esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cam Wave Generators with Free Deformation in Non-Working Area
### Design of Cam Wave Generator  
Because the wall thickness diameter ratio of the flexspline's cup is relatively low, the force and deformation of the flexspline can be approximately treated as a thin-walled circular ring. Here, the circular ring is assumed to be deformed by a known convex curve in two symmetrical arcs, and the shape of the thin-walled circular ring after deformation is in a continuous fitting state with the convex curve, as shown in Figure 4a.  
Figure 3: New deformation model of the flexspline.  
The forced deformation of the ring by a symmetric convex curve is equivalent to the continuous load \(p(\varphi)\) and \(q(\varphi)\) acting on the convex curve, while the non-load section of the ring deforms freely. Based on the symmetry of deformation, a quarter section of a convex curve is taken for analysis. The elastic deformation curve equation and the deflection curve of the ring \(u(\varphi)\) at this segment can be gained by solving the follow differential equation [26; 27]:  
\[\frac{d^{5}u}{d\varphi^{5}}+2\frac{d^{3}u}{d\varphi^{3}}+\frac{du}{d\varphi}= \frac{R^{4}}{EI}[\frac{dp(\varphi)}{d\varphi}+q(\varphi)]\ \ \varphi=\left[\alpha,\frac{\pi}{2}\right] \tag{1}\]  
u Here, the tangential load \(q(\varphi)\) and the radial load \(p(\varphi)\) are determined by the contour shape of the convex curve. is the radial deformation at any point of the ring curve and \(R\) is the radius of the ring. \(EI\) is the bending stiffness of the ring.  
When \(\phi=[0,\alpha]\), the deformation curve of the ring is the same as that of the known convex curve and the equation of the convex curve in this section can be expressed:  
\[\rho=\rho(\phi) \tag{2}\]  
Figure 4: Cam wave generator design in a new deformation model.  
The radial deformation \(\mathbf{u}\) corresponding to the convex curve section is expressed as:  
\[u=\rho(\varphi)-R \tag{3}\]  
According to the forced deformation of the convex curve segment and the deflection equation of the free deformation segment, the complete curve equation of the deformed ring can be obtained by:  
\[\rho=\left\{\begin{array}{lr}\rho(\phi)&0<\phi<\alpha\\ R+u(\phi)&\alpha<\phi<\frac{\pi}{2}\end{array}\right. \tag{4}\]  
Here, point C is the dividing point between the non-working section and the working section, and \(\alpha\) is the angle between O-x and OC.  
So far, if the convex curve of the working section is determined, the complete deformation curve of the ring can be obtained by solving the deflection equation. The contour shape of the cam wave generator in the non-working area can be obtained by using the equidistant curve of the complete deformation curve of the ring.
## Appendix A Artificial Neural Network
### Bayesian Neural Network  
The Bayesian neural network (BNN) has the same architecture as of the ANN (described in Appendix A.1), but additionally it uses Sequential Model Based Optimization (SMBO) to determine the hyperparameters of an ANN such as learning rate, number of neurons, etc. A detailed flowchart of SMBO is shown in Figure A2. The machine learning algorithms are generally termed as black box algorithms and optimizing their hyperparameters for performance enhancement could be termed as black art which requires extensive experience or brute force search of solution space. The evaluation for optimum selection of hyperparameters for a function \(f(x)\) is computationally extensive. Therefore, it is important to minimize the number of samples drawn. Bayesian optimization incorporates prior belief about \(f(x)\) and updates the prior with samples drawn from \(f(x)\) to get posterior that better approximates it. This method had been used previously to tune the hyperparameters of Markov Chain Monte Carlo (MCMC) by [PERSON] et al., [64], and [PERSON] et al. [65] used it to develop strategies for configuring the satisfiability of a mixed integer programing solver using random forest. Application of Sequential Model Based Optimization (SMBO) has been established by [PERSON] el al. [66] for optimizing hyperparameters for SVM and convolution neural networks. SMBO method iterates between fitting a surrogate model to approximate the objective function \(f(x)\) and an acquisition function that directs the sampling to the area where an improvement over the current sample observation. In this study, SMBO has been used to optimize the hyperparameters of ANN such as number of neurons, number of hidden layers, drop out ratio and learning rate for predicting the pressure loss.  
