esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reduced Element
### Error Analysis  
Since the full order solution \(u^{h}\) of degree \(m+1\) is a conventional FE solution, and hence, from Equation (12), it gains convergence \(O(h^{m+2})\) on element and \(O(h^{2m+2})\) at element end nodes, i.e.,  
\[\begin{array}{c}\max_{0\leq t\leq T}\Big{|}u(t)-u^{h}(t)\Big{|}\leq Ch^{m+2} \text{ on elements,}\\ \Big{|}u_{i}-u_{i}^{h}\Big{|}\leq Ch^{2m+2}\text{ at mesh point }i(=0,1,\ldots,N_{e}). \end{array} \tag{14}\]  
To estimate the error in the reduced order solution \(u_{\text{R}}^{h}\), it is easy to see that  
\[\Big{|}u-u_{\text{R}}^{h}\Big{|}=\Big{|}u-u^{h}+u^{h}-u_{\text{R}}^{h}\Big{|} \leq\Big{|}u-u^{h}\Big{|}+\Big{|}u^{h}-u_{\text{R}}^{h}\Big{|}. \tag{15}\]  
It follows from Equation (14) that the first term \(\Big{|}u-u^{h}\Big{|}\) in Equation (15) is \(O(h^{m+2})\). And, the error \(\Big{|}u^{h}-u_{\text{R}}^{h}\Big{|}\) of the second term in Equation (15) is purely a polynomial interpolation error, which is well known to be of \(O(h^{m+1})\). Thus, it is concluded that  
\[\begin{array}{c}\max_{0\leq t\leq T}\Big{|}u(t)-u_{\text{R}}^{h}(t)\Big{|} \leq Ch^{m+1}\text{ on elements,}\\ \Big{|}u_{i}-u_{\text{R},t}^{h}\Big{|}\leq Ch^{2m+2}\text{ at end nodes. }\end{array} \tag{16}\]  
Note that the nodal solutions of the reduced element are inherited from the full order solutions of the full order element, which ideally raises the accuracy ratio to 2 for the reduced element. Furthermore, the above error analysis validates the fact that the full solution, since it is at least one order higher than the reduced solution, is well qualified to serve as a pointwise error estimator for the reduced solution, which is in a very simple form as  
\[\varepsilon_{\text{R}}^{h}(t)=\bar{N}_{m+1}(t)u_{m+2}^{h}. \tag{17}\]  
Without pointwise searching, the maximum error can be directly calculated as  
\[\max_{\varepsilon}\Big{|}\varepsilon_{\text{R}}^{h}(t)\Big{|}=\max\big{|} \hat{N}_{m+1}(t)\big{|}\cdot\Big{|}u_{m+2}^{h}\Big{|}\text{ with max}\big{|}\hat{N}_{2}(t)\big{|}=3/2,\text{ max}\big{|}\hat{N}_{3}(t)\big{|}=5\sqrt{3}/9,\ldots. \tag{18}\]
# Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis
## 4 Test Analysis
### Analysis of Fault Characteristic Data
Figure 7b shows the distribution of the eigenvalues of the torque signal, whose signal characteristics are similar to those of the pump vibration signal. The distribution of the eigenvalues of the motor vibration signal can be seen in Figure 7c, where the signal characteristics of the loose foot bolt are much larger than those of the other states. Figure 7d shown is the distribution of the eigenvalues of the oscillation signal, though contrary to the above, the signal characteristics of severe cavitation and slight cavitation are much larger than those of the other states. In addition, the multiple fractal spectra feature parameters \(\Delta f\), \(\alpha_{0}\), \(\Delta\alpha\), \(\alpha_{\text{min}}\), and \(\alpha_{\text{max}}\) were sensitive to the signal to different degrees. In general, \(\alpha_{0}\) and \(\alpha_{\text{max}}\) present with an improvement in distinguishing the pump operation state. Figure 6 demonstrates the improved MFDFA method is able to extract the multifractal spectrum feature parameters \(\Delta f\), \(\alpha_{0}\), \(\Delta\alpha\), \(\alpha_{\text{min}}\), and \(\alpha_{\text{max}}\), which are very sensitive to the changes of the fault state, while avoiding any loss of the embedded fault information of each signal. Thus, the different operating conditions' feature parameters for the four types of sensors can better constitute the feature vector for the BP neural network preliminary fault diagnosis, obtain the basic trust distribution function of the operating conditions, and apply the D-S evidence theory based on a multi-sensor fault diagnosis. In summary, it can be seen that the classical feature extraction method is not ideal for distinguishing different fault signals of complex centrifugal pumps, and there will be discriminant errors. However, the improved MFDFA can integrate multiple characteristic parameters of the multi-fractal spectrum to distinguish different working conditions, and the results show that the effect is good. Figure 6: Quality index of vibration signal of centrifugal pump.
