esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, the injection processes and spray behavior of diesel fuel, rapeseed oil and their fuel blends have been qualitatively and quantitatively analyzed to identify the differences in the injection rate and the spray parameters. The volumetric and mass injection rates, the spray penetration, the spray cone angle, and the spray area under non-evaporation conditions in a nitrogen-filled camer were analyzed. The following conclusions can be drawn from this investigation:  
1. Compared to diesel fuel, the peak volumetric injection rate of rapeseed oil was 22.0, 20.6, 11.3 and 9.1% lower by injection of the same amount of fuel at the injection pressure of 24.0, 40.0, 60.0 and 80.0 MPa, correspondently. Moreover, rapeseed oil and its blends with diesel fuel show a slower rise in the volumetric injection rate after the start of injection caused by its higher density and viscosity. Despite the higher density of rapeseed oil and its blend with diesel fuel, at low pressure, their mass flow rates remain lower than that of diesel fuel. The differences in maximum mass flow rates between diesel fuel, rape-seed oil and fuel blends become negligible at the higher injection pressures of 60.0 and 80.0 MPa due to about 10% higher density of RO that compensates for the lower volumetric flow rate.
2. The spray development of RO demonstrates 26.9, 25.5, 14.0 and 7.4% longer tip penetration than diesel fuel at the injection pressures of 24.0, 40.0, 60.0 and 80.0 MPa after the same time after the start of injection (1 ms). The spray tip penetrations of the fuel blends RO25, RO50 and RO75 were arranged between the values of DF and RO. As the injection pressure increases the differences in the spray tip penetrations diminish.
3. The spray cone angle of RO was 61.4%, 50.1%, 37.3% and 31.7% smaller than diesel fuel develops for the injection pressures of 24.0, 40.0, 60.0 and 80.0 MPa in the spray chamber. The fuel blends suggest smaller cone angles than diesel. The spray cone angles increase to maximum just after the start of injection and then decrease and stabilize around lower values.
4. The spray area of RO is always 32.0-20.0% lower than that of diesel fuel and varies within a wide range of 300 to 400 mm\({}^{2}\) while the spray area of diesel fuel less depends on the variation of injection pressure from 24.0 to 80.0 MPa and changes within a narrow range 460-500 mm\({}^{2}\) for the same spray tip penetration of 50 mm. A smaller spray area would be expected to provide a poor distribution of RO droplets.  
The noted changes in the spray macroscopic characteristics of pure rapeseed oil and its blends with diesel fuel can be reasonably applied to sunflower, cottonseed and other vegetable oils since their physical properties do not differ much from those.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
## 2 Case Study  
As the temperature of PV cells increases, the cell voltage and the overall efficiency of any PV module, panel, or array decrease, so it is preferable to install the PV modules so that the heat removal from them is as efficient as possible. By constructing a ventilation channel below or behind the PV modules themselves, it is possible to remove heat from them, where the air velocity and the efficiency of the system depend on the dimensions of that channel, the ambient temperature, and the solar irradiance [12; 38]. To improve air circulation in the ventilation channel, the so-called solar chimney effect can usually be utilized.  
The solar chimney operates on the basis of creating additional buoyancy force and increasing the air velocity in the channel, which improves the heat removal from the back surfaces of the PV modules [36; 39]. The buoyancy force is proportional to the magnitude of the temperature gradient and causes the air to move upward along the channel. This way of cooling the PV modules does not require any additional source of electricity, which is an advantage compared to other cooling systems, by means of cold water circulation or forced ventilation. A solar chimney consists of absorber and top sections. The absorber section is actually an absorber of solar radiation in which additional heating of the air occurs. The absorber is painted in matte black, which intensifies the absorption of solar heat and rises the temperature of air in it. The top section is an extension of the absorber section, and its height affects the buoyancy in the solar chimney, as with a regular chimney. The solar chimney made in this way is then installed as a continuation of the channel that goes under the section with PV modules in order to intensify the air circulation along the back surfaces of the PV modules. Accordingly, Figure 1 shows a segment of the building facade composed of these three sections together with the associated heat transfer processes.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work  
The main goal and scope of this research are to channel estimation accuracy, beamforming efficiency, training speed, and overall network throughput. The overall architecture of the proposed method is illustrated in Figure 1. Here, the proposed methodology is detailed and discussed below:* Network protocol;
* Channel estimation;
* Train the beamforming;
* Bottleneck-aware congestion mitigation.
