esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Inference Methods
### The Leak Probability  
Since a large number of signal mechanisms are involved in the NILM, the diagnosis network cannot be built explicitly, containing every possible factor. The symptoms of fault that may not be established as nodes in the Bayesian network should be considered. Leak probability can solve that problem in the diagnosis model, improving accuracy. In the Bayesian network, a leak node is added as a parent node \(X_{L}\), as shown in the upper layer (Figure 4).  
The causes that are not included by any other parent nodes are set by the node \(X_{L}\), which can trigger the child node. For the Bayesian network shown in Figure 4, there is  
\[P(Y|X_{i})=P_{i}+P_{all}-P_{i}P_{all} \tag{7}\]  
given that  
\[P_{all}=P\big{(}Y\big{|}\overline{X_{l}}\big{)} \tag{8}\]  
\[P_{i}=\frac{P(Y|X_{i})-P\big{(}Y\big{|}\overline{X_{l}}\big{)}}{1-P\big{(}Y \big{|}\overline{X_{l}}\big{)}} \tag{9}\]  
Figure 4: A Bayesian network with leak probability node.  
According to [PERSON] [44], it can be concluded that  
\[P(X=T|Y)=1-\prod_{i:X_{i}\in H^{+}}(1-P_{i}) \tag{10}\]  
where \(T\) represents the true state while \(F\) represents the false state, and the node of truth is presented by \(H^{+}\). So  
\[\begin{array}{c}\mathrm{P}\big{(}\overline{Y}\big{|}X_{i}=F\big{)}=\frac{ \sum_{H|X_{i}\in H^{-}}P(\overline{Y}\big{|}H)P(H)}{P(X_{i}=F)}=\\ \frac{\sum_{H|X_{i}\in H^{-}}\big{(}1-P_{i}^{i}\big{)}\prod_{X_{j}\in H^{+}} \big{(}1-P_{i}\big{)}\prod_{X_{j}\in H^{+}}P(X_{j})\prod_{X_{j}\in H^{-}}P( \overline{X}_{j})}{P(X_{i})}\end{array} \tag{11}\]  
The \(P_{i}\) can be obtained using Equation (12).  
\[P_{i}=\sum_{i}\big{[}1-P(X_{i})\big{]}P_{i}^{i} \tag{12}\]  
The conditional probability distributions with leak probability can be calculated using Equation (13).  
\[P(Y=T|H)=1-(1-P_{i})\prod_{i:X_{i}\in H^{+}}(1-P_{i}) \tag{13}\]  
The type of device can be determined when the identification probability is greater than a certain threshold, \(\varepsilon\). The threshold \(\varepsilon\) is a constant value and can be optimized during the process. It is suggested that it be set at 80% at the initial stage [46].  
By using the Bayesian network model, the obtained electrical signals are analyzed. When the probability value of the output result of a certain electrical device exceeds the set threshold, it can be determined that the signal contains the operating characteristics of the device, and the device is in operation at this time.
# The Optimization Algorithm of the Forced Current Catholic Protection Base on Simulated Annealing +
## 1 Introduction  
A substation grounding grid is an essential facility for working grounding, lightning protection grounding and protective grounding. It is important for ensuring the safety of people, equipment and systems. It acts as a discharge and equalization for lightning protection, static electricity and fault current [1; 2; 3; 4; 5]. When an accident occurs, if the grounding grid is defective, the short-circuit current cannot be fully diffused in the soil. This will cause the grounding grid to rise in potential and cause the metal housing of the grounding device to carry a high voltage. This jeopardizes personal safety and breaks through the insulation of the secondary protection device. In severe cases, it will damage the equipment, expanding the accident and damaging the stability of the power grid system [6; 7; 8; 9; 10; 11]. Forced current cathodic protection is an effective grounding grid protection system. How to optimize the forced current cathodic protection effect? The parameters of the auxiliary anode system are critical in the grounding grid [12; 13; 14].  
