esa-sceva/satcom-chunk-collection huggingface dataset:
### Contributions  
The aim of this work is to develop a data fragmentation model using the Red Fox Optimization-based Support Vector Machine (RFO-SVM) algorithm. The goal is to address the limitations and challenges of data fragmentation in DDBMS and provide an optimized solution for horizontal fragmentation. The primary contributions of this paper include the following:  
* The development of an innovative data fragmentation model that integrates the Red Fox Optimization with Support Vector Machine (RFO-SVM) to optimize horizontal fragmentation in distributed database management systems (DDBMS).
* Enhanced data preprocessing techniques that ensure high data quality and effective handling of missing data.
* Advanced classification methods using optimized SVM parameters to accurately identify data fragments.  
Effective allocation of classified data fragments to various nodes using a Genetic Algorithm (GA), optimizing total allocation cost.
## 3 Results
### Characterization of Microcapsules
#### 3.2.7 Microstructural Features of the Dried Powders via Scanning Electron Microscopy  
The morphology of all powders was studied using a scanning electron microscope (SEM). The micrographs shown in Figure 5 reveal a noticeable variation in structure and particle size between the SD and FD powders. All the analyzed SD samples were found to be almost spherical with some surface dents. Size polydispersity was observed and is consistent with the results obtained from laser light scattering using a Mastersizer instrument, which showed the presence of particles of different sizes. Some cracks were also observed, indicating the loss of essential oils as volatile compounds. Similar results have been reported by several authors [35; 36; 37]. The micrographs of FD powders showed an irregular shape similar to broken glass. Porosity was also observed in FD powders. The porosity could be explained by the formation of essential oil cavitations resulting in volatilization of the oil during air exposure. This result is in agreement with the encapsulation of rosemary oil performed by [PERSON] et al. [38]. They observed more pores in a formulation with maltodextrin compared to whey protein. The same results were observed by [PERSON] et al. [39], who used freeze-drying for microencapsulation of polyphenol compounds using different wall materials. The particles presented a structure with porosity and a smooth surface which could be attributed to the degree of polymerization of the maltodextrin as encapsulating wall [40]. No significant difference was observed in the morphology of the particles obtained with different formulations of the samples that were dried with the same drying method.  
Figure 4: Antimicrobial activity of encapsulated _trans_-cinnamaldehyde before and after drying (spray-drying and freeze-drying). For results with the same letter, the difference between the means is not statistically significant.
# The Influence of Sebum on Directional Reflectance of the Skin
## 2 Materials and Methods  
The amount of sebum and the directional reflectance were measured at 48 points on the skin. Measurement points were set on the forehead, chin, chest, and forearm of 12 volunteers aged 23-25. All volunteers were women characterized by II and III skin phototypes. The volunteers did not sunbathe for at least 2 months before the study and did not use any cosmetics on the day of the study. The research was conducted in June 2022. The temperature and humidity of the rooms were controlled and amounted to: temperature 21 \({}^{\circ}\)C, humidity 40%. The choice of measurement points was associated with physiological amounts of sebum.  
A Sebumeter(r) SM 815, part of the MPA(r) multi-probe adapter by Courage + Khazaka, (Koln, Germany) was used to test the amount of sebum. The Sebumeter consists of a cassette with plastic film and a measuring unit with spectrometer. The film was applied to each test point with a force of 10 N for 30 s, and then its transparency was determined using a photocell. The amount of sebum was presented in micrograms of sebum per square centimeter and ranged from 0 to 350. At each point, the Sebumeter measurement was repeated twice, and the reflectometer measurement three times. The obtained results were the average of all measurements and the subject of further analysis.  
A 410-Solar Reflectometer (SOC 410 Directional Hemispherical Reflectometer) from Surface Optics Corporation (San Diego, CA, USA) was used to test the directional reflectance of the skin. It allows measurement of the reflectance values of sunlight in seven discrete spectral bands, from ultraviolet to near infrared, in the following ranges: 335-380 nm, 400-540 nm, 480-600 nm, 590-720 nm, 700-1100 nm, 1000-1700 nm, 1700-2500 nm. Sunrays that fall on the surface of the skin are reflected and absorbed in varying intensity. The reflectance coefficient indicates the amount of sunlight that is absorbed and reflected from the test point on the skin. In this study, total reflectance was analyzed. A reflectometer that works in the spectral range of 335-2500 nm, with an incidence angle of 60 degrees, was used because it faithfully reflects skin exposure to natural solar radiation. In addition, the vast majority of radiation emitters (lasers, IPL, diode lamps, and others) work in this spectral range because it covers the full range of the so-called absorption maxima for the main skin chromophores, i.e., water, hemoglobin, and melanin.  
