esa-sceva/satcom-chunk-collection huggingface dataset:
# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 4 Experiment, Analysis and Discussion
### Experimental Results and Discussion
Compared with CNN-TS, only m data are added, but the rumor recognition effect is enhanced. The details are shown in Table 5.  
CNN-TS + sentiment + Attention is less than the CNN from 59,138 \(\times\) 50 to 20 \(\times\) 51, which can reduce the calculation of the model and accelerate the training speed of the model. In addition, although the input parameters are reduced, the experimental results show that the rumor recognition effect is increased, indicating that the packet processing of time series is effective, and the compressed input can still express the rumor features well. With the input of CNN-TS, although 20 data are added, our model is superior to the CNN-TS model in the four important evaluation indexes of rumor recognition. Additionally, this small amount of parameter increase is almost negligible. Reducing the input parameters can greatly reduce the amount of calculation of the model and reduce the number of iterations of model training. Therefore, the training speed of the model is accelerated. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Parameter** \\ \hline CNN & 59,138 \(\times\) 50 \\ \hline CNN-TS & 20 \(\times\) 50 \\ \hline CNN-TS + Sentiment & 20 \(\times\) 51 \\ \hline CNN-TS + Sentiment + Attention & 20 \(\times\)51 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of parameters of the rumor detection models. Figure 5: Input comparison of the CNN, CNN-TS, CNN-TS + Sentiment + Attention. Last, we analyze on epochs of compared methods. The loss of the model is the difference between the predicted value and the real value which can be used to measure the quality of a model. In this experiment, we use 10-fold cross validation to train and test the model. In order to compare the effects of iterations in CNN-TS, CNN-TS + Sentiment and CNN-TS + Sentiment + Attention on the model, we select the first-fold test loss value. See Figure 6 for details. It can be seen from Figure 6 that the loss value of CNN-TS model proposed by [PERSON] is reduced to 0.2530 after 64 iterations under the first fold data. In order to prevent overfitting, the model takes an early-stopping operation. The CNN-TS + Sentiment proposed in this paper only needs 42 iterations and the loss value reaches stability and stops training. The improved CNN-TS + Sentiment + Attention is more optimized. It only needs 18 iterations, after which the model stops training. Additionally, the loss value remains unchanged. This verifies the superiority of our model in training speed.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 2 Categorization of Bridge Boost Converter Topologies  
BBC topologies with PFC based on Boost converter are presented in this section. BBC converters are mainly composed of a full rectifier bridge; however, all BBC topologies presented in this section need a line EMI filter between the feeding source and the bridge to comply with international normatives regarding \(THD_{i}\) (lower than 6%), regardless of the control system [25]. Nevertheless, sizing of the EMI filter depends on the number of switches, control strategy, voltage level, power level, and paths to handle the converter currents.
## 5 System Optimization  
As mentioned before, EREVs have complex architectures that contain multidisciplinary technologies. Since the EREV performance can be affected by many multidisciplinary, interrelated factors, computer simulations are the most critical technology with which to optimize performance and reduction costs. Additionally, EREV simulations can help manufactures to minimize prototyping costs and rapidly evaluate their concepts. Since an EREV's system consists of various subsystems clustered together by mechanical, electrical, control, and thermal links, the simulation should be a parameterized mixed-signal one. Hence, optimization is at the system level, at which there are many tradeoffs among various subsystem criteria. The preferred system criteria generally involve numerous iterative processes. In summary, the system-level simulation and optimization of EREVs should consider the following key issues.  
1. As the interactions among various subsystems significantly affect the performances of EREVs, the significance of those interactions should be analyzed and taken into account.
2. The model's accuracy is usually correlated with the model's complexity, but the latter may run counter to usability, tradeoffs among the accuracy, complexity, usability, and simulation time should be considered.
3. The system voltage generally causes contradictory issues for EREV design. For example, the battery weight (higher voltage requires more battery modules in series, and hence more weight for the battery case). Similarly, motor drive voltage and current ratings, auxiliary power unit range, energy generated, acceleration performance, driving range, and safety should be optimized at the system level.
4. The adoption of multiple energy sources helps to increase the driving range. For the EREV case, the APU should be optimized based on the vehicle's performance and cost requirements.
# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Human RBCs**} & \multicolumn{2}{c}{**Pseudo-Leucocytes**} & \multicolumn{2}{c}{**Pseudo-Platelets (10\%/L) (\(\alpha\) = 3)**} \\  & \multicolumn{2}{c}{**(10\%/L) (\(\alpha\) = 3)**} & \multicolumn{2}{c}{**Pseudo-Platelets (10\%/L) (\(\alpha\) = 3)**} & \multicolumn{2}{c}{**Pseudo-Platelets (10\%/L) (\(\alpha\) = 3)**} \\ \cline{2-10}  & **ABX** & **CellDyn1700** & **Midray** & **ABX** & **CellDyn1700** & **Midray** & **BC3000** & **ABX** & **CellDyn1700** & **Midray** & **BC3000** \\ \hline
1 month & 4.20 \(\pm\) 0.08 & 4.15 \(\pm\) 0.09 & 4.22 \(\pm\) 0.09 & 6.40 \(\pm\) 0.10 & 6.45 \(\pm\) 0.05 & 6.36 \(\pm\) 0.04 & 189.00 \(\pm\) 1.00 & 188.33 \(\pm\) 0.58 & 186.00 \(\pm\) 1.73 \\
2 months & 3.88 \(\pm\) 0.19 & 4.05 \(\pm\) 0.06 & 4.11 \(\pm\) 0.07 & 6.15 \(\pm\) 0.09 & 6.40 \(\pm\) 0.14 & 6.30 \(\pm\) 0.10 & 181.67 \(\pm\) 1.15 & 181.67 \(\pm\) 2.08 & 184.33 \(\pm\) 1.53 \\
3 months & 3.60 \(\pm\) 0.12 & 3.85 \(\pm\) 0.09 & 3.95 \(\pm\) 0.09 & 6.01 \(\pm\) 0.11 & 6.23 \(\pm\) 0.06 & 6.23 \(\pm\) 0.06 & 181.33 \(\pm\) 1.53 & 181.33 \(\pm\) 2.65 & 182.67 \(\pm\) 1.53 \\ \hline \end{tabular}
\end{table} 
Table 5: The stability of the storing samples was read on different machine models. A Plackett-Burman design is an effective way to screen for the main parameters of many elements of the optimization process. The RSM-CCD method was performed to optimize the value of the most influential screening factors. This method is experimental planning with many factors based on mathematical models. This result has very important significance, and the research will help create hematology EQA samples with the same components as actual blood, with a long storage time (3 months at 2-8 \({}^{\circ}\)C) compared with whole blood samples that have a short storage time. This study attempted to establish an optimal formula for manufacturing of hematological reference samples for EQA in hematology, helping hematology laboratories in Vietnam with quality assurance of tests.
## 2 Results and Discussions
### DoE  
The conditions for extraction time and exposure temperature were optimized using a Design of Experiments (DoE) approach. The relative areas of five key VOCs--2,4,5-trimethyl-1,3-dioxolane, 3-methylbutyl alcohol, hexanal, ethyl 2-methylbutanoate, and 3-methylbutyl ethanoate--across the three ripening stages of copoazu were selected as response variables for the modeling of 3D response surface graphs.  
A Pareto chart of standardized effects was generated for each compound (Figures 1a and S3a-56a). For 2,4,5-trimethyl-1,3-dioxolane (Figure 1a), temperature (T) (40-80 \({}^{\circ}\)C) and the quadratic temperature term (T \(\times\) T) had a statistically significant effect on the response variable (\(p<0.05\)). However, the time factor (t) and the interaction between time and temperature (t \(\times\) T) were not significant. Interestingly, both temperature and time showed negative effects, suggesting that lowering these parameters increased the relative peak area of this compound. Similar trends were observed for 3-methylbutyl alcohol, ethyl 2-methylbutanoate, and 3-methylbutyl ethanoate, where temperature and its quadratic term were the primary significant factors, as shown in the respective Pareto charts (Figures S3a, S5a, and S6a). In contrast, for hexanal (Figure S4a), none of the studied factors had a statistically significant effect on the response variable.  
Figure 1b depicts the response surface plot of the area of 2,4,5-trimethyl-1,3-dioxolane as a function of extraction time and temperature. The compound area reached optimal values at 15 min and at 40 \({}^{\circ}\)C, with an increasing trend at temperatures below 60 \({}^{\circ}\)C. The same behavior was observed in the response surface plot of the areas of 3-methylbutyl alcohol, ethyl 2-methylbutanoate, and 3-methylbutyl ethanoate as a function of the extraction time and temperature (Figures S3b, S5b, and S6b). Hexanal reached the optimal temperature and time values across the entire studied interval (40-80 \({}^{\circ}\)C) and (15-45 min), respectively, with a trend of increasing up to 30 min and 60 \({}^{\circ}\)C and then decreasing (Figure S4b). The optimal conditions depended on the compound. Therefore, based on the response optimization of the five VOCs, the optimal extraction time and temperature were determined to be 27 min and 40 \({}^{\circ}\)C, respectively.
## 10 Concluding Discussion  
Large-scale data sharing in a distributed smart city environment requires an increased attention to data security and reliability issues. Methods that ensure data security at the required level with high reliability and speed are very important.  
