esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Girder Flaring on Load Effect in Girders of Composite Steel Bridges
## 3 Approach
### Location of Maximum Dead Load Effect  
Determination of the dead load effect in a flared girder within a bridge is a straightforward task. It starts by isolating an individual girder bounded by the tributary width of the slab supported by the girder. The major component of dead load includes the self-weight of the girder and part of the deck slab above it. As per the specifications, the weight of the parapets and wearing surface can be assumed to be shared equally by all the girders if they are placed after the concrete in the deck slab has cured and hardened. In addition to the distributed load along the length, the individual girder is also subjected to concentrated loads from the weight of the cross-bracings at their locations. If the latter load is taken as a small percentage of the girder's self-weight, as is normally done in practice, the resulting dead load on the girder becomes uniformly distributed in the shape of a trapezoid along the girder's length. The shear force and bending moment in the girder can then be computed by determining the support reactions, isolating a free body diagram of part of the girder at some distance away from one of the supports, and using statics if the structure is statically determinate. For a simply supported bridge, the maximum shear in the girder is equal to the support reaction at the bridge's end, where the girder spacing is largest. The critical bending moment does not occur at the midspan due to the varying width of the bridge deck slab, which causes the critical flexural effect in the girder to be shifted toward the region with larger girder spacing, where the shear is zero. Figure 4 shows a single interior girder isolated from a simply supported splayed girder bridge and loaded by its self-weight, exclusive of the superimposed dead load, together with the corresponding shear and bending moment diagrams. The shift in the location of the maximum moment from midspan represents the location of zero shear. Conducting a three-dimensional finite element analysis on various splayed girder bridge systems yielded virtually the same result as the line element approach. Note that for the reference bridge B1, the self-weight of the steel girder is 5.43 kN/m, while the weight of the concrete deck slab over the tributary width is 11.65 kN/m at the smallest girder spacing and 23.3 kN/m at the largest girder spacing. For the interior girder in the reference bridge B1, this loading causes a maximum shear of 480.3 kN at the left support and a maximum bending moment of 4438.8 kN/m at 19,165 mm from the left support.
###### Abstract  
This paper presents a novel approach to enhancing the performance of artificial muscle fibers by incorporating air gaps within the bulk dielectric material. Building on previous models, the COMSOL simulation was developed to investigate the effects of varying the inner ligament width ('w3') and air gap width ('w2') on force production. Results indicated that an air gap width of 50 \(\upmu\)m is optimal, balancing improved force output with manufacturability constraints. A longitudinal array sweep was conducted to determine force density saturation in long fiber arrays, comparing the gap model with a traditional non-gap model. The gap model demonstrated superior performance, achieving higher force densities and better energy efficiency. The inclusion of air gaps reduced overall weight, enhanced flexibility, and improved the force-to-weight ratio, making the design particularly suitable for applications in prosthetics, exoskeletons, and soft robotics. These findings suggest that the air gap design represents a significant advancement in artificial muscle technology, offering a practical and efficient solution for various biomedical and robotic applications.  
actuator; muscle; fiber; biomimetic; electrostatic; force; simulations; COMSOL; gaps Article
## 5 Proposed Operational Model: Real-Time Sustainability Monitoring Dashboard
### Model Structuring  
The operating model focuses on the continuous monitoring of key sustainability indicators, including water and carbon footprints. Through a system based on big data and predictive analytics, the dashboard will provide critical information to support strategic decisions along the supply chain. In addition, the model will promote a collaborative approach, involving all actors in the textile supply chain, including producers, distributors, and consumers. Using technologies such as blockchain to track and authenticate data, transparency will be guaranteed throughout the product life cycle.  
To create an effective real-time sustainability monitoring dashboard, a structured process must be followed, which consists of three main features:  
* F#1. Defining sustainability indicators.
* F#2. Data collection and management.
* F#3. Information visualization.  
Each phase plays a crucial role in ensuring that the platform provides accurate and useful data to support informed decisions and promote sustainable business practices.
