esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of a Rectangular-Cut Kirigami Pattern for Soft Material Tuning
## 1 Introduction
[34] conducted a study using a pattern inspired by rectangular kirigami cuts distributed on TPU material plates manufactured with thicknesses ranging from 2 to 10 mm. In this study, the walls of the test plate containing the rectangular cuts were fully enclosed, which restricts material deformation, although subsequently, the study was conducted on a cylindrical scheme onto which the rectangular cutting pattern was projected. In this study, material stiffness was estimated against wall thickness, and unit deformation was estimated against the reaction force presented by the plate with cuts. Ref. [35], on the other hand, presented an inflatable actuator, which studies various cutting patterns, including the pattern inspired by rectangular cuts. In their research, they created actuators that, in their uninflated form, are configured with the pattern of rectangular cuts. Subsequently, by joining the edges of two plates with rectangular cuts and the central part forming conduction channels, the actuator was subjected to tensile tests and then inflated to characterize its response, mainly its contraction response, thereby achieving behavior similar to that of muscles. The pattern of rectangular cuts has also been studied for sensor development, primarily in its single-column configuration, as shown in the work developed by [36]. In their study, they used a rectangular cut-bonding material-rectangular cut configuration similar to the configuration used by [33], all distributed on a polyamide base as a substrate with synthesized graphene on a copper sheet via low-pressure CVD. In this study, the aim was to characterize the material's resistance behavior as it deforms since the objective was to use the plate as a stress-strain sensor for use on human skin, and no other mechanical tests were conducted in greater depth. In this study, we propose an investigation into a pattern inspired by rectangular kirigami cuts to characterize properties such as elasticity and stiffness. This investigation involves varying the width of rectangular cuts applied to a 1 mm thick TPU plate. The configuration of the pattern differs from those previously reported, featuring a greater number of rectangular cutting cells distributed horizontally, with free edges following the rectangular cutting pattern. This approach aims to facilitate the development of mechanisms capable of rapidly adapting to specific needs in applications such as robotics, sensors, and actuators based on thermoplastic polyurethane.
# Recent Developments on Biomineralization for Erosion Control
###### Abstract  
Erosion poses significant threats to infrastructures and ecosystems, exacerbated by climate change-driven sea-level rise and intensified wave actions. Microbially induced calcium carbonate precipitation (MICP) has emerged as a promising, sustainable, and eco-friendly solution for erosion mitigation. This review synthesizes recent advancements in optimizing biomineralization efficiency, multi-scale erosion control, and field-scale MICP implementations in marine dynamic conditions. Key findings include the following: (1) Kinetic analysis of Ca\({}^{2+}\) conversion confirmed complete ion utilization within 24 h under optimized PA concentration (3%), resulting in a compressive strength of 2.76 MPa after five treatment cycles in ISO-standard sand. (2) Field validations in Ahoskie and Sanya demonstrated the efficacy of MICP in coastal erosion control through tailored delivery systems and environmental adaptations. Sanya's studies highlighted seawater-compatible MICP solutions, achieving maximum 1743 kPa penetration resistance in the atmospheric zone and layered \"M-shaped\" CaCO\({}_{3}\) precipitation in tidal regions. (3) Experimental studies revealed that MICP treatments (2-4 cycles) reduced maximum scour depth by 84-100% under unidirectional currents (0.3 m/s) with the maximum surface CaCO\({}_{3}\) content reaching 3.8%. (4) Numerical simulations revealed MICP enhanced seabed stability by increasing vertical effective stress and reducing pore pressure. Comparative analysis demonstrates that while the destabilization depth of untreated seabed exhibits a linear correlation with wave height increments, MICP-treated seabed formations maintain exceptional stability through cohesion-enhancing properties, even when subjected to progressively intensified wave forces. This review supports the use of biomineralization as a sustainable alternative for shoreline protection, seabed stabilization, and offshore foundation integrity.  
m acrobially induced calcium carbonate precipitation (MICP); sustainability; aerosol control; biomineralization; ocean engineering; scour protection
# Monitoring of [PERSON]'s Disease Based on Wireless Sensing Technology
## 5 The Data Processing
### Data Preprocessing  
#### 5.1.1 Remove Outliers  
In the process of data collection, due to the influence of environmental noise or internal voltage fluctuation of the device, there was a large number of outliers in the original signal. These outliers seriously distort the original signal and must be removed.  
