esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Compatibility  
Compatibility evaluates co-existence with other software tools and interoperability. Interoperability evaluates the importation process; the steps and time resources are detailed in Table 3. In this case, objective experiments were performed using the same 3D SketchUp model. The results revealed that each software tool requires different types of files and models for successful model importation. Although the results from objective experiments highly depend on software compatibility with SketchUp, other software tools such as Autodesk Revit and ArchiCAD have comparable issues with information loss. Climate adaptation tools currently support the importing of climate-specific information such as materiality. Therefore, the importation process relies on compatible formats adding climate relative information in climate adaptation software tools.  
Firstly, Ladybug and GreenScenario are mostly compatible as both are based on the Rhino interface, which is compatible with a number of other planning software tools--From Autodesk to Graphisoft. However, GreenScenario requires a 2D file with closed and grouped polylines, which are extruded when assigning information of materials, levels of each building and vegetation. CitySim is compatible with AutoCAD software, requiring a model compounded from 3D faces. In this case, the SketchUp model was exported to AutoCAD, where the faces were converted to 3D faces and saved as.dxf files. ENVI-met is the only software that suggests a plug-in for SketchUp, which allows files to be prepared for export. However, the experiment revealed some inefficiencies causing errors and information loss leading to the necessity of remodelling. The AST tool fails to integrate the SketchUp model, and is rather designed for renovation projects than for new developments. Therefore, the design had to be redrawn due to failure to achieve precision.
## 5 Discussion
### Limitations of EFSDAF  
EFSDAF can predict both gradual change and abrupt change events, but it cannot accurately monitor tiny changes (e.g., small flood information in this study) because tiny changes are not shown in one MODIS pixel. In other words, the changes of \(20\times 20\) Landsat pixels can be displayed in one MODIS pixel in this study. Therefore, the prediction accuracy of tiny changes can be improved by blending Landsat images (30 m) and Sentinel 3 OLCI images (300 m), but the predicted images still cannot monitor all actual changes. In addition, the computational efficiency of EFSDAF is the slowest compared with other three methods due to the complexity of the calculation process. However, the computational efficiency of fusion methods will be ignored in the future with the rapid development of high-performance computing platforms like Google Earth Engine (GEE) [46].
# Tension Force Detection of Short Suspender by Using Laser Doppler Vibrometer
## 1 Introduction
Therefore, based on the fact that the durability of steel rod type short suspenders is higher than that of strand type, three types of prestressed steel rebars with aspect ratios of 14, 20.8, and 37.8 were used to simulate steel rod-type short suspenders, and their tension force state was detected in real time by using Laser Doppler Vibrometer (LDV) and vibration frequency analysis. Meanwhile, the theoretical calculation model for tension force of steel rod-type short suspenders according to vibration frequency was established, without considering mass, stiffness, and boundary condition.
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 2 Materials and Methods
### Variable Selection Methods and Classification Models
#### 2.6.2 Classification Models  
The least squares-support vector machine (LS-SVM) is an improved version of the support vector machine (SVM) algorithm. This method changes the inequality constraint in the SVM to an equation constraint, maps the selected nonlinear vector to the high-dimensional space, and constructs the optimal classification hyperplane (optimal decision function). It is also a modeling method that can handle linear and nonlinear information simultaneously. In addition, the LS-SVM overcomes the structure complexity and error-prone nature of neural network networks. It does not only decompose the linearity of the original space, but also realizes the classification of the data. It is especially suitable for small sample sizes in quantitative and qualitative analysis.  
The principle of this method is as follows [20]:  
Assuming the given training set is \(\{(x_{i},y_{i}),\cdots,(x_{N},y_{N})\}\), the objective optimization function of LS-SVM is:  
\[\left\{\begin{array}{l}J_{1}(w,e)=\frac{\mu}{Z}w^{T}w+\frac{\gamma}{Z}\sum \limits_{i=1}^{n}e_{i}^{2}\\ y_{i}=w^{T}\varphi(x_{i})+b+e_{i},i=1,2,\cdots,N\end{array}\right. \tag{2}\]  
where the mapping function of the \(\varphi(x_{i})\) kernel space is the weight vector; \(e\) is the error variable; \(b\) is the offset; and \(\mu\) and \(\gamma\) are adjustable parameters.  
