esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Heat Treatment  
In Figure 2a, the dilatation curves of the specimens during the heating are shown. The linear course of all curves from a temperature of 825 \({}^{\circ}\)C indicates that, for all specimens, full austenitization is achieved. Figure 2b shows the corresponding dilatation curves during the quenching. Concerning the martensite start temperature, no significant influence of the quenching rate is observed. However, the yellow curve corresponding to \(t_{85}=30.5\) s has a different gradient, which indicates the formation of a non-martensitic phase at high temperature. All other curves show a parallel course. Thus, the critical time interval is \(t_{85}=20.0\) s, corresponding to a quenching rate of \(\dot{T}_{85}=15.0\,^{\circ}\text{Cs}^{-1}\).  
The temperature curves with respect to time in Figure 2c are extended by the temperature measurements during the water bath quenching. The reduction of the temperature rate at approximately the martensite start temperature is due to the exothermic austenite-martensite transformation. The black dashed lines indicate the corresponding ideal linear course. Based on this diagram, the parameters \(t_{85}\) and \(\dot{T}\) can be determined. Accordingly, by using water as quenching medium, a time interval of \(t_{85}=0.62\) s corresponding to a quenching rate of \(\dot{T}_{85}=484\,^{\circ}\text{Cs}^{-1}\) is achieved inside the sample.  
Four heat treatments were selected for the next analysis step, which are summarized in Table 2 with their respective cooling times. The further investigated test runs corresponded to the lowest possible quenching rate to form a fully martensitic microstructure, the maximal possible quenching rate with specimen types A and B with the dilatometer setup and the water bath quenching rate, which is the overall maximum rate investigated in this work. Note that the dilatometer with the chosen quenching medium could not keep the specified cooling rate below the martensite start temperature. As a result, the sample from analysis A2 experienced slower cooling below \(T_{\text{MS}}\).
## 3 Optimal Load Shedding Model for the IEWS
### Power System Constraints  
\[\begin{array}{l}\sum\limits_{i\in\Omega_{\rm{E}}^{\rm{T}}}P_{i,t}+\sum \limits_{w\in\Omega_{\rm{W}}^{\rm{W}}}(P_{w,t}^{\rm{wn}}-P_{w,t}^{\rm{wc}})- \left(D_{n,t}^{\rm{EDS}}-\Delta D_{n,t}^{\rm{EDS}}\right)\\ -\sum\limits_{h\in\Omega_{\rm{WDS}}^{\rm{WDS}}}P_{h,t}^{\rm{WDS}}=\sum\limits _{m\in\Lambda_{\rm{E}}^{\rm{E}}}B_{n,m}(\theta_{n,t}-\theta_{m,t}),\forall n \in\Lambda^{\rm{E}}\\ |P_{i,t}-P_{i,t-1}|\leq R_{i}\phi_{i,t-1}+P_{i}^{\rm{gmin}}(\phi_{i,t}-\phi_{i,t-1})+\\ P_{i}^{\rm{gmax}}(1-\phi_{i,t}),\forall i\in\Omega^{\rm{T}}\\ \end{array} \tag{11}\]  
\[P_{i}^{\rm{gmin}}\phi_{i,t}\leq P_{i,t}\leq P_{i}^{\rm{gmax}}\phi_{i,t},\forall i \in\Omega^{\rm{T}} \tag{13}\]  
\[0\leq P_{w,t}^{\rm{wc}}\leq P_{w,t}^{\rm{wn}},\forall w\in\Omega^{\rm{W}} \tag{14}\]  
\[-S_{n,m}\leq B_{n,m}(\theta_{n,t}-\theta_{m,t})\leq S_{n,m},\forall(n,m)\in \Lambda^{\rm{E}} \tag{15}\]  
\[\theta_{ref,t}=0 \tag{16}\]  
where \(P_{i,t}\) is the node power of the power system, \(P_{w,t}^{\rm{wn}}\) and \(D_{n,t}^{\rm{EDS}}\) are the predicted value of the wind power and conventional electric load, \(P_{h,t}^{\rm{WDS}}\) is the power consumed by the WS, \(n\) and \(m\) are both nodes of the water system, \(n\) represents the node where the water flows into the pipeline, and \(m\) represents the node where the water flows out of the pipeline. \(B\) represents the susceptance of transmission line, \(\theta\) represents the phase angle of the node, and \(R_{i}\) is ramp rate for thermal power generating unit. \(S_{n,m}\) is the transmission capacity. \(\Lambda_{n}^{\rm{E}}\) is the collection of transmission system nodes, \(\Lambda^{\rm{E}}\) is the collection of power transmission lines, \(\Omega^{\rm{T}}\) is the collection of thermal power generating units, and \(\Omega^{\rm{W}}\) is the collection of wind turbines.  
