esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation of Service Performance  
The dynamic stresses on the track structure, as induced by HAL trains, represent a critical factor that must be considered in the design of HAL railways. In this work, an integrated analysis of all the above mentioned numerical and empirical results was performed to evaluate the service performance. As mentioned previously, the dynamic stresses on the top layer surface are influenced by \(v\) and \(F\), and this influence can be expressed using Equations (8) and (10), respectively. Considering the vertical peak dynamic stress on the top layer influenced by the two factors presented above, a function of the variation in \(v\) and \(F\) is stated:  
\[\sigma_{d,\max,v-F}=\sigma_{0}\times f(v)\times f(F) \tag{13}\]  
where \(\sigma_{0}\) is a model parameter, and \(f(v)\) and \(f(F)\) depend on \(v\) and \(F\), respectively. Based on Equations (8) and (10), a nonlinear regression analysis of these numerical results in the following equation:  
\[\sigma_{d,\max,v-F}=0.0078\times(0.26v+110.6)\times(0.649F+0.021) \tag{14}\]  
By combining Equations (12) and (14), the distribution of \(\sigma_{d,max}\) under the track structure with varying \(v\) and \(F\) can be predicted.  
Figure 13 shows the ratio of \(\sigma_{d,max}\) to the self-weight stress (\(\sigma_{s}\)) at the bottom layer (2.5 m below the top layer surface) with varying \(v\) and \(F\) for the locations directly below the track centerline. According to the Code for the Design of Heavy Haul Railway [36] of China, \(\sigma_{d,max}/\sigma_{s}\) should be less than 0.20 to maintain the long-term stability of a railway subgrade. Consequently, the thickness of the top and bottom layers considered in the existing HAL railway network can likely satisfy the requirements to realize a heavy-haul train with a certain v and F within the available zone (see gray zone of Figure 13); however, the operation of HAL trains with v and F outside the available zone cannot be realized unless the top and bottom layers are reinforced. It should be noted that the operation of HAL trains with different \(v\) and \(F\) for the existing railway lines [7; 25; 37; 38] lies in the predicted available zone (see Figure 13), which demonstrates that the proposed service performance evaluation method is valid and accurate.  
Figure 13: Effects of train speed (\(v\)) and axle load (\(F\)) on long-term stability of the existing heavy-haul railway system [7; 25; 35; 36].
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 3 Composition of ECMs from Various Tissues
### Bone ECM  
The ECM of bone contains both organic and inorganic compounds, which make up 40% and 60% of the total matrix, respectively [73]. The organic component consists of type I collagen (90%), along with non-collagenous proteins (10%) [73], while the inorganic component of the matrix consists primarily of hydroxyapatite and trace elements such as magnesium and zinc [74]. Type I, type III and type V collagens are abundant in the organic component of the ECM of bone. Like most other ECMs, collagen in bone tissue serves to provide mechanical support and forms a protective network for bone cells to reside. Type I collagen forms triple helical structures, which can be assembled to form higher-order fibril structures (Figure 1). Although types III and V collagen are present in smaller amounts, they also play a crucial role in regulating the formation of collagen fibres [73].  
In addition to collagen, non-collagenous proteins are arranged into an ordered and specific network [75] in the ECM of bone. These include proteoglycans or small leucine-rich proteoglycans (SLRPs), such as decorin or keratocan. These play an important role in regulating bone homeostasis and controlling cellular behaviours by interacting with cell surface receptors to promote cell proliferation, osteogenesis, mineral deposition and bone remodelling. Osteocalcin, a \(\gamma\)-carboxosyltransferaseductamic acid-containing protein is an additional important organic component of bone and is a marker of bone formation. Finally, Small Integrin-Binding ligand N-linked Glycoprotein (SIBLINGS) are a group of glycopoproteins, including bone sialoprotein (BSP) and osteopontin, which are found in the organic matrix of bone [73].
## 4 Results and Discussion  
### Virtual Reality System Design  
CATIA V5 software was used for creating a 3D model of the fuel system of the Dornier 228 [20; 21] as shown in Figure 3. The horizontal elevator and the fuel system were designed for a detailed inspection. The 3D model is imported to Unity 3D software after changing the file type to *.fbx. Then, the preselected software development kits were installed for designing the complete AMVR system. Finally, after completing the design, the AMVR system was evaluated to know whether the system design met the requirements or whether further modifications were required. The results of the evaluation process are shown in Figures 4-7. The A questionnaire survey was conducted to evaluate the efficiency of the AMVR before and after students experienced the AMVR system for inspecting the aircraft.  
