esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Smart Gate Processes
Going into the specifics of the process of entering the terminal, it begins, as mentioned above, with the need for an entity to deliver/collect the goods and with the hauler/driver requesting authorization to access the terminal for his/her lorry driver and making or not making a prior appointment for the service. The driver then heads for the terminal and then to the four different checkpoints in the case of normallories. For large exceptional vehicles, access is via a parallel road. Non-exceptionallories go to checkpoint 1 without stopping and then to checkpoint 2, stopping at the kiosk. Depending on his/her situation, the driver can have two different outcomes: (i) go to checkpoint 4 if everything is fine, or (ii) go to checkpoint 3 if he/she cannot solve the problem in the short term. At checkpoint 3, if the driver manages to resolve his/her situation, he/she should proceed to checkpoint 2; if not, he/she should return to the road. In the end, when everything is in order, the driver goes to gate IN and enters the terminal. Thus, the driver and his/her cargo will only enter the terminal when three conditions are met: container, driver, and vehicle are identified and authorized. Figure 3 illustrates the terminal access process. After the general explanation of the process, the input process will now be detailed. Firstly, Figure 4 illustrates the layout of the proposal for the first checks. This detailed explanation will be divided into four checkpoints, and each of these parts is explained in this subsection. Figure 4: Representation of the layout of the four checkpoints. Figure 3: BPMN of the smart terminal access process (BPMN 2.0). 2.1. Checkpoint 1--Non-Stop Station for Checking Vehicle Type and Reading Truck/Container/Galley Data Automatically  
The driver goes to the terminal and, depending on the type of vehicle/load identified through a recognition system (OCR), the driver must follow the route indicated for the purpose. If the driver has an authorized heavy goods vehicle, he/she goes to checkpoint 2. This phase also involves the automatic reading and collection of (i) truck and galley registration using LPR and (ii) container data through ACCR. By collecting these data and comparing them with the information in the schedules, the system can validate the existence or non-existence of a schedule. After this check, the driver will be told which route to take (it differs according to whether he/she has an appointment or not):  
* If the driver has an appointment, there will be signs directing him/her to the left-hand lanes;
* If the driver does not have an appointment, he/she goes to those on the right. In addition, information about the request to weigh the goods will be identified at checkpoint 1, and if there is no prior request to weigh the goods, they will proceed to the lane without a weightridge; otherwise, the driver will proceed to the lane with a weightridge.
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
## 2 Related Work
### Source-Based Structure  
Some researches on the sink mobility exploit source-based structure. In the approach, a source detecting an event initiates construction of a structure. The structure provides the route tending to the source on whole network. A mobile sink might loads location update message thereby delivering the message to the source. The source reports detecting data to the sink through the reverse route. The most representative is Two-Tier Data Dissemination (TTDD) model for large-scale WSNs [21].  
In TTDD, once an event is detected, the source pro-actively builds a virtual grid structure, as shown in Figure 5 left. The source chooses itself as the starting grid point in the grid structure and then sends notification to its four adjacent grid points of the structure. The grid point nodes receiving the notification message become the downstream nodes of the source. The notification process continues until the grid structure is completely constructed on whole network. A sink propagates its location to the closest grid point node by local flooding. The grid point node sends the message to its upstream node. This location update continues until the source receives the message. Once the source receives the location update message from one of its downstream grid point nodes, the source sends out data to this grid point node. The data report process continues until the data reach the mobile sink. In individual object tracking, TTDD supports sink mobility without both flooding and intermediary.  
On the other hands, in continuous object tracking, TTDD might causes a serious dissipation of energy. As mentioned above, in TTDD, the grid structure is constructed by a source as the initiator. In continuous object tracking, a number of sources are generated because a continuous object is large and diffusing continuously. Therefore, the sources initiate the construction of grid structures respectively like in Figure 5 right. The overhead to construct grid structures dramatically increases in proportion to the number of sources. The construction overhead might shorten the network lifetime due to the excessive consumption of energy.
###### Abstract  
Recently, the array-invariant method was proposed to passively localize sources of opportunity in shallow water. It exploits multiple arrivals which are different in terms of beam angle and travel time. Conventional plane-wave beamforming in the existing array-invariant method is used to obtain beam-time migration. The resolution capability of conventional plane-wave beamforming is determined by array aperture, which, however, limits the localization accuracy of the existing array-invariant method. To improve the localization accuracy, this study proposes the use of two-dimensional (2D) deconvolution to obtain a better beam-time migration than in conventional plane-wave beamforming. Our simulation with a small horizontal array showed that the range estimation error of the proposed array-invariant method based on 2D deconvolution was only one-third of that of the existing method. The experiment also demonstrated the validity of our proposed method.  