## 4 Optimization of Microseismic Monitoring Network Design Based on Particle Swarm Optimization
### Objective Function Construction  
To sum up, the microseismic events are locally concentrated and distributed discretely in the whole area due to the existence of faults and broken zones and the diversity of mining techniques. The distribution characteristics are directly related to mine production and safety. It is necessary to increase the number of sensors in key areas. While it is only necessary to monitor large-scale events such as mine earthquakes, a few sensors can be arranged in some areas. Therefore, factors such as the importance of the monitoring area and the possibility of microseismic occurrence need to be considered.  
According to the above analysis, the microseismic probability factor \(F_{e}\), the microseismic importance factor \(F_{Q}\), and the effective range factor \(F_{V}\) are introduced to establish the objective function of the optimal network layout scheme. The microseismic monitoring network optimization problem can be transformed into the \(D\)-value optimization problem shown in Equation (9):  
\[obj=\min\left[P_{V}\left(\sum_{i=1}^{n_{Q}}\sum_{i=1}^{ne}P_{Qj}(h_{i})P_{e}(h _{i})\lambda x_{0}(h_{i})\lambda y_{0}(h_{i})\lambda z_{0}(h_{i})\lambda t_{0} (h_{i})\right)\right] \tag{9}\]  
In the equation, \(F_{V}\) is the effective range factor. \(F_{Qj}\) is the importance factor in the \(j^{\text{th}}\) area of the microseismic monitoring space. \(F_{e}\) is the microseismic probability factor. \(n_{Q}\) is the number of areas divided into the monitoring space, and other parameters are as in Equation (3).
## 3 Results and Discussion
The results indicated that the mean anxiety scores decreased significantly in the VR group, with 34.17 before the intervention and 24.77 after the intervention, unlike the control group, which had a score of 34.08 for mean anxiety at the beginning of the intervention and 27.98 at the end of the intervention. Other information was gathered by monitoring cardiac signs, which showed that the VR group had a reduced frequency and the control group had increased results. They demonstrated that the impact of VR on pain perception goes beyond distraction. Shifting attention away from the environment reduced the sensation of pain and anxiety; in addition, shorter work times were identified in the VR group. Research published in 2021 by [PERSON] et al. recruited a total of 50 patients, who were randomly divided into two groups: a test group in which VR goggles with a headset connected to a mobile phone were used, with the other 25 patients in a control group in which conventional distraction techniques were used [38]. The data obtained by the authors were statistically significant, starting with the following results: in the test group, the score was 2.07 for pain perception, and in the control group, it was 3.97, but when the VR goggles were applied, pain perception was 2.40, and in the control group, it was 2.72. Another parameter was heart rate: in the test group, the result was 91.20, and in the control group, it was 85.48, but it was not statistically significant. They concluded that the use of VR goggles has a beneficial effect as a novel method of distraction and has a greater relevance on heart rate levels and pain uptake. Table 1 summarizes all of the articles and their findings (Table 1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
This study has its limitations. As a narrative review, there was no statistical analysis of the articles we searched, and we could have missed some articles with our exclusion and inclusion criteria. We are looking to perform broader studies in this area of pediatric dentistry, as we can see that it is not only an important part of children's dental treatment but also a great area for the development of the VR industry. We also believe that noise reduction devices and computerized anesthesia instruments could make VR goggles more effective in order to help children undergoing dental procedures [40; 41]. We suggest that VR goggles be correctly used according to the manufacturer's instructions and the correct disinfection protocols be applied; in our case, we suggest not more than 25 min of goggle use and the use of disinfection control for all dental materials. We believe that narrative reviews are a good tool to give a scientific overview of a part of the huge number of articles that today are being written and a great help for clinicians who want to improve their clinical work.