Designing a grounding grid auxiliary anode system parameters has always been a difficult problem and many experts at home and abroad have done a lot of research. In [15], the multi-electrode system was calculated by the finite element method and the reliability of the finite element method in the field of corrosion was verified for the first time. In [16], the finite element method was usedto simulate the cathodic protection system and compared with the experimental results. In [17], the forced current cathodic protection system of the ship was modeled, and the influence of the shape and position of the auxiliary anode on the cathodic protection effect was analyzed. In [18], the forced current cathodic protection system of reinforced concrete is modeled and analyzed, and the parameters such as current output and position of the auxiliary anode are optimized. In [19], the forced current cathodic protection system of the offshore platform in seawater was modeled and analyzed to find out the influence of the auxiliary anode output current, anode position, seawater conductivity and biofouling coverage on cathodic protection.  
Although scholars have done a lot of research on this aspect, there is little research on the inverse problem of grounding grid cathodic protection, that is, how to optimize the parameters of the grounding grid cathodic protection system. Therefore, an optimization of the parameter of the grounding grid auxiliary anode system is carried out in this paper. A grounding grid auxiliary anode system parameter optimization algorithm (SAOA), based on a simulated annealing finite element, is proposed and verified.
# Analysis of the Interaction Effects of Shield Structure Oblique Passing under an Existing Tunnel
###### Abstract  
The interaction mechanism between a two-lane shield tunnel and an existing tunnel during oblique underpass is a matter of widespread concern in the engineering community, and knowledge in this area remains crude. In the construction of subway tunnels in mountainous cities with huge topographical fluctuations, internal forces and deformations are inevitable in existing tunnels. To verify the applicability of existing shield construction technology and empirical parameters to the Guiyang area, a systematic and refined numerical analysis was conducted on the shield passing under the existing tunnel section of the Tao-Hua interval of Guiyang Metro Line 3. In this paper, the accuracy of the numerical simulation is verified by comparing the calculated results with the data measured in the field; the settlement pattern that appeared above the existing tunnel during the construction of the shield with slurry hardening is analyzed; the internal forces, lateral deformation, and torsional deformation of the existing tunnel caused during the excavation of the new tunnel are obtained based on the numerical simulation results; finally, the effect of the old and new tunnels on the torsional deformation and settlement of the existing tunnel under different spatial intersection angles is studied. The results show that the internal forces, lateral deformation, and surface settlement of the existing tunnel due to the diagonal underpass show obvious asymmetric characteristics. Additionally, the existing tunnel experiences local irrecoverable torsional deformation, with the maximum torsional deformation occurring at the intersection of the old and new tunnels, and the spatial intersection angle of the old and new tunnels has a great influence on the maximum settlement of the tunnel vault and arch bottom, which shows a negative correlation.  
double-lane shield tunnel; oblique passing under; lining torsional deformation; grouting hardening; composite rock stratum +
Footnote †: journal: Applied Sciences  
Article
# Climate Perspectives in the Intra-Americas Seas
## 4 Climate Variability
### Decadal and Multi-Decadal Variability  
The spatial pattern of the Pacific Decadal Oscillation (PDO) is similar to that of ENSO, and it is not entirely clear whether the PDO is a truly independent mode [189; 190]. Although the impacts of the PDO occur mainly in the North Pacific, it also affects the tropics. There is a correlation between the PDO index and precipitation anomalies in the IAS region [191]. Warm PDO phases are associated with anomalously dry periods in Central America and northern South America, whilst the same phase was related to wetter than normal conditions in Mexico. It is important to note that as a Pacific mode, the PDO has the potential to act jointly with ENSO, and depending on the phasing, the PDO can enhance ENSO conditions. Precipitation in Mexico between November and April remains in phase with the PDO and positive (negative) PDO increases (decreases), while during May-October the relationship is reversed [192]. The combined effects of the PDO and ENSO over Mexico in warm and cold seasons has previously been investigated. The El Nino was found to favour wet conditions during summers of the PDO cold phase and during winters of the PDO warm phase throughout Mexico. During winter in Mesoamerica, the PDO forcing was more prominent when in association with El Nino phases [191]. When ENSO and PDO are in phase (out of phase), the sea level pressure anomalies over the Caribbean are enhanced (weakened or cancelled). This affects the CLLJ anomalies in direction and intensity, and changes the spatial distribution of precipitation [193].  