###### Abstract  
High internal phase emulsions (HIPEs), in which the dispersed water phase exceeds 70%, play a critical role in enhancing oil recovery through in situ permeability modification. However, their stability remains a major challenge due to frequent phase inversion and coalescence. In this study, the formation and stabilization mechanisms of water-in-oil HIPEs were investigated using a multiscale modeling approach that combines dissipative particle dynamics (DPD), molecular dynamics (MD), and density functional theory (DFT). Fourteen oil types and six polyaromatic emulsifiers with varying side-chain configurations and polar functional groups were modeled. Emulsifier performance was evaluated across 42 DPD-simulated systems with 70% and 80% water content. The results showed that emulsifiers with moderate dipole moments (~6 Debye) and spatially distributed heteroatoms achieved the most stable emulsion structures, forming continuous interfacial films or micelle-bridged networks. In contrast, emulsifiers with weak polarity (<1 Debye) or excessive stacking tendencies failed to prevent phase separation. The HOMO-LUMO energy gap and cohesive energy density (CED) were found to be poor predictors of emulsification performance. Four distinct stabilization mechanisms were identified, including interfacial film co-construction with oils and steric stabilization via side-chain architecture. The findings demonstrate that dipole moment is a reliable molecular descriptor for emulsifier design. This study provides a theoretical foundation for the rational development of high-performance emulsifiers in petroleum-based HIPE systems and highlights the potential of multiscale simulations in guiding formulation strategies.  
HIPE; polyaromatic sheet emulsifier; asphaltene; polarity of the emulsifier; dissipative particle dynamics simulation +
Footnote †: journal: Applied Sciences  
Article
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum MIMO Transmission and Quantum Channel
#### 3.4.5 Phase Damping Noise  
Phase damping (or dephasing) captures the loss of quantum coherence without affecting population probabilities. This is particularly relevant in systems where environmental interactions degrade phase information. The noise model is described in Equation (21).  
\[\mathcal{N}_{p}(\rho)=L_{0}\rho L_{0}^{\dagger}+L_{1}\rho L_{1}^{\dagger} \tag{21}\]The Kraus operators are represented in Equation (22).  
\[L_{0}=\begin{pmatrix}1&0\\ 0&\sqrt{1-p_{P}}\end{pmatrix},\quad L_{1}=\begin{pmatrix}0&0\\ 0&\sqrt{p_{P}}\end{pmatrix} \tag{22}\]
## 3 Aerodynamic Drag and Noise of Cavities with Different Shapes
### Flow Field  
Figures 4 and 5 show the streamlined distribution around the modified cavity. It can be seen that a large recirculation zone is formed inside the rectangular cavity, which is caused by the backflow generated by the airflow separating from the cavity edge and then hitting the downstream surface and flowing to the bottom. When the cavity edges are sloped or rounded, the position where the upstream airflow produces flow separation changes, and the speed of the high-speed airflow decreases, which leads to a decrease in the recirculation zone. With the increase of \(\theta\), the flow separation weakens or even disappears, which is beneficial to the drag reduction of the cavity itself, but the shielding effect on the object located in the center of the cavity decreases, which is not conducive to the overall drag reduction. With the increase of \(R/D\), the position point of the airflow impacting the bottom of the cavity moves upstream. When \(R/D\) is about 0.8, the recirculation zone is the smallest. However, when the round continues to increase, another recirculation zone will appear. Flow separation and reattachment also occur at the rear edge of the cavity, resulting in a recirculation zone, as shown in Figures 6 and 7. The recirculation zone at the trailing edge of the cavity can be weakened or eliminated by sloped or rounded edges, which also avoids potential aerodynamic noise sources.  
Figure 8 and 9 show the turbulent kinetic energy (\(k\)) distribution around the modified cavity. Flow separation occurs when the incoming flow passes through the upstream surface of the cavity, resulting in the formation of unstable shear layer airflow. The highly unstable airflow will act on the bottom of the cavity and the downstream surface, thereby forming a highly unstable fluid flow region. It is worth noting that the aerodynamic radiation noise comes from these key locations in the highly turbulent kinetic energy region. Optimizing the shape of the cavity edge to be sloped or rounded can significantly attenuate the flow separation.  
Figure 5: Mean streamline around the modified cavity with different rounded. (**a**) \(R/D=0\) (**b**) \(R/D=0.4\) (**c**) \(R/D=0.8\) (**d**) \(R/D=1.0\).  
