esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlation of Gravity and Magnetic Field Changes Preceding Strong Earthquakes in Yunnan Province
## 2 Data Processing and Accuracy
### Data of Gravity and Geomagnetic Total Intensity  
#### 2.2.1 Gravity Data Processing and Accuracy  
Every year, the Yunnan Seismological Bureau carries out two relative gravity observations in the whole province and conducts joint surveys with 10 absolute gravity points in the survey area. The gravity value of each point is obtained through adjustment calculation. In the adjustment calculation of gravity data, the observation data are first adjusted by a free network, and the observation accuracy of each instrument is preliminarily understood. Second, according to the observation accuracy of each instrument, the weight of each instrument is determined reasonably. Finally, absolute gravity control was used to carry out classical adjustment for the pretreatment results [15]. The accuracy of the adjustment calculation is shown in Table 1.  
In Table 1, the average accuracy of the 20 data points from 2011 to 2021 is (6.6-10.5) \(\times\) 10\({}^{-8}\) m\(\cdot\)s\({}^{-2}\), the average point value accuracy is 8.5 \(\times\) 10\({}^{-8}\) m\(\cdot\)s\({}^{-2}\), and the difference between the posterior median error and the prior median error is small, reflecting the reliable quality of gravity observation data, which can be used to reflect the space-time dynamic evolution process of the regional gravity field.  
Figure 2: Distribution map of gravity survey points, geomagnetic survey points, and faults in Yunnan Province.
## Abbreviations  
Graphitic carbon nitride (g-C\({}_{3}\)N\({}_{4}\)); Graphitic carbon nitride co-doped with Bi-Fe oxide (Bi@Fe/CN); Peroxymonosulfate (PMS); Sulfamethoxazole (SMX); Specific surface area (SAX); Sulfate radicals (SO\({}_{4}\)\({}^{-}\)); Tert-butanol (TBA); 5;5-dimethyl-1-pyrrolidine N-oxide (DMPO); 2,2,6,6-tetramethyl-4-piperidin (TEMP); Methanol (MeOH); X-ray diffractometer (XRD); Scanning electron microscope (SEM); X-ray photoelectron spectroscopy (XPS); Infrared absorption spectrum (FTIR); Reactive oxidative species (ROS); Electron paramagnetic resonance (EPR); High-performance liquid chromatography (HPLC); Chloride (Cl\({}^{-}\)); Nitrate (NO\({}_{3}\)\({}^{-}\)); bicarbonate (HCO\({}_{3}\)\({}^{-}\)); monohydric phosphate (HPO\({}_{4}\)\({}^{2-}\)); single oxygen (\({}^{1}\)O\({}_{2}\)); Hydroxyl radicals (\({}^{\bullet}\)OH).
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 5 Performance Evaluation of OFDM Navigation Signals
### Receive Bandwidth  
For the GNSS receivers, the bandwidth selection of the receiver has a great impact on the navigation performance. In order to ensure the tracking performance of the navigation signals, the RF front end of the general receiver should contain at least 90% power [29]. Figure 11 shows the power ratio versus the RF front-end bandwidth. For BOC(1,1), the RF front-end bandwidth at a 90% power ratio is the smallest, about 8 MHz. The RF front-end bandwidth at the same power ratio in OFDM(128) and OFDM(8) is 14 MHz and 16 MHz, respectively, which is slightly larger than the 8 MHz and 12 MHz of BOC(1,1) and BPSK-R(10), respectively; it is much smaller than the 50 MHz of BOC(10,5), so the RF front-end bandwidth of 36 MHz cannot fully receive the signal power of BOC(10,5). Based on the above analysis, it can be concluded that BOC(10,5) requires a large bandwidth to achieve a high accuracy, while the OFDM-modulated navigation signals only require a small bandwidth to achieve a relatively high accuracy.  
Figure 11: Relationship between the power ratio and bandwidth. The blue dash line is 90% power containment.  
Figure 10: Comparison between the navigation signal and traditional signal. (**a**) Lower bound of code ring tracking accuracy; (**b**) Incoherent lead–lag code loop tracking error.
