esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
## 1 Introduction
Some of the algorithms mentioned above cannot fully exploit the system block-sparsity and others do not deal well with multi-clustering block-sparsity. The BS-PNLMS algorithm proposed in [26] inserts a penalty of a mixed \(l_{2,1}\) norm to the cost function of the PNLMS algorithm. This algorithm further exploits the block-sparsity of the estimated system as compared to the BS-LMS, and it can serve for the multi-clustering case in addition to the single-clustering case. In spite of this, the performance in terms of convergence rate, estimation error, and computational complexity still needs to be improved. In recent decades, the set-membership (SM) filtering technique has gained extensive attention because of its good estimation performance and computational burden reduction [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. The SM  
Figure 1: Sparse system response type: (**a**) generalized sparse response; (**b**) single-clustering block-sparse response; (**c**) two-clustering block-sparse response. filtering technique sets a model space in which there are input and output vector pairs. The updating of filter taps only occurs when the error of estimation is larger than the preset upper limit [33]. In this paper, we use the SM principle and the mixed \(l_{2,1}\) norm to devise the block-sparse proportionate adaptive filtering algorithm to improve the estimation performances of the previous block-sparse algorithms. We replace the elements of the step size assignment matrix with the \(l_{2}\) norm of the blocks instead of the absolute value. Therefore, the step size distribution of the new algorithm is based on the whole block value rather than the single coefficient value, which speeds up the convergence. In addition, the introduction of the SM principle also can help to improve the estimation accuracy and computation burden reduction. The proposed block-sparse SM-PNLMS (BS-SMPNLMS) algorithm, and its variant the improved BS-SMPNLMS (BS-SMIPNLMS) algorithm are well deduced and analyzed in detail. The performances of the devised BS-SMPNLMS and BS-SMIPNLMS algorithms are discussed in terms of the estimation error and tracking. The obtained results show that the two new block-sparse algorithms converge faster and provide smaller estimation errors compared with related algorithms. The structure of this paper can be summarized as follows. In Section 2, we review the SM principle and the PNLMS algorithm. In Section 3, the BS-SMPNLMS and BS-SMIPNLMS algorithms are proposed within the framework of the block-sparse and set-membership theories. Section 4 presents the evaluation performances of both the BS-SMPNLMS and BS-SMIPNLMS algorithms in block-sparse signal processing. Section 5 summarizes the full text.
## 6 Conclusions  
In summary, synthesis of PVDF-g-PHEA graft copolymer is confirmed using FT-IR and NMR data. More specifically, hydroxyl and ketone groups of PVDF-g-PHEA graft copolymer, as well as three peaks connected to PHEA at 4.2, 3.5 and 3.3 ppm, are observed using FT-IR and NMR, respectively. As an evaluation result, the grafting ratio of PHEA in PVDF-g-PHEA was 22.6% in a mass basis. TGA data for PVDF-g-PHEA indicates that PVDF was successfully polymerized with 2-hydroxyethyl acrylate. Contact angle is one of the important and brief criteria to recognize the hydrophilicity of the membrane. As a result of measurement, contact angles of 9.5% and 10% PVDF pristine membranes were 68.6\({}^{\circ}\) and 68.0\({}^{\circ}\), respectively. Contact angles of PVDF membranes blended with 0.6% and 0.9% PVDF-g-PHEA were 64.3\({}^{\circ}\) and 61.8\({}^{\circ}\), respectively. In conclusion, the contact angle was decreased by blending of  
Figure 12: Rejection % of membranes at 6 ppm (**a**) and 20 ppm (**b**) humic acid.  
