esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 3 Semiflooding Location Service
### Algorithms  
The semifiododing location service can be formalized using two algorithms running asynchronously on all nodes in the network. The first algorithm (see Figure 2(a)) aims at periodically broadcasting local location information. The second (see Figure 2(b)) aims at updating the local location information of other nodes when an update packet that is received and forwarding it 1 in \(q\).  
Each node in the network stores an array (node) to save information about the other nodes. This array is indexed by the node ID. Information for each node includes:  
* position, the most recent position issued by the node and received locally.
* timestamp, the time the position was issued. This timestamp is the one given by the issuer node and not by the receiver. Synchronization is not an issue since only timestamps related to the same node are compared.
* done, a Boolean value that can be used to make sure that, the first time a location position is issued for a node, this message is forwarded up to the boundary of the network. This property is only available for the probabilistic version of the algorithm.
* counter, a value used to determine when a location update has to be forwarded. It is incremented every time a packet is received. When its value becomes larger than \(q\), the packet is forwarded, and \(q\) is subtracted from the counter. This property is only available for the deterministic version of the algorithm.  
Figure 3: Semifooding-location-service (SFLS) algorithms.  
For the description of the algorithms, functions broadcast and receive have the following meaning:  
* broadcast (id, position, time) sends the node ID, the position, and the timestamp associated with the position to all nodes at one hop.
* receive (& id, & position, & time) waits for the next broadcast message to be received, and returns the node ID, the position, and the timestamp from the content of the packet.
## 4 Discussion  
### Novelty of VCMS  
This study introduced our newly developed web-based ventilator monitoring system and demonstrated the quality of real-time data transmission and usability in the VCMS, which consists of central monitoring and remote mobile applications. The novelty of the proposed VCMS is that it can monitor multiple ventilators and monitoring devices of patients based on the web browser. Existing IoT-based ventilator monitoring research has focused on single ventilators, with approaches for contactless monitoring to prevent infection by medical staff and lower prices [51; 52]. The VCMS makes it possible to respond to ICU patients quickly, and the ventilator information can be accessed and closely monitored remotely, making it beneficial for patient management and reducing medical staff fatigue when monitoring multiple ventilators and ICU patient monitoring devices. In particular, the web-based integrated monitoring platform allows medical staff to manage ICU patients without being distracted by location. Figure 9 shows the real-time web-based display in the VCMS between the ventilator device and mobile application. The proposed VCMS provides a comprehensive diagnosis by recording and displaying different biometric signals and vital signs. Providing event and trend reviews overcomes the limitations of judgments based on temporary phenomena, thereby enabling evidence-based care.  
Figure 8: Bar graph of the satisfaction survey results by scenario according to Table 5 of participants who performed medical software validations on the developed VCMS.  
VMS development increases viability in terms of task completion rate and satisfaction considering the convenience and familiarity of the clinical staff. Lack of experience can be an important issue for ICU nurses. When these issues are combined with problems such as a lack of clinical staff and poor supervision, the likelihood of errors increases [53]. Thus, repeated training and hands-on experience should be performed. However, training clinical staff on various equipment and devices requires considerable time. With the VCMS, the adaptation period for using new equipment related to the quality of care was minimized. The same operation was performed with the patient monitor and ventilator equipment for the VCMS. Based on the medical software validation survey results, feedback was used to shorten the training period for additional VCMS tasks (e.g., hospitalization, events, and trend review operations). In the medical software validation, a task completion rate of >80% and high satisfaction of 4.67 points with only 20 min of task introduction and use training indicated that participants could easily use the VCMS without much difficulty.  
The developed VCMS might play an important role in implementing tele-ICUs successfully. Central monitoring, remote mobile application monitoring, and managing each ventilator might improve the concentration of the clinical staff on the patient. These advantages may contribute to lower ICU maintenance costs and staff fatigue.
