esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Correlation Study  
Table 6 provided the Pearson correlation coefficients between the main FA parameters. Those correlations demonstrated significantly high positive correlations between PUFA and USEAs (r = 0.908, \(p\) < 0.033), USEAs and PUFA/SFAs (r = 0.9502, \(p\) < 0.013), and PUFA and PUFA/SFAs (r = 0.9859, \(p\) < 0.001), as shown in Table 6. SFAs had significant negative correlations with USEAs (r = \(-\)0.9966, \(p\) < 0.001), PUFA (r = \(-\)0.9010, \(p\) < 0.011) and PUFA/SFAs (r = \(-\)0.9369, \(p\) < 0.018). Omega-3 had a significantly negative correlation with omega-6 (r = \(-\)0.9573, \(p\) < 0.024) (Figure S2). Our results showed that SFAs had no positive correlation between all parameters, except omega-6 (0.10), which may have low concentrations of the MUFA ratio from most of the VOs. Omega-6/omega-3 presented a  
Figure 4: Dendrograms of the clustering analyses of seven VOs based on each oil’s FA composition. The vertical distance shows the Euclidean distance.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Safflower** & **Sunflower** & **Maize** & **Linseed** & **Cottonseed** & **Soybean** & **Rapeseed** \\ \hline Safflower & 0.0 & 9.0 & 14.4 & 81.1 & 23.6 & 26.5 & 75.7 \\ Sunflower & 9.0 & 0.0 & 6.6 & 75.6 & 18.1 & 18.2 & 68.0 \\ Maize & 14.4 & 6.6 & 0.0 & 72.9 & 16.2 & 13.2 & 61.8 \\ Linseed & 81.1 & 75.6 & 72.9 & 0.0 & 70.9 & 62.7 & 69.3 \\ Cottonseed & 23.6 & 18.1 & 16.2 & 70.9 & 0.0 & 15.5 & 64.7 \\ Soybean & 26.5 & 18.2 & 13.2 & 62.7 & 15.5 & 0.0 & 52.8 \\ Rapeseed & 75.7 & 68.0 & 61.8 & 69.3 & 64.7 & 52.8 & 0.0 \\ \hline \end{tabular}
\end{table}
Table 5: Euclidean distances.  
very similar correlation to all of FA ratios. Strong positive and negative correlations were reported between FA groups [69,70].
# Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm
## 1 Introduction
On the other hand, the Bishop model improves the representation of the cell behavior at the second quadrant, but eight parameters of an implicit model must be determined. However, to the best of the authors' knowledge, procedures for estimating its parameters are not reported in the literature. Instead, most of the works concerning PV cell or array analysis based on Bishop model adopt the parameters from other studies previously reported. Therefore, the purpose of this paper is to propose a methodology to estimate the parameters of the Bishop mathematical model to represent a PV cell in both the first and second quadrants. Because the high number of parameters to be identified and the implicit and non-linear relationship between output current and voltage of Bishop model, an optimization approach based on a Chu-Beasley genetic algorithm was used. The estimation process is divided into two stages; in the first stage, the curve in the first quadrant is modeled based on the estimation of SDM parameters; then, using the first five calculated parameters, the Bishop parameters are estimated to reproduce the I-V curve in both the first and second quadrants. In the second stage, the behavior in the first and second quadrants is modeled entirely by the Bishop model. Both estimation stages are validated based on the I-V curves of two PV cells (that are constructed with different technologies) using the root mean square error (RMSE). The rest of the paper is organized as follows: Section 2 presents the Bishop model using an equivalent circuit, describing the equation that relates the output current and voltage of a PV cell and defining the set of parameters to be estimated. In addition, Section 2 also defines the objective function and the search ranges for the parameters. Then, Section 3 describes the GA method implemented in this work to perform the proposed parameter estimation, which is based on the [PERSON] mathematical model. Section 4 presents the experimental analysis and reports the results of the parameter estimation process of two PV cells (one mc-SI and one pc-SI). Those results are analyzed with the average values and standard deviation of the parameters, the objective function value, the computation time and the evaluation of accuracy metrics, such as RMSE, MAPE and MBE. Moreover, that section also provides a summary of the proposed methodology. Finally, the last section discusses the most relevant findings of this study in the conclusions.