The AMO is a natural mode of variability in the North Atlantic on multi-decadal time scales [194; 195]. In general, the positive AMO phase favours tropical convective activity over the Caribbean, bringing much of the rainfall during the boreal summer wet season [196]. Wetter (drier) conditions over Central America during summer (winter) were modulated by the AMO [197]. The AMO  
Figure 7: Correlation between the (**A**) Atlantic meridional mode (AMM) index and (**B**) NAO index computed with ERA–5 rainfall anomalies, with dotted areas indicating significant correlations \(p<0.05\). The blue shading shows areas in which positive AMM and NAO leads to rainfall reductions, while orange shading corresponds to regions that experience rainfall increases during positive AMM and NAO phases.  
is also important in terms of influencing Atlantic TC activity at decadal time scales [198]. The impact of the AMO on seasonal TC activity manifests itself through the AMM as the former can activate the AMM at decadal time scales [182]. The AMO is also important for the low-frequency variability of precipitation as it modulates the distribution of moisture and extreme rainfall events. A direct relationship between droughts and the variations of AMO and PDO and the rainy (dry) periods in the north (south) of Mexico has also been identified [199]. Positive (negative) AMO and negative (positive) PDO were related to wet (dry) conditions in north (south) Mexico.
# The Recycling of Carbon Components and the Reuse of Carbon Fibers for Concrete Reinforcements
## 3 Experimental Investigation
### Impregnation of the Staple Yarns  
The yarns, made of recycled carbon fibers, must be impregnated with a matrix before they are used in construction. The matrix creates an internal bond between the yarns, and thus ensures an ideal power transmission. Three different impregnation processes were selected for the initial test runs. The first impregnation is a duromer, the second one is a thermoplastic and the third matrix a geopolymer.  
The duromer was an epoxy resin from Gremolith, based on an epoxy-bisphenol-A-vinylester resin dissolved in styrene. Due to the properties of the resin, this could only be applied manually to the rCF yarn. Mechanical impregnation was not possible as part of the tests. After impregnation, the surface was cured with UV light. The final strength of the impregnated yarn was achieved in the drying oven at 140 \({}^{\circ}\)C for 24 h.  
The second wetting was performed using a polymer called TECOSIT CC 1000 from CHT Germany GmbH. To wet the individual yarns reproducibly, a wetting machine developed at ITM was used [12]. The machine shown in Figure 4 is used for the production of profiled yarns [13; 14]. The wetting, shaping and curing are carried out in a continuous process. In the case of the first wetting experiments and for better comparability, the yarn was wetted and cured in the laboratory unit.  
The third impregnation was developed at the Institute of Building Materials (IfB) at the Technical University of Dresden [15].This geopolymer impregnation is based on a mixture of silicon and aluminum oxide. The impregnation of the yarn was also carried out in a laboratory unit specially developed for the continuous process [15]. The yarn is deflected over several rollers to achieve optimal impregnation. Subsequently, the still wet yarn is stretched on a rotating frame and dried for at least 24 h. To accelerate the drying speed, the drying temperature can be increased.
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 4 Simulation and Results
### Simulation
According to the five evaluation parameters proposed in Section 3, during the network simulation process, we directly store the relevant data in the LinkQoset information table from the hello message packets exchanged between nodes or calculate and store them. In \"olsr-routing-protocol.h\" and \"olsr-routing-protocol.cpp\" files, we calculate the link duration of node based on the speed and location information in the hello data packets, and compute the load of link between two nodes based on the load of node.
## 5 Surface Subsidence Prediction Method for UCG
### Basic Roof Subsidence Space Calculate
#### 5.1.4. Roof and Floor Compression Caused by Stress Concentration  
From the analysis in Section 4.2.3, the compression amount at different locations of the coal pillar is also different, which indicates that the stress concentration of the overburdened load on the coal pillar at different locations is also different. Assuming that the additional load caused by stress concentration is uniform, the additional stresses in the overburdened rock carried by the coal pillar are calculated from the coal pillar compression amount as follows:  
\[\sigma_{c}(x)=w_{c}(x)*E_{c}/h\ (0<x<n(gl^{\prime}+cl^{\prime})) \tag{16}\]  
The stress concentration at the coal pillar will cause compression of the coal seam and the rock at the roof and floor. Moreover, the additional stress at depth \(z\) at any point in the foundation for a strip foundation under uniform load can be derived from [PERSON]'s formula, expressed by the following equation:  
\[\sigma_{z}=\frac{2}{\pi}(\frac{2n}{1+4N^{2}}+\arctan\frac{1}{2N})\sigma_{c}(x) \tag{17}\]  
\(N\) is the ratio of the depth at any point in the foundation to the width of the strip foundation, \(N=\frac{z}{cl^{\prime}}\).  
