esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
DNA binding assays and computational docking data have highlighted a competitive mechanism between SQLs and the viral DNA end, which leads to the inhibition of the 3'-processing and makes SQLs representative compounds of the INBI group. In cells, the stable IN-DNA interaction within the preintegration complex (PIC) might preclude inhibitors from acting as competitor of viral DNA from being very effective in the cellular context. Together with the dramatic effectiveness of INSTI compounds, this consideration questions the relevance of further developing INBIs.  
Yet, the following observations temper this conclusion. First, although most 3'-processing inhibitors actually lack antiviral effect, this is not the case for SQLs, which show a moderate activity _ex vivo_[44, 50, 51]. Second, investigation of the mechanism of viral inhibition by compound **77u** in cells revealed the possibility that the inability of integrase to bind to the viral DNA impairs the formation of the IN/DNA nucleoprotein complex. Finally, the lead compound **77u** proved also to be active against viruses resistant to AZT, lamivudine (3 TC), nevriapine, efavirenz and against INSTIs-resistant integrase[50]. _In vitro_ selection of resistant virus identified the two mutants C280Y and (V165I V249I) which differ from those conferring resistance to Raltegravir (N155H, G140S, Q148H, Y143) [52, 53]. It is not clear how these mutations promote resistance to SQLs. V165 has been proposed to participate in the nuclear translocation of the integrase [54], a step that might be impaired in cells by SQL as observed _in vitro_[51]. In agreement with this difference of mechanism of action, SQLs demonstrated an additive to moderate synergic effect with INSTIs [55].  
Altogether, these results evidence that INBIs could be used in combination with other antiretroviral drugs. Considering that resistance has already emerged against INSTIs, they justify pursuing the effort to improve SQLs.
## 5 Challenges and Future Research Directions  
The flexibility and mobility of UAVs significantly enhance the capabilities of integrated networks. Future technological breakthroughs and cross-domain collaborations will create more opportunities for this field. In this section, we will analyze the main challenges and future development directions in UAV-assisted SAGINs.
## 2 Materials and Methods
### Conditions for HPLC/PDA  
The quantitative HPLC/PDA analysis was conducted using a Waters Alliance e2695 HPLC Separation Module and Waters 2998 PDA detector. A J'sphere ODS-H80 column was selected as the stationary phase. The flow rate was maintained at 1 mL/min, the injection volume was set at 10 uL, and the ultraviolet (UV) wavelength was fixed at 254 nm. For the mobile phase, 0.1% of trifluoroacetic acid in water (A) and acetonitrile (B) were used, and the gradient elution system was arranged as follows: 88% A between 0 and 8 min, 80% A at 20 min, 75% A at 40 min, 0% A at 45 to 50 min, and 88% A between 53 and 65 min.
# An Accelerating Approach for Blockchain Information Transmission Based on NDN
## 4 Findings: The Accelerating Strategy  
### Secondary Nodes  
Before discussing the information acceleration transmission strategy proposed in this paper, we need to introduce the concept of the secondary node. In a random NDN network, we select some nodes to become the secondary nodes, and assign them higher bandwidth and larger cache to speed up the packet transmission in the network. These nodes usually have higher transmission capacity and communication weight, are connected to more nodes, and undertake more frequent data packet forwarding tasks, as shown in Figure 2 (Figure 2 does not represent the real topology network). Therefore, the secondary nodes are the routers with higher bandwidth and larger cache for storing the updating data of the producers first and pushing these data to all the receivers attending to the blockchain-based application, which is different from ordinary routers of the NDN.  
Figure 2: The secondary nodes are generally connected to more nodes, have higher transmission capacity and communication weight and undertake more frequent data packet forwarding tasks.  
communication weight of the data link follow the normal distribution. Thus, Equation (5) is given as:  
\[\left\{\begin{array}{cc}T_{j}&\sim&N\big{(}\mu_{0},\sigma_{0}^{2}\big{)}\\ W_{(i,j)}&\sim&N\big{(}\mu_{1},\sigma_{1}^{2}\big{)}\end{array}\right. \tag{5}\]  
where \(\mu_{0}\), \(\sigma_{0}\), \(\mu_{1},\sigma_{1}\) represents the constants of the normal distribution.  
