esa-sceva/satcom-chunk-collection huggingface dataset:
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 2 Theoretical Model of Specular Returns from Inland Waters
### The Spatial Extent of Specular Echoes and Its Implications  
The spatial extent of radar echoes can be generally thought as infinite, but, for practical purposes, it is mostly limited to the radiator's main lobe, where the illuminated target is treated as a radiator. In general, the size of the main lobe first decreases with the increasing size of the water target, in a similar fashion to the law of the radiation pattern of an aperture antenna. Then, at some point, the relationship reverses and the lobe width increases with the size of the target. Given the parameters in Table 1, Equation (1) was exercised for a range of target widths in order to identify where such transition occurs. Figure 2 shows a turning point at about 100 m in size of the target, assuming the main lobe width at 10 dB below peak. Consequently, very narrow rivers can be characterized by main lobes that are much wider than the river itself, and inversely proportional to the river width. Instead, water bodies with a spatial extent (along track) higher than 100 m are expected to produce a strong echo for a duration that coincides with the track crossing the water. A deeper analysis of this behavior falls outside the scope of this paper, and it is the intention of the authors to develop a dedicated work for probing further into this aspect and its implications.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Orbital height & 773 km \\ Orbital inclination & 98.276\({}^{\circ}\) \\ Radar frequency & 13.5753 GHz \\ Doppler velocity Nyquist & 9.91 m/s \\ Range gate & 0.4688 m \\ PRF & 1795 \\ Number of echoes in a record & 1984 (1.1 s) \\ Ground distance between echoes & 3.8 m \\ \hline \end{tabular}
\end{table}
Table 1: Relevant parameters characterizing the Envisat collection of Individual Echoes.  
Figure 2: Main lobe width (at 10 dB below peak) as a function of river width.  
The examples discussed in the next two sections hint at the said two conditions. In Section 2.3, we discuss the behavior of water bodies smaller than 100 m. It is shown that the width of the main lobe is inversely proportional to the physical size of the water surface. In Section 2.4, we discuss larger water bodies, where the spatial scale is proportional to the size of the water body. A possible practical implication for the analysis of a wide range of inland waters is to assume a main lobe width of at least 100 m, or 26 IEs, which will fit the true behavior of the radiator, in most cases.
# M2 ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios
## 4 Discussion
### Limitations and Future Work  
In real-world scenarios, a variety of factors can cause modality absence, such as the faces in the video not appearing within the range of the camera at some moments. The datasets we used are also affected by modality absence, which definitely affects the accuracy of the modality. In future work, we will focus on addressing the issue of modality absence, which may enhance M2 ER.  
The proposed method involves multiple components, including face detection, face recognition, and attention-based fusion; while these components enhance the effectiveness of the model, they also introduce complexity that undoubtedly increases the difficulty of implementation for others.  
Another limitation is that only two datasets were utilized for the experiment, without further testing the generalization of the model. Due to the potential impact of different data sources on performance, it is necessary to explore how well the proposed method generalizes to other datasets due to the potential impact of diverse data sources on performance in the future.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Example** & **Speaker** & **T** & **V** & **A** & **Authentic Emotion** & **Predicting Emotion** \\ \hline A & [PERSON] & Ohh! I’m gonna be on the news. & & & high pitch, cheerful tone & Positive & Positive \\ \hline B & [PERSON] & So, I hear you, you hate me! & & & slow pace, downcast tone & Negative & Negative \\ \hline C & [PERSON] & Look! I did not feel like dancing. Okay? & & & downcast tone, high pitch & Positive & Positive \\ \hline \hline \end{tabular}
\end{table}
Table 10: Examples of multimodal emotion recognition. \(T\) represents the dialogue text, \(V\) represents the speaker’s visual information, and \(A\) represents the audio information in the video.
