esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this research, the relative content of VBs/VPs was quantitatively analyzed using GC-TOF-MS technology, and the key VB/VP components contributing to the aromatic quality of oolong tea were identified based on the OAV calculation. Furthermore, the key _CsAAAT_ genes were screened by combining a tea genome database and a transcriptome database of oolong tea processing samples. The following conclusions were drawn:  
1. A total of 13 types of VBs/VPs were measured in the samples, including 9 types of VPs and 4 types of VBs, and the mechanical force of turnover was the key exogenous stress inducing VBs/VPs (excluding methyl phenylacetate).
2. The OAVs of 2-hydroxy benzoic acid methyl ester and phenylethyl alcohol were greater than 1.0; therefore, these specific VB/VP components could be considered key components contributing to the aromatic quality of oolong tea.
3. Six key _CsAAAT_ genes closely related to VB/VP formation were screened, namely, _CsAAAT1-1_, _CsAAAT1-3_, _CsAAAT1-9_, _CsAAAT2-1_, _CsAAAT2-3_, and _CsAAAT2-6_.
4. The upregulation of the expression level of _CsAAAT2_-type genes may respond to light stress during solar-withering, in addition to the induction of the mechanical force of turnover.  
of total RNA of tea leaf samples during the post-harvest processing steps. Table S1: The specific primers for qRT-PCR. Table S2: The concentration and A260/A280 ratios of the total RNA of samples. Table S3: The nucleic acid sequence of 16 candidate CsAAAT genes. Table S4: Sensory evaluation results of raw oolong tea.  
Conceptualization, Y.S.; software, S.Z.; validation, L.C.; resources, H.R.; writing--original draft and reviewing, Z.Z.; writing--original draft and reviewing, Q.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Fujian, China (2021)05271), the China Agriculture Research System of MOF and MARA (CARS-19), and the Scientific Research Foundation of Ningde Normal University (2022Y05).  
Not applicable.  
Data Availability Statement: Data are contained within the article or the Supplementary Materials.  
The authors declare no conflict of interest.
# Comparing the Performance of Robust Controllers for Vibration Suppression in Long Rotor Systems
## 2 Open-Loop Transfer Function Matrix Estimation
 \tag{6}\]  
In (6), \(y_{1}(s)\) and \(y_{2}(s)\) are the transformed functions of the measured vibration amplitude via sensors 1 and 2, and \(u_{1}(s)\) and \(u_{2}(s)\) are the transformed functions of control forces applied via the actuators 1 and 2. The individual transfer functions in (6) were estimated via [12]  
\[G_{11}(s)=\frac{0.146s^{3}+15.76s^{2}+3229.1s+511750}{s^{3}+213.06s^{2}+13904s+2574 700} \tag{7}\]  
\[G_{22}(s)=\frac{-0.038s^{2}+3.47s+840.04}{s^{2}+6.78s+12435} \tag{8}\]  
and  
\[G_{21}(s)=\frac{0.103s^{2}+1.92s+2576.7}{s^{2}+6.303s+12501} \tag{9}\]  
\[G_{12}(s)=\frac{-0.049s^{2}-5.39s+832.7}{s^{2}+6.91s+12617} \tag{10}\]  
The input control effort vector is \(\mathbf{u}(s)\), and the measurement variable vector is \(\mathbf{y}(s)\). The controller designated by the matrix \(\mathbf{K}(s)\) should be designed to apply the control force \(\mathbf{u}(s)\) to the rotor system \(\mathbf{G}(s)\) via the actuators. In the terminology of robust control that will be described in Section 4, the \(\mathbf{G}(s)\) is a part of the plant that is designated by the matrix \(\mathbf{P}(s)\).