The Lagrange function is:  
\[L=J(w,e)-\sum\limits_{i=1}^{n}a_{i}\Big{\{}w^{T}\varphi(x_{i})+b+e_{i}-y_{i} \Big{\}} \tag{3}\]  
where \(a_{i}\) is the Lagrange operator; \(a_{i}\in R\) is the input vector \(x_{i}\) corresponding \(0<a_{i}<C\). Then \(x_{i}\) is the support vector.  
Calculate the partial derivative of Equation (3) to obtain:  
\[\left\{\begin{array}{l}\frac{\partial L}{\partial w}=0\to w=\sum\limits_{i= 1}^{n}a_{i}\varphi(x_{i})\\ \frac{\partial L}{\partial b}=0\to\sum\limits_{i=1}^{N}a_{i}=0\\ \frac{\partial L}{\partial e_{i}}=0\to a_{i}=\gamma e_{i}\\ \frac{\partial L}{\partial a_{i}}=0\to w^{T}\varphi(x_{i})+b+e_{i},i=1,2, \cdots,N\end{array}\right. \tag{4}\]  
The mapping function of kernel space is defined as:  
\[K(x,x_{k})=\Phi(x)^{T}\Phi(x_{k}) \tag{5}\]  
This study uses the RBF function as the kernel function of LS-SVM. The calculation formula is as follows:  
\[K(x,x_{k})=\exp\left(-\frac{\left\|x-x_{k}\right\|^{2}}{2\sigma^{2}}\right) \tag{6}\]  
where \(\sigma^{2}\) is the square of the band width.  
The variables w and a are eliminated to obtain the Equation (7):  
\[\begin{bmatrix}0&1_{v}^{T}\\ 1_{v}&\Omega+\frac{1}{7}I_{N}\end{bmatrix}\times\begin{bmatrix}b\\ a\end{bmatrix}=\begin{bmatrix}0\\ y\end{bmatrix} \tag{7}\]  
where \(y=[y_{1},\cdots,y_{N}],1_{v}=[1,\cdots,1],a=[a_{1},\cdots,a_{N}]\), and \(\Omega=k(x_{i},x_{l}),i,l=1,2,\cdots,N\).  
If the LS-SVM algorithm is used to determine the category of unknown vector \(x\), the corresponding discriminant function is Equation (8):  
\[y(x)=sign\left[\sum\limits_{k=1}^{N}a_{k}y_{k}K(x,x_{k})+b\right] \tag{8}\]
# Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios
## 4 Experiments
### Ablation Study  
In order to further improve final recognition performance, we propose to binarize the correlation matrix \(P\), because \(P_{ij}\) may be a noisy edge if \(P_{ij}\) has a very small value. Specifically, we use the threshold \(t\) to filter noisy edges:  
\[A_{ij}=\left\{\begin{array}{ll}0,&if\ P_{ij}<t\\ 1,&if\ P_{ij}\geq t\end{array}\right.. \tag{14}\]  
As shown in Figure 4, it will achieve a better recognition performance than the initial model when the threshold is about 0.6 and obviously it gets top results when the threshold is about 0.9. But it may cause sparse problems, for example, if t = 0.9, then most of edges will be filtered, so we propose to assign proportion \(p\), which means the weight between the node itself and its neighbor nodes are:  
\[A_{ij}=\frac{p}{\sum_{j=1}^{C}A_{ij}+\varepsilon}A_{ij}, \tag{15}\]  
where \(\varepsilon\) is a very small number, notice that if the proportion is too small, it will ignore the information from the neighbor nodes; if proportion is too large, it will ignore the feature of the node itself. As shown in Figure 5, our model will get better performance if proportion is close to 0.5, but the proportion strategy does not work for improving overall performance.  
