esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dataset
#### 2.1.2 Synthetic Dataset Generation  
To be able to tackle the scarcity of data, we propose to synthetically enlarge the dataset of baseline-follow-up image pairs presenting HE. We do so by utilizing the existing baseline images and introducing some variations in hematoma appearance, e.g., by slightly altering its shape, size, morphology, and intensity. The idea is to generate novel baseline images without losing the main information regarding the hematoma growth pattern.  
Our image synthesis is inspired by the work of [23], where the authors propose an approach to produce synthetic cortical atrophy, generating a longitudinal ground-truth dataset specifically designed for accuracy validation of surface-based CT measurements. Following the same procedure, we apply slight deformations to the baseline hematoma to obtain different versions of the lesion, while preserving the same follow-up image in order to keep the expansion behavior as much as possible.  
To achieve this, we follow several steps, as shown in Figure 1. The first one is to generate the target hematoma mask, which will represent our desired lesion appearance in one of the variants of the baseline image. Similarly to the work of [23], we utilize morphological operations for this purpose, using different binary morphological operations (with only 1-2 iterations) to introduce small changes in the baseline hematoma mask (see Figure 1). For our task of predicting hematoma expansion, we generate 5 different versions of each baseline image, to produce more baseline-follow-up image pairs for training. All of them are produced using the same binary erosion operation but with different 3D structuring elements of dimensions \(3\times 3\times 3\), which are chosen randomly and some of which are repeated two times. This way, the final mask of each new version is slightly different from all the other masks of the same case (and between different patients), avoiding repetitive patterns or biasing the training data, which could affect the models.  
Once this step is complete, we move to the second stage and calculate a transformation to deform the baseline image accordingly. Following the same approach as in [23], we achieve this by deformably registering the original baseline mask to the eroded one by using the publicly available Greedy software [28]. This results in a deformation field from the real baseline to the desired one (see Figure 1C). In the third step, we apply this resulting deformation field to the original baseline CT image to artificially shrink the hemorrhagic stroke lesion (Figure 1E). The intensities in the resulting image are obtained with linear interpolation. Finally, our training set includes 6 baseline-follow-up pairs generated from one patient: the original pair and 5 more pairs where the baseline lesion has a different appearance. Note that the follow-up images are the same for all the pairs within the same patient.
## 2 Materials and Methods
### Theoretical Calculations  
DFT calculations were performed using periodic Pwscf code of Quantum ESPRESSO (QE) (v. 6.0) [30]. Employed ultrasoft pseudopotentials were based on GGA-PBE approximation for exchange-correlation functional [31]. Cutoff energy for plane waves was set to 40 Ry, while charge density cutoff was 600 Ry. The electronic convergence criterion was set to 10\({}^{-6}\) eV. The vacuum between slabs was set to at least 25 A to minimize interaction between periodic images. The relaxation was performed until the residual forces were below 0.005 eV A\({}^{-1}\). Van der Waals interactions were included through the DFT + D3 scheme of [PERSON] [32]. Graphene slab cells (3 \(\times\) 3) and (6 \(\times\) 6) consisted of 32 and 128 atoms, respectively. A sampling of k-points was performed using the Monkhorst-Pack scheme (4 \(\times\) 4 \(\times\) 1 k-points for 32-atom cell and 2 \(\times\) 2 \(\times\) 1 k-points for 128-atom cell) [33]. The isolated dimethoate molecule was optimized in a 50 \(\times\) 50 \(\times\) 50 A\({}^{3}\) cell, using only gamma  
Figure 1: 3D structure of DMT. Horizontal and vertical lines represent the dimensions of the rectangular box used for estimation of the size of the adsorption site (Section 3.1.1). Color code: Carbon= black, sulfur = light yellow, phosphorus = yellow, oxygen = dark red, hydrogen = turquoise, nitrogen = grey.  
k-point and Martyna-Tuckermann correction for isolated molecules, as implemented in QE [30]. Adsorption energy (\(E_{ads}\)) was calculated from the difference between total energy of the graphene with adsorbed organophosphate (\(E_{graphene+OP}\)), and the sum of total energies of the empty graphene slab (\(E_{graphene}\)) and isolated organophosphate molecule (\(E_{OP,isol}\)):  
\[E_{ads}=E_{graphene+OP}-\left(E_{graphene}+E_{OP,isol}\right) \tag{1}\]  
The electronic structure of bands was calculated using the projwfc.x code of QE, while the Bader [34] code was used to calculate charge transfer. Molecular structures are visualized using XCrysDen software (v.1.6.2) [35].
