esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Systematic Literature Review (SLR)
### SLR Result and Findings
#### 2.2.3 Fusion Levels  
In the field of multimodal biometric systems, various fusion strategies are employed to integrate information from multiple biometric sources with the goal of enhancing authentication performance and system security. Recent studies demonstrated that researchers have adopted different levels of fusion, including feature, score, decision, and hybrid fusion,  
\begin{table}
\begin{tabular}{l l l l} \hline
**Dataset** & **Number of Studies** & **Modalities** & **References** \\ \hline SDUMLA-HMT & 7 & Face, Iris, FV, FP, Gait & [6; 7; 8; 9; 10; 11; 12] \\ FVC (2000–2004) & 4 & FP & [14; 15; 16; 17] \\ CASIA & 3 & Iris, Palm, FV & [12; 13; 19] \\ NUPT-FPV & 2 & FP, FV & [20; 21] \\ PolyU Palm Vein & 1 & Palm Vein & [12] \\ IITD Palm Print & 1 & Palmprint & [18] \\ \hline \end{tabular}
\end{table}
Table 1: Biometric datasets from related studies.  
\begin{table}
\begin{tabular}{c c c} \hline
**Biometric Type** & **Number of Studies** & **References** \\ \hline Face & 20 & [1,2,8,10,11,14–17,22–29] \\ Fingerprint (FP) & 14 & [1,4,9,12–17,20,25,28,30,31] \\ Iris & 13 & [1,6,8,10–13,19,22,27,28,31,32] \\ Finger Vein (FV) & 11 & [1,10,12–12,20,21,33–37] \\ Palmprint & 8 & [12,13,18,23,32,34,38,39] \\ \hline \end{tabular}
\end{table}
Table 2: Biometric type traits across related studies.  
each offering distinct advantages relative to improving recognition accuracy and system robustness [1,6].  
Table 3 presents an overview of the fusion types most commonly applied in the reviewed studies, highlighting their prevalence and rationale behind their use in contemporary multimodal biometric authentication frameworks.  
The findings presented in Table 3 indicate that feature-level fusion was the most commonly employed strategy, observed in twenty-five studies. This approach is widely favored for its ability to combine rich low-level features from multiple modalities early in the processing pipeline, which often results in higher recognition accuracy and more discriminative representations.  
Score-level fusion appeared in sixteen studies and is appreciated for its simplicity and flexibility, particularly in systems integrating heterogeneous biometric modalities. Although it operates at a later stage and may lose some fine-grained information, its ease of implementation makes it a practical choice in many scenarios.  
Decision-level fusion was used in six studies. This approach, while simpler to implement, is generally considered less effective due to the potential loss of modality-specific information when only final decisions are fused.  
A total of five studies reported the use of both feature-level and score-level fusion techniques within the same work.
# Measuring the Effect of Circular Public Procurement on Government's Environmental Impact
## 3 Results  
### Text Analysis  
Analysis of the tender texts showed 62 applications of CPP criteria in 31 of the 72 tenders (Figure 3). In total, 24 different user-defined procurement criteria were distinguished. Thus, CPP was applied in 43% of the analyzed tenders, and it seemed that unique criteria were being used often without much repetition. Circularity-related procurement criteria occurred in absolute numbers the least for cars (1 criterion in 1 tender) and the most for furniture (26 criteria applied in 6 tenders) and ICT hardware (10 criteria applied in 8 tenders). Furthermore, analysis of the applied criteria showed that they were mostly related to (Figure 3) (1) repair (R4, 22 applications), applied mainly in tenders on ICT hardware and furniture, (2) longer life span (R2, 13 applications), applied mainly in tenders on furniture, and (3) recycling after use (R8b, 16 applications), applied mainly in tenders on solar panels. Criteria on the refuse (R0), rethink (R1), remanufacture (R6) and repurpose (R7) strategies were not found. For refuse, this was to be expected, because this strategy does not lead to a tender and hence is not found in e-procurement databases. The other strategies can, in theory, be stimulated via procurement criteria, but that was not found in this study.
