esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Setup
### Training and Testing Database  
#### 3.2.1 Interpatient Database  
The main reference for the division of the database into training and testing for the interpatient approach was a study by [44], who proposed dividing the MIT-BIH database into two sets, DS1 (training) and DS2 (testing). This division is widely used for the development of classification systems proposed in the literature, thus facilitating a comparison of the results. However, some studies revealed problems with this division and proposed improvements that were also adopted in our study. Refs. [45; 46] reported imbalances between the classes of the two sets, leading the authors to suggest the addition of two medical records for the same patient in the two sets, DS1 and DS2, an approach also adopted by [23; 47]. Ref. [44] reported concerns regarding the use of unbalanced register 232 in DS2, which contains more than 75% of class S heartbeats with incorrect rhythm annotations in some segments. To maintain the integrity and reliability of the study results, the authors decided to remove record 232 from DS2 to avoid distortions in the evaluation results of the algorithms.  
Table 3 shows the organization of training and test data for the interpatient paradigm used in this study, where only classes N, S and V were selected for classification. Refs. [22; 48; 49; 50; 51] indicated that, as observed in practice, the F class is considered difficult to classify owing to the complex nature of the QRS complexes in ECG signals, where its morphology varies significantly, as the fusion of normal heartbeats and ventricular premature heartbeats creates labeling ambiguities for this class of arrhythmia.
# Systematic Review of Mecanum and Omni Wheel Technologies for Motor Impairments
## 3 Results
### Energy Efficiency and Drive Systems  
Another challenge is the efficiency of the drive system and power consumption in Mecanum/omni-based wheelchairs. Multidirectional drive systems often exhibit higher energy losses (e.g., due to slippage) and complex motor control requirements. In response, [PERSON] et al. [41] implemented field-oriented control (FOC) in a detachable power module for a manual wheelchair. This method increased motor efficiency and provided consistent torque at low speeds, resulting in smoother driving and reduced battery load. [PERSON] et al. [42] used advanced kinematic simulations of a Mecanum platform (via RecurDyn) to optimize wheel configuration and control logic. Their results emphasized that both mechanical layout and power distribution algorithms are critical for smooth and energy-efficient omnidirectional movement. Real-world prototypes have also addressed powertrain concerns--[PERSON] et al. [43] built a functional Mecanum-wheel using standard components such as 24 V DC motors and microcontrollers. Recommendations focus on enhancing device functionality, including safety (with additional sensors), efficiency (battery improvements), and comfort (speed control and smooth operation). Additionally, machine learning and field testing are emphasized for continuous improvement and real-world validation. The data were collected and summarized in Table 3.
## 2 Theoretical Analysis
### Residual Shear and Normal and Stresses Calculation
For the case of \(\varphi=0^{\circ}\), by substituting Equation (3) into Equations (5) and (6), the following equations can be obtained:  
\[\varepsilon_{\alpha 1,\varphi=0}=-\frac{1+\
u}{E}[(\sigma_{xx}-\sigma_{zz})\sin 2 \psi+\tau_{zx}\cos 2\psi]\sin 2\eta\cos\alpha \tag{7}\]  
\[\varepsilon_{\alpha 2,\varphi=0}=2\frac{1+\
u}{E}[\tau_{xy}\sin\psi+\tau_{yz} \cos\psi]\sin 2\eta\sin\alpha \tag{8}\]  
From Equation (7), a linear relationship between \(\varepsilon_{\alpha 1}\) and \(\cos\alpha\) can be observed. Thus, the normal stress \(\sigma_{\varphi=0}\) can be determined:  
\[\sigma_{\varphi=0}=(\sigma_{x}-\sigma_{z})+2\tau_{zx}\cot 2\psi=-\frac{E}{1+ \
u}\frac{1}{\sin 2\eta\sin 2\psi}\frac{\partial\varepsilon_{\alpha 1,\varphi=0}}{ \partial\cos\alpha} \tag{9}\]  
