esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antibacterial Activity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **RT** & **Abundance (\%)** & **KI \({}^{\text{a}}\)** & **KI \({}^{\text{b}}\)** & **Boiling Point (\({}^{\text{c}}\)C)** \\ \hline \(\alpha\)-Terpineol & 12.4 & 4.46 & 1143 & 1189 & 219.2 \({}^{\text{c}}\) \\ \(\beta\)-Isosafrole & 15.1 & 17.39 & 1345 & 1288 & 253.0 \({}^{\text{c}}\) \\ Phenol, 2-methoxy-3- & 16.9 & 0.61 & 1392 & 1356 & 286.4 \({}^{\text{e}}\) \\ (2-propenyl) & 18.2 & 10.33 & 1361 & 1405 & 262.6 \({}^{\text{c}}\) \\ Arthapedrene & 18.5 & 0.52 & 1386 & 1416 & 257.0 \({}^{\text{d}}\) \\ \(\alpha\)-Amorphene & 20.0 & 0.58 & 1440 & 1478 & 303.2 \({}^{\text{e}}\) \\ \(\gamma\)-Elemene & 20.4 & 1.08 & 1465 & 1494 & 258.0 \({}^{\text{d}}\) \\ \hline \hline \end{tabular} RT = Retention time. \({}^{\text{a}}\) Kovats indices obtained from databases NIST08 and NIST08. \({}^{\text{b}}\) Kovats indices are relative to the n-alkane (C7-C30) standard in a capillary column HP-5 MS. \({}^{\text{c}}\) Data obtained from the National Center for Biotechnology Information (NCBI), PubChem database. \({}^{\text{d}}\) Data obtained from the Good Sents Company Information System (TGSC) database. \({}^{\text{e}}\) Data obtained from the Chemoe database.  
\end{table} 
Table 1: _Cont._Figure 1: Bacterial growth inhibition in the presence of \(L\). _philippiana_ essential oil and mineral oil. The effect of different volume/volume ratio of essential oil (solid line) and mineral oil (segmented line) was evaluated using (**A**) \(S\). _aureus_ and (**B**) \(K\). _pneumoniae_. Results were averaged from 3 independent experiments (\(n=3\)). Statistically significant differences (\(p<0.05\)) compared essential oil and mineral oil treatments are indicated with an asterisk (*).
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
###### Abstract  
Nanosatellites have established their importance in low-Earth orbit (LEO), and it is common for student teams to build them for educational and technology demonstration purposes. The next challenge is the technology maturity for deep-space missions. The LEO serves as a relevant environment for maturing the spacecraft design. Here we present the ESTCube-2 mission, which will be launched onboard VEGA-C VV23. The satellite was developed as a technology demonstrator for the future deep-space mission by the Estonian Student Satellite Program. The ultimate vision of the program is to use the electric solar wind sail (E-sail) technology in an interplanetary environment to traverse the solar system using lightweight propulsion means. Additional experiments were added to demonstrate all necessary technologies to use the E-sail payload onboard ESTCube-3, the next nanospacecraft targeting the lunar orbit. The E-sail demonstration requires a high-angular velocity spin-up to deploy a tether, resulting in a need for a custom satellite bus. In addition, the satellite includes deep-space prototypes: deployable structures; compact avionics stack electronics (including side panels); star tracker; reaction wheels;
# Development of Highly Sensitive Temperature Microsensors for Localized Measurements
## 4 Conclusions  
This manuscript describes the development, fabrication and characterization of temperature microsensors to be integrated in a OoC. The simulations and experimental results show that the best geometry for the required application was a platinum meander-shaped RTD with 24 windings, featuring 200 nm platinum film thickness, 10 \(\upmu\)m of line width and spacing and a RTD area of 0.38 mm\({}^{2}\). Moreover, the performance of the microsensor results in high sensitivity to temperature changes, \(\sim\)0.3542 mV/\({}^{\circ}\)C. The small size of each sensor will allow one to integrate multiple sensors in a microfluidic chamber for real-time temperature measurements with high precision. In the future, these measurements will allow evaluation of the temperature of the microenvironment, which will add essential information for assessing the efficiency of drug nanocarriers triggered by mild temperature within the OoC.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11093864/s1](https://www.mdpi.com/article/10.3390/app11093864/s1)]([https://www.mdpi.com/article/10.3390/app11093864/s1](https://www.mdpi.com/article/10.3390/app11093864/s1)), Figure S1: Simulated temperature and electric potential distribution for a sensor with 24 windings, with an excitation current of 100 \(\upmu\)A and temperature of 45 \({}^{\circ}\)C, Figure S2: Simulated temperature and electric potential distribution for a sensor with 24 windings, with an excitation current of 100 \(\upmu\)A and temperature of 45 \({}^{\circ}\)C, Figure S3: Experimental sensitivity of the sensors for the different fabricated RTDs according to the windings number. Orange line represents an increase temperature from 35 to 45 \({}^{\circ}\)C, and black line a decrease temperature from 45 to 35 \({}^{\circ}\)C.  
