esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Influence of the Measured Signal's Oscillation
The result of this filtering process is shown in Figure 7, where it is possible to view the reference signal alongside the filtered signal. After the filtering process, numerical integrations were performed on both the reference signal and the filtered signals, in accordance with the requirements of the ABNT NBR 8167 standard [20], to evaluate their impact on the total impulse calculation. The results of these integrations are presented in Table 5, providing a comparative view of how filters influence the measurement outcomes. Figure 5: Thrust vs. time for a static firing test. Figure 6: Thrust signal frequency spectrum over the DC—1000 Hz band. The data analysis in the table reveals that the effect of the oscillations in the total impulse is a negligible 0.04% of the total impulse value. The application of digital filters, often used to eliminate noise or smooth the captured signals, had a negligible effect on the calculated value of the total impulse. Thus, the stability of the results suggests that the measurement process is consistent, even when some noises or variations could be filtered. This low impact of the digital filters on calculating of the total impulse indicates that the raw data already present high quality or that the filters are adjusted in a way that does not introduce significant distortions. Therefore, filters serve more to improve the graphical visualization of the results than as a tool to correct large disturbances. The low influence of the filters reinforces confidence in the data and in the methods used to calculate the total impulse. Nevertheless, if the maximum value of the thrust is needed for further analysis, the filtering has a large impact, reducing the maximum value from 1226.68 N to 970.85 N, a difference of 20.86%. This fact could justify employing digital filters to improve raw data quality.
## 3 System Model and Problem Formulation
### Computing Model
#### 3.2.2 Offloading Model from Active Users to the AP  
Consider the offloading computing for executing \(q_{i}(t)\) offloaded bits in any time slot. The mobile device offloads its task to the AP by the FDMA offloading strategy. Then, the offloading computing will be performed by the edge server integrated with the AP. The data transmission rate for user \(i\) in the \(t\)-th time block is expressed as:  
\[v_{i,\rho}(t)=\beta_{i,\rho}(t)\log_{2}{(1+\frac{p_{i,\rho}(t)h_{i,u}(t)}{ \sigma^{2}})},\forall i\in K,\forall t\in\mathcal{T} \tag{8}\]  
where \(p_{i,\rho}(t)\) is the uplink data transmission power from the user \(i\) to the MEC. \(\sigma^{2}\) denotes the noise power at the AP receiver, \(\beta_{i,\rho}(t)\) denotes the bandwidth allocation vector for the mobile device \(i\) in each time block \(t\). As the shared bandwidth of \(K\) users cannot exceed the total bandwidth, the following constraints can be obtained:  
\[\sum_{i=1}^{K}\beta_{i,\rho}(t)\leq B,\forall i\in K,\forall t\in\mathcal{T} \tag{9}\]where \(B\) denotes the total bandwidth of the channel. Then, the offloading time from the \(MD_{i}\) to the AP can be expressed as:  
\[T_{i,o}(t)=\frac{q_{i}(t)}{v_{i,o}(t)},\forall i\in K,\forall t\in\mathcal{T} \tag{10}\]  
As harvesting and offloading time cannot exceed the time slot length \(T\), there are the following constraints:  
\[T_{i,h}(t)+\frac{q_{i}(t)}{v_{i,o}(t)}\leq T,\forall i\in K,\forall t\in \mathcal{T} \tag{11}\]  
Computation offloading incurs energy consumption. In addition to the transmission power \(p_{i,o}(t)\) the constant circuit power \(p_{c,i}>0\) is consumed. It is obtained that the energy consumption (J) of task offloading for user \(i\) can be expressed as:  
\[E_{i,off}(t)=p_{i,o}(t)T_{i,o}(t)+p_{c,i}T_{i,o}(t),\forall i\in K,\forall t\in \mathcal{T} \tag{12}\]  
Consider the limit of the computation capability of the MEC server. \(Q_{\max}\) denotes the maximum number of computation cycles executed at the edge server during \(t\)-th time block. Then, we have:  
\[\sum_{i=1}^{K}q_{i}(t)C_{i}\leq Q_{\max},\forall i\in K,\forall t\in\mathcal{T} \tag{13}\]  
This constraint can guarantee that the MEC computation latency is negligible.
