esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Roads for Autonomous Accident Detection and Warnings
## 5 Experiments
### Experiments for Finding Locations  
The GPS 8M module [69] is used for recording location information and saving it permanently. Because it takes longer to establish the first fix, once the location is determined that information is saved to the Arduino Uno board [70]. The temporary connection of the GPS 8M module with the Arduino Uno board is shown in Figure 14.  
Figure 12: Smoke detector’s waveform when there is no smoke.  
Figure 13: Smoke detector’s waveform when there is smoke.  
A sketch is uploaded to the Arduino Uno board (as shown in Figure 15) to obtain the location information on the serial monitor.  
When the GPS module is connected to the board, it sends the received data to a serial port. On the serial monitor, the data are viewed in the form of NMEA sentences. When it starts, the data received are not complete, as shown in the text box.  
It takes (10 to 15 mins) to the module after a cold restart to obtain and lock in the data. The data are shown in another text box.  
\(\$\)GSNVTG\({}_{\textit{\tiny{{{{{\textit{{\textit{{\textit{\textit{\textit{\textit{{\textit  
SGNGSA,A,3,79,73,81,.......,1.72,1.02,1.39*13  
SGPGSV,3,1,12,01,18,042,18,02,09,198,23,03,03,084,28,06,36,175,20\"71  
SGPGSV,3,2,12,15,12,271,25,17,68,012,25,19,71,278,27,21,00,034,71  
SGPGSV,3,3,12,22,01,058,24,22,315,38,28,59,048,28,30,35,156,\"7B  
SGLGSV,2,1,07,73,14,322,34,78,01,131,_79,47,111,25,80,60,352,19\"60  
SGLGSV,2,2,07,81,28,030,26,82,81,025,_83,46,208,\"51  
SGNGLL,3336.52621,N,07306.48790,E,070234.00,A,\"72  
SGNRMC,070235.00,A,3336.52625,N,07306.48784,E,0.382,_280421,_,\"A*6F  
We stored the latitude, longitude, and address permanently (in code) to the Arduino Uno as a pre-saved location. After obtaining and saving the information, the GPS module was uninstalled and was no longer part of the system. The pre-saved location is used when an accident occurs. After the successful detection of an accident, the message on the location is sent to the immediately adjacent node.  
The original NMEA sentence is:  
$GNGLL,3336.52621,N,07306.48790,E,070234.00,A,A*72  
The following output can be obtained from the execution of the above line:  
33\"36\"31.33\"N, 73\"6\"629.57\"E  
The following latitude and longitude values are shown when above coordinates are searched on the Google Maps:  
33.608703, 73.108214  
Finally, the following physical address is shown against the above latitude and longitude values:  
\"Street Number 7, Dhoke Raj Muhammad Khan, Rawalpindi, Islamabad Capital Territory 46000, Pakistan\"  
For the complete code for one node of the AALS, see Appendix B.
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
###### Abstract  
Antarctic conditions demand that wireless sensor nodes are operational all year round and that they provide a large communication range of several tens of kilometers. LoRa technology operating in sub-GHz frequency bands implements these wireless links with minimal power consumption. The employed chirp spread spectrum modulation provides a large link budget, combined with the excellent radio-wave propagation characteristics in these bands. In this paper, an experimental wireless link from a mobile vehicle which transmits sensor data to a base station is measured and analyzed in terms of signal-to-noise ratio and packet loss. These measurements confirm the usefulness of LoRa technology for wireless sensor systems in polar regions. By deploying directional antennas at the base station, a range of up to 30 km is covered in case of Line-of-Sight radio propagation in both the 434 and 868 MHz bands. Varying terrain elevation is shown to be the dominating factor influencing the propagation, sometimes causing the Line-of-Sight path to be obstructed. Tropospheric radio propagation effects were not apparent in the measurements.  
