esa-sceva/satcom-chunk-collection huggingface dataset:
## Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree  
_Myoelectric Prosthetic Hand_  
[PERSON][PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Department of Robotics and Mechatronics, Tokyo Denki University, Tokyo 120-8551, Japan;  
[EMAIL_ADDRESS] (E.N.); [PERSON]ADDRESS] (C.L.); [PERSON]DDRESS] (J.I.)  
* Correspondence: [EMAIL_ADDRESS]  
Received: 4 October 2017; Accepted: 8 November 2017; Published: 13 November 2017  
Featured Application: This work is intended for the development of myoelectric prosthetic hand systems. Furthermore, the outcome of this study may also benefit other electromyographic based human-machine interfaces.  
**Abstract:** The myoelectric prosthetic hand is a powerful tool developed to help people with upper limb loss restore the functions of a biological hand. Recognizing multiple hand motions from only a few electromyography (EMG) sensors is one of the requirements for the development of prosthetic hands with high level of usability. This task is highly challenging because both classification rate and misclassification rate worsen with additional hand motions. This paper presents a signal processing technique that uses spectral features and an artificial neural network to classify 17 voluntary movements from EMG signals. The main highlight will be on the use of a small set of low-cost EMG sensor for classification of a reasonably large number of hand movements. The aim of this work is to extend the capabilities to recognize and produce multiple movements beyond what is currently feasible. This work will also show and discuss about how tailoring the number of hand motions for a specific task can help develop a more reliable prosthetic hand system. Online classification experiments have been conducted on seven male and five female participants to evaluate the validity of the proposed method. The proposed algorithm achieves an overall correct classification rate of up to 83%, thus, demonstrating the potential to classify 17 movements from 6 EMG sensors. Furthermore, classifying 9 motions using this method could achieve an accuracy of up to 92%. These results show that if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability.  
**Keywords:** artificial neural network; brain machine interface (BMI); electromyogram (EMG); prosthetic hand; spectral analysis
## 3 VM Consolidation Model
### Objective Function  
The proposed VM consolidation model aims to reduce power consumption and avoid VM migration for stable DC operations. To evaluate the results of the VM consolidation model, the consolidation score, which unifies the values for the amount of energy and migration cost, is defined and calculated as shown in Equation (9).  
\[\textbf{minimize}\ f=\ \alpha\cdot\bigg{(}1-\frac{W(x)\ -\ W(x^{\prime})}{Min(W)} \bigg{)}+(1-\alpha)\cdot\frac{MC(x^{\prime})}{Max(MC)} \tag{9}\]  
The VM migration cost is evaluated by normalizing the number of migrations compared to the worst case, when all VMs are migrated. The variables evaluating the power consumption is normalized and the power saved is compared to the maximum power savings (\(Min(W)\)) based on the specification of the current PM and VM [37]. The maximum power savings concept is demonstrated in previous studies. Two of the multiobjective variables are calculated as one objective function using the weight variable \(\alpha\). This weight variable represents the conservatism of DC management; the low value of \(\alpha\) chooses an operational plan to reduce the amount of energy by enduring some penalties and high value of \(\alpha\) represents the small interest in reducing the amount of energy. The value of \(\alpha\) is adjusted by the decision maker who determines the operation strategy of the DC.
