esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Intent Detection and Slot Filling
#### 3.2.2 Slot Label Extractor  
In order to carry out the task of slot filling in Chinese medical dialogues, this paper designs the SLE, which interacts with the fusion of character and word information through multi-head attention to achieve the extraction of important tags in patient texts. Firstly, an LSTM with the decoding function of Equation (7) is used to generate the hidden state \(h_{t}^{c,s}\). Then, this paper uses a SLE to integrate the character and word information, as shown in Figure 5, to make use of character and word information interaction for better slot label extraction. Firstly, the hidden states of character information and word information are taken as inputs, and both are spliced to join the multi-head attention; the common multi-head attention weight is calculated; then the attention weight acts on the hidden states of both; and finally the fusion interaction is carried out to realize the extraction of slot labels, which improves the filling of the correct slot labels. The formula is as follows:  
\[\text{SLE}(C,W)=A((1-\lambda)\cdot C+W) \tag{15}\]  
\[\lambda=\textit{sigmoid}(C^{T}N_{f}W) \tag{16}\]  
Figure 4: Example diagram of an IIA.  
Finally, slot filling is performed using integration \(V_{t}^{S}\) instead of \(h_{t}^{c,s}\) that contains character information and word information. The probability \(P\) is obtained by the _softmax_ function. The formula is as follows:  
\[V_{t}^{S}=\text{SLE}(h_{t}^{c,s},h_{t}^{w,s}) \tag{17}\]  
\[P\left(\tilde{y}=j\mid V_{t}^{S}\right)=softmax\Big{(}W^{S}V_{t}^{S}+b^{S}\Big{)} \tag{18}\]
## 2 Materials and Methods  
### Sample Preparation  
A light brown bitumen (penetration grade 50/70), produced in Saudi Arabia and kindly supplied by Loprete Costruzioni Stradali (Terranova Sappo Minulio (RC), Italy), was used as base asphalt binder to test the effectiveness of natural compounds selected as anti-aging additives. The natural antioxidant additives selected for the present research were: (a) commercial mix of phospholipids in form of light yellow powder (hereafter LCS) provided by Kimical srl (Rende (CS), Italy); (b) Vitamin C, also known as ascorbic acid (hereafter VC) provided by Sigma-Aldrich (Milano, Italy); (c) rice husk (hereafter RH) obtained from producers located in Mantova, Italy. The raw RH was first washed with distilled water and dried at 60 \({}^{\circ}\)C and then ground for 10 min in an electric mixer and sieved with 0.5 mm sieve to get fine powder prior mixing with bitumen. Three aliquots of the starting bitumen were first heated at 140-160 \({}^{\circ}\)C and then mixed with 2% by weight (\(w/w\)) of LCS, VC and RH, respectively. A fourth aliquot of the same bitumen source was used as reference (hereafter, CTRL). Each compound was separately added to bitumen, under vigorous stirring at the temperature of 150 \({}^{\circ}\)C and the stirring was maintained for a period of 30 min. Once the mixing procedure was finished, each of the resulting bitumen mixtures was separately poured into small sealed containers and then stored in a darkened thermostat at 25 \({}^{\circ}\)C to preserve morphology.  
Figure 1: Flowchart of the experimental approach. The tested binders both neat bitumen as control CTRL, and bitumen modified with 2% \(w/w\) of natural antioxidants, namely, a mix of phospholipids (LCS), Vitamin C (VC) and rice husk (RH), are subjected to artificial thermal treatment by Rolling Thin Film Oven Test (RTFOT). Then, Dynamic Shear Rheological (DSR) tests carried out on artificially aged binders are compared to those performed on aliquots of unaged samples. A parallel EPR study is performed on both the asphaltene and maltene fractions obtained, respectively, from unaged binders and bitumens subjected to short-term aging by RTFOT.
# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
## 1 Introduction
Finally, in case of the successful development of an ANN-based pipeline for recognizing meteor signals in the Mini-EUSO data, it can be applied for a search of track-like signals of different nature, including those that mimic extensive air showers. In what follows, we present a pipeline consisting of two basic neural networks that demonstrate high performance and can be trained on an ordinary PC. The work continues a series of studies fulfilled within the JEM-EUSO collaboration on application of machine learning and neural networks to analysis of data of fluorescencetelescopes [24; 25; 26; 27]. We do not present any results of applying the suggested method to data analysis since this will be covered in detail in a dedicated paper.
