esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of a Moderate Fire inside a Segment of a Subway Station
## 3 Transient Simulation of Non-Stationary Heat Conduction
### Uniform Prescription of One Temperature History  
At the entire inner surface of the simulated structure, the same temperature history was prescribed; see the blue dotted graphs, labeled \"BC\", in Figure 11. The prescribed evolution of the surface temperature was obtained by averaging the readings of the two thermocouples that were positioned at a distance of 2 mm from the heated inner surface; see the dotted black lines in Figure 11a,d. The other graphs shown in Figure 11a refer to the simulated (label \"Sim\") or measured (label \"Meas\") evolutions of temperature changes at positions 30 mm, 68 mm and 105 mm away from the heated inner surface; see also Figure 5.  
The results of the numerical simulations (Figures 12 and 13) overestimated the experimentally measured temperatures at the right wall, while underestimations were obtained at all other positions that were equipped with thermocouples; see Figure 11. The largest percent underestimation, obtained 30 min after the start of the fire test, amounted to some 70% and referred to the temperature evolution of the column; see Figure 11f. This was the motivation to refine the numerical simulations by means of updating of the model.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Property** & **Value** \\ \hline Specific heat capacity (J/(kgK)) & 900 \\ Thermal conductivity (W/(mK)) & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermal properties of concrete at room temperature.  
Figure 11: _Cont._  
Figure 11: Comparison of simulated evolutions of the temperature inside the analyzed structure, obtained by prescribing a spatially-uniform temperature history along the entire inner surface, with experimental data: (**a**) top slab, at the midspan of the right cell, (**b**) top slab, at the midspan of the middle cell, (**c**) top slab, at the midspan of the left cell, (**d**) center of the right wall (**e**) bottom slab, at the midspan of the middle cell, and (**f**) core of the right column. Sim, simulated; Meas, measured.  
Figure 12: Temperature distribution obtained by prescribing a uniform surface temperature history, 30 min after the start of the thermal loading: detail showing the connection of the left wall to the top slab.
## 4 Conclusions  
The presented study aimed to research the impact of non-activated and Dex-activated Per and Verm fillers on the processing times, microstructure, thermal conductivity, water absorption, and strength properties of PUR foam composites. Applied activation resulted in the development of Per and Verm fillers' surfaces containing hydroxyl groups.  
Dex-activated Per and Verm fillers were introduced into a PUR matrix. The introduction of non-activated and Dex-activated fillers resulted in an elongation of processing times compared to control PUR foam premixes. Such an effect was primarily due to the increased viscosity of the resulting premixes caused by the introduction of solid particles. However, the surface activation with Dex caused a positive effect due to the availability of hydroxyl groups. Changes in processing times were reflected in the cellular structure of PUR foam composites.  
For PUR/PerDex and PUR/VermDex foam composites, a typical increase in apparent density, closed cell content, wall thickness, and reduction in average cell size were observed. At the same time, Dex activation catalyzed the polymerization reaction compared to non-activated Per and Verm fillers. The quality of cellular structure directly affected the compressive and tensile strengths. Therefore, superior compressive strength was observed for PUR/PerDex and tensile strength for PUR/VermDex foam composites at 10 wt.% PerDex and VermDex loadings due to the changed surface interaction abilities of the Dex-activated fillers.  
Regarding thermal conductivity, the maximum reduction in the parameter was achieved at 2.5 wt.% non-activated and Dex-activated fillers loadings. Considering water resistance, changes in the surface of Dex-activated Per and Verm fillers were reflected in the water absorption and contact angle properties of the PUR foam composites. Generally, the presented results indicate that the Dex activation of Per and Verm surface results in the formation of Dex-Si-O-Si-Dex linkages, which inhibit the filler's participation in further reaction with isocyanate to form the strong interface between filler particles and polymer matrix. Such an effect resulted in reduced water absorption and increased water contact angles.  
Conceptualization, A.K.; methodology, A.K.; software, A.L.; validation, A.K. and A.L.; formal analysis, A.K.
## 4 Monitoring System Framework
### Components of the Multi-Source Monitoring System
#### 4.1.4 Pressure Monitoring of Cut Top Support  
A new type of hydraulic support with high initial supporting force and high working resistance is used to temporarily strengthen the roof under this coal mining method. The pressure on the support is sensed by the FBG hydraulic sensor shown in Figure 9 and its specific parameter models are shown in the Table 5. It gets a data in a minute.  