## 3 Results  
### Hotspot Analysis  
Hotspots were found to be prominent in camps with drainage lines (see Camps A1, B2, B3, B4, C2, and C4 in Figure 3). When the sheep were allocated to camps with no drainage lines (Camps A2, A3, A4, B1, C1, and C3), no significant clustering occurred. The season and time of year do not seem to influence when the animals congregate in drainage areas. Cold spots were mostly associated with low NDVI values outside of the drainage lines. The hotspots indicated that the sheep spent significantly more time grazing in a relatively small portion (17.3%) of available grazing land (Table 2). This finding could alter perceptions of the true impact of stocking density and possible overutilization of portions within camps.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 4 LEO Augmentation
### Horizontal Protection Level
#### 4.4.1 Ambiguity-Float HPLs  
The ambiguity-float HPLs converge with the processing time. As discussed in [14], the speed of convergence is correlated with both the multipath and the ionospheric conditions. In this sub-section, the influences of the LEO augmentation on the ambiguity-float HPLs are discussed for different scenarios and constellation combinations.  
Figure 14 shows the ambiguity-float HPLs for network 1 under quiet multipath and ionospheric conditions (Category A), and for network 3 under active multipath and ionospheric conditions (Category D). It is noted that the convergence of the ambiguity-float HPLs is not only related to the satellite number, but also to their configurations, especially at the beginning phase. The increased overbounding variances of the interpolated ionospheric delays, which do not consider the elevation-dependence in this study, could degrade the positional components in the two horizontal directions with different propotions. In addition, the accumulated bias projects onto the two horizontal directions with an absolute projection matrix (see Equation (37) in [14]). This could also lead to different behaviors in the HPLs compared to the horizontal precision. As such, for Figure 14, the focus is intended to be put on the differences between the cases without and with the LEO augmentation, i.e., the solid and dashed lines in Figure 14.  
From Figure 14 it can be observed that adding LEO satellites leads to a dramatic reduction in the HPLs. With the LEO augmentation, the improvements in the HPLs have reached more than 60% after convergence for the period of about 5 to 30 min, with the HPLs brought down to about 0.2 and 1 m at around 5 min after the processing start for the two tested scenarios. The HPLs for the two test scenarios are shown in Table 4 at different convergence times. The rapid convergence with LEO augmentation is not only caused by the increased satellite number, but is also a result of the rapid geometry change generated by the LEO satellites.  
Figure 14: Ambiguity-float HPLs without and with LEO augmentation for: (**a**) Network 1 under quiet multipath and ionospheric conditions (Category A); (**b**) Network 3 under active multipath and ionospheric conditions (Category D). The solid lines refer to the case without LEO, and the dotted lines represent the HPLs with LEO augmentation. Note that the y-scales are different in the two sub-figures.
## 5 Discussion  
### Differences in the Seismic Environment of the Linfen Basin  
A low-velocity layer exists at a depth of approximately 16-21 km in the southern part of the Linfen Basin (Figures 6 and 10). This feature is commonly found in other basins in the North China region. These low-velocity layers are considered the boundary between the upper and lower crust [59; 67; 68; 69].  
Figure 10: Three-dimensional seismic structural model for the two large earthquakes in the Linfen Basin. The yellow spheres represent the 1303 Hongtong earthquake, and the green spheres represent the 1695 Linfen earthquake. The solid red line at the top of the fault represents the fault trace, and the solid grey lines represent burial depth contour lines. The yellow three-dimensional body represents the low-velocity body. The Hongtong–Subao Fault is the boundary between the northern and southern parts of the basin.  
