esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Emerging Trends and Future Directions  
### Current Research Trends in Data Analysis for Maritime IoT  
The field of data analysis for maritime IoT is rapidly evolving, fueled by advancements in technology and increasing demands for efficiency, safety, and environmental sustainability in the maritime industry. The current researc75h trends indicate a progressive shift towards more advanced and sophisticated data analytics techniques and tools [8] (Figure 6).  
* Edge and Fog Computing: To address the challenges of big data processing in maritime IoT, edge and fog computing are emerging as promising solutions. By processing data at the network edge or in a fog node, close to where it is generated, these techniques can reduce the latency, bandwidth usage, and load on the central servers, enhancing the efficiency and responsiveness of data analysis [95].
* Advanced Machine Learning and AI Techniques: Machine learning (ML) and artificial intelligence (AI) techniques are being increasingly applied to analyze maritime IoT data. These techniques can automate the analysis process and extract complex patterns and insights from large datasets that might be missed by traditional analysis methods. Current research is exploring various ML and AI techniques, including deep learning, reinforcement learning, and predictive analytics, for different maritime IoT applications [96].
* Real-time Analytics: As maritime operations become more dynamic and time-critical, the need for real-time data analysis is growing. Real-time analytics can provide instant insights from IoT data, enabling quick decision-making and responses to changing conditions. This is especially important for applications such as navigation, safety monitoring, and incident response, where delays in data analysis can have serious consequences [97].
* Cybersecurity Analytics: With the increasing reliance on IoT and data analysis, cybersecurity has become a critical concern in the maritime industry. Cybersecurity analytics is an emerging field that uses data analysis to detect and prevent cyber threats. This includes analyzing network traffic, system logs, and user behavior to identify suspicious activities and potential security breaches [98].
* Data Fusion and Multi-Source Analysis: Maritime IoT systems often involve multiple sensors and data sources. Data fusion techniques, which combine data from different sources to achieve more accurate and comprehensive insights, are being actively researched. This includes sensor fusion for improved navigation and monitoring, and fusion of operational, environmental, and financial data for strategic decision-making [99].  
Figure 6: Emerging Trends in Maritime Operations.  
Privacy-Preserving Data Analysis: With the increasing concerns about data privacy, research is focusing on developing techniques for privacy-preserving data analysis. These techniques aim to analyze data without compromising the privacy of individuals or revealing sensitive information. This is especially relevant for personal data collected by IoT devices on crew members and passengers, and confidential operational data [100].
# Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks
## 4 Preallocated Sequential Routing
### Proposed Algorithm
In Algorithm 1 initialization, each BS connects to MBS and one antenna is achieved, i.e., \(s_{2}=s_{3}=s_{4}=s_{5}=s_{6}=s_{7}=1\), as shown in Figure 2(a). Because there is no BS closer to \(BS_{1}\), \(BS_{2}\) connects to \(BS_{1}\) directly. As the link between \(BS_{2}\) and \(BS_{3}\) requires more antennas than the link between \(BS_{1}\) and \(BS_{3}\), \(BS_{2}\) cannot possibly become the candidate forwarder of \(BS_{3}\). Therefore, there is a set of alternative nodes of \(BS_{3}\), i.e., \(V_{3}=\{\}\). As for \(BS_{4}\), its candidate set is \(V_{4}=\{BS_{2},\ BS_{3}\}\). As shown by the white arrow in Figure 2(b), PSR first connects \(BS_{2}\) and adds \(s_{4}\) to \(s_{2}\), \(s_{4}=0\), \(s_{2}=2\), which leads to \(A_{2}=\{5,\ 2\},\ A_{4}=\{6,1\}\), so the performance gain of this topology is \(\Delta U=l_{2,1}(5,2)\ +l_{4,2}(6,1)\ -l_{2,1}(6,1)\ -l_{4,1}(6,1)\). Similarly, the algorithm calculates the difference when \(BS_{4}\) connects to \(BS_{3}\) for comparison. Assuming that connecting to \(BS_{2}\) yields higher utility, the parent node of \(BS_{4}\) is finally set as \(BS_{2}\). The parent nodes of \(BS_{5}\), \(BS_{6}\), and \(BS_{7}\) are also selected in the same way. As shown in Figure 2(c), \(BS_{5}\) is determined to connect to \(BS_{3}\). \(BS_{6}\) has candidate set of parent nodes \(V_{6}=\{BS_{2},\ BS_{4}\}\) and we assume that \(BS_{2}\) is also a better choice, thus PSR determines the parent node of \(BS_{6}\) as \(BS_{2}\). Finally, as shown in Figure 2(d), \(BS_{7}\) is connected to \(BS_{4}\), and the connection of the entire tree topology is completed.
