esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2015**, _15_, 19331-19350; doi:10.3390/s150819331  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks**  
**[PERSON] and [PERSON] ***  
Department of Electronic Engineering, Kyungpook National University, Daegu 702-701, Korea;  
E-Mail: [EMAIL_ADDRESS]c.kr  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-53-950-7577; Fax: +82-53-950-5505.  
Academic Editor: [PERSON]  
_Received: 5 June 2015 / Accepted: 3 August 2015 / Published: 6 August 2015_  
**Abstract:** In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node's energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network.  
**Keywords:** underwater sensor network; autonomous underwater vehicle; TDMA; clustering; Voronoi region
## 3 Results and Discussion
### Stability of the Catalyst  
In a heterogeneous PF like catalyst, the stability of the catalysts is of great significant due to the economic and environmental effects. The stability was investigated through recycling of the catalyst and the results are shown in Figure 10. The reaction conditions deployed entailed the catalyst dosage, 0.3 g/L, the PS, 5 mM/L, pH value 6.5, with the initial RR198 dye concentration being 25 mg/L (Figure 10).  
After each reuse cycle, the catalyst was washed with deionized water and added into the process. The removal efficiency of RR198 decreased slightly with the increase of the number of cycle runs. During eight consecutive runs (Figure 10), the removal efficiency of RR198 and COD decreased from 99.2% to 86%. This might be due to the adsorption of byproducts on the surface of AC/CFO. The limiting parameter for the Fenton-like catalyst process is the production cost of the solid catalyst [26]. Higher catalyst loading would increase the removal cost prohibitively; higher concentration would cause the generation of a large volume of sludge, which would necessitate the need for the sludge disposal process [24].  
Figure 9: Influence of two radical scavengers on removal efficiency of RR198 under optimum conditions.
# Intersatellite and Downlink Agile Attitude Maneuvers
## 6 Attitude Maneuvering for Intersatellite Connection
### Command Attitude  
The spacecraft attitude is referred again to the Earth-centered inertial frame (ECI), associated with vectrix \(\underline{N}\). The final objective is in defining the commanded attitude, identified by \(\underline{C}\), in both phases, i.e., tracking and reorientation. In order to achieve correct pointing, a specified spacecraft body axis (i.e., \(\hat{l}_{c}\)) must be aligned with the relative position vector that points from S to T.  
In the ECI-frame, the position vectors \(\vec{r}_{S}\) and \(\vec{r}_{T}\) can be written in terms of orbit elements, using Equation (41) for both S and T.  
Figure 16: Non-visibility condition (\(\theta>\theta_{S}+\theta_{T}\)).  
Figure 17: Visibility condition (\(\theta<\theta_{S}+\theta_{T}\)).  
The commanded attitude is defined through the following relations:  
\[\hat{t}_{c}=\frac{\overline{r}_{T}-\overline{r}_{S}}{||\overline{r}_{T}-\overline {r}_{S}||} \tag{70}\]  
\[\hat{J}_{c}=\frac{\hat{t}_{c}\times\hat{c}_{3}}{||\hat{t}_{c}\times\hat{c}_{3}||} \tag{71}\]  
\[\hat{k}_{c}=\hat{t}_{c}\times\hat{J}_{c} \tag{72}\]  
For this problem, the commanded attitude reference frame is illustrated in Figure 18. Using Equations (70)-(72), the unit vectors of the commanded attitude are resolved in the ECI-frame, and the rotation matrix that relates the commanded reference frame \(\underline{\underline{C}}\) to the inertial reference frame \(\underline{\underline{N}}\), denoted with \(\underset{c\gets N}{R}\), can be found in closed form.  
