esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Frame and Materials
### Theoretical Frame
This carrier (depending on the modulation system) can lead to baud rates around 200 kbps, which are higher than the previous baud rates reported in the literature for PLC within PV plants, so even when this mode of propagation represents the lower baud rate for our model, it still supposedly should provide an improvement on the speeds previously reported. In addition, the resonance condition can still be achieved for this case because even when the impedance variations related to auto interference are almost not present, there is an inductance associated with the line and reactive impedances representing the transceivers (capacitive or inductive depending on the coupling chosen) that must be compensated for with a lumped element in order to push the loop to resonance. For the higher-frequency (lower-wavelength) option, as a starting point, we can consider a model of signal propagation along a closed loop similar to the ones previously mentioned regarding loop antennas, which show series resonances (impedance close to zero) at frequencies whose wavelengths are integer fractions of the loop lengths [20]. In this way, a constructive interference gives rise to a spatial resonance, and a maximum in the signal amplitude is observed. Since one key goal of a communications system is the integrity of the signal, we must satisfy the above condition in our cable loop in order to work at an optimal point with the best SNR possible. For a chosen carrier frequency, a loop length is fixed to fulfil the above condition; however, the length of the loop is an imposed parameter depending on the physical dimensions of the installation, and therefore it is necessary to find a way to adjust the natural loop resonance to match the frequency of the carrier. The insertion of a coil in series with the cable loop has the effect of increasing the electrical length seen by the signal (length expressed as a wavelength multiple), that is to say, the loop will resonate at lower frequencies. The opposite of this behaviour is seen with the insertion of a capacitor. As such, the insertion of a reactive component in series with the loop could be used to perform the matching between the carrier and loop resonance frequencies. This effect is shown in Figure 3 from our measurements over a typical PV string cable loop 20 m long with a vector network analyser (VNA) connected to the loop, where the resonance condition is recognized by the minimum in the modulus of the S11 scattering parameter (maximum power sent to the loop) and a sudden change in its phase. Figure 3 shows curves around the first resonance frequency (9.192 MHz without compensation) corresponding to the connection in series of a coil, three values of capacitors and the raw cable. These measurements have been used to determine more precisely the phase velocity in our PV cable. Since the first cable resonance (9.192 MHz) corresponds to one wavelength in the 20 m long cable:  
Figure 1:2
# Assessment of Daily Personal PM2.5 Exposure Level According to Four Major Activities among Children
## 2 Materials and Methods
### Measurement of Daily Personal PM2.5 Exposure Amount  
Using 10 s real-time PM2.5 concentrations and applying the average daily inhalation rate (0.011 m\({}^{3}\)/min) for children aged 6 to 11 years, we calculated the daily PM2.5 exposure amount per activity per person according to Equation (1) [20].  
Exposure Amount for an activity.  
\[(E_{j})\ =\ \frac{\text{Conc.}\left(\frac{\text{ug}}{\text{m}^{3}}\right) \times\ \text{Inh.Rate}\left(0.011\frac{\text{m}^{3}}{\text{min}}\right)\times\text{ Exp.Duration}\ \ (\text{sec})}{60\ \text{sec}/1\ \text{min}} \tag{1}\]  
where Conc. (ug/m\({}^{3}\)): PM2.5 concentration per activity per person measured every 10 s.  
Inh. Rate (0.011 m\({}^{3}\)/min): Inhalation rate of children aged 6 to 11 years, 99 percentile value  
Exp. Duration (second): Exposure duration per activity per person  
\(j\): Type of activities (\(j\) = 1 to 11)  
Equation (1) indicates the accumulated amount of PM2.5 exposure per activity per child. We set an initial time of 0 and the daily measurement lasted for 24 h. The daily total exposure duration (seconds) should be 24 h \(\times\) 60 min/h \(\times\) 60 s/min.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activity** & **No. of Participant** & **No. of Events** \\ \hline
1. Commuting with parents’ car & 12 & 71 \\
2. Visiting a commercial building other than restaurant & 14 & 58 \\
3. Spending time in an education institute & 12 & 48 \\ (either school, kinder-garden, or private institute) & 15 & 129 \\
4. Spending time at home without cooking & 15 & 129 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of individuals who experienced the activity predefined and the pooled number of the activity by all participants.  
