esa-sceva/satcom-chunk-collection huggingface dataset:
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
## 4 Discussion
On the one hand, the attitude control error, attitude measurement error, and antenna electronic miss-pointing of GF-3 need further analysis. Therefore, we look forward to arranging special GF-3 missions in the future and obtaining corresponding dual channel antenna patterns to better verify and find out the reasons for the phase imbalance of GF-3 ATI mode. The following data are expected to be obtained, such as ATI data of the land scene with the same scanning angle and different observation directions, preferably GF-3 ATI data of tropical rainforests and the equatorial region. On the other hand, the ocean current measured by the GF-3 ATI data must rely on the land scene to eliminate the phase imbalances due to GF-3 dual channel phase imbalance. Therefore, accurate modeling and analysis of channel phase imbalance are needed to get rid of the dependence on the land scene in the subsequent research. Moreover, how to accurately eliminate the contribution of sea surface wind and Bragg phase velocity to Doppler velocity is also an essential research content.
# Two-Party Quantum Private Comparison Protocol Based on Rotational Encryption
## 1 Introduction
To address the aforementioned challenges, we propose an efficient two-party QPC protocol that employs single photons as quantum resources and utilizes rotational encryption to safeguard the privacy of the inputs, making it both effective and straightforward to implement. In this protocol, two participants encode their inputs as single photons and encrypt them using rotational encryption. The encrypted single photons are then sent to a semi-honest third party (TP), which can act independently but cannot collude with any participants. TP performs quantum measurements to derive the comparison results. Simulations conducted on the IBM Quantum Platform demonstrate the feasibility of our protocol, while security analysis confirms its resilience against both external attacks and insider threats. Compared to existing QPC protocols that rely on multi-qubit states or d-dimensional states, our approach exhibits superior efficiency and practicality. Specifically, our protocol utilizes rotational encryption and single photons, which are more practical than those based on multiple-qubit or d-dimensional quantum states due to the simpler manipulation and preparation of single photons. On the other hand, our protocol achieves a qubit efficiency of 50% by using two single photons to compare one bit of classical information, surpassing many existing QPC protocols. The structure of this paper is organized as follows: Section 2 introduces the concept of rotational encryption. Sections 3 and 4 outline the detailed steps of the proposed QPC protocol and its simulations, respectively. Section 5 presents the security analysis, while Section 6 gives a discussion. Finally, we summarize our findings in Section 7.
## 4 Results  
The proposed reference architecture for electric vehicle (EV) charging optimization consists of several key components. These include the EV itself, the charging station (CS), and the charging station control center (CSCC), each playing a critical role in managing energy flow and ensuring system efficiency. Below, we outline the components and their interactions.  
EV represents the consumer side of the system and is powered entirely by electricity. The EV's technical characteristics directly influence the efficiency and performance of the charging process, including the following:  
* Battery capacity: The amount of energy stored in the EV's battery, affecting how long it can operate before needing to be recharged.
* Battery performance: This refers to how efficiently the battery charges and discharges, which impacts both the cost and speed of charging.
* Charge level limits: Each EV has maximum and minimum allowable charge levels to protect battery health and ensure optimal performance.
* Charge/discharge rates: These rates determine the speed at which the EV's battery can charge or discharge, influencing how quickly the vehicle can be powered or contribute to the grid.  
While a deeper technical analysis can be found in sources like [48], this section focuses on the essential components and their interactions with the grid.  
Charging stations (CSs) are responsible for delivering power to the EVs and come in two primary types:  
* Slow charging, typically, around 3.3 kW, suited for overnight or long-term charging sessions.
* Fast charging, ranges from 60 to 150 kW, designed for rapid charging in shorter periods.  
The power flow from the charging station to the EV is not continuous and is limited by standards and manufacturer specifications. Charging stations must also maintain communication with the CSCC via the internet, allowing for real-time updates on charging status, grid demands, and changes in electricity tariffs.  
CSCC functions as an aggregator and controller at the local area (LA) level. It plays several key roles, including the following:  
* Data aggregation: The CSCC receives real-time data from both EVs and the grid, processing them to make informed decisions.
* Control signal generation: Based on this data, the CSCC sends control signals to the charging stations, adjusting charging rates according to current grid conditions, user preferences, and market signals.