## 1 Introduction
[3] were able to improve on the ARIMA results for daily patient attendances in Singapore General Hospital by incorporating other variables such as public holidays, periodicities and air quality. Another option is to use proprietary models. One such, Prophet, is a forecasting model developed at Facebook Research, an R&D branch of the company. [PERSON] & [PERSON] [10] observed that ARIMA forecasts can be prone to large trend errors, particularly when a change in trend occurs close to the cut-off period, and often fail to capture any seasonality. In experiments with Prophet, [PERSON] and [PERSON] [10] predicted the daily number of Facebook events in a 30-day horizon with 25% more accuracy than ARIMA. The accurate prediction of these daily Facebook events, which are captured by an indicator with similar characteristics as some of the healthcare indicators, particularly in terms of in trend, seasonality and holidays, and which are at the heart of the Prophet model decomposition, motivated their use in this study. Finally, a number of Machine Learning models are used to predict data, specifically for time-series processes, and in particular Neural Network variants have presented encouraging results for classification problems, e.g., [11; 12]. In this study, after fitting models of LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network) and RBN (Radial Basis Network), we present the results for the model found to best fit the data sample in our simulations, that is RBN, and in particular GRNN (Generalised Regression Neural Networks). The rest of the paper is organised as follows: Sections 2-4 give more details of ARIMA, Prophet and GRNN, respectively. Section 5 describes the data and Section 6 gives details of how COVID has impacted on it. The main parts of the paper are Section 7 which describesthe experimentation, and Section 8 that analyses those results. Finally, Section 9 presents conclusions and suggestions for further work.
## 1 Introduction
Minimizing energy consumption and maximizing the network lifetime of a wireless sensor network (WSN) problem using the HS algorithm was closely studied [27; 28; 29; 30]. [PERSON] [31] optimized the frame size of truss structures by harmony search algorithm. Compared with the genetic algorithm for max-cut problem [32], the harmony search algorithm has advantages of generating new vectors after considering all of the existing vectors and parameters. [PERSON] and [PERSON] [33] proposed a harmony search algorithm with the additional improvement of harmony memory for asymmetric traveling salesman problems, which eliminates the imperfectness revealed in the previous research. [PERSON] et al. [34] researched the portfolio optimization problem using a mean-semi variance approach based on harmony search and an artificial bee colony. HSA [35] was used in reservoir engineering-assisted history, matching questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. However, HS and its variants usually have the following drawbacks, which are also our research motivation. 1. For the pitch adjustment operator of HS, a larger bandwidth is easier to jump out of the local optimum, while a smaller bandwidth biases to find a promising solution for the firing search. Therefore, a fixed step size is not an ideal choice. 2. It is difficult to find the optimal solution with a constant execution probability and an adaptive adjusting method is required. 3. Parameter _HMS_ has an important influence on the performance of algorithms. An adaptive sizing _HMS_ is possible to enhance the performance of the algorithm. Therefore, an adaptive harmony search algorithm is proposed with differential evolution mutation, periodic learning and linear population size reduction (aHSDE). The main contributions of this paper are as follows. 1. The pitch adjustment strategy is implemented with differential mutation. Adjust the pitch adjusting rate _PAR_ and the scaling factor \(F\) with periodic learning strategy. Linear population size reduction strategy is adopted for _HMS_ changing scheme. 2. The cooperation and effects of several strategies are analyzed step by step. The organization of this paper is as follows. Section 2 reviews the canonical HS and several improved variants. In Section 3, the composite strategies and algorithm aHSDE are proposed. In Section 4, the effects and the cooperation of the proposed strategies and parameters analysis are presented. The comprehensive performance comparison with other HS variants and other state-of-the-art EAs are presented in Section 5. Finally, Section 6 concludes the paper.
# Analysis of the Dynamic Stability of Tailing Dams:
## 4 Hysteresis Curve Analysis for Tailing Silt under Cyclic Loading  
### Hysteresis Curve Analysis of Typical Tailing Silt  
The hysteresis curve shows the stress-strain relationship of a sample under cyclic loading, and its horizontal and vertical coordinates represent the axial and dynamic stresses of the sample, respectively, which can better indicate the dynamic strength and energy dissipation characteristics of soil particles. In this section, we examine the hysteresis curve of tailing silt at different confining pressures.  
