esa-sceva/satcom-chunk-collection huggingface dataset:
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 6 Simulation Results
### Competing Algorithms beyond CSD
#### 6.4.2 Multiplierless Matrix-Vector Multiplication  
The ideas of subexpression sharing in multiple constant multiplication are applied in several lines of work concerned with matrix-vector multiplication. Early studies have focused on structured matrices. Reference [32] is the first one to report results on unstructured matrices. For random matrices of size \(16\times 16\) and \(8\)-bit precision (corresponding to a distortion level of \(-48\) dB) an average number of 412.4 additions is reported in Table 7 in [32]. This means 1.61 additions per matrix entry. Similar to [29], the number of required additions in [32] hardly depends on the size of the random matrix and the complexity of the preprocessing scales with the fourth power of matrix size. This means that the algorithm in [32] outperforms our algorithm in terms of number of additions for \(16\times 16\) matrices. For matrix size \(64\times 64\), it already falls behind our method.  
Improvements can be found in subsequent work on subexpression sharing: In Table VIII in [33] only 338.3 additions on average are reported for the same setting as above. This means 1.32 additions per matrix entry. This is better than our results for matrix size \(64\times 64\), but worse than what we achieve for matrix size \(256\times 256\). This reduced number of additions in [33] comes at a high price: The complexity of the preprocessing scales approximately with the fifth to sixth power of the matrix size, see Figure 2c in [34]. The best result for subexpression sharing we could find in the literature is due to [34]. Allowing for exponential complexity in the preprocessing, Figure 2a in [34] reports only 1.15 additions per matrix entry on average. This beats our method for size \(256\times 256\), though it is more than questionable whether the NP-complete algorithm can be implemented for that matrix size. For matrices of size \(1024\times 1024\) or larger, our algorithm requires fewer additions.  
The method in Figure 4a in [35] requires about 2.1 additions per entry for an 8-bit matrix of size \(10\times 10\). This is not competitive with the methods described above. However, the optimization goal of this paper also cares to reduce the overall delay in the signal flow which is important for pipelining in FPGAs. A problem which often occurs in subexpression sharing is as follows: Some intermediate results are ready earlier than others, thus computing units need to wait for others to finish before they can continue. This problem does not occur in our approach, if we use the vertical decomposition. In that case, all computing paths have exactly the same lengths and all computations can be pipelined.
## 2 Selection Method of Wavelet Parameters  
Assuming that there is a signal \(u(t)\), the FT of \(u(t)\) can be expressed as follows [23]:  
\[U(f_{k})=\left\langle e^{j2\pi f_{k}t},u(t)\right\rangle \tag{1}\]  
where \(j=\sqrt{-1}\) and \(\left\langle e^{j2\pi f_{k}t},u(t)\right\rangle\) represents the inner product of two functions \(e^{j2\pi f_{k}t}\), \(u(t)\). It can be seen from Equation (1) that the essence of FT is projecting \(u(t)\) on the typical complex exponential function \(e^{j2\pi f_{k}t}\). Moreover, \(U(f_{k})\) can be considered as the intensity of the target frequency \(f_{k}\) in the signal \(u(t)\). Assuming that the duration of \(u(t)\) is \(T\), let \(u(t)\) be sampled at equal intervals with the sampling frequency of \(f_{s}\), and the total number of sampling points (\(N\)) is \(N=Tf_{s}\). Then the signal value \(x(n)\) of the \(n\)-th sampling point is:  
\[x(n)=u(nT_{s}),0\leq n\leq N-1 \tag{2}\]  
where \(T_{s}\) represents the sampling period, and its value is \(1/f_{s}\). The Discrete Fourier Transform of \(x(n)\) can be expressed as [24]:  
\[X(k)=\sum_{n=0}^{N-1}x(n)e^{-j2\pi(\frac{k}{N})n}=\sum_{n=0}^{N-1}x(n)e^{-2 \pi(\frac{kf_{k}}{N})(n\frac{1}{f_{s}})} \tag{3}\]  
where \(k\) is an integer, and \(1\leq k\leq N\). By comparing Equations (1) and (3), it can be found that \(f_{k}=ky_{s}/N\), namely \(k/N=f_{k}f_{s}\).
