esa-sceva/satcom-chunk-collection huggingface dataset:
# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
## 1 Introduction
In this paper, we present a novel approach for constructing RBF networks for pattern classification problems. An iterative self-constructing clustering algorithm is used to produce a desired number of clusters from the training data. Accordingly, the number of nodes in the hidden layer is determined. Basis functions are then formed, and their centers and deviations are initialized to be the centers and deviations of the corresponding clusters. Then the parameters of the network are refined with a hybrid learning strategy, involving hyperbolic tangent sigmoid functions, steepest descent backpropagation and least squares method. As a result, optimized RBF networks are obtained. Our approach can offer advantages in practicality. The number of nodes in the hidden layer is determined and basis functions are derived automatically, and higher classification rates can be achieved through the hybrid learning process. Furthermore, the approach is applicable to construct RBF networks for solving both single-label and multi-label pattern classification problems. Experimental results have shown that the proposed approach can be used to solve classification tasks effectively. We have been working on RBF networks for years, and have developed different techniques [26; 27; 36; 37]. This paper is mainly based on the MS thesis of the first author, [PERSON] (who preferred to use a different English name, [PERSON], in the thesis) [38], supervised by [PERSON], with the following contributions:  
1. An iterative self-constructing clustering algorithm is applied to determine the number of hidden nodes and associated basis functions. 2. The centers and deviations of the basis functions are refined through the steepest descent backpropagation method. 3. Tikhonov regularization is applied in the optimization process to maintain the robustness of the output parameters. The hyperbolic tangent sigmoid function is used as the activation function of the output nodes when learning the parameters of basis functions. The rest of this paper is organized as follows: Section 2 gives an overview of the related work. The proposed approach is described in detail in Sections 3-5. Basis functions are derived by clustering from training data and a hybrid learning is used to optimize network parameters. Experimental results are presented in Section 6. The effectiveness of the proposed approach on single-label and multi-label classification is demonstrated. Finally, concluding remarks are given in Section 7.
## 3 Adaptive Distributed Student's T EKF
### Adaptive Distributed Student's T Filter  
Using Allan variance and the distributed Student's t EKF, we can now introduce our adaptive distributed Student's t EKF, as outlined in the following algorithm. From Algorithm 1, we can see that, firstly, we employ the local t EKF to estimate the target object's position by fusing \(r_{t}^{(i)}\) (lines 4-10); then, we compute \(\hat{\mathbf{R}}_{t}^{(i)}\) by Equation (9) (line 11). With \(\hat{\mathbf{X}}_{t}^{(i)}\) and \(\hat{\mathbf{P}}_{t}^{(i)}\), we can then obtain the final output of the filter.  
```