As shown in Figure 9, at \(\rho=1.62\) g/cm\({}^{3}\) and \(\sigma^{\prime}_{o}=200\) kPa, the hysteresis curves of typical tailing silt at each stage showed axial compression as a whole cyclic loading and the strain increased with an increase in cyclic vibrations, which was followed by plastic failure; this reflects the deformation accumulation characteristics of tailing silt. With the application of cyclic load, the hysteresis curves of typical tailing silt can be divided into the elastic strain stage, plastic failure stage, and softening stable stage. As shown in Figure 9a, at the elastic strain stage, the overall shape of the hysteresis curves was relatively regular and symmetrical about the origin, indicating that the strain produced by tailing silt with an increase in the number of vibrations  
small and it can withstand high dynamic load and exhibit high liquefaction resistance. Additionally, this stage accounted for the largest number of vibrations required for the liquefaction of the sample. At this stage, the soil particles were primarily staggered and the deformation was small. Figure 9 depicts the plastic failure stage. At this stage, the maximum value of the hysteresis curves steadily diminished, the sample's capacity to endure dynamic stress gradually declined, and the strain gradually increased. In addition, the tailing silt exhibited irreversible plastic deformation and obvious damage. The effective tension between many particles eventually passed to the pore water pressure, reducing the duration, and the sample entered the softening stable stage (as shown in Figure 9c) after a given number of vibrations. At this stage, the tailing silt accumulated multiple irreversible plastic deformations and the sample structure was completely deformed and showed softening. In addition, the dynamic stress of the sample remained unchanged with an increase in the number of vibrations and the dynamic strain increased steadily. This shows that the sample exhibits a certain cyclic activity.
# Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning
## 3 Tests and Evaluation
### Data Quality Analysis  
The residuals of UWB data calculated by different algorithms based on both simulation experiments and measured experiments were presented in Figure 3. It can be seen that the residual distributions for the simulation data basically obeyed the normal distribution. For the measured data, they were significantly nonnormal distributions. This appearance was mainly caused by the NLOS error. However, the residual distributions of PF and RPF were closer to normal distribution. According to the statistics in Tables 3 and 4, the residuals indexes for LS, EKF, UKF, CKF, PF, and RPF were close to each other and a little better than Bancroft in simulation test in terms of average, STD, and Maximum. Such appearance can also be found in the field test. The difference in visible was that these three indexes in the field test were much bigger than those in the simulation test. It can be concluded that the conventional adjustment methods and the presented method had similar distribution characters, while the UWB data only contained Gauss noise. However, these adjustments will result in residual with obvious differences while suffering NLOS noise.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **LinkTrack P** \\ \hline Size & 60.3 \(\times\) 29 \(\times\) 9 mm \\ Weight & 33.3 g \\ Maximum Communication Distance & 500 m \\ Recommended Distance & 300 m \\ Maximum Sampling Rate & 200 Hz \\ Recommended Sampling Rate & 20 Hz \\ One, two-dimensional Accuracy & 10 cm \\ Three-dimensional Accuracy & 30 cm \\ Frequencies & 4/4.5 GHz \\ Band-wide & 499.2 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification parameters of devices ([[https://www.nooploop.com/en/](https://www.nooploop.com/en/)]([https://www.nooploop.com/en/](https://www.nooploop.com/en/)), accessed on 9 May 2023).  
Figure 3: Residual histogram of UWB data in the simulation experiment (**a**) and these in the field experiment (**b**).
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
###### Abstract  
This study collects all strong motion records from 134 seismic events in the Loess Plateau region of China between 2020 and 2023. The corresponding ground motion attenuation dataset is constructed through data processing and screening, including 1149 soil horizontal strong ground motion records. Based on this dataset, two ground motion attenuation models are developed. Specifically, through magnitude grouping and weighted processing, two-step fitting methods are used to establish prediction equations for the peak ground acceleration (PGA), effective peak acceleration (EPA), and peak ground velocity (PGV) at the horizontal soil layers. These equations can estimate ground motion parameters with magnitudes ranging from \(M_{\text{S}}\) 3.0 to \(M_{\text{S}}\) 6.5 and epicentral distances between 0 and 100 km. Furthermore, attenuation curves are plotted based on the prediction equations. A comparison between the actual observed values and the predicted curves and an evaluation of the fitting residuals assess our models' fitting performance. The attenuation characteristics of horizontal ground motion parameters in the Loess Plateau region are systematically summarized.  
