esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Key Technologies of Data Processing Mechanisms in Ternary Optical Computer
## 2 Characteristics Analysis of TOC
### MSD Number System  
In conventional numeral systems, an \(n\)-radix has \(n\) numeral symbols representing values from 0 to \(n\)-1, with carry-over every \(n\). If each digit in an \(n\)-radix has \(n+r\) numeral symbols (where \(n\) and \(r\) are positive integers) and still carries over every \(n\), then each digit can represent \(n+r\)-1 values, known as redundant counting. Clearly, in redundant counting, a numerical value can have multiple representations, which is inconvenient for human use but convenient in multi-valued computers. In ternary optical computing, only redundant counting with \(n=2\) and \(r=1\) is feasible, with the most convenient form being MSD counting. This method was proposed by [PERSON] et al., in 1961 [31] and introduced into optical computing by [PERSON] et al. in 1986 [32]. Ternary optical computers employ the MSD number system, which represents a signed ternary binary number allowing multiple representations of any decimal number, providing the theoretical foundation for carry-free addition. In theory, the physical states of the TOC and the MSD symbol expressed by them are equivalent.  
Nevertheless, dark light is used to express the symbol 0, one polarized state of light the symbol 1, and the other polarization symbol -1. Therefore, each MSD digit is expressed  
Figure 1: Abstract diagram of the physical form of TOP bits.  
explicitly by hardware in the TOC. The symbol set of the MSD number system is {2, 0, 1}, where 2 represents the value -1. Any decimal number D can be expressed in the MSD number system with the expression: \(\sum_{i}a_{i}2^{i}\). For ease of description, in this paper, decimal numbers are denoted by subscript D, MSD numbers by subscript M, and binary numbers by subscript B. For the number 6: \((6)_{D}=(110)_{B}=(1020)_{M}=(1210)_{M}=(12020)_{M}\). For the number -6: \((-6)_{D}=(-110)_{B}=(2010)_{M}=(2120)_{M}=(21010)_{M}\). Comparing the MSD representations of these two numbers reveals that the representation of a number in MSD is obtained by inverting the representation of its corresponding negative number. This characteristic is generalizable across MSD representations.
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
###### Abstract  
This paper presents a 3D vision-based autonomous navigation system for wheeled mobile robots equipped with an RGB-D camera. The system integrates SLAM (simultaneous localization and mapping), motion planning, and obstacle avoidance to operate in both static and dynamic environments. A real-time pipeline is developed to construct sparse and dense maps for precise localization and path planning. Navigation meshes (NavMeshes) derived from 3D reconstructions facilitate efficient A* path planning. Additionally, a dynamic \"U-map\" generated from depth data identifies obstacles, enabling rapid NavMesh updates for obstacle avoidance. The proposed system achieves real-time performance and robust navigation across diverse terrains, including uneven surfaces and ramps, offering a comprehensive solution for 3D vision-guided robotic navigation.  
Keywords:3D vision-based navigation; mesh reconstruction; navigation mesh (NavMesh); obstacle avoidance; SLAM +
Footnote †: Academie Editors: [PERSON], [PERSON] and [PERSON]  
Received: 22 January 2025  
Revised: 19 February 2025  
Accepted: 19 February 2025  
Published: 21 February 2025  
robot movements, further enhanced by via-point control strategies. In additional, the integration of NavMeshes with real-time \"U-map\" allows for updates for dynamic obstacle avoidance. Clearly positioning the work within the broader field of robotic navigation will enhance its impact.  
Our primary objectives are twofold:  
1. To construct comprehensive 3D maps and convert them into navigation meshes, facilitating seamless navigation in intricate 3D settings.
2. To demonstrate efficient path replanning with reduced node complexity, enabling adaptation to dynamic environments.  
This framework, designed for mobile robots equipped with RGB-D cameras, addresses the intertwined challenges of SLAM, motion planning, and obstacle avoidance, ensuring robust operation in both static and dynamic scenarios.
## 2 Materials and Methods
### Analysis Methods  
A data analysis was conducted using MATLAB R2023a (MathWorks, Natick, MA, USA). The calculations were executed over two distinct time periods. Initially, baseline hemodynamic variables were used to compute the parameters during a 10 min period before the onset of VF. The subsequent set of calculations focused on a 2 min timespan after VF was induced; this was selected as the timeframe for data analysis and parameter fitting processes. This 2 min interval following VF initiation represented a period during which no actions, such as CPR or drug administration, were undertaken.  
