esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Bluetooth-Based Contact Tracing  
Alternative methods of contact tracing have emerged to address the problems of the GPS-based method described above, where contact is detected by exchanging signals based on Bluetooth. The Bluetooth-based contact tracing method collects only anonymized information from devices that are in proximity, utilizing Bluetooth signals, and has the advantage of protecting personal information [16]. Private automated contact tracing (PACT) [17] detects exposure to an infected person based on estimating the distance between users by utilizing Bluetooth. GAEN, which is compatible with Android and iOS, is a representative Bluetooth-based contact detection framework that determines the status of exposure to COVID-19 [8; 9]. GAEN exchanges tokens by interacting with other smartphones through Bluetooth. GAEN measures the signal strength between devices using Bluetooth, and the distance between two smartphones is estimated based on the signal strength. If it is determined that the distance between the smartphones is shorter than a specified distance for a set duration (e.g., 10 min), the beacon information from nearby smartphones starts to save. GAEN conducts exposure detection through the distance-based token exchange but does not reflect the transmission characteristics of the COVID-19 virus. Using this approach, GAEN makes judgments simply based on contact distance, without considering the transmission characteristics of indoor or outdoor environments. Therefore, numerous contact alerts, including casual contacts with a very low transmission probability, may occur in indoor and outdoor environments [10; 11; 18]. However, to capture the nuanced risk of virus transmission, IntelliTrace moves beyond mere distance estimation to a spatial-based contact detection method that considers the environmental characteristics conducive to virus spread.
# Low-Cost Digitalization Solution through Scalable IIoT Prototypes
###### Abstract  
Industry 4.0 is fast becoming a mainstream goal, and many companies are lining up to join the Fourth Industrial Revolution. Small and medium-sized enterprises, especially in the manufacturing industry, are the most heavily challenged in adopting new technology. One of the reasons why these enterprises are lagging behind is the motivation of the key personnel, the decision-makers. The factories in question often do not have a pressing need for advancing to Industry 4.0 and are wary of the risk in doing so. The authors present a rapid, low-cost prototyping solution for the manufacturing companies with legacy machinery intending to adopt the Industry 4.0 paradigm with a low-risk initial step. The legacy machines are retrofitted through the Industrial Internet of Things, making these machines both connectable and capable of providing data, thus enabling process monitoring. The machine chosen as the digitization target was not connectable, and the retrofit was extensive. The choice was made to present the benefits of digitization to the stakeholders quickly and effectively. Indeed, the solution provides immediate results within manufacturing industrial settings, with the ultimate goal being the digital transformation of the entire factory. This work presents an implementation cycle for digitizing an industrial broaching machine, supported by state-of-the-art literature analysis. The methodology utilized in this work is based on the well-known DMAIC strategy customized for the specifics of this case study.  
