esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Protocol and Measurements  
After monitoring, the animals received 0.9% sodium chloride at 4 mL/kg/h and were randomly allocated to the different groups. After a 30-min resting period, sepsis was induced at \(t\) = 0 h by the administration of an intravenous infusion of live _Escherichia coli_ (2 \(\times\) 10\({}^{8}\) CFU/mL mL/kg, serotype O26:H11, isolated from blood cultures from a patient with septic shock) over 30 min. The non-septic animals (_n_ = 15) received an equivalent amount of saline over 30 min. From \(t\) = 0 h and during the remaining 5 h, the non-septic animals received 0.9% sodium chloride at 4 mL/kg/h throughout the experiment. The septic animals, depending on the resuscitation group, received 0.9% sodium chloride at 4, 10, or 17 mL/kg/h (_n_ = 8 for each group) starting at \(t\) = 0. This septic challenge used induced the biochemical, hematological, and hemodynamic changes characteristic of human sepsis.  
At \(t\) = 0 h (right before the infusion of _E. coli_ or the placebo) and then hourly, we measured the different hemodynamic parameters. The pressures were measured and displayed (Hewlett Packard monitor, Madrid, Spain). We registered the mean systemic arterial pressure (MAP), mean pulmonary artery pressure (MPAP), right atrial pressure (RAP), and pulmonary artery occlusion pressure (PAOP). Cardiac output (Q\({}_{\text{TOT}}\)) was measured by the thermodilution technique (CO computer; Abbott Labs: Chicago, IL 60064, USA). Systemic (SVR) and pulmonary (PVR) vascular resistances were calculated (MAP-RAP/QTOT and PAP-MPAP/QTOT, respectively) and expressed as arbitrary units.  
Blood samples were obtained at \(t=0\) h, \(t=1\) h, \(t=3\) h, and \(t=5\) h for measurement of the creatinine and arterial lactate concentrations (Hitachi 91; Boehringer, Mannheim, Germany) and arterial blood gases (ABL 500; Radiometer, Copenhagen, Denmark). A serum sample obtained at the end of the experiment was frozen and stored at \(-80^{\circ}\) for later measurement of the cytokines and miRNAs.  
The experiment was terminated at \(t=5\) h or if severe hypotension (MAP < 20 mm Hg) ensued. At that point, the animal was sacrificed by an intravenous injection of thiopental (15 mg/kg intravenously). After the animal's death, the left kidney fossa was accessed through a left-sided lumbotomy, and samples of the renal cortex and the renal medulla were cut and stored at \(-80^{\circ}\) until further analysis.  
We followed the current European and national legislation on the use of laboratory animals (Principles of Laboratory Animal Care, 2010/63/UE and Real Decreto 53/2013 BOE-08/02). This study was approved by the institutional and the local government animal research ethics committees.
## 2 Materials and Methods
### Sealing Ring Parameters
#### 2.2.2 Determination of Material Parameters  
The GS seal ring consists of a step seal ring and an O-ring. The step seal ring is made of copper-filled polytetrafluoroethylene (PTFE), and the O-ring is made of modified nitrile rubber (NBR). According to the requirements of GB/T 1041-1992 [18] 'Test Method for Plastic Compression Properties', cylindrical specimens of copper-filled PTFE with a diameter of 12 mm and a length of 30 mm were prepared, totaling 5 specimens, and compressed at a speed of 1 mm/min. Following the requirements of GB/T 7757-2009 [19] 'Determination of Compression Stress-strain Properties of Sulfur-Cured Rubber or Thermoplastic Rubber', cylindrical specimens of nitrile rubber rod with a diameter of 29 mm and a length of 12 mm were prepared, totaling 3 specimens, and compressed at a speed of 10 mm/min. The test materials were supplied by the seal ring manufacturer. Figure 4a shows the PTFE specimen, and Figure 4b shows the NBR specimen.  
