esa-sceva/satcom-chunk-collection huggingface dataset:
# Behavior Characteristics of Hazardous Gas and Scattering Coal Dust in Coal Storage Sheds
## 5 Results and Discussion  
### Analysis of Internal Flow and Ventilation  
Figure 4 shows the velocity contours that appeared from the CFD analysis results of the outdoor air-measurement data, used to analyze the flow characteristics of shed-type coal storage. When the inflow velocity was \(V_{Max}\), the outside air from the first-floor louver and window rose by the coal pile and the central wall, and flowed out to the monitor louver. Moreover, a recirculation area generated in the upper part of the outside air inflow direction. For \(V_{low}\), the velocity of the inflow reduced, and the upper recirculation area gradually reduced. The discharge to the monitor louver also reduced rapidly.  
The coal storage shed did not have mechanical ventilation. The discharge of pollutants was not smooth, due to the structural characteristics of generating a large amount of coal dust and harmful gas in the coal pile. Therefore, to improve the indoor working environment of the field workers inside the indoor coal yard, the amount of ventilation must be analyzed for each outdoor air inlet and outlet. The mass flow rate that flows in and out of the natural ventilation system due to changes in wind speed is illustrated in Figure 5. When the inflow velocity of the outside air was 1.0 m/s, 1634.1 m/s of the outside air was discharged from the first-floor louver. In addition, 1084.4 m/s of the outside air flowed out to the upper monitor louver. The amount of ventilation for each natural ventilation device is described in Figure 6. When the total outdoor air outflow was 100%, 51.9% of the outdoor air flowed through the monitor louver. Additionally, 33.8% was emitted by the first-floor louver, and 5.0% was by the window. When the inflow velocity was 4.8 m/s, 79.0% of the total outdoor air inflow flowed into the first-floor louver. About 54.3% of the outflow flowed to the upper monitor louver, and 31.9% flowed out of the first-floor louver.  
In addition, the air change rate per hour (ACH) in coal storage was analyzed; at the maximum speed, the ventilation volume was 13.1 ACH. At the minimum speed, the ventilation volume was 4.4 ACH. Even at a relatively low wind speed, more than 4.4 ACH were secured. However, it may be difficult to ensure sufficient ventilation because the amount of ventilation decreased sharply if the inflow velocity was further reduced, due to the nature of the indoor coal mine, where substantial harmful gas and dust is generated. Therefore, additional analysis is required on the concentration characteristics of local pollutants when spontaneous ignition occurs.  
Figure 4: Computational analysis results of the internal flow characteristics of a coal storage shed (velocity vector): (**a**) velocity vector with west wind and inflow velocity of 6.0 m/s; (**b**) velocity vector with west wind and inflow velocity of 2.0 m/s.
# Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data
## 4 Evaluation Setup
### Comparison  
As a comparison with the TL-based fault diagnosis method, we apply two common classification models, SVM and CNN, for our fault diagnosis problems. We refer to our approach as CNN-TL to distinguish the CNN model trained from scratch.  
* SVM: A machine learning technique known as SVM relies on the structural risk minimization problem [29]. It can perform well in a high-dimensional non-linear problem with limited samples. Various signal processing techniques for the time and frequency domains are adopted to manually extract the features of the signals. We use various statistical metrics of the time domain analysis, including mean, standard deviation, root mean square, kurtosis, maximum-to-minimum difference, and signal median absolute deviation. Furthermore, the spectral analysis extracts useful features for predicting faults, such as bearings, gears, and engines [21]. We consider the cumulative powers in the low-frequency range of 10-20 Hz, mid-frequency range of 40-60 Hz, and high-frequency range above 100 Hz, as well as the frequency of the peak magnitude and spectral kurtosis peak. Note that spectrum condition indicators of various frequency ranges are based on the expected harmonics due to the specifications of the triplex reciprocating pump model, as we will discuss in Section 5.
