esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Human Ground Control Performance in Unmanned Aerial Systems
## 3 The Ground Control Station Prototype
### Ground Control Station Architecture  
For the development of the project, we opted for the use of NodeJS technology, this allowed us to work in an innovative environment and take advantage of the flexibility that this technology exhibits. Indeed, a web app was created with all the pro deriving from it: we can in fact use it from any operating system through a browser but what interests us even more is the capability to use it on different devices; more specifically it has been designed for use on normal desktop or MultiTouch wall systems.  
The application is real-time: it receives updates on drones' status data in real-time and updates the interface to allow the operator to react immediately to changes. We decided to adopt a three-tier architecture:  
* The first level consists of the data source; in the experiments, a JSON-coded scenario was used which contains all the initial information on the mission and how the system should behave but in real cases, the data source will be the drones that communicate through MaxLink protocol;
* The second level consists of the controllers; we need a controller that receives events from the drones and updates the interface and a controller that instead receives events from the operators and communicates them to the level below to send direct commands to the drone;
* The third level takes care of the graphical interface and sends the user inputs in the form of events to the associated controller.  
The word \"event\" has often been used; this is because the NodeJS environment is asynchronous and allows event-driven programming.  
For the user interface of the application, we opted for the use of HTML5, Sass (compiled in CSS3), and JavaScript; this stack of technologies allows us to develop a working prototype in a short time (just think the limitations posed by the development of a graphical interface in java).  
Another technology used for the project is Webpack: it is a module bundler that has allowed us to implement a workflow such as: speeding up development, ensuring simple and intelligent code organization, and optimizing the size of the app in production.  
Figure 4: The GCS interface during the execution of the mission with the visualization of the artificial horizon.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 2 The Use of Neural Networks in the Global Positioning System
### Processing for Navigation States for GPS Receivers
Finally, using computer simulation to base tests on a single epoch and execute kinematic positioning, researchers looked at how well the least absolute and minimax approaches fared in terms of outlier resistance when compared to the least squares method.
## 3 Framework of Forecasting AOG Models in Aviation
### Advancements in Predictive Maintenance for AOG Prevention Aviation  
**What Is Predictive Maintenance?**  
Airlines can enhance operational performance and minimize AOG incidents by analyzing historical aircraft operations and maintenance data. AI and machine learning algorithms are critical for identifying patterns in complex systems with multiple failure points. These systems evolve, becoming more accurate as they process larger datasets. By simulating equipment failures and utilizing time series data, predictive maintenance enables companies to shift from reactive or random maintenance to planned, timely interventions, improving asset reliability and operational efficiency.  
Predictive maintenance differs from preventive maintenance because it relies on real-time data rather than fixed schedules. It incorporates condition-monitoring techniques such as vibration analysis, thermal imaging, ultrasonic acoustics, and fluid testing to detect anomalies early. These techniques allow for proactive problem-solving, reducing the risk of costly disruptions and optimizing maintenance schedules. Figure 2 demonstrates a classification of maintenance strategies.  
Predictive maintenance (PdM) technologies are increasingly being adopted in aviation, with Aircraft Health Management (AHM) systems now essential for airframe, engine, and component maintenance. However, challenges persist, particularly with data reconciliation, as Original Equipment Manufacturers (OEMs) aim to control data-sharing frameworks. This is particularly critical for multi-sourcing airlines that outsource maintenance to various  
\begin{table}
\begin{tabular}{c c c} \hline
**Financial Factor** & **Estimated Cost Range (EUR)** & **Factor Metrics** \\  & **per Day** & **crew overtime or replacement, rebooking passengers on alternative flights.** \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Aircraft maintenance strategies [20].  
providers. Although airlines now own their maintenance data, they must ensure effective sharing with third-party providers. The global aviation fleet generates nearly 2 million terabytes of data annually through technologies like Flight Data Recorders (FDR) and Aircraft Health Monitoring (AHM). This figure is projected to surge to 98 million terabytes per year by 2026, driven by digitization advancements, reduced sensors and communication technology costs, and rapid IT evolution. Managing such vast data volumes requires efficient methods, as manual processes are no longer sufficient.  
