esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Beidou-Binocular Cooperative Navigation Model
### Intelligent Collaborative Fusion and Path Planning Module
#### 2.3.3 Dynamic Obstacle Avoidance and Optimization Control  
In dynamic environments, robots must avoid dynamic obstacles in real time and adjust their paths. This paper introduces the Riemannian Motion Policies (RMP) framework for dynamic obstacle avoidance and path optimization.  
RMP describes the robot's dynamic path adjustment through task acceleration fields, with the optimization goal as follows:  
\[\dot{x}=\arg\min_{\dot{x}}\sum_{i}\parallel\mathbf{f}_{i}(\mathbf{x},\dot{ \mathbf{x}})-\dot{x}\parallel_{\mathbf{M}_{i}(\mathbf{x})}^{2} \tag{20}\]  
where \(\mathbf{f}_{i}\) is the acceleration field for task \(i\), and \(\mathbf{M}_{i}\) is the weight matrix. RMP decomposes path planning into multiple tasks, including guiding the robot towards the target point, generating avoidance acceleration fields near obstacles, and optimizing path smoothness via curvature.  
The intelligent collaborative fusion and path planning module provides efficient path planning and obstacle avoidance capabilities for robots in complex environments through data fusion, global-local coordination, and dynamic optimization. The integration of this module ensures seamless connection between the Beidou navigation module and the binocular vision module, ensuring stable operation and flexibility in response to dynamic changes in substation environments.
# Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
## 6 Discussion  
In this work, we proposed a deep kernel method for the reconstruction of free-breathing and ungated cardiac MRI. A GRE sequence with spiral readouts is implemented to acquire the highly undersampled free-breathing and ungated data.  
The non-Cartesian spiral k-space trajectories with golden angle increment are used for data acquisition because it is more robust to motion effects compared to the Cartesian acquisition. Furthermore, the long spiral trajectories enable more data samples in the center k-space region, and the longer repetition time from the spiral trajectories offers enhanced inflow contrast between the myocardium and blood pool. Another advantage of the spiral trajectories is that they are less sensitive to the eddy-current effects compared to other non-Cartesian trajectories, such as radial trajectories. Hence we did not perform k-space trajectories correction in this work. In addition to the spiral k-space trajectories, the spoiled GRE sequence for the data acquisition in this work does not suffer from banding artifacts, which are usually seen in the images acquired using the bSSFP sequence without shimming.  
Even the spiral GRE sequence has a few advantages, we can see from the image quality assessment done by experts that the images acquired from the bSSFP sequence have the best image quality. Indeed, the bSSFP sequence is widely used in clinical cardiac MRI due to the short acquisition time and high contrast between the blood pool and myocardium. So one direction in the future is to design a better non-Cartesian sequence based on the bSSFP sequence for free-breathing and ungated cardiac MRI to get further improved image quality with even shorter acquisition time.  
Five volunteers were recruited for this study: three of them are healthy volunteers, and two of them have heart problems. In the future, we plan to have more subjects with different health conditions to study the feasibility of real-time free-breathing and ungated cardiac MRI using the proposed deep kernel framework.  
We demonstrate the deep kernel framework in free-breathing and ungated real-time cardiac MRI reconstruction. We note that we may also apply the framework to free-breathing and ungated first-pass perfusion, \(T_{1}\) mapping, \(T_{2}\) mapping, and late gadolinium enhancement imaging. We will investigate the application of the deep kernel method in different applications in the future work.  
Figure 8: SNR and CNR comparison of the images reconstructed from the proposed scheme and the SToRM method. The average SNR and CNR of the images reconstructed from the proposed method are 37.99 dB and 26.28 dB. While the average SNR and CNR of the images reconstructed from SToRM are 36.01 dB and 24.89 dB. From the quantitative comparison, we can see that the proposed deep kernel method can provide improved image quality compared to the existing kernel method.
## 3 Methodology
### Data Collection Methods  
The research data collection procedure aimed to acquire complete systematic information regarding student performance levels alongside their engagement activities and AI-generated feedback interactions. Researchers combined quantitative system logs and survey data with student and instructor qualitative feedback into their research approach. For 20 weeks 700 undergraduate students participated, including 350 students who received AI adaptive feedback as well as 350 students in the control group who received feedback from instructors.