The specimens were subjected to uniaxial compression tests using the WAW-600D (SUNPOC(tm), Shenzhen, China) servo-hydraulic universal testing machine, as shown in Figure 5. The stress-strain curves of the specimens were obtained. By analyzing the stress-strain curves of five PTFE specimens, the average elastic modulus of PTFE was calculated to be 292 MPa, with a Poisson's ratio of 0.45. The stress-strain curve test data for NBR were imported into the ABAQUS property module, and a constitutive model for NBR material was fitted using the material evaluation function. In this study, the Mooney-Rivlin hyperelastic model was chosen, with a strain energy density function as follows:  
\[W=C_{10}(I_{1}-3)+C_{01}(I_{2}-3)+\frac{1}{2}D(I_{3}-1)^{2} \tag{1}\]  
In the equation, \(W\) represents the strain energy density function, \(I_{1}\) and \(I_{2}\) are the stress tensor invariants, and \(I_{3}\) and \(D\) reflect the material's deformation behavior. \(C_{10}\), \(C_{01}\), and \(D\) are material constants, which were calculated based on material test data imported into the ABAQUS 2022 software, resulting in \(C_{10}\) = 1.183 MPa; \(C_{01}\) = 0.479 MPa; \(D\) = 0.004.  
Figure 3: Geometric dimensions of GS seal ring cross-section (unit: mm): (**a**) step seal ring, (**b**) O-ring.  
Figure 2: GS seal ring.
###### Abstract  
Driving environment perception for automated vehicles is typically achieved by the use of automotive remote sensors such as radars and cameras. A vehicular wireless communication system can be viewed as a new type of remote sensor that plays a central role in connected and automated vehicles (CAVs), which are capable of sharing information with each other and also with the surrounding infrastructure. In this paper, we present the design and implementation of driving environment perception based on the fusion of vehicular wireless communications and automotive remote sensors. A track-to-track fusion of high-level sensor data and vehicular wireless communication data was performed to accurately and reliably locate the remote target in the vehicle surroundings and predict the future trajectory. The proposed approach was implemented and evaluated in vehicle tests conducted at a proving ground. The experimental results demonstrate that using vehicular wireless communications in conjunction with the on-board sensors enables improved perception of the surrounding vehicle located at varying longitudinal and lateral distances. The results also indicate that vehicle future trajectory and potential crash involvement can be reliably predicted with the proposed system in different cut-in driving scenarios.  
connected and automated vehicle; cooperative perception; V2X communications; sensor fusion; advanced driver assistance system; trajectory prediction; risk assessment; collision warning 2021
## 4 Computational Model for Surface-Fractal Intersections  
So far, we have a tabular representation of the tree-like fractals, and the question is how to trim the fractal such that it does not pass through the CAD shell (defined or approximated by a surface in this model). The following inputs are defined:  
1. A surface mesh with a given set of points denoted \(PTS\) and triangulation denoted \(TR\).
2. A tabular fractal representation.  
To determine if a given branch intersects the surface, we check if the branch intersects any triangle (given by \(PTS\) set as vertices and \(TR\) as triangle definition). First, the ray/triangle intersection algorithm (Moller and Trumbore) is used [19], which outputs the intersection point of a ray (infinite line defined by the two points of the branch) and a triangle (with vertices within \(PTS\) set). To evaluate whether the line segment is intersecting the triangle, we compare \(D_{1}\) with \(D_{2}\), where \(I_{P}\) is the intersection point (with the triangle, and is an output for the ray/triangle intersection algorithm), \(O_{P}\) is the origin of the ray (or branch), and \(F_{P}\) is the final point in the branch:  
\[\begin{split} D_{1}&=\text{norm}(I_{P}-O_{P})\\ D_{2}&=\text{norm}(F_{P}-O_{P})\end{split} \tag{18}\]  
If \(D_{1}\geq D_{2}\), there is no intersection; otherwise, the intersection point (output for the ray/triangle intersection algorithm) is stored in Table 4 to overwrite the final value on the branch. Next, all the children of the final node of the intersecting branch are considered NaN. An example is given in Table 5. The yellow cell is the branch that intersects a triangle, and \(P_{2,2}\)(x,y,z) was changed to the intersection I\({}_{\text{P}2,2}\)(x,y,z). The red cells are the children of \(P_{2,2}\)(x,y,z). Table 5 is the final representation of the trimmed fractals (after they intersect a surface), and is the output of Step#2 (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \(P_{0}\) & \(ORG\)(x,y,z) & NaN & NaN & NaN & NaN & NaN & NaN \\ \hline \(P_{1}\) & \(\overline  
It is important to note that the algorithm requires surface data (points and triangulation), which are assumed to form the input. If an STL file is provided, either a surface mesh is also provided for the intersection, or the trimming is performed via a CAD method (see Section 6).  
