esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 6 Applications
### Human-Robot Interaction  
[PERSON] et al. [147] describe various levels of human-robot relationships, from mere coexistence to interaction, cooperation, and finally collaboration. Human engagement can bridge all these relationships [5; 148]. In social, industrial, and domestic HRI, identifying and recognizing engaged users acts as a prior for robots to function and to establish situational awareness. In the highest level, _collaboration_, it typically requires a coordinated, synchronous activity to achieve a certain shared goal, and this includes physical contact between human and robot. They highlight the challenges of capturing humans' intents, as well as ensuring safety--human engagement is fundamental for both.  
As part of Industry 5.0, it is envisioned that collaborative robots (cobots) will work with humans in close proximity on the shop floor [149]. The inability to perceive engaged users is a long-standing hurdle in designing cobots which can safely contact humans, smoothly switch interlocutor, and collaborate seamlessly with humans to complete tasks. Capturing humans' intents is often performed using gesture recognition [150], implicitly assuming engagement. [PERSON] et al. [151] presented a comprehensive review in safety and related standards for industrial cobots. ISO 10218-1/2 [152] is the relevant standard for robots and robotic devices, and ISO 15066 [153] is designed specifically for cobots. Traditionally, safety could be ensured by requiring the human to be outside of the robot's workspace, but this paradigm is broken in human-robot collaboration, where physical contact is desired. Instead, safety is implemented by limiting contact forces [154], safety-rated stops, and speed restriction [149]. For example, ISO 15066 specifies admissible pressures and forces on different areas of the human's body to limit the severeness of possible injuries. We argue that engagement-informed safety mechanisms and protocols are a promising yet under-explored avenue for preventing forceful contact in the first place.  
Understanding human attention and intention accurately is paramount for safe and trustworthy HRI in social and domestic context as well. [PERSON] et al. [15] aimed to use cobots in assisted living with a focus on human intention and mental state modelling. [PERSON] et al. [14] employed a tour guide robot in a public museum for data collection and user engagement analysis. There has been ongoing research in social and conversational intelligence for years to endow robotic agents to interact with humans in a socially compliant manner [33; 78; 82; 89].
# Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology +
## 4 Assessment of the GNSS-Based Distances Accuracy
### Description of the Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **ME5000-SI** & \(\mathbf{U}_{k=2}\) & **GNSS-SI** & \(U_{k=2}\) & **GNSS-ME5000** & \(\mathbf{U}_{k=2}\) \\ \hline
2013 & 0.25 mm & 0.30 mm & 0.12 mm & 0.42 mm & \(-0.13\) mm & 0.42 mm \\
2014 & \(-0.64\) mm & 0.30 mm & \(-0.36\) mm & 0.40 mm & 0.28 mm & 0.40 mm \\
2015 & \(-0.13\) mm & 0.30 mm & \(-0.11\) mm & 0.45 mm & 0.02 mm & 0.45 mm \\
2016 & \(-0.28\) mm & 0.30 mm & 0.05 mm & 0.54 mm & 0.33 mm & 0.54 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Differences between the ME5000, SI-traced and GNSS distances, and their corresponding expanded uncertainties \(U\) (\(k=2\)). to be conducted in order to assess the validity of this degree of agreement and possibly reduce the standard deviation of the GNSS distances computed.
## 4 Adaptive DC Voltage Droop Control at VSC Terminal
It is observed that when the DC voltage deviation \(\Delta U_{\text{dc}}\) is small and close to the dead zone range, the droop coefficient is maintained at a constant value \(K_{0}=0\), indicating fixed active power control; when the DC voltage deviation \(\Delta U_{\text{dc}}\) is large, adjustments are based on the changes in the available power at the converter station, ensuring that voltage control dominates to quickly stabilize the DC voltage. In essence, adaptive DC voltage droop control combines fixed active power control and fixed DC voltage control, allowing for rapid adjustment of DC power without the need to switch control modes.