The Linfen earthquake is located in the Linfen Sag in the southern part of the Linfen Basin. Its epicentre lies in the velocity transition zone above the low-velocity body [20; 21; 51; 65; 66]. This interface medium easily accumulates strain energy, and when the strain energy exceeds its bearing limit, the medium fractures, releasing energy and causing earthquakes [37]. The distribution of small earthquakes in the southern part of the basin is relatively scattered, with small earthquakes primarily distributed at depths of less than 25 km (Figures 6 and 9). The Hongtong earthquake occurred in the Hongtong Sag in the northern part of the Linfen Basin, with its epicentre in a high-velocity structural area with high P-wave velocities. Small earthquakes in this area are relatively dense and show good clustering, primarily distributed at depths of 15-35 km (Figures 6 and 8). The two major earthquakes occurred in different velocity structure regions (Figure 6). From the distribution of small earthquakes (Figures 6 and 7) and the characteristics of velocity changes, it is observed that the southern and northern parts of the Linfen Basin have significantly different seismic environments. We propose that the low-velocity layer in the southern part leads to the differences in seismic environments between the southern and northern parts of the basin.  
The Huoshan Fault has exhibited vertical motion of 1-1.5 mm/year over the past 7000 years [33]; however, the Fushan Fault has shown a vertical motion of only 0.56 mm/year [70]. The vertical motions in the southern and northern parts of the basin differ substantially. The vertical motions of various faults directly correlate with extensional tectonics in the North China region [33; 70]. Mantle upwelling has induced extension in the region [71; 72; 73]. Therefore, we posit that the low-velocity layer in the southern Linfen Basin reduces the effect of mantle upwelling on vertical fault motion, resulting in the observed disparity in the vertical motion between the southern and northern parts of the basin.
## 4 Model Checking Fidelity Properties
### Complexity of the FCTL Model Checking Problem  
Since FCTL differs from CTL, PCTL, and QCTL only for one operator, the detailed complexity analysis available in literature (see, e.g., [26, 27, 58]) for this kind of logic carries over to FCTL [50]. However, the presence of the fidelity operator \(\mathbb{F}_{\sim\tau}\) increases the resulting complexity from polynomial to exponential [50] (Theorem 5.12).
## 2 Understanding WebAssembly
### Interfacing with the Host Environment
* **How Web Assembly Interacts with the Host** Wasm modules are designed to be embedded within a host environment, which in most cases is a web browser [16]. A Wasm module cannot interact with the host environment directly; instead, it must interact through JavaScript using a set of explicit imports and exports. This mechanism allows Wasm to interact with JavaScript APIs, access the Document Object Model (DOM), and more, all while maintaining a clear boundary between the Wasm module and the host environment.
* **Web APIs and JavaScript Glue Code** To interact with web APIs, Wasm relies on JavaScript \"glue\" code that serves as an intermediary. This glue code can call web APIs and pass the results back to the Wasm module. This mechanism allows Wasm to take advantage of the wide range of APIs available on the web, such as those for accessing hardware capabilities, networking, storage, and more.
* **WebAssembly JavaScript API** Wasm provides its own JavaScript API, which is used to load, compile, and instantiate Wasm modules. This API allows developers to interact with Wasm directly from their JavaScript code, providing a high degree of control over the lifecycle of Wasm modules.
* **Direct DOM Access** While Wasm cannot currently access the DOM directly, and must rely on JavaScript, there is an ongoing proposal to add direct DOM access to Wasm. This would allow Wasm to manipulate the DOM without having to go through JavaScript, potentially leading to performance improvements for certain types of applications.
* **Beyond the Browser** While the primary host environment for Wasm is the web browser, it is not limited to this context. Wasm can be hosted in non-web environments, for instance, standalone runtimes such as Wasmer and Wasmtime, or embedded in other applications. Thisflexibility makes Wasm an appealing choice for a variety of use cases, from server-side applications to IoT devices and more.
# Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
## 5 Methodology
### Development of the MGWR-Based Model  
The GWR model aims to examine the potential spatial instability in relationships and provides a measure of the spatial scale at which processes operate optimally by determining bandwidth. The classical GWR-based model assumes that all modeled processes operate at the same spatial scale. However, the MGWR-based model overcomes this limitation by assuming that each variable has a unique spatial bandwidth. This enables the model to remove the constraints that all explanatory variable relationships in the GWR-based model are different at the same spatial scale. As a result, the error bias in parameter estimation can be rationally reduced, and the problem of multicollinearity is appropriately reduced [24]. The essential equation of the MGWR-based model is presented by:  
\[y_{i}=\beta_{bw0}+\sum_{k=1}^{m}\beta_{bwk}(u_{i},v_{i})x_{ik}+\varepsilon_{i} \tag{5}\]  
where \(\beta_{\mathrm{bw0}}\) denotes the intercept of regression of the \(i\)-th region, \(\beta_{\mathrm{bwk}}(u_{i},v_{i})\) is the coefficient of the influence of the \(k\)-th riding demand influence factor on the bike-sharing trip demand at the regression of the \(i\)-th region, and \(bwk\) denotes the bandwidth of the \(k\)-th influence riding demand factor on the bike-sharing trip demand.  
The \(\beta_{\mathrm{bwk}}(u_{i},v_{i})\) estimation method of the MGWR-based model is similar to that of the GWR-based model. The weight calculation is calculated by the following relation:  
\[w_{ij}=\exp(-\frac{1}{2}(\frac{d_{ij}}{bwk})^{2}) \tag{6}\]
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 2 GNSS-Based Bistatic Radar System Geometry Signal Model
It should be noticed that the signal bandwidth of the GNSS signal is reasonably small and the range-curvature in the envelope term can be ignored. Hence, the signal model can be rewritten as  
\[s(r,t_{m})=AQ[r-(R_{0}+\lambda f_{dc}t_{m})]\exp(-j\frac{2\pi}{\lambda}(R_{0}+ \lambda f_{dc}t_{m}+\frac{1}{2}\lambda f_{dmr}t_{m}^{2})) \tag{7}\]  
After the above approximation, the range migration reduces to a linear term. Keystone transform (KT) [32] can be applied to remove the couple between the range and frequency domain. The detailed procedures of KT have been presented in [32], so only a brief description will be provided here. Firstly, a Fourier transform (FT) is applied along the fast time and the data are converted to the \((f_{\tau},t_{m})\) domain, and then interpolated to a new slow-time domain \((f_{\tau},\hat{t}_{m})\), (where \(t_{m}=\frac{f_{\tau}}{f_{\tau}+\hat{f}_{\tau}}\hat{t}_{m}\)), finally an inverse FT is used to come back to the range and slow-time domain. Subsequently, the range cell migration corrected data can be described as  
\[s(r,\hat{t}_{m})=AQ[r-R_{0}]\exp(-j\frac{2\pi}{\lambda}(R_{0}+\lambda f_{dc} \hat{t}_{m}+\frac{1}{2}\lambda f_{dmr}\hat{t}_{m}^{2})) \tag{8}\]  
Figure 2: Two-dimensional rearranged data. Assuming that the range cell contains target echo has been selected, we focus on the azimuth signal and the envelope is neglected in the following discussion. Subsequently, (8) can be written as  
\[s(r,\tilde{t}_{m})=AQ[r-R_{0}]\exp(-j\frac{2\pi}{\lambda}(R_{0}+\lambda f_{dc} \tilde{t}_{m}+\frac{1}{2}\lambda f_{dmr}\tilde{t}_{m}^{2})) \tag{9}\]  
The DRM will seriously impact the traditional target detection performance. Next, we focus on the compensation of the quadratic phase term that is caused by DRM.
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 5 Evaluation
### Impact of GMN Parameters  
Due to the fact that the performance of deep neural network is usually affected by its hyper-parameters, in this experiment, we investigated the impacts of different parameters on the performance of vulnerability detection. Considering the fact that our two approaches utilize the same training model, we only analyzed _ASGVulDetector_ in detail, and gave the experimental results of _BASGVulDetector_ straightforwardly. We considered four key parameters, i.e., number of epochs, embedding dimension, number of hidden layers, and learning rate. The proper values obtained in this experiment were applied to performance evaluation as aforementioned. We chose F1 score to reveal the above impacts; the results of _ASGVulDetector_ are summarized in Figure 8.  
