esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 3 Methodology
### Evaluation Criteria  
To evaluate the model under various conditions and to select an appropriate mapping topology, we apply objective metrics to the data during the dimensionality reduction and reconstruction processes. These allow us to evaluate the extent to which (1) the dimensionality reduction technique retains the structure of the high-dimensional data (_trustworthiness, continuity, K-Nearest Neighbours (K-NN)_), (2) the classes are separable in low-dimensional space (_Jeffries-Matusita Distance_), and (3) the system accurately reconstructs the high-dimensional parameter space (_reconstruction error_).
## 7 Compliance with Maritime Regulations
### Integration of Compliance Mechanisms  
Regulatory compliance in autonomous maritime systems is not achieved through isolated processes; it requires a synchronized framework where emission control, monitoring, reporting, and data protection operate in unison. AI agents are the central orchestrators of this integrated compliance architecture [120].  
In practice, AI agents ensure that emission optimization, telemetry monitoring, and cybersecurity modules communicate seamlessly. A change in one system--such as a shift to eco-routing--automatically updates emission logs, triggers the recalculation of CII scores, and adjusts real-time reporting parameters [137].  
For example, after rerouting to avoid a storm (increasing distance), the agent recalculates expected fuel use, pre-emptively adjusts the vessel's speed profile, and flags the change in the compliance report, all while maintaining encrypted data transfer logs.  
This tight coupling enables automated regulatory traceability; every decision taken for environmental or safety reasons is recorded, time-stamped, and stored securely, ready for audit or inspection. Simultaneously, data governance ensures compliance with privacy and retention policies [137; 138; 139; 140; 141; 142; 143; 144].  
The agent also maintains a feedback loop, evaluating compliance performance, identifying recurring near-violations, and adjusting optimization parameters accordingly. This continuous self-improvement loop reinforces both efficiency and regulatory trustworthiness.  
The result is a vessel that does not just \"follow rules\" but continuously learns how to comply better.  
Through this holistic integration, AI agents transform fragmented compliance efforts into a unified, intelligent ecosystem, reducing human workload, minimizing compliance risk, and enabling scalable maritime autonomy.
## 3 Operational Data Analysis: Methods and Results
\[R(\mathbf{x}_{i})=y(\mathbf{x}_{i})-\hat{y}(\mathbf{x}_{i}). \tag{3}\]  
For \(i=1,2\), one computes:  
\[\Delta_{i}=100*\frac{\sum_{\mathbf{x}\in Data_{i}}\left(y(\mathbf{x})-\hat{y}(\mathbf{x}) \right)}{\sum_{\mathbf{x}\in Data_{i}}y(\mathbf{x})}. \tag{4}\]  
The quantity \(\Delta=\Delta_{2}-\Delta_{1}\) is a percentage estimate of the production improvement. It is possible to repeat the above procedure several times: This kind of bootstrap technique allows to simulate the repeatability of the tests. This is done as follows: At each run of the model, a different D0 (training set) and D1 (pre-upgrade validation dataset) are selected, while the whole D2 is kept as a validation data set at each run. One could decide to randomly select a subset of D2 as the post-upgrade validation data set but, in this work, the decision has been to take it fixed as a whole because it is shorter than the pre-upgrade data sets. In Figure 3, for a sample selection of D1, the sets \(R(\mathbf{x}_{1})\) and \(R(\mathbf{x}_{2})\) are plotted after being averaged in intervals of 10% of the rated power. Figure 3 is consistent with the intuitive expectation about the upgrade of interest: The effect of the upgrade is visible below rated power (i.e., before the rpms saturate) as a change in the mean difference between measurements and model estimates. When the rpms saturate, approaching rated power, the residuals between model estimates and measurements have a similar behavior because the upgrade is not effective. Figure 3: The average difference between measurements and simulation (Equation (1)) of the power of T7, for data sets D1 and D2 and for a sample run of the model. Basically, for each run of the model, an estimate of the energy improvement \(\Delta\) can be obtained from Equation (4): This procedure has been repeated several times and the average energy improvement is computed as \(\bar{\Delta}=1.0\%\). In other words, the estimate is that T7 has produced, after the adoption of the control system upgrade, \(1.0\%\) more than it would have done without the upgrade. Recall that this percentage refers to the production below rated power, consistently with the adopted criteria for filtering the data sets. If one wants to estimate how much this improvement amounts to in terms of annual energy production, one can assume a certain wind distribution or deduce it from long-term data and project the \(\bar{\Delta}\) estimate on the data set of interest.
# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 1 Introduction
Therefore, this paper selected the geometric relationship method to complete the distance measurement task of vehicles on two-way and two-lane roads. Numerous researchers have conducted in-depth research on this method. [PERSON] [16] proposed the basic model of the geometric ranging method and discussed the influence of pixel error on ranging accuracy. In order to improve ranging accuracy, [PERSON] and [PERSON] [17; 18] considered the influence of the pitch angle of the camera on ranging and established a traditional geometric ranging method considering the pitch angle of the camera. Since then, many research and practical applications have been completed with the support of this method [19; 20; 21; 22; 23]. Although the above research on vehicle distance measurement methods based on geometric relationships has achieved good results, the influence of the roll angle caused by camera rotation around its optical axis on this method has been ignored. In order to measure the longitudinal distance between the vehicle and the forward vehicle, as well as the longitudinal distance between the vehicle and the opposite vehicle, on the two-way two-lane roads, and improve the ranging accuracy, the main contributions of this paper are as follows:  
* The vehicle detection model suitable for two-way two-lane roads was trained. * The influence of the roll angle of the camera on ranging results using the traditional geometric ranging method was analyzed. When the roll angle of the camera exists, the ranging result will deviate from the normal value. The degree of deviation will vary with the vehicle positions. Moreover, the larger the roll angle, the greater the deviation. * The improved geometric ranging method considering the roll angle of the camera was proposed. Through experimental verification and method comparison, the proposed method is effective, and the improved geometric ranging method has higher ranging accuracy than the other two methods on two-way two-lane roads.
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 2 Environmental Vibration Model of Metro Depot
### Train Model  
The train model is a Chinese Type A train with an eight-car formation. A schematic diagram of the Type A metro train is shown in Figure 6.  
The speed limit inside the operation depot is set at 5 km/h. The speed in the throat area is approximately 10 to 20 km/h. The section of the metro depot simulated in this study is located near the depot entrance. It is connected to the straight track of the throat area at a level crossing, where the speed is higher. To consider the worst-case scenario of vibrations from the over-track building, the train speed is set at 20 km/h. An eight-car train returning to the depot is simulated. Table 5 presents the parameters of the Type A metro train.  
Figure 5: Track-soil-building finite element model.  
Figure 6: Schematic diagram of Type A metro train.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Numerical Value** & **Unit** \\ \hline Vehicle body mass & 23,825 & kg \\ Bogie frame mass & 3970 & kg \\ Wheelset mass & 1654 & kg \\ Primary suspension vertical stiffness & 1.26 \(\times\) 10\({}^{6}\) & N/m \\ Primary suspension longitudinal stiffness & 1.0 \(\times\) 10\({}^{7}\) & N/m \\ Primary suspension lateral stiffness & 6.5 \(\times\) 10\({}^{6}\) & N/m \\ Secondary suspension vertical stiffness & 4.9 \(\times\) 10\({}^{5}\) & N/m \\ Secondary suspension longitudinal stiffness & 2.31 \(\times\) 10\({}^{5}\) & N/m \\ Secondary suspension lateral stiffness & 2.31 \(\times\) 10\({}^{5}\) & N/m \\ Primary suspension vertical damping & 10,626 & N-s/m \\ Primary suspension longitudinal damping & 0 & N-s/m \\ Primary suspension lateral damping & 0 & N-s/m \\ Secondary suspension vertical damping & 20,590 & N-s/m \\ Secondary suspension lateral damping & 40,000 & N-s/m \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of Type A metro train.  
Train-induced vibrations are generated by moving loads caused by the weight of the train and track irregularities. In this study, the train load is simulated using the SIMPACK multibody dynamics software. Due to the use of seamless rails and high construction quality in the operation depot area, the overall track smoothness is relatively good. Therefore, the American Class 6 track spectrum is adopted in the model to impose irregularities on the rails [35]. Figure 7 shows the simulated wheel-rail forces of the train wheelsets in this study.  
In the track-soil-building finite element model, the train load is applied under the assumption that the wheel-rail forces on both rails are identical. The wheel-rail forces are sequentially applied as moving loads to the rail nodes located above the fasteners.
