esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Tips and DIPS Creation Using 3D Image Guidance  
Following the pre-procedural imaging, the animals were transported to the interventional imaging suite, which was equipped with rotational CBCT angiography and 3D reconstruction-rendered real-time angiography. All procedures were performed using an angiographic C-arm imaging system that utilized a flat-panel x-ray detector ([PERSON], Siemens, Forcheim, Germany). The animals were placed under the C-arm and general anesthesia was maintained with 1-3.5% isoflurane. All procedures were carried out by the same interventional radiologist who had 10 years of post-fellowship experience, including over 300 TIPS procedures. Next, the right internal jugular vein (RIJV) was accessed under US guidance. A 10F sheath was then advanced through the RIJV access point and either a glide catheter or multipurpose angiography (MPA) catheter was used to catheterize the right hepatic vein (HV). A Rosch-Uchida TIPS needle (Cook Inc., Bloomington, IN, USA) was then placed over the wire in place of the glide/MPA catheter. Once the Rosch-Uchida needle was placed in the right HV, a CBCT (DynaCT, Siemens, Forcheim, Germany) was obtained using the rotational angiography apparatus.  
The pre-procedural CT or MR images were retrieved from a PACS system at the angiographic system workstation (Syngo X-Workplace, Siemens, Forcheim, Germany). The 3D-reconstructed CT or MR images were loaded into the InSpace(r) application (Siemens, Forcheim, Germany) and subsequently fused with the 3D-reconstructed CBCT images using the iGuide(r) software (Siemens, Forcheim, Germany). This fused 3D-reconstructed roadmap was overlaid onto the real-time fluoroscopic image. The tip of the Rosch-Uchidaneedle and the target portal branch were identified on the images and the optimal path from the needle to the target puncture site of the PV was automatically mapped out by the iGuide(r) software. Under real-time 3D fluoroscopic guidance, the operator then advanced the TIPS needle into the PV following the determined path. Once the iGuide(r) PV puncture was made, venography was performed to confirm access to the portal system. For the DIPS procedures, the tip of the Rosch-Ushida needle was placed in the intrahepatic IVC instead of the right HV and the imaging fusion, 3D fluoroscopic guidance and portal puncture were performed as in the TIPS procedure.
## 3 Results
### Quantitative Findings
#### 3.1.2 Tracked Interactions (In-Game)  
All voluntary interactions with the target sounds were tracked and sound intensity as well as duration of the interaction were recorded and analysed. Figure 5 displays the change in time each participant voluntarily interacted with target auditory stimuli between their first quartile of recorded interaction and their last quartile of interactions during the study (the total number of voluntary interactions with target stimuli varied between participants, with a range of 4-14 interactions). Out of the seven participants, four participants showed an increase in interaction time over the course of the study. On average, the total increase was 33 s, which is 28% of the average initial interaction length.  
Figure 4: Quantitative changes in SRER between baseline and 10-week assessment, with associated target sounds and interacting participants. SRER scale ranges from 1 (very happy) to 6 (very unhappy); all target sounds had a SRER \(\geq\) 4 at baseline. Negative values in the graph represent a reduction in sensitivity, while positive values indicate an increase in sensitivity.  
Figure 5: Changes in length of interaction with target stimuli for each participant, comparing the first quartile of interactions to the last quartile of interactions. Data are shown in seconds.  
In addition to recording the time participants voluntarily interacted with target stimuli, data were also captured for the intensity of the exposure level, 1 being the lowest and 4 being the highest (see Table 1). Figure 6 shows the change in intensity between the first quarter of recorded interactions and the last quarter of interactions. It is noticeable that four of the seven participants chose to lower the exposure level over the course of the trial, with only one participant increasing the intensity and two remaining constant. On average, the intensity was lowered from 2.9 to 2.3 over the course of the study.
