esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 3 BPNN Model for Noise Limits Prediction during Vehicle Acceleration
### Training of the Neural Networks  
Before conducting the training, the parameters of the BPNN need to be set and initialised. The training target accuracy was set to 0.001. Levenberg-Marquardt algorithm with an adaptive learning rate were selected for the training function. The Levenberg-Marquardt algorithm combines two numerical minimisation algorithms: The gradient descent method and the Gauss-Newton method. It can automatically adapt its learning rate to train the model with input data and is used to solve nonlinear least squares curve-fitting problems [26]. The performance of the learning algorithm is very sensitive to the selection of the learning rate. If the learning rate is too large, the algorithm may oscillate and unstable; while the learning rate is too low, the convergence speed is slow, and the training time is long.  
Like the quasi-Newton methods, the Levenberg-Marquardt algorithm is developed to have a second-order training speed without computing the Hessian matrix (H). When the performance function has the form of a sum of squares, then the Hessian matrix can be approximated as  
\[H=J^{T}J \tag{4}\]  
and the gradient can be computed as  
\[g=J^{T}e \tag{5}\]  
where, \(J\) is the Jacobian matrix that contains first derivatives of the network errors with respect to the weights and biases, and \(e\) is a vector of network errors and it is equal n product MSE. The Jacobian matrix can be computed through a standard back-propagation technique that is much less complex than computing the Hessian matrix.  
The Levenberg-Marquardt algorithm uses this approximation to the Hessian matrix in the following Newton-like update with the following expression.  
\[W_{k+1}=W_{k}-[J^{T}J+\mu I]^{-1}J^{T}e \tag{6}\]  
where, \(\mu\) is a learning rate that ensures the positiveness of the Hessian matrix and \(I\) is the identity matrix, respectively. When the learning rate \(\mu\) is zero, it degenerates to the Newton method, using the approximate Hessian matrix. When \(\mu\) is large, it becomes the gradient descent method with a small step size. The Newton method is faster and more accurate, and thus, the aim is to get it close to the Newton method as quickly as possible. Thus, \(\mu\) is set to be decreased after each successful step (reduction in the performance function) and to be increased only when a tentative step would increase the performance function. In this way, the performance function is always reduced at each iteration of the algorithm to accelerate the training.
# Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells
## 4 Case Study
### Discussion  
The results in Figures 12 and 13 and Table 2 show that the data knowledge dual-driven method proposed in this paper has good prediction accuracy in horizontal wells. Numerically, the RF method has an RMSE of 1.08 and an R\({}^{2}\) of 0.98, which meet the needs of drilling engineering. Compared with the measured ROP, the R\({}^{2}\) of the proposed data knowledge dual-driven method is on average 30% better than the conventional data-driven ROP prediction method, and the RMSE and MAE are reduced by 50%. It is important to highlight that while techniques like data noise reduction, feature selection, ensemble learning, 10-fold cross-validation, and their integration can enhance the prediction accuracy of ROP, they do not address the fundamental challenge in horizontal well ROP prediction. The core issue lies in the direct impact of drilling parameters on the training data, which essentially determines the upper limit of model accuracy. Building the model based on downhole conditions requires considering factors like the friction coefficient, survey data, and drilling string, which need to be taken into account when applying the model to other wells.
