esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Dataset Preparation  
The basic data utilized in the study include the digital elevation model (DEM) of the study area with a spatial resolution of 10 m; a geological map with the scale of 1:100,000; aerial photographs taken by Ministry of Land and Resources. After field identification and interpretation of the color aerial photographs, 973 landslide points are identified, and the distribution is as shown in Figure 1.  
Owing to that a variety of factors make a contribution to the landslide occurrence, an adequate selection of the effective parameters is essential to identify areas prone to landslide. We choose these factors in accordance with the well-illustrated criterion in many previous studies (as listed in Table 1). In detail, firstly, the actual conditions of research area are considered. The study area has a complex topography, so topographic factors, such as slope and slope height, provide terrain conditions  
Figure 2: Methodology of research applied in this study.  
for landslides. The alternating appearance of soft and hard lithology provides a material basis for the development of landslides. Besides, there are two large faults across the study area, and the landslide frequency may increase as the distance to a major or minor fault decreased [26]. There are also many river systems in the study area. In the dense-river-areas, with the increase of catchment area and reduction of erosion basis, the shearing force of river flow is enhanced, which gives rise to the steep gorges in the downstream and provides premise conditions for the growth of geological disasters.  
Based on the analysis of the research area, the factors commonly used in previous studies on landslide susceptibility are further referenced (Table 1), and 7 variables (elevation, slope, slope height, aspect, distance-to-stream, distance-to-fault, and lithology) are finally identified for modeling. All these factors are processed within ArcGIS. Slope, elevation, slope height, and aspect are calculated using DEM. All data are directly obtained by ArcGIS without data transformation. Lithology map is digitized from the existing geological map. Lithology map is digitized from the existing geological map. Distance-to-fault is obtained by calculating the distance to the nearest fault, and distance-to-stream is the distance to the nearest stream.
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 3 Results and Discussion  
This section presents and discusses the results obtained from the proposed method. The results of the crack segmentations, which were achieved through image processing techniques, are presented first. Their impact on the accuracy of crack width measurement and the overall performance of the method are then discussed. Finally, the results of the concrete crack width measurement are presented, discussed, and compared to other approaches in the literature.
## 2 Materials and Methods
### Unsupervised Machine Learning
#### 2.7.2 DBSCAN Clustering  
The DBSCAN algorithm was first proposed by [PERSON] et al. [51] in 1996. It is a typical density-based clustering algorithm that relies on two parameters, the neighbourhood radius \(\varepsilon\) and the minimum number of points minPts, to create a dense area. Two points are considered neighbours if the distance between them is less than or equal to \(\varepsilon\). The key ideas are to identify dense regions and to expand them to form clusters. Based on these two parameters, some important concepts are defined below:  
A core point is defined as a point that satisfies the criterion of having a minimum of minPts points (including itself) within a radius of \(\varepsilon\). On the other hand, a border point is a point that is reachable from a core point but does not meet the minimum density requirement of having at least minPts points within a \(\varepsilon\) radius. Any point that does not satisfy either of the two conditions, being a core point or being reachable from a core point, is considered an outlier or noise.  
The detailed steps of the algorithm are presented by [PERSON] et al. [52] The main steps are listed as follows:  
* Locate all the points within the \(\epsilon\) radius of each point.
* Identify the core points that have at least minPts neighbours.
* Find all the core points' connected components in the neighbour graph.
* Assign each non-core point to a cluster if it is within the \(\epsilon\) neighbourhood of the cluster.
* Any remaining points are considered outliers or noise.  
The advantage of DBSCAN is that it works well with clusters of arbitrary shapes and sizes [25]. Unlike many other unsupervised machine learning algorithms, it does not require setting the parameter of the desired number of clusters.
# Single-Switch Non-Isolated Resonant DC-DC Converter for Single-Input Dual-Output Applications
## 3 Model and Experimental Prototype
### Simulation Results
Figure 8: Output voltage variation with \(R_{Load_{1}}\) and \(R_{Load_{2}}\) 10 \(\Omega\), \(V_{g}\) = 14 V, with a change in the switching frequency from 411 kHz to 200 kHz at 0.075 s.  
