esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The anterior leaflet bulging distance indicates how the SC influence the blood outflow from the left ventricle. Systolic anterior motion (SAM) refers to the clinical observation such that the distal portion of the anterior leaflet is placed obstructing the left ventricular outflow area. When the SC restrict the anterior leaflet mobility, SAM can occur [6]. The SC prevent the lateral portion of the anterior leaflet from bulging toward the left ventricular outflow tract (LVOT) by acting like a sail. The top-narrow MV model demonstrated the shortest anterior leaflet bulging distance, indicating that the SC prevent the entire anterior leaflet bulging toward the LVOT to develop an orifice area at diastole smaller than the others (Figure 7). The top-narrow MV model also showed lower stresses across the anterior leaflet (Figure 4) and the lowest maximum stresses (Figure 5). Therefore, in principle, this top-narrow MC model could be assumed to have the most suitable SC insertion location from the valvular functional perspective. However, in the case of functional mitral regurgitation accompanied by distal displacement of the papillary muscles, the tethering force in the SC increases; this could result in tenting and regurgitation [35]. The parametric MV modeling strategy employed in the present study has several limitations. Although recent studies demonstrated and emphasized the importance of patient-specific chordae modeling [41; 42; 43], true geometric configurations of the complete chordae tendineae structure are still not available from 3D echocardiographic data due to the restricted spatial and temporal resolution issue of the currently available clinical imaging modality. The parametric MV model was created based on the average dimensions of normal human MVs. The key focus of the present study is to evaluate the effect of varying the SC insertion location on normal MV function and dynamics to better understand the complex MV structures from the biomechanical perspectives. The length of the SC is not directly configured but determined by quantitating the distance between the papillary muscle tips and the locations where the SC were inserted in this study. Therefore, the simulated variations of the SC insertion location may not implement directly on evaluation of patient MV models. Nevertheless, valuable information can be obtained from assessing the relative dimensional comparisons of these MV apparatus deformations to better understand the effect of the SC insertion locations on MV function.
## 2 Attempts of Nucleation Triggering and of Discharge at an Acceptable Power
### Successful Xylitol Nucleation and Crystallization: Preliminary Tests
#### 2.3.1 Undercooled Xylitol Crystallization Induced by Mechanical Agitation  
In industrial crystallization, mechanical agitation is often used in combination with seeding. The primary objectives of this method are to create and maintain a good dispersion of the seeds in the bulk and to enhance the rate of mass transfer between the solid and liquid phases [19]. Mechanical agitation without seeding has been here considered to crystallize undercooled Xylitol.  
A glass beaker with a diameter of 85 mm and a height of 105 mm is filled with Xylitol initially in powder form and placed into an oven up to its molten liquid state. Small volumes (\(\sim\)400 mL) of undercooled Xylitol are used for testing. Then, the beaker is put out of the oven and remains at room temperature to make it naturally cool down to the selected starting temperature. Finally, the mixer is started with a power of 125 W at a constant and low speed and for a short duration of 5 s. This allows the agitation of the undercooled Xylitol.  
The form and the position of the mixer within the undercooled Xylitol can be seen in Figure 1. The crystallization of the sample is observed with a high speed CCD camera (HM1024, Genie, Teledyne DALSA, Waterloo, ON, Canada). Simultaneously, a thermocouple glued to the wall of the beaker provides useful information regarding the sample temperature evolution with time. Several starting temperatures were investigated.  
Figure 1 illustrates the device used for mechanical agitation and the snapshots in Figure 2 show an example of tests performed on highly undercooled melts at a starting temperature of 50 \({}^{\circ}\)C (T0 = 50 \({}^{\circ}\)C), i.e., with an undercooling of 45 \({}^{\circ}\)C (\(\Delta\)T = 45 \({}^{\circ}\)C) regarding the melting temperature of 95 \({}^{\circ}\)C.  
