esa-sceva/satcom-chunk-collection huggingface dataset:
# Micro-Actuators with Light and Magnetism Dual-Control for Maneuverable Movements
###### Abstract  
Recent studies have highlighted the advantageous applications of the Marangoni effect in interfacial propulsion systems. Among these, optically driven Marangoni systems are particularly promising owing to their precise controllability and eco-friendly operation. Nevertheless, among these actuators, free movement still is limited by the interaction between light and actuators. In this work, we present a facile fabrication method for photothermal composites comprising polydimethylsiloxane (PDMS) matrices embedded with carbon nanoparticles and Fe\({}_{3}\)O\({}_{4}\) microparticles to achieve a dual-control micro-actuator. Experimental characterization confirmed the superior photothermal conversion efficiency of the composite material. Symmetrical structural configurations were engineered to achieve long-range (>15 cm), directionally programmable, and rotational motion under continuous near-infrared laser irradiation (808 nm, 2 W/cm\({}^{2}\)), while exhibiting magnetically responsive capabilities for trajectory modulation. Furthermore, the inherent viscoelasticity, mechanical flexibility, and enhanced tensile strength (up to 1.8 MPa) of the composite material enable propulsion of macroscopic payloads exceeding 50 g. The fabrication process demonstrates cost-effective, scalable, and environmentally sustainable characteristics, requiring neither complex equipment nor organic solvents. This strategy provides a paradigm shift for designing Marangoni effect-based photothermal actuators, with transformative potential in autonomous surface robotics and microfluidics applications.  
optical actuators; Fe\({}_{3}\)O\({}_{4}\); composite materials; Marangoni effect; magnetic Article
# Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes
## 4 Conclusions  
Aiming at having a better understanding of the thermal failure propagation of battery modules, this work carried out two groups of experiments to investigate the influence of discharging treatment on the thermal failure propagation in battery modules with various shapes, including triangle, rectangle, parallelogram, line, hexagon, and square. Specific information, such as experimental photograph, battery surface temperature, thermal runaway time, propagation speed, and mass loss, was analyzed.  
According to the results, the evident domino effect in the thermal failure propagation of battery modules can be found. The failure propagation process can be divided into several phases and failure propagates to the next phase of batteries through the former phase. The number of phases varied with the shape of module and it exhibited two phases, three phases, three phases, two phases, and three phases, respectively, corresponding to the modules listed before. Based on the results it revealed, the module with discharging treatment when it was heated behaved with an earlier thermal failure, a quicker failure propagation, and a larger mass loss compared to the results of modules without discharging treatment. It indicates that discharging treatment will aggravate the thermal failure propagation of battery modules, namely, battery modules under work possess a higher hazard once failure occurs. Besides that, it can be seen that the triangular module and the linear module presented much better safety, and their propagation speeds were smaller than the others. Combined with the results of space utilization, it is revealed that the triangular module may be the best choice of battery module due to its lower failure propagation hazard and higher space utilization.  
Figure 24: The propagation speeds of modules with various shapes.  
Figure 25: The space occupancy of triangular module and linear module.  
**Author Contributions:** [PERSON] wrote this paper; [PERSON], M.C., [PERSON] and [PERSON] revised it.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by the National Key R&D Program of China (2016 YFC0802500) and the Open Foundation of State Key Laboratory of Fire Science (No. HZ2016-KF13).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods  
### Raw Materials  
#### 2.1.1 Trial Soil  
The soft soil used in the experiment (Figure 1) was sourced from Davideng in Xiamen, China (Figure 2). It was extracted from the original mudflats and consisted mainly of fine-grained clay, mud, or muddy clay. The salt content was high, the texture was sticky and heavy, the structural integrity was poor, the specific gravity and bulk density were large, and it had characteristics of high compressibility, low bearing capacity, and large settlement. Jiangsu Zhugang Construction Group Co., Ltd. (Lianyungang, China) analyzed the natural specific gravity (\(\gamma\)), liquid limit (LL), plastic limit (PL), initial moisture content (W), salt content (Sc), organic matter content (Oc), alkalinity (ESP), porosity ratio (e), and compression coefficient (a) of the soft soil. The physical and chemical properties of the test soil are shown in Table 1, while its chemical composition is shown in Table 2. The XRD analysis is presented in Figure 3, and the microscopic morphology is depicted in Figure 4.  
Figure 1: Marine soft soil.  
Figure 4: SEM of dried-out marine soft soil.  
Figure 3: XRD of marine soft soil.  
