esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Mathematical Model
The antigen concentration (\(G\) (pmol/mgprot)) variation over time for Model 1 is:  
\[\frac{dG(t)}{dt}=\ k_{G}\ \ -\ \ \left(k_{XG}+k_{XGZ}\ e^{-\lambda_{GZ}\ Z(t)} \right)\ G(t),\ \ \ G(t_{0})\ \ =\ G_{0} \tag{3}\]  
where \(k_{G}=[k_{XG}+k_{XGZ}]\ G_{0}\ =k_{XG}^{tot}\ G_{0}\) (pmol/mgprot/h) is the antigen production rate (assumed constant over the time of the experiments); \(k_{XG}\) (/h) is the minimal, irreducible elimination rate of antigen even at infinite ZA concentrations (\(Z(t)\rightarrow\infty\)); \(k_{XGZ}\) (/h) is the (maximum) antigen elimination rate that can be suppressed (down to zero) in the presence of ZA; and \(\lambda_{GZ}\) (/pmol/mgprot) is the rate of exponential decay of antigen elimination rate with increasing ZA concentrations. In Model 2, the enzyme dynamics are described by Equations (4) and (5), which represent the enzyme's bound and unbound percentages, respectively:  
\[\frac{dB(t)}{dt}=\ k_{BUZ}\ Z(t)\ U(t)\ \ -\ \ k_{UBG}\ e^{-\lambda_{UBG}\ G(t)}\ B(t),\ \ \ \ B(t_{0})\ \ =\ 0 \tag{4}\]  
Figure 1: Block diagram of Model 1 (panel **A**) and Model 2 (panel **B**). According to the mathematical formulation used for two models, the biological variables considered are: the zoledronate concentration in the medium (Y(\(\mu\)M)); the zoledronic acid (ZA) intracellular concentration (Z (pmol/mg prot)); the antigen (IPP) concentration (G (pmol/mg prot)); the percentage of bound enzyme (B (%)) and the percentage of unbound enzyme (U (%)). Blocks with a continuous contour represent differential variables (Z, Y, G, B), and the block with a dashed contour represents the algebraic variable (U). Arrows in continuous lines indicate mass transfer, while dashed arrows indicate enhancement (arrow endpoints) or suppression (circle endpoints). \[U(t)=\ U(t_{0})\ -\ B(t),\quad U(t_{0})=100 \tag{5}\]  
where \(k_{BUZ}\) (/h/(pmol/mgprot)) is the second order bound enzyme formation rate, depending on both zoledronate and unbound enzyme availability; \(k_{UBG}\) (/h) is the maximum bound enzyme dissociation rate; and \(\lambda_{UBG}\) (/(pmol/mgprot)) is the rate of exponential decay of the bound enzyme dissociation rate with increasing IPP antigen concentrations. The IPP concentration variation over time for Model 2 is thus represented as follows:  
\[\frac{dG(t)}{dt}=\ k_{G}\ -\ \left(k_{XGH}U(t)+k_{XG}\right)\ G(t)\,\quad G(t_{0})\ =\ G_{0} \tag{6}\]  
where \(k_{G}=\left(k_{XGH}*U(t_{0})+k_{XG}\right)G_{0}=k_{XG}^{tot}\), \(G_{0}\) (pmol/mgprot/h) is once again the antigen production rate (assumed constant over the time of the experiments); \(k_{XG}\) (/h) is again the minimal, irreducible elimination rate of antigen even at zero percent unbound enzyme (\(U(t)\to 0\)); and \(k_{XGH}\) (/h/%) is the rate of decrease in antigen per percent of unbound enzyme. Notice that the parameters \(k_{G}\), \(k_{XG}\) and \(k_{XG}^{tot}\) have the same meaning in the two models, but are estimated or computed in a model-specific way in each case.
