esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed System: Sana
### Progress and Performance Feedback  
During the development of the Sana system, the progress dashboard, as shown in Figure 13, had to be thoughtfully designed. While it may be easy to present progress and performance data to a patient through a digital interface only, this had to be achieved in such a way that would continuously keep patients motivated rather than overwhelmed. This is especially important considering that ACLR rehabilitation can take up to 12 months. Progress data are presented simply and unobtrusively via progress bars and colloquial syntax (e.g., \"You're 30% there!\"). Through this approach, patients perceive the system as friendly and human-oriented, thereby increasing user engagement and long-term adherence to the rehabilitation program. Behavioral design and nudge theory are two fundamental pillars of the dashboard design. Positive reinforcement is used throughout to elicit feelings of personal accomplishment and to boost self-efficacy. [PERSON] et al. [8] highlighted that positive reinforcement methods lead to higher levels of rehabilitation compliance; however, feedback provided by the primary caregiver is irreplaceable. The Sana system facilitates direct patient-caregiver communication by incorporating a chat facility. [PERSON] et al. [8] also stated that patients who are aware of their clinicians' feelings towards their performance and progress are more likely to strive for positive rehabilitation outcomes.  
Finally, a summative evaluation was conducted with an occupational therapist to assess the proposed system against the design specification and relevant usability heuristics. Overall, the concept was well-received. The participant stated that being able to review performance remotely was extremely beneficial for rehabilitation purposes. Regarding visual design, the therapist highlighted the simplicity, unobtrusive and friendly nature of the system as favorable characteristics which would lead to high adoption rates. Similarly, the participant did not experience any issues with navigation or performing set tasks, reflecting a good overall user experience. The participant agreed with the idea of using the uninjured knee to calibrate exercises to determine patients' baseline, describing it as \"genius\", and went on to suggest that they could see applications of this technology in other aspects of rehabilitation, such as shoulder and hand therapy.  
Figure 13: Screen print of the progress and feedback element of the Sana system.
# Bending Performance of Concrete Sandwich Walls with Actual Boundary Conditions
## 3 Test Results
### Load-Deflection Curves  
Several key points were selected to analyze their deflection during the test. The deflections of the key points were measured by the LVDTs, and the load was recorded by the pressure sensor. For specimens WP and WN, the deflections of five points were recorded in the test. For specimens WPO and WNO, the deflections of six points were recorded, as can be seen in Figure 7.  
The load-deflection curves of test specimens are presented in Figure 8. For specimens WP and WN, similar load-deflection curves were displayed at the five points. Overall, the results showed that the deflection increased with the decrease in the distance between the points and middle span. In comparison, the deflection of specimen WP was larger than that of specimen WN, owing to the fact that the steel beam provided additional vertical constraints for the wall specimens when subjected to negative bending conditions. Moreover, the load-deflection curve of specimen WP reached the first inflection point when the uniform load (\(q\)) equaled to 3 kN/m\({}^{2}\) due to the concrete cracking, whereas that of specimen WN reached the first inflection point when \(q\) reached 9 kN/m\({}^{2}\). The load-deflection curve of specimen WP reached the second inflection point when \(q\) was 13 kN/m\({}^{2}\), and that of specimen reached the second inflection point when \(q\) was 13.5 kN/m\({}^{2}\). This also indicated that the cracking load of specimen WN was higher than that of specimen WP.  
Figure 8 depicts the load-displacement curves of specimens WPO and WNO. It also demonstrated that the deflection of the points increased with the decrease in the distance between the point and the middle span. However, the maximum deflection of specimen WPO was almost equal to that of specimen WNO. In comparison, the inflection on the load-displacement curve of specimen WPO appeared when \(q\) equaled to 3 kN/m\({}^{2}\), indicating that the concrete started cracking, while that of specimen WNO emerged when \(q\) was about 7.5 kN/m\({}^{2}\). Afterwards, the load-displacement curves of specimens WPO and WNO both exhibited their second points of inflection when \(q\) increased to 12.5 kN/m\({}^{2}\). Therefore, it could be determined that the cracking load of specimen WPO was lower than that of WNO. It also showed that the deflections on S5 and S6 were significantly larger than those of S1 and S2. This could be explained by the fact that the center of the eight loading points was closer to S5 and S6.  
Figure 7: Selected deflection points. (**a**) Deflection points of specimens WP and WN. (**b**) Deflection points of specimens WPO and WNO.
