esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model  
The UWSN routing protocols usually involve four phases: network deployment, neighbor discovery, relay node selection, and communication phase. The scope of this work is limited to relay node selection by using decision making. Figure 1 shows a node deployment model (3D UWSN), where nodes are located randomly at various locations and depths, except for the gateway (GW) node that resides on the surface (random location too). After the deployment task, we can consider for analysis purposes that nodes will remain static (anchored to the seafloor or fixed to a buoy) and be placed far enough to prevent sending the same packet from multiple nodes to the sink or avoid undesired overlaps (minimum distance between two adjacent nodes or neighbor nodes).  
In the creation of a path from a node to the gateway, the next forward node selection has been performed by taking into account input parameters such as distance between nodes, number of hops to reach the GW, number of neighbor nodes, and the transmission range by applying a fuzzy process (based on FAHP technique) that will be explained later. As a result, the last node of the path chooses as next relay node the node having the highest score (weights in FAHP) among the rest of candidate nodes.  
Figure 1: Random node deployment in 3D UWSNs. Data: 25 nodes (including gateway on surface), deployment zone: 10 \(\times\) 6 \(\times\) 10 km, minimum separation between nodes: 1 km.  
Energy Consumption Model  
The sensor nodes are usually powered through batteries, and it is inconvenient to replace or recharge them when they are depleted, so designing an efficient routing scheme is a key challenge. The energy used to send the data from one node to another node over distance d is given by  
\[E_{d}=E_{t}(d)+E_{r}(d) \tag{1}\]  
where \(E_{t}(d)\) is the transmission energy and \(E_{r}(d)\) is the reception energy. Both energies are affected by certain parameters given in the following equations:  
\[E_{t}(d)=k\big{(}E_{\text{elec}}\ +\varepsilon_{\text{amp}}\ \big{)}+P_{t}\frac{k}{ \alpha\cdot B(d)} \tag{2}\]  
\[E_{r}(d)=k(E_{\text{elec}}\ +E_{DA})+P_{r}\frac{k}{\alpha\cdot B(d)} \tag{3}\]  
where \(P_{t}\) and \(P_{r}\) are the transmission and reception powers, respectively; \(k\) indicates the packet length; \(E_{\text{elec}}\) is the electronic energy consumed; \(\varepsilon_{\text{amp}}\) is the amplifier energy; \(E_{DA}\) is the energy consumed throughout the data aggregation process; \(\alpha\) is the modulation efficiency; and \(B(d)\) is the available bandwidth.
## 5 Results
### Ground-Based Direct Validation
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PROBA-V SA v1.0** & **MCD43A3 C6** \\ \hline
**\% optimal (GCOS)** & 15.5 & 23.7 \\
**\% target (C3S KPI)** & 28.6 & 45.5 \\
**\% threshold** & 44.2 & 58.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Compliance matrix of the direct validation of the C3S PROBA-V SA v1.0 and MODIS MCD43A3 C6 blue-sky albedo satellite retrievals (best quality pixels, Table 3) versus ground values (N = 643) from 20 GBOV stations during the 2014–2018 period. N stands for the number of samples. Figure 11: Direct validation of the best quality pixels of the C3S PROBA-V SA v1.0 (**a**) and MODIS MCD43A3 C6 (**b**) blue-sky albedo satellite products versus the ground measurements (RMs) at 20 GBOV stations during the 2014–2018 period. The continuous black lines correspond to the 1:1 lines and the dashed lines correspond to the optimal (GCOS uncertainty requirement), target (C3S KPI) and predefined threshold levels. The red lines correspond to the Major Axis Regression (MAR).
# Buckling Analysis of CNTRC Curved Sandwich Nanobeams in Thermal Environment
## 6 Conclusions  
A novel quasi-3D higher-order shear deformation theory was proposed in this work to study the buckling response of CNTRC curved sandwich nanobeams for the first time. The problem was tackled theoretically, based on a Galerkin procedure, accounting for different boundary conditions and size-dependent effects. The material properties of CNTRC sheets were here assumed to be temperature-dependent, in agreement with the Toulouian principle.  
A parametric study was performed systematically, to check for the influence of some significant parameters on the buckling response of CNTRC curved sandwich nanobeams, namely the CNTs reinforcement patterns and the nonlocal and length scale parameter, together with the geometric parameters. Based on the parametric investigation, it seems that the critical buckling load decreases for an increased temperature because of a global reduction in the stiffness of CNTRC curved sandwich nanobeams. Possible size effects can reduce the overall stiffness of CNTRC curved sandwich nanobeams, whereby the dimensionless critical buckling load decreases for an increased nonlocal parameter \(\mu\). Unlike the nonlocality effect, an increased length scale parameter \(\lambda\) leads to an increased buckling stability. More flexible elastic foundations and boundary conditions can reduce significantly the overall structural stability, which is also largely affected by a varying core-to-face sheet thickness ratio \(h_{c}/h_{f}\), opening angle \(\alpha\), and CNT volume fractions. The results obtained by neglecting the effect of thickness stretching (\(\varepsilon=0\)) are perfectly in line with predictions from the literature, thus confirming the good accuracy of the proposed method to handle similar problems. The results obtained in this work, could represent valid benchmarks for engineers and researchers to validate different numerical methods as well as for practical design purposes of nanostructures.  