### State-of-the-Art Space Plasma Instruments  
Traditionally, most instruments measuring charged particles in the solar wind fall within one of two families: Faraday cups and electrostatic analyzers (ESAs). The Parker Solar Probe SWEAP instrument suite includes a Faraday cup (SPC) [12; 13] that achieves measurements with a cadence of 16 Hz for a full distribution and greater than 128 Hz in high-cadence mode (only one energy/charge window near the peak of the distribution is measured). The instrument has a 28\({}^{\circ}\) field of view (FOV) with 1\({}^{\circ}\) target resolution and achieves radial 1D measurements of the VDF. ESAs are often capable of performing 3D measurements of the distribution. The typical acquisition time of a full 3D VDF for an ESA is higher than the typical acquisition time of an 1D VDF for a Faraday cup. For instance, the Hot Ion Analyzer (HIA) on board Cluster samples a full 3D VDF in 4 s, while the Proton Alpha Sensor (PAS) on board Solar Orbiter samples a full 3D VDF in 1 s [14; 15]. These longer acquisition times are a consequence of the measurement cycle in these instruments. Our instrument concept aims instead at high energy and time resolutions at the expense of a full 3D coverage. It is based on the successfully flown Magnetic Electron Ion Spectrometer (MagEIS) instrument onboard the Van Allen Probes, which was designed for the measurement of energetic electrons in the Earth's radiation belts [16]. In the MagEIS design, the magnetic field created by two permanent magnets deflects the incoming electrons toward a set of silicon detectors. The hit positions of the electrons on the detector area are related to their energy according to the definition of the relativistic gyro-radius. The magnetic field is used to select energy bands for the energy-sensitive silicon detectors placed in the focal plane [16]. Our instrument design uses the same operation principle, although it relies on the measurement of the curvature radius by a position-sensitive sensor as its sole determination of the particle energies, which is sufficient for non-relativistic particles such as thermal ions in most space-plasma applications.
# Correlation Plenoptic Imaging: An Overview
## 4 A Different Architecture of CPI with Chaotic Light
### Depth-of-Field Improvement  
Let us compare the depth of field of CPI with entangled photons with the one of PI and SI devices, following the same strategy as in Section 2.3, namely by evaluating the visibility of the images of a double-slit mask having width \(a\) and center-to-center distance \(d=2a\), as retrieved at different axial positions \(z_{b}\), The minimum resolved distance \(d\) is defined as the one at which the visibility of the slits image falls below 10%.  
In Figure 14a, we plot the minimum resolved distance \(d\), as a function of the defocusing parameter \(z_{b}-z_{bF}\), for ghost imaging (orange), plenoptic imaging with \(N_{u}=3\) (green) and CPI (blue). In all cases, we choose the same numerical aperture (\(\mathrm{NA}=0.3\)). CPI clearly enables refocusing in a wider range than both standard imaging (SI) and standard plenoptic imaging (PI) without losing the high resolution of the focused image. Points \(A\) and \(B\) correspond to the examples shown in Figure 14b. In case \(A\), the slit distance and \(z_{b}\) are chosen to be very close to the boundary of the CPI refocusing range: both SI and PI are completely out-of-focus, while CPI still partially resolves the object. Point \(B\) lies outside the PI refocusing range but well below the CPI limits, and the refocused image thus appears to be well-resolved. Interference effects in the simulated results of Figure 14b are due to the coherent nature of CPI.
## 2 Related Works
More specifically, all sectors have equal width and sector time; in each sector, nodes attempt communications by beamforming toward the intended receiver with an angle \(\alpha_{i}\) for the entire duration of the sector time; the systems operates in a circular way by enabling (for communications) one sector at a time; the sector in which the beamforming is currently enabled is denoted as _current active sector_. Figure 1: Round-robin MAC principle. A non-negligible matter in directional MAC contexts is certainly represented by the handoff problem that involves the mobility of nodes. For example, in [33], the authors attempt to limit the handoff problem through the use of an efficient beam control mechanism; a similar approach is proposed in [34] in which handoff is reduced using a predictive location model that is able to estimate future movements of nodes. Similarly, the work [35] presents a predictive location algorithm using in-frame priority scheduling in order to reduce the handoff.
