esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work  
The performance of the opportunistic spectrum access (OSA)-based I-CRNs has been lavishly published. A substantial amount of research has been dedicated to the CR architecture, to its operating principle, mobility, and spectrum sensing performance both in perfect and realistic imperfect sensing scenarios [28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. Nevertheless, analytical modelling of the spectrum utilization due to dynamic nature of the PU is a challenging task [30; 31; 32; 33; 34; 35; 36; 37]. In our own prior contribution [19], we studied the improvement of the sum rate by optimizing the sensing operating point, e.g., the sensing threshold, in conventional orthogonal CR networks, because sensing errors, i.e., missed-detection events and false alarms, cannot be avoided in real-time spectrum sensing. We also investigated the optimal sensing operating points, adaptive to different sensing parameters, to maximize the achievable sum rates of the considered networks. [PERSON] et al. [30] discussed the operation of the CR networks over a dynamic bandwidth in both time and space. The integration of users in CRN requires SUs to be capable of efficient sensing and keeping precise track of the primary transmissions. The unique characteristic of SUs co-existing with PUs is the dynamic nature of the spectrum availability. The CRN operates over a dynamic bandwidth (i.e., both in time and space) that inherently forms clusters. Hence, it is important to consider both the spatial and temporal dimensions of spectrum opportunities on the basis of PU activities. The network topologies, based on the interaction between the integrated users, depend on the physical proximity and availability of the spectrum holes. Specifically, [PERSON] et al. [34], and [PERSON] et al. [37] discussed the utilization of spectrum for different network topologies. For the communicating SU nodes, the packet must be transmitted to the corresponding receiver without any significant error, along with the correct detection of spatial-temporal spectral opportunity. However, the transmitted packet may be severely damaged by the poor channel conditions, thus degrading the transmission reliability. Moreover, [PERSON] et al. [31] analyzed the impact of spectrum handoff on the performance of considered CRN. Spectrum handoff occurs when a channel is being utilized by a SU and a PU appears. Thus, the SU needs to vacate the channel and migrate to another channel in order to transmit a packet successfully. In a nutshell, spectrum-aware communication in CRNs requires timely and accurate detection of the spatial-temporal spectrum access opportunities, efficient spectrum mobility (spectrum handoff) when required, and successful transmission of the SU and PU packets.
###### Abstract  
This paper introduces a direct predictive torque control (DPTC) method to minimize the torque ripple and copper loss of the switched reluctance motor (SRM). Generally, there is a problem with torque ripple during the SRM's commutation. In this method, the commutation optimization is provided by a direct predictive torque control algorithm. Firstly, the reachable range of phase torques is predicted and the boundary of two continuous phases is modified. By dividing the torque range directly to determine the torque allocation set, the suppression of torque ripple becomes simple. Secondly, considering the optimization problem of copper loss during commutation, a cost function only related to the phase current is constructed. Further, the minimization of copper loss can be achieved by solving the cost function, and the work of setting weight parameters is not required. Finally, the proposed DPTC method is tested by simulation and experiment in a three-phase 12/8-pole SRM drive system and the results are compared with the existing predictive torque control methods. The results show that the proposed method has less torque ripple and copper loss, which effectively improves the torque control performance.  
