esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis
## 3. The Architecture and the Core Components
### Network Infrastructure  
Figure 4 shows the network infrastructure based on the information interplay between financial institutes, investors, and policymakers. Electronic Data Interchange (EDI) can take place between the various stakeholders through the big data processing and analysis services of the BIMA framework. This allows the policymakers to make their policies up-to-date, which can be reviewed by the investors and decide on the market trends to make decisions on investment. The financial institute is the one that makes the end choices rather than the one in the middle to manipulate, hide, and delay the processing of information and decisions.  
There are additional features offered by the architecture. First, security has been blended successfully to ensure all transactions to be safe and resilient. This can be achieved by the combined uses of the firewall to protect the services, XML to define access control, user privileges, and advanced security rules. Network security can be fully integrated with web services languages and rules set by XML. Second, the transaction costs can be managed based on the demands and time of transactions, which can be low as $0.05 per transaction. Payments can be made via a fair, open, and market-acceptable platform such as secure cloud computing. Third, there is a comprehensive payment system, which includes the following:  
* The customer pays with a credit card after the vendor presents the amount of money to be paid and a reference number.
* Advanced Point of Sale (POS) terminal application: payments can be made via secure browsers.
* All results based on the market trends can be calculated in real-time by big data processing and analysis services (BDPAS).  
The Interpay ecosystem consists of the collection and payment agents to ensure the delivery of web services in the BIMA framework to be smooth and successful. It supports multi-tenancy for hundreds of users, thus lowering operational costs in the long run. The Interpay, as part of the BIMA framework, also suggests that add-ins can be developed and integrated into web services. Additionally, web services can be used as \"Add-ons\" to existing Enterprise Resource Planning (ERP) systems such as Systems, Applications & Products in Data Processing (SAP). Once the web service is integrated into the ERP, it can now collaborate with other ERP modules (i.e., Supplier Relationship Management (SRM), Supplier Customer Relationship Management (SCM), and Customer Relationship Management (CRM)) to manage suppliers and data can be refreshed in almost real time (15-s refresh).  
Figure 4: General network infrastructure (based on information of Interpay).
# Precise Channel Estimation Approach for a mmWave MIMO System
## 7 Simulation Results and Discussion
### System Model  
The HBF system architecture was adopted as described in [45]. A total of 64 transmitting and receiving antennas, i.e., \(\rm N_{T}=N_{R}=64\), were considered at the BS and MS. The antennas were assumed to be in a ULA configuration. Laplacian distributions with 55\({}^{\circ}\) standard deviation were used to produce the Azimuth angle of the AoAs and AoDs. Phase shifters had the quantized phase, whereas spacing between antennas was determined by \(\rm d=\frac{\lambda}{2}\).
## 4 Performance Analysis
### Simulation Results
#### 4.3.2 Convergence Time  
Table 2 demonstrates that EEABR, IACO and SensorAnt generally obtain stable optimal routes after about 130 seconds for a 100-sensor node network scenario. According to these findings, FSACO had 28.8%, 19.7% and 28.4% faster convergence speed than EEABR, IACO and SensorAnt, respectively. As mentioned in Section 2, the ACO-based route discovery algorithm used in EEABR, IACO and SensorAnt is prone to premature, stagnation phenomenon, whilst further route discovery is hindered, which results in sub-optimal routes. In FSACO, we introduced the dynamic crowd factor (Equations (6) and (7)) to help the number of artificial ants in the overcrowded link be controlled. This hybrid scheme could prevent the ants from prematurely aggregating on a path with high pheromone to cause premature routing. Ultimately, FSACO would accelerate the convergence of a stable optimal route, as reported in Table 2.  
Intuitively, as the network scale increased, routing protocols took more time to find the optimal route, which means that the convergence time would increase as a consequence. This has been proven in other applications of ant colony optimisation algorithms [16; 41]. In Figure 2, we can observe that with the increase of network sensor nodes, FSACO, EEABR, IACO and SensorAnt required more time to find the stable optimal route; moreover, FSACO always achieved the fastest convergence speed in different network densities. Furthermore, with the increase of network scale, this difference became more apparent. In a 500-sensor-node network, the convergence performance of FSACO was 31.2%, 22.3% and 29.8% higher than EEABR, IACO and SensorAnt, respectively.  
