esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Framework
### Bayesian Primer  
To overcome the drawbacks of the weak duality and the oscillatory behavior of Lagrange relaxation, the Lagrange multipliers are modeled to form a Gaussian process.  
This constitutes a stochastic process that governs the properties of functions rather than describing random variables, which are scalars or vectors. Consequently, the new population is produced by adding the Gaussian number \(\mathcal{N}(0,\sigma^{2})\) with a mean of zero and a pre-determined standard deviation to each multiplier such that:  
\[\lambda^{k+1}=\lambda^{k}+\mathcal{N}\Big{(}0,\sigma^{2}\Big{)} \tag{23}\]  
\[\mu^{k+1}=\mu^{k}+\mathcal{N}\Big{(}0,\sigma^{2}\Big{)} \tag{24}\]  
This allows for giving a prior probability to every feasible function, with higher probabilities being given to functions that are more likely to be seen. Then, Bayesian inference techniques can be used in order to make progressive observations towards the final solution. In this realization, \(X\) represents the pairs of binary states \(S_{i}^{t}\) and the continuous output \(P_{i}^{t}\), whereas \(Y\) is the scalar objective value of cost [34]. According to [PERSON] theorem, the updated posterior \(p(f|\,Y,X)\) is given considering an observed likelihood \(p(Y|\,f)\), the prior \(p(f|\,X)\) and the marginal likelihood \(p(Y|\,X)\), which will optimize the kernels and normalize the posterior such that:  
\[p(f|Y,X)=\frac{p(f|X)p(Y|f)}{p(Y|X)} \tag{25}\]  
Any set of the correlated, real values of independent and identically distributed random variables \(X\) and the postulated prior imposed over \(f(x)\) is expressed as \(p(f|X)\sim\mathcal{N}(0,K)\), where \(K\) represents the kernel, covariance matrix [35]. In this work, variational Bayes is taken into account due to its notably better scalability related to computational costs, which is of vital importance when having to deal with large datasets. Assuming \(D\) to be the set of the model parameters over which a prior distribution has been imposed, and considering \(\Xi|\,x_{i}\) as the set of all model-hyperparameter priors and kernel functions, variational Bayesian inference provides a distribution \(q(D)\) in order to approximate the actual posterior \(p(D|\,\Xi,X,Y)\), which is computationally intractable, yielding:  
\[\log p(X,Y)=\mathcal{L}(q)+KL(q|p) \tag{26}\]  
\[\mathcal{L}(q)=\int dD_{q}(D)\,\log\frac{p(X,\,Y,\,D|\Xi)}{q(D)} \tag{27}\]  
where \(KL(q|p)\) represents the Kullback-Leibler divergence between the actual posterior \(p(D|\,\Xi,X,Y)\) and the approximate variational posterior, \(q(D)\). Since KL divergence is non-negative, \(\mathcal{L}(q)\) forms a strict lower bound of the log evidence and would become exact if \(q(D)=p(D|\,\Xi,X,Y)\). Therefore, by maximizing this lower bound \(\mathcal{L}(q)\) such that it becomes as tight as possible, not only can the KL-divergence be minimized between the true and the variational posterior, but the unknown \(D\) can also implicitly be integrated out [36].
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 3 Basic System Design
### Skyline Query
#### 3.5.1 Tree Structure Construction  
Each SH constructs a tree considering itself as the root using Algorithm 3. Figures 8 and 9 illustrate an example of the formation of a tree rooted at the \(13\)th (\(2\), \(3\)) Sector. Sector (\(2\), \(0\)), (\(2\), \(1\)), (\(2\), \(2\)), (\(2\), \(3\)), (\(2\), \(4\)) and (\(2\), \(5\)) are the child nodes of Sector (\(2\), \(3\)). The sectors lying at the same column but upper row and lower row of each of the child nodes of root node are added to the left and right branch, respectively. For example, Sector (\(1\), \(0\)), (\(0\), \(0\)) and (\(3\), \(0\)), (\(4\), \(0\)) are added to the left and right branch, respectively, of Sector (\(2\), \(0\)). The hierarchy is further maintained among segments inside each sector, as shown in Figure 10.
# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
###### Abstract  
Deep learning approaches require a large amount of data to be transferred to centralized entities. However, this is often not a feasible option in healthcare, as it raises privacy concerns over sharing sensitive information. Federated Learning (FL) aims to address this issue by allowing machine learning without transferring the data to a centralized entity. FL has shown great potential to ensure privacy in digital healthcare while maintaining performance. Despite this, there is a lack of research on the impact of different types of data heterogeneity on the results. In this study, we research the robustness of various FL strategies on different data distributions and data quality for glaucoma diagnosis using retinal fundus images. We use RetinaQualEvaluator to generate quality labels for the datasets and then a data distributor to achieve our desired distributions. Finally, we evaluate the performance of the different strategies on local data and an independent test dataset. We observe that federated learning shows the potential to enable high-performance models without compromising sensitive data. Furthermore, we infer that FedProx is more suitable to scenarios where the distributions and quality of the data of the participating clients is diverse with less communication cost.  
federated learning; image processing; fundus images; retina quality; glaucoma
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 1 Introduction
Therefore, CR can provide UAVs with promising features for their massive deployment, such as reduced energy consumption and delay, opportunistic spectrum use based on application requirements, and a security enhancement, as CR does not suffer the effects of some conventional attacks. These features would allow CR-based UAVs to perform in situations where traditional UAVs face limitations, or are often subject to being hacked. Moreover, CR alongside 5G would permit the UAVs to work in an IoT role within an IoT context. This would fulfill the increasing growth of applications requiring highly connected devices, such as Smart Cities [13]. However, although UAVs and CR are well-established research fields, the integration between the two is fairly unexplored [9]. Many issues remain open, such as the impact of the UAVs mobility on CR and the definition of hardware compatible with both. These open issues allow new research opportunities and advances into the state-of-the-art. In this paper, we provide a comprehensive overview of background on CR for UAV communication, and the ongoing research regarding the integration between the two. We also specify the steps to build a simple and low-cost CR-based UAV testbed. Finally, we present key opportunities and future challenges in the field.
# Energy Optimisation of Industrial Limestone Grinding Using ANN
###### Abstract  
This paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. Two key process evaluation indicators were developed: specific electric energy consumption and an extended indicator that also includes gas usage. Using process data collected from the SCADA system and results from industrial factorial experiments, regression artificial neural network models were developed, with controllable process parameters used as inputs. In the next phase, black-box optimisation was performed using Bayesian and genetic algorithms to identify optimal mill operating settings. The results demonstrate significant improvements in energy efficiency, with energy savings reaching up to 48% in selected cases. The proposed methodology can be effectively applied to enhance energy performance in similar industrial grinding processes.  
energy efficiency; artificial neural networks; industrial process optimisation; Bayesian optimisation; limestone grinding 2020 IEEEexample: accepted: 2020 IEEE  
process optimisation\" [8]. The dynamic simulation work of [PERSON] [9] and [PERSON] [10] introduced multi-loop PI control tuning guidelines that are still embedded in many distributed control systems (DCSs). Model-based predictive control (MPC) subsequently emerged as a powerful alternative, offering the explicit handling of process delays and constraints [11; 12]. These classical contributions form a well-established benchmark against which any claimed improvement--whether evolutionary algorithms, adaptive neuro-fuzzy inference systems, or deep learning--must be rigorously compared.
# ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning
## 5 Conclusions  
To achieve super-resolution ISAR images for maneuvering targets, a novel ISAR imaging method based on deep learning assistance is proposed. The neural network integrates the structure of CapsNet and is trained on samples to learn the inherent mapping function. The trained CapsNet effectively suppresses background noise. Numerical experimental results demonstrate the effectiveness of the proposed method. A novel composite structure based on a corner reflector, Luneberg lens reflector sphere plate, and array technology is proposed. The ISAR image formed through this combination is widely recognized as the aircraft target, which proves that the combination method can effectively simulate the ISAR image result of the aircraft target.  