Since \(T_{j}\) and \(W_{(i,j)}\) follow a normal distribution, we have Equation (6):  
\[\big{(}\alpha_{2}T_{j}+\alpha_{3}|W_{j}|\big{)}~{}\sim~{}N\Big{(}\mu_{2},\sigma _{2}^{2}\Big{)} \tag{6}\]  
where \(\mu_{2},\sigma_{2}^{2}\) represents the constants of the normal distribution.  
Then, because the \((\alpha_{2}T_{j}+\alpha_{3}|W_{j}|)\) follow the normal distribution, so its value can be within a certain range. When considering the number of secondary nodes, the influence of \(T_{j}\) and \(W_{j}\) can be ignored, and only the number of paths connected by the nodes will be considered. We set \(L_{j}\) as the probability of the node connecting more than or equal to \(j\) nodes, so obviously there is Equation (6):  
\[L_{j}=1-\sum_{0}^{j-1}P_{j} \tag{7}\]  
After giving a specific \(P\), the corresponding \(L_{j}\) can be calculated according to \(P\), and an appropriate number of nodes are selected as the secondary nodes according to the value of \(L_{j}\), which are configured high bandwidth and large caches to accelerate the data transmission.
# Numerical Modeling of Bowel Sound Propagation: Impact of Abdominal Tissue Properties
###### Abstract  
Bowel sounds, produced by intestinal peristalsis, are essential for diagnosing gastrointestinal disorders. However, acquiring and analyzing bowel sounds is challenging due to their unpredictable nature and individual variability. Biological tissues can affect bowel sounds during propagation, resulting in varying degrees of signal attenuation between the sound source and the transducer. This study aims to develop a numerical model of bowel sound propagation in the abdominal cavity, focusing on the impact of different biological layers on signal attenuation. Validation of the model demonstrated strong consistency between simulated and actual bowel sound signals, confirming the model's accuracy and reliability. The model accounted for adipose tissue thickness, ranging from 5 to 20 mm across individuals, while muscle and skin thicknesses remained constant. Results indicated that signal attenuation increases with both the propagation distance and adipose tissue thickness. These findings provide insights into how tissue layers influence bowel sound propagation, offering a theoretical foundation for developing personalized and precise monitoring devices.  
Keywords:bowel sounds; individual wave component; signal attenuation; numerical modeling; tissue properties +
Footnote †: journal: Applied sciences
## 3 Case Study
### TEP Dataset Analysis
#### 3.1.5 Training Process and Performance Stability  
To evaluate the training efficiency and performance stability of the ATASML model, we conducted detailed experiments with specific settings. The training process was configured with a total of 20,000 epochs and a batch size of 64, using the Adam optimizer with a learning rate of \(5\times 10^{-4}\). The 3-way 6-shot task was selected to represent a complex fault-diagnosis scenario.  
The experiment results demonstrate that the model approaches a stable performance around 15,000 epochs, with diagnostic accuracy peaking at 98.47%. Figure 7 illustrates the training dynamics of the model over the epochs, detailing both the accuracy and loss metrics. The accuracy graph (Figure 7a) reveals a consistent improvement, reaching near-maximal levels, while the loss graph (Figure 7b) showcases a significant reduction in classification loss, stabilizing at a lower rate, which is indicative of the model's ability to effectively learn and generalize from the training data.
## 6 Discussion
### The Reliability of the Trihear Model and Limit Analysis  
It should be noted that this study involves only one case. The geometry (such as diameter, inclination), thickness, and depth of the sill, as well as the mechanical properties of the host rock (such as cohesion, density, and internal friction angle), are fixed. However, these parameters can vary in different cases. Previous studies show that sills in the central Tarim Basin range from saucer-shaped and strata-concordant to hybrid geometries [22].  
Figure 8: Shear dissipation map that predicts the damage distribution of the model. The white lines are the modeled sill. The energy dissipation by shear damage is represented by the color scale in kJ. Vtip and Vsurf are measured at the sill tip and at the surface, respectively.  