## 2 Literature Review
### Measuring Effects of Non-IEEE 802.11 Interference on the Performance of IEEE 802.11 Net-Works  
Several studies have examined the effects of interference from various non-IEEE 802.11 devices on the performance of IEEE 802.11 networks. For example, one study found that microwave ovens decreased throughput by 28-49%, depending on proximity to the network (1-3 m), whereas Bluetooth loudspeakers reduced it by 5-9% [22]. Another study even found that microwave ovens could stop network throughput entirely [7]. Interference from microwave ovens can be mitigated using a \"cognitive radio\" technique [23]. In other work, Bluetooth devices were shown to substantially increase the bit error rate of IEEE 802.11b networks [24], while Zigbee (IEEE 802.15.4), a wireless communication protocol intended for smart homes and other personal area networks with relatively low throughput and energy consumption, reduced throughput by 20-40% [25]. One study found that, when sufficiently close to an IEEE 802.11 network, a Bluetooth handset reduced throughput by 26.5%, and a videophone reduced it by 7.5% [26]. On the other hand, another study [27] concluded that neither microwave ovens nor Bluetooth devices affect throughput of down- or uploads, but that they do weaken IEEE 802.11 signal strength as well as the wall between the client and access points.  
Radiofrequency noise from a laptop computer can reduce throughput and other aspects of IEEE 802.11 network performance [28]. Analogue wireless video cameras and analogue cordless phones can reduce network throughput by 90-100% because they transmit continuously and therefore interfere with network traffic most of the time [7].  
Despite these studies of various types of non-IEEE 802.11 interference, we are unaware of studies analyzing the effects of wireless audio transmitters on the throughput of IEEE 802.11 wireless networks. Wireless audio transmitters and wireless audio handsets may be used for warehouse sound systems or augmented reality purposes in Industry 4.0 contexts [29; 30]. Therefore, the present study measured the effects of a Line6 Relay G30 wireless audio transmitter on throughput of an IEEE 802.11 network in a laboratory environment. We selected this transmitter model as a reasonable proxy for the range of wireless audio devices that can be found in Industry 4.0 environments.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 3 Literature Review
### Vulnerabilities  
**AMQP-Specific Vulnerabilities**:  
1. **Broker Overload**: AMQP's reliance on brokers introduces risks such as exhaustion of resources during Distributed Denial-of-Service (DDoS) attacks [5].
2. **Replay Attacks**: AMQP's session-oriented nature is susceptible to packet replay, where attackers resend captured packets to disrupt communication [10].
3. **Queue Manipulation**: Attackers may exploit unsecured queues to reroute, drop, or duplicate messages, compromising data integrity [11].
4. **Man-in-the-Middle (MitM) Attacks**: Without robust encryption, AMQP is vulnerable to MitM attacks, allowing adversaries to intercept and alter messages [14]. **MQTT Vulnerabilities**:  
1. Limited authentication mechanisms make it prone to brute force attacks and unauthorized access [16].
2. Susceptibility to low-rate DoS attacks like SlowITe, exploiting MQTT's lightweight nature [19]. **CoAP Vulnerabilities**:  
1. Susceptibility to amplification attacks due to large responses to small requests, exploiting the lack of mandatory encryption [20].
