esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Research Methodology
#### 3.2.1 Laboratory and Industrial Experiments  
Experimental studies played a key role in the analysis of combustion processes and emissions. In Ref. [46], a specialized Macro TGA device was used to test the combustion of centimeter samples of charred biomass (pine) under conditions simulating industrial furnaces. This work aimed to map the behavior of large particles in real combustion systems and demonstrated consistency with the results of the zero-dimensional model. Ref. [59], on the other hand, focused on experiments with a ternary fuel (biodiesel karanja, biobutanol and diesel) with the addition of cerium oxide nanoparticles (CeO\({}_{2}\)) in the CRDI engine. Based on the experimental results, an MLP model was built to optimize operating parameters, reducing fuel consumption and emissions. In Ref. [60], the combustion of poultry manure was analyzed by thermogravimetry (TGA), determining the ignition and burnout parameters. This data was compared with the results of isoconversion and ML models, which allowed for optimization of the waste biofuel combustion process. An experimental approach was also used in a study [61] that analyzed the effect of CNG (10-40%) in a fuel mixture with microalgae biodiesel in a diesel engine. Real-world NO\({}_{\text{x}}\) emissions, smoke emissions, and efficiency were measured and used to train ML models. The aim was to extend the range of stable operation of this type of engine. Emissions and efficiency measurements were taken at different fuel ratios and used to train the ANN model. In Ref. [62], ML was used to analyze CFD data to optimize injector design in GDI systems, reducing the need for costly simulations. In Ref. [63], a random forest was used to analyze the effect of flame topology and pre-ignition flow metrics on cycle variability (CCV) in an SI gasoline engine. The model predicted peak cylinder pressure based on data from 123 LES cycles.
# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 4 Evaluation
### Evaluating Churn Classifier Performance  
#### 4.2.1 Data  
For our experiments and to evaluate the effectiveness of applying the feature engineering derived from ChurnKB, we need a churn-related dataset that includes both textual and other data types, such as categorical and numerical data. The only available dataset that meets these criteria is the churn prediction with text and interpretability dataset ([[https://github.com/aws-samples/churn-prediction-with-text-and-](https://github.com/aws-samples/churn-prediction-with-text-and-)]([https://github.com/aws-samples/churn-prediction-with-text-and-](https://github.com/aws-samples/churn-prediction-with-text-and-))  
Figure 4: (**A–D**) Snapshots of sample questions and corresponding responses used to evaluate H1, H2, H3, and H4, respectively. (**E**) A snapshot of the _negative word-of-mouth_ sub-concept, along with its corresponding instances and sub-instances, included in the second version of ChurnKB.  
Figure 5: Questions related to the relevance of two ChurnKB sub-instances and their corresponding words and phrases that received an ‘irrelevant’ rating of more than 20% from experts and were, therefore, removed from the initial version of ChurnKB.  
interpretability/blob/main/README.md (accessed on 13 April 2025)). While the dataset is specific to the telecom industry, the underlying methodology of ChurnKB is designed to be adaptable across various sectors. Customer churn behaviours, though differing in industry-specific manifestations, share common cognitive and behavioural indicators, such as dissatisfaction, reduced engagement, and negative sentiment patterns, that ChurnKB could potentially capture.  
The dataset contains 3333 rows and 21 columns. Of these, 2850 samples (85.50%) belong to the 'churn = no' class, while 483 samples (14.50%) belong to the 'churn = yes' class. Each column represents a different feature type. The features include categorical variables such as _state_, _international plan_, and _voice-mail plan_, and numerical variables like _account length_ and _total length of day calls_. Additionally, the dataset includes a specific column for text data, which contains chat logs between customers and agents.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 4 System Optimization Scheme Based on A Multi-Constraint Multi-Objective Genetic Algorithm  
In this section, the three optimization objectives of maximizing function utilization, maximizing load balancing, and minimizing total cost are first introduced, and then a multi-objective constrained genetic algorithm (MMGA) is proposed to optimize the system objectives to achieve a dynamic balance of resources and task demands under the constraints of system resources.
