esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Data Storage and Retrieval with IPFS and Smart Contract
The gas consumption results for the two scenarios are shown in Figure 15. As can be seen, for Scenario (2), the gas consumption increases linearly with the data entry sizes for both the write and read transactions. The rate of increase is much larger for the write transactions than that for the read transactions (i.e., the two curves labeled \"Contract Write/Read Full\"). In contrast, the gas consumption for Scenario (1) for both the write and read transactions remains constant (except the very first transaction during the run), where the gas consumption for the write transactions is much lower than that for Scenario (2) as the data entry sizes increase, and the gas consumption for the read transactions is also lower than that for Scenario (2) because the returned data are shorter. These results demonstrate that it is advantageous to use the proposed approach (in terms of financial cost savings) that integrates with IPFS and stores only the IPFS hash of the data stored in IPFS in the smart contract. It is also informative to compare the total latency for read and write of data in Scenario (1) and Scenario (2). The ratio of the total latency between Scenario (1) and Scenario (2) is shown in Figure 16. If the ratio is below 1, it means that the total latency for IPFS read/write and contract read/write for the integrated approach (i.e., Scenario (1) is lower than the corresponding latency for Scenario (2). As can be seen, the ratio is below 1 consistently if the original data entry size is 1250 bytes or higher. Figure 14: Latency in interaction with IPFS. Figure 15: Gas consumption with and without IPFS integration.
## 5 Conclusions  
Thermal-sensitive barcodes are widely used in areas such as commodity distribution and logistics management, where the quality of their printing significantly impacts information accuracy and circulation efficiency. Improving prediction accuracy and refining the printing process can help minimize errors and losses associated with barcode quality issues, thereby enhancing overall operational efficiency. As neural network technology continues to advance, research focused on predicting the printing quality of thermal barcodes through advanced PSO-BP neural networks aligns with technological trends and provides innovative solutions to the practical challenges of barcode printing quality. This study investigates the structure, algorithms, and parameter designs of BP neural networks to conduct comprehensive experiments aimed at achieving optimal results under various conditions. The model considers multiple influencing factors to accurately forecast the printing quality of thermal-sensitive barcodes, which can lead to increased production efficiency and higher customer satisfaction. Furthermore, this research can serve as a valuable reference for the thermal printing industry regarding material selection and equipment maintenance strategies.  
This research demonstrates promising outcomes in predicting printing quality by using BP-PSO. However, the inclusion of data volumes and multiple parameters presents some challenges for industrial applications. In the future, we will aim to identify superior models to expand the range of data collected from manufacturers, including substrate materials and ambient humidity. The goal is not only to enhance but also to optimize energy efficiency and minimize material waste. Consequently, our research will focus on multi-objective algorithms to derive production parameters that align with low-carbon and energy-saving principles, making it more beneficial for industrial applications.  
Conceptualization, C.-L.H.; methodology, Z.W. and S.Q.; validation, C.-L.H. and T.-C.C.; formal analysis, Z.W.; resources, Z.W.; data curation, T.-C.C. and S.Q.; writing--original draft preparation, Z.W.; writing--review and editing, C.-L.H. and S.Q.; visualization, T.-C.C.; supervision, C.-L.H.; funding acquisition, C.-L.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are available on request from the authors.  
The authors declare no conflicts of interest.
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 5 Results
### Results on Public Datasets
#### 5.2.1 CD4  
CD4 data, a classical form of functional data, have received attention in [1,11,13]. CD4 cell counts gauge the immune system's response to human immunodeficiency virus (HIV) infection, which leads to a progressive reduction in CD4 cell counts. The Multicenter AIDS Cohort Study (MACS) [42] provided the CD4 data. This dataset consists of CD4 percentages from 283 male human subjects that were HIV-positive, each with 1 to 14 repeated measurements over time in years. Subjects were scheduled for reevaluation at least semiannually. However, missed visits caused the sparse and uneven distribution of measurements. The proposed method used five length scales selected from cross-validation and k-means clustering. Finally, the model selected nine basis functions. Figure 6 displays the estimated mean function, eigenfunctions, and curves of the observations. The mean function reflects the overall decreasing tendency with the progression of the disease. The eigenfunctions are obtained by applying the singular value decomposition of the covariance operator that is discretized (for visualization purposes only) with a grid of 50 evenly spaced points over the whole timeline. The first eigenfunction is relatively flat and mainly captures the subject-specific average magnitude of the CD4 counts, consistent with the findings of [1,11,13]. The second eigenfunction captures the simple linear trend of the variations, as described in [13]. The third eigenfunction captures the piece-wise linear time trend with a breakpoint near 2.5 years from the baseline. Refs. [1,11] found similar eigenfunctions.
