esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
While researchers have extensively studied the initial decomposition mechanism of Monomethylhydrazine (MMH, CH\({}_{3}\)NHNH\({}_{2}\)) in the MMH/dinitrogen tetroide (NTO) system, the investigation of Unsymmetrical Dimethylhydrazine (UDMH, (CH\({}_{3}\))\({}_{2}\)NNH\({}_{2}\)) has been limited due to its high toxicity, corrosiveness, and deterioration rate. Hence, the effects of UDMH's deterioration products on combustion performance and gas-phase combustion reaction mechanisms remain unclear. This comprehensive review examines the existing research on the reaction kinetics of the three widely used hydrazine-based self-ignition propellants: Hydrazine (HZ, N\({}_{2}\)H\({}_{4}\)): MMH: and UDMH, emphasizing the necessity for further investigation into the reaction kinetics and mechanisms of UDMH. It also discusses the implications of these findings for developing safer and more efficient rocket propulsion systems. Additionally, this review underscores the importance of utilizing computational chemistry theory to analyze hydrazine-based fuels' combustion and decomposition properties, constructing detailed pyrolysis and combustion reaction mechanisms to optimize rocket engine fuel performance and environmental concerns.  
hydrazine-based fuels; rate constants; thermochemistry; reaction kinetics; kinetic model development; experimental studies
## 4 Results
### Land Surface Classification Results
#### 4.2.2 Result with the Multi-Spectral ISS Moonlight Images  
It is found that Calgary was covered by snow on 21 November and 27 November 2015. The snow-covered areas (Snow) and forests/trees (Forests) were accurately classified (Figure 10) with an overall accuracy of 87.16% and a kappa coefficient of 0.77. The classification results of the Komsomsolsk-na-Amure imagery showed that the overall accuracy and kappa coefficients were 91.49% and 0.85, respectively (Figure 11). It is evident that moonlight remote sensing has tremendous potential in extracting land surfaces, such as snow-covered lands and forests, under faint lunar illumination in winter.  
Figure 8: Three cases of the UAV moonlight remote sensing image with reference to the high-resolution optical image (SuperView-1) taken on 17 December 2019.  
Figure 11: The classification result in the low-light areas of Komsomsolsk-na-Amure.  
Figure 10: The classification result in the low-light areas of the Calgary image.  
Figure 9: The classification result obtained in the low-light areas of the VIIIRS/DNB moonlight image.  
Although astronauts aboard the ISS have taken numerous nightlight photos, we only found limited scenes with moonlight, mainly because their focus was on city lights. In the future, it would be very useful if astronauts aboard the ISS or China's Tiangong space station in the near future could help to take photos while paying attention to moonlit areas that are apart from urban areas.
## 3 Segmentation of Nanostructured Materials for Gas Sensing
### Tin Oxide (SnO\({}_{2}\))
This method has shown promising results in enhancing sensitivity and extending the operating range of SnO\({}_{2}\) sensors, especially for gases like NO\({}_{2}\) and CO that require precise, low-level detection [85]. Composite materials combining SnO\({}_{2}\) with other metal oxides, such as ZnO, WO\({}_{3}\), or TiO\({}_{2}\), have demonstrated enhanced gas sensing properties through synergistic effects. SnO\({}_{2}\)-ZnO composites, for instance, leverage the high surface area of ZnO and the electronic properties of SnO\({}_{2}\) to achieve superior sensitivity and selectivity for gases like ethanol and acetone. Similarly, SnO\({}_{2}\)-TiO\({}_{2}\) composites improve stability and sensitivity to NOx and CO by forming heterojunctions that enhance charge transfer and promote selective gas adsorption. These composite sensors are particularly useful in environmental monitoring, where a broad range of gases need to be detected accurately and reliably [86; 87]. SnO\({}_{2}\)'s high sensitivity, tunable selectivity, and adaptability to nanostructuring and doping make it a leading material in gas sensor technology. Its effectiveness in detecting a broad spectrum of gases has made it a key material for sensors in environmental monitoring, industrial safety, and healthcare. Ongoing research into doping, nanostructuring, and composite formation continues to expand the capabilities of SnO\({}_{2}\)-based sensors, supporting the development of high-performance, low-power devices for next-generation gas sensing applications. Figure 9: (**top**): SEM images of tin oxide nanofibers and nanoribbons doped with PG after calcination at 450 \({}^{\circ}\)C. (**a**) Low and (**b**) high magnifications. (**bottom**): Sensor responses to 2 ppm ethanol, 4 ppm acetone, 5 ppm CO, and 100 ppb NO at different operating temperatures (25, 100, 200, and 300 \({}^{\circ}\)C) (reproduced from [82], MDPI, 2020).