Figure 11: Recognition results of the aircraft ISAR imaging.  
**Author Contributions:** Conceptualization, [PERSON]; methodology, J.H.; investigation, S.Y.; formal analysis, H.G.; software, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by (Ministry of Education of the People's Republic of China grant) number [20190209400011].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Software Implementation Notes  
All numerical computation tools are based on a set of Python libraries. _Odeint_, _interpolate_, _ffrpack_, _ffthstiff_, _splrep_, _splev_, _symbols_, _Eq_, _solve_, and _findpeaks_.  
The most challenging element in deploying _Odeint_ and _interpolate_ libraries is the internal curve smoothing we don't control. Hence, we found that using external smoothing (_splrep_, _splev_) to limit the impact of the internal curve smoothing. It is essential to recognize that not any set of values for \(\{N_{0},\,a_{0},\,a_{1}\}\) produces a realizable filter solution \(h_{f}\) (general and  
Figure 7: Use Case III: \(r_{sig}(t)\) with colored AGTN and ISI–[Equation (22) Numerical solution for Harbour water]. **(a)** three optimum \(h_{f}(t)\) solution scenarios with different \(\{N_{0},\,a_{0},\,a_{1}\}\) value sets, **(b)** the corresponding outputs \(r_{f}(t)\), **(c)** the frequency response for the three \(h_{f}(t)\) solutions, **(d)** the frequency response for \(h_{c}(t)\), optimum \(h_{f}(t)\), & \(r_{f}(t)\), **(e)** the eye diagram for the transmitted optical pulse \(h_{t}(t)\) with pw = 0.25, and **(f)** the eye diagram for the filter output \(r_{f}(t)\).  
optimum). Table 1 exhibits a working map of the three use cases, associated equations, and diagrams. Therefore, we will not repeat the description of the sub-figures and equations in this section. Regarding the numerical solution of Equation (22), the _Odeint_ Python library has 25 parameters, including the _args_(0) and the _func_. The function is preferred to be a monotonically non-decreasing or non-increasing function. Therefore, we used half of the intended array, and later, we completed the solution using a bespoke data symmetry method. This helped us to cut the processing time to half.  
The other aspect of using the _Odeint_ library is the tolerances. We noticed that tightening the tolerances will not necessarily make the numerical half-solution monotonic. There is no way to guarantee that. If we are approaching a steady state monotonically, tightening tolerances will limit false oscillations (expressly, it will limit their amplitude, but not their total variation). However, it did not eliminate them for sure. Consequently, we noticed amplified undershoots for the \(h_{p}(t)\) solution in Figure 6a,d.  
As part of the MGF model reliability check, we worked on finding valid and safe solutions because they provide realizable and causal filters. We decided to find general solutions besides the corresponding optimum ones to bridge the gap between valid and safe solutions. In this manner, we can examine the new MGF designing model frame of reliability by changing the configuration solution set defined by {\(N_{0}\), \(a_{0}\), \(a_{1}\), \(pw\)} plus the ISI depth.
## 3 Results & Observations
### Acknowledgements, Retransmissions and Gateway Availability
In our experiments we noticed that the messages that were not delivered successfully due to GW TX condition, when only one GW is deployed, are about 5-15% of the total transmissions and their number is based on the network size (100 to 3000 EDs respectively).
# Optimal Control of an Ultraviolet Water Disinfection System
## 3 Disinfection System's Optimal Control
### Optimum Criteria  
Obviously, the most important optimum criterion refers to the level of inactivation of microbiological pathogens, i.e., a certain value or a minimum value for the bacterial abatement \(A\). We can call it the disinfection criterion. The following constraints may express this criterion:  
\[A(t_{f})\ =\ A_{0} \tag{11}\]  
or  
\[A(t_{f})\ \geq\ A_{0}. \tag{12}\]  
The notation \(t_{f}\) is the final moment of the control (simulation) horizon [\(0\ t_{f}\)].  
