esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This article considers the results of comparing two algorithms for representing data from the complex space based on an excess basis using the \(L1\) norm. Algorithm 1 based on the sparse representation method for complex data SL0 (smoothed \(L0\) norm), based on the smoothed \(L0\) norm, and Algorithm 2 based on the method using a modified MFNN are analyzed. Using numerical modeling for determining the DOA and direction of sources  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \(\mu\), \(\alpha\) & \multicolumn{3}{c}{**Coefficients Quantity**} \\ \hline  & & **\textgreater{}0.1** & **\textgreater{}0.01** & **\textgreater{}0.001** \\ \hline
1 & - & 7 & 10 & 12 \\
2 & \(\mu=1.5\), \(\alpha=0.3\) & 14 & 24 & 151 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of excess basis coefficients with a level exceeding 0.1, 0.01, and 0.001 for six close sources.  
received by an equidistant antenna array (ULA--Uniform Linear Array), it is shown that the solutions implemented in the form of Algorithm 1, based on the SL0 method, are dependent on the choice of the values of the coefficients \(\upmu\), \(\upalpha\). It should be underlined that the choice of universal optimal values of these coefficients is difficult. At the same time, the modified MFNN method allows for high accuracy in finding the optimal solution for different values of positions, amplitudes, phases, and the number of sources, and the convergence rate of the modified MFNN method implemented in the form of Algorithm 2 is in some cases higher than the convergence rate of the SL0 method. Thus, it can be concluded that algorithm 2 is preferable in terms of the stability and accuracy of the solution, and its convergence rate characteristics are close to those of Algorithm 1.  
In subsequent studies, there is a plan to conduct a detailed analysis of the convergence rate and stability of the method, as well as an experimental study of its characteristics for data processing using dictionaries based on functions other than exponential ones.  
Methodology, N.V.P.; software, N.V.P. and I.A.K.; writing--original draft preparation, N.V.P. and A.Y.N.; writing--review and editing, N.V.P. and A.V.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Higher Education of the Russian Federation within the framework of state assignment No. FZRR-2023-0008.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble  
### Introduction  
Traditional techniques for diagnosing eye diseases are mainly related to the professional expertise and skills of ophthalmologists, which, unfortunately, may lead to a higher rate of misdiagnosis and underutilization of medical data.  
The combination of ophthalmology and AI has the capability to objectively revolutionize monitoring, diagnostics, and models of management of many eye diseases.  
Traditionally, an eye exam involves describing the results by means of words, drawings, and pictures accompanied by a medical verdict. However, this general procedure of diagnosis is subjective, qualitative, and often inappropriate. Most diagnostics in ophthalmology are based on image processing; therefore, much depends on the analysis and quantification of various parameters in the images.  
Medical image processing usually extracts characteristics (features), which can be hard to visually evaluate for the specialist. In general, there are two types of features:  
1. The semantic (or syntactic) features, expressed by specialists;
2. The numerical characteristics, represented by mathematical formulas.  
The use of big data and analytics has increased, and neural networks are developing, so computers have helped in the detection, extraction, learning combinations, and permutations of important features.  
Note: _Big data_ refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that continue to grow over time. _Analytics_ is the process of discovering, interpreting, and communicating meaningful patterns in data. It helps us see insights, patterns, and meaningful data that we might not otherwise detect.
# LieTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories
## 4 Experimental Results and Analysis
### Comparative Analysis of Tomato Detection Performance
#### 4.3.2 Computational Complexity  
The computational efficiency of LiteTom-RTDETR was rigorously validated through comprehensive benchmarking against the Faster-RCNN, YOLOv8m, and original RTDETR architectures. This performance evaluation utilized the tomato image dataset described in Section 3.1 to ensure the validity of the comparison. The experimental environment and hyperparameters for benchmarking are detailed in Tables 5 and 6. All four models were trained under identical conditions and uniformly saved in PyTorch format (*.pt). The benchmarked complexity metrics comprised parameter counts, GFLOPs, inference speed, and model size. Specifically, parameter counts, GFLOPs, and inference speed were measured using the PyTorch Profiler toolkit, while model size refers to the storage space occupied by the PyTorch-format saved model. These four model complexity evaluation metrics are recorded in Table 10.  
