esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks
## 3 Experimental Setup
### Proposed BCI Methodology  
Figure 2 illustrates the five stages of the proposed EEG classification methodology for BCI. Firstly, \(B=17\) band-pass filters decompose each trial using five-ordered Butterworth filters of 4 Hz bandwidth and overlapping 2 Hz within [4,40] Hz [25]. Secondly, the spatial filters, optimized in Section 2.3, project the band-wise covariance matrix into the lower-dimensional Riemannian manifold \(\mathbb{P}_{Q}\). Thirdly, the Stein kernel assesses the similarity between the projected test and training covariances, relying on Equation (11). Further, the multikernel linearly combines the 17 similarities for a test trial into a single kernel value. Lastly, a support vector machine, fed by the learned kernel, labels the recordings into one of the four classes according to a one-vs-one scheme.  
The proposed methodology contains three sets of parameters, namely, the band-wise spatial filters, the kernel bandwidth, and the kernel mixing weights. For the former two, the gradient descent algorithm, updating the parameter according to Equation (18), finds the channel-to-component space projections and gamma values enhancing class separability at each band. For the latter, the solution to the quadratic programming problem in Equation (22) defines the contribution of each spectral component to the supervised discrimination task. Besides, the single hyperparameter of the proposal corresponds to \(Q\), defining the number of columns for matrices \(\mathbf{W}_{b}\), that is, the number of spatial filters. A cross-validated grid-search approach fixes the optimal \(Q\) by maximizing the classification performance within the range [2, 10]. Specifically, we assess the performance through [PERSON]'s kappa score defined in Equation (24), where \(p_{o}\) corresponds to the empirical probability of agreement on the label assigned to any sample (the observed agreement ratio), and \(p_{e}\) determines the expected agreement at classification by chance.  
\[\kappa=\frac{p_{o}-p_{e}}{1-p_{e}} \tag{24}\]  
Figure 1: BCI competition IV acquisition setup. (**a**) EEG montage. (**b**) Paradigm time scheme.  
Figure 2: Proposed EEG classification methodology based on filter-banked Stein-kernel trial similarities.
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 2 Efficient Fuzzy MPC Algorithms with the Advanced Construction of the Dynamic Matrix  
The complications resulting from the need to solve a nonlinear optimization task by the MPC algorithm in each time step can be avoided by using an approximation of the process model carried out in each time step. Then, a nonlinear model is used to obtain the free response in such a way that the prediction is linear relative to decision variables, but mimics the nonlinearity of the process very well. An easy-to-obtain fuzzy model is used to get the dynamic matrix.  
The dynamic matrix can be constructed in different ways. In Section 2.2, it is described how to do it in such a way that it fits the nonlinearity of the process better than in the standard approach. The method is based on a skillful utilization of the free response (generated using the nonlinear process model). Then, using both elements needed to obtain the prediction, the free response and the dynamic matrix, the optimization problem (1)-(4), solved in each time step, is formulated as a quadratic optimization problem. Such a problem is easy to solve using commonly available optimization routines. Moreover, the simplified versions of the algorithms, in which a control law is obtained, are also discussed.  
The proposed algorithms are the generalization of their counterparts proposed in [12]. They use the same free response generation (reminded in Section 2.1) and the same formulation of the optimization problems solved at each time step (reminded in Section 2.3). The difference is in the way the dynamic matrix is constructed (the topic detailed in Section 2.2.3).
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 1 Introduction
### Motivation and Contributions  
In-network caching specifically for streaming delivery (e.g., video delivery, audio broadcasting and file distribution) over ICN architectures raises new challenges. Firstly, the cached object has a different granule size. This variation leads to inadequacy in simply extending the existing file-oriented caching research results to ICN caching studies, such as the popularity of cached objects and the request feature [38]. Additionally, for streaming content delivery over ICN, the chunk request sequence for a file is well ordered rather than generated independently. Thus, the access probability among chunks in the same file, which we call chunk-level internal probability, is traceable and calculable once the file is requested. In the case of streaming content delivery over ICN, the correlation in the occurrences of requests for chunks in the same file needs to be considered. A cache replacement algorithm for layered video content is proposed [39]. However, the experiment is conducted on a straightforward and linear network with five nodes, thus the scalability of this proposal is unclear. Two-level popularity oriented time-to-hold policy (TLP-TTH) [40] also adopts the two levels of popularity and future requests prediction. Nevertheless, the chunk-level internal probability is not included, which may further improve the streaming content delivery performance.  