LoRa; antenna; propagation; measurement; Antarctic +
Footnote †: journal: Sensors
## 5 Empirical Studies: Industrial Cases
### Body Control Module  
The body control module (BCM) core module controls all vehicle electronics via CAN and LIN communication featuring analog and digital inputs and output. The BCM model studied is that of the Kia Ceed. The BCM controls the tailgate lamp, burglar alarm, wipers, washers, side mirrors, power windows, seat belt reminders, central door control, remote key entry, and flasher lamp. The BCM software features 270 states and 610 transitions (Table 6). Each submodule features up to 73 state transitions depending on its complexity. As described in Section 5.1, the UML-based model was limited to 100 mutants for each submodule. Mutation-based testing employed 1000 to 2000 test cases for each submodule depending on submodule complexity and mutant type. However, the number of test cases was limited, but adequate in terms of coverage. The test suites were prioritized using the various methods. As Figure 5 shows, the prioritization test results of the more complex submodules exhibited lower APFDs than the tests of simpler submodules. A low APFD indicates that it is difficult to kill many mutants at once during test case creation. We group the methods as follows: Group 1: Random, SfTotal, and StAddtl; Group 2: FepTotal, FepAddtl, and AVM. The APFD values of the first group were 60 percent of those of the second group for complex submodules, and 90 percent for simple submodules. This is because the prioritization methods of group 1 do not adequately expose faults, as mentioned in Section 4. It is essential to prioritize the test suite appropriately because it is difficult to kill multiple mutants simultaneously in more complex modules. Therefore, Random, SfTotal, and StAddtl did not yield good APFD values.
## 3 Proposed Joint Decoding (JD) Framework for Video
### Fusion Approach
#### 3.2.2 Residual Reconstruction  
In this step, the predicted frames \(\text{F}_{\text{P}}\) is projected onto the measurement basis \(\text{Y}_{\text{P}}=\Phi_{\text{x}}\text{ }\text{I}_{\text{P}}\). Once, the predicted measurements \(\text{Y}_{\text{P}}\) is generated, they are then differentiated from the given frame measurements \(\text{Y}_{\text{x}}\) to produce the residual measurement \(\text{Y}_{\text{r}}\) as in Equation (10):  
\[\text{Y}_{\text{r}}=\text{Y}_{\text{x}}-\text{Y}_{\text{P}} \tag{10}\]  
The obtained residual measurements \(\text{Y}_{\text{r}}\) are then decoded by using BCS-TV-AL3 method to generate the residual frames \(\text{F}_{\text{r}}\).
###### Abstract  
The biological effect of sound on microorganisms has been a field of interest for many years, with studies mostly focusing on ultrasonic and infrasonic vibrations. In the audible range (20 Hz to 20 kHz), sound has been shown to both increase colony formation and disrupt microbial growth, depending upon the organism and frequency of sound used. In the brewer's yeast _Saccharomyces cerevisiae_, sound has been shown to significantly alter growth, increase alcohol production, and affect the metabolite profile. In this study, _S. cerevisiae_ was exposed to a continuous 90 dB @ 20 uPa tone at different frequencies (0.1 kHz, 10 kHz, and silence). Fermentation characteristics were monitored over a 50-h fermentation in liquid malt extract, with a focus on growth rate and biomass yield. The profile of volatile metabolites at the subsequent stationary phase of the ferment was characterised by headspace gas chromatography-mass spectrometry. Sound treatments resulted in a 23% increase in growth rate compared to that of silence. Subsequent analysis showed significant differences in the volatilomes between all experimental conditions. Specifically, aroma compounds associated with citrus notes were upregulated with the application of sound. Furthermore, there was a pronounced difference in the metabolites produced in high- versus low-frequency sounds. This suggests industrial processes, such as beer brewing, could be modulated by the application of audible sound at specific frequencies during growth.  
Keywords:yeast; sonic vibration; sonic pressure; aromas; Solid-Phase Microextraction (SPME); beverage; wine; beer; metabolism +
Footnote †: journal: _metabolites_
## 7 Effect of MgO Modifier  
MgO is one of the most commonly used materials in medical applications, when added to glass it decreases the crystallization rates, lowers the melting temperatures, and enhances the performance in the glass-forming zone [54]. Table 5 summarizes the mass attenuation coefficients of the studied glass with different fractions of MgO at a wide photon energy range from 10 keV to 20 MeV. On the other hand, Table 6 summarizes the neutron effective removal cross sections of the studied glass with different fractions of MgO. MgO decreased the photon-shielding capabilities of glass while it enhanced the neutron-shielding capabilities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Neutron Energy (keV)**} & \multirow{2}{*}{**Pure Glass**} & \multicolumn{5}{c}{**Glass with MgO Modifier**} \\ \cline{3-7}  & & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** \\ \cline{2-7}  & & \multicolumn{5}{c}{**Removal Cross Section (mm2/g)**} \\ \hline
10 & 8.7100 & 8.7329 & 8.8259 & 8.9421 & 9.0585 & 9.1745 \\
20 & 30.9580 & 31.2500 & 30.5490 & 29.7570 & 29.7380 & 29.9910 \\
30 & 8.9307 & 8.9533 & 9.0339 & 9.1349 & 9.2350 & 9.3364 \\
40 & 8.9773 & 8.9941 & 9.0707 & 9.1669 & 9.2630 & 9.3582 \\
50 & 8.5718 & 8.5991 & 8.6959 & 8.8177 & 8.9412 & 9.0611 \\
60 & 9.1666 & 9.1905 & 9.2878 & 9.4095 & 9.5316 & 9.6529 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The neutron effective removal cross sections of the studied glass with MgO modifier.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Photon Energy (keV)**} & \multirow{2}{*}{**Pure Glass**} & \multicolumn{5}{c}{**Glass with MgO Modifier**} \\ \cline{3-7}  & & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** \\ \cline{2-7}  & & \multicolumn{5}{c}{**Mass Attenuation Coefficients (cm2/g)**} \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: The photon mass attenuation coefficients of the studied glass with MgO modifier.