industrial IoT; Industry 4.0; prototyping; retrofitting solutions; embedded solutions; low-cost Article
## 4 Case Study
### Result Analysis and Discussion
Figure 16 illustrates the quality assessment results based on the following metric thresholds: a vertical distance of 50 cm between two dabs, a horizontal distance between dabs, a ribbon width of 30 cm, a dab diameter of 10 cm, and an adhesive area of 40%. Items that did not meet these criteria were marked in red. The proposed model can derive inspection results in seconds with only the collected photos, which can help automate the existing labor-intensive and time-consuming inspection work and increase the productivity of inspection work. Figure 14: Examples of heavy tilt (**a**) segmentation of Type 1 (**b**) segmentation of Type 5. Figure 15: Examples of thickness distortion: (**a**) the original image and (**b**) the aligned image after the image transformation process. It can be observed that heavily tilted images become distorted after the image transformation process, especially in regions farther from the camera.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 5 Evaluation
### Testbed Results
#### 5.3.1 \(\delta\) Selection
Simultaneously, the results also clearly indicate that the heuristic does not necessarily need the most reliable links to perform well, but can benefit from using faster  
Figure 11: PDR values for different \(\delta\) values for 261 ms and 423 ms slotframe lengths, comparing the heuristic with the GA scheduler and the GA scheduler that can only use the 50 kbps PHY. The \(\mathbf{x}\) denotes the mean value. The diamond shapes represent outliers that are values smaller than the minimum (i.e., the lower whisker), or larger than the maximum, value (i.e., the top whisker). Figure 12: Average number of PHY allocations per iteration for the GA scheduler and the heuristic with different \(\delta\) values, for scenario 2. The error bars in the figures represent the standard deviation. and less reliable PHYs that leave more free space for possible extra slots for retransmissions or other nodes' allocations. Additionally, Figure 11 shows the benefits of using multiple PHYs when only allowing the 50 kbps PHY results in PDRs of 0.36 and 0.82 for scenario 1 and PDRs of 0.36 and 0.82 for scenario 2, for the 261 ms and 423 ms slotframe, respectively. This is explained by the fact that the 50 kbps PHY requires bonded slots with a length of four regular slots, thereby occupying a lot of space in the schedule per transmission attempt and not allowing sufficient free space for extra slots or other nodes to allocate slots. The results for only using the 1000 PHY are not shown as that did not allow for fully connected topologies.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping  
The designed aerial manipulator system achieves autonomous positioning and navigation through LiDAR-based SLAM map construction after the UAV takes off. It performs target search, gathers target object features using the depth camera D435 and laser sensor TFmini, extracts target feature information, obtains the target's coordinate information, and transmits control signals to adjust the swinging angle of the manipulator to achieve accurate target grasping. Below, we discuss the SLAM-based autonomous positioning, visual navigation algorithm for grasping targets, and control method for aerial manipulator operations guided by vision.
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 4 Conclusions  
In this paper, we proposed a self-calibrating RGB-D semantic segmentation neural network model based on an improved residual network for the semantic segmentation of multimodal information. First, we used IResNet [6] to extract features from RGB and predicted depth images. Then, our designed self-calibration network performed multimodal fusion of the depth information and RGB image features and utilized the feature pyramid attention structure to fuse multiscale semantic information. Finally, we presented a bilinear interpolation structure as a decoder module to generate segmentation results with high resolution and rich semantic information. The experimental results for the publicly available Cityscapes dataset [27] and collected forest scene images show that our method outperformed the competitive methods.  
Our model trained with the estimated depth information could achieve comparable performance to the ground truth depth map in improving the accuracy of the semantic segmentation task. However, the reasoning process of our semantic segmentation was divided into two parts; that is, it first predicted the depth map and then performed3 semantic segmentation. Although our method improved the prediction accuracy, it also correspondingly increased the time of single image segmentation. Second, the fusion of RGB images and depth images with the self-calibrating fusion architecture and feature pyramid attention mechanism increased the computational cost of the model.  
In future work, we will focus on semantic segmentation of 3D objects to solve fundamental problems such as unmanned driving, smart healthcare and 3D object recognition.  
* S.C.: writing--original draft, methodology, visualization, investigation, validation and formal analysis. M.T.: methodology, writing--review and editing, conceptualization, supervision and funding acquisition. R.D. and J.K.: supervision, methodology and writing--review and editing. All authors have read and agreed to the published version of the manuscript.
* This work was funded by the National Natural Science Foundation of China (Grant number 32071680); the Science and Technology Fund of Henan Province (Grant number 222102110189); Research and Innovation Initiatives of WHPU; and research funding from Wuhan Polytechnic University (Grant number 2023Y46). The authors gratefully acknowledge this support.
* The data are available from the corresponding author upon request.
* This research was sponsored by Wuhan Polytechnic University and Beijing Forestry University. The authors gratefully acknowledge this support.  
* The authors declare no conflict of interest.