array invariant; underwater target location; 2D deconvolution +
Footnote †: journal: Applied  
with conventional plane-wave beamforming to obtain the high-resolution beam-time migration. The more accurate source range can thus be estimated from the high-resolution beam-time migration. The existing array-invariant method [11] involving blind deconvolution and conventional plane-wave beamforming is referred to as the reference method in this study. The simulation with a small horizontal array in shallow water shows that the source-range estimation error of our proposed method based on the 2D deconvolution is only one third of that of the reference method. The experiment with a small horizontal array in the Yellow Sea of China also proves the validity of our proposed method. Additionally, the results in this study also demonstrate that the wide beam-time migration obtained with conventional plane-wave beamforming in the array-invariant method is one of the main reasons behind source-range estimation errors.  
This brief report is organized as follows: Section 2 describes the Green's function and blind deconvolution for the horizontal array. In Section 3.1, conventional plane-wave beamforming is reviewed. In Section 3.2, the 2D deconvolution method is proposed to deconvolve the beam-time migration obtained with conventional plane-wave beamforming. The 2D deconvolution method can obtain high-resolution beam-time migration. The simulation and the experiment are described in Sections 4 and 5, respectively. The simulation and the experiment demonstrate the validity of our proposed method. Finally, our conclusion is provided in Section 6.
# Downregulation of Squalene Synthase Broadly Impacts Isoprenoid Biosynthesis in Guayule
## 4 Materials and Methods
### Agrobacteriumium and Leaf Tissue Transformation  
_Agrobacteriumium_ and leaf tissue transformation were performed based on [PERSON] et al. methods [12; 69]. For _Agrobacteriumium_ transformation, the overnight _Agrobacteriumium_ culture was prepared by inoculating 20 uL glycerol stock into a 50 mL Falcon tube containing 5 mL Luria-Bertani (LB) medium plus 40 mg/L rifampicin and 200 mg/L streptomycin, shaking vigorously at 200 rpm at 28 \({}^{\circ}\)C for 16 h. The suspension was then centrifuged for 15 min at 3500 rpm at room temperature. The supernatant was discarded, and the pellet was re-suspended in 25 mL of inoculation solution [1/10 Murashige and Skoog (MS) salts [70] plus 6-benzylaminopurine (BA; 2 mg/L), 1-naphthaleneacetic acid (NAA; 0.5 mg/L), glucose (10 g), acetosyringone (200 uM), pluronic F-68 (0.05%), and pH 5.2].  
For leaf transformation, leaves were cut from plants grown in 1/2 MSI0.1 [1/2 MS containing indole-3-butyric acid (IBA 0.1 mg/L), sucrose 15 g/L, agar 8 g/L, pH 5.8] in Magenta boxes]. The adaxial side of each leaf was placed facing up in a Petri dish containing 5 mL _Agrobacteriumium_ suspension. The leaf was cut into \(\sim\)10 mm strips and immediately placed in an empty Petri dish in a non-overlapping manner. When this Petri dish was full, all leaf strips were blotted with the filter paper and placed into another empty Petri dish. The Petri dish was sealed by parafilm and placed in the dark. After 3 days, the leaf strips were transferred to MSB1T400 [MS medium with BA 1 mg/L, NAA 0.25 mg/L, sucrose 30 g/L, agar 8 g/L, and timentin 400 mg/L] for recovery under low light. After 5 days, the leaf strips were then transferred to MSB0.75 TK30 [MS medium containing BA 0.75 mg/L, NAA 0.25 mg/L, sucrose 35 g/L, agar 8 g/L, timentin 250 mg/L, and Kanamycin 30 mg/L] for selection under low light. After two weeks, the leaf strips were subcultured to the same MSB0.75 TK30 medium but grown under high light. Subculturing was performed every 2 weeks under high light until green shoots emerged. Green shoots 10 mm and longer were transferred to 1/2 MSI0.1 TK10 [same as 1/2 MSI0.1 but with timentin (200 mg/L) and kanamycin (10 mg/L)] for root development. After 1-2 months, shoot tips of the rooted plantlets were transferred to 1/2 MSI0.1 TK10 for micropropagation or transplanted into soil.
## Case Study: Data Analysis and Results
### Bayesian Statistics and Hierarchical Model Fitting  
The output of the previous processing step is a table which reports a relative point target power per target and scene. A graphical representation is shown in Figure 8. This is the input data to derive the transponder ERCS and its confidence interval.  
In order to estimate the target powers from all available data, daily RADARSAT-2 and transponder drifts need to be estimated and compensated, as described in the following Section 4.3.1. Then, the transponder ERCS can be computed with the measurement model Equation (4). The final, combined uncertainty shall include the uncertainties which are incorporated in each step. This problem is solved with Bayesian statistics, which directly allows to estimate the uncertainty for all derived parameters.
