esa-sceva/satcom-chunk-collection huggingface dataset:
[PERSON] and [PERSON] [36] showed that the adoption of region-specific aftershock parameters can improve forecast estimates, as the information from the tectonic region is particularly useful, and suggest the determination of clustering features in smaller regions where high-quality earthquake data are available. More recently, [PERSON] et al. [37] updated the generic parameters of sequences in California incorporating the regionalization of the former work for their determination. In this way, there was an improvement of the aftershock forecasts' accuracy. The temporal ETAS model assumes that seismicity is evolving in the form of independent events (background seismicity) who generate their aftershocks with each one producing their own. It incorporates two empirical laws, the Omori-Utsu law [38] and the productivity law used to explain the distribution of aftershocks, as well as a stable Poissonian rate for the background seismicity. Our work utilizes the estimated parameters of the ETAS model to investigate regional variabilities in the productivity of the sequences and gain insights into the involved triggering mechanisms [39; 40; 41]. The aim of this study is firstly to demonstrate the efficiency of the proposed clustering method to separate triggered from background seismicity and subsequently to investigate and compare the clustering properties among three major seismic zones of Greece. In particular, we focus on the statistical analysis of the detected clusters based on the ETAS model producing generic and sequence specific parameters for each area. The paper is organized as follows. In Section 2, the MAP-DBSCAN method for the identification of the clusters is described and simulation results for the evaluation of the method are deployed. MAP is used as a tool for the detection of changes in the seismicity rate and DBSCAN is implemented to reveal spatially high-density areas. In Section 3,the study area along with the datasets used in the application are presented. Finally, in Section 4, details on the detected clusters for each area are given and the ETAS regional parameters based on the identified clusters are derived. The regional variability of the clustering properties among the three areas is investigated based on the generic estimates of ETAS parameters (\(a\), \(K\), \(p\), \(c\) and \(\mu\)) and sequence-specific parameters. A brief discussion of the results is given in Section 5 and the main conclusions are presented in Section 6.
# Thermal Energy Storage for Building Load Management: Application to Electrically Heated Floor
## 2 Materials and Methods
### EHF in TRNSYS
#### 2.1.3 Model Verification  
The implementation of this model was applied to a residential building in Montreal (QC, Canada), which had been previously validated for electric baseboard with field measurement data [21] and used in previous studies [22]. To validate the model, the main consideration was to be sure that the creation of a new zone \"fictitious\" was done properly and the proposed wall gain approach simulates the behavior of the system accurately.  
To validate the integration of the fictitious zone, the _wall gain_ was set to zero, and electrical baseboards were used to heat the building with the same control strategy as the one used earlier [21] (operating all day as function of the indoor temperature with a set-point at 21 \({}^{\circ}\)C). Simulations were performed for one month (in January), for the reference building (equipped with electrical baseboards only) and the modified one (with electrical baseboards and with the EHF structure with the _wall gain_ set to 0). The energy consumption of the two heating systems were then compared in calculating the Normalized Mean Bias Error (NMBE). Details on the calculation of this statistical index are accessible on the ASHRAE Guideline 14-2014 [23]. Results are presented in Table 1. The passive EHF model is shown not to have adversely affected the building performance.  
To validate the assumption of perfect contact between the two concrete layers, the temperature on the bottom of Floor_1 was compared to the temperature on the top of Floor_2 (see Figure 2). These temperatures are outputs of TRNSYS. The temperature difference between the upper side of Floor_2  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistical index** & **Monthly** & **Hourly** \\ \hline NMBE & 0.20\% & 1.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical index to compare energy consumptions of the reference building and the modified one.  
Figure 1: Conventional configuration of a room in TRNSYS (**a**); and configuration with the added fictitious zone in TRNSYS (**b**).  
Figure 2: Conventional configuration of an EHF (electrically heated floors) (**a**); and configuration of the floor in TRNSYS with the added fictitious zone and the wall gain (**b**).  
and the lower side of Floor_1 was calculated for 10 days, and only the last eight days were taken for analysis (to ignore the initial condition effect): results showed a NMBE of less than 1%.  
