esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \(a\) & \(b\) & \(\frac{a}{p\text{-Value}}\) & \(b\)_p_-Value & \(a\) 95\% CI & \(b\) 95\% CI & \(\mathbf{R^{2}}\) & \(\mathbf{R\_p}\)**-Value** & \(\mathbf{R\_95\% CI}\) \\ \hline \multirow{2}{*}{3-class MRAC} & TOF & 0.966 & 89.1 & \(<\)0.001 & 0.142 & [0.9595, 0.9717] & [\(-\)30.06, 208.32] & 0.996 & \(<\)0.001 & [0.9978, 0.9986] \\  & non-TOF & 0.960 & 134 & \(<\)0.001 & 0.030 & [0.9544, 0.9656] & [127, 254.80] & 0.997 & \(<\)0.001 & [0.9981, 0.9988] \\ \multirow{2}{*}{2-class MRAC} & TOF & 0.938 & 152 & \(<\)0.001 & 0.115 & [0.9286, 0.9479] & [\(-\)37.25, 341.09] & 0.991 & \(<\)0.001 & [0.9942, 0.9962] \\  & non-TOF & 0.932 & 256 & \(<\)0.001 & 0.002 & [0.9216, 0.9418] & [37.25, 474.32] & 0.990 & \(<\)0.001 & [0.9935, 0.9957] \\ \multirow{2}{*}{2-class MRAC} & TOF & 0.943 & 211 & \(<\)0.001 & 0.027 & [0.9336, 0.9527] & [235, 399.48] & 0.991 & \(<\)0.001 & [0.9943, 0.9963] \\ \multirow{2}{*}{(no air cavities)} & non-TOF & 0.934 & 320 & \(<\)0.001 & 0.003 & [0.9241, 0.9439] & [104.95, 534.89] & 0.990 & \(<\)0.001 & [0.9938, 0.9959] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fitting coefficients \(a\) (slope) and \(b\) (intersect) for linear regression and R\({}^{2}\) values from the correlation analysis. Confidence intervals (CI) and \(p\)-values for regression and correlation coefficients are also presented. Coefficient \(b\) does not differ significantly from zero for 3-class MRAC TOF and 2-class MRAC TOF. Coefficient \(a\) and R\({}^{2}\) value are statistically significant for all methods. Figure 4: Bland–Altman plots of non-TOF (marked as blue ‘o’ symbol, and dashed lines) and TOF (marked as red ‘\(\times\)’ symbol and solid lines) reconstructed activity values with different MRAC methods versus CTAC reconstructed PET. (**A**) 3–class MRAC, (**B**) 2 – class MRAC, (**C**) 2 –class MRAC without air cavities. The horizontal lines correspond to mean bias and limits of agreement (LoA). Figure 6 shows the bias atlas images with all the methods. Local improvements can be seen with the TOF reconstruction especially in the sinus region. The bias is generally reduced in the cortical regions and across the brain with TOF. Figure 7 shows the bias standard deviation images with all the methods. Small local improvements can be seen with the TOF reconstruction with a systematic reduction of standard deviation throughout the brain. Figure 6: Mean bias atlas images from non\(-\)TOF and TOF reconstructed PET. A clear reduction of bias is seen in the TOF reconstructed images across the brain compared to non\(-\)TOF reconstructed PET. (**A**) 3\(-\)class MRAC, (**B**) 2\(-\)class MRAC, (**C**) 2\(-\)class MRAC without air cavities using non\(-\)TOF reconstruction. (**D**) 3\(-\)class MRAC, (**E**) 2\(-\)class MRAC, (**F**) 2\(-\)class MRAC without air cavities using TOF reconstruction.
## 3 Proposed Reliable Routing Mechanism  
This section introduces several basic assumptions for the proposed network model. The cluster-based border gateway routing protocol divides the routing into two schemes, intra-cluster routing and inter-cluster routing. The cluster structure describes in detail the handling of the reliability routing paths of wireless computing.  
Figure 6: Network elements connected with parallel configuration.  
This study is based on the following assumptions:  
* All sensor nodes are stationary at their initial locations after they are deployed.
* Every sensor node has limited energy.
