esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 6 Nanoparticle Toughening Method
### Hybrid Epoxy Nanocomposite
#### 6.3.1 Liquid Rubber/Epoxy Resin Nanocomposite  
[PERSON] et al. [10] successfully attached carboxyl-terminated poly (acrylonitrile-butadiene) copolymer (CTBN) to graphene oxide (GO) surfaces via anionic polymerization, forming a novel nanocomposite (GCTBN), which aims to enhance the composite performance. The results demonstrated that GCTBN exhibits excellent thermal stability and significantly improves the tensile strength, modulus, and fracture toughness of the composites, showing increases of 25%, 34%, and 128%, respectively, compared to neat epoxy resin.  
[PERSON] et al. [106] introduced potassium aluminum oxide (K-Al\({}_{2}\)O\({}_{3}\)) nanosheets and carboxyl-terminated butadiene-acrylonitrile (CTBN) liquid rubber into an epoxy resin system to fabricate novel K-Al\({}_{2}\)O\({}_{3}\)/CTBN/epoxy resin composites. They systematically investigated the effects of K-Al\({}_{2}\)O\({}_{3}\) nanosheet content on the thermal and mechanical properties and morphology of the composites. The results indicated that the tensile modulus and elongation at break of the composites exhibited an initial increase followed by a decrease with increasing K-Al\({}_{2}\)O\({}_{3}\) nanosheet content, reaching optimal values at 5 phr K-Al\({}_{2}\)O\({}_{3}\) nanosheets. Specifically, these values increased by approximately 22% and 50%, respectively, compared to those of the pure epoxy resin. Additionally, the K-Al\({}_{2}\)O\({}_{3}\) nanosheets effectively enhanced the thermal stability and glass transition temperature (Tg) of the composites.  
[PERSON] et al. [107] investigated the morphology and properties of epoxy resin composites synthesized via a high-pressure mixing method, incorporating organically modified montmorillonite (OMMT) and carboxyl-terminated butadiene-acrylonitrile (CTBN) rubber. Experimental results demonstrated that, compared to the pristine resin, the fracture toughness parameters (KIC and GIC) exhibited maximum increases of 2.2-fold and 7.6-fold, respectively, at loadings of 6 phr OMMT and 20 phr CTBN. A positive synergistic effect on fracture toughness was observed in the epoxy matrix modified with both rubber and OMMT. Additionally, the ultimate strength, yield strength, and glass transition temperature (Tg) of the OMMT/CTBN-modified epoxy resin were also enhanced.
## 4 DBP-EP Pile Soil Thermomechanical Response Test
### DBP-EP Pile Top Displacement Analysis  
Figure 9 illustrates the changes in pile top displacement over time under various operating conditions, specifically with constant pile top loads of 0 kN, 0.26 kN, and 0.78 kN. The data presented in the graph reveal that during the initial phase of the DBP-EP operation, there is a rapid increase in pile top displacement. However, this rate of increase begins to decline significantly after approximately 8 h of operation. Moreover, it is observed that as the magnitude of the constant load applied to the pile top increases, the resulting pile top displacement correspondingly decreases. A particularly noteworthy observation is that when the constant load is set at 0.78 kN, the pile top displacement stabilizes after around 2 h. This stabilization effect can be attributed to the constant load's ability to limit the axial elongation that results from the thermal expansion of the pile during the initial stages of energy pile operation. Nonetheless, as the operation proceeds, the thermal expansion of the pile gradually begins to surpass the restrictions imposed by the constant load, ultimately leading to the attainment of a stable state after approximately 24 h.  
As the constant load at the top of the pile is raised from 0 kN to 0.26 kN, the corresponding final displacement of the pile top is reduced by 0.31%D, 0.46%D, and 0.65%D, respectively. In a similar fashion, increasing the constant load from 0.26 kN to 0.78 kN results in decreases of 0.36%D, 0.47%D, and 0.66%D in the final displacement. Notably, for each 1 \({}^{\circ}\)C rise in the temperature of the inlet water, the final displacement at the pile top diminishes by roughly 0.03%D. A review of this data indicates that substantial displacements at the top of the pile can be reduced by modifying the water inlet temperature, adjusting the load on the pile top, and ensuring the selection of suitable operating conditions.
