esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Arctic-Based Field Observations
In an effort to obtain a rock sample, the Arktika-2012 expedition was initiated, for which several sequence steps were taken, i.e., detecting the location of bedrock exposure, determining site parameters, and conducting video-photometric and sonar detection (see the summarized illustration in Figure 19). Acquisition of gravity and magnetic data was conducted later using two gravimeters, Chekan-AM and Shelf-E (manufactured by Elektobribor), which were mounted on two involved research vessels in the Arktika-2014 expedition. Additionally, the SeaPath 330 satellite integrated navigation system was used for offshore geodetic data collection. As backup secondary systems, C-Nav-2050R and C-Nav-3050 were also set to confirm the designated location reading. Figure 19: The technical process of rock sampling in Arctic expeditions [106]. Figure 18: Measurements of the bow and WII: (**a**) wave height and (**b**) crossing period (based on data in [105]). Similarly to the discussed work, researchers from Japan and Australia performed co-research, but on a quite different subject. As presented by [PERSON] et al. [107], the consortium conducted a field survey during the R/V Mirai Arctic expedition in 2019 to observe on-ice wind waves. The specific location was also designated, i.e., the western part of the Arctic Ocean. In the expedition, near-surface atmospheric and oceanographic variables were measured using the Shipboard Oceanographic and Atmospheric Radiation (SOAR) measurement system. Specifically, the air temperature and wind characteristics were recorded using an anemometer (Model 05106). To provide atmospheric data, including land and oceanic climate per hour, ERA5 (atmospheric reanalysis introduced by European Centre for Medium-Range Weather Forecasts--ECMWF) was deployed. Several buoys, namely, spotter buoys (low-cost, compact, and solar-powered buoy developed by Sofar Technologies, Inc. (San Francisco, CA, USA), were deployed in a designated location (see Figure 20) to measure the spectral distinction between the wave characteristics in the non-ice ocean and those in the sea ice-covered area. Satellite instrumentation was also considered in this work. Synthetic aperture radar (SAR) was used to provide different images of the sea ice-covered ocean, such as drifting of sea ice, and ice classification. Finally, passive microwave measurement from the satellite was performed to obtain information regarding the sea ice concentration (SIC) in the marginal ice zone (MIZ). The images from the SAR data acquisition were compared to the retrieved SIC data to quantify wave prediction uncertainties. In this step, the SIC dataset was created by JAXA using the Advanced Microwave Scanning Radiometer 2 (AMSR2).
## 5 Conclusions and Prospects  
In this paper, we investigated the relay selection problem in a cooperative DF-FSO system, which was modeled as a Markov decision process. A DQN-RS relay selection scheme was proposed based on the dueling DQN method, where the channel capacity was derived and maximized by considering the switching loss between the relay nodes. In our DQN-RS algorithm, a state contained both the previous selected relay and the current CSI. An action presented the current selected relay, where the corresponding reward was equal to the derived channel capacity. On the basis of the simulation results, the proposed DQN-RS algorithm outperformed the greedy method. As the switching loss increased, our algorithm became increasingly superior to the greedy method. It was also found that the channel capacity did not increase monotonically with the number of relay nodes, which is different from the traditional case where the switching loss is ignored. It indicates that there is a tradeoff between the number of relay nodes and the switching loss. In addition, it should be mentioned that our algorithm has the potential of practical application through complexity analysis.  
Although both the feasibility and effectiveness of the algorithm have been verified, more work can be carried out. Two aspects of our study should be highlighted. First of all, this paper assumed that perfect channel information can be obtained. When the channel information is not perfect, how to optimize the algorithm is a topic worthy of study in order to achieve better performance. In addition, how to speed up the convergence is another point of interest. Potential improvements include, but are not limited to, optimizing the network structure and the training methods.  
Figure 7: Channel capacity versus the number of relay nodes.  
