esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method of Intelligent Driving-Style Recognition Using Natural Driving Data
## 6 Conclusions
### Shortcomings and Prospects  
Based on the NGSIM dataset, this paper proposes a driving-style recognition method using naturalistic driving data, employing PCA, K-means clustering, and SVM. While some significant results have been achieved, further improvements are required to enhance the robustness and applicability of the method. The key limitations and areas for future research are as follows:  
1. Dataset Limitation: The NGSIM dataset used in this study is designed for a single environment on highways, which may reflect one type of driving style. Therefore, the proposed method should be validated across different environments, such as urban and rural roads, to ensure its generalizability to various traffic conditions.
2. State Simplification: The driving-style classification method proposed in this paper primarily considers two driving states: car-following and lane-changing, excluding other states such as free driving and turning. Additionally, lane-changing behavior can be further subdivided into categories such as forced, selective, and free lane-changing. Future research should address these gaps by including a wider range of driving states and refining the analysis of driving styles under diverse conditions, which will improve classification accuracy.
3. Classification Optimization: In the current driving-style classifications, the overall type is determined solely based on the minimum distance between states, without considering additional parameters such as the frequency of car-following and lane-changing. Future work could incorporate multi-criteria decision-making methods, such as the Analytic Hierarchy Process (AHP) and the entropy weight method, to assign appropriate weights to different driving states. This approach would lead to more realistic and balanced classifications, enhancing the robustness of the overall driving-style recognition system.
4. Clustering Method: The clustering method used in this paper relies exclusively on the K-means algorithm, without considering alternative clustering techniques. Future research should evaluate other clustering algorithms, such as hierarchical clustering and DBSCAN, to assess their performance in driving-style recognition. After improvement and optimization, the method with superior clustering performance should selected for driving-style recognition.  
Conceptualization, Z.Z.; methodology, Z.Z.; validation, Z.Z.; formal analysis, Z.Z.; investigation, Z.Z.; data curation, Z.Z.; writing--original draft preparation, Z.Z. and C.Z.; writing--review and editing, Z.Z., C.Z. and S.Z.; visualization, Z.Z.; supervision, Z.Z., C.Z. and S.Z.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities [grant number 22120240086].
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 2 Related Work
### Two-Stage MOT  
With the development of deep learning, object detection has achieved relatively high accuracy and effect. One-stage MOT methods are not optimal in accuracy. MOT based on detecting largely benefit from the development of target detection, so they are better than one-stage methods in accuracy.A novel end-to-end learning neural network, MATNet [10], leverages motion cues as a bottom-up signal to guide the perception of object appearance. SORT [11] is a typical tracking by detecting the MOT method, using a simple Kalman filter and Hungarian algorithm for tracking, which achieving the most accurate results at that time. This paper refers to ByteTrack [12], which divides the detection boxes into two categories, including high-scoring boxes and low-scoring boxes, and performs matching strategies, respectively, which significantly improves the metrics of multi-target tracking. In TransMOT [13], a sparse weighted graph is used to represent the spatial relationship between objects, encoding and decoding are performed according to transFormer, and an allocation matrix is generated for matching. Ref. [14] propose two-stage methods for pixel-level tracking.
## 2 Materials and Methods
### Determination of Total Phenolic Content (TPC) and Antioxidant Activity (AO)  
TPC and AO were determined in the hydrophilic fraction extracted from olive oil samples as described above, making a correction for the addition of the internal standard, based on extraction and determination of TPC and AO in refined sunflower oil spiked with the internal standard solution.  
