esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Objective Degree-Based Network Anonymization Method
## 3 Generating \(k\)-Anonymous Degree Sequences
### Choosing \(k\)  
As mentioned before, the anonymity value \(k\) is chosen based on the purpose of publishing the data and the desired privacy level. The larger the value of \(k\), the more resistant to attacks the data will be, but the more perturbation to the data set which will be performed. Hence, the challenge in deciding on the value of \(k\) is to keep the balance between maintaining privacy and utility simultaneously. The literature does not suggest any formula to set the value of \(k\), other than by experimenting. In this paper, we suggest and apply a statistical formula to decide on the privacy level that is the most convenient for each data set, based on the distribution of the degrees of nodes. The formula is centered around the standard deviation of degrees of nodes. The standard deviation tells how the values in the degree sequence vector are spread out from the average. \(k\) is proportional to the standard deviation of degrees, which will have a good impact on data utility preservation. Formally:\[k=\left\lceil\frac{\sigma}{\sqrt{n-1}}\right\rceil\]  
where \(\sigma\) is the standard deviation of the values in D. To assure that the final value is a natural whole number, we take the ceiling of the calculated number. If the degrees are relatively close to each other, i.e., the difference between them is small, then our formula would suggest a small value for \(k\), because the standard deviation is insignificant in this case. Working with a small \(k\) can guarantee the best solution in terms of utility because the data set does not need major alterations to be anonymized. However, if there is a vast difference between the degrees, i.e., their standard deviation is remarkable, then k would be too large to guarantee a good privacy level.  
```
Input:\(G=(V,E),k,t\) ;  Generate \(D(G);D^{\prime}\longleftarrow D(G)\); \(n\longleftarrow|V|;add\longleftarrow 1;delete\longleftarrow 1\); for\(i=1;i<n;i=i+k\)do \(a\longleftarrow|D^{\prime}[i]-D^{\prime}[i+k-1]-2*t|\); \(d\longleftarrow a\); \(avg\longleftarrow\) Average of degrees; \(j\longleftarrow\) Index of the closest node to avg; \(D^{\prime}[i]\longleftarrow D^{\prime}[j]\); \(lowerbd\longleftarrow D^{\prime}[i]-t\); \(upperbd\longleftarrow D^{\prime}[i]+t\); foreach\(z\in[i+1,i+k]\)do if\(D^{\prime}[z]\
otin[lowbd,upperbd]\)then \(changes\longleftarrow|lowerbd-D^{\prime}[z+1]|\) if\(changes\leq a\)then while\(add\leq changes\)do \(D^{\prime}[z+1]\longleftarrow D^{\prime}[z+1]+add\); \(add++\);  end if  end for if\(changes>a\)then while\(delete\leq d\);\(add\leq a\)and\(D^{\prime}[z]\geq D^{\prime}[z+1]\)do \(D^{\prime}[z]\longleftarrow D^{\prime}[z]-delete\); \(D^{\prime}[z+1]\longleftarrow D^{\prime}[z+1]+add\); \(add++\); \(delete++\);  end if  end for  end for \(D^{\prime}[z+1]\longleftarrow D^{\prime}[z]\);  end for  end for return\(D^{\prime}\)
```  
**Algorithm 1**Multi-parameterized \(k\)-degree anonymization
## 5 Numerical Evaluation Results
### Separated Peak Demands
#### 5.2.4 Impact of Random Traffic Bursts at Operators
When a larger number \(O\) of operators shares the overall resource, then the performance of the SDN control would further improve in accordance with the classical statistical multiplexing gains for many variable bitrate traffic streams sharing a common resource [28; 61; 92; 93; 94]. In this and the preceding evaluation scenarios, traffic bursts were generated on a per-operator basis, i.e., an independent Markov chain for each operator \(o,\ o=1,2\), determined the Poisson packet traffic rates (whereby the eNBs at an operator contributed equally to the operator traffic load). This per-operator traffic burst scenario reflects situations where traffic demands shift among operators, e.g., as large groups of users move among different nearby sub-networks, e.g., from lecture halls to restaurants (whereby the lecture halls and the restaurants have different operators) in a campus setting. In the next section, we consider per-eNB Markov chain modulated Poisson packet traffic rates that reflect situations where each eNB generates traffic bursts independently, e.g., when individual users conduct bursty Internet transactions, e.g., upload files.
# Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation
## 2 Methods
### Network Architecture
#### 2.1.1 Common Baseline  
We start from the plain 2D U-Net, which is composed of an encoder, a decoder, and skip connections. The encoder extracts image features by using convolution layers. Max-pooling layers are located at the end of each level and shrink the input resolution in half. They enable the convolutional operations to extract abundant image features under the various input resolutions. The decoder predicts the blood vessel area and restores the image resolution to the original input size. For prediction accuracy, the up-sampling paths, which double the input size, are composed of deconvolution layers. As the deconvolution layers are trainable, they are dynamically optimized to predict a better segmentation mask during training. Skip connections are designed to improve the performance of the decoder by passing additional image features from each level of the encoder. The softmax function located at the end of the baseline model produces the probability map which indicates whether each pixel is a blood vessel or not. To make the final segmentation mask, pixels with a probability larger than 0.5 are marked as blood vessels.  
The overall architecture of the baseline U-Net is shown in Figure 1. The total network depth is set to 5, and each level of the encoder and decoder is linked with skip connections. When the serial arrangement of a convolutional layer, a rectified linear unit, and a batch normalization is denoted as a basic layer set (BLS), each level of the encoder is implemented with two BLS and a max-pooling layer, and the decoder is composed of two BLS and a deconvolutional layer per level. A single BLS is placed in the middle of the encoder and decoder, and the last convolutional layer is activated by the softmax function. This whole architecture is denoted as \(B\) and is adopted for the common baseline of Spider U-Net and the other comparative models.
## 6 Mediating Variable Analysis  
Hypotheses 16-22 state that CL mediates the relationship between H16: PS \(\rightarrow\) CL \(\rightarrow\) CP, H17: PV \(\rightarrow\) CL \(\rightarrow\) CP, H18: SE \(\rightarrow\) CL \(\rightarrow\) CP, H19: RE \(\rightarrow\) CL \(\rightarrow\) CP, H20: RC \(\rightarrow\) CL \(\rightarrow\) CP, H21: OFT \(\rightarrow\) CL \(\rightarrow\) CP, and H22: GF \(\rightarrow\) CL \(\rightarrow\) CP. In this regard, the two-step empirical investigations were conducted in PLS to examine the mediating effect based on the indirect effect between independent and dependent variables via a mediating variable. The first step involves applying path coefficients, t-statistics, and _p_-value to verify the significance of direct and indirect effects. According to [116], full mediation occurs when the mediated effect is significant, but not the direct effect, while partial mediation occurs when a mediator variable partially explains the relationship between an exogenous and an endogenous construct in the presence of a significant direct effect. Therefore, and as shown in Table 14, Cybersecurity Leadership (CL) has a full indirect mediating effect between the dependent variable Cyberattack Prevention (CP) and the five independent variables Self-Efficacy (SE), Perceived Severity (PS), Perceived Vulnerability (PV), Response Cost (RC) and Organization Training (OFT). Further, Cybersecurity Leadership (CL) has a partial mediation effect between Cyberattack Prevention (CP) and only two independent variables, Response Efficacy (RE) and Government Frequent Alerting (GF).
# Evaluation of Condition of Concrete Structures Using Ultrasonic Pulse Velocity Method
## 5 Discussion  
Based on the experiment results, this study attempted to estimate the compressive strengths of concrete structures by identifying the correlation between the concrete compressive strength and the ultrasonic pulse velocity according to age. Figure 13 shows the correlation between the ultrasonic pulse velocity and the compressive strength derived from the 123 concrete specimens fabricated by setting nine variables based on the ages of 16, 20, 24, 48, 72, 120, 168, 360, and 672 h for the designed strengths of 24, 30, and 40 MPa. From the derived correlation, a concrete compressive strength estimation equation is proposed as Equation (6).  
\[\mathrm{y}=832.75\ln(\mathrm{x})+844.9,\ R^{2}=0.94 \tag{6}\]  
where  
x: concrete compressive strength (MPa)  
y: ultrasonic pulse velocity (m/s)  
Figure 12: Relationship of ultrasonic pulse velocity and compressive strength (40 MPa).  