## 3 Results and Discussion
### ANOVA of SRD for Algorithm Combinations  
Uncertainties can be rendered to the algorithm combinations of (Figures 1 and 5) if the ranking is repeated ten times leaving one row from the input matrix out at a time (each row once and only once). One new factor is introduced to factorial ANOVA: the leave-one-out (LOO) cross-validation (ten levels), whereas the countries (F1, five levels) disappear from the factors. LOO is a random factor and is, in fact, a repetition of ranking. Only robust methods are suitable, as the input data (\(r\) and RMSE of Tables 3-10 in ref. [2]) are contaminated by heavy outliers, and they are on vastly different scales. As the key step of SRD is a rank transformation, all SRD values placed on the same scale; namely, they are measured in %. All factors are significant (F2, models to be compared, F3, global optimization algorithms, and their coupling F2*F3). In Figure 9, F3 is shown only.  
Figure 9: Significant differences between global optimization algorithms: genetic algorithm is the worst; particle swarm optimization is somewhat better and grey wolf optimization is the best option as follows from SRD analysis of \(r\) and RMSE data of Tables 3–10 in ref. [2].  
It should be noted that the SRD results are in (partial) accordance with the original authors' findings: \"GWO provided the highest accuracy (smallest RMSE and largest correlation coefficient) and smallest processing time compared to PSO and GA for fitting the logistic, linear, logarithmic, quadratic, cubic, power, compound, and exponential equations for all five countries. It can be suggested that GWO is a sustainable optimizer...\" [2].
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 3 Results and Discussion
### Instantaneous Net Radiation Validation
In the same study and for the same date, the use of the MODIS/Aqua sensor revealed values of around 613.0 to 669.0 W\(\cdot\)m\({}^{-2}\) in the dense savanna region; for an area of exposed soil or very sparse native vegetation, estimates were in the range of approximately 510.0 to 572.0 W\(\cdot\)m\({}^{-2}\). For the present study, the average Rn observed was 614 W\(\cdot\)m\({}^{-2}\) and 655.1 W\(\cdot\)m\({}^{-2}\) in the DC and SC areas, respectively. With estimated average Rn 1 and Rn 2 of 663.1 W\(\cdot\)m\({}^{-2}\) and 647.7 W\(\cdot\)m\({}^{-2}\) for the DC area and 589.4 W\(\cdot\)m\({}^{-2}\) and 575.1 W\(\cdot\)m\({}^{-2}\) for the SC area. The authors of [25], in a study conducted in the Serido ecological station, obtained values between 500 and 700 W\(\cdot\)m\({}^{-2}\) for the Rn estimated exclusively via MODIS data. This corroborates with the results presented in Table 1 for DC.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 3 Minimum Distortion Optimization  
### Investigation and Motivation  
In pseudo-analog transmission, every scaled DCT chunk is given its own slot in the transmission channel. An advantage of the compacting nature of DCT is that low variance coefficients dominate the high spatial frequencies. Chunks with the lowest variance are considered the least important chunks, denoted LP (low-priority) because they contribute least to the video quality. Therefore, discarding these chunks to meet the constrained resource requirement will not lead to a significant quality degradation. The power saved from chunk dropping is re-allocated to other more important HP (high-priority) chunks for efficient resource utilization, particularly in stringent power-constraint conditions. Therefore, we have a joint problem of power allocation and chunk selection for 3D video transmission.
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 3 Collection Methodology
### LoRaWAN Antwerp  
Lastly, a large dataset of LoRaWAN messages was obtained in the city center of Antwerp. It holds 123,529 messages which are collected from 17 November 2017 until 5 February 2018, the spatial and temporal spread of the dataset can be found in Figures 7 and 8 respectively. The City of Things hardware that is used to create the urban Sigfox dataset (Figure 6) is also used to collect the urban LoRaWAN dataset: twenty cars of Antwerp's postal service drove around in the city center while carrying this hardware; the Firefly X1 GPS receiver continuously acquired the current latitude and longitude of the car, as well as the HDOP of the GPS signal. This location information is sent in a LoRaWAN message via the IM880B-L radio module. Compared to Sigfox, LoRaWAN implements a much wider bandwidth and higher data rate. Therefore, the radio modules were able to send a new message every minute. Also, additional information such as the HDOP could be included in the message because of the larger maximum payload. Concisely, more data could be sent more frequently, which is why the urban LoRaWAN dataset is significantly larger than the Sigfox datasets. Including the HDOP in the message allows the user of this dataset to omit messages with poor GPS signal quality. However, the GPS coordinates at receiving time could still differ from the GPS coordinates at transmission time, especially if the transmitter is moving at high speeds.  
