esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fundamental Properties of RP
### Physical and Chemical Properties of RP
#### 3.4.2 Appearance and Microstructure  
The appearance and the micro-structure of RP are generally determined by equipment such as scanning electron microscopy (SEM), environmental scanning electron microscopy (ESEM), and electron dispersive spectroscopy (EDS).  
RP is a dry powder which has an off-white, grey color, and its look is comparable to that of cement, FA, SF, and limestone powder (LP) [6; 34; 35; 115; 116; 128; 154; 166; 178; 192; 193; 194]. On the other hand, RP particles exhibit non-uniform shapes and uneven edges with sharp angles [6; 10; 32; 35; 45; 52; 56; 59; 65; 58; 82; 84; 87; 98; 102; 116; 127; 128; 140; 143; 147; 152; 164; 166; 171; 172; 192; 193; 195; 196; 197; 198; 199; 200; 201; 202], contrary to cement [6; 35; 98; 154; 166; 192; 193], FA [116; 154; 178; 194], SF [115], and LP [128] particles.  
RP contains quartz (SiO\({}_{2}\)) from sand and residues of coarse aggregate, as well as calcite (CaCO\({}_{3}\)). RP also consists of the hydration products portlandite (CH) and calcium silicate hydrate (C-S-H) gel, which are formed from the hardened cement paste, as well as belite (C\({}_{2}\)S) and alite (C\({}_{3}\)S), which primarily originate from the unhydrated cement particles in the hardened cement [6; 24; 38; 56; 67; 68; 82; 84; 87; 128; 140; 147; 153; 154; 164; 168; 192; 198; 199; 201; 202; 203; 204; 205; 206]. The maximum and the minimum values of major components (SiO\({}_{2}\), CaO, Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\)) of RP are presented in Figure 7.  
RP particles exhibit rough texture surfaces, a large number of pores, and microcracks [5; 13; 56; 59; 127; 128; 140; 147; 153; 164; 172; 184; 194; 195; 202]. The surface of RP particles exhibits a higher degree of roughness compared to cement particles due to the presence of porous hydration products such as CH within the RP particles [59; 62; 87; 127; 140; 194]. Therefore, RP is a material with higher permeability than cement [13; 147; 164; 178; 195; 202; 203]. Furthermore, the presence of plate-like and needle-bar-like structures can be observed within the RP due to the inclusion of hydration products [6; 140; 195; 207].  
Despite its irregular microstructure, larger particles are often surrounded by many fine particles [84; 122; 172; 205]. The acute angles and rough surface of the RP particles enhance reactivity and facilitate hydration processes [129; 194; 203] while fine particles enhance the specific surface area, leading to increased activity [59; 66; 147]. However, the non-uniform arrangement of particles and the high SSA of RP leads to a reduction in the workability of the mixture [45; 59; 116; 184; 194].  
Figure 6: Density (g/cm\({}^{3}\)) for RP, cement, and FA.