## 2 Results and Discussion
### Results
#### 2.1.5 Suppression of Male-Specific _Imp_ Pre-mRNA Splicing by Inhibitors of Nucleotide Biosynthesis  
Sensitivity to MPA and the base analog 6 AU are hallmarks of transcription elongation defects in yeast [37, 38, 39]. Both drugs cause the depletion of cellular nucleotide substrate pools required by RNA polymerases [40, 41]. _In vitro_, RNAP II complexes pause and arrest more frequently under conditions of limiting amounts of nucleotides [42], suggesting that the _in vivo_ hypersensitivity to these compounds is due to increased dependence on factors that promote elongation by RNAP II. Treatment with these drugs causes inhibition of exon skipping [36, 43] because the extent of skipping of alternative exons correlates with the elongation rate of RNAP II [36]. Based on these reports, we investigated the effects of these inhibitors on male-specific splicing of _Imp_ pre-mRNA. Male cultured cells (NIAS-Bm-M1) that were not growth-arrested were treated with these inhibitors at concentrations established previously [43]. The efficiency of male-specific splicing was estimated by the ratio of \(\textit{Imp}^{M}/\textit{Imp}^{C}\) mRNA. The \(\textit{Imp}^{M}/\textit{Imp}^{C}\) ratio in male cells treated with each drug relative to that in the negative control cells is indicated in Figure 5D. In the MPA experiment, an approximately threefold decrease in the \(\textit{Imp}^{M}/\textit{Imp}^{C}\) ratio was observed. A similarly high reduction in the \(\textit{Imp}^{M}/\textit{Imp}^{C}\) ratio was observed when the male cells were treated with 6 AU. Importantly, no significant difference was detected in the level of total _Imp_ mRNA between control cells and inhibitor-treated cells, indicating that the reduction in the \(\textit{Imp}^{M}/\textit{Imp}^{C}\) ratio represented simply the reduction in male-specific _Imp_ mRNA expression. Taken together with measurements of RNAP II processivity in Figure 5B, the simplest explanation for these results is that drug-induced reduction in transcript elongation led to the inhibition of the male-specific splicing of _Imp_ pre-mRNA.
# Biochar from Agro-Forest Residue: Application Perspective Based on Decision Support Analysis
## 2 Materials and Methods
### Experimental Methods Used for Biochar Characterization  
Thermogravimetric analysis (TGA) of the obtained biochar was carried out with TA Instruments SDT 2960 V3.0F (New Castle, DE, USA). The apparatus has a weighing precision of \(\pm\)0.01% and sensitivity in the mass measurements of 0.1 mg. The thermogravimetric tests were performed using a sample weight between 7 to 11 mg, depending on the density of the biomass used [39]. Each biochar sample was heated from 25 to 1100 \({}^{\circ}\)C, in air and at four heating rates, namely 10, 20, 30, 40 \({}^{\circ}\)C/min. The temperature was measured with an experimental uncertainty of \(\pm\)1 \({}^{\circ}\)C.  
The biochar morphology was examined with the use of a conventional Scanning Electron Microscope (SEM)--Hitachi, (Tokyo, Japan) S2400 model, with an acceleration voltage of 20 kV, with digital image acquisition with Esprit software, with Bruker (Billerica, MA, USA) coupled light element EDS SDD detector. The chemical composition was depicted through an Energy Dispersive X-ray Spectroscopy (EDS) detector.  
