esa-sceva/satcom-chunk-collection huggingface dataset:
# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
## 6 Comparison with Existing Systems  
To contextualize the above results, Table 1 summarizes key performance metrics of the proposed 5G teleoperation system alongside representative state-of-the-art systems from the literature. These include latency measures, path-following accuracy, braking (stopping) distances, and fallback response characteristics. All metrics are reported under experimental conditions comparable to our field trials (low to moderate speeds on closed-road circuits or testbeds). Figure 12: Comparison of instantaneous and latency-affected braking distances relative to vehicle speed. Figure 13: Relationship between braking distance and vehicle speed: (**a**) at the highest latency; (**b**) at an average latency. Despite variations in experimental setup, latency performance remains comparable to recent 5G teleoperation trials. The measured average end-to-end delay (~110 ms) is well below the 170 ms threshold typically associated with minimal driving impact and is lower than the 202 ms G2G latency reported in Ref. [24]. The worst-case delay (~317 ms) falls within the range where remote control begins to degrade, reinforcing the need for fallback mechanisms. Lateral control accuracy (~0.2-0.3 m deviation) closely matches autonomous mode performance and aligns with sub-0.3 m benchmarks achieved using 5G mmWave and INS fusion [36; 37]. This precision indicates that remote operation via 5G can maintain near-autonomous path-following accuracy under low-speed conditions. Emergency braking distance increased from 1.42 m (no delay) to 2.37 m under peak latency (~316 ms), aligning with prior studies suggesting an increase of ~0.5-1 m per 200 ms delay [24]. Automated latency-aware emergency stopping avoids added delay from human reaction time (typically 0.5-1.5 s), which remains a limitation in manual-only systems. Compared to existing implementations, the system integrates real-time latency monitoring and autonomous braking, reducing risk without relying solely on operator intervention. Although tested at low speeds (~2.78 m/s), performance remains robust across control stability, latency management, and fallback activation. Several limitations remain: operation under higher speeds or poor network conditions (~500 ms latency) may exceed system capabilities. Advanced predictive HMIs (e.g., GAN-based video prediction [25], EKF-driven overlays [26]) and curve-aware fallback maneuvers (e.g., LQR-based [33])) represent potential areas for future enhancement. Additionally, handoff to onboard autonomy--as recommended in Level 4 guidance [31]--could support service continuity during link loss. In addition to latency considerations discussed above, other network impairments must also be acknowledged. Beyond latency spikes, real-world teleoperation is affected by
## 3 Results
### HCA Discrimination Based on the NIR Spectra of Waters under Temperature Perturbation  
An experiment was conducted to discriminate between the various types of waters using NIR spectra in the region of the first overtone of water (1300-1600 nm). NIR absorbance spectra of different waters at temperatures varying from 30 to 60 \({}^{\circ}\)C (Figure S1), show that the peak of the first overtone spectra experiences a shift towards lower wavelengths (\"blue shift\") [3; 24] from 1450 to 1428 nm with an increasing temperature, with an isosbestic point around 1442 nm (magnified view in Figure S1), which is consistent with the well-known influence of temperature on water spectra [5; 25].  
The results of the HCA analysis performed on the SNV transformed spectra of waters at a constant temperature (30 \({}^{\circ}\)C) are presented in Figure 2. At the level of similarity of 0.8, four groups of waters can be observed in the dendrogram. The clustering of waters based on the NIR spectra is different compared to the clustering based on the conventional index (Figure 1a,b), which suggests that the NIR spectra of waters are taking into account more than just the ion content. However, at a constant temperature, it was still not possible to successfully discriminate between the waters, and thus the temperature protocol is proposed to perturb each water and enhance subtle differences between spectra.  
Figure 1: Dendrograms of HCA analysis performed using auto-scaled physico-chemical parameters (Table 1) (Panel **a,b**). The nine different waters were classified into 6 groups based on ion type (panel **a**) or 8 groups when pH and \({}^{17}\)O-NMR values were used in addition to the ion content (panel **b**). The similarity between the thus found clusters was 0.8.
