esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### The Effectiveness of DDPM in Segmentation Tasks  
Researchers applied DDPM to denoise and segment magnetic resonance imaging (MRI) images, enhancing the visibility of tissue boundaries. The model's probabilistic framework provided uncertainty estimates, aiding in distinguishing tissue regions from MRI images. As shown in Figure 2, segmenting tissue in MRI scans is a critical task for treatment planning and monitoring disease progression [13; 42].  
DDPM was employed to denoise and segment histopathology images, enabling precise identification of cancerous regions. The generative capabilities of DDPM also facilitated data augmentation for training robust segmentation models. Segmentation of cancerous lesions in histopathology images assists pathologists in diagnosing and grading tumors accurately [42; 53].  
DDPM was employed to enhance the quality of electron microscopy images and segment neural synapses. The model's denoising capabilities contributed to the accurate identification of synaptic structures. Segmentation of neural synapses in electron microscopy images is crucial for understanding neural circuitry and synaptic connectivity [13; 55].  
These case studies exemplify the versatility and effectiveness of DDPM in various segmentation tasks. Whether applied to medical imaging, histopathology, satellite imagery, electron microscopy, or industrial quality control, DDPM consistently demonstrates its ability to enhance segmentation accuracy, provide uncertainty estimates, and contribute to improved decision-making in diverse domains.
## 2 Literature Review
(2021)), based on spatial indexing and grid technology. In recent years, numerous spatial clustering algorithms based on DBSCAN have emerged. For example, OPTICS, [PERSON] et al. (2015) [22], enhanced density processing capabilities, and PDBSCAN [23], [PERSON] et al. (2011), handled data of varying densities through partitioning, laying the groundwork for the development of HDBSCAN. Additionally, HPDBSCAN, [PERSON] et al. (2015) [24], introduced parallel computing to accelerate big data processing; Fuzzy-DBSCAN, [PERSON] et al. (2018) [25], incorporated fuzzy theory to allow for fuzzy cores and overlapping boundaries; ST-DBSCAN, [PERSON] et al. (2016) [26], added a temporal dimension; DBSCAN+, [PERSON] et al. (2021) [27], strengthened noise and boundary handling; and K-DBSCAN, [PERSON] et al. (2021) [28], utilized K-means for preliminary classification before applying DBSCAN for finer segmentation to speed up computation. Among these numerous algorithms, the literature on HDBSCAN has accumulated the highest citation count, indicating its significant influence in both academia and practical applications, which is an important consideration for us in choosing HDBSCAN.
# Effects of Age and Sex on the Kinematics of the Sprinting Technique in the Maximum Velocity Phase
###### Abstract  
The purpose of this study was to compare the step parameters, the Body Center of Mass (BCM) kinematics, as well as the angular and linear kinematics of the lower extremities' joints of prepubescent and adult sprinters of both sexes. A total of forty-two athletes were examined, including adult men (AM) and women (AF) as well as preadolescent boys (PPB) and girls (PPG). A 2D-DLT analysis was conducted on video recordings (sampling frequency: 100 fps) of the participants' maximum effort trial while in their maximum sprinting velocity phase. A 2 (age) \(\times\) 2 (sex) ANOVA revealed significant (\(p<0.05\)) effects of the factors age and sex, and an interaction of age \(\times\) sex in the sprint running velocity. It was also found that the spatiotemporal structure of the step parameters was significantly (\(p<0.05\)) different between AM and AF but not between PPB and PPG. Also, a significant (\(p<0.05\)) main effect of age was evident mainly for the ankle joint of both legs, as well as for the angular kinematics of the swing leg's knee joint. In conclusion, apart from the possible disparities in training experience, differences due to age and sex exist in the step parameters and the kinematics of the sprinting technique due to differences in the technical elements related to coordination, strength application capability, and stiffness.  
tractable and field; biomechanics; step parameters; joint angular kinematics; maturation; development; sport performance; sport technique Article
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 5 Informal Security Analysis
### Sensor Node and Gateway Capture Attacks
data in the TLS channel in the registration stage and 2048-bit data in the open channel in the authentication stage as examples, a total of 1000 tests were conducted to obtain the average values. Table 5 shows the test results. The measured results indicated that the time overhead for a single transmission and reception was on the microsecond level (1 \(\upmu\)s = 10\({}^{-3}\) ms = 10\({}^{-6}\) s). The transmission delay was much lower than the hardware operation, so in the analysis of time complexity, we ignored the transmission delay. Since the time for computing the XOR operation and string concatenation could be ignored, as compared with other cryptographic primitive-based operations, we only considered the time to calculate the one-way hash function (\(T_{h}\)), deterministic reproduction function of fuzzy extractor (\(T_{f}\)), Chebyshev chaotic map polynomial (\(T_{c}\)), elliptic curve point multiplication (\(T_{e}\)), modular multiplication (\(T_{m}\)), and symmetric encryption/decryption (\(T_{s}\)). In the environment of Windows 7 64-bit AMD 860K CPU @ 3.7 GHz 8 GB RAM, the computational times were approximately 0.068 ms, 8.038 ms, 3.084 ms, 8.038 ms, 16.076 ms, and 0.56 ms, respectively. From Tables 3-5, we can see that our scheme had a lower computation cost and higher security.
