esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A method for identifying mapping completion, ensuring that the exploration process effectively detects when the environment has been fully mapped. To visually illustrate the structure of MARS-SLAM and its contributions, Figure 1 presents a mind map summarizing the main components of our methodology. This diagram provides an intuitive overview of the proposed approach, highlighting its core mechanisms and objectives. Despite its advantages, MARS-SLAM faces challenges in highly dynamic environments, where frequent and unpredictable obstacles can interfere with the correct inclusion of markers. In highly dynamic environments, the main difficulty lies in the ever-changing nature of the scenario, which can compromise the reliability of the marking system and, consequently, the accuracy of navigation. Since MARS-SLAM relies on the placement and tracking of virtual markers to guide exploration, any unexpected change in the environment can render previously registered markers obsolete. For example, if a marker is placed in an area that later becomes inaccessible due to the emergence of a moving obstacle, the robot may fail in its attempt to reach that region, requiring constant reassessments of the exploration strategy. Thus, the research presented in this paper aims to answer the following question: How can the use of markers to identify unexplored regions optimize the SLAM process, ensuring completeness and accuracy of mapping in unknown environments? Section 2 reviews related works, exploring existing SLAM approaches and identifying the gaps that our methodology seeks to address. In Section 3, a detailed description of MARS-SLAM is provided, including the flowchart and strategies implemented for the method's operation. Section 4 presents the experimental results obtained in various scenarios. Finally, Section 5 concludes the paper, discussing the contributions and limitations of MARS-SLAM, as well as suggesting potential directions for future research.
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
## 2 Module-Level FRM  
### Definition of Variable Set of FRM  
Based on fault inference process of electronic equipment and characteristics of the model based on BN, the FRM based on fuzzy BN was proposed to express fault states and correlations of modules in electronic equipment and transmission modes of faults. FRM includes relevant parameter and test information, such as topological structure of network, and probability distribution in graph theory. The model of electronic equipment is expressed through a five-element group \(<S,F,A,P,T>\) of a variable set, where \(S=\{S_{1},S_{2},\ \ldots,\ S_{n}\}\) indicates the node set in fuzzy BN, which represents fault set of each function or circuit module in electronic equipment. Each node shows \(S=\{F_{1},F_{2},\ \ldots,\ F_{m}\}\ m\) fault states. If there are two fault states, namely \(F=\{normal,\ fault\}\), for the convenience of modeling, it is designated that \(F=\{0,1\}\), that is, normal state and fault state are represented by 0 and 1. This research discusses multi-state systems, that is \(F=\{0,1,2,\ \ldots,i,\ \ldots\}\). In other words, normal state represents zero state and faults 1 and 2 separately indicate states 1 and 2.  
\(A=\left\{[a_{ij}],\ i=1,\ 2,\ \ldots,\ n;\ j=1,\ 2,\ \ldots,\ n\right\}\) represents the set of directed edges between nodes, showing the correlations between faults of modules. If there is no directed edge between two modules,it means that they are independent in fault inference. Moreover, \(a_{ij}\) denotes a directed edge from node \(a_{i}\) to node \(a_{j}\), and demonstrates that the state of node \(a_{i}\) influences fault state of node \(a_{j}\).  
\(P=\{P(S_{i}|pa(S_{i})),\ S_{i}\in S\}\) stands for the conditional probability table (CPT) of variables in BN; \(pa(S_{i})\) is the father node of \(S_{i}\); that is, \(P=\{P(S_{i}|pa(S_{i})),S_{i}\in S\}\) refers to the conditional probability for the occurrence of event \(S_{i}\) in premise of knowing fault of the father node. For electronic equipment, owing to interference of operating environment of electronic system to test data and limited recognition on expert knowledge of complex system, the CPT shows fuzziness. In other words, the probability \(\widetilde{p_{ij}}\) of node \(S_{i}(1\leq i\leq n)\) in fault \(F_{j}(1\leq j\leq m)\) is a fuzzy number.  
\(T=\{t_{1},t_{2},\ldots,t_{k}\}\) represents the set of test indicators of node \(S\). For electronic equipment, due to limited test points and different types of output signals of each module, different test and measurement instruments are needed which also have distinct test indicators. For example, direct-current voltage and current can be measured by using a universal meter, while signal frequency is obtained through a spectrum analyzer. Sometimes, different indicators with non-unified units and large ranges need to be tested at a point simultaneously, so they are required further processing.