## 1 Introduction
In addition, for discontinuous issues, this method needs to regenerate a new particle at the point where fracture occurs, causing the computation time to increase again. Different from the aforementioned methods derived from continuum mechanics, the theoretical basis of Member Discrete Element Method (MDEM) presented by Ye team [22] according to conventional Particle Discrete Element [23] is non-continuum mechanics. In the MDEM, a member is represented by a set of finite particle rather than by continuum. The motion equation of each particle is solved by the central difference method and arbitrarily adjacent particles are connected by the contact constitutive model. The difference between the MDEM and conventional DEM lies in theestablishment of the contact constitutive model. Therefore, the MDEM not only extends the application range of conventional DEM, but also inherits its advantages such as no assembly of stiffness matrix and iterative solution of motion equations, no special treatment for nonlinear issues, no re-meshing for fracture problems, and no convergence difficulty. At present, the method has been successfully applied to geometric nonlinearity [24] and nonlinear dynamic analysis [25] of member structures as well as progressive collapse simulation of reticulated shells [22]. These results show that the MDEM is a simple and effective tool to process geometrically large deformation, material nonlinearity and fracture issues. However, no report that the MDEM is adopted for static and dynamic response analysis of steel frames with semi-rigid joints exists. A simple and effective numerical approach is proposed based on the MDEM for static and dynamic response analysis of steel frames with semi-rigid joints. Brief details of the basic theory for the MDEM are first presented including particle motion equations, particle internal forces, geometric nonlinearity modelling and fracture modeling. The semi-rigid connection is simulated by a virtual spring element, and then the modified formula of the contact element stiffness adjacent to the semi-rigid connection is derived. The nonlinear behaviors of semi-rigid connections are captured by the bilinear semi-rigid connection model and Richard-Abbort four-parameter model. Furthermore, its hysteresis behavior is traced by the independent hardening model. The correctness and accuracy of the numerical procedure proposed are verified by complex behaviors analyses of steel frames with semi-rigid connections, such as geometric nonlinearity, snap-through buckling, dynamic responses and fracture.
## 3 Proposed Ranging-Compensated Temperature-Sensing Sensor Design Method  
### Architectural Overview  
The architecture of the proposed ranging compensation temperature-sensing sensor is shown in Figure 3, which includes the temperature-sensing circuit, ADC, data control and processing terminal. The temperature-monitoring circuit converts the ambient temperature into an analog voltage signal. The ADC converts the collected voltage signal into a digital signal. The data control and processing terminal is responsible for controlling the multichannel ADC and converting the digital signal into transmitted and received delay compensation data of the ISL payload.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **TC, Unit ps/\({}^{o}\)C** & **Comments** \\ \hline HPA & \(-5\) (\(\pm 2\)) & Transmission path \\ LNA and filter & \(-5\) to +2 (\(\pm 2\)) & Reception path \\ Mixer and isolator & \(-18\) (\(\pm 2\)) & Transmission path or reception path \\ Up-Converters & \(-4\) to +1 (\(\pm 2\)) & Transmission path \\ Down-Converters & +2 to +10 (\(\pm 2\)) & Reception path \\ \hline \hline \end{tabular}
\end{table}
Table 1: Temperature coefficients (TC) of group delay for modules (measured at +5–+45 \({}^{\circ}\)C, 40% relative humidity).
## 2 Methods
### Step 2  
#### 2.4.1 Position Accuracy Assessment  
In Step 2, the same GNSS data as in Step 1 were used, but, in contrast, the data from the entire run (12 turns) were assessed. The analysis of the spatial differences started shortly after the start gate when the individual skiers reached a speed of 2 m/s (using the Doppler-speed measurement of method _A_). The analysis ended when the skiers passed the last gate. The analysis in Step 1 revealed that method \(A\) under the condition 10\({}^{\circ}\) (_A10_) was the most accurate and consistent (see results Step 1). Consequently,  
Figure 3: Overview of the geodetic measurement setup. Skier trajectory is drawn in green, the digital terrain model in gray. The gray scale is darker with decreasing altitude. Fixpoints 1 and 2, the tachymeter position and the GNSS base stations are marked with black dots. The geodetic network is drawn in red. The black dots along the skier trajectory represent the calibration points used for the video-based photogrammetry at turn 7. The graph is shown in the LCS.  
