esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Anomaly detection (AD) has captured a significant amount of focus from the research field in recent years, with the rise of the Internet of Things (IoT) application. Anomalies, often known as outliers, are defined as the discovery of anomalous occurrences or observations that differ considerably from the mainstream of the data. The IoT which is described as a network of Internet-based digital sensors that continuously generate massive volumes of data and use to communicate with one another the Message Queuing Telemetry Transport (MQTT) protocol. Brute-force, Denial-of-Service (DoS), Malformed, Flood, and Slowite attacks are the most common in the MQTT network. One of the significant factors in IoT AD is the time consumed to predict an attack and take preemptive measures. For instance, if an attack is detected late, the loss of attack is irreversible. This paper investigates the time to detect an attack using machine learning approaches and proposes a novel approach that applies correlation analysis to reduce the training and testing time of these algorithms. The new approach has been evaluated on Random Forest, Decision Tree, Naive Bayes, Multi-Layer Perceptron, Artificial Neural Network, Logistic Regression, and Gradient Boost. The findings indicate that the correlation analysis is significantly beneficial in the process of feature engineering, primarily to determine the most relevant features in the MQTT dataset. This is, to the best of our knowledge, the first study on MQTSet that reduces the prediction time for DoS 0.92 (95% CI \(-\)0.378, 2.22) reduced to 0.77 (95% CI \(-\)0.414, 1.97) and for Malformed 2.92 (95% CI \(-\)2.6, 8.44) reduced to 0.49 (95% CI \(-\)0.273, 1.25).  
Internet of Things; dataset; MQTT; machine learning; detection system; artificial intelligence; anomaly detection +
Footnote †: journal: Applied Sciences
# High-Throughput Bit-Pattern Matching under Heavy Interference on FPGA
## 2 Fundamentals  
The main purpose of bit-vector pattern matching is the matching of an input pattern to one known pattern. Usually, the known patterns are referred to as rules and the size of the set of rules is known as the rule set. Rule sets vary in size; generally, they are lower than or equal to 1000 [7]. In other implementations, the patterns are broken down further into sub-patterns, but this will not be the case in our circuit. Table 1 presents an input pattern, along with the rule set used. The rule size is 5 and the length of the pattern is 10.  
In this particular case, the input pattern does not match any entry exactly, but it can be matched with Rule 2 since it is the closest in terms of Hamming distance. Determining the closest bit vector in terms of distance can be simply achieved by  
\begin{table}
\begin{tabular}{c c} \hline
**Input Pattern** & **0011011101** \\ \hline Rule 1 & 1101001101 \\ Rule 2 & 0001011101 \\ Rule 3 & 100101111 \\ Rule 4 & 111110101 \\ Rule 5 & 1011110100 \\ \hline \end{tabular}
\end{table}
Table 1: Rule set.  
respective positions pass through xnor gates) the input pattern with each memory entry and then summing the product bits. Table 2 depicts this operation.  
The highest value for the sum is the number of bits when both patterns are the same and is set at 0 when they are complements. The simplicity of this method fits the minimalistic tone of the circuit. Achieving its implementation with the fewest possible resources and the highest possible frequency, while providing maximum accuracy, will be key to success. Before moving on to the hardware requirements, we are going to present two basic circuits that will be used extensively in the architecture.
## 3 Key Technologies of UAV-Assisted SAGINs
### Deployment of UAVs
#### 3.5.3 Multi-UAV Deployment  
Most deployment methods are designed for multi-UAV scenarios. Compared to single-UAV deployment, multi-UAV deployment is more complex. Theoretically, there is an upper limit to the number of UAVs that can be deployed in a specific area, beyond which communication quality degrades. [PERSON] et al. [136] used a novel AAP layout algorithm based on regular polygons to determine the horizontal positions of AAPs and calculated the exact upper limit for deployable AAPs within a given circular area. When multiple UAVs collaborate, more channel capacity is consumed. [PERSON] et al. [137] designed a decentralized learning algorithm to maximize the optimal Nash equilibrium of multiple-input-multiple-output (MIMO) systems, achieving higher UAV channel capacity. [PERSON] et al. [138] optimized UAV deployment by reducing the average distance between heterogeneous UAV swarms and users, validating the approach's effectiveness in non-line-of-sight scenarios.
