esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
It is possible to obtain with the method of moments, blind estimators for signal power, noise power and signal-to-noise ratio when a deterministic real sinusoid is corrupted by additive white Gaussian noise. We have derived new estimators by providing a general formula for the moments of the observed signal of any even order that is valid for a sufficiently large number of samples observed.  
The performances of the proposed estimators have been studied through analytic derivation of asymptotic variances and Cramer-Rao lower bounds and confirmed through Monte-Carlo simulations. A general expression for the covariance matrix of the even-order sample moments has also been derived to get the asymptotic performances of the proposed estimators. Such an expression is valid asymptotically and can be useful to evaluate performances of any moment-based estimator for the same signal model, when a deterministic real sinusoid is corrupted by additive noise.  
Analytical and numerical results show the estimator of signal power \(\hat{S}\) results to be asymptotically efficient and consistent for a large range of SNR values obtained by varying the signal power \(S\) and with \(N=1\). On the other hand, at low SNRs and with \(N=1\), below approximately 10 dB, the estimator \(\hat{S}\) is not efficient, though still consistent. Similar results have been obtained with the noise power estimator \(\hat{N}\). The SNR estimator results were consistent in all the cases we studied. However, it is not efficient and we observed a performance degradation with respect to the corresponding CRLB that becomes larger as the signal power \(S\) increases and \(N=1\).  
As a general consideration, we found that the performances can be acceptable in a relatively wide range of SNRs. However, since we have found that there exists a strong dependence between \(S\) and \(N\) to be estimated and the number of observations, special care in the choice of \(K\) is required when this estimator is used in practice to get acceptable performances. Numerical examples show that in some cases the SNR estimator has very good performances and then it can be used to obtain an accuracy very close to the CRLB. The asymptotic results presented in this paper represent a useful mathematical tool that may provide useful guidance to expected performances before any practical implementation, letting any practitioner assess the performances, and then, usefulness, of the proposed estimators based on his application needs and operating conditions.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Evaluation of Surface Quality, Material Utilization, and Efficiency
### Evaluation of Surface Quality
#### 3.1.3 Developing and Validating the Regression Models  
Based on the results of the measured bead width given in Table 2, the regression model (second-order) that describes the dependence of \(B_{\mathrm{W}}\) on \(W_{\mathrm{FR}}\), \(T_{\mathrm{S}}\) and \(W_{\mathrm{V}}\) is obtained with the aid of the software Design-Expert (Version 6.0, State-Ease, Minneapolis, MN, USA, 2005) as follows:  
\[\begin{array}{ll}B_{\mathrm{W}}&=\ 9.73+1.23W_{\mathrm{FR}}-0.58T_{ \mathrm{S}}-0.019W_{\mathrm{V}}-0.12W_{\mathrm{FR}}T_{\mathrm{S}}-0.17W_{ \mathrm{FR}}W_{\mathrm{V}}-0.15T_{\mathrm{S}}W_{\mathrm{V}}\\ &\ +0.12W_{\mathrm{FR}}^{2}+0.21T_{\mathrm{S}}^{2}+0.03W_{\mathrm{V}}^{2}\end{array} \tag{1}\]  
Then ANOVA is undertaken for validating this model and the results are given in Column 1-3 of Table 3. It is seen that the \(F\)-value of the model is 33.54, much higher than \(F_{0.05}\)(9, 10) = 3.179, indicating that this model is significant at a 95% confidence level, whereas \(F\)-value of lack of fit is 1.52, lower than \(F_{0.05}\)(5, 5) = 5.05, indicating that lack of fit is not significant. Moreover, the coefficient of determination \(R^{2}\) is very close to 1, i.e., \(R^{2}\) = 0.9679, which means that the model clarifies 96.79% of all deviations. Thus, we can conclude that this obtained regression model is credible and accurate. At a 95% confidence level, only \(p\)-values of \(T_{\mathrm{S}}\), \(W_{\mathrm{FR}}\), and \(T_{\mathrm{S}}\)\({}^{2}\) term are all lower than 0.05, which indicate that only their effects on \(B_{\mathrm{W}}\) are significant. After omitting the insignificant terms, this regression model is simplified to:  
\[B_{\mathrm{W}}\ =\ 9.73+1.23W_{FR}-0.58T_{\mathrm{S}}+0.21T_{S}^{2} \tag{2}\]  
Analogously, the regression model (second-order) that describes the dependence of \(R_{\mathrm{a}}\) on \(S_{\mathrm{S}}\), \(T_{\mathrm{FR}}\) and \(C_{\mathrm{D}}\) is also obtained as follows:  