## 3 Results and Discussion
### Volatile Fatty Acids
However, the VFAs produced in the HRT4 assay were composed of 81% acetic acid and butyric acid. It was also found that the HRT4 trial showed the highest increase from the influent to the effluent in the total concentration of acetic acid and butyric acid compared to the other trials (HRT4: increase of 21.4%; HRT5: increase of 14.3%; HRT6: increase of 18.1%). The increase in the OLR in the HRT4 assay may have led to an unstable DF process through the accumulation of VFAs, namely acetic acid, with an inhibition of bioH\({}_{2}\) production. [PERSON] et al. [39] studied the effect of pH, OLR, and temperature in the DF of food wastes under batch conditions. Their results revealed that the maximum production of VFAs occurred at pH 6 and the acetic and butyric acids corresponded to approximately 77% of the overall VFAs produced. These authors observed that the concentration of VFAs increased as the OLR increased from 5 g TS.L\({}^{-1}\).d\({}^{-1}\) (VFAs = 13.27 g.L\({}^{-1}\)) to 16 g TS.L\({}^{-1}\).d\({}^{-1}\) (VFAs = 24.93 g.L\({}^{-1}\)). At the highest OLR, 65% of the produced VFAs were constituted by acetic and butyric acids. The produced VFAs in the current work, with HRTs of 4, 5, and 6 d were not as high as in the study by [PERSON] et al. [39] because the feeding conditions were different. [PERSON] et al. used batch conditions, while the current work was performed under continuous feeding. The latter conditions tendentially present lower concentrations of the final products as the HRT tested are shorter than under batch conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Molar Ratio** & **HRT4** & **HRT5** & **HRT6** & **Optimum Value** \\ \hline BA/AA & 0.26 & 0.40 & 0.26 & \textgreater{}2.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Butyric acid to acetic acid (BA/AA) molar ratio for the three HRTs.
## 5 Field Experiment
### Qualified Rate of Planting Depth, Planting Posture, and Film Side Outcrop  
In order to measure the planting depth, planting posture, and outcrop distance on the membrane side of Codonopsis seedlings, before data collection, the horizontal distance \(L_{s}\) between the seedling head and the membrane edge after the membrane edge points vertically downward and the covering soil is removed was set as the measurement standard for the membrane side outcrop. The angle between the naked seedling and the horizontal plane was set as \(\varphi\), and the distance between the deepest part of the naked seedling and the ridge surface was measured as \(H_{s}\) (Figure 21).  
According to the agronomic requirements of Codonopsis seedling transplanting, when the planting depth meets the standard 4 cm \(\leq H_{s}\leq\) 8 cm, the planting depth can be regarded as qualified. When the angle between the naked seedling and the horizontal plane meets the standard \(10^{\circ}\leq\varphi\leq 20^{\circ}\), the planting posture can be regarded as qualified. When the horizontal distance between the seedling head and the membrane edge meets the standard 3 cm \(\leq L_{s}\leq\) 5 cm, film side outcrop can be regarded as qualified. Supposing the total number of planted seedlings is \(N_{t}\), the number of seedlings which meet the standard of planting depth is \(N_{s}\), the number of seedlings meeting the standard of planting posture is \(N_{z}\), and the number of seedlings which meet the standard of film side outcrop is \(N_{l}\), then the calculation method of the qualified rate of planting depth \(P_{s}\), the qualified rate of planting posture \(P_{z}\), and the qualified rate of the film side outcrop \(P_{l}\) are, respectively, as follows:  
\[P_{s}=\frac{N_{s}}{N_{t}}\times 100\% \tag{33}\]  
\[P_{z}=\frac{N_{z}}{N_{t}}\times 100\% \tag{34}\]  
\[P_{l}=\frac{N_{l}}{N_{t}}\times 100\% \tag{35}\]  
The test results obtained from the above formula are shown in Table 5.  