**Author Contributions:** Writing--original draft preparation, Y.-T.L.; writing--review and editing, Y.-T.L., S.-J.G. and T.-W.G.; visualization, S.-J.G. and T.-W.G.; supervision, Y.-T.L.; project administration, S.-J.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China (NO. 62101527); Funding Program of Innovation Labs by CIOMP.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors also gratefully acknowledge the Optical Communication Laboratory of CIOMP for the use of their equipment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Investigation and Design of the Transonic Laminar Flow Characteristics in a Laminar Aircraft
## 3 Results and Discussion of Sensitivity Analysis  
### Sensitivity Analysis of FSTI  
The calculated states of this section are shown in Table 3. Y/Span represents the spanwise \(C_{p}\) curve section. The spanwise Cp curve sections are mainly 5%, 15% and 25% in Figure 9a. From the \(C_{p}\) calculation results in Figure 9, it can be seen that the variation in the \(FSTI\) has basically no effect on the \(C_{p}\) characteristics of the central experimental wing. On the contrary, from the calculation results of Figures 10 and 11, the length of the laminar flow zone is influenced strongly by the \(FSTI\). When \(FSTI\) is 0.2%, the transition position shown in Figure 10a (about 50% of the chord length from the leading edge of the central experiment wing) is near the starting point of the adverse pressure gradient shown in Figure 9a. When \(FSTI\) is 0.6%, the transition in Figure 10b (about 30% of the chord length from the leading edge of the central experiment wing) occurs in the weak favorable pressure gradient segment shown in Figure 9b. When \(FSTI\) is 1.0%, the transition position in Figure 10c (about 18% of the chord length from the leading edge of the central experiment wing) is near the strong stop point of the favorable pressure gradient shown in Figure 9c. The evidence from the above phenomenon suggests that the transition position varies mainly with the FSTI. At this time, the main factor affecting the transition is \(FSTI\), and the existence of a laminar flow zone cannot be guaranteed by the weak favorable pressure gradient. The transition mode under different \(FSTI\) is the natural transition formed by the sudden Tollmien-Schlichting instability. Therefore, the favorable pressure gradient is not a sufficient condition for the maintenance of laminar flow. The increase in \(FSTI\) leads to a significant advance in the transition position at each station in the spreading direction. The transition positions at each \(Y\)/\(Span\) are significantly advanced due to the increase in the \(FSTI\). The most obvious finding to emerge from this section is that \(FSTI\) has a strong destructive effect on the maintenance of the laminar zone of the wing, and the greater \(FSTI\), the more likely TS instability occurs.
# Improvement Methods for Reduction of the High Stress of Ultra-High Asphal Concrete Core Dams
## 1 Introduction  
Comparing with clay core rockfill dams (CCRDs), the development of asphalt concrete core rockfill dams (ACCRDs) is relatively late. The latter has become a very competitive type of dam because of its good seepage resistance, earthquake resistance, low cost, and ability to adapt to deformation [1; 2; 3; 4; 5; 6; 7; 8]. Since the Kleine Dhuenn dam (it is the world's first compacted asphalt concrete core rockfill dam and the dam height is 35 m) was built in Germany in 1962, there has been significant interest in the construction of ACCRDs all around the world such as the Finstertal Dam (the dam height is 149 m and the core height is 96 m) in Austria, the Storlomvatn Dam (the dam height is 125 m and the core height is 123 m) in Norway, and the Maopingsxi Dam (the dam height is 104 m and the core height is 94 m) and Yele Dam (the dam height is 124.5 m and the core height is 120 m) in China. To date, more than 200 such dams have been built. The International Commission on Large Dams (ICOLD) [9], [PERSON] [10], and [PERSON] [11] summarized the knowledge of ACCRDs in terms of design, construction, and performance. With the depth of research and the accumulation of damming experience [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23], the Quxue Dam [24] was built in China in 2017 (the dam height is 174.2 m and the core height is 132m), which is the highest asphalt concrete core rockfill dam (ACCRD) in the world. China has made a remarkable contribution to the development of ACCRDs. With the deepening of China's western development strategy and the promotion of the Belt and Road (B&R) strategy, ACCRDs will move toward the construction of ultra-high asphalt concrete core rockfill dams (UACCRDs). However, there are few practical experiences and suitable design codes for UACCRDs. Furthermore, there are still numerous mechanical properties of UACCRDs that are not very clear.  
