esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Films Characterization
#### 2.4.3 Barrier Properties  
Water absorption of films was determined, in triplicate, according to UNE-EN ISO 62:2008 standard [36]. Samples (\(80\times 10\times 4\) mm) were dried in a vacuum oven at 23 \({}^{\circ}\)C for 4 h, cooled in a desiccator, and then immediately weighed to the nearest 0.001 g. Then, samples were immersed in distilled water and maintained at 23 \({}^{\circ}\)C and 50% RH. Samples were taken out at different times, wiped out properly and then reweighed. Water absorption was calculated according to the Equation (2):  
\[\text{WA}=[(\text{W}_{\text{t}}-\text{W}_{0})/\text{W}_{\text{t}}]\times 100 \tag{2}\]  
where W\({}_{0}\) was the sample weight prior to water adsorption experiment and W\({}_{\text{t}}\) was the final mass at a pre-determined time t.  
Water vapour permeability (WVP) was determined according to UNE 53097:2002 standard [37]. Samples of 95 mm diameter were fixed with paraffin on the top of aluminium capsules containing anhydrous calcium chloride, and they were placed in a climate chamber (Dycometal, Barcelona, Spain) at \(20\pm 1\) \({}^{\circ}\)C and \(50\pm 2\)% RH. Capsules were periodically weighed until the steady state was reached. WVP measurements were performed in triplicate.  
Oxygen transmission rate (OTR) tests were carried out with an oxygen permeation analyser (8500 model Systech Instruments, Metrotec S.A, Spain). Circular films of 14 cm diameter were clamped in the diffusion chamber at 25 \({}^{\circ}\)C. Tests were performed in triplicate by introducing O\({}_{2}\) (99.9% purity) into the upper half of the diffusion chamber while N\({}_{2}\) was injected into the lower half, where an oxygen sensor was located. Results were expressed as oxygen transmission rate per film thickness (OTR-e).
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 4 Result and Discussion
### Analysis of Input Parameters
#### 4.3.2 Sensitivity Analysis of Excavation Parameters  
When geological parameters are unchangeable, the nine excavation parameters selected in this paper have varying degrees of influence on the shield's roll angle and pitch angle. The greater the importance of a certain excavation parameter, the higher its impact level on the roll angle and pitch angle. Therefore, analyzing the sensitivity of excavation parameters to determine their relative importance shows significant guidance in practical engineering applications.  
The fundamental principle of sensitivity analysis is to determine which input variables are most critical for the model or decision-making process by evaluating the impact of changes in these input variables on the output results. For the well-constructed PCA-PSO-LGBM model, the sensitivity of each input parameter can be calculated using the following equation:  
\[S(\%)=\frac{1}{n}\sum_{i=1}^{n}\big{(}\frac{Output\;\text{change}\%}{Input\; \text{change}\%}\big{)}_{i}\times 100, \tag{21}\]  
where \(n\) represents the sample size, and the sensitivity is obtained by changing each excavation parameter by 10%. Figure 18 illustrates the sensitivity of shield tunnelling roll angle and pitch angle to various excavation parameters. Overall, the pitch angle generally exhibits higher sensitivity to tunnelling parameters compared to the roll angle. Hinged pull, hinged thrust, and cutterhead speed are particularly significant factors affecting the pitch angle. The sensitivity of the six segment propulsion pressures in shield tunnelling varies significantly, with the lower segments--B, C, and D--showing greater sensitivity than the upper segments--A, E, and F. This aligns with our experience: the lower propulsion cylinders bear more soil pressure and the weight of the shield itself. To prevent \"nosediving\" and ensure the pitch angle matches the tunnel gradient settings, it is crucial to adjust the lower cylinder propulsion pressures in response to changing geological conditions. For the roll angle, except for the high sensitivity of hinged pull, the sensitivity of other tunnelling parameters remains relatively low. In summary, during the adjustment of shield tunnelling parameters, priority should be given to hinged pull, hinged thrust, lower cylinder propulsion pressures, and cutterhead speed, with a particular focus on changes in the pitch angle.  
Figure 17: The prediction results of the surrounding rock class as a geological parameter: (**a**) roll angle; (**b**) pitch angle.