### Discussion  
There are many research papers that have attempted to use the SEER program in combination with machine learning and survival analysis to predict various tasks, such as tumor metastasis, patients' disease status or survival status. Although these papers have  
Figure 16: Feature importance of ET in stage 2.  
Figure 15: Feature importance of XGB in stage 2.  
different data sources, features, data cleaning, data modeling and prediction tasks, there are some useful materials or methods that can be referenced.  
Focusing on RCC, [PERSON] et al. used the best performing machine learning algorithm, XGB, to evaluate the risk of RCC with liver metastasis, achieving an AUC of 94.7% [44]. In terms of the features in their research, they only used six variables--grade, T stage, N stage, tumor size, bone metastasis, and lung metastasis--as inputs for all models. They only used the P value and Lasso regression to filter the variables. In comparison to our research, we modify the inputs of the models based on the modalities of the data. [PERSON] et al. also visualized the importance of the variables for the six models, but they relied solely on the importance ranking method from the models themselves, rather than using the SHAP package.  
Some other papers have focused on the prognosis of cancer. [PERSON] et al. concentrated on the 5-year survival status prognosis of non-metastatic cervical cancer patients by using features such as age, marital status, race, histology, stage, surgery and more [45]. [PERSON] et al. conducted a 10-year prognosis for differentiated thyroid cancer with similar features [46]. However, both papers lacked detailed tables of categorical indicators for machine learning classification problems, making it challenging to compare them with our study. In the prognosis of lung cancer [47], [PERSON] et al. employed a similar approach to our study. They achieved an accuracy ranging between 70% and 90% with seven machine learning models to classify the survival probability at 1 year, 3 years and 5 years by predicting the prognosis of lung cancer patients at different time points. They also predicted the prognosis of lung cancer for male and female patients. In terms of variable treatment, [PERSON] et al. used common variables such as age, race, marital status, grade, T stage, N stage, M stage, and surgery. Moreover, in the training process, their research required retraining the machine learning model for prediction due to changes in labels for classification. In contrast, our research addresses the problem of dividing the prognostic year in the regression stage of stage 2, where we only need to convert the regression results into different prognostic years.
# Train-Induced Unsteady Airflow in a Metro Tunnel with a Ventilation Shaft
## 2 Theoretical Analysis
### PW-Wys
#### 2.2.1. Ppw-Tet  
When the subway train starts entering the tunnel from outside, as shown in Figure 2a, the following occurs:  
(1) The Bernoulli equation for fluid flow between cross-sections 1 and 2 is expressed as Equation (4):  
\[P_{2}=P_{a}+\Big{(}\xi_{hin}+\lambda_{h}\frac{l_{12}}{D_{h}}+\xi_{bout}\Big{)} \frac{1}{2}\rho v_{h1}^{2}+\rho l_{12}\frac{dv_{h1}}{dt} \tag{4}\]  
where \(l_{12}=V_{T}t\), \(v_{h1}=\frac{V_{T}-v_{h}}{1-a}\), \(\frac{dv_{h1}}{dt}=\frac{-1}{1-a}\frac{dv_{h}}{dt}\).  
(2) The Bernoulli equation for fluid flow between cross-sections 2 and 3 is expressed as Equation (5):  
\[P_{2}=P_{3}+\lambda_{L}\frac{l_{23}}{D_{L}}\frac{1}{2}\rho{v_{h}}^{2}+\rho l_{ 23}\frac{dv_{h}}{dt} \tag{5}\]  
(3) The Bernoulli equation for fluid flow between cross-sections 3 and 4 is expressed as Equation (6):  
\[P_{3}+\frac{1}{2}\rho{v_{h}}^{2}=P_{a}+\Big{(}\xi_{3-4}+\lambda_{L}\frac{l_{3 4}}{D_{L}}+\xi_{out}\Big{)}\frac{1}{2}\rho{v_{d}}^{2}+\rho l_{34}\frac{dv_{d} }{dt} \tag{6}\]  
(4) The Bernoulli equation for fluid flow between cross-sections 3 and 5 is expressed as Equation (7):  