In an ideal case, PM2.5 was recorded every 10 s; however, some time periods had missing values because there was no signal. For the case in which the total measurement data with valid values did not reach to 8640 per day (24 h x 60 min/h x 6 data/min) owing to missing signals, adjustments were made using a weight factor for the exposure duration by dividing the accumulated amount of PM2.5 by the ratio of the actual exposure duration over the total period for each activity. Finally, we obtained the daily total exposure amount (Etotal) by summing up the exposure amount for different activities (Figure 2).
## 4 Results and Discussion
### Modeling of SVAR for Experimental Data
#### 4.5.1 Effect of Generator Temperature on COP of SVAR and Efficiency of Solar Collector  
Figure 12 shows the variation of the SVAR COP and the efficiency of the CPC collector for a range of specified SVAR generator temperatures. From the above figure, it is clear that the COP of the SVAR system increases with the increase in the generator temperature, and the efficiency of the solar CPC collector decreases with an increase in the generator temperature. The maximum COP of 0.72 is obtained at the generator temperature of 99.55 \({}^{\circ}\)C, whereas the minimum of 0.22 is obtained at the generator temperature of 80.78 \({}^{\circ}\)C. The minimum CPC collector efficiency of 0.29 is obtained at the generator temperature of 99.55 \({}^{\circ}\)C, and the maximum is recorded at 0.31 with a generator temperature of 80.78 \({}^{\circ}\)C.
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 2 Materials and Methods
### Methods  
The PWV retrieval algorithm developed for the DPC in this study is mainly composed of three parts: (1) the construction of the DPC water vapor retrieval lookup table, (2) the calculation of WVAT in the DPC 910 nm band, and (3) DPC PWV retrieval. The flowchart of DPC PWV retrieval is shown in Figure 2.
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 4 Results
### Video Description and Moving Object Detection  
The video was captured at a frame rate of 30 fps by a Zenmuse X5 mounted on an Inspire 2. The urban and suburban environment in the video includes a highway interchange, a toll gate, structures, and farms. The drone hovered at a height of 400 m with the camera pointing directly at the ground. There was a total of 55 moving vehicles for approximately 20 s. The frame size is 3840 \(\times\) 2160 pixels, and the space to pixel ratio is 6 pixel/m. The actual frame rate is 10 fps as every third frame was processed for efficient detection processing; a total of 202 frames were considered, and \(N_{K}\) is 201 because the detection process starts with two frames (\(k\) = 0 and \(k\) = 1). It is noted that the last frame (\(k\) = 201) corresponds to the 604 th frame of the video. Figure 6a shows Targets 1-33 at \(k\) = 1. As shown in Figure 6b, a total of 46 targets appeared until \(k\) = 101; targets 1, 6, 20, 32, 33, and 43 either disappeared or were occluded. A total of 53 targets appeared until \(k\) = 151 as shown in Figure 6c; targets 1, 6, 20, 32, 33, 43, 45, and 46 are missing in Figure 6c. A total of 55 targets appeared until the last frame, \(k\) = 201 as shown in Figure 6d; targets 1, 3, 6, 20, 26, 31-33, 43, 45, and 52 are not shown in Figure 6d. It can be seen that the bridge above the road in the center and the toll gate at the bottom right occlude the vehicles.  
For the object detection, \(\theta_{T}\) in Equation (1) is set to 30. The structure element for the morphological operation is set at \([1]_{2\times 2}\). The minimum size of the basic rectangle is set  
Figure 5: Illustration of three tracks associated with one target.  
Figure 6: (a) 33 targets at \(k\) = 1; (b) 40 targets at \(k\) = 101; (c) 45 targets at \(k\) = 151; and (d) 44 targets at \(k\) = 201.  
to 3 m\({}^{2}\), which corresponds to 108 pixels. The minimum squareness and rectangularity are set to 0.2 and 0.3, respectively. Figure 7 shows the intermediate results of the detection process of Figure 6a. Figure 7a is a binary image generated by the frame subtraction and thresholding. Figure 7b shows the object area after applying the morphological operation to Figure 7a. Figure 7c shows the basic rectangles shown as blue contours after removing false alarms in Figure 7b. Figure 7d shows the centroid of the basic rectangle of the detected objects. A total of 120 objects were detected, of which 92 detections were false alarms. Figure 8a-c are the detection results of Figure 6b-d, respectively. The detection counts are 57, 92, and 60 in Figure 8a-c, respectively. Among them, the number of false alarms is 13, 45, and 13, respectively. Figure 9 shows
## 7 Antenna Qualification and Acceptance Test
### Verification of Results After Qualification Tests
Following the completion of antenna qualification tests, the flight model of the antenna (FM) is integrated into the Leader-S satellite (see Figure 22b), which has passed all acceptance tests. The Leader-S satellite is scheduled for integration and launch aboard a SpaceX Falcon rocket [36] in January 2025.
