esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 4 Proposed Method  
The proposed model comprises two stages: (i) model generation and (ii) model selection. The model generation stage performs the training of all forecasting methods (ARIMA, GB, RF, SVR, DBN, ELM, MLP) to produce a pool of models. The model generation stage is crucial to the ensemble, considering that the models should be as accurate and diverse as possible. The accuracy is achieved through a search methodology over the space of the possible parameter configurations of all models, and diversity is achieved because different models with distinct learning algorithms are used in the pool. The model generation architecture is presented in Figure 1.  
The dynamic model selection stage occurs as new test instances arrive at the proposed model, HetDS(\(m\),\(k\)) (source code available at: [[https://github.com/domingos108/solar_forecasting](https://github.com/domingos108/solar_forecasting)]([https://github.com/domingos108/solar_forecasting](https://github.com/domingos108/solar_forecasting)), accessed on 5 March 2022), standing for a heterogeneous ensemble dynamic selection. Based on the local performance, the proposed method selects the best set models with a size \(m\) from the pool for each new test instance. It defines a region of competence of a size \(k\), composed of the \(k\) nearest instances in the validation set. Therefore, all models in the pool are evaluated in this region employing the RMSE metric, and the \(m\) models with the lowest RMSE are selected to perform the forecast of the test instance.  
The Euclidean distance metric is employed to determine the \(k\) nearest instances on the validation set. Therefore, different models can be selected for each test instance to improve the forecast accuracy. The architecture of the dynamic model selection is presented in Figure 2. The configuration of the number of models \(m\) used in the dynamic ensemble and the number of nearest instances \(k\) plays a major role in the performance of the proposed system.  
Lower and higher values of m increase the chance of selecting inappropriate model(s) since there is no guarantee that the best model(s) in the region of competence (validation set) will also demonstrate an improved generalization capacity on the test set. In the first scenario, when the value of \(m\) is near 1, the selected model may not achieve an improved performance on the test set, whereas in the second situation, when the value of near the size of the pool, it increases the chance of selecting inaccurate models for the ensemble combination.  
Moreover, solar irradiation data presents dynamic characteristics due to exogenous variables. In addition, the value of \(k\) defines the size of the region of competence, which should represent the dynamics of the system. The models are combined through a median operator [64] since it is more robust to outliers. It is essential to mention that if the value of \(m=1\), then no combination is performed.  
Figure 1: Model generation stage.  
Figure 2: Dynamic model selection stage.
## 2 Materials and Methods
### Human Joint Rotation Angle Estimation Model Based on SMA-BLS
#### 2.2.1 Broad Learning System  
The Broad Learning System (BLS) (Algorithm 1) represents an innovative machine learning architecture, introduced as a substitute for conventional deep learning methodologies. Its primary objectives are to streamline the complexity of the model structure and to expedite the training phase. The hypothesis was introduced by a team of investigators from Guangdong University of Technology in the year 2017. The BLS has been architected to deliver elevated performance across diverse computational tasks such as classification, regression, and clustering. Notably, it accomplishes this with a substantial reduction in computational resource demands, in contrast to the more intensive requirements of deep neural networks. The pseudo code of the BLS is shown in Figure 10. The structure of the BLS is shown in Figure 10. The following is the pseudo code of the BLS.  