# Mapping of Kill Erosion of the Middle Volga (Russia) Region Using Deep Neural Network
## 3 Materials and Methods
### Remote Sensing Data  
Data from the Sentinel-2 satellite constellation was used for recognition. Unlike other sources of multispectral remote sensing data, such as Landsat, MODIS, SPOT, RapidEye and so on, Sentinel-2 imagery provides the best resolution in the target bands (10 m) at no charge. The cloudless composite data of the Sentinel-2 mission for the spring period, April-June, were used as input data to develop the fill erosion recognition technique. Near-infrared images with baseline resolution (10 m) were used for recognition. The composite was created using the Google Earth Engine (GEE) [62], product \"Sentinel-2 MSI: MultiSpectral Instrument, Level-2A\" (COPERNICUS/S2_SR). GEE allows the processing of big remote sensing data and facilitates some routine operations. A total of 2988 scenes from both S2A and S2B satellites were used to create the composite. To create the composite, the 2018-2019 images were filtered by date, images were cleaned from clouds and shadows, median pixel brightness values were calculated, and imagery was clipped to the boundaries of the study area and reprojected into the WGS 84/Pseudo-Mercator (EPSG:3857) projection coordinate system. A cloud probability raster was used to clear the imagery from the clouds. The S2 cloud probability is created using the sentinel2-cloud-detector library (LightGBM [63]). All bands are upsampled using bilinear interpolation to 10 m resolution before the gradient boost base algorithm is applied. The resulting 0 to 1 floating point probability is scaled to 0-100 and stored as a UINT8. Areas missing any or all of the bands are masked out. Higher values are more likely to be clouds or highly reflective surfaces (e.g., roof tops or snow). A 15% probability of having clouds on the scene was used as the cloud threshold.
The well-known mobility protocols for localized mobility are Fast Handover Proxy Mobile IPv6 (FPMIPv6) and Network-Based Proxy Mobile IPv6 (PMIPv6) [10; 11], standardized by the IETF NETLMM working group, and the telecommunication and Internet communities have started to pay close attention to it. The aim of this article is to describe a secure and efficient PMIPv6 architecture that can be used to provide the intra-domain and inter-domain mobility handover of vehicles in a VANET. Originally, PMIPv6-supported schemes provided mobility in a localized domain. The proposed architecture, which is based on IP multicasting, can provide both inter- and intra-domain handoff. The remaining parts of the work are structured as follows. Relevant work in the mobility management of vehicles is reviewed in Section 2. Section 3 discusses the model for the estimation of the total cost during intra-domain handoff and inter-domain handoff and proposes the multicast-enabled mobility handover strategy. Section 4 emphasizes the mathematical analysis of costs for inter-domain handoff, along with the costs of intra-domain handoff, whereas Section 5 presents a qualitative outcome analysis based on several metrics, and a comparison with the current mobility management program with the proposed architecture is discussed. Lastly, Section 6 closes the work by mentioning future directions in mobility management.
## 2 Methodology
### Methods for Collection and Processing of Respiration Signals  
#### 2.1.1 Working Principle of Millimeter-Wave Radar for Biosignal Detection  
The principle of bio-signal detection with millimeter-wave radar relies on the Doppler effect, where the received signal reflects distance variations between the radar antenna and the participant's chest wall, driven by cardiac and respiratory movements. The radar continuously emits a digitally generated sinusoidal carrier wave, and for objects with periodic motion, the radar receiver component receives echoes that vary periodically over time. This characteristic makes it possible to detect vital signs such as the periodic movement of the chest caused by breathing or heartbeats, from which physiological information such as heart and respiration rates can be extracted [29].  
We selected a 77 GHz millimeter-wave radar sensor (Figure 2) for this study. The radar sensor based on frequency-modulated continuous wave (FMCW) signals for radar detection, featuring a 2-transmitter, 4-receiver, multiple-input multiple-output (MIMO) antenna configuration. This system achieves a detection range of 0.1 to 2 m and operates independently of environmental conditions, including temperature, humidity, noise, airflow, dust, and lighting. The high integration and bandwidth characteristics of this sensor chip enable flexible applications with a compact size. The sampling rate was set to 32 Hz to facilitate data processing.  