## 2 Materials and Methods
### Variance Compensation Adaptive Kalman Filter for High-Rate GNSS and SM Loose Integration  
The variance compensation method is a recursive algorithm in real time that calculates a closer approximation to the actual state valuation by estimating the system noise variance vector with its own information [48]. According to the analysis of the loose integration, the state one-step prediction equation can be assumed as:  
\[\hat{x}_{k}^{-}=A\hat{x}_{k-1}^{-}+B\alpha_{k-1} \tag{9}\]  
where the hat notation \"\(\cdots\)\" represents the estimated value of the state vector. The right superscript \"\(-\)\" represents the prior value of the state vector before the update step. Define the corresponding predicted state covariance matrix as:  
\[P_{k}^{-}=AP_{k-1}A^{T}+Q \tag{10}\]  
and the filtering gain matrix as:  
\[G_{k}=P_{k}^{-}H^{T}[HP_{k}^{-}H^{T}+R]^{-1} \tag{11}\]  
Then, the state optimal estimation can be written as:  
\[\hat{x}_{k}=\hat{x}_{k}^{-}+G_{k}[z_{k}-H\hat{x}_{k}^{-}] \tag{12}\]  
where \(\hat{x}_{k}\) is the filter value, and \(z_{k}-H\hat{x}_{k}^{-}\) is the forecast residual value. Consequently, the corresponding predicted state covariance matrix is redefined as:  
\[P_{k}=(I-G_{k}H)P_{k}^{-} \tag{13}\]  
Following [PERSON] et al. [50], the prediction residual is presented as:  
\[V_{k}=z_{k}-\hat{z}_{k} \tag{14}\]  
where \(z_{k}\) is the observation vector at epoch \(k\), and \(\hat{z}_{k}\) is the best predicted value at epoch \(k\). According to Equations (5) and (14), let:  
\[E=V_{k}^{T}V_{k}-tr(HAP_{k}A^{T}H^{T})-tr(R) \tag{15}\]The estimate of \(Q\) can be denoted as:  
\[\left\{\begin{array}{l}Q={{(O^{T}O)}^{-1}}{O^{T}E}\\ O=HAC\end{array}\right. \tag{16}\]  
From Equations (15) and (16), VC-AKF can further optimize the loose integration of high-rate GNSS and SM, which not only has the ability to estimate the system noise covariance matrix \(Q\) in real time but also can appropriately increase the weight of better-quality GNSS data in the loose integration system when the SM baseline drifts severely. The detailed steps of VC-AKF for high-rate GNSS and SM loose integration can be described as follows.  
1. Take high-rate GNSS time series displacement and SM time series acceleration as input data.
2. Determine the initial value of the VC-AKF model, including: initial state vector \(x_{0}=0\) and its covariance matrix \(P_{0}=I\) and the system noise matrix \(Q_{0}\).
3. Build the VC-AKF model according to Equations (4)-(16).
4. Calculate the one-step prediction value \(\hat{x}_{k}^{-}\), predicted covariance value \(P_{k}^{-}\) and gain matrix \(G_{k}\).
5. Implement the VC-AKF model, calculate and correct the system noise covariance matrix \(Q\).
6. Return to (4), recursive calculations.
7. Obtain the filtered value \(\hat{x}_{k}\) and the covariance matrix \(P_{k}\) after each calculation.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 4 Results
### Analysis of Estimated Drifts in TOC Products
#### 4.3.3 Statistical Analysis of Azimuthal Asymmetry
The parameters of the boxplot chart are those used and described in Figure 12.  
classes. This reduction is particularly evident for the NIR and SWIR bands, where the drift difference decreases by more than one order of magnitude. The same analysis was also conducted for the temporal series of TOC NDVI. The results (see Supplementary Materials, Section S.4) show that the evident azimuthal asymmetry verified in the Red and NIR bands are largely smoothed out in the NDVI ratio. The statistical distribution of the estimated NDVI drifts in the two angular conditions is broad with no obvious bias. The BRDF normalization procedure only slightly impacts the statistical distribution of the TOC NDVI estimated drifts in the two scattering directions. This finding is consistent with similar investigations into MODIS-derived vegetation indices [10], showing that NDVI is more robust to changes in azimuthal configuration with respect to other indices, notably EVI.
## 4 Experiment Results
### Feature Selection Results  
Figure 3 shows the example in CTDD using original features as we determine m%. As m% is 10%, clearly, accuracy is the best. For the other CTDI index cases, the results are similar. Thus, m% is set to 10%, and the 16 features are selected for each index from the 154 features.  
Table 4 shows the feature selection results. For original features, the selected features are similar, so EFS can have 18 features from them. This shows the selected 18 features are representative since the three indexes reach a high consensus on these features. The Auto Encoder features are extracted from the original features, and their features are less similar than the original features. EFS selects six features from them. Overall, EFS in CTDI selects 24 features from both the original and Auto Encoder features.  
