esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Working Fluids Selection  
There is a trade-off between the thermodynamic specification, economic aspects, and environmental limits to select ORC working fluid [35]. The selection of a working fluid delimits the thermal efficiencies of the cycle and the heat recovered from the exhaust gas of the engine, and there is not a defined criterion for the working fluid selection [36; 37]. Therefore, a systematic methodology has been proposed as a guide, taking into account chemical characteristics such as thermal stability, molecular weight, critical conditions, and safety and environmental features.  
Only the Alkanes have been considered for their favorable critical conditions at high temperatures [38], and they are environmentally friendly, as well as Siloxanes. Chloro Fluoro Carbons (CFCs), and Hydro Chloro Fluoro Carbons (HCFCs) were not considered as candidates due to their effects on the environment according to the Montreal Protocol [39]. Likewise, Hydro Fluoro Carbons (HFCs) were excluded according to the Kyoto protocol [40].  
From the criteria presented, the methodology proposed to select the organic working fluid at high operating temperatures is shown in Figure 2. The application of these methodology allows the identification of a group of fluids under particular operational conditions of the process.  
Additionally, Table 1 presents some recent works, focused on the study of different fluids in ORC systems in MCI, where the presence of alkane groups, alkane cycles, and alcohols in high-temperature applications can be highlighted.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Engine** & **Temp [\({}^{\circ}\)C]** & **Working Fluids** & **Reference** \\ \hline
2015 & Diesel & 519 & Methanol \({}^{\circ}\), Toluene & [41] \\
2015 & Diesel & 275–300 & Cyclohexane \({}^{\circ}\), Benzene, Toluene & [42] \\
2016 & Diesel & 473.2 & Toluene \({}^{\circ}\), Acetone, Benzene, Dichlorohexane & [43] \\
2016 & Diesel & 212.5–581 & Acetone \({}^{\circ}\), Ethanol, D4, MDM, MM, MD2M, Glycol & [44] \\
2016 & Diesel & 373 & n-Pentane \({}^{\circ}\), n-Hexane & [45] \\
2017 & Diesel & 466.8–484.5 & Acetone \({}^{\circ}\), n-Octane, n-Hexane, MDM, Toluene & [46] \\ \hline \hline \end{tabular}  
* Working Fluids that presented a high performance in the system  
\end{table}
Table 1: Review of organic Rankine cycle (ORC) coupled to Internal Combustion Engine (ICE).  
Figure 2: Methodology of working fluids selection.  
There is not an ideal fluid that fulfills all these criteria, but the aim is to select the best one for this particular application. Therefore, following the methodology shown in Figure 2, the acetone, toluene, and cyclohexane, which are dry and isentropic fluids with a high critical temperature, have been chosen as working fluids of ORC to be studied in this work, because of the low global warming potential (GWP) and low ozone depletion potential (ODP), to figure out environmental limits. The physical, chemical, and environmental properties of the selected fluids are presented in Table 2.
###### Abstract  
The carbon/TiO\({}_{2}\) hybrid dots (C/TiO\({}_{2}\)-Dots) are structurally TiO\({}_{2}\) nanoparticles (in the order of 25 nm in diameter from commercially available colloidal TiO\({}_{2}\) samples) surface-attached by nanoscale carbon domains with organic moieties, thus equivalent to hybrids of individual TiO\({}_{2}\) nanoparticles each decorated with many carbon dots. These hybrid dots with exposure to visible light exhibit potent antibacterial properties, similar to those found in neat carbon dots with the same light activation. The results from the use of established scavengers for reactive oxygen species (ROS) to \"quench\" the antibacterial activities, an indication for shared mechanistic origins, are also similar. The findings in experiments on probing biological consequences of the antibacterial action suggest that the visible light-activated C/TiO\({}_{2}\)-Dots cause significant damage to the bacterial cell membrane, resulting in higher permeability, with the associated oxidative stress leading to lipid peroxidation, inhibiting bacterial growth. The induced bacterial cell damage could be observed more directly in the transmission electron microscopy (TEM) imaging. Opportunities for the further development of the hybrid dots platform for a variety of antibacterial applications are discussed.  
