esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Image Processing: Image Segmentation and Geometrical Modelling  
The patient underwent pre-operative computed tomography (CT) of the pelvis, sacrum and proximal femur, and magnetic resonance imaging (MRI) of the pelvis regions involved in the tumor lesion. The acquired images were exported in Digital Imaging and Communications in Medicine (DICOM) files and imported into an image visualization and processing software, Amira (Zuse Institute Berlin ZIB, Dahlem, Berlin, Germany--Thermo Fisher Scientific, Waltham, MA, USA). A semi-automatic segmentation was performed (Figure 1A) [40], for anatomical structure reconstructions. For every slice of the scan, the external surface of the bones and the tumor were identified and depicted, to obtain their 3D models (Figure 1B) by merging these 2D segmented silhouettes.  
Several tools of the Amira software package were used to obtain accurate patient-specific models of the above reported anatomical structures. In more details, among these tools, thresholding was preferred, according to the Hounsfield Unit (HU) values for the bones and the tumor, which was set slice by slice, also depending on the quality of the corresponding CT or MRI medical image. The 3D model of the bones was generated from segmentation of the former (Figure 2A), the 3D model of the bones and tumor was generated from segmentation of the latter (Figure 2B). The full segmentation and modelling process was performed by a single expert operator. These two 3D models were exported in binary stereolithography format (STL), and then imported into a reverse engineering software, Geomagic Control (2014.3.0.1781, 3D Systems, Rock Hill, SC, USA).  
Figure 1: The process of geometrical modeling of the bone shape: image segmentation in the 2D images (**A**) and the final 3D model (**B**) via Amira software.
# Research on Variable-Step-Size Adaptive Filter Algorithm with a Momentum Term
## 1 Introduction
The adaptive filtering algorithm shares a core principle with the backpropagation (BP) neural network, in which a cost function is constructed using the error, and the optimal weight coefficient that minimizes the error is obtained through iterative solutions [22]. One specific algorithm proposed in this paper is the variable-step-size adaptive filter algorithm, which incorporates the logistic function and a momentum term. This algorithm builds upon the standard LMS algorithm and derives a new weight update formula under the Lagrange function constraint, i.e., the normalization LMS algorithm (NLMS). Due to the inherent contradiction between the convergence rate and convergence error in the LMS algorithm, the logistic function model is introduced into the NLMS algorithm as a function of the step size varying with the error, leading to the variable-step normalization LMS algorithm (VSNLMS). Numerical examples demonstrate that the VSNLMS effectively reduces the convergence error. Furthermore, a momentum term is introduced during the weight iteration process of the VSNLMS algorithm, which consists of a forgetting factor and historical weight. This enhancement enables the algorithm to converge faster, resulting in the momentum-variable step normalization LMS algorithm (M-VSNLMS). Through numerical examples, it is demonstrated that this algorithm achieves a smaller convergence error and shorter steady-state convergence compared to those of the LMS algorithm. To verify the applicability of the proposed algorithm in practical applications, system identification is performed on a dynamic system mathematical model that incorporates noise. The results show that the proposed algorithm exhibits a higher convergence rate and smaller convergence error than those of the traditional LMS algorithm. A system identification was conducted on a dynamic system in the Aeroelasticity Laboratory of the China Aerodynamics Research and Development Center (CARDC). The results showed that the data fitting accuracy of the algorithm was higher than that of the LMS algorithm and traditional N4 SID and ARMAX algorithms; thus, it has value in engineering applications and provides strong support for the subsequent implementation of active control.
# An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet
## 4 Experiment and Results Analysis
### Results Analysis  
We evaluated the proposed experimental method through a large number of experiments, and compared them quantitatively through PSNR, SSIM, and UQI indicators. The higher the three values, the better the image enhancement effect.  
