esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Trajectory Tracking
The \(WP_{Radius}\) is the distance in meters from a WP when the algorithm considers the WP has been reached and determines when the unit will proceed to the next WP. After reaching the first WP, the unit starts the L1 tracking mode (positions \(USV_{2}\), \(USV_{4}\)). In the L1 tracking mode a point L is tracked and this point is dynamically located on the line between the last and next WP. The L point is the intersection point between the track line and the circle with the radius equal to the distance \(L_{1}\) defined by the user. When point B is reached the next WP (C) is acknowledged to be tracked (Next WP Tracking Area). The procedure is repeated until the unit reaches the last WP when it stops and waits for the next command, either from the operator or the autonomous system. \[a_{s}=2\frac{V^{2}}{L_{1}}\sin\eta \tag{1}\]0 Guidance logic trajectory tracking for an unmanned surface vehicle (USV). The L1 method calculates the lateral acceleration to be executed by the unit. The \(L_{1}\) acceleration is translated to the motors and the steer command using the Steering LA and Motor Control methods (Figure 1). The Steering LA method is based on a PID controller. The automatic (mission control) algorithms, according to research [25] were demonstrated to control speed and course of the surface vessel. The PID controller continuously attempts to minimize the error \(e(t)\) over time by adjustment of a control variable \(u(t)\). The error value function \(e(t)\) is the difference between a desired set point \(r(t)\) and the measured process variable \(y(t)\) (\(e(t)~{}=~{}r(t)-y(t)\)). Process variable is represented by the value that is being controlled (e.g., actual speed or actual course). The PID controller can be expressed as:  
\[a_{s}=2\frac{V^{2}}{L_{1}}\sin\eta \tag{1}\]1  
where \(K_{p}\), \(K_{i}\), and \(K_{d}\) are non-negative and denote the coefficients for the proportional, integral, and derivative terms, respectively. The parameters were set experimentally during test trials. The PID desired and the PID achieved were monitored and displayed and the coefficients can be adjusted to achieve the appropriate object response and parameter (course and speed) stabilization.
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 1 Introduction
In this method some gateway nodes are placed at the border of the area. Cluster heads, which are inside the network, calculate distance between itself and all gateway nodes and then select the gateway node which is nearest to it. Then cluster heads transmit data to the gateway node. This approach also improves the network's lifetime, but there are some issues with this protocol. Its major requirement is heterogeneous network which is always not possible; it takes an approximate number of gateway nodes. The number of gateway nodes deployed is not determined in a logical way. A small number of gateway nodes can be inefficient for a larger area and the large number of gateways is unnecessary in a small area. Gateway nodes are positioned at the border of an area; as a result nodes in this area consume less power in transmission but nodes in remote area consume more power. As the size of area increases this situation gets worst. The rest of the paper is organized as follows: Section 2 presents the related work. A node deployment model has been presented in Section 3. Section 4 describes the energy consumption model. Section 5 explains the proposed protocol. Section 6 provides the proposed algorithm. Section 7 gives the usefulness of the protocol in real life. Section 8 provides an energy consumption analysis of the MGBEHA protocol. Section 9 provides simulation and results analysis. Section 10 gives the details of future work and Section 11 concludes the paper.
