esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
Substitute Equations (A4) and (A7) into Equation (A1), the mode shape function of the ME plate under cantilever boundary condition is given by  
\[\begin{array}{c}W_{p}=X_{1}\cdot Y_{1}\cdot a_{11}+X_{1}\cdot Y_{2}\cdot a_{ 12}+X_{1}\cdot Y_{3}\cdot a_{13}\\ +X_{2}\cdot Y_{1}\cdot a_{21}+X_{2}\cdot Y_{2}\cdot a_{22}+X_{2}\cdot Y_{3} \cdot a_{  
Since the accurate theoretical results can hardly be determined by the analytical method, the Ritz method was adopted in this paper. It is an approximate solution method according to the energy variation principle, which could obtain the results by transforming functional extremum problem to the extremum problem of multivariate function. Due to the vibration excitation, the plate would be deformed, producing stress and strain. Therefore, there is deformation energy varying with time existing in the plate. The strain energy density can be expressed by  
\[W_{p}=\frac{1}{2}\Big{(}\sigma_{X}\varepsilon_{X}+\sigma_{Y}\varepsilon_{Y}+ \tau_{xy}\gamma_{xy}\Big{)}. \tag{10}\]  
Set the deformation function of central plane as \(w_{0}(x,y,t)\), the displacement function of any point in the plate can be given by  
\[ \begin{array}{l}u(x,\ y,z,t)=-z\frac{\partial u_{0}(x,y,t)}{\partial x},\\ v(x,\ y,z,t)=-z\frac{\partial u_{0}(x,y,t)}{\partial y}.\end{array}  \tag{11}\]  
According to geometric equation, the strain of any point in the plate can be obtained as  
\[ \begin{array}{l}\varepsilon_{x}=\frac{\partial u}{\partial x}=-z\frac{ \partial^{2}w}{\partial x^{2}}=-z\kappa_{x},\\ \varepsilon_{y}=\frac{\partial v}{\partial y}=-z\frac{\partial^{2}w}{\partial y }=-z\kappa_{y},\\ \gamma_{xy}=\frac{\partial u}{\partial y}+\frac{\partial v}{\partial x}=2z \frac{\partial^{2}w}{\partial x\partial y}=-2z\kappa_{xy},\end{array}  \tag{12}\]  
where \(\kappa_{x}\), \(\kappa_{y}\) and \(\kappa_{xy}\) are the curvature and distortion of the central plane. According to stress-strain relationship, the stress of any point in the plate could be expressed by  
\[ \begin{array}{l}\sigma_{x}=\frac{\mathrm{E}}{1-\
u^{2}}\Big{(}\varepsilon_{ x}+\
u\varepsilon_{y}\Big{)}=-\frac{\mathrm{E}}{1-\
u^{2}}z\Big{(}\frac{ \partial^{2}w}{\partial x^{2}}+\
u\frac{\partial^{2}w}{\partial x^{2}}\Big{)},\\ \sigma_{y}=\frac{\mathrm{E}}{1-\
u^{2}}\Big{(}\varepsilon_{y}+\
u\varepsilon_{ y}\Big{)}=-\frac{\mathrm{E}}{1-\
u^{2}}z\Big{(}\frac{\partial^{2}w}{ \partial x^{2}}+\
u\frac{\partial^{2}w}{\partial x^{2}}\Big{)},\\ \tau_{xy}=\mathrm{G}\gamma_{xy}=-\mathrm{G}\,\mathrm{G}\,\frac{\partial^{2}w}{ \partial x\partial y},\end{array}  \tag{13}\]  
where \(\
u\) is poisson's ratio, \(\mathrm{E}\) is elastic modulus and \(\mathrm{G}\) is shear modulus. Substituting Equations (12) and (13) into Equation (11) gives  
\[ \begin{array}{l}W_{p}=\frac{1}{2}\Big{[}\frac{1}{\mathrm{E}}\Big{(}q_{x}^{2} +\sigma_{y}^{2}-2\
u\sigma_{x}\sigma_{y}\Big{)}+\frac{1}{\mathrm{G}}\tau_{xy} \Big{]}\\ =\frac{\mathrm{E}^{2}}{2(1-\
u^{2})}\Bigg{[}\Big{(}\frac{\partial^{2}w}{ \partial x^{2}}\Big{)}^{2}+\Big{(}\frac{\partial^{2}w}{\partial y^{2}}\Big{)} ^{2}+2(1-\
u)\Big{(}\frac{\partial^{2}w}{\partial x\partial y}\Big{)}^{2} \Bigg{]}.\end{array}
### Objective, Contribution, and Structure  
This study aims to investigate a geometry-based stochastic channel-modeling scheme for next-generation 3D IoT networks in terms of wireless channel quality and network performance. The 3D IoT networks operate at mmWave and are complemented by aerial APs mounted on UAVs. This research contributes to next-generation mobile networks in several aspects, as follows:* Unlike existing studies that use path loss or standard stochastic channel models, a sophisticated geometry-based stochastic channel modeling scheme is developed.
