esa-sceva/satcom-chunk-collection huggingface dataset:
# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
## 2 Literature Review
### Cable Fire Mechanisms  
Many studies have addressed the ignition and combustion mechanisms of cable fires, by a large number of experimental analyses. For instance, [PERSON] [4; 5] and [PERSON] [6], investigated the mechanisms of cable fires caused by arc faults, overheating of cable cores, and external heating. [PERSON] [7] and [PERSON] [8], revealed the ignition mechanisms and failure criteria of cable fires by performing cable fire characterization experiments. [PERSON] [9] investigated cable fires caused by cable looseness or corrosion, high resistance connection, and wire insulation moisture absorption. [PERSON] [10], [PERSON] [11], and [PERSON] [12], studied the influence of polyvinyl chloride (PVC) cable aging on the fire risk. The above studies revealed that the mechanisms of cable fires were associated with intrinsic factors. However, the occurrence of cable fires is generally attributed to a complex combination of factors. Moreover, conventional methods cannot simultaneously predict the occurrence probability and the consequences of cable fires. In this sense, a cable risk analysis model should be implemented, for incorporating the impact of multi-factor coupling and for quantifying the occurrence probability and the severity of fire consequences.
## 3 Search Results and Classification
### Construction Costs
Studies P29, P66, P72, P74, and P75 highlighted the relevance of incorporating new technologies, such as 3D printing, augmented reality (AR), and building information modeling (BIM). These innovations improve accuracy, reduce errors, and increase the efficiency of construction processes, resulting in more profitable and sustainable projects. Studies P64 and P71 mentioned that industrialized construction guarantees a longer life with lower operating and life cycle costs, providing viable and sustainable long-term housing solutions. This is of great interest as the modernization of buildings using prefabricated and modular systems also contributes to addressing the housing affordability crisis. In this regard, studies M11 and P43 mentioned that the supply of industrialized housing is crucial for economic growth in both developed and developing countries. Implementing the latest technologies in the construction industry enhances economic growth by improving productivity and efficiency in project management. Despite all these statements, studies M6, P1, P44, P56, P60, and P62 indicated that modular and prefabricated construction face high initial investment costs for implementation, which can make project financing difficult for many companies. Additionally, the profitability of these methods can be affected by the project's location and logistics costs. In this context, the same studies (M6, P1, P44, P56, P60, and P62) declared that under certain circumstances, the cost of prefabricated and modular solutions could be higher than traditional solutions.
## 3 Results and Discussion
### Cost-Efficiency Analysis  
As shown in Table 3, the biomass energy produced from the co-digestions fed with Treat-RCS, Treat-CSA, Treat-CSB, Treat-CSC and Treat-CSD was 4681.76, 6170.87, 6043.21 and 5371.55 kJ/kg initial TS, respectively. The digester with RCS could harvest energy of 4694.75 kJ/kg TS, which would incur revenue of $136.62/ton TS from electricity generation. When 0.1 g NaOH was added in the CS pretreatment (Treat-CSA), the energy production could be significantly improved to 6170.87 kJ/kg TS and the revenue from electricity generation was increased to $179.57/ton TS. Therefore, Treat-CSA could earn more revenue ($42.96/ton TS) compared to the digestion of RCS. However, the chemical cost ($45/ton TS) associated with the pretreatment made the net benefit negative (i.e., -$2.04/ton TS).  
On the other hand, when the NaOH dose was decreased to 0.075 g NaOH by adding 0.05 g CaO in the CS pretreatment (Treat-CSB), the energy production of the biomass was significantly improved to 6040 kJ/kg TS and the benefit from electricity generation was increased to $176/ton TS. Therefore, Treat-CSB could incur more revenue ($39.0/ton TS), compared to the digestion with RCS. Considering the chemical cost of $37.4/ton TS, the net benefit could be calculated as $1.9/ton TS. Since the relatively lower biogas production, Treat-CSC and Treat-CSD also resulted in negative net revenues: -$6.4 and -$18.8/ton TS for Treat-CSC and Treat-CSD, respectively. A similar study has been carried out to report that NaOH pretreatment of CS resulted in a negative net benefit while CS pretreated with Ca(OH)\({}_{2}\) not only improved the methane yield by up to 1.4-fold, but also obtained a net benefit of $1.1-5.8/ton dry biomass [40].