2. High eavesdropping risks, as it typically uses UDP and lacks transport-layer encryption by default [21].
## 5 Results
From the so-obtained \(G_{APD}(T,V_{bias})\), it is possible to determine the \(V_{bias}\) control curve that can be used to keep \(G_{APD}\) constant during typical \(T\) variations of orbiting CubeSats (from \(-20\)\({}^{\circ}\)C to \(60\)\({}^{\circ}\)C [22; 23]). This is obtained slicing the \(G_{APD}(T,V_{bias})\) surface at fixed \(G_{APD}\) values:  
\[V_{bias}=\frac{1}{c}(\ln\left(G_{APD}-a\right)-\ln\left(b\right)-dT)\,. \tag{8}\]  
The so-obtained curves are shown for \(G_{APD}=\{50;100;150;200\}\) in Figure 13. These are an example of the curves that can be obtained with the here-designed system. No uncertainties are evaluated on the control curves due to their application: define the \(V_{bias}\) to apply to the APD when a certain \(T\) is measured. Their slope is \(\alpha=-d/c\), and thus APDs with large \(c\) (sensitivity to \(V_{bias}\)) and small \(|d|\) (sensitivity to \(T\)) are desired in order to reduce the span of the \(V_{bias}\) values needed to stabilize \(G_{APD}\). However, the uncertainties in \(G_{APD}(T,V_{bias})\) are detrimental for the determination of \(G_{APD}\) that can be obtained following one of the curves in Figure 13. To give an estimate of this detrimental effect, \(V_{bias}(T)\) from Equation (8) is substituted in Equation (2) in order to evaluate the obtainable \(G_{APD}\). Given the sensitivity coefficient vector:  
\[\mathbf{C_{p}}=\left(\frac{\partial G_{APD}}{\partial a},\ \frac{\partial G_{APD}}{ \partial b},\ \frac{\partial G_{APD}}{\partial c},\ \frac{\partial G_{APD}}{ \partial d}\right)=\left(1,\ \mathrm{e}^{(eV+dT)},\ bV\mathrm{e}^{(eV+dT)},\ bT \mathrm{e}^{(eV+dT)}\right)\,, \tag{9}\]  
\[u(G_{APD})\]  
can be evaluated as follows [39]:  
\[u(G_{APD})=(\mathbf{C_{p}U_{p}C_{p}}^{T})^{0.5}\,. \tag{10}\]  
The temperature variation of \(u(G_{APD})/G_{APD}\) for fixed \(G_{APD}\) values is well below the uncertainty of the evaluated uncertainty, and thus \(u(G_{APD})/G_{APD}\) here is assumed constant in temperature. Hence, the dependence of the relative standard uncertainty \(u(G_{APD})/G_{APD}\) on \(G_{APD}\) is studied and the results are shown in Figure 14. A minimum is found in \(u(G_{APD})/G_{APD}\) at \(G_{APD}=17\), and the uncertainty starts increasing rapidly as \(G_{APD}\) is increased. A reduction in this uncertainty can be obtained with a more accurate measurement of \(G_{APD}\), \(T\), and \(V_{bias}\), a denser \(T\) and \(V_{bias}\) measurement grid, and the use of a \(G_{APD}(T,V_{bias})\) function that can better minimize \(\mathbf{U_{p}}\). Figure 12: APD gain \(G_{APD}\) measured for different biasing voltages \(V_{bias}\) and temperatures \(T\), placing the APD in direct contact with the \({}^{55}\)Fe source. The experimental data are fitted with the function shown in Equation (2). Figure 13: APD voltage bias \(V_{bias}\) control curves for the stabilization of the gain \(G_{APD}\) from \(-20\)\({}^{\circ}\)C to \(60\)\({}^{\circ}\)C.
## 2 Materials and Methods
### Shoreline Data Sources and Processing
Furthermore, in this study, we employ the fractal geometry method to describe shoreline evolution, which can also effectively avoid differences in shoreline measurement caused by spatial scale problems. Furthermore, to avoid the influence of seasonal differences on the extraction results, all data are from the same season (see Table 1). All relevant images have completed data preprocessing such as radiation calibration, atmospheric correction, and geometric correction in ENVI, and the images have been enhanced through methods such as histogram equalization. According to the interpretation signs of different types of coastlines, we divide the coastline attributes into natural coastline and artificial coastline. Among them, the artificial coastline was mainly obtained directly based on its geological characteristics, remote sensing image characteristics, and land feature adjacency characteristics using ArcGIS platform (version:10.8), and the results were corrected through field investigations. In order to address the problems of blurred boundaries and difficult positioning of natural coastlines, a human-computer interactive interpretation solution with multiple means is adopted. To effectively reduce the impact of vegetation, soil, and other objects in the image on shoreline extraction and highlight the ocean water information, we first calculated the normalized difference water index (NDWI). Water has relatively high reflectance in the green band and very low reflectance in the near-infrared band. By calculating the NDWI, it can effectively enhance the contrast between water bodies and other land cover types such as vegetation and soil, which can effectively improve the distinction between land and water. For areas where it is more difficult to identify shorelines, other information is used, such as Google Earth data and in situ data, and the coastline position is determined by artificial visual interpretation. To eliminate the shoreline scale effect introduced by the difference in image resolution and the \"double-eyelid\" phenomenon caused by multi-temporal images, we take the extraction results in 2000 as the background data and only dynamically update the areas with coastline changes when extracting the 2020 coastline. \[NDWI=\frac{R_{\text{Green}}-R_{\text{NIR}}}{R_{\text{Green}}+R_{\text{NIR}}} \tag{1}\]where \(R_{\text{Green}}\) and \(R_{\text{NIR}}\) are the radiation values of the pixel in the green band and near-infrared band, respectively.
