esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
## 2 Materials and Methods
### Rock Bolts
It has been confirmed that the deformation of surrounding rocks drives the deformation of bolts by means of the transfer capability of the connection of both the grout and bearing plate, not only in traditional pull-out testing but also when the block of rock and bolts are cooperating [10].
## 4 Data Representation: Runtime Events and Dataset
### Execution Trace (Level 2)  
At this second level (mid-level) of information, the represented execution trace gives more details about the runtime behavior of the test case. For example, considering the _Resources and Allocations_ major category represented in one character \(A\) in the first level (\(L_{1}\)). At this second level, it is represented or expanded into six subcategories (six different classes) that can specifically depict the allocated resource type. In particular, the data-structure allocations are expressed in their type at this level, such as list allocations, table allocations, and set allocations represented in the letters \(H\), \(U\), and \(Z\), respectively. Moreover, objects' allocations are represented by the letter \(Y\), strings' allocations are representedby the letter \(X\), and every other allocation (Miscellaneous Allocations) is represented in the letter \(M\). In other words, the letter \(A\) from the \(L_{1}\) expands into six letters \(\{H,U,Z,Y,X,M\}\) at level \(L_{2}\), each of which represents one allocation type that the test case can use. The alphabets described in this category are expressed in Equation (2) and Table 2. Other subcategories are expressed in Equations (3)-(5).  
In summary, at the top level, we have one letter to depict the complete 17 runtime events in each major category; at this second level, each subcategory has a different number of letters based on its type. In particular, the **first** subcategory \(\sum_{2:1}\) that represents the _allocation and resources_ has six different letters \(\{H,U,Z,Y,X,M\}\), each of which represents one of the six subcategories; see Equation (2). Likewise, the **second** subcategory \(\sum_{2:2}\) that represents the _data-structure operations_ has four letters \(\{L,T,C,O\}\), each of which represents one of the four subcategories; see Equation (3); the **third** subcategory \(\sum_{2:3}\) that represents the _string operations_ and has two letters \(\{N,J\}\), each of which represents one of the two subcategories; see Equation (4); and finally the **fourth** subcategory \(\sum_{2:4}\) that represents the _general evaluations and operations_ has four letters \(\{G,V,P,F\}\), each of which represents one of the four subcategories; see Equation (5).  
\[\sum_{2:1}=\{H,U,Z,Y,X,M\} \tag{2}\]  
_where_ \(H\): _List Allocations, U: _Table Allocations, Z: Set Allocations,_  
_Y: Objects/Record Allocations, X: String Allocations, M: Miscellaneous Allocations_  
\[\sum_{2:2}=\{L,T,C,O\} \tag{3}\]  
_where_ \(L\): _List Operations, T: _Table Operations, C: Set Operations, O: _Object/Record Operations_  
\[\sum_{2:3}=\{N,J\} \tag{4}\]  
_where_ \(N\): _String-Scanning Operations, J: Pattern-Matching Operations_  
\[\sum_{2:4}=\{G,V,P,F\} \tag{5}\]  
_where_ \(G\): _Built-in Operators, V: Variable Read/Write, P: User Procedures, F: Built-in Functions_
## 3 Testing  
### Numerical Modeling of Motion Sensor  
Only heave-motion signal at the center of gravity (CG) of FPSO was used. Time domain simulation software, such as CHARM3D, can be used to get the ship's motion [27]. However, in this paper, the motion data is generated by superposition for the testing i.e., they are generated from the motion spectrum by Equations (28) and (29). The wave spectrum was assumed to be single-peaked and narrow-banded. The data sampling frequency is 5 Hz.  
