esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In order to improve the E-AHF model which has been proposed in previous work, we modified the LOO-CV method, which is used to build the surrogate library in E-AHF. By moving a validation point around random normal distributions rather than leaving it out, the move-one-away cross validation (MOA-CV) is proposed. Four surrogate techniques are used to construct models; errors generated by MOA-CV and LOO-CV methods, i.e., MOA-CVrror and LOO-CVrror, respectively, were used to select the best model from those four surrogate models through 20 standard mathematical functions and one engineering problem. We used the \(R^{2}\) which is reliable to evaluate the accuracy of a model to be a calibration of MOA-CV and LOO-CV. The accurate rate of selecting the best and worst surrogate was used to assess and compare the performance of MOA-CV and LOO-CV.  
Results show that with the increase in sample sizes, MOA-CV and LOO-CV are more likely to select the best models. MOA-CV performs much better than LOO-CV in selecting best models, especially when the sample size is larger. For HD functions, with small samples, neither the MOA-CV nor the LOO-CV method can find the best model. Similar to selecting the best models, MOA-CV and LOO-CV also perform quite poor with smaller samples for HD functions.  
From the average accurate rates over ten sets of DoEs, it is concluded that with increase in sample sizes, the MOA-CV model may become better in selecting best and worst models. In the engineering problem, both the MOA-CV and LOO-CV can choose the worst models, and in most cases, MOA-CV has a higher probability to select the best model than LOO-CV.  
Conceptualization, L.L. and X.S.; methodology, L.L.; writing--original draft preparation, L.L.; writing--review and editing, X.S. and W.S.; project administration, X.S. and W.S.; funding acquisition, X.S. and W.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, Grant Number U1608256 and the National Key Research and Development Program of China, Grant Number 2018 YFB1700704. The authors declare no conflict of interest.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 1 Introduction
In some newer applications, the issue of scarce data has been offset by the use of synthetic data to augment existing datasets [11]. [PERSON] et al. [12] made a synthetic review of hull and wake detection, focusing specifically on the use of infrared bands in multi-spectral applications. However, many new applications have been reported, especially when considering the broader field of interest in both optical and radar applications. Moreover, both traditional and DL-based solutions have been proposed over the years, with a wide spectrum of ideas that have influenced the evolution of the field. Applications using both multi-spectral and synthetic aperture radar (SAR) spaceborne imagery are taken into account, with the objective of providing the reader with a thorough and complete review of the state of the field of wake detection in all its aspects. For that reason, this work can be effectively considered the first thorough overview of the field, and, hopefully, serve as a point of reference for future researchers interested in the topic. The review is organized as follows:  
* Section 2 provides an overview of the materials and methodology used while conducting the research and data gathering required for this review;
* Section 3 provides the results of the review. In particular, it first covers an overview of the characteristics of ship wakes, then moves onto traditional and state-of-the-art DL-based methods of ship wake detection;
* Section 4 contains a more high-level discussion of the field as a whole, as well as the challenges posed by ship wake detection, and the strengths and weaknesses of the state-of-the-art approaches. A meta-analysis is also provided in this section. * Section 5 contains the conclusions of the review.