## 4 Experimental Analysis and Verification
### Test II  
To verify the effectiveness of the proposed method for bearing fault diagnoses under different types and variable working conditions, the proposed method, DBN, S-Alexnet, IG-CWT-CNN, TF-MDA, ICPW-HPF-CNN, ML, JDA, TCA-DBN, and DSAN were used for cross types and cross-working condition fault diagnoses on datasets A, E, F, G, and H. There were four tasks of cross types and cross-working condition fault diagnoses, namely A \(\rightarrow\) E, A \(\rightarrow\) F, A \(\rightarrow\) G, and A \(\rightarrow\) H. The dataset allocation and parameter settings for each method in Test II are the same as for Test I. The fault diagnoses results are shown in Table 4 and Figure 7.  
Figure 6: Radar chart of cross-working conditions fault diagnoses results using different methods.  
As shown in Table 4 and Figure 7, the average accuracy values of the DBN, S-Alexnet, IG-CWT-CNN, TF-MDA, ICPW-HPF-CNN, ML, JDA, TCA-DBN, DSAN, and ITCA-DBN were 74.375%, 75.313, 73.125%, 75.938%, 70.156%, 64.01%, 80.886%, 91.719%, 97.916%, and 98.906%, respectively. The JDA and TCA-DBN that achieve global domain adaptation can obtain better performance than the DBN, IG-CWT-CNN, TF-MDA, ICPW-HPF-CNN, ML, and S-Alexnet without using transfer learning. The accuracy of DSAN that achieves subdomain adaptation was higher than that of both JDA and TCA-DBN. The accuracy of the proposed method was 0.99% higher than that of DSAN. Due to changes in the bearing types and working conditions, the discrepancies in the data distribution between the source and target domains became more pronounced than just the changes in the working conditions, resulting in a decrease in the accuracy of all methods. Compared with the cross-working condition fault diagnosis test, the accuracy of the DBN, S-Alexnet, IG-CWT-CNN, TF-MDA, ICPW-HPF-CNN, ML, JDA, TCA-DBN, DSAN, and ITCA-DBN decreased by 10.278%, 10.069%, 9.541%, 10.072%, 10.538%, 9.013%, 8.767%, 3.663%, 0.799%, and 0.539%, respectively. The proposed method had the highest accuracy and the lowest reduction in accuracy, indicating the best performance. Based on the performance of ITCA-DBN, it can be seen that the proposed method can effectively achieve bearing fault diagnoses across types and cross-working conditions.
## 2 Materials and Methods  
### Basic Principles of Effective Medium Theory  
We assume that rock is a micro-inhomogeneous material that can be in general macroscopically anisotropic. Macroscopic properties (or effective properties) of this material averaged over a representative volume (REV) are controlled by the properties of mineral grains and materials filling pores and cracks (inclusions), and also by inclusion's shape, orientation, and spatial distribution. REV should be a statistically homogeneous media. In addition, REV should be large enough compared to the characteristic size of heterogeneities and, on the contrary, REV should be small enough compared to the fluctuation length of macroscopic fields (specifically, wavelength). Thereafter the EMT methods provide a determination of the physical properties (elastic and transport) of heterogeneous media.  