## 4 Integrated Fixed Time Sliding Mode Control for Motion and Vibration
### Fixed Time Sliding Mode Control
The sliding mode reaching law is designed as follows:  
\[\dot{s}_{i}=-\frac{\gamma_{2}}{\sqrt{2}}\bigg{(}\frac{s_{i}}{\sqrt{2}}\bigg{)} ^{2a_{3}-1}-\frac{\beta_{2}}{\sqrt{2}}\bigg{(}\frac{s_{i}}{\sqrt{2}}\bigg{)} ^{2a_{4}-1},\ i=0,1,2, \tag{26}\]  
where \(a_{3}=m_{2}/n_{2}\), \(a_{4}=p_{2}/q_{2}\gamma_{2}\) and \(\beta_{2}\) are normal numbers, \(m_{2},n_{2},p_{2},q_{2}\) are positive odd number and satisfies \(m_{2}>n_{2}\) and \(p_{2}<q_{2}\). Taking the derivative of Equation (25) and substituting Equation (21) into it, we have:  
\[ \begin{array}{rcl}\dot{s}_{i}&=&\ddot{e}_{i}+\gamma_{1}a_{1}\dot{e}_{i}^{a_ {1}-1}\dot{e}_{i}+\beta_{1}a_{2}\dot{e}_{i}^{a_{2}-1}\dot{e}_{i}\\ &=&\Gamma_{i}+\Phi_{i}+h_{i}-\ddot{q}_{\rm d}i+\gamma_{1}a_{1}\dot{e}_{i}^{a_ {1}-1}\dot{e}_{i}+\beta_{1}a_{2}\dot{e}_{i}^{a_{2}-1}\dot{e}_{i},i=0,1,2\end{array}  \tag{27}\]  
Equation (26) is associated with Equation (27); we have:  
\[ \begin{array}{rcl}\Gamma_{i}=&-\Phi_{i}-h_{i}+\ddot{q}_{\rm d}i-\gamma_{1}a _{1}\dot{e}_{i}^{a_{1}-1}\dot{e}_{i}-\\ &&\beta_{1}a_{2}\dot{e}_{i}^{a_{2}-1}\dot{e}_{i}-\frac{\gamma_{2}}{\sqrt{2}} \bigg{(}\frac{s_{i}}{\sqrt{2}}\bigg{)}^{2a_{3}-1}-\frac{\beta_{2}}{\sqrt{2}} \bigg{(}\frac{s_{i}}{\sqrt{2}}\bigg{)}^{2a_{4}-1},i=0,1,2\end{array}  \tag{28}\]  
The fixed time sliding mode controller is as follows:  
\[\tau_{\rm oj}=M_{0}\Gamma, \tag{29}\]  
where the elements of \(\Gamma\) are shown in Equation (28). **Theorem 1**. : _For system (21), the system is globally stable and the tracking error \(\mathbf{e}\) converges to zero in a fixed time when the switching function (25) and the controller (28) are elected._  
**Proof 1**. : _Selecting Lyapunov function as:_  
\[V_{i}=\frac{1}{2}s_{i}^{2}. \tag{30}\]Taking the derivative of \(V_{i}\) and substituting Equation (26) into it, we have:  
\[ \begin{array}{l}\dot{V}_{i}=\dot{s}_{i}s_{i}\\ =-\frac{\gamma_{2}}{\sqrt{2}}\Big{(}\frac{\dot{s}_{i}}{\sqrt{2}}\Big{)}^{2a_{3}- 1}s_{i}-\frac{\beta_{2}}{\sqrt{2}}\Big{(}\frac{\dot{s}_{i}}{\sqrt{2}}\Big{)}^{2 a_{4}-1}s_{i}\\ =-\gamma_{2}V_{i}^{a_{3}}-\beta_{2}V_{i}^{a_{4}}\end{array}  \tag{31}\]  
According to the lemma, the reaching time of the system is as follows:  
\[t_{\text{fc1}}\leq t_{\text{max1}}:=\frac{1}{\gamma_{2}(a_{3}-1)}+\frac{1}{ \beta_{2}(1-a_{4})}. \tag{32}\]  
When the system reaches the sliding surface, according to Equation (25), we have:  
\[\dot{e}_{i}=-\gamma_{1}e_{i}^{a_{1}}-\beta_{1}e_{i}^{a_{2}}. \tag{33}\]  
According to lemma and Equation (33), the sliding mode motion time of the system is as follows:  
\[t_{\text{fc2}}\leq t_{\text{max2}}:=\frac{1}{\gamma_{1}(a_{1}-1)}+\frac{1}{ \beta_{1}(1-a_{2})}. \tag{34}\]  
Because of \(V_{i}\geq 0\), \(\dot{V}_{i}\leq 0\), the system is globally stable and the convergence time is as follows:  
\[T_{\text{fc}}= t_{\text{max1}}+t_{\text{max2}}=\frac{1}{\gamma_{2}(a_{3}-1)}+ \tag{35}\] \[\frac{1}{\beta_{2}(1-a_{4})}+\frac{1}{\gamma_{1}(a_{1}-1)}+\frac{ 1}{\beta_{1}(1-a_{2})}\]
###### Abstract  
This paper addresses the issue of the difficulty in obtaining inter-turn fault (ITF) samples in electric motors, specifically in permanent magnet-synchronous motors (PMSMs), where the number of ITF samples in the stator windings is severely lacking compared to healthy samples. To effectively identify these faults, an improved fault diagnosis method based on the combination of a cycle-generative adversarial network (GAN) and a deep autoencoder (DAE) is proposed. In this method, the Cycle GAN is used to expand the collection of fault samples for PMSMs, while the DAE enhances the capability to extract and analyze these fault samples, thus improving the accuracy of fault diagnosis. The experimental results demonstrate that Cycle GAN exhibits an excellent capability to generate ITF fault samples. The proposed method achieves a diagnostic accuracy rate of up to 98.73% for ITF problems.  