PSNR is used to measure the difference between the two images. These include compressed image and original image, evaluation of the quality of the compressed image, restoration image and real image, evaluation of restoration algorithm performance, etc. The formula is:  
\[\text{PSNR}=10\times\text{log}\Bigg{(}\frac{\text{MaxValue}^{2}}{\text{MSE}} \Bigg{)} \tag{4}\]  
where MSE is the mean square error of two images; MaxValue is the maximum value of image pixels.  
Structural similarity is the most widely used standard for referenced image quality evaluation; SSIM is used to highlight the brightness, contrast, and structural similarity between two images. The value range is 0-1; the closer the value is to 1, the more similar the two images are. Assuming that \(x\) and \(y\) are two input images, the formula is:  
\[\text{SSIM}(x,y)=[l(x,y)]^{a}[C(x,y)]^{\beta}[S(x,y)]^{\gamma} \tag{5}\]where \(l(x,y)\) is brightness comparison, \(C(x,y)\) is contrast comparison, and \(S(x,y)\) is structural comparison. \(l(x,y),\ C(x,y),\ \text{and}\ S(x,y)\) are the following formulas, respectively:  
\[I(x,y)=\frac{2\mu_{x}\mu_{y}+c_{1}}{\mu x^{2}+\mu y^{2}+c_{1}} \tag{6}\]  
\[C(x,y)=\frac{2\sigma_{xy}+c_{2}}{\sigma x^{2}+\sigma y^{2}+c_{2}} \tag{7}\]  
\[S(x,y)=\frac{\sigma_{xy}+c_{3}}{\sigma_{x}\sigma_{y}+c_{3}} \tag{8}\]  
In the formula, \(\mu_{x}\) and \(\mu_{y}\) respectively represent the average value of the two images, \(\sigma_{x}\) and \(\sigma_{y}\) represent the standard deviations of the two images, \(\sigma_{xy}\) represents the covariance of the two images, \(c_{1}\), \(c_{2}\) and \(c_{3}\) are constants to avoid 0 in the denominator.  
The UQI is also called the image universal quality index. It can be easily calculated and applied to a variety of image processing applications, mainly combined with three factors to calculate: correlation loss, brightness distortion, and contrast distortion.  
The calculation formula of the UQI index is as follows:  
\[\text{UQI}=\frac{4\sigma_{xy}\overline{xy}}{\left(\sigma_{x}{}^{2}+\sigma_{y} {}^{2}\right)\left[\left(\overline{x}\right){}^{2}+\left(\overline{y}\right) {}^{2}\right]} \tag{9}\]  
where \(x=\{x_{i}|i=1,2,\ldots,N\}\), is the original image signal; \(y=\{y_{i}|i=1,2,\ldots,N\}\), is the test image signal.  
\[\overline{x}=\frac{1}{N}\sum_{i=1}^{N}x_{i},\ \overline{y}=\frac{1}{N}\sum_{i=1}^{N }y_{i} \tag{10}\]  
\[\sigma_{x}{}^{2}=\frac{1}{N-1}\sum_{i=1}^{N}(x_{i}-\overline{x}){}^{2},\ \sigma_{y}{}^{2}=\frac{1}{N-1}\sum_{i=1}^{N}(y_{i}-\overline{y}){}^{2} \tag{11}\]  
\[\sigma_{xy}=\frac{1}{N-1}\sum_{i=1}^{N}(x_{i}-\overline{x})(y_{i}-\overline{y}) \tag{12}\]
# JIT Production Strategy and Maintenance for Quality Deteriorating Systems
## 4 Simulation-Optimization Approach  
This section discusses the simulation-optimization approach used to determine the optimal control parameters. In particular, our approach combines simulation and optimization techniques to accurately model the stochastic behavior of the production system. We combine an analytical model with the descriptive capacities of simulation techniques, and statistical analysis based on the design of experiments and response surface methodology. This approach has been successfully applied in cases where analytical solutions are not available, such as in [PERSON] et al. (2017). We adopted a simulation-optimization approach because it provides effective capabilities of modeling and modifying complex systems, such as those studied in this paper. Additionally, such an approach is closely related to stochastic systems and so they are appropriate when studying real-world phenomena, as stated in [PERSON] et al. (2018) and [PERSON] et al. (2019). With the proposed approach, we managed to implement the stochastic nature of real production systems. The approach is applied to the control problem of Section 3 and consists of the following sequential steps:  
* Step 1-Mathematical model: In this step, we provide rigorous modeling to analytically formulate the production system under study, and define the decision variables, the objective function to be minimized, and the control parameters of the joint control policy.