Table 5 shows the names of selected original and Auto Encoder features in the CTDI. The #8 feature, frame length, and #66 feature, WLAN frame control type, are selected in both original and Auto Encoder features, so they are recognized as the critical features to distinguish the attack from normal behaviors. The #66 feature, WLAN frame control type,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Selected Features** & **Number of Features** \\ \hline \multicolumn{3}{c}{Original Feature} \\ \hline CTFD & 4, 7, 8, 9, 38, 47, 50, 51, 64, 67, 70, 71, 73, 140, 142, 154 & 16 \\ \hline CTMD & 8, 9, 47, 50, 51, 64, 66, 67, 68, 70, 71, 73, 90, 118, 142, 154 & 16 \\ \hline CTDD & 4, 7, 8, 9, 47, 50, 51, 66, 67, 68, 71, 82, 118, 140, 142, 154 & 16 \\ \hline EFS & 4, 7, 8, 9, 47, 50, 51, 64, 66, 67, 68, 70, 71, 73, 118, 140, 142, 154 & 18 \\ \hline \multicolumn{3}{c}{Auto Encoder Feature} \\ \hline CTFD & 10, 16, 24, 27, 35, 49, 51, 55, 68, 76, 78, 82, 87, 109, 139, 143 & 16 \\ \hline CTMD & 8, 13, 22, 30, 39, 42, 66, 69, 76, 78, 100, 103, 115, 118, 139, 141 & 16 \\ \hline CTD & 5, 8, 9, 31, 39, 41, 50, 54, 66, 71, 75, 80, 86, 92, 98, 102, 112, 147 & 16 \\ \hline EFS & 8, 39, 66, 76, 78, 139 & 6 \\ \hline CTDI & Original: 4, 7, 8, 9, 47, 50, 51, 64, 66, 67, 68, 70, 71, 73, 118, 140, 142, 154; Auto Encoder: 8, 39, 66, 76, 78, 139 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature selection results.  
Figure 3: The accuracy for different m%.  
is clearly the key feature to identify wireless network behaviors by its definition. Some type-related features, such as #50, #51, #64, and #67, are also selected to distinguish the attack behaviors. It shows that the CTDI model can identify features with clear differences between normal and attack behaviors.  
The names of selected original and Auto Encoder features in CTDI.  
The packet length features #8 frame.len, #9 frame.cap_len, and #154 data.len are also selected to detect the attack behaviors. Other selected original features are related to notable wireless parameters, such as time (#4 and #7), frame control (#70, #71, and #73), and WEP (#140 and #142). Auto Encoder features are extracted from the original features, so their selected features show four different wireless parameters (#39, #76, #78, and #139).
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
## 3 Results and Discussions
### One-Dimensional Examples
#### 3.1.2 1D Advection-Diffusion  
In this example, a property \(\phi\) is transported by a constant velocity of 0.001 m/s by advection-diffusion through the 1D domain. The boundary conditions are \(\phi\) x = 0 = 373 K and \(\phi\) x = L = 298 K, where L = 1 m. The property has 1.2 kg/m\({}^{3}\) of density, 1.005 kJ/kg.K of specific heat, and 0.026 W/m.K of thermal conductivity. Due to the advection velocity of the current example is constant, the accuracy criterion does not come to the picture. The 1D advection-diffusion equation can be expressed as follows:  
\[\rho C_{p}\frac{\partial\phi}{\partial t}+\rho C_{pl}\frac{\partial\phi}{ \partial x}=k\frac{\partial^{2}\phi}{\partial x^{2}}. \tag{76}\]  
The analytical solution of the steady-state condition is given by [23]:  
\[\phi=\phi_{x=0}+(\phi_{x=L}-\phi_{x=0})\left[\frac{exp\left(\rho C_{p}\mu x/k \right)-1}{exp\left(\rho C_{p}\mu L/k\right)-1}\right]. \tag{77}\]  
The following data are used in the LBM modeling:  
\[\tau_{e}=1, \tag{78}\]  
\[u^{eq}=u_{(LS)}, \tag{79}\]  
and  
\[S_{i}=0, \tag{80}\]  
where \(u_{(LS)}\) is the advection velocity at the lattice-scale, which is constant.  
