esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 2 Related Work
### Vehicle Dynamic Modeling  
We mark the notation with the bicycle model, as illustrated in Figure 8. The MPC models of host vehicles can be expressed in a state-space equation. As in [34], we used the 2-degrees of freedom (DOF) bicycle model for the MPC bicycle system. The lateral velocity of the vehicle is assumed to be constant and the slip angle is assumed to be zero. As this paper aims to develop an obstacle avoidance path planning in real-time, the vehicle model was constructed as a linearized model. The nonlinearized model approximately doubled the computational cost of the linearized model, but there was no significant difference in the generated path. However, this condition is only complete when the vehicle speed is lower than 10 m/s. When vehicle speed exceed the limit, the linearized model does not follow the nonlinear model [35].  
The model of the host vehicle is expressed by the state space equation according to the velocity and the position shown in (11) and (12):  
\[\dot{\mathbf{x}}=A\mathbf{x}+B\mathbf{u}, \tag{11}\]  
Figure 8: Vehicle bicycle model for path planning.  
Figure 7: Obstacle-dependent Gaussian model prediction control architecture.  
\[\mathbf{x}=[p_{{}_{X}},p_{{}_{X}},p_{{}_{Y}},v_{{}_{Y}}]^{T},\ \ \ \mathbf{u}=[a_{{}_{X}},a_{{}_{Y}}]^{T}, \tag{12}\]  
where \(\mathbf{x}\) is the state of the vehicle vector, and \(\mathbf{u}\) is the acceleration vector. The controlled system is usually modeled on the basis of a discrete state-space model of MPC [36]. Because the Ego vehicle model must predict the physical longitude of the next step, the continuous time model was converted into the discrete-time model with the sampling time \(T_{s}\)[37].  
The dynamic discrete-time model of the operation point of the vehicle can be described as the state space, and the shape of the operation point of the vehicle can be linearized as follows:  
\[\mathbf{x}(k+1)=A_{d}\mathbf{x}(k)+B_{d}\mathbf{u}(k), \tag{13}\]  
\[\mathbf{y}(k)=C_{d}\mathbf{x}(k), \tag{14}\]  
\[A_{d}=\left[\begin{array}{cccc}1&T_{s}&0&0\\ 0&1&0&0\\ 0&0&1&T_{s}\\ 0&0&0&1\end{array}\right],\ \ \ B_{d}=\left[\begin{array}{cccc}\frac{1}{2}T_{s}^{2}&0 \\ 0&T_{s}\\ \frac{1}{2}T_{s}^{2}&0\\ 0&T_{s}\end{array}\right],\ \ C_{d}=\left[\begin{array}{cccc}1&0&0&0\\ 0&0&1&0\end{array}\right], \tag{15}\]  
\[\mathbf{y}(k)=[p_{{}_{X}},p_{{}_{Y}}]^{T}, \tag{16}\]  
where \(A_{d}\) is a system matrix, \(B_{d}\) is an input matrix, and \(C_{d}\) is an output matrix. In addition, \(\mathbf{x}(k)\) is the \(k\)th vehicle state, and \(\mathbf{y}(k)\) denotes the latitude and longitude position.
# Material Characterization for Sustainable Concrete Paving Blocks
## 3 Results and Discussion
### Tensile Splitting Strength  
Figure 9 demonstrates the relationships between the replacement levels and the tensile splitting strength of concrete paving blocks for various types of waste materials. Specimens mixed with RCCA had an even higher tensile splitting strength of 11.73 MPa at a replacement level of 20%. However, the tensile splitting strength decreased in an almost linear trend from 11.73 MPa to 8.80 MPa when the replacement level increased from 20% to 80%. The lowest splitting tensile strength is 6.37 MPa with 100% RCCA in the specimen. A positive effect on the blocks' tensile splitting strength was observed when crushed glass was used as the replacement for coarse aggregates. The overall tendency was that the tensile splitting strength kept increasing with the replacement level varying from 10% to 40% and reached the highest value of 12.79 MPa at 40%. When the replacement level was 30%, the tensile splitting strength was only 12.45 MPa. However, this value was still higher than the blocks mixed with natural aggregates (11.34 MPa). The tensile splitting strength of the blocks mixed with RCFA declined directly from 10.80 MPa to 7.41 MPa as the replacement level rose from 10% to 30%. Similar to the trend observed for the compressive strength, the tensile splitting strength of specimens containing recycle rubber also remarkably declined with the increase of replacement level. It reached the lowest at 5.53 MPa when the crumb rubber's content is 3%. The tensile splitting strength of blocks mixed with GGBS maintained the same level (12.81 MPa and 13.06 MPa) when the replacement levels rose from 30% to 50%. However, when the percentage of GGBS increased to 70%, the tensile splitting strength was only 9.53 MPa.  