Furthermore, we take the GCN layers into consideration to see if the deeper layer can get higher accuracy. We stack more layers to see how the layer numbers affect the model performance. As shown in Figure 6, our model achieves best performance when layer equals to 3. As shown, when the number of graph convolution layers increases over 4, recognition performance drops quickly. The possible reason for the performance drop may be that when using more GCN layers, the propagation between nodes will be accumulated, which can result in over-smoothing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Method** & \multicolumn{4}{c}{**RAP**} \\ \cline{3-6}
**Metric** & **mA** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline ACN & 69.66 & 62.61 & 80.12 & 72.26 & 75.98 \\ DeepMar & 73.79 & 62.02 & 74.92 & 76.21 & 75.56 \\ HydraPlus-Net & 76.12 & 65.39 & 77.33 & 78.79 & 78.05 \\ JRL & 77.81 & - & 78.11 & 78.98 & 78.58 \\ VeSPA & 77.70 & 67.35 & 79.51 & 79.67 & 79.59 \\ Ours & 74.04 & 65.78 & 80.12 & 76.88 & 78.47 \\ Ours-improved & 75.97 & **68.99** & **81.48** & **79.97** & **80.72** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison against previous methods.
## 3 Performance of NS RA-Modified Asphalt  
### Index Tests  
For comparison, the index tests of NA RA modified asphalt and NS RA modified asphalt for softening point, penetration and penetration ration before and after aging were conducted in the study. RA was added to the base asphalt at the weight ratios of 0%, 2%, 4%, 6% and 8%, respectively. Aging was addressed by a rolling thin-film oven (RTFO) (air flow at 4 L/min, 163 \({}^{\circ}\)C and 85 min) according to American Society for Testing Materials D2872. The index tests were carried out according to the stand testing methods of bitumen and bituminous mixtures for highway engineering (JTG E20-2011) in China. Figure 2 shows the results.  
It can be seen from Figure 2 that for the same base asphalt, whether original asphalt or aging asphalt, the change trends of the softening point, penetration and penetration ration of NS RA modified asphalt were same as those of NA RA-modified asphalt. In comparison with NA RA, NS RA also had a good modification effect. However, there were performance differences to a certain extent,  
Figure 2: Index test results of the RA-modified asphalt.  
for different RA-modified asphalts. The penetration value reflects the soft and hard extents and consistencies of asphalt. This has an important influence on the high-temperature stability and the low-temperature crack resistance of asphalt pavement. Figure 2a,b show that whether it is original asphalt or aging asphalt, the softening point value of NS RA-modified asphalt was larger than that of base asphalt, and that it is greatly increased with the increase of the NS RA content. Therefore, the NS RA can enhance the high-temperature deformation resistance of the asphalt binder. At the same time, the high-temperature performance became greater with increasing NS RA content. It can be seen from Figure 2c,d that whether it was original asphalt or aging asphalt, the penetration value of NS RA-modified asphalt was lower than that of base asphalt, and the penetration value was greatly decreased with the increase of NS RA content. Therefore, the NS RA can obviously improve the consistency and the deformation resistance of asphalt binder. As seen in Figure 2e, the penetration ratio was elevated with an increase in NS RA, which indicates that the anti-aging performance of NS RA-modified asphalt is improved with the increase of NS RA content.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 3 Idealized Model of Mimblewimble-Based Blockchain
### Transactions  
Given two fixed generator points \(G\) and \(H\) on the elliptic curve \(\mathcal{C}\) of prime order \(n\) (whose discrete logarithms relative to each other are unknown), we define a single transaction between two parties as follows:  
**Definition 3** (Transaction).: _A single transaction \(t\) is a tuple of type:_  
\[\text{Transaction}\stackrel{{\text{def}}}{{=}}\{i:I^{*},o:O^{*},tk: TxKernel,tko:\text{KOffset},\}\]  
_with \(X^{*}\) representing the lists of elements of type \(X\) and where:_  
* \(i=(c_{1},\ldots,c_{n})\) _and_ \(o=(o_{1},\ldots,o_{m})\) _are the lists of inputs and outputs. Each input_ \(c_{i}\) _and output_ \(o_{i}\) _are points over the curve_ \(\mathcal{C}\) _and they are the result of computing the Pedersen commitment_ \(r.G+v.H\) _with_ \(r\) _the blinding factor and_ \(v\) _the transactional value in the finit field_ \(\mathbb{F}_{n}\)_._
* \(tk=\{rp,ke,\sigma\}\) _is the transaction kernel where:_
* \(rp\) _is a list of range proofs of the outputs._
* \(ke\) _is the transaction excess represented by_ \((\sum_{1}^{m}r^{\prime}-\sum_{1}^{n}r-tko).G\)_._
* \(\sigma\) _is the kernel signature (for simplicity, fees are left aside)._
* \(tko\in\mathbb{F}_{n}\) _is the transaction kernel offset._  
The transaction kernel offset will be used in the construction of a block to satisfy security properties.  
