esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Evaluation Parameters and Numerical Modeling
#### 2.2.2 Flexible Hydrofoil Fluid-Solid Coupling Process  
Regarding the flexible deformation of hydrofoils, [PERSON] et al. [10] investigated the effects of parameters such as the active flexible deformation mode, flexibility coefficient, and chord-wise deformation length on the propulsive characteristics of flexible hydrofoils by combining different forms of chord-wise deformation equations. Because in practical applications, active flexibility is usually used for hovering foils and is different from the actual hydrofoil motion. Therefore, in this paper, passive flexible deformation based on the bidirectional fluid-structure interaction technique was used. The deformation of the mesh was determined according to the transient structural force, as shown in Figure 4, which is a schematic diagram of the mesh in the fluid domain and the solid domain during the flexible deformation of the hydrofoil in this study, where \(D_{d}\) is the chord-wise deformation of the hydrofoil in m. The deformation of the hydrofoil when subjected to the fluid force was performed by the transient structural solver. The force on the hydrofoil in the transient structure solver was derived from the fluid domain solver, then the data were transferred to the fluid domain part through the system coupling, and finally the results were calculated to convergence through the coupled iteration method. In addition, since the bidirectional fluid-structure interaction method does not support calculation in a 2D plane, an extra thickness must be extended in the normal direction of the plane; but, in the meshing, a single layer boundary process was carried out in the direction of the thickness to avoid the extra 3D inaccuracy.  
There are currently two main approaches to solving fluid-structure interaction (FSI) problems: monolithic coupling and partitioned iterative coupling. Monolithic coupling constructs coupled equations for both the fluid and solid domains and solves all variables simultaneously within a single time step. However, when the density ratio between the solid and fluid is large, this method requires a high-quality mesh. In contrast, partitioned iterative coupling offers more flexibility in terms of mesh quality requirements and is more widely used in FSI solutions. In partitioned iterative coupling, results are exchanged between the fluid and solid domains via interface coupling equations. Within a single time step, pressure data from the fluid solver are passed to the structural solver and then displacement data from the solid solver are passed back to the fluid solver, achieving bi-directional FSI. This process is illustrated in Figure 5.
###### Abstract  
Delay-and-sum (DAS) beamforming of backscattered echoes is used for conventional ultrasound imaging. Although DAS beamforming is well suited for imaging in soft tissues, refraction, scattering, and absorption, porous mineralized tissues cause phase aberrations of reflected echoes and subsequent image degradation. The recently developed refraction corrected multi-focus technique uses subsequent focusing of waves at variable depths, the tracking of travel times of waves reflected from outer and inner cortical bone interfaces, the estimation of the shift needed to focus from one interface to another to determine cortical thickness (_Ct.Th_), and the speed of sound propagating in a radial bone direction (_Ct.v11_). The method was validated previously in silico and ex vivo on plate shaped samples. The aim of this study was to correct phase aberration caused by bone geometry (i.e., curvature and tilt with respect to the transducer array) and intracortical pores for the multi-focus approach. The phase aberration correction methods are based on time delay estimation via bone geometry differences to flat bone plates and via the autocorrelation and cross correlation of the reflected ultrasound waves from the endosteal bone interface. We evaluate the multi-focus approach by incorporating the phase aberration correction methods by numerical simulation and one experiment on a human tibia bone, and analyze the precision and accuracy of measuring _Ct.Th_ and _Ct.v11_. Site-matched reference values of the cortical thickness of the human tibia bone were obtained from high-resolution peripheral computed tomography. The phase aberration correction methods resulted in a more precise (coefficient of variation of 5.7%) and accurate (root mean square error of 6.3%) estimation of _Ct.Th_, and a more precise (9.8%) and accurate (3.4%) _Ct.v11_ estimation, than without any phase aberration correction. The developed multi-focus method including phase aberration corrections provides local estimations of both cortical thickness and sound velocity and is proposed as a biomarker of cortical bone quality with high clinical potential for the prevention of osteoporonic fractures.  
m 2022
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 6 Conclusions  
In this paper, we propose an alternative method to reconfigure the number of RFB sub-bands based on spectrum-sensing results and sub-bands ISP. First, a P-DFTFB is especially designed for uniform division of the wideband signals into multiple sub-band signals, and the energy detection method is applied to confirm the occupancy status of each sub-band. Moreover, the number of RFB sub-bands is reset to come up with the ISP of the current detection. That is, for a higher sub-band ISP, more RFB sub-bands may be divided to achieve the required detection performance. If the ISP is low, the number of RFB sub-bands would be reduced to ease the burden of calculation. The simulation results show that the proposed wideband spectrum-sensing method with a RFB outperforms the energy detection method. Specifically, the number of RFB sub-bands may well be adjusted with the ISP, and ultimately, will greatly improve the detection performance along with less calculation work.  