# An Improved Product Code-Based Data Hiding Scheme
## 4 An Improved Product Code-Based Data Hiding Scheme
### Embedding
Suppose that the bit 1 is located in the \(i\)-th component of \(\mathbf{e}\), once codeword \(\mathbf{c}\) is also added into the \(i\)-th column of \(\mathbf{T}\), then the syndrome of the new toggle array remains the same as \(\mathbf{T}\). Denote the new toggle array as \(\mathbf{T}^{\prime}\). If the Hamming weight of \(\mathbf{T}^{\prime}\) is not smaller than that of \(\mathbf{T}\), \(\mathbf{T}^{\prime}\) is changed back to \(\mathbf{T}\). _Step 6_: Utilize the technique of weight approximation to further reduce the number of 1s in \(\mathbf{T}^{\prime}\) to get the toggle array \(\mathbf{T}^{\prime\prime}\). _Step 7_: Perform \(\mathbf{X}\oplus\mathbf{T}^{\prime\prime}\) = \(\mathbf{Y}\) to finish the embedding. **Theorem 2**. : _The technique described in Step 5 can guarantee the syndrome of \(\mathbf{T}^{\prime}\) will be the same as that of \(\mathbf{T}\)._  
**Proof. ** In _Step 5_, first the task of adding \(\mathbf{e}\) respectively to those changed rows can be easily accomplished by adding the codeword \(\mathbf{c}\) into the \(i\)-th column of \(\mathbf{T}\); second, \(\mathbf{w}\oplus\mathbf{e}\) is a codeword. Then, the net vector added into any row or column of \(\mathbf{T}\) is a codeword; hence, the syndrome remains unchanged.\(\square\)  
Suppose the probability of each secret bit with a value of 1 or 0 equals 1/2 (this can be done easily by encrypting the secret bit sequence with a pseudo-random bit sequence by the XOR operation). Whatever the probability of each cover bit 1 or 0, the probability of each toggle syndrome bit with the value of 1 or 0 will equal 1/2. That is, each coset leader will be evenly selected. Hence, in Figure 6, the last four rows of \(\mathbf{T}\) (i.e., the four coset leaders) will most likely differ. That is, the last four bits of each column of the rightmost four columns in the toggle array \(\mathbf{T}\) own at most one bit 1. Then, we have:  
**Property 1**. : _For each of the four toggle sequences \(\mathbf{t}_{i}^{\mathsf{T}},5\leq i\leq 8\), in upright form, most of the bits 1 are concentrated in the top three bits. Hence, the resultant sequence constructed by performing the component-wise OR operation on the toggle sequences with the Hamming weight greater than 2 is almost the same as each of the original toggle sequences. Therefore, the number of 1s in \(\mathbf{T}\) can be reduced almost at each time through the technique described in Step 5 and most of the bits (1s) are located in the upper left corner of 3 \(\times\) 3 sub-array of the new toggle array \(\mathbf{T}^{\prime}\)._  
Further, due to the property 2, while applying the technique of weight approximation to the toggle array \(\mathbf{T}^{\prime}\), the number of 1s in \(\mathbf{T}^{\prime}\) can be further reduced. **Property 2**. : _For each of the 16 chosen code arrays shown in Figure 5, the bits in the upper left corner of 3 \(\times\) 3 sub-array of the code array have a higher density of 1._
## 3 Proposed Methodology
### Implementation Procedure
#### 3.3.1 AUVs and Sensor Nodes in the Marine Environment  
To begin with, an advanced version of the ant colony optimization algorithm is developed to optimize the path planning of an AUV operating in a marine sensor network environment. The AUV needs to traverse through sensor nodes that are spread across different depths and regions in the underwater area. Each sensor node records specific environmental events and possesses a corresponding Vol based on the importance and relevance of the data captured. The behavior of the node is assumed to sense and record information and wait for the AUV to arrive to collect the recorded information optically. During the collection of information, we assume that the AUV can receive information within r meters far from the sensor node without collection delay. Thus, the optimization of the AUV's path and the appropriate dock location for data recording leads to energy conservation during its journey, as well as an increase in the Vol gathered from the sensor nodes. The MACO algorithm, which was originally meant to address path optimization problems by simulating the foraging behavior of ants, is altered with hybrid improvement optimization techniques for bettering the path and the most important dock location for the AUV.
