esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Water Damage in Insulation Layer  
To evaluate the resulting damage of added water, HIH-5030 humidity sensors were embedded in the insulation material, as shown in Figure 4. For EP, XP, and GW, this was accomplished with drilling holes of 3 cm diameter and depths varying from 50% to 75% of the respective insulation thickness. Top sealing was attained with waterproof tape. For the fine-grained PS, drilling holes were not necessary because the sensors could be placed easily.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Material** & **Thickness \(D\) [cm]** & **Density [g\(\cdot\)cm\({}^{-3}\)]** & **Porosity * [\%]** \\ \hline Cement screed (CT) & 5, 6, 7 & 1.92 & 20.76 \\ Anhydrite screed (CA) & 5, 6, & 2.05 & 27.18 \\ \hline Expanded polysterene (EP) & 2, 5, 7, 10 & 0.027 & - \\ Extruded polysterene (XP) & 2, 5, 7, 10 & 0.037 & - \\ Glass wool (GW) & 2, 6, 10 & 0.061 & - \\ Perlites (PS) & 2, 6, 10 & 0.092 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Used materials and layer thicknesses for the screed (top) and insulation layer (bottom).  
Figure 3: Modular test specimen with screed, insulation, and concrete base layer.  
After adding equal amounts of water in all four sides of the setup, the moisture could spread for at least 12 hours to ensure stable conditions. In practical investigations, a threshold of 80% relative humidity is often considered as an adequate reason for renovations, since it provides optimum growth conditions for mould [22]. Following that, a setup was labeled as \"damaged\" only if all three sensors S3 to S5 exceeded this critical value. Thereafter, the measurement procedure, which is discussed in Section 2.4, was conducted for each of the six exceeds.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 3 Material and Methods
### Communication Protocols  
The protocols used for the prototype are NMEA (National Marine Electronics Association) 0183, UBX, and RTCM version 3, which are briefly described below.
## 3 Results and Discussion  
### Image Quality Assessment  
In this section, we present the results obtained from the overall quality assessment of raw GPR images (Section 3.1.1) and the assessment of the quality of GPR images for the detection of hyperbolas representing underground utilities (Section 3.1.2).
# Dataset Transformation System for Sign Language Recognition Based on Image Classification Network
## 3 Materials and Methods
### STmap Generator  
A spatial-temporal map (STmap) was created to make the data converted from the x-y coordinate system to the color coordinate system into an image with a certain rule. Figure 5 represents the information input to each pixel constituting the image.  
Figure 4: The structure of the proposed method and its application to the sign language recognition model.  
One pixel in the STmap image corresponds to one extracted keypoint. The horizontal axis constituting the image is a series of keypoints, and one row consists of 135 keypoints extracted from the body. The order is from left to right, that is, left hand (21 keypoints), right hand (21 keypoints), face (70 keypoints) and body (25 keypoints). The vertical axis means time, and moving to the next row is the same as moving to the next frame of the video. Figure 6 shows the result of converting the word 'burn' in Korean sign language into an STmap image.  
In Figure 6, the color of the image changes as the movement of a person appears. The change becomes more noticeable in the image generated as the result of normalization. As shown above, the characteristics of the generated image are vivid. The image pattern changes a lot due to the large movement of both hands because of the nature of the sign language. Facial movement contains important information in sign language, but the pattern did not appear strongly in the image, so the keypoints of the face were excluded in this experiment.
## 1 Introduction  
The interactions between ground conditions and wheeled or tracked vehicles have long been studied for the design and operation of off-road vehicles and terrain-working machinery, which has come to be known as terramechanics [1]. One of the major objectives of this research area is to investigate the performance of vehicles or machines associated with their operational environment, which is not only limited to various ground conditions on Earth but has been extended to the lunar surface [2]. Due to the ARTEMIS mission [3], increasing attention has been paid to the mobility of lunar roving vehicles (e.g., [4, 5]). Demanding subsea industrial activities, such as underwater construction and mining, have also brought interest in the performance of underwater tracked vehicles against seabed conditions (e.g., [6, 7]).  
One of the most critical components in the performance of wheeled or tracked vehicles is the tractive performance, or mobility, on the natural terrain surface. This tractive performance indicates how a vehicle can effectively thrust off of unpaved ground surfaces; vehicles can move forward when the thrust force can overcome the resistance to motion [4]. For off-road tracked vehicles, the shear resistance of the soil along the soil-track system constitutes the thrust force, which is known as soil thrust [1]. One of the pioneering studies on the soil thrust of track systems was that of the study in [8]. This study derived theoretical expressions of soil thrust under a rectangular block failure using elasticity theory. Considering the plastic state of soils underneath the track system, empirical relationships for the soil thrust were presented for sandy soils based on experimental studies (e.g., [9, 10, 11]). Recently, the soil thrust of clay ground was analytically derived through an upper-bound limit analysis followed by experimental verification [12]. However, little attention has been paid to soil thrust considering sloped ground.  