In order to assess the performance impact of the AFSA optimisation on the FSACO routing algorithm, we divided FSACO into two variants and analysed the different performance in terms of the first route setup time and routing convergence time. The results are reported in Figure 3. Here, original FSACO adopted the hybrid routing algorithm combined with AFSA and ACO, while degenerated FSACO only employed the pseudo random proportional rule in Section 3.4 and  
Figure 1: Impact of network scale on first route setup time.  
global pheromone trail update strategy in Section 3.5. Figure 2(a) demonstrates that the original FSACO had a shorter first route setup time than degenerated FSACO under the different node densities. This result proved that AFSA-based initial route discovery could help the routing algorithm find the feasible route to the sink node quickly. Figure 2(b) indicates that original FSACO sped up the routing convergence time over degenerated FSACO due to the hybrid route discovery algorithm. The reason is that the crowd factor in AFSA could effectively prevent the routing algorithm from falling into a local optimum. Furthermore, this effect was more pronounced in large-scale networks.
## 3 The Diurnal Reference Check (DRC)
### Quality of the L4 SST in the Presence of Diurnal SST Variations  
Figure 7 shows the statistics of \(T_{\text{{NIGHT}}}\)\(-\)\(T_{\text{{L4}}}\), \(T_{\text{{MAX}}}\)\(-\)\(T_{\text{{L4}}}\), and \(T_{\text{{MIN}}}\)\(-\)\(T_{\text{{L4}}}\), averaged over the daily segments with ADS falling within \(D\)\(\pm\) 0.25 K bins in 2012-2018, as a function of \(D\). Figure 7b,c show the corresponding statistics for \(T_{\text{{MAX}}}\)\(-\)\(T_{\text{{NIGHT}}}\) and \(T_{\text{{MIN}}}\)\(-\)\(T_{\text{{NIGHT}}}\).  
In Figure 7a, the median, \(M(T_{\text{{NIGHT}}}\)\(-\)\(T_{\text{{L4}}})\), is as small as 0.01 K, and the RSD, \(S(T_{\text{{NIGHT}}}\)\(-\)\(T_{\text{{L4}}})\) = 0.2 K within the range 0 K \(\leq D\)\(\leq\) 0.5 K. This indicates a close consistency between the \(T_{\text{{NIGHT}}}\) and \(T_{\text{{L4}}}\), when the diurnal warming is limited. However, when \(D\) grows, the \(M(T_{\text{{NIGHT}}}\)\(-\)\(T_{\text{{L4}}})\) decreases and the \(S(T_{\text{{NIGHT}}}\)\(-\)\(T_{\text{{L4}}})\) increases. One concludes that in the presence of the DS, the CMC SST becomes less consistent with the nighttime SSTs and less stable, likely due to residual diurnal variations in the assimilated daytime SSTs.  
In Figure 7b, the two medians, \(M(T_{MIN}-T_{NIGHT})\) and, more so, \(M(T_{MIN}-T_{L4})\), are both negative and decrease when \(D\) increases. This suggests a direct link between larger ADS and more intensive nighttime cooling of the ocean at the buoys' depth. The deviations of \(T_{MAX}\) and \(T_{MIN}\) from \(T_{L4}\) are colder than the corresponding deviations from \(T_{NIGHT}\), consistent with curve 1 in Figure 7a. In Figure 7c, all four RSDs, \(S(T_{MAX}-T_{L4})\), \(S(T_{MIN}-T_{L4})\), \(S(T_{MAX}-T_{NIGHT})\), and \(S(T_{MIN}-T_{NIGHT})\), increase with \(D\). However, the \(S(T_{MAX}-T_{NIGHT})\) and \(S(T_{MIN}-T_{NIGHT})\) are much smaller than the \(S(T_{MAX}-T_{L4})\) and \(S(T_{MIN}-T_{L4})\). Considering that variations of \(T_{IS}-T_{NIGHT}\) are less dependent on the ADS compared to \(T_{IS}-T_{L4}\), we conclude that the comparison of \(T_{IS}\) with \(T_{NIGHT}\) provides a more stable screening of individual \(T_{IS}\) outliers than the comparison of \(T_{IS}\) with \(T_{L4}\).