## 3 Experimental Results
### Discussion
#### 3.4.2 Reducing the Influence of Cloud Thickness  
The proposed method can reduce the influence of cloud thickness and the cloud detection results. Thin and thick clouds have different thicknesses. On sunny days, they have different influences on ground objects in terms of shading. These phenomena cause image information loss and imbalance. Moreover, the detection of a cloud shadow is much more challenging and difficult as a result. If the non-cloud areas are recognized as clouds mistakenly, since our proposed method considers multiple-level information, including local super-pixel information, the over-compensation would not appear even if the non-cloud-shadow region is compensated for. This will reduce the effectiveness of compensation in terms of cloud shadow detection accuracy and make it more adaptive to be used in different conditions. As shown in Figure 12, based on the original cloud shadow detection results in Figure 12(a1), mask 2 in Figure 12(a2) and mask 3 in Figure 12(a3) are acquired by dilating the original mask 5 and 10 pixels, respectively. As a result, there are some non-cloud-shadow regions included in mask 2 and mask 3. By adopting the different compensation results in these three masks, the ability to deal with shaded regions of different extents of shading can be compared. From the second row in Figure 12b to the seventh row in Figure 12g, these shadow masks are compensated for via LCC, HM, GT, CSR, WLC, and our methods. The compensation results of LCC, HM, GT, CSR are not able to solve the problem of imbalance. If the non-shadows are included in the shadow mask, they will be over-compensated for. Especially for HM and GT, the insufficiency and over-compensation is obvious. WLC considers small window information from Wallis and LCC aspects, reducing problem of imbalance However, the contrast is over-enhanced. There is more noise in the compensation results. Our method obtains the most balanced compensation results. Even if some non-shadows are included in the shadow mask, they will not be over-compensated for. The center of a cloud shadow with the most shaded loss also can be sufficiently compensated for. This is owed to the super-pixel local information  
Figure 11: Histogram comparison of shadow compensation results using shadow information of different regions. (**a**) Histogram of image #1 using shadow region information; u = 1 and v = 0. (**b**) Histogram of image #1 using super-pixel information; u = 0 and v = 1. (**c**) Histogram of image #1 combining multi-level information; u = 0.5 and v = 0.5.  
around the pixel and the multi-level information combination strategy. Therefore, the proposed method can adaptively compensate for information affected by uneven shading, as long as the cloud shadow can be detected. The wrong detection of non-shadows has little effect on the final compensation results.  
Figure 12: _Cont._
## 5 Experiments
### Experiment Setting
#### 5.1.3 Implementation Details  
**Backbone.** We adopt the ResNet-50 network as the backbone, and to better accommodate our proposed method, we made several customized modifications to the backbone network. First, we set the _in_channel_ of the network's initial _conv2_ layer to 4. Second, we adjusted the _stride_ of the conv2 layer and the downsample layer in Layer4 of the network to 1. Third, during the training, we set the _out_feature_ of the fully connected (FC) layer to match the number of IDs in the corresponding dataset.  
