esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Manual Segmentation  
All manual segmentations were generated following the protocol described in the supplementary material, which uses the T1-weighted images and diffusion tensor maps simultaneously, thereby circumventing the low contrast limitation and achieving a more reliable segmentation of the thalamus.  
In summary, the thalamus manual segmentation protocol employed in this work considers the T1-weighted image and diffusion tensor maps, and the three views (axial, coronal, and sagittal), to facilitate specialized decision-making processes. It also used the ITK-SNAP software [42] to simultaneously display multiple grayscale images (e.g., T1-weighted and FA) along with the segmentation.  
The proposed manual segmentation protocol was designed to facilitate the task and stabilize the segmentation considering the relatively high inter- and intra-rater variability in thalamic manual segmentation for T1 images alone, as demonstrated in [7]. Results of the  
Figure 5: Age range and sex distributions for HCP dataset and test subset.  
Figure 6: Axial view of the thalamic masks generated by different methods overlaid on silver standard (in blue): (**A**) FSL—green; (**B**) FreeSurfer—red; (**C**) QuickNAT—yellow; (**D**) manual segmentation—purple.  
proposed manual segmentation protocol are not intended to differ from the segmentation in T1, as they refer to the same structure.  
Of the 60 subjects examined in the manual segmentation task, 16 were classified by a non-expert rater, and the remaining 44 by a physician with 15 years of experience in brain structure and manual segmentation on MRI. All manual segmentations performed by the non-expert also followed protocol and were used only to fine-tune the CNN. Only manual segmentations were considered for model evaluation, with a total of 20 subjects.  
To evaluate the variability of segmentation between the expert and non-expert raters, five additional subjects were segmented by the latter, and certain metrics were computed (Section 2.7). The comparison (Table 1) indicates that the manual segmentations from the two raters are closer to each other than to the proposed silver standard, especially when considering the overlap (Dice coefficient) and volumetric similarity.
## 3 Results and Discussion
### The Correlation Analysis between Volatile Compounds and Amino Acids in Grape Berries and Wines  
The analysis correlation ship between volatile compounds and amino acids in grape berries and wines was shown in Figure S2. The RDI-2 and RDI-3 treatment significantly increased the level of leucine in the two vintages berries and wines (Tables 2 and 3). The concentration of leucine in berries was positively related to the concentration of leucine in wines (Figure S2). The concentration of leucine was closely related to the concentrations of 2-methyl-1-butyl acetate, benzaldehyde, 3-methyl-1-pentanol and 3-methyl-1-butanol in wines (Figure S2). RDI-3 treatment significantly enhanced the concentrations of arginine, alanine, valine, and isoleucine in both vintages wines and RDI-2 significantly increased the concentrations of valine and isoleucine in both vintages wines (Table 3). The concentrations of arginine, alanine, valine, and isoleucine were positively related to the accumulation of volatile compounds--such as 3_4_-dimethyl-benzoic acid, 3-hydroxy-2-butanone, 1-butoxy-2-propanol, 2-methyl-1-butyl acetate, and 3-methyl-1-pentanol--in wines (Figure S2). The accumulation of volatile compounds, after RDI treatments, was closely related to the amino acid concentrations--especially isoleucine, valine, and leucine--in wines.  
Figure 1: Clustered heatmaps of amino acids and volatile compounds derived from grape berries subjected to different treatments. Data were normalized by a pooled sample from the control group.
## 4 Concept
### Data Processing
#### 4.3.2 Data Preprocessing  
Data preprocessing has the goal of producing a dataset that is ready for analysis. This includes tasks such as the integration of data from the different sources, the calculation of so-called aggregated data from the individual raw data, or the generation of extraction and filtering.  
Potentially, data quality can be affected along the transmission path from sensors to storage. For example, performance limits can lead to jitter fluctuations, which can cause problems for analysis algorithms based on constant time step sizes. It is essential to check the microscopic availability of the data. This means, for example, checking whether the signal traces are without gaps or whether they contain the outliers. Data containing gaps and outliers have to be completed or corrected if this is necessary and possible. Another example of the subsequent influence on sensor data is represented by interference in the measurement signals when shields of cable connections are insufficient or defective.  
During data preprocessing, coding inconsistencies and value inconsistencies must also be checked and eliminated if necessary. Coding inconsistencies are deviating uses of units of measurement. Value inconsistencies are, for example, simultaneous uses of terms in metadata. In addition, typographical errors, i.e., spelling errors during manual data entry, must be cleaned up. Figure 4 shows examples of some DQ problems.  
