esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
General purpose FEM solvers like ABAQUS and Ansys can also be used in geengineering [34; 35; 36; 37]. A disadvantage of previously described commercial general purpose and specialized FEM software is its difficult integration into DMSs. Custom-made FEM solvers, such as 3 DFE, have been developed for automatic monitoring data analysis and dynamic analysis of dam-reservoir-foundation systems, using a solid-fluid coupled formulation to account for dam-water interactions, and include modules for complex modal and seismic analysis [38]. In commercial software such as Ansys or ABAQUS, meshing/remeshing is performed in just a few mouse clicks, but, unfortunately, academic, research-oriented, open source, and highly specialized FEM software lack such capabilities. The algorithm presented in this paper can easily be added to any FEM software, increasing simulation accuracy, while removing the need for intricate remeshing. This paper presents a novel and original implementation of an interpolation algorithm used as a solution to the problem of subsequent modification of existing FEM dam models in DMSs. Its relevance and contribution lie in the possibility of improving countless FEMcodes with a simple algorithm which fills the narrow gap between commercial FEM solvers and academic, research-oriented, open source, and highly specialized commercial software, such as that used for DMS. This algorithm can be used in the analysis of other FEM problems as well, not just for geomechanics, but it is applicable for FEM only; other numerical methods such as FVM or SPH use similar yet different discretization of continua, which means the interpolation showcased in this paper cannot be implemented. The remainder of this paper is structured as follows:  
Section 2, \"Materials and Methods\", describes the Hydropower Plant Djerdap and the finite element model used for its DMS. Section 3, \"Natural coordinate of the point in tetrahedral element\", showcases the interpolation functions for the tetrahedral finite element and presents the algorithm for determining the local coordinates of a particular point within that element. Section 4, \"Calculation of potential at measurement points position\", outlines the procedure for calculating the potential at measurement points. In Section 5, \"Results and Discussion\", the results of pore pressure calculation and temperature analysis are presented, and improvements over the old methodology using approximated values calculated at nodes are discussed. Finally, Section 6, \"Conclusions\", summarizes the motivation behind this work, improvements to non-commercial software, and implications for DMS accuracy.
## 3 Results
### Screw Stress  
In flexion, the screw stress on Nt35 increased by 1% when compared with Nt45, whereas the other models decreased by 4-6%. The screw stress increased by 5-7% and 11-15% during extension and lateral bending, respectively, and decreased by 23-53% in torsion. For all motions, the screw stress of all models ranged between 98.9 and 211 MPa, and the maximum occurred on Nt45 in torsion (Table 7).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Level**} & \multicolumn{2}{c}{**Flexion**} & \multicolumn{2}{c}{**Extension**} & \multicolumn{2}{c}{**Torsion**} & \multicolumn{2}{c}{**Bending**} \\ \cline{3-10}  & & **Left** & **Right** & **Left** & **Right** & **Left** & **Right** & **Left** & **Right** \\ \hline INT & L2–L3 & 0 & 0 & 94 & 94 & 0 & 336 & 53 & 31 \\  & L3–L4 & 0 & 0 & 105 & 105 & 0 & 336 & 41 & 9 \\ \hline Nt45 & L2–L3 & 0 & 0 & 117 & 117 & 0 & 374 & 82 & 38 \\  & L3–L4 & 0 & 0 & 6 & 5 & 0 & 213 & 0 & 0 \\ \hline Nt35 & L2–L3 & 0 & 0 & 118 & 118 & 0 & 366 & 82 & 37 \\  & L3–L4 & 0 & 0 & 7 & 7 & 0 & 245 & 0 & 0 \\ \hline Nt25 & L2–L3 & 0 & 0 & 118 & 118 & 0 & 351 & 80 & 35 \\  & L3–L4 & 0 & 0 & 11 & 11 & 0 & 285 & 1 & 0 \\ \hline Nt15 & L2–L3 & 0 & 0 & 115 & 115 & 0 & 343 & 74 & 33 \\  & L3–L4 & 0 & 0 & 14 & 15 & 0 & 323 & 9 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of facet joint contact forces (N) in finite element