**Training Models.** In the training process, the following settings were applied: First, a mini batch of P (P = 64) images were randomly selected from the dataset. Then, the following preprocessing steps were applied to each image in this mini batch: The images were first resized to \(256\times 128\), followed by data augmentation techniques such as horizontal flipping, random erasing, and color enhancement. Finally, the pixel values of the images were normalized to have a mean of 0 and a variance of 1. To effectively train the neural network model, we used the Adam optimizer to optimize the model parameters, with weight decay and momentum factors set to \(5\times 10^{-4}\) and 0.9, respectively. Additionally, we employed a joint learning rate adjustment strategy: For the first ten epochs, we used warm-up techniques to increase the learning rate from \(1\times 10^{-6}\) to 0.05. After the warm-up, we applied a cosine annealing learning rate adjustment technique, gradually decreasing the learning rate from 0.05 over \(1/4\) of a cosine cycle until reaching the minimum value. The change in learning rate can be expressed using the following formula:  
\[lr=\begin{cases}\frac{0.05\text{+}epoch}{5}+1\cdot 10^{-6}&epoch<5\\ 0.05\text{-}\cos\left(\frac{epoch-5}{epoch_{total}-5}\cdot\pi\right)&5\leq epoch \leq 80\end{cases} \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Released**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Query Set**} & \multicolumn{2}{c}{**Gallery Set**} & \multirow{2}{*}{**Cameras**} \\ \cline{3-3} \cline{5-10}  & & **Images** & **IDs** & **Images** & **IDs** & **Images** & **IDs** & \\ \hline Market-1501 & 2015 & 12,936 & 751 & 3368 & 750 & 16,364 & 750 & 6 \\ DukeMTMC-reID & 2017 & 16,522 & 702 & 2228 & 702 & 17,661 & 702 & 8 \\ MSMT17 & 2018 & 32,621 & 4101 & 11,659 & 3060 & 82,161 & 3060 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets details induction.  
where _epoch_ is the current epoch during the training.  
In addition, we set the number of pedestrian split \(N\) to 8 and the balance parameters \(\alpha\) and \(\beta\) to 0.3 and 0.7, respectively [37]. The Canny edge generator is implemented using the canny function from the Kornia library with the following parameter settings: low_threshold = 0.2, high_threshold = 0.7, and kernel_size = 3. All other parameters are kept as default.
# How Should the Transition from Underwater to Surface Swimming Be Performed by Competitive Swimmers?
## 4 Discussion  
The aim of the current study was to examine the factors relevant for swimming performance during transition from underwater to surface swimming by applying correlation and regression analysis. Previously, it had been indicated that swimming velocities of elite swimmers after emersion from underwater were faster than during mid-pool swimming [2]. However, no information about how swimmers should perform this transition phase had been revealed. Our results indicate that body position and coordinative swimming parameters (besides SR and SL) have an influence on the transition performance, which magnitude depends on the swimming strokes.
## 2 Online 3D Trajectory Generation for Deburring Processing  
During metal processing, procedures such as casting, cutting, and forging create burrs on the surfaces of workpieces, which necessitates subsequent deburring. Because deburring performed by robotic arms is influenced by numerous variables (e.g., cast workpiece dimensional deviations, irregular burr distributions, and low robotic arm dynamic accuracy), deburring is still mainly performed manually. Although solutions to the aforementioned problems have been developed, automated deburring is expensive and time-consuming and requires a sufficient number of trained technicians to perform various preoperational processes, making large-scale use of automated deburring difficult. To address this problem, the method proposed herein uses automated carriers, deburring cutters, and linear contour scanning sensors to detect burrs and generate automatic deburring trajectories. In executing the method, a CAD model is used to analyze the cross-sectional contours of the workpiece and obtain the mathematical models for the contour curves. Subsequently, curve fitting is performed using linear scan information to obtain the boundary equations for detecting burrs online and generating deburring trajectories. The use of the burr trajectory tracking method can improve robotic arm dynamic accuracy and minimize workpiece dimensional deviations.  