\(\mathbf{A_{10}}\) served as the reference method in Step 2 using the same approach as in Step 1, the spatial differences between \(\mathbf{A}\)-\(\mathbf{E}\) and \(\mathbf{A_{10}}\) were calculated each 0.02 s from start to finish for the elevation masks 10\({}^{\circ}\), 30\({}^{\circ}\) and 40\({}^{\circ}\). GNSS measurements of the unit on the athletes were started (warm start) on average 69 s (minimum 53 s, maximum 92 s) before skiers reached the speed of 2 m/s, while the measurement on the base station unit was started earlier. Between the start of the GNSS measurement and the time point when skiers left the start gate, skiers stood in an upright position in an open area with no obstacles other than the topography affecting satellite signal reception. The same statistics as in Step 1 were used to characterize the spatial differences between \(\mathbf{A}\)-\(\mathbf{E}\) and \(\mathbf{A_{10}}\).
## 4 The COASYM-MAC Protocol
### Data Collection
#### 4.4.3 In Case of Data Loss Due to Link Asymmetry  
Link asymmetry can sometimes also happen from child \(u\) to parent \(P_{u}\). Child \(u\) is waiting for the parent's beacon to send its data. Once it successfully wins the channel through random backoff, child \(u\) sends its data but parent \(P_{u}\) cannot receive the data because of an asymmetric link. The sender will assume it is an asymmetric link because it will wait \(t\) time (half of the beacon interval) but cannot get an ACK from the receiver. Child \(u\) will then choose a cooperative node, as described in Algorithm 5. Figure 8 displays the timing diagram for the scenario where data are lost because of link asymmetry.  
Algorithm 5 presents pseudo-code to summarize selecting a relay in the case of beacon loss (receiver-to-sender asymmetry) and data loss (sender-to-receiver asymmetry).  
Figure 6: Relay selection among sibling.  
Figure 7: Beacon loss situation.
## 9 Discussion and Simulation Results
### Case 2: \(L_{2}n<(1-r)n\)  
Since, as said above, for the examples considered in the paper, \(1-r=1/2\), it follows that the degree distributions fulfilling the condition \(L_{2}n<(1-r)n\) are those with \(L_{2}n<n/2\), i.e., \(L_{2}<1/2\)  
Figure 1: BER with respect to \(E_{b}/N_{0}\) in dB with \(d_{l}=4\) and \(d_{l}=8\), \(n=1000\), and iterations number \(I=50\).  
Thus, in conclusion, the randomly generated code having a degree distribution with \(d_{l}=9\) (Figure 3) is the best since as it has the highest value of \(L_{2}\) (0.49128) even if it presents an intermediate value of the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\) (1.69321). Moreover, the code with the lowest value of the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\) (1.59749), i.e., the one with \(d_{l}=10\) also presents, as expected from (39), the highest value of \(\sigma^{*}_{\rm Jensen}\) (1.03314).
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 2 Background
### Domain Analysis
#### 2.2.3 Tracking and Communication  
The overall integration of departments and sections of a warehouse is necessary for efficient communication among departments. Traditionally, communication is maintained using manual pen and paper methods such as generating sales orders, demand orders, etc., and sending them to other departments through electronic mail or manually. As for smart warehouses, the IoT uses communication protocols to link every section and item to each other [27]. This ensures smooth communication within the warehouse and that the information is transmitted and received in time.
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 2 History  
In a major contribution to MALDI development, [PERSON] and his colleagues used a mass spectrometer to demonstrate the technique of matrix-assisted laser desorption/ionization (MALDI) in 1985 [22]. [PERSON] and [PERSON] were awarded the 2002 Prize in Chemistry for their development of soft desorption ionization methods for mass spectrometric analyses of biological macromolecules; specifically, soft laser desorption by [PERSON] and the electrospray by [PERSON]. The method using MALDI-TOF MS was reported by [PERSON] et al. in 1994 using protein profiling with \(\alpha\)-cyano-4-hydroxycinnamic acid as a matrix material and was reported to be useful for rapid identification of bacteria [23]. However, at that time, MALDI-TOF MS was still performed using pretreated bacteria. Later, it was shown that the method could be applied directly to colonies applied to the MS plate without special pretreatment [24; 25], paving the way for the method's practical application. However, it took many years for the results at the laboratory level to reach clinical practice, and it was not until 2009 that the results of the application of this method were first reported by a clinical bacteriology laboratory [26]. The first report from a laboratory was published in 2009, indicating that it takes many years for laboratory-level results to be translated into clinical practice. There are several reasons for this apparent delay, including: (1) concern that the proteome changes dynamically in vivo and is easily affected by culture conditions; (2) doubts about whether the differences in MS spectra are fully consistent with currently established taxonomies; (3) the lack of a complete database; (4) the fact that, from the bacteriologist's point of view, microbial identification is an arduous task that requires a certain level of training in bacteriology; and (5) early papers were published in journals that may have been too focused on MS techniques (as opposed to applications). Development also required a period of validation for clinical application. Since 2009, the number of clinical report papers has been increasing year by year (Figure 1), and MALDI-TOF MS is gaining attention in hospital bacteriology laboratories.
