esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Performance Comparison  
Using our real ID card character testing dataset, we compared our proposed approach to EasyOCR [19] and PadddleOCR [18], and evaluated the recognition performance. EasyOCR and PaddleOCR exhibited recognition accuracies of 55.22% and 0.85%, respectively. Our system had a 99.39% success rate. Despite the apparent disparity, the input image  
Figure 14: Procedure of projection method.  
Figure 13: Some error misclassification samples: (a) Contaminated and similar error (Ground Truth: “East” and Ground Truth: “West”) and (b) cropping error (Ground Truth: “Three” and Ground Truth: “Two”).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Training Samples** & **Original** & **Projection** \\ \hline Font model & 600 & 83.72\% & **85.70\%** \\ Mixed model & 600 & 91.95\% & **96.54\%** \\ Transfer model & 400 & 95.01\% & **97.53\%** \\ \hline \hline \end{tabular}
\end{table}
Table 9: New evaluation results of the proposed models on the 13,070 characters classification task using the projection rectification testing set.  
formats were different. EasyOCR and PaddleOCR takes an image of a line of text as input, whereas our approach takes an image of a single character as input to perform recognition on each character sequentially. A single-character picture collected from real IDs forms the basis of our testing dataset. The evaluation and performance comparison may be skewed, as few alternative methods for recognizing printed Chinese characters on ID cards have been reported in the literature. To the best of our knowledge, [PERSON] et al.'s most recent work achieved an average character recognition accuracy of 98.57%. Our proposed technique outperformed their approach (99.39% vs. 98.57%). The testing dataset, however, differed notably. Hence, making a comparison is difficult owing to the lack of a consistent baseline to evaluate the benefits of new technologies.
## 3 Experimental Results
### Database Setup
As seen in Table 4, the accuracy of the classification below 150 BPM was 100% and thus ideal for being used in the real world. However, as the speed reached 150 BPM and higher, the accuracy dropped. The total accuracy at each speed is shown in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Musical Gesture** & \begin{tabular}{c} **Number of** \\ **Testing Data** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Correctly** \\ **Classified Gestures** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Misclassified Gestures** \\ \end{tabular} & **Accuracy** \\ \hline Duple & 1750 & 1633 & 117 & 93.31\% \\ Triple & 1750 & 1571 & 179 & 89.77\% \\ Quad & 1750 & 1458 & 292 & 83.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of musical conducting gesture recognition. Figure 16: Screenshots of a single musical conducting gesture (quad) being made while facing the camera, including the 2D video, a skeleton model of the musical conductor, and the depth video. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Musical Gestures** & \begin{tabular}{c} **Number of** \\ **Testing Data** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Correctly** \\ **Classified Data** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Misclassified Data** \\ \end{tabular} & **Accuracy** \\ \hline Duple (150 BPM) & 350 & 334 & 16 & 95.43\% \\ Triple (150 BPM) & 350 & 319 & 31 & 91.14\% \\ Quad (150 BPM) & 350 & 295 & 55 & 84.29\% \\ Duple (200 BPM) & 350 & 249 & 101 & 71.14\% \\ Triple (200 BPM) & 350 & 202 & 148 & 57.71\% \\ Quad (200 BPM) & 350 & 113 & 237 & 32.29\% \\ Other & 350 & 316 & 34 & 90.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy of musical conducting gesture recognition classified by BPM. As seen in the above table, the accuracy at 150 BPM started to drop, and at 200 BPM, the accuracy dropped dramatically. As mentioned before, the main reason for the drop in accuracy was that the camera filmed at 30 frames per second, and the resolution of a gesture decreased as the BPM increased. Fortunately, in real-life applications, there are very few musical pieces that actually go up to 200 BPM, and thus, our proposed system is usable on most occasions. Our experiment comprised conducting gesture recognition not only at different speeds but also at different angles. Our system can accurately recognize the gesture as long as the palms are clearly captured by the depth camera, because it recognizes the gestures based on the vertical movement of the palms. The accuracy of our musical conducting recognition system at different viewing angles is shown in Table 5.  
