esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper presents a comparative study on using supervised machine learning classifiers to detect and localize leaks in a water distribution network; the network was divided into two hydraulic zones with leak scenarios in each zone. The capacity of three supervised machine learning classifier tools has been studied for leak detection and localization in the water distribution network of the University of Port Harcourt Choba campus. For the development of the three machine learning models, as well as for the training and testing of the model, the study employed the sklearn library, which is a machine learning analysis tool written in the Python programming language.  
The literature shows that the studies applying machine learning models to water distribution network problems are still in the research stage. The research indicates that the combination of support vector machine, K-nearest neighbor, and artificial neural network machine learning techniques for leak detection have yet to be explored. This study contributed to closing this knowledge gap by advancing the deployment of a machine-learning model for detecting and localizing leaks in the water distribution system.  
The data used in the training and testing of the models were generated using EPANET software, where uncertainty conditions for the water distribution network were also factored into simulating the network model on EPANET. The uncertainty conditions in the model development correspond to uncertainty of demand and leak noise [4; 5]. The study showed the excellent performance of the support vector machine over K-nearest neighbor and the artificial neural network in leakage detection and localization when pressure residuals data were used. The results obtained from each model are based on a dataset generated from the EPANET software across the network nodes. The study did not cover the use of the selected machine learning models to determine the magnitude of the detected and localized leaks in the water distribution network. Future research should focus on adding fault warnings when a leak is discovered in the network, enhancing the study's applicability. Also, in the future, researchers should consider evaluating the influence of sensor placement in a network, because the topology of the water distribution network was not considered in this analysis.  
C.U.O. and D.O.A. were involved in the conceptualization, methodology and analysis, validation, and writing and editing of the original draft. S.S. assist in the writing and editing of the original draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
## 2 Basis and Fundamentals  
Image degradation is a common situation in transmitting image information and recording digital images, but in many practical applications, high-quality images are necessary, so effective methods to improve and recover degraded images are worthy of our study. In this section, we will briefly describe some important image denoising models based on partial differential equations and anisotropic diffusion.
## 4 Life Cycle Inventory Results
### WCO In Situ Production Inventory
Taking into account the annual vegetable oil consumption of the Caribbean region [100], the recovery ratio determined by [PERSON] (2013) [101], and the urban population in each island, we estimate a hypothetical availability of 124 million liters per year of regenerated WCO or 4.2 GJ which could be used to replace 44% of diesel imports for electricity production. The current population of the Galapagos Islands is 25,500 people [102]. In addition, according to the annual Visitor Report for the protected areas of Galapagos [103], an average of 228,306 tourists (floating population) visit the archipelago every year with average permanency of 7 days; 77% of the total, 159,814 tourists, stay on Santa Cruz Island. On the other hand, the average annual amount of edible cooking oil used per capita in the Latin American region is 20 kg [104]. The food's oil absorption ratio is 25% [105]. Nevertheless, the real WCO recovery ratio in the region is estimated to be from 20% to 45% [101; 106]. Thus, we can determine an average WCO potential recovery of between 114,259.70 and 257,084.33 kg, or between 121,287.99 and 272,898 L, per year on Santa Cruz Island. According to the Galapagos energy balance, Floreana Island uses 74,944 L of diesel and 18,367 L of vegetable oil (_Jatropha curcas_) per year as fuels in its dual electricity generators [40]. This fuel demand can be easily covered by WCO produced on Santa Cruz Island. In addition, the implementation of a WCO value chain could develop a new local industry, creating local employment and reducing foreign exchange expenditures on energy. In situ and bibliographical data were collected for each of the following production stages: (i) washing containers, (ii) WCO collection and transportation to plant, (iii) delivery of WCO from \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **RPO** & **WCO** & **Diesel** \\ \hline Viscosity (eSt) (40 \({}^{\circ}\)C) & 39–43 & 31–50 & 2.5–4.5 \\ Density (15 \({}^{\circ}\)C) (kg/m\({}^{3}\)) & 860–920 & 910–943 & 820–860 \\ Heating value (MJ/kg) & 36.5–40.1 & 32.2–41.8 & 43.0–46.0 \\ Cetane number & 42–49 & 36–37 & 45–56 \\ Flash point (40 \({}^{\circ}\)C) & 267–304 & \textgreater{}250 & \textgreater{}52.0 \\ Iodine value & 35–66 & 98–128 & – \\ \hline Sources & [88; 89; 90; 91; 92] & [93; 94; 95] & [81; 96; 97; 98] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the physical and chemical properties of refined palm oil (RPO), Waste Cooking Oil (WCO), and diesel. the collection point to the plant, (iv) pre-treatment, (v) processing at the cleaning facility, and (v) transportation to the electricity plant.
