esa-sceva/satcom-chunk-collection huggingface dataset:
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 4 System Efficiency
### Pylos  
The PYLOS subsurface (1 m) temperature and salinity measurements demonstrate the same performance as the E1-M3A (Figure 14). The 600 m and 1000 m CT above 50% percentage of NON-OPERATION status were caused by the periodic absence of available sensor and sensor failures immediately after the deployment, even though it was just delivered from the manufacturer. The lower percentage of QC-rejected data compared to the E1M3A can be explained by the wider filtering criteria.
# Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning
## 3 Material and Experimental Methods
### Random Forest  
RF is a powerful and widely used supervised learning algorithm that leverages the collective intelligence of multiple decision trees to address both classification and regression problems. As a versatile ensemble method, it harnesses the strength of individual decision trees and combines their outputs to arrive at robust and accurate predictions. In the context of classification tasks, an RF constructs multiple decision trees, each trained on different subsets of the data with replacement (bootstrapping). Each tree is trained using a random subset of features, ensuring diversity and reducing overfitting. The final classification decision is made based on a majority vote from all the individual trees. For regression problems, the RF method operates similarly, but, instead of a majority vote, it averages the outputs of all the decision trees to arrive at the final regression prediction.  
One of the advantages of using an RF is its ability to handle high-dimensional data and large datasets. Additionally, the RF approach is less prone to overfitting compared with single decision trees. In this study, the RF method was employed with specific hyperparameter settings. To optimize these hyperparameters effectively, the GridSearchCV method was utilized. GridSearchCV performs cross-validation, which involves splitting the data into multiple subsets (folds) and training the model on different combinations  
Figure 5: Decision tree analysis: a visual representation of the decision-making process.  
of the training and validation data. This process ensures more reliable evaluation of the hyperparameters' impact on the model's performance. Moreover, a 5-fold cross-validation was applied, meaning that the dataset was partitioned into five subsets, and the model was trained and validated five times, with each subset serving as a validation set once. This procedure helps to obtain a more robust evaluation of the model's performance and generalizability. The combination of RF with hyperparameter tuning using GridSearchCV and cross-validation ensures that the model is well optimized and capable of delivering accurate and reliable predictions for the specific classification or regression problem under study [64; 65].  
Figure 6 illustrates the general structure of the RF classifier (RFC) model used in this research. The RF is an ensemble learning method that combines multiple decision trees to create a more robust and accurate classification model. The figure showcases the architecture of the RFC model, starting with the collection of individual decision trees, represented as tree nodes. Each decision tree is constructed based on a random subset of the training data and a random subset of features.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 5 PAGWO-IDWA Algorithm  
### Overall Framework  
Figure 3 presents the main steps of the proposed path planning framework, from environment initialization to generating a safe and smooth path. First, the environment is represented as raster maps and relevant parameters are defined. The mobile robot's start and goal positions, as well as any obstacles, are then specified. Next, a fitness function that combines Euclidean distance with an obstacle avoidance penalty is established to evaluate candidate paths. The improved PAGWO is subsequently used to determine the globally optimal path, and the resulting waypoints are provided to the improved DWA for local trajectory planning. By integrating PAGWO's global guidance with DWA's local maneuvering, the method ensures efficient, collision-free navigation in dynamic environments.  
Figure 2: Robot Obstacle Avoidance Strategies. (**a**) The mobile robot’s angle detection range; (**b**) the mobile robot accelerates to the left to bypass obstacle; (**c**) the mobile robot accelerates to the right to bypass obstacle; (**d**) non-head-on conflict.  
In the following section, we present the detailed algorithm flowchart and pseudo-code for the PAGWO-IDWA framework. This comprehensive algorithmic description elucidates the step-by-step procedures, key components, and decision-making logic that underpin the proposed method, thereby providing a clear pathway for both implementation and further analysis.
# Insights into Organizational Security Readiness: Lessons Learned from Cyber-Attack Case Studies
## 5 Overall Observation  
The following is the synopsis of the overall observation from our case study:  
1. Out of the four main human factors, ignorance and negligence are the two human behaviors that appear to be linked to most of the cyber threats.
2. Financial Institutes, Retail and Entertainment industries are the main targets for hackers, in addition to Technology, Health and Energy. This is especially true when financial gains are the key factor behind an attack.