## 2 Modeling and Research Methods
### Robustness
#### 2.3.1 Attack Strategies  
Network attack methods can be categorized as either random failure or deliberate attack [31; 32]. In aviation networks, random destruction resulting from non-subjective factors, such as the operation status of internal and external infrastructure within the aviation system and occasional extreme weather conditions, is classified as a random failure. The Rand function was utilized to generate random numbers, and the authors then applied the function that removes airport nodes and routes randomly. The network'sperformance was measured following the attack until the metric was smaller than or equal to the predetermined threshold. Exploring the effects of random failure and deliberate attacks is vital in understanding the resilience of a network and can guide the development of more efficient security mechanisms for network protection.  
In addition to objective factors such as weather and equipment, the aviation network is also vulnerable to various forms of subjective interference, often in the form of biased attacks where critical nodes with higher importance are prioritized as preferred targets. The importance of a node depends on the extent of the decline in connectivity of the airline network following an attack. To assess node importance, centrality is utilized in deliberate attacks. This study employed four types of centrality, namely degree centrality (the number of connections between nodes), the nearest neighborhood degree centrality (local clustering measure), betweenness centrality (the extent of the node used as an intermediary between other nodes), and closeness centrality (the average distance between a node and all other nodes in the network). Formulas (7)-(10) show the computational methods for these centrality types, respectively:  
\[C_{\text{D}}(t,v)=d(t,v) \tag{7}\]  
where \(d(t,v)\) refers to the degree of node \(v\) at time \(t\), i.e., the number of edges connected to node \(v\) at time \(t\).  
\[C_{\text{D}_{\text{nn}}}(t,v)=\frac{\sum_{i\in N_{i}}d(t,i)}{d(t,v)} \tag{8}\]  
where \(N_{i}\) represents the set of neighboring nodes of node \(i\).  
\[C_{\text{B}}(t,v)=\sum_{\begin{subarray}{c}i=1\\ i\
eq v\end{subarray}}^{n}\sum_{\begin{subarray}{c}j=1\\ j\
eq v\end{subarray}}^{n}\frac{\sigma_{ij}(t,v)}{\sigma_{ij}(t)} \tag{9}\]  
where \(\sigma_{ij}(t)\) stands for the number of shortest paths from node \(i\) to node \(j\) at time \(t\), while \(\sigma_{ij}(t,v)\) signifies the number of such paths that pass through node \(v\).  
\[C_{\text{C}}(t,v)=\frac{N-1}{\sum_{\begin{subarray}{c}i=1\\ i\
eq v\end{subarray}}^{n}l_{vi}(t)} \tag{10}\]  
where \(l_{vi}(t)\) is the length of the shortest path from node \(i\) to node \(v\) at time t.
# Implementation of MS Circle Map in Digital Image Encryption
## 3 Results and Discussion
### Key Sensitivity  
The key sensitivity of the MS Circle Map function can be tested by decrypting the encrypted image using a different key parameter value (fake key) from the original key when used in the encryption process. The goal is to find out how much the difference in value between the original and fake keys is so that the encrypted image can be restored to the original image. The difference in value is stated as key sensitivity.  
Table 7 illustrates an example of a key sensitivity test on four images: two color images and two grayscale images. In the first column is the original image, and the second column shows the encrypted image using the key parameter values: \(\lambda=2.14365\), \(\Omega=0.5984736\), K = 1000, r = 3.85942, and the initial value with a precision of 16 digits \(x_{0}=0.9182736459283746\). In the last three columns are the decrypted images using the same \(\lambda\), \(\Omega\), K, and r values as during the encryption process, but with different \(x_{0}\) values. The third column visualizes the decrypted image using the value \(x_{0}+10^{-7}\) or \(x_{0}=0.9182737459283746\), where this value has a difference of \(10^{-7}\) from its original key value. Despite a difference of \(10^{-7}\), all encrypted images were not successfully opened. The fourth column presents the encrypted image using the value \(x_{0}+10^{-15}\) or \(x_{0}=0.9182736459283756\), where this value has a difference of \(10^{-15}\) from its original key value. Even with a smaller difference of \(10^{-15}\), the encrypted image is still protected and cannot be restored to the original image. Furthermore, if the \(x_{0}\) value is used with a difference of \(10^{-16}\) (\(x_{0}=0.9182736459283747\)) or smaller than \(10^{-16}\), the encrypted image is successfully cracked, and the information contained in it can be known. The decryption results are shown in the fifth column.  