It has been observed that mechanical agitation results in a very fast response. Indeed, nucleation is reached in only 5 s of mixing all over the agitated region. Then, the crystallization progresses downwards from the agitated region. As shown in Figure 2, crystal clusters detach from the crystallization front and fall by gravity, thus contributing to accelerate the process of crystallization. After 1 min 30 s an increase in temperature of the beaker exterior wall is measured (25 \({}^{\circ}\)C).  
The obtained results suggest that mechanical agitation could be an efficient solution for discharging the storage system when necessary and at appropriate speeds. However, this technique shows a solidification which is less homogeneous from a spatial viewpoint than the bubbling one (Figure 2 versus Figure 3). Besides, the mechanical agitation is intrusive, and would require a specific reactors design and would probably lead to a significant extra-cost.
# Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
## 3 Results and Discussion
### Diurnal Variation  
Figure 12 illustrates a polar view of the trough occurrence depending on the magnetic local time (MLT) and the magnetic latitude for the northern (Figure 12a-c) and southern (Figure 12d-f) hemispheres. The trough minimum position was binned with a resolution of \(2^{\circ}\) latitude \(\times 1\) h. The percentage occurrence was calculated by dividing the trough minimum position in each sector by the total number of available profiles in the sector and multiplying by 100. Figure 12 shows that the trough polar plot for each season expands in latitude from 60 to 50 MLAT and elongates from dusk to dawn. This occurs more often at night and in the morning. [PERSON] et al. (1992) suggested that a boundary between the dusk and morning trough should exist due to different flow regimes [9]. For the northern hemisphere, the trough occurs more often during the December solstice and equinoxes. For the southern hemisphere, it occurs more often during equinoxes and the June solstice, as discussed in the previous section.  
Figure 13 shows the variation in mean trough minimum position (marked by black dots) as a function of magnetic local time (MLT). The corresponding mean trough widths are plotted as error bars. The trough width is bounded by trough equatorward and poleward breakpoints (see Figure 13).  
For the northern hemisphere, the mean trough widths are smaller in the poleward direction, indicating a sharp slope or high gradient. The trough widths are comparatively larger in the equatorward direction, indicating a less sharp slope. A similar trend is observed in the southern hemisphere, although the widths are comparatively smaller in both directions.  
Diurnal variations in the trough width and depth are represented in Figure 14.  
Figure 12: Polar view of the northern (**a**–**c**) and southern (**d**–**f**) hemisphere trough occurrence rates in the magnetic local time and magnetic latitude for three seasons: December solstice, June solstice, and equinoxes.  
Figure 13: Diurnal variation in trough position for the northern (**a**) and southern (**b**) hemispheres as a function of the magnetic local time.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 9 Genetic and Metabolic Diversity in Legumes
### Marker-Assisted Breeding for Common Vetch  
Key contributions of the study are as follows:  
Genetic diversity assessment: Using start codon-targeted (SCoT) markers, the study identified high levels of genetic polymorphism among four advanced lines and two commercial varieties of _Vicia sativa_.  
Metabolomics integration: The metabolomic analysis revealed significant differences in seed composition, linking specific metabolites to yield and biomass traits. Key metabolites, such as fatty acids and amino acids, were identified as potential biomarkers for breeding.  
Marker-trait associations: Stepwise multiple regression analysis (MRA) identified SCoT marker bands associated with seed quality traits, providing a basis for marker-assisted selection.