The acquisition function in this study is Expected Improvement (EI), given in Equation (A2a).  
\[EI(x)=\mathbb{E}\ max\Big{(}f(x)-f\big{(}x^{+}\big{)},\ 0\Big{)}\] (A2a)where \(f(x^{+})\) is the value of the best sample so far and \(x^{+}\) is the location of that sample and the expected improvement for Gaussian model can be defined using Equation (A2b) and Equation (A2c).  
\[EI(x)=\left\{\begin{array}{ll}\left(\mu(x)-f(x^{+})-\xi\right)\Phi(x)+\sigma(x) \phi(z)&\quad\text{if}\ \ \sigma(x)>0\\ 0&\quad\text{if}\ \ \sigma(x)=0\end{array}\right.\] (A2b)  
\[Z=\left\{\begin{array}{ll}\frac{\mu(x)-f(x^{+})-\xi}{\sigma(x)}&\quad\text{ if}\ \sigma(x)<0\\ 0&\quad\text{if}\ \sigma(x)=0\end{array}\right.\] (A2c)  
where \(\sigma(x)\) and \(\mu(x)\) are standard deviation and mean of the posterior prediction at \(x\), respectively, \(\Phi\) and \(\phi\) are the Cumulative Distribution Function and Probability Distribution Function of the standard normal distribution and \(\xi\) is the constant of exploration during optimization and the recommended default value is 0.01. An objective function is shown in Figure A3 represented by yellow dashed line and a Gaussian based surrogate function which approximates the objective function (shown in blue) and the samples are the outcome of the expected improvement for sample selection (marked in black).
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 5 Experimental Evaluation
### Results  
Three tests were performed to assess the feasibility of the 5 GT system and validate its components, which are the system capacity, the maximum latency and the maximum packet loss. More specifically, the system capacity refers to the maximum number of containers that can be supported by the 5 GT system. To measure this number, we rely on the maximum number of messages (i.e., NB-IoT transmissions) that can be sent from the CTDs, without breaking, to the OneM2M platform during the experimental tests. The latency refers to the time elapsed between the sending of a message by the CTD and its publication on the OneM2M platform. For each CTD, we only report the minimum and maximum registered values then we calculate the average as follows. The average minimum latency is equal to the sum of the measured minimum values of all the CTDs over the total number of CTDs, while the average maximum latency is equal to the sum of the maximum measured values of all the CTDs over the total number of CTDs. Finally, the packet loss is calculated as the number of received messages over the number of transmitted ones.
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
###### Abstract
We first use an RoF CNN to simulate the generic RoF connection and then use this to train the proposed DPD CNN by backpropagating the mistakes. 4. A comparative experimental investigation was conducted in which the previously proposed ILA-based GMP method was benchmarked against the CNN technique and compared utilising a 5G NR multiband signal. Error Vector Magnitude (EVM) and multiplications and coefficients required measuring complexities are used to evaluate the performance.
