esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Statistical Processing Methods for Hypothesis Testing
#### 3.3.1 Assessment of Measurement Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Constructs** & **Items** & \(\lambda\) & **Cronbach’s \(\alpha\)** & **rho\_A** & **AVE** & **CR** & **VIF** \\ \hline \multirow{4}{*}{Intimacy} & PAI1 & 0.839 & & & & & 2.263 \\  & PAI2 & 0.838 & & & & & 2.295 \\  & PAI3 & 0.868 & 0.908 & 0.908 & 0.731 & 0.931 & 2.682 \\  & PAI4 & 0.873 & & & & & 2.685 \\  & PAI5 & 0.856 & & & & & 2.492 \\ \hline \multirow{4}{*}{Morality} & PAM1 & 0.849 & & & & & 2.415 \\  & PAM2 & 0.855 & & & & & 2.352 \\  & PAM3 & 0.823 & 0.901 & 0.908 & 0.716 & 0.927 & 2.191 \\  & PAM4 & 0.844 & & & & & 2.322 \\  & PAM5 & 0.862 & & & & & 2.359 \\ \hline \multirow{4}{*}{Dependency} & PAD1 & 0.818 & & & & & 1.879 \\  & PAD2 & 0.821 & & & & & 1.983 \\  & PAD3 & 0.850 & & & & & 2.054 \\  & PAD4 & 0.848 & & & & & 1.916 \\ \hline \multirow{4}{*}{Information Privacy Risk} & IPR1 & 0.830 & & & & & 2.137 \\  & IPR2 & 0.824 & & & & & 2.074 \\  & IPR3 & 0.822 & 0.883 & 0.884 & 0.681 & 0.914 & 2.103 \\  & IPR4 & 0.827 & & & & & 2.055 \\  & IPR5 & 0.823 & & & & & 2.004 \\ \hline \multirow{4}{*}{Parasocial Interaction} & PSI1 & 0.834 & & & & & 2.304 \\  & PSI2 & 0.806 & & & & & 2.024 \\  & PSI3 & 0.824 & & & & & 2.249 \\  & PSI4 & 0.833 & & & & & 2.307 \\  & PSI5 & 0.808 & & & & & 2.109 \\  & PSI6 & 0.823 & & & & & 2.205 \\ \hline \multirow{4}{*}{Trust} & TRS1 & 0.781 & & & & & 2.140 \\  & TRS2 & 0.812 & & & & & 2.368 \\  & TRS3 & 0.804 & & & & & 2.331 \\  & TRS4 & 0.843 & & & & & 2.691 \\  & TRS5 & 0.828 & 0.938 & 0.939 & 0.668 & 0.948 & 2.581 \\  & TRS6 & 0.808 & & & & & 2.339 \\  & TRS7 & 0.833 & & & & & 2.636 \\  & TRS8 & 0.841 & & & & & 2.704 \\  & TRS9 & 0.804 & & & & & 2.291 \\ \hline \multirow{4}{*}{Trust} & CUI1 & 0.834 & & & & & 1.944 \\  & CUI2 & 0.810 & & & & & 1.771 \\ \cline{1-1}  & CUI3 & 0.834 & 0.847 & 0.847 & 0.685 & 0.897 & 1.957 \\ \cline{1-1}  & CUI4 & 0.832 & & & & & 1.874 \\ \hline \hline \end{tabular} Source(s): Table by the Author. Note: \(\lambda\): Outer Loading; AVE: Average Variance Extracted; CR: Composite Reliability; Abbreviations: PAI = Perceived Anthropomorphism–Intimacy; PAM = Perceived Agency–Morality; PAD = Perceived Agency–Dependency; IPR = Perceived Risk–Information Privacy Risk; PSI = Parasocial interaction; TRS = Trust; CUI = Continuance usage intention.  
\end{table} 
Table 3: Construct validity. below 0.85 indicate strong discriminant validity between two reflective constructs [104]. As presented in Table 4, all values fall within acceptable ranges. Furthermore, to avoid potential issues with multicollinearity, we utilized the Variance Inflation Factor (VIF) to assess multicollinearity in the structural model [105]. The analysis results, as shown in Table 3, indicate that all VIF values are below the threshold value of 3.3, confirming that there is no issue of multicollinearity among the constructs [89].
