esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 1 Introduction
There are two types of orbits for Earth observation satellites, polar orbiting and geostationary, with different revisit time, and while polar-orbiting satellites typically have a revisit time of 2-3 days, geostationary satellites operate in time scales of hours, which is key to be able to capture diurnal variation in phytoplankton abundance and productivity. South Korea's (GOCI) instrument on board the COMS-1 satellite, launched on 26 June 2010, is the first ocean-colour sensor in a geostationary orbit. The important factors to consider when designing an observation strategy and a detection technique are the scales of variability for these algal blooms [26]. Blooms usually have spatially distinct patchy distributions that should also be considered to select the suitable spatial scale of satellite sensors [23]. Data from remote sensing monitoring can inform decision-makers where to apply their sampling effort to verify the presence of a bloom [27]. Sample measurements at the spatial and temporal scale that are made possible with satellite imagery are not possible with in situ observations [28]. Bibliometrics is a useful tool for quantitatively evaluating the present situation and development of scientific production in specific study fields that has been applied in various fields [29; 30; 31]. This paper aims to provide a systematic analysis of research on remote sensing of phytoplankton blooms during 1999-2019 via bibliometric technique. This study aims to reveal the limitations of current remote sensing of phytoplankton bloom studies to analyse the climatic variability effect and propose suggestions for the direction of future research.
# Birth Allocation and Query Crane Assignment Considering the Uncertain Maintenance Requirements
## 2 Literature Review
While various approaches such as buffer times and redundancy have been employed to address uncertainties in the Berth Allocation Problem (BAP) and Quay Crane Scheduling Problem (QCSP), previous research has yet to simulate real-world scenarios that account for quay crane availability and breakdowns. Moreover, many studies have neglected the inclusion of hard time window constraints based on reliability, a critical factor that enhances the realism of scheduling models. Additionally, the uneven workload distribution among cranes and vessel bays often results in bottlenecks, necessitating prioritization in operations. To address these gaps, we propose a novel scheduling approach that integrates these overlooked factors. Our study introduces a proactive-reactive method that incorporates a reliability-based model into the Swarm Optimization with Differential Evolution (SWO-DE) algorithm, enhancing quay crane scheduling by accounting for crane reliability and operational disruptions. This integrated approach aims to balance operational efficiency while mitigating the impact of crane breakdowns, offering a more realistic and robust solution to the QCSP.
# High-Speed Cell Assembly with Piezo-Driven Two-Finger Microhand
## 4 Automated Assembly Strategy for Cells and Experimental Results  
### Visual Recognition of the Operational Target and End-Effector  
The Hough circle detection method can be employed for microsphere targets. However, this method exhibits a low success rate in recognizing cells due to their irregular circular shape. In this paper, a combination of contour detection and various image processing algorithms is adopted for cell recognition. Firstly, the image undergoes Gaussian filtering and binary conversion. After binary processing, almost every cell in the images has many holes in the center, which is caused by the semi-transparent state of cells in the liquid. To make the region where cells are located a closed black area, morphological operations are employed. Subsequently, the image is eroded, followed by dilation, and then edge detection and contour detection are performed sequentially. After contour detection, it is necessary to exclude interference caused by other backgrounds with closed contours. The area of dust is generally small, while cell fragments often have irregular shapes and exist in large patches. The areas of all contours in the image are calculated, and a filtering process is applied. Contours that match the cell size criteria will be retained, while contours that do not match will be eliminated from the image. The minimum circular contour bounding algorithm is applied to the filtered contours, which can determine the smallest circular region that can encompass the entire cell contour. The obtained center positions and radii of cells are prepared for subsequent automated grasping operations on the cells. Through multiple iterations of image processing, the successful recognition rate for cells reaches 98%.  
