esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Aspects
### A Nonlinear Hybrid Control Strategy Combining Dry Friction and Active Control Force
#### 2.2.3 Stability of the Proposed Nonlinear Control Law  
The stability of nonlinear control law is generally determined by the Lyapunov criterion. In this framework, a generalized energy function \(V\) is required to be defined, which is the Lyapunov function. For an arbitrary vector \(\mathbf{Z}\), if \(V(\mathbf{Z})>0\) and \(\dot{V}(\mathbf{Z})<0\), the system is asymptotically stable.  
In order to check the stability of the above control law, the Lyapunov function of the controlled system motion equation is investigated in this section.  
The new variable in the second state space is noted as  
\[\mathbf{Z}=\left[\begin{array}{c}\mathbf{Y}\\ \dot{\mathbf{Y}}\end{array}\right]. \tag{15}\]  
The new governing motion equation, along with the control law in the second state space, is found to be  
\[\left[\begin{array}{cc}\mathbf{K_{m}}&0\\ 0&\mathbf{M_{m}}\end{array}\right]\left[\begin{array}{c}\dot{\mathbf{Y}}\\ \ddot{\mathbf{Y}}\end{array}\right]=\left[\begin{array}{cc}0&\mathbf{K_{m}}\\ -\mathbf{K_{m}}&0\end{array}\right]\left[\begin{array}{c}\mathbf{Y}\\ \dot{\mathbf{Y}}\end{array}\right]+\left[\begin{array}{c}0\\ \mathbf{F_{nl_{m}}}\end{array}\right]+\left[\begin{array}{c}0\\ \mathbf{F_{c_{m}}}\end{array}\right], \tag{16}\]  
\[V(\mathbf{Z})=\frac{1}{2}\mathbf{Z}^{\top}\left[\begin{array}{cc}\mathbf{K_{m}}&0\\ 0&\mathbf{M_{m}}\end{array}\right]\mathbf{Z}>0, \tag{17}\]  
\[\dot{V}(\mathbf{Z})=\mathbf{Z}^{\top}\left[\begin{array}{cc}\mathbf{K_{m}}&0\\ 0&\mathbf{M_{m}}\end{array}\right]\dot{\mathbf{Z}}. \tag{18}\]  
According to Equation (6), we have  
\[\dot{\mathbf{Z}}=\left[\begin{array}{cc}\mathbf{K_{m}}&0\\ 0&\mathbf{M_{m}}\end{array}\right]^{-1}\left(\left[\begin{array}{cc}0&\mathbf{K_{m}} \\ -\mathbf{K_{m}}&0\end{array}\right]\left[\begin{array}{c}\mathbf{Y}\\ \dot{\mathbf{Y}}\end{array}\right]+\left[\begin{array}{c}0\\ \mathbf{F_{nl_{m}}}\end{array}\right]+\left[\begin{array}{c}0\\ \mathbf{F_{c_{m}}}\end{array}\right]\right), \tag{19}\]  
\[\begin{array}{ll}\dot{V}(\mathbf{Z})&=\mathbf{Z}^{\top}\left(\left[\begin{array}{ cc}0&\mathbf{K_{m}}\\ -\mathbf{K_{m}}&0\end{array}\right]\left[\begin{array}{c}\mathbf{Y}\\ \dot{\mathbf{Y}}\end{array}\right]+\left[\begin{array}{c}0\\ \mathbf{F_{nl_{m}}}\end{array}\right]+\left[\begin{array}{c}0\\ \mathbf{F_{c_{m}}}\end{array}\right]\right)=\mathbf{Y}^{T}\mathbf{K_{m}}\dot{\mathbf{Y}}-\dot{ \mathbf{Y}}^{T}\mathbf{K_{m}}\dot{\mathbf{Y}}^{\dagger}\mathbf{\mathrm{sign}}\left(\dot{\mathbf{Y} }\right)-\dot{\mathbf{Y}}^{T}\mathbf{C}\dot{\mathbf{Y}}\\ &=-\dot{\mathbf{Y}}^{T}\mathbf{k}\left|\dot{\mathbf{Y}}\right|^{\alpha}\mathrm{sign} \left(\dot{\mathbf{Y}}\right)-\dot{\mathbf{Y}}^{T}\mathbf{C}\dot{\mathbf{Y}}.\end{array} \tag{20}\]  
Since \(\mathbf{C}\) is positive definite, and the dry friction distribution matrix \(\mathbf{k}\) is semi-positive definite, we can conclude that \(\dot{V}(\mathbf{Z})<0\). According to the Lyapunov criterion, the system is asymptotically stable.