Figure 2: Millimeter-wave radar sensor.
## 4 Discussion
### Limitations and Potential  
In this study, our Computer-Aided Diagnosis (CADx) system successfully distinguishes between T-stages 1 and 4 of gastric cancer, early gastric cancer, and normal cases. However, it is essential to acknowledge that our system's classification does not encompass the entire spectrum of T-stages, given the intricate nature of cancer staging, which involves factors such as tumor size, depth of invasion, and extent of metastasis. Moreover, the sole reliance on gastroscopy images for staging poses challenges due to the complex interplay of these variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Classification Class** & **Purpose** & **Performance** \\ \hline [PERSON] et al. [14] & EGC and NOR & Classification & AUC: 0.81 \\ [PERSON] et al. [15] & EGC and NOR & Classification & Acc: 0.77 \\ [PERSON] et al. [16] & BGU, AGC, EGC & Classification & Acc: 0.82 \\  & Depth of invasion & Classification & Acc: 0.72 \\ [PERSON] et al. [17] & Depth of invasion & Classification & Acc: 0.77 \\ Proposed method & T-stage 1 and 4 & Classification & Acc: 0.83 \\  & EGC and NOR & Classification & Acc: 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of previous studies and our study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Metrics** & **Original** & **AutoAugment** & **Cut and Paste** \\ \hline  & Precision & 0.7990 & 0.8683 & 0.9114 \\  & Sensitivity & 0.7750 & 0.8667 & 0.9000 \\ Dataset B & FPR & 0.2857 & 0.1563 & 0.1571 \\  & F1-score & 0.7868 & 0.8675 & 0.9057 \\  & Accuracy & 0.7750 & 0.8667 & 0.9000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Gastric cancer classification results using TTA.  
The limitations of our current CADx system also pertain to its inability to encompass all possible lesion cases. Notwithstanding these challenges, our research underscores the value of data augmentation techniques, such as Cut and Paste, in improving the performance of the CADx system. This method enables more diverse and comprehensive feature learning by generating gastric cancer tissue samples that faithfully replicate the distinctive characteristics of lesions. These factors collectively contribute to the robust and generalizable performance of our CADx system.  
Furthermore, the improved performance of the CADx system holds the potential to offer specialists consistent and highly accurate second opinions. This aspect represents a significant strength of our proposed method in the context of patient care.
# Multi-Body Model of Agricultural Tractor for Vibration Transmission Investigation
## 3 Results and Discussion
### Case Study 3: Rear Spring-Dampers  
Modal analysis revealed that the resonance frequencies of rear spring-dampers are located in frequency bands from 1.25 Hz to 2 Hz, which interferes with critical frequency bands (from 1.6 Hz to 2.5 Hz). A case study was performed to determine if modification of damping curves can improve overall suspension performance. The damping curves used in this case study were achieved by 80 % and 120 % multiplication of the experimentally measured damping curve used for model validation, shown in Figure 4.  
A minimal difference in vibration level at the 1.6 Hz frequency band was found (see Figure 18). The 80 % damping simulation has the lowest, and 120 % has the highest vibration levels. The opposite trend was expected as this frequency band is in the resonance region. This unexpected result may be attributed to the character of the ISO rough track, where the bumps vary both in amplitude and frequency. This means the suspension elements are permanently in transient regimes, and no steady-state vibrations are present.
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion
### Performance Evaluation Parameters
#### 4.1.4 Throughput  
Throughput characterizes the quantity of data handled within the given time or transmitted from one location to another. To examine the overall throughput of the algorithm, it is also compared with the size of the network as shown in Figure 8. The number of nodes participating in the activity at the launch of algorithm throughput is lower due to communication overhead. More number of packets start reaching their destination as the size of the network decreases with the passage of rounds.  
Figure 6: Packet delivery ratio with rounds.  
Figure 7: Latency with rounds.