In this study, to further improve the ICN streaming content delivery performance of previous studies, we propose the RXI-based cache replacement algorithm, which aims to enhance cache utilization and to reduce the content retrieval time and unnecessary network traffic. RXI considers both the file-level and chunk-level internal request probability and estimates the possible future request probability according to the dynamically varied request status at each ICN router. Subsequently, the RXI-based algorithm evicts the cached chunk that has the smallest request expectation in the future in order to keep a high cache utilization. Moreover, the RXI based algorithm avoids the additional management overhead, such as messages or control packet transmission, which increases the network burden to some extent. We have evaluated the proposed algorithm compared with LRU, LFU, and TLP-TTH in both a hierarchical topology and a hybrid topology. The results indicate that, in both scenarios, the RXI-based algorithm improves several aspects of network performance and can be deployed in complexity network scenarios.
# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 6 Results
### Performance Comparison  
#### 6.2.1 Baselines  
We compare the performance of our proposed model with some baselines, which include the state-of-the-art classification algorithms, a sybil detection method (SybilSCAR) and a text classification method (HAN).  
* LR [29]: Logistic Regression (LR) is a simple classification model that adds a logistic function upon the linear regression. We feed the selected features in Section 4.1 into LR to predict the labels.
* DT [30]: Decision tree (DT) is one of the classic machine learning algorithms, which divides the final classification outcomes into a series of related choices on the features. Here, we use DT to separate the mis-configured authors profiles and reliable ones based on the selected features mentioned in Section 4.1.
* RF [31]: Random forest (RF) utilizes an ensemble of DTs trained with the bagging method to make a more accurate and stable prediction. Selected features mentioned in Section 4.1 are used for classification.
* XGBoost [31]: XGBoost is a scalable end-to-end tree boosting system widely used in the machine learning competitions. It provides a parallel boosting, which is highly efficient, flexible and portable, to solve the classification problem.  
LightGBM: We leverage LightGBM for mis-configuration detection based on the features extracted and selected in Section 4.1.
* SybilSCAR [32]: SybilSCAR is a state-of-the-art algorithm for scalable sybil detection that unifies Random-Walk-based and Loop-Belief-Propagation-based methods. It is suitable for large-scaled networks and is robust to noise. We apply this method to the co-author network and the prior probability of each node to be mis-configured is obtained by XGBoost.
* HAN [33]: HAN is a hierarchical attention neural network framework to deal with the content data, which is originally proposed for fake news detection. We leverage this method based on the paper list of each scholar.
# Improved Matching Algorithm with Anchor Argument for Rotate Target Detection
## 4 Results  
### Datasets  
In order to evaluate the effect of the structure we designed, we chose to test on two public remote sensing datasets.  
DOTA is a large remote sensing dataset released in 2018. The whole dataset contains 2806 aerial images with image sizes ranging from \(800*800\) to \(4000*4000\). There are 15 categories of targets, including planes, ships, storage tanks, basements, diamonds, tennis courts, swimming pools, ground track fields, harbors, bridges, large vehicles, small vehicles, helicopters, roundabouts, soccer ball fields and basketball courts, with a total of 188,282 instances.  
HRSC2016 was released in 2016 for ship detection. All the images in the dataset are from six famous ports, and the resolution of most images is greater than \(1000*600\). The dataset contains 1070 images and 2976 samples in total.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Advantages of TinyML  
* Reduced Latency: Data processing on Raspberry Pi eliminates the lag associated with transmitting data to a centralized server and then fetching results. This ensures real-time or near-real-time responses.
* Decreased Bandwidth Consumption: Only crucial or processed data may be sent to the central server, reducing network load.
