esa-sceva/satcom-chunk-collection huggingface dataset:
# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 3 Method
### Semantic Segmentation  
The original semantic segmentation algorithm LinkNet [17] used in this paper is based on the implementation of Pytorch [60]. The network uses the autoencoder idea to connect each encoder and decoder directly without using pooling index or full convolution to recover the stepwise convolution, removing unnecessary spatial information and greatly reducing the processing time and the number of parameters required by the network. On the basis of such a lightweight real-time segmentation algorithm, this paper uses Intel's Open Visual Inference & Neural Network Optimization (OpenVINO) tool, as it provides a complete deep learning inference toolkit (DLDT), which can be used for a variety of trained models are deployed, but models trained by Pytorch deployment are not yet supported by it, so it needs to be preprocessed in advance to convert the pretrained model format to Open Neural Network Exchange (ONNN). The pretrained model is first optimized using Model Optimizer to obtain a file containing the specific network topology and a binary model file containing the weights and biases. The Inference Engine is then used to read the above two files and repredict the model. Finally, parallel operation is achieved using single instruction multiple data (SIMD) on the CPU to meet the real time, and the corresponding optimized semantic segmentation algorithm framework is shown in Figure 4.  
Each frame of the input color map is predicted using an optimized model that returns semantic information for each pixel in real time, where the semantic information includes the semantic color (the color given in advance to the object category) and the confidence. An optimal value comparison method is introduced to preprocess the semantic information for each pixel, and the algorithmic structure of this method is given below (Algorithm 1).  
```
1:functionOptimal Value Comparison (\(semantic_{1}\),\(semantic_{2}\))
2:if\(semantic_{1}\).color\(==\)\(semantic_{2}\).color
3:\(semantic_{optimal}\).color\(=semantic_{1}\).color
4:\(semantic_{optimal}\).conf\(=\frac{semantic_{1}\).conf\(+semantic_{2}\).conf}{2}\)
5:else
6:if\(semantic_{1}\).conf\(>semantic_{2}\).conf
7:\(semantic_{optimal}\).color\(=semantic_{1}\).color
8:\(semantic_{optimal}\).conf\(=semantic_{1}\).conf
9:\(semantic_{optimal}\).conf\(=semantic_{optimal}\).conf\(\times\) 0.95
10:else
11:\(semantic_{optimal}\).color\(=semantic_{2}\).color
12:\(semantic_{optimal}\).conf\(=semantic_{2}\).conf
13:\(semantic_{optimal}\).conf\(=semantic_{optimal}\).conf\(\times\) 0.95
14:return\(semantic_{optimal}\)
```  
**Algorithm 1**Optimal value comparison
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## Materials
### Remotely Sensed Data  
The remotely sensed data include two sets of original imagery data and one set of the retrieved ice concentration from the brightness temperature data. The first set is true-color images from the moderate-resolution imaging spectroradiometer (MODIS) comprising three bands: 1, 4 and 3 with wavelength 645 nm, 555 nm, and 469 nm, respectively. Image sharpening using Band 1 is applied to Bands 4 and 3 to obtain composite color images at 250 m resolution [16].  
The second set is high-resolution C-Band SAR data. We accessed both ASAR (Advanced Synthetic Aperture Radar) data from the European Envisat (available prior to its decommissioning in April 2012) and SAR data from the Canadian Radarsat-2. In general, SAR data has a limited coverage (100-500 km swath width), which reduces their availability at specific times and locations. For this study of ice navigation in Prydz Bay four ASAR and two Radarsat-2 SAR images were acquired (Table 1). SAR images were obtained at level 1B, which were then georeferenced, calibrated and speckle filtered. Image processing was implemented using ESA's (European Space Agency) NEST (the Next ESA SAR Toolbox) software at Beijing Normal University (BNU). The map projection was UTM (Universal Transverse Mercator) Zone 43S with WGS84 Datum. The calibrated images (radar backscatter (\(\sigma^{\circ}\)) in  
Figure 2: RV _Xuelong_ navigation speed (knots) versus sea ice concentration in the Prydz Bay. Data are based on previous RV _Xuelong_ sailing in the region.  
decibels; dB) were sent to ice pilots on board RV _Xuelong_. All remote sensing data were transformed into layers and analyzed in ArcGIS 10.0 software. It is noted that when near-coincident SAR data were not available, MODIS imagery was used instead.  