The specific surface area of the obtained biochar was estimated through the Brunauer-Emmett-Teller (BET) method. To this end, the samples were degassed in apparatus FlowPrep 60, Micromerities (Norcross, GA, USA), under continuous flow of helium. FlowPrep 60 combines flowing gas and a vacuum with heat to remove atmospheric contaminants, such as water vapor and adsorbed gases, from the sample's surface and pores. After this  
Figure 1: Experimental apparatus and scheme of the HTF.  
preparation, the samples' specific surface areas were measured through TriSTAR II 3020, Micromeritics. It calculates BET specific surface area by measuring the gas adsorption of carbon dioxide (CO\({}_{2}\)).  
Biochar proximate, ultimate, calorimetric, and bulk analyses were carried out following the standards and procedures applied for the biomass analysis [40; 41; 42; 43]. The parameters pH, electric conductivity (EC), and salt content were determined according to [44], using a multi-parameter measuring device (T; pH; EC and salt content) of type Combo HANNA Instruments HI 98129 (Woonsocket, RI, USA). It was equipped with a combined pH electrode and calibrated with pH-buffer solutions (HI 73127) having pH = 4, 7, and 10 (20 mL) and EC-buffer solution (HI 70031P) having an _EC_ of 1413 \(\upmu\)S/cm (20 mL). The salt content was obtained in ppm. The biochar aqueous extracts were prepared with deionized water in a ratio of 1:20. The sample extraction was carried out in graduated tubes, shaking the samples for 1 h.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Data Collection and Processing
#### 2.2.1. Agronomic Data  
The agronomic data contains several parameters collected from each site. In addition to yield data (the target feature), yearly irrigation information was also acquired from each grower, recorded as binary values (0 for no irrigation and 1 for irrigation). Irrigation is recognized as a vital factor that significantly influences the optimal growth and development of trees, consequently affecting crop productivity [23]. The availability and efficient distribution of water directly affects physiological processes, such as transpiration and nutrient uptake, which are critical for trees to reach their full yield potential. Moreover, appropriate irrigation practices can help alleviate the adverse effects of environmental stressors, such as droughts or heat waves, which are becoming increasingly prevalent due to climate change [24]. Furthermore, data regarding the average tree age (plantation date) were incorporated as a feature of the dataset. The age of almond trees is of paramount importance for productivity, as older trees tend to have more extensive root systems, established canopies, and enhanced nutrient storage, leading to increased almond production and increased overall orchard yield [20]. The data were pre-processed for each orchard separately to filter the outliers, based on distribution analysis.
# Advances in Simulating Radiative Transfer in Complex Environments
## 3 Results and Discussion
As thermal comfort indices take MRT into account and MRT is solely based on longwave radiation during nighttime, the high accuracy in modeling longwave radiation budgets, when using IVS compared to AVF, improves the accuracy and reliability of thermal comfort indices. High accuracy for modeling nighttime urban heat stress is especially needed for metropolitan regions where thermal stress at nighttime can lead to health implications such as insomnia or cardio-vascular diseases [59]. In the comparison of PET results at pedestrian level for 03:00 (Figure 9b), we indeed find some discrepancies in defining thermal hotspots between both scenarios. By including more precise MRT values into thermal comfort calculations, PET is refined and mostly decreased in almost all parts of the model area. Cooling influences of Central Park for the built-up areas, however, cannot be observed as the wind flows in from the top of the model area and leaves at the open park side at the bottom of the model area. Since the influence of both shortwave and longwave radiation patterns on thermal comfort proves to be very large, the new IVS scheme seems to be able to contribute to a more accurate analysis of urban thermal comfort in general. Figure 7: Comparison of proof-of-concept simulation results between AVF and IVS for reflected shortwave radiation received from the lower hemisphere at 09.00 in a height of 82.5 m (**a**) and for reflected shortwave radiation received from the upper hemisphere at 13.00 in a height of 1.5 m (**b**), respectively. Figure 8: Comparison of proof-of-concept simulation results between AVF and IVS at 13.00 in a height of 1.5 m for potential air temperature (**a**), for mean radiant temperature (**b**), and for PET (**c**), respectively.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 4 Results and Discussion  
### Overall Effectiveness  
The overall effectiveness of VR-supported online learning is shown in Table 2. A meta-analysis of 37 studies was conducted, and the heterogeneity analysis revealed a statistically significant \(Q\) value (\(Q\) (36) = 430.196, \(p\) < 0.001), which suggests considerable heterogeneity across the effect sizes. The REM analysis result indicated that VR had a large and positive effect on online learning (\(g\) = 0.913, \(SE\) = 0.148, 95% CI = [0.624, 1.203]). This finding supports the idea that VR can enhance learning outcomes [61]. The \(l^{2}\) of the overall model showed high heterogeneity (\(l^{2}\) = 0.92), which indicated the need for moderator analysis to explain this heterogeneity.  