Equation (11) is the nodal power balance constraint, (12) is the ramp rate constraint of the thermal power generating unit, (13) is the output power constraint of the thermal power generating unit, (14) is the wind abandonment power constraint, (15) is the power flow constraint of the power lines, and (16) is the phase angle constraint of the balancing node.
## 5 Simulation Results and Analysis  
To illustrate the effectiveness of the proposed adaptive identification model and dynamic inverse control, simulation studies were carried out under stochastic constant control and dynamic inverse control. The parameters of the identification model are selected as \(A=-0.001I,B=0.1I\), \(m_{1}=0.5\), \(m_{2}=1.3\), \(\delta_{1}=\delta_{2}=0.0001\), \(k=0.4\), \(\eta_{i}=1,i=1,2,\cdots,11\), and \(\lambda_{i}=1,i=1,2,\cdots,11\). The initial velocity and altitude errors of the real system and identification system are taken as 10 and 30, respectively.
## 4 Discussion
However, if the comparison only considers the common RB EMSs, this bias is avoided. Another finding of the review was that although the validation of the implementation of the EMS in real time should be a significant part of these works, there is a clear absence of this. There are several works that fail to provide any kind of online validation of their EMS' performance. After the review, the following approaches were identified for this purpose: HIL simulation platform based on dSPACE real-time simulator [24; 26], non-commercial HIL test benches [31; 40], measuring the elapsed time of simulations and checking if they fit for real-time operation [30; 32; 38; 42; 46] and driver-in-the-loop (DIL) using the IPG Carmaker platform [37]. Results related to the maintenance of SOC were not considered for comparison since each work considered different ranges. These ranges sometimes depend on the battery technology used in the HEV studied or simply on the experience of the EMS designers. Furthermore, it is difficult to score this variable since an EMS that obtains a higher value of SOC at the end of the driving cycle does not necessarily perform better. According to the authors, maintaining the SOC within a specific range is an obligatory requirement that all the EMSs must meet. It would be interesting for future works to propose indexes that evaluate the SOC trajectories compared to the optimum. Aspects related to the time spent around the reference value (as a positive index) or the time spent at the SOC limits (as negative indexes since they would mean battery overcharge or depletion) could be considered. Other research gaps found were related to some variables that were considered in the works reviewed and there is no consensus about their measurement. Avoiding battery degradation, for example, is one of the common objectives proposed for the EMSs. However, there is no reference to battery lifespan modeling. Efficiency improvement (overall or specific to a defined component) is also proposed as an objective. It would be necessary to specify clearly how this efficiency is calculated. Figure 8: Fuel consumption improvement with respect to commercial RB EMSs.
# Research on Interval Optimal Scheduling Strategy of Virtual Power Plants with Electric Vehicles
## 2 Structure and Principle of Virtual Power Plant with Electric Vehicles
### Principle of a Virtual Power Plant with Electric Vehicles
The EV charging process has a dispatchable potential, as shown in Figure 2. The charging energy trajectory between the upper energy boundary and the lower energy boundary is the feasible energy trajectory, and the area between the upper and lower boundaries is the dispatchable potential of a single EV. By aggregating groups of EVs, a huge dispatchable potential can be obtained. Taking 1000 EVs as an example, based on the probability density distribution of the daily driving distance and return time of the EVs, the initial SOC of the EVs and the time distribution of the EVs connected to the grid can be obtained by drawing samples through the Monte Carlo method, as shown in Figures 3 and 4, respectively, and then the adjustable potential of the EV population can be obtained, as shown in Figure 5.  