The time derivative of matrix \(\underset{c\gets N}{R}\) is given by  
\[\underset{c\gets N}{\hat{R}}=\left(\frac{\partial\underset{c\gets N} {R}}{\partial\theta_{*T}}+\frac{\partial\underset{c\gets N}{R}}{\partial \theta_{tT}}\right)\theta_{*T}+\left(\frac{\partial\underset{c\gets N}{R} }{\partial\theta_{*S}}+\frac{\partial\underset{c\gets N}{R}}{\partial \theta_{tS}}\right)\theta_{*S} \tag{73}\]  
Equations (47) and (50) still hold in this context. Finally, the derivative of the commanded angular velocity is given by  
\[\underline{\underline{\omega}}_{c}^{\text{(C)}}=\left(\frac{\partial\underline {\omega}_{c}^{\text{(C)}}}{\partial\theta_{*T}}+\frac{\partial\underline{ \omega}_{c}^{\text{(C)}}}{\partial\theta_{tT}}\right)\theta_{*T}+\left(\frac{ \partial\underline{\omega}_{c}^{\text{(C)}}}{\partial\theta_{*S}}+\frac{ \partial\underline{\omega}_{c}^{\text{(C)}}}{\partial\theta_{tS}}\right)\hat{ \theta}_{*S} \tag{74}\]  
The preceding developments lead to finding the desired attitude and angular rate in tracking intervals. The desired attitude in reorientation arcs can be found by predicting the inertial position of T at the time when it becomes visible; in this context, \(\underline{\underline{\omega}}_{c}^{\text{(C)}}\) is set to \(\underline{0}\), as well as its time derivative.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 2 Methods
### Decision Tree (DT)  
Decision tree is a typical supervised classification method that approximates discrete function values to classify data. First, it processes data and uses an inductive algorithm to generate readable rules and decision trees. Then, it uses the decision to analyze new data. Essentially, decision trees classify data through a series of rules.
## 6 Conclusions  
The proposed method of impedance matching based on the full current mode for optimizing wireless power transfer efficiency has significant advantages in terms of energy transfer efficiency and tracking detection. This system uses an innovative approach to transferring energy to the target device. It achieves optimized energy transfer efficiency by controlling the conduction and shutdown of the switch transistor. In turn, the tracking effect expands and extends.  
When using the full current mode impedance matching method, closely monitoring changes in transmission efficiency through real-time monitoring of system parameters such as input power, output power, and transmission distance is crucial. By analyzing and optimizing these parameters, the system can achieve maximum efficiency and optimal energy utilization results. The impedance matching method for wireless power transfer described in this paper, based on full current mode, achieved excellent efficiency tracking in both modes. Whether tracking current or efficiency, the matching degree was controlled within a range of 92-95%.  
Furthermore, the impedance matching method based on the full current mode for optimizing wireless power transfer efficiency has great potential for further expansion. By modifying the system's circuit parameters and structure, transmission efficiency and distance can be further optimized. This method can be extended to various applications, including wearable and IoT devices. Therefore, the application and optimization of this system should be explored and studied to achieve broader applications and more efficient energy utilization.  
In summary, the impedance matching method based on the full current mode for optimizing wireless power transfer efficiency has significant advantages in terms of energy transfer efficiency, and tracking detection. It also has great expansion potential. In future research, we will continue to explore and optimize this system to achieve better results and broader applications.  
Conceptualization, Y.X. and T.W.; methodology, Y.X. and T.W.; validation, Y.X., Y.Z. and T.W.; formal analysis, Y.X.; investigation, Y.Z.; resources, Y.X. and T.W.; data curation, Y.Z.; writing--original draft preparation, Y.X. and Y.Z.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, Y.X. and T.W. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the National Natural Science Foundation of China (Grant No. 51677058).  
Not applicable.  
The authors have no competing interests or other interests that might influence the results and/or discussion reported in this paper.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 2 Microbial Chassis as Cell Factories for Edible Terpenoids
### Prokaryotic Microorganisms
#### 2.1.4 Cyanobacteria  
Cyanobacteria are a kind of photosynthetic autotrophic microorganism that can grow using sunlight and trace minerals without an additional carbon source. Due to their endogenous MEP pathway, cyanobacteria have been recognized as novel promising hosts as a large-scale platform for light-driven terpenoid biosynthesis [47; 48].  
