esa-sceva/satcom-chunk-collection huggingface dataset:
# A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
## 3 Validation by Model Experiment
### Experimental Design  
The experimental apparatus consiste of three parts: the test bin, the loading system, and the monition system, as shown in Figure 3. The dimensions of the test bin were 1 m in length, 0.6 m in width, and 1 m in depth. The loading system consisted of a frame, wire rope, fixed pulleys, and weights. The frame provided the reaction force, the wire rope transmitted the tensile force, the fixed pulleys changed the direction of the force, and the weights generated the tensile load. The monitoring system comprised a force sensor, data acquisition device, displacement sensor, and a computer. The force sensor was placed between the steel wire rope and the anchor cable to measure the actual applied load. The displacement sensor was placed below the weights to measure the anchor cable's pull-out displacement, as shown in Figure 3.  
The boundary effects plays an important role in obtaining reasonable experimental data [34; 35]. [PERSON] [35] and [PERSON] [36] indicated that boundary effects can be neglected when the distance from the boundary to the failure zone is more than 10 times the size of the failure zone. [PERSON] [37] and [PERSON] [34] stated that boundary effects become negligible when the distance between the anchor cable and the test bin boundary exceeds 10 times the anchor diameter in sandy soils. Thus, the test bin dimensions (1 m \(\times\) 0.6 m \(\times\) 1 m) were approximately 30 times the diameter of the predicted failure zone in our experiments, which means that the boundary effects can be ignored in the experiment.  
Table 1 presents details of the experimental scheme. Five groups of pull-out tests were conducted on anchor cables with different inclinations, while the burial depth and fixed anchor length were kept the same. As shown in Figure 3, the fixed anchor length was 12 cm, and the buried depth was 7 cm.  
Figure 3: Experimental apparatus.
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 2 System Overview  
The schematic representation of the proposed system is shown in Figure 1. It can be divided into two major modules: (1) automatic selection of navigation goal; (2) segmented global path planning. The first module is used to select a navigation goal based on human-robot speech interaction and affordance theory. The second module is used to generate ANPs and segment the global path planning into each topological area.  
First, object semantic grid map and semantic topological map are built for the indoor environment. Semantic topological map can provide the rooms' semantic information and topological information. Object semantic grid map contains the objects' semantic information, occupied space, and semantic information of object surrounding space (affordance and nonaffordance space). Then, using the users' natural language as input, the system runs speech recognition and word segmentation to obtain the navigation goal's room, object, and spatial relationship (front or beside). By querying object semantic grid map and semantic topological map, the location of the target home and object can be determined. Next, according to the target spatial relationship, robot will choose affordance or nonaffordance space as the goal space and select a point from it as the navigation goal point. The orientation of navigation goal, points to the coordinate of the target object. Afterward, to improve the navigation success rate and decrease the effects of sensor noise, localization error, and motion error, we use the coordinates of the door endpoint and the dominant direction of the room, which are generated in the process of building the topological map, to generate auxiliary navigation points (ANPs) on both sides of the door. ANPs can be used to correct the robot's trajectory through door. Finally, to improve the efficiency of global path planning, we carry on topological path planning in the topological map and get the global topological path. We segment the global path into each topological area and run path planning respectively by using ANPs as the end of every part of the global path.  
In additional, considering that theories for local path planning is already achieved, we directly adopt dynamic window approach algorithm [36] to create the kinematic trajectory and focus on the automatic selection of navigation goal and segmented global path planning.  
Figure 1: System overview. The system contains two major parts: automatic selection of navigation goal and segmented global path planning.
## 2 Background and Motivation
### Motivation  
Object based big data transfer systems (OBDTS) leverage the layout architecture and employ layout- and congestion-aware object scheduling to improve data transfer rates. However, these enhanced scheduling algorithms transfer file objects and dataset files out of order. This out-of-order nature of the data transfer necessitates higher memory and complex sorting algorithms to generate consistent file, and dataset level signatures for data integrity verification. Consequently, one of the main objectives of end-to-end data integrity verification frameworks is to minimize the computational and memory overheads  
Figure 2: Standard Bloom filter. (**a**) Bloom filter insert operation. (**b**) Bloom filter query operation.  
associated with data integrity verification, thereby optimizing the overall data transfer performance. The TPBF method of data integrity [17] addressed these issues by exploiting Bloom filter's memory efficiency and insertion-order-independent features.  