Loess Plateau of China; ground motion attenuation; strong motion records; attenuation model; peak ground acceleration (PGA); effective peak acceleration (EPA); peak ground velocity (PGV) +
Footnote †: journal: Applied sciences
## 4 Proposed Protocol
### CH Selection  
The flexibility and fault tolerance of fuzzy logic enable simultaneous consideration of multiple factors when selecting the appropriate CH. The applicability of CHs can be evaluated comprehensively by defining appropriate membership functions and fuzzy rules. In the proposed FQA, we utilized an FIS (fuzzy inference system) with the classic Mamdani model to select the appropriate CHs based on the well-tuned fuzzy rule. In each round, the BS collects the information of all nodes in the network to select the set of CHs.
# Bit Error Rate of Multi-Gaussian Correlated Asymmetric Bessel Beam Through Turbulent Ocean
## 2 Theoretical Analysis
\(N_{0}/P_{TX}\) reflects the level of background noise relative to the transmission power, which is usually given by the basal signal-to-noise ratio \(SNR_{0}\) in decibel through the conversion of \(N_{0}/P_{TX}=10^{-SNR_{0}(\text{dB})/10}\). Under the condition of signal modulation, the BER of the system is closely related to \(SNR\). As for the modulation format of QAM with constellation points labeled by Gray code, the expression of BER has the form of [18]  
\[ \begin{split} BER&=\frac{2}{\log_{2}M}\Big{(}1-\frac{1}{ \sqrt{M}}\Big{)}erfc\sqrt{\frac{3\log_{2}M}{2(M-1)}SNR}\\ &\times\bigg{[}1-\frac{1}{2}\Big{(}1-\frac{1}{\sqrt{M}}\Big{)} erfc\sqrt{\frac{3\log_{2}M}{2(M-1)}SNR}\bigg{]}\end{split}  \tag{21}\]  
where \(M\) is the modulation order and \(erfc(\cdot)\) indicates the complementary error function.
# Spatiotemporal Image Fusion in Remote Sensing
## 3 Synthesis: Challenges and Opportunities  
The spatiotemporal image fusion methods reviewed in this paper generate images at a higher temporal resolution which can be further used for land surface monitoring applications, especially in cloudy areas (e.g., tropical areas), where only a few cloud-free images per year are available. [PERSON] [66] reported, for example, that there is a 10% probability of acquiring cloud-free images such as Landsat for a certain time interval. The problem of data gaps caused by the presence of clouds can be addressed by combining microwave images with optical images similar to the approach proposed by [PERSON] et al. [27], who proposed fusing Advanced Microwave Scanning Radiometer (AMSR) series with MODIS images followed by MODIS-Landsat fusion.  
Developed spatiotemporal image fusion methods have been successfully used to fuse optical images collected by different remote sensing platforms. [PERSON] et al. [67] reconstructed daily 30 m remote sensing data from Huanjing satellite constellation, Gaofen satellite, Landsat, and MODIS data using the spatiotemporal methods developed by [PERSON] et al. [50]. Generated remote sensing data are efficient for extracting vegetation phenology and for mapping crops with an overall accuracy higher than those obtained from multi-temporal Landsat NDVI data. [PERSON] et al. [68] proposed a method to fuse Landsat, MODIS and geostationary satellites to 100 m resolution and one-hour interval. This method was compared with those developed by [PERSON] et al. [32], [PERSON] et al. [44], and [PERSON] et al. (2019) and proved to perform better over heterogeneous landscapes and changing land cover types. [PERSON] et al. (2019) evaluated how existing spatiotemporal image fusion methods perform for fusing Planet and WorldView images scenarios, and emphasized the importance of reducing the magnitude differences in the reflectance values between the two input sensors products and of aligning them to avoid misregistration errors. [PERSON] et al. (2019) fused MSI and OLI sensors for a study dedicated to land cover/land use mapping and proved that land cover change accuracy increases when Landsat-8 panchromatic band is used in the image fusion task. This is one of the few studies which fuses the two sensors products.
