esa-sceva/satcom-chunk-collection huggingface dataset:
# The Importance of Modeling Path Choice Behavior in the Vehicle Routing Problem
## 2 Transport System Analysis
### Actors  
Considering a transport system, divided into supply and demand subsystems, three main categories of actors can be identified:  
1. Decision-takers define the objectives to be achieved and define (or must respect) constraints of a technical or regulatory nature; they also take the policy decisions to be implemented for the vehicle routing problem and/or for demand management;
2. Analysts specify and apply models and procedures for the analysis of the supply-demand interaction (the analysis of network performance with supply models, user behavior with demand models; interactions with supply-demand interaction models), the network design (i.e., a better configuration of the transport network in terms of topology and capacity, vehicle routing reported in this document); demand management (i.e., information on the expected status of the transport system); they also make technical decisions to support decision takers;
3. Users (citizen) move in the transport system and make pretrip and en route decisions (user behavior) in relation to the supply performance, experience and information received on the system state; users move on the supply designed and the latter is influenced by demand management actions.  
In this paper the decision takers and the analysts' points of view are considered.
# A Systematic Review and Future Development of Automotive Classis Control Technology
## 2 Automotive Steering-By-Wire System
### The State of the Art
Currently, the mainstream power supply for the vehicle is 12V, and 48V will be the future development trend. According to the number, arrangement position, and control method of steering motors, the typical layout of wire-controlled steering systems can be divided into five categories, namely single motor-front wheel-steering, dual-motor front-wheel steering, dual-motor independent front-wheel steering, rear-wheel wire-controlled steering, and four-wheel independent steering. The dual motor front wheel steering arrangement has good redundancy and requires less power for a single motor. Infiniti Q50 adopts this arrangement, but the cost of parts in this form increases and the redundancy algorithm is complex. The working schematic of wire-controlled steering is shown in Figure 2. When the driver turns the steering wheel, the angle displacement sensor converts the driver's intention into a digital signal, along with other signals of the vehicle, such as speed signals. Transmitted to the ECU through the bus, the ECU calculates the front wheel angle based on the set algorithm and transmits this signal to the steering motor to complete the steering. In addition, after obtaining the steering resistance information through the steering resistance sensor, the ECU transmits the magnitude of the return torque to the driver for road feel feedback based on the return torque algorithm.
## 3 Different Objective Functions
### Energy Life-Cycle Cost Objective Function  
The third criterion is the objective function of the energy life-cycle costs, Equation (3), which is given here in the following form:  
\[\text{Minimize }ELCC=DC+\sum\limits_{i}C_{i}\cdot(1+r)^{y\alpha r_{i}}/(e \cdot L) \tag{3}\]  
where \(ELCC\) represents the energy life-cycle costs of the composite system per unit of its useable surface, \(DC\) are the direct costs (Equation (2)), \(C_{0i}\) denotes the structure operating costs, and \(r\) stands for the discount rate of money where \(year_{i}\) indicates the years in which the corresponding costs are incurred, and the index \(i=1,2,\dots,n\) is the year counter. It should be noted that \(year_{1}=0\) is adopted for the initial year, while the following years aredefined as \(year_{2}=1\), \(year_{3}=2\) and so on. The structure operating costs \(Co_{i}\) are defined by Equation (4):  
\[Co_{i}=C_{HVAC}\cdot p_{i}\cdot H\cdot e\cdot L \tag{4}\]  
where \(C_{HVAC}\) represents the current (i.e., at the initial year, \(year_{1}=0\)) yearly cost of heating, ventilation and air conditioning (HVAC) per m\({}^{3}\) of the air space of the structure volume, while \(H\) denotes the height of the structure. The parameter \(p_{i}\) is determined in order to take into account the anticipated influence of the electricity market sale price evolution on the structure's operating costs during its design working life. The variable \(C_{HVAC}\) is calculated by Equation (5):  
\[C_{HVAC}=ECC\cdot EP/(CH+h+d) \tag{5}\]  
where \(ECC\) is the energy consumption class parameter in kWh/m\({}^{2}\) (energy efficient case EEC, energy inefficient case EIC, and high energy consumption case HEC), \(EP\) is the electricity cost parameter in EUR /kWh, \(CH\) is the clear ceiling height (3.0 m), \(h\) is the height of the steel I beam, and \(d\) is the depth of the concrete slab.  