## 1 Introduction
The primary objective of this study is to improve the process of detection and georeferencing of graffiti in Lisbon through a system that automatically identifies and classifies an image as having illegal graffiti (Figure 1), street art (Figure 2), or no graffiti. In addition, in cases where the image is classified as illegal, the system also detects the region of the image associated with the graffiti to notify the Urban Hygiene teams of which places need to be cleaned. This system would allow the allocation of the work done by the Lisbon City Council members in selecting the places to be cleaned to other more critical tasks. Thus, a tedious and time-consuming process can become a simple, easy, and effective process that only requires uploading images to an application. The grand ambition of the  
Figure 1: Examples of images with illegal graffiti. Figure 2: Examples of images with street art graffiti. City Council is to develop a system that, through a camera system implemented in cars driving around the city, can detect walls that need cleaning and automatically notify the corresponding team of their location. This research work developed a contribution/proof of concept to understand the viability of the identification and classification of graffiti and, in this way, obtain the necessary certainty and confidence to determine whether it makes sense to invest in a system of automatic identification and classification in real time of the existing graffiti in the city of Lisbon. Our major contributions are (1) a deep learning model that successfully discriminates the differences between street art and illegal; and (2) determining how accurate the automatic identification and location of illegal graffiti on images acquired in the streets of Lisbon under loose controlled conditions. The system development methodology was based on the Cross Industry Standard Process for Data Mining (CRISP-DM). CRISP-DM is a methodology that provides an overview of the life cycle of a data mining project, describing all phases of the project [12]. However, it was necessary to adjust the methodology to comply with the needs and characteristics of the problem and the data in question. The main difference to the problem at hand is the type of data. In this case, since it is a computer vision problem, the data consist of images, which require a different kind of processing and collection. These adjustments can be observed in Figure 3.
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
## 2 Materials and Methods
### FEM Modeling  
A finite element model (FEM) was adopted to simulate the dynamic response of the rock bolts to a pulse excitation applied to the protruding head. The model considered both the mechanical properties of the steel rod and the properties of the grouting.  
To calculate the dynamic response of the bolt, we applied the finite element model to overcome the main limitations of the previous and simplified analysis where the bolt was simulated using simple beam elements. These elements were not appropriate to describe grouted rock bolts because they did not consider the stiffness of the grout.  
A 3D finite element analysis (FEA) of the rock bolt and grouting was performed to simulate the dynamic response of the system. The typical FEA workflow included importing or creating the geometry of the rock bolt and grouting, generating the mesh, defining the physics of problems with a load, defining the boundary and initial conditions, and obtaining and visualizing the results.
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios
#### 4.2.1 Human Digital Twin  
Currently, digital technology is primarily employed in detecting common indicators and preventing major diseases through the analysis of human body structure. However, there is a need to enhance the real-time availability and accuracy of this process. As 6G technology progresses and interdisciplinary sciences like bioscience, materials science, and bioelectronic medicine converge, the prospect of generating digital twins of the human body becomes increasingly plausible. This endeavor aims to develop a holistic virtual model of the human body, facilitating real-time monitoring of personalized health metrics.  
With more than 100 smart sensors per person, these digital twins will accurately and instantaneously represent the health of critical organs, the nervous system, the respiratory system, the urinary system, the musculoskeletal system, and the emotional state. These digital twins may also incorporate professional medical imaging methods like magnetic resonance imaging (MRI), computed tomography (CT), color Doppler ultrasound, blood tests, and urine biochemistry by utilizing 6G capabilities. Individuals can obtain a precise evaluation of their health state and timely actions by integrating these data.  