In the case of elasticity, the macroscopic properties of a heterogeneous medium are described by the effective stiffness tensor \(\mathbf{C}^{*}\) that relates, via the Hook law, the strain \(\varepsilon\) and stress \(\sigma\) fields averaged over the REV. The problem of determination of effective properties leads to a multiparticle problem that can be solved only approximately. Consequently, for random media only approximation methods exist. The majority of EMT methods are based on the Eshelby problem solution for the strain filed in ellipsoidal inclusions placed in the infinite matrix [33]. Thereafter, all heterogeneities have the shape of general ellipsoids that can be characterized by two aspect ratios. For simplicity, ellipsoids of revolution are used whose shape can be specified by a single aspect ratio \(\alpha\). Among a variety of EMT methods we choose the following ones: the self-consistent method of [PERSON] [34], differential effective medium (DEM) [35], and two versions (self-consistent and \(f\)-model) of generalized singular approximation (GSA) [28,29]. Besides, we suggest our own version of [PERSON]'s method to parametrize the void's connectivity via the \(f\)-parameter.  
Our choice is motivated by the method's ability to take into account the connection of inclusion implicitly or explicitly.
## 3 Results
### Effect of the Capsule Size on the Viability of Encapsulated Cells  
Next, we evaluated the effect of the capsule size on the viability of the encapsulated cells. C3H10T1/2/Nluc cells were suspended in PBS at 5 \(\times\) 10\({}^{5}\) cells/mL, and sodium alginate was added to prepare a 1% alginate solution. The cell-suspended alginate solution was collected using a micropipette, and 2.5, 5, 10, 25, or 50 \(\mu\)L of the collected solution was dropped into 10% calcium chloride solution and immersed for 10 s to prepare the alginate capsules. Capsules of different sizes were obtained (Figure 2a). C3H10T1/2/Nluc cell-encapsulated alginate capsules of different sizes were cultured at 37 \({}^{\circ}\)C in humidified air containing 5% CO2, and the relative light units in the culture supernatant were measured every two days (Figure 2b). The relative light units of the 50 \(\mu\)L alginate capsules gradually decreased from day 2, and the relative light units on day 12 was one quarter of that observed on day 2. Conversely, the relative light units of the 25, 10, and 5 \(\mu\)L alginate capsules were almost constant from days 2 to 12. The relative light units of the 2.5 \(\mu\)L alginate capsules gradually decreased from days 2 to 12. Based on these results, the suitable volume range for the encapsulated C3H10T1/2/Nluc cells was 5-25 \(\mu\)L. In subsequent studies, 10 \(\mu\)L of alginate solution was used owing to the ease of preparing the alginate capsules.  
Figure 1: Preparation of alginate capsules. **(a)** Alginate capsules were prepared by dropping 0.1, 0.25, 0.5, or 1% alginate solution (10 \(\mu\)L) into 1, 2.5, 5, 10, or 25% calcium chloride solution (2 mL). A 0.5% trypan blue solution was added to the sodium alginate solution at a ratio of 10%. **(b)** Bright-field and fluorescence images of an alginate capsule encapsulating 5 \(\times\) 10\({}^{3}\) Dil-labeled C3H10T1/2 cells, prepared with 1% alginate solution and 10% calcium chloride solution. The scale bars indicate 500 \(\mu\)m.  
Figure 2: Effect of the capsule size on the viability of encapsulated cells. Sodium alginate (10 mg) was dissolved in 1 mL phosphate-buffered saline (PBS) containing C3H10T1/2/Nluc cells at a density of 5 \(\times\) 10\({}^{5}\) cells/mL. The alginate solution was collected using a micropipette, and 2.5, 5, 10, 25, or 50 \(\mu\)L of the collected solution was immersed in 10% calcium chloride solution for 10 s to prepare alginate capsules. **(a)** Typical image of capsules prepared using 2.5, 5, 10, 25, and 50 \(\mu\)L alginate solution (fromleft to right). (**b**) Relative light units in the culture supernatant of C3H10T1/2/Nluc cell-containing alginate capsules with different sizes. Results are expressed as the mean \(\pm\) standard deviation (SD) (\(n\) = 3-4).