Finally, [PERSON] et al. [24] modeled the same EHF using 2D finite-element analysis. Their results are very close to the 1D TRNSYS model results presented in this paper: the predicted temperature at the heating cable height from the TRNSYS model was 32.38 \({}^{\circ}\)C, when the average temperature on the 2D model of was 31.11 \({}^{\circ}\)C. Similarly, results of the 2D model show a difference in the floor surface temperature in comparison with the TRNSYS model of approximately 1 \({}^{\circ}\)C. Moreover, the hypothesis of an isothermal floor surface was validated.
# Non-Cooperative Target Ranging Based on High-Orbit Single-Star Temporal-Spatial Characteristics
## 2 Establishment of the Distance Measurement Model
Assuming a short time interval between two samples (e.g., 1 min), it can be considered that the two samples' true anomaly (f) values are close to each other, leading to small changes in the Z-axis. This gives the expression in Equation (7). From Equation (11), the following calculation can be performed:  
\[x_{J2000}^{2}+y_{J2000}^{2}=a^{2}\cos^{2}(\omega+f)+a^{2}\cos^{2}(i)\sin^{2}( \omega+f) \tag{13}\]  
Furthermore, when i is small, we can approximate that \(\cos^{2}(i)=1\), yielding Equation (14):  
\[x_{J2000}^{2}+y_{J2000}^{2}=a^{2} \tag{14}\]  
Thus, when the change in the true anomaly (f) is small, the equation can be written as in Equation (9). Based on Equations (1), (3)-(7) and (9), the coefficient \(k_{2}\) can be solved to obtain the absolute values of the two solutions, \(d1_{abs}\) and \(d2_{abs}\). Consequently, Equation (15) can be used to select the minimum coefficient. \[d=\min(d1_{abs},d2_{abs}) \tag{15}\]  
As shown in Figure 3, when the two satellites move to position 2 (establishing equations using positions 1 and 2), the calculated coefficient \(k_{2}\) for the pointing vector assumes two values. This is the case because, according to Equations (7) and (9), the pointing vector intersects the surface of the cylinder where the target satellite T is located at two points. The minimum value of the coefficient represents the observable target satellite, and another point is located in a position that cannot be observed in the line of sight. Thus, by selecting the minimum coefficient, the distance from satellite S to target satellite T can be determined using Equation (16). \[d_{last}=d\sqrt{(v_{x2}^{2}+v_{y2}^{2}+v_{z2}^{2})} \tag{16}\]
## 4 Results  
### Discriminability Analysis  
Table 2 shows the discriminability metrics of all the architectures. In Approaches 0 and 1, overall, discriminability was high, with notable recall (i.e., true positives), precision, and Cohen's Kappa (i.e., balance true positives and negatives), even though including the attention layer (Approach 1) led to varying degrees of decrease in all metrics except recall. The attention layer in (2) was chosen for further experiments due to its slightly higher PR-AUC and minimal decrease in metrics (0.98%) compared to (1) and Approach 0, respectively.  
Considering the different feature-domain processing methods in Approach 2 (Section 3.1.4), the simplest ones (Dense, GRU, and CNN down-sampling) showed similar, best results. On the other hand, the down-up CNN performed worse in true negatives, with higher recall but lower Cohen's Kappa and precision. The architecture with the best discriminability, i.e., the one processing the features using a GRU, showed a 6.39% decrease in discriminability (averaging the changes in all the metrics) compared to the best from Approach 1, mainly due to decreased performance in predicting true negatives (1-year survival), e.g., the precision decreased by 10.34%.  
Approach 3 (Section 3.1.5), employing hierarchical attention, compared the feature-domain processing methods again. The down-sampling CNN proved best in terms of discriminability, though slightly lower than the best of Approach 2 (by 1.60%), Approach 1 (7.85%), and baseline Approach 0 (8.72%). As in Approach 2, the drop in discriminability affected only the detection of true negatives, while recall was maintained or even increased. Using a single FC layer for feature processing decreased model discriminability, likely due to class imbalance and more complex processing methods' ability to predict the outcome of interest.