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture
### Use Cases
This information can also be provided when Near-RT RIC performs radio resource allocation for on-demand coverage for a UAV, by considering the radio channel condition, flight path information, and other information. Another category II AI-enabled RAN use case related to UAV is for UAV radio resource allocation. In UAV control scenario, the O-RAN architecture meets the need for wireless resource adjustment [68]. Rotor UAV usually flies at low altitude and low speed, while carrying mounted cameras and sensors. These UAVs have asymmetry requirements between UL and DL services. For UL, the 5G network has to support service to receive the 4K high-definition video UP data from the UAV. On the other hand, there is only a small amount of control data interaction requirement. This introduces a new requirement for the gNB. In addition, the existing network management platform could not optimize parameters for individual users. In O-RAN architecture, Near-RT RIC function module provides RRM functions and radio resource requirements for different terminals to O-CU and O-DU  
Besides AI-enabled RAN use cases published in category II, there is also the virtual RAN use case. The use case for the virtual RAN network is RAN sharing. RAN sharing is an efficient and sustainable way to reduce the network deployment costs while increasing the capacity and the coverage of the network [68]. O-RAN architecture can accelerate the development of RAN sharing solutions because of its open and multivendor architecture. The O-RAN architecture also enables each operator to separately control their own VNFs in shared hardware. Remote O1, O2, and E2 interfaces can be introduced so that each operator could use their own SMO and Near RT RIC to independently monitor and remote control their O-DU in shared network resources. O-RAN architecture introduces freedom and ease in the RAN sharing process  
In the previous paragraphs, we have explained several AI-enabled RAN use cases. O-RAN prioritized some of these use cases in [42]. The prioritized AI-enabled RAN use cases are TS; QoS and QoE optimization; RAN slicing and SLA assurance; and massive MIMO optimization. There are also new additional AI-enabled RAN use cases introduced, such as multi-vendor slices, O-RAN dynamic spectrum sharing, RAN slice resource allocation optimization, local indoor positioning in RAN, massive MIMO single user/multi user-MIMO grouping optimization, O-RAN signaling storm protection, congestion prediction and management, and industrial IoT optimization. Another new use case introduced is the O-DU pooling use case.
## 2 Active Distribution Network Communication Demand Analysis  
### Active Distribution Network Business Traffic Demand  
The communication system of the active distribution network is an important part of it. Firstly, the bandwidth required for each service of the active distribution network is discussed from the perspective of the communication system. According to a previous study [20], the communication services of the active distribution network can be classified as the following categories: vertical network protection (VNP), advanced distribution automation (ADA), DER access control (AC), load management (LM), and other services (e.g., video surveillance). Considering that the State Grid Corporation standardizes the importance of the power consumption area into six categories of A+-E, the terminal equipment configuration levels are different in different areas, and the bandwidth requirements are different. Combined with the traffic analysis methods of different business sections proposed in another study [21], the 110 kV substation and 35 kV substation are set to cover various numbers of nodes, and the bandwidth required for different substations to meet the corresponding node services is calculated. The results are shown in Table 1.  