* CNN: Figure 3 depicts the structure and the configuration of the CNN, consisting of 18 layers. The hidden layer mainly consists of the convolutional layer, the batch normalization layer, the activation layer, the sub-sampling layer, and the dropout layer. We adopt the rectified linear units (ReLUs) as an activation function to improve the training time. The output of the convolutional layer is fed to the max pooling of the sub-sampling layer. The softmax function is applied to the output of the last fully connected layer and returns the distribution of eight class labels corresponding to the fault types. The classification accuracy of the CNN considerably depends on the configuration parameters, including input image size, activation function, filter size, sampling method, and iteration number. The network parameter optimization method is adopted to optimize the configuration parameters for the classification accuracy [30].
# The Inapproximability of \(k\)-DominatingSet for Parameterized AC\({}^{0}\) Circuits +
## 2 Preliminaries
### Problem Definitions  
The decision problems studied in this paper are listed below:* In the \(k\)-DominatingSet (\(k\)-DomSet) problem, our goal is to decide if there is a dominating set of size \(k\) in the given graph \(G\).
* In the \(k\)-SetCover problem, we are given a bipartite graph \(I=(S,U,E)\) and the goal is to decide whether there is a subset \(X\) of \(S\) with cardinality \(k\) such that for each vertex \(v\) in \(U\), there exists a vertex \(u\) in \(X\) that covers \(v\), i.e., \(\{u,v\}\in E\).
* In the \(k\)-Clique problem, our goal is to determine if there is a clique of size \(k\) in the given graph \(G\).
* In the \(3\)-CNF-SAT problem, we are given a propositional formula \(\varphi\) in which every clause contains at most \(3\) literals and the goal is to decide whether \(\varphi\) is satisfiable.  
We say a set cover instance \(I=(S,U,E)\) has set cover number \(m\) if the size of a minimum set \(X\subseteq S\) such that \(X\) could cover \(U\) is \(m\). Similarly, we say a graph \(G\) has dominating number \(m\) if the size of a minimum dominating set of \(G\) is \(m\).  
As we mentioned, the dominating set problem is tightly connected to the set cover problem. Given a \(k\)-DomSet instance \(G=(V,E)\), we can construct a \(k\)-SetCover instance \(I=(S,U,E^{\prime})\) with \(S=V\), \(U=V\) and \(E^{\prime}=\bigcup_{\{u,v\}\in E}\{\{u_{S},u_{U}\},\{v_{S},v_{U}\},\{u_{S},v_{ V}\},\{v_{S},u_{U}\}\}\); here, for each vertex \(v\in V\), we denote the corresponding vertices in \(S,U\) by \(v_{S}\) and \(v_{U}\), respectively. It is quite clear that \(G\) has dominating number \(k\) if and only if \(I\) has set cover number \(k\). Also, given a \(k\)-SetCover instance \(I=(S,U,E)\), we can construct a \(k\)-DomSet instance \(G=(D\cup U_{1}\cup U_{2},C\cup E_{1}\cup E_{2})\) by letting \(D=S\), \(U_{1}=U_{2}=U\), \(C=\{\{u,v\}\mid u,v\in D\}\), \(E_{1}=\{\{s,u\}\mid s\in D,u\in U_{1}\}\) and \(E_{2}=\{\{s,u\}\mid s\in D,u\in U_{2}\}\). It is trivial that \(I\) has set cover number \(k\) if and only if \(G\) has dominating number \(k\). The reductions can also be found in the work of [PERSON] and [PERSON] [29].  
It is notable that each hard instance with gap reduced from a Clique or \(3\)-CNF-SAT instance satisfies that the size of the sets \(M\) is at most \(\operatorname{poly}(N)\) where \(N\) is the size of the universe. Hence, it is safe to tacitly apply the inapproximability of \(k\)-SetCover to the \(k\)-DomSet problem.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 5 Energy-Balancing Techniques
### Energy-Efficient Routing Protocols
By performing aggregation on data within the cluster, the energy consumed in the network is far less than when all the raw data are sent to the base station [111]. The rationale behind this approach is rooted in the desire to reduce overall communication distances, minimize energy consumption, and distribute energy-intensive tasks among the nodes effectively. Energy-aware routing is another popular energy-balancing technique that has the ability to achieve uniform energy consumption. Energy-aware routing involves making routing decisions based on the current energy levels of sensor nodes. Nodes with higher residual energy are favored in routing decisions to evenly distribute the energy consumption across the network. The rationale is grounded in the necessity of preventing certain nodes from depleting their energy quickly, thus avoiding premature network failure. As shown in Table 6, the energy-efficient routing techniques (cluster-based, multi-path, and energy-aware routing) primarily designed for traditional WSNs are less effective in LWSNs due to the lack of multiple routing paths imposed by the linear topology.