The walk-around inspection included preflight check, subsequent flight check, and daily check. All the maintenance check requirements were performed by maintenance personnel according to the maintenance practices. Small screens were designed to assist students to inspect and understand the operation of various components extensively. The aircraft inspection was carried out for wings, fuselage, propellers, and power supply. During the walk-around inspection, students followed the inspection procedure and filled out a checklist based on instructions provided on the display.  
Figure 3: 3D modeling: (**a**) 3D model of the Dornier 228; (**b**) The components use to connect fuselage with horizontal stabilizer; (**c**) 3D model of the Dornier 228’s fuel system.  
For fuel system inspection, the schematic diagram, location, and the number of components were displayed as images and 3D models, helping students observe the system with HMDs. When maintaining the horizontal stabilizer, students disassembled and reassembled the horizontal stabilizer according to the instructions of the aircraft maintenance manual displayed on the display.  
Figure 4: Walk-around inspection screen.  
Figure 5: Fuel system observation scene. The green arrow indicates parts location of fuel system.  
Figure 6: Maintenance scene of horizontal stabilizer.  
In all three scenes, a user interface object called \"Hint\" was used to guide how to use and be familiar with the controllers. The other UI object called \"Switch Scene\" was used to change the training scenes or exit the VR training system, as shown in Figure 7.
# Laboratory Investigations of Mold Growth on Transverse and Longitudinal Wood Surfaces
## 4 Discussion  
### The Experimental Results  
The experimental results showed that a relative humidity of 75% is too low for mold growth on solid wood. This is consistent with previous findings [11; 13; 15; 19]. The genera _Penicillium_ spp. and _Aspergillus_ spp. grew at relative humidity of 87 and 95%. The growth of _Alternaria alternata_ was not observed at a relative humidity of 87%, and surprisingly not even at 95%, although the minimum relative humidity to initiate its growth on wood is reported between 85 and 89% [15]. This genus is commonly found on plants, but also colonizes building materials, including wood [14; 20; 21; 25]. [PERSON] [14] reported that the relative humidity required for the growth of _Alternaria_ spp. on woodchip wallpaper is greater than 90%. We interpret that _Alternaria alternata_ does not grow on a clean and planed surface of wood. The explanation may be that this genus requires an additional source of nutrients, for example, in the form of dust.  
With respect to the main anatomical surfaces of the wood, two patterns were noticed in the mold growth curves. The maximum delay in mold growth was found on the transversal surface of spruce specimens. The shortest time of reaching the maximum mold index was found for the tangential surface of both species. With respect to the latter, a possible explanation might be that the tangential surfaces were almost purely composed of earlywood. On the other hand, the explanation could also be that mold growth on tangential surface is easier to observe in a microscope due to the flat surface without tree rings. It should be stressed that both mentioned trends are not very apparent and should be interpreted with care due to high uncertainty of characteristic times (due to the low frequency of observation and somewhat limited number of test specimens).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Species**} & \multicolumn{4}{c}{**Pine**} & \multicolumn{4}{c}{**Spruce**} \\ \hline
**RH [\%]** & & **87** & & & **95** & & & **87** & & & **95** & \\ \hline Specimens & PT-87 & PX-87 & PR-87 & PT-95 & PX-95 & PR-95 & ST-87 & SX-87 & SR-87 & ST-95 & SX-95 & SR-95 \\ \hline \(Ml=1\) [day] & 7 & 7 & 7 & 12 & 7 & 12 & 7 & 19 & 7 & 12 & 19 & 12 \\ \hline \(Ml=3\) [day] & 24 & 24 & 31 & 19 & 31 & 24 & 31 & 52 & 52 & 24 & 31 & 39 \\ \hline Max \(Ml\) [day] & 66 & 45 & 31 & 39 & 39 & 24 & 31 & 52 & 52 & 24 & 31 & 39 \\ (\(Ml\) value) & (4) & (3) & (3) & (4) & (4) & (3) & (3) & (3) & (3) & (3) & (3) \\ \hline Decay [day] & - & 59 & 66 & - & 66 & 66 & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristic times in mold growth on test specimens. The values are expressed in days and represent: \(Ml=1\), the time of the first microscopic signs of the growth; \(Ml=3\), the time of the first macroscopic signs of growth; Max \(Ml\), the time when the mold index was achieved and its value in brackets; Decay, the time when the decay of sporanguium maximum was first observed.