## 4 Results and Discussion
### Experiment Analysis  
In order to further verify the accuracy of the numerical simulation, an experimental study was conducted. The test bench is shown in Figure 7a. K-type thermocouples are placed on the internal parts to measure the temperature changes during braking. The schematic diagram of the positions of various temperature measurement points is shown in Figure 7b.  
The experimental design conditions are as follows: the aircraft brake disc speed is 22 r/s and the braking time is 15 s, after which the aircraft stops, and the brake system enters the natural convection heat dissipation state. The energy is approximately 33 MJ when heat begins to dissipate using normal grounding without reverse thrust. The temperature change curves of each component in the experiment are drawn according to the temperature change of the thermocouple and compared with the simulated temperature change curves, as shown in Figure 8. Comparing and analyzing the temperature curve experimental data and simulation results of the rotor disc, it was found that the two were in good agreement. The maximum absolute error between the experiment and simulation is 29 K, and the maximum relative error does not exceed 4.5%, further confirming the reliability of the simulation model.  
Figure 8: Comparison of temperature changes between experiments and simulations under natural convection.  
Figure 7: Experimental diagram: (**a**) photo of thermocouple layout; (**b**) schematic diagram of thermocouple layout.  
There are three main sources of error: (1) the simplification of material properties: numerical simulations often treat the thermal physical parameters of materials, such as specific heat capacity and thermal conductivity, as constant and isotropic, ignoring their temperature-dependent characteristics, resulting in deviations between simulation results and actual situations; (2) boundary condition setting: in numerical simulations, the convective heat transfer coefficient is set based on the empirical average value, but, in reality, this coefficient dynamically changes over time and has a spatial distribution, causing errors; (3) experimental measurement: during the experiment, errors may be introduced in terms of thermocouple accuracy, measurement location selection, and environmental factors such as air flow and radiation heat loss.  
To compare the cooling effects of natural convection and forced convection, the fan is turned on for cooling after the vehicle stops. Figure 9 combines both the simulated temperature change curve and the experimental results for direct comparison; the simulation and experimental results are in good agreement. In this condition, the upper limit of energy for melting the fusible plug is adopted, which is approximately 48 MJ. It can be concluded that the cooling efficiency of forced convection is higher than that of natural convection.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 3 Instruments for TD-NIRS
### Other New TD-NIRS Systems
#### 3.3.3 Compact TD-NIRS Systems Using MPPCs (or SiPMs)  
A multi-pixel photon counter (MPPC), or a silicon photomultiplier (SiPM) for another name, is a new type of photon counting semiconductor device consisting of a high-density matrix of avalanche photodiodes, enabling single-photon detection and offering high-photon detection efficiency, excellent timing resolution, low bias voltage operation, ruggedness, resistance to excess light, and immunity to magnetic fields. Using MPPCs (or SiPMs), compact TD-NIRS systems have been developed and commercialized recently.  
A dual-channel TD-NIRS system for non-invasive monitoring of oxygenation in the brain (tNIRS-1, Hamamatsu Photonics K.K., Japan) was manufactured and marketed as a medical instrument in 2014 [55]. Figure 12 shows its photograph. It employs semi-conductor devices of cooled multi-pixel photon counters (MPPCs) for light detection and time-to-digital converters (TDCs) for TR measurement with a design concept of compactness and usability, resulting in a size of 292 mm (width) \(\times\) 291 mm (height) \(\times\) 207 mm (depth) and a weight of about 7.5 kg.  
In 2016, the group from Politecnico di Milano, Italy, developed a compact (size of 160 mm (width) \(\times\) 50 mm (height) \(\times\) 200 mm (depth)), low power consumption, dual-wavelength TD-NIRS system as shown in Figure 13[56]. It employs two pulsed laser diodes with wavelengths of 670 nm and 830 nm for the light sources, a silicon photomultiplier (SiPM) with an active area of 1 mm\({}^{2}\) for the detector, and a home-made TDC with a temporal resolution of 10 ps for TR measurements. For TD-NIRS, the same group also developed a compact detector probe integrating a fast SiPM and its electronics, which can be directly put in contact with the skin [57]. Many compact detector probes can be installed into a head-cap without the need for optical fibers for collecting light.