## 3 Test Results
### Bearing Capacity and Ductility  
Table 3 shows the yield load \(P_{\mathrm{y}}\), peak load \(P_{\mathrm{u}}\), and ductility coefficient \(\mu\) of all specimens. For the convenience of comparative analysis, the bearing capacity coefficient \(SI\) was analyzed. The calculation method for \(SI\) and \(\mu\) are as follows:  
\[SI=\frac{P_{\mathrm{u}}}{P_{\mathrm{s}}} \tag{1}\]  
\[P_{\mathrm{s}}=A_{\mathrm{a}}f_{0.2}+A_{\mathrm{c}}f_{\mathrm{c}} \tag{2}\]  
where \(P_{\mathrm{s}}\) is the calculated sum of the bearing capacity of concrete and circular aluminum tubes. \(P_{\mathrm{u}}\) is the ultimate bearing capacity obtained from the experiment. \(A_{\mathrm{a}}\) and \(f_{0.2}\) are the cross-sectional area and yield strength of the aluminum tube, respectively. \(A_{\mathrm{c}}\) and \(f_{\mathrm{c}}\) are the cross-sectional area and compressive strength of the core concrete, respectively.  
\[\mu=\frac{\Delta_{85\%}}{\Delta_{\mathrm{y}}} \tag{3}\]  
where \(\Delta_{85\%}\) is the axial displacement when the bearing capacity decreases to \(85\%\) of the ultimate bearing capacity. \(\Delta_{\mathrm{y}}\) is the yield displacement, which is calculated using the equal energy method.  
Figure 4: Axial load–displacement curve.  
The table reveals that, as the _a/b_ increased, the improvement coefficient of bearing capacity and ductility coefficient of the specimen decreased. When the _a/b_ reached 2.5 and 4.0, the improvement coefficient fell below 1.0, indicating a deterioration of the specimen at that point. This was because the specimen with a large _a/b_ was prone to overall buckling around the weak axis of the section and the aluminum tube was not provided with a restraint specimen before instability failure occurred. The critical _a/b_ was between 2.0 and 2.5. Among specimens with the same _a/b_ (CA100-0 and CA130-0), the specimen with a higher aluminum content in the section (CA100-0) exhibited a smaller increase coefficient. This could be attributed to the thicker wall of the aluminum tube, leading to poorer interaction between the aluminum tube and the core concrete. Therefore, it was recommended that the optimal aluminum content range be between 8.5% and 13.5%. However, the ductility increased by 8.9% due to the increased aluminum content. This could be attributed to the excellent plasticity of aluminum alloys, where an increase in aluminum content enhanced restraint after concrete crushing, resulting in improved resistance to deformation and enhanced ductility.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reinforcement Learning Energy-Efficient Routing Protocols (RL-Based E2 RPs)
#### 5.4.8 Comparison of Depth-Based Energy-Efficient Routing Protocols (D-b E2 RPs)  
For routing protocol techniques in UWSNs, depth-based E2 RPs are regarded as the foundational elements. All other E2 RPs strategies used in UWSNs are built around this concept. The sensor Rx nodes in the network are deployed with respect to different depth levels in the undersea, with sea level depth being the most basic aspect of the underwater habitat. This crucial quality is considered by depth-based E2 RPs, which are created based on depth in UWSNs. Although the routing techniques on the basis of depth are very old, Table 12 shows the current E2 RPs which are based on depth. The first energy efficient routing protocol on the basis of depth was proposed in 2012, which was the enhanced DBR version that can handle communicating data packet redundant transmission. The main technique of both routing protocols is similar with each other. However, the main difference between these routing protocols is that the E2D-bRP can introduce new communicating nodes as an idle node in the network at the middle level of depth in UWSNs. When the communicating nodes loses their energy during the communication, the idle node supportsthese normal nodes at the middle level of depth. The E3 DBRP performance ID evaluated on the basis of different number of nodes, which are constant in the network.
## 4 Numerical Simulation Analysis
### Analysis of Rock Damage Law
From Figure 11a, it can be seen that in the simultaneous detonation method, the explosives in the blast holes detonate at the same time. The size and morphology of the damage caused to the rock mass around the three blast holes are roughly the same, exhibiting a certain symmetry in the overall damage. From Figure 11b-d, it is evident that in the sequential detonation method, the explosives in the blast holes detonate separately at the set delay times. Except for the first detonated hole, where the damage does not change significantly compared to Figure 11a, the subsequently detonated holes exhibit significant changes in the size and morphology of the damage caused to the rock mass around them due to the influence of the previously detonated holes. Moreover, the damage caused to the rock mass around the subsequently detonated holes is smaller than that around the first detonated hole. The similarities among Figure 11a-d are due to the failure characteristics of the rock mass around the blast holes in simultaneous detonation pre-splitting blasting being similar to those in sequential hole pre-splitting blasting. The differences are that, compared to the two detonation methods, simultaneous detonation pre-splitting blasting results in a larger damage range and a wider damage zone between the holes; meanwhile, sequential hole pre-splitting blasting produces a smaller damage range and a narrower damage zone between the holes. In sequential hole pre-splitting blasting, because the subsequently detonated holes can achieve connectivity with the previously detonated holes, the damage range tends to decrease. The depth and extent of blasting damage caused to the retained rock mass are smaller, which ensures the stability of the retained rock mass. At the same time, it can be seen that the choice of delay time has a relatively small impact on the damage caused to the rock mass around the blast holes. In order to ensure complete connectivity between the holes, we should strive to minimize the damage range, thereby reducing the impact on the stability of the retained rock mass. Figure 11: Model damage cloud from bottom view.
