esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Component Cost and Financial Assumption  
### System Component Cost  
For the simulation analysis, the cost information of required components in this case is provided by the Taiwan system integration company, as shown in Table 4. The capital cost of the PV system is $1.31 \(\times\) 10\({}^{3}\)/kW including PV module, PV inverter, mounting hardware, and other balance of system costs. The annual operation and maintenance (O&M) cost is 1% of PV system capital costs, with 0.25 MWp as a level for simulation. For the battery system, each three battery cabinets (639 kWh) are used as the simulation level. The price per kWh is $290/kWh, which includes DC and AC switchboards, fire protection equipment, air conditioning systems, lighting systems and energy management systems, and 20-foot containers. O&M cost per kWh per year is 1.5% of storage system capital cost, and replacement cost to the total system is 60% of storage system capital cost which is considered based on once replacement in 20 years. For PCS part, the simulation setting and analysis is relative to 0.25 MWp as each level with the price of $123/kW, and then the replacement cost to the total system is 60% capital cost which is considered based on once replacement in 20 years. The above equipment prices include transportation and installation costs. The project lifetime of the simulation analysis in this study is set to 20 years, and the residual value of the equipment is not included in the calculation.
## 5 Integration Options
### Integration at Infrastructure Level  
A first integration approach would be to consider the usage of infrastructure from a different provider, nominally an infrastructure provider. This situation can be assimilated to an Infrastructure-as-a-Service (IaaS) offering in the cloud computing business. The business motivation for this kind of integration could be that of an MEC provider requiring increasing its footprint in a given geographical area with restrictions for deploying new infrastructure, then leveraging on some available infrastructure, for instance provided by a municipality. Alternatively, it could be the case of an initial and fast deployment of MEC services in a certain location while the own infrastructure is being built for that same area. Since MEC makes use of NFVI environments for hosting the applications and other virtualized functions, this scenario leads as well to an integration of NFVI environments, probably requiring the interconnection of the overall NFVI substrates used by the MEC provider. Figure 2 represents the administrative boundary among providers in this model.  
In this alternative, the MEP from Domain A will control and program the virtualization infrastructure through the MD-Mp2 reference point following SDN principles, then it is important that the resources allocated from Domain B to Domain A remain isolated from some other resources in Domain B in order to avoid any kind of conflicting configuration action. This could be performed, e.g., by providing a specific resource slice to Domain A.  
Figure 2: Integration of MEC providers at infrastructure level following an Infrastructure-as-a-Service (IaaS) approach.  
The MD-Mm4 and MD-Mm6 interfaces will depend on the VIM used by the infrastructure provider. It can be assumed the usage of some open solution for the VIM, e.g., OpenStack.  
One component that could be considered apart is the VIM itself. The VIM could be provided or not by the infrastructure provider, that is, Domain B. Alternatively, the MEC provider in Domain A could leverage on the concept of VIM-on-demand [31] for instantiating a VIM on top of the virtualization infrastructure fully under control of the MEC provider. This could facilitate the integration, since the VIM-on-demand could be prepared in advance with the necessary capabilities for making the integration smooth. This would simplify (or even remove) the requirements to be supported by the MD-Mm4 and MD-Mm6 interfaces, since they could appear as being part of the same domain of the MEC provider.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
###### Abstract  
Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.  
p  
pavement management system; road pavement distresses; automated detection; low-cost technologies; deep learning; pavement condition monitoring
# Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
## 3 Finite Element Simulation of Corroded Reinforcement
### Results of Finite Element Simulation  
#### 3.2.1. GUW Signals  
The stress contour of the elastic wave propagating within the reinforcement is shown in Figure 6. The ultrasonic wave forms significant GUWs at the boundaries after multiple reflections, and the axial time domain waveforms of the central node at the end of the reinforcement are extracted for analysis at different corrosion degrees.  
Taking the excitation signal with a frequency of 100 kHz as an example, the GUW signals received at the center of the reinforcement end under different corrosion degrees are shown in Figure 7a, where the direct waveform is shown in Figure 7b, and the reflected waveform is shown in Figure 7c. From the GUW waveforms of different corrosion degrees, it can be seen that the waveforms show a regular change as the corrosion degree increases; that is, the amplitude decreases, and the waveform moves forward, indicating that the  
Figure 5: Geometric models of reinforcement with different corrosion degrees (\(\eta\)).  