Figure 8: Relationship between the different replacement levels of materials and the compressive strength of specimens.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 7 RAN Slicing in 5G  
We discussed the 5G architecture in Section 2.1, and it can be observed that it is comprised of actual physical and virtual resources of the RAN, networking nodes, cloud nodes, and associated links. Through virtualization, the flexibility of network resources can be achieved, which helps to create the core network slices. Spectrum resources can be shared, and flexible management of these resources can be achieved by slicing the RAN. Resource scheduling plays an essential role to meet the demands of RAN slicing and it can be very challenging due to different service requirements. In [43], authors present an intelligent resource scheduling scheme for 5G RAN slicing based on DL and RL. Both algorithms are used in collaborative manner in order to predict traffic pattern, which also results in improving the resources multiplexing gains. Network slicing partitions a physical network into different multiple logical networks according to their specific application requirements, and has become a key idea in addressing the heterogeneity requirements of future wireless networks.  
In [44], the idea of multi-cell RAN was analyzed for the different applications that have been distributed among different tenants. It can be challenging because isolation, such as traffic isolation and the radio-electrical isolation, is required between different applications. In [45], authors have discussed the 5G RAN architecture, functions, and logical network architecture to support network slicing. Allocation of the radio resources is the main challenge in the implementation of network slicing. In RAN slicing, adaptation of conventional allocation strategies is an open research issue along with the development of new resource allocation schemes. Sharing the same radio resources can be challenging in RAN slicing for different heterogeneous applications. In [46], it has been discussed that in order to meet the heterogeneous service requirements, radio resources can be shared by tiling and puncturing in RAN slicing.  
Figure 7: Unsliced network (**left**) vs. sliced network (**Right**).
# Fatigue Crack Detection Based on Semantic Segmentation Using DeepLabV3+ for Steel Girder Bridges
## 5 Pixel Category Threshold Optimization  
The abovementioned analysis shows that the proposed model tends to classify more pixels as crack ones, indicating a relatively good classification accuracy and poor IoU for the crack pixels. Therefore, in order to further improve the effect of semantic segmentation, this paper sets the crack pixel category determination threshold to be greater than 0.5.  
Prior to setting this appropriate threshold, one must examine whether the relationships between the model performance and category threshold exhibit similarity for the training and test datasets. This similarity implies its generalization; an optimal threshold chosen from the training dataset can be adopted for the test dataset. To reveal this, the classification accuracy and IoU for the crack pixels and the MIoU for the model are evaluated with different thresholds using the training and test datasets, which are depicted in Figure 6. It can be observed that (i) the performance indicators for the training dataset are always superior to those for the test dataset; (ii) the ideal similarity exists between two datasets, and thus good generalization is expected for this threshold; (iii) as the threshold increases, the accuracy of the crack pixel detection always decreases, whereas the IoU and the MIoU increase with the threshold \(<\)0.9 and decline with the threshold \(>\)0.9. Actually, a larger  
Figure 5: Cracks predicted by different models: (**a**,**d**,**g**) Model 1; (**b**,**e**,**h**) proposed model; (**c**,**f**,**i**) ground truth.  
threshold means that fewer pixels are classified as crack, indicating fewer true positive crack pixels and lower accuracy. As defined in Equation (1), the IoU increases in that its denominator is effectively reduced. Notably, the threshold step herein is 0.05 to save computation time.  
It can be seen from Figure 6 that the maximum values of the IoU and MIoU must correspond to a threshold around 0.9. In order to precisely identify the optimal threshold, a smaller sampling step, namely, 0.01, is chosen and adopted for analysis, with the results plotted in Figure 7. The results show that when the threshold is 0.89, the highest MIoU is 0.7466 for the training dataset and 0.7231 for the test dataset. This means that 0.89 is the optimal threshold value.
