esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Constitutive Model and Soil Properties  
In this study, a composite system used to support a wind turbine is considered, which consists of a hollow circular pipe pile with pile diameter \(D_{\rm P}\) = 4 m, wall thickness \(t\) = 0.2 m and a solid wheel with the thickness \(T_{\rm w}\) = 2 m. The wheel diameter \(D_{\rm w}\) and pile embedment depth \(L\) are determined as ratios of \(D_{\rm w}/L\) and \(L/D_{\rm P}\) varying from 0 to 0.95 and 7 to 13, and the effects of wheel thickness (\(T_{\rm w}\)) and height of wheel collar (\(T_{\rm collar}\)) are also investigated. The steel pile and wheel are modelled as an elastic-perfectly material with the Young's modulus of 210 GPa, Poisson's ratio \(\
u\) of 0.17, and unit weight of 78.5 \(\rm{kN/m^{3}}\). This assumption of the foundation has also been widely used in many other researches [11; 28; 31; 32], and good predictions of the bearing performance have been obtained compared to that of the real structure. The sand is assumed to have the buoyant unit weight \(\gamma^{\prime}_{\rm s}\) of 9.8 \(\rm{kN/m^{3}}\), internal friction angle \(\varphi\) of 33\({}^{\circ}\), [PERSON]'s ratio (\(\
u\)) of 0.3, [PERSON]'s modulus \(E_{\rm s}\) of 40 MPa, dilation angle \(\psi\) of 3\({}^{\circ}\), and cohesive force \(c\) of zero, which are obtained from the triaxial tests. The friction coefficient between the sand and foundation is measured as 0.3 from the lab tests as reference [32]. By adopting a perfect contact with [PERSON]'s tangent friction model, interactions between the foundation and the sand are simulated allowing detachment and sliding. The buoyant unit weight of clay \(\gamma^{\prime}_{\rm c}\) is taken as 8.5 \(\rm{kN/m^{3}}\)[32]. A uniform stiffness ratio \(E_{\rm c}/s_{\rm u}\) = 500 (where \(E_{\rm c}\) is the Young's modulus of the clay, \(s_{\rm u}\) is the undrained shear strength of clay) [43] is adopted throughout the soil domain and the Poisson's ratio \(\
u\) is set as 0.49 to model the fully undrained condition. The undrained shear strength of clay is described with a normalized soil strength gradient in a linearly increasing strength profile, as follows:  
\[s_{\rm u}=s_{\rm um}+kz, \tag{1}\]  
where \(s_{\rm um}\) is the undrained shear strength at soil surface, \(k\) is the shear strength gradient with the submerged depth \(z\) and is taken as 1.75 \(\rm{kPa/m}\) in the following analyses [4; 44]. The shear strength of the nodal joint elements near the clay/pile interface is limited to \(\rm{\alpha s_{u}}\) (\(\alpha\) is the interface friction coefficient which is set as 0.3 for the composite foundation/clay interface). This method has been successfully adopted in simulating the interaction between pile and clay in small strain analyses, where good predictions were obtained [45; 46]. The gravity is initially applied to the soil to establish geostatic stresses state in soils, and the coefficient of lateral earth pressure at rest is set as \(K_{0}\) = 1 \(-\) sin\(\varphi\).
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### Boredom  
SCCs operators have to look at screens for several hours, which can lead to a high level of boredom. A study of operators of remotely piloted aircraft [37] found that 92% reported \"_moderate_\" to \"_total_\" boredom compared to 62% of sensor operators. Likewise, [PERSON] et al. [23] highlighted boredom as a human factor challenge for unmanned ship operations.
## 4 Experiments
### Evaluation Metrics  
To compare the quantitative results generated by different methods, here we use four popular metrics to evaluate our model against other unsupervised methods.  
**Region Similarity**\(F\). To measure the similarity of matching regions from two salient segmentation maps, \(F\) is defined as:  
\[F=\frac{\left(1+\beta^{2}\right)\text{ Precision }\times\text{Recall}}{\beta^{2} \text{Precision }+\text{Recall}} \tag{6}\]  
where \(\beta^{2}=0.3\) to balance between _recall_ and _precision_.  