In this study, the upper-bound limit analysis for the soil thrust of a single-track system over flat clay ground by [12] was extended to account for sloped ground conditions. A typical continuous-track system interacting with clay ground was adopted, and three potential failure modes associated with each single-track system, a track place and a grouser, were investigated to evaluate the soil thrust along the clay slope. The upper bound solution was based on the plane-strain conditions under undrained conditions considering the shearing mechanism between the clay and the single-track system. The following notations and symbols are used throughout: bold-faced letters denote tensors, and the symbol '\(\cdot\)' denotes a single contraction of adjacent indices of two tensors (e.g., \(\mathbf{a}\cdot\mathbf{b}=a_{i}b_{i}\) or \(\mathbf{c}\cdot\mathbf{d}=c_{ij}d_{jk}\)).
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 4 Robust Watermarking Algorithms Based on Original Videos
### Video Watermarking in Transform Domain
#### 4.2.3 SVD-Based Watermarking Algorithms
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Type** & **Watermark Preprocessing** & **Embedding Position** \\ \hline
[44] & Blind & Arnold transform & Middle-frequency DWT coefficients of Y component \\ \hline
[45] & Blind & Pseudorandom generator & Middle-frequency DWT coefficients of Y component \\ \hline
[46] & Blind & Arnold transform & High-frequency DWT coefficients of Y component \\ \hline
[47] & Non-blind & Chaotic encryption & Selected blocks of Y component based on human visual masking threshold \\ \hline
[48] & Blind & Spread-spectrum technique & LH\({}_{2}\), HL\({}_{2}\), and HH\({}_{2}\) sub-bands of Y component \\ \hline
[49] & Blind & Pseudorandom generator & LH\({}_{2}\), HL\({}_{2}\), and HH\({}_{2}\) sub-bands of Y component \\ \hline
[50] & Blind & Resize & LL and HL sub-bands \\ \hline
[51] & Blind & Spread-spectrum technique & 3D coefficients of HL, LH, and HH sub-bands \\ \hline
[52] & Blind & Random shuffling & LH\({}_{3}\) sub-band of luminance component \\ \hline \end{tabular}
\end{table} 
Table 4: Summary comparison of several watermarking algorithms in DWT domain.
# Visual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environments
###### Abstract  
Estimating the location of unmanned aerial vehicles (UAVs) within a global coordinate system can be achieved by correlating known world points with their corresponding image projections captured by the vehicle's camera. Reducing the number of required world points may lower the computational requirements needed for such estimation. This paper introduces a novel method for determining the absolute position of aerial vehicles using only two known coordinate points that reduce the calculation complexity and, therefore, the computation time. The essential parameters for this calculation include the camera's focal length, detector dimensions, and the Euler angles for Pitch and Roll. The Yaw angle is not required, which is beneficial because Yaw is more susceptible to inaccuracies due to environmental factors. The vehicle's position is determined through a sequence of straightforward rigid transformations, eliminating the need for additional points or iterative processes for verification. The proposed method was tested using a Digital Elevation Model (DEM) created via LiDAR and 11 aerial images captured by a UAV. The results were compared against Global Navigation Satellite Systems (GNSSs) data and other common image pose estimation methodologies. While the available data did not permit precise error quantification, the method demonstrated performance comparable to GNSS-based approaches.  
G 2024 14 9493 1
## 3 Results  
### Variation in CO\({}_{2}\) Fluxes  
#### 3.1.1 Diurnal Variation in CO\({}_{2}\) Fluxes  
The results from the current investigation indicated the diurnal variations in CO\({}_{2}\) fluxes during the growing season in the Middle Tien Shan grassland from April to September of 2018. NEE and GPP showed a \"U\"-shaped and inverted \"U\"-shaped trend, respectively, while Reco remained relatively constant with a mean value of 0.1 mg m\({}^{-2}\) s\({}^{-1}\) (Figure 5a). At 5:00, NEE showed a decreasing trend, while GPP showed an increasing trend. As the absorption rate of CO\({}_{2}\) increased, the concentration of CO\({}_{2}\) in the air gradually decreased, eventually causing NEE to reach a minimum value of \(-0.37\) mg \({}^{-2}\) s\({}^{-1}\) at 11:00. After that, GPP gradually decreased as solar radiation weakened and photosynthesis in the grassland ecosystem slowed down, causing Reco to become the main source of CO\({}_{2}\) fluxes at night and the concentration of CO\({}_{2}\) in the air to rise. Overall, from 6:00 to 18:00,  
Figure 4: Flowchart of the methodology in this study.  
the grassland ecosystem acted as a carbon sink, while for the rest of the time, it acted as a carbon source.  