Sensitivity testing for other key parameters was also carried out as described above. All of them showed a sensitivity level of \(10^{-15}\). Thus, it can be concluded that the MS Circle Map has a key sensitivity of \(10^{-15}\). This means that an encrypted image can only be decrypted if all key parameter values are found to be exactly the same or have a difference of less than \(10^{-15}\) compared to the original key parameter values. This also proves that a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Original Image**} & \multirow{2}{*}{**Encrypted Image**} & \multicolumn{2}{c}{**Decrypted Image**} \\ \cline{3-4}  & & \(x_{0}+10^{-7}\) & \(x_{0}+10^{-15}\) & \(x_{0}+10^{-16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Initial value sensitivity.  
small change in the key parameter value will produce a very different keystream and can minimize the possibility of a brute force attack.
# Study on Airflow Field Distribution and Dust Distribution Characteristics at Blast Piles
## 2 Methods
### Sources of Dust Generation and Dust Formation Mechanisms at Open-Pit Mine Blast Piles
#### 2.1.2 Particle Size Distribution of Dust at Blast Piles  
Dust particle size is a physical parameter that characterizes the magnitude of dust particles, and dust particles of different sizes pose varying degrees of harm to human health. To gain a clearer understanding of the sources and particle size distribution of dust from blast piles, this study conducted field sampling of blast pile dust and analyzed its particle size distribution. The blast pile samples were collected from the Heidaigou open-pit coal mine (Inner Mongolia, China), and the dust particle size was tested using the S3500 laser particle size analyzer from Microtrac (Haan, Germany). The instrument measures the particle size according to the diffraction of dust particles to light. In principle, it adopts the classical static light scattering technology, which has the characteristics of high precision and fast processing efficiency.
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 3 Blockchain Technology
The underlying layer uses decentralized ledger technology, called Tangle, to make transactions [29]. The speed is faster. Tangle is also the first decentralized ledger system that does not require a fee. By distributing the work of verifying transactions to each trader, Tangle has saved the commission, which in turn increases work efficiency. Compared with other platforms, IOTA has a shorter time and there are still some imperfections waiting for the development team to improve  
The combination of blockchain and IoT, which have constrained power and storage capacity, presents a hurdle due to blockchain's complexity, which also includes significant  
Figure 2: Blockchain ledger. computational costs and delays. The difficulties encountered with managing IoT data on a blockchain are shown and outlined below:  
1. **The trade-off between performance, security, and power consumption:**The development of these technology-based apps on resource-constrained devices has increased significantly due to the high computing power needed to execute blockchain algorithms [30]. Researchers have also questioned how well the blockchain performs while processing data from the Internet of Things and advised improving its core algorithms to produce more verified blocks per second [31]. For instance, getting rid of the blockchain's PoW consensus algorithm can enhance efficiency and minimize power use [32]
2. **IoT Connectivity Challenges:** To exchange IoT data with possible stakeholders, the IoT devices are anticipated to be connected to high computational, storage, and networking capabilities [33]. The Internet of Things (IoT) has limited capacity to link with blockchain technology to offer fresh business prospects for the development of new applications and services in several domains. 3. **Data concurrency and throughput issue:** IoT systems have high concurrency because IoT devices transmit data continually [34]. Due to its intricate cryptographic security protocol and consensus procedures, the blockchain's throughput is constrained. Increased bandwidth is needed to quickly synchronize new blocks between blockchain nodes in a chain-structured ledger, which can increase blockchain throughput. 4. **Blockchain Regulating in IoT:** Decentralization, immutability, anonymity, and automation are some of the blockchain's promised security properties for a variety of IoT applications, but these traits taken together provide several new regulatory issues [35]. The distributed transaction ledger (DTL) immutability characteristic indicates that data are permanently published there and cannot be changed or removed. Additionally, because there is no governance, documents cannot be vetted to preserve privacy before being published on the blockchain. In this paper, the local blockchain will be used to protect the IoT device's identity and data integrity.