In this work, we propose a DT-RRNS routing solution for the WSN and MANET complex dynamic hierarchical heterogeneous networks for improving data transmission. To design efficient mechanisms, we consider reliability and security as close concepts. Increased security and reliability are achieved with an effective data recovery mechanism of RRNS with moduli of compact sequences of coprime numbers.  
This mechanism together with adaptive multipath routing increases the resistance of the sensor network to attacks of various types, including unauthorized interception, message falsification, errors, node and network connection failures, information loss in case of attacks or accidents, etc.  
This approach does not have the limitations of the traditional encryption methods for secured data transmission. The secret key management is solved by the SSS.  
In addition, this solution reduces data redundancy, resulting in less use of large equipment, energy consumption, and message storage capacity. These properties are important when deploying IoT.  
In the DT-RRNS, each participant receives shares of a smaller size than the original data. It improves transmission speed, resulting in better support for big data sensing and processing, in contrast to the Asmuth-Bloom scheme.  
The promising direction for future work is the development of computationally efficient methods for generating dynamic RRNS parameters and dynamic routes due to loss of sensors, connections, loss of functionality, errors by contamination, vibration, shocks, high temperatures, etc. It is important to study the problem of selecting moduli for dynamic adaptation to changing network topology and characteristics. To further improve efficiency and reliability, we will consider specialized multipath routing protocols based on a weighted version of DT-RRNS.  
A.G.: conceptualization, methodology, software, validation, research, writing; E.S.: conceptualization, methodology, software, validation, research, writing; A.T.: conceptualization, methodology, research, writing, supervision; M.D.: methodology, research, writing; M.B.: conceptualization, methodology, research, writing, supervision; S.N.: conceptualization, methodology, research, writing. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Redundancy.  
**Funding:** This work was supported by the Ministry of Education and Science of the Russian Federation (Project 075-15-2020-788).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimentation Setup
### Jobs and Cluster Used in the Evaluation  
**Jobs.** There are several DL model architectures, such as Recurrent Neural Networks (RNNs) [28] and Convolutional Neural Networks (CNNs) [29]. In our evaluation, we adopt CNN as the DL model, trained with the CIFAR-10 dataset. This selection is based on the extensive popularity of CNNs in image processing and the wide accessibility of the CIFAR-10 dataset. Our evaluation contains a diverse set of 21 CNN models, each varying in characteristics such as model size, number of parameters, and the number of layers. The selection of models used in our evaluation is based on the availability of pre-built models in the Keras framework [30], covering a range from small to large models. The batch size for each model is configured within the range of 32 to 512 (maximum at 512 due to GPU memory constraint).  
Jobs are defined as a 4-tuple: DL model name, batch size, GPU architecture, and GPU count, as detailed in a set in Table 2. An example of a job is the training of a VGG16 model with a batch size of 32 on an A100 GPU architecture with 1 GPU. In this study, it gives us a total of 1680 possible combinations.  
The number of jobs is configured in four settings: 25, 50, 100, and 200. They are uniformly sampled from the job table, shown in Table 2. The jobs are submitted at a regular pace over a given period of time. The selected scenarios include a 0-15 min span and a 0-7 min span, with all jobs arriving simultaneously at time 0.  
**Cluster.** This study considers a heterogeneous GPU cluster comprising six different GPU architectures: Nvidia A100, A40, RTX2080 Ti, RTX1080 Ti, K40M, and T4. The machine of each GPU architecture contains multiple GPUs, with its count indicated in Table 2. Due to the accessibility constraints of the GPU cluster, the experiments in this work are conducted through simulation. In our study, the simulation specifically focuses on two key aspects: the performance of each GPU architecture and the memory specifications of the GPUs. The reference training time of all jobs on the six different GPU architectures was recorded on a real cluster (Grid'5000 [31]) and reused in the following simulations.
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 2 Materials and Methods
The workflow of the methodology followed in the test for each GNSS individually is provided in Figure 2, so each of its 10,652 runs is executed as follows:  
* Selection of the recording and the attenuation level;
* Execution of a cold start in the receiver and playback of the signal;
* Acquisition of the GSV and GGA NMEA sentences until the first fix. In this study, due to constraints in bandwidth recording, data storage, and execution time, the test is limited to the most common frequency band and a widely used low-cost receiver. For this reason, the results are only useful for comparative analysis between  
Figure 2: Test methodology workflow for each GNSS. GNSSs, and it is not intended to provide absolute claims about them. Nonetheless, since the proposed methodology relies on general NMEA messages, it can be applied to any other recorded signals and receivers of interest.
## 4 RIS Implementation
Another experiment showed that an RIS between a transmitter and a receiver could act as a relay and increase the signal strength and coverage area [53]. In sensing and imaging applications, RISs have been used to enhance the resolution and accuracy of the imaging system. For example, an RIS can be designed and fabricated as a lens or a mirror that can focus or reflect electromagnetic waves. In one experiment, an RIS enhanced the resolution of a synthetic aperture radar (SAR) imaging system by improving the signal-to-noise ratio and reducing the imaging artifacts [54].