## 4 Methodology
### Reinforcement-Learning-Based Spaced Repetition Optimization
#### 4.4.1 DQN Algorithm  
The main idea behind DQN is that if we had a Q function \(Q^{*}:\mathcal{A}\times\mathcal{S}\rightarrow\mathbb{R}\) to compute the reward if the policy to take an action \(a\) in a given state \(s\), to maximize the reward, we have the equation:  
\[\pi^{*}(s)=\arg\max_{a}Q^{*}(s,a). \tag{8}\]Since the optimal Q function is unknown, we use a neural network \(Q^{\pi}\) to approximate it. For each training step, we have:  
\[Q^{\pi}(s,a)=r+\gamma Q^{\pi}(s^{\prime},\pi(s^{\prime})), \tag{9}\]  
where \(r\) is the step reward, and \(s^{\prime}\) is the previous state. The difference between two consecutive steps is the temporal difference error \(\delta\):  
\[\delta=Q(s,a)-(r+\max_{a}Q(s^{\prime},a)). \tag{10}\]  
To minimize the temporal difference error, DQN optimizes the Huber loss \(\mathcal{L}\) over a batch of data sampled from the replay buffer:  
\[\mathcal{L}=\frac{1}{|B|}\sum_{(s,a,s^{\prime},s^{\prime})\in B}\mathcal{L}( \delta), \tag{11}\]  
where:  
\[\mathcal{L}(\delta)=\begin{cases}\frac{1}{2}\delta^{2}&\text{for}|\delta|\leq 1 \\ |\delta|-\frac{1}{2}&\text{otherwise}\end{cases}. \tag{12}\]
## 2 Experimental Preparation  
### Large-Scale Model Test Platform  
The experimental equipment used is the HDR fracturing model test device, independently developed by the Wuhan Institute of Rock and Soil Mechanics (Wuhan, China). It can be used to conduct fracturing tests on cubic rock samples with a maximum side length of 450 mm. The schematic diagram of its composition is shown in Figure 1a, which mainly consists of three major parts: the temperature and pressure loading system, the fluid injection system (or ignition system), and the control and data acquisition system.  
Figure 1: The schematic and physical diagram of the model experimental device for HDR fracturing. **(a)** Schematic diagram of test device connection; **(b)** Model experimental platform for large-sized samples; **(c)** The hand-operated pump; **(d)** SSCO plunger pump, produced by Teledyne ISCO (Lincoln, NE, USA); **(e)** The computer for control and data acquisition.  
The temperature and pressure loading system encompasses the above HDR fracturing model experimental device and a hand-operated pump (Figure 1c) that provides axial loading. Electrical heating plates are positioned on the bottom and lateral sides of the device, directly contacting the rock sample to heat it. Between the heating plates and the loading plates, rigid insulation boards are installed to maintain the temperature. Vertical (Z-direction) loading on the rock sample is achieved by injecting high-temperature-resistant silicone oil into a stainless-steel bladder-type pressure container via a hand-operated pump. Lateral loading, on the other hand, is accomplished through passive compression by two wedge plates on the side, which can also be considered as imposing fixed displacement boundary conditions.  
The fluid injection system can provide a maximum injection pressure of 60 MPa, with an injection flow rate ranging from \(10^{-3}\) to 100 mL/min. When performing deflagration fracturing experiments, the fluid injection system is replaced with an ignition system and the connection to the wellbore changes from fluid pipes to electrical wires. The data acquisition system (Figure 1e) continuously collects and stores data such as the temperatures on the bottom and four sides of the rock sample, the injecting rate, and the fluid pressure within the wellbore of the rock sample.