esa-sceva/satcom-chunk-collection huggingface dataset:
# A Model for Oxygen Transport from Blood in Microessels to Tissue
###### Abstract  
Oxygen is vital for cellular energetics and metabolism in the human body. Blood transports oxygen to the tissues with hemoglobin in red blood cells playing a key role in the transportation of oxygen. To account for the Fahraeus and Fahraeus-Lindqvist effects, we use [PERSON] marginal zone concept, which subdivides each microvessel into a cell free layer surrounding a core region of uniform red blood cells concentration. The marginal zone concept is used to develop a steady state model for the transport of oxygen from blood to tissue where chemical reaction of oxygen occurs to produce energy. The approach is based on fundamentals of fluid flow and mass transfer in the two zones while accounting for the role of hemoglobin in the transport process and including mass transfer and chemical reaction in the tissue to produce energy using the Krogh cylinder concept. In contrast to transport modeling of solutes such as glucose, the present model includes the key role of hemoglobin in the transport of oxygen from blood to tissue. The model is analytical and provides analytical expressions for the oxygen level profiles in the blood cell free layer, the core zone, and the Krogh cylinder. The results are found to agree with published results in the literature for oxygen transport from blood in capillary size microvessel to its Krogh tissue cylinder. The model is not restricted to transport from capillaries and includes transport of oxygen from microvessels to tissue in general. Extensions of the model include further investigations in the case where changes in the blood microvessel or red blood cells occur due to pathological conditions.  
Fahraeus effect; oxygen; blood flow; tissue; Krogh cylinder; marginal zone theory +
Footnote †: journal: Applied  
definition  
The model of [PERSON] [3] for oxygen transport accounts for oxygen transport in capillaries using a uniform velocity profile while accounting for transport of oxygen in plasma and the role of hemoglobin in the transport process, with Krogh model used for transport by diffusion and oxygen consumption in the tissue side. On the other hand, the particulate models account for mass transport in microvessels with each cross-sectional area containing no more than one RBC. In a microvessel, there is a cell-free layer in which no RBCs are present, with a core region where RBCs concentrate. This segregation is considered in the present analysis of oxygen transport. The present model accounts for mass transport in both zones while including the role of hemoglobin in transport of oxygen in the core region. In addition, non-uniformity of the velocity profiles in the two zones and transport of oxygen in the tissue side are included in the present analysis of oxygen transport from blood to tissue. In contrast to a previously published model for solute transport (such as glucose) from blood to tissue [37], the present model includes the key role of hemoglobin in the transport of oxygen.
## 3 Path Loss Models
### Empirical Models  
Empirical models used to predict path loss rely on recorded measurements of signal strength in various environmental conditions. These models use empirical data to establish a relationship between the transmitter and receiver. Numerous empirical models have been created for the measured environment, but when used in other measurement contexts and experimental setups, they are found to be ineffective. To enable its services and use cases, the 5G standard provided significant improvements. The empirical model is categorized into early and current empirical models [32].
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 6 Applicability of the Proposed SEPIC Converter in a PV System  
For proving the usefulness of the proposed SEPIC-based converter in a PV application, a PV system with the block diagram presented in Figure 24, was built.  
The structure of the PV application is similar with the block diagram presented in [64]. The chosen maximum power point (MPP) algorithm was perturb and observe (P&O), and it was implemented in an ADuCino development board equipped with the ADuCM360 ARM microcontroller (Analog Devices, Greater Boston, MA, USA) [68]. The current transducer used in this application was LEM HO-8-NSM/SP33 [69]. A resistive divider was used to measure the voltage across the PV modules. The MPPT algorithm is described in detail in the authors' work [64].
# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
## 5 Experimental Results and Discussion
### Machine Learning  
In this study, five machine learning (ML) algorithms were used. The algorithms were, namely, support vector machine (SVM), naive bayes (NB), K-near neighbours (KNN), decision tree classifier (DT), and random forest (RF). These algorithms have been observed to be popularly chosen among researchers of affective computing [14; 20; 21; 54; 69]. GridSearchCV [70] was utilized to tune the hyperparameters of the algorithms, and the model was fit with the optimal parameters. This study also utilised the KFold cross-validation technique, with 10 folds. The accuracy of each ML in classifying the emotions were then compared.
