esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 2 Renewable Energy Integration
### Current State of Solar and Wind Energy Technologies
#### 2.2.4 Integrating Solar and Wind Renewable Energy Systems  
The integration of wind and solar energy technologies has become a focal point in the push for more reliable and sustainable energy generation. Traditionally, wind and solar energy have operated independently, each contributing to the renewable energy mix according to their unique strengths--wind turbines capturing kinetic energy from wind and photovoltaic panels converting sunlight into electricity. However, recent innovations have led to the creation of hybrid energy systems that integrate wind and solar technologies, offering a more stable and reliable energy supply. Figure 4 shows a typical component of a hybrid energy system consisting of solar and wind energies.  
A combination of these renewable energy sources will ensure a steadier supply, minimizing the chances of power shortages during challenging weather conditions [34]. These systems leverage the complementary strengths of wind and solar energy, with solar power usually reaching its peak during the day, while wind power tends to be more reliable at night or in varying seasons [24]. Current technologies incorporate advanced energy storage  
Figure 4: Fundamental components of a standard solar–wind hybrid energy system.  
Figure 3: Wind energy technological innovations studied by NREL.  
solutions like lithium-ion batteries to store the surplus energy produced during peak times to be used when demand surges or when wind and solar resources are unavailable. Also, smart grid technologies further enhance these systems by using real-time data analytics to optimize the distribution and usage of generated power, ensuring a stable and efficient energy supply [35]. Floating solar panels, combined with offshore wind farms, represent another cutting-edge innovation, maximizing the use of available space and resources [36]. These integrated systems have charted the route for a resilient and adaptable infrastructure capable of meeting the growing global demand for clean energy. For example, Figure 5 highlights a schematic diagram of a basic power system that integrates various renewable energy sources.
## 3 Automated BIM-Based Alarm System
### Developed Tool  
In this section, we introduce an innovative methodology aimed at accomplishing the two following objectives: (1) automating the periodic integration and visualization of weather information, including hazardous conditions, into BIM digital twins, and (2) enabling the automatic delivery of alerts to stakeholders upon the detection of hazardous weather conditions.  
The proposed methodology is based on the four different RPA protocols defined in Figure 3. These protocols are as follows:  
1. Weather information extraction: This RPA protocol is configured to periodically access the established weather websites and extract the weather data. Then, this information is organized and stored within an Excel spreadsheet located in the Google Drive cloud (Google Sheets). This sheet is programmed with the hazardous weather criteria to identify potential alerts. It is worth noting that the frequency with which the weather information is updated varies across different sites, necessitating distinct time-triggers within the RPA program;
2. Alert dispatch: In this step, the RPA protocol checks the weather analyses in Excel. In those cases where the predefined thresholds are surpassed, an RPA is programmed to transmit digital alerts to the stakeholders via email using Gmail;
3. Integration of weather data into the BIM model: This RPA protocol automates the introduction of the weather data collected in Step 1 into the BIM model;
4. Integration of BIM information into the weather database: This RPA protocol facilitates the export of BIM information that may be necessary for analyzing hazardous weather conditions to the Excel sheet containing the weather data.  
The RPA protocol depicted in Figure 3 employs Dynamo scripts to enable the exchange of information between the Excel sheet and the BIM software Revit. As an example, the fundamental components of the script used to import data into Revit is outlined in Figure 4, encompassing the following steps:  
1. Access the Google Sheet containing the weather data and read the parameters, alerts along with their respective values/severity levels;
2. Create shared parameters for the weather data and incorporate their respective values/severity levels;
3. Create filters for the BIM mass model to depict the severity of the weather alerts and apply the data to different scenarios.
