esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
### Summary of Key Findings  
The results indicate that the model combining Linear Discriminant Analysis with the Support Vector Machine (LDA-SVM) exhibited the highest accuracy in classifying EMG signals for prosthetic control. Initially, with data from a single session, the LDA-SVM model achieved an accuracy of 86.98%. When the dataset was expanded to include three sessions, accuracy improved to 90.69%, demonstrating the benefits of a larger, more comprehensive dataset. This enhancement underscores the model's robustness and its ability to generalize across different sessions and participants. The confusion matrix and performance metrics, such as precision, sensitivity, and F-measure, provided deeper insights into the classification effectiveness. For instance, the precision and sensitivity metrics indicated that the model not only accurately predicted the correct class but also had a high true positive rate across various hand gestures. Comparing the results with other machine learning models like K-Nearest Neighbor (KNN) and Naive Bayes (NB), the SVM consistently outperformed these classifiers, particularly in handling high-dimensional data and maximizing the margin between classes. This highlights the importance of choosing appropriate dimensionality reduction and classification techniques tailored to the dataset's characteristics. The analysis also demonstrated the impact of using a diverse and detailed dataset like GRABMyo, which includes a wide range of gestures and diverse participants, on improving model performance and applicability in real-world scenarios. These findings suggest that advanced machine learning models, particularly those using SVM with LDA, can significantly enhance the functionality and intuitiveness of prosthetic control systems, paving the way for more adaptable and user-friendly prosthetic devices. Furthermore, it introduces innovative methodologies for processing EMG signals, with the goal of creating artificial intelligence systems capable of decoding brain activity to control arm movements. The research entails a comprehensive investigation into signal processing techniques and machine learning classification algorithms, all implemented on the GRABMyo dataset. The findings indicate that the employed range of techniques, signal filtering and discrete wavelet transform (DWT), alongside a composite feature set (MAV + WL + ZC + SSC + RMS + EWLs + EMAV) followed by LDA dimensionality reduction and SVM classification, yielded optimal performance. In summary, each stage of EMG signal processing is crucial for determining accuracy, highlighting the importance of careful methods selection and processing.
## 3 Results
### Fourier Series Decomposition of Case I  
An important aspect when obtaining this signal is to evaluate how close our generated square signal is to the sinusoidal signal. For this, it is necessary to calculate the Fourier series corresponding to the waveform obtained. Since our signal is quarter-wave symmetry, the coefficients \(\mathrm{a}_{0}\) and \(\mathrm{a}_{\mathrm{n}}\) of the Fourier series are equal to zero, which leaves only the coefficient \(\mathrm{b}_{\mathrm{n}}\). The equation to calculate this coefficient would be  
\[b_{nodd}=\frac{4V_{dc}}{n\pi\left[\sum\limits_{k=1}^{N_{dc}-1}\left[\cos(n \beta_{k})-\cos(n\beta_{kf})\right]+\cos(n\beta_{Na})\right]} \tag{28}\]  
where \(V_{dc}\) is the dc voltage feeding the inverter, \(n\) represents the odd harmonics, \(\beta_{\mathrm{k}}\) represents the angles corresponding to the positive pulses, \(\beta_{\mathrm{k}f}\) represents the angles corresponding to the negative pulses, and \(\beta_{\mathrm{Na}}\) is the last angle calculated for a quarter-wave. In this case, being three pulses, the equation would be simplified as follows:  
\[b_{nodd}=\frac{4V_{dc}}{n\pi}\left[\cos(n\beta_{1})-\cos(n\beta_{1f})+\cos(n \beta_{2})-\cos(n\beta_{2f})+\cos(n\beta_{3})\right] \tag{29}\]  
With Equation (30), we can calculate the total harmonic distortion (THD) up to the 49 th harmonic to evaluate the quality of our signal concerning the sine wave, obtaining the following THD equation:  
\[\%THD=\frac{\sqrt{\sum\limits_{n=1}^{\mathrm{q9}}\left(b_{nodd}^{2}\right)}}{ b_{1}}100 \tag{30}\]  
Thus, the following graph is obtained, as shown in Figure 8.  