## 3 Security Analysis
The reason is that the large number of the exchanged signals results in the perfect signals parameter estimation. However, it is infeasible to achieve the infinite number of the exchanged signals in practice. Nonetheless, it still has resulted in an improvement, compared with the traditional system in the finite-size regime, as shown in Figure 8.  
Figure 8: Finite-size key rate of CVQKD involving the ZPC operation for \(\varepsilon=0.01\), where the (**a**) represents eight-state DM source preparation, and inset (**b**) represents four-state DM source preparation. Figure 7: Finite-size key rate of CVQKD involving the ZPC operation for \(\varepsilon=0.01\), where solid lines represent the eight-state DM source preparation and dotted lines represent the four-state DM source preparation, respectively. In addition, we show the effect of the ZPC-involved source on the tolerable excess noise of CVQKD, as shown in Figure 9. We find that CVQKD involving the ZPC-involved source performs better than the traditional system in terms of maximal transmission distance. Moreover, reconciliation efficiency is the other important parameter that has an effect on the performance of CVQKD. Figure 10 suggests the effect of the ZPC-based source on the secret key rate of CVQKD related to the reconciliation efficiency. We find that the ZPC-based source can tolerate low reconciliation efficiency. Therefore, the ZPC-based source preparation can reduce the requirements for reconciliation efficiency, thereby reducing costs of practical implementation of the high-rate long-distance quantum communications. Figure 10: The secret key rate of the CVQKD system involving the ZPC operation with \(L\) = 50 km and \(\epsilon\) = 0.01 for the traditional CVQKD system (dotted lines) and the ZPC-based CVQKD system (solid lines), where (**a**) represents eight-state DM source preparation, and inset (**b**) represents four-state DM source preparation. Figure 9: The maximal tolerable excess noise of CVQKD involving the ZPC operation (dotted lines and solid lines) for the traditional CVQKD (dotted lines) and the ZPC-based CVQKD (solid lines), where (**a**) represents eight-state DM source preparation, and inset (**b**) represents four-state DM source preparation.
# Design of a Bionic Spider Robot with a Two-Degrees-of-Freedom Leg Structure and Body Frame
## 3 Design and Analysis of Structure
### Design and Analysis of Leg Structure
#### 3.1.3 Kinematic Analysis
 \tag{12}\]  
The \(i\)-th leg coordinate system \(\sum O_{A}^{i}\) is represented in the body coordinate system \(\sum O_{B}\) as Equation (13). \[ \begin{array}{c}A\overset{A}{}T^{i}=\begin{bmatrix}\cos\phi_{i}&-\sin\phi_{i} &0&p_{AX}^{i}\\ \sin\phi_{i}&\cos\phi_{i}&0&p_{AV}^{i}\\ 0&0&1&p_{Az}^{i}\\ 0&0&0&1\end{bmatrix}\end{array}  \tag{13}\]  
In Equation (13), \(\phi_{1}\) represents the angle between the x-axis of leg coordinate frame \(\sum O_{A}^{i}\) and the x-axis of the body coordinate frame \(\sum O_{B}\), which is the initial installation angle. The coordinates \((p_{AX}^{i},p_{AV}^{i},p_{Az}^{i})\) denote the position of the base of \(i\)-th leg, which is the origin of the leg coordinate frame, in the body coordinate frame. Furthermore, the transformation matrix between the leg coordinate frame and the body coordinate frame is analyzed, as shown in Equations (14) and (15). \[\overset{2}{0}T^{i}_{\ B}=\overset{A}{}T^{i}\overset{2}{}\overset{2}{}T_{A} \tag{14}\]  
\[\overset{2}{0}T^{i}_{\ B}= \begin{bmatrix}\cos(\phi_{i}+\gamma_{1})&-\sin(\phi _{i}+\gamma_{1})&0&\cos\phi_{i}\cdot(l_{1}+d_{1})+p_{AX}^{i}\\ \sin(\phi_{i}+\gamma_{1})&\cos(\phi_{i}+\gamma_{1})&0&\sin\phi_{i}\cdot(l_{1} +d_{1})+p_{AV}^{i}\\ 0&0&1&p_{Az}^{i}\\ 0&0&0&1\end{bmatrix}  \tag{15}\]  
Consequently, through forward kinematics, the position of the foot tip in the body can be determined. The foot tip coordinates \((x_{3},y_{3},z_{3})\) are transformed into \((x_{B},y_{B},z_{B})\), as shown in Equation (16). \[ \begin{cases}x_{B}=x_{3}\cdot\cos(\phi_{i}+\gamma_{1})-y_{3}\cdot\sin(\phi_{i }+\gamma_{1})+P_{Ax}^{i}\\ y_{B}=x_{3}\cdot\sin(\phi_{i}+\gamma_{1})+y_{3}\cdot\cos(\phi_{i}+\gamma_{1})+P _{AV}^{i}\\ z_{B}=z_{3}+P_{Az}^{i}\end{cases}  \tag{16}\]  