## 1 Introduction
The main application of HAOPs is pretreatment prior to membrane filtration. [PERSON] et al. (2020) reported that the implementation of a DM composed of HAOPs protects the membrane the DM has been deposited on [1]. Additionally, they reported that HAOPs could be used as a coating layer on a porous surface such as ultrafiltration membranes and proved to be efficient in removal of NOM compounds from water [19; 20; 21; 22; 25; 26; 27; 28; 29]. Since the deposition of HAOPs involves the integration of a granular media filtration and a packed bed adsorption, the feed water is required to pass through a layer of pre-deposited adsorbent on a filtering media. Such DM adsorber has been reported to be very efficient in the reduction in both NOM and the fouling [19; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. Although DMs containing HAOPs have been investigated for the removal of phosphorus from the effluent produced by a membrane bioreactor (MBR) [20; 24], they have not yet been investigated for removal of phosphorus from the surface water. On the one hand, [PERSON] et al. (2008) reported that iron oxides have superior adsorption affinity to phosphorus, when compared to HAOPs [21]. At the same time, [PERSON] et al. (2019) outlined that the use of HAOPs as the adsorbent produces higher-quality treated permeate using lower adsorbent doses [24]. Additionally, [PERSON] et al. (2019) reported that sludge generated from fouled DM layers was extremely easy to dewater and dry through vacuum filtering and drying [24]. Lately, [PERSON] et al. (2019) observed a significant removal of color from feed water when testing the application of HAOPs in a fully automated pilot system. NOM is a mixture of macromolecules and humic acid (HA) is the one that is responsible for the water color [29]. In that study, HAOPs were pre-coated/pre-deposited on 10 \(\upmu\)m nylon mesh tube in a recycling mode to limit the loss of HAOPs [29]. However, it was not stated whether it was possible to recover and re-use HAOPs. Sludge management as well as energy consumption of the process were not evaluated either. This work investigates the use of a pre-coated DM adsorber containing HAOPs for the simultaneous removal of NOM and phosphorus from surface water. The manuscript is focused on both NOM and phosphorus removal from surface water since biological stability of water is a new topic of importance in water technology and low concentrations of both carbon and phosphorus compounds are considered to be limiting factors to this phenomenon. The major purpose of the presented paper is to explore new areas of application of DM adsorber made of HAOPs.
###### Abstract  
In recent decades, precision agriculture and geospatial technologies have made it possible to ensure sustainability in an olive-growing sector. The main goal of this study is the extraction of olive tree canopies by comparing two approaches, the first of which is related to geographic object-based analysis (GEOBIA), while the second one is based on the use of vegetation indices (VIs). The research area is a micro-location within the Lun olives garden, on the island of Pag. The unmanned aerial vehicle (UAV) with a multispectral (MS) sensor was used for generating a very high-resolution (VHR) UAV\({}_{\text{MS}}\) model, while another mission was performed to create a VHR digital orthophoto (DOP). When implementing the GEOBIA approach in the extraction of the olive canopy, user-defined parameters and classification algorithms support vector machine (SVM), maximum likelihood classifier (MLC), and random trees classifier (RTC) were evaluated. The RTC algorithm achieved the highest overall accuracy (OA) of 0.7565 and kappa coefficient (KC) of 0.4615. The second approach included five different VIs models (NDVI, NDRE, GNDVI, MCARI2, and RDVI2) which are optimized using the proposed VITO (VI Threshold Optimizer) tool. The NDRE index model was selected as the most accurate one, according to the ROC accuracy measure with a result of 0.888 for the area under curve (AUC).  
geospatial technologies; Lun olive proves; object-based image analysis; classification algorithms; machine learning; accuracy assessment
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 2 Core Principles of SLAM: Front-End and Back-End
### SLAM Back-End
#### 2.2.2 Optimization-Based SLAM  
Optimization-based SLAM formulates the problem as graph optimization, with poses and landmarks as nodes and measurements as constraints. The goal is to minimize constraint violations for accurate maps and trajectories. A prominent example of this approach is graph-based SLAM, which uses optimization algorithms to identify the most likely configuration of the graph that represents the robot's trajectory and the environment. An extension of this is factor-graph SLAM, which incorporates sensor noise and uncertainties into the optimization process, refining the system's robustness.  
Key contributions include the following:  
* Klein and Murray [21] introduced a real-time tracking system separating tracking and mapping.
