esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
###### Abstract  
It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51 automotive noise limit regulations.  
UNECE R51; automotive noise limits; machine learning; BPNN; Levenberg-Marquardt algorithm; quadratic regression Article
## 2 Related Works
### Plane Detection-Based Methods  
Surface reconstruction has been a popular research topic for decades [1; 44]. Indoor interior reconstruction is closely related to outdoor reconstruction [45], which has been more thoroughly studied [1; 14], although the addressed issues are different. Early research on indoor reconstruction focused on detecting planes to reconstruct indoor environments, similar to methods of outdoor reconstruction.  
A Random Sample Consensus (RANSAC)-based algorithm to extract various shapes from raw point clouds was proposed by [PERSON] et al. [25]. The weakness of [PERSON]'s algorithm is its highly variable point density and strong anisotropy [1]. Furthermore, this algorithm is not robust on outliers and noise points that are caused by the uncertainty of randomly sampling the minimum subset with three points [2; 46].  
Principal component analysis and model fitting were proposed to reconstruct interior planes, and this method addresses floor planes, wall planes, and stair planes [26]. Interior planes are successively extracted by region-growing segmentation and the least-squares fitting algorithm [47]. The least-squares fitting algorithm is based on RANSAC and alpha-shape-based algorithms. However, this method cannot rebuild a complete wall or ceiling planes when missing data and it cannot model a watertight structure. Moreover, this method suffers from the same weakness as [PERSON]'s method because of issues with missing and noisy data.  
A method for wall detection and reconstruction via supervised learning [31; 4; 32] was proposed to reconstruct the missing portions of walls. This method first labels wall surfaces as cluttered, occupied, or empty areas by studying the relationship between the scanner points and the wall points. Then, a supervised learning method is used to distinguish walls from clutter. Although this method can export a watertight model, indoor structures are restricted to very simple shapes in this type of research.
# Investigation of the Motion Characteristics of Parts on a Platform Subjected to Planar Oscillations
## 1 Introduction
[44] showed that the surface with fin-like asperities of a longitudinal oscillating trough can be applied for the directional motion of particles achieved due to the net force created by the fin-like asperities. Recently, a technique for manipulation and transportation of various parts was demonstrated where the motion was achieved by the means of a periodic asymmetry of the frictional forces between the part and the harmonically oscillating surface that are dynamically controlled in a certain manner with respect to the period of the harmonic excitation [45; 46]. The presented paper proposes a technique for positioning applications, which is accomplished by employing a platform subjected to planar oscillations along circular, elliptical, and complex trajectories. The aim of this research is to investigate the motion characteristics of a part on this platform. By adjusting the parameters that control the oscillations of the platform, the part can be moved in any direction at a given velocity and can be subjected to complex dense positioning trajectories. Therefore, such a platform can be applied for feeding, positioning, and manipulation tasks.
# An Overview of GEO Satellite Communication Simulation Systems
## 4 Comparative Evaluation of Simulation Tools
### Co-Simulation Interfaces and Integration  
Given the modular strengths and complementary capabilities of existing simulation tools, an effective GEO satellite simulation system necessitates hybrid workflows involving multiple specialized software packages. Integration among these tools is achieved through various co-simulation interfaces, each enabling robust information transfer and interoperability. These interfaces include command-driven APIs such as STK/Connect, standardized model exchange methods like Functional Mock-up Units (FMUs), real-time communication channels using network protocols such as Transmission Control Protocol/User Datagram Protocol (TCP/UDP) sockets, and structured file-based data exchanges (e.g., Comma-Separated Values (CSV), JavaScript Object Notation (JSON), ephemeris files).  
For example, STK can export precise ephemeris and satellite visibility data in standard formats to MATLAB, which subsequently utilizes this information for detailed physical-layer analyses, such as evaluating SNR, bit error rates (BERs), and link budgets. The resultant performance metrics generated by MATLAB can then be seamlessly transferred through TCP/UDP sockets or structured files (e.g., JSON) to network simulators such as NS-3 and OMNeT++, enabling comprehensive end-to-end performance assessment.  
As illustrated in Figure 2, the co-simulation framework clarifies how each interface type functions within the hybrid environment. Specifically, it delineates the precise direction of information flow, identifies which data formats are involved at each step, and highlights the roles each tool assumes within the integrated workflow. Such detailed visualization and explanation of interface mechanisms not only promote clarity but also underscore their practical significance in achieving accurate and reliable GEO satellite simulation systems. These interfaces collectively ensure consistent data synchronization, model fidelity, and ultimately facilitate the scalability and flexibility required for complex mission planning scenarios.
