esa-sceva/satcom-chunk-collection huggingface dataset:
# Tip Vortex Cavitation and Induced Noise Characteristics of Hydrofoils
## 2 Experimental Setup  
Experiments were carried out at the cavitation tunnel of the Chungnam National University (CNU-CT). The size of the test section is 100 mm \(\times\) 100 mm \(\times\) 1400 mm (H \(\times\) B \(\times\) L), and the maximum flow speed is 20 m/s. Pressure inside the tunnel can be changed from 10 to 300 kPa. Figure 1 shows the schematic of the experimental setup. Two hydrofoils of elliptic planform with different cross-sections, NACA 16-020 and 66(2)-415, were tested. Aspect ratio of the foil is 1.5 and the length of the chord and span is 80 mm and 60 mm respectively. The cavitating hydrofoil was imaged using a high speed camera, capturing videos at the rates up to 80,000 fps at the resolution of 1280 \(\times\) 1024 pixels on a complementary metal-oxide-semiconductor (CMOS) sensor. Minimum exposure time of the camera shutter is 3.9 \(\upmu\)s. Forces exerted on a hydrofoil were measured by using a dynamometer. The three-component dynamometer is capable of measuring forces along two perpendicular directions and moment along one direction. The dynamometer is capable of force measurement up to 20 kgf along the streamwise direction and 50 kgf along the cross-stream direction. Maximum moment measurable is 1 kgf.m about the perpendicular. The dynamometer was calibrated before measurements and linear response curve was obtained for their operating range. The dynamometer also exhibited no hysteresis and had a good repeatability; performance characteristics of the dynamometer is tabulated in Table 1. The pressure fluctuations were measured at the top of the test section and analyzed in the frequency domain according to the test conditions. The unsteady pressure transducer used in the experiments can measure absolute pressure up to 2 bar at the sampling rate of 50,000 Hz. A customized LABVIEW interface was utilized to operate the cavitation tunnel, record the high-speed images, obtain the voltage output of the dynamometer and the unsteady pressure signals from the pressure transducer in a synchronous manner. The key parameter is the cavitation number, \(\sigma=(P_{\infty}-P_{\mathrm{p}})/(1/2\rho V_{\infty}^{2})\), where, \(P_{\infty},P_{\mathrm{p}},\rho,V_{\infty}\) are the free-stream wall static pressure measured at the test section entrance, water vapor pressure at the fluid temperature, water density and flow speed respectively.
## 2 Antenna Configuration  
### Antenna Model  
The proposed triple-band UWB antenna with dual-notch band characteristics is shown in Figure 1a-c. Basically, the antenna consists of an inverted triangular radiating patch with a triangular slot in the middle, a 50 \(\Omega\) microstrip feeding mechanism, a rectangular partial ground plane on the back side of the substrate, and three SSRRs with stub embedded in the radiating patch.  
A triangular slot and 3-SSRR with stub embedded in the radiation patch allow for triple-band operation with dual-band rejection. The range of dual-band rejection can be varied by adjusting the dimensions of the slot and SSRRs. The proposed dual-notch band UWB antenna is designed on an FR4 substrate having a thickness (\(h\)) of 1.6 mm and dielectric constant (\(\varepsilon_{r}\)) of 4.3 with a loss-tangent of 0.025, respectively. The overall size of the proposed antenna is 28 mm \(\times\) 40 mm. The antenna parameters are designed and analyzed using the CST Microwave Studio (CST-MWS). Table 1 shows the proposed antenna dimensions.
# Stability of MSW Landfill Slopes Reinforced with Geogrids
## 3 Results and Discussion
### Reinforced Analyses
#### 3.2.2 Effect of Cohesion on the Reinforced Slope  
In order to investigate the effect of cohesion (c) on the optimum reinforced slope stability, FE analyses were carried out for different cohesion values of the MSW landfill. In the analyses, the other parameters were chosen as \(\phi=15^{\circ}\); \(\psi=0^{\circ}\); E = 6500 kN/m\({}^{2}\); \(\
u=0.45\); \(\gamma=9\) kN/m\({}^{3}\); \(\beta=26.56^{\circ}\) (1V/2H); and N = 4. The FS values for different cohesion values obtained as a result of the FEM analyses are shown in Table 10 and Figure 16.  