# New Insights in Potato Leaf Freezing by Infrared Thermography
## Materials and Methods  
### Plant Material  
Freezing processes were studied in leaves of _Solanum tuberosum_ (var. Agria Bio). During the summer season of 2018 (May till August) potato plants were cultivated in a field plot (2 m\({}^{2}\)) of the Botanical Garden of the University of Innsbruck (47\({}^{\circ}\)16\({}^{\circ}\)5\" N, 11\({}^{\circ}\)22\({}^{\prime}\)52\" E, 610 m a.s.l.). The field plot contained natural soil (sandy silt) with modest compost fertilization. Plants were regularly watered. Sampling of leaves was started right after the maximum canopy height had been reached. Only young but fully expanded leaves from the canopy surface were used for the experiments. Leaves were selected randomly and always detached in the morning before experiments were performed. The leaf petiole was immediately submersed and cut under water before the transport to the laboratory, which lasted less than 3 min. The leaves remained well watered till the onset of the experiments.  
Air temperatures were recorded by a TidBit data logger (Onset, Bourne, MA, USA). Monthly mean/minimum temperatures were in May 17.2/8.3 \({}^{\circ}\)C, in June 18.7/9.6 \({}^{\circ}\)C, in July 20.5/11.4 \({}^{\circ}\)C and in August 20.9/6.1 \({}^{\circ}\)C.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 2 Materials and Methods
### Extending VRAE to Include Temporal Information  
Based on the effectiveness of deep stochastic models, we propose two VRAE extensions that incorporate the time information in the dimensionality reduction of light curve time series, based on the time processing proposed by [10]. The first model is a natural extension from VRAE that includes the temporal information of the signal, while the second model adds the information of the Flux standard deviation for the dimensionality reduction task.
## 2 Materials and Methods
### Plate Topology Optimization
#### 2.2.1 Finite Element Procedure  
In order to achieve custom-fit optimized plate, the finite element analysis in the optimization procedure considered a bone-screw-plate model (i.e., an assembled bone-plate construct) as shown in Figure 2. Although the SIMP approach is not affected by the magnitude of the load, the loading conditions are considered to simulate a 10% bodyweight of the patient's weight (i.e., -70 N) during swing phase and subjected to the proximal tibia region (Figure 2a) [37; 38; 39]. The distal tibia region was fully constrained in order to prevent the model floating. Following the standard practice of fracture fixation, two different screw configurations (i.e., location and number of screws) was considered (Figure 2c,d) [40]. The screw configuration is expected to influence fracture stability and topology optimization procedure [40]. The locking head screws were modelled in Autodesk Inventor 2019 (Autodesk, San Francisco, CA, USA), each screw has a 3.5 mm diameter head and a main body of 2.5 mm in diameter and 35 mm in total length. For simplicity, screw threads were neglected. Fixation plate and screws were applied with an elastic modulus of 120 GPa, and a Poisson's ratio of 0.3, assuming Ti-6 Al-4V as the material. In terms of the bone, only the cortical region was considered in this simulation with a Young's modulus of 18 GPa and a Poisson's ratio of 0.3 [41]. The implant holes, screws and bone models were considered as non-design regions. Based on a previous mesh convergence study [24], all models in the finite element analysis considered quadratic tetrahedron elements C3D10, and the considered number of elements is shown in Table 1. In order to simulate the no friction or contact of the Locking Compression Plate technique, a gap of 0.5 mm was imposed between the bone and plate. The screw head were securely locked (i.e., tie-contact) to the plate, and the bone was secured to the screws [41].  
Figure 2: Cont.
###### Abstract  
Patients with brain metastasis (BM) have a better prognosis when it is detected early. However, current guidelines recommend brain imaging only when there are central nervous system symptoms or abnormal experimental values. Therefore, metastases are discovered later in asymptomatic patients. As a result, there is a need for an algorithm that predicts the possibility of BM using clinical data and machine learning (ML). Data from 3153 patients with renal cell carcinoma (RCC) were collected from the 11-institution Korean Renal Cancer Study group (KRoCS) database. To predict BM, clinical information of 1282 patients was extracted from the database and used to compare the performance of six ML algorithms. The final model selection was based on the area under the receiver operating characteristic (AUROC) curve. After optimizing the hyperparameters for each model, the adaptive boosting (AdaBoost) model outperformed the others, with an AUROC of 0.716. We developed an algorithm to predict the probability of BM in patients with RCC. Using the developed predictive model, it is possible to avoid detection delays by performing computed tomography scans on potentially asymptomatic patients.  