## 3 Demand-Side Management Strategy and Resource Forecasting
### Wind and Solar Forecasting  
The strong correlation between the received ground-level solar irradiance, and the generated output power of the PV system provides the flexibly to utilize the predictions of solar irradiance in estimating the output power of the PV in smart grid applications [22]. The same approach is also adopted in predicting the wind power output based on the wind speed forecasts. Therefore, solar irradiance and wind speed prediction are also equally crucial to the implementation of energy management in the smart grid [23]. The wind and solar power forecasting model implemented in this study considered the following input parameters; wind speed, solar irradiance, humidity, temperature, pressure and wind direction.  
The model fitting or training of data-driven forecasting model is achieved by establishing underlying patterns among historical input variables [24]. Thus, its success relies heavily on appropriate input parameters selection, and the data pre-possessing. The candidate input data set for the model consists of a number of variables such as humidity, wind speed, pressure, solar irradiance, temperature, _etc._ A data-driven framework is based on the standard methodology of data science, and the main stages are as outlined; data pre-processing, model training, validation and final deployment. Data pre-processing might integrate all or some of these standard techniques: transformation, integration, normalization, cleaning etc. The subsequent process includes modeling, performance evaluation, and final deployment [25]. The schematic forecasting framework adopted in this study is shown in Figure 3.
# Shadow Fleets: A Growing Challenge in Global
## 4 Approaches to Detecting Dark and Gray Fleet Characteristics  
The covert nature of dark and gray fleets poses significant challenges to traditional methods of monitoring and regulation. Without the right tools, identifying vessels engaged in sanctioned cargo transportation becomes a daunting task, as these fleets invest substantial effort in concealing their actions. However, advancements in technology provide a promising avenue for enhancing detection capabilities and safeguarding the integrity of global maritime operations. The identification and monitoring of such vessels require a nuanced understanding of their operating characteristics and behaviors.  
Despite its more sophisticated operational model, the gray fleet faces challenges like those of the dark fleet, particularly concerning vessel age and maintenance. A substantial portion of gray fleet vessels exceed 15 years of age, mirroring the trend observed in the dark fleet. This aging infrastructure raises significant concerns about operational safety and environmental risk mitigation. The use of older vessels not only increases the likelihood of mechanical failures and accidents but also poses potential environmental hazards due to the increased risk of oil spills and other maritime incidents.
## 2 Modelling Methods
### Decomposer  
Generally, the conversion of a fuel during gasification is described by three subsequent mechanisms: drying, pyrolysis and gasification [6]. While the gasification step is modelled in the FR, the former two mechanisms are modelled in the decomposer block in this study. As drying solely encompasses the release of moisture from the fuel [6; 46], the main focus of this section is placed on fuel pyrolysis. [PERSON] et al. [25] applied the pyrolysis model of [PERSON] et al. [47] to predict the pyrolysis product composition from coal proximate and ultimate analysis parameters. Although it is reported that the basic mechanism of coal and biomass pyrolysis are similar [6; 7], it was decided to employ a pyrolysis model specifically tailored for biomass feedstocks, as this study is focused on the conversion of biomass-based fuels. [PERSON] et al. [48] devised a pyrolysis model for biomass feedstock built on the basis of an extensive experimental database. Similar to the pyrolysis model by [PERSON] et al. [47], this model solely requires information on the feedstock composition (C, H, O and char content) to estimate the final chemical composition of the organics after pyrolysis, allowing for its straight forward implementation into the existing Aspen Plus(tm) model. [PERSON] et al. [49] found that the formation of tar and larger hydrocarbons (>C1) is negligible in the presence of ilmenite and steam/CO\({}_{2}\). Therefore, the assumption by [PERSON] et al. [25] and [PERSON] et al. [50] that tars and larger hydrocarbons are directly converted to methane and carbon monoxide was also adopted in this study. Moreover, oxygen and hydrogen contained in the char were converted to syngas, resulting in a char solely consisting of carbon. As the FR is modelled based on chemical equilibrium, these simplifications do not have an impact on the final simulation results.  
By applying these assumptions, the product compositions after pyrolysis were calculated on the basis of the proximate and ultimate analysis of wood pellets, being the model feedstock for all subsequent considerations (see Table 1).  
