esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cost Components
### Road User Cost
#### 2.2.2 Work Zone-Related User Costs
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Type** & **Property Damage Only** & **Injury Accident** & **Fatal Accident** \\ \hline passenger car & \(\left[0.682^{(\mathrm{IRI}-0.3)}+1.018\right]\cdot\mathrm{K}\) & \(\left[0.769^{(\mathrm{IRI}-0.3)}+1.527\right]\cdot\mathrm{K}\) & \(\left[0.738^{(\mathrm{IRI}-0.3)}+1.982\right]\cdot\mathrm{K}\) \\ bus & \(\left[0.683^{(\mathrm{IRI}-0.3)}+1.114\right]\cdot\mathrm{K}\) & \(\left[0.684^{(\mathrm{IRI}-0.3)}+1.257\right]\cdot\mathrm{K}\) & \(\left[0.718^{(\mathrm{IRI}-0.3)}+1.327\right]\cdot\mathrm{K}\) \\ small truck & \(\left[0.738^{(\mathrm{IRI}-0.3)}+0.828\right]\cdot\mathrm{K}\) & \(\left[0.746^{(\mathrm{IRI}-0.3)}+1.498\right]\cdot\mathrm{K}\) & \(\left[0.749^{(\mathrm{IRI}-0.3)}+1.511\right]\cdot\mathrm{K}\) \\ big truck & \(\left[0.782^{(\mathrm{IRI}-0.3)}+1.028\right]\cdot\mathrm{K}\) & \(\left[0.805^{(\mathrm{IRI}-0.3)}+1.847\right]\cdot\mathrm{K}\) & \(\left[0.823^{(\mathrm{IRI}-0.3)}+2.156\right]\cdot\mathrm{K}\) \\ \hline \hline \end{tabular} where K—traffic density.  
\end{table} 
Table 7: Relationship between Annual Accidents of Different Vehicle Types. L\({}_{\rm q}\)--queue length, km. Unlike the methods used in the FHWA report to determine various fuel consumption costs related to speed changes, this study used a micro-vehicle-based model. This model provides a better method to estimate vehicle fuel consumption under changing vehicle speed conditions, which can consider the fuel consumption caused by acceleration and deceleration modes at various speeds [30]. This specific model was developed by combining on-site experiments with the comprehensive mode emission model (CMEM). On-site experiments collected second-by-second vehicle speed and acceleration data on highway sections during peak hours and working hours, and then CMEM generated second-by-second emissions of light-duty vehicles and high-duty vehicles. For more information on this method, please refer to the publication in [30]. Therefore, the additional fuel consumption cost of vehicles in the work zone area is calculated as shown in Equation (15). \[\rm{FCC_{WZ}=P_{F}\times FC_{WZ}} \tag{15}\]  
According to the FHWA report, Life-Cycle Cost Analysis in Pavement Design [31], the following Formulas (16) and (17) can be used to calculate the delay time of work zones and queues. For more information on this method, view the publication of [31]. \[\rm{DT_{WZ}=\frac{work\ zone\ length}{work\ zone\ speed}-\frac{work\ zone\ length}{upstream\ speed}} \tag{16}\]  
\[\rm{DT_{queue}=\frac{queue\ length}{queue\ speed}-\frac{queue\ length}{upstream\ speed}} \tag{17}\]  
Therefore, the additional time cost for the work zone is calculated as shown in Equation (18). \[\rm{DTC}=\theta\times\left(\rm{DT_{WZ}+DT_{queue}}\right) \tag{18}\]
## 2 Materials and Methods
### Optimization
#### 2.6.3 Nsga-ii  
After the ANN models were trained, the optimization process was carried out via the multi-objective genetic algorithm (MOGA). The MOGA is an evolutionary algorithm used to optimize multiple objectives simultaneously [53]. In this study, the NSGA-II aimed to determine the optimal design variables using the predictions of ANN models, and the population size was 100 in the implementation of the NSGA-II. The algorithm aims to optimize three objectives simultaneously. The fitness function of the NSGA-II was calculated via the average of the predictions of 50 ANN models. This approach aims to obtain more reliable results by using the collective prediction of many models rather than relying on a single ANN model. A set of Pareto-optimal solutions was obtained at the end of the optimization process. These solutions are in a trade-off relationship with each other and ensure comprehension into the optimal design space and support the selection of the most appropriate combination of input parameters on the basis of desired performance criteria.
