esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Determination of Muffin Texture
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Hardness (N)**} \\ \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & **\(p\)-Value** \\ \hline Model & 3.85 & 9 & 0.4281 & 23.05 & 0.0006 \\ \hline A—Substituted sugar & 1.45 & 1 & 1.45 & 78.00 & 0.0001 \\ \hline B—Substituted oil & 0.0099 & 1 & 0.0099 & 0.5314 & 0.4935 \\ \hline C—Temperature & 0.6833 & 1 & 0.6833 & 36.80 & 0.0009 \\ \hline AB & 0.2764 & 1 & 0.2764 & 14.89 & 0.0084 \\ \hline AC & 0.1647 & 1 & 0.1647 & 8.87 & 0.0247 \\ \hline BC & 0.0690 & 1 & 0.0690 & 3.72 & 0.1021 \\ \hline A\({}^{2}\) & 1.18 & 1 & 1.18 & 63.41 & 0.0002 \\ \hline B\({}^{2}\) & 0.7080 & 1 & 0.7080 & 38.13 & 0.0008 \\ \hline C\({}^{2}\) & 0.0165 & 1 & 0.0165 & 0.8880 & 0.3824 \\ \hline R\({}^{2}\) & \multicolumn{5}{c}{0.971} \\ \hline  & \multicolumn{5}{c}{**Chewiness (N)**} \\ \hline Model & 2.12 & 9 & 0.2355 & 36.75 & 0.0001 \\ \hline A—Substituted sugar & 0.0312 & 1 & 0.0312 & 4.88 & 0.0693 \\ \hline B—Substituted oil & 0.0164 & 1 & 0.0164 & 2.56 & 0.1604 \\ \hline C—Temperature & 0.0834 & 1 & 0.0834 & 13.01 & 0.0113 \\ \hline AB & 0.0066 & 1 & 0.0066 & 1.03 & 0.3500 \\ \hline AC & 0.2034 & 1 & 0.2034 & 31.74 & 0.0013 \\ \hline BC & 1.43 & 1 & 1.43 & 223.10 & \textless{0.0001} \\ \hline A\({}^{2}\) & 0.0197 & 1 & 0.0197 & 3.08 & 0.1297 \\ \hline B\({}^{2}\) & 0.5772 & 1 & 0.5772 & 90.06 & \textless{0.0001} \\ \hline C\({}^{2}\) & 0.0123 & 1 & 0.0123 & 1.92 & 0.2156 \\ \hline R\({}^{2}\) & \multicolumn{5}{c}{0.982} \\ \hline  & \multicolumn{5}{c}{**Cohesiveness (/)**} \\ \hline Model & 0.0307 & 9 & 0.0034 & 32.15 & 0.0002 \\ \hline A—Sugar & 0.0003 & 1 & 0.0003 & 2.44 & 0.1691 \\ \hline B—Oil & 0.0037 & 1 & 0.0037 & 35.29 & 0.0010 \\ \hline C—Temperature & 0.0026 & 1 & 0.0026 & 24.38 & 0.0026 \\ \hline AB & 0.0076 & 1 & 0.0076 & 71.78 & 0.0001 \\ \hline AC & 0.0086 & 1 & 0.0086 & 80.99 & 0.0001 \\ \hline BC & 0.0030 & 1 & 0.0030 & 28.76 & 0.0017 \\ \hline A\({}^{2}\) & 0.0069 & 1 & 0.0069 & 65.29 & 0.0002 \\ \hline B\({}^{2}\) & 0.0000 & 1 & 0.0000 & 0.4502 & 0.5272 \\ \hline C\({}^{2}\) & 0.0002 & 1 & 0.0002 & 1.64 & 0.2474 \\ \hline R\({}^{2}\) & \multicolumn{5}{c}{0.9679} \\ \hline  & \multicolumn{5}{c}{} & \multicolumn{5}{c}{} & \multicolumn{1}{c}{} \\ \hline \end{tabular}
\end{table} 
Table 5: Analysis of variance (ANOVA) for the polynomial model in terms of muffin hardness, chewiness and cohesiveness. To make predictions about the hardness, chewiness and cohesiveness of muffins studied for the levels of each factor, the coded factor Equations (3)-(5) can be used. Analyzing Equations (3)-(5) corresponding to the model for each factor, a ranking of the significant factors is observed in the order of importance of their impact on the hardness, chewiness and cohesiveness of the muffins.