**Pixel-wise Accuracy**\(\varepsilon\). \(F\) does not consider true negative saliency predictions. We define the normalized (\([0,1]\)) mean absolute error (MAE) between predicted salient segmentation maps and ground truth masks as:  
\[\varepsilon=\frac{1}{W\times H}\sum_{x=1}^{W}\sum_{y=1}^{H}\|M(x,y)-G(x,y)\| \tag{7}\]  
where \(W\) and \(H\) are the width and height of images, respectively.  
**Enhanced Alignment Measure**\(E\). Proposed by [28], using the enhanced alignment matrix \(\phi_{FM}\) to measure the two properties (pixel-level matching and image-level statistics) of a binary map, \(E\) is defined as:  
\[Q_{FM}=\frac{1}{w\times h}\sum_{x=1}^{w}\sum_{y=1}^{h}\phi_{FM}(x,y) \tag{8}\]  
where \(h\) and \(w\) are the height and width of the map, respectively.  
**Structural Similarity**\(S\). \(S\) proposed by [29] evaluates the structural similarity by considering both regions and objects. Since saliency of potential spacial structures is crucial to biomedical images, we additionally use \(S\) to comprehensively evaluate the structural similarity of biomedical images.
## 3 The MARISMA Framework
* _Activity A1.4--Generating artefact library tables:_ T1.4.1--Selecting rules, T1.4.2--Selecting procedures, T1.4.3--Selecting registers, T1.4.4--Selecting templates, T1.4.5--Selecting technical instructions, T1.4.6--Selecting metrics, T1.4.7--Establishing relationships between rules and artefacts, T1.4.8--Establishing relationships between rules and controls, T1.4.9--Establishing relationships between artefacts and controls, T1.4.10--Establishing relationships between procedures and artefacts. * _SMSyG--Security Management System Generation_: The principal objective of this sub-process is the creation of an ISMS that is appropriate for a company by using an already-existing schema. Figure 2: Sub-processes of the methodology. _Activity A2.1--Establishing the framework of the ISMS:_ T2.1.1--Requesting the valid interlocutor, T2.1.2--Requesting the company's organigram, T2.1.3--Obtaining the list of information system users and their roles. _Activity A2.2--Establishing the maturity level:_ T2.2.1--Gathering business information, T2.2.2--Gathering technical information from the information system, T2.2.3--Obtaining the security maturity level. _Activity A2.3--Carrying out the risk analysis:_ T2.3.1--Identifying assets, T2.3.2--Generating the risk matrix and the improvement plan. _Activity A2.4--Generation of the ISMS:_ T2.4.1--Generation of ISMS objects, T2.4.2--Presentation of results to interlocutor. * _SMSyM--Security Management System Maintenance:_ The principal objective of this sub-process is to maintain and manage the security of the company's information system, providing information that is updated in the time of an ISMS generated. _Activity A3.1--Obtaining and renewing safety culture certificate:_ T3.1.1--Taking safety culture test. _Activity A3.2--Executing ISMS procedures:_ T3.2.1--Activate general procedure, T3.2.2--Activate report procedure. _Activity A3.3--Following up the fulfilment of the ISMS:_ T3.3.1--Managing security scorecard, T3.3.2--Managing periodicity of procedures, T3.3.3--Managing security violations, T3.3.4--Managing safety culture certificates, T3.3.5--Realisation of periodical audits, T3.3.6--Realisation of general metrics, T3.3.7--Managing the alarm system. The process followed to obtain the various activities and tasks of which the MARISMA methodology is composed is analysed in the following section. This is done by using the action research method and adapting it to the characteristics of SMEs [47; 48].