## 2 Internet of Vehicles and Smart Cities
### Main Challenges
#### 2.4.4 Node Mobility  
In IoV environments, the nodes (vehicles) are constantly moving as they move in the real world. This regular mobility and the rapid topology changes present a challenge when trying to keep vehicles connected to the network and the Internet since they lead to packet loss, link failures, and even frequent network disconnections. Nevertheless, there is a difference between IoV and regular networks affected by mobility. Specifically, IoV mobility is somewhat predictable since vehicles and their movement are limited to road layout and topology, road signs, and other vehicles.
## 2 Materials and Methods  
This work is greatly based on bibliographic research. Complementary results are obtained through the analysis of two ionospheric characteristics; the peak electron density in terms of the critical frequency foF2, and the height of the peak electron density hmF2 obtained by a network of European stations listed in Table 1. foF2 is related to the F-peak electron density NmF2 by NmF2/m\({}^{-3}\) = 1.24 \(\times\) 10\({}^{10}\)\(\times\) (foF2/MHz)\({}^{2}\). The analyzed period includes the years 2008-2020 that fully cover the evolution of solar cycle 24 (see also Figure 1). Ionospheric disturbances are justified through the comparison between the observed values with their monthly medians. Disturbances are quantified by the following metric: DfoF2% = [(foF2 obs \(-\) foF2 median)/foF2 median] \(\times\) 100 for foF2.  
Ionospheric observations were retrieved from the Global Ionosphere Radio Observatory (GIRO--[[http://giro.uml.edu/](http://giro.uml.edu/)]([http://giro.uml.edu/](http://giro.uml.edu/)), accessed on 30 December 2021) data repository [5], particularly the FastChart database. The level of the geomagnetic activity is determined by records of Dst index available from the World Data Center (WDC) for Geomagnetism, Kyoto ([[http://wdc.kugi.kyoto-u.ac.jp/dstdir/](http://wdc.kugi.kyoto-u.ac.jp/dstdir/)]([http://wdc.kugi.kyoto-u.ac.jp/dstdir/](http://wdc.kugi.kyoto-u.ac.jp/dstdir/)), accessed on 30 December 2021). Geomagnetic storms may be grouped with respect to their intensity, as in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Station Name** & **Geographic Longitude (\({}^{\circ}\)E)** & **Geographic Latitude (\({}^{\circ}\)N)** \\ \hline Chilton & 359.4 & 51.5 \\ \hline Dourbes & 4.6 & 50.1 \\ \hline Juliusruh & 13.4 & 54.6 \\ \hline Pruhonice & 14.6 & 50.0 \\ \hline Ebre & 0.5 & 40.8 \\ \hline Rome & 12.5 & 41.9 \\ \hline San Vito & 17.8 & 40.6 \\ \hline Athens & 23.5 & 38.0 \\ \hline \end{tabular}
\end{table}
Table 1: List of the ionospheric stations used in the analysis and their geographic coordinates.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 2 Methods  
A systematic literature review focused on peer-reviewed studies on nanosatellite EO missions and propulsion systems. Relevant studies and technical reports were identified based on their focus on nanosatellite missions, EO applications, and propulsion technologies.  
A total of approximately 137 relevant publications were initially identified, drawing attention to nanosatellites, sensor technologies, propulsion systems, and mission strategies.  
To refine the selection, a meticulous filtration process was undertaken, with a particular emphasis on articles discussing nanosatellite remote sensing, specialized applications, and challenges. Subsequently, 113 papers were meticulously categorized based on the survey's scope.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Related Work**} & **Emphasis** & **Exploration of** & **Mention of Large** & **Evaluation of** \\  & **on EO** & **Limitations** & **Constellations** & **Compatibility with** \\  & & & & **Scientific Requirements** \\ \hline [PERSON] and [PERSON] [6] & ✓ & \(\bigtimes\) & ✓ & \(\bigtimes\) \\ [PERSON] et al. [7] & ✓ & \(\bigtimes\) & ✓ & \(\bigtimes\) \\ [PERSON] and [PERSON] [8] & ✓ & \(\bigtimes\) & ✓ & \(\bigtimes\) \\ [PERSON] [9] & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & ✓ \\ [PERSON] et al. [10] & \(\bigtimes\) & ✓ & ✓ & \(\bigtimes\) \\ [PERSON] and [PERSON] [11] & ✓ & ✓ & ✓ & \(\bigtimes\) \\ Our work & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Related surveys comparison.  
