esa-sceva/satcom-chunk-collection huggingface dataset:
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 5 Results and Discussion
### Average Temperature Rise  
Figure 7 shows the average temperature rise performance of all nodes in different rounds. The communication process of each sensor node will cause the node temperature to rise, thereby causing the generation of hot nodes. TSAER is significantly better than the other two protocols, mainly because of its temperature perception and threshold control. M-ATTEMPT adopts a temperature sensing method to adopt a backoff mechanism for nodes that reach the temperature threshold. A large number of retransmissions also cause the nodes' temperature to rise. Since iM-SIMPLE does not have any measures to prevent the formation of hot nodes, the nodes' temperature continues to rise, and the number of nodes that eventually become hot nodes is more than the other two protocols.  
Figure 6: Analysis of throughput.
## 6 Simulation Results
### Scenario with a Large Number Of Devices  
In scenarios involving more than three categories of IoT devices, the PFs may not be easily demonstrated; yet, it is still crucial to monitor the performance of algorithms in environments with a multitude of device types. Consequently, we constructed a scenario comprising ten distinct categories of IoT devices, with each category containing ten devices,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Number of IoT Devices per Category** & **10** & **30** & **50** & **70** & **100** & **200** & **300** & **400** & **1000** \\ \hline ADSR(kb/s) scenario A & 0.03453 & 0.00859 & 0.00519 & 0.00388 & 0.00318 & 0.00199 & 0.73537 & 1.86991 & **2.28121** \\ ADSR(kb/s) scenario B & 1.47435 & 3.94150 & 1.15200 & 1.44833 & 3.28922 & 4.67727 & 5.10086 & 3.94150 & **2.08684** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sensitivity analysis of number of IoT devices.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number of UAV** & **1** & **2** & **3** & **5** & **10** & **15** & **20** & **25** \\ \hline ADSR(kb/s) scenario C & 0.00859 & 2.29062 & 1.74026 & 0.96567 & 1.63528 & 2.24172 & 2.57945 & **2.30610** \\ ADSR(kb/s) scenario D & 3.94150 & 3.35178 & 1.67385 & 1.82613 & 4.42636 & 6.20919 & 6.19233 & **9.56433** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Sensitivity analysis of number of UAVs.  
totaling one hundred IoT devices, and one UAV. The population size was set to 120, with all other settings consistent with Table 4. We listed the final ADSR for algorithm iterations of 10, 20, 30, 40, and 50 in Table 9. It was observed that as the number of iterations increased, the number of feasible solutions also increased, while the ADSR decreased. When the iteration count reaches 50, the ADSR is 2.54977 kb/s. Comparing this with Table 7, where each category of IoT devices had ten units, the ADSR is 0.03453 kb/s in scenario A (with two device types) and 1.47435 kb/s in scenario B (with three device types). As the number of device types increases, the ADSR experiences a modest increase, which can be attributed to the increased complexity of the algorithm. However, this increase remains within an acceptable range.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
###### Abstract  
To address the limitations of traditional predictive maintenance for large wind turbines, a fault prediction method that combines a gated recurrent unit (GRU) network with an improved ant lion optimization (IALO) algorithm is proposed. Traditional fault monitoring primarily relies on the supervisory control and data acquisition (SCADA) system to monitor parameters such as oil temperature using threshold-based alarm methods. However, this approach suffers from low accuracy in judgment and delayed fault detection. To enhance the accuracy and timeliness of fault warnings, this paper selects SCADA feature variables using the Pearson correlation coefficient (PCC) and optimizes the hyperparameters of the GRU model using the IALO algorithm, which is enhanced by Latin hypercube sampling and random sampling ranking. The method is based on historical data during normal operation, and the residuals and normal distribution are used to set warning thresholds for fault prediction. The results indicate that this method overcomes the issue of traditional hyperparameter tuning falling into local optima and surpasses conventional methods in terms of prediction accuracy and timeliness. It can effectively improve the gearbox fault-warning performance.  