## 2 Materials and Methods
### Chemical Analysis of Plant Material
#### 2.2.4 GC Analysis  
Determination of the total essential oil content was carried out according to the Analytica-EBC 7.10 method. Briefly, 100 g of dry and ground plant tissues was mixed with 1000 mL of deionized water and steam distilled for 3 h using a Clevenger distillation unit. Identification and quantification of the components of essential oils were carried out according to the Analytica-EBC 7.12 method. First, 0.1 mL of collected oil was diluted with 2 mL of hexane and separated by GC analysis. An Agilent 6890 series GC system equipped with a flame ionization detector and a HP-1 capillary column (30 m \(\times\) 0.25 mm, 25 \(\upmu\)m, manufacturer: Agilent Technologies, city: Santa Clara, CA, USA) with 5.0 helium as the carrier gas with a flow rate of 0.5 mL min\({}^{-1}\) was used. One microliter of solution was injected in the injector at a temperature of 200 \({}^{\circ}\)C. The temperature programme was 1 min at 60 \({}^{\circ}\)C, 2.5 \({}^{\circ}\)C min\({}^{-1}\) to 190 \({}^{\circ}\)C, 70 \({}^{\circ}\)C min\({}^{-1}\) to 240 \({}^{\circ}\)C, and 11 min at 240 \({}^{\circ}\)C. Detection was carried out on a flame ionization detector set at 260 \({}^{\circ}\)C. All solvents were of analytical grade or higher purity and were purchased from Sigma-Aldrich, Germany. The identified and quantified components were \(\alpha\)-pinene, camphene, sabinene, \(\beta\)-pinene, \(\beta\)-myrcene, \(\alpha\)-phellandrene, p-ocymene, limonene, trans ocymene, linalool, nonanal, borneol, \(\alpha\)-terpineol, dodecane, bornylacetate, \(\alpha\)-cubehene, \(\alpha\)-copaene, \(\beta\)-bourbonene, \(\beta\)-elemene, cyprene, \(\beta\)-caryophyllene, \(\alpha\)-caryophyllene, \(\gamma\)-gurjunene, \(\beta\)-copabene, \(\sigma\)-cadinene, bicyclogermacrene, germacrene-D, \(\beta\)-selinene, \(\beta\)-ionene, \(\alpha\)-selinene, \(\alpha\)-murolene, \(\gamma\)-cadinene, \(\beta\)-bisabolene, \(\gamma\)-murolene, isoledene, \(\beta\)-copaene, \(\gamma\)-gurjunene, \(\alpha\)-bergamotene, \(\beta\)-seline, cadina-1(10,\(A\)-diene, \(\beta\)-sesquiphellandrene, germacren B, spathulenol, aromaderdrene, \(\beta\)-turmerone, cypperone, farnesyl acetone, 1,2-hexadecen-1-ol,3,7,11,15-tetramethyl, and 3,7,11,15-tetramethyl-2-hexadecen-1-ol. For some components, identification was performed with the use of standards by standard addition and through comparison of retention times. For those that were not available, identification was performed by the use of GC/MS and the NIST library of mass spectra with a probability of more than 80%. Standard compounds used for identification were purchased from Sigma-Aldrich, Germany.
## 4 Dual-Comb Doppler Reflectometer System in LHD
### First Observation in LHD Plasma  
The dual-comb Doppler reflectometer system is installed in the LHD to measure the turbulent plasma velocity in the high-temperature plasma experiment. This is carried out under the condition that the magnetic axis position in the vacuum field is \(R_{\mathrm{ax}}\) = 3.60 m, the magnetic field strength is \(B_{\mathrm{t}}\) = 2.75 T, the helical coil pitch parameter \(\gamma\) = 1.2538, and the ratio of the quadrature field \(B_{\mathrm{q}}\) = 100%. For plasma heating, 5 MW ECH and 15 MW NBI are used. During the plasma discharge (which shot number is #148725) the electron temperature is kept above 1.0 keV. The example of the frequency spectrum of the observed scattered signal is shown in Figure 11a. This spectrum is obtained by performing FFT processing on 22 (=4,194,304) data points of the Mixer 3 output signal, collected by a wideband digital oscilloscope. Some visible peaks in the figure correspond to the injected frequency comb components. Figure 11b is the enlarged view of one frequency comb component with an IF frequency of 120 MHz. The center frequency of 120 MHz in this spectrum corresponds to the 30.5 GHz carrier frequency component injected into the plasma in this frequency comb system. A clear frequency shift due to the presence of plasma turbulence is observed. The time variation of the Doppler shift frequency can be obtained by IQ detection using the method in Reference 10. The plasma flow velocity can then be estimated from this Doppler shift frequency. Figure 12 shows the plasma flow velocity estimated by the Doppler reflectometer in this plasma discharge, where the plasma is initiated by ECH at \(t\) = 3.3 s and additional heating is provided by perpendicular NBI (\(p\)-NBI) and tangential NBI (\(t\)-NBI). The change in flow velocity can be observed in response to changes in the plasma state. The observed signal agrees well with that obtained from the different Doppler reflectometer, using a frequency hopping method [5] which uses the same antenna, indicating that the dual comb system can be used to measure the plasma flow velocity.  