permanent magnet-synchronous motor; cycle-generation adversarial network; deep autoencoder; dataset expansion; inter-turn fault; fault diagnosis Article
## 3 Dimensional Analysis
### Dimensionless Parameters
This approach is especially beneficial for comparing and evaluating the performance of leveees of different scales, and it can enhance the understanding of various leveee designs while contributing to the integration of design standards. Ratio of superelevation to leveee height, \(\frac{\Delta y}{H}\)  
Using the ratio of superelevation to leveee height is one of the key indicators for assessing the risk of leveee overtopping in river curves. Superelevation refers to the phenomenon where water levels rise on the outer side and decrease on the inner side of a river curve due to centrifugal forces. The magnitude of superelevation is determined by the radius of curvature and the velocity of the river. The ratio of superelevation to leveee height indicates how much higher the superelevation is compared to the leveee height, and it varies with the radius of curvature and flow velocity. In river curves with larger radii of curvature and higher velocities, superelevation becomes more pronounced, thereby increasing the likelihood of overtopping. This ratio plays a crucial role in quantitatively evaluating the influence of river curves in leveee design and in predicting overtopping risks in advance. Ratio of water depth to leveee height, \(\frac{h}{H}\)  
The ratio of water depth to leveee height is another dimensionless variable that can be used to assess the likelihood of leveee overtopping intuitively. Water depth reflects the flow characteristics of the river, and the higher the water depth relative to leveee height, the greater the probability of overtopping. This ratio is helpful in leveee design for easily comparing overtopping risks based on water depth conditions and for determining appropriate leveee heights. Particularly in river curves, variations in water depth can strongly influence overtopping, as the curved channel geometry often leads to significant velocity gradients and uneven flow distribution. Consequently, analyzing the ratio of water depth to leveee height provides a more systematic approach to evaluating overtopping stability in these areas. Froude number, \(Fr\)  
The Froude number is a representative dimensionless variable that characterizes the interaction between velocity and gravity, and it is widely used in fluid dynamics to evaluate flow characteristics. The Froude number is expressed by Equation (12), which compares the inertial forces to gravitational forces to determine whether the flow is subcritical, critical, or supercritical. \[Fr=\frac{V}{\sqrt{gh}} \tag{12}\]where \(V\) is the velocity, \(g\) is the gravitational acceleration, and \(h\) is the water depth. A high Froude number indicates a relatively fast velocity, and inertial forces dominate over gravity. In river curves, a higher Froude number tends to cause the flow to shift outward, which can accelerate leveee overtopping and erosion. Therefore, the Froude number plays a crucial role in quantitatively assessing the flow characteristics of rivers and the risk of leveee overtopping, as well as in establishing design criteria for these applications.