* Step 2-Simulation model: We develop a combined discrete-continuous simulation model that uses the control parameters \((Z_{p},B_{JIT},A_{0})\) as inputs to evaluate the stochastic behavior and the performance of the production system, defined in this case by the total incurred cost. The complete description of the simulation model is provided in the next section.
* Step 3-Statistical analysis: In this step, we perform a design of experiments (DOE) to fit the total incurred cost reported from the simulation model with a second-order regression model. Additionally, the obtained regression model is statistically analyzed to determine the main effects and significant interactions that must be included in the optimization phase.
* Step 4-Optimization: The obtained cost regression model is optimized in this step within the feasible domain of the control parameters, defining the optimal values of \(\left(Z_{p}\ast,B_{JIT}^{\ast},A_{0}^{\ast}\right)\) and the optimal total cost. In this step, the optimal control policy is also analyzed extensively in a sensitivity analysis to validate the obtained results.
## 1 Introduction
Furthermore, adults' preference for cross-sensory mappings has been discovered in speed discrimination tasks as well [13]. Uncertainty remains as to whether object brightness and sound pitch independently cue mass in collision events and as a result affect adults' perception of dynamic events. The cross-sensory literature highlights the association between object brightness and pitch sound suggest a pairing based on similarity in weight or mass [9; 10; 11; 12]. Moreover, adults consider object properties and masses in assessment of object momentums in dynamic events [22; 23]. [PERSON] and [PERSON] [22] demonstrated material properties (namely mass and elasticity) of objects influenced adults' judgments of collision event outcomes. For example, collisions between objects of low elasticity such as 'plasticine' were expected to produce less displacement than collisions between objects of high elasticity such as 'wooden' objects [22]. Similarly, [PERSON] and [PERSON] [23] reported similar links between judgements of elasticity of an object and momentum patterns of that object. Other object properties and masses are also considered in adults' assessment of object momentums in dynamic events [22]. However, little is known about adults' perception of the mass cues of object brightness and pitch sound in collision events, and how these influence judgements of object momentums in dynamic events. For example, how far an object of a certain brightness or pitch sound associated with a certain mass can displace another fixed property object. This paper aimed to investigate whether adults make brightness-mass and pitch sound-mass associations in computer-generated collision events. Specifically, we tested whether dark coloured objects and objects that elicited low pitch sounds would be expected to displace another object further than bright coloured objects and objects which elicited high pitch sounds. The present investigation considered the object properties brightness and pitch sound, designed as a precursor for an infant audience. Thus, the two experiments with adults herein served as a validation process prior to use with infants. The experiment followed a child friendly design by [PERSON] and [PERSON] (2016), but a computer-generated approach similar to [PERSON] et al. (2016). In this context, adults were presented with two different collision outcomes for each object property; a shorter and longer travelled distance of stationary object after collision with an object. In a collision event between object A (agent) and object B (patient), we tested whether object A brightness (Experiment 1) or the pitch of sound it produced (Experiment 2) would affect adults' judgement of the exerted force on object B. We hypothesised a priori collision event outcomes aligned with cued mass of object A (through brightness or sound pitch) would be rated higher (i.e., more likely) than collision event outcomes that did not. Adults rated events on a Likert scale based on how real-life they were from 1 (very unlikely) to 10 (very likely).