In this study, the design working life of the structure is set to 50 years (i.e., \(i=1\), 2,..., 50), which is in accordance with the recommendations of Eurocode 0 [31]. As far as the life-cycle cost optimization of structures is concerned, [PERSON] and [PERSON] [37] suggest setting the discount rate of money at 2 to 3% above inflation. Here, the mentioned parameter is determined with the frequently considered value \(r=0.04\) (i.e., 4% discount rate). In terms of the anticipated influence of the price evolution in the electricity market sale, the parameter \(p_{i}\) is in the optimization model set on the basis of the data presented by [PERSON] and [PERSON] [38] and uses the following polynomial approximation function in Equation (6):  
\[p_{i}=1+0.0187\cdot year_{i}+0.0012\cdot year_{i}\textsuperscript{2} \tag{6}\]
###### Abstract  
Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.  
electroencephalography; functional connectivity; resting-state networks; head modelling; electrode localization; head tissue segmentation Article
## 4 Discussion
The aroma of the fruit is influenced by multiple compounds, and a higher content does not necessarily mean a greater contribution to the aroma. Only certain characteristic compounds have a significant impact on the fruit's aroma [33; 34]. According to the differences in sensory effects, fruit aromans can be classified into fruits, green, spicy, woody, aldehyde, and others [35]. Esters endow fruits with floral and fruity aromans, belonging to fruity compounds. Aldehydes and alcohols have a green aroma of green grass and leaves, while terpen are the main components of floral fragraces [36]. From the types and aroma values of characteristic aromans, 'Hongshou', Aitianli, 98-1-6, XY, XY 7-2, XY 7-1, Konglongdan, Great Rosa, and Richard Early are varieties mainly composed of aldehydes, belonging to the aldehyde fragrance type. Dadanguo, Xiangjlaoli, Hongsoring, Taiyang, Taoxinli, Zhushali, Jisheng, Hongxinli, 10-27, Dahongpoa, Hongbayi, Jinxiaiangiaoli, Suili 3, Qiuji, Xingguang 2, Guofeng 2, Giulizi, Angeleno, and Liwang are of the aldehyde alcohol mixed type. Lixingazhong, Zaoli, Gaida, Jihong, Huangganhe, Heibali, Wanhuang, and Friar Plum are mainly composed of aldehydes, esters, and alcohols, forming a mixed aroma of aldehyde, ester, and alcohol. Jizaofeng is mainly composed of aldehydes, esters, and ketones, with a mixed aroma of aldehydes, esters, and ketones. Fortune belongs to the ester fragrance type. (E)-2-hexenal (green, aldehyde, fruity, spicy, and fatty), and 2-hexenal (leafy), butyl acetate (fruity), and (Z)-3-hexen-1-ol acetate (strong banana) are common aroma compounds and primary components in most plum varieties. Additionally, each variety has its own unique aromans, such as octanal (extremely diluted with aromans like rose and orange peel) in Hongxing fruit, acetic acid pentyl ester (fruity and sweet) in Dadanguo fruit, and 1-octen-3-one (herbs, mushrooms, soil) in Jizaofeng fruit. These findings highlight the diversity of fruit aroma components, similar to research on the diversity of peach germplasm aromans [37]. They are all excellent plum resources rich in unique aromans, and are good parents for aroma breeding. Hybridizing with plum resources containing unique aroma components can improve the efficiency of aroma breeding. The excavation of these unique aroma plum resources can provide an important reference for the breeding of new plum varieties (lines) rich in various aroma types, and can also provide reference for the regulation of aroma substances such as octanal and acetic acid pentyl ester.