PVDF-g-PHEA additive in PVDF casting solution. Moreover, macrovoid holes are discovered in cross sectional SEM images of PVDF UF membrane containing PVDF-g-PHEA. The final humic acid rejection percent of PVDF membranes blended with PVDF-g-PHEA additive was higher than that of PVDF pristine membranes at 6 ppm and 20 ppm. Average pore size of membranes was the key factor to influence the humic acid rejection percent. Finally, permeate flux indicates increase by blending of PVDF-g-PHEA additive, which has hydrophilic characteristics. Based on above characterizations, it suggests that PVDF-g-PHEA graft copolymer is successfully synthesized, thereby resulting in the formation of hydrophilic PVDF UF membrane. Also, this indicates the possibility of using PVDF-g-PHEA to fabricate membranes with improved flux during water treatment containing humic acid.  
This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20154010200810).  
[PERSON] and [PERSON] contributed equally to this work in order to conduct the experiments, fabricate the membranes, write first draft and complete the article; [PERSON] and [PERSON] reviewed the data; [PERSON] and [PERSON] advised to complete this article.  
The authors declare no conflict of interest.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Ubiquitous Networking for the Internet of Things
#### 2.1.1 Urban Scenario
To address this problem in [21], an architecture, \(mPat\), is proposed to explore issues related with multi-source data applied to the specific case of human mobility. The third challenge is data privacy. In project [22], authors analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and USA state scale. Nevertheless, social media data are relatively less privacy-concerning compared to passive data-collecting approaches that include phone calls, cellular records, and smart cards. Researchers applied methods that identified and aggregated before application. Then, understanding the needs of citizens, visitors, and employees is another challenge. An expert interviewed in [23] highlighted that the design and development of platforms for big data processing should be a priority, with a focus on citizen-oriented management. In terms of geographic information delivery methods, data should be analyzed according to a geographic unit (e.g., region and building). For example, [24] proposes a series of standard modifications to the models, such as using travel time instead of physical distance and adding a multiplicative term to correct the underestimated urban-to-urban trip volumes. Finally, designing a data-based smart city service is crucial because this task integrates all of the outcomes from the data analytics, ideation, and information content design for a smart city [25]. Figure 3: Challenges in transforming data into information for smart cities.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 4 Proposed Mitigation Mechanism for Jamming Attacks
### Detection of Nodes under Jamming  
A node that is experiencing a jamming attack might detect such condition by using a heuristic anomaly model. In this way, the node observes a set of variables which are compared to the values of the same variables under normal conditions (without attack).  
The jamming detection model considers energy consumption, an average number of packet re-transmissions and the average number of retrials in the inner loop of the CSMA/CA algorithm. These three metrics have previously shown to be affected by jamming conditions [10; 11].  
First, we create a network's metrics model by training each metric with a database of the observed values obtained when the network was working under an attack-free scenario. Our model is trained locally for each node as follows; we compute the range \([v_{min},\ v_{max}]\) for a given variable \(v\), where the \(k\)-th training value of \(v\) is denoted as \(v_{k}\in[v_{min},\ v_{max}]\), see Algorithm 1. For reducing the number of false positives we have smoothed the input data with a moving average filter of length \(m\), where the output of the filter is the training value \(v_{k}\) of the detection algorithm, this filtering is also done with the testing data.  
```
for each node do (a) moving average filtering of input data; (b) compute the min and max value of each variable: Energy, Retransmissions, CSMA-retries. endfor
```  
**Algorithm 1**Training of the detection algorithm.  
For testing the detection model, a decision algorithm is run locally in each node, the \(i\)-th test value \(v_{i}\) of \(v\) is compared to its corresponding \(v_{max}\) value, if \(v_{i}>v_{max}+\epsilon\), where \(\epsilon\) is a constant, then the node could be under a jamming attack. In order to reduce the false alarms we use a majority vote rule, i.e., an indication of jamming is produced when a majority (two or three) of the variables considered in the model exceeds the corresponding thresholds. Once the jamming detection algorithm produces a positive result, then a notification of jamming attack is emitted to the neighbor nodes by sending a control packet, see Algorithm 2.  