## 3 Results and Discussion
### Analysis of Numerical Simulation Results
#### 3.2.4 Analysis of Damage Patterns of Specimens
In contrast, the crack formed on the left side was minor in width and did not penetrate the specimen's center. The above analyses show that the crack extension patterns within the specimens exhibit significant differences with freeze-thaw cycles. When the number of freeze-thaw cycles is high, the specimens are prone to X-shaped conjugate plane shear damage. On the contrary, the specimens are prone to monoclinic shear damage. The difference in damage patterns results from weakened cementation between rock particles. The freeze-thaw effect on the specimens is from the outside to the inside, with the outer surface showing more drastic cementation damage. Therefore, cracks are more likely to extend to the outer surface, leading to block spalling.
## 1 Introduction
The purpose of this work is to expand on the research presented in a previous publication [39] that concerned the effect of vanadium compounds (Vanadium(IV)-Oxy Acetylacetonate and Vanadium(III) Acetylacetonate) on DPPC lecithin model membranes. In this work, studies were carried out on the effect of large molecules--three vanadium complexes of different structures--on the structural properties of membranes obtained by sonication of DPPC and EYL lecithin. To date, the influence of these compounds has not been studied in terms of their effect on membranes, in particular on liposome membranes serving as a good model of biological membranes. At physiological temperatures, the lipid bilayer of DPPC liposomes is in the gel phase, while the lipid bilayer of EYL liposomes is in the liquid crystalline phase. The diffusion of vanadium complexes into the centre of the bilayer is likely to be more difficult in the case of DPPC membranes as compared to EYL membranes in the liquid crystalline phase. As EYL and DPPC membranes are very similar (identical in the surface layer), EPR studies using the appropriate spin probes allow for the comparison of the effect of the complexes on the fluidity of the membranes in both phases. Three vanadium complexes with Schiff base-type tetradentate ligands were selected for this study. Schiff bases containing an azomethine (-HC=N-) or imine (>C=N-) group are some of the most important classes of organic ligands for transition metal complexes because they are able to stabilize many metals in various oxidation states [7; 8]. Their structure can be easily changed to modify the electronic and steric environment around the metal, and thus modify the performance of the synthesized complexes. The studied complexes of general formula N,N\({}^{\prime}\)-1,2-cyclohexis(3,5-dichlorosalicylideneimate) (**V1**); LVCl\({}_{2}\) (L = N,N\({}^{\prime}\)-1,2-cyclohexis(5-chlorosalicylideneiminate) (**V2**); N,N\({}^{\prime}\)-1,3-propylenebis (salicylideneiminate) (**V3**) are shown in Figure 1. As can be seen, complexes **V1** and **V2** have a similar molecular structure, differing only in substituent (Cl or H) at position 3 on the aromatic rings. Complex **V3** has a slightly different structure. It contains a propylene bridge between nitrogen atoms instead of a cyclohexylene linker, and it has unsubstituted aromatic rings. The studied vanadium complexes, upon activation with an appropriate organoaluminium compound, were previously used in the polymerization of olefins (ethylene and 1-octene) [40; 41; 42]. It was demonstrated that structural changes of the tetradentate Schiff base ligand in the complex (both the type of bridge and the type of substituent on the aryl rings) affect the activity of the catalytic system in ethylene polymerization. The presence of the cyclohexylene bridge and the electron-withdrawing group on the phenolate rings proved beneficial.