###### Abstract  
In order to accurately identify the pipeline leak fault of a mine air compressor, a novel intelligent diagnosis method is presented based on the integration of an adaptive wavelet threshold denoising (WTD) algorithm, improved firefly algorithm (IFA), Otsu-Grabcut image segmentation algorithm, histogram of oriented gradient (HOG), gray-level co-occurrence matrix (GLCM) and support vector machine (SVM). In the proposed method, the adaptive step strategy and local optimal firefly self-search strategy for the basic firefly algorithm (FA) are used to improve the optimization effect. The infrared thermal image is denoised by using wavelet threshold algorithm which is optimized by IFA (WTD-IFA). The Otsu-Grabcut algorithm is used to segment the image and extract the target. The HOG and GLCM are calculated to reveal the intrinsic characteristics of the infrared thermal image to extract feature vectors. Then the IFA is utilized to optimize the parameters of SVM so as to construct an optimal classifier for fault diagnosis. Finally, the proposed fault diagnosis method is fully evaluated by experimentation and the results verify its feasibility and superiority.  
Keywords:mine air compressor; pipeline leak recognition; infrared image; support vector machine +
Footnote †: journal: Applied Sciences
## 1 Introduction
Unlike the LS based relative motion compensation methods that makes use of pseudo-range, carrier phase and even Doppler observations [15, 16], the proposed method only requires pseudo-range observations and ephemeris information, and the error originates from relative motion could be calculated by means of one communication link. Third, it proposes a cost-effective and energy-effective way to achieve the desired synchronization and syntonization performance of satellite systems. The paper is organized as follows: in Section 2 we propose an autonomous time-synchronization system and illustrate the design in detail. In Section 3, a software-defined simulator is presented to investigate the performance of this system. Related simulation results are provided and analyzed. Finally, Section 4 concludes the paper and describes the advantages and disadvantages compared with existing time synchronization systems. Recommendations for further improvement are also provided.
## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management
#### 3.4.4 SDN/NFV Technology
In addition to heuristic methods, ML techniques have also made significant advancements in network resource management. DRL algorithms, in particular, are driving the intelligence of controllers. In this context, resource allocation problems are often modeled as joint optimization problems to meet QoS requirements. [PERSON] et al. [124] proposed a DRL-based multi-domain network resource orchestration algorithm, which improves the accuracy and efficiency of cross-domain VNE through the segmental modeling of the network. However, the dynamic topology of SAGINs complicates the weight allocation strategy in joint optimization problems, significantly affecting model performance and convergence speed. One solution is to incorporate fairness in task and resource competition during optimization. For example, [PERSON] et al. [23] introduced a DRL method optimized with a distributed soft actor-critic (DNAC) algorithm for resource allocation in SAGINs and aggregation weight optimization in federated learning. Although this method improves performance, its increased complexity makes scalability challenging. Furthermore, to address the limitations of static VNE algorithms in dynamic environments, [PERSON] et al. [126] proposed a heuristic VNE scheme combined with RL-based optimization. The RL agent selects the appropriate VNE algorithms based on topology changes and service demands. In summary, the integration of SDN and NFV offers innovative solutions for resource management in UAV-assisted SAGINs, showcasing vast potential for research and practical applications.
## 2 Related Work
### Clustering
#### 2.2.2 Bisecting K-Means  
Bisecting K-means is a variation of the K-means algorithm. It combines the advantages of bottom-up hierarchical clustering and K-means [25]. In this method, the data are considered as a single cluster which then progressively splits. First, the entire amount of data are viewed as one cluster, which is then divided into two clusters using K-means. Then, the largest of the partitioned clusters is selected and split using K-means, and this process is repeated until the desired number of clusters is achieved [26].  
Figure 2 compares the results obtained by the K-means and bisecting K-means algorithms. The red dots represent the centroids of each cluster, providing a visual representation of how the data is divided based on the centroids between the clusters. In K-means and Bisecting K-means, the four clusters are separated by four colors each, while the eight clusters represent a finer-grained distribution of clusters with eight colors. This makes it easy to see the difference between the two algorithms and the distribution of the clustering results. The clustering results are more stable than K-means; while K-means can produce different results depending on the initial centroid settings, bisecting K-means can gradually divide the clusters to produce balanced results. The latter works especially well on large datasets by breaking large clusters into smaller units. Further, bisecting K-means does not require the specification of an initial number of clusters in advance; while the traditional K-means algorithm requires the user to set the number of clusters, K, in advance, bisecting K-means can dynamically adjust the number of clusters, making the algorithm flexible and responsive to the data structure. However, initial partitioning can affect the results. An incorrect initial partitioning can negatively affect the overall performance, while a sequential approach to dividing large clusters leads to balanced and consistent clustering results [27; 28; 29].  