## 6 Application Scenarios of the Proposed Approach
### Applications in Conferencing Scenarios  
During video conferencing, the quality of speech can become severely degraded due to background noise, reverberation, the number of recording microphones, the geometry of the microphone array, the acoustic and circuit design of the microphone arrays, the interference due to speakers, etc. [58]. Therefore, speech enhancement is a critical component of the overall speech processing system and plays an important role in video conferencing applications. As compared with a microphone placed very close to a speaker, microphone arrays offer better speech intelligibility by offering significant directional gain as well as effective noise reduction, thereby leading to an improvement in overall audio experience for the participants in the conference. Due to the motion of the speakers, it is necessary to track them and switch the focus towards the desired speaker in a dynamic manner. For this reason, microphone array-based speech enhancement techniques which incorporate localization and tracking methods are ideal for ensuring clear and intelligible communication in conference rooms, rooms which involve participants changing their positions or intermittently speaking from different locations. The proposed approach in this paper involves multiple microphone arrays with an MS-GLMB filtering algorithm to track the speakers and deep neural architecture-based speech enhancement. Such an approach can be leveraged to dynamically enhance the targeted speech in a conference environment.
## 3 Unified White-Box ML Watermarking Framework
### Dynamic White-Box Watermarking Schemes  
[PERSON] et al. Scheme (DeepSigns) [51]  
Unlike previous works that directly embed watermarks into static model content (weights), DeepSigns embeds watermarks into the probability density functions (PDFs) of activation maps of a secret selected layer. The watermarking process is both data- and model-dependent, meaning the watermark is embedded into the dynamic behavior of the DNN, triggered only by passing specific input data to the model.  
Feature extraction process: DeepSigns watermarks model layers independently through the following steps:  
1. Feed the model with all training data \((X_{\text{train}},Y_{\text{train}})\) to obtain activation maps \(\{f^{l}(x,w)\}_{x\in X_{\text{train}}}\) for layer \(l\), where \(f^{l}(x,w)\in\mathbb{R}^{m}\);Define a Gaussian Mixture Model (GMM) of \(S\) Gaussians fitted to the activation maps, where \(S\) equals the number of classification classes;  
Secretly select \(s\) Gaussians from the mixture, identified by indices \(T\), and compute their mean values \(\{\mu_{i}^{l}\}_{i\in T}\);  
Secretly select a subset of training data to build the trigger set \((X_{\text{key}},Y_{\text{key}})\) from the Gaussian classes selected.  
Framework integration: In our framework, the extraction function of DeepSigns is defined as:  
\[\text{Ext}(M,K_{\text{ext}})=\{\{f^{l}(x,w)\}_{x\in X_{\text{key}}\&y_{\text{ train}}=i}\}_{i\in T} \tag{14}\]  
where \(T\) indexes secretly selected GMM classes, and \(K_{\text{ext}}\) corresponds to the trigger set.  
Loss function: The global DeepSigns loss function is  
\[E=E_{0}(X_{\text{Train}},Y_{\text{Train}})+\lambda_{1}E_{\text{wat}}-\lambda_ {2}E_{\text{sep}} \tag{15}\]  
where \(E_{\text{sep}}\) prevents watermarked parameters from converging to non-watermarked GMM means.  
Advantages and limitations: DeepSigns provides higher security than static methods by relying on the secret matrix and trigger set and secretly selected GMM classes. It demonstrates resistance to fine-tuning, pruning, and overwriting attacks for small insertion capacities. However, its main weakness lies in its limited insertion capacity, where increasing watermark length makes it vulnerable to FTA and overwriting attacks. Additionally, the method's reliance on GMM clustering becomes questionable for large watermark capacities due to the lack of constraints, ensuring that watermarked parameters remain close to the original GMM means.
## 4 Semantic Understanding of Object Layer
### Local Area Semantics
#### 4.2.3 Probabilistic Topic Model  
In order to improve the performance of image semantic expression, a visual language model is proposed [69], which is inspired by the probabilistic topic model (PTM) of natural language understanding. Based on the visual language model, an image can be divided into many blocks as visual words according to certain rules, and these visual words have certain grammatical rules and spatial dependencies, also called visual grammar. The semantic information is represented by the co-occurrence frequency and spatial dependence of local features in the image. Common PTMs include probabilistic Latent Semantic Analysis (pLSA) [70] and Latent Dirichlet Allocation (LDA) [71].  