## 2 Materials and Methods
### Sample Collection and Test  
In this study, the satellite image data of Weigan River Basin oasis in April 2018 is used as the reference map, and the precision of remote sensing image is 30 m. Sampling points are arranged in Weigan River Basin oasis through GIS to cover the whole oasis, and field sampling is conducted from 5 April to 5 May 2020. No large-scale irrigation activities were carried out in the Weigan River Basin oasis during this period, and the evaporation was strong; therefore, it was a typical salinization period in this area and was representative. In the sampling process, the soil texture and vegetation type in the study area are fully considered, and the sample points arranged are appropriately modified according to the actual land use conditions in the study area, so as to achieve uniform sampling and facilitate geostatistical analysis. A total of 92 sampling points is arranged (Figure 2). In order to reduce the soil difference, five sampling points are arranged with the sampling point as the center and 10 m as the radius to form a quincunx distribution. The hand-held undisturbed sampling drill (Model: HW-T30, drill diameter: 38 mm) is used to carry out sampling in the layers between 0 m and 0.25 m, between 1 m and 2 m, and between 2 m and 3 m. The  
Figure 1: Salinization landscape in the study area: reclamation of saline alkali land (**a**); oasis edge salinization (**b**); TDS of river water increases (**c**); salinization in the lower reaches of the river (**d**).  
corresponding layers of soil at the five sampling points are mixed and about 0.5 kg soil samples are taken back to the laboratory by quartering method.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 6 Conclusions  
The purpose of this research work is to increase the number of processed tracks of a two-tier hierarchical fusion process in a fusion cycle by creating a balanced fusion tree. The balanced fusion tree can balance fusion workload across local fusion nodes. Clustering sensor nodes is an effective topology control approach [16]; and it can be used to create fusion tree for the two-tier hierarchical fusion process. However, the normal clustering methods for wireless sensor networks do not guarantee to create a balanced fusion tree. In [12], we proposed the fusion tree generation (FTG) algorithm which considers fusion workload. However, the FTG models the fusion workload of a radar by counting the number of its neighbor nodes. Therefore, the FTG does not guarantee to create a balanced fusion tree when targets are non-uniformly distributed.  
In this paper, we have proposed the non-uniform FTG (NU-FTG) algorithm executed in a fully distributed manner. In the NU-FTG, the fusion workload of a node is in proportion to the number of its own and its neighbors' tracks. With this approach, the NU-FTG is guaranteed to create a balanced fusion tree not only when targets are uniformly distributed, but also when targets are non-uniformly  
Figure 6: Results of experiments for an unsynchronized start time.  
distributed. The performance of the NU-FTG was compared to the performance of FTG [23] and HEED [16] in the OPNET (Optimized Network Engineering Tool), network simulator; the simulation results are presented in Section 5. In Section 5.2, the NU-FTG performs better than FTG and HEED as the number of deployed targets increases. This result shows that the NU-FTG is more robust than the others in varied number of targets. In Section 5.3, the NU-FTG performs better than FTG and HEED in the non-uniform distribution of targets. In Section 5.4, the performance of the NU-FTG is evaluated for the unsynchronized start time of radars. Section 5.4 shows that the NU-FTG can guarantee the performance of two-tier fusion process, even though the start time of radars are unsynchronized.  
The future work is to design the algorithm which considers the dynamic environments: movement and failure of nodes, and movement of targets as a large group. Additionally, we will design a constraint programming model to solve FTG problems; this model can be used to verify the performance of FTG algorithms.
## 3 Experimental Verification
### Monitoring Performance Analysis
#### 3.2.3 Performance Analysis of Climbing Scaffold Status Warning  
During the monitoring of high-rise building climbing scaffolds, in addition to analyzing the current movement status, it is crucial that we utilize monitoring data for early the warning of potential risks, such as scaffold detachment or falling hazards. Timely warning detection is essential for ensuring construction safety, as it helps mitigate potential losses by providing predictive alerts. Since the monitoring accuracy and risk standards vary across the three directions (E, N, and U), three different levels of early warning thresholds are set for each direction. The following figure presents the three-level warning results in a static state for each direction.  