\[P_{3}+\frac{1}{2}\rho{v_{h}}^{2}=P_{a}+\Big{(}\xi_{3-5}+\lambda_{s}\frac{H_{s }}{D_{s}}+\xi_{sout}\Big{)}\frac{1}{2}\rho{v_{s}}^{2}+\rho H_{s}\frac{dv_{s}}{dt} \tag{7}\]  
As shown in Figure 2a, at the ventilation shaft, the continuity equation is used to obtain the following results, expressed as Equations (8) and (9):  
\[A_{L}v_{u}=A_{L}v_{d}+A_{s}v_{s} \tag{8}\]  
\[v_{s}=\frac{A_{L}v_{u}-A_{L}v_{d}}{A_{s}}=\frac{v_{u}-v_{d}}{\beta} \tag{9}\]  
where \(\beta=A_{s}/A_{L}\), \(\frac{dv_{s}}{dt}=\frac{1}{2}\frac{dv_{s}}{dt}-\frac{1}{\beta}\frac{dv_{d}}{dt}\);  
By combining the above equations, Equation (10) is obtained:  
\[\left\{\begin{array}{l}A\frac{dv_{u}}{dt}+l_{34}\frac{dv_{d}}{dt}=B{v_{h}}^{ 2}-\frac{k_{h}}{2}v_{d}^{2}+Cv_{u}+D\\ \frac{H_{s}}{\beta}\frac{dv_{u}}{dt}+E\frac{dv_{d}}{dt}=-\frac{k_{h}}{2\beta}v _{u}^{2}+F{v_{d}}^{2}+\frac{k_{h}}{\beta}v_{u}v_{d}\end{array}\right.\ t\in \left[0,\frac{L\tau}{V_{T}}\right] \tag{10}\]  
where \(A=\frac{l_{12}+l_{23}(1-a)}{1-a}\), \(B=\frac{(1-k_{h})(1-a)^{2}+k_{h}}{2(1-a)^{2}}\), \(C=-\frac{2k_{h}V_{T}}{2(1-a)^{2}}\), \(D=\frac{k_{h}V_{T}^{2}}{2(1-a)^{2}}\), \(E=-\frac{H_{s}+\beta l_{34}}{\beta}\), \(F=\frac{\beta^{2}k_{d}-k_{h}}{2\beta^{2}}\), \(k_{u}=\lambda_{L}\frac{l_{23}}{D_{L}}\), \(k_{h}=\xi_{hin}+\lambda_{h}\frac{l_{12}}{D_{h}}+\xi_{bout}\), \(k_{d}=\xi_{3-4}+\lambda_{L}\frac{l_{34}}{D_{L}}+\xi_{out}\), \(k_{s}=\xi_{3-5}+\lambda_{s}\frac{H_{s}}{D_{s}}+\xi_{sout}\).
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
## 2 Materials and Methods  
### Utilised Methods  
The research in the article focused preferentially on elevation models to define the strengths and weaknesses of the models without any external influences. The comparison of the DEMs was based on preparing a suitable representative sample of data and determining a proper method of evaluation. Data from the Czech Republic was selected to test the accuracy of the slopes that were calculated from DEMs: Digital Terrain Model 3 rd generation, 4 th generation and 5 th generation (DTM 3, DTM 4, DTM 5) and Digital Terrain Elevation Data 2 (DTED 2). The selection of the models was based on their occurrence in different parts of the globe. Whilst models with a resolution of up to 1 m predominate in Europe, areas of potential foreign operations are covered predominantly at most with elevation models with a maximum resolution of 30 m. Naturally, more accurate models might be locally available as well, e.g., laser scanning data. Nonetheless, from a military operation perspective, short notice ready-to-use terrain models with global coverage are the ultimate goal in the military environment. This excludes potential surface models that have other disadvantages for accurate CCM analyses. The challenge for elevation databases and their versatile usage is that any additional extensive processing of data prevents it from being operationally usable.  
Although the key part of this research was based on the proper selection of elevation data, its usability in CCM analyses was guaranteed only with a suitable processing method. This article introduced its own approach to elevation data evaluation. This method was based on generating random points and general statistical comparisons of their slope values. The second part of the study was a comparison of the reliability of data, which was achieved by evaluating passability with a raster analysis for selected military vehicles. Five operationally representative areas were selected for the tests.  
The analyses in the study were undertaken with the software ArcGIS 10.4.1 with its Toolbox functionalities; for points statistics, Extract values to points was used; and for areal statistics, Slope and Zonal statistics were used. Microsoft Excel 2019 was utilised for generating and evaluating statistical functions, e.g., mean slope and standard deviation. A major method used was the comparative method of map results or statistical results. The overview of the methodology and steps used in this research is in Figure 1.
