esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Numerical Tool Development
### The Development of the Simulation Strategy
#### 2.1.4 Resin Flow and Compaction  
Resin flow is crucial in determining the fiber volume fraction distribution, influencing the laminate's mechanical properties and the component's final dimensions [27]. Due to the non-uniform nature of resin injection, areas with varying resin concentrations--both resin-rich and resin-poor--can develop. A bleeder can be utilized within the vacuum bag during laminate manufacturing to achieve a more consistent resin distribution. This approach enables liquid resins to migrate and bleed through the thickness of the laminate. Flat laminates are most prone to warpage and distortion, as shown in Figure 4. Thin-walled structures with no stiffeners are susceptible to warping.  
In the case of curved components, the phenomenon that leads to a non-uniform distribution of fiber content in the through-thickness direction is referred to as fiber bridging. During the compaction process, as the thickness of the component diminishes, the friction  
Figure 4: Effect of resin flow on the warpage in flat laminates.  
Figure 3: Effect of tool–part interaction on distortion: (**a**) flat parts; (**b**) parts that have corner sections.  
between the prepreg layers inhibits their ability to adapt to the tool's shape, particularly at the corners. Consequently, the pressure applied is ineffective at these corners due to fiber bridging. This results in the formation of a low-pressure zone at the tool's corner, which is subsequently infiltrated by resin, thereby increasing the thickness of the component in that area and creating a region rich in resin, as illustrated in Figure 5.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 4 Conclusions  
Based on the VBS-RTK positioning technology, this work has developed a GNSS buoy for monitoring water surface elevations in estuaries and coastal areas. Both laboratory and field tests were performed to test the capability of this buoy. For the field tests, the GNSS buoy was deployed in the waters off Suao (Taiwan) for ten months. The accuracy of the monitored water surface elevations obtained from the GNSS buoy was confirmed by comparing the collected tide and wave data with those obtained from the conventional tide gauge and the accelerometer-tilt-compass (ATC) sensor.  
Figure 12: Scatter plot and regression line for the dominant wave directions.  
Figure 13: Directional wave spectra obtained at 23:00 on 21 August 2013 from the (**a**) ATC sensor and (**b**) GNSS buoy.  
The tide levels obtained from the GNSS buoy were consistent with those from a neighboring tide station. The RMSE of the tide data was within 10 cm. The water surface elevations, significant wave heights, zero-crossing periods, one-dimensional wave spectra, directional wave spectra, and peak wave directions derived from the GNSS buoy were consistent with those obtained from the ATC sensor. The field tests demonstrated that the developed GNSS buoy can be utilized as an operational network for providing long-term and real-time tide and wave data in estuaries and coastal areas. Finally, detailed information on the procedures for deriving the water surface elevations from the accelerations measured by an ATC sensor are provided in Appendix A. The procedures for determining the directional wave spectrum and the dominant wave direction from the data provided by a GNSS buoy and an ATC sensor are provided in Appendix B.  
The authors thank the Water Resources Agency (WRA), Ministry of Economic Affairs, Taiwan for allowing the GNSS receiver to be installed on the WRA Suao data buoy.  
[PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] revised the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study, the collection, analyses, or interpretation of the data, the writing of the manuscript; or the decision to publish the results.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 1 Introduction
Thanks to advancements in edge computing and mobile internet technology, the neural network model can now be deployed in real time on edge devices. For example, the neural network model can be deployed on the control box of a wind turbine [6], resulting in a significant improvement in the response speed of bearing fault monitoring. In summary, neural networks have shown huge application potential in the monitoring of wind turbine bearing failure monitoring. In order to achieve the real-time detection of wind turbine bearing failure, this study aimed to apply machine learning methods in a Raspberry Pi to achieve high-precision and low-delay real-time monitoring with this portable device, which is of great significance for improving the reliability and utilization rate of wind power equipment. Our model can accurately identify patterns by training the physical characteristics of health and fault bearings. The data were divided into smaller segments so that the model can quickly analyze each segment and generate high-speed predictions. In addition, in order to achieve a minimal delay treatment, high-efficiency algorithms were developed. The network was trained and, subsequently, the NN algorithm was embedded into the Raspberry Pi. The network has two fully connected layers, and the tested time was 0.06 milliseconds on the Raspberry Pi. The results of this study indicate that the model can accurately detect wind turbine bearing faults and provide real-time predictions within milliseconds of the fault occurrence. This model possesses the capability to generate real-time predictions and assess the holistic health condition of bearings, thereby substantially diminishing maintenance expenses and enhancing the accessibility and efficacy of wind turbines. In essence, this study demonstrates the potential of AI-driven solutions in optimizing the generation of renewable energy and mitigating reliance on fossil fuels.