## 3 Model
### Case Study  
A typical office building located in subtropical city Guangzhou is considered. There are ten floors in this building. The area of each floor is 384 m\({}^{2}\) so that the total area is 3840 m\({}^{2}\). Furthermore, the heat transfer coefficient of wall, window and roof is 1.081 W/(m\({}^{2}\) K), 2.7 W/(m\({}^{2}\) K) and 0.812 W/(m\({}^{2}\) K), respectively. The daily cooling period spans from 8:00 a.m. to 6:00 p.m. The cooling season is from April to October. The hourly cooling demand is calculated by the DeST software [30]. The annual data are displayed in Figures 3-5. It should be mentioned that the meteorological data is the monthly mean values from the reference [31]. The design and operation condition of SASCHCS used in the above-mentioned office building is listed in Table 2. It should be mentioned that the initial temperature of the storage tank is equal to the ambient temperature, and the simulation time step is set to 1 min. In addition, the thermodynamic properties of water and R410A are calculated by the software Refprop9 [32].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline \hline
**ETC** & & \\ \hline \hline Aperture area of ETC & m\({}^{2}\) & 270 \\ \hline \hline Absorber tube & Outer diameter & m & 0.037 \\ Thickness & m & 0.0012 \\ \hline Glass tube & Outer diameter & m & 0.047 \\ Thickness & m & 0.0012 \\ \hline Outer diameter of U-tube & m & 0.008 \\ Thickness of air layer & m & 0.001 \\ Thickness of copper fin & m & 0.0006 \\ Length of the header pipe per one U-tube & m & 1.2 \\ Titled angle & & 20 \\ \hline \hline
**Storage tank** & & \\ \hline \hline Aspect ratio & & 3.5 \\ Heat loss coefficient & W/(m\({}^{2}\)\({}^{\circ}\)C) & 0.83 \\ Volume of storage tank & m\({}^{3}\) & 2.7 \\ \hline \hline
**Absorption subsystem** & & \\ \hline \hline Nominal cooling capacity & kW & 45 \\ Cooling water flow rate in condenser & m\({}^{3}\)/h & 8/9/10.5 \\
1/absorber/subcooler & m\({}^{3}\)/h & 8/9/10.5 \\ Inlet temperature of cooling water & \({}^{\circ}\)C & 32 \\ \hline \hline
**Compression subsystem** & & \\ \hline Nominal cooling capacity & kW & 500 \\ Cooling water flow rate in condenser 2 & m\({}^{3}\)/h & 98.1 \\ Inlet/outlet temperature of chilled water & \({}^{\circ}\)C & 12/7 \\ Inlet temperature of cooling water & \({}^{\circ}\)C & 32 \\ Isentropic efficiency of compressor & \% & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design and operation parameters of SASCHCS.  
Figure 5: Cooling load.
## 3 Results and Discussion  
As an initial step, all four methods (i.e., rpm and spark duration, each in median and odd/even mode) were investigated at the outliers of the engine load range: idle (0.0 kW electric load, with the engine running at 4000 rpm) and full load (1.0 kW, at 5200 rpm). Once the methods that feature true cycle-to-cycle capabilities were identified, they were compared for all five load points that were investigated. Engine load was characterized by 1.35, 3.19, 4.74, 6.06, and 6.64 bar indicating the mean effective pressure (IMEP), with a coefficient of variation (COV) of 27.55, 5.32, 2.07, 1.82, and 1.49%, respectively for 0.0, 0.3, 0.5, 0.8, and 1.0 kW generator output.  
The rpm-based method was taken as the benchmark, as it is often employed for indirect diagnostic (reduced hardware requirements). The peculiarity of its application in the current study is that only one point per turn is available for measuring rpm, i.e., each time a spark occurs. The spark duration method was developed by considering the observations found in [37] and improving its applicability by adding cycle-to-cycle capabilities.  