**Definition 4** (Ownership).: _Given a transaction \(t\), we say \(S\) owns the output \(o\) if \(S\) knows the opening \((r,v)\) for the Pedersen commitment \(o=r.G+v.H\)._  
The strength of this security definition is directly related to the difficulty of solving the logarithm problem. If the elliptic curve discrete logarithm problem in \(C\) is hard then given a multiple \(Q\) of \(G\), it is computationally infeasible to find an integer \(r\) such that \(Q=r.G\).  
It is important to notice that during the construction of the transaction the sender and the receiver do not learn their respective blinding factors. Instead, they build a Schnorr signature that is used to guarantee the authenticity of the transaction excess value.  
**Definition 5** (Balanced Transaction).: _A transaction \(t=\{i,o,tk,tko\}\), with transaction kernel \(tk=\{rp,ke,\sigma\}\), is balanced if the following holds:_  
\[\sum_{\phi_{j}\in o}o_{j}-\sum_{c_{j}\in i}c_{i}=ke+tko.G.\]  
A balanced transaction guarantees no money is created from thin air and the transaction was honestly constructed.  
**Property 1** (Valid Transaction).: _A transaction \(t\) is valid (valid_transaction\((t)\)) if \(t\) satisfies:_  
1. _The range proofs of all the outputs are valid._
2. _The transaction is balanced._
3. _The kernel signature_ \(\sigma\) _is valid for the excess._  
These three properties have a straightforward interpretation in our model. Due to limitations of space, in this paper we only formalize and analyze some of the properties mentioned throughout the document.
# Generating Process Models by Interacting with Chatbots--A Literature Review
## 6 Discussion
### Need for an Interactive Chatbot Generating Process Models  
The presented SLR has shed light on the current state of research in the domain of using interactive chatbots for generating process models. Through the comprehensive analysis of scholarly articles, it becomes evident that there is a gap in the existing body of knowledge. No study has been found that perfectly aligns with the specific requirements for an interactive chatbot capable of generating and modifying process models to meet user needs.  
The prevailing research trends primarily focus on distinct facets related to process modeling and chatbot technology. These trends include, but are not limited to, endeavors aiming at extracting information from textual inputs to generate process models or generating natural language text from process models. Nevertheless, there is a notable gap in the literature, as there is no single chatbot demonstrating both. The absence of such a comprehensive solution raises fundamental questions about the existing state of technology in this field. Investigating communication technologies in specialized settings, such as those highlighted in the study 'Development of underground communication system for data transmission using Wi-Fi Direct and power line communication' [30], could offer valuable insights for deploying advanced technologies like interactive chatbots. Understanding how hybrid communication systems perform in challenging environments can provide a broader context for enhancing technological applications in industrial or complex information systems. In essence, the SLR underscores the pressing need for a comprehensive solution--an interactive chatbot that possesses the capability to generate process models from natural textual input and adapt them in response to user requests but also for the opposite way. This unmet need represents a fertile ground for future research and development, opening up exciting opportunities to bridge the gap between chatbot technology and process modeling, thereby revolutionizing the way organizations handle their process management needs. Future research endeavors in this direction will undoubtedly contribute significantly to the fields of AI, LLM, NLP, and Business Process Management.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 2 Theoretical Background
### Non-Blocking I/O  
The concept of non-blocking I/O refers to the ability of an application thread to perform other tasks instead of waiting for a response to a previously initiated request [13]. This allows for more efficient utilization of system resources, which can lead to improved performance and scalability. The NIO library, which provides classes such as _Selector_ and _Channel_ for handling non-blocking I/O operations, is mainly used in Java for interfacing with these scenarios [7]. In addition, non-blocking I/O operations can be handled using the asynchronous paradigm, to further increase efficiency, employing methods such as event-based programming or the use of _Futures_ and _Executors_.