###### Abstract  
In smart cities, unmanned aerial vehicles (UAVs) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model's effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases is superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-score (99.92%).  
unknowned aerial vehicles (UAVs); intrusion detection; privacy preserving; Internet of UAVs; federated learning; real-time decision mechanism +
Footnote †: journal: Sensors  
0023
## 3 Results
### Parameters for Performance Evaluation
#### 3.1.2 Cumulative Distribution Function (CDF)  
To define how accurately the random variables are distributed, the CDF is used. The CDF aims is to finds out the likelihood that the random variable '\(A\)' may take equal or less value to '\(a\)' as shown in (12) [14].  
\[F_{A}(a)=Pr(A<a)\forall\ a\in R \tag{12}\]  
Here:  
\(A\) = Random Variable,  
\(F_{A}(a)\) = CDF,  
\(a\) = Distance Error,  
\(Pr(A\leq a)\) is the probability that the random variable \(A\) takes on a value less than or equal to \(a\).
# Bayesian Modeling of Traffic Accident Rates in Poland Based on Weather Conditions
## 2 Materials and Methods
### Models
#### 2.2.3 Model 3: Nationwide Weather Model  
Transitioning to a finer temporal resolution, the first weekly nationwide model examines the relationship between nationwide accident counts and weekly meteorological conditions. This model aggregates accident data across all counties for each week of the study period and relates these counts to country-average weather conditions. The weekly granularity allows for capturing seasonal patterns and shorter-term weather effects.  
The linear predictor for this model is structured as:  
\[\log(\lambda_{i})=\alpha +\beta_{1}\cdot weekly\_mean\_temperature_{i}+ \tag{6}\] \[+\beta_{2}\cdot weekly\_mean\_humidity_{i}+\] \[+\beta_{3}\cdot weekly\_total\_precipitation_{i}\]  
where \(\lambda_{t}\) represents the expected nationwide accident count for week \(t\).
# Mechanisms of Fluoride Toxicity: From Enzymes to Underlying Integrative Networks
## 3 Mechanisms of Fluoride Toxicity on Cellular Level
### What Do the Studies of Fluoride Effects on Enzymes In Vitro Tell Us?
#### 3.5.1 G Protein Signaling Cascade  
Heterotrimeric G protein is formed by alpha (G\(\alpha\)), beta, and gamma subunits (G\(\beta\gamma\)) [80; 81; 82]. The name of G protein was selected because the G\(\alpha\) subunit binds guanosine diphosphate (GDP). A hormone-activated receptor triggers the exchange of GDP to bind guanosine triphosphate (GTP) in the G\(\alpha\), the alpha subunit dissociates from the G\(\beta\gamma\) and interacts with AC, which is an effector molecule. The result of a hormone signal binding to its receptor is elevated concentration of cAMP in the cell (see Figure 3).  
All eukaryotes use G proteins for signaling and have evolved a large diversity of G proteins. For example, humans encode 18 different G\(\alpha\) proteins, 5 G\(\beta\) proteins, and 12 G\(\gamma\) proteins. There are many classes of G\(\alpha\) subunits, such as G\(\alpha\)s (activation of AC), G\(\alpha\)i (inhibition of certain AC isoforms), and G\(\alpha\)q/11 (phospholipase C activation) [83; 84]. The diversity of G proteins and effector molecules broaden enormously the possibilities of molecular interactions during signal transduction.  
Figure 2: The effect of fluoride on the adenylyl cyclase according to [PERSON] ([[https://www.nobelprize.org/prizes/medicine/1994/[PERSON]/lecture/79](https://www.nobelprize.org/prizes/medicine/1994/[PERSON]/lecture/79)]([https://www.nobelprize.org/prizes/medicine/1994/[PERSON]/lecture/79](https://www.nobelprize.org/prizes/medicine/1994/[PERSON]/lecture/79))).  
Figure 3: The message of AlFx is greatly amplified in G protein signaling cascade. Effector enzymes are adenylyl cyclase (AC) or phospholipase C, the second messenger molecule could be cAMP, inositol 1,4,5-trisphosphate, and diacylglycerol.
# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 6 Discussion
### Answers to the Research Questions  
Based on the RQs posed, after this comparison, they can be answered.  
