esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Saccade Distance Calculation Procedure  
To calculate the saccade distance, we conducted the following steps:  
Coordinate Extraction: We extracted the coordinates of gaze positions in image space from the raw eye tracking data. These coordinates were measured in pixels, allowing us to precisely capture the movements of the participants' gaze.  
Pupil Trajectory Positions: For each participant and condition, we obtained the coordinates (\(X_{i}\), \(Y_{i}\)) of the \(i\)-th pupil trajectory position and the coordinates (\(X_{i+1}\), \(Y_{i+1}\)) of the \((i+1)\)-th pupil trajectory position.  
Saccade Distance Calculation: The saccade distance was computed as the Euclidean distance between the \((i+1)\)-th and \(i\)-th pupil trajectory positions. This distance represents the magnitude of the saccadic eye movement between consecutive fixations, as illustrated in Figure 3.  
Figure 2: Flowchart of eye tracking experiment.  
The formula used to calculate the saccade distance between two consecutive pupil trajectory positions is as follows:  
\[\text{Saccadic distance}=\sqrt{\left(X_{i+1}-X_{i}\right)^{2}+\left(Y_{i+1}-Y_{i} \right)^{2}}\]  
We have calculated 11 features related to saccade distance, including Minimum, First Quartile, Mean, Median, Skewness, Kurtosis, Standard Deviation, Coefficient of Variation, Maximum, Third Quartile, and Mode. These saccade distance-related features provide a comprehensive view of saccadic behavior. The specific code used for these calculations can be found in Appendix A.
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 4 An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
### An Efficient Heuristic for EOS Integrated Scheduling
#### 4.3.1 General Framework  
The proposed heuristic is essentially a stochastic local search algorithm [25]. Starting from an initial solution provided by a greedy construction heuristic, the algorithm alternates between an improvement phase and a perturbation phase. In the improvement phase, the algorithm conducts a directed and focused search by accepting only improving insertions. When a local optimum is reached in the improvement phase, the algorithm triggers a perturbation phase to help the search process escape from the local trap and restarts an improvement procedure in a different zone of the search space. Such an Improvement-Perturbation cycle is iterated for \(\pi\) rounds. The best solution is updated each time a new local optimum is attained at the end of the improvement phase. The first half of the iterations are run with a small violation penalty multiplier \(c_{1}\) to diversify the search. While for the other half of the iterations, the algorithm is run with a large violation penalty \(c_{2}\) to guarantee the feasibility of the resulting schedule. The general framework of the SFHA is shown in Algorithm 1.  
```
0: P: An AEOS scheduling problem; \(\pi\): the max number of iterations allowed for Improvement-Perturbation;
0: The best solution \(\sigma^{*}\)
1:\(\sigma\leftarrow\)Greedy-Construction( \(P\) )
2:\(\sigma^{*}\leftarrow\sigma\)
3:\(count\gets 0\)
```  
**Algorithm 1**Main Procedure of Our Algorithm  
4.3.2. Solution Initialization  
The initial solution is produced by a greedy construction algorithm. The algorithm first sorts all VTWs (associated with requests) in decreasing order of their profits and in ascending order of their start time. For each VTW, the algorithm arranges observation of the associated request at the earliest possible start time that satisfies all imperative constraints. If the memory level drops below zero at a new VTW, the algorithm finds the nearest download window and inserts a download activity into the schedule. Observation activities that conflict with the download activity are then removed from the schedule to ensure the feasibility. This process is terminated until all requests are examined.