Figure 21: Schematic diagram of measurement of planting posture.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Functional Validation
#### 4.3.2. Provision and Configuration of NFV Sites  
The realization of the scenario shown in Figure 6 requires three independent NFV sites, to host the functions of the IPTV head end and the two video-serving offices. To deploy this target scenario, we have used a multi-site NFV ecosystem that is available at the 5G Telefonica Open Network Innovation Centre (5 TONIC) [9]. The ecosystem includes a production-quality MANO software stack, based on the ETSI-hosted Open-Source MANO (OSM) project, along with two independent NFVIs, each under the control of an OpenStack VIM (this NFV ecosystem is described in [30]). In addition, we have created a third NFVI, setting up a new OpenStack controller and a set of portable mini-ITX computers as compute nodes. This NFVI has been connected to the Internet through a fiber-optic access provided by a commercial Internet service provider. Following the methodology presented in [31], we have integrated this portable NFVI into the multi-site NFV ecosystem, making use of the VPN service offered by the 5 TONIC laboratory. This way, all the aforementioned NFVIs were interconnected at layer-3, by means of the 5 TONIC routing infrastructure and the VPN service.  
With this, our validation scenario presents three independent sites. The first two sites (identified as \(A\), \(B\) in Figure 6) are hosted within 5 TONIC, whereas a site with a portable NFVI (site \(C\)) is available from an external location. In addition, a virtual link in the form of a VLAN has been pre-created at each of the NFVIs (VL\({}_{1}\), VL\({}_{2}\) and VL\({}_{3}\)) in Figure 6), making use of their corresponding OpenStack VIMs. These virtual links will be used to support inter-site communications among VNFs of the IPTV service. Following our previous analysis, we set the MTU of these virtual links to 1400 bytes to avoid fragmentation and reassembly processes at the L2S VNFs. An additional virtual link has been pre-created as a VLAN in sites \(B\) and \(C\) (VL\({}_{4}\) and VL\({}_{5}\) in Figure 6, respectively), to support the connectivity of an IPTV subscriber equipment. In site \(C\), this equipment has been represented by a laptop, which has been connected to the pre-created VLAN through an Ethernet Port of a mini-ITX computer. The IPTV subscriber equipment of site \(B\) has been represented as a virtual machine.  
Figure 6. Overview of the validation scenario.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### Potential Applications
#### 4.2.1 Internet of Flying Things  
The increased demand for highly connected devices originated the concept of IoT. In IoT, a massive number of \"things\" (i.e., devices) are connected to the Internet, in order to exchange information for the most diverse applications and purposes, from gains in efficiency to expansion of functions.  
Because of the IoT rise, there is a major demand for integrating UAVs with this network. However, due to the special features involving Flying Ad hoc Networks (FANETS) and these aircraft, the IoT was created. IoT is a new research field that differs to IoT in scope of research, despite being related areas.  
IoT specific challenges range from regulatory issues to hardware and software limitations. Regarding security, as the UAVs are critical embedded systems, so a network security issue may generate a safety issue. The security challenges in IoT are diverse, involving the physical, data link, transport, session, and application layers [7].
## 5 Conclusions  
Reducing transportation costs and improving production efficiency are crucial methods for enhancing mining productivity. Researching mine transportation scheduling is a key approach to achieving these goals. Unlike civil engineering academic textbooks, which remain limited to fossil fuel-based mining equipment, with a primary focus on improving transportation efficiency [34; 35], this study combines the problem of battery-swapping truck scheduling with multi-objective optimization algorithms, constructing a transportation scheduling optimization model suitable for the new transportation mode of battery-swapping trucks in open-pit coal mines. The model is solved using both genetic algorithms and adaptive genetic algorithms. The results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively solve the multi-objective scheduling model for the new transportation mode of battery-swapping trucks in open-pit coal mines. The generated scheduling plans, truck-shovel configurations, and transportation paths for battery-swapping trucks provide two optimized scheduling solutions for vehicle transportation under the new transportation mode. Both solutions meet the mining  
Figure 6: Comparison of the basic and adaptive algorithms.  
production requirements; however, the solution obtained using the adaptive genetic algorithm is slightly superior to that of the basic genetic algorithm. In the solutions identified by the sensitivity analysis of the multi-objective weights, the adaptive genetic algorithm reduced the total transportation costs and total waiting time by 5.6% and 17.4%, respectively, and reduced the number of battery swaps and transportation distance by 15.8% and 1.2% compared to the basic genetic algorithm. The proposed vehicle transportation scheduling plan for the new transportation mode of battery-swapping trucks in open-pit mines addresses the lack of scheduling solutions for this mode. The study establishes a multi-objective scheduling optimization model tailored to the battery-swapping mode, achieving the optimization of battery-swapping truck transportation scheduling in open-pit mines. This provides technical support for efficient production and intelligent development in open-pit mines.  