There are three model cyanobacteria, _Synechococcus elongatus_ PCC 7942, _Synechococcus_ sp. PCC 7002, and _Synechocystis_ sp. PCC 6803 [38]. Scientists compared the potential of two models, \(S\). PCC 7002 and \(S\). PCC 7942, in producing terpen. They expressed the corresponding terpene synthase by the same strong promoter in the plasmid into the two hosts and measured their terpene yield. It was found that different chassis have different product spectrums. It is suggested that the choice of host is a crucial step when designing and constructing synthesizing terpene cell factories [49]. Using \(S\). PCC 7942 as the host, it only requires the expression of codon-optimized cineole synthase (CnsA) to directly convert intracellular GPP into 1,8-cineole [38]. Similarly, when the gene of pinene synthase originated from _Abias grandis_ (_Ag_PS) was integrated into the chromosome, the pinene titer of \(S\). PCC 7002 was achieved at 1.525 mg/L under the condition of CO\({}_{2}\) aeration and continuous illumination [39]. In \(S\). PCC 6803, [PERSON] et al. knocked out squalene synthase and squalene hopene cyclase coding sequences for the FPP branch pathway to avoid squalene by-products. Furthermore, they knocked down the key gene _crtE_ in the GGPP derivative pathway and introduced a heterologous FPP synthase (IspA) to increase the supply of precursor FPP. In order to further improve the conversion rate, they fused IspA and valencene synthase (_Ct_VS) for co-expression. The resulting strain produced 19 mg/g DCW of valencene [40]. Unlike other work producing terpenoids using traditional enzyme engineering and pathway engineering strategies, [PERSON] and co-workers focused on optimizing cultivation conditions in the isoprene production in cyanobacteria. They found that the yield of isoprene increased under violet light and elevated temperature. This provides a new strategy for other cyanobacteria-based cell factories to produce terpenoids [50].
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 3 Experimental Validation of Selected Fault Features  
### Configuration of a USV Fault Diagnosis System  
In this study, a fault diagnosis system in a USV was designed and developed, as shown in Figure 4, to diagnose faults, specifically the breakage of the thruster blade and entanglement of floating objects. The USV consisted of a battery, a control system, two thrusters and a fault diagnosis system, all of which were placed in a rubber boat. The fault diagnosis system consisted of different sensors to measure the data of fault features and a data acquisition (DAQ) system for data collection and transmission.  
Table 1 summarizes the types of sensors for acquiring data on the fault features and DAQ modules connected to each sensor.
# A Low Cost Vision-Based Road-Following System for Mobile Robots
## 6 Experiments
In order to evaluate the performance of the proposed system, the variance error is compared based on the measured line, detected line and estimated UKF lines; see Figure 14 for an example of an experiment with asphalt road in a parkway under sunshine illumination. The red line is the error of the measured lines detected by the color-based module and the texture line detector. The green line is the error of robot's motion model that varies with time, which is based in the accuracy of the robot's odometer. The error of estimated UKF lines is shown as the blue line. As indicated in Figure 14, the filter keeps the inertia of the estimated state and filters the high variance of the measurements and the motion model. The maximum error of UKF lines is 352 mm, and the average error of UKF lines is 33.6 mm. As a conclusion, the UKF produces minimal error and more stable road detection and tracking target position, even in the frames where the road boundaries are occluded. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-Process** & **Time (ms)** & **Percentage (\%)** \\ \hline Extraction of the region of interest & 4.4915 & 6.03\% \\ Smooth the image & 0.47 & 0.63\% \\ Apply white balance filter & 10.31625 & 13.84\% \\ Apply RGB normalization & 5.6225 & 7.55\% \\ Convert to HSV color space & 2.70725 & 3.63\% \\ Extraction of road window & 0.403 & 0.54\% \\ Image color segmentation & 1.4385 & 1.93\% \\ Morphological filtering & 3.4315 & 4.61\% \\ Border validation & 0.457 & 0.61\% \\ Road border estimation & 1.17075 & 1.57\% \\ IPM & 11.79625 & 15.83\% \\ Extraction of straight lines & 12.57375 & 16.88\% \\ Fusion of color-based borders and texture borders & 9.503 & 12.76\% \\ UKF computation & 5.16875 & 6.94\% \\ Draw IPM lines, 3D points and Kalman borders & 4.951 & 6.65\% \\ Total computation time [ms] & 74.5 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The required computation time for image processing. Figure 14: Comparison of the error of the estimated UKF with the errors of the motion model and the measure mode. Figure 13: Road detection and target position of sequence frames. Estimated road borders (red). 2 \(\times\) 5 m grid (green). Road tracking target position (black square). (**a**) Sidewalk; (**b**) Bikeway; (**c**) Parkway; (**d**) Parkway with sunset.