## 3 Preliminary Analysis and Design Direction
### Preliminary Analysis Conditions
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specification** \\ \hline Software & ANSYS Mechanical 2023 R2 \\ Analysis Type & Static Structural, 2D Axisymmetric \\ Element Type & 2D Axisymmetric 4-node structural solid elements \\  & (e.g., PLANE182 or equivalent) with plasticity and contact capabilities \\ Material Model (Plunger) & PAI: Multilinear Kinematic Hardening (MKIN) model (data from) \\ Material Model (Seat) & Aluminum Bronze: Linear Elastic (Properties in Table 1) \\ Contact Algorithm & Augmented Lagrangian \\ Friction Coefficient & 0.2 (Plunger-Seat Interface) \\ Contact Behavior & Asymmetric (due to differing material stiffness) \\ Mesh Statistics & Approx. 10,000 nodes, 3500 elements \\ Mesh Size (Contact Area) & Min. element size: 4.29 \(\upmu\)m \\ Mesh Quality Metric & Average Element Quality: 0.93 \\  & - Seat: Fixed support on outer boundary (Figure 5b) \\ Boundary Conditions & - Plunger: Uniformly distributed pressure (0–87.5 MPa) on upper surface \\  & (Figure 5a) \\ Solver Settings & - Large Deflection: On (to account for geometric nonlinearity) \\  & - Newton-Raphson method for nonlinear solution \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of finite element analysis setup. contact zone is considered sufficient to yield reliable stress and contact pressure results for the geometries analyzed. The frictional contact interface between the plunger and seat surfaces, highlighted in Figure 5b, was modeled using the augmented Lagrangian method. A friction coefficient of 0.2 was applied, reflecting typical values for the contact between polymer (PAI) and metallic (bronze) surfaces, assuming a conservative dry contact scenario for this study. The applied boundary conditions and loading are depicted in Figure 5c and summarized in Table 3. A fixed support constraint was applied to the outer boundary of the seat. A uniformly distributed pressure, ranging from 0 MPa to 87.5 MPa, was applied to the upper surface of the plunger. The FEA simulations incorporated both material nonlinearity and large deflection effects. The combination of these modeling choices provides a comprehensive setup for the preliminary FEA.
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 3 On the Performance Improvement of Hybrid NoCs
### Adaptive Routing
#### 3.2.1 Router Pipeline  
The adaptive router is mainly composed of the input buffer, crossbar and allocators. If a received packet cannot bypass the current router, it is written to the input buffer (BW), and meanwhile, route computation (RC) is performed. Adaptive selection is done automatically by masking the congested output port similar to [15]. The crossbar in this router is implemented using two sets of multiplexers like those in [17; 50] to be cost effective. The SA process thus contains the arbitration for the multiplexer of the input buffer (SA-I) and that of the output port (SA-II). The winner of SA-II will then transmit a flit to the output link (LT). An idle VC of the output port is also assigned to the SA-II winner, which forms the VA procedure. Figure 7 shows the pipeline of this adaptive routing process. To support bypassing, upon receiving a packet, we need to perform bypassing control (BC) to determine if the packet should be written to the buffer, so there is a BC procedure before the BW operation in the pipeline. If the packet can bypass the current router, it follows the single-stage bypassing traversal (ST + LT).
## 4 Simulations
### Simulation Scenario  
We consider a rectangular area of 100 \(\times\) 100 m\({}^{2}\) in which 100 sensor nodes are deployed with network connectivity. It means that first each sensor node has at least one path to reach the BS. 10% of the nodes are chosen randomly as CHs. A simple scenario in monitoring application is simulated, which is that each sensor node transmits its sensed data to the BS every 25 s. The length of data packet is fixed at 2,048 bits and the initial energy of each node is \(10^{-3}\) J. To examine the performance influenced by the scanning angle, which is the main factor of directional communication, the scanning angle is set to \(\dfrac{\pi}{6}\), \(\dfrac{\pi}{4}\), \(\dfrac{\pi}{3}\), \(\dfrac{\pi}{2}\), respectively. Ten different topologies are generated and the results are based on the average. An example of the simulated topology is shown in Figure 8.  
Figure 8: Example of simulated topologies.