Research by EXSYN [9] identifies three key stages for airlines to transition into predictive, data-driven organizations (Figure 3) where airlines adopting predictive maintenance move through three stages: reporting, monitoring, and data-driven. PdM and AHM systems are no longer luxuries but necessities, providing critical support for maintenance and operational efficiency. The challenge lies in balancing OEM strategies with airline needs to ensure seamless data-sharing and stakeholder collaboration.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 7 Research Trends and Directions
For instance, the _robust availability_ could be a metric computing the average operation time (dependable coverage) that a set of cameras cover at least \(k\) targets (spatial \(k\)-coverage) integrating the distance and the angle of view from each camera to the covered targets (content coverage);
* _Metric standardization_: although the categorization proposed in this work facilitates the comparison of visual networks, this task could be enhanced through standardized metrics. This would allow fairer and more accurate analysis of the compared networks and metrics. For this, the researchers of the topic of quality of visual monitoringshould establish a fundamental set of quality metrics that would be respected as basis of comparison. This would be similar to works addressing QoS in communication networks, which establish comparisons based on common and well-defined metrics (bandwidth, latency, jitter, error rate);
* _Mobile visual nodes_: when mobility is added to the visual nodes, a highly dynamic context is created, which yields monitoring issues equally dynamics. The existing quality metrics should be adapted and new metrics should be created to consider this constant changing of the monitoring scenario. A special challenge in this case is to deal with real-time requirements whilst guaranteeing the quality of monitoring. For this a new metric should be proposed, the _coverage lifetime_, which would be the duration that a coverage scheme remains valid in a network. Overall, visual monitoring using distributed sensor nodes will be valuable to support a great number of applications in emerging multi-parameters scenarios such as in Smart Cities, Industry 4.0, and Vehicular Networks. Although promising, however, the adoption of multiple interconnected cameras will impose important challenges when trying to provide minimum levels of quality, especially when some of the aforementioned issues are considered. In the coming years, we believe that research efforts in this area should better support achieving quality metrics and methodologies that may be useful when improving new generations of wireless visual sensor networks.
# Experimental Study on Partially Coherent Optical Coherent Detection
## 3 Experimental Study
### Partial Coherent Polarization Measurement and the Coherent Detection Experiment
Circularly polarized EGSM beams have higher sensitivity when EGSM beams with different polarization states are used for coherent detection, although there may be slight variations in the signal light's power and its deviations when adjusting the polarizer. In the case of EGSM beams, their output power can be affected by the polarization states of the beams. When the polarization states are different, it implies that the electric field vectors of the beams are oriented at different angles, or that they have different circular polarization directions. These differences in polarization states can result in an unequal coupling efficiency and unbalance interactions between the optical components, such as the mixers mentioned earlier. As a result, there may be variations in the power levels of the EGSM beams with different polarization states.
## 4 Case Study
### Integration of Case Study
#### 4.2.2 Application Effect in the Main Venue Project  
In the main venue project of Xiong'an Park, this methodology has reaped remarkable dividends. The contractor, in accordance with contractual stipulations, converts model files into the XDB format and extracts data in strict adherence to standards, furnishing the construction unit with standardized data for efficacious project control. During the construction process, real-time data accessibility empowers the construction unit to vigilantly monitor progress and quality and expeditiously address emergent issues, such as steel structure installation deviations.  
The construction unit imports model files into the SuperMap platform in compliance with regulatory requirements, integrating data for the government's benefit. This enables the government to conduct visual planning and management, execute efficient data analysis, and ensure the project's conformity with regional planning imperatives. When appraising the project plan, the government can astutely assess the building-environment compatibility and indicator compliance through the platform, thereby facilitating scientifically informed decision-making.  
In summation, this methodology has achieved multi-unit and multi-scale information collaboration within the project, shattered information silos, substantially enhanced information management efficiency, provided robust support for project and regional development, and proffered invaluable references for analogous projects, thereby evincing its prodigious potential and value in large-scale construction undertakings.
## 3 Materials and Methods
### Data Processing and Statistical Analysis  
LECO ChromaTOF Version 4.73.3.0 software (Leco Corporation, St. Joseph, MO, USA) was used for instrument control, data acquisition and processing. Each chromatograph peak was compared to National Institute of Standards and Technology (NIST2017) library and the minimum similarity is 800.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pear Cultivars** & **Abbreviation** & **Producing Area** & **Sampling Time** \\ \hline Longyuanyangli & longyuanyangli & Qiqhaer city of Heilongjiang province & 07 September 2018 \\ Packham’s Triumph & Packham & Weihai city of Shandong Province & 27 September 2018 \\ [PERSON] [PERSON] & Yantai city of Shandong Province & 01 August 2018 \\ Clapp’s Favorite & Clapp & Yantai city of Shandong Province & 07 August 2018 \\ Starkrimson & Stark & Yantai city of Shandong Province & 01 August 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The cultivars, abbreviation, producing area and sampling time of 5 pears.  