Figure 6: Mean streamline around the trailing edge of the modified cavity with different sloping angles. (**a**) \(\theta=0^{\circ}\) (**b**) \(\theta=15^{\circ}\) (**c**) \(\theta=30^{\circ}\) (**d**) \(\theta=60^{\circ}\).  
the turbulence intensity around the cavity. With the increase of sloping angle (\(\theta\)) and rounded (\(R/D\)), the high turbulent kinetic energy region formed by the collision of the airflow upstream of the cavity with the downstream surface can be reduced.
## 2 Pointing Strategy and Model Establishment
### Dynamic Model
#### 2.2.2 Coordinate System Definition  
To facilitate the establishment of the dynamic model of the telescope pointing mechanism, we established the following coordinate systems as reference frames, and the spatial relationships among these coordinate systems are illustrated in Figure 4.  
* The Constellation Reference Frame, CRF = {O\({}_{\odot}\)_c_s_x_x_y_z_z}, with the origin located at the center of mass of the spacecraft, where _c_z is perpendicular to the plane formed by the incident laser vectors L2 and L3, _c_x is the bisector of the angles defined by the two incident lasers, and _c_y satisfies the right-hand rule;
* Two Optical Reference Frames, ORF = {O\({}_{\odot}\)_o_j_,_o_j_,_o_j_,_o_j_}. The coordinate origin is defined on the rotation axis of the optical subassembly, and the center of mass of the optical subassembly is also located at this point. The _o_j_ axis is oriented along the symmetry axis of the optical subassembly and points toward the laser emission direction. The _o_j_ axis is perpendicular to the _o_j_ axis and points in the opposite direction of the angle between the two optical subassemblies. The _o_z_j axis coincides with the rotation axis of the optical subassembly, following the right-hand rule;
* The Spacecraft Reference Frame, SRF = {O\({}_{\odot}\)_s_x_,_s_y_,_s_z_j}. The coordinate origin is defined at the center of mass of the entire spacecraft. The _s_x axis direction is between the two telescopes, pointing in a direction that forms a 30\({}^{\circ}\) angle with the optical reference frames' _o_z_j axis direction. The _s_y axis direction is perpendicular to the _s_x axis direction and coincides with the planes of the two optical components. The _s_z axis direction is perpendicular to both the planes of the optical subassembly and follows the right-hand rule.  
Figure 4: Reference systems.  
Figure 3: Three-dimensional model of the telescope pointing mechanism.
# The Additive Manufacturing of Glass: A Critical Review
## 3 A Focus on Semi-Solid Processing for Glass AM
### Process Overview
The rapid cooling of freshly deposited material is important in unsupported regions, such as bridged and overhanging sections (in the case of the printing of polymeric materials). To address this challenge in semi-solid glass AM, printing speeds may be limited as necessary to allowadequate time for the cooling of deposited glass layers. To obtain strong interlayer adhesion, nozzle temperature, ambient temperature, and printing speed are important variables to consider. High-quality interlayer adhesion is characterised by a homogenous bond, where layers are seamlessly fused together. Elevated processing temperatures provide a stronger bond through increased molecular diffusion and improved surface wetting between layers. Combined with slower printing speeds, this allows more time for the layers to merge and form a continuous bond. The final step for the semi-solid processing of glass generally involves removing the deposited glass from the printing platform. As the glass cools and thermally contracts at a different rate compared to the printing platform, there is separation between the two. In cases where separation does not occur, a release agent such as 'kilin wash' may be applied before printing to ensure reliable removal, or mechanical removal may be employed. Process control across the various methods [85; 86; 87; 88; 89] remains comparable, with differences primarily observed in hardware and feedstock type. There are two distinct methods of glass AM, depicted in Figure 14. The first involves a bulk high-temperature, low-viscosity feedstock (i.e., container of molten glass) with a secondary system controlling deposition. Conversely, the other method uses a low-temperature, high-viscosity feedstock (i.e., solid glass rods/filament), subsequently heated and deposited as needed.