The results clearly indicate that LiteTom-RTDETR exhibited the best computational efficiency metrics for agricultural vision applications, with 13.3 \(\times\) 10\({}^{6}\) parameters, 36.3 GFLOPs, a 52.2 FPS inference speed, and a 26.4 MB model size. The LiteTom-RTDETR architecture demonstrated significant efficiency improvements compared to Faster-RCNN,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Parameters** & \multirow{2}{*}{**GFLOPs**} & **Inference** & **Model** \\  & **(Millions)** & & **Speed (FPS)** & **Size (MB)** \\ \hline Faster-RCNN & 28.3 & 174.1 & 28.3 & 108.2 \\ YOLOv8m & 25.8 & 78.7 & 39.8 & 49.6 \\ RTDETR & 19.9 & 56.9 & 45.2 & 38.6 \\ LiteTom-RTDETR & 13.3 & 36.3 & 52.2 & 26.4 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Complexity evaluation of different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & _P (\%)_ & _R (\%)_ & _F1 (\%)_ & _mAP50 (\%)_ \\ \hline Faster-RCNN & 61.2 & 76.9 & 68.2 & 74.8 \\ YOLOv8m & 87.9 & 82.0 & 84.8 & 88.1 \\ RTDETR & 87.7 & 82.3 & 84.9 & 87.6 \\ LiteTom-RTDETR & 89.6 & 81.1 & 85.1 & 88.2 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative analysis of recognition models.  
YOLOv8m, and baseline RTDETR, with parameter count reductions of 53%, 48.4%, and 33.2%, respectively, GFLOPs reductions of 79.1%, 53.9%, and 36.2%, respectively, FPS improvements of 84.5%, 31.2%, and 15.5%, respectively, and model size reductions of 75.6%, 46.8%, and 31.6%, respectively. These results confirm the ability of LiteTom-RTDETR to balance recognition accuracy with operational efficiency while simultaneously maintaining high precision and achieving lightweight deployment capabilities, all of which are critical for real-time field applications.
## 3 Supervised ML Models
### Tree-Based Ensembles and Decision Forest Classifier Models
#### 3.1.1 Random Decision Forest (RDF) and Boosted Decision Trees (BDT)  
The random decision forest regressor or classifier model is composed of an ensemble of various non-parametric decision trees. An individual tree in an RDF would output a Gaussian distribution as a prediction. The aggregation of trees is performed over the tree combination to search for the closest Gaussian distribution to the combined total distribution of all the trees in the decision model. The boosted decision tree model combines individual trees to reach a decision by using the technique of \"boosting\" in order to increase the accuracy of prediction. Boosting simply means that each succeeding tree is dependent on the preceding on. Hence, the algorithm or model learns by fitting each tree's residual that precedes it.  
3.1.2. Meta-Heuristic Ensembles and Voting Ensemble (VE)  
In order to further improve ML predictions, meta-ensembles are used. These are constructed by an aggregation of several models or hyperparameters and thus are also referred to as model of models. Some of the hyperparameters of the forgoing models were combined through averaging by the voting and stacking systems. The voting ensemble estimates the average predictive output of the sum of aggregated models through a majority voting exhibited by the model with the highest prediction confidence or the model that has the most popular output. A typical structure of VE is shown in Figure 5.  
While the stacking ensemble (SE) is an adjunct of the averaging system where the hyperparameters learn and choose when to depend on themselves to enable a more generalised multistage prediction. Hence, the output of preceding models would become or serve as inputs for the subsequent models and predictions are being made as shown in Figure 6.  
Figure 4: Typical decision trees.  
Figure 5: Typical structure of voting ensemble.
