esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
With the increasing application of AIS, a large amount of ship data has been provided for maritime surveillance. The interval of a ship broadcasting AIS messages is determined by the information type and navigation status. The reporting interval of static information is 6 min, and the reporting interval of dynamic information is between 2 s and 3 min [37]. This means that the development of high-performance ship classification and identification algorithms to handle such massive information is challenging. This paper comprehensively extracts ship features and studies the ship classification and anomaly detection method for spaceborne AIS data based on machine learning methods. The experimental results show that the proposed method can effectively improve the performance of spaceborne AIS data mining and the application levels of maritime surveillance by considering ship motion features.
## 1 Introduction
* Proposed a new technique based on the improved version of synchrosqueezing transform called wavelet transform synchrosqueezed transform (WT-SST) to extract tight time-frequency features and then classify with the proposed 2D deep CNN. The rest of this paper is organized as follows. A review of relevant and related work is discussed in Section 2. Section 3 discusses the background concepts, and Section 4 presents the experimental system methodology of the proposed technique. Section 5 presents the results of the experiment, while Section 6 discusses the findings of the proposed models. Finally, Section 7 presents the conclusion, system improvement, and future direction.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 5 Signal Technologies
### Radiofrequency-Based Systems (RF)
The coordinator is an FFD and is responsible for the overall management of the network, starting the network, setting the parameters of the network, and transferring the packets of the application. It can also be used as a router. The router is used in tree and mesh topologies to expand network coverage. It is usually located either in the central area of the network. Its function is to find the best route to the destination to transfer a message. It can perform the same functions as the coordinator except for the start of the network. The terminal device can be an RFD responsible for collecting and transmitting data. ZigBee nodes consume almost no energy when at rest. ZigBee technology provides three structures: star, tree, and mesh. The star structure is the simplest and consists of a coordinator and a few end devices. The end devices are all connected to a single coordinator node, and all communications pass through this coordinator. The tree topology consists of a coordinator, a few routers, and end devices. Routers allow extending network coverage; end devices are connected in groups to each router and can only communicate with their coordinator. In the case of disabling the latter, the nodes connected to it remain isolated and cannot communicate even with the nearest nodes. The mesh topology, a peer-to-peer network, consists of a coordinator, several routers, and end devices. A mesh topology is self-healing, which means that the node will find an alternative path to the destination during transmission if a path fails. The range of the network can be easily changed by adding or removing multiple devices. Figure 13 shows the three topologies. Commonly, the RSS signal is used to estimate the distance between two or more ZigBee sensor devices [50]. Figure 13: Network topologies.
## 3 Results and Discussion
### Enzyme Inhibitory Properties
[PERSON] et al [22] reported that the different solvent extracts (n-hexane, dichloromethane, acetone, ethyl acetate, methanol and water) from _C. officinalis_ leaves and flowers exhibited the low inhibition values on AChE and BChE. The ethyl acetate extracts exhibited the best inhibitory effects among them and the findings could indicate that the plant has a complex phytochemical profile. Amentoflavone, identified in the roots, was previously identified in other plants possessing cholinesterase inhibitory properties [34; 35]. Amentoflavone inhibited AChE in scopolamine-induced memory impaired mice [36]. Cynarin, characterized from the maceration extract of _C. officinalis_ roots (0.042 mg/g extract), was previously reported to fit to AChE catalytic triad [37]. The hydroxycinnamic acid derivative was reported to inhibit AChE with an IC\({}_{50}\) value of 243.67 nM [38]. The ability of the _C. officinalis_ extracts to inhibit tyrosinase was also investigated. Topical cream enriched with _C. officinalis_ ethanol extract was reported to reduce skin melanin content [39]. However, the authors did not mention the exact plant part used for this study. Ethanolic extract of the leaves of _C. officinalis_ was reported to possess \(\alpha\)-amylase inhibitory activity [40]. However, findings gathered from this study showed that _C. officinalis_ extracts exhibited weak inhibition against \(\alpha\)-amylase and \(\alpha\)-glucosidase. _C. officinalis_ flower extract exhibited \(<\)30% and \(<\)45% inhibition at 20 mg/mL and 500 ug/mL, for \(\alpha\)-amylase and \(\alpha\)-glucosidase, respectively [40]. As shown in Table 4, in general the flower extract showed highest inhibition against \(\alpha\)-amylase, followed by the leaf extracts and finally the root extracts, while the root extracts were more active against \(\alpha\)-glucosidase and flower extracts were the least active. Following our literature survey, it was noted that scientific information regarding the enzyme inhibition properties of _C. officinalis_ is scarce. Thus, our presented findings could provide valuable information for novel applications with _C. officinalis_.