# The Applications of Plasma Techniques
### Supply Systems of Non-Thermal Plasma Reactors. Construction Review with Examples of Applications  
[PERSON] in [7] performed a review of the power supply systems of non-thermal plasma reactors (NTPR) with dielectric barrier discharge (DBD), atmospheric pressure plasma jets (APP), and gliding arc discharge (GAD). This choice is due to the following reasons: these types of electrical discharge produce non-thermal plasmas at atmospheric pressure; the reactor design is well developed and relatively simple; the potential area of application is large, especially in environmental protection processes and biotechnologies currently under development; and these reactors can be powered from similar sources using non-linear transformer magnetic circuits and power electronics systems. The author stated that research on the properties and applications of non-thermal and non-equilibrium plasmas at atmospheric pressure requires the interdisciplinary cooperation of scientists who represent both basic and applied sciences, namely: plasma chemistry and physics, biochemistry, microbiology, medicine, chemical technology, environmental engineering, material engineering, agricultural engineering, bioengineering, metrology, and electrical engineering. The latter is represented by the author of this review, who, together with a team from the Department of Electrical Engineering and Electrotechnology of the Lublin University of Technology, has conducted research for over 30 years on the generation of non-thermal plasma by means of electrical discharges. In particular, the author's research has focused on the power supply systems of these special energy receivers in cooperation with the above-mentioned representatives of the basic and applied sciences. The research has resulted in many monographs, publications, doctorates, and patents, most of which are cited in this review.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 1 Introduction
Firstly, the \(k\)-means algorithm [17] is used to group the users and, then, taking into account the service, a subset of centroids are selected to be served by a UAV at a given altitude. In our paper, a novel ABBS autonomous positioning strategy is proposed for the multiple UAVs scenario. As opposed to previous works, the decisions concerning the new 3-D positions of the UAVs are taken under a communication criterion, namely the maximization of the rate delivered to the users subject to fairness and a maximum backhaul bandwidth to be shared among all the UAVs following a frequency division multiplexing (FDM) approach. Some other challenges such as the specific payload and the regulations limiting the altitudes of the ABBSs are deferred to a future work. The paper is organized as follows. In Section 2, a system overview is given as a background to understand the whole proposed strategy. In Section 3, two different time division multiple access (TDMA) schedulers are evaluated to cover the multiple access challenge for the coverage area corresponding to each individual UAV. Section 4 derives the proposed navigation strategy for the multiple UAVs as an optimization problem where a fair distribution of the resources in the entire scenario is subject to limited rate backhaul constraints that have to be fulfilled. In Section 5, an instantaneous mechanical power consumption model for the UAVs is proposed, and the energy expenditure is estimated. In Section 6, the proposed strategy is evaluated and compared to other works of the state of the art to characterize its behavior. Finally, in Section 7, some conclusions are abstracted and the future work is outlined.
## 3 Polyaryletterketone  
A new alternative to metal-based materials is polymeric biomaterials such as those based on PAEK [26]. PAEK is a semicrystalline, thermoplastic polymer that contains both ether and ketone groups. Its structure gives it a glass transition temperature of around 157 \({}^{\circ}\)C and a melting point of 370 \({}^{\circ}\)C. The physical properties of PAEK are affected by the ratio and arrangement of ether and ketone groups, with a greater number of ketone groups  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Young’s Modulus (GPa)** & **Yield Strength (MPa)** & **Tensile Strength (MPa)** & **Fatigue Limit (MPa)** \\ \hline Stainless steel & 190 & 221–1213 & 586–1352 & 241–820 \\ Co-Cr alloys & 210–253 & 448–1606 & 655–1896 & 207–950 \\ Titanium (II) & 110 & 485 & 760 & 300 \\ Cortical Bone & 15–30 & 30–70 & 70–150 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of biomaterials [20].  
leading to increased polarity and stiffness. As a result, both the glass transition temperature and the melting point are higher [1].  
Polyetheretherketone (PEEK) and Polyetherketoneketone (PEKK) are two materials that belong to the PAEK family. Both the materials have high-temperature resistance, fatigue resistance, and have the ability to be sterilized, making them perfect candidates for healthcare applications [27; 28; 29]. In contrast to metals, PAEKs do not cause artifacts in CT/MRI scans. However, their material composition can lead to differences in certain characteristics, such as their degree of crystallinity and the speed of crystallization [30]. Figure 1 shows the structures of PEEK and PEKK.  