* Strasdat [22] showed keyframe bundle adjustment's superior accuracy.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Aspect** & **Filtering-Based** & **Optimization-Based** \\ \hline Core Principle & \multicolumn{2}{c}{Probabilistic state estimation} & \multicolumn{2}{c}{Graph-based minimization} \\ \hline \multirow{3}{*}{Key Methods} & \(\bullet\) & EKF/UKF SLAM & \(\bullet\) & Graph-based SLAM \\  & \(\bullet\) & Particle filter (FastSLAM) & \(\bullet\) & Factor-graph SLAM \\  & \(\bullet\) & RBPF SLAM & \(\bullet\) & Bundle adjustment \\ \hline \multirow{3}{*}{Strengths} & \(\bullet\) & Online processing & \(\bullet\) & Higher accuracy \\  & \(\bullet\) & Computationally efficient & \(\bullet\) & Better loop closure \\  & \(\bullet\) & Handles real-time updates & \(\bullet\) & Handles non-linearities \\ \hline \multirow{3}{*}{Limitations} & \(\bullet\) & Linearization errors & \(\bullet\) & Higher computation \\  & \(\bullet\) & Particle degeneracy & \(\bullet\) & Batch processing needed \\  & \(\bullet\) & Drift accumulation & \(\bullet\) & Memory intensive \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Back-end SLAM Approaches.  
Latif et al. [23] proposed a method for robust loop closure detection.
* Li et al. [24] developed an NN-based FastSLAM to reduce errors.
* Bustos et al. [25] used rotation averaging for simplified SLAM.
* Liu et al. [26] presented a bundle adjustment for LiDAR SLAM reducing drift.  
In conclusion, the SLAM back-end is critical for refining pose and map accuracy. Filtering-based methods are efficient but may struggle with non-linearities, while optimization-based approaches offer robustness at higher computational cost. The choice depends on application needs. These principles, established in terrestrial robotics, require adaptation for underwater environments due to unique challenges. The next section explores these challenges, introducing a classification of traditional underwater SLAM methods based on UWSNs and the growing importance of UWSNs in advancing the field.
## 2 Materials and Methods
### Demonstration of the Model's Potential for Application as a Decision Support Tool
#### 2.2.6 Medfly Behavior and Dispersal  
Medfly is most active around mid-day, optimally at air temperature close to 25 \({}^{\circ}\)C. Based on the literature [38] and our observations, the required conditions were estimated as follows: air temperature ranging from 15 to 35 \({}^{\circ}\)C, sunny conditions (solar radiation above approx. 150 W/m\({}^{2}\)), no or weak wind (below approx. 8 m/s) and no precipitation or very minimal (below approx. 0.5-0.8 mm/h). In contrast to most other models, the dispersal of individual medflies, although stochastic, is not completely random and is dependent on the topography of the site. The starting cohort of 10 wild females and their progeny that emerged within the site, as well as the released sterile males, was able to disperse outside the simulated area. It was assumed that 40% of these out-migrating individuals return to the site, while the rest of the cohort \"vanishes\" in the surrounding landscape.
## 2 Method
### Bayesian Hierarchical Model  
Bayesian hierarchical technique could be used to account for the heterogeneity resulted from the data structures. This technique was especially implemented in this study to account for the heterogeneity, by including the identified clusters based on the FMM, and measuring the amount of the heterogeneity that the clusters or classes could account for. Researchers account for the structure of the dataset based on the subjective decisions: including various spatial measures such as highway system or temporal correlation such as seasonality. However, a most viable and rational method is to let the advanced statistical method, e.g., the FMM, to disaggregate the dataset based on the underlying distributions.  
After implementing the hierarchical analysis based on identified clusters through the FMM, an arbitrary highway grouping, in another analysis, was also used to account for spatial heterogeneity. This would provide some measures for comparison when a hierarchy is selected subjectively. The highway system was chosen as a possible dependency of observations across various highway systems. Strong spatial correlation across various highway systems might be expected. The correlations across identified clusters and highway system were implemented in the model via a set of random effects.  
In the Bayesian hierarchical model, there is a random intercept for level 2 corresponding to the highway systems, which could be written as follows:  
\[\log(y_{i})=\sum_{p=1}^{p=P}\beta_{pj}\times x_{ijp}+\gamma+\vartheta \tag{17}\]  
where \(x_{ijp}\) is covariate \(p\) for crash \(i\) in highway system \(j\). \(\gamma\), and \(\vartheta\) are intercepts related to the levels considered in the model. In the case of the BHFMM, the clusters would be related to the clusters assigned by the FMM. It should be noted that the interpretation of the BHFMM is similar to the standard Bayesian hierarchical technique in Equation (17), with changes in the hierarchy.  