## 3 Finite Element Method (FEM)  
Heat transfer problems that include solar radiation are difficult to solve analytically, as they require transient solutions of complex nonlinear partial differential equations [19]. Thereby, FEM has become attractive for these iterative processes. This numerical modeling  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multirow{2}{*}{**Day of Inspection**} & \multirow{2}{*}{**Duration of the Inspection**} & \multirow{2}{*}{**Solar Radiation**} & \multirow{2}{*}{**Wind Speed**} & \multirow{2}{*}{**Average Temperature**} & \multicolumn{2}{c}{**Half Diurnal**} \\  & & & & & (\(\mathbf{W/m^{2}\cdot s}\)) & (\(\mathbf{{}^{\circ}}\)C) & **Temperature Variation** \\ \hline November & 22 November 2018 & 7 h to 21 h & 574.02 & 3.21 & 23.95 & 6.90 \\ February & 23 February 2019 & 7 h to 21 h & 524.14 & 1.30 & 27.00 & 8.85 \\ April & 18 April 2019 & 7 h to 21 h & 464.19 & 1.71 & 17.55 & 5.80 \\ June & 14 June 2019 & 7 h to 21 h & 276.48 & 2.85 & 20.28 & 5.60 \\ July & 06 July 2019 & 7 h to 21 h & 408.58 & 1.38 & 3.81 & 6.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inspection and meteorological data used in the numerical simulation of the field tests.  
Figure 2: Experiment details: (**a**) Concrete sample after curing; (**b**) location of the samples in the university site.  
method includes reproducing the tested geometry, dividing it into finite elements (mesh), and exposing the sample to the heat source and boundary conditions through the time of interest. Then, the governing differential equation of the heat transfer problem is replaced by finite algebraic equations at the finite points created in the element at the given time.
## 1 Introduction
Finally, an expression governing wander of a Gaussian-beam wave along a slant path valid in the weak fluctuations regime is presented. This work lays a scientific foundation for the establishment of an atmospheric turbulence compensation model and has certain scientific significance for improving the performance of a satellite-ground laser communications system, on-orbit experiment and practical application.
## 2 Research Methodology
### Overview of the Study Area and Data Sources
#### 2.1.2 Data Sources and Preprocessing  
The data involved in this study include elevation data, satellite remote sensing data, precipitation and temperature data, soil data, land use data, road, rainfall erosivity data, and water system data. The source and resolution/scale of the data are shown in Table 1.  
The data were uniformly converted using the CGCS2000 coordinate system and the Gauss Kruger projection. For raster data, they were converted to a size of 30 \(\times\) 30 m by resampling; for vector data, such as water systems and roads, buffer analysis and clipping were carried out, and then converted to raster data with a resolution of 30 m.