Furthermore, 6G will make it easier for professional medical facilities to integrate artificial intelligence (AI), enabling accurate diagnosis and providing recommendations for individualized surgical procedures.
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 3 Multi-Feature Fusion Image Dehazing Network
### Gated Pixel Attention  
The pixel attention mechanism adjusts the weight of each pixel in the image, thereby directing the algorithm's focus towards areas with higher fog density. MFID-Net incorporates a gated pixel attention mechanism (GPA) to foster cooperation and competition among pixel features, enhancing the efficiency of defogging. The specific structure is depicted in Figure 6. Initially, the GPA sequentially conducts convolution, pooling, and convolution operations on the feature map \(\widehat{\chi}\) to preliminarily extract features. Subsequently, the degree of activation for the gating mechanism is controlled utilizing weight parameters \(\delta=[\delta_{1},\delta_{2},...,\delta_{\mathbb{C}}]\) and biases \(\mu=[\mu_{1},\mu_{2},...,\mu_{\mathbb{C}}]\). During the activation process, the GPA employs the _tanh_ function to modulate pixel-level features. Ultimately, the features processed by the activation function are subjected to multiplication operations and residual connections with the original feature map \(\widehat{\chi}\), resulting in the feature map \(X^{*}\), as manifested in Equation (13).  
\[X^{*}_{c}=\widehat{\chi_{c}}(1+tanh(\delta_{c}(Conv(Pool(Conv(\widehat{\chi_{c} }))))+\mu_{c})) \tag{13}\]  
where in the shallow layers of the algorithm, \(\delta_{c}\) is set to be less than zero, indicating that they can promote collaboration between pixel features, which aids in more comprehensively capturing and utilizing image information. Conversely, in the deeper layers of the algorithm, \(\delta_{c}\) is set to be greater than zero, leading to competition between pixel positions, which helps to highlight key features and suppress unnecessary information. Empirical evidence demonstrates that GPA optimizes the defogging effect of MFID-Net.  
Figure 6: Structure of gated pixel attention mechanism.
## 2 Related Work  
With the advancements in machine learning (ML) algorithms, geospatial technologies and computational power, production of LS maps have become less challenging. Among the common ML algorithms used for the LS mapping in the literature, the artificial neural networks (ANN), logistic regression (LR), deep learning methods, decision trees, random forest (RF), naive Bayes tree, fuzzy logic and support vector machine (SVM) can be listed (e.g., see [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]). The ML techniques can increase the accuracy of the LS maps [23]. [PERSON] et al. [27] evaluated the suitability and the performances of several ML methods in the literature and found that the tree-based ensemble algorithms achieve excellent results compared to the other ML algorithms. Although such techniques are effective and useful for reducing losses caused by landslides, the performances of the data-driven methods are dependent on the data quality and the suitability of the selected method [15; 27]. However, with the increase in the amount and quality of data, more studies can be conducted, which will eventually lead to improved conclusions.  
Although various supervised ML algorithms have been used for LS mapping in the literature as mentioned above; the use of XGBoost method [9] is relatively new and limited. [PERSON] and [PERSON] [28] compared the accuracy results of XGBoost (76.73%) and the deep neural network (83.71%) methods for LS mapping in two catchments in South Korea. [PERSON] [29] performed a comparative analysis with various gradient boosting methods (GBM), such as categorical boosting (CatBoost), XGBoost, LightGBM, GBM and RF for LS mapping in the northern part of Turkey and found that CatBoost yielded the best overall accuracy (85.03%) followed by the XGBoost method (83.36%). [PERSON] et al. [30] assessed the performance of the XGBoost algorithm for landslide, debris, unstable slope and rock-fall hazards in Jiuzhaigou, China; and compared with the RF and the SVM results. The XGBoost algorithm outperformed the RF, and provided slightly worse results than the SVM for LS mapping. However, the method produced the best accuracy results for the other three hazards assessed. [PERSON] et al. [31] applied a combination of genetic algorithm-XGBoost (GE-XGBoost) methods for gully erosion susceptibility mapping in Iran and compared with the RF, SVM and LR methods. The GE-XGBoost method provided higher classification accuracy (89.56%) in comparison to the RF, SVM and LR methods for the gully erosion susceptibility mapping. Similarly, [PERSON] et al. [32] obtained the best results for debris flow susceptibility with XGBoost method in Shigatse Area, China, when compared with the neural networks, decision trees and the RF.