* Enhanced Privacy and Security: On-device processing ensures data privacy. Additionally, Raspberry Pis can be equipped with encryption tools to secure data before any transmission.
* Energy Efficiency: Although Raspberry Pis consume more energy than simple sensors, they are far more efficient than transmitting vast amounts of data to a distant server.
* Operational Resilience: Raspberry Pis equipped with TinyML can continue operations even when there is no network connectivity.
* Scalability and Flexibility: Raspberry Pis can be equipped with a variety of tools and software, allowing custom solutions for different data types and processing needs.
## 3 Result Analysis
### Spatial Differences Analysis
Compared with Figure 4, Figure 5 shows that the implementation intention is higher than the support intention in multiple regions, both before and after release-type communication. After release-type communication, the implementation intention increases in 14 regions (such as Gansu, Tibet, Xinjiang, and Hebei), while the implementation intention decreases in 18 regions in varying degrees (such as Beijing, Jiangsu, Zhejiang, and Shandong). Among them, Shanghai and Shandong have the most significant changes in implementation intention (Shanghai increases markedly, and Shandong decreases dramatically). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Support Intention**} & \multicolumn{6}{c}{**Implementation Intention**} \\ \hline
**Provinces** & **Means** & **Provinces** & **Means** & **Provinces** & **Means** & **Provinces** & **Means** \\ \hline Gansu & 0.3 & Shandong & 0.07 & Hebei & 0.34 & Chongqing & 0.18 \\ Taiwan & 0.27 & Henan & 0.07 & Tibet & 0.31 & Sichuan & 0.18 \\ Tibet & 0.23 & Anhui & 0.05 & Shanxi & 0.31 & Taiwan & 0.18 \\ Hunan & 0.23 & Hubei & 0.05 & Gansu & 0.26 & Macau & 0.18 \\ Guizhou & 0.22 & Shanxi & 0.05 & Shanghai & 0.26 & Jilin & 0.16 \\ Qinghai & 0.2 & Shanghai & 0.05 & Xinjiang & 0.24 & Jiangsu & 0.16 \\ Hebei & 0.2 & Fujian & 0.04 & Hubei & 0.24 & Tianjin & 0.16 \\ Guangxi & 0.2 & Chongqing & 0.04 & Jiangxi & 0.24 & Zhejiang & 0.14 \\ Xinjiang & 0.19 & Zhejiang & 0.02 & Henan & 0.23 & Guangdong & 0.13 \\ Sichuan & 0.17 & Jiangsu & 0.01 & Shaanxi & 0.23 & Yunnan & 0.12 \\ Jiangxi & 0.17 & Liaoning & 0.01 & Fujian & 0.23 & Shandong & 0.12 \\ Heilongjiang & 0.15 & Guangdong & \(-\)0.01 & Guizhou & 0.22 & Guangxi & 0.11 \\ Tianjin & 0.13 & Macau & \(-\)0.09 & Hunan & 0.21 & Anhui & 0.11 \\ Inner Mongolia & 0.11 & Hainan & \(-\)0.12 & Ningxia & 0.2 & Inner Mongolia & 0.11 \\ Shaanxi & 0.11 & Yunnan & \(-\)0.15 & Qinghai & 0.2 & Beijing & 0.1 \\ Ningxia & 0.1 & Beijing & \(-\)0.15 & Liaoning & 0.19 & Hainan & 0.08 \\ Jilin & 0.08 & Hong Kong & \(-\)0.21 & Heilongjiang & 0.18 & Hong Kong & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Means of support intention and implementation intention after release-type communication. Figure 4: Temporal and spatial differences in support intention before and after release-type communication. Note: * means \(p<0.05\); *** means \(p<0.001\). Figure 5: Temporal and spatial differences in implementation intention before and after release-type communication. Note: * means \(p<0.05\).
