esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Description  
There are four core questions that must be addressed to achieve over-the-horizon PTV detection on the expressway:  
* Question 1: How do we process the transaction data generated by the ETC gantry?  
An ETC gantry can generate a large amount of data in a short period of time. However, due to equipment failures, network issues, and other reasons, the generated data may have errors. How to effectively process this data to ensure its accuracy and reliability is one of the challenges we currently face. In addition, we also need to address the challenge of converting ETC transaction data into data suitable for PTV detection. This conversion process involves multiple technical challenges such as data format, accuracy, and timeliness, which require in-depth research and resolution.  
* Question 2: How do we obtain the speed of vehicles traveling in transit?  
In the ETC system, vehicles only generate transaction records when passing through the ETC gantry, so we cannot calculate the vehicle's speed between the entrance gantry of a certain section and an exit gantry they did not pass. However, as one of the core features of PTV detection, vehicle speed is crucial for accurately evaluating the safety of vehicles. Therefore, we need to study how to obtain speed information of vehicles traveling on the road to elevate the precision of the evaluation.  
* Question 3: How do we effectively evaluate the positions of vehicles in transit?  
Due to the deployment intervals of ETC gantry, ranging from a few kilometers to more than ten kilometers, and the fact that these gantries only collect data when the vehicle passes through them, they cannot continuously track the vehicle's position throughout the entire driving process. Therefore, in some cases, it is difficult to accurately evaluate the position of the vehicle.  
* Question 4: How do we effectively detect PTVs beyond the line-of-sight range?  
Effectively detecting PTVs beyond visual range helps drivers take timely preventive measures to avoid traffic accidents. However, when vehicles are traveling at high speeds, it is difficult to accurately detect them in a short period of time. In particular, complex road environments such as bends and obstacles may also interfere with the accuracy of the detection system.
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 3 Proposed Framework--AMAN
### Breast Cancer Classification Using BI-RADS Descriptors
#### 3.5.1 Preprocessing  
The BI-RADS system (Figure 27) employs specific descriptors to characterize tumors. These are the density, shape, and margin descriptors. Each of these has its own category, as illustrated in Table 3. Our strategy is to classify mammography findings using these descriptors as features. In addition, patient age was added as a predictive variable because it has been established as a significant risk factor for breast cancer. Several preprocessing approaches were applied to our KFUH dataset to obtain the best results possible.  
To begin, the hospital reports were collected in \"_._docx\" format; to help the operations, these reports were converted to \"_._txt\" files using the doc2 txt library. Following that, we designed a data extraction tool that converts unstructured reports to structured tabular data in an Excel sheet using BI-RADS descriptors as columns and their categories as rows, as seen in Figure 28. The tool was created by combining text processing techniques and Python  
\begin{table}
\begin{tabular}{c c} \hline
**BI-RADS Category** & **Description** \\ \hline BI-RADS 0 & Incomplete \\ BI-RADS 1 & Negative, no mass \\  & Benign \\ BI-RADS 2 & - 0\% probability of malignancy \\  & Probably benign \\ BI-RADS 3 & - \textless{}2\% probability of malignancy \\  & Suspicion of malignancy \\  & - 2–94\% probability of malignancy \\  & For mammography, it can be divided into: \\ BI-RADS 4 & - BI-RADS 4A: low suspicion for malignancy (2–9\%) \\  & - BI-RADS 4B: moderate suspicion of malignancy (10–49\%) \\  & - BI-RADS 4C: high suspicion of malignancy (50–94\%) \\  & Highly suggestive of malignancy \\ BI-RADS 5 & - \textgreater{}95\% probability of malignancy \\ BI-RADS 6 & Biopsy-proven malignancy \\ \hline \end{tabular}
\end{table}
Table 3: BI-RADS Categories.  
Figure 27: BI-RADS categories.  
libraries such as NumPy and Pandas. Although this tool was robust enough to collect all available data, we found a few difficulties since various radiologists describe their findings differently. As a result, there was a great deal of noise and missing data that required substantial cleaning and organization. For instance, one of the primary characteristics that supported our approach was the patient's age. However, the reports only revealed the patient's date of birth, which required us to calculate the patient's age. Additionally, to improve classification, we switched the age from a numerical to a categorical variable. This was accomplished by classifying individuals' ages into three categories: those under the age of 50, those between the ages of 50 and 64, and those over the age of 64. This method has been used successfully in previous studies [97].  
After completing the necessary preprocessing, only 114 reports remained, consisting of the class distribution as seen in Figure 29.
