esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Composite Docking Test Platform  
### Analyzing the Space Docking Process  
After the target spacecraft and tracking spacecraft have reached about the same speed and location on the track, the active docking mechanism on the tracking spacecraft extends to grab the passive docking mechanism on the target. Next, the two spacecraft were linked flawlessly using buffering and locking. The space docking was then concluded [30]. As depicted in Figure 2, the space docking procedure was performed in perfect weightlessness with three degrees of freedom and three degrees of rotational flexibility for both the target and tracking spacecraft. Consequently, the space docking process between the two spacecraft is a dynamic process with 12 degrees of freedom (That is, displacement and rotation along the \(x\)-axis, displacement and rotation along the \(y\)-axis, and displacement and rotation along the \(z\)-axis), making it very difficult for ground semi-physical simulation test systems to measure the attitude.  
Realizing the relationship between the simulator's relative position and attitude facilitated the simplification of each spacecraft's freedom of movement. Thus, a motion simulator with six degrees of freedom and both vertical and horizontal docking modes can be created and used to model docking attitudes for various space modes. Here, the motion simulator was separated into active and passive simulators; the active simulator can realize two degrees of freedom, while the passive simulator can realize four degrees of freedom.
# Super-Wideband Monopole Printed Antenna with Half-Elliptical-Shaped Patch
## 1 Introduction
The latter type of slit shows the lowest resonant frequency of 1.44 GHz. Utilizing a tapered microstrip feedline in conjunction with a small gap between the partial ground plane and the antenna presents another intriguing approach for improving the bandwidth in super-wideband (SWB) antennas. Studies such as [28, 29, 30, 31, 32] have demonstrated the effectiveness of this technique in increasing the maximum resonant frequency up to 70 GHz. Combining this with a semicircular shape, as described in [33, 34, 35, 36], can reduce the minimum resonant frequency to as low as 1.3 GHz and 0.3 GHz, respectively, while maintaining bandwidth ratios exceeding 10:1. These techniques have also been studied in the Thz region [37], achieving a similar bandwidth enhancement to that in the mmWave region. The development of fractal antenna designs also contributes to the advancement of super-wideband (SWB) antennas. As shown in [38, 39], octagonal fractal geometries can achieve a wide resonant frequency range, spanning from 10 GHz to 50 GHz. Furthermore, incorporating a Sierpinski fractal within the octagonal shape and introducing an inset at the partial ground plane, as presented in [40], can further reduce the minimum frequency of the SWB antenna. The benefits of employing elliptical partial ground planes extend to fractal antenna designs, as demonstrated in [41, 42, 43]. These studies report that hexagonal- and star-shaped fractal antennas achieve bandwidth ratios exceeding 11:1 when incorporating elliptical partial ground planes. While the aforementioned discussion on fractal patterns highlights their potential application in SWB antenna design, the complexity of designing such elements presents a significant challenge. This paper presents a novel super-wideband (SWB) antenna design utilizing a half-elliptical radiator and a modified partial ground plane with a triangular inset. The proposed antenna exhibits a bandwidth ratio exceeding that of previously cited references and without design complexity. The proposed antenna achieves a bandwidth ratio greater than 10:1 across the observed frequency range of 0.5-23.5 GHz. The proposed antenna's low operating frequency of 0.5 GHz, coupled with a bandwidth ratio exceeding 10:1, makes it suitable for a wide range of applications, including UHF/VHF applications, mobile communication systems, and radar systems.
# Research Progress on Flexible Oxide-Based Thin Film Transistors
## 3 Approaches to Flexible Oxide-Based TFTs
### Optimized Ways for the Reduction of Mechanical Stress  
Since the performance of TFTs is compromised under mechanical bending stress, its impact needs to be minimized. To achieve this, two options are at hand: the first involves structure optimization of the TFTs and the second optimization of the device layout.  
Turning to the first option, an additional layer can be applied to the top of the TFT device as reported in Section 3.1. Such devices exhibit good stability after 10,000 bending cycles. The experimental results show that the stress coming from the bottom films (closer to the substrate) can be effectively absorbed by the layer on top and thus protect the TFT device.  
