esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Chaotic dynamical systems are applicable in a wide range of domains such as encryption [1, 2, 3], secure communication [4, 5, 6], robotics [7, 8, 9], lasers [10], chemical reactors [11], memristors [12, 13, 14], etc. [PERSON] et al. [1] reported an optical image encryption algorithm in gyrator transform domain. [PERSON] [2] devised a new unified image encryption system with identical encryption and decryption algorithms. [PERSON] et al. [3] developed a new color image encryption algorithm using a 2-D logistic tent modular map. [PERSON] et al. [4] designed a chaotic secure communication system with the help of a brain emotional learning-based intelligent controller. [PERSON] et al. [5] proposed a chaotic evolutionary amino acid codons Model for image encryption. [PERSON] et al. [6] discussed chaos-based multiple encryption algorithms for speech recordings. Chaotic systems find use in secure communication devices and engineering applications featuring the implementations of analog or digital electronics [15].  
Many types of chaotic dynamical systems have been studied in the literature such as jerk systems [16], butterfly attractors [17], multi-scroll systems [18, 19, 20], no-equilibrium chaotic systems [21, 22, 23], etc.  
In the recent years, many papers have discussed the modeling of chaotic dynamical systems equipped with special curves having an infinite number of balance points [24].  
Table 1 lists various dynamical systems exhibiting chaotic behavior with special curves of balance points such as hyperbola [25], circle [26], apple [27], axe [28], conch [29] and peanut [30].  
A main contribution of this work is the finding of a new chaotic dynamical system with a butterfly-like curve of balance points. We perform a bifurcation analysis of the new chaotic system and analyze its intrinsic properties such as multi-stability and transient chaos. Multi-stability refers to the coexistence of attractors of the new chaotic system, generated with different initial states of the system under fixed values of the parameters [31; 32; 33]. Transient chaos phenomenon occurs in a dynamical system when trajectories starting from randomly chosen initial conditions seem chaotic up to a certain time interval and then collapses into a periodic orbit or balance point with the evolution of time [34; 35; 36].  
Furthermore, we carry out field-programmable gate array (FPGA) realization of the new chaotic system with a butterfly-like curve of balance points. Chaotic systems can be implemented in FPGA using numerical analysis and computer algorithms [37; 38; 39; 40]. Realizations of chaotic systems using FPGA are useful for engineering applications such as generators of random numbers [41], secure devices [42], etc.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 3 Literature Review
### Security Measures for AMQP  
Given its critical role in enterprise-grade IoT systems, AMQP requires robust security measures tailored to its architecture:  
* **Replay Protection**: Implementing message sequencing or unique identifiers to detect and discard replayed packets [10].
* **Broker Hardening**: Rate-limiting, load balancing, and resource allocation to prevent broker exhaustion during DDoS attacks [5].
* **Encrypted Queues**: Using TLS for secure message queuing and routing, ensuring end-to-end confidentiality [7].
* **Access Controls**: Role-based access control (RBAC) for restricting unauthorized queue access.
* **Enhanced Authentication**: Adopting mutual TLS or token-based mechanisms to prevent impersonation [9].
* **Activity Monitoring**: Real-time monitoring of message queues to detect anomalies indicative of manipulation [8].  
As shown in Table 1, while MQTT and CoAP are optimized for constrained environments, AMQP's focus on high-throughput, transactional reliability introduces distinctvulnerabilities. Addressing these challenges through robust countermeasures ensures AMQP can serve as a secure and resilient foundation for IoT applications in critical enterprise settings.
## 3 Experiments and Results  
Certain experiments can be conducted to check whether the proposed methodology is valid. This article also takes advantage of a previous experiment by the same authors, according to article [4], to compare technology improvements and revise the dataset. Furthermore, it reinforces the proposal to reduce false positives using two datasets, this time using the same technology.  
Two main actions are applied in the experiment: The first consists of updating the technology used, from version 4 of YOLO, initially used in the first article [4], to version 8, which consists of the evolution of the previous technology. The second consists of reviewing the dataset used, CODEBRIM [13], in the first article to identify possible inconsistencies and correct them to allow the neural network to learn better. The classification dataset was also trained with YOLO version 8 instead of the customized network used in the first article. Regarding this last point, the idea of the first article is maintained, making it possible to use a specific dataset containing more images to identify false positives from detection.