At 90\({}^{\circ}\), a part of the conductor's body was blocked, so the accuracy of the gesture was slightly lower. Our method can recognize three basic gestures made at 60\(-\)200 BPM and can recognize the gestures at any angle within 45\({}^{\circ}\) upward or downward and within 90\({}^{\circ}\) to the left or right.
# An Algebraic Approach to Identifiability
## 3 Observability and Identifiability
### Global Identifiability  
When one wants to decide if a system is identifiable without bothering if the initial condition can be restored from the output trajectory, the statement at the end of Section 3.1 is too restrictive. However, instead of comparing the sets \(\mathcal{I}\) and \(\mathcal{J}\) that contain the full extended state, one may compare these sets in their projections on the parameter plane  
\[\{(x,\bar{x},p,\bar{p})\in\mathcal{Z}\,|\,x=\bar{x}=0\}.\]  
This corresponds algebraically to the comparison of the _elimination ideals_  
\[\mathfrak{I}\cap\mathbb{R}[p,\bar{p}],\quad\mathfrak{I}\cap\mathbb{R}[p,\bar {p}].\]  
The system is globally identifiable if and only if these ideals are equal, regardless of the initial condition.
# Using Wearable Sensors to Estimate Vertical Ground Reaction Force Based on a Transformer
## 6 Conclusions  
In this work, we use a _transformer_ as a feature extractor to address the problem of predicting GFR using wearable sensors. First, we introduce the dataset used in this paper, which contains lower extremity motion data and vGRF in multiple motion situations, solving the problems of high cost and limited range of motion associated with the use of ergometers in the past and can be applied to outdoor situations. Then we introduce the advantages of using transformer as a feature extractor, which performs better than the traditional neural network models RNN and LSTM. Finally, we refer to Gate_MSE to solve the problem of large peak errors in the GRF prediction task. Meanwhile, this paper explores the effects of the number of transformer encoders, the data format, and the wearing mode or number of inertial sensors worn on GRF prediction, which can be replaced flexibly in practice.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wearing Method** & **MSE** & **MAPE** \\ \hline Ankle & 0.0294 & 83.1910 \\ Ankle + Pelvis & 0.0218 & 79.2751 \\ Ankle + Pelvis + Thigh & 0.0205 & 78.7815 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of the wearing method on results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data format** & **MSE** & **MAPE** \\ \hline Acceleration + Rotation & 0.0325 & 90.5268 \\ Acceleration + Joint position & 0.0237 & 81.5452 \\ Acceleration + Joint position + PCA & 0.0218 & 79.2751 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of the data format on results.  
**Author Contributions:** Conceptualization, Y.Z. and H.Z.; Methodology, Y.Z.; Writing--original draft, Y.Z.; Writing--review & editing, D.X. and H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Fundamental Research Funds for the Central Universities, China, under grant XDJK2020B029. This work was supported by the Major Transverse Project, China, under Grant SWU41015718 and Grant SWU2071095.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to our data only collect the actions of the subjects.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Artificial-Intelligence-Based Charger Deployment in Wireless Reshargeable Sensor Networks
## 3 Problem Definition
### Wireless Charging Model  
The most important deployment problem is that the power consumption of each sensor node differs because each sensor has its own sensing function and power consumption. Therefore, we must calculate the received power of each sensor:  
\[P^{S}_{i,j}(d)=\frac{G_{c}G_{s}\eta}{L_{p}}\left(\frac{\lambda}{4\pi\left(d_{i,j}+\beta\right)}\right)^{2}P^{C}_{i,j}, \tag{1}\]  
where \(P^{S}_{i,j}(d)\) represents that the \(j^{th}\) sensor's power received from the \(i^{th}\) charger; \(P^{C}_{i,j}\) represents power transferred from the \(j^{th}\) charger to the \(i^{th}\) sensor; \(G_{c}\) is a value of antenna gains of the chargers; \(G_{s}\) represents a value of antenna gains of sensor nodes; \(\eta\) is a value of rectifier efficiency; \(L_{p}\) is polarization loss; \(\lambda\) is the wavelength of RF; \(\beta\) is a unique adjustable parameter in an indoor environment. To ensure each sensor can be charged, the first step is to find the amount of power which the most power-hungry sensor needs. If the provided power can meet the requirements of the most power-hungry sensor, all of the sensors can receive enough power in this scenario. In view of this, we further define the ECD, as shown in Equation (2)  
\[R=\frac{\lambda}{4\pi\sqrt{\left(\frac{W_{Max}L_{p}}{nG_{s}G_{s}\eta P^{S}_{i,j}(d)}\right)}}-\beta, \tag{2}\]  
where \(R\) is ECD; \(W_{Max}\) is the maximum power consumption of all sensors; \(N\) is the number of chargers needed for the most power-hungry sensor. We assume \(P^{S}_{i,j}(d)=W_{Max}\); it represents that the most power-hungry sensor will run out of power received from the  
Figure 1: The chargers were deployed on the ceiling.  