To derive numerical results for the parameters, we employed the inverse of the nonlinear version of the hemodynamic model using the MATLAB function _fmincon_. As inputs, the model employed total hemoglobin measurements from the hNIRS and CBFV indices captured using LDF. Its objective was to optimize the aforementioned hemometabolic parameters to ensure alignment between its outputs (HbO\({}_{2}\) and HHb) and the actual NIRS readings. The calculated parameters and their respective ranges were as follows: \(ctHb\) (2-2.6 mM), \(\alpha_{o}\) (0.6-1.0 s\({}^{-1}\)), \(F^{(c)}\) (0.6-0.9), \(S_{0}^{(a)}\) (0.8-0.99%), \(Fp^{(c)}\) (0.2-0.8), \(fc^{(c)}\) (0.1-0.7), \(L^{(c)}\) (0.4-0.8 mm), \(L^{(v)}\) (0.5-1.2 mm), \(r^{(c)}\) (0.6-1.0 mm/s), and contributions to baseline blood volume from arterioles (0.001-0.05), capillaries (0.005-0.05), and venules (0.001-0.05) [9].  
The cost function for the optimization was the reduced \(X^{2}\) for the combined HbO\({}_{2}\) and HHb fits. We also calculated \(R^{2}\) values for the combined HbO\({}_{2}\) and HHb fits. For the final calculation results, we only included the cases for which \(R^{2}\) > 0.5.  
We employed a paired \(t\)-test to compare the average parameter values during baseline and after CA.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 3 Key Enabling Technologies: An Overview
AI and machine learning can facilitate interoperability by enabling systems to understand and adapt to different environments [91]. However, achieving true interoperability requires collaboration between developers, platform providers, and standardization bodies to establish common frameworks and protocols. To enable seamless interactions, it is required to integrate diverse technologies, such as blockchain, AI, and edge computing, and the need for standardized interfaces to enable seamless interactions [19]. Without interoperability, the metaverse risks becoming fragmented, with isolated virtual environments that cannot communicate with each other. The metaverse holds significant potential for transforming education by creating immersive and interactive learning environments [92]. The metaverse can enable virtual classrooms where students and teachers interact in a shared virtual space, enhancing the learning experience through interactive simulations and collaborative projects. The flexibility and accessibility of the metaverse can democratize education, making high-quality learning resources available to a global audience. The concept of distance online learning in the metaverse can be expanded through evidence-based insights by leveraging immersive technologies like social virtual reality environments (SVREs) and AR. Research highlights that SVREs foster deep and meaningful learning (DML) by enabling collaborative, authentic interactions among learners [93]. These virtual environments allow students to engage in social and cognitive activities that parallel in-person experiences, thus addressing traditional distance learning challenges like isolation and disengagement [94]. AR and VRtechnologies further support DML by creating simulations that encourage active, reflective, and goal-oriented learning, which enhances knowledge retention and motivation. Moreover, studies indicate that integrating SVREs into distance learning can foster a strong sense of presence and co-presence, leading to richer educational experiences that replicate the dynamics of physical classrooms [93]. Beyond technological advancements, the metaverse presents several social and ethical considerations [95]. Some issues, such as user addiction, digital harassment, and equitable representation of avatars, are highlighted. The immersive nature of the metaverse can exacerbate these issues, necessitating the development of guidelines and regulations to protect users and promote a healthy virtual environment [96]. Additionally, integrating AI and blockchain raises data privacy and security concerns, which must be addressed to ensure user trust and safety in the metaverse.
## 2 Materials and Methods
### Observation Windows and Precision Levels  
The study further evaluated 7 observation windows (1-7 days). For each observation window, three precision levels were evaluated (calendar day, 24-h period, and 24-h period plus a 4-h margin). The 4-h margin was added to account for variation in time of SCr measurements between days (Supplementary data, Text S1).
## 4 Results and Discussion
### Rice Area Maps
Sivaganga (Figure 5j) was a challenging site to classify because of the sparse rice area and the semi-dry rice crop establishment practice. The irregularity in monsoon rains is one reason for the variation in crop establishment date across the site, resulting in a mixture of early/late rice and rice. Muang Yang (Figure 5k) is an extensive rainfed system with homogeneous planting dates due to plentiful and timely rainfall in most areas during the monitored season. Field sizes are much larger than those in Suphan Buri, and coarser spatial resolution imagery (in the region of 20 m) than used here would be more suitable for rice crop monitoring in the northeast region of Thailand. Suphan Buri (Figure 5l) was another challenging area to map, since the continuous availability of irrigation water and the stable seasonal conditions mean that rice can be established at almost any time, in patterns of double crop, triple crop or five crops in two years. This complexity in crop calendars means that a short-duration acquisition period cannot capture the full rice signature over the entire footprint. Furthermore, the small field sizes at the site were difficult to capture with the coarser spatial resolution used in this large footprint. Longer-duration monitoring or even continuous monitoring with higher spatial resolution would be required to better represent the rice area here. Nueva Ecija (Figure 5m) is the major rice-growing area of the Philippines, and it relies on water provided by major irrigation facilities--the NIA-UPRIIS (National Irrigation Administration-Upper Pampanga River Integrated Irrigation Systems) service area--for much of the footprint. The water source is the Pantabangan Dam (the reservoir is clearly visible in East of the image), which is one of the largest irrigation facilities in the Philippines. The planting in this season was relatively homogeneous and aligned with the acquisitions, with only a few areas showing early or late planting.