In the final analysis, 85 papers and technical reports were deemed most pertinent to the scope of this review paper, providing a detailed examination of nanosatellite applications, remote-sensing for EO, propulsion systems, and limitations. These selected papers contributed significantly to the enriched and refined content of the present survey, offering valuable insights into the current state and future prospects of nanosatellite technology.  
It is important to note that many studies reviewed focus on conceptual frameworks or simulations with limited experimental validation or in-orbit deployment. While these studies advance theoretical knowledge, caution should be exercised when extrapolating their findings to operational scenarios.
## 2 Materials and Methods
### Description of the XMed-Dry Network and the Multiplatform Observations
#### 2.1.2 PM\({}_{10}\) Data  
In this study daily mean PM\({}_{10}\) (particulate matter with aerodynamic diameter less than 10 \(\upmu\)m) concentration values have been obtained by regional or municipal air quality monitoring networks in Lecce, Athens, and Malta by automatic devices. In particular, daily mean PM\({}_{10}\) values in Lecce have been obtained by the air quality monitoring network of the environmental protection regional agency (ARPA-Puglia) at the Lecce-Garigliano traffic urban monitoring station (18.1\({}^{\circ}\) E, 40.3\({}^{\circ}\) N). The second PM\({}_{10}\) measurement point was localized at a rural background site in G\(\hbar\)arb, Gozo (14.2\({}^{\circ}\) E, 36.1\({}^{\circ}\) N). The third measurement point is located at the Demokritos suburban monitoring station in Agia Paraskevi, Attiki, Greece (23.8\({}^{\circ}\) E, 38.0\({}^{\circ}\) N).
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 4 Analysis and Discussion
### Sea Levels  
Derived tidal heights relative to chart datum for tidal cycle A are shown in Figure 10, whereas Figure 11 shows the corresponding results for tidal cycle B. Tide gauge data from Liverpool and Heysham are added for comparison with those obtained from the GNSS buoy. A 15 min (900 s 'boxcar') moving average was applied to the buoy data before comparison with the 15 min Liverpool and Heysham tide gauge data. Both Figures 10 and 11 emphasise the periods of good data during the ebb tide (e.g., comparison with GNSS 900 s average and Liverpool 15 min data; mean difference \(-0.02\) m, standard deviation difference 0.03 m and RMSE difference 0.04 m) and poorer data during the flood tide. Although the tidal characteristics between Liverpool, Heysham and Rossall are similar, there are small but significant differences in the tidal elevations and times of LW and HW (Table 1); thus, these comparisons are considered indicative of data quality, i.e., good data quality during the ebb periods.  
Figure 10: Comparison of the GNSS buoy derived sea levels for tidal cycle A and established tide gauges at Liverpool and Heysham. Note: after high water the 900 s boxcar moving average can be seen to match the 15 min Liverpool tidal data.  
During the ebb tide the 15 min GNSS buoy data matches the Liverpool tide gauge data more closely in both timing and elevation, even though the Heysham tide gauge is nearer. The larger differences between the GNSS buoy and the Heysham tide gauge data are probably due to Heysham being at the edge of Morecambe Bay (with the accompanying dynamics of a larger intertidal zone, and the water being initially directed along the Lune Deep channel at low water). However, on the ebb tide there are some good data between 18:30 and 19:30 on 28 August (Figure 10), which indicate timing differences compared with the Liverpool tidal data. Again, both Figures 10 and 11 show there are poorer data as the water at the buoy approaches 'drying out'.  
This low cost GNSS buoy is suitable for providing sea-level (and wave) data to support X-band radar monitoring systems of coastal morphology changes over time scales from days to years. During the tidal cycle, the radar system records the advancing and retreating shorelines (using the water-line method [1]) which are then assigned altitudes based on corresponding water-levels from a local tide gauge. Previous research [1] indicates that the accuracy of the beach-derived morphology maps is greatly improved if tidal measurements are available within the radar footprint.
