolyte and natural substrate for trypsin, electrostatically interacts with the tetra-anionic A16 molecules, causing their aggregation and the formation of supramolecular complexes (A16/PrS). In the presence of trypsin, PrS is enzymatically degraded, leading to the disintegration of the supramolecular assembly and enabling trypsin detection. When methyl paraoxon (POM) is present, trypsin activity is inhibited, and the fluorescence of the A16/PrS system is restored, allowing for the detection of OPs. This probe, with a detection limit of 500 \(\upmu\)M for POM, offers  
Figure 14: (**a**) Schematic illustration of the A14 probe for the colorimetric and ratiometric detection of FPN (Inset: Structures of LIQ-TPA-TZ and FPN); (**b**) Preparation and testing procedures of the A14-based paper strips; inset: Photographs of the A14-based paper strip in the absence (left) and presence (right) of FPN under a UV lamp (365 nm). (Reproduced with permission from ref. [80]. copyright 2023 Royal Society Chemistry Publishing). a simple and convenient method for detecting OPs and shows significant potential for practical applications. Figure 16: Schematic representation of PrS-dependent AIE of A16 and its application for the detection of trypsin and OPs. Inset: Chemical structure of A16 (Reproduced with permission from ref. [82]. copyright 2021 Elsevier). Figure 15: (**a**) Schematic structure of the compound A15 and illustration of the paraquat sensing process; (**b**) Fluorescence intensity changes of A15-based test strips after treatment with varying concentrations of paraquat (0–120 \(\upmu\)M). (Reproduced with permission from ref. [81]. copyright 2022 Elsevier). The studies utilize materials with AIE properties and supramolecular characteristics (e.g., kaempfer tetraacetate, LIQ-TPA-TZ@HSA, TPE-4P, and Su-TPE/PrS) to design fluorescent sensors. By employing mechanisms like the AIE principle and host-guest interactions, they achieve sensitive and selective detection of pesticides and biological enzymes. Portable detection platforms, such as test strips, have been developed, emphasizing the potential for co-developing supramolecular and AIE materials.
## 3 Topside Effective Scale Height Calculation Methodology
### Topside Effective Scale Height Vertical Gradient Modeling
Such seasonal differences support the need to include seasonal dependence in the modeling. The mean \(\mathrm{d}H/\mathrm{d}z\) values seen in Figure 2 are used to apply the linear scale height formulation to CSES--01 and Swarm B observations. Figure 1: Panel (**a**), topside \(N_{e}\) values measured by COSMIC—1 (blue points) and modeled by using the topside linear scale height (\(H_{\rm Linear}\)) (red line). Panel (**b**), topside scale height values \(H_{\rm Epstein}\) (blue points) obtained from the COSMIC—1 measured \(N_{e}\) profile and corresponding modeling through a linear fit (red line). Data from COSMIC—1 ionPrf_C001.2011.284.10.20.G23_2013.3520_nc file, measured on the \(11\)th of October 2011 at 10:19:45 UT at latitude = 39.86\({}^{\circ}\) N and longitude = 0.69\({}^{\circ}\) E are used. Modeling the Effective Scale Height at the F2-Layer Peak through In Situ LEO \(N_{e}\) Observations  
We model the topside effective scale height in the linear approximation assumption (6), with the vertical gradient provided by COSMIC--1 RO data, through the NeQuick topside formulation (4). The information given by CSES--01 and Swarm B in the topside and by IRI model at the F2-layer peak are used to calculate the effective scale height at the F2-layer peak, i.e., \(H_{0}\). Figure 3 depicts an example applied to a CSES--01 measurement performed on the 24 th of January 2020 at 12:55:10 UT--latitude = \(-26.88^{\circ}\), longitude = \(10.77^{\  
