esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Girder Flaring on Load Effect in Girders of Composite Steel Bridges
## 3 Approach
### Truck Positioning and GDF
#### 3.4.2 Transverse Truck Position  
The bridge is loaded by single or multiple side-by-side trucks placed within the transverse lanes to maximize the live load flexural and shear effects in the exterior or critical interior girder. The AASHTO LRFD specifications [1] account for the lower probability of simultaneous presence of multiple trucks over a bridge by employing a multiple presence factor. To find the maximum live load effect in the exterior or interior girders, different locations should be checked in the transverse direction for maximum effect in the member under consideration. The truck is first placed at a minimum clear distance from the edge of the interior face of the parapet and is then moved transversely in small increments toward the parapet on the opposite side of the bridge until the considered girder reaches the greatest live load effect.  
In this study, variable spacing of the girders has an effect on truck positioning in the transverse direction, where both the position of the truck and the truck plan orientation angle affect the structural analysis results. By giving the truck the correct angle in the proper position, the live load effect can be maximized. Finite element analysis of the bridge systems showed that the truck orientation on the bridge influences both the interior and exterior girders but has a higher effect on the exterior girder, especially when the overhang width is large. By loading a splayed girder bridge with a truck oriented perpendicular to the line of supports, with a minimum wheel distance of 600 mm from the parapet, as required by AASHTO, only the wheel at the front or rear axle of the truck will maintain this minimum distance, while the other two axles will have a greater distance from the parapet, thus resulting in a lower flexural effect on the exterior girder. If the truck is oriented parallel to the parapet with a constant wheel distance of 600 mm from the parapet, the flexural effect of the truck on the exterior girder will be maximized because all of the truck wheels will contribute more toward the load effect in the girder. With regard to an interior girder, maximum flexural load effect is attained if the truck axle is oriented perpendicular to the centerline of the girder under consideration, as shown in Figure 6.  
To maximize the shear effect due to live load, the truck is placed just off the support at the end of the bridge in the longitudinal direction. However, like in flexure, the truck should be moved in the transverse direction starting from 600 mm away from the face of the parapet until the maximum effect is reached in the girder under consideration. Finite element analysis showed that rotating the truck to maximize the shear effect was not critical in the interior girders, where keeping the truck perpendicular to the end support produced the maximum shear effect for such girders. Alternatively, orienting the truck in a position parallel to the parapet resulted in the maximum shear effect in the exterior girder.
# A Next-Generation Core Network Architecture for Mobile Networks
## 5 Basic Operations
### UE Attachment and SIP Registration
#### 5.1.2 Scenario with eeNodeB  
Figure 12 shows UE attachment and SIP registration when using an eeNodeB. In such a scenario, the eeNodeB can directly \"talk\" to the P2P core network and directly query the DHT in order to find the SN responsible for a specific UE: in other words, no iDNS is necessary. Furthermore, when using eeNodeBs, we do not have IP-in-IP tunneling and, therefore, the eeNodeB will only have to setup MPLS routes with the SN which are then used to route IP packets between the UE and the SN. The only bearer left, when using an eeNodeB, is the radio bearer on the \(Uu\) interface between UE and eeNodeB. For establishing MPLS routes, the SN behaves as an SDN controller for that UE and takes care of establishing such routes by leveraging OpenFlow [22], ReSource Reservation Protocol-Traffic Engineering (RSVP-TE) [23] or any other similar protocol.  
In both scenarios (i.e., with eNodeB and with eeNodeB), once the UE performs an SIP registration with the SN, the SN updates the P2 PSIP overlay with such information.  
Figure 11: UE attach and SIP registration with eNodeB.
## 5 Experimentation, Results, and Analysis
### Experimental Setup
#### 5.1.1 Hyperparameters  
The selection of hyperparameters for the DCAT model involved a meticulous process of experimentation and optimization. Through preliminary experiments and grid searches,we identified the optimal settings to ensure the model's effectiveness and efficiency. The summary of hyperparameter settings for proposed DCAT model is presented in Table 4. The following subsections detail the rationale behind each hyperparameter choice, the challenges faced during their selection, and considerations for their adaptability across different datasets.  
