esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Relation between Chloride Penetration Depth and Compressive Strength  
Figure 9a shows the chloride penetration depth of each concrete mix by age. The chloride penetration depth could not be measured at 3 days of age, but chloride had started penetrating the concrete at 7 days. From 7 days, C35_O10 exhibited larger chloride penetration depths than C24_O3_E5_O2. This difference reached a maximum at 28 days and then slowly decreased.  
Figure 9b shows the relation between the compressive strength and chloride penetration depth. Under the same strength development conditions, C24_O3_E5_O2 exhibited smaller chloride penetration depths. In general, the concentration of airborne chloride on the structure is not high. However, the concentration of seawater with a high value of 2.8 M or more may affect the concrete while curing. The relationships between compressive strength and chloride penetration depth in Figure 9b show that the chloride penetration increases even as the compressive strength increases. Therefore, high surface watertightness is required in order to reduce the penetration depth of chlorine when  
Figure 8: (a) CaO/SO\({}_{3}\) and (b) SO\({}_{3}\)/Al\({}_{2}\)O\({}_{3}\) ratios according to binder type.  
Figure 9: (a) Chloride penetration depth by age; (b) relation between compressive strength and chloride penetration depth.  
the concrete is in contact with high concentrations of chloride over long periods. In the scope of this study, C24_O3_E5_O2 tends to have a lower chloride penetration depth than C35_O10 as the age increases, and a low value of about 60% after 91 days. This means that the designed durability service life of the structure can be extended beyond that of the C35_O35 formulation, while using an economical mixture design.  
Chloride penetrated to significantly different depths until 28 days of age because of the development of early strength owing to the EPC and PC-based superplasticizer, and the high fineness increased the watertightness of the concrete surface. After 28 days, the increased watertightness of the concrete caused by the action of GGBS [22; 23; 24; 25] appears to have reduced the chloride penetration depth.
# Approaches to Parameter Estimation from Model Neurons and Biological Neurons
## 2 Model Neurons: Lessons Learnt from Well-Posed Models
### Noise and Effect of Assimilation Window over Parameter Distribution Adaptive Sampling  
Noise in the membrane voltage introduces uncertainty in the parameter field. This is observed in the broadening of the posterior distribution function of extracted parameters (Figure 2), which is generally non-Gaussian [11]. Gaussian noise added to the current protocol has a comparatively small effect on parameter estimates, as it is integrated by the conductance model.  
Figure 2: **Effect of the length of the data assimilation window on parameter accuracy. (a) The model data (black line) were obtained by stimulating the model equations with the current protocol (blue line). A small amount of Gaussian noise was added to introduce uncertainty in the parameters inferred back from the data. Parameters were estimated from three assimilation windows of increasing duration but incorporating the same number of data points, thanks to an adaptive step size \(\Delta t_{2}\) applied to subthreshold oscillations. (b) Posterior distribution functions of one model parameter (gate recovery time constant) corresponding to the three assimilation windows [11].**  
Measurement noise or intrinsic noise [75] may be smoothed [7,8] to improve parameter accuracy. Parameter accuracy may also be enhanced by increasing the duration of the assimilation window. In Figure 2, the effect of increasing the assimilation window is to narrow the posterior distribution function [11,76]. The assimilation window can be widened without increasing the size of the problem, using an adaptive mesh size. This works by selectively sampling action potentials at a faster rate than subthreshold oscillations across the assimilation window [11].
## 1 Introduction
Laser scanning directly acquires three-dimensional (3D) point cloud data at a high accuracy level, with 2 to 6 mm at 50 m [8; 9]. According to the existing studies [10; 11; 12], which conducted comparisons between laser scanning methods and vision-based approaches, the laser scanning approaches offer better accuracy than vision-based methods. Building information modeling (BIM) combined with prefabricated modular components has enabled integrated construction and engineering workflows on quality control. Using BIM to coordinate, document, and control the quality in modular constructions appears to be a practical approach [13]. However, the previous research focused on the quality control of production processes [14] and modular components [7]. There is a research gap on how to control the quality in the install stage of modular construction with the integrated method of BIM and laser scanning. This paper introduces a framework that integrates the use of BIM and 3D laser scanning technology to enable rapid analysis of the assemble quality of anchor plates and other components for a removable floodwall. The researchers first conducted a thorough literature review of laser scanning and BIM in modular construction quality control. Then, this study proposes a framework of the modular floodwall installation and quality control process. The following sections describe the data capture, quality inspection procedure, and the data storage and delivery methods accordingly. The study utilizes the data of a real project located in a riverside city in China for validating the level of technical feasibility and accuracy of the presented methods. The results indicate that the proposed integration of BIM and 3D laser scanning can produce a rapid and reliable method to proactively control the assemble quality of anchor plates for removable floodwall during field installations.