From Equation (8), a linear relationship between \(\varepsilon_{\alpha 1}\) and \(\sin\alpha\) can be observed. The shear stress \(\tau_{\varphi=0}\) can be determined:  
\[\tau_{\varphi=0}=\tau_{xy}+\tau_{yz}\cot\psi=\frac{E}{2(1+\
u)}\frac{1}{\sin 2 \eta\sin\psi}\frac{\partial\varepsilon_{\alpha 2,\varphi=0}}{\partial\sin\alpha} \tag{10}\]  
Similarly, the normal stress and shear stress can be determined for the case of \(\varphi=90^{\circ}\), \(\varphi=180^{\circ}\), and \(\varphi=270^{\circ}\), respectively. For the case of \(\varphi=90^{\circ}\):  
\[\sigma_{\varphi=90}=\left(\sigma_{yy}-\sigma_{zz}\right)+2\tau_{yz}\cot 2 \psi=-\frac{E}{1+\
u}\frac{1}{\sin 2\eta\sin 2\psi}\frac{\partial\varepsilon_{ \alpha 1,\varphi=90}}{\partial\cos\alpha} \tag{11}\]  
\[\tau_{\varphi=90}=-\tau_{xy}-\tau_{zx}\cot\psi=\frac{E}{2(1+\
u)}\frac{1}{\sin 2 \eta\sin\psi}\frac{\partial\varepsilon_{\alpha 2,\varphi=90}}{\partial\sin\alpha} \tag{12}\]  
Figure 1:0 Residual stress determination by using \(\cos\alpha\) method. For the case of \(\varphi=180^{\circ}\):  
Figure 1:1  
Figure 1:2  
and for the case of \(\varphi=270^{\circ}\):  
Figure 1:3  
Figure 1:4  
Consequently, stress components can be determined by the following equations:  
Figure 1:5  
Figure 1:6  
Figure 1:7  
As the penetration depth of X-rays for Inconel 718 is about 5 \(\upmu\)m during measurements [1; 5], the stress state is usually assumed to be a plane stress state as illustrated in Figure 1c. The normal stress in the \(z\)-axis is zero. Therefore, the normal stress and shear stress can be determined by Equations (17)-(19).
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Risk Parameters for the Environment Outside the Cableway
#### 2.3.1 Proximity to Oil and Gas Pipelines  
The cable channel is adjacent to storage containers of flammable or corrosive media or transmission pipelines, and the distance does not meet the standard requirements. Directly buried cables are located directly above or directly below the oil and gas pipeline. The minimum distance between cables and oil and gas pipelines is less than 1 m when they are parallel, and less than 0.5 m when they are crossed. The distance between the cable and the oil and gas pipeline is less than 0.25 m when the cable is separated by a partition or when the cable is threaded through a pipe.
## 3 Inertial Aiding Method Based on a Nonlinear Tracking Differentiator  
### Jerk Aided Kalman-PLL  
The process noise covariance matrix \(\mathbf{Q}_{k}\) of Kalman-PLL is demonstrated as [21]:  
\[\mathbf{Q}_{k}=\begin{bmatrix}0&0&0\\ 0&0&0\\ 0&0&n_{\Delta\dot{f},k}^{2}\tau\end{bmatrix} \tag{12}\]  
where \(n_{\Delta\dot{f}}^{2}\) denotes the Doppler shift rate PSD, that is:  
\[n_{\Delta\dot{f},k}^{2}=\sigma^{2}(\Delta\dot{f}_{k}-\Delta\dot{f}_{k-1})/\tau \tag{13}\]  
The Doppler shift rate value is related to satellite-receiver line-of-sight acceleration. Parameters of process noise covariance matrix \(\mathbf{Q}_{k}\) are related to the line-of-sight jerk. When the line-of-sight jerk is constant or it varies slowly, \(n_{\Delta\dot{f}}^{2}\) can be set as a very small constant. However, if the line-of-sight jerk is large, the process noise characteristic is unknown, and filtering divergence might occur. There are two solutions to such a problem: the first one is to estimate the receiver's dynamic characteristics through adaptive algorithms, and adjust the coefficients of process noise covariance matrix [23; 24]; the second one is to introduce inertial aiding information [25], and carry out real time correction of the process noise covariance matrix during the prediction step.  
The Doppler shift rate is related to satellite-receiver line-of-sight acceleration, is demonstrated as:  
\[\begin{split} n_{\vec{\mathcal{M}},k}^{2}&=\sigma^{2} \left(\Delta\dot{\vec{f}}_{k}-\Delta\dot{\vec{f}}_{k-1}\right)/\tau\\ &=\frac{\sigma^{2}\left(\frac{f_{ca}}{c}\cdot\mathbf{a}_{k}-\frac {f_{ca}}{c}\cdot\mathbf{a}_{k-1}\right)}{\tau}\\ &=\sigma^{2}(\frac{f_{ca}}{c}\cdot\mathbf{j}_{k})\tau\end{split} \tag{14}\]  
where \(f_{ca}\) is the carrier frequency, \(c\) is the light velocity, \(\mathbf{a}_{k}\) is the satellite-receiver line-of-sight acceleration vector.  
