esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Visual Exploration System with Fault Tolerance Structure
###### Abstract  
Reliability of visual tracking and mapping is a challenging problem in robotics research, and it limits the promotion of vision-based mobile robot applications to a great extent. In this paper, we propose to improve the reliability of visual exploration in terms of its fault tolerance. Three modules are involved in our visual exploration system: visual localization and mapping, active controller and termination condition. High maintainability of mapping is obtained by the submap-based visual mapping module, persistent driving is achieved by a semantic segmentation based active controller, and robustness of re-localization is guaranteed by a novel completeness evaluation method in the termination condition. All the modules are integrated tightly for maintaining mapping and improving visual tracking. The system is verified with simulations and real world experiments, and all the solutions to fault tolerance are verified to overcome the failure conditions of visual tracking and mapping.  
Keywords:visual exploration; visual tracking and mapping; fault tolerance +
Footnote †: journal: Applied
## 4 Results
### Comparison of Critical Values of \(t\) Testing  
The default method for the \(t\)-testing is that the 95% (\(\alpha=0.05\)) confidence interval corresponds to a default critical value of 1.96; the 99% (\(\alpha=0.001\)) confidence interval corresponds to a default critical value of 2.58. In the case of expressway transport flow, using the adjusted critical value of the \(t\)-testing method mentioned above, the 95% confidence interval for FGWR corresponded to an adjusted critical value of 3.641 and the adjusted \(\alpha\) value was 0.00028. In MFGWR, the 95% confidence interval corresponded to an adjusted critical value of 3.6234, and the adjusted \(\alpha\) value was 0.00029. The 95% confidence interval values for the four covariates in MFGWR corresponded to the following adjusted critical values and adjusted \(\alpha\) values respectively; 3.6888 and 0.000229 for intercept, 3.6596 and 0.000256 for Topp, 3.7367 and 0.000189 for GDP, 1.9606 and 0.05 for Distance (see details in Table 6).  
Figure 6: Histograms of \(t\)-statistics for three parameter estimations.  
Obviously, only the critical value of the distance explanatory variable in MFGWR was similar to the default critical value, and the other adjusted critical values were larger than the default critical value.  
Table 7 shows the number of significant \(t\)-statistics after filtering the \(t\)-statistics of FGWR and MFGWR using the default critical value and the adjusted critical value. In FGWR, since the adjusted critical value was larger than the default critical value (3.641 compared with 1.96 at the 95% confidence interval), the number of significant \(t\)-statistics after filtering is significantly reduced. In MFGWR, except for the significant number of significant \(t\)-statistics for intercept, the number of significant \(t\)-statistics of the other three explanatory variables remains unchanged.
## 5 Conclusions  
To improve the stability of the immersed boundary-lattice Boltzmann (IB-LB) coupling scheme, we proposed effective and efficient approaches which are based on iterative correction to the external force term. Several typical cases are simulated to verify and validate the scheme. The main conclusions are as follows: (1) Directly introducing the iterative process to the IB-LB framework will induce numerical fluctuation around an analytical solution, which will not improve the stability. By introducing the AWI approach, most of the fluctuation can be eliminated, and both numerical stability and accuracy are improved. (2) The proposed method can significantly improve the band of material parameters in the framework of the IB-LB coupling scheme. The dimensionless stretch coefficient, fastening coefficient, and bending rigidity can be improved by 3, 12, and 8000 times, respectively. There is qualitative improvement in the choice of bending rigidity. (3) Because of the stability improvement of the proposed method, the inertial force can be directly discretized, which helps in avoiding the user-defined parameter. (4) Even when the fluid viscosity is notably small, stability can be maintained by the proposed method, which makes this method even more versatile.  