## 3 Results  
In the study group, there were 103 (41%) children with scoliosis, 141 (56.17%) with scoliotic posture, and seven (3.0%) with normal posture. The values of the position and dispersion measures for the curves had variable distributions in girls and boys in the scoliotic, scoliotic posture, and norm groups (Table 1). The greatest absolute differences in the angle of curvature occurred in the scoliosis group, both in girls (S = 10.72) and boys (S = 8.42). One-way analysis of variance showed significant differences between groups in the angle of curvature both in girls (F = 46.6, \(p=0.001\)) and boys (F = 61.81, \(p=0.001\)). This means that the values of the curves were significantly different between the scoliosis, scoliotic posture, and norm groups (Table 1). The values of the position and dispersion measures for SEMG amplitude of the erector spinae had diverse distributions in both girls and boys in the scoliosis, scoliotic posture, and normal posture groups. Among girls, the greatest absolute differentiation occurred for the variables supine position, lumbar section, right side in the scoliosis (S = 47.58), scoliotic posture (S = 47.22), and normal posture groups (S = 56.48). Among boys, the greatest absolute differentiation also occurred in the case of the variables supine position, lumbar section, right side, both in the scoliosis (S = 51.33) and scoliotic posture groups (S = 47.00) (Table 2). Whereas in the group with the norm among boys, the highest absolute differentiation of values for the trunk variable was observed in the chest segment, right side (S = 48.29) (Table 3). In girls, the highest generalized amplitude was present in scoliosis (x = 47.175 mV), and also in boys with scoliosis (x = 48.726 mV). One-way analysis of variance showed significant differences between groups in the size of SEMG amplitude of the erector spinae only in the case of boys. This concerned the amplitude in the test lower limbs up, lumbar section, left side (\(p=0.03\)). The values of SEMG amplitude distribution in this test differed significantly between the scoliosis, scoliotic posture and norm groups, and the significance level was less than 0.5 (\(p<0.05\)) (Table 4). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Scoliosis Angle in Girls**} \\ \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Mean**} & \multicolumn{2}{c}{**Confidence**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**ANOVA**} \\ \cline{3-4} \cline{6-7}  & & **-95.00\%** & & & & & \\ \hline Scoliosis & 22.64 & 19.17 & 26.12 & 9.00 & 51.00 & 10.72 & \multirow{2}{*}{\
\begin{tabular}{c} F = 46.6; \\ \(p=\mathbf{0.001}\) \\ \end{tabular} } \\ \cline{3-4} \cline{6-7} \multirow{2}{*}{Scoliotic posture} & 10.27 & 9.65 & 10.90 & 4.00 & 18.00 & 2.62 & \multirow{2}{*}{
\begin{tabular}{c} F = 46.6; \\ \(p=\mathbf{0.001}\) \\ \end{tabular} } \\ \cline{3-4} \cline{6-7}  & 6.25 & 4.25 & 8.25 & 5.00 & 8.00 & 1.26 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences in one-way Analysis of Variance (ANOVA) among scoliosis angle in girls and boys.
## 2 Literature Review
### Brief Background of Techniques
#### 2.3.3 Time2 Vector  
Time is crucial for synchronous and asynchronous events. Time2 Vec represents time as a vector in model layers, enhancing performance across frameworks. It captures data patterns and improves prediction accuracy by integrating with deep learning models like convolutional neural networks and recurrent neural networks. Time2 Vec is utilized in forecasting, anomaly detection, and time series analysis in fields like stock prediction, healthcare, and sales forecasting [26]. The Time2 Vec representation enhances the performance of the final model compared with traditional time [26]. Furthermore, Time2 Vec representation establishes a foundation for learning suitable time functions directly from the data, eliminating the need for manually defined time functions. It is simple to incorporate time vector representations into existing deep learning architectures. It has three properties: periodicity, simplicity, and an ability to perform remeasurements at a fixed time [26]. For a given standard concept of time \(\tau\), the Time2 Vec of \(\tau\), denoted as t2v(\(\tau\)), is a vector of size k + 1, as defined by the following equation:  
\[\tau\mathrm{2v}(\tau)[i]=\begin{cases}\omega i\tau+\phi i\ :if\ i=0\\ \mathrm{F}(\omega i\tau+\phi i):if\ 1\ \leq\ i\ \leq\ \mathrm{k}\end{cases} \tag{1}\]  
where F is a periodic activation sine function, \(\omega\) and \(\phi\) are learnable parameters, and t2v(\(\tau\))[\(i\)] is the ith element of t2v(\(\tau\)).  