# Improvement of a Tunable Stiffness Organ-Grasping Device by Design of a Wavy-Shaped Beam Structure
## 3 FEM Study  
### Simulation Settings  
We compared the stiffness gain of proposed designs with the gain of other designs by simulation using finite element method (FEM). Figure 6 shows the four models we compared, which were a homogeneous beam (Homogeneous), the principal-stress-line (PSL)-based design with uniform pitch (PSL-pitch), the PSL-based design with uniform bottom thickness (PSL-thickness), and a beam using uniform pitch pillars (Pillars). These models were composed of the two beams made with high-stiffness material ([PERSON]'s modulus 20 MPa, Poisson's ratio 0.45, maximum thickness 1.94 mm) and friction sheets ([PERSON]'s modulus 3.0 MPa, Poisson's ratio 0.49, thickness 0.5 mm). Spaces between high-stiffness material and friction sheets were kept void. The length, height and width of the device were 50.0 mm, 4.88 mm and 8.0 mm, respectively. The edge curves of the two PSL-based models consisted of two sets of streamlines that were symmetrically aligned about the boundary surfaces of the two beams. The thickness of troughs in PSL-thickness was 0.81 mm, which was the same as the previously implemented device [13]. Lines for the edge of PSL-pitch intersected the boundary surfaces of the two beams at points 5.0 mm apart, whose distance from the fixed edge were 0.0, 5.0,..., 50.0 mm. These two models were designed based on the curves drawn by MATLAB (The MathWorks Inc., Natick, MA, USA) and imported to Fusion 360 (Autodesk Inc., San Rafael, CA, USA). The pitch of the pillars in Pillars design was set to 5.0 mm to match with the PSL-pitch design. The roots of the pillars were filleted by exponential curves.  
In the simulation, a vertical point load was applied on the right edge of the device fixed by the left edge. The load was 0.1 N in a high-stiffness state and 0.01 N in a low-stiffness state. Fusion 360 was used for FEM computation. Mesh elements were quadratic tetrahedra with the mean size 0.5 mm. The contact surfaces of the two beams were bonded in a high-stiffness state and set as sliding surfaces in a low-stiffness state. The bending stiffness in the two states was given as:  
\[EI=\frac{FL^{3}}{3\delta_{\text{max}}}, \tag{11}\]  
Figure 6: Models compared in FEM study. (**a**) Homogeneous material (Homogeneous). (**b**) Principal-stress-line (PSL)-based design with uniform pitch (PSL-pitch). (**c**) PSL-based design with uniform bottom thickness (PSL-thickness). (**d**) Beam using uniform pitch pillars (Pillars).  
where \(F\) was the point load and \(\delta_{\text{max}}\) was the maximum vertical deflection. The stiffness gain was calculated from the higher stiffness \(EI_{\text{high}}\) and the lower stiffness \(EI_{\text{low}}\) as  
\[G=\frac{EI_{\text{high}}}{EI_{\text{low}}}. \tag{12}\]
## 6 Conclusions  
In order to detect the foreign objects suspended on transmission lines, this paper proposes a method combining multi-network feature fusion and random forest. A case is carried out on foreign object detection and the influences of different samples, feature extraction layers, CNN models and feature fusion schemes are compared and analyzed. The conclusions are drawn as follows:  
1. Target region extraction based on Otus binarization threshold segmentation and morphology processing can reduce the influence and distraction of complex background factors in the inspection images and improve the detection accuracy.
2. The optimal feature fusion scheme by fusing the features extracted by GoogLeNet, EfficientNet-B0 and DenseNet-201 can achieve the highest accuracy of 95.88%. It can be seen through Grad-CAM that the fused features can compensate for the differences of different networks and fully reflect the features of the foreign object images, thus improving the generalization ability of the detection model.  
The proposed detection method combining multi-network feature fusion and random forest can realize accurate detection of foreign objects on transmission lines, which can avoid problems such as insufficient samples, low performance of equipment and long time consuming, and assist transmission line inspection personnel to detect and remove foreign objects suspended on transmission lines.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Detection Method** & **Accuracy (\%)** \\ \hline GMM + K-means clustering [9] & 78.00 \\ Combination features + cascade classifier [11] & 97.33 \\ SSD [13] & 85.20 \\ Faster R-CNN [15] & 93.09 \\ FODN4 PS Net [16] & 85.00 \\ RetinaNet [18] & 94.10 \\ Improved YOLOv3 [19] & 92.60 \\ YOLOv4 [21] & 81.72 \\ The proposed method & 95.88 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different detection methods.  