As mentioned above, in this case, the predicted shear zone by limit analysis is not sensitive to the mechanical properties of the host rock. However, this may not be true in other cases. Previous studies showed that for tabular sills, the diameter and depth of the sill, as well as the cohesion and internal friction angle of the host rock, can significantly influence the shape of the failure zone at the sill tip [3; 9; 10]. Therefore, the results suggesting a 90\({}^{\circ}\) dip angle for the peripheral fault may only account for sill 1, which is saucer-shaped and emplaced in a depth of \(\sim\)2.5 km. The value may not be right in other cases.  
A systematic test of kinetic modeling and limit analysis may provide robust reliability for these approaches based on a wide range of sills in the study area. This test can also provide more general insights into the structure of peripheral faults and how sill geometry, depth, and mechanical properties of the host rock control the fault geometry, similar to flat sills [3; 9; 10]. It should be noted that there are large differences between this study and previous works that used the limit analysis on the damage zone at the sill tips [3; 9; 10]. The damage zones in these previous works were used for inclined sheets of saucer-shaped sills. This study focuses on faulting at the tips of a saucer-shaped sill.  
It is possible that both inward dipping reverse faults and outward can occur at the sill tips, as observed from the Black Mesa and Trachyte Mesa intrusions in Henry Mountains, Utah, United States [4; 39; 40]. However, it should be noted that the intrusions in these areas mainly consist of diorites. The structural relief of the associated uplifts often comprises several hundred meters, which is much larger than those in this study at tens of meters.
## 4 Methodology
### Domain Clustering Based on Multi-Path Transmission
#### 4.2.2 Network Topology and Path Reliability  
Although the domain clustering method effectively improves network reliability, path selection randomness still exists, especially when the link reliability is unstable. Traditional methods often overlook the coupling effects between links. Therefore, accurately evaluating path reliability and making reasonable path selection decisions becomes the next key problem to address.  
To this end, we introduce the conventional reliability \(\mu_{E}^{r}\) and irregular reliability \(\mu_{E}^{ir}\) of the links, to model the link states. The link reliability considers the weighted combination of historical data and the current state:  
\[\mu_{ij}^{E}=(1-\xi)\cdot\mu_{E}^{r}+\xi\cdot\mu_{E}^{ir} \tag{32}\]  
where \(\xi\) is the adjustment factor, and \(\mu_{E}^{r}\) and \(\mu_{E}^{ir}\) represent the conventional and irregular reliability, respectively.  
The path reliability is then represented as the product of the reliabilities of all links in the path:  
\[R_{h}=\prod_{E_{ij}\in PFi}\mu_{ij}^{E} \tag{33}\]  
**Theorem 5**.: _Given the link reliability model, the reliability of a path is the product of the reliabilities of all the links in the path, and path selection can achieve optimality through dynamic adjustment._  
Proof.: Suppose that all paths in the network consist of a series of links \(E_{ij}\), and the reliability of each path can be calculated as the product of the reliabilities of the links in that path. Therefore, the path reliability \(R_{h}\) is  
\[R_{h}=\prod_{E_{ij}\in PFi}\mu_{ij}^{E} \tag{34}\]  
To ensure optimal path selection, nodes need to select the path with the highest reliability. By comparing the reliability values of different paths, nodes will choose the path with the highest reliability for data transmission. Since the path reliability is the product of link reliabilities, optimizing this product value can adjust the path selection strategy.
# Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks
## 5 The Dataset  
The dataset used for this study was the Next Generation Simulation (NGSIM) Vehicle Trajectories Dataset [7]. NGSIM is an open source publicly available dataset with a collection of real-world vehicles' trajectories collected by smart vehicles. It contains a detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data in NGSIM were collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provides the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Moreover, NGSIM consists of many patterns representing different drive situations and driver behavior [7]. In addition, NGSIM provides high-quality contextual data that describe realistic real-world scenarios on different road sections [19]. Particularly, NGSIM was built by collecting data from vehicles moving on a road section with 500 m-long and seven-lane highway. For each vehicle, the data are collected (recorded) for 45 min using 16 sensors. Each record in the dataset contains s set of basic elements regarding the vehicle like position, speed, time, direction, and acceleration. Although there are similar datasets such as the Connected Vehicles Pilot (CVP), the NGSIM dataset was chosen in this study to be consistent when comparing with the related works as they used the NGSIM as well.  