The shear stress (stress level) and tensile stress of ACC will increase with dam height. If the shear stress (stress level) and tensile stress of ultra-high asphalt concrete core (UACC) exceed its shear strength (ultimate stress level) and tensile strength, the UACC will experience shear failure and tensile failure. These situations are hazardous for UACCRDs. Thus, improvement methods that reduce the high shear stress (high stress level) and high tensile stress of UACC are proposed and investigated in this paper. The purpose of this paper is to provide a reference for the construction of UACCRDs in the future.
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 3 Theoretical Background
### Traffic Characteristics  
For our prediction, we used two traffic datasets. The first dataset came from a large telecommunication operator, which collected roughly 21 h of traces in Ireland. We divided them into a fixed scenario, where the UE remained in the same position, and a mobile one, where the UE changed its position. A detailed traffic generation and capture methodology is available in [38].  
Cellular system operation has diverse characteristics that depend on the specific network deployment details, e.g., antenna heights or cell numbers in an area. Furthermore, the geographical environment also impacts the system. For example, in a city, radio waves are more obstructed by infrastructure than in rural areas. These factors can contribute to unique properties in the collected network traffic. Therefore, we also conducted measurements of a 5G system. For the collection of the network throughput data, we employed G-NetTrack Lite. This is a network monitor and a test tool for mobile networks that runs on a smartphone. To obtain the throughput, we repeatedly downloaded from a server a 1 GB file. The large file allowed the TCP sending window to extend to its maximum size. The network monitor logged the throughput with 1 s granularity. In the case of the mobile scenario, denoted as M2, we drove a car between multiple locations separated by an average distance of 2 km. In each location, we stayed for a random time interval, with an average duration of 5 min. This simulated a typical bus or car drive in an urban area with crossroads, traffic lights, traffic jams, and bus stops. We captured the traffic, and after data cleaning, we had 600 min for a fixed scenario and another 600 min for a mobile one.  
Our traces, F2 and M2, had a higher spread between the minimum and maximum values but lower average throughput than the public traces F1 and M1, as shown in Table 2. When examined more closely, both traffic traces had high variability and no clear repeating patterns, as shown in Figure 2. The mobile scenario trace (M2) had slightly higher oscillations and achieved higher throughput as some measurement points were closer to the BS compared to the fixed scenario trace (F2). These high-throughput times were separated by lower-throughput periods where the UE was farther from the BS or was moving. Both traces could be divided into multiple regimes that could be statistically described based on their periods of low and high throughput, as well as periods of low and high variability. The M2 trace exhibited these regimes more clearly, which can be attributed to the switching of the measurement points.  
Figure 2: Throughput measured in 5G network. **Upper figure**—traffic for a fixed UE scenario (F2 trace); **Bottom figure**—traffic for a mobile UE scenario (M2 trace).
## 4 Results
### Numerical Results
#### 4.1.3 Group Structural Similarity Index Measure  
The Group SSIM method significantly improved the error rates compared to the PSNR methods, particularly at depth 1, where only 27 errors were observed. Additionally, the SSIM methods exhibited distinct patterns in Periods 1 and 4, with narrower value spreads. The Group SSIM method with a depth of 1 had an average execution time of 0.2075 s (see Figure 8d).  
Increasing the depth to 2 resulted in a further slight reduction in errors to 25, but the execution time increased to 0.6648 s. Similar to the Group PSNR with depth 2, this method showed a clustering of High-Quality measurements within the Low-Quality range in Period 3 (see Figure 8e).  