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
## 2 System Model  
### System Description  
The block diagram of the proposed noncoherent UAC scheme is shown in Figure 1. At the transmitter, the information bits are first encoded via a pre-designed IrCC encoder and then interleaved and fed to an ACC. The ACC is a unit-rate code encoder with the generating polynomial \(g=1/\left(1+D\right)\). It introduces correlations among codewords without increasing their length, leading to an OOK modulation with memory. Attributed to the ACC, turbo iteration between the decoder and the demapper is thus enabled on the receiver side [19]. The IrCC is designed by matching the EXIT curve of the ACC-OOK soft demapper over the Rayleigh fading channel. The OOK symbols are partitioned into blocks, and each block is passed through an inverse fast Fourier transform (IFFT) module to obtain an orthogonal frequency-division multiplexing (OFDM) symbol. Guard intervals are inserted among OFDM symbols to avoid inter-block interference.  
At the receiver, OFDM demodulation is performed via an FFT operation. After that, iterative detection is performed. The iterative detector consists of of a component ACC-OOK demapper and a component IrCC decoder. At each iteration, an LLR bias is evaluated based on the proposed ITE algorithm, as will be detailed in Section 3. Meanwhile, the channel fading information collected during the demodulation process is utilized to update the IrCC design, which is fed back to the transmitter for the next transmission. It is noted that: (1) the IrCC redesign is performed only when the channel fading characteristics undergo considerable change (e.g., every 3-4 h); (2) only subcode weighting coefficients of the redesigned IrCC need to be fed back and the involved payload is merely 200 bits. Hence, this feedback transmission can be implemented via a noncoherent UAC with MFSK modulation.
###### Abstract  
Phase contrast computed tomography (PCCT) provides an effective non-destructive testing tool for weak absorption objects. Limited by the phase stepping principle and radiation dose requirement, sparse-view sampling is usually performed in PCCT, introducing severe artifacts in reconstruction. In this paper, we report a dual-domain (i.e., the projection sinogram domain and image domain) enhancement framework based on deep learning (DL) for PCCT with sparse-view projections. It consists of two convolutional neural networks (CNN) in dual domains and the phase contrast Radon inversion layer (PCRIL) to connect them. PCRIL can achieve PCCT reconstruction, and it allows the gradients to backpropagate from the image domain to the projection sinogram domain while training. Therefore, parameters of CNNs in dual domains are updated simultaneously. It could overcome the limitations that the enhancement in the image domain causes blurred images and the enhancement in the projection sinogram domain introduces unpredictable artifacts. Considering the grating-based PCCT as an example, the proposed framework is validated and demonstrated with experiments of the simulated datasets and experimental datasets. This work can generate high-quality PCCT images with given incomplete projections and has the potential to push the applications of PCCT techniques in the field of composite imaging and biomedical imaging.  
Keywords:phase contrast computed tomography; sparse-view sampling; dual domain; convolutional neural network; radon inversion layer +
Footnote †: journal: Medical Imaging
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Case Studies  
#### 4.5.1 Real-Time Weather Analysis  
The integration of Large Language Models (LLMs) in maritime operations can enhance safety and efficiency by processing diverse data sources such as weather forecasts, Automatic Identification System (AIS) data, and navigational charts. LLMs can analyze weather forecasts to identify severe weather conditions like storms and suggest alternative routes to avoid these hazards, thereby improving crew safety [140; 141]. By processing AIS data, LLMs can detect congested sea lanes and recommend routes that minimize the risk of collisions and delays, enhancing operational efficiency [142; 143]. The ability to synthesize information from navigational charts further aids in providing comprehensive situational awareness, allowing for informed decision-making in real time [144; 145]. This integration of data sources enables LLMs to offer actionable insights that can significantly improve maritime navigation and safety. However, the effectiveness of such systems depends on the accuracy and timeliness of the data inputs as well as the robustness of the LLM algorithms in interpreting and integrating this information.