## 2 Materials and Methods
### Data Sources  
The dataset used in this study is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Type** & **Data Name** & \begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Data Source** & **Year** & **Note** \\ \hline NPP data & MOD17A3 HGF & 8-day and annual & 500 m & [[https://lpdac.usgs.gov/](https://lpdac.usgs.gov/)]([https://lpdac.usgs.gov/](https://lpdac.usgs.gov/)) (accessed on 7 August & 2001–2020 & \\  & & & & (accessed on 7 August & 2023) & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Land cover data \\ \end{tabular} } & Landsat 7 ETM & 16-day & 30 m & https://eartheplorer. & 2001–2013 & All land cover data are \\  & Landsat 8 OLI & 16-day & 30 m & usgs:gov/ (accessed on 7 August 2023) & 2014–2020 & derived from data with \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Meteorological data \\ \end{tabular} } & China Surface & Monthly & - & [[http://www.mnic.cn/](http://www.mnic.cn/)]([http://www.mnic.cn/](http://www.mnic.cn/)) (accessed on 7 August & 2001–2020 & Meteorological data include PRE (mm), TEM (”C), SUN (h), and EVA (mm) for 40 meteorological stations in and around the Songnen Plain \\ \cline{1-1}  & Meteorological & Monthly & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & [[http://www.esrl.noaa](http://www.esrl.noaa)]([http://www.esrl.noaa](http://www.esrl.noaa)). & 2001–2020 & \\ \cline{1-1}  & & V2 & & gov/psd/data/gridded/ & & \\ \cline{1-1}  & data & & & data.cpsoli.html & & \\ \cline{1-1}  & & & & (accessed on 7 August & & \\ \cline{1-1} \cline{2-6} Terrain data & NASADEM & - & 30 m & [[https://search.earthdata](https://search.earthdata)]([https://search.earthdata](https://search.earthdata)). & - & \\ \cline{1-1}  & & & & nasa.gov/ (accessed on 7 August 2023) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data Source Information Table.  
Figure 2: The geographic location of the Songnen Plain, China: (**a**) geographical map of the Songnen Plain; (**b**) geographical distribution of Songnen Plain major land cover.
# Simple Approach for Evaluation of Abrasive Mixing Efficiency for Abrasive Waterjet Rock Cutting
## 3 Experimental Program  
### Test Cases and Setup  
To evaluate the mixing efficiency and cutting performance according to the DBP in the focus, the main variables used were the focus diameter, the orifice diameter, and the AFR. The focus diameter and the terminal velocity of the abrasive were controlled. Toevaluate these variables considering the applicability, commercial product sizes, which are popular in the waterjet industry, were used. Figure 3 shows the orifices and focuses used in the experiment. Sapphire orifices with diameters of 0.15, 0.254, and 0.33 mm and WFRs of 10.67, 29.50, and 50.00 mL/s were utilized at a pressure of 320 MPa. For each WFR, tungsten carbide focuses with diameters of 0.76, 0.91, and 1.02 mm were employed. The AFR was controlled by Venturi suction according to the WFR and pressure from a minimum of 3.3 g/s to a maximum of 29 g/s. Table 2 lists the details of the experimental cases, including the WFR, the AFR, and the focus diameter.  
An abrasive injection waterjet (AWJ) consists of a pump section and an abrasive waterjet system. Figure 4 depicts the intensifier pump and the abrasive waterjet system used in the experiment. The pump capacity used was 50 HP of hydraulic power and the waterjet could produce 6 mL/s of water with a maximum pressure of 420 MPa. In a suspension waterjet (slurry waterjet; SWJ), a high-pressure pump applies pressure to a mixture of water and abrasive, whereas in an AWJ, which was used in this study for rock cutting, the pump applies pressure only to the water and the water accelerates the abrasive. AWJs are more suitable for civil engineering purposes, such as rock excavation, because the system is relatively simple and occupies a small space.
## 5 The Agent-Based Model
### Configuration Phase
#### 5.1.1 Social Classes  
The agent households were characterized with a set of attributes, whose values permitted to define the category of the family; these attributes were: income, size (number of components), education level, age class (average value among all components), number of earners, yearly energy consumption and social class. The attribute values assigned to the households followed the real distribution in the Emilia-Romagna region, obtained from the Survey on Household Income and Wealth (SHIW) provided by Bank of Italy ([[https://www.bancaditalia.it/statistiche/indcamp/bifait/](https://www.bancaditalia.it/statistiche/indcamp/bifait/)]([https://www.bancaditalia.it/statistiche/indcamp/bifait/](https://www.bancaditalia.it/statistiche/indcamp/bifait/))). On the basis of its attribute values, each agent possesses an associated budget for installing a PV panel. Clearly, the spending capability of a household is directly related to its income class, and this influences the price that each family is willing to pay for purchasing the PV panel. Households with higher income will accept to pay more for the initial investment, while a lower income was associated to a lower budget to invest in a PV system. Generally speaking, households that belonged to the same category (class) made similar decisions when deciding whether to install a PV panel. In practice, the proposed model assumed that this available budget of each family was determined by the attributes defining the family category. A linear regression model was then used to correlate the budget to the explanatory variables obtained from the SHIW data.  