* **Learning Rate (\(\eta\))**: The learning rate is crucial for controlling the speed of convergence during training. After experimenting with various rates including \(\mathbf{1\times 10^{-3}}\), \(\mathbf{5\times 10^{-4}}\), and \(\mathbf{1\times 10^{-5}}\), a learning rate of \(\mathbf{1\times 10^{-4}}\) was found to offer the best balance between rapid convergence and stability of the training process. Selecting this rate was challenging due to the trade-off between convergence speed and the risk of overshooting the global minimum. Additionally, this rate might require adjustments if applied to datasets of differing complexity or size.
* **Batch Size**: The batch size affects both the computational efficiency and the generalization capability of the model. A batch size of 64 was chosen to optimally utilize our hardware resources while ensuring adequate model generalization. This choice stemmed from the challenge of balancing the hardware constraints with the need for robust model training. For datasets with significantly different characteristics, the optimal batch size may vary to accommodate memory constraints or differing data distributions.
* **Epochs**: Determining the appropriate number of training epochs was crucial to prevent overfitting while ensuring comprehensive learning. We settled on 50 epochs, as further training showed diminishing returns in terms of performance improvement. This number of epochs was particularly challenging to establish, as it had to accommodate the varying complexities and sizes of different datasets, requiring extensive experimentation.
* **Custom Loss Function Hyperparameters (\(\alpha\) and \(\beta\))**: The custom loss function, a combination of traditional captioning loss and contextual loss, is controlled by \(\alpha\) and \(\beta\). We set these to 0.7 and 0.3, respectively, to emphasize linguistic accuracy while also incorporating contextual depth. Balancing these parameters was challenging, as it required a nuanced understanding of the trade-off between linguistic precision and the depth of contextual understanding. These parameters might need fine-tuning when adapting the model to datasets with different characteristics or where the emphasis between linguistic accuracy and contextual relevance needs to be shifted.
## 5 Overview of 5G-Enabling Technologies
### Blockchain-Based Security in 5G Network  
Security of the 5G network is a top concern for the communication system since the network is dynamic and radio frequency signals are transmitted on-air without adequate protection. An attacker who is close to the broadcast signal can easily take advantage of this and carry out several malicious activities, including stealing sensitive data, listening in on conversations, modifying the signal, or even disrupting the entire connection. Apart from the inherent weakness of wireless communication, the 5G network has security issues due to the numerous application and technologies it supports, as these technologies and applications are vulnerable to several attacks, such as Man-in-the-Middle (MITM) attacks, eavesdropping attacks, hijacking attacks, malware attacks, etc. [133; 134]. This has drawn the attention and interest of several authors who have tried to design and develop various security algorithms and schemes to defend against these attacks in the 5G network over the years. Blockchain technology is a widely utilized technology for providing security in the 5G network. It refers to a chain of blocks where each block contains some verified records, and all blocks are connected using their crypto-hashes [135]. This technology is also well-known in bitcoin cryptocurrency transactions. Blockchain technology is a decentralized, invariable, and encrypted ledger that guarantees trust between untrustworthy parties without the use of a centralized third party. This decentralized framework allows efficient and reliable blockchain operations, with fraud protection and no single point-of-failure weaknesses [136]. Furthermore, blockchain technology has several security benefits, including providing full control of information when transmitted over an untrusted or unprotected network, improving the performance of the overall system, as it does not require any centralized system to function, and simplifying the network. Figure 12 depicts the integration of blockchain and the 5G network.  
Figure 12: Integration of 5G Network and Blockchain Technology for Improved Security.
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Hybrid  
This section presents two hybrid routing schemes for FANETs.