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 2 Differential Precise Time and Frequency Transfer  
The key problem to be solved by the differential precise time and frequency transfer of GNSS is to obtain high-precision inter-station clock offset. First, based on the GNSS observations of the time reference station and the user station, the DD mathematical model is established, and the solution of DD carrier-phase ambiguities and the corresponding variance matrix are obtained. Secondly, the reference satellite is selected to calculate the receiver clock offset through the SD pseudorange observation, then the inter-station SD carrier-phase ambiguity of the reference satellite is estimated, and, further, the inter-station SD carrier-phase ambiguities of all visible satellites are estimated. Affected by pseudorange noise and multipath, there may be a bias in the estimation of SD carrier-phase ambiguity by the integer rounding estimator. Then, we determine the final SD integer carrier-phase ambiguities by the K-means clustering method [20; 21]. Once the ambiguities are determined, the carrier phase can provide the \"absolute\" time difference between stations. Finally, the accurate time transfer based on the SD carrier-phase observation is realized. In the following, the detailed DPT method will be introduced.
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 2. Bistatic ISAR Signal Model with Non-Ideal Channel
The channel calibration should be carried out in practical bistatic ISAR systems.
## 2 Feature Engineering  
Considering the importance of feature extraction for effective fault diagnosis, this investigation adopts a unique approach, relying exclusively on voltage and current phasors captured directly from the terminals of the machine under test. Even though feature extraction provides valuable insights into machine status, it results in data sets that are susceptible to multicollinearity. Given the uncertainty surrounding the optimal feature set, however, this stage aims to collect as many features as possible and subsequently employ the developed algorithm to identify the optimal subset. Therefore, in addition to three-phase winding impedance calculations, a set of features is derived and covered in the following subsections. The selected features, namely the fast Fourier transform (FFT) spectrum of voltage and current signals, total harmonic distortion (THD), and the harmonic voltage factor (HVF), were chosen based on their strong theoretical foundation and proven effectiveness in power quality assessment and electrical fault diagnostics. Specifically, the FFT enables transformation of time-domain phasor data into frequency-domain representations, thereby revealing harmonic components and frequency signatures associated with specific fault types [14]. Similarly, THD, as defined by IEEE Std. 519-2022 [35], quantifies the cumulative distortion effect of higher-order harmonics on the fundamental waveform. THD is particularly sensitive to nonlinear loading and imbalance scenarios, making it a robust indicator of both electrical and mechanical anomalies [22].  
Additionally, the HVF provides a normalized index for evaluating the relative contribution of individual harmonic orders to the overall signal. By evaluating the ratio of specific harmonic magnitudes to the fundamental waveform, HVF enables the detection of harmonic-rich conditions that are often overlooked by traditional metrics, according to IEC 60034-1 [36; 37].  
Together, these features create a multidimensional input space, capturing both linear and nonlinear fault behaviors. This ensures that the feature selection algorithm can effectively isolate the most discriminative attributes, ultimately improving fault classification accuracy and system reliability.
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 4 Simulation Setup for TSN Synchronization over 5G Scenarios
### Simulation Scenarios
#### 4.2.1 Simulation Scenario 1  
In the first scenario, the GM is placed on the server side of the 5G network. That means synchronization messages are transferred in the downlink. Both TSN switches are 5G-TSN devices; the first, named TSN Switch, encapsulates the time synchronization messages and forwards them to the 5G network. The second 5G-TSN device, named TSN device 2, acts as a TSN device capable of synchronizing to the GM through the 5G network. It receives encapsulated time synchronization messages, decapsulates them and synchronizes itself with the GM via the extracted messages. From the perspective of TSN devices, the 5G network is simply a link between TSN Switch and the TSN device 2. This simulation scenario is shown in Figure 1(a).  
The scenario can be expanded by connecting TSN devices to the GM (TSN Master), TSN Switch, and TSN device 2 if it takes the role of a TSN Bridge in Figure 1(a), resulting in a hierarchical structure.
