esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Crowd Density and Movement Analysis  
A micro-road network refers to a network structure composed of passages or streets with different functions, levels, and locations (typically with widths ranging from 2.4 m to 4.2 m) in large event areas. These networks are formed with a certain density and an appropriate layout. During events, such networks bear the burden of large crowds gathering and moving, posing a significant risk of crowd congestion. Crowd density estimation remains one of the most commonly employed approaches in crowd behavior analysis. Initial works in this area largely relied on crowd flow models and density maps to identify potential risks. For instance, [PERSON] et al. proposed a method for crowd density estimation that leveraged deep learning models to predict crowd congestion in real-time crowd density estimation [16], which was effective for detecting congestion in open spaces. However, it failed to account for the behavioral nuances of crowd panic, such as sudden changes in movement patterns and emotional escalation that make it difficult to identify the behavioral cues indicative of panic, which are often characterized by sudden, irregular movements and deviations from the general flow of the crowd [17]. Similarly, [PERSON] et al. introduced a method using convolutional neural networks (CNNs) to estimate crowd density, achieving promising results in detecting crowded areas [18]. However, their model could not assess sudden shifts in behavior that could signal an emergency or panic situation.
# Stacked-GRU Based Power System Transient Stability Assessment Method
## 2 Methodology
### Stacked-Gru  
GRU is a shallow model with weak capability of feature extraction, and the stacked-GRU is composed of several GRU units, as shown in Figure 2. Specifically, the input of first layer in stacked-GRU is the original data, and the formulas is the same as the GRU unit in Section 2.1.  
The input of each GRU unit in the middle is the output of the hidden layer of the upper layer GRU unit:  
\[\left\{\begin{array}{l}\mathbf{z}_{t}^{i}=\sigma\Big{(}\mathbf{W}_{z}^{i}\Big{[}\mathbf{h}_ {t-1}^{i},\mathbf{h}_{t}^{i-1}\Big{]}\Big{)}\\ \mathbf{r}_{t}^{i}=\sigma\Big{(}\mathbf{W}_{t}^{i}\Big{[}\mathbf{h}_{t-1}^{i},\mathbf{h}_{t}^{i- 1}\Big{]}\Big{)}\\ \widetilde{\mathbf{h}}_{t}^{i}=\tanh\Big{(}\mathbf{W}^{i}\Big{[}\mathbf{r}_{t}^{i}\odot\mathbf{ h}_{t-1}^{i},\mathbf{h}_{t}^{i-1}\Big{]}\\ \mathbf{h}_{t}^{i}=\mathbf{z}_{t}^{i}\odot\mathbf{h}_{t-1}^{i}+(1-\mathbf{z}_{t}^{i})\odot\mathbf{ h}_{t}^{i-1}\end{array}\right. \tag{7}\]  
Among them, the superscript represents the \(i\)-th GRU unit, and the subscript represents the moment \(t\). The hidden layer of the last layer in GRU unit adds a layer of sigmoid as the classifier to provide output:  
\[\widetilde{y}_{last}=\sigma(\mathbf{W}_{o}^{n}\mathbf{h}_{o}^{n}+\mathbf{b}_{o}^{n}) \tag{8}\]  
In the formula, \(\widetilde{y}_{last}^{(i)}\) is predicted label at the last moment of \(i\)-th sample, \(\mathbf{W}_{o}^{n}\) is weight of output layer, and \(\mathbf{b}_{o}^{n}\) is the bias of the \(n\)-th GRU unit. The training method of stacked-GRU and the optimized objective function are the same as a single GRU. The objective function is:  
\[L=\sum_{i}^{N}y^{(i)}ln(\widetilde{y}_{last}^{(i)}) \tag{9}\]  
On the one hand, such a deep structure can efficiently discover high-level feature from limited data. On the other hand, it can more fully utilize information in the time series, which can effectively improve the performance of the classifier. In addition, the stacked-GRU model parameters are independent of time, so the trade-off between time and precision in reference [18] can be avoided.
# Bending Fatigue Behavior of 17-4 PH Gears Produced by Additive Manufacturing
## 6 Results Discussion  
The present work is a part of a project aimed at the mechanical characterization of AM materials [50; 51; 52], specifically the determination of the fatigue limit, and their application to a classical machine element such as gears. In this context, it is of interest to compare the results of the present experimental campaign with the classical gear literature data aiming at evaluating the applicability of AM technologies in the context of gearbox design. ISO 6336-5:2016 [23] presents fatigue limits for steel which are like the one obtained in this experimental campaign. As an example, according to the aforementioned code, the bending fatigue limit \(\sigma_{\text{FE}}\) in the case of hardened wrought steel of medium quality (ME), at 1% failure probabilities and without considering any correction factors, typically ranges between 425 and 500 MPa, values comparable with the ones found in this experimental campaign.  