Data:\(r_{t}^{(i)}\), \(\hat{\mathbf{s}}_{t}\), \(\hat{\mathbf{P}}_{t}\), \(\mathbf{Q}_{0}^{(i)}\), \(\mathbf{R}_{0}^{(i)}\) Result:\(\hat{\mathbf{s}}_{t}\), \(\hat{\mathbf{P}}_{t}\) begin for\(t=1:\infty\)do for\(i=1:N\)do \(\hat{\mathbf{s}}_{t}^{(i)}=\mathbf{s}_{t}\); \(\hat{\mathbf{P}}_{t}^{(i)}=\mathbf{P}_{t}\); \(\hat{\mathbf{s}}_{t}^{(i)-}{=}\mathbf{A}\hat{\mathbf{s}}_{t}^{(i)}\); \(\hat{\mathbf{P}}_{t}^{(i)-}{=}\mathbf{A}\hat{\mathbf{p}}_{t}^{(i)}{\mathbf{A} }^{\mp}\mathbf{Q}_{t}^{(i)}\); \(\eta_{3,t}=\eta_{3,t-1}+m_{r_{t}^{(i)}}\); \(\hat{\mathbf{s}}_{t}^{(i)}=\) \(\hat{\mathbf{s}}_{t}^{(i)-}+\hat{\mathbf{P}}_{t}^{(i)-}\Big{(}\mathbf{G}_{t} ^{(i)}\Big{)}^{T}\Big{(}\mathbf{G}_{t}^{(i)}\hat{\mathbf{P}}_{t}^{(i)-}\Big{(} \mathbf{G}_{t}^{(i)}\Big{)}^{T}+\mathbf{R}_{t}^{(i)}\Big{)}^{-1}\Big{(}r_{t}^{ (i)}-g\Big{(}\mathbf{s}_{t}^{(i)-}\Big{)}\Big{)}\); \(\hat{\mathbf{P}}_{t}^{(i)}=\) \(\frac{\eta_{3,t-1}+\Lambda_{r_{t}^{(i)}}^{2}}{\eta_{3,t-1}+m_{r_{t}^{(i)}}^{ (i)}}\bigg{[}\hat{\mathbf{P}}_{t}^{(i)-}-\hat{\mathbf{P}}_{t}^{(i)-}\Big{(} \mathbf{G}_{t}^{(i)}\Big{)}^{T}\bigg{(}\mathbf{G}_{t}^{(i)}\hat{\mathbf{P}}_{t }^{(i)-}\Big{(}\mathbf{G}_{t}^{(i)}\Big{)}^{T}+\mathbf{R}_{t}^{(i)}\bigg{)}^{- 1}\mathbf{G}_{t}^{(i)}\hat{\mathbf{P}}_{t}^{(i)-}\bigg{]}\);  Compute \(\hat{\mathbf{R}}_{t}^{(i)}\) by Equation (9); endfor \(\hat{\mathbf{P}}_{t}=\Big{(}\hat{\mathbf{P}}_{t}^{(1)}\Big{)}^{-1}+\Big{(} \hat{\mathbf{P}}_{t}^{(2)}\Big{)}^{-1}+\cdots+\Big{(}\hat{\mathbf{P}}_{t}^{(N )}\Big{)}^{-1}\); \(\hat{\mathbf{s}}_{t}{=}\hat{\mathbf{P}}_{t}\bigg{[}\Big{(}\hat{\mathbf{P}}_{t }^{(1)}\Big{)}^{-1}\hat{\mathbf{s}}_{t}^{(1)}+\Big{(}\hat{\mathbf{P}}_{t}^{(2) }\Big{)}^{-1}\hat{\mathbf{s}}_{t}^{(2)}+\cdots+\Big{(}\hat{\mathbf{P}}_{t}^{(N )}\Big{)}^{-1}\hat{\mathbf{s}}_{t}^{(N)}\bigg{]}\);  endfor end
```  
**Algorithm 1**Adaptive distributed Student's t extended Kalman filter for model (2) and (3)
## 2 Theoretical Background
### Wavelet Denoising  
Vibration signals are frequently affected by diverse sources and levels of noise, which can significantly hinder the extraction of useful information from the signal. Among the various denoising techniques, wavelet-based denoising has demonstrated excellent performance in various applications [27; 28; 29; 30] due to (i) its inherent capability to handle both stationary and nonstationary signals and (ii) its bank filter-based computation. The steps that describe this process are as follows:  
1. Introduce a noisy signal into the process. A noisy signal is described as follows [27]: \[s_{i}=f+\sigma e_{i}\] (3) where \(f\) is the original signal, \(e_{i}\) is a Gaussian white noise, and \(\sigma\) is the noise level.
2. In the next step, the noisy signal is decomposed using the discrete _WT_ (_DWT_). This method allows for the decomposition of the signal into sets of coefficients at different frequency levels according to the following [30]: \[\begin{array}{c}DWT\big{[}n,\ a^{j}\big{]}=\sum\limits_{m=0}^{N-1}x[m]\psi_ {j}^{*}[m-n]\\ \psi_{j}[n]=\frac{1}{\sqrt{a}}\psi\Big{(}\frac{n}{a^{j}}\Big{)}\end{array}\] (4) where \(x[m]\) is the original discretized signal, \(\psi\) is the discretized mother wavelet, \(a\) is a scaling parameter, \(n\) is the shift parameter that determines the location of the wavelet, and the parameter \(j\) is the decomposition level.
3. To eliminate the noise at each decomposition level, a threshold value is necessary. There are different threshold estimators, e.g., _rigsure_, _gatvolog_, _hearsure_, and _minimaxi_. Once the threshold estimator has been selected, either hard or soft thresholding can be applied [27; 28; 29; 30].
4. Finally, the inverse DWT is used to reconstruct the denoised signal using the newly thresholded coefficients. Further details can be found in [27; 28; 29; 30].  
Once the noise has been removed from the signal, useful frequency information can be extracted more effectively.