Figure 2: Marine soft soil sampling site.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\mathbf{\gamma}\) & **LL** & **PL** & **W** & **Sc** & **Oc** & **ESP** & **e** & **a** \\ (**g/cm\({}^{3}\))** & **(\%)** & **(\%)** & **(\%)** & **(g/kg)** & **(g/kg)** & **(\%)** & **(–)** & **(MPa\({}^{-1}\))** \\ \hline
16.40 & 49.70 & 31.20 & 60.60 & 30.23 & 1.84 & 28.80 & 1.56 & 1.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and chemical properties of marine soft soil.
## 1 Introduction
[PERSON] (2010) analyzed the TBM selection of super-long tunnels by investigating a large number of documents [23]. [PERSON] (2006) believed that TBM should be considered according to stratum conditions [24]. Previous studies have focused on adverse geology, essentially examining the utilization rate of TBM in adverse geology. However, the influence of geological conditions and the radius of the tunnel plane surface on TBM selection is also noteworthy. If geological conditions and the radius of the tunnel's flat surface are not considered, misjudgment will easily occur in TBM selection. For example, in a tunnel containing only extremely hard rock, open TBM is usually preferred according to previous studies. However, in actual construction, the efficiency and economy of open TBM are much lower than that of the NATM method. If a tunnel with a small curvature radius is built in a stratum with more adverse geology, shield TBM is usually selected according to previous research methods. However, shield TBM is difficult to pass through the small curvature section according to the design route. In order to solve the above problems, this paper takes geological conditions, adverse geology and tunnel design as evaluation indexes and uses fuzzy theory to quantify the indexes. In this paper, the MAHP method is proposed by combining the modified weighting method with the AHP method. This can improve the accuracy of index weights. Finally, this paper combines the MAHP method and the TOPSIS method to build a TBM selection decision-making model. In order to verify the accuracy and advantages of the model, this paper conducts a case study and comparative study. The results show that the decision-making results are consistent with the actual situation and more comprehensive and accurate than the previous methods. The model can be further transformed into a program, which has a certain reference value for tunnels constructed by the TBM method.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 3 System Calibration and Image Reconstruction Methods
### Modeling of the Practical Scanning Trace
Define the line equation of the rotation axis using Equation (3) with parameters \(\theta_{r}\) and \(\varphi_{r}\);
3. Calculate the plane equation of the sample surface after rotation about the rotation axis using Equation (4) with the parameter \(\omega\);
4. Define the line equation of the illumination line using Equation (5) with parameters \(x_{i},y_{i},\theta_{i},\varphi_{i}\) and \(v\);
5. Calculate the intersection point of the sample plane and the illumination line;
6. Transform the coordinate of the intersection point from the system coordinate system to the surface coordinate system using Equation (6) with parameters \(\theta_{r},\varphi_{r}\), and \(\omega\);
7. Transform the surface coordinate of the intersection point to the image coordinate using Equation (2) with parameters \(v\) and \(\omega\). It can be found that the ideal scanning trace, introduced in Section 2.2, is a special case of the practical scanning trace with the ideal system parameter vector \(\boldsymbol{\psi}_{0}=(0,0,\theta_{i0},\varphi_{i0},0,0,v_{0},\omega_{0},f_{0})\), where all the parameters are the setting values of the system, and the specific expression of the ideal mapping relationship \(\mathbf{g}_{\boldsymbol{\psi}_{0}}\) is given by Equations (1) and (2). Apparently, to reconstruct an undistorted image, an accurate system parameter vector is required for establishing the actual scanning trace and mapping relationship.
# Visualization of Caregiving Posture and Risk Evaluation of Discomfort and Injury
## 3 Results
### The Difference in COG  
We used a camera to capture the entire process of patient transfer and implemented COG trajectory visualization (Figure 4a). The Exp group showed a trajectory of the COG first dropping, then rising, and then dropping again during patient transfer (Figure 4b). They tended to lower their COG to keep their upper body upright for reducing the torque in the waist, and to keep their trunk as straight as possible. Conversely, the COG trajectory of the Inexp group showed an initial rise before directly dropping (Figure 4c). Inexp group caregivers, unfamiliar with power positions, tended to bend their trunk as much as possible to lift the patient.  
The height changes of the COG were visualized, where notable differences between the Exp and Inexp groups were found during patient transfer (Figure 5a). The Exp group showed a downward trend in COG changes during Stage 2 of patient transfer, but the Inexp group showed an opposite upward trend. During Stage 2, the Exp group used a backward body movement to lower the COG and maintained an upright trunk to increase their body stability. The Inexp group lifted the patient by bending over directly, which  
Figure 4: The difference in the trajectory of the pixel COG between the Exp and Inexp groups: (**a**) The representative COG trajectory map during different stages of caregiving. The pixel COG of each frame consists of pixel coordinates (x, z), where x represents the image width and z represents the image height. The COG coordinates of many frames were fused to obtain the COG trajectory. (**b**) Two representative trajectories of the pixel COG of the Exp group. (**c**) Two representative trajectories of the pixel COG of the Inexp group.  