## 2 Methodology
### Kaist-Tmd  
There have been many efforts to develop an S-CO\({}_{2}\) compressor design and analysis tool. Most of the researchers utilized the existing commercial codes with minor modifications. For this reason, many codes have convergence issues near the critical point [17]. The main differences between KAIST-TMD and the previous ones are modification of correlations based on ideal gas assumption and integration of real gas property database NIST [18] and the design tool. As a result, the calculation procedure is mainly based on calculating enthalpy and pressure, which is more straightforward and has less error for adopting the definition based static to stagnation conversion method directly. Equation (1), which was commonly used in the previous tools, has a faster calculation speed but it is accompanied by large errors near the critical points where thermodynamic properties change sharply [19]. On the other hand, Equation (2), which is used in KAIST-TMD, has a slower calculation speed but reflects thermodynamic properties of S-CO\({}_{2}\) even near the critical point [20].  
\[\frac{P_{o}}{P_{s}}=\left(\frac{T_{o}}{T_{s}}\right)^{\frac{\gamma-1}{\gamma} }=\left(1+\frac{\gamma-1}{2}M^{2}\right)^{\frac{\gamma}{\gamma-1}} \tag{1}\]  
\[h_{o}=h_{s}+\frac{V^{2}}{2} \tag{2}\]  
Even though the computational fluid dynamics (CFD) is a powerful tool to analyze turbomachinery, the 1D mean streamline analysis method is still a very important tool because it determines key design parameters. Since the flow mechanism in a compressor is complicated, the simplified design method is generally preferred for compressor design process. The 1D mean streamline method on the basis of basic aerodynamic design and empirical correlations is widely applied. It is a design process to determine velocity triangles with Euler turbine equation and continuity equation as shown in Equation (3), Equation (4). Empirical correlations such as loss models and slip factor which cause entropy generation are applied. KAIST-TMD adopts the following 1D mean streamline analysis method.  
\[h_{o2}-h_{o1}=U_{2}C_{w2}-U_{1}C_{w1} \tag{3}\]  
\[\dot{m}=\rho(h_{s},P_{s})AV \tag{4}\]  
For valeness diffuser design, Equations (5)-(8) which are derived from the conservation equations in the vaneless diffuser region were utilized in KAIST-TMD [21]. Figure 4 represents a flow chart of its main algorithm. This tool generates a compressor having the highest isentropic efficiency while meeting the outlet pressure conditions.  
\[C_{m}\frac{dC_{m}}{dr}-\frac{C_{w}^{2}}{r}+C_{f}\frac{C^{2}\cos\alpha}{b\sin \phi}+\frac{dp}{\rho dr}=0 \tag{5}\]  
\[C_{m}\frac{dC_{w}}{dr}+\frac{C_{m}C_{w}}{r}+C_{f}\frac{C^{2}\sin\alpha}{b\sin \phi}=0 \tag{6}\]  
\[\frac{d\rho}{\rho dr}+\frac{dC_{m}}{C_{m}dr}+\frac{dh}{hdr}+\frac{1}{r}=0 \tag{7}\]  
\[\frac{dh}{dr}+C_{m}\frac{dC_{m}}{dr}+C_{w}\frac{dC_{w}}{dr}=0 \tag{8}\]
## 1 Introduction  
The emerging communication systems use higher frequency spectrum and wider bands to transmit at higher data rates. Transmission at higher frequencies limits the range. Additionally, cell sizes are also kept very small to increase frequency reuse and reduce interference. Thus, there is always a need for efficient spectrum utilization, as well as range extension to meet today's communication requirements. The concept of relays is not new in wireless networks, which were initially used to increase the range, but with the exponential growth in usage of the electromagnetic spectrum, it is also used for improving the spectral efficiency and interference mitigation. The relaying was first standardized in IEEE802.16j [1], and later, in LTE, the use of relays was thoroughly deliberated and various advance relaying strategies considered to meet throughput and coverage requirements [2; 3]. Cooperative relaying is yet another unique method for wireless networks that enhances throughput and extends the coverage of the network. In this, the job of relay selection serves as a building block to realize cooperative relaying. In earlier networks, the relays were operated in half-duplex mode to minimize problems arising due to transmission and reception in the same frequency band and same time slot. The relaying may solve some of the above-mentioned problems in wireless networks, but we still need to address the problem of efficient utilization of the frequency spectrum. According to the FCC, the existing frequency bands are not efficiently utilized in time and space. This encourages the unlicensed users to use the spectrum opportunistically.  