## 4 Discussion
Finally, this approach offers flexibility to surgeons, as they have the option to choose their preferred commercial implants when utilizing the 3D template printing method. Implants consisting of a mixture of two or more materials, such as the porous polyethylene with embedded titanium implants, are difficult to manufacture using direct 3D printing. In addition to 3D printing technology, the use of intraoperative navigation surgery utilizing 3D CT prove to be valuable tools in the identification of current implant positions and the confirmation of immediate postoperative orbit status, particularly in the context of complex orbital fractures [27; 28]. The synchronous positioning of instruments with 3D CT images offers a valuable approach, allowing for precise and safe surgery on orbital fractures, especially those located around the optic canal. Visualizing the locations indicated by the intraoperative navigation pointer enhances the accuracy and safety of the surgical procedure. In the future, it is essential to conduct studies comparing the effectiveness of surgery that combines implants using 3D printing technology and 3D navigation during surgery with conventional methods in correcting fractures and reducing complications. Such research will provide valuable insights into the potential benefits and advantages of utilizing these advanced techniques in fracture correction and surgical outcomes. One of the limitations of the present study was its retrospective design, as opposed to being a randomized controlled trial. Second, the number of patients differed between the groups. This difference might have an impact on the standard deviation of the results. Third, postoperative orbital CT scans were conducted immediately after surgery. However, [PERSON] et al. [24] reported that there was no considerable alteration in orbital volume when comparing measurements taken immediately after surgery to those taken during a final follow up at a minimum of 6 months after orbital wall reconstruction. This suggests that the postoperative orbital tissue volume within the bony orbit is unlikely to undergo any clinically significant changes, regardless of the duration of the follow-up period. In this study, we evaluated pre- and postoperative orbital volume but did not analyze qualitative metrics such as pain. Future prospective studies should explore pain and swelling reductions with 3D implants for a more comprehensive understanding of their benefits in orbital wall fracture surgery.
## 4 Experimental Setup
### Considered Conditions under Evaluation  
As aforementioned and according to the theoretical basis of the bearing operation, impacts are induced over the whole bearing system when bearings are working under the influence of defects in the inner race and/or outer race, such impacts appear when the balls get in contact with the damaged area during the rotating operation of bearings. Thus, these impacts usually appear at intervals of time that are influenced by the severity (size) and shape of the fault. Therefore, regarding the assessed conditions, different fault severities in one of the bearing elements of the IM are experimentally tested under different rotational speed conditions; specifically, the end-drive bearing of the IM (model 6203) is that bearing under evaluation.  
Consequently, six different bearing conditions are taken into account during the experimentation. The evaluated conditions are the healthy condition (HC) and five different fault severities that are artificially induced on the outer race of the bearing. To generate the different fault severities, five identical bearings are damaged with different tungsten drill bits with diameters of 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm; thus, by drilling a  
Figure 4: A flow chart of the electromechanical system based on a pulley-belt system and its wiring to perform the acquisition of vibrations signals and the stator current signature.  
completely through-hole that passes the outer race of each identical bearing, five bearing fault severities (S1, S2, S3, S4, and S5, respectively) are produced. In Figure 5, the damaged bearings are shown.  
Accordingly, during the experimentation, different values are considered to be set as the supply frequency in the VFD, aiming to produce different rotational speeds in the IM; thus, each bearing condition (HC, S1 to S5) is iteratively tested under different values of supply frequency that are set in the VFD, that is, 5 Hz, 15 Hz, 50 Hz, and 60 Hz. In this sense, for each supply frequency, the vibration signals and the stator current are continuously measured, acquired, and stored in a personal computer for posterior analysis. Therefore, each bearing condition is tested 15 times under each considered supply frequency, and the vibrations and current signals are acquired during 10 s of the steady-state operation of the IM; as a result, for each bearing condition, 150 s of the measured signals are acquired.