## 3 Proposed Method
### Hybrid-Based Feature Calculation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Statistical Time-Domain Feature** & **Mathematical Equation** \\ \hline Mean & \(T_{1}=\frac{1}{N}\cdot\sum_{i=1}^{N}\left|x_{i}\right|\) (3) \\ Maximum value & \(T_{2}=max(x)\) (4) \\ Root mean square & \(T_{3}=\sqrt{\frac{1}{N}\cdot\sum_{i=1}^{N}\left(x_{i}\right)^{2}}\) (5) \\ Square root mean & \(T_{4}=\left(\frac{1}{N}\cdot\sum_{i=1}^{N}\sqrt{\left|x_{i}\right|}\right)^{2}\) (6) \\ Standard deviation & \(T_{5}=\sqrt{\frac{1}{N}\cdot\sum_{i=1}^{N}\left(x_{i}-T_{1}\right)^{2}}\) (7) \\ Variance & \(T_{6}=\frac{1}{N}\cdot\sum_{i=1}^{N}\left(x_{i}-T_{1}\right)^{2}\) (8) \\ RMS Shape factor & \(T_{7}=\frac{T_{3}}{\frac{1}{N}\cdot\sum_{i=1}^{N}\left|x_{i}\right|}\) (9) \\ SRM Shape factor & \(T_{8}=\frac{T_{4}}{\frac{1}{N}\cdot\sum_{i=1}^{N}\left|x_{i}\right|}\) (10) \\ Crest factor & \(T_{9}=\frac{T_{2}}{T_{3}}\) (11) \\ Latitude factor & \(T_{10}=\frac{T_{2}}{T_{4}}\) (12) \\ Impulse factor & \(T_{11}=\frac{T_{2}}{\frac{1}{N}\cdot\sum_{i=1}^{N}\left|x_{i}\right|}\) (13) \\ Skewness & \(T_{12}=\frac{\sum\left[\left(x_{i}-T_{1}\right)^{2}\right]}{T_{3}^{2}}\) (14) \\ Kurtosis & \(T_{13}=\frac{\sum\left[\left(x_{i}-T_{1}\right)^{4}\right]}{T_{3}^{2}}\) (15) \\ Fifth moment & \(T_{14}=\frac{\sum\left[\left(x_{i}-T_{1}\right)^{5}\right]}{T_{3}^{2}}\) (16) \\ Sixth moment & \(T_{15}=\frac{\sum\left[\left(x_{i}-T_{1}\right)^{4}\right]}{T_{3}^{2}}\) (17) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The proposed set of statistical features for the characterization of the available signals during the processing in the time-domain analysis, where _x(i)_ is a sample for \(i\) = 1, 2,..., \(N\), and \(N\) is the number of points for each acquired signal. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistical Feature** & **Mathematical Equation** \\ \hline Mean & \(F_{1}=\frac{1}{M}\cdot\sum_{j=1}^{M}s(j)\) & (18) \\ Variance & \(F_{2}=\frac{1}{M^{-1}}\cdot\sum_{j=1}^{M}(s(j)-F_{1})^{2}\) & (19) \\ Third moment & \(F_{3}=\frac{1}{M\left(\sqrt{F_{2}}\right)^{2}}\cdot\sum_{j=1}^{M}(s(j)-F_{1})^{3}\) (20) \\ Fourth moment & \(F_{4}=\frac{1}{M\left(\sqrt{
# Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
###### Abstract  
Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors--namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)--are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images.  
Keywords:computer vision; biometrics; cattle identification; bag-of-visual-words; muzzle print images +
Footnote †: journal: Applied Sciences
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Results and Analysis  
To assess the road extraction capability of the HRU-Net model, we carried out a comparative analysis between HRU-Net and other cutting-edge segmentation algorithms, such as U-Net, ResNet, DeepLabV3, ResUnet, and HRNet. Throughout the experiment, each compared network, along with our proposed network, utilized the same virtual environment and parameter settings during the training, validation, and prediction process.
# Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring
## 3 Modeling Method
### Analytical Modeling  
A simply supported beam with an open crack was used to simulate the application of the damage detection algorithm presented in Section 2. The proposed algorithm utilized an optimization technique for detecting damage by using the frequency response. The beam shown in Figure 2 has a transverse crack (with crack length, \(a\)) that is located at \(l_{a}\). Damage  
Figure 1: Flowchart for the adaptive multi-stage algorithm.  
is modeled as a torsional spring between the two sides of the beam. The simply supported beam is subjected to a sinusoidal load with forcing frequency, \(\omega\) located at \(l_{f}\). Parameters such as crack depth and crack location have been varied to generate several damage cases that were used to evaluate the effectiveness of the proposed algorithm.  
