esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Temperature and Salinity Measurement Using Frequency Comb
## 3 Materials and Methods
### Temperature Measurement Principle  
There are various formulas that exclaim the relations of temperature and salinity. In this presented work, we adopt the Quan-Fry formula [32]:  
\[n(S,T,\lambda)=n_{0}+(n_{1}+n_{2}T+n_{3}T^{2})S+n_{4}T^{2}+\frac{(n_{5}+n_{6} S+n_{7}T)}{\lambda}+\frac{n_{8}}{\lambda^{2}}+\frac{n_{9}}{\lambda^{3}} \tag{8}\]  
In this empirical Equation, \(S\) is the salinity in \(\%\), \(T\) is the temperature in degrees Celsius and \(\lambda\) is the wavelength in nanometers. Here are the coefficient values:  
\(n_{0}=1.31405\), \(n_{1}=1.779\times 10^{-4}\), \(n_{2}=-1.05\times 10^{-6}\), \(n_{3}=1.6\times 10^{-8}\), \(n_{4}=-2.02\times 10^{-6}\), \(n_{5}=15.868\), \(n_{6}=0.01155\), \(n_{7}=-0.00423\), \(n_{8}=-4382\), \(n_{9}=1.1456\times 10^{-6}\).  
With this formula, the phase refractive index \(n\) can be obtained.  
The relation between the group refractive index \(n_{w1}\) and phase refractive index \(n\) is:  
\[n_{w1}=n-\lambda\frac{dn}{d\lambda} \tag{9}\]  
By Equations (8) and (9), the group refractive index \(n_{w1}\) can be indicated as:  
\[n_{w1}=n_{0}+(n_{1}+n_{2}T+n_{3}T^{2})S+n_{4}T^{2}+\frac{2(n_{5}+n_{6}S+n_{7} T)}{\lambda}+\frac{3n_{8}}{\lambda^{2}}+\frac{4n_{9}}{\lambda^{3}} \tag{10}\]  
Hence, the temperature \(T\) can be expressed as:  
\[T=\frac{-(n_{2}S+\frac{2n_{7}}{\lambda})+\sqrt{(n_{2}S+\frac{2n_
## 3 Recent Scientific Highlights and New Instruments
We compared our system with another timing monitor based on the THz streaking method constructed by the PSI group, which assured a relative accuracy of 16.7 fs [44]. This system is now routinely used for pump-probe experiments and contributes to improving time resolution down to a few tens of fs. The two-color generation configuration based on the split-undulator technique can be combined with the two-stage tight X-ray focusing system, enabling researchers to investigate the non-linear interactions between intense X-ray fields and matter [32; 38]. The generation of a Cu K\(\alpha\) laser marked a significant achievement. The Cu target was excited with ultra-intense 9-keV X-rays to produce K-shell vacancies and to form the population inversion condition, leading to the generation of amplified spontaneous emission on Cu K\(\alpha\) lines [26]. Furthermore, while operating SACLA in the two-color mode, we observed the efficient amplification of 8 keV X-rays induced as a seeding. In this two-color mode, the temporal separation of two XFEL pulses could be tuned with a sub-fs resolution by using a small chicane of the electron beam in the middle of the undulator line. In the summer of 2016, the maximum separation of 40 fs for the 8 GeV electron beam was extended to ~300 fs by increasing the maximum current for the chicane magnets. Based on this scheme, an X-ray pump and X-ray probe experiment was performed to investigate the fundamental damage processes in a diamond induced by intense 6.1 keV X-rays [45]. It was found that the diffraction signal of the 5.9 keV probe pulse decreased after 20 fs following pump pulse irradiation with an intensity of 10\({}^{19}\) W/cm\({}^{2}\) due to the X-ray-induced atomic displacement. This finding offers a valuable opportunity for experimental evaluation of the \"diffraction-before-destruction\" scheme. We developed a hard X-ray split-and-delay optical (SDO) system based on the Bragg diffraction in crystal optics for generating two split pulses with a variable temporal separation [46; 47; 48]. To achieve both high stability and operational flexibility, the SDO system was designed to include both variable-delay and fixed-delay branches. As key optical elements, we fabricated high-quality thin crystals and channel-cut crystals by applying the plasma chemical vaporization machining technique. The SDO system using Si(220) crystals covered a photon energy range of 6.5-11.5 keV and a delay time range from a negative value to ~45 ps over the photon energy range (up to 220 ps at 6.5 keV). We developed a simple alignment method for achieving a spatial overlap between the split pulses. This SDO system was tested at BL29 XU of SPring-8 in combination with a focusing system. We achieved an excellent overlap with an accuracy of 30 nm for ~200 nm focused beams in both the horizontal and vertical directions. This result marks a milestone towards the realization of time-resolved studies using multiple X-ray pulses with a time range from femtosecond to sub-nanosecond scales at XFEL facilities.