From Figure 10, it is clear that warnings are triggered at peak fluctuation moments (i.e., significant displacement events) and during continuous descent periods (i.e., potential fall risks). This method effectively accumulates minor fluctuations in the displacement sequence, enabling the identification of both abrupt and small-scale anomalous changes. The figure also indicates that monitoring accuracy in the elevation (U) direction is relatively low, while the N direction exhibits the highest accuracy. Consequently, the precision thresholds for warnings should be adjusted accordingly. By establishing different warning standards within the same direction, the system categorizes warning levels based on displacement severity. Furthermore, differentiated thresholds are set across directions to align with the monitoring accuracy and directional requirements, ensuring the precise surveillance of the climbing scaffold's status. In our implementation, the warning thresholds for the Level 1, Level 2, and Level 3 alerts are set to 0.03, 0.05, and 0.07, respectively. It can be observed that, as the warning threshold increases, the frequency of warnings decreases; certain cumulative displacements that previously triggered a warning are no longer considered significant enough to do so under the defined probability criteria.  
Figure 10: E/N/U direction early warning result charts: (**a**) E direction; (**b**) N direction; and (**c**) U direction.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 4 Experiment and Results
### Experimental Results
Our proposed method has demonstrated outstanding performance in haze removal and ground detail preservation. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Image** & **DCP** & **DehazeNet** & **AOD-Net** & **GridDehazeNet** & **MSBDN** & **Proposed** \\ \hline Band 5 & 0.744 & 0.732 & 0.792 & 0.856 & 0.827 & 0.922 \\ Band 6 & 0.766 & 0.782 & 0.812 & 0.866 & 0.834 & 0.903 \\ Band 7 & 0.713 & 0.722 & 0.775 &  
Figure 9: Dehazing results on images with slight haze interference. (**a**) Hazy image; (**b**) DCP; (**c**) DehazeNet; (**d**) AOD-NET; (**e**) GridDehazeNet; (**f**) MSBDN; (**g**) proposed method; (**h**) haze-free image. Figure 10: Dehazing results on images with moderate haze interference. (**a**) Hazy image; (**b**) DCP; (**c**) DehazeNet; (**d**) AOD-NET; (**e**) GridDehazeNet; (**f**) MSBDN; (**g**) proposed method; (**h**) haze-free image. Tables 5-7 list the PSNR, SSIM and FSIM of the above experiments. It can be seen that for the images with slight haze interference, the PSNR and FSIM values are close. DCP, DehazeNet and AOD-Net achieve relatively close results. MSBDN is slightly better and DehazeNet is even better. Our proposed method outperforms all these methods. Figure 11: Dehazing results on images with heavy haze interference. (**a**) Hazy image; (**b**) DCP; (**c**) DehazeNet; (**d**) AOD-NET; (**e**) GridDehazeNet; (**f**) MSBDN; (**g**) proposed method; (**h**) haze-free image.
## 5 Discussion
### Correlation between InSAR Deformation Results and Multiple Factors
#### 5.1.2 Lake Area Factor  
Figure 10 presents Landsat optical images of the South Lhonak Lake region spanning from January 2021 to October 2023, with the final subgraph obtained on 15 October 2023, after the GLOF. These images visually and clearly depict the changes in the lake over time, illustrating the evolution of the glacial lake. Figure 11 depicts the correlation between the time-series deformation of point P1 derived from the descending images before the disaster and the variation in the area of South Lhonak Lake from January 2021 to July 2023. Through Figure 11, it is evident that the variation in glacial lake area exhibits periodicity. Around October each year, the lake area begins to decrease rapidly, reaching its minimum during winter. Subsequently, from approximately January, the area starts to expand rapidly, while during the months of April to September, the lake area remains relatively stable. Overall, the lake area shows a yearly increasing trend. At the same time, point P1 exhibits periods of rapid deformation from January to May 2022 and from February to May 2023 (indicated by the gray area in Figure 11), with deformation rates higher than those observed during other periods. It was observed that during 2022 and 2023, the lake area experienced rapid growth from January to March, corresponding to a significant increase in the deformation rate of P1 from January to May. However, the onset of deformation acceleration slightly lagged behind the rapid expansion of the lake area. Therefore, we believe there is a correlation between the deformation rate of P1 and the variation in the area of the glacial lake. Furthermore, the relatively high rainfall in April and May may have contributed to the accelerated deformation observed during these months.  
Figure 9: The monthly average rainfall of North Sikkim from 2018 to 2022 and five-year average rainfall.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods
### Model Performance Evaluation Metrics  
Semantic segmentation is pixel-level classification, and lake water body extraction is the scope of semantic segmentation, and the commonly used performance evaluation metrics include pixel accuracy (_PA_), mean pixel accuracy (_MPA_), and mean intersection over union (_MloU_), and these metrics are calculated based on the confusion matrix, which is used to count the number of samples categorized into right and wrong categories [32; 33]. The higher the values of _PA_, _MPA_ and _MIoU_, the better the model is for the extraction of the predicted targets, and the three metrics are calculated as follows.