## 2 System Modeling
The transfer functions of the system are given by  
\[G_{1}(s)=\frac{\Omega_{1}(s)}{\tau_{mn}(s)}=\frac{1}{J_{2n}s}\Bigg{[}\frac{ \frac{B_{2n}}{J_{1n}}+\frac{K_{n}}{J_{1n}}}{s^{2}+\frac{B_{1n}+B_{2n}}{J_{1n}J_ {2n}}s}+\frac{K_{n}}{J_{1n}J_{2n}}\Bigg{]} \tag{4}\]  
\[G_{2}(s)=\frac{\Omega_{2}(s)}{\tau_{mn}(s)}= \frac{1}{J_{1n}s}\Bigg{[}\frac{\frac{B_{1n}}{J_{2n}}+\frac{K_{n}}{J_ {2n}}}{s^{2}+\frac{B_{1n}+B_{2n}}{J_{1n}J_{2n}}s}+\frac{K_{n}}{J_{1n}J_{2n}} \Bigg{]} \tag{5}\]  
The parameters to be identified in the 2 MM system include the normalized motor and load inertias (\(J_{1n}\), \(J_{2n}\)), the normalized torsional stiffness (\(K_{n}\)), the normalized Coulomb  
Figure 1: Open-loop control structure of two-mass model (2 MM) system. friction coefficients (\(C_{1n}\), \(C_{2n}\)) and the normalized viscous damping coefficients (\(B_{1n}\), \(B_{2n}\)). These parameters can be represented in vector notation as given below:  
\[\mathbf{\theta}=[J_{1n},\ J_{2n},\ K_{n},\ C_{1n},\ C_{2n},\ B_{1n},\ B_{2n}] \tag{6}\]  
The goal of parameter identification is to estimate the values of these parameters in order to accurately model the 2 MM system.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 1 Introduction
### Contributions
Specifically, the following major contributions are provided:  
* An ANN-based UFBS placement framework is established in order to improve the overall communication quality of a UAV-NOMA and D2D cooperative network. Towards this end, supervised ML algorithms (ANN) and unsupervised ML algorithms (k-means and k-medoids) are combined. * State-of-the-art data mining strategies are presented to transform raw data into an intelligible format for ANN algorithms and avoid underfitting and overfitting drawbacks. To the best of our knowledge, it is the first time that specific strategies have been provided in the field of UAV-NOMA and D2D cooperative networks. * A step-by-step approach on how to handle the issue of hyperparameter tuning in ANN models is provided to enhance the predictability of the UFBS placement procedure. * For the UFBS NOMA transmission, an optimal power allocation and user pairing strategy is considered [26]. Also, the proposed scheme promotes the cooperation between aerial and D2D networks.
## 3 Proposed Algorithm
### GAMP-SBL-Based Channel Estimation
In this process, the inverse of the matrix no longer needs to be calculated. The main operation in each iteration is simple matrix multiplication, and then EM is used to update the hyperparameters. The steps of the above GAMP-SBL algorithm are noted as Algorithm 1, where \(\varphi_{mn}\) represents the (\(m\), \(n\))-th entry, and \(\mu_{n}^{c}(k)\) and \(\phi_{n}^{c}(k)\) represent the posterior mean and variance of \(c_{n}\) at the \(k\)-th iteration, respectively. ```
0:\(\mathbf{\Phi},\varepsilon\)
0:\(\mathbf{\hat{c}},\widehat{\sigma}^{2}\)
1. Initialization: \(\mu^{c}(0)=\mathbf{\Phi}^{c}(0)=\mathbf{I}_{N},\sigma^{2}(0)=1\), \(s_{m}(-1)=0\)
2. for\(k=0,1,\cdots,k_{\text{max}}\)
3. E-Step:
4. \(\forall m\in\{1,\cdots MN\}:\)
5. \(z_{m}(k)=\sum\limits_{n}\varphi_{mn}\mu_{n}^{c}(k)\)
6. \(\tau_{m}^{p}=\sum\limits_{n}\sigma_{mn}^{p}\phi_{n}^{c}(k)\)
7. \(\hat{p}_{m}(k)=z_{m}(k)-\tau_{m}^{p}(k)s_{m}(k-1)\)
8. \(s_{m}(k)=g_{\text{out}}\left(\hat{p}_{m}(k),\tau_{m}^{p}(k),\theta\right)\)
9. \(\tau_{m}^{s}(k)=-\frac{\partial}{\partial\hat{p}_{m}}g_{\text{out}}\left(\hat{ p}_{m}(k),\tau_{m}^{p}(k),\theta\right)\)
10. \(\forall n\in\{1,\cdots QL\}:\)
11. \(\tau_{n}^{r}(k)=\left(\sum\limits_{m}\varphi_{mn}^{2}\tau_{m}^{s}(k)\right)^{-1}\)
12. \(\hat{r}_{n}(k)=\mu_{n}^{c}(k)+\tau_{n}^{r}(k)\sum\limits_{m}\varphi_{mn}s_{m}(k)\)
13. \(\mu_{n}^{c}(k+1)=g_{\text{in}}(\hat{r}_{n}(k),\tau_{n}^{r}(k),\theta)\)
14. \(\phi_{n}^{c}(k+1)=\tau_{n}^{r}(k)\frac{\partial}{\partial\hat{r}_{n}}g_{\text{in }}(\hat{r}_{n}(k),\tau_{n}^{r}(k),\theta)\)
15. M-Step:
16. Update the hyperparameters according to Equations (30) and (31)
17. If \(\sum\limits_{n}\left|\mu_{n}^{c}(k+1)-\mu_{n}^{c}(k)\right|^{2}\leq\varepsilon\), break
18. end
```  