\[S_{z}(\omega)=RAO^{2}\cdot S_{\zeta}(\omega) \tag{28}\]  
\[Z(t)=\sum_{n=1}^{N}\sqrt{2S_{z}(\omega_{n})\bigtriangleup\omega}\cdot\cos( \omega_{n}t+\varepsilon_{n}+\varphi_{n}) \tag{29}\]  
where  
\(S_{z}(\omega)\): motion spectrum  
\(S_{\zeta}(\omega)\): wave spectrum  
\(\mathbf{z}\): vessel motion  
\(\mathbf{\omega}\): intrinsic frequency of harmonic  
\(\Delta\omega\): frequency interval  
\(\varepsilon_{n}\): random phase  
\(\varphi_{n}\): phase of TF  
In this paper, the frequency range of 0.1\(\sim\)2.0 with 0.02 rad/s interval is used. Subsequently, the number of entry frequencies is 96 and the number of states to be obtained from the filter is 192. It was compared to the cases with different \(\Delta\omega\) to investigate the relationship between frequency intervals and estimation performance. Figure 21 shows that the real-time estimated wave spectra reasonably match, regardless of the two employed frequency intervals.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Description** & **Dimension** \\ \hline x & State Vector & \(n_{x}\times 1\) \\ z & Measurement Vector & \(n_{z}\times 1\) \\ \(\phi\) & State Transition Matrix & \(n_{x}\times n_{x}\) \\ H & Output Matrix & \(n_{z}\times n_{x}\) \\ P & Error Covariance Matrix & \(n_{x}\times n_{x}\) \\ R & Measurement Error Covariance & \(n_{z}\times n_{z}\) \\  & Matrix & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimension of Variables.  
When superposing sinusoidal components, the randomly-perturbed frequency interval \(\Delta\omega\) was used by multiplying the random value \(\alpha\), as shown below in order to prevent the signal repetition. The Kalman filter then ran for 1000 s.  
\[\Delta\omega=0.1\times\alpha\;[\mathrm{rad}/\mathrm{s}]\;(-0.5(\alpha\langle 0.5\rangle \tag{30}\]  
Estimation tests were performed for various sea states to confirm whether the proposed modified TF can be applied to a wide range of applications. In the later part of this section, another vessel (OC4 semisubmersible e.g., [28; 29]) with different RAO will be tested. The applied Kalman-filter technique equally worked well for the new vessel, which shows that the developed algorithm is not sensitive to vessel type either. To observe the sensitivity against various wave conditions, a series of sea states applied are shown in Table 2, as below. In this table, No. 1 to 5 cases were selected as reasonable sea states [30]. On the other hand, No. 6 and 7 cases are particular sea states that correspnd to the two areas marked in Figure 2.
## 6 Experimental Results and Simulations
### Results with Feature Selection
#### 6.3.3 Comparison of Top Variants WOA-S2 and WOA-V4

Table 10: Evaluation results of WOA using four V-shaped TFs in terms of average and standard deviation of AUC, No. selected features, and fitness values.
## 2 Smart Homes
### Home Application Areas
#### 2.2.4 Advanced Multimedia Services  
Media consumption within the home has been growing over the years and new forms of domestic entertainment are very popular, forever changing how we act and relate. Such a category of smart home shows the enormous development potential.  
A main promoter for the evolution of future Home Area Media Networks (HAMNs) is the emergence of beyond High Definition (HD) media formats. These formats oblige far greater demands on networks for low latency, high-capacity and rigorous Quality-of-Service (QoS) in comparison to other existing formats. Furthermore, their data-intensiveness will require real-time interconnection of multiple, probably distributed, high performance media processing and storage resources. In order to be able to satisfy this, novel networked architectures are required [32].  
Ultra-High-Definition (UHD) embodies the next generation of digital media _i.e._, past High Definition (HD) as 4 K and 8 K have four (4096 \(\times\) 2160) and sixteen (7680 \(\times\) 4320) times the spatial resolution of HD respectively [32].  
As a result, large-scale networked circulation of UHD content demands high bandwidth interconnections normally found in optical networks. UHD formats are also data intensive and,as such, it results in a direct correlation with the quantity of processing capacity that it is necessary. Consequently, high performance media processing resources and high capacity networked storage are also required for large-scale UHD HAMNs. Additionally, there is a paradigm shift headed for user-centric HAMNs.  
The purpose of this paradigm is to set up flexible customizable network-based media communication platforms that support distinct media users and tools. In addition, it allows the creation and generation of new media content and services on-the-fly, and supports the transmission of content across several media and network environments [32, 33].  
[PERSON] _et al._ developed a framework for resource allocation in a Cognitive Digital Home (CDH) with a multiplicity of radio access technologies (RAT) such as cognitive radios and legacy radio devices supporting heterogeneous applications [11]. [PERSON] and [PERSON] designed and implemented an integrated architecture that supports the outdoor remote control to home devices and the sharing of digital media among indoor and outdoor devices [34]. [PERSON] _et al._ proposed a smart furniture prototype for the smart home, a magic mirror table that has a camera to capture the viewer's facial expression. The system is able to determine the emotion of the viewer and then act accordingly to alleviate his/her emotion [35].