# Impact of VR Application in an Academic Context
## 2 Materials and Methods
### Voice Recognition Accuracy: Voice Samples  
To assess EduAssistant's voice recognition capabilities, we simulated the experience using a total of 200 voice samples. The samples were divided as presented in Table 1. Two approaches were used for the simulation phase: human voice interaction and machine generated samples. The voice samples were collected from both male and female subjects. Given the diverse environmental conditions under which students interacted with the 3D application, we also took the presence of background noise into account for the purpose of quality assurance. The machine samples were generated using typecast.ai and were acquired from native speakers [34]. A total of 100 samples were generated, with 50 using female voice and 50 using male voices. All auto-generated samples were recorded in a noisy environment. The following emotions were considered: happiness, sadness, anger, and surprise. In total, 30 of the samples were equally divided between low, medium, and high frequencies [35; 36]. In terms of human voice, for testing purposes we used both a male and a female. All human voice samples used were from non-native speakers. The subjects performed 50 voice searches each in normal and noisy environments using the same set of emotions stated above. In total, 80 of the searches were made using words or phrases derived from dictionary list and the other 20 were performed using words or phrases that have no correspondence in the dictionary. For each voice sample a test scenario was created and the result considered two aspects: capacity of the system to understand and to return results. The performance of the system was evaluated considering two main criteria: its ability to understand the input and its accuracy in returning results. Expected  
Figure 14: Hardware equipment setup (source: [22]).  
results were established for each test case scenario, and the actual results were measured to evaluate their correspondence. Actual results were evaluated with 0--fail and 1--success. Scenarios with different results than the expected outcome were considered failed. Results that matched the expected outcome were considered successful.
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
###### Abstract  
Most electrochemical sensing requires affordable, portable and easy-to-use electrochemical devices for use in point-of-care testing and resource-limited settings. This work presents the design and evaluates the analytical performance of a near-field communication (NFC) potentiostat, a flat card-sized electrochemical device containing a microchip for electrical analysis and an NFC antenna for smartphone connection. The NFC interface is a wireless connection between the microchip and smartphone to simplify measuring units and make the potentiostat into a passive operated device, running without a battery. The proposed potentiostat can perform the common electrochemical techniques including cyclic voltammetry and chronoamperometry with a current range and voltage range of \(\pm 20\)\(\mu\)A and \(\pm 0.8\) V. The performance of the NFC potentiostat is compared to a commercial benchtop potentiostat using ferricyanide as a standard solution. The results show that the NFC potentiostat is comparable to a commercial benchtop potentiostat for both cyclic voltammetry and chronoamperometry measurements. The application of the proposed potentiostat is demonstrated by measuring ascorbic acid concentration. As described, the NFC potentiostat, which is compatible with a smartphone, is low-cost, small in size and user-friendly. Thus, the device can be developed for on-site measurement to apply in various fields.  
potentiostat; battery-less; near-field communication; smartphone 202111399210390/app1101039210390/  
19 December 2020  
10 December 2020  
3 December 2020  
10 January 2022  
**Subisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Interactive Evolutionary Algorithm
### Interactive Differential Evolution Algorithm
#### 3.2.2 Mutation Operation  
During each iteration, a mutation vector is generated by the mutation operation. Mutation operation plays an significant role in the performance of differential evolution algorithm. Therefore, since the differential evolution algorithm was first proposed, a great deal of researchers have conducted research on mutation operators and proposed a large number of mutation operators. The most classic mutation operators are as follows.  
(1) DE/rand/1:  
\[V_{j,t}=X_{r1,t}+F_{j}\times(X_{r2,t}-X_{r3,t})\] (2) DE/best/1:  
\[V_{j,t}=X_{best,t}+F_{j}\times(X_{r1,t}-X_{r2,t})\] (3) DE/current-to-best/1:  
\[V_{j,t}=X_{j,t}+F_{j}\times\left(X_{best,t}-X_{j,t}\right)+F_{j}\times(X_{r1,t }-X_{r2,t}) \tag{4}\]  
Figure 2: An iteration of IDE.  
