esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Major Areas of Discussion in UR Publications
#### 5.1.2 Disaster Risk Assessment and Treatment  
The United Nations office for Disaster Risk Reduction (UNDRR) promotes the analysis of possible hazards and the assessment of current exposure and susceptibility circumstances that collectively potentially affect people, property, services, livelihoods, and the environment over which they rely. This can be done using qualitative or quantitative techniques [111]. Disaster risk assessments involve the following steps: (i) identifying hazards; reviewing technical aspects of hazards, such as their location, intensity, frequency, and probability; (ii) analyzing exposure and vulnerability, along with the physical, social, health, environmental, and economic dimensions; (iii) assessing the efficacy of existing and alternative coping mechanisms in light of likely risk scenarios.  
UNDRR also discussed disaster risk management as the use of policies and techniques for reducing disaster risk in order to avoid new disaster risks, lower current disaster risks, and manage residual risks. This helps to increase disaster resilience and cut down on disaster losses [111]. It is possible to distinguish among prospective, corrective, and compensating disaster risk management--also known as residual risk management--actions in disaster risk management.  
Many communities are vulnerable to natural disasters, resulting in economic, social, and environmental damages as a result of insufficient investment and planning. Cities must alter their institutional frameworks in order to foster a culture of Disaster Risk Reduction (DRR) and collect and distribute knowledge for sound decision-making [112]. Investing in early warning systems, developing risk assessments and vulnerability maps through financing for social services and infrastructure, and developing and enforcing land usepolicies to reduce hazards and regulate construction rules for safer human settlements are all important steps toward improving UR.  
Risks and vulnerabilities are considered in urban planning, considering human habitation of hazard zones, hazard analysis and the creation of hazard maps, control over unauthorized development, scenario-based planning, the use of action and reaction characteristics, stakeholder engagement, proactive planning, level of flexibility, land, and appropriate acquisition [97; 113].  
The key results of the world energy council are that (1) for market tools, technology and data solutions, collaborations and partnerships, and communications, short-term agility is crucial; (2) lack of coordination, complicated backup plans, underused communication, and escalating failure costs are major obstacles to the dynamic resilience of whole energy systems in transition. The primary facilitators of dynamic resilience are improved climate change scenario modeling and weather forecasts in determining long-term adaptation needs; (3) Building resilience across more intricate and embedded energy systems requires a larger role for simulated and shared experiences, participatory preparation planning, and other best-practice learning methods [66].
## 4 Discussions
### Predicted vs. Measured Student Activity Level  
A prediction model for SA level was proposed by [PERSON] (2016) as follows:  
\[SA\simeq 83.0+10.0\log N-34.4\log A\quad(\mathrm{dB}) \tag{3}\]  
where  
* \(N\) is the occupancy;
* \(A\) is the absorption area of the classroom in occupied conditions, in m\({}^{2}\).  
Based on \(N\), and \(A\) values in occupied data for each lesson (see Figure 2), it is possible to compare predictive data from Equation (3) with measured data; see Figure 7.  
Figure 6: SA level as a function of V/N ratio and N.  
There are several observations to be made. The values derived from [PERSON]'s formula appear to \"saturate\" around a range of 40-45 dB, whereas the measured values exhibit a wider range. This discrepancy could be attributed to the fact that [PERSON]'s empirical formula is based on measurements conducted in a context where the Lombard effect does not manifest. Specifically, in the formula in Equation (3), the Lombard effect is implicitly ignored: the factor multiplying logN is 10, while in the case of the Lombard effect, the factor becomes \(10/(1-c)\), where \(c\) is the Lombard slope (see ISO 9921 [40]). When considering the Lombard slope, the SA values undergo an expansion.  
Moreover, [PERSON]'s formula seems to imply a certain mechanism of the cocktail party effect. The cocktail party effect is a cognitive phenomenon that enables individuals to focus on a single stream of auditory information, such as a conversation, while ignoring other surrounding sounds in a crowded and noisy environment. This effect demonstrates the brain's selective processing ability [41]. As a consequence, people (i.e., students) tend to converse in groups, and a tendency has been observed for groups to have similar sizes depending on the type of individuals, the environment, and the spatial geometry [42]. This implies the definition of a group size \(g\), which represents the ratio between talking students and silent students.This can be inferred from the fact that Equation (3) accounts for all N students and incorporates the value \(-10\log g\) (\(g\) is the group size) as part of the first constant 83.0.  