* Optimization: The CSCC uses algorithms to balance EV charging needs with grid stability requirements and economic opportunities, ensuring an efficient and cost-effective charging process.  
In conclusion, the key points are (i) integration: the CSCC plays a crucial role in integrating EVs into the grid; (ii) flexibility: slow charging is often preferred for grid flexibility due to its ability to better match supply and demand; (iii) communication: reliable communication between EVs, CSs, and the CSCC is essential for efficient operation (see Figure 1).
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 3 Materials and Methods
### MODIS Data Products
#### 3.3.1 MOD09 GA Product  
The MOD09 GA product offers an estimation of the surface spectral reflectance for MODIS bands 1 to 7 at a resolution of 500 m. This estimation takes into account atmospheric conditions, including gases, aerosols, and Rayleigh scattering [27]. Additionally, it includes observation and quality layers consisting of ten 1 km observation bands and geolocation flags. The reflectance layers obtained from the MOD09 GA product serve as the foundational data for numerous MODIS land products. This product is used to determine the vegetative cover of land surface from red and near-infrared reflectance factors, corresponding to MODIS band 1 (0.65 um) and band 2 (0.86 um) images. For example, the Normalized Difference Vegetation Index (NDVI) is one of the vegetation indices to quantify vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs). NDVI is computed following Equation (1), where \(\rho_{Red}\) and \(\rho_{NIR}\) are red and near-infrared reflectance factors, respectively. NDVI ranges from \(-1\) to \(+1\). A negative NDVI value is highly likely water while an NDVI value close to \(+1\) presents a high possibility of dense green leaves. But when NDVI is close to zero, there are likely no green leaves and it could even be a built-up area.  
\[\text{NVDI}=\frac{\rho_{NIR}-\rho_{Red}}{\rho_{NIR}+\rho_{Red}} \tag{1}\]
# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 1 Introduction  
As the magnitude of an earthquake increases, its destructiveness also does. The need to predict an earthquake is particularly relevant when its magnitude is large. About one million earthquakes of magnitude \(M_{s}=2.0\) occur annually across the world. However, there are only seven recorded earthquakes with a magnitude equal to or greater than 9. The low frequency with which large-scale earthquakes occur is an added difficulty for the study of their prediction, as modeled in Gutenberg-Richter law.  
In machine learning, this problem is widely known as imbalanced classification. A dataset is imbalanced when there exists one class with one label to which the majority of instances belong to (typically 70% or higher). Alternatively, a small number of instances are assigned to the other label (minority), usually the one with higher interest [1]. When the dataset exhibits such data distribution, standard classification algorithms, which search for a global performance, are not able to accurately predict instances with minority presence in the dataset. That is, they tend to assign minority instances to the label containing the majority of instances.  
In the case at hand, the minority label is related to the occurrence of large magnitude earthquakes and the goal is to predict if an earthquake of large magnitude will occur during the next five days. In order to manage this problem, it is proposed a novel methodology based on algorithms specialized in imbalanced learning. To carry out this methodology, datasets that collect the seismic activity of several cities in Chile have been used. Furthermore, ensemble learning has been applied in order to make more accurate predictions [2], as a result of combining the strengths of the different imbalanced classifiers here analyzed.  
The remainder of the paper is structured as follows. The state-of-the-art is reviewed in Section 2, in which both general-purpose imbalanced classifiers and approaches particularly designed forearthquake prediction are discussed. Section 3 describes the proposed methodology, including the algorithms evaluated and the ensembles generated. Results for different zones in Chile are reported in Section 4. Finally, the conclusions drawn from this study are summarized in Section 5.
# Quantum Computing for Healthcare: A Review
## 3 Applications of Quantum Computing for Healthcare
### Drug Research and Discovery  
Quantum computing enables medical practitioners to model atomic-level molecular interactions, which is necessary for medical research [61]. This will be particularly essential for diagnosis, treatment, drug discovery, and analytics. Due to the advancements in quantum computing, it is now possible to encode tens of thousands of proteins and simulate their interactions with drugs, which has not been possible before [35]. Quantum computing helps process this information more effectively by orders of magnitude as compared with conventional computing capabilities [62]. Quantum computing allows doctors to simultaneously compare large collections of data and their permutations to identify the best patterns. Detection of biomarkers specific to a disease in the blood is now possible through gold nanoparticles by using known methods, such as bio-barcode assay. In this situation, the goal could be to exploit the comparisons used to help the identification of a diagnosis [63].  
