esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories
## 3 Method  
The main idea of our method, called NaPoCo, is to calculate a simple average from subsets of the points from the trajectory set. We use the order of the points in trajectories to determine the subsets for averaging. The input data for this algorithm is created by slicing the original trajectories into intermediate parts between the estimated intersection. This means that we are only analysing a small period of time in which the cars would behave similarly in terms of speed acceleration and moving shape due to traffic regulations and the shape of the road. The variation of sampling rates has only little influence on the number of points per trajectory. In the evaluated data set, most trajectories with deviating number of points are outlier where the vehicle was not moving on the assumed road segment (for example, Figures 12 and 13). Ignoring the outlier trajectories, we assume that the order of points correlates with their position along the segment. The application Douglas-Peucker (DP) [15] helps to reduce the influence of sampling rate, as the resulting number of points is dependent on the complexity of the underlying shape. The correspondences between the points in different trajectories are even more clear as the points in optimal case refer to the start point, endpoint, and curves.  
Given a set of trajectories \(T\), the algorithm generates the averaged segment based on the following steps in Figure 1:  
1. Generalize single trajectories using the DP algorithm. DP takes a curve composed of segments defined by 2D points and creates a sketch by repeatedly adding the point with maximal orthogonal error to the sketch unless this error falls below a fixed threshold \(\epsilon\).
2. Determine the trajectory \(T_{max}\) that contains the most points. Reverse the order of the points in some trajectories to achieve identical orientation. The order of indices in a trajectory \(t_{i}\) is reversed if the distance between the endpoint of \(t_{i}\) and start point of \(t_{max}\) is smaller than the distance between the start point of \(t_{i}\) and the start point of \(t_{max}\).
3. Align indices of the points in trajectories with a smaller number of points along \(T_{max}\) using linear interpolation. This step was part of the contribution to the contest, but as described in the Experiments and Discussion part this step is not always contributing to the performance improvement.
4. Calculate the centroid of all points with the same index by averaging coordinates.
## 4 Results and Discussion
### Discussion on Throughput  
This study comprehensively compares the throughput (FPS) of different data protection strategies across two tasks: medical image classification and financial prediction regression. The experimental results clearly reveal the differences in throughput and their underlying reasons. Throughput directly reflects the operational efficiency of a model and serves as a crucial metric for evaluating the practical applicability of privacy protection techniques. The results show that traditional methods, such as FHE and DP, have significantly lower throughput compared to other methods due to their inherent design characteristics. FHE's high computational complexity in encryption and decryption processes results in FPS values of only 23 and 25 for medical image classification and financial prediction, respectively, making it unsuitable for real-time tasks. DP achieves a slight improvement in throughput by introducing noise but remains constrained by the computational overhead of noise generation. In contrast, MPC improves computational efficiency through distributed processing, raising throughput to 30 and 35 for the two tasks. However, its communication overhead poses a major limitation in complex scenarios. Federated learning significantly enhances throughput to 45 in both tasks by leveraging local modeling and avoiding direct data transmission. Nevertheless, the proposed method achieves the highest throughput, reaching 57 and 54 for medical image classification and financial prediction, respectively. This improvement is attributed to its lightweight design, including the probability density extraction module and the information entropy fusion module, as well as the optimization of the fusion loss function, which reduces the computational complexity of data obfuscation. This high throughput not only highlights the superiority of the proposed method's theoretical design but also demonstrates its practical value in scenarios requiring efficient processing of large-scale data.
# Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning
## 4 Experimental Analysis  
### Experimental Datasets  
The article conducts experiments on the publicly available datasets DuEE1.0 and Title2 Event. As shown in Table 1, DuEE1.0 [33] is a Chinese event extraction dataset released by Baidu, consisting of 17,000 sentences with event information across 65 event types. The event types were selected based on the hot topics from Baidu's Top Searches, ensuring strong representativeness. These 65 event types include not only common types such as \"marriage\", \"resignation\", and \"earthquake\", typically found in traditional event extraction evaluations, but also current event types such as \"likes\" that reflect the current era. Title2 Event [34], on the other hand, is a Chinese title dataset released by Deng, designed for open event extraction tasks. This dataset comprises 42,915 news titles collected from Chinese web pages across 34 domains. It includes 70,947 manually annotated event triplets and 24,231 unique predicates. The dataset has undergone two rounds of expert reviews, making it the largest manually annotated Chinese dataset for sentence-level event extraction currently available. The data presentation of two publicly available Chinese event extraction datasets is shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & **Number of** & **Number of** & **Number of Test** & **Number of** \\  & **Training Sets** & **Validation Sets** & **Sets** & **Events** \\ \hline DuEE1.0 & 13,478 & 1790 & 4372 & 19,640 \\ Title2 Event & 34,295 & 4286 & 4288 & 70,879 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset information. Figure 3: Generative input template construction diagram. Firstly, from the two datasets of DuEE1.0 and Title2 Event, we obtain the event schema, including trigger words and corresponding event arguments, and then randomly extract the trigger words in the event schema, we can obtain the event extraction annotation corpus by generating a given number of training corpora through ChatGPT. The text contained in the event corpus can be categorized into six themes, including six categories such as social, financial, sports, current affairs, science and technology, and others, and each theme contains more than 500 instances. Among them, the proportion of other categories is more than 50%. Table 3 below provides some examples of the corpus. In the given instances, ChatGPT provides event prompts to guide the consistency of event arguments. For example, in sentence 1, under the event prompt \"charity event\", the generated event arguments \"donors\" and \"vulnerable groups\" maintain good consistency. However, ChatGPT can also make errors and deviate from the trigger words. For instance, in sentence 2, the trigger word changed from \"support\" to \"guarantee\". The reason behind this is that when dealing with complex questions, ChatGPT may encounter ambiguity in the semantic or contextual information present in the data, leading to errors or confusion in identifying trigger words. Additionally, when ChatGPT generates event arguments as task
# New Approach of the Variable Fractional-Order Model of a Robot Arm
## 4 The Robot Arm's Mathematical Models
### Variable Fractional-Order Mathematical Model (VFOM) of the Robot Arm  
The VFOBD may will be applied to model the robot arm. From the general form of a linear, time-invariant variable fractional-order difference equation:  
\[\sum_{i=0}^{n}A_{i0}^{\text{ }CL}\Delta_{k}^{(u_{k})}y_{k}=\sum_{i=0}^{m}B_{i0}^{ \text{ }CL}\Delta_{k}^{(\mu_{k})}u_{k} \tag{35}\]  
where  
\(A_{i}\), \(B_{i}\)--constant coefficients with \(A_{n}=1\),  
\(v_{n,k}>v_{n-1,k}>\dots>v_{1,k}>v_{0,k}=0\)--order functions,  
\(\mu_{m,k}>\mu_{m-1,k}>\dots>\mu_{1,k}>\mu_{0,k}\geq 0\)--order functions,  
\(u_{k},y_{k}\)--the robot arm input and output functions.  
The robot arm possesses the integration element properties. Hence, in the first approach one considers (14) with \(n=1,m=0\) and \(\mu_{k}=0\). Then, from (35) one gets the equation:  
\[{}_{0}^{\text{ }CL}\Delta_{k}^{(u_{k})}y_{k}=B_{0}u_{k} \tag{36}\]  
describing a variable fractional-order integrator (VFO-I) [25]. In such simple model structure, there are two parameters to select: a coefficient \(B_{0}\) and an order function \(\
u_{k}\). Note that this is a generalization of the FO discrete integrator, which in turn is a generalization of the classical (first-order) integrator. By (10) and (11), a response of the VFO-I is of the form:  
\[y_{k}=-\begin{bmatrix}a_{1}^{(u_{k})}&a_{2}^{(v_{k})}&\dots&a_{k}^{(u_{k})} \end{bmatrix}\begin{bmatrix}y_{k-1}\\ y_{k-2}\\ \vdots\\ y_{0}\end{bmatrix}+B_{0}u_{k} \tag{37}\]  
It is easy to prove that Equation (37) can be transformed to an equivalent form  
\[y_{k}  
To the robot arm electrical drive input, several constant voltages were supplied. Zero initial conditions were assumed although the initial angle is nonzero. This is caused by the robot construction. Hence, there were measured step responses of the robot arm. The input constant voltages are collected in a set U:  
\[\mathbb{U}=\{3,4,5,6,7,8,9,10,15,20,25\}\ [\mathrm{V}] \tag{40}\]  
The responses are presented in Figure 15. It can be seen in Figure 15 that higher input voltages cause the faster responses of the robot arm.
