esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method of Intelligent Driving-Style Recognition Using Natural Driving Data
## 5 Discussion  
This paper proposes a novel driving-style recognition method based on the NGSIM dataset, which utilizes machine learning techniques to establish a driving-style classification and recognition model. The experimental results are discussed in depth to verify the effectiveness and accuracy of the proposed method, providing valuable insights into the development of autonomous driving and intelligent transportation systems. The experimental results are analyzed and discussed below.  
* Driving-Style Clustering Using the K-means algorithm.  
Figure 11: Accuracy of model recognition.  
The principal component clustering centers for car-following and lane-changing states are obtained, categorizing drivers into three types: conservative, moderate, and radical. Label data are constructed for the subsequent development of the driving-style recognition model. The statistical values of the data features before dimensionality reduction are consistent with the results of principal component clustering, as shown in Tables 4 and 5, validating the effectiveness of K-means clustering and PCA.  
The comprehensive driving-style tendency of drivers in both states is calculated based on clustering results and the minimum distance principle, as depicted in Figure 10. The comprehensive evaluation results of driving styles obtained by this method are relatively reasonable compared to individual driving styles in car-following and lane-changing states.  
* Establish a Driving-Style Recognition Model Using SVM.  
The recognition accuracy of the SVM model is the highest for both car-following and lane-changing scenarios, regardless of whether the driving-style feature parameters are reduced or not. The recognition accuracy of the car-following scenario improved from 0.99 to 1, and the accuracy of the lane-changing scenario improved from 0.915 to 0.979, as shown in Figure 11. This demonstrates that the SVM model has superior accuracy for the high-dimensional, small-sample number dataset used in this study. Compared to RF and KNN algorithms, the SVM model provides better recognition of driving style. Additionally, dimensionality reduction significantly improves the performance of SVM, RF, and KNN models. This may be attributed to the use of reduced-dimension data in K-means clustering, resulting in higher recognition accuracy post-dimension reduction.  
Through the entire experiment, this paper verifies the proposed driving-style recognition method and its accuracy. In the future, this method can be applied to driver risk rating and driver operation training. It also holds promise for application in vehicle behavior safety evaluation in autonomous driving, lane-changing decisions, dangerous driving early warning, and intelligent personalized driving of vehicles.
## 2 Background and Related Work
Different approaches, algorithms, and models are suggested and developed to minimize the CAPEX, IMPEX, and OPEX to enhance the QoS of 5G networks. Open mobile edge cloud vision was introduced in the white paper [42] and focuses on the impact of SDN and Network Functions Virtualization (NFV) on telecommunication networks. Technical challenges have been investigated while migration and security policy issues are based on the IEEE SDN Initiative. A techno-economic analysis is performed in [43] to improve indoor coverage by using a small cell network, and this approach is economically viable and managed by a third-party micro-operator as a neutral host. Priority-based parameters such as performance, business, and sub-criteria such as low latency, high reliability, and high data rate underperformance, privacy, and security under acceptance and SDN and NFV under technology are identified in [44]. This results from road-mapping activities of technologies, financial, regulatory, and standardization issues, and surveys of pairwise comparison based on the fuzzy Analytical Hierarchy Process (AHP) method among experts in the CHARISHMA project of the EU. A techno-economic analysis of the 5G network is accomplished in [45] for the urban area of South London using macro, micro, and hotspots together with 802.11 ac access points for the indoor and outdoor environments. Cost modeling incorporating the cost of base stations, transport, spectrum, operation, and maintenance has been carried out at different frequencies. The cost of a 5G network in urban areas increased significantly compared to 4G LTE, but capacity and coverage improved significantly. Using the frequency of a 700 MHz macro cell, blanket coverage can be provided but cannot offer 100 Mbps capacity. Using a 3.5 GHz micro cell, the outdoor capacity becomes significant but relatively lowers indoor capacity. Using a millimeter wave in a 5G network, the capacity can be increased to a hundred or thousand times higher with the integration of the 820.11 ac access point but at the expense of higher cost. The demand-driven and supply-driven analysis is conducted in [46] for the different users experiencing demand of 30, 100, and 300 Mbps using the Netherlands' use case scenario. Providing higher speed in all areas is not cost-effective, and targeted small-cell deployment is cost-effective in dense areas. The first 30% most densely populated area covers 5% of investment, the next 40% population of the suburban area accounts for 20% of investment, and the remaining 30% rural population needs 75% of total investment. An improvement of 40% is possible by utilizing a spectrum integration strategy through supply-driven analysis. The existing literature lacks a study on the proper usage of existing 4G infrastructure and its impact while deploying 5G networks. We propose deploying 5G networks with or without using existing infrastructure and subsequent impact. We are motivated by the revision carried out in this section to design an optimized mixed cell architecture to provide the required data rate and meet coverage constraints.