## 5 Discussion  
### Complementary Advantages of Multi-Models  
The experimental results in Table 4 demonstrate that the performance of MS-RRBR is significantly superior to that of the individual models, UniformV2 or InternVL. This improvement can be attributed to the multi-model synergetic strategy, which fully leverages the complementary strengths of different models. Initially, UniformV2, with its exceptional spatiotemporal feature modeling capability, produces relatively accurate preliminary classification results during the category candidate stage. It performs particularly well in recognizing movements with distinct characteristics, such as \"body spinning\" and \"head shaking\". Subsequently, the general MLLM, InternVL, demonstrates strong performance in recognizing simple, routine actions such as \"clapping\" and \"jumping\", which alleviates some of the shortcomings of UniformEV2 in handling these actions due to insufficient training data. Ultimately, GPT-4o leverages its extensive language comprehension to generate more discriminative questions, further guiding InternVL to focus on more specific behavioral details and thereby compensating for MLLM's visual encoding shortcomings in complex tasks. MS-RRBR facilitates object-level interactions across models, from spatiotemporal fine-grained features to language descriptions, effectively enhancing the accuracy of behavior recognition. The multi-model combination strategy has been similarly shown to be effective in other domains. For example, [PERSON] et al. [45] proposed a multi-model ensemble learning architecture for semantic code search, which combines learners trained from different code perspectives. This approach has also demonstrated the efficacy of multi-model strategies, aligning with the conclusions drawn in our study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Example Sets (Questions)** & **Accuracy** \\ \hline
0 (0) & 88.60\% \\
1 (5) & 93.67\% \\
2 (10) & 94.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of prompt examples.  
\begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Action Type**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**Precision**} \\ \cline{2-5}  & **Uniformerv2** & **MS-RRBR** & **Uniformerv2** & **MS-RRBR** \\ \hline Arm Flapping & 92.31\% & 100\% & 80.00\% & 100\% \\ Hand Action & 90.00\% & 100\% & 100\% & 100\% \\ Clapping & 0\% & 100\% & 0\% & 100\% \\ Body Spinning & 100\% & 100\% & 80.95\% & 100\% \\ Jumping & 0\% & 50\% & 0\% & 100\% \\ Head Shaking & 100\% & 100\% & 82.35\% & 82.35\% \\ Object Manipulation & 91.67\% & 100\% & 100\% & 100\% \\ Object Rejection & 0\% & 33.33\% & 0\% & 50\% \\ Head Self-Harm & 100\% & 100\% & 75.00\% & 100\% \\ Self-Biting & 66.67\% & 66.67\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of recognition results for specific behavior types.