This hydraulic sensor adopts the cylindrical thin-wall sensing principle. According to generalized [PERSON]'s law and the strain characteristics of an FBG, it can be stated that:  
\[\Delta\lambda_{B}=\frac{0.39d\lambda_{B}}{Eh}(1-\frac{1}{2}\mu)p \tag{3}\]  
In the formula, \(E\) is the elastic modulus of the material, \(h\) is the thickness of the thin wall, \(d\) is the inner diameter of the cylinder, \(p\) is the pressure and \(\mu\) is the Poisson's ratio of the optical fiber.  
There is a linear relationship between the drift of the center wavelength of the grating and the measured pressure. The range of pressure measurement can be changed by adjusting the inner diameter of the cylinder and the wall thickness. The principle is shown in Figure 10.  
Figure 10: Working principle of an FBG hydraulic sensor.  
Figure 9: FBG hydraulic sensor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Type** & **Parameter Values** & **Parameter Type** & **Parameter Values** \\ \hline Range (MPa) & 0–60 & Reflectivity (\%) & \(\geq\)90 \\ Resolution & 0.1\%\(\mathrm{\SIUnit
## 4 Conclusions  
In this study, the performance benefit of LH\({}_{2}\) compared with jet fuel (kerosene) has been discussed. The performance results show a 64% decrease in the fuel of LH\({}_{2}\) compared to jet fuel, due to the high LHV of LH\({}_{2}\). This also allows the turbofan engine to run on lower TET, thereby extending the engine life by 15%. The study also discussed the exergy analysis, with LH\({}_{2}\) showing better exergy than jet fuel. Although there are several drawbacks to LH\({}_{2}\) owing to its technology being still at a TRL less than 7, this study did not identify any insurmountable difficulties in the potential certification of a hydrogen-fueled engine in the future. Potential issues that need to be overcome include the fuel system, tank configuration and control system design. NOx and water vapor emission must be eliminated or optimized in the combustor design for reduced and acceptable emission; however, at lower than 10,000 m altitude, research has shown that water vapor does not have an adverse impact.  
Another external concern mentioned in this paper is the cost of LH\({}_{2}\) production, which is currently less competitive than kerosene. LH\({}_{2}\) requires a large amount of energy, and most of the energy source for production comes from fossil fuel, which also adds to total environmental emission. To resolve the emission issue, hydrogen must be produced from water using a renewable or nuclear energy source. Whether we consider other production options for LH\({}_{2}\), like electrolysis, which has less impact on the environment, the need to account for high energy losses from new infrastructure builds and the liquefaction process needs justifying to make economic sense.  
Some of this study's limitations include the assumptions made on turbofan engine hardware and operating conditions used during the assessment. A detailed combustor performance assessment using an advanced computational fluid tool is also proposed in future work. Other future work includes component exergy and creep life assessment on turbine NGV and downstream components.  
Conceptualization, E.O.O. and A.G.-B.; methodology, E.O.O.; software, E.O.O.; validation, A.G.-B. and E.O.O.; formal analysis, E.O.O.; investigation, E.O.O.; resources, T.N., B.S., SJ. and P.P.; data curation, E.O.O.; writing--original draft preparation, E.O.O.; writing--review and editing, A.G.-B.; visualization, E.O.O.; supervision, T.N.; project administration, A.G.-B.; funding acquisition, A.G.-B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article. Any data not presented are available on request from the corresponding author.  
The authors wish to acknowledge EGB Engineering and Cranfield University for technical and moral support during this study.  
The authors declare no conflict of interest.  
Figure 5: Creep life estimation.
## 3 Materials and Methods
### Analysis of Volatile Organic Compounds
#### 3.4.2 HS-SPME-GC\(\times\)GC-TOF Analysis  
GC\(\times\)GC was performed by a flow modulation apparatus consisting of an Agilent 7890B GC (Agilent Technologies, Palo Alto, CA, USA), with capillary flow modulator device for 2D separation, coupled with a time-of-flight mass spectrometer (TOF-DS Markes International Ltd., Llantrisant, UK)). SPME sampling was carried out at the same conditions described in 3.4 for mono-dimensional GC-MS analysis. Chromatographic separation was performed using a first dimension (1D) HP-5 column (0.18 \(\upmu\)m d.f. \(\times\) 0.18 mm i.d., 20 m) and an InnoWAX second dimension (2D) column (0.23 \(\upmu\)m d.f. \(\times\) 0.32 mm i.d., 5 m). Flow modulation was performed with a modulation period of 3 s. Helium was used as carrier gas (99.999% purity) at flow rates of 0.4 and 10 mL min\({}^{-1}\) in first and second dimensions, respectively. The chromatographic conditions were--oven temperature program, 40 \({}^{\circ}\)C, increased at 4 \({}^{\circ}\)C min\({}^{-1}\) to 220 \({}^{\circ}\)C, increased at 10 \({}^{\circ}\)C min\({}^{-1}\) to 260 \({}^{\circ}\)C (hold 1 min); injector temperature, 260 \({}^{\circ}\)C; split ratio 1:5. The inlet of the 2D column was maintained under vacuum by a deactivated fused silica (15 cm 0.10 mm i.d.) placed immediately before the column, after the flow modulator. TOFMS parameters--the ion source temperature was 230 \({}^{\circ}\)C; the transfer line temperature was 280 \({}^{\circ}\)C; ionization, \(-\)70 eV. A mass range of 43-500 Da was used, with data rate of 50 Hz. TOF-DS TM software, version 2.0 (Markes International Ltd.; Llantrisant, UK, 2016) was used for data acquisition. GC IMAGE version R2.5 GCGC (64 bit) software (GC IMAGE; LCC-Lincoln, Lincoln, NE, USA, 2014) was used for data processing.  