Methodology, C.Z. and T.L.; Software, C.Z. and T.L.; Validation, C.Z. and T.L.; Formal analysis, J.H.; Resources, J.H. and Q.Z.; Data curation, C.Z., T.L., W.M. and P.P.; Writing--original draft, C.Z., T.L. and W.M.; Writing--review & editing, C.Z., T.L., J.H., Q.Z., Q.M. and P.P.; Visualization, C.Z., T.L. and Q.Z.; Project administration, Q.Z.; Funding acquisition, J.H. and Q.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China (Grant No. 52109007 and 52109150), Natural Science Foundation of Chongqing (Grant No. cstc2020 jcyj-msxmX0965), the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN201900748), and the Open-End Foundation of National Engineering Research Center for Inland Waterway Regulation and Key Laboratory of Water Resources and Water Transport Engineering of Ministry of Education of China (Grant No. SLK2021B11).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
LBM Lattice Boltzmann method  
IBM Immersed boundary method  
AWI Averagely weighted iteration  
DI Directly iterative
## 2 Nanofluid Thermal Conductivity  
Experimentation has shown that nanofluid thermal conductivity is dependent upon base liquid [3; 9; 10; 11; 12; 13; 14; 15], possible surfactant [16; 17; 18; 19; 20; 21; 22; 23], concentration [1; 3; 4; 7; 8; 9; 10; 11; 13; 15; 16; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40], temperature [5; 8; 10; 12; 13; 25; 28; 33; 38; 39; 40], and characteristics of nanoparticles, such as nanoparticle size [3; 4; 7; 11; 20; 25; 28; 32; 35; 36; 42] and nanoparticle shape [16; 25; 43]. Interestingly, in some cases, experimental results are controversial. It has been observed that nanoparticle size and shape significantly influence the thermal conductivity of nanofluids whereas, surprisingly, nanoparticle material does not have a significant impact. It has been observed that temperature and nanoparticle volume concentration also significantly influence the thermal conductivity of nanofluids. The following section will discuss the aforementioned parameters which influence nanofluid thermal conductivity.
## 2 Materials and Methods
### Feature Extraction with Deep MLP  
The Deep Multi-Layer Perceptron (Deep MLP) with feature extraction was used in this work due to its ability to capture complex patterns through multiple hidden layers. The Deep MLP steps and training stages are as follows:  
* Architecture Design: The network has many hidden layers designed to capture complex data structures.
* Starting Weights: Starting weights are used to start the training program.
* Activation function: A Rectified Linear Unit (ReLU) is used for nonlinear conversion.
* Batch normalization: Layers are added to normalize the data within the network, thereby accelerating training.
* Optimization: An adaptive moment estimation (ADAM) optimizer was used for dynamic learning rate variation.
* Hyperparameter tuning: The number of layers and neurons, the dropout rate, etc., are changed
* Loss functions: Log-loss or squared loss functions are used to measure performance.
* Training and Validation: Training losses are monitored over epochs, and measures are implemented to prevent overfitting, such as dropout and early dropout.  
Mathematically, the deep MLP for feature extraction can be described as follows [11]:  
Figure 1: The comprehensive breast cancer diagnosis algorithm comprises a deep MLP, a feature-fused autoencoder neural network, and a weight-tuned cross-validation decision tree.  
Let the input data be  
\[\mathbf{X}=\left\{\mathbf{x}^{(1)},\mathbf{x}^{(2)},\ldots,\mathbf{x}^{(N)} \right\},\mathbf{x}^{(i)}\in\mathbb{R}^{d} \tag{1}\]  
Pass each input through a deep MLP:  
\[\mathbf{h}^{(i)}=f_{\text{MLP}}\Big{(}\mathbf{x}^{(i)}\Big{)}=\sigma_{L}\Big{(} \mathbf{W}_{L}\cdots\sigma_{1}\Big{(}\mathbf{W}_{1}\mathbf{x}^{(i)}+\mathbf{ b}_{1}\Big{)}+\cdots+\mathbf{b}_{L}\Big{)} \tag{2}\]  
where  
\(\sigma_{j}\) denotes the activation function (e.g., ReLU);  
\(\mathbf{W}_{j},\mathbf{b}_{j}\) are weights and biases in layer \(j\);  
\(\mathbf{h}^{(i)}\in\mathbb{R}^{dd^{\prime}}\) denotes the extracted feature vector.
