esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology to Complete Missing Spatial and Geometrical Attributes
### Detailed Methodology and Illustrative Examples
#### 2.2.3 Bound Calibration Strategy  
The third step of the process is called \"_Bound_\" and applies probability density functions triggered by completion rules, as well as confidence levels associated to positioning attributes, in order to propose a secondary (bounding) geometry. This geometry envelops the primary object and represents the volume in which the subsurface object shall be found with a given probability.  
A tree root class object serves as an illustrative example. Topographic and geometric attributes of the object are linked with a precision. The position of the tree (and thereby the tree root) has been measured with an accuracy of \(+/-10\) cm. A triangle distribution, with an overall degree of confidence of 95%, is assigned to the positioning attributes. As presented in Section 2.2.2, the confidence linked to the depth attribute of the tree root is represented by a stair-type probability density function. All these probability density functions are applied to model the secondary object (Figure 10) and consolidate precision on an attribute level to precision on an object level.  
Figure 9: Calibration of a minimum ground cover when no data is available (**left** side) or when local is available (**right** side).  
For natural gas networks, the secondary object is modeled by a bounding rectangle around the pipeline diameter. The maximal lateral limits of the secondary object are obtained using the triangle probability function, since x and y coordinates are known through measurements. The upper and lower bounds of the secondary object are calculated by adding a second component, obtained by subtracting the measurement uncertainty from the PERT coeff a (for the upper limit) and by adding the measurement uncertainty to PERT coeff c (for the lower limit), see also Figure 11. The size of the secondary object depends on the degree of confidence defined by the user: the bigger the precision confidence interval, the larger the boundaries of the secondary object.  
Figure 11: Bounding box for a natural gas network secondary object.  
Figure 10: Secondary geometry and radius calibration based on the triangle probability density function along the X-axis.
# P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images
## 3 Experimental Results
### Evaluation Results  
The proposed algorithm was evaluated using the MIT-BIH AF database. The MIT-BIH AF database [29; 30] contains ECG signals with no P-waves in the case of AFIB, sawtooth P-waves in the case of AFL, and normal P-waves in other cases. Figure 5 indicates that the proposed algorithm is able to successfully detect the P-waves, even when the P- and T-waves are merged in the ECG signals. It correctly located P-waves in normal and abnormal cases. The abnormal cases have overlapping P and T waves, the presence of U-waves between P- and T-waves, and negative polarization. It can also be deduced that the proposed algorithm is not affected by noise and that it is thus a robust algorithm.  
Figure 5: Demonstrating the performance of the proposed P-wave detection algorithm on the MIT-BIH atrial fibrillation database. The P-wave is detected in electrocardiogram (ECG) signals that contain: (**a**) high-frequency noise; (**b**) normal sinus rhythm without U-waves; (**c**) normal sinus rhythm with U-waves; (**d**) normal sinus rhythm with negative polarization; (**e**) merged P- and T-waves. Here, the red circle asterisk represents detection of merged P- and T-waves. (**f**) No P-wave detected in case of premature atrial fibrillation.  
Table 2 illustrates the evaluation results obtained over the test data in 23 records of the MIT-BIH AF database. These results were obtained by training the model using 460 images of ECG recordings, each consisting of 10 s for 23 patients (00735 and 03665 are unavailable signals), and tested by using 230 images of the same specifications for the same patients. The proposed algorithm achieved a training accuracy of 99.8%, a testing accuracy of 98.56% after 20 epochs, a sensitivity of 98.78%, and a positive predictivity of 97.41%.