# Investigating Wearable Fitness Applications: Data Privacy and Digital Forensics Analysis on Android
## 5 Discussion
### GPS Locations  
It is widespread in modern fitness trackers and smartwatches that track the GPS locations of the user to enable many insightful features that allow for a better overall calculation and improve the user's awareness of their movements. There are two main ways these devices acquire GPS: by using the built-in GPS in the devices or using the paired phone GPS signal to determine the location when they lack the built-in GPS capabilities.  
On the other hand, our study found that these apps allow us to infer the location from other pieces of data (e.g., Garmin vvosmart(r) 4 that gets information from the nearest weather station). Through this weather notification, the app recorded the station's name which is near the user's real location. Another example within the Garmin Connect app, where the activities performed by the user are named using the city/county name when the user did a walk, for example. Furthermore, the Mobvoi app recorded IP addresses that could be used to infer device locations, which can help the app to determine user location with city-level accuracy even without using or enabling GPS functionality. Although these are unorthodox methods to determine user locations, they could be essential for investigators to cross-validate the evidence found in the device. Although forensic investigators may be thrilled by the availability of such location data within these apps, Connect and Mobvoi app users may not be aware that their devices are tracking their location whenever they venture to a new/different place. This tracking may occur even without the user explicitly tracking a workout activity and thus may facilitate placing a suspect within the vicinity of an incident in question.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 4 ETSI PoC
### Security Assets
#### 4.1.3 Proof of Transit (PoT)  
PoT is a security asset designed to provide trust in the routing path followed by a packet. Based on the IETF draft [47] and the mathematical operations outlined in [48], PoT adds a small portion of operational data to each packet traversing the network devices that form the infrastructure. These data enable the determination of the path's properties and the detection of any missing conditions. In the PoC, PoT ensures trust for traffic expected to traverse a protected channel. It verifies that encrypted traffic arriving at one of the endpoints--in our PoC, the RAN and the core--has followed the desired hops. PoT denies traffic that fails to meet this condition and identifies where the failure occurred. Nodes with PoT enabled are managed by a centralized controller.  
The policy model expansion involves extending the monitoring capability to include a configuration action definition for Proof of Transit. This allows specification of the allowable traffic profile for specific interfaces. For example, it can be used to detect plain traffic appearing on an interface where all traffic is expected to be encrypted. The plugin for configuration creation is responsible for supplying the controller with the endpoints of the nodes involved. The driver utilizes the REST API defined by the controller to process the configuration requests. The controller then generates Shamir's Secret Sharing Scheme (SSSS) keys and symmetric keys for each pair of nodes [48]. These configurations are forwarded to the nodes using the IETF PoT profile YANG model via NETCONF.  
Once the final agents receive and validate the configuration, they initiate probes to verify the trustworthiness of the path. This process ensures the integrity of the traffic and enables detection of any deviations from the specified routing path.
## 4 Scheme Design: MPMF
### Relay Selection
#### 4.2.2 Case 2  
To select a one-hop relay node, its location and destination's movement can be exploited to take a forwarding decision. This situation arises when a message-carrying node encounters only one-hop relay node during a message transfer session.  
Let \(s\) encounters \(n_{i}\) at any time instant. Without other node at the one-hop distance from \(s\), \(\theta_{i}\) is calculated according to (1). Let \(\overrightarrow{d}\) be a directional vector of \(d\). Given the next location of \(d\) is known to \(s\), then \(\theta_{d}\) is calculated with respect to \(\overrightarrow{d}\) and \(\overrightarrow{ds}\). For the two geometric angles, i.e., \(\theta_{i}\) and \(\theta_{d}\), the conditions for one-hop relay selection are that \(\theta_{i}\) and \(\theta_{d}<\frac{\pi}{2}\) and \(n_{i}\) and \(d\) are moving in the same direction. The value obtained in \(\frac{\overrightarrow{ds}\times\overrightarrow{ds}}{\overrightarrow{ds} \times\overrightarrow{d}}\) determines whether both the nodes are moving in the same direction or not. If \(\frac{\overrightarrow{ds}\times\overrightarrow{ds}}{\overrightarrow{ds} \times\overrightarrow{d}}<0\), the nodes are moving in the same direction.  