Figure 7: Waveforms of the ZVS-QR SEPIC–Cuk converter in step-down mode. Figure 10: Waveforms of the ZVS-QR SEPIC–Cuk converter in step-up mode. Figure 9: Output voltage variation at switching frequency from 411 kHz, \(V_{g}=14\) V, with a change in \(R_{Load_{1}}\) and \(R_{Load_{2}}\) from 20 \(\Omega\) to 10 \(\Omega\) at 0.075 s.  
On the other hand, the results shown in Figure 11 correspond to \(V_{g}\) = 10 V, \(R_{Load_{1}}\) and \(R_{Load_{2}}\) of 10 \(\Omega\), but with a change in the switching frequency from 200 kHz to 290 kHz at 0.075 s. In this case, due to the change in frequency, the output voltages vary between \(\pm\)11.9 and \(\pm\)16.2 V, and the converter remains in step-up mode. In addition, the results shown in Figure 12 also correspond to \(V_{g}\) = 10 V, with a switching frequency of 290 kHz, but with a change in \(R_{Load_{1}}\) and \(R_{Load_{2}}\) from 20 \(\Omega\) to 10 \(\Omega\) also at 0.075 s. In this case, the output voltages vary between \(\pm\)12.1 and \(\pm\)16.4 V, and the converter does not change its operating mode either, remaining in step-up.
## 3 Dynamic Anchor
### Quality Branch  
The detectors usually assign anchors to the ground-truth boxes based on the maximum IoU criterion, which means that an anchor with a higher IoU score is more important. The locations near the bounding box center will produce high-quality anchors in the feature maps, while those away from the center will produce low-quality anchors. However, the loss weights of all predicted anchors are equal when calculating the anchor regression loss, which is unreasonable. To suppress the low-quality anchors, we issued an effective strategy.  
Figure 4: Illustration of the feature enhancement module.  
Specifically, we added a branch to predict the quality of the predicted anchors in parallel with the regression branch.  
As shown in Figure 5, the prediction head of the network is composed of three convolution branches to complete the prediction tasks of category, coordinate, and quality, respectively. The decoupling prediction structure greatly reduces the difficulty of classification and regression, and is conducive to parameter optimization. At the same time, in order to improve the reasoning speed, each branch contains only one convolution operation, which significantly reduces the amount of operation. For the input feature map, the prediction head will output three prediction matrices, including Class, Object, and Quality.  
Given a ground-truth box \(gt_{i}=\big{(}x_{gt},y_{gt},w_{gt},h_{gt}\big{)}\) and its corresponding anchor box \(anchor_{i}=(x,y,w,h)\), the quality target is defined as:  
\[\text{quality}(gt_{i},anchor_{i})=\frac{|gt_{i}\cap anchor_{i}|}{|gt_{i}\cup anchor _{i}|} \tag{14}\]  
At the training stage, the quality is trained with the binary cross entropy (BCE) loss, and the quality target is used as the weight of anchor loss. The final confidence score is computed by multiplying the classification score with the corresponding predicted quality in testing. Therefore, the quality can reduce the scores of the low-quality anchors. By the non-maximum suppression (NMS) [32] process, the most low-quality predicted boxes might be filtered out, improving performance.
## 2 Materials and Methods
### Related Works
#### 2.2.4 Health Monitoring of Aviation Systems  
The evolution of modern aviation hydraulic systems is characterized by trends towards higher pressures, speeds, and power outputs [44], significantly enhancing system efficiency but also introducing pronounced hydraulic pipeline vibrations. These vibrations are primarily induced by the pulsating forces from hydraulic pumps and the structural connections within the system, compounded by fluid-solid interactions within the pipelines that further complexity vibration dynamics.  
The study of these vibration characteristics holds substantial value for both scientific research and practical engineering applications [45].  
The paper [46] reviews diagnostic methods for the hydraulically powered flight control actuation system in aircraft, focusing on the challenges of diagnosing intermittent and incipient faults and the integration of subsystems for overall system health monitoring. It discusses various diagnostic approaches, including model-based, statistical mapping, and algorithmic methods, and highlights the need for extensive data for effective fault diagnosis. The complexity of subsystem integration increases the likelihood of faults, emphasizing the importance of Integrated Vehicle Health Management tools. However, the scarcity of accessible, detailed data from manufacturers and users complicates the use of data-driven diagnostics, presenting significant opportunities and challenges in the field of aircraft system health monitoring and diagnostics.  