We used the \"Hampel\" function in MATLAB to remove the outliers of the original signal. For each sampling point of the original signal, the function calculates the median of the window consisting of the sampling point and the three sampling points on the left and right sides. Then the absolute value of the median is used to estimate the standard deviation of the median at each sampling point. If a sample is more than three standard deviations away from the median, the sample is replaced with the median [24]. The outliers contained in the original signal are shown in Figure 5. The original signal after removing the outliers is shown in Figure 6.  
Outliers can affect signal de-noising. After removing the outliers of the original signal, we can de-noise the signal, which is described in the next section.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **Age** & **Gender** & **Weight (kg)** & **Height (cm)** \\ \hline
1 & 26 & Male & 72 & 181 \\
2 & 45 & Male & 53 & 173 \\
3 & 30 & Male & 64 & 168 \\
4 & 32 & Female & 50 & 155 \\
5 & 24 & Female & 56 & 165 \\
6 & 25 & Male & 62 & 164 \\
7 & 36 & Male & 70 & 174 \\
8 & 48 & Male & 65 & 175 \\
9 & 25 & Female & 51 & 161 \\
10 & 40 & Female & 57 & 163 \\ \hline \end{tabular}
\end{table}
Table 2: Details of volunteers.  
Figure 5: Outliers contained in the original signal.
# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 2 Related Work  
### Improved Routing Protocols Based on RL  
The core advantage of the RL algorithm lies in the design of its abstract formulation, which enables the algorithm to be independent of the specific prediction of topology and the precise estimation of channel conditions, endowing the algorithm with strong versatility and adaptability, and showing excellent application advantages under dynamic or unknown network conditions.  
Recent literature [22] has introduced Q-Learning (QL) into routing protocol studies, proposing the Q-Routing Protocol, which outperforms non-adaptive algorithms based on counting shortest paths and routes efficiently even with changing network loads. Study [23] proposed the Q-Learning-based Geographic Routing Protocol (QGeo). Experiments show that QGeo outperforms traditional location-based protocols in terms of delivery rate and network overhead in high-mobility UAV scenarios. However, QGeo does not balance exploration and exploitation strategies to find better relay UAVs, nor does it consider energy consumption. Further literature [24] proposed Reward Function Learning for QL-Based Geographic Routing Protocol (RFLQGEO). RFLQGEO offers fewer retransmissions, lower average end-to-end delay, and a higher delivery rate than QGeo. Its limitations include not considering mobility control mechanisms, exploration and exploitation methods, or energy consumption. Study [25] designed the Geolocation Ad Hoc Network (GLAN) routing system using geolocation information and developed the Adaptive GLAN (AGLAN) system. This system applies RL to adapt to changing environments and introduces a pseudo-attention function in RL, improving learning efficiency. However, it only considers geolocation conditions during design.
## 4 Application of Pavement Management Platform and Validation with Practical Cases  
### Application of Pavement Management Platform  
This study establishes a pavement management platform to manage pavement inspection and maintenance decision-making. It creates a database for each pavement unit based on the inspection indicators and integrates the maintenance decision-making methods developed in this study to assist managers in formulating maintenance strategies.  
* Inspection Data Module  
A database is established for the inspection indicators used in this study, as shown in Table 15, and imported into the management platform. Managers can use the system to query and analyze inspection data for tracking pavement inspection indicators. The platform interface is shown in Figure 8.  
* Integration of Maintenance Decisions for Management  
Next, the pavement management platform is used for maintenance decision-making. Managers input the grading thresholds from Section 3.3 into the platform, which can automatically filter pavement units into \"Maintenance Sections\", \"Continuous Monitoring Sections\", and \"Good Condition Sections\", effectively enhancing maintenance decision efficiency, as shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Direction** & **Pavement Unit** & **Lane** & **Year** & **Crack Rate (\%)** &
\begin{tabular}{c} **Average Rutting** \\ **Depth (mm)** \\ \end{tabular} & **IRI** \\ \hline Forward & 16K+000–16k+100 & 1 & 2023 & 5.66 & 6.04 & 2.50 \\ \hline Forward & 16k+100–16k+200 & 1 & 2023 & 6.92 & 6.06 & 2.70 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Inspection database.  
Figure 8: Importing inspection data into pavement management platform.