The proposed method comprises three stages. The first stage involves matching the mathematical model used for CAD-based cross-sectional contour feature extraction with that used for modeling boundary contour curves (S110, S120). The burr distribution is determined through linear contour scanning, and a mathematical model is established using cross-sectional contour data to obtain boundary equations and perform burr detection and trajectory generation. The second stage involves fitting the equations of boundary contour curves in linear contour scanning (S130, S140). Boundary contours are determined using linear contour scanning sensors to detect the actual position of the workpiece according to the manufacturing tolerance of the process, to compensate for the dynamic errors of robotic arms online, and to increase the control accuracy of robotic arms. The third stage involvesdetecting burrs and generating deburring trajectories (S150, S160). After the contour data obtained using linear contour scanning sensors are processed and curve fitting is completed, the cross-sectional boundary equations and the curves of the workpiece are obtained. These equations can be used to divide the space into two parts to detect the burr distribution and extract deburring trajectories. The procedures in the proposed method are described in Figure 1.
# Machine Learning Based Representative Spatio-Temporal Event Documents Classification
## 5 Result and Discussion
### Learning Curve Analysis  
To measure the performance of the model according to the training data size (ratio), we plot the learning curves for accuracy and F1 score according to the size of the training data, as shown in Figure 4. CNN and BiLSTM models were trained by dividing the entire data into 20%, 40%, 60%, 80%, and 100%. After 10 experiments were conducted for each model, the average result was derived.  
Figure 3: Receiver operating characteristic curves of CNN, LSTM, and BiLSTM.  
Figure 2: Comparison of precision, recall, accuracy, F1 score, AUC, and MCC performance for each model.  
Both the F1 score and accuracy increase linearly as the data size increases. In terms of accuracy, BiLSTM outperforms CNNs at all data sizes while maintaining a constant spacing with CNNs. In terms of F1 score, CNN outperforms BiLSTM when the data size is less than 40%, but BiLSTM outperforms CNN when the data size is 60% or more. In the case of CNN, the F1 score increases gradually from 0.554 to 0.605, whereas in the case of BiLSTM, the F1 score increases relatively rapidly from 0.497 to 0.631. It can be seen that the performance of BiLSTM is more affected by the data size.
# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
## 5 Performance Analysis of CNN Based Robot Face Recognition Models  
In this section, we present the results of the performance of the previously discussed CNN models and the impact of the encryption algorithms on the accuracy of these models. For each CNN model, we measure its accuracy first with clear images and then with encrypted images using one of the studied encryption algorithms. Moreover, we have also evaluated the performance in robot and cloud environment.  
To study the impact of the encryption algorithms on the accuracy of the studied CNN models, we proceed as follows. First, we encrypt the images, then the studied CNN model is trained using these encrypted images, and finally, the CNN model is tested using a subset of the encrypted images and consequently, the accuracy is measured.  
We studied the performance of the previously nine CNN models namely, Lenet, AlexNet, ResNet, VGG16 Net, GoogleNet, DenseNet, MobileFaceNet, ShuffleNet, and EffNEt.  
During the pre-processing step, the dataset images are resized to a size suitable for each CNN model and gray scale images are converted to RGB images.  