The paper [47] introduces a fault detection and isolation method for aircraft gas turbines, utilizing modular convolutional neural networks combined with a physics-driven trend monitoring system. The approach effectively identifies and classifies gas-path faults at the component level, leveraging fault signatures derived from performance degradation monitoring. The study highlights the significant potential of modular neural network architectures in enhancing the accuracy of gas turbine diagnostics.  
The study [48] introduces a digital twin framework for aviation systems performance diagnosis, combining component-level mechanism models with data-driven models through a novel integration of particle swarm optimization-extreme gradient boosting and low-rank multimodal fusion, further enhanced by a sparse stacked autoencoder. High-lighting its economic benefits, the framework promises to significantly enhance engine reliability, availability, and efficiency in practical engineering applications.
## 4 Bioremediation of PAH-Contaminated Soil by Composting
### Effect of Temperature
Under these conditions, it is assumed that a longer incubation period under the mesophilic phase could facilitate PAH removal, due to the richest microbial diversity and possible increased microbial activity [27]. According to these studies, and others in the literature, mesophilic temperatures demonstrated their viability and were found to be more favorable for degrading LMW PAHs, with great success in many cases due to the large microbial diversity;however, these temperatures were not found to be so efficient in the degradation of recalcitrant PAHs [69; 75; 88; 90]. On the other hand, thermophilic ranges have been documented as enhancing PAH degradation. During the composting of hydrocarbon-polluted sediments (total petroleum hydrocarbons (TPH) = 40.3 gkg\({}^{-1}\) dw) with different organic co-substrates, [PERSON] et al. [91] point out that fish sludge achieved higher temperatures and was able to maintain thermophilic temperature longer than other amendments, which ultimately led to greater TPH removal rates (39.5%). It was assumed that such conditions are conducive to develop fungal communities and exert a surfactant effect, thus promoting the degradation rates. Similarly, [PERSON] et al. [92] proposed that enhanced solubility under thermophilic conditions could explain the higher removal rate (46%) of benzo[a]pyrene in composting treatments compared to 29% under mesophilic ones. However, whether the increased solubility or microbial community changes contribute to the high-temperature impacts needs further investigation. [PERSON] et al. [82] further demonstrated that the elevated temperature during the thermophilic phase of composting enhanced the solubilization rates of phenanthrene, and hence its degradation. Based on the aforementioned observations, it is clear that the impact of composting temperature is correlated to the physiochemical properties of the targeted PAH, as the corresponding degrading microorganism are specific to temperature [93]. Generally, and despite of the different observations, mesophilic temperatures and the dominant microorganisms under these conditions are believed to be more preferable for the degradation of such compounds [69; 78; 83; 84; 85; 86; 94].
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
### Limited Testing Resources  
With the advancement of integration technology, in order to meet the requirements of shortening development cycles and reducing costs, a variety of Intellectual Property (IP) cores are integrated into a single integrated circuit, forming an SoC device. Due to the continuous increase in the scale of integrated circuits and the addition of functions, the types of integrated IP cores are diverse. Each type of IP core has different testing conditions, which undoubtedly increases the complexity of the integrated circuit testing schemes. As the number of integrated cores on the chip continues to increase, while the number of I/O pins remains largely unchanged, accessing the internal circuits during integrated circuit testing becomes extremely difficult. Considering that IP cores do not have packaged physical pins, in order to ensure the effective transmission of test stimuli and test responses, it is necessary to design dedicated Test Access Mechanisms (TAM) and Test Wrappers (TW) for the IP cores to be tested. The transmission between the TW and the test interface adheres to the IEEE P1500 [3] and P1450 [4] standards. When designing TAM and TW, a significant amount of testing resources is required, presenting new challenges for the effective utilization of testing resources [5].  
Figure 1: Trends in microprocessor development and the evolution of manufacturing processes [2].
## 7 Experimentation and Analysis
### Analysis and Discussion  
On the one hand, from the simulation experiment results of the above four offloading strategies, the resource optimization strategy based on the GLSOAG achieves a certain advantage among similar algorithms, which can effectively reduce the system energy consumption and delay overhead, and then improve the execution efficiency of emergency rescue tasks. On the other hand, in different task scenarios, facing the actual demand of different task volumes, although our approach does not have obvious advantages when the task volume is small, it is because the lightweight tasks do not need to be offloaded to the edge side, and can be processed locally in the device, and the tasks do not use most of the components of the system that we designed, so the system cannot be fully functional. However, when the task volume is increased, the emergency rescue system based on the GLSOAG is able to show superiority, which proves that the system we designed and the optimization method have a certain degree of generality.  