## 6 Limitations and Future Directions  
NILM has made significant progress but still faces key limitations. There is no universally optimal algorithm, as performance depends on the type of device and signal conditions. Deep learning models, while accurate, require high computational costs that hinder their use in real-time applications or resource-constrained systems. Additionally, the lack of standardization and heterogeneity in datasets complicates comparisons and reproducibility, while the complexity of consumption patterns limits the robustness of algorithms.  
Regarding hardware, current devices are often expensive and proprietary, with variability in precision and constraints for edge computing implementations. However, open hardware presents opportunities for improvement, especially in the power supply and reliability of measurement modules.  
To advance the field, it is necessary to develop algorithms that integrate deep learning and edge computing to enhance efficiency and adaptability. Expanding and validating more representative datasets, optimizing open hardware design for real-time measurements, and deepening the analysis of high-frequency and transient data are also essential. Ultimately, expanding practical applications in smart homes, industry, and smart grids will enhance NILM's impact.  
Conceptualization, F.G.M. and A.A.; methodology, C.R.-N., F.P., and A.A.; software, C.R.-N. and A.A.; formal analysis, C.R.-N.; investigation, C.R.-N. and F.P.; resources, F.G.M. and A.A.; data curation, C.R.-N.; writing--original draft preparation, C.R.-N. and F.P.; writing--review and editing, C.R.-N., F.P., F.G.M., and A.A.; visualization, C.R.-N. and F.P.; supervision, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 8 Concluding Remarks  
Accurate and reliable information about the spatial distribution of tropical forest biomass is imperative for managing carbon stocks, mitigating climate change and to understand the role of the tropical forests in the global carbon budget. Remote sensing plays a pivotal role in the estimation of the extent and biomass of tropical forests. The emergence of high-performance computing has enabled upscaling of the remote-sensing-based biomass estimation methods, from localized site-specific studies to pan-tropical and global-scale AGB estimate at increasingly high spatial resolutions. In the last decade, many studies were carried to estimate pan-tropical carbon stock maps by combining field inventories with multi-source remote sensing data such as NOAA, MODIS, Landsat, SPOT, Sentinel-1 and 2, ICESat-1 and 2, and PALSAR-2. In the tropics, integration of ICESat-1's GLAS with the established data products from other satellites has generated several significant pan-tropical biomass maps at a 500 m-1 km spatial resolutions [105, 156, 159, 162, 163, 186]. Global forest and non-forest mosaics at a 25 m spatial resolution have been generated from ALOS-PALSAR from 2007 to 2017 [76] and has been integrated with Sentinel-SAR images and ICESat LiDAR data to generate global AGB maps of 2017 at a 100 m spatial resolution [164]. To further enhance the accuracy and continuous development of space-based estimates and monitoring of the AGB of the forest, special biomass measurement missions have been launched and/or planned to be launched in the near future such as GEDI (LiDAR) in 2018, MOLI (Multi-footprint LiDAR) in 2022, BIOMASS (P-band SAR) in 2022, PALSAR-3 (L-band SAR)in 2021, NISAR (S and L--band SAR) in 2021 and Tandem-L (L-band SAR) in 2022. LiDAR-based observation from the GEDI and MOLI on canopy height and vertical structure is critical in improving the accuracy using remote sensing. Overlapping operational time of these missions with different sensors (LiDAR, P, S, and L--band SAR) will open new research domains of sensors' integration and synchronizations to develop a variety of accurate products for forest biomass measurements, which will help to estimate and monitor changes in tropical forests due to deforestation, degradation, and most significantly, the recovery of the tropical forests due to abandonment. The coming decades will be revolutionary for pan-tropical biomass monitoring, due to high-performance cloud computing and the new satellite missions focusing on biomass mapping. This research was supported in part by the grant (1-BBWD) from the Research Institute for Sustainable Urban Development, the Hong Kong Polytechnic University; and grant of G-YBU3 from the Hong Kong Polytechnic University. We thank the five anonymous reviewers whose critical reading and comments/suggestions helped improve this manuscript. The authors are grateful to the NASA, Copernicus Sentinel, ESA, World Resources Institute (WRI), Greenpeace, Copernicus Global Land Service--Land Cover 100 (CGLS-LC100)/ESA, GLCF, UMD, IGBP, and Global Land Analysis and Discovery (GLAD). The authors declare no conflict of interest.
# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
###### Abstract  
Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.  
cellular networks; location-awareness; positioning; failure management +
Footnote †: journal: Sensors
# Reversal of Photoinduced Bending of Crystals Due to Internal Refraction of Light
## 4 Model of the Interaction of Light with a Crystal  
### Simple Qualitative Model  
The influence of light refraction on the direction of the crystal bend can be qualitatively explained using the geometrical optics approximation shown in Figure 5.  
If light falls on a crystal having a convex surface, then the layers located on opposite sides of the neutral plane (vertical dashed line in Figure 5) receive different amounts of light flux. Layers located closer to the irradiated surface (layer highlighted in blue) receive a portion of the light flux proportional to their height \(\Delta y\). Layers behind the neutral line, such as the red layer in Figure 5, receive almost the entire luminous flux incident on the crystal. If we neglect the effects of reflection and absorption, then the average light intensity in the layers in front of the neutral plane is approximately equal to \(l_{0}\), while in the layers behind the neutral plane it is equal to \(l\!l_{0}/\Delta y\). That is, the light intensity is approximately constant in the front half of the crystal section, and increases with \(x\) in the back half, resulting in the negative bend direction, according to Equation (7). A slight absorption of light will lead to definite decrease of the light intensity with \(x\), which will lead to a decrease in the magnitude of intensity moment in Equation (7). At some critical value of the absorption coefficient, the intensity moment becomes zero and the crystal will not bend during the transformation. At a higher absorption coefficient, the crystal will bend in the usual direction, given by the sign of \(\varepsilon_{0}\) and the light absorption law. To accurately describe this phenomenon, taking into account all the effects of wave optics, including reflection, refraction, diffraction, and interference, it is necessary to directly simulate the interaction of a plane wave with a crystal.
## 3 Results
### Effects of Scan Rate  
To assess the electrocatalytic behaviors of BPA at Rh\({}_{2}\)O\({}_{3}\)-rGO/GCE, scan rates from 10 to 270 mV s\({}^{-1}\) were studied (Figure 4). Oxidation peak current (\(I\), \(\mu\)A) of BPA was linearly increased with the scan rate (\(v\), mV s\({}^{-1}\)) with R\({}^{2}\) = 0.9938, and the relationship can be expressed by the linear regression Equation (1).  
\[I=0.0489\ \times\ v+2.7458, \tag{1}\]  
The oxidation of BPA on the Rh\({}_{2}\)O\({}_{3}\)-rGO/GCE surface was an adsorption-controlled process. Meanwhile, the oxidation peak potential (\(E\), V) increased linearly with natural logarithm of the scan rate (\(\ln\upsilon\), mV s\({}^{-1}\)) with R\({}^{2}\) = 0.9931, and the Equation (2) is as follows:  
\[E=0.0246\ \times\ln\upsilon+0.4543, \tag{2}\]  
Figure 3: (**a**) CVs of Rh\({}_{2}\)O\({}_{3}\)-rGO/GCE detected BPA with different pH; (**b**) effects of pH on the peak current of BPA at Rh\({}_{2}\)O\({}_{3}\)-rGO/GCE.  
Figure 2: (**A**) CVs of (**a**) bare GCE, (**b**) rGO/GCE, and (**c**) Rh\({}_{2}\)O\({}_{3}\)–rGO/GCE in the solution of 1.0 mM [Fe(CN)6]\({}^{3-}\)/\({}^{4-}\) (1:1) and 0.1 M KCl; (**B**) CVs of (**a**) bare GCE, (**b**) rGO/GCE, and (**c**) Rh\({}_{2}\)O\({}_{3}\)–rGO/GCE in the solution of 20 \(\mu\)M BPA.  