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 3 Experiments
### Experimental Setup  
The experiments were conducted using two GeForce RTX 3090 GPUs. The experimental environment was built on Ubuntu 20.04.6 LTS, CUDA 11.6, Python 3.9.19, and PyTorch 1.13. During training, the learning rate was set to 0.006, with a weight decay of 0.01. The AdamW optimizer was used in combination with the Poly learning rate strategy, where the power parameter was set to 0.9. The model was trained for 50 epochs, with a batch size of 8 for both the TZMD_NUC and SemanticKITTI datasets.  
The hyperparameters in our model were chosen through a combination of empirical evaluation and references to existing literature. For activation functions, ReLU was used in 3D convolutional layers due to its simplicity and efficiency, while GELU was applied within Mamba modules to enhance training stability and representation quality for long-range dependencies. To determine the number of Mamba blocks, we performed a grid search over [1, 2, 3,...] blocks per stage and observed that using two blocks per encoder/decoder stage achieved the best trade-off between segmentation accuracy and computational cost on our deployment device.  
During data preprocessing, the input scene range for the TZMD_NUC outdoor mining dataset was limited to \([-65\,\text{m},-65\,\text{m},-8\,\text{m}]\) to \([65\,\text{m},65\,\text{m},7\,\text{m}]\), while for the SemanticKITTI dataset, the input scene range was \([-51.2\,\text{m},-51.2\,\text{m},-4\,\text{m}]\) to \([51.2\,\text{m},51.2\,\text{m},2.4\,\text{m}]\) in order to adapt to the characteristics and scale of each dataset.  
Figure 4: Mining operation environments.  
Regarding network architecture, to ensure a fair evaluation of the proposed method, we removed the attention structure from the SphereFormer [15] network and used the remaining network as the baseline model. Downsampling in the U-Net structure was implemented as a five-stage hierarchical structure, with the number of feature channels progressively increasing as [32; 64; 128; 256; 256], whereas upsampling uses a mirror structure to gradually reduce the number of channels from 256 to 32.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 4 Experimental Results and Analysis
### Assessment  
In order to evaluate the proposed method, the uncertainty of the signals and the similarity between them were evaluated.  
* Uncertainty: The uncertainty of the signals was assessed quantitatively through the entropy and the disorder level (\(DL\)). Entropy measures the information content of the signal, and it can be calculated using the Shannon entropy (Equation (12)) [14]. \[H(X)=-\sum_{i}^{M}P(x_{i})\log_{2}(P(x_{i})),\] (12)  
Figure 1: Example of the proposed method (signals in time domain).  
where \(X\) is the entire speech signal, \(x_{i}\) is the \(i\)th sample of the signal, \(P(x_{i})\) is the probability of the occurrence of \(x_{i}\), and \(M\) is the finite number of samples. The maximum entropy value is reached when all amplitudes of the signal are of equal likelihood, in which case it is equal to the number of bits per sample. On the contrary, if the signal has a unique symbol, the entropy is zero. Thus, entropy is a measure of the uncertainty of data [15], with higher entropy values indicating higher uncertainty. In a complementary way, the disorder level (\(DL\)) is a metric that compares each sample with its previous and subsequent sample. For a signal with a low uncertainty level, the values of the adjacent samples will be very similar, whereas in a signal with a high uncertainty level, the difference between adjacent samples will be significant. \(DL\) is calculated using Equation (13) [16]. \[DL=\frac{\sum_{i=2}^{M-1}\sqrt{\left|x_{i}-x_{i+1}\right|+\left|x_{i}-x_{i-1} \right|}}{M-2},\] (13) where, \(x_{i}\) is the \(i\)th sample of the voice signal and \(M\) is the number of samples. According to the above, the higher the \(DL\) value, the lower the intelligibility of the signal. The maximum \(DL\) value is \(\sqrt{2 Vpp}\), where \(Vpp\) is the signal's peak-to-peak amplitude (i.e., 2 for signals ranging between \(-1\) and \(1\)).