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 3 Experimental Results
### Dataset Preparation Techniques
#### 3.1.1 Separation Per Function  
The dataset provided by NIST contains source code snippets per each CWE. Most of the source code snippets contain both vulnerable and not vulnerable functions for each CWE included in the dataset. This is created intentionally, to verify the ability of Static Analysis Security Testing tools to correctly identify true positives and to not consider false negative as weaknesses.  
Figure 2: Control flow tokens clusters.  
We select the samples for vulnerable code per each CWE by selecting only the vulnerable function, named \"bad (0)\" as a convention by NIST. In the same way, we select the samples for non-vulnerable code to the specific CWE by selecting only the not-vulnerable function, named \"good (0)\". An example of a Java vulnerable function is displayed in Figure 3.  
The advantage of this approach is that the Intermediate Representation can capture in detail the very tiny differences between a non-vulnerable and a vulnerable piece of code, as NIST uses very similar lexical and semantical structure of both functions (vulnerable and not vulnerable), modifying only the CWE relevant lines of code.  
The second advantage is that the number of lines of code remains very small, making the IR data flow and control flow vector able to be represented in smaller dimensions for the classifiers, with impact on processing and accuracy.  
The disadvantage is that the number and diversity of non-vulnerable code snippets is lower, making the non-vulnerable dataset similar in size with the vulnerable dataset. In reality, vulnerable and non-vulnerable codes are unbalanced, for example one specific CWE vulnerability (defined in 5-10 lines of code) may be present several times only, in a program sized at more than 50,000 lines of code.  
We use this approach to separate vulnerable code from non-vulnerable code in our experiments with the entire NIST database for Java code.
# Dual-Band Single-Layer Fractal Frequency Selective Surface for 5G Applications
## 2 Self-Similar Structures and Fractal Antennas  
The first time fractals were looked at as something useful to improve real-life applications was by the mathematician [PERSON] in his book 'The fractal geometry of nature' [15], where the occurrence of fractals in nature and their relation to roughness are explained. A couple of years later, [PERSON] and [PERSON] published the first paper on fractal antenna theory [16]. After that, [PERSON] made the first real-life fractal antenna [17]. [PERSON] and [PERSON] compiled [PERSON]'s research on fractal antenna design [18]. More general fractal antenna design rules were also compiled in a book by [PERSON] et al. [19].  
Among these fractal proposals, a well-known self-similar geometric shape is the Sierpinski triangle, of which the first four different stages are shown in Figure 1. This shape was used in previous antenna and FSS designs. [PERSON] and [PERSON] [11] used the triangle to create a bow tie FSS that works for two frequencies. This dual-band design forms the base of the design used in this research.
# Laboratory Evaluation of Rejuvenating Agent on Reclaimed SBS Modified Asphalt Pavement
###### Abstract  
To evaluate the effect of rejuvenating agent on reclaimed SBS (styrene-butadiene-styrene) modified asphalt pavement (RSMAP) material, different tests of asphalt and mixtures were conducted. Firstly, the improvement effect of rejuvenating agents on the aged modified asphalt was tested at macroscopic and microscope level. Then the properties of hot mix asphalt (HMA) with different RSMAP contents (0%, 30%, 50% and 70%) were evaluated by conducting freeze-thaw split, semi-circular bending (SCB) and dynamic modulus (DM). The results indicate that rejuvenating agent can recycle the properties of aged modified asphalt effectively. The initial phase structure of the aged modified asphalt is not changed by adding rejuvenating agents. Moreover, the SBS particles area ratio of modified asphalt blends is significantly correlated with tenacity as the proportion of rejuvenating agent increases. For mixtures, RSMAP is harmful to moisture susceptibility and low-temperature cracking resistance of recycled mixture, especially with high RSMAP content. Moreover, the two properties can be improved by adding rejuvenating agents, but the recycled mixtures with high RSMAP content are not easy to recover to HMA mixture level. In general, the rejuvenating agent has an effect on the stiffness of the recycled mixture, but this is effect not obvious. When loading frequency reaches a higher value, the DM of recycled mixtures and HMA mixture tend to be consistent including high RSMAP content.  