## 2 Improved Homotopy Analysis Method
### Improved Homotopy Analysis Method
#### 2.3.2 Construction of High-Order Homotopy Equation  
With the new nonlinear operator \(\overline{\mathcal{N}}\) in Equation (28) and the selected linear operator \(\mathcal{L}\) in Equation (10), the homotopy operator is defined as follows,  
\[\overline{\mathrm{H}}\big{[}\overline{\Psi}(\xi,q)\big{]}=(1-q)\mathrm{L} \big{[}\overline{\Psi}(\xi,q)-w_{0}(\xi)\big{]}-q\mathrm{h}\overline{\mathrm{ N}}\big{[}\overline{\Psi}(\xi,q)\big{]} \tag{29}\]  
The improved homotopy analysis method replaces the homotopy operator \(\mathcal{H}\) in Equation (13) with the new homotopy operator \(\overline{\mathcal{H}}\) in Equation (29).  
When \(\varepsilon=0\), the new homotopy operator \(\overline{\mathcal{H}}\) in Equation (29) degenerates to the homotopy operator \(\mathcal{H}\) in Equation (13).  
With definition of a vector  
\[\overline{w}_{n}=\{w_{0}(\xi),\overline{w}_{1}(\xi),\overline{w}_{2}(\xi), \cdots,\overline{w}_{n}(\xi)\} \tag{30}\]  
a new homotopy equation of high order is similarly constructed  
\[\mathrm{L}[\overline{w}_{n}(\xi)-\chi_{n}\overline{w}_{n-1}(\xi)]=\mathrm{h} \overline{R}_{n}(\overline{w}_{n-1},\xi) \tag{31}\]in conjunction with the boundary conditions  
\[w(0)=0,w^{\prime}(1)=0 \tag{32}\]  
where  
\[\overline{R}_{n}(\overline{w}_{n-1},\xi)=\left.\frac{1}{(n-1)!}\frac{\partial^{n- 1}\overline{\mathrm{N}}[\overline{\Psi}(\xi,q)]}{\partial q^{n-1}}\right|_{q=0} \tag{33}\]  
and  
\[\chi_{n}=\left\{\begin{array}{ll}0,\ n\leq 1\\ 1,\ n>1\end{array}\right. \tag{34}\]  
The improved homotopy analysis method replaces the homotopy Equation (14) with the new homotopy Equation (31) for high-order problem.  
To solve Equations (31) and (32), the result of \(n\)th-order approximation are given by  
\[\overline{w}(\xi)\approx w_{0}(\xi)+\sum_{m=1}^{n}\overline{w}_{m}(\xi) \tag{35}\]
## 4 Data Pre-Processing
### Data Analysis and Processing
#### 4.2.1 Classification and Calculation of Coastal Passenger Ship Routes and Operation Frequency  
First, we sequentially connected point-shaped data representing waypoints to construct linear coastal passenger ship routes. Next, we visualized the locations and extents of MTSA-assessed project sites from 2010 to 2014 using GIS software. Based on the range of each project site, we calculated the centroid of the objects and generated circular ranges with a radius of 3 NM from the centroid, as per the distance criterion mentioned in Section 3.2.1.  
By overlaying these two spatial information layers, we measured whether the distance between each route and the location of past MTSA-assessed project sites was within 3 NM. If the distance was 3 NM or less, we classified the route as an \"MTSA-related route;\" if the distance exceeded 3 NM, we categorized it as an \"MTSA-unrelated route.\" These classification methods are summarized in Figure 4.  
Figure 3: Overview of Data U set composition.  
To organize the operational information for the classified routes, we combined annual sailing information based on port calls for each route and port data for each route to derive sailing information from 2017 to 2022 for both \"MTSA-related routes\" and \"MTSA-unrelated routes.\" The derived results are shown in Figure 5 and in Table 5.
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 4 Simulations and Result Analysis
### Mars Aerocapture Mission
#### 4.2.2 Parameter Impact Analysis and Comparison  
Similarly, the impacts of entry flight path angle \(\gamma_{0}\) and the spacecraft's lift-to-drag ratio \(L/D\) on the guidance algorithm's performance are studied.  
Consider that the dispersion of the entry flight path angle is uniformly distributed on [\(-0.5\), \(0.5\)] deg, and \(1000\) Monte Carlo simulations are carried out in the Mars scenario. The guidance results of Mode 1 and Mode 2 are shown in Figure 12. It can be seen that both two guidance algorithms can adapt to a wide range of variation in entry flight path angle, but the velocity increment's dispersion obtained in Mode 1 is significantly smaller. Given that the \(\Delta V\)'s standard deviation in Mode 1 is reduced by 53.6% compared with that in Mode 2, the method proposed in this paper has better robustness.  