[PERSON] designed the RFB and wideband spectrum-sensing method, and wrote the paper. [PERSON] helped with analysis the sensing model. [PERSON] helped to perform the data processing and simulation model. [PERSON] helped with data analysis and the editing of this paper.  
This research is funded by the National Key Research and Development Project under Grant 2016 YFF0104000, in part by the Science and Technology Planning Project of Sichuan Province under Grant 2019 YJ0309, in part by the Sichuan Education Department Funded Project under Grant 18 ZB0611, in part by the Longshan Academic Research Support of Southwest University of Science and Technology under Grant 17 LZX318 and 17 LZX319.  
We are grateful for the Spectrum Data Archive of the Institute of Networked Systems at RWTH Aachen University for providing wireless data.  
The authors declare no conflict of interest.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 2 Preliminaries
### Measurements with Multiple-Step Random Delays  
The measurements obtained from Equation (2) need to proceed to the data processor (filter). However, the phenomenon of measurements transmission along unreliable communication channels, due to the occurrence of equipment failures, bandwidth limitations, environmental disturbances [32], etc., could not be ignored, as these delays result in the ideal measurements \(z_{k}\) generated by the sensors and the actual measurements \(y_{k}\) received by the filter becoming asynchronous. Figure 1 represents a schematic diagram of the system with multiple-step randomly delayed measurements, where the filter performs state estimation (SE) at point A and the associated measurements should reach the buffer before A. Due to the unreliability of the data transmission channel, there is one-step or multiple-step delay in the measurements received from sensor 1, where the solid line indicates that the measurements are synchronized and the dashed line indicates that the measurements are delayed. In the case of the \(d\)-step delay, the filter receives the actual measurements \(y_{k}\), which could be \(z_{k-i}(0\leq i\leq d)\). Therefore, the actual measurements \(y_{k}\) can be described as follows:  
\[\begin{array}{l}y_{k}=(1-\tau_{1})z_{k}+\tau_{1}(1-\tau_{2})z_{k-1}+\tau_{1 }\tau_{2}(1-\tau_{3})z_{k-2}+\\ \cdots+\left(\prod\limits_{s=1}^{d-1}\tau_{s}\right)(1-\tau_{d})z_{k-d+1}+[1-( 1-\tau_{1})-\\ \tau_{1}(1-\tau_{2})-\cdots-\left(\prod\limits_{s=1}^{d-1}\tau_{s}\right)(1- \tau_{d})]z_{k-d}\\ =\sum\limits_{s=0}^{d}\tau^{(s,j)}z_{k-s}\end{array} \tag{3}\]  
where \(\tau_{0}=1\) and \(\tau_{i}\in[0,1]\) are independent Bernoulli random variables, which satisfy the following:  
\[\begin{array}{l}p(\tau_{i}=1)=E(\tau_{i})=p_{i}\\ p(\tau_{i}=0)=1-p_{i}\\ E\Big{[}(\tau_{i}-p_{i})^{2}\Big{]}=(1-p_{i})p_{i}\end{array} \tag{4}\]  
where \(p_{i}\) is the expectation of \(\tau_{i}=1\). \(\tau^{(s,j)}\) is then the following:  
\[\tau^{(s,j)}=\left\{\begin{array}{ll}\left(\prod\limits_{j=0}^{s}\tau_{j} \right)(1-\tau_{s+1}),&0\leq s\leq d-1\\ \prod\limits_{j=0}^{d}\tau_{j},&s=d\end{array}\right. \tag{5}\]  
The probability of delayed measurements for \(s(0\leq s\leq d)\) step [33] is the following:  
\[\begin{array}{l}p^{(s,j)}=\left(\prod\limits_{j=0}^{s}p_{j}\right)(1-p_{s+1 }),\quad s=0,1,2,\cdots,d-1\\ p^{(d,j)}=\prod\limits
## 2 Determination of Computational Model
### Grid Segmentation and Irrelevance Validation  
The mesh delineation of the flow field and the structural field is the key to realizing the two-way fluid-solid coupling, and the data transfer in the fluid-solid coupling is transmitted through the mesh on the fluid-solid coupling surface. For the completely corresponding mesh, the data can be transferred well, and this paper adopts the Mesh module in ANSYS Workbench for unstructured meshing. Due to the complex and irregular structure of the wind turbine as a whole, the structural field is delineated with a more adaptive unstructured mesh in Meshing. The flow field mesh is meshed according to the rotational and non-rotational domains to simulate the rotational effect of the complex impeller surface structure. While the rotational domain is finely meshed, the air domain is generated with a lower-density mesh using adaptive meshing techniques.  