Figure 8 shows the harmonics up to harmonic 49, where the first harmonic is in blue, and the odd harmonics are in green. These harmonics are expressed in percentage and normalized concerning the first harmonic, which has a value of 100%. It is observed that the most significant and largest harmonics will appear on the opposite sides of the carrier frequency, which in this case is 12. The largest harmonics are in 11 th and 13 th place, gradually decreasing to the ninth and fifteenth. The 11 th and 13 th harmonics are about 20% of the fundamentals, while the ninth and fifteenth are about 17% and 18%. From these harmonics onwards, the other harmonics decrease in amplitude. The largest harmonics,  
Figure 8: Odd harmonics and their percentage concerning the fundamental component.  
always appearing on the sides of the carrier frequency, are the ones of interest to eliminate. This can be achieved by placing a filter at the output, which will significantly reduce their amplitude and improve the efficiency and cost of the system. An important parameter to calculate is the THD, which is the sum of the squares of all harmonics up to 49, according to IEEE 519. In this case, the THD obtained is 52.63%.  
The obtained results are congruent with the characteristic behavior of the natural or digital SPWM [8]. Therefore, the behavior validates the assessment of the proposed algorithm.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### PV-Powered Charging Station with V2G Service with Energy Cost Optimization
#### 2.2.3 Energy Cost Optimization  
This layer interacts with the prediction layer and the HMI to carry out the optimization to minimize the total energy cost. This layer, the power references for the stationary storage, the public grid, and the EVs, which are the decision variables, are sent to the operational layer. The optimization has several benefits, such as minimizing the energy cost, determining the optimal contribution of the stationary storage or the public grid, and avoiding EV and PV shedding. The energy pricing is considered for on-peak and off-periods with fixed tariffs, and the limits for public grid power injection and supply are defined with the public grid operators. Moreover, the physical limitations of the stationary storage should be respected to avoid its damage. The energy cost optimization problem is detailed in Section 3.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 4 Functions of the Fog Server in Details
### Data Uploading  
Figure 7 shows how a client uploads data to the fog server.  
1. A client sends \"data upload\" request to the fog server. To process the received request, the client has to pass the authentication process first (authentication steps are detailed in Section 4.1).
2. Data will be sent from the accessing module to the processing module.
3. The processing module will send the received data to the storage module to store.
# Face Gender and Age Classification Based on Multi-Task, Multi-Instance and Multi-Scale Learning
## 4 Conclusions and Future Work  
We present a novel deep-learning-enhanced multi-task random forest method for facial gender and age recognition. The facial robust features are extracted using multi-instances and multi-scale deep learning, and the facial gender and age are recognized together using a multi-task random forest. The proposed approach achieves good results owing to transfer learning, multi-instance multi-scale learning and multi-task conditional random forest learning. The multi-instance multi-scale learning features can alleviate the problem of intra-person variation, such as low image resolution, illumination and occlusion; the multi-task random forest can alleviate the inter-subject variations existing due to different personal attributes, such as gender, ethnic backgrounds and level of expressiveness.  
In the future, we plan to consider other factors in our model. In reality, facial age is not only related to gender, but also to other attributes, such as ethnicity, expressions and poses, etc. If we can take all facial attributes into account and learn the relationship between the attributes and age, it will definitely help us to improve the facial age grouping accuracy. In addition, using the interdependence between face attributes, multi-task learning can identify multiple attributes, such as gender, race, age, expression and the pose of a face at one time, to achieve the goal of a double win.  
Conceptualization, H.L. and L.Y.; methodology, H.L. and M.W.; validation, H.L., L.Z. and G.J.; formal analysis, H.L. and M.W.; writing--original draft preparation, H.L. and M.W.; writing--review and editing, L.Z., G.J. and N.X.; supervision, M.W., G.J. and N.X.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the natural science foundation of Hubei province, grant number No. 2021 CFB388, the outstanding young and middle-aged science and technology innovation team of universities in Hubei Province, grant number No. T2022032, the innovation team of Hubei University of Science and Technology, grant number No. 2022T04 and the science and technology planning project of Xianning city, grant number No. 2022 GXYF056.  