On the LoRaWAN backend server, a callback function is configured to forward the payload of each message, with additional network information attached, to the local data server. Table 4 shows how this information was stored in the urban LoRaWAN dataset. Similar to the Sigfox datasets, the first columns indicate the receiving base stations and their respective RSSI. In the urban area that was explored for this dataset, 68 LoRaWAN base stations are detected. In the next columns, the receiving time, spreading factor, HDOP, latitude and longitude are stored.  
Figure 7: The urban LoRaWAN dataset was collected in the city center of Antwerp.
## 6 Validation in Real PR Trials  
### IDEPAR Demonstrator and Trials Scenario  
IDEPAR is the PR technological demonstrator developed at the University of Alcala [19]. The designed reflectarray is integrated in its surveillance channel for DVB-S PR trials while a commercial dish antenna of 80 cm diameter is employed in the reference channel to acquire the direct signal from the _Hispast 30W-5_ satellite. The horn of both channels is the same, as well as the LNBs to downsample the Ku signal to frequencies about 1.5 GHz that can be acquired by an _USRP X-310_ board. The selected central frequency is 11.3 GHz with an acquisition bandwidth of 100 MHz covering 3 DVB-S channels: one of 30 MHz and two of 10 MHz.  
To validate the reflectarray design, the IDEPAR demonstrator was deployed in two semi-urban terrestrial scenarios located in the External Campus of the University of Alcala (Figure 11).  
The first deployment is next to a Nursing School with its surveillance area covering a straight street surrounded by medium-height buildings, some trees and a metallic fence that surrounds the sports facilities. The main beam covers all the street up to its ends at range bin 200 (\(\sim\)330 m from the PR).  
The second deployments is at the border of the campus, next to the \(M\)-121 road which will be the region of interest for ground traffic monitoring. In this scenario, there are some buildings on the right side of the road and crops on the left side. The beamwidth covers the road from range bins 100 to 360, in which a target in the road will be at 160 m and 600 m from the PR, respectively.  
Figure 11: Terrestrial scenarios employed to validate the reflectarray antenna and the PR deployment in each of them.  
Figure 10: Reflectarray characterization results.
## 3 Results
### Results of Sensitivity Analyses
#### 3.2.2 Sensitivity to Acquisition Cost of PV Generation  
For this analysis, the acquisition costs of the elements of PV generation, such as panels and inverters, were multiplied by factors from 0.1 to 2. As expected, in all cases, the higher cost of the elements, the higher cost of the system.  
Regarding PV systems, Figure 13a shows that in the irrigation pumping case, the NPC line of optimal system with Li-ion batteries has a greater slope than that with lead-acid ones, even crossing it, i.e., the system with Li-ion batteries was more sensitive to variations in the acquisition cost of PV generation. Thus, in this case and in the interval studied, lower acquisition cost for PV elements (panels and inverters) favored the presence of Li-ion batteries over lead-acid batteries in the absolute optimum, and vice versa. However, Figure 13b shows that in the case of the winery, this behavior was very attenuated, both lines being virtually parallel, i.e., the acquisition costs of the PV elements (panels and inverters) did not affect the type of battery present in the absolute optimum system of the case and in the interval studied. The rest of the cases showed intermediate behaviors between the two described. In none of the cases, the reduction in cost of PV elements favored lead-acid batteries.  
With respect to hybrid systems, Figure 14a shows that in the irrigation pumping case, both lines are virtually superimposed, keeping very little distance between them. In fact, their NPCs in the base case differ only by 0.97%. Figure 14b refers to the case of the Single-family home, where both lines are also virtually parallel. All other cases had the same behavior. Thus, unlike optimal PV systems, in hybrid systems the type of battery (lead-acid or Li-ion) present in the absolute optimum was not affected by the acquisition costs of the PV generator.  