## 2 Current Supervisory or Regulatory Technologies
### Definition of Several Concepts: Fintech, RegTech, SupTech
#### 2.1.2 RegTech  
RegTech was first presented in 2015 by the Financial Conduct Authority (FCA) [11]. As quite a new industry, it is defined as \"the use of technologies to solve regulatory and compliance requirements more effectively and efficiently\" by the Institute of International Finance (IIF) [12]. RegTech focuses specifically on leveraging technology to address regulatory compliance challenges faced by organizations, particularly in highly regulated industries like finance. It involves using advanced technologies such as AI, machine learning (ML), natural language processing (NLP), and data analytics to streamline compliance processes, automate regulatory reporting, monitor risks, ensure data privacy and security, and manage regulatory changes effectively. RegTech solutions aim to help organizations meet their compliance obligations more efficiently, accurately, and cost-effectively. The basic differences between Fintech and RegTech are shown in Table 1.  
The life cycle of products in certain industries, such as electronic products, is becoming shorter and shorter [13]. Companies must constantly design new products to replace old ones to maintain their market share. New products need to be designed to meet changing market demands and comply with regulations. Implementing RegTech ensures that designers are aware of whether the new products are compliant at the design stage so that they can be successfully launched after mass production.  
At the same time, competition in new industries is increasing. A larger number of companies are startups developing new products. This creates a risk for consumers as the final products may not be of acceptable quality and/or the companies may not be able to meet the regulatory requirements while maintaining design and functional originality in the long run. Having an in-built RegTech application from the start of product design for such companies can ensure that companies are aware of quality requirements and confidently design and sell their products with high quality assurance. Larger companies, on the other hand, will benefit proportionately less from RegTech for new product design but can still ensure that the production and logistics themselves have acceptable regulatory compliance.
# Weighted-CAPIC Caching Algorithm for Priority Traffic in Named Data Network
## 3 Related Works: Caching Algorithms on Named Data Network
However, we need to improve the performance of priority classes while increasing total system performance to give the best experience for priority consumers by using the system's resources efficiently. If we exploit all resources efficiently, this will provide enormous value to the development of caching techniques. Because of this issue, in this research, we propose a Weighted Cache based on Popularity and Class (Weighted-CAPIC) to improve the performance of the main priority class and the whole system in the mobile environment.
## 1 Introduction
In the published literature, the response of LaBr\({}_{3}\):Ce under X-ray excitation has been carried out using different X-ray sources and in a different energy range than the one used in the current work. Specifically, the energy resolution of the crystal was calculated with X-rays at 17.4 KeV from the \({}^{241}\)Am/Mo source [5]. X-ray measurements of the crystal were performed at energies between 9 KeV and 100 KeV at the Hamburger Synchrotron (HASYLAB synchrotron radiation facility) [23; 24]. Additionally, the emission spectrum of LaBr\({}_{3}\):Ce for various concentrations of Ce was measured by exciting the crystal with X-rays from the copper target of a Philips X-ray tube at 30-35 KV and 15-25 mA [6; 7; 12]. Our investigation is complementary to the aforementioned works in the sense that the LaBr\({}_{3}\):Ce response is studied in an higher X-ray tube-voltage range, and properties such as effective efficiency and DQE(0) examined. Our results were compared with results for Bi\({}_{4}\)Ge\({}_{3}\)O\({}_{12}\) (BGO), Lu\({}_{2}\)SiO\({}_{5}\):Ce (LSO), CdWO\({}_{4}\) and LaCl\({}_{3}\):Ce single crystals that are commonly employed in several imaging systems [25; 26; 27]. In Table 1, some of the properties of the aforementioned crystals are presented. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Properties** & **BGO** & **LSO:Ce** & **CdWO\({}_{4}\)** & **LaCl\({}_{3}\):Ce** & **LaBr\({}_{3}\):Ce** \\ \hline Wavelength, max emission (nm) & 480 & 420 & 490 & 350 & 380 \\ Decay Time (ns) & 300 & 40 & 5000 & 28 & 25 \\ Light Yield (photons/MeV) & 8900 & 30,000 & 28,000 & 49,000 & 63,000 \\ Radiation Length (cm) & 1.118 & 1.14 & 1.06 & 2.813 & 1.881 \\ Density (g/cm\({}^{3}\)) & 7.13 & 7.4 & 7.9 & 3.86 & 5.2 \\ Effective Atomic Number & 74 & 75 & 74 & 47 & 46 \\ Melting Point (\({}^{\circ}\)C) & 1044 & 2050 & 1325 & 1135 & 1116 \\ Hardness (Mho) & 5 & 5.8 & 4-4.5 & 3 & 3 \\ Hygroscopicity & No & No & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BGO, LSO, CdWO\({}_{4}\), LaCl\({}_{3}\):Ce and LaBr\({}_{3}\):Ce crystal properties [18; 26; 28; 29; 30].