## 5 Results and Discussion
### Short-Term Analysis
The Italian severe weather emergency plan is, now, based on rainfall alarm thresholds, defining three possible degrees of criticality (yellow, orange and red). The linking of rainfall thresholds to PWV evaluation would make it possible to anticipate the actions envisaged in the emergency plan. With regard to the experience gained in our study, although limited to only three events, we can assume that an alarm system should be set to both absolute values (thresholds) and steep increases after a threshold value is reached. In other words, further analysis will have to aim at achieving a robust statistics in order to determine a value above which a steep increase in PWV triggers rain. Further studies, on a sufficient number of events, might confirm, or not, our hypothesis. The TAI1 station, which is equipped with a GNSS receiver-chokering antenna system, enabled us to investigate the differential response (tropospheric delay) of the signals emitted by different satellite systems (Figure 11). The three systems analysed (GPS, GLONASS, GALILEO) showed no significant differences. In fact, calculating the difference in PWV with respect to the GPS system (Figure 11 below) yielded values ranging around \(\pm 1.5\) mm. In only a few time intervals did the difference between the various systems appear significant, and this is likely due to the different number of satellites visible at the station. Figure 9: Thirty-second values of PWV, obtained with CSRS-PPP analysis, for a selection of stations of NeVoCGPS network close to MAFE, where weather observations are collected, and rainfall amounts (grey curve); NGL and Gamit solutions for TAI1 and MAFE stations, respectively, are also plotted as a reference. Such inter-system biases are in agreement with other studies. For example, [PERSON] et al. [56], processing observations (for about six months) from 80 International GNSS Service (IGS) stations, found RMS values of ZTD differences between GPS and GLONASS ranging \(5\div 13\) mm (which is equivalent to a PWV of 0.8 \(\div\) 2 mm). In addition, [PERSON] et al. [57], in developing a multi-GNSS model to improve real-time ZTD/IWV, retrieved RMS values for GPS and GLONASS differences of 3.2 and 5.5 mm, respectively. Figure 11: Hourly PWV values retrieved from Gamit analysis at the GNSS station TAI1 for different satellite constellations. The inter-constellation difference and rainfall is also plotted. Figure 10: Thirty-second values of PWV, obtained with CRS-PPP analysis, for a selection of stations of NeVoCGPS network located at different heights and rainfall amounts (grey curve); NGL and Gamit solutions for TAI1 and MAFE stations, respectively, are also plotted too a reference.
## 4 Discussion  
### Moment-Rotation Hysteresis Curves  
Figure 10 describes the moment-rotation hysteresis curves of the connections. The shapes of the hysteresis moment-rotation curves for these specimens are relatively similar until the maximum moments are reached. The moment-rotation relationship of the connection can be considered to be roughly linear in the early initial elastic stage. In the inelastic stage, an obvious pinching effect can be observed. As the drift ratio increased, the pinching effect becomes more obvious. This change can be due to the severe deformation of the tube face and the end-plate, the bolt slippage and deformation, the local buckling of the steel beam, and the concrete crushing, representing the failure modes of the different specimens.  
The specimens (ST-1, ST-2, and ST-5) that failed in mode I have low flexural resistance, but their hysteresis performance is better than those that failed in mode II, which is attributed to progressive concrete crushing. With increasing drift ratio, a sudden drop in the moment can be observed, which reflects the simultaneous anchorage failure of the blind bolts and crushing of the concrete. In subsequent cycles of loading, the moment for the thin end-plate connection (ST-1) continues to decrease, but the moment for the thick end-plate connections (ST-2 and ST-5) first increases and then slowly decreases due to the considerable restriction of the thick end-plate on the rotation of the connection. The energy dissipation of the joints is mainly achieved by the crushing of concrete in the panel zone and the deformation of the steel tube walls. However, those failure modes are not expected in practical engineering. In addition, for the same reason mentioned above, obvious stiffness degradation was found at a larger drift ratio.  
The specimens (ST-3, ST-4, and ST-6) that failed in mode II have high flexural resistance, but the hysteresis performance is worse than that of the specimens that failed in mode I. In particular, for specimen ST-4, bolt brittle fracture occurs before a drift ratio of 4% was reached, and the hysteresis loop is flat. Reductions in the end-plate thickness (ST-3 and ST-4) and steel beam section (ST-4 and ST-6) widen the hysteresis curve and produce relatively good hysteresis performance. The energy dissipation is mainly realized by the deformation of the blind bolts, steel beams, and end-plates. Moreover, a thicker steel tube wall relieved the stiffness degradation of the connection more, as infill concrete was effectively confined by the steel tube and failure was prevented even though its unconfined capacity was achieved.
## 5 Deflection Slope Test Error Analysis for Inverted Asphalt  
### Pavement Structure  
In the design of the TSD equipment, the S3500 sensor was set as the reference sensor based on the assumption that the deflection response at this location for traditional asphalt pavement structures tends to zero. However, as shown in the previous analysis, for the inverted pavement structure, the actual measurement value from the Doppler sensor at the S3500 position is not zero. The complex structural response characteristics at this location invalidate this assumption. As a result, the accuracy of the entire measurement system is affected. This limitation in the design assumption leads to deviations in the measurement data from other sensor locations, which in turn impacts the overall deflection measurement results for the inverted asphalt pavement.  
