esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Determination of Relevant Visual Information
#### 3.2.6 Alternate Camera Placement  
Other views that were suggested included a camera at the end of the boom facing forward (24%), a close-up view of the nozzle and its tip (24%), a view from under the sprayer facing backward to see the spray pattern behind the sprayer and wheels (10%), dashboards or displays (10%), and a camera that would focus on the wheel to show how well an operator was either following old tire tracks or steering within the rows of a corn crop (7%).
## 3 Results  
Table 3 shows the maximum von Mises stress for the implant, abutment, and abutment screw and the maximum first principal strain for cortical bone. The simulated results show that the unilateral implant neck wall thickness was the main factor which significantly influenced the magnitude of mechanical responses for the implant, abutment, and abutment screw stresses and bone strain (Table 4). Generally, the main effect plot showed that increased unilateral neck wall thickness can decrease the implant, abutment, and abutment screw stresses and bone strain (Figure 2a). When the thickness of the implant neck wall increased from 0.45 mm to 1.00 mm, the maximum equivalent stresses on the implant, abutment, and abutment screw and the maximum first principal strain of the cortical bone decreased to 58%, 48%, 54%, and 70%, respectively. However, abutment screw size and the interaction between abutment screw size and implant were only significantly influenced by abutment screw stress. The maximum equivalent stress tended to gradually decrease as the screw diameter increased. The decrease rates were 10% and 29% when the screw diameter increased from 1.40 mm to 1.6 mm and 1.80 mm (Figure 2b).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** \\ \hline Cortical bone & 13.7 & 0.3 \\ Cancellous bone & 1.37 & 0.3 \\ Implant (Ti grade 4) & 105 & 0.3 \\ Abutment (Ti6 Al4V) & 110 & 0.35 \\ Abutment screw (Ti6 Al4V) & 110 & 0.35 \\ Loading device & 193 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties adopted in our simulations.  
The highest von Mises stresses were found on the compressive side at the neck region for implant thread region tension side for the abutment screw and the hexagon connection junction for the abutment. Also, the highest bone strain values were found at the crestal cortex region that received tensile stress (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Implant Stress** & **Abutment Stress** & **Abutment Screw Stress** & **Bone Strain** \\ \hline Implant neck wall thickness & \textless{}0.001 & 0.039 & 0.002 & 0.002 \\ \hline Abutment screw size & \textless{}0.001 & 0.961 & 0.960 & 0.817 \\ \hline Implant neck wall thickness \({}
# Some Thoughts about Infrared Radiation Response Characteristics during Loading of Sandstone Samples
###### Abstract  
The infrared radiation response characteristics and mechanisms of sandstone samples under uniaxial compression were investigated using infrared thermal imaging and strain measurement techniques. The stress-strain curves were divided into different damage stages (i.e., crack closure, elastic deformation, stable crack growth, unstable crack growth, and post-failure) by the damage thresholds determined using the transverse and vertical strains. Experimental results show that the infrared radiation response characteristics of sandstone include the superposition of different mechanisms, which lead to either temperature increase or decrease. The response mechanisms of infrared radiation vary depending on the damage stage. During crack closure and elastic deformation stage, temperature variations are mainly governed by thermoelastic effects and the nearly adiabatic compression of air in the pore space. In the stable and unstable crack growth phase, the temperature variations are dominated by two reverse acting processes: heat production by frictional sliding and cooling by the expansion of gases in pore spaces. This leads to complex inhomogeneous radiation and consequently temperature distribution at the sample surface with local hotspots.  
infrared radiation; uniaxial loading; deformation; damage evolution; failure; temperature
## 1 Introduction
### Prior Works
In 2020, the artificial neural network (ANN) was first adopted in solving the UC problem [17]. The proposed ANN-based UC is first trained with the datasets generated using the BF-S method, the scheme is then used to predict the cluster formations in the testing phase. Empirically, it is revealed that the throughput achievement is much better than the PSO-based UC, especially in a NOMA network with a large number of users. Furthermore, it is also observed that the ANN-based UC is very adaptive and flexible because the ANN model is trained to fully explore the channel heterogeneity and diversity among the NOMA users so that it can output an optimal clustering solution under any circumstances.