# A Frequency-Selective Reconfigurable Antenna for Wireless Applications in the S and C Bands
## 4 Experimental Results and Discussion
### Comparative Study  
As we have already mentioned, there are several techniques to design and implement a reconfigurable antenna. The comparative study is based on antennas being similar to the one presented in this work. They are all printed on an FR-4 substrate and utilize PIN diodes to attain the reconfiguration. Table 4 shows the key features of the antenna presented in each work, specifically, the dimensions of the antenna, the number of PIN diodes used, and the place of their installation, i.e., whether it is on the patch and/or on the ground plane. The table also includes the operating frequencies of each antenna, as well as its maximum gain, and whether it assumes the advantages of Defected Ground Structure (DGS) or not. Table 4 clearly illustrates the prevalence of employing PIN diodes in prior studies. Multiple diodes were used in the majority of them. In contrast, this study distinguishes itself by utilizing a single PIN diode. Conventionally, reconfigurable antennas tap into the two states of the PIN diodes, ON (forward bias) and OFF (reverse bias). Notably, the antenna proposed herein demonstrates three distinctive modes of operation (based on the two states of the diode). It is worth mentioning that varying the forward biasing voltage can result in multiple modes. The use of a singular PIN diode and the low forward biasing voltage it requires offer convenience, and the process of reverting between the three modes can be very straightforward to automate with devices such as a microcontroller. Moreover, this work employed a Defected Ground Structure (DGS), a feature scarcely used in previous works. By and large, the proposed antenna exhibits an omnidirectional radiation pattern across all frequencies, along with low return loss, a broad bandwidth, and exceptionally high maximum gain. Lastly, the compact size of the antenna makes it convenient for
## 4 Results
### Performance Comparison
The fact that the DR methods require more samples, especially under the fixed 1 s update criteria, implies that more frequent sampling might be necessary to maintain a certain level of performance, whereas the 8B-PGDM method can achieve good performance with far fewer samples. In summary, compared with other traditional methods in Figure 8, our method has the advantage of having fewer sampling times, smooth curve estimation, and small cumulative error.
## 5 Structural Properties of the Electrodeposited Nickel Parts
### Hardness  
The hardness of an electrodeposited nickel part is strongly influenced by the electrolyte chemistry and operating parameters [51; 52; 53]. In electroplating processes with a Watts-type electrolyte, the addition of chloride ions, \(Cl^{-}\), is essential for maintaining the anode's efficiency by preventing passivation--i.e., the formation of a non-conductive layer that disrupts the dissolution of nickel ions--thus ensuring a uniform current distribution and consistent deposition quality [54]. Along with chloride ions, various additives such as grain refiners, leveling agents, and brighteners are employed in the literature [50; 55; 56] to modify the plating chemistry and promote a fine-grained microstructure, which, according to the Hall-Petch effect, increases tensile strength [57], and thus hardness. For example, it has been reported that addition of saccharin in the Watts electrolyte can lead to an increase in the hardness of the deposit from 270 HV to 527 HV [17]. Operating conditions also affect the hardness of the deposited material: it is reported that moderately low deposition temperatures (around 30-40 \({}^{\circ}\)C) enhance the nucleation of deposit and yield a higher density, while higher deposition temperatures may coarsen the grains and reduce the hardness [48; 58].  
Electric current density during the deposition also plays a critical role in the hardness of the deposited material. Moderate current densities (-1-5 A/m\({}^{2}\)) can generate refined microstructures and a hardness between 200 and 300 HV, whereas excessively high current densities can lead to hydrogen evolution, porosity, and dendritic formations that diminish hardness [29; 48; 59; 60]. Therefore, a stable, ripple-free DC rectifier is vital to supply a consistent current density, as fluctuations can lead to uneven deposition and defects that undermine hardness.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 3 System Design
### NDN Mobility Support Design  
As responders need to move a lot during their missions in the disaster response phase, mobility support is an essential feature that our network architecture should provide. Our proposed design is shown in Figure 7.  