Furthermore, if we assume the starting point of the Lombard effect to be at 45 dBA of ambient noise, it becomes evident that even around 45 dBA, the measured level is higher than predicted by several dB. This bias could be due to local factors and/or the educational context. For example, Italian university students might tend to speak more due to factors such as longer lecture fragmentation, or local cultural aspects causing Italian students to use a different sound power level compared to U.S. [5] or Korean [36] students.
## 3 Low-Power Electronic Converters  
### Single-Phase Rectifiers  
In this section, various types of voltage rectifiers are considered, which are used as input stages in low-power AC-to-DC converters. The review begins with the well-known circuits of the half-wave rectifiers and bridge rectifiers or full-wave rectifiers since all other electronic circuits of this class are obtained from them. Furthermore, they serve as basic circuit configurations and are used in most cases when performing comparative analysis.
## 2 Material and Methods
### Numerical Analyses  
Based on long-term records (March-October, 2007-2018) of LSASAF LST and the SMAI (referred to root zone depth horizons), a stochastic graphical analysis (boxplots statistics, Pearson correlation coefficient, Pearson's r, and linear regression techniques) was performed using two approaches--quantitative site-scale comparison, and spatiotemporal variability consistency evaluation. The consistency in the behavior of the two indices was tested in terms of mean seasonal course, spatiotemporal variability on a monthly and annual basis, as well as their anomalous distribution and relations. To perform the comparisons, R language for statistical computing was used [49].  
Figure 1: Example of color-coded maps of SMAI over Bulgaria for (a) 0–20 cm soil layer; (b) 0–50 cm soil layer. The symbol (\(\cdot\)) indicates the location of synoptic stations used for SVAT model running. Mountain regions are excluded.  
The data for comparative analysis are available at a different spatial resolution from that used in the operational Land Surface Analysis Scheme at NIMH. They are applied for the current study as follows:  
SMAI values are calculated on the basis of inputs from meteorological measurements at 36 synoptic stations of the NIMH operational network (for the purposes of this study, only one Synop station is used for each administrative region). Soil moisture can vary significantly with depth and the anomalies at the surface can propagate and influence the dynamics of the entire soil profile [50]. For that reason, SMAI is calculated at 20, 50, and 100 cm, and the mean between 05 and 100 cm soil layers.  
LST is derived at MSG pixel resolution and only values in the 1400 grid points of the European Centre for Medium-Range Weather forecasts (ECMWF) global NWP model IFS version O1280 (about 9 km spatial resolution) that cover the region of Bulgaria are used.  
At each of these ECMWF grid points, the SMAI values at the nearest synoptic station are considered for calculation and comparison purposes.  
Site-scale assessment of mean monthly SMAI at synoptic stations are compared with the averaged LST values from corresponding MSG pixels covering the region of the station using boxplots analyses and Pearson's r statistics. Color-coded maps for each one of indices (monthly/annual means) and their anomalies (towards 2007-2018) were developed to depict the consistency in spatial-temporal variability between the two indices, including during 'dry' and 'wet' conditions.  
Vulnerability of drought-prone areas to potential fire occurrence over Bulgaria was illustrated using biomass burning detections as shown by the LSASAF Fire Radiative Power-Pixel product for two sample years, 2016 and 2017.
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 5 Future Roadmap and Implementation  
### Phased Implementation Strategy  
The successful deployment of a CubeSat-based contrail monitoring system will require a carefully planned, phased approach that addresses the technical, logistical, and regulatory challenges involved. The implementation strategy could be divided into several key phases, each building on the progress of the previous phase and gradually expanding the system's capabilities and coverage.  
The implementation of the CubeSat-based contrail monitoring system is structured into a phased strategy, as illustrated in Figure 9. This approach begins with a pilot deployment to validate technology, followed by expanded regional integration, a global rollout, and a final phase focused on sustaining and upgrading the system while fostering international collaboration. This phased strategy ensures scalability and adaptability to changing environmental and technological requirements.