Secondly, in addition to structure optimization, changes in the TFT layout are another effective way to reduce the effect of mechanical stress. Such changes involve using different patterns for the design of each electrode and the active layer. Figure 16[181] shows three different types of patterning: a TFT design with conventional S/G/D patterns (Figure 16a), a design with a conventional S/G/D pattern but a strip semiconductor layer underneath (Figure 16b). Both structures permanently fail as cracks occur on the electrode films after 5000 bending cycles.  
Figure 14: (a) variation of compressive and tensile strain with inverse bending radius [162], variations in V\({}_{\text{TH}}\) (b) and \(\mu_{\text{sat}}\) (c) and their percentage variations [81].  
Figure 15: Electrical transport behaviors under two kinds of strains; (a) compressive stain and (b) tensile stain in flexible TFTs [81].  
In comparison, a TFT with metal mesh electrodes and a strip semiconductor film underneath shows high mechanical stability without any cracks after bending.  
As discussed in Section 3.2.2, TFTs bent along the channel width exhibit higher stability and performance than those bent along the channel length during the bending test. Hence, the layout of TFTs should be rationally designed along the channel width, especially in applications with fixed a bending radius and bending in one direction. While in the case of bending in any direction, a tradeoff in the TFT channel design should be considered. It is suggested that TFTs might achieve a relatively high performance when the channel of the TFT and the bending direction form an angle of 45 degrees. More effort is needed to demonstrate this possibility.
# Friction Factor Correlation for Regenerator Working
## 4 Computational Model
### Computational Domain
In an attempt to minimize the effect of temperature on the pressure readings, the development of temperature gradient has been controlled using cold heat exchanger. For all regenerator samples tested here, the recorded temperature gradient varies in a range of \(10\)\({}^{\circ}C\leq\Delta T\leq 21\)\({}^{\circ}C\). The values of temperature from experiments are used in the model so that the effect of the small temperature variations on pressure readings are being modelled following the experimental findings. The contribution of pressure loss from the heat exchanger has been calculated using the pressure drop prediction for a compact heat exchanger as presented in [8]:  
\[\frac{\Delta p}{p_{1}}=\frac{v_{1}^{2}/2g}{p_{1}/\rho_{1}}\bigg{[}\Big{(}K_{c} +1-\phi^{2}\Big{)}+2\bigg{(}\frac{1}{\rho}-1\bigg{)}+f\frac{L}{r_{h}}\rho- \frac{1}{\rho}\Big{(}1-\phi^{2}-K_{e}\Big{)}\bigg{]}. \tag{26}\]  
The subscript 1 refers to parameters obtained at the inlet of the heat exchanger. The terms \(p\), \(v\), \(\rho\), \(g\), \(\phi\), and \(r_{h}\) refer to pressure, velocity, fluid density, gravity, porosity and hydraulic radius, respectively. Parameters \(K_{c}\), \(K_{e}\) and \(f\) are the entrance losses, exit losses and Fanning friction losses, respectively, as given in [8]. The calculations showed that the contribution of cold heat exchanger losses was within 1%-5% of the whole pressure drop measured between locations P2 and P5. The loss varies according to the range of velocity and pressure drop achieved in the experiment. The overall contribution of losses is small because of the low speed of flow. The calculated loss was deducted from the total pressure measured in the experiment. The hydrodynamic condition of the regenerator for CFD modelling was gained by determining the permeability coefficient that gives a pressure drop similar to the experimentally measured value.
# Laboratory Evaluation of Eco-Friendly Pervious Concrete Pavement Material Containing Silica Fume
###### Abstract  
Pervious concretes, such as sustainable pavement materials, have great advantages in solving urban flooding, promoting urban ecological balance, and alleviating urban heat island effect, due to its special porous structure. However, pervious concrete typically has high porosity and low strength. The insufficient strength and poor freeze-thaw durability are important factors that restrict its wide application, especially in seasonal frozen areas. Improving the strength and freeze-thaw resistance of pervious concrete will expand its application. Silica fumes, as an industrial by-product waste and supplementary cementitious material, play an important role in improving concrete performance. The objective of this paper was to study the effects of silica fumes on properties of sustainable pervious concrete. Silica fumes were used to replace cement with the equivalent volume method at different levels (3%, 6%, 9%, and 12%). The control pervious concrete and silica fume-modified pervious concrete mixtures were prepared in the lab. The porosity, permeability, compressive strength, flexural strength, and freeze-thaw resistance properties of all mixtures were tested. The results indicated that the addition of silica fumes significantly improved the strength and freeze-thaw resistance of pervious concrete. The porosity and permeability of all pervious concrete mixtures changed little with the content of silica fumes due to the adoption of the equal volume replacement method.  