# Reconditioning of Diamond Coated Tools and Its Impact on Cutting Performance for CFRP Laminates
## 2 Materials and Methods
### Decoating by the RIBE  
Diamond-film RIBE decoating was performed with a commercial ion etching system (IE400, ShinMaywa Industries, Hyogo, Japan). The experimental setup is shown in Figure 3. The diamond etching was achieved through oxygen ion beams generated with CDIS. The CDIS beam generation relies on the crossed electric and magnetic field discharge, where electrons are trapped in a circular region near the magnetic trap while ions are freely accelerated by the electrical field. More details of the CDIS principle and structure can be found elsewhere [19].  
The tools for decoating were placed in a vacuum chamber and exposed to a CDIS concentric array. The beam size of CDIS was \(\sim\)100 mm in diameter and covered the whole coated length of the processed tools, so that the whole coating was etched with a good uniformity along the vertical axis. In addition, the tools were rotated planetary in order to provide an even ion/radical flux exposure in the horizontal plane, which was previously shown to result in a good etching uniformity inside the flutes [20].  
Pure oxygen was used as a beam-forming gas. The accelerating voltage was set to 4.0 kV, which generated a discharge current of \(\sim\)100 mA at a 0.3 Pa pressure.  
The RIBE rate was evaluated in a previous work and was about 1.7 \(\upmu\)m/h for the microcrystalline diamond film. Wherever the ion beam contacts with the uncoated tool  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Boron-Doped Micro-Crystalline Diamond (B-MCD)** \\ \hline Flow of H\({}_{2}\) (sccm) & 700 \\ Flow of CH\({}_{4}\) (sccm) & 15 \\ B/C atomic ratio (ppm) & 8000 \\ Process pressure (Pa) & 600 \\ Substrate temperature (\({}^{\circ}\)C) & 800 \(\pm\) 20 \\ Duration (h) & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Deposition process parameters.  
Figure 3: Reactive ion beam etching (RIBE) experimental setup [14].  
parts, the WC-Co sputtering also took place but with a much slower rate due to the primarily chemical nature of the process [14]. The applied tools were etched for 6 h in the same batch. The temperature during the etching did not exceed 300 \({}^{\circ}\)C.
# Microbes in Health and Disease: Human Gut Microbiota
## 3 Multifunction of the Human Gut Microbiota
### Development, Education, and Regulation of Immunity  
The gut microbiota participates in the development, improvement, and regulation of immunity as well as in the regulation of inflammation [27; 72]. The relevance of the gut microbiota to the development of immunity has been demonstrated widely in various germ-free animal model experiments and epidemiological studies [72; 73; 74; 75; 76]. For example, compared with conventional animals, a germ-free animal model demonstrated various defects in gut mucosal and systemic immunity, including relatively lower weights of lymph nodes in thesmall intestine [77], smaller Peyer's patches, thinner lamina propria, and reduced numbers of CD4+ T cells and IgA-producing plasma cells in the intestine, and these defects were found to be reversible by colonization by the commensal microbiota [74; 76]. Reduced numbers of some myeloid cells, differentiated T cells, and B cells in the spleen, liver, etc., were also observed in germ-free or antimicrobial-treated animal models [73]. In epidemiological studies, an increased incidence of immune diseases (e.g., allergic disease) in childhood and infectious diseases has been reported, especially in regions with improved hygien lifestyles, which is suggested to be associated with decreased exposure to microorganisms or reduced biodiversity of the gut microbiota [78; 79]. Together, these findings highlight the relevance of the gut microbiota to the development of normal immunity.  