Finally, it should be noted that the non-informative priors were set for all parameters. These include multivariate normal distributions with means of 0 and large variances, or short precisions. The same distribution was employed for the mean of random intercept, while the random intercept variance was defined as the gamma distribution with low mean and precision.
# Initial Positioning Assessment of BDS New Satellites and New Signals
## 3 Signal in Space Range Error
For BDS-3, with the contribution of a more stable satellite clock and a new ISL technique, SISRE of BDS-3 is superior to BDS-2, where the average SISRE is 0.71 m versus 0.96 m after TGD correction. If DCB correction is applied, the average SISRE is 0.57 mversus 0.90 m, respectively, which indicates that the TGD accuracy of BDS-2 is similar to DCB, while the TGD accuracy for BDS-3 is slightly worse. As it can be seen from the lower subplot of Figure 4, some satellites show an obvious poor SISRE after TGD correction. This may be due to the accuracy of the TGD on these satellites. Meanwhile, it is interesting that the SISRE of C35-C37 are apparently worse than other BDS-3 satellites. As reported by [PERSON] [30], receivers able to track C35-C37 are much less than other BDS-3 satellites, which would lead to lower accuracy of precise orbit and clocks for C35-C37. If C35-C37 are excluded, SISRE of BDS-3 would reduce to 0.62 m (TGD correction) or 0.45 m (DCB correction), which is at the similar level of GPS and Galileo.
## 7 Discussion
### Trends and Insights  
Appendices B.4-B.6 present the optimization results of the three objectives obtained using the proposed method under different scenarios. Based on Figures 12 and 13, the overall trend of changes can be further analyzed, providing valuable insights.  
Figure 12 presents a comparative analysis of the global cumulative results. Overall, as the number of patrol units increases and the patrol intervals are further segmented, both risk coverage and the patrolled street length exhibit a significant upward trend. However, the transfer distance also increases correspondingly, partially offsetting these benefits.  
Figure 13 further illustrates that with an increasing number of patrol units and the finer segmentation of patrol intervals, the efficiency of individual units per time interval generally declines. This trend is most pronounced in risk coverage, while the impact on the patrolled street length is less significant. The transfer distance, on the other hand, initially decreases significantly, but then stabilizes or even increases when \(\delta=1\).  
Based on the above analysis, the following recommendations can be provided for decision makers.  
First, deploying a minimum of \(n=2\) or a maximum of \(n=10\) patrol units is not highly recommended. A small number of units fail to cover sufficient risk areas, whereas an excessive number of units leads to a decline in operational efficiency.  
Secondly, from the perspective of balancing efficiency and risk coverage, the following two scenarios are recommended: \(n=6\) and \(\delta=2\) or \(n=8\) and \(\delta=2\). The rationale is as follows: the overall risk coverage is relatively high (1.3539 for the former and 1.6690 for the latter), and the average risk coverage is also satisfactory (0.0047 for the former and 0.0043 for the latter). Additionally, the total transfer distance remains acceptable compared to high-risk coverage scenarios (469,950 for the former and 610,580 for the latter). Both scenarios are suitable for decision makers seeking high patrol efficiency while avoiding excessive transfer distances.  
Thirdly, from the perspective of minimizing the transfer distance while maintaining a relatively high risk coverage, the following two scenarios are recommended: \(n=4\), \(\delta=1\) or \(n=6\), \(\delta=1\). The rationale is that while the overall risk coverage is not the highest, it remains reasonable (0.7026 for the former and 0.9179 for the latter). More importantly, the total transfer distance is significantly lower than other high-risk coverage scenarios (131,940 for the former and 195,220 for the latter), and the average transfer distance is  
Figure 12: Comparison chart of cumulative risk coverage, street length patrolled, and transfer distance under various scenarios.  
among the lowest (1434.13 for the former and 1414.64 for the latter). Both scenarios are suitable for decision makers who need to consider transfer costs while maintaining a reasonable level of risk coverage.