## 3 Transfer Function of the Measurement System  
The calculation of the transfer function of the measurement system requires the prior knowledge of the dynamic behaviour of vehicle and track systems in the frequency domain. To this extent, the rail irregularity \(z_{irr}\) is assumed as a harmonic excitation at the wheel-rail interface that can be expressed as a function of the spatial coordinate \(x\):  
\[z_{irr}(x)=Z_{0}e^{\frac{2\pi}{\lambda}x}, \tag{2}\]  
Figure 1: Accelerometers mounted on the axlebox of a wheelset of the instrumented trailer bogie (the same setup is repeated on the other wheelset): (**a**) side A, vertical axlebox accelerometer; (**b**) side B, vertical and lateral axlebox accelerometers.  
where \(Z_{0}\) is the amplitude of the rail roughness and \(\lambda\) is the wavelength. The wheel running over the irregularity with a specified speed \(v\) defines an excitation frequency \(f\) according to the following relationship:  
\[f=\frac{v}{\lambda}, \tag{3}\]  
Since the spatial coordinate \(x\) can be expressed by the product of the speed and the time, the rail irregularity in time domain assumes the following form:  
\[z(t)=Z_{0}e^{\Omega t}\quad\Omega=2\pi f, \tag{4}\]  
where \(\Omega\) is the circular frequency of excitation.  
In high-frequency models for the wheel-rail interaction [12; 13; 14; 15], the generic contact variable \(a(t)\) can be seen as the superposition of two different contributions:  
\[a(t)=a_{0}+\Delta a(t), \tag{5}\]  
The \(a_{0}\) term is the steady-state component of the variable, while the second term \(\Delta a(t)\) represents its dynamic fluctuation, varying harmonically as the irregularity. It is assumed in this work that rail irregularity generates fluctuations only in the vertical component of wheel-rail contact force and consequently in the vertical axlebox acceleration.  
As a first step, the dynamic behaviour of the train and the track as standalone systems is obtained in terms of vertical direct receptance, i.e., the vertical displacement of the contact point on the wheel and the rail per unit vertical contact force. Concerning the vehicle dynamics, only the wheelset resting on the primary suspension is considered [16], because carbodies and bogies can be assumed dynamically decoupled from the wheelset given that the primary suspensions isolate bogie vibration in the frequency range of interest for rail corrugation (from 30 up to 300 Hz).
# A Method for Extracting Acoustic Water Surface Waves Based on Phase Compensation
Aiming at the problem of traditional methods finding it difficult to accurately extract acoustic water surface waves when the phase is discontinuous, this article innovatively proposes a method for extracting acoustic water surface waves based on phase compensation, which compensates the discontinuity points of the phase history according to the change in range bins. Then, the low-frequency water surface fluctuations and discontinuous points are filtered by second-order differential joint outlier removal. Simulations and experiments verify the effectiveness of the proposed method and its superiority in communication quality.
## Article  
Effects of Home Exercise and Manual Therapy or Supervised Exercise on Nonspecific Chronic Low Back Pain and Disability  
**[PERSON] \({}^{\star}\)\({}^{\copyright}\) and [PERSON]**  
Department of Health Promotion and Rehabilitation, Lithuanian Sports University, 44221 Kaunas, Lithuania  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
Featured Application: In this study, we describe a specific exercise program for individuals with nonspecific chronic low back pain. The exercises can be performed by individuals at home as a home exercise program. Physicians, physiotherapists, and researchers can apply the results and conclusions of this study in their therapeutic and scientific practice.  