```
Input: Raw MMG signals \(X\); Joint angle data \(Y\). Hyperparameters: \(N\) (feature nodes); \(D\) (projection dim); \(\lambda\) (regularization) Trained weights \(Beta\). Output: Predicted values \(\hat{Y}\). (1) Data Preparation: Extract upper envelope MMG via preprocessing  Combine envelopes to form input matrix \(X\) (2) Initialize Feature Nodes: Generate random projection matrix \(W\in\mathbb{R}^{d\times D}\)  Compute feature nodes: \(Z\gets X\cdot W\) (3) Generate Enhancement Nodes: Create random weights \(W_{e}\) and bias \(\beta_{e}\)  Compute nonlinear transform: \(E\leftarrow\xi(Z\cdot W_{e}+\beta_{e})\)  Concatenate features: \(H\leftarrow[Z\mid E]\) (4) Ridge Regression: Compute regularized weights: \(Beta\leftarrow\left(H^{T}H+\lambda I\right)^{-1}H^{T}Y\) (5) Training & Prediction: Training: Store \(H\) and \(Beta\) Prediction for new \(X_{\text{new}}\): \(Z_{\text{new}}\gets X_{\text{new}}\cdot W\) \(E_{\text{new}}\leftarrow\xi(Z_{\text{new}}\cdot W_{e}+\beta_{e})\)
```  
**Algorithm 1** Broad Learning System  
The following is the pseudo code of the BLS.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
## 1 Introduction
Most often, single BER tester (BERT) comprising both the PRBS generator and the receiver, is used whereas the loop-back is made at the far end of the fiber. Now, back to OTDR trace, if it exhibits enough large dynamic range and, consequently, large SNR, the fiber transmission performance--effectively the BER floor, can be regarded as being dominantly determined by fiber time dispersion, i.e., by the OTDR-identified reflective events along the fiber, Figure 4.  
So, the question of interest here is: can we (reliably) estimate the fiber BER floor from the related OTDR trace in this case? With this respect, still some (mostly older) OTDR units cannot fulfill the above-elaborated condition for residual BER estimation, due to their insufficient dynamic range. Therefore, in this paper, we propose a simple and cost effective solution to extend the OTDR dynamic range for given laser saturation energy determined by the chosen pulse width, which is lowering the OTDR noise floor. This makes sense, as the OTDR receiver noise figure is, to a large extent, determined by the (large) one of the ODC (as of the passive  
Figure 3: Various OTDR trace events’ signatures. device) [3]--the very first block which the returning backscatter passes through before entering the detector. Thus, we inserted a low-noise high-gain optical preamplifier as the OTDR front end, justifiably expecting that the noise figure of the series would be dominantly determined by the (lower) noise figure of such a front end, which, according to our preliminary tests in the framework of a network operator environment, enabled larger OTDR dynamic range and thus SNR, where the latter is a prerequisite for reliable OTDR aided BER floor estimation. In Section 2, we review the relevant OTDR basic analytical model to relate the according residual BER expression to the crucial time dispersion parameter--root-mean-square (rms) delay spread of the optical channel delay profile that we consider conforming to the OTDR trace. Then, we review the model for OTDR dynamic range extension by inserting the low-noise high-gain optical amplifier (OA) as the external front end, and thus fulfil the condition for estimating the residual BER. In Section 3, we provide the according analysis to qualify matching between the OTDR-based and directly measured BER floor values. We present and discuss the preliminary test results, and then summarize the conclusions in Section 4.
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
## 3 FEA Based on Concrete Damage  
### Element Type  
Based on testing research, the ABAQUS finite element software was adopted to analyze the influencing factors of the prefabricated shearing studs. Therefore, by finding the major factors which influence the bearing capacity of prefabricated shearing studs, we might provide more data support for future design. Firstly, according to the geometrical dimensions of the testing specimens, the fundamental FEM HS-MS was established, as shown in Figure 6. The 3D solid element C3D8R [24] was employed with adaptive meshing, to balance accuracy and efficiency. The element size was defined as 2 mm in the \(50\times 50\) mm region around the studs and then gradually increased to 10 mm outside the region.  
Then, based on the fundamental model, three parameters, including the concrete strength, the stud diameter, and the arrangement distance, were modified, and numerical analysis was performed. Considering the obvious slip and material nonlinearity in the push-out tests, eight-node hexahedron-reduced integral elements were applied for the studs, steel plates, and concrete. In this type of element, each node has eight degrees of freedom, which is suitable for modeling specimens with complex nonlinearity.  
Figure 6: FEA push-out model.