Conceptualization, A.A.D., M.S.A.H., B.K., R.D. and F.T.; Data curation, A.A.D. and B.K.; Formal analysis, M.S.A.H., B.K., R.D. and F.T.; Investigation, A.A.D., R.D. and F.T.; Methodology, M.S.A.H., M.A.E., R.D. and F.T.; Supervision, R.D. and F.T.; Validation, A.A.D., B.K., M.A.E., R.D. and F.T.; Writing--original draft, A.A.D., M.S.A.H., B.K. and M.A.E.; Writing--review & editing, R.D. and F.T. All authors have read and agreed to the published version of the manuscript.  
This research receive no funding.  
Figure 9: Effect of thickness ratio and elastic foundation on the dimensionless buckling load.
## 1 Introduction
[PERSON] et al. used limestone or zeolite to remove heavy metals from constructed wetlands with a removal efficiency of 98% [7]. Some studies have shown that construction bricks have a good anchoring effect on heavy metals [8], but the research is blurry with regards to the chemical form and content of heavy metals in the media, and the stability after fixing is worth exploring. There are also some studies to remove heavy metals from water using hazelnut and almond shells [9], but these adsorbents are present in small quantities. In addition, there is little research on the existing form of heavy metals after being fixed by the percolation facility and after the environmental risk has been evaluated. Some studies have shown that the shape of the breakthrough curve is affected by the linear velocity, the concentration of metal ions in the solution, and the height of the bed [10; 11; 12; 13; 14]. A study found that continuous column experiments can provide good references for environmental remediation applications such as infiltration facilities like permeable pavements [15]. Various models such as the Adams-Bohart model and the Thomas model have been applied to analyze and interpret the experimental data and predict the effects of different process variables on the efficiency of the adsorption process [16]. [PERSON] et al. found that the Adams-Bohart model is essentially the same as the Thomas model [17]. However, heavy metal removal by wastesbricks should be explored and the proper fitting models should be evaluated in order to better predict the removal behavior. In the present study, the cost-effective lime sand bricks were selected as the adsorbent, their removal efficiency towards Cu(II) in aqueous solution was systematically studied through the static batch experiments and fixed bed continuous column experiments. The objectives of this study are (i) to study the adsorption mechanism of Cu(II) by lime sand bricks using batch experiments; (ii) to examine the effects of several process parameters such as the bed depth, solution concentration and residence time on removal efficiency of Cu(II); (iii) to analyze the effect of different process variables on the removal of Cu(II) by lime sand bricks and their applicability with the fitting model; (iv) to analyze the chemical forms of copper in the medium of lime sand bricks for the evaluation of the environmental risk.
###### Abstract
This advanced capability helps systems accurately identify and locate surrounding vehicles, pedestrians, and obstacles in real time [8]. Three-dimensional LiDAR also demonstrates exceptional performance in the detection range, typically identifying objects hundreds of meters away. It is a crucial capability for high-speed vehicles operating on highways or in complex urban environments. In autonomous driving scenarios, the early detection of distant obstacles and traffic conditions helps vehicles better adjust speed and route, significantly improving overall safety margins. Therefore, this study develops an autonomous driving solution adapted for 3D-LiDAR sensors. Three-dimensional LiDAR outputs point cloud data, a three-dimensional format fundamentally different from conventional visual images. In this study, an algorithm based on PointNet combined with an attention mechanism is used in the processing stage of the point cloud data to perceive the environment and feed refined environmental data into the reinforcement learning framework. The scientific novelty and contributions of this research in autonomous driving technology manifest in several significant aspects:  
1. This study demonstrates that DDPG reinforcement learning networks can be effectively trained and applied to autonomous driving technology, achieving unsupervised learning tasks with 3D-LiDAR sensors. 2. In the processing stage of point cloud data, this study adopts the classical PointNet-based algorithm to directly process the point cloud data instead of voxelization and combines with self-attention to optimize the data, which are inputted into the reinforcement learning network to cope with the challenge of 3D-LiDAR data in a realistic environment. 3. After the implementation of unsupervised autonomous driving in a simulator, this study successfully built and deployed a realistic 1/10-scale experimental vehicle, named F1 tenth, to complete autonomous driving tasks in real environments, advancing real-road condition applications and bridging the gap between simulation and reality. The remainder of this article is structured as follows. Section 2 provides a review of related research on applying reinforcement learning for autonomous driving and the implementation of 3D-LiDAR in autonomous driving. Section 3 presents the detailed methodology of the DDPG reinforcement learning network implementation and point cloud data processing solutions, including architectural decisions and optimization strategies. Section 4 states experimental processes and results in both simulated and real environments. Section 5 summarizes the research and proposes future directions for related work, including potential improvements and extensions. This study aims to advance the practical application of unsupervised learning solutions in autonomous driving technology, contributing to the goal of safer and more efficient transportation systems.