Since the pyrolysis product composition is highly temperature dependent [6, 7, 48], a constant temperature representing the FR temperature during CLG was selected as the input for the pyrolysis model (\(T_{devol.}=900\)\({}^{\circ}\)C). A summary of the final product composition after de-volatilization, which was implemented into the process model, is given in Table 2.
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## Appendix A Planetary Parameters of Mars  
In this study, the following fundamental planetary parameters are used:  
\[\begin{array}{ll}\text{equatorial radius :}&R_{M}=3396\text{ km}\\ \text{gravitational parameter :}&\mu=42,828\text{ km}^{3}/\text{s}^{2}\\ \text{harmonics of the neopotential :}&\text{$J_{2}=1.957\cdot 10^{-3}$, }&\text{$J_{3}=3.147\cdot 10^{-5}$, }&\text{$J_{4}=-1.539\cdot 10^{-5}$,}\\ \text{$J_{2,2}=6.311\cdot 10^{-5}$, }&\text{$\lambda_{g,2,2}=1.309$, }&\text{$J_{3,1}=2.750\cdot 10^{-5}$, }&\text{$\lambda_{g,3,1}=1.421$}\end{array}\]  
where the symbol \(\lambda_{g,i,j}\) denotes the characteristic geographical longitude associated with harmonic \(\text{$I_{i,j}$}\).
## 4 Discussion  
The findings in this study suggest that although AI technologies are becoming ubiquitous in society, their role in law enforcement from the perspective of police officers varies considerably depending on general familiarity with the concept of AI and how much individual jurisdictions employ these technologies in their communities. In addition, this study reveals that integrating technological advancements to include autonomous vehicles could impact the relationships between communities and police jurisdictions from a public safety and traffic enforcement perspective. To expand on these previous points, this study offers critical considerations for developing ethics and procedural training for police officers who employ and increasingly interact with artificial intelligence technologies.  
At the same time, there are objective prerequisites and reasons for applying AI technologies in the course of law enforcement work. They are conditioned upon the fact that modern policing is required to solve many issues--reducing crime, optimization of law enforcement agencies, improving the efficiency of resources to ensure the activities of law enforcement agencies, increasing public confidence in law enforcement, and reduction in corruption. All the participants in this study echoed these requirements in their description of the role of policing in general and when considering how AI impacts law enforcement. The police officers' perceptions captured in this study begs the question, could AI create congruency with established norms and rules set forth by public policy?  
Nevertheless, police officers believe that AI technologies perform a limited role in the law enforcement domain, with the consensus among the participants that the technologies will expand and become widespread in the next 5 to 10 years. The evidence from the findings establishes that police officers think AI technologies positively impact law enforcement, improve public safety, reduce crime, and increase policing capability and capacity. Conversely, the evidence suggests that law enforcement professionals believe AI  
Figure 4: Self-driving technology implications for policing.  
technologies will not necessarily increase police and community trust. Ethical concerns that were raised include autonomy, privacy, affective empathy, and the potential to infringe on civil rights if the technology is not used responsibly.
## 6 Results and Discussion
### Comparing Our Findings with Existing Results  
To our knowledge, Table 6 lists the recent machine models that researchers have developed to detect or classify the IoTID20 dataset. The table lists two types of classification used by researchers: detection (binary classification) and classification (multiclass classification). For machine learning, it is generally simpler to perform binary classification than multiclass classification [44]. The reason is that in binary classification, the ML needs to select from two decisions, i.e., 0 or 1; however, with multiclass classification, ML needs to choose from more than two decisions and perform sub-binary classification.  
We can observe that our results slightly exceed other results due to the comprehensive data engineering process conducted in this research, as stated earlier in Features Engineering subsection (Section 4.2). Additionally, it is worth saying that in this research [35], the authors used several machine learning classifiers such as DT, SVM, and ensemble to detect and classify network activities in the IoTID20 dataset. They claimed they reached 100% using DT for detection and classification models. However, they accomplished low accuracy using SVM (less than 80% in the detection model and less than 50% in the case of the classification model); we reached an accuracy of 100% for the detection model and 99.80% for the classification model using SVM due to the feature engineering we discussed earlier.
## 1 Introduction
### Problem Statement  
Through a comprehensive review of previous research, there are still some unresolved issues:* There has been no research conducted on the durability variation of high-power fuel cells exceeding 100 kW.