After moving, NDN name prefixes from responders' devices need to be advertised to the NDN network to continue message delivery. To minimize the handover time, we add two pairs of client and server at user equipment and border edge so that this advertisement process can be performed immediately at the node that the responder device connects to. Normally, when responders join a group channel, the group prefix and the message prefix will be sent from the advertising client to the advertising server at the edge node. They will be advertised to the NDN network by the NLSR engine here. These prefixes will also be saved in the local database of the advertising client. When moving to a new location and connecting to a new border edge node, the advertising client will detect the network change and inform the ndn-autoconfig [29] client. Ndn-autoconfig client will automatically create an NDN connection with the corresponding server running at the edge node. After  
Figure 6: Multiple concurrent NDN group messaging channels in Seoul disaster scenario.  
Figure 7: NDN mobility support design.  
the NDN connection is established, those saved prefixes at the advertising client will be sent to the server for advertising to the NDN network. At the same time, routes back to user equipment for these prefixes will also be created. At this moment, the responder can send and receive NDN messages normally.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Coordination of Traffic Flows through Intelligent Intersections
#### 3.1.2 Mechanisms for Multiple Intersections
In terms of average travel times, CTA is the best performing policy. CA-CTA has a slightly inferior performance, but it can be shown that it enforces an inverse relationship between bid value and delay, similar to the results presented in the previous section [27]. The fact that both CA-CTA and CTA outperform FCFS is an indication that, in general, a traffic assignment strategy (the \"CTA\" component of both policies) improves travel time. In fact, FCFS drivers always select the shortest route, which in some cases is not the best route choice. Furthermore, granting reservations through an auction (the \"CA\" component of the CA-CTA policy) ensures that bid value and delay reduction are correlated. Figure 6: Urban road network.
# A Point Cloud Registration Algorithm Based on Weighting Strategy for 3D Indoor Spaces
###### Abstract  
Point cloud registration is a technology that aligns point cloud data from different viewpoints by computing coordinate transformations to integrate them into a specified coordinate system. Many cutting-edge fields, including autonomous driving, industrial automation, and augmented reality, require the registration of point cloud data generated by millimeter-wave radar for map reconstruction and path planning in 3D environments. This paper proposes a novel point cloud registration algorithm based on a weighting strategy to enhance the accuracy and efficiency of point cloud registration in 3D environments. This method combines a statistical weighting strategy with a point cloud registration algorithm, which can improve registration accuracy while also increasing computational efficiency. First, in 3D indoor spaces, we apply PointNet to the semantic segmentation of the point cloud. We then propose an objective weighting strategy to assign different weights to the segmented parts of the point cloud. The Iterative Closest Point (ICP) algorithm uses these weights as reference values to register the entire 3D indoor space's point cloud. We also show a new way to perform nonlinear calculations that yield exact closed-form answers for the ICP algorithm in generalized 3D measurements. We test the proposed algorithm's accuracy and efficiency by registering point clouds on public datasets of 3D indoor spaces. The results show that it works better in both qualitative and quantitative assessments.  
point cloud registration; millimeter-wave radar; semantic segmentation; PointNet; ICP; objective weighting strategy +
Footnote †: journal: Applied sciences
## 1 Introduction
Through the exchange of information between vehicles, roads, and the cloud [14], the ETC gantry of highways is used to collect vehicle traffic information, and the intervention of cloud platforms can process and analyze large-scale vehicle data and road condition information, thereby detecting potential threat factors outside the driver's line-of-sight range and transmitting this warning information to corresponding vehicles subscribing to beyond-line-of-sight services. This enables drivers to comprehensively assess the surrounding environment, perceive potential hazards in advance, and minimize the potential risk of traffic accidents [15]. For example, if the vehicle behind approaches the intersection quickly, the system can alert drivers who have subscribed to the beyond-line-of-sight service in advance to avoid potential intersection accidents. This not only helps to improve the decision-making level of drivers, but also lays a solid foundation for the development of future intelligent driving assistance systems. The structure of this article is as follows. The second section summarizes the current domestic and international research status. The third section introduces the description of the problem and provides relevant definitions. The fourth section constructs an expressway over-the-horizon PTV detection algorithm. The fifth section provides the results and analysis of the experiments. The sixth section summarizes the entire text.
