esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Raw Materials: SU-8 2025 resist was purchased from Microchem. Propylene glycol methyl ether acetate (PGMEA), tetrahydrofuran (THF), and isopropanol alcohol (IPA) were purchased from Sigma-Aldrich. All chemicals were used as received.  
Synthesis of PFBT and preparation of PFBT/SU-8 mixture: We introduced chirality in PFBT by attaching (S)-3,7-dimethyloctyl substituents at the 9-position of the fluorene monomer. Chiral PFBT was synthesized using palladium-catalyzed Suzuki polycondensation and the product was purified with Soxhlet extraction to obtain high molecular weight (Mn = 13,000, Mw = 30,000). The PFBT was dissolved in THF by stirring with a magnetic stirrer for \(\sim\)24 h. SU-8 2025 was then added to the PFBT/THF solution and stirred for an additional 24 h with a magnetic stirrer.  
Fabrication: Figure 1 shows the overall process for photopatterning of the PFBT/SU-8 2025 nanocomposite. The PFBT/SU-8 2025 solution, prepared as described above, was spin-coated at 1000 RPM onto a glass substrate, then baked at 65 \({}^{\circ}\)C for 60 min, then baked at 95 \({}^{\circ}\)C for 10 min, and finally allowed to cool to room temperature for 2 h. A [PERSON] UV aligner (365 nm wavelength) and chrome photo mask were used for the UV exposure process with a total exposure dose of 225 mJ/cm\({}^{2}\). After exposure, samples were baked at 65 \({}^{\circ}\)C for 10 min, then at 95 \({}^{\circ}\)C for 10 min to accelerate the crosslinking of the polymer. They were allowed to cool for 2 h to room temperature before development. To develop the UV exposed samples, they were submerged in PGMEA for 5 h to remove unpolymerized material. IPA and deionized water were used to rinse the developed samples. After development and rinsing, samples were annealed at 150 \({}^{\circ}\)C for 15 min in an inert atmosphere glove box, then gradually cooled to room temperature.  
Characterization. The patterned structures were analyzed by scanning electron microscopy (SU-4000, Hitachi, Japan) and circular dichroism (CD) spectroscopy (J-815, Jasco, UK).
## 4 Data and Methodology  
### Data Sources  
In this study, we used only the _in-situ_ meteorological synoptic stations within the four study areas that have undergone quality control by the Iran Meteorological Organization (IMO). However, the stations with large data gaps were removed. The GPM mission's Precipitation Processing System (PPS) at NASA's Goddard Space Flight Center released the IMERG data to the public in late February, 2015. The data set includes precipitation rates since mid-March 2014. Current and future data sets are freely available to users from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) website [33]. At present, the IMERG data are available from 12 March 2014 to present, thus providing IMERG-V03D data for March 2014 to February 2015 period. The 3B42-V7 from TRMM and other satellites at GES DISC and ERA-Interim data sets from ECMWF [34] are available to the public and are free of charge.  
We should mention that further works are needed to evaluate the interannual variation of IMERG data relying on larger samples.  
The satellite products were compared with the measured precipitation data of 43 meteorological synoptic stations (Figure 3) at daily, monthly and seasonal time scales for different climate conditions in Iran. At the time of data analysis, we had access only to this number of stations.  
In this research, the data of the nearest grid point in the satellite grid (less than 0.05 degrees off the grid points) is compared with that corresponding to the ground point observation (_i.e._, the meteorological synoptic station). In cases where the grid points were close to the stations, the comparison was carried out directly between them. However, in cases where the ground station was surrounded by four grid cells but not particularly close to none, an average of the four grid points around the station was used as the basis for comparison. Table 1 indicates the characteristics of data sets and Table 2 shows the number of stations and pixels in each area as used in this study.  
Figure 2: (**a**) Elevation map of Iran; (**b**) mean annual precipitation (mm) across Iran, 1961–1990 [11].