## 4 Methodology
### Key Generation by Neural Key Exchange Protocol  
1: Let key1 be the key shared between two parties and key2 be the new key generated by the proposed algorithm.  
\[key2\ \to func(key1)\]  
Key2 is generated using a neural key exchange protocol. Random input vector and initial random weights for TPM are generated by key1, which means that the new key, key2, is a function of key1.  
2: Define the structure of NN by choosing the number of neurons in the input and hidden layer.  
The structure of the neural network is defined by the length of the key shared between two parties. For example, if we take a 16 byte key, i.e., a 128 bit key, we can have:  
Input layer--64/32/16 neurons, hidden layer--2/4/8 neurons, and output layer--1 neuron.  
3: Consider two tree parity machines (TPM), for instance, TPM-X and TPM-Y. Both TPMs need to agree on the structure of NN based on key1.  
In the process of key generation, the tree parity machine is constructed by both parties. They take the common input vectors I, H, M, and L. Using Algorithm 1, a TPM is constructed, and outputs are generated. Synchronization of weights is carried out if output \(\tau_{1}\) is equal to \(\tau_{2}\). After the synchronization, the Hebbian learning rule is used to update the weights. This process is carried out until the weights are equal. Respective weights are used as keys after full synchronization.  
Tree parity machine (TPM)  
\(\tau\) is the final output. The output \(\tau_{1}\) and \(\tau_{2}\) generated by TPM-X and TPM-Y by Algorithm 1, are compared for equality. If they are equal, then the synchronization of weights is carried out using Algorithm 2. Individual weights are used as keys only if synchronization is achieved between the two parity machines.  
Synchronizing the weights  
The Hebbian learning rule is used for updating weights and is given in Algorithm 2.  
Figure 3: Detailed flow diagram.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 4 Results
### Trend Analysis of the Annual Mean LST  
The MK trend test results (Table 2) of LST for the Pakistan showed positive and negative trends during the time span of 2018 to 2021. In this section, the positive values represent increasing, and the negative values represent decreasing trends in the regions, the \(p\)-value decides the level of significance, and the slope indicates the magnitude of LST. The highest increasing trend (0.019 \({}^{\circ}\)C/year) at a 10% level of significance was observed in region V. The other regions also showed increasing trends, but these were insignificant. The lowest decreasing trend was observed in region I at a 5% level of significance, with a magnitude of \(-\)0.013 \({}^{\circ}\)C/year, which was lowest among the other regions. However, as evidenced from the minimum and maximum values of Table 2 and Figure 7, there are lot of areas that experience significant increasing and decreasing trends, but these are very small in area. Figure 7 shows the spatial distribution of the MK parameters. The most important significant trends can be observed from Figure 7d, which shows increasing trends in the northern areas and decreasing in the southern areas. The slope of the LST per year throughout the study region ranged between \(-\)0.013 and 0.019 \({}^{\circ}\)C/Year.  
Figure 6: Spatial distribution of annual mean LST in Pakistan. (**a–d**) Mean LSTs of 2018, 2019, 2020, and 2021. (**e**) LST difference between 2018 and 2021 on spatial scales. (**f**) Bar chart for mean LST difference for sub-regions of Pakistan.