# Valorization Potential of Oilseed Cakes by Subcritical Water Extraction
## 3 Results  
### Extraction Yield  
Pumpkin, hemp and flax seed cakes were treated by subcritical water under three different operating conditions in order to assess simultaneous extraction of components and partial decomposition of the sample matrix. Samples denoted with \"1\" were treated with pure water in nitrogen atmosphere, whereas samples denoted with \"3\" were treated in carbon dioxide atmosphere. Samples \"2\" were treated in nitrogen atmosphere with the addition of 0.05 mol/L HCl to potentiate hydrolytical reactions. Obtained extraction yields are presented in Table 1 as mean values of three replicates \(\pm\) 2 SD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Atmosphere**} & \multirow{2}{*}{**EY (\%)**} & \multicolumn{2}{c}{**Total lipid Content**} & \multirow{2}{*}{**Total Protein Content**} \\  & & & **(g/100 g Dry Extract)** & & **(g/100 g Dry Extract)** \\ \hline \multirow{3}{*}{Pumpkin} & N\({}_{2}\) & 57.51 \(\pm\) 0.19 & 28.54 \(\pm\) 0.18 & 1.94 \(\pm\) 0.04 \\  & N\({}_{2}\) + 0.05 mol/L HCl & 60.10 \(\pm\) 0.18 & 48.55 \(\pm\) 0.23 & 3.14 \(\pm\) 0.06 \\  & CO\({}_{2}\) & 57.13 \(\pm\) 0.19 & 38.87 \(\pm\) 0.22 & 4.87 \(\pm\) 0.08 \\ \hline \multirow{3}{*}{Hemp} & N\({}_{2}\) & 42.07 \(\pm\) 0.18 & 10.10 \(\pm\) 0.21 & 4.83 \(\pm\) 0.06 \\  & N\({}_{2}\) + 0.05 mol/L HCl & 40.45 \(\pm\) 0.18 & 50.75 \(\pm\) 0.21 & 6.59 \(\pm\) 0.09 \\  & CO\({}_{2}\) & 44.23 \(\pm\) 0.17 & 32.04 \(\pm\) 0.18 & 6.83 \(\pm\) 0.09 \\ \hline \multirow{3}{*}{Fax} & N\({}_{2}\) & 50.33 \(\pm\) 0.18 & 6.97 \(\pm\) 0.10 & 1.30 \(\pm\) 0.01 \\  & N\({}_{2}\) + 0.05 mol/L HCl & 53.02 \(\pm\) 0.15 & 32.87 \(\pm\) 0.18 & 2.52 \(\pm\) 0.02 \\ \cline{1-1}  & CO\({}_{2}\) & 51.73 \(\pm\) 0.17 & 23.49 \(\pm\) 0.17 & 2.84 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Extraction yields (EY, g dry extract/100 g seed cake) and extract composition (lipid and protein content, g/100 dry extract) of pumpkin, hemp and linseed cakes obtained with subcritical water extraction at 160 \({}^{\circ}\)C, 20 bar for 1 h under different atmospheres.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Simulation in Simulation Software
#### 4.1.1 Simulation of Link Management Protocol  
First, the link management protocol runs and begins to build control channels and collect local resources. We used node 4 (node 0x 0004) as an example.  
Figure 12 shows the local resources collected by the protocol in node 4 (0x 0004), including the bandwidths of the available links and the negotiated wavelengths of available ports which could be used to transmit service data. This means that the link management protocol has built control channels and collected local resources successfully.
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
###### Abstract  
The size distribution of rock fragments significantly influences subsequent operations in geotechnical and mining engineering projects. Thus, accurate prediction of this distribution according to the relevant blasting design parameters is essential. This study employs artificial intelligence methods to predict the fragmentation of open-pit bench blasting. The study employed a dataset comprising 97 blast fragment samples. Random forest and XGBoost models were utilized as base learners. A prediction model was developed using the stacking integrated strategy to enhance predictive performance. The model's performance was evaluated using the coefficient of determination (\(R^{2}\)), the mean square error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The results indicated that the model achieved the highest prediction accuracy, with an \(R^{2}\) of 0.943. In the training set, the model achieved MSE, RMSE, and MAE values of 0.00269, 0.05187, and 0.03320, while in the testing set, these values were 0.00197, 0.04435, and 0.03687, respectively. The model was validated using five sets of actual blasting block data from a northeastern mining area, which yielded more accurate prediction results. These findings demonstrate that the stacking strategy effectively enhances the prediction performance of a single model and offers innovative approaches to predicting blasting block size.  
blasting fragmentation prediction; stacking integrated strategy; random forest; XGBoost Article  
16 December 202414 January 202524 January 202526 January 202526 January 202526 January 202526 January 2025
## 3 System Requirements for BVLOS Flight Operations
### RAVEN-Proposed Small UAS DAA System  
For safe UAS operations, aviation authorities have required the installation of DAA systems for UAS to maintain \"well clear\" with other air traffic during BVLOS flight operations [3; 4]. For this requirement and SWaP limitations on small UAS, the RAVEN project has proposed a small UAS DAA system as shown in Figure 8, where ADS-B and ground portable radar systems are integrated together to detect cooperative and non-cooperative traffic near UAS operating areas, and then those tracked intruders are fed into the Avoid module to identify hazards and carry out collision mitigation for small UAS to avoid Near Mid-Air Collisions (NMACs) during BVLOS flight operations.  
Figure 8: Block diagram for the RAVEN-proposed small UAS DAA system.  
Figure 7: Geo-fencing setup in the autopilot [13]. (**a**) Geo Fence in a Box; (**b**) Example Flight.