# Study on Wear Resistance of Ti-6 Al-4V Alloy Composite Coating Prepared by Laser Alloying
## 1 Introduction
The results showed that TiN/Ti\({}_{3}\)Al intermetallic compound coating could effectively improve the high temperature oxidation resistance and mechanical properties of Ti-6 Al-4V titanium alloy. It can be observed that aluminum and silicon elements play an important role in improving the surface properties of titanium alloys. According to the survey, TiB [18], TiN [19], TiC [20], and Ti\({}_{5}\)Si\({}_{3}\)[21] have high hardness, high strength, and high wear resistance, which are often used as reinforcing phases of composite materials. Moreover, TiB and TiN reinforcements have little influence on the toughness of Ti-6 Al-4V alloy, which can effectively improve the wear resistance of metals without destroying their toughness. Ni60A contains 3.0-4.5% element B. under the action of higher laser energy, Ni60A almost completely decomposes. Although the chemical bond between Ti and N is strong, tin is still partially decomposed, resulting in the existence of free Ti element, decomposed B, and added Si element to synthesize new phase TiB and Ti\({}_{5}\)Si\({}_{3}\) in situ. The new phase TiB and UN decomposed tin constitute the reinforcement phase of the alloying coating, which improves the wear resistance of the titanium alloy. Therefore, TiB, tin, Al\({}_{3}\)Ti, and other composite phases can be obtained at the same time,  
Figure 1: (a) Surface wear morphology of Ti-6 Al-4V alloy; (b) cross section morphology of oxide layer. which can improve the oxidation resistance and wear resistance of Ti-6 Al-4V alloy at the same time. Therefore, Ni60A/TiN-Al-Si composite powder was planned to be used in this study. The tribological properties of the composite coating and Ti-6 Al-4V under different loads and temperatures were compared, and the mechanism of different wear results was analyzed. This has certain guiding significance for improving the wear resistance and high temperature resistance of the titanium alloy Ti-6 Al-4V under harsh conditions, such as high temperature.
# Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing
## 6 Discussion  
This study focuses on using Turkish Natural Language Processing for the formation control of mobile robot systems, particularly focusing on the leader-follower approach. Through experiments conducted in the Gazebo simulation environment with Turtlebot 3 Burger robots, this study demonstrates the effectiveness of interpreting and executing Turkish commands for navigation and formation control. In this study, by preprocessing natural Turkish commands and employing path-planning algorithms, the robots successfully navigate to desired locations and maintain their formation in multi-robot scenarios. The agglutinative structure of the Turkish language presents challenges in the context of NLP for mobile robot control. The grammar structure of Turkish may be defined by its extensive use of suffixes, which are appended to root words, especially to verbs, to convey complex meanings and grammatical functions. This agglutinative structure has highly variable and context-dependent command structures, making it challenging to parse and interpret commands for robot control accurately. Moreover, Turkish exhibits rich morphological features, such as case markings and verb conjugations, which further complicate language-understanding processes. As a result, developing robust and reliable approaches for preprocessing Turkish commands and extracting meaningful information for NLP applications poses significant difficulties. Addressing these challenges necessitates a comprehensive understanding of Turkish grammar and morphology to increase robot-human interaction and develop innovative NLP approaches tailored to agglutinative languages' intricacies. By integrating language-processing capabilities into multiple-robot formation control, we propose an approach for more intuitive human-robot interaction, potentially expanding the accessibility and usability of robotic technology in Turkish language applications. Furthermore, this study establishes a foundation for future research in Turkish NLP applications and robot control aimed at enhancing the robustness and versatility of natural language-based control systems for mobile robots.
## 5 Conclusions  
In this paper, we compared two phase aberration correction techniques (near-field phase-screen modeling (PS) and geometry-based reconstruction technique (GB)) when an ultrafast imaging sequence (five plane waves tilted from \(-15\) to \(15\) degrees in cutaneous tissue) was used for data acquisition. The numerical results showed that the PS technique is strongly affected by speckle noise caused by a heterogeneous brain tissue model and fails to reveal many scatterers in the reconstructed image, while the GB method detects nearly all the scatterers. Compared to the PS approach, the GB method improves the signal-to-clutter ratio by 7.5 dB and reduces the localization error by up to 2 mm in our experimental study. The spatial lateral resolution is only slightly improved by 0.1 mm with the GB method in the experimental study compared with the PS approach. This work indicates that the GB method outperforms the PS method in the case of ultrafast transcranial ultrasound imaging with a single phased-array transducer used for data acquisition.  
Conceptualization, M.M. and G.R.; methodology, M.M. and G.R.; software, M.M. and G.R.; validation, M.M., G.R., N.d.J. and M.D.V.; formal analysis, M.M., G.R., N.d.J. and M.D.V.; investigation, M.M., G.R. and N.d.J.; resources, M.M., G.R., N.d.J. and M.D.V.; data curation, M.M. and G.R.; writing--original draft preparation, M.M. and G.R.; writing--review and editing, G.R., N.d.J. and M.D.V.; visualization, G.R., N.d.J. and M.D.V.; supervision, G.R., N.d.J. and M.D.V.; project administration, N.d.J. and M.D.V.; funding acquisition, N.d.J. and M.D.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a joint grant from the Netherlands Organisation for Scientific Research (NWO)/the Netherlands Organisation for Health Research and Development (ZonMw) and the Department of Biotechnology (Government of India) under the program Medical Devices for Affordable Health (MDAH) as Project Imaging Needles (Grant Number 116310008).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The numerical and experimental data used in this study will be provided upon request.  