# Coregistration and Spatial Compounding of
## 2 Materials and Methods
### Heartbeat Frequency and Phase Estimation  
The heart and breathing rates were first estimated by calculating the mean Fourier transform of the time profiles of each voxel of the reconstructed three-dimensional images. The fundamental frequency of the heart could clearly be differentiated from the breathing frequency and its harmonics (Figure 1b). Three-dimensional phase maps were then calculated by considering the phase of the Fourier transform for the estimated calculated heart frequency. This enables monitoring the state of the heart during the cardiac cycle assuming that the heart beats regularly and in a cyclical manner.  
Figure 1: Methodology for image coregistration and compounding. (**a**) Lay-out of the experimental system. (**b**) Amplitude of the temporal Fourier transform of the average of all image voxels for 1000 (top) and 100 (bottom) frames. B—Fundamental breathing frequency, B2, B3—Second and third harmonics, H—Fundamental heart frequency, H2, H3—second and third harmonics. (**c**) Three-dimensional views of the reconstructed optoacoustic images for three consecutive scanning positions in the vertical direction. (**d**) Phase maps for the cross-sections indicated in (**c**) along with the compounded phase map after compensating for the phase differences between frames. RA—Right atrium, LA—Left atrium, RV—Right ventricle, LV—Left ventricle.
## 2 Related Works
Because of this, LoRa can be used for a transit vehicle tracking service. However, Ref. [24] features no field tests. In [25], the authors focus mainly on the relationship between range and scalability of LoRa. They state a single gateway can cover a range of a few kilometers in an urban area and up to 30 km in a suburban area (considering typical densities of end devices). Ref. [25] considers the Doppler effect for moving devices, with a propagation factor of 12; they conclude that at a relative speed greater than 40 km/h, performance impairment in communication is evident. For speeds of up to 25 km/h communication is reliable enough, which allows its use in people monitoring and tracking applications. Thus, the experiments performed in the present paper take into account a 25 km/h speed limit to avoid degradation due to Doppler effect. In the literature revision, no work was found that integrated the characteristics of the present research. These integrated characteristics are what determine the novelty of this research. The work presented in this paper proposes a prototype of a transit vehicle tracking service, based on ITS and using LoRa technology in the critical communication that occurs between vehicles and network devices located on the roads. The proposed technology is evaluated through six experiments that use a considerable number of settings, some of these experiments (the last three) emulate the environment the evaluated service may have (in terms of packet rate, routes traveled, vehicle speeds and number of devices operating simultaneously).
# Risk Assessment of Soil Heavy Metals in the Jiahe River Basin of Yantai City, China
## 3 Results  
### Concentrations of Heavy Metals  
Histograms with box plots of the concentrations of 11 heavy metals and the pH in 213 soil samples are illustrated in Figure 2. Basically, the concentrations of the 11 heavy metals follow a log-normal distribution. However, significant outliers, particularly larger ones, are evident in the box plots. Therefore, geometric means without outliers were used herein to represent the average values of the heavy metal concentrations; these are listed in Table 1, along with other statistical parameters.  
Figure 1: Location of the study area in China (**a**) and its geological sketch map (modified from the J-51–20 geological map from China Geological Survey) with the locations of samples (**b**). Notes in (**b**): 1—Quaternary clay, sand, gravel; 2—Neogene gabbro; 3—Sinian slate, marble, marl, quartzite, phyllite, metasandstone; 4—Lower Proterozoic schist, leptite, granulite, marble, tremolite, diopsidite; 5—Archean gneiss, amphibolite, leptite, granulite, schist; 6—Mesozoic quartz diorte porphyry; 7—Mesozoic granite porphyry; 8—Mesoproterozoic fine-grained monzogranite; 9—Paleoproterozoic plagioamphibolite (metagabbro); 10—Lithological boundary; 11—Fault; 12—Main residential place; 13—Location of soil sample.  
Figure 2: Histograms with box plots of concentrations of heavy metals and pH.  
The pH values of the 213 soil samples from the Jiahe River Basin range from 4.44 to 9.01, with an average of 6.42. Regarding the concentrations of heavy metals in the 213 samples compared with those in greater China, the level of Hg in the Jiahe River Basin is lower, that of Pb is comparable, and the others are slightly greater (Table 1).