As described by [PERSON] et al. and [PERSON] et al., PEEK is a semicrystalline, thermoplastic, polycyclic, aromatic polymer synthesized through the reaction of 4,4'-difluorobenzophenone with disodium hydroquinone at 300 \({}^{\circ}\)C, with a melting point of 335 \({}^{\circ}\)C [1]. PEKK, on the other hand, is synthesized from diphenyl ether and terephthalaloyl chloride, with aluminum chloride and nitrobenzene used as catalysts. The key difference between PEKK and PEEK lies in the presence of an additional ketone group in PEKK's aromatic ring. This structural variation increases the polarity and backbone rigidity of the material [1; 31].  
PEEK and PEKK share physical and mechanical characteristics that resemble those of bone, resulting in high impact resistance [1]. Tables 3 and 4 compare the properties and differences of PEEK and PEKK.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 3 Proposed System  
### System Overview  
The proposed system addresses the challenges associated with road sign recognition by leveraging a modular approach that integrates object detection, text recognition, and user-specific contextual information. Road sign images are diverse in structure and content, typically comprising textual information such as distances and destinations, symbolic elements like directional arrows and warning icons, and distinctive colors that denote road categories. These images are often captured under varying environmental conditions, including different lighting scenarios, weather conditions such as rain and fog, and potential occlusions, making robust detection and recognition essential [1; 2].  
The dataset used in this study consists of 4754 annotated road sign images, including 10,846 labeled objects categorized into text, symbols, and directional elements, as shown in Figure 2. The images were captured using vehicle-mounted image sensors, inherently resulting in relatively lower resolutions reflecting real-world capturing conditions. Images were collected from real-world road environments using vehicle-mounted cameras, ensuring diversity in lighting conditions (daylight: 55%, low-light/night: 20%, rain/fog: 15%, other: 10%). The annotation process followed standard labeling procedures, manually verifying each object's position and class. The dataset structure aligns with established road sign recognition benchmarks to support reliable model training and evaluation.  
To address the complexity of road sign images, the system is designed with three primary components. Object detection is carried out using Faster-RCNN, which is optimized for detecting small and overlapping features like text and symbols [1]. Text recognition is handled by the CRAFT model, which detects and converts text into machine-readable formats [8]. Finally, contextual information integration combines user data, such as location, speed, and destination, with the detected road sign information, ensuring that the outputs are actionable and tailored to the user's immediate needs [11].  
Figure 3 illustrates an example where the text detection model encountered false positives. The misaligned annotations in the figure are not formatting issues but represent incorrect text detection cases. The dataset used in this study was collected from real-world vehicle-mounted cameras, and images were processed at a resolution that matches actual on-road conditions. As a result, some images appear lower in resolution due to the inherent quality of the captured data.  
Figure 2: Examples of real-world road sign images. The images naturally contain Korean text as they were collected from actual road signs in the Republic of Korea (as explained in Figure 1). The white boxes highlight individual object detection results within the road sign images, demonstrating the object detection process.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 4 Deep-Learning Techniques
### Connectionist Temporal Classification (CTC) [44]  
Connectionist temporal classification is an approach to the modeling of the labeling of unsegmented sequence data, which can be a problem for RNN and encoder-decoder models. It is used in automatic speech recognition and handwritten-text-recognition tasks. It allows the training of RNNs without the need to manually segment the data and direct training for sequence labeling; therefore, it solves the problem of the requirement of an alignment between the input and the output. With an input of an arbitrary length, it merges the spaces, special blanks (epsilons), and repeated characters to form a word. A CTC loss function is used, which aims to have a conditional probability that marginalizes over a valid set of alignments, computing in each step the probability of each alignment. To elucidate this point, consider an input with a length of 6, Rayyan, perceived by the RNN in eight steps, making different distributions for each character of the set, [R, a, y, n, epsilon], and then compute the probability of different sequences, such as [R, a, y, epsilon, y, a, n, n], [R, a, y, y, a, n, epsilon, n], and outputs (Rayyan, Rayann). Next, by marginalizing over the alignments, distributions over the outputs are obtained, and the Rayyan sequence has the highest probability.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Determining the Best Network Configuration and Setup  
The first set of experiments was carried out to find the best network configuration. Specifically, the study analyzed the model performance for different optimizers as well as various values of the learning rate, momentum, and class weight. As recommended by the authors of the Inception network [35], the experiments started off by using the stochastic gradient descent (SGD) with a learning rate of \(10^{-2}\), the momentum of 0.9, and an L2 weight decay of 0.0002. After experimenting with a few choices for the learning rate with the SGD optimizer, it was replaced for Adam with an initial learning rate of \(10^{-3}\). In this experiment, a scheduled learning rate decay was applied by using learning rates of \(10^{-3}\), \(10^{-4}\), and \(10^{-5}\) in epochs 1-30, 31-50, and 50-70, respectively. As this setting yielded the best results, the approach used the same model on the testing set.  