# Research on VVC Intra-Frame Bit Allocation Scheme Based on Significance Detection
## 3 Model and Proposed Method
### Options for Improving CTUs
Therefore, this experiment uses the Canny algorithm with SATD-based coding complexity together with the Laplacian operator to guide the selection of improved CTUs. The Canny significance value, color contrast significance value, and coding complexity information obtained for mv5 as an example are shown in Figure 5. From the metrics obtained in the three ways, it can be seen that the metrics converging to half of all CTUs in the frame are the more obvious ones, and they should be assigned more than the average number of bits, whereas all three metrics of the last CTU are significantly low, not even up to the average, and it is clearly not reasonable to assign the extreme bit cost to the last CTU. After obtaining the relevant information about each CTU using each of the three methods, they are sorted in descending order and the CTU serial number that co-occurs in the first \(K\) values is selected as the serial number of the improved CTU, with the value of \(K\) being determined by the following:  
\[K=round\left(\frac{1}{5}\left(\frac{1}{2}N_{CTU}+\frac{1}{4}\sum_{i=1}^{4}T_{ F,i}\frac{1}{24000}\right)\right).5 \tag{9}\]  
where \(N_{CTU}\) is the total number of CTUs in the frame, and \(T_{F,i}\) is the actual number of target bits allocated by the encoder for the frame under the condition of 4 different target bit numbers. The value of \(K\) will be taken as a multiple of 5 of the closest \(K\). Subsequently, the serial number of the improved CTUs will be determined by \(K\). In order to prevent the  
Figure 4: Significance information extraction using mv5 as an example. (**a**) original image; (**b**) grayscale image; (**c**) significant map based on Canny algorithm; and (**d**) significant map based on color contrast algorithm. occurrence of overflow or imbalance between the number of allocated bits and the number of CTUs, they are trimmed after the decision of \(K\). The trimming is shown in Table 2.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 1 Introduction
This study proposes a traffic information detection method at a single intersection based on Wi-Fi data. The method tracks the vehicle trajectory through the Wi-Fi data MAC address so as to realize the space mean speed and vehicle steering ratio detection at the intersection entrance. Compared to traditional detection technology, this method can expand the type of conventional traffic information detection. The use of a Wi-Fi data MAC address as a track label has a higher penetration rate than satellite positioning technology, and the track positioning accuracy is higher than that of mobile signaling data [33; 34]. The detection accuracy of traffic information was greatly improved [35; 36]. The detection device is cheap and easy to install and maintain. The original data format is simple and can quickly be processed and stored. The detection targets are various. The detection device is convenient for networking and has a comprehensive detection range. The rest of this study is organized as follows: Section 2 summarizes the proposed method's system framework and key techniques. In Section 3, the traffic information collection process is given, and the prediction model is established. In Section 4, the prediction model is verified and evaluated. Finally, a conclusion is drawn in Section 5.
# Real-Time 3D Reconstruction Method for Holographic Telepresence
## 2 Real-Time 3D Reconstruction Method
### Background Removal  
An RGB-D camera has been utilized during the background removal process to capture the local user. The Kinect V2 sensors have captured the colour and depth images. From the depth image, the body index image contains a 2D grid where each coordinate gives a simple 0 to 5 integer representing which body the sensor associates with that coordinate.  
The background removal from the depth image to generate the body index image is required as this research only focuses on reconstructing the human. Thus, the foreground extraction step is needed and can be done by segmenting the foreground pixels from the background pixels in depth images. In order to extract foreground pixels, the foreground or background information is converted into binary ones and zeros, respectively.  
The body index image includes the instance segmentation map for each body in the depth camera capture. Each pixel maps to the corresponding pixel in the depth or IR image. The value for each pixel represents which body the pixel belongs to. It can be either background or the index of a detected body. We obtained the human body segmentation mask using this body index image mapped together with the colour image.
# Flexural Strength and Stiffness of Donut-Type Voided Slab
## 4 Test Results and Discussion  
### Failure Behaviour and Crack Pattern  
Figure 11 shows the load-deflection curve of specimen, and Figure 12 shows the crack pattern of specimen after the failure. The donut-type voided slab specimens showed a ductile flexural failure mode such as 'Solid', regardless of the fixing method. However, the non-donut-type voided slab specimen (OF-V-S-R) failed with the inclined flexural-shear crack opening after the yield of bottom rebar without fully crushing of concrete in compression zone. It was caused by 'OF-V-S-R' having a lower shear strength than flexural strength.  