# Video Error-Resilience Research Based on Error-Resilient Screen Content
## 3 Proposed Method
### Region Division  
The HEVC-SCC standard is developed on the basis of the HEVC coding framework. The reliability can be ensured by judging the abrupt frames in the sequence of screen content coding and implementing the redundant coding. The judgment is made by utilizing the proportion of the complexity of the current frame in the entire sliding window. The complexity of the current frame refers to the SAD (sum of absolute differences) value of the current frame and the reference frame. \(\frac{3\lambda C_{pre}}{N_{pre}}\) represents three times the average SAD value for all previous coding frames, which is used as the judgment threshold to determine whether the current frame is a mutation frame. \(C_{pre}\) represents the sum of the complexity of all encoded frames before the current frame, and \(N_{pre}\) denotes the number of currently encoded frames. Note that \(\frac{3\lambda C_{pre}}{N_{pre}}\) is set due to numerical experience.  
Since the mean value can better reflect the complexity of the already encoded frame, and test, the decision threshold can accurately identify the abrupt frame. When the complexity of the current frame is greater than three times the average value of all previously encoded frames \(C_{pre}\), the current frame is an abrupt frame, and vice versa. If the current frame is an abrupt frame, the current frame and subsequent frames (four frames) are subjected to redundant coding; otherwise, the code is controlled according to the original code rate control model.  
Figure 2 exhibits the flowchart of region division. Note that region division aims at determining whether the current frame is the abrupt frame. According to the decision results, different actions will be taken just as mentioned above.  
Figure 2: The flowchart of region division.
## 4 Dataset and Evaluation Metrics  
### Dataset  
In this study, we test our approach and other portfolios on the Vietnam stock market, one of the emerging markets in Asia. Particularly, we use historical trading data of all stocks traded on the Ho Chi Minh City Stock Exchange (HOSE) which are available on their website. The period of our experiment is over seven years, from 2013 to 2019, with 1744 trading days in total. According to their trading regulation, short-sell is not allowed and we only focus on long-only portfolios. A statistical summary of data is described in Table 1, unit for average volume is \(10^{5}\) shares and for others is a basis point.
## 4 Analysis
### Cellular Automata Model for Dar es Salaam Port Traffic Flow
#### 4.2.3 Simulation Setup for Dar es Salaam Port Traffic  
Initial Conditions:  
* Road length: 20 km (2670 cells).
* Vehicle density: 900 vehicles distributed along the 20 km road (60% heavy trucks, 40% smaller trucks, and passenger vehicles).
* Construction zones: Narrow the road to 1 lane in some areas, reducing road capacity.  
Steps of the Simulation:  
Initialization: Vehicles are placed randomly on the road, each with an initial speed. The model applies the acceleration, deceleration, random slowdown, and movement rules iteratively.  
Traffic Flow: Over each time step, the position and speed of every vehicle are updated based on the above equations.  
Data Collection: We track the average speed, lane-changing frequency, traffic density, and travel times during the simulation.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 2 Groundwork
### Groundwork on Disaster Robotics  
Even though there are currently plenty of work focusing on robotics and DM [18], the presence of USVs [7; 19] directly associated with DM seems to have fallen behind those of other platforms [8; 9], such as UAVs [10] and UUVs [11; 12]. For instance, [PERSON] et al. [20] address robotics and automation systems for harsh environments, but briefly discusses USVs with a generalist focus. The reference book from [PERSON] [18] is an important reference for disaster robotics, covering different unmanned systems, including USVs and UUVs where they were actually used. In fact, [PERSON] uses the umbrella term Unmanned Marine Vehicles (UMVs) for such group of robots. There, UUVs seem to be predominant  
Figure 1: The DM cycle. Mitigation and Preparedness occur before the disaster indeed, while Response is immediately before and after it. Recovery always happens after the occurrence of a disaster. Note that the DM cycle tends to make affected regions better prepared when another disaster strikes.  
when compared with the use of USVs. [PERSON] et al. [21] present a book focusing mainly on marine vessels for SAR, where the authors point out the low prevalence of research involving marine vehicles for SAR, with a strong focus on the authors projects.  