wind turbine gearbox; gated recurrent unit (GRU) network; ant lion optimization (ALO) algorithm; fault warning; predictive maintenance
# Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
## 2 Background and Related Work
### Unsharp Masking  
Unsharp masking is a classical sharpness-enhancement technique [12]. The enhancement framework can be summarized as follows. The original image is decomposed into two layers by applying a linear shift-invariant low-pass filter (e.g., the Gaussian filter) [27]. The resulting image is called the base layer, containing the main structure of the original image. The difference produced between the base layer and the original image is called the detail layer, revealing the fine details of the original image. It can be described by  
\[\hat{I}=\lambda(I-\Phi_{L}(I))+I, \tag{6}\]  
where \(\hat{I}\) represents the enhanced image and \(I\) is the original input image. \((I-\Phi_{L}(I))\) denotes an unsharp mask, where \(\Phi_{L}\) represents a low-pass filter. \(\lambda\) controls the effect of enhancement achieved at the output and is the only coefficient to be estimated. Essentially, guided filtering has the same edge enhancement function as unsharp masking by transferring structure from an additional guidance image.  
Based on the information above, we derived a novel guided point-cloud denoising formulation from the original guided filter, with the estimation of only one coefficient, akin to the formulation of unsharp masking. To overcome the halo artifacts caused by \(\varepsilon\) being a constant value, as described in Section 2.3, an edge-aware weighted method is also proposed to perform edge enhancement while smoothing the edge of the point cloud.
## 2 Proposed System Description  
The studied hybrid microgrid is composed of a photovoltaic array, together with batteries, and AC loads. These systems form a hybrid microgrid interconnected to a low voltage distribution. Figure 1 illustrates the configuration of a hybrid power system for the distribution microgrid.  
The IEEE 14 bus feeder is the basis of the test platform for the hybrid microgrid developed in this report. The test feeder has been significantly modified to include diesel generators, detailed domestic dynamic multi-home models, battery storage systems and photovoltaic. The network diagram of the proposed distribution system is shown in Figure 1. The location of the photovoltaic generator, batteries and diesel generator used in this article is also indicated. It consists of a substation, 14 lines, 14 buses or nodes and 9 houses. The house consists of a combination of constant current, continuous impedance and constant loads of power. The network proposed incorporates a microgrid with an electric transmission system of 69 kV. There were two voltage output stages. The primary voltage was 13.8 kV and the secondary voltage was 0.22 kV. In the proposed system, three sub-microgrids have been given: AC microgrid 1 is an AC microgrid 2-related area of 0.22 kV through lines 4, 5, 6. In addition, AC microgrid 1 provides electricity to 4 houses and is powered by a diesel generator. AC microgrid 2 is another area with battery power storage 2 and photovoltaic 2 feature. The two AC microgrids run at 50 Hz. Ultimately, the third zone is the photovoltaic 1 DC microgrid and a battery power storage system 1. The battery power storage system 1 is connected by a two-way buck-boost converter, while photovoltaic 2 and DC connections are connected by a boost converter. The DC Bus and the AC microgrid 2 are connected by two parallel bidirectional transformers, which can be used as inverters or rectifiers for two transformers to swap reactive and active power. Finally, this hybrid AC/DC system also has a load equivalent grid network and diesel generators.
# Effect of Drying-Wetting Cycle and Vibration on Strength Properties of Granite Residual Soil
###### Abstract
[29] investigated the impact of vibration on the thixotropic strength recovery of silt. They found that, at the same frequency and rate, longer vibration times result in greater disturbance. The absolute strength recovery of silt was positively correlated with its disturbance degree, while its relative strength recovery was negatively correlated under the same static time. Based on this research, [PERSON] et al. [30] examined the effect of different water contents on the strength recovery of disturbed silt. When the soil is vibrated for the same duration, the degree of disturbance is positively correlated with the water content, and the strength recovery is negatively correlated with water content. [PERSON] et al. [31] employed dynamic and static triaxial instruments to investigate the changes in static shear strength of silt clay under different vibration loads, perimeter pressures and overconsolidation ratios. The results indicated that the specimen behaved similarly to the overconsolidated soil under vibration loads. When the similar over-consolidation ratio (SOCR) was less than 1.25, vibration had a minimal effect on the shear strength. However, when the SOCR exceeded 1.25, the shear strength rapidly decreased. In summary, the impact of DW cycles on GRS has been thoroughly investigated from macroscopic to mesoscopic to microscopic levels. However, the research on the effects of vibration on GRS is still inadequate, and studies on the combined influence of both factors are exceedingly rare. Nevertheless, under the interaction between DW cycles and vibrations, GRS will undergo multi-field coupling and discrete damage processes, resulting in a complex deterioration process of its strength characteristics. Consequently, this work focuses on the strength characteristics of GRS under DW cycles and vibrations. DW cycle tests and vibration tests are conducted to simulate the effects of hot and rainy climates as well as mechanical vibration on GRS. Straight shear tests and UCS tests are utilized to explore the effect of DW cycles and vibrations on the strength characteristics of GRS. Based on these tests, the aim of the present research is to contribute more reliable calculation results when applied to the stability analysis of GRS foundations affected by hot and rainy weather and mechanical vibration.