The template matching method was employed for the recognition of end-effector and the final success rate also reached 98%. Template matching recognizes targets by detecting the position of a given template in the overall image. This method is suitable for scenarios where the main motion of the target is translation in a two-dimensional plane, as applied in  
Figure 4: (**a**) Horizontal release displacement deviation (negative values indicate a leftward horizontal direction). (**b**) Vertical release displacement deviation. (**c**) Horizontal release displacement deviation after compensation.  
this paper. The end-effector of the microhand is primarily involved in translational motion in the operation plane during cell manipulation without significant rotational movements.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis
### Read Delay Comparison  
Read delay, also referred to as read access time (RAT), is defined as the time interval from when the WL voltage reaches 50% of VDD to the moment a voltage difference of 50 mV is established between the BL and the complementary BLB [15]. This metric is significantly influenced by the read current through the access and pull-down transistors, as well as the capacitance associated with the bitlines. The proposed EWS-16T cell includes two pairs of access transistors; however, only one pair is utilized during read operations to connect the storage nodes to the bitlines. The remaining pair remains inactive and introduces parasitic capacitance, which degrades the read current path and increases the overall read delay. Additionally, the internal feedback structure, which is optimized for soft-error resilience and write performance, adds resistance and capacitance to the read path, further impacting read speed.  
As illustrated in Figure 8, the EWS-16T cell exhibits a longer read delay compared to other reference cells, including QCCS, SCCS, SAW16T, HP16T, RHSCC16T, and S8P8N. This reflects a structural trade-off, where enhancements in write performance are achieved at the expense of read delay due to parasitic and feedback-related effects during the read operation.
## 2 Research Methods  
### Uniaxial Compression Test  
To ensure the tests closely reflect the actual project conditions, natural granite samples from Jining City, Shandong Province, are utilized. For the simulation of the anchor system, 8.8-grade low carbon alloy steel screws are selected, whose composition includes carbon (C), phosphorus (P), sulfur (S), and boron (B). The screws have an effective diameter of 3.88 mm, a length of 140 mm, and an elastic modulus of 121 GPa. The preparation of the granite-anchor composite samples involves several steps: initially, waterjet technology is employed to drill holes with a 6 mm diameter into the granite samples. The screws are then inserted into these holes, and a syringe is used to inject an anchoring filler. The filler is the anchoring agent, that fills the gap between the screw and the inner wall of the hole, ensuring the anchor adheres firmly to the hole. After allowing the anchors to cure for 1-2 days, shims, trays, and nuts are installed to complete the assembly. Figure 1 illustrates the schematic diagram of the final granite-anchor sample.  
Considering the stress state of the granite samples and facilitating the recording of the deformation and rupture process of the samples, the MTS816 electro-hydraulic servo rock tester (MTS Systems Corporation, Eden Prairie, MN, USA) was used in this test. The maximum axial compression force of MTS816 is 1459 kN, which is used for uniaxial compression experiments on the granite samples, as shown in Figure 2. The test process was loaded by axial displacement control, with a displacement loading rate of \(1.5\times 10^{-3}\) mm/s and a preset compression contact force of 1.0 kN.  
The monitoring tools used in this study were digital image correlation (DIC), computerized tomography (CT), and polarized light microscopy.
## 3 Results and Discussions
### Emission Characteristics
#### 3.2.2. HC Emission  
Figure 9 describes the effect of four different fuels on HC emission at different loads.  
As shown in Figure 9, the HC emission is significantly decreased as the percentage of the palm oil is increased at all engine loads. At 30 Nm, the HC emission of BP10, BP30 and BP50 decreases by 15.15%, 36.36% and 42.42% compared with that of BP 0, respectively. At 60 Nm, the HC emission of BP10, BP30 and BP50 decreases by 18.75%, 40.63% and 53.13% compared with that of BP0, respectively. At 90 Nm, the HC emission of BP10, BP30 and BP50 decreases by 5.41%, 43.24% and 43.24% compared with that of BP0, respectively. In general, the generation of HC is mainly related to the following four reasons: (i) misfires and partial burns; (ii) flame quenching in crevice volumes; (iii) wall quenching and deposits; (iv) oil absorption [30]. In addition, fuel properties such as density, viscosity, surface tension, cetane number and oxygen content are also the main factors affecting HC emission.  