3. Biometric threats have serious consequences. When our biometrics are compromised, unfortunately, the mediation from these threats becomes very minimal, because we cannot change the configuration of our fingerprint.
4. Cyber threats usually have high customer impacts. The financial loss can be immense, and organizational credibility is at stake.
5. Insider threats are perhaps the most difficult to predict or detect. If the internal resources include an individual with bad intentions who may have significant access to the system, it becomes very difficult to protect the organizational assets.
6. Having a strong cybersecurity policy is crucial to safeguard from cyber threats. We may have the most sophisticated technology, but with a weakly articulated cyber policy and less governance, an IT environment can be vulnerable to different types of cyber threats.
7. We observed strong linkages between weak/no cyber policy and less investment on technology, no technology adoption, minimal training and education, and cyber awareness. Among these factors, technology adoption and human factors are the two dominant contributors. In other words, to make the digital assets safe, we need to invest adequately in security, train our employees, build and govern strong cybersecurity policy, and above all adopt and keep up with technology to overcome new and existing security threats. Developing and adopting a strong security policy will help increase awareness and provide opportunities for learning and establishing protocols to prevent cyber threats.
## 2 Materials and Methods
### SOM Technique  
The SOM technique also calculates the four parameters referred to in [PERSON]'s criteria; however, it combines the four parameters into an input vector to train the SOM. The SOM is a realistic model based on the function of the biological brain ([PERSON], 2007). Formally, the SOM commonly arranges output map units in the form of one, two, or three dimensions and connects them to input vectors via weights. Close input vectors within the space are clustered into units that are also close ([PERSON], 2007; [PERSON], 2008). This means that  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Slope** &
\begin{tabular}{c} **Cross-Sectional** \\ **Curvature** \\ \end{tabular} & **Maximum Curvature** & **Minimum Curvature** & **Class Output** \\ \hline \(>\)Slope tolerance & \(>\)Curvature tolerance & - & - & Ridge \\ (Sloping surface) & \(<\) – Curvature tolerance & - & - & Channel \\  & \(>\) – Curvature tolerance & - & - & Plane \\ \(<\)Slope tolerance & - & \(>\)Curvature tolerance & \(>\)Curvature tolerance & Peak \\ (Horizontal surface) & - & \(>\)Curvature tolerance & \(<\) – Curvature tolerance & Pass \\  & - & \(<\) – Curvature tolerance & \(<\) – Curvature tolerance & Pit \\  & - & \(>\) – Curvature tolerance & \(<\) – Curvature tolerance & Channel \\  & - & \(<\)Curvature tolerance & \(>\) – Curvature tolerance & Plane \\  & - & \(>\)Curvature tolerance & \(<\) Curvature tolerance & Ridge \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wood’s criteria for the classification of geomorphic elements.  
surface points can be considered to be the same geomorphic element if their input vectors are very close compared to others.  
In this study, the SOM technique was used in MATLAB 2019b (The MathWorks, Natick, MA, USA) via the command \"newosom.\" Prior to learning, four input parameters were normalized to the range of 0-1, as indicated in previous researches [20; 36]. The number of iterations affects the amount of computation required; therefore, to reduce the mapping time, it is necessary to control the iterations. The iteration values of rough tuning and fine tuning were thus set as 100 and 100, respectively. The number and combination of output map units was preset, as it is impossible to determine which number will provide a meaningful classification. Following the learning phase, the trained SOM was used to classify the geomorphic elements based on the input vectors for each cell, and the optimal number and the corresponding output were selected after analysis. One of the aims of this study was to determine a suitable map unit combination that enabled the identification of bedforms' features.
## 5 Verification of Real-time Hybrid Control System  
### Three-DOF Structural Dynamic Test with Steel Structure  
#### 5.1.1 Test Set-Up and Input Properties  
To confirm the simplified hybrid test control system, a steel frame specimen, one of the homogeneous materials predicting the nonlinear behavior, was used. As shown in Figure 5, the size of the specimen is 2.45 m wide and the height of the first and second floors is 2.4 m and 2 m, respectively, and a numerical analysis model was constructed with six beam-column elements under the same conditions. As shown in Figure 6, the physical model for the real-time hybrid test was prepared by constructing a column element on the first floor of the numerical model and installing one actuator horizontally and two actuators vertically in order to simulate a displacement and a rotation at the node.  