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 2 Analysis of Video Transmission Latency  
The latency caused by the camera and the display is relatively independent. Through proper selection of the camera and the display, this will minimize the impact on the overall delay. The key to reducing the real-time video transmission latency lies in video codecs and video transmission.  
Transmission and codecs are interrelated. On one hand, a burst data stream output by the encoder going into the transmission network will increase network congestion. On the other hand, it is necessary to reduce the video bit rate to ensure the real-time stream transmission when the transmission conditions are limited. Hence the introduction of a cache and rate control throughout the transmission path. The total latency (D_latency) of the transmission system, including encoding, decoding, and transmission, is represented by the encoder latency (D_latency). This system delay is effectively the accumulation of the following components: encoder latency (D_enc), transmission latency (D_net), decoding latency (D_dec), and the latency caused by the buffer of each stage (D_cache). This shown in Figure 1 and represented in Equation (1).
## 2 Simulation Model  
Figure 2 depicts the sealing chambers around the gear circumference divided by the meshing gears and the crescent fillers: the suction chamber, the transitional chamber, the discharge chamber and the trapped chamber (if it exists), where the suction chamber is formed by the gear profiles between the meshing point C\({}_{2}\) and the intersections A\({}_{1}\) and A\({}_{2}\); the transitional chamber denotes the tooth space (TS) located in the transitional stage from the suction chamber to the discharge chamber; the discharge chamber is formed by the gear profiles between the meshing point C\({}_{1}\) and the intersections B\({}_{1}\) and B\({}_{2}\); and the trapped chamber is formed by the gear profiles between the two meshing points C\({}_{1}\) and C\({}_{2}\). Flow exchange was observed between the adjacent sealing chambers via the clearances and the grooves machined on the floating plate, namely the triangular grooves and the relief grooves, due to the pressure difference, as shown in Figure 3. To account for the flow characteristics, an evaluation of the pressure evolution around the gear circumference, where the pressure in suction chamber can be treated as the inlet pressure (atmospheric pressure) was needed; the pressure with respect to time in the other three chambers can be evaluated by applying the mass conservation equation.  
\[\frac{\mathrm{d}p}{\mathrm{d}t}=\frac{\beta}{V}\biggl{(}\sum q_{\mathrm{in}}- \sum q_{\mathrm{out}}-\frac{\mathrm{d}V}{\mathrm{d}t}\biggr{)} \tag{1}\]  
In Equation (1), \(\beta\) denotes the bulk modulus of the fluid. It should be noted that in this work, the oil temperature was set as a constant value (40 \({}^{\circ}\)C); hence the oil bulk modulus \(\beta\) was dependent on the pressure, as addressed by [PERSON] et al. [25]. Regarding the other terms, \(V\) denotes the chamber's volume; \(q_{\mathrm{in}}\) and \(q_{\mathrm{out}}\) denote the flow into and out of the chamber, respectively; and \(\mathrm{d}V/\mathrm{d}t\) denotes the time derivative of the chamber's volume.  
By applying Equation (1) to each chamber, the pressure can be estimated after evaluations of the terms in the right hand side of Equation (1), and the related flow characteristics can be obtained. Details are described in the following sub-sections on the evaluations of the related terms and their implementation in Matlab.  
Figure 2: Definition of the sealing chambers around the gear circumference.
## 4 Laboratory Experiments and Parameter Identification  
### Laboratory Experiments  
In order to observe the dynamic stiffness, damping, fluid, and seal friction properties of this HPS system, as well as the significance of temperature and excitation frequency, laboratory experiments were conducted on an HPS prototype to acquire its response characteristics under sinusoidal excitations at different operating temperatures. The temperature of the HPS system during the experiments was maintained within certain desired ranges via a cooling fan, as seen in Figure 3. Three different HPS body temperatures (30 \(\pm\) 2 \({}^{\circ}\)C, 40 \(\pm\) 2 \({}^{\circ}\)C, and 50 \(\pm\) 2 \({}^{\circ}\)C) were selected under sinusoidal excitation ranging from 0.5 to 8 Hz. Briefly, the force transducer was mounted between the cross beam and the prototype to acquire the total force, while the deflection was measured using a linear variable differential transformer installed within an electro-hydraulic exciter. Two pressure sensors were installed in order to measure the pressure of the fluids. A thermocouple was attached to the exterior surface to monitor the body temperature.