Figure 6: Stress contour for reinforcement.  
amplitude decreases with the increase in the corrosion degree, and the arrival time also decreases. This is generally consistent with current research findings [35], and some studies [36; 37] have shown a more obvious trend in the amplitude relative to the acoustic time difference.
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 3 Results
### Visualization of Results  
To evaluate the classification performance of our method more intuitively, we visualize the classification maps in Figure 7. The results show that our method has a good effect on the details of classification targets, especially the highlighted areas marked by rectangles in the figures where the bedrock exhibits slight changes (Figure 7a,c) and the layers change continuously (Figure 7b,d). We further compare our method with other classification method of ice subsurface targets (i.e., reference [10]). Considering that [10] takes OA as the evaluation of window sample classification, which is not suitable for comparison with the OA used to measure the more detailed image pixel classification in this paper, we only qualitatively visualize the classification results of the two methods, as shown in Figure 8. Compared with the bedrock area in the radargram, the classification results of the method reported in [10] in the bedrock area are noticeably wider (Figure 8a,c), which can be caused by fixed window classification. However, our method gets more accurate results in bedrock areas (Figure 8b). Moreover, our method is very fast, taking only an average of 2 s to infer each image on a computer with Intel Core i7-7700 @ 3.6 GHZ and NVIDIA GTX1080 Ti GPU card. The method of [10] needs about 45 s to infer each image by using a cluster of 192 CPUs (at 2.05 GHz).
## 2 Methodology
### Adaptive DEM Flight Space Layering Model
To solve this problem, this study proposes selecting the optimal drone cruising altitude based on the obstacle density value. The definition of obstacle density at a given altitude is as follows:  
\[Dens_{k}=\frac{\sum_{i=0}^{m}\sum_{j=0}^{n}Obs_{k}(i,j)}{m\times n},k=1,2, \ldots,N-1 \tag{6}\]  
The physical meaning of the above equation refers to the percentage of the obstacle occupying the assigned cruising altitude. The _Dens_ threshold parameter exhibits the following trade-offs:  
A higher _Dens_ threshold prioritizes stealth by selecting flight planes closer to the terrain base, but this increases obstacle density and may render path planning infeasible. A lower _Dens_ threshold improves path optimality by selecting higher-altitude flight planes (reducing obstacles) at the cost of increased detection risks. By reasonably selecting the _Dens_ threshold, the corresponding optimal fly_plane \(L_{b}\) can be obtained, with b representing the serial number of the best cruising level. The next higher level serves as the backup_plane used for obstacle avoidance when crossing mountainous terrain. To reduce the computational load, the layered obstacle density calculation begins at \(H_{max}\) and approaches \(H_{min}\) in increments of \(\Delta\)H.
# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 2 Theoretical Background
### Attention Mechanism  
Traditional neural networks cannot pay good attention to the degree mapped by different features in the feature extraction process. Adding attention mechanism to the network can focus on vital information and decrease the attention to redundant information, thus improving the accuracy and efficiency of feature extraction. In this paper, we use a global attention mechanism (GAM) which considers not only the channel information but also the cross-dimensional interaction information of channel information and spatial information [26]. It corrects the initial feature map by channel attention feature map (CA), and then corrects the correction result again by spatial attention feature map (SA) to obtain the final feature map. The GAM's network structure displayed in Figure 4.
## 3 Filter-Level Pruning Based on RL
### DCNN Pruning as a Markov Decision Process
#### 3.1.1 State Representation  
A tuple of features is used to represent the network state, i.e., \(s_{t}=(l_{t},n_{t},k_{t},acc_{t},f_{t})\), where \(l_{t}\) is the index of the layer being pruned, \(n_{t}\) is the number of filters, \(k_{t}\) is the kernel size, \(acc_{t}\) is the network accuracy, and \(f_{t}\) corresponds to the reduction rate of FLOPs.
## 5 Resolving Questions about Dance Language and Scent Detection  
Now that we have covered bees as biosensors and contemporary biosensor technologies, we can return to the premise posed in our introduction: that _scent-directed search is under-valued by the focus on a dance language_. Harmonic radar technology and, we suspect, bee mapping LIDAR, have finally provided technologies for answering long-standing questions about the honey bee dance language.  