# The Sun and Space Weather
## 2 Basic Properties of CMEs  
### Morphological Properties  
CMEs appear as excess material newly appearing in the corona and moving away from the Sun. Although white-light coronagraphs are the most commonly used instruments to detect CMEs, extreme ultraviolet (EUV) imagers have become key instruments in observing CMEs closer to the solar surface. The combination of inner coronal images in EUV or other wavelengths, and white-light images, provide the full picture of CMEs early in their life. A CME typically starts with a slightly larger initial size than the solar source, grows in size as it expands, and becomes a large-scale coherent structure in the coronagraphic field of view. A typical CME has a number of substructures with different densities, temperatures, and magnetic field strengths [8]. CMEs often show a three-part structure consisting of a bright front followed by a dark void and a bright core [9; 10]. The bright front is the compressed coronal material caused by the outward motion of the dark void interpreted as a magnetic flux rope, while the bright core in the CME interior is the eruptive prominence. The three-part structure is somewhat of an incomplete description when it comes to shock-driving CMEs: a shock forms ahead of the bright front with a compressed sheath behind the shock. Although the shock is too thin to be discerned from white-light observations, the sheath is identified as a diffuse feature surrounding the bright front [11; 12; 13; 14; 15; 16].  
The CME in Figure 1 has all the substructures: shock sheath, bright front, void, and core observed by the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO) mission. The bright front in Figure 1b is thought to indicate the outline of a magnetic flux rope identified with the void region. The presence of a shock can be inferred from the kink (marked S in Figure 1b) in the nearby streamer. The shock sheath can be seen better in the difference (event minus pre-event) image in Figure 1c as a diffuse structure surrounding the CME flux rope [17]. The outer edge of the sheath is taken as the shock, because the shock is too thin compared to the spatial resolution of the coronagraph images. Different substructures have different space weather consequences. When the sheath and/or the flux rope contains a south-pointing, out-of-the-ecliptic magnetic field component, a geomagnetic storm ensues upon arrival at the magnetosphere. The bright prominence core has high density, and so it can enhance a geomagnetic storm if the density enhancement occurs during an interval in which the CME flux rope has a southward component (see, e.g., [18; 19; 20]). On the other hand, the outermost structure, viz., the CME-driven shock, is responsible for accelerating particles to high energies (e.g., [21; 22]).
## 2 Material and Methods
### MPHAC Characterization  
Scanning electron microscope (SEM), X-ray powder diffraction (XRD), FTIR measurements, energy-dispersive X-ray spectroscopy (EDS), and vibrating sample magnetometry (VSM) was performed to determine the surface morphology, crystallization, the surface functional groups, chemical characterization, and magnetic properties of MPHAC, respectively. The surface area (\(S_{BET}\)) and pore size were determined by the Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) methods. In addition, the drift method was employed to determine the pH of point of zero charge (pH\({}_{\text{PZC}}\)) of MPHAC.
# Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose
## 3 Results and Discuss  
### Quantitative Analysis of Volatile Organic Compounds in Livestock House  
In order to quantitatively characterize the volatile components in different times and spaces of the livestock house, the gas samples from the four sampling points (nursery-middle-morning, nursery-middle-afternoon, nursery-window-afternoon, chicken-middle-morning, chicken-middle-afternoon, and chicken-window-afternoon) were detected by GC-MS. The relative contents of volatile compounds were calculated by the peak area normalization method.  
More than 70 kinds of VOCs were detected in the nursery piggery, including alkanes, aldehydes, ketones, alcohols, esters, amides, naphthalene, alkynes, indene, benzene, alkenes, etc. More than 30 kinds of VOCs were detected in the chicken house, including alkanes, aldehydes, amides, indene, esters, alcohols, mercaptans, naphthalene, alkenes, benzene, alkynes, etc. Figure 2 shows the classification and relative content of VOCs in six samples.  