## 2 GaAs-based VCSELs  
Gallium-arsenide (GaAs) materials possess properties that are conducive to VCSEL lasing. These materials can be used to fabricate high-reflectivity mirrors and it is thus possible to obtain highly reflective DBRs using arsenide semiconductors via epitaxial growth techniques [18]. One common approach to fabricating GaAs-based VCSELs is to employ AlGaAs materials having two different compositions [19]. Varying the composition of AlGaAs leads to negligible changes in its lattice constant, so that AlGaAs DBR pairs can be grown in large numbers, which is required to achieve a reflectance close to 100%. In addition, GaAs-based materials provide a suitable means of establishing current paths in VCSELs, since AlGaAs DBRs are conductive in both p- and n-type layers. Therefore, a metallic electrode deposited on the outside of each DBR can provide a path for current injection into active regions located between p- and n-type layers. In addition, a layer of an arsenide such as AlAs placed next to the active layers is readily oxidized, thus allowing lateral confinement of the current [20]. The resulting structures give devices capable of concentrating carriers within the limited physical space defined by the active layers so as to enhance population inversion, which is essential for lasing. The third advantage of GaAs-based VCSELs is that a short cavity length can be readily obtained by epitaxial growth. All layers, including both DBRs, p-type and n-type layers and quantum wells, can be grown directly on a GaAs substrate using an epitaxial growth technique such as metal organic chemical vapor deposition (MOCVD).
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### XFEM/GFEM Enrichment Functions for LEFM
#### 3.2.4 Blending  
As already mentioned, enrichment in the XFEM and GFEM is mostly performed locally to increase efficiency. This leads to situations where only some of the nodes of an element are enriched with a specific enrichment function and the remaining nodes are either not enriched at all or enriched with a different enrichment function. In these elements, the shape functions pre-multiplying the enrichment functions no longer form a partition of unity leading to increased errors, also called \"blending\" errors. For the enrichment functions used in LEFM, these errors only result in some loss of accuracy, leaving the convergence rates unaffected. For other types of enrichment functions however, the convergence rate can also be affected [117].  
Due to the above reasons, the \"blending\" problem has been extensively studied and several solutions have been proposed involving a variety of techniques such as assumed/enhanced strain formulations [117; 118; 119], directly matching displacements between the enriched and non enriched part of the approximation [105; 120; 121] and the use of weight functions [122; 123; 124] to smoothly blend different parts of the approximation. The later approach, also known as the corrected XFEM, is likely the most successful due to its relative simplicity and effectiveness.
## 4 Results and Discussion
### Implications  
The results of this study suggested that ANN was the best forecasting technique to handle and infer future lumpy demand. Previous studies considered the data feature extraction along with the recorded data based on flying hours and operational intensity. The hybrid neural networks were also used by combining traditional techniques with neural networks [68]. However, all these approaches led to magnified forecasting error due to a lack of data pertaining to fleet performance and its approximation.  
During the research, quantity demanded instead of quantity issued was taken into consideration. Forecasting future consumption was performed with the prior information that there were no spare parts available in the warehouse. Furthermore, a detailed survey was performed to choose CSPs from a whole collection of spare parts, which was a challenge solved by demand categorization based on the extent of lumpiness as shown in Table 2. The spare parts were sorted and selected, which showed variability in the demand interval and demand value. Hence, planning for the worst-case scenario was performed to forecast lumpy demand.  
The training MSE of the ANN model was found to decrease, which showed perfect training. The trained and validated model was then posed with a new set of inputs to evaluate the forecasted value of 7.989 as shown in Table 3, which was comparable with the value of nine actually demanded in the years 2014-2015.
# Design of XOR Photonic Gate Using Highly Nonlinear Fiber
## 1 Introduction
Overall, HNLF-based gates have the potential of monolithic integration, offering advantages such as compactness, increased reliability, and cost reduction [12]. This paper is organized as follows. Section 2 presents the design concept for the proposed logic gate and the various components used in this design along with the signal flow sequence at each stage. Then, the HNLF output and the BPF calibration settings are shown in Section 3. The BPFs output results are presented in Section 4. This is followed by simulation results and performance analysis in Section 5, along with conclusions in Section 6.