esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Current Status
### Isothermal UWCAES  
[PERSON] and [PERSON] et al. from North Carolina State University and Baylor University continue their studies on UWCAES while concentrating on investigating isothermal compression technologies [29; 30; 31; 32; 33]. Similarly, a team from the University of Nantes and SEGULA Technologies is also developing a UWCAES project \"REMORA\" and focuses on isothermal compression/expansion [34; 35; 36]. There is little doubt that the round-trip energy efficiency could be significantly improved with isothermal compression and expansion. Beyond this, requisite thermal energy storage facilities could be omitted by taking advantage of the highly accessible water heat sink. Overall, for enhancing heat transfer and achieving isothermal processes, most studies are based on the liquid piston concept accompanied by liquid spray, wire mesh, porous media, water-gas two-phase foam, etc. From quasi-steady-state theoretical studies and low-speed experiments, a very high exergy efficiency of compression could be achieved in the range of 85-95% [37; 38]. However, the performance degenerates when considering the transient operation of the system and the off-design operation of hydraulic facilities. There is still a shortage of studies that consider real operating conditions. Further, it is very difficult to achieve isothermal compression/expansion when the rotational speed of the liquid piston compressor is close to the engineering practical rotational speed. The bankruptcies of well-known SustainX and Lightsail highlight the uncertainties surrounding isothermal CAES.
# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 3 Forecasting the Risk of Forest Wildfires Using a Case-Based Approach
### Results (Employed Works)  
Our research included stages and steps that can be presented in the form of the following sequence (Figure 4):  
* Data preprocessing. This stage's primary goal is the transition from thermal points to forest fires;
* Formalization. This stage's main result is the structure of a case model;  
Figure 3: A map of Irkutsk Oblast displaying thermal points for 2017–2020.  
Conceptualization. This stage is dedicated to specifying a case model for the domain. In our case, the domain is the forecasting the risk of forest wildfires for Irkutsk Oblast;
* Prototyping a case base. This stage's primary goal is to create and debug a case base based on information about fires and a specified case model;
* Forest fire risk forecasting. This stage's main result is fire risk assessments for forest quarters.  
The first stages can be performed in parallel. Next, the stages are presented in more detail.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 5 Real Scenarios Evaluation
### Analysis of Results  
In this section, we will show and analyze the results obtained for the different bit streams configured above. The first two sets of experiments in the vehicular scenario (in Sections 5.2.1 and 5.2.2) evaluate the situation under ideal conditions (when no background traffic is injected), with and without FEC protection. In the other two sets of experiments (in Sections 5.2.3 and 5.2.4), background traffic load has been used, also with and without FEC protection. In the background traffic tests, a moderate (62 pps/512 bytes) and a high background traffic load (125 pps/512 bytes) have been used.
## 3 Methodology  
This study focuses on developing XGBoost and Bayesian GLMs for predicting Vs. Figure 1 presents the methodology used for the development and evaluation of these predictive models. The process begins with the preprocessing of SCPT and SCPTu datasets. The preprocessed dataset was used for the development of the XGBoost model, conducting partial dependence analysis to identify impactful features, and fitting the Bayesian GLM. Model evaluations include PDPs and SHAP analysis for the XGBoost model and a comparison of the predictive performance of both models. The following sub-sections provide a detailed description of the research data, performance metrics, and the assessment of the models' predictive capabilities.