# SACRB-MAC: A High-Capacity MAC Protocol for Cognitive Radio Sensor Networks in Smart Grid
## 3 Analytical Model  
The CR nodes can only access the licensed channel when no PU activities are found. Therefore, each CR user has a probability to access some licensed channel. Let \(\mathcal{P}_{ac}^{j,i}\) denote the probability for CR users to access the \(i^{th}\) channel in the \(jth\) band, which can be given by:  
\[\mathcal{P}_{ac}^{j,i}=P_{busy}^{j,i}\left(1-P_{d}^{i}\right)+P_{ i\_{d}e}^{j,i}\left(1-P_{f}^{i}\right),\;\;i\in\mathbf{S} \tag{13}\]  
In the SACRB-MAC, the probability of single hop transmission failure over the \(i^{th}\) channel in the \(j^{th}\) band is given by:  
\[\mathcal{P}_{fail}^{j,i}=\mathcal{P}_{ac}^{j,i}\left[1-\left(1-p_{b}\right)^{ d+m}\right] \tag{14}\]  
where \(d\) and \(m\) denote the sizes of data frame and micro-frame in bits, respectively; \(p_{b}\) is the bit error probability.  
Reliability is an important property of a network and usually evaluated in terms of _Packet Delivery Ratio_ (PDR) [10], which is defined as the fraction of the numbers of packets received and packets sent. Analytically, the reliability for SACRB-MAC (with \(N_{R}\) valid receivers) on the selected channel set \(SC\) is given by:  
\[\mathcal{PDR}=1-\left[1-\prod_{s=1}^{S}\left(1-P_{fail}^{s}\right)\right]^{N_ {R}\left(\chi+1\right)} \tag{15}\]  
where \(S\) denotes the number of selected channels, and \(P_{fail}^{s}\) denotes the probability of single hop transmission failure over the \(s^{th}\) selected channel.  
The single hop delay for SACRB-MAC over the selected channels is given by:  
\[\mathcal{D}=\chi\cdot\left(T_{pr}+T_{d}+T_{\text{CW}}\right)+\chi_{ss}\cdot T _{ss}+\Delta t_{y}+T_{CI}\cdot\left(\chi_{ss}-1\right) \tag{16}\]  
where \(T_{pr}\) denotes the duration of the preamble frame, \(T_{d}\) denotes the duration of data frame, \(\chi_{ss}\) denotes the number of spectrum sensing events, and \(\chi\) denotes the retransmission count, which is given by:  
\[\chi=\sum_{k=1}^{K}k\cdot P_{k}=\left(1-\prod_{s=1}^{S}\left(1-P_{fail}^{s} \right)\right)^{k\cdot N_{R}}\left[1-\left(1-\prod_{s=1}^{S}\left(1-P_{fail}^{ s}\right)\right)^{N_{R}}\right] \tag{17}\]  
where \(P_{k}\) denotes the probability that the transmission is successful after \(k\) retransmissions, and \(K\) denotes the maximum number of retransmissions. For a multi-hop scenario, the end-to-end delay over \(Q\) hops is given by \(\sum_{q=1}^{Q}\mathcal{D}\).  
The potential bandwidth of the \(i^{th}\) channel in the \(j^{th}\) band can be represented by:  
\[W_{x,y}^{j,i}=\mathcal{P}_{ac}^{j,i}B_{x,y}^{j,i} \tag{18}\]  
Therefore, the total capacity of the link between \(x\) and \(y\) is given by:  
\[C_{x,y}=\sum_{s=1}^{S}\mathcal{P}_{ac}^{s}\cdot C_{x,y}^{s} \tag{19}\]  
where \(C_{x,y}^{s}\) denotes the capacity of the \(s^{th}\) selected channel, and \(\mathcal{P}_{ac}^{s}\) denotes the probability to access the \(s^{th}\) selected channel.
# Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory
###### Abstract  
In this paper, we propose 2D dynamic visual servoing (Dynamic IBVS), where a quadrotor UAV tries to track a moving target using a single facing-down perspective camera. As an application, we propose the tracking of a car-type vehicle. In this case, data related to the altitude and the lateral angles have no importance for the visual system. Indeed, to perform the tracking, we only need to know the longitudinal displacements (along the \(x\) and \(y\) axes) and the orientation along the \(z\)-axis. However, those data are necessary for the quadrotor's guidance problem. Thanks to the concept of differential flatness, we demonstrate that if we manage to extract the displacements according to the three axes and the orientation according to the yaw angle (the vertical axis) of the quadrotor, we can control all the other variables of the system. For this, we consider a camera equipped with a vertical stabilizer that keeps it in a vertical position during its movement (a gimbaled camera). Other specialized sensors measure information regarding altitude and lateral angles. In the case of classic 2D visual servoing, the elaboration of the kinematic torsor of the quadrotor in no way guarantees the physical realization of instructions, given that the quadrotor is an under-actuated system. Indeed, the setpoint has a dimension equal to six, while the quadrotor is controlled only by four inputs. In addition, the dynamics of a quadrotor are generally very fast, which requires a high-frequency control law. Furthermore, the complexity of the image processing stage can cause delays in motion control, which can lead to target loss. A new dynamic 2D visual servoing method (Dynamic IBVS) is proposed. This method makes it possible to generate in real time the necessary movements for the quadrotor in order to carry out the tracking of the target (vehicle) using a single point of this target as visual information. This point can represent the center of gravity of the target or any other part of it. A control by flatness has been proposed, which guarantees the controllability of the system and ensures the asymptotic convergence of the generated trajectory in the image plane. Numerical simulations are presented to show the effectiveness of the proposed control strategy.  