# Drag Reduction in Compressible Channel Turbulence with Periodic Interval Blowing and Suction
## 3 Results and Discussions
### Skin Friction Distribution  
Figure 5 depicts the skin friction coefficient distribution at \(Ma=0.8\) and \(Ma=1.5\). In contrast to global control, the skin friction coefficient \(C_{\text{f}}\) in the cases with interval control varies along the streamwise direction on account of the staggered arrangement of the controlled region and the uncontrolled region. In Figure 5a, the \(C_{\text{f}}\) curves of C3-C5 begin to decline/rise before the blowing/suction region, and the \(C_{\text{f}}\) value at the control starting position in C5 exhibits the greatest disparity from that of the basic flow. In the controlled region, the \(C_{\text{f}}\) curves reach the valley/peak and then recover. At the end point of the controlled region, the \(C_{\text{f}}\) value is lower than that at the start position on the suction side and higher on the blowing side. The valley/peak value of \(C_{\text{f}}\) in C5 is the lowest/highest because of the greatest blowing/suction intensity. The \(C_{\text{f}}\) curves on the blowing side and the suction side are roughly symmetrical about the basic flow curve.  
In Figure 5b, the distribution of the skin friction coefficient becomes complicated at the supersonic speed. Oscillation is found after the control starting position. The \(C_{\text{f}}\) values at the control starting point and the end point are almost equivalent in C8 and C9. However, as the \(L_{\text{c}}\) shrinks to \(L_{\text{x}}/5\) in C5, the \(C_{\text{f}}\) value at the end point is much greater than that at the control starting point on the blowing side, and lesser on the suction side. The valley/peak of the \(C_{\text{f}}\) curves shows asymmetry in C5, and the effect of suction is stronger than blowing. This phenomenon is different from the subsonic cases.
# Development of a Novel De-NOX Technology for the Aftertreatment of Ship Exhaust Gases
###### Abstract  
The shipping industry is the most fuel-efficient means of transporting goods, carrying more than 90% of the global freight task. Ships generally use low quality fuel to reduce costs and, as a result, the sulfur content in the exhaust gas stream is high. Emissions of sulfur oxides (SO\({}_{\rm x}\)) and nitrogen oxides (NO\({}_{\rm x}\)) from ships represent about 13% and 12%, respectively, of the global anthropogenic SO\({}_{\rm x}\) and NO\({}_{\rm x}\) emissions. In total, 95% of the total maritime NOx emissions are NO (nitric oxide) and 5% are NO\({}_{\rm 2}\) (nitrogen dioxide). The present work focuses on the development and pilot operation of an advanced novel Selective Catalytic Reduction of NOx with H\({}_{2}\) (H\({}_{2}\)-SCR) technology for the elimination of Nitrogen Oxides (NO\({}_{\rm x}\)) emitted from ship exhaust gases. For the proper operation of the novel H\({}_{2}\)-SCR de-NO\({}_{\rm x}\) unit, two additional conventional technologies were employed for the removal of SO\({}_{\rm 2}\) and Particulate Matter (PM). In particular, the proposed novel H\({}_{2}\)-SCR de-NO\({}_{\rm x}\) technology was combined with a Sea Water Absorption (SWA) unit and an oxidative catalytic system. A pilot unit has been successfully designed, assembled and implemented on a cruise ship for the abovementioned purposes. This effort is considered to be pioneering and is here attempted for the first time worldwide. It was proven, for the first time ever, that the Selective Catalytic Reduction of NOx with the use of H\({}_{2}\) as a reducing agent in combination with a suitable catalyst can be considered a suitable NOx-pollution control technology for ships. In particular, it was found that more than 80% of NOx (to N\({}_{\rm 2}\)), 99.8% of SO\({}_{\rm 2}\) and 72% of PM can be reduced by using the present combined SWA and H\({}_{2}\)-SCR technologies.  
Keywords:H\({}_{2}\)-SCR; ship emissions; depollution; sea water adsorption +
Footnote †: journal: Applied
# Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems
## 3 Modeling of the Milling Machine  
### Modeling of the Joint/Interface  
#### 3.1.1 Rolling Interface  
According to Hertzian theory, the contact force between a rolling element and the raceways can be related to the local deformation at the contact point as the expression (Figure 2):  
\[Q=K_{h}\delta^{3/2} \tag{1}\]  
where \(Q\) denotes the contact force and \(\delta\) is the elastic deformation at the contact point. \(K_{h}\) represents the Hertz constant, which is determined by the contact geometry of the ball groove or raceway and the material properties of the contacting components. Details are available in the literatures [26,27]. The contact stiffness at the contact point can further be determined from Equation (2).  