It can be seen from Figure 3 that the VOC contents in different times and spaces of livestock house are quite different. The VOCs of the nursery piggery house are more complex than that of the chicken house. From Figure 3a-c, it can be seen that the percentages of alkanes and aldehydes were the highest, which indicates that alkanes and aldehydes were the most important types of VOC pollution in nursery pigs and should be paid attention to because those were of great significance for understanding emission characteristics of the VOCs in the nursery piggeries. The content of alkanes in the chicken house was the highest, and alkanes were the main type of VOC pollution in the chicken house. It can be noted from Figure 3e that chickens ate, indicated by more kinds of VOCs, which indicated that eating had a significant impact on the content of VOCs in the chicken house.
# Study on Shear Mechanism of Bolted Jointed Rocks: Experiments and CZM-Based FEM Simulations
## 4 Numerical Simulation  
Since the shear experiments can only reflect the macroscopic shear process and the failure characteristics of the rock, its meso-mechanical deformation and failure mechanisms, especially the interaction between bolt and jointed rock during the shear process, cannot be fully analyzed in the laboratory. In order to overcome this limitation, here we propose a new shear simulation method based on the commercial finite element software ABAQUS. The shearing process is reproduced by numerical simulation to better understand the deformation characteristics of bolted joints. The novelty of this simulation is that the rock is simulated by a globally embedded zero-thickness cohesive element, and the bolt is simulated by the conventional finite element method.
## 3 Results and Discussion
### Method Validation
#### 3.4.2 Linearity  
The linearity of the method was validated rigorously through linear regression analysis, with the correlation coefficients (\(r^{2}\)) derived from the five-point calibration curves for the respective fat-soluble vitamins. The resulting linearity ranges were 100-300 ng/band for vitamin \(\rm D_{2}\), \(\rm D_{3}\) and E and 20-60 ng/band for vitamin \(\rm K_{1}\). The \(r^{2}\) values of three replicate experiments for all analyzed fat-soluble vitamins were found to be more than 0.98, confirming adequate linearity of the method in the defined concentration range (Table 4).
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Network Function Virtualisation (NFV)
#### 3.2.3 Advantages of NFV  
NFV streamlines the administration and orchestration of network functions throughout their lifecycle. This capability streamlines deployment, scaling, and maintenance processes, leading to improved operational efficiency and reduced management overhead [48]. NFV's decoupling of software from hardware provides flexibility in deploying network functions, which is particularly advantageous for evolving technologies like 5G networks. This flexibility allows operators to respond swiftly to evolving network requirements and launch new services quickly and efficiently [49; 50]. NFV's dynamic operation makes it well suited for dynamic network environments, such as those found in 5G networks. By dynamically allocating resources and scaling network functions in real time, NFV enables operators to meet fluctuating traffic demands and ensure optimal network performance [50].
## 5 Case Study and Simulation Results
### Converter Is connected to a Non-Ideal Grid (Non-Zero Impedance)  
In this part, the converter is connected to a non-ideal grid with \(L_{s}=0.4\) mH which corresponds to \(SCR\approx 22\). Accordingly, the resonance frequency of the system is changed to 662 Hz. The scenarios and the applied reference commands are the same to those of the previous sub-section. The simulation results are presented in Figure 9. As expected, the non-zero grid impedance has a negligible effect on the performance of the proposed control scheme. According to the results, the fast response, the high stability margin of the system, with negligible coupling between the active and reactive power channels, are achieved by using the proposed control scheme. As shown in Figure 9c, the changes of the active and reactive power affect the grid voltage, which consequently results in a small coupling (slightly more than the previous cases).  
Figure 8: The response of the system connected to a zero-impedance grid; (**a**) the active and the reactive power, (**b**) the DC-link voltage, and (**c**) grid-side currents.  
Converter Is Connected to a Non-Ideal Grid (Non-Zero Impedance) Considering Uncertainties in the Filter Impedances  
In this subsection, the robustness of the proposed controller in the presence of uncertainties in the system parameters is evaluated. It is supposed that there is 20% uncertainty in the filter inductances, i.e., their values are \(L_{1}=0.9\) mH and \(L_{2}=0.48\) mH, whereas the controller is designed for \(L_{1}=1.1\) mH and \(L_{1}=0.6\) mH. The grid inductance is also considered \(L_{s}=0.4\) mH. The simulation results are presented in Figure 10. The fast and proper dynamic response of the system are concluded from the results. Similar to the previous subsections, there is a negligible interaction between the active and the  
Figure 9: Response of the simulated system with \(L_{s}=0.4\) mH; (**a**) the active and the reactive powers, (**b**) the DC-link voltage, (**c**) grid-side currents, and (**d**) PCC voltage magnitude.  