* Current empirical and semi-empirical models do not account for variations in fuel cell electrochemistry and operating conditions, as they uniformly apply the same trends on polarization curves.
* Most studies on fuel cell durability lack experimental data support.
* There is a lack of literature regarding the durability variation of fuel cells when applied in vehicles.
* Existing fuel cell durability models proposed in the literature are only applicable to laboratory testing conditions and lack adaptability for real vehicles.
## 4 Theoretical Analysis of Double-Column Self-Centering Piers with Shear Links
### Theoretical Model Parameters  
The parameters in the theoretical model of the double-column self-centering piers with shear links can be divided into the following five groups:  
1. Geometrical dimensions: \(h\) and \(A_{\text{pier}}\) are the height and cross section of the columns, respectively; \(d\) is the distance between columns and \(A_{\text{PT}}\) is the area of steel strands.
2. Material properties: \(E_{\text{c}}\) and \(f_{\text{c}}\) are the elastic modulus and axial compressive strength of concrete, respectively; \(E_{\text{PT}}\) and \(f_{\text{PT}}\) are the elastic modulus and the ultimate strength of steel strands, respectively.
3. Gravitational forces: \(W_{\text{beam}}\) and \(W_{\text{pier}}\) are superstructure gravity and column gravity, respectively.
4. Shear link parameters: \(k_{\text{SL}}\) and \(F_{\text{ySL}}\) are the initial stiffness and yield strength of shear links, respectively.
5. Other: \(F_{\text{PT0}}\) is the initial prestress of the steel strands. All of these parameters are illustrated in Figure 12.  
Figure 11: Results of different spring models.  
Figure 12: Parameters of the double-column self-centering pier with shear links.
## 3 Results
### In-Orbit Results
#### 3.2.4 Material Mission (MM)  
Data from the material mission have shown that it is possible to obtain information regarding the CTE change of each of the samples from strain and temperature sensors. A comparison with the preliminary ground tests and data measured in orbit for Sample 3 (described in Section 2.3) is shown in Figure 14. The data show that CTE does not remain constant and is dependent on the temperature changes as well as the carbon fiber direction.  
Figure 12: Geographic locations of the CPD missions conducted on November 2018.  
Strain was measured in two orthogonal, in-plane directions in each sample. While manufacturing the samples with embedded strain gauges, the alignment of the gauges with respect to the direction of the carbon fibers was also shown to modify the readings of the applied strain. The preliminary results from the MM appear to match the previous tests [31]. To have a complete understanding of the material degradation, a correlation of the orbital data with the ground calibration information will be performed. This will be included in a future publication regarding the material mission results.
# Smart Sensors and Smart Data for Precision Agriculture: A Review
## Abbreviations  
\begin{tabular}{l l l l} ADC & Analog-to-Digital Converter & MLR & Multiple Regression Analysis \\ AI & Artificial Intelligence & N & Nitrogen \\ ANFIS & Adaptive Periodic Threshold-sensitive Energy & NC & Network Coordinator \\  & Efficient sensor Network protocol & & \\ APTEEN & Adaptive Periodic Threshold-sensitive Energy & NCS & Neural Compute Stick \\  & Efficient sensor Network protocol & & NDVI & Normalized Difference Vegetation Index \\ AWS & Amazon Web Services & & & Optimized Algorithm of Sensor Node Deployment \\ BOP & Beacon Only Period & OASNDFA & for intelligent Agricultural monitoring \\ CDC & Complex Dielectric Constant & OSRS & Open-Source Remote Sensing \\ CNN & Convolutional Neural Network & PA & Precision Agriculture \\  & Convolutional Neural Support Vector Machine & PAF & Precision Agriculture and Farming \\  & Classifier & & & \\ CRF & Controlled-Release Fertilizer & PAR & Photosynthetically Active