esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimum Value Computing
#### 2.2.3 Simulation of Single Obstacle Avoidance  
Figure 9 presents the simulation results of the obstacle avoidance approach. The red line indicates the trajectory of the ROV with the result of obstacle avoidance by optimum value computing. The heading (degree) of the destination is defined as the north of the map. The ROV started toward the destination from origin point (0, 0). At point \"a\", an obstacle was detected and the ROV turned right 60\({}^{\circ}\) to avoid the obstacle. At point \"c\", no obstacle was detected, and the ROV traveled toward the destination.  
Figure 8: Destination and obstacle aligned and not aligned.  
Figure 7: Flowchart for the obstacle avoidance approach and FLC.  
Figure 10 presents the cost function values of optimum value computing. As shown in Figure 10, 7 points were defined as follows: \(-60^{\circ}\) (red), \(-40^{\circ}\) (orange), \(-20^{\circ}\) (yellow), \(0^{\circ}\) (green), \(20^{\circ}\) (sky blue), \(40^{\circ}\) (navy blue), and \(60^{\circ}\) (purple). The total time of the simulation was 42 s. According to Equations (8) and (9), the cost function values of the destination and obstacle were negative and positive, respectively. Therefore, the sum of the cost function values from Equation (7) was negative, which meant that no obstacles were present inside the range of the scanning sonar, and the ROV traveled toward the destination. The ROV traveled toward the destination from 0 s to 17 s. At 17 s, point \"a\", an obstacle was detected, and the cost function values suddenly changed from negative to positive. The maximum value was 0.0009 for \(-60^{\circ}\) (red line), and the minimum value was 0.00055 for \(60^{\circ}\) (purple line). This meant that the obstacle was located to the left of the ROV, and it should turn right to avoid it. This situation continued from 17 s to 24 s. At 20 s, point \"b\", the cost function values were updated again because the ROV was nearing the obstacle and the maximum value had changed from \(-60^{\circ}\) (red line) to \(-20^{\circ}\) (yellow line). However, the minimum value, 0.0015, remained at \(60^{\circ}\) (purple line), and the ROV continued to travel in the direction of \(60^{\circ}\). Finally, no obstacle was detected (at point \"c\"). The cost function values became negative again, and the ROV turned to move toward the destination.  
In the simulated result, the obstacle avoidance approach successfully enabled the ROV to avoid the obstacle. Next, we used the approach in an experiment with multiple obstacles in a real environment.  
Figure 9: Simulated trajectory of the ROV’s obstacle avoidance. The points of the scan map are marked by blue lower-case letters. The red line signifies the trajectory of the ROV.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 5 Physical Network Optimization
### State Space  
The state space of the proposed model is the current crowd situation of the network which is sent in the form of periodic packages to the mobile devices. The definition of this part is similar to that of Q-learning. The difference is that here, we focus more on the overhead of the network as a state factor.  
\[\mathbf{S(t)}=\mathbf{A(}t),k\in K,b\in B \tag{32}\]  
Here, in addition to slicing state information similar to traditional Q-learning and DQN, we introduce some real-time user state information in order to better allocate network resources (e.x. user location, transmitting power, user data amount, etc.). Thus, the state space can be summarized as follows:  
\[\mathbf{S(t)}=[\mathbf{A(}t),\mathbf{P}_{kb}(t),\mathbf{D}_{k}(t),\mathbf{R^{ \prime}}_{k}(t)],k\in K,b\in B \tag{33}\]  
where \(\mathbf{D}_{k}(t)=\{\mathbf{L_{c}(}t),\mathbf{L_{u}(}t),\rho(t)\}\), which is the location status parameter of the mobile device and represents the shortest distance from the mobile station to the cellular and UAV network access point and MS density in certain areas, respectively, collected and analyzed by the satellite navigation module.  
\[L_{c,2,1}(t)=\sqrt{d_{2,1}(t)^{2}+\left(h_{2}(t)-h_{1}(t)\right)^{2}} \tag{34}\]  
\[\rho(t)=\ \frac{card(U(t))}{\pi R^{2}} \tag{35}\]  
The parameters in the above two formulas can be obtained in the following ways. The position relationships between mobile stations are shown in Figure 6.  