## 3 Results and Discussion
### Effectiveness Analysis
#### 3.2.5 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**VR Design** & \(p\)**-Value** & **Impact** \\ \hline Level of ease & 0.068 & No significant impact. \\ \hline Level of memorability & 0.365 & No significant impact. \\ \hline Level of intuitiveness (visualize data) & 0.048 & There is an impact. \\ \hline Level of intuitiveness (subset data) & 0.330 & No significant impact. \\ \hline Level of intuitiveness (merge data) & 490 & No significant impact. \\ \hline System preference & 0.053 & No significant impact. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Impact of final questions on total completion time in VR. traditional methods. In addition, 2D representations only offer partial information about the data, and it is hard for users to make sense of the data when it is not visualized as-is. The newly developed VR environment provides additional features, including an infinite virtual space and 3D representations. It offers better methods for manipulating and examining data. Hence, IVEE allows for the as-is visualization of multivariate data and their manipulation via the use of virtual hands--while impersonating an avatar--without the need to program it. All menu options within IVEE are tied to no more than two buttons. This greatly reduces clutter and helps users avoid complicated software editors. This also reduces the users' cognitive load because they do not have to recall complex menu systems and button combinations. To select an option, all users must do is press the A button on the right-hand touch controller and swipe to select. In summary, most of the participants (80%) preferred the IVEE to the R environment for completing the assigned tasks because they managed to complete them almost effortlessly, without having to type complicated lines of code. Even with a cheat sheet, the participants encountered various compiler errors, usually because of a character missing in their program that they could not spot, which resulted in a significant loss of time. IVEE eliminates these frustrating compiler errors by eliminating any input other than users' gestural interactions. Furthermore, IVEE allows for the generation of clear, sizable visualization in the form of virtual 3D objects, which users can easily navigate for precise examination.
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis
### Total Efficiency  
The total efficiency graph at Figure 10b illustrates the antenna performance across frequencies, showing values from 63% to 92%. Most frequencies maintain high efficiency (above 78%), with a notable dip to around 63% at certain points. This indicates strong radiation efficiency with minimal losses. The drop-in efficiency may relate to specific resonances or bandwidth limitations from the ground plane slots. Overall, the antenna generally performs well, though some frequencies warrant further investigation. Overall, the design effectively supports the intended frequency range, and DGS enhances performance through improved impedance matching and radiation characteristics.
## 1 Introduction
### Organization  
The remained of this paper is structured as follows. Following the presentation of an introduction and overview in the initial section, Section 2 is dedicated to the discussion of the conceptual model. Following this, Section 3 features the presentation of the mathematical formulation of the proposed method. Additionally, Section 4 addresses the modeling of the proposed method within the framework of a multi-objective optimization method, accompanied by a flowchart. Section 5 centers on the simulation and the subsequent discussion of the results derived from it. Finally, the main conclusions of the article are presented in the fifth section.
## 3 Results and Discussion
### Tribological Properties  
Figure 4 presents mass loss (Figure 4a) and wear rate (Figure 4b) for the epoxy/GNPs nanocomposites studied. Both wear parameters decreased when GNPs were added to the epoxy resin, regardless of the type. This decrease was more pronounced for materials with 5 wt.% and 8 wt.% of GNPs. When analysing the nanocomposites with 8 wt.% of the two types of GNPs, the mass loss and the wear rate were in values of 0.2 mg and 0.1\(\cdot\)10\({}^{-3}\) mm\({}^{3}\)/m, respectively. Also, Figure 4 shows the differences between the two types of GNPs. It can be observed that the analysed values were slightly lower when f-GNPs were used. However, considering the error of measurements, these differences were insignificant. In view of these results, it can be concluded that the increase of GNPs content in the epoxy resin, regardless of the type of GNPs, significantly reduced its mass loss and wear.  