Total phenolic content (TPC) in olive oil samples was estimated based on the reaction of coloration of phenols with the Folin-Ciocalteu reagent (FCR) and sodium carbonate [44],following the spectrophotometric reading of light absorbance at 725 nm (Carry UV/Vis 50, Varian Inc., Harbor City, CA, USA). The procedure was as follows: 1.0 mL of sample or standard solution/blank was diluted with 5 mL of water to which 0.5 mL of FCR was added and stirred. After exactly 3 min, 1 mL of sodium carbonate saturated solution (20 g/L) was added. The reaction mixture was stirred and left for exactly 1 h until spectrophotometric reading [45]. Gallic acid (GA) was used as a reference. The stock solution was prepared by weighing 0.1 g GA and dilution in 100 mL of water. A calibration curve was built with 10 standard solutions of GA prepared in 25 mL volumetric flasks by adding aliquots of GA stock solutions in order to fit within the linear range of absorbance readings of the spectrophotometer (0.1-0.8 AU). The results are expressed in milligrams of GA equivalents per kilogram of oil (mg GAE/kg). Measurements were conducted in duplicate, and average values were used for statistical elaboration. Folin-Ciocalteu reagent and sodium carbonate were obtained from Kemika (Zagreb, Croatia).  
Antioxidant activity (AO) was determined as free radical scavenging capacity based on the principle that scavenging of the free radical DPPH\(\bullet\) by samples or standards cause a reduction in its absorbance [46; 47]. A sufficient amount of the stable radical DPPH\(\bullet\) in methanol was prepared (0.025 g/L) for absorbance at around 0.8 AU. Trolox was used as a reference. The concentration of the stock solution of Trolox was 200 \(\upmu\)mol (0.05 g/L) and standard solutions were prepared in the range from 10 to 100 \(\upmu\)mol. Calibration curves were constructed with seven concentration levels and in the absorbance range of 0.1-0.8 AU. One hour after addition of sample extract/standard solution/blank (0.1 mL) to the DPPH\(\bullet\) solution (4.9 mL), the absorbance of remaining DPPH\(\bullet\) was determined at 515 nm, and the results were calculated using the regression equation between the Trolox concentration and the percentage of DPPH inhibition obtained from the calibration curve, expressed as mmol Trolox equivalents per kilogram of oil (mmol TE/kg oil). Analytical measurements were conducted in duplicate for each sample, and average values were used in further statistical elaboration. Gallic acid, DPPH\(\bullet\), and Trolox were products of Sigma-Aldrich-Merck KGaA, Darmstadt, Germany.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 3 Results and Discussion
### Optimum Fiber Length Variation with Temperature
#### 3.4.2 Dependency of L\({}_{\text{opt}}\) on Input Pump Power  
The effect of input pump power on L\({}_{\text{opt}}\) variation with temperature is studied in Figure 6. Three different input pump powers are chosen: 50 mW, 100 mW, and 200 mW, while the corresponding L\({}_{\text{opt}}\) is calculated using Equation (12). To accomplish the simulation, an input signal power of 10 \(\mu\)W is used. An increase in L\({}_{\text{opt}}\) is noticed with increasing input pump power, which supports the best fiber length design that is needed to acquire the highest possible gain.  
Figure 5: Variation of L\({}_{\text{opt}}\) with temperature for three different input signal powers: 1 \(\mu\)W, 5 \(\mu\)W, and 10 \(\mu\)W.  
Also, it is noticed that a certain temperature value exists at which the value of L\({}_{\rm opt}\) is maximized, as already stated in the last section. At lower temperature values, the value of L\({}_{\rm opt}\) increases with the temperature increase. However, at higher temperature values, a decrease in the corresponding value of L\({}_{\rm opt}\) is noticed. Concerning this limiting temperature at which L\({}_{\rm opt}\) is at its maximum, its value decreases with the increase in the driven input pump power, as illustrated in Figure 6.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Electrode Processing Techniques
#### 3.3.3 Additive Incorporation  
Incorporating additives during electrode processing is an effective approach to reduce sticking in MXene electrodes. Additives can include polymers, surfactants, dispersants, or binding agents. These additives alter the interfacial properties between MXene flakes, hindering their agglomeration and reducing sticking. Surfactants or dispersants can enhance the electrostatic or steric repulsion between flakes, improving their dispersion stability. Meanwhile, polymers or binding agents can act as a matrix to prevent direct flake-to-flake contact, thereby minimizing sticking [61].  