* Using a spatial channel model, standard CDL channel profiles defined by 3 GPP are customized based on the geometry of the environment. To the best of our knowledge, this is the first study that incorporates a spatial channel model into a CDL channel in the context of UAV-assisted networks.
* Although many studies consider a homogeneous setup in which BSs are located in hexagonal grids and UTs are uniformly distributed, this research follows a more realistic geometry-dependent approach to modeling the locations of APs and sensor nodes (SNs).
* The evaluation of cellular network performance can be computationally complex, particularly for heterogeneous networks with many UTs. This study proposes a simplified approach by identifying the relationship between radio conditions and the modulation and coding scheme (MCS). Specifically, the signal-to-interference-plus-noise ratio (SINR) and channel profiles are mapped to channel quality indicator (CQI) indices, and such a mapping approach can be generalized to other environments.  
In our previous study [40], a CQI versus SINR table was proposed for a single UT. This work is extended in four main directions. First, the areas of interest are characterized by statistical parameters by considering many UTs, or equivalently, SNs. Second, the altitude of the UAVs is optimized to ensure LoS and achieve the highest spectral efficiency. Third, the network performance is evaluated in terms of downlink (DL) throughput. Fourth, it is shown that results accuracy can be improved considerably when customizing CDL channel profiles instead of following the standard channels.  
The remainder of this paper is organized as follows. In Section 2, a reference model consisting of terrestrial APs and state-of-the-art technology is presented. Subsequently, a new system model is proposed to incorporate flying APs in the form of UAVs. Section 3 presents the simulation results, and Section 4 provides concluding remarks.
###### Abstract  
The analysis of electrocardiogram (ECG) signals is profoundly affected by the presence of electromyographic (EMG) noise, which can lead to substantial misinterpretations in healthcare applications. To address this challenge, we present ECGnet, an innovative architecture based on Transformer technology, specifically engineered to denoise multi-channel ECG signals. By leveraging multi-head self-attention mechanisms, positional embeddings, and an advanced sequence-to-sequence processing architecture, ECGnet effectively captures both local and global temporal dependencies inherent in cardiac signals. Experimental validation on real-world datasets demonstrates ECGnet's remarkable efficacy in noise suppression, achieving a Signal-to-Noise Ratio (SNR) of 19.83, a Normalized Mean Squared Error (NMSE) of 0.9842, a Reconstruction Error (RE) of 0.0158, and a Pearson Correlation Coefficient (PCC) of 0.9924. These results represent significant improvements from traditional deep learning approaches while maintaining complex signal morphology and effectively mitigating noise interference.  
electrocardiogram; denoising signal; self-attention; transformer +
Footnote †: journal: Medical Imaging
## 5 Evaluation and Discussion
### Evaluation Based on Existing Work  
Evaluation based on existing work is depicted in Table 5. We have compared IRSA ZTF Images GUI, Datawnt0 GUI with our Proposed Systems GUI. Datawnt0 does not have data support for reference images and catalog files, although it contains some features such as Multiobject search and it supports relate and join functions unlike IRSA. The proposed system has few parameters similar to IRSA, such as providing data support for reference images and catalog files and filtering option in result table, although it has some parameters such as QBE support and this system can be used by novice users (who do not have any knowledge of SQL), unlike IRSA and Datawnt0.
