esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Models and Settings
### Transformer Models  
We compared the composed architecture with some recent popular language models. We used a dedicated tokenizer belonging to each of the models. To obtain the desired classification result, we connected each transformer model's output to a feedforward neural network--a fully connected layer with 128 neurons and regularisation layer dropout (15%). The last fully-connected layer represents the output layer of the neural network and consists of six output class (toxicity type in the input text). The architecture of this setup is depicted in the Table 3.  
Figure 8: Architecture of the composed BiLSTM + CNN model for multi-label classification.  
* BERT : we used BERT tokenizer, and following pre-trained models:
* BERT model (Cased/Uncased): we used a pre-trained BERT model for sequence classification, which contains a sequence classification/regression head on top (a linear layer on top of the pooled output). In this model, the transformer is pre-trained, and the sequence classification head is only initialized and has to be trained. We used only BERT-base pre-trained model, with BERT-large, we experienced the stability issues with its training. Output of the classification head was used and fed into the FF network for classification.
* Bare BERT (Cased/Uncased): in this case, we used pre-trained BERT model transformer, which is outputting raw hidden-states without any specific head on top. We used BERT-base and BERT-large versions of the model, both cased and uncased versions. We used pooled output of the model for the classification.
* DistilBERT: we used distilbert-base tokenizer, and we created DistilBERT model (Cased/Uncased) based on the architecture shown in Table 3. We used model with a sequence classification/regression head on top (a linear layer on top of the pooled output).
* XLNet: we used xnet-base-cased tokenizer, and we created XLNet model based on the architecture shown in Table 3. We used model with a sequence classification/regression head on top (a linear layer on top of the pooled output). XLNet model doesn't provide a pre-trained and uncased version.
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 4 Results and Discussions
### Change Analysis Results  
The unmatched features were processed further for change analysis. Pixel-based change magnitude maps were generated for each test dataset by applying the MAD algorithm. Changed pixels were determined by choosing a threshold value for the magnitude of pixel changes. As mentioned before, we performed feature-based change detection experiments repeatedly with various thresholds.  
Feature change magnitude was defined as the ratio of changed pixels within the ROIs of the features. Changed features can be determined by choosing an appropriate threshold value for the magnitude of feature change. In this paper, we used ROIs with the size of \(3\times 3\) pixels and a feature-change threshold value of 0.5. We classified feature points into changed and unchanged features using the threshold.  
Figures 8-10 show original images, pixel-based change maps for a given pixel threshold value, and feature-based change maps overlaid on the pixel-based change maps. The meaning of orange and green polygons is the same as before. We can compare the differences in pixel-based and feature-based change detection results. Many areas judged as changes in the pixel-based approach were determined to be unchanged in the feature-based approach. Feature-based change detection reduced false detection of unchanged objects while maintaining correct detection in changed objects. In Incheon, it was possible to detect changes in ships and shipments, and remove the falsely detected areas of unchanged buildings. In Chilgok and Seoul, it was possible to detect changes in small buses, trucks, and vehicles in parking lots and roads, and to remove errors from unchanged objects and areas of natural change. As mentioned earlier, several featureless changed objects of very small size were not detected. From the images, we can visually confirm that the use of feature points improves change detection performance with small objects, and compensates for the disadvantages of pixel-based approaches.  
Figure 8: Comparison of pixel-based and feature point-based change detection results for Incheon: (**a**) before images, (**b**) after images, (**c**) pixel-based results, and feature-based results using (**d**) SIFT, and (**e**) AKAZE.