The statistical analysis was carried out using STATISTICA 13 software (Statsoft, Tulsa, OK, USA). The Shapiro-Wilk test was used to verify the normality of variable distribution. [PERSON]'s ANOVA test and [PERSON]'s post hoc test were used to assess the differences in directional reflectance of the skin at different wavelength ranges. The relation between amount of sebum and directional reflectance at a given wavelength range were assessed using [PERSON]'s rank-order correlation. Significance was set at \(p\) less than 0.05.
# Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection
## 3 Experimental Results and Analysis
### XceptionNet  
The attention-based XceptionNet architecture includes three dense layers: the first and second layers each have 64 units with ReLU activation and an \(L_{2}\) regularizer of 0.001. The final layer has 3 units with softmax activation while the traditional XceptionNet architecture consists of six dense layers: the first five layers each have 512 units with ReLU activation and an \(L_{2}\) regularizer of 0.001, while the sixth layer has 3 units with softmax activation. The XceptionNet model uses 2,101,251 parameters to attain an accuracy of 93.62%. A similar accuracy of 92.70% is achieved by the attention-based XceptionNet model which uses 1,190,821 trainable parameters. The attention-based approach reduces the number of parameters by approximately 44%.  
Table 11 shows the accuracy distribution for the MobileNet model with and without the attention approach.  
\begin{table}
\begin{tabular}{c c c} \hline  & **With Attention** & **Without Attention** \\ \hline Precision & 96.84 & 94.82 \\ \hline Recall & 97.15 & 92.90 \\ \hline \(F_{1}\) Score & 96.99 & 93.70 \\ \hline \end{tabular}
\end{table}
Table 10: Summary table for MobileNet.  
Figure 7: MobileNet loss curve for attention and without attention module.  
Figure 8 illustrates the decrease in loss for the XceptionNet with and without the attention approach. Figure 8a shows the loss reduction when the attention approach is combined with XceptionNet, while Figure 8b depicts the loss decrease for the XceptionNet model without any form of attention. It is evident that after 10 epochs, the attention-based technique results in a more substantial loss reduction, bringing it below 0.35, whereas the model without attention remains around a loss value of 0.4. Table 12 depicts the loss values after 10 epochs of training.  
The results can be further evaluated using other metrics such as precision, recall, and \(F_{1}\) score. The attention-based XceptionNet model achieved a precision score of approximately 92.92% compared to 92.88% for the model without attention. For recall, the attention-based module scored 93.54%, while the model without attention achieved 93.07%. The attention-based XceptionNet model attained an \(F_{1}\) score of 93.21%, whereas the model without attention achieved 92.96%. These metrics are calculated by averaging the values for each individual class. Table 13 shows the distribution of these metrics.
# Clinical Mass Spectrometry in Immunosuppressant Analysis: Toward a Full Automation?
## 2 Materials and Methods
### Chromatographic and Mass Spectrometry Analyses  
Open LC-MS/MS analysis (Shimadzu-Recipe): the tubes were carefully shaken through tube inversion then the erythrocyte membranes were precipitated by adding 220 uL of a mixture of precipitant and labeled internal standards (Recipe) to 100 uL of each sample. Then, samples were vortexed for 1 min, centrifuged at 10,000 rpm for 5 min, and the supernatant was dispensed in vials and 30 uL was subjected to LC-MS/MS analysis by using a Shimadzu Nexera X2 HPLC interfaced with a TripleQuad 8040 mass spectrometer (Shimadzu). The HPLC system was equipped both with a solid-phase-extraction column (SPE) for sample extraction and purification, and with an analytical column (Recipe) maintained at 60 \({}^{\circ}\)C. SPE flow rates were set as follows: 0.00-0.01 min at 0.1 mL/min (loading phase), 0.01-0.50 min at 2.5 mL/min (injection phase), 0.51-1.50 min at 0.1 mL/min, 1.51-1.99 min at 2.5 mL/min, and equilibration at 0.1 mL/min until 2.00 min. As regards the analytical column conditions, the chromatographic parameters were set as follows: 0.00-1.30 min at 0.5 mL/min, 1.35-1.55 min at 1 mL/min, 1.65-2.00 min at 0.5 mL/min. Subsequently, the analytes were transferred to MS operating in positive modality (desolvation temperature 220 \({}^{\circ}\)C, interface voltage 2500 V, nebulizer gas flow 3 L/min, and drying gas flow 15 L/min), monitoring the transitions indicated in Table 1.  