Figure 8a shows the impact of different epoch settings. With the increasing number of epochs, the F1 scores of the four types of vulnerabilities all grow dramatically before the data point of 80. After that, the detection performance enters into a stable state, suggesting that 80 epochs are sufficient to train the neural network. Figure 8b demonstrates the impact of different embedding dimensions. At the beginning of all four cases, F1 scores increase significantly with the embedding dimension. However, as the dimension grows larger than 110, the values of the F1 scores drop sharply. This can be explained by small dimensions limiting the expressiveness of the embedding, but too-large ones suffering from overfitting. Figure 8c shows the impact of different numbers of hidden layers in GMN. Hidden layers are of key importance to capture the internal features of a contract graph, but a large number of hidden layers will significantly increase the complexity of the model. This rule can be derived from this experiment, where four hidden layers are enough to extract internal connections inside contract graphs. Figure 8d reveals the impact of different learning rates. The learning rate is another key hyper-parameter of the neural network, which helps with network convergence and thus influences detection performance. In this experiment, we started from a large learning rate, i.e., 0.1, and scaled the value down to find an appropriate rate, which is suggested to be 0.001.  
Figure 7: Comparison of detection time for _BASGVulDetector_.  
In _BASGVulDetector_, recommended values for the four parameters, i.e., number of epochs, embedding dimension, number of hidden layers, and learning rate, are 70, 90, 4, and 0.001, respectively. It is worth noting that _BASGVulDetector_ requires fewer epochs and embedding dimensions than _ASGVulDetector_ since the contract graph in _BASGVulDetector_ is less complicated than that in _ASGVulDetector_.
## 1 Introduction
Due to the reflection and absorption of electromagnetic waves by the target, there will be a high energy attenuation area behind the target. When other targets exist in this shadow area, they will be difficult to be detected. Recently, deep learning technology has gained huge advances in the remote sensing community due to its powerful feature extraction capabilities [37; 38; 39; 40; 41]. Convolutional neural network (CNN) is originally designed to deal with the image-format datasets, which can also be adjusted to handle radar signals. CNN can extract hidden features from input signals, which replaces handcrafted features. A Doppler-Radar-based hand gesture recognition system using CNN was proposed in [42]. A residual neural network was used in [43] to identify the moving target number in through-wall radar. In this paper, a multiple stationary human targets detection method based on CNN in through-wall UWB radar is proposed. First, we pre-process the raw data collected by the developed UWB impulse radar prototype to achieve the suppression of background and noise signals and the enhancement of weak respiration signal. Then, a six-layer CNN is designed to extract the target point of interest (TPOI) from the range-slow-time matrix. Finally, the clustering algorithm is used to simplify the TPOIs to achieve the accurate detection of multiple targets behind the wall. To the best of our knowledge, this is the first work to automatically identify and extract multiple stationary human targets behind the wall using CNN. The effectiveness of the proposed method is verified by the experimental data. This paper is organized as follows. In Section 2, the UWB time domain respiration signal model is demonstrated. Section 3 details the proposed multiple stationary human targets detection method based on CNN. Section 4 describes the UWB impulse radar system developed and explains the conducted experiments settings. Section 5 presents the analyses and discussions of the experimental results. Finally, Section 6 concludes this paper.
## 3 Results and Discussion
### Bioprinted Material Characterization Test Results
#### 3.1.3 Analysis of the Mechanical Properties of the Materials  
The strength and toughness of the material have become indispensable parameters for material selection, evaluation, and study of modification laws. Therefore, to investigate the effect of the addition of CNC on the mechanical properties of the CNC/PCL blended system, tensile tests were conducted on the samples with different CNC contents. As shown in Figure 7, when the content of CNC was increased to 1%, the tensile strength increased by 4% relative to PCL, and when the content of CNC was increased to 3%, the tensile strength increased by 13% relative to pure PCL. As the CNC content continues to increase, up to 5%, the tensile strength can reach 19%. This shows that the addition of CNC has an enhanced effect on the tensile properties of PCL, which is consistent with the results observed in XRD (see supplementary material Figure S5). When comparing the results of pure PCL, we can see that when CNC is added and the content is increased to a certain level, the enhancement effect of CNC is more obvious [37]. Therefore, it proves the feasibility of CNC/PCL materials in terms of mechanical properties, and the results have important reference significance for the study of self-healing properties of CNC/PCL blended systems.  
Figure 6: Variation of viscosity with shear rate (**a**), the storage modulus (**b**), and loss modulus(**c**) as a function of angular frequency in pure PCL and PCL/CNC composites with different contents of CNC.