## 4 Proposed Approach
### Design Methodology
#### 4.2.6 Legal and Ethical Compliance  
AURORA MINDS ensures full legal and ethical compliance with data protection regulations, including the General Data Protection Regulation (GDPR). All users are informed about the processing of their data through clear and transparent privacy notices, outlining how their data will be processed and the purposes for which it is collected. Consent is obtained before any data is collected, with users given the option to withdraw consent at any time, ensuring that data collection is legally justified. The processing of personal data is limited strictly to the purposes of ADHD diagnosis, adhering to the principle of purpose limitation. Users are notified of retention periods, with personal data stored for a maximum of two years before being anonymized or deleted, in line with the data retention policy. Additionally, data accuracy is maintained through regular checks and updates to ensure compliance with GDPR's requirement for data to be kept accurate and up to date.  
Strong technical measures, such as encryption of personal data in transfer and at rest, secure access controls, and regular security audits, are implemented to protect data confidentiality and integrity. All data processors are bound by agreements that include confidentiality clauses and adherence to GDPR standards. The system ensures that users have rights such as access, correction, and deletion, with requests being processed within 30 days. Personal data is not transferred to third countries unless adequate protection measures are in place. A record of processing activities is meticulously maintained, ensuring accountability and compliance with relevant legal and ethical standards. Furthermore, necessary approvals and ethical reviews from competent authorities have been obtained to ensure full regulatory compliance in data processing.
## 4 Case Study and Analysis
### Wind Tunnel Test
#### 4.2.2 Results and Analysis of the Test  
Two force measurement tests of the scaled model of Prop A and Prop B in different Reynolds number ranges were carried out, as shown in Table 6. One test was performed with a freestream velocity of 4.2 m/s (\(Re_{\text{average}}\approx\) 4 \(\times\) 10\({}^{4}\)), which corresponded to the design point of the propeller. In addition, the other test was performed with a freestream velocity of 16 m/s, which corresponded to the working state of the propeller at a higher Reynolds number of about 1.6 \(\times\) 10\({}^{5}\). Limited by the maximum measuring range of the force measurement device, tests under higher Reynolds numbers were not performed, but the results of these two tests were sufficient.  
The comparison of the wind tunnel test and CFD results under \(Re\approx\) 4 \(\times\) 10\({}^{4}\) and \(Re\approx\) 1.6 \(\times\) 10\({}^{5}\) are shown in Figures 28 and 29, respectively. It can be seen that the thrust coefficient, power coefficient, and efficiency obtained from the wind tunnel test were slightly larger than the CFD results, which may be caused by the fact that the actual turbulence of the airflow at such low velocity in the test was larger than expected. However, the variation trend of performance data with advance ratio by CFD method is consistent with the results of the wind tunnel test. Besides, as shown in Figure 28, the propeller with a Gurney flap had about 1% higher efficiency at the advance ratio of about 1.0 than that of the propeller without a Gurney flap under a Reynolds number of about 4 \(\times\) 10\({}^{4}\), while at the Reynolds number of about 1.6 \(\times\) 10\({}^{5}\), the propeller with a Gurney flap no longer had an efficiency advantage relative to the propeller without a Gurney flap (see Figure 29). This is also in good agreement with the optimized results by the CFD method. Thus, the CFD method has sufficient reliability for the optimization of the propeller under low Reynolds numbers.  
Figure 26: Test propellers installed in the wind tunnel: **(a)** The test propeller without a Gurney flap (Prop A); **(b)** The test propeller with a Gurney flap (Prop B).