Figure 6 shows the simulation results for the given example using the listed data in Table 5. Excellent agreements with the analytical solutions at the lattice-scale (Figure 6a) and the physical-scale (Figure 6b) are shown. As mentioned, in such problems, the LBM results are not affected by the advection velocity. Therefore, the lattice Mach number (\(Ma_{(LS)}\)) does not affect the results. However, the results of the conventional computational fluid dynamic (CFD) methods are significantly affected by the value of the advection velocity, which causes dispersion error [23]. Figure 7 shows a comparison between the LBM and finite difference method (FDM) at the physical-scale for different mesh sizes. The LBM solution is achieved at the lattice-scale then converted to the physical-scale. As shown, the error between the analytical and FDM solutions is significantly affected by mesh size, unlike the LBM.  
Figure 6: Numerical and analytical solutions of the steady-state temperature distribution along the domain; (**a**) solutions at the lattice-scale (the analytical solution is achieved using Equation (77) at the lattice-scale), and (**b**) solutions at the physical-scale (the numerical solution is achieved by the LBM then converted to the physical-scale).
# Construction of Cultural Heritage Knowledge Graph Based on Graph Attention Neural Network
## 7 Discussion and Outlook
### Effectiveness of Model Construction  
The research combines graph attention networks (GAT) and BERT with the training model for joint extraction, significantly enhancing the ability to represent and integrate complex knowledge systems. By deeply encoding the text data related to gold and silver artifacts using the BERT model, its bidirectional contextual understanding capability precisely captures subtle semantic information within the text, ensuring the accurate extraction of each entity and its related descriptions. This process is particularly suitable for the complex and multilayered descriptive structures of gold and silver artifacts, from the overall shape of the artifact to the detailed features of its patterns and connections, all of which BERT can effectively identify.  
After semantic encoding is completed, the dependency analysis module further parses the structural relationships between entities. For instance, in the description of the two key components \"cup body\" and \"foot ring\", the dependency analysis can clearly determine their connection method and the interaction between them. Through this analysis, the model is not only able to recognize individual entities but can also systematically extract the structural dependency information between entities.  
The graph attention network (GAT) further processes these entity nodes and their relationships by assigning weights. When handling complex descriptions of gold and silver artifacts, GAT excels at capturing the interdependencies between elements such as shape, pattern, and material. For example, in the case of the \"silver cup with pointed lotus petals and wild goose patterns\", the cup body and high foot ring are treated as key modules and set as nodes in the knowledge graph while their connection and pattern details are precisely represented through edges and attributes in the graph structure. Through this structured approach, the model not only faithfully reconstructs the dependency relationships between complex entities but also ensures that the representation of this knowledge in the graph is more intuitive and organized.  
By combining semantic analysis with multidimensional graph structure processing, the joint application of GAT and BERT, compared to traditional knowledge extraction methods, not only automates the extraction of entities and relationships but also ensures the stable operation of the entire knowledge system through deep exploration of the text's semantics and graph structures.
# From DevOps to MLOps: Overview and Application to Electricity Market Forecasting
## 4 Case Study 1: Forecasting an Hourly Day-Ahead Electricity Market Using an MLOps Pipeline
### Prediction Model  
For the energy price predictions in ML, ANNs are widely used. For instance, based on the architecture defined in [173], Recurrent Neural Networks (RNNs) and feed-forward neural networks are the two major ANN categories. RNNs can predict the high energy spikes better, whereas feed-forward networks can predict the spot market prices for day-ahead prediction [174]. An ANN was employed to predict ancillary market prices by considering different data sources where the results outperformed Support-Vector Regression (SVR) and Autoregressive Integrated Moving Average (ARIMA) [164]. For implementingthe FCR-N market price forecasting using the MLOps pipeline, this case study uses the Temporal Fusion Transformer (TFT) model structure defined in [175]. Energy price prediction datasets have a time component, and forecasting the future price values can provide significant value for multi-horizon forecasting, i.e., predicting variables of interest at multiple future time steps. Deep Neural Networks (DNNs) have been used in multi-horizon forecasting, showing substantial performance improvements over traditional time-series models. However, most existing RNN models often do not consider the different inputs commonly present in multi-horizon forecasting, and either assume that all exogenous inputs are known in the future or do not consider static covariates. Conventional time-series models are influenced by complex nonlinear interactions between many parameters, making it difficult to explain how such models arrive at their predictions. Attention-based models are proposed for sequential data such as energy price prediction datasets. However, multi-horizon forecasting has many different types of inputs, and attention-based models can provide an understanding of appropriate time steps, but they cannot contrast the importance of different features at a given time step, and TFT solves these issues in terms of accuracy and interpretability.  