## 4 Results and Discussion
#### 4.1.4 Cross Validation  
A 5-fold cross-validation was conducted to assess the generalizability and stability of all models. The Random Forest with Extra Trees model achieved the strongest average performance (R\({}^{2}\) = 0.98, MAE = 2.43, MSE = 12.87), followed by Linear Regression (R\({}^{2}\) = 1.00, MAE = 4.44, MSE = 49.68). The ANN model performed well with an average R\({}^{2}\) of 0.95, MAE = 35.24, and MSE = 1842.16, while the LSTM showed good temporal learning capability with an average R\({}^{2}\) = 0.96, MAE = 11.65, and MSE = 860.92. These results confirm the consistency and reliability of the models across different data splits. A comparison table (Table 3) summarizes our work in relation to previous related studies.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 7 Conclusions  
This work explored fundamental principles concerning the baseline crossing phenomenon for a forward scatter radar configuration. It was highlighted that a unique time-frequency behavior occurs, from which deductions about the target location in relation to a specific receiver can be made. Zero Doppler is accompanied by a magnitude reduction at the baseline crossing point. Maximum signal reflection magnitude occurs just before and after a baseline crossing event. Maximum Doppler is displayed at the visible points furthest from the receiver. In addition, the self-mixing signal processing technique was investigated and shown to be an efficient method for extracting Doppler information when utilized in conjunction with analog or digital filters and the FFT. Doppler extraction  
Figure 9: FFT performance at selected SNR values after coherent summation.  
is maximized when using a linear array. When one element records zero Doppler, the other elements will record discrete Doppler differences. Future efforts will be towards target localization and harnessing the Doppler and direction information to build space debris angular tracks.  
Conceptualization, R.M.N.; methodology, J.K.A.H. and R.M.N.; software, J.K.A.H.; validation, J.K.A.H. and R.M.N.; formal analysis, J.K.A.H.; investigation, J.K.A.H. and R.M.N.; resources, R.M.N.; data curation, J.K.A.H.; writing--original draft preparation, J.K.A.H.; writing--review and editing, J.K.A.H. and R.M.N.; visualization, J.K.A.H. and R.M.N.; supervision, R.M.N.; project administration, R.M.N.; funding acquisition, R.M.N. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded through the US Air Force Office of Scientific Research Grant Number FA9550-20-1-0176.  
The datasets presented in this article are not readily available because of sponsor restrictions.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### Digital Holographic Imaging and Analysis  
The cells were allowed to attach for 24 h prior to start of the experiment. For NMuMG cells, compounds were added as described above immediately before imaging. The standard lid of the Petri dish was replaced with a HoloLidTM 71,110 for Petri dishes with 35 mm diameter (Phase Holographic Imaging AB (PHI), Lund, Sweden) or HoloLidTM 71,120 for 6-well plates (PHI). The cells were then imaged using the HoloMonitor(r) M4 with a motorized stage (PHI). The HoloMonitor(r) M4 is a quantitative imaging system based on digital holographic microscopy [24, 26]. For imaging, the software HStudioTM (PHI) was used. Images were acquired at three to five locations (image frames) per well, at time-intervals ranging from every hour to every 24 h, for a total time of 48-72 h.  
The images were analyzed using HStudioTM as follows. First, each image was segmented using thresholding that identified each individual cell in that image. Second, based upon this segmentation, morphological parameters for each cell in each image frame were computationally calculated. In the HStudioTM analysis tool, values for the 27 morphological parameters considered here are presented for each individual cell. To develop the EMP score, we used the average values for each image frame at each timepoint. Data were analyzed for correlations using the free software R (R Core Team, 2015). To generate the score, we first selected four features that positively correlated with mesenchymal conditions in the dataset obtained from a 72-hour time-lapse imaging of NMuMG cells with and without exposure to 0.5 ng/mL TGF\(\beta\): average cell eccentricity, average cell hull convexity, average cell roughness skewness, and average cell optical thickness max. We centered each feature set by dividing each value within that set by the mean feature value in control conditions. We then performedstandard min max feature scaling (Equation (1)) to restrict all values to the range 0-1 that were then summed to generate a score in the range 0-4 with increasing values correlating with a more mesenchymal phenotype.  