Keywords:brain metastasis; machine learning; prediction; renal cell carcinoma +
Footnote †: journal: Medical Informatics
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 5 Conclusions  
This study investigates benchmark tests for different application scenarios in ground and space missions. The benchmark tests for high-performance processors in space applications provided by the European Space Agency (ESA), GPU4S, and OBPMARK, were selected. The performance of the Jetson AGX Xavier embedded processor, Loongson platform, and Asus FL8000 laptop was systematically tested. We conducted tests on representative computational modules and application scenarios on the CPU and GPU of the three experimental platforms, including fast Fourier transform, matrix multiplication, CIFAR-10 inference chain, image calibration processing, AES encryption, etc. The performance of the Jetson AGX Xavier and Loongson platforms on different computationally intensive operators was analyzed in detail.  
Through experimental research on the implementation ability of these architectures and the advantages of different architectures in space missions, this study provides important references for gradually adopting more advanced architectures to better meet the computational requirements of future space missions.  
Conceptualization, L.W.; data curation, M.X. and M.M.; formal analysis, P.Z.; methodology, M.X. and L.W.; writing--original draft, M.X.; writing--review and editing, L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2022 YFF0503903).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 2 Materials and Experimental Characterizations
### Juncus Maritimus and Solid Mortar Volume Fraction Determination  
The Juncus maritimus volume fraction, \(\varphi_{JM,\
u}\), contained in the composite materials is determined using the density values, \(\rho_{C}\), \(\rho_{s,JM}\), and the fiber mass fraction, \(\varphi_{m,JM}\). It is expressed as follows:  
\[\varphi_{JM,\
u}=\frac{\rho_{c}}{\rho_{s,JM}}\varphi_{m,JM} \tag{2}\]  
The dense mortar volume fraction, \(\varphi_{m,\
u}\), was also determined using the solid density, \(\rho_{s}\), of mortar matrix, its mass fraction, \(\varphi_{m,m}\), and composite density, \(\rho_{c}\). It is expressed as follows:  
\[\varphi_{m,\
u}=\frac{\rho_{c}}{\rho_{s}}\varphi_{m,m} \tag{3}\]  
where the solid density of mortar, \(\rho_{s}\), was determined from the dry density, \(\rho_{0}\) and the measured porosity, \(\phi_{0}\), of the reference mortar sample (without fibers) and it is expressed as follows:  
\[\rho_{0}=(1-\phi_{0})\rho_{s} \tag{4}\]  
Indeed, the effective porosity of the reference mortar (without fibers), \(\phi_{0}\), was measured directly using the Helium gas expansion porosimeter-UltraPoreTM300 [27]. The pore volume was determined using the isothermal Helium expansion and by application of the Boyle's law.  
The value obtained for the porosity, \(\phi_{0}\), and the solid density, \(\rho_{s}\), of reference mortar are 27% and 2647.94 kg/m\({}^{3}\), respectively.  