As for an adsorption-controlled and totally irreversible electrode process, \(E\) was defined by Equation (3) [43].  
\[\mathrm{E=E^{\Theta}+(RT/\alpha nF)\times ln((RT\mathrm{\^{\Theta}})/\alpha nF)+(RT/ \alpha nF)\times ln}, \tag{3}\]  
where \(\alpha\), \(\mathrm{k^{\Theta}}\), \(n\), \(\upsilon\), \(\mathrm{E^{\Theta}}\), \(R\), T and F represent transfer coefficient, standard rate constant of the reaction, electron transfer number (involved in rate determining step), scan rate, formal redox potential, the gas constant, the absolute temperature, and the Faraday constant, respectively. According to Equation (2), the value of RT/\(\alpha\)nF was 0.0246. Generally, \(\alpha\) is assumed to be 0.5 in a totally irreversible electrode process [44]. Therefore, n was calculated to be 2.09, indicating that the electron transfer number for BPA oxidation is 2.
## Broadband Wireless Communication Systems for Vacuum Tube High-Speed Flying Train  
**[PERSON] 1, [PERSON] 1,*(c), [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1,2**  
1 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; [EMAIL_ADDRESS] (C.Q.); [EMAIL_ADDRESS] (B.H.); [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (T.Z.)  
2 The Center of National Railway Intelligent Transportation System Engineering and Technology, China Academy of Railway Sciences, Beijing 100081, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-138-1189-3516  
Received: 4 January 2020; Accepted: 14 February 2020; Published: 18 February 2020  
A vectrain (or vacuum tube high-speed flying train) is considered as a novel proposed rail transportation approach in the ultra-high-speed scenario. The maglev train can run with low mechanical friction, low air resistance, and low noise mode at a speed exceeding 1000 km/h inside the vacuum tube regardless of weather conditions. Currently, there is no research on train-to-ground wireless communication system for vactrain. In this paper, we first summarize a list of the unique challenges and opportunities associated with the wireless communication for vactrain, then analyze the bandwidth and Quality of Service (QoS) requirements of vactrain's train-to-ground communication services quantitatively. To address these challenges and utilize the unique opportunities, a leaky waveguide solution with simple architecture but excellent performance is proposed for wireless coverage for vactrains. The simulation of the leaky waveguide is conducted, and the results show the uniform phase distribution along the horizontal direction of the tube, but also the smooth field distribution at the point far away from the leaky waveguide, which can suppress Doppler frequency shift, indicating that the time-varying frequency-selective fading channel could be approximated as a stationary channel. Furthermore, the train-to-ground wireless access architectures based on leaky waveguide are studied and analyzed. Finally, the moving scheme is adopted based on centralized, cooperative, cloud Radio Access Network (C-RAN), so as to deal with the extremely frequent handoff issue.  
vacuum tube high-speed flying train; train-to-ground communication; leaky waveguide; C-RAN; moving cell
# Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks
###### Abstract
They integrated the following multimodal information to analyze sentiment: the bag-of-words representation of the video transcripts, the duration of smiles detected from video clips by a commercial software package, and the vocal intensity features of the audio track computed by an open source software project. [PERSON] _et al._[17; 18; 19] built models that used multimodal content including audio, visual, and textual information to harvest sentiments from videos posted to YouTube. They leveraged SenticNet 3.0 [4] and EmoSenticNet [20] for textual sentiment analysis and extracted facial expression features from video clips and vocal intensity features from the audio track. They used both feature- and decision-level fusion methods to merge the affective information extracted from these multiple modalities. [PERSON] _et al._[21] presented an approach to perform sentiment analysis of news videos based on multimodal features extracted from closed captions, facial expressions and the participants' speeches. Some researchers have concentrated on  
Figure 1: Examples of textual microblog messages with related images from Sina Weibo. (**a**–**c**) are three types of micro-blogs. sentiment analysis of microblogs, which typically include a short text and one or more related images in each post. [PERSON] _et al._[22] and [PERSON] _et al._[23; 24; 25] employed both text and images to predict sentiment. The former focused mainly on detecting sentiment from English-language social media, while the latter established a dataset extracted from a Chinese social network (Sina Weibo) and provided a benchmark for combining visual and textual sentiment prediction from Chinese social media. In this paper, we focus on analyzing sentiment via both textual and visual content in the benchmark dataset mentioned above. We adopt deep convolutional neural networks (DNNs) with DropConnect [26] for visual sentiment analysis and train another CNN on word vectors using the short text for textual sentiment analysis and then analyze the complete sentiment by combining these two prediction results. Our textual results alone surpass the fusion results from [23] and we improve that performance even further by introducing the visual content component, thus achieving state of art performance on this benchmark. The rest of this paper is organized as follows. In Section 2, we introduce the research status of textual and visual sentiment analysis. In Section 3, we introduce the architecture of our approach consisting of a textual model, a visual model and a fusion model. We conduct experiments to evaluate the performance of different models on sentiment analysis in Section 4. Finally, we summarize this article and mention possible future work in Section 5.