In pseudocode, we can define Step#2 as follows (Algorithm 2):  
```
0. Inputs: STL file or surface mesh.
1. if the input is an STL file, and if no surface mesh is provided, then break else goto 2. for every cell in Table 4, do: for every triangle from the mesh with vertices in \(PTS\) and triangulation in \(TR\): compute intersection point \(I_{P}\) if\(I_{P}\) exists: \[C_{m,t}=\{P_{m,n}|m=i+c,\ n=2 cj-2^{c-1}-1\ldots 2^{c}j\}=\text{NaN}\] break endfor
3. Outputs: table representation of the trimmed fractal (Table 5).
```  
**Algorithm 2**Table representation for trimmed tree-like fractals.
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 3 Continuous Glucose Monitoring Systems
### Fully-Implantalbe CGMS with Fluorescence Sensors  
Another big step forward in CGMS is the FDA-approved, fully implantable, 90-day CGMS by Senseconics [43; 44]. Although CGMSs with enzyme electrochemical sensors have reached to 14-day use with zero finger-pricking, efforts on long-term CGMS have been conducted, mainly due to the short lifespan of enzyme-based CGMS. A promising glucose-biosensor is boronic-acid based sensors because boronic acid has high-sensitivity, high-biocompatibility, solubility in aqueous solution, reversible-binding to glucose, and long-lifespan in vivo. Since the [PERSON] group synthesized a saccharide-sensitive probe based on bis-boronic acid in 1994 [45], there have been efforts on developing CGMS with boronic-acid based glucose-sensors in academia and industry. The [PERSON] group immobilized fluorescence-conjugated diboronic-acids (Figure 3) into small-sized hydrogels [46; 47]. [PERSON] et al. implanted the fluorescent hydrogels under the skin of mouse ears and constantly detected fluorescence intensity through skin. Using this sensing concept, [PERSON] et al. succeeded to continuous glucose monitoring for up to 140 days (Figure 4) [47]. Such results prove that boronic-acid based sensors are stable in vivo for a long period of over three months. Senseconics has been developed small-sized, all-integrated, fully implantable CGMS, and obtained FDA approval in 2018. Eversense(Senseonics) is designed to implant under the skin of upper arms for 90 days [43; 44]. The wearable transmitter wirelessly activates a light source in the system. Fluorescence-conjugated diboronic acids are excited by the light. The fluorescence intensity is measured by the integrated photodetectors. The integrated RF systems sends measured data to the wearable transmitter. Although Eversense(
# A Latent-Factor System Model for Real-Time Electricity Prices in Texas
## 2 Data and Variables
### Geographical Scope  
The current ERCOT market with its eight zones is the focus of the paper; see Figure 1 for a map of ERCOT. The North and Houston zones account for about 37% and 27%, respectively, of ERCOT market energy sales, while the South and West zones contribute 12% and 9%. Further, these four zones account for nearly all of the state's retail competition, and most of the competitive generation resides within these zones.
## 4 Results
### Decision Trees (DTs)  
The following describes the steps to apply the DT model and create a report and graphical representations. The training results include the features and their importance alongside plots that show the model predictions against the actual values.  