Figure 3: Initial SOC of the electric vehicles. Figure 2: Dispatchable potential of a single electric vehicle. (5) Power-to-gas equipment model  
The core unit of power-to-gas technology is an electrolyzer, which produces oxygen and hydrogen by electrolyzing water, after which the methane reactor absorbs carbon dioxide to produce methane through catalysis, with the following chemical reactions [36]:  
\[2H_{2}O\xrightarrow{\text{High temperature, energizing, catalyst}}O_{2}+2H_{2}\]  
\[CO_{2}+4H_{2}\xrightarrow{\text{High temperature, high pressure}}CH_{4}+H_{2}O\]  
In this paper, we take a proton exchange membrane electrolyzer as an example and model it with a methane reactor. The hydrogen produced is:  
\[G_{H_{2}}=\alpha_{P2H_{2}}\cdot\frac{P_{P2H_{2}}}{P_{P2H_{2},rated}}\cdot V_{ cc} \tag{10}\]  
where \(G_{H_{2}}\) is the output of H\({}_{2}\), m\({}^{3}\), \(P_{P2H_{2}}\) and \(P_{P2H_{2},rated}\) are the electrical power consumed by the electrolyzer and the rated power, kW, respectively, \(\alpha_{P2H_{2}}\) is the electrolytic efficiency factor, and \(V_{cc}\) is the rated capacity, m\({}^{3}\). The natural gas produced by the methane reactor is:  
\[G_{CH_{4}}=\frac{\eta_{P2 CH_{4}}\cdot G_{H_{2}}\cdot H_{CH_{4}}\cdot\beta}{m_{ CH_{4}}} \tag{11}\]
## 3 NB-IoT Frame Structure
### Npbch  
NPBCH is transmitted in the subframe 0 of every radio frame and the first three symbols are unused. The data of NPBCH is identical in eight consecutive radio frames. In in-band mode operation, the LTE cell-specific reference signal (CRS) can also be deployed in the NPBCH subframe. So, in addition to NPBCH data, CRS, NRS and channel control signals can also be included in NPBCH subframes.
## 4 Dispersing the Light
### Fourier Transform Spectrograph
#### 4.3.3 Challenges and the Future  
Astrophotonic FTSs are in their infancy and will see a boost as other photonic technologies mature. There are several challenges that need to be resolved to enable astronomical spectroscopy with photonic FTSs, the most important being the throughput. The traditionally used SOI waveguides have higher propagation losses (\(\sim\)1-2 db/cm) due to the sharp index contrast between the Si core and SiO\({}_{2}\) cladding. In addition, doping and introduction of metals further degrade the transmission efficiency of the waveguides (by \(>\)0.5 dB). Recently, a photonic FTS has been demonstrated in a more efficient silicon nitride platform (propagation loss \(\sim\)0.1 dB/cm) [96]. Such developments show promise for boosting the throughput. Another important issue with FTSs is the birefringence and high dispersion of materials such as Si, SiO\({}_{2}\), and Si\({}_{3}\)N\({}_{4}\) (in optical and near-IR). Since optical path modulation is achieved by modulating the refractive index, the optical path changes by a different amount for different polarizations as well as different wavebands. This creates a trade-off between the spectral resolution and bandwidth of the spectrograph. However, precise knowledge/calibration of the wavelength dependence of the thermal modulation can, in principle, alleviate this issue. Several new techniques have recently been demonstrated to effectively correct for temperature drifts and non-linearity of the thermal/electro-optic modulators [85; 97; 98]. FTSs are also being demonstrated in the mid-IR range [99] (\(\lambda\sim\) 3.7 \(\upmu\)m, spectral resolution \(\sim\)5000), which is particularly useful for exoplanet characterization, as described above. All of these developments indicate a rapidly growing potential of photonic FTSs for astronomical spectroscopy.
## 6 Conclusions  
### Main Conclusions  
1. An analysis was conducted on the distribution of temperature, humidity, noise, wind speed, and other factors throughout the monitoring period, along with their relationship with dust concentration. A notable negative correlation was found between dust concentration and temperature, humidity, rainfall, and wind speed. Conversely, a strong positive correlation exists between the intensity of mine production (noise) and dust concentration. The fluctuations in PM2.5 and PM10 concentrations are largely similar.
2. A new feature importance screening technique utilizing the random forest (RF) algorithm was introduced. The importance scores for the eight influencing factors, ranked from highest to lowest, are as follows: stripping amount (1.43), temperature (0.88), humidity (0.67), noise (0.61), wind direction (0.54), rainfall (0.41), wind speed (0.20), and wind force (0.08). Ultimately, the best combination of indicators for forecasting dust  
Figure 15: Model learning curve.  
concentration consists of temperature, humidity, stripping amount, wind direction, and wind speed.