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 5 Computational Setup  
### Flow Solver and Mesh Configuration  
The 2D axisymmetric NS equations are solved by using an in-house code EllipSys2D [44; 45] developed at Technical University of Denmark (DTU). The code is a general incompressible flow solver with multi-block and multi-grid strategy. The equations are discretized with a second-order finite volume method. In the spatial discretization, a central difference scheme is applied to the diffusive terms and the QUICK (Quadratic Upstream Interpolation for Convective Kinematics) upwind scheme is applied to the convective terms. The SIMPLEC (Semi-Implicit Method for Pressure-Linked Equations) scheme is used for the velocity-pressure decoupling. The turbulence flow is simulated using the method of Reynolds-averaged Navier-Stokes equations (RANS) in which the \(k\)-\(\omega\) turbulence model of [PERSON] [46] is employed with a modification for a better simulation of the turbulence quantities in the free-stream flow [47; 48].  
The coordinate for the AD/NS simulations is defined in the \(z\)-\(y\) plane. A computational mesh is generated, as shown in Figure 3 where the inflow, outflow and axisymmetric boundaries are indicated, the length of the computational domain is 30, which is nondimensionalized with the rotor radius. The blade is positioned at \(z=0\) and the grid cells are clustered around \(z=0\) and \(y=1\) to ensure a better resolution near the blade tip. The mesh is composed of four blocks where \(64\times 64\) grid points are used for each block. There are 64 grid points on the AD along the radial direction, and 20 points in the range of [-0.02, 0.02] in the axial direction where the Gaussian distribution of the body force plays a significant role. Since the axisymmetric boundary condition is applied, the 2D flow solution is regarded as an azimuthal slice of a full 3D field.  
The above computational setup has been proven to perform well in AD/NS simulations, as shown in the previous work of [PERSON] et al. [49] where both blade loads and wake flows were validated against experiments.
## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring
### Persistent Scatterer Interferometry
The method involves the following steps, as reported in Figure 7:  
* co-registration and interferograms generation;
* removing the topography phase terms using an external DEM;
* identification of the PSC points from the SAR amplitude statistics by computation of the Amplitude Dispersion Index (ADI);
* using the PS points to detect linear deformations and atmospheric effects;
* generating the APSs for each interferogram;removing the atmospheric noise contribution from each interferogram;
* computing differential phase interferograms, identifying PS points through phase statistics and coherence values;
* geocoding phase: converting the outputs in geographical coordinates and exporting the outputs into other formats (e.g., kml, csv, shp), for further incorporation into a GIS platform. The PSI technique allows measurement of the rate of deformation along the LOS of the sensor, i.e., the direction connecting the sensor to the object, related to the incidence angle of the SAR sensors (Figure 8). Several approaches have been presented to solve this problem using the decomposition of the velocity vector combining SAR data collected in different acquisition geometries, (i.e., ascending and descending) [79].
# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 4 Results and Discussion  
In our previous work [9], we report on an initial evaluation of the BF approach. Hereafter, referring to [9], we further analyze our system with a real-world dataset to verify our initial findings that maintaining a BF structure does not incur a significant burden in terms of processing, memory, and bandwidth consumption and can be used for encoding MCS data in Section 4.1. In addition, we include novel results and evaluate the overall behavior of the proposed BF algorithm for different input scenarios by considering the overhead incurred in communication between ME host and end-user devices when the user changes its location or transmits redundant data readings in Section 4.2. We have also extended our initial solution so that multiple data readings of the same type can be accepted simultaneously on the user device and the ME host.  