Figure 1: Comparison of the original data and the data after K-means clustering.
# A Novel 64 QAM-OFDM Optical Access System Based on Bit Reconstruction
## 3 Simulation Setup and Results
The numerical study shows that the probability of the transmitter output signal in the high-power region fluctuates between 13.91 and 14.62%. The reason for this situation is that information is re-output to the level signal with four different amplitudes 0, 1, 2 and 3, and the setting of the reference bits constitutes most of the information flow tilt to the low-level amplitude signal. When filling bits in the partition mapping part, we still pay attention to the power range where the low-level signal corresponds to the bit. Therefore, the bits in the low-power region correspond to the low-level amplitude signal as much as possible. The illusion of probability shaping occurs in the constellations.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 2 Methodology
### Error Budget  
Using Equation (3) and the accuracy of the system's components, the error budget for the WSE epoch solution \(\sigma_{wl_{sys}}\) is quantified as follows:  
\[\sigma_{wl_{sys}}^{2}=\sigma_{apc}^{2}+\sigma_{uls}^{2}+\sigma_{tilt}^{2}+ \sigma_{noise}^{2} \tag{12}\]  
The descriptions and approximate values of these components are provided in Table 3. \(\sigma_{apc}\) is set to 0.015 m for the short baseline, such as <1 km kinematic positioning, based on prior research and empirical data [32; 33; 34; 35; 36]. \(\sigma_{uls}\) and \(\sigma_{tilt}\) are given as 0.020 m and 0.005 m, respectively. \(\sigma_{noise}\) includes random noise and other contributing factors, e.g., Non-Tidal Atmospheric Loading (NTAL) [21]. Therefore, once the ambiguity is fixed correctly, the short-baseline WSE epoch solution is expected to be within 3 cm.
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 4 Performance Analysis
### DOA Estimation Results  
During the training stage, two broadband BPSK signals in the far-field are assumed, with their angular interval set to \([9^{\circ},11^{\circ},\ldots,35^{\circ}]\), resulting in 14 distinct angle intervals in total. The signal sources are located within the angular range of \([-60^{\circ},60^{\circ}]\) with an SNR of 10 dB, and the angular step is \(1^{\circ}\). The snapshot number is set to 1024. In total, 1386 training samples are generated for model training. In the testing phase, the angle range of the two signals remains \([-60^{\circ},60^{\circ}]\), while only three angle intervals \([10^{\circ},20^{\circ},30^{\circ}]\) are considered. The angular step is also \(1^{\circ}\). The test signal adopts the same BPSK modulation and number of snapshots as the training stage. The SNR is 6 dB. A total of 303 test samples are generated. A comparative analysis is conducted between the DOA estimation results and errors of the proposed algorithm and those obtained using the RSS\(-\)MUSIC, L1\(-\)SVD, and CWSVR algorithms. Here, the estimation error is used as the evaluation metric, and it is defined as the difference between the estimated angle and the true value. The DOA estimation results are shown in Figure 5 (the solid lines represent the true DOAs), and the corresponding errors are shown in Figure 6. The predicted angle of the RSS-MUSIC method is determined using conventional beamforming, and the other experiments are equivalent.  
Figure 5: The direction-finding results: (**a**) RSS-MUSIC; (**b**) L1-SVD; (**c**) CWSVR; (**d**) proposed method.  
Figure 6: The DOA estimation error: (**a**) RSS-MUSIC; (**b**) L1-SVD; (**c**) CWSVR; (**d**) proposed method.  
The proposed method has high accuracy, as shown in Figure 6, and the majority of its estimation errors are concentrated within \([-0.5^{\circ},0.5^{\circ}]\). The CWSVR method also achieves favorable estimation performance across all signal intervals. However, its estimation errors are consistently larger than those of the proposed algorithm, remaining within \({}_{2^{\circ}}\). The L1-SVD method exhibits larger errors when the signal angular intervals are small, while its estimation performance improves as the intervals increase. The performance of the RSS-MUSIC method is influenced by the predicted angle, resulting in a wider fluctuation in estimation errors with small angular intervals.