**Algorithm 1** Basis coefficient estimation based on GAMP-SBL
## 3. Freestanding Position-Based Routing Protocol
### Location Service
#### 3.4.2 Redundancy Strategy
These factors are used to adjust \(d(FN_{n},FN_{n+1})\) by means of MFWAR. If the MFWAR mechanism cannot find a \(BN\) within the required range, then the current forwarder node is chosen as the \(BN\) node. As previously mentioned, after the selection of a \(BN\) node its ID is included in the corresponding table in the discovery packet. Figure 4: Backup node (\(BN\)) selection through the MFWAR mechanism. The \(BC_{2}\) node is chosen as \(BN\) because it is the nearest to the border of the adjusted distance \(\beta_{R_{c}}*d(FN_{n},FN_{n+1})\). Figure 3: Example of the Light Acknowledgement Mechanism implemented in FPBR. (**a**) The primary relay node successfully receives the discovery packet; (**b**) The \(BN\) node successfully receives the implicit acknowledgment from \(FN_{n+1}\). An example of the \(BN\) node selection through the MFWAR mechanism is schematized in Figure 4. Assume that the distance from the original transmitter, \(FN_{n}\), to the primary relay node, \(FN_{n+1}\), is \(d(FN_{n},FN_{n+1})\). As both backup nodes candidates in Figure 4 are moving in the same direction as \(FN_{n}\), \(\beta_{R_{c}}\) is used to adjust \(d(FN_{n},FN_{n+1})\). In Figure 4 the \(BC_{2}\) node is nearer to the border of the adjusted distance than the \(BC_{1}\) node. Therefore, \(BC_{2}\) is chosen as \(BN\) for this example.
## 3 Results and Discussion  
By applying the extracted probability density distributions and related assumptions in the NPV function, the probability distributions of the NPV in both density (PDF) and cumulative (CDF) forms are obtained, as presented in Figure 10.  
Figure 8: The probability density distribution of the activity level of petrochemical plant (source: authors calculation, using Crystal Ball software.  
Figure 9: The probability density distribution of the annual growth rate of electricity price (source: authors calculation, using Crystal Ball software).  
The upper graphs represent the case which ESCO benefit was just obtained based on the produced electricity (Elec), but in the lower graphs, the benefit of the sold certificates has been included as well (Elec&Sec). The calculations have been done during the periods of 5, 7, 9, and 11 years as the periods that the ESCO is allowed by the regulator to sell the certificates in the market. The shift of the probability distributions to the right in the lower graphs compared with the upper graphs, indicates the considerable effectiveness of the certificates sale on risk reduction that is expressed by the numerical criteria in Table 5.  
So, now the important issue is to find the appropriate time period that should be specified by the market regulator for selling the certificates. For this purpose, the following two cases have been compared for different time periods.  
Figure 10: The probability distributions of the NPV (source: authors calculation, using Crystal Ball software).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Unit**} & \multicolumn{3}{c}{**T = 5**} & \multicolumn{3}{c}{**T = 7**} & \multicolumn{3}{c}{**T = 9**} & \multicolumn{3}{c}{**T = 11**} \\ \cline{3-10}  & & **Elec** & **Elec \& Sec** & **Elec \& Sec** & **Elec \& Sec** & **Elec \& Sec** \\ \hline The probability & \multirow{3}{*}{Percentage positive NPV} & \multirow{3}{*}{\(0\)} & \multirow{3}{*}{44} & \multirow{3}{*}{0} & \multirow{3}{*}{99.1} & \multirow{3}{*}{71.6} & \multirow{3}{*}{100} & \multirow{3}{*}{96.7} & \multirow{3}{*}{100} \\ of obtaining positive NPV & & & & & & & & \\ Maximum NPV & & & & & & & & \\  & USD & \(-\)0.53 & 1.04 & \(-\)0.03 & 1.95 & 0.47 & 2.73 & 0.86 & 3.67 \\  & \
\begin{tabular}{c} Million \\ USD \\ \end{tabular} & \(-\)0.74 & \(-\)0.02 & \(-\)0.23 & 0.71 & 0.1 & 1.34 & 0.43 & 1.87 \\  &
\begin{tabular}{c} Million \\ USD \\ \end{tabular} & 0.1 & 0.25 & 0.14 & 0.36 & 0.18 & 2.8 & 0.21 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The quantities of the statistical and probabilistic criteria.