Identifying small molecules, macromolecules, and other molecular formations that develop into drugs that treat or cure diseases is a core activity of pharmaceutical companies. Many important drugs have been discovered in the past by way of scientific fortuity, with some notable examples being penicillin, chlor hydrate, LSD, and the smallpox vaccine. To tackle modern-day challenges such as those related to climate change and the COVID-19 virus, chemists cannot rely on luck alone. Modern-day drug discovery requires precise modeling of the energies dissipated in chemical reactions. Classical computers rely heavily on approximations for this, because even just calculating the quantum behavior of a couple of electrons involves very time-consuming computation. This reduces the precision and value of the model and puts the onus on the chemist to guide the model and validate the resultsin the lab. Converselt, quantum computers are already reliably modeling the properties of small molecules, such as lithium hydride [64], and have been shown to benefit quantum chemistry calculations requiring an explicit depiction of the wave function [65] because of high system entanglement and because they simulate properties at high accuracies. Finally, researchers have developed several quantum algorithms for chemistry, such as those that estimate the ground-state energies of molecular Hamiltonians and those that compute molecular reaction rates that are superior to their classical computing counterparts.  
Research by [PERSON] et al. [66] has demonstrated that it is even possible to accurately compute circuitry exhibiting noise with quantum chemistry. The researchers utilized a maximum of 16 qubits on Google's 53-qubit quantum computer to run a Monte Carlo simulation developed for physics models consisting of fermions that comprise electrons. The H4 molecules, molecular nitrogen, and solid diamond involving a maximum of 120 electron orbitals were simulated.
## 3 The Principle of VMD-DWT-Based Denoising  
During the late 1990s, [PERSON] introduced the EMD algorithm [8]. It is widely used to recursively decompose a signal into different unknown and separate spectral bands. However, the properties of the EMD method includes a sensitivity to sampling noise and a lack of mathematical theory, drawbacks that limit its application in signal de-noising. Though some attempts have been made to address these issues, such as empirical wavelets and the recursive VMD mode, they continue to persist. In 2014, [PERSON] [22] introduced VMD, an entirely non-recursive variational decomposition mode. In his research, VMD was described as different from the existing decomposition in that it can extract the mode concurrently from the input signal online. Moreover, the mode is an ensemble of modes, respecting central frequencies \(w_{k}\). The basic modes are obtained by applying the VMD algorithm to the input signals, called band-limited intrinsic mode functions (BLIMFs) \(u_{k}\) with respect to the sub-energy of the signal. As the decomposition number is predefined, the decomposition level decides the energy distribution in each basic mode. The authors in Reference [4] show the high-order modes \(u_{k}\) representing fast oscillations and the low-order modes \(u_{k}\) representing slow oscillations. The noise is almost in the high-frequency domain, so the experiment signal should be separated into several high-order modes. For the purpose of enhancing denoising performance, inspired by the translation invariant wavelet, VMD-based denoising techniques were developed. The wavelet-changed thresholding principle was employed in the decomposition basic modes. The VMD-changed thresholding algorithm is described in this section.
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 4 Proposed CLIC Protocol  
Assume that the nodes know their location and the location of the sink node. Localization algorithms [24; 25] can be used to obtain these geographic coordinates. Using the vector hydrophone localization algorithm, nodes can determine the relative positions of neighboring nodes [26].  
Based on the routing algorithms and MAC designs for interference and congestion avoidance, the CLIC forms a route that adaptively bypasses nodes with high packet collision probability and high congestion, reducing duplicate packet forwarding and end-to-end delay.
