esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 UTM Monitoring Framework
### Machine Learning Algorithm  
Besides statistical analysis, machine learning analysis was applied to the flight test data. Based on the studies in the surveyed literature, which conducted a flight data analysis using machine learning, the random forest algorithm is recommended to be used for parameter identification in aviation. For this context, the random forest algorithm is superior in accuracy, collinear resistance, overfitting resistance, and parametric robustness compared with other algorithms, such as the boosting Ensemble, decision trees, K-nearest methods, and naive Bayes [37].  
The random forest algorithm is a combination of multiple decision trees processing data. The algorithm comprises a root node, a few internal nodes, and leaf nodes that consists of a subset of randomly selected features, as shown in Figure 5.  
The procedure used by the random forest algorithm consists of three main parts, which are the input, training, and prediction process. The input consists of sample training set x, which consists of the flight parameters data, and the response y, which is the target parameter. The training process is a loop for each dataset as follows [39]:  
For \(t=1\) to T:  
Figure 5: Random Forest classifier algorithm description [38].  
Take a bootstrap sample \([x_{t},y_{t}]\) of size N from \([\text{x},\text{y}]\);
2. Use \([x_{t},y_{t}]\) as the training data to train the t-th decision nodes by using a binary recursive function;
3. Repeat the following steps recursively for each unsplit node until the stopping criteria are met. The prediction process predicts the y target parameters based on the new \(x_{i}\) test data. There are two application types of the random forest algorithm, which are the regression and classification problems. The regression problem is related to a continuous target parameter. It is solved using the mean square error (MSE), as shown in Equation (8) [40]:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(f_{i}-y_{i})^{2} \tag{8}\]  
where \(N\)--the number of data points;  
\(f_{i}\)--the value returned by the model;  
\(y_{i}\)--the actual value for data point \(i\).  
In contrast, the classification problem is related to a discrete target parameter. It is solved using the GINI index, which is calculated using Equation (9):  
\[GINI=1-\sum_{i=1}^{C}(p_{i})^{2} \tag{9}\]  
where \(p_{i}\)--relative frequency of the class of the dataset;  
\(C\)--the number of classes.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Deep Learning Models
#### 2.2.2 Attention Networks  
Attention mechanisms have emerged as a powerful augmentation of traditional neural network architectures, enabling models to selectively focus on relevant parts of the input data [59]. Attention mechanisms in the context of AD detection can significantly improve the performance of CNNs by allowing the model to prioritize regions of MRI images that are most indicative of neurodegenerative changes. A dual-attention multi-instance DL (DA-MIDL) model is proposed for discriminative pathological localization and diagnosis of AD in [60]. Research by [19] demonstrates the effectiveness of attention mechanisms in various applications, including image classification and natural language processing. In the detection of AD, attention-based models have been shown to improve feature representation, leading to improved classification accuracy. For example, the integration of spatial and channel attention mechanisms can help focus on important areas of the brain, thereby improving the diagnostic process. Adaptive Hybrid Attention Network (AHANet) is proposed in the article [61]. The hybrid network operates on two attention modules, namely enhanced non-local attention (ENLA) and focal attention. The Adaptive Features Fusion (AFA) module is also proposed to fuse features from both global and local levels. The proposed network demonstrated better performance compared to existing works. The network was trained and tested on the ADNI dataset and produced 98.53% classification accuracy. Although attention mechanisms improve explainability, they do not completely solve the problem of the \"black box\" nature of DL models [26].