# Integration of ERT, IP and SP Methods in Hard Rock Engineering
## 1 Introduction
The electrical resistivity obtained by ERT was used to delineate the distinct subsurface layers, such as the topsoil layer, weathered layer, weathered/unweathered transition zone and the unweathered rock at the bottom. Zones of higher clay content were delineated with the integration between ERT and IP. The groundwater flow path was inferred by SP. The 2D models of integrated geophysical methods provide a thorough insight into the subsurface. Compared with conventional geotechnical studies, our contribution reduces the uncertainty caused by the spatially limited data, provides a more accurate geological model via lateral and vertical (2D) imaging of the large area, and suggests the most suitable places for the development of engineering structures in highly heterogeneous hard-rock terrain.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 5 Implementation
### The Protection of IDS  
We implement multiple enclave protected IDS solutions based on vNIDS [27]. vNIDS is an innovative NIDS architecture to address the challenges of efficient intrusion detection and the monolithic NIDS configuration in virtualized NIDS. vNIDS has three microservices: head-based detection, protocol-based parsing, and payload-based detection. The three microservices of vNIDS are implemented based on the Click modular router software. Click provides rich networking processing elements, which can be leveraged to construct a wide range of network functions. In order to achieve better detection results, vNIDS is implemented based on ClickOS [26] and ported the event engine of Zeek [28] (previously named Bro). Zeek is a passive open source network traffic analyzer which supports a variety of traffic analysis tasks, even outside the security domain. Zeek's scripting language facilitates a broader approach to discovering malicious activities, including semantic misuse detection, anomaly detection, and behavior analysis.  
Based on our architecture, we use SGX and Click to implement four different protocol analyzer elements (HTTPAnalyzer, SSHAnalyzer, FITAnalyzer, and DNSAnalyzer) to detect whether it is subject to session hijacking, DNS tunneling attacks, and Trojan horses. When a packet is sent to the IDS, it is first classified based on the header information. Packets belonging to different application protocols are sent to different protocol analyzers for further analysis. The protocol analyzer then generates an event based on the analysis results. The IDS then passes these events to the event engine. The event engine sends the packet to the behavior element, such as discard, delay, etc. or sends it to the next virtual function for further processing.
# Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning +
## 3 Training Results and Evaluation
### Non-Uniform Team Rewards  
The next step was to train agents so that each one had a different kind of personality trait. We allocated the initial number of agents based on the 25 percent ratio of introverts to extroverts in a community [25] to also the Neurotic and Non-Neurotic traits.  
Based on this hypothesis, we conducted an experiment to observe the impact of introducing one Introvert into a team of three Extroverts (red) and one Extrovert into a team of three Introverts (green), comparing their performance to the previous teams. As shown in Figure 8, the team comprising three Introverts and one Extrovert (green) outperformed all other configurations. The presence of the Extrovert agent proved beneficial, as they naturally assumed a leadership role, effectively organizing and directing the Introverts, whose preference for reflective and deliberate actions complemented the Extrovert's initiative and energy.  
In contrast, the team with three Extroverts and one Introvert (red) performed similarly to the all-introvert team but outperformed the all-extrovert team. The Extroverts' competitive nature caused inefficiencies, but the Introverts introduced a stabilizing element, reducing competition. This allowed the team to work more cohesively and efficiently than the all-extrovert team.  
Overall, these findings highlight the importance of balancing team dynamics with diverse personality traits. The Extrovert's leadership can harness the strengths of Introverts, while an Introvert's presence in an Extrovert-dominated team can introduce a calming influence, fostering a more harmonious and effective team environment.  
In the same way as before, we showcase the results of other teams and with a variety of personalities, while their results, diagrams, and complementary analysis can be found in the Appendix A.  
As we can see in Table 5, there was a significant change of the success rates of the teams when there was more than one personality in the agents. For example, when we included 1 Introvert, the team improved by 10% but on the other hand, when we introduced 1 Neurotic to a Non-Neurotic team, the effectiveness dropped by 5%.  