## 3 Results and Discussion
On the other hand, the ability of the ANVC system to simultaneously reduce the contribution of other vibrating interior parts to the SPL at the virtual error microphones can be considered an advantage. A combined structural and acoustic error sensing scheme could lead to a compromise between global sound power and local sound pressure reduction. The realization of such a scheme is beyond the scope of this work and remains a future task. Research work for an industry-compatible integration of the active components into the lining base structure is currently being carried out in the EU-project ACASIAS [21]. To test the performance of the smart lining under real aircraft conditions, flight tests in a propeller-driven aircraft are planned. Of special interest are the noise reduction performance and the robustness of the smart lining under varying temperature and pressure conditions. Initial robustness tests under variable temperature conditions in the laboratory will be done in the near future within the ACASIAS project. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Virtual Microphones** & \(\Delta SPL_{mean}\)** **dB/dB(A)** & \(\Delta SPL_{max}\)** **dB/dB(A)** \\ \hline
2 & 24,26 & \(-1.8/-7.5\) & 29/31 \\
3 & 4,6,24,26,44,46 & 4.8/1.3 & 15/13 \\
4 & 4,6,14,16,24,26,34,36,44,46,54,56 & 6.7/4.0 & 16/16 \\
5 & all & 8.0/5.3 & 13/12 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean and maximum SPL reductions of the configurations averaged over all 60 microphones. Figure 7: Vibration level distribution on the lining surface. Two different virtual microphone configurations (2 and 5) are compared to the the uncontrolled case (1). **Funding:** The research work documented in this manuscript is part of the project Advanced Concepts for Aero-Structures with Integrated Antennas and Sensors (ACASIAS) funded by the European Union under Grant No. 723167. **Acknowledgments:** The author gratefully acknowledges the support of [PERSON], [PERSON], [PERSON] and [PERSON] from DIEHL Aviation Laupleim. **Conflicts of Interest:** The author declares no conflict of interest.
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
###### Abstract  
High-frequency (HF) communication is critical for applications such as over-the-horizon positioning and ionospheric detection. However, precise time-delay estimation in complex HF channels faces significant challenges from multipath fading, Doppler shifts, and noise. This paper proposes a Modulation Signal-based Adaptive Time-Delay Estimation (MATE) algorithm, which effectively decouples carrier and modulation signals and integrates phase-locked loop (PLL) and delay-locked loop (DLL) techniques. By leveraging the autocorrelation properties of 8 PSK (Eight-Phase Shift Keying) signals, MATE compensates for carrier frequency deviations and mitigates multipath interference. Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. Validation with actual measurement data further confirms the robustness of MATE against channel variations. MATE offers a high-precision, low-complexity solution for HF time-delay estimation, significantly benefiting applications in HF communication systems. This advancement is particularly valuable for enhancing the accuracy and reliability of time-of-arrival (TOA) detection in HF-based sensor networks and remote sensing systems.  
high frequency (HF); time-delay estimation (TDE); time-of-arrival (TOA); delay lock loop (DLL) 2025  
3
## 4 Results and Validation
### Assessment of Homogeneous Receivers
Receivers with homogeneous type are used as rover sites. These statistics reflect two findings. First, the IFCB of selected rover stations agrees well with that of the base stations used to calculate the WL and NL UPDs. Second, the proposed method can even achieve better performance than the homogeneous receivers.