Figure 13: Estimation equation of compressive strength.
## 2 Methodology
### Assessment of Consistency between Model and Observation Data Sets  
Several analyses were performed over various temporal and geographical extents. Diurnal cycle was performed over the Bartlett Experimental Forest flux tower site from 2005 to 2006, seasonal cycle and spatial patterns were performed globally from 2003 to 2005, and long-term trends were conducted globally over the 28-year period of 1982-2009.  
Prior to the analysis, data were resampled by averaging at 0.5 \({}^{\circ}\)resolution if needed, grids defined as non-vegetated in MODIS, and dominant vegetation distribution was masked in all datasets. To assess the consistency between the model simulated FPAR and observations, several sets of analyses were performed as follows:
# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 3 Methodology  
This paper proposes a novel traffic congestion detection method specifically designed for the surveillance scenario of expressways, considering challenges such as small vehicle sizes and vehicle occlusion. Firstly, we collected a dataset of surveillance videos from the Nanjing Raoyue expressway. Then, we improved the vehicle detection rate by introducing the Involution operator and BiFPN module into the CrowdDet algorithm, resulting in the IBCDet algorithm. To achieve more accurate traffic congestion detection, we incorporated a tracking algorithm into the IBCDet algorithm to measure vehicle speeds. By combining the measured speeds with the LoS criteria for the Chinese expressway, we categorized the vehicle speeds into congestion levels, enabling effective traffic congestion detection. The overall architecture of the traffic congestion detection method is illustrated in Figure 1.  
This paper describes the generation of training and testing datasets for the detection of traffic congestion on one-way roads by extracting, filtering, and labeling frames from surveillance videos of the Nanjing Raoyue expressway. To improve the accuracy of the detection results, the original video images are cropped to eliminate irrelevant background information, and the areas of interest are manually delineated. In addition, uninteresting areas are masked to prevent interference from roadside disturbances on the detection results. To prevent overfitting, data augmentation techniques are used to expand the small number of samples.  
Figure 1: Traffic congestion detection architecture diagram.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 2 RADIANCE: Theoretical Basis and General Calculation Methodology
### Calculating View Factors
\[E_{ind}(\theta_{i},\varphi_{i})=\int\limits_{\varphi=0}^{2\pi}\int_{\theta=0}^ {\pi/2}L_{i}(\theta_{i},\varphi_{i})\ \cos\theta_{i}\sin\theta_{i}d\theta_{i}d\varphi_{i} \tag{5}\]  
As indicated by Figure 3b, the amount of stochastic sampling of a particular surface is controllable by increasing or decreasing N, the value for the ambient divisions (-ad) parameter, which in turn necessitates decreasing or increasing the limit weight (-lw) parameter respectively. Ambient divisions directly control the number of rays used for sampling and limit weight controls the contribution of each ray to the final result. So, the value of 1/N for limit weight indicates that each ray is given equal weightage. As is expected of any Monte Carlo raytracing method, higher sampling leads to a better estimation of the result. Based on the illustrations in Figure 3, the functionality and methodology for calculation of view factors can be broadly communicated in simple terms. However, the specific inputs provided to _rcontrib_ need to be in a format that is compatible with RADIANCE and the outputs received from _rcontrib_ need to be post-processed to derive the actual view factor. Accordingly, the invocation of _rcontrib_ is preceded and followed by a sequence of steps that is shown in Figure 5.  
Figure 5 introduces an additional RADIANCE program called _oconv_. _Conv_ creates an octree data-structure that facilitates efficient raytracing by reducing the number of ray-surface intersections [27]. The octree data-structure is a pre-requisite for all the raytracing programs in RADIANCE [28]. The \"post-processing\" box shown in the figure relates to extracting the results from _rcontrib_, dividing the results by \(\pi\) as described earlier and finally performing case-appropriate view factor algebra operations. The methodol  
Figure 4: An illustration of the original sampling pattern employed in RADIANCE. The dots represent individual samples over the projected hemisphere [23]. ogy for specifying inputs, and deriving view factors from outputs, is explained in the following section.