# The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches
## 3 Research Methodology
### Rapid Lane Change
\begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Normal & 0.97 & 0.99 & 0.98 \\ LC (S1) & 0.95 & 0.92 & 0.93 \\ LC (S2) & 0.89 & 0.80 & 0.84 \\ LC (S3) & 0.97 & 0.84 & 0.90 \\ LC (S4) & 0.85 & 0.88 & 0.87 \\ \hline \multicolumn{4}{c}{Accuracy} & 0.95 \\ \hline \end{tabular}
\end{table} 
Table 4: Table for evaluating CNN-LSTM classification model results for rapid lane change. LC is the rapid lane-change class, and S1 to S4 are in that stage. \begin{table}
\begin{tabular}{c c} \hline
**Raw Dataset** \\ \hline Location & Lateral(x) and longitudinal(y) position of the vehicle \\ Gyroscope & Angular velocity (x, y, z-axis) from IMU sensor in Carla simulator \\ Acceleration & Acceleration (x, y, z-axis) from IMU sensor in Carla simulator \\ Speed & Speed of the vehicle \\ \hline \multicolumn{4}{c}{Feature Engineering} \\ \hline Jerk, Snap & The second derivative on acceleration and the rate of change of \\ Jerk, respectively & Moving windows (Mean, Max, Min) with time intervals of 1 s, 2 s, \\ Mean, std, max, min, & Means and standard deviations of variables under different \\ quantile values & window sizes \\ Angle & Angle of the roads under different window sizes (0.5 s, 1 s, and 2 s) \\ Percentage change & Percentage change in variables (speed, acceleration, and \\  & angular velocity) \\ Log ratio & Log ratio on variables (speed, acceleration, and angular velocity) \\ \hline \end{tabular}
\end{table} 
Table 2: Table of raw dataset and feature engineering results. \begin{table}
\begin{tabular}{c c c} \hline
**Rank of Feature** & **Notation of Feature** & **Definition** \\
**Importance** & & \\ \hline
1 & acc-y\({}_{\text{mmax500}}\) & Moving max acceleration of y-axis with 5 s \\
2 & gyro-z\({}_{\text{mmax100}}\) & Moving max angular velocity of z-axis with 1 s \\
3 & gyro-x\({}_{\text{mmax100}}\) & Moving max angular velocity of x-axis with 1 s \\
4 & acc-z\({}_{\text{mmax100}}\) & Moving max acceleration of z-axis with 1 s \\
5 & angle\({}_{\text{mmax50}}\) & Moving max angle with 0.5 s \\
6 & speed\({}_{\text{mstd500}}\) & Moving standard deviation of speed with 5 s \\
7 & acc-z\({}_{\text{mstd500}}\) & Moving standard deviation acceleration of z-axis \\
8 & angle\({}_{\text{mmax100}}\) & Moving max angle with 1 s \\
9 & speed\({}_{\text{ma}}\) & Moving average speed under different \\
10 & speed\({}_{\text{mmax500}}\) & Moving max speed with 5 s \\ \hline \end{tabular}
\end{table} 
Table 3: Table for selected features.
## 4 Experimental Results and Its Analysis in Real-Time Environment
### Real-Time Qualitative Analysis  
For real-time qualitative evaluation, we used grid search algorithms to find the top three mean test scores, such as 90.38, 85.58, and 79.4, that helped to achieve the highest accuracy, as seen in Table 7. By using the highest test score of 90.38, we were able to achieve the best hyperparameters used for training models, such as learning rate of 0.01, epochs as 250, batch-size as 32, kernel-size as 1, hidden-units as 32, regularisation dropout as 0.05, and optimizer as (referenced in Table 8). Where the following parameters are stated as:  
* Learning Rate: states how many weights are modified in the loss gradient model;
* Batch Size: Specified are run through the model at any particular moment;
* Epochs: Defines the number of times the machine learning model is performed on the same dataset;
* Dropout: Is a method of regularization to reduce the issue of overfitting by dropping some of the random nodes during the training phase and improving generalization error in NN.  
**Remark 3**.: _Adam is a stochastic gradient optimizer for the training of deep learning models, and it is a combination of the best features of AdaGrad and RmsProp, so it can solve problems with low gradients or a lot of background noise._
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology
### Distributed Storage of Trust Values  
In this phase, we utilize the IOTA Tangle to securely store the agreed trust scores of all nodes in a distributed and immutable manner. This ensures that no node in the network presents a fake trust value for malicious intention. It also stores the values in a distributed fashion, so that any other node in the network can access and verify the trust scores of another node conveniently. We configure the IOTA SDK ([[https://github.com/iotaledger/iota-sdk](https://github.com/iotaledger/iota-sdk)]([https://github.com/iotaledger/iota-sdk](https://github.com/iotaledger/iota-sdk)) (accessed on 25 May 2025)) for the Shimmer network by setting up the client libraries and establishing a connection to the Shimmer testnet. The integration of the IOTA ledger involves the following steps.  