## 2 Materials and Methods  
### Dataset and Pre-Processing  
The dataset consisted of five classes, including maize (_Zea mays_) and four weed species commonly identified in maize production systems, namely, Charlock (_Sinapis arveensis_), Fat Hen (_Chenopodium album_), Shepherd's purse (_Capsella bursa-pastoris_), and Small-flowered Cranesbill (_Geranium pusillum_). The dataset was derived from [PERSON]'s image data of the crop and weed seedlings at different growth stages, a public image dataset offered by [PERSON] et al. (2016). Each class contained 200 RGB images at various growth stages (5-8 weeks) and illumination effects. These images were manually and binary segmented at pixel level using the Image Segmenter app of _MATLAB R2020a_ and the Image Processing toolbox to distinguish the vegetation from the background. These semantic-segmented images were arranged in class-wise folders, as such so that the sequence of images matched the corresponding real image folder. This allowed for the easier pairing of real and segmented images, which was necessary during the cGAN training process. Figure 1 shows some sample images from the dataset along with their binary-segmented counterparts. The images were resized to a size of 256 \(\times\) 256 pixels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Purpose** & **Crop Production** & **Image Synthesis** & **Results/Conclusion** & **Reference** \\ \hline Synthetic RGB images of individual tomato and black night-shade plants were generated for improving classification performance. & Tomato & Conventional GANs & \begin{tabular}{c} \(F1\)-score of 0.86 was obtained \\ when GAN-based \\ augmentation was \\ performed, compared to 0.84 \\ without the artificial dataset. \\ \end{tabular} & [37] \\ \hline Generation of multi-spectral images of agricultural fields for semantic segmentation of crop/weeds. & Sugarbeet & \begin{tabular}{c} Conditional GAN \\ (cGAN) \\ \end{tabular} & \
\begin{tabular}{c} Intersection over union \\ (mIoU) value was improved \\ to 0.98 from 0.94 for \\ background class and to 0.89 \\ from 0.76 for vegetation. \\ \end{tabular} & [38] \\ \hline Artificial data were generated using UAV-acquired images for supporting crop/weed species identification at an early stage. & Strawberry and peas & Semi-supervised GAN (SGAN) &
\begin{tabular}{c} Classification accuracy of \\ 90\% was achieved using only \\ 20\% of labelled dataset. \\ \end{tabular} & [39] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of previous studies on the application of GANs for crop/weeds identification tasks.
## 5 Vulnerability Detection
### Detailed Comparative Analysis on Vulnerability Detection Methods
#### 5.2.5 Datasets and Evaluation-Based Methods  
Datasets and evaluation-based methods provide detailed, comprehensive insights into vulnerabilities. For instance, DAppSCAN [89] plays a crucial role in improving our understanding of smart contract weaknesses. Fine-grained vulnerability detection techniques [90] maintain granular labels (e.g., re-entrancy, integer overflow) for each function or code block, thereby enhancing training and evaluation processes. Tools such as Oyente and Mythril [73; 92] have contributed partial datasets for benchmarking new solutions, while SmartFast [78] and ReDefender [94] offer curated test cases focusing on common vulnerabilities. Despite these advances, there remains a need for continuous updating and expansion of vulnerability databases, such as the SWC Registry [107], to ensure comprehensive vulnerability coverage.
## 3 Results and Discussion
### Dust Emission and Distribution Characteristics during Coal Charging  
#### 3.3.1 Dust Emission  
In order to observe and explore the superiority of the new coal charging device to control dust emission control during coal charging, the experiment was based on the traditional coal charging and new coal charging method, the dust concentration sensor was used to track and measure the dust emission near the coal charging hole at the top of the furnace and in the carbonization room when the coke oven was not working. The dust emission characteristics of the two methods were compared, and the specific experimental results are shown in Figure 10, the moisture content of the experimental coal was 5%.  