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 2 Explicative Head-Mounted Fault Display System Design
### System Architecture  
The solution follows the architecture illustrated in Figure 1 designed to function on a VR headset as well as on a computer, guaranteeing that the user can access the application even in the case that no functional headset is available. In other terms, the application works similarly on the computer and the xHMFD system versions, the only difference being the movement and interaction controls.  
As illustrated in Figure 1, the system is divided into three components: (i) the failure diagnoser server and model, and (ii) the Unity server, part of the system core, for overall implementation. A robust computer system capable of handling VR applications and real-time data processing is particularly selected in this case to support the application. (iii) The last part is the clients using the application. One selects a VR headset that offers high resolution, comfortable wear, and reliable tracking. The interaction between the different parts is as follows. First, the failure diagnoser model receives data from the wind turbines via the Unity server and uses the deep learning model to predict potential issues. Second, the Unity server acts as the core of the application, storing all turbine information, which it then passes on to the various clients and the anomaly diagnoser server. Finally, the clients are the application instances running on virtual reality devices, displaying the wind farm and the associated data collected via the application running on the computer. Let us notice that sensor data from the turbines are sent to the Unity server, which serves as a mediator between the clients and the anomaly detection server. When the server receives new data, it updates the clients with the latest information. Additionally, it sends the data to the fault diagnoser server, which predicts the current state of the turbines and compares it with the actual status. Any identified anomalies are reported to the Unity server, allowing the computer application to visualize them and provide this information to clients for further action.
## 4 Numerical Results and Discussion
### Out-of-Plane Simulation
#### 4.1.1 Numerical Model
As shown for the non-damaged and damaged situations in Figure 36a,b under 0.6 MPa and 1.0 MPa loading, respectively, the value, wave length, and trend of simulation strain were similar to the features of experiment strain.
## 3 Results and Discussion  
### Characterization  
The adsorption-desorption isotherms and the pore size distribution curve of D311 and AEE-3 are displayed in Figure 1. The isotherm plot of two resins can be considered type IV with a hysteresis loop in the relative pressure range of 0.8-1.0, hinting at the existence of both mesopores and macropores in these resins. Table 1 lists the essential properties of D311 and AEE-3.  
The FT-IR spectra of D311 and AEE-3 samples are shown in Figure 2. Notably, two bands with peaks at 1366 cm- and 2930 cm- are ascribed to asymmetric bands of the alkyl groups (-CH2-and-CH3), and the peak at 1478 cm- is assigned to C-N group. The appearance of a new band at 1407 cm-is associated with -CH\({}_{2}\)-of-CH\({}_{2}\)-N\({}^{+}\)R\({}_{3}\) group [7]. On the other side, the fact that the strong base ion-exchange capacity of the resin increased from 0 to 3.42 mmol/g implied that propyl groups were successfully introduced onto the primary amine after alkylation. These observations proved AEE-3 had been synthesized successfully.  
Figure 1: Nitrogen adsorption–desorption isotherms of D311 (**a**) and AEE-3 (**b**) with inset showing the corresponding pore size distribution.
## 5 Discussion  
As observed in the examined designs, a ship with 1400 kW maximum load requirement can be sufficiently supplied by a single 1500 kW generator, assuming that no redundancy and reliability considerations exist.  
When reliability begins to matter, one might expect a solution of two 750 kW engines. However, the proposed combination was three 500 kW engines. Although the total power supply capability remained the same, the larger number of engines is obviously more reliable.  
Despite the fact that in the first case, the large engine ran most of the time at a load less than 30% of its nominal value, it was still more economical than the combination of the second case, which probably ran more efficiently per engine.  
Next, when full redundancy became a requirement, as was expected, the scheme of the first case (without reliability) doubled, even though again only one generator was running at any certain time.  
The same did not occur when both full redundancy and reliability were required, and the scheme of the second case was not doubled like before. Instead, four engines with 750 kW nominal power were selected as more efficient. This configuration is seen in many types of ships. Again, although the total power in both plants with full redundancy was the same, the cost of the reliable one was higher.
# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 4 Experimental Setup
### Classification Methods and Algorithms  
In our study, various widely used and well-known machine learning methods for classification were used to train binary models for logging activity acoustic detection. These machine learning algorithms were:  
* the support vector machine (SVM) that used the sequential minimal optimization algorithm with a radial basis function kernel [23];
* the widely used three-layer multilayer perceptron (MLP) neural network with a neuron architecture of 18-10-1, the neurons were all sigmoid and the MLP was trained with 50,000 iterations [24];
* the pruned C4.5 decision tree (J48) was set to three-fold for pruning the tree and seven-fold for growing the tree [25];
* the k-nearest neighbors classifier with linear search of the nearest neighbor and without weighting of the distance, referred to here to as an instance-based classifier (IBk) [26];
* the Bayes network learning (BN) using a simple data-based estimator for finding the conditional probability table of the network and hill-climbing for searching network structures [27].  
In the study, the Weka [27] software toolkit was employed for the implementation of the aforementioned machine learning algorithms. In all the evaluated algorithms, the free parameters that were not mentioned above were set to their default values.
## 5 Conclusions  
Aiming to solve the trajectory tracking problem of distributed driving vehicles under extreme conditions, such as high speed and low adhesion, this paper designs a trajectory tracking control system to ensure the vehicle's stability, improve the trajectory tracking accuracy, and realize the coordinated optimization of dual control objectives. The simulation experiments of related working conditions are carried out using Carsim-Simulink co-simulation. The results show that the coordinated control strategy proposed in this paper can simultaneously ensure trajectory tracking accuracy and improve the vehicle's driving stability under the limited working conditions of high speed and low adhesion. However, the particle swarm optimization algorithm designed in this paper only optimizes the two weight coefficients Q and R in real time. It does not consider the influence of the control and prediction time domains on the control effect. In addition, the working conditions selected in this experiment are established scenarios, while actual road conditions are changeable. Therefore, future research can introduce a state parameter estimator that combines visual images with dynamic information to achieve adaptive stable tracking control for different road conditions and explore new PSO algorithms to control the control and prediction time domains. In addition, this study has only stayed in the simulation stage without actual testing, and the combination of different control strategies has certain complexity, which may bring challenges to the actual application and calibration.  
Conceptualization, Y.S.; Data curation, H.W. and J.Z.; Investigation, H.W., C.W. and J.Z.; Methodology, Y.S.; Resources, H.W.; Software, H.W., C.W. and J.Z.; Supervision, Y.S.; Validation, H.N.; Visualization, H.N. and C.W.; Writing--original draft, H.N.; Writing--review and editing, Y.S. and H.N. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Hebei Province (Grant No. F 2021402011).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.
# Power Modeling and Experiment Study of Large Flapping-Wing Flying Robot during Forward Flight
## 2 Materials and Methods
### Power Model of the Flapping-Wing Air Vehicle in Forward Flight
#### 2.3.2 Inertial Power  
The inertial power is the power required to flap the wings of the FWAV, and is zero when the wings remain stationary. Calculating the inertia of the wings around the flapping axis is the most important component for determining the inertia power.  
The inertia of a wing contains two parts; one is the inertia \(I_{w}\) of the wing itself flapping reciprocally around the flapping axis. As depicted in Figure 5, the wing is decomposed into \(n\) units of width \(a\), and the distance of the centerline of the wing unit from the flapping axis is defined as \(d_{i}\), and the mass of each unit is \(m_{i}\).  
Obviously, \(I_{w}\) is obtained by using the parallel axis principle to calculate the inertia of each strip relative to the flapping axis, if the number of units is large.  