The idea of cognitive radio (CR), first proposed by [PERSON] [4], allows unlicensed users, also called secondary users (SUs), to share the licensed spectrum in time and space without interfering with primary users' (PUs) communication, thus increasing the spectral efficiency. The opportunistic use of the spectrum by the secondary users can be categorized on the basis of its existence along with the primary users [5], which could be in three possible ways, i.e., (i) underlay, (ii) overlay, and (iii) interweave. In underlay mode, which is of prime interest in our work, the SUs are allowed to transmit their data in the licensed spectrum band when PUs are also transmitting. The interference temperature model is imposed on SUs' transmission power so that the interference at a PU receiver is within the interference temperature limit and PUs communication unaffected or minimally affected. The first cognitive radio wireless regional area network standard, IEEE 802.22 [6], was developed by the IEEE 802 LAN/MAN Standard Committee (LMSC) and published in 2011. This standard uses geo-location and spectrum sensing for spectral awareness. It was visualized that if we incorporate relaying in the cognitive radio network and somehow reduce the delays because of successive relaying, then we can get closer to the objective of the most effective utilization of the frequency spectrum.
## 3 Sensing Performance Tests and Discussions  
### Temperature Response  
To examine the temperature response, the hybrid grating was placed in a temperature-controllable chamber with a calibrated temperature probe. As the temperature increased, the grating period and RI  
Figure 1: (**a**) Schematic model of the hybrid grating with an FBG and an excessively tilted fiber grating (Ex-TFG) in the reduced-diameter fiber (RDF); (**b**) optical microscopic images of the FBG and Ex-TFG fringes, and (**c**) transmission spectrum of the hybrid grating.  
Figure 2: Experimental setup for testing spectrum properties of the hybrid grating. PC: Polarization controller; TC chamber: Temperature-control chamber; OSA: Optical spectral analyzer.  
modulation changed due to thermal expansion and thermal-optic effects, resulting in a shift in the wavelengths of Bragg resonance and cladding mode resonances. The hybrid grating was heated from 25-80 \({}^{\circ}\)C, and its spectrum was recorded with a step of 5 \({}^{\circ}\)C. The results are shown in Figure 3a. Clearly, the spectrum showed a global red-shift with the temperature increasing. In a detailed examination, we selected the cladding mode with resonance wavelength at 1561.4 nm and the Bragg resonance to investigate the temperature response, as shown in Figure 3b. The results show that the Bragg and cladding mode resonances shift linearly with the temperature, with response coefficients of 9.68 pm/\({}^{\circ}\)C and 39.65 pm/\({}^{\circ}\)C, respectively.