## 2 Noncontact Mechanical Property Testing Methods
### Photoacoustic (PA) Techniques
The results show that a change of elastic properties by means of tensile stress exists even for linear deformation, although a corresponding explanation of the stress effect was not given in the published report. Another limitation of the proposed setup was the requirement for water coupling. If the ultrasonic wave is detected with an optical method, the PA stress-strain measurement can be truly noncontact. As proposed by [PERSON] and [PERSON] [65], a high sensitivity laser interferometric system was developed to measure stress-induced delamination in carbon-fiber reinforced polymer. Based on the principle of time-of-flight approach [65], [PERSON] et al. measured superficial and subsurface residual stresses in a metal with laser-induced ultrasound [66]. Not only lasers, but also incoherent  
Figure 5: The scanning photoacoustic signal amplitude change. The visible defect is between 0 and 97 μm (“X” marking); reproduced from [60] with permission from The American Institute of Physics © 2000. Figure 6: PA stress-strain test schematic with water coupling (left) and the stress-strain curve on the basis of PA phase (right); reproduced from [64] with permission from Springer Nature © 2017.  
sources such as a helium lamp, can be used in stress-strain analysis. [PERSON] et al. developed a PA spectroscopic methodology to detect the stress level in some transparent materials such as a thin polymer film [67]. A Fourier Transform Infrared (FTIR) spectrometer [68; 69] was adopted in the test. The presence of stress can slightly shift absorption peaks of the film, which lays a basis for quantitative stress measurement by spectral analysis. Photoacoustic techniques have inherent advantages for mechanical strength evaluation. Ultrasonic wave mode propagation velocities exhibit a clear theoretical dependence on the stress-strain level [70], which makes PA techniques sensitive and fast. Optical generation and detection schemes can produce multi-physical and totally noncontact NDT methods. The main disadvantage of PA techniques is their low energy conversion due to thermoelasticity and thus sophisticated apparatuses must be adopted. In addition, they are not sensitive to stress accumulation and are hard to perform local tests with, because ultrasonic waves travel fast in elastic media.
## 3 FSS Analysis  
### Equivalent Circuit Model Analysis  
To better understand the operation principle, we simulated the surface current and surface electric field to construct the equivalent circuit model. Figure 4 shows the distribution of surface current at normal incidence at frequencies of 7.3 GHz, 17.1 GHz, and 23.9 GHz. It can be seen from Figure 4 that at 7.3 GHz, the currents are mainly distributed around the outer branch and the central cross branch; at 17.1 GHz, the currents are mainly distributed around the central cross branch; and at 23.9 GHz, the currents are mainly distributed around the other small branch. According to observations, due to the branch structure design, three different resonance paths are provided. Then, four stopbands will be produced. The results show that the short arm with a smaller electrical length helps to produce higher resonance, and the increase of electrical length helps to produce lower resonance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \(\mathbf{H}\) & \(\mathbf{G}\) & \(\mathbf{W_{1}}\) & \(\mathbf{W_{2}}\) & \(\mathbf{D_{\mathrm{x}}}\) & \(\mathbf{D_{\mathrm{y}}}\) \\ \hline
**Value** & & & & & & \\
**(mm)** & 0.80 & 0.16 & 0.10 & 0.10 & 3.94 & 3.94 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values shown in Figure 2.  
Figure 3: Perspective of overall structure of the proposed unit.  
When the proposed FSS structure is illuminated by the 7.3 GHz incident wave, the surface electric field and surface current appear as shown in Figures 4a and 5a. Near the outer branch and the central cross branch, the surface electric field is weak and the surface current is strong. However, on the other small branches, the surface electric field is strong and the surface current is weak, so the equivalent circuit of this structure at 7.3 GHz could be modeled by a series \(L\)-C resonator which is shown in Figure 5b. C indicates the capacitance in the central zone of an element and \(L\) is the inductance of a whole convoluted and interdigitated stripes.  