Compounds were tentatively identified comparing mass spectra of each peak with those reported in mass spectral databases; identification was confirmed by their retention index.
# Blockchain Technology Application in Security: A Systematic Review
## 1 Introduction  
Blockchain technology is a novel idea that was first introduced by [PERSON] with the invention of the Bitcoin [1]. Bitcoin is a digital currency that has revolutionized financial trading. Blockchain is a distributed Hyperledger technology with many intrinsic features, including security, decentralization, transparency, immutability, and provenance [2; 3; 4]. These features create unique blockchain applications beyond the financial market.  
Blockchain is categorized into three types: public, private, and consortium. Public blockchains are permissionless blockchains where every user can enter the network, compared to the private network, where only authorized users by one organization can access the network. A consortium blockchain is a combination of public and private where there is more than one organization in the network. The best selection of blockchain categorization type according to various requirements and conditions of different use cases is very important [5; 6].  
Consensus mechanisms are a feature that provides a fair and decentralized network. The main goal of the consensus algorithm is to reach consensus without the need for a central authority or third parties, which makes the blockchain decentralized [7]. There are different techniques for reaching consensus, such as proof of work (PoW), which is the most famous algorithm and is used in the Bitcoin network, Practical Byzantine Fault Tolerance (PBFT), and proof of authority (PoA). Each algorithm has its own features, specific time, and energy consumption. Consequently, choosing the right consensus algorithm depends on the specific needs of the project's applications.  
Nowadays, blockchain is widely used in a variety of areas such as the Internet of Things (IoT), healthcare, energy market, and industrial projects to provide solutions for their security attacks and challenges, and there is an increasing trend of using blockchain to provide security frameworks in different fields [8; 9; 10; 11; 12].  
This systematic review focuses on recent research that applied blockchain as their security framework and concerns their categorization type, consensus algorithms, usage of smart contracts, and integration with other technologies. We deeply investigate this area and use our insight to propose future research for scholars and academics interested in working in this field.
# Shearer-Positioning Method Based on Non-Holonomic Constraints
## 3 Experimental Validation  
### Scheme Design  
To verify the feasibility and positioning accuracy of the algorithm, and to compare it with the traditional INS/ODO combined-positioning scheme, this paper reports the experiments on a laboratory-simulation platform. The experimental equipment used an inertial measurement unit featuring an ICM42605 gyroscope and an ADXI355 accelerometer. Table 1 shows the relevant parameters of the components. The E6H-CWZ6C encoder was installed on the traction gear of the shearer as the observation source data of the odometer, and the encoder resolution was 3600 P/R. The inertial measurement unit and coder were installed according to the requirements described in Section 1. The data of the inertial measurement unit and coder were collected in real time over the course of the experiment. The data-collection frequency was 100 Hz, and the data were processed after the experiment.  
The experimental process simulated the process of the slanted shearer cutting and triangular coal cutting in the long-wall face. The cutting distance was 0.3 m and the total length of the whole process was 30 m. In order to facilitate the comparison and observation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gyroscope** & \multicolumn{2}{c}{**Accelerometer**} \\ \hline Dynamic range (\(\lx@math@degree\)/s) & \(\pm\)500 & Dynamic range (g) & \(\pm\)2 \\ Zero bias (\(\lx@math@degree\)/s) & \(\pm\)0.5 & Zero bias (g) & \(\pm\)0.025 \\ Random error (\(\lx@math@degree\)/ \(\sqrt{h}\) ) & 0.24 & Random error (\(\mu\)m/s\(\cdot\sqrt{h}\)) & 9 \\ Scale-factor error (\%) & \(\pm\)0.82 & Scale-factor error (\%) & \(\pm\)1.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance parameters of IMU.  
of the experimental results, a face-coordinate system (F system) was established, with the starting position of the shearer as the origin, the heading direction of the shearer as the x-axis, the advancing direction of the face as the y-axis, and the z-axis conforming to the right-hand rule. The positioning results were transformed from the N system to the F system. During the movement process of the shearer, a series of marker points were made on the moving track of the shearer artificially and irregularly, the coordinates of the marker points in the coordinate system of the long-wall face were measured, the marker time was recorded, and the marker points were numbered. In this way, a series of marker points for the shearer's actual moving track were obtained. Tagged points were used as location-reference points, and the corresponding point coordinates of the trajectory calculated by the algorithm presented in this paper were compared and analyzed.