# A Low-Band Multi-Gain LNA Design for Diversity Receive Module with 1.2 dB NF
## 2 Structure of the Designed LNA
The transistors of these switches have been stacked to handle the Absolute Maximum Rating (AMR) condition which is \(25\) dBm power at the RF_INx port of Figure 2. The loss of this switch in the simulation level for all frequencies below \(1\) GHz remains below \(0.2\) dB. For different frequency bands, the output capacitors are coarse-tuned to keep the S\({}_{22}\) in the desired level for the corresponding band. The band selection and tuning are controlled using digital circuitry applied for the whole chip.
# Rainfall Standard of Disaster Prediction for Agricultural Droughs in S. Korea
## 2 Materials and Methods
### Local Rainfall Observatories and Rainfall Status
\begin{table}
\begin{tabular}{c c c c} \hline
**Month** & **Rainfall (mm)** & **Cumulative Rainfall (mm)** & **Ratio (\%)** \\ \hline
1 & 27.2 & 27.2 & 2.1 \\
2 & 35.4 & 62.6 & 2.8 \\
3 & 55.4 & 118.0 & 4.4 \\
4 & 89.0 & 207.0 & 7.0 \\
5 & 95.3 & 302.3 & 7.5 \\
6 & 143.7 & 446.0 & 11.3 \\
7 & 285.8 & 731.9 & 22.5 \\
8 & 259.4 & 991.2 & 20.4 \\
9 & 147.1 & 1138.3 & 11.6 \\
10 & 57.8 & 1196.1 & 4.5 \\
11 & 49.1 & 1245.2 & 3.9 \\
12 & 26.1 & 1271.4 & 2.1 \\ \hline \end{tabular}
\end{table} 
Table 3: Average monthly rainfall trends (1965–2018). South Korea's agricultural drought occurs in the form of a 1-year-cycle short-term, rather than long-term, drought and ends in the course of the country's monsoon season, lasting from June to September. In this study, we comparatively analyzed the past agricultural drought damage versus the average rainfall of drought-free years to set up the disaster-prevention rainfall for predicting the agricultural droughts. For the reference points for disaster-prevention rainfall, we analyzed the extent to which the rainfall in the preceding year fell short of the rainfall in the year prior to the occurrence of agricultural droughts. Agricultural droughts end in the course of the monsoon season as early as July or in September at the latest when a certain amount of rain falls. To set up the reference points for disaster-reduction rainfall, we analyzed the average monthly rainfall for the months preceding and following the point of time when agricultural drought damage ended. Given agricultural droughts hardly end with a single rainfall event but above a certain amount of rainfall, we analyzed the average monthly rainfall for the months preceding and following the point of time when droughts ended. Thus, in this study, we took into account the past agricultural drought damage data, to set up the reference points of rainfall for predictable disaster prevention and reduction. We analyzed the rainfall to set up the reference points for disaster-prediction rainfall, as shown in Figure 6.
# A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
###### Abstract  
The combination of permanent magnet synchronous motor (PMSM) and inverter is the key electric drive system (EDS) of electric vehicles (EVs), and its overall efficiency seriously affects the energy consumption of EVs. In order to further improve the efficiency of PMSM-inverter, the influence of a special control object current angle \(\beta\) on EDS was studied and the general rule between \(\beta\) and EDS efficiency was obtained in this paper. Then, the golden section search (GSS) method was used to obtain optimal \(\beta\) and its corresponding stator current \(i_{s}\), which can realize EDS working in optimal efficiency in the whole EDS working area. On this basis, an overall efficiency optimization control strategy for EDS based on the current angle \(\beta\) look-up table was proposed in this paper. To verify the effectiveness of the proposed control strategy, simulation considering iron loss and copper loss of motor and inverter loss was completed, which showed that compared with traditional control, the control strategy proposed in this paper can effectively improve the working efficiency of EDS under steady state and transient state.  
PMSM-inverter; electric drive system; efficiency optimization; current angle; loss model; look-up table +
Footnote †: journal: Applied Sciences