All steps of data preprocessing must be documented in the data cleaning report to ensure the traceability and trustworthiness of the ML application.  
Figure 4: Examples of data quality problems, covered by the data preparation part of the quality assurance approach.  
All of the above content imperfections in the data affect the contextual analysis accuracy. During data preprocessing, the content control of data quality and the establishment of freedom from errors is performed.
## 3 Results  
### Bleeding on Probing (BoP, %)  
As shown in Table 3, BoP significantly decreased after baseline in every group, including Group 4 (control group). No significant intergroup differences were assessed at baseline, and the highest intragroup decrease occurred in Group 2 and Group 3, whereas the lowest one occurred in the control group. No significant intragroup differences were assessed between T1 and T3 in any group, except in Group 3 where BoP further decreased at the last time considered.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group** & **Time** & **Mean** & **St Dev** & **Min** & **Median** & **Max** & **Significance \({}^{*}\)** \\ \hline
1—15\%MR & T0 & 91.85 & 12.42 & 55 & 100 & 100 & A \\  & T1 & 50.75 & 13.70 & 25 & 52 & 75 & B \\  & T2 & 47.95 & 12.77 & 23 & 49.5 & 67 & B \\  & T3 & 44.85 & 13.39 & 20 & 43.5 & 65 & B \\
2—20\%MR & T0 & 89.45 & 14.21 & 55 & 97.5 & 100 & A \\  & T1 & 29.75 & 8.17 & 15 & 27.5 & 50 & C \\  & T2 & 28.30 & 8.89 & 13 & 28 & 48 & C \\  & T3 & 25.55 & 8.11 & 10 & 25 & 43 & C \\
3—20\%MR+Paraprobiotics & T0 & 77.00 & 21.30 & 40 & 80 & 100 & A \\  & T1 & 24.00 & 17.59 & 0 & 20 & 60 & C \\  & T2 & 20.40 & 14.56 & 0 & 10 & 48 & C \\  & T3 & 13.15 & 11.94 & 0 & 10 & 37 & D \\
4—Control & T0 & 81.25 & 17.23 & 50 & 80 & 100 & A \\  & T1 & 69.25 & 18.157 & 40 & 67.5 & 100 & E \\  & T2 & 68.15 & 16.468 & 43 & 69 & 98 & E \\  & T3 & 57.95 & 9.8434 & 40 & 58 & 75 & E \\ \hline \hline \end{tabular} \({}^{*}\) Different letters show statistically significant differences among the groups (\(p<0.05\)).  
\end{table}
Table 3: Descriptive statistics of bleeding on probing (BoP) index.
# Plenty of Room at the Bottom: Nanotechnology as Solution to an Old Issue in Enhanced Oil Recovery
## 4 Nanocatalysts
### Factors Affecting Nanocatalysts in EOR Applications
#### 4.2.2 Heat Transfer  
The thermal properties of the heavy oil/bitumen are an important limitation for the development of energy-efficient thermally enhanced recovery techniques. Better thermal properties obtained with nanoparticles can result in a faster heat distribution during thermal EOR processes. The thermal conductivity enhancement by means of metallic particles with high thermal conductivities has been studied by several authors [43]. Recent work on liquids' thermal conductivity enhancement using nanoparticles has led to further examination of the nanofluids' thermal conductivity [171,172]. However, these studies showed different results, from anomalous thermal conductivity enhancement using nanoparticles to results showing thermal properties according to the values predicted. It is advised to carry out further research to determine the origin of these anomalous results. Experimental  
Figure 42: Viscosity reduction ratio of nano-NiWMoC catalyst synthesized at different milling times (**left**) and viscosity reduction ratio vs. particle size (**right**). Figures reproduced from Refs. [144] (left) and [149] (right).  
Figure 43: Environmental scanning electron microscope (ESEM) microphotographs of Ni-Mo-W nanoparticles deposited on low-permeability oil sands at the (**a**) top and (**b**) bottom of the reactor column at a temperature of 320 \({}^{\circ}\)C. Figure reproduced from Ref. [37].  
data and measurement methods are still lacking consistency, and additional experiments are deemed necessary [173].