Figure 14: (**a**) N = 1; (**b**) N = 2; (**c**) N = 3; (**d**) N = 4. Slip surfaces of the reinforced slope obtained by LEM analysis.  
Figure 15: Comparison of FEM and LEM results (N = 4).  
From the analyses performed to determine the effect of cohesion (c) on the reinforced slope stability, it can be seen that the FS of the slope increases linearly with an increasing cohesion value.
## 5 Discussion
### Limitations  
Despite the enthusiasm for SCAN-ECHO among specialists and remote primary care providers, lack of financial support (reserved/protected time) appears to be the key encumbrance to attendance. Primary care providers are assigned substantial clinical caseloads, which takes precedence over other valuable professional activities, even those involving clinical education, which has the potential to serve as a multiplier effect (e.g., the answer to one case consultation may be applicable to numerous similar cases). Given these priorities, SCAN-ECHO consultation submissions and attendance have been relatively low. This low level of engagement has relegated us to offering only one session per month; this delay could also be an inhibiting factor. Administrative support, in the form of specifically allocated time(s) for SCAN-ECHO attendance, could potentially address both of these issues; our specialists have expressed a desire to offer more than one session per month, should the referral load warrant that.  
Another constraining factor involves the proprietary telehealth network that is currently in use. In order to use this system, costly telehealth terminals must be deployed and maintained. Such terminals are hard-wired into the telehealth communication network; hence, users must access these terminals in the room in which they have been installed, which has resulted in scheduling conflicts. A potential solution to this would be to use a secured Internet-based videoconferencing service, which would enable SCAN-ECHO attendees to engage in these sessions via a variety of readily accessible devices (e.g., a PC equipped with a webcam, laptop, smartphone, tablet, _etc._), thereby alleviating the need to reserve specific rooms, affording additional access (e.g., those on travel/remote assignment). Additionally, utilization of a secured Internet-based videoconferencing system would substantially reduce the need for technical support staff at the MCU and each clinic; a centralized help-desk may be sufficient to support such a system.
# PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems
## 1 Introduction
[PERSON] [22] proposed a method for describing the spatial variance of Zernike polynomials. This approach is derived from the description provided by [PERSON] [19], and laid the foundations for truly space-variant Zernike polynomials. Of course, there is also the problem of space-variant PSF. In fact, we cannot be limited to rotationally symmetric imaging systems only. [PERSON] [27] and [PERSON] [28] came up with a more general description of the pupil function for elliptical or rotationally non-symmetrical Zernike polynomials. This description can be used for calculating the wave aberration in optical materials such as calomel for acousto-optical elements [29]. The first optical approach to wave aberration estimation was described by [PERSON] et al. [30]. The search for the best description of UWFOV systems is not limited to astronomical imaging. Microscopy is another area where the UWFOV type of lens is used. Measurements of the special spherical aberration using a shearing interferometer were described in [31]. A promising new approach was formulated in [32]. It is based on aberration measurements of photolithographic lenses with the use of hybrid diffractive photomasks. The aperture exit wavefront deformation is modeled in [33] for wide field-of-view fluorescence image deconvolution with aberration-estimation from Fourier ptychography. UWFOV cameras are also used in surveillance systems. However, an image affected by aberrations can have a negative effect in criminal investigations [6]. [PERSON] et al. [5] face this issue, and they propose an approach that estimates a matrix of coefficients of the wavefront aberrations. Many authors have reported on investigations of space-variant imaging systems. An estimate of the PSF field dependency is critical for restoring the degraded image. [PERSON] and [PERSON] proposed their linear space-variant model [34] and focused on the restoration algorithm for imaging systems. [PERSON] et al. [35] proposed techniques for removing aberration artifacts using blind deconvolution for the imaging system with a simple lens. In our work, we propose a modeling method for PSF estimation for space-variant systems. Since we would like to use this model for general optical systems, the method is based on modeling the PSF of the system without knowledge of the wavefront. Thus, the method can be an alternative to the Shack-Hartmann interferometer [4; 36], or to other direct wavefront measurement methods, since we can estimate wavefront aberrations from fitting PSF in the image plane. The following section begins with a description of wavefront aberrations.