## 3 Results and Discussion  
The proposed system model is tested in both simulated as well as the real-time environment and its tracking performance is analyzed in the presence of disturbance. The images are subjected to translating,  
Figure 10: Flow chart for adaptive threshold technique (ATT).  
rotating, and scaling perturbations that commonly occurs in the leader-follower configuration. BER is considered as a performance index to evaluate the proposed system in all these scenarios.  
Additionally, the LFSR based key generation technique is implemented for secure communication. The incoming data stream is encrypted by stream ciphers using this generated key. The security of the system is evaluated based on the randomness of the key. Various tests proposed by the National Institute of Standards and Technology (NIST) suite are used for determining the randomness of the generated key as listed in Table 1. These tests predominately measure the probability of the given key. A benchmark of 0.01 is considered for the probability of passing the test. Proportion and probability values are used as performance metrics. It is observed that the key generated using the proposed method has proportion values of 10/10 with the probability value greater than the benchmark (>0.01). These results show that the generated key is random, which is capable of generating a robust cipher stream. In both the environments, the data given to the LEA transmitter is encrypted using the generated random key.  
Figure 11: Grouping of lightemitting diodes (LEDs) (**a**) Group-1 LEDs (**b**) Group-2 LEDs (**c**) Group-3 LEDs.
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 5 Challenges and Countermeasures  
### Data Issues and Privacy Security  
At present, the research of water conservancy big data theory and technology is still in its infancy, facing the problems of data barriers and analysis methods that have not played their advantages. In order to achieve scale benefits of water conservancy big data, water conservancy authorities need to organize governments, enterprises, universities and scientific research institutions to cooperate and jointly promote the healthy and orderly development of water conservancy big data [94]. In recent years, the digital watershed has developed into a smart watershed. The key technology is to use a wireless sensor network to realize the construction of a smart watershed. The wireless sensor nodes of automatic measurement and reporting rain stations, weather stations and hydrology stations play an important role in obtaining hydrological data, and optimizing the layout has become a research hotspot and difficulty in smart basin IoT, especially under the influence of global climate change and human activities [95].  
An intelligent water conservancy system involves a large number of sensitive data, such as water level, water quality, hydrology and other information, and the leakage, tampering and malicious attack of these sensitive data may lead to disastrous consequences. Therefore, it is necessary to strengthen data encryption, permission control, security monitoring and other measures to ensure the confidentiality, integrity and availability of data. For the protection of user privacy, the system needs to design a reasonable access control mechanism to ensure that only authorized personnel can access specific data. At the same time, establishing a security audit mechanism to monitor the access and use of data and detecting abnormal behaviors in a timely manner are also required.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 1 Introduction
In the Copernicus Global Flood Monitoring service, the implemented algorithms based on Sentinel-1 data are referenced and well described [9; 10; 11]. Regarding burnt areas, the algorithm developed by CIMA for Sentinel-2 automatic processing is fully described in a dedicated article [12]. The level of description of the thematic algorithms integrated in EEO will be lower in this technical note, a note which also covers the description of the operational pipeline required to implement global, multi-sensor, and multi-resolution thematic chains. In the last few years, SERIT has developed and operationally implemented a Sentinel-2- and Landsat-8-based fire mapping pipeline that sources the imagery and extracts burnt areas. The system requests the optimal days from which to extract the burnt area layer. The layers are then rapidly verified by experienced operators before integrating crisis mapping databases and delivery to users. This pipeline was applied to vast areas in eastern Australia during the 2019-2020 fire season to test the system, and was well suited to mega-fire mapping. EEO's flood and water body mapping pipelines can cover vast areas by ingesting any type of optical or SAR data, and uses the Global Surface Water (GSW) [13] layer to automatically provide adapted samples before automatically providing floodwater layers to validate. Deep time series are also processed to provide lake water extent curves. This paper will present the current architecture of EEO, the algorithms and performances of both thematic processing chains, and possible perspectives for improvement in the near future.