## 5 Simulation Model and Results
To be able to apply the performance results of this protocol to any general network configuration, and not to any fixed network topology, the location of the _CNodes_ are changed at the beginning of every simulation run, by randomly redistributing them in the cooperative region; the source and destination remain fixed at their positions. Further, to obtain averaged and confident values, the simulations are executed over enough number of runs (100-150), with each run delivering 1,000-1,500 data packets from source to destination, so that the final performance metrics plots are definite and smooth. Moreover, to validate the accuracy and precision of the obtained results 95% confidence interval is also shown, but only in one plot (Figure 5) due to lack of space in other figures. Nonetheless, the confidence level of the 95% confidence interval applies to each and every plot. Finally, the obtained results and their discussions are as follows.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 2 Overview of Existing KF RAIM
### Fault Detection Test Statistics
Thus, the test statistics shown in Equation (1) are changed with time-correlated errors in the carrier-smoothed pseudorange measurements as  
\[\alpha_{j}^{2}=\Delta\underline{\tilde{\mathbf{\rho}}}^{*T}\tilde{\mathbf{S}}^{ \prime}\tilde{\mathbf{S}}^{\prime T}\mathbf{\Theta}\tilde{\mathbf{S}}^{\prime} \mathbf{\
abla}^{\prime T}\Delta\underline{\tilde{\mathbf{\rho}}}^{*}+\sum_{\ell=p_{j }}^{m_{j}}\left(\Delta\underline{\tilde{\mathbf{\rho}}}_{\ell}^{T}\mathbf{W}_{rr,\ \ell}{}^{-1}(\mathbf{I}-\mathbf{G}_{\ell}^{\prime T})\Delta\underline{\tilde{ \mathbf{\rho}}}_{\ell}\right) \tag{6}\]  
where \(j=1\), \(2\). At an epoch, if \(\alpha_{1}\) crosses its threshold \(T_{th,\ 1}\), or \(\alpha_{2}\) is greater than its threshold \(T_{th,\ 2}\), or both occur, a fault is detected. The third test is not formulated. Necessary modifications for not performing the third test are made to PLs. \(\alpha_{1}\) is formed with terms of \(M\) recent epochs including the current one, whereas \(\alpha_{2}\) has terms of \((N-M)\) previous epochs. Both \(M\) and \(N\) are calculated adaptively at each time epoch. The matrices \(\mathbf{\
abla}\) and \(\mathbf{\Theta}\) are briefly described next. \(\mathbf{\
abla}\) can be re-arranged into a block diagonal matrix, with \(i\)th block for satellite \(i\) given as \(\mathbf{\
abla}_{\text{sub}}^{i}= \begin{bmatrix}1&a_{11}&a_{11}a_{21}&a_{11}a_{ 21}a_{31}&\dots\\ a_{11}&1&a_{21}&a_{21}a_{31}&\dots\\ a_{11}a_{21}&a_{21}&1&a_{31}&\dots\\ &\vdots&&\end{bmatrix} \), where \(a_{\ell 1}\) at an epoch \(t_{\ell}\) takes a value between \(a_{\min}\) and \(a_{\max}\); \(a_{\min}=0.9890\); and \(a_{\max}=0.9911\) with a \(5^{\circ}\) elevation mask angle. In this context, another matrix \(\mathbf{\
abla}_{\max}\) is also defined. It has the same structure as that of \(\mathbf{\
abla}\), but its sub-matrix \(\mathbf{\
abla}_{\text{sub,\ max}}^{i}\) is formed with \(a_{\ell 1}\) always set as \(a_{\max}\). \(\mathbf{\Theta}\) is given by \((c\mathbf{I}+\mathbf{\
abla})^{-1}\) for \(\alpha_{1}^{\prime}\), with \(c\) being a positive multiplication factor calculated adaptively to ensure that \(\lambda^{\max}_{\mathbf{\Theta},2}\) is less than or equal to one. For \(\alpha_{2}^{\prime}\), \(\mathbf{\Theta}\) is \((c\mathbf{I}+\mathbf{\
abla}_{\max})^{-1}\), with \(c\) as \((\lambda^{\max}_{\mathbf{\
abla},\max}-\lambda^{\min}_{\mathbf{\
abla},\max})\). \(\lambda^{\max}_{\mathbf{\
abla},\max}\) and \(\lambda^{\min}_{\mathbf{\
abla},\max}\) are the maximum and minimum eigenvalues of \(\mathbf{\
abla}_{\max}\).