# Generating Image Descriptions of Rice Diseases and Pests Based on DeiT Feature Encoder
## 3 Image Caption Model
### Model Structure
#### 3.1.1 DeiT  
To compensate for the lack of data in the image description dataset of rice diseases and pests, we used the distillation pre-trained DeiT [36] as the encoder of this model. The DeiT model is small and effective. Using it as an encoder in the model can significantly reduce the training time of the model on the rice pest image description generation task and achieve great training results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & \multicolumn{2}{c}{**Manually Annotated Reference Sentences**} \\ \hline  & 1. & Bacterial blight of rice in which the leaves have yellow stripes \\  & 2. & Rice bacterial leaf blight, rice leaf with yellow stripes \\  & 3. & Bacterial blight of rice in which the leaves have yellow \\  & 4. & Rice bacterial leaf blight, rice leaf lesions with yellow stripes. \\ Bacterial blight & 5. & Bacterial blight disease of rice in which the leaves show \\  & & yellow stripe lesions. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of rice pest and disease image descriptions.  
Figure 2: Rice pest and disease image description model.  
The DeiT model was used for the image classification task. It was trained by the teacher-student training strategy. The distillation training process is shown in Figure 3. The teacher model is typically selected as a high-performing image classification model, often a CNN-based model like RegNet. Meanwhile, the student model is based on the fundamental structure of ViT (Vision Transformer). Compared with the ViT model, the DeiT model has an additional distillation token through which the student model continuously extracts from the teacher model to learn the generalization ability of the teacher model.  
There are two types of distillation methods, soft distillation and hard-label distillation, of which soft distillation is:  
\[\mathcal{L}_{global}=(1-\lambda)\ \mathcal{L}_{CE}(\varphi(Z_{s}),\mathcal{Y})+ \lambda\tau^{2}KL(\varphi(Z_{s}/\tau),\varphi(Z_{t}/\tau)) \tag{1}\]  
The loss is calculated from the output of the student model and the true label. The KL (Kullback-Leibler) divergence loss is calculated from the output of the student model and the output of the teacher model. \(\lambda\) is the coefficient that balances the KL divergence loss. The cross-entropy (\(\mathcal{L}_{\text{CE}}\)) is on ground-truth label \(\mathcal{Y}\), and \(\varphi\) is the softmax function. \(\mathcal{L}_{\text{global}}\) guides the whole distillation training process.  
The hard-label distillation is:  
\[\mathcal{L}_{global}^{hardDistill}=\frac{1}{2}\ \mathcal{L}_{CE}(\varphi(Z_{s}), \mathcal{Y})+\frac{1}{2}\ \mathcal{L}_{CE}(\varphi(Z_{s}),\mathcal{Y}_{t}) \tag{2}\]  
The first loss is calculated from the output of the student model and the true label. The second loss is calculated from the output of the student model and the teacher model. \(\mathcal{L}_{global}^{hardDistill}\) is obtained by adding the two.
## 3 Problem Definition and Solution Method
### Choosing a Reactive Scheduling Approach  
Our problem requires basic scheduling at the beginning of the production schedule and rescheduling as a result of disruptions caused by production errors or the incorrect estimation of production time.  
In our article, periodic and complete rescheduling methods will be studied using the scheduling approach known as predictive-reactive scheduling in the literature. The predictive reactive scheduling approach was preferred because it responds more flexibly to real-time events and allows strategy transitions.  
The completely reactive scheduling approach is not suitable for our problem because the main purpose of our thesis is to provide the manager with a production plan and estimated performance evaluations at time \(t_{0}\). In other words, it should include a result prediction before production begins.  
Again, robust proactive scheduling is also not suitable for our problem. The difference between predictive-reactive approaches and robust-proactive approaches is mainly due to the fact that in robust-proactive approaches rescheduling is not conducted online; instead, one of several previously estimated schedule solutions is selected. The fact that there are too many variables and random situations in our problem makes it impossible to make robust scheduling for every possibility in advance.