The effect of graphene nanoplatelets content on the friction coefficient is presented in Figure 4c. It can be observed that at a low GNP percentage, the friction coefficient was similar to the neat epoxy resin with values near 0.5. However, when the percentages were higher (8 wt.%), a sharp drop in the  
Figure 3: Variation of microhardness (columns) and density (blue circles) of epoxy/GNPs nanocomposites as a function of GNPs content.  
friction coefficient occurred, reaching values near 0.25 for the two types of GNPs. This low friction coefficient observed in the materials containing 8 wt.% of GNPs can be related to the lubricating effect of the GNPs. They facilitated a relative movement between surfaces, i.e., they reduced the friction force needed to overcome the friction generated between the pin and the material with respect to the unlubricated surfaces. Other authors have already confirmed this effect of graphene on the friction coefficient decrease. [PERSON] et al. [34] showed that the addition of 0.1 wt.% of graphene particles to epoxy resin reduced the friction coefficient by 70.26%, and [PERSON] et al. [7] demonstrated that the lubrication of a steel surface with a layer of liquid ethanol with graphene also reduced the friction coefficient by four orders of magnitude.  
Regarding the influence of the type of GNPs, it can be observed that the friction coefficient of the materials containing f-GNPs was slightly higher than that of the materials with GNPs. As discussed in the analysis of the worn surfaces, a tribological layer (debris) was formed on these materials. The epoxy resin detached with graphene nanoplatelets torn during the test due to the higher interface resistance created between amino functionalised GNPs and the matrix. The epoxy resin constituted this debris. The debris can act as a third body in the wear mechanism, thus increasing the real contact surface and producing this slight increase in friction coefficient [35; 36].
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Healthcare Scenario: MC Children's Hospital
#### 4.1.1 Scenario Description  
A closed-world scenario involving a healthcare provider, namely Mount Cedar (MC) Children's Hospital [12, 35], was utilized to implement the proposed risk-aware RBAC and ABAC model. In hospital environments, patient information is commonly managed and stored as structured datasets, each uniquely identified by an object identifier (OID). These datasets can be grouped into classes, allowing for collective reference under a common designation. Each class is associated with an object profile, which is a metadata structure that encapsulates descriptive and contextual information about the dataset, thereby supporting more granular access control and data management decisions. While the core structure of the scenario, such as the hospital setting, roles (e.g., doctors, nurses, administrative staff), and types of access requests, remains consistent with [12; 35], we have made specific modifications to align the scenario with the requirements of our proposed model. For instance, we extended the contextual and behavioral parameters associated with each access decision, refined the assignment of risk scores based on cumulative risk analysis, and incorporated more nuanced access patterns to reflect hybrid access control conditions.  
Suppose the MC hospital has now received a four-year-old child called [PERSON], who was brought into the MC's first aid clinic by his mother, [PERSON], late Wednesday evening. The admitting staff observed that [PERSON] suffered from several bruises all over his body, a fractured rib, and a distorted shoulder [12; 35]. Let us walk through the events that would occur in this situation. Initially, [PERSON]'s doctor in the first aid clinic, Dr. [PERSON], made an access request to the system to view or read [PERSON]'s history file in the Electronic Patient Record (EPR). He also assigned [PERSON] to a care team involving a set of nurses and ordered a series of examinations. The leader nurse of the care team made an access request to the system to read [PERSON]'s file in the patient's EPR.  
When the examination results were returned, Dr. [PERSON] wrote the diagnosis and the required medication for [PERSON] and called social workers and policemen to investigate the incident, as he suspected child abuse had occurred. Therefore, one of the social workers who was responsible for helping the children in the case of abuse and a police officer requested to access [PERSON]'s medical information for investigation purposes.
## 4 Experimental Verification and Discussion  
### Prototype Fabrication  
As shown in Figure 17, 16 magnets were embedded in the rotor, which was produced by a 3D printed model in two arrangements (a Halbach array and a two-directional array). The magnet array was connected to the rotating shaft while the coil was fixed on the three-coordinate sliding table. The different rotation speeds of the rotor were provided by the machine. The gap between the coil and the magnet array was controlled by a three-coordinate sliding table using a FZ-740 motor machine manufactured by Feizhi Power Tools Co., Ltd., Ningbo, China. The three-coordinate sliding table was an LD40 produced by PDV corporation. The induced voltage was measured by an oscilloscope (Tektronix MDO3024). The structural parameters of the REMEH are shown in Table 2.