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 3 Experimental Data and Parameters
### Models Training  
To evaluate the performance of defect detection in nuclear graphite components, five mainstream models--Faster R-CNN [18], Mask R-CNN [19], Efficient Net [36], RT-DETR [37], and YOLOv11 [38]--are compared with the proposed Defect R-CNN. Functionally, Faster R-CNN and RT-DETR are employed for target detection, while Mask R-CNN, Efficient Net, YOLOv11, and Defect R-CNN perform both detection and segmentation. Structurally, Efficient Net, RT-DETR, and YOLOv11 are one-stage models that directly predict the target category and location, offering faster inference speeds, and making them suitable for real-time detection. In contrast, Faster R-CNN, Mask R-CNN, and Defect R-CNN are two-stage models that generate candidate regions for classification, yielding higher detection accuracy but with slower inference.  
The experiments are conducted using the open-source object detection framework MMDetection [39]. The backbone network is initialized with weights pre-trained on the MS COCO dataset [40] to accelerate convergence and improve generalization. Except for the backbone network, all hyperparameters follow the default settings of the original Mask R-CNN. The model is trained using stochastic gradient descent (SGD) with a momentum of 0.9 and a weight decay of 0.0001. The initial learning rate is set to 0.01 and is reduced by a factor of 10 at the 40 th and 70 th epochs following a step decay schedule. The total number of training epochs is 100, and the batch size is set to 50 per iteration. The model is trained on a workstation equipped with an Intel i7-12700 KF CPU and an NVIDIA GeForce RTX 3090 GPU.  
The Faster R-CNN, Mask R-CNN, Efficient Net, RT-DETR, YOLOv11, and Defect R-CNN models are trained on the same training dataset. The final loss curve is shown in Figure 9.  
From the curves, it is evident that although all models exhibit a significant decrease in loss values during the early training stages, their convergence behaviors differ. Efficient Net, RT-DETR, and YOLOv11 converge quickly with minimal fluctuation, reflecting efficient and stable early learning. In contrast, Faster R-CNN, Mask R-CNN, and Defect R-CNN show larger fluctuations in loss values during the initial phase. These fluctuations arise as these models, initialized with pre-trained weights, adapt to the new data and adjust their parameters. Despite initial instability, Defect R-CNN demonstrates competitive convergence, eventually effectively minimizing loss and attaining strong final convergence.
# Design and Analysis of the Composite Stability Control of the Reflective Optoelectronic Platform
## 2 Problem Description  
### The Principle Assumption of the LOS Stability  
This study utilizes the typical two-axis four-frame structure dynamics model [3; 4; 9] to ensure system stability. The basic principle assumption of the model is illustrated in Figure 1, where the detector and fixed reflector (Figure 1a) are fixed on the base, and the adjustable reflector can adjust its attitude relative to it. The imaging stability of the detector is maintained by adjusting the adjustable reflector when the base's motion causes the inertial instability of the line of sight (LOS).  
The adjustable reflector consists of two components: the traditional two-degree-of-freedom reflector and the fast steering reflector. As depicted in Figure 1b, the traditional reflector utilizes two frame axes to provide coarse-stable axes, while the fast steering reflector employs two motion axes as fine-stable axes. The coarse-stable pitch frame of the traditional reflector comprises a driving axis and a driven axis, which necessitates a mechanism with a 2-to-1 transmission ratio [4; 10]. On the other hand, the fast steering reflector is mounted on the driven axis for improved efficiency.