switched reluctance motor (SRM); direct predictive torque control (DPTC); commutation optimization; torque ripple and copper loss
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 2 GNSS and IMU Integrated Filter Design
### Error State Kalman filter
#### 2.2.4 The ESKF Prediction Process  
The error states and the covariance prediction process can be obtained as  
\[\begin{split}\widehat{\mathbf{\eta}}\widetilde{\mathbf{\chi}}_{k+1}^{-}& =\mathbf{F}_{\pm k}(\widetilde{\mathbf{\chi}}_{k},\mathbf{u}_{mk})\mathbf{f}\mathbf{\chi}_{k}\\ \widetilde{\mathbf{P}}_{(k+1)}^{-}&=\mathbf{F}_{\pm k}\widetilde{\mathbf{P}}_{k}\mathbf{F}_{ \pm k}^{T}+\mathbf{\tau}_{\pm k}\mathbf{Q}_{\mathbf{w}}\mathbf{F}_{wk}^{T}\end{split} \tag{18}\]  
where \(\mathbf{Q}_{w}\) represents the noise covariance matrix of the form:  
\[\mathbf{Q}_{w}=\begin{bmatrix}\mathbf{W}_{v}&0&0&0\\ 0&\mathbf{W}_{\theta}&0&0\\ 0&0&\mathbf{W}_{\alpha}&0\\ 0&0&0&\mathbf{W}_{\Omega}\end{bmatrix}. \tag{19}\]
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 3 Materials and Methods
### System Model and Problem Formulation
#### 3.1.2 Security Requirements  
Mutual authentication, between the drone and the approved User/Controller, which authenticate each other's identities before creating a secure communication channel, must be an aspect of the IoD authentication protocol. It must ensure data integrity to prevent transmitted information from being tampered with and offer data confidentiality by encrypting all data being communicated to prevent unlawful access by eavesdroppers. To avoid denial of participation in communication by the parties, non-repudiation should also be included in the protocol. Forward secrecy should be utilized for further security in a way that past communication is not affected by the compromise of current session keys. Lastly, to make the communication of the IoD both reliable and of integrity, the protocol should be resistant against commonly known attacks like replay, man-in-the-middle, and impersonation attacks.
# Fabrication and Photo-Detecting Performance of 2D ZnO Inverse Opal Films
## 1 Introduction  
Two-dimensional (2D) inverse opals (IOs) with periodical porous structures always have large surface area and unique optical properties [1]. Therefore, they have demonstrated great potential in a variety of application fields, such as sensors, light-emitting diodes (LEDs), electrochromic devices, and surface-enhanced Raman spectroscopy (SERS) [2; 3; 4; 5].  
Basically, fabrication of 2D IOs often requires multiple steps: (1) formation of a monolayer colloidal crystal (MCC) template film; (2) infiltration of precursors or matrix materials into the voids of the template film; (3) removal of the colloidal templates by dissolution or calcination. As recently reviewed in detail by [PERSON] et al. [6], MCC templates can be achieved by a variety of methods, including dip-coating, spin-coating, slope self-assembly, etc. [7; 8; 9; 10; 11; 12]. Regarding infiltration and replication strategies, evaporation deposition, electrochemical deposition, and solution/sol-based infiltration methods are mainly included [13; 14; 15]. Among them, the solution/sol based method has been widely used for its advantages: low-cost, simplicity, and versatility. To date, 2D IOs with various chemical compositions, including Ag, In\({}_{2}\)O\({}_{3}\), ZnO, CeO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), etc. have been successfully achieved through this method [5; 16; 17]. However, there still remain some problems to be solved. For instance, poor regularity and cracks may arise from the infiltration step or the subsequent calcination process due to solution flow, shrinkage, or change in surface tension.  
In order to overcome these drawbacks, a novel strategy that uses floating MCCs as templates has been developed [4; 18; 19]. Since template colloids are loosely connected with each other, prior-prepared crystalline nanoparticles can be easily introduced into the interstitial spaces without damaging the template. Floating MCCs can also serve as masks for in situ interfacial reaction to fabricate 2D IOs, including PbS, Ag, and CaCO\({}_{3}\) nanonets [20].  
Oil-water interfacial assembly has been proven to be an effective way of obtaining high-quality floating MCC templates [21]. Very recently, we fabricated Ni\({}_{x}\)Mg\({}_{1.7}\)O nanobowal array photo-detecting devices via the oil-water interfacial assembly of polystyrene (PS) colloids and metal oxide precursors, followed by calcination [22]. However, for the device constructed by Ni\({}_{x}\)Mg\({}_{1.7}\)O, the photocurrent and selectivity still need to be improved.  