Figure 13: Sensitivity analysis of the NPC of the optimal PV systems with lead-acid and Li-ion batteries, based on the acquisition cost of PV generation: (**a**) Irrigation pumping case; (**b**) Winery case.  
Regarding the elements of the optimal systems, in the five cases both PV and hybrid, the variation in the acquisition cost of the PV generator influenced their size. Figure 15 shows the graphs corresponding to the winery case, both PV (a) and hybrid (b), with Li-ion batteries. The behaviors presented some similarities to those observed in the sensitivity analysis on the cost of Li-ion batteries. In the PV system (a), as the acquisition cost of the PV generator increased, its size tended to decrease and that of the battery to increase. Again, a lower production of the PV generator was compensated by taking advantage of part of the surplus energy, through a battery with a higher capacity.  
However, as shown in Figure 15b, in the hybrid system, the decrease in the power of PV generation was proportionally greater than in the PV system. Again, a higher output from the diesel genset compensated for the lower output from the PV generator.
## 3 Tests Results and Discussion
### Microstructure and Phase Analysis
#### 3.4.2 Results of the EDS Analysis  
The EDS spectra of each group of samples at different target temperatures are shown in Figures 19-21. The EDS analysis indicated that as the ambient temperature and sampling location varied, the main elements (mass fraction) in the sample also varied. Overall, the elements contained in the precipitate samples of each group were mainly C, O, and Ca. In addition, some samples also contained other elements such as Mg and Al. Observing the samples of Group C, it can be seen that after 28 days of self-healing, Ca, C, and O bonded to form CaCO\({}_{3}\), which filled the crack area. It can be concluded that the large number of polygonal blocks observed in the previous FESEM images were CaCO\({}_{3}\) crystals. Based on this, it can be reasonably inferred that there should be microbial mineralization in the specimens of Group C. This is consistent with the results of [PERSON] and Deshannavar [60]. Therefore, Group C achieved a better performance than Group A. In the case of 500 \({}^{\circ}\)C, compared with the block in the center, there was a large difference in the amount of CaCO\({}_{3}\) precipitate in the blocks at the edges. This corroborated the inference that the strains near the surface of the test specimen died at a high temperature of 500 \({}^{\circ}\)C and that the strains inside could partially survive. This could reasonably explain the results of the pull-out  
Figure 17: Images of the crack healing of the pull-out specimens.  
test in Group C. The EDS analysis results showed that there were CaCO\({}_{3}\) crystals in the samples of the experimental group. It is worth noting that a higher mass fraction of calcium was found in the experimental group samples. Moreover, the ratio of the atom fractions of calcium to silicon is an important parameter. It can be used to reflect the changes in the chemical composition of C-S-H colloids in ITZ and cement matrix [62]. As shown in Figure 21, in the Group A sample, the Ca/Si ratio was 0.04; in the Group B sample, the Ca/Si ratio was 0.07; and in the Group C sample, the Ca/Si ratio was 0.26.  
Figure 18: FESEM images of the surface blocks of LWAC samples: (**a**) at room temperature; (**b**) after exposure to 300 \({}^{\circ}\)C; (**c**) after exposure to 500 \({}^{\circ}\)C.  
Figure 19: EDS analysis of the center block of each group of samples (after being subjected to 300 \({}^{\circ}\)C) and with a healing period of 28 days): (**a**) Group A; (**b**) Group B; (**c**) Group C.  
Figure 20: EDS analysis of the center block of each group of samples (after being subjected to a temperature of 500 \({}^{\circ}\)C and with a healing period of 28 days): (**a**) Group A; (**b**) Group B; (**c**) Group C.
## 5 Flow Validation with PIV-CFD Comparison  
In this study, Converge CFD commercial software was employed with the adaptive mesh refinement (AMR) algorithm to accurately capture velocity and temperature gradients within the transient CFD model. Base mesh sizes of 3 mm, 4 mm, and 5 mm were utilized to investigate mesh independence for Sandia's PIV validation case, thereby ensuring the accuracy of the research.  