## 2 Materials and Methods
### Datasets
#### 2.1.3 Other Geographical Covariates  
(1) NPP-VIIRS dataset: This dataset provides high-resolution global nighttime light data. It is jointly maintained and published by NASA and the NOAA National Centers for Environmental Information (NCEI). The dataset has a spatial resolution of 0.004\({}^{\circ}\)\(\times\) 0.004\({}^{\circ}\) and can be obtained as monthly composite products from the Earth Observation Group(EOG) Website at [[https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)]([https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)) ([last access: 26 June 2023]). (2) DEM: STRM DEM 90m data were collected from the Resource and Environment Science and Data Center ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) [last access: 2 April 2021]). (3) Longitude/Latitude: Previous studies have demonstrated that the XCO2 distribution has obvious latitudinal differences [34], so we extracted the latitude and longitude information of each data point as auxiliary variables in this study. The above datasets were upscaled to 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) by resampling to standardize the spatial resolution of all variables.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### GNSS-Based Technologies
#### 3.1.2 RTK GNSS  
One of the most recent innovations in using the GPS signals is the RTK GPS or RTK GNSS. It is a technology that provides position accuracy close to that achievable with conventional carrier-phase positioning, but in real-time. The RTK GPS employs a method of carrier-phase differential GPS positioning with centimeter-level positioning accuracy. It consists of a reference station and a roving station with a single or dual frequency GPS receiver. The GPS reference station sends carrier-phase and pseudorange data over a radio link to the roving station. Both single and dual frequency GPS receivers can be used, however, dual-frequency systems usually have faster ambiguity resolution and higher positioning accuracy over longer distances. To achieve the best results, the reference station antenna should be mounted in a location with less barrier and the radio link antennas should be as high as possible in order to maximize the link coverage [22].  
Some RTK installations use combined GPS receivers at the reference and roving stations. Besides, a faster ambiguity resolution and higher positioning accuracies can be achieved by using GLONASS data in addition to GPS. However, the data collected at the reference station reaches the rover after some delays which causes some troubles for a real-time operation. This is mostly because of the need for the data to be formatted, packetized, transmitted over the link, decoded, and passed on to the roving receiver's software. This cannot all occur simultaneously and so there are some delays (latency) which depending on the link data rate might be up to two seconds. This delay might be acceptable for some static-point surveying applications, but it is not appropriate for kinematicsurveys or for equipment navigation. To address this issue, the rover can extrapolate reference station measurements based on its own current measurements with an appropriate filter using an intelligent algorithm. This method can reduce solution latency to less than a quarter of a second, but accuracies are still limited to a few centimeters [22].
# Development of Hybrid Airlift-Jet Pump with Its Performance Analysis
## 6 Conclusions  
Numerical simulations of 18 cases were performed to investigate the performance of the preliminary design of a hybrid airlift-jet pump under various operational conditions. The water mass flow rate increases with the higher total pressure boundary conditions at the air inlet. With the same air inlet conditions, an improvement in pumping ability can be achieved by shortening the suction height, releasing air into the water inlet to form a hybrid pump case, and applying a combined case of the abovementioned settings. Using the efficiency definition applicable for the current study, it is shown that the efficiency for each air inlet boundary condition improves when a shorter suction length is applied or a volume fraction of water is decreased in the hybrid pump phase. The best volume fraction rate of water to obtain a large mass flow rate is 0.1 for the current design.  
The static pressure distribution shows an area of negative pressure from the outlet of the air nozzle to the diffuser in each case. The velocity distribution indicates that despite the different pressure values in the air nozzle, the air travels at almost the same speed for the different cases. The difference in velocity only occurs near the outlet of the air nozzle.  
The fluctuating mass flow rates of water in different cases, as well as the volume fraction counters, are analyzed, which shows a periodic fluctuating phenomenon of the water flow. The inlet water mass flow rate fluctuates, thus forming a pulsating pump system. This phenomenon is attributable to the air being periodically blocked by the water and weakened in the hybrid pump cases.  
These results confirm that the performance of the jet or airlift pump can be enhanced by employing the hybrid airlift-jet pump newly proposed in the present study. In the future, this preliminary conceptual design will be improved by optimizing design factors, such as the nozzle, the mixing chamber and the suction pipe, on a basis of more intensive and extensive numerical and experimental analysis on flow characteristics of the hybrid pump. The numerical methods also need to be improved by modelling the airlift part in a more realistic way: an air-injection boundary condition will be considered instead of the water volume fraction boundary condition.  
[PERSON] proposed the initial concept of new hybrid pump. [PERSON] refined the initial concept of hybrid pump and supervised the entire research. [PERSON] performed the related numerical computations and prepared the first draft of paper. [PERSON] carried out the related experiments. [PERSON] reran all of the numerical simulations in the preparation of revised manuscript.  
This research received no external funding.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2016 RID1A1A09918456).  
The authors declare no conflicts of interest.