Here, we further extend the oil-water interfacial assembly method to fabricate a PS/citric acid (CA)-zinc acetate (ZA) precursor monolayer film, and crack-free 2D ZnO IO films were obtained after calcination of the PS/precursor film. Moreover, photo-detecting devices constructed on such films showed high photocurrent, excellent spectrum selectivity and reversible response to ultraviolet (UV) lights.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
###### Abstract  
The 3 rd Generation Partnership Project (3 GPP) evolution of mobile communication technologies from 5G to 6G has been a transformative journey spanning a decade, shaped by six releases from Release 15 to Release 20. This article provides a retrospective of this evolution, highlighting the technical advancements, challenges, and milestones that have defined the transition from the foundational 5G era to the emergence of 6G. Starting with Release 15, which marked the birth of 5G and its New Radio (NR) air interface, the journey progressed through Release 16, where 5G was qualified as an International Mobile Telecommunications-2020 (IMT-2020) technology, and Release 17, which expanded 5G into new domains such as non-terrestrial networks. Release 18 ushered in the 5G-Advanced era, incorporating novel technologies like artificial intelligence. Releases 19 and 20 continue this momentum, focusing on commercially driven enhancements while laying the groundwork for the 6G era. This article explores how 3 GPP technology evolution has shaped the telecommunications landscape over the past decade, bridging two mobile generations. It concludes with insights into learned lessons, future challenges, and opportunities, offering guidelines on 6G evolution for 2030 and beyond.  
3 GPP; 5G; 6G; cellular networks; communications standards +
Footnote †: journal: _Telecom_  
2025  
3 GPP; 5G; 6G; cellular networks; communications standards
## 2 Literature Review and Methodology  
In this study, the increasing scientific interest in magnetic gear systems is examined through a structured analytical process from both thematic and methodological perspectives.  
During the literature review, a comprehensive search was conducted using major academic databases, such as Google Scholar, IEEE Xplore, ScienceDirect, Web of Science, and Scopus. The search employed keywords and logical combinations, including \"magnetic gear,\" \"coaxial magnetic gear,\" \"flux modulation gear,\" \"magnetic gear torque density,\" \"magnetic losses,\" and \"magnetic gear material comparison.\" Although the review focused primarily on studies published after the year 2000, earlier foundational research works were also included. The scope of the review encompasses original research articles, review papers, and publications involving experimental and numerical analyses.  
The collected literature was categorized under four main thematic areas based on a content-oriented analysis. The first category addresses topological diversity, analyzing modulated and non-modulated magnetic gear structures, magnet and rotor configurations, and carrier geometry design parameters. The second category focuses on material-based evaluations, comparing the magnetic and thermal characteristics of core materials such as electrical steels, soft magnetic composites (SMCs), and cobalt-based high-performance steels. Additionally, the suitability of these core materials for manufacturing methods, such as machining, wire EDM, investment (precision) casting, and powder metallurgy, was evaluated in terms of surface machinability, tolerance to geometric complexity, and impact on production precision, providing a more comprehensive analysis of their applicability in magnetic gear systems. The Section 3 discusses the performance and loss analysis, with particular attention to volumetric torque density (VTD), core and solid losses, and overall system efficiency. Finally, the fourth category reviews application-oriented studies, evaluating the implementation potential and operational advantages of magnetic gear systems in various engineering domains, including renewable energy, electric vehicles, robotics, aerospace, and marine technologies.