Intercalation technology is widely acknowledged for its ability to prevent the stacking of MXene nanosheets. The interaction between intercalators and MXene, relying on van der Waals forces, holds significant importance for MXene-based composite development. [PERSON] et al. achieved enhanced mechanical properties in MXene/cellulose nanofiber composite membranes by intercalating silver ions. Additionally, simple deposition methods have been employed to load metal oxides onto MXene nanosheets, serving as superior hosts through intermolecular forces. This not only prevents the accumulation of MXene layers but also facilitates electron transport. Solution casting, a method for mixing polymer matrices and fillers in solvents, results in well-dispersed composite materials after solvent evaporation and mold pouring. Thorough stirring during the solution casting process is essential for achieving a homogeneous mixture. MXene and PVA were successfully mixed under ultrasonic and stirring conditions, resulting in a composite material with excellent dispersibility. Similarly, PVA/MXene nanocomposites have been obtained using the same approach, resulting in MXene with a homogeneous dispersion. This method was also utilized to fabricate Ti\({}_{3}\)C\({}_{2}\)/TPU composites with exceptional thermal conductivity and mechanical properties [62; 63; 64; 65]. The described approaches are illustrated in Figure 11.
## 4 Materials and Methods
### Parameter Extraction
#### 4.3.2 Sample Entropy (SaE)  
The SaE parameter represents a type of entropy that tries to determine the regularity inherent in a signal irrespective of the signal length. Its value can be computed as given in Equation (4) [54]. A high value of SaE corresponds to a significantly unpredictable signal and vice versa.  
\[\text{SaE}=-\log(\frac{M}{N}) \tag{4}\]  
where \(M\) and \(N\) indicate the vector pair having lengths of \(a+1\) and \(a\), respectively. The vector length \(a\) was set to 2 during the calculation of SaE in our study.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 4 Jamming Utilization Method Based on IMSFH
### Frequency-Detection Network
#### 4.3.3 Algorithm Complexity Analysis  
Considering the actual computing power scheduling situation, an algorithm complexity analysis was conducted on the network model. The network structure is shown in Table 1. The convolutional layer is responsible for extracting low-level features (such as edges, textures, etc.) from the spectral waterfall image, and scanning the input image through a filter to generate a feature map. The pooling layer is subsampled by the feature map through a max pooling operation, reducing computation and memory usage while preserving important feature information. The fully connected layer integrates and classifies the features extracted by the convolutional layer and pooling layer, and outputs the final classification result. The Dropout layer randomly discards a portion of the neurons to prevent overfitting and improve the model's generalization ability. In this paper, hard decision is adopted, where each output layer generates decision values for each frequency category.  
Based on the parameters in Table 1, the computational complexity of this network model is approximately \(63.3\times 10^{6}\) FLOPs.
## 3 Architecture
### Power-Quality-Evaluation Component  
Figure 6 shows the power-quality-evaluation component of MIRD. This component takes as input the one-week-ahead predicted values of PQ variables per PQM and a set of PQ evaluation parameters stored in a relational database. SparkSQL scripts in this module calculate the values of the selected PQ indexes (power factor, voltage interval, and current unbalance) for each PQM, at a 1-h resolution, for a week ahead. The database stores the results of the detailed PQ evaluation, labeled on the PQM class as either circuit or node. A PQ aggregates evaluation uses this information at four geographical levels in the power distribution network: individual device (PQM), substation, area, and zone. MIRD executes the power-quality-evaluation component downstream of the forecasting component, i.e., weekly.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 4 Conclusions  
In this paper, material-specific transparent antennas from nano carbon to metallic nanostructures are reviewed. Transparent antennas were first devised by National Aeronautics and Space Administration (NASA) for integration with solar cells. Transparent antennas need to be consistently developed autonomously as well as embedded in active display panels to compensate for the low efficiency of electrically small antennas in mobile devices in the 5G world. In other words, a transparent antenna could be a core technology used in the 5G era and should be studied with the concept of Antenna-on-Display (AoD). In addition, to compensate for 5G millimeter-wave degraded channel characteristics, the authors estimate the transparent antenna to be an array consisting of bandwidth >1 GHz, antenna array gain >10 dBi, and far-field radiation efficiency >60%. It is essential to use an antenna array to obtain high gain (high efficiency) so that AoD can easily utilize the entire display area for transparent arrays. Furthermore, the electrical sheet resistance of TCFs must be below a few \(\Omega\)/sq to secure the gain of a single antenna element and the optical transmittance must be higher than 85.0% to be transparent at the visible spectrum. Optical degradations such as haze and moire effects when placed above active display panels should be considered when TCFs are selected for transparent antennas. Along with Antenna-in-Package (AiP) and Antenna-on-Chip (AoC) research, AoD needs to be researched as one of critical technologies in 5G communications.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (2017R1C1B501).  