Automatic LC-MS/MS analysis (Cascadion, product code 99990000): sample preparation was completely automated starting from the primary tube due to the integration of a liquid handler system equipped with a centrifuge, a couple of LCs working in \"duplex\" modality, and a triple quadrupole MS [13; 14; 15]. Briefly, after tube shaking, the internal standards were added to the sample. Subsequently, samples were mixed by the stirrer, precipitated by Thermo Scientific precipitant solutions, centrifuged, and the supernatant was injected into one of the 2 LC channels. Chromatography was carried out by using an easily exchangeable module consisting of a pre-purification column (TurboFlow technology, Thermo Fisher Scientific), operating in an aqueous solvent mix, coupled to an analytical column set (Quick Connect Cartridge, Thermo Fisher Scientific) where analytes were separated using an organic gradient (phase B). Subsequently, analytes were transferred to the MS whereas the TurboFlow column was cleaned using solvent C and then reconditioned with a mix of solvent A and B before next injection on the respective channel. Mass transitions were not accessible in the Cascadion software. Quantitation was achieved by using 6 calibration levels and 3 quality controls.
## 8 Image Quality Analysis
### Catphan(r) 500  
The CATPHAN(r) 500 [15], as shown in Figure 9, has four modules enclosed in a 20 cm housing. Each module is used for performing different image quality metrics, such as geometry alignment, _uniformity_, _noise_ and _low contrast_. Before describing the modules, we introduce the Hounsfield unit (HU), also referred as the \"_CT number_\". It is the relative quantitative measurement of radio density [49]. Radiologists uses it in the interpretation of CT images because different body tissues have different densities.  
We scanned the different modules with the same CT scanner configuration. Based on the pre-selected metrics, we used the three following modules:  
* **CTP515 Low Contrast Module**: This module consists of a series of cylindrical rods of various diameters and three contrast levels to measure low contrast performance [15]. The roads, as shown in Figure 10, are provided on z-axis positions, for avoiding any volume-averaging errors [50]. The different low contrast are useful for identifying small low contrast objects, such as tumors. Subslice targets have a nominal 1.0% contrast and z-axis lengths of 3, 5, and 7 mm. For each of these lengths, there are targets with diameters of 3, 5, 7 and 9 mm [50]. We acquired this phantom section to perform the _low contrast_ image quality for the different data formats.  
Figure 9: CATPHAN® 500.  
* **CTP486 Uniformity Module**: This module is cast from a uniform material with a \"_CT number_\" designed to be within 2% of water's density under standard scanning protocols [15]. This module is used for measurements of spatial uniformity, which means CT number and noise value. As shown in Figure 11, this module has a different region of interest (ROI) that can be targeted for measuring the _uniformity_ of the different areas of a phantom section. In fact, the mean CT number and standard deviation of a large number of points, in a given ROI of the scan, is determined for central and peripheral locations within the scan image for each format of the scanning protocol [50].
* **CTP401 Slice Geometry and Sensitometry Module**: This module is used to verify the phantom position. The module, as shown in Figure 12, includes four sensitometry targets (Teflon, Acrylic, LDPE and Air) to measure the CT number linearity [15]. The module also contains five acrylic spheres to evaluate the scanner's imaging of subslice spherical volumes. The diameters of the acrylic spheres are 2 mm, 4 mm, 6 mm, 8 mm, and 10 mm. We used this phantom for a human visual analysis of the CT images, in relation to the different materials and the size of the spheres.  
Figure 11: CTP486 uniformity module.  
Figure 10: Section of the CTP515 low contrast module.