## 4 Experiments
### Calibration Effects  
In this subsection, we assess the calibration effectiveness of the proposed CSD by ECE. For a more intuitive presentation, reliability diagrams [62] are provided in Figure 6, which plot the expected accuracy as a function of the model confidence (the dashed diagonal line indicates perfect calibration [55]). We note an interesting phenomenon: that CSD substantially improves the calibration on two regular datasets while impairing the model calibration on three fine-grained datasets. The lower calibration implies lower confidence in the predictions of the model. But it is not a bad phenomenon, because in many fine-grained tasks where the classes are very similar, we often need to know the association between the sample classes to perceive the label hierarchy [63]. The lower prediction confidence with a sufficiently high accuracy indicates that CSD can provide better class similarity relationships. In addition, as shown in Table 4, the CSD achieves the best Top-5 values on the three fine-grained datasets, indicating that the prediction confidence is spread across several similar classes, which is beneficial for the portrayal of class relations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\lambda\) & **0** & **0.1** & **0.3** & **0.5** & **0.7** & **0.9** \\ \hline Top-1 & \(24.71\pm 0.24\) & \(23.92\pm 0.19\) & \(23.19\pm 0.11\) & \(22.52\pm 0.07\) & \(21.98\pm 0.09\) & \(21.19\pm 0.05\) \\ Mstd & \(5.53\pm 0.42\) & \(5.59\pm 0.30\) & \(5.65\pm 0.23\) & \(5.71\pm 0.19\) & \(5.79\pm 0.19\) & \(5.84\pm 0.16\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top-1 error and Mstd of ResNet-18 on various lamda values in CIFAR-100 classification.  
Figure 6: Reliability diagram of multiple methods on different datasets. ECE metrics are reported in the illustrated labels.
## 4 Software System
### Path Planning
#### 4.2.1 Identifying the Upper's Foot Orientation (Left or Right) and Size  
As mentioned earlier, the 3D point cloud model of the lasted upper is generated using three scanning systems positioned at different orientations. The laser scanning system mounted above, which captures the bottom of the lasted upper, serves three main functions. First, it enables the seamless integration of laser data from the top region with those from the left and right scanning systems, thereby supporting the reconstruction of a complete 3D model of the side surfaces. Second, it provides the pixel-to-millimeter conversion factor based on variations in the surface height, ensuring an accurate 3D model of the bottom of the lasted upper. These two functions have already been utilized in the previous section.  
The third function is to use the depth image captured from the bottom of the lasted upper to determine its size and identify whether it belongs to the left or right foot in real time. As shown in Figure 17, for a given depth image, the width of the lasted upper, L1, is measured at the 7/10 position of the model. By connecting its center position, P1, to the toe tip, P2, the slope of vector \(\overrightarrow{P_{1}P_{2}}\) is found to be positive; then, it is a left- foot upper. Conversely, if the slope is negative, the lasted upper is identified as a right foot. Additionally, by measuring the distance from the toe tip to the heel, the shoe size can be determined. Based on the estimated size and foot orientation, the corresponding outsole  
Figure 16: Merged 3D point cloud of the lasted upper, constructed from three scanning views and aligned with the robotic coordinate system. Color coding: green-left side; red-right side; blue-bottom.  
3D model is retrieved from the database. The 3D model of the lasted upper is then aligned and merged with its matched outside model. This combined geometry serves as the basis for generating the adhesive dispensing path.
## 5 Discussion
### Comparison with the Results of Traditional Methods
The traditional method left view, front view, and top view consecutively illustrate that traditional DBSCAN clustering outcomes contain a lot of noise, mistakenly classifying more positioning points as noise. The modified DBSCAN algorithm correctly identifies strong sidelobe point clusters diffused in the whole data and separates them from the target and strong side lobe point clusters. The modified DBSCAN algorithm reduces noise significantly and extracts more accurate target spatial distribution information. Hence, precise extraction of a 3D SAR image target point cloud is achieved. Figure 20: Comparison results of 3D point cloud clustering.