carbon/TiO\({}_{2}\) hybrid dots; light-activated; bacteria; antimicrobial; mechanism Article
###### Abstract  
This study presents an assessment of familial hypercholesterolemiaemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. The primary objective is to explore an enhanced method for estimating FH probability, surpassing the currently recommended Dutch Lipid Clinic Network (DLCN) Score. The models were trained using the largest Polish cohort of patients enrolled in an FH clinic, all of whom underwent genetic testing for FH-associated mutations. The initial dataset comprised over 100 parameters per patient, which was reduced to 48 clinically accessible features to ensure applicability in routine outpatient settings. To preserve balance, the data were stratified according to DLCN score ranges (<0-2>, <3-5>, <6-8>, and \(\geq\)9), representing varying levels of FH likelihood. The dataset was then split into training and test sets with an 80/20 ratio. Machine-learning models were trained, with hyperparameters optimized via grid search. The accuracy of the DLCN score in predicting FH was first evaluated by examining the proportion of patients with positive DNA tests relative to those with a DLCN score of 6 and above, the threshold for genetic testing. The DLCN score demonstrated an accuracy of approximately 40%. In contrast, the CatBoost model and its ensembles achieved over 80% accuracy. While the DLCN score remains a clinically valuable tool, its diagnostic accuracy is limited. The findings indicate that the ML models offer a substantial improvement in the precision of FH diagnosis, demonstrating its potential to enhance clinical decision making in identifying patients with FH.  
machine learning; familial hypercholesterolemiaemia; DLCN; model ensembles +
Footnote †: journal: Applied sciences  
1
Footnote 1: [[https://doi.org/10.3390/appl42311187](https://doi.org/10.3390/appl42311187)]([https://doi.org/10.3390/appl42311187](https://doi.org/10.3390/appl42311187))
## 3 Results
### Specific Cognitive Performance  
In each study, specific cognitive performance was selected as the evaluation index. Considering the small number of intervention studies selected, a subgroup analysis was conducted only for the 15 cross-sectional studies. Studies measuring the same type of cognitive ability were used to explore specific subfunctions of CSE and OSE to fully understand the ability of these two modes of exercise to promote cognitive performance. It is worth noting that each study adopted the same or similar paradigm for the measurement of specific subfunctions. The final results are presented in Table 3 Raw data for Tables 3 and 4 can be obtained in the Supplementary Materials. The overall effect size for OSE versus CSE on inhibition was 0.247 (95% CI (\(-\)0.173, 1.213); \(p=0.042\)). In addition, no significant heterogeneity was observed among the studies (Q(6) = 10.529; \(p>0.05\); I\({}^{2}\) = 43.01%). Regarding cognitive flexibility, the combined effect size was 0.360 (95% CI (0.036, 0.923); \(p=0.013\)), and no significant heterogeneity was observed (Q(4) = 5.382; \(p>0.05\); I\({}^{2}\) = 25.68%). Furthermore, no significant differences in visuospatial attention or processing speed were found between OSE and CSE.  
Figure 2: Forest plot for the efficacy of OSE compared to CSE in cross-sectional studies.
# Sentiment Analysis of Online New Energy Vehicle Reviews
## 2 Related Works
### CNN Model  
Convolutional Neural Networks (CNNs) are a type of feedforward neural network widely employed in image processing. In recent years, CNNs have also shown progress in the field of natural language processing (NLP). The NLP process, depicted in Figure 2, shares similarities with image processing and primarily involves convolutional layers, pooling layers, and fully connected layers [31]. The input to CNN channels is a text vector obtained through an embedding layer.  
However, a single convolutional kernel can only extract features of a specific category, which may result in inadequate feature extraction. On the other hand, employing too many convolutional kernels can lengthen the model's training time. Experimental results have indicated that the optimal model performance is achieved when utilizing three  
Figure 2: Schematic diagram of the TextCNN model structure.  
parallel CNN channels. The pooling layer comprises average pooling and max pooling. Average pooling reduces the amplification of variance caused by the limited neighborhood size, while max pooling mitigates the bias in the mean estimation induced by errors in convolutional layer parameters. The combination of these pooling techniques facilitates improved feature extraction, reducing errors in text feature extraction.  
Although TextCNN is a straightforward and effective model, it still possesses limitations in handling lengthy texts, modeling semantic relationships, and comprehending domain-specific terms and contextual dependencies in sentiment analyses of new energy vehicle reviews. To overcome these limitations, it may be beneficial to consider combining other models or adopting more intricate deep learning models to enhance the performance of sentiment analysis.
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
## 5 Simulator Architecture  
In this section, the simulator architecture is introduced. The simulator is developed to generate the numerical results presented in Section 6. The Network Simulator-3 (NS-3) is utilized based on the NS-3.33 version. NS-3 is a discrete-event-based network simulator that operates based on a series of events created by a specific network topology [33]. New classes and methods are implemented to create the WAN IoT simulator along with the specific PHY model, as presented in Section 3. Modifications are applied to the simulator to incorporate the PHY and MAC layer models presented in Section 2. The classes created for each layer of the protocol stack are depicted in Figure 16. The application layer manages the events for packet generation and schedules them down to the MAC layer. The MAC layer at the GW is responsible for beacon creation, frequency hopping, and switching among various radio modes. The MAC layer at the EN is responsible for scanning for beacons, receiving a beacon, or a data packet coming from the GW in the DL, and setting the channel, etc. The event scheduler manages the MAC and PHY layers, i.e., it manages switching among various radio modes such as sleep, wake-up, standby, CAD, active RX, and active TX, etc. The PHY layers at the EN and GW are interconnected via the wireless channel, i.e., the wireless air interface. The wireless air interface computes the distances between the GWs and ENs and determines the corresponding RSSs. The C/I ratio per link at a given time is calculated using the interference helper function. This function calculates the air times per packet and determines all the overlapping collision events over which the C/I value is determined as described in Section 2. The PSR is determined based on the C/I and RSS levels experienced by each packet. The PSR is calculated as the ratio of the total number of packets successfully received versus the total number of packets sent.  