# Development of a Hardware-in-the-Loop Platform for a Teleoperation Flexibility Robotic System
## 2 Description of HIL Simulation System
### Kinematics of Simulator
#### 2.3.2 Master Device  
This haptic device comprises a base, an end-effector, and three legs. The original coordinate system of this device is centered at its base, as depicted in Figure 6a. To clarify the kinematics of Novint Falcon, the kinematics of one of its legs are illustrated in Figure 6b,c.  
In Figure 6b,c O is the center of the XYZ coordinate system of the base, and P is the position of the end-effector of the haptic device. Moreover, \(\text{U}_{\text{i}}\text{V}_{\text{i}}\text{W}_{\text{i}}\) denote the relationship between the kinematics of \(i\)th leg and the original coordinate system of the device. \(\theta_{\text{i}}\) is the angle of each joint of the \(i\)th leg (where \(i=1,\,2,\,3\)).  
The inverse kinematics mathematics are computed to determine the angle of each joint given the position of the end-effector. \(\theta_{3\text{i}}\) can be obtained as follows [27]:  
\[\theta_{3\text{i}}=\pm\text{a}\cos\left(\frac{\text{P}_{\text{vi}}+\text{f}}{ \text{b}}\right) \tag{7}\]The angle \(\theta_{\rm 1i}\) can be computed as follows:  
\[\theta_{\rm 1i}=2\tan^{-1}\left(\frac{\rm B\pm\sqrt{B^{2}+A^{2}-C^{2}}}{\rm A+C}\right) \tag{8}\]  
where \(\rm A=2(P_{\rm ui}+c)a\), \(\rm B=2P_{\rm w}a\), and \(\rm C=-(P_{\rm ui}+c)^{2}-a^{2}-P_{\rm wi}^{2}-(e+b\sin\theta_{\rm 3i}+d)^{2}\)[27].  
After calculating \(\theta_{\rm 3i}\) and \(\theta_{\rm 1i}\), the \(\theta_{\rm 2i}\) angle can be defined as follows:  
\[\theta_{\rm 2i}=2\tan^{-1}\left(\frac{\rm P_{wi}-a\sin\theta_{\rm 1i}}{\rm P_{ \rm ui}+c-a\cos\theta_{\rm 1i}}\right) \tag{9}\]  
The position of the end-effector can be obtained from the forward kinematics when the angle of each joint is known. However, because of the fact that the robot has a parallel structure, standard methods cannot be used to calculate these joint angles. Therefore, the equation of intersection of three spheres is used to determine the position of the haptic device [28].
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 4 Simulator
### Topology Module: Generating Topologies  
One of the main properties of each topology is its _density_ that is related to the number of links between nodes. Density highly influences the number of alternative paths between  
Figure 3: Module architecture of the simulator.  
any two nodes in the network. Together with the total number of nodes, density is the most important parameter to observe when observing the behavior of routing mechanisms. To describe the density, we borrowed the definition from the graph theory. We use the _average vertex degree (AVD)_, which tells us how many neighbors each node has on average.  
When generating a topology for a given 2D random distribution of node positions, we want to create a connected topology and, at the same time, we want to achieve a specific density. We decided to use the topology control algorithm called Lune \(\beta\)-skeleton [27]. The performance of this algorithm is influenced by parameter \(\beta\) that produces the topologies with various densities. A smaller \(\beta\) value creates a higher density network while a greater value produces a lower density network. An example of the difference between topologies created with various \(\beta\) parameters is in Figure 4. \(\beta=1\) produces a topology with more links and higher _AVD_ compared to \(\beta=2\).  
The advantage of this method is that, for \(\beta\leq 2\), it is ensured that the resulting topology is always connected if it was connected before the application of the algorithm. In our case, we started with the complete graph topology and then reduced the number of links with the help of this algorithm.  
The resulting _AVD_ of each topology depends on the \(\beta\) parameter and on the initial positions of all nodes. Therefore, the same \(\beta\) value produces topologies with slightly different _AVD_s. The dependency of the average vertex degree on the \(\beta\) value is shown in Figure 5.  