charges. In other words, all of sensors will receive enough power when the distance of sensor to charger is between \(R\). According this assumption, \(R\) is also equal to \(d_{i,j}\). We can deduce Equation (1) by Equation (2).  
We assume that all of chargers will provide the same power to have better fairness. The charging efficiency is better when the effective charging distance is less than \(R\), and vice versa.
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
## 2 Materials and Methods  
### Adsorbent Characterization  
For this study, commercially available natural zeolite of the clinoptiolite type, specifically FERTCEL zeolite (Zeocel Ltda., Agueda, Portugal), was employed. Widely utilized in agriculture to regulate soil fertility, maximize nutrient utilization, and qualitatively and quantitatively improve crop yields, this zeolite boasts a high cation-exchange capacity (CTC) exceeding 150 mEq/100 g, temporarily retaining ions and releasing them as required by plants. According to the manufacturer's data, it reduces nitrogen losses in the soil, prevents phosphorus fixation by iron, aluminum, and manganese in acidic soils, enhances potassium efficiency, and mitigates soil acidity. Additionally, it can absorb up to 40% of its weight in water. The physicochemical and mineralogical characterization of clinoptiolite zeolite (hydrated aluminosilicate) was conducted in an independent laboratory specializing in chemical-agricultural analyses, accredited by the National Accreditation Entity (ENAC, Madrid, Spain), ensuring the objectivity of the results.  
In this context, the crystalline phases of the zeolite were assessed through X-ray diffraction (XRD, Bruker-AXR D8 Advance) under the following conditions: \(\Delta 2\theta=5\)-\(65^{\circ}\), \(2^{\circ}\) increments, integration time = 0.05 s, Cu K\(\alpha\)1 radiation, \(\lambda=1.540598\) A [20]. The obtained diffractograms were analyzed and compared with the Crystallography Open Database (COD, available at [[http://www.crystallography.net/](http://www.crystallography.net/)]([http://www.crystallography.net/](http://www.crystallography.net/)) (accessed on 27 December 2023) using the specialized software Match! version 3.16. The chemical composition was determined through Fourier-transform infrared spectroscopy (FTIR-4700 type A, FTIR spectrometer JASCO Quick Start System with ATR Pro One as an accessory). Spectra were collected with a resolution of 4 cm\({}^{-1}\) in the range of 400 cm\({}^{-1}\) to 1400 cm\({}^{-1}\) and analyzed using OriginPro 9.5 Software (OriginLab Corporation, Northampton, USA) [21; 22]. Furthermore, laser diffraction techniques (Master Sizer 2000, APA2000 model from Malvern Instruments Ltd., Cambridge, UK) in the range of 0.01 to 3500 um were employed [23].
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 3 Influence Analysis of Error Source
### Satellite Attitude Error  
The SAR satellite attitude control error can lead to polarimetric error, because the polarimetric state of incident and scattering wave can be expanded with a certain polarimetric basis corresponding to the desired polarization antennas. Under the influence of the satellite attitude control error, the antenna rotates from the desired attitude, then the polarimetric basis will be rotated by a certain angle \(\varphi\)  
Figure 6: Phase imbalance caused by antenna array under different view angle.  
which is defined as POA [26]. The POA can be calculated by Equation (6) using the attitude angle (yaw, roll and pitch) [7]:  
\[\varphi=\tan^{-1}\bigg{(}\frac{\sin\phi\sin\theta_{p}-\cos\phi\cos\theta_{p}\sin \theta_{y}}{\cos\theta_{p}\cos\theta_{y}}\bigg{)} \tag{6}\]  
where \(\theta_{p}\), \(\theta_{y}\) are the pitch angle, the yaw angle difference from the desired angle, and \(\phi\) is the look angle. Here, the desired attitude is according to the yaw and pitch steering performed by GF-3 satellite, which is aimed at minimizing the Doppler centroid caused by earth rotation [27].  