# A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures
## 3 Experiment
### Experiments and Data Analysis  
We chose to test the navigation system in a dry coal shed of the Fengcheng Thermal Power Plant Phase I in Jiangxi Province, China. This is a typical GNSS-denied environment with a spatial grid structure in industrial production. During the experiment, the UAV equipped with the navigation system was manually controlled for flight. Various data were collected and recorded during the experiment. We analyzed the recorded data and compared the proposed navigation system with other systems in terms of the following: feature extraction, localization, and mapping.
# An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats
## 4 Numerical Simulations
### Monte Carlo Simulation  
A set of 100 Monte Carlo runs has been prepared for the second simulation, varying the initial conditions \(\hat{x}_{0|0}\in\mathcal{N}(x_{0},\sqrt{P_{0|0}})\), with \(P_{0|0}\) selected as:  
\[P_{0|0}=diag[\sigma_{r}^{2},\quad\sigma_{\xi}^{2},\quad\sigma_{\phi}^{2},\quad \sigma_{v_{r}}^{2},\quad\sigma_{v_{r}}^{2},\quad\sigma_{v_{k}}^{2}], \tag{12}\]  
where the variances \(\sigma\) are given in Table 1.  
The cumulative distribution of the miss distance obtained in the simulation campaign is shown in Figure 4. The results indicate that a miss distance shorter than 1 m is obtained in 85% of the cases, in line with the requirement on the final miss distance expressed in [29]. The accuracy obtained by the relative navigation filter in reconstructing the estimated state (9) is reported in Table 2. As can be seen by comparing the values of Table 2 with the variance of the initial guess and the level of the measurement noise, the filter is able to converge to a more accurate estimate of the variable. The only variable whose estimation accuracy has not been improved by the filter is the out-of-plane component of the velocity. This can be explained by the fact that there is no out-of-plane motion of the spacecraft in this case, as the orbit is assumed totally Keplerian. Thus, the out-of-plane motion is completely unobservable and the estimate of \(\hat{v}_{k_{T}}\) remains bounded by the variance \(\sigma_{v_{k}}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Accuracy** & **Variable** & **Accuracy** & **Variable** & **Accuracy** \\ \hline \(r\) & 20 m & \(\hat{v}^{\text{s}}\) & 0.001\({}^{\circ}\) & \(\phi\) & 0.001\({}^{\circ}\) \\ \hline \(v_{r}\) & 0.25 m/s & \(v_{t}\) & 0.4 m/s & \(v_{k}\) & 1 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimation accuracy.  
Figure 3: Velocity errors in the radial and transversal directions.
## 4 Discussion
### How to Choose Smoothing Methods  
Since there is no single method that is always optimal, selecting a suitable smoothing method depends on the data quality, signal dynamics, and the intended level of generalization of the smoothing. We found that in general, the function fitting methods are robust for extracting phenological parameters or reconstructing strongly generalized seasonal curves. Function fitting methods are also the prior choice for smoothing data with very low quality (e.g., frequent noise and gaps). However, these methods have a limitation when it comes to capturing curve details and risk of underfitting, thereby compelling us to sometimes use local filtering methods. If the purpose is to analyze intra-seasonal variations (e.g., in disturbance analyses), local filtering methods are recommended, because they are able to capture seasonal details. Also, if there are multiple peaks during the season (e.g., due to multiple harvesting in agricultural lands), local filtering methods may be more useful because their smoothing process is freer. Function fitting methods may work also with multiple cropping but a difficulty is to provide initial positions and shapes for the functions of all seasons.