This simulator has been validated with the Quality Assurance (QA) test setup, where the PER obtained with the simulator is compared to that of the QA test result. The PHY model is developed based on the measurements obtained from the field, as presented in the earlier section on the PHY model with GFSK.
# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## 5 Mathematical Model
### P2P System Infected by Malware with Countermeasure  
In this case, we assume that every time a leecher downloads a chunk, it passes through a revision process, looking for malware. An example of this kind of measure could be antivirus protocols for verifying the origin of the files or quarantine protocols for files that are not known to be trusted. However, not all malware can be detected, and not all peers may have the latest version of the antivirus software, which causes some malware to remain undetected and infect lechers, as in the previous case.  
Then, in this chain, the model accounts for the malware revision procedure in the sense that the nodes do not get infected just by downloading an infected chunk. Rather, they become infected only if these countermeasures fail. Then, \(P_{I}\) is the probability that a non-infected node downloading from an Infected node does become contaminated. The closer this parameter is to one, the higher the probability of contagion, and, when it is closer to zero, this indicates that the assumed measures are more effective. Building on this, the Markov chain, depicted in Figure 6, describes the use of countermeasures in a malware attack as very similar to the previously described chain, except that whenever there is a risk of getting infected, the security software avoids infection with the probability \(P_{I}\). Then, only transitions to the following states are modified:  
* \((X_{n}-1,X_{i}+1,Y_{n},Y_{i})\) when a leecher downloads an infected chunk and cannot avoid infection at the rate \(P_{I}\tau_{c}\)
* \((X_{n}-1,X_{i},Y_{n},Y_{i}+1)\) at the rate \(P_{I}\tau_{ni}\) when a non-infected leecher cannot prevent infection and becomes an infected seed.  
Figure 5: Markov chain of the infection process in a P2P system.  
Figure 6: Markov chain of the infection process in a P2P system with countermeasures.
## 5 Power System Design
### Power Storage Component Selection  
The chosen battery is the Clyde-Space 40 Whr CubeSat battery [10]. This version uses the company's latest lithium ion technology to provide an extremely high energy density storage solution. The product is comprised of two lithium polymer pouch cells in a 2s4p arrangement [10] with a nominal voltage of 7.6 V. To ensure adherence to the CubeSat regulations, high-and low-side solid state inhibits as well as battery operation monitors are installed [1]. The cell is qualified to NASA EP-Wi-032 and has various protective safeguards for scenarios including under-temperature, overcharge, and overvoltage.  
Lithium-Ion technology was chosen for its superior performance compared to rival technologies and suitability to the CubeSat design. The technology has an extremely high energy density. Although expensive, this allows significant amounts of power to be stored in a small volume, which is at a premium in the limited CubeSat dimensions. The \"useable capacity\" of an equivalent lead acid battery is only 50%, whereas a lithium ion is approximately 90%. The lithium ion batteries used on board CubeSats are typically lithium-ion phosphate, as this compound has a high combustibility resistance compared to alternatives such as lithium cobalt oxide. The battery also has a long cycle life, which is extremely important for extended the maximum duration of the mission. A typical lithium ion phosphate bank can perform 2000-5000 discharge cycles, whereas a similar lead acid battery would only perform 500 cycles.  
Another key advantage of lithium ion technology is that no Voltage Sag is present. The cell has a flat discharge curve and therefore can provide the same voltage output for any value of charge. For example, the same discharge voltage is produced at 20% charge compared to 80% whereas most battery technologies including lead acid, have much lower discharge voltages at low charge values. This will ensure sufficient power is delivered to the CubeSat always, particularly towards the end of the eclipse period when power draw is at its maximum and the battery is at a low charge value. The technology is also fast-charging, with no final absorption phase; with a large input, current batteries can typically charge in 30 min, which is useful for the CubeSat as fast charging will occur during the power generation phase due to incoming solar flux. Failure to fully charge also does not cause damage to the cell.  
The key advantage of this technology it has an extremely high charging efficiency, around 97% [10]. It is also suited to cold environments, such as the cold space background temperature, especially during the eclipse phase where no heating effect occurs due to radiation. A lithium-ion cell also requires very little maintenance and does not need to be stored upright, allowing for more configurations for integration into the CubeSat's available space [10].  