QD latitude = \(-\)35.95\({}^{\circ}\)--along a daytime orbit. Specifically, for that time, CSES--01 LP measures an electron density value of 95,496 cm\({}^{-3}\) at the height of 507.0 km (green point in Figure 3); at the same time and geographical location, IRI models an F2-layer peak with \(Nm\)F2 = 416,130 cm\({}^{-3}\) at \(hm\)F2 = 254.3 km (red point in Figure 3). These two points represent the topside and F2-layer peak anchor points, respectively. For those daytime conditions, month, and QD latitude, from Figure 2, we obtain a value of \(\mathrm{d}H/\mathrm{d}z\) equal to 0.147. At this point, starting from the F2-layer peak anchor point and having fixed \(\mathrm{d}H/\mathrm{d}z\) = 0.147, we vary \(H_{0}\) in Equation (6) and input \(H\) in the NeQuick topside formulation (4) until we obtain a match with the measured topside \(N_{\mathrm{e}}\) anchor point. In Figure 3, \(H_{0}\) = 55.4 km is the optimal value found for \(H_{0}\), i.e., the value for joining the two anchor points with the NeQuick topside formulation with a linear scale height. The topside \(N_{\mathrm{e}}\) profile is the blue curve in Figure 3. The procedure just described has been applied to every CSES--01 and Swarm B LPs measurement in the datasets described in Section 2. This allowed for the obtainment of a large dataset of modeled \(H_{0}\) values from both CSES--01 and Swarm B datasets. Moreover, we used both calibrated and non-calibrated CSES--01 and Swarm B observations for comparison purposes.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Classifiers  
The sensed and collected data have been categorized into predefined categories, as discussed in the introduction. As many works have been analyzed in the literature review, the following classifiers have been implemented in this work. For fault detection from the sensed data, SVM has been implemented in WSN, and for the classification of sensed data, the CNN has been implemented and have used the RF mechanism to find and category the faults of sensed data, have used PNN to find the faulty sensor nodes from the WSN.
## 3 Results and Discussion
### Viscosity  
The Brookfield rotational viscosity test gauges asphalt binder workability at temperatures of 120 and 135 \({}^{\circ}\)C, determining their capacity to withstand shear forces. Figure 6 displays viscosities for virgin and modified asphalt binders. At the higher test temperature of 135 \({}^{\circ}\)C, asphalt binders demonstrated reduced viscosity values. CR asphalt binders exhibited the highest viscosity values at both temperatures, attributed to crumb rubber particle behavior. Elevated viscosity of aged binders can decline asphalt mixture workability, causing reduced adhesion between aggregates and binders [45]. Higher viscosity demands elevated laying, compaction, and mixing temperatures, consuming more energy, as noted by [PERSON] and [PERSON] [46]. WMA additives lowered viscosities across asphalt binders. Sasobit had the most significant influence on binder workability, whereas Rediset had a limited effect. Viscosity in all asphalt binders, with crumb rubber and WMA additives, was akin to virgin asphalt binders. SECR asphalt binder exhibited the lowest viscosity values at both temperatures due to Sasobit's substantial workability enhancement, consistent with [PERSON] and [PERSON]'s findings [47].  
Figure 5: The softening point result of the asphalt binders.  
Figure 6: The viscosity of the asphalt binders at 120 and 135 \({}^{\circ}\)C.
# Volatiles in Berries: Biosynthesis, Composition, Bioavailability, and Health Benefits
## 3 The Chemical Composition of Volatile Compounds in Berries
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Compound** & **Strawberry** & **Blueberry** & **Raspberry** & **Blackberry** & **Cranberry** \\ \hline Ethyl hexanoate & [66,72] & & & [73] & \\ Ethyl 2-methylbutanoate & [67] & [74] & & [71,73] & [75] \\ Ethyl 2-methylpropanoate & [67] & & [76] & & \\
3-methylbutylacetate & [67] & & & & \\ Hexyl butanoate & [69] & & & & \\ Ethyl propanoate & & [74] & & & \\ Methyl 2-methylbutanoate & & [74] & & & \\ Methyl 3-methylbutanoate & & [74] & & & \\ Ethyl 3-methylbutanoate & & [74] & & & \\ (Z)-3-hexyl acetate & & [74] & & & \\ (E)-2-hexyl acetate & & [74] & & & \\ Geranyl acetate & & [74] & & & \\