# A Novel 3D Analytical Scattering Model
## 6 Conclusions  
A novel 3D geometric model has been proposed for the A2G/G2A radio propagation environments for the characterization of the Doppler spectrum and the spatial spread and the second order fading statistics. Mathematical expressions for joint and marginal PDFs of the Doppler shift, power and AoA have been derived. Furthermore, an analysis of SFs and second order fading statistics for A2G/G2A communication environments has been presented. The considered SFs are angular spread, angular constriction and the direction of maximum fading. The second order fading statistics considered in the presented analysis are LCR, AFD, auto-covariance and coherence distance. The SFs are computed using the knowledge of the PDFs of AoA and observed at both AS and GS in correspondence with both the azimuth and elevation planes. The second order statistics are computed using their relationship in terms of SFs. The impact of the azimuthal and elevational direction of AS's motion, the height of AS, the height of GS and the delay of the longest propagation path on the distribution characteristics of the Doppler shift, SFs and second order fading statistics has been thoroughly analysed. To show the validity of the proposed model, a comparison of the proposed analytical results with the performed simulation results for the Doppler spectrum has been presented.  
The proposed work can be used as a base to conduct a study for establishing a realistic choice of scattering objects' distribution for spatial modelling of the vicinity of the ground station. The proposed work can also be used to study the impact of realistic antenna beam patterns on the channel characteristics.  
A part of this work was funded by the EU research project, \"Advancing the state of the art of MIMO: The key to the successful evolution of wireless networks\"; (EU ATOM -690750) approved under the call H2020-MSCA-RISE-2015. All authors discussed and agreed on the idea and scientific contribution. [PERSON], [PERSON], and [PERSON] did the mathematical modelling, performed the simulations and contributed to the writing. [PERSON] and [PERSON] contributed to the revisions and discussion of the results. The authors declare no conflict of interest.
## 3 Description of the Problem and Assumptions  
### Analysis Problem  
The traditional full-length and short-turn route mode needs to determine the number of marshalled vehicles of full-length trains according to the passenger flow of the whole line, but with the application of virtual coupling trains, the transportation organization mode of full-length and short-turn routes can be more flexible. The number of marshalled vehicles of short-turn trains can be determined according to the passenger flow in the non-overlapping sections of full-length and short-turn routes. Therefore, virtual coupling trains can use a smaller number of marshalled vehicles of full-length trains, which reduces the cost to enterprises. In this paper, the full-length train and the short-turn train are coupled at the turn-back station of the short-turn routing, and the two trains operate on the short-turn routing to provide more transport capacity. When the two trains arrive at another turn-back station of the short-turn routing, they will be uncoupled. The full-length train continues to run forward, while the short-turn train turns back at the turn-back station, forming the full-length and short-turn routings. The transportation organization mode of virtual coupling trains can reduce enterprise operating costs and improve service levels.  
This paper aims at optimizing the train operation plan with full-length and short-turn routes of virtual coupling trains. It establishes a two-level optimization model. The upper model is used to minimize passenger travel time and enterprise operation cost, and the lower model to optimize the equilibrium of train load rate on short-turn routes. The upper model adopts the departure frequency of the train and the location of the turn-back station of the short-turn as the decision variables, and the lower model takes the number of marshalled trains as the decision variable. Since the objective function of the upper model and that of the lower model are not in the same order of magnitude, they are considered separately. The two-layer optimization model is designed according to the two optimization objectives, which can better take into account the factors of cost and full load rate.  
As shown in Figure 1, there are \(N\) stations in the line, and the direction of trains running from station 1 to station \(N\) is upward. The short-turn train only runs between station \(a\) and station \(b\). In this paper, stations 1 to \(a\) are regarded as section I. Then, stations \(a\) to \(b\) are recorded as section II, and stations \(b\) to N are recorded as section III. \(f_{1}\) is departure frequency of the full-length train, and \(f_{2}\) is departure frequency of the short-turn train. \(f_{1}\), \(f_{2}\), \(a\), \(b\) are decision variables of the upper model. \(n_{1}\) is the number of marshalled vehicles of the full-length train, and \(n_{2}\) is the number of marshalled vehicles of the short-turn train. \(n_{1}\) and \(n_{2}\) are decision variables of the lower model.