The third set is made up of daily SIC maps, generated using the Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) algorithm [17]. Data from the 91 GHz channel of the Special Sensor Microwave Imager/Sounder (SSMIS) were used for the maps of the 28\({}^{\text{th}}\) CHINARE (in 2011) and data from the 89 GHz channel of the Advanced Microwave Scanning Radiometer 2 (AMSR-2) were used for the maps of the 29\({}^{\text{th}}\) and 30\({}^{\text{th}}\) CHINARE (in 2012 and 2013). Grid spacing of 6.25 km is maintained for both cases.
## 3 Results of Simulation Experiments and Discussion
### Feasibility Analysis with Large-Scale Problems
We also checked the generated paths of each robot in a simulation environment developed with MATLAB 2023a, as shown in Figure 11, and no conflicting paths are found. Figure 9: The relationship between normalized total consumption (\(TC\)) and number of tasks (\(n\)) for our proposed methods with different numbers of robots (\(r\)), weight of time consumption (\(w_{t}\)), and weight of energy consumption (\(w_{e}\)): (**a**) \(r=5,w_{t}=0.6,w_{e}=0.4\); (**b**) \(r=10,w_{t}=0.6,w_{e}=0.4\); (**c**) \(r=5,w_{t}=0.4,w_{e}=0.6\); (**d**) \(r=10,w_{t}=0.4,w_{e}=0.6\).
# Assessment and Categorization of Biological Effects and
## 3 RF-EMF Exposure
### Characteristics of Sources and Interactions  
The sources of RF-EMF are of two categories, devices functioning nearby the human body, occasionally a near field exposure interacting highly constrained in a part of the body, and sources functioning distantly from the body that create an entire-body uniform exposure. Approximately, far field corresponds to transmitter-receiver distance greater than a wavelength and its strength diminishes quickly with distance. Classic near field sources are mobile and cordless phones. Characteristic far field sources include TV posts, mobile and cordless phones base stations, Wi-Fi access points or neighboring mobile phones.  
Interaction of RF-EMF with a human body is affected by the frequency, the field intensity, the exposure interval, the field polarization and the dielectric properties of absorbing matters. Moreover, in case of simultaneous activation of several EMF radiating sources nearby the body we have to account for such complex interactions. The nature of interactions can produce different BE. These effects can be due to short or long-term exposure. The most common effect is the thermal short-term one. There are also lesser frequent short and long-terms effects. The non-thermal complex effects fall in this category, as well as the cases exposing atypical symptoms.  
The thermal effects and the cases exposing atypical symptoms will be detailed in the next subsections while the non-thermal effects will be discussed later in Section 4.
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
## 7 Conclusions  
A new 2D PV coding scheme was proposed based on a WHTS coding approach and possesses several advantages over the existing families, such as simplicity of code construction, no limitation of the selection of any positive integer weight, and high degree of cardinality with increased flexibility. The proposed code can be employed for both synchronous and asynchronous incoherent OCDMA environment. Results indicated that 2D W-T PV code had improved system performance due to reduced error probability with MAI and phase induced intensity noise (PIIN) effects suppression and easier code implementation capabilities. For future implementations, simple direct detection method can be employed in the receiver design, which is cost effective and less complex due to the utilization of single-photo diode and low-pass filter circuit. The numerical results indicated that the 2D PV code provided better performance in terms of system parameters such as transmitted power at the source, cardinality, BER, and data rate. The simulation results demonstrated successful implementation of the 2D PV-based OCDMA scheme and make it applicable for all optical networks in practice by maintaining optimal BER (\(10^{-9}\)) required for error-free transmission at minimum data rate.  
[PERSON], [PERSON] and [PERSON] designed the research. [PERSON] and [PERSON] performed the code programming. [PERSON] carried out simulation analysis. [PERSON], [PERSON] and [PERSON] performed the analysis of the results and the results' interpretation. [PERSON], [PERSON] and [PERSON] wrote the first draft together, supported by [PERSON], [PERSON] and [PERSON] commented on the draft and all authors finalized it. [PERSON] and [PERSON] were corresponding authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Automatic 2D Floorplan CAD Generation from 3D Point Clouds
## 3 Proposed Method
### IFC File and 2D Floorplan CAD Generation  
Using the proposed method, walls are identified successfully with information that includes the position, direction and altitudes, but they are still in the form of 3D point clouds. Therefore, 3D reconstruction of the structural elements of the building must be performed. There are two necessary steps when reconstructing walls and exporting the result into a BIM format. In the first step, we describe the 3D geometry of the elements based on information for which the wall is described as either a volume composed of eight points and six facets or as a surface composed four points and  
Figure 3: Result of the wall detection process for seventh floor of the Robot Convergence Building at Korea University: (**a**) result of the depth image, (**b**) results of horizontal and vertical lines, and (**c**) results of walls.  
one facet. The second step consists of translating the 3D geometry of the elements into an IFC file. The elements describe what is expected in the building information model. A conversion phase is thus necessary to be able to integrate the results in BIM software. The IFC format is chosen as the format for the output in this approach. The IFC format is a standardized object-based file format used by the AEC industry to facilitate interoperability between building actors. Both elements and relationships between elements are described in this type of file. In order to generate a file in the IFC format from a 3D geometry of structural elements, we use the open-source library IfcOpenShell [28].  