3-cis-hexenyl formate & & & & [77] & \\ Ethyl benzoate & & & & [69] \\ \hline _Ketones_ & & & & [77] \\
2-heptanone & [67] & [74,78] & [79] & [73] & \\
2,3-butanedione & [67] & & & [80] & \\
1-octen-3-one & & [74] & & & \\
2-nonanone & & [74] & & & \\
6-methyl-5-hepten-2-one & & [74] & & & \\ Raspberry ketone & & & [76] & & \\ \(\beta\)-damascenone & & & [76] & [71] & \\
3-hydroxy-2-butanone & & & & [80] & \\
2-undecanone & & & & [73] & \\ Isophorone & & & & [69] \\ \hline _Terpenes_ & & & & [69,73] & \\ Limonone & [65] & [74] & & [69,73] & \\ \(\alpha\)-terpinene & [65] & [69,74,78] & [67,76] & [71,73] & [69] \\ Linalool & [65,66] & & [69,74,78] & [67,76] & [71,73] & [69] \\ Nerolidol & [66] & & [69] & [69,73] & \\ Myrtenol & [69] & [68,74] & [76,79] & [71] & \\ Geraniol & & [74,81] & & & [69] \\ Citronellol & & [69,74,81] & [69,76] & [70] & [69,77,82,83] \\ Nerol & & [74] & [76] & & [69,83] \\ Eucalyptption (1,8-cineo) & & [74,78] & & & [69,83] \\ Divydrolinalool oxide & & [74] & & & \\ \(\delta\)-elemene & & [78] & & & \\ (E)-caryophyllene & & [78] & & & \\ Caryophyllene oxide & & [78] & & & \\ Linalo oxide & & [69] & & & [83] \\ \(\beta\)-ionone & & [69] & [76,79] & [73] & [75] \\ \(\alpha\)-ionone & & & [69,76,79] & [73] & \\ \(\beta\)-pinene & & & [76] & & \\ Terpinen-4-ol & & & [79] & & \\ \(\alpha\)-pinene & & & & [73] & \\ p-cymene & & & & [73] & \\ Sabinene & & & & [73] & \\ \hline _Acids_ & & & & [77] \\ Butanoic acid & [69] & & & [69] & [77] \\ Hexanoic acid & & [78] & & & \\ Octanoic acid & & [78] & & & \\ Nonanoic acid & & [78] & & & \\ Decanoic acid & & [78] & & & \\
3-methylbutanoic acid & & & [69] & [73] & \\
2-methylbutanoic acid & & & [73,80] & [69,75] \\ Acetic acid & & & & [73] & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# A Review of Milk Frauds and Adulterations from a Technological Perspective
## 3 Results and Discussion
### The Most Common Milk Adultrations
#### 3.1.2 Adulterations Involving the Increased Protein Content
The amount of protein in the feed ration and the wellbeing of the animals have a direct impact on the concentration of urea in milk, which varies from farm to farm. It has been demonstrated that the content of urea in milk increases with the amount of protein in the feed taken over the previous 24 h. According to the Food Safety and Standards Authority of India (FSSAI), the maximum permissible limit for urea in milk is 70 mg/100 mL. The adulteration of milk with urea could be performed in two ways: by intentionally adding urea or by adding synthetic milk to natural milk. Contrary to the addition of water, which decreases the density of the adulterated milk, the addition of urea enhances it. Because of this, it's common practice to preserve the density of tainted milk by mixing water and urea. For instance, diluted milk (20%) has a density of 1022 kg/m\({}^{3}\), and adding 3% urea to diluted milk (20%) makes it nearly as dense (1030 kg/m\({}^{3}\)) as unadulterated milk. The milk, water, and urea combination becomes denser as the urea concentration rises for a certain level of dilution [10; 11; 13; 18; 30]. Adulteration of Milk with Proteins Resulting from the Activity of Genetically  
Modified Yeasts  
The technology for obtaining these types of proteins allows them to be applied anywhere, even in urbanized areas. This technology is still being studied and the aim is to make these caseins allergen-free. Since these proteins are based on GMO technology, no milk protein produced in this way has so far received regulatory approval in Europe. In the US, products containing proteins identical to those of natural milk, produced by a controlled fermentation process, are already on the market. In the future, it is likely that these proteins will also be the main ingredient in synthetic milk as a response to the huge demand for the product, the drawbacks of factory farming, and concerns about lactose allergies, hormones, and antibiotics [10; 31].