\[K_{n}=\frac{\mathrm{d}Q}{\mathrm{d}\delta}=\frac{3}{2}K_{h}\delta^{1/2}=\frac {3}{2}K_{h}^{2/3}Q^{1/3} \tag{2}\]  
For a roller guide, the relationship between the contact force \(Q\) and the local deformation \(\delta\) at the contact point of the roller to the flat raceway can be described as  
\[\delta=\frac{2Q}{\pi LE_{eq}}\mathrm{ln}(\frac{\pi L^{2}E_{eq}}{Q}) \tag{3}\]  
\[E_{eq}=2/((1-\mu_{a}^{2})/E_{a}+(1-\mu_{b}^{2})/E_{b})\]  
Figure 1: Schematics of a horizontal milling machine with moving column and hybrid guideways. (**a**) Main structure modules of the milling machine, (**b**) \(Y\)-axis guideway system, (**c**) \(X\) and \(Z\)-axis guideway system.  
In above equation, \(E_{eq}\) is the relative elastic stiffness between the contacting bodies. \(L\) is the length of the roller. The normal stiffness is thus given as  
\[K_{A}=\left[\frac{2}{\pi LE_{eq}}\text{ln}(\frac{\pi L^{2}E_{eq}}{Q})-\frac{2}{ \pi LE_{eq}}^{-1}\right] \tag{4}\]  
To reduce the complexity in model creation and mesh generation of the motion components, such as the ball bearings in the spindle unit, the contact configuration between rolling ball and the raceway is modeled as a two-point contact mode. The outer and inner raceways were respectively simplified as a part of the spindle shaft and housing in geometry. The outer and inner raceways are directly connected using a series of spring elements by neglecting the rolling elements, as shown in Figure 2.
## 5 Conclusions  
The aim of the study was to investigate the effects of breathing exercises and a combination of breathing exercises and aerobic program on changes in basic spirometry parameters in standardized diagnostics in patients with bronchial asthma in the presence of a clinical immunologist. In the research work, we applied a three-group time-parallel experiment in which we monitored selected spirometry parameters using a standardized spirometer. In each separate patient group, we observed significant improvements in all parameters examined when comparing pre-training and post-training diagnostics. Comparing the post-training values of the individual groups, we found that participants in experimental group 1, which performed breathing exercises, significantly improved in FEV1 parameters and in FEV1/FVC compared to the control group. Patients in experimental group 2, who performed a combination of breathing exercises with an aerobic program, significantly improved in the parameters FEV1, FEV1/FVC, PEF and FEF25-75 compared to the control group. Based on these facts, we can state that when patients with bronchial asthma perform breathing exercises, there is a significant positive effect on improving their lung function in the aforementioned parameters. However, the application of a program combining breathing exercises with an aerobic program seems to be an option that brings significant improvements in spirometry parameters. When comparing the effects of experimental factors, we did not find significant differences between the groups. The reason for this finding may be the nature of aerobic activity, which was performed at higher pulse values and contained higher frequencies of breath cycles than breathing exercises, which allowed patients to train in a more intense flow zone by walking or running.  
Conceptualization, L.G., D.S. and I.M.; methodology, L.G. and J.L.; investigation, D.S.; data curation, L.O.; writing--original draft preparation, L.O.; writing--review and editing, I.M., L.G., J.L. and L.O.; visualization, L.O.; supervision, L.G. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences ([PERSON]: 1/0089/20).  
All protocols were approved by the Ethics Committee of the Faculty of Physical Education and Sport, Comenius University in Bratislava (number 1/2020). Participants provided written informed consent.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical and privacy restrictions.
## 3 QoS Aware MAC Protocols for WMSNs
### Pa-Mac  
PA-MAC [45] is an IEEE 802.15.4 based multi-channel MAC protocol. The superframe structure of PA-MAC is similar to IEEE 802.15.4. But a dedicated channel is used for the beacon frame for transmission of control information such as GTS slot request, traffic priority class, and data channel assignment. PA-MAC classifies traffic into these four categories in a decreasing order of priority: Medical traffic including emergency traffic, on-demand traffic, normal traffic, and non-medical traffic such as audio and video. Accordingly, the contention access period (CAP) is sequentially divided into four sub-phases. In favor of medical data, nodes with higher priority traffic is granted with access to the slots in the sub-phases for low priority traffic. The length of each sub-phase is controlled by the proportion of nodes with respective category of traffic. As for nodes with on-demand traffic and non-medical traffic, their CAP sub-phases are only used to send GTS slot requests to the coordinator while the actual data transmission takes place in the contention free period (CFP). After data transmission, nodes enter a sleep state until the next superframe begins.  