## 2 Related Work
### Traditional Knowledge Graph Reasoning Methods  
Traditional knowledge graph reasoning methods mainly contain two streams: (1) distributed representation-based methods. These methods embed entities and relations into low-dimensional continuous vector space, and the plausibility of triples can be formulated as vector computation. TransE [16] is a representative distributed representation learning model; the relation of a triple is modeled as the translation process from the head entity to tail entity. Then, TransH [17], TransR [18], and TransD [19] further extend the method to interpret 1-\(N\), \(N\)-1, and \(N\)-\(N\) relations. In order to model more complex relation patterns of knowledge graphs, some studies project the entities and relations into complex vector space. ComplEx [20] introduces the complex vector to model the symmetric and antisymmetric relations. RotatE [21] transforms the relations as rotational operations from head entities to tail entities.  
(2) Logical rule-based methods. The main idea of logical rule-based methods is to learn the frequency patterns of knowledge graphs and represent the patterns by logical rules. Then, the rules are applied to existing triples and the new triples can be obtained through reasoning by logical rules. Amie [22], Amie+ [23], and Amie3 [24] utilize a machine learning method to mine logical rules for specific relations, and prune the rules with low confidence and coverage. RLvLR [25] defines new criteria for the logical rules and enables the method to extend to large-scale knowledge graphs. NeuralLP [26] transforms the logical rule learning to a differentiable process and the process of rule mining can be conducted by the gradient-based optimization. AnyBURL [27] proposes a bottom-up rule mining method; it can learn fuzzy and uncertain rules and can extend to large-scale knowledge graphs with relatively low resource consumption.  
However, these methods are mainly data-driven methods, and highly rely on the existing triples in the knowledge graph. The incompleteness problem of knowledge graphs may impair the performance of reasoning [16; 22; 23].
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 5 Numerical Results
The graph is plotted for the salty water type and lower bubble level and temperature gradient with pointing error \(=2\), for moderately turbulent atmospheric conditions with AOA fluctuation \(=7\) mrad. It is observed that in the simulation results, the asymptotic outage plots are in perfect agreement with the theoretical analysis and the Monte Carlo simulation, hence validating the analytical modeling of the three-hop mixed RF/FSO/UWOC system. Figure 7 shows the influence of atmospheric turbulence on the outage probability of the system w.r.t. the average SNR in dB at the destination node. We assume direct detection at the receiver with \(\gamma_{th}\) at 0 dB. The outage probability of the system is analyzed in different atmospheric turbulence conditions for \(\xi=2\). The outage probability in highly turbulent conditions is more and it decreases from moderately to weakly turbulent conditions. From the figure it can be seen that the outage probability of the three-hop system reduces as the atmospheric turbulence changes from high- to low-turbulence scenarios. However, for the proposed model, the changes in the outage performance are not significant, and hence the system also slightly outperforms in highly turbulent conditions. Figure 4: Outage probability vs. average SNR (dB) with variation of Nakagami fading factor \(m\) in RF link. Figure 5: Outage performance of the system under varying bubble levels (BLs) in different water types. Figure 6: Outage probability of the system at high and low SNR regime.
# Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry
## 1 Introduction
In this paper, the sea SWH is estimated from CYGNSS DDM SNR data, which is then compared with buoys and satellite altimeter SWH observations. In Section 2, observation data and processing methods are introduced. Main results are presented in Section 3, and finally conclusions are given in Section 4.
## 5 Our Proposed Scheme
### Role of a Border Sensor Node  
The node that resides in the borderline of the sensing field or closer to it is considered as a border node. The node selected as CH must operate and communicate with its neighbors efficiently. When clusters are formed, almost in circular fashion, each CH broadcasts its  
Figure 3: LEACH-PRO working principle.  