## 6 Other Approaches
### Analysis of Multivariate Designed Data
In particular, by assuming that the experimental design at hand involves two factors (generically named A and B), the data matrix **X** made up of all the spectra collected at the different design points after mean centering (i.e., after subtraction of the mean spectrum calculated across the entire dataset) is partitioned as follows:  
\[\textbf{X}=\textbf{X}_{\text{A}}+\textbf{X}_{\text{B}}+\textbf{X}_{\text{AB} }+\textbf{X}_{\text{res}} \tag{34}\]  
where \(\textbf{X}_{\text{A}}\), \(\textbf{X}_{\text{B}}\), and \(\textbf{X}_{\text{AB}}\) account for the effect of the two factors and their interaction, respectively, while \(\textbf{X}_{\text{res}}\) contains the residual variance in **X** not explained by the model. More specifically, \(\textbf{X}_{\text{A}}\), \(\textbf{X}_{\text{B}}\), and \(\textbf{X}_{\text{AB}}\) carry identical replicates of the mean spectra corresponding to the different factor or interaction levels. Starting from the common ground defined by Equation (33), the various methods developed for the analysis of multivariate designed data proceed along different directions. Among these techniques, the most popular is surely ANOVA--Simultaneous Component Analysis (ASCA [253; 254]), due to its relative simplicity and remarkable interpretability properties. In ASCA, the effect of each design term (factor or interaction) is estimated through the sum of squares of the elements of the corresponding matrix in Equation (29). For example, the effect of factor A is quantified as:  
\[SSQ_{\text{A}}=\sum\
olimits_{m}\sum\
olimits_{j}x_{n,j,\text{A}}^{2}=\textbf {X}_{\text{A}}{}^{2} \tag{35}\]  
and its statistical significance is evaluated non-parametrically by comparing \(SSQ_{\text{A}}\) with its null-distribution retrieved by permutations [255; 256]. The interpretation of the significant effects is finally carried out by applying PCA to the respective matrices. Alternative techniques described in the literature are ANOVA-PCA (APCA [257; 258]), ANOVA-Target Projection (ANOVA-TP [259; 260; 261]), ANOVA-Common Dimensions (AComDim [262]) and regularized MANOVA (rMANOVA [263]). Considering their underlying model structure, the methodologies outlined in this subsection have been widely exploited to obtain insights into how certain technological factors affect food-related processes like ageing (see Table 4); ASCA, for instance, has often been coupled to MIR and/or NIR spectroscopy to determine and examine the influence of (i) drying temperature and time on dried egg-pasta manufacturing [76], (ii) varietal origin and roasting time on coffee bean spectral properties [150], and (iii) storage temperature and conditions on Cheddar cheese ripening [264]. In all these case studies, the application of ASCA has generally permitted to highlight in a rapid, non-destructive, relatively cheap and green fashion the occurrence of specific phenomena (e.g., proteo- and lyophilis) ongoing along with the evolution of the monitored process itself.
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 5 Evaluation
### Performance Evaluation
#### 5.2.4 Intrusion Detection with Different Moving Patterns  
The most important problem that RDFID solves is human detection under different moving patterns. In consequence, to evaluate the ability of RDFID in this problem, the data of different moving patterns is used in this evaluation. To address the importance of this problem, RDFID is compared to FRID, PADS, and FIMD [21]. A 30 s moving data segment of the first volunteer in scenario (b) under regular moving pattern is randomly divided as training data, while the data of the other three volunteers in scenario (b) under three different moving patterns is used as the test data. The results of the three approaches are the mean values of the three volunteers, and the window size is 5 s.  
The FN and FP rate of the four approaches under different moving patterns is shown in Table 5. It can be seen from the table that the FN rate of RDFID remains stable under different moving patterns. However, the FN rate of the other three approaches raises significantly when the volunteers creep on the floor. FRID, PADS, and FIMD are affected more significantly because the influence of the human body to the transmission of the wireless signal becomes weak when the volunteers creep on the floor. The FN rate of RDFID has a small fluctuation because the extracted feature is related to the periodicity of human motion. It can detect human at a high accuracy as long as there exists a periodicity of human motion.  
The FP rate of RDFID is still stable under the three moving patterns, while the change trend of the FP rate of the other three approaches is the opposite to that of the FN rate. The reason is the same that the influence of human body to the transmission of the wireless signal becomes less when the person creeps on the floor. The low FP rate of the other three approaches is on the cost of the high FN rate. In consequence, RDFID has the ability to detect human of different moving patterns. It has the advancement of human detection especially when the person moves in an irregular pattern. The robustness of RDFID is higher that the detection performance is less affected by different moving patterns.  
As illustrated in Figure 11, the PD of RDFID is the highest and stable under the three different moving patterns benefiting from the frequency-domain feature.  