## 4 Experiments and Analysis
### Experimental Environment  
The method presented in this paper is implemented using the PyTorch deep learning framework, and all experiments are carried out on an NVIDIA RTX 1080 Ti GPU with 11 GB of memory. The initial learning rate for training is configured at 0.000025, and the training process employs the SGD optimizer with a momentum value of 0.9. The learning rate is subjected to an equal interval reduction strategy. The model undergoes training for a cumulative 150 epochs across the complete dataset, employing a batch size of 2 for training images.
###### Abstract  
In an era marked by technological advancement, the rising reliance on Virtual Private Networks (VPNs) necessitates sophisticated forensic analysis techniques to investigate VPN traffic, especially in mobile environments. This research introduces an innovative approach utilizing Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) for classifying VPN traffic, aiding forensic investigators in precisely identifying applications or websites accessed via VPN connections. By leveraging the combined strengths of CNNs and GNNs, our method provides an effective solution for discerning user activities during VPN sessions. Further extending this framework, we incorporate blockchain technology to meticulously record all mobile VPN transactions, ensuring a tamper-proof and transparent ledger that significantly bolsters the integrity and admissibility of forensic evidence in legal scenarios. A specific use-case demonstrates this methodology in mobile forensics, where our integrated approach not only accurately classifies data traffic but also securely logs transactional details on the blockchain, offering an unprecedented level of detail and reliability in forensic investigations. Extensive real-world VPN dataset experiments validate our approach, highlighting its potential to achieve high accuracy and offering invaluable insights for both technological and legal domains in the context of mobile VPN usage.  
Virtual Private Network; blockchain; VPN; deep learning; Convolutional Neural Network +
Footnote †: journal: Applied sciences
## 3 Test Results and Analysis
### The Energy Dissipation Capacity  
Under the earthquake, the structure will crack and yield, thereby consuming and absorbing the seismic energy, showing the characteristics of elastic-plastic restoring force. In this study, the energy dissipation capacity of the specimen was represented using cumulative energy dissipation (\(S_{\text{sum}}\)):  
\[S_{\text{sum}}=\sum_{i}^{n}S_{i} \tag{3}\]  
where \(n\) was the total number of cycles.  
Figure 16 illustrates that there were almost no differences in the elastic stage. When the displacement was less than 80 mm, SFZP-1's energy consumption was less than that of XJ, indicating that the linear steel fiber reduced the RPC matrix's damage resistance. In the later loading stage, a plastic hinge area formed at the end of the beam and became the main energy dissipation area. SFZP-3's energy dissipation capability was better than that of SFZP-2. Because the superposition effect of the PP fiber and steel fiber interface layer [34] strengthened the PP fiber interface (although SF-3 had a smaller volume and a larger number), the interface layer's superposition effect was better. Due to wavy steel fibers' continuous irregular shape and longer length, the sliding friction force between wavy steel fiber and the matrix was greater under cyclic loading, consuming more energy. SFZP-4 had the best energy dissipation capacity.  
Among the four types of steel fibers, the wavy and end-bent steel fibers have the best improvement effect on the energy consumption of assembled beam-column connections, while the straight steel fibers are slightly worse.  
Figure 15: Stiffness degradation.
# Binary Mixes of Self-Compacting Concrete with Municipal Solid Waste Incinerator Bottom Ash
## 1 Introduction
This study belongs to a state-funded research project, the mission of which is to further valorise MIBA in new building materials, thereby reducing the environmental impacts of the waste management and construction sectors. The main objective of the present work is to evaluate the properties of binary mixes of self-compacting concrete (SCC) with cement and MIBA in the fresh and hardened state, to verify the applicability of this addition in the production of concrete. Different combinations in mix design were tried (i.e., 0%, 20%, 30%, 40% and 50% of MIBA instead of cement). The requirements in the fresh state were evaluated based on fluidity, viscosity, filling capacity and flowability in accordance with current standards. The tests in the hardened state were divided into two main groups (i.e., mechanical and durability-related performance). Mechanical properties included uniaxial compressive strength in cubic and cylindrical specimens, splitting tensile strength, modulus of elasticity and shrinkage. Durability properties included the evaluation of open porosity through the water absorption by immersion and by capillary action tests, chloride ion penetration, electrical resistivity and carbonation. The ultrasonic pulse velocity test was also carried out to evaluate the specimens' densification.