Thus, the porosity of composite samples is deduced and is expressed as follows:  
\[\phi=\frac{V_{p}}{V_{t}}=\frac{V_{t}-V_{JM}-V_{m}}{V_{t}}=1-\varphi_{JM,\
u}- \varphi_{m,\
u} \tag{5}\]
## 4 Discussion
### Variations in ET and WUE within the WD Period
A wealth of literature is available on the climatic drivers of ET and GPP, but to date, information about drivers of WUE during drought periods in tropical forests have been wanting. In our long-term study, we found that the weak variations in WUE observed during the WD periods were mainly controlled by global radiation and soil temperature, not by soil water (Table 2). In contrast to the effects of Rg on ET and GPP, Rg explained little of the variability of WUE during the WD period (Table 2). However, the Rg/WUE relationship was negative. This suggests that under future climate conditions with longer WD periods, less cloudy conditions, higher CO\({}_{2}\) concentration, and greater solar radiation [2], tropical forest ecosystems may adjust their CO\({}_{2}\) and H\({}_{2}\)O flux control and become less efficient, transpiring a greater amount of water as compared to the CO\({}_{2}\) absorbed for growth. Such a scenario must be considered with great care, however, as, again, the observed variations in WUE during WD periods were very low. When the influence of Rg on WUE during the WD periods was removed (Rg-normalized WUE), the decrease in REW induced a slight increase in normalized WUE for most WD periods (Figure 6). Such a pattern of normalized WUE increasing with soil water deficit is consistent with the general literature on WUE/soil water content effects [23; 44]. The observed diverging effects of Rg and soil water content on WUE during drought periods once again illustrates the complexity of the drivers of seasonal variations in WUE and the challenges that must be overcome before ecosystem process models will be able to precisely simulate these variations. The origin of the patterns we found can be discussed based on our knowledge of leaf-level responses of CO\({}_{2}\) and H\({}_{2}\)O exchanges to drought. At leaf level, stomatal closure during drought usually induces a stronger down-regulation of transpiration than of photosynthesis [47]. Photosynthesis is usually reduced less than transpiration during severe dry conditions, since transpiration is linearly related to stomatal conductance, whereas photosynthesis may be limited by a variety of other factors and does not respond linearly to instantaneous changes in stomatal conductance [49]. The decline in Rg-normalized WUE during water depleted periods suggests that these mechanisms might indeed drive the observed ecosystem-level patterns observed at our site.
## 4 Experimental Verification  
To verify the feasibility of this method, the AE source location experiment is carried out in this paper. The acoustic wave is collected by a _DSS-16C Holographic Acoustic Emission Signal Analyzer_ (made by Beijing SoftLand Scientific and Technology Co., Ltd., Beijing, China). AE sources are generated by pencil-lead breaks. The HB pencil lead is 0.5 mm in diameter, and the pencil lead is broken at 30\({}^{\circ}\) on the surface of the specimen, according to the requirements of _Metal Pressure Vessel Acoustic Emission Testing and Result Evaluation (GB/T18182--2000)_. The granite specimen that is 200 mm \(\times\) 179 mm \(\times\) 84 mm in size and an average velocity of 4600 m/s is used as a monitoring system. Sixteen RS-2A piezoelectric ceramic resonant sensors with the coordinates of (10, 10, 84), (190, 10, 84), (190, 170, 84), (12, 170, 84), (0, 80, 74), (110, 0, 74), (200, 80, 74), (90, 180, 74), (0, 170, 10), (0, 90, 10), (10, 0, 10), (100, 0, 10), (190, 0, 10), (200, 90, 10), (190, 180, 10), and (100, 180, 10) (in mm) respectively are mounted on the surface of the monitoring system. Sensors are numbered as \(S_{i}\) (\(i\) = 0, 1, \(\cdots\), 15)  
Figure 3: Flow charts of preconditioned closed-form solution based on weight estimation (PCFWE) method location process.  
according to the arrival orders of the AE signals, and the nearest sensor \(S_{0}\) from the AE source is regarded as the reference sensor. Later, 15 TDOA measurements \(\triangle t_{i}\) (\(i=1,2,\)\(\cdots,15\)) between the sensor pairs of \(S_{0}\) and \(S_{i}\) are obtained. In addition, there are four AE sources generated by pencil-lead breaks, and their coordinates are \(P\) (80, 120, 84), \(Q\) (160, 60, 84), \(R\) (0, 120, 42), and \(T\) (120, 0, 42) respectively. Before signal acquisition, the voltage threshold of each channel is set as 10 mv, and the preamplifier gain is 40 dB.  
Figure 4 shows the received AE waveforms by two sensors, in which \(t_{0}\) and \(t_{2}\) are the arrival times at sensor \(S_{0}\) and sensor \(S_{2}\), respectively. Then, TDOA measurement is formed by using the arrival times at the sensor pair \(S_{0}\) and \(S_{2}\), i.e., \(At_{2}=t_{2}-t_{0}\). The arrival times of the acoustic waves are picked manually according to the waveform diagram to ensure a high picking quality.