To ameliorate the robustness of the visual language model to the change of target scale, [PERSON] et al. extended the original model to multi-scale, and proposed the scale-invariant visual language model (m-VLM) [72]. [PERSON] et al. use LDA to realize the scene classification of optical remote sensing images and compare it with the bag of visual words (BOVW) model [73]. The results show that LDA can provide more concise and abundant semantic information for image representation. In the parameter training stage, the probability of a visual language model is estimated by counting the frequency of a visual word or visual word combinations in the image. This approach equates the visual words in the target area of the image with the visual words in the background area, thus ignoring the negative impact of background noise on the target semantic representation [74]. Therefore, if we can distinguish the visual words in the background and assign the weight according to their contribution to the target, we can enhance the resolution of the visual language model to image semantic representation.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 6 Reception Results
### Reception of Terrestrial and LEO Satellite Antenna System
#### 6.2.2. Inmarsat4-F2 with Wideband Grid Antenna  
Inmarsat4-F2, or International Mobile Satellite Organization 4-F2, is a satellite that is part of the Inmarsat system, providing global telecommunications services, especially in areas without effective ground connectivity [30]. Signal reception from Inmarsat satellites in this research was divided into two parts:  
* STD-C or standard-C data from the satellite, operating at a frequency of 1541.45 MHz, is a communication standard designed for messaging and other technical data. Figure 21 provides an example of receiving STD-C data from Inmarsat-4 F2, showcasing the interface of the SatDump program used for processing these data. The figure highlights the frequency and signal spectrum section, which visually represents the received signal's strength across the frequency range, with peaks indicating active transmissions. Additionally, the STD-C Packets panel on the right displays decoded messages, including maritime safety information, weather forecasts, and navigational alerts, illustrating the practical applications of this communication standard.
* Inmarsat Aero data are satellite communication data for aircraft at a frequency of 1545.211 MHz. Figure 22 provides an example of receiving Immarsat Aero data from Inmarsat-4 F2, showcasing the interface of the SatDump program, which is designed to receive, monitor, and decode aviation communication signals transmitted via the Inmarsat satellite network. These data provide real-time information for aircraft tracking, messaging, and safety communications, such as ACARS messages (Aircraft Communications Addressing and Reporting System). The figure includes a frequency spectrum that shows the power of the received signal across the frequency range, with noticeable peaks indicating active transmissions. Additionally, the waterfall display visualizes the signal's strength over time, offering insights into its stability and activity.  
Figure 21. STD\(-\)C data from Inmarsat4\(-\)F2.  
Figure 20. Satellite images from NOAA-19 after the process from WXtoImg (**a**) Natural color composite image showing cloud cover and land features. (**b**) False-color composite image highlighting cloud intensity and convection areas.  
The parameters listed in Table 10 outline the specifications for receiving signals from Inmarsat4-F2. Currently, the orbit of the Inmarsat4-F2 satellite is a GEO with an inclination angle of 4.2\({}^{\circ}\). Due to being in operation for over 18 years, the pointing of the antenna varies in azimuth and elevation at different times of the day. Therefore, the position of the satellite should be tracked regularly to ensure correct pointing.
## 4 Numerical Examples
### Example 6--Reliability Analysis--Effects of Boundary Conditions and Self-Weight on Bridge Topology
The computational cost of SESO is significantly lower than that of BESO. However, BESO achieves lower compliance for the analyzed reliability indices. This behavior is illustrated in Figure 14 which also demonstrates that as the level of reliability increases (i.e., as the value of \(\beta\) increases), the stiffness of the structure decreases, as predicted by [30]). Furthermore, an increase in \(\beta\), with the same boundary conditions, geometry, and random variables, does not significantly alter the optimal configurations, as shown in Table 2. Under these conditions, what changes is the solution of the problem known as the design point P*, as illustrated in Figure 15.  
One approach to identify a good approximation point is to first transform the original random variable space into an independent Gaussian space. Then, the Most Probable Point (MPP) is identified as the approximation point for the g-function, as illustrated in Figure 15. In the transformed u-space, the MPP is the point of minimum distance from the origin to the \(g=0\) surface. This minimum distance \(\beta\) is called the reliability index, and in the FORM, the solution is usually referring to a linear approximation in the u-space, and it can show that the corresponding exact \(P_{f}\) is given by  
\[P_{f}=\Phi(-\beta) \tag{11}\] \begin{table}
\begin{tabular}{c c c} \hline
**Opt.** & **Time RBTO-BESO** & **Time RBTO-SESO** \\
**Techniques** & **RBTO-BESO** & **RBTO-SESO** \\  & **(Hours)/Compliance/Iteration** & **(Hours)/Compliance/Iteration** \\  & & **FORM** \\ \hline \multirow{3}{*}{\(\beta_{t}=1\)} & Time = 2.22 & Time = 1.86 \\  & \(C=3.58\times 10^{7}\) & \(C=3.68\times 10^{7}\) \\  & \(iter=1\) & \(iter=1\) \\ \hline \multirow{3}{*}{\(\beta_{t}=2\)} & Time = 1.62 & Time = 1.33 \\  & \(C=2.61\times 10^{7}\) & \(C=2.27\times 10^{7}\) \\  & \(iter=3\) & \(iter=3\) \\ \hline \multirow{3}{*}{\(\beta_{t}=3\)} & Time = 1.37 & Time = 1.19 \\  & \(C=2.53\times 10^{7}\) & \(C=2.71\times 10^{7}\) \\  & \(iter=4\) & \(iter=4\) \\ \hline \multirow{3}{*}{\(\beta_{t}=4\)} & Time = 1.56 & Time = 1.24 \\  & \(C=2.45\times 10^{7}\) & \(C=2.67\times 10^{7}\) \\  & \(iter=6\) & \(iter=6\) \\ \hline \multirow{3}{*}{\(\beta_{t}=5\)} & Time = 1.88 & Time = 1.25 \\  & \(C=2.29\times 10^{7}\) & \(C=2.57\times 10^{7}\) \\  & \(iter=8\) & \(iter=8\) \\ \hline \multirow{3}{*}{\(\beta_{t}=6\)} & Time = 1.83 & Time = 1.25 \\  & \(C=2.15\times 10^{7}\) & \(C=2.38\times 10^{7}\) \\ \cline{1-1}  & \(iter=9\) & \(iter=9\) \\ \hline \end{tabular}
\end{table} 
Table 2: Influence of the reliability index \(\beta_{t}\) on Topology Optimization.