The Monte Carlo simulation results that vary the lift-to-drag ratio in the Mars scenario are shown in Figure 13. Through comparison, the velocity increment does not change significantly with the large-scale variation of the lift-to-drag ratio in both Mode 1 and Mode 2, but its dispersion obtained by the method proposed in this paper is significantly smaller. The standard deviation of the velocity increment in Mode 1 is reduced by 66.4% compared with that in Mode 2, indicating the proposed method's better performance.  
From the simulation results in the Earth scenario and the Mars scenario, we can see that the guidance algorithm proposed in this paper not only ensures near-optimal performance, but also has better robustness than the comparison algorithm. Furthermore, the guidance performance of our method is relatively less affected by the large-scale variations of entry flight path angle and lift-to-drag ratio, indicating strong adaptability.  
Figure 12: Dispersion of the velocity increments with the different lift-to-drag ratios for Mars aerocapture.
## 5 Case Study: Experimental Results
### Thematic and Temporal Analysis
#### 5.1.2 Qualitative Topic Assessment from Top Articles  
We can qualitatively determine which thematic subjects belong to a topic by examining the articles whose engaged users mostly have the topic assigned to them. From the topic assignment we can easily compute the probability \(P(\tau_{k}|a_{i})\), which indicates the portion of topic \(\tau_{k}\) in article \(a_{i}\). Table 5 shows the top-5 articles per topic in terms of probability \(P(\tau_{k}|a_{i})\) from the Slashdot dataset (SR) with \(K=4\). Even though we did not use any textual information in our modeling, we observe that the resulting topics show thematic consistency in their textual contents. This demonstrates our underlying assumption that users would behave according to the thematic subjects with their own interests. More specifically, Topics 2 and 4 clearly show their related thematic topics from the titles of the top engaged articles. The articles of Topic 2 deal with environmental issues such as climate change. Although its top article does not seem to show this topic in its title, some users opened the discussion on this topic with the election pledges and statements of the presidential election candidates. Topic 4 deals with subjects in software development, such as operating systems and related software. Meanwhile, articles on Topic 3 are observed to be related to technology, economics and business, which cover a broader area than Topics 2 and 4. Lastly, articles on Topic 1 have a variety of subjects and are likely to be affected by temporal factors, as shown in Figure 2, where the distribution of Topic-1 articles is biased towards the first half of the dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Topic** & **Temporality** & **Class** & **Subjects** \\ \hline
1 & 3.44 & Temporal & Mixed \\
2 & 1.76 & Thematic & Climate Change \\
3 & 2.44 & Thematic + Temporal & Technology, Business \\
4 & 1.11 & Thematic & OS Development \\ \hline \end{tabular}
\end{table}
Table 4: Qualitative assessment of topics in Figure 2.
## 4 Simulation Results
### Scenario Three: EVs, PV and ESS  
Scenario three considers the cooperation among EVs, PV and ESS to reduce the building's power consumption. Compared to scenario one and scenario two, EVs were able to discharge part of their batteries in order to support the load demands of the building by providing power during peak periods. Figure 8 shows the simulation result considering 50 EVs, a PV system of 10 kWh and an ESS of 30 kWh. The proposed algorithm showed a reduction in the building's power consumption during the period between 11:00 a.m. and 7:00 p.m. During that time, the output power from PV panels was enough to support part of the EV charging during their parking time. We noticed that power consumption during the remaining periods was similar to the initial building power consumption.  
Figure 6: Results of scenario one with immediate charging.
# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
## 3 Proposed Method
### Position Pre-Estimation
#### 3.1.2 Pre-Estimate Coordinates  
To obtain all possible row and column coordinates, the following three judgments were made:  
\[SPs=\{sp_{1},sp_{2},\cdots,sp_{n}\} \tag{5}\]  
Figure 3: Process of defect detection.  