## 3 Proposed Work
### Message Flow Diagram of the Initial Connection in PMIPv6 for VANET  
When a vehicle connects itself for the first time to the PMIPv6 domain, the sequence of messages that are exchanged among network entities MAG/LMA, TTP [15], and the  
Figure 1: Multicast authentication information by TTP server.  
vehicle are as shown in Figure 3. For the first time, the vehicle connects to an access point that is affixed to one of the MAGs; the MAG forwards the authentication request to the TTP with the vehicle ID and MAG ID in step 2. Vehicle authentication and authorization, as well as that of network entities such as MAGs and LMAs, are handled by the TTP. After authentication is completed, in step 3, the TTP will send the authentication response consisting of the vehicle and MAG profiles to the MAG and LMA [16]. In addition to this, the TTP also multicasts this profile information to the neighboring TTPs so that, later, during the inter-domain handover, this information can be used. On receiving the router solicitation message from the vehicle in step 4, in step 5, the PBU message with the vehicle ID is sent by the MAG to the LMA. Following the updating of the binding cache entry (BCE) in step 6, the MAG is identified as being permitted to send PBU messages. The LMA in step 7 then transmits a PBA message along with the vehicle's HNP. Step 8 creates a bi-directional tunnel to the MAG [17; 18], which makes it accessible for the vehicle. In step 9, the MAG transmits to the vehicle a router advertisement (RA) message. After obtaining the RA message, the vehicle produces its home address by fusing the HNP with its interface address. In PMIPv6, which only employs the per vehicle prefix technique, each vehicle is assigned a unique home network prefix. In contrast to MIPv6, a vehicle traveling within a PMIPv6 domain receives a distinctive home address. The bidirectional tunnel enables all messages sent by the vehicle to be forwarded to the LMA. The LMA, which serves as the topological anchor point for the domain, receives all communications meant for the vehicle. The LMA then transmits the received message through the tunnel to the MAG. After the outer header is removed, the message is sent to the vehicle via the MAG at the opposite end of the tunnel.
## 1 Introduction
This subject is addressed in [22], where authors focus on the optimized charging control strategies taking into account the efficiency and safety of the battery cells. Higher efficiency is achieved by the reduction of energy losses. The leader-followers-based approach was proposed to enable optimal charging control. The challenge related to the battery cells equalizers is addressed in [23]. The authors develop and evaluate new equalizer topologies that offer high speed and efficiency with a compact size. The challenge of the battery thermal management is addressed in [24]. In this paper, a charging control strategy that applies a developed coupled thermoelectric model is proposed. The battery pack can be effectively used for driving and V2H services only if the drive inverter meets versatile requirements, such as high efficiency and reliability for an affordable price. The study presented in [25] is focused on the development of inverter topology design. Authors proposed an inverter topology that has a lower efficiency compared to other topologies; however, it reduces cost and size by utilizing low rating components at higher-voltage gain operations. The papers mentioned above provided relevant contributions to the concept of future smart networks with a high deployment of EVs (assuming their large-scale integration into buildings and the supplying network). However, the authors of the cited research either focused on the technical issues or potential effects on the supplying network rather than on the benefits for prosumers. Since the discussed resources (PVs, ESSs, and EVs) are part of the prosumer installation, this paper focuses on the possible benefits for its owner. Our study aims to show the benefits of using EVs drive inverter, RESs, and stationary storage technology for coordinated energy management, as a small-scale project that can be realized in the prosumer installation without extensive costs. The presented \"fast-home\" charging functionality does not offer the charging speed typical for super and extra fast public chargers. However, the introduced system can be used to speed up the charging process at home and be applied for services that require bi-directional power flow. To sum up, the main focus is on the prosumer benefits and other curtailments are not considered. In the following sections, the elements of generic prosumer installation and a typical EV user's behavior are presented (Section 2). What follows are the assumptions, the description of the control strategy, and the control algorithm (Section 3). Next, there are the results of the simulations with a description (Section 4) and finally the conclusions (Section 5).
# A Comprehensive Review of Micro UAV Charging Techniques
## 3 Battery Charging Techniques for UAVs
### Battery-Powered UAVs  
Small drones are typically battery-powered and batteries are critical components of these drones [29]. They improve the flexibility and simplicity of the propulsion system. Moreover, battery-powered drones offer cost-effectiveness and enhanced flight time. However, small UAVs have limited battery endurance due to low weight carrying capacity and can travel up to 90 min on LiPo batteries [30]. Small scale UAVs are mostly suitable for commercial applications. Furthermore, Lithium batteries are installed in small UAVs due to their high energy and light weight. Table 2 illustrates a comparison of different batteries. [PERSON] et al. [31] evaluated different batteries on the basis of state of charge (SOC) for a desired operation. Similarly, the author in [32] investigated parameters which impact the performance of battery-powered UAVs. Battery-powered UAVs face the critical challenge of reduced autonomy. Several research studies have focused on enhancing battery performance to extend UAV mission time. Moreover, safety and stability concerns have been reported as a result of energy density enhancement [33]. Thus, several approaches have been proposed to overcome these limitations, such as super capacitors, solar cells, fuel cells and hybrid charging technologies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristics** & **Li-S** & **LiPo** & **Ni-Mh** & **Ni-Cd** \\ \hline Specific power (W/kg) & 600 & 2800 & 900 & 300 \\ \hline Energy density (Wh/L) & 350 & 300 & 300 & 100 \\ \hline Specific energy (Wh/kg) & 350 & 180 & 80 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different batteries [34].