## 4 Experimental Settings and Results
### Results of LSTM

Table 3: Accuracy of experiments with different LSTMs on the Persian and English test set. In addition, Figure 7 demonstrates the average accuracy of different architectures in this paper. Table 7 shows a confusion matrix of nearly 4680 characters of test set, font Calibri and size 14 which has low accuracy on the best model. Since the initial table contains around 86 rows, some Persian and English characters with precision and recall of 100 are eliminated. The first row shows information of characters that are deleted, inserted, or \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Font** & **12** & **14** & **18** & **Average of Fonts** \\ \hline Arial & 93.97 & 96.90 & 97.74 & 96.20 \\ Calibri & 92.21 & 94.16 & 94.81 & 93.73 \\ Homa & 94.92 & 95.23 & 97.53 & 95.89 \\ Lotus & 93.89 & 97.30 & 97.86 & 96.35 \\ Mitra & 94.27 & 97.33 & 97.65 & 96.42 \\ Nazanin & 95.85 & 97.62 & 97.73 & 97.07 \\ Tahoma & 92.09 & 96.30 & 97.74 & 95.38 \\ Times New Roman & 93.82 & 96.25 & 97.39 & 95.82 \\ Traffic & 92.99 & 96.03 & 97.74 & 95.59 \\ Yagut & 96.36 & 97.70 & 97.96 & 97.34 \\ Zar & 95.72 & 97.29 & 97.69 & 96.90 \\ \hline Average of Accuracy & 94.19 & 96.55 & 97.44 & 96.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Deep BLSTM accuracy on Persian only test set with four layers and 64, 128, 256 and 512 BLSTM blocks. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Font** & **12** & **14** & **18** & **Average of Fonts** \\ \hline Arial & 96.78 & 96.92 & 68.17 & 87.29 \\ Calibri & 96.42 & 96.54 & 68.09 & 87.00 \\ Homa & 92.16 & 97.87 & 96.95 & 95.66 \\ Lotus & 97.51 & 98.13 & 96.01 & 97.21 \\ Mitra & 96.66 & 98.01 & 72.39 & 89.00 \\ Nazanin & 96.53 & 98.29 & 96.58 & 97.13 \\ Tahoma & 96.72 & 96.90 & 68.34 & 87.32 \\ Times New Roman & 96.29 & 96.84 & 68.06 & 87.00 \\ Traffic & 97.79 & 97.00 & 96.58 & 97.12 \\ Yagut & 98.37 & 98.16 & 96.81 & 97.78 \\ Zar & 96.53 & 97.8 & 95.95 & 96.76 \\ \hline Average of Accuracy & 96.52 & 97.49 & 83.99 & 92.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Character level accuracy of Deep BLSTM with four layers of 64, 128, 256 and 512 BLSTM blocks on Persian and English test set. Figure 7: Average of the accuracy in different models. replaced by the model (Section 4.2). The model was able to recognize the character \"\(\_\)\" correctly 129 times and was not able to recognize \"\(\_\)\" one time when it was in the labels. The character \"\(\_\)\" was recognized two times which was not in labels. Finally, the model could not recognize the characters correctly 4533 times. The proposed model was tested and evaluated on a dataset [51; 52] of real images. The dataset contains images of different books with different fonts. Images were taken by a cell phone in an ordinary condition. The dataset also contains Persian words, punctuation, and numbers. Our model has successfully achieved the accuracy of 90.51%. The results of this experiment are reported in Table 8. Figure 8 illustrates an image of the dataset, and Figure 9 demonstrates its output. The model has difficulty to recognize \"\(\_\)\", \"\(\_\)\", \"\(\_\)\".
## 5 Models Behavior and Feature Importance  
The Pearson and Spearman Correlations of the four continuous features and average CQI are shown in Table 2. As can be seen in this table, surrounding PRB utilization has a slight but discernible correlation with average CQI. This correlation is stronger when taking total surrounding PRB utilization rather than the average. The Pearson and Spearman correlation coefficients are similar for all four features. This indicates the correlation is approximately linear.  
The time taken to train the models is shown in Table 4. Linear regression is the fastest, followed by XGBoost, and finally the neural network. Training time will vary slightly each run; however, the several order of magnitude differences between the models are unlikely to change. All experiments were run using an Intel i7-8565U CPU. It may be possible to train the neural network faster using a Graphics Processing Unit (GPU).  