When \(1\leq i\leq\mathrm{k}\), \(\omega i\) and \(\phi i\) are the frequency and phase shifts of the sine function [26].
## 7 Applications of the Typical Medium DCA to Systems with Disorder
### Results for the Anderson Model
#### 7.1.2 Cluster Size Convergence  
We now consider how the critical disorder strength \(W_{c}\) converges with the cluster size \(N_{c}\). Since \(W_{c}\) is defined by the vanishing \(TDOS(\omega=0)=0\), in Figure 32 we plot the local \(TDOS(\omega=0)\) at the band center as a function of disorder strength \(W\) for several clusters \(N_{c}\). The presented results are obtained using Ansatz 1. We also did calculations with Ansatz 2 (data not shown) and obtained very similar results. Our results show that as cluster size \(N_{c}\) increases, the \(W_{c}\) systematically increases until it converges to \(W_{c}\approx 2.25\) which is in good agreement with the \(W_{c}\approx 2.1\) values reported in the literature [209]. The data presented in Ref. [174] for large cluster sizes does not attain full self-consistency. We pay extra attention to the convergence of the self-energy and redo the calculations for the data as shown in Figures 31 and 32.  
Figure 31: Phase diagram of the Anderson localization transition in 3D obtained from TMDCA simulations. As \(N_{c}\) increases, a systematic improvement of the trajectory of the mobility edge is achieved. At large enough \(N_{c}\) and within computation error, our results converge to those determined by the TMM [209].  
Figure 32: The TDOS at the band center \(TDOS(\omega=0)\) vs. disorder strength \(W\) for the 3D Anderson model calculated with the TMDCA using Ansatz 1 for different cluster sizes \(N_{c}=1,10,12,38,92\) with units where \(4t=1\). The \(TDOS(\omega=0)\) vanishes at the critical disorder strength \(W_{c}\) when all states become localized. For \(N_{c}=1\), which corresponds to the TMT method, the critical disorder strength \(W_{c}(N_{c}=1)\approx 1.65\). As cluster size \(N_{c}\) increases, the critical disorder strength \(W_{c}\) increases quickly to \(\approx 2.25\), which is in very good agreement with the results from the TMM \(W_{c}\approx 2.1\)[210].
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 5 Simulations and Results
### Result
#### 5.2.2 Environment 2
Therefore, with the increase in node movement speed, CND-AODV shows some improvement in network performance. Figure 21: Relationship between delay and number of nodes for different protocols under uniform distribution. Figure 22: Throughput performance with respect to the speed of nodes for different protocols. As depicted in Figures 22 and 23, it can be observed that as the speed of nodes increases, the throughput and packet delivery ratio of the three routing methods, including AODV, ND-AODV, and the proposed CND-AODV, all exhibit a decline. However, CND-AODV shows the smallest decline in performance. As shown in Figure 24, regarding the delay, with the continuous increase in the speed of nodes, the network delay for all three methods demonstrates an upward trend. Nevertheless, it is evident that CND-AODV outperforms AODV and ND-AODV in terms of transmission delay during the process of increasing delays. Therefore, in scenarios with higher node mobility speeds, CND-AODV shows a certain level of improvement in network performance. Figure 13:0 Packet delivery ratio performance with respect to the speed of nodes for different protocols. Figure 13:1 Delay performance with respect to the speed of nodes for different protocols.