target and feature tracking; autonomous unmanned aerial vehicles; quadrotor; dynamic image-based visual servoing; flatness control 2023
# An Active Path-Associated Cache Scheme for Mobile Scenes
## 5 Simulation Results and Analysis
### Setting of Experimental Environment  
We use Icarus as the simulation software [47]. Icarus is an ICN simulation using python, and it is licensed under the terms of the GNU GPLv2 license. Object-oriented encapsulation allows users to easily expand many useful modules and functions on its original basis. In addition, it can efficiently simulate a large number of requests in the network and support the implementation of various designed caching methods. To fully evaluate the performance of our method, we have conducted extensive experiments on various real networks, including WIDE (Japanese Academic Network), GARR (Italian Academic Network), and GEANT (European Academic Network). It is worth mentioning that APs are internal nodes with degree = 1 in the topology.  
To fully test the effect of our strategy, we selected the cache hit ratio, link load, and latency as our evaluation indicators. To ensure the confidence of experimental statistics, each experiment is run five rounds, and then the results of the experiment are averaged. For specific experimental parameters, to simulate a real scenario, we set the requested data volume to \(6\times 10^{5}\) (here refers to the total request volume of mobile users) and set the content in the network to \(3\times 10^{5}\). Before actually running the experiment, we set preheating data to \(10^{6}\). Setting a higher preheat value will help subsequent experiments obtain more stable results. In addition, we assume that the mobile users conform to the Poisson distribution, and the number of users accessing new APs per second is 10. Like other conventional ICN cache research, we also assume that the content requested by users usually also meets the Zipf popularity distribution, so we set the parameter skewness  
Figure 6: The process of deciding nodes and caching content.  
(\(\alpha\)) of the Zipf distribution to have a range of [0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]. At the same time, the size of the cache space is also a key factor affecting performance, and lower cache capacity usually means higher cache competition. Therefore, we set the range of the ratio of the cache space to the total content size to be [0.004, 0.002, 0.01, 0.02, 0.03, 0.04, 0.05]. Regarding cache replacement, we use LRU as the cache eviction strategy by default for all cache strategies. All configuration contents are shown in Table 2.
# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 1 Introduction  
Query running time prediction is useful for effective resource management, query optimization, accurate scheduling and user experience management [1]. Some prediction algorithms have been proposed in the technical literature mainly for database systems [1, 2, 3, 4, 5, 6, 7]. In this paper, we focus on query running time prediction in Web search engines (WSE). Large-scale Web search engines are designed to process hundreds of thousands of queries per second where each query has to be processed within a fraction of a second. To achieve this goal, search engines are composed of services capable of processing large amounts of data. One of these services is the index service which is responsible for calculating the top-k documents for user queries. The index service executes a ranking algorithm on a data structure called inverted index or inverted file [8, 9]. For each term, the inverted index keeps a posting list with the document identifiers where the term appears in and data for document ranking such as the frequency of the term in the document. Document ranking algorithms compute a similarity score for every document that contains any of the query terms.  
A dynamic pruning technique named Weighted AND (WAND) is a strategy that first runs a fast-approximate evaluation on candidate documents, and then makes a full costly evaluation limited to the promising candidates only. This algorithm enables many documents to be skipped and thereby it is able to achieve efficient performance by reducing the total number of full document score evaluations. The BM-WAND extends the WANDby skipping consecutive sets of documents by using a block-wise inverted index where each posting list block has a maximum score.
# Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture
## 3 Plants Light Requirements
### Ultraviolet (IV) Light Effect  
UV light is at the range of 100-400 nm in the non-visible spectrum of light. UV light can stimulate the production of important chemical compounds like flavonoids and alkaloids in plants [27]. Flavonoids are responsible for the colors of many fruits and flowers and are proven to be beneficial for human health. They are antioxidants,and they reduce damage caused by free radicals, in addition to having anti-cancer, anti-inflammatory, and anti-viral properties. Moreover, they increase the nutritional value and shelf life of products. Alkaloids, on the other hand, are used in medicine for their sedative, analgesic (pain-relieving), and stimulant effects. However, other alkaloids are toxic and even deadly when taken in high doses. UV light alerts the plant defense system, increases its stress, and reduces growth and yield [64]. This is why only low intensities of UV light in the range of 1-10 umol/m2s are applied to plants in small durations. The exact values are determined by monitoring plants' stress when exposing them to different UV light intensities and durations.
# Efficient Power Control Framework for Small-Cell Heterogeneous Networks
## 5 Conclusions and Future Work  
Heterogeneous networks are going to play a key role beyond 5G networks. In this regard, a feasible solution to the power control problem of small-cell heterogeneous networks is provided in this work. More specifically, we have developed an optimization framework that allows the small-cell BSs to switched off when the traffic demands are not critical. The sparse structure of the problem helps in guiding the selection of small-cell BSs that needs to be switched off and improve user admission. The simulation results demonstrate the effectiveness of the proposed solution. When compared to the conventional coordinated beamforming approach, it has been shown that the proposed approach requires a lesser number of active small-cell BSs. Furthermore, the mean power consumption of a coordinated beamforming approach is also 20 W higher than the proposed approach. Besides, it has also been shown that the proposed solution improves the number of admitted UEs when compared to the membership deflation approach. Thus, it has been shown that the proposed solution outperforms the said approaches in terms of reduction in power consumption and improvement in user admission.  
Though the proposed solution for power controlling significantly improves the performance of the network, many extensions can further improve the state-of-the-art. For instance, the proposed solution can be extended for imperfect and outdated channel estimation. This can have an impact on the sparsity of the problem which requires further investigation. Moreover, the proposed solution can be extended for the delay-sensitive mobile network. In this case, the user admission control would require a much more robust framework. These problems are interesting and challenging and left for future work.  
**Author Contributions:** Conceptualization: O.A.S., Q.A.; Investigation, A.A.; Supervision: A.S.; Validation: I.K. and Q.A.; Visualization: L.A.-T.; Data Curation: L.A.-T. and O.A.S.; Writing--original draft: A.A. and Q.A.; Writing--review & editing: A.S. and Q.A.; Funding Acquisition: A.S.; Formal Analysis: I.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020-UIDP/50008/2020.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 4: Total admitted user equipments (UEs) versus target SINR.
## 4 Discussion
### Comprehensive Performance Assessment  
The results from this study demonstrate that wearable robotic exoskeletons provide substantial benefits across multiple performance dimensions in construction activities.  
Figure 10: Exoskeleton productivity advantage over time.  
When integrating the findings from productivity analyses, stability assessments, and time-dependent performance evaluations, several key patterns emerge.  
First, the exoskeleton's impact varied significantly by task type and intensity. While all tasks showed improved performance with exoskeleton assistance, the benefits were most pronounced in high-intensity conditions. For instance, in the masonry task under high-intensity conditions, productivity improved by 32.7% initially, growing to 59.5% by the end of the 2 h period, and cycle consistency improved by 34.5%, compared to 20.0% and 27.6% improvements respectively under low-intensity conditions. This suggests that exoskeletons provide the greatest value in more physically demanding scenarios, where workers face higher physical strain and fatigue potential.  
Second, the temporal analysis revealed important adaptation patterns that have practical implications for exoskeleton implementation. The increasing productivity advantage observed throughout the 2 h period (from 23.9% to 40.1% on average) indicates both a learning curve as workers adapt to technology and the increasing benefits of fatigue reduction over time. This adaptation pattern varied by task complexity, with the masonry task showing the most pronounced improvement over time. This finding suggests that training programs and adequate adaptation periods are essential when introducing exoskeletons to construction environments.  
Third, the comprehensive analysis of both productivity and stability metrics allows for a more nuanced understanding of exoskeleton benefits. While productivity improvements were substantial (ranging from 20.0% to 59.5% across conditions), the improvements in postural stability (24.8% to 35.4% reduction in sway area) and movement consistency (27.6% to 39.9% reduction in cycle duration CV) were equally significant. This suggests that exoskeletons provide dual benefits: increasing output while simultaneously reducing physical strain and enhancing motion quality.