# Experimental Study of Coaxial Jets Mixing Enhancement Using Synthetic Jets
## 2 The Experimental Method
### The Synthetic Jet Actuators  
The diaphragm type piezoelectric ceramic synthetic jet actuator was used in the experiment, which converted electric energy into diaphragm vibration kinetic energy, and then periodically induced the synthetic jet. It had the advantages of simple structure, light weight and convenient control. The structure of the actuator is shown in Figure 1a. Round copper with a thickness of 0.1 mm and a diameter of 50 mm was used as the diaphragm. The piezoelectric ceramics on the diaphragm had a thickness of 0.2 mm and a diameter of 35 mm. The diaphragm frequency used in the experiment was 310 Hz. The outlet size was 0.5 mm \(\times\) 20 mm. The average velocity distribution on the outlet centerline of the actuator is shown in Figure 1d. The highest velocity on the centerline was reached at a distance of 3 mm from the outlet. This velocity was used to represent the actuator outlet velocity, denoted by U\({}_{\text{s}}\). For actuators whose diaphragm material, geometric structure, and diaphragm frequency were already determined, different velocities at the outlet were obtained by adjusting the diaphragm voltage. Before the experiment, through constant attempts, the diaphragm voltage corresponding to the outlet velocity in every case were found.  
Four identical actuators were evenly mounted on a circular disk with an inner diameter equal to the nozzle outer diameter to form a combined actuators disk. Since two different outer diameter nozzles were used in the experiment, there were also two types of combined actuators disks, but the actuators inside them were the same. The disk was installed at the outlet of the nozzle. The synthetic jets of these actuators were perpendicular to the nozzle jet and worked in the same phase to prevent mainstream deflection. The number of actuators used in comparison experiments is represented by \(n\) in Table 1.
## 3 Experimental Scheme
### Polarimetric Calibration of CP-SAR System  
The scattering matrix of all multi-polarimetric radar systems is easily contaminated by system-introduced distortions such as polarimetric channel imbalances, crosstalk, antenna gain, and impurity of polarization. This contamination inevitably degrades the polarimetric decomposition results, and thus polarimetric calibration should be performed in all cases. Here, we introduce the FLP calibration method proposed by [PERSON] et al. [25] as a suitable and an effective way for calibrating the ground-based LP-radar systems. To apply this method to CP-radar, an error model in circular polarization basis is constructed [26]:  
\[\left[\begin{array}{cc}S_{LL}^{m}&S_{LR}^{m}\\ S_{RL}^{m}&S_{RR}^{m}\end{array}\right]=\left[\begin{array}{cc}R_{LL}&R_{LR} \\ R_{RL}&R_{RR}\end{array}\right]\left[\begin{array}{cc}S_{LL}^{c}&S_{LR}^{c} \\ S_{RL}^{c}&S_{RR}^{c}\end{array}\right]\left[\begin{array}{cc}T_{LL}&T_{LR} \\ T_{RL}&T_{RR}\end{array}\right]+\left[\begin{array}{cc}I_{LL}&I_{LR}\\ I_{RL}&I_{RR}\end{array}\right]. \tag{18}\]  
The model includes receive \([\mathbf{R}]\) and transmit \([\mathbf{T}]\) distortion matrices; an isolation distortion matrix \([\mathbf{I}]\); and correct \([\mathbf{S}^{c}]\) and measured \([\mathbf{S}^{\mathbf{m}}]\) scattering matrices. With using three types of canonical reflectors, namely, _circular plate_, _dihedral_, and 45\({}^{\circ}\)_inclined dihedral_, the error coefficients in distortion matrices \([\mathbf{R}]\) and \([\mathbf{T}]\) can be estimated, as explained in detail in our previous study [26]. Empty room calibration is also needed within the steps to extract the isolation distortion matrix \([\mathbf{I}]\), which can be performed easily by measuring a target free scene.  
Figure 3: Experimental geometry inside an anechoic chamber.