Judgment 1: Whether there is a chip before the first pre-estimated starting point. In Formula (5), _SPs_ are the coordinates of qualified contour, and \(D\) is the distance between two adjacent chips, so _PTs_1 is the possible coordinates calculated.  
\[\textit{PTs1}=\{pt|pt=sp_{1}-D\times(N-k),k\in[0,N1)\} \tag{6}\]  
\[N1=\frac{sp_{1}}{D} \tag{7}\]  
Judgment 2: Whether there is a chip between the pre-estimated starting points. _PTs_2 is the possible coordinates calculated.  
\[\textit{PTs2}=\{pt|pt=sp_{i}+D\times(k+1),k\in[0,N2)\} \tag{8}\]  
\[N2=\frac{sp_{i+1}-sp_{i}}{D},i\in[1,n) \tag{9}\]  
Judgment 3: Whether there is a chip from the last pre-estimated starting point to the edge of the picture. _PTs_3 is the possible coordinates calculated. In Formula (11), \(SZ\) is the size of the picture.  
\[\textit{PTs3}=\{pt|pt=sp_{n}+D\times(k+1),k\in[0,N3)\} \tag{10}\]  
\[N3=\frac{SZ-sp_{n}}{D} \tag{11}\]  
By combining the possible coordinates calculated and _SPs_ to obtain all the pre-estimated coordinates:  
\[Result=\{\textit{SPs}\cup\textit{PTs1}\cup\textit{PTs2}\cup\textit{PTs3}\} \tag{12}\]
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 4 Random Errors
### Processing-Induced Decorrelation (\(\delta_{processing}\))  
Processing-induced decorrelation differs from all other error causes identified thus far. The errors in the aforementioned scenarios are caused by the sensor, orbit positioning, propagation path, or surface/scatterer motion. Processing-induced decorrelation is introduced during InSAR processing. The two most prevalent forms are interpolation error and coregistration error.  
Resampling is the first step in InSAR processing which helps to coregister the first image onto the second one. To resample radar images, an interpolation kernel is applied for the purpose of convolution. Different interpolation kernels create aliasing terms at varying scales and are treated as interpolation noises [153] which reduce coherence.  
Coregistration is one of the most important steps in InSAR processing. In this step, two radar images are aligned so that an interferogram can be properly generated in subsequent steps. Good alignment symbolizes optimal coherence conditions. On the other hand, misalignment reduces coherence. A shift of an entire resolution cell will result in a complete loss of coherence since it means there is no overlap within the resolution cells between the two radar scans. Consequently, subpixel coregistration accuracy is required to obtain coherent interferometric products.  
As the algorithms applied for InSAR processing have been developed for several decades, the interpolation error and coregistration error are subtle enough to be ignored. For example, the cubic convolution method can achieve mean total coherence at over 0.99, and a coregistration accuracy of 0.1 resolution cell can achieve a mean total coherence at 0.96 [28]. For the equations to calculate mean total coherence, one can reference [PERSON] [28].
## 4 Discussions  
In the previous section, five nonlinear examples were used to prove the performance of the proposed AFSL algorithm in both effectiveness and convergence aspects, by comparison the calculated reliability index and number of iterations with the HL-RF, STM, HLRF-BFGS, RHL-RF, and FSL algorithms are used for comparisons in each example. The results globally are listed in Table 8. It can be concluded that the HL-RF algorithm has the worst robustness. The HL-RF algorithm converges only in Example 4. In Examples 1-3 and 5, its iterative processes are trapped in a periodic loop. The HLRF-BFGS algorithm fails to converge in Examples 1 and 3. Furthermore, its convergence rate is the lowest in Examples 4 and 5. Other improved HL-RF algorithms, i.e  
The selection of adjusting coefficient in the proposed AFSL algorithm is studied as well. Figure 12 shows the number of iterations of the proposed AFSL algorithm with different adjusting coefficients. It can be seen that the adjusting coefficient \(c\) does not have significant effects on the iterations of the proposed AFSL algorithm. For the recommended value of \(c\in[0.5,\ 0.6]\), the proposed AFSL algorithm can converge effectively.  
Figure 11: The comparisons of iterative process for Example 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Example 1** & **Example 2** & **Example 3** & **Example 4** & **Example 5** \\ \hline FSL & 121\(\backslash\)121 & 55\(\backslash\)55 & 184\(\backslash\)184 & 9\(\backslash\)9 & 290\(\backslash\)290 \\ Proposed AFSL & 114\(\backslash\)48 & 50\(\backslash\)36 & 32\(\backslash\)29 & 9\(\backslash\)9 & 113\(\backslash\)82 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparisons (i.e., \(G\)-evaluations\(\backslash\)VG -evaluations) of the FSL and AFSL algorithms.  