\[\begin{array}{ll}R_{\mathrm{a}}&=\ 1.60-0.23S_{\mathrm{S}}+0.063T_{ \mathrm{FR}}+0.079C_{\mathrm{D}}-0.017S_{\mathrm{S}}T_{\mathrm{FR}}+0.014S_{ \mathrm{S}}C_{\mathrm{D}}+0.010T_{\mathrm{FR}}C_{\mathrm{D}}\\ &\ +0.11S_{\mathrm{S}}^{2}+0.061T_{\mathrm{FR}}^{2}+0.027C_{\mathrm{D}}^{2} \end{array} \tag{3}\]  
The corresponding ANOVA results are given in Column 4-6 of Table 3, which prove that this regression model is also credible and accurate. Only \(S_{\mathrm{S}}\), \(T_{\mathrm{FR}}\), \(C_{\mathrm{D}}\), \(S_{\mathrm{S}}\)\({}^{2}\) and \(T_{\mathrm{FR}}\)\({}^{2}\) have significant effects on \(R_{\mathrm{a}}\) at 95% confidence level and as a result the simplified regression model is  
\[R_{\mathrm{a}}\ =\ 1.60-0.23S_{\mathrm{S}}+0.063T_{\mathrm{FR}}+0.079C_{ \mathrm{D}}+0.11S_{\mathrm{S}}^{2}+0.061T_{\mathrm{FR}}^{2} \tag{4}\]
## 6 Results  
The main findings of this study can be summarized as follows:  
Current applicability: The study positively evaluates the current applicability of VPL technology in structural design. It is found to be highly effective in creating complex geometries for building structures, particularly those with asymmetrical shapes that are challenging to design using traditional methods. This is highly effective when multiple design options are at the conceptual stage.  
Optimization: By using an algorithmic-parametric approach to design, and in particular generative design, there are great opportunities for structural optimization. The ongoing development of optimization methods, such as the use of genetic algorithms, in VPL-based design is noteworthy.  
VPL-BIM environments and integration: The development of VPL environments dedicated to structural design, along with their integration with design systems such as Building Information Modeling (BIM), is seen as a positive advancement. This integration improves the overall design process. A noticeable development is the use of VPL in BIM, 4D, 5D, and higher. This allows for interoperability in iBIM.  
Limitations in structural computing: While VPL technology is beneficial for form creation, it has some limitations in structural computing. Existing systems mainly allow for simple static analyses, and more advanced capabilities, such as automatic structural optimization, are lacking. Current optimization tasks often require the use of individual scripts and sophisticated methods.  
Designer competencies: The awareness and experience of a wide range of designers of the use of VPL in structural design need to be improved. The theoretical foundation for VPL-based design requires increased educational efforts during academic studies and professional work. This also applies to the need for preparing and updating teaching literature.  
Continuous development: VPL environments are continuously evolving, and their theoretical operating algorithms are being improved over time. This highlights the ongoing progress in VPL technology.
## 1 Introduction
Moreover, the algorithms applied for subtracting iodine content from acquisitions, called virtual non-contrast (VNC), can be used to evaluate unenhanced vessels instead of true non-contrast (TNC) acquisitions [24; 38]. At present, several software tools and clinical applications of DECT have been developed. VNC algorithms may be applied to DECT images to identify the iodine content of every single voxel and to remove the iodine component of the CT number to create images lacking in contrast material enhancement. This analysis is based on a three-material decomposition including iodine, soft tissue, and another material in relation with the anatomical constricted studied. The VNC algorithm is also able to quantify the iodine amount generating Iodine distribution maps. The selective removal of iodine content from post-contrast images, preserving calcium or other metallic devices is also performed with a good specificity. In vascular studies, this application may help to obtain a reconstruction of vessels without contrast from a post-contrast DE-CTA. It has not been well established if the VNC is of acceptable quality to replace the TNC in arterial disease evaluation. The purpose of our study is therefore to evaluate the diagnostic reliability of \"virtual non-contrast\" (VNC) images, obtained from the reprocessing of DE-CTA arterial scans acquired with third-generation scanners compared to baseline acquisition obtained without intravenous administration of iodinated contrast (TNC) in the study of PAD.