# Microwave Photonic Signal Processing and Sensing Based on Optical Filtering
## 2 Optical Filtering for Single Sideband Modulation and Spectral Characterization
In Reference [36], the use of the weak EIT-like design has enabled enhancement of the bandwidth of an integrated silicon-on-insulator (SOI) filter while maintaining a high rejection ratio and high slope steepness, thus increasing the range of operating frequencies possible for the generation of OSSB modulation. Figure 1a shows experiment setup that was performed to verify the principle of the weak EIT-like notch micro-ring filter for optical sideband suppression. This has achieved better than 29 dB of optical sideband suppression ratio for high frequency signals (17-35 GHz), as shown by the experimental results in Figure 1b-d, while realizing electrical power ripple reduction to less than \(\pm\)1.25 dB for RF frequency up to 30 GHz. It has also been used to demonstrate the high-resolution performance of an optical vector network analyser, by measuring the SBS filter amplitude and phase response, and by performing wide-band measurements over 10-30 GHz of an on-chip SOI-based micro-ring resonator.
## 7 Future Directions  
Despite their current state of development, mainly in point-to-point connections, the future of terahertz (THz) communication systems, especially in hybrid setups, can be seen in the desire to advance these systems while eliminating their current limitations and making them fit for other applications. Presently, these limitations can be overcome by the upcoming technologies and strategies that will make THz communication more effective. The future of THz-FSO communication relies heavily on overcoming the inherent challenges posed by atmospheric absorption, dispersion, and security vulnerabilities. One of the most critical aspects is the accurate modelling of THz propagation characteristics in real-world atmospheric conditions. Recent studies have shown that, beyond 500 GHz, the atmospheric attenuation surpasses 100 dB/km, severely limiting long-range THz communications. However, [PERSON] et al. (2024) [107] conducted an in-depth analysis of THz channel behavior under various atmospheric conditions, highlighting the significant impact of humidity, temperature variations, and oxygen absorption on the signal degradation and security performance of the system. Their findings emphasize that adaptive multiband switching and hybrid THz-FSO-RF systems could mitigate propagation losses by dynamically selecting the optimal frequency bands based on environmental conditions.
## 4 Case Study and Result
### Examples of Data Exploration with the Geovisual Analytical System  
#### 4.2.1 Detecting Spatial Variations of Multiple Variables Using Maps  
Spatial variations can be detected from maps showing mean values over geographical region. The ensemble means of 5-year annual mean (1956-1960) from 300 model runs are used for analyzing spatial variation in this experiment. Four highly related variables including net thermal radiation at the top of the atmosphere (TOA) (trnf), net solar radiation at TOA (smf), net radiation of planet at TOA (net_rad_planet) and surface air temperature (tsurf) are selected as examples from the control panel and added into four different maps (Figure 7). By comparing the four maps through synchronized map operations, we can find that smf, turf and tsurf have high values within medium latitude area, but net_rad_planet has high values at mid-latitudes. Both net_rad_planet and smf have obviously high value clusters over tropical ocean areas, but the values of tsurf evenly distribute along latitude belts and decrease towards the poles.  
Figure 7: Data exploration on multiple Google Earths (GEs).
## 3 Simulation and Verification
These results were compared with the FEA results in Abaqus. In Abaqus, all links use the C3D8R element. The surface contact was set between the outer and inner tube surfaces as hard and frictionless contact. The \(U\) and \(S\) pairs were simulated by the U JOINT connector and JOIN + CARDAN connector, respectively. The boundary force \(\mathbf{F}_{f}\) of the theoretical results and simulation results are compared in Table 1. The relative error is less than 3%. The Von Mises stress distributions of the two results are compared in Figure 5, which shows a good match. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Theoretical Results** & **Simulation Results** & **Relative Error (\%)** \\ \hline \(F_{x}(\text{N})\) & \(-3525.19\) & \(-3541.30\) & \(-0.46\) \\ \(F_{y}(\text{N})\) & \(-2060.13\) & \(-2073.82\) & \(-0.66\) \\ \(F_{z}(\text{N})\) & \(435.27\) & \(425.617\) & \(2.27\) \\ \(M_{x}(\text{N}\cdot\text{M})\) & \(48.07\) & \(48.53\)With stiffness matrix \(K_{11}\) and mass matrix \(M_{11}\), the \(i\)th theoretical natural frequency \(\omega_{i}\) and the corresponding modal \(\mathbf{\phi}_{i}=\left[\mathbf{\phi}_{1};\mathbf{u}_{2}^{0}\right]\) can be obtained as follows. \[\left|K_{11}-\omega_{i}^{2}M_{11}\right|=\mathbf{0} \tag{28}\]  