1. Transaction Creation: * Encoding the agreed trust score data into a suitable format for IOTA transactions. This ensures that the data payload adheres to the protocol's requirements. * Creating a transaction object in the IOTA SDK to embed the encoded trust score data into the transaction message. * Attaching the metadata about the respective node to the transaction to facilitate easy retrieval and identification. 2. Transaction Submission: * Submitting the transaction to the IOTA network via the IOTA SDK client. * Monitoring the network response to ensure that the transaction is successfully broadcasted and confirmed by the network. 3. Verification and Confirmation: * Verifying the transaction status using the transaction ID provided by the IOTA network. * Confirming the immutability of the trust score data by ensuring it is included in a milestone, making it a permanent part of the IOTA ledger. 4. Broadcasting and Retrieval:* Broadcasting the transaction ID to the stored trust score to all nodes within the network.
* Nodes can retrieve and verify the trust score directly from the IOTA ledger using the provided transaction ID, ensuring transparency and trust in the stored trust data.  
In the proposed framework, each node's trust update is packaged as an individual transaction, signed, and submitted directly to the Tangle. There is no logical or temporal dependency on block creation. Finality is achieved once the transaction is referenced by a milestone. This ensures that the transaction becomes an immutable part of the ledger. This lightweight, transaction-first model aligns with the low-latency and high-throughput requirements of IoT trust management. Moreover, the integration of the IOTA Tangle ensures that trust scores are securely stored in an immutable, decentralized manner. This enhances the reliability, security, and transparency of the trust evaluation framework, providing a robust solution for managing trust in IoT environments.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Cobalt Tin Sulfate Films  
The formation of CoSnS thin films took place on a soda lime glass slide [94] by using cobalt sulfate, tin chloride, ammonia (complexing agent), and thioacetamide (C\({}_{2}\)H\({}_{5}\)NS) (rinsing time = 10 s, immersion times = 20 s). The XRD studies indicated that polycrystalline with the diffraction peaks could be observed in (200), (201), (211), and (221) planes. However, secondary phases, such as hexagonal CoS, orthorhombic Sn\({}_{2}\)S\({}_{3}\), cubic Co\({}_{9}\)S\({}_{8}\), and hexagonal SnS\({}_{2}\) could be detected in the XRD patterns also. Optical investigations confirmed that absorption was very high when the wavelength was less than 350 nm; however, absorption was reduced in the visible and near-infrared regions. Based on the findings, the refractive index reduced (3.11 to 2.94), but the band gap (1.22 to 1.52 eV) and electronegativity (1.6928 to 1.7168) increased when increased with increasing the deposition cycles from 40 to 80 cycles.
## 2 Data Acquisition and Preprocessing
### Prediction Model
#### 2.4.1 Machine Learning Models  
The artificial neural network (ANN) is a computational model that mimics the connection mode of neurons in the human brain and is widely used to address various complex pattern recognition and prediction problems [19]. It comprises a large number of neurons arranged in a hierarchical structure, where information is transmitted through weighted connections. Random forest (RF), on the other hand, generates multiple decision trees by randomly selecting samples and features, thereby reducing overfitting and enhancing generalization ability. During the generation of each tree, data subsets are extracted via the bootstrap sampling method, and random feature subsets are selected to construct decision trees [20]. The final prediction result is obtained by aggregating the outputs of multiple decision trees through voting or averaging.  
XGBOOST is an efficient implementation of the boosting tree. It uses parallel processing and multiple optimization techniques, making significant improvements in speed and memory efficiency [21].  
CATBOOST is a machine learning library based on gradient boosting trees introduced in 2018. The primary design objective of CATBOOST is to streamline the machine learning workflow while enhancing performance, particularly in scenarios with rich feature sets [22]. It employs the ordered boosting method, which reduces bias in the gradient boosting algorithm by randomly permuting the training data during each iteration. Additionally, it offers a variety of optimization algorithms and feature engineering tools, enabling automatic selection of the optimal feature combination.  