The authors declare no conflict of interest.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 2 Basics
### Machine Learning Basics
In the context of routing optimization, RL algorithms operate on the principle of trial and error, where agents learn to make routing decisions by interacting with the network and receiving rewards or penalties based on the outcomes of their actions. For instance, techniques like Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) allow the model to learn optimal routing paths by continuously updating its policy based on real-time network performance metrics, such as end-to-end delay and throughput [59]. Figure 4: The general framework of applying supervised learning for routing optimization [10]. Each router or SDN controller is an agent with a different routing policy. The routing policy defines the routing optimization objective, for example, delay minimization or throughput maximization. The action is the specific operation to achieve the corresponding optimization objective, for example, the next hop selection probability or the traffic split ratio for multi-path routing, which is typically the output of a neural network model used by the agent. The state is the network status history data, e.g., the link utilization ratio in the past few rounds, which is usually the input of the neural network model. The reward is direct feedback from the network environment to the agent, which is usually a mapping function from different network metrics to a single value. The agent updates the model parameters based on the reward obtained after performing an action and interacting with the environment. RL-based routing optimization is a sequential decision process, and the action in the current round directly affects the state and action of the next round. Thus, the design of the cumulative reward function is essential for tuning an effective agent [60].
# The Law and Mechanism of the Effect of Surface Roughness on Microwave-Assisted Rock Breaking
## 3 Numerical Experiment  
### Modelling  
In order to study the influence of roughness on the effect of microwave-assisted rock breaking more systematically, the electromagnetic field, temperature distribution, and plastic zone were analyzed through numerical tests so as to make up for the shortcomings of physical tests in determining key characteristics, such as the electromagnetic field and plastic zone.  
The numerical model was the same size as the rock sample, as shown in Figure 9. The selected physical and mechanical parameters were derived from the existing literature [29; 30]. The 11 contour lines were imported into MATLAB to extract the data points and then imported into COMSOL5.6 to generate an equiscale model. Finally, electromagnetic-thermal-mechanical coupling calculations were performed to analyze the electric field contours, temperature fields, and plastic zone distributions. The microwave frequency, irradiation power, and irradiation time are consistent with the physical test. The boundary conditions were consistent with the physical test, the mechanical boundary was the fixed-end bottom surface, and the surrounding and top surfaces were the free ends. The electromagnetic boundary condition was that the side surface and the bottom surface were the scattering boundary.  
Figure 8: P-wave velocities and wave velocity differences of the samples before and after irradiation.  
Figure 9: Numerical model building process. (**a**) Rock sample. (**b**) Contour extraction, calculation of roughness, extraction of data points. (**c**) Mesh. (**d**) Cloud diagram of the temperature, electric field, and plastic zone was obtained.  
smaller values than those on the surface of the flat rock. The rough surface reduced the loss of electric field strength and improved the effect of microwave irradiation on the rocks.
# Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment
## 3 Simulation
### Simulation Results
However, at 25\({}^{\circ}\) cut off elevation, observed horizontal integrity failures are two times longer than the TTA limit: 42 s and 210 s. Besides, vertical integrity failures are observed too: at 15\({}^{\circ}\) as a continuous event of duration 672 s and at 20\({}^{\circ}\)--continuously over a period of 408 s.  