Conceptualization, Y.X. and B.L.; methodology, Y.X.; software, K.Y. and Y.Y.; writing--original draft preparation, Y.X.; writing--review and editing, B.L. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the \"Fundamental Research Funds for the Central Universities (2024 QN11004),\" which was led by the corresponding author, [PERSON].  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.  
Author [PERSON] was employed by the company China Coal Technology Engineering Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Comparison Metrics  
Three statistical indices were used to quantify degree of agreement/disagreement between the GHI estimates of Heliosat-2 (\(\hat{y}_{i}\)) and ground measurements (y\({}_{i}\)): the coefficient of determination (R\({}^{2}\)), mean bias (MB), and root mean square error (RMSE); these equations are given below.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i=1}^{n} \left(y_{i}-\overline{y}\right)^{2}} \tag{8}\]  
where, \(\overline{y}\) represents the mean of the ground measurements.  
\[MB=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i}) \tag{9}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{10}\]  
R\({}^{2}\) quantifies the proportion of variability in the ground GHI measurement that is explained by the Heliosat-2 estimates, facilitating model evaluation regardless of scale. MB assesses the average error between Heliosat-2 and ground GHI values, providing insight into overall prediction accuracy. In contrast, RMSE encapsulates the average magnitude of the differences between Heliosat-2 and ground GHI values, with lower values indicating better model performance. RMSE's sensitivity to large errors, interpretability in the units of the predicted variable, and robustness to outliers render it a versatile and informative metric for assessing predictive model accuracy.
## 5 Numerical Results
### Example 2  
In this example, the data set (\(M=9261\)) has been trained for obtaining a neural constitutive model with the partial derivatives of the components of the stress tensor obtained via automatic differentiation. In Figure 9, the components of the stress tensor are presented, and in Figures 10-12, the partial derivatives obtained from the components are sketched, respectively. The Sobolev error function (6) (\(K=1\)) is also used to train the neural network with the use of AD for the computation of the partial derivatives. Figures 10-12 show that the accuracy is better in the case of using the Sobolev metrics with the same number of training iterations. The neural network, which consists of four hidden layers with [15; 20; 15; 20] neurons with 5000 epochs, has been trained. The batch size is 64 and the data set has 9261 values for the components of the stress tensor (from which 4631 are used for the training and 4630 are used for the test validation). The test data are used for the comparison and validation of the results. In Table 2, the results of numerical experiments with different numbers of layers, neurons, and epochs are presented. It is noted that the accuracy of computations increases not only with an increase in the number of epochs but also with an increase in the number of layers and neurons.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **Neurons** & **Epochs** & **Batch Size** & **MSE\(\sigma\)** & **Time (min.)** \\ \hline
2 & [15;15] & 1000 & 64 & \(2.0\times 10^{-4}\) & 13.64 \\ \hline
2  
The numerical experiments show that with a larger number of epochs, hidden layers, and neurons, the results are more accurate. In the case when derivative data are not available or [PERSON]'s error is not included in the process, more training iterations are required in order to obtain good accuracy for the derivatives.  
Figure 9: The components of the stress tensor after training of the neural network (Figure 5) and from the data set (\(M=9261\)) obtained from the representative volume elements.  
Figure 10: The partial derivatives of the components of the stress tensor \(\sigma_{xx}\) after training of the neural network (Figure 5) and from the data set \(M=9261\) obtained from representative volume elements.  
Figure 11: The partial derivatives of the components of the stress tensor \(\sigma_{yy}\) after training of the neural network Figure 5 and from the data set \(M=9261\) obtained from representative volume elements.  
From Figures 10-12, we can see the decreasing error after using the Sobolev metrics in the loss function of the training process. Therefore, if the data for a stiffness matrix are available, the calculations of the components of the stress tensor and its derivatives are more accurate.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### Path Cost Estimation
#### 3.2.1 Routing Table (RT)  
When nodes receive the hello packets from their neighboring nodes, they update their NT which is used to update the RT. If a packet is received for the first time from a sender node, a new entry is created in the RT. The RT contains 'neighbor IDs' and 'path cost' values of each neighbor node. The next-hop is selected based on the least value of the PCF.