The modal properties of a uniform beam can be obtained by using the equation of motion (EOM) derived from the Euler-Bernoulli formulation [29] as follows:  
\[EI\frac{\partial^{4}y}{\partial x^{4}}(x,t)+\rho A\frac{\partial^{2}y}{ \partial x^{2}}(x,t)=f(x,t) \tag{8}\]  
In Equation (8), \(E\) is the Young's modulus of the material, \(\rho\) is the material density, \(y(x,t)\) is the lateral displacement at time \(t\) and location \(x\), \(f(x,t)\) is the external load, \(A\) is the cross-sectional area of the beam, and \(I\) is the moment of inertia. The system response is determined by finding the steady-state solution and the transient solution of the beam. The general solution of Equation (8) including the steady-state solution as well as the particular solution, \(y_{p}\), is:  
\[y_{1}(x,t)=\left[C_{1}\cos\gamma x+C_{2}\sin\gamma x+C_{3}\cosh\gamma x+C_{4} \text{sinh}\gamma x+y_{p}\right]\sin\omega t\ 0\leq x\leq l_{a} \tag{9}\]  
\[y_{2}(x,t)=\left[D_{1}\cos\gamma x+D_{2}\sin\gamma x+D_{3}\cosh\gamma x+D_{4} \text{sinh}\gamma x\right]\sin\omega t\ l_{a}\leq x\leq L \tag{10}\]  
The coefficients, \(C_{1},\ C_{2},\ \ldots\ D_{3},\ D_{4}\) in Equations (9) and (10) can be determined by applying the boundary conditions and the compatibility conditions. The frequency response function is defined as the ratio of the output amplitude, \(\overline{y}(\omega)\) to the input amplitude \(\overline{f}(\omega)\). Therefore, the frequency response function is expressed as:  
\[H(\omega)=\frac{\overline{y}(\omega)}{\overline{f}(\omega)} \tag{11}\]
## 2 Materials and Methods
### Regression Model I  
In creating a multiple regression model for two types of noise, we considered the apparent sound reduction index variable as a function of two input variables--frequency and excitation interval--i.e., we consider the function  
\[R^{\prime}=f(F,I) \tag{4}\]  
where \(R^{\prime}\) is the apaprent sound reduction index, \(F\) is the frequency (Hz) and \(I\) is the mean value of excitation interval (dB). The frequency \(F\) is varied between 50 and 1000 Hz. For the excitation Interval I, we considered six intervals: 75-78, 80-83, 85-88, 90-93, 95-98, and 98-102 dB.  
In the case of frequency Interval I (frequency from 50 to 100 Hz), the best regression model (Model Ia) has the form (regardless of noise type)  
\[R^{\prime}=b_{0}+b_{1}F+b_{2}I \tag{5}\]  
For the Frequency Interval II (frequency from 120 to 500 Hz), as well as for the Frequency Interval III (frequency from 630 to 1000 Hz), the best regression model (Model Ib) has the form (regardless of noise type)  
\[R^{\prime}=b_{0}+b_{1}F \tag{6}\]  
Point estimates of parameters of multiple regression models and values of determination indices are given in Table 3.  
To verify the statistical significance of all abovementioned models, we used the F-test of the model statistical significance. In all models, the resulting p-value is less than the considered level of significance \(\alpha\) (\(\alpha\) = 0.05), so we can assume that the proposed regression models are statistically significant. We verified the statistical significance of the parameters of the regression models using the test of statistical significance of regression parameter. The results show that all parameters of  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\mathbf{b_{0}}\) & \(\mathbf{b_{1}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{r^{2}\text{+}100\%}\) \\ \hline \multicolumn{5}{c}{**White**} \\ Interval I & \(-\)26.54 & 0.255 & 0.316 & 84.3 \\ Interval II & 26.75 & 0.023 & - & 76.2 \\ Interval III & 29.59 & 0.009 & - & 87.6 \\
**Pink** & & & & \\ Interval I & 3.428 & 0.171 & 0.082 & 78.2 \\ Interval II & 27.35 & 0.024 & - & 76.8 \\ Interval III & 30.03 & 0.008 & - & 71.8 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of Regression Model I.  
the proposed regression models are statistically significant. In all the cases considered, there is a strong dependence.  
Regression Model I shows a significant increase in the coefficient b\({}_{1}\) (see Table 3), which is related to the significant effect of low frequencies. We consider the difference in this coefficient to be significant in this regression model at frequencies up to 100 Hz (Interval I: white noise 0.255, pink noise 0.171). Conversely, at higher frequencies, this difference is negligible (Interval II: White noise 0.023, pink noise 0.024; Interval III: white noise 0.009, pink noise 0.008). The minimal difference of the coefficients is evident, as well as a significant decrease in the values of these coefficients themselves.