**Author Contributions:** Conceptualization, Y.Y., Z.Q. and X.Z.; methodology, Y.Y., Z.Q. and X.Z.; software, X.Z.; validation, Y.Y. and Z.W.; formal analysis, X.Z.; investigation, Y.Y., X.Z. and Z.Z.; resources, H.L. and Z.W.; data curation, X.Z. and H.L.; writing--original draft preparation, Y.Y. and X.Z.; writing--review and editing, Z.Q. and X.Z.; supervision, H.L. and Z.W.; project administration, Z.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by Jiangxi \"Double Thousand Plan\" Innovative Leading Talents Long-term Project, grant number jssq2019101071, and Jiangxi College Student Innovation and Entrepreneurship Training Program, grant number 202110403086.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data used in this research can be provided upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 3 BBR Operation Principles
### Detailed Description of BBR v1  
BBR encompasses four distinct control states within a finite state machine, with each dedicated to a different purpose:* STARTUP--initiating a search for maximum bandwidth \(BW\);
* DRAIN--draining the built-up queue;
* PROBE BW--exploring additional bandwidth, draining the queue, and sending data using the estimated \(BW\),
* PROBE RTT--refreshing the \(RTT_{min}\).  
The BBRv1 state machine and the conditions for transitioning between states are presented in Figure 2. The STARTUP phase is similar to the slow start phase of the TCP CUBIC. BBR seeks the maximum available bandwidth by exponentially increasing the congestion window. During this phase, the pacing_gain and cwnd_gain are set to 2.89, which restricts BBR inflight almost to 3*BDP. The STARTUP phase concludes if the estimated bandwidth fails to exceed 1.25 times the previous bandwidth for three consecutive RTIs. Upon meeting this criterion, BBR transitions to the DRAIN phase, which allows for emptying of the queue that built up during the STARTUP phase. The pacing_gain is set inversely to the STARTUP phase. BBR transitions from DRAIN to PROBE BW when the inflight data drop below the estimated BDP.  
Long-lived BBR flows spend most of their time in the PROBE BW phase. At every eighth RTT, the pacing_gain is increased to 1.25 for one RTT interval only to promptly revert to 0.75 for the subsequent RTT interval. For the remaining 6 RTIs, it maintains a consistent sending rate equivalent to the windowed maximum filter of the delivery rate from the previous 10 RTIs (pacing_gain = 1). Meanwhile, the cwnd_gain is fixed at 2 to ensure sufficient inflight data.  
If \(RTT_{min}\) has not been updated for at least 10 s, BBR transitions into the PROBE RTT phase. During this phase, the congestion window is reduced to four segments, and BBR attempts to measure the shortest RTT. This phase persists for 200 milliseconds. The significant reduction in the transmission rate allows for the draining of the queue to refresh the minimum RTT. Following the PROBE RT phase, BBR reverts to the PROBE BW phase or to STARTUP if it failed to reach the bandwidth plateau earlier.  
The initial results were very promising; however, the shortcomings of BBRv1 soon became apparent. Its insensitivity to packet loss contributed to elevated drop ratios and retransmission rates, particularly in scenarios involving shallow buffers. It also became evident that this protocol exhibits a more aggressive behavior than CUBIC and fails to ensure equitable bandwidth allocation across numerous scenarios.
# A Polygonal Model to Design and Fabricate Ion-Exchanged Diffraction Gratings
## 4 Design and Fabrication of a Grating with Suppressed Zero Order and a Three Splitter
### Design and Fabrication of a Grating with Suppressed Zero-Order  
The cancellation of the zero order implies \(\eta_{0}=0\). According to the blue curve (efficiency of the zero order) of Figure 5, this condition is fulfilled for \(t\approx 26\) min. Therefore, we fabricated a new grating using this ion-exchange time. After fabrication, we measured the diffraction efficiencies and obtained the results shown in Table 1.  