To take full advantage of the freedom of movement that is also an advantage of UAVs, this study focuses on UAVs that can operate wirelessly without earth connection cables. Therefore, each UAV must be equipped with a battery. This battery must power not only the UAV's flight but also the communications of various wireless interfaces. There is a tradeoff: increasing battery capacity increases the relative weight of the UAV while also increases the power supplied to the UAV. However, the issue of UAVs' power consumption is beyond the scope of this paper, knowing that there is research and development of lightweight, low-power wireless components and lightweight, high-capacity batteries, or even wireless power transmission for UAVs. Wireless communications at the mmWave frequency band are critical for 5G wireless networks [45]. In addition, understanding radio propagation characteristics in the airspace domain is essential for the efficient use and development of air-to-ground mobile communication systems in the future. However, to the authors' best knowledge, there are only a few studies focusing on the use of the mmWave frequency band for low-altitude platforms such as UAVs [37]. Therefore, this paper examines the construction of a UAV BS network using mmWave frequencies and demonstrates its effectiveness via both empirical experiments and validating theoretical studies. The major contributions are briefly outlined below. 1. We are the first to propose, design, and implement a UAV system equipped with a mmWave device. 2. We conducted an outdoor proof-of-concept (PoC) experiment with multiple drones equipped with mmWave communication devices, evaluating the proposed system over various scenarios. 3. We discussed the radio propagation model for mmWave communications between UAVs, based on the data collected. The validity of the proposed model is demonstrated by comparing the outdoor experimental results with those from theoretical studies. The structure of this paper is presented as follows. Section 2 provides detailed technical descriptions and relevant prior studies. This section begins by examining mmWave wireless communication, followed by a discussion on the dynamic deployment approach for mmWave UAV access, and concludes with an exposition on mmWave antenna polarization. Section 3 presents an in-depth look at our proposed mmWave-powered UAV system, describing the experimental methods and presenting the results. Section 4 describes the numerical evaluation model, including the setup and the resulting numerical data. Section 5 compares the experiment and numerical results. Finally, Section 6 concludes the paper with a summary and discussion of future work. This paper is an extended version of a conference paper presented at The 14 th International Conference on Ubiquitous and Future Networks (ICUFN 2024) held in Paris, France in July 2023 [46].
# Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems
## 2 System Model  
The paper considers the downlink transmission in a cellular network consisting of \(J\) cells, each having an \(N\) multi-antenna BS to transmit independent messages to one active single-antenna MS, as shown in Figure 1. It is assumed that all BSs share the same narrowband spectrum for downlink transmission. This is assuming that the exact locations of the base station (BS) and the reference node (RN) are fixed and known after their deployments. Let us denote \(\mathcal{J}\overset{\Delta}{=}\{1,2,\ldots,J\}\) as the set of the BS, the location of the \(j\)th BS is \(\mathbf{u}_{j}=[u_{j,x},u_{j,y}]^{T}\), and the location of the RN is \(\mathbf{p}_{f}=[p_{f,x},p_{f,y}]^{T}\). Each user equipment (UE) is equipped with a transmitting module for broadcasting and transmitting UWB pulse signals, and the location of the \(k\)th UE is expressed as \(\mathbf{p}_{k}=[p_{k,x},p_{k,y}]^{T}\).  
Each symbol is conveyed by repeating one pulse per frame (frame duration \(T_{f}\gg T_{w}\)) over \(N_{f}\) frames, resulting in a low duty cycle transmission form that is beneficial for distinguishing the multipath signal in terms of time resolution; \(T_{w}\) denotes each pulse  
Figure 1: The considered UWB communication system model.  
duration. The transmitted signal of this system adopts the DS-UWB modulation mode, and the transmitted symbol from the \(j^{th}\) BS to the \(k\)th UE is expressed as follows [21]:  
\[s_{j,k}(t)=\sum_{m=0}^{\infty}d_{j,m}^{(k)}\sum_{i=0}^{N_{f}-1}A_{m}^{(k)}p \Big{(}t-mT_{s}-iT_{f}\Big{)}. \tag{1}\]  
where \(T_{s}=N_{f}T_{f}\) indicates the symbol duration, \(d_{j,m}^{(k)}\) is the \(m\)th message sent to \(k\)th UE, and \(A_{m}^{(k)}\in\{\pm 1\}\) is the spread spectrum sequence corresponding to the \(k\)th UE. The second-order derivative of the Gaussian function has been widely used in practice and is used as the model of UWB signal \(p(t)\). Subsequently, we will explain in detail the geometrical model, the TDOA correction method, and the CSI estimation error model.