Node \(s\) will continue to carry its messages when \(n_{i}\) is not moving in the direction of \(d\). The message delivery services may suffer if \(s\) continues to keep the message without forwarding it because of the absence of better candidate nodes. Therefore, when a one- and two-hop relay does not qualify for receiving a message, a centrality metric value is used instead. Let the inter-contact between \(n_{i}\) and \(n_{j}\) follow an exponential distribution with a meeting rate \(\lambda_{i,j}\)[27], where \(\lambda_{i,j}\) is calculated by \(\frac{u}{\sum_{j=1}^{n}t_{ij}^{l}}\) corresponding to \(u\) inter-contact time samples, i.e., \(t_{i,j}^{1},t_{i,j}^{2},\ldots,t_{i,j}^{u}\) between \(n_{i}\) and \(n_{j}\) in a given time span \(t\). The contact probability between \(n_{i}\) and \(n_{j}\) within time \(t\) is given by \(1-e^{-\lambda_{i,j}t}\). Given \(N\) nodes in the network, the average probability that a node randomly contacts \(n_{i}\) within \(t\) is given by (9).  
\[C_{i}=1-\frac{1}{N-1}\sum_{j=1,j\
eq i}e^{\lambda_{i,j}t}. \tag{9}\]  
In (9), two nodes can compare their respective centrality values. A message-carrying node compares its centrality with the one-hop relay node. If its centrality value is smaller, the relay node will receive a message \(m_{i}\) from the message-carrying node. The above relay selection can be written as follows:  
\[d(m_{i})=\begin{cases}1,&\left((\theta_{i}\wedge\theta_{d})\leq\frac{\pi}{2} \wedge\frac{\overrightarrow{ds}\times\overrightarrow{ds}}{\overrightarrow{ds} \times\overrightarrow{ds}}<0\right)\ \vee\ \left(C_{s}\leq C_{i}\right),\\ 0,&\text{otherwise}.\end{cases} \tag{10}\]  
Note that in (9), \(t\) is replaced by the current time-to-live of \(m_{i}\) to compute the centrality.
## 4 A Deep Learning-Based CSI Feedback for New Radio
### NR-CsiNet Decoder  
As for the encoder, the NR-CsiNet decoder (Figure 5b) utilizes the implementation scheme of the CsiNet decoder presented in [12]. The first layer is a fully connected layer which receives as input the codeword \(\mathbf{s}\) produced by the encoder at the UE side, sent to the BS. The result of this layer is a coarse reconstruction of the real and imaginary part of the channel matrix \(\mathbf{H}_{m}^{id,t}\) in the form of a vector of size \(T\times 1\). A reshape layer transforms the vector back to the two matrices corresponding to the real and imaginary part of the channel, i.e., two matrices of size \(C\times N\) each. In order to improve these initial estimates, a particular set of blocks, called the RefiNet Unit (Figure 5c), is inserted in the decoder. Each RefineNet unit is composed of three convolutional layers using a kernel of size \(3\times 3\). The first and second layers generate 8 and 16 feature maps, respectively, while the third outputs the final reconstruction of the \(\mathbf{H}_{m}^{id,t}\) matrices (in the form of two feature maps). The feature maps produced by the three layers of a RefineNet unit are forced by a zero-padding technique to have the same size as the input channel matrix (\(C\times N\)). Moreover, each layer is followed by a rectified linear unit (ReLU) [49; 50] and a batch normalisation layer [50; 51]. Before the last leaky ReLU computation, the input of the RefiNet is added to the output of the last CNN (after proper batch normalisation). This step is necessary since, as reported in the literature [52], the sequence of many CNN layers may lead to a vanishing gradient problem. To identify a suitable trade-off between the complexity and reconstruction capabilities, the authors of [12] investigated the impact of the number of RefiNet units, determining that two RefineNet units were sufficient. In our work, we investigated the effect of adopting different numbers of RefiNet units and observed that exploiting more than two RefiNet units did not significantly impact the reconstruction quality, though at the expense of greater complexity. An additional convolutional layer follows the RefiNet Units and, finally, a softmax layer [50; 53] scales the reconstructed coefficients into the \([0,1]\) range.  
Figure 5: NR-CsiNet representation. (**a**) Block scheme of the NR-CsiNet encoder. (**b**) Block scheme of the NR-CsiNet decoder. (**c**) Block scheme of the RefineNet unit.