Figure 3: Evaluation results of caregiving posture with the REBA method; \(n=8\) in the Exp group, \(n=10\) in the Inexp group. NS = not significant. The red line represents a REBA score of 8 points.  
increased the burden on their waist. The differences in Stages 2 (\(p<0.001\)), 3 (\(p=0.002\)), and 4 (\(p=0.003\)) were statistically significant, suggesting that Stages 2, 3, and 4 might be meaningful intervention stages for caregiving posture (Figure 5b). The visualization results provided a reference for the Inexp group, which reminded them to adjust their caregiving posture and reduce the risk of musculoskeletal discomfort and injury.  
In addition, the COG heights from Stage 1 to the highest lifting position (Stage 3 or Stage 4) were also compared between the Exp and Inexp groups. The Exp group utilized power positions to control the COG height in Stage 1, minimizing the fluctuation and reducing the work performed by gravity and the waist burden. The Inexp group showed an overall tendency of higher COG heights from Stage 1 to Stage 3 and Stage 4, indicating that the Inexp group held the patient at a higher height during the caregiving task. This also increased the waist burden and the risk of injury.
# Dynamic Storage Optimization for Communication between AI Agents
## 6 Discussion  
Here, we have presented a novel communication model that uses a multi-layered ontology approach to improve communication between AI agents in a decentralized, multi-organization context.  
We examined the suitability of various blockchain and decentralized storage technologies, such as Tendermint Cosmos, Hyperledger Fabric, and IPFS, as means of communication for AI agents. We identified the solutions that provide the best outcomes for upholding ontology concepts and highlighted possible communication and resource usage bottlenecks through benchmarking and profiling.  
Figure 6: Hyperledger results. (**a**) Hyperledger read results. (**b**) Hyperledger write results.  
By breaking down the monolithic ontology model into semantic layers, our proposed design enables flexibility, mobility, and efficiency in data processing. This flexibility allows for significant optimization of read and write latencies since, in machine-to-machine communication, only the core layers are necessary. Thus, the template, versioning, instruction, and naming layers can be excluded (see Figure 1).  
We observed robust improvements to IPFS and Tendermint across both read (30-40% and 30-35%; see Observations 2 and 5, respectively) and write (20-25% and 55-59%, see Observations 2 and 5, respectively) speeds. However, for Hyperledger Fabric, the read improvement was only 9-12% (Observation 7), and the improvement for writing was even less, at 5%. A similar pattern was observed when comparing read and write latencies (i.e., independent of the optimization). Hyperledger read concepts only 10-18% faster than writing them (see Observation 6). In contrast, reading was 200% faster on IPFS (see Observation 1) and 300% faster on Tendermint (see Observation 5). This discrepancy suggests that a component responsible for transporting the information in Hyperledger heavily impacts overall system performance.  
This research study utilizes ontology principles to improve AI agent communication in decentralized systems. Blockchain technology inherently involves substantial resource and energy costs. However, our proposed multi-layered architecture mitigates these issues by ensuring that each agent can efficiently access the necessary information without incurring the overhead associated with storing or retrieving extraneous data. By optimizing data access and minimizing unnecessary storage, we aim to balance the benefits of blockchain's decentralized nature with a pragmatic approach to resource management. The multi-layered framework streamlines the interaction and setup between humans and machines and promotes fast communication between machines, enabling easy data exchange and identification among AI entities. This nuanced use of blockchain addresses some typical concerns regarding its sustainability and operational efficiency in practical applications. The proposed model provides a thorough methodology that can open the door for a broader use of AI across various applications.
# TRAP: A Three-Way Handshake Server for TCP Connection Establishment
## 4 System Design
### System Overview
#### 4.1.1 TRAP Server  
A TRAP server is a server program listening for incoming requests. When TRAP receives incoming SYN packets, it will attempt to establish a TCP connection via the TCP three-way-handshake with the incoming client via a TCP three-way handshaking process by sending a SYN-ACK packet.  
Figure 6: System overview.  
If a client successfully established a TCP connection with the TRAP server, the TRAP server will then send back a redirect command packet (TCP reset (RST) packet in our implementation) to redirect the client to a new server IP address and a secret hash utilizing the TCP header options.  
The TRAP server will also send a control message contained in a TCP RST packet to the new server, but the IP address in the TCP header option are replaced with the client's IP address, indicating a new client will attempt to connect soon.  
The TRAP server will reject any packets other than packets required for TCP connection establishment (SYN packets and ACK packets).