```
for each node do \(n=0\); \(e:constant>0\);  moving average filtering of input data with 10 s of step; for each variable: Energy, Retransmissions, CSMA-retries. do if test value exceeds max \(+\epsilon\)then \(n++\); else  DO nothing; endif endfor if\(n\geq 2\)then  jamming alarm:  SEND control packet for notification; else  DO nothing; endif endfor
```  
**Algorithm 2**Testing of the detection algorithm.
## 4 Network Approaches to Optimize 360-Degree Video Streaming
### Information-Centric Networking (ICN)  
[PERSON] [67] demonstrated how Information-Centric Networking (ICN) may alleviate the network latency problems related to 360-degree video streaming. ICN is a network architecture that transitions from the traditional host-oriented communication model to a content-centric model, relying on location-independent naming schemes, in-network pervasive caching, and content-based routing to enable effective content distribution across the network, allowing content retrieval via any available network interfaces [68].
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 3 Materials and Methods
### Maximum Likelihood Estimator (MLE)
To find the solution, several searching techniques can be used for the MLE:  
* Nelder-Mead: is the most widely used algorithm in direct search method for solving the unconstrained optimisation problem. The Nelder-Mead method iteratively generates a sequence of tetrahedrons to approach the optimal point which can reflect,expand, contract, and shrink. The algorithm is designed for small search spaces because it quickly stalls [27];
* Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS): is designed for large non-linear optimisation problems. The algorithm handles bounds on the variables and solves unconstrained problems. However, the convergence is slow and non-optimal for real time cases [28];
* Truncated Newton (TNC): utilises rougher estimations of the optimal search direction for efficiency. As a drawback, the algorithm appears to rapidly stall [29];
* Constrained optimisation by linear approximation (COBYLA): is a direct search method which only incorporates linear models about the objective and the constrains with quick searching time [30];
* Sequential least squares programming (SLSQP): is an iterative method in which the objective and constraints functions demand to be triple continuously differentiable. The method reduces the non-linear optimisation problems by sequential iterations to trim the convergence time [31]. In this paper, the SciPy [32] package implementation of these algorithms was used.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 5 Design Procedure and Application Example
### Calculation of \(K_{\varphi}\)  
The fourth step is to calculate the \(K_{\varphi}\) parameter of the SMC using expression (36), which must fulfill the stability conditions given in (24), (29), and (30). Figure 9 shows the \(K_{\varphi}\) values for different settling times and bus capacitances, taking into account that such \(K_{\varphi}\) values fulfill the stability conditions. Finally, in that figure are highlighted the \(C_{dc}=\) 50 [uF] value selected in the previous subsection, and the maximum settling time \(t_{s}=\) 1 [ms], which results in the \(K_{\varphi}=\) 0.2 [A/V] value. Since the selected condition [\(t_{s}=\) 1 ms, \(C_{dc}=\) 50 uF, \(K_{\varphi}=\) 0.2 A/V] is within the feasibility zone defined by the stability conditions, \(K_{\varphi}=\) 0.2 [A/V] ensures both global stability of the SMC and the desired settling time \(t_{s}=\) 1 ms for the bus voltage.  
Finally, the fourth step described in this section enables to design a stable battery charger/discharger, based on the flyback topology, fulfilling the conditions required to ensure a safe operation for both sources and loads connected to a DC bus.  
Figure 9: \(K_{\varphi}\) calculation for different settling times and bus capacitances.
# Reversible Image Processing for Color Images with Flexible Control
## 2 Related Works  
### Contrast Enhancement Methods for Grayscale Images  
Reversible CE methods [1; 2; 3; 4; 5; 6; 7; 8; 9; 10] have been widely researched for grayscale images. Their main purpose is to reversibly enhance the contrast without increasing the data amount for each image. Reversible CE methods perfectly reconstruct raw images by using RDH techniques.  
