esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of TLQoS
### Delay Module  
The delay module aims for choosing a potential forwarder for delay-constrained and critical traffic type. Algorithm 1 presents route selection procedure used by delay module.  
**INPUT** (a packet \(p\in Cr,Dc\), NT)  
```
1.if\(p.HC>HC^{max}\){Condition-2}then
2.\(\gamma\)=\(p.\gamma\)
3.\(\gamma=\gamma-0.1\)
4.\(p.\gamma\)=\(\gamma\)
5.else
6.\(\gamma\) =\(p.\gamma\)
7.endif
8.for all\(n_{j}\) in NT do
9.if\(T_{n_{j}}^{avg}<T^{thr}\){Hotspot Avoidance}then
10.if\(n_{j}\
otin RS\){Condition-1}then
11.\(c_{n_{i},n_{j}}=dst_{n_{i},n_{j}}\)
12. Calculate \(P_{n_{i}}^{d}\) and \(P_{n_{j}}^{d}\) according to Equations (4) and (5)
13. Calculate \(P_{n_{j}}^{Hd}\) according to Equation (12)
14. Calculate \(F_{h}(n_{i},n_{j})\) according to Equation (3)
15. Calculate \(F_{d}(n_{i},n_{j})\) according to Equation (6)
16. Calculate \(F_{Hd}(n_{i},n_{j})\) according to Equation (18)
17.endif
18.endif
19.endfor
20. Select \(n_{j}\) with MAX-\(F_{Hd}(n_{i},n_{j})\), MIN-\(P_{n_{j}}^{Hd}\), MIN-\(P_{n_{j}}^{h}\), Random in turn
21.if p.PacketType=Cr then
22. Call Reliability Module with \(p\in Cr\) as parameter
23.else
24. Call Queuing Manager with \(p\in Dc\) as parameter
25.endif
```  
**Algorithm 1** Route selection algorithm at delay module  
The algorithm first checks the condition-2 for the delay sensitive packets. The recent value of \(\gamma\) is extracted from the packet header. If the hop-count value of the packet exceeds maximum hop-count threshold, it resets the value of \(\gamma\) (line-2,3). The current value of \(\gamma\) is also updated in the packet (line-4). Otherwise, the \(\gamma\) will not change. The delay module avoids the hotspot by ignoring the neighbors for which the average temperature exceeds some hotspot threshold, \(T^{thr}\) (line-9). Condition-1 is applied in line-10 to avoid the routing loop with recent sender, \(RS\). For the remaining neighbors other than the hotspot(s) and recent sender, the algorithm measures the required potentials and forces (line 11-16). Finally, it chooses the neighbor with maximum hybrid delay force as its potential forwarder. However, if more than one neighbor possesses the same maximum force, then the potential forwarder will be selectedbased on minimum hybrid delay potential, minimum hop-count potential successively. If the forwarder can not be determined even after this, the algorithm chooses it randomly.  
After selecting the suitable forwarder node, the algorithm checks the packet type. For a _critical_ packet, reliability module is called for further forwarder selection that ensures the packet reliability. However, if the packet type is _delay constrained_, the queuing manager module will be called to place the packet in a proper queue for transmission.
## 3 Materials and Methods
### Methods
#### 3.2.4 Based on ML Approach to Detect Group Characteristics
Random forest enables many weak or weakly-correlated classifiers to form a strong classifier [36]. Finally, the Bayesian network is one of the most effective classifiers [37] and is very useful tool to define logical relationships among variables in complex models. It is a probabilistic graphical model that expresses the relationships among a set of variables that quantify the links between variables based on their conditional probabilistic relationship [38]. For the machine learning analysis, the open software Weka [39] was used. Different training experiments were implemented in order to choose the most adequate prediction features and the optimum machine learning parameters. For the assessment of the classification \(k\)-fold cross-validation was employed, which splits the training data into \(k\) equal-sized partitions [40]. If the applied machine learning algorithm can classify successfully, this implies that there is a questionnaire response pattern for each group (mechanical, electrical or electronic engineering). Namely, it will be possible to predict the student's degree as a function of the different measured characteristics. The machine learning classification results are evaluated in terms of the overall accuracy, the [PERSON]'s Kappa index and the receiving operating characteristic (ROC) curve area. The Kappa statistic measures the degree of agreement of categorized data [41]. It is defined in the range of \(-1\)-\(1\), with zero being the expected value for a random classification, \(1\) being a perfect agreement, and negative values indicating no agreement, although they are unlikely in practice. The area under the ROC curve (AUC) provides a comparison between the predicted and actual instance values in a classification by measuring the precision and the recall [42]. It measures the probability that randomly chosen instances will be correctly classified. It is widely used for model comparison, since it describes the model performance for a complete range of classification thresholds. The ROC area is defined in the range \(0\)-\(1\), with \(0.5\) being the expected value for a random classification. Therefore, AUC values equal to or less than \(0.5\) imply that the classifier has an efficiency that is not superior to randomness, not being effective in this case.