# Dynamics and Control of Typical Orbits around Saturn
## 6 Conclusions  
In our paper, we analyze the sun-synchronous orbits, repeating ground track orbits, frozen orbits, and stationary orbits around Saturn and corresponding control strategies based on the mean element theory while zonal harmonic coefficients \(J_{2}\) and \(J_{4}\) of Saturnian gravitational field are considered.  
For sun-synchronous orbits, we analyze the existence of orbits, find the relation between inclination with eccentricity and semi major axis, and calculate the local time drift caused by solar gravitation and atmospheric drag. After that, we take two inclination-biased control strategies to damp the local time drift. The initial inclination-biased method suits the short-time spacecraft. The periodic inclination biased method has a better effect on long-period missions.  
For repeating ground track orbits, we find the meaningful range of repetition parameter \(Q\). Then we find the relation between the inclination and semimajor axis. After that, we calculate the compensation for semimajor axis and maneuver period.  
For frozen orbits, we learn that only when \(\omega=270\) deg can we be sure of the eccentricity positive for any given inclination and semimajor axis.  
For stationary orbits, we first calculate the radius using the conditions of equilibrium point. Then, we analyze the perturbations caused by solar gravitation and solar radiation pressure. Finally, we take corresponding maneuver strategies to control the inclination and eccentricity.  
Conceptualization, C.Z. and Y.L.; methodology, C.Z. and Y.J.; software, C.Z.; validation, C.Z. and Y.J.; formal analysis, H.L.; resources, Y.J.; data curation, C.Z. and Y.L.; writing--original draft preparation, data curation, and writing--review and editing, H.L. and Y.J.; visualization, C.Z. and Y.L.; supervision, H.L. and Y.J.; project administration, H.L. and Y.J.; and funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 11772356, No. U21B2050).  
Not applicable.  
The authors gratefully acknowledge the reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially.  
The authors declare no conflict of interest.
## 3 Simulation Experiments and Analysis
### Performance Analysis with Traditional Methods  
In this section, we conduct a performance comparison between the proposed method and traditional networks under the condition of few-shot learning. The compared methods include CNN [31], LSTM [32], and Gated Recurrent Unit (GRU) [33]. The objective is to verify whether the proposed method exhibits strong robustness and generalization capability in handling few-shot problems and whether it outperforms traditional network structures. The traditional networks extracted 600 samples in each training session, while the proposed method is evaluated under the 5-way 5-shot condition with 100 samples. The recognition accuracies of each method as the SNR ratio varies are shown in Figure 8.  
It is observed that LSTM and GRU employed gating mechanisms to selectively retain and forget information, enabling them to possess strong contextual memory capabilities. This, in turn, led to better results in the test outcomes compared to traditional CNN. Due to the ability of LSTM's memory cells to effectively retain and update long-term information, it is suitable for conducting in-depth analysis on long sequences. In contrast, GRU has only one hidden state, resulting in slightly inferior performance compared to LSTM. In contrast to conventional machine learning networks, the approach utilized in this study effectively leveraged the distinctive features of the communication signals and enhanced the model's learning ability. Our method demonstrated improved accuracy compared to traditional neural networks. Meanwhile, traditional methods still exhibited fluctuations within a broad range, potentially stemming from the limitations in handling small-sample training and model structures inherent in these approaches. This rendered traditional methods  
Figure 7: Variation of recognition accuracy with SNR ratio for different N values.  
more susceptible to disturbances caused by a limited number of samples, resulting in unstable outcomes.  
In addition to comparing with traditional methods, such as CNN, LSTM, and GRU, we introduced a comparison with an intermediate approach between the traditional methods and the model proposed in this paper. This approach utilizes the same feature extraction and classification techniques as relation network, but it does not employ an epoch-based training strategy like our proposed model. Instead, it follows the training strategy of traditional machine learning methods, without resorting to meta-learning techniques. From the experimental results, it is evident that even when employing similar feature extraction and classification techniques, not using a meta-learning training strategy still hinders the attainment of the desired level of classification accuracy. This further underscores the advantage of the approach proposed in this paper in addressing the few-shot AMC problem.