reactive power control channels (slightly more than the previous cases) due to the time delay, observer operation, grid impedance, and uncertainty in the control loop.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Miniaturization
#### 2.2.5 High Frequency Band  
The use of a higher frequency band can reduce the size of the in-body antenna significantly. Higher frequencies of operation have shorter wavelengths, which leads to a decrease in antenna size [2; 17]. Alternatively, if the antenna size is reduced, the resonant frequency of the antenna will move to a higher band and vice versa. Higher frequencies with a wide bandwidth are also desirable for better data communication [35]. However, they suffer from higher tissue attenuation and loss compared to lower frequencies, which affect the overall in-body antenna performance by inducing more losses [36]. It is also necessary to maintain the operation frequency band specification, as described in Section 2.1.
# CL-MMAD: A Contrastive Learning Based Multimodal Software Runtime Anomaly Detection Method
## 3 Preliminaries
### Assumptions and Problem Formulation  
The basic principle of the CL-MMAD model proposed in this work is attempting to maximize the likelihood probability of the normal samples in the training datasets inspired by prior work [26]. The main objective of the CL-MMAD model is to learn an end-to-end scoring mechanism for detecting anomalies by directly pairing data segments from different modalities. In the subsequent sections, we provide a detailed explanation of the problem formulation in CL-MMAD.  
**Assumption 1:**_Anomalies and normal instances are, respectively, low- and high-probability events. Multimodal data of normal samples have stronger consistency across modalities compared to abnormal samples._  
CL-MMAD learns an end-to-end scoring mechanism for anomaly detection by directly pairing the data segments from different modalities. Particularly, the problem is framed as:  
\[\Theta^{*}=\operatorname*{argmax}\sum_{x\in\mathcal{X}}\log p(x;\Theta) \tag{1}\]  
where \(p(x;\Theta)\) is the probability of the event \(x\) with the parameters \(\Theta\) to be learned. Event \(x\) denotes that data segments from different modalities of one sample are pairable. SoftMax function is used for easy optimization, as:  
\[p(x;\Theta)=\frac{\exp(\tau(x;\Theta))}{\sum_{x\in\mathcal{X}}\exp(\tau(x; \Theta))} \tag{2}\]  
where \(\tau(x;\Theta)\) is a similarity measure used to validate if one multimodal sample is consistent across modalities and can also be used to capture the pairwise feature interactions during training phases:  
\[\tau(x;\Theta)=\sum_{i,j,k,\ldots}\left(W_{ijk,\ldots}Z_{i}Z_{j}Z_{k}\ldots\right) \tag{3}\]  
where \(i,j,k,\ldots\) denote different modalities, and \(W_{ijk,\ldots}\) denote the weights added for the similarities between different modalities and are learnable parameters. The embedding \(Z_{i}\) denotes the projection of the \(i\)th modal feature vector of the sample projected onto the representation space \(\mathcal{Z}\). Since \(\sum_{x\in\mathcal{X}}\exp(\tau(x;\Theta))\) is a normalization term, learning the likelihood function \(p\) is equivalent to directly optimizing the similarity \(\tau\). The similarity \(\tau\) can be calculated using cosine similarity (commonly used in contrastive learning framework) or Euclidean distance (used in this work).
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
###### Abstract  
This paper proposes a positioning strategy for a fleet of unmanned aerial vehicles (UAVs) airlifting wireless base stations driven by communication constraints. First, two schedulers that model the distribution of resources among users within a single cell are analyzed. Then, an UAV autonomous positioning strategy is developed, based on a fair distribution of the radio resources among all the users of all the cells in a given scenario, in such a way that the user bitrate is the same regardless the users' distribution and spatial density. Moreover, two realistic constraints are added related to capacity of the backhaul link among the UAVs and the ground station: the bitrate delivered per UAV and the total backhaul bandwidth shared among all the UAVs. Additionally, an energy consumption model is considered to evaluate the efficiency and viability of the proposed strategy. Finally, numerical results in different scenarios are provided to assess both the schedulers performance and the proposed coordinated positioning strategy for the UAVs.  