## 4 Discussion
### Potential Limitations and Challenges Associated with Proposed CMSs  
In discussing the potential limitations and challenges associated with the proposed CMSS, various concerns can be articulated. A primary issue is that the CMSS exhibits some limitations in validating the accuracy and operational relevance of the data it processes. This constraint may hamper the reliability and effectiveness of the system in decision-making scenarios. Additionally, the system currently lacks an automated mechanism to identify and filter out obsolete information data. Consequently, this deficiency necessitates manual intervention for data updates, which is sometimes inefficient and in certain cases could diminish the system's operational value for expert users. Moreover, the CMSS is not always able to effectively identify instances where information is incomplete, particularly in the context of critical operational data. This shortcoming may interfere with the ability of experts to make accurate and informed assessments, as critical data elements may be missing. Furthermore, the system currently lacks a cybersecurity sub-system to mitigate hacking risks. Lastly, the CMSS does not incorporate AI solutions, both in terms of thematic decomposition, where machine-learning algorithms for clustering could be utilized, and in the user interface, where Large Language Models (LLMs) embedded in chatbots could enhance interactions with users (operators or actionable actors).
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 5 Towards Algorithm Development: Propagation Concepts, Path Loss and Communication Graph
### Air-to-Ground Channel (A2G)
#### 5.3.1 Path Loss Modelling  
Further, one must consider the path loss model for air-to-ground channels. In the literature, a consistent number of air-to-ground PL models were proposed. In [67], 16 path loss models are reviewed, while in [68], a comparison of the most significant air-to-ground channel models is presented in terms of proposed scenarios, advantages and drawbacks.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.5 Impact of Research Focus on Observed Model Performance  
While intrinsic data characteristics undoubtedly influence the model performance, it is also important to study how research priorities and biases shape the model performance. The papers surveyed in this study exhibit diverse objectives and emphases, ranging from developing hybrid deep neural networks to benchmarking specific advanced TBML models against DL models, or conducting balanced evaluations across a variety of ML and DL approaches. This subsection examines whether the primary focus of the research--categorized as TBML models, DL models, or a balanced approach--affects the observed performance outcomes of the evaluated models. Figures 14 and 15 present the FPA and WRA scores, respectively, for TBML and DL models under each research focus category. The results reveal a noticeable bias in performance outcomes depending on the primary focus of the papers:  
1. Deep Learning-Focused Papers: When the primary focus of the paper is on deep learning models, DL models outperform TBML models significantly. The FPA score for DL models is 33.79% higher, and the WRA score is 0.2891 points higher than TBML models. This finding suggests that papers with a DL emphasis may introduce methodological, architectural, or experimental advantages tailored to highlight the DL performance.
2. Tree-Based Model-Focused Papers: Conversely, when papers focus on TBML models, the observed performance skews in favor of TBML models. In this category, TBML models achieve a 66.67% higher FPA score and a 0.5694 higher WRA score compared to DL models. These results indicate that TBML-focused research often optimizes conditions or design choices that favor these methods.
3. Balanced Focus Papers: In papers with no specific emphasis on either model class, TBML models slightly outperform DL models. The FPA score for TBML models is 15.23% higher, and the WRA score is 0.1771 points higher than DL models. This finding suggests that when research is conducted without bias toward a specific model class, TBML models may have a slight advantage, potentially due to their relative simplicity and robustness in a range of scenarios. Besides exploring inherent data characteristics or potential biases that may affect a model's performance, it is important to investigate the computational cost required to train a model.