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviations** & **The Full Designation** & **Abbreviations** & **The Full Designation** \\ \hline MLR & Multiple Linear Regression & HTT & Hydraulic-Temperature-Time \\ SR & Stepwise Regression & HT\({}_{\text{A}}\)T & Hydraulic-Air temperature-Time \\  & Radial Basis Function & HHST & Hydraulic-Hysteresis-Seasonal-Time \\  & Network & PCA & Principal Component Analysis \\  & Support Vector Machine & HT\({}_{\text{H}}\)T & Hydraulic-TemperatureHysteresis-Time \\ ELM & Extreme Learning Machine & RF & Random Forest \\  & Gated Recurrent Unit & MIMO & Multiple-Input Multiple-Output \\  & Neural Network & & \\ LSTM & Long and Short-Term & MCSST & Multiple Correlation-based Structural Stack \\  & Memory Neural Network & & Test \\ LSSVM & Least Squares Support & BPNN & Back Propagation Neural Network \\  & Vector Machine & & LSSVM optimized by PSO \\  & Genetic Algorithm & PSO-LSSVM & Multi-input Multi-output Least Squares \\  & Fruit Fly Optimization & MIMO-PSO-LSSVM & Support Vector Machine with Particle \\  & Algorithm & & Swarm Optimization \\  & & & Root Mean Square Error \\  & & & \\  & & & Mean Absolute Error \\  & Particle Swarm & & \\  & Optimization Algorithm & & \\  & HST & Hydraulic-Seasonal-Time & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations. the complexity and multiple contributions of the model, improve prediction accuracy, and provide a better interpretation of the importance of the influencing factors on the displacements. 3. Following consideration of the spatial correlation of measurement points, a unified set of factors suitable for displacement prediction with multiple outputs is determined. LSSVM is combined with multi-objective regression, and the PSO is used to select the hyperparameters of the model. The result is the proposal of a displacement prediction model, MIMO-PSO-LSSVM, which achieves synchronous displacement prediction at multiple measurement points. The superiority of the model performance in terms of both accuracy and efficiency is verified through an engineering case study.
## 4 Synthesis and Summary
### Future Directions
#### 4.4.2 Highly Integrated Sensor Systems and Multi-Modal Task Execution  
Recent advances in robotic design, autonomy and sensor integration create solutions for the exploration of deep-sea environments [78]. As marine exploration and exploitation continue to advance, coupled with the strides made in mechanical intelligence, there is a notable emphasis on the utilization of USVs and the intricate design of their guidance systems. A newly developed module in 2021, designated as the Three-Dimensional Perception Module (PMTD), employs a combination of camera and LiDAR technology to assimilate multi-dimensional environmental data. This module attains centimeter-level localization accuracy through the integration of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) technologies [79]. A new integrated USV and unmanned underwater vehicle (UUV) platform connected via underwater cables capable of acquiring real-time underwater data and long-time operation was developed to gather more sensory data for decision making [80].  
Additionally, new technology was presented in a novel recovery system: one a piece of hardware that ensures the attainment of unique attitude requirements and improves the effectiveness of stern chute recovery [81]. A new controller for dynamic positioning was then developed, combining model predictive control with predictions of short-term wave movements. This approach effectively compensated for wave effects, thereby significantly enhancing the operational capabilities of the vessel [82]. With more and more adjuncts devised, USVs will be expected to be equipped with a greater variety of and higher resolution sensors, such as high-resolution cameras, LiDAR, sonar, radar and more, to enhance their environmental perception capabilities [83]. This will contribute to a broader range of applications, including ocean surveys, marine conservation, military operations and emergency response.  
In the meanwhile, USVs will be capable of executing a variety of tasks, including underwater exploration, subsea operations, cargo transportation, search and rescue, etc. This will necessitate advanced adaptive control systems and task-planning algorithms.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 2: Multi-Temporal Input
#### 2.2.2 Long Series Data Input
This study demonstrates the advantages of multi-temporal satellite images in supporting spatiotemporal studies of urban forests and the urban heat island. However, the insufficient number of high-quality periodic satellite images for a 20-year analysis constitutes a critical limitation in this study. Overall, for long-term high-frequency monitoring it is difficult to ensure that all images are cloud-free, despite the tools available on Earth Explorer and Google Earth Engine that allow researchers to select the most cloud-free scenes from the library. Sensor anomalies, as found with Landsat ([[https://www.usgs.gov/land-resources/nli/landsat/landsat-known-issues](https://www.usgs.gov/land-resources/nli/landsat/landsat-known-issues)]([https://www.usgs.gov/land-resources/nli/landsat/landsat-known-issues](https://www.usgs.gov/land-resources/nli/landsat/landsat-known-issues))), may also impact long-term data collection. Accordingly, multi-source data are sometimes required to compensate the need for high-quality images.