## 1 Introduction
### Study Objectives  
This study aims to thoroughly examine security concerns and strategies within 5G mobile technology while also investigating ML and DL's roles in addressing these security issues. Thus, this study seeks to identify the distinctive security challenges inherent to the 5G system, comprehend their broader implications, and critically assess the efficacy of potential solutions. Further, this research provides a broad overview of 5G telecommunications technology, highlighting its technical aspects, architecture, and technical aspects. Secondly, this study examines the primacy of security in 5G networks and discusses the privacy concerns and the importance of ensuring the reliability and integrity of services. Thirdly, this research explores the role of ML and DL in enhancing 5G mobile technology security. Also, this research aims to dissect the theoretical underpinnings of existing security mechanisms using AI, shedding light on the concepts and principles that undergird the current security landscape of 5G networks.  
This research aims to answer the following key questions:  
1. What are the unique security challenges posed by 5G telecommunications technology?
2. What are the solutions for the security challenges of 5G telecommunications technology?What is the role of machine learning and deep learning in enhancing 5G mobile technology security?
## 2 Friction Coefficient Estimation
### Tire Force and Effective Radius Estimation  
The estimated vertical load force on each tire can be expressed as [27; 28; 29]:  
\[\left\{\begin{array}{l}\hat{F}_{zfl}=\frac{mgl_{r}}{2L}-\frac{ma_{x}h_{c}}{2L}- \frac{ma_{y}h_{c}}{2l_{s}}\\ \hat{F}_{zfr}=\frac{mgl_{r}}{2L}-\frac{ma_{x}h_{c}}{2L}+\frac{ma_{y}h_{c}}{2l_{s }}\\ \hat{F}_{zrl}=\frac{mgl_{f}}{2L}+\frac{ma_{x}h_{c}}{2L}-\frac{ma_{y}h_{c}}{2l_{s }}\\ \hat{F}_{zrr}=\frac{mgl_{f}}{2L}+\frac{ma_{x}h_{c}}{2L}+\frac{ma_{y}h_{c}}{2l_{s }}\\ \end{array}\right., \tag{1}\]  
where \(a_{x}\) and \(a_{y}\) represent the longitudinal and lateral accelerations, respectively, at the CG, which can be obtained by means of integrated GPS/INS sensors [30]; \(L=l_{f}+l_{r}\); \(h_{c}\) and \(m\) are the height of the CG and the total mass, respectively; and \(g\) denotes the acceleration due to gravity.  
Under normal conditions, the effective radius of each tire is mainly determined by the vertical force on that tire and can be estimated as [31]:  
\[\hat{R}_{wi}=R_{g}-\frac{d_{ri}}{3}, \tag{2}\]  
where \(i=fl\), \(fr\), \(rl\), and \(rr\); \(\hat{R}_{wi}\) is the estimated effective radius of the corresponding tire; \(R_{g}\) is the geometric radius of the tire; and \(d_{ri}=\frac{\hat{F}_{zi}}{k_{z}}\) is the deflection of the tire under the vertical force acting on it, where \(k_{z}\) is the vertical stiffness of the tire.  
Based on the dynamics of each wheel, the longitudinal force on each wheel can be estimated as:  
\[\hat{F}_{xi}=\frac{T_{i}-J_{w}\dot{\omega}_{i}}{\hat{R}_{wi}}+\hat{F}_{zi}f_{r}, \tag{3}\]  
where \(i=fl\), \(fr\), \(rl\), and \(rr\); \(J_{w}\) and \(f_{r}\) are the rotational inertia and rolling resistance coefficient, respectively, of each wheel, which are constant factors; and \(T_{i}\) and \(\dot{\omega}_{i}\) are the drive torque and angular acceleration, respectively, of the corresponding wheel. The drive torque can be obtained from the motor control system, and the angular acceleration can be obtained from the wheel angular acceleration sensors.  
Figure 3: Vehicle structure model used for estimation.  
Due to the coupling of the lateral forces on the tires on the same axle, it is difficult to calculate the lateral force on each tire separately. However, the lateral forces on the front and rear axles can be estimated based on the vehicle dynamics [32]:  
\[\left\{\begin{aligned} \hat{F}_{yf}&=\frac{1}{l_{f}+l_{r}}(l_{ f}ma_{y}-I_{z}\dot{\omega}_{r})\\ \hat{F}_{yr}&=\frac{1}{l_{f}+l_{r}}(l_{r}ma_{y}-I_{z} \dot{\omega}_{r})\end{aligned}\right., \tag{4}\]  
where \(\hat{F}_{yf}\) and \(\hat{F}_{yr}\) are the estimated lateral forces on the front and rear axles, respectively, and \(I_{z}\) denotes the moment of inertia around the vertical axle.