models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Implant** & **Motion** & **N45** & **N35** & **Nt25** & **Ntt5** \\ \hline \multirow{4}{*}{Screw} & Flexion & 139 & 140 & 134 & 130 \\  & Extension & 105 & 110 & 112 & 110 \\  & Torsion & 211 & 162 & 107 & 98.9 \\  & Lateral bending & 158 & 178 & 181 & 175 \\ \hline \multirow{4}{*}{PCU shell} & Flexion & 4.7 & 3 & 3.5 & 4.9 \\  & Extension & 5.1 & 2.7 & 3.1 & 4.9 \\  & Torsion & 17.4 & 7.6 & 6 & 4.6 \\  & Lateral bending & 6.8 & 5.1 & 5 & 7.2 \\ \hline \multirow{4}{*}{Nitinol stick} & Flexion & 44 & 54.6 & 72.4 & 118 \\  & Extension & 39.5 & 46 & 58.4 & 101 \\ \cline{1-1}  & Torsion & 219 & 324 & 383 & 381 \\ \cline{1-1}  & Lateral bending & 127 & 145 & 187 & 275 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the maximum von Mises stress on rods (MPa) in finite element models. There was no obvious trend in outer PS stress as the NS diameter changed. The stress on the inner nitinol stick increased as the NS diameter decreased.
## 3 Method
### Structured Prediction for Accurate Event Localization
#### 3.4.2 CRF Post-Processing  
As the proposed model can produce segment-level predictions, we can determine the border of audio events through post-processing. A common approach is to smooth the segment-level predictions and threshold them for boundary decision. However, since this approach does not take dependency between the segments into account, it is not easy to determine the borders of audio events precisely. To address this issue, we apply CRF for post-processing the segment-level predictions. To reflect the full relationship among segments, we incorporate the fully connected CRF model proposed in [34] into our system.  
In the conventional approach, segment-level predictions \(y_{i}\) are smoothed and thresholded for segment-level classification. The threshold value \(th_{v}\) is determined to have the best F1 score on the validation set. In the CRF post-processing approach, label assignment probability of each class for the \(i\)th segment \(P(i)\) is calculated as  
\[P(i)=sigmoid((y_{i}-th_{v})). \tag{9}\]  
The energy function for the fully connected CRF is given as  
\[E(x)=\sum_{i}\theta_{i}+\sum_{ij}\theta_{ij}, \tag{10}\]\[\theta_{i}=-\log(P(i)), \tag{11}\]  
\[\theta_{ij}=\mu(i,j)*[w_{mel}*\exp(-\frac{||m_{i}-m_{j}||^{2}}{2\sigma_{mel}^{2}}) \tag{12}\] \[+w_{pos}*\exp(-\frac{||p_{i}-p_{j}||^{2}}{2\sigma_{pos}^{2}})],\]  
where \(\theta_{i}\) represents the unary potential at the \(i\)th segment and \(\theta_{ij}\) is the pairwise potential between the \(i\)th and \(j\)th segments. In the pairwise potential, \(\mu(i,j)=1\) if the \(i\)th and \(j\)th segments have different label assignments, and zero otherwise. \(p_{i}\) denotes the temporal position and \(m_{i}\) is the log mel spectrum of the \(i\)th segment. The hyperparameters \(w_{mel}\), \(\sigma_{mel}\), \(w_{pos}\), and \(\sigma_{pos}\) control the Gaussian kernels. The pairwise potential penalizes segments with similar log mel spectra and positions having different labels. This model can efficiently infer the probabilities using mean field approximation and efficient message passing through high-dimensional filtering [34].
# A Low BER DB-PAM4 Adaptive Equalizer for Large Channel Loss in Wireline Receivers
###### Abstract
Signals of the duobinary type have proven to be very efficient modulation formats on high-loss channels. In order to reduce the overall equalization strength and reduce the bandwidth required by the receiver, this paper adopts a new generation method of DB-PAM4 and a joint adaptive algorithm. The DB-PAM4 signal is not directly generated at the transmitting end, but uses the roll-off characteristic of the channel itself and then is adjusted by the adaptive CTLE to produce the effect of binary processing. The rest of this article is divided into three parts. The second part describes the overall architecture of the equalizer, including the analysis and implementation of the DB-PAM4 signal. The third part introduces the key technology of the adaptive equalizer. The fourth section is the experiment and verification of the algorithm. The last is the conclusion of this paper.