In Equation (16), \((x_{B},y_{B},z_{B})\) represents the coordinates of the foot tip in the body coordinate frame \(\sum O_{B}\).
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 2 Research Methodology
### Search Strategy  
The preferred reporting items for systematic review and meta-analysis (PRISMA) [12] were used to direct the reporting of the search for articles, the extraction of data, and the synthesis of results. A broad search process was conducted in the following digital databases: IEEE Xplore (145), PubMed (139), and Scopus (446). The search was performed between June 2024 and early July 2024, covering 13 years of publication (2012-2024) to showcase the most recent BCI technology. The search string used to search for relevant literature was the following: ((\"BCI\" OR \"brain-computer interface\") AND (\"mixed reality\" OR \"augmented reality\" OR \"MR\" OR \"AR\")). The next step was to exclude duplicate publications using the Rayyan software [13] which is a free web tool designed to help researchers with systematic reviews.
###### Abstract  
The purpose of this study was to observe the changes in soil nitrogen pools (active organic and inorganic nitrogen fractions) after applying organic materials under drip irrigation with film mulching in a semi-arid soil. The two-year field experiment included four fertilization treatments: maize straw, fodder grass, sheep manure, and a control treatment with no organic material applied (CK). The results showed that the application of maize straw and sheep manure significantly increased the soil total nitrogen content by 9.02-13.33% and 11.02-17.27%, respectively, while no significant improvement was found with the use of fodder grass. Compared with CK, returning organic materials to the field increased the soil active nitrogen pools, except for ammonium nitrogen content. Meanwhile, the content of particulate organic nitrogen was significantly increased by 42.11-78.85% under the application of organic materials, which took the highest proportion of active nitrogen pools and was sensitive to fertilization treatments. Among the three organic materials, maize straw and sheep manure increased the soil active nitrogen content the most. In conclusion, both maize straw and sheep manure are the optimum organic materials, which could be applied for improving soil nitrogen storage under drip irrigation with film mulching within semi-arid areas.  
organic fertilization; soil fertility; soil nitrogen management; sustainable agriculture
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
###### Abstract  
Remote-sensing image dehazing (RSID) is crucial for applications such as military surveillance and disaster assessment. However, current methods often rely on complex network architectures, compromising computational efficiency and scalability. Furthermore, the scarcity of annotated remote-sensing-dehazing datasets hinders model development. To address these issues, a Dual-View Knowledge Transfer (DVKT) framework is proposed to generate a lightweight and efficient student network by distilling knowledge from a pre-trained teacher network on natural image dehazing datasets. The DVKT framework includes two novel knowledge-transfer modules: Intra-layer Transfer (Intra-KT) and Inter-layer Knowledge Transfer (Inter-KT) modules. Specifically, the Intra-KT module is designed to correct the learning bias of the student network by distilling and transferring knowledge from a well-trained teacher network. The Inter-KT module is devised to distill and transfer knowledge about cross-layer correlations. This enables the student network to learn hierarchical and cross-layer dehazing knowledge from the teacher network, thereby extracting compact and effective features. Evaluation results on benchmark datasets demonstrate that the proposed DVKT framework achieves superior performance for RSID. In particular, the distilled model achieves a significant speedup with less than 6% of the parameters and computational cost of the original model, while maintaining a state-of-the-art dehazing performance.  