## 5 Results and Discussion
### Improvement of Velocity Estimates at Observed Locations  
DA improvement of the model estimates of velocity at all observed locations was tested. To evaluate the impact of velocity data on the efficiency of assimilation, an absolute error indicator was used. Figure 7a examines the observation minus modelled velocity components in the domain that was covered by drifters and for the x (left panels) and y components (right panels). The absolute error between observations (i.e., drifter velocities) and modelled velocities derived from the Free-Run (i.e., model without assimilation) is plotted in the top panel and the absolute error between observations and modelled velocity after assimilation (i.e., Base-Test) is shown in the bottom panel. Clearly, the DA enhanced model performance with most of the error values near zero after assimilation. The Free-Run appears to have a higher error at observed locations while the assimilation appears to have reduced this error significantly, bringing the modelled velocity close to the observed velocity. To better exhibit the general performance of the DA, Figure 7b shows the time series of absolute error for the modelled velocity with and without assimilation (i.e., Base-Test and Free-Run, respectively) with respect to drifter velocity. It is evident that assimilation of drifter velocities can successfully reduce the errors in the modelled velocity during the assimilation period.  
Figure 6: Comparison between modelled velocities and the velocities of non-assimilated drifter data.  
Figure 7: **(a) Absolute error between modelled and observed velocities before assimilation (top) and after assimilation (bottom) for the \(V_{x}\) (left) and \(V_{y}\) components (right) of velocity. Numbers in brackets indicate the frequency of data in each category. **(b)** Time series of absolute error between modelled velocities and observations before and after assimilation.
# Disaster-Resilient Optical Network Survivability: A Comprehensive Survey
## 6 Challenges in Disaster-Resilient Optical Network Survivability
As in disaster scenarios, network resources face sudden traffic floods and such techniques cannot provide the resilience or survivability to networks. Hence this opens a new research challenge for the research community. Capacity exhaustion has become an acute problem due to post-disaster traffic floods as indicated in Reference [123]. To deal with the critical demand of ICT services during traffic fluctuations and upsurges, Least-Congested Distributed Light path Allocation (LC-DLA), as proposed in Reference [119] for large-scale networks, should be studied to mitigate capacity exhaustion problem while considering a huge number of connection requests and heavy traffic loads.
## 1 Introduction
AM combines different fields of natural language processing, such as information extraction, knowledge representation, and discourse analysis [13]. In addition to those fields, sentiment analysis can also be performed in AM, be it a document, sentence, or aspect level with different outputs, binary (positive or negative), or multi-level [12]. Sentiment analysis performed at the aspect level is called aspect-based sentiment analysis (ABSA) and intends to classify opinions at the aspect level and identify the elements of an opinion [12]. To make GDSS more accessible to its users, the automatic identification of elements of an opinion is a step needed to reduce the time necessary for decision makers to set themselves up on these systems. For example, the automatic identification of alternatives and criteria on natural language text used in discussions should be a feature in future GDSSs to make them more acceptable to organizations. Some work has been done in the GDM context to achieve this solution. For example, machine learning classifiers can automatically classify the direction (relation) between two arguments [14] or create intelligent reports where an algorithm selects which information topics should be reported to decision makers. These features improve decision makers' perception of the problem they are deciding on through the ability to present accurate and relevant information [14]. All these advancements in AM applied to the GDM context will lead to a better understanding of the conversations. They will result in a more intelligent way to organize and display the conversation to the decision maker, enhancing the capabilities of the GDSSs. This work intends to apply unsupervised techniques, most concretely clustering, to group arguments to organize the discussion dynamically. This work's output will enhance the capabilities of the GDSS by offering an ML-based dynamic organization of ideas that could outperform a standard filter, since these standard filters need annotations to be viable. Unsupervised techniques ditch the need for annotated data. They can detect comparisons being made and group them, as well as detecting micro-discussions about a small group of alternatives and group them together. The rest of the paper is organized in the following order: Section 2 presents some related works on clustering in natural language processing, Section 3 presents the methodology to solve the problem addressed in this article, Section 4 presents the obtained experiments results, and Section 5 presents a discussion about the obtained results. In the last section, some conclusions are presented, alongside suggestions for work to be done afterward.