## 2 Vehicle and Tire Model
### Nonlinear Duggoff Tire Model  
Unlike analytical tire models established on pure physical theories, semi-empirical tire models that combine theoretical models with experimental statistics provide advantages such as simpler calculations and higher accuracy [36]. At present, the magic tire model and the Duggoff tire model are widely used. The nonlinear Duggoff tire model applied for this paper does not rely on empirical parameters, ignores tire camber, and considers only longitudinal and lateral tire forces. The calculation equation, after a reasonable simplification of the model, is as follows:  
\[F_{xij}=\mu\cdot F_{z}\cdot C_{xij}\cdot\frac{\lambda_{ij}}{1-\lambda_{ij}} \cdot f(L) \tag{10}\]  
\[F_{yij}=\mu\cdot F_{z}\cdot C_{yij}\cdot\frac{tan\left(\alpha_{ij}\right)}{1 -\lambda_{ij}}\cdot f(L) \tag{11}\]  
where  
\[f(L)=\left\{\begin{matrix}L\cdot(2-L),&L<1\\ 1,&L\geq 1\end{matrix}\right. \tag{12}\]  
\[L=\frac{1-\lambda_{ij}}{2\sqrt{C_{xij}^{2}\cdot\lambda_{ij}^{2}+C_{yij}^{2} \cdot\left(\tan\left(\alpha_{ij}\right)\right)^{2}}} \tag{13}\]  
where \(\mu\) is the road adhesion coefficient, and \(C_{xij}\) and \(C_{yij}\) are the longitudinal stiffness and lateral stiffness obtained through checking tables for each tire under different vertical loads, respectively. \(\lambda_{ij}\) is the longitudinal slip rate, \(\alpha_{ij}\) shows the tire slip angle, \(v_{cij}\) is the wheel center velocity, and the boundary value \(L\) is used to determine whether each tire is currently in a linear or non-linear state.  
The speed of each wheel center is expressed as:  
\[v_{cfl,cfr}=\left(v_{x}\mp\frac{B_{f}}{2}r\right)\cdot cos\delta+\left(v_{y}+ ar\right)\cdot sin\delta \tag{14}\]\[v_{crl,crr}=v_{x}\mp\frac{B_{r}}{2}r \tag{15}\]  
The longitudinal slip rate for each wheel can be calculated by bringing the obtained wheel speeds into the following equation:  
\[\lambda_{ij}=\frac{\omega_{ij}\cdot R_{0}-v_{cij}}{v_{cij}} \tag{16}\]  
Use the following equations to obtain the tire side slip angle, and substitute it with the longitudinal slip rate into Equations (10)-(13) to obtain the longitudinal and lateral tire forces.  
\[\alpha_{rl,fr}=\delta-arctan\left(\frac{v_{y}+ar}{v_{x}\mp\frac{B_{r}}{2}r}\right) \tag{17}\]  
\[\alpha_{rl,rr}=-arctan\left(\frac{v_{y}-br}{v_{x}\mp\frac{B_{r}}{2}r}\right) \tag{18}\]
## 2 Monitoring of Nutritional Status in CKD Patients: Affected Parameters
### Main Nutritional Score Evaluations
Unfortunately, this costly device, which is nonportable and relies on operator proficiency, cannot be used as a practical or accessible bedside tool. Edema or hyperhydration render DXA less reliable than BIA in the precise detection of different body compartments, in particular total body water (TBW), in chronic HD patients. It should, moreover, be highlighted how these parameters should be detected only in advanced CKD stages by expert doctors or nutritionists through dietary recall and, more recently, dietitian-led telehealth coaching intervention, by applying the Healthy Eating Index. Actual calorie intake and degree of compliance of interviewed patients is hard to establish [63; 74]. An additional method of proven validity is represented by the calculation of urea nitrogen appearance (UNA) [75]. Selected patients with a good RKF undergoing once-weekly hemodialysis showing good compliance with low protein intake (0.6/g/kg/day) were assessed over a six-day interdialytic program [76; 77] and UNA calculated; this method provides an indirect estimate of dietary intake based on urinary nitrogen output, fecal output and body area nitrogen.