## 2 DNSGA Algorithm  
### Algorithm Framework  
There are two aspects of shortcomings for the NSGA-II algorithm. On the one hand, it is easy to fall into local optimal solutions and into premature convergence due to its focus on convergence. On the other hand, when some individuals have large differences in decision variables while their objective function values are approximately equal, the adoption of NSGA-II for the crowdedness ranking may remove potentially excellent individuals, which is unfavourable to the diversity of the population. The DNSGA algorithm is proposed to remedy the shortcomings, which essentially increases the optional range of individual decision space by improving the non-dominated ranking and spatial density ranking. This is conducive to the inheritance of excellent individuals to the next generation, thus improving the diversity of individuals in the population.  
The next generation of DNSGA is selected in three main steps. First, the parents and offspring are divided into three groups (\(M1\), \(M2\) and \(M3\)) according to the non-dominance result. The number of individuals in \(M1\) does not exceed 50% of the population size. The number of individuals in \(M2\) is not smaller than the population size. The remaining individuals are divided into \(M3\). Second, \(M3\) is deleted. \(M1\) and \(M2\) are combined into group \(Q\). Finally, the individuals with the lowest spatial density in \(M2\) are sequentially deleted according to the SD until the number of individuals in \(Q\) is equal to the population size.  
The main workflow of the algorithm is shown in Figure 1, and the specific steps are as follows:  
1. Randomly generate an initial population \(P_{0}\) to start the evolution;
2. Perform the binary tournament selection, simulated binary crossover and polynomial variation on \(P_{0}\) to generate new offspring \(P_{S}^{*}\);
3. Evaluate the fitness function so that multi-objective values are obtained for each individual;
4. Sort all individuals in \(P_{S}\) and \(P_{S}^{*}\) into \(F_{1}\), \(F_{2}\), \(F_{3}\)...... \(F_{n}\) based on non-dominance.
5. Divide \(F_{1}\), \(F_{2}\), \(F_{3}\)...... \(F_{n}\) into the three groups of \(M1\), \(M2\) and \(M3\);
6. Generate the next population \(P_{S+1}\) according to the spatial density operator ordering;
7. Return to step 2 until the end of the maximum number of iterations.
## 4 Results and Discussion
### Equivalent Inductance of the 23 Discharging Bricks  
When all the 23 discharging bricks in the same FLT cavity begin to release the stored energy in the capacitors, due to the discharge current flow direction, all the mutual flux-linkages among different discharging bricks have the azimuthal direction, resulting in the increased total equivalent inductance of the discharging bricks. As depicted in Figure 17, the bricks No. 2 to 12 are symmetric to the bricks  
Figure 16: Cross-sectional view of the magnetic field vector at the instant of maximum brick current.  
Figure 14: Cross-sectional view of the current density vector at the instant of maximum brick current.  
Figure 15: Cross-sectional view of the electric field vector at the instant of maximum brick current.  
No. 23 to 13. Thus, only inductances of No. 1 to 12 bricks are calculated in simulation and the results are tabulated in Table 2.  
It can be observed from Table 2 that brick No. 1 has the largest total inductance while brick No. 12 has the smallest total inductance. Obviously, brick No. 1 has the largest mutual flux-linkage since it located in the middle of all the discharging bricks, bricks No. 12 and 13 have the smallest mutual flux-linkage because of the location of the triggering brick. Therefore, when the FLTD induction cavity is releasing the stored energy, the actual equivalent inductance of the discharging brick is \(\sim\)195 nH rather than 160 nH. This will lead to a larger load current rise time. The influence of increased brick's equivalent inductance should be considered in the design of FLTD cavity.