esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis on the Features of 2D-DPRCML
### Return Map and Uniformity Analysis  
The return maps can be employed to assess the uniformity of the sequence generated by a chaotic system. Furthermore, the character of a return map can be employed to judge whether the chaotic system is able to withstand attacks by the return maps approach [32; 43].  
We assign \(\mu\) to 4. The sequences selected for describing return maps are generated by lattice \(x\) (5, 5) in these four systems. The return maps are listed in Figure 7.  
From the results displayed in Figure 7a-c, the points in the maps progressively scatter as \(\varepsilon\) increases. Nevertheless, the shape of the return map is clearly recognizable. Thus, an attacker can easily attack these three systems through the return map approach. Moreover, the return maps are prone to change with \(\varepsilon\) in Figure 7a-c. It implies that the change of coupling coefficient can influence the chaotic behaviors of 2D-CML, 2D-NLCML and 2D-MCPML systems. In contrast, the return map in 2D-DPRCML is insensitive to \(\varepsilon\) because the points in Figure 7d are uniformly distributed in phase space irrespective of the \(\varepsilon\). Hence, the space of the optional coupling coefficient in the proposed system has been enlarged. Furthermore, the unrecognizable shape of the return map means that the return maps method or phase-space reconstruction is ineffective for the 2D-DPRCML system.  
In addition to analyzing return maps in phase space, the uniformity of the chaotic system's output sequence is another essential measure for evaluating the system's randomness. In this paper, we set iteration number \(n=5000\), coupling coefficient \(\varepsilon=0.375\) and control parameter \(\mu=4\) in chaotic systems. Then, the 100 sequences that are 5000 in length produced by the 100 lattices in the 4 chaotic systems mentioned above are acquired, respectively. The range from 0 to 1 is equally partitioned into 200 segments, and the points of the sequences acquired in every segment are measured. Statistical results are illustrated in Figure 8.  
Figure 6: Bifurcation diagrams of (**a**) 2D-CML, (**b**) 2D-NLCML, (**c**) 2D-MCPML and (**d**) 2D-DPRCML.  
Figure 7: Return maps of (**a**) 2D-CML, (**b**) 2D-NLCML, (**c**) 2D-MCPML and (**d**) 2D-DPRCML at \(\varepsilon=0.125\), 0.625 and 0.875.  
In Figure 8, the \(X\)-axis denotes the segments in the range (0, 1), the \(Y\)-axis denotes the indexes of the 10 \(\times\) 10 lattices and the \(Z\)-axis represents the number of points in a segment. It is clear that the points of sequences generated by the first two systems shown in Figure 8a,b are primarily centered around the zones 0.8 and 0.5. Moreover, in Figure 8c, the sequence points produced by 2D-MCPML are mostly distributed in the interval [0.5, 0.8]. On the contrary, the points in Figure 8d uniformly distribute throughout the interval (0, 1), indicating that the sequences produced by any lattice in 2D-DPRCML possess better uniformity. Consequently, the uniformity of our system outperforms that of the system in Figure 8a-c.
## 4 Meta-Learning-Based INDI Control and Convergence Analysis
### Convergence Analysis  
We define a metric termed ACE to evaluate the performance of learning and control:  
\[ACE=\frac{1}{TN}\Sigma_{i=1}^{N}\Sigma_{t=1}^{T}\parallel p_{t}^{(i)}-s_{t}^{( i)}\parallel, \tag{25}\]  
where \(s_{t}^{(i)}\in\mathbb{R}^{3}\) denotes the desired position of the quadrotor at time \(t\) in the \(i\)th environment. ACE describes the tracking error under \(N\) iterations of environmental conditions, with each condition persisting for \(T\) time steps.  