In this study, we aimed to evaluate the effects of two therapeutic methods: home-based exercise and manual therapy (MT) or supervised exercise on pain and functional status in individuals with chronic nonspecific low back pain. In total, 60 individuals with LBP were included in this randomized study. All participants were treated for 6 weeks in one of two groups: the manual therapy group or the exercise therapy group. In addition, all participants were asked to perform an exercise program at home independently. Levels of pain, spinal mobility, disability, and abdominal and back muscle endurance were measured before and after 6 weeks of intervention. Results: After MT, disability was reduced by 70% (F = 42.2; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.99\)) and pain was reduced by 78% (F = 4.9; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.51\)). After exercise therapy, disability and pain were reduced by 78% (F = 11.5; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.78\)) and 68% (F = 9.4; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.41\)), respectively. Muscle endurance and lumbar spine mobility values were significantly higher in both groups after 6 weeks of intervention (\(p<0.00\)). After 12 MT interventions, lumbar spine mobility increased by 40% (F = 1.9; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.24\)) and after exercise therapy by 38% (F = 28.4; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.82\)). Abdominal muscle endurance improved by 29% (F = 24.2; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.79\)) after MT and by 34% (F = 57.6; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.67\)) after exercise therapy; back muscle endurance improved by 18% (F = 48.6; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.78\)) after MT and by 20% (F = 14.2; \(p<0.00\); \(\eta_{\text{p}}^{2}=0.76\)) after exercise therapy. After 6 weeks of intervention, there was no statistically significant difference between the pain, disability, and spinal mobility groups (\(p>0.05\)). However, differences between groups in kinesiophobia (\(p=0.02\)), back (\(p<0.02\)) and abdominal (\(p<0.03\)) muscle endurance values were statistically significant. Following the home exercise program and manual therapy, or the home exercise program and supervised exercise, LBP and disability had clinically significant reductions and functional status showed improvement.  
**Keywords: chronic low back pain; musculoskeletal pain; disability; exercise therapy; manual therapy**
### Contribution  
This paper describes the orbit control of the formation-flying technology developed for the CANYVAL-C mission. The mission operates the satellites in low-Earth orbit, maintaining a string-of-pearls configuration rather than a typical circular formation. Hence, the reference trajectory is defined by describing the observation target in a relative coordinate system, and the consecutive observation periods and alignment possibilities are evaluated based on this information. Because of the limited performance of cube satellites, control algorithms and complementary strategies have been developed and validated. The detector satellite is constructed as 1U, the minimum standard for cube spacecraft, whereas the occulter satellite has a propulsion system and an occulter in the 2U standard. A strategy was devised to minimize the degradation of mission performance due to differences in perturbations, and the mission success rate was verified to be 3 sigma or higher. Because the orbit control system was meant to be flight proven, Monte Carlo simulations were employed to study the effects of the other components of the GNC system, such as the navigation, attitude determination, attitude control, and propulsion system.
###### Abstract  
Obstructive sleep apnea-hypopnea syndrome (OSAHS) affects more than 936 million people worldwide and is the most common sleep-related breathing disorder; almost 80% of potential patients remain undiagnosed. To treat moderate to severe OSAHS as early as possible, the use of fewer sensing channels is recommended to screen for OSAHS and shorten waiting lists for the gold standard polysomnography (PSG). Hence, an effective out-of-clinic detection method may provide a solution to hospital overburden and associated health care costs. Applying single-channel signals to simultaneously detect apnea and hypopnea remains challenging. Among the various physiological signals used for sleep apnea-hypopnea detection, respiratory signals are relatively easy to apply. In this study, a fusion method using fuzzy logic and two single-channel respiratory indexes was proposed. A total of 12,391 apnea or hypopnea episodes were included. The proposed algorithm successfully fused standard deviation of airflow signals (SDA) and amplitude changes of peaks (ACP) indexes to detect apnea-hypopnea events, with overall sensitivity of 74%, specificity of 100%, and accuracy of 80% for mild to moderate OSAHS. For different apnea-hypopnea severity levels, the results indicated that the algorithm is superior to other methods; it also provides risk scores as percentages, which are especially accurate for mild hypopnea. The algorithm may provide rapid screening for early diagnosis and treatment.  