## 3 Approach
### Collision Avoidance Algorithm  
The control of the collision avoidance for USVs is generally divided into two layers [21]. The first layer is global collision avoidance, which is to construct a proper environment model based on the information of known obstacles or danger areas and then plan a safe path. The second layer is the short-range real-time collision avoidance based on the real-time sensor information, which enables USVs to quickly avoid the obstacle that appears suddenly in the surrounding environment. Therefore, short-range real-time collision avoidance is the last line of defense to ensure the safety of USVs.  
Velocity obstacle (VO) algorithm is a kind of short-range real-time collision avoidance algorithm commonly used in USVs. However, the traditional VO algorithm does not consider the influence of the kinematic performance of USVs and the error of obstacle movement information, nor does it specify when to start collision avoidance and when to complete. Therefore, in order to overcome these problems, we combined a dynamic window algorithm with elliptic VO algorithm, gave the judgment conditions for when to start and end collision avoidance, and used the virtual obstacle method to reduce the influence of the error of obstacle movement information.
## 2 Platform Architecture
### Particle Waveform Data Software Preprocessing Module
Based on the above, it can be seen that the waveforms generated by semiconductor sensors interacting with particles or scintillators interacting with particles have a certain degree of variability in the rising or falling edges; thus, the training dataset needs to include information on the rising edges, falling edges, and full waveforms in order to comprehensively extract the characteristics of the waveforms and further expand the training dataset by means of data enhancement methods, such as random sampling, random cropping, noise addition, and frequency-domain transformations [18], in order to improve the model's generalization and robustness. The data augmentation methods are implemented as follows: random downsampling of training data by controlling the extraction interval; random cropping of waveforms by randomizing the starting position and fixing the waveform length; adding Gaussian white noise to simulate real-world random noise; and frequency domain transformation using Fourier transform. The overall implementation roadmap of the software's preprocessing is shown in Figure 3.
## 3 Method
### A Fast Testing Method for DSP Undocumented Instructions  
In this section, we provide a method to automatically test the undefined instruction \(H_{u}\) one by one. As shown in Formula (2), it is assumed that the undefined instruction set \(H_{u}\) contains \(M\) undefined instructions to be tested. In general, according to the result of instruction execution, \(H_{ui}\) can be further attributed to the undocumented instruction set \(H_{h}\) or the illegal instruction set \(H_{i}\), as shown in Formula (3).  
\[H_{u} =\left\{H_{u1},H_{u2},H_{u3},\ldots,H_{uM}\right\}\,(1\leq i\leq M) \tag{2}\]  
\[H_{u}=\left\{\begin{array}{l}H_{i},\ H_{i}\in I_{i}(\text{illegal instructions})\\ H_{h},\ H_{h}\in I_{h}(\text{undocumented instructions})\end{array}\right. \tag{3}\]  
Our approach to distinguishing the undocumented instruction \(H_{h}\) from the illegal instruction \(H_{i}\) is as follows. Firstly, we make use of unused memory space to store an instruction to be tested \(H_{ui}\) followed by a jump instruction, so that the original program address can be returned after the instruction under test is executed. Then, we observe whether the processor can recognize the instructions under test and execute them. If the instruction under test is executable, the instruction belongs to undocumented instruction. Otherwise, the instruction is an illegal instruction, and an \"illegal opcode\" error will be reported on the development software or an unexcepted interrupt will happen on the DSP chip. In this way, the undocumented instructions and the illegal instructions can be quickly distinguished.
## 3 Results and Discussion  
### Bioprinted Material Characterization Test Results  
#### 3.1.1 SEM Observation of Surface Morphology  
The cross-section of the solid sample of PCL/CNC was ruptured to observe the dispersion of CNC in the PCL matrix. As shown in Figure 5, the CNC is uniformly dispersed in the PCL and exposed as small dots (approximately 100 nm in diameter). This is due to the rupture of the sample along the cross-section perpendicular to the melt flow direction during the molding process. The injection flow causes the CNCs to align along the flow direction [34]. Besides, as the CNC content increases, more bulges, such as elevations, appear in the image, indicating that CNCs in other directions are buried in the PCL matrix. In particular, in 5% of the CNC samples (Figure 6d), some bumps merge with dots, indicating that the CNCs are aggregated within the PCL matrix, As the CNC content increased, more CNC particles were observed. In addition, no significant voids were observed in the images, thus indicating a strong interfacial interaction between CNC and PCL.  