**Assumption 1**.: \(\forall x_{t}^{(i)}\)_, \(d_{t}^{(i)}\leq D\) and \(s_{t}^{(i)}\leq S\). \(D\) and \(S\) are finite constants._  
**Theorem 1**.: _Let constants \(\gamma\geq 0\) and \(0\leq\rho<1\). The ACE has a non-asymptotic convergence guarantee as:_  
\[ACE\leq\frac{\gamma}{1-\rho}\sqrt{\frac{1}{TN}}\sqrt{\Sigma_{i=1}^{N}\Sigma_{t =1}^{T}\parallel G(B_{t}^{(i)},u_{t}^{(i)})-G(L_{t}^{(i)},u_{t}^{(i)})+d_{t}^{ (i)}-X_{t}^{(i)}\parallel^{2}}_{[1:3]}. \tag{26}\]  
Proof.: To prove the convergence of ACE, we replace the position vector in ACE with the state \(x_{t}^{(i)}\). The desired state \(X_{t}^{(i)}\in\mathbb{R}^{6}\) is bounded. Let constant \(\beta\geq 0\). From the exponentially input to state stable property of (22) we can obtain:  
\[\frac{1}{TN}\Sigma_{i=1}^{N}\Sigma_{t=1}^{T}\parallel x_{t}^{(i) }-X_{t}^{(i)}\parallel\] \[\stackrel{{(a)}}{{\leq}}\frac{1}{TN}\Sigma_{i=1}^{N} \Sigma_{t=1}^{T}(\beta\rho^{t-1}\parallel x_{1}^{(i)}\parallel+\gamma\Sigma_ {k=1}^{t-1}\rho^{t-1-k}\parallel G(B_{k}^{(i)},u_{k}^{(i)})-G(L_{k}^{(i)},u_{ k}^{(i)})+d_{k}^{(i)}-X_{k}^{(i)}\parallel)\] \[\stackrel{{(b)}}{{=}}\frac{1}{TN}\Sigma_{i=1}^{N} \Sigma_{t=1}^{T}\gamma\Sigma_{k=1}^{t-1}\rho^{t-1-k}\parallel G(B_{k}^{(i)},u _{k}^{(i)})-G(L_{k}^{(i)},u_{k}^{(i)})+d_{k}^{(i)}-X_{k}^{(i)}\parallel\] \[\stackrel{{(c)}}{{\leq}}\frac{\gamma}{1-\rho}\frac{1 }{TN}\Sigma_{i=1}^{N}\Sigma_{t=1}^{T-1}\parallel G(B_{t}^{(i)},u_{t}^{(i)})-G (L_{t}^{(i)},u_{t}^{(i)})+d_{t}^{(i)}-X_{t}^{(i)}\parallel\] \[\stackrel{{(d)}}{{\leq}}\frac{\gamma}{1-\rho}\sqrt{ \frac{1}{TN}}\sqrt{\Sigma_{i=1}^{N}\Sigma_{t=1}^{T}\parallel G(B_{t}^{(i)},u _{t}^{(i)})-G(L_{t}^{(i)},u_{t}^{(i)})+d_{t}^{(i)}-X_{t}^{(i)}\parallel^{2}}. \tag{27}\]  
(a) and (b) are based on the assumptions \(X_{1}^{(i)}=0\) and \(x_{1}^{(i)}=0\), respectively. (c) is from the geometric series and (d) is from the Cauchy-Schwarz inequality. \(u_{t}^{(i)}\) is derived from  
\[u=(R\begin{bmatrix}0\\ 0\\ B(1:)\end{bmatrix})^{\dagger}(F_{0}+m(-k_{x}e_{p}-k_{v}e_{v}-v_{0}+v_{d})), \tag{28}\]  
and \(L_{t}^{(i)}\) is related to the learned results \(\phi\) and \(\hat{c}^{(i)}\), which has a convergence guarantee. When the rotation angles are not large, it is evident that \(R\) is bounded. By appropriately setting the value of \(k_{x}\) and \(k_{v}\), we ensure that the error from \(X_{t}^{(i)}\) in (27) is bounded. We can infer that ACE converges non-asymptotically. Further, we can also deduce that the improvement in the control law and learning performance significantly reduces ACE, as demonstrated in the subsequent section.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.1 Medical Data Security and Privacy  
Data security is very crucial in telemedicine designs in order to avoid incorrect diagnoses. The high proliferation of medical data on the cloud due to ease of access and telemedicine deployment has increased data vulnerability. Potential attacks such as information disclosure, Denial of Service attacks (DoS), cloud malware injection attacks, man-in-the middle cryptographic attacks, spoofing, and collusion attacks, as seen in [134], must be addressed in telemedicine deployment. This is a major barrier to the proper implementation of telemedicine, as noted by [135].