Keywords:road engineering; reclaimed SBS modified asphalt pavement; rejuvenating agent; phase structure; fracture energy; fracture toughness; stiffness +
Footnote †: journal: Applied Physics Letters
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 3 Proposed Method  
In this paper, we propose a multi-scale contextual information enhancement network (MCIE-Net) for crack segmentation, and its structure is shown in Figure 1. In crack segmentation, there are strict requirements to accurately localize fine-grained spatial information, such as crack edges, which means that the network must extract sufficient figurative spatial information, as well as abstract semantic information. Since the plain skip connection of U-Net cannot fully utilize the multi-level features information generated in the encoder, MCIE-Net designs a new skip connection structure, so that the deep layer of the decoder network can aggregate feature information from multiple shallower layers. By fusing the multi-scale information, the decoder can obtain a richer representation of semantic information, and, more importantly, the decoder can make full use of the low-level features to improve the network's ability to restore detailed spatial information. Due to the limitation of fixed sampling scales, the plain convolutional neural networks cannot provide enough contextual scene information, which leads to the weak ability of the network to distinguish the segmented object from the interference information in the background. Therefore, we propose the contextual feature enhancement module, through which the network can extract contextual features at multiple scales and obtain a richer representation of contextual scene information. The contextual feature enhancement module is placed in the deep layers of the decoder network instead of all layers, because the shallow layers do not explore the contextual features and the feature enhancement methods may dilute the fine-grained spatial information in them, affecting the network's segmentation capability. Our experiments in Section 4.5.2 demonstrate that when both performance and efficiency are taken into account, it is the best choice to place the module in the fourth and fifth layers of the decoder network. Additionally, in order to better extract the diverse morphological features of cracks, we improve the feature extraction network by introducing the deformable convolution into the encoder network to change the size of the receptive field through adaptive sampling area adjustment. Similar to [49], we do not replace all the layers of the encoder network with deformable convolution layers because there will be a limited improvement in network performance while using the deformable convolution in the shallow layers, but a significant increase in computational cost. Our experiments in Section 4.5.3 show that replacing the plain convolution of the fourth and fifth layers of the encoder network with deformable convolution is the optimal choice to achieve both performance and efficiency.
However, the optimization periods of these two methods are relatively short and it is difficult to obtain an overall optimal solution. In order to eliminate the deviation generated by the build of the approximation model, the researchers used the natural heuristic algorithm to optimize the performance globally. For example, [PERSON] et al. [24] utilized a genetic algorithm to optimize the geometry of a spiral axial-flow mixing pump. In recent years, a lot of intelligence algorithms have been proposed. Because there is no crossover and mutation process in the genetic algorithm, the convergence speed is greatly accelerated [25; 26]. In summary, the pump optimization methods have undergone an improvement process from single target to multi-objective optimization, from experience to theory. Currently, with the advance of computer technology, automation, and artificial intelligence, automatic design methods without human intervention are being developed and applied in various fields. This study focuses on the hydraulic optimization of automotive electronic pumps. Firstly, numerical simulations for a given model were carried out to build the prediction process of pump efficiency. Secondly, the target of this multi-objective optimization was established by weighting analysis. Then, an automatic optimization platform was established including parametric design, CFD and DOE. Orthogonal design, a kind of DOE method, was implemented in advance to estimate the influence of four selected factors on pump efficiency and head. The optimized ranges of these parameters were also determined. After, one kind of DOE namely the multi-island genetic algorithm (MIGA) was utilized to drive the platform for optimizing the weighted average efficiency under three operating flowrates. Finally, the optimized hydraulic scheme was obtained in a limited 375 h by using a normal desktop computer without running in parallel and was proven to improve the pump efficiency by 4.2% through the experiment. The proposed method may provide some theoretical guidance for hydraulic optimization of fluid machinery.