Using results from Figure 9, the smallest number of visible satellites can be established for each cut-off elevation. It can be seen that at 20\({}^{\circ}\) cut-off elevation at least five satellites are visible. At 15\({}^{\circ}\) cut-off elevation, this number increases to six. At 10\({}^{\circ}\) and 5\({}^{\circ}\) cut-off elevations, there are seven and eight visible satellites during the 12 h simulation period, respectively. It can also be seen that the required minimum of four satellites cannot be guaranteed at all times at 25\({}^{\circ}\) cut-off elevation. They are only visible for approximately 96% of the simulation time. Both statistics and time history of DOP results show that cut-off elevation can be used as a parameter during the integrity monitoring due to its complementarity to DOP variation in terms of enhanced knowledge on positioning accuracy. Nevertheless, analysis of cut-off elevation limits may struggle in the differentiation of scenarios with high DOP and issues due to the insufficient number of satellites. So, we need to introduce another parameter to guarantee that the alert limits are not exceeded when cut-off elevation is high. Variation of DOP is not only dependent on cut-off elevation in urban environments but is also directly related to the number of visible satellites. In order to demonstrate this effect, let us consider together Figure 8, which shows a time history of DOP variation over a 12 h simulation period and Figure 9, showing how the number of visible satellites changes during the same period of time for each cut-off elevation. It can be seen from these figures that poor satellite visibility causes sharp increases of HDOP and VDOP.
## 4 Discussion  
Once the TMD was optimized, and the behavior of the masonry chimney with TMD was investigated considering even the operation of the device over the elastic behavior of the chimney, a standard approach through the execution of nonlinear time-history analysis was executed to confirm the results obtained by the proposed procedure. The seismic action used is represented by Accelerogram 1 of the seven spectrum-compatible accelerograms mentioned in Section 2 (see Figure 20). Figures 21 and 22 show the trend of the top displacement and the base shear of the chimney, with and without TMD, when the structure remains within the elastic range. However, the showed results are valid to the Step\({}_{\text{fun}}\). The optimized TMD benefits are confirmed by significant reductions in the response obtained both in terms of top displacement and base shear. Consequently, the use of the TMD is an effective solution to reduce the effects of the seismic action. This reveals the TMD capabilities to protect slender historical masonry structures (as the chimney), not only for concrete and/or steel structures. Furthermore, the procedure proposed represents an alternative to the nonlinear time-history analyses, commonly used by the designers to optimize the TMD parameters.  
Figure 19: M- diagram of the base cross-section.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Section** & **Stiffness Ratio** \\
**(m)** & **(\%)** \\ \hline
0–5 & 70\% \\
5–10 & 78\% \\
10–15 & 83\% \\
15–20 & 83\% \\
20–25 & 89\% \\
25–30 & 92\% \\
30–35 & 100\% \\
35–40 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ratio between the calculated stiffness obtained in correspondence to the Step\({}_{\text{fun}}\) and the elastic stiffness (Step\({}_{\text{el,lim}}\)).  
Finally, Figures 23 and 24 show, respectively, the trend of the displacement and the acceleration of the TMD. It is believed that the use of the TMD system to improve the seismic behavior of slender historical masonry structures is vital as it can protect against collapse. The TMD can protect the primary structure against the formation of widespread crack patterns, which can significantly reduce the bending moment acting on the structure and the resulting decompression effects. Furthermore, the proposed device configurations represent a reversible system that is applicable to structures characterized by a particular historical value.  
Figure 21: Comparison of the top displacement obtained by the nonlinear time-history analysis when the structure remains within the elastic range.  
Figure 22: Comparison of the base shear obtained by the nonlinear time-history analysis when the structure remains within the elastic range.  
The further development of this study is to apply the proposed procedure to designing TMDs for masonry structures characterized by more complex dynamic behavior.