Support Vector Regression (SVR) differs from traditional regression models in that it incorporates the principles of support vector machines and predicts outcomes by identifying an \"optimal hyperplane\". Its primary objective is to minimize the deviation between predicted and actual values, and through specific techniques such as kernel tricks and regularization, the model achieves strong generalization capabilities [23].
## 1 Introduction
This is crucial for a high precision application such as machining. Second, it aims at comparing the elastic displacements of the manipulator at hand obtained by Rayleigh-Ritz and finite element methods based on FFRF. A simple geometry of the manipulator which can be modeled by beams is used to enable the utilization of both the methods. After an acceptable agreement between the two methods is verified in this study, the use of only finite element method with 3D elements for a similar manipulator with a complex geometry, in which Rayleigh-Ritz method is no more applicable, can be performed in the future. For this future work, a nonsymmetric planar 3 PRR manipulator with a complex geometry which will be described in Section 3 was built for machining application. For convenience, the formulation of FFRF using the Rayleigh-Ritz and finite element methods will be presented here briefly. First, the kinematics of a general flexible multibody system is presented. Second, the components of the flexible body dynamics which include the inertia matrix, the quadratic velocity vector, the external force vector and the elastic force vector are described. These components should be defined for each body and subsequently an assembly should be performed to obtain the components at the system level. For simplicity, friction forces are not taken into consideration. Since the system is constrained, the constraint equations should also be defined. Finally, by incorporating the constraint equations into the system equations of motion, a constrained flexible body dynamics for the system is composed. In order to solve the obtained flexible body dynamics, a set of boundary conditions should be imposed to the system. In this work, the elastic displacements of all of the flexible RR links in the manipulator at hand are solved by applying a fixed-free boundary condition to the links. The links are modeled by using Euler-Bernoulli beam model. The limitation of this link model is its lack of applicability to complex geometry. In a case where links with complex geometry should be used, one should apply another model such as finite element model. However, the finite element model for complex geometry typically results in a huge number of degrees of freedom (DOFs). For a pure analysis, this is acceptable. But for control purpose, the computation involving the huge number of DOFs is intensive while a control should be performed in real time. In this case, either a model which provides a reasonably small number of DOFs should be used or a model order reduction should be performed to a model which provides the huge number of DOFs to obtain a smaller number of DOFs.
# Attention-Based Spatiotemporal-Aware Network for Fine-Grained Visual Recognition
## 2 Related Work
### Micro-Expression Recognition Based on Action Units  
The key to improving micro-expression recognition lies in capturing and extracting more comprehensive and discriminative micro-expression information. Besides the spatiotemporal dimensions of micro-expression information, the movement changes of facial muscles during micro-expressions are also related. Therefore, analyzing micro-expressions through the combination of action units (AUs) is an effective method.  
For instance, [PERSON] et al. (2016) used a 3D convolutional neural network to extract AU-related features and then utilized a graph convolutional network to connect AU nodes with complex dependencies for emotion classification. This method focuses on local action changes but employs a model with a large number of parameters. The designed adjacency matrix is calculated based on the co-occurrence rate of AUs in the training set, making it susceptible to noisy actions and lacking robustness.  
Similarly, [PERSON] et al. (2017) proposed AGACN, which combines AU information with micro-expression labels, modeling AUs in different regions based on facial muscle deformation and relational information. [PERSON] et al. (2018) represented micro-expressions based on partial facial key points and used depthwise convolution (DConv) to learn key point features, or node learning, and employed the Transformer encoder to learn relationships between nodes, or edge learning. The AU-GCN module in this method constructs and learns the AU feature matrix and adjacency matrix through word embedding and GCN. This method combines facial features with AU information, yielding good recognition results.  
[PERSON] et al. (2019) proposed the MERASTC method, which encodes the subtle changes in facial muscles in micro-expression image sequences by combining AU, key points, and appearance features during micro-expressions. They also proposed a face normalization method based on neutral features to accelerate the network's micro-expression recognition efficiency. Although MERASTC alleviates the network overfitting issue, it requires high sample data quality (the image sequence must contain neutral frames), thus having certain limitations.  