## 4. Clamping Resonators
### Reflection from the Clamping Resonator Array  
To determine whether the clamping resonators reflect or attenuate the incident S0 Lamb wave, the transducer setup was changed to measure and calculate the 2 DFFT of possible reflections as shown in Figure 6a. The incident/reflection A-scans were used to calculate the 2 DFFT shown in Figure 6b. Extracting a slice of the data parallel to the wavenumber axis at a frequency of 53.9 kHz yields the wavenumber spectrum shown in Figure 6c. The wavenumber spectrum indicates that about 62% of the incident wave was reflected from the resonator array. It should be noted that the plot shown in Figure 6c is not directly comparable to those in Figure 4b because different transducer arrangements were leveraged.  
Figure 5: A-scans measured 69 cm from the transmitting transducer. The A-scan (black) from the plate with clamping resonators has a reduced amplitude around 200 \(\upmu\)s when compared to the baseline (grey). These A-scan examples were some of the noisier signals that were measured.
## 2 Materials and Methods
### Validation of Parallel PSO Algorithm  
Implementing the parallel PSO algorithm is based on the formulae previously presented in Section 2.1. The block diagram of this algorithm is presented below in Figure 8.  
Figure 6: Pressure to temperature chart of a real gas [16].  
Figure 7: Hampson-Linde cryogenic cooler functional diagram [17].  
When using variables from the Matlab workspace, it was necessary for each parallel instance of this algorithm to access them. The Matlab workspace is different from the ones used by each parallel instance. Considering that the CPU core can handle one Simulink simulation for each physical core, the total optimization time could be calculated using Equation (5), which reduced the optimization time when many CPU cores were used. The optimization time increased based on the model complexity (simulation time), the number of agents given as input for PSO, and the maximum iteration number--these factors increased the chance for an acceptable result. The total optimization time was:  
\[t_{opt}=\frac{t_{sim}\cdot n_{agents}\cdot n_{\max,iter}}{n_{CPU\_cores}}, \tag{5}\]  
by considering the simulation time, the number of agents, maximum iteration number, and the number of CPU (Central Processing Unit) cores. For validating the parallel PSO algorithm, the function from Equation (6) was used, obtaining the result from Figure 9 by using 5 optimization agents over 20 iterations:  
\[f(x,y)=(x+2y-7)^{2}+(2x+y-5)^{2} \tag{6}\]  
Figure 8: Parallel PSO algorithm block diagram.  
Figure 9: Global best cost value over several iterations for the previous function.  
For evaluating the error returned to the PSO algorithm in Matlab, the formula used in Simulink has to correspond to the topology chosen inside the PSO algorithm, the global optimum could be global minimum or global maximum. In this case, the search was run for global minimum, both for the validation stage and the Hampson-Linde system.  
Both this function and the Hampson-Linde system model implemented the following objective function in Simulink, which will be evaluated for minimum by each particle, as shown in Equation (7):  
\[E=\int[S_{ideal}-S_{model}]dt, \tag{7}\]  
where:  
* \(E\)--Computed error at the end of model simulation;
* \(S_{ideal}\)--Reference signal or value, which, in most cases, remain unchanged until the optimization is finished;
* \(S_{model}\)--Model output for randomly initialized particles; the initialization space is given as input by the user and defines the upper and lower boundaries of the searching space for each dimension.  
For the previous formula, the Equation used in defining the objective function would cause the model and reference outputs to almost superimpose when an acceptable result had been obtained.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 5 Results and Discussion  
In this study, Matlab R2018a was used to simulate the protocol proposed in this paper. Simulation refers to Nordic nRF2401 and tissue characteristic parameters [15; 34]. Simulation parameters and their values are shown in Table 2. The results are compared with those of two existing protocols, M-ATTMPT and iM-SIMPLE, to verify the performance of TSAER. It is worth noting that both M-ATTMPT and iM-SIMPLE are explicitly designed for wearable medical sensors, whose key metrics (energy efficiency, temperature) are in line with the focus of this paper, and secondly, as cluster-based layered protocols, they enable a fair assessment of routing efficiency. In the simulation, 10 sensor nodes were placed on different body parts, and 1 sink was deployed at the waist of the human body. The experiment mainly analyzed the network lifetime, stable period, residual energy, throughput, and average temperature rise in these protocols.  