\[\left(K_{11}-\omega_{i}^{2}M_{11}\right)\mathbf{\phi}_{1}=\mathbf{0} \tag{29}\]  
The first 10 natural frequency and modalities between the FEA results and theoretical results are compared in Figures 6 and 7, which show a good match. Figure 5: The statistical analysis results of the lander: (**a**) theoretical results and (**b**) FEA simulation results. Figure 6: The theoretical natural frequency and FEA simulation natural frequency. Figure 7: The first ten modal of the lander. \(a_{1}\sim a_{10}\), theoretical results. \(b_{1}\sim b_{10}\), FEA results.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 5 Experimental Setup and Methodology  
An extensive review of datasets publicly available for batteries is presented in [40]. The dataset selected herein is the one available in [41] (under 'CC BY 4.0'), which has been cited in multiple publications, such as in [11,42,43]. Four typical driving cycles are covered within the battery testing dataset, UDDS, US06, LA92, and HWFET, followed by a mix of other cycles. Data is related to a single NCA (Lithium Nickel Cobalt Aluminium Oxide) Panasonic 18650 PF cell with a nominal capacity of 2.9 \(Ah\). Further details of the battery testing setup and used methodology is presented in [36].  
The dataset contains characterization data from Hybrid Power Pulse Characterisation (HPPC) and EIS (Electrochemical Impedance Spectroscopy) tests, and in-cycle measurements from the driving cycles. The HPPC profile consists of pulses of 10 s equivalent to 0.5C, 1C, 2C, 4C, and 6C, where C is the battery nominal capacity, with each pulse followed by a rest period of 20 min. Five pulse discharge HPPC tests (0.5, 1, 2, 4, 6C) are performed at intervals varying between [100:5:0] percent of the SOC.  
A series of nine drive cycles were performed in the following order: Cycle 1, Cycle 2, Cycle 3, Cycle 4, US06, HWFET, UDDS, LA92, neural network (NN). Cycles 1-4 consist of random mixes of UDDS, US06, LA92, HWFET, and NN drive cycles (these emission test cycles have been regulated by American authorities [44]). A neural network drive cycle is a combination of US06 and LA92. The drive cycle power profile is calculated for an electric Ford F150 truck with a 35 kWh battery pack scaled down for a single NCA Panasonic 18650 PF cell. The drive cycle tests are terminated when voltage first hits 2.5 V for 25 \({}^{\circ}\)C.
## 1 Introduction
### Related Works
This dual optimization approach, combined with the innovative use of angular-based feedback in DRL for mm-wave multi-user mMIMO hybrid precoding, lies in its ability to dynamically optimize EE/SE through AB-HP and maximum RBT, significantly reducing the beam training overhead and RF chain usage while adapting to real-time network dynamics. Existing studies on mm-wave systems have thoroughly examined the difficulties in beamforming, channel estimation, and RF chain utilization. Although the number of RF chains has been reduced with the introduction of hybrid beamforming, this method still necessitates a high pilot power signal and has high beam training complexity. Additionally, even though RBT-DRL has been used to enhance beamforming and RA in mm-wave systems, many solutions ignore the critical problem of effective beam training and RF chain usage. Recent developments that optimize beamforming and reduce the RF chain, such as the AB-HP technique and DRL approaches, have made headway in overcoming these constraints. However, only a few studies have completely combined these methods to address channel estimation difficulty, RF chain use, and beam training overhead, which is a key gap that the proposed AB-HP framework aims to fill. The RBT-DRL techniques have demonstrated their effectiveness in numerous fields, including developing low-complexity PA techniques that achieve near-optimal system capacity, making them suitable for real-time applications in HP-based multi-user MIMO systems based on deploying AB-HP to reduce the number of RF chains and the channel estimation overhead size. The authors of [28] recommend looking at state-of-the-art techniqueslike dimension reduction and feature engineering [5] that can reduce the state vector and action space while maintaining the deep neural network (DNN) performance. Through this, we want to accomplish efficient and effective beam training for mm-wave systems while also drastically cutting down on the DNN's training period.