###### Abstract  
Future autonomous transportation is one of the most demanding application areas in terms of connectivity, as it has to simultaneously meet stringent criteria that do not typically go hand in hand, such as high throughput, low latency, high coverage/availability, high positioning and sensing accuracies, high security and robustness to interferences, etc. In order to meet the future demands of challenging applications, such as applications relying on autonomous vehicles, terrestrial networks are no longer sufficient and are to be augmented in the future with satellite-based networks. Among the emerging satellite networks, Low Earth Orbit (LEO) networks are able to provide advantages over traditional Medium Earth Orbit (MEO) and Geo-Stationary Earth Orbit (GEO) networks in terms of signal latency, cost, and performance. Nevertheless, several challenges exist in LEO system design, which have not been fully addressed in the existing literature. In particular, the problem of LEO-system optimization of design parameters is a multi-dimensional problem with many aspects to be considered. This paper offers a comprehensive survey of the LEO-system design parameters, of the challenges in LEO system design process, and of the optimization methods for satellite communication, positioning, and sensing applications, as well as a summarizing discussion on the design considerations for LEO-based networks to support future autonomous transportation.  
Low Earth Orbit (LEO) satellite networks; autonomous transportation; optimization criteria; multi-target design; space segment; ground segment; user segment +
Footnote †: journal: Sensors
# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 4 The Proposed Adaptive HARQ Q-Learning Model
### Q-Learning Model with the 5G NR-V2X System  
Referring to the study by [23], we adapt it as our asynchronous framework for the proposed A-HARQ architecture. Figure 4 illustrates the two primary stages: offline training and online decision making. From a global perspective, they operate on the same Q-Table. The offline training stage aims at building a basic Q-Table to provide support for the online decision stage. The offline training process is completed in advance, and its outcomes are compiled into the MATLAB logging file. The online decision stage focuses on both the processes of making the decision on what action will be performed and updating the Q-Table to help improve future decisions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action** & \(R_{\text{CQI}}\) & \(R_{\text{Distance}}\) & \(R_{\text{RBs}}\) \\ \hline A1 & \(\left(1-\frac{\text{CQI}_{i}}{15}\right)*25\) & \(\left(1-\frac{Distance_{i}}{2000}\right)*10\) & \(\left(1-\frac{RBs_{i}}{120}\right)*10\) \\ \hline A2 & \(\left(1-\frac{\text{CQI}_{i}}{15}\right)*28\) & \(\left(1-\frac{Distance_{i}}{2000}\right)*8\) & \(\left(1-\frac{RBs_{i}}{120}\right)*8\) \\ \hline A3 & \(\frac{\text{CQI}_{i}}{15}*10\) & \(\frac{Distance_{i}}{2000}*25\) & \(\frac{RBS_{i}}{120}*10\) \\ \hline A4 & \(\frac{\text{CQI}_{i}}{15}*8\) & \(\frac{Distance_{i}}{2000}*28\) & \(\frac{RBS_{i}}{120}*8\) \\ \hline A5 & \(\left(1-\frac{\text{CQI}_{i}}{15}\right)*18\) & \(\frac{Distance_{i}}{2000}*15\) & \(\left(1-\frac{RBs_{i}}{120}\right)*10\) \\ \hline A6 & \(\left(1-\frac{\text{CQI}_{i}}{15}\right)*15\) & \(\frac{Distance_{i}}{2000}*18\) & \(\left(1-\frac{RBs_{i}}{120}\right)*8\) \\ \hline A7 & \(\left(1-\frac{\text{CQI}_{i}}{15}\right)*15\) & \(\frac{Distance_{i}}{2000}*10\) & \(\left(1-\frac{RBs_{i}}{120}\right)*10\) \\ \hline A8 & \(\left(1-\frac{\text{CQI}_{i}}{15}\right)*10\) & \(\frac{Distance_{i}}{2000}*15\) & \(\left(1-\frac{RBs_{i}}{120}\right)*8\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The weight factor of the parameter.