By constantly adjusting the number of grids and non-constant calculation of their output power, the difference in the calculated output power of different grids is not very large, with a maximum error of 0.69%, while meeting the requirements of grid quality Max = 1, Average = 0.85, and Skewness = 0.81, so the final number of grids is 4.67 million.  
After verifying the grid independence of the structural field model for calculating the deformation and equivalent force under rated wind turbine operating conditions, it was found that the maximum equivalent force and the maximum deformation of the blades stabilized and no longer increased significantly when the number of grids was increased to 210,000 under the rated wind speed condition. Therefore, it was decided to determine the number of grids in the structural field of the solid wind turbine model as 210,000.
# Ensemble-Based Knowledge Distillation for Video Anomaly Detection
## 3 Proposed Approaches
### Confidence Based Maximum Selection (CBMS) Approach  
In this approach, a selection mechanism has been integrated within the loss function compared to the EWC approach as shown in Figure 3:  
\[L_{Total}=(1-\alpha)*L_{GT}+L_{CBMS} \tag{3}\]  
Figure 2: Visual overview of Equally Weighted Combination approach.  
\[L_{CBMS}=\alpha*argmax(H(\sigma(Z_{t_{1}};T=\tau), \tag{4}\] \[\sigma(Z_{s};T=\tau)),H(\sigma(Z_{t_{2}};T=\tau),\sigma(Z_{s};T= \tau)))\]  
\[C_{T_{i}}=\begin{cases}\frac{0.5-\sigma_{T_{i}}}{0.5}&\text{if $\sigma_{T_{i}}<0.5$}\\ \frac{\sigma_{T_{i}}-0.5}{0.5}&\text{if $\sigma_{T_{i}}\geq 0.5$}\end{cases} \tag{5}\]  
where \(C_{T_{i}}\) represents the confidence scores for the GCN and AR-Net teacher models calculated from sigmoid outputs (\(\sigma_{T_{i}}\)) of the GCN and AR-Net teacher models. Because directly using sigmoid outputs is inappropriate, this transformation method is designed.  
Unlike our previous approach, CMBS deploys a selection process, in which the approach forces the student model to choose a single feedback signal from one of the teacher models (GCN or AR-Net) for a specific input sample. We designed this selection mechanism by comparing confidence scores obtained from teacher models' sigmoid outputs and choosing the teacher model with a bigger confidence score for information transfer to the student model. Since directly using the sigmoid output of a teacher model is not appropriate for determining confidence level, we developed a special function (Equation (5)) that transforms the sigmoid output of each teacher model to a confidence score, which represents how much each teacher trusts itself for determining the class (anomalous or normal) of a specific sample. Please notice that the function naturally reaches its minimum value on the \"0.5\" sigmoid function value, which represents the lowest level of confidence and reaches its maximum values on \"0\" and \"1\" sigmoid function values, which represent the highest level of confidence.  
Compared to the EWC approach, this method provides a student model to select one of the teacher models (GCN or AR-Net) with respect to their current confidence scores calculated on their end outputs.