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 3 Controller Design of USV  
### Disturbance Observer Design  
The external environmental disturbance is treated as the combined effect of the uncertainty of the model and disturbances by the wind, waves, and the current in the external marine environment. The environmental disturbance force can be expressed as follows:  
\[\mathbf{\tau}_{E}=\left[\tau_{uu}\ \tau_{uv}\ \tau_{uv}\right]^{T} \tag{14}\]  
The disturbance is regarded as an uncertain time-varying disturbance with upper bound and slow variation. \(\tau_{uu}\), \(\tau_{uv}\), and \(\tau_{uv}\) represent the influence of ocean disturbances and model uncertainty acting on the surge, sway, and yaw directions, respectively.  
In this paper, the disturbance in the surge and yaw directions was estimated online, and the trajectory tracking controller with disturbance compensation was designed in combination with the output of the disturbance observer:  
\[\left\{\begin{array}{l}\tilde{\mathbf{\tau}}_{\mathbf{e}}=\mathbf{\mu}+\mathbf{K}_{e}\mathbf{M} \mathbf{v}\\ \dot{\mathbf{\mu}}=\mathbf{K}_{e}(\mathbf{C}(\mathbf{v})\mathbf{v}+\mathbf{D}(\mathbf{v})\mathbf{v}-\mathbf{\tau}- \mathbf{K}_{e}\mathbf{M}\mathbf{v})-\mathbf{K}_{e}\mathbf{\mu}\end{array}\right. \tag{15}\]  
where \(\tilde{\mathbf{\tau}}_  
The estimation error of each degree of freedom disturbance variable is defined as follows:  
\[\left\{\begin{array}{l}\widetilde{\tau}_{wu}=\tau_{wu}-\hat{\tau}_{wu}\\ \widetilde{\tau}_{v}=\tau_{v}-\hat{\tau}_{v}\\ \widetilde{\tau}_{ur}=\tau_{ur}-\hat{\tau}_{ur}\end{array}\right. \tag{16}\]  
The Lyapunov function is designed as follows:  
\[V_{wu}=\frac{1}{2}\widehat{\tau}_{wu}^{2} \tag{17}\]  
Taking the derivation of both sides of Equation (17) and substituting Equation (15) into it, we obtain the following:  
\[\begin{array}{l}\dot{V}_{wu}=\widetilde{\tau}_{wu}\dot{\widetilde{\tau}}_{wu }=\widetilde{\tau}_{wu}(\dot{\tau}_{wu}-\dot{\tau}_{wu})\\ =\widetilde{\tau}_{wu}\dot{\tau}_{wu}+k_{wu}\widetilde{\tau}_{wu}\big{\{} \mathbf{C}(\mathbf{v})\mathbf{v}+\mathbf{D}(\mathbf{v})\mathbf{v}-\mathbf{\tau}-k_{wu}\mathbf{M}\mathbf{v}+\bm {\mu}+\mathbf{M}\dot{\mathbf{v}}\big{\}}\\ =-k_{wu}\widetilde{\tau}_{wu}^{2}\\ \leq 0\end{array} \tag{18}\]  
Therefore, the longitudinal disturbance observation error \(\hat{\tau}_{wu}\) is globally exponentially stable, and similarly, the disturbance observations \(\hat{\tau}_{ww}\) and \(\hat{\tau}_{ur}\) can also accurately estimate the actual disturbance. The disturbance observer and control algorithm designed are independent from each other; the accuracy of disturbance estimation does not affect the controller design.
## 5 Conclusions  
The time-domain curves of the vibration velocity of the geocell-reinforced aeolian sand side and the gravelly soil side are both N-shaped curves. The wave peak's appearance time corresponds to the vehicle axle's arrival time. The response period of traveling load vibration velocity is around 1.5 s. In addition, the geocell-reinforced aeolian sand side has a somewhat longer response duration than the gravelly soil side.  
The majority of the frequency distribution of road vibrations induced by the test vehicle is mainly within the 30 Hz range. As the test vehicle's speed diminishes, the impact frequency steadily lowers to between 0-15 Hz. The single peak value within the region of influence is between 0-5 Hz. In terms of vibration amplitude decay in the horizontal direction, geocell-reinforced aeolian sand is slower, but smoother, than gravelly soil.  