* The sensor network is organized into clusters with any clustering method, and every node has a unique identity for determining its cluster.  
A large-scale region is covered by a large number of homogeneous sensor nodes. These sensor nodes communicate with each other through short-range radios, and wireless channels are bidirectional. Long distance data forwarding is achieved across multiple hops.
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Cardiovascular Protection  
By lowering insulin resistance, TZDs can have cardioprotective and anti-atherosclerotic activity, and prevent ischemic events to an extent in patients with prior history of insulin resistance [28; 29; 30]. Additionally, it has been noted that, in murine models and in vitro studies, piogltazone has demonstrated the capability of limiting cardiac hypertrophy in addition to arresting cardiac fibrosis and providing protection against oxidative stress [31; 32; 33].
# Research on a Dynamic Calibration Method for Photogrammetry Based on Rotary Motion
## 5 Calibration System Uncertainty Analysis  
The measurement uncertainty analysis of the device includes the run-out uncertainty of the turntable, the error caused by the installation of the encoder, and the run-out error of the turntable caused by the table body. In theory, the points on the turntable should always move around a fixed axis, but the fact that the axis is in constant motion causes the points on the turntable to move in an irregular circle. The radial run-out can be ignored compared with the radius, so the resulting speed error for the turntable can be ignored. The measurement uncertainty analysis of the device is as follows:  
1. Uncertainty introduced by rotational consistency  
The rotational speed error is calculated as the maximum error of 0.02% of the measured value, which is a uniform distribution estimation, and the introduced uncertainty component is calculated as follows:  
\[u_{1}=\frac{0.002\%}{\sqrt{3}}\approx 0.012\%, \tag{9}\]
2. Uncertainty introduced by measurement repeatability  
According to the measured data, the rotational speed is set to 600 r/min, and the corresponding introduction uncertainty is calculated as follows:  
\[u_{2}=\frac{0.01\%}{3}\approx 0.003\%, \tag{10}\]
3. The change in the length perpendicular to the direction of rotation introduced by the rotating shaft system  
An analysis of the change in the length perpendicular to the direction of rotation introduced by the rotation axis shows that the change is less than 0.1 mm, which is estimated based on a uniform distribution.  
\[u_{3}=\frac{0.1\text{ mm}}{500\text{ mm}\times\sqrt{3}}\times 100\%\approx 0.012\%, \tag{11}\]
4. Relative standard uncertainty of synthesis  
Regardless of the correlation between the above terms, the relative uncertainty of the synthesis is as follows:  
\[u_{rel}=\sqrt{\sum_{i=1}^{8}u_{i}^{2}}\approx 0.017\%, \tag{12}\]
5. Relative expansion uncertainty  
If the inclusion factor \(k=2\) is used, the relative extension uncertainty is as follows:  
\[U_{rel}=ku_{rel}\approx 0.03\%(k=2), \tag{13}\]
## 3 Modeling the Relationship between Field AGB and Satellite Data
### Machine Learning Methods
#### 3.2.1 Multi-Layer Perception Neural Network  
As a nonparametric mathematical model, ANN is inspired by biological neural networks and it has strong abilities for linear and nonlinear fitting ([PERSON] et al., 2000; [PERSON] et al., 2001). The ANN considered in this study was the multi-layer perception neural network (MLPNN). The architecture of the MLPNN consists of an input layer containing predictors, one or more hidden layers, and an output layer containing the response variable, along with interconnection weights characterizing the connection strength between these layers (Figure 3). The algorithm chosen was the back-propagation (BP) learning rule, an iterative gradient descent algorithm that was designed to minimize the mean square errorbetween the desired target and the actual output vectors [77; 78]. The initial weights were assigned randomly, and when developing the network, the interconnection weights were adjusted to minimize the prediction error [79; 80].
# Lower-Latency Screen Updates over QUIC with Forward Error Correction
## 4 Design and Implementation
### Partial Ordering for Screen Updates over QUIC  
We want to relax ordering for the messages of each update but keep the order of the updates as they have been sent. The original QUIC cannot help with the partial ordering requirements of screen updates. The reason is that to relax the ordering of messages within each update, we need to send them over different QUIC streams. However, since there is no ordering among streams, we cannot continue the same strategy for the messages of the next update and make sure that, for example, we start delivering the messages of the second update after completely delivering the messages of the first update.  