## 3 Results and Analysis
### Data Processing and Analysis
#### 3.2.3 Convergence Performance and Positioning Accuracy Assessment  
To further assess the convergence time and positioning accuracy in RT PPP using our proposed approach, the method of priori variance and without constraints, as listed in Table 1, are compared with a weight factors searching algorithm using datasets collected at 31 MGEX stations over 30 consecutive days from 23 January 2018 to 23 February 2018. A total of 11,160 sets of results are used to derive a statistical estimate on the convergence time as well as the positioning accuracy. The definition of the convergence time is the same as that described in Section 3.2. The distribution of the 11,160 sets of convergence times is plotted in minutes in Figure 7. It is observed that the performance of convergence can be improved in all positioning modes by using ionospheric constraints, but only to a slight degree. Using our proposed weighting approach, the convergence time of RT PPP is significantly decreased. The percentage of position solutions converging within 20 min is up to about 54%, 39%, 52%, and 50% in the dual-frequency static, dual-frequency kinematic, single-frequency static, and single-frequency kinematic positioning modes, respectively. The statistical results in terms of mean convergence time are also given in Figure 7. According to the mean values, the improvement of the new method on the convergence time is about 35.9%, 25.9%, 20.4%, and 25.2% over the method of priori variance in four positioning modes, respectively.  
During the converging procedure of the RT PPP position filter, the size of position errors over time can also reflect the converging speed of the position filter [17]. Figure 8 illustrates the RMS statistical values of vertical and horizontal for all stations at the beginning of 30 min (left) and all day (right). Since the position solutions in the first two hours are still in the converging stage, they are not used for the accuracy statistics of all day. From the statistical results of 30 min, it is noted that positioning errors in four positioning modes sharply decrease after using RT ionospheric products at the beginning of the filter processing, especially in the single-frequency case. There is no denying that RT ionospheric products play an important role in accelerating filter convergence. But the method of priori variance exhibits slightly worse performance than the other two methods in the RMS statistical values of all day, as shown in Figure 8 (right), which indicates the negative impact of unreliable weighting on the convergence position accuracy. In terms of the RMS statistical values of all day, our proposed weighting approach can achieve the same precision as the method without constraints.  
Figure 7: Distribution of convergence time for dual-frequency static, dual-frequency kinematic, single-frequency static, and single-frequency kinematic using datasets at 31 MGEX stations over thirty days.
## 2 Materials and Methods
### Cables and Antennas  
All cables required to build and operate this unit are available on the market and can be summarized as follows: (1) power cables used to power the reader and the laptop, (2) a VGA cable to link the adaptor kit to the reader, (3) ethermet cables linking the adaptor to each hub, (4) radio-frequency cables linking the reader to each hub for signal transmission from the beehive, and (5) antenna cables that transmit the signal from the transponder (at the beehive entrance) to the hubs (Figures 2 and 3b). The 1800 W power generator is provided with all necessary cabling to operate, including a charging cable from the solar panel to the generator and a cable for charging the generator directly from an electric outlet.  
The second crucial part of this unit is the antenna, whose role is to capture the bee tag (transponder) signal at the hive entrance and guide it to the hub and subsequently to the reader. There are different types and sizes of antennas commercially available; however, the 5 dBi precision antenna is the most adapted for our purpose (Table 1). In order for the antenna to accurately detect and transmit the bee entry/exit from the hive, funneling bees through an entrance tunnel is required, as shown in Figures 1 and 3a.
# Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond
## 3 System Model
### Candidate Locations for ABSs  
A candidate position is a location where an ABS can be deployed. We assume that each candidate position consists of a platform where an ABS can perch while it is deployed there and that the ABS can receive power at that location; so, when an ABS reaches its deployed candidate location, it does not need to hover. Furthermore, it means that there is no risk of a UAV or small cell running out of energy while it is operating at a candidate location. Furthermore, the batteries of a UAV can be charged while it is perched and operating at the candidate location. We assume that each candidate position has sufficient backhaul capacity to support the maximum possible traffic load generated at an ABS.
# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 3 Deep Learning Methods
### Multimodal Approaches  
Recently, a few works have leveraged auxiliary types of data in order to obtain better pose estimations and alleviate depth ambiguity and finger occlusions. Information, such as depth maps, heatmaps, point clouds, or even motion capture (MoCap) data, is better suited to capture the structureof the hand pose than RGB images. There are two ways of incorporating additional modalities into the framework: either as supplementary information available only during training in order to boost the RGB inference (unimodal inference) or as accessible information throughout the whole procedure (multimodal inference).
# Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Number of Robots** & **Algorithm** & **Base Placement Objective** \\ \hline ([PERSON] et al., 2018) & Single & Genetic Algorithm & Optimizing manipulability and torque profiles \\ \hline ([PERSON] et al., 2018) & Multiple & Hybrid: Genetic Algorithm and simulated annealing & Maximizing the coverage while minimizing the makespan \\ \hline ([PERSON] et al., 2018) & Single & Particle Swarm Optimization & Maximizing reachability while avoiding singularities and collision \\ \hline ([PERSON] et al., 2018) & Multiple & Hybrid: greedy, genetic, and simulated annealing & Minimizing the number of robots while maximizing the coverage \\ \hline ([PERSON] et al., 2021) & Single & Sequential quadratic programming & Maximizing the stiffness performance index while avoiding singularities and collision \\ \hline ([PERSON] et al., 2021) & Multiple & Whale and Ant Colony Optimization & Ensuring kinematic feasibility while checking for collision-free trajectories \\ \hline ([PERSON] et al., 2021) & Single & Genetic Algorithm & Maximizing the available workspace \\ \hline ([PERSON] et al., 2021) & Multiple & Metaheuristic Search & Energy efficiency in additive manufacturing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A sample overview of robotic base placement algorithms proposed in the literature. **Relationship to our previous work:** The current article is independent from and complements our previous work [18], in which we investigated the design of motion planning algorithms for collaborative robotic car painting. In that work, we were assuming that the manufacturer would provide a suitable collection of base positions, usually from experience and/or heuristics. However, a poor choice of base positions for painting robots resulted in the failure of the motion/trajectory planning algorithms. The base placement algorithm proposed in the current paper makes the process completely automatic, whereby the computed optimal base positions can be utilized within any available robotic painting motion/trajectory planner including but not limited to [18]. The rest of the paper is organized as follows. After presenting our assumptions and problem statement, we present our high-level solution strategy in Section 2. Next, we present the pre-processing computational step associated with our algorithm, which entails a one-time 3D point cloud rendering and ellipsoidal fitting for the reachable workspace of the robotic arms in Section 3. Thereafter, we present the two types of base placement cost functions tailored to the multi-robot car-painting application in Section 4 and Section 5. Subsequently, in Section 6, we present the multi-objective optimization framework and our overall algorithm. We demonstrate our simulation results in Section 7. Finally, we conclude this article with some final remarks and future research directions in Section 8.