The compression of each rock layer in the roof and floor can be calculated by the following formula:  
\[s_{i}=\varphi\int_{z_{i}}^{z_{i+1}}\frac{\sigma_{z}}{E_{i}}dz \tag{18}\]  
\[w_{r}=\sum_{i=1}^{n}s_{i} \tag{19}\]  
\(s_{i}\) is the compression of layer \(i\) at the roof or floor of the coal pillar.  
\(z_{i}\) is the distance between the upper surface of layer \(i\) at the bottom of the coal pillar and the coal seam floor or the distance between the lower surface of layer \(i\) at the top of the coal pillar and the roof of the coal seam.  
\(z_{i+1}\) is the distance between the upper surface of layer \(i+1\) at the bottom of the coal pillar and the coal seam floor, or the distance between the lower surface of layer \(i+1\) at the top of the coal pillar and the roof of the coal seam.  
\(E_{i}\) is the elastic modulus of the combustion rock strata in the \(i\) layer of the coal pillar roof or floor.  
\(w_{r}\) is the total compression of \(n\) beds.  
\(\Phi\) is the empirical coefficient in the calculation of foundation compression, which can be obtained from Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Elastic modulus of rock in foundation (MPa) & 2.5 & 4.0 & 7.0 & 15.0 & 20.0 \\ \hline Empirical coefficient \(\Phi\) & 1.4 & 1.3 & 1.0 & 0.4 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 3: Empirical coefficient of foundation compression calculation [42].
# Controlled-Atmosphere Sintering of KNbO\({}_{3}\)
## 1 Introduction  
KNbO\({}_{3}\) is a ferroelectric perovskite material and is one of the end members of the KNbO\({}_{3}\)-NaNbO\({}_{3}\) pseudo-binary system [1; 2]. It has an orthorhombic \(Amm2\) unit cell at room temperature, with phase transitions to a tetragonal \(P4 mm\) and a cubic \(Pm\overline{3}m\) phase at \(\sim\)225 and \(\sim\)435 \({}^{\circ}\)C, respectively [3; 4; 5; 6]. KNbO\({}_{3}\) has many potential applications including electro-optic modulators and frequency convertors [7; 8], photocatalysts [9; 10], photovoltaic materials [11; 10; 12], phosphor hosts [13; 14; 15], and in biomedical applications [16; 17; 18; 19]. KNbO\({}_{3}\) has also been studied as a potential lead-free piezoelectric material for the replacement of Pb(Zr,Ti)O\({}_{3}\)[12; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31], although it has received less attention than its cousin (K\({}_{0.5}\)Na\({}_{0.5}\))NbO\({}_{3}\) due to its inferior piezoelectric properties [32]. KNbO\({}_{3}\) shares the same challenges in processing (hygroscopic starting materials, difficulty in sintering to high density, the formation of water-soluble second phases, abnormal grain growth) as (K\({}_{0.5}\)Na\({}_{0.5}\))NbO\({}_{3}\)[21; 25; 26; 30; 33; 34; 35]. However, it has the advantages of a lower sintering temperature (1040 \({}^{\circ}\)C vs. 1100 \({}^{\circ}\)C) and less tendency towards abnormal grain growth. It also has unusually good shear mode piezoelectric properties (\(k_{15}\) = 0.55, \(d_{15}\) = 207 pC/N), making it a candidate for actuators and high-power applications [31]. An advantage of (K\({}_{0.5}\)Na\({}_{0.5}\))NbO\({}_{3}\) over Pb(Zr,Ti)O\({}_{3}\) is its stability during sintering in reducing atmospheres [36; 37; 38]. This allows the possibility of co-firing with lower cost base-metal electrodes [39; 40; 41; 42]. Sintering in an inert atmosphere also reduces the nonlinear behavior of (K\({}_{0.5}\)Na\({}_{0.5}\))NbO\({}_{3}\)-based materials through the formation of oxygen vacancies that pin the domain walls [43]. It is known that the use of reducing sintering atmospheres can change the densification behavior, grain growth behavior, and also the structure of (K\({}_{0.5}\)Na\({}_{0.5}\))NbO\({}_{3}\)[44; 45; 46; 47], but the behavior of KNbO\({}_{3}\) during sintering in reducing atmospheres has not previously been studied. The effect of different reducing atmospheres on the densification, grain growth behavior, and structure of KNbO\({}_{3}\) is examined in this work.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 4 Evaluation Methods