Figure 10: (**a**) Example of frequency spectrum of signal scattered from rotating drum whose rotating speed is 600 rpm. Black line is result of Gaussian fitting. (**b**) Estimated Doppler shift frequency as function of rotating speed of drum. Black dotted line is the line estimated from equation \(f_{D}=r\omega\cdot 2k\ \sin\theta\ /2\pi\).  
Figure 11: Frequency spectra of (**a**) entire scattered signal from LHD plasma (shot number is #147825), and (**b**) magnified view of 120 MHz IF frequency component (the area enclosed by the dotted square in (**a**)). (monitor point 6).  
Figure 12: Temporal behavior of plasma perpendicular flow velocity obtained with (**a**) dual comb system at 30.5 GHz and (**b**) frequency-hopping Doppler reflectometer system operated at fixed 30.0 GHz, respectively. Shot number of LHD plasma is #148725. The injection timing of heating power for ECH, p-NBI, and t-NBI is shown above in color bar.
## 3 Monopulse Ratio Curve Improvement
### Monopulse Ratio Curve for the Presented Tile  
In this section, the monopulse ratio curve for a single tile was analyzed based on the antenna of the system configuration discussed in Section 2.  
Figure 5 shows the sum and difference patterns of Equations (5) and (6) according to the presented tile configuration. The HPBW of the sum pattern is 9.6\({}^{\circ}\).  
Table 1 shows the optimum \(c_{r}\) and the location estimation accuracy, which are calculated based on Equations (7) and (8) for the presented tile.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\phi_{c}\) & 3\({}^{\circ}\) & 3.6\({}^{\circ}\) & 4.2\({}^{\circ}\) & 4.8\({}^{\circ}\) & 5.4\({}^{\circ}\) & 6.0\({}^{\circ}\) \\ \hline \(c_{r}^{opt}\) & 6.634 & 6.534 & 6.418 & 6.126 & 5.948 & 5.746 \\ MAE mean & 0.026 & 0.044 & 0.065 & 0.129 & 0.180 & 0.244 \\ MAE maximum & 0.075 & 0.123 & 0.192 & 0.404 & 0.562 & 0.769 \\ \hline \hline \end{tabular}
\end{table}
Table 1: For presented tile, estimation accuracy for monopulse ratio curves according to \(\phi_{c}\).  
Figure 5: Single-tile radiation characteristics in (a) polar plot and (b) rectangular plot.  
As the estimation range of the communication counterpart's locations expands, it becomes difficult to optimize the linear characteristics of the monopulse ratio curve; thus, the location estimation accuracy decreases. Figure 6 shows the monopulse ratio curves for \(\phi_{c}=3.6^{\circ}\) and \(\phi_{c}=6.0^{\circ}\).
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 3 Key Technologies
### High-Reliability Data Transmission Design
#### 3.2.2 Redundant Design of Water Surface Communication  
The air link for data transmission through satellite communication devices already has mature communication technologies and applications, such as the Iridium communications service, maritime satellites, BeiDou short messages, and Tiantong satellites, to complete data transmission. This article uses the BeiDou satellite for data transmission. In the data transmission subsystem, the BeiDou communication antenna is installed inside the surface communication buoy and connected to the underwater unit through communication cables. The data collector is integrated into the underwater unit and has a certain computing capability. It can receive real-time data collected by the water level gauge and calculate pressure data. When a tsunami wave is detected, the device sends the data to the BeiDou satellite antenna through a communication cable and sends it back to the remote data center.  
To ensure the reliability of tsunami data transmission, the small communication buoy adopts a redundant design. The underwater unit can carry six small communication buoys as backups for the BeiDou satellite antenna. The design of the small surface-communication buoy is shown in Figure 5a, and the underwater unit is shown in Figure 5b,c. After the system is deployed, the first communication buoy is released onto the sea surface for data communication. The other five communication buoys are stored inside the underwater unit and fixed to the installation structure of the buoy through a release device. The installation structure of the buoy is fixed to the bottom of the underwater unit through bolts. The release device of the underwater buoy is shown in Figure 5d.  