## 3 Results
### Area-Based Approaches  
The local model obtained by fitting a power-law model to the data available from SAFE oil palm plantations yielded the following relationship between top of canopy height (TCH) and aboveground carbon density (Figure 5a):  
\[\text{ACD}_{\text{HTC}}=1.494\text{TCH}^{1.107} \tag{7}\]  
A TCH exponent of just over 1 indicates a near-linear relationship between TCH and carbon density in oil palm plantations, as expected from the straight-line relationship between individual biomass and height (i.e., Equation (1)). The relationship between ACD and CC follows instead an exponential relationship (Figure 5b):  
\[\text{ACD}_{\text{cc}}=22.991\text{CC}^{2.744} \tag{8}\]  
In comparison, when ACD was expressed a function of both TCH and residual CC, we obtained the following model:  
\[\text{ACD}=1.31\text{ TCH}^{1.17}\ (1+3.52\text{ CCres}) \tag{9}\]  
where CCres can be obtained by subtracting the actual CC by the model that predicted CC from logistic regression with TCH as the predictor variable:  
\[\text{CCres}=\text{ CC}-\frac{1}{1+\text{ e}^{0.77\text{ TCH}^{-0.37}}} \tag{10}\]  
Figure 4: Spearman correlation (\(r\)) between field-measured average tree height per plot and LiDAR-derived (**a**) top-of-canopy-height (TCH), and (**b**) canopy cover (CC) at 3 m height, as well as between field-measured tree density (#.ha\({}^{-1}\)) and (**c**) TCH and (**d**) CC.  
The model based on TCH generated unbiased ACD predictions with RMSE of 26.3%, whereas the model based on CC showed a bias of 1.2% with similar RMSE of 26.4%, with both models yielding an R\({}^{2}\) = 0.62 (Figure 6a,b). The inclusion of both TCH and residual canopy cover into the model reduced RMSE to 23.9% and improved the strength of relationship to R\({}^{2}\) = 0.69 (Figure 6c). The predictions were obtained from the validation datasets using the leave-one-out procedure. Neither approach showed any trend with oil palm plantation age (e.g., plots deviating from the 1:1 line), which indicates that the equations can be applied to plantations of different ages and to faster and slower growing stands of the same age.
## 4 Results and Discussion
### First Part: Factorial Design Multivariate Statistical Analysis
#### 4.1.2 Factor Regression Analysis  
By analyzing the factor regression (Table 8), the coefficient column shows the magnitude value of all the factors and their interactions, which denotes the effect strength of these factors on the air bubble defect or in the formation of the air bubble area. Among these factors, the strongest effect was contributed by the interaction factor combination of melt temperature*screw speed at levels of 180 \({}^{\circ}\)C and 30% with a magnitude value of 3428, followed by the main factor back pressure at the level of 2 bar, and finally by the combined interaction of melt temperature*back pressure*screw speed with variable values of 180 \({}^{\circ}\)C, 2 bar, and 30%.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Term** & **Coef** & \(t\)**-Value** & \(p\)**-Value** & **VIF** \\ \hline Melt temperature & & & & \\
180 & \(-\)3563 & \(-\)6.82 & 0.000 & 1.00 \\ Back pressure & & & & \\
2 & 2433 & 4.66 & 0.000 & 1.00 \\ Screw speed & & & & \\
30 & & & & \\ Melt temperature*Back pressure & & & & \\
180*2 & & & & \\ Melt temperature*Screw speed & & & & \\
180*30 & & 3428 & 6.56 & 0.000 & 1.00 \\ Back pressure*Screw speed & & & & \\
2*30 & & & & \\ Melt temperature*Back pressure*Screw speed & & & & \\ speed & & & & \\
180*2*30 & & 2297 & 4.40 & 0.000 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Factor regression analysis.  
Figure 4: Main factors and their interaction effects with the output response.
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Model for Prediction
#### 4.4.6 K-Nearest Neighbors  
The K-nearest neighbors (KNN) algorithm is a simple instance-based machine learning algorithm. In the scenario of building energy consumption prediction, for a building energy consumption sample to be predicted, the KNN algorithm will find the K samples in the training dataset that are most similar in features to this sample (usually measured by Euclidean distance, Manhattan distance, etc.). Then, it predicts the energy consumption of the target sample based on the energy consumption values of these K neighbor samples. For example, if K is set to 5, it will find the 5 samples closest to the sample to be predicted. If the energy consumption of 3 out of these 5 samples is within a certain range, then the energy consumption of the sample to be predicted is likely to be predicted within that range as well.  