# Reliability of Automated Intracranial Volume Measurements by Synthetic Brain MRI in Children
## 2 Materials and Methods
### Manual Volume Measurements  
In order to analyse the reliability of the SyMRI measurements, all examinations were additionally conducted manually with regard to the volume of the CSF, the BPV, and the ICV. The images were pre-processed using software to enable faster results as follows: after exporting all native T2 images in Digital Imaging and Communications in Medicine (DICOM) format from our database to a local hard drive, we utilized the Mango viewer (ver. 4.1) to convert them to the NIFTI file format [19]. This step was necessary to be able to use available freeware software tools for post-processing. The NIFTI images were then  
Figure 1: Example SyMRI maps for (**a**) intracranial volume (ICV), (**b**) cerebrospinal fluid (CSF), (**c**) grey matter, and (**d**) white matter. The sum of grey and white matter represents the brain parenchyma volume (BPV).  
Figure 2: The image shows SyMRI reference curves for ICV, BPV, and CSF in a sample patient. Note that the CSF volume is increased above the normal amount while the BPV is at the lower threshold, which can be visually determined from the curve. The present constellation of findings suggests the onset of brain atrophy.  
processed using the FreeSurfer [20] (in our case, ver. 7.4.1 under Windows Subsystem for Linux) integrated tool SynthStrip [21], which provides highly consistent skull stripping for all MRI sequences. Each image was then processed individually using the command lines provided by the developers. Each brain extraction was then manually compared to the original image to ensure that no areas of the brain were lost during the extraction. In the case of incomplete extraction, we used SynthStrip's inbuilt settings to increase the extraction boundary, using the \(-\)b0 flag, where the number in mm corresponds to the adjusted extraction boundary, as specified by the developers. However, the more conservative extraction settings left some notable imperfections in the non-brain material which had to be manually filtered out in the next steps. Finally, we used 3 DSlicer [22] (ver. 4.10.2) to perform a threshold-based segmentation of the CSF and parenchyma, which was then manually reviewed and corrected by a pediatric radiologist with 11 years of experience (S.T.) (compare Figure 3); this was especially important in cases of brain lesions. Despite the need for manual corrections, in the search for the appropriate threshold, we found the maximum entropy threshold algorithm for CSF segmentation and the triangle algorithm for total ICV particularly useful. The volumes of the two respective segments were then subtracted from each other to estimate the BPV.
# A Review of Milk Frauds and Adulterations from a Technological Perspective
## 3 Results and Discussion
### The Most Common Milk Adultrations
#### 3.1.3 Counterfeiting Involving the Addition of Unusual Substances  
This practice is applied in order to hide another type of falsification and involves the addition of the following compounds:  
Detergents or Soap  
Milk fat is an important component of food and plays a significant role in the economics, nutrition, and physical and chemical properties of milk. About 70% of the milk fat is made up of saturated fatty acids, whereas 30% is unsaturated fatty acids. Diet, lactation, individual characteristics, breed, state of health, and environmental conditions all have an impact on an animal's fatty acid composition. The most significant factor is the nutrition of dairy cows. However, the incorporation of vegetable oils alters the content, type, and distribution of fat droplets in the protein network, causing changes in the microstructure and textural behavior of dairy products. Olive oil in particular is used to replace milk fat, and it is the added element after the fat content is removed. As a result of having a fatty acid profile that is nearly equal to milk fat, palm oil is yet another frequently utilized oil. In order to identify blends of vegetable oils in animal fats, cholesterol has also frequently been utilized. Typically, milk fat contains 204.3 to 382.4 mg/100 g of cholesterol. Due to the high cholesterol content of milk fat, adulteration with foreign oils will be obvious due to the change in cholesterol levels. Since milk fatty acids are short-chain fatty acids (caprylic, capric, and butyric) and vegetable fats are long-chain fatty acids, only a simple analysis of the fatty acid profile by chromatographic method can show that milk has been adulterated with vegetable oils [11; 12; 33; 34].