# Comparison and Identification of the Aroma-Active Compounds in the Root of _Angelica dahurica_
## 1 Introduction  
_Angelica dahurica_ (Baizhi) is a traditional herb in China, according to the location of origin the _A. dahurica_ has 5 types: Hangbaizhi, Qibaizhi, Chuanbiazhi, Yubaizhi, and Bobaizhi [1]. The _Angelica dahurica_ is a dual-purpose plant with medicinal and edible value. On the one hand, with the increasing health consciousness, _A. dahurica_ will be increasingly popular, as it has anti-inflammatory, antioxidant, antibacterial, and anticancer properties [2; 3; 4; 5]. Root of _Angelica dahurica_ was defined in the 2010 edition of the China Pharmacopoeaia (2010) [6] as the dry root of _Angelica dahurica_ (Fisch.ex Hoffm.) Benth.et Hook.f. or _Angelica dahurica_ (Fisch.ex Hoffm.) Benth.et Hook.f. var. formosana (Boiss.) [PERSON]. On the other hand, its edible parts are used as a spice for flavoring, seasoning, or imparting aroma. In previous work, out of the very few studies on the flavor of the root of _A. dahurica_, [PERSON], [PERSON] and [PERSON] [7] use gas chromatography-mass spectrometry (GC-MS) to analyze the volatile components, such as 1,\(\beta\)-cineol, Imonene and caryophyllene (SPME). Furthermore, the terpenes,\(\alpha\)-limonene, 3-decene, and 1-\(R\)-\(\alpha\)-pinene were identified in the volatile oil of _A. dahurica_[8], and [PERSON] et al. [9] found that dodecanal is the most abundant species in the volatile oil of Chuanbaizhi and Hangbaizhi and found that volatile oils have many different flavors.  
However, a comprehensive analysis of the key aroma-active compounds of the root of _A. dahurica_ has not been reported. In further investigations focusing on the flavor, some scholars have studied the aroma components of botanical seasonings [10; 11; 12; 13]. [PERSON] et al. [12] used dynamic headspace sampling-gas chromatography-mass spectrometry (DHS-GC-MS) to analyze the monoterpenoids in four aromatic plants [14]. [PERSON] et al. [13] identified the key aroma-active components in rocket leaves by combining headspace solid-phase microextraction (HS-SPME), stir bar sportive extraction (SBSE) with GC-MS, and gas chromatography-olfactometry (GC-O); they found that the most potent odor-active compounds identified included (\(Z\))-3-hexenal, (\(E\))-3-hexenal, (\(Z\))-1,5-ocatadien-3-one, and 4-mercapobutyl [15]. However, the summary on the characteristics of odorens on the different of types _A. dahurica_, especially, qualification and quantification on the aroma compounds was still fragmentary.  
The main objectives of this study were, firstly, to compare the four headspace analysis methods (SPME, P&T, SBSE, and DHS) to identify the volatile flavor compounds from the three types of _A. dahurica_. Secondly, SPME-AEDA and flavor dilution (FD) factors were employed to identify and quantify ten key aroma-active compounds shared by the three types of _A. dahurica_. Finally, the _A. dahurica_ samples were divided into three groups by principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) was used to identify the aroma-active compounds that most strongly influenced the classification.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
###### Abstract  
Represented by reactive security defense mechanisms, cyber defense possesses a static, reactive, and deterministic nature, with overwhelmingly high costs to defend against ever-changing attackers. To change this situation, researchers have proposed moving target defense (MTD), which introduces the concept of an attack surface to define cyber defense in a brand-new manner, aiming to provide a dynamic, continuous, and proactive defense mechanism. With the increasing use of machine learning in networking, researchers have discovered that MTD techniques based on machine learning can provide omni-bearing defense capabilities and reduce defense costs at multiple levels. However, research in this area remains incomplete and fragmented, and significant progress is yet to be made in constructing a defense mechanism that is both robust and available. Therefore, we conducted a comprehensive survey on MTD research, summarizing the background, design mechanisms, and shortcomings of MTD, as well as relevant features of intelligent MTD that are designed to overcome these limitations. We aim to provide researchers seeking the future development of MTD with insight into building an intelligently affordable, optimized, and self-adaptive defense mechanism.  