**Author Contributions:** Conceptualization, P.J.S., V.C.P., R.O.R. and G.M.; funding acquisition, G.M.; methodology, P.J.S., V.C.P., V.H.M. and G.M.; supervision, P.C.S. and G.M.; writing--original draft, P.J.S., V.H.M. and V.C.P.; writing--review and editing, P.C.S., R.O.R. and G.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is the result of the project NORTE-01-0145-FEDER-029394, RTChip4 Theranostics, and was supported by Programa Operacional Regional do Norte-Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement through the European Regional Development Fund (FEDER) and by Fundacao para a Ciencia e Tecnologia (FCT), IP, project reference PTDC/EMD-EMD/29394/2017. The authors also acknowledge the partial financial support by the projects UIDB/04436/2020 and UIDP/04436/2020.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
Figure 12: (**a**) Microsensor response with implemented electronics to temperature variation from 15 to 50 \({}^{\circ}\)C. (**b**) Dynamic test.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 5 Evaluation
### Evaluation of Buffer Hardware  
We evaluated the buffer design in D-SWIM and compared it with the previous work of SWIM, which tackles the similar BRAM-misalignment issue for the multi-pixel block but only supports static image sizes that are pre-defined before FPGA synthesis. In the experiment, we configure the SWIM and D-SWIM with different parameter sets for a complete evaluation. As Table 3 shows, the parameters (\(N_{line}\), \(H\), and \(N_{blk}\)) are set to different values, and the implementation results are listed. In Configurations 1-6, we set the image width (\(N_{line}\)) to arbitrary values. The window height (\(H\)) was set to 3 or 5, which are frequently used in image applications. The pixel number of one input block (\(N_{blk}\)) was set to \(8,16,32\). Note that in the SWIM method, the number of LB (\(N_{LB}\)) was deduced by \(N_{blk}\) and \(N_{line}\); thus, it may exceed \(H\), whereas \(N_{LB}\) is equal to \(H\) in D-SWIM. The optimization technique of multi-pass BRAM partitioning in SWIM was also invoked to reduced \(N_{LB}\), and the corresponding results are shown in the SWIM-2 pass column of Table 3.  
Figure 11: Buffer-read logic.
## 1 Introduction
### Contributions and Organization  
[PERSON] et al. [37] systematically put forward the design process of BTMS: (1) determine the design objective and layout boundary; (2) determine the heat generation rate and thermal characteristic parameters of the battery; (3) determine the channel structure and preliminary formulation scheme; (4) determine the feasibility scheme through numerical simulation; and (5) experiment verification and design optimization.  
In this paper, a performance-optimized and engineering-oriented properly liquid cooled plate was designed to meet the requirements of a battery pack in a certain hybrid SUV. In addition to the five steps before, this paper particularly emphasizes the role of theoretical calculation, which, together with numerical simulation, effectively reduces development costs, shortens development cycle, and improves development success probability and maturity. The organization of this study is introduced in Figure 1.  