The area of the base peak was used for quantification. The quantitative analysis for aroma components was carried out by internal standard method using 2-nonanone as an internal standard. Therefore, the concentration of each volatile compound was normalized to that of 2-nonanone. The formula for volatile compounds quantification is as follows:  
\[C_{a}=\frac{\frac{PA_{a}}{PA_{is}}\times C_{is}\times 5\;\mu L}{m}\]  
\(C_{a}\): the concentration of aroma components (ngg); \(PA_{a}\): peak area of aroma components; \(PA_{is}\): peak area of internal standard; \(C_{is}\): the concentration of internal standard (gmL); \(m\): mass of sample (g). The concentration of the 2-nonanone was 10 \(\mu\)g/mL and the mass of sample was 6.0 g. Data are means \(\pm\) SD of three replications. Cluster analysis (CA) was performed using the SPSS Statistical 19.0.
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 2 Materials and Methods  
### Study Area  
The NHAR (104\({}^{\circ}\)17\({}^{\prime\prime}\)-107\({}^{\circ}\)39\({}^{\circ}\)E, 35\({}^{\circ}\)14\({}^{\prime}\)-39\({}^{\circ}\)23\({}^{\prime}\)N) is located in the middle and upper reaches of the Yellow River in northwest China (Figure 1a). It encompasses five prefecture-level cities with a total area of 6.64 million ha. The terrain of the NHAR slopes gradually from southwest to northwest, with ravines and gullies (Figure 1b). The geographical structure is complex and diverse, surrounded by deserts on three sides and connected to the Loess Plateau in the south. It exists in the agro-pastoral transition zone, where natural forest resources are scarce and intensively distributed in Mount Helan, Mount Luo, and Mount Liupan, with Qinghai spruce (_Picea crassifolia_), Chinese pine (_Pinus tabuliformis_), and [PERSON]'s larch (_Larix principis-rupprechtii_) as the dominant species [29; 30]. Affected by the continental arid and semi-arid climate, the average annual precipitation (305 mm) is low and concentrated in the summer, yet the mean evapotranspiration (1800 mm) is high. Drought is an overriding limitation of plant growth in ecologically fragile regions [31]. Trees and shrubs, such as black locust (_Robinia pseudoaccia_ L.), apricot (_Prunus armeniaca_ L.), and aspen (_Populus tremuloides Michx._), were established here to balance the ecological and economic benefits [32; 33; 34].  
Figure 1: Location of the study area: (**a**) general location in China; (**b**) digital elevation model (DEM) of the study area; and (**c**) forest area of Ningxia Hui Autonomous Region in 2020.
# Simple Thalidomide Analogs in Melanoma: Synthesis and Biological Activity
## Materials and Methods
### Chemistry
Yield: 52%: mp 199-200 \({}^{\circ}\)C; GC/MS (70 eV) _m/z_ (%): 259 (M\({}^{+}\), 100); \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)): $ 7.22-7.39 (m, 3H, Ar), 7.78-7.84 (m, 2H, Ar), 7.93-7.99 (m, 2H, Ar); \({}^{13}\)C NMR (75 MHz, CDCl\({}_{3}\)): $ 116.0 (d, \(J_{\rm CF}\) = 19.4 Hz, 1C), 117.5 (d, \(J=18.6\) Hz, 1C), 112.6 (d, \(J_{\rm CF}\) = 4.6 Hz, 1C), 122.7 (d, \(J_{\rm CF}\) = 3.4 Hz, 1C), 123.9 (2C), 127.9 (1C), 131.4 (2C), 134.6 (2C), 148.3 (dd, \(J_{\rm CF}\) = 29.8, 12.6 Hz, 1C), 151.6 (dd, \(J=28.0\), 13.2 Hz, 1C), 166.8 (2C). Anal C\({}_{14}\)H\({}_{7}\)FNO\({}_{2}\)-0.20 H\({}_{2}\)O (262.64): calcd %: C 63.98; H 2.84; N 5.33. Found %: C 64.32; H 2.69; N 5.38. _Synthesis of 2-[4-(2-chlorophenylorphenyl)-1H-isionid-1,3(2H)-dione_ (**2i**). Prepared as described for **2a** starting from 4-(2-chlorophenylorphenoxy)aniline. Grey solid. Yield: 55%: mp 135-136 \({}^{\circ}\)C (CHCl\({}_{3}\)/hexane); GC/MS (70 eV) _m/z_ (%): 349 (M\({}^{+}\), 100); \({}^{1}\)H NMR (500 MHz, CDCl\({}_{3}\)): $ 7.11-7.15 (m, 4H, Ar), 7.24-7.28 (m, 1H, Ar), 7.37-7.40 (m, 2H, Ar), 7.47-7.48 (m, 1H, Ar), 7.78-7.80 (m, 2H, Ar), 7.93-7.96 (m, 2H, Ar); \({}^{13}\)C NMR (125 MHz, CDCl\({}_{3}\)): $ 117.9 (2C), 121.7 (1C), 123.8 (2C), 125.4 (1C), 126.4 (1C), 126.5 (1C), 128.0 (1C), 128.1 (1C), 130.9 (2C), 131.7 (2C), 134.4 (2C), 151.8 (1C), 156.7 (1C), 167.4 (2C). Anal. C\({}_{20}\)H\({}_{21}\)CINO\({}_{3}\)-0.50 H\({}_{2}\)O (257.02): calcd %: C 66.95; H 3.65; N 3.90. Found %: C 67.32; H 3.36; N 4.02. _Synthesis of 2-[4-(2-methoxyphenoxy)phenyl]-1H-isionid-1,3(2H)-dione_ (**2j**). Prepared as described for **2a** starting from 4-(2-methoxyphenoxy)aniline. Light brown solid. Yield: 75%: mp 148-150 \({}^{\circ}\)C (AcOEt/hexane). GC/MS (70 eV) _m/z_ (%) 345 (M\({}^{+}\), 100), 133 (100); \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)): $ 3.84 (s, 3H, CH\({}_{3}\)), 6.93-7.09 (m, 5H, Ar), 7.15-7.22 (m, 1H, Ar), 7.31-7.36 (m, 2H, Ar), 7.76-7.81 (m, 2H, Ar), 7.92-7.97 (m, 2H, Ar); \({}^{13}\)C NMR (125 MHz, CDCl\({}_{3}\)): $ 55.9 (1C), 113.0 (1C), 117.1 (2C), 121.1 (1C), 122.0 (1C) 123.7 (2C), 125.5 (1C), 125.6 (1C), 127.9 (2C) 131.8 (2C), 134.4 (2C), 144.0 (1C), 151.7 (1C), 157.9 (1C), 167.5 (2C). Anal C\({}_{21}\)H\({}_{15}\)NO\({}_{4}\):H\({}_{2}\)O (363.35): calcd %: C 69.63; H 4.91; N 3.54. Found: C 69.41; H 4.72; N 3.85. _Synthesis of 2-[4-(2-methylphenoxy)-phenyl]-1H-isionid-1,3(2H)-dione_ (**2k**). Prepared as described for **2a** starting from 4-(2-methylphenoxy)aniline. White solid. Yield: 33%: mp 123-124 \({}^{\circ}\)C (AcOEt/hexane); GC/MS (70 eV) _m/z_ (%) 329 (M\({}^{+}\), 100); \({}^{1}\)H NMR (500 MHz, CDCl\({}_{3}\)): $ 2.25 (s, 3H, CH\({}_{3}\)), 6.97-7.02 (m, 3H, Ar), 7.11 (t, \(l\) = 7.4 Hz, 1H, Ar), 7.21 (t, \(l\) = 7.4 Hz, 1H, Ar), 7.25-7.29 (m, 1H, Ar), 7.32-7.36 (m, 2H, Ar), 7.77-7.81 (m, 2H, Ar), 7.92-7.97 (m, 2H, Ar); \({}^{13}\)C NMR (75 MHz, CDCl\({}_{3}\)): $ 16.1 (1C), 117.3 (2C), 120.5 (1C), 123.7 (2C), 124.6 (1C), 125.6 (1C), 127.3 (1C), 127.9 (2C), 130.3 (1C), 131.6 (2C), 131.8 (2C), 134.3 (1C), 15
## 3 Experimental Results
### Strain Results under Thermal Load
#### 3.3.2 CA Mortar Strain  
Figure 13 shows the variation of the temperature-strain plot of the CA mortar with the number of thermal loading cycles. It can be seen from Figure 13a,b that, with the increase of thermal loading cycles, the temperature-strain plot of the CA mortar at the fixed end section and that at the mid-span section gradually shift toward the strain reduction direction. In the three stages of initiation, expansion and stabilization of the interlayer gap, the plots exhibit a dense-sparse-dense distribution. The CA mortar temperature-strain plot at the sliding end section is opposite to the strain increase direction. However, a dense-sparse-density distribution is also observed at the sliding end section, as shown in Figure 13c.  