remote-sensing image dehazing; knowledge distill; model efficiency +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Remote Sensing Image Dehazing via Dual-View Knowledge Transfer. _Appl. Sci._**2024**, 14, 8633. [[https://doi.org/10.3390/app14198633](https://doi.org/10.3390/app14198633)]([https://doi.org/10.3390/app14198633](https://doi.org/10.3390/app14198633))
## 7 Results and Discussion
### Discussion  
Previous works presented different techniques for cluster head (CH) selection and routing paths for transmission. Different protocols, such as FBFCP, TEEN and LEACH, considered various factors (e.g., minimum distance, available connections between nodes, and residual energy) to prolong the lifetime of the network. However, due to more processing and disconnection of links, these protocols consume more energy, decreasing the network lifetime and packet delivery ratio. In our schemes, early identification of the next hopping node with minimum distance, high energy level with reinforcement learning, and optimum cluster head (CH) selection with game theory improves network lifetime, packet delivery ratio, and decreased energy consumption and end-to-end delay.  
Figure 11: End-to-End delay vs number of rounds.  
In our proposed schemes, each cluster head (CH) selection is based on a distance between the node and base station, node mobility, link connection with other nodes, and residual energy. In this scheme, the optimum cluster head (CH) selection with game theory and best route path selection with reinforcement learning (RL) were used to prevent overhead in communication and allow smooth transmission of data between clusters heads (CHs) and the base station. In terms of throughput, residual energy, packet delivery ratio, end-to-end delay, and network lifetime, our scheme outperforms the FBFCP, TEEN, and LEACH schemes. This is because our scheme selects the optimal cluster head (CH) and optimal route path for data transmission from nodes to cluster head (CH) and from the cluster head to the base station. On the other hand, the random cluster head (CH) and route path selection need more calculation and a long transmission range, leading to energy wastage, which is prevented in our proposed schemes. Therefore, the proposed scheme minimizes the energy consumption of the nodes in the network. In this manner, the energy dissipation reduces and improves the success of the energy-saving of the nodes.
# A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter
## 4 Results and Analysis  
To verify the UKF algorithm based on the maximum cross-correlation entropy criterion proposed in this paper, a simulation and experimental verification were designed.
## 5 Results and Discussion
### Cooperative Task between UAV and UGVs
#### 5.2.1 Experiment #1  
In the first experiment, the weight of the actuator and the payload is 90 g and 72 g, respectively, which corresponds to about 32% of the weight of the drone. The video presenting the entire experiment can be found at [[https://youtu.be/0](https://youtu.be/0) UIOjn5_a5s]([https://youtu.be/0](https://youtu.be/0) UIOjn5_a5s) (accessed on 6 December 2022). Table 2 presents in chronological sequence in which the robots perform each action cooperatively.  
Experiment #1 is divided temporally into five regions: transportation, setup, lifting, returning, and transportation again. These regions make up the complete realization of the experiment. In addition, Figure 11 highlights the actions that [PERSON] performs at each region transition: take off, docked, released and landing, respectively.  
At the beginning of the experiment, only P1 starts its motion by carrying C1 and B1 through the T1 trailer. This step comprises the transportation region and occurs in the interval from 0 to 42 s. After 20 s, [PERSON] reaches a previously defined desired point in an orientation perpendicular to the obstacle. At this instant, [PERSON] comes from rest and starts moving to reach a position in front of [PERSON] and also perpendicular to the obstacle.  
Starting at 42 s, the setup region is initiated and occurs in the interval from 42 to 55 s. During this interval, B1 takes off, aiming to position itself under C1 in order to take it. The change in altitude of B1 in Figure 11 confirms such an action. Note also that before taking off, B1 is at a height close to 30 cm; after all this is the height of T1 added to the height of B1's center of gravity relative to T1.  
Figure 10: Cargo transported in experiments.  