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 2 Model Description
### Baseline Models  
#### 2.2.1 Traditional Baseline Models  
The linear model is a basic and simple model since it takes only the temporal evolution of radar echoes into account. It calculates the linear trend between 2 radar images for each pixel and then interpolates or extrapolates these pixels using the corresponding trends. The optical flow model was constructed based on the Dense Inverse Search (DIS) algorithm.  
The DIS algorithm is a global optical flow algorithm proposed by [PERSON] et al. [47], which allows the explicit estimation of the velocity of each image pixel based on an analysis of 2 adjacent radar images. The DIS algorithm was selected as a benchmark because of its high accuracy in the extrapolation and interpolation of radar images [12]. The implementation of the optical flow model can be summarized as follows:  
1. Calculate velocity field using the global DIS optical flow algorithm [47] based on the radar images at times \(t_{a}\) and \(t_{b}\).
2. Use a backward constant-vector [48] to interpolate or extrapolate each pixel according to the velocity field.
3. Obtain an irregular point cloud that consists of the original radar pixels. Then interpolate the intensity of these displaced pixels in the original radar grid. The inverse distance weighted interpolation technique is used in the interpolation.  
Figure 1: (**a**) Network architecture of the convolutional neural networks (CNNs) combined with bi-directional convolutional long short-term memory networks (CNN-BiConvLSTM) model. (**b**) Network architecture of bi-directional recurrent neural network (BiRNN) in (**a**). X denotes input radar images, E denotes features encoded by the CNN encoder, O denotes features encoded by the BiRNN encoder, and s denotes strides of the convolution.  
Figure 2 demonstrates an example of interpolating 3 radar frames from 2 adjacent radar images. The features of Radar (t) were propagated according to the calculated velocity field to generate intermediate frames Radar (t + 1), Radar (t + 2), and Radar (t + 3). Apparently, the observed radar image Radar (t + 4) was only used in velocity field calculation, and the shape of the echoes was fully dependent on the first frame.
## 2 Materials and Methods
### Measurement of Electrocardiogram and Electromyogram Signal from Human Skin  
For electrophysiological signal monitoring, the electromyogram (EMG) and electrocardiogram (ECG) signals were recorded using a bio-signal amplifier (Bio Amp FE231, AD Instruments, Dunedin, New Zealand) and a data acquisition device (PowerLab 8/35, AD Instruments). Action potential signals were filtered as the International Society of Electrophysiology and Kinesiology (ISEK) standard (1500-Hz low-pass filter). The sensing (SE) and reference (RE) electrodes were prepared by cutting the Au-SEBS electrode, and a commercial gel electrode (3M 2660 Ag/AgCl gel electrode) was used as the ground electrode.  
For measuring the EMG sensing, the SE and RE were attached to the flexor digitorum superficialis [39; 40] of the right forearm, whereas the reference gel electrode was attached to the opposite side of the forearm. The volunteer was required to perform two simple hand gestures--that is, grasping and releasing of the fingers for 30 s each.  
For measuring the electrocardiogram (ECG) sensing, the cardiac signal was measured by ECG equipment using the three proposed electrodes based on the standard 12-lead measurement method. The positive and negative electrodes were attached to the left and right forearms, respectively, and the reference electrode was attached to the left ankle. The electrodes were positioned based on [PERSON]'s triangle positioning for representative ECG electrode placement on humans [41; 42].
## 3 Materials and Methods
### Microplastic Staining  
Microplastic identification using dyes has become a popular method to quickly and efficiently identify plastic particles by providing specific complexes with which to differentiate them from organic and mineral matter. Individual dyes differ in their affinity for the plastic, fluorescence intensity, incubation time, and the type of polymers that they react with, and the most commonly described include oil red (Sudan-nitrogen dye), eosin (acid dye), Bengal pink, hostasol yellow, Nile Red (lipid dye) [32], alkali blue (bromothymd dye), crystal violet (aniline dye), neutral red (neutral red), and trifan blue [33]. The current literature indicates that the most widely used and effective dye is Nile Red, which provides high fluorescence in plastics, with a wide range of microplastics; therefore, it has found applications in translocation of microplastics in soil and living organisms (no toxicity to living cells, including human), in field monitoring of microplastics, and in many laboratory techniques [34]. According to the current knowledge, there is still no uniform methodology for dealing with all the stages of staining, and as the authors point out, this gap needs to be filled, especially with environmental studies [35].  