pervious concrete; silica fume; freeze-thaw resistance; porosity; permeability; compressive strength; flexural strength Article
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 4 Results
### Comparison with State-of-the-Art
#### 4.2.1 Comparison on MOT17  
As shown in Table 1, the method proposed in this paper is best on MOTA, IDF1, ML, and FN on the MOT17 dataset, and MT is second only to the TransMOT. It is proved that the method in this paper can effectively reduce false negatives and most lost while having a good tracking effect.
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 2 Materials and Methods
### Geolocation  
Conventional imaging generally requires an intricate co-registration process to perform pixel-to-pixel matching for pushframe imagery taken at different rows on an FPA. This procedure is especially critical for images collected from an aircraft due to the continual yawing/pitching motions, even during straight, and level flight on autopilot. Figure 6 depicts the data collection and processing pipeline for the aircraft flights.  
Because changes in aircraft attitude can artificially skew features by hundreds of pixels, which complicates stereo matching, it was necessary to track the position and orientation of the CMIS camera relative to an earth-fixed frame. This was done by rigidly attaching a high precision NovaTel Inertial Measurement Unit (IMU) to the camera mounting plate and calculating the global camera pose at the acquisition of each camera frame.  
The initial engineering flight was used to geometrically calibrate the camera by matching 150 key points in each pushframe view to known points on the ground. This helped determine the fixed offset and rotation matrices between the IMU and the CMIS sensor, as well as the camera lens distortion parameters. When combined with the IMU position and orientation, pixels in the camera image plane could therefore be projected onto the WGS-84 ellipsoid to provide motion-compensated, geo-registered imagery for stereo matching.  
Position and orientation data from the primary NovaTel IMU was used to analyze Science Flights #1 and #2. However, data from a backup IMU was used to analyze Science Flight #3. Approximately 90 min into the four-hour flight (23:00Z on 8 February 2021), the primary IMU lost GPS lock and failed to provide a \"fine steering\" solution. Efforts to correct the problem in-flight were not successful. Fortunately, the CMIS imager continued to acquire imagery during the primary IMU dropout, and a logging application saved position and orientation data from a navigation IMU attached to the NASA Gulfstream III aircraft's ARINC-429 bus. The primary and backup IMU poses were aligned to each other by shifting the orientation data streams in time during the first part of Science Flight #3 when both sources of data were available, and verifying that the geo-registered images matched before the primary IMU dropout. To remove the chance for an abrupt data transition between the two IMUs at the 23:00Z boundary, only the backup IMU was used to geo-register Science Flight #3.
## 4 Simulation Results
### Small Sinusoidal Oscillations and Step down Load Active Power  
In the previous case, before the perturbation, the reserve was entirely available to stabilize the frequency due to its load power constancy. This is far from the reality of a power system: the load continuously oscillates, entailing that a part of the reserve is already involved in the stabilization of the frequency. This means that if a sudden perturbation of the load power happens the system has less reserve available to face the variation.  
The aim of this second type of simulations is to address how the continuous and small variability of the load affects the primary frequency control and its capability to face a step in load power, as well as to understand if the designed control for renewables is able to catch and follow these small load oscillations. The load power oscillations introduced in this simulation have an amplitude of 0.005 pu. The resulting frequency of Simulation 5 is presented in Figure 16.  
It can be observed that the system with a _complete FC_ needs some seconds to adapt to the sinusoidal variation of the load, but then it is able to follow it without problems. It is also possible to appreciate the huge reduction in the frequency nadir with respect to the base scenario, which is a fact that testifies one more time to the effectiveness of the updated control of the renewable generators. After the frequency peak, both systems are able to follow the sinusoidal oscillations of the load, but of course in the _complete FC_ scenario the stabilization of the frequency is more evident and requires less time to be reached.  
This simulation is particularly important for the validation of the innovative control of the renewables, since it is closer to the normal behavior of the load power.  