The authors declare no conflict of interest.  
Figure 7: Age grouping with and without gender-aligned conditional probability.
# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 2 Related Work
### Recommendation Algorithms Based on Knowledge Graph Representation Learning
#### 2.1.1 Embedding-Based Methods  
The core idea of embedding-based methods is to map entities and relationships in the knowledge graph into a low-dimensional vector space, thereby enhancing the representation capabilities of items or users by leveraging rich auxiliary information. For example, the CKE model proposed by [PERSON] et al. [14] learns item embeddings with the participation of the knowledge graph, effectively linking items to entities and relationships in the knowledge graph by fusing collaborative filtering and knowledge graph embeddings. [PERSON] et al. [15] proposed a Knowledge Graph Embedding Attention Network model, which introduces the translation distance method into high-order knowledge propagation, effectively embedding knowledge into a set of triples and achieving efficient fusion of collaborative information and auxiliary knowledge. These methods can transform complex knowledge graph structures into compact vector representations through embedding learning, facilitating subsequent recommendation task processing.  
However, embedding-based methods also have some limitations. Firstly, these methods often struggle to capture high-order relationships between entities because they primarily focus on direct embeddings of entities and relationships, ignoring complex path information and multi-hop relationships in the knowledge graph. Secondly, the learned entity embeddings may not be entirely suitable for recommendation tasks, as there may be a discrepancy between the objectives of embedding learning and recommendation tasks. Additionally, embedding methods may face challenges in computational efficiency and storage costs when dealing with large-scale knowledge graphs.
## 9 Performance Evaluators
### Relation between Number of Messages and Estimation Errors  
Figures 4-6 indicate the relation between the number of messages and the estimation error for localization and clock parameters to analyze the performance of the proposed algorithm. In our  
Figure 3: Dimension of nodes having the area of 500 m \(\times\) 500 m \(\times\) 1000 m with randomly placed anchor and sensor.  
simulations, the proposed algorithm has been executed a number of times ranging from 20 to 80 along with the algorithms discussed in state of art STSL, GN-JSL, and JSL. As a result of the implementation, we have seen that the overall performance of the proposed method is much better than other methods. The reason behind this performance is that both STSL and JSL use linear regression to estimate clock skew and offset and the accuracy of their calculation is based upon the number of message exchanges. Where GN-JSL is based upon the more accurate estimation of initial position and estimation of initial position is also based upon the large number of messages involved. However, as it is seen in Figure 4 that \(E^{2}JSL\) localization accuracy is much better than the STSL [13] and JSL [11], because both these algorithms did not consider the stratification effect, thus increasing the number of messages can't give an accurate estimation. Whereas the performance of GN-JSL [19] is slightly better than both these algorithms because of the consideration of the stratification effect. Similarly, the clock parameters accuracy estimated by \(E^{2}JSL\) is better than other algorithms given by Figures 5 and 6, JSL and STSL are almost unaffected by the number of messages. So, we can conclude that the proposed algorithm that initially estimates the stratification