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline
**Building Type** & **Permissible Vibration (cm s\({}^{-1}\))** \\ \hline Vibration-sensitive structures (cultural heritage) & 0.3 \\ Structures with masonry infill walls and timber ceilings & 1.0 \\ (traditional houses and low-rise general houses) & 2.0 \\ Masonry buildings with underground foundations and & 3.0 \\ concrete slabs (row houses) & 3.0 \\ Medium and small buildings with reinforced concrete & 3.0 \\ framework and slabs (low-rise apartments) & 3.0 \\ Large buildings with reinforced concrete, reinforced & 5.0 \\ framework, and slabs (high-rise apartments) & \\ \hline \end{tabular}
\end{table} 
Table 2: Tunnel standard specifications for permissible blasting vibration [13]. \begin{table}
\begin{tabular}{c c} \hline
**Classification** & **Building Type** & **Permissible Vibration (cm s\({}^{-1}\))** \\ \hline \multirow{4}{*}{Seoul subway} & Cultural heritage and buildings & 0.2 \\  & with precision machinery & 0.5 \\  & Houses and apartments & 0.5 \\  & Shopping complexes, offices, and & 1.0 \\  & public buildings & 4.0 \\  & Reinforced concrete and steel frame factories & 4.0 \\  & Cultural heritage such as relics or ancient sites & 0.2 \\  & Nearby computer facilities & 0.2 \\ Busan subway & Houses and apartments & 0.5 \\  & Shopping complexes & 1.0 \\  & Reinforced concrete buildings and factories & 1.0–4.0 \\ \hline \end{tabular}
\end{table} 
Table 3: blasting vibration standards for subway construction in Seoul and Busan [14]. Excavations using TBMs are generally considered vibration-free, and the vibration and noise created by the machine are deemed not to be a significant issue aboveground. A summary of the Final Environmental Impact Assessment Report from the \"Access to the Region's Core\" project in New Jersey, USA, is provided in Table 5[16]. This report contains vibration measurements taken aboveground for various TBM diameters, ground conditions, and drilling depths. Based on the information in Table 5, it is considered that vibration values that are lower than those generated by the TBM itself will have a low impact on the TBM.
## 3 Experimental Methods and Model Parameters
### Critical Volume Fraction of Mineral Filler in Asphalt Mastic
Specifically, the critical volume fractions for limestone asphalt mastic, fly ash asphalt mastic, and coal gangue asphalt mastic are 30.65%, 44.96%, and 41.15%, respectively. To minimize the influence of interactions between mineral filler particles on the analysis, subsequent investigations into the interaction mechanisms between asphalt and mineral filler should be restricted to the critical volume fraction ranges identified above. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Filler Type**} & \multicolumn{6}{c}{**Fitting Parameters**} \\ \cline{2-7}  & \(G_{1}\) & \(\phi_{\rm c}\) & \(G_{2}\) & \(a_{1}\) & \(a_{2}\) &
\begin{tabular}{c} **Correlation** \\ **Coefficient R2** \\ \end{tabular} \\ \hline Limestone filler & 2.36 & 30.65\% & 0.62 & 0.03 & 0.45 & 0.916 \\ Fly ash & 1.83 & 44.96\% & 5.28 & 2.33 & 2.83 & 0.981 \\ Coal gangue filler & 4.29 & 41.15\% & 3.06 & 0.05 & 4.55 & 0.998 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fitting results and goodness of fit for critical volume fraction of filler.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM
### Vision-Based SLAM
#### 5.1.3 Image Enhancement Techniques  
Deep learning-based image enhancement has revolutionized underwater visual SLAM by addressing fundamental visibility challenges. These techniques specifically target key problems: turbidity compensation, low-light enhancement, and real-time processing. Liu et al. [54] developed adaptive filtering methods that improve feature-matching accuracy. Modern implementations balance enhancement quality with computational efficiency [55]. These advancements have expanded the operational envelope of vision-based SLAM to previously challenging environments, though they still face limitations in extreme turbidity or complete darkness.  
As research continues, these integrated strategies--improved image processing, sensor fusion, and continuous real-time mapping--make vision-based SLAM increasingly viable across various underwater scenarios. Whether used for detailed inspections, ecological surveys, or the exploration of wreck sites, vision-based SLAM systems are steadily evolving to meet the complex demands of underwater environments. For further insights into current methods, findings, and challenges, Table 4 summarizes recent studies in this field.