## 3 Materials and Methods
### Data Analyses  
#### 3.3.1 Vulnerability Indexes  
This work is based on a composite vulnerability index developed by [20; 21; 39]. This index was created with the intention of being used in the context of African agricultural growing. This composite index is a combination of three sub-indexes that are subcomponents of the vulnerability index (sensitivity index, exposure index, and adaptive capacity index). These indexes are used to determine the degree of vulnerability of (wheat, barley, and maize) in Morocco and to incorporate some crop parameters such as yield, precipitation, and socio-economic proxies (poverty and literacy rates). This methodology is based on other vulnerability indexes, including the Notre Dame Global Adaptation Index (NDGAIN) [51], the crop-drought indicator [52], and the water-poverty index [53]. Equation (1), shown below, was used to compute the vulnerability index:  
\[VU_{xinsn}=SE_{xinsn}+EX_{xinsn}-ADC_{xinsn} \tag{1}\]  
where \(VU_{xinsn}\) is the crop yield vulnerability index for wheat, barley, and maize at the national and sub-national scale, \(SE_{xinsn}\) is the crop yield sensitivity index at both the national and sub-national scale, and \(EX_{yinsn}\) is the yield crop exposure index--also at the national and sub-national scale. \(ADC_{xinsn}\) is the crop yield adaptive capacity index at both the national and sub-national scale, while \(x\) indicates the year and \(i\) is the given crop.
## 2 State of the Art
### Modeling Mobile Systems with Petri Nets  
The use of Petri nets as a modeling tool has proven crucial for analyzing and optimizing the behavior of complex mobile applications. [PERSON] and [PERSON] [11] conducted a comprehensive review on modeling mobile computing systems using Petri nets, highlighting their ability to represent key aspects such as concurrency, parallelism, process synchronization, and asynchronous behavior. These characteristics position Petri nets as widely used tools in concurrent and distributed systems, particularly in the context of mobile applications that require continuous asynchronous interaction between the user and the server [22]. In this scenario, modeling system behavior through Petri nets allows for a deeper understanding of the processes involved, as well as effective optimization of concurrency and event synchronization [10].  
[PERSON] et al. [23] addressed the modeling and analysis of three critical properties in interactive mobile systems using Petri nets. Their research demonstrates how these tools enable formal verification of these properties, which is essential for ensuring the reliability and robustness of mobile applications. The value of Petri nets lies in their ability to model and analyze complex systems, such as mobile applications interacting with an API in real-time. This approach facilitates the analysis of critical properties such as reachability and deadlock prevention in communication, optimizing the exchange of information between the mobile application and the API. Furthermore, its applicability in modeling proximity systems allows for anticipating and mitigating delays in content delivery, ensuring accurate and timely communication. [PERSON] et al. [24] highlighted the flexibility of Petri nets for modeling mobile applications that require efficient management of asynchronous processes, thus laying the groundwork for extending their use in proximity applications, such as those presented in this study.  
[PERSON] et al. [25] proposed a one-time password (OTP) scheme based on stochastic Petri nets, specifically designed to enhance security in authentication systems using mobile devices. Their approach leverages Petri net modeling to accurately represent the processes of OTP generation, transfer, and real-time verification, incorporating intermediate mathematical calculations performed by the user.