# Hot-Pressing of Ti-Al-N Multiphase Composite: Microstructure and Properties
## 1 Introduction
### \(\mu\)CT Data Acquisition and Analysis  
Industrial tomographic imaging devices (such as XCT, \(\mu\)CT, and X-ray CT) function based on the principles of X-ray spectroscopy, as extensively detailed in various publications [21, 24]. Despite being developed over two decades ago, the technology's core mechanisms remain essential and widely utilized. The imaging process involves analyzing a target object placed in a specialized chamber positioned between the radiation source (commonly referred to as the tube) and the detector. The device operates by precisely adjusting the object's position relative to the source and detector, setting the appropriate voltage and current intensity to control the radiation output, and configuring detector parameters. Fine-tuning these settings ensures the collection of high-quality data suitable for quality analysis and enables detection of material defects like pores in the whole volume of the material. For this study, data were collected using a microfocus X-ray tube operating at 100 kV and 95 \(\mu\)A, yielding voxel dimensions of 9.53 \(\mu\)m\({}^{3}\).  
The next phase entails reconstructing the 3D structure of the scanned object. As the sample completes a full rotation, the system generates numerous high-resolution grayscale images, such as those produced by the GE Phoenix v! tomex! m system (General Electric Company, Hurth, Germany), which features a 14-bit grayscale depth. Advanced algorithms are employed during reconstruction, incorporating features such as beam hardening correction, automated geometry calibration, and geometry refinement [25, 26]. The resulting reconstructed models enable detailed analyses, including 2D and 3D cross-sectional imaging and volumetric studies. In this study, reconstruction was performed using an established algorithm that applied the BHC+ filter (factor 6.4), automatic AGC, and optimized reconstruction parameters. The volumetric analysis commenced with identifying the material's isosurface, distinguished by its higher density. Porosity was evaluated using the VGDefX procedure in VGStudio MAX 3.5.1. Total sample porosity was quantified, with  
Figure 3: Hot pressing—progress of the pressure and temperature during process.  
significant defects defined as regions exceeding eight voxels. After further filtering, only large-scale defects (0.02 mm\({}^{3}\) or larger) were included in the final analysis.
## 5 Results and Discussion
### Results in Few-Shot Setting  
To assess the performance of the model in scenarios with limited resources, we randomly sampled a fixed number of instances for each dataset as K-shot, where K = 10, 20, 50, 100, 200, 500. The few-shot setting follows [10]. The experimental results in the OntoNotes few-shot dataset are shown in Table 4. The experimental results in the MRSA few-shot dataset are shown in Table 5. The experimental results in the Weibo few-shot dataset are shown in Table 6. The experimental results in the GA few-shot dataset are shown in Table 7.  
From Tables 4-7, we can find that the models based on prompt generally exhibited better performance than others did in the few-shot setting. The prompt template designed in this paper could better stimulate the a priori knowledge of PLM and get better results than other models. After fusion with boundary information features, the MPBCNER model achieved optimal results with limited resources. From the above experimental results, we can get the following three conclusions:  
1. Comparing the approach based on sequence labeling and the approach based on span, it can be found that the prompt-based model had a significant advantage because the prompt template could provide the necessary context for large-scale pre-training of the language model, helping the model to better understand the downstream task requirements and therefore achieve better performance in a few-shot setting;
2. Comparing the approach based on machine reading comprehension and the approach based on sequence generation, it can be found that these two methods, although they performed better than the sequence labeling-based method did in the few-shot setting, did not perform as well as the prompt-based method in the experimental results because of insufficient deep mining of the prior knowledge of the pre-trained model;
3. Comparing the approach based on prompts, it can be found that the prompt-based method was more advantageous in the few-shot setting, but due to the lack of feature learning on Chinese entity boundaries, it was less effective on Chinese datasets, especially on nested datasets. MPBCNER, which incorporates boundary features and entity location features, could better handle the NER task in the Chinese small-sample setting.