In addition, with the increase in emergency rescue equipment, the demand for data fusion processing is further increased, and by adding MEC nodes with different arithmetic power to the system, the system can be flexibly expanded, and our optimization method is also applicable. As a whole, the emergency rescue system we designed has good generality and scalability, and our proposed resource optimization strategy based on the GLSOAG has good performance.
## 4 Parameter Sensitivity Analysis
### Effect of Density on Wave Behaviour  
Apart from the effect of elastic modulus on wave behaviour, the parameter of density is also investigated. Similar to the elastic modulus, the densities used in this study are selected within the range of density for Spotted Gum, which are 950 kg/m\({}^{3}\), 1050 kg/m\({}^{3}\) and 1150 kg/m\({}^{3}\). The isotropic and orthotropic models are imported, respectively. The same measurement point as the elastic modulus study is used to record the time history of wave propagation.  
Figures 16 and 17 display the behaviours of wave propagation with different densities for isotropic and orthotropic material models, respectively while Tables 6 and 7 give the corresponding wave velocities. It is clearly seen from the figures that higher wave velocity can be obtained from the model with lower density regardless of the material characteristics. It should also be noticed that decay of wood will lead to the strength loss at first and then a gradual decrease in the rate of the strength loss at higher weight losses. In this study, the density range is within the intact pole of Spotted Gum. The lower limit of the density is still higher than intact soft wood. If there is decay in the wood, the density of the decayed wood should be much lower than the intact wood. Accordingly, a wider range of density parameter should be studied in future for damage detection.  
Figure 14: Effect of elastic modulus in the radial direction on wave behaviour in the orthotropic model.  
Figure 15: Effect of elastic modulus in the tangential direction on wave behaviour in the orthotropic model.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.1 Pearson III Distribution (PE3)  
For estimation with MOM, the distribution parameters have the following expressions [10]:  
\[\alpha=\left(\frac{2}{\text{C}_{\text{s}}}\right)^{2} \tag{42}\]  
\[\beta=\frac{\sigma}{2}\text{C}_{\text{s}} \tag{43}\]  
\[\gamma=\mu-\alpha\cdot\beta \tag{44}\]  
where C\({}_{\text{s}}\) represents the skewness coefficient.  
The parameter \(\alpha\) can be estimated using the rational function presented by [PERSON] in 1997, named in this section the \"Hosking approximation\", or based on an approximation made up of two polynomial functions and one rational, depending on the definition domain of the estimated parameter, named here \"a better approximation\".  
Thus, for the estimation with the L-moments, the shape parameter \(\alpha\) can be evaluated numerically with the following approximate forms, depending on L-skewness (\(\tau_{3}\)):  
The Hosking approximation, rational function [11; 14]:  
\[\alpha=\frac{1+2.73884\cdot\tau_{3}^{2}}{9.424778\cdot\tau_{3}^{2}+16.7171359 \cdot\tau_{3}^{4}+37.0028906\cdot\tau_{3}^{6}} \tag{45}\]  
if \(\frac{1}{3}\leq|\tau_{3}|<1\):  
\[\alpha=\frac{0.36067\cdot(1-|\tau_{3}|)-0.59567\cdot(1-|\tau_{3}|)^{2}+0.25361 \cdot(1-|\tau_{3}|)^{3}}{1-2.78861\cdot(1-|\tau_{3}|)+2.56096\cdot(1-|\tau_{3} |)^{2}-0.77045\cdot(1-|\tau_{3}|)^{3}} \tag{46}\]  
better approximation, polynomial and rational functions:  
If \(0<|\tau_{3}|\leq\frac{1}{3}\):  