Based on the results, it is safe to say that the personality traits of the agents of each team do play a significant role in how the group operates, how efficient it is, and how quickly they manage to escape. This means that the reward functions and how they are set can indeed replicate and emulate how the traits and behaviors are exhibited in real life based on logic.  
Figure 7: Agents mean play time (Openness (O), Non-Openness (NO), Conscientiousness (C), Non-Conscientiousness (NC), Extrovert (E), Non-Extrovert (NE) Neurotic (N), Non-Neurotic (NN) agents).
# A Graph-Based Approach for Modelling Quantum Circuits
## 6 Evaluation of the Proposal
### Application to Other Quantum Computing Models  
As described in this paper, the proposed metamodel for representing quantum circuits provides a standardized way of describing the structure and behaviour of quantum programs. According to [38], there are five models in which the principles of quantum mechanics, including superposition and entanglement, can be leveraged to perform computation: Quantum Gate Array, One-Way Quantum Computer, Zidan's Model, Topological Quantum Computer, and Adiabatic Quantum Computer. In this context, for each of the above-mentioned models, the possibilities of adopting the proposed metamodel are outlined.  
Quantum Gate Array (Unitary Circuit Model). This model is based on the use of quantum gates to manipulate qubits, and is the model of computation considered in this paper.  
One-Way Quantum Computer [39]. This model of computation is based on the concept of measurement-based quantum computation, where the program is represented as a graph of entangled qubits and measurement operations. The proposed metamodel could capture the graph structure, the entanglement relationships, and the measurement outcomes, providing a comprehensive representation of the program's dependencies and measurement results.  
Zidan's Model [40]. This is a novel approach to quantum computing that aims to utilize the degree of entanglement between qubits to perform quantum computations. This model offers a different perspective on quantum computing. The proposed metamodel provides the quantum gates employed by [PERSON]'s model and can capture the graph structure of the program and the entanglement relationships between qubits, allowing for the representation of the whole system entanglement.  
Topological Quantum Computer [41]. This model of quantum computing is based on the concept of topological qubits, which are robust against noise and decoherence. Topological quantum computers have the potential to be highly fault-tolerant and scalable. Here, the representation and structure of quantum programmes are not based on graphs of quantum circuits, but on topological concepts and manipulations of anyons on a two-dimensional topological surface. Therefore, this model of QC is not compatible with the proposed approach.  
Adiabatic Quantum Computer [42]: Adiabatic quantum computing is based on the adiabatic theorem of quantum mechanics. Adiabatic quantum computers are particularly suited for solving optimization problems. Since programs in this model are represented as the continuous evolution of a Hamiltonian system, the proposed metamodel can not capture the time-dependent changes in the system's state, so the representation of the program's dynamics could not be possible.
## 3 RS Applications in Ocean
### Ocean Wave Height (OWH)
#### 3.3.3. Altimeter  
Generally, altimeters measure the traveling time (\(T\)) of the transmitted radar pulses at regular intervals defined by the Pulse Repetition Frequency (PRF) from a nadir-pointed antenna to the ocean surface and back to the receiver onboard the satellite, according to the following equation:  
\[T=\frac{2R}{c} \tag{1}\]  
where \(c\) is the speed of light and \(R\) is defined as the range from the sensor to the ocean surface. R can be calculated using Equation (2).  
\[R=\frac{1}{2}cT \tag{2}\]  
\(R\) is an uncorrelated range and should be modified based on Equation (3) to take into account various ranges of atmospheric and geophysical corrections, including wet and dry tropospheric correction, ionospheric correction, and solid and pole earth tide correction [177].  
\[R_{cor}=\frac{1}{2}cT-\sum_{i}\Delta R_{i} \tag{3}\]  
where \(\Delta R_{i}\) is related to the aforementioned range corrections. The data collected by Jason-3, Chinese HY-2A/B, Satellite with ARgos and ALtiKa (SARAL), and Sentinel-3 altimeters have been widely used in OWH estimation. These satellites offer global observations under all weather conditions [178]. For example, [PERSON] et al. [178] estimated OWH based on observations from HY2B. They used the data collected by the National Data Buoy Center (NDBC) from April 2019 to April 2020 for validation purposes. In this study, numerous DL algorithms were utilized to approximate the waveform of HY2B with high accuracy. Moreover, [PERSON] and [PERSON] [179] enhanced the Brown model to estimate OWH based on three years of observations from the Jason-1 data. The results were validated against observations from eight buoys and compared with the retrieved OWH by the four-parameter Maximum Likelihood Estimator (MLE) retracing method. Moreover, SARAL (i.e., the first Ka-band altimeter) observations were used to retrieve the OWH in the coastal ocean and inland water bodies. Validation of the results with the in situ measurements demonstrated a significant correlation (0.98) between field measurements and satellite measurements in the coastal ocean; the performance was remarkably stable across various coastal zones.