## 4 Conclusions  
Perovskites can be prepared using a low-cost and simple process, thus becoming a potential candidate material for PDs. As a deep-ultraviolet photodetector material, Ga\({}_{2}\)O\({}_{3}\) has also attracted great attention from many researchers due to its solar-blind nature. To enhance the UV detectivity of perovskite PD, we combined the Ga\({}_{2}\)O\({}_{3}\) with a CsPbBr\({}_{3}\) perovskite to prepare a heterojunction PD. We used a low-cost method to prepare the \(\beta\)-Ga\({}_{2}\)O\({}_{3}\) using Mist-CVD, and the CsPbBr\({}_{3}\) exhibited good coverage on the \(\beta\)-Ga\({}_{2}\)O\({}_{3}\) film. This Ga\({}_{2}\)O\({}_{3}\)/CsPbBr\({}_{3}\) heterojunction PD exhibited a high response for 254 nm lights, and the R of the PD increased and reached 0.016 mA/W under a 5 V bias and a light density of 500 uW/cm\({}^{2}\). The detectivity of the Ga\({}_{2}\)O\({}_{3}\)/CsPbBr\({}_{3}\) heterojunction PD achieved 1.04 \(\times\) 10\({}^{12}\) Jones. The PD exhibited a good linear dependence between light power densities and photocurrents in the UV region. It also achieved increased relaxation times of 2.22 s (slow) and 0.31 s (fast). Our work hopes to offer an effective method for enhancing the performance of perovskite detectors at the UV wavelength.  
Z.Z. and Y.B.: methodology, original draft, and formal analysis; P.Y.: methodology and investigation; W.Z.: visualization; D.C., Q.S. and C.Z.: review and editing of writing, supervision, project administration, methodology, funding acquisition, and conceptualization; and Y.Z. and Y.H.: project administration, investigation and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (No. 2022 YFB3605402); the National Natural Science Foundation of China under Grant 62274132; the Key Area R&D Program of Guangdong Province (Grants 2019B010127001, 2020B010170001, 2020B0909030003); the Natural Science Basic Research Program of Shaanxi under Program No. 2021 JC-24; the Key Research and Development Program of Shaanxi (Grant No. 2021 GY-007); the Innovation Capability Support Program of Shaanxi (Grant No. 2021 TD-04); and the Key Research and Development Program of Shaanxi (Grant 2020 ZDLGY03-07).  
Not applicable.  
**Acknowledgments:** This work was supported by the Wide Bandgap Semiconductor Technology Disciplines State Key Laboratory.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### GPS Acquisition
#### 2.5.3 Acquisition Aiding from Navigation  
For a standalone receiver, some aiding can still be obtained from the navigation result. The _a priori_ knowledge consists of:  
* Position (from the last known position stored in memory),
* Time (from the real-time clock),
* Reference frequency (since the receiver oscillator offset is determined by the navigation solution)
* Approximate GNSS satellite positions and velocities (calculated from the almanac data stored in memory).  
Time Uncertainty  
By time, we mean the time of week (TOW) from the navigation data. This can be delivered as the GPS week and seconds of the week. Provided the receiver is tracking at least one satellite above 15 dB-Hz the time can be decoded and considered to be known within 1 s. The rate of change of the Doppler frequency is up to 4 Hz/s according to Figure 7. Therefore, for 1 s of error in time, the corresponding error in the Doppler estimation is up to 4 Hz. Once a navigation fix is achieved, as we are using a stable OCXO our time knowledge will not drift quickly in periods of poor GNSS visibility (around 1 ms/day) and so is likely to be much better.  
According to our post processing simulations, whose assumptions are described in [25], when the GPS solution is not filtered through any orbital forces model, the receiver velocity uncertainty can reach approximately 700 m/s at Moon altitude, that means that the corresponding frequency uncertainty is 700 m/s/0.19 m = 3684 Hz (0.19 m being the wavelength of the L1 C/A signal).  
According to our simulations, when the GPS solution is not filtered through any orbital forces model, the receiver position uncertainty can reach peaks of 80 km at Moon altitude (neglecting a very few higher peaks). Using the equations given in Section 3.6.5 of [27], the corresponding frequency uncertainty is (3800 m/s \(\times\) 80 km)/(380 000 km \(\times\) 0.19 m) = 4.2 Hz, where 3800 m/s is the GPS satellite speed, 380,000 km is the distance between the Earth and the Moon, and 0.19 m is the wavelength of the L1 C/A signal.  
The precision of the ephemeris is very high, so the frequency uncertainty induced by the ephemeris can be ignored. According to Section 3.6.6 of [27], if the almanac data are used for frequency assistance, the frequency uncertainty is 60 Hz.  
Total Search Uncertainty  
According to the analysis above, the total frequency uncertainty is about \(\pm\)3692 Hz. Note that for the position and velocity uncertainty, to be conservative we have considered the worst case that is obtained at Moon altitude; in fact it is the highest altitude of the trajectory and it corresponds to the highest GDOP and accordingly to the highest position and velocity errors.
# Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering
## 4 Applications of Antimicrobial Nanoparticles
### Healthcare Applications  
Contaminated surfaces are one of the leading causes of hospital infection transmission; some bacteria and fungi can survive for up to four months on inanimate surfaces [137; 138]. Paints have been treated with nanoparticles to produce an antimicrobial coating finish that protects and prevents surfaces from supporting bacterial or fungi growth to reduce pathogen contamination on surfaces. Oil-based antimicrobial paints are increasingly used in hospitals to provide a reduced pathogenic environment on wooden, glass and polystyrene surfaces [139]. Equally, antimicrobial polymer coating has been developed to protect devices made from stainless steel, glass or polyvinyl fluoride. However, studies have shown that AMNP containing paints and coating are not effective against all types of bacteria [140]. In addition, the physical properties of antimicrobial paint may not be suitable for hospital settings. For example, NanoCote (commercialised antimicrobial paint with Cu NPs) was investigated by [PERSON] et al., who found that the paint coating resulted in a slippery surface, colour loss after water contact and unattractive appearance [141].  
Nanoparticles can be coated on medical sutures and implant devices. [PERSON] et al. investigated the effectiveness of sutures coated in silver nanoparticles and found that they produced antimicrobial activity against _E. coli_ and had an anti-inflammatory effect [142]. Some medical implant devices have AMNP coating to reduce the risk of secondary infections and/or surgical complications. For example, TiO\({}_{2}\) nanoparticle coating on heart valve implants is used to inhibit Streptococcus species and _E. coli_[143; 144]. Other types of implants made with nanoparticles include Ag incorporated polymer catheters [145; 146]. Furthermore, [PERSON] et al. coated polymers of urinary catheters with silver nanoparticles. This produced antimicrobial activity against both Gram-negative and Gram-positive bacteria, in addition to bacterial adherence and formation of biofilms on the surface of the catheter [147]. However, both examples are currently researched applications rather than commercialised products.  
Lastly, wound dressing infused with nanoparticles has been reported to be particularly useful in decreasing the risk of infections in wounds and aiding the healing process. The current commercial dressing, Biatain(r) AG non-adhesive foam dressing, contains a Ag complex that is effective at inhibiting _S. aureus_ and other bacteria [148; 149]. The utilities of Ag NPs in both artificial implants and wound dressings have been developed with antimicrobial features to help reduce infections and inflammation caused by microbes and biofilm formation [150].
## 2 Torsional Thrust Balance Description
### Torsional Balance Design  
To reduce the longitudinal dimension of the torsional balance, an asymmetric arm structure about the rotational axis was adopted. The arm was made of hard aluminum alloy to ensure sufficient structural strength and small moment of inertia. The thruster to be measured will be mounted on the longer end of the arm. The center of the thruster mounting table was designed to be 270 mm away from the rotational axis, and the ratio to the distance between the electrostatic comb attached position and the rotational axis was 2.5. To reduce the influence of the deviation of the center of mass of the entire arm from the rotational axis [5], a counterweight structure that can be orthogonally adjusted in the plane perpendicular to the rotational axis was designed on the arm's short end. By adjusting the counterweight, the arm can be balanced and make the mass center of the arm as close to the rotational axis as possible.  
To make the torsional balance more compact and easier to assemble, only one C-Flex model F10 flexural pivot (torsional spring rate is 42.08 N\(\cdot\)um/rad) was employed to provide the torsional restoring force. It was chosen mainly because of its good compatibility with vacuum, its frictionless rotation, and its low hysteresis. The shift of geometric center of the pivot will be minimal on the order of 0.02% of its diameter for small rotation angles.
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 2 Types of Data
### Quantitative Data  
A quantitative variable represents a numerical value designating how much, how often, or how many of an item is assigned to a specific sample, thus providing information about quantities of a specific object [31; 32]. In food safety experiments, enumeration of bacteria is the most common example of quantitative data, as it provides the number of a specific bacterium in a sample; however, quantitative data can be further classified into two different types: discrete and continuous data. Continuous data represent a numerical value that can be calculated, and they have an infinite number of probable values expected withing a range such as height, weight, and pH values. Discrete data contain a finite number of probable values; thus, they can only be counted as whole integers, such as the number of bacteria in a specific sample.  
In statistics, distribution functions and their properties are usually discussed; however, it is important to understand what a distribution represents. When a large number of measurements are collected during an experiment, those observations can be organized into groups or classes that have the same interval size, resulting in groups with a certain number of observations or frequencies [32]. If plotted, those groups and frequencies will result in a histogram, in which the height of the bars will represent the frequency of each class. Frequency distributions can be used as models for experimental data where appropriate [33]. The most common models used for microbiological analysis include the normal, lognormal, and binomial distributions.
# Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks
## 5 Evaluation and Discussion
### Evaluation Results
By calculating these metrics, we can gain a comprehensive understanding of the model's performance in terms of accuracy, precision, recall, and overall balance between precision and recall using the F1-score. Table 6 illustrates performance metrics extracted from the confusion matrix for distinct eight individual models in addition to the three implementations of the ensemble model. The application of an ensemble model based on bagging, boosting, and stacking is compared with standard individual algorithms. Moreover, Table 7 illustrates the detailed performance metrics for Bagging, Boosting, and Stacking mechanisms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 0.94 & 0.95 & 0.95 & 0.93 \\ \hline LR & 0.94 & 0.95 & 0.92 & 0.93 \\ \hline DT & 0.93 & 0.99 & 0.93 & 0.94 \\ \hline KNN & 0.88 & 0.92 & 0.90 & 0.90 \\ \hline SVM & 0.89 & 0.91 & 0.91 & 0.92 \\ \hline XG & 0.90 & 0.93 & 0.92 & 0.92 \\ \hline CB & 0.90 & 0.94 & 0.91 & 0.91 \\ \hline ANN & 0.91 & 0.94 & 0.91 & 0.90 \\ \hline
**Bagging** & 0.99 & 0.97 & 0.98 & 0.99 \\ \hline
**Boosting** & 0.99 & 0.98 & 0.98 & 0.98 \\ \hline
**stacking** & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance results of Individual and ensemble models. The methods of bagging and boosting have also performed well with a percentage that is almost identical to the stacking mechanism. Furthermore, great accuracy is accomplished by balanced precision-recall trade-offs. We have analyzed various models to predict accurately unknown security events. Experimental results of the proposed model combination demonstrate that ensemble algorithms achieve better performance compared with regular processing of ML algorithms. However, the selection of base models and feature space size plays a significant role in achieving higher performance. It is also noticeable that individual algorithms also have more accurate detection compared to other studies in the literature. This indicates that our method in feature engineering and dataset pre-processing has contributed to producing effective learning operations. The development of optimal IDS capable of detecting unknown and advanced attacks can be constructed using reliable datasets in addition to an extensive analysis of traffic data.
## 4 The Virtual Astronaut for Santa Maria  
The first release of the Virtual Astronaut covers Opportunity's Santa Maria campaign. Santa Maria is an impact crater on Mars that is about 90m in diameter and is located at 2.172\({}^{\circ}\) S, 5.445\({}^{\circ}\) W in Meridiani Planum. The crater sits northwest of the much larger Endeavour Crater. Before resuming its long-term trek toward Endeavour, Opportunity investigated Santa Maria from 16 December 2010 to 22 March 2011 (Martian days or sols 2451-2545). During the campaign, Opportunity conducted a number of experiments around Santa Maria. The interior and exterior of the crater were extensively imaged from several viewpoints to study the structure of the crater. These image sequences provided multispectral data. Topography was also derived from the stereo observations. In addition, two rock targets, Luis De Torres and Ruiz Garcia, where investigated in detail with the instruments on the rover's arm. Data collection for the rock targets included close-up images and elemental oxide abundances.  
Data integration is an important step to build texture models for the VA. Figure 3 illustrates the data source used and their integration for the VA at Santa Maria Crater. Raw data were originally obtained from the PDS. Derived products were developed in-house and by third parties. These derived products were registered and integrated into a texture model.  
Figure 3: Data source and data integration.
## 3 Context and Background Information
### Overview of the Madeira Island Electric Grid  
Madeira is the main island of the Madeira archipelago in the North Atlantic Ocean. Madeira is located about 1000 km southwest of mainland Portugal. It has a population of almost 270,000, from which around 110,000 live in the capital city of Funchal. Madeira is a total energy island, meaning that it is not electrically connected to the mainland. Therefore, all the energy consumed on the island is generated on-site and managed by a single distribution system operator (DSO) responsible for the entire grid infrastructure and large-scale energy generation, transmission and distribution [8].  
With a little more than 134,000 registered consumers, the 2018 total net demand from the Public Electricity Supply System of Madeira Island was 831.08 GWh. From these, 759.55 GWh were delivered to the end-consumer, amounting to 70.45 GWh of emission losses (8.6 %) [24]. The non-domestic sector (e.g., services and commerce) was responsible for near 46% of that consumption, whereas the domestic sector was responsible for about 33% [24].  
The electric grid on Madeira Island is fed by five energy sources, namely: hydro, wind, photovoltaic, solid waste incineration and thermal energy from burning fossil fuels such as diesel and natural gas. In 2018, thermal accommodated almost 70% of the net demand, whereas RES covered the remaining 30%: wind (12.1%), hydro (11.6%), waste/biomass (4.2%) and solar PV (3.5%).