Figure 5: Design of underwater unit and communication buoy structure. (**a**) Small communication buoy, (**b**) Underwater unit, (**c**) Installation structure of small communication buoys, (**d**) Release device.  
The release of the communication buoys is controlled by a data collector equipped with a data acquisition and processing subsystem inside the underwater unit. When the collector loses contact with the communication buoy, communication testing or the release of new sea surface buoys will be carried out to ensure the reliable transmission of tsunami data. The release mechanism process of the small surface-communication buoy is shown in Figure 6.
## 5 Disturbance Rejection and Tracking Reference
### Tracking Reference  
The following result guarantees that the controller is able to generate a response allowing reference tracking.  
**Lemma 5**.: _Let \(\tau<\frac{1}{a}-\frac{1}{b_{1}}+\sqrt{\frac{1}{a^{2}}+\frac{1}{b_{1}^{2}}}\). Consider the system given by (1); the observer-predictor scheme given by Figure 2, \(C(s)\) described by (22); and a step reference \(R(s)=\frac{\delta}{s}\). Then, \(lim_{t\to\infty}y(t)=\delta\)._  
Proof.: The transfer function in Figure 2\(Y(s)/R(s)\) is given by  
\[\frac{Y(s)}{R(s)}=\frac{C(s)G(s)\mathrm{e}^{-\tau\mathrm{s}}}{C(s)G(s)-1}. \tag{23}\]  
Applying the final value theorem to (23), one obtains that  
\[\lim_{t\to\infty}y(t)=\lim_{s\to 0}sY(s)=\delta. \tag{24}\]  
That is, the scheme shown in Figure 2 is capable of following a step type reference.
## 2 Explaining BBRefinement
### Problem with a Naive Single Object Detector  
A neural network is trained to minimize a loss function between its output and the ground truth. If such a network can detect a single BB only, but the input image includes several BBs, then the loss function is minimized when the network produces a BB, that is, the mean of them. Thus, with no guarantee of a single object's presence only, the trivial solution cannot be used. This problem is addressed later by a sliding deformable models/window technique [10], two-stage techniques such as (Fast/Faster) R-CNN [3], single-stage techniques with anchors such as SSD [11] or YOLO [12], anchor-free techniques that are mainly keypoint-based [13], and finally by involving Transformer into the architecture and realizing a bipartite loss [14]. Each such approach affects the architecture of a neural network and is related to a specific model.  
To solve the problem of multiple objects in an image, BBRefinement takes as input an image with the information about the object, which should be refined, namely, its class and the coordinates of its center. Note that there may be more objects in a crop because such a bounding box for a non-rectangular object will also involve some background containing other objects. To illustrate the situation, we show crops from the COCO dataset. In Figure 2, we show an easy case where only one object is presented, and in Figure 3 we show a hard case where more overlapping objects are presented. In particular, the COCO dataset includes 1.7M boxes, in which 47% of all boxes have an intersection with a box of the same class, and 84% of the boxes have an intersection with an arbitrary class box.  
Figure 1: A scheme of coupling BBRefinement with one-stage (top row) and two-stage (bottom row) object detectors.  
Figure 2: The figure shows crops that can be refined even with the naive way because the crop includes only one, nicely visible object.
# A Study on the Seismic Performance of Steel H-Column and T-Beam-Bolted Joints
## 2 Establishment and Verification of Model  
### Node Size  
The whole section of the beam is considered to reach the plastic state. This assumption aligns with seismic design principles [15], where beam plastic hinges are intended to form in advance of connection failure to ensure ductile energy dissipation. To prevent connection overstrength, a connection coefficient of 1.35 is adopted, following the'strong connection-weak member' philosophy [16]. The coefficient 1.35 is used to correct the theoretical strength of the joint, taking into account factors such as construction defects, stress concentration, or corrosion. The ultimate bearing capacity of the actual design value is divided by this coefficient. Therefore, in the design of bolts and T-shaped parts, the connection coefficient of 1.35 is used to design and calculate the components. The steel samples are all Q355B, represent steel with a yield strength of 355 MPa, and impact absorption work greater than 34 J at 20 \({}^{\circ}\)C. Single side-bolt-type is a nested 10.9 class M24 single-side high-strength bolt; due to the existence of the outer sleeve, its standard bolt aperture is 34 mm. The calculation method of single-side high-strength bolts is the same as that of ordinary high-strength bolts. The main component information of the node is shown in Table 1, and the specific dimensions are shown in Figure 1. The node structure is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Specification (mm)** & **Quantity** \\ \hline Square-steel column & 400 \(\times\) 400 \(\times\) 14 & 1 \\ H-shaped steel beam & HN400 \(\times\) 200 \(\times\) 8 \(\times\) 13 & 4 \\ T-part 1 & T320 \(\times\) 200 \(\times\) 16 \(\times\) 24 & 8 \\ T-part 2 & T180 \(\times\) 200 \(\times\) 8 \(\times\) 12 & 4 \\ M24 high-strength bolt & \(\Phi\)24 & 56 \\ M24 single-side bolt & \(\Phi\)24 & 48 \\ \hline \end{tabular}
\end{table}
Table 1: Main component list.