## 2 Methodology
### ACF Algorithm  
One of the goals of the ACF detector is to achieve a rich representation of the image without high computational cost. The fractal features of images allow reliable prediction of their structures across scales. Figure 2 shows the ACF algorithm.  
Figure 2: ACF algorithm [30].  
In the first stage, features such as the LUV color space, gradient magnitude, and HOG are extracted from input image \(I\). The results were then smoothed to obtain low-resolution channels (aggregations). In the second stage, a multi-scale feature pyramid is constructed. To achieve this, image \(I\) is resized to scale \(s\). Subsequently, linear and non-linear transformations (\(\Omega\)) are applied to the resized image \(I\). Equation (1) shows the steps in the second stage.  
\[C_{s}=\Omega(I_{s})=\Omega(R(I,s)), \tag{1}\]  
where \(C_{s}\) is the feature channel at scale \(s\), \(\Omega\) represents linear and non-linear transformations, and \(R\) is the resampling function. In the third stage, AdaBoost is used to train and combine the decision trees on the features. Subsequently, a multi-scale sliding window was applied for detection.
# Infection Control with Antimicrobial Solid-State ZnO Nanoparticles on Silk Fiborin Gauze
## 1 Introduction
ZnO NPs are highly biocompatible, biodegradable, and have low toxicity in topical applications since they do not penetrate human skin [18; 19; 20]. These properties make ZnO NPs an effective antibacterial agent, comparable to Al2O3 and SiO2, and a promising candidate for broadening the use of metal-oxide NPs in biomedical materials [21]. Silk fibroin (SF), derived from natural _Bombyx mori_, offers excellent compatibility with biological tissues, structural integrity, and simplicity of chemical surface alterations. Additionally, SF enhances skin tissue regeneration and healing processes [22; 23]. In this research, we seek to improve the antibacterial properties of electrospun silk fibroin gauze by incorporating antimicrobial ZnO nanoparticles through a cost-effective and widely applicable electroless deposition method. Given that the electroless technique is a chemical reduction process, it allows a controlled and uniform deposition of ZnO nanostructure onto fibrous structures such as electrospun silk fibroin gauze. Electroless deposition does not need an external power source and/or a high temperature for metal deposition, making the process adequate for the medical textile industry. ZnO is chosen for its well-known antimicrobial activity, low toxicity, and biodegradability. The study aims to assess the coated gauze's efficacy in preventing hospital-acquired infections by testing its antimicrobial activity against _S. aureus_ and _P. aeruginosa_ and quantifying the percentage of bacteria death after 72 h. Additionally, a viability assay with CHO cells is conducted to evaluate cytotoxicity. The ultimate goal of this study is to confirm ZnO as a promising antimicrobial coating for medical textiles that is bioresorbable and drug-free, contributing to the development of practical solutions for healthcare infection prevention.