# Model Experimental Study on De-Icing Method of Bridge Pylon Beam Based on Electric Heating
## 3 Pre-Tests of Icing and Analysis of Results
### Icing Distribution  
As can be seen in Figure 9, in the five groups of icing tests the icing position and the shape of the beam were roughly the same, but the icing degree was different. The top and front surfaces of the beam were all iced, and massive icicles were attached below the beam. At the same ambient temperature, the ice layer on the wall surface was harder and denser than that on the top surface, but the ice thickness was not uniform. With the deterioration of the environment, the icing degree of the beam increased significantly. The icicles tilted, especially after the wind was supplied.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group Number**} & **Precipitation** & **Ambient** & \multirow{2}{*}{**Wind Velocity (m/s)**} \\  & **(mm/min)** & **Temperature (\({}^{\circ}\)C)** & \\ \hline
1 & 1 & \(-\)5 & 0 \\
2 & 1 & \(-\)10 & 0 \\
3 & 1 & \(-\)15 & 0 \\
4 & 1 & \(-\)15 & 3 \\
5 & 1 & \(-\)15 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The test conditions of icing tests.  
The top and side surface of the beam in the spray range were almost completely frozen, and a large number of icicles were attached to the bottom edge. If one attempted to set up de-icing modules in all the icing areas, the workload would undoubtedly be considerable, whether in the test or in the practical project. Therefore, it was beneficial to select a suitable area for the deicing tests. In general, the ice on the beam can be divided into three parts: the ice accumulation on the top surface of the beam; the ice layer stuck to the wall surface of the beam; and the icicles adhering to the bottom side of the beam. In comparison, the damage caused by falling sharp icicles is obviously more serious. Therefore, this paper chose to install the heating system at the bottom edge of the beam and focus on the de-icing strategy in this area.
# Class Activation Map Guided Backpropagation for Discriminative Explanations
###### Abstract  
The interpretability of neural networks has garnered significant attention. In the domain of computer vision, gradient-based feature attribution techniques like RectGrad have been proposed to utilize saliency maps to demonstrate feature contributions to predictions. Despite advancements, RectGrad falls short in category discrimination, producing similar saliency maps across categories. This paper pinpoints the ineffectiveness of threshold-based strategies in RectGrad for distinguishing feature gradients and introduces Class activation map Guided BackPropagation (CGBP) to tackle the issue. CGBP leverages class activation maps during backpropagation to enhance gradient selection, achieving consistent improvements across four models (VGG16, VGG19, ResNet50, and ResNet101) on ImageNet's validation set. Notably, on VGG16, CGBP improves SIC, AIC, and IS scores by 10.3%, 11.5%, and 4.5%, respectively, compared to RectGrad while maintaining competitive DS performance. Moreover, CGBP demonstrates greater sensitivity to model parameter changes than RectGrad, as confirmed by a sanity check. The proposed method has broad applicability in scenarios like model debugging, where it identifies causes of misclassification, and medical image diagnosis, where it enhances user trust by aligning visual explanations with clinical insights.  
interpretability; gradient-based feature attribution; class activation map +
Footnote †: journal: Applied sciences
## 3 Mathematical Modeling
### Isosteric Heat of Adsorption from the D-A Model  
#### 3.2.1 Ideal Approach  
The isosteric heat of adsorption \(q_{st}\) is normally expressed in the form of heat released by the diminutive amount of bulk molecules adhered to the finite surfaces of the adsorbent at the constant temperature and pressure. Typically, the general expression of the isosteric heat of adsorption can be written as the following Equation (20) [48; 49; 50].  
\[q_{st}=\ T\bigl{(}v_{g}-v_{a}\bigr{)}\frac{dP}{dT} \tag{20}\]  
When the adsorbed phase volume is neglected, and the ideal gas assumption is invoked as in most of the cases, the equation takes the following form:  
\[q_{st}=RT^{2}\frac{d\ln P}{dT} \tag{21}\]  
At the same time, the specific enthalpy of evaporation \(q_{fg}\) (kJ/kg) can be expressed as follows:  
\[q_{fg}=RT^{2}\frac{d\ln P_{0}}{dT} \tag{22}\]  
From the D-A equation, \(\ln P\) is expressed as follows:  
\[\ln P=-\frac{E_{a}}{RT}\left[\ln\Bigl{(}\frac{w_{0}}{w}\Bigr{)}^{\frac{1}{2} }\right]+\ln P_{0} \tag{23}\]  
Finally, combining Equation (21) with Equation (23) and after some algebraic manipulation, the ideal approach for the heat of adsorption is obtained in Equation (24):  
\[q_{st}=\ q_{fg}+E_{a}\Bigl{[}\ln\Bigl{(}\frac{w_{0}}{w}\Bigr{)}\Bigr{]}^{1/n} \tag{24}\]where the second term then combines the activation energy of the adsorption pair \(E_{a}\) with adsorption potential expressed as a reversed ratio of the maximum adsorption \(w_{0}\) over the isosteric amount \(w\). This means the energy yield is naturally higher at less surface coverage.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 2 Material and Methods  
### Study Area  
The study area is located in the state of Paraiba, northeastern Brazil. Figure 1 shows the land use and land cover (LULC) for the state of Paraiba in the year 2020, and the location of the two micrometeorological towers used in this study. In the study area, the dominant soil type is Lithic Neosol, characterized as shallow, with a maximum depth of 10 cm, excessively drained, stony, and gently wavy [28]. Based on the Koppen classification, the region's climate is semiarid (type BSh) with low latitude and altitude [29]. The rainy season starts in February/March and lasts until July/August. The average annual rainfall is \(\sim\)783 mm\(\cdot\)year\({}^{-1}\), from 1935 to 2014 [5].
# Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems
## 3 Dynamic Access Control and Pilot Allocation Algorithm
### Optimization Problem and Algorithm
#### 3.1.1 Estimation Phase  
Idle pilots can be identified by detecting the signal of pilots. A BS determine the number of idle pilots \(\hat{S}_{idle,i}\) in the \(i\)th time slot among the total number of available pilots \(\tau_{p}\). The probability of the pilots can be computed as  
\[\tilde{p}_{idle,i}=\frac{\hat{S}_{idle,i}}{\tau_{p}}. \tag{1}\]  
Moreover, we also compute the probability of the pilots being idle by \(p_{idle,i}=(1-\frac{1}{\tau_{p}})^{K_{C,i}}\). Let \(\tilde{p}_{idle,i}=p_{idle,i}\), the number of contending MTCDs \(\hat{K}_{C,i}\) in \(i\)th time slot can be estimated as  
\[\hat{K}_{C,i}=\frac{\log(\frac{S_{idle,i}}{\tau_{p}})}{\log(1-\frac{1}{\tau_{p }})}. \tag{2}\]  
From \(\hat{K}_{C,i}\) and the barring factor \(p_{\text{ACB},i}\) in \(i\)th slot, the number of MTCDs in \((i+1)\)th time slot can be estimated by  
\[\hat{K}_{i+1}=\frac{\hat{K}_{C,i}}{p_{\text{ACB},i}}. \tag{3}\]  
Figure 3: Random access procedure of the strongest-user collision resolution (SUCRe) with dynamic access class barring (ACB).
# Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images
## 2 Materials and Method
### [PERSON] and [PERSON]'s Method  
[PERSON] and [PERSON] [24] suggested using digital RGB photoelastic measurements to find out how strong the contact force chains are inside the particle assembly. By tracking the changes in R, G, and B under axial load, periodic fluctuations in intensity can be observed. [PERSON] and [PERSON] divided this into three sections to obtain linear equations. To apply each linear equation, the mean and standard deviation of the R, G, and B values must be considered. Equations (8) to (10) show the linear equations derived based on the photoelastic experiment images obtained in this study, following the methods of [PERSON] and [PERSON].  
\[F_{P}=1.676\mu_{R}-175.9,\ if\ \mu_{G}-\mu_{R}>23\ and\ \sigma_{B}<57 \tag{8}\]  
Figure 1: Pixel point illustration for G\({}^{2}\) calculation in a photoelastic image.  
Figure 2: Results of applying the G\({}^{2}\) method to the photoelastic response image.  
\[F_{P} =-2.892\mu_{R}+813.7,\;if\;\mu_{G}-\mu_{R}<23\;and\;\sigma_{B}>57 \tag{9}\] \[F_{P} =0.1143\mu_{R}-1256.5,\;if\;\mu_{G}-\mu_{R}<23\;and\;\sigma_{B}<57 \tag{10}\]  
where:  
\(F_{P}\) = the particle force.  
\(\mu_{R}\) = the mean of the intensities of red.  
\(\mu_{G}\) = the mean of the intensities of green.  
\(\sigma_{B}\) = the standard deviation of the intensities of blue.  
Utilizing all RGB color information, [PERSON]'s method accurately calculates contact forces, but a new set of linear equations must be established if the experimental method changes. Additionally, there is a limitation that the three linear equations alone cannot track changes if the axial load exceeds the third section and new patterns of RGB value changes appear.