###### Abstract  
To effectively plan the travel paths of automated guided vehicles (AGVs) in complex manufacturing scenarios and avoid dynamic obstacles, this paper proposes a pathfinding strategy that integrates macro-control and micro-autonomy. At the macro level, a central system employs a modified A* algorithm for preliminary pathfinding, guiding the AGVs toward their targets. At the micro level, a distributed system incorporates a navigation and obstacle avoidance strategy trained by Prioritized Experience Replay Double Dueling Deep Q-Network with \(\varepsilon\)-Dataset Aggregation (PER-D3 QN-EDAgger). Each AGV integrates its current state with information from the central system and the neighboring AGVs to make autonomous pathfinding decisions. The experimental results indicate that this strategy exhibits a strong adaptability to diverse environments, low path costs, and rapid solution speeds. It effectively avoids the neighboring AGVs and other dynamic obstacles, and maintains a high task completion rate of over 95% when the number of AGVs is below 200 and the obstacle density is below 0.5. This approach combines the advantages of centralized pathfinding, which ensures high path quality, with distributed planning, which enhances adaptability to dynamic environments.  
AGV; MAPF; PER-D3 QN-EDAgger +
Footnote †: journal: Applied sciences
# Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems
## 1 Introduction
### Contribution  
The prolonged sitting required for work or study causes a loss of muscle strength. A sedentary lifestyle harms the human body, and poor posture can cause neck, back, and shoulder discomfort if ignored.  
Consequently, it is significant to manage human behavior to ensure safety and health at work and in the field of studies. The study describes five significant contributions that are described in the following, considering the need:This study implemented an innovative InceptionV3 and SVM technique to automatically identify the posture of a human. It is worth stating that the deep learning TL technique does not require hand-crafted features, unlike the ML models.
2. The proposed technique used an L2 regularizer of 0.01 and L1 regularization (LASSO FS).
3. To advance the accuracy of the suggested method, the study used different techniques during the data preprocessing phase. The techniques include the use of data augmentation to prevent model overfitting and the use of the LASSO (L1 regularization) feature selection (FS) algorithm to improve model training, validation, and testing accuracy.
4. The layers of the DCNN model (InceptionV3) were also fine-tuned to achieve better training, validation, and testing accuracy.
5. A thorough comparison of the experimental results is made using cutting-edge methods to assess how well our suggested technique performs.  
The DCNN technique utilized in this work project is to improve the performance of human posture recognition. The sitting, bending, standing, and laying positions can be identified. The ability of humans to monitor their activities when sitting for an extended time or standing for a short period makes sitting and standing postures crucial to detect.  
The remaining portions of this article are arranged as follows: Related work in the area of sensor-based motion detection is described in depth in Section 2. The approach used for the experimentation of this research is presented in Section 3. The results are covered in Section 4, and the conclusion is found in Section 5.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Implementation Details
#### 3.2.4 Comparative Models  
To evaluate the superiority and effectiveness of the proposed DMLU-Net, we compared it with various known mainstream object detection algorithms using the five performance indicators described in Section 3.2.1. The specific models included U-Net [34], DeepLabv3+ [35], SwinUNet [36], TransUNet [37], MU-Net [22], and QTU-net [21]. Among them, the MU-Net and QTU-net models have been advanced in water body recognition work in recent years. Uniform experimental conditions were maintained for the training and testing of all networks, and the indicators of each batch were averaged to obtain the final indicator results.
# The Use of Radio and Telemedicine by TMAS Centers in
## 2 Methodology
### Quality and Risk Bias Assessment  
During the review process, each author independently screened papers and assessed their quality and relevance to the research. Papers were selected based on consensus among reviewers and conformity to inclusion and exclusion criteria. Upon reading the abstracts, the authors compiled a list of articles that they believed qualified in primer appraisal. Study quality was assessed using the Newcastle-Ottawa scale (NOS), which is a quality assessment tool that ranks studies and assesses the risk of bias [16]. It is fairly simple to interpret the NOS scale, which rates studies as poor (0-4), fair (5-6), and good (7-9). The studies that scored \(\text{NOS}\geq 7\) were considered for final review.