Performance introduction:  
1. Network lifetime: the time interval from the beginning of the network to the death of the last node;
2. Network stability period: the time interval from the beginning of the network to the death of the first node;
3. Residual energy: the average residual energy of nodes in the network;
4. Throughput: the number of data packets effectively transmitted to the destination node;
5. Average temperature rise: the calculated average temperature rise in all nodes.
# Measurement of the Resonant Magneto-Optical Kerr Effect Using a Free Electron Laser
## 4 MOKE Measurement Scheme  
### Visible MOKE  
In the visible region, the MOKE is typically measured using the polarization plane modulation technique shown in Figure 4. A Faraday cell set between a polarizer and an analyzer compensates for the rotation of the polarization plane due to the Kerr effect in the sample. In the magneto-optic measurement field, the polarizer that is positioned after the sample is called the \"analyzer\". To enhance the sensitivity to the Kerr rotation angle, an AC current for modulation is added to the DC current, and the output signal from a detector is input to a lock-in amplifier; its output signal is then given as feedback to the current for the Faraday cell. Anisotropic materials such as LiF and MgF\({}_{2}\) can be used for the polarizer because they either show birefringence or dichroism [67; 68]. Figure 5 shows an experimental example of the Ta (2 nm)/Gd\({}_{21}\)(Fe\({}_{90}\)Co\({}_{10}\))\({}_{79}\) (20 nm)/Ta (10 nm)/SiO\({}_{2}\) sample. The material is ferrimagnetic; its magnetization saturates at \(\pm\)0.1 kOe (0.01 T), and the magneto-optical Kerr rotation angle reaches \(\pm\)0.24\({}^{\circ}\). Data from the sample are used as examples throughout this article, and the sample is described in detail in the next section.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 3 The Architectural Model
### The Control Plane--Tiered Tunnel Automation
#### 3.2.2 Tier 2--The Announcements of VNF Locations and SFCs  
The second Tier is a full mesh of one-to-one control channels between all network controllers involved in an SFC. The channels are utilized to exchange information about the route locations of the VNFs that reflect the SFC header 2 routes. The BGP announcements are split into two parts. The announcement of the location of the VNFs and the announcement of the SFC.  
The full mesh of Transport Links allows BGP route distribution of flow-specific routes to be scaled down, since the routes are transmitted only to relevant network controllers, according to the network topology established in Tier 1.
# Optimization of Multi-Day Flexible EMU Routing Plan for High-Speed Rail Networks
## 1 Introduction  
The rapid expansion and increasing complexity of China's high-speed railway (HSR) network present significant challenges in optimizing Electric Multiple Unit (EMU) routing plans. Traditional optimization methods have predominantly focused on single-day planning, addressing operational requirements within a fixed 24 h timeframe. However, the considerable variability in passenger demand--particularly evident between weekday commuter patterns and intensified weekend travel--highlights the inadequacy of these conventional single-day optimization methods. Therefore, there is an urgent need to develop multi-day EMU routing optimization strategies capable of dynamically responding to varying operational demands within China's extensive HSR network.  
Recent advancements in standardized EMUs, such as China's \"Fuxing\" series, have significantly enhanced operational flexibility through standardized critical components and interfaces [1]. These improvements enable more adaptable routing optimization strategies, including multi-day flexible storage and maintenance planning, as well as the dynamic coupling and decoupling of trains at intermediate stations. Exploiting this versatility can substantially enhance EMU utilization efficiency and reduce overall operational expenditures.  
In this study, we focus on multi-day EMU routing optimization within China's high-speed railway network, incorporating flexible storage, maintenance planning, and station-based dynamic coupling and decoupling operations. By considering multi-day fluctuations in passenger demand, we propose a connection-based optimization model designed to effectively align EMU assignments with varying operational requirements. The model explicitly incorporates the flexible selection of inspection locations and the operational feasibility of coupling and decoupling at stations. The objective is to minimize connection times and empty-running distances, thereby significantly enhancing the adaptability and overall efficiency of EMU routing plans.