## 4 Results And Discussion
\begin{table}
\begin{tabular}{l c c c} \hline  & **Estimate** & **Non Adjusted** & **Adj.** \\  & & _p_**-Values** & _p_**-Values** \\ \hline Intercept & 381.74 & & \\ d-block & \(-\)52.08 & 0.04950 & 0.3465 \\ d-page-c2 & \(-\)39.17 & 0.5439 & 0.8541 \\ d-page-c3 & \(-\)74.83 & 0.2477 & 0.8099 \\ d-page-c4 & \(-\)101.92 & 0.1126 & 0.5629 \\ d-page-c5 & \(-\)161.83 & 0.0121 & **0.0968** \\ d-page-c6 & \(-\)239 & 0.0004 & **0.0048** \\ d-page-c7 & \(-\)82.67 & 0.2025 & 0.8099 \\ d-page-c8 & \(-\)113.42 & 0.0790 & 0.4740 \\ d-page-c9 & \(-\)190.08 & 0.0033 & **0.0363** \\ d-page-c10 & \(-\)188.83 & 0.0044 & **0.0396** \\ d-page-c11 & \(-\)189.33 & 0.0039 & **0.0390** \\ d-page-c12 & \(-\)247.58 & 0.0002 & **0.0026** \\ d-tlcpage-1 & \(-\)25.48 & 0.4271 & 0.8541 \\ d-tlcpage-2 & 129.12 & 0.0001 & **0.0015** \\ PE-cycles & 189.67 & 0.0001 & **0.0015** \\ \hline \end{tabular}
\end{table} 
Table 2: Estimation of the coefficients, partial _p_-values on the significance of the coefficients estimates, non-adjusted and adjusted with the Bonferroni–Holm method (significance in bold).
## 4 Results and Discussion
### Thermal Expansion Behavior over the Rotating and Static Assemblies  
Figure 13 shows the thermal expansion behavior along the rotating assembly. The displacement at the inner rear bearing ring surface is 1.656 mm. Figure 14 shows the thermal expansion behavior along the static assembly. The calculated displacement at the outer rear bearing ring surface is 1.891 mm. These analyses were performed by considering the design point conditions at maximum rotational speed. The rear bearing is a critical component regarding the thermal expansion effects. The thermal expansion variation between the rotating and static assemblies indicates that the rear bearing slides 0.235 mm towards the front bearing. The prediction of this movement allows the appropriate rolling bearing specification and its correct position in the whole bearing assembly.  
Figure 11: Temperature distribution over the compressor stator vanes.  
Figure 12: Temperature distribution near the combustion chamber.  
Figure 10: Temperature distribution over one eighth section of the static assembly.  
Figure 15a shows the clearance in the coupling of the combustion chamber outer wall with the turbine stator at room temperature. The effect caused by thermal expansion at the design point conditions can be seen in Figure 15b. At room temperature the clearance measures 3.21 mm. After thermal expansion, the clearance reduces to 1.2 mm.  
The lower part of the combustion chamber is simply supported by the turbine stator. The combustion chamber can slide, due to thermal expansion, over the turbine stator. Figure 16a shows the assembly position at room temperature. Figure 16b shows the new position after thermal expansion. After thermal expansion, the lower part of the combustion chamber slides 2.526 mm towards the turbine stator v  
Figure 16: Coupling of the combustion chamber inner wall with the turbine stator. (**a**) Position at room temperature; (**b**) new position after thermal expansion.  