As shown in Figure 7, El Centro seismic wave was applied as the external load, and total loading time is about 30 s and the peak ground acceleration value was 0.7553 g. Figure 8 demonstrated the installation of an accelerometer and strain gauges on the steel frame specimen and Table 1 presented the detail dimension and properties of the specimen [31].  
In order to accurately predict the nonlinear behavior of the steel frame specimen during the real-time hybrid testing, the elastic modulus (230 GPa) and yield stress (300 MPa) were measured through a tensile test with universal testing machine (MTS 322), and the Giuffre-Menegotto-Pinto model was used as the nonlinear material model.  
Figure 4: Quadratic Interpolation.  
Figure 5: 1-bay, 2-story steel structure model.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 4 Potential Research Directions
### Security in ITS  
Although security issues relating to UAV-enabled systems have been extensively addressed in many research works, ensuring the ultimate security is still under investigation. The future ITS will involve interconnected systems of UAVs, vehicles (air, ground, water, and rail), and roadside infrastructure. The UAVs can carry, generate, and transfer valuable data and sensitive information about both vehicle users and UAV operators. This information includes vehicle information, location, and traffic data. As UAVs and vehicles are connected to a communication network, they can be tracked, and the information can be leaked for malicious purposes [37]. Besides, some UAVs can be manipulated to execute attacks on sensitive ITSs data. Any of these data needs to be adequately protected against unknown intruders. Providing security to prevent any unexpected intrusion would be a significant challenge. Moreover, the privacy of the users should be ensured with the emerging ITS technology. Although cybersecurity has always been a widely explored area, it still needs more advancements as the future ITS will build upon a UAV-vehicle network.
# Neighborhood Granule Classifiers
## 3 The Model of Neighborhood Granule Classifiers
### Granular Distance between Neighborhood Granules  
Suppose \(CS=(U,A,D,\delta)\) is a classification system. For \(\forall B\subseteq A\) and \(\forall x,y\in U\), suppose \(p=g_{B}^{\delta}(x)\), \(q=g_{B}^{\delta}(y)\) are two neighborhood granules, then the relative distance between two neighborhood granules is defined as:  
\[d(p,q)=\frac{|p\oplus q|}{|p\lor q|}=\frac{|(g_{B}^{\delta}(x)\cup g_{B}^{ \delta}(y))-(g_{B}^{\delta}(x)\cap g_{B}^{\delta}(y))|}{|g_{B}^{\delta}(x)\cup g _{B}^{\delta}(y)|}. \tag{8}\]  
**Example 2**: _There are two neighborhood granules such as \(g_{1}=\{x_{1},x_{2}\}\) and \(g_{2}=\{x_{1},x_{2},x_{3}\}\), respectively. We can obtain their relative granular distance: \(d(g_{1},g_{2})=\frac{|\{x_{1},x_{2}\}|\cup\{x_{1},x_{2},x_{3}\}|-(\{x_{1},x_{2 }\}\cap\{x_{1},x_{2},x_{3}\})|}{|\{x_{1},x_{2}\}\cup\{x_{1},x_{2},x_{3}\}|}= \frac{|\{x_{3}\}|}{|\{x_{1},x_{2},x_{3}\}|}=1/3\)._  
**Proposition 1**: _The relative distance between two neighborhood granules is a distance metric, which satisfies the following three properties:_  
\begin{tabular}{l l} _(1)_ & \(d(p,q)\geq 0\)_, non-negativity;_ \\ _(2)_ & \(d(p,q)=d(q,p)\)_, symmetry;_ \\ _(3)_ & \(d(p,k)+d(k,q)\geq d(p,q)\)_, triangle inequality._ \\ \end{tabular}  
**Proof.** The two properties of (1) and (2) are easily proven by the definition of the relative distance of neighborhood granules. The following proves the property (3).  
Given any \(a,b,c\), let \(a\geq b>0\), \(c\geq 0\), then \(\frac{b}{a}\leq\frac{b+c}{a+c}\). According to the definition of the relative distance between neighborhood granules, we can know \(d(p,k)+d(k,q)-d(p,q)=\frac{|p\cup k|-|p\cap k|}{|p\cup k|}+\  
Proof.: According to the definition of the absolute distance of neighborhood granules, it is easy to prove.  