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 2 Literature Review
### Overall Analysis  
Specifically, the signals from IoT devices serve two purposes: the first is data aggregation based on signals from several devices, and the second is information transmission based on signals from a single device. IoT devices should have adequate energy to concurrently realize precise computing and effective communication. Energy supply for a large IoT, however, is not an easy operation. The enormous human cost and environmental burden make it unaffordable for huge IoT to change batteries frequently. It is therefore enticing to use the WPT to achieve one-to-many charging by taking advantage of the wireless broadcast channel's open nature. In actuality, information transmission, data aggregation and energy supply are all interrelated. The existing method involves more processing and offers less accurate localization. In those challenging circumstances, the wireless signal-based approaches might be less limited, which would improve sensing precision. By using variance-based integration, all of the aforementioned issues have been resolved. An energy, communication and computation (ECC)-based variance-based integrating model for the 6G-IoT method is suggested in order to increase overall performance with constrained wireless resources.
# Experimental Investigation of Column Separation Using Rapid Closure of an Upstream Valve
## 3 Observations and Results
### Influence of Quantities on Column Separation Events
#### 3.3.4 Changing Trends of Maximum Pipeline Pressure  
To summarize the relationship between the maximum pipeline pressure and the initial flow velocity, reservoir static pressure head, and pipeline length, a comparison was made regarding the ratio of maximum pipeline pressure to the static pressure in the downstream reservoir, Tank 2, as shown in Figure 14. In all test cases, the \(H_{max}/H_{T2}\) ratio exceeded 150. With constant reservoir static pressure, an increase in initial flow velocity initially led to an increase in maximum pipeline pressure followed by a decrease and subsequent increase. This changing trend became more pronounced with decreasing reservoir static pressure. For nearly constant initial flow velocity, a lower reservoir static pressure resulted in a larger ratio of \(H_{max}/H_{T2}\). Additionally, when comparing cases with constant reservoir static pressure and initial flow velocity, shorter pipelines exhibited greater changes in maximum pressure compared to longer pipelines.  
Figure 13: Pressure histories for different pipeline lengths, \(H_{T2}\) = 3.55 m: (**a**) \(V_{0}\) is around 0.65 m/s; (**b**) \(V_{0}\) is around 1.15 m/s.
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 1 Introduction
Scholars have conducted in-depth studies on these batch-learning models [16, 17, 18, 19]. Most research on slope stability prediction has relied on batch learning that, despite satisfactory performance, requires comprehensive retraining with each new data point integration, leading to increased training times as data volumes grow. Additionally, batch learning often fails to optimally utilize the relationship between new data and historical cases [20]. This inefficiency highlights the need for continuous and efficient prediction methods. Incremental learning retains old knowledge while incorporating new data, enabling continuous learning and improved performance [21]. Noteworthy models include incremental support vector machines, neural networks, and Bayesian models, with the naive Bayesian model standing out for its simplicity, speed, and accuracy. Widely used in text classification, image recognition, and lithology identification, the naive Bayesian model is ideal for slope stability analysis combined with incremental learning theory. This study develops the incremental learning Bayesian (ILB) model by integrating incremental learning theory with the naive Bayesian model for continuous slope stability prediction. To enhance prediction accuracy and efficiency, this study integrates six key factors for assessing slope stability: slope height, slope angle, unit weight, cohesion, internal friction angle, and pore water ratio. To validate these factors, this study compiled and reviewed relevant literature that frequently identifies these six factors as critical for slope stability. The database includes 242 slope samples from both China and other regions, which not only provides a solid foundation for model development but also enhances the reliability and accuracy of the predictions. By leveraging the strengths of incremental learning, the ILB model aims to address the limitations of traditional batch learning methods, offering a more efficient and adaptive solution for slope stability prediction. Implementing the ILB model not only enhances the capability to process and learn from new data promptly but also ensures the effective utilization of existing historical data, leading to more accurate and reliable predictions. The continuous improvement of the model through incremental learning holds significant promise for advancing the field of geotechnical engineering, providing a robust tool for mitigating the risks associated with slope instability and safeguarding both lives and infrastructure in mountainous regions.