## 3 Results and Discussion
The transition between the transitional-flow and slip-flow regimes occurs at \(M/\sqrt{Re}=0.1\). With data points colored according to \(M/\sqrt{Re}\), it is apparent that data approaching the slip-flow regime show better agreement with the Carlson-Hoglund model. Since the model is a function of \(Re\) and \(M\), it is provided in Figure 4 as a shaded range for 1.2 \(<M<\) 4. The data in best agreement with the model are the \(D_{\text{P}}=\) 7.6 \(\upmu\)m, \(P=\) 760 Torr data, which fall within the slip-flow regime (0.01 \(<M/\sqrt{Re}<\) 0.1). These findings join other studies [1; 43; 44; 45; 46; 43] in illustrating that \(C_{D}\) is not solely a function of \(Re\), but also of metrics that reflect changing flow conditions, such as \(M\) and \(Kn\). The increasing deviation of the \(M>\) 1.2 empirical \(C_{D}\) data from the Carlson-Hoglund model in Figure 4 is attributed to the suppression of flow characteristics in increasingly rarefied conditions. For instance, the drag force incurred by the bow shock may be diminished in increasingly rarefied supersonic flow. Thus, while Figure 3 attests to the Carlson-Hoglund model's accuracy for modeling \(C_{D}\) at \(M<\) 0.8, Figure 4 indicates that the model is less accurate for \(M>\) 1.2 rarefied flow conditions, including the transitional-flow regime.
## 4 Methodologies and Materials  
### Dataset  
The experiments are conducted on two publicly available benchmarks: the SemEval2007 [18] and ReLocaR [8] datasets. Unlike WiMCor [35] and GWN [36] that contains huge amount of instances, SemEval2007 and ReLocaR are relatively smaller. The samples lay into two classes: literal and metonymic. SemEval contains 925 training and 908 test instances, while ReLocaR comprises a train (1026 samples) and a test (1000 samples) dataset. The class distribution of SemEval is approx 80% literal, 20% metonymic. To eliminate the high class bias of SemEval, the class distribution of ReLocaR sets to be 50% literal, 50% metonymic.  
Since the MR task is still in its infancy, there is no available MR dataset for Chinese. English datasets SemEval and ReLocaR are employed to construct a Chinese MR dataset through text translation, manual adjustment, and labeling.  
1. Text translation: examples of SemEval and ReLocaR are translated using API on the Internet and finally obtain independent Chinese samples;
2. Manual adjustment: Considering the poor quality of the dataset obtained from API, all examples are corrected and well selected to meet the Chinese expression norms;
3. Labeling: Inserting a pair of indicators to mark the entity.  
After the above steps, an MR dataset called CMR in Chinese is constructed to verify the model performance on Chinese texts. Finally, the dataset contains 1986 entity-tagged instances, of which 1192 are randomly divided as a training set and 794 as a test set. Each instance contains a sentence with the entity tag and a classification tag of literal or metonymic.
# Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
## 2 Methodology
### BPT Building  
The creation of BPT consists of two stages: region modelling and region merging. The region model \(M_{R_{i}}\) defines how regions are represented and how to model the union of two regions. The CLUS-BPT framework uses the first-order parametric model for region modelling due to its simplicity in definition leading to straightforward merging order process [27]. Given a hyperspectral region \(R\) formed by \(N_{R_{p}}\) spectra containing \(N_{n}\) different radiance values, the first-order parametric model \(M_{R}\) is defined as a vector of \(N_{n}\) components, which corresponds to the average of the values of all spectra \(p\in R\) in each band \(\lambda_{i}\), as follows:  
\[M_{R}(\lambda_{i})=\frac{1}{N_{R_{p}}}\sum_{j=1}^{N_{R_{p}}}H_{\lambda_{i}}(p_ {j})\qquad i\in[1,\dots,N_{n}] \tag{1}\]  
where \(H_{\lambda_{i}}(p_{j})\) represents the radiance value in the wavelength \(\lambda_{i}\) of the pixel with spatial coordinates \(p_{j}\).  