We used a real-world dataset presented in [10] for the evaluation purpose, which contains locations collected from 85 users in South Korea from March 2011 to September 2012. On average, a single user utilized a mobile application for tagging sites for 79 days. In total, users have tagged 13,500 different locations. The dataset was initially designed for autonomous place detection. Therefore, we had to modify it to model the motions of a broad user base suitable for MCS deployments. We note that the dataset does not include information about energy consumption. First, we initially filtered the dataset to exclude entries that did not have an accurate location or timestamp associated with them. There were 151,649 user check-ins from 67 different individuals in the filtered dataset. Because the number of unique users is too small to analyze a realistic MCS scenario, we separated the filtered set based on user identity and date. As a result, we generated numerous virtual user traces from a single genuine user's trace for a single day. Each slice (a user-day slice is a sequence of user check-ins for a single day) represented a virtual user's movement pattern for that day. Our evaluation is based on a total of 7724 virtual users and their accompanying user-day traces. We constructed additional check-ins (similar to the linear interpolation method presented in [51]) with a sampling frequency of 1 min by interpolating the expected user location between two consecutive check-ins because the data collection did not contain user location information with a high sampling frequency as required in MCS. That resulted in a realistic MCS data-trace with 4.7 million user-location records in total (on average, 600 location entries per user).
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
## 5 Numerical Simulation of Thermal-Structural Coupling of the Subsea Connector  
The simulation analysis of the coupling stress of the subsea connector requires that loads such as oil and gas temperature, pre-tightening force, and oil pressure be applied to the connector, which increases the complexity of the numerical simulation. The simulation analysis model of the subsea connector is limited by constraints, with a degree of freedom of 1 and set as a 2D axisymmetric structure. The plane has 912 elements and 3165 nodes, of which the element is mainly \"quad 8\". This article first carried out a numerical simulation of the temperature field of the subsea connector and then the node temperature of each component was used as a static load and was applied to the connector with the rest of the load. The thermal-structural coupling stress state of the connector under the action of the steady-state temperature field was obtained by statics. In the simulation, F22 material was used for hubs and fingers, and Incoloy 825 nickel-chromium iron corrosion resistant alloy material was selected for the sealing gasket.
## 3 Suppression Methods for Low-Frequency Pressure Fluctuation
### Active Control Methods  
The active control method for low-frequency pressure fluctuations in open-jet wind tunnels typically involves the use of loudspeakers and movable flaps to inject energy into the flow field, thereby controlling the vortex structure and altering the frequency and intensity of the edgetone feedback. Alternatively, anti-phase sound waves emitted by loudspeakers can be used directly to eliminate pressure pulsations at specific frequencies [19; 27].  
The literature [26; 32] presented an active control system known as ARC (Active Resonance Control), which consists of a plenum-mounted microphone to capture pressure pulsations, a wall-mounted loudspeaker on the wind tunnel circuit, and a signal conditioner to adjust the phase relationship between the microphone signal and the loudspeaker output. Experimental evaluations conducted in both the Audi aeroacoustic wind tunnel and its scaled wind tunnel in Germany showed that the ARC system effectively eliminated low-frequency pulsations caused by pipe resonances without introducing additional noise into the wind tunnel. However, it should be noted that this system is expensive and requires a sophisticated control scheme [32].  
[PERSON] et al. [19] proposed an active flow control method that incorporated electrically driven movable flaps (FKFS flaps) at the nozzle of the IVK-MWT wind tunnel. By inducing forced perturbations in the shear layer at a frequency different from acoustic resonance, these movable flaps prevented vortex shedding from synchronizing with acoustic resonance, resulting in a reduction of the pressure pulsation amplitude from 134 dB to 108 dB.  
[PERSON] et al. [27] investigated the control of low-frequency pressure fluctuations using synthetic jet exciters in a 3/4-scale open-jet wind tunnel at the Shanghai Automotive Wind Tunnel Center. The synthetic jet exciter consisted of loudspeakers arranged around the nozzle. It was observed that a certain excitation frequency can effectively suppress the low-frequency pressure fluctuations, and when the excitation frequency was close to the edgetone frequency, it led to a strong amplification of the pressure oscillation.  
[PERSON] et al. [50] used numerical methods to study the so-called \"flow-follow device\", which injects air flow around the nozzle. The results showed that the technique can change the feedback frequency of the edgetone, thereby preventing resonance between the edgetone and the standing wave mode in the wind tunnel loop, ultimately reducing low-frequency pressure fluctuations.