esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
## 3 Case Study  
### Point Cloud Data Processing  
The subject of investigation for this study was the Haerwusu open-pit coal mine in China. Figure 8 presents the point cloud model representing the mine. The point cloud model comprises 2,541,887 discrete data points. The coal deposits within the mine are primarily characterized by horizontal deposition, and the haulage roads predominantly follow near-horizontal roads.  
Part of the point cloud data of the road captured by the UAV is shown in Figure 8(a). The point cloud model is a filtered model with some erroneous points removed. The model is complete, but it is difficult to obtain continuous point cloud elevations with consistent length intervals because some points are filtered. Therefore, this study constructed a road DEM, as shown in Figure 8(b), based on the TIN (Triangulated Irregular Network) method, which is usually used for high-precision modeling of smaller areas [31], which makes the discrete point cloud road model, into a continuous surface model, and facilitates the acquisition of the continuous elevation changes in the road.
# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 3 Proposed Method
### Overall  
To cope with the performance degradation of one-stream trackers caused by an unconstrained interaction, a feature-enhanced transformer-based network for robust visual object tracking (FETrack) is proposed in this paper. Specifically, the proposed FETrack adopts a transformer-based encoder-decoder structure, as illustrated in Figure 1. The encoder is used to extract visual features of the template and the search images, while the decoder is used to fuse the extracted visual features and generate the bounding box token sequence autoregressively. Figure 2 depicts the structure of the encoder and decoder modules. In the encoder, we leverage the advantages of a one-stream and two-stream pipeline to construct a modeling scheme that consists of an inherent template stream and a bidirectional template-search stream. This scheme prevents template features from suffering from background noise while realizing the deep interaction between the template and the search region features. In the decoder, a causal transformer is employed to generate the bounding box. Specifically, a causal self-attention mask restricts the current token to attend only to preceding tokens, and visual features from the encoder are leveraged to increase the accuracy of the bounding box prediction. After obtaining the predicted bounding box, we evaluate the results to determine whether to use the currently predicted search image as a new dynamic template.  
Figure 1: Network architecture of the proposed FETrack. The components of FETrack consist of a transformer-based encoder and decoder. The former is responsible for capturing visual features of input images, while the latter generates the sequence of bounding box tokens autoregressively.  
Figure 2: The structure of encoder and decoder modules. In the encoder, an independent template stream is embedded to prevent potential background interference. In the decoder, a masked multi-head self-attention layer is used to maintain the causality of the token sequence, and a multi-head cross-attention layer is used to integrate visual features from the encoder. The symbols C and + represent concatenate and addition, respectively.
# Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System
## 4 Experimental Results and Analysis
### Random Disturbance  
It is assumed that all control areas are equipped with a PVG functioning as a negative load and the variation in its irradiance is considered to be random. The load disturbance signals for each control area are illustrated in Figure 10. These signals consist of step, triangular, and parabolic patterns. It can be seen from Tables 2-4 that the proportion of new energy added in the three regions is basically balanced, and the same disturbance signal has little disturbance to the three regions. Figure 10 verifies the robustness of the proposed method to the three types of disturbance signals.  
The effectiveness of using the proposed optimal ADRC strategy is demonstrated in Figure 11. The proposed LFC strategy successfully keeps the frequency deviation near zero in all regions, even in the presence of load disturbances and unpredictable fluctuations in the functioning of the PVGs acting as negative loads. The area where the maximum frequency deviation is observed is area 3, with a value of only 3.7 \(\times\) 10\({}^{-4}\) Hz. Additionally, the proposed strategy effectively governs the power exchange between control areas through the tie-line in Figure 12. The maximum deviation in tie-line power is approximately 9.8 \(\times\) 10\({}^{-3}\)\(p.u.\)  
It can be seen from Figures 11 and 12 that the control strategy proposed in this paper has a good effect on the three different disturbance signals. However, it can also be seen that these three signals have different effects on the system. The robustness of the system to the step signal is the strongest, and basically, the system has no fluctuation. For the other two nonlinear interference signals, the control effect is not as good as the step signal. Although the peak value is not large, it is also the direction of further improvement in the future.  
Figure 10: Load disturbance \(\Delta P_{L,i}\) for each control area.