Households social classes serve to mimic the different adoption rates of innovative technologies observed in many scenarios [51]. For instance, the so called S-shape curve [52] has been widely used to describe the adoption of an innovation: initially, the adoption rate of a new technology is slow since it is not well understood and its benefits are unclear (or not fully perceived). In a second phase, the adoption rate rises together with the spread of the technology and associated knowledge (mass market phase). Beyond a certain point, the market gets saturated and the adoption rate flattens. [PERSON] [52] identifies five categories of different adopters: (1) innovators, (2) early adopters, (3) early majority, (4) late majority and (5) laggards. The different categories were usually reflected on characteristics defining each adopter, such as socio-economic status (i.e., high-income individuals can afford to invest more on new and not yet well-established technologies). Each house owner fell in one of the five adopters categories, depending on three of its attributes (the most important features): age class, education level and income. \(K\)-means was used to identify the five clusters and group the agents belonging to the same class.
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 3 Ophthalmological Examination: A Key Tool in the Diagnosis and Management of Sphingolipidoses
### Spectral-Domain Optical Coherence Tomography  
SD-OCT is a noninvasive imaging method that employs low-coherence light to generate cross-sectional images of the retina, providing both qualitative and quantitative insight into its microstructure. With resolutions reaching 3-5 um in swept-source OCT, this technology is indispensable for assessing retinal and optic nerve pathologies [22]. It has been used to identify biomarkers in neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease in evaluating the RNFL and GCL [23, 24, 25, 26]. This instrumental examination is useful for analyzing retinal alterations related to the accumulation of sphingolipids in the retinal layers, facilitating diagnosis, monitoring disease-related changes over time, and assessing potential therapeutic responses.  
SD-OCT has proven particularly useful for evaluating neurodegenerative processes at an early stage. For example, in TSD and SD, thinning of the RNFL and thickening of the macular GCL have been observed, reflecting the neurodegenerative process affecting both the retina and the central nervous system [27]. Similarly in GD, thinning of the peripapillary RNFL and thickening of the macular ganglion cell complex has been described, suggesting the potential role of SD-OCT as a non-invasive tool for the identification of early neurodegeneration biomarkers [28,29].  
SD-OCT exhibits limitations in the differential diagnosis of conditions characterized by a cherry-red macula, particularly when multiple pathologies share similar findings [6]. In these instances, a thorough diagnostic approach is essential, and collaboration with a multidisciplinary medical team is recommended to enhance diagnostic accuracy and inform appropriate management strategies [30]. Additional limitations associated with this imaging modality include a decrease in image contrast in the presence of media opacities, such as those induced by cataracts or corneal opacities [31]. Furthermore, difficulty in performing the examination is often encountered in patients with compromised fixation stability, which is prevalent in conditions associated with progressive optic nerve atrophy and subsequent vision loss. Additionally, ocular motility disorders, including nystagmus, can further hinder the execution of the procedure [32; 33; 34; 35; 22; 36].
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 4 Differential Privacy
### Methods to Implement Differential Privacy
#### 4.2.2 Global Differential Privacy  
GDP generates noise to be added to the final results of a query by the central aggregator before sharing them with any third party [42]. In this model, each user shares their actual data with a central aggregator without adding noise. To add noise to the entire dataset, the central aggregator uses a differential privacy method. Global differential privacy makes sure that the presence or absence of an individual in the dataset does not alter the probability distribution in the final output. Consider a function (\(f\)) that calculates a sum over a dataset (\(O\)) defined as \(f(O)=\sum_{i=1}^{n}x_{i}\). The Laplace mechanism adds noise taken from the Laplace distribution to achieve global differential privacy [43].  
\[P(O)=f(O)+\text{Lap}\bigg{(}\frac{\Delta f}{\epsilon}\bigg{)} \tag{10}\]  
As the central aggregator has access to the real dataset, requires trust in the data collector. This model's primary benefit lies in the fact that low values of epsilon (\(\epsilon\)) can yield useful results without requiring a significant amount of noise. But before sharing the data, the trust of users in the data collector is required. If the data aggregator is compromised, the data can be leaked, increasing the risk of privacy failure.