[PERSON] et al. (2019) combined AU with facial key points to construct four regions of interest: eyes, nose, cheeks and mouth. They then weighted the corresponding regionsand extracted features using the proposed MER-AMRE network, improving the network's ability to extract local motion information.  
To explore the relationship between facial expressions and AUs, [PERSON] et al. [21] segmented micro-expression videos into multiple adjacent video clips, revealing the spatiotemporal feature changes in the three-dimensional neighborhood. They combined this with the proposed Multi-Task Facial Activity Pattern Learning Framework (MFAPLF) to promote the aggregation of similar micro-expressions.
## 7 Conclusions, Challenges, and Future Research
### Limitations  
Despite these promising results, there are notable limitations that must be addressed. First, our evaluation was confined to a specific set of benchmark functions and engineering problems. Although these problems are widely accepted in the optimization field, they may not represent the full spectrum of real-world challenges. Therefore, the results may not fully generalize to all problem types or industries, particularly for those that deviate from the tested scenarios.  
Second, the parameter settings employed in our tests were fixed after initial tuning, which worked well under the tested conditions. However, it is conceivable that further fine-tuning or employing adaptive parameter control mechanisms could yield even better results. This underscores the need for a more detailed investigation into dynamic parameter settings that could adapt to the unique demands of different optimization problems.  
Additionally, the scope of this study did not include testing the MRSO on large-scale, highly complex engineering problems, which may limit its immediate application to such contexts. Addressing large-scale optimization scenarios will be crucial for future research to determine how scalable the MRSO is when dealing with computationally intensive problems. Lastly, the comparative algorithms used in our evaluation, while competitive, were not exhaustive. A more comprehensive comparison involving newer or more established algorithms could provide deeper insights into the MRSO's performance advantages and its areas of improvement.
## 4 Numerical Experiment and Analysis
### Impact of Measurement Error  
To assess the impact of measurement error, the number of measuring points was set as \(M=\) 6 and fine groups of measurement error of 0%, \(\pm\)0.3%, \(\pm\)0.5%, \(\pm\)1%, and \(\pm\)2% were used. The values of \(\lambda\) were calculated by the least square method; the relative error is shown in Table 2. Results are as follows:  
* when measurement error was 0%, the average relative error of \(\lambda\) was 0.00%;
* when measurement error was \(\pm\)0.3%, the average relative error of \(\lambda\) was 0.50%;
* when measurement error was \(\pm\)0.5%, the average relative error of \(\lambda\) was 0.84%;
* when measurement error was \(\pm\)1.0%, the average relative error of \(\lambda\) was \(-\)1.40%;
* when measurement error was \(\pm\)2.0%, the average relative error of \(\lambda\) was \(-\)1.94%.  
It can be seen that there were some measurement errors under the premise of a large amount of measurement data, and the inversion results were still satisfactory. However, the larger the measurement error, the more distorted the inversion results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Points**} & \multicolumn{2}{c}{**Calculated Value of \(\lambda\) Relative Error (\%)**} & \multicolumn{2}{c}{**Calculated Value of \(\lambda\) Relative Error (\%)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Measurement Error = 0**} & \multicolumn{2}{c}{**Measurement Error = \(\pm\) 0.1\%**} \\ \hline
2 & 11.9610 & \(-\)0.33 & 11.9490 & \(-\)0.43 \\
4 & 12.0049 & 0.04 & 11.9700 & \(-\)0.25 \\
6 & 12.0000 & 0.00 & 11.9871 & \(-\)0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The value of \(\lambda\) and the relative error.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Measurement Error (\%)**} & \multicolumn{2}{c}{**Calculated Value of \(\lambda\)**} & \multicolumn{2}{c}{**Relative Error (\%)**} \\ \hline
0 & 12.0000 & 0.00 \\ \(\pm\)0.3 & 12.0600 & 0.50 \\ \(\pm\)0.5 & 12.1005 & 0.84 \\ \(\pm\)1.0 & 11.8316 & \(-\)1.40 \\ \(\pm\)2.0 & 11.7669 & \(-\)1.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The value of \(\lambda\) and the relative error.