Figure 10: Test set-up.  
Figure 11: Load–deflection relationship of the test specimens.  
Figure 12: Crack pattern and failure mode. (**a**) Solid; (**b**) OF-V-S-D; (**c**) OF-V-M-D; (**d**) OF-V-S-R.  
In 'Solid', flexural cracks were first observed in the center of lower part of specimen at a load of 48.41 kN. With the increase of the load, flexural cracks spread into upper parts and diffused from the center to the supports gradually. In addition to major cracks at the center, multiple hair cracks were also observed. The specimen failed with the crushing of upper concrete at the ultimate load after the yield of bottom rebar.  
In 'OF-V-M-D', a donut-type voided slab with the merged-type fixing method, flexural cracks were firstly observed in the center of lower part of specimen at a load of 45.50 kN, and the failure behavior and crack pattern were similar to 'Solid'. With the increase of the load, flexural cracks spread into the upper parts and diffused from the center to the supports gradually. In addition to major cracks at the center, multiple hair cracks were observed. The specimen also failed with the crushing of upper concrete at the ultimate load after the yield of bottom rebar.  
In 'OF-V-S-D', donut-type voided slab with the spacer-type fixing method, flexural cracks were firstly observed at relatively lower load of 36.69 kN, the failure behavior and crack pattern were different to 'Solid' and 'OF-V-M-D'. As the increase of the load, major cracks spread into upper parts, and diffused from the center to the supports gradually, while significantly fewer hair cracks occurred. In addition, flexural-shear cracks in a diagonal direction occurred at the end of test after the yield of bottom rebar and ultimate flexural strength, and the region of concrete crushing was smaller than that of 'Solid' and 'OF-V-M-D'. It seemed to be caused by the reduction of shear strength by voids.  
In 'OF-V-S-R', non-donut-type voided slab with the spacer-type fixing method, flexural cracks were firstly observed at a load of 36.29 kN, which was similar to that of 'OF-V-S-D'. With the increase of the load, major flexural cracks gradually propagated with few hair cracks until bottom rebar yielding. After the yield of rebar, flexural-shear cracks suddenly occurred, and the specimen failed in shear. It was deduced that 'OF-V-S-R' had a lower shear strength than flexural strength.
## 3 Multiagent DRL Design for Proposed Method
### Proposed Algorithm  
The proposed training and the implementation algorithm are shown in Figure 6. Algorithm 1 presents the pseudocode of the training algorithm in detail. The closed-loop training dataset comprises 3000 episodes, where each episode comprises 50,400 1-s intervals (from 6 to 20 o'clock assuming that the PV system generates active power during daytime) of raw data. For each episode, the corresponding decisions and rewards are saved in batches to calculate the gradient descent to update the NN. After day-ahead training, NN has an optimal policy to act properly.  
The PV generation data and load data were selected from the historical yearly PV generation data of the Korea Power Exchange [23]. Figure 7 shows the normalized PV generation and load consumption, which are applied to various PV generation and load consumption per unit.  
Figure 6: Information flow of the day-ahead training in the digital twin and implementation in the actual grid.  
At each time step \(t\) (1 s), PV generation and load consumption, including their Gaussian noise, are used to consider the difference between the forecast and actual active power of the PV generation load consumption. The stochastic variations in PV generation and load consumption follow normal distributions. Equation (12) shows a normal distribution function.  
\[f(x)=\frac{1}{\delta\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{x-\mu}{\delta} \right)^{2}} \tag{12}\]  
where \(\mu\) denotes the mean value of PV generation and load consumption at time \(t\), and \(\delta\) denotes the standard deviation of PV generation and load consumption.  