It can be seen from Table 1 that in the case of DER large-scale access to the grid, distribution systems require lower communication delays and a higher bandwidth. The communication network of the active distribution network should be able to meet the delay and bandwidth requirements of various services. In the process of building a communication network, it is not only possible to consider this from the perspective of the replacement of communication equipment, but also on the basis of the existing communication network, from communication scheduling, data priority transmission, data compression transmission, and mixing multiple communication methods. Based on this, ways to meet the needs of an active distribution network communication can be found.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Fungal Aegerolysins from Basidiomycota--Agaricomycotina
#### 2.1.1. Aegerolysins from Pleurotus ostreatus
ostreatus_ was produced in recombinant form and named recombinant ostreolysin (**rObjA**) [88], which has an amino acid substitution at position 50 (valine to isoleucine) compared to OlyA6. **rObjA** has not been used as a lipid raft marker, but it has been shown to have anti-proliferative and pro-apoptotic effects against colon cancer cells or can be used the treatment of metabolic disorders, which was attributed to its lipid raft binding properties [88]. **rObjA** induced apoptosis of human and mouse cancer cells in vitro but was not toxic to normal intestinal cells [88]. After endocytosis, **rObjA** colocalized with \(\beta\)-III tubulin, which probably impaired microtubule dynamics and led to cell death. These results were confirmed by in vivo mouse models in which **rObjA** reduced tumor cell growth. Further, through its lipid raft binding and interfering with signaling cascades, **rObjA** can induce browning of white preadipocytes used for energy storage in vitro, resulting in a brown-like phenotype with increased mitochondrial biogenesis. **rObjA** has therefore been proposed as a candidate for regulating metabolic disorders such as obesity, hyperlipidemia, and non-alcoholic fatty liver disease [89; 90]. In the genomes of _P. ostreatus_ PC9 or PC15, a bi-directional pair of genes with 5\({}^{\prime}\)-5\({}^{\prime}\) orientation of the **plyA (priA)** and **plyB** genes was observed [91; 92]; no difference among the two strains was observed in the sequences of PlyA.
## 5 Results and Discussion  
This section demonstrates the performance of HCGC and baseline measures through SIR spreading dynamics experiments on both public real networks and ThaiNet datasets. These baseline measures consist of degree centrality (DC), closeness centrality (CC), betweenness centrality (BC), eigenvector centrality (EC), DC\({}^{+}\)[29], GGC [26], LGI [34], DCGM\({}^{++}\)[29], HGSM [28], NPIC [35], and NHM [33]. The results obtained from SIR can be used to identify the influential nodes in the complex network. All experiments were run on an Intel(R) Core(TM) i5-12450H CPU 2.00 GHz processor with 16 GB of memory.
# Reliable Visual Exploration System with Fault Tolerance Structure
## 2 Fault-Tolerant Visual Robot Navigation
### Exploration System  
The structure of our system is shown in Figure 1. The input of our system is the view from a monocular camera fixed on the mobile robot; the output is the control command for platform driving and the model of explored environment. Three modules are involved in our exploration system: active controller based on semantic scene segmentation, visual localization and mapping, and termination condition.  
For maintaining continuous robot motion, an active controller is introduced. Thanks to the advance in deep learning-based image segmentation, a reactive controlling strategy can be designed. We plan the goal point in the image space with the help of ground recognition, which is accomplished by a pre-trained CNN (convolutional neural network). In addition, the main advantage of our active controller is that no real-time localization is needed, and the mobile platform is able to continue its motion and capture the view of the environment.  
After image capturing, the images from the camera are used for environment modeling. The SfM (Structure from Motion) module in our system is labeled as VLM (visual localization and mapping). A pose-graph back-end is employed to estimate robot poses visited by the robot in its environment, and submap idea is introduced for robustness improving, which maintains a graph update within one of the submaps instead of keeping tracking in a single global graph.  
From the aspect of the termination condition, after obtaining the pose graph from VLM, a mapping completeness evaluation is conducted. A coarse-to-fine evaluation is used for this purpose. The coarse completeness evaluation is calculated in terms of the coverage density of robot poses, represented by the vertexes within the pose graph. After the density satisfies the coarse evaluation, a fine evaluation is performed in terms of the distribution of existing keyframes. Only when both evaluations are satisfied will the exploration be terminated.