As previously mentioned in Section 2.2.1, the bacterial abatement enters into the  
Figure 4: Static gains generation using procedure \(A(I_{0},Q_{0},t)\).  
saturation zone at the moment \(t_{\rm exp}\) given by Equation (2). Consequently, the function \(A(I_{0},\)\(Q_{0},\)\(t)\) has to meet the equality:  
\[A(I_{0},Q_{0},t_{f})\ =\ A(I_{0},Q_{0},t_{\rm exp}),\ {\rm for}\ t_{f}\ \geq\ t_{\rm exp}. \tag{13}\]  
Another optimum criterion that may be considered is to minimize the consumption of energy necessary to provide the UV dose. We can call it the energy criterion. The energy is used for powering the UV discharge lamp. Considering the unit of irradiation dose, i.e., mW\(\cdot\)s/cm\({}^{2}\), one can identify an equivalent surface \(S\) such that:  
\[\Delta E\ \cong\ D\cdot S. \tag{14}\]  
\(\Delta E\) denotes the energy consumed for irradiation in a single passage. It can be expressed in mW\(\cdot\)s, if \(S\) is given in cm\({}^{2}\). The equivalent surface \(S\) depends on the constructive parameters of the UVDS under consideration. Equation (14) allows us to state that \(\Delta E\) is proportional to the UV dose. Therefore, the minimization of energy is equivalent to minimizing the dose \(D\) received during the exposure time.
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 7 Conclusions  
This paper proposes an enhanced DV-Hop node localization method based on the Grey Wolf Optimization algorithm to mitigate the reduced localization accuracy caused by the non-uniform distribution of nodes in IoT networks. The impact of non-uniform node distribution on hop count and average hop distance is analyzed, and a binary GWO-based anchor node selection mechanism is designed to filter superior anchor nodes, enabling precise localization of unknown nodes. During the multilateration phase, the continuous GWO algorithm replaces the least squares method for localization optimization, greatly enhancing the accuracy of the distance equations. Simulation results demonstrate that the improved algorithm effectively mitigates the cumulative effects of hop count and average hop distance errors caused by non-uniform node distribution, leading to significant improvements in localization accuracy in non-uniform topologies. This approach offers an efficient and precise solution for node localization in IoT networks.  
Conceptualization, T.C. and S.H.; methodology, M.I.A.; software, Y.L.; validation, H.N., S.H. and T.C.; formal analysis, H.N. and S.H.; investigation, T.C.; resources, M.I.A.; writing--original draft preparation, H.N. and S.H.; writing--review and editing, H.N., M.G. and T.C.; visualization, Y.L.; supervision, M.I.A.; project administration, L.S.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Natural Science Foundation of China (grant number: 62173127, 62472143), Key R&D Special Projects in Henan Province (grant number: 241111521000), Program for Scientific and Technological Innovation Team in Universities of Henan Province (grant number: 25 IRTSTHN021), Top Young Talents in Central Plains (grant number: (2023)11), and Cultivation Project of Tuoxin Team in Henan University of Technology (grant number: 2024 TXTD17).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# Non-Contact Tilapia Mass Estimation Method Based on Underwater Binocular Vision
## 5 Conclusions  
In this paper, a non-contact tilapia mass estimation method based on underwater binocular vision is proposed. Firstly, a platform for acquiring underwater tilapia images was built. The underwater tilapia images were acquired by a binocular vision system, and the underwater camera was calibrated, and the aberration was corrected. The image was preprocessed using a Retinex-based image enhancement algorithm, combined with stereo matching to obtain depth information. Image segmentation was performed on the acquired tilapia images, and the coordinates of key points were labeled. Thus, the three-dimensional reconstruction of the underwater tilapia was realized. The estimation model between body length and mass of tilapia was established, thus realizing the mass estimation of non-contact tilapia. The MRE of the tilapia mass estimation model developed in this paper ranged from 5.34% to 12.14%. Experimental results show that the method proposed in this paper has high accuracy and stability. The \(R\) of the estimated value of the mass estimation model to the manually measured value was 0.99, and the MRE was 5.90%. The mass estimation of underwater tilapia can be carried out effectively. The tilapia mass estimation model established by this study can estimate the mass of tilapia without contact, which improves the measurement efficiency. It provides technical support for the rapid estimation of body length and mass of underwater free-swimming tilapia.  