```
1:Initialize network
2:Initialize the critic and actor networks with weights
3:for episode = 1 to N do
4:for time step = 360 min (6 h) to 1200 min (20 h) do
5: each agent selects an action \(a_{i}^{t}\) for each \(s^{t}\in S\)
6: Execute \(a_{i}^{t}\) in environment
7: for time step = 0 s (0 min) to 60 s (1 min) do
8: Solve power flow with proposed volt-var curve
9: Calculate \(VUF^{proposed}\) at PCC
10: Solve power flow with fixed volt-var curve
11: Calculate \(VUF^{fixed}\) at PCC
12: reward calculate \(-\left\{VUF^{proposed}-VUF^{fixed}\right\}\)
13:end
14: Send a set of states \(s^{t+1}\) and reward \(r^{t}\) to each agent
15: Each agent selects \(a_{i}^{t+1}\) through \(s^{t+1}\)
16: Store the transition pairs in replay buffer
17: Sample a random minibatch
18: Update target critic and actor by stochastic gradient descent to the loss function of network
19: step+ =1
20: end
21:end
```  
**Algorithm 1**Training algorithm based on PPO.
## 3 Principals of Routing Protocols and Their Breakdowns
### Acar [57]
#### 3.3.6. MRP [64]  
The authors suggest MRP as a multi-layer routing protocol in [64]. According to the paper, this protocol has two operating modes, firstly the layering phase, where all the nodes are arranged in layers, these layers have a leader or as the authors called it a supernode. In the next phase, the data that is collected is forwarded. For this, all the nodes forward the data to the layer leader. The leader then sends the data to the next layer leader until it reaches the sink. As the leader has to forward the data and collect it from other nodes, the author suggested that this node should have more energy than other nodes in that layer.  
* Comparison  
Here, all the protocols have a void avoidance mechanism except EERA-CA. All protocols have different routing techniques as illustrated in Table 15. Only MRP has multiple sinks hence it has lesser delay than others.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Classification** & **Protocol Year** & **Key Idea** & **Advantage** & **Disadvantage** \\ \hline Depth-based & DBR (2008) & Uses depth information to forward the data to the sink & High delivery ratio & No void detection mechanism \\  & LDBR (2018) & Uses node residual energy to optimize energy & Balanced energy load & Low packet delivery rate \\  & SORP (2018) & Detect and avoid communication void zones & Void detection & Redundant re-transmission of data, and high delay \\  & RSAR (2019) & Reliable and stability-aware routing using energy-assigned nodes & Stable network, and balanced energy distribution & No void detection, and uses single sink to transmit data \\  & RD (2018) & balances the energy consumption using energy as a metric for data forwarding & High packet delivery ratio & High delay, and energy consumption \\  & LETR (2018) & Avoids void communication zone using location error, and load balancing & Longer node lifespan & Energy consumption, and low packet delivery rate \\ \hline \hline \end{tabular}
\end{table}
Table 16. Comparison of geographic routing protocols.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocol** & **Routing** & **Void Avoidance** & **Forwarding Method** & **Simulation Tool** & **No. of Sink** & **Energy Consumption** & **Delay** \\ \hline EERA-CA & Layer & No & Location & NS2 & Single & Low & High \\ CMSE2R & Geo-cast & Yes & Greedy & Not given & Single & Medium & High \\ MRP & Multi-layer & Yes & Super nodes & NS2 & Multiple & Low & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 15. Comparison of cluster-based routing protocol.
# RoseCliff Algorithm: Making Passwords Dynamic
## 2 Encryption
### Quantum-Resistant Cryptographic Algorithms  
According to NIST, a part of the U.S. Department of Commerce responsible for setting standards in digital security, existing encryption technologies face threats from quantum computers, as these quantum machines could undermine the security of daily digital activities. They chose four encryption algorithms as the postquantum cryptographic standard, which is set to be finalized in about two years. These algorithms focus on two primary encryption functions: general encryption for securing data over public networks and digital signatures for identity verification. They were collaboratively developed by experts from various countries and institutions. NIST has picked the CRYSTALS-Kyber algorithm for general encryption, which is known for its compact keys and operational efficiency. In the realm of digital signatures, NIST has selected three algorithms: CRYSTALS-Dilithium, FALCON, and SPHNCS+ (pronounced \"Sphincs plus\"). CRYSTALS-Dilithium is the primary choice, FALCON suits smaller signatures, and SPHNCS+ serves as a unique backup due to its distinct mathematical approach. The three algorithms are rooted in structured lattices, while SPHNCS+ relies on hash functions. Four additional algorithms are under consideration for general encryption, each with distinct approaches [31].