## 2 Materials and Methods
### Photoacoustic Imaging Experimentation and Machine Learning-Based Crack Detection
#### 2.1.3 Machine Learning-Based Crack Length and Width Prediction Framework  
For the goal of predicting the length and width of the cracks, machine learning capabilities of CNN models are combined with statistical techniques and regression models. The concept is to extract deep features from the input PA images through three deep CNN models (GoogleNet, ResNet50, and VGG16) using transfer learning. Each of these CNN models exhibits unique architectural characteristics to extract discriminative features from input images [26]. VGG16 is characterized by a simpler architecture featuring smaller filter sizes, albeit with a substantial number of trainable parameters [27]. ResNet50 [28] introduces the concept of residual learning to address the challenge of vanishing gradients. GoogleNet, on the other hand, prioritizes the simultaneous capture of multiscale features across successive layers [29]. The CNN models are pretrained on the famous dataset ImageNet [30] containing millions of images and several diverse classes. Since the feature space from these models is quite rich and diverse from each other, a dimensionality reduction statistical technique called principal component analysis (PCA) is utilized [31]. The extracted features from the penultimate layers of each of these models are fed into the PCA block for dimensionality reduction. In this way, the complexity of the proposed scheme is significantly reduced while keeping the maximum information intact [32; 33]. The working principle of PCA can be divided into two major steps: (1) the covariance matrix computation for the dataset and (2) identification of the principal components by singular value decomposition of the covariance matrix [34]. The eigenvalue represents the maximum variance in the dataset, and the eigenvector gives the direction of the maximum variance. The order of the variances gives the most vital principal components. Using PCA on the deep features extracted using the 3 deep CNN models, the most significant 128 PCA features are retained. This 128-dimensional vector is then input to the three different regression models (linear, SVM, and random forest regression models) used. The complete schematic is presented in Figure 5.  
Figure 5: Schematic diagram for combinatorial application of deep CNN models for feature extraction, PCA for dimensionality reduction, and machine learning regression models to predict crack length and width.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Augmentation Technique** & **Parameters** \\ \hline Rotation & \([0,350]\) degrees \\ Reflection & X and Y: \([-1,1]\) \\ Scaling & \([0.5,1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data augmentation techniques and their corresponding parameter values for generating sufficient volumetric data for a deep learning model.
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 2 Related Work
### Machine Learning-Based Time Series Prediction Methods  
Machine learning methodologies, particularly linear regression techniques [PERSON] (2016), are significantly linked to time series prediction tasks. Linear regression employs a linear model to predict the future trajectory of a series, presuming a linear relationship between the input and output variables. SVM [PERSON] (2016), grounded in statistical learning theory, employs kernel functions to map data to high-dimensional spaces, thus overcoming issues related to [PERSON]. SVR [PERSON] (2016), a variant of SVM functioning as regression analysis, transforms data nonlinearly within this feature-rich space. SVR determines a function that precisely represents the relationship between input and output data, thus facilitating time series fitting and prediction. MLP [PERSON] (2016), characterized by its hidden layers and output layer, generates predictions through the iterative adjustment of weights and biases via backpropagation. The utilization of nonlinear activation functions and multiple layers in MLP enables it to learn and represent complex nonlinear relationships. HMMs [PERSON] (2016) provide a probabilistic framework for the modeling of multivariate time series predictions. HMMs utilize hidden Markov chains, which represent underlying stochastic processes and can be estimated through a sequence of observations.
## 3 Experiment and Results
### Real-Vehicle Testing  
#### 3.2.1 Real-Vehicle Test Results  
After the simulation, a series of in-vehicle experiments were conducted to validate the system's stability and measurement accuracy. The tests were performed on a Honda CRV test vehicle configured with a 0.5 engine-to-alternator transmission ratio. A sampling frequency of 5 kHz was implemented, with power being supplied through the cigarette lighter interface. During testing, the transmission was kept in neutral position, with engine speed being controlled through manual throttle manipulation.  
The reference engine speeds displayed on the vehicle dashboard were systematically compared with the calculated values obtained from the measurement system. In the implemented architecture, dual-channel acquisition was performed by the STM32F407 microcontroller (STMicroelectronics; sourced from Shenzhen JLCPCB Technology Development Co., Ltd., China), with the channel that exhibited a superior signal amplitude being selected for subsequent processing. The amplitude characteristics of the fitted signal are presented in Figure 9.  