# Dynamic Characterization of Cohesive Material Based on Wave Velocity Measurements
## 2 Methods and Materials
### Characterization of Test Material  
In order to investigate the elastic modulus of cohesive material, associated with the passage of seismic waves (an S-wave and a P-wave), silty clay (siCl) samples, from the test site located in the center of Warsaw (the capital of Poland), recognized by particle size analysis using the sieves and hydrometer methods (European Standard Eurocode 7, [34]), were used. The description of the research area can be found in [25]. The index properties of the examined soils are summarized in Table 1. In order to compute the fundamental indices (\(w_{\text{P}}\), \(w_{\text{L}}\)), standard test methods were employed, namely plastic limit test of soil, Casagrande liquid limit test and fall cone liquid limit test. The grain size distribution of the soil specimens is shown in Figure 6. The proportional content of each fraction is as follows: Gr = 0%, Sa = 13%, Si = 66%, Cl = 21%. The soil used during the tests, which is of Quaternary origin, was sampled in an undisturbed state using a standard Shelby tube. All of the samples were acquired at a depth of around 6.0 m and selected cautiously with the consideration of the soil structure uniformity, their physical properties and their double phase. Tubes were pressed carefully and gently into the pre-drilled holes. Subsequently, the samples were sealed and stored in a humidity room until needed [35].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(w\) (\%) & 17.52 \\ \(w_{\text{P}}\) (\%) & 17.14 \\ \(w_{\text{L}}\) (\%) & 33.00 \\ \(I_{\text{P}}\) (\%) & 15.86 \\ \(I_{\text{L}}\) (\%) & 0.02 \\ \(I_{\text{C}}\) (\%) & 0.98 \\ \(\rho\) (kg/m\({}^{3}\)) & 2140 \\ \hline \hline \end{tabular}
## 3 Risk Assessment-Based Dynamic Access Model
### Request a Risk Assessment
#### 3.1.3 Indicator Weight Calculation
\begin{matrix}r_{11}&r_{12}&\dots&r_{1n}\\ r_{21}&r_{22}&\dots&r_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ r_{n1}&r_{n2}&\dots&r_{nm}\end{matrix} \right\}\]  
Calculate the weight vector \(W_{C_{i}}\) of \(n\) evidences in attribute \(C_{i}\):  
\[W_{C_{i}}=\{w_{C_{1}},w_{C_{2}},...,w_{C_{n}}\}^{T} \tag{5}\]  
where \(w_{i}\) is:  
\[w_{C_{i}}=\frac{\sum_{j=1}^{n}a_{ij}}{\sum_{i=1}^{n}\sum_{j=1}^{n}a_{ij}} \tag{6}\]  
(2) Attribute Layer--Target Layer:  
Similarly, first, calculate the attribute layer to determine the initial matrix \(EQ^{\prime}\). \[EQ^{\prime}=\left\{ \begin{matrix}b_{11}&b_{12}&\dots&b_{1n}\\ b_{21}&b_{22}&\dots&b_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ b_{n1}&b_{n2}&\dots&b_{nm}\end{matrix} \right\}\]  
Then the initial judgment matrix EQ is transformed into a fuzzy consistency matrix \(Q^{\prime}\) according to Equations (3) and (4), and then the weight vector \(W\) of each attribute \(B_{i}\) of risk is calculated:  
\[W=\{w_{1},w_{2},...,w_{n}\}^{T} \tag{7}\]  
(3) Risk calculation:  
From Equations (5) and (7), the value of risk \(R\) can be obtained:  
\[R=W_{C_{i}}\cdot W^{T} \tag{8}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Scale** & **Meaning** \\ \hline
1 & The former is extremely less important than the latter \\
3 & The former is slightly less important than the latter \\
5 & Two factors are of equal importance compared to each other \\
7 & The former is significantly more crucial than the latter \\
9 & The former is far more crucial than the latter. \\