We implemented the CF model over QUIC by sending each update over a separate QUIC stream. Then, we obtained a buffer at the client side to store all the messages from the next updates/streams. Also, we used the new user-level callback that we added to QUIC to send the frames in arrival order to the HTTP layer at the client side so we could relax the order of messages in each update and be able to apply FEC, discussed in the next section, to reduce the completion time of each update and start the next update sooner.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 6 Results and Discussion
### Examples of Compensation Control Strategy
#### 4.1.4 Terminal Time is Fixed  
The terminal time is set as 212 s.  
Figure 15 shows the average ZEM distance of Monte Carlo runs in three fixed-time scenarios: the evader plays without time delay, without compensation for time delay, and with compensation for time delay. When the evader plays without time delay, the average terminal distance between the two players reaches 0.00005056 m, which is less than the safe distance. When the evader plays without compensation for time delay, the average terminal distance between the two players reaches 0.00005425 m, which is less than the safe distance. When the evader plays with compensation for time delay, the terminal distance between the two players reaches 0.00004705 m, which is less than the safe distance. According to Figure 15b, when the evader has delayed information, the zero-effort miss distance at the beginning of the confrontation declines faster than that in the no-delay scenario. The reason is that the evader adopts no control at the beginning of the confrontation due to the lack of information. At the terminal time, the pursuer can successfully intercept the evader in all scenarios, and the terminal distances are close to each other.  
Figure 13: The average \(t_{go}\) and \(\Delta t_{go}\) of 100 Monte Carlo runs in the scenario that the evader plays (**a**) without time delay; (**b**) without compensation for time delay; and (**c**) with compensation for time delay.  
Figure 14: The average control variable of 100 Monte Carlo runs in the scenario that the evader plays (**a**) without time delay; (**b**) without compensation for time delay; and (**c**) with compensation for time delay.  
Figure 15: The average ZEM distance of 100 Monte Carlo runs: (**a**) the ZEM distance changing with time; zoom in on the curve at (**b**) the beginning of the confrontation and (**c**) the end of the confrontation.  
Example 2 shows that when the pursuer has delayed information, the compensation control strategy proposed in this paper can shorten the relative distance between the players and increase the possibility of intercepting the evader. Example 3 shows that when the evader has delayed information, the compensation control strategy proposed in this paper prolongs the time that the pursuer takes to intercept the evader.
# Analysis of the Failure Area of the Slope Using the Slip Line Method
## 2 Slip Line Method  
### Application of Slip Line Method of Coulomb Material  
To apply the slip line method, the following assumptions are made:  
1. The soil body is an ideal hard plastic body.
2. Mohr-Coulomb yield criteria are followed.
3. When a load is applied, the plastic area of the ground can move freely.
4. The plastic deformation of the ground is large; therefore, elasticity can be neglected.  
As shown in Figure 1, under the plane strain condition, a point on the plane always has principal stresses perpendicular to each other.  
If the lines indicating the direction of the principal stress are continuously connected at each point, curves orthogonal to each other can be obtained. This line is called the principal stress trajectory, and there are two failure surfaces at each point, as shown in Figures 2b and 3. By successively connecting the failure surfaces (or slip lines) of each point, two curves can be obtained, and the curves are called slip lines.  
Figure 2a shows the slip line \(\alpha\) and \(\beta\) using Mohr circle in Coulomb material. In Figure 2a, \(p\) is the average stress, and \(R\) is the radius of the stress circle. In Figure 2b, \(\sigma_{1}\) is the maximum principal stress, and \(\sigma_{3}\) is the minimum principal stress. \(\alpha\), \(\beta\) denote the slip line (shear failure surface), and \(\mu\) is the angle between the maximum principal stress and the slip line (shear failure surface).  
Figure 1: Coordinate plane for plane strain problem [27].  
Figure 2: Mohr circle notation of plastic stress state of Coulomb material [27]. (**a**) Slip line represented by Mohr circle; (**b**) stress state at a point.  