## 2 Review and Analysis
### Vacuum Solar Panels  
Let us consider vacuum solar panels (VSP) [20]. A modern VSP is usually equipped with 10-30 vacuum tubes located parallel to each other. The number of VSPs depends on the needs of the consumer, but usually, one or two are enough; in some cases, four or more are needed (depending on the heat load and the direction of use of the accumulated heat).  
The conversion of solar radiation energy into heat is carried out due to vacuum tubes, which are the most effective from the point of view of obtaining radiation heat, and the received heat is transferred to water, which is directly used by the heat consumer, or for the heat-carrying medium, which is used to heat water for heat-supply systems [21].  
There are four main types of vacuum tubes with which the panels are equipped (Figure 2) [20], (Figure 3) [21].  
Figure 3: Scheme of a vacuum solar panel. 1—vacuum; 2—aluminum foil; 3—copper heat pipe; 4—hermetic plug; 5—working circuit; 6—working fluid; 7—condensed steam.  
Figure 2: Types of vacuum tubes: (**a**) simple glass vacuum tube; (**b**) 3-core solar vacuum tube; (**c**) heat tube; (**d**) super heat tube.  
Water tube. Each solar panel vacuum tube consists of two glass tubes, between which a vacuum is created to minimize heat loss. The outer tube is made of transparent, strong borosilicate glass that can withstand hail with a diameter of 25 mm. The inner tube is also made of translucent borosilicate glass covered with a special selective coating that provides heat absorption with minimal reflectivity. Under the influence of solar radiation, the water in the tubes heats up and enters the heater, being replaced by cold water from the heater. Damage to the tube results in the leakage of the heat-carrying medium from the system, and seasonal use significantly reduces the efficiency, but these tubes have a much lower cost compared to thermal ones.  
The heat tube consists of two tubes: an outer glass tube made of borosilicate glass and an inner copper tube with a small content of light-boiling liquid. When heated, steam rises to the tip (condenser) of the heat pipe. Having given heat to the water moving through the pipeline, it condenses and returns back through the heat pipe. This is how the cycle is carried out. If one of the tubes is damaged, the collector continues to work, because heat transfer occurs through the copper \"sleeve\" of the receiver, due to which the heating circuit is separated from the tubes. The process of replacing the tubes is very simple, and there is no need to remove the antifreeze from the circuit of the heat exchanger. The easy replacement of the tube and the possibility of system operation in the case of its damage, as well as the use of a copper tube with a small content of easily boiling liquid, increases the efficiency compared to the water tube.
## 2 Evolution of Maritime Communication Networks
### Five/Sixth-Generation-Enabling Technologies
In maritime domains, ISAC-equipped nodes can sense vessel positions, sea surface states, or object proximity while maintaining robust connectivity, which is crucial for safety and navigation. In the computation domain, advancements such as Mobile Edge Computing (MEC) and task offloading enable near-real-time processing of data onboard ships, UAVs, and buoys, reducing dependence on high-latency satellite links [38; 39]. Techniques like federated learning and digital twins enable decentralized, privacy-preserving AI and simulation-driven planning, which is essential for remote and autonomous operations at sea [40]. On the one hand, MEC nodes deployed on ships, UAVs, or sea platforms reduce reliance on distant cloud infrastructures by enabling local, low-latency processing. In this concept, dynamic task offloading strategies optimize latency, energy, and reliability trade-offs, allowing maritime assets to delegate computation to edge or cloud resources. On the other hand, digital twins provide real-time virtual replicas of maritime assets or fleets, enabling simulation-driven decision-making for predictive maintenance, navigation, and energy optimization. AI-based solutions contribute to adaptive control, resource optimization, and decision-making in MCNs [41]. These include reinforcement learning for dynamic routing and power control, AI-based spectrum sensing for interference avoidance, and energy management systems to extend the operational lifetime of UAVs and AUVs. Federated learning enables privacy-preserving collaborative learning across ships, buoys, or UAVs without sharing raw data, which is particularly important for maritime stakeholders across international boundaries. Reinforcement learning agents can adaptively learn optimal resource allocation (e.g., routing, power control) based on environmental feedback, especially under uncertain or dynamic sea conditions. Regarding the semantic communications aspect, instead of transmitting raw bits, 6G leverages semantic and goal-oriented communication, where meaning and intent are prioritized. This is particularly beneficial for bandwidth-constrained scenarios like underwater acoustic communications or satellite uplinks. By embedding intelligence into the network stack, these tools provide the scalability and agility required for large-scale maritime deployments. Collectively, the integration of these technologies positions 6G as a transformative enabler for future maritime networks, helping overcome bottlenecks and enabling a new generation of smart, autonomous, and resilient maritime services.