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
## 3 Experimental Results and Discussion
### Up to 64 Gbs Experiment Employing a VCSEL-Based Transceiver  
The acquired PAM-4 optical eye diagrams for different transmission speeds and distances are shown Figure 8. The diagrams were captured by a 70 GHz equivalent time oscilloscope. As expected, the eye diagrams exhibit better eye opening for lower baud rates. Moreover, propagation through the SSMF, especially in the case of a reach of 2 km, introduces degradation to the eye diagram of the multi-level signal as a result of fiber dispersion at 1550 nm.  
The acquired BER values at 25, 28, and 32 Gbaud for the aforementioned transmission distances were plotted against the average received optical power that was calculated from the measured  
Figure 8: PAM-4 optical eye diagrams at the output of the linear receiver at 25, 28, and 32 Gbaud for back-to-back configuration and propagation in 500 m and 2 km of SSMF.  
Figure 7: Bit error rate (BER) curves at 32 and 40 Gbaud in back-to-back configuration and after propagation over 500 m.  
photocurrent (Figure 9). The BER curves depicted by the crosses correspond to the obtained 25 Gbaud signal in back-to-back configuration and after propagation over 500 m and 2 km. Following the same rationale, the black circles and the x-marks refer to the received signals at 28 and 32 Gbaud, respectively, according to the aforementioned transmission conditions (back-to-back configuration and after transmission over 500 m). It is evident that for lower baud rates, the received PAM-4 optical signal exhibits better performance in terms of BER. It is also clear that an extension in the transmission distance leads to the degradation of the obtained BER curves. As observed for cases of 25 Gbaud and 28 Gbaud, the achieved BER values lie well below the FEC limit for all the transmission scenarios. Moreover, the respective power penalties for transmission over 500 m and 2 km SSMF at 25 Gbaud were 0.2 dB and 0.6 dB for a BER equal to \(6\times 10^{-4}\). Similarly, the received 28 Gbaud signal exhibited a power penalty of 1.8 dB for a BER in the order of \(5\times 10^{-4}\). Finally, in the case of 32 Gbaud, only the back-to-back configuration achieved acceptable BER values, whereas propagation through 500 m of SSMF was severely affected by the effect of chromatic dispersion and, as a result, the signal failed to reach the FEC limit.
## 3 Influence of Boundary Distance in the Integral Response Deformation Method  
In the traditional IRDM, exterior fixed boundaries are used to simulate the influence of semi-infinite medium domains. Consequently, these boundaries must be positioned sufficiently distantly to ensure that non-free-field displacements at interface S, induced by  
Figure 1: Computational model and load components of the IRDM.  
Figure 2: Auxiliary free-field model for computing equivalent seismic loads in the IRDM.  
the uncoordinated movement of the soil structure, undergo adequate attenuation. However, current design codes lack explicit guidance on boundary setting. In practice, empirical approaches are commonly adopted, typically setting the bottom boundary at the bedrock surface and lateral boundaries at approximately three times the structural span. While this method generally ensures acceptable accuracy, its universal validity remains unverified. In specific scenarios, such as when structures are located far from bedrock or those with large transverse dimensions, this boundary configuration may result in excessively large models, significantly increasing computational complexity and resource demands. To address these limitations, this study investigates the impact of boundary distance on the computational accuracy of the IRDM, aiming to optimize boundary placement strategies and achieve a balance between precision and efficiency.  