## 2 Experimental Section
### Photocatalytic Activity Evaluation  
#### 2.3.1 Photocatalytic Degradation Experiments  
Tetracycline hydrochloride (TCH), known for its high detection rate in water, was chosen to assess the effectiveness of the synthesized photocatalysts for degrading antibiotic-contaminated wastewater. The experiments utilized a TCH solution prepared with a fixed volume of ultrapure water. The photocatalytic degradation experiments are given in the Supplementary Materials (Figure S1). The efficiency of TCH photodegradation in aqueous solution is determined through the following formula [20]: \(\eta\)= 1 \(-\)\(C\)/\(C_{0}\), where \(\eta\) represents the degradation efficiency, while \(C_{0}\) and \(C\) denote the initial and real-time concentrations of the TCH. TCH degradation in this study adheres to pseudo-first-order kinetics, represented by the equation [20]: \(-\ln(C/C_{0})=\)\(k\), with \(k\) (min\({}^{-1}\)) being the rate constant of photodegradation. To determine the average value, the experiment was repeated three times independently. If the standard error was less than 5%, the experiment was not retested.
## 4 Exact Algorithm
### Eccentricity Bounds  
We propose to use the following bounds on the eccentricity of all nodes \(w\in V\), when the eccentricity of one node \(v\in V\) has been computed:  
**Observation 1**: _For nodes \(v,w\in V\) we have \(\max(\varepsilon(v)-d(v,w),d(v,w))\leq\varepsilon(w)\leq\varepsilon(v)+d(v,w)\)._  
The upper bound can be understood as follows: if node \(w\) is at distance \(d(v,w)\) from node \(v\), it can always employ \(v\) to get to every other node in \(\varepsilon(v)\) steps. To get to node \(v\), exactly \(d(v,w)\) steps are needed, totalling \(\varepsilon(v)+d(v,w)\) steps to get to any other node. The first part of the lower bound (\(\varepsilon(v)-d(v,w)\)) can be derived in the same way, by interchanging \(v\) and \(w\) in the previous statement. The second part of the lower bound, \(d(v,w)\) itself, simply states that the eccentricity of \(w\) is at least equal to some found distance to \(w\). The bounds from Observation 1 were suggested in [6] as a way to determine which nodes could contribute to a graph's diameter. We extend the method proposed in that paper by using these bounds to compute the full eccentricity distribution of the graph. The approach is outlined in Algorithm 2. First, the candidate set \(W\) and the lower and upper eccentricity bounds are initialized (lines 3-8). In the main loop of the algorithm, a node \(v\) is repeatedly selected (line 10) from \(W\), its eccentricity is determined (line 11), and finally all candidate nodes are updated (line 12-19) according to Observation 1. Note that the value of \(d(v,w)\) which is used in the updating process does not have to be computed, as it is already known because it was calculated for all \(w\) during the computation of the eccentricity of \(v\). If the lower and upper eccentricity bounds for a node have become equal, then the eccentricity of that node has been derived and it is removed from \(W\) (lines 15-18). Algorithm 2 returns a vector containing the exact eccentricity value of each node. Counting the number of occurrences of each eccentricity value results in the eccentricity distribution. An overview of possible selection strategies for the function SelectFrom can be found in [6]. In line with results presented in that work, we found that when determining the eccentricity distribution, interchanging the selection of a node with a small lower bound and a node with a large upper bound, breaking ties by taking a node with the highest degree, yielded by far the best results. As described in [6], examples of graphs in which this algorithm would definitely not work are complete graphs and circle-shaped graphs. However, most real-world graphs adhere to the small world property [17], and in these graphs the eccentricity distribution is sufficiently diverse so that the eccentricity lower and upper bounds can effectively be utilized.