In this sense, previous studies investigating the prone plank exercise have reported how the activity of the abdominal wall musculature, as well as the rating of perceived exertion (RPE), can be influenced depending on the position of certain joint regions, linking their specific positioning to the magnitude of the abdominal and RPE responses [39; 40; 41]. Based on these studies, it seems that the specific pelvic position strongly modulates the activity of the abdominal musculature and the perceived effort during abdominal tasks and bridging exercises [39; 42; 43; 44; 37]. Likewise, it has been found that the scapular position during the prone plank performance has also an important influence increasing the activation of the core muscles and the exertion rating values, specifically when a posterior pelvic tilt position is adopted, and the scapulae are in adduction [39]. In this context, it would be important to monitor and evaluate whether other spinal segments, such as the cranio-cervical region, could also influence and modulate the abdominal response and the perceived neuromuscular fatigue in this bridging exercise. Preliminary studies have revealed how specific variations in head and gaze orientation, mainly by adopting a flexed position, affected trunk muscle activity and lumbar motion during exercises performed in the standing and supine hook-lying positions [45; 46] or during controlled tasks such as the active leg straight raising test or the forward trunk flexion movement [47; 48]. Interestingly, while executing the prone plank, it is often observed that in some untrained individuals, in individuals under fatigue, or when a plank variation is too challenging and causes too high core demands, they often shift towards a head flexion posture. However, limited evidence is currently available on the effect that different head postures have in modulating the activity of the core muscles and the perceived exertion during the performance of the traditional plank. Consequently, in this study, we aim to investigate whether the specific position of the head and the cervical spine influences the magnitude of the activity of the core muscles and the extent of perceived neuromuscular fatigue during the prone plank exercise. It was hypothesised that performing head flexion would provide greater activation on overall abdominal musculature. In comparison, head extension would elicit higher activation on the lumbar extensor musculature. Additionally, it was hypothesised that both flexion and extension postures would elicit more perceived effort on participants than the traditional head neutral posture.
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 2 State of Art of Estimation Models for Solar Radiation
### Estimation Model for Beam and Diffuse Solar Radiation  
The beam solar radiation is related to the solar altitude, atmospheric transparency, cloud cover, altitude and so on [39]. The diffuse solar radiation is related to the incident angle of solar radiation, atmospheric condition and so on [39]. Literature on the beam or diffuse solar radiation are less frequent compared to the global solar radiation. Most of the research is focused on estimation models for beam and diffuse solar radiation under clear conditions. Moreover, most such estimation models are based on the hourly time scale. The model suggested by American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE model) is a widely used estimation model for beam and diffuse solar radiation in a clear day [47]. Some modifications have been proposed to improve its accuracy [48; 49; 50].  
A relationship between the transmission ratio of beam solar radiation \(\tau_{\rm b}\) and the transmission ratio of diffuse solar radiation \(\tau_{\rm d}\) was proposed by [PERSON] and [PERSON] in 1960 [38] under the assumption that the atmosphere is transparent.  
\[\tau_{\rm d}=0.2710-0.2939\tau_{\rm b} \tag{5}\]  
where \(\tau_{\rm d}=I_{\rm d}/I_{0}\), \(\tau_{\rm b}=I_{\rm b}/I_{0}\), \(I_{\rm d}\) is the hourly diffuse solar radiation, \(I_{\rm b}\) is the hourly beam solar radiation, and \(I_{0}\) is the hourly extraterrestrial solar radiation on a horizontal surface.  
The following equation was proposed by [PERSON] in 1976 for estimating the transmission ratio of beam solar radiation based on the solar altitude angle and geographic altitude [51].  
\[\tau_{\rm b}=a_{0}+a_{1}\mathrm{exp}(-k/\mathrm{sin}h) \tag{6}\]  
where \(a_{0}\), \(a_{1}\) and \(k\) are empirical coefficients considering the altitude and meteorological conditions, and \(h\) is the solar altitude angle. Other meteorological factors, such as ambient temperature and relative humidity, as well as the artificial intelligence approach, were considered by some researchers to establish a numerical relationship for beam solar radiation and diffuse solar radiation [52; 53; 53]. However, a combination of the Liu and Jordan equation and Hottel equation has been used by researchers to estimate the hourly beam and diffuse solar radiation values.
## 2 Materials and Methods
### EV Isolation and Characterization  
Blood samples were collected following a standard procedure as previously described [43; 44]. EVs isolation from plasma was carried out by several steps of differential ultracentrifugation and by ultrafiltration. Briefly, 3 mL of plasma were centrifuged at 11,000\(\times\) g for 30 min to remove cell debris. The supernatant was diluted with PBS, then filtered through a 0.2 \(\upmu\)m filter (Milker GP, Millipore, Darmstadt, Germany), followed by a two-step ultracentrifugation at 110,000\(\times\) g for 2 h to pellet the EVs. The EVs were then washed in cold PBS and resuspended in 100 \(\upmu\)l of PBS for morphological evaluations or in 70 \(\upmu\)l of RIPA (radioimmunoprecipitation assay) lysis buffer (0.3M NaCl, 0.1% SDS, 25 mm HEPES pH 7.5, 1.5 mm MgCl2, 0.2 mm EDTA, 1% Triton X-100, 0.5 mm DTT, 0.5% sodium deoxycholate) for Western blotting (WB) [44].  