The dataset represents the ground truth information and each vehicle represents a cITS node. In real-world deployment, the dataset needs to be fed each cITS node. That is, each node should have a copy of the dataset to run its own applications and adjust its communication or driving behavior. As such, the collection of accurate and reliable context information is crucial. The context information in the dataset combines two types of messages, cooperative awareness message (CAM) and decentralized environmental notification message (DENM) into a basic safety message (BSM). While CAMs are sent periodically, DENMs are event-driven that only sent when an event has occurred. The CAM consists of information about the vehicles such as the position, size, speed, and steering wheel angle.  
In contrast, DENM contains information about a certain event such as lane changing and sudden braking. BSM combines CAM and DENM messages. The first part of BSM, as well as CAM in the European standard, carries information about position, heading, speed, acceleration, steering wheel angle, vehicle role, vehicle size, and the status of vehicle lights [4; 27; 28]. Unlike the first part of BSM that is included in all BSM messages, the second part of BSM (which corresponds to DENM in the European standard) is only included when an event happens, to carry information about such an event.
## 3 Message Transmission and Notification  
### Swarm  
In Section 2.2, we have discussed a little the general part of the process of the system and have shown an illustration in Figure 2. Now, upon closer look at the flowchart in Figure 7, we can see that there are more hidden steps. First, let us start with the SWARM Technologies' devices and the existing system.  
1. Dev 1 in Figure 7 describes a SWARM device that is collecting some data in the remote region while also automatically sending the data (with some time interval) to the satellite. It connects without any Internet usage and uses the VHF radio to send the data. 2. When the satellite receives the data, it sends them to the HIVE cloud (developed by SWARM Technologies).
3. The HIVE cloud is then connected to our AWS through the API. The HIVE cloud will be discussed more in the next section.  
Figure 7: The flowchart shows the full process, including the AWS processes. Dashed arrows show the connection and data transfer between devices, satellites, and the HIVE cloud, which takes some time based on experiment values. The solid arrows indicate the process performed in the AWS cloud, which is an instant transfer and is outside the main transmission system (physical). The process starts at Dev 1, sending a message to the satellite. Then, following number #1, the API is used to receive a message and then processed at the AWS cloud, where response message #2 can then be followed back up to Dev 2.  
After the HIVE cloud receives feedback or a message that it should be delivered to the next device, it automatically sends it to the satellites.
* Then, the closest satellite to Dev 2 in the region will try to deliver the message until the confirmation is received from Dev 2.  
There could be more than two devices \"talking\" to each other since all of the speed for the data transfer depends on how many satellites are flying in the area above the devices. Each device is programmed through the SWARM Tile for the following options:  
1. Dev 1: * Constantly try to send data to a satellite as the new data collected; * Try to send data over a specific period.
2. Dev 2: * Constantly ask for new messages (responses from Dev 1) from the nearby flying satellite; * Send the response about the message being received to the device.
## 3 Research Methodology
### Application of MCDM Methods
#### 3.2.4 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
\[PIS=A^{+}=V_{1}^{+},V_{2}^{+},\ldots,V_{n}^{+},\ \ \text{where}\ \ V_{j}^{+}=(max_{i}(V_{ij})iffj\in J);(min_{i}(V_{ij})iffj \in J^{{}^{\prime}}), \tag{11}\]  
\[NIS=A^{-}=V_{1}^{-},V_{2}^{-},\ldots,V_{n}^{-},\ \ \text{where}\ \ V_{j}^{-}=(min_{i}(V_{ij})iffj\in J);(max_{i}(V_{ij})iffj \in J^{{}^{\prime}}), \tag{12}\]  
where \(A^{+}\) and \(A^{-}\) are positive and negative ideal solutions, respectively, \(J\) and \(J^{{}^{\prime}}\) are variables or parameters with positive as well as negative effect on the alternatives, respectively. Step 5: Computation of Separation Distance:  
After finding the positive and negative ideal solutions, the separation distance of each alternative from both positive (\(S^{+}\)) and negative (\(S^{-}\)) ideal solutions will be found. This can be done by applying root mean square to the difference of entities in weighted normalized decision matrix to the positive and ideal solution values of the corresponding column or criteria (Equations (13) and (14)). \[S^{+}=\sqrt{\sum_{j=1}^{n}\left(V_{j}^{+}-V_{ij}\right)^{2}}\ \ \text{where}\ \ i=1,\ldots,m, \tag{13}\]  
\[S^{-}=\sqrt{\sum_{j=1}^{n}\left(V_{j}^{-}-V_{ij}\right)^{2}}\ \ \text{where}\ \ i=1,\ldots,m, \tag{14}\]  
where (\(S^{+}\)) and (\(S^{-}\)) are separation distances for each alternative from ideal and negative ideal solutions, respectively. Step 6: Measuring the Relative Closeness to the Positive Ideal Solution:  
For each alternative, a relative closeness (\(C_{i}\)) to an ideal solution can be found by using Equation (15). \[C_{i}=\frac{S_{i}^{-}}{(S_{i}^{+}+S_{i}^{-})},0\leq C_{i}\leq 1. \tag{15}\]  
Step 7: Ranking the Preference Order:  
Depending on the relative closeness values, an alternative with maximum \(C_{i}\) will be the best alternative among the available alternatives and other alternatives will be ranked in a descending order.