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 2 Materials and Methods
### Classification Methods for Detecting Moldy Maize Kernels  
#### 2.4.1 Dataset Creation  
In this study, the ResNet50 model was fine-tuned for detecting corn mildew, and 2191 grey-scale images were divided into two classes. Images showing mold spores, low gray values of the embryo, or defects were categorized as images with mildew characteristics. Conversely, images without mildew characteristics were also identified. Among these, 1084 images exhibited no mildew characteristics, while 1107 images showed mildew characteristics, as illustrated in Figure 2. The dataset was subdivided into a training set (80% of the images) and a validation set (20% of the images); the training set consisted of 867 grayscale images without mildew features and 886 images with mold features and the validation set consisted of 217 grayscale images without mildew features and 221 images with mildew features.
## 2 Experimental Study
### Measuring Point Arrangement  
The arrangement of sensors inside the box girder section is illustrated in Figure 1b. To accurately capture the temperature gradient, the sensors are densely positioned at the corner haunches of both the top and bottom girders, with a spacing of 6 cm between adjacent sensors. Considering minimal temperature variation in the web plate, a spacing adjustment to 15 cm is made between the measuring points. The sensor positions are adjusted according to reinforcement placement, and each individual sensor's measuring point position is meticulously corrected prior to concrete pouring.  
Additionally, a meteorological station was established within the experimental area to collect real-time meteorological data for the tested model. The station is equipped with sensors measuring atmospheric temperature and humidity and wind speed and direction, as well as a solar radiation meter. Data collection occurs at a sampling frequency of 1/60 Hz.
## 2 Materials and Methods  
### Experimental Research  
#### 2.1.1 Experimental Specimens  
The complete procedure of the newly proposed method of cut-off splicing of additional bar-shaped GFRP reinforcement for beam strengthening, called double-strap joint NSM FRP, has been given in the authors' previous paper [16]. The beam specimens have been tested under a four-point load disposition (Figure 1a), where the following beams have been tested: (1) B-CON beam, with steel reinforcement only and without FRP strengthening (Figure 1b); (2) B-G1 beam, strengthened within the concrete cover via NSM GFRP without a cut-off and a diameter of \(\mathcal{O}\)10 mm (Figure 1c); (3) B-G2 beam, with a cut-off of the strengthening bar in the midspan of the beam (L/2) and without bypassing (Figure 1c); (4) B-G3 beam, with a cut-off of the strengthening bar in L/2 and with bypassing on both sides, 2 \(\times\)\(\mathcal{O}\)10, and 20 cm long (Figure 1d); and (5) B-G4 beam, with a cut-off of the strengthening bar in L/2 and with bypassing on both sides, 2 \(\times\)\(\mathcal{O}\)10, and 40 cm long (Figure 1d). The specimens were made of concrete with a compression strength of 31.6 MPa and with steel reinforcement with a yield strength of 400 MPa. The groove for the strengthening was cut in the specimens and GFRP bars were placed, while a MapeWrap11 epoxy was used (Figure 2). Considering that the maximal bending moment is in the middle third of the beam span, and that it decreases towards the beam supports, the development length of the straight FRP bar in the ending third of the span is sufficient for the anchoring and force transfer from the FRP bar to the epoxy and then on concrete. Consequently, the bond mechanism [29; 33] provides composite action of the concrete, epoxy, and FRP bar.  
Figure 1: Experimental specimens: (**a**) beam disposition; (**b**) cross-section of an unstrengthened beam (model B-CON); (**c**) cross-section of a strengthened beam without a cut-off (model B-G1) and a strengthened beam with a cut-off and without bypassing (model B-G2); and (**d**) cross-section of a strengthened beam with a cut-off and with different lengths of bypassing (models B-G3 and B-G4).