## 2 Proposed Slepian Sequences-Based 16-4D LDPC-Coded Modulation Scheme  
The demands for using higher order and more complex modulation formats are increasing rapidly to satisfy never ending data requirements. To meet such transmission formats needs, more advanced DSP upgrading is required, which will bring us a large amount of implement costs. The Slepian sequence (SS), which is a discrete prolate spheroidal sequence, can offer us some new approaches to reduce DSP complexity and improve both BER performance and spectral efficiency without extra expensive hardware costs. The Slepian sequences \(\left\{s_{n}^{(j)}(N,W)\right\}\) of the \(j\)-th order are defined as a real-valued solution to the following system of discrete Equation (1):  
\[\sum_{i=0}^{N-1}\frac{\sin 2\pi W(n-i)}{\pi(n-i)}s_{n}^{(j)}(N,W)=\mu_{j}(N,W)s_ {n}^{(j)}(N,W);\ n\in N \tag{1}\]  
where \(i\) and \(n\) denote the particular sample in each Slepian sequence, \(N\) is the sequence length, and \(j\) denotes the particular sequence order out of the set of sequences. The shaping factors \(\mu_{j}(N,W)\) are ordered eigenvalues of the systems of Equation (1) corresponding to the concentration of each SS within the desired time interval of length \(N\); thus \(0<\mu_{j}\leq 1\), with 1 occurring when the sequence energy is entirely included in the desired time interval. \(W\) is a discrete bandwidth [1; 3].  
We can easily generate the Slepian sequences of different orders, which are orthogonal to each other and as such they can be used as a new degree-of-freedom. Figure 1 shows the first 4 orders of the Slepian sequences. The Slepian Sequence we used is aimed to recover the transmitted sequence only with very simple DSP, to efficiently reduce the cost and complexity of the system.  
Our proposed Slepian sequence-based 4D LDPC-coded transmitter is shown in Figure 2. A pseudorandom binary sequence (PRBS) is generated and split to 4 parallel sequences as our 4D signal. After the LDPC encoder, the encoded 4 parallel sequences are mapped to our 4D constellation points. The 4D constellation points take values of \(\pm 1\) for every dimension. The corresponding output is then sent to the Slepian sequence mapping module, and finally we impose the 4D modulated signal on 2D I/Q modulator, by mapping the first two coordinates to the in-phase (I) channel and the last two coordinates to the quadrature (Q) channel of an electro-optical I/Q modulator.  
As an example, to transmit a constellation point of (+1, +1, +1, -1), we first map the symbol to a group of Slepian sequences:  
\[\{+(1\text{st order}),+(2\text{nd order}),+(3\text{rd order}),-(4\text{th order})\},\]  
Figure 1: Different orders of Slepian sequences with 32 samples/waveform used in experimental verification.  