unmanned aerial vehicle; wireless communications; positioning strategies; navigation; energy consumption; radio resource allocation +
Footnote †: journal: Sensors
## 3 Simulation Results and Analysis
### Effect of Temperature and Water Content Coupling on Methane Adsorption Performance
#### 3.3.2 Analysis of Extreme Variance of Test Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Temperature** & \multicolumn{4}{c}{**a/(mmol/g)**} & \multicolumn{4}{c}{**b/MPa\({}^{-1}\)**} \\ \cline{2-9}
**(K)** & **0.00\%(L/J)*** & **1.10\% (L/J)** & **4.27\%(L/J)** & **8.31\% (L/J)** & **0.00\% (L/J)** & **1.10\% (L/J)** & **4.27\% (L/J)** & **8.31\% (L/J)** \\ \hline
223.00 & 5.35/5.33 & 4.63/4.53 & 3.99/3.94 & 3.35/3.33 & 0.09/0.07 & 0.10/0.09 & 0.11/0.12 & 0.15/0.14 \\
243.00 & 4.07/4.15 & 3.95/3.97 & 3.56/3.62 & 3.20/3.24 & 0.11/0.11 & 0.15/0.14 & 0.16/0.15 & 0.13/0.15 \\
263.00 & 4.03/3.99 & 3.66/3.72 & 3.01/3.00 & 2.60/2.59 & 0.16/0.15 & 0.14/0.12 & 0.18/0.17 & 0.24/0.23 \\
303.00 & 3.88/3.86 & 3.29/3.20 & 2.79/2.69 & 2.31/2.30 & 0.10/0.12 & 0.23/0.25 & 0.22/0.22 & 0.26/0.28 \\
313.00 & 3.57/3.47 & 2.98/2.90 & 2.64/2.66 & 2.14/2.16 & 0.11/0.10 & 0.15/0.17 & 0.19/0.16 & 0.17/0.15 \\
343.00 & 3.46/3.39 & 2.61/2.60 & 2.30/2.34 & 1.55/1.59 & 0.19/0.18 & 0.23/0.27 & 0.22/0.21 & 0.25/0.25 \\
363.00 & 2.98/2.99 & 2.37/2.34 & 1.69/1.68 & 1.24/1.25 & 0.22/0.20 & 0.38/0.36 & 0.30/0.29 & 0.39/0.37 \\ \hline \hline \end{tabular} \({}^{*}\) L is the Langmuir equation fitting, and \(\mid\) j is the value obtained by the empirical formula fitting.  
\end{table} 
Table 6: Coupling effect Langmuir equation fitting parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Level** & **Factor 1 (Water Content)** & **Factor 2 (Temperature)** \\ \hline  & 1 & 8.52 & 5.57 \\  & 2 & 8.02 & 5.25 \\ K & 3 & 7.10 & 4.54 \\  & 4 & - & 3.15 \\  & 5 & - & 2.65 \\  & 6 & - & 2.47 \\ \hline  & 1 & 1.42 & 1.86 \\  & 2 & 1.34 & 1.75 \\ k & 3 & 1.18 & 1.51 \\  & 4 & - & 1.05 \\  & 5 & - & 0.88 \\  & 6 & - & 0.82 \\ \hline R & & 0.24 & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Range analysis.
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 4 Improved Algorithms
### Accuracy Assessment  
The BDS ultra-rapid clock offset products of GBM and ISC can be downloaded from the public websites [57; 58]. In order to verify the accuracy and applicability of the improved model, the observations and predictions of the BDS clock offset products were used to test and analyze. The time span of the data was from 1 January 2017 to 30 January 2017, with a sampling interval of 5 min.  
The accuracy of the clock offset is described as the standard deviation (STD) \(\delta^{s}\), the specific formula is:  
\[\delta^{s}=\sqrt{\sum_{i=1}^{n}\left(\Delta_{i}^{s}-\overline{\Delta^{s}} \right)^{2}/n}, \tag{13}\]  
\[x_{i}=\sum_{s=1}^{m_{i}}(t_{i}^{s}-t_{0i}^{s})/m_{i}, \tag{14}\]  
\[\Delta_{i}^{s}=t_{i}^{s}-t_{0i}^{s}-x_{i} \tag{15}\]  
where \(i\) is the epoch of the clocks offset and \(s\) is the satellite number. \(\Delta_{i}^{s}=t_{i}^{s}-t_{0i}^{s}-x_{i}\) means the difference of the deviation between different reference clocks which is eliminated. \(x_{i}\) is the difference between the clock offset of the real-time prediction and the precise observations. \(t_{i}^{s}\) and \(t_{0i}^{s}\) are the real-time prediction clock offsets and the precise observations of satellite \(s\) in epoch \(i\), \(n\) is the number of the clock offset predictions, and \(m_{i}\) is the satellite number of epoch \(i\).