The jerk aided PLL involves performing time difference of line-of-sight acceleration, obtaining the line-of-sight jerk, and correcting the process noise matrix relevant coefficient \(n_{\Delta\dot{\vec{f}},k}^{2}\) of the Kalman-PLL according to Equations (12) and (14).
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
## 3 Conventional Detection Schemes
### Qrd-M  
In Equation (9), \(|S|\) times SEDs between \(Z_{N}\) and reference symbol \(C_{k}\), \(k=1,2,\cdots|S|\) are calculated at the \(N\)-th layer, where \(|S|\) is constellation size. The SED between \(Z_{N}\) and the \(k\)-th reference symbol \(C_{k}\) is as follows,  
\[E_{N,k}=|V_{N}-R_{NN}C_{k}|^{2}. \tag{12}\]  
With respect to \(k\) in Equation (12), the SED vector \(\mathbf{E}_{N}\) is as follows,  
\[\mathbf{E}_{N}=\Big{[}\begin{array}{cccc}E_{N,1}&E_{N,2}&\cdots&E_{N,|S|} \end{array}\Big{]}. \tag{13}\]  
In Equation (13), the small \(M\) values are selected and its corresponding symbols are selected as candidate symbols at the \(N\)-th layer. The selected \(M\) candidates are extended to the \((N-1)\)-th layer. At the \(m(1\leq m\leq N-1)\)-th layer, \(M|S|\) times SEDs are calculated. However, accumulated SED (ASED) is calculated for accurate estimation. The ASED between \(V_{m}\) and \(C_{k}\), which considers the ASED for the \(d_{m+1}\)-th candidate symbol, is as follows,  
\[E_{m,k}^{d_{m+1}}=\left|V_{m}-\left(R_{mm}C_{k}+\sum_{i=m+1}^{N_{t}}R_{mi} \hat{X}_{i}^{d_{i}}\right)\right|^{2}+E_{m+1,d_{m+1}}^{d_{m+2}}, \tag{14}\]  
where \(\hat{X}_{i}^{d_{i}}\) is temporarily estimated symbol at the \(i\)-th layer corresponding to the \(d_{i}\)-th candidate symbol. At the first layer, transmit symbols are estimated by selecting the candidate that has the smallest ASED with respect to \(k\).  
Figure 1 shows the tree structure of the conventional QRD-\(M\) (\(M=2\)) in \(4\times 4\) MIMO-OFDM system using quadrature phase shift keying (QPSK) modulation. In Figure 1, bold circles are candidate symbols at each layer. The complexity of the conventional QRD-\(M\) increases exponentially in huge MIMO-OFDM systems because the conventional QRD-\(M\) calculates \(M|S|\) times ASEDs at each layer.
## 3 Results and Analysis
### Microparticle Flow Velocity  
The results of the analysis of flow characteristics for the bottom and central sides/centers of Capillary tubes for copper contaminants are shown in Figure 9. The flow rate at the bottom of the capillary tube was about \(1.0\times 10^{-4}\) cm/s, which is slower than the inflow rate of \(1.0\times 10^{-3}\) cm/s, and the flow rate at the center was analyzed to be \(1.0\times 10^{-3}\) cm/s similar to the inflow rate. In addition, it was analyzed that the flow rate from the center to the center appeared somewhat faster than the side. When copper contaminants pass through the filter,  
Figure 8: Micro particles distance (\(\upmu\)m/s).  
Figure 11: CFD numerical analysis results (pH 6): (**a**) contour; (**b**) vector.  
Figure 12: CFD numerical analysis results (pH 8): (**a**) contour; (**b**) vector.  
Figure 13: CFD numerical analysis results (pH 10): (**a**) contour; (**b**) vector.  