moving target defense; cyber security; affordable defense; self-adaptive defense; intelligent defense +
Footnote †: journal: Applied sciences
## 4 Experiments and Results Analysis
### Evaluation Metrics  
To evaluate the performance of POI recommendation methods, three evaluation metrics are adopted in this paper. Precision of a group is described as the ratio of the number of POIs the group actually visited to the total number of POIs in the recommendation list. Recall of a group is described as the ratio of the number of POIs the group visited to the total number of POIs. Generally, given a group \(g\), we denote the set of POIs which group actually visited in the testing set as \(T_{g}\), and the set of POI recommendation list by a method as \(R_{g}(N)\). Therefore, we use the average precision \(Precision@N\) of each group to indicates the overall precision. Furthermore, the average recall \(Recall@N\) of each group to indicate the overall recall. The corresponding formulas are shown in Equations (18) and (19). In the experiment, the length of recommended POI list \(N\) is set 5, 10 and 20, respectively.  
\[Precision@N=\frac{1}{|G|}\sum_{g\in G}\frac{|R_{g}(N)\bigcap T_{g}|}{|R_{g}(N)|} \tag{18}\]  
\[Recall@N=\frac{1}{|G|}\sum_{g\in G}\frac{|R_{g}(N)\bigcap T_{g}|}{|T_{g}|} \tag{19}\]
## 3 Method
### Wavelet Domain Selective Kernel Network  
This section provides an overview of our method, as shown in Figure 4. The overall architecture is built upon recent popular structures [15; 18; 34; 43]. The Wavelet-Domain Adaptive Receptive Field Network (WDARFNet) architecture consists of four stages, and the number of WDARFNet blocks in each stage is \(\{3,\ 4,\ 6,\ 3\}\). Correspondingly, the feature channel numbers in each stage are \(\{64,\ 128,\ 256,\ 512\}\). Each WDARF block (Figure 4) consists of a Wavelet Domain Selective Kernel (WDSK) sub-block and a Feed-forward Network sub-block. The core WDARF module (Figure 5) is embedded in the WDARF block, consisting of three modules: Selective Kernel Convolutions Module, Wavelet Domain Channel Selection Module (WDCSM), and Wavelet Domain Spatial Selection Module (WDSSM). The Selective Kernel Convolutions Module consists of two branches with large convolution kernels, explicitly constructing a series of feature maps with different receptive field ranges. Subsequently, the WDCSM and WDSSM select appropriate features in the channel and spatial dimensions, respectively. This allows the model to adjust the receptive field  
Figure 3: Illustration of image using Haar wavelet transformation. The image on the left is the original image with added Gaussian noise. The image on the right displays the four sub-bands that are produced as the result of the 2D Discrete Wavelet Transform. \(A\) is the low-frequency component, \(H\) is the horizontal high-frequency component, \(V\) is the vertical high-frequency component, and \(D\) is the diagonal high-frequency component.  
Figure 2: Flowchart of the 2D Discrete Wavelet Transform.  
field size for each object as needed, which will be detailed later. The Feed-forward Network sub-block is used for channel mixing and feature refinement.
## 3 Applications of Game Theory in WSN
### Game Theory for WSN Security  
Due to the limited capabilities of sensor nodes in terms of computation, communication, and energy, providing security to WSN has increasingly become one of the most interesting areas of research in recent years. WSN security is a primarily important and critical issue before WSN can be widely used. There usually exist two mechanisms of intrusion prevention and detection in WSN security. GT provides a mathematical method for analyzing and modeling WSN security problems for it considers scenarios where multiple players with contradictory objectives compete with each other. [PERSON] _et al_. [6] had proposed a taxonomy which divides current existing typical game theory approaches for WSN security into four categories: preventing DoS attacks, intrusion detection, strengthening security, and coexistence with malicious sensor nodes. They pointed out some future research areas for ensuring WSN security based on game theory, including Base Station credibility, IDS efficiency, WSN mobility, WSN QoS, real-world applicability, energy consumption, sensor nodes learning, expanding game theory applications, and different games.