## 3 RL-Based Jamming Algorithm
### Reward  
With the setting of reward value, the intelligent agent can know which actions are effective in a given environment and which are not. When jamming successfully, reward \(R\) is 1. If not, \(R\) should be 0. Then the agent learns to make the more effective decision in the corresponding state. As shown in Figure 3, the horizontal axis represents frequency, and the longitudinal axis represents time. To simplify analysis, we set \(b_{u}=b_{j}\). In this case, state is the user's communication frequency band denoted as \(u_{t}\) and action is the jamming frequency band denoted as \(a_{t}\). Note decision \(a_{t-1}\) is made at time \(t-1\), and execute at time \(t\). Figure 3 shows the relation of state and action, and \(R\) can be described as:  
\[R=\left\{\begin{array}{ll}1&u_{t}=a_{t-1}\\ 0&u_{t}\
eq a_{t-1}\end{array}\right.. \tag{5}\]  
Note that, in an actual situation, it is hard for a jammer to judge if \(u_{t}=a_{t-1}\). In order to know the jamming effect, the following methods can be used.  
1. Detect negative acknowledgement (NACK). In ref. [33], NACK is used as reward standard. If NACK is detected, we affirm \(u_{t}=a_{t-1}\), \(R=1\). Since the communication protocol of civil communication system is public, jammers can easily recognize NACK. However, in the actual situation, the users' communication protocol is unknown. Auxiliary methods are needed for the jammers.
2. Detect change of communication power. In ref. [34], the detection of increasing power is the sign of successful jamming. Many wireless communication devices are designed to increase power when it gets the interference or noise in the environment. Take a cell phone for example, when it can not communicate well to base station, a cell phone increases the communication power. By sensing the changing transmit power of users, we could affirm \(u_{t}=a_{t-1}\), \(R=1\).  
Figure 3: Time-frequency two-dimensional diagram of jammer and user.  
Figure 2: Mechanism of reinforcement learning.  
Detect users switching channels. In actual communication, switching channels usually requires negotiation between the transmitter and receiver and often consumes more energy. Due to the limited energy, users are more inclined to stay on the current channel to communication. Therefore, detecting the channel switching can also evaluate jammer effect.
## 2 Materials and Methods
### Methodology
#### 2.4.2 Extraction of SAO Structure Keywords of Patent Text by Dependency Parsing  
The extraction of SAO structure keywords from patent texts is carried out through the Python package Spacy. Spacy is a natural language processing library developed by the Explosion AI team. It is mainly used for word segmentation, part-of-speech restoration, part-of-speech tagging, dependency analysis, etc. The steps are explained as follows:  
1. Import patent text: The import of data is the first step of natural language processing. Through the Pandas package, the patent as shown in Figure 3 can be imported into the compiler for subsequent analysis.
2. Dependency parsing analysis: Since the evolutionary benefit is based on the word structure of the binary relation, the SAO structure keywords of the binary relation are extracted from the patent text by using Spacy to perform dependency parsing. The dependencies \"amod\" adjective modifiers and \"dobi\" direct objects are extracted through Spacy, which are related to the SAO structure. Figure 4 is partially demonstrated the output as shown below.  
\begin{table}
\begin{tabular}{c c c} \hline
**Trend Line** & **Evolutionary Stage** & **Evolving Benefits** \\ \hline \multirow{3}{*}{Space Segmentation (S2)} & Structure with multiple hollow (S2.3) 1  & Improve surface area \\ \cline{2-3}  & Capillary/porous (S2.4) & Improve strength/weight ratio \\ \hline \end{tabular}
\end{table}
Table 2: Evolutionary benefits of the _Space Segmentation_ trend (partially presented).  
\begin{table}
\begin{tabular}{c c c} \hline
**Evolving Benefit** & **keyword 1** & **Keyword 2** \\ \hline Improve surface area & improve, increase, better, enhance, raise & surface area, area, surface \\ \hline \end{tabular}
\end{table}
Table 3: Example of synonym expansion for evolutionary benefits dictionary.