\[\alpha=\exp\left(\begin{array}{l}-3.164791927-5.108735285\cdot\ln(|\tau_{3 }|)-4.116014079\cdot\ln(|\tau_{3}|)^{2}-\\ 2.985250105\cdot\ln(|\tau_{3}|)^{3}-1.327399577\cdot\ln(|\tau_{3}|)^{4}-0.3739 44875\cdot\ln(|\tau_{3}|)^{5}-\\ 0.065421611\cdot\ln(|\tau_{3}|)^{6}-0.006508037\cdot\ln(|\tau_{3}|)^{7}-0.00028 1969\cdot\ln(|\tau_{3}|)^{8}\end{array}\right) \tag{47}\]\[\text{if }\frac{1}{3}<|\tau_{3}|\leq\frac{2}{3}\text{:}\] \[\alpha=\exp\left(\begin{array}{l}-3.9918551-10.781466\cdot\ln(| \tau_{3}|)-21.557807\cdot\ln(|\tau_{3}|)^{2}-\\ 33.8752604\cdot\ln(|\tau_{3}|)^{3}-35.0641585\cdot\ln(|\tau_{3}|)^{4}-22.921163 \cdot\ln(|\tau_{3}|)^{5}-\\ 8.5491823\cdot\ln(|\tau_{3}|)^{6}-1.3855653\cdot\ln(|\tau_{3}|)^{7}\end{array}\right) \tag{48}\]  
\[\text{if }\frac{2}{3}<|\tau_{3}|<1\text{:}\] \[\alpha=\frac{5.17817436-26.209448756\cdot|\tau_{3}|+62.12494027 \cdot\tau_{3}^{2}-84.39423264\cdot|\tau_{3}|^{3}+}{1+0.0005134\cdot|\tau_{3}| \text{:} \tag{49}\]  
The scale parameter \(\beta\) and the position parameter \(\gamma\) are determined with the following expressions [14]:  
\[\beta=\text{L}_{2}\cdot\sqrt{\pi}\cdot\frac{\Gamma(\alpha)}{\Gamma\left( \alpha+\frac{1}{2}\right)} \tag{50}\]  
\[\gamma=\text{L}_{1}-\alpha\cdot\beta \tag{51}\]  
where \(\text{L}_{1},\text{L}_{2},\tau_{3}\) are the sample L-moments and L-skewness, [7; 11; 14].
## 2 Regulatory Aspects
### European BPA Limits in Foods  
In 2006 the European Food Safety Authority (EFSA) completed its first full risk assessment on the use of BPA. As a result, EFSA recommended the TDI and reference dose for BPA to be set to 50 \(\upmu\)g/kg bW)/day [25]. New scientific information was evaluated by EFSA in 2008, 2010, 2013, and 2015 resulting in a reduction of the TDI to 4 \(\upmu\)g of BPA /kg bW/day in January 2015 [5]. Scientific experts have started to evaluate the recent toxicological data on BPA and the assessment has been scheduled for update in 2020 but has not yet been published.  
Apart from these EU-wide regulatory aspects, some national measures have also been adopted under the safeguard measures of EU framework regulations. For example, Denmark, France, Sweden, and Belgium have restricted the use of BPA in food packaging for young children. Denmark imposed a ban on the use of BPA for the manufacturing of plastic materials in contact with food intended for children aged 0-3 years [28]. In 2012, France adopted a law of suspending all BPA containing materials coming into contact with foods intended for children of 0-3 years of age from January 2013, and all other food contact materials from January 2015 [29].
## 2 The Proposed Method
### Invertible Denoising Network
#### 2.2.3 Forward Pass  
The InvDN initially employs a supervised methodology to guide the network in transforming digital images into high- and low-frequency signals. After undergoing an invertible transformation g, the noisy image is converted into its corresponding low-resolution clean image and high-frequency representation. The forward pass can be expressed as follows:  
\[\mathrm{L}_{\mathrm{forw}}(\mathrm{y},\mathrm{x}_{\mathrm{LR}})=\frac{1}{ \mathrm{M}}\sum_{i=1}^{\mathrm{M}}||\mathrm{g}(\mathrm{y})_{\mathrm{LR}}- \mathrm{x}_{\mathrm{LR}}||_{\mathrm{m}} \tag{8}\]  
In this expression, \(\mathrm{g}(\mathrm{y})_{\mathrm{LR}}\) represents the low-frequency signal learned by the network, which corresponds to the three channels in the output representation during forward propagation. Here, M denotes the number of pixels and \(\mid\!\!\mid\!\!\mid\mathrm{m}\) refers to the m-norm, where m can be either 1 or 2.