During feature selection, XGBoost models trained using total surrounding PRB utilizations and average surrounding PRB utilization were compared. Average surrounding PRB utilization had a better performance, with a MAPE of 5.504% in comparison to 5.507%. This runs contrary to the correlation coefficient findings. It was found that eliminating any feature produced worse results. The MAPE using a definition of neighboring cells of 2.5 km, 5 km, and 10 km were 5.509%, 5.504%, and 5.577%, respectively.  
The feature importance is shown in Figure 3 for the best performing XGBoost model.  
The feature importance is found for a single decision tree by calculating the degree to which the split points using that feature improve the performance measure, when weighted by the number of samples being split. As XGBoost is an ensemble of decision trees it takes the average feature of importance across the trees [21]. The features in descending order of importance are the heavy data factor (f2), higher order modulation (f3), average surrounding PRB utilization (f4), frequency band (f0), number of neighbors (f1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Training Time (Seconds)** \\ \hline Linear Regression & 0.004 \\ XGBoost & 0.500 \\ Feed-Forward Neural Net & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training Time.  
Figure 3: Feature importance.
## 3 Methods
### Supervised Classification
#### 3.4.1 Support Vector Machines  
Binary SVMs set a hyperplane by exploiting the feature information of each entity in the training set. This hyperplane would be the decision boundary in the classification process. Optimally, it is defined to maximize the distance between itself and the nearest training entity of any class (functional margin), with larger margins relating to lower generalization errors [18]. In this case, multiple binary classifiers for all different 36 class pairs (1-to-1 mapping) are combined to construct a single multi-class classifier.  
Since the classification problem in this study is not linearly solvable, a quadratic kernel is used to transform the original feature space onto higher dimensions where the crop classes become linearly separable. However, the data are not perfectly separable and the algorithm allows for some misclassification in the training set, which is applied by the box constraint parameter (C). Specifically, higher box constraint values suggest stricter data separation. The model was built using the MATLAB function _fitececo_, where the kernel scale parameter was set to 'auto' mode for optimization. The box constraint parameter was selected via hyperparameter optimization, based on the minimization of the 10-fold cross-validation loss, as shown in Table 2. Thirty different objective evaluations were performed, of which an indicative subset is presented. The final selection was made under both cross-validation loss minimization and processing time considerations.  
It is evident that a box constraint of approximately 1, as in the 1 st Evaluation, provides the optimal 10-fold cross-validation loss, while it also proves superior in terms of processing efficiency, as compared to the evaluations of comparable cross-validation loss (i.e., Evaluations 2-5).
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 3 Methodology
### Data Collection and Preparation  
Data collection and preparation involved a series of steps to address inconsistencies and improve data quality for model reliability. Key challenges in the sales dataset included:  
* Data Distribution and Quality Assessment: The dataset presented skewed distributions across products and customer accounts. Skewed distributions can significantly impact model bias and generalizability, especially in predictive analytics for logistics [16]. Addressing these imbalances was crucial for developing robust predictive models.
* Data Cleaning: To handle inconsistencies, negative quantity values--often reflecting promotional adjustments--were converted to positive values to maintain consistency. Temporal gaps were flagged to avoid skewing ARIMA forecasts, and down-sampling was applied to customers and products with low occurrence frequencies to ensure a representative dataset, following best practices in data preprocessing for predictive modeling [17].
## 3 Results and Discussion  
### Fluidity  
The incorporation of PCE can significantly improve the initial flow of HBCSA cement, as shown in Figure 3. The initial flow rate reached 250 mm when the PCE admixture was at 0.8%. With an increase in admixture, the initial flow rate also increased; when the admixture reached 1.0%, the flow rate increased most significantly to 310 mm. The flow rate increased slowly after the admixture of PCE exceeded 1.0%, the admixture reached adsorptionsaturation at 1.4%, and the flow rate did not continue to grow. From the viewpoint of the longitudinal flow rate, the flow rate of single-doping PCE has a large longitudinal loss, the flow rate at 15 min decreases by about 50%, and the flow rate disappears after 15 min. Although PCE can improve the initial fluidity of HBCSA, the loss of liquidity is high. It has been reported [26] that this is due to the high activity of HBCSA, which makes the dispersion of PCE lose quickly afterwards, and other studies reported [22] that it may be caused by ettrinigite produced rapidly resulting from special molecular structure of PCE and its effect of steric hindrance. Therefore, retarders can be used to overcome the rapid loss of PCE dispersion efficiency.