## 3 Challenges and Limitations  
The removal of H\({}_{2}\)S by metal oxides in a packed bed configuration is an established technology. However, some challenges still remain. In regard to practical applications, these are, in particular, the regeneration of the sorbent and the presence of other compounds in the gas feed. It is difficult to ensure that the sulfur removal capacity of the sorbent does not decline during desulfurization-regeneration cycles. Morphological changes and fusion of particles into each other are among the main reasons for incomplete regeneration [57; 58; 59]. Also, other compounds in the gas phase can dramatically change the removal efficiency [36]. This can be due to a shift in the equilibrium of the sulfidation reaction (Equation (1)) towards the reactants in the case of wet gas streams, deposition of materials and coking of the sorbent in the case of the feed stream containing tars and particulates and adsorption of foreign species onto the sorbent surfaces.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 2 Related Studies
[49] used several machine learning algorithms to train and test samples, and these included KNN, logistic regression, and the decision tree ML algorithm. The detection results obtained were good, and for the Ramnit.gen!A malware, an accuracy of 79.495% was achieved, and for the Ramnit.gen!C malware, an accuracy of 95.473% was achieved. Similar research was conducted by [50], who created a machine learning model that was able to classify and identify malware and also able to group malware variants based on their relationships. These included several variants of the banking malware Ramnit. Ref. [50] extracted features from the malware samples using an approach called static analysis. This approach enabled them to extract features without having to execute or run the malware executable. The authors claimed that this approach allowed them to achieve a better performance with a low computational risk. Using the static approach, features could be extracted from two files within the executable, the hex file or the byte code file. During their research, ref. [50] extracted the features from the byte code files. This was performed using the n-gram feature extraction approach, which analyzed the byte sequence or opcode patterns within the malware executable files and then represented these patterns as words, in this case using the hex format. Ref. [50] used the K-Nearest Neighbor, logistic regression, random forest, and XGBoost machine learning algorithms to train, test, and classify the malware, and for the evaluation, accuracy and log-loss were used to measure the performance of the machine learning algorithms. Table 9 shows the classification results obtained when classifying the malware using the byte file features. Table 9 shows that the XGBoost and decision tree ML algorithms performed the best, achieving an accuracy of 98.76 and 97.98, respectively.
## 3 Methodology  
The process was initiated by preparing blood smear slides from 50 patients at Shaukat Khanum Hospital and Research Center (SKMCH&RC), Pakistan. Out of 50 patients, 25 were non-anemia, and 25 were anemic patients. After that, the Olympus Dp27 8.9-megapixel CMOS sensor captured RBC images with 4 K resolution with a microscopic digital camera. The resolution of the collected microscopic RBC was 1920 \(\times\) 1080. After pre-processing, the collected images were given as input to the CNN-based model to identify healthy and anemic images. The proposed model was executed on GEFORCE RTX-3060 GPU with 12 GB of RAM using a Python 3.7 and PyTorch v1.10 environment.  
After the ethical committee's approval of SKMCH&RC, we collected real-time microscopic images of a peripheral blood smear. These images were used to train and test the proposed CNN-based model to diagnose anemia and its severity level from a digital image like an expert medical consultant. Before the training and testing process, collected images were annotated and manually classified into relevant classes under the supervision of a medical consultant, as shown in Figures 3 and 4. The methodology section comprises the following steps.  
Figure 3: (**a,b**) Healthy RBCs image, (**c,d**) Mild stage RBC image (**e,f**) Chronic stage RBCs image.  
* Image collection
* Pre-processing
* Dataset arrangement
* Proposed CNN model architecture
* Loss function