## 2 Geological Overview of the Study Area  
The Kaiping coalfield is located in the Kailuan mining area of Hebei Province, China, and lies in the northeastern part of the North China Basin. The strata on the western limb are steeply inclined and locally overturned, while those on the southeastern limb are relatively gentle and characterized by more folds. The coal-bearing Paleozoic strata in and around the coalfield have undergone multiple phases of tectonic changes, experiencing various processes of geological burial and deformation. The current structural state is the result of the superposition of these tectonic phases, which can be divided into four stages of tectonic evolution: the initial stage (Hercynian-Indosinian period), the development stage (early to mid-Yanshanian period), the maturity stage (late Yanshanian to the first phase of the Himalayan period), and the final stage (second phase of the Himalayan period to the present) [20; 21; 22]. Several folds have developed in the coalfield from north to south, including the Nianzizhuang West Anticline, Gaogezhuang North Syncline, Xiaoqigongzhuang West Anticline, and Yangjiapu Anticline. The overall distribution of faults in the coalfield predominantly trends in the northeast direction, with a secondary trend in the northwest direction (Figure 1a). The study area includes two mining sites at the turning end of the Kaiping syncline, namely the Zhaogezhuang mine and the Linxi mine, with their locations shown in Figure 1a.  
The Kaiping coalfield is part of the North China stratigraphic region, with widespread Paleozoicic strata. The upper Carboniferous-Permanian system forms the coal-bearing strata, with most formations in the coalfield in either unconformable or paraconformable contact with one another. While most of the coal-bearing strata are covered by Quaternary loess, scattered outcrops are found throughout the area. Based on drilling and stratigraphic profiles within the mining area, the strata, from oldest to youngest, include the Ordovician, Carboniferous, Permian, and Quaternary systems (Figure 1b).  
The coal-bearing strata belong to the Carboniferous and Permian systems, with the coal measured underlain by Ordovician limestone (Figure 1b). The total thickness of the coal measures in the two mining fields is approximately 500 m, occurring within the Carboniferous and Permian systems, with a total coal steam thickness ranging from 20 to 28 m. The main minable coal seams are Nos. 5, 7, 8, 9, 11, and 12, which are located in the Lower Permian Damiaozhuang and Zhaogezhuang Formations, with a combined thickness of about 10.74 m.
# Line-of-Sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification
## 3 Multi-Link LOS Pointing Model
### The Principle of LOS Pointing
#### 3.1.1 Coordinate System Definition  
WGS-84 coordinate system e: With the center of mass of the earth as the coordinate origin, the Z-axis is found along the polar axis, the X-axis is located at the intersection of the prime meridian and the equatorial plane, and the Y-, Z-, and X-axes form a right-handed coordinate system.  
East-north-up (ENU) coordinate system: Taking the station center or terminal center as the origin, the X-axis points east along the local latitude, the Y-axis points north along the local meridian, and the Z-, X-, and Y-axes form a right-handed coordinate system.  
Carrier coordinate system b: This coordinate system is concentric with the northeast celestial coordinate system. The X-axis is located to the right along the horizontal axis of the carrier, the Y-axis is found to the front along the vertical axis of the carrier, and the Z-axis is perpendicular to the X-axis and the Y-axis, forming a right-handed coordinate system.  
LOS coordinate system: Taking the center of the optical antenna as the origin, the r-axis is found along the optical LOS of the capture antenna, e points along the distance direction, the d and r-axes are orthogonal axes, and the three axes r, e, and d form a right-handed coordinate system.  
Since the networking system is a multi-LOS system, there are multiple LOS coordinate systems, and the relationship between each LOS coordinate system and the carrier coordinate system needs to be calibrated in advance.