## 2 Related Work
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Highlighted**} & \multicolumn{5}{c}{**Applications**} \\ \cline{3-6}  & & **Collaboration-Based** & **DT-Based** & **Industry 4.0-Based** & **Operational Data-Based** \\ \hline
[5] (2019) & The homogeneous and heterogeneous of multi-robot collaboration to & ✓ & X & X & X \\  & perform their complex task in decentralized fashion with blockchain technology & & & & \\ \hline
[6] (2019) & The importance of machine learning of multi-robot cooperation by keeping connectivity & ✓ & X & X & ✓ \\  & and improving mobility in performing tasks. & & & & \\ \hline
[7] (2019) & The collaboration between drone and IoT devices for improving Industry 4.0 applications, & ✓ & X & ✓ & X \\  & such as smart city, smart line & & & & \\ \hline
[8] (2018) & DT monitoring that for monitoring and development of wind farms. & X & ✓ & X & ✓ \\ \hline
[9] (2017) & An approach for identifying the network physical vulnerabilities in Industry 4.0 systems. & X & ✓ & X & ✓ \\ \hline
[10] (2014) & AI-based supervisory control and data acquisition method for prediction and fault & X & X & X & ✓ \\  & diagnosis of wind turbines & & & & \\ \hline
[13] (2019) & A conceptual architecture and model for smart manufacturing relying on service-based DIs & X & ✓ & ✓ & X \\ \hline
[14] (2019) & Advanced physics-based modeling approach for predictive maintenance using DIs & X & ✓ & ✓ & ✓ \\ \hline
[15] (2016) & A model-based machine predictive maintenance-based on DIs and a simulation platform & X & ✓ & ✓ & ✓ \\ \hline
[16] (2018) & A modular-based corrective maintenance methodology using DTs to automate decision & X & ✓ & ✓ & ✓ \\  & making in complex systems & & & & \\ \hline
[17] (2018) & Discussion of the DT and big data in smart manufacturing in terms of applications, & X & ✓ & ✓ & ✓ \\  & production, manufacturing, maintenance prediction & & & & \\ \hline
[18] (2019) & A tool and technologies for DT in smart manufacturing & X & ✓ & ✓ & X \\ \hline
[19] (2020) & A DT-driven sustainable technique smart manufacturing & X & ✓ & ✓ & X \\ \hline
[20] (2020) & Focusing on DIs with Industry 4.0 and data analytics & X & ✓ & ✓ & ✓ \\ \hline
[21] (2018) & Investigation wind farm and power consumption for smart manufacturing using IoT & X & ✓ & ✓ & ✓ \\  & and DIs to perform wind turbines maintenance. & & & & \\ \hline
[22] (2020) & Collaboration of drone and IoT to enhance smartness of smart cities applications. & ✓ & X & X & X \\ \hline Our work & A conceptual framework for DTs collaboration to provide an auto-detection of & ✓ & ✓ & ✓ & ✓ \\  & erratic operational data by utilizing the intelligence of operational data in the & ✓ & ✓ & ✓ & ✓ \\  & manufacturing systems. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing work and the present work.
# Evaluating the Degradation of WE43 for Implant Applications: Optical and Mechanical Insights
## 3 Materials and Methods
### Experimental Setup and Preparation of the Experiments
The pH value of the body replacement fluid was initially set to 7.4 \(\pm\) 0.05 to simulate the human body pH value [16]. Before the experiments, the sensors were calibrated using the supplied liquids, which were assigned a specific pH value. The calibration solutions used had a pH value of both 7.0 and 4.0. [PERSON]'s balanced salt solution (HBSS) was used as the body replacement fluid. It consists of the following inorganic salts: KCl, KH\({}_{2}\)PO\({}_{4}\), NaCl, Mg\({}_{2}\)SO\({}_{4}\), CaCl\({}_{2}\), and D-glucose. It should be emphasised that no buffer was used to regulate the pH value. The aim was to assess pH changes during degradation to identify potential adverse effects on future applications. In bone environments, rapid pH correction may not be feasible. The lack of a buffer system enables an estimation of the timeframe for a critical pH increase, thereby excluding applications that cannot accommodate such pH adjustments. In addition, this allows a comparison with other work such as in [PERSON] et al. [19] or [PERSON] et al. [16]. Additionally, sample holders were integrated into the respective experimental chambers (Figure 4). The sample holders were designed in such a way that only a small surface area was covered by the holder. Design considerations ensured the exposure of samples to fluid flow at the cross-sectional area rather than at the mantle surface. The cross-sectional surface is easier to analyse in terms of other parameters such as degradation form. In addition, this surface corresponds more to the processing condition as it will be in the later application than the mantle surface of the sample. The samples are cylinders with a diameter of 18 mm and a height of 10 mm. These samples were extruded and then machined at the front end. Figure 3: CAD representation of the experimental setup.
## 3 Description of the Systematic Impact/Aptitude Land Planning Model
### Inventory of the Physical Environment of the Study Area  
#### 4.1.1 Inventory of the Abiotic Physical Environment of the Study Area  
The study area is a semi-arid environment with a very warm climate, characterized by dry periods and heavy rains in autumn and spring. Consequently, the ecosystem is subject to prolonged periods of water stress, while water erosion occurs during storms. The Martonne Bioclimatic Index of Aridity is a bioclimatic index that indicates that the lower the value of the index, the more arid the climate. It has been calculated for the period (2001-2019), obtaining a value of 11.60, which means a climatic zone of steppes and dry Mediterranean countries.  