One possible issue that was considered is that noise could affect evaluations. Figure 4 shows raw in-cylinder pressure and secondary current traces recorded during two consecutive cycles. There is an evident effect of noise at the point of ignition for the trace acquired during the 5 th cycle. Applying linear interpolation for identifying the 2-mA threshold causes a slight overestimation of the spark duration by approximately 16 \(\upmu\)s, even if the event is practically the same in terms of duration for both cycles. Nonetheless, the actual difference in the result is within the resolution of the measurement (20 \(\upmu\)s at an acquisition frequency of 50 kHz), and therefore, no significant influence was exerted by signal noise. A median filter with a 5-point integration window was applied in the LabVIEW VI for further reduction of this effect.
# A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations
## 1 Introduction
This method offers greater flexibility in performing localized and independent cell refinement, and more readily allows for control over the size of distributed grid cells. Compared to block-structured AMR, cell-based AMR has advantages such as saving the number of grid cells required and providing a more flexible adaptation process, making it better suited to handling transient flow and fluid-structure interaction problems [25; 26]. Although the benefits of this type of AMR are clear, several issues remain unresolved. On one hand, the flexible refinement approach can lead to a more prevalent occurrence of mismatched levels of adjacent grid cells (hanging grids), compromising computational accuracy. Most previous AMR simulation studies have been based on the finite volume method, which requires special treatment at the interfaces of grids of differing sizes. On the other hand, criteria selection for adaptive Cartesian grid methods under varying flow velocities and characteristics, as well as an assessment of the computational efficiency gains and accuracy improvements brought about by cell-based AMR, have not been fully explored. In this paper, we build upon cell-based AMR technology and employ a distance-weighted least squares interpolation method to implement finite difference discretization. Additionally, we utilize an immersed boundary method near wall boundaries to conveniently accommodate a variety of flow conditions in both transient and steady-state problems. The organization of the paper is as follows: Section 2 describes the adaptive Cartesian grid technique developed herein, including the grid generation process, criteria for flow field adaptation, grid smoothing, and the assignment of new cell values. Section 3 details the numerical methods used, the discretization of governing equations, handling of hanging grids, and the treatment of immersed boundaries. In Section 4, the capabilities of the adaptive Cartesian grid are analyzed and evaluated through inviscid supersonic/subsonic and high Reynolds number test cases. Finally, conclusions are presented in Section 5.
## 5 Discussion  
From the abovementioned research, it could be found that the effect of the fracture on the UCS was greater than the elastic modulus, which is consistent with the results of [PERSON] et al. [23]. Additionally, the mechanical parameters of the fractured rock mass were sensitive to the fracture traces, which is in agreement with [PERSON] and [PERSON] [26]. However, [PERSON] et al. [23] and [PERSON] et al. [24] found that fracture orientation was the most effective parameter among the fracture geometrical parameters, which is inconsistent with the results of this paper. The main reason could be that the fractured rock mass was generated based on a 2D PFC and the fracture orientation (dip angle) was considered in 2D space by [PERSON] et al. [23]. Moreover, although a three-dimensional numerical model was applied by [PERSON] et al. [25], the fractures in the rock mass were assumed to be parallel and only the fracture orientation (dip angle) was changed in the calculation results. Fractures in the rock mass are three-dimensional and their orientation is determined by the inclination and dip angle.  
Additionally, the geometric parameters involved in this research do not completely exactly agree with those of other authors. The sensitivity orders of the rock mass mechanical parameters to the rock mass geometry parameters were also different from other authors. To increase the credibility of the conclusions, another orthogonal test was performed. The study methodology, test scheme, and analysis method are consistent with parts III and IV of the paper. Only the level values of the trace length were changed, as shown in Table 5.  
The specific calculation results are not described in detail here. Extreme differences and F ratios of the elastic modulus and UCS for various influencing factors in the Group 2 test are presented in Figure 8. The results showed that the influence of the fracture geometry parameters in the UCS was greater than that of the elastic modulus. The results are consistent with the results of the Group 1 test.  