## 7 Conclusions  
Due to the dynamic, complicated, and harsh characteristics of the underwater habitat, UWSNs faces various difficulties. Despite these challenges, one of the most critical issues to address for IoUT that can be used generally is energy efficiency/ energy consumption. During the previous couple of decades, a lot of research efforts have been directed to dealing with these challenges of energy efficiency/energy consumption in UWSNs. After doing a comprehensive survey we found out that our study can be divided in to two subcategories, i.e., energy-efficient MAC and routing protocols for UWSNs. We compared different MAC and routing protocols that are used for energy efficiency by different research groups, based on their design. It is concluded from the survey that a homogenous ideal solution in terms of energy efficiency for MAC and routing protocol cannot exist for UWSNs, due to the changing need according to the change in communication environment. However, we find out from this study that the performance analysis of Buffering_slotted_Aloha is best from the other aforementioned methods opted for UWSNs, because after the comparison with other ALOHA protocols the Buffering_slotted_Aloha protocol achieves significant performance in terms of average delay, network throughput ratio and energy efficiency/energy consumption. Furthermore, Artificial intelligence-based routing solutions have gained popularity because of their capacity for adaptation in a dynamic environment and their ability to satisfy a variety of application needs. The existing energy-efficient MAC and routing strategies for every class were also compared and summarized. Compared to other categories, routing protocols based on cooperative reliability, cluster, and MAC protocols based on ALOHA have received the most research in this article. Finally, the issues that are still needed to be resolved and the challenges in conducting research on E2 RPs in UWSNs are emphasized as directions for future study.  
Conceptualization: Q.G. and Z.U.K.; methodology: Q.G. and Z.U.K.; software: Z.U.K.; validation: Q.G.; formal analysis: A.M., Z.U.K. and M.M.; investigation: Q.G.; resources: Q.G. and Z.U.K.; data curation: A.M., M.E.A. and M.M.; writing--original draft preparation: Z.U.K.; writing--review and editing: A.M., S.U.K., J.K. and M.M.; supervision: Q.G.; project administration: M.E.A.; funding acquisition: M.E.A., G.A. and I.U. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the College of Underwater Acoustics Engineering, Harbin Engineering University, Heilongjiang, Harbin, China, National Natural Science Foundation of China (NSFC) under grant no U1806201.  
Not Applicable.  
The data presented in this study will be available on request from the corresponding author.  
This work was supported by the EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh Saudi Arabia.  
It is declared that authors have no conflict of interest.
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 3 Scalable TCP for 5G MmWave Networks
### MmWave Scalable TCP
#### 3.3.2 Cwnd Decrease Mechanism of mm-Scalable TCP
* While repeated slowstart phases by S-TCP caused many duplicated ACKs, mmS-TCP was able to significantly reduce the number of duplicated ACKs at the beginning of the connection;
* Improved network stability and slightly increased throughput;
* After slowstart, mmS-TCP was able to improve fairness with other TCP CCAs because the ssthresh was not too high.
# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
## 1 Introduction
The rest of this paper is organized as follows. Section 2 provides related work on the channel estimation and equalization of CPM transmission over frequency-selective channels. In Section 3, the burst structure, communications system, and channel model are designed and formulated for typical UAV-aided IoT communications scenarios. The new low-complexity blind equalization based on EMLVA, as well as the turbo scheme are introduced and described in Section 4. The performances of the proposed turbo blind equalizer are evaluated and analyzed based on the link-level simulation platform in Section 5. In Section 6, we discuss the application prospects of reflecting intelligent surfaces (RISs) in UAV-aided IoT communications systems. Finally, the paper is concluded in Section 7. \begin{table}
\begin{tabular}{c c l} \hline \hline
**Classification** & **Ref.** & **Contribution/Methodology** \\ \hline \multirow{4}{*}{Data-aided} & [14] & A generalized pilot symbol-aided demodulation method is proposed in a flat fading channel. The optimal filters for channel estimation are also presented. \\ \cline{2-3}  & [15] & The estimate of the channel is realized by local B-splines. \\ \hline \multirow{4}{*}{Statistics} & [16] & The second-order statistics of the signal for channel estimation is extracted for CPM by TXK. \\ \cline{2-3}  & [17] & A fourth-order cross-cumulant matrix is extracted by the eigenvector method. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related work.