After creating the IFC format, a 2D floorplan CAD file, such as an SVG format file, is generated using the IfcConvert method in an IfcOpenShell library. The IfcConvert method converts IFC files into various output formats, which include geometrical models and which are tessellated and easy to understand.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases
### Use Case 8: Military Logistic Report  
This UC holds paramount importance in military operations, as it guarantees the precise allocation of resources to units for their military endeavors. Among the technologies exerting a substantial influence on logistics, IoT stands out. These devices enhance security in overseeing logistic procedures, elevate transparency in the supply chain, and most importantly, deliver real-time updates on the status of products and services. In addition, they provide great energy efficiency due to the low-consumption technologies with which they are designed, thus also reducing costs. The tactical internet stands out as another noteworthy technology. It facilitates communication services that link strategic decision makers with commanders, enabling real-time access to information (supplied by neuromorphic processors) regarding the service's status. Additionally, it ensures dependable and secure communications, preventing the enemy from intercepting them and exploiting resource status during a military operation.
## 3 Application of Functionality Loss Evaluation Model for Railway Network Components
### Construction and Visualization of the Railway Network Model
#### 3.3.2 Derivation of Functionality Loss for Railway Network Components Under the Earthquake Scenario
To quantitatively evaluate the socioeconomic impact of earthquakes, such as disruptions in logistics, caused by functionality loss in railway networks, it is essential to accurately calculate the functionality loss ratio. While this study proposes a preliminary method based on functionality loss of components, future research on the spatial correlation of structural damage caused by earthquakes is expected to enable more precise evaluations of the impact of component functionality loss on overall network functionality and socioeconomic losses.
## 3 Results and Discussion  
Using the approach shown in Figure 11, we tested a variety of products and models using GPP derived from a consistent sample of eddy covariance sites to both train GPP satellite-driven models and evaluate them. A particular satellite data-driven model is given by \(\mathrm{GPP}=bx\), where a model parameter \(x\), such as \(\mathrm{NDVI}\times\mathrm{SW}_{\mathrm{TOA}}\), is used to predict flux tower estimates of GPP and \(b\) is determined by fitting to a subset of flux tower data.  
We investigated the impact of using different MODIS datasets in Appendix A. Generally, MCD43D provided superior results as compared to most other datasets. Our results in the remainder of this paper focus on the MCD43D reflectances that can be used at their highest resolution (\(\sim\)1 km) or degraded to be comparable to the resolution of other datasets used for benchmarking.  
We also tested the use of different radiation datasets in Appendix C. The use of top-of-atmosphere shortwave (\(\mathrm{SW}_{\mathrm{TOA}}\)), computed as being proportional to cosine of the solar zenith angle (SZA) integrated over a day, as compared with the satellite-derived all-sky SW produced the best results with Equation (2) where LUE was assumed constant. These results are consistent with those of [PERSON] et al. (2009) and [PERSON] et al. (2010), who used a limited number of flux tower sites. Those studies found that GPP did not follow \(\mathrm{PAR}_{\mathrm{in}}\), but rather that other factors affect GPP, and that the use of \(\mathrm{PAR}_{\mathrm{pot}}\) reduces some of the variability resulting from the dependence of LUE on cloudiness. This would occur if LUE is not constant, but rather anti-correlated with \(\mathrm{PAR}_{\mathrm{in}}\) (or correlated with cloudiness), such that various combinations of \(\mathrm{PAR}_{\mathrm{in}}\) and LUE produce a relatively constant GPP over a range of cloud conditions and for a given fixed value of \(\mathrm{GAPAR}_{\mathrm{chl}}\). It is well known that LUE is higher in situations with more diffuse light (e.g., ([PERSON] et al., 2012)) (i.e., cloudy conditions) as compared with clear-sky conditions. One exception is very low light conditions where LUE saturates.  