# Video Super Resolution Using a Selective Edge Aggregation Network
###### Abstract  
An edge map is a feature map representing the contours of the object in the image. There was a Single Image Super Resolution (SISR) method using the edge map, which achieved a notable SSIM performance improvement. Unlike SISR, Video Super Resolution (VSR) uses video, which consists of consecutive images with temporal features. Therefore, some VSR models adopted motion estimation and motion compensation to apply spatio-temporal feature maps. Unlike the models above, we tried a different method by adding edge structure information and its related post-processing to the existing model. Our model \"Video Super Resolution Using a Selective Edge Aggregation Network (SEAN)\" consists of a total of two stages. First, the model selectively generates an edge map using the target frame and also the neighboring frame. At this stage, we adopt the magnitude loss function so that the output of SEAN more clearly learns the contours of each object. Second, the final output is generated using the refinement (post-processing) module. SEAN shows more distinct object contours and better color correction compared to other existing models.  
computer vision; video super resolution; edge detection +
Footnote †: journal: 2022, 12, 2492. [[https://doi.org/10.3390/app12052492](https://doi.org/10.3390/app12052492)]([https://doi.org/10.3390/app12052492](https://doi.org/10.3390/app12052492))
## 6 Key Challenges and Mitigation Strategies
### Data Heterogeneity and Fairness  
The performance and fairness of FL systems are significantly impacted by data heterogeneity, commonly referred to as Non-IID data, which is prevalent in IoT environments [113]. Data distributions often vary substantially across devices due to differing local environments, usage patterns, or sensor types (e.g., label or feature skew). This heterogeneity can hinder the convergence of standard FL algorithms like FedAvg and lead to a global model that performs poorly for specific clients. Furthermore, biases present in local data or even within the pre-trained base LLM can be amplified or unfairly distributed across participants through the FL process, and measuring or mitigating such biases in a decentralized manner remains difficult [37]. Strategies to address Non-IID data and promote fairness include using Robust Aggregation algorithms (like FedProx), designed to be less sensitive to diverging updates [44], and employing PFL techniques that tailor parts  
Figure 5: Trade-off between trainable parameter ratio and downstream task performance (e.g., typical accuracy observed on natural language understanding tasks from benchmarks like GLUE) for various LLM fine-tuning methods. Full fine-tuning updates 100% of parameters, whereas PEFT approaches such as LoRA (1%), adapter (2%), and prompt tuning (<0.1%) offer large savings in parameter updates at the cost of some performance.  
of the model to local data, although this adds complexity [73; 74]. Fairness-aware algorithms explicitly try to balance performance across different client groups, sometimes at the cost of overall average accuracy. Another approach involves augmenting local data with synthetic data (potentially generated by LLMs) or relevant public data, but this requires careful consideration of privacy implications [103; 104].
###### Abstract  
The preferences of travelers determines the utility of daily activity plans. Decision-makers can affect the preference of travelers when they force private car users to use park-and-ride (P&R) facilities as a way of decreasing traffic in city centers. The P&R system has been shown to be effective in reducing uninterrupted increases in traffic congestion, especially in city centers. Therefore, the impacts of P&R on travel behavior and the daily activity plans of both worker and shopper travelers were studied in this paper. Moreover, autonomous vehicles (AVs) are a promising technology for the coming decade. A simulation of the AV as part of a multimodal system, when the P&R system was integrated in the daily activity plans, was carried out to determine the required AV fleet size needed to fulfill a certain demand and to study the impacts of AVs on the behavior of travelers (trip time and distance). Specifically, a group of travelers, who use private cars as their transport mode, was studied, and certain modifications to their daily activity plans, including P&R facilities and changing their transport mode, were introduced. Using the MATSim open-source tool, four scenarios were simulated based on the mentioned modifications. The four scenarios included (1) a simulation of the existing transport modes of the travelers, (2) a simulation of their daily activity plans when their transport modes were changed to AVs, (3) a simulation of the travelers, when P&R facilities were included in their activity chain plans, and (4) a simulation of their daily activity plans, when both P&R and AVs were included in their activity chain plans. The result showed that using the P&R system increased overall travel time, compared with using a private car. The results also demonstrated that using AVs as a replacement for conventional cars reduced travel time. In conclusion, the impact of P&R and AVs on the travel behavior of certain travelers was evaluated in this paper.  
autonomous vehicle; MATSim; optimization; park and ride; utility function +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Methods: Algorithms and Machine Learning Models
#### 5.1.3 Random Forest  
Random Forest (RF) algorithms are based on the construction of a large number of decision trees to create a learning model. Each decision tree decides a class and the most common class ends up being the final prediction of the model. Its use in indoor positioning has been decreasing, and in the year 2021 no articles were detected that used it. In 2020, there were only three articles that used it, and none of these used scenarios with obstacles to perform the experiments.  
The best accuracy found with RF is 1.68, from [PERSON] et al. [36] in 2020, in a space of 112 m\({}^{2}\). However, in 2018 the authors of [37] claimed an accuracy of 1.20 m in a space 75 m\({}^{2}\), and the authors of [38] obtained an accuracy of 0.4033 m in a space of 80 m\({}^{2}\). From these results it seems that RF is an algorithm suitable for small spaces.
