esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
In this paper, we proposed a novel method for the continuous monitoring of the volumetric parameters of dough piece during fermentation. We showed that a proper segmentation of a three-dimensional topography and a reliable volume estimation could be achieved by the combination of machine learning and 3D model fitting. Using our proposed method, an essential and promising advancement of the current state of the art has been proposed, which finally enables the possibility of parallel monitoring of multiple dough pieces, instead of single ones, to get a better overview of the complete fermentation interior. In that way, certain planes of fermenting dough pieces can be analyzed individually and independently from each other. We found out that the optimal fermentation state cannot be determined only by considering the volume gradient. It remains to be examined whether other parameters like the surface moisture of the dough can be analyzed to obtain the optimal time automatically. With our method, a predefined desired final volume can be detected automatically, which already can relieve the baking staff. Due to the fact that, using our system, the fermentation chamber does not have to be opened during the fermentation to check the state, a much smoother process without interruptions can be established.  
Future work will concentrate on optimizing the model fitting method and minimizing the processing time, which currently takes approximately three seconds on average per object. That is of great interest, since a fermentation chamber contains several dozens of dough pieces whose volumes have to be monitored simultaneously. A difference of one second of computation time for one object applied to 50 dough pieces would lead to a reduction of 50 s altogether, which could make the difference between optimal and over-fermentation. For the acceleration of the calculation, the complete transfer of the process to hardware, like field programmable gate array (FPGA), is conceivable. Another future research task lies in the concurrent detection of different kinds of dough object types like pretzels, donuts, and so on. Therefore, more data has to be captured and our neural network has to be trained to be able to perform the classification of different classes with a high classification rate and accuracy. It should be examined if it is possible to use simulated point clouds for the training, which would lead to an unlimited amount of training data and hence to a much more precise network.
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 4 Results
### Potentially Hydrocarbon Supporting Faults (F7-F10)  
#### 4.2.1 Fault Structure  
In contrast to faults F1-F6, faults F7-F10 are situated in proximity to the basin boundary Zhu III South Fault and are closely related to it. The F7 fault predominantly trends in a northeast direction and dips northwest, forming a segmented fault pattern in the plan view (Figure 1c). In the cross-section, it appears as a steeply dipping planar fault with a limited scale, extending downward into the Enping Formation and upward into the N122 strata.  
In contrast, the F8 fault is oriented near east-west and dips to the north. Vertically, it assumes a slightly listic fault shape with a steep dip angle and features a relatively larger fault scale. This fault cuts downward into the Enping Formation and has the potential to extend upward into the Engliang Formation.  
Faults F9 and F10 share a similar geometry, both trending near parallel in a northeast-east direction and dipping northwest. In the cross-section, both faults exhibit a small-scale planar fault configuration with a relatively gentle dip angle. These faults cut downward into the Zhuhai Formation and are truncated upward by the T60 seismic reflection interface.  
Figure 7: Plot illustrating the relationship between Shale-Gouge-Ratio and column heights for the hydrocarbon sealing faults of the three studied fields. For each fault, the SGR at the structurally highest point is given, as well as the measured and predicted column heights. Note that there is no clear correlation between SGR and column heights.
###### Abstract
Driven by the latest advances in algorithms, computing power, and big data, artificial intelligence has made substantial breakthroughs in a wide range of fields [18]. Deep reinforcement learning technology enables agents to learn from past experience to make the best decisions, and does not require massive labeled data. Deep reinforcement learning obtains data from the interaction with the environment to train, which makes RL widely used in automatic control, resource management, network optimization, and other fields [19; 20; 21]. As the most popular deep reinforcement learning algorithm, Proximal Policy Optimization (PPO) has good stability and has become the baseline algorithm of OpenAI [22; 23]. Deep reinforcement learning algorithms such as PPO also have great research value in software-defined sensor networks. By learning the distribution characteristics and task requirements of sensor networks, the network can effectively overcome the influence of dynamic environment and heterogeneous characteristics, and adaptively realize the scheduling of sensor network resources. Motivated by the above work, we consider an edge computing network with heterogeneous edge computing nodes and heterogeneous task requirements, and establish a software-defined heterogeneous edge computing network architecture. Furthermore, in order to optimize network energy consumption and network load balancing, we establish a multi-objective optimization problem and further model the problem as a Markov Decision Problem (MDP) process. The PPO resource scheduling algorithm based on deep reinforcement learning is designed to solve the combinatorial optimization problem. Specifically, our contributions are as follows. * We propose a heterogeneous edge computing network architecture based on software-defined network. The heterogeneous edge computing network architecture realizesthe separation of control layer and data layer, and can effectively improve the efficiency of resource scheduling through centralized control strategy. * We establish an optimization model for the heterogeneous edge computing networks considering network energy consumption and network load balancing. We decouple the edge computing network tasks into subtasks according to different requirements, establish a task alliance, and propose a task scheduling problem model. * We model the heterogeneous edge computing network resource scheduling problem as an MDP, and introduce PPO algorithm to solve the scheduling strategy. The proposed PPO resource scheduling algorithm has good performance under the dual goals of network energy consumption and network load balancing. The arrangement of this article is as follows. We first describe the heterogeneous edge computing network, establish the software-defined heterogeneous edge computing network architecture, and propose a multi-objective optimization model in the Section 2. In the Section 3, we first model the resource scheduling problem as an MDP process, and design a reinforcement learning algorithm based on PPO. The simulation analysis and conclusion are given in Sections 4 and 5, respectively.