**Acknowledgments:** This work was supported by a joint grant from the Netherlands Organisation for Scientific Research (NWO)/the Netherlands Organisation for Health Research and Development (ZonMw) and the Department of Biotechnology (Government of India) under the program Medical Devices for Affordable Health (MDAH) as Project Imaging Needles (Grant Number 116310008).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Analysis of RT Solutions
### Occupation Times  
In order to verify the effect of the session duration on RT solutions, the GNSS-based multiple-epoch network RTK (mRTN) solutions were obtained for short baselines (\(\sim\)2 km) in Phase 1. The solutions were alternatively obtained for different occupation of five, 30, 60, 180, 300, and 600 s, respectively. The time series of the ellipsoidal heights for each site is plotted in Figure 12, along with the overall statistics. All observations are grouped according to the session duration and assigned the same color code for two subsequent occupation intervals.  
The overall statistics of mean values show a relatively large bias for NORTH and SOUTH due to the surrounding environment, as expected, and their precisions are equivalent. There is no obvious trend in the mean values, except for longer spans, depending on the duration of multiple-epoch solutions, but the variance keeps decreasing as the session duration increases (Table 3).
## 3 Machine Learning and Fault Diagnosis
### Simple Fault Diagnosis Models
#### Artificial Neural Networks (ANN)
The results of the simulation and experimental study show a good correlation with a classification error rate of 2.7%. [PERSON] et al. also found that ANNs are useful in the detection of bypass diode faults in short circuits and open circuits, with the model being 96.4% and 92.6% accurate in detecting short-circuit and open-circuit bypass diode faults, respectively [89]. In a review of the application of ANNs in the diagnosis of PV systems, [PERSON] et al. emphasized the importance of ANNs in the field of solar photovoltaics [21]. Finally, a new approach to diagnosing short-circuits faults and disconnected strings based on ANNs in a PV system was found to have an accuracy of 98.6% [86].
## 4 Conclusions  
We performed sliding tests on dry sands which were subjected to constant shear loads, and the normal loads were linearly unloaded. Normal force, shear force, and shear displacement were recorded during the unloading process. The main conclusions drawn were as follows:  
The granular sample discontinuously slides during the unloading of normal force, and the slip velocity does not increase uniformly with the elapsed time. Due to the increase in inner material friction strength caused by the accumulated shear displacement, the sliding velocity of dry sands fluctuates, and temporary sliding intermission occurs. With different normal unloading rates and different constant shear forces applied, the dry sands begin to slide under almost the same corresponding normal force. The corresponding normal force decreases with larger unloading rates and increases with larger shear force at different sliding velocities in the final fast sliding stage. These normal force values are less than the values given by [PERSON]'s theory based on conventional displacement-controlled shear tests. The temporal variation in the apparent friction coefficient under the same normal unloading rate satisfies the inverse proportional function relation during the sliding, and the greater the constant shear force, the variation range of friction coefficient is narrower. Under a specified sliding velocity (the maximum experimental velocity), the apparent friction coefficient (i.e., the maximum friction coefficient) decreases with a higher normal unloading rate, and increases with larger shear force.  
Regarding the limitations of this study, the sliding intermission phenomenon is not analyzed quantitatively, and the hypothesis in this paper can be verified by FEM and DEM numerical simulations in the future. Further investigations considering more environmental factors (e.g., temperatures and fluids [28; 34]) are expected to better reflect the conditions in situ.  
Conceptualization, W.D., K.T., J.F. and B.W.; methodology, K.T.; software, K.T.; data curation, W.D.; writing--original draft preparation, K.T.; writing--review and editing, W.D. and J.F.; visualization, K.T.; supervision, W.D.; project administration, J.F. and B.W.; funding acquisition, J.F. and B.W. All authors have read and agreed to the published version of the manuscript.  
This research is financially supported by the National Natural Science Foundation of China (No. 52474122), the Open Foundation of National Engineering Laboratory for High-Speed Railway Construction (HSR202105), the open research fund of the State Key Laboratory of HydraulicEngineering Simulation and Safety, Tianjin University (No. HESS-2317), and the Natural Science Foundation of Guangdong Province of China (2022A1515240009).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.