###### Abstract  
Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky-Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety.  
acoustic emission; leak detection; Savitzky-Golay; convolutional neural network; long short-term memory; hybrid model +
Footnote †: journal: applied sciences
# RF Exposure Assessment for Various Poses of Patient Assistant in Open MRI Environment
## 3 Results and Discussion
In this scenario, the SAR levels of the patient assistant were close to 90.5% those of the patient, as shown Table 5. As the patient's body size decreases, the poses or contact position of the patient assistant with the patient become located closer to the RF coils, and thus the SAR levels of the patient assistant become more similar to those of the patient. If the subject of the MRI scan is a neonate/child or pet (e.g., dog, cat), it is more likely that the MRI scan will be accompanied by a patient assistant. Therefore, in these cases, the patient assistant should be particularly careful and aware of the RF energy exposure. Furthermore, more careful attention is required if the field strength (B\({}_{0}\)) of an open MRI system is increased (up to 1.2 Tesla, www.hitachi-medical-systems.eu/products/mri/oasis-12t.html (Accessed on 28 May 2021) because the overall SAR level tends to increase in proportion to the square of the increase in B\({}_{0}\)[32]. That is, when the field strength of the open MRI system increases, it can be predicted that the SAR level for the patient assistant having the same pose as in scenario 5 will be much higher. Therefore, although the ICNIRP guidelines for EMF protection [4] are not currently applied to patient assistants, the results of this study suggest that open MRI systems require the preparation of safety rules for them as well.
###### Abstract
By eliminating the need for batteries, backscatter communication supports the development of sustainable, low-power IoT networks, making it ideal for large-scale deployments in smart cities, industrial automation, and environmental monitoring where energy efficiency is crucial [16].
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 3 Proposed System Explanation  
### Magnetic Field in Tunnel Environment  
Figure 3 depicts the test bed, which comprised three tunnels with a total length of approximately 12 km. The magnetic field values obtained for the test bed are presented in Figure 4. The magnetic field value was measured from the three axes of the magnetic sensor in the smartphone. The blue, red, and yellow lines represent the x, y, and z axis values of the magnetic sensor, respectively. The results of the repeated experiments in the space marked in Figure 4 are depicted in Figure 5. The magnetic sensor values suggested adequate repeatability, and the repeated experiments with various time periods using the same device demonstrated that the stored magnetic sensor values were similar. Figure 5a depicts that the magnetic values of all the axes fluctuated significantly. When the vehicle passes through a bridge, the fluctuation of magnetic field values occurs. There is a considerably high repeatability in such situations. The yellow ellipses in Figure 5b depict the recurrence of a specific pattern for the magnetic field values, which enables the accurate position estimation using these features. This pattern occurs when the vehicle passes by an emergency parking lot in the tunnel.  
Figure 3: Test bed for tunnel navigation experiment.  
Figure 2: Block diagram of the proposed system.
## 5 Discussion  
### Discussion on the Exclusion of Non-Building Height Variation  
The elimination of elevation changes of non-building factors has always been the bottleneck. At present, remote sensing images are used to extract building land areas, and only the height change of the building area is calculated in the statistics. However, due to the limited accuracy of extracting construction land from images, some non-building height change factors will inevitably interfere with the calculation results. On the other hand, setting the elevation change threshold to exclude the non-building height change makes it difficult to exclude a sizeable non-building height change in particular areas. Therefore, combining the two methods to obtain more objective results is better.