Pros and Cons. PA-MAC is proposed for wireless body area networks (WBANs) that can be considered as a sub-category of WMSNs in which multimedia data has lower priority instead. PA-MAC reduces the collision ratio of traditional IEEE 802.15.4 by using service differentiation, and by transmitting continuous multimedia data in the CFP period. However, the collision ratio is still high compared to unslotted CSMA/CA, especially when node density increases, and the protocol is not suitable for regular WMSNs.
## 3 Analysis of the Dual-Peak Resonance Restructured by Thermal Strains
### Analysis of Type A SMC-FBG
#### 3.1.1. Wavelength Shift \(\Delta\lambda_{dT}\) Caused by the Double-Layer Coating  
Because of \(\alpha_{1}<\alpha_{3}<\alpha_{2}\) and no relative displacement exists between the inner coating and FBG, the factual elongation of the inner coating will be smaller than \(\Delta L_{2}\), the factual elongation of FBG will be greater than \(\Delta L_{1}\), and the factual elongation of the outer coating will be greater than \(\Delta L_{3}\). At the same time, the thermal stress is satisfied by Equation (3a). The factual elongations of the outer coating, inner coating and FBG are satisfied by Equations (3b) and (3c).  
\[\sigma_{dzz}A_{2}=\sigma_{dzz}A_{1}+\sigma_{dzz}A_{3} \tag{3a}\] \[\alpha_{1}\Delta T\frac{L_{1}}{2}+\frac{\sigma_{dz1z}}{E_{1}} \frac{L_{1}}{2}=\alpha_{2}\Delta T\frac{L_{2}}{2}-\frac{\sigma_{dzz}}{E_{2}} \frac{L_{2}}{2}\] (3b) \[\alpha_{2}\Delta T\frac{L_{2}}{2}-\frac{\sigma_{dzz}}{E_{2}} \frac{L_{2}}{2}=\alpha_{3}\Delta TL_{3}+\frac{\sigma_{dzz}}{E_{3}}L_{3} \tag{3c}\]  
where \(\sigma_{dz}\) (\(i=1,2,3\)) represents the thermal stress of No. \(i\) layer of coating induced by the double-layer coating. \(A_{i}\) (\(i=1,2,3\)) is the cross section of No. \(i\) layer. Let \(\varepsilon_{dz1z}\) represent the axial thermal strain of the FBG induced by the double-layer coating. From the Equations (3a)-(3c), \(\varepsilon_{dz1z}\) can be expressed by Equation (4),  
\[\varepsilon_{dz1z}=\frac{\sigma_{dz1z}}{E_{1}}=\frac{(E_{2}A_{2}+E_{3}A_{3})( \alpha_{2}-\alpha_{1})\Delta T-E_{3}A_{3}(\alpha_{2}-\alpha_{3})\Delta T}{E_{ 1}A_{1}+E_{2}A_{2}+E_{3}A_{3}} \tag{4}\]  
From Equation (1b), the corresponding wavelength shift \(\Delta\lambda_{dT}\) is expressed using Equation (5)  
\[\Delta\lambda_{dT}=\lambda_{B}(\alpha+\xi)\Delta T+\lambda_{B}(1- p_{\rm e})\varepsilon_{dz1z}\] \[=\lambda_{B}*\Delta T\Big{[}(\alpha+\xi)+(1-p_{\rm e})*\frac{(E_ {2}A_{2}+E_{3}A_{3})(\alpha_{2}-\alpha_{1})-E_{3}A_{3}(\alpha_{2}-\alpha_{3})} {E_{1}A_{1}+E_{2}A_{2}+E_{3}A_{3}}\Big{]} \tag{5}\] \[=K_{dT}*\Delta T\]  
where \(K_{dT}\) is the temperature sensitivity of the SMC-FBG with the double-layer coating.  
\[K_{dT}=\lambda_{B}*\Big{[}(\alpha+\xi)+(1-p_{e})*\frac{(E_{2}A_{2}+E_{3}A_{3}) (\alpha_{2}-\alpha_{1})-E_{3}A_{3}(\alpha_{2}-\alpha_{3})}{E_{1}A_{1}+E_{2}A_ {2}+E_{3}A_{3}}\Big{]} \tag{6}\]
## 11 Further Research Directions and Future Considerations
### Software-Defined Optical Transmission  
As previously mentioned, there has been considerable advancement in the area of COH optical transceivers as there are high-capacity COH transceivers such as 100 Gb/s and 200 Gb/s that are commercially available. Semiconductor companies are relentlessly working on the application-specific integrated circuit (ASIC) development for 400 Gb/s and beyond transceivers [161; 162; 175]. It is noteworthy that the 100 Gb/s-based transmission systems are compatible with the 50 GHz ITU wavelength grid. However, bit rates that are more than 100 Gb/s may not fit into the fixed 50 GHz spectrum slots. Therefore, a flexible grid model is required to meet future bandwidth demands. [175].  