Figure 3 shows the principal stress trajectories and slip lines of the Coulomb material. Curves 1 and 2 are the principal stress trajectories, and \(\theta\) is the angle between the maximum principal stress and the \(x\)-axis. Each point has two failure surfaces, as shown in Figure 2. Two slip lines can be obtained by continuously connecting the failure surfaces of each point. These slip lines are line \(\alpha\) and line \(\beta\) in Figure 3.
# OTFS-Based Handover Triggering in UAV Networks
## 7 Numerical Analysis
### Studying the Effects of \(n^{\prime}\) and \(P_{th}\)
#### 7.1.2 Effect of \(P_{th}\)  
Figures 6-8 show the assessed handover metrices of the compared schemes against \(P_{th}\) using \(n^{\prime}=12\), which is the rated \(n^{\prime}\) value for the HST scenario, as stated in [8], based on practical measurements. Generally, the handover overhead and the ping-pong ratio of all compared schemes are decreasing when increasing \(P_{th}\), while their average throughput is increasing. This can be attributed to the trade-off coming from increasing the value of \(P_{th}\). On the one hand, increasing \(P_{th}\) will always link the HST with a certain UAV for a long time period, decreasing both the handover duration and the ping pong ratio. On the other hand, the linkage with a certain UAV for a long time period will decrease the spectral efficiency of the HST, as it will be unable to connect with other UAVs with higher spectral efficiencies. Decreasing handover duration and the ping-pong ratio will increase the average throughput, while decreasing the spectral efficiency will decrease it for constant available bandwidth \((M\Delta f)\). As the handover overhead has a dominant effect over the spectral efficiency, its decrease due to increasing \(P_{th}\) will always result in increasing the achievable throughput till reaching a saturation point. The saturation happens when there are no other handovers between the current UAV and the other UAVs, resulting in \(\Psi=M\Delta f\psi_{\hat{\mathcal{H}}}\), as given in (55). The proposed OTFS-based strategy has superior handover performances over the other benchmarks due to its constant DD channel gain over the whole OTFS symbol duration. From these figures, when \(P_{th}=0\) dB, the handover overhead of the proposed OTFS strategy is about 0.1286 (0.067) of that belonging to OFDM (RSS), respectively, and these values become 0.0036 (7.8 \(\times\) 10\({}^{-5}\)) when \(P_{th}=4\) dB. For the ping-pong ratio, these values are 0.0854 (9.16 \(\times\) 10\({}^{-4}\)) when \(P_{th}=0\) dB. For average throughput, the proposed OTFS-based handover strategy has higher throughput than OFDM (RSS) by 5.81 (10.8) times when \(P_{th}=0\) dB, and these values become 1.74 (15.64) when \(P_{th}=4\) dB. Also, we can conclude that the optimal \(P_{th}\) value for the proposed scheme, causing a saturation in the average throughput, is equal to 3.5 dB.
# Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm
## 4 Practical Application and Result Analysis
### Optimization and Comparison of Algorithm Parameters  
In this paper, it is assumed that Well M has the complete DTS, and the DTS of Well M is used as the training set to establish the ELM, DELM, PSO-DELM, GA-DELM, SSA-DELM, and ISSA-DELM model. The \"missing\" DTS corresponding to the Well N is predicted. The objective function is established based on the RMSE of the training sample (Well M). The optimization algorithms (GA, PSO, SSA, and ISSA) are used to optimize the input weights of DELM. The optimization iteration curve of the proposed algorithms is shown in Figure 7.  
As illustrated in Figure 7, GA is most likely to achieve a local optimum during the optimization process. PSO is unable to obtain higher diagnostic accuracy due to its defects of poor handling of discrete optimization problems. ISSA increases the population's diversity and widens the search window by adding the logistic chaos mapping to the population's initialization. To prevent the SSA trapped in a local optimum, the firefly disturbance approach is employed to update the ideal sparrow's location in time. The ISSA has the fastest convergence time and the lowest error when compared to the other optimization algorithms. As a result, optimizing input weights in DELM via ISSA is more favorable.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 3 Proposed Improvement for Fault Diagnosis Models
### Training the Models  
The training convergence of a deep neural network depends heavily on the training algorithm design. With suitable choices, the sample efficiency and the test performance are likely to increase. This work deployed various techniques, such as time window division, learning rate scheduling and early stopping. The following discusses briefly the major choices. The repository for this study is linked in Appendix A for further evaluation.  