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 6 Performance Evaluation
### Analysis of Autonomous Load Regulation Mechanism Performance  
In order to verify the performance of the autonomous load regulation mechanism to extend the network lifetime, we compared the ALRMR with the ALRMR-Non Radius Control (ALRMR-NRC) for different node numbers in the range of 100 to 250 with a step of 50 and \(P=\) 0.3 W. Compared with the ALRMR, the ALRMR-NRC does not apply an autonomous load regulation mechanism and it omits the step of determining the available forward neighbor. Algorithm 3 shows its specific route establishment process and Figure 6 shows the corresponding contrast results.  
The network lifetime decreases with the increasing number of nodes. However, the network lifetime of the ALRMR is longer than that of ALRMR-NRC if the node number is the same, as shown in Figure 6. This is because the autonomous load regulation mechanism enhances the energy balancing efficiency. Figure 7 demonstrates the above analysis. When the autonomous load regulating mechanism is not applied, the network load balancing effect is poor and the energy consumption of key nodes varies  
Figure 5: Mean residual energy of key nodes (MREKN) with different values of \(P\).  
Figure 6: Lifetime with different node numbers.  
greatly, which means that the energy utilization efficiency of key nodes is low. Hence, the MREKN of the ALRMR-NRC is highest and the lifetime of the ALRMR-NRC is the smallest. Using the autonomous load regulating mechanism, the network load balancing effect is greatly enhanced, and the energy consumption of key nodes is more balanced. In the ALRMR, the MREKN is significantly lower than that in the ALRMR-NRC and the lifetime is also longer. However, when \(\gamma=0\), the load regulation accuracy is too high, which causes the data relay radius of many nodes to become too short. Many nodes must choose next hop based on residual energy and thus can easily and quickly become overloaded, which leads to a higher MREKN and smaller lifetime. The above analysis demonstrates that the autonomous load regulation mechanism can extend the network lifetime by balancing the load of key nodes efficiently.  
```
Inputs: Sensor list \(V\), state information of all nodes; Outputs: Network routing \(V\_next\); for all\(v_{i}\in V\) \(FN(i)=\left\{j\big{|}d_{j_{i}}<d_{i\mu},d_{ij}<R,j\in V\right\}\); \(v_{i}\_next\leftarrow\arg\max_{v_{j\in FN(i)}}\left[LS_{ij}\right]\) endfor \(V\_next=[v_{i}\_next]\forall v_{i}\in V\)
```  
**Algorithm 3** Routing setup and update strategy of the autonomous load regulation mechanism-based energy balanced routing algorithm--non radius control (ALRMR-NRC)
## 4 Results and Discussion
Average delay in seconds with different Buffer Sizes, generating 2 MB messages every 1 to 2 h, (**a**) MAPs moving randomly on the path; (**b**) MAPs following the path in only one direction. DFA: Distributed Forwarding Algorithm. Figure 9:6 Results for scenario 2.4. Average hop count with different Buffer Sizes, generating 2 MB messages every 1 to 2 h, (**a**) MAPs moving randomly on the path; (**b**) MAPs following the path in only one direction. DFA: Distributed Forwarding Algorithm.
## 3 Methods
### Investigation of Missing MODIS FRP Due to Sampling Limitations
#### 3.2.1 Extraction of Contemporaneous Fire Detections  
Daytime fire detections that were contemporaneously observed by MODIS and VIIRS were extracted first for comparison purposes because fire is highly dynamic and evolves very fast as environmental conditions (i.e., topography, fuel conditions, and fire weather) change [34]. This study considered only daytime fire detections because fires burn mainly during daytime in most ecosystems across Africa [52]. Specifically, for each MODIS ascending orbit (daytime) overpassing the Africa continent, VIIRS fire detections were first clipped using the MODIS swath boundaries to exclude VIIRS fire detections observed inside the equatorial MODIS swath gaps. Then, fire detections observed contemporaneously by Aqua MODIS and S-NPP VIIRS within an observing time difference of \(\pm\)5 min were extracted by assuming that FRP on the ground and cloud cover remained the same.
## 3 Result and Discussion
### Parametric Study
#### 3.2.2 Effects of the Operating Parameters
Simultaneously, the system exergy efficiency \(\varepsilon_{tot}\) increases firstly and then decreases monotonically corresponding to \(\hat{E}_{\text{D},tot}\). Tendencies of \(\hat{E}_{\text{D},tot}^{\text{EN}}\), \(\hat{E}_{\text{D},tot}^{\text{AV}}\) and \(\hat{E}_{\text{D},tot}^{\text{AV},\text{EN}}\) are similar with \(\hat{E}_{\text{D},tot}\).