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
## 6 Conclusions  
This work develops a method to study the detailed melt pattern in the AP by using Sentinel-1 imagery. Based on a practical method of RGZ classification introduced in this study, the two open access Sentinel-1 satellites can provide timely accessible RGZs, as well as a way to study the dynamic changes in the detailed patterns of snowmelt at a spatial resolution of 40 m. The variances of DSL suggest the occurrences of heavy snowmelt in the AP, indicating the extreme events of polar climate. RGZ and DSL mapped by Sentinel-1 in this study are not validated directly because of the lack of in  
Figure 13: Misclassifications of RGZs in Agassiz Cape on 14 November 2015. (**a**) shows the location of Agassiz Cape and the misclassifications of the crevasses and mountains. Blue and yellow areas represent bare ice and dry snow, respectively. The base map is a Landsat 8 image from 2 November 2015 derived from the United States Geological Survey (USGS, [[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/))). (**b**) illustrates the ice crevasses on the corresponding SAR image. (**c**) shows the local incidence angle.  
situ observations. However, the results agree well with the snowmelt detected by the SSM/I. In view of the dramatic collapses of the Larsen A and B ice shelves, the strong rising trend of the surface snowmelt threatens the stabilities of the ice shelves in the AP. With the subsequent launches of Sentinel 1C and 1D, RGZs and DSL detected by Sentinel-1 can reveal the exhaustive melting condition in the AP, and also provide input and output validations for the climate models.  
The authors would like to thank the European Space Agency and the National Snow and Ice Data Center for providing the Sentinel-1 images and SSM/I brightness temperature data respectively. We also thank [PERSON] (University of Innsbruck) for providing consultations on the MEMLS3&a. This research was supported in part by the National Natural Science Foundation of China (NSFC) (Grant no. 41531069 and 41376187) and in part by the Chinese Polar Environment Comprehensive Investigation & Assessment Programme (CHINARE2017-02-05).  
[PERSON] and [PERSON] conceived the study and carried out the design of the method. [PERSON] processed the Sentinel-1 SAR images and conducted the manuscript preparation. [PERSON] processed the SSM/I data and revised the manuscript.  
The authors declare no conflict of interest.
# A Comparative Analysis of High Availability for Linux Container Infrastructures
## 3 Materials and Methods
### Containerd, Docker, and Docker Swarm as a Container Orchestrator  
Containerd is an abstraction layer that separates the OS-specific system calls and functions needed to run and virtualize containers. It provides simplified and abstract functions so that users do not have to work directly with system calls. Containerd does not provide management of network elements and settings, which is the responsibility of the superstructures above it. Containerd is based on an API that allows access and management of containers [29; 30].  
Docker is a tool and a superstructure on top of Containerd that provides application automation in containers. The Docker engine is the application deployment layer of containers. Docker makes it easy to test and deploy applications and is based on existing container virtualization methods such as Containerd and libvirt. Docker provides isolation of containers from the host system but requires additional steps to secure the installation. Docker consists of a client and server, Docker containers, Docker registries, and Docker images. A container is an isolated environment where applications run on the basis of Docker images that define instructions for the container. Docker registries are used to store finished Docker images [13; 31; 32], see Figure 2.  
Figure 1: Proxmox Virtual Environment Architecture.  
Figure 2: Docker Architecture—Host, Client, and Registry.  
Docker Swarm is a tool for distributing containers and services, clustering them, and managing resources. It is often used for high-availability solutions where multiple instances of the same service are run and the user load is balanced. Docker Swarm's main features encompass cluster management, tightly integrated with the Docker engine, decentralized design, declarative service models, scalability, service health monitoring, multihost networking, load balancing, and easy application updates. Docker Compose also offers inter-container networking capabilities and the definition of external data sources [33; 34].  
Security in Docker is an important topic because Docker is not a fully isolated system like a virtual machine. The applications in the container can run under the main admin user, which poses a risk of attacks. To secure Docker, there are security profiles and namespaces to define access rights and isolate containers from the main operating system [35; 36; 37].