Keywords: obstructive sleep apnea-hypopnea syndrome; amplitude changes of peaks (ACP) algorithm; fuzzy logic fusion; single channel detection +
Footnote †: journal: Applied Sciences
# Automatic Essay Evaluation Technologies in Chinese Writing--A Systematic Literature Review
## 3 Results
### Feature Extraction and Selection (RQ2)
#### 3.2.9 Embedding-Based Feature  
The embedding technology maps textual data into a fixed-length vector space as informative representations of the text while preserving certain semantic information from the original text. These informative representations can be utilized for feature extraction ordirectly inputted into machine learning models such as SVM and Deep Neural Network (DNN) to predict essay scores. In some studies, text embeddings are also referred to as \"embedding-based features\".  
The Vector Space Model (VSM) [41] is an early form of the embedding model. Each vector dimension corresponds to a word, and the value represents the frequency of the word occurrence in the text (usually represented by TF-IDF). Latent Semantic Analysis (LSA) [44], also known as Latent Semantic Indexing (LSI), is an improvement upon the traditional VSM. LSA utilizes singular value decomposition to transform the Bag-of-Words (BoW) vector into a lower-dimensional space called the Latent Semantic Vector Space (LS-VS). LS-VS represents the semantic similarity of words and effectively resolves the synonymy issue. However, LSA ignores the order of words and contextual information. Some researchers have proposed improvements to address these limitations. [PERSON], [PERSON] et al. [41] divided essays into multiple ordered parts, representing each part by the LS-VS model, and combined them into composition vectors in order, thereby adding sequential information into the semantic features. [PERSON], [PERSON] et al. [38] proposed a Contextualized Latent Semantic Index (CLSI) method that utilizes Weighted Finite-State Transducers (WFST) to extract local contextual information of words in sentences for obtaining feature representation vectors to construct the LS-VS model.  
In recent years, deep neural network-based embedding models such as word2 vec (word to vector) and BERT, which incorporate sequence features and multidimensional text features, have been widely applied in ACEE feature extraction and scoring tasks. Some studies directly use the one-hot representation of the raw essay texts [50; 60]. In contrast, others employ word embedding vectors pre-trained on large-scale corpora to encode essays to obtain more comprehensive semantic information [34; 45]. Pretrained language models such as BERT in Chinese [34; 45], HBiLSTM [45], and NEZHA [34] have provided convenience for evaluating essays using deep learning methods. Most studies adopted multi-level embedding representations to extract features at multiple levels. [PERSON], [PERSON] and [PERSON], [PERSON] et al. [45; 60] used a sentence-paragraph two-level neural network model to represent text. At the same time, [PERSON], [PERSON] et al. [50] incorporated two levels of Bi-LSTM layers on top of BERT-encoded sentences to obtain a three-level embedding feature representation of sentences, paragraphs, and essays, capturing the text's local and global contextual information.
## 2 Materials and Methods
### Improved Unsupervised Homography Estimation Model
#### 2.2.1 Improved HomographyNet Model for Enhanced Rotational Invariance  
The task of unsupervised homography estimation is a pixel-level task, where pixels outside the receptive field do not affect the network output. To ensure that important information is not ignored during decision-making and to better handle images with significant perspective changes, it is generally considered that a larger receptive field is better. Deepening the network can increase the receptive field, but when the network depth reaches a certain level, continuing to add convolutional layers increases computational load and risks network degradation [22; 23]. Pooling operations can also increase the receptive field but at the cost of reduced spatial resolution. Dilated convolution is another method to increase the receptive field, as it can expand the receptive field without losing spatial resolution.  
This paper introduces a special deformable convolution module [24], which, like dilated convolution, can expand the receptive field without reducing resolution. Additionally, it enhances the network's ability to extract deformations, thereby improving rotational invariance in homography estimation. As shown in Figure 3, this module differs from regular convolution and dilated convolution, demonstrating its ability to learn rotational characteristics. Introducing the special deformable convolution too early can result in the loss of local information, while introducing it too late makes it difficult for the network to learn rotational invariance. This paper finds that the best approach is to introduce the special deformable convolution module to replace regular convolution right after the first downsampling, enhancing the backbone network's ability to learn rotational invariance in homography estimation tasks.  