## 3 Literature Review Results
### Systematic Review
#### 3.2.2 Mobility Services other than FMCS
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Article** & **Communication** & **Service** & **Based on** & **Implementation** & **Applied** & **Use of** \\  & **Technology** & **TTS** & **Type** & **Environment** & **Security** \\ \hline Internet of Bikes: A DTN & & & & & Medium-sized & \\ Protocol with Data & & & & & city for & \\ Aggregation for Urban & & & & & European & \\ Data Collection [51]. & & & & & context & \\ \hline Heterogeneous Intelligent & & & & & & \\ Transportation System in & & & & & & \\ Quito city under the & & & & & & \\ paradigm of the & & & & & & \\ SWE-SOS standard and & & & & & & \\ IoT notifications [52]. & & & & & & \\ \hline A vehicular & & & & & & \\ network-based intelligent & & & & & & \\ transport system for & & & traffic & & Asian context & Yes \\ Smart cities [53]. & & & & & & \\ \hline Cloudthink: a scalable & & & & & & \\ secure platform for & & & & & & \\ mirroring transportation & & & & & & \\ systems in the cloud [54]. & & & & & & \\ \hline A mobile platform system & & & & & & \\ for driving assistance [55]. & & & & & & \\  & & & & & \\  & & & & & \\ \hline Fleet Management & & & & & & \\ System for Truck & & & & & \\ Platoons—Generating an & & & & & & \\ Optimum Route in Terms & & & & & \\ of Fuel Consumption [56]. & & & & & \\ \hline Fleet Management & & & & & \\ Cooperative Systems for & & & & & \\ Commercial Vehicles [57]. & & & & & \\ \hline Smart fleet monitoring & & & & & \\ system using Internet of & & & & & \\ Things (IoT) [58]. & & & & & \\ \hline Intelligent transportation & & & & & \\ system based on the & & & & & \\ principles of & & & & & \\ service-oriented & & & & & \\ architecture [59]. & & & & & \\ \hline Analysis of handshake & & & & & \\ time for Bluetooth & & & & & \\ communications to be & & & & & \\ implemented in vehicular & & & & & \\ environments [60]. & & & & & \\ \hline Field testing of Bluetooth & & & & & \\ and ZigBee & & & & & \\ technologies for & & ZigBee & & & \\ vehicle-to-infrastructure & & & & & \\ applications [61]. & & & & & \\ \hline The Application of & & & & & \\ ZigBee Technology to the & & & & & \\ Intelligent Bus Query & & & & & \\ System [62]. & & & & & \\ \hline A novel approach of & & & & & \\ using security-enabled & & & & & \\ ZigBee in vehicular & & & & & \\ communication [63]. & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Documents of mobility services other than FMCS. Regarding the use of security, there is also an increase in participation, reaching up to 33%, and greater use of ITS services or architectures (53%), although there is a reduction in the implementation of the proposals in real environments (63%).
# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 3 News Text Summarization Model Based on Framing Theory
### A Summarization Model Incorporating Framework Information (FrameSum)
#### 3.3.1 Encoder Design Incorporating Framework Information  
1. Frame Feature Representation  
Through the automatic framework identification model, the news text \(t\) obtains framework labels \(\hat{l}\in L\). To transform the discrete framework labels into continuous vector representations, we map the framework labels \(\hat{l}\) through the word embedding matrix to obtain a \(d_{f}\)-dimensional framework embedding vector \(f\), as follows:  
\[f=Embedding_{frame}\Big{(}\ \hat{l}\ \Big{)} \tag{5}\]  
To introduce the semantic features of the framework into the encoder, we need to transform the semantic feature vector of the framework into a fixed-dimensional embedding representation. Specifically, we map the semantic feature vector \(\Phi\) corresponding to framework \(\hat{l}\) through a linear transformation layer to obtain a \(d_{f}\)-dimensional semantic embedding vector \(s\),  
\[s=W_{s}\Phi+b_{s} \tag{6}\]  
where \(W_{s}\in R^{d_{\Phi}\times d_{s}}\) and \(b_{s}\in R^{d_{f}}\) are learnable parameters. Finally, we concatenate \(f\) and \(s\) to obtain the framework information representation \(r=[f,s]\) of the news text \(t\).  
2. Framework-Aware Attention Mechanism  
To enable the encoder to extract key information based on the framework type of the news text, we introduce framework information \(r\) to modulate the attention weights in each layer of self-attention in the transformer encoder. For the \(l\)-th layer and the \(i\)-th position, the attention weight \(\alpha_{ij}^{l}\) to the \(j\)-th position is calculated as follows:  
\[\begin{split} e_{ij}^{l}=\frac{\left(\frac{u^{l}W_{q}^{l}+r_{i}W_{ q}^{l}}{\sqrt{d_{k}}}\right)\left(\frac{u^{l}W_{q}^{l}}{\sqrt{d_{k}}}\right)^{T}}{ \sqrt{d_{k}}}\ (i,\ j=1,\ 2,\ \dots,\ n)\\ \alpha_{ij}^{l}=\frac{exp\left(\frac{d_{j}}{\sqrt{d_{k}}}\right) }{\sum_{k=1}^{k}exp\left(\frac{d_{k}^{l}}{\sqrt{d_{k}}}\right)}\\ h_{i}^{l}=\sum_{j=1}^{n}\alpha_{ij}^{l}h_{j}^{l}W_{p}^{l}\end{split} \tag{7}\]  
where \(W_{r}^{l}\in R^{d_{model}\times 2d_{f}}\) are learnable parameters and \(d_{k}\) is the dimension of the attention head. By adding the news framework representation \(r_{i}\) to the query vector \(h_{i}^{l}W_{q}^{l}\), the attention mechanism captures semantic relationships while considering the framework attributes of the current news.  