###### Abstract  
To mitigate the three-dimensional (3D) coupling interference of electric field sensors, a novel MEMS 3D electric field sensor with a dual-orthogonal induction structure and its spatial decoupling method is proposed. The sensor is designed with a cylindrical structure, in which two pairs of induction electrodes are orthogonally arranged to suppress common-mode interference. MEMS electric field sensing chips are utilized to achieve 3D electric field measurement. Furthermore, a spatial decoupling calibration model is established based on the structural characteristics of the sensor. The Cramer-Rao lower bound of the linear model is calculated to obtain the optimal decoupled calibration matrix, enabling precise 3D electric field decoupling. Experimental results showed that within an electric field range of 0-50 kV/m, the linearity of the three decoupled electric field components was 2.60%, 1.20%, and 1.78%, respectively, while the synthesized electric field achieved a linearity of 0.74% with a maximum full-scale error of 0.80%. Under varying angles and field intensities, the maximum and average relative errors of the decoupled synthesized electric field were 1.20% and 0.43%, respectively, representing reductions of 61.8% and 56.1% compared to the conventional matrix inversion method. These results confirmed that the proposed method effectively suppressed coupling interference and enhanced 3D electric field measurement accuracy.  
3D electric field; MEMS electric field sensor; decoupled calibration matrix; coupling interference; dual-orthogonal induction; Cramer-Rao lower bound
## 3 Results and Discussion
### Effect of Pressure Difference Derived by Vacuum Pump
It is observed that, for the same vacuum (\(\Delta\)p = 0.75 bar), two different set-point scenarios (75 and 80\({}^{\circ}\)C) lead to a COP with a very small difference when the recirculation ratio is up to 80%, while for the recirculation ratio higher than 80%, only a small part comes from RO with a low temperature, and the set-point of 75 \({}^{\circ}\)C can be more easily reached, leading to reduced power consumption and higher COP. Finally, the specific energy consumption for all these scenarios is presented in Figure 14. It is observed that the specific energy consumption reduces significantly as the recirculation ratio increases, presenting a minimum of 170 kWh/m3 of produced water for a recirculation ratio of 98%. Moreover, when the pressure difference is higher, at 0.8 bar, the specific energy consumption is significantly lower for low recirculation ratios compared to \(\Delta\)p = 0.75 bar and \(\Delta\)p = 0.7 bar, while this difference reduces when the recirculation ratio increases. Furthermore, for the same vacuum, the heat pump set-point seems to present the same performance for a recirculation ratio lower than 85%, but for higher values, the specific energy consumption is higher in the case of higher set-point temperatures. As the brine  
Figure 12: Power consumption of the vacuum pump, heat pump and the whole system for \(\Delta\)p = 0.75 and 0.8 bar at 75 \({}^{\circ}\)C (a) and for \(\Delta\)p = 0.7 and 0.75 bar at 80 \({}^{\circ}\)C (b). inlet temperature increases, the energy that is lost to the sea discharge is highly increased, since at the set-point of 75 \({}^{\circ}\)C, the outlet temperature of the flash tank is 4-5 \({}^{\circ}\)C higher than the saturation temperature, while at 80 and 85\({}^{\circ}\)C, the temperature difference is much higher, and a part of this brine flow is rejected to the sea, leading to increased specific energy consumption.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
### Enhanced Security Analysis
#### 4.5.2 Information-Theoretic Security  
The use of true random sequences ensures that the mutual information between the transmitted signal and any intercepted signal by an adversary is minimized. The entropy \(H\) of the frequency sequence is maximized, making the system secure against attackers with unlimited computational resources.  
\[I(\text{Transmitted Signal; Intercepted Signal})=0 \tag{12}\]  
This implies that without knowledge of the true random sequence or successful synchronization, an adversary gains no useful information from intercepted signals.
## 5 Research Challenges and Future Directions  
In this section, we discuss the open research issues and practical challenges in the passive wake-up radio. While we mentioned earlier that most of the reported systems and protocols are evaluated based on theoretical analysis and simulation, we recommend moving forward with the development of testbed prototypes to accelerate the real-world implementation of wake-up radio technology. We also discuss the future research direction that can be engaged to solve these challenges.
# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
## 2 Materials and Methods  
The composite material used for this research work is a prepreg unidirectional carbon fiber reinforced plastic. The matrix of the understudy composite is a di-glycidyl ether of bisphenol A (DGEBA) epoxy system, a class of thermoset polymer. DGEBA is used as a binding matrix in high-performance fiber-reinforced composites and it is known for excellent mechanical properties, dimensional stability, chemical resistance, and ease of processing [19; 43]. During curing, the epoxy converts into a hard rigid solid by chemical cross-linking. A two-stage CC is recommended by the prepreg CFRP manufacturer. The glass transition temperature with the maximum degree of conversion (T\({}_{\text{goo}}\)) is 140 \({}^{\circ}\)C according to the manufacturer. Stage-1 comprises 1-3 \({}^{\circ}\)C/min heating rate from ambient temperature to 80 \({}^{\circ}\)C and holding for 30 min. During stage-1, polymerization initiates. Stage-2 recommends increasing curing temperature at the same heating rate from 80 \({}^{\circ}\)C to 120 \({}^{\circ}\)C for an additional time of 120 min to accelerate the polymerization process under autoclave pressure of 7 bar. After 120 min, a cooling rate of 2-5 \({}^{\circ}\)C/min is recommended to bring the laminate to 30 \({}^{\circ}\)C. A vacuum (\(-\)0.8 bar) is recommended inside the laminate bag during curing. Figure 1 presents a manufacturer-recommended curing cycle (MRCC) comprising curing temperature (\(T_{c}\) = 120 \({}^{\circ}\)C), curing time (\(t_{c}\) = 120 min), and autoclave pressure (\(P_{au}\) = 7 bar). This research work is confined to the second stage of curing (MRCC) only. It is pertinent to mention that MRCC results in a set of CFRP properties for general-purpose application. The MRCC needed modification to achieve optimum properties for an aircraft skin application.
# CFD Assessment of Car Park Ventilation System in Case of Fire Event
## 2 Materials and Methods
### Simulation Setup in OpenFOAM  
#### 2.3.1 Turbulence Model  
The selection of a turbulence model for CFD analysis plays a critical role in accurately simulating fluid flows. Turbulence models allow for accounting for the turbulent nature of flows, which is pervasive in many fluid dynamics scenarios. In the realm of CFD, several turbulence models are available, including Reynolds-averaged Navier-Stokes (RANS), large eddy simulations (LES), unsteady RANS (URANS), hybrid RANS-LES, and direct numerical simulation (DNS). Each model comes with its own computational cost and level of accuracy. In the context of this study, the \(k-\varepsilon\) turbulence model, belonging to the RANS family, is chosen, as it is widely employed in indoor environment simulations [29]. It consists of two transport equations for turbulent kinetic energy (\(k\)) [m\({}^{2}\)s\({}^{-2}\)] and turbulence dissipation rate (\(\varepsilon\)) [m\({}^{2}\)s\({}^{-3}\)]. The \(k\) equation is as follows [30]:  
\[\frac{D}{Dt}(\rho k)=\
abla\cdot(\rho D_{k}\
abla k)+P-\rho\varepsilon \tag{4}\]  
where \(D_{k}\) is the effective diffusivity for \(k\) [-], and \(P\) is the turbulent kinetic energy production rate [m\({}^{2}\)s\({}^{-3}\)]. The \(\varepsilon\) equation is as follows [30]:  
\[\frac{D}{Dt}(\rho\varepsilon)=\
abla\cdot(\rho D_{\varepsilon}\
abla \varepsilon)+\frac{C_{1}\varepsilon}{k}\left(P+C_{3}\frac{2}{3}k\
abla\cdot u \right)-C_{2}\rho\frac{\varepsilon^{2}}{k} \tag{5}\]  
where \(D_{\varepsilon}\) is effective diffusivity for \(\varepsilon\) [-], \(C_{1}\) and \(C_{2}\) are model coefficient [-]. The \(k-\varepsilon\) turbulence model is robust and reduces the memory needed for the analysis while maintaining reasonable accuracy. This model assumes the flow to be fully turbulent, disregarding the molecular viscosity. The adoption of the \(k-\varepsilon\) turbulence model is justified by its favorable balance between computational efficiency and accuracy, aligning well with the study's focus on initial monitoring and iterative investigations. This choice optimizes resources for timely insights into dominant turbulent flow behavior, while acknowledging the model's limitations in pressure gradients and streamline curvature [31]. Top of Form.