(4) DE/current-to-pbest/1:  
\[V_{j,t}=X_{j,t}+F_{j}\times\left(X_{pbest,t}-X_{j,t}\right)+F_{j}\times\left(X_{ r1,t}-X_{r2,t}\right) \tag{5}\]  
where \(X_{j,t}\) refers to the \(j\)th individual in the population at the \(t\)th iteration, \(V_{j,t}\) is the corresponding mutation vector, \(r1\), \(r2\), \(r3\) are randomly selected from \(\{1,2,\ldots,N\}\) and are not equal to \(j\). \(X_{best,t}\) is the best individual at \(t\)th iteration, \(X_{pbest,t}\) is chosen at random from the top \(p\)% individuals, \(F_{j}\) is the scaling factor of the \(j\)th individual, meaning that the scale factors of each individual are independent of each other. At the same time, the adaptive strategy can also be applied to \(F_{j}\).  
\[F_{j}=F_{l}+(F_{u}-F_{l})(f_{m}-f_{b})/(f_{w}-f_{b}) \tag{6}\]  
The three randomly selected individuals \(X_{r1,t}\), \(X_{r2,t}\) and \(X_{pbest,t}\) in the mutation operator are sorted from good to bad, corresponding to fitness \(f_{b}\), \(f_{w}\) and \(f_{m}\). Where \(F_{l}=0.1\) and \(F_{u}=0.9\).
## 5 Results and Discussion
### Annual Precipitation
Moreover, the value of CC of GPM IMERG was 0.90, which is in high agreement with the rain gauges, indicating that GPM can also capture temporal variations at the national scale. In addition, since POD (0.79), FAR (0.21) and CSI (0.70) were consistent, the GPM captured most precipitation events correctly. To summarize, the performance of the GPM IMERG precipitation product is satisfactory at both annual and national scales but shows a slight underestimation due to most precipitation having happened in low-altitude sites or mistaken precipitation measurements by rain gauges. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scale** & **Year** & **CC** & **RMSE (mm)** & **MAE** & **BIAS (\%)** & **POD** & **FAR** & **CSI** \\ \hline \multirow{3}{*}{Regional} & 2015 & 0.91 & 416.29 & 0.31 & \(-\)4.01 & 0.86 & 0.19 & 0.73 \\  & 2016 & 0.88 & 406.79 & 0.29 & \(-\)7.97 & 0.82 & 0.22 & 0.62 \\  & 2017 & 0.90 & 183.91 & 0.33 & \(-\)8.63 & 0.73 & 0.25 & 0.55 \\ \hline National & 2015-2017 & 0.90 & 298.94 & 0.30 & \(-\)6.87 & 0.79 & 0.21 & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of evaluation metrics for GPM IMERG product, which was calculated based on mean regional and national scale precipitation. Figure 9: Mean annual precipitation measured by (**a**) rain gauges and (**b**) GPM (2015–2017).
Sensors_**2012**, _12_, 14887-14913; doi:10.3390/s121114887  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Qualitative Comparison of Different Logical Topologies for Wireless Sensor Networks**  
**[PERSON]**  
School of Computing and Mathematics, Charles Sturt University, New South Wales 2678, Australia;  
E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +61-269-334-725  
_Received: 1 October 2012; in revised form: 23 October 2012 / Accepted: 24 October 2012 / Published: 5 November 2012_  
**Abstract:** Wireless Sensor Networks (WSNs) are formed by a large collection of power-conscious wireless-capable sensors without the support of pre-existing infrastructure, possibly by unplanned deployment. With a sheer number of sensor nodes, their unattended deployment and hostile environment very often preclude reliance on physical configuration or physical topology. It is, therefore, often necessary to depend on the logical topology. Logical topologies govern how a sensor node communicates with other nodes in the network. In this way, logical topologies play a vital role for resource-constraint sensor networks. It is thus more intuitive to approach the constraint minimizing problems from (logical) topological point of view. Hence, this paper aims to study the logical topologies of WSNs. In doing so, a set of performance metrics is identified first. We identify various logical topologies from different application protocols of WSNs, and then compare the topologies using the set of performance metrics.  
**Keywords:** logical topology; performance metric; flat topology; cluster topology; tree topology; chain topology