This phenomenon reveals a key issue within the TSD system: when the Doppler sensor at the S3500 position is used to calibrate the readings from other sensors, significant measurement errors may be introduced. To accurately assess this error, this study used the TSD calibration principles and fitting by the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) method to calculate the deflection error when applying TSD to Inverted Pavement Structure 1. As shown in Figure 7, the calculation results show that for the inverted pavement application, the deflection errors at the S100 to S1500 Doppler sensor positions relative to the deflection values obtained from the finite element simulation are 59.3%, 70.4%, 67.7%, 73.8%, 75.2%, and 80.1%, with the maximum error reaching 80.1%.  
The substantial deflection errors observed when applying TSD to inverted asphalt pavement structures have important practical implications. Since the TSD system underestimates actual deflection due to incorrect sensor calibration, it may significantly overestimate the structural bearing capacity of the pavement. This misjudgment can lead to under-designed maintenance interventions or deferred rehabilitation schedules, increasing the risk of unexpected pavement failures and long-term maintenance costs. Furthermore, inaccurate TSD data, when used in network-level pavement management systems, may  
Figure 7: A comparison of TSD method fitting values and finite-element calculation values for Inverted Pavement Structure 1.  
compromise the reliability of condition assessments and disrupt optimal resource allocation strategies.
# A Comparison of Energy Management System for a DC Microgrid
## 4 Results
The results show that the SOC of the battery is consistent with the behavior of the battery charge and discharge graph of Figure 6. We could observe that at all hours of time the SOC of the battery in Cancun was higher due to the production of PV power in this city being greater due to the available solar radiation and therefore there was an opportunity to charge the battery more. For example, from 8:00 a.m. to 10:00 a.m. the battery in Cancun has a higher SOC the behavior was very similar from 10:00 a.m. to 4:00 p.m. at 5:00 p.m. the battery in Baglan managed to have an SOC similar to the battery of Cancun since at this time the production of PV in Baglan was greater. For the discharge we observed that at 9:30 a.m. the battery in Baglan is discharged to the maximum possible 20% while in Cancun the battery is discharged at 40%, this behavior is similar at 11:30, 13:00, 15:00 and 16:30. Figure 6: Result of the powers in generators for a clear summer day (Cancun and Baglan). This comparison shows us that having a greater availability of solar radiation the battery in an isolated micro network has less wear due to the lower number of use cycles. In Figure 8 shows the behavior of the nodal voltages within the microgrid. It is observed that the voltage levels were maintained in the minimum and maximum permissible values of 95%-105%, in this way it was verified that the inequality constraints to variable were preserved. Figure 9 shows that the two methods IP-GA and PSO, the IP-GA method gave a better fitness value than the PSO method. The IP-GA was faster than PSO, reducing the computing time about 300 seg. as the Figure 10 shows. Figure 8: Result of the nodal voltages. Figure 7: Result of the state of charge (SOC). Although the GA-IP was faster, the two methods used to solve the optimization problem in the microgrid could be considered like an effective method to solve the optimization problem and worked like a tertiary control within the microgrid. This is possible due the energy cost from the battery for the optimization methods being the most expensive for all times.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 5 Discussion
### Summary of the Process:  
* [leftmargin=  
The Integration Workflow of the SAAS Model with VR NPCs illustrates the sequential procedure starting from user engagement inside the VR environment and concluding with the provision of voice-based suggestions by NPCs. The workflow commences when the user chooses or interacts with an item within the virtual reality (VR) environment, hence initiating the real-time acquisition of data. The SAAS model utilizes the interaction data to provide tailored recommendations. The text suggestions generated are transformed into speech using a text-to-speech engine. Ultimately, the non-player character (NPC) obtains the audio output and conveys the idea to the user, resulting in a captivating and engaging shopping experience, as depicted in Figure 10.