## 5 Numerical Results
### Completeness Effect  
To study the completeness effect on the solution cost, let  
\[t=\sum_{k\in K}l_{kk}\]  
which denotes the number of terminals in a solution. Then, we define the \"Completeness rate\" of inter-terminals induced graph as follows:  
\[r=\frac{\sum_{k,m\in K}l_{km}}{\frac{t(t-1)}{2}} \tag{13}\]  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Instance** & **Best Gap \%** & **Average Gap \%** & **Time (s)** & **\# Terminals** \\ \hline  
& 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulated Annealing results.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Instance** & **Solution by [(27) (\(\times 10^{6}\))]** & **Our Solution (\(\times 10^{6}\))** & **Gap** \\ \hline
20C40L & 47.76 & 45.1 & 6\% \\
20C50L & 42.87 & 41.36 & 4\% \\
20C60L & 48.57 & 43.12 & 13\% \\
20C70L & 39.31 & 39.19 & 0\% \\
20C80L & 43.58 & 41.84 & 4\% \\
20C90L & 38.74 & 40.88 & \(-5\%\) \\
20C100L & 48.54 & 46.54 & 4\% \\
30C10L & 117.72 & 105.84 & 11\% \\
30C20L & 113.5 & 116.51 & \(-3\%\) \\
30C30L & 117.03 & 119.74 & \(-2\%\) \\
30C40L & 91.31 & 88.14 & 4\% \\
30C50L & 105.48 & 102.94 & 2\% \\
30C60L & 103.24 & 114.64 & \(-10\%\) \\
30C70L & 113.53 & 90.57 & 25\% \\
30C80L & 188.55 & 105.55 & 2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: SA results versus Best found solutions.  
We modify the linear program introduced in the Section 3 by changing Equation (7) to become:  
\[\sum_{k,m\in K}l_{km}\leq l \tag{14}\]  
As shown in Figure 6, the completeness rate of the SA solutions is overall between 50% and 100%. This means two important things: (1) a sufficient number of rail links is necessary to reduce the total transportation cost as the rail mode enables to achieve scale economy by transporting huge volumes of flows; (2) a fully connected terminal network is unnecessary and increases the total transportation cost.  
To further study the completeness rate effect on the generated solutions, we plot in Figure 7 the completeness rate over iterations of the archived best solutions for an instance with 40 nodes. This figure highlights that, while a sufficient number of rail links is necessary to reduce the total transportation cost (more than 50%), a fully connected rail network may not achieve an efficient transportation plan.  
Figure 6: The completeness rate of the SA solutions.  
Figure 7: The completeness rate over iterations for the instance 40C10L2 TL.
## 4 Analysis and Discussion of Experimental Results
### Result Analysis
#### 4.3.1 Evaluation Analysis of the Average Satisfaction Rate  
Figure 10 presents a comparative chart illustrating the average satisfaction rates among the five job recommendation methods. The graph clearly indicates that JRSR attains a notably higher average satisfaction rate when contrasted with the remaining four job recommendation methods. Among these five approaches, TF-C demonstrates the lowest average satisfaction rate, registering at a mere 2.631. Meanwhile, the average satisfaction rates for the other three job recommendation methods, namely, MS, GERS, and RSCF, exhibit relatively close values. The job recommendation method introduced in this paper, JRSR, records an average satisfaction rate of 3.743. In comparison to the four benchmark methods, it showcases a maximum improvement of 1.112 and a minimum improvement of 0.502. This compellingly suggests that the job recommendation method presented in this study, JRSR, excels in discerning the job preferences of college graduates, ultimately resulting in more gratifying job recommendations.  
Figure 10: Average satisfaction rate.  
Figure 9: Social network for college graduates and graduated students.  
The experimental data in this paper were non-normally distributed. To test for a significant difference in the AR@5 assessment values of the JRSR, TF-C, MS, GERS, and RSCF methods, the Wilcoxon signed-rank test was used. As indicated in Table 3 (\(p<0.05\)), there is a significant difference between the assessed values of JRSR and the other four methods.