## 4 Realization of PQ Comprehensive Evaluation  
1. Solve the weighted normalized decision matrix:  
Let \(\omega_{j}\) = [\(w_{1}\), \(w_{2}\),..., \(w_{n}\)]T be the combined weight vector of each PQ index, then the weighted normalized decision matrix \(\mathcal{C}\) = (\(c_{ij}\))m*n can be obtained as follows:  
\[c_{ij}=w_{j}a_{ij},\;\;i=1,2,\ldots,m;\;j=1,2,\ldots,n. \tag{10}\]
2. Determine the absolute ideal solution of the evaluation system:  
According to Equation (10), the weighted normalized matrix \(\mathcal{C}\) = (\(c_{ij}\))\({}_{m}\)*n is obtained, and, according to the basic principle of the TOPSIS method, two vectors of positive ideal solution \(\mathbf{A_{j}}^{+}\) and negative ideal solution \(\mathbf{A_{j}}^{-}\) are constructed:  
\[A_{j}^{+}=\left\{\begin{array}{ll}1,\;j\in T1\\ 0,\;j\in T2\end{array}\right., \tag{11}\]  
\[A_{j}^{-}=\left\{\begin{array}{ll}0,\;j\in T1\\ 1,\;j\in T2\end{array}\right., \tag{12}\]  
where, 1 and 0 respectively represent the maximum and minimum values of the corresponding indices. \(T1\) means the benefit attribute index, that is, the positive index; \(T2\) means the cost-based attribute index, that is, the inverse index.  
3. Calculate the Mahalanobis distance:  
Let the multi-index vector \(x\) = (\(x_{1}\), \(x_{2}\),..., \(x_{p}\)) whose mean vector is \(\mu\) = (\(\mu_{1}\), \(\mu_{2}\),..., \(\mu_{p}\)), and \(s^{-1}\) be the co-inverse matrix of \(x\), then define the Mahalanobis distance equation for \(x\) as follows:  
\[M(x)=\sqrt{(x-\mu)^{T}s^{-1}(x-\mu)}. \tag{13}\]  
Combined with Equation (13), the Mahalanobis distance of the \(i\)th PQ evaluation sample to the positive and negative ideal solutions can be respectively defined as follows:  
\[\widetilde{M}(A_{i},A^{+})=\sqrt{(A_{i}-A^{+})^{T}s^{-1}(A_{i}-A^{+})}, \tag{14}\]  
\[\widetilde{M}(A_{i},A^{-})=\sqrt{(A_{i}-A^{-})^{T}s^{-1}(A_{i}-A^{-})}. \tag{15}\]  
4. Calculate the proximity of the \(i\)th evaluation sample:  
Let \(c_{i}\) be the relative proximity of the \(i\)th evaluation index to the ideal solution, and sort the evaluation sample according to the values of proximity \(c_{i}\). The larger the \(c_{i}\), the better the evaluation sample. Conversely, the smaller the \(c_{i}\), the worse the evaluation sample. The \(c_{i}\) can be obtained as follows:  
\[c_{i}=\frac{\widetilde{M}(A_{i},A^{-})}{\widetilde{M}(A_{i},A^{-})+\widetilde{ M}(A_{i},A^{+})},\;i=1,2,\ldots,m. \tag{16}\]  
5. An improved TOPSIS method for comprehensive evaluation of PQ considering the correlation between indices is shown in Figure 6:
## 4 Verification Numerical Examples and Discussion  
Two examples of cracked structures on two-parametric elastic soil are presented in order to verify the discussed solutions. Transverse displacements, bending moments, and shear forces along the structure length were studied. The results were compared to the exact results from the solutions of the simplified model governing differential equations that were solved for each considered example. These solutions were obtained by implementing the known analytical solutions for the non-cracked parts (Equations (5) or (6)) using the same continuity conditions at crack location as in the derivation of new interpolation functions.  