## 3 Proposed Dynamic Graph Processing Scheme
### Graph Preprocessor  
For an input graph to be processed by the GPU, it must first undergo preprocessing to fit within the GPU's limited memory. Since the size of the input graph often exceeds the capacity of the GPU's global memory, the Graph Preprocessor divides the input graph into fine-grained partitions that can fit within this memory constraint. During partitioning, a vertex-cut method is employed to segment the graph into sizes optimized for GPU memory usage. This approach ensures that each partition remains within the available memory capacity while maintaining computational efficiency. Equation (1) calculates the size of each partition, ensuring that the total size of N partitions, \(\mid\)_Partition_\(\mid\times N\) does not exceed the size of the global memory capacity, \(\mid\)_Global_\(\mid\).  
\[\mid\)_Partition_\(\mid\ \leq\ \frac{\mid\)_Global_\(\mid\)  
The resulting partitions are assigned to the GPU's SMs for parallel processing. The vertex-cut method is designed to preserve the graph's structural integrity while optimizing memory utilization. Through this process, the Graph Preprocessor ensures that the input graph is efficiently prepared for GPU-based processing. The graph partitioning approach ensures that the input graph is properly divided to maximize GPU efficiency. In the event of graph updates (e.g., edge or vertex additions and deletions), only the affected portions of the graph are processed. This selective update mechanism minimizes CPU-GPU data transfer costs by moving only the necessary data for graph updates, thereby reducing redundant data transfers and improving overall processing efficiency.
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 3 Results
### Comparision with Ground Rain Gauge Data  
To demonstrate the advantages and value of the proposed model in applications, the ground rain gauge data with a higher accuracy are selected for independent validation for the testing dataset. Before the evaluation, we match the rain gauge data with the retrieval data. Firstly, we convert Beijing time to UTC time. Because the temporal resolution of the rain gauge data is one hour, the average of the retrieval data in one hour is matched with the rain gauge data at this hour. Secondly, we search for the retrieval data closest to the ground rain gauge station [49]. The evaluation indicators are shown in Figures 20 and 21. The results show that when using ground rain gauge data as our reference, the accuracy of the precipitation retrieval is lower than that of the GPM IMERG product, but the overall trend is consistent. In the precipitation identification model, the POD, CSI and ETS scores are all higher than those of the FY-4B/AGRI operational product, and in the precipitation estimation model, the retrieval error is also smaller. Overall, the model is better at identifying and estimating precipitation.  
Figure 19: RMSE comparison of different underlying surfaces: (**a**) land and sea classification, (**b**) land classification.  
Figure 20: Evaluation indicators of precipitation identification model and FY-4B/AGRI operational precipitation product based on the ground rain gauge data and GPM IMERG product (**a**) during the day and (**b**) at night.
# Effect of Pouring Time on the Integrity of Pulverized Fuel Ash Concrete
## 3 Results
### Relationship between Compressive Strength and Pulse Velocity  
Analysis of experimental results presented in Sections 3.1 and 3.2 showed that the compressive strength of concrete had a positive correlation with ultrasonic pulse velocity. In this regard, many researchers use the model shown in Equation (1) to estimate the relationship between the compressive strength and pulse velocity of concrete samples.  
Where a and b are empirical parameters obtained by the least squares method, it is worth noting that different researchers have obtained different parameters [26; 27; 28; 29]. This shows that factors affecting the performance of concrete also affect the relationship between its compressive strength and ultrasonic pulse velocity. It is well known that when PFA is added to concrete, its workability, compactness, and degree of hydration change. Therefore, in this experiment, a new fitting and analysis of the relationship between the compressive strength and ultrasonic pulse velocity of PFA concrete were made.  
\[y=ae^{bx} \tag{1}\]  
According to experimental results presented in Sections 3.1 and 3.2, the relationship between the compressive strength and pulse velocity of concrete with different pouring time intervals at different curing ages was plotted. Resulting data for concrete samples with a 0.55 w/c were selected and analyzed, as shown in Figure 5. It can be seen in Figure 5 that the relationship between the compressive strength and pulse velocity of concrete samples performed to the law of Equation (1) after 14, 28, and 56 days of curing. Moreover, the correlation coefficient \(R^{2}\) of all fitting equations was high (greater than 90%). This shows that Equation (2) can be used with pulse velocity data to estimate the strength at different locations in the concrete. However, it is worth noting that when the pouring time interval and the curing age were different, constants of the fitting formula were not consistent. Moreover, by comparison with research presented in the literature, it was found that there were also differences from constants in the results of this study when the coefficient \(a\) was in the range of 0.3-3.5 and the coefficient \(b\) was in the range of 0.5-1.1. This may have been due to a number of factors, including concrete types, ratios, curing environments, etc. In order to reduce the error and improve the accuracy of the fitting equation, all data in Figure 5a-c were integrated and fitted by Equation (1); results are shown in Figure 5d, while the fitting formula is shown in Equation (2). It can be seen in Figure 5d that the coefficient \(a\) was 0.63, \(b\) was 0.95, and the fitting degree \(R^{2}\) was higher than 95%, indicating that the fitting formula was consistent with the development relationships of compressive strength and pulse velocity of PFA concrete when the water-cement ratio was 0.55.  