Figure 10a represents the changing law of dust emission with time during the charging process of the new method, and Figure 10b represents the dust emission law of the traditional method. (A-the dust emission near coke oven charging hole; B-the dust emission in the coking chamber). It can be seen in Figure 10a, at 0-60 min, that the dust emission at position (A) reached its peak value, which was about 0.21 g/m\({}^{3}\); however, the amount of dust at position (B) was almost very small. After 60 s, the amount of dust in position (A) began to decline, the dust content at position B began to increase gradually, reaching the peak around 150 s, and then tended to be stable. This indicated that from 0 to 60 s, the cylinder fell to the bottom of the carbonization chamber, and the process of filling the cylinder with coking coal was a free-falling motion. In this process, coal was subjected to air resistance, the small particles floated up and were discharged from the joint gap between the cylinder and the cylinder; however in the carbonization chamber, due to the resistance of the cylinder wall, dust could not escape to the carbonization chamber, so during the period of filling the cylinder with coal, the dust emission of the carbonization chamber was almost very small. When the cylinder was lifted and coal fell, the coal gradually flowed along the length of the chamber, and dust began to escape to the chamber, this resulted in an increase in the amount of dust in the carbonization chamber. In addition, it was difficult for small particle dust to settle in a short time, thus, the measured dust value tended to be stable within this period.  
For traditional coal charging, it can be seen from Figure 10b, compared to the new coal charging method, the dust emission of A and B positions increased significantly. Starting from about 90 min, the dust emission in the carbonization chamber gradually exceeded that near the coal charging hole of the coke oven. The dust emission reached the highest at 120-180 s, with an average of 2.2 g\(\cdot\)m\({}^{-3}\). After that, due to the factor of particle sedimentation, the dust quantity began to decline.  
Figure 10: The dust amount of coal during loading varied with time under different charging methods. (**a**) New method of coal charging; (**b**) Traditional method of coal charging.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 3 System Model
### The Energy Model  
As the literature records [37; 38], the energy consumption of sensors used in transmission is about 1000 times higher than the energy used for other purposes. Therefore, we only consider energy consumption used for transmission in our model, which is shown as Figure 2.  
As Figure 2 describes, the energy used in transmission is generally composed of two parts, the sending and receiving parts. The energy used in sending depends on the transmission distance. Once the signal is sent by the transmitter, it will be strengthened by the amplifier, and the amplifier will choose two different power levels to strengthen the signal according to the transmission distance. Therefore, the energy model used in sending data is also classified into two different types, the free  
Figure 1: The network model.  
Figure 2: The energy model.  
space model (\(d^{2}\) power loss) and multi-path fading model (\(d^{4}\) power loss). The energy used for sending the \(l\)-bit data package can be calculated as:  
\[E_{Tx}(l,d)=\left\{\begin{array}{ll}l\cdot E_{elec}+l\cdot\varepsilon_{fs} \cdot d^{2}&if,d<d_{0}\\ l\cdot E_{elec}+l\cdot\varepsilon_{mp}\cdot d^{4}&if,d\geq d_{0}\end{array}\right. \tag{1}\]  
where \(E_{elec}\) is the energy use of the transmitter and receiver unit. \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) denote the amplification coefficients for two different models, respectively. \(d_{0}\) is the distance threshold for choosing different amplifier powers, which can be calculated as:  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{2}\]  
The energy used for the receiver is simpler compared to the sending part, and its power is constant, the same as the transmitter. The energy used for receiving the \(l\)-bit data package can be calculated as:  
\[E_{Rx}=l\cdot E_{elec} \tag{3}\]
# An Implementation of the HDBSCAN* Clustering Algorithm
## 3 Materials and Methods
### Compare the HDBSCAN* Implementations
#### 3.5.1 Cluster Assignments and Outlier Scores  
The first comparison to make is between the reference implementation and Tribuo Hdbscan. A model is trained for both implementations using the same dataset, and the resulting cluster assignments and outlier scores are compared. This process is repeated with three different datasets. The first and second datasets both use Gaussians, or generated isotropic Gaussian blobs, which are simply sets of points normally distributed around a defined number of centroids. The first dataset uses Gaussians with four centroids and 2000 points, where each point has three features. The second dataset uses gaussians with three centroids and 4000 points, where each point has seven features. The third dataset is more of a real-world dataset, which is the usage behavior of credit card holders over a six-month period [18]. After data preprocessing, this dataset contains 8949 records where each record has seventeen features.  