Comparison metrics are essential for evaluating the flow distribution consistency between CFD simulations and PIV measurements within the cylinder. Accordingly, following the approach proposed by [PERSON] et al. [8], both global and local structure index metrics were employed in this study.  
The Global Structure Index (GSI), defined in Equation (1), quantifies the average vectorial directional agreement (Vector Angle) across the measurement plane at corresponding nodes between CFD and PIV data. Similarly, the Global Magnitude Index (GMI), described in Equation (2), represents the average match in vectorial magnitudes (Vector Magnitude) between the two datasets at the respective nodes.  
These indices are derived based on vector similarity principles, where GSI uses the cosine of the angle between velocity vectors and GMI evaluates the normalized magnitude difference at each node.  
Furthermore, the sub-vectorial component formulations for both GSI and GMI metrics are presented in Equation (3), providing a detailed basis for assessing local flow field similarities.  
\[\text{GSI}=\frac{u.v}{\|u\|\|v\|} \tag{1}\]  
\[\text{GMI}=1-\frac{\|u\|-\|v\|}{\|u\|+\|v\|} \tag{2}\]  
\[\|u\|=\sqrt{\sum_{k=1}^{n}\left(u_{k,1}^{2}+u_{k,2}^{2}\right)} \tag{3}\]  
\[\|v\|=\sqrt{\sum_{k=1}^{n}\left(v_{k,1}^{2}+v_{k,2}^{2}\right)} \tag{4}\]  
This formulation [8] was implemented in MATLAB v2024a code by interpolating from the CFD environment to the PIV measurement results. Thus, the CFD vector field results were compared and quantified using non-dimensional GSI and GMI metrics.
# GaN-on-Si: Monolithically Integrated All-GaN Drivers for High-Voltage DC-DC Power Conversion
## 1 Introduction
To efficiently drive E-mode GaN transistors as output stages of power converters, this paper proposes an EED inverter-based high-side driver to directly drive GaN power transistors on the GaN-on-Si process. Rather than relying solely on a conventional level shifter combined with a bootstrap circuit scheme, the EED structure modifies the D/E-mode inverter to achieve a high-efficiency and low-EMI high-side gate driver for E-mode power transistors. Through the driver design principle proposed in this paper, the high-voltage GaN power switch can operate at frequencies greater than 1 Mhz, the switching voltage exceeds 200 V, and the waveform has no overshoot at turn-on and no oscillation at turn-off, indicating that the design effectively drives high-voltage E-mode power transistors. The experimental results show that the rise and fall times of a 200 V switching pulse generated by the half-bridge driver based on EED architecture are 54.4 ns and 57.6 ns, respectively. The driver has been verified in a buck converter, and the overall efficiency of the system can reach more than 90% under the output power of 8-15 W, and the overall power consumption of the EED driver is about 0.4-0.6 W.
# Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis
## 3 Results
### Evaluation on Transfer Learning-Based Dungan Acoustic Models  
#### 3.2.1 Corpus  
In the experiment, we utilized recordings of nine female and thirty-one male speakers from the Tsinghua Chinese 30-hour database [45] (totaling 13,389 sentences) as the Mandarin corpus. For the Dungan corpus, we selected five male speakers' recordings (923 per person, totaling 4615 sentences and 6 h). The Dungan corpus encompasses all initial and final pronunciations of the Dungan language. The average sentence length is 18 syllables, with an average duration of 10 s. All recordings were converted to a monochannel 16 kHz sampling frequency with 16-bit quantization accuracy.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Attention layers \(N_{x}\) & 6 \\ Heads & 8 \\ Batch size & 32 \\ Hidden & 513 \\ Dropout & 0.1 \\ Learning rate & 0.0001 \\ \hline \end{tabular}
\end{table}
Table 2: The hyperparameters of Transformer-based Character-to-unit conversion model.  
\begin{table}
\begin{tabular}{c c c} \hline
**Precision** & **Recall** & **F1** \\ \hline
90.12 & 89.91 & 90.01 \\ \hline \end{tabular}
\end{table}
Table 3: The results of Transformer-based Dungan character-to-unit conversion.