\[d_{2,1}(t)=2 rarcsin\sqrt{sin^{2}\frac{\Delta\phi_{2,1}}{2}+cos\phi_{1}cos\phi _{2}sin^{2}\frac{\Delta\theta_{2,1}}{2}} \tag{36}\]  
\[\Delta\phi_{2,1}=\phi_{2}-\phi_{1} \tag{37}\]  
\[\Delta\theta_{2,1}=\theta_{2}-\theta_{1} \tag{38}\]  
\[U(t)=\{(x,y)|\sqrt{(x_{i}-x)^{2}+(y_{i}-y)^{2}}<R\} \tag{39}\]  
Figure 6: Schematic diagram of user density per unit area.
# Image Analysis and Processing for Generating Camouflages from Digital Earth Photographs
## 2 Selected Practical Aspects of Camouflage
### Principles of Camouflage Operation  
Depending on the used technique, the following types of camouflage can be distinguished [4; 5; 9]:  
* Decorative --it is based on the use of artificial masking structures, which are used to show a different state of the object than its true state. It can be, for example, an imitation of a destroyed building above a combat post, which makes it uninteresting to the enemy, while maintaining its defensive potential.
* Imitative--it consists of making a given stationary object similar to another harmless object of the surroundings, e.g., a camouflage showing the position of a soldier hidden under a trunk, which is a natural object that does not arouse suspicion found in forests.  
* Color or classic camouflage--by changing the colors of the object, as well as the appropriate deforming patterns, it blends the object into its surroundings, e.g., the need for at least a minimal similarity of the object's color to its surroundings. The example in Figure 1 shows the difference between no camouflage and color masking.
* Natural and vegetal--it consists of using camouflage to mask the natural properties of the terrain, e.g., fog, as well as live or cut vegetation. Live vegetation is used to mask stationary positions, which makes it difficult to see objects from the air and land. The cut vegetation allows masking moving and smaller objects. Such a camouflage is effective in the area from which the vegetation was obtained, and its durability, which translates into effectiveness, decreases with time, e.g., the use of grass to disguise the soldier and his weapons. The example in Figure 2 shows the use of grass to camouflage a soldier and his weapon.  
There are also other methods of camouflage, but their purposes do not coincide with the purpose of the camouflage presented in this paper, so they were omitted. The main technique used in the work was color camouflage, which, due to the ease of application on objects and the possibility of staying in motion of camouflaged objects, is the best masking technique, adapted to the needs of the recipients.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 4 Experiments
### Training Settings  
IDR\({}^{2}\)ENet proposed in this paper is implemented on PyTorch, and all experiments are executed on an NVIDIA GeForce GTX 2080 Ti GPU. During training, images in the GoPro dataset are randomly flipped and rotated horizontally during data expansion, and then are further cropped into patches of size \(256\times 256\), with the batch size set to 2. We use the Adam solver as the optimizer for IDR\({}^{2}\)ENet with hyperparameters set to \(\beta_{1}=0.9\), \(\beta_{2}=0.99\), and \(\epsilon=10^{-8}\). The learning rate \(\gamma\) is initially set as \(10^{-4}\) and decreased to \(10^{-6}\) when training stops.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 7 Responses to Research Questions
### Response to Research Question 1  
As mentioned in the pre-SLR section, the same approach was adopted to search the literature published before the year 2018, and several key enabling technologies were found that were employed in smart healthcare applications a brief overview of those technologies is given below.  