Finally, the Group SSIM with a depth of 3 further exacerbated the pattern observed in Period 3, with a larger number of High-Quality values clustered within the Low-Quality range. This method produced 71 errors and had an average execution time of 1.6794 s (seeFigure 8f). Overall, the values appeared less structured than the G-SSIM methods with smaller depths.
# Urban Heritage Facility Management: A Scoping Review
## 4 Results
### Overview of the Results  
#### 4.2.1 Mapping Resources  
The discussion around the first step of the HUL approach, mapping resources, was dominated by the usage of building information modelling (BIM, H-BIM, ACTIVe3D, BIM4 FM) as an information management tool within the \"civic engagement\", \"knowledge and planning\", and \"regulatory systems\" [36; 37; 54; 62; 63; 65]. The usage of BIM technology was not stated by any author regarding the financial aspect of the HUL supporting tools within the mapping resources step, although it is important for efficiency [58] and cost-saving in the long run. As argued by [PERSON] et al. [66], the discussion around financial instruments showed the potential of expanding the PPP model into PPPP (Table 1). At the same time, another author discussed more on the characteristics that might affect the heritage property price and value [67].  
The potential of big data, social media, the Internet of Things (IoT), and artificial intelligence [42; 68] in facilitating people to engage in the mapping resources step voluntarily within the UHFM context was also discussed among authors. The effort to integrate the interoperability of BIM and geographic information systems (GIS) could be a breakthrough for urban information modelling (UIM) [37; 54; 56; 62], or even further, urban heritage information modelling (UHIM). Implementation of the 3D modelling through HBIM (historic-BIM) in heritage buildings' interventions made it possible for stakeholders to understand the significance and necessary actions required in the process [36; 54; 56; 63] and made it easier for the facility managers to project and plan ahead for the future maintenance needs [36; 63; 69; 70]. The authorities could create new requirements on permit application of renovating protected buildings by obligating the stakeholder to provide BIM-friendly data of the building to be added to the heritage database as a part of regulatory systems to accelerate the usage of BIMs [54].  
The review showed the lack of discussion on natural and cultural mapping and identification. Most authors only addressed the mapping of human resources and the processes involved in FM and conservation. It is understandable because the rigid scoping review process produced a very concentrated topic within the UHFM field.
## 6 Conclusions  
This paper presents a sliding mode switch control strategy for both the ride comfort and handling stability of the adjustable hydro-pneumatic suspension system of a rescue vehicle. The switch index of the Switched SMC is defined according to the value of LTR. Three different SMC controllers are designed respectively and tested through the simulations of the half-car model based on MATLAB/Simulink. AHP is applied to calculate the weight coefficients of the objective functions, and the parameters of the controller are tuned by PACSA, the optimization ability of which proved better than that of GA. The performance of the switch strategy is proved through simulations with different road input (random road and bump road), different vehicle speeds, and different steering maneuvers (J-turn steering and Fishhook steering).  
Compared with the conventional SMC controlled hydro-pneumatic suspension system, the simulation results show that the proposed switch control strategy has the following advantages:  
1. The PACSA performs better than a genetic algorithm in terms of parameter optimization of the SMC.
2. The proposed switch control strategy can simultaneously address concerns on both ride comfort and handling stability under different road surfaces and driving conditions through switch actions.  
In the future work, on the one hand, it is essential to reduce the shocks induced by the switch action, which will make the switch control strategy work better. On the other hand, we will apply the proposed control strategy based on related algorithms to a real fire rescue prototype to validate the proposed strategy when the prototype is completed in the future.  
conceptualization, C.Z. and X.L.; methodology, F.X., C.Z. and W.C.; software, C.Z.; validation, F.X., C.Z. and W.C.; writing--original draft preparation, C.Z.; writing--review and editing, C.Z. and F.X.; visualization, C.Z.; supervision, W.C.; project administration, C.Z. and X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Key Research and Development Program of China under Grant No. 2016 YFC0802900.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 6 Training and Validation
### Training and Validation Metrics
#### 6.2.3 Framework Validation  
The overall performance of the framework was validated using video samples captured while creating intentionally missed welds, that were unseen by the framework. The framework predictions were compared with the ground truth. The detector confidence score threshold was 0.25, and the non-maximum suppression (NMS) threshold was 0.3. As presented in Table 9, the confusion matrix, precision, and recall were calculated for each class as metrics. The framework inference time was 0.0167 s.  