On the whole, the impact factors affecting HC emission are divided into positive impact factors (i.e., oxygen) and negative impact factors (i.e., high viscosity, high surface tension). The positive impact factor is beneficial to reduce HC emission, while the negative impact factor is to increase HC emission. As shown in Figure 9, adding palm oil to diesel  
Figure 8: CO emissions for all test fuels according to various engine loads.  
Figure 9: HC emissions for all test fuels according to various engine loads.  
reduces HC emission because the influence of positive factors is stronger than negative factors. Palm oil is an oxygenated fuel. The oxygen carried by palm oil changes the fuel/air equivalence ratio and reduces the possibility of flame quenching in crevice volumes. Moreover, in this study, the addition of palm oil reduced HC emission, but increased CO emission due to the difference of formation mechanism. Similar findings have been reported by [PERSON] et al. [2] Moreover, the average HC emissions of all test fuels first decrease and then increase with the engine load increasing from 30 Nm to 90 Nm. As explained earlier, the increase of engine load leads to the increase of temperature and pressure in the cylinder and the increase of turbulence rate, so as to promote the formation of a more uniform mixture of fuel and air, improve combustion efficiency and finally reduce HC emission [31].
###### Abstract  
This study investigates the coordinated control problem of Euler-Lagrange systems with model uncertainties in environments containing obstacles when escorting a target. Using an outer-inner loop control structure, a null-space-based behavioral (NSB) control architecture was proposed in the outer loop considering obstacles. This architecture generates the desired velocity for the inner loop. The adaptive proportional derivative sliding mode control (APD-SMC) law was applied to the inner loop to ensure fast convergence and robustness. All the robots were distributed around the target evenly and escorted the target at a specified distance while avoiding obstacles in a \(p-\) dimensional space (where \(p\geq 2\) is a positive integer). Stability and convergence analyses were conducted rigorously using a Lyapunov-based approach. The simulation results of three scenarios verified the effectiveness and high-precision performance of the proposed control algorithm compared to that of the adaptive sliding mode control (ASMC) in both two-dimensional and three-dimensional space. It is shown that all the robots can move into appropriate positions on the surface of a sphere/circle during an escort mission and reconfigure the formation automatically when an obstacle avoidance mission is active.  
multiple Euler-Lagrange systems; null-space-based behavioral control; adaptive proportional derivative sliding mode control; escort mission; obstacle avoidance mission Article
###### Abstract  
This paper studies the integration of data collection and offloading for maritime Internet of Things (IoT) systems with multiple unmanned aerial vehicles (UAVs). In the considered multi-UAV maritime IoT system, the UAVs act as the aerial base stations to complete the missions of data collection from buoys and data offloading to the offshore base station (OBS). In this case, the UAVs need to adaptively select the mission mode between data collection and data offloading according to the network resources and mission requirements. In this paper, we aimed to minimize the completion time of data collection and offloading missions for all UAVs by jointly optimizing the UAV trajectories, mission mode selection, transmit power of buoys, and association relationships between the UAVs and buoy/OBS. In order to solve the mixed-integer non-convex minimization problem, we first designed a multi-agent deep reinforcement learning algorithm based on a hybrid discrete and continuous action space to preliminarily obtain the UAV trajectories, mission mode selection, and the transmit power of buoys. Then, we propose an algorithm based on the stable marriage problem to determine the buoy-UAV and UAV-OBS association relationships. Finally, the simulation results show that the proposed algorithms can effectively shorten the total mission completion time of data collection and offloading for the multi-UAV-assisted maritime IoT system.  
maritime IoT; deep reinforcement learning; UAV trajectory optimization; hybrid proximal policy optimization +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Adaptive Data Collection and Offloading in Multi-UAV-Assisted Maritime IoT Systems: A Deep Reinforcement Learning Approach.  