## 3 Results and Discussion
### Parameter Settings  
In the MSAMT, for 10D, 20D, and 30D test functions, the initial population size \(N\) was set to \(5\times D\), the maximum number of exact function evaluations \(NFE_{max}\) was set to \(11\times D\), and the sample size \(ND\) for training the ensemble surrogate was set to \(2\times D\), which were the same as used in [28]. For 50D and 100D test functions, \(N\) and \(ND\) were both set to \(\frac{11}{10}D\), and \(NFE_{max}\) was set to 400. In the G-MFA, the evolutionary population size \(NP\) was set to 100 as used in [31], the iteration number for population evolution was set as \(k_{max}=30\), and the mating probability was set as \(rmp=0.4\). In addition, a vital parameter in the MSAMT is the number of clusters \(n\), which was fixed at 2. To avoid randomness, each SAEAs was performed independently 20 times in MATLAB R2021b on the Intel Core i5-10400F with a 2.90 GHz processor to obtain the comparison results, and the MSAMT was compared to the other SAEAs using the Wilcoxon rank sum test (the significance level was set as \(\alpha=0.05\)) on fourteen test functions. The symbols, i.e., \"+\", \"-\", and \"=\", show that the proposed MSAMT is superior, inferior, and competitive with the corresponding algorithms, respectively.
# 4-Class MI-EEG Signal Generation and Recognition with CVAE-GAN
## 4 Experimental Results
### Generation Evaluation  
Figure 5 plots the average of the real and generated (labeled as fake) samples of each individual subject (S1-S3) from D1. The blue and the green colors represent the real and the artificial fake data, respectively. It can be observed that there is a high match between them. Furthermore, we can find the diversity generated from an individual which suggests that the proposed model can learn a complicated temporal variation from real samples rather than copy the same ones repeatedly. This result can pave a smooth way for exploring the possibilities of the practical application of generating EEG samples considering individual diversity.  
We evaluate the proposed CVAE-GAN-based approach with the following frameworks: (1) CVAE, (2) convolutional encoder (CNN), (3) conditional GAN (CGAN) [28]. For each dataset, 70% of normal samples are randomly used to constitute the training set. The testing set is composed of all abnormal and the rest normal samples. Table 3 shows metric results for different architectures. Inception score (IS) is a common method to provide information for the quality of the trained generator. The Frechet inception distance (FID) is also used to give a better evaluation to the quality of the generated samples through calculating the distance distribution between the real and generated samples. The sliced Wasserstein distance (SWD) [29] is employed to approximates the Wasserstein distance by computing projections of the two distributions. These metrics give evidence for the quality of the generated model to some extent. The test accuracy of the classifier for calculating the IS and FID was 86.14%. Obviously, CVAE-GAN performs the best in IS and SWD metrics. CNN outperforms in FID but is relatively worse in other metrics. Overall, CVAE-GAN and CGAN are the better architecture choices for classified EEG generation.  
Figure 4: The topographic maps of the real and the generated EEG samples power.  
Figure 3: The average feature power on topographic maps.
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 3 Results
### Related Work on Organ
After data enhancement, we processed the chaotic dataset by removing the background-only images, and the total number of images released was 300. The data images with only the liver were removed. The number of removed images is 60. Experiments were conducted for this dataset using CI-UNet and the results are shown in Table 7.  
By observing Table 7, we can find that the indicators demonstrate a slight performance improvement, and we analyze the advantages of balanced datasets as follows:  
1. Improving model performance: When the number of samples in different categories in a dataset is unbalanced, the model tends to predict the categories with higher numbers and ignore those with lower numbers. By balancing the dataset, the number of samples in each category can be made closer, thus reducing bias and improving the learning ability of the model for each category, improving the model performance;
2. Preventing overfitting: Dataset imbalance may lead to model overfitting, i.e., learning better for a more significant number of categories and worse for a smaller number of categories. It may affect the generalization ability of the model. By balancing the dataset, the risk of overfitting can be reduced, and the model can be better adapted to the samples of each category;
3. Improving the accuracy of evaluation metrics: Commonly used evaluation metrics such as accuracy can give misleading results when the dataset is unbalanced. By \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Attention** & **mIoU (\%)** & **mPA (\%)** & **PA (\%)** \\ \hline CI-UNet & VGG16 & PSA & 83.07 & 90.74 & 98.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CI-UNet results with balanced dataset. Figure 13: Segmentation prediction results of CI-UNet for typical images. balancing the dataset, the evaluation metrics can be made to reflect the model's performance in each category more accurately. The results of Grad-CAM used for the organ image are shown in Figure 14.  
By observing Figure 14, it can be seen that the attention for each individual organ is basically focused on the organ attachment structures, and similar identical structures receive some lower ranking attention.