Additionally, the framework ability to track the objects was assessed. If the tracker module could keep track of a node until it reached the last 15 percent of the FoV width, we considered the tracking successful. The tracker module was successful for 6073 nodes and it failed for 34 nodes in 20 h of production. Therefore, the success rate of the tracker module was 0.994.
## 3 Experimental Program  
### In Situ Creep Test Device  
A special loading system was designed in order to carry out in situ uniaxial compression creep tests with X-ray micro-photography imaging [43]. As shown in Figure 3a, the experimental system included a manual axial loading frame, a long transparent tube, a stiff base, a force sensor and a data logger. The transparent tube was made of polycarbonate with a high X-ray transmission and a good mechanical stability. The use of such a long tube allowed placing the sample as close as possible to the X-ray source in order to obtain a high resolution. The range of the force sensor was 100 kg and its accuracy was about 0.01 kg. In order to reduce the friction at the top and bottom surfaces of sample and also to obtain clear boundaries in X-ray scanning, the tested sample was placed between two transparent polycarbonate pistons, as shown in Figure 3b. The axial force was manually applied with a slow rate.
## 4 Characterization Framework
### Security  
In an MSA, security is actually gathered under two main headings as in all other systems, as shown in Figure 10. These are authorization and authentication. As shown in Figure 10, both are concepts to be addressed. However, handling these processes in MSA can create a more complex structure compared to the monolithic architecture. There are some best practices and patterns for this.  
For authentication, a token is usually given to the user by performing an identity check through a structure that is developed into a communication task between the external world and the internal world, such as the API Gateway. This token contains the information that the user has authenticated to the system and what his/her permissions are. Thanks to this structure, the user can authenticate from a single point to a structure with many services. This eliminates the disadvantage that many services have relations with the outside world. Moreover, in this way, the services inside will have the convenience of talking to each other with HTTP instead of HTTPS as an example. In this case, however, it should not be forgotten that the API Gateway is a centralized failure point and the design for failure should be carried out accordingly. Another option, for each microservice to run, is having its own authentication and authorization processes locally, as opposed to global management. This situation requires the requests to be authenticated separately for each microservice and the complexity increases. However, each service can use different authentication and authorization methods according to preference and, at this point, a more fine-grained mechanism should be designed.
# Deep Learning Methods for Space Situational Awareness
## 2 Related Works
### DL-Based Methods
#### 2.2.2 YOLO Networks  
The YOLO algorithm uses neural networks to deliver real-time object detection, performing object detection as a regression problem. Because of its precision and velocity, Yolo is used in many object detection applications. In [103], a simple framework for recognizing moving objects is proposed. The framework consists of two steps that are applied sequentially to each video frame. In the first step, using simple background subtraction, regions of interest (ROIs) are found in a video frame. In the second step, a YOLO network (namely YOLOv2 [104]) is used to rank the detected ROIs based on a set of predefined criteria. The framework offers the advantage of reusing background subtraction. In fact, the real processing time dedicated only to the detection of objects in the framework is the CNN inference time. YOLO is also used for machine vision target detection of unmanned aerial vehicles (UAVs). YOLOv3-Tiny optimizes the YOLOv3-based network structure and reduces the output by one scale. In this work, human images are used to train YOLOv3-Tiny to recognize humans as tracking targets. Then, YOLOv3-Tiny analyzes the images collected by the UAV for target tracking. Finally, the tracking system control signal is sent to the flight controller of the UAV [105].