Radiation \\ D-ATR & Diamond Attenuated Total Internal Reflectance & PCR & Principal Component Regression \\ DCTA & Dynamic Converge cast Tree Algorithm & PIS & Passive Infrared Sensor \\ DSS & Decision Support Systems & PLC & Programmable Logic Controller \\ EC & Electrical Conductivity & PLSR & Partial Least Squares Regression \\ ET & Evapotranspiration & PWM & Pulse-Width Modulation \\ FLFL & Five-Layer, Fifteen-Level & reNUVI & red-edge NDVI \\ FTIR & Fourier Transform Infrared Spectroscopy & RFEH & Radio Frequency Energy Harvesting \\ GCPs & Ground Control Points & RMSE & Root Mean Square Error \\ GD & Gradient Descent & RNN & Recurrent Neural Network \\ GHS & Greenhouse Gas & RTU & Remote Terminal Unit \\ G-IoT & Green IoT & SDGs & Sustainable Development Goals \\ GIS & Geographic Information Systems & SIS-PAF & Smart Irrigation System for Precision Agriculture \\ GNDVI & Green Normalized Difference Vegetation Index & SNR & Signal-To-Noise Ratio \\ GPR & Gaussian Process Regression & SPAD & Soil Plant Analysis Development \\ GPS & Global Positioning Systems & SSIM & Structural Similarity Index \\ GPU & Graphics Processing Unit & SVM & Support Vector Machines \\ IAS & Irrigation Advisory Services & TAK & Title, Abstract, and Keywords \\ IoE & Internet of Everything & TVWS & TV Whitespace \\ IoT & Internet of Things & UAVs & Unmanned Aerial Vehicles \\ IWUE & Irrigation Water Use Efficiency & UWB & Ultra-Wide Band \\ Lasso & Least absolute shrinkage and selection operator & VLRGD & Variable Learning Rate Gradient Descent \\ LCIS & Low-cost System for agricultural Irrigation & VWC & Volumetric Water Content \\  & Support & & \\ \end{tabular}
# The Influence of UV on the Production of Free Trepenes in _Vitis vinifera_ cv. [PERSON]
## 3 Results and Discussion  
### Basic Chemical Parameters of the Shiraz Grapes  
Variations in grape TSS were observed among treatment groups, and no statistically significant difference could be established between individual treatment and control likely due to variations among field replicates (Table 1). Previous studies on Pinot noir grapes [16] and \"Jingxiangyu\" grapes [28] reported slight reductions in TSS in UV and light reduction treatments, which could not be confirmed in the current study. In the current study, UV-attenuated treatments, especially from intermediate ripeness to harvest, tended to have lower TA and higher pH (Table 1). This was different from previous studies where lower grape pH was observed in UV-reduced treatment [16], while no significant differences were observed among slight reduction treatments [28; 29]. Total grape phenolics measured at 280 nm increased in UV-attenuated treatments, which was in accordance with previous research that the concentrations of hydroxycinnamic acids and hydroxybenzoic acids would be promoted by UV attenuation [30]. The same study also reported significant reduction in all types of grape anthocyanins, total flavonoids and total flavanols in UV-attenuated treatment at harvest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & **Total Soluble** & \multicolumn{2}{c}{**Tittrable Acid**} & \multicolumn{2}{c}{**Total Phenolic**} \\  & **Solid’ (Brix)** & **pH** & **(g/L)** & **(Absorbance Units Per Gram)** \\ \hline Control & 21.7 \(\pm\) 0.7 abc & 3.93 \(\pm\) 0.11 ab & 4.7 \(\pm\) 0.3 cd & 1.20 \(\pm\) 0.09 a \\ TH PC & 21.4 \(\pm\) 1.1 ab & 4.03 \(\pm\) 0.18 ab & 3.7 \(\pm\) 0.8 a & 1.31 \(\pm\) 0.16 abc \\ TH PETG & 22.7 \(\pm\) 1.0 bc & 4.03 \(\pm\) 0.11 ab & 3.7 \(\pm\) 0.3 a & 1.32 \(\pm\) 0.11 abc \\ TI PC & 21.9 \(\pm\) 0.8 abc & 4.03 \(\pm\) 0.09 ab & 4.0 \(\pm\) 0.4 abc & 1.32 \(\pm\) 0.16 abc \\ TI PETG & 21.6 \(\pm\) 0.3 abc & 4.00 \(\pm\) 0.18 ab & 4.5 \(\pm\) 0.8 bcd & 1.48 \(\pm\) 0.07 c \\ TV PC & 22.0 \(\pm\) 1.7 abc & 3.97 \(\pm\) 0.11 ab & 4.6 \(\pm\) 0.6 bcd & 1.40 \(\pm\) 0.16 bc \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conventional chemical parameters of Shiraz grapes under different UV attenuation treatments in 2017 *.