An SSI model is established as depicted in Figure 3. The lateral boundary distance \(L_{S}\) denotes the horizontal distance from the outer wall of the structure to the lateral boundary, whereas the bottom boundary distance \(H_{B}\) denotes the vertical distance from the structural floor to the lower boundary. A single-story, double-span structure is adopted for modeling, featuring a span of \(L=10\) m and height of \(H=5\) m, as illustrated in Figure 3b. The burial depth of the underground structure remains fixed at \(H_{D}=5\) m. The soil domain is discretized using plane strain elements \(0.5\) m \(\times\)\(0.5\) m in size, characterized by a mass density of \(2000\) kg/m\({}^{3}\), a shear wave velocity of \(300\) m/s, and a Poisson's ratio of \(0.3\). The reinforced concrete structure, which is composed of C30-grade concrete, is modeled with a mass density of \(2500\) kg/m\({}^{3}\), an elastic modulus of \(30\) GPa, and a Poisson's ratio of \(0.15\). Shear-deformable beam elements with a mesh size of \(0.5\) m are used for structural discretization. The Kobe earthquake record (Figure 4) is selected as the seismic input, with a total duration of \(40.95\) s.  
Figure 4: Kobe wave.  
Figure 3: Schematic diagram of the SSI model.  
To evaluate the computational accuracy of the IRDM, the dynamic time history method is employed as the benchmark in this study. For dynamic time history analysis, viscoelastic artificial boundaries are applied to simulate wave radiation effects in semi-infinite media, and seismic motions are introduced via the seismic wave input method, which is based on the substructure of the artificial boundary.
# An In-Line Coaxial-to-Waveguide Transition for Q-Band Single-Feed-Per-Beam Antenna Systems
## 3 Results
### Performance  
The performance was evaluated in terms of scattering parameters between the transverse section of the coaxial cable upstream the air gap and the transverse section of the hollow waveguide, far enough from the Chebyshev transformer to avoid issue due to higher modes in the ridges' edges.  
Figure 7 shows the return loss and the insertion loss. The RL was higher than 25 dB over the whole Q-band (BW% \(>\) 40%), guaranteeing a very good matching between the horn and the coaxial. However, the most remarkable result was the IL lower than 0.06 dB over the whole Q-band. This was significantly smaller compared with the microstrip-fed transitions, where values around 0.25 dB were typical [33], because the latter had a larger fraction of (lossy) dielectric, and, moreover, the electric field in the dielectric was far larger than the field of a coaxial cable. These results were also validated by using CST Microwave Studio, and it resulted in a deviation less than 1 dB in the whole frequency band (this evaluation is reported in Appendix A).
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 3 Method
### Hypergraph Construction  
In this paper, we use the hypergraph method to construct point cloud data. We sample the original point cloud using farthest point sampling (FPS) to identify representative centroids. Furthermore, we use the ball query to generate hyperedges that effectively capture the local and global structural relationships between point clouds, as shown in Figure 1. First of all, a vertex set \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) is obtained from point clouds \(X=\{x_{1},x_{2},\ldots,x_{N}\}\) with \(N\) points, then the representative vertex \(V_{F}=\{v_{F,1},v_{F,2},\cdots,v_{F,p}\}\in V\) is obtained using the furthest point sampling algorithm, \(n_{c}\leq p\leq N\), where \(p\) represents the number of points sampled, and \(n_{c}\) represents the number of target classes. Next, we use a ball query to divide the entire point set into a set of hyperedges. Specifically, a sampled point \(v_{F,i}\in V_{F}\) is chosen as the centroid at a time, and all nodes within the ball of radius \(R\) (including the centroid) are found and used to generate hyperedges. An important basis for choosing a radius \(R\) is the construction of a connected hypergraph, which means a reachable path exists between any pair of vertices within the hypergraph. The sampling radius of a sampling point \(v_{F,i}\) is therefore restricted between its own maximum and minimum distances from other sampling points. The sampling radius distance of a sampled point \(v_{F,i}\) is  
\[R_{\min}(v_{F,i})\leq R\leq R_{\max}(v_{F,i}) \tag{7}\]  
where the maximum sampling radius is denoted as \(\max(\text{dist}(v_{F,i},V_{F}))\). The minimum sampling radius is denoted as \(\min(\text{dist}(v_{F,i},V_{F}))\), \(V_{F^{\prime}}\) denotes the need to remove\((\backslash)\) the starting point \(v_{F,1}\), the center point \(v_{F,i}\) and the sampling point \(v_{F,j_{1}},\cdots,v_{F,j_{q}}\) that has been used by other nodes to generate the radius. \(V_{F^{\prime}}\) is expressed as  
\[V_{F}=V_{F}\backslash\left\{(v_{F,1}),(v_{F,i}),\left(v_{F,j_{1}},\cdots,v_{ F,j_{q}}\right)\right\} \tag{8}\]  
In this way, we obtain the hypergraph \(G=(V_{F},E)\) generated from point clouds, where \(V_{F}\) denotes the vertex set, and \(E\) denotes the hyperedge set. The method is more suitable for point cloud data with uneven distribution, irregularity, and other characteristics than kNN for constructing hypergraphs. It also reduces the complexity of computing distances.