Besides, in the Group 2 test, the sensitivity degree of the rock mass elastic modulus and UCS to the four influencing factors was in the order of trace length \(>\) bulk density \(>\) dip angle \(>\) inclination, which are different than the results of the Group 1 test. Therefore, when the influencing factors take different data in the range of values, the order of the sensitivity of the rock mass mechanical parameters to the fracture geometry parameters may be different. To fully understand the problem, the global sensitivity analysis method  
Figure 8: Extreme difference values and F ratios under various influencing factors of the Group 2 test.  
can be used [32; 33]. The Monte Carlo method was applied in the global sensitivity analysis method to generate a random set of data over a reasonable range of parameters to determine the influencing factors. The parameters of different influencing factors were then combined to establish a numerical model and acquire results. Generally, hundreds or even thousands of time-valued simulations are required.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature/Metric** & **YOLOv7-W6-Pose** & **YOLOv11** & **Advantages of YOLOv11** \\ \hline \multirow{2}{*}{Architecture Type} & CNN-based with pose & Hybrid CNN + & Enhanced feature extraction and \\  & head & Transformer & context modelling via Transformers \\ \hline \multirow{2}{*}{Pose Estimation Support} & Extended with a & \multirow{2}{*}{Native integration} & Simplified, end-to-end pose estimation \\  & separate model & & without extra modules \\ \hline \multirow{2}{*}{Model Size} & Large & \multirow{2}{*}{Optimized (\(\sim\)60 M parameters)} & More efficient with fewer parameters, \\  & (\(\sim\)75 M parameters) & & reducing computational load \\ \hline \multirow{2}{*}{Detection Accuracy (mAP@50)} & \multirow{2}{*}{\(\sim\)56–58\%} & \multirow{2}{*}{\(\sim\)62–65\%} & Higher accuracy improves detection \\  & & & and pose estimation reliability \\ \hline \multirow{2}{*}{Inference Speed (FPS)} & \multirow{2}{*}{\(\sim\)30–40 (on GPU)} & \multirow{2}{*}{\(\sim\)45–50 (on GPU)} & Faster inference enables better \\  & & & real-time performance \\ \hline \multirow{2}{*}{Low-Light/Occlusion Handling} & \multirow{2}{*}{Moderate} & Improved (via attention & Better robustness in challenging \\  & & layers) & environments \\ \hline \multirow{2}{*}{Ease of Integration} & \multirow{2}{*}{Requires an additional pose module} & End-to-end unified model & Streamlined deployment and reduced \\  & & model & engineering complexity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between YOLOv7-W6-Pose and YOLOv11.
# Multi-Objective Optimisation of Welding Parameters for AZ91D/AA6082 Rotary Friction Welded Joints
## 6 Conclusions  
In the present study, the first sound joint was attained between the AZ91D magnesium alloy and AA6082 aluminium alloy using a conventional friction welding method. Additionally, a new approach in multi-criteria optimisation of welding parameters was presented to obtain maximum joint strength and minimum shortening of joints. The following conclusions can be drawn from the research:  
1. A neural network with nine neurons in the hidden layer and a log-sigmoidal activation function were the best approximators in formulating a multi-objective function for the genetic algorithm.
2. The _NSGA-2 genetic_ algorithm found a set of welding parameters for which the joint strength increases from 24 to 81 MPA, and the joint shortening decreases from 8.25 to 0.23 mm. These solutions are in constant conflict with each other.
3. The increase in welding time and upsetting force has a positive effect on the increase in the tensile strength of the welded joint and a negative effect on material losses.
4. The results show that a low friction force (35 kN) and upsetting force (42 kN) give a high value of tensile strength (78 MPa) and the lowest shortening (3.5 mm) of the bimetal joints.
5. During the verification of the model for the new welding parameters, its error did not exceed 3%.  
Figure 10: Variation in friction force (**a**), upsetting force (**b**), and (**c**) friction time in the Pareto solution (in order according to decreasing metal loss) of the AZ91/6082 joints.  
This study can be extended to the microstructure and crack behaviour at the joint interface. The influence of welding parameters on the formation of intermetallic compound (IMC) layers and microcracks, reported in the references [42; 43; 44] for this pair of joined materials, should be investigated.  
R.W.: Proposal of the research topic; formal analysis; experiments; optimisation; writing of the manuscript; review and editing. A.S.: Formal analysis; experiments. P.S.: Formal analysis: experiments. 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 are contained within the article.  
The authors declare no conflicts of interest.