## 7 Results and Analysis
### Dataset Limitations and Biases  
The DAWN Dataset and OpenRAN Gym, while instrumental in simulating adversarial conditions within O-RAN environments, exhibit inherent limitations that require critical examination. The DAWN Dataset, in particular, focuses on predefined adversarial scenarios such as 15% data poisoning or signaling storms. While these scenarios offer valuable insights, they only represent a narrow subset of the broader threat landscape. For example, the dataset does not encompass sophisticated multi-vector attacks, such as adversarial perturbations combined with E2 interface exploitation, nor does it account for zero-day exploits that may target emergent vulnerabilities in AI-driven RAN components. Additionally, although the dataset supports 1 ms temporal granularity, its synthetic traffic traces may oversimplify real-world conditions by failing to capture the burstiness, spatial variability, and user mobility patterns observed in dense urban deployments or highly dynamic radio environments. Consequently, critical edge cases such as sudden traffic surges during large-scale events or emergency situations may be underrepresented.  
Similarly, the OpenRAN Gym's emulated environment abstracts many hardware-specific nuances that can influence system behavior and resilience. For instance, the platform simplifies latency variations that are vendor-dependent, particularly those affecting fronthaul links, and overlooks hardware constraints such as thermal throttling on Distributed Unit (DU) energy consumption. Despite its adherence to O-RAN specifications, the platform's scalability assumptions--such as homogeneous xApp deployments--may inadequately reflect the operational heterogeneity inherent in large-scale O-RAN deployments where mixed-vendor equipment and diverse configurations are commonplace. As a result, the OpenRAN Gym may inadvertently produce optimistic estimates of cross-domain interoperability and system robustness.
## 2 Related Works
### Routing Strategies in Pub/Sub Systems for MANET  
In recent years, many works [11, 12] have been developed Pub/Sub systems in mobile _ad hoc_ networks. For example, in [11], [PERSON] _et al._ propose a structure-less routing protocol, where a distributed implementation of the dispatching service is realized by running a broker on each mobile node of the MANET. Unlike traditional cases, that paper leverages off the broadcast communications available in a MANET to forward events to multiple destinations and lets each receiving broker autonomously decide if and when re-forwarding the event on the basis of an estimation of its proximity to potential subscribers for that event. However, event delivery delay of that protocol is very long. Furthermore, continuous broadcasting messages in the network would cause large communication overhead and poor network performance.  
In [12], [PERSON] and [PERSON] discuss a model with event sources, event brokers and event displays, and modify it to be feasible in mobile networks. However, this method is based on the assumption that broker servers can always be organized into a multicast tree. An optimal Pub/Sub tree is proposed in [13] for routing events from the source to all interested recipients. Since each node knows its successors' subscriptions, flooding events can be avoided. However, the scalability of that approach is limited. As the network scale is growing or nodes' subscriptions change over time, tree maintenance incurs costs that are too high to be practical in the reality.  
Moreover, EMMA [14] has focused on reliable event delivery by adopting an epidemic-style mechanism in a mobile ad hoc network. There are many unnecessary deliveries of events in that approach since that forwarding style does not consider the relationships of each node's subscriptions. [PERSON] _et al._[15] propose a scheme to build and maintain event delivery structures for content-based routing in mobile ad hoc networks. They adopt and extend the MAODV (Multicast Ad hoc On-Demand Distance Vector) tree maintenance mechanism [16].
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 2 Related Work
### Vision-Based Waterline Detection  
In general, vision-based waterline detection refers to the task of applying computer vision techniques to analyze image data with the purpose of estimating the boundary line of the water area (i.e., identifying a waterline) from the images obtained by various sensing devices [25; 26]. At present, in the maritime domain, synthetic aperture radar (SAR) has been also the most commonly used sensing device to obtain image data, except for optical sensors such as a camera. Due to its advantage of being independent of weather conditions (e.g., the images can be obtained day or night, even in stormy weather and through clouds), there are numerous waterline detection applications via SAR, such as [27]. Nevertheless, since SAR usually has to be mounted on a moving platform such as an aircraft or spacecraft, there exist limitations to its application for the USVs, especially for the low-cost or lightweight ones within inland waters. What is more, SAR is usually not good at obtaining high-revolution images at short ranges, which is not beneficial to aiding the USVs to carry out some specific missions within cramped inland water, such as navigation or obstacle avoidance.  