1. The training set was utilized to train the DTs model.
2. The evaluation of the model was carried out using the testing framework.
3. The importance of attributes will be discussed according to the model's findings (Table 4).
4. After the model has been implemented, reports and graphical representations were created to show the model's predictions against the test set's values (Figures 2 and 3).
5. A Python code was also used to conduct DTs analysis using Cassandra data through Apache Spark (Table A9) [34; 35; 36; 37; 38].  
Figure 2 illustrates the performance of the DTs model by comparing predicted fish mortality values against the actual mortality values. The red line represents the ideal scenario where predictions perfectly match the true values (i.e., the line of perfect fit). Each data point (marked by green 'x') corresponds to an actual value plotted against its respective prediction. The scatter of points around the red line indicates the level of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Coefficient Value** \\ \hline RMSE & 64.21 \\ R2 & \(-\)1.16 \\ MSE & 4123.37 \\ C-index & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: DT results analysis.  
Figure 3: DTs predicted factors affecting fish mortality.  
prediction error. While some points align closely with the ideal line, reflecting accurate predictions, a significant number deviates from it, particularly in the higher and lower ranges, which suggests that the model struggles with extreme mortality values. This visualization highlights both the strengths and limitations of the DT model in this context, where it may underperform in predicting the more extreme outcomes.  
* The RMSE value is 64.21, indicating a significant variance between the predicted values and the actual data. A lower RMSE would indicate greater model accuracy, suggesting there is room for improvement in prediction precision (Table 4).
* The R\({}^{2}\) value is \(-1.16\), which is significantly lower than the ideal value of 1.0. A negative \(R^{2}\) indicates that the model performs worse than simply predicting the mean of the data, suggesting that the current model does not explain the variance in the dataset effectively (Table 4).
* The MSE value is 4123.37, representing the average squared difference between the predicted and actual values. A high MSE suggests that the model makes substantial prediction errors, further highlighting the need for optimization or an alternative modeling approach (Table 4) [34; 35; 36; 37; 38].
* The C-index of 0.65 reflects its moderate ranking accuracy (Table 4).
# Quantitative Analysis of Drag Reduction Methods for Blunt Shaped Automobiles
## 2 Literature Review
### Aerodynamic Drag
#### 2.1.1 Particle Image Velocimetry  
To examine negative pressure zones, there are two preferred methods: wind tunnels and computational fluid dynamics (CFD). In wind tunnels, by injecting smoke in front of the examination zone, flow can be examined. Particle Image Velocimetry (PIV) is a more advanced version of this method where the measured particles in the air are digitalized in a way that velocity can be calculated. The PIV system consists of a laser (which highlights small intentionally added particles in the flow), a camera, and software, which helps to observe the turbulent flow. In the turbulent flow, velocity distribution and wake formation can be examined [13]. With this method, drag cannot be calculated. For drag calculation, force sensors are required, which will be able to determine the F\({}_{\text{D}}\) change on the entire examined body. There are cases [14; 15] when pressure sensor arrays are placed on the rear surfaces of the vehicles, yet only partial drag reduction can be mentioned.
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 2 PV Module Imaging System Using Drone Equipped with Dual Camera
### Gimbal System  
In this work, a three-axis brushless gimbal stabilizer alloy was applied to control the camera's proper angle. The applied gimbal consists of three sets of BLDC motor for the function of pan, roll, and tilt, and a 32-bit microprocessor integrated with the GCB, a gimbal IMU (inertial measurement unit) with a three-axis accelerometer and a three-axis gyroscope, a wireless receiver for external PWM signal input, a slip ring for 360-degree continuous rotation, and an aluminum alloy frame for light weight (Figure 4). The full rotation axis symmetrical design is adapted for bearing and uses no virtual spaces in order to minimize the vibration. The gimbal functionality is controlled by a GCB, which calculates corrections for the camera position based on the IMU's horizon deflection mounted on the camera holder.