The advantages of the KNN algorithm are its simplicity and intuitiveness. It does not require a complex training process, and the model update is easy, only needing to add new training samples. However, it has a high computational complexity, as it needs to traverse the entire training dataset to calculate the distances during prediction. Moreover, the selection of the K value has a significant impact on the prediction results. If the K value is too small, the model is sensitive to noise; if the K value is too large, the model may become vague and unable to accurately reflect local features. In building energy consumption prediction, it is necessary to select an appropriate K value according to the characteristics of the specific dataset and prediction requirements through methods such as cross-validation to improve the prediction accuracy.
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
###### Abstract  
In the context of globalization and the Belt and Road Initiative, maintaining the stability and security of the China Railway Express network (CRN) is critical for international logistics operations. However, unexpected events can lead to node and edge failures within the CRN, potentially triggering cascading failures that critically compromise network performance. This study introduces a Coupled Map Lattice model that incorporates cargo flow dynamics, distributing cargo based on distance and the residual capacity of neighboring nodes. We analyze cascading failures in the CRN under three scenarios, isolated node failure, isolated edge disruption, and simultaneous node and edge failure, to assess the network's vulnerability during emergencies. Our findings show that deliberate attacks targeting cities with high node strength result in more significant damage than attacks on cities with a high node degree or betweenness. Additionally, when edges are disrupted by unexpected events, the impact of edge removals on cascading failures depends on their strategic position and connections within the network, not just their betweenness and weight. The study further reveals that removing collinear edges can effectively slow the propagation of cascading failures in response to deliberate attacks. Furthermore, a single-factor cargo flow allocation method significantly enhances the network's resilience against edge failures compared to node failures. These insights provide practical guidance and strategic support for the CR Express in mitigating the effects of both unforeseen events and intentional attacks.  
China Railway Express; Cascading Failures; Coupled Map Lattice Model; Vulnerability; Emergency Scenarios
# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 3 System Model  
In this paper, we consider a segment of a bidirectional multi-lane highway that contains \(N\) number of vehicles and several RSUs located along the road. The RSUs serve as fog nodes, thus providing computing services to the vehicles, as shown in Figure 1. The RSUs are connected to the cloud server via optical fiber links. The time taken to transmit tasks between neighboring RSUs and from RSUs to the cloud is considered negligible. Each vehicle is equipped with a communication device that allows for communication with nearby vehicles and other RSUs.  
Figure 1: Illustration of the considered system model.
# Hydrogeochemistry of Fault-Related Hot Springs in the Qaidam Basin, China
###### Abstract  
Hydrogeochemical characterization studies are regarded as an important method for determining the origin of hot springs. The major elements, trace elements, and stable isotopes of four groups of hot spring water samples and two groups of gas samples collected from the intersection of the Altyn Tagh fault zone and the East Kunlun fault belt were investigated in this study. The hot spring water temperature ranged between 6 \({}^{\circ}\)C and 14 \({}^{\circ}\)C. The water chemistry types of the hot springs were Na-Mg-Cl, Mg-Na-Cl-HCO\({}_{3}\), Na-Cl-SO\({}_{4}\), and Na-Cl-HCO\({}_{3}\). The \(\delta\)D values ranged from \(-50.00\%\) to \(-68.60\%\), while the \(\delta\)18\(\mathrm{O}\) values ranged from \(-6.90\%\) to \(-8.60\%\). The hot spring water was recharged mainly by infiltrating precipitation, with a recharge elevation of 3390-3676 m. The heat storage temperature ranged from 66.7 to 164.9 \({}^{\circ}\)C. The circulation depth was estimated to range between 1043 and 2679 m. The strontium isotopic composition of the water samples in response to the main weathering sources comprised carbonate and sulfate. CO\({}_{2}\) was the main component in the hot spring gas in the study region, and its content was over 95%. The \({}^{3}\)He/\({}^{4}\)He-R/Ra relationship diagram revealed that the mantle-sourced helium from the Yitunbulake spring was 3.06%. In comparison, that from the Aiken spring was 7.38%, which indicated an intrusion of mantle-source material mixed into the hot springs in the study region. The crustal marine limestone contributed significantly to the carbon inventory of the hot spring gas samples (?5%). The dissolution of the marl aquifer resulted in the release of CO\({}_{2}\). Carbon was primarily obtained through metamorphism and hydrothermal reactions in the basement lithologies. The Yitunbulake and Aiken hot springs are found near the intersection of the Arjin and East Kunlun fractures, where the water-rock response is relatively strong and the depth of circulation and thermal storage temperature are both high. This causes relatively high ambient pressure to be released from the deep fluid, resulting in microseismic activity in this region. The continuous observation of Aiken spring water chemistry allows for the monitoring of fracture activity in the region. The results of the study could serve as a foundation for further exploration of the relationship between geothermal water and deep faults, shallow geological formations, hydrogeological conditions, and geothermal resource development in the region.  