# ATLAS: Latest Advancements and First Observations
## 2 Materials and Methods
### Calibration Procedure
The elevation angle is affected by changes in the refractivity of the atmosphere with altitude. The authors of [28] provided an extensive explanation of the equations necessary for calculating the refractivity index and compensating for the apparent elevation obtained in (1). These equations usually require access to local meteorological parameters such as ambient temperature and pressure. The effect of refraction should be taken into account, especially when collecting data at low elevations (<10 deg [26]). Measurement of solar noise is a relatively straightforward, accurate, reproducible, and convenient method for assessing receiver performance. It consists of measuring the ratio between the power received by the antenna when pointed at the Sun and when pointed at the cold sky [33]. This metric proves valuable in receiver optimization because a higher measured solar noise correlates with improved G/T, allowing for the detection of weaker signals. The basic principle of pointing calibration with the Sun consists of scanning the azimuth/elevation positions around the predicted position of the Sun and measuring the received power [16]. When the antenna is pointing to the center of the solar disk, it will receive the maximum power. The difference between the sensor angle readings at the peak power and the predicted position of the Sun corresponds to the bias in the positioning system. The cross-scanning routine consists of the following steps:  
1. Define a vector of N offset angles \(\theta_{ofs}=[\theta_{1},\theta_{2}\ldots\theta_{N}]\). 2. Define a vector of N time instants \(T=[t_{1},t_{2},\ldots t_{N}]\). 3. Predict the azimuth positions of the Sun for the defined time instants: \(A_{z}(T)=[A_{z}(t_{1}),A_{z}(t_{2}),\ldots A_{z}(t_{N})]\). 4. Compute the scanning positions: \(S=A_{z}+\theta_{ofs}\). 5. Point the antenna to positions \(S\) at time instants \(T\) and compute the power received for each position. 6. Locate the peak power and update the offset with the new angle value. 7. Repeat for the elevation angle. The scan should be performed by alternating between azimuth and elevation to avoid the coupling error of moving azimuth and elevation simultaneously and for several iterations until the obtained offsets converge within a given uncertainty. This routine ensures that the calculated positions compensate for the Sun's movement during the scan. The operator should make sure the timestamps between points are long enough to be able to move the antenna and record the signal at the correct time. This method should only be used when the beamdwith is larger than the diameter of the Sun. In cases where the beamwidth is larger, the scanning will return the beam pattern of the antenna, and we can confidently say that the maximum noise reading corresponds to the center of the Sun. However, if the beamwidth is smaller than the diameter of the Sun, there will be multiple maximum values when passing inside the solar disk, which will result in a beam pattern with a flat top instead of a peak.
# Comparing Link Budget Requirements for Future Space-Based Interferometers
###### Abstract  
Inter-satellite interferometric missions are critical in the ongoing monitoring of climate change. Next-generation Earth geodesy missions are opportunities to improve on mission cost and measurement sensitivity through revised design. To be considered feasible, mission architectures must meet an optical power requirement that factors in both shot noise and laser frequency noise. Reference-transponder mission configurations, like the Gravity Recovery and Climate Experiment-Follow On (GRACE-FO) mission, are designed for measurement down to a received carrier-to-noise density ratio of 70 dB-Hz--1.9 picowatts in shot-noise-limited detection. This work shows, through modeling and simulation, that the optical power level required to perform robust measurement varies significantly between mission configurations. Alternate configurations, such as retro-reflector-based schemes, can operate robustly down to much lower carrier-to-noise density ratios, with the example parameters considered here: down to 29 dB-Hz--150 attowatts in shot-noise-limited detection. These results motivate exploration of alternate missions configurations with revised optical power requirements, increasing the feasibility of new designs.  
GRACE-FO; GRACE-C; NGGM; laser interferometery; phasemeter; femtowatt measurement, space-based interferometers +
Footnote †: journal: Remote Sensing  
1
Footnote †: journal: Remote Sensing  
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Footnote 1: institutetext: Centre for Gravitational Astrophysics, Research School of Physics, The Australian National University,  
Canberra, ACT 2600, Australia; [EMAIL_ADDRESS] (K.M.)  
* Correspondence: [EMAIL_ADDRESS]