# Intelligent TCP Congestion Control Policy Optimization
## 5 Conclusions  
Aiming at the problems of poor adaptability of mainstream TCP congestion control algorithms and inability to effectively use virtual data space network borrowing, a TCP  
Figure 6: Under the condition that only the queue size is changed, comparison of the latency of the traditional congestion control strategy and the congestion control strategy based on deep reinforcement learning.  
Figure 7: Under the condition that only the packet loss rate is changed, comparison of the latency of the traditional congestion control strategy and the congestion control strategy based on deep reinforcement learning.  
congestion control strategy based on deep reinforcement learning is proposed, which effectively improves the data transmission efficiency. The main conclusions of this paper are as follows:  
1. Optimize the traditional TCP congestion control strategy by using the near-end policy optimization algorithm, map the system's send rate to the behavior of deep reinforcement learning, set the reward function by balancing throughput, latency, and packet loss, and use a simple deep neural network to approximate the final strategy. Through the comparison of a large number of experimental data, the parameters such as the number of neural network layers, the number of neurons, and the length of the history were determined, and the optimization of TCP-PPO2 was successfully realized.
2. Through Mininet simulation experiments, it is determined that the TCP congestion control algorithm based on the proximity policy optimization adapts to network changes faster than the traditional TCP congestion control algorithm, changes the real-time congestion window size, improves transmission efficiency, and reduces the data transmission delay by 11.7-87.5%.  
Conceptualization, H.S.; methodology, H.S.; software, H.S.; validation, H.S. and J.W.; formal analysis, H.S. and J.W.; investigation, H.S. and J.W.; resources, H.S. and J.W.; data curation, H.S.; writing--original draft preparation, H.S.; writing--review and editing, J.W.; visualization, H.S.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Xi'an Key Laboratory of Clean Energy: 2019219914 SYS014C G036; Key R&D Program of Shaanxi Province: 2023-ZDLGY-24.  
Not applicable.  
The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 5 Experimentation and Analysis
### Evaluation Indices
Subsequently, the desired accuracy is calculated and estimated by calculating the marginal distributions of the rows and columns of the confusion matrix and the total number of samples. Finally, Kappa is calculated based on the observed accuracy and the desired accuracy. Higher Kappa means higher consistency between the predicted segmentation results and the ground truth and better segmentation results. The expression is given in the following equation:  
\[Kappa=\frac{p_{o}-p_{e}}{1-p_{e}} \tag{11}\]  
where \(p_{o}\) represents the calculated observed accuracy and \(p_{e}\) represents the calculated expected accuracy.
## 4 Conclusions  
In summary, the Czochralski method was employed to grow Cu:Fe:LiNbO\({}_{3}\) crystals doped with varying concentrations of Zn\({}^{2+}\) ions. Infrared spectroscopy revealed that when the Zn\({}^{2+}\) ion doping concentration reached 7 mol%, Fe\({}^{3+}\) and Cu\({}^{2+}\) ions occupied Nb sites in the lattice, leading to the formation of defect structures Fe\({}_{\rm Nb}^{2-}\) and Cu\({}_{\rm Nb}^{3-}\). The incorporation of Zn\({}^{2+}\) ions significantly enhanced the exposure energy of the crystals. Notably, sample Zn7 emerged as the optimal material for mitigating light-induced scattering effects, exhibiting an _Er_ value of 786.55 J/cm\({}^{2}\), approximately 416 times higher than that of Zn1. The two-wavelength coupling experiments demonstrated that the holographic storage properties improved with increasing Zn\({}^{2+}\) ion concentration. Among the tested samples, Zn7 exhibited the shortest response time (196.4 s) and the highest values for key performance metrics: a dynamic range (\(M\)/#) of 9.81, a saturation diffraction efficiency (\(\eta_{s}\)) of 66.7%, and a sensitivity (\(S\)) of 1.04. These parameters are critical for advancing volume holographic data storage technologies. Overall, these findings underscore the potential of Zn:Cu:Fe:LiNbO\({}_{3}\) crystal, particularly at a Zn\({}^{2+}\) ion doping concentration of 7 mol%, as a high-performance material for holographic storage applications. These advancements could pave the way for more efficient, reliable, and high-capacity holographic storage systems, addressing the growing demand for advanced data storage solutions in the era of big data and beyond.  