The examples setup is given in Figure 2. Both examples consider a beam with two transverse cracks of relative depth \(\delta^{\prime}=0.5\) (with \(F(\delta^{\prime}=0.5)=0.582914\) obtained from Equation (2)), which are loaded with two uniformly distributed loads as well as with two transverse concentrated loads at both ends. The cross-section is a rectangle of width \(b=0.5\) m, while the heights of both examples differ: the values considered are \(h=0.8\) m and \(h=0.1\) m, respectively.  
At first, the solutions of corresponding governing differential equations (GDEs) were found just to obtain the benchmark values for both examples. The presence of two cracks and an in-field change of the applied transverse continuous load requires four elastic segments in the corresponding computational model of the beam. Consequently, four coupled differential equations were solved to obtain the model exact solutions. Afterwards, several FE meshes consisting completely of recently presented cb2 psFE elements were analyzed, starting with the smallest model possible. Due to the presence of two cracks, two cb2 psFE finite elements were required as a minimum.
## 5 Conclusions
More research is required to fully understand this difference, however. In conclusion, the general agreement between the bottom-up GNSS-RO profiles with both FIRI and ionosonde profiles indicates that this method is capable of providing global coverage of the D- and E-region ionosphere. The agreement between methods is strongest at lower altitudes near \(fmin\) with larger separation at higher altitudes near \(hmE\), suggesting that the RO profiles are more trustworthy at lower altitudes and become less uncertain as altitude increases. These results reflect the optimal estimation design used for the bottom-up method that focuses on reducing negative electron density oscillations at the bottom of the profile. Further tuning of the optimal estimation weighting balance is the focus of an ongoing effort, which may help to reduce these uncertainties at higher altitudes. However, this technique may be used in its current form to provide global D- and E-region estimates for HF operations and analyses of global ionospheric dynamics at lower altitudes. Due to the target \(fmin\) value and the time required to hand-scale ionograms, the dataset used for this research was limited. Ideally, this study would have included a seasonal comparison and morning-afternoon comparison at all sites, but this was not possible given the relatively small number of profiles. Future research should include a much larger dataset, which would require automatically scaled ionograms instead of the hand-scaled approach used here. Further, since this was the first in-depth comparison of the bottom-up method following the preliminary comparison performed in [23], the primary focus was on the quiet ionosphere. Therefore, future research should also include comparisons for the disturbed ionosphere, when sporadic-E is present. Formal analysis and investigation, D.J.S., D.L.W., N.S., A.L.F., E.V.D. and D.J.E. ; conceptualization, D.J.S., D.L.W., N.S., A.L.F., E.V.D. and D.J.E. ; writing--original draft preparation, D.J.S., D.L.W., N.S., A.L.F., E.V.D. and D.J.E. ; writing--review and editing, D.J.S., D.L.W., N.S., A.L.F., E.V.D. and D.J.E. All authors have read and agreed to the published version of the manuscript. This work was supported by NASA's Living With Star and Sun-Climate research funds to the Goddard Space Flight Center (GSFC) under WBS 936723.02.01.12.48. Not applicable. The GNS radio occultation data presented here can be found at the COSMIC Data Analysis and Archive Center (CDAAC): [[https://cdaac-www.cosmic.ucar.edu/](https://cdaac-www.cosmic.ucar.edu/)]([https://cdaac-www.cosmic.ucar.edu/](https://cdaac-www.cosmic.ucar.edu/)) (accessed on 1 December 2022). Additionally, the ionosonde data can be found through the Digital Ionogram Database (DIDBase): [[https://giro.uml.edu/didbase/](https://giro.uml.edu/didbase/)]([https://giro.uml.edu/didbase/](https://giro.uml.edu/didbase/)) (accessed on 1 December 2022). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} GNSS & Global navigation satellite system \\ RO & Radio Occultation \\ EDP & Electron Density Profile \\ \end{tabular}