Analyzing the impact of POA on dihedral and triangle corner reflectors, we find that this error does not affect the measurement of trihedral corner reflector, but results in poor isolation of dihedral corner reflector. Crosstalk of GF-3 PolSAR is required to be less than \(-35\) dB, then the POA should be controlled within \(0.5^{\circ}\). The attitude control error of GF-3 is within \(0.03^{\circ}\), so the POA caused by the attitude control error is quite small. We get the measured attitude and the desired attitude, and calculated the POAs of PARCs in 4 scenes of GF-3 polarization data. Results, shown in Table 3, suggest that the theoretical POA is very small and produces a crosstalk less than \(-75\) dB which can be ignored.  
On the other hand, the actual POA in imagery can be estimated by the PARC. After the calibration of channel imbalances, the measured scattering matrix \(M_{PARC}\) of PARC, the real scattering matrix of which is \([0,0;1,0]\) and becomes  
\[M_{PARC}\approx Ae^{j\Theta}\cos(\Omega+\varphi_{R})\cos(\Omega-\varphi_{R}) \left[\begin{array}{cc}\tan(\Omega+\varphi_{R})+\delta_{3}&0\\ 1&\tan(\Omega-\varphi_{R})+\delta_{2}\end{array}\right] \tag{7}\]  
where \(\varphi_{R}\) means the real POA, other parameters have the same meaning as in Equation (1). Equation (7) suggests that the difference between HH and VV is caused by \(\varphi_{R}\) and \(\delta_{2}-\delta_{3}\). The amplitudes of the PARC scattering matrix in Table 4 imply that difference between HH and VV is very small. Therefore, it can be considered that the actual POA introduced by attitude has a negligible effect on GF-3 polarization observation.
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 2 Wireless Communications Technologies for m-Health  
Mobile Health (m-Health) is broadly defined as a medical practice supported by mobile devices, such as mobile phones, health monitoring devices, Personal Digital Assistants (PDAs), and other wireless devices. Connectivity plays an important role in patient monitoring, and it has become particularly critical in those applications where it is necessary to move patient data to data centers. A wide range of innovative wireless technologies, such as Bluetooth Low Energy (BLE), Infrared (IR), Wireless Local Area Network (WLAN), ZigBee, and VLC are increasingly being used in medical devices, especially those used for patient monitoring applications [4].  
As shown in Figure 1, the essential requirements for m-Health monitoring devices are small size, low power consumption, positioning capacity, and the ability to efficiently and accurately transfer data to remote monitoring stations [5].  
Figure 1: m-Health Requirements.
## 2 Development of a New Analytical Model Based on the Virtual Solid Flame Concept
### Evaluation of the Steel Temperature of a Structural Member Placed in the Compartment  
The steel temperature is computed according to EN 1993-1-2 equation 4.25 [22], given below as Equations (27) and (28), respectively. For a given time-step \(\Delta t\), the Equation (27) allows to compute the corresponding rise in steel temperature \(\Delta T\). The following notations are used:  
* \(A_{m}\) the surface area of the member per unit length [m\({}^{2}\)/m];
* \(V\) the volume of the member per unit length [m\({}^{3}\)/m];
* \(A_{m}\)/\(V\) the section factor for unprotected steel members [m\({}^{-1}\)];  
Figure 7: Model of the virtual solid flame: 3 cases (linear scheme according to x coordinate).  
\((A_{m}/V)_{b}\) the box value of the section factor (box refers here to the rectangular envelope around the steel member) [m\({}^{-1}\)];
* \(c_{a}\) the specific heat of steel [Jkg\({}^{-1}\)K\({}^{-1}\)] as given in EN1993-1-2 $ 3.4.1.2 [22];
* \(F_{tot}\) the design value of the net heat flux per unit area [W/m\({}^{2}\)];
* \(\Delta t\) the time interval [s];
* \(\rho_{a}=7850\) kg/m\({}^{3}\) the unit mass of steel.  