* Similarity: The idea here is to evaluate if the encrypted signal has traces of the original signal (ideally the similarity must be low), and if the content of the recovered signal corresponds to that of the original signal. Thus, the similarity between the signals must be assessed both in time domain (correlation coefficient (CC), Equation (14)) [17] and frequency domain (spectral distortion (\(S_{p}D\)), Equation (15)) [18]. If \(X\) and \(Y\) are the original and encrypted signals, \(\sigma_{X}\) and \(\sigma_{Y}\) their standard deviations, \(CC\) is given by, \[CC\left(X,Y\right)=\frac{cov\left(X,Y\right)}{\sigma_{X}\sigma_{Y}}.\] (14) If \(V_{x}(j)\) and \(V_{y}(j)\) are the spectra (dB) of the original and encrypted signals, \(J\) the number of points used in the transform, \(S_{p}D\) (dB) is given by, \[S_{p}D=\frac{1}{J}\sum_{j=0}^{J-1}\big{|}V_{x}\left(j\right)-V_{y}\left(j\right) \big{|}.\] (15)
## 4 Results and Discussion
Simultaneously, with lag = 5, the AST2 FP-OHDBN method has gained lower RT value of 5.07 s whereas the LS-SVM system, GA-LSSVM approach, PSO-LSSVM method, FFO-LSSVM technique, and hybrid LSSVM models have resulted in ineffectual outcomes with higher RT values of 7.89 s, 22 s, 12.24 s, 15.31 s, and 10.44 s, correspondingly. From the detailed result analysis, it is concluded that the AST2 FP-OHDBN model has reached an effectual traffic flow forecasting performance.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 6 Conclusions  
This study focused on the optimal route problem for truck-drone delivery, which is a classic FSTSP. We considered the scenario that the drones may arrive at rendezvous points later than the trucks. This problem was formulated as a single-objective mixed-integer nonlinear programming model subject to time constraints and route constraints, aiming at minimization of total delivery time. Then, we developed a variable neighborhood tabu search algorithm to solve the model. Finally, a testing delivery network was used to illustrate the proposed method. The optimal truck-drone delivery route included the sequence of all delivery tasks, and the total delivery time. The key findings are as follows:  
1. Compared with traditional truck delivery, truck-drone delivery can save delivery time by 20.1% in the numerical experiment, which can guide logistics enterprises to complete the delivery tasks in less time.
2. More trucks and drones can effectively reduce the total delivery time, but the marginal benefit gradually decreases. Only increasing either the truck speed or drone speed cannot minimize the delivery time, and only when the truck speed and drone speed increase synchronously can the delivery time be significantly reduced.  
The results of the numerical experiment indicate that the proposed method can generate the optimal route for truck-drone delivery, taking truck waiting time at rendezvous points into consideration, which provides a reference for logistics enterprises in planning delivery routes. However, there are still some limitations of this study. Future work will focus on: (1) taking uncertainties, e.g., truck and drone speed and service time at customer points into account during the entire delivery time; (2) designing more heuristic algorithms and comparing these algorithms to find a more efficient model solution.  
[PERSON] wrote the manuscript; [PERSON] collected the data; [PERSON] edited and revised the manuscript; [PERSON] drew the figures; [PERSON] designed research methods; [PERSON] analyzed the data. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities (Grant Number 300102341513) and the Natural Science Research Program of Shaanxi Province (Grant Number 2020)Q-360) and the National Natural Science Foundation of China (Grant Number 52102374) and the Transportation Science and Technology Research Project of Hebei Province (Grant Number JX-202006).  
Not applicable.  
Data Availability StatementData are contained within the article. The data presented in this study can be requested from the authors.  
The authors declare no conflict of interest.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Embedding Layer  
As shown in Figure 2, a key function of the embedding layer is to convert tokenized URL tokens into dense numerical vectors. By collecting semantic similarities between tokens and enabling a deeper comprehension of the context within the neural network model, these vectors offer a richer representation of the input text.  
Figure 2: Demonstration of the embedding layer [21].