Figure 4: Two networks with the same distribution of nodes in the area but with different densities controlled by the \(\beta\) parameter.  
Figure 5: Average vertex degree as a function of the \(\beta\) parameter.
# Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
## 5 Evaluation
### Effect of Classifiers and Custom Permissions
permissions and custom permissions were used. Thus, the null hypotheses are given as \(H_{0}:MCC(built\_in)=MCC(all)\) and \(H_{0}:BAC(built\_in)=BAC(all)\), where _all_ includes both built-in and custom permissions. For the two-sided t-test, we conducted 5-fold cross-validation 30 times for LightGBM and then obtained 30 MCCs and BACs. The means of 30 MCCs were 0.9337700 and 0.9474933 for built-in and all permission sets, respectively. The relevant \(p\)-value was \(5.931\times 10^{-39}\), which is much less than 0.0001. Thus, \(MCC(all)\) was significantly bigger than \(MCC(built\_in)\). The means of 30 BACs were 0.9090167 and 0.9229400 for built-in and all permission sets, respectively. The relevant \(p\)-value was \(4.518\times 10^{-20}\), which is much less than 0.0001. Thus, \(BAC(all)\) was significantly bigger than \(BAC(built\_in)\). Therefore, we noticed that using custom permissions together with built-in permissions was more effective than using only built-in permissions, and then, custom permissions contributed to the Android malware family classification. We also performed the normality test by using the scipy.stats.shapiro() function for the Shapiro-Wilk test, which is known to be a reliable test for normality, on 30 MCCs and BACs of LightGBM. The result of the normality test showed that the data followed a normal distribution. Figure 8 shows the performance of LightGBM for each permission set. The measured metrics for S96 and S64 (permission sets containing custom permissions) were higher than S87 and S56 (permission sets not containing custom permissions), respectively. This was the same for the other classifiers. Including custom permissions improved the classification performance. On the other hand, the effect of 0-importance permissions was not consistent. Excluding 0-importance permissions slightly improved the classification performance in the majority of cases. However, in some cases, excluding 0-importance permissions degraded the classification performance.
# High Performance Receiver Design for RX Carrier Aggregation
## 2 Receiver Architecture
Prior to that, in this section, we delve into the architectural issues and concerns to enable a high-performance receiver with large dynamic range. Figure 2: Carrier aggregation (CA) receiver architectures: (**a**) Inter-band CA receiver; (**b**) Intra-band CA receiver.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 2 The Rural Paradigm Shift
### Outdoor Perspective
Moreover, LEO and SCPs significantly enable coverage increase and do not require new terrestrial towers. Therefore, these options can offer highly reliable data rate services while demanding simple but special maintenance attention with respect to its tracing [26]. Figure 3 states a feasible implementation of the reviewed solutions in the countryside for outdoor areas, aiming to develop new tendencies considered in Section 2.3. Figure 3 entails the use of alternative-to-terrestrial wireless connectivity aimed at rural coverage. To promote the solutions mentioned previously, the SCPs bring several attributes in offering a low-cost and widespread array of services in the countryside. Among them, in [27], the author listed the advantages of deploying these airships--either a constellation or a singular aerial unit--which may be summarized into three categories: seamless countryside coverage, power consumption trade-offs, and a higher speed of transmission. Since SCPs enable an extension of coverage, the unobstructed free-of-shadowing LOS roaming service would boost roaming services [27], leveraging the autonomous and discrete features of these devices. For instance, [PERSON] et all. led a feasibility study in [28] for two Peruvian rural areas to promote connectivity, aiming at the underserved/unserved communities despite the rough landscape of either the Andes range or the Amazon rainforest. The authors used stratospheric balloons to serve the growing customer and speed demand for up to eight years. After that, an LTE-based network complemented the established structure. Figure 3: Some solutions for outdoor networks issues. Nonetheless, the fully fledged satellite-based networks carried on a cooperative relay scheme to accomplish the ongoing high demands of reliable, seamless, and high-rate transmissions. Therefore, the space-air-ground integrated network [29]--named SAGIN--combines LEO satellites, SCPs, and terrestrial BS to achieve the future requirements of 6G communications by addressing the provided-by-satellite coverage probabilities enhanced with terrestrial gateways as the authors in acknowledged in [30] in addition to real-time dynamic adjustment of HAP-based networks.