NEE, GPP, and Reco varied in each month of the growing season (Figure 5b-d). As solar height and solar radiation are stronger from June to August than in other months, the changes in NEE and GPP were most pronounced in June and July, while the changes in Reco were most pronounced in July and August. Thus, carbon sources and sinks in grassland ecosystems showed greater changes in June and July than in other months. The daily average values of NEE ranged from \(-\)0.24 to 0.004 mg m\({}^{-2}\) s\({}^{-1}\), GPP ranged from 0.03 to 0.37 mg m\({}^{-2}\) s\({}^{-1}\), and Reco ranged from 0.03 to 0.17 mg m\({}^{-2}\) s\({}^{-1}\) from April to September of 2018.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC Standalone PKE Protocol
#### 5.1.4 Replay Protection  
Replay attacks involve an attacker capturing and retransmitting a valid encrypted message to disrupt communication or impersonate a legitimate sender.  
Mechanism  
To mitigate replay attacks, the protocol implements the following mechanisms:  
1. Sequence numbers are incorporated into each encrypted message.
2. The receiver tracks these sequence numbers to ensure they are unique and monotonically increasing.
3. Messages with duplicate or out-of-order sequence numbers are rejected.  
Security Assumption  
This approach assumes that sequence numbers are generated uniquely and managed correctly by both parties during communication.  
Formal Statement  
If sequence numbers are generated uniquely and monotonically increasing, replayed messages will be detected and rejected, preventing replay attacks.
## 3 Application Examples
### Nontargeted Analysis of Hairy Root Exudates in Hyoscyamus niger: Comparison to Shaking Flasks  
Hairy root cultures of \(H\). _niger_ were grown in the bioreactor operated in a bubble mode and in traditional shaken flask cultures. After 30 days, growth media were collected, extracted with ethyl acetate and the extracts were analyzed by HPLC. Because the components of the exudates were unknown, an evaporative light scattering detector (ELSD) was used, which allows for an approximate comparison of concentrations without authentic standards because the response factor of the detector is largely independent of the chemical structure of the analyte [67].  
The results of the analysis of hairy root exudates by HPLC-ELSD are shown in Table 3. The average standard error of the intensity of HPLC signals in exudates obtained from bioreactors was 20%, compared to the average standard error of 28% for exudates from shaken flasks. Thus the reproducibility of the composition of hairy root exudates collected in bioreactors appears higher than the reproducibility of exudates collected in shaking flasks. The total intensity of HPLC-ELSD signals detected in exudates from bioreactors (average total per accession of 9024) was 1.6-times larger than the total intensity of signals from shaking flasks (average total per accession of 5692), indicating that hairy roots exuded larger amounts of metabolites in bioreactors as compared to shaking flasks.
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space
### The Formation of Space Blocs  
The escalating geopolitical tensions and the rebalancing of global power dynamics that have characterized recent years have precipitated profound changes in the structure of the space system. In the contemporary geopolitical landscape, the distribution of power is becoming increasingly polarized along terrestrial lines.  
The historically predominant status of the United States as a space power is currently being contested by emerging competitors who explicitly aspire to lead the contemporary space race. Since the end of the Cold War, space has been an arena of collaboration among nations; however, in recent decades, the nature of cooperation has been limited to bilateral and regional agreements led by major space powers. In this context, the emergence of new coalitions and alliances among space-faring nations signifies a shift towards repolarization.  
In the current global context, the United States and China are at the heads of a coalition of countries that are willing to cooperate in the space domain, with each other, and in opposition to the other alliance. This phenomenon is creating an incipient but clear division among two polarized groups, also known as a \"space bloc\" [93].  
Since October 2020, the United States has assumed a leadership role in a coalition of countries under the Artemis Accords, a multilateral agreement between the U.S. and other states participating in the Artemis program, the new American space project to return to the Moon and then to Mars. The initial international agreement was endorsed by eight nations in 2020, and, to date, it has garnered the participation of more than 52 countries, including a diverse array of nations such as Brazil, Saudi Arabia, India, France, and Japan.  