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Extraction Factor Contribution in the RNN Model  
To compute the contribution of each factor in predicting the severity of traffic accidents, the profile method [39; 40] was used. This technique examines the evolution of each factor with a scale of values, while the remaining factors keep their values fixed. Each factor \(x_{i}\) takes 11 values resulting from the division of the range, between its minimum and maximum value, into 10 equal intervals. All factors except one were initially fixed at their minimum value and then were fixed successively at their first quartile, median, third quartile and maximum values. Five values of the response variable were obtained for each 11 value and adopted by \(x_{i}\), and the median of those five values was calculated. Finally, a curve with the profile of variation was obtained for every factor.  
Table 7 shows the calculated weight of each factor by the profile method implemented in Python. The results indicate that the road bound and accident time have a significant effect on injury severity; the drivers had a greater risk of injuries during daytime along the southbound lanes of the NSE. Also, dry surface conditions were found to be more dangerous than a wet surface, as far as driver injury severity is concerned. During rainy times (surface is wet), drivers decrease their speed due to traffic jams, hence decreasing the severity level of driver injury. Lighting conditions, such as dark with and without street lights, increase the potential of possible injury and fatality, whereas daylight reduces the severity level of driver injury. The negative weight of the accident location factor indicates that severe accidents are most likely to happen in the section 220-225 km of the expressway. When vehicle types such as cars and motorcycles are involved in crashes, drivers are more prone to possible injury and fatality than for heavy vehicles and buses. Fatigue and speeding can give the driver a greater chance of experiencing a severe injury than other causes reported in the dataset. Accidents at the entry and exit ramps, toll plaza, and main route are more dangerous than accidents in other zones. Collision type also plays a significant role in increasing or decreasing the severity level of an accident. The analysis in this paper shows that collisions such as out of control and rear collisions increase the injury severity, whereas right angle side collision and sideswipe decrease the injury severity on the NSE.
# A Novel Hybrid Path Planning Method Based on Q-Learning and Neural Network for Robot Arm
## 2 Method Description
### Angle-Finding by Trained Neural Network (Passive)
Since during path planning, we only use the trained weights, which are in memory, we call it the passive phase. Therefore, savings in processing time would be significant. Additionally, using a neural network is efficient than IK and table lookup. This is because calculating joints angles are often faster and cheaper than carrying out an expensive computation like the IK method for each step or saving a large table to use in the table lookup method. To summarize, the proposed method includes following five steps:  
1. Capturing a picture from the top view of the 2D workspace;
2. KNN image processing to detect start, target, and obstacle cells;
3. Using the Q-learning algorithm to find the best path from start to target while avoiding collision with the obstacle (Active phase);
4. Using the trained neural network to find the joints angles of the found actions (Passive phase);
5. Implementation of the obtained sequence of joints angles on the robot arm in the real world or simulation world. Figure 3 illustrates these steps. Figure 3: Method overview: 1. capturing picture, 2. image processing, 3. Q-learning, 4. neural network, 5. implementation. Figure 2: (**a**) Structure of the neural network. (**b**) Data acquisition sample through the vicinity cells. As it can be seen, in each step of our hybrid method, we try to use the simplest and fastest way because it would result in a fast and simple path planning method overall. In detail, in step 1, we can use a low-resolution image to increase the speed of image analysis because the start, obstacle, and target cells are distinguished by their colors which can be detected even in low-resolution images. In step 2, we use the KNN algorithm because it is one of the simplest classification algorithms which does not need to be trained. In step 3, we use a simple action space to model it in a programming software without needing a linking the simulator with programming software; we additionally use Q-learning because it can help us to find the optimal path. In step 4, instead of using IK and table lookup, we use a trained neural network that is fast. These simple tasks do not interfere with path planning goals. The performance of our hybrid method is evaluated and tested in simulation and real world in the next section.