## 2 Why the Electroencephalogram (EEG)?
### qEEG and Neurotransmitters  
Several studies have suggested a relation between different qEEG oscillations and neuromodulator balance [5; 205]. This is because peculiarities of qEEG features result from the interaction of numerous resonance loops within the cortex and between the cortex and subcortical structures, and these interactions are significantly influenced by neurotransmitter concentrations in the brain [206]. Indeed, the levels of activity of different neurotransmitter systems (acetylcholineergic (ACh-ergic), noradrenalineergic (NA-ergic), dopaminergic (DA-ergic), serotonergic (ST-ergic), and GABA-ergic), as well as the patterns of their interaction, are important drivers of qEEG oscillations. For example, the activation of the NA-ergic system is associated with the desynchronization of qEEGs during behavioral excitation [207] and an increase in high-frequency qEEG oscillations [208]. It is also believed that the increased activity of the DA-ergic cerebral systems results in shifts of the frequencies of qEEG oscillations toward higher ranges and facilitates the reaction of desynchronization [209]. Additionally, posterior vs. anterior distribution of qEEG theta oscillations is informative on DA levels [210]. Low ST levels result in a higher power of low-frequency qEEG components [206]; conversely, high ST levels result in the decreased power of low-frequency qEEG components and the higher power of high-frequency qEEG components [211]. Higher relative levels of ACh promote qEEG alpha oscillations, whereas an increased tone of inhibitory monoamine receptors is associated with qEEG delta oscillations (_delta rhythm_ is electromagnetic oscillations in the frequency range of brain activity between 1.5 and 3.5 Hz) [205]. It seems that for each qEEG oscillatory pattern, there is a correlated neurotransmitter mix [212].  
Deficiencies or excesses of any of the neurotransmitters will produce a marked departure from homeostatically regulated normative qEEG oscillatory patterns and may contribute to neuro-psycho pathophysiology [199; 205]. Indeed, a large body of data suggests that it is possible to unravel distinctive abnormal qEEG oscillatory profiles in terms of specific neurochemical imbalances in particular brain regions [213].
## 5 Data Analysis and Results
### Analysis of Model Fit Tests
#### 5.3.3 Distinguishing Validity  
The data in Table 7 shows that all observed variables have correlation coefficients less than 0.9 and that all of the square roots of the construct AVEs on the diagonal have stronger correlations than their correlations with other variables. This suggests that the scale has strong discriminant validity because there is some association between the observed variables and a relatively high degree of differentiation.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 5 Discussions and Research Directions
### Comparative Analysis: Emerging Technologies
To overcome the challenge of high propagation loss, LC is modeled to construct antenna structures that radiate efficiently, obtaining large tunability and fast switching [139]. As they can direct THz waves dynamically, LC antennas are an interesting solution for THz communications. The work in [140] proposed a programmable metasurface based on LC, where a phase change could be flexible by switching between 0 and 1 states of each element. Besides FAS and LC antennas, and different from conventional mMIMO systems, CF mMIMO is a distributed architecture that has also been introduced as a promising technology for enabling future 6G networks [141]. This network architecture comprises numerous AP in a coverage area to serve users close to the APs. In [131], the IS-aided CF mMIMO system is based on the CF architecture with an additional IS layer between the AP and its users. The authors analyzed the network performance over spatially correlated fading and the blocking probability of the direct AP-user links. Finally, machine learning or AI must be considered as another fundamental 6G enabler, where simple and complex algorithms can be trained to provide intelligent solutions and efficient multiple access resource allocation under the premise of maintaining the coexistence between THz and lower frequency bands [142].
## 3 Full-Scale Testing
### Results
#### 3.4.1 Load-End Shortening Relationships  
Graphs of applied load against end shortening are shown in Figures 12 and 13 for the PLA and ABS elliptical tube specimens, respectively; the results for experimental ultimate load N\({}_{u,\text{exp}}\) are shown in Table 8. In Figure 12b-d, graphs for the PLA batch 1 specimens (EHS01 to EHS08) are plotted in lighter colours while those for PLA batch 2 (EHS09 to EHS17) are plotted in darker colours. It can be seen that almost all PLA specimens have undergone linear elastic deformation up to a sudden failure with a negligible amount of softening visible; EHS11-PLA2-90-60-3.0, which is the stockiest specimen, has shown a more gradual softening prior to achieving its ultimate load, indicating that yielding has occurred prior to rupture of the material.  