A disadvantage of deep neural network solutions for fault diagnosis originates from their need for a large number of training samples in order to converge to a satisfying optimum. Fortunately, the number of training samples can be increased with data augmentation. This work employed overlapping time window division for data augmentation. That is, the time series samples were split into shorter time windows including time steps over multiple rotor revolutions. Figure 5 demonstrates this overlapping time window division technique. The extracted time windows correspond to an input sample of 2048 time steps in this study.  
The loss function for multi-categorical fault diagnosis tasks is cross-entropy loss (CE loss), as shown in Equation (20). The CE loss is a measure for the difference of the target distribution and the estimated distribution over \(K\) categories averaged over \(N\) samples in a batch. In Equation (20), \(y_{j}^{n}\in\{0,1\}\) are the \(K\) values in the target distribution with the correct category encoded as 1 and \(f_{j}^{n}\in[0,1]\) are the \(K\) probabilities in the estimated distribution. The loss function for binary fault detection tasks is the binary cross-entropy loss (BCE loss), as shown in Equation (21). Similarly to CE loss, the BCE loss is averaged over the \(N\) samples in the batch. In Equation (21), \(y^{n}\in\{0,1\}\) is the correct label and \(f^{n}\in[0,1]\) is the probability of a fault.  
\[CE\ loss=-\frac{1}{N}\sum_{n=1}^{N}\sum_{j=1}^{K}y_{j}^{n}log(f_{j}^{n}) \tag{20}\]  
\[BCE\ loss=-\frac{1}{N}\sum_{n=1}^{N}(y^{n}log(f^{n})+(1-y^{n})(1-log(f^{n})) \tag{21}\]  
The gradient optimisation steps of all models were controlled with Adam optimiser [42] and decaying learning rate. The learning rate decayed by half every tenth epoch. Furthermore, all models were optimised with early stopping and checkpoints. That is, at the end of every epoch, if the validation loss decreases, the model weights are saved. If the validation loss increases, the previous best weights are loaded to the model. The optimisation ends after a maximum number of epochs or after the validation loss increases seven times. A maximum number of epochs and other training and model related hyperparameters are detailed in Appendix B.
# Research on Magnetic Coupling Flywheel Energy Storage Device for Vehicles
## 3 Core Component Performance Test
### Efficiency Characteristics  
According to the working principle of common permanent magnet couplers, the loss of energy is caused by the difference in rotational speed between the input and output [24]. In the magnetic coupling device proposed in this paper, the magnetic ring transmits torque and adjusts the rotational speed by moving in the axial direction. When the magnetic ring moves, there is a rotational speed difference between the main and driven shafts, which will cause energy consumption.  
For a constant torque load, the power calculation formula of the drive motor is as follows:  
\[P=\frac{T\cdot n}{\eta} \tag{1}\]  
In the formula, \(\eta\) is the total efficiency of the transmission system.  
The transmission efficiency of the magnetic coupling mechanism is defined as follows:  
\[\eta=\frac{P_{out}}{P_{in}}=\frac{P_{out}}{P_{out}+P_{0}} \tag{2}\]  
In the formula, \(P_{out}\) is the output power, \(P_{in}\) is the input power, and \(P_{0}\) is the power loss.  
As shown in Figure 8, we adjusted the driving shaft at four rotational speeds of 400 rpm, 600 rpm, 800 rpm and 1200 rpm to obtain different transmission torques through axial movement and tested the operating efficiency under the corresponding working conditions.  
It can be seen from the experimental data that at a certain speed, the efficiency of the mechanism is positively related to the transmission torque. When the magnetic ring is in  
Figure 8: Efficiency changes with torque and speed.  
Figure 7: Relation between ring position and torque.  
the middle of the two shafts, the transmission torque is the largest, and the transmission efficiency is the highest at this time. Similarly, when the transmission torque is certain, the efficiency is also positively related to the rotational speed. When the transmission torque is 240 N-m and the speed is 1200 rpm, the transmission efficiency can reach up to 93%, and the energy saving effect is obvious.