# A Voxel Generator Based on Autoencoder
## 1 Introduction
The first is called the encoder; this is responsible for extracting features from the input image. The second part is called the decoder; this is responsible for mapping the 2D image features into 3D structures. These methods have achieved an acceptable level for some object categories such as cars and airplanes while they leave a significant room of improvement for many other object categories. Accordingly, the design of the encoder and decoder architecture is an emergency research issue for 2D to 3D model reconstruction. With this in mind, in this paper, we propose a voxel generator called VoxGen, based on the autoencoder architecture. The architectural characteristics of this voxel generator may be described as follows. First, VoxGen uses VGG16 [18] and ResNet18 [19] for image feature extraction. The VGG16 network consists of 13 convolutional layers and 5 pooling layers. The ResNet18 network consists of 17 convolutional layers and one pooling layer. Consequently, VoxGen can extract more image features with a deeper network and effectively avoid the problem of gradient disappearance through the residual network. Moreover, VoxGen modifies VGG16 and ResNet18 by deepening and widening the fully connected network in order to learn the mapping between image features into latent variables. Second, VoxGen provides an option of using a global average pooling layer or a fully-flattened layer in the flattened layer of the encoder architecture. This option allows users to minimize the time cost of training the voxel generator or optimize the accuracy of the 3D model reconstruction. Third, VoxGen mixes deconvolution and convolution in the decoder architecture to map image features into a voxel model. Past methods often use up-pooling or deconvolution to map feature values to voxels. However, up-pooling fills the null value of the enlarged voxel map with 0 or a specific value. Although it is fast, it is not effective for accurate voxel reconstructions. By contrast, deconvolution is superior to up-pooling in terms of the accuracy of the model because it can properly estimate the values in the enlarged voxel map by training the filter parameters. Therefore, VoxGen also uses deconvolution to construct voxels while adding convolutional layers to enhance the features of the reconstructed voxels, thereby further improving the accuracy of the generated voxels. In this paper, we have implemented the proposed voxel generator and have evaluated the performance impact of the above-mentioned architectural features. We also have compared the performance of the proposed voxel generator with other related approaches. The experimental results show that our proposed generator has better performance than the others in terms of the accuracy of generated voxels. The rest of this paper is organized as follows. Section 2 discusses related research; Section 3 introduces the architecture of VoxGen; Section 4 discusses the results of the performance evaluation; and finally, Section 5 presents the conclusions of this research and discusses future work.
# GAN-Based Approaches for Generating Structured Data in the Medical Domain
## 3 Results
### Breast Cancer Coimbra-BCC  
To verify the performance of the GAN models, we conducted further experiments with the BCC dataset. The latter is a smaller dataset and has fewer features than the BCW. Figure 5 demonstrates the accuracy of binary classification (malignant/benign) trained on extended data generated from GAN variants using the BCC dataset. The accuracy of SVM and MLP classifiers at different sizes of Train data used with RS and RE sampling are shown. The Silver Standard exhibits an apparent increase in accuracy as the size of the training data increases, but the overall accuracy is lower compared to the BCW. This behavior is similarly observed for some GAN variants as well. The results for the BCC also show higher standard errors compared to the corresponding points in the previous dataset, possibly due to the smaller size of the Train dataset with a reduced number of features, making the GAN models more unstable. We note that the results for the BCC are shown over a wider range of training sizes.  
Contrary to the results for the BCW, here, the GAN data yield some better results, especially for MLP, where the accuracy increases with increasing training size. However, the accuracy obtained for the GAN data remains the least accurate for SVM. Some improvements are noted in the CGAN results, mainly for the SVM classifier with RE sampling. This is also in contrast to the BCW, where the CGAN results are always below the Silver Standard. The accuracy curves for CTGANs and CopulaGANs are more unstable here, and they even display the worst results for the MLP classifier. It appears that more complicated models in their standard form have difficulty in generating synthetic data that are sufficient to improve classification accuracy. This is reflected in the larger standard errors as well, which means that the results for each point vary more. Interestingly, the WGANGP results illustrate quite different behaviors from the BCW. While they do not exceed the Silver Standard accuracy for MLP, their results for SVM have become more stable and show a more consistent improvement as the amount of training data grows. Additionally, they demonstrate improved accuracy over a wider range of sizes of training data, with better performance for smaller data subsets, likely due to less complicated data. Furtheranalysis is still required to examine the statistics of the generated data by GAN variants using different datasets, and to verify how corresponding synthetic data might improve the classification accuracy. This also allows us to determine how robust the results of the various GAN models are, even when only a small amount of data is available.
# High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes
## 2 Preliminaries
### LDPC Codes in ATSC 3.0  
The LDPC codes in the ATSC 3.0 standard adopt two different lengths, commonly referred to as short codes (16,200) and long codes (64,800). There are two different coding structures for each code length, i.e., irregular repeat accumulate (IRA) and MET. For each code rate, the coding structure is determined by its bit error rate (BER) and FER performance over binary phase-shift keying/quadrature phase-shift keying modulation. The IRA structure shows better performance at high code rates while the MET structure shows better performance at low code rates [24]. The structure applied for each code rate and code length is shown in Table 1.  
As shown in Figure 2, the IRA-structured parity-check matrix \(H\), whose size is \(M\times N\), is composed of the information part \(A\) of size \(M\times K\) and the parity part \(D\) of size \(M\times M\). The \(A\) part has a \(Q_{1}\)-shift structure while \(D\) part has a dual-diagonal structure.  