[PERSON] et al. [1] designed a reversible CE method for grayscale images by using a histogram shifting (HS)-based RDH technique. This method embeds recovery information into an image histogram, and the raw images are perfectly restored. On the basis of this fundamental method, many extended approaches have been published [2; 3; 4; 5; 6; 7; 8; 9; 10] in which the CE function is refined in terms of local contrast, output image quality, CE effect, application to medical images, automation, or brightness preservation. The local contrast was improved by [PERSON] et al. [2]. In their method, k-means clustering is conducted by using the correlation among each pixel and its three neighboring pixels. [PERSON] et al. [3] built upon [PERSON] et al.'s method [2] to improve the effectiveness of k-means clustering. Their method refers to four neighboring pixels for each pixel and considers the correlation among them using multiple elements, such as the variance, mean, and median. [PERSON] et al. [4] modified the preprocessing of the CE function [1]. This method prevents perceptual distortion caused by preprocessing, so the output images have a high image quality. To refine the CE effect, a two-dimensional image histogram was used by [PERSON] et al. [5]. Their method can obtain the correlation among pixels with higher accuracy and provide more effective CE. For medical images, several CE methods [6; 7; 8] have been proposed. These methods first apply a region segmentation process to a raw image and divide the image into regions of interest (ROIs) and non-ROIs. The CE function is subsequently executed on the ROIs only. [PERSON] et al. [9] developed an automatic CE method. Their method automatically repeats the CE function until the amount of recovery information exceeds the hiding capacity. The automatic CE method, however, causes an excessive enhancement effect and cannot preserve the brightness. To improve the performance of this method, an extended method [10] was designed. Their method optimally defines the direction in which to shift the image histogram in each HS sequence so as to maintain the mean of the brightness. Additionally, when the CE function excess sively changes the brightness, their method breaks the repetition process. Consequently, this method can preserve the brightness and prevent the excessive enhancement effect.  
The above CE methods are designed for grayscale images. If these methods are applied to color images, then hue distortion occurs. To tackle this issue, several CE methods [11; 12] have been considered for color images.
## 4 Experiments
### Comparisons  
There are few public domain datasets in the field of remote sensing, and in particular, multi-class segmentation public datasets, suitable for training a deep model, are rare. In this section, we show the results of the comparison between our method and the results associated with the only public dataset available [20]. Furthermore, we did an analysis on the use of different ResNet used as backbone of the bottom-up block of the proposed model. The results are reported in Table 3 and show that as the complexity of the bottom-up block increases, the accuracy of the classification improves. We have not used more complex models due to our limited hardware availability, but we should probably get better results using more powerful models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Backbone** & **acc.** & **MSE Loss** \\ \hline ResNet101 & 93.48\% & no \\ \hline ResNet101 & 93.71\% & yes \\ ResNet50 & 93.62\% & yes \\ ResNet34 & 92.19\% & yes \\ ResNet18 & 91.57\% & yes \\ ResNet10 & 91.37\% & yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results on Class Activation Intervals (CAIs) using MSELoss+CELoss while changing the bottom-up backbone ConvNet. All the models were trained on the Munich dataset for 300 epochs.  
Figure 7: Precision of the FPN 3D model as the batch size varies on the dataset Munich. The plot on the top shows how the precision for each class changes as the batch size in the training phase changes when the loss function weights are calculated for each batch. The plot on the bottom compares three different loss function weighting techniques when the batch size is 2 and 32.  
In Table 1, we report the comparison results made with what was published in paper of [PERSON] and [PERSON] (2018) and we can conclude that our proposed model behaves better from the point of view of all the metrics used in this comparison. Although the cardinality per class is not the same, we can see that the behavior of the (3+2)D FPN is very similar on both the validation and the test set.