## 3 Enhanced Interpolation Approach
### Time-Frequency Decoupling with POD Interpolation
Once \(a_{q}^{jj}(\widetilde{t})\) and \(\Phi_{q}^{jj}(\widetilde{\omega})(q=1,2,\ldots,N_{\Phi})\) were determined, the interpolation approach could be used to approximately estimate \(a_{q}^{jj}(t)\) and \(\Phi_{q}^{jj}(\omega)(q=1,2,\ldots,N_{\Phi})\). Then, the square root of the EPSD at all time-frequency points could be reconstructed by  
\[\sqrt{S_{jj}(\omega,t)}\approx\sum_{q=1}^{N_{\Phi}}a_{q}^{jj}(t)\Phi_{q}^{jj} (\omega),\,j=1,2,\cdots,n \tag{34}\]  
Figure 1 depicts the aforementioned decoupling procedure using POD interpolation. It can be seen that there were mainly three steps for the decoupling procedure. First, the square root of the EPSD at the time-frequency interpolation points \(\sqrt{S_{jj}(\widetilde{\omega},\widetilde{t})}\) was calculated. Then, \(\sqrt{S_{jj}(\widetilde{\omega},\widetilde{t})}\) was decoupled as some time and frequency functions \(a_{q}^{jj}(\widetilde{t})\) and \(\Phi_{q}^{jj}(\widetilde{\omega})\) with POD. Finally, the time and frequency functions \(a_{q}^{jj}(t)\) and \(\Phi_{q}^{jj}(\omega)\) could be obtained with interpolation. POD is only utilized for \(N_{\widetilde{\omega}}\times N_{\bar{t}}\) dimensional small matrices and only \(2 nN_{\Phi}\) one-dimensional interpolations are required in this approach while it was on \(N\times M\) dimensional matrices in the existing study [21; 28]. As a result, POD interpolation outperformed previous time-frequency decoupling approaches.
## 1 Introduction
The temporal and spatial variation of rain height plays a significant role in the determination of the magnitude of rain-induced attenuation which is responsible for CPA [20; 21; 22]. Hence, it is important to consider the impacts of seasonal variation of rain heights and seasonal rain-induced attenuation on CPA in any location. This is because CPA quantifies the loss of power in the co-polarized component due to rain-induced attenuation. Cross-polarization discrimination (XPD) is a parameter used to quantify the ability of a system to distinguish between the desired polarization (co-polarization) and the undesired orthogonal polarization (cross-polarization) [24]. Proper knowledge of XPD due to rain is crucial in wireless networks, especially in dual-polarized satellite communication systems, where one polarization channel is used for transmission and the orthogonal one for reception. Such systems are bound to experience interference between the two channels due to cross-polarization caused mainly by raindrops. The instantaneous XPD of a communication system is greatly influenced by the prevailing CPA, which is a function of the atmospheric conditions. For instance, XPD reduces drastically during heavy thunderstorms due to high CPA and vice versa. As CPA increases due to rain, XPD decreases, leading to potential interference and signal degradation. By understanding and mitigating these effects, system performance can be optimized for reliable communication under varying atmospheric conditions. Hence, it is crucial to estimate the required XPD needed for optimum satellite communication services in any geographical location. The ITU-R P.618 recommendation incorporates polarization effects in satellite link design and acknowledges the advantages of circular polarization in mitigating depolarization and maintaining high XPD values. The recommendation is updated periodically based on recent research findings. The latest version, ITU-R P618-14 [23] was used for this study.
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 4 Results
### Estimated Performance Evaluation of Critical Latency
#### 4.3.1 Evaluation of Estimated Performance
In Figure 13(b), OS-TS relative distance RD\({}_{\text{DR}}\) is shown to converge at 0 (zero) at Points M (7.08 min) and C (7.62 min), and it is shown to appear earlier at Point M than at Point C. In addition, the time difference between M and C was 0.54 min (7.62\(-\)7.08), which was the smallest in all scenarios.