## 7 Security Analysis and Cryptographic Guarantees
### Mutual Authentication Guarantees
#### 7.2.4 Resistance to Replay and Impersonation Attacks  
Each session involves freshly generated \(\kappa\) values and thus unique public components \(\Gamma_{1}\) and \(\Psi_{1}\), producing session-specific commitments. An attacker replaying old messages will fail to pass hash verification due to a mismatch with expected inputs. Furthermore, impersonation attempts without possession of valid private keys result in authentication failure.
# 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
## 3 Experimental System and Results
### Gaze Estimation Accuracy Comparison of Different Methods
#### 3.5.1 Gaze Estimation Accuracy without Considering Error Compensation  
Figure 10 shows a comparison of gaze estimation accuracy and standard deviation calculated through the proposed method and other neural network methods, respectively, without considering error compensation. The proposed method can provide an accuracy of 1.29\({}^{\circ}\), 0.89\({}^{\circ}\), 0.52\({}^{\circ}\), and 0.39\({}^{\circ}\) when a four-, six-, nine-, or 16-marker calibration model is utilized for calibration, respectively. The maximum gaze estimation error through the proposed method for a four-, six-, nine-, or 16-marker calibration model is, respectively, 2.45\({}^{\circ}\), 1.98\({}^{\circ}\), 1.21\({}^{\circ}\), and 0.82\({}^{\circ}\). The specific results are shown in Table A1.
## 2 Materials and Methods
### Raw Materials
#### 2.1.2 Anti-Rutting Modifiers  
This study selected two kinds of anti-rutting polymer modifiers, including a flexibilizer (M-9467) and a high-modulus additive (PR module), as shown in Figure 1. Their properties are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Values**} \\ \cline{2-7}
**Properties** & \multicolumn{2}{c}{**AH-70**} & \multicolumn{2}{c}{**SBS**} & \multicolumn{2}{c}{**AR**} \\ \cline{2-7}  & **Test** & **Standard** & **Test** & **Standard** & **Test** & **Standard** \\  & **Value** & **Value** & **Value** & **Value** & **Value** & **Value** \\ \hline Penetration at 25 \({}^{\circ}\)C, 100 g, 5 s (0.1 mm) & 69 & 60–80 & 65 & 60–80 & 72 & 60–80 \\ Penetration index (PI) & \(-\)0.8 & \(-\)1.5–+1.0 & 0.2 & \(\geq\)\(-\)0.4 & 0.5 & \(\geq\)0 \\ Ductility at 10 \({}^{\circ}\)C, 5 cm/min (cm) & 55 & \(\geq\)25 & - & & - & \\ Ductility at 5 \({}^{\circ}\)C, 5 cm/min (cm) & - & 62 & \(\geq\)30 & 36 & \(\geq\)25 \\ Softening point (\({}^{\circ}\)C) & 46.5 & \(\geq\)45 & 60.0 & \(\geq\)55 & 68.0 & \(\geq\)65 \\ Flash point (\({}^{\circ}\)C) & 282 & \(\geq\)260 & 275 & \(\geq\)230 & 266 & \(\geq\)240 \\ Dynamic viscosity at 60 \({}^{\circ}\)C (Pa-s) & 216 & \(\geq\)160 & - & & - & \\ Kinematic viscosity at 135 \({}^{\circ}\)C (Pa-s) & - & 1.8 & \(\leq\)3 & - & \\ Kinematic viscosity at 175 \({}^{\circ}\)C (Pa-s) & - & - & 2.6 & 1.0–4.0 \\ Elastic recovery at 25 \({}^{\circ}\)C (\%) & - & 88 & \(\geq\)65 & 92 & \(\geq\)85 \\ Relative density of bitumen at 15 \({}^{\circ}\)C (g/cm\({}^{3}\)) & 1.008 & \multicolumn{2}{c}{measured} & - & - & \\ Storage stability segregation, 48 h softening & - & 2.4 & \(\leq\)2.5 & 5.0 & \(\leq\)5.5 \\ point difference & \multicolumn{2}{c}{Changes in quality (\%)} & \(-\)0.3 & \(\leq\)\(\pm\)0.8 & \(-\)0.2 & \(\leq\)\(\pm\)1.0 & \(-\)0.2 & \(\leq\)\(\pm\)1.0 \\ RTFO test \(\emptyset\) & Residual penetration ratio at 70 & \(\geq\)61 & 74 & \(\geq\)60 & 76 & \(\geq\)70 \\
163 \({}^{\circ}\)C & 25 \({}^{\circ}\)C (\%) & 22 & \(\geq\)6 & - & & - \\  & Residual ductility at 10 \({}^{\circ}\)C (cm) & - & & 27 & \(\geq\)20 & 20 & \(\geq\)10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic properties of bitumen.  