The Council of the European Union's Directive 91/676/EEC of 12 December 1991 on the protection of water against pollution produced by nitrates from agricultural sources, establishes the obligation to designate as vulnerable areas for nitrate pollution (ZVCN) those that, due to the effects of runoff or percolation, contribute to the contamination above. Monforte del Cid is a municipality designated as ZVCN from agricultural sources (Decree 86/2018, of June 22, of the Valencian Community Council), thus requiring the implementation of prevention measures to reduce nitrate pollution.  
The municipality of Monforte del Cid is situated in the transitional zone between the Sub-Betic range corridors and the arid pre-coastal plains of Alicante. The study area is situated at a relatively low altitude, ranging from 233 to 333 m above sea level. The geomorphology of the terrain is largely influenced by the slope of the terrain, which has undergone significant changes. The study area encompasses a variety of soil types, including agricultural terraces and small hills with varying slope gradients, which have led to the formation of scrub areas. The lithology is primarily composed of carbonate rocks, including limestone, sandy limestone, and marly limestone (upper age: Cenomaniense; lower age: Albiesne).  
In the study area, water erosion is linked to torrential rains, which cause significant transformations and modeling of the morphology of the terrain. Superficial runoff possesses considerable power, which causes the dragging of silty or sandy materials with lower density, which are deposited in depressed areas.  
The group of soils, presented in the small elevations that surround the agricultural plots, are Leptosols (soils with limited rooting that are or have been strongly influenced by water). In agricultural plots that have been cultivated for decades, the reference group is Anthrosols, which are soils that have been modified by human activity and have acquired characteristics derived from human activity, in this case, agriculture [31].
## 4 Deep Reinforcement Learning-Based Efficient Federated Learning Policy Optimization
### Resource Optimization
#### 4.1.1 Virtualized Edge-Assisted Framework
To deal with the heterogeneity of IoT taxonomies and services, [71] presented a graph neural network (GNN) and DRL-assisted efficient VNF-FGs placement in SDN- and NFV-enabled IoT environments, which provided a great lesson on the applicability of modifying the FL-based IoT services to be optimized via a DRL-based algorithm. The states of resource capacities and actions on link placement are correlated in NFV-enabled FL service deployment. Furthermore, in [72], a hierarchical DRL that relied on the GNN algorithm was extensively studied for improving resource management for efficient service chain control. The lower- and upper-level DRL were designed for minimizing the resource utilization and blockage probability, respectively. The collaboration between lower and upper levels greatly assists the training execution and outputs a cost-efficient policy. In [73], an energy-efficient FL technique was studied at the wireless network edge by aiming to optimize the communication (e.g., power and bandwidth) and computation (e.g., CPU capacities at the central server) resource usage. The weighted models ideally consider  
Figure 6: SDN-assisted control architecture for distributing DNNs. Figure 7: Agent-assisted orchestrator for adaptive action in optimizing FL resources. tradeoffs between energy, delay, and FL performance that are delivered in wireless networks. Stochastic gradient descent (SGD)-based FL and Lyapunov stochastic optimization were formulated in the system models to assist the developing phases of the adaptive FL-enabled resource allocation technique. In this perspective, DRL-based control can be used to observe the radio/computation states for reassignment and allocation in an experience-driven orchestration. An FL-enabled vehicular EC faces the same problems with client (vehicle) selection and resource optimization. In [74], the authors addressed these issues and formulated an optimization model considering geographical position and mobility to achieve sufficient computation resources, transmission power, and accuracy. These features are the primary characteristics of resource optimization in executing FL-based applications. The authors comprehensively designed in-depth system models of autonomous vehicles, FL transitions, computation, and communication models. The resource allocation algorithms are complemented by Lagrange multipliers update. The solutions achieved remarkable performance in optimizing the system costs and fairness. However, in this scenario, DRL-based selection can assist with state-specific resource orchestration to tackle the tradeoffs and feedback in an efficient weighted sum model.