ADV message to its neighbors. Such message is transmitted to all members of each cluster. A border node can cover its communication range either side of the sensing field, therefore, if it is selected as CH, it cannot communicate with its CMs effectively. To avoid such issue, LEACH-PRO excludes border nodes from participating in the CH selection process to improve the overall performance.  
\[x_{f-1} \geq x_{i} \geq x_{0+1}\] \[y_{f-1} \geq y_{i} \geq y_{0+1} \tag{9}\]  
Let us assume that the coordinates of each node \(\left(x_{i}y_{i}\right)\) are predetermined when the nodes are distributed in the sensing field. Thus, any node that satisfies the conditions set in Equation (9) is eligible to participate in the CH selection process; otherwise, it is excluded. These conditions are defined based on Figure 4 which illustrates the area in the sensing field where border nodes are located. Nodes in this area cannot be selected as CH in next rounds and are declared as CMs.
## 3 Results and Discussions
### Chlorophylll Determination
#### 3.4.3 Chlorophyll Quantification with Spectrophotometer  
The spectrophotometer analysis in t1, for Untreat, Treat 2 and Treat 3 showed the highest amount of chlorophyll A content; instead in t2 no significant differences were highlighted (Figure 3C,D). Chlorophyll B showed the highest value in Treat 2 in the first period (t1), when Treat 4 and Treat 1 had the lowest value (Figure 3C). In t2, instead, a different trend is shown: Untreat and Treat 2 have the higher ChB values (Figure 3C). Caroenoid content did not show significant differences in t1; in t2, instead, Treat 1, Treat 3 and Treat 4 showed higher amounts than Untreat and Treat 2 (Figure 3D). The carotenoid content tended to decrease during the vegetative period [45].  
Fe fertilization also increases chlorophyll concentration in plants [48], but in our study the treatment with the highest Fe concentration (Table 1) did not correspond to the treatment with the highest chlorophyll accumulation, showing in this case a non-dominant role of Fe on hop chlorophyll content.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
###### Abstract  
Under dynamic conditions, when the measured magnetic field is projected onto the sensitive axis of the fluxgate magnetometer, it will show strong dynamic characteristics, resulting in errors or delayed interpretation of the measured physical process. Considering the dynamic characteristics of the three-axis fluxgate magnetometer, this paper proposes a series dynamic compensator based on system identification to carry out system identification and modeling of the dynamic characteristics of the three-axis fluxgate magnetometer and proposes a system parameter calculating method based on hybrid optimization of the genetic algorithm and the simulated annealing algorithm (GA-SA). With these methods, not only can the parameter optimization process effectively avoid falling into the local optimal, but they can also greatly improve the search efficiency and shorten the time of solving the optimal solution. Thus, the full-space search ability of the algorithm is improved, and the accuracy of the dynamic magnetic measurements is improved to the maximum extent. The simulation results of the step signal show that the response time is reduced to 18.22% of the pre-compensation time. The standard signal test results of the step signal and sinusoidal signal, respectively, show that the response time of the step signal is reduced to 5.5% and, in the 0.01-10 Hz frequency band, the maximum attenuation decreases from 0.24% to 0.10%, and the phase difference is less than \(-0.5^{\circ}\). Moreover, the experimental results of the geomagnetic dynamic measurements show that the dynamic compensated data are mostly close to the real geomagnetic field values. In addition, the standard deviation errors are reduced from 95.30/277.35/108.51 nT to 3.41/2.36/2.75 nT in three geomagnetic vector components. The results of this work show the ability of dynamic measurement accuracy improvement of fluxgate magnetometers and are useful for geomagnetic dynamic measurement and related applications.  
dynamic magnetic measurement; fluxgate magnetometer system identification; GA-SA algorithm 2023  
22 January 2023  
20 February 2023  
22 February 2023  
20 February 2023
# Landslide Prediction with Model Switching +
###### Abstract  
Landslides could cause huge damages to properties and severe loss of lives. Landslides can be detected by analyzing the environmental data collected by wireless sensor networks (WSNs). However, environmental data are usually complex and undergo rapid changes. Thus, if landslides can be predicted, people can leave the hazardous areas earlier. A good prediction mechanism is, thus, critical. Currently, a widely-used method is Artificial Neural Networks (ANNs), which give accurate predictions and exhibit high learning ability. Through training, the ANN weight coefficients can be made precise enough such that the network works in analogy to a human brain. However, when there is an imbalanced distribution of data, an ANN will not be able to learn the pattern of the minority class; that is, the class having very few data samples. As a result, the predictions could be inaccurate. To overcome this shortcoming of ANNs, this work proposes a model switching strategy that can choose between different predictors, according to environmental states. In addition, ANN-based error models have also been designed to predict future errors from prediction models and to compensate for these errors in the prediction phase. As a result, our proposed method can improve prediction performance, and the landslide prediction system can give warnings, on average, 44.2 min prior to the occurrence of a landslide.  
Keywords:landslide prediction; machine learning; neural networks; model switching +
Footnote †: _Applied Sciences_