## 4 Robots Modeling and Control
### The Control Strategy for the UGVs  
Our UGVs are a unicycle-type mobile robot, whose kinematic model is given by  
\[\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{\psi}\end{bmatrix}=\begin{bmatrix}\cos\psi&-a\sin\psi\\ \sin\psi&a\cos\psi\\ 0&1\end{bmatrix}\begin{bmatrix}u\\ \omega\end{bmatrix}. \tag{1}\]  
Figure 5: Sequence of way-points.  
Observing Figure 6, \(\mathbf{x}=\begin{bmatrix}x&y\end{bmatrix}^{\top}\) is the robot position, \(\psi\) is its heading with respect to \(x\)-axis of the global frame \(\langle W\rangle\), and \(a\) is the linear distance between the point of control and the robot axle. Finally, \(u\) and \(\omega\) are its linear and angular velocities, respectively.  
Representing the robot navigation in polar coordinates, we have  
\[\rho=\sqrt{(x_{d}-x)^{2}+(y_{d}-y)^{2}} \tag{2a}\] \[\theta=\arctan\frac{y_{d}-y}{x_{d}-x}\] (2b) \[\alpha=\theta-\psi, \tag{2c}\]  
where \(\rho\) is the robot-destination distance, \(\theta\) is the desired orientation, and \(\alpha\) is the heading error to the destination.  
In this work, the navigation circuit is given by a set of way-points, without time constraints. Consequently, the robot performs a regulation (positioning) task, in which temporal derivatives of the reference positions are equal to zero. Having this in mind, let us take the first time derivative of (2). By replacing (1), we get the kinematic model in polar coordinates  
\[\begin{bmatrix}\dot{\rho}\\ \dot{\alpha}\end{bmatrix}=\begin{bmatrix}-\cos\alpha&-a\sin\alpha\\ \frac{\sin\alpha}{\rho}&-a\frac{\cos\alpha}{\rho}-1\end{bmatrix}\begin{bmatrix} u\\ \omega\end{bmatrix},\quad\text{or in the compact form}\quad\dot{\mathbf{h}}= \mathbf{B}\mathbf{u}, \tag{3}\]  
with \(\dot{\theta}=\dot{\alpha}+\omega\).  
Assuming that the sequence of way-points favors task-oriented arrival, let us design a controller with no final orientation. In this case, theta is a free variable, with the constraint of being finally bounded. Thus, based on [PERSON] theory, let us take the following radially unlimited candidate function  
\[V(\mathbf{h})=\frac{1}{2}\mathbf{h}^{\top}\mathbf{h}>0. \tag{4}\]  
By applying the feedback linearization technique, we can propose the control signal  
\[\mathbf{u}=-\mathbf{B}^{-1}\mathbf{G}\tanh\mathbf{h}, \tag{5}\]  
with \(\mathbf{G}\) being a positive defined gain matrix.  
Figure 6: The UGV seeking for its destination \(\mathbf{x}_{d}\).  
After applying its first time derivative in (4), and replacing (3) and (5), we get  
\[\dot{V}(\mathbf{h})=\mathbf{h}^{\mathsf{T}}\dot{\mathbf{h}}=\mathbf{h}^{\mathsf{ T}}\mathbf{B}\mathbf{u}=-\mathbf{h}^{\mathsf{T}}\mathbf{G}\tanh\mathbf{h}<0. \tag{6}\]  
Therefore, it is demonstrated that the global asymptotic convergence of \(\mathbf{h}\to 0\), for \(t\to\infty\), which means that the robot will reach its goal in the absence of obstacles. In addition, as a consequence, \(\mathbf{u}\to 0\), also for \(t\to\infty\).
## 12 Lessons Learnt from Recent Tsunami Events  
### 12.1. 20 July 2017 Bodrum-Kos Earthquake and Tsunami  
An assessment of the operational performance of NEAMTWS-TSPs for this event was discussed in detail in [32]. Although the Mw 6.6 Bodrum-Kos earthquake and resulting tsunami was moderate with little damage to properties, it was the first noticeable tsunami in the Mediterranean Basin since the tsunami triggered by the 21 May 2003 Boumerdes Mw 6.8 earthquake in the Western Mediterranean. Tsunami warnings were issued by INGV (Italy) within 10 min, by NOA (Greece) within 18 min and by KOERI (Turkey) within 19 min. Apart from INGV, the responses from NOA and KOERI were not adequate enough to trigger any potential tsunami evacuation in the Bodrum-Kos area. Post-mortem analysis revealed that for KOERI, the instability of the earthquake magnitude determined through the automatic systems was the main result of the message dissemination latency. This event led to the revision of the duty officer's SOP and an additional visual interface was  
Figure 9: Events with magnitudes and corresponding alert levels for the events in the Eastern Mediterranean that necessitated dissemination of tsunami bulletins by KOERI-RETMC since its operationalization as a candidate tsunami service provider as of 1 July 2012.  