Figure 6: Long exposure photography of the experimental setup. On the right side, a polarizer, before one of the fabricated gratings, was placed to modify the intensity of the beam. On the left side, you can see the detector head, with a diaphragm on its front. The stabilized He-Ne laser used in the measurements is not shown in the picture.  
Figure 7: Close-up photography of one of the fabricated gratings (in the middle of the picture). Behind, you can see a periodic object to better observe the dimensions of the grating with respect to the glass substrate. The grating grooves are arranged horizontally.  
The result was very good. The efficiency of the zero order did not reach the theoretical minimum, but was very close, and the efficiency of the first orders (\(\pm 1\)) was higher than expected. This closeness between the predicted and obtained values was not surprising because of the low sensitivity of the diffraction efficiency \(\eta_{0}\), at its minimum, with the ion-exchange time (see Figure 5). Furthermore, the efficiency obtained at the first orders was slightly higher than the value (27%) that a sinusoidal phase grating provides. If higher efficiencies were necessary, we could stack a second grating with half the period to obtain a non-symmetrical phase profile. A proper design of these two phase elements would result in an improved efficiency for the +1 order at the expense of the \(-1\) order. On the other hand, the small discrepancy between the experimental and theoretical values of the efficiencies can be attributed to the polygonal model, which does not perfectly reproduce the real phase profile. Anyway, the small values \(\eta_{0}\) that we obtained validate this ion-exchange technique to fabricate this kind of gratings and the polygonal model to represent its phase profile.
## 3 Experimental Setup and Results
### Data Augmentation and Implementation Details  
The generalization issue with deep learning methods was addressed by data augmentation, which enables the network to learn variations and generalize its findings to previously unobserved images while preventing overfitting on small datasets. This method has already been shown to be effective for classifying medical images [28]. So, the following operations served as the direction for each image:  
* Rotation: rotating the image with an angle between zero and ten in the clockwise or counterclockwise direction;
* Scaling: sampling the scale of the frame size of the image randomly between 80% and 110%;
* Translation: translating the image horizontally and vertically between \(-10\%\) and 10%;
* Horizontal flip: horizontally flipping the image with a probability of 0.5.  
For this study, all thermal pictures were normalized and resized. In the testing stage, the model and tune of the hyperparameters were evaluated, and generalized results were obtained. For training, the following hyperparameters are used: Batch size = 32; Optimizer = \"adam\"; epochs = 25. Batch size is the number of images that are processed in one iteration, \"adam\" is the type of optimizer which is an extension to the Stochastic Gradient Descent, and epoch is passing the data forward and backward one time in a neural network. The first callback list was also defined as follows. The Early Stopping function had to be determined for efficient learning. To avoid overfitting, this function was used. The \"loss\" value is tracked in this CNN model, and the \"patience\" of the three epochs is found. It means that once the loss value reaches a minimum, and if the loss value increases in the next three epochs, training will be terminated at that epoch. Another adherence strategy is to slow down the learning rate. As a result, once the metric reaches a plateau, the learning rate slows. For this callback, patience is set to two epochs. If no improvement is detected, the learning rate is reduced by a factor of 0.3 because the loss value will gradually decrease until it reaches its lowest value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Training** & **Cross-Validation** & **Testing** & **Total** \\ \hline Healthy & 122 & 18 & 38 & 178 \\ Sick & 90 & 12 & 26 & 128 \\ Total & 212 & 30 & 64 & 306 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of training, cross-validation, and testing dataset for the classification model.  
Figure 4: Sample thermal images of breasts from the dataset: (**a**) sick (Hospital of Nur-Sultan), (**b**) healthy (Hospital of Nur-Sultan), (**c**) sick (DMR), and (**d**) healthy (DMR).  
Class weight was another critical parameter to define. Because the dataset primarily consisted of patients who did not have breast cancer, it was necessary to give minority classes a higher-class weight so that they could learn from all classes in a balanced manner [29].  
The study was performed on a machine with the characteristics given in Table 3.