In order to estimate their morphology, EVs were examined with Transmission Electron Microscopy (TEM) (JEOL JEM 1220 TEM at 120 kV); Atomic Force Microscopy (AFM), using a multimode scanning probe microscope driven by a nanoscope V controller (Digital Instruments, Bruker, Kennewick, WA, USA); and Dynamic Light Scattering (DLS), using a Brookhaven Instrument BI200-SM goniometer; as previous described [43; 44].  
Western Blot was performed to detect the EVs markers Alix (mouse anti-Alix, 1A12 clone, Santa Cruz Biotechnology, Inc., Dallas, TX, USA); Hsp70/Hsc70 (mouse anti-Hsp70/Hsc70, W27 clone, Santa Cruz Biotechnology); and CD81 (mouse anti-CD81, B-11 clone, Santa Cruz Biotechnology) [43; 44].
## 3 An Instance-Based Learning (IBL) Approach
### Formulation of the RUL Estimation Problem  
Given a learning instance (denoted by superscript (\(l\))) of the vibration signal, the run-to-failure degradation process can be represented by  
\[\left\{\left(\mathbf{x}_{i}^{(l)},\;\;RUL_{i}^{(l)}\right)\right\}i=0,\;1,\;2, \;\ldots,M \tag{1}\]  
where \(\mathbf{x}_{i}^{(l)}\) represents the prognostic feature vectors of dimension \(K\) at time cycle \(i\) indexed from the beginning of bearing usage. \(M\) denotes the end of useful life.  
\[\mathbf{x}_{i}^{(l)}=\left(x_{1},\;x_{2},\ldots,\;x_{K}\right)_{i}^{(l)} \tag{2}\]  
Features are extracted from raw signals at every time cycle. Recall that the PRONOSTIA datasets provide recordings of 0.1 s duration at intervals of every \(\Delta t=10\) s. In Equation (1), \(RUL_{i}^{(l)}\) is the RUL at time cycle \(i\). Hence:  
\[RUL_{i}^{(l)}=M-i \tag{3}\]For a test instance, the degradation process can be represented much the same way except we use superscript (\(p\)) to distinguish test from learning instances  
\[\left\{\left(\mathbf{x}_{j}^{(p)},\ RUL_{j}^{(p)}\right)\right\}j=0,\ 1,\ 2,\ \ldots,N \tag{4}\]  
with feature vectors represented by:  
\[\mathbf{x}_{j}^{(p)}=(x_{1},\ x_{2},\ldots,\ x_{K})_{j}^{(p)} \tag{5}\]  
In the above, index \(N\) denotes the last data recording. Obviously, \(RUL_{j}^{(p)}\) is to be estimated and finding \(RUL_{N}^{(p)}\) is the ultimate goal.  
The key to the IBL approach lies in the selection of a learning instance from historical datasets, whose degradation process is deemed similar to the given test instance. Once a learning instance has been identified, the RUL problem can be solved by identifying the location of the current time cycle in the life cycle of the learning instance in order to determine the remaining useful life \(RUL_{N}^{(p)}\).
# Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats
## 2 Study Related to Regulations  
Many countries have been following the standard regulations to ensure the security and privacy implications of drones. Many of them have started to propose several step-by-step procedures to license their UAVs [12; 13]. If these regulations are not followed, then unlicensed drones are taken under custody and proper legal action is taken against the pilot [12]. As per the media news broadcasted by the British Broadcasting Corporation (BBC), the CAA and FAA have declared some standard operating procedures (SOPs) to maneuver the UAVs at a low altitude [14], which are mentioned below:  
* The users or operators of a registered UAV must carry the proof of license while operating the UAV.
* The maximum height at which the UAV can maneuver is 400 feet only.
* UAVs must be kept away from the airfields and, in case of necessity, one may acquire the written permission from relevant boards or authorities.
* In the case of a UAV crash, legal action can be taken against any harmful actions or the damage that occurred from UAV failure.
* UAVs with computer vision or camera surveillance are not allowed to maneuver within 50 m of people or any crowd.