## 4 Results  
### Bulk Density  
In the absence of any treatment, both soils had too high a bulk density (\(>\)1.2 g/cm\({}^{3}\)), denoting a poor SPQ with reference to this parameter. In particular, the \(BD\) was equal to 1.39 g/cm\({}^{3}\) at CAS and 1.29 g/cm\({}^{3}\) at GIN, with a percentage difference between the two soils, \(\Delta\) = +8.0%. Therefore, the amendment addition was effective if it determined a decrease of the \(BD\).  
For both soils and the three amendments (COM, VER, BIO), the \(BD\) decreased significantly as the \(ar\) increased, with coefficients of determination, \(R^{2}\), of the fitted \(BD\) vs. the \(ar\) relationship varying between 0.91 and 0.95, depending on the soil and the amendment (Figure 2 and Table 1). In particular, relatively low amounts of the amendment were generally enough to make the SPQ good, since the \(BD\) decreased below the upper limit of the range of the optimal values. Instead, high \(ar\) values deteriorated the SPQ, since the \(BD\) became less than the lower limit of the optimal range. Therefore, both a minimum, \(ar_{min}\), and a maximum, \(ar_{max}\), \(ar\) value were defined for each soil/amendment combination. In particular, the \(ar_{min}\) was the minimum amount of amendment necessary to obtain a good SPQ (\(BD\) = 1.2 g/cm\({}^{3}\)) starting from the poor condition of a too compacted soil (\(BD\) \(>\) 1.2 g/cm\({}^{3}\)). Instead, the \(ar_{max}\) was the amount of the amendment beyond which the condition was reversed, and the passage was from a good SPQ (0.9 \(\leq BD\)\(\leq\) 1.2 g/cm\({}^{3}\)) to a poor SPQ (\(BD\) \(<\) 0.9 g/cm\({}^{3}\)). The SPQ was good for \(ar_{min}\)\(\leq\)\(ar\)\(\leq\)\(ar_{max}\) and poor for both \(ar\)\(<\)\(ar_{min}\) and \(ar\)\(>\)\(ar_{max}\).  
At CAS, the \(ar_{min}\) was equal to 54-58 t/ha for the COM and the VER, and 36 t/ha for the BIO. At GIN, \(BD\) = 1.2 g/cm\({}^{3}\) was obtained with 23-24 t/ha of COM and VER, and 14 t/ha of BIO. Therefore, for both soils, a relatively low amount of the BIO had the same effect of higher amounts of COM and VER. Depending on the amendment, the dose to be used at CAS was 2.3-2.6 times higher than that necessary at GIN. Likely, this result occurred because, in the absence of any treatment, the \(BD\) was higher at CAS than at GIN.  
Figure 2: Dry soil bulk density, \(BD\), against the amendment rate for the three tested amendments (COM = compost; VER = vermicompost; BIO = biochar) at (**a**) the Castellaneta and (**b**) the Ginosa field sites. For a given site, the continuous lines are the significant linear regression lines (black for COM; red for VER; blue for BIO). The dashed grey lines define the range of values inside which the soil physical quality is considered good.  
The maximum amendment rate, not be exceeded to avoid an SPQ deterioration (\(BD\) \(<\) 0.9 g/cm\({}^{3}\)), was equal to 132-162 t/ha at CAS and 125-159 t/ha at GIN, depending on the amendment. The maximum rate for CAS was equal to 1.0-1.3 times that of GIN, depending on the soil amendment. Therefore, the \(ar_{max}\) varied with both the amendment and the soil less than the \(ar_{min}\).
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 6 Experimental Evaluation
### Models
This fact happens because GOSSIPING randomly selects the sensor nodes to forward the packets, and the reliability is directly related to the energy level of the sensor node. For example, consider that protocol GOSSIPING selects nodes \(X\) and \(Y\) to forward packets at times \(T_{0}\) and \(T_{1}\), respectively. If node \(Y\) has more energy than node \(X\), GOSSIPING has reliability at time \(T_{1}\) greater than \(T_{0}\). In other words, the reliability increases from  
Figure 16: Power consumption of the network. Figure 17: Results of the WSN reliability. to \(T_{1}\). A worse scenario would be if node \(X\) has 0% of energy (reliability = 0) and node \(Y\) has 100% of energy (reliability = 1). The reliability of GOSSIPING would change from 0 to 1. This atypical behaviour makes difficult to compare its reliability with other protocols.