## 2 Data Analysis and Preprocessing
### Data Preprocessing
#### 2.4.2 Missing Data Filling  
In the process of collecting NWP data, due to various reasons such as abnormal communication or equipment failure, data loss often occurs. A deep learning model normally cannot handle this kind of incomplete data input, so filling in missing data is necessary [18]. In order to ensure the quality of the filled data and avoid the interference of low-quality data on the model prediction, this paper adopts the K-Nearest Neighbor (KNN) filling algorithm [35]. The data points with missing values are filled with K adjacencies, and the distance matrix between the data points with missing values and other data points without missing values is calculated, and the K data points with the nearest Euclidean distance are selected. Filling the vacancy value in the data with the field average corresponding to the selected K-nearest neighbor data points, the data filling is finally achieved. The Euclidean distance formula is as follows:  
\[d_{X,Y}=\sqrt{\sum_{i=1}^{n}\left(X_{i}-Y_{i}\right)^{2}} \tag{3}\]
## 3 Results and Discussion
### Semi-Quantitative Analysis and Standard Curve
To analyze the relationship between I\({}_{\text{CTBN}}\) and the CTBN content, correlation analysis was performed; the results indicated that the correlation is 0.978 and that the _p_-value was <0.0001, which is lower than 0.05, indicating that the effect of the CTBN content on I\({}_{\text{CTBN}}\) is statistically significant. In addition to correlation analysis, one-way ANOVA was performed using [PERSON]'s method of multiple comparisons to evaluate the effect of the CTBN content on I\({}_{\text{CTBN}}\) with a confidence interval of 95% (\(\alpha\) = 0.05) [37; 38; 39]. The one-way ANOVA results are summarized in Table 6, and the multiple comparisons results are shown in Table 7.  
The one-way ANOVA results in Table 6 display a _p_-value < 0.0001, which means that the effect of the CTBN content on I\({}_{\text{CTBN}}\) is statistically significant. According to multiple comparisons, for each pair of CTBN content groups, if the confidence interval of their mean difference excludes zero, the two CTBN content groups are significantly different; otherwise, the two CTBN content groups are not significantly different. As shown in Table 7, all 95% confidence intervals of the I\({}_{\text{CTBN}}\) differences exclude zero, indicating that each pair of I\({}_{\text{CTBN}}\) values is significantly different and that the CTBN content significantly affects the I\({}_{\text{CTBN}}\) of the virgin bitumen. Considering that the motivation of this study is to explore the blending degree of virgin and RAP binders in RAM and that the mineral powder and aggregate have significant effects on the absorption peak area (as shown in Figure 3), the virgin AC-13 asphalt mixtures, to which virgin bitumen with 1 wt. %, 5 wt. %, 10 wt. %, and 15 wt. % CTBN were added, were tested by FTIR. FTIR samples were scraped from the virgin asphalt mixtures at randomly selected locations, and each kind of virgin asphalt mixture with a different CTBN content was measured three times; the outer, middle, and inner layers of each sample were each measured once at a certain point. The data were analyzed to calculate the MV, SD and CV of each sample, and the results are listed in Table 8. The SD and CV data indicate that CTBN was evenly distributed throughout the virgin asphalt mixtures. Meanwhile, one-way ANOVA was performed using [PERSON]'s method of multiple comparisons with a confidence interval of 95% (\(\alpha\) = 0.05). The one-way ANOVA results of the virgin asphalt mixtures are summarized in Table 9, and the multiple comparisons results of these mixtures are shown in Table 10. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Source of Variation** & **Sum of Squares** & **Degrees of Freedom** & **Mean Square** & **F Ratio** & _p_**-Value** \\ \hline Between groups & 28.472 & 2 & 14.236 & 537.381 & \textless{}0.0001 \\ Within groups & 0.159 & 6 & 0.026 & – & – \\ Total & 28.631 & 8 & – & – & – \\ \hline \end{tabular}
\end{table} 
Table 6: The values of the one-way ANOVA analysis of I\({}_{\text{CTBN}}\) for the virgin bitumen.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Data Normalization  
After the extraction and integration of the different features, two different analyses were performed, i.e., one with raw features, and another one with the normalized features. According to the literature, there are different normalization techniques, but the most adapted to the implementation of the Deep Neural Networks (DNN) method with the DeepLearning4j framework [29] consists in the use of the mean and standard deviation [30].