As demonstrated in Figure 9, noise and frequency mixing effects were effectively suppressed through the fitting algorithm. Consequently, a more accurate frequency determination was achieved when phase information derived from the fitted signal was utilized. During the fitting process, the instantaneous phase was simultaneously obtained using Equation (6). However, since the calculated phase was wrapped, phase unwrapping was performed according to Equation (7), yielding continuous phase data. Following phase continuity restoration, the fundamental frequency was precisely calculated by applying Equation (9) to any two corresponding phase points. Finally, the determined frequency value was substituted into Equation (2) to obtain the rotational speed measurement.  
Following analysis of the results of several tests, the data records of this algorithm under the unsteady condition are shown in Table 3. Under typical engine operating speeds, the measurement speed error is less than 0.4%, which shows high accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Metric** & **Value** & **Threshold** & **Compliance** \\ \hline Mean & 2048.2 & - & - \\ Skewness (\(\gamma_{1}\)) & 0.15 & [\(-\)0.5, +0.5] & Yes \\ Kurtois (\(\gamma_{2}\)) & \(-\)0.82 & [\(-\)1.0, +1.0] & Yes \\ Outliers Detected & 3.2\% & \(<\)5\% & Yes \\ Shapiro–Wilk & 0.062 & \(>\)0.05 & Yes (marginally) \\ \(p\)-value & & & \\ \hline \end{tabular} Note: “-” denotes no threshold or compliance requirement.  
\end{table}
Table 2: Verification results of simulation data.  
Figure 9: Honda CRV vibration signal fitting results.
## 2 Methodology
### BERT Text Features  
The text information corresponding to speech is the most fundamental and intuitive carrier of emotion, often used to infer the emotional state of the speaker [44]. The process of emotion state recognition based on text is illustrated in Figure 2, primarily encompassing data preprocessing, text feature extraction, model training, and emotion recognition.  
Text emotion recognition first requires collecting text data from various sources, such as social media, comments, news articles, etc. The collected text data are then subjected to preprocessing operations, including cleaning, tokenization, the removal of stop words, stemming, and punctuation handling. Text feature extraction involves converting the text into numerical features suitable for machine learning algorithms. Common feature extraction methods include the bag-of-words model, TF-IDF (Term Frequency-Inverse Document Frequency) [45] features, word embedding [46] of syntactic features, etc.  
Word embedding is currently the most commonly used method in text feature extraction, aiming to map words from the text data to a continuous vector space. This mapping process is achieved by capturing the contextual relationships between words. Common word embedding techniques include Word2 Vec [47], Glove (Global Vectors for Word Representation) [48], Elmo (Embeddings from Language Models) [49], etc. However, it is important to note that using a single vector to represent a word in different contexts may lead to some semantic understanding errors.  
Considering these factors, this paper adopts the language representation model BERT for feature extraction. BERT is a pre-trained language model based on the Transformer architecture, utilizing a bidirectional training approach during pre-training, allowing it to consider both the left and right context information simultaneously. BERT's strength lies in its powerful context modeling and multi-task pre-training, enabling the model to learn richer, more universal semantic representations, resulting in outstanding performance across various natural language processing tasks. BERT, proposed by Google in 2018 as an alternative to Word2 Vec, is essentially composed of stacked Transformer encoders. It follows a two-phase framework comprising pre-training and fine-tuning on specific tasks. BERT's innovation lies in using two pre-training tasks: a Masked Language Model (MLM), which predicts masked words in a sequence, and Next Sentence Prediction (NSP), which predicts whether the next sentence is related to the current one. This addresses the issue of different semantic expressions for the same word in different contexts. The structure of the BERT model is illustrated in Figure 3.  
Figure 3: Structure diagram of the BERT model.  
Figure 2: Text emotion recognition process.