## 1 Introduction
The most significant benefits of this approach include reduced drying time, reduced energy consumption, consistent temperature distribution, improved dried product quality, ease of application, high heat transfer coefficients, environmental friendliness, and space savings [19; 20]. Heat-trapped water is created by the ability of infrared radiation to penetrate deep inside moist materials [21], resulting in more efficient moisture removal than traditional heating [22; 23]. In comparison to conventional drying processes, the infrared drying system's shorter drying time and consistent temperature distribution contribute to higher dried product quality [24]. Specifically, in the study of [PERSON] et al. (2005) on heat transfer on crust formation of bread, the jet impingement and infrared heating improved the color development of products compared to conventional heating [25]. Another study on efficiency of different drying of bitter gourd slices showed that although freeze-drying provided better results in terms of color, appearance, rehydration ratio, and microstructure than infrared drying of bitter gourd slices, infrared drying gave the best results in the bioactivities and physicochemical characteristics of dried bitter gourd slices in comparison with other drying methods such as hot air drying or freeze-drying [26]. Infrared drying, on the other hand, does not appear to be suitable for drying essential oil-rich materials. [PERSON] et al. (2018) reported that infrared drying caused more loss in yield as well as antioxidant and antibacterial activity of essential oil than hot air drying and natural drying [27]. According to a comprehensive review of [PERSON] (2020), many reports of infrared drying of fruits and vegetables were published, but there is still no publication on avocados [28]. The advantages of infrared drying make it ideal for producing high-quality dried avocados. Therefore, the aims of this study were to investigate the impact of infrared drying conditions on the quality of avocado pulp with different formulations. The experimental data were used to analyze and evaluate the efficiency of the infrared drying method based on nutritional value in the drying of superfoods such as avocado flesh. To dry food with high nutritional content, a new drying technique with suitable drying parameters was applied.
## 3 Results
### Discussion
#### 3.4.2 Effect of Debonding on the Global Failure  
Apart from the impact on the long-term durability, the potential effect of premature interfacial debonding on the damage progress should also be considered. The reader is reminded that AE and DIC inspection is limited to 50% of the ultimate load of the panels, therefore the total AE hits activity and DIC displacement at half of the ultimate load are comparatively discussed. Based on Figure 2c, the total number of AE hits stands at the same range for both panels, although the REF panel has 14% more hits than the IID one, a difference that can be neglected, considering the complex fracture progress. Additionally, it is evident that the damage mode does not change; in both cases, the crack saturation point is reached, and the textiles actively resist failure occurring at about 10 kN/m\({}^{2}\). At half of the ultimate load, both panels are fully cracked and, as shown in Figure 5e, their out-of-plane displacement of the bottom skin is identical. The displacement tracking along a diagonal line that crosses the previously debonded zone proves that at higher load levels and at crack saturation level debonding and cracking occur simultaneously, and the structure compensates and adjusts, leading to a homogeneous strain distribution (red zone Figure 5a at 5 \(\mathrm{kN/m^{2}}\)). It is concluded that the ultimate failure is identical for both samples (catastrophic tensile failure of the bottom TRC skin and shear failure of the insulation core at approximately 10 \(\mathrm{kN/m^{2}}\)). However, it should be noted that in service, and due to weathering conditions, the water/humidity invasion through the debonded interface at the early load stage can potentially enhance the autogenous sealing at the TRC matrix or, on the contrary, additionally degrade the TRC-core interface due to debonding extension. Future research will investigate these hypotheses and assess the effect of premature interfacial debonding on the panels' long-term durability.  
Figure 5: Out-of-plane displacement (mm) (**a**) at each step of incremental load for REF and IID panels, (**b**) at the point of maximum displacement, (**c**) along a diagonal line at 1.5 \(\mathrm{kN/m^{2}}\), (**d**) at the middle of the panel, (**e**) along a diagonal line at 5 \(\mathrm{kN/m^{2}}\).