\[z=\frac{x-minimum\left(x\right)}{maximum\left(x\right)-minimum\left(x\right)} \tag{1}\]
# Optimized Travel Itineraries: Combining Mandatory Visits and Personalized Activities
## 3 Methodology
### Adaptive Neighborhood Simulated Annealing Algorithm (ANSA)  
#### 3.3.1. ANSA Parameters  
The proposed ANSA algorithm requires five parameters, like those used in SA: \(I_{lit}\), \(T_{0}\), \(T_{f}\), \(\alpha\), and \(K\). The explanations are as follows:  
* The number of iterations (\(I_{lit}\)) performed at a particular temperature. This parameter dictates how many solutions are evaluated and updated before the temperature is reduced according to the cooling schedule. It impacts the depth of the search at each temperature level and helps balance exploration and exploitation.
* Initial Temperature (\(T_{0}\)) is the starting point for the annealing process. It determines the initial level of randomness or the likelihood of accepting worse solutions early in  
Figure 4: Example of the solution.  
Figure 5: Example of the route construction and activity selection.  
the search. A higher initial temperature allows for more exploration of the solution space, while a lower temperature restricts the search more quickly.
* Final Temperature (\(T_{f}\)) is the threshold temperature at which the algorithm terminates. Once the temperature drops below this value, the search process ends, and the algorithm finalizes the best solution found.
* \(\alpha\) is a coefficient that defines how quickly the temperature decreases over time. It controls the rate at which the algorithm transitions from exploration to exploitation. A slower cooling rate allows for more thorough exploration of the solution space, while a faster cooling rate accelerates convergence but may miss better solutions.
* Finally, K is the Boltzmann constant used in the calculation of the probability of accepting a worse solution in ANSA.
# Effects of \"S\"-Type Bowed Guide Vanes on Unsteady Flow in 1.5-Stage Axial Compressors
## 1 Introduction
[24] studied the effect of variable-camber inlet guide vanes on the performance of compressors using a numerical method. Results show that the stall margin and the total pressure rise of the compressor rotor are sensitive to the span range of the variable-camber. [PERSON] et al. [25] studied the effect of bowed outlet guide vanes in gas turbines on the heat conduction and aerodynamic performance near the endwall using numerical simulations. It was found that the bowed vanes can effectively reduce the endwall heat transfer. [PERSON] et al. [26] found that forward-swept and positive-leaned vanes can reduce the exciting force and make the output power more stable for gas turbines using numerical calculations. [PERSON] et al. [27] conducted numerical and aeroacoustic studies on axial flow compressors with different lean angles of the stator, and found that a positive leaned stator has better noise reduction effect than the negative. [PERSON] et al. [28] found that the combination of blade lean and blade clocking can improve unsteady aerodynamic performance and adiabatic efficiency through the numerical simulation of 1.5-stage axial turbines. Through the above research, it can be found that most of the researches are aimed at improving unsteady aerodynamic performance or reducing aerodynamic noise. However, in order to ensure the safe and stable operation of the compressor, the unsteady force on the vane is also very important. In addition, changing the vane stacking line in the tangential direction has a greater impact on the unsteady aerodynamic performance of fluid machinery, and the effect of reducing unsteady aerodynamic force is more obvious. However, most of the designs are leaned vanes and \"C\"-type bowed vanes. This study improved the structure of the guide vane of the axial compressor with an \"S\"-type bowed guide vane, and the improvement is validated on a 1.5-stage axial compressor model. For five guide vane models with different bending coefficients, the unsteady numerical simulation was carried out under design conditions and near-blockage conditions, respectively. The influence of the \"S\"-type bowed guide vane on the aerodynamic performance and the aerodynamic exciting force on moving vanes are both studied to explore the optimal structure of the guide vane of the 1.5-stage axial compressor.