###### Abstract  
Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks. To address this problem, in this paper a new initialization mechanism is proposed to establish a communication link and set up a sensor network without adopting spectrum holes to convey control information. Specifically, firstly a transmission channel model for analyzing the maximum accessible capacity for three different polices in a fading environment is discussed. Secondly, a hybrid spectrum access algorithm based on a reinforcement learning model is proposed for the power allocation problem of both the transmission channel and the control channel. Finally, extensive simulations have been conducted and simulation results show that this new algorithm provides a significant improvement in terms of the tradeoff between the control channel reliability and the efficiency of the transmission channel.  
dynamic spectrum access; control channel; power allocation; reinforcement learning +
Footnote †: journal: Sensors
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 2 Materials and Methods  
### The Proposed Welding Defect Identification Method  
The arc sound signal in the welding process is a non-stationary time series signal. Short-time Fourier transform (STFT) is an effective method for the time-frequency analysis of non-stationary signals [25]. STFT and differential operation can be used to transform 1D raw arc sound signals into a 3D multi-channel time-frequency spectrum, which contains static and dynamic characteristics of the sound. The multi-channel time-frequency spectrum of an arc sound contains a significant amount of information about weld quality, but it is difficult to extract features from the multi-channel time-frequency spectrum using traditional manual methods. Compared with traditional methods, the CNN (convolutional neural network) has adaptive feature extraction capability. Therefore, a CNN with a multi-channel time-frequency spectrum as the input was designed for CMT welding defect identification. The processing procedures of the welding defect identification method are listed below.  
1. Arc sound signal is divided into sound segments, each with a sound length of 2742 points. The original dataset including different weld seam states is constructed.
2. Each sample in the original dataset is transformed using STFT to obtain the time-frequency spectrum dataset. STFT uses a Hanning window with a length of 255 and 205 overlap points.
3. The samples in the time-frequency mapping dataset are computed using first-order difference and second-order difference operations, and the computed results are overlaid with the original time-frequency spectrum to compose a multi-channel time-frequency dataset.  
The multi-channel time-frequency dataset is divided into a training set (70%) and a test set (30%). We can use the training set to input a custom convolutional neural network for multiple rounds of training, and then use the test set to test the trained model.
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 3 Experiments and Analysis of Results
### Ablation Study
#### 3.5.2 Does ResNet18 Provide the Best Multi-Scale Deep Feature Extraction?  
The rationale behind our selection of ResNet18 for multi-scale deep feature extraction can be attributed to the findings of a comparative analysis conducted on Dataset A, which involved various commonly used classical convolutional neural networks. This analysis revealed that ResNet18 demonstrated optimal performance across log-spectrogram, log-wavelet spectrogram and log-Mel spectrogram features, as illustrated in Table 6. The selection of ResNet50 was not made on the basis that its performance is comparable to that of ResNet18; rather, its greater number of parameters renders it less suitable for real-time inference tasks, especially with portable devices. The training loss curves for the various models are shown in Figure A1 of Appendix A.
## 4 Conclusions  
Based on theoretical derivation and simulation analysis, the conclusions are as follows:  
(1) In contrast to existing methods limited to sea-skimming aircraft, the proposed method offers a significant advancement by being applicable to aircraft at any altitude.  
(2) With the input parameters, the proposed method can effectively calculate the probability \(P_{\mathit{ident}}\) of the target closed to MGF being correctly identified by the radar, rather than a binary result of \"identified\" or \"unidentified\" from existing methods, which helps provide more precise and intuitive decision-making support for aircraft mission planners.  
However, we acknowledge that our current model does not account for dynamic environmental factors such as sea state or weather conditions, nor does it consider the correlations between the mentioned errors. Future work will focus on incorporating these elements to enhance model accuracy and robustness. Specifically, we plan to analyze error correlations to refine our method, which means that the distribution for actual radar activation point \(S^{\prime}(X_{S^{\prime}},Y_{S^{\prime}},Z_{S^{\prime}})\) would consider the covariances between MGFs and aircraft caused by dynamic environmental factors, and each Monte Carlo simulation would then sample from this refined distribution, yielding more accurate and robust probability results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RTA Determination Factors** & **MGF Data Utilization** & **Applicable Range** & **Error Consideration** & **Result** \\ \hline Existing methods & Range resolution & 2D & Sea-skimming contour & No & Binary \\ \hline Proposed method & Range resolution & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of two methods.  
**Author Contributions:** Conceptualization, H.M. and L.-H.Z.; methodology, H.M. and L.-H.Z.; validation, H.H., S.-J.R. and B.-H.G.; formal analysis, H.M.; writing--original draft preparation, H.M.; writing--review and editing, L.-H.Z.; visualization, H.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.