###### Abstract
According to the recent literature, recent advances in machine learning and deep learning have significantly improved crash prediction accuracy than traditional statistical methods [16; 17; 18]. Moreover, existing studies must find appropriate evaluation metrics for the family of machine and deep learning-based approaches. Many of the previous works have used different approaches to explain the relationship between each variable and to provide a more detailed interpretation of the output of the models. Therefore, many researchers adopted interpretation methods, such as Shapley additive explanations (SHAP) or partial dependent plots (PDP), for analysis in machine learning models [19; 20; 21]. By applying these methods, it was possible to figure out the individual contribution of variables to the prediction result of the classification model, and various interpretations of the classification result models were shown. As a result, the machine learning models were able to compensate for the relatively insufficient interpretation power compared to the traditional statistical model. This research aims to analyze the factors of highway accident occurrence and specific solutions by predicting highway accident data using a classification model. For this, we tried to design the model to show the optimal performance. Considering the characteristic of imbalanced data, oversampling (SMOTE and ADASYN) [22], undersampling (Tomek-Link and ENN), and hybrid sampling (SMOTE, SMOTETomek, and SMOTEENN) techniques were applied [23]. Regarding the classification model, we used Logistic Regression, Support Vector Machine, Random Forest, Gradient Boost, and XGBoost for highway accident classification. Additionally, for better performance, we applied Bayesian optimization for the hyperparameter optimization technique. In the case of evaluation metrics of the model, evaluation was conducted using many evaluation metrics, such as recall,precision, f1-score, specificity, and accuracy, which are often used to evaluate the existing classification performance. Finally, by using the interpretable machine learning methods SHAP and PDP, we could precisely interpret the classification model's result in more detail. As a result, we tried to propose a method to identify the main factors in accidents and to improve safety by designing an analysis model for highway accident data and applying an interpretation model that can explain the results in detail.
## 5 Use Cases
### Integration of Large Language Models
#### 5.9.1 Enhanced Pattern Analysis  
LLMs can be leveraged to:  
* Generate natural language explanations of complex visualization patterns.
* Identify non-obvious correlations across multiple visualization outputs.
* Provide context-aware interpretations of temporal and spatial trends.
* Synthesize insights from multiple visualization types into coherent narratives.
## 2 Methodology
### Numerical Method  
Various methods, such as Large Eddy Simulation (LES) and Detached Eddy Simulation (DES), show a major accuracy advantage in representing the precise shift inflow structures when simulating flow fields with large Reynolds numbers. However, as the mesh resolution and time-step become more stringent, computational efficiency suffers, and these methods are challenging to complete the work mentioned in this analysis. Furthermore, the Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations turbulence model is commonly used in simulating flow structures similar to those in this analysis. As a result, in the present investigation, the 3D unsteady RNG k-\(\epsilon\) turbulence model was used to simulate the flow field as HSTs travel through the bridge.  
The STAR CCM+ 13.06 commercial software was used to perform the calculations. A pressure-based solver based on the finite volume method was used to obtain the pressure data. A second-order upwind scheme was applied to discretize the convection and diffusion concepts. An implied scheme with second-order accuracy was used to deal with the time term, and the time-step was set as 5 \(\times\) 10\({}^{-4}\) s was the physical time stage scale. Each time stage had 20 iterations, and the residual of each turbulent equation was at least 10 \(\times\) 10\({}^{-5}\) at each step.  