# A Method to Estimate Dynamic Pore Water Pressure Growth of Saturated Sand-Gravel Materials
## 4 The Estimation Model of the Number of Cycles Required to Reach Liquefaction, \(N_{\rm L}\)  
### The Verification of \(N_{\rm L}\)  
\(N_{\rm L}\) is the number of cycles required to reach liquefaction. \(N_{\rm L}\) is mostly obtained from the liquefaction test curve or empirical formula, such as that proposed by [PERSON] [30]:  
\[N_{\rm L}=10^{a-b\frac{\tau_{\rm d}}{\sigma_{0}^{\prime}}};\ {\rm lgN_{\rm L}}=a-b \frac{\tau_{\rm d}}{\sigma_{0}^{\prime}}=a-b\frac{\sigma_{\rm d}}{(1+K_{\rm c} )\sigma_{\rm 3c}} \tag{3}\]  
where \(a\) and \(b\) are constants, \(\tau_{\rm d}\) is dynamic shear stress, \(\sigma_{0}^{\prime}\) is the initial normal stress on the \(45^{\circ}\) slope of the sample, \(\sigma_{\rm 3c}\) is the initial effective confining pressure, \(\sigma_{\rm d}\) is the axial cyclic stress, and \(K_{\rm c}\) is the initial consolidation stress ratio. The least squares method was employed to fit the experimental data, resulting in the values of \(a\) and \(b\). Under different conditions of \(K_{\rm c}\) and \(\sigma_{\rm 3c}\), one relationship between \(N_{\rm L}\) and \(\sigma_{\rm d}\) can be established. It has been widely used in the field of engineering practice [30; 31; 32; 33]. Figure 9 verifies the accuracy of the empirical formula by using the [PERSON] et al. [34] test data. This empirical formula provides the relationship curves of the different groups of test materials, and the goodness of fit is greater than 0.99. This formula can, however, not consider the influence of other factors, such as relative density, on the cyclic numbers required to reach liquefaction. The number of cycles required to cause liquefaction is often related to various factors such as the soil's relative density, porosity, consolidation ratio, and initial effective consolidation stress. Therefore, this empirical formula must be corrected to consider these factors.
# Analysis of the Energy Efficiency of a Pneumatic Booster Regulator with Energy Recovery
## 5 Results and Discussion
### Energy Efficiency of VBA-R
#### 5.3.3 Effect of Operate Conditions  
There are different demands in practical applications, and, according to the different operating conditions, we hope to achieve higher energy efficiency. We made a comparison between two kinds of operation conditions, as shown in Figure 17. In these two conditions, the energy efficiency difference was up to 20%. We analysed previously that, as the supply pressure or flow rate increased, the energy efficiency decreased. Thus, in a practical application, it is better to use a booster regulator with a high boost ratio.  
In Figure 18, the influence of boost ratio was further verified, and the result was consistent with the analysis according to Equation (6), which indicated that the energy efficiency gets higher with decreasing supply pressure or increasing boost ratio. However, the boost ratio gets smaller with decreasing supply pressure, so we should comprehensively consider these factors in order to obtain higher efficiency.  
Figure 16: Relationship between energy efficiency and diameter ratio.  
Figure 17: Efficiency under different operating conditions.