## 4 Experiments
### Results
#### 4.3.2 SORT and Deep-SORT on Skipped Frames  
In real scenarios, sometimes due to hardware constraints, it is not always possible (or needed) to run the algorithms at 30 FPS, which is a standard value on image acquisition from cameras. Hence, to evaluate the tracking performance on such conditions, experiments by skipping 1, 2, and 3 images, representing an image acquisition at 15, 10, and 7.5 FPS, respectively, were performed.  
Table 5 shows the SORT and Deep-SORT results attained on the ISR Tracking dataset using non-consecutive frames. As expected, as the image gap increased, the object tracking performance decreased. This happens due to a greater displacement of the objects, which increases the difficulty in predicting and associating objects. Nevertheless, promising results were achieved by the proposed \(A\) data association metric on both tracking methods, outperforming the default \(IoU\) data association metric, especially on MOTA, IDs, and MT evaluation metrics. The best overall performance was reached by the SORT method with the proposed \(A\) data association cost matrix with an accuracy of 86.43% and 58.2% of mostly tracked object sequences. The SORT method using the \(IoU\) data association metric attained the best results on the MOTP and FP evaluation metrics, while the Deep-SORT method with the proposed \(A\) data association metric achieved the best results on the TP and FN evaluation metrics. Note that, in these conditions, a significant improvement was achieved by the \(A\) data association cost matrix compared to the \(IoU\) association metric, showing its capacity to hold the object track.
## 4 Simulations and Results  
### Estimation of External Torque Using SPO  
All the simulations are performed in MATLAB, Simulink, and Simscape Multibody toolbox. To implement the collision detection algorithm, Equation (12) is used to estimate the external disturbance torque at each joint of the robot manipulator. When collision does not occur, the sum of the estimated external torque at all joints is zero. The sum of these torques at all joints considering different speeds can be seen in the no-collision case in Figure 10.  
In Figure 10, deflection from zero can be observed at different speeds and periods. Deflection occurs because the base of the robot manipulator is at the center of the RVI. To perform cutting, the end-effector must be close to the wall of the RVI. The trajectory of the end-effector causes slight deflection. The solution to this deflection is to set the threshold value at different speeds. If the external estimated torque exceeds the set threshold, then  
Figure 10: Estimated external torque at different speeds with no collision.  
Figure 9: Methodology.  
collision has occurred. The deflection increases with the increase in speed because the inertia and Coriolis force terms have been neglected. Nevertheless, deflection has a negligible impact at low speeds, as discussed in previous section. In Figure 11, when the robot end effector collides with the obstacle (RVI wall) twice, the estimated external torque exhibits two spikes. Note that the obstacle is a wall and is considered a continuous obstacle.  
Next, the collision detection is considered as the end effector reaction force. At the time of collision with the obstacle, the robot scans the obstacle and records its position. Equation (15) is used at that moment to estimate the contact force for verification purposes. The estimated force is also compared with the contact force library in the Simulink. Both results were compared and found to be the same, which validates the contact estimation algorithm. The Figure 12 shows the results of the estimated and toolbox force.
###### Abstract  
Understanding the spatiotemporal characteristics of merging behavior is crucial for the advancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merging patterns, and investigate how various factors, such as merging scenarios and vehicle types, influence driving behavior. Initially, a framework based on a high-definition (HD) map is developed to extract trajectory information in a meticulous manner. Subsequently, eight distinct merging patterns are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane's spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifies high-risk situations. Utilizing the latest aerial dataset, exiD, which provides HD map data, the study presents novel findings. Specifically, it uncovers patterns where the following vehicle in the target lane chooses to accelerate and overtake rather than cutting in front of the merging vehicle, resulting in Time-to-Collision (TTC) values of less than 2.5 s, indicating a significantly higher risk. Moreover, the study finds that differences in merging speed, distance, and duration can be disregarded in patterns where vehicles are present both ahead and behind, or solely ahead, suggesting these patterns could be integrated for simulation to streamline analysis and model development. Additionally, the practice of truck platooning has a significant impact on vehicle merging behavior. Overall, this study enhances the understanding of merging behavior, facilitating autonomous vehicles' ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic management, and enabling the effective integration of autonomous driving systems with human drivers.  