3. A predictive model for dust concentration was developed utilizing genetic optimization and least squares support vector machine techniques. The model's input variables consist of temperature, humidity, stripping amount, wind direction, and wind speed. The primary output variable is PM2.5 concentration, while PM10 concentration serves as a reference auxiliary variable. The model's sample library was created using 10 days of data collected in August from the Weijiamao open-pit coal mine in Inner Mongolia, with training and testing samples divided in a 7:3 ratio.
4. In comparison with LSSVM, PSO-LSSVM, ISSA-LSSVM, GWO-LSSVM, and other models, the GA-LSSVM model achieves fitting degrees (R\({}^{2}\)) of 0.872 for PM2.5 and 0.913 for PM10, along with root mean square errors (RMSE) of 8.592 and 7.476, respectively. The GA-LSSVM model shows superior overall performance, showcasing excellent predictive abilities with minimal error and high fitting accuracy.
## 3 Results  
### Results of XRD and DSC Analysis of Synthesized Glasses  
All synthesized samples were analyzed using X-ray powder diffraction technique to ensure their glassy state. As one can see from the diffraction patterns presented in Figure 1a, one should consider all phosphate glass samples as fully amorphous materials. The lack of crystalline peaks is observed for all samples, even for PPG_Tb30 sample, containing relatively high contribution (3.0%, \(w/w\)) of Tb\({}_{2}\)O\({}_{3}\) oxide. The presented XRD patterns show that for all samples only wide scattering peaks (so called \"halo\") are observed at ca. 21\({}^{\circ}\) and 31\({}^{\circ}\) 2 theta angles. In the low range of 2 theta angle (5\({}^{\circ}\) to 16\({}^{\circ}\)), one can also observe an increased background intensity due to more efficient X-ray scattering in air. Obtained X-ray patterns are similar to those that can be find in literature [31; 32; 33]. The results of DSC analysis are presented in Figure 1b. Thermal analyses were carried out within the temperature range of 40-550 \({}^{\circ}\)C and confirmed the glassy nature of the investigated materials by existing of the glass transitions within the temperature range of 407-423 \({}^{\circ}\)C. For pure, undoped phosphate glass, the glass transition temperature was determined at 423 \({}^{\circ}\)C, whereas for the terbium-doped samples the transition temperatures were shifted towards lower values between 408-410 \({}^{\circ}\)C. In terms of DSC, for undoped phosphate glass exhibits there was similar Tg to that reported in literature [34]. Decreasing of Tg temperature for terbium-doped glasses may be caused by a global weakening of the glass network induced by relatively large Tb\({}^{3+}\) ion incorporation, which can be ascribed to the increase in non-bridging oxygens due to the cleavage of P-O-P bonds to form P-O-Tb\({}^{2+}\)[35; 36; 37; 38]. However, this issue requires further research using other research methods, for example, NMR.
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 5 Experiment and Analysis
### Delay Dispersion Characteristic  
In practical applications, it is vital to consider data offloading to ensure that the delay difference between nodes requesting data can be managed effectively. Rapid data offloading without delay difference is essential for services requiring cloud-edge collaboration, as individuals in different regions jointly handle these services. The insufficient coordination of data can result in errors in information and eventual service failure. Our study calculated the standard deviation and variance of delay for WFO and FCFS in various CC bandwidth states.  
Figure 11 illustrates the delay standard deviation and variance of WFO and FCFS with CC bandwidths ranging from 100-550 M. The blue line represents the variance of WFO, while the red line represents the variance of FCFS. The figure shows that for varying CC bandwidths, the delay variance of WFO is less severe than that of FCFS, which proves that the WFO strategy can effectively reduce the discrete delay between nodes.