hot spring; isotopes; hydrogeochemistry; gas geochemistry; Altun Tagh fault; Kunlun fault belt
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 2 Background and Related Work
### Colocation Tool Support  
Colocation is the use of new telecommunication devices with an existing mast of a mobile telecommunication operator for the purpose of resource reduction through sharing or new customer integration. For the process to be successful, some optimization work typically needs to be carried out with the aid of supporting tools such as NETMAX or Atoll, which are advanced tools for analyzing the performance of a network. communication network analysis (CNA) is an intelligent wireless network tool that supports geographic information system (GIS) and data analysis functions. Geographical Resources Analysis Support System Radio Planning Tool (GRASS-RaPlaT) is an open-source tool with various modules for sectorization due to a given number of antenna radiation patterns that can be used to store complete radio network coverage data. Drones can be used for panoramic views, to survey the area for wide angular view and ensure that there is no blockage or obstruction of the line of sight. Google Earth is another relevant tool to check for elevation and coordinates of the geographical area. To assist with identification of the transmitter to mast allocations to support colocation, in this work several software tools were developed. The tools are described, starting with the underlying modelling and definition of the problem statement, in following section.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
###### Abstract  
Smart Rings (SRs) are user-friendly devices capable of measuring various health parameters, making them suitable for remote continuous monitoring in diverse clinical settings. Since the available evidence on the accuracy of SRs recording health data is highly heterogeneous, this systematic review, conducted in accordance with PRISMA guidelines, searched for articles evaluating the efficacy of SRs for sleep, respiratory, and cardiovascular monitoring across the PubMed, SCOPUS, and ProQuest databases. Meta-analyses were conducted for health outcomes evaluated in at least three studies with a comparable study population and design, and the same comparison device. Nineteen articles were included: eleven analyses focused on sleep quality, eight on cardiovascular parameters, and one on oxygen saturation. Studies analysing cardiovascular outcomes found a good accuracy of SRs in measuring heart rate (HR) with a mean bias of \(-0.4\) bpms (limits of agreement (LoAs): \(-2.7\); \(1.8\)). The meta-analyses showed variability in SRs' efficacy in monitoring total sleep time (mean bias: \(-21.3\) min, LoAs: \(-69.9\), \(27.4\)) and REM duration (mean bias: \(-18.2\) min, LoAs: \(-33.3\), \(-3.1\)). The results highlighted the promising potential of SRs for HR monitoring. Further research is needed to clarify the reliability of SRs in monitoring sleep quality and their use directed to a broader range of health parameters. With further development, SRs could become valuable tools for healthcare professionals.  
smart ring; IoMT; sleep; cardiovascular; digital health; health monitoring; digital twin
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 3 Simulation
### Comparison of Fitting in Geomagnetic and Modip Latitudes  
The residual maps of fitted global TEC in geomagnetic latitude and modip latitude at 18:00 UT on the day of year (DOY) 265, 2014 are shown in Figure 2. In geomagnetic latitude, the fitting residuals are approximately (\(-6.60\), \(7.44\)) TECU, and the large values mainly occur at equatorial regions and parallel to the magnetic equator, which has a relation with the position of the equatorial ionospheric anomaly (EIA). The fitting residuals in the modip latitude range from 1.86 to \(-2.50\) TECU, which are much smaller compared with the results of fitting in the geomagnetic latitude, and the larger deviations occur in the equatorial area (EA) and the South Atlantic Magnetic Anomaly (SAMA) region.  
The RMSE statistics for using ED data in geomagnetic and modip coordinate systems over time and different latitude bands on DOY 265, 2014 are depicted in Figure 3. The mean reductions of RMSE are 49.57% and 46.55% in time and latitude, respectively, suggesting the fitting can be improved by over 40% with modip latit  
Figure 2: Residual maps of fitting in geomagnetic latitude (**left**) and modip latitude (**right**) at 18:00 UT, day of year (DOY) 265, 2014.