developed and installed in the operational system, which would allow the duty officer to visually investigate and compare various magnitude values of different types derived from automatic sources, so that the earthquake magnitude can be selected from this interface directly and a corresponding tsunami message can be created and disseminated. As pointed out in [32], even if a warning time of ~10 min was achieved by KOERI and NOA, it would not have led to any evacuation, simply due to the fact that there was (and still is) no publicly available tsunami warning infrastructure, which remains the major responsibility of the respective civil protection authorities. Another striking aspect is that the tsunami modeling results indicated two-fold nearshore maximum water elevations for an earthquake Mw of 6.8, which is not an unlikely scenario for the Bodrum-Kos area. In addition, the tsunami could have resulted in loss of lives if the earthquake had happened during the daytime in the middle of the tourist season [33]. In fact, this event was a bitter reminder of the inefficiencies of NEAMTWS in dealing with local and near-field tsunami threats and the need for possible supplementary mechanisms [34; 35].
# Development of a Simplified Human Body Model for Movement Simulations
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Joint**} & \multirow{2}{*}{**Left/Right**} & **Measured Movement** & **Angle Min.** & **Angle Max.** & **Torque Min.** & **Torque Max.** \\  & & & [\({}^{\circ}\)] & & [\({}^{\circ}\)] & **[Nm]** & **[Nm]** \\ \hline \multirow{4}{*}{Hip} & \multirow{4}{*}{Left} & F/E & \(-\)5.84 & 93.90 & 30.90 & 144.85 \\  & & A/A & 1.80 & 9.71 & 4.18 & 37.44 \\  & & Rot & \(-\)0.05 & 16.40 & \(-\)11.17 & 7.39 \\ \cline{2-7}  & \multirow{4}{*}{Right} & F/E & \(-\)3.84 & 93.86 & 28.21 & 136.20 \\  & & A/A & \(-\)2.09 & 8.58 & 2.96 & 58.90 \\  & & Rot & \(-\)6.65 & 3.41 & \(-\)10.06 & 9.87 \\ \hline \multirow{4}{*}{Knee} & \multirow{4}{*}{Left} & F/E & 0.06 & 51.62 & 31.90 & 60.36 \\  & & A/A & \(-\)2.20 & 0.46 & \(-\)30.34 & \(-\)4.88 \\  & & Rot & \(-\)6.69 & 2.12 & \(-\)7.35 & \(-\)2.52 \\ \cline{2-7}  & \multirow{4}{*}{Right} & F/E & \(-\)1.69 & 54.29 & 31.22 & 62.36 \\  & & A/A & \(-\)8.04 & 1.48 & \(-\)31.93 & \(-\)10.08 \\  & & Rot & \(-\)7.88 & 2.68 & 2.07 & 7.52 \\ \hline \multirow{4}{*}{Ankle} & \multirow{4}{*}{Left} & F/E & \(-\)7.17 & 2.29 & \(-\)52.14 & \(-\)51.72 \\  & & A/A & \(-\)8.00 & \(-\)2.75 & \(-\)0.51 & 3.43 \\  & & Rot & \(-\)3.64 & 0.66 & \(-\)0.27 & 0.33 \\ \cline{2-7}  & \multirow{4}{*}{Right} & F/E & \(-\)6.58 & 3.13 & \(-\)52.16 & \(-\)51.48 \\  & & A/A & \(-\)7.62 & \(-\)3.70 & \(-\)4.99 & 0.73 \\  & & Rot & \(-\)5.25 & 0.64 & \(-\)0.69 & 0.50 \\ \hline \multirow{4}{*}{Pelvis} & \multirow{4}{*}{-} & F/E & \(-\)47.34 & 6.58 & 14.18 & 157.84 \\  & & A/A & \(-\)12.53 & 6.21 & \(-\)75.74 & 86.78 \\  & & Rot & \(-\)7.46 & 2.15 & \(-\)106.79 & 18.77 \\ \hline \multirow{4}{*}{Thorax} & \multirow{4}{*}{-} & F/E & 1.59 & 50.04 & \(-\)18.40 & 137.24 \\  & & A/A & \(-\)2.71 & 4.68 & \(-\)54.49 & 21.78 \\ \cline{1-1}  & & Rot & \(-\)7.81 & 4.93 & \(-\)17.98 & 17.97 \\ \hline \multirow{4}{*}{Head} & \multirow{4}{*}{-} & F/E & \(-\)6.16 & 35.15 & \(-\)3.46 & 7.35 \\ \cline{1-1}  & & A/A & \(-\)3.27 & 28.19 & \(-\)2.20 & 2.00 \\ \cline{1-1}  & & Rot & \(-\)5.83 & 21.86 & \(-\)0.12 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Extreme values of the angular displacements and moments of forces for the part of the developed model obtained from the simulation based on measurements using the IMU system. are generally much smaller than for F/E or A/A, the highest for Thorax (nearly 18 Nm). Overall, the movements obtained were very close to those observed in reality, and the results were consistent with the assumptions. This proves that it is possible to reproduce movements using the developed human body model and that measurements from IMU sensors can be used for this purpose. More importantly, the developed model allows for the simulation and analysis of various parameters with regard to both kinematics (movement) and dynamics (moment of force).