## 6 Conclusions  
To investigate the subsurface structure of the northern Pingtung Plain, an MT survey was conducted with ten local observation stations and one remote reference station. Given the substantial presence of noise, careful data selection and processing were essential. Spectral analysis was employed to identify time segments with minimal noise, and in this study, the nighttime data were chosen for analysis due to their lower noise content. Static noise, such as the 60 Hz powerline signal, was mitigated using a notch filter to reduce its effect on the corresponding frequencies, while random noise was minimized by employing a remote reference station located away from major noise sources. Despite this, the obtained data, supported by existing datasets, allowed for the adequate imaging of the subsurface structure through both 1D and 2D inversions, to depths of up to 4 km. The resistivity profile beneath the study area generally displays a conductive shallow layer overlying a resistive layer. From the inversion results, we identified the boundary between the Quaternary sediment and the underlying bedrock, which possibly dates from the Pliocene to Miocene periods. This boundary was located at various depths of up to 500 m. Our results suggest that the sediments thin out toward the northeast and southwest directions. A series of step-fault structures assumed to be caused by the thrusting of the Chaochou fault are observed. However, due to the lack of available supporting data, its true existence remains speculative at the moment. Thus, it can serve as preliminary insight into the deep subsurface information in the area. To further validate these findings, we recommend increasing the density of MT soundings, especially in areas where these structures are observed. Additionally, the use of complementary geophysical methods would provide more detailed subsurface information and help confirm the deep structural characteristics of the northern Pingtung Plain.  
Conceptualization, H.H.A. and P.-Y.C.; Methodology, H.H.A. and P.-Y.C.; Validation, P.-Y.C.; Formal Analysis, H.H.A., P.-Y.C., D.-J.L., J.M.P. and Y.W.; Investigation, H.H.A. and D.-J.L.; Resources, P.-Y.C., D.-J.L., J.M.P. and Y.W.; Data Curation, H.H.A., P.-Y.C. and D.-J.L.; Writing--Original Draft Preparation, H.H.A.; Writing--Review & Editing, H.H.A. and P.-Y.C.; Visualization, H.H.A.; Supervision, P.-Y.C.; Project Administration, P.-Y.C.; Funding Acquisition, P.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Central Geological Survey of the Ministry of Economy, R.O.C. (Taiwan), under the project \"The Investigation of Hydrogeology and Groundwater Resources--The Utilization Improvement and Capacity Assessment of Underground Reservoir (3/4)\" (Project Number: 108-5226904000-01-02-01).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors thank [PERSON], [PERSON], and [PERSON] for their help with data acquisition.  
The authors declare no conflicts of interest.
## 1 Introduction  
Warm carrier-based techniques associated with different sealers have been shown to be effective and offer clinical advantages due to their ease of use, short learning curve, and reliable technical and clinical outcomes [1]. Warm procedures have traditionally been associated with the use of epoxy resin-based sealers, which are still considered the \"gold standard\" and revealing success rates from 81% to 96% after 3-5 years [2; 3; 4]. Unfortunately, epoxy-resin sealers are highly hydrophobic and require a root canal with no moisture in order to achieve a stable seal with no voids [5].  
In the last few years, calcium silicate-based materials have shown increasing popularity in endodontic treatment. The introduction of calcium silicate-based materials as endodontic sealers is particularly attractive for their chemical and physical properties. These materials are able to set in the presence of moisture and blood, such as in wide apexes [6; 7]. Calcium silicate-based sealers are biocompatible and osteeconductive towards circulating and periapical MSC populations, as observed in recent histological and in vitro studies [8]. The use of these sealers was proposed in combination with the cold single cone technique [9; 10], thanks to their specific and innovative properties such as the ability to expand into the root canal, the capacity to produce new apatite formation and to seal the discrepancies between guttapercha and dentinal walls [11; 12; 13; 14; 15]. Reconsidering the application of these sealers with a warm technique, such as the carrier-based technique, could be an interesting new perspective in an attempt to simplify the technique.  
A new category of premixed CaSi-based or containing sealers has been recently developed and characterised by different compositions [16; 17; 18]. AH Plus Bioceramic sealer (Dentsply, Konstanz, Germany) is a novel premixed ready-to-be-used sealer with hydraulic properties and adequate flowability. The chemical and physical properties have been investigated in some recent studies, which have reported the ability of the sealer to release calcium and alkalise the environment [19; 20; 21] and the ability to induce the formation of small apatite deposits when immersed in simulated body fluids [19]. These properties showed the sealer suitability for clinical use.  
Despite their promising characteristics and considering the recent introduction on the market, a limited number of short-term clinical investigations have been conducted [9; 22; 23; 24]. Further research is needed to fully understand their performance, clinical outcomes, and occurrence of post-operative pain after filling.  
The aim of this clinical pilot prospective cohort study was to evaluate the 12-month outcome, survival, and periapical healing rate of endodontically affected teeth filled with AH Plus Bioceramic sealer in association with warm carrier-based technique. Post-operative pain after root canal filling was assessed after 1 day, 1 week, and 1 month.