After geocell reinforcement of aeolian sand, the vibration velocity primary frequency amplitude per attenuation ratio increased substantially. Additionally, the value increase was between 0.52% and 2.26%. At speeds ranging from 20 to 120 km/h, the equivalent amplitude attenuation ratio increased by 11.66, 2.57, 2.83, 5.58, 7.32, and 9.73 times, respectively.  
At different driving speeds, the depth of vibration effect of the geocell-reinforced sand side is smaller than that of the gravel side, with a maximum difference of 0.55 m. Therefore, the 15 cm geocell-reinforced sand can replace the 30 cm gravel as the upper roadbed regarding vibration velocity and energy attenuation.  
[PERSON] and [PERSON] contributed to analysis and manuscript preparation. [PERSON], [PERSON] and [PERSON] performed the data analyses and wrote the manuscript. [PERSON] and [PERSON] helped perform the analysis through constructive discussions. All authors have read and agreed to the published version of the manuscript.  
This study received funding from Xinjiang Transport Planning Survey and Design Institute Co. Ltd. (Grant Nos KY2020112301; KY2021100901) and CSXEC Xinjiang Construction & Engineering (Group) Co. Ltd. (Grant Nos 65000022859700210197).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.  
**Acknowledgments:** The authors would like to thank [PERSON] of Chang'an University for helpful discussions on topics related to this work.  
**Conflicts of Interest:** Author [PERSON] is employed by Xinjiang Academy of Architectural Science (Limited Liability Company) which is a subsidiary of CSXEC Xinjiang Construction & Engineering (Group) Co. Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. All authors declare no other competing interest.
# Forensic Analysis of Blackhole Attack in Wireless Sensor Networks/Internet of Things
## 4 Simulation and Results
### Analysis of Simulation Outcomes
#### 4.1.2 Analysis of the PCAP Files  
Wireshark works by putting the network interface card (NIC) into a promiscuous mode, and it tells NIC to accept every packet it receives. It helps to analyze the traffic patterns in real-time.  
Figure 9 shows the packets captured at node number 5 and their sender and receiver IP addresses. We can observe that the senders broadcast RREQs to 10.1.1.255 (Network) and other packets to the nodes. We have included communication of only one node due to the restriction of space.  
Figure 8: Packets Lost Ratio.  
Figure 9: Communication at Node 5.  
We analyzed the 50 PCAP files of all nodes and examine their network protocol hierarchy and conversations patterns. We plotted the nodes' I/O graphs over time and analyzed the behavior of attack at the nodes level. We analyzed the behavior of attack at the network IP level in the previous Flow Monitor part. All PCAP files were merged to compose one combined file and then plot a graph of that file. Another reason for doing this is the similar behavior of all malicious nodes with respect to time, because the traffic pattern at those nodes is almost the same.  
We can observe the number and hierarchy of User Datagram Protocol (UDP) data and AODV packets from Figure 10. Their comparison shows that AODV control packets are far greater in numbers than data packets. The BH attack creates chaos in the network, which causes exponential growth in control packets traffic, causing damage to the cumulative performance of the network. From Figure 11, we observe traffic patterns of the packets captured at all nodes and their protocol hierarchy. The first 100 s is the network convergence time, so there is no traffic of data packets till this time except a little traffic of protocols' control packets. After that, the data packets' transmission starts and eventually tries to grow in the network. However, the BH attack starts and affects the performance of the network. Now, an interesting pattern is observed. The attack causes a major flood of AODV control packets in the network, almost threefold to the data packets. This creates an immense network overhead, and then the damage reaches its maximum at the time span from 130 to 140 s, where the data packets decline to 0 packets per s. This is the worst situation. The BH attack behaves like a smurf attack and affects the end-to-end delay and efficiency of the network. In this time period, the attack is at its extreme extent. Again, similar behavior is observed from span 160 to 170 s, where data packets declined. Therefore, the AODV protocol behaves in an \"up and down\" manner. It tries to re-establish routes again, but the BH again causes the same result. As the attack takes place no matter the scale, it disturbs the long-term performance of the network.