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 2 Proposed Methodology  
Voice activity detection usually addresses a binary decision in the presence of speech for each frame of the noisy signal. The proposed VAD block diagram is shown in Figure 1. The proposed VAD method is explained in detail in the following subsections. The motivation for the proposal is to identify a robust feature that would improve the accuracy of the VAD. The implementation steps of the proposed VAD are explained in detail as follows.
## 6 Experimental Setup and Results
### Overall Performance
We introduce color maps to investigate the predicted values on each grid further, emphasizing the accuracy levels for different tasks, including task max, task avg, and task min. In Figure 10, we observe that the T-DenseNet and ARIMA methods exhibit lower predicted values in the low-flow regions of the 27 \(\times\) 27 grid. In contrast, the central region with a higher traffic flow displays higher predicted values than our method. This pattern implies that these methods might capture high-traffic areas more accurately but fail to predict low-flow regions precisely. On the contrary, our method predicts the average value across the entire grid, resulting in a predominantly reddish color map. This signifies that our method achieves an accuracy of over 70% in a significant portion of the grids, delivering a more balanced and, on the whole, superior accuracy prediction. Figure 9: Cumulative comparison of different methods of network traffic prediction. (**a**) Task Max: Accurate cumulative distribution, (**b**) Task Avg: Accurate cumulative distribution, (**c**) Task Min: Accurate cumulative distribution. In conclusion, the color maps provide visual insights into the predicted values, underscoring the superior performance of our method in securing a higher accuracy across the entire grid, particularly in the task average.
###### Abstract  
This work presents a method for measuring flame temperatures through an imaging technique that combines spectral analysis with two-color pyrometry. Initially, we employed Laser-Induced Breakdown Spectroscopy (LIBS) to analyze the radiation spectrum of nitrocellulose, selecting 694 nm and 768 nm as the two spectral lines for temperature measurement. Subsequently, we constructed a temperature measurement system utilizing two sCMOS cameras and conducted calibration within the range of 600 to 1000 \({}^{\circ}\)C, achieving a maximum temperature measurement uncertainty of 3.43%. Finally, we successfully performed two-dimensional temperature field detection and imaging of nitrocellulose flames of varying qualities, achieving a flame image resolution of \(2048\,\mathrm{(H)}\times 2048\,\mathrm{(V)}\). In comparison to traditional two-color infrared thermometers and Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology, the maximum relative temperature measurement error was 2.1%. This work provides technical insights into the development of high-resolution, low-cost flame temperature imaging technology applicable across a wide range of fields.  
sCMOS sensors; optical imaging; temperature measurement; spectral analysis; two-color pyrometry +
Footnote †: journal: Applied sciences
## 5 Innovative Approaches for Enzyme Immobilization
### Nanomaterial-Based Enzyme immobilization
#### 5.1.1 Polymer-Based Enzyme Immobilization  
Immobilizing enzymes on polymer nanostructures creates an intermolecular network that can alter enzyme structure, metabolic pathways, and active site accessibility. Both natural (e.g., cellulose, chitosan, alginate) and synthetic (e.g., polypropylene, polyamide, polyethylene) polymers are used for this purpose [109]. Common synthetic polymers include polyvinyl alcohol (PVA), polyethylene oxide (PEO), polymethyl methacrylate (PMMA), and polylactic acid (PLA), which are often electrospun into fibers for enzyme immobilization.  
Figure 3: Latest approaches of enzyme immobilization and their benefits.  
Polymer-based enzyme immobilization enhances stability, catalytic efficiency, reusability and storability. Studies show that nanofiber mats (NFM), PMMA/Fe3O4 nanofibers, spongin scaffolds, and polystyrene fibers effectively immobilize lacase and glucose oxidase [110; 111; 112; 113].  
Despite its benefits, polymer-based enzyme immobilization faces challenges including diffusion barriers that hinder substrate/product accessibility [114], the high costs of premium polymers and reagents, and potential enzyme performance impairment due to degradation or impurity accumulation [115]. Additionally, some polymers or cross-linkers pose biocompatibility concerns due to toxicity.  
In conclusion, this approach is versatile, affordable, and biocompatible, with broad biotechnological and industrial potential, although efficiency, stability, and environmental impact challenges require further optimization.