## 3 Modeling of Backup Systems
### Hydrogen Storage System
#### 3.1.1 Electrolyzer  
The electrolyzer is the cornerstone of the hydrogen storage system. It utilizes excess electricity from RESs to separate water into hydrogen and oxygen through electrolysis. This process ensures that any excess energy, which would otherwise be wasted, is converted into a storable form. The efficiency of the electrolyzer significantly impacts the overall performance of the HSS. Different electrolyzer types, such as alkaline, proton exchange membrane, and solid oxide, offer varying levels of efficiency, cost, and scalability. The choice of electrolyzer depends on several factors, including the specific requirements of the energy system, economic considerations, and the intended scale of hydrogen production. The ability of the electrolyzer to convert surplus renewable energy into hydrogen enhances the sustainability and efficiency of the HEMS by ensuring that no generated energy is wasted. Through this process, water is broken down into oxygen (O\({}_{2}\)) and hydrogen (H\({}_{2}\)), as illustrated below:  
\[Electricity+H_{2}O=H_{2}+\frac{1}{2}O_{2} \tag{1}\]  
The amount of \(H_{2}\) generated by the electrolyzer and sent to the tank can be calculated through the following formula [50; 51]:  
\[m_{H_{2},EL}(\Delta t)=\left\{\begin{array}{ll}\frac{P_{EL}(t)\times\Delta t \times\eta_{de}}{a_{ele}}&\qquad if\ P_{EL}(t)>0\\ 0&\qquad if\ P_{EL}(t)=0\end{array}\right. \tag{2}\]  
The given equation describes the hydrogen production rate of an electrolyzer, which depends on the power supplied to it and the efficiency of the conversion process. When power is supplied (\(P_{EL}(t)>0\)), the electrolyzer produces hydrogen, with the amount generated over a time interval \(\Delta t\) calculated based on the power input and the electrolyzer's efficiency (\(\eta_{ele}\)). If no power is supplied (\(P_{EL}(t)=0\)), the hydrogen production is zero. This equation ensures that hydrogen production only occurs when the electrolyzer is active, effectively linking energy input to hydrogen output, which is critical for optimizing the use of electrolyzers in energy systems, especially when integrated with renewable energy sources.
## 3 Experimental Study
### Experimental Setup
#### 3.1.7 Evaluation Metrics  
In this study, two performance metrics were utilized to evaluate the performance of the proposed approach for RUL prediction, namely, the scoring function and RMSE. Both metrics utilize the definition of \(d_{i}=RUL_{predicted}-RUL_{actual}\), which denotes the difference between the predicted RUL and the actual RUL  
The scoring function proposed by the International Conference on Prognostics and Health Management (PHM08) Data Challenge [60] is defined by  
\[score=\sum_{i=1}^{N}S_{i},\ \ S_{i}=\left\{\begin{array}{ll}e^{-\frac{d_{i}}{ \Omega}}-1,&d_{i}<0\\ e^{\frac{d_{i}}{\Omega}}-1,&d_{i}\geq\ 0\end{array}\right. \tag{15}\]  
where \(N\) is the number of testing samples. The scoring function is asymmetric and gives a larger penalty to late predictions than early predictions due to the severe consequences of system failure.  
The _RMSE_, which is a standard evaluation metric for prediction, gives equal penalties for both early and late predictions, and it is calculated as follows:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}d_{i}^{2}} \tag{16}\]  
The lower the scoring function and _RMSE_ values, the better the prediction result.
# Identification of Key Performance Indicators for T20--A Novel Hybrid Analytical Approach
## 4 Discussion
### Limitations of the Study  
Despite the valuable insights provided by this study, it is not without limitations. Firstly, the number of performance indicators (PIs) exceeds the number of observations (78 matches), which can negatively impact the analysis. Secondly, the dataset is confined specifically to the T20 cricket competition, potentially limiting its applicability to other competitions or women's cricket, due to differences in playing conditions and strategies. Moreover, the matches used in the study occurred without spectators, during the COVID-19 pandemic, which can influence a home team's advantage and overall match performance. Finally, the dataset used in the study lacks detailed contextual features, such as pitch conditions, weather conditions, and specific information on deliveries and shots played on the field. Future research should consider incorporating these contextual features to enhance the understanding of field placement strategies against different types of batters to improve prediction accuracy. Furthermore, future research should contemplate including data from diverse T20 leagues, enriching models with context, employing more robust validation strategies, and utilising alternative feature selection methods. Furthermore, Emphasis on interpretability and data integrity will further enhance model utility and practical relevance.