The internal parameters of this cooling plate, including the cross-sectional area ratio of the main pipe to the branch pipes and the number of internal supporting walls in the branch pipe, were analyzed and studied. In addition to these inner parameters, the boundary conditions for the flow uniformity, pressure loss, flow rate, differential currents, differential inlet temperatures, and temperature performance of the pipe-based mixed parallel-series liquid-cooling system were also  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technology Readiness Level** & **Air** & **Liquid** & **PCM** & **HP** & **Emerging** \\ \hline TRL 9—Complete industrialization & O & O & & & \\ TRL 8—Optimization & O & O & & & \\ TRL 7—Entry into production & O & O & & & \\ TRL 6—Application development in product & O & O & O & O & \\ TRL 5—Verification in production equipment & O & O & O & O & \\ TRL 4—Verification in representative prototype & O & O & O & O & \\ TRL 3—Verification in the laboratory & O & O & O & O & O \\ TRL 2—Feasibility and profitability analysis & O & O & O & O & O \\ TRL 1—Investigation of the fundamentals of technology & O & O & O & O & O \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technology readiness level (TRL) of battery thermal management system (BTMS) in vehicles.  
analyzed and studied. Three-dimensional finite element models of the packs' cooling systems and batteries were set up and numerically simulated. Then, a temperature test platform with the battery pack, battery tester, and cooling tank was established to verify the theoretical design and simulation.
## 2 Numerical Simulation Methods
### Aerodynamic/Structural/Motion Coupled Solution Strategy  
The previous section completed the flight dynamics and generalized force modeling based on CFD. In the next section, the specific solution process will be introduced. Among this, the TPS [26] method, also known as the Thin Plate Spline method, is widely used in the field of aeroelastic analysis; in this paper, it is used for the data exchange of the fluid-structure interaction interface. The solution flowchart is shown in Figure 2. The simulation procedure can be described as follows.  
1. Initialing the flow field for unsteady calculations based on the steady flow solution;
2. Calculating the aerodynamic force and moment at the current physical time step by integrating the distributed force on the wall surface;  
Figure 2: Simulation procedure of the rigid–flexible problem.  
3. Obtaining the aerodynamic forces and moment, and at the same time, using the TPS method to complete the interpolation of the distributed aerodynamic force (imposed on the wall boundary of CFD grid) to structure grid.
4. Solving the rigid-flexible equation to obtain the whole motion and structure deformation;
5. Updating the time step, outputting the calculated parameters including centroid displacement, attitude angles, and generalized displacement, and then proceeding to the next time step iteration calculation.
## 1 Introduction
[3] investigated Fenton and ozone pretreatment of oil refinery wastewater containing DEA with the concept of sequential AOP and biological wastewater treatment process (BWTP). [PERSON] et al. [13] also reported ozone pretreatment of metalworking fluids' wastewater containing MEA and TEA showing the biodegradation results of ozone-pretreated effluents. According to our literature survey, however, there is no report to deal with AOP pretreatment of MDEA with the scheme of AOP-combined BWTP. In addition, the effect of AOP-pretreated effluent on the performance of BWTP has been rarely explored despite its significance. In this study, an AOP-combined BWTP scheme is investigated for the treatment of reclaimer wastewater generated in a steel-making industry. In this scheme, the reclaimer wastewater containing extraordinarily high concentration MDEA is first pretreated by AOP and then further treated in a BWTP currently treating cookes wastewater, which is the largest BWTP in the field of the steel-making industry [20; 24]. Three AOPs, i.e., Fenton, ozone alone, and peroxone, were tested as candidate pretreatment methods, and the most proper one was chosen based on their performances and chemical reagent costs required. To assess the effect of AOP-pretreated wastewater on the BWTP, microbial toxicity, biodegradability, and pollutant biodegradation kinetics were also investigated using a respirometer system.