Figure 14: Thermal expansion along the static assembly.  
Figure 13: Thermal expansion along the rotating assembly.  
Figure 15: Coupling of the combustion chamber outer wall with the turbine stator: (**a**) Position at room temperature; (**b**) new position after thermal expansion.  
It is also important to evaluate the clearances between the turbine rotor and the static components. Figure 17 shows the dimensions D1 and D2 evaluated at room temperature and after the thermal expansion using the design point conditions at the maximum rotational speed. D1 represents the clearance between the turbine disc and the turbine stator, and D2 represents the clearance between the turbine disc and the exhaustion duct. Dimension D1 measures 4.296 mm at room temperature and it has a reduction of 0.761 mm after thermal expansion. In the same way, dimension D2 measures 3.067 mm at room temperature and it has a reduction of 0.494 mm after thermal expansion.
## 3 Methodology
### Maintenance and Crew Cost  
This study defined and considered three maintenance scenario assumptions. These consisted of high, medium, and low scenarios dependent on the Moore Maritime Index. For a high scenario, the assumption of all maintenance and crew costs is increased by 10%. Meanwhile, for the medium scenario, there is no difference between existing and retrofit vessels, and for the low medium, the lubricating oil and spare parts are reduced by 50%, as opposed to maintenance costs by 33% less than the initial. The maintenance and crew costs are the most significant operational expenditure, besides fuel oil prices.  
Table 1 shows the information of these three scenarios with operational cost in accordance with Moore Maritime Index 2021 [37] on an average level.
## 2 Materials
### 2.2. Parcel Level Aggregation
#### 2.2.3 Overbuilt and Underbuilt Area Measurement  
It is not legally allowed to compensate overbuilt volumes with underbuilt ones inside a parcel, and accordingly aggregate calculations had to be performed independently for both situations to avoid the aggregate operations adding positive and negative numbers. This operation would be mathematically correct but it is not possible according to the regulations, which state that an unwarranted height in a part of a building is an offence independently of the same parcel having another part below the authorized height.  
Formulae 2 to 6 show the aggregation operations to calculate for each parcel: the total built area (2), the maximum allowed built area in zones (3), the overbuilt area in zones (4), the underbuilt area in zones (5), and the overbuilt area in systems (6).  
\[Real\ Built\ Area_{\text{Parc}}=\sum_{\text{SubpeParc}}HB_{\text{Subp}} \cdot A_{\text{Subp}} \tag{2}\]  
\[Allowed\ Built\ Area_{\text{Parc}}=\sum_{\text{FragZeParc}}HP_{\text{FragZ}} \cdot A_{\text{FragZ}} \tag{3}\]  
\[Overbuilt_{\text{Parc}}=\sum_{\text{FragZeParc}}\left(HB_{\text{FragZ}}-\ HP_{\text{FragZ}}\right)\cdot A_{\text{FragZ}} \tag{4}\]  
\[Underbuilt_{\text{Parc}}=\sum_{\text{FragZeParc}}\left|HB_{\text{FragZ}}-\ HP_{\text{FragZ}}\right|\cdot A_{\text{FragZ}} \tag{5}\]  
\[Overbuilt\ in\ Systems_{\text{Parc}}=\sum_{\text{FragSeParc}}HB_{\text{FragS}} \cdot A_{\text{FragS}} \tag{6}\]Using the following notation:  
* _HB_: Real height of the building, measured in number of floors above street level.
* _HP_: Maximum height according to the plan, measured in number of floors above street level.
* _A_: Area of a polygon entity (surface of land occupied).
* Parc: Set of parcels inside the area of study.
* Subp: Set of sub-parcels.
* FragZ: Set of fragments from the spatial intersection between parcel and planning layers (in zones).
* FragS: Set of fragments from the spatial difference between parcel and planning layers (in systems).