In addition, the scalability and flexibility of most of the transceivers in certain operation scenarios such as SDN and flexible grid DWDM links are debatable [1; 161; 162; 175]. It should be noted that optical technologies that leverage the SDN can help the realization and optimization of on-demand infrastructure and service provisioning to subscribers in an efficient manner [176]. This is mainly owed to the SDN inherent features such as centralized controllers, data plane open interfaces, the global network view, as well as separate data and control planes [176; 177]. With native support for software-defined optical transmission by the transceiver, the system throughput and flexibility can be significantly improved [1; 161; 162; 175]. For instance, optimal use of the transceiver can be achieved in some cases if it can be sliced into portions. In this regard, each portion can be dynamically used to support different applications in accordance with their respective need. In general, adaptive and flexible networks that are well equipped with flexible transceivers and network elements that are capable of adapting to the actual traffic needs are required to address the challenges appropriately [175]. This section focuses on research directions and considerations for software-defined optical transmission systems.
# Study on the Mechanism of Stress Sensitivity Changes in Ultra-Deep Carbonate Reservoirs
###### Abstract  
Quantitative evaluation of stress sensitivity of ultra-deep carbonate reservoirs has been one of the challenges in exploration and development, and the problem of permeability loss law in ultra-deep carbonates under variable stress conditions has not been solved so far and further research is urgently needed. Through experimental and numerical simulation methods, the stress-sensitive evaluation equations were established based on matrix-type carbonate and fractured carbonate reservoirs, the stress-sensitive changes under different Young's modulus were discussed, and the degree of permeability loss under different stresses was evaluated. Finally, the dual-media model of ultra-deep carbonate was established, and the practical application was carried out in the Shunbei area of the Tarim Basin. Studies have shown that (1) under the same effective stress, the stress sensitivity of matrix-type and fracture-type carbonate reservoirs is related to the Young's modulus of the rock skeleton. In matrix-type carbonate reservoirs, rocks with a larger Young's modulus have smaller rigidity and stronger stress sensitivity. In fracture-type carbonate reservoirs, the stress sensitivity is relatively weak under a smaller Young's modulus, and relatively strong under a larger Young's modulus. (2) Measured under the conditions of 87 MPa of peripheral pressure, 50 MPa of flow pressure, and 120 \({}^{\circ}\)C, the effective stress of matrix-type carbonate reservoirs has an exponential relationship with the permeability of reservoirs. The degree of stress sensitivity for fracture-type is generally higher than that of matrix-type reservoirs, and the smaller the Young's modulus, the larger the difference in stress sensitivity. (3) The stress sensitivity of typical ultra-deep carbonates in the Shunbei area of the Tarim Basin is higher by establishing a dual-porosity model based on the initiating pressure gradient, which supports new evidence for the characteristics of ultra-deep carbonates with high-stress sensitivity. In actual production, the impact of stress sensitivity on the reservoir volume calculation and efficient development of ultra-deep carbonate reservoirs requires critical attention.  
Keywords:stress-sensitive; ultra-deep carbonate reservoirs; dual-porosity model; Tarim Basin +
Footnote †: journal: Applied Sciences
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 4 Algorithm Design
### PPO-Based SFC Migration Algorithm  
In the study of VNF migration problems, traditional heuristic algorithms are widely used in practical scenarios due to their simplicity and low computational complexity. However, in high-dimensional and complex decision spaces, these algorithms tend to get stuck in local optima. This limits the overall effectiveness of migration strategy optimization.  
Deep reinforcement learning (DRL) algorithms, with their self-learning capabilities and extensive data analysis power, can explore a more comprehensive strategy space in high-dimensional decision environments. This allows DRL algorithms to find superior migration strategies compared to heuristic algorithms in complex and dynamic network environments. Although training deep neural networks requires significant computational resources and time, they offer notable advantages in decision accuracy and migration strategy optimization for complex networks involving numerous network nodes, links, and service functions [4]. Additionally, the use of experience replay and parallel training techniques can improve training efficiency and reduce resource consumption.  
Therefore, we propose a DRL algorithm based on Proximal Policy Optimization to solve the VNF migration problem. This algorithm converts the optimization objectives into a Markov Decision Process (MDP) and then uses the PPO algorithm to solve the MDP model, obtaining a near-optimal solution.