So far as the class weights are concerned, the weights w1, w2, w3 are important to increment the importance of the three classes of abnormal behavior, because the _none_ class (as reflected in the results of precision and recall values) was found to be learned better than the other classes. However, if one considers all three classes of abnormal behavior versus the _none_ class, the recall of the model is particularly important. Specifically, some false positives are more acceptable than false negatives. Hence, by adjusting the class weights, the system tried to reach a situation where the model is performing better for the three classes of abnormal behavior even at cost of having some false alarms. In this case, a value higher than 1.0 for w1, w2, w3 improved results. Therefore, the base values for these weights were set to 2.0 and incremented or decremented based on results. In this way, the most promising results for one class (i.e., loitering) were achieved with a value slightly higher than 2.0 but if a standard value for all three was to be selected, a value of 2.0 is deemed adequate in general.  
As previously mentioned in Section 4.1, some initial trials of the approach were based on a _basic setup_ that included randomly selected videos from the datasets. This means that specific movement patterns of each behavior were not taken into account. Also, videos from only one dataset were used in training for loitering (Cavair-DS) and violence (Hockey-DS). Moreover, this basic experimental setup tried to solve the problem of unbalanced datasets by applying Synthetic Minority Over-sampling Technique (SMOTE) [64] to generate synthetic samples from the minority class. Later, the study enhanced the dataset as previously described and obtained the best configuration setup results.
# Perception of Risks and Usefulness of Smart Video Surveillance Systems
## 1 Introduction
### Technical Principles of the Smart Subsystem  
The description of the general workflow of the abnormality detection software is presented in Figure 1[2; 34]. The process is divided into three parts: The input of the neural network represents the used image material as well as sequences of images collected by video cameras. The first step of a general solution approach is the extraction of the posture of several human bodies simultaneously from this image material. This step is also called Multi-Person Pose Estimation [35; 36] or just Human Pose Estimation [29; 37]. The outputs are keypoints of the digital skeleton (see Figure 2), which relate to various body parts such as joints or some relevant body parts such as eyes or nose. That way, the resulting skeleton correlates with the posture of the corresponding human being, i.e., its body. In the next step, the movements of the body are identified by capturing the keypoints over several timesteps, which can be completed by using optical flow estimation or other tracking algorithms. This results in motion features.  
Based on this extracted motion information, a neural network is used to determine whether an abnormality is present or not. The achieved output of the generally represented procedure is an anomaly feature. The exact kind of feature depends on the chosen algorithm and can range from heatmaps [2] to classified bounding boxes [12; 13; 31; 32].  
Figure 1: Simplified schematic block diagram showing a possible workflow in smart video surveillance as an aiding system. A video management system (VMS) is responsible for the data acquisition and provides the data to the smart subsystem. The output of the subsystem is then provided to the VMS, which presents it to the human operator. The human operator then takes these results and decides whether to perform additional steps (e.g., send a police officer to the location) or ignore them.