## 4 Experimental Results
### System Calibration  
As shown in Figure 11a, a standard block was designed to calibrate the TFTP system. There are six standard cylinders located evenly on a plane. Each standard cylinder consists of four cylinders with different heights and diameters. The distance between adjacent parallel planes is 150 mm, 200 mm, 200 mm and 250 mm. A slope connecting two adjacent parallel planes was designed to ensure the continuity of the phase distribution between planes with different heights. One of the deformed fringe patterns is shown in Figure 11b. FFT, band-pass filtering and inverse transform operations were performed along the temporal axis pixel by pixel. The corresponding wrapped phase is shown in Figure 11c. The moment \(t_{0}\) = 8 was selected as the initial moment. The wrapped phase of the standard block was unwrapped and guided by its modulation distribution [15]. The corresponding unwrapped phase distribution is shown in Figure 11d.  
Figure 10: Deformed fringe patterns. (**a**) A sequence of deformed fringe pattern captured by the camera. (**b**) Intensity distribution along the temporal axis.  
Figure 9: Experimental setup.  
Phase distribution with the same height but in six different locations was extracted to fit a ternary cubic surface by least squares method. Then the phase distributions of five different heights were fitted. By defining one plane as the reference plane, the distances between the other four planes and the reference plane were known. Finally, with the calculated phase difference relative to the reference plane and substituting the phase difference into Equation (8), the height-to-phase mapping coefficients can be obtained to recover the 3D shape of the tested object.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 4 Conclusions  
This paper develops the three-channel filters to solve the problem that the conventional parity space approach cannot detect soft fault correctly after quantization. It proposes the constraint conditions of the filter design and implements them theoretically at a deep level, using the parity space approach. The proposed theory can provide a theoretical reference to improve the performance of the FDI system.  
(1) Regarding the step fault, the Monte Carlo simulation results confirm that with the increases of time constant, the false alarm rate and the false isolation rate are both reduced but have a longer time delay. Therefore, when the fault amplitude is smaller, the FDI method needs a longer time for fault  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time Constant** & **3** & **3.85** & **5** & **8** & **10** & **20** \\ \hline
**False alarm rate** & 0.420 & **0.140** & 0.080 & 0.000 & 0.000 & **0.000** \\
**False isolation rate** & 0.448 & **0.442** & 0.130 & 0.160 & 0.080 & **0.122** \\
**PCD1** & 0.580 & **0.780** & 0.340 & 0.000 & 0.000 & **0.000** \\
**PCD2** & 0.000 & **0.080** & 0.580 & 1.000 & 1.000 & **0.440** \\
**PCD3** & 0.000 & **0.000** & 0.000 & 0.000 & 0.000 & **0.560** \\
**PCD4** & 0.000 & **0.000** & 0.000 & 0.000 & 0.000 & **0.000** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The relationship between time constant and the effect on detecting faults (square wave fault).  
Figure 16: Second-order channel filter. (**a**) Fault detection; (**b**) Fault isolation.  
tolerance and we can choose a bigger parameter for the second-order channel filter according to the application environment.  
(2) The relationship between time constant and the effect on detecting step fault is also studied in this paper using enumeration. Additionally, the parameters for detecting or isolating the typical step fault in different sampling times are recommended.  
(3) The extended experiments have shown that the method could also detect and isolate the ramp and square wave faults. The time constant of the filters should be prolonged to achieve a low false alarm rate and low false isolation rate. Furthermore, the time constants of the ramp fault and the square wave fault require further investigation.  
[PERSON] presented the concept, designed simulation and wrote the paper; [PERSON] analyzed the data; [PERSON] contributed the statement modification.  
The authors wish to express their appreciation to the National Natural Science Foundation of China (61603297). Its abstract was published in the 68 th International Astronautic Congress.  
The authors declare no conflict of interest.