# Comparative Studies on Nanocellulose as a Bio-Based Consolidating Agent for Ancient Wood
## 1 Introduction
(2017) [19]; alternatively, even the addition of alkaline nanoparticles in these products, such as calcium or magnesium hydroxide, provides an effective action for the deacidification of paper and canvas, as shown by [PERSON] et al. (2002), or for the deacidification of waterlogged wood, as described by [PERSON] et al. (2016) [20; 21]. However, these types of chemical products very often do not protect cellulosic materials from aging processes and introduce a lot of problems with toxicity, being harmful to human health [22; 23]. In this context, CNC represents the optimal solution for overcoming these problems since, thanks to its affinity with the initial degraded materials, it certainly gives better results compared to synthetic products, and moreover, it is not toxic and does not alter the visual aspect of artistic surfaces [24]. The fields of application for this innovative material and its composite derivatives are numerous [11], such as the deacidification and consolidation of strongly degraded cellulosic artworks [16], the recovery and consolidation of decayed wood [25] and waterlogged wooden artifacts [15], and structural reinforcement for canvases degraded by aging [17]. For example, recent studies by [PERSON] et al. (2022) demonstrated the protective capacity of green nanocomposites obtained by combining cellulose nanofibrils (CNF), cellulose nanocrystals (CNC), and lignin nanoparticles [26]. Other studies on the use of nanocellulose as a filler for nanocomposite materials have been conducted by [PERSON] et al. (2023), which demonstrated the improvement of mechanical strength in wood artifacts coated with a Klucel E/CNC film. More recently, the same research team ([PERSON] et al., 2024) showed the possibility of using cellulose derivatives, in particular ethers and esters, for the synthesis of nanocomposites useful for the treatment of archaeological wood [27; 28]. This work aimed to evaluate the efficacy of CNC in the consolidation treatment of degraded wood compared with traditional products such as acrylic, aliphatic, and aromatic resins, which often alter the visual aspect of artistic surfaces. On the contrary, nanocellulose, with its excellent properties and its non-toxicity, seems to be a promising candidate as a bio-inspired consolidating agent for wood, with better yields in terms of its stiffness and homogeneity with the starting material [25]. First of all, in this work, CNC was selected as a renewable bio-product, and it was applied as a reinforcing and protective agent on decayed wooden sheet samples with a thickness of 1-2 mm, and was strongly perforated by woodworm [25; 29].
## 5 D-MSR Management Phases
### Coping with Dynamicity, Reservation Conflict and Interference in the Network (Phase-6)  
#### 5.6.1 Coping with Dynamicity in the Network  
In order to cope with network dynamicity, such as node or edge failure, the connection manager in a node (_i.e._, incident nodes of the broken edge or adjacent nodes of the failed node that are part of end-to-end connections), transmits _Release_ message(s) toward the source node(s) or destination node(s) by applying the D-SAR protocol. In case of edge failure, the incident nodes of the broken edge (node A and B in Figure 14) transmit the _Release_ messages, including the traffic ID information, toward the end-points of each connection that passed through the broken edge.  
The process of releasing the reserved communication resources, which is identified by the traffic ID, is executed for each of the connections containing the broken edge. At this stage, all the resources previously allocated to that connection will be released and become free. This means that the related links and superframes are deleted from the communication tables of each device in the former route.  
Figure 14: Releasing the allocated communication resources after edge failure.  
Figure 13: ATM concepts in link definition between nodes A and B.  
As Figure 15 illustrates, in case of node failure, the adjacent nodes which joining edges are part of an end-to-end connection, release the allocated resources by transmitting the release messages toward the sources or destinations of the end-to-end connections. Exchanging the _Release_ and _Release Complete_ messages and releasing the resources follows the same procedure of edge failure.  
The routing layer repairs the former routes. Upon receiving the _Release_ message, the connection manager in the source node will re-establish a new connection and reserve the new resources along the new path once more by using the D-SAR protocol.  
In case of a node or an edge failure in centralized approaches like WirelessHART or ISA100.11a, the failure should be reported to the network manager. Subsequently, the network manager establishes new routes, releases the previous communication schedule, and constructs new schedules.
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 3 Results
### Comparison of ELISA and Folin-Ciocalteu Method  
Figure 4A represents the correlation between IF concentrations obtained from soybean extracts by ELISA and the phenolic compound concentrations measured by the Folin-Ciocalteu method. It can be seen that the data points are completely dispersed, without any apparent correlation. In fact, it is impossible to quantify the reduction of IFs in soy matter using the Folin-Ciocalteu method. On the contrary, in Figure 4B, which represents the ELISA results plotted against the Folin-Ciocalteu data obtained from treatment water samples, the correlation is strong (R = 0.995). Interestingly, and confirming this result, the correlation between the decrease rate of the total IFs in dehulled beans determined by the ELISA and the polyphenol content in treatment water determined by the Folin-Ciocalteu method is significant, as shown in Figure 4C. Additionally, it is noted that using magnetic stirring, the R correlation coefficient is 0.946, while as mentioned previously, data obtained using orbital stirring are inconsistent.  
Figure 3: (**A**) Water and mass evolutions during the thick bed-drying process. (**B**) Evolution of the bed height during drying. Operating conditions: air temperature 55 \({}^{\circ}\)C; air velocity 1.5 m/s; initial water activity (Wini) 65.0% and final water activity (Wfin) 8.3%.