## 6 Result and Discussion
### Optimization Result
#### 6.1.2 Maneuver Requirement
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fault Condition**} & \multicolumn{6}{c}{**Parameters (Angles in Degree)**} & \multirow{2}{*}{**Cost Function**} \\ \cline{2-2} \cline{4-9}  & \multicolumn{2}{c}{**Initial Value**} & \multicolumn{6}{c}{**Optimization**} & \multirow{2}{*}{**Cost Function**} \\ \hline Actuator 1 failed & \(\begin{bmatrix}0\\ 15\\ 15\\ 15\\ 15\\ 15\\ 15\\ 15\\ 15\\ \end{bmatrix}\) & \(\begin{bmatrix}30&0&-20&20&20&20&20&-20\\ 30&20&0&20&-20&20&10\\ 30&20&20&0&20&20&-12\\ 30&20&20&0&20&20&-12\\ 30&20&20&-20&0&20&20\\ 30&20&-5&-20&20&0&-10\\ 30&20&17&20&20&-20&0\\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mission-based parameter optimization results for single actuator failure at a time.
## 4 Experiment
### Parameter Sensitivity
#### 4.7.3 Impact of Knowledge Propagation Layers and Hop  
In the experiments, the impact of neighborhood information on the performance of the algorithm in this paper was analyzed by varying the number of hops in the knowledge graph. As shown in Figure 9, the AUC peaks when the number of layers on MovieLens-1M, MovieLens-20M, BookCrossing, and Last.FM are 2, 2, 3, and 2, respectively. This is due to the fact that the more hops away from the user's historical click items in the knowledge graph, the lower the relevance of the entity to the user. However, when the number of hops is too small, it is difficult to obtain additional information from the knowledge graph.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 1 Introduction
### Contributions of the Article  
In this paper, we propose a classification scheme that, to our knowledge, has never been modeled for classifying IRS-enabled GEO satellite signals, namely, M-QAM and M-PSK signals. The classification scenario is based on a Gabor filter network (GFN). The GFN has three parameters, i.e., shift, scale, and modulation parameters; these parameters are tuned and optimized for the accurate classification of the modulation formats. First, we describe the system model that characterizes the signal received at the receiver end. Then, GFN parameters are optimized using a genetic algorithm and its local search variants, such as GA-Fminsearch, GA-Pattern Search, GA-Fminunc, and GA-Fmincon. The genetic algorithm-assisted GFN provides the optimized values of GFN parameters for classifying M-QAM and M-PSK signals. The simulations investigate the performance trade-off between various approaches in the system. The impact of increasing the sample size is also explored. The major contributions include the following:  
1. We introduce a scheme for IRS-equipped GEO satellite signals with proper channel modeling and system characterization.
2. The parameters of the GFN are analyzed and optimized for the classification of M-QAM and M-PSK signals.
3. Finally, a performance analysis of the proposed classifier structure was conducted with and without the IRS.
4. The findings show that the classification accuracy of the proposed IRS-based methodology is better than that of the benchmark without the IRS-based methodology.
# A Grey Wolf-Based Method for Mammographic Mass Classification
## 3 The Proposed Method  
The proposed method, as explained in Figure 1, consists of four phases: Region of interest (ROI) segmentation, feature extraction, feature selection and classification.  
In phase 1, the ROI is manually segmented in a way that the given centre of abnormality is the centre of the cropped ROI. In phase 2, features are extracted using wavelet and curvelet. Using a concatenation method, a fusion of wavelet and curvelet features are constructed. This gave three types of features: wavelet, curvelet and fused wavelet and curvelet features.  
The curvelet and wavelet transform have proved their capability to represent the image in the frequency domain accurately. It decomposed the ROI into several sub-bands. The statistical features described in Section 2.3 are calculated for each sub-band to construct the feature vectors. In this study, we have the following three scenarios.  
The first one is to compute the ten features from the curvelet sub-bands (i.e., wedge). In order to achieve this task, the curvelet transform is applied to ROI, as shown in Figure 2. The ten features (described above) for each wedge are calculated using Equations (8)-(17). In this work, the curvelet is applied using four scales with 16 angles. Hence, the curvelet decomposition produced 81 wedges, so a total of 810 features are calculated to construct a features vector of the curvelet.  
The second scenario is to employ the wavelet decomposition of 16 bands to extract each band's ten features. This is accomplished as follows. Daubechies 4 (DB4) wavelet is proved to be efficient as reported in [6]. This is why it was used in our proposed method to decompose the ROI image into four levels. Each of these levels composes of four sub-bands: approximation (A), horizontal (H), vertical (V) and diagonal (D). Hence, a total of 16 sub-bands can be obtained, see Figure 3. For each sub-band, the ten features are calculated. Therefore, a total of 160 features will be extracted from each ROI (i.e., 16 sub-bands \(\times\) 10 Features) producing the wavelet feature vector.  