_Remote Sens._**2023**, _15_, _292_. [[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))  
doi/10.3390/rs15020292  
Academic Editors [PERSON] and [PERSON]  
Received: 9 November 2022  
Revised: 21 December 2022  
Accepted: 29 December 2022  
Published: 4 January 2023  
**Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 2 Mathematical Basis
### Mutual Information  
Since its introduction by [PERSON] [59], mutual information has been used across various fields to quantify coupling or information transmission between systems [60]. In neuroscience, several studies have employed mutual information analysis to investigate information transfer in the brain. For example, [PERSON] et al. [41] applied this method to multi-channel EEG data to assess information flow between cortical regions in Alzheimer's disease (AD) patients.  
Cross mutual information (CMI) measures the information gained about one system from observing another, in contrast to auto mutual information, which quantifies mutual information between two parts of he same time series \(x(t)\) separated by a lag \(\tau\). Unlike traditional correlation functions, which capture only linear dependencies, CMI detects both linear and nonlinear statistical relationships between time series. CMI between measurement \(x_{i}\) from system \(X\) and \(y_{j}\) from system \(Y\) represents the amount of information \(x_{i}\) provides about \(y_{j}\). Thus, CMI serves as a measure of dynamical coupling or information transmission between \(X\) and \(Y\). When applied to EEG data, it can be interpreted as an indicator of functional connectivity between brain regions. If two systems are entirely independent, their CMI is zero, meaning no information is transmitted between them. Therefore, in our case, CMI quantifies the information flow between different brain areas.  
CMI is calculated as  
\[Inf(X,Y)=\sum_{x\in X}\sum_{y\in Y}p(x,y)\log_{2}\biggl{(}\frac{p(x,y)}{p(x)p( y)}\biggr{)}, \tag{11}\]  
where \(p(x)\) and \(p(y)\) are the normalized histograms of the distributions of measurements \(x\) and \(y\), respectively, and \(p(x,y)\) denotes their joint probability density.  
In this study, we compute the time-delayed CMI, \(Inf(X(t),Y(t+\tau))\), which quantifies mutual information between EEG signals from each pair of channels as a function of a time delay. To assess information transmission between different cortical areas, we select the peak CMI value within a time delay range of \(0\pm 500\) ms for each electrode pair.  
CMI measures the degree of dependence between \(X\) and \(Y\), ranging from 0 to entropy \(H\) of \(X\). A value of \(Inf(X,Y)=0\) indicates mutual independence, whereas \(Inf(X,Y)=H(X)\) signifies that one signal completely determines the other. Similar to coherence and correlation measures, changes in CMI due to attention (\(\Delta I\)) can be either positive or negative. Specifically, positive values (\(\Delta I>0\)) indicate _information gain_, while negative values (\(\Delta I<0\)) correspond to _information loss_.
## 7 Performance Evaluation
### Simulation Configurations
#### 7.2.2 DRRF and GWF  
DRRF was chosen to show the effect of GWF. Therefore, the set up is similar to the DRRF simulation configuration. However, we only used 4 MSs with 5 Mbps CBR traffic rate. These 4 MSs can saturate the link capacity so that the fairness is exercised. Although we simulated four CBR flows to demonstrate the effect of GWF, with more mobile stations or number of flows, intuitively the effect of weighted fairness will be more obvious. Moreover, we set the weight values at 0.0, 0.5, and 1.0.
## 3 Problem Formulation
### Variables  
The same as the other optimization problems in power systems, such as Optimal Power Flow (OPF), two types of variables, named control variables and state variables, are defined for the RPP.  
Normally, for a typical RPP, control variables are defined as generators' voltage magnitudes, transformers tap settings, and output reactive power of VAR sources. Considering a scenario-based approach to model the uncertainty of the problem, the control variables set (\(U\)) for a probabilistic RPP are expressed as Equation (8) [14; 15; 16; 17].  
\[U=\left\{\begin{array}{ll}V_{S_{i\wedge}},&i\in\Omega_{g},\,s\in\Omega_{s} \\ t_{k_{i}s},&i\in\Omega_{Tap\text{C}t},\,s\in\Omega_{s}\\ Q_{C_{i\wedge}},&i\in\Omega_{Comp},\,s\in\Omega_{s}\end{array}\right. \tag{8}\]where \(V_{g_{i,s}}\) shows the voltage magnitude of the \(i^{th}\) generator for the \(s^{th}\) scenario, \(t_{k_{i,s}}\) is used to assign the settings of the \(i^{th}\) tap-changing transformer for the \(s^{th}\) scenario, and \(Q_{C_{i,s}}\) shows the output reactive power of the \(i^{th}\) VAR compensator device for the \(s^{th}\) scenario. Likewise, \(\Omega_{g}\), \(\Omega_{s}\), \(\Omega_{TapC_{i}}\), and \(\Omega_{Comp}\) symbolize the set of generators, set of scenarios, set of tap-changing transformers, and set of VAR compensator devices, respectively.  