Figure 11: PD of human detection under different moving patterns.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**FN**} & \multicolumn{3}{c}{**FP**} \\ \hline
**Moving** & **Regular** & **Bending** & **Creping** & **Regular** & **Bending** & **Creping** \\
**Pattern** & **Walking** & **Down** & **Creping** & **Walking** & **Down** & **Creping** \\ \hline RDFID & 2.3 & 2.5 & 2.6 & 1.7 & 1.7 & 1.5 \\ \hline FRID & 4.8 & 5.2 & 9.8 & 7.8 & 6.2 & 3.6 \\ \hline PADS & 4.3 & 4.8 & 6.4 & 4.3 & 4.1 & 2.8 \\ \hline FIMD & 5.4 & 5.7 & 12.5 & 6.8 & 6.4 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: FN/FP of human detection under different moving patterns (%).
# Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics
## 2 State of the Art
### Advances in Gaussian-Process Models  
Sparsed GP (sGP) approaches tackle the approximation of the full GP using auxiliary variables instead of the similarity measurement \(\mathbf{K}\). This can be done using a subset of the training data [42] or pseudo inputs [43; 44]. Both approaches aim to identify \(m\) variables which can be used instead of \(n\) original data points. In the research work in [43] the authors use variational Bayes (VB) [35] as the current state-of-the-art approach for statistical models such as deep GPs [33] or GP latent variable models [34].  
This VB-based approximation converts the mean GP prediction procedure to \(GP_{\mu}(.)=\vec{k}_{xm}\mathbf{K}_{mm}^{-1}\vec{\mu}\), where \(\vec{\mu}\) is the result of the approximation procedure and \(\mathbf{K}_{mm}\) is a similarity matrix of the auxiliary variables. Similar to \(\vec{k}_{*}\), \(\vec{k}_{xm}\in\mathbb{R}^{1\times m}\) links the input to the kernel matrix. The number of auxiliary variables needed for the application can be estimated based on the evaluation of the lower bound [35]. Due to the reduction of the training set, where \(m\ll n\), the prediction is accelerated.  
For autonomous vehicles, sGP can easily be implemented in Bayes filters [8] for movement and measurement models. As we previously mentioned, this results in a black box approach. To overcome this problem, we contribute to the field of research and present in this work a method that combines sGP-based Bayes filters and kinematics to derive vehicle parameters, thereby providing insights on the functionality of sGP in autonomous driving. Our method makes it possible to follow and reproduce the internal processes so that the model behavior can be analyzed later.
# Microseismic Location Error Due to Eikonal Traveltime Calculation
## 2 Methods  
### Diffraction Stacking  
First, we briefly review diffraction stacking--the preferred industrial methodology for surface monitoring technique used for detection and localization of microearthquakes from dense arrays (hundreds to thousands of receivers) [1]. At the initial step, we select a volume of interest, which coincides with the zone of expected microseismic activity. We discretize the volume and consider each grid node to be a potential source position. Then for each point \(\mathbf{x}\) we compute P-wave traveltimes \(T_{R}(\mathbf{x})\) for the given velocity model as if this point was a microseismic event. Since the origin time of events is unknown, we also consider all possible values of origin time \(t\). After stacking amplitudes of all \(M\) receivers (typically vertical component only) along the predicted hodographs \(T_{R}(\mathbf{x})\) we obtain a 4D image function:  
\[F(\mathbf{x},t)=\Big{|}\sum_{R=1}^{M}\phi_{R}(\mathbf{x})A_{R}(t+T_{R}( \mathbf{x}))\Big{|}. \tag{1}\]  
In order to account for the source radiation pattern and ensure constructive interference of signals with reversed polarities, we introduce an amplitude sign correction \(\phi_{R}(\mathbf{x})\), which is equal to +1 or \(-1\). At every 4D point \((\mathbf{x},t)\), we use least-squares inversion to estimate the moment tensor based on the distribution of the summed amplitudes \(A_{R}(t+T_{R}(\mathbf{x}))\) across all receivers, evaluate the weights \(\phi_{R}(\mathbf{x})\) for each receiver, and correct for the source mechanism prior to stacking.  