# Demystifying Quantum Gate Fidelity for Electronics Engineers
## 5 Brief Case Studies
### Entanglement Fidelity  
Finally, it is worth noting that the authors in [45] expressed the fidelity in Equation (42) in terms of the entanglement fidelity \(F_{e}\):  
\[\mathbf{F}=\frac{\mathbf{n}\mathbf{F_{e}}+\mathbf{1}}{\mathbf{n}+\mathbf{1}} \tag{56}\]  
where \(F_{e}\) quantifies the exactness in teleporting information coded in a quantum state over a quantum transmission channel formed by entangled states (e.g., Bell states). Equation (56) further reinforces the analogy proposed in Figure 1 between a quantum gate and a transmission channel. The substitution of Equation (42) into Equation (56) yields:  
\[\mathbf{F_{e}}=\frac{\left|\mathbf{Tr}\{A_{I}^{\dagger}A_{R}\}\right|^{2}}{\mathbf{n}^{2}} \tag{57}\]  
which is the mathematical expression used in [28] to investigate the impact of the non-ideal behavior of the electronics on quantum fidelity. In the above case studies with the Pauli X and CNOT quantum gates, the substitution of Equation (48) into Equation (57) yields the entanglement fidelity \(F_{e,X}\) for the Pauli X quantum gate:  
\[\mathbf{F_{e,X}}=\frac{\mathbf{4}\mathbf{cos^{2}}\left(\frac{\varepsilon}{2}\right)}{\mathbf{ 2}^{2}}=\mathbf{cos^{2}}\left(\frac{\varepsilon}{2}\right)=\mathbf{1}-\mathbf{sin^{2}} \left(\frac{\varepsilon}{2}\right) \tag{58}\]  
and the subsititution of Equation (54) into Equation (57) yields the entanglement fidelity \(F_{e,CNOT}\) for the CNOT quantum gate:  
\[\begin{array}{c}\mathbf{F_{e,CNOT}}=\frac{4\left[1+\cos\left(\frac{\varepsilon}{ 2}\right)\right]^{2}}{\mathbf{4}^{2}}=\frac{2-\mathbf{sin^{2}}\left(\frac{\varepsilon }{2}\right)+\mathbf{2 cos}\left(\frac{\varepsilon}{2}\right)}{\mathbf{4}}\\ =\frac{1-\mathbf{sin^{2}}\left(\frac{\varepsilon}{2}\right)+\cos\left(\frac{ \varepsilon}{2}\right)}{\mathbf{2}}\end{array} \tag{59}\]  
For small values of \(\varepsilon\), \(\mathbf{sin}\left(\
icefrac{{\varepsilon}}{{2}}\right)\) can be approximated with \(\
icefrac{{\varepsilon}}{{2}}\) and \(\mathbf{cos}\left(\
icefrac{{\varepsilon}}{{2}}\right)\) with \(\mathbf{1}\), so that, from Equations (58) and (59), respectively, you obtain:  
\[\mathbf{F_{e,X}}\cong\mathbf{1}-\left(\frac{\varepsilon}{2}\right)^{2}=\mathbf{1}-\frac{ \varepsilon^{2}}{\mathbf{4}} \tag{60}\]  
\[\mathbf{F_{e,CNOT}}\cong\frac{\mathbf{1}-\frac{\mathbf{1}}{2}\left(\frac{\varepsilon}{2} \right)^{2}+\mathbf{1}}{\mathbf{2}}=\mathbf{1}-\frac{\varepsilon^{2}}{\mathbf{16}} \tag{61}\]  
Equation (60) agrees with the mathematical form claimed in [24,28] for one-qubit quantum gates facing phase errors in the controlling microwave signal.