* UAVs will be summoned if they are not flown within the operator's line of sight.
* UAVs will be summoned if they are flown at night without proper lighting.  
The above standard rules and SOPs ensure the secure operation of drones.  
It is also noted that with a dramatic rise in the drone industry, various countries have inducted their own rules as well [15]. Mainly, to operate a UAV, there are three fundamental components: the first is the ground control room (GCR); the second one is the communication method, for example, satellite, radio frequency, etc., as illustrated in Figure 2; and last, but certainly not the least, is the UAV itself. There are three different methods to communicate with a drone, i.e., satellite, radio signal, and internet, as shown in Figure 2[15]. The essential license-exempted radio equipment, along with the frequencies, is mentioned in Table 1[16].  
The standard bandwidth through which a communication is established between a UAV and the ground control room (GCR) is mentioned in Table 1, whereas the other standards are still in progress for the safe operations of a UAV in any vicinity [17].
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Feature Extraction from PPG Waveforms  
Fifteen PPG-based general features were extracted at each of the three wavelengths. The 15 features are zero-crossing rate (ZCR), autocorrelation (ACR), kurtosis (kurt), variance (var), and mean of power spectral density (PSD); kurtosis (kurt), variance (var), mean, and skewness (skew) of Kaiser-Teager energy (KTE); kurtosis (kurt) and skewness (skew) of spectral analysis (spec); mean of wavelet analysis; autoregressive (AR) coefficients; skewness and sum of absolute difference (SAD). The feature vector and detailed explanation  
Figure 4: R1, R2, and R3 versus Glucose levels for 34 subjects.  
of these PPG waveform-based features can be found in [9]. One of these 15 features is auto-regressive coefficient feature, and we employed the Yule-Walker method to determine the auto-regressive coefficients with an order of 2, yielding 3 additional features. Therefore, a total of 48 PPG waveform-based features were extracted for three wavelengths, consisting of 16 features for each wavelength. In addition to this, 3 AC/DCs and 3 ratios (R1, R2, R3) were also included as features, and a total of 54 features were considered in this study. Figure 5 shows the top 20 important features out of PPG waveform-based 54 features.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods  
Significant progress has been made in the synthesis of magnetic nanoparticles (MNPs) in the past decade. The emphasis has been on creating different techniques capable of generating MNPs with certain attributes such as size, shape, magnetic properties, and stability. Compatibility with living organisms is a very important issue that needs to be addressed especially in the case of their use in biological applications. A wide range of synthesis methods were developed over time. These synthesis methods can be divided into physical, chemical, and biological processes (Figure 1). The methods encompassed in this study are ball milling [4], laser ablation, and other physical techniques, coprecipitation, thermal decomposition, hydrothermal synthesis, microemulsion, and the sol-gel technique. Other non-thermal and biological synthesis methods are also mentioned. Each of these methods possesses distinct mechanisms and circumstances for generating MNPs, customized to meet certain requirements and applications [5].
## 3 Results and Discussion
### Response Surface Optimization ANOVA for the Ultrasound-Assisted Enzymatic Extraction of SDF
#### 3.2.2 Establishment and Analysis of Response Surface Regression Model  
The data in Table 2 were analyzed to derive the regression model equation for the extraction rate of SDF (Y) versus enzyme addition amount (A), material-liquid ratio (B), and reaction time (C), as follows:  
Y = 76.1 + 4.85A + 3.34B + 2.01C - 0.775 AB - 0.525 AC - 1.1 BC - 6.6A\({}^{2}\) - 8.67B\({}^{2}\) - 8.13C\({}^{2}\)  
The correctness of the regression equation was tested using ANOVA.  
It can be seen from Table 3 that the F value of the model was 543.51 and the \(p\) value was less than 0.001, indicating that the model was highly significant. The \(p\) value of the lack-of-fit term was 0.6222 > 0.05, indicating that the effect of the extra-experimental factors was not significant. The predicted R\({}^{2}\) of 0.9910 was in reasonable agreement with the adjusted R\({}^{2}\) of 0.9967, indicating that this model was highly credible. A, B, and C had highly significant effects on the extraction of SDF from sauce residue (\(p\) < 0.01). The secondary terms BC, A\({}^{2}\), B\({}^{2}\), and C\({}^{2}\) reached highly significant levels (\(p\) < 0.01), and AB reached a significant level (\(p\) < 0.05). The influence order of each factor was A > B > C, i.e., enzyme addition amount > material-liquid ratio > reaction time.