For following comparisons between models and flux tower data, results are typically shown in terms of scatter diagrams or 2D histograms with accompanying statistics. For completeness, we also provide probability distribution functions in Appendix D. These show the degree to which the differences follow a normal distribution that is assumed in statistical significance tests. While the differences deviated somewhat from normal distributions, the number of samples was large enough in most cases that the significance tests (when \(p\)-values were very small) were assumed to be valid.
## 2 Theoretical Background
### Imputation Methods
#### 2.1.2 Multiple Imputation  
Multiple imputation algorithms create a number of different imputed values for each missing record. Unlike single imputation methods, this approach allows for the statistical uncertainty that arises from imputing. **Multivariate normal imputation (MVNI)** and **Multiple Imputation by Chained Equations (MICE)**[29; 30; 31] are the two main techniques for multiple imputation. In MVNI, all features are fitted to a unified imputation model. Each feature is assumed to follow a multivariate normal distribution. Missing values can be imputed by drawing random samples from the conditional distribution of missing values based on the observed values after the parameters of this distribution have been estimated. This method is particularly effective when there are complex dependencies between variables. It captures correlations and joint distributions more accurately than univariate approaches. However, MVNI can require larger sample sizes and may not be suitable for datasets with non-normal distributions or strong non-linear relationships [32; 33; 34]. On the other hand, MICE is an iterative method. It imputes missing values by generating multiple plausible imputation records. For each missing variable, a separate imputation model is developed based on observations of other variables. The MICE algorithm imputes missing values in an iterative fashion, repeating the process until convergence is reached or a pre-defined stopping criterion is met. This iterative approach generates multiple imputations during each cycle. These are then combined into a single imputed dataset. MICE is often preferred to MVNI due to its lower reliance on distributional assumptions and reduced computational requirements [15; 35; 36].
## 4 Signal Processing
### Respiratory Signal Extraction  
Auto-correlation presents the correlation strength of a signal at different time lag. It is defined as  
\[R(\tau)=\int_{-\infty}^{\infty}r(t)r(t+\tau)dt \tag{24}\]  
where \(r(t)\) denotes respiratory signal and \(\tau\) denotes time delay. For periodic signals, the auto-correlation coefficient also changes periodically [32]. The respiratory signal of humans is a regular periodic signal with good time correlation, and other independent components (ICs) describe the movement of radar and interference; they are random signals. In this case, the respiratory signal extraction method is based on the comparison of auto-correlation between ICs; then the wanted IC that represents the vital signal of injured people can be effectively extracted.
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 2 Description of the Problem and Concept of the Solution
### Reinforcement Learning  
Reinforcement Learning (RL) offers a promising solution to the limitations of traditional heuristic approaches in lift control. This specific field of machine learning introduces a dynamic approach in which an agent learns through active interaction with its environment. RL finds widespread application in various fields, including robotics, finance, healthcare, and game playing [10; 11]. In robotics, RL is widely employed to enable adaptive and autonomous behavior in robots, allowing them to navigate and perform tasks in unstructured and dynamic environments [12]. It offers the unique advantage of exploring complex environments to find intricate strategies that are often beyond the scope of simple rule-based traditional solutions. This adaptability makes RL well-suited for lift control systems, where the dynamic nature of traffic patterns and user demands requires an intelligent and evolving decision-making approach.  
The environment can be represented as a Markov Decision Process (MDP). This MDP is composed of several components: a set of possible states \(S\), a set of available actions \(A\), and two functions--the transition function \(T(s,a,s^{\prime})\) and the reward function \(R(s,a,s^{\prime})\). The transition function captures the dynamics of the environment, determining the probability of transitioning from state \(s\) to \(s^{\prime}\) when taking action
## 3 Adaptive Routing Optimization Algorithm  
### Network Model  
In our study, the OppNets for mHealth is designed as shown in Figure 1. The network consists of two different types of mobile nodes including ordinary and ferry nodes. The ordinary node is a common mobile intelligent terminal with several pieces of software that can send or accept messages and record logs of conversations, and the embedded communication device such as Bluetooth. It can be divided into different communities according to a certain metric, for example, the relationship between nodes. Compared to the ordinary node, the ferry node can reach a longer communication distance and don't need to join the community.  
The transmission mechanism of the network meets the following rules: (1) The forwarding of a message in the community is dependent on the ordinary nodes, while the communication between communities is carried out by the ferry nodes. (2) An encounter node is chosen as the relay node if it satisfies a certain condition, such as being more active than the source node, one of the ferry nodes, and the same community with the destination node.