## 4 Computational Simulations and Experimental Results
### Hardware Experimental Testing  
Experimental tests were conducted on the system both within laboratory settings and outdoors. To filter out ambient light noise mixed in the detection signal and minimize background noise, we have added a UV optical filter in front of the PMT with a transmittance of over 34% at 261 nm, with an FOV of \(5^{\circ}\). The wavelength of the mercury lamp light source used in our system is 254 nm. It is imperative to acknowledge that owing to the physical constraints inherent to the ultraviolet optical filter utilized, there exists a marginal deviation between the wavelength of the ultraviolet light source and the peak wavelength of the optical filter. The variation in transmittance of the optical filter due to this difference leads to a slight change, resulting in signal loss and a decrease in received signal intensity, thereby reducing the system's detection sensitivity and signal-to-noise ratio, and thereby impacting the performance of the system to some extent. Additionally, this deviation can also cause misalignment in the system's frequency characteristics, thereby affecting the frequency response and signal processing capability of the system. Nevertheless, this disparity does not engender a significant deterioration in system performance, attributable to the bandwidth of the UV optical filter employed. Should the wavelength of the optical filter align with that of the UV light source, it would ostensibly augment communication efficacy.
## 2 Overview of SMOTE Schemes and Ensemble Learning Schemes
### RF Algorithms  
The RF scheme was created in 1995 [8] in which DTs are aggregated to improve the performance of a single DT. As shown in Figure 3, RFs comprise DTs on randomly selected training data sets; in this approach, predictions are obtained from each tree and the best solution is selected by means of majority votes in ensemble algorithms. RFs typically provide a fairly accurate indicator of the feature's importance [9]. The RF algorithm creates \(n\) DTs on randomly selected data points and synthesizes the prediction results from each DT (basic classifier) through group voting to reduce the variance of classification results. Put simply, an RF is an ensemble learning method that is better than a single DT because it reduces the overfitting outcomes by averaging the results of DTs (Figure 4). Figure 2: An example of resampling process of SMOTE. (**a**) Minority instances of the imbalanced data (**b**) Select five nearest neighbors (**c**) Generated two neighbors along the line segments.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 4 System Architecture  
To address the lack of internet connectivity and the distance between connected devices, and the inability to establish an Ethernet connection, we propose a flexible fog network architecture where the primary communication channel for long-distance traffic is based on LoRaWAN. In Figure 1, we present a sample connectivity scheme. IoT devices in a home, including sensors, can have two modes of connectivity. They could either be connected to an in-house healthcare gateway or have embedded LoRa transceiver radio and communicate to the neighboring gateway. Wearable devices with LoRa radio transmit can communicate with any healthcare gateway in the network. The process for sending data is shown in Figure 2.  
Unreliable electricity and difficulty to set up charging outlets and the risk of them being unplugged by the house occupants is addressed by relying only on battery-powered IoT devices. The healthcare gateways also require reliable battery operations with recharging the batteries either via the power grid when electricity is available or using renewable energy sources such as solar panels. The need for battery operation and the long servicing time poses energy conservation as the primary challenge in our design, and we address this in the following sections.  
To ensure cost efficiency from small implementation to scaling up, we propose inexpensive off-the-shelf components integrated into sensor enclosures for small quantity deployments. Then, when a need arises for larger deployments, some components can be reduced or combined to save material costs. An example of such cost saving through consolidation is creating custom boards with multiple sensors used by an IoT device, built-in sleep timers and clock chips, and a single power supply circuit.  
The proposed architecture has several built-in constraints resulting from batteries as the primary energy source and LoRa as the dominant protocol for distant communication. LoRa is designed to be energy efficient; however, that comes at the cost of very low bandwidth. With the requirement of a long-lasting battery, energy conservation is the primary challenge. Ensuring maximal energy conservation is accomplished by reducing
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 4 Predicting Signal Strength
### The Role of Antenna Gain Patterns  
The gain pattern of an antenna is a function of the antenna radiation efficiency and directivity. It shows how efficiently a transmitting antenna can convert electrical power to radio waves and, analogously, how a receiving antenna can convert radio waves into electrical power in different angles of departure and arrival. As it can be inferred from Equation (1), the power received at the receiving antenna is a function of the gain pattern of both transmitting and receiving antennas.  