## 4 Conclusions  
In summary, TiO\({}_{2}\) NS-filler/monolayer-based TIMs were fabricated by spin-coating a TiO\({}_{2}\) NS/PDMS mixture on a TiO\({}_{2}\) NS-monolayer with transfer via the icing method. The thermal conductivities of the TIMs were enhanced by increasing the loading fractions of the TiO\({}_{2}\) NS-fillers in PDMS from 0 wt% to 40 wt%, which lowered the GPU chipset temperature from 62 \({}^{\circ}\)C to 50 \({}^{\circ}\)C. In all TiO\({}_{2}\) NS/PDMS fillers with different loading fractions, it was found that an additional TiO\({}_{2}\) NS-monolayer improved the thermal conductivity of TIM and lowered the GPU chipset temperature by about 1-7 \({}^{\circ}\)C compared to TIMs without an NS-monolayer. The interesting thing is that including only the TiO\({}_{2}\) NS-monolayer in PDMS (0 wt% filler) achieved dramatic heat conduction improvement, similar to that for the 30 wt% TiO\({}_{2}\) NS-filler in PDMS. The thermal conductivity enhancement via the TiO\({}_{2}\) NS-monolayer was analyzed using simulations via COMSOL Multiphysics 5.6. The TiO\({}_{2}\) NS-monolayer can effectively absorb larger quantities of heat from the heat source than polymers with small thermal resistance, and can effectively dissipate the absorbed heat to the outside because of the large surface areas of the spheres. Based on our results and considering that the TiO\({}_{2}\) NS-monolayer's loading fraction is only 0.314 wt%, the TIM consisting of TiO\({}_{2}\) NSs and an NS-monolayer provides an efficient way to achieve higher thermal conductivity with low additional filler loading. Our proposed metal oxide NS-monolayer structure has great potential for improving TIM performance.  
Investigation, writing-original draft preparation, data curation and software, J.M. and U.J.; methodology and validation, B.J.; writing-review and editing, supervision, project administration, funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript. This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1A4A10520850) and Samsung Electronics Co., Ltd. (Suwon-si, Korea). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
Figure 7: Schematic illustrations of the heat conduction process with the absence or presence of TiO\({}_{2}\) NS-fillers and -monolayers in PDMS matrix.
## 3 Results
### Inversions Based on the Minimum Root Means Square Error (RMSE) Criterion  
Using each LUT, the difference (RMSE) between the simulated and measured canopy reflectance obtained from the corresponding image was calculated, and the results were sorted using the minimum RMSE criterion. The simulated reflectance from the LUT which yield the smallest RMSE with a measured reflectance (obtained from the RapidEye and Sentinel-2 images) was regarded as the best fit or best-simulated canopy reflectance. Accordingly, the set of model input parameters corresponding to the \"best fit\" from the LUT were regarded as the solution. Figure 5 demonstrates the canopy reflectance spectra obtained from Sentinel-2 and RapidEye data and the so-called best-fit reflectance from the LUTs calculated in this way for two plots in 2015 and two plots in 2016 with different LAI values.  
Figure 4: Sensitivity analysis highlighting the effect of variation of leaf area index (LAI) on simulated canopy reflectance of RapidEye and Sentinel-2 using PROSAIL. Reflectance values are at distinct spectral wavelengths; lines are used to help the interpretation.  
As Figure 5 illustrates, the measured reflectance from RapidEye and Sentinel-2 are modelled differently using PROSAIL. For Sentinel-2, the observed mismatch between the two canopy reflectance is more noticeable for the NIR and green bands while for the RapidEye data this mismatch is more pronounced in the red band.  
To examine these discrepancies between the measured and modelled spectral reflectance from RapidEye and Sentinel-2 the AAEs were considered [21]. In Figure 6, the AAEs between the reflectance from RapidEye and Sentinel-2, and their corresponding best-fit spectra (resulting in the lowest RMSE among the image and simulated canopy reflectance) from the two LUTs are shown. It shows that generally the dissimilarities between RapidEye and Sentinel-2 measured and modelled reflectance follow a similar trend. The AAEs calculated for the data from the two sensors demonstrate comparable drift in the visible region, with the green band showing the highest error in this region. While using Sentinel-2 data the infrared and the third red edge bands present relatively larger errors.  
Figure 5: Measured and modelled canopy reflectance spectra of two sample plots with different LAI values using RapidEye (2015) and Sentinel-2 (2016) data. Reflectance values belong to distinct spectral bands; lines are used to help the interpretation.  