Figure 5: SEM phase morphology of (**a**) neat PCL, (**b**) CNC-1/PCL, (**c**) CNC-3/PCL, and (**d**) CNC-5/PCL samples.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
## 3 Feature Extraction Method Based on WPD and SE-Isomap
### Feature Extraction  
#### 3.3.1 Statistical Features Based on OWP  
A series of WPD coefficients will be obtained after the MI-EEG signal is decomposed by WPD, and they carry the important time-frequency feature information. Here, the OWPs with the top \(q\) mean variance difference are found, and their coefficients are used in the calculation of statistical features. Let \(d_{l,j}^{n,i}(k)\) be the decomposition coefficients of the \(s(j,n)\) subspace for the sample \(x_{i}\) over channel \(Cl\), then their statistics are defined as follows:  
\[\overline{\mu}_{n}^{l}=\frac{1}{K}\sum_{k=1}^{K}d_{l,j}^{n,i}(k) \tag{19}\]  
\[\overline{E}_{n}^{l}=\frac{1}{K}\sum_{k=1}^{K}\left(d_{l,j}^{n,i}(k)\right)^{2} \tag{20}\]  
\[\overline{S}_{n}^{l}=\sqrt{\frac{1}{K}\sum_{k=1}^{K}\left(d_{l,j}^{n,i}(k)- \overline{\mu}_{n}^{l}\right)^{2}} \tag{21}\]  
where \(\overline{\mu}_{n}^{l}\) represents the mean of the coefficients in the frequency band corresponding to the \(s(j,n)\) subspace and \(\overline{E}_{n}^{l}\) and \(\overline{S}_{n}^{l}\) denote the mean energy and mean standard deviation, respectively.  
The ERS/ERD phenomenon can be expressed by C3 and C4 electrodes when the brain is implementing the imagination of left or right hand movement. To have the maximum separability for two motor imagery tasks, the time-frequency characteristics are defined as follows:  
\[F_{1}=\left\{\overline{\mu}_{n_{1}}^{3}-\overline{\mu}_{n_{1}}^{4},\overline {E}_{n_{1}}^{3}-\overline{E}_{n_{1}}^{4},\overline{S}_{n_{1}}^{3}-\overline{ S}_{n_{1}}^{4},\ldots,\overline{\mu}_{n_{q}}^{3}-\overline{\mu}_{n_{q}}^{4}, \overline{E}_{n_{q}}^{3}-\overline{E}_{n_{q}}^{4},\overline{S}_{n_{q}}^{3}- \overline{S}_{n_{q}}^{4}\right\}R^{1\times(3\times q)} \tag{22}\]  
where \(n_{1},\ n_{2},\ldots,n_{q}\) denote the indexes of the selected OWPs.
# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 3 Methodology
### Generative Adversarial Structure  
We employed a segmentation network with a dual-encoder as the generator, working in tandem with the discriminator to constitute adversarial training. The training process is depicted in Figure 4.  
In this network structure, CBCT images are initially fed into the segmentation network of the generator. After a series of operations, including downsampling, upsampling, and skip connections, the generator produces predicted labels. To further enhance segmentation accuracy, we concatenate CBCT images with the predicted labels and input them as constraint conditions into the discriminator. By calculating the loss between the discriminator output and a matrix of all ones, and then performing backpropagation, we update the weights of the generator. This process helps the generator better fit the real labels, thus improving its performance in image segmentation tasks. In this way, one training update process for the generator is completed.  
Subsequently, we freeze the parameters of the generator and proceed to train the discriminator. The discriminator receives input either with CBCT images and real labels or with CBCT images and concatenated predicted labels. It calculates the loss between the discriminator output and a matrix of all ones or all zeros. Through backpropagation, the weights of the discriminator are updated to enhance its ability to distinguish between real and fake labels. By iteratively updating the parameters of the generator and discriminator, the network becomes more adept at the CBCT tooth image segmentation task, improving its overall performance. This alternating training strategy effectively propels the learning process of the network, gradually optimizing it to achieve higher segmentation accuracy.  
Overall, our approach fully leverages the collaborative training between the generator and discriminator. Through an effective network structure and alternating update strategy, it demonstrates improved performance in three-dimensional tooth segmentation tasks.