In the interval from 52 to 55 s, it can be seen that E1 and C1 have the same vertical coordinate, i.e., the actuator is in contact with the load. This situation is maintained until the load is released.  
Upon entering the lifting region, which occurs after 55 s, B1 grasps C1 and makes its transposition between P1 and P2, over the obstacle. It is worth remembering that P1 and P2 wait at rest until the transposition of the load is successfully completed. Notice in Figure 11 that the altitude of E1 and C1 are identical during this stage, which confirms that the payload is being lifted by the UAV. Note also that the distance between B1 and C1 represents the length of the suspended cable.  
At the end of the lifting stage, note that C1 returns to the same altitude value that it had before being lifted from P1. This demonstrates that the load has been delivered to P2,
## 1 Introduction
presented an OFDI system based on two independent interferometer reference arms, each having an acousto-optic frequency shifter operating at a distinct frequency and an independent round-trip delay [12]. Through combination of a dual-channel OCT system focusing on different segments of the eye, [PERSON] et al. extended the imaging depth using two OCT setups [13; 14]. For the sequential acquisition approach, [PERSON] et al. proposed a simple and inexpensive method for increasing the effective imaging depth range of the SDOCT system, which was realized by employing a high-speed fiber optic switch to serially access two reference paths with different offset delays [2]. Recently, [PERSON] al. enabled OCT imaging of multi-frames at four depths without sacrificing axial resolution by implementing a galvo scanner-based mechanical optical switch in the reference arm [15]. They utilized the system to image the whole eye from anterior to retina. More recently, a SDOCT system capable of deep depth imaging using a \(3\times 3\) fiber coupler, and a mechanical shutter was demonstrated [16]. To conclude, the simultaneous acquisition approaches can extend the imaging depth of the OCT system without sacrificing the effective imaging rate. However, they use either expensive frequency modulators or two OCT systems, which increase the complexity and cost of the imaging system. Furthermore, due to splitting the light source power into two channels, the simultaneous approaches suffer from a 3 dB loss of the imaging sensitivity [9]. On the other hand, the sequential acquisition approaches extend the ranging depth with no sensitivity penalty, but with the penalty of halving the effective image acquisition rate, and it cannot be applied in some applications that need to image the different depth regions of the sample simultaneously. In this paper, we present a simultaneous acquisition approach for extending the imaging depth range of a SDOCT system. The method, which is termed spatial frequency domain multiplexing method, is based on two pivot-offset galvo scanners in two separate reference arms. Galvo scanners are popular devices that offer a good scanning speed, a good field of scan, high precision, and repeatability [17]. Thus, the presented approach can image the different depth regions of the sample simultaneously without any expensive acousto-optic or electro-optic modulation device. In the following sections, we theoretically derive the parameter choice criteria for the high signal/crosstalk ratio (SCR) reconstruction and demonstrate the extended imaging depth performances of the system experimentally.
# Analysis of Inductive Displacement Sensors with Large Range and Nanoscale Resolution
###### Abstract  
With the advantages of high resolution, structural simplicity, reliability, compact size, and high sensitivity, inductive sensors have been widely used in nanopositioning systems. However, the measuring range of traditional inductive sensors are usually limited to 0.2 mm. A novel analysis and design methodology of the miniaturized inductive sensor with large measuring range and nanoscale resolution is proposed. Firstly, an accurate leakage inductance model is established. Secondly, a design rule of armature size is proposed by considering the fringing effect. Then, the error terms introduced by the measurement circuit of differential inductive sensors are analyzed and the corresponding error suppression methods are illustrated. Moreover, A design rule of selecting the optimal excitation frequency is proposed to meet the requirements of high \(Q\) value and high bandwidth, and to minimize the impact of core loss resistance on the performance of the sensor. Validated by the experiments, the proposed analysis and design method can effectively guide the design of the miniaturized inductive sensor with nanoscale resolution in the measuring range of \(\pm\)0.5 mm. The overall size of the fabricated sensor prototypes is less than 6 mm \(\times\) 6 mm \(\times\) 3 mm. Combined with large range, high resolution and ideal miniaturization, this inductive sensor can be well suitable for compact and large stroke nanopositioning systems.  
inductive sensor; nanopositioning; large range; design method; nanoscale resolution +
Footnote †: journal: Applied Sciences