As shown in Figure 13, there is a considerable amount of post-peak deformation recorded for the more ductile ABS specimens. The load-end shortening curve for EHS21 suggests a loss of the initial linear stiffness after only a small amount of end shortening. It should be noted that COV(\(t\)) for EHS21 is over 15%, suggesting a higher level of imperfection during the fabrication process, which can manifest as a lower relative stiffness of the material during compression testing.  
Figure 12: Load–end shortening graphs for PLA EHS specimens in compression; (**a**) all specimens, (**b**) \(a/b=1.5\), (**c**) \(a/b=2.0\), (**d**) \(a/b=3.0\). Light colours: PLA batch 1; dark colours: PLA batch 2.
## 1 Introduction
### Era of Agile  
However, nowadays, the majority of the so-called \"big IT\" software development teams and specialists are eager to use iterative and/or incremental software development models, i.e., based on agile processes, for example, SCRUM (see Figure 4 or Kanban).  
Figure 3: ”Waterfall” SDLC reinforced by the V-model.  
Such an approach means that the whole CubeSat software is broken down into chunks of \"features\" (functions) that are ready to be implemented, tested, and demonstrated as a separate stand-alone function. Looking into the research \"Using the Event-B Formal Method for Disciplined agile Delivery of Safety-critical Systems\" [33] available, it became clear that there are many alternations of this classical SCRUM process that fit better into mission-critical software development that might be more applicable for the CubeSat projects and shall be further investigated. One such example is a development framework called disciplined agile (DA) and presented by the Figure 5, which represents the process parts shown below.  
Based on the statements and found problems of the waterfall and the advantages of the agile (SCRUM, DA) development models, the authors suggest moving the CubeSat software development and overall integration of the satellite to the agile model while the hardware parts mature via the classical waterfall model. Accordingly, the authors suggest using the hybrid model, which is becoming commonly used in embedded electronics development. A simple visualization of the benefits is shown below in Figure 6.  
Figure 4: Typical agile-like SCRUM SDLC.  
Figure 5: Disciplined agile as the mission-critical systems development response to the classical SCRUM process.  
Figure 6: Comparison of the agile and waterfall development models and their value delivery.  
As it could be seen, the main difference is shown by the question mark symbol. It emphasizes the problem of too late verification of the entirely developed software, rather than testing smaller but completed deliveries (as shown in the Agile part of the figure).
## 1 Introduction
Some researchers have simulated earthquake-induced damage to cross-fault slopes by setting up equivalent fault surfaces (rupture zones) in shaking table model boxes and inputting seismic motion records. However, due to the limitations of shake table equipment, it is difficult to replicate the relative dislocation between the two fault blocks. On the other hand, fault dislocation devices have become quite advanced. Whether using normal gravity or centrifuge models, there has been abundant experimental research on the rupture responses of buildings and their overburden soil sites under the bedrock fault dislocation. However, experimental studies on cross-fault slopes under bedrock fault dislocation remain quite scarce. Centrifuge model tests face challenges such as small model size, high costs, and complex testing procedures. Therefore, based on the case study of slope damage induced by bedrock fault dislocation, this paper independently developed a large-scale normal gravity bedrock dislocation device to simulate the failure mechanisms and evolution process of cross-fault slopes under bedrock fault dislocation. At the same time, microseismic monitoring has been widely applied in the entire process of the initiation, development, and propagation of microfractures within the slope's geotechnical materials, ultimately leading to macroscopic instability and rupture [54; 55; 56]. To further validate the rationale of field site investigations and hypotheses, this study introduces microseismic monitoring technology to reveal the process and patterns of fault rupture evolution within the slope. Based on the above research, the strong earthquake rupture zone (fault softening surface) directly affects the slope damage in the region adjacent to the strong earthquake fault and controls the failure mode and damage scale of the landslide. Therefore, this paper presents physical model tests conducted on soil slopes across normal faults using a self-developed large-scale bedrock dislocation testing platform. The research results aim to reveal the deformation patterns and instability mechanisms of slopes across faults, promote the development of dynamic theories for slopes under seismic action, and provide technical support for seismic design in practical slope engineering.