# 3D-STARNET: Spatial-Temporal Attention Residual Network for Robust Action Recognition
## 2 Related Work
### Three-Dimensional CNN-Based Action Recognition  
It is widely acknowledged that CNN has made great achievements in processing two-dimensional images, such as object detection [16; 17] and instance segmentation [18]. However, it still faces considerable challenges in tasks based on skeletons and other objects with spatiotemporal information. In the field of skeleton-based action recognition, GCN has always occupied a mainstream position. In order to explore the potential of 3D CNN in capturing the spatiotemporal features of skeleton sequences, many researchers have conducted research on action recognition based on 3D CNN.  
[PERSON] et al. [19] pioneered a new 3D CNN model for action recognition, which extracts features from the temporal and spatial dimensions through 3D convolution, thereby capturing motion encoding information in multiple adjacent frames. However, Ref. [19] ignored the long-term spatiotemporal dependencies of videos. To solve this problem, [PERSON] et al. [20] designed a temporal 3D convolutional network that can densely and efficiently capture short-, medium-, and long-term appearance and temporal information. [PERSON] et al. [21] introduced the SlowFast network for video recognition, in which the slow path of the network runs at a low frame rate to capture spatial semantics, and the fast path runs at a high frame rate to capture motion with fine temporal resolution. By reducing the channel capacity, the fast path can be made very lightweight and can fully learn temporal information. In their subsequent work X3D [22], a tiny 2D image classification architecture is gradually expanded in spatial, temporal, width, and depth dimensions. A simple step-wise network expansion method is used, which expands one axis at each step, achieving a good accuracy and complexity trade-off. In order to find a balance between model size and inference efficiency, [PERSON] et al. [23] proposed a skeleton-based double-feature double-motion network (DD-NET), which achieves ultra-fast inference speed by lightweighting the network.  
In order to construct 3D input data, previous studies either converted the pose distance matrices at different time spans into pseudo images and then stacked these pseudo images in time series [24], or summed up the 3D skeletons to obtain 3D data [25]. However, these methods suffer from the problem of information loss. Our study converts the skeleton sequence into a 2D heat map and then stacks it along the time dimension to form a 3D heat map volume to preserve all the information.
## 5 Experiments and Results  
### Simulation Environment  
In this section, the computational experiments and algorithms are constructed and implemented to analyze the quality of the heuristic being developed. Our experiments are developed in C++, and implemented in a platform with Intel Core2 Quad 2.4 GHz, 1 GB RAM, and Windows Server 2003 Standard with SP2. Table 3 illustrates experiment parameters.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Topology & Grid network \\ Number of Nodes & 49 \\ Number of groups & 2\(\sim\)5 \\ Range of requested bandwidth & 1\(\sim\)3 \\ Number of destinations in a group & 2\(\sim\)5 \\ Node Capacity & 30 \\ Number of iteration & 1000 \\ Improvement counter & 80 \\ Initial upper bound & 0 \\ Initial value of multipliers & 0 \\  & CPU: Intel Core2 Quad 2.4 GHz \\ Test platform & RAM: 1 GB RAM \\  & OS: Windows Server 2003 with SP2 \\ Development tool & Eclipse with g++ \\ \hline \end{tabular}
\end{table}
Table 3: Experiment environment and parameters.  
Figure 10: Flow chart of getting primal feasible solution.  
A grid topology is designed with 49 nodes for experiments. To display the characteristics of our proposed algorithm, each source and their corresponding destinations are deployed as far as possible.  
In order to compare the performance of LR optimal solution, we propose a simple algorithm, SA, for a simulation to show the benchmark comparison between not optimal, near optimal or optimal solutions. SA procedures of experiments are illustrated in Table 4.