By 2004, [PERSON] had concluded that floral scents induce recall of visual and navigational memories in honey bees [113]. Then, in 2005 and 2007 [56,114], [PERSON] and his team reported that the dance communication/flight navigation system of the honey bee was used by forager bees to get them to the vicinity of a food source, but not to the specific location. Only 2 out of 19 tracked bees located the feeder that the dance communication indicated. The remaining bees initiated local searching maneuvers that ranged as far as 200 m from the feeder and lasted as long as 20 min. Other recent researchers using harmonic radar had similar difficulty in showing the dance communication/flight navigation system's ability to direct foragers to unscented food sources [115].  
The dance communication/flight navigation system of the honey bee is not precise. At best it only provides general distance and direction information. The language appears to be only as helpful as directing a person to an office building, but not informing that person where in the building to go. To discover the location of the appropriate office, the person would need to rely on other information such as a building directory. For the honey bee that information would be the scent she gleaned from the dancing forager, her own individual foraging experience, and presumably both scent and visual clues that she uses near the location of the food source.  
Despite historical anecdotes that bees have a poor sense of smell, using PERS, video imaging of free-flying bees, and LIDAR, we have learned much about bee foraging and have published in Technical Reports to the US military Receiver Operating Characteristic curves (ROCs) for honey bee detection of chemicals that one might not expect them to be able to discern. The term Receiver Operating Characteristic came from World War II radar operators who tested the ability to determine whether a blip on the screen represented a signal (object) or noise.  
Our ROCs for scent detection by free-flying bees were independently verified by US Army scientists, based on extensive video documentation, and by Sandia scientists who provided the chemical concentrations in the air just above buried substances. Published by the Rand Corporation [12], the ROCs for bee detection of 2-4 dinitrotoluene (DNT) in soils at PPB and at PPT levels were nearly perfect (Figure 14). A perfect sensor would have a value of 1.0 for the area under the curve (AUC), a theoretically perfect test (appearing in the upper left hand corner of chart). An AUC of 0.5 indicates no discriminative value. Bees at PPB and PPT scent concentrations scored above 99.9% for instantaneous scent detection.
## 3 Results and Discussion
### Response Surface
Figure 13: Parametric Mapping of Power Dynamics in [W]: Contour Analysis Across Angular Velocity, Shaft Diameter and Clearance with Hold Values from Response Optimization. Conversely, negligible wear on the reverse side of the screw blade was not taken into consideration. To approximate the quantitative wear results, the Archard model was utilized, relying on the predicted wear coefficient as depicted in Figure 14 (left). It's crucial to emphasize that the wear coefficient was not calibrated but rather assumed based on a literature review. Moving forward, calibration of this coefficient would be essential to ensure accurate quantitative representation in the model. Our investigation expanded beyond the minimization of wear depth to encompass an evaluation of the structural integrity of the screw blade. Figure 15. showcases the stress field data obtained near the support location. The initial conditions involved connecting the screw blade with the shaft to fixed support on specifically created surfaces on each side of the shaft. On one side of the shaft, a cylindrical support was added, with only the radial component fixed. This setup allows rotation and movement in the axial direction while constraining radial movement. In order to apply loading conditions, node forces were employed, which were transferred from DEM simulations. The measuring point is positioned away from the edge of the shaft. Notably, the measurement point is distinctly identified with an additional label. The intention behind this measurement was to avoid any singular points and acquire stress values that are not realistic. By employing FEM analysis at all time steps, we discovered that the highest levels of stress for this specific geometry occur near the support, where the transmission of torque takes place, as well as in the middle of the shaft. Furthermore, the responsive surface is depicted in Figure 15. demonstrates that as the clearance distances increase, so do the stresses. During the design phase, the stresses were found to be insignificant, so they were not considered back in the optimization process, which solely focused on the wear depth and mass flow. However, it would be interesting to explore the potential impact of incorporating stress optimization into the analysis. Similarly, our exploration extended to deformation analysis, delving into the structural integrity of the system. Figure 16. display case for the deformation field, particularly emphasizing maximal deformations occurring at the midpoint and increasing with greater clearance distances. Deformations were also negligible and not included in the optimization process. However, considering their potential impact on design, further exploration could provide valuable insights into structural considerations. Figure 14: Qualitative screw blade wear (**left**), Parametric Mapping of Shear Intensity Dynamics in [W/m\({}^{2}\)]: Contour Analysis Across Pitch, Angular Velocity, Shaft Diameter, Screw Diameter, and Clearance with Hold Values from Response Optimization (**right**).