## 1 Introduction
### Literature Review  
The effective width method, introduced by [PERSON] [5], and subsequently modified by [PERSON] [6], has been the primary design approach for cold-formed steel members in the AISI Specifications, including the latest edition of the North American Specification for the Design of Cold-Formed Steel Structural Members (AISI S100-16) [7]. In the effective width method, the effective width is defined through iterative calculations considering the post-buckling capacity of the member section. This method has some dimensional limitations. The limitations originate from the work of [PERSON] [8], after which the limits of the stiffened elements in bending were updated in 1980, based upon studies conducted at the University of Missouri-Lola in the 1970s [9; 10; 11; 12; 13; 14; 15]. To some extent, these dimensional limitations are arbitrary; however, the limitations of AISI S100-16 hinder the applicability of the design methods.  
Studies [16; 17; 18; 19] that tested and evaluated cold-formed steel-sheathed shear walls have shown that these walls have lengths ranging from 610 to 1830 mm, and heights from 2440 mm. In this case, even if the thickness of the steel sheet is 1.6 mm, the aspect ratio of the web becomes 380 or more, when an integral member is included. This value exceeds the member's dimension limit (web slim (height-to-thickness) ratio \(<\) 200).  
Studies have been conducted to improve the seismic performance of CFS shear walls. [PERSON] et al. [20] investigated a different type of steel shear wall, containing double-skin steel panels. The test results showed moderate ductility and energy dissipation, attributed to the post-buckling strength of the wall. However, it is difficult to machine openings for electrical points in the panels,  
Figure 1: Conceptual diagram of an integrated panel system derived from a steel-sheathed wall system: (**a**) Steel-sheathed wall system, (**b**) Integrated panel system.  
thus limiting its practical use. [PERSON] et al. [21] proposed steel-sheathed CFS trussed shear walls, which exhibited a significantly higher ultimate strength than conventional shear walls. However, the numbers of members and joints constituting the steel-sheathed CFS trussed shear wall were too high for on-site panel construction. [PERSON] et al. [22] investigated the seismic behavior and parametric influence of cold-formed, thin-walled, steel tube truss shear walls. The experimental data showed that the shear walls exhibited good hysteretic behavior, ductility and energy dissipation. However, the construction of a very large number of galvanized V-shaped connectors on-site requires extended construction periods, which is a disadvantage. Although many studies have focused on improving the seismic performance of CFS shear walls, few have analyzed its constructability.
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 4 Growth of GaN Nanorods Using MSE  
GaN nanorods hold several advantages compared to their thin film counterpart. Due to their high surface to volume ratio, GaN nanorods can grow on substrates with lattice and thermal mismatch while maintaining high crystal quality. The threading dislocations commonly associated with mismatch between the GaN and substrate can be eliminated via the nanorod's sidewalls [77]. In addition, the three-dimensional structure allows the nanorod to relieve strain, resulting in better carrier recombination efficiency for quantum confined structures [78].  
In contrast to the vapor-liquid-solid (VLS) mechanism of catalyst-driven GaN nanorod growth in CVD systems [79; 80], nanorods can grow without the need for catalysts in MSE. MSE-grown nanorods can grow either through a self-assembled growth mode or in a selective-area growth (SAG) mode. The former is driven by the anisotropy in GaN surface facet, and is analogous to self-assembled nanorods grown using MBE. On the other hand, the latter is driven by anisotropy of sticking coefficient between the substrate and SAG mask material, and has been observed in both MBE and MOCVD-based systems. Both methods have been subjects of many research activities, resulting in functional devices. The mechanism behind both the self-assembled and SAG growth mode is elaborated in the following section.
# SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory
## 5 Attack Design
### Finding Gadgets for the ROP Payload  
As explained in the Intel(r) Software Guard Extensions (Intel(r) SGX) SDK for Windows OS, SGX does not allow dynamic linking, meaning that all necessary functions' binaries are copied to the enclave code at build time [6]. Each enclave needs its own function sets at different locations. This is why it is almost impossible to construct a silver bullet ROP payload that will work for any enclave applications. Therefore, an adversary has to build a customized ROP payload for each target enclave.  