## 2 Research Status on Anhydrous Hydrazine Fuel
### Critical Assessment of the Research on Hydrazine
\begin{table}
\begin{tabular}{c c} \hline
**Researchers** & **Details** \\ \hline [PERSON] et al. (2014) & Constructed a model for hydrazine decomposition \\ \hline [PERSON] (2015) & Conducted a series of studies on the N\({}_{2}\)H\({}_{4}\)/NTO combination fuel \\ \hline [PERSON] and [PERSON] et al. (2014) & Studied the gas-phase reactions of N\({}_{2}\)H\({}_{4}\) with O\({}_{2}\), NO, and NO\({}_{2}\) \\ \hline [PERSON] et al. (2016) & Adjusted the reaction rate constant for the reaction N\({}_{2}\)H\({}_{4}\) + NH\({}_{2}\)\(\rightarrow\) N\({}_{2}\)H\({}_{3}\) + NH\({}_{3}\) \\  & based on the decomposition of N\({}_{2}\)H\({}_{4}\) and flame experimental data \\ \hline [PERSON] et al. (2016) & Developed a kinetic reaction mechanism for the decomposition of hydrazine at high temperatures, containing 51 reactions and 11 substances \\ \hline [PERSON] et al. (2016) & Developed a gas-phase chemical reaction mechanism of N\({}_{2}\)H\({}_{4}\)/NTO, containing 64 and 22 chemical reactions, and performed a series of CFD simulations \\ \hline [PERSON] et al. (2016) & Investigated the nature and mechanism of contact explosions of spontaneous liquid propellants \\ \hline [PERSON] et al. (2016) & Presented a chemical mechanism for the early stage of hypergolic ignition in liquid hydrazine/nitrogen tetroxide mixtures \\ \hline [PERSON] et al. (2017) & Studied the decomposition of hydrazine fuels at pressures of 0.1–10 mTorr by the ultra-low-pressure pyrolysis technique (VLPP). They measured the reaction rate constants for the decomposition of Hydrazine single molecules due to collisions \\ \hline \end{tabular}
\end{table} 
Table 2: Details of Hydrazine and combination with other fuels research. Figure 6: Current Limitations on Hydrazine Research.
## 5 Results and Analysis
### Thermal Prediction Under Variable Radial Load Conditions
To further verify the transient accuracy of the thermal prediction model under variable radial load conditions, uncertainty analysis is conducted on the predicted and real temperatures of the reference nodes at 4000 N-m and 5500 N-m, as shown in Figure 17.  
Based on Figure 17a, it can be observed that under a radial load of 4000 N-m, the transient predicted temperature on the outer surface of the outer ring shows a high goodness-of-fit with the real temperature (R\({}^{2}\) = 0.9947), with real temperature data points largely falling within the 95% prediction intervals. However, in the other three subfigures, the goodness-of-fit decreases to 0.9567, 0.9786, and 0.95, respectively, indicating reduced prediction accuracy under these conditions. Real temperature points also intermittently fall outside the 95% prediction intervals (marked by blue circulars). These results suggest that the proposed prediction framework requires further enhancement in accuracy under variable load conditions. Additionally, the real temperature data exhibits minor fluctuations under high-load conditions, which may be related to sensor performance. The reliability of sample data remains a critical factor for ensuring accurate parameter identification and early-warning results.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 3 Applications
### LoRaWAN Applications
[132] address the health and well being monitoring systems using testbeds. Study shows that sent frames are received by GW with a ratio of 96%. Different work effort put such as generic sensor framework [133] for IoT, analysis of LoRa for smart metering [134; 135], undermine and soils [136; 137; 138; 139], propagation analysis of indoor (Smart Buildings) [140] and campuses [141], industrial use cases [142; 143], aerospace [144] and satellite communications [145], ventricular smart city applications [146], remote on/off switches [147], multi hop monitoring system [148] and enegery efficient monitoring systems [149], military use cases for troop tracking [150] and public use case for tracking kids [151].