## 2 Force Analysis and Power Calculation
### Landing Phase  
The landing phase primarily consists of four segments: the descent segment from cruising altitude, the flare and deceleration segment, the touchdown segment, and the ground roll segment. The descent segment has already been analyzed in detail.  
When the UAV descends from cruising altitude to the final descent altitude (buffer altitude), it transitions into the flare and deceleration segment. The specific force and power conditions during this segment are as follows:  
(1) Force analysis:  
\[\left\{\begin{array}{l}L_{fd}=W,\;\textit{Vertical direction}\\ F_{a,fd}=D_{fd}-T_{a,fd},\;\textit{Horizontal direction}\end{array}\right. \tag{17}\]  
(2) Power situation:  
\[P=P_{fd}=T_{a,fd}\cdot V(t) \tag{18}\]  
During the landing segment, the UAV decelerates and descends from the buffer altitude to the ground. Due to the relatively low buffer altitude, this motion can be approximated as near-horizontal linear movement. The corresponding force and power conditions are as follows:  
(1) Force analysis:  
\[\left\{\begin{array}{l}L_{l}\approx W,\;\textit{Vertical direction}\\ F_{a,l}=D_{l}-T_{a,l},\;\textit{Horizontal direction}\end{array}\right. \tag{19}\]  
(2) Power situation:  
\[P=P_{l}=T_{a,l}\cdot V(t) \tag{20}\]  
During the ground roll phase, after the UAV has landed, the motor is turned off, and the UAV gradually comes to a stop due to ground friction and air resistance. At this stage, no power is consumed, and the force conditions are as follows:  
Force analysis:  
\[F_{a,s}=\mu W+D_{s} \tag{21}\]
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Stochastic Models
#### 3.2.5 Null Space Monte Carlo  
In the realm of numerical groundwater modelling, outcomes are inherently approximate due to the inherent uncertainties. However, techniques exist to complement deterministic groundwater models by identifying and assessing these uncertainties. One such method is the Null Space Monte Carlo method (NSMC), which leverages the Monte Carlo approach and calibration-constrained parameters to compute parameter fields for model validation [471]. NSMC implementation can be facilitated through the PEST code [472], which is integrated into the GMS software. Consequently, users typically start by acquiring the deterministic groundwater model (e.g., MODFLOW) alongside flow-path delineation (particle tracking method). Subsequently, they employ PEST for parameter calibration and uncertainty evaluation. The method has been effectively used to identify areas potentially contaminated by chlorinated hydrocarbons, and to determine the most likely sources of contamination in the vicinity of water stations in Milan [473].
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 2 Pipeline Safety Monitoring Theory
### Corrosion Monitoring  
For pipeline corrosion monitoring, it is assumed that \(P\) and \(R\) are the constants since pipelines usually work in the condition of steady pressure and the radius of the pipeline is a constant. Therefore, the hoop strain is inversely proportional to the wall thickness of pipeline based on Equation (1). According to this theory, the wall thickness reduction, which is the most direct phenomenon caused by corrosion, can be reflected by the hoop strain directly.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 3 Network and Power Control Model  
### Network Model  
The WSN investigated in this study is designed to be mainly used in certain coalmine tunnels that are, in particular, within the haulage roadway and that have a long strip shape. As the WSN has a length that is much greater than its width, several sinks are needed within the chain. In addition, the communication radius and transmission power between all the sink nodes are negotiated to ensure WSN coverage in the coalmine tunnel. In relation to the coverage of each sink, unequal clustering is established in individual regions to optimize network topology, reduce the overall energy consumption of the network and clustering interference, and prolong the network lifetime with the aim of ensuring the best connectivity.  