## 2 Basic Principles and Concepts in Time Series Analysis
### Time Series Modeling  
As mentioned above, time series analysis focuses on modeling a phenomenon \(y\) from time \(t\) backwards \(\{y_{t},y_{t-1},y_{t-2},\)\(ldots\}\), with the objective of forecasting the following values of \(y\) up to a _prediction horizon_\(s\). For predicting \(y_{t+s}\), one can assume a functional model \(f\) based on the historical records \(y_{t},y_{t-1},y_{t-2},\ldots,y_{t-d+1}\):  
\[y_{t+s}=f(y_{t},y_{t-1},y_{t-2},\ldots,y_{t-d+1}) \tag{1}\]  
Equation (1) is a function approximation problem, and it can be solved by applying the following steps (see [23]):  
1. **Functional model**\(f\): Suppose a function \(f\) that represents the dependence of \(y_{t}\) related to \(y_{t-1},y_{t-2},\ldots,y_{t-d+1}\);
2. **Training phase**: For each past value \(y_{t_{k}}\), train \(f\) using as _inputs_ the values \(y_{t_{k}-1}\),\(y_{t_{k}-2}\) and \(\ldots\),\(y_{t_{k}-d+1}\), and as _target_\(y_{t_{k}}\);
3. **Predict value**\(\hat{y}_{t+1}\): Apply the trained functional model \(f\) to predict \(y_{t+1}\) from \(y_{t},y_{t-1},\)\(y_{t-2},\ldots,y_{t-d+1}\).  
The training phase step should be repeated until all predictions \(\hat{y}_{t_{k}+1}\) are close enough to their corresponding target values \(y_{t_{k}+1}\). If the functional model \(f\) is properly trained, it will produce accurate forecasts, \(\hat{y}_{t+1}\approx y_{t+1}\). The above steps can be applied to forecast any horizon \(s\), replacing \(y_{t_{k}}\) with \(y_{t_{k}+s}\) as the target. The three-step procedure presented above is known as an _autoregressive_ (AR) model.
## 2 Existing Studies Related to the Level of Service in Metro Stations
For example, the LOS in walkways goes from Level A (free flow with no conflicts) to Level F (critical density, sporadic flow, frequent stops and physical contact), where Level E is equal to capacity. The LOS can be obtained similarly in waiting areas and stairs. In other words, with the LOS, any macroscopic representation will only need the number of pedestrians to determine what space (e.g., width of platform) is needed [21; 22]. The LOS is an important tool to identify problems of congestion in walkways, waiting areas, and stairs. However, the LOS is based on a global view or macroscopic, in which the flow of pedestrians is understood as \"fluid dynamics\", i.e., pedestrians are analysed only by physical variables already explained before (speed, density, and flow). Moreover, some authors [23] state that pedestrians are not like fluids, and they must be analysed by taking into consideration each individual's characteristics and preferences, because individual pedestrians can overtake (pass each other), be stuck in a bottleneck, or move in different directions. With respect to density in metro stations, it seems that using general values of densities (overall number of passengers in a physical space such as a platform) is not the ideal way to measure the interaction between passengers. According to [PERSON] et al. [24], it is necessary to identify where and how passengers are moving in these spaces to better analyse the interaction between them. Moreover, an interaction index could represent the level of service in metro stations which could be divided into different locations such as exits, stairways, corridors, and open areas [25]. According to [26], the headway of trains and the location of the ticket purchase affect the volume of passengers, and therefore the level of service. In the case of the Shangai metro system, ref. [27] measured the speed and flow in walkways and stairways, which was affected by the space used by passengers. Other studies [28] obtained the speed, walking time, flow, and density of passengers, taking the Swiss Federal Railway as a case study. Recent studies [29] have shown the use of online data to study the level of service in metro stations. In [30; 31], the authors studied boarding and alighting behaviour, and their effect on the dwell time, in which passengers boarding while alighting was in process affected the level of service of passengers. According to [32], the interior of the train layout (e.g., handrails or seats) may affect the number of touches as well as the density inside the train (e.g., crowding), which is related to the level of service. Similarly, the seat layout inside the train carriage may affect capacity, and therefore the level of service [33]. Despite the relevant research reported in the literature, there is a lack of studies related to metro stations in the context of developing cities such as Valparaiso, in which the level of service should be analysed according to the passengers' characteristics and perspectives.