In future works, the effects of the various mechanical and thermal treatments on which the tooth root can undergo, will be studied. In this work, gears have been tested after hobbing and without any thermal treatment to set a reference for future studies. Therefore, there is still room for the increase of the tooth root bearing capacity by improving the tooth root roughness and adopting proper thermal treatment. For instance, ageing and Hot Isostatic Pressing (HIP) have beneficial effects on the fatigue behavior of an AM 17-4 Ph specimen [53; 54].  
Despite the degree of freedom given by the design of the parts via AM, the low geometrical accuracy often requires the machining of the parts. This aspect is even more crucial in gears because the tooth profile must be as close as possible to the theoretical involute to avoid undesired levels of noise, vibration, wear, and fatigue phenomena. Thus, gear postprocess machining of the meshing parts remains necessary. Regarding the real advantages of producing small batches of full body gears, AM remains necessary when aiming at a gear tuning that wants to overcome the limits of the classic manufacturing  
Figure 12: Tooth B15—the nucleation zone is remarked in (**a**), where large fusion defect (about 240 \(\upmu\)m deep) with unmelted particles is clearly visible on the outer surface (**b**,**c**).  
processes and become an affordable technique for prototypes. Within this context, the evaluated fatigue resistance properties are a key positive factor.
## 6 Conclusions  
In this paper, we proposed an integrated approach to robot vision--the key frame-based skeleton feature estimation and action recognition network (KFSENet)--which incorporates action recognition with face and emotion recognition to afford social robots a more personal interaction with humans. We propose a key frame-based approach to the extraction of human skeleton features or keypoints using pose estimation models. The key frames are selected based upon the gradient of a proposed total motion metric that is computed using dense optical flow. We use the human skeleton features extracted from the key frames to train a deep neural network (i.e., the double-feature double-motion network (DDNet)) for action recognition. The proposed KFSENet utilizes a simpler model to learn about different action classes, is computationally simpler and more efficient and yields better action recognition performance compared with existing methods. The use of key frames allows the proposed method to eliminate unnecessary and redundant frames, thereby improving classification accuracy and decreasing the computational cost. The proposed method was tested and compared to existing methods using both standard benchmark datasets and self-collected datasets. Furthermore, we integrated face and emotion recognition into the proposed framework to enable social robots to engage in more personal interaction with humans.  
Conceptualization, H.H.H.; Data curation, V.-A.T.; Formal analysis, T.-H.N.; Funding acquisition, D.-S.L. and T.-H.N.; Investigation, T.-H.N.; Methodology, D.-S.L.; Resources, H.H.H.; Software, H.-H.P. and V.-A.T.; Validation, H.-H.H. and D.-Q.N.; Writing--original draft, D.-S.L., H.-H.P. and D.-Q.N.; Writing--review and editing, D.-Q.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Vietnam national project No. KC-4.0-04/19-25.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline IR50 Backbone Network [50] + Softmax Classifier & 73.04 \\ Residual Masking Network & 74.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation of different methods for facial emotion recognition on the FER2013 dataset.
## 2 Materials and Methods
### Mechanical Characterization  
The 3D printed scaffolds (cell-free) were mechanically characterized using a uniaxial unconfined compression test using the LLOYD Instruments LR5k compression machine (AMETEK test & calibration instruments). The PCL frame, hydrogel, and the combined scaffolds (PCL and hydrogel) were tested separately using a 100 N load cell with a 0.1 N preload, a 1 mm deflection, and a strain rate of 0.002 s\({}^{-1}\) (i.e., a crosshead speed of 0.36 mm/min). From the load-deflection curve generated by the machine on the Nexygen software, 200 data points were exported per test for further analysis. Each of the data points included the recorded time (s), load (N), crosshead travel (mm), and deflection from the preload (mm), which were used for the generation of the stress-strain curves and the calculation of the compressive stiffness of the scaffolds. The stress was calculated by dividing the compression force by the cross-section area, and the strain was defined as the ratio of the crosshead travel to the initial length of the specimens. The stiffness calculations were performed using a moving regression algorithm generated with Gnu R [22] that was used to calculate the linear line with the steepest slope fit of the stress-strain curve. The slope of the linear curve was taken as the value for the compressive stiffness of the scaffolds (E = \(\sigma/\varepsilon\), MPa).