## 2 Related Works
It is proved that CS based data collection methods reduce the communication cost with high efficiency, but the data decoding usually involves high computation and the energy efficiency generally predominates in large scale WSNs. In addition, during the process of data collection, it generally demands complete sampling for all nodes in each round. Once there exist data missing, error or outliers, the result of data reconstruction would be greatly compromised. As an efficient data recovery method, the LRMA model is widely used in many scenarios, and especially the fast algorithm for the solution of low rank minimal optimization was proposed in [25]. A CS method combined with low rank approximation was proposed to interpolate the Internet traffic matrices, in which the spatio-temporal properties of the matrices were considered [26]. The low rank completion method was adopted to reconstruct the fingerprint from a small subset of training samples [27]. The spatial correlation structure was combined with low rank completion for Received Signal Strength Indication (RSSI) recovery [28]. The basic LRMA model is first introduced to reduce energy consumption in data collection in [14]. Compared with the globally random sampling of the CS based data collection method which realizes energy efficiency by reducing the communication cost, the random sampling scheme of the LRMA based method reduces both the communication and sensing cost at nodes, which is the main virtue of the LRMA based method. However, in the basic LRMA model, the spatial or temporal correlation of the sensing data is not exploited. To obtain better data recovery results, a temporally constrained LRMA model for data collection was proposed in [15]. The similar idea was shared by our previous work [16]. However, the spatial properties of the sensing data are not well explored in any currently existing methods. In this paper, we consider both the temporal and spatial correlation of the sensing data simultaneously to propose an improved LRMA based data collection method. Furthermore, the issues of optimizing sensing node selection and balancing network energy distribution are also discussed.
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Real Data
#### 3.2.7 Analyses of Overall System Performance Using an Image-Based Metric  
An alternative, image-based metric was designed to evaluate the overall performance of the system without relying on the manual alignment procedure and alignment tool calibration (described in Section 3.2.2) to provide a reference value, allowing us to rule out errors in those processes. However, this metric expresses not only the accuracy of our system and the ability of the robot's CS to report its position correctly, but also its ability to reach the target position. Therefore, we can expect the overall errors to be potentially higher.  
The procedure starts by capturing an image at the initial position and calculating the transformation between the left camera and the target, \(T_{\text{C1}_{j}}^{K}\), using our developed methods. As a second step, the base-to-end-effector transformation that brings the camera to the desired position, \(T_{W}^{TCP_{j}}\), is determined by:  
\[T_{W}^{TCP_{j}}=T_{W}^{TCP_{j}}\ T_{\text{TCP}}^{\text{C1}_{1}}\ T_{\text{ C1}_{1}}^{K}\ T_{\text{C1}_{j}}^{-1}\ T_{\text{TCP}}^{\text{C1}_{1}}\,, \tag{14}\]  
where \(T_{\text{C1}_{j}}^{K}\), the transformation between the camera and the target in the desired position, has the value:  
\[T_{\text{C1}_{j}}^{K}=\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&1&250\\ 0&0&0&1\end{bmatrix}, \tag{15}\]  
Figure 21: Error in each of the X and Y axis for the estimation provided by the stereo ICP based method from earlier work, with different amounts of initial position deviation from ground truth. Offset values of \(|\Delta a|=5\) deg, \(|\Delta t|=25\) mm (**a**). Offset values of \(|\Delta a|=10\) deg, \(|\Delta t|=50\) mm (**b**). Each colour point represents a different position of the camera in relation to the target. The continuous black circle represents the error tolerance of the application.  
which corresponds to aligning the camera and target plane at a distance of 250 mm from the target. The command is given to the robot controller, which moves the camera to the calculated position, where a full-resolution image of the target is taken (Figure 22).  
If the alignment is correct, the center of the reference marker is expected to coincide with the center of the image. Therefore, an alignment error can be measured in pixels, i.e., as the difference between the center of the marker and the center of the image (Figure 23). The measured error can be converted into millimeters, since the dimensions of the target are known (e.g., we know that the diameter of one bead corresponds to 1.95 mm).  
In Figure 24, we show the calculated error using this method for the set of 20 initial camera positions that we have been analyzing so far.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 2 Related Works
### Visual Position Recognition  
In recent years, with the development of deep learning technology, position recognition based on computer vision has made great breakthroughs. The visual place recognition technology is not limited by application scenarios, and can intuitively provide users their locations. [PERSON] et al. [25] used pre-trained CNNs that are trained for objection detection to realize a position recognition task. [PERSON] et al. [26] obtained better performance using ImageNet to extract features from images than methods of SIFT features in recognizing locations. [PERSON] et al. [27] designed a new CNN architecture to train a place recognition task in an end-to-end manner; their method outperforms existing feature descriptors. [PERSON] et al. [28] considered place recognition as a classification task and built a massive specific dataset for VPR. Most of the currently used VPRs are applied to outdoor scenes; however, indoor scenes have many feature points, and the features are more homogeneous. Therefore, we need to make special designs for VPR in this regard to adapt to indoor positioning.