* Patient Demographics: Age, gender, and other demographic variables;
- Clinical Features: Diagnosis and severity of illness;
- Operational Factors: Admission status and pre-operative hospital LoS;
- Data Preprocessing Techniques: Feature selection, normalization, and handling of missing data.
* Regression Models: Linear regression, ridge regression, Lasso regression, Elastic-Net regression, and multiple linear regression;
- Neural Network: Various configurations of neural networks optimized through techniques like grid search and random search;
- Decision Trees and Ensemble Methods: Decision trees, random forest, and gradient boost (e.g., XGBoost);
- Bayesian Methods and Advanced ML Algorithms: Bayesian methods, support vector machine, and advanced ensemble techniques;
- Text-Based Models: GPT-3 and other neural network language models.
* ElasticNet Regression: Shows a superior performance among regression models, with a lower mean absolute error (MAE) compared to simple linear regression;
- Neural Network: Achieves high accuracy rates (e.g., 94.74% in one study), but its performance is highly dependent on extensive preprocessing and parameter optimization;
- Ensemble Methods: XGBoost outperforms traditional methods when handling complex relationships within large datasets, demonstrating lower prediction errors;
- Bayesian Methods and Support Vector Machine: Provide high accuracy for non-disease-specific predictions, with the support vector machine often outperforming other models, like naive Bayes and random forest;
- Text-Based Models (GPT-3): Show potential, but may not yet surpass expert human predictions in accuracy.
* Benefits: Improved accuracy, operational efficiency, and data-driven decisions;
- Limitations: Complexity, interpretability, data quality, and availability.  
Our review of the literature revealed that the neural network algorithm is one of the most popular, with a high degree of adaptability and applicability to a wide variety of predictions. As a result, the analysis determined that the neural network algorithm is the best-performing algorithm in this study on the datasets tested. However, the selection of the optimal algorithm for hospital LoS predictions may depend on the specific characteristics of the dataset and the intended application context.  
It is worth noting that the papers analyzed also described the use of other algorithms (as shown in Tables 3 and 4), but none of them stood out as much or achieved such positive results as the neural network algorithm.
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 3 Materials and Testing
### Specim Preparation  
Two types of cylindrical specimens were prepared for this study, both with a diameter of 101.6 mm (4 inches) but with different heights. For the Marshall mix design, the specimen height was 63 mm (2.5 inches), whereas for permanent deformation evaluation, the height was 203.2 mm (8 inches). Aggregates received from the mixing plant were separated into fractions based on their retention on specific sieves (37.5, 25.0, 19.0, 12.5, 9.5, 4.75, 23.6, 0.3, and 0.075 mm). The aggregates retained in the pan were discarded and replaced with an equivalent amount of limestone dust as a mineral filler. To meet the gradation requirements shown in Figure 1, 1150 g of aggregate was weighed for the Marshall specimens, while 3800 g was weighed for the permanent deformation specimens. Both were then heated to 150 \({}^{\circ}\)C and placed in a mixing bowl. The asphalt cement was separately heated to 140-145 \({}^{\circ}\)C, corresponding to a viscosity of 170 \(\pm\) 20 cSt, and then added to the preheated aggregates in the mixing bowl. The materials were thoroughly mixed on a hot plate for two minutes to ensure uniform coating. The mixture was then placed back in an oven at 140 \({}^{\circ}\)C for 10 min to maintain a uniform compaction temperature. Meanwhile, the compaction molds were preheated to 100 \({}^{\circ}\)C. A 4-inch paper disk was placed at the base of each mold, and the mold's interior edges were lubricated to facilitate specimen extraction. Half of the heated mixture was poured into the mold using a funnel. To ensure a homogeneous blend, the mixture was vigorously spaded with a heated spatula 15 times around the perimeter and 10 times over the interior before compaction, following ASTM D1559. The same procedure was repeated for the remaining portion of the mixture.  
For the Marshall specimens, compaction was performed using 75 blows per side of a Marshall hammer, as per ASTM D1559. In contrast, the double plunger method was employed for the permanent deformation specimens, in which a 65,000 lb (29,491 kg) load was applied to each end of the specimen for one minute using a hydraulic compression machine. Finally, the compacted specimens were cooled overnight on a smooth, flat surface at room temperature before being extracted and stored for testing.