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 2 Two-Way Ranging (TWR) Overview
### Propagation Model  
A signal \(x\) is transmitted through a line-of-sight channel, during which it is distorted before the signal \(z\) is received. The traditional propagation model [20] for this channel is  
\[z=ax(t-\tau)e^{j2\pi f_{c}\tau}, \tag{3}\]  
where \(a\) is the complex channel attenuation, \(\tau\) is the propagation delay, and \(f_{c}\) is the carrier frequency. We assume a basic line-of-sight channel attenuation [44]  
\[a^{2}=\bigg{(}\frac{\lambda}{4\pi d}\bigg{)}^{2}G_{Tx}G_{Rx}, \tag{4}\]  
where \(G_{Tx}\) and \(G_{Rx}\) are the transmitter and receiver antenna gains, \(\lambda\) is the signal wavelength, and \(d\) is the distance between the two users.  
Advanced ToA estimators require a much higher fidelity propagation model, which was previously explored in significant detail in [45]. The simplified model presented in Equation (3), however, is sufficient to motivate the following discussions and for the remainder of this manuscript.
## 4 Imaging System Characterization and Performance
### Camera Signal-To-Noise Ratio  
Besides selecting or developing the microscope objective and light source, image quality also relates to the signal-to-noise ratio (SNR) of the imaging device. Under specific conditions, such as the high-speed requirements investigated in this work, the SNR may become critical when gain is applied to the images within the imaging device (in this case at the sensor or camera front-end) to maximize its light sensitivity. Gain generally comes with noise, and too much noise may compromise the image quality, taxing the image processing methods to extract information from the acquired data. We therefore measured the SNR as a function of digital level (8-bit scale) using the following expression,  
\[\text{SNR}=20\cdot\log\left(\frac{\mu_{\text{S}}}{\sigma_{\text{S}}}\right). \tag{2}\]  
In Equation (2), \(\mu_{\text{S}}\) and \(\sigma_{\text{S}}\) are the mean and standard deviation of the signal S, respectively. To measure the SNR across the dynamic range of the camera, the sensor was illuminated with a diagonal intensity gradient covering the entire dynamic range. The SNR was then computed applying Equation (2) on all pixels of the sensor, except when pixels reached the top-end of the 8-bit digital scale (255 Cs). We repeated the experiments at several gain settings, from a gain level of 1 (or no gain), to a gain level of 3. The results are reported in Figure 6, showing the SNR for all pixels, represented by the dots, as well as the mean profile (dashed-lines) as function of digital intensity.  
The results of Figure 6 demonstrate that the camera exhibits excellent SNR, with a mean peak SNR of 41.3 dB with a conversion gain of 1. Current standards for digital camera systems consider 40 dB as excellent SNR, whereas 10 dB would represent an acceptable SNR level. This represents a dynamic range of 120:1, supporting the 8-bit (256 levels) ADC operation as the appropriate digitization mode. At the same time, it would be interesting to evaluate the same camera with the sensor's ADCs operating at 10- and 12-bit digitization level to conclude whether the increased bit-depth effectively provides usable image information. As expected, SNR drops with higher gain settings, by approximately 3 dB for a gain of 2 and a total of 5 dB from baseline to the SNR measured with a gain of 3; though these still represent good SNR behavior. Looking at the SNR data at the low-end of the graph, we observe  
Figure 6: Signal-to-noise ratio as a function of digital intensity for the camera at various gain settings. The data points have been calculated from Equation (2), and the dashed-line profiles represent the mean of the scatter distributions.  
that the background level increases with higher gain settings, which is in fact where a large part of the difference in top-end SNR comes from. Despite not being observable on the scatter results of Figure 6, all three gain-specific distributions present similar dispersion, with a marginal increase in standard deviation for the SNR at a gain level of 3.