Figure 7 presents scatterplots illustrating the relationship between measured and retrieved LAI using the smallest RMSE criterion. In general, the retrieved and measured LAI had somehow reasonable relationships, while this relationship was stronger in 2016 using Sentinel-2 data. The measured and estimated LAI in 2016 had a higher coefficient of determination (R\({}^{2}\) = 0.55) and lower error (NRMSE = 0.17) in comparison to those from RapidEye in 2015. The deviation of estimated LAI in 2015 from one to one line was more toward the higher values.
## 4 Field test  
A magnetic survey was carried out in the Gandia marsh, near Gandia, a city located about 100 km south of Valencia (Spain). A Roman cemetery is thought to be buried in the survey area. The area is divided in two sectors due to grove layout. The sectors are small, approximately 10 by 5 meters (Figure 2).  
One second was the time interval selected for data collection at 0.1-0.2 m/s walk speed, so the sample interval was about 0.25 m. The Active Geodetic GPS/GNSS network for the Valencia region [16,17] was used for VRS kinematic positioning. The data were collected walking in the same direction for every sector, so the orientation of the antenna is always the same.  
Magnetic data were reduced for diurnal correction, magnetic anomalies were calculated by subtracting the mean magnetic value from the data and reduction to the pole was achieved. Kriging interpolation was used in order to display the magnetic anomalies in a 0.25 m x 0.25 m grid.  
Comparison of coordinate accuracy can be seen in Figure 3. Some control points located in sector 1 have been observed with the high precision GPS VRS antenna (black points) and with the low precision GPS magnetometer antenna (red points). Taking the path through the area of reference, the low accuracy survey presents data points inside the path, but in the survey the limit of the path was never crossed. Based on this example, it can be concluded that the limits and forms of the magnetic anomalies can vary substantially depending on the precision of the coordinates of the observed points, which can lead to mistakes in their interpretation.  
Figures 4 and 5 are the final plot of the magnetic survey for the first and second sector with high position accuracy. Original UTM coordinates have been translated and rotated only for  
In the first sector a marked magnetic anomaly is presented in the form of 11 by 1 meter corridor crossing the area horizontally. In the second sector some corridors can be seen crossing the plot vertically; these corridors have a mean width of approximately 1 meter.
## 2 Numerical Analysis
### Boundary and Initial Conditions  
The boundary conditions (BCs) at the cylinder liner, cylinder head, and piston surface were defined as impermeable walls. The geometry of the combustion chamber is axially symmetric, so the cutting surfaces of the cutting portion of the computational domain were assigned as cyclic BCs. The simulation was started from the SPC of 30 crank angle degrees (CADs) after the bottom dead center (BDC) to the EVO of 30 CADs before the bottom dead center (BBDC) of the engine. The start of injection (SOI) of the pilot and gaseous fuels were 12 and 10 CAD BTDC, respectively. The injection duration of the pilot and gaseous fuels were 12 and 30 CADs, respectively. The single-injection method with step impulse function was applied for both pilot and gas fuel injections. The injection processes take place from the SOI to the EOI continuously with constant mass flow rates. A schematic of the working process of the engine is shown in Figure 4. The BC\({}_{5}\) and initial conditions for the numerical CFD simulations in this study were obtained from the engine shop test experiments and are summarized in Table 6.  
Figure 4: Schematic of the working process of the engine.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Description** & \\ \hline \multirow{2}{*}{Combustion} & \multicolumn{2}{l}{Non-premixed combustion model coupling with the PDF} \\  & \multicolumn{2}{l}{reduced chemical mechanism} \\ \hline \multirow{2}{*}{Emission} & NO & Extended Zeldovich \\ \cline{2-3}  & \multicolumn{2}{l}{Soot} & \multicolumn{2}{l}{Moss–Brookes model} \\ \hline \multirow{3}{*}{Fuel injection} & Spray & Discrete Phase Model (DPM) \\ \cline{2-3}  & Breakup & KMRT \\ \cline{2-3}  & Ignition & Auto-ignition \\ \hline Collision & Stochastic Collision model & \\ \hline Turbulence & Standard k-\(\epsilon\) model & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the CFD models.