2, 4, 6, 8 & indicate the midpoints between the aforementioned neighboring judgments. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scale values. Compare the calculated risk value with the membership degrees of risk levels in Table 3, judge the risk level, and make a decision to allow or deny the request according to the specific situation of the system.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**Difference between Final and Initial**} & \multicolumn{2}{c}{**95\% Confidence**} & \multicolumn{2}{c}{**ANOVA 2 \(\times\) 2**} \\  & \multicolumn{2}{c}{**between Final and Initial**} & \multicolumn{2}{c}{**Interval (Lower; Upper)**} & \multicolumn{2}{c}{**Time (Pre and Post) \(\times\) Group**} \\ \hline Strength of lower train & & & & & \\ Five sit to stand (s) & GE & 0.488 & \(-\)2.847; \(-\)3.824 & F\({}_{1.91}\) = 0.066; \(p\) = 0.798; \(\eta^{2}\) = 0.01 \\ \hline Dynamic balance & & & & \\ Time up and go (s) & GE & 3.919 & \(-\)3.262; 11.100 & F\({}_{1.91}\) = 1.302; \(p\) = 0.028; \(\eta^{2}\) = 0.12 \\ Time up and go + cognitive task (s) & GE & 6.048 & \(-\)4.295; 16.291 & F\({}_{1.91}\) = 2.577; \(p\) = 0.015; \(\eta^{2}\) = 0.16 \\ \hline Tinetti scale & & & & \\ Balance (pts) & GE & \(-\)2.105 & \(-\)3.851; \(-\)0.359 & F\({}_{1.91}\) = 1.927; \(p\) = 0.016; \(\eta^{2}\) = 0.18 \\ Gait (pts) & GE & \(-\)1.892 & \(-\)2.834; \(-\)0.951 & F\({}_{1.91}\) = 6.024; \(p\) = 0.016; \(\eta^{2}\) = 0.07 \\  & GC & 0.450 & \(-\)1.445; 2.345 & F\({}_{1.91}\) = 6.024; \(p\) = 0.016; \(\eta^{2}\) = 0.07 \\ Total (pts) & GE & \(-\)3.998 & \(-\)6.421: \(-\)1.574 & F\({}_{1.91}\) = 3.753; \(p\) = 0.046; \(\eta^{2}\) = 0.05 \\ \hline PDQ-39 & & & & \\ Mobility & GE & 5.110 & \(-\)5.862; 16.082 G & F\({}_{1.96}\) = 0.002; \(p\) = 0.968; \(\eta^{2}\) = 0.001 \\  & GC & 4.718 & \(-\)13.126; 22.075 & F\({}_{1.96}\) = 0.002; \(p\) = 0.968; \(\eta^{2}\) = 0.001 \\ Activities of daily living & GC & 1.881 & \(-\)8.195; 11. As far as the specific results for the IVR component are concerned, it should be noted that there were no adverse effects related to cybersickness generated by the immersive application. The majority of the participants reported a total absence of the main symptoms of cybersickness, and those who did experience some symptoms described them as being mild. By size, 7% of the sample reported feeling mild symptomatology related to nausea, 2.93% experienced mild symptomatology related to oculomotor symptoms, and 6.29% reported symptoms related to disorientation. Post-game experiences evaluated with the GEQ highlighted the predominance of positive experiences (2.92/4) over negative experiences (0.04/4), fatigue (0.23/4), and return to reality (0.15/4) with residual values. Finally, the hardware and software usability were considered to be quite good (SUS scores: 82.90/100).
## 4 Discussion
### Environmental Controls as Estimated by Two ML Algorithms
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**No. of Sites**} & \multirow{2}{*}{**Biome**} & \multirow{2}{*}{**Cor**} & \multirow{2}{*}{**R2**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{5-8}  & & & & & **(g C m\({}^{-2}\) day\({}^{-1}\))** & & \\ \hline GBR & & & & 0.90 & 0.54 & 0.69 & \\ SVM & & & & 0.85 & 1.13 & 1.49 & \\ SGD & 2 & ENF & & 0.82 & 1.86 & 1.66 & \\ BR & & & & 0.76 & 1.99 & 2.24 & \\ RF & 1 & EBF & & 0.68 & & 0.58 & [52] \\ MTE & & & 0.7 & & & & [36] \\ SVR & 144 & All & 0.62–0.66 & & & & [32] \\  & 54 & All & 0.42 & & 1.40 & \\  & 11 & ENF & & 0.29 & & 1.28 & \\  & 7 & EBF & & 0.00 & & 1.86 & \\  & 8 & DNF & & 0.54 & & 1.42 & \\ SVR & 4 & DBF & & 0.78 & & 1.50 & [33] \\  & 7 & MF & & 0.37 & & 1.27 & \\  & 8 & GRA & & 0.37 & & 1.09 & \\  & 4 & TUN & & 0.66 & & 0.68 & \\  & 5 & CRO & & 0.60 & & 1.59 & \\ ANFIS & & & DBF & & & & \\ ELM & 8 & ENF & & 0.59–0.80 & 0.35–0.97 & 0.52–1.32 & [39] \\ ANN & & & MF & & & & & \\ SVM & & & & & & & \\ \cline{5-8}  & & & & & & & \\ \cline{5-8}  & & & & & & & \\ \cline{5-8}  & 12 & DBF & 0.89 & 0.90 & 0.80 & 0.80 & 0.96 & 0.98 & 1.49 & 1.46 \\  & 5 & EBF & 0.76 & 0.76 & 0.58 & 0.58 & 1.30 & 1.31 & 1.74 & 1.74 \\  & 17 & ENF & 0.89 & 0.89 & 0.78 & 0.79 & 0.78 & 0.77 & 1.10 & 1.08 \\  & 4 & MF & 0.84 & 0.82 & 0.70 & 0.67 & 0.46 & 0.49 & 0.79 & 0.83 \\ RF & 15 & GRA & 0.76 & 0.76 & 0.58 & 0.57 & 0.65 & 0.67 & 1.08 & 1.10 \\  & 5 & CRO & 0.65 & 0.66 & 0.42 & 0.43 & 1.65 & 1.66 & 2.55 & 2.52 \\ XGBoost & 2 & OSH & 0.56 & 0.52 & 0.31 & 0.27 & 0.27 & 0.28 & 0.41 & 0.43 \\  & 1 & CSH & 0.86 & 0.86 & 0.73 & 0.73 & 0.56 & 0.57 & 0.72 & 0.73 \\  & 4 & SAV & 0.80 & 0.78 & 0.65 & 0.61 & 0.30 & 0.33 & 0.45 & 0.47 \\  & 3 & WSA & 0.75 & 0.71 & 0.56 & 0.50 & 0.41 & 0.45 & 0.65 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Synthesis of NEE predicted by data-drive methods. List of acronyms: Cor, correlation coefficient; R\({}^{2}\), determination; MAE, mean absolute error; RMSE, root mean square error; DBF, deciduous broadleaf forest; EBF, evergreen broadleaf forest; ENF, evergreen needleleaf forest; MF, mixed forest; GRA, grassland; CRO, cropland; OSH, open shrulands; CSH, closed shrulands; SAV, savannas; WSA, woody savannas; TUN, tundra; GBR, gradient boosting regression; SVM, support vector machine; SGD, stochastic gradient descent; BR, Bayesian ridge; RF, Random Forest; Model tree ensembles, MTE; SVR, support vector regression; SVR, support vector regression; ANFIS, adaptive neuro-fuzzy inference system; ELM, extreme learning machine; ANN, artificial neural network; SVM, support vector machine; XGBoost, Extreme Gradient Boosting.
## 1 Introduction  
Until relatively recently, the diagnosis of Alzheimer's disease (AD) during life was mainly clinical and given to patients with an amnestic dementia syndrome according to criteria introduced 30 years ago [1]. With time, due to the results of pathological studies, it became evident that AD patients may present at pre-dementia stages [2] or with atypical pictures in the form of various syndromes, including primary progressive aphasia (PPA) [3], corticobasal syndrome (CBS) [4], posterior cortical atrophy (PCA) [5], and frontotemporal dementia (FTD)-like syndrome [6], or a mixture of other conditions, including cerebrovascular [7] and Lewy body pathology [8]. A major contribution for the in vivo recognition of these unusual presentations of AD came from the use of imaging [9] and, especially, cerebrospinal fluid (CSF) biomarkers [10, 11], which can identify the biochemical \"fingerprint\" of AD during life. This expanded concept of AD [12] has been at least partly incorporated in recent diagnostic criteria [13, 14, 15]. Early and accurate diagnosis of unusual clinical presentations of AD, as well as exclusion of other primary [16] or secondary [17, 18] causes of dementia, reduces uncertainty and may have significant implications in therapeutic decisions and predictions of prognosis.  
However, the relative frequencies of typical and atypical or mixed presentations within the entire spectrum of AD remain unclear, while some mixed or atypical presentations may have not received adequate attention for them to be included in recent criteria. The aim of the present study was to explore the spectrum of clinical presentations in patients with the AD CSF biomarker profile who were hospitalized in a tertiary academic center.