Figure 1: Geometric models employed in the numerical simulation: **(a)** The train model; **(b)** A realistic CRH3 train; **(c)** The bridge model and location of the train; **(d)** The length components of the train.
## 1 Introduction
It leverages the transformer architecture to capture deep bidirectional context, making it well-suited for understanding the nuances of user purchase behavior. Our main idea is to describe each order as a text sentence, capturing the product names, their attribute values, corresponding category names, and whether multiple products were purchased together in a single order (i.e., a concurrent purchase). This allows us to treat a user's purchase history as a document composed of sentences. We fine-tune these sentences for BERT's Next Sentence Prediction formulation. This method not only diverges from traditional techniques but also sets a new precedent for leveraging advanced language models to enhance the accuracy of next product prediction, thereby significantly improving the predictive performance of recommendation systems. By reformulating orders as natural language sentences, our approach captures the nuances of concurrent purchases, aiming to craft a recommendation system with enhanced prediction capabilities for the next purchase a user is likely to make [7; 8]. Our research demonstrates the effectiveness of our proposed methodology by empirically validating it on a real-world e-commerce platform. The transformer-based BERT model, trained on order information including concurrent purchases represented as text sentences, shows improved performance in predicting the next product purchase compared to conventional approaches. The inclusion of concurrent purchase indicators proves to be crucial in capturing the context of user history, leading to enhanced prediction performance. This study highlights the potential of incorporating concurrent purchase data in e-commerce recommendation systems and showcases the applicability of transformer-based language modeling in understanding user purchase behavior. Our work lays the foundation for future research on integrating complex purchasing patterns with advanced machine learning techniques, aiming to develop more effective and personalized recommendation systems. This paper is organized as follows. Section 2 describes related studies. In Section 3, the problem of our main concern is defined, the way a user's shopping history is formulated as a natural language sentence under the consideration of concurrent purchases will be presented, and the adoption of BERT's Next Sentence Prediction as a next purchase product prediction will be derived. In Section 4, we present the experimental results on real-world e-commerce datasets. This includes an ablation study to demonstrate the effectiveness of the proposed methodology. In Section 5, we examine the advantages and limitations of the proposed method and suggest possibilities for future work to improve upon it. Finally, Section 6 presents the overall findings and outlines future directions for research in this area.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 4 Analysis of the Improved PBFT Consensus Algorithm  
With the growing popularity of blockchain research, consensus algorithms, as a key technology for implementing blockchain, have garnered widespread attention from researchers. As the number of nodes increases, the transaction throughput of traditional PBFT significantly decreases, while communication overhead and transaction delays increase substantially. To address these issues, researchers both domestically and internationally have explored the limitations of traditional PBFT, focusing primarily on four aspects: hierarchical group consensus methods, the introduction of reward and punishment mechanisms to reduce the number of consensus nodes, improvements to multi-leader consensus algorithms, and improvements to consistency protocols based on optimistic node assumptions [90].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization Scheme** & **Core Mechanism** & **Advantages** & **Disadvantage** & **Application Scenario** \\ \hline The Hierarchical Group & Adopting an intra-group & Reducing & Requires an additional & \\ Consensus Algorithm & consensus with & reduction & communication complexity & intra-group & Large-scale network \\ \hline Select High-Quality Nodes for & Selecting only & Mitigating the impact of & May lead to a trend & \\ Participation in Consensus & consensus participation & & malicious nodes & toward centralization & Supply chain management \\ \hline Improvements to & & & & \\ Multi-Leader & Multiple leaders processing & Alleviating the bottleneck & & Requires additional & \\ Consensus Algorithms & consensus in parallel & of the primary node & coordination, increasing & High-throughput blockchain \\ \hline Optimized Consensus & Enhancing honesty through a & & & \\ Algorithm Based on & staking mechanism and a & Preventing Sybil attacks & Requires additional economic & Finance and traceability systems \\ Optimistic Mechanism & reward-penalty system & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: A summary of PBFT consensus algorithm optimization strategies.