## 3 Case Study
### Topological Optimization Set-Up  
As previously described, the first guess concept assumes, as design space (DS), a bulk 3D filling of the statue, surrounding the central beam from the feet up to the base of the neck. Equivalent loads of the central body are applied at the bottom of the central beam. Rigid links are used to transfer force and moment. Table 2 summarizes design intent in terms of TO problem set-up and numerical constraints as derived by the DRs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Loads} & \multirow{2}{*}{Central Body Equivalent loads @ basement} & Force: (0, 0, 2111) N \\  & & Moment: (\(-\)53,740, 25,310, 0) Nm \\  & Arms and wings Equivalent loads @ basement & Force: (0, 0, 866) N \\  & & Moment: (203,600, 65,050, \(-\)56,890) Nm \\ \hline \multirow{2}{*}{Stress-Strain Constraints} & Max displacement at neck & \textless{0.9 mm} \\  & Maximum stress at hip & Less than 40 MPa \\  & Maximum stress at neck & Less than 40 MPa \\ \hline Mass Constraint & Volume Fraction (\(\%\)) & Less than 10\% \\ \hline Objective Function & Central Body compliance & To be minimized \\ \hline \hline \end{tabular}
\end{table}
Table 2: TO condition derived from DRs (for boundary conditions see Figure 4b).  
Figure 4: (**a**) Lateral view of DS and central beam NDS; (**b**) Boundary conditions (numbers represent locked DOFs: 1 = DOF locked in x direction, 2 = DOF locked in y, 3 = DOF locked in z). (**c**) von Mises equivalent stress (MPa).  
The first guess concept minimizes compliance of the design space, looking for a volume fraction of the DS less than 0.1. The adoption of this extreme threshold derives from the assembly requirements DR8 and OR8.2, which highlight a wireframe structure as preferred.  
For what concerns TO solver options, using Optistruct, we set up the usage of SIMP approach with a dual optimization algorithm for achieving convergence, in addition to any limit in terms of minimum member size. This choice represents an advisable set-up for concept level optimization, especially when many design variables are involved.
# Distributed Uniform Streaming Framework:
## 3 DUST Core for Event Streaming
### Core Functionality  
The DUST Core will connect the application interface module with the middleware modules and offer the general functionality that every application component requires. The DUST Core offers functionality that is available over every message protocol within DUST.  
The first functionality DUST offers is event message batching. The goal of batching is to group messages and send them together to minimize the impact on the network. This is important when a lot of small messages are being sent as in an IoT environment. The disadvantage of this approach is that some message need to wait before they are being sent. The DUST Core allows the user to configure the size of the batch before they are being send.  
The next functionality the DUST Core offers is that it allows the coordinator to change the middleware during runtime to optimize the distributed resource consumption. Changing the middleware during runtime increases the chance that a message gets lost while the system is in transition. To minimize this problem, the DUST Core will implement a basic QoS level. The DUST Core will add an ID to every message (see Table 1). This ID is used to validate which messages are missing at the receiver side. When a message is missing, the DUST Core will notify the application which messages are missing. This is an important feature when we move application components and cannot guarantee that messages will arrive. The final QoS that is achieved by using the DUST Core will depend on the used middleware in combination with the added QoS that is built on top of the middleware. The QoS of the middleware can be adapted in the configuration or in the middleware specific implementation.  
The DUST-Core library also implements the behavior required for the component monitoring. The library makes measurements such as CPU usage, RAM usage, and the number of messages sent between application components. These measurements are sent to the DUST Coordinator to be used in the distributed resource optimization process. The communication between the coordinator is also used to send commands from the coordinator to the application component such as a middleware change or a request to stop the process.