## 2 Methods
### Bayesian Neural Networks and Bayesian Parameter Estimation  
In a neural network for binary TSC we want to estimate parameters or weights \(\boldsymbol{\omega}\) that best determine which class the time series belongs to. For a Bayesian Neural Network, any knowledge we have on the weights before training are referred to as the prior and denoted \(p(\boldsymbol{\omega})\). The prior can be updated after observing a new time series \((\mathbf{X},\mathbf{Y})\), to reflect the most likely values of \(\boldsymbol{\omega}\)  
\[p(\boldsymbol{\omega}|\mathbf{X},\mathbf{Y})=\frac{p(\boldsymbol{\omega})p( \mathbf{Y}|\mathbf{X},\boldsymbol{\omega})}{p(\mathbf{Y}|\mathbf{X})}.\]  
Here, \(p(\boldsymbol{\omega}|\mathbf{X},\mathbf{Y})\) is called the posterior distribution of \(\boldsymbol{\omega}\), while \(p(\mathbf{Y}|\mathbf{X},\boldsymbol{\omega})\) is referred to as the model (here the neural network architecture) or likelihood function. The marginal likelihood \(p(\mathbf{Y}|\mathbf{X})\) is defined as the integral  
\[p(\mathbf{Y}|\mathbf{X})=\int p(\mathbf{Y}|\mathbf{X},\boldsymbol{\omega})p( \boldsymbol{\omega})d\boldsymbol{\omega}.\]  
The posterior predictive distribution is defined such that,  
\[p(\mathbf{y}^{*}|\mathbf{x}^{*},\mathbf{X},\mathbf{Y})=\int p(\mathbf{y}^{*}| \mathbf{x}^{*},\boldsymbol{\omega})p(\boldsymbol{\omega}|\mathbf{X},\mathbf{Y })d\boldsymbol{\omega}, \tag{2}\]  
where \(\mathbf{x}^{*}\) represent a new observation with unknown target value \(\mathbf{y}^{*}\). By varying \(\boldsymbol{\omega}\), Equation (2), can be viewed as an ensemble of models weighted by \(p(\boldsymbol{\omega}|\mathbf{X},\mathbf{Y})\). It is difficult and sometimes impossible to solve Equation (2) analytically, thus we often resort to Monte Carlo (MC) sampling. If we have the distribution over the weights in the neural network we can simply sample from the distribution of these to get the posterior predictive distribution, i.e., estimate of the uncertainty of the time series class predicted by the neural network. If the Monte Carlo sampling becomes too computational costly, we can turn to other approximation methods such as variational inference or MC dropout.
## 4 Description of the Application Problem and Employed Multi-Fidelity Models
### Determination of Significant Parameters
#### 5.2.2 Anova and Removal of Non-Significant Parameters  
The analysis of variance described in Section 3.2 compares the variability of each metamodeled variable \(z\) regarding the main effects produced by each model parameter. Table 6 presents these values in terms of the F-statistic defined in Equation (3).  
Comparing the results shown in Table 6 with the corresponding critical value, three non-significant parameters (inlet turbulent intensity, EGR turbulent intensity, and outlet pressure) were identified and excluded from subsequent analysis. It is worth noting the model parameters that were deemed significant (usually with the highest confidence level of 99%) for every variable \(z\), namely, inlet mass flow, EGR temperature, EGR humidity, and EGR rate. The remaining parameters (inlet temperature and relative humidity are significant or not depending on the variable considered, so they will not be dismissed, as the best variable to construct a surrogate is not known yet.  
\begin{table}
\begin{tabular}{l r r r r r} \hline \hline
**Parameter** & \(\mathbf{F(in_{cond\_3D})}\) & \(\mathbf{F(w_{cond\_3D})}\) & \(\mathbf{F(\varphi)}\) & \(\mathbf{F(\Delta)}\) & \(\mathbf{F(\eta)}\) \\ \hline Inlet air mass flow & 59.6 ** & 31.1 ** & 38.8 ** & 87.9 ** & 38.7 ** \\ Inlet temperature & 4.4 * & 16.1 ** & 35.8 ** & 0.2 & 52.5 ** \\ Inlet relative humidity & 0.5 & 0.9 & 27.2 ** & 0.5 & 15.4 ** \\ Inlet turbulent intensity & 0.1 & 0.0 & 0.0 & 0.1 & 0.0 \\ EGR temperature & 11.8 ** & 27.1 ** & 24.7 ** & 18.7 ** & 25.2 ** \\ EGR rate & 53.4 ** & 103.1 ** & 143.1 ** & 22.0 ** & 128.9 ** \\ EGR specific humidity & 38.8 ** & 85.8 ** & 61.3 ** & 53.0 ** & 77.7 ** \\ EGR turbulent intensity & 0.2 & 0.4 & 0.0 & 0.3 & 0.1 \\ Outlet pressure & 0.03 & 0.3 & 1.00 & 0.6 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Identification of significant main effects according to ANOVA F-statistics for different HF model parameters and metamodeled variables.  