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 3 System and Noise Model
### LDACS System Model
\[I_{Dp}(f)=\sqrt{\frac{8\pi}{\alpha}}e^{\frac{2\alpha^{2}t^{2}}{\alpha}}\cdot e ^{-j\pi f\Delta t}cos(\pi f\Delta t) \tag{4}\]  
The baseband DME pulse pairs are modulated to the relative carrier frequency of the channel to 0.5 MHz left and to the 0.5 MHz right of the LDACS system bandwidth. The total interference signal \(I_{Dp}(t)\) at LDACS system for a time interval '\(t\)' is from N DME stations that are operating on the 0: 5 MHz offset to the center frequency of the LDACS system. Then, the DME interfering signal that affects the LDACS system can be denoted as in (5) [12]. \[I_{Dp}(t)=\Sigma_{i=0}^{N_{Dp-1}}\Sigma_{l=0}^{N_{l}-1}\sqrt{P_{i,l}P(t-t_{i, l})}e^{2\pi f_{c,i}t+j\overline{\
u}_{i,l}} \tag{5}\]  
where \(N_{Dp}\) is the total number of interfering DME stations, \(N_{l}\) is the total number of pulse pairs in the particular time interval for the \(i^{th}\) interfering DME station, and \(P_{(i,l)}\) and \(\psi_{(i,l)}\) are the power and phase of the pulse pair respectively. \(f_{(c,i)}\) is the relative carrier frequency of the \(i^{th}\) interfering DME station, and \(t_{(i,l)}\) is the starting time of the \(l^{th}\) pulse pair of the \(i^{th}\) DME station. DME noise can be reduced with the same techniques, which are used to reduce the impulse noise. Figure 4: Proposed LDACS receiver block diagram. The detailed block diagram of the LDACS FL AS receiver with the proposed interference mitigation method is as shown in Figure 4. The first block in the receiver removes the cyclic prefix bits associated with the received vector \(y_{t}^{\prime}=[y_{t}(0),y_{t}(1),\ldots,y_{t}(N+N_{C_{p-1}})]^{T}\) resulting in vector \(y_{t}=[y_{t}(0),y_{t}(1),\ldots y_{t}(N-1)]^{T}\). The nonlinear device present at the receiver block accomplishes suppression of DME pulses by processing the signal \(y_{t}^{\prime}\) in the time domain. In general, the resulting estimated vector can be defined as \(\hat{x}_{t}=f(y_{t};\pi_{t})\), where \(f(. )\) is the nonlinear estimator for the time domain processing of input \(y_{t}\) with \(\pi_{t}\). The vector \(\pi_{t}\) holds the values of parameters of \(t^{th}\) OFDM block, such as the signal power \(\sigma^{2}x_{t}\), noise power \(\sigma^{2}i_{t}\). The nonlinear estimators discussed in this paper process the signal \(y_{t}\) in dissimilar ways. Hence, the definition of function \(f(. )\) varies with each proposed model. The nonlinear device is operated on signal \(y_{t}\) before the FFT processing to block the dispersion of sparse time domain impulses \(D_{t}[n]\) over all the OFDM carriers in the frequency domain. Considering the low complexity requirement, we disregard any conceivable correlation between different time domain impulses and focus on instantaneous devices as stated by \(\hat{x}_{t}[n]=f(y_{t}[n];\pi_{t})\).
## 3 Methodology
### MDDA Augmented GNSS/INS Integration
#### 3.6.1 IMU Calibration  
In the realm of smartphone sensors, calibration holds paramount importance as it facilitates the computation of deterministic errors. The process of calibration entails assessing sensor errors in a controlled laboratory environment. The calibration requirements may vary depending on the specific technology employed in an IMU. The precise determination of all parameters necessitates the use of specialized calibration instruments, such as three-axial turntables, to conduct a six-position static test. In [11], calibration was carried out specifically for the gyroscope (gyro) and accelerometer (acc) of the smartphone. This study primarily focused on investigating the impact of scale factor and bias, two prominent sources of deterministic errors, on the IMU solution and the resulting position error \(dx\) as follows:  
\[dx_{acc}\cong\ \frac{1}{2}\ b_{acc}\ t^{2} \tag{19}\]  
\[dx_{gyro}\cong\ \frac{1}{6}\ b_{gyro}g\ t^{3} \tag{20}\]  
where \(b_{acc}\) and \(b_{gyro}\) are biases in the acc and gyro, which are responsible for the position errors \(dx_{acc}\) and \(dx_{gyro}\), respectively. \(t\) is the mechanization time of IMU and \(g\) is the Earth's gravity. In other words, as shown in Equations (19) and (20), this bias introduces a second-order and third-order error in position, respectively [3,11,28,47].  