###### Abstract  
The development of small-satellite technologies allows the low Earth orbit intersatellite link (LEO-LEO) occultation method to observe the Earth's atmosphere with global coverage and acceptable costs using electromagnetic signals, in which the L/X/K/M band and short-wave infrared band signals have been well demonstrated to be suitable. We hence need to investigate the impacts of orbital and constellation parameters on the number and spatiotemporal distribution of LEO-LEO occultation events for best-possible LEO-LEO occultation mission design and optimization at the targeted mission size. In this study, firstly, an occultation events location simulation model accounting for the right ascension of the ascending node (RAAN) precession was set up and the concept of a time-dependent global coverage fraction of occultation events was defined. Secondly, numerical experiments were designed to investigate the orbital parameters' impacts and to assess the performance of LEO-LEO occultation constellations, in which the Earth is divided into \(5^{\circ}\times 5^{\circ}\) latitude and longitude cells. Finally, the number, timeliness, and global coverage fraction of occultation events for two-orbit and multi-orbit LEO-LEO constellations were calculated and analyzed. The results show that: 1 the orbit inclination and RAAN are the main impacting parameters followed by orbital height, while the RAAN precession is a relevant modulation factor; 2 co-planar counter-rotating receiving and transmitting satellite orbits are confirmed to be ideal for a two-satellite LEO-LEO constellation; 3 polar and near-polar orbit constellations most readily achieve global coverage of occultation events; near-equator orbit constellations with supplementary receiving and transmitting satellite orbit planes also readily form the occultation event geometry, though the occultation events are mainly distributed over low and low-to-middle latitude zones; and 4 a well-designed larger LEO-LEO occultation constellation, composed of 36-72 satellites, can meet the basic requirements of global numerical weather prediction for occultation numbers and timeliness, yielding 23,000-38,000 occultation events per day and achieving 100% global coverage in 12-18 h.  
LEO-LEO occultation; occultation event numbers; spatiotemporal coverage; orbit elements; constellation design 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021-2022,
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 4 Design
### The VULA and VULA Algorithm Comparison
#### 4.7.2 Signal Resolution  
The resolution of received target signal source on a certain azimuth angle is related to the change rate of direction vector around this azimuth angle when leveraging the antenna array to detect the direction of arrival. The resolution and the change rate are positively related. Thus, we can measure the resolution by:  
\[D(\theta)=\left|\left|\frac{dA(\theta)}{d\theta}\right|\right|_{F}. \tag{32}\]  
Thus, the resolution of VULA system is  
\[D_{VULA}(\theta)=\sqrt{\sum_{i=-N}^{N}i^{2}\frac{2\pi fvT}{c}|cos\theta|} \propto|cos\theta|. \tag{33}\]According to Equation (33), the resolution of the VULA system is changing in the form of cosine. It means when the signals source's azimuth angle is perpendicular to the moving trajectory of moving smartphone, the resolution reaches the highest point. However, the resolution becomes lower and lower until the direction of the target signal becomes parallel to the moving trajectory.  
On the other hand, the resolution of VUCA system is  
\[D_{VUCA}(\theta)=\sqrt{\sum_{i=-N}^{N}i^{2}}=k, \tag{34}\]  
where \(k\) is a constant. It represents the VUCA system that has the same resolution of the received target signal source around \(360^{\circ}\), which makes the VUCA system more stable than the VULA system during daily use.
# Digital Image Processing: Advanced Technologies and Applications
## 1 Introduction  
A few decades ago, conventional image processing methods mostly focused on basic tasks such as image enhancement, registration, or edge detection. Early attempts to achieve these tasks mostly utilized grayscale images. Over time, simple methods to process grayscale images resulted in performance degradation for RGB images [1]. Ultimately, RGB image processing received more attention and subsequent advancements were made, including color preservation and fusion-based processing [2]. Currently, deep learning is used extensively in various fields, such as speech recognition and healthcare domains, with encouraging outcomes in image processing, such as image classification and segmentation [3]. A recent study showed that deep learning-based approaches significantly improve the performance of many image-related tasks, such as object detection, recognition, or segmentation compared, compared to conventional methods.  