## 3 Methods
### Random Forest Model Construction and Parameters Optimization  
#### 3.3.1 Model Construction  
The random forest algorithm is a machine learning technique that involves training and predicting with numerous decision trees collectively. It is primarily based on bagging ensemble learning theory, where decision trees are constructed by randomly drawing an equivalent training sample with replacement from the sample set. The algorithm employs a random subspace method to select a subset of features from the entire feature set and chooses the optimal features to split the nodes of the decision tree. The final result is determined collectively with multiple independent decision trees. This approach effectively constructs complex nonlinear relationships between population data and geo graphical factors. These random selection processes ensure better feature acquisition, improving model robustness and preventing overfitting. Additionally, due to the independence between decision trees, they can be generated in parallel during the tree-building process, enhancing the efficiency of the algorithm.  
We constructed the population spatialization prediction model based on random forest algorithm using the Python language and the Scikit-learn open-source library. A sample dataset comprising 183 county-level administrative units of Sichuan Province will be used as the training sample. A total of 27 highly correlated features are selected as independent variables, while population data of county-level administrative units are used as the dependent variable. In total, 80% of the samples are used as training data, and the remaining 20% constitute the validation samples. The dataset is divided with fixed random numbers to ensure the randomness of dataset division and the replicability of experiments.
## 3 System Descriptions and Assumptions
### Cache Chord Mechanism
#### 3.3.1 Identifier Mapping  
The initial step involves performing a consistent hashing operation on the \(NID\) of server nodes, and \(KID\) of application resource identifiers to construct a ring topology. The Hash algorithm maps these identifier data into a space of size \(2^{m}\), creating a unique m-bit string. The data in this space are arranged in ascending order, proceeding clockwise, to form a virtual circular \"ring\". This ring is numerically labeled with integers within the range of \([0,2^{m}-1]\), assigning numbers to both the server and application data, which follows  
\[X=\mathrm{Hash}\langle\mathrm{NID}\rangle\bmod m, \tag{4}\] \[Y=\mathrm{Hash}\langle\mathrm{KID}\rangle\bmod m, \tag{5}\]  
where \(\mathrm{Hash}\langle\rangle\) is the mapping function. Both random variables \(X\) and \(Y\) are defined as positive integers within interval \([0,2^{m}-1]\). The position of server node within the ring is denoted by \(\mathrm{Nod}X\), while the location of application node is represented by \(\mathrm{Key}Y\).  
In the ring network, resource \(\mathrm{Key}Y\) is updated by the first server node \(\mathrm{Nod}X\) encountered in the clockwise direction. \(\mathrm{Nod}X\) maintains a cache IP list for resource localization. Specifically, the list comprises a dataset formed by tuple \(\langle\mathrm{Key}Y,\mathrm{ServerIP}_{Y}\rangle\), the first element of this tuple represents the position \(Y\) of the application data within the ring, the IP address of each physical device that stores application data \(Y\) at time \(t\) is reperensted by  
\[\mathrm{ServerIP}_{Y}^{t}=\{nid_{1},nid_{2},...,nid_{S}\}\cup\{0\},\forall nid _{s}\in\mathrm{NID}, \tag{6}\]  
where \(\{0\}\) signifies that the application data do not exist in edge layer. Utilizing this table, any user requesting service \(k\) for application data \(\omega_{k}\) can determine the resource's position in the ring network by processing its resource identifier \(kid_{k}\) through Formula (5), and index which machine stored the application data through \(\mathrm{ServerIP}_{Y}^{t}\). It should be clarified that the positions and adjacent relationships of nodes within the Cache Chord merely represent a logical topology at the overlay network level, rather than actual storage locations. Additionally, the cache IP list on the server stores only indices of certain application data, not the application data themselves within the cache pool.
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems
### Industrial Control Systems  
Most industrial control systems have been designed and programmed in a manner that is independent of the security requirements, and data are transmitted in plain text [3; 12]. Component security should be shared by vendors who maintain secure development structures and operators who configure components in accordance with industry standards and best practices. Either way, it often assumes the opponent's fault and takes no action, exposing many critical flaws for attackers to exploit [47]. Designers and operators of ICSs must understand the system's limitations, as well as the weaknesses of their components and protocols, as these are critical for the vessel's safety. The ship's distributed information technology network allows these control systems to communicate with one another. Continuous communication between the IT network and the website enables remote monitoring, troubleshooting, and debugging, while also lowering field travel costs and simplifying data collection and evaluation. One of the major concerns is that operators and engineers frequently disregard safety for the sake of convenience and efficiency, which can have far-reaching consequences throughout the whole shipping industry [48]. This conduct is caused by commercial pressure to save time and circumvent security policies [24; 39; 48].