Figure 11: Track slab strain variation with the thermal load.  
Figure 12: Variation of the track slab strain with the thermal loading cycle.  
Figure 13: CA mortar strain variation with the thermal load.  
The variation of the CA mortar strain with the thermal loading cycles under the three temperature rising conditions of 10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C and 30 \({}^{\circ}\)C is shown in Figure 14, which shows that under the same temperature load condition, the CA mortar strains at the fixed end section and the mid-span section decrease gradually, while that at the sliding end section increases gradually with the increase of the number of thermal loading cycles. The CA mortar strain range is substantially consistent with the expansion rate of the gap length during the three stages of interlayer gap development. It can be seen from Figure 14b that the CA mortar strain in the mid-span section abruptly changes between the 5 th and 6 th thermal loading cycle, which coincides with the time of occurrence of the interlayer gap.
## 2 Related Work  
### Blockchain-Enabled IoV System  
Blockchain technology has been recognized as a promising solution for addressing trustworthy data sharing in the IoV. [PERSON] et al. [6] integrated blockchain with public key infrastructure (PKI) to manage vehicle identity information in SIoV, proposing a checkpoint-based dynamic consensus mechanism to achieve trusted vehicle access control. [PERSON] et al. [14] tackled the high computational and communication overhead in blockchain-enabled IoV by introducing an SDN-based lightweight blockchain security protocol, which modifies the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm [15] to reduce computational and communication costs in secure message propagation. [PERSON] et al. [16] developed a P2P computing resource trading system for IoV, utilizing a two-stage Stackelberg game mechanism to optimize pricing utilities for buyers and sellers under consortium blockchain environments.  
The above-mentioned works primarily focus on security and trust management in IoV [6, 14, 16], often customizing blockchain consensus mechanisms to fit vehicular network scenarios. However, the storage scalability challenges introduced by blockchain in IoV systems remain largely overlooked. For RSUs with limited and heterogeneous storage resources, efficiently managing ever-growing blockchain data while providing low-access-cost services to vehicles remains an open and complex problem. We address blockchain storage allocation in SIoV by proposing a collaborative storage approach based on RSU clustering. By leveraging cooperative storage, our method enhances RSU storage efficiency and optimizes overall network service quality.
###### Abstract  
In contrast to the US Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS) and the European Galileo, the developing Chinese BeiDou satellite navigation system (BDS) consists of not only Medium Earth Orbit (MEO), but also Geostationary Orbit (GEO) as well as Inclined Geosynchronous Orbit (IGSO) satellites. In this study, the Precise Point Positioning (PPP) and PPP with Integer Ambiguity Resolution (IAR) are obtained. The contributions of these three different types of BDS satellites to PPP in Asia-Pacific region are assessed using data from selected 20 sites over more than four weeks. By using various PPP cases with different satellite combinations, in general, the largest contribution of BDS IGSO among the three kinds of BDS satellites to the reduction of convergence time and the improvement of positioning accuracy, particularly in the east direction, is identified. These PPP cases include static BDS only solutions and static/kinematic ambiguity-float and -fixed PPP with the combination of GPS and BDS. The statistical results demonstrate that the inclusion of BDS GEO and MEO satellites can improve the observation condition and result in better PPP performance as well. When combined with GPS, the contribution of BDS to the reduction of convergence time is, however, not as significant as that of GLONASS. As far as the positioning accuracy is concerned, GLONASS improves the accuracy in vertical component more than BDS does, whereas similar improvement in horizontal component can be achieved by inclusion of BDS IGSO and MEO as GLONASS.  
BDS; precise point positioning; integer ambiguity resolution; convergence time; multi-GNSS 2015; 15, 29970-29983; doi:10.3390/s151229780