Figure 1: Appearances of anti-rutting modifiers: (**a**) M-9467; (**b**) PR module.
## 3 Results and Discussion
### Initial Conditions
#### 3.3.3. Modal-Derived Initial Conditions and Summary of the Test Cases  
Taking into account the stability results, namely that 0 Ln modes display the most unstable behavior, the modal initial conditions are chosen to excite the following modes:  
1. All unstable modes
2. Mode 0 Ln, with \(n=0,\cdots,8\)  
Table 3 presents a compact overview of the initial conditions imposed to the modal contributions vector \(\mathbf{q}_{0}\) and its derivative \(\dot{\mathbf{q}}_{0}\), for all test cases, which belong to either the physically-derived initial conditions or the modal-derived ones. If unspecified, the value of the initial velocity \(\dot{u}_{0}\) used is \(0.1\alpha V_{x}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Name** & \(\mathbf{q}_{0}\) & \(\dot{\mathbf{q}}_{0}\) \\ \hline \multicolumn{3}{c}{Modal-derived initial conditions} \\ \hline All\_unstable\_V0.1 & \(\mathbf{0}\) & \(q_{0,i}=\frac{\epsilon V_{x}}{\Theta_{i}\pi t}\) \\  & & With \(i\in\) Unstable modes \\  & & and \(\epsilon=0.1\) \\ All\_unstable\_V1.0 & \(\mathbf{0}\) & \(\cdots\)\(i\in\) Unstable modes \\  & & and \(\epsilon=1\) \\ All\_unstable\_V2.0 & \(\mathbf{0}\) & \(\cdots\)\(i\in\) Unstable modes \\  & & and \(\epsilon=2\) \\
0L0 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L0” \\
0L1 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L1” \\
0L2 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L2” \\
0L3 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L3” \\
0L4 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L4” \\
0L5 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L5” \\
0L6 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L6” \\
0L7 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L7” \\
0L8 & \(\mathbf{0}\) & \(\cdots\)\(i=\) “mode 0L8” \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the test cases.  
Figure 6: Distribution of disturbance \(\epsilon\) over the modes.
# Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection
###### Abstract  
Cloud detection is an important step in the processing of optical satellite remote-sensing data. In recent years, deep learning methods have achieved excellent results in cloud detection tasks. However, most of the current models have difficulties to accurately classify similar objects (e.g., clouds and snow) and to accurately detect clouds that occupy a few pixels in an image. To solve these problems, a cloud-detection framework (Cloudformer) combining CNN and Transformer is being proposed to achieve high-precision cloud detection in optical remote-sensing images. The framework achieves accurate detection of thin and small clouds using a pyramidal structure encoder. It also achieves accurate classification of similar objects using a dual-path decoder structure of CNN and Transformer, reducing the rate of missed detections and false alarms. In addition, since the Transformer model lacks the perception of location information, an asynchronous position-encoding method is being proposed to enhance the position information of the data entering the Transformer module and to optimize the detection results. Cloudformer is experimented on two datasets, AIR-CD and 38-Cloud, and the results show that it has state-of-the-art performance.  
cloud detection; mask classification; remote-sensing images; transformer