The Bloom filter is a simple, space-efficient, and insertion-order-independent probabilistic data structure that can be used for both insertion and lookup operations in constant time [18; 21]. This data structure is based on hashing and is similar to a hash table that uses hash functions to map an element in the set to a bucket. However, unlike a hash table, a Bloom filter does not store the element in that bucket; instead, it simply marks the bucket as filled. Hence, multiple elements in the set may map to the same filled bucket, resulting in false-positive errors. In this work, we aim to avoid the false-positive errors by exploiting the lower false-positive characteristics of the cross-referencing Bloom filter data structure [22].
# Corrosion Behavior of Zn-Al-Mg-Si Coatings in Sulfur Dioxide-Containing Environment
## 3 Results and Discussion
### Cross-Section Microstructure of Coatings  
Figure 3 and Table 3 show the cross-sectional microstructure of Zn-Al-Mg-Si coating and corresponding EDS analysis. The general image of the coating shows a two-layer structure: outer layer and diffusion layer (Figure 3a). The main structural components of the outer layer are Al-rich dendrites (point 1), MgZn\({}_{2}\) intermetallic and lamellar eutectic structure in interdendritic areas. The microstructure of lamellar eutectic is shown in Figure 1c. It consists of light lamellars of Zn-rich phase (point 4) and dark lamellars of MgZn\({}_{2}\) intermetallic. Si precipitates (point 3) are also located in the outer layer. The structure of the outer layer can, therefore, be described as dendrites of the Al(\(\beta\)) solid solution with interdendritic spaces filled by MgZn\({}_{2}\) intermetallic and the Zn/MgZn\({}_{2}\) eutectic, in which Si precipitates occur locally.  
The diffusion layer of the coating consists of two zones (Figure 3d). The compact layer of low thickness that forms near the substrate (point 7) turns into a zone of crystals with a regular structure (point 6). The structural components of these zones, although they show different morphologies, have similar chemical compositions, and EDS analysis allows the identification of Al, Fe, Zn and Si in their micro-area (Table 3, points 6 and 7). These results correspond very well with the content of components in the diffusion layer of ZnAl12 Mg3 Si0.3 coatings in FeAl\({}_{3}\) intermetallics [15]. Si and Zn have high solubility in Fe-Al intermetallics in the solid state [24]. As the authors show in their work [15], the total content in at.% (Al+Si+Zn) is close to 75 at.%, which, with a Fe content close to 25 at.%, gives the atomic ratio (Al+Si+Zn)/Fe corresponding to FeAl\({}_{3}\) intermetallics. The atomic ratio (Al+Si+Zn)/Fe at points 6 and 7 is 3.01 and 2.93, respectively. This may suggest that  
Figure 2: XRD spectra of top surface of the Zn-Al-Mg-Si coating.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point No.** & **Mg (at. \%)** & **Al (at. \%)** & **Si (at. \%)** & **Fe (at. \%)** & **Zn (at. \%)** & **Phase** \\ \hline
1 & - & 89.3 & - & - & 10.7 & Al-rich phase \\
2 & 34.9 & - & - & - & 65.1 & MgZn\({}_{2}\) intermetallic \\
3 & - & 6.3 & 91.6 & - & 2.1 & Si particles \\
4 & 0.6 & 11.1 & - & - & 88.3 & Zn-rich phase \\
5 & 6.7 & 3.2 & - & - & 90.1 & MgZn\({}_{2}\) intermetallic \\
6 & - & 60.9 & 5.8 & 24.9 & 8.4 & Fe(Al,Si,Zn)\({}_{3}\) \\
7 & - & 59.1 & 6.1 & 25.4 & 9.4 & Fe(Al,Si,Zn)\({}_{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of EDS analysis of a cross-section of Zn-Al-Mg-Si coatings and the corresponding phase (analysis points as shown in Figure 2).  
Si and Zn atoms replace Al atoms; therefore, as presented by [PERSON] et al. [25], the phase forming the diffusion layer of the coating can be written as Fe(Al,Si,Zn)\({}_{3}\) intermetallic.
## 5 Conclusions  
This study investigates the effects of three main design parameters of the WGB, including the main wind deflector plate \(\theta_{1}\), the upper deflector angle size \(h_{1}\), and the root width \(B_{1}\), on the aerodynamic characteristics of a train-bridge system through wind tunnel experiments. This study combines analysis of the mean and fluctuating wind pressure coefficients on the train and bridge surfaces, tests of the aerodynamics of the bridge and barrier, and wind field analysis on the bridge deck to draw the following conclusions:  
(1) The WGB is a better wind barrier solution for bridges. It provides comparable windproof performance for the train while reducing barrier resistance and the resistance of the bridge-barrier system by 32% and 12%, respectively.  