## 6 Conclusions  
An innovative low-orbit satellite network design is put forth in this research. With this design, beam hopping, a crucial regenerative network method, is combined with the benefits of non-regenerative networks. It is the first architecture to support a multi-hop bent-pipe LEO satellite network employing beam hopping in the DVB-S2X protocol. To increase the adaptability of satellite constellation networks, it incorporates beam-hopping into the inter-satellite routing algorithm of LEO satellites. To enable this design's deployment, it also uses AWGR devices. The test results of the vector network analyzer verify 1. a decrease of the phased-array antennas' beam pointing offset during broad-band wide-angle scanning; 2. wavelength control of the antenna pointing direction; and 3. high-precision beam steering over a wide range of scanning angles. This research has great potential application possibilities, it enhances the flexibility of bent-pipe LEO satellite network architecture, reduces the weight of a single satellite, and ensures user data security.  
Conceptualization, S.X., M.Z., P.L. and G.L.; methodology, S.X., M.Z.; experiment, S.X. and M.Z.; validation, S.X., M.Z. and P.L.; investigation, Z.C., F.S., J.J. and H.W.; resources, P.L. and G.L.; writing--original draft preparation, S.X., P.L., M.Z., F.S. and Z.C.; writing--review and editing, M.Z., P.L., C.Z., F.S., J.J., and H.W.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. and M.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National special support plan for high-level talents (no. WRJH19 DH01), and the China Postdoctoral Science Foundation (2020M682863). Not applicable. Not applicable. Not applicable.  
The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.
## 4 Conclusions  
In this study, the accurate identification of the suitable storage status of paddy during storage using NIR (1000-1800 nm) technology combined with the feature selection algorithms of NRS, SPA, and VCPA was investigated. The storage statuses of paddy samples were divided into three grades (suitable storage, mildly unsuitable storage, severely unsuitable storage) based on the values of measured fatty acid as the reference. The different spectral feature wavelengths were extracted separately using NRS-a, NRS-b, SPA, and VCPA. Based on the feature wavelengths, three classifiers of RF, ELM, and SIMCA were used to establish the classification models for identifying the storage statuses of the paddy. The analysis showed that the nine spectral wavelengths selected by the NRS-a algorithm coupled with the RF classifier to build the NRS-a-RF model had the best classification ability, with high CCRs of 96.31% and 93.68% in the calibration and test sets, respectively. Additionally, the indicators of sensitivity and specificity were 0.93-0.99. Thus, the integrated NRS and RF algorithms based on NIR technology could effectively improve the identification ability for paddy storage status compared to other methods. This study is expected to provide theoretical guidelines and new ideas for the development of on-site rapid inspection technology for grain quality and safety.  
Modeling, data testing and validation, writing--original manuscript by [PERSON]; software by [PERSON]; data collection and preprocessing by [PERSON] and [PERSON]; supervision and revision of manuscript by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Special Scientific Research Surplus Fund of the Academy of National Food and Strategic Reserves Administration of China (Item No: Y2302).  
The study did not require ethical approval.  
The study did not involve humans.  
The datasets generated for this study are available on request to the corresponding author.  
The authors declare no conflict of interest.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Combinatorial Fusion Method  
As seen from the above, different fusion methods have different fusion characteristics. The combination fusion method combines two or more methods to take their strengths. For example, the transform domain method can be combined with the region segmentation method, which can extract more details and enhance the fusion efficiency.  
[PERSON] et al. [30] proposed an image fusion scheme based on image cartoon texture decomposition and sparse representation. Aiming at the proposed sparse representation-based fusion method, they trained a dictionary with a strong representation ability to fuse the animation and texture components. [PERSON] et al. [31] proposed a multi-focus image fusion framework based on non-subsampled contourlet transform form (NSCT) and sparse representation (SR). [PERSON] et al. [32] proposed a multi-focus image fusion algorithm based on spatial frequency-driven parameter adaptive pulse-coupled neural network (SF-PAPCNN) and an improved non-subsampled Shear-wave transform (NSST) domain summing modified Laplace Transform (ISML).  
Figure 9: Typical structure diagram of PCNN [61]. Reprinted/adapted with permission from Ref. [61]. 2022, MDPI.