The third scenario is to fuse the feature vector of both the curvelet and the wavelet. This produced a feature vector with length (810 + 160 = 970 features). The obtained feature vectors could improve the classification rate of mammographic images.  
Figure 1: The proposed method for mammographic image classification.  
In phase 3, the BGWO algorithm, described in Section 2.4, is applied to select the best set of features given the best performance which is measured in the classification phase (i.e., phase 4). In this phase, the random forest, introduced earlier in Section 2.5, has been used to classify the selected features into normal or abnormal. The same set of features is also used to differentiate between benign or malignant cases.
## 4 Cost Prediction Performance Analysis
### Accuracy by R-Squared Analysis  
The coefficient of determination is calculated as the square of the correlation coefficient. Unlike the correlation coefficient, the coefficient of determination is a value expressed by quantifying the degree of influence between variables or the degree of causal relationship. The coefficient of determination is a number used in regression analysis, not correlation analysis, and is an evaluation index showing the performance of the regression model.  
R-square is generally denoted by \(R^{2}\). It is an indicator of how well the independent variable explains the dependent variable in a regression model. The higher the coefficient of determination, the better the independent variable explains the dependent variable, and its increases as the number of independent variables increases. Therefore, it may be inappropriate to evaluate the regression model by relying only on the coefficient of determination. Adjusted R-squared is sometimes used to compensate for this. The R-squared, \(R^{2}\) score has a range between 0 and 1, and the closer it is to 1, the higher the regression model is associated with the data. The \(R^{2}\) score can be calculated as in Equation (8).  
\[R^{2}score=\frac{\text{SSE}}{\text{SST}}=1-\frac{\text{SSR}}{\text{SST}} \tag{8}\]  
Here, SST represents the sum of total squares, and SSE represents the sum of regression squares, which is the difference between the average of the estimated and observed values of the regression equation, and SSR represents the sum of whole squares. These SST, SSE, and SSR can be calculated as in Equations (9)-(11).  
\[\text{SST}=\sum_{i=1}^{n}(y_{i}-\overline{y})^{2} \tag{9}\]  
\[\text{SSE}=\sum_{i=1}^{n}(g_{i}-\overline{y})^{2} \tag{10}\]  
Figure 5: Feature Importance.  
\[\text{SSR}=\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2} \tag{11}\]  
Here, \(y_{i}\) is the observed value, \(\overline{y}\) is the average of the observed value, and \(\hat{y_{i}}\) is the predicted value from regression.
## 1 Introduction  
### Background and Motivation  
The Volt/Var control (VVC) is defined as the regulation of a voltage profile over the distribution feeders and optimum reactive power flows in the system [1,2]. The traditional VVC scheme was employed to find appropriate coordination between the on-load tap changer (OLTC), voltage regulators, and all of the switched shunt capacitors (Sh.Cs) in distribution networks.  
Due to the increasing penetration of DGs, it appears reasonable to suppose that some ancillary services, such as reserve and reactive power support, could be provided by DGs in an efficient and economical manner [3, 4, 5]. Consequently, DGs could incorporate the VVC problem. At the present time, most of the DGs are generally operated at unity or at a fixed power factor for avoiding interference with the other Volt/Var regulation devices connected to distribution systems [5,6]. Since the distribution systems usually have a radial structure, which comprises the distribution lines with low X/R ratios, the active power of DGs has a significant effect on the VVC performance [7]. Generally, VVC in a distribution system can be implemented in a centralized or decentralizedmanner. Centralized offline control have been implemented to determine dispatching schedules for switched Sh.Cs, OLTC settings at substations and the active/reactive power of DGs for the day ahead, according to an optimization problem based on load demand forecasts [7; 8; 9; 10; 11; 12], while research on decentralized real-time control have aimed to control the VVC devices based on real-time and local measurements [13; 14; 15].  
Although, much research on the reactive power market in a restructured power system, at the wholesale level, has been performed [16; 17; 18; 19], in few works the potential of expansion of a reactive power market at the distribution level with DGs has been reported [3; 20; 21]. Additionally, the reactive power capability of DG has not been considered in the previous research on the VVC problem. Therefore, by ever-increasing integration of DGs into the distribution networks, development of a market-based approach for active and reactive power scheduling in distribution systems with DGs is gaining more importance.