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum Encoder
#### 3.3.2 Hadamard Gate for Superposition  
The Hadamard gate (\(H\)) is a fundamental single-qubit quantum gate that is used to create superposition. It transforms the computational basis states \(\ket{0}\) and \(\ket{1}\) into equal superpositions, enabling quantum parallelism [45]. The matrix representation of the Hadamard gate is shown in Equation (4).  
\[H=\frac{1}{\sqrt{2}}\begin{pmatrix}1&1\\ 1&-1\end{pmatrix} \tag{4}\]  
The Hadamard transform operates on the basis states as in Equations (5) and (6).  
\[H\ket{0}=\frac{1}{\sqrt{2}}(\ket{0}+\ket{1}) \tag{5}\]  
\[H\ket{1}=\frac{1}{\sqrt{2}}(\ket{0}-\ket{1}) \tag{6}\]  
These transformations prepare the qubit in a coherent superposition, which is essential for achieving quantum parallelism in quantum information processing.
# A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
###### Abstract  
Imbalanced classification is one of the most important problems of machine learning and data mining, existing in many real datasets. In the past, many basic classifiers such as SVM, KNN, and so on have been used for imbalanced datasets in which the number of one sample is larger than that of another, but the classification effect is not ideal. Some data preprocessing methods have been proposed to reduce the imbalance ratio of data sets and combine with the basic classifiers to get better performance. In order to improve the whole classification accuracy, we propose a novel classifier ensemble framework based on K-means and resampling technique (EKR). First, we divide the data samples in the majority class into several sub-clusters using K-means, \(k\)-value is determined by Average Silhouette Coefficient, and then adjust the number of data samples of each sub-cluster to be the same as that of the minority classes through resampling technology, after that each adjusted sub-cluster and the minority class are combined into several balanced subsets, the base classifier is trained on each balanced subset separately, and finally integrated into a strong ensemble classifier. In this paper, the extensive experimental results on 16 imbalanced datasets demonstrate the effectiveness and feasibility of the proposed algorithm in terms of multiple evaluation criteria, and EKR can achieve better performance when compared with several classical imbalanced classification algorithms using different data preprocessing methods.  
imbalanced classification; K-means; resampling Article
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 5 Targeted Safety Interventions
### Weather, Environmental, and Temporal Factors  
[PERSON] et al. [103] employed Palm probability to relate crash risk to the time spent on a segment; relative risk is the highest in icy rain and on slippery surfaces, with single-vehicle crashes being particularly sensitive.  
[PERSON] et al. [104] linked roadway lighting to night-to-day crash ratios, finding observed ratio drops (\(\leq\)13%) smaller than the oft-cited 30%, likely because of uncontrolled covariates in earlier studies.  
[PERSON] et al. [105] built a spatial multinomial-logit injury-severity model with real-time weather, identifying vertical grade, visibility, EMS response time, and vehicle type as key factors; spatial correlation improves fit and predictive accuracy.  
Environmental factor research demonstrates the complexity of weather and visibility effects on crash risk, with important methodological implications for intervention assessment. [PERSON] et al.'s finding of the highest risk during icy rain conditions, combined with the particular sensitivity of single-vehicle crashes, suggests that environmental interventions must target specific weather-crash type combinations rather than general adverse conditions. The lighting research by [PERSON] et al. reveals a significant methodological issue: the observed safety benefits (\(\geq\)13%) are substantially smaller than commonly cited values (30%), highlighting how uncontrolled covariates can overstate intervention effectiveness. [PERSON] et al.'s integration of real-time weather data with spatial modeling demonstrates that environmental factors interact with infrastructure characteristics (vertical grade) and emergency response capabilities, suggesting that effective environmental safety interventions require integrated approaches considering multiple interacting factors. The consistent emphasis on spatial correlation across studies indicates that environmental effects vary significantly by location, challenging universal intervention strategies.
## 2 Materials and Methods
### Proposed Load-Flow Approach  
In this study, the proposed load flow algorithm uses the distribution network tree to determine the direction of the currents flowing through different branches and then calculates their values as well as the various node voltage profiles. The load flow algorithm is based on two matrices derived from the network topology: the bus injection branch current (BIBC) matrix and the branch current bus voltage (BCBV) matrix. A flowchart of the proposed load flow is shown in Figure 1. For the proposed load-flow method, each node is supplied by a unique source node; however, a source node can supply more than one bus. Initially, the node voltages were considered equal to the unity, with the exception of the source bus voltage, which was assumed to be equal to its nominal value. Moreover, it was assumed that the shunt capacitances were negligible.