Figure 15: Generator power variations in Simulation 4.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 4 Experiments
### Generalization Evaluations  
To further evaluate the cross-domain generalization performance of the proposed MUIR, the zero-shot generalization evaluation is conducted on the KITTI2015, Middlebury Stereo Evaluation 3 and ETH3d training sets. The results are shown in Table 3.  
In Table 3, rows 2 and 6 show that, compared to the baseline, the disparity EPE of MUIR fast is reduced by about 19.2% in Middlebury Eval 3 and 39.4% in ETH3D. To further study the applicability of MUIR to other RAFT-based variants, the baseline is replaced with IGEV-Stereo fast (row 5) and EAI-Stereo fast (row 6), fast versions of [14,32] running at 1/8 resolution with seven iterations. The modified models are denoted MUIR-IGEV fast (row 5) and MUIR-EAI fast (row 6). Compared to each baseline, the MUIR-IGEV fast achieves 7.6% and 9.8% outlier proportion reduction in Middlebury Eval 3 and ETH3D, respectively, and \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**EPE (px)**} & \multicolumn{4}{c}{**Bad.10 (\%)**} \\ \cline{3-13}  & & **Init 1** & **3 rd ir 2** & **5 th ir** & **7 th ir** & **10 th ir** & **15 th ir** & **Init 1** & **3 rd ir2** & **5 th ir** & **7 th ir** & **10 th ir** & **15 th ir** \\ \hline (1) & Baseline fast [12] & 1.576 & 0.957 & 0.882 & 0.866 & - & - & 21.054 & 11.025 & 9.847 & 9.573 & - & - \\ (2) & +MUD & 1.558 & 0.891 & 0.817 & 0.797 & - & - & 20.920 & 9.890 & 8.678 & 8.390 & - & - \\ (3) & +MUD+URI & **1.142** & 0.853 & 0.783 & 0.766 & - & - & **14.362** & 9.307 & 8.267 & 8.010 & - & - \\ (4) & MUIR fast & 1.156 & **0.817** & **0.736** & **0.711** & - & - & 14.541 & **9.032** & **8.028** & **7.769** & - & - \\ \hline (5) & Baseline [12] & 2.159 & 0.924 & 0.786 & 0.743 & 0.706 & 0.690 & 25.844 & 10.641 & 8.347 & 7.668 & 7.344 & 7.225 \\ (6) & +MUD & 2.218 & 0.873 & 0.723 & 0.678 & 0.655 & 0.643 & 27.393 & 9.481 & 7.260 & 6.614 & 6.319 & 6.197 \\ (7) & +MUD+URI & **1.182** & 0.801 & 0.699 & 0.686 & 0.631 & 0.617 & **15.851** & **8.480** & 6.811 & 6.294 & 6.060 & 5.957 \\ (8) & MUIR & 1.239 & **0.758** & **0.636** & **0.593** & **0.573** & **0.570** & 16.746 & 8.599 & **6.710** & **6.149** & **5.893** & **5.819** \\ \hline \hline \end{tabular}  
* 1 \"Init\" is short for initialization; it is also treated as 1 st iteration in other works [14,32]. 2 \"itr\" is short for iteration.  
\end{table} 
Table 2: Ablation study for models with different iterations on the Scene Flow test set. Rows (1–4) and rows (5–8) shows models’ performance at \(1/8\) and \(1/4\) resolution, respectively. The best results in each category are bolded.
## 4 Case Study
### Simulation Analysis of Accessibility Measurement
Despite this fragmentation, these subnetworks retain a certain degree of accessibility, which results in a relatively flat \"long tail\" effect in the efficiency decline curve. Simultaneously, these subnetworks may possess distinct efficiency characteristics, leading to an overall increase in efficiency instead of a decrease. 3. The rapid decline in efficiency in the initial stage is attributed to the removal of highly connected nodes, and the subsequent gradual change is because the remaining nodes contribute relatively less to the network efficiency. 4. Fluctuations in the efficiency curve may arise from the network's reorganization following disturbance or from the varying degrees of adaptability exhibited by the network under different disturbance simulations. According to Formula (6), the above three types of simulation are processed as measures of accessibility, and the results are presented in Figure 8.  