## 3 Experimental Dataset and Results  
### Dataset  
The experimental dataset was performed on the audio-visual database we had created, and 10 independent digital English utterances (numbers from zero to nine) were gathered from six different speakers (three males and three females). Each speaker pronounced each word up to 100 times. The dataset was based on American English pronunciation and the pronunciation of each number was divided into separate video clips. Each independent speaker was not trained in professional pronunciation, and their first language was not always English. Thus, there might be some differences in the lip movements of individual utterances. We collected numerous non-standard samples for the dataset in order to facilitate a more extensive study of the lip-reading recognition system. We collected videos from the frontal perspective of individual speakers who were sitting naturally without any actions. The size of each frame of the original images was \(1920\times 1080\) resolution, approximately 25 frames per second. In order to accurately locate the beginning and end of each utterance unit, we used audio as an aid to separate each uttered word, each word lasted about 1 s. Then, each isolated word video was further extracted into a fixed length of 10 frames. After processing each video frame, we obtained a fix dimension of ROI at \(224\times 224\) pixels as standard inputs of CNN model.
## 2 Description of the Shipboard Microgrid under Study  
The general scheme of the system studied in the present work is shown in Figure 1 composed of a ship's DG, a proton exchange membrane fuel cell (PEMFC), and RESs such as sea wave energy (SWE) and photovoltaic [39]. The shipboard MG also consists of an energy storage system, which includes a flywheel energy storage system (FESS) and a battery energy storage system (BESS). Furthermore, the system takes into account a time delay (\(\tau_{d}\)). Time delays are a significant non-linear variable that can impact the effectiveness of control mechanisms. Such delays can occur due to communication line congestion or data blockages, leading to undesirable outcomes. The controller relies on the communication network to exchange information, which means there are limitations on the data that can be transmitted and received. One of these constraints is \(\tau_{d}\), which has been recognized as a factor that can affect the system's overall performance.
# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 3 Proposed Methodology  
In multi-label learning, each example is described by a feature vector while being associated with multiple-class labels simultaneously. \(X=\mathbb{R}^{d}\) is the dimension of features and \(Y=\mathbb{R}^{q}\) is the dimension of labels. Given a multi-label data \(D=\{(X_{i},Y_{i})|1\leq i\leq N\}\), where \(X_{i}=(x_{1},x_{2},\ldots x_{d})\) denotes a \(d\)-dimensional feature vector of the example, \(i\) and \(x_{i}^{j}\) are the values of \(X_{i}\) in feature \(f_{j}\), and \(Y_{i}=(y_{1},y_{2},\ldots y_{q})\) denotes the label vector of the example \(i\). \(y_{i}^{j}=1\) when \(X_{i}\) has label \(l_{j}\); otherwise, \(y_{i}^{j}=0\). The task of multi-label learning is to learn a multi-label classifier \(h:X\to 2^{Y}\) from \(D\), which maps the space of feature vectors to the space of label vectors. In addition, most of the existing multi-label learning methods do not fully consider the class imbalance among labels. For class label, the positive training examples are denoted by \(D_{j}^{+}=\{(x_{i},1)|y_{i}\in Y_{i},1\leq i\leq N\}\) and the negative training examples are denoted by \(D_{j}^{-}=\{(x_{i},0)|y_{i}\in Y_{i},1\leq i\leq N\}\). As a general rule, it is possible for the imbalance ratio \(\text{Im}R=\text{max}(|D_{j}^{+}|,|D_{j}^{-}|)/\text{min}(|D_{j}^{+}|,|D_{j}^{ -}|)\) to become high because \(|D_{j}^{+}|\) is less than \(|D_{j}^{-}|\) in most cases. Therefore, the corresponding imbalance ratio is used to measure the imbalance of multi-label data. Considering multi-label imbalanced data sets, COCOA is an effective multi-label learning approach to train an imbalanced clinical data set in the proposed technique. In this study, a regularized ensemble approach integrated into multi-class classification process of COCOA named as COCOA-RE was developed to improve the performance of COCOA.