(2) The effect of changing \(\theta_{1}\) on the aerodynamic characteristics of the train-bridge system is not significant. The design parameters \(B_{1}\) and \(h_{1}\) for the WGB have a significant  
Figure 16: Effect of various WGB cases on turbulence intensity for the bridge deck wind field: (**a**) Case 2; (**b**) Case 3; (**c**) Case 4; (**d**) Case 5; (**e**) Case 6; (**f**) Case 7.  
impact on the mean aerodynamic coefficients of the train. Changing each installation parameter will affect the pulsating wind pressure at the shoulder of the train, and the extreme aerodynamic coefficients of the trains can also change, reaching up to 60%.  
(3) The WGB can effectively reduce the wind velocity on the bridge and has a strong guiding effect on the airflow. The max values of the transverse wind velocity ratio and vertical wind velocity ratio behind the WGB can reach 0.25 and 0.17, respectively.  
(4) Changing \(\theta_{1}\) has a small effect on the wind field on the bridge, while \(h_{1}\) has a significant effect on the transverse and vertical guiding effects. When \(h_{1}\) is reduced, the max value of the transverse wind velocity ratio increases by more than 0.1. Increasing \(B_{1}\) enhances the blocking and guiding effects and suppresses the intensity of the jet near the bridge surface, which is beneficial for the windproofing of the train.  
Conceptualization, D.G., S.J. and Y.Z.; methodology, X.H.; software, D.G. and S.J.; resources, X.H.; writing--original draft preparation, D.G. and S.J.; writing--review and editing, Y.Z., X.H. and Q.L.; visualization, Q.L.; supervision, S.J.; funding acquisition, Y.Z. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundations of China (Grant No. 51925808), the Natural Science Foundation of Hunan Province (Grant No. 2022 JJ10082), the Science and Technology Innovation Program of Hunan Province (Grant No. 2021 RC3016), the Fundamental Research Funds for the Central Universities of Central South University (No. 2023 ZZTS0952).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Numerical Analysis of Bicycle Helmet under Blunt Behavior
## 3 Results
### Head Acceleration Response Validation in Standardized Impacts Results
#### 3.1.3 Helmet-Head Numerical Simulation Validation  
The results of the flat and curbstone anvil impacts agree with previous experimental studies in terms of loading and unloading curves, maximum head acceleration, and effective impact duration with less than a 16% difference in these parameters, validating the simulations.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 2 State of the Art
### Related Works
#### 2.3.3 Comparison of Real Measurement and Corresponding Simulations  
This part deals with papers where the network simulations correlate with a real measurement of a network, such as a smart grid. Ref. [51] was mentioned in Section 2.3.1 as a reference to HPAV simulations in NS-2. The HPAV recommendations are not intended for smart grid application, but the simulations should match real scenarios of BPLC systems. The simulation setup is presented in detail where the medium access method, data segmentation of UDP, TCP traffic and queue processing techniques were used for performance evaluation. The paper presents the developed HPAV model in a validation process by the average throughput comparison of the simulated model and the real system. The real HPAV system configuration is not described and the specific name of the HPAV device is not given, but only the information that the configuration of used devices is based on the default parameters specification of the HPAV. This paper demonstrates the fact that an NS-2 simulator can produce almost the same simulation results as the measured results of a real BPLC system.  
Ref. [59] is another paper where the comparison of PLC simulation and real measurement is provided. This examines the standardized NPLC frequency band (CENELEC A) intended for advanced metering infrastructure (AMI). The overhead middle-voltage line was used for testing. Results of the comparison show the change in attenuation depending on the frequency of the simulation and measurement. The maximum deviation between the simulated and measured values is approximately 2 dB. The article does not state a specific method of performing the simulation. The main goal of the work is focused on the problems of measuring and procedures for characterization of MV grids for NPLC.  
Ref. [56], described above, is also related to this section. Advanced BPLC simulation of the G.hn standard was performed with topology, which was created on the basis of a real BPLC network. Table 4 shows a paper overview of the works dealing with comparison of performed measurements under real conditions and corresponding simulations.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 7 Recent Advances in Deep Learning-Based SLAM
### Pose Estimation for Underwater SLAM  
Accurate pose estimation, the process of determining an underwater vehicle's 3D position and orientation, is vital for underwater SLAM. This section explores recent advances in DL-based pose estimation methods, significantly improving underwater SLAM performance.  
Hou et al. [132] introduce the AMB-SLAM online algorithm for underwater localization in featureless seabeds using acoustic and magnetic beacons. AMB-SLAM utilizes dense neural networks to map between beacon positions and the vehicle position. Another approach proposed by Risholm et al. [133] leverages an EfficientNet CNN feature extractor with bi-directional feature pyramid network for identifying Aruco markers and subsequently obtaining the vehicle's position.  