\[I_{m}=\sum_{i=1}^{n}m_{i}d_{i}^{2} \tag{20}\]  
In addition, the air motion caused by wing flapping must be taken into consideration, which means that the virtual mass of air \(m_{v}\) must be attached to the wings. The virtual mass of each segment of the wing can be calculated as:  
\[\mathrm{d}m_{v}=\rho\pi(\frac{c(y)}{2})^{2}\mathrm{d}y \tag{21}\]  
Then, the total virtual mass is described as:  
\[m_{v}=\int_{0}^{l}\mathrm{d}m_{v}=\int_{0}^{l}\rho\pi(\frac{c(y)}{2})^{2} \mathrm{d}y=\frac{\rho\pi}{4}\int_{0}^{l}c(y)^{2}\mathrm{d}y \tag{22}\]  
As a result, the total inertia of the wings around the flapping axis is:  
\[I=I_{v}+I_{w}=\sum_{i=1}^{n}m_{i}d_{i}^{2}+\int_{0}^{l}m_{v}r^{2}\mathrm{d}r \tag{23}\]  
It is worth noting that birds in nature can considerably reduce the wing inertia during upward flapping by folding their wings. However, because this article solely considers a single segment wing of FWAV, the case of folded wings is not addressed here.  
The power required when the wings flap at a specific angular velocity equals the rate of change of kinetic energy \(0.5I\omega^{2}\) against time, hence the inertial power is expressed as:  
\[P_{iner}=(I_{v}+I_{w})\omega\dot{\omega} \tag{24}\]  
In summary, since the mechanical efficiency of the FWAV is not 100%, then the required power can be calculated as:  
\[P_{tot}=\left(P_{ind}+P_{pro}+P_{pur}+P_{inter}\right)/\eta \tag{25}\]  
where \(\eta\) is the mechanical efficiency of the FWAVs.  
The mechanical efficiency \(\eta\) is defined as a lumped efficiency, which includes the drive unit transmission efficiency and conversion efficiency. Since the load applied on the wing varies during the flapping cycle, for this brushless DC motor, its mechanical efficiency also varies with the load on the wing during the flapping cycle, but it is difficult for us to obtain its actual value through experiment. Therefore, the lumped efficiency is regarded as a constant value [17; 25]. In this paper, the mechanical efficiency \(\eta\) is set at 0.85.  
Figure 5: Wing strip for calculating inertia of a flapping wing.
## 2 Materials and Methods
### Terminal Restriction Length Polymorphism Analysis of Csd Gene  
The analysis of the Csd gene through Terminal Restriction Length Polymorphism (T-RFLP) involved a two-step nested PCR reaction with 30 cycles at a 51 \({}^{\circ}\)C annealing temperature. The amplification targeted the fragment encoding the potential specifying the domain, utilizing specified oligo-nucleotide primers [23] complementary to the Csd gene (NC_037640.1). The primer sequences were as follows: csf1: 5\({}^{\prime}\)AGACrATATGAAAAATTA CACAATGA, csf1R1: 5\({}^{\prime}\)TCATwTTTCATTATCA, csf2: 5\({}^{\prime}\)-HEX-TATCGAGAAAAsATCGAAAGAACGAT, csf2R2: 5\({}^{\prime}\)6 AM-ATTGAAAATCCAAAGGTCCCATTGGT, and the PCR Mix  
Figure 1: _Apis mellifera Csd locus._ The honeybee _Csd_ gene _locus_ is depicted, with rectangles representing exons and shaded rectangles or their fragments denoting the coding segments of the gene. Arrows indicate the positions of the primers employed in this study. The green and blue sections of exons 7 and 8 illustrate the localization of the amplicon and its corresponding restriction fragments, which were analyzed using the T-RFLP method in this research.  
Plus kit from A&A Biotechnology was used. Between amplifications, exonuclease I (0.05 U, Thermofisher Scientific, Waltham, MA, USA) digestion was performed (30 min 37 \({}^{\circ}\)C, 15 min 85 \({}^{\circ}\)C) in order to remove primers. Subsequently, 1-2 uL of the second amplification product underwent digestion in a 10 uL reaction mixture with 2.5 U of the VspI restriction enzyme (Thermo Scientific), followed by a 1 h incubation at 37 \({}^{\circ}\)C. A denaturation step with Hi-Di formamide and 0.25 uL of 350 bp Rox standard (Life Technologies, Carlsbad, CA, USA) was applied to 1 uL of the digestion product, followed by capillary electrophoresis on an ABI Prism 310 apparatus.
## 3 Datasets and Methods  
In this study, we used five high-resolution multispectral (MS) images, acquired by the Pleiades-1 satellite constellation, aerial and drone photographs, centered on the study area, Global Navigation Satellite Systems (GNSS) data, collected as ground truth validation, radiometric measurements of the features likely to appear in the classifications and georeferenced photographs.