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 2 Antenna Design Methodology
### Antenna's Radiation Efficiency  
The simulated and measured antenna's radiation efficiency values compared at the human chest are shown in Figure 20. The simulated radiation efficiencies at 1.8 GHz, 2.45 GHz, and 5.8 GHz are 62.5%, 67.9%, and 70.1%, respectively. The measured radiation efficiencies at 1.8 GHz, 2.45 GHz, and 5.8 GHz are 60.6%, 62.6%, and 68.3%, respectively. The measured values of radiation efficiencies are close to the simulated values; some differences might be due to the fabrication tolerances, the large size of the chest, and the comparatively small chest phantom in the simulator.
###### Abstract  
The extraction of buildings from remote sensing images is of crucial significance in urban management and planning, but it remains difficult to automatically extract buildings with precise boundaries from remote sensing images. In this paper, we propose the FEPA-Net network model, which integrates the feature extraction and position attention module for the extraction of buildings in remote sensing images. The suggested model is implemented by employing U-Net as a base model. Firstly, the number of convolutional operations in the model was increased to extract more abstract features of the objects on the ground; secondly, within the network, the ordinary convolution is substituted with the dilated convolution. This substitution aims to broaden the receptive field, with the primary intention of enabling the output of each convolution layer to incorporate a broader spectrum of feature information. Additionally, a feature extraction module is added to mitigate the loss of detailed features. Finally, the position attention module is introduced to obtain more context information. The model undergoes validation and analysis using the Massachusetts dataset and the WHU dataset. The experimental results demonstrate that the FEPA-Net model outperforms other comparative methods in quantitative evaluation. Specifically, compared to the U-Net model, the average cross-merge ratio on the two datasets improves by 1.41% and 1.43%, respectively. The comparison of the results shows that the FEPA-Net model effectively improves the accuracy of building extraction, reduces the phenomenon of wrong detection and omission, and can more clearly identify the building outline.  
Keywords:building extraction; dilated convolution; feature extraction module; position attention module +
Footnote †: journal: Applied sciences
## 3 Results
### Game Design Elements
#### 3.2.2 Preference/Favorite  
As concerns the preferences for the implemented GDEs, challenges and adaptation were consistently the top preference for the three games, as can be seen in Table 4. However, this result for the latter GDE was a rather unexpected outcome, given that adaptation had only been implemented in _Tesoros_. One-way ANOVA followed by Tukey post hoc comparisons were used to identify the differences between all games. These comparisons revealed a significant difference for rewards (\(F_{2,104}~{}=~{}3.351\), \(p<0.05\)). Post hoc tests indicated significant between-group differences for _Agentes_ and _Anotatlon_. Similarly, there was a significant difference in storyline (\(F_{2,78}~{}=~{}6.257\), \(p<0.05\)). Again, post hoc tests indicated significant between-group differences for _Tesoros-Agentes_ and _Anotatlon-Agentes_.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l} \hline \multicolumn{2}{c}{**GDE**} & \multicolumn{4}{c}{_Agentes_} & \multicolumn{4}{c}{_Tesoros_} & \multicolumn{4}{c}{_Anotatlon_} \\ \hline  & **Mean** & **SD** & **INT.ENJ** & **COMP** & **Mean** & **SD** & **INT.ENJ** & **EFE.IMP** & **Mean** & **SD** & **INT.ENJ** & **COMP** & **EFE.IMP** & **TEN.PRES** \\ \hline Adaptation & 6.97 & 2.34 & 0.50 * & **7.61** & 2.37 & 0.49 * & 0.39 * & 7.53 & 2.20 & & & & & \\ Avatar & 2.74 & 2.49 & & & 6.72 & 2.95 & & 0.42 * & 6.05 & 3.55 & & & & \\ Challenges & **7.43** & 2.13 & 0.36 * & & 7.31 & 2.24 & 0.57 *** & 0.60 *** & **7.91** & 2.08 & 0.60 *** & & & 0.49 * & \\ Customization & 3.29 & 2.88 & & & 5.00 & 3.56 & 0.46 * & & & **8.03** & 2.99 & 0.41 * & & & \(-\)0.39 * \\ Leaderboards & **7.91** & 1.84 & & & 7.47 & 2.57 & & 0.46 * & 