TFT is an attention-based architecture that combines multi-horizon forecasting with interpretable insights into temporal dynamics. TFT utilizes recurrent layers for local processing and interpretable self-attention layers for learning long-term dependencies with the knowledge of temporal relationships at diverse scales [175]. The major components of TFT are summarized below.  
* _Gating mechanisms_ skip any unused components of the model.
* _Variable selection networks_ choose relevant input variables at each time step.
* Static features can have a meaningful impact on forecasts, and _static covariate encoders_ integrate such features based on which temporal dynamics are modeled.
* A sequence-to-sequence _temporal processing_ layer to learn long-term and short-term temporal relationships.  
Table 2 provides additional information on TFT hyperparameters for practitioners who may wish to reproduce the results.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 7 Conclusions
### Efficiency of the Algorithms  
Algorithm 1 can be implemented with a runtime that increases linearly with the size of its input, so it is efficient. The trivial running time estimation of Algorithm 2 is proportional to the product of the sizes of the command sets \(A\) and \(B\)--assuming that the \"above\" relation in the node structure \(\mathbb{N}\) can be checked in constant time, as generating the conflict graph takes that much time. Section 6.3 discusses some possible improvements by exploiting the structure of the conflict graph. As to whether the running time of the algorithm can be improved significantly, or it is substantially quadratic, our conjecture is that the algorithm can be made sub-quadratic.
## 4 Discussion
Moreover, in the case of studies like the present, the environmental conditions present in the room (temperature, humidity, air currents) must be taken into account, as well as the sampling procedure itself. All such factors form a set of features that can interfere with the final results. The fungal action identified on the garment investigated could be pathological for humans, especially for the personnel handling the garment. Although the fungi of the _Aspergillus_ spp. family are generally harmless, they can cause various pathologies in those with compromised immune systems, underlying lung disease or asthma, who inhale the relevant fungal spores. The pathologies resulting from infections with such a fungus, which usually affect the respiratory system, have greatly varying signs and severity. In some (such as asthmatics), the spores trigger an allergic reaction, whereas others tend to develop mild to severe lung infections. The most serious form of aspergillosis, invasive aspergillosis, occurs when the infection spreads through the blood vessels, with the signs and symptoms of the infection varying, depending on the type of pathology that the patients concerned develop, and with it not being contagious. Daily exposure to _Aspergillus_ is rarely a problem for people with healthy immune systems [86; 87; 88]. Fungi from the _Candida guilliermodii_ family appear to be one of the most common pathogens in localised infections of the nails (onychomycosis) and/or of the skin on the toes. In patients with low immunity, infections with such a fungus can be a potential cause of fungal infections in the blood, namely fungemia, especially in patients with haematological malignancies, organ transplants and central venous catheters. This type of fungus is one of the most common opportunistic agents in severely immunocompromised patients. In such cases, depending on the associated pathologies, fungemia can prove fatal [89]. Fungal infections of the _Cladosporium_ spp. family either aggravate the symptoms of an existing asthma, or are associated with atopy located mainly in the upper respiratory tract (allergic rhinitis/sinusitis) and less often in the skin (superficial or deep skin lesions). The impact of patients' symptoms (dyspnoea, sneezing, stuffy nose, pruritus, nasal/ocular secretions) was significantly correlated with spore concentrations. In patients with weakened immune systems, infections with this type of fungus can even cause disseminated infections, called fungemia [90; 91; 92]. Fungi of the genus _Penicillium_ spp., which are occasionally the cause of infection in humans, can be isolated from patients with keratitis, endophthalmitis, otolomycosis, necrosis esophagitis, pneumonia, endocarditis, peritonitis and urinary tract infections. Most _Penicillium_ infections occur in immunosuppressed hosts, and the mechanism of infection with this type of fungus is either by means of inhalation (most commonly resulting in fungemia) or post-traumatic [93; 94].