Moreover, the waterline usually means the boundary line distinguishing the water area and non-water area. According to the difference of water areas, various waterlines fall into two broad categories, i.e., the coastal waterline and the inland waterline, which can be further detailed as coastline (or shoreline), sea-sky-line, water-land-line, water-sky-line, and so on. Compared with the coastal waterline, the inland waterline (e.g., lakeshore, riverside) usually presents more visual complexity, such as its own irregularity and versatility, as well as the diversity and dynamics of its surroundings. Most current works [2; 3; 4; 5] on vision based waterline detection focus on the coastal waterline, and only a few concern about the waterline within inland water, i.e., the inland waterline, such as [6; 7; 8].  
Accordingly, to facilitate the USVs sailing in inland water with a certain visual skill, in this paper, we focus on the inland waterline detection making use of general digital cameras for USVs.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 3 Problem Statement and Design Considerations  
In this section, we present the basic principles of machine learning and the challenges associated with its applications in IoT networks. The proposed approach to overcoming these challenges and the associated design considerations are also explained.  
In a typical machine learning scenario, as shown in Figure 2, the data from all the sources (sensors) are sent to the central server via a gateway, where the model resides. The model is trained on the collected data from various sources. Once the model is trained, the test data are sent to the central server where decisions or predictions are made. It can be inferred that, in both the training and testing phases, the data must be collected at the central server. In the IoT ecosystem, doing so poses some challenges that are discussed in the following subsection.
# Localization of Radio Sources Using High Altitude Platform Station (HAPS)
## 1 Introduction
### Related Research  
Various studies have been conducted on radio source localization. In a study in [5], a method using UAVs, such as drones, estimates the location of radio sources by leveraging pre-trained data, including signal strength. This approach achieves high localization accuracy even in environments with poor line of sight to the transmission source. Additionally, in a study in [11], a location fingerprinting method was used for localization in a simulated outdoor environment, demonstrating that optimizing the UAV's trajectory improves estimation accuracy. However, these studies focus on localization within outdoor environments or university campuses, which does not address the challenges faced by the current DEURAS radio monitoring system in large-scale search areas. Moreover, large-scale exploration requires an extensive amount of training data.  
Interestingly, ref. [12] explored the integration of artificial intelligence (AI) into radio localization techniques for outdoor environments. The authors introduced LocUNet, a convolutional neural network designed for real-time outdoor localization using received signal strength (RSS) from base stations. Unfortunately, machine learning-based approaches require high computation and storage complexity for both the training and estimation phases, which is not suitable for the large-scale scenarios considered in this paper.  
In the studies in [13; 14], experiments were conducted on the localization of ground-based radio sources using high-altitude unmanned aircraft. Both studies utilized direction-of-arrival (DOA) estimation and demonstrated high accuracy. However, these experiments were conducted in environments with clear line of sight and no obstacles. Additionally, the maximum altitude tested was only about 3 km, leaving the effectiveness of localization at higher altitudes unverified.  
In contrast, our previous research [15] explored ground-based radio source localization using HAPSs at an altitude of 20 km. However, this study was limited to simulations with a single transmission source and did not consider multiple sources. Furthermore, although multiple HAPS units were used for localization, it is unlikely that multiple HAPSs would be available simultaneously for a single transmission source when considering their coverage as communication base stations. Therefore, in this study, we focus on localization using a single HAPS.  
As outlined above, previous research has not thoroughly addressed localization in wide-area, obstacle-dense environments. To address this gap, this paper demonstrates through simulations that using a HAPS enables high-accuracy localization, even in urban environments with dense buildings and large coverage areas.
## 5 Prediction Modeling and System Optimization  
Fortunately, by studying the dynamic characteristics and the degradation law of the system and combining the actual operation experience, it is found that there are corresponding relations among exhaust flow, inlet and outlet pressure, inlet temperature, and compressor load. The load prediction model can be constructed by using the inlet and outlet pressure \(P_{i}\), the inlet and outlet temperature \(T_{i}\), and the exhaust flow \(Q\). Under capacity regulation condition, the performance of the SCRS can be predicted, and the estimated value \(\overline{\eta}_{i}\) of the actual operating load of the cylinder can be obtained. Through the estimated value of the load \(\overline{\eta}_{i}\), the control parameters of the SCRS can be optimized online.