These granular distances of neighborhood granules can be used as the similarity measures of granules, which indicate the degree of similarity of neighborhood granules. The smaller the granular distance is, the greater the similarity is. On the contrary, the larger the granular distance is, the smaller the similarity is.
# A Massively Parallel SMC Sampler for Decision Trees +
## 1 Introduction  
### Motivation  
Decision tree (DT) models are widely used algorithms when addressing classification problems in machine learning (ML). Their versatility spans numerous fields, including medicine [1], biology [2], chemistry [3], and engineering [4]. Although real-world applications often provide access to large datasets, this typically imposes significant challenges in terms of both accuracy and runtime performance.  
In recent years, Markov Chain Monte Carlo (MCMC) methods have gained popularity as a Bayesian approach to DTs [5, 6, 7, 8], often outperforming traditional classification algorithms such as decision forests in terms of accuracy [9; 10]. Despite achieving state-of-the-art accuracy, MCMC is computationally demanding. This challenge is typically mitigated by leveraging parallel computing on modern multi-core computer architectures, such as shared memory (SM) and distributed memory (DM) systems. However, the inherently sequential nature of MCMC makes it difficult to efficiently address modern classification problems while maintaining both accuracy and speed. Consequently, a Bayesian alternative to MCMC for DTs that is both parallelizable and accurate would be highly beneficial in solving ML classification tasks.
## 4 Evaluation Criteria
### Ground Truth Correlation (GTC)  
A Pearson correlation coefficient's matrix is computed for each dimension pair \((s,\phi(s))\):  
\[\rho_{\phi(s),s}=\frac{\mathbb{E}\big{[}\Big{(}\phi(s)-\mu_{\phi(s)}\Big{)}(s- \mu_{s})\big{]}}{\sigma_{\phi(s)}\sigma_{s}} \tag{14}\]  
where \(s\) is the ground truth state, \(\phi(s)\) is the learned state. \(\mu_{s}\) and \(\sigma_{s}\) are the mean and standard deviation of state \(s\) respectively.  
We can visualize the correlation matrix to quantitatively assess the ability of a model to encode relevant information in the states learned. However, the visualization idea is impractical when meeting high-dimensional spaces such as those in our experiments. As a result, [PERSON] et al. proposed another two metrics: Ground Truth Correlation (\(GTC\)) and the mean of that--\(GTC_{mean}\)[52]. They can be used to assess a model's capacity to encode important information, measuring the similarity between the state representation \(\phi(s)\) and the ground-truth state \(s\). The only difference between \(GTC\) and \(GTC_{mean}\) is that that \(GTC_{mean}\) just need one scalar value to evaluate the learned state. More specifically, for each component \(s_{i}\) of \(s\), \(GTC_{i}\) gives the maximum absolute correlation value between \(s_{i}\) and any component of the predicted states \(\phi(s)\):  
\[GTC_{i}=\max_{j}\Big{|}\rho_{\phi(s),s}(i,j)\Big{|}\in[0,1] \tag{15}\]  
\[GTC_{\text{mean}}\ =\ \mathbb{E}[GTC] \tag{16}\]  
with \(i\in[0,|s|],j\in[0,|\phi(s)|],s=[s_{1},\dots;s_{n}]\), and \(s_{k}\) being the \(k^{th}\) dimension of the ground truth state vector. Taking the Mobile Robot 2D navigation with one random target task as an example, the the ground-truth state have a dimension of 4 (\(|s|=4\)), which is made up of the two-dimensional robot position and the two-dimensional target position.
## 5 Performance Evaluation
### Network Lifetime  
The DT method also illustrates the multilevel (i.e., first stage, second stage and third level stage) performance term of network lifetime. It is a time period when mobile nodes are deployed till the first mobile nodes die because of the usage of battery. In the MWSN, the amount of the mobile nodes is enhanced and varied between 50, 100, 150 and 200. Figure 12 explains that the network lifetime is  
Figure 11: Energy consumption at different stages of the DT method.  
Figure 10: Average amount of messages delivered per mobile node.  
stable for 50 nodes. However, as the number of the mobile nodes increases, the lifetime of network decreases due to the usage of fixed batteries.