Figure 2: Slepian sequences-based 4D LDPC-coded transmitter.  
then we have the first and second order Slepian sequences mapped to the I-channel, while the third and fourth order Slepian sequences mapped to Q-channel as follows:  
\[\left\{\begin{array}{l}\text{I}=(1\text{st order})\text{ and }(2\text{nd order})\\ \text{Q}=(3\text{rd order})\text{ and }(4\text{th order})\end{array}\right.\]
## 3 Result and Discussion
### Statistics of Behavior in Mechanical Ventilation Operation
#### 3.1.3 Operation Ventilation Flow Rate of Split and Centralized Mechanical Ventilation Systems  
Figure 7 shows the boxplots of the ventilation flow rates of the two types of systems in different seasons. For the split system, the highest average ventilation volume was observed in spring, at 25.7 m\({}^{3}\)/h, while the lowest was observed in summer, at 13 m\({}^{3}\)/h. For the centralized system, the highest was observed in winter, at 94.4 m\({}^{3}\)/h, while the lowest was observed in autumn, at 50.4 m\({}^{3}\)/h. According to the Indoor Air Quality Standard (GB/T 18883-2002), the fresh air volume of residential buildings should not be less than 30 m\({}^{3}\)/h per person. Thus, the minimum fresh air volume of households with the split system should be 60 m\({}^{3}\)/h for two individuals, and the centralized system is 90 m\({}^{3}\)/h for three individuals. Since there are no window opening data in this study, without considering natural ventilation, it seemed that only the average mechanical ventilation volume of most households could not meet the fresh air need of human health. Relatively, the actual ventilation volume per person of households with the centralized system was better.  
In addition, it was found that the variation of the mechanical ventilation volume of the centralized system in summer was larger than in other seasons, indicating that there were obvious differences (with \(p\)-value being 0.018 less than 0.05) in the usage behavior among different households in summer. Some households chose not to run the system or kept lower ventilation volumes probably because the air conditioner was turned on in summer, and the introduction of outdoor air through mechanical ventilation system would increase indoor temperatures and lead to a waste of energy. However, some occupants were willing to spend money on energy when the health requirement could be met by mechanical ventilation [9].
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 5 Results and Discussion
### Effect of Filter Type on System Performance  
Although the results in Table 2 show that MFWF outperforms MFMVDR when used alone, the effect of both on the overall system performance needs to be further verified. For this purpose, we trained two models on WHAM!, both of which have the same configuration except for the different types of filters used. Both filters set the total number of frames to 13, with 6 frames on both sides of the target frame.  
The performance of the two models on the WHAM! test set is shown in Table 3. The WER metric in the table is obtained by feeding the speech enhanced by the model to an ASR system. The ASR system used in this study is a joint Connectionist Temporal Classification-Attention (CTC-Attention) model trained on wsj0 corpus resampled to 8 kHz. In the decoding stage, a word-level language model is used to improve the decoding results.  
It is observed that the system using MFMVDR as the filtering module performs slightly better than the one using MFWF, which is different from the conclusion in [9]. The scores on the enhancement metrics are the same for both, but the system using MFMVDR scores better on the WER metric than the one using MFWF. We suppose this may be related to the non-distortion property of MFMVDR; the model may learn this property from MFMVDR in the second training stage so that the enhancement results of the system contain less distortion. As the system using MFMVDR performs better, all models in subsequent experiments will use the MFMVDR filter.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 4 Proposed Tree Routing Protocol
### Rate of Parent Change  
Each node should update its parent node periodically because the network condition frequently changes in WSNs. For example, if the parent node is not updated for a long period of time, an excessive load occurs on a specific node. To achieve a stable topology, each node periodically updates its parent node based on hello message interval. However, if the node frequently changes the parent node, an additional overhead may occur. In the next section, we present the effect of the rate of parent change on the network performance.
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
## 3 Materials and Methods
### Materials
#### 3.1.1 RAP from Old Roads  
Three sources of RAP (RAP-A, RAP-B and RAP-C) from different old roads were used to study the influence of preheating temperature on the activation of RA binder. The characteristics of RAP was evaluated in terms of asphalt content, gradations of RAP and extracted RAP aggregates and properties of RA binder.  