Figure 4: TC integration architecture.  
Figure 3: LC integration architecture.  
Table 1 presents the specification and deterministic error of the BMI160 BOSCH gyro and acc, which were installed on the REALME RMX2030 smartphone [48].
## 2 Structure and Design of The BPF
### Analysis of Lower Path Ii  
The lower path II consists of a stub-loaded anti-parallel coupling section connected by microstrip lines, as is shown in Figure 3. Here the impedance and electric length of connecting microstrip lines are \(Z_{3}\) and \(\theta_{3}\). The anti-parallel coupled line has even and odd impedance of Z\({}_{e2}\) and \(Z_{o2}\) and the electric length of \(\theta_{3}\) with loaded stubs of \(Z_{4}\) and \(\theta_{4}\). Formula Equation (6) gives the ABCD matrix of lower path II.  
\[T=\begin{pmatrix}\cos\theta_{3}&jZ_{3}\sin\theta_{3}\\ jY_{3}\sin\theta_{3}&\cos\theta_{1}\end{pmatrix}\begin{pmatrix}A_{c}&B_{c}\\ C_{c}&D_{c}\end{pmatrix}\begin{pmatrix}\cos\theta_{3}&jZ_{3}\sin\theta_{3}\\ jY_{3}\sin\theta_{3}&\cos\theta_{1}\end{pmatrix} \tag{6}\]  
A\({}_{c}\), B\({}_{c}\), C\({}_{c}\) and D\({}_{c}\) in Equation (6) are the ABCD matrix's elements of the stub-loaded anti-parallel coupled line. The method to calculate these elements is same as the one used for coupling networks T2 or T4 [17] as Equation (7).  
\[A_{c} =\frac{1}{G}[(c+aZ_{s})(a+eZ_{s})-(d+bZ_{s})(b+fZ_{s})] \tag{7a}\] \[B_{c} =\frac{1}{G}[(c+aZ_{s})(c+aZ_{s})-(d+bZ_{s})(d+bZ_{s})]\] (7b) \[C_{c} =\frac{1}{G}[(a+eZ_{s})(a+eZ_{s})-(b+fZ_{s})(b+fZ_{s})]\] (7c) \[D_{c} =\frac{1}{G}[(a+eZ_{s})(c+aZ_{s})-(b+fZ_{s})(d+bZ_{s})] \tag{7d}\]  
where  
\[G=(d+bZ_{s})(a+eZ_{s})-(b+fZ_{s})(c+aZ_{s}) \tag{8}\]  
and  
\[Z_{s}=-jZ_{4}cot\theta_{4} \tag{9}\]  
Similarly, a to f in Equation (7) and Equation (8) are same as Equation (5) but the even and odd parameters are replaced by \(\theta_{5e}\), \(\theta_{5o}\), Z\({}_{c2}\), Z\({}_{o2}\), Y\({}_{c2}\) and Y\({}_{o2}\). Formula Equation (9) is the input impedance of the open stubs.  
MATLAB can also be used to calculate the response of combined upper and lower path. However, the formulas will be very complicated and the simulated result will not be clear enough to demonstrate the function of lower path II. Thus full-wave simulation software Ansys HFSS 19 is used. Blue line in Figure 4 shows the HFSS simulated results of upper path I while the red line gives the ones of lower path II. As is shown in Figure 4, the upper path I has bandpass response with the second and third harmonics suppressed by TZ2 and TZ3, and another transmission zero TZ1 located at lower sideband is introduced by inductive source-load coupling as is mentioned in the last subsection. It can also be seen that the response of lower path II has same amplitude as upper path I in some frequencies but at the same time they are out of phase. So in these frequencies, transmission zeros will appear because of signal cancellation. In this design, three extra transmission zeros TZ4, TZ5 and TZ6 are introduced due to signal cancellation. TZ5 located at upper sideband is used to improve the selectivity while the lower and upper stopband is improved by TZ4 and TZ6. However, TZ2 and TZ6 are both at around 2f\({}_{0}\). So overall, there are five TZs used to improve selectivity and out-of-band suppression performance.  
Figure 3: Equivalent circuit of anti-parallel coupling network in lower path II.