## 2 Data Collection and Processing
### Data Preprocessing  
The process of data extraction, processing, and AI model training in an IoT system is designed for power forecasting. The whole data processing and model training process is shown in a flow chart in Figure 2. Initially, cloud database access is configured using PostgreSQL, allowing secure and efficient data retrieval through specific SQL queries. After retrieving the data from the database, data are cleaned by removing null values, filling NaN values through interpolation, and filtering to handle heterogeneous datasets. The term heterogeneous data refers to datasets that consist of diverse types of information, such as numerical, categorical, and temporal data, or data from different sources like energy consumption per appliance and environmental conditions such as room temperature and humidity.  
This addresses the complexity of varied data types, such as energy consumption per appliance and room temperature/humidity. Filtering heterogeneous data is essential in data processing to identify data entries, ensure uniqueness, and align the data with predefined structures required by databases. Before filtering, the dataset contained 513 days of data for most appliances. After the filtering process, the data were reduced to 451 days for the water dispenser, 489 days for the refrigerator, and 438 days for the air conditioner (AC). For environmental factors, such as room temperature and humidity, the dataset was reduced to 438 and 336 days, respectively. These reductions were necessary to ensure the data were used for analysis and AI model training. These steps ensure that the data are accurate and relevant, preparing it for the next phase of analysis. However, only one year (365 days) of data were used for AI model training to preserve seasonal patterns and maintain the seasonal relationship. Data from 6 months were used to test the model. The cleaned data are then processed to calculate daily energy consumption from the second energy consumption for each appliance and average room conditions, which are crucial for accurate power forecasting. Subsequently, the data are extended and prepared for training AI models. By scaling and splitting the data into test and train sets, and defining LSTM, GRU, and the hybrid LSTM-GRU models, the system focuses on sequential patterns crucial for forecasting future energy consumption. Training individual deep learning models for each data type helps manage the complexity of varied data inputs and improves prediction accuracy. This method not only enhances model performance but also ensures reliable day-ahead power forecasting, a critical function for optimizing energy management and planning.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 2 Materials and Methods
### Model Evaluation Index  
Accuracy rate, prediction rate, recall rate, F1 score, and confusion matrix are five commonly used evaluation indicators for image classification [31]. The accuracy rate indicates the proportion of correctly classified samples to the total number of samples. The precision rate indicates the proportion of the number of samples predicted to be positive and indeed positive to the number of samples predicted to be positive. The recall rate indicates the proportion of the number of samples that are predicted to be positive and are indeed positive to the number of samples that are indeed positive. The F1 score represents the harmonic mean of precision and recall. The confusion matrix represents the classification between different categories. The horizontal axis is the predicted category, and the vertical axis is the real category. The elements on the diagonal represent the number of samples that are correctly classified, and the elements on the off-diagonal represent the number of samples that are incorrectly classified.  
\[Precision(P)=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall(R)=\frac{TP}{TP+FN} \tag{11}\]  
\[Accuracy=\frac{TP+TN}{P+N} \tag{12}\]  
\[F1-score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{13}\]  
where \(TP\) is the true case, \(TN\) is the true negative case, \(P\) is the total number of positive cases, \(N\) is the total number of negative cases, \(FP\) is the false positive case, \(FN\) is the false negative case.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### State-of-the-Art Comparison  
This paper mainly uses the meningiomas dataset and the BraTS2021 dataset to verify the results of ECA in medical image tumor segmentation. We compare these results with SOTA 3D image segmentation models, including UNETR [10], SwinUNETR [20], nnFormer [35], UNETR++ [30], dResU-Net [36],CU-Net [37], and nnUNet [9]. In order to validate the lightweight nature of ECA, it is compared based on FLOPs and Params with other 3D attention mechanisms, including 3D CBAM [12], 3D SA [28], HDC [24], and TDPC [13]. To ensure fairness of results, the same input and preprocessing strategies are applied to each model. Regarding the hyperparameters of the comparison models, since UNETR, SwinUNETR, nnFormer, and UNETR++ were trained and validated against the BraTS dataset in their original papers, the recommended hyperparameters for the BraTS dataset on the Nvidia GeForce RTX 3090 in their respective codes are used in this paper. All hyperparameters for the models are available in the official code. In addition, to ensure the fairness of the experiments, we retrained each model on both datasets using the same training and testing sets.