During the attack preparation step, the adversary will search for a set of gadgets. We use ROPgadget [31] for finding all gadgets that can be used to build ROP payloads. Since certain library functions are likely to be included in many enclaves, it is usually better to use gadgets from SGX's trusted library. However, as we mentioned above, each enclave uses its own set of library functions, so there may be cases in which gadgets that the adversary wants to use are not available in any of the enclave's binary code. We discovered that there are various combinations of gadgets that perform the same operation. For example, one gadget, _inc addr_, in mbedTLS-SGX, increments the value stored at _addr_. However, Graphene-SGX does not have that gadget. Instead, we used six other gadgets and composed an ROP sequence shown in the first nine lines of Listing 4, which performs an equivalent operation to _inc addr_. In this fashion, we successfully built the SGXDump ROP payloads for mbedTLS-SGX and Graphene-SGX, despite them having completely different sets of gadgets. To demonstrate that the SGXDump ROP payload can be created in other SGX applications, we constructed payloads in two additional SGX applications, Asylo [32] and Mystikos [33], and presented their gadgets in Appendix A. Based on our experience of finding gadgets from four SGX applications, it is very likely that SGXDump ROP payloads can be constructed for most enclave applications.  
Before going further, we make the case that our attack can work well even when address space layout randomization (ASLR) is enabled. In an ASLR environment, the same enclave is loaded at different base addresses, causing a gadget address found in one execution to change in the next execution. However, it is worth noting that while base addresses are random at every execution, the relative addresses of the text and data sections inside the enclave memory do not change. This means that a gadget's distance from the base address is always the same. Therefore, a new version of the ROP payload can be constructed correctly when the base address of the target enclave becomes available.  
**Identifying the Enclave's Base Address.** To find out the base address of the Enclave, we make use of the _/proc/pid/maps_ file. This file contains the address information of the _/dev/isgx_ file, which has the device name of the SGX kernel module [34]. The address is identical to the base address of the loaded enclave in the target process.  
Figure 3: Contents of _/proc/pid/maps_ file.
## 2 Experiment Description  
### Material  
The 3D RF material applied in this study was sintered using a few multite fibers and a substantial amount of high purity silica fibers. The 3D RF material's average porosity and density were 87% and 0.28 g/cm\({}^{3}\), respectively. The high porosity made the 3D RF material have a low weight, which also reduced the mechanical properties of the material. The detailed sintering process of this material is shown in [26]; only a short introduction is given here. Firstly, the multite fibers and silica fibers were cut into short fibers (the averaging fiber length and diameter were \(\sim\)0.6 and \(\sim\)0.01 mm respectively), then the sintering additive, such as B\({}_{4}\)C powder and soluble starch, were mixed with the short fibers to produce the slurry. The slurry was evaporated in the vacuum. Then, the fiber body was sintered at 1473 K in a furnace for about 2 h. In this sintering process, the fibers in the body were randomly bonded together. Finally, an RF material with a high porosity was constructed. Moreover, a scanning electron microscope (SEM) image for this 3D RF material's cross-sectional area viewing along the TTT directions is given in Figure 1. Obviously, this 3D RF material had a high fiber spacing and the fibers were distributed randomly along the TTT direction.
## 5 Conclusions  
In this paper, we propose a multi-dimensional resource allocation strategy to facilitate covert communication within the multi-beam LEO satellite network. Our approach ensures the rate requirements of different user equipments while preventing the detection of communication signals by an eavesdropping GEO satellite. Specifically, we formulate an optimization problem that jointly optimizes satellite beam-hopping scheduling, frequency band allocation, and the transmit power of different user equipments, under the covertness constraint. Then, we transform the optimization problem into a MILP problem by introducing additional auxiliary binary variables, which allows us to utilize machine learning-based techniques for efficient solution finding. Specifically, we apply a TMDP method to address this MILP problem. Simulation results demonstrate that our proposed scheme significantly improves the communication efficiency in the multi-beam LEO satellite network.  