If a microseismic event has sufficiently strong signal, it will create a local maximum of the image function in space and time. When velocity model is correct and the traveltimes are computed accurately, this maximum is at the true location and origin time of the microseismic event. In real life application, the velocity model normally introduces location errors, since it always describes only some approximation of elastic properties of the subsurface. Since in this work we are interested in location errors caused by incorrect traveltime computations, in the examples section we consider only rather simple synthetic models in order to exclude other factors affecting location accuracy.
## 2 Characteristic Analysis of the Iridium System  
This section comprehensively analyzes epoch characteristics in LEO SOP positioning from three perspectives: satellite, epoch, and signal levels. These characteristics collectively impact positioning accuracy and must be considered in the following algorithm design.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 4 Methodology  
To accomplish the proposed research, the following high-level methodological steps are mechanized. The high-level steps are illustrated in Figure 2. From the raw traffic data, features are extracted for a different number of packets and are, subsequently, fed to different ensemble algorithms. It is worth mentioning that we trained all models in both holdout and cross-validation setting to come up with reliable models with impressive performance. The testing scores are analyzed and compared using four different evaluation metrics.
## 3 Reclassification of Emergency Messages Dissemination Approaches
### Simulation Parameters and Setup  
In order to illustrate the operation of our protocol, we propose an environment with a central crossroad where a single incident happens and ten routes, with three of them directly obstructed and where/when emergency messages (EMs) will prevent a further traffic jam, as observed in Figure 3 for the space setup. The same environment is used to represent basis cases throughout the proposed protocol and comparison mechanisms of flooding in order to disseminate EMs. Two flooding variants are compared: flooding constrained to one and only one relay of an EM, and flooding that relays or forwards every message received. Both mechanisms are addressed as flooding-c and flooding-r, respectively. Meanwhile, the pheromone follows the system model at the start of Section 4.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 1 Introduction
We assess the reliability and potential discrepancies of the MODIS satellite-driven top-down versus the terrestrial bottom-up NPP estimates from continental to national scale and across different gradients like location, elevation or stand density. This will provide a better understanding of the reliability of remote sensing based NPP estimates, which could be used also for regions, where no terrestrial measurements are available.
## 2 Background Theory
### Radiative Transfer Equation  
The radiative transport equation (RTE) [32; 33] is the globally accepted analytical model to simulate accurate photon propagation in biological tissues. In steady state, the RTE is given as,  
\[\left(\hat{n}\cdot\
abla+\mu_{a}(\vec{x})+\mu_{s}(\vec{x})\right)I(\vec{x}, \hat{n})-\mu_{s}(\vec{x})\int_{S^{2}}I(\vec{x},\hat{n}^{\prime})P(\hat{n},\hat {n}^{\prime})d\omega^{\prime}=q(\vec{x},\hat{n}), \tag{1}\]  
where \(\
abla\) is the gradient operator, \(\mu_{a}\) and \(\mu_{s}\) are absorption and scattering coefficients of the medium, \(I(\vec{x},\hat{n})\) is the radiance at a position \(\vec{x}\) inside the medium in \(\hat{n}\) direction, and \(q(\vec{x},\hat{n})\) represents the source term. The phase function \(P(\hat{n},\hat{n}^{\prime})\) is the probability density function governing the scattering of a beam incident from \(\hat{n}\) into \(\hat{n}^{\prime}\) direction. For this work, we have utilized the commonly used \"Henyey-Greenstein phase function\" [33].  
\[P(\hat{n},\hat{n}^{\prime})=\frac{1}{2^{n-1}\pi}\frac{1-g^{2}}{(1+g^{2}-2g( \hat{n},\hat{n}^{\prime}))^{d/2}} \tag{2}\]  
where \(g\) is known as the anisotropy factor, mathematically defined as \(<\hat{n}.\hat{n}^{\prime}>\) and \(d\) takes the values 2 and 3 for 2D and 3D RTE respectively.  
The RTE is an integro-differential equation and solving it numerically is mathematically challenging as well as computationally expensive. Typically, the tissue is dominantly a scattering medium where RTE reduces to the diffusion equation [33], a partial differential equation, much easier to solve as compared to RTE. However, to investigate superficial regions such as the epithelium from where light captured does not go through large scattering events, RTE is more appropriate. A numerical equivalent of solving the RTE is the MC simulation [33; 34].