Once \(\Delta T\) is known, it is possible to evaluate \(T_{t}\), the steel temperature at time \(t\) through an iterative procedure, see Equation (28).  
\[\Delta T=\frac{(A_{m}/V)_{b}}{c_{a}\rho_{a}}F_{tot}\Delta t \tag{27}\]  
\[T_{t}=T_{t-1}+\frac{(A_{m}/V)_{b}}{c_{a}\rho_{a}}F_{tot}\Delta t \tag{28}\]  
Since the steel temperature depends on the heat fluxes and that in the present methodology, heat fluxes vary within the compartment (see Figures 4 and 5), the model allows to represent the spatially transient heating of the steel structure. Nevertheless, it assumes a uniform steel temperature for a given cross section, i.e., there is no cross-sectional temperature gradient.
## 6 Evaluation
### Only Fully Connected Layers Pruning
By combining pruning and quantization, the model is reduced by factor 11 with the same accuracy drop of 7.99. Figure 4: Weights sum for fully connected layers (orange) and convolutional layers (blue). With AlexNet (see Table 7), using only 1% of weights in the fully connected layers, the model size is reduced by a factor of 21.1 with a gain in accuracy of 1.79. By combining pruning and quantization, the model is reduced by a factor of 85.2 with a gain in accuracy of 2.15. Using LeNet5 (see Table 8), only 1% of weights in the fully connected layers is used. The model size is reduced by factor 12.01 with a drop of 1.3 in accuracy. However, by combining pruning and quantization, the model is reduced by factor 49.4 with a gain in accuracy of 3.14. By observing the above results, sometime pruning only the fully connected layers can be good enough if the goal is only the footprint reduction. Pruning only the fully connected layers with high sparsity level results in high compression of the whole model.
# A Hybrid CNN and RNN Variant Model for Music Classification
###### Abstract  
Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features to identify unique patterns but are still unable to determine the original music characteristics. Comparatively, music classification using deep learning models has been shown to be dynamic and effective. Among the many neural networks, the combination of a convolutional neural network (CNN) and variants of a recurrent neural network (RNN) has not been significantly considered. Additionally, addressing the flaws in the particular neural network classification model, this paper proposes a hybrid architecture of CNN and variants of RNN such as long short-term memory (LSTM), Bi-LSTM, gated recurrent unit (GRU), and Bi-GRU. We also compared the performance based on Mel-spectrogram and Mel-frequency cepstral coefficient (MFCC) features. Empirically, the proposed hybrid architecture of CNN and Bi-GRU using Mel-spectrogram achieved the best accuracy at 89.30%, whereas the hybridization of CNN and LSTM using MFCC achieved the best accuracy at 76.40%.  
music classification; music information retrieval; convolutional neural network; recurrent neural network; Mel-spectrogram +
Footnote †: journal: Medical Image Analysis  
Deep learning assists in the design of end-to-end systems for a wide range of applications. These systems can automatically extract features without biases and outperform traditional techniques. CNN and RNN are two of the most effective approaches to classifying music data [3; 4], where CNN is better at recording spatial dependencies in feature domains [5; 6], and RNN satisfactorily handles sequential data temporal dependencies [7; 8]. In literature, many of the works classified GTZAN [9] dataset, which became the musical analysis benchmark. Further, a computational model that can automatically explore the digital content of the large and growing library of music is still lacking. Another reason to apply the CNN and variants of RNN jointly is to learn features incorporated with estimated parameters, in addition to using sequential modeling for neural networks that should be capable enough to analyze the inherently sequential nature of music files. The following are the main contributions of this work:  
* Utilizing MFCC and Mel-spectrograms to determine which neural architecture is most effective for classifying music.
* For the classification of music, multiple hybridization such as CNN-LSTM, CNN-Bi-LSTM, CNN-GRU, and CNN-Bi-GRU are taken into consideration.
* Lastly, the performance of the various hybridizations for the various extracted features were also evaluated using the same musical dataset.  
The remainder of this paper is structured as follows: Section 2 contains related work, while Section 3 elaborates on the methodology and proposed architecture. Section 4 is made up of data descriptions and experiments. Section 5 has the results of extracted features and discussion, and the work is concluded in Section 6.