## 3 Numerical Simulation
### Distribution Characteristics of Flow Field around Subgrade  
Distribution characteristics of flow field around embankment when sand-carrying wind flows through flat terrain and where dunes exist are shown in Figure 9. It can be found that the change of terrain has a serious influence on the structure of the highway flow field. The sand-carrying air flows in from the leftmost entrance of the model. When wind flows to the windward slope foot of embankment or dune, the airflow deceleration zone appears at the windward slope foot due to the impediment of the sudden change of terrain and then the airflow rises along the slope with speed increasing gradually, leading to the formation of an airflow acceleration zone. When the airflow climbs to the top of the windward side embankment, the energy continuously gathers on the surface of the embankment, forming a high-speed zone. While on the leeward side of the dune, on account of the terrain declining sharply, the airflow velocity on the upper part of the highway surface is low, forming pressure difference in this area according to [PERSON]'s principle, further resulting in vortex on the surface of the embankment. Then the air flow moves forward continuously and small fluctuations of wind speed occur at the depression in the center of the surface of the embankment. An airflow vortex is formed on account of the adverse pressure gradient generated by the impact of terrain when airflow passes through the leeward slope of the embankment. After that, the sandy air current crosses the cross section of the embankment, the energy changes gradually stabilize, and the original wind speed is restored. Finally, the sandy air current flows out of the air outlet on the right side of the model.  
Figure 8: Comparison of wind speed profiles between wind tunnel test and numerical simulation.  
According to the variation characteristics of sandy air current in transit mentioned above, the grits pass slowly and tend to deposit in the deceleration zone, while in the acceleration zone, the grits tend to rise. At the high-speed zone, grits often pass quickly, while in the vortex region, the velocity of grits changes and can be sedimented or raised. When there are dunes around the embankment, the area of low-speed zone and vortex zone around the embankment both increase, providing more space for the sand-carrying wind to decelerate, in which case it is easy to cause the separation of grits and airflow.
# On Ad Hoc Communication in Industrial Environments
## 5 Examination of the Industrial Ad Hoc Channels
### Directional Channel Probability  
Consider a transmitter \(A\) sent a message to receiver \(B\) at time \(T_{AB}\). In this work a channel is classified as unidirectional if a transmission at time \(T_{BA}\in[T_{AB}-dt,T_{AB}+dt]\) from \(B\) was not received at \(A\). If the transmission is received, the channel is classified as bidirectional. Possible reasons for unidirectional channels are changes in the propagation path within \(dt\) or interference with other communication networks. The office reference test shows that interference within the tracing protocol is unlikely.  
Many common routing protocols (e.g., DSR [37], AODV [38]) expect bidirectional connections. Routing protocols can be enhanced to work in the presence of unidirectional channels at the cost of higher overhead [39]. The percentage of unidirectional connection is therefore highly relevant in the evaluation of the applicability of ad hoc routing protocols to the industrial environment. We assume that such protocols need at least about 200 ms for route search and establishment; therefore, the chosen 0.2 s NEP time resolution is sufficient for the examined application.  
NEPs that were extracted from the proposed trace protocol can be used to determine this probability of a channel being unidirectional. It is defined by Equation (9) using the same parameters as Equation (8).  
\[P_{u}(t)=\frac{\sum_{i=1}^{N}\sum_{j=1}^{N}|C(i,j,t)-C(j,i,t)|}{2\cdot\left( \sum_{i=1}^{N}\sum_{j=1}^{N}C(i,j,t)-N\right)} \tag{9}\]  
Figure 2: Network connectedness in different scenarios as extracted from the nodal encounter pattern (NEP).  
Previously shown, Figure 1 illustrated examples for the percentage of channels that are unidirectional. In the central graph three connections exist. One of these connections has no reverse connection. Therefore, one-third of all connections are unidirectional.  
As seen in Figure 3, the percentage of unidirectional connections is much higher in mobile industrial scenarios than in the reference use case. In the static reference measurement, unidirectional connections are very rare and only of short duration. On average only 0.52% of all connections are unidirectional. In contrast, about 33.6% of all connections are unidirectional in the mobile industrial scenario. In the static industrial scenario, on average about 3.8% of all connections are unidirectional. The results therefore support the previous observations that node mobility has a higher impact on the wireless channel than the industrial environment. Overall, such channel characteristics have to be taken into account when selecting or designing a routing protocol for the industrial use case. Another important aspect for this task is the route lifetime.