Experimental results are conducted using the ORL database [35]. The Olivetti Research Laboratory (ORL) face dataset contains a set of face images.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 3 Materials and Methods
### System Model and Problem Formulation
#### 3.1.3 Threat Model and Problem Formulation  
The threat model considers both active attackers (Aa), who intercept, modify, or destroy messages, impersonate parties, launch denial-of-service (DoS) attacks, or take control of drones, and passive attackers (Ap), who listen in on and inspect communications. The probability that an attack will succeed is expressed as:  
\[\text{Pattack}=\text{Pdetection}\times\text{Pexploitation}\times(1-\text{P mitigation})\]  
where Pdetection is the probability of detecting a vulnerability, Pexploitation is the probability of successful exploitation, and Pmitigation is the system's mitigation probability. The authentication problem in the Internet of Drones (IoD) can be posed as an optimization problem that balances security strength and resource utilization. The objective function F is presented as:  
\[\text{F}=\text{max}\ \{\text{w1}\times\text{S}-\text{w2}\times\text{C}-\text{w3 }\times\text{L}-\text{w4}\times\text{M}-\text{w5}\times\text{E}\}\]  
concerning the constraints:  
\[\text{S}\geq\text{Smin},\text{C}\leq\text{Cmax},\text{L}\leq\text{Lmax},\text {M}\leq\text{Mmax},\text{E}\leq\text{Emax}\]  
where w1, w2, w3, w4, and w5 are weighting factors representing different protocol features of importance, Smin is the minimum security level acceptable, and Cmax, Lmax, Mmax, and Emax are maximum permissible constraints for computational overhead, authentication latency, memory use, and energy consumption, respectively. The authentication process between a drone Diand a user Uj is expressed as:  
\[\text{Autth(Di,Uj)}=\{\text{m1, m2,..., mn}\}\]  
Figure 2: Internet of Drones network architecture.  
where m1, m2,..., mn are the sequence of authentication messages. The security strength S is quantified as:  
\[S=\Sigma_{k=1}^{k}a_{k}-(1-P_{attack,k})\]  
where \(K\) is the number of available attack vectors, \(\alpha_{k}\) is the effect of the \(k\)-th attack vector, and \(P_{attack,k}\) is the probability of success of an attack via the \(k\)-th vector. The computational overhead \(C\) is expressed as:  
\[\sum_{i=1}^{n}c_{crypto,\ i}+c_{ML,\ i}\]  
where \(c_{crypto,\ i}\) is the cost of cryptographic computations, and \(c_{ML,\ i}\) is the cost of computations for machine learning at the \(i\)-th step. The optimization framework ensures that authentication in IoD is extremely secure while minimizing the usage of resources.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ3--Frameworks for Edge Intelligence
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Framework**} & \multicolumn{2}{c}{**Groups of**} \\  & **Techniques or** & **Comments** \\  & **Strategies** & \\ \hline Minerva [143] & Data Quantization Model compression & Quantization analysis minimizes bit widths without exceeding a strict prediction error bound. Compared to a 16-bit fixed-point baseline, Minerva reduces power consumption by 1.5\(\times\). Minerva identifies operands that are close to zero and removes them from the prediction computation such that model accuracy is not affected. Selective pruning further reduces power consumption by 2.0\(\times\) on top of bit width quantization. & Quantization analysis minimizes bit widths without exceeding a strict prediction error bound. Compared to a 16-bit fixed-point baseline, Minerva reduces power consumption by 1.5\(\times\). Minerva identifies operands that are close to zero and removes them from the prediction computation such that model accuracy is not affected. Selective pruning further reduces power consumption by 2.0\(\times\) on top of bit width quantization. & Automatically selects a combination of compression techniques for a given DNN that will lead to an optimal balance between user-specified performance goals and resource constraints. AdaDeep enables up to 9.8\(\times\) latency reduction, 4.3\(\times\) energy efficiency improvement, and 38\(\times\) storage reduction in DNNs while incurring negligible accuracy loss. \\ \hline JALAD [145] & Data Quantization Model Partitioning & Data compression by jointly considering compression rate and model accuracy. A latency-aware deep decoupling strategy to minimize the overall execution latency is employed. Decouples a deep NN to run a part of it at edge devices and the other part inside the conventional cloud. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._EI techniques tackle latency problems when part of the entire process is realized on edge devices, decreasing data traffic on the network and, consequently, decreasing the inherent delay in data transmission. Regarding security and privacy issues, it is possible to train and infer on sensitive data partially or fully at the edge, preventing their risky propagation throughout the network, where they are susceptible to attacks.