The cost per unit of this product is PS3420 [10].
## 3 Materials and Methods
### Feature Selection  
With the continuous expansion of datasets in scale, diversity, and complexity, reducing data size has become vital to maintaining efficient performance. Features are often divided into four distinct groups: unnecessary, irrelevant, strongly relevant to the target, or weakly relevant but non-redundant [6; 7]. Feature selection focuses on building an effective model with reduced computational complexity by prioritizing the most relevant and unique features for classification and prediction. With feature selection, a subgroup of features is determined, with the features obtained using statistical methods used to create a model. In the literature, the FSMs are generally collected into three categories [8; 9]. In addition to these methods, hybrid methods have also been developed [35]. Among the widely adopted techniques are the filter method, appreciated for its computational efficiency, and the wrapper method, which, although computationally expensive, is renowned for its reliability [36]. These methods integrate feature selection with modeling techniques to systematically assess features. Conversely, embedded methods merge feature selection and classification within a unified framework, executing both processes concurrently [37]. Feature selection has numerous applications across diverse fields such as engineering (fault detection), climate (weather prediction), health (disease diagnosis), ecology (species distribution modeling), environment (pollution forecasting), finance (fraud detection), tech
# A Data Processing Framework for Polar Performance Diagrams
## 2 Problem Formulations and Theoretical Backgrounds  
In its purest mathematical form, polar diagrams are (partial) functions mapping (TWA, TWS) pairs to BSP values. We sometimes use the notation BSP(TWA, TWS) to refer to this function. Since functions with two-dimensional input are harder to handle, the TWS value often will be fixed, reducing the polar diagram to a function of TWA to BSP. Such a reduced function is called a _slice_ of the polar diagram at a certain constant TWS. A slice is traditionally visualised by a polar plot.  
In addition to the natural interpretation, we can also use the following interpretation of a polar diagram slice: we imagine the TWS and the wind direction to be fixed (from top to bottom in the polar diagram); then, the polar plot gives us the exact boat direction vectors (BD) for each possible direction the vessel could take (see Figure 3).  
Figure 3: How to use a polar diagram slice to obtain the boat direction (BD).
## 2 Materials and Methods
### miR-320a Quantification by qPCR  
To evaluate the miR-320a expression levels in hOBs, U2 OS, and MG-63, total RNA was extracted using the miRNeasy Mini Kit (QIAGEN Iberia SL; Madrid, Spain) according to manufacturer instructions. Then, 1 \(\upmu\)g of total RNA was reverse-transcribed using the miRCTP II RT Kit (QIAGEN Iberia SL; Madrid, Spain). cDNA was assayed in 10 \(\upmu\)L qPCR reactions in 384-well plates using MiScript SYBR Green PCR Kit according to the protocol. The mature miR-320a sequence was used as a forward primer, and the Universal primer was used as a reverse primer. Amplification was performed in the QuantStudio 12K Flex Real-Time PCR System (Applied Biosystems; Waltham,MA, USA), and the ExpressionSuite software v.1.0.3 (Life Technologies; Carlsbad, CA, USA) was used for the determination of relative quantification (RQ) by 2-\(\Delta\)\(\Delta\)Ct method. Global normalization was used to normalize qPCR results.
## 3 Results
### Pulse Phase-Resolved Deconvolution  
In order to determine if the 305 s pulsation detected in the observation can also be identified in the deconvolved images, we created additional pulse phase-resolved images. This is done by stacking up events in each quarter of the phase (i.e., in intervals of 76.25 s) of the total spin period, and then by producing individual images from each phase-specific event file. Unfortunately, the individual images did not have enough photon counts (SNR), and the deconvolutions resulted in artifacts and increased noise. Applying this phase-driven technique to real X-ray images will require either brighter sources in close proximity or much longer exposure times in order to collect more photons during the observations.  
As an addendum, we demonstrate how this technique works using simulated sources for which we again use the _NuSTAR_ calibrated PSF. For each phase bin, a synthetic image was constructed by simulating two point sources at a separation of 10''--one pulsating and one static source. The phase bins are associated with the spin of the pulsating source. The pulsating source exhibited sinusoidal intensity variations with a peak-to-peak amplitude of 2.0, while the static source remained at a fixed intensity of 1.0. The maximum intensity of the pulse occurs at phase 0.0 (and phase 1.0), and the minimum intensity occurs at phase 0.5 (see the pulse profile at the bottom of Figure 4).  
The simulated images were first convolved with the _NuSTAR_ PSF to generate phase-resolved 'observed' images, which were then deconvolved using the RL algorithm. The results from the simulation are shown in Figure 4. We see that the source on the right shows the variation of intensity as expected from the plotted pulse profile, and the source on the left remains at a fixed intensity in all images. Furthermore, the pulsating source is not discernible at the phase of 0.5 (where its intensity drops to zero).