7.75 & 2.41 & 0.37 * & 0.41 * & & & \\ Levels & **7.15** & 2.58 & 0.45 ** & 7.31 & 2.84 & 0.60 *** & 0.38 * & 7.40 & 2.78 & 0.36 * & & & 0.40 * & \\ Progress & 5.88 & 3.09 & & & 7.57 & 2.68 & & & 6.50 & 3.06 & & & & & \\ Rewards & 6.89 & 2.76 & & & **8.69** & 1.72 & 0.36 * & 0.38 * & **8.39** & 1.68 & & & & & \\ Storyline & 5.58 & 2.88 & & & 5.11 & 2.59 & & & 4.53 & 2.84 & & & & & \\ Theme & 6.47 & 2.55 & 0.42 * & & **7.69** & 2.10 & 0.56 *** & & & 7.29 & 2.97 & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics and correlation matrix between the GDE implementation and the IMI. Bold numbers show the highest-rated GDE implementations. INT.ENJ = Interest/Enjoyment, COMP = Competence, EFF.IMP = Effort/Importance, TEN.PRES = Tension/Pressure. * indicates \(p<0.05\), ** indicates \(p<0.01\), *** indicates \(p<0.001\).
###### Abstract  
Secondary roads represent the largest part of the road network. However, due to the absence of clearly defined edges, presence of occlusions, and differences in widths, monitoring and mapping them represents a great effort for public administration. We believe that recent advancements in machine vision allow the extraction of these types of roads from high-resolution remotely sensed imagery and can enable the automation of the mapping operation. In this work, we leverage these advances and propose a deep learning-based solution capable of efficiently extracting the surface area of secondary roads at a large scale. The solution is based on hybrid segmentation models trained with high-resolution remote sensing imagery divided in tiles of \(256\times 256\) pixels and their correspondent segmentation masks, resulting in increases in performance metrics of \(2.7\)-\(3.5\%\) when compared to the original architectures. The best performing model achieved Intersection over Union and F1 scores of maximum \(0.5790\) and \(0.7120\), respectively, with a minimum loss of \(0.4985\) and was integrated on a web platform which handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles' format and the generation of geogtiff results compatible with geospatial databases.  
a _Article_  
A Deep Learning-Based Solution for Large-Scale Extraction of the Secondary Road Network from High-Resolution Aerial Orthoimagery
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 3 Our Proposed AMC Method
### Details of the Lightweight CVResNet
#### 3.2.3 Depthwise Separable Convolution  
Despite the powerful feature extraction capabilities of complex-convolutional residual networks, the complex structure, large number of parameters, and long training time make it difficult to deploy the complex, convolutional residual networks on actual lightweight devices for AMC. To solve the problem, this paper combines complex convolution with depthwise separable convolution and proposes a lightweight depthwise separable complex convolution. It is done by replacing the real convolution in the complex convolution with a depthwise separable convolution [56; 57]; this method can significantly reduce the number of parameters of convolutional layers. As shown in Figure 5, the depthwise separable convolution consists of depthwise convolution and pointwise convolution.  
Each convolution kernel is responsible for one channel, and each channel is convolved by only one convolution kernel. This process generates the same number of channels in the feature map as the number of channels in the input. The operation of pointwise convolution is similar to the regular convolution operation, with a convolution kernel of size \(1\times 1\times W\), where \(W\) is the number of channels in the previous layer. Here, the convolution operation combines the maps from the previous layer in the depth direction with weighting to generate a new feature map.  
Although depthwise separable convolution reduces computational complexity and parameter count, it can provide similar performance to ordinary convolutions in practical applications. This is mainly because--in many automatic modulation recognition tasks--the correlation between spatial features and channel features is relatively weak. By separating these two types of features, depthwise separable convolution can significantly reduce computational costs with minimal performance loss.