The numerical analysis shows that there was no change in velocity at pH 4, pH 6 and pH 8, and that there was a small decrease in velocity at pH 10, but the difference between the actual flow rate and the actual flow rate was confirmed to be caused by pH. Based on the experimental results, the velocity coefficient was presented. At this time, the diffusion coefficient was calculated by regression analysis of the flow rate measured for the copper contaminant and expressed in the rate coefficient Equations 1 to 3 according to the pH.  
\[v_{b}=0.0005e^{-0.335 pH} \tag{1}\]  
\[v_{sc}=0.0013e^{-0.115 pH} \tag{2}\]  
\[v_{mc}=0.0031e^{-0.192 pH} \tag{3}\]  
Here, \(v_{b}\): velocity coefficient at the bottom of the capillary tube, \(v_{sc}\): velocity coefficient at the central side of the capillary tube, \(v_{mc}\): speed factor at the center of the capillary tube, and \(pH\): \(pH\) of heavy metal contaminants are shown.
# Improvement of the Zienkiewicz-Zhu Error Recovery Technique Using a Patch Configuration
## 7 Square Plate with a Circular Opening Example  
The finite element analysis of the plate with an opening example, employing the improved ZZ recovery technique was also carried out to compare the analysis results obtained using the classical ZZ recovery and least-squares displacement recovery techniques in a stress concentration condition. Due to symmetry, only the upper left square quadrant of the plate is modeled. Along the symmetry line, the shear stress and the normal displacement component are zero. Equations (24) to (26) show the analytical solution for stresses in the plate with opening [4]:  
\[\sigma_{x}=\sigma_{\infty}\bigg{[}1-\frac{a^{2}}{r^{2}}(1.5\cos 2\
## 3 Application Description and Requirements
### Core Features  
Collaboration within a document collaboration system should mimic real-life interactions, enabling one or more users to engage with document content in near real time. Besides interacting with the actual text document and sharing resources with peers, users must be able to observe the other user's changes, safely store their content, and get notified of meaningful events that occurred while they were not part of a collaboration session. (e.g., A document shared with them was deleted or their access to a resource was revoked).  
Figure 1: Development methodology: from requirements to architecture validation.  
To accommodate varying user demands, the system must be scalable and capable of adapting to high usage volumes. All of the functionalities described should be accessible through a user interface that optimally utilizes real-time collaboration features, enhancing user experience and productivity. As reference numbers, this work aims to have a final product capable of supporting 1000 concurrent user interactions.  
To better understand the application requirements, a list of the main system features is provided.
# Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours
## 3 Results  
### Features Based on the Dct and the Dft. a Comparative Study  
#### 3.1.1 Leave-One-Out Cross-Validation  
A significant limitation we deal with in this work is the limited number of classified images available for each species used for the training. In the binary classification problem, the two classes are balanced, with 23 individuals in each group. However, in the multi-classification problem, this limitation is more severe, as we have to deal with an even more reduced set of data, which is also unbalanced for the nine groups that need to be classified, meaning that there are different numbers of individuals in each of these groups, as shown in Figure 5.  
Despite having these nine classes unbalanced and some of them badly under-represented, the same methodology has been applied in both problems. The _leave-one-out cross-validation_ (LOO-CV) strategy is commonly used to evaluate the performance of the classifier when dealing with small data-sets [48]. This strategy uses a single observation taken from the original data-set to validate the data, while the remaining observations are used for the training phase. This way, the model is built with all the labeled elements except one, which is used for validation. Once the element has been tested, it is added to the group again, and the next element is extracted, repeating the process until all elements have been used for the test. Thus, all the elements are tested against the rest of the known classified elements. The advantage of this method is that all observations are used for both training and validation, and each observation is used for validation exactly once.
## 4 Discussion
### Comparison of Simulation Results with Known Ones  
A visual comparison of the results of modeling metal artifacts in X-ray CT with the data of other authors [7; 8; 25; 27; 47; 48] showed their closeness for similar phantoms.  
We also note the confirmation of the main statements cited above.  
Works [11; 18; 47; 52] note the effect of \"photon starvation,\" that is, a low level of the product of radioscopic transparency and the number of initial photons, which leads to the appearance of metallic artifacts. Images from [47] illustrate the conclusion that a decrease in radioscopic transparency is associated with an increase in the significance of metal artifacts. Works [7; 49; 53] emphasize the importance of choosing the energy of the X-ray source and filtering the beam to exclude metallic artifacts. Metal artifacts significantly complicate the interpretation of the final images in other implementations of computed tomography [54; 55; 56].  