The parity-check matrix of the MET-structured LDPC code is shown in Figure 3, which is composed of six parts where \([A\ D]\) in the upper left corner of the parity-check matrix has the IRA structure shown in Figure 2, \(Z\) part is a zero matrix of size \(M_{1}\times M_{2}\), \(I\) part is an identity matrix of size \(M_{2}\times M_{2}\) and \([B\ C]\) is a \(Q_{2}\)-shift structure of size \(M_{2}\times(K+M_{1})\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Code Rate** & **Coding Structures of 64,800** & **Coding Structures of 16,200** \\ \hline
2/15 & MET & MET \\
3/15 & MET & MET \\
4/15 & MET & MET \\
5/15 & MET & MET \\
6/15 & IRA & IRA \\
7/15 & MET & IRA \\
8/15 & IRA & IRA \\
9/15 & IRA & IRA \\
10/15 & IRA & IRA \\
11/15 & IRA & IRA \\
12/15 & IRA & IRA \\
13/15 & IRA & IRA \\ \hline \hline \end{tabular}
\end{table}
Table 1: The coding structures corresponding to code length and code rates.  
Figure 2: The parity-check matrix \(H\) of irregular repeat accumulate (IRA)-structured LDPC code.  
Note that the MET-structured LDPC code can be seen as a concatenation of the IRA-structured LDPC code and many single parity-check codes which can provide an efficient encoding algorithm. In addition, the identity matrix has only one element in every row. Therefore, parity bits corresponding to the part \(I\) can be obtained in parallel and encoding latency can be correspondingly decreased [25].  
In next section, we introduce the construction methods of our proposed parity-check matrices, which are used to correct errors in the noise version of secret key so that [PERSON] and [PERSON] can share the symmetrical secret key.
## 3 Materials and Methods
### Classification  
Step 1: Then, the test stage is performed. For this, a test image is required (image with any gesture of the hand) \((Z)\) from which its weight \((w_{i})\) is calculated through the multiplication of Eigen vector \((v_{i})\) with the image difference; this is described in the following equation:  
\[w_{i}=v_{i}^{T}\cdot(Z-\Psi) \tag{9}\]  
In the same way, the test image is represented by the weight vector and is calculated as:  
\[\mu=\left[w_{1},w_{2},w_{3},...,w_{M}\right]^{T} \tag{10}\]  
Step 2: Calculate the Euclidean distance between the weight vector of the test image \((\mu)\) and the weight vectors of the training image database \((\mu_{j})\).  
\[e_{d}=min\left\|\mu-\mu_{j}\right\| \tag{11}\]  
Finally, a type of gesture is determined by presenting a minimum Euclidean distance with respect to the test gesture (which is the test image). Thus, to classify the input image (test image) with the correct match of the training database the k-nearest neighbor algorithm (also named K-Nearest Neighbors (KNN) Classifier) is used. Therefore, KNN algorithm is used in this project to classify a test image based on the similarity measure of a set of training image database. KNN algorithm calculates the distance between each unknown instance (test image) and all known instances (training image), and the k-nearest neighbor in known instance sets are selected. The nearest neighbors can be determined by calculating based on distinct distance metrics, such as Euclidean distance [33]. This distance metric is used in this work where the classification is performed by finding the nearest Euclidean distance of the transformed weights of the test image, i.e. a neighbor is deemed nearest if it has the smallest distance, in the Euclidean sense. The choice of the number of neighbors (\(k\)) is discretionary and up to the choice of the users. On this occasion, \(k=1\) is chosen, because the algorithm classifies the class of the nearest neighbor. Typically, the test image is classified according to the labels of its k-nearest neighbors by a majority of votes. Then, if \(k=1\), the object (test image) is classified as the class of the object (some training image) closest to it and also presents greater precision in having good results. In this work, \(k>1\) is not chosen because with more \(k\) points the algorithm takes longer to process and receive a response, while a quick response is desired because the gesture detection is in real time. In addition, the training image closest to the image test is searched, thus more \(k\) points are not considered, only the nearest one that appears in \(k=1\). Figure 1 shows the operation of the k-nearest neighbor algorithm.  
The steps described above, namely data acquisition, segmentation and pre-processing, feature extraction and classification, are described in Figure 2, where the hand gesture recognition processing is observed.