The overall framework of our proposed resource scheduling strategy is novel, and there are several directions of extension for future research. Firstly, additional performance metrics such as energy efficiency and queuing delay can be incorporated to enhance the resource allocation strategy. Additionally, refining the communication requirements of user equipments to account for sudden business demands, regional variations, and other factors can improve the adaptability of our strategy. In order to establish a more accurate channel model, factors such as the satellite movement speed, multi-path fading, and rain/fog fading can also be considered. Furthermore, the corresponding solving algorithms should be optimized, and the strategies for covert communication between multi-beam LEO satellites are also worth studying. Based on these considerations, we aim to further explore and refine the multi-beam LEO satellite resource allocation strategy for covert communication in our future work.  
Figure 6: The comparison between different schemes.  
**Author Contributions:** Conceptualization, R.W.; methodology, R.W.; software, M.C.; validation, M.C. and L.X.; formal analysis, R.W. and M.C.; investigation, M.C., Z.W. and Y.W.; resources, Y.W. and L.X.; data curation, S.L.; writing--original draft preparation, R.W. and M.C.; writing--review and editing, M.C.; visualization, L.X. and S.L.; supervision, Z.W.; project administration, Z.W. and S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grant 92367201.  
**Data Availability Statement:** The data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Advances in Tissue Engineering for Disc Repair
## 3 Tissue Engineering for IVD Regeneration
### Tissue Engineering for AF and NP Restoration and Maintenance
#### 3.2.2 NP Regeneration and Tissue Engineering  
NP is composed of type II collagen and PGs and contains 77% water. In recent years, bioengineered scaffolds that resemble the native NP structure and its mechanical properties have attracted attention [11]. Easy to inject high molecular weight hyaluronic acid-gelatin-adipic acid dehydazide (oxi-HAG-ADH) hydrogels with anti-inflammatory and immunosuppressive activities, low viscosity, viscoelasticity similar to that of NP tissue, and expression of NP ECM genes have been fabricated [91]. [PERSON] et al. generated hyaluronic-methylcellulose (HAMC) hydrogels loaded with [PERSON]'s jelly-derived MSCs (WJ-MSCs), which significantly promoted degenerated disc repair by improving NP cell viability and decreasing ECM degradation [92]. [PERSON] et al. generated a hydrogel with dextran and gelatin as the first network and PEG as the second network to produce hydrogels, forming the optimal 3D interpenetrating network hydrogel. This increased NP cell proliferation, long-term cell retention and survival, and promoted rehydration and regeneration of degenerative NP in animal models [93]. Laminin is the main component of the NP ECM and directly interacts with NP cells to regulate their function. Several laminin mimetic peptides bound to polyacrylamide gels have been reported to be able to support an immature and healthy NP phenotype. These hydrogel scaffolds provided a favorable environment for NP cell proliferation [94]. [PERSON] et al. manufactured a biocompatible self-assembled peptide hydrogel (SAPH) with easily modifiable properties and nanofibrous architecture. They reported that the SAPH scaffold was as strong as native tissue, injectable, and that it restored the IVD cell phenotype and stimulated deposition of aggrecan and type II collagen, which are key NP ECM components [95].
###### Abstract  
The term \"soft computing\" refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models.  
soft computing; cardiovascular diseases (CVDs); machine learning models +
Footnote †: journal:  
leading causes of death globally and can be triggered by various factors, such as age, gender, smoking, and alcohol consumption. Some of these include underemployment, stress, and anxiety. The prevalence of these diseases has increased in the population due to the busy lifestyles of people currently, and the level of consciousness of their health has become quite worrying [1].  
Out of the 17.9 million deaths due to cardiovascular diseases (CVDs) in 2016, 85% were caused by heart attack and stroke. Almost all CVD deaths occur in low- and middle-income countries, where raised blood pressure is considered one of the most common risk factors. One-quarter of all deaths in India are caused by CVDs, and other causes are Ischemic heart disease and stroke [2]. It is very important that people regularly monitor their health and recover from any chronic diseases. In most cases, uncertainties and biases occur during the healthcare decision-making process. Therefore, the use of soft techniques has become crucial [2]. Automated and cost-effective soft computing solutions can democratize otherwise expensive medical services, therefore saving many lives in developing countries. This research contributes to understanding the insights and performance of soft computing automation in heart disease monitoring, with immense societal impacts.