## 3 Research Model Design
### Measurement of China's Agricultural Green Development Level
#### 3.1.3 Inspection of the Indicator System for Measuring the Development Level of Agricultural Green
(2) Overall inspection of elements  
The overall inspection of the agricultural green development level measurement index system mainly starts with the specific inspection from three aspects: consistency, completeness and necessity. Specifically, consistency measures the consistency of the range of the index calculation and the calculation method. The completeness of the indicators reflects the input end, output end and the important content involved in the operation process of the green agricultural production system. Necessity tests the irreplaceability of each index element from the perspective of the whole index system. This paper focuses on the overall necessity of eliminating indicators that are not representative or highly correlated by calculating identification and redundancy. In particular, the recognition degree is calculated by the minimum mean square error method, and the formula is as follows:  
\[S_{j}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(x_{ij}-\overline{x}_{j})^{2}}},j =1,2,3,\cdots m \tag{1}\]  
In the formula, \(S_{j}\) is the evaluation object, and \(x_{ij}\) is the \(j\)th index value of the \(i\)th evaluated object. If \(k_{0}(1\leq k_{0}\leq m)\) exists, then \(S_{k_{0}}=\min\limits_{1\leq j\leq m}\left\{\,S_{j}\right\}\). Then, the evaluation index \(x_{k_{0}}\) corresponding to \(S_{k_{0}}\) can be eliminated. It can be seen from Table 2 that the standard deviation calculated for the total agricultural ammonia nitrogen emission index is 2.05, which is the smallest standard deviation among all indicators, indicating that this indicator is not representative. Although it can indicate the degree of resource and environmental friendliness in theoretical analysis, the scores of green agricultural development in China's 30 provinces from 2006 to 2019 are comparable for this indicator; that is, the impact of this indicator on the ranking of units is negligible, so it can be eliminated. Proceeding from the four first-level indicators of economic and social development, resource-saving investment, resource recycling and resource and environmental friendliness, the irreplaceability of various indicators is ensured by performing a layered measurement of redundancy. The principle is to select independent indicators with very little correlation with other indicators. The specific steps are as follows:  
Step 1: Calculate the correlation coefficient matrix \(R=(r_{ij}),r_{ij}=\frac{S_{ij}}{\sqrt{S_{ii}\cdot S_{jj}}},i,j=1,2,\cdots,m\). Here, \(S_{ij}(i\
eq j)\) is the covariance, and \(S_{ii}\) and \(S_{jj}\) are the variance. Step 2: Calculate the correlation degree between the \(i\)th index and the other \(m-1\) indexes in each layer; that is, the multiple correlation coefficient, which is defined as \(\delta_{i}\). Replace the \(i\)th row and \(j\)th column of matrix \(R\) to the last row and last column of the matrix
## 4 Results and Discussion
### Adsorption Data  
#### 4.3.1 Initial Concentration Effect  
Figure 4a,b show the effect of the initial concentration on the uptake of Cr(VI) and MB. The results show that as the concentration of the Cr(VI) and MB solution increases from 20 to 200 mg/L, the adsorption capacity also increases. The increase in adsorption is due to an increase in the mass ratio of contaminant to adsorbent, which leads to an increase in the proportion of surface coverage [28]. At a higher initial concentration, the number of Cr(VI) and MB ions present in the aqueous solution is greater than at the lower initial concentrations, resulting in a stronger repulsion between the ions. This, therefore, leads to a stronger interaction between the Cr(VI)/MB solution and the paper-based adsorbent and thus a higher adsorption capacity [29]. It was observed that the paper-based adsorbent activated with nitric acid (APWN) has a higher adsorption capacity for both Cr(VI) and MB compared to APWP, APWH, and CPW.  
Figure 4: Initial concentration effect for (**a**) Cr(VI) and (**b**) MB.  
Figure 3: Raman analysis.
## 4 Discussion
### Theoretical Implications  
* Multisensory Integration Theory [52]: The findings from the studies contribute to advancing multisensory integration theory and support the need to further explore how combining multiple sensory inputs--visual, auditory, and olfactory--can enhance the user experience and improve outcomes such as memory, stress reduction, and emotional well-being.
* Need-driven behavior model: More attention should be paid to theoretical advancements in VR technology and sensory therapy in future studies by investigating how technology like VR with aromatherapy can serve as a non-pharmacological approach to address unmet needs in people with dementia and reduce behavioural and psychological symptoms of dementia. According to need-driven behavior theory, many challenging behaviors in dementia stem from unmet sensory, social, or emotional needs. By leveraging immersive VR experiences to stimulate sensory engagement and pairing them with the soothing effects of aromatherapy, these interventions can provide tailored sensory inputs, meeting individual needs and mitigating stress, agitation, or apathy. Future research should also investigate the effectiveness of such interventions across diverse populations and care settings, further validating their role in improving the quality of life for individuals with dementia.