Internet of Things technology permits for the integration of medical devices, such as wearables and sensors, with healthcare systems to enable remote monitoring and communication [60]. It is covered more thoroughly in Section 4 in considerable detail. Big data in smart healthcare refers to the use of large and complex data sets to improve healthcare delivery, medical research, and public health. Smart healthcare systems generate and collect a vast amount of data from various sources such as electronic health records, medical devices, and wearables. By using advanced analytics and machine learning techniques on this data, healthcare professionals can gain insights that can improve patient outcomes and reduce costs. For example, big data can be used to identify patterns in patient data that indicate a potential health problem, such as a disease outbreak or an adverse drug reaction. It can also be used to optimize treatment plans and improve population health management. In medical research, big data can be used to identify new drug targets and biomarkers and to accelerate the discovery of new therapies. However, Big data in healthcare also poses some challenges, such as data privacy and security, data integration, and data governance. It is important to ensure that data is collected, stored, and analyzed in compliance with relevant regulations and guidelines for patient data privacy and security [61]. Large volumes of medical data can be stored, managed, and processed thanks to cloud computing. It also allows for remote access to health information and facilitates communication and collaboration among healthcare providers [62]. As mentioned and discussed in the pre-SLR section, the current research focus in smart healthcare is still very similar, but artificial intelligence and more specifically machine learning are quite prominent, along with IoT and cloud computing.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 3 Machine Learning
### Unsupervised Learning  
Unsupervised learning algorithms are given a set of unlabeled data to correctly predict the output, which is the basic difference with the supervised learning approach. These algorithms are mostly used for clustering and aggregation problems, but can also achieve great results for regression problems. Some typical unsupervised algorithms include K-means, Self-Organizing Maps (SOMs), Hidden Markov Model (HMM), Auto Encoders (AEs), Principal Component Analysis (PCA), Restricted Boltzmann Machine (RBM), fuzzy C-means etc. Furthermore, unsupervised ML have been applied to enhance the performance of Deep Learning (DL) algorithms such as Convolutional Neural Networks (CNNs) and Long short-term memory (LSTM) algorithms [16]. * K-means: It is a widely used method to classify unlabeled raw input data into different clusters. K-means algorithm assigns each new data point to a cluster, based on its distance from the nearest associated centroid. The centroids are updated based on the previously assigned data point and the procedure is repeated until there is no alteration in the input data points and the centroids. K represents the number of desired clusters and can greatly impact the performance of the algorithm [16]. * Self-Organizing Map (SOM): This approach is mostly used for data clustering and dimensionality reduction. The model has one input layer and a map layer, with each layer containing many neurons and a different weight vector is assigned to each neuron. During the training process, SOM builds the map by using an unsupervised competitive learning approach. The winning neuron from this competition determines the cluster in which any new input vector is classified [16]. Figure 2 displays the architecture of a traditional Self Organizing Map model. * Autoencoders: learning circuits that copy inputs into outputs, aiming to have the least possible deviation. The have great results on both classification and regression problems. Autoencoders are stacked approaches and are trained unsupervised bottom
# RFID Technology as a Low-Cost and Passive Way to Digitize Industrial Analog Indicators
###### Abstract  
Simple analog devices like manometers, manual valves, etc., have been ignored in the digitization process that has characterized the transition towards Industry 4.0. The reason behind this is that their substitution with the equivalent digital versions is high cost and needs re-wiring. This study introduces a low-cost wireless and passive model aligned with the Industry 4.0 paradigm to digitize analog indicators. The concept is based on electromagnetic (EM) shielding of the manometer's embedded radio frequency identification (RFID) tag. We designed and tuned a new tiny RFID tag to be embedded into analog devices. Finally, a digitized manometer by RFID electromagnetic shielding concept is simulated in the Ansys HFSS modeling environment.  
tiny RFID; metallic electromagnetic isolation; analog manometer; Ansys HFSS simulator; passive digitization +
Footnote †: journal:  
design and embed small passive RFID tags inside the analog devices, keeping an acceptable reading range. Many researchers have discussed RFID limitations and challenges dealing with the environment and substances and their influences like reading range reductions and wave reflections and suggested different possible solutions [6; 7; 8]. The general concept of the methodology is based on the material behavior and its impact on the RFID system. Hence, the radio waves pass through air, plastic, wood, and ceramics efficiently, while there is a considerable reduction and reflection with other materials such as conductors (e.g., metals). The use of metals in industrial environments creates blind spots for RFID readers and disrupts their performance [9; 10]. Covering the RFID tag with metals causes electromagnetic (EM) shielding, but a movement of the metal cover removes the isolation and turns the RFID chip on. The adverse effects of metals on RFID performance have also been investigated by [PERSON] [11].  