\[F_{c}=0.63e^{0.95 Vc} \tag{2}\]
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 3 Results
### GF3-02 SAR Wind Speed from VV Polarization  
#### 3.1.1 Compared with NDBC Wind Speed  
Figure 2(a) shows the comparison between wind speed retrieval results from GF3-02 SAR VV polarization data using CMOD_IFR2, CMOD5, CMOD5.N, CSARMOD2, and CMODH_VV models and the matched NDBC wind speed. Among them, the abscissa is the wind speed of NDBC, and the ordinate is the wind speed retrieved by the GF3-02 SAR. The RMSE of GF3-02 SAR wind speed retrieval results are 2.03 m/s, 1.78 m/s, 1.40 m/s, 1.82 m/s, and 1.63 m/s, respectively. Based on this, the GF3-02 SAR wind speed retrieval results using the CMOD5.N model are optimal with an RMSE of 1.40 m/s. Furthermore, compared with the proposed retrieval model based on SAR data, the RMSE of the CMODH_VV is 1.63 m/s, which is better than those of the CSARMOD2.
## 4 Results and Analysis
### Spatiotemporal Evolution Characteristics of Thalweg
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Year** \\ **Location** \\ \end{tabular} & **1993–1996** & **1996–2000** & **2000–2004** & **2004–2008** & **2008–2012** & **2012–2016** & **2016–2020** & **2020–2024** & \begin{tabular}{c} **Cumulative** \\ **Migration** \\ **Distance (m)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Tarim Farm Area \\ 5 Reach \\ \end{tabular} & 497.9 & \(-\)1097.2 & 1160.8 & \(-\)1298.2 & 480.1 & 560.9 & \(-\)191.2 & \(-\)914.3 & \(-\)801.2 \\ \hline \begin{tabular}{c} Paman Reservoir \\ In Tarim \\ Township Reach \\ \end{tabular} & \(-\)855.1 & 1014.9 & \(-\)899.6 & \(-\)220.6 & \(-\)59.3 & \(-\)174.7 & \(-\)35.7 & 20.2 & \(-\)1209.9 \\ \hline \begin{tabular}{c} Pama Reach \\ \end{tabular} & \(-\)189.6 & \(-\)258.9 & 667.7 & \(-\)1892.6 & \(-\)131.3 & \(-\)59.8 & \(-\)173.1 & \(-\)105.8 & \(-\)2143.4 \\ \hline \
\begin{tabular}{c} Kuerqiaoer Reach \\ \end{tabular} & 38.3 & 77.9 & \(-\)101.2 & \(-\)11.1 & \(-\)2.9 & 2507.7 & 74.1 & 36.6 & 2619.4 \\ \hline
\begin{tabular}{c} Yueliangwan \\ Scenic Area \\ Reach \\ \end{tabular} & 218.1 & 129.1 & 968 & 12.4 & 12.6 & 57.6 & \(-\)36.4 & \(-\)85.9 & 1275.5 \\ \hline \hline \end{tabular} Note: A minus sign (\(-\)) indicates northward migration (toward the Tianshan Mountains); a plus sign (\(+\)) indicates southward migration (toward the Taklamakan Desert). Units are in meters (m).  
\end{table} 
Table 2: Thalweg migration distance in typical areas of the Xinjaman–Kelelik segment (1993–2024). 49.11 m/a to 4.03 m/a, which provided valuable empirical data for understanding the effectiveness of artificial regulation measures in river geomorphology management in arid areas, and supplemented [PERSON]'s [12] observation on the impact of engineering on river activity, while highlighting the necessity of targeted engineering interventions in fragile ecological areas (such as proposed spur dikes, diversion walls, and vegetation buffer zones). In addition, the maximum thalweg line offset (2619.4 m) is much larger than the 1200 m offset of the Ganges River [9], highlighting the synergistic amplification effect of loose riparian sediments and flood events.