## 2 Topology Optimization Method
### Performance Characterization of ITPS  
ITPS panels form the outer skin of the aircraft to protect the underlying structure from a severe environment at high speed. They are designed to fulfill both thermal insulation and load-bearing function.  
The thermal insulation capacity of the ITPS can be characterized by the equivalent thermal conductivity. The smaller the equivalent thermal conductivity, the better ITPS thermal insulation capacity. According to [PERSON]'s law, heat flux across \(z\)-direction in a steady state is:  
\[q_{in}=-k\frac{\partial T}{\partial z}=-\lambda\frac{\Delta T}{h} \tag{7}\]  
In these functions, \(q_{in}\) is the heat flux, \(\Delta T\) is the temperature difference between the TFS and BFS, and \(\lambda\) is the equivalent thermal conductivity of the ITPS. The heat transfer rate can be represented as:  
\[Q=\lambda\frac{T_{out}-T_{in}}{h}L=\int k\frac{\partial T}{\partial z}Ldh \tag{8}\]  
The heat transfer rate \(Q\) is proportional to \(\lambda\). When \(Q\) is minimized, \(\lambda\) is minimized as well, and the best thermal insulation capacity of the ITPS can be achieved.  
As for optimizing the load-bearing capacity of the ITPS, it can be realized by maximizing equivalent stiffness. This problem can be transformed into the problem of minimizing elastic strain energy. The strain energy for thermo-mechanical coupling problem can be expressed as:  
\[W=\frac{1}{2}\int(\varepsilon-\varepsilon_{th})^{T}\mathbf{D}(\varepsilon- \varepsilon_{th})dV \tag{9}\]  
\(\mathbf{D}\) is the stiffness matrix of material:  
\[\mathbf{D}=\frac{E}{1-\mu^{2}}\left(\begin{array}{ccc}1&\mu&0\\ \mu&1&0\\ 0&0&\frac{1}{2}(1-\mu)\end{array}\right) \tag{10}\]  
The best way to reduce thermal short is to remove the webs. Under such conditions, thermal stress can be eliminated as well since there would be no thermal expansion mismatch. However, the load-bearing capacity could be diminished dramatically as a consequence. Therefore, appropriate objectives and constraints should be employed for ITPS optimizations.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 2 Wind Turbine Model Description
### High-Frequency Transformer Model  
As transformers play a significant role in the energy transmission, it is extremely important to study their performance under different conditions, especially in high frequencies. Because of the increased effect of distributed capacitance in high frequencies, distributed capacitance should be considered in modeling transformers in transient studies to obtain accurate transient responses; however, there are difficulties in determining the capacitance [33; 34].  
The high-frequency transformer model [30] and its parameters are as follows:  
\(C_{H}\) is the capacitance between high voltage windings and the ground system;  
\(C_{L}\) is the capacitance between low voltage windings and the ground system;  
\(C_{HL}\) is the capacitance between the high voltage and low voltage windings.  
Generally, it is assumed that \(C_{HL}\) and \(C_{L}\) will be larger than \(C_{H}\), not only to reduce the cost of insulation between the transformer core and windings, but also to decrease the size of the transformer.