\begin{table}
\begin{tabular}{l r r r r r} \hline \hline
**Working Point** & \(\mathbf{in_{cond\_3D}}\) [kg/h] & \(\mathbf{w_{cond\_3D}}\) [g/kg] & \(\mathbf{\eta}\) [\%] & \(\Delta\) [kg/h] & \(\mathbf{\varphi}\) [-] \\ \hline A (Low condensation) & 0.2406 & 0.8023 & 79.65 & 0.9417 & 0.2035 \\ B (High condensation) & 1.651 & 5.505 & 38.24 & 1.022 & 0.6176 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Values of target and intermediate variables for two working points.
## 6 Simulation Results
DBR has more alive nodes than CoDBR. In DBR, only a single node is used for data forwarding, which consumes less energy and tends to be alive for more time, while in CoDBR, due to cooperation for a single packet transmission, three nodes are required, which drained the energy at a high rate, and they tend to die earlier than DBR. The aforesaid discussion for the alive nodes is conversely true for the number of dead nodes, as shown in Figure 11. The number of alive nodes is in the order: SiM-RPO > CoSiM-RPO > ODBR > DBR > CoDBR. Figure 10: Number of alive nodes. The latency of all the algorithms is demonstrated in Figure 7.0 CoDBR has the greatest latency compared to all the other schemes. This is due to the data reception at the final destination through multiple paths using cooperation, which takes time to combine the data received from different forwarders, while in CoSiM-RPO, the data is forwarded in a cooperative manner directly to the MS, which takes less time compared to CoDBR. The other schemes are non-cooperative, transmitting data through a single path, leading to less time consumption. The latency of ODBR is greater than DBR, SiM-RPO, and CoSiM-RPO. This is due to the many eligible forwarders in ODBR, which consumes time to deliver data to the final destination, while in DBR, the nodes at the surface die soon and has a fewer number of eligible forwarders, tending to consume less time. In the proposed schemes, the delivery is done directly without any relaying data, which reduces the latency. Figure 7.1 Number of dead nodes. Figure 7.2 Latency of the network. DBR has the greatest delay compared to the proposed schemes due to the multiple paths routing, while in the proposed scheme, information is exchanged directly, which is delivered in less time. Time comparison between the proposed schemes shows that CoSiM-RPO transfers data with a greater amount of time compared to SiM-RPO. This is due to the retransmission of the data in CoSiM-RPO, which takes time, while there is no retransmission in the SiM-RPO algorithm. The latency is in the order: CoDBR > ODBR > DBR > CoSiM-RPO > SiM-RPO.
## 3 Gas Turbine Rotor System Fault Diagnosis Method Based on Improved DCGAN
### The Proposed Gas Turbine Rotor System Fault Diagnosis Method  
In traditional Generative Adversarial Networks, random data are used as the encoding source to generate samples. In order to improve network training stability and the generated signals' quality, the inputs introduce fault features in relation to the original signals. According to the Improved DCGAN structure and optimized loss function, an improved method is proposed, which can be applied to gas turbine rotor fault diagnosis under different unknown conditions with small labeled samples, with the purpose of generating effective sample data and supplying a dataset for fault diagnosis. Figure 4 is a flow chart of the proposed gas turbine rotor fault diagnosis method. The whole process is summarized as follows:  
Firstly, accelerometers installed on different parts of the gas turbine are adopted to collect vibration data, and then the obtained signals are preprocessed by time domain and frequency domain analysis. Combining the analysis with expert diagnosis, labeled samples are obtained and the data are divided into the training set and testing set.  
Figure 4: Flow chart of the proposed gas turbine rotor fault diagnosis method.  
Secondly, the fault samples are adopted as the input of the proposed DCGAN network to generate fault samples. The one-dimensional CNN network suitable for vibration signal feature extraction is constructed, and the network and parameters are used for the generator of the Improved DCGAN network, and a new generator for feature extraction of the actual sample data is formed to continually generate new associated samples.  
Thirdly, the sample maximum mean difference (MMD) method is used to determine the mean difference between the generated signal and the real signal, and combine the generated data that meet the requirements with the original small sample data to form a new dataset.  
Lastly, fault classification and diagnosis are performed on the generated new sample set, and comparisons of results and accuracy analyses are performed to verify the method.