## 2 Related Work  
### Dynamic Motion Estimation  
The challenge in video prediction is to predict future video frames based on past observations. Accurately predicting future video frames remains an open problem because of the unpredictability and high dimensionality of spatiotemporal dynamics, as well as the uncertain nature of future events. Spatiotemporal prediction has been applied in several fields, such as precipitation forecasting [1; 13; 14; 15; 16], traffic prediction [17; 18; 19; 20; 21; 22], human action [23; 24], air quality [25; 26; 27; 28; 29; 30; 31], and motion planning [32; 33; 34; 35; 36].  
Therefore, several deep learning models have been proposed to address this problem and make better decisions. These include convolutional neural network (CNN) models [37] that capture short-range spatial features, recurrent models such as LSTM and gated recurrent unit (GRU), adversarial generative networks (GANs), variational autoencoders (VAEs), and a mixture of these models [38].
## 4 Optimal Design of Membership Function
### Fitness Evaluation  
When the detector has training samples and randomly generated membership function, it can obtain the corresponding failure probability by fuzzy theory. However, if the membership function is designed improperly, misdiagnosis can be caused. In order to avoid this problem, the fitness function must be set properly for genetic algorithm to search the global optimal solution. The fitness evaluation for this research refers to the fuzzy inference results, with the fuzzy rules formulated as follows:  
Figure 5: The conceptual diagram for data collection.  
Figure 8: The composition for the single individual.  
Figure 6: Triangular Membership Function.  
Figure 7: The input membership function.  
1. If the left characteristic frequency energy is \"Normal\", then the state of the rotor is \"Normal\".
2. If the left characteristic frequency energy is \"Abnormal\", then the state of the rotor is \"Abnormal\".
3. If the right characteristic frequency energy is \"Normal\", then the state of the rotor is \"Normal\".
4. If the right characteristic frequency energy is \"Abnormal\", then the state of the rotor is \"Abnormal\".  
The output membership function is shown in Figure 9. Aggregation is formed by multiple fuzzy rules and the center of gravity method is used to solve defuzzification, as shown in Equation (5), and the rotor failure probability can be obtained.  
\[z^{*}=\frac{\sum\limits_{i=1}^{L}\mu(\mathbf{z}_{i})\cdot\mathbf{z}_{i}}{\sum \limits_{i=1}^{L}\mu(\mathbf{z}_{i})} \tag{5}\]  
The fitness function is formulated as follows:  
\[\begin{array}{c}\mathit{Fitness}=\left\{\begin{matrix}0,&\mathit{if}\ f_{ normal}\ \mathit{or}\ f_{abnormal}<0\\ f_{normal}+f_{abnormal},&\mathit{otherwise}\end{matrix}\right.\\ \mathit{where}&\mathit{f}_{normal}=\min\{0.5-z_{i},\ i=1,\ 2,\ \dots,\ 25\}\\ &\mathit{f}_{abnormal}=\min\{z_{i}-0.5,\ i=26,\ 27,\ \dots,\ 50\}\end{array} \tag{6}\]  
where \(z_{i}\) is the inference result of training sample \(i\), \(f_{normal}\) is the worst inference result of normal motors, and \(f_{abnormal}\) is the worst inference result of abnormal motors.
# Basins of Convergence in a Multi-Perturbed CR3 BP +
## 1 Introduction
Studies investigating the BCs within the framework of different restricted problems with three bodies can be found in [12; 19; 20], where the effects of radiation pressure and oblateness have been studied using the iterative NR method. Also, the BCs associated with the equilibrium points in an \(N\)-body problem (\(N=3,4,5\)) under different types of perturbing accelerations can be found in [12; 21; 22]. The basins of the out-of-plane equilibrium points are also studied in [17]. In this paper, a modified CR3 BP simultaneously including the effect of radiation forces, the oblateness of the primary bodies, and the effects of relativity is studied. Moreover, the presence of natural satellites is also considered to obtain a more accurate model, mainly when the relation between the primary mass and its satellites is significant. Under these conditions, the BCs are calculated by means of an adaptive algorithm based on the iterative NR method. This paper builds upon and significantly expands our previous work [23], including validation of the results and providing a more comprehensive analysis of our findings. This paper is structured as follows: In Section 2, a description of the dynamical model is provided, with both classical and modified formulations. In this section, the modifications implemented in the model are presented. In Section 3, the basins of convergence, the Lagrange points, and the chosen iterative method are introduced. The development of the algorithm and its validation are also presented in this section. Next, in Section 4, an application of the model to the Sun-Mars system is obtained considering the different effects and analyzing the speed of convergence of the implemented code. Finally, in Section 5, the conclusions can be found.