In addition, the parameters of each layer in the convolutional neural network change continuously during training. These parameter changes alter the probability distribution of input data for the next layer, necessitating weight initialization and reduced learning rates. Therefore, this paper augments the original network by adding GN normalization layers after each convolutional layer. Unlike Batch Normalization (BN), which depends on batch size, GN normalizes by computing the mean and variance across channels for each sample group, reducing dependency on batch size. This allows the model to maintain stable training, even with smaller batches, making it suitable for training with irregular input sizes and high-resolution images, which are more applicable to the training scenarios in this paper. The improved network with the introduction of the special deformable convolution module and GN normalization is depicted in Figure 4.  
Figure 3: Different convolution sampling methods: (**a**) regular convolution; (**b**) dilated convolution; (**c**) special deformable convolution.
## 3 Influence of PWM Pulse Frequency on Overvoltage Distribution
### The Influence of PWM Frequency on the Inter-Turn Voltage Distribution  
The inter-turn voltage waveforms at three frequencies of 100 kHz, 50 kHz, and 10 kHz are the same as the first pulse rising edge overvoltage waveform, and the difference is still the overvoltage waveform generated by the second pulse. With the decrease in frequency, the frequency is reduced to 50 kHz and below. The inter-turn voltage waveform generated by the two pulse waves in coil 1 tends to be consistent, as shown in Figure 10.  
Figure 10: Distribution characteristics of winding inter-turn voltage at different frequencies.  
Figure 9: The maximum ground voltage distribution characteristics of windings at different frequencies.  
As shown in Figure 10, the first pulse (black curve) marked in the figure represents the overvoltage generated by the first pulse wave at different frequencies. It can be seen from the figure that the inter-turn voltage distribution of adjacent conductors in different coils of the stator winding of the permanent magnet wind turbine is the same. The inter-turn overvoltage generated by the first pulse wave at different frequencies is the same. The maximum voltage drop is 263 V, which is about 8.8 times the average voltage of the winding. It is located between the 2-3 conductors in the first coil 1 of the stator winding. The change law of the inter-turn voltage between 10 kHz and 50 kHz is consistent with the overvoltage generated by the first pulse wave, and the maximum inter-turn voltage is also about 263 V. When the frequency is 100 kHz, the maximum inter-turn overvoltage is located between the 2-3 conductors in the first coil 1, and the size is 276 V, which is about 9.2 times the average voltage of the winding.
# Lightning Protection Methods for Wind Turbine Blades: An Alternative Approach
## 2 Lightning Discharges and Existing Lightning Protection Systems for Wind Turbine  
### Mechanism of Lightning Discharge  
Lightning discharge from cloud to ground stems from a stepped leader initiated in a cloud and increases the electric field within its path. When a grounded object is in that electric field, it generates a leader towards the stepped leader, and it is called a connecting leader. If the downward moving leader has a negative charge, then the connecting leader is positive. If the downward leader is negative, then the connecting leader is positive [3].  
As a stepped leader approaches ground level or the tip of the grounded structures, the electric field increases to such an extent that it discharges, and connecting leaders starts to propagate towards the downward leader in an attempt to connect, to equal the potential difference. Taller structures generate longer connecting leaders due to the field enhancement caused by the accumulation of positive charge on the structure [1; 3; 4].  
The stepped leader channel is at cloud potential, approximately 50 MV [1; 3; 4; 5; 6; 7; 8] and with the final connecting jump, a near of ground potential travels along the channel in the direction of the cloud, which is called return stroke. The flow of charge generates a large current with an average peak of 30 kA [1; 3] to 80 kA [1]. Due to the rapid generation of heat of around 30,000 K [1; 3] in the channel, a pressure is created of 10 atm or above [3]. In some instances, new charges from the cloud forming another electrical discharge called dart leaders, creating subsequent return strokes with an average peak current of about 10-15 kA [1; 3].  
Most negative cloud-to-ground flashes contain more than one stroke, generally 3-4 [1] and, in major cases, the first stroke is usually 2-3 [3] times larger than the following subsequent strokes. On the other hand, occasionally in multiple stroke flashes there is at least one subsequent stroke which is greater than the first return stroke [3].