A similar comparison is made between Tribuo Hdbscan and the Python module hdbscan [4]. A model is trained for both implementations using the same dataset, and the resulting cluster assignments and outlier scores are compared. The process is repeated using the same three different datasets just described in the previous paragraph. The HDBSCAN* algorithm is deterministic, which means that the algorithm produces the same cluster assignments and outlier scores when the input remains fixed. Therefore, there is no need to execute each individual test multiple times.
# Surrogate-Based Optimization of a Centrifugal Pump with
## 3 Geometry Parametrization
### Impeller  
The impeller geometry considered in this parametrization has an axial inflow and a radial outflow. The parametrization is based on 17 DVs, which can be divided in four sub-sets: 3 DVs define the external dimensions of the impeller (DV\({}_{i,a}\)); 9 DVs define the chamber line at the hub and shroud (DV\({}_{i,b}\)); 4 DVs define the meridional channel (DV\({}_{i,c}\)); 1 DV defines the number of blades (DV\({}_{i,d}\)).  
DV\({}_{i,a}\) includes the impeller outer diameter (\(D_{2}\)), the inlet pipe diameter (\(D_{0}\)), and the blade height at the impeller outlet, (\(b_{2}\)). The bearing diameter is fixed equal to 6 mm. DV\({}_{i,b}\) includes the impeller diameter at the leading edge for the hub, \(D_{1,b}\), and the shroud, \(D_{1,s}\), the blade angle at the inlet for the hub, \(\beta_{b1,b}\), and the shroud, \(\beta_{b1,s}\), the blade angle at the outlet for the hub, \(\beta_{b2,b}\), and the shroud, \(\beta_{b2,s}\), the stagger angle for the hub, \(\gamma_{l}\), and the shroud, \(\gamma_{s}\), and the relative twisting angle of the leading edge for the shroud with respect to the hub, \(\delta_{s}\). The blade profiles at hub and shroud are built from the camber line and a thickness function. The camber lines (their projection is described on a plane perpendicular to the rotation axis) are Bezier curves of fourth order (see Figure 3). The blade surface is defined as a linear interpolation between the profiles at hub and shroud.  
The axial position of the control points 1, 2, 6, 7, i.e., \(z_{1}\), \(z_{2}\), \(z_{6}\) and \(z_{7}\) (see Figure 4) can be derived from DV\({}_{i,c}\) and are used to define the two Bezier curves adopted to represent the hub and the shroud for the meridional channel. The points 3, 4, 5 are fixed at \(z=0\) and points 8, 9, 10 at \(z=b_{2}\) to obtain a radial outlet. The inlet channel before the blade is controlled by four control points: d and h correspond to the leading edge at the hub and shroud; points a, b, c and e, f, g are aligned in order to guarantee an axial inflow; furthermore, the axial position of points c and g is set to guarantee the continuity of the derivative of the meridional channel at the leading edge for both the hub and shroud.  
Figure 2: Main symbols for the impeller geometry (**top**), and impeller blade (**middle**, **bottom**).  
After defining the control points, the blade camber surface is generated (see Figure 5) and a thickness function is added (see Figure 6): the leading edge is defined with a symmetrical 4-digit NACA profile (NACA0012 has been adopted for this work) with a maximum thickness of 2 mm; when the maximum thickness is reached, this value is kept constant along the rest of the blade. The blade ends with a blunt trailing edge. The outlet of the impeller region is placed at 1 mm from the outer diameter \(D_{2}\). DV\({}_{i,d}\) includes the number of impeller blades \(n_{b}\).  
Figure 4: Control points for the meridional channel.  
Figure 3: Control points for the camber surface at the hub and shroud.