Figure 12: Iterations of the proposed AFSL algorithm with different adjusting coefficients.
# Communication Transport Protocol Strategies for Rail Applications
###### Abstract  
Current technologies for managing rail traffic such as the Global System for Mobile communications for Railway (GSM-R) will be no longer be available within the upcoming years. The European Shift2 Rail Joint Undertaking (S2R-JU) proposed the Adaptable Communication System (ACS) to overcome this problem. In this work, we model the ACS by abstracting it at the Internet Protocol (IP) level, using tunnels for datagrams' transmission as a communication bearer is available along the rail. Then, to evaluate its performance, an ACS emulator has been implemented. The core part of it is a Tunnel Manager which can establish pseudo-virtual circuits through multi-bearer tunnels, forcing datagrams on a service-basis to follow specific paths between gateways (i.e., from on-board to a train to the network-side rail control center and vice versa). The Tunnel Manager can properly select a given tunnel/bearer for sending messages (and duplicating them on redundant paths) of critical rail applications for train traffic management, relying on tunnels based on either connection-oriented protocol (i.e., the Transport Control Protocol, TCP), connectionless protocol (i.e., the User Datagram Protocol, UDP) or a mix of them. In this paper, we investigate the best solutions in terms of transport protocols for implementing tunnels through the bearers. Results are based on two main use cases: i. the position report/movement authority messages for the European Rail Traffic Management System (ERMTs) and ii. the critical file transmission, considering either TCP or UDP as tunnel transport protocol. For the first rail application, one UDP bearer can be selected only if the end-to-end channel delay is lower than 100 ms and the experienced packet loss is lower than 4% in the whole crossed network. Two UDP bearers, one TCP bearer or two mixed UDP/TCP bearers should be selected in case the channel delay is greater than 300 ms and the experienced packet loss is greater than 15%. Considering the critical file transfer in the rail scenario, TCP should be selected with two bearers to have a throughput greater than 50 Mbit/s even for a packet loss of 1%.  
Adaptable Communication System; transport protocols; ERTMS/ETCS; rail applications 20221230313390/app12063013390/app12063013390/app1206301390/
## 5 Future Scope and Applicability of the Proposed Model in Seaports
### Expansion to Diverse Seaport Operations  
While the primary focus of this research revolves around lane identification and parking spot detection, the underlying deep learning model can be adapted and extended to address various other seaport operations. For example, its object detection capabilities can be applied to facilitate efficient container handling, identify different container types, and monitor cargo movements. Additionally, it can be utilized in security surveillance to detect unauthorized access or suspicious activities within the seaport premises. The model's versatility and adaptability make it an asset for a wide range of seaport management and operational tasks.
## 2 Materials and Methods
### Identification Methods
#### 2.3.1 Statistical Analysis and Spectral Analysis
It provides an intuitive display of the frequency components and energy distribution of the signal, aiding in observing spectral features. (5) Band energy features: by dividing the spectrum into different frequency bands and calculating the energy or power in each band, band energy features can be extracted. These features reflect the strength or energy distribution of the signal in different frequency ranges, facilitating the identification and classification of different types of microseismic signals. Spectrum analysis methods reveal the frequency characteristics of microseismic signals, helping to identify different frequency components and energy distributions, thus enabling waveform identification. It has widespread applications in microseismic monitoring, seismology, structural health monitoring, and other fields. The processing workflow involved in statistical and spectral analysis methods is clearly depicted in Figure 4. Manual recognition methods typically rely on the expertise and intuition of domain experts to judge the event type by manually selecting and extracting waveform features. Traditional machine learning methods automate this process by training models to learn the features of different event types and then classify new waveform data. These methods perform well in some cases but still depend on manually designed features, which may have limitations in the classification and recognition of complex waveforms. In contrast, deep learning methods, as emerging technologies, possess powerful automatic feature learning capabilities and can directly extract features from raw waveform data, thus having greater potential in microseismic event recognition. Figure 4: Workflow for recognizing different microseismic events using statistical and spectral analyses. (**a**) Statistical method (**b**) Spectral method.