With the evolution of convolutional neural networks (CNNs), supervised learning techniques were used to train CNNs, which aimed to extract efficient features to meet their gold label requirements [4]. The performance of these methods strictly relied on the available training data. Subsequently, the limited annotated training data failed to acquire the particulars of the image details. Since the supervised learning approaches learned nonlinear mapping, they tended to primarily focus on the limited training data. As a result, the trained model struggled to yield encouraging results on unseen image data [5].  
The domain of digital image processing has experienced amazing advancements, particularly through the evolution of deep learning-based algorithms, which have enhanced capabilities in many real-life applications, such as image object detection [6], recognition [7], segmentation [8], edge detection [9], and restoration [10]. Despite these advances, critical gaps remain in research and knowledge, especially in the applications and exploration of deep learning models' robustness in several challenging situations. Deep learning models also have great ease and efficiency in processing high-dimensional data [11].  
This Special Issue entitled \"Digital Image Processing: Advanced Technologies and Applications\" addresses these challenges by collecting 15 state-of-the-art research contributions that reinforce current methodologies and offer inventive solutions and novel perspectives. Looking ahead, future research will likely focus on developing more robust and explainable AI models to enhance the feasibility of image processing systems.  
Future research can also focus on exploring the potential of quantum computing to process progressively complex image data, in addition to current deep learning models. These directions will not only satisfy existing knowledge gaps but also open new possibilities for advanced applications and technologies in digital image processing.
# The Power System and Microgrid Protection--A Review
## 3 Protection Scheme of Microgrid
### Modifications of Scheme
#### 3.1.2 Fault Current Limiter  
A fault current limiter is a series device used to limit the fault current and does nothing in normal operation; fast action is needed to limit the short circuit current level using a preset value by inserting a series of high impedance values under fault condition [62]. Several FCL technologies and applications have been reported in References [70; 71]. The FCLs have been classified into four groups: the superconducting FCLs (SFCLs), solid-state FCLs (SSFCLs), hybrid FCLs (HFCLs), and other technologies [72]. Figure 9 shows the classification of different types of FCL.  
Inductive FCLs have superior performance and higher operation speed than other FCLs [73]. Inductive and resistive FCLs are examined to limit the current drawn from DGs during a fault somewhere in the power system. An FCL was implemented at the beginning of a radial distribution feeder equipped with a DG [62]. In Reference [58], a solution was presented using FCLs to restore the relay coordination in looped distribution networks. Figure 10 shows a fault current limiter in the distribution network with a DG.  
Figure 10: Fault current limiter–DG system interconnection.  
Figure 9: Classification of different types of FCLs [72].
## 1 Introduction
Morphed LE and TE shapes are approximated using Bezier curves derived from a number of 11 and 9 control points, locally defined in the vicinity of the LE and TE regions, respectively. The vertical positions of the control points constitute the design variables of a design optimization loop, which is thereafter followed in order to obtain the LE and TE shapes, which provide maximum \(\rm C_{L}\) and stall AoA. The procedure of defining the search space of the control points is described in detail and may constitute a guideline for future works on design optimization. In the optimization process, candidate morphed solutions are requested to satisfy certain geometrical constraints regarding smoothness of the surface curvature, while their aerodynamic performance characteristics (lift and drag polars) are obtained using Foil2w, a fast viscous-inviscid interaction in-house solver [9]. The objective function of the optimization process is the maximum lift coefficient \(\rm C_{L,max}\). In order to reduce the computational cost and render the entire process efficient, three different categories of optimization algorithms are tested. Apart from the traditional and most commonly used in design problems, Genetic Algorithm, the alternative of Evolutionary Strategies and the more modern Particle Swarm Optimizer are applied and evaluated. Optimum designs stemming from the design optimization loop are verified through simulations using the in-house Unsteady Reynolds Averaged Navier-Stokes (URANS) solver MaPFLow [10]. In addition to the verification purpose, CFD aerodynamic simulations (a) provide better understanding and physical insight of the local flow characteristics over the modified portions of the airfoil section, which are directly linked to the resulting distributed and overall aerodynamic loads and (b) estimate the change in the aerodynamic performance (\(\rm C_{L}/\rm C_{D}\)) using the morphed airfoils. Hence, CFD simulations serve the scope of interpreting the results obtained, but also ensure the overall aerodynamic effectiveness of the design. The following text is organized as follows: In Section 2, the optimization procedure along with the solvers employed for the flow simulations are described. The application and the results of the methodology are presented in Section 3. Section 4 refers to the verification of the aerodynamic results obtained through the optimization chain for selected morphing designs using the CFD solver. Finally, in the last section, the main conclusions of the work are reported and discussed.