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 4 Experimental Settings
### Experimental Environment  
The hardware environment for the experiment is Intel Core I7 and above processor, 16 GB of memory, and 256 GB of solid-state drive (SSD). The software environments are Windows 10 operating system, Anaconda 3.0, and Python 3.7. The experiment is primarily implemented using the Python programming language, leveraging several third-party Python libraries. Specifically, numpy and pandas are employed for data processing and analysis, seaborn and matplotlib are utilized for data visualization, scikit-learn, LightGBM, and XGBoost are applied in model training, and scipy is used for statistical analysis.
# Argininemia: Pathophysiology and Novel Methods for Evaluation of the Disease
## 4 Novel Methods of Evaluation
### Novel Evaluation Methods  
#### 4.2.1 Use of Arginine/Ornithine Ratio  
In newborns with ARG1-D, plasma arginine levels may appear normal or near-normal due to the lingering effects of maternal arginase or due to arginase 2, which can increase greatly when arginase 1 is deficient [3; 80]. Therefore, relying solely on elevated plasma arginine for the diagnosis of argininemia may potentially lead to the omission of some positive cases, something that would understandably be associated with detrimental consequences for the neurophysiological development of the falsely diagnosed individuals [80].  
To address the aforementioned issue, the arginine to ornithine ratio (Arg/Orn ratio) has been investigated as a secondary diagnostic marker in newborns with a positive NBS result for hyperargininemia and arginase deficiency [81]. It was revealed that increased plasma arginine when combined with an Arg/Orn ratio of \(\geq\)1.4 correctly identified all the arginase cases in the study. Furthermore, it was observed that during the first 31 days of life, plasma arginine increased at a rate of 0.94 umol/L/day, while ornithine remained essentially unchanged. Consequently, the Arg/Orn ratio exhibited a similar increasing trend (0.01/day) [81].  
The researchers concluded that the combination of both plasma arginine and plasma Arg/Orn ratio serves as a more effective diagnostic marker compared to plasma arginine alone. This improved diagnostic approach enhances sensitivity for identifying ARG1-D in newborns with hyperargininemia, allowing for a timely diagnosis and early initiation of adequate treatment before the onset of symptoms [80; 81].
## 5 Simulation Results
### Multi-Domain Informative Path Planning for Unknown Obstacles and Current Fields
#### 5.2.1. Replan Methods  
The time complexity of KD tree construction is \(O(log^{2}n)\)--the insert has \(O(log^{2}n)\) complexity; searching in k-dimensions is \(O(n^{1-\frac{1}{k}}+1)\)-complex, and using a scapegoat tree to insert can be generalized to \(O(logn)\). Paths replanned by the scapegoat tree and the reconstructed tree were the same most of the time. For each round of _obset_ searching, the performances of the two methods were different: when inserting/reconstructing many discrete target points, the scapegoat method performed better; when coping with dense, small numbers of points, the reconstructed tree method performed better. A possible reason is: consider the additional time taken to reconstruct a whole tree compared to a few insertions; but traversal through and insertion into a sub-tree is time consuming, so many points could reverse the situation. The geometric density of obstacle points can lead to unbalancing of the KD tree. When inserted into a tree, they are high-probability in the same sub-tree. Similarly, when there are a small number of discrete obstacles on a relatively large-scale map, few reconstruction processes for sub-tree are required. Another probable reason for our observations is that, since a map is much larger than any obstacle, the time taken to insert an obstacle into the map is longer than that taken to reconstruct a new tree for obstacle information.  
In conclusion, the scapegoat tree should be used for small areas with discretely located obstacles having simple geometrical features. In large maps with dense obstacles, reconstructing a KD tree would improve performance.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Active BCI
#### 4.3.2 Signal Processing and Feature Extraction  
For the active BCI category, a low-pass filter at 40 Hz was applied by [51], while ref. [54] employed a bandpass filter between 13 and 30 Hz. To extract the desired features for the classifier, ref. [54] utilized CSP. The filtering and feature extraction techniques are crucial to enhancing signal quality and improving classification accuracy.