With the method above, the equivalent circuit model of the proposed FSS structure can be obtained, as shown in Figure 6. The effectiveness of the equivalent circuit model is proved by the performance comparison between the reflection coefficient calculated by ADS and HFSS. It can be observed in Figure 7 that both curves fit well in the range of 0-30 GHz.  
Figure 4: Surface current diagram at: (**a**)\(f_{1}\) = 7.3 GHz, (**b**)\(f_{2}\) = 17.1 GHz, (**c**)\(f_{3}\) = 23.9 GHz.  
Figure 5: Resonance of the stripe at 7.3 GHz: (**a**) schematic diagram of surface electric field (**b**) equivalent circuit model diagram.
# Safety-Aware Optimal Attitude Pointing for Low-Thrust Satellites
## 2 Methods
### Communications Pointing Maintenance  
In addition to the telescope in the previous section, we considered that the spacecraft has a communications antenna with a beam pencil centered on the Z-axis with pitch \(\phi\). We wished to ensure that the beam pencil does not pass out of contact with a ground station directly at the nadir; i.e., the z-axis in the body-fixed frame must remain within \(\sin 2\phi\) of the Z-axis in the LVLH frame. This criterion requires that the dot product of the antenna axis must fall within the set defined by the inequality:  
\[\mathcal{S}_{2}=\{\mathbf{v}\in\mathbb{R}^{3}\mid[0,0,1]\bullet\mathbf{v}\leq sin 2\phi,\ \|\mathbf{v}\|=1\}, \tag{6}\]  
The boundary of this region is a cone in \(\mathbb{R}^{3}\), and the intersection of this cone with the sphere \(\mathbb{S}^{2}\) describes the boundary of the region in which the antenna axis must remain. In this paper, the antenna axis is denoted by \(\mathbf{b}=[0,0,1]\), and the trajectory traced by the antenna is then given by:  
\[\mathbf{b}(t)=R(t)\cdot\mathbf{b}. \tag{7}\]  
This trajectory \(\mathbf{b}(t)\) must lie within the set \(\mathcal{S}_{2}\) at all times.  
In what follows, the maintenance of the pointing set means that:  
\[\mathbf{b}(t)\in\mathcal{S}_{2}\ \ \wedge\ \ \mathbf{a}(t)\
otin\mathcal{S}_{1} \tag{8}\]  
for all \(t\in[0,1]\).  
The boundaries of these two sets are shown on the sphere in Figure 2.  
Figure 2: The boundaries of the pointing set. The bright spot set that needs to be avoided (\(\mathcal{S}_{2}\)) is the interior of the sphere with the dashed boundary, while the set where the antenna needs to remain (\(\mathcal{S}_{1}\)) is the interior of the sphere with the solid boundary.
## 3 Results and Discussion
### Adsorption Mechanisms  
The adsorption mechanism of BCs toward MB was proposed (Figure 11). MB, a cationic organic dye, contains aromatic and heterocyclic rings. The N and S atoms, as well as its cationic characteristics, greatly affected the interactions between MB and BCs. The adsorption of MB by CPP-derived BCs was achieved through electrostatic interactions, hydrogen bonding, ion exchange and \(\pi\)-\(\pi\) stacking interactions [58]. FT-IR analyses of CPP200 pre- and after-adsorption showed characteristic peaks of MB at 881.6 cm\({}^{-1}\) (C-S stretching vibrations), at 1372.8 cm\({}^{-1}\) and 1322.8 cm\({}^{-1}\) (C-N stretching vibrations) and at 789.8 cm\({}^{-1}\) and 667.9 cm\({}^{-1}\) (in-plane bending vibrations of aromatic hydrocarbons). Furthermore, EDS confirmed the N and S contents in CPP200-MB.  