## 3 Machine Learning for IRS-Assisted Communication Systems
### Supervised Learning for IRS-Enhanced Communication Systems
The authors in [82] proposed an RIS-assisted drone communication network where the drone is connected to the base station via IRS. An SL-based approach was implemented to find the consistent communication link between the base station and drone. The study in [83] proposed an ML-based beam management framework for IRS-assisted mmWave networks. Environmental and mobility awareness were used to maximize the beamforming accuracy.
# A Hybrid Autoencoder Network for Unsupervised Image Clustering
## 5 Discussion  
Image clustering is an important issue in the fields of machine learning and computer vision. Traditional methods, such as \(k\)-means or the Wald method, cannot perform well due to their restricted capability with high-dimensional pixels. With the successful introduction of AE-based models such as SAE, CAE, and AAE, the clustering accuracy is dramatically increased. However, a single model may not have sufficient capability to extract all important feature representations from images, since each algorithm has its specialty for extracting a specific feature space. Hence, we consider a hybrid AE-based algorithm to extract a more complete feature space of images.  
The characteristics of the proposed algorithm have the following advantages. First, we consider three distinct AE-based algorithms to extract the feature space as abundantly as possible. Second, the proposed algorithm considers the deep structure of the models to capture high-level feature representations. Furthermore, the proposed algorithm provides flexibility for further applications, such as classification or regression. For example, we can add a density layer behind the hidden code and use softmax or a linear function to consider the classification or the regression problem, respectively.  
In addition, the results of the numerical experiments show that the proposed algorithm outperforms other methods with respect to the criteria of ACC, NMI, and ARI. Furthermore, although the proposed algorithm is combined with CAE and AAE, the performance of the proposed algorithms is better than that of CAE and AAE and justify the reason why we should consider a hybrid algorithm. However, the parameters of the model also significantly increase, and more time is needed to obtain convergent results. Future study may consider more different AE-based algorithms, such as the denoising autoencoder, sparse autoencoder, etc., to survey which combinations would be best for a specific dataset.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.3 Pick and Place  
In [71], the authors introduce a technique to allow a collaborative robot to grasp items for a pick-and-place task without requiring prior item position knowledge of the objects involved. The authors aimed to create a learning system capable of adjusting to variations in the robot's setup and unexpected alterations in the environment. The authors effectively deployed their approach in both simulated and real-world environments, enabling future experimentation on a physical robot. Their approach utilizes a camera for visual input, and the DQN algorithm determines the best grasp positions of different items. This work tackles the constraints of typical pick-and-place robotic systems that frequently rely on human programming or predefined models for item manipulation.  
The authors in [71] used the DQN RL algorithm to train an agent to control pick-and-place tasks, where the agent learns from rewards for successful grasps and penalties for unsuccessful ones. The authors used the \(\epsilon\)-greedy strategy, which balances exploration and exploitation. An ROS played a vital role in the system design by enabling seamless communication among various components and simplifying data exchange through ROS topics and services. As shown in Figure 9, the process is divided into two processes: the learning process, located on the right side of the diagram and which includes the modules, and the execution process, located on the left side and which includes the ROS drivers. This technique helps the robots to handle a larger variety of objects in dynamic situations where their locations may vary. This approach provides the flexibility and adaptability of the robotic system, allowing it to handle diverse objects in dynamic scenarios.  
The above-mentioned work effectively connects DRL with robotic manipulation, showcasing the potential for future advancements in industries like manufacturing. Implementing this strategy in real-world applications, especially with complex environment and unpredictable conditions, could lead to improved automation efficiency and reduced reliance on human labor in pick-and-place operations. Adding more complex perception  
Figure 9: RL architecture for pick-and-place application. For more information, please refer to Table 4.  
systems, such as various perception inputs, could help robots better understand their environment and perform tasks with more accuracy and flexibility.