The PCEB-MS protocol used in the coalmine tunnel, which is discussed in this paper, sets a sink at random intervals in the center of the link, which is not greater than the maximum communication radius of the sink. Each sink has its own location by means of the UWB technology, and it broadcasts a power control message (PCM) within its communication radius. Sink nodes communicate with one another using a special mine communication cable buried in the ground. If we assume that two sink nodes are able to communicate in the initial stage, each sink then receives monitoring information from a sensor node within a certain region, which ensures the PCM exchange between both sinks.  
In this paper, we suppose that the number of sensor nodes in the network is N, and these are distributed in a long-strip region measuring \(L\times W\) with \(L>>W\). As soon as the sink and sensor node are deployed, the location is fixed and it no longer changes, but the transmission power of the sensor nodes and the sink is controllable. The distance between nodes and the receiving power are calculated on the basis of the intensity of the receiving signal. The receiving power is more than the minimum power threshold of the normal receiving data \(PR_{\text{min}}\). The sensor nodes are isomorphic with the same initial energy, and they also have data fusion function and self-sensing of residual energy. Furthermore, there is unrestricted sink energy.
# Ultra-High-Cycle Fatigue Life Prediction of Metallic Materials Based on Machine Learning
## 5 Conclusions  
To predict the ultra-high-cycle fatigue life of metallic materials, two ultra-high-cycle fatigue life prediction models were developed based on the GB and RF algorithms. The fatigue life of materials was obtained by inputting the partial mechanical properties of the materials and the geometry of ultrasonic fatigue specimens, largely reducing the cost of obtaining the fatigue life of metallic materials. The main observations of this work are presented below.  
1. The training data size significantly affected the accuracy of the models. As the proportion of training data increased, the prediction ability of both GB and RF models was significantly improved.
2. The GB and RF models manifested different characteristics in predicting the fatigue life of metallic materials. The GB model had higher prediction accuracy than the RF model, whereas the RF model had better stability. In practical applications, they could be used alone or in combination to suit different scenarios.
3. Local similarities were introduced to the models. The fatigue life prediction ability of the GB and RF models for new materials was significantly improved by adding data of new materials to the training database.  
Validation, M.S.; Formal analysis, D.H.; Data curation, Z.W.; Writing - original draft, X.Z.; Writing - review & editing, N.Y.; Funding acquisition, F.L. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (#51775388).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Application of the OW-Fxlms Algorithm to ANC of Interior Noise  
### Experimental Setup and Procedure  
A sport utility vehicle was used for the application of ANC to interior noise. The test car is SNATAFE made by Hyundai motor company as shown in Figure 12. A 2.2 \(L\) diesel engine was used in the powertrain of this vehicle. The instruments used for the ANC were loaded inside the vehicle. The equipment used in the ANC system is shown in Figure 13.  
Four microphones were used for the measurement of error signals, and were attached to the roof of the vehicle. Five speakers were used as the secondary sound sources required to cancel the primary noise sources inside the vehicle [26]. The error signals were transferred to the input channels of a DSP control board (Ds 1104, dSPACE, Paderborn, Germany) via a signal conditioner (441-A42, PCB Company, Depew, NY, USA). The software used to implement the adaptive algorithm is MATLAB (Simulink, MathWorks, Natick, MA, USA). The pulse signal, including the speed data of the engine, was detected by a camshaft position sensor and transferred to the input channels of the dSPACE control board. Both input data streams were transferred to the control computer via an analog-to-digital (A/D) converter in the dSPACE control board. The pulse signal was used for the calculation of the instantaneous frequency and reference signal. The reference signal was convolved by the estimated impulse response of the secondary path. The signal was the input \(\mathbf{x}^{\prime}(n)\) of the LMS algorithm. The adaptive filter \(\mathbf{w}(n)\) was updated by using error data \(e(n)\) and input data \(\mathbf{x}^{\prime}(n)\).  
Figure 12: Test car for the active control of interior noise (**a**) error microphone; (**b**) the secondary speaker and controller (**c**) controller monitor.  
Figure 13: Equipment in the ANC system for active control of interior noise in a test vehicle.