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 5 Role of Network Slicing in 6G Technology
### 6G-LEGO Framework for 6G Network Slices  
In [17], the ETSI-NFV-MANO paradigm is used by most modern network slicing technologies, with some modifications like multi-domain slicing, slice choice, etc. The architecture is a component of the independent 5G network that adheres to 3 GPP standards. Nevertheless, there are substantial disadvantages to the current network slicing for 5G implementation, especially in terms of management and orchestration: Slice management isolation is not completely handled, slice tenant management capabilities are constrained, and centralization poses major scalability and reliability concerns. Because there is an insufficient separation of concerns, slice-level activities are also not properly separated from other processes. As a consequence, several 5G network elements communicate with the overall network-slicing architecture. In this research, a novel architecture for the 6G network is presented that uses a method to maintain identity and self-orchestrated slices. To achieve this, a modular architecture called 6G-LEGO is proposed, in which single-domain slices rely on different slice-agnostic orchestrators, whereas slices with multiple domains have built-in management and orchestration abilities. The framework enables the very easy and innovative creation of additional slices by joining single domain slices.
## 6 Results and Discussions
### Scenario 4: Nakagami-m--Nakagami-q Fading  
In this scenario, unlike the previous scenarios, we studied the outage performance of the system with (a) various values of the fading parameter \(m\), constant \(q=0.5\), \(\bar{\gamma}=10\) dB and (b) \(m=3\), \(q=0.1\), \(0.5\), \((\gamma_{th})=1\) dB, \(5\) dB, \(15\) dB, as illustrated in Figure 5. The system was studied with the parameter \(m\) set at values of 0.5, 1.0, 1.5, 2.0, and 2.5, as shown in Figure 5a. By setting \(q<1\), one link always experienced worse-than-Rayleigh fading. In the other link, values of \(m<1,m=1\) and \(m>1\) indicated worse-than-Rayleigh, Rayleigh, and better-than-Rayleigh fading, respectively.  
We observed that as \(m\) increased, the performance of the system improved, as was the case in the Rayleigh-generalized gamma scenario and other previous scenarios. However, at high values of \((\gamma_{th})>12\) dB, the outage performance equaled one for all cases. Furthermore, as shown in Figure 5b, the performance of the system was also affected by changes in the fading parameter \(q\). For example, taking a fixed value of \((\gamma_{th})=1\) dB and an average SNR = 10 dB, the outage probability was calculated to be 0.2776 and 0.1409 for \(q=0.5\) and \(1\), respectively.
## 6 Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Number of Batches**} \\ \cline{2-4}  & **100** & **1000** & **7000** \\ \hline Train MAE & 57.21248041152757 & 60.34714927690382 & 10.869020548775927 \\ Train RMSE & 135.27171651469044 & 108.08156944244062 & 87.87709345147564 \\ Test MAE & 49.284213431742664 & 70.8143694844377 & 4.640554866524584 \\ Test RMSE & 52.16111599740313 & 74.90565143407075 & 7.078313259118829 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MAE and RMSE for batch number 100, 1000 and 7000. Figure 11: Train and test loss with batch number = 1000. Figure 12: Train and test loss with batch number = 7000.  
with 80% and 20% data points respectively. The train set was used to fit the model and generate a prediction for every element on the test part of the dataset. For the ARIMA rolling forecast, we manually inspect all observations in a list, namely history, which is fed with the training data and to which new observations are appended for each iteration. The results of the comparison between the LSTM and the ARIMA is shown in Figure 13. Here, it can be seen that the ARIMA model predictions are very close to the actual values. However, the LSTM does not perform very badly, being close to the actual values as well. For this problem, the ARIMA model seems to be better; however, in future work, the two models will be evaluated to multivariate problems. The ARIMA model [26] works quite well with the PM concentrations and the predicted values are close to the actual values, which verifies our results. Moreover, the LSTM, in this work [24], works quite well in predicting the value that is under investigation. The reader can check the value error percentage, which is indeed quite good. This also verifies the results of our paper because the LSTM does perform significantly well when predicting the CO values.