In opposition to this new space bloc, China signed a Memorandum of Understanding with the Russian Federation in 2021 for the creation of the International Lunar Research Station. This strategic bilateral agreement has evolved into a new bloc that aims to challenge the Artemis program. In April 2023, China formally established the International Lunar Research Station Cooperation Organization (ILRSCO), entrusted with the responsibility ofoverseeing and administrating the project. In contrast to the Artemis Accords, which is a nonbinding multilateral agreement, the ILRSCO is to be established as a formal organization with leadership and bureaucracy, both of which are likely to be sourced from China. As of January 2025, 13 states have joined the bloc, including Egypt, Serbia, and Venezuela.  
Since that time, both coalitions have made attempts to attract nation-states, making the competition between space blocs more evident. These groups facilitate collaboration among nations within their respective blocs, while also fostering competition among the blocs themselves, resulting in the emergence of an emerging bipolar structure.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 3 Result and Discussion
### Model Validation  
Figure 7 shows the performance of the good fit learning curve for training loss and validation loss, and it shows that the values were closely grouped around the range of 1-3%. Therefore, the model exhibited a strong aptitude for learning. This model can be utilized to make precise predictions for previously unseen data. Alternatively, it can be referred to as a model that exhibits the ability to apply its knowledge to unfamiliar data. We divided the 100,000 datasets into 60% training data, 20% testing data, and 20% validation data, for our proposed method.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 4 Security Issues of UWSNs
### Security Mechanisms
#### 4.2.4 Localization Security  
Location estimation is a vital component in the source detection and tracking applications. The underwater sensor nodes get the location information and speed of mobile nodes during the localization phase. The location and motion information would be used in the routing protocol to select the best relay node to forward data. Without the location information, the sink node cannot identify where the received data comes from.  
Due to the characteristics of the underwater channel (long propagation delays, Doppler effect, limited bandwidth, node mobility and multipath), localization protocols proposed for WSNs cannot work in underwater applications. With regard to the mechanisms used for location estimation, underwater localization algorithms can be classified into three categories: range-based schemes, range-free schemes, and hierarchical schemes [72]. In the range-based location algorithms, distance or angle estimates with neighbors will be used for calculating node locations. In the range-free location algorithms, due to the energy consumption and hardware limitation, the neighbor distances or angle information are assumed to be unavailable for positioning. The hierarchical schemes consist of four types of nodes, which are surface buoys, detachable elevator transceivers (DETs), anchor nodes and ordinary nodes. A surface buoy is assumed to be equipped with GPS on the water surface. A DET is attached to a surface buoy and can rise and down to broadcast its position. The anchor nodes can compute their positions based on the position information from the DETs and the measurement of distance to the DETs.  
Some localization-specific attacks e.g., Sybil attack, black hole attack and wormhole attack can cause great damage by utilizing or modifying the localization information. Most of the existing localization protocols do not take security issues into account when being designed. The secure localization scheme should be able to determine the location of sensors even in the presence of Sybil and wormhole attacks, and the scheme should be able to node mobility in UWSNs. To defend against injecting false localization information in UWSNs, effective and efficient cryptographic algorithms need to be developed [73; 74; 75; 76].
## 1 Introduction
In this paper, based on a SIP traces (Call Data Records (CDRs)) dataset obtained from an Egyptian mobile network operator, we analyze different call failures' use cases. From the analysis, we conclude that the failure in any protocol inside the IMS domain will lead to SIP protocol failure. Then, a further analysis for SIP traces is conducted to identify the features inside the SIP that classify the success and failure calls. After that, we will build eight different ML prediction models to predict whether the ongoing call will succeed/fail. We compare four different ML classifiers: decision tree, naive Bayes, K-Nearest Neighbor (KNN) and SVM with using two feature selection methods. The feature selection methods are (i) the Filter Selection method using the Relieff algorithm and (ii) the Wrapper Model using the Slime Mould Algorithm (SMA) and Whale Optimization Algorithm (WOA). Predicting the failure enables the operator to prevent the failure by redirecting the call to another radio access technique by initiating Circuit Switching fallback (CS-fallback) through a 380 SIP error response sent to the handset [12]. The advantage of the model is not limited to call failure prediction, but also to know the reason (root causes) behind the failure; more specifically, the multi-factorial reasons by using machine learning, which cannot be obtained using the traditional method (manual tracking of the traces). The rest of the paper is organized as follows. Section 2 discusses related works. Section 3 presents different VoLTE call failures use cases. Section 4 gives a brief discussion on the SIP protocol analysis. Section 5 presents the dataset used in the machine learning models. Section 6 introduces our proposed call failure prediction machine learning models. The results are discussed in Section 7. The paper is concluded in Section 8.