The main objective of the present work is to map the actual contributions of USVs for DM.
# Auditory Coding of Reaching Space
## 2 Materials and Methods
### Experimental Setup  
The participants were seated on a height-adjustable chair in front of an experimental apparatus resting on a table and wore circumaural headphones (Beyer Dynamic, DT 100, 30-20.000 Hz) as well as inertial sensors.  
A digitizing tablet (WACOM Intuosa  XL PTK-1240, active surface 487.7 mm \(\times\) 304.8 mm) was positioned successively in the \(xy\)-plane (with the long tablet side along the \(y\)-axis), \(yz\)-plane (with the long tablet side along the \(z\)-axis), or \(xz\)-plane (with the long tablet side along the \(x\)-axis) as shown in Figure 1.  
The digitizing pen was positioned next to the tablet. A chin rest, positioning the chin at a height of 30 cm from table surface, in combination with a height-adjustable chair ensured a firm and standardized head position throughout the whole experiment. An arm rest provoking approximately a 90-degree abduction in the right shoulder joint was used in the calibration process of the inertial  
Figure 1: The tablet positions in the three used planes. \(X\)-axis is later called “transverse axis”, \(y\)-axis is later called “longitudinal axis” and \(z\)-axis is later called “sagittal axis”. The subject sits at the front edge of the table, looking in the direction of the \(z\)-axis.  
sensors. The subjects were instructed to execute reaching actions with a digitizing pen in their right hand towards invisible, merely acoustically coded target positions on the tablet (see Figure 2).
## 2 Literature Review
### Related Work
All the research identified in Table 1 lacks one or more focused attribute areas. However, two significant technical gaps were observed when comparing and evaluating the other papers with this study. Firstly, there is limited use of real-world devices and infrastructure data in existing research papers. This is crucial to ensure research findings remain critical and applicable to link with real-world environments. This lack of comprehensive analysis with device and infrastructure real-world data usage leads to a disconnect when addressing real-world challenges. This gap is comprehensively investigated in this study. Secondly, there are limited studies on data analysis and optimisation with multiple performance metrics in the existing research. This study addresses and explores this gap to ensure crucial assessment of various performance metrics for efficient, effective, and scalable solutions in a real-world smart building environment founded on Cloud- and Fog-based computing scenarios. Furthermore, addressing these gaps in this study will help improve the robustness of outcomes that align with real-world challenges and requirements. Therefore, this study significantly extends the state-of-the-art Fog-Cloud computing research area using real-world IoT device data, infrastructure optimisation, and extensive evaluation metrics.
# Minimization of the Decoupling Circuit (DC-Link) in Induction Motors
## 2 Energy Flow Analysis of the Study Case
### Development of a Mathematical Model
as well as the influence of the modulation in them, is obtained (15), which is used to calculate the time in which the maximum energy of the critical case is returned. Equation (15) represents the ratio of the complete interval, from T0 to T6 concerning the switching frequency. Figure 9:4  
where \(f_{sw}\) is the modulation switching frequency. Once the dimension of the interval increment \(\Delta t\) is defined, the relation concerning \(t_{4}\) and \(t_{6}\) where the energy return occurs was determined, with (16) and (17), respectively. Equations (16) and (17) show the relation between the inverter switching frequency and the intervals in which energy is returned to the decoupling system. This returned energy is added to the energy managed by the DC-link, resulting in the critical case of maximum energy storage by the DC-link (critical case). Figure 9:5  
Figure 9:6  
Equations (15)-(17) are used to determine the exact duration of the intervals when the switching frequency in the inverter varies. Finally, substituting (14) in (5) and considering (18), the required capacitance equation is obtained with (19). Figure 9:7  
Figure 9:8  
The negative sign of the \(K\)-factors represents the energy returned from the load to the source, which is added to the energy delivered by the source and together they represent the total energy stored in the link capacitor at that instant.