Previous research reported that the adsorption of MB by activated carbon was seriously affected by the electrostatic interactions between the adsorbent and adsorbate [59]. An effect of pH on the adsorption process indicated that electrostatic interactions dominated the adsorption of MB by CPP200. Under low pyrolysis temperatures (\(\leq\)350 \({}^{\circ}\)C), the adsorption  
Figure 11: A proposed mechanism for MB adsorption by CPP200.  
capacities of BCs toward MB were affected by the oxygen-containing functional groups which were responsible for the negative charges of BCs [60]. In addition, hydrogen bonds between MB and different oxygen-containing functional groups such as -COOH and -OH in BCs that were prepared at low temperatures were formed [61; 62]. The intermolecular \(\pi\)-\(\pi\) interactions may be caused by the \(\pi\) electrons and the aromatic/heterocyclic rings of MB and CPP200. Obviously, the \(\pi\)-\(\pi\) interactions tended to increase with an increase in pyrolysis temperature [63]. As a result of stronger electrostatic interactions, hydrogen bonding and ion exchange dominated the adsorption capacity of CPP-derived BCs, the \(\pi\)-\(\pi\) interactions could consequently be ignored or excluded [64].
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 5 Results and Discussion
### Wave Tank Experiment  
As explained in Section 4, three waves with different periods and heights were generated, and the movement of the buoy was monitored by a data collection module. In order to estimate the power generation, classification was performed beforehand so that the corresponding regression model could be applied. The detailed results of data segmentation and prediction are presented below.
# The Bearing Performance and Sectional Design Method of Yielding U-Shaped Steel Support
## 3 Indoor Experimental Research on the Bearing Performance of Yielding U-Shaped Steel Support
### Analysis of Test Results
#### 3.2.1 Analysis of the Deformation and Yielding Process
Post-peak Bearing Stage (B1C1, B2C2): After the supports reach their ultimate bearing capacity, with the continuous loading the supports enter the yielding state, the deformation increases rapidly, and the bearing capacity drops rapidly. It can be seen from Figure 11 that the ultimate bearing capacities of the three-section and four-section support are 512 kN and 502 kN, respectively, and the maximum deformation corresponding to the ultimate bearing capacities are 183 mm and 234 mm, respectively. Compared with the three-section support, the yieldable performance of the four-section support is increased by 21.8% and the bearing capacity is only reduced by 1.9%. It shows that the four-section support improves the yieldable performance without a significant reduction in the bearing capacity, which verifies the rationality of the proposed sectional design method for yielding U-shaped steel support.
# Low-Noise Programmable Voltage Source
## 5 Conclusions  
In this paper the design, realization and testing of a low-noise programmable voltage source have been presented. The source is characterized by excellent noise performances when compared with other realizations presented in the literature. In the experimented system configuration, the output voltage can be set in the range from 0 to 3.3 V and a VSD as low as 3 nV/\(\surd\)Hz (1 Hz), 0.6 nV/\(\surd\)Hz (10 Hz) and 0.4 nV/\(\surd\)Hz (100 Hz) when setting the output voltage to 1 V is observed. Comparing the obtained results with earlier published low-noise voltage sources, better results were obtained, especially at frequencies above a few Hz.  
The limitation in the maximum output voltage is a direct consequence of the fact that the supply voltage of the JFET-based output stage must ensure that the gate leakage current remains very small, as the leakage current depends on the drain-to-gate voltage difference. However, it is conceivable that, in future development, the possibility of changing the drain supply voltage can be changed as a function of the voltage range to be generated, thus extending the output range.  
While the residual noise at frequencies above 10 Hz appears to be set by the type and number of JFETs used in the output buffer stage, the noise in the lower portion of the spectrum appears to be set by the DA section in the device. Future work will be devoted to improving the noise performances of the DA section.  
The results of noise measurements on advanced devices have been used to prove the advantages that can be obtained with a very low-noise programmable voltage reference.  
Conceptualization, K.A.; methodology, K.A. and C.C.; software, K.A.; validation, C.C., J.M., Z.B. and G.S.; formal analysis, J.M., Z.B., C.C. and G.S.; investigation, K.A.; data curation, K.A.; writing--original draft preparation, K.A.; writing--review and editing, G.S., J.M., C.C. and Z.B.; supervision, C.C., Z.B. All authors participated in the discussion and proofread the work. All authors read and approved the final manuscript.  
This work was funded by Military University of Technology, grants number ZBW/08-896/2020/WAT.  
The authors declare no conflict of interest.