Not applicable.
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.1 Thermal-Aware Routing Algorithm (TARA)  
In [28], [PERSON] _et al._, proposed a thermal-aware routing algorithm that aims to reduce the possibility of overheating in implanted sensor networks. The implanted sensor network is a thermally sensitive application environment. The authors of [28] assumed that there is no sensor inside the bio-medical sensor nodes to measure the temperature of the node, because of the size and simplicity of bio-medical sensor nodes. In the setup phase each node observes the activities of its neighbor nodes, counts the received/transmitted packets and then evaluates their communication radiations and power consumption. Based on these observations each node estimates the changes in the temperature of its neighbors. Hotspots are the nodes having temperatures beyond the threshold value. In the routing phase the data packets whose destination is not the hotspot nodes are routed through nodes other than the hotspot nodes using withdrawal strategy. In the withdrawal strategy the nodes having hotspots as neighbor nodes send back the data packets to the sender node to find alternative routes to forward the data packet, as shown in Figure 9, redrawn from [15]. A data packet having a hotspot node as destination node, is buffered until the temperature of the destination node drops to a certain value.  
Figure 9: An example of TARA [15].  
In the TARA system model [28], bio-medical sensor nodes are physically implanted and the dielectric and perfusion properties of the tissues are assumed to be known. The implanted bio-medical sensors continuously sense the data and send it to the gateway which is responsible for aggregating the data and sending to the base station located outside the body. This algorithm calculates the Specific Absorption Rate (SAR) and temperature of each node using Finite Difference Time Domain (FDTD) discussed in [53] and Pennes Bioheat Equation given in [54].
## 4 Results and Discussion
### Practical Implications and Research Limitations  
The methodology presented in this paper is generic and can be applied to any group decision-making problem where the input from experts and stakeholders becomes relevant. The incorporation of survey data from a diverse group of experts enables the involvement of multiple perspectives in the decision-making in a rational way. This approach can be very useful in multifaceted industries such as the construction industry, where multiple players with diverse sensitivities are involved. In the specific case of the eco-material selection application presented in this article, the methodology provided has direct practical implications for contractors and designers interested in green and sustainable construction. By using the proposed methodology, they can identify and prioritize candidate eco-materials that align with qualitative (sustainability, aesthetic, etc.) and quantitative (durability, cost, etc.) goals in a structured and data-driven way. Moreover, considering the impact of the selected eco-materials on the United Nations' Sustainable Development Goals (SDGs) has a significant practical implication. It encourages decision-makers to align construction projects with broader global sustainability objectives, making it relevant for policy makers, government agencies, and funding organizations aiming to meet their SDG commitments.  
However, the data-driven nature of the proposed methodology becomes also its main limitation. More specifically, the final output is entirely dependent on the quantity and quality of the survey data, and care needs to be taken when designing the data collection process, ensuring the selection of knowledgeable and diverse experts and addressing potential biases in the responses. To mitigate such a limitation, the proposed methodology includes a probabilistic assessment and ranking of eco-material candidate selection, which accounts for variability and uncertainty (e.g., lack of knowledge) in the survey data. This practical aspect is very relevant in dealing with real-world uncertainties in construction projects, enhancing the robustness and rigorousness of the selection decisions.  
In summary, the practical implications of this research extend to various stakeholders in the construction industry, including contractors and designers, as well as policy makers and organizations committed to sustainability. The methodology and principles discussed are generic and can easily adapted to other decision-making processes, particularly when dealing with survey data and sustainability criteria.
## 1 Introduction
### Add Start Frame  
A preamble symbol is a signal applied in communications to synchronize and detect the start frame, which can be called a header. This is a special bit sequence, as the preamble length can affect the performance of the system. This is because the redundant part (that does not carry the desired data) is added to synchronize the start frame. Then, the preamble length needs to be carefully considered. The preamble should be configured so that it is suitable for the RLL code, which is used in the data packet, as given in Table 1.