## 3 Micromanipulation by Vibration-Induced Acoustic Wave
### Manipulation by SAW
#### 3.1.2 SSAW Devices
The 2D standing wave field generates a potential well at the intersection of two sets of 1D standing wave nodes perpendicular to each other, where the particles are concentrated. When the particles need to be moved, the position of the nodes can be changed by changing the phase relationship of the standing wave field in the corresponding direction, thus changing the position of the particles. The acoustic streaming in the standing wave field is still ubiquitous in each potential well, so the particles located within it are continually making a rotating motion. However, particles in a single potential well cannot be operated independently. Thus, it is almost impossible to control multiple particles to move along different paths simultaneously using this device. Both TSAW and SSAW devices are important branches of in-planar manipulation methods. TSAW is more conducive to particle transport and classification, whereas SSAW has better performance in particle patterning and precise translating. In terms of the driving frequency, particle size, input power, and operating object, no pronounced difference exists between them, as shown in Table 1. Both TSAW and SSAW share characteristic limitations. These two devices are applied only inside the plane, but do not provide control in the vertical direction. However, neither of these devices can control the movement of individual particles along different paths when there are a large number of particles in the acoustic field. In recent years, new solutions have emerged that combine TSAW and SSAW. In the system proposed by [PERSON] et al. [50], the transducers are smaller units that can be controlled separately by the upper controller. In this work, the authors used the mixed effect of ARF and acoustic streaming in the water layer to push the floating object following the directions of SAW propagation. When a small transducer unit (referred to as a micro-pump) is activated, an upward jet is generated in the +z direction to suspend the particles. Simultaneously, a high acoustic field gradient is generated around the edges of this transducer, and leaky waves simultaneously occur. Under the combined effect of acoustic streaming and the leaky wave generated by the acoustic field gradient, particles can be pushed away from this micropump. As shown in Figure 2d, each of the unit IDTs in an array embedded beneath the carrier fluid serves as a micro-pump for fluids along the x and y axes. Therefore, an object can be programmed to move in predesigned routes following the sequential triggering of hydrodynamic gradients by selectively switching different unit IDTs on and off.
## 2 System Description
### Flight Controller  
The main task of the flight controller is to control the UAV according to the commands received from the flight coordination control unit. To implement this functionality, we use a product called Pixhawk2, which is an open-source flight control system designed for UAVs and other robotic systems. The flight controller operates an autopilot software stack and provides the ability to control various flight parameters of the UAV, such as altitude, speed, and direction. More details about the core of the flight controller can be found on the Pixhawk2 product website.
# Nonwoven Textile Waste Added with PCM for Building Applications
## 2 Materials and Methods
### Samples Preparation  
Several test samples were made in the Building Physics Lab of the Polytechnic University of Bari, in order to identify the best proportions of wool, binder and PCM. The starting point for all the samples was wool carding, a manual process performed by two hand carders and through which cloth strips were decomposed into its elementary fibers. The carded wool is soaked into a water and gum arabic solution (Figure 4) and suitably squeezed by passing small quantities through two rollers in order to eliminate the excess binder. Then, tufts of wool are placed inside a cylindrical mold, with a diameter of 10 cm and a height of 5 cm. During this step, PCM powder is homogeneously added between the various layers of wool, so that it can remain trapped between the meshes of the sample. The latter is finally dried in an oven for about 12 h at 60 \({}^{\circ}\)C.  
Three specimens were made (Figure 5), one being the reference (without PCM) and the other two having different weight percentages of phase change material (Tables 1 and 2).  
Figure 4: Materials: (**a**) Carded wool; (**b**) Gum arabic; (**c**) phase change material (PCM); (**d**) Water.  
The amount of wool and binder always remained the same, so that, with equal volumes, when PCM increased the total weight, sample density increased too.  
From the analysis of scanning electron microscope (SEM) images of the specimen surface, with a magnification rate of 200 and 500\(\times\), the interaction between the PCM microcapsules, the wool fibers and the gum arabic was observed (Figure 6), showing that microcapsules often tended to cluster rather than being uniformly distributed among the fibers, possibly having implications on making phase change less efficient because of lower surface-to-volume ratio.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PCM\_00** & **PCM\_26** & **PCM\_41** \\ \hline Wool (g) & 25 & 25 & 25 \\ Gum arabic (g) & 20 & 20 & 20 \\ Water (mL) & 100 & 100 & 100 \\ PCM (g) & / & 12.50 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantity of materials used in the specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PCM\_0** & **PCM\_26** & **PCM\_41** \\ \hline Wool (\%) & 73.19 & 52.55 & 40.72 \\ Gum arabic (\%) & 26.81 & 21.17 & 18.57 \\ PCM (\%) & / & 26.28 & 40.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weigh percentage of the materials present in the specimens after squeezing.  
Figure 5: From left to right: PCM_00, PCM_26, PCM_41.  
Figure 6: SEM images of PCM_41 specimen with magnification 200\(\times\) (**a**) and 500\(\times\) (**b**).