Generally, RAP from the old road is divided into two or three grades according to the particle size. In order to form a specimen composed only of RAP, it is necessary to redesign the gradation of the mixture and combine each grade of RAP in a certain proportion. According to the nominal maximum particle size of different sources of RAP, asphalt concrete 25 (AC-25) was selected as the designated gradation of mixture. The asphalt content, gradations of RAP and extracted RAP aggregates of each source of RAP are shown in Figure 1.  
It can be seen from Figure 1 that the gradations of extracted RAP aggregates and the gradations of RAP are different. When the two curves are close to each other, it means the gradation is relatively stable; otherwise, the variability of gradation is large. Due to the influence of various factors, the gradation variability of RAP obtained from different sources may also vary greatly. For RAP-A (Figure 1a) and RAP-C (Figure 1c), their grading curves before and after extraction are quite different, while the difference in RAP-B is relatively small.  
The properties of RA binder, such as softening point, penetration at 25 \({}^{\circ}\)C and ductility at 15 \({}^{\circ}\)C, were tested according to the relevant requirements in the Chinese standard [41]. Table 1 shows the characteristics of RA binder from different sources of RAP.
## 4 Results and Discussion
### Effect of Confining and Injection Pressures on Intact and Fracture Permeability
It should be noted that, under experimental conditions, the permeability of the fully fractured specimen is significantly higher due to the fact that the fracture is throughout the sample. This could not be observed under field conditions and therefore, permeability enhancement may be less than the experimental values. However, for the partially fractured sample, although the final permeability is governed by the rock matrix, it is 10 times higher than that of the intact rock. This may be due to the reopening of micro-fractures in the matrix portion, and these micro-fractures can reopen at certain pressures and can be fully closed under normal atmospheric conditions. [PERSON] [49] observed similar behaviour in shale samples fractured using water. However, as explained earlier, although fracture permeability is significantly higher than intact permeability, when the confining pressure is increased from 5 to 25 MPa at 1 MPa injection pressure, the permeability reductions for intact, partially fractured and fractured rock permeability are less than 1 MPa. This is consistent with the fact that fracture is not fully fractured, and the fracture is not fully fractured, and the fracture is not fully fractured. Figure 14: Variation of permeability for fractured sample with 7 MPa confining pressure (**a**) with injection pressure and (**b**) with confining pressure. fully fractured samples are about 24%, 56% and 81%, respectively. This implies that the fractured rock layer may be more significantly affected by reservoir depth than an intact or poorly fractured rock layer, and this needs to be considered in the fracturing process. Furthermore, the permeability of the fractured rock mass may only be high at low depths or confining pressures, and may reduce drastically with increasing depth, due to the associated crack closure. Another aspect related to reservoir depth is the additional driving force for the production gas, which is created by the increased pore pressure gradient due to the increase in reservoir depth. However, in this study, the results show that the Klinkenberg slip flow effect is dominant at low injection pressures and as a result, the fracture closure effect is more significant than the pore pressure gradient with respect to the reservoir depth. These issues can be minimised by using some effective applications. This indicates the importance of further studies on the role of proppants in the permeability enhancement of reservoir rocks.
## 3 Results
### Data Distribution Analysis  
We discovered that the repeated acquired data in one B-frame were not normally distributed, as required by the ED analysis. One B-frame with four repeated scans was randomly chosen to apply the data distribution analysis to, with different selections of depth ranges after being flattened. This is shown in Figure 2. Here, we processed the distributions of the windowed B-scan frame, not A-lines, because there would be too few data points on each A-line to generate the distributions after applying the windows. The distributions of the selected data were plotted on the right, which were used to generate a fitted Gaussian. Lastly, for each window, the correlation coefficients between the distribution and the fitted Gaussian were calculated. In Figure 2, region of interest (ROI) (A), (B), (C), and (D) were the windows applied to the same B-scan frame. The axial windows' sizes were 340, 170, 40, and 5 pixels in depth, respectively, which generated corresponding correlation coefficients of 0.67, 0.86, 0.99, and 0.72. Therefore, applying a window to the input data can improve the performance of ED statistical analysis, though the smallest window did not necessarily produce the best results.