This work aims to digitize analog devices by integrating new tiny passive RFID tags [9]. We performed experiments in large dimensions with commercial RFID tags to evaluate the technical feasibility of the concept. A tag antenna was designed, virtually localized in an analog manometer, and numerically simulated using Ansys HFSS V2021 R2 software.  
Then, the EM shielding cover was optimized, and the final digitized analog manometer performance was simulated. The following sections illustrate the state of the art, then the methodology will be explained in detail. Later, in the experiments, Ansys HFSS simulation practices and results are described, and finally, conclusions are drawn.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 6 Scheme Analysis  
### Security Analysis  
#### 6.1.1 Medical Data Security  
All users requiring access to the data must be authenticated. Data access control is enforced based on the identity of the DU, preventing unauthorized access. The encrypted medical data generated by the system are stored in the IPFS distributed storage servers. Data sharing is protected through proxy re-encryption, ensuring that IPFS nodes only provide computational power for ciphertext transformation and cannot access the plaintext medical data.  
For data integrity, on one hand, the IPFS utilizes content addressing, ensuring the integrity of the data. On the other hand, the cryptographic hash of the medical data ciphertext is persistently stored on the blockchain ledger. When users access the data, they are required to sign and verify the integrity of the medical data. Additionally, usage records are uploaded to the blockchain for storage, ensuring that encrypted data are not altered and that usage is traceable.
## 4 Experiments and Results
### Hyperparameter Tuning  
In the experiments, we adopt the default hyperparameters for scikit-learn and XGBoost to build a robust baseline of various machine-learned models for detecting DDoS attacks in IoV systems. These default configurations were selected to maintain coherence and fairness among these models, with which we compare their base functionality without the added complexity provided by ample tuning. For instance, the depth of the decision Tree was not limited and used the Gini criterion for splitting. By the same token, Random Forest utilized 100 estimators with the Gini criterion. By default, XGBoost had a learning rate of 0.3 and also had 100 estimators, while the Gradient Boosting, with a learning rate of 0.1 and 100 estimators, was chosen in order to trade-off between model complexity and computational efficiency. By default, we have also used five neighbors for the K-Nearest Neighbors model with a distance metric of Minkowski. Though these defaults provide a fair starting point, we are aware that hyperparameter optimization is a necessary investment to increase model performance and efficiency.
## 6 Numerical Study and Comparison
### Geometry and Mesh  
The typical finite element mesh of a PCCSW is shown in Figure 4, which is generated by using lines and areas. Finite element mesh sizes of 80 mm \(\times\) 80 mm are used for each longitudinal panel and inclined panel of the PCCSW. In the mesh partition stage, the number of elements belong to each panel segment of the PCCSW is more reasonable in the finite element simulation. Additionally, mapped meshing is adopted. Four elements were used for meshing longitudinal and inclined panels due to the transfer of longitudinal loads along the edge of the web from the inclined panel to the longitudinal panel.  
Numerical analysis using large general-purpose software ANSYS and theoretical verification are performed in the following analysis. The data are shown in the following tables. The finite element model is shown in Figure 4.
# Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model
## 3 Experiments and Discussion
### Long-Term Structural State Trend Forecasting
Figure 11: Structural state trend forecasting based on long FFT–Informer on the ASCE benchmark data set. Figure 10: Structural state trend forecasting separated by half a year at different structure status. In order to conduct a comparative experiment, these feature extraction methods are combined with Informer to compare with FFT-Informer forecasting, which also shows the advantages and disadvantages of each feature extraction method combined with Informer. Table 3 indicates that: (1) compared with the Informer model, the MAE of the FFT-Informer model is reduced by 17%, and the MSE of the FFT-Informer model is reduced by 29% on average on two data sets, respectively. It indicates that FFT has a better ability to improve the prediction ability of the Informer model. (2) Compared with other feature extraction methods-based Informer models, the MAE and MSE of the FFT-Informer is the smallest, which indicates that FFT has a better ability to extract features and aid in prediction. Figure 7:0 Structural state trend forecasting on the ASCE benchmark data set separated by half a year at different structure status. Figure 7:1 Structural state forecasting based on the FFT–Informer on 10 and 15 forecasting lengths. In addition, forecast length is increased to 15, and the predicting accuracy of the FFT-Informer model is compared with the Informer model and some commonly used forecasting models, including LSTM, the echo state network (ESN), and the autoregressive integrated moving average model (ARIMA) ARIMA. Table 4 indicate that: (1) Compared with the ARIMA model, which is a statistical regression model, the MAE of the FFT-Informer model is reduced by 61%,and the MSE of the FFT-Informer model is reduced by 59% on average. (2) Compared with the ESN and LSTM model, which are machine learning forecasting methods, the MAE of the FFT-Informer model is reduced by 43% and 37%, and the MSE of the FFT-Informer model is reduced by 49% and 41% on average. (3) With the increase of prediction length, the prediction ability of the FFT-Informer can remain relatively stable compared with others. These indicate the FFT-Informer has good prediction performance and stability.
# Application of Computational Fluid Dynamics Analysis after Bimaxillary Orthognathic Surgery
## 1 Introduction  
Orthognathic surgery is widely used to improve skeletal facial asymmetry and malocclusion [1]. In Asians, who tend to exhibit a higher proportion of skeletal class III malocclusion, amelioration of mandibular protrusion has significant esthetic and functional effects [2]. Mandibular setback or bimaxillary orthognathic surgery are used to resolve mandibular prognathism. Bimaxillary orthognathic surgery is more likely to be performed because upper and lower jaw complexes are more esthetically effective than mandibular setback alone.  
Movement of the jaw also affects the shape of the soft tissue attached to it [3]. The most influential structures in this regard include the tongue base and hyoid bone. Changes in the position of these structures affect the shape of the airway [4], and the changes in airway can cause oxydative stress. Oxydative stress can cause the sensation of chronic pain [5]. Moreover, postoperative reduction of the airway in skeletal class III patients may cause obstructive sleep apnea (OSA). Many studies investigating postoperative airway changes have involved patients undergoing orthognathic surgery. [PERSON] et al. compared the effects of mandibular setback and bimaxillary surgery on the airway and found that mandibular setback surgery caused long-term changes in the airway. However, bimaxillary surgery was confirmed to not cause significant changes [6]. Most of these studies focused mainly on measuringvolume changes after two-dimensional or three-dimensional (3D) reconstruction of airway changes in the sagittal plane [7]. It is known that the computational fluid dynamics (CFD) model applied to the upper airway is advantageous in capturing the characteristics of airflow [8]. Using this simulation model, we can observe how changes in air flow characteristics affect the soft tissue structures of the airway [9]. For this reason, CFD is an effective tool for identifying changes in airflow characteristics and the resulting airway changes that occur after bimaxillary setback surgery. Given the increase in the incidence and prevalence of OSA, attempts have been made to evaluate functional changes after airway changes. [PERSON] et al., observed airway changes using CFD after maxillomandibularismessment (MMA) in OSA patients. As a result, it was found that the laminar and turbulent air flows were reduced at all locations in the airway [10]. [PERSON] et al., used CFD methods to analyze airway changes observed after mandibular setback surgery in class III patients [11]. However, the study by [PERSON] et al. only confirmed changes in a single jawbone (i.e., the mandible). As described above, the movement of the upper and lower jaw complex may occur in more directions than simply up, down, left, and right. Accordingly, airway changes can also assume many forms. The purpose of this study, therefore, was to use CFD simulation to analyze changes in the airway observed in a patient who underwent bimaxillary orthognathic surgery.