## 2 Data and Methodology
### Methodology
#### 2.3.2 Sentinel-1 Time Series Curve Composition  
Figure 5 illustrates the Sentinel-1 time series curves of garlic and winter wheat based on 5000 sample pixels. Figure 5a presents the time series data of VH/VV for garlic and winter wheat, while Figure 5b displays the time series data of VH and VV for garlic and winter wheat. Analysis of Figure 5b indicates that the time series data of VV for garlic and winter wheat exhibit a similar pattern of change to the time series data of VH, with more significant differences observed around the month of April, particularly in the troughs and the timing of their occurrence. Conversely, examination of Figure 5a suggests that the trend of the time series data of VH/VV for garlic and winter wheat is similar, although the difference is significant from February to May. Notably, the peak for garlic occurs around February, whereas for winter wheat, it is around April.
## 4 Simulation Analysis
### Algorithm Comparison
#### 4.3.2 Comparison of Integrated Navigation and Positioning Algorithm with MEO and IGSO Constellation  
To compare the navigation and positioning performance of LEO, MEO and IGSO constellations based on ICN technology, and to explore a reference constellation that is more suitable for ICN solutions, it is necessary to compare and analyze these constellations with different orbital altitudes. Without loss of generality, we only compare the 5 s alternate switching algorithms based on the same orbital surface and adjacent orbital surface. The simulation result of the comparison among the INS+LEO2-satellite 5 s, INS+MEO2-satellite 5 s and INS+IGSO2-satellite 5 s alternate switching ranging integrated navigation algorithms is shown in Figure 8.  
Figure 7: Error statistics.  
Figure 8 shows that the INS+LEO2-satellite 5 s alternate switching ranging integrated navigation algorithm based on the same orbit and adjacent orbit surface is significantly better than the INS+MEO2-satellite 5 s and the INS+IGSO2-satellite 5 s alternate switching ranging integrated navigation algorithm based on the same and adjacent orbit surface. Figure 9 shows the statistical results of the positioning errors of the INS+LEO2-satellite 5 s, the INS+MEO2-satellite 5 s and the INS+IGSO2-satellite 5 s switching algorithm.  
Figure 8: Comparison curve between the INS+LEO2-satellite 5 s alternate switching ranging algorithm and INS+MEO2-satellite 5 s alternate switching algorithm.  
The statistical results in Figure 9 are easily interpreted, because in terms of orbital height, IGSO > MEO > LEO, therefore the propagation delay is similar. Moreover, taking into account multipath and noise interference on the propagation path, as well as the ionospheric and tropospheric factors, it is not difficult to comprehend that the IGSO constellation navigation error is greater than that of the MEO constellation, which in turn is greater than that of the LEO constellation. This result is in line with expectations, so we can conclude that the LEO constellation is more suitable for our algorithm than the MEO and IGSO constellations.
## 2 Method and Data Characteristics
### Risk Level of Ground Subsidence  
The risk levels used as the output data in this study were divided into three levels according to the number of times ground subsidence occurred in the grid. Since there are no quantifiable measures to categorize the risk level, we build datasets by selecting different numbers of data belonging to risk level 2 according to the number of occurrences of ground subsidence in the grid. Thus, the number of data varies according to the category based on the number of occurrences of ground subsidence of each dataset, which is presented in Table 3. As presented in Table 3, the ratio of Risk Level 1 data was the highest (57%), and the ratios of Risk Level 2 and 3 data varied according to the conditions. As such, the composition of the data shows unbalanced features, and we applied SMOTE, an oversampling technique, to the 12 datasets to balance the data [12; 13; 14].
# Circular Regression Applied to GNSS-R Phase Altimetry
## 1 Introduction
In recent years, several techniques, based on \(2\pi\) periodic distributions, have been defined for the statistical processing of circular signals. For example, using a von Mises distribution, a circular median filter is defined in [33] and applied to color images processing. The same distribution is used to derive a circular recursive filter (or Kalman-like filter) for angle measurements in [34]. In the context of dynamic estimation, a circular filter was proposed for GNSS phase smoothing in [35] and GNSS phase positioning with cycle slip detection and repair in [35]. In [36], a von Mises-based Probability Hypothesis Densities (PHD) filter is proposed and applied to the problem of multiple angular targets tracking. In [37], it is the Bingham distribution that is used for deriving a recursive filter dedicated to \(\pi\) symmetric circular problems. The optimality of such estimation techniques according to various circular error criteria and further techniques for reaching such optimality have been investigated in [38; 39]. In this article, using the von Mises distribution, we propose to define the maximum likelihood estimator that provides a direct estimation of the height with observations defined in the circular domain. The proposed estimator is defined as a linear-circular regression [30; 40] in the time reference frame of the sine of the satellite elevation and allows one to avoid the phase unwrapping step. It is based on a loss function derived from the measurement likelihood. This loss function is maximized using a Newton-Raphson algorithm. The paper is organized as follows. The phase observation model is presented Section 2. Section 3 is dedicated to the linear-circular regression height estimator. In Section 4, the proposed approach is assessed on synthetic and real data. The last section is the conclusion.