The second stage of BPT construction is the merging criterion \(O(M_{R_{i}},M_{R_{j}})\), which defines the similarity of neighboring regions as a distance measure between the region models \(M_{R_{i}}\) and \(M_{R_{j}}\) and it determines the order in which regions are to be merged. In [27], different region models with their compatible region merging criterion for BPT construction are described. A merging criterion that is defined as the spectral angle distance \(d_{SAD}\) between the average values of any two adjacent regions is defined as:  
\[O(M_{R_{a}},M_{R_{b}})=d_{SAD}=arccos\left(\frac{R_{a}^{T}R_{b}}{||R_{a}||||R_ {b}||}\right). \tag{2}\]  
Given the initial partition of small regions, the distances between each region and its neighbouring regions are computed and sorted. The smallest distance between the current region and neighboring regions indicates the candidate region for merging. Figure 2 depicts the BPT building process, where the leaf nodes correspond to the initial partition of the image. From this initial partition, an iterative  
Figure 1: Flowchart of the proposed method.  
bottom-up region merging algorithm is applied by keeping track of the merging steps until only one region remains. The last region that corresponds to the whole image is the root of the tree.  
Before the merging stage, small regions in the initial partition may result in a spatially unbalanced tree during BPT construction. Those regions are prioritized to be merged first by determining whether their size is smaller than a given percentage (typically 15%) of the average region size of the initial partition [27].
# Study on Composite Fracture Characteristics and Hydraulic Fracturing Behavior of Hard Rock
## 5 True Triaxial Hydraulic Fracturing Test Verification
### Verification of \(p_{b}\)  
This study revealed that under conditions where the water pressure exceeds the critical fracturing pressure (\(p_{\text{b}}\)), the fracture angle of hydraulic fractures decreases with increasing water pressure. In such cases, the influence of the critical fracture distance on hydraulic fractures diminishes. The hydraulic fracturing indoor experiments conducted in this study utilized a constant-flow water injection method, leading to an increase in internal water pressure over time within the fractures. The specimen experiences instability at the most vulnerable point, i.e., the initiation of the pre-existing crack. Therefore, considering the fracture pressure at this moment as the critical fracturing pressure, it is justified. When \(\beta=0^{\circ}\), the specimen undergoes a pure Mode I fracture, and the Mode I stress intensity factor is equivalent to the pure Mode I fracture toughness. By substituting the experimental parameters into Equation (23), the theoretical prediction for the critical fracturing pressure (\(p_{\text{b}}\)) is calculated. Comparing the experimental fracturing pressure values with the theoretical predictions, as shown in Figure 15, it can be observed that the derived formula for critical fracturing pressure performs well in predicting the hydraulic fracturing pressure for \(\beta=0^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\). However, due to the heterogeneity of rocks, some experimental data points exhibit deviations from the predicted values.  
Figure 14: Morphologies of hydraulic fractures.  
Figure 15: Comparison between the theoretical and experimental values of \(p_{\text{b}}\).
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 5 Discussion
### Comparison between Techniques in Geophysical Exploration
#### 5.3.2 Euclidean Methods  
Traditional Euclidean methods, including ray tracing based on the Eikonal equation, have been widely used in geophysical exploration due to their relative simplicity and computational efficiency.  
Strengths
* **Simplicity and Speed.** Euclidean methods are generally simpler to implement and computationally less demanding, making them suitable for real-time applications.
* **Widely Established.** These methods are well-established and widely used in the industry, with extensive resources and tools available for practitioners.  
Weaknesses
* **Limited by Simplifications.** Euclidean methods often assume straight-line propagation of seismic waves, which can lead to inaccuracies in complex geological settings.
* **Less Accurate in Curved Spaces.** These methods struggle with accurately modelling wave propagation in highly curved and heterogeneous environments, such as salt domes.