## 2 Related Works
### Classification of Interruption Coordination Modes  
How and when to deal with interruption is a major topic in interruption research and a criterion for classifying interruption types. For example, [PERSON] et al. [PERSON] (2013) proposed an auditory notification interface support to find opportune moments to interrupt primary tasks and to facilitate the resumption of interrupted tasks. This interface reduces the cost of interruption by additional sensory inputs and prepared consequent activities at the moments of lower cognitive workload. Though this approach needed the consideration of the different effort levels in interruption handling, it may provide the possibility to understand the abstract level of interruption interactions. [PERSON] (2011) suggested the interruption management stage model (IMSM), which suggests interruption management strategies on the basis of the information processing stages. IMSM acknowledges cognitive processes such as the requirement that the annunciation stimulus must be detected, and acknowledges the flexibility that the interrupting peripheral task may also be intentionally dismissed or scheduled to be completed at a later stage [PERSON] (2011).  
[PERSON] introduced four modes to coordinate interruptions in human-computer interactions [PERSON] and [PERSON] (2013). Among them, the immediate mode indicates that an interruption is promptly delivered regardless of the status or progress of the primary task and disrupts the recipient's primary task without warning or notification. In the negotiated mode, the user is aware of the onset of interruption by notification;  
Figure 1: Time course of interruption.  
thus, the user can manipulate when to accept the interruption. The scheduled mode delivers the interruption according to a predetermined schedule. Users can expect when interruptions happen, but they cannot administer specific interventions to control them. In the mediated mode, a mediation agent is searching for an opportune moment for interrupting and suggests the timing to users, and the user decides and conducts the peripheral task processing.  
Several studies examined and compared [PERSON]'s four interruption coordination modes, and the outcomes were inconsistent. The negotiated and the mediated modes in controlled laboratory experiments showed better efficiency and accuracy and less interruptivity [32; 33]. [PERSON] et al. [34] claimed that the negotiated mode is better at debugging tasks than the immediate mode. However, [PERSON] and [PERSON] [35] found that the immediate mode is favorable compared to the negotiated mode in their head-mounted display (HMD) experiment. However, these results were obtained without consideration of different types of tasks. [PERSON] and [PERSON] [11] compared the immediate mode to the mediated mode, and confirmed that the effects of interruption varied by task type.
## 2 The Proposed Approach
### Multi-Scale Input  
As opposed to [23; 24], which constructed multi-scale input by employing average pooling layer, we constructed multi-scale input by taking the first value (in the upper left corner) for every \(2\times 2\), \(4\times 4\), \(8\times 8\) and \(16\times 16\) non-overlapping area, respectively. We take the way to construct input\({}^{(1)}\)in Figure 1 as an example. Different from max pooling layer with stride equal to 2 by getting max values for every \(2\times 2\) non-overlapping area, we take the first value(in the upper left corner) for every \(2\times 2\) non-overlapping area. The way we construct input\({}^{(1)}\) is shown in Figure 2.  
After experiencing a convolution block, the feature maps of current input are simply added to the encoder layers. Let \(in^{i}(i\in\{1,2,3,4\})\) denote the output of node \(\text{In}^{i}\). input\({}^{(i)}(i\in\{0,1,2,3,4\})\) denotes the input in the \(i\) down-sampling layer along the encoder, and \(x_{i,0}(i\in\{0,1,2,3,4\})\) denotes the output of node \(\text{X}_{i,0}(i\in\{0,1,2,3,4\})\). The formulation of \(in^{i}(i\in\{1,2,3,4\})\) and \(x_{i,0}(i\in\{0,1,2,3,4\})\) can be calculated as:  
\[in^{i}=F(input^{(i)})\quad(i\in\{1,2,3,4\})\]  
\[x_{i,0}=\begin{cases}F(input^{(i)})&\text{\emph{i=0}}\\ F(M(x_{i-1,0}))+in^{i}&\text{\emph{i=1,2,3,4}}\end{cases}\]  
Figure 1: An overview of the proposed MDAN-UNet architecture. The blue and green parts denote re-designed skip pathways [21] and red line indicates deep supervision.  
Figure 2: Illustration of how to construct input\({}^{(1)}\). We take the first value indicated by the red circle (in the upper left corner) for every \(2\times 2\) non-overlapping area.  
where \(F(\cdot)\) is the convolution block shown in Figure 1, consisting of two identical operations, for each operation including convolution layer with kernel size 3 \(\times\) 3, batch normalization, ReLU layer in sequence. Moreover, \(M(\cdot)\) denotes the down-sampling operation which is a max pooling layer with stride equal to 2. The main advantage of our multi-scale input is: it can achieve multiple level sizes of receptive field so that it can enhance the ability of multi-scale detection; more specifically, it can effectively strengthen the ability of multi-scale lesion segmentation.
## 2 Case Study: Energy Harvesting Floor
### Testing Piezoelectric Transducers  
In this paper, we considered and studied different interdigitated piezoelectric models and a piezoelectric disk which was necessary to perform the tests in the same conditions to analyze the piezoelectric transducers. The experiments used an oscilloscope to measure the output voltage of the piezoelectric transducers when a weight of 3 kg (29.43 N) was applied to the transducer.