Based on a visual comparison of images of ideal and real distributions of the linear X-ray attenuation coefficient over the cross sections of test samples, we proved that for the integral photon detection mode, the reduction of metal artifacts in X-ray computed tomography is achieved by increasing the intensity of the X-ray source, increasing its maximum energy, and increasing the ADC bit depth. The development of criteria for the rational choice of the noted parameters is beyond the scope of this article; however, in the context of the discussion, we will propose several ideas for future research.
## 1 Introduction
Application of remote sensing data for urban areas requires imagery with moderate until high spatial resolution. Remote sensing is a powerful tool for mapping and monitoring regional landscapes, and high-resolution imagery has proved effective for understanding land properties (e.g., ecosystems and hydrology) [21] and vegetative cover [22] in urban areas. Landsat imagery with a moderate spatial resolution of 30 m has been effectively used to classify homogeneous landscapes; however, the accuracy of such techniques may diminish in regions with highly heterogeneous landscapes [22]. Urban areas provide unique challenges to satellite remote sensing techniques. In urban landscapes, accurate flux measurements are complicated by surface heterogeneity, and NDVI is less useful for estimating CO\({}_{2}\) exchange [23]. The operational potential of urban remote sensing depends on the capacity of remote sensing to capture small-scale objects over heterogeneous surfaces in urban areas [24]. We therefore require imagery with spatial resolution higher than that of Landsat to classify and identify the heterogeneous landscapes characteristic of urban areas. ALOS/AVNIR-2 and Aster are two types of images that have higher spatial resolution than that of Landsat. ALOS/AVNIR-2 has a spatial resolution of 10 m and Aster 15 m in the red and near infrared (NIR) bands. The spatial resolution of the remote sensing data is crucial for discriminating fluxes for the different land cover types and hence avoiding significant errors due to application of a land surface model to a mixed pixel containing large contrasts in vegetation cover [25]. The aim of this research is to analyze the potential of GIS and remote sensing for estimating GPP, in particular over urban scales. In this study, we focus our analysis on the distribution of annual GPP, which effectively measures CO\({}_{2}\) assimilation by vegetation in urban areas. We further use two types of satellite data (ALOS/AVNIR-2 and Aster) to assess the impact of spatial resolution on the detection various ecosystem processes in Denpasar, an urban city in Bali Island, Indonesia.
## 4 Research and Analysis
### Autoencoder  
Another method to reduce the fluctuation of RSSI data is a denoising autoencoder. The denoising autoencoder is one type of autoencoder. Autoencoders are often used for RSSI pre-processing [41; 42]. The structure of the autoencoder consists of an encoder and a decoder. The encoder parts make input data into compressed data, which is a small dimension compared with the dimension of input data. Then, the parts of the decoder restore the original data using compressed data. This structure can be expressed as  
\[X^{\prime}=D(E(X)) \tag{5}\]  
where \(X\) and \(X^{\prime}\) are the input and output of the autoencoder, respectively. \(E()\) expresses a part of the encoder process and \(D()\) expresses a part of the decoder process.  
The autoencoder is a type of neural network that consists of several layers. The outputs of the perceptron are calculated with the outputs of the previous layer and their weight values. Expressing this as an equation, it is  
\[X^{i}=\phi(X^{i-1}W^{i-1}+b^{i}) \tag{6}\]  
where \(X^{i}\) is the output of the \(i\)th layer. \(W_{i}\) and \(b^{i}\) are the weight matrix and the bias vector between the \(i\)th layer and the \(i+1\)th layer, respectively. \(\phi\) is the activation function.  
The denoising autoencoder has the same dimensions of input data and output data, but different values. The input data are data with noise and the output data are data from which noise has been removed. We define noise as fluctuation in RSSI data. Therefore, the input data and output data for denoising autoencoder learning are raw data that have fluctuation and data with fluctuations removed, respectively. Output data without fluctuations are made manually. The method to make output data is the following. Values that differ by more than three from the average value in the input data set are replaced with the average value of the input data set. A method to make output data uses Equation (7).  
\[In_{i}=\left\{\begin{array}{cc}In_{i},&if\ |In_{i}-AVG(In)|\leq 3\\ AVG(In),&if\ |In_{i}-AVG(In)|>3\end{array}\right., \tag{7}\]  
where \(In_{i}\) is the ith data in the input data. \(AVG(In)\) is the average value of input data.  
The Kalman filter implemented autoencoder is used similarly to the denoising autoencoder. The difference with the denoising autoencoder is that data applied with the Kalman