The state variables in a typical RPP consist of the generated active power by the slack bus, the generated reactive power by each of the existing generators, the voltage magnitude of the load buses, and the flow of the transmission lines.  
Using a scenario-based approach to model the uncertainty of the problem, the state variables set (\(X\)) for a probabilistic RPP are expressed as Equation (9) [14; 15; 16; 17].  
\[X=\left\{\begin{array}{ll}P_{G_{Slud,s}},&s\in\Omega_{s}\\ Q_{G_{i,s}},&i\in\Omega_{g},\,s\in\Omega_{s}\\ V_{L_{i,s}},&i\in\Omega_{TQ},\,s\in\Omega_{s}\\ S^{From}_{I_{s,s}},&l\in\Omega_{Lines},\,s\in\Omega_{s}\\ S^{To}_{I_{s,s}},&l\in\Omega_{Lines},\,s\in\Omega_{s}\\ \end{array}\right. \tag{9}\]  
where \(P_{G_{Slud,s}}\) indicates the generated active power by the slack generator (bus) for the \(s^{th}\) scenario, \(Q_{G_{i,s}}\) is used to denote the generated reactive power by the \(i^{th}\) generator for the \(s^{th}\) scenario, \(V_{L_{i,s}}\) shows the voltage magnitude of the \(i^{th}\) load bus for the \(s^{th}\) scenario, and \(S^{From}_{I_{s}}\) and \(S^{To}_{I_{s}}\) show the apparent power flow of the sending and receiving ends of the \(l^{th}\) line for the \(s^{th}\) scenario, respectively. Additionally, \(\Omega_{PQ}\) and \(\Omega_{Lines}\) specify the set of the load buses and the set of transmission lines, respectively.
## 2 Collection of Arc Fault Data in Series  
### Arc Occurrence Platform Construction and Current Signal Collection  
According to GB/T31143--2014 \"Arc Fault Protection Appliances (General Requirements for AFDD),\" [18] an independently designed and constructed AC arc fault signal acquisition platform suitable for 220 V has been established. The arc fault device consists of a fixed electrode and a moving electrode controlled by a stepper motor. The material of the fixed electrode is a stone grinding rod, while the moving electrode is a copper rod. The experimental platform mainly includes an experimental circuit and measuring equipment. The experimental circuit primarily comprises a 220 V/50 Hz AC power supply, an arc fault simulator, and a load in series. Among these, the arc fault simulator is connected to the load and oscilloscope, generating arcs when the electrical appliance operates; the oscilloscope is used to collect the load current signal; and high-frequency current transformers (cut-off frequency: 10 KHz) and low-frequency current transformers (cut-off frequency: 5 KHz) connected to the oscilloscope are linked to the live wire of the load, obtaining the operating current of the load. The torque of the stepper motor is 0.55 N.m and the step angle is 1.8. The experimental platform is shown in Figure 1.  
Common household low-voltage AC appliances are used as experimental loads. At the start of the experiment, normal operating signals are collected. The two electrodes are in contact, and switch 1 is closed to form a circuit. Then, the distance between the two electrodes is controlled by a stepper motor. When a certain distance is reached, a sustained arc will be generated. During the experiment, first, the current signal when the load is operating normally is collected; then, the current signal under the arc generation state of the arc generator is collected, and finally, signal processing and analysis are performed. AC fault arc protection is mainly applied in homes or office settings, so different household appliances are selected as experimental loads. The load parameters used in the experiment are shown in Table 1.