Conceptualization, G.F., M.C. and B.P.; methodology, B.P.; software, B.P.; validation, G.F. and B.P.; resources, G.F.; data curation, G.F. and B.P.; writing--original draft preparation, G.F. and B.P.; writing--review and editing, G.F. and B.P.; supervision, G.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2022 YFD2400800) and Jiangsu Modern Agricultural Industry Key Technology Innovation Planning, No. CX (20) 2028.  
Not applicable.  
**Data Availability Statement:** Experimental data related to this paper can be requested from the authors by email if any researcher is in need of the dataset, email: [EMAIL_ADDRESS].  
The authors are very grateful to [PERSON] of Nantong Longyang Aquatic Co. in Jiangsu Province, China, for providing us with the experimental data and the data collection site.  
The authors declare no conflicts of interest.
# Multi-Layered Interactive Target Guidance with Visual Safety in Convex-Shaped Obstacle Environments
## 4 Planning Layer for Interactive Guidance
However, in the SCvx algorithm, the solution cannot be obtained unless the gradients of the constraints are linearly independent, and the occlusion avoidance control barrier function \(h_{occ,k}\) does not satisfy this condition inside an obstacle, i.e., \(p_{wd}\in\mathcal{O}_{k}\). Therefore, each obstacle is approximated as a sphere. Let the center of the sphere be \(p_{k}\) and the radius be \(r\). In (34), \(l_{c,k}\) is approximated as \(\|p_{wc}-p_{k}\|-r\) and \(l_{o,k}\) as \(\|p_{w0}-p_{k}\|-r\). Then, the constraint \(h_{occ,k}(x_{pe})\geq 0\) is replaced by  
\[g_{occ,k}[j\mid i]\coloneqq(\|p_{wd}[j]-p_{k}\|-r)+(\|p_{w0}[i]-p_{k}\|-r)-\|p_ {w0}[j]-p_{w0}[i]\|\geq 0. \tag{62}\]  
For simplicity, \(\gamma\) and \(\Delta_{occ}\) in (34) are set to 0. Based on the above, the optimization problem at time \(t_{i}\) is formulated as follows:  
\[\underset{\{p_{nd}[j]\}_{j=i}^{i+N-1},~{}\{v_{nd}[j]\}_{j=i}^{i+N-1 }}{\text{minimize}} \sum_{j=i}^{i+N-1}\mathcal{J}[j\mid i]\] (63a) subject to \[g_{dyn}[j]=0, \tag{63b}\] \[g_{occ,k}[j\mid i]\geq 0,~{}~{}\forall k=1,\ldots,n_{O}. \tag{63c}\]  
Only the trajectory for \(j=i,\ldots,i+\ell-1\) is passed to the safety layer, and the problem is resolved every \(\ell T\). Here, \(\ell\) is a natural number satisfying \(0<\ell\leq N\). When \(\ell=1\), it corresponds to model predictive control. Though the safety guarantees are also considered in the planning layer, we place the QP block in the safety layer in order to guarantee visual safety within discrete intervals. Inaddition, dealing with dynamic changes in the target's position is not considered in the optimal control problem. Furthermore, the QP block allows simplification of the conditions in the optimal control problem. For example, because the QP block guarantees visual safety against obstacles modeled in detail, it is possible to consider only obstacles approximated as spheres in (63c), which reduces computation time.