3. Text Representation Incorporating Framework Information  
In the last layer \(L\) of the transformer encoder, the hidden state \(h_{i}^{L}\) at the \(i\)-th position is concatenated with the framework representation \(r_{i}\) to obtain the final text representation incorporating framework information:  
\[D_{i}=\left[h_{i}^{L},r_{i}\right] \tag{8}\]  
As the output of the encoder, \(D_{i}\) not only incorporates the semantic content at the \(i\)-th position but also the attribute information of the news framework.
## 4 Modeling and Validation
### Individual Modeling
#### 4.1.6 Support Vector Machine  
The support vector machine algorithm works to find a hyperplane that maximizes the separation of the data points to their potential classes in an n-dimensional space. The dimension of the hyperplane depends upon the number of features. If the number of input features is three, then the hyperplane becomes a 2D plane. When the number of features exceeds three, like in the case of the gene dataset, it becomes difficult to imagine the hyperplane. For the sklearn support vector classification (SVC) model, care must be taken when selecting the kernel function to avoid over-fitting when the number of features is much greater than the number of samples (as is the case with the gene dataset) [25]. Optimum classification results for the sklearn SVC model were achieved using the radial basis function (RBF) kernel.
# Leaky Wave Generation Through a Phased-Patch Array
## 1 Introduction
The reflected field, on the other hand, keeps propagating inside the waveguide. Again, the remaining power will only be a portion of the initial impinging field, due to the leakage. This leakage rate per meter is the attenuation constant that, due to the conservation of the tangential component of the electric field at the interface, is present in the field radiated in the air. It is well known that the behaviour of the field inside the guide is frequency-dependent. In fact, starting from the cut-off frequency at which the field does not travel inside the dielectric (the stop-band region), by increasing the frequency the propagation vector points  
Figure 1: Pictorial representation of leaky radiation principle. more and more towards the propagation direction shown in Figure 1 (i.e., x direction). Again, this is a good explanation of how a leaky-wave antenna operates by varying the frequency: the higher the frequency, the more the field is radiated towards endfire. To analyse the numerous types of existing leaky-wave antennas was beyond the scope of this paper. We only considered a uniform monodimensional LWA, where the cut-off of the guided mode of interest coincides with the stop band. The analysis and design of a given LWA requires the derivation of its dispersion diagram and the calculation of the attenuation and propagation constants related to the geometrical and electromagnetic characteristics of the waveguide and its asymmetries. Finally, the attenuation and propagation constant are related to the far-field properties. The following approximate relationships are present in the literature, valid for \(\alpha<<\beta\), where \(\alpha\) is the absolute value of the attenuation vector and \(\beta\) is the absolute value of the phase vector:  
\[\theta_{0}\cong sin^{-1}(\beta_{z}/k_{0}) \tag{1}\]  
\[\theta_{3 dB}\cong\frac{0.91}{(L/\lambda_{0})cos\theta_{0}}\propto\frac{\alpha }{k_{0}} \tag{2}\]  
In the equations illustrated above, \(\theta_{0}\) is the angle between the normal and the antenna aperture and the radiated beam, \(\theta_{3 dB}\) represents the beamwidth, \(\lambda\) is the wavelength, \(\beta_{z}\) is the \(z\) component of the phase vector, and \(k_{0}\) is the wave number in vacuum. These relations imply that the pointing angle is decided by the value of the propagation constant \(\beta\), while the beamwidth is decided by the attenuation constant \(\alpha\). The intuitive explanation of the frequency behaviour has been provided above. As far as the relationship between the attenuation constant and the beamwidth is concerned, it is sufficient to note that a larger attenuation results in an earlier decay of the electromagnetic field and so in a shorter electrical length and, as is known, a wider beam.