While dense neural networks are reliable in some applications, they do not factor in the temporal aspect of the input information. Hence, recurrent neural networks (RNN) have proven their reliability in localization since they take into account the time series information of the input inertial, visual, and other sensor data. Specifically, long short-term memory (LSTM) networks have proven their superiority in underwater dead reckoning navigation [134], while gated recurrent unit (GRU) networks have shown enhanced performance as particle filters for underwater target state estimation [135].  
Research work by Teixeira et al. [136] leverages a CNN-LSTM network for underwater pose estimation utilizing a single frame at a time. Similarly, Sudevan et al. [137] evaluate the performance of visual-selective visual-inertial odometry (VS-VIO), a hybrid learning-based multimodal pose estimation framework shown in Figure 12, in underwater environments characterized by low lighting and high turbidity. Unlike in previous work, the proposed network feeds multiple image sequences to the CNN-LSTM network at a time. When testing on the AQUALOC dataset, findings indicate that VS-VIO can dynamically reduce visual modality usage while maintaining accuracy. More recently, the attention mechanism has emerged as a powerful tool for sequence processing. Research by Li et al. [138] showed that a CNN-attention network to process an underwater MEMS IMU sensor can significantly reduce the overall trajectory error.  
Moreover, recent research has aimed to utilize DL with DVL sensors for more accurate pose estimates. For instance, BeamsNet [139] is a neural network of dense 1-dimensional convolutions that combines extracted features a gyroscope, and an accelerometer with the DVL readings. Similarly, Topini et al. [140] evaluate different network architectures, including 1-dimensional convolutions, LSTM, dense layers, and Conv-LSTM layers for vehicle velocity estimations in case of temporary DVL failure.  
These advancements highlight the growing capability of CNNs when utilized along with recurrent networks for accurate pose estimation in underwater SLAM.
## 3 Motivation and System Model  
### Motivation  
V2C communication enables information exchange between vehicles and cloud servers. V2C communication plays a significant role in the periodical transmission of vehicle information such as vehicle position and speed, as well as sensing information obtained from the camera, LiDAR, and GPS receiver mounted at the vehicle. Hence, the reliability of V2C communication is associated with driving safety, collision avoidance, and coordination among vehicles.  
For a V2C path, packet loss may occur due to wireless communication issues at the vehicle side and potential congestion at the cloud server side. Hence, reliability in terms of packet losses on a single V2C path is constrained to support high-reliability communication between the vehicle and the cloud server. To improve V2C reliability, duplicated packet transmission across multiple paths is a way to improve reliability. First, duplicated delivery of packets across multiple paths increases the probability of receiving information at the cloud server. Second, with advances in multipath transport protocols and the low cost of wireless interfaces for vehicles, multipath delivery of V2C packets is practical.  
On the other hand, improving reliability with multipath-based duplicated packet delivery comes with the cost of path redundancy at both the sender and receiver sides. Therefore, we attempt to address the issue of enabling V2C reliability while minimizing unnecessary redundancy. This issue is important due to the increasing number of cloud-connected vehicles and the increasing role of V2C in promoting driving safety, automated driving, and intelligent transport.
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### The 5G Project Use Cases
#### 3.2.10 Security Issues  
Although 5G standards are still being defined and large-scale commercial implementations are not expected before 2020, a sure challenge is that the market acceptance and success of 5G services will require a gradual improvement in security. Any perception that 5G technology relies on uncertain security aspects will decrease user confidence and will expose system operators and service providers to lower investments and to legal and regulatory implications. In this context, mobile security and managed security services are one of the most intensive sectors in terms of innovation, to respond to the challenges in the field of cyber-security inherent to digital transformation for the Communication Service Providers and their customers. In particular, agility and automation are the basic elements of digital transformation for the Communication Service Provider and its customers. Fundamental to any service, security must integrate into these agile and automated ecosystems.  
Furthermore, the 5G promises to reshape services and promote innovation. With increased bandwidth, multitenant edge computing, and several innovative services, from high-bandwidth connectivity to machine learning and AI, security has never played a major role in the mobile operator's infrastructure. The protection of the virtual infrastructure of mobile operators in 5G requires a powerful, scalable, and agile security.  
In summary, the main 5G security challenges are as follows:  
* protecting a virtual and agile mobile core with 5G;
* integrating security services into new innovative services offered through 5G such as mobile edge computing (MEC) and network slicing;
* providing a complete ecosystem of security services to generate new revenue, create competitive advantage, and increase customer loyalty.