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 4 Collision Avoidance System
### Control Behaviors and Prediction of the USV Trajectory  
For the USV system, the control behavior \(S\) is the combination of propulsion angle \(\delta\) (which replaces the command propulsion angle \(\delta_{r}\) later) and thruster speed \(n\). The actuator of the USV has a lot of limitations, such as maximum value \(\delta_{m}\) and \(n_{max}\), change rate \(\dot{\delta}\), and the \(\dot{n}\) of the two control behaviors. In the control update interval \(\Delta t\), the scope of the control behaviors is  
\[S=\left\{\left(\delta,n\right)\Biggm{|}\begin{array}{l}\delta\in\left[\delta- \dot{\delta}\Delta t,\delta+\dot{\delta}\Delta t\right]\\ n\in\left[n-\dot{n}\Delta t,n+\dot{n}\Delta t\right]\end{array}\right\} \tag{12}\]  
and  
\[\left\{\begin{array}{l}|\delta|\leq\delta_{m}\\ 0\leq n\leq n_{\max}\end{array}\right.\]  
Then, by the discrete propulsion angle \(\delta_{d}\) and thruster speed \(n_{d}\), the maximum number of control behaviors is \(c=(2\dot{\delta}\Delta t/\delta_{d})\times(2\dot{n}\Delta t/n_{d})\). With the decrease of \(\delta_{d}\) and \(n_{d}\), the control behaviors will be more precise, which can improve the performance of the system, but the calculation complexity will increase accordingly. In terms of the control performance and safety, it is obvious that there should be as many control behaviors as possible, but from an information processing perspective, the calculation complexity should be reduced within the computational capacity. Obviously, there is a tradeoff between the number of the control behaviors and the computational complexity with respect to time discretization, the control update interval, the prediction horizon, and the computation time. In addition, a very useful feature of these control behaviors is that they should remain constant throughout the prediction horizon.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 5 Discussion
### Practical and Theoretical Implications
#### 5.1.1 Practical Implications  
By integrating AI-driven NPCs in VR shopping environments, our model aims to replicate real-life interactions, thereby enhancing user engagement and satisfaction. The SAAS model demonstrates high scalability, making it suitable for implementation across various e-commerce platforms, potentially transforming how online retail is conducted. Additionally, the use of deep learning models such as GPT-3, BERT, and T5 allows for highly personalized shopping recommendations, which can lead to increased customer loyalty and sales.
# Mapping Based Quality Metrics for Mesh Deformation Algorithms Using Radial Basis Functions
## 3 Mapping-Based Metrics  
The quality of deformed mesh is evaluated by the mesh quality metrics. Most of the present research studies give quality metrics that require the topology of the initial mesh or deformed mesh. It is based on a set of Jacobian matrices containing information on mesh element qualities such as size and skewness. In this section, an efficient mesh quality metrics generation method based on a mesh deformation algorithm is introduced.
# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
###### Abstract  
Chatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. A formidable challenge is that chatbot dialogue systems have difficulty handling queries with unknown intents due to the technical bottleneck and restricted user-intent answering scope. Furthermore, the wide variation in a user's consultation needs and levels of medical knowledge further complicates the chatbot's ability to understand natural human language. Failure to deal with unknown intents may lead to a significant risk of incorrect information acquisition. In this study, we develop an unknown intent detection model to facilitate chatbots' decisions in responding to uncertain queries. Our work focuses on algorithmic innovation for high-risk healthcare scenarios, where asymmetric knowledge between patients and experts exacerbates intent recognition challenges. Given the multi-role context, we propose a novel query representation learning approach involving multiple views from chatbot users, medical experts, and system developers. Unknown intent detection is then accomplished through the transformed representation of each query, leveraging adaptive determination of intent decision boundaries. We conducted laboratory-level experiments and empirically validated the proposed method based on the real-world user query data from the Tianchi lab and medical information from the Xunyiwenyao website. Across all tested unknown intent ratios (25%, 50%, and 75%), our multi-view boundary learning method was proven to outperform all benchmark models on the metrics of accuracy score, macro F1-score, and macro F1-scores over known intent classes and over the unknown intent class.  
healthcare chatbot; intent detection; multi-view learning; decision boundary; representation learning 2020