merging patterns; driving heterogeneity; HD map +
Footnote †: journal: 2024, 14, 2344. [[https://doi.org/10.3390/app14062344](https://doi.org/10.3390/app14062344)]([https://doi.org/10.3390/app14062344](https://doi.org/10.3390/app14062344))
## 4 Simulation Results and Discussion
### The Impact of Different Optimization Approaches on System Performance  
In this example, we explore the probability of further improving the system performance under the dual-layer decoding architecture. The mutual information versus SNR for the PNC scheme with QPSK modulation is shown in Figure 9. The Shannon capacity limit for the relay node to attain 1.9 bits/channel-use is observed (node \(N_{1}\) corresponds to 1.0 bits/channel-use and node \(N_{2}\) corresponds to 0.9 bits/channel-use) to be 5 dB. Conse  
Figure 8: This is the performance comparison between CD and DC algorithms under the configuration \(L_{1}=L_{2}=200\). The code rates of the BMST encoders with memory 5 and 6 are 0.4878 and 0.4854, respectively.  
quently, node \(N_{1}\) and node \(N_{2}\) achieve code rates of 0.5 and 0.45, respectively. Based on the above analysis, we construct an RSPC code \(\mathcal{C}[20,1]^{10}\times\mathcal{C}[20,19]^{8}\) with a code rate of \(\mathcal{R}=0.45\) (marked as \(\mathcal{C}[360,162]\)).  
As shown in Figure 10, we plot the BER curve of the BMST-RSPC-PNC system with \(m_{1}=m_{2}=6\) and \(\mathcal{R}_{1}=\mathcal{R}_{2}=0.5\) (marked as SM-SR) as a benchmark. We find that error propagation significantly degrades system performance in low-SNR regimes, while its impact gradually diminishes in high-SNR regions.  
For the scheme with the same memory and different code rates (\(m_{1}=m_{2}=6\), \(\mathcal{R}_{1}=0.5\), and \(\mathcal{R}_{2}=0.45\)), marked as SM-DR, we take \(\mathcal{C}[360,180]^{10}\) and \(\mathcal{C}[360,162]^{10}\) as the basic codes for BMST encoders at node \(N_{1}\) and node \(N_{2}\), respectively. For the \(L_{1}=L_{2}=200\) setup, the code rates \(\mathcal{R}_{1}^{\prime}\) and \(\mathcal{R}_{2}^{\prime}\) are 0.4854 and 0.4369, respectively. For the scheme with  
Figure 10: This is the BER performance of the BMST-RSPC-PNC system with different parameter configurations under the two-layer decoding structure.  
Figure 9: Mutual information for PNC scheme with QPSK modulation by Monte Carlo simulation (\(\mu_{K,1}=10.0\) dB; \(\mu_{K,2}=3.0\) dB).  
different memories and the same code rate (\(m_{1}=6\), \(m_{2}=7\), and \(\mathcal{R}_{1}=\mathcal{R}_{2}=0.5\)), marked as DM-SR, the basic codes for both BMST encoders are \(\mathcal{C}[360,180]\)[10]. To keep the number of decoding layers the same, we set \(L_{1}=200\) and \(L_{2}=199\). Hence, the code rates \(\mathcal{R}^{\prime}_{1}\) and \(\mathcal{R}^{\prime}_{2}\) are 0.4854 and 0.4830, respectively. Compared with the SM-SR scheme, the SM-DR scheme has a more significant performance improvement in the waterfall region. We also find that, when the SNR exceeds 6.5 dB, the DM-SR scheme has a lower error floor. This is because increasing the memory (the repeated transmission numbers) of the BMST system can lower its error floor, hence mitigating the impact of the inferior-performance side on the overall performance of the system. The performance evaluation shows that, at the target BER of \(10^{-5}\), both the SM-DR and DM-SR schemes yield an extra 0.2 dB coding gain.