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 4 Mobile LiDAR Sensing Circuit and Chip Design
### CORDIC Angle Calculator
#### 4.3.4 XYZ Iteration Architecture  
This architecture is a pipelined operation. When the operation proceeds from outputting values to the next iteration level, the data will be input at the same time. The number of layers in the iterative architecture is determined by selecting the angle of Table 3 and then determining the number of layers. However, since the hardware architecture cannot be different every time, the maximum number of rotations is fixed, at seven layers. _Xi_ is the origin of the COS and substitutes 1. _Yi_ is the origin of the SIN and is substituted for 0. _Zi_ is the input angle, or the angle at which the SIN and COS values need to be found, and \(\mathbf{\theta}\)_i_ is the angle to be compared for input fixation. The highest bit of the input angle of _Zi_ and the highest bit of the fixed input angle of \(\mathbf{\theta}\)_i_ are used as the ratio. This will determine the initial comparison input angle, After the comparison, we select the number of displacements \(i\), and determine the operation of the addition and subtraction of _Xi_ and _Yi_. After the calculation, the values _Xi_, _Yi_ and _Zi_ continue to be substituted into the next-order iterative generation to repeat the operation until the value of the input angle _Zi_ is less than 0.001, and the iterative operation is ended. When  
Figure 10: CORDIC vertical angle calculator.  
**Xi**, **Yi** and **Zi** are converted into the second-order iterative generation from the first-order iterative generation, the data will be re-substituted into the first-order iterative generation to achieve timely operation, as shown in Figures 11 and 12.  
The CORDIC hardware circuit data processing is presented in Tables 5 and 6 When the vertical angle or horizontal angle is input into the system, **Xi** and **Yi** will input the initial rotation value at the same time, and the data will be input continuously.  
Figure 11: Horizontal angle iteration architecture.
## 4 Conclusions  
The present study indicated that there were significant differences in the contents of phenols and polysaccharides and volatile profiles of noni juice produced under different fermentation time and scale conditions. The flavonoid content in noni juice showed an increasing trend throughout the fermentation process and reached its highest level at L63 and F49 (36.73 \(\pm\) 2.51 mg RE/100 mL and 43.28 \(\pm\) 1.66 mg RE/100 mL, respectively). A total of 73 volatile compounds were identified in fresh and fermented noni juice, in which methyl hexanoate (1.39-3.33%), 3-methyl-3-buten-1-ol (0.47-5.57%), octanoic acid (28.88-43.75%), hexanoic acid (12.20-36.93%) and 2-heptanone (3.59-39.82%) were the major aroma components. The contents of hexanoic acid and octanoic acid in F0 and F63 decreased from 34.08 and 39.69% to 12.20 and 33.41% in noni juice after factory fermentation, respectively, which resulted in a less pungent aroma. To the best of our knowledge, this is the first report on the changes of volatile profiles in noni juice during the fermentation process. The results of the present study may contribute to the improvement of noni juice quality.  
Conceptualization, W.L.; methodology, R.D.; formal analysis, K.C.; investigation, Z.W. and R.D.; resources, C.L. and R.Y.; data curation, K.C., Z.W. and R.D.; writing--original draft preparation, Z.W. and R.D.; writing--review and editing, W.L. and R.Y.; validation, K.C.; funding acquisition, W.L. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hainan Province, China (grant number: 320 RC511) and the National Natural Science Foundation of China (grant number: 31760456).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflict of interest.  
Samples of the compounds are available from the authors.