## 3 Results and Discussion
### Microhardness and Nanoindentation Analysis of Al-BiSn Composites  
The microhardness variation of the Al-BiSn composites with BiSn amount is shown in Figure 7a. The microhardness of the extruded Al-BiSn composites increases with the increasing BiSn content. The hardness increases from 36 \(\pm\) 2 (pure Al) to 78 \(\pm\) 4 HV (15 wt.% of BiSn), indicating an increment of 117%. The increasing trend of the hardness in Al-BiSn composites with an increasing amount of BiSn reflects the efficient and uniform dispersion of BiSn particles in the Al matrix. This improvement in  
Figure 6: Two-dimensional and three-dimensional AFM micrographs of (**a**) pure Al and (**b**) Al-(5 wt.% BiSn), (**c**) Al-(10 wt.% BiSn), and (**d**) Al-(15 wt.% BiSn) extruded composites.  
hardness with increasing concentration of BiSn particles can also be ascribed to the strengthening effects of BiSn reinforcement. Furthermore, near-dense materials synthesized with the powder metallurgy method, microwave sintering, and hot extrusion may have also contributed to enhancing the hardness of the developed Al-BiSn composites.  
Nanoindentation analysis of extruded Al-BiSn composites was further conducted to have more insight into the mechanical behavior of the developed composites. The load-displacement curves are presented in Figure 7b. A lower depth of penetration is noted for the developed Al-BiSn composites when compared to pure Al. Moreover, the depth of penetration decreases with the increasing amount of BiSn particles, suggesting an increase in hardness with an increasing amount of reinforcement (Figure 7c). The increase in the hardness and decrease in the indentation depth with the increasing BiSn volume fraction show that hard BiSn particles are homogeneously dispersed in the Al matrix and offer a hindering effect to the indenter. The highest resistance offered to penetration is \(83\pm 2\) GPa for Al-BiSn composites containing 15 wt.% BiSn. The nanoindentation results of Al-BiSn composites are in line with their Vickers hardness results.
# Studying Semi-TCP and Its Application in Marine Internet
## 1 Introduction  
Marine Internet aims to provide Internet services in marine environments for users and applications on the water surface and under water. One type of user comes from civil sectors, including seamen, fishermen, and yacht passengers. These users have to be provided with seamless Internet access and kept connected with the rest of the world during their sea voyages. Another kind of user is from industry sectors, such as maritime transportation and offshore oil industry. TCP protocol has good performance for terrestrial Internet, but its performance in marine Internet declines significantly. The traditional TCP protocol always considers that the loss of data packets is caused by network congestion, but in the marine Internet, which consists of shore-based networks, ship ad hoc networks, high-altitude communication platforms, and satellite networks [1; 2; 3], it is easy to cause packet loss due to channel loss and dynamic routing rather than by congestion. When TCP is applied to wireless ad hoc networks, such non-congestion packet loss will lead to misjudgment of the TCP congestion and reduction of the transmission rate. Finally, it causes a waste of channel resources when there is no congestion, and is not conducive to relieving congestion quickly when there is congestion. Aiming at solving the above problems, people have proposed a variety of TCP improvement schemes for wireless ad hoc networks [4; 5]. But, most of the improvements are based on the original TCP protocol structure, such as ADTCP [6; 7; 8], TCP-AP [8], and so on; their congestion control [9; 10] function is still implemented in the transport layer [11], they show certain advantages only in specific environment or applications, but the overall performance is not satisfying.  
Different from the above-mentioned TCP variants proposed for wireless networks, Semi-TCP puts the congestion control function originally in the transport layer to the data link layer [12] so that the controller can learn accurately and quickly the congestion status of the network. Through hop-by-hop control, it can improve the efficiency of congestion control. The marine Internet has the characteristics of low node density, high channel loss rate, and relatively stable node movement speed [13]. Combined with the characteristics of the Marine Internet, this paper enhances the congestion control method of Semi-TCP using RTS/CTS (Request To Send/Clear To Send) to improve TCP transmission performance in the Marine Internet.