## 4 Proposed Antenna Codebook and MAB Approach
### Proposed Nested Two-Stage MAB Approach  
Jointly optimizing the values of \(\mathbf{\Phi}\) and \(\mathbf{f}\) using the prementioned codebook design will consume a considerable BT overhead due to the search over \(|\mathcal{R}||\mathcal{F}|\) different candidate beams. Instead, an online learning approach is proposed to successively obtain the optimal solution over the time horizon. This results in considerably reducing the BT overhead as only one pair \(\mathbf{\Phi}_{i}\) and \(\mathbf{f}_{j}\) will be tested at a time. In this context, an online single-player MAB game is constructed to address this problem efficiently. In this formulation, the BS is considered as the player of the bandit game; the available joint values of \(\mathbf{\Phi}_{i}\) and \(\mathbf{f}_{j}\) are the arms of the bandit; and the achievable spectral efficiency at the UE, i.e., \(\mathbf{\psi}_{\mathbf{\Phi}_{i}\mathbf{f}_{j}}\), is the reward. This MAB-based optimization problem can be mathematically formulated as follows:  
\[\max_{\mathbb{I}(1),\ldots,\mathbb{I}(T_{H})}\frac{1}{T_{H}}\sum_{t}\sum_{i,j} \mathbb{I}_{ii_{t}j_{t}}\left(\mathbf{\psi}_{\mathbf{\Phi}_{i}\mathbf{f}_{j_{t}}}\right), \tag{9}\]  
s.t.  
1. \(T_{H}\in(0,\ Z^{+})\),
2. \(\sum_{ij}\mathbb{I}_{ii_{t}j_{t}}=1\),  
where \(T_{H}\) indicates the time horizon and \(Z^{+}\) is the set of all positive integers. \(\mathbf{\psi}_{\mathbf{\Phi}_{i}\mathbf{f}_{j_{t}}}\) is the spectral efficiency resulting from using \(\mathbf{\Phi}_{i}\) and \(\mathbf{f}_{j}\) combination at time \(t\), i.e., \(\mathbf{\Phi}_{i_{t}}\) and \(\mathbf{f}_{j_{t}}\). \(\mathbb{I}_{ii_{t}j_{t}}\) is a selection indicator, which is equal to 1 if the combination \(\mathbf{\Phi}_{i}\) and \(\mathbf{f}_{j}\) is selected at time \(t\) and 0 otherwise. The constraint \(\sum_{ij}\mathbb{I}_{ii_{t}j_{t}}=1\) means that only one \(\mathbf{\Phi}\) and \(\mathbf{f}\) combination is allowed to be selected at time \(t\). To reduce the computational complexity of the constructed MAB game, a nested two-stage MAB strategy is proposed. In the proposed algorithm, the value of \(\mathbf{\Phi}\) is adjusted in the first MAB stage for a particular value of \(\mathbf{f}\). Then, based on the adjusted value of \(\mathbf{\Phi}\), the value of \(\mathbf{f}\) is adjusted in the second MAB stage, and so on over the time horizon. In this context, two common MAB algorithms are proposed to implement the suggested nested two-stage MAB approach, namely TS and UCB algorithms.
## 5 Main Program Design
### Data Transmission Component  
The data transmission process of this program uses MQTT v3.1.1 as the transmission protocol. MQTT is a client-server-based message publish/subscribe protocol, which is characterized as lightweight, simple, open, and easy to implement, and therefore is widely used in IoT devices. In MQTT protocol communication, the two sides of the communication are divided into the server and the client, in which the MQTT server's main function is to receive information sent by the client and also send information to the MQTT client; in addition, the server is also responsible for managing the MQTT client, which is needed to ensure that the communication is smooth and that the information is correctly accepted and accurately delivered. The client, on the other hand, is able to send information to the server and also receive information from the server. The process in which the client establishes a connection with the server includes creating a client, opening a client connection, creating a connection, subscribing to topics, and publishing data.