## 4 Result and Discussion
### Pipeline Stratum Model under 3D Effect
#### 4.1.2 Pipeline Size  
To understand the influence of the pipeline size on the 3D effect, this study set two kinds of stratum models with different pipeline sizes, where the section size of the pipelines was 1.5 (W) \(\times\) 2 (D) m and 3 (W) \(\times\) 4 (D) m, respectively. As seen in Section 4.1.1, when n was more than 0.1, the 3D effect became weaker. Therefore, this study fixed the resistivity ratio to explore the 3D effect under different conditions of pipeline sizes.  
Figure 6 shows the results of testing the 2D ERT near two different pipeline sizes. Due to the pipeline sizes, the distances between the L1-L5 lines of the small-scale pipeline and the boundary of the pipeline were 1.5, 0, 3, 6, and 9 m, respectively, and the distances between the L1-L6 lines of the large size pipeline and the boundary of the pipeline were 0 (the left side of the pipeline), 0 (the right side of the pipeline), 3, 6, 9, and 12 m, respectively. In the case of the small pipeline, there was a nonsignificant 3D effect outside the line of the boundary, but in the large size pipeline, line L4 (6 m away from the edge of the pipeline) shows the mapping results of adjacent pipelines. The results show that the influence of the 3D effect increases with the size of the pipeline.  
Furthermore, we observed that the mapping depth was deeper than the actual depth of the pipeline. The results showed that the mapping mechanism was not horizontally mapped, and that the mapping depth was related to the distance between the pipeline and the survey line. When the distance was over 6-9 m, the result showed that it was almost unaffected by the 3D effect.
## 3 Optimal Power Allocation (PA), Cooperative Relaying and Channel Models in PD-NOMA
### Fuzzy Inference System for Optimal PA
#### 3.1.3 Defuzzification  
In the defuzzification, the center of area (COA) technique [42] is used to calculate the optimal power factors using the proposed FRBS system. For various numbers of fuzzy rules, the defuzzifier's output is obtained by:  
\[p_{i}^{\prime}=\frac{\sum_{i}\mu_{Ci}(\Delta\mu)\Delta\mu(Ci)}{\sum_{i}\Delta \mu(Ci)}. \tag{9}\]  
For the rule \(Ci\), \(\Delta\mu(Ci)\) specifies the peak value of the fuzzy set's membership degree and \(\mu_{Ci}\) represents the centroid of the associated output membership function. Using Equation (9), each user is assigned an absolute power factor based on the FRM, which is then normalized through Equation (2) to produce real power factors. Figure 7 shows the graphical representation of the FIS system's output as a function of \(d\) and \(SNR\). The foliage depth is kept constant at \(d_{f}=14\) m for a 3D graphical view. The output power factors for input \(d\), \(SNR\), and \(d_{f}\) are maximum for combinations (H, L, H) and minimum for combinations (L, H, L). For a better understanding of Table 1 in terms of the FIS system's outcome for the different power levels, we assumed the input arrangements for row 21 (H, L, and H). The FIS system will result in maximum power levels for each user if the distance is high (H), the SNR is low (L), and the foliage depth is high (H). The same intuition is tabulated for all the power levels calculated.  
Figure 7: Graphical view of the FIS system’s output.