# Stochastic Analysis of a Large-Span Continuous
## 2 Analysis Methodology  
### Equation of Structural Motion  
In the global coordinate system, the motion equation for a structural system that has n degrees of freedom (DoFs) subjected to support motions can be represented as the following partitioned matrix form [32]:  
\[\begin{bmatrix}\mathbf{M}_{\mathrm{s}}&0\\ 0&\mathbf{M}_{\mathrm{b}}\end{bmatrix}\begin{cases}\dot{\mathbf{X}}_{\mathrm{s }}\\ \dot{\mathbf{X}}_{\mathrm{b}}\end{cases}+\begin{bmatrix}\mathbf{C}_{\mathrm{s}} &\mathbf{C}_{sb}\\ \mathbf{C}_{sb}^{T}&\mathbf{C}_{b}\end{cases}\begin{cases}\dot{\mathbf{X}}_{ \mathrm{s}}\\ \dot{\mathbf{X}}_{b}\end{cases}+\begin{bmatrix}\mathbf{K}_{\mathrm{s}}&\mathbf{ K}_{sb}\\ \mathbf{K}_{sb}^{T}&\mathbf{K}_{b}\end{bmatrix}\begin{cases}\mathbf{X}_{ \mathrm{s}}\\ \mathbf{X}_{b}\end{cases}=\begin{cases}0\\ \mathbf{F}_{\mathrm{b}}\end{cases}, \tag{1}\]  
where subscript \(s\) represents the slave DoFs; subscript \(b\) represents the master DoFs; \(\mathbf{M}\), \(\mathbf{C}\) and \(\mathbf{K}\) represent the mass, damping and stiffness matrices, respectively; \(\mathbf{F}_{b}\) represents the seismic forces at structural supports; \(\ddot{\mathbf{X}}\), \(\dot{\mathbf{X}}\) and \(\mathbf{X}\) represent the vector of the nodal acceleration, velocity and displacement, respectively, in the global coordinate system; \(\mathbf{C}_{sb}\) and \(\mathbf{K}_{sb}\) represent the coupled damping and stiffness matrices, respectively; \(T\) denotes transpose.  
Furthermore, the nodal absolute displacement \(\mathbf{X}_{\mathrm{s}}\) can be split into the quasi-static displacement \(\mathbf{X}_{\mathrm{s}}^{s}\) and the dynamic displacement \(\mathbf{X}_{\mathrm{s}}^{d}\).  
\[\begin{cases}\mathbf{X}_{\mathrm{s}}\\ \mathbf{X}_{b}\end{cases}=\begin{cases}\mathbf{X}_{\mathrm{s}}^{s}\\ \mathbf{X}_{b}\end{cases}+\begin{cases}\mathbf{X}_{\mathrm{s}}^{d}\\ 0\end{cases}, \tag{2}\]  
The quasi-static displacement \(\mathbf{X}_{\mathrm{s}}^{s}\) is caused by the non-uniform displacements of the supports and can be solved by:  
\[\mathbf{X}_{\mathrm{s}}^{s}=-\mathbf{K}_{\mathrm{s}}^{-1}\mathbf{K}_{sb} \mathbf{X}_{b}=\mathbf{R}_{\mathrm{s}}\mathbf{X}_{b}, \tag{3}\]  
where \(\mathbf{R}_{\mathrm{s}}\) is the static influence matrix. The dynamic displacement \(\mathbf{X}_{\mathrm{s}}^{d}\) satisfies the following equation [26]:  
\[\mathbf{M}_{\mathrm{s}}\ddot{\mathbf{X}}_{\mathrm{s}}^{d}+\mathbf{C}_{\mathrm{ s}}\dot{\mathbf{X}}_{\mathrm{s}}^{d}+\mathbf{K}_{\mathrm{s}}\mathbf{X}_{\mathrm{s}}^{d}=- \mathbf{M}_{\mathrm{s}}\mathbf{R}_{\mathrm{s}}\ddot{\mathbf{X}}_{b}, \tag{4}\]  
According to Equation (4), the dynamic displacement \(\mathbf{X}_{\mathrm{s}}^{d}\) can be obtained in terms of the acceleration time-histories of each support.