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 5 Discussions and Perspectives
### Future Trends of Silicon Photonic Edge Couplers
#### 5.4.3 Inverse Design Method for Nanophotonics  
The use of electronic design automation (EDA) software to assist electronic integration circuit design is both appropriate and has signaled an advance. Since photonic circuits are often analogized with electronic circuits, it is natural to come up with the idea of photonics design automation. All the design techniques mentioned in the previous sections are forward approaches that obey the logic following which a prototype of an edge coupler is proposed, and then its performance is evaluated via numerical simulation and experimental measurements. Recently, researchers have begun to use computer-aid design methods to exploit the performance advancement of edge couplers. Contrary to traditional forward design, performance metrics are firstly put forward, and then device structures are obtained by numerical calculation accordingly; thus, this is called inverse design. [PERSON] et al. from Stanford University have firstly proposed the concept of the objective-first inverse design method applied for photonics design in 2007 [143] and after that, increasingly more groups have caught up to focus on the inverse design method [144; 145; 146; 147; 148; 149; 150; 151; 152; 153]. The inverse design procedure acts similar to a black box in which the input is the desired performance and the output is a certain structure. An arbitrarily initial structure is chosen, and specific requirements of performance are set such as a given coupling efficiency for an edge coupler; then, calculation and iteration will start under predefined optimization algorithms. In each iteration, a temporary intermediate structure is obtained, and the algorithm will determine whether this structure satisfies the predefined shutoff criterion. If the criterion is met, a final structure is achieved, and its performance will be evaluated regarding whether it has met the performance requirements. If the criterion is not satisfied, the design procedure will loop back to reach out a new intermediate structure, and the iteration goes on. The output performance will be compared with the predefined criterion at the end of each iteration step, and it will continue to be optimized on the basis of the existing structure. Consequently, the optimal result can always be achieved as long as enough iterations are run. The inverse design can closely approach the internal performance limits of silicon photonic devices.
## 3 Results
### Characteristics of Spatial and Temporal Changes in Drought
#### 3.3.4 Characteristics of Spatial and Temporal Changes in Drought Severity
These deeper root systems enable them to draw water from deeper soil layers and rely less on surface moisture [57; 58]. Wetlands not only had lower drought susceptibility but also exhibited more stable and gentle fluctuations. Their resistance to drought was comparable to that of forested land, and they demonstrated a higher degree of overall stability. This can be attributed to the excellent hydrological conditions and ecosystem regulation functions of wetlands. Located in water-rich areas, wetlands possess a strong soil water-holding capacity, enabling them to effectively cope with environmental stresses such as drought [59]. It is worth noting that in 2022, woodlands and grasslands experienced extreme dryness, with the drought intensity reaching the highest level in sixteen years. In contrast, dry cropland and wetlands were less significantly affected by dry conditions in 2022. Although the Northeast experienced a high overall level of precipitation in 2022, its distribution was uneven, particularly in areas with dense vegetation cover such as woodlands and grasslands. Under the influence of the La Nina effect, the Northeast continued to experience high temperatures in 2022. The substantial transpiration from vegetation consumed large amounts of water, thereby exacerbating drought conditions. In contrast, dry cropland has sparse vegetative cover, while wetlands, with their greater water content, are less impacted. This study not only effectively identified the responses of different surface cover types to drought but also achieved significant improvements in precision and accuracy. More accurate results were obtained using daily-scale precipitation data with a spatial resolution of 2 km. This approach not only verified the reliability of previous studies but also provided a more reliable database for subsequent research.