### Rms  
RMS (root mean square) is a simple statistical measure for determining image quality based on contrast. It does not use reference images, but gives a quality assessment solely based on the information contained in the decolorized image. The formula for calculating the quality estimate using this method is given by Expression (13), where M and N represent the width and height of the image, \(\bar{I}\) denotes the mean value of the image intensity, and \(I[x,y]\) symbolizes the intensity value of a single pixel [9].  
\[RMS=\sqrt{\frac{1}{M\cdot N}\sum_{x=0}^{N-1}\sum_{y=0}^{M-1}(I[x,y]-\bar{I})^ {2}} \tag{13}\]  
The results of this measure are based on the contrast within the image. If there are many pixels whose intensities deviate from the mean intensity value, the contrast of the image is higher. For this reason, a higher RMS value is interpreted as a better quality image, but this does not always have to be the case, because too high contrast can also cause a degradation in image quality. Due to the obvious limitation of this simple measure, it is necessary to reach the other quality assessment measures, which will also take into account the reference (color) image.
## 6 Numerical Model Calibration
### Calibration Based on Temperature
Overall, the stratification profiles in Figure 18 confirm that the model accurately captures the vertical temperature gradient, especially during the active phase and in the upper part of the enclosure. Despite some underestimations near the floor during the generalized phase, the results remain within acceptable accuracy margins, supporting the model's use for simulating vertical thermal dynamics in compartment fires. Temperature discrepancies arose primarily from four thermocouples that displayed errors over 40%, as these were positioned near the door where local turbulent eddies and sustained smoke flow were prevalent throughout the experiment. Although FDS, with its Very Large Eddy Simulation (VLES) capabilities, models fluid flows accurately, it can occasionally miss capturing such turbulence, leading to discrepancies. However, with a greater number of thermocouples, these negative effects can be mitigated. By excluding these outliers, the mean error was reduced from 17.79% to 6.9%. In the post-experiment analysis, it was observed that employing 96-120 thermocouples would provide a more comprehensive capture of thermal gradients, thus enhancing the overall predictive accuracy of the simulation model.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 4 Results and Discussion
### Discussion
#### 4.2.5 Compare with Other Methods  
To further validate the feasibility of the proposed method, a comparison was made with previous studies on UFZs identification in Beijing, as shown in Table 7. Most of these studies fuse remote sensing images and data that can reflect human activities to identify UFZs. In cities with complex functions, such fusion approaches generally exhibit higher accuracy compared to using remote sensing images. However, it is worth noting that while medium-resolution remote sensing images are more readily available for large-scale UFZs identification, their recognition accuracy tends to be lower than that achieved with high-resolution images due to the lack of detailed ground object information. In  
Figure 12: Cumulative variance contribution rate and recognition accuracy of image features in different dimensions: (**a**) cumulative variance contribution rate, (**b**) overall accuracy, and (**c**) kappa coefficient.  
addition, our method demonstrates slightly lower accuracy than the second and fourth methods. This is primarily due to the lower identification accuracy in the categories of public service and education, which limits the OA of UFZs identification. However, when compared to the first and the third methods, our method achieves 4.2% and 7.2% higher OA, respectively. Overall, although our method does not attain optimal classification accuracy, it still possesses certain advantages, particularly in terms of its simplicity and ease of model training.