The results in Table 3 indicate that online learning enhanced by VC platforms has a significant impact on students' learning outcomes, with a small effect size (\(g\) = 0.284, \(SE\) = 0.071, 95% CI = [0.146, 0.423], \(p\) < 0.001). The analysis revealed substantial heterogeneity in effect sizes (\(Q\) (34) = 245.828, \(l^{2}\) = 0.86), indicating that 86% of the difference was due to true heterogeneity. Therefore, additional moderator analyses were necessary to explore the factors contributing to this heterogeneity.
## 4 Data Description  
Initially, the CO values of the complete dataset have been plotted, in order to determine whether a large deviation in the data will be noticed. In Figure 3, we can see some extreme values that lead to a rapid increase in the CO values. Here we see measurements that exceed the 80,000 value, whereas almost the entire dataset is within the range of 0 and 6000. Particularly, there are 81 records that exceed the value 6000 and 67 values over 10,000. In addition, 23 values are over the value of 60,000. Note that the first 17 and the last 6 of them are consecutive. In Figure 4, we can see the deviation of the values, which shows a rapid increase and a more gradual decrease to what we describe as smaller values. What we see is a rapid increase with a small number of values and a smoother decrease. This may indicate the presence of the event.  
Figure 1: Parts of the environmental station.  
Figure 2: Deployed device in the greater area of the port of Igoumentitsa.  
Moreover, the dataset included a few negative values, which also constituted a problem, and we removed them. Thereafter we split the dataset into 6-h intervals, in order to identify when the extreme values occurred. As we can see in Figure 5, the values between 00:00-06:00 form the upper limit of the values that do not exceed the value 1500, and greater values are not observed. The values of the measurements seem to have a meaningful difference because the increase and decrease of the values appear to be within an acceptable range.  
Figure 4: Sample of rapid increase and gradual decrease.  
Figure 3: The CO data collected from the entire dataset—full time.  
Here we present the values in the time window between 00:00-06:00 and 06:00-12:00, and we omit the remaining two subsets. The reason is that we only wish to show the extreme rise in the values. In particular, in Figure 6 the values exhibit some extreme rise, which the authors attribute to a sudden event. In particular, we see individual values that exceed 80,000 for the times 06:00-12:00. The point of the aforementioned plots was to show that there were extremely high values for some hourly readings in the port. The aforementioned finding requires further investigation, such that the reason for the increase of values needs to be identified.
## 5 Discussions
### Validation for Color Correction  
Since our method can be used to heighten any component to recover the shadow information, choosing efficient components to raise the shadow information closer to its original content is significant. In general, \(H\), \(S\), and \(I\) were all used together to compensate in several studies, but this approach is not effective for maintaining the original color of the shadow objects. Because it almost raises all of the components to similar values, the results show a gray color that does not reflect the original colors, as shown in Figure 14a,d. In fact, during the experiments and in our previous research [31], component \(I\) was rather efficient for cloud shadows compensation to recover the original information, leading to some color loss in ground object shadows compensation. As shown in Figure 14b,e, the results were greater with the proposed model for compensating component \(I\); and within the building shadows, there is a color deviation from the original color that does not occur in the cloud shadow compensation results. As shown in Figure 14c,f, the results optimized by the color correction strategy show the original colors were well maintained.  
Figure 13: Relationships between compensation parameters and quality in different regions. (**a**) Relationship between \(\alpha\) and \(Q^{B+T}\); (**b**) Relationship between \(\beta\) and \(Q^{B+T}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area Name** & \(\alpha\) **Automatic Value** & \(\beta\) **Automatic Value** & \(\alpha\) **Ideal Value** & \(\beta\) **Ideal Value** \\ \hline
1 & 1.295 & 1.036 & 1.35 & 1 \\
2 & 1.264 & 0.926 & 1.3 & 1 \\
3 & 1.091 & 9.831 & 1.1 & 10 \\
4 & 1.0863 & 6.321 & 1.05 & 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of \(\alpha\) and \(\beta\) values calculated automatically with its ideal value of different regions.