Due to the application of normalization processing, all indicators have been scaled to the same range. This preprocessing step enables the direct comparison of different metrics within a single chart; however, it may also exaggerate certain fluctuations, particularly when the absolute changes in the original data are minimal. Despite this, the chart clearly reveals that the curves associated with targeted disturbances--such as external attacks and fluctuations in passenger and cargo traffic--are significantly steeper than those corresponding to random disturbances, including natural disasters and technical malfunctions. This indicates that deliberate disturbances inflict more severe damage on the network,  
Figure 7: (**a**) Trend chart of network efficiency under natural disasters and technical fault simulations. (**b**) Trend chart of network efficiency under external attacks and passenger and cargo flow fluctuation simulations. which is in line with expectations. As the number of simulations increases, the accessibility measures for both types of disturbances tend towards zero, signifying that the network eventually loses its functionality.
## 3 Results
### Relationship between Perceived Control and Available Control
\begin{table}
\begin{tabular}{l c c c c}  & **Perceived Control** & **Perceived Control** & **Perceived Control** & **Perceived Control** \\  & **over Temperature** & **over Shading** & **over Light** & **over Van Ventilation** \\ \hline
**Type of Available Temperature Controls** & & & & \\ Manual industry industry & 3.38 & & & \\ Local thermostat at mid/networking unit & 3.96 & & & \\ Local thermostat (i.e., on wall) & 3.53 & & & \\ Central sensor & 3.52 & & & \\ Feedback sensor(s)-i.e., outside temperature & 4.12 & & & \\ Zone sensor(s) & 4.03 & & & \\ Manual control in room(s) & 3.70 & & & \\ \(p\)-value **Gronak-Wallis test** & 0.166 & & & \\ \hline
**Availability of shading devices** & & & & \\ None & & & & \\ Internal & & & & \\ Internal & & & & \\ External & & & & \\ \(p\)-value **Gronak-Wallis test)** & & & & \\ \hline
**Type of control of the available solar shading devices** & & & & \\ No control (fixed) & 3.45 & 3.90 & 4.53 & \\ Individual & & 3.60 & 4.43 & 4.43 \\ Central down, individual up & 3.25 & 4.46 & 3.76 & \\ Automatic & 3.77 & 4.18 & 4.61 & \\ \(p\)-value (Gronak-Wallis test) & 0.758 & 0.17 & 0.710 & \\ Type of operable windows & & & & \\ Yes & & & & \\ Yes, some & & & & \\ Yes, but occupants are not allowed to open them & & & \\ No & & & & \\ \(p\)-value **Gronak-Wallis test)** & & & & \\ \hline
**Type of available light controls** & & & & \\ Manual & & & & \\ Demand control: Occupants (motion sensors) & & & & \\ Demand control: Dwight (photocols) & & & & \\ Automatic with manual and control (building/face/zone) & & & & \\ Automately to time (building/face/zone) & & & & \\ Automately to time (building/face/zone) & & & & \\ \(p\)-value **Gronak-Wallis test)** & & & & \\ \hline
**Type of available mechanical ventilation controls** & & & & \\ Central–Mass (mod/off) & 3.81 & & & 3.33 \\ Central–Clock & 3.62 & & & 3.23 \\ Central–Demand control (temperature, CO\({}_{2}\) other pollutant, relative humidity) & 3.85 & & & 3.27 \\ Local–Mass (mod/off) & & & & 4.11 & \\ Local–Clock & & & & 3.78 \\ Local–Demand control (temperature, CO\({}_{2}\) other pollutant, relative humidity) & 4.42 & & & 3.73 \\ \(p\)-value **Gronak-Wallis test)** & & & & 0.105 \\ \hline \end{tabular}
\end{table} 
Table 3: Available control at building level and occupants’ perceived control (from 1 = no control at all to 7 = full control) with \(p>0.05\).
## 4 Results and Discussion
### Reservoir Space Characterization
#### 4.1.2 Intraparticle Pores  
Intraparticle pores within a shale reservoir are formed as a result of selective dissolution within brittle mineral particles, including calcite and feldspar (Figure 2F). Based on pore size distribution, the intraparticle pores of Long 1-1 were in the range of mesopore-macroopore, but the development degree of intraparticle pores was generally low.