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 4 Proposed Algorithm (Elastic Hop Count Trickle Timer Algorithm)  
According to the prior section, the primary drawback of the original trickle algorithm is the listen-only period. We are curious if there is a link between the number of hops (rank) and the length of the listen-only period. The central concept was investigated to see if the number of hops has an impact on the listening period.  
A novel trickle algorithm was introduced in this study called the \"Elastic Hop Count Trickle Timer Algorithm.\" It was carried out to let the original trickle algorithm work in a dynamic manner.  
The primary contribution is the ability to dynamically choose the listen-only duration based on the number of hops needed to reach a node. Every time a node starts running, the number of hops is calculated. Every main interval starts with an Imin value, which is also the beginning of the first sub-interval. The node transmits a message within a short time of listening; the idea was implemented to make it easier for a node to join the DODAG quickly. This is the first step in making the algorithm behave in an elastic and flexible manner.  
The listen-only period of the sub-interval is determined based on the node's rank. The listen-only period will be short when the rank value is low, and vice versa. For example, suppose nodes are called n1, n2, and n3. If the rank (r1) of n1 is less than the rank (r2) of n2 then the algorithm will dynamically make the listen-only period (lp1) of n1 shorter than the listen-only period (lp2). As a relation, if r1 < r2 < r3, then this leads to the formation of lp1 < lp2 < lp3. By allowing sufficient time for the nodes to be heard, this concept helps prevent collisions.  
The node utilizes the listening time to determine if its state is consistent or inconsistent based on the listen-only period that is dynamically selected. In case a node receives a new message with an unknown ID, and other nodes exchange the same message, it is considered to be in a consistent state. However, the node is regarded as inconsistent if the unknown ID of the received message is lower than the ones being exchanged by neighboring nodes.  
In addition, the node tests the counter value and compares it to the k value; if the counter value is smaller than k, the node sends the message. Otherwise, the node will remain muted until the new sub-interval has been doubled and the current sub-interval has ended, as shown in Figure 3.  
The flowchart of the Elastic Hop Count Trickle Timer Algorithm is illustrated in Figure 4a,b.  
Figure 3: Elastic Hop Count Trickle Timer Algorithm of a node.  
Figure 4: _Cont._
## 3 Methodology
### Data Processing Methods  
#### 3.3.1 Data Preprocessing  
Data preprocessing comprises data cleaning, data integration, data transformation, and data reduction. In this research, the data are preprocessed as follows.  
1. One-hot. The categorical features are one-hot encoded because numeric coding will cause a large number of errors in this work.
2. Standardization. In this work, maximum and minimum standardization are used to map the initial data to the interval [0, 1].
3. Missing value processing. In this work, the regression tree model is used to fill in the missing values.
4. Outlier handling. The data set is constructed using the sliding window method [34]. Then, we calculate the mean and standard deviation and determine the data outside  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Sequence**} & \multicolumn{2}{c}{**Description**} \\ \hline Step 1—Raw data collection & \multicolumn{2}{c}{Use marketing data tools or data crawlers to collect the sales volume data.} \\ \hline \multirow{5}{*}{Step 2—Data processing} & (1) & Data cleaning and data transformation; \\  & (2) & The seasonal trend decomposition procedures based on loess (STL) are used for data fluctuation analysis; \\  & (3) & Use RFECV to selected key features. \\ \hline \multirow{3}{*}{Step 3—Prediction using GRU-LightGBM} & \multicolumn{2}{c}{The prediction model is a stacking model developed based on the integration of GRU and LightGBM. GRU is used to extract trends and fluctuations in time series data and LightGBM is used to solve multivariable problems.} \\ \cline{1-1}  & \multicolumn{2}{c}{Series data and LightGBM is used to solve multivariable problems.} \\ \hline Step 4—Prediction output analysis & \multicolumn{2}{c}{Deliver prediction outputs and generate sales strategies for organization.} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The steps of applying GRU-LightGBM in sales prediction.  
the range of three standard deviations from the mean as outliers. The outliers are handled as missing values.