* Satellite  
Table 2 shows the RHCP values of \(G_{t}\) used in this study for GPS satellites. The gain pattern of the GPS satellites depend on the type of the space vehicle transmitting the PRN code [51]. The angle of departure (AoD) can be calculated from the _Off-Nadir_ angle \(\beta\):  
\[\beta=sin^{-1}\left(\frac{R_{Earth}sin(El+90^{\circ})}{R_{SV}}\right), \tag{11}\]where \(R_{Earth}\) is the radius of the Earth and \(R_{SV}\) is the radius of the space vehicle from the center of the Earth. \(El\) denotes the elevation angle of the satellite. The Off-Nadir angle can change from \(0^{\circ}\) to \(15^{\circ}\), for which the corresponding value can be selected from Table 2. The LHCP values which are needed in Equation (2), can be calculated from  
\[G_{lhcp}=G_{rhcp}\frac{-Ax+1}{-Ax-1}. \tag{12}\]  
The axial ratio (\(Ax\)) of 1.2 dB is applied for GPS satellites. It can be denoted here that for GLONASS satellites, there are no exact gain patterns available to be used for this calculation; rather, some mean values, e.g., 14 dB for L1, can be used [51].  
* Receiver  
The power received at the receiving antenna is attenuated based on the angle in which the signal impinges the antenna (the receiving antenna is assumed to be levelled). This attenuation depends on the gain pattern of the antenna, which usually degrades by decreasing the elevation angle. Here, the gain pattern is assumed to be only a function of elevation and not azimuth; thus, side lobes are neglected. Unfortunately, the gain pattern of the receiving antenna (Septentrio Altus NR3) is not known, and the gain pattern of a GNSS 800 series antenna is used. For evaluating the performance of the Altus receiver, 24 h data are collected in an open sky situation. On the other hand, a 24 h simulation was also conducted to see the results for different elevation angles. Comparing the measured C/N\({}_{0}\) data with the theoretical C/N\({}_{0}\) calculated from a simulation offers the possibility to adapt the used gain pattern to better fit the real data. Therefore, a modified version of the gain pattern is introduced, and the simulation is conducted with this modified version as shown in Figure 9b.
## 4 Attribute Weight Calculation Based on Type-2 Hesitant Pythagorean Fuzzy Sets  
To facilitate the subsequent evaluation, the attribute weight calculation will be mainly introduced. The distance measurement of Type-2 hesitant Pythagorean fuzzy elements and the entropy calculation formula were described successively and then proved, respectively. Next, how to calculate the weight value of each attribute using the entropy of Type-2 hesitant Pythagorean fuzzy elements will be introduced.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 10 Proposed Solution: Background, Implementation, and Simulation Results
### Proof-of-Concept Simulation Results
On the third interval (30 to 40 s), these users suffer a small loss, but the gain from hotspot users compensates, resulting in a gain in the system average throughput. Summarizing, the throughput percentage gain is presented in Figures 21 and 22, for scenarios 1 and 2, respectively. The smallest gain obtained for hotspot users was 5.4% in scenario 1 and 1.7% in scenario 2. On the other hand, the highest gain for these users was 59.8% in scenario 1 and 12.5% in scenario 2. Users outside the hotspots presented loss in some intervals for scenario 2. Nevertheless, all intervals presented gain for hotspot users. These users were not present in the plots for the first interval because there were no active hotspots users. The worst and best percentage gain for each scenario and group of users are summarized in Table 12.  
Figure 21: Scenario 1 of the proposed solution: throughput gain (%) for each group of users. Figure 22: Scenario 2 of the proposed solution: throughput gain (%) for each group of users.
# Influence of Pre-Ionized Plasma on the Dynamics of a Tin Laser-Triggered Discharge-Plasma
###### Abstract  
The effect of laser-current delay on extreme ultraviolet emission by laser-triggered discharge-plasma has been investigated. Typical waveforms for current, voltage, laser signals, and X-ray signals have been compared. Theoretical tin spectra were simulated among the electron temperature ranges from 10 to 50 eV to compare with the experimental results. The results show that longer laser-current delay time is propitious to increase the steady-state time of plasma at high temperatures, and it increases the intensity and spectral purity of 13.5 nm emission in 2% band. The 13.5 nm radiation intensity increases about 120% with the delay increasing from 0.7 to 5 us, and the extreme ultraviolet (EUV) emission conversion efficiency (CE) increases from 0.5% to 1.1%.  
EUV emission; laser-triggered discharge plasma; pre-ionized plasma; tin spectra; 13.5 nm emission +
Footnote †: journal: Applied Sciences