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 5 Results and Discussion
### Performance Evaluation in Scenario 1
#### 5.2.2 Network Lifetime  
Figures 17-19 present a comparison of the network lifecycle performance of EEUC, CHEF, and FQ-UCR in Scenario 1. For the 100-node network, EEUC resulted in the shortest lifetime, while CHEF attempted to improve this but still underperformed. FQ-UCR, however, achieved the longest network lifetime. With 200 nodes, FQ-UCR surpassed both EEUC and CHEF in network lifetime. For the 400-node scenario, FQ-UCR extended the network lifetime to 2499 rounds, while CHEF and EEUC reached 1326 and 938 rounds, respectively. FQ-UCR clearly surpassed the other two protocols in network lifetime across varying node counts.
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
###### Abstract  
Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.  
data quality; large-scale; high-dimensionality; linear discriminant analysis; random projection; bootstrapping +
Footnote †: journal:  
In this paper, we propose three new quality measures called \(M_{sep}\), \(M_{var}\), and \(M_{var}\), and their computation methods that overcome the above limitations. Our approach is inspired by [PERSON]'s linear discriminant analysis (LDA) [4], which is mathematically well-defined for finding a feature subspace that maximizes class separability. Our computation method makes use of the techniques from statistics, random projection [5] and bootstrapping [6], to compute the measure for large-scale high-dimensional data efficiently.  
The contributions of our paper are summarized as follows:  
* We propose three new data quality measures that can be computed directly from a given dataset and can be compared across datasets with different numbers of classes, examples, and features. Although our approach takes ideas from LDA (linear discriminant analysis) and PCA (principal component analysis), the techniques by themselves do not produce single numbers that are comparable across different datasets.
* We provide efficient algorithms to approximate the suggested data quality measures, making them available for large-scale high-dimensional data.
* The proposed class separability measure \(M_{sep}\) is strongly correlated with the actual classification performance of linear and non-linear classifiers in our experiments.
* The proposed in-class variability measures \(M_{var}\) and \(M_{var}\) quantify the diversity of data within each class and can be used to analyze redundancy or outlier issues.
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
## 4 Optimization and Simulation Model
### Performance-Improving Strategies
#### 4.3.2 Recovery from Delays  
The second type of strategy tries to exploit operational flexibility to recover from delays. If weather permits, a vessel can increase its speed to reduce or even eliminate a delay. [PERSON] et al. [30], for example, show that speed-up can be used as a recovery action when considering disruptions in planning fleet deployment in roll-on roll-off liner shipping. Though speeding-up allows recovering from delays in some cases, it requires increased power output, which in turn will increase fuel consumption and thus bunker costs. The ability to speed-up is limited by the maximum propulsion power of the vessels.
## 2 Theoretical Background
### Mesh Morphing and Radial Basis Functions (RBFs)  
Mesh morphing [21; 22; 23; 24] consists of defining a mathematical law that controls the deformation of the mesh. In particular, several studies have pointed out that RBFs are very flexible and interesting tools to deform the mesh. The idea is to define source points and apply a known displacement to them. RBFs are used to map the deformation defined on the mesh nodes using a proximity criterion. The interpolation quality and its behavior depend on the chosen RBFs.  
A linear system with an order equal to the number of introduced source points must be solved to calculate the coefficients. Once these unknown coefficients are determined, the motion of any arbitrary point, whether inside or outside the domain (interpolation/extrapolation), is represented as the sum of the radial contributions from each source point, provided the point falls within the influence domain. An interpolation function, consisting of a radial basis and a polynomial, is defined as follows:  
\[s(x)=\sum_{i=1}^{N}\gamma_{i}\phi(\|x-x_{i}\|)+h(x) \tag{1}\]  
The minimal degree of the polynomial depends on the choice of the basis function. A unique interpolant exists if the basis function is conditionally positive definite. If the basis functions are conditionally positive definite of order m = 2, a linear polynomial can be used:  
\[h(x)=\beta+\beta_{1}x+\beta_{2}y+\beta_{3}z \tag{2}\]  
The values for the coefficients of RBF and the coefficients of the linear polynomial can be obtained by solving the following system:  
\[\begin{pmatrix}M&P\\ P^{T}&0\end{pmatrix}\begin{pmatrix}\gamma\\ \beta\end{pmatrix}=\begin{pmatrix}\delta\\ 0\end{pmatrix} \tag{3}\]  
where \(g\) are the known values at the source points. \(M\) is the interpolation matrix defined by calculating all the radial interactions between source points, and \(P\) is the constraint matrix.  
The radial basis function is a meshless method. Only the grid points are moved, regardless of the connected elements, making it suitable for parallel implementation.