The gut microbiota regulates immune innate and adaptive responses via gut microbiota-associated cellular compounds or metabolites (e.g., short-chain fatty acids and bile acids) [72; 80]. For example, segmented filamentous bacteria are an important group of beneficial gut microbes. They attach to epithelial cells in the ileum and trigger epithelial cells to secrete humoral factors (serum amyloid A) that promote Th17 cell differentiation in the lamina propria (Figure 3), and the production of Th17 effector cytokines (e.g., IL-17 and IL-22) is associated with increased resistance against the intestinal pathogen _Citrobacter radentium_[81]. As important gut fermentation products, short-chain fatty acids regulate the differentiation of T cells into regulatory T cells (e.g., Tregs) and effector T cells (e.g., Th1 and Th17 cells). While effector T cells combat invading microbes and produce proinflammatory cytokines, regulatory T cells control the excessive responses of effector T cells and produce anti-inflammatory cytokines (e.g., IL-10) to limit inflammation and maintain immune tolerance. A delicate balance exists between effector T cells and regulatory T cells in the normal immune system. Short-chain fatty acids inhibit histone deacetylases (HDACs) and subsequently increase the activity of the mTOR pathway, which is important for promoting T-cell differentiation into regulatory T cells (e.g., Tregs) and effector T cells (e.g., Th1 and Th17 cells) and the expression of their cytokines (e.g., IL-10, IFN-\(\gamma\), and IL-17) [71; 82].
## 4 Testbed Development
### Testbed Architecture  
The testbed architecture is outlined in Figure 7. _Machine (A)_ and _VM_ correspond to physical computers, whereas _VM_ is a Virtual Machine (VM) instance. The physical nodes comprise Asus PN51 equipped with 64 GB of memory and 1 TB of disk space. The VM has 1 virtual CPU, 8 GB of memory, and 20 GB of disk space. Each machine runs Ubuntu Server 22.04 with kernel v5.15.0.  
The integration of the mega-constellation leads to the existence of two distinct LANs. While _VM_ and _Machine (B)_ are hosted in the fiber-based LAN of our laboratory, _Machine (A)_ is hosted in the LAN created by the Starlink _User terminal_. This distribution enables the models to communicate with the components of both LANs through mega-constellation connectivity.  
The deployment of the proposed architectures involves always maintaining the UE on the Starlink side and the 5 GC on the opposite side, thereby generating traffic from the former LAN. This allows us to simulate a scenario where the UE and the 5 GC are geographically distant. To this end, we opted to install OAI [52] in _Machine (A)_ and _VM_ and Open5 GS [53] in _Machine (B)_. The decision of utilizing OAI is that, unlike srsRAN, it offers an updated implementation method for the 5G UE, as well as the RFS simulator tool [54]. This tool encapsulates the I/Q samples, which would originally reach the radio's frontend, in TCP packets to be sent through an Internet Protocol (IP) connection, enabling the transmission of the sampled NR signal over Starlink. We installed the latest code versions of that moment, namely 2024.w22 by OAI (compliant with Release 15) and v2.6.2 by Open5 GS. The resulting testbed distributes 5G components across the nodes to implement specific network architectures. Particularly in this work, we move the full or split gNB  
Figure 6: Linear graphs of metrics collected by (**a**) the UDP session and (**b**) 1-stream TCP connection tests. Vertical bars highlight the time interval during which metrics do not suffer sharp fluctuations, indicating the duration of potential contact windows.  
between _Machine (A)_ and _VM_ to implement the proposed architectures, as detailed in Section 5.  
Nonetheless, the present testbed entails noteworthy considerations which may introduce technical limitations during experimentation. Some of them are unavoidable, but others are addressable with the introduction of auxiliary tools or the implementation of additional software features. Below, we explain them and, if possible, provide a solution.
# Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools
## 5 Classification
### A Note on Threat Modelling Tools and Methodologies  
The PTES classification shows that the number of tools identified for _Threat Modelling_ is relatively small. However, the research field is somewhat active, but some contributions only propose new methodologies without implementing specific tools, so we did not include them in the classification. Some threat modelling methodologies discussed here cover different frameworks, each designed to improve security in cyber-physical systems (CPS), information technology, and critical infrastructure areas.  
[PERSON] et al.'s [146] framework integrates vulnerability assessment with reliability and threat analysis (both external and internal) within a unified model focused on critical infrastructures integrated with CPS. Similarly, [PERSON] et al. [147] present a novel method for modelling cyber and physical interactions within IoT networks. The study emphasises the identification of unexpected event chains that could lead to security vulnerabilities. Additionally, [PERSON] et al. [148] proposed a hazard-driven threat modelling methodology tailored for CPS, focusing on the interplay between security, reliability, and safety.  
To highlight the critical role of human factors in information security, [PERSON] et al. [149] introduce a methodology that systematically evaluates information security incidents caused by human error, adopting the HEART methodology from high-reliability sectors like aviation and energy. Also, [PERSON] et al. [150] propose using timed automata to model socio-technical attacks, offering a method that incorporates time and cost into analysing socio-technical systems and attacks.  
Furthermore, using formal methods for security analysis, [PERSON] et al. [151] introduce an algorithm that transforms Attack Trees into Markov Decision Process models, aiming to address the limitations of scalability, state explosion, and manual interaction inherent in Attack Trees.  
Collectively, these methodologies demonstrate a shift towards integrating diverse analytical tools and perspectives, from human factors to formal methods and system theory, to address the increasingly complex and interconnected nature of modern systems.
# A Systematic Review: To Increase Transportation Infrastructure Resilience to Flooding Events
## 3 Results
(2019) also used social media as well as satellite imagery to determine which roads were passable during floods [146]. [PERSON], et al. (2020) established a method of sharing flood information to road users during floods in Virginia Beach, Virginia, USA. This implementation required communication between the traffic management center of flooding presence and the in-vehicle systems to determine if the vehicle was at risk and possible alternative pathways [141]. There are many studies that investigated how floods impact people on the streets. [PERSON], [PERSON], et al. (2020) found the ways how flooding events affect roadway users by considering altered departure times, mode of travel, or travel cancellation. They conducted a survey with public transit commuters to investigate how flood events within the last ten years impacted their morning commutes in Metro Manila, Philippines [144]. [PERSON], et al. (2016) evaluated exposure of road users to flooding in the Gard region, France by: (1) combining the density of roads and average distance driven to certain points to find the number of road users and (2) applying a traffic attribution to census data [155]. Main research categories and relevant studies were briefly summarized and explained in this section. The Discussion section will elaborate how each category's studies can be included to contribute to studying the increase of transportation infrastructure resilience to flood events.
###### Abstract  
Nowadays, some of the most well-deployed infrastructures are Virtual Private Networks (VPNs) and Overlay Networks (ONs). They consist of hardware and software components designed to build private/secure channels, typically over the Internet. They are currently among the most reliable technologies for achieving this objective. VPNs are well-established and can be patched to address security vulnerabilities, while overlay networks represent the next-generation solution for secure communication. In this paper, for both VPNs and ONs, we analyze some important network performance components (RTT and bandwidth) while varying the type of overlay networks utilized for interconnecting traffic between two or more hosts (in the same data center, in different data centers in the same building, or over the Internet). These networks establish connections between KVM (Kernel-based Virtual Machine) instances rather than the typical Docker/LXC/Podman containers. The first analysis aims to assess network performance as it is, without any overlay channels. Meanwhile, the second establishes various channels without encryption and the final analysis encapsulates overlay traffic via IPsec (Transport mode), where encrypted channels like VTI are not already available for use. A deep set of traffic simulation campaigns shows the obtained performance.  
IPsec; Linux; OpenWrt; Overlay; LibreSwan; IKE; TLS; Cybersecurity +
Footnote †: journal:  
0000-0001-4607-2278-22011