Nile Red is a very stable photochemical dye, classified as a hydrophobic, heterocyclic, and metachromatic compound, and is almost insoluble in water [36]. Both its emission and quantum properties depend on the type of solvent used, and an increase in polarity most often leads to a red shift in fluorescence. The wide solvatochromic range and the effect of the environment on the color was described in detail by [PERSON] and [PERSON] [37]. This study used acetone as a solvent at a concentration of 1 mg/mL, a volume ratio of 0.1-0.5 mL of dye solution per 100 mL of water sample, and an exposure time of 30 min.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Radio Resource Management  
With the future 6G networks, the density of small-cell networks increases drastically. Consequently, radio resource management has emerged for the system-level management of co-channel interference, radio resources, and other radio transmission characteristics in a wireless communication system to utilize the radio spectrum efficiently. With the increasing dynamicity and complexity of network generations towards 6G, the traditional heuristic-based approaches for radio resource management become inaccurate. ML/DL-based approaches are explored as an alternative solution. [PERSON] et al. [58] proposed graph neural networks for radio resource management in a large-scale network by modeling the wireless network as a wireless channel graph and then formulating the resource management as a graph optimization problem. The neural network consisted of three layers, an adam optimizer, and a learning rate of 0.001. [PERSON] et al. [59] proposed a DNN framework for radio resource management to minimize the energy consumption of the network constrained by power limitation, inference limitation, and the QoS. A DNN model with three layers, 800 neurons per layer, a 0.01 learning rate, and an adam optimizer is employed  
Figure 9: Deep Reinforcement Learning (DRL)-based intelligent routing in the Internet of Things (IoT) environment.  
for a power optimization scheme, whereas a DNN model with four layers, 80 neurons per layer, a 0.05 learning rate, and an RMSProp optimizer is used for sub-channel allocation.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 5 Application of AR to PHM
### AR Human-Computer Interaction
In this technological framework, they skillfully used deep learning models to achieve highly accurate predictions of the remaining useful life (RUL) of equipment. They used AR as an aid to superimpose the prediction results and maintenance guidance information onto the real world. Through a series of rigorous validations, this study not only demonstrated the feasibility and effectiveness of AR in PHM but also proved its significant advantages over the traditional methods [37]. Delving into using augmented reality (AR) technology in fault diagnosis and maintenance is indispensable. This innovation could enhance the precision and expediency of fault diagnosis while yielding substantial reductions in maintenance expenses.
# Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets
## 4 Experiments and Results
### Pixel-Based Rhythm Segmentation  
The standard HR network and DeepLabV3+ networks are designed for the semantic segmentation of images that are applicable in MIR if audio representation is a time-frequency spectrogram. In this work, the HR network is used in its original form and with minor modifications. We termed the modified HR network for semantic segmentation as a _2 Dto1D HR network_ in which the frequency dimension is squeezed to one by max-pooling/average pooling after upsampling the encoded feature maps in the final layer. The modified version of the HR network outperformed the original HR network but did not exceed the DeepLabV3+'s capabilities, as shown in Table 4. For the semantic segmentation network, the input audio representation had a size of (1920, 32) with a single channel. Here, 32 represents the number of Mel-bins, and 1920 represents the temporal length of audio in spectral representation that is equivalent to one-minute audio.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Test\_IOU** & **Test\_Acc** & **MSE** & **Test\_Pixel\_Acc** \\ \hline DeepLabV3 & **0.81580** & **0.9829** & **0.0038** & **0.9829** \\ HR network 2 Dto1D & 00.7987 & 0.9428 & 0.0145 & 0.9428 \\ HR network & 0.67242 & 0.8886 & 0.0287 & 0.8886 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Semantic segmentation based on rhythmic segmentation.  
Figure 4: Segmented output on test sample using classification-based segmentation method. (ground truth: Aniri 0 to 17 s and [PERSON] 18 to 93 s).  
Figure 3: Confusion matrix of our three classifiers using Pansori classification dataset of seven classes.  
The evaluation metrics used in the semantic segmentation experiment are intersection over union (IoU), class accuracy, mean square error (MSE), and pixel accuracy scores. The DeepLabV3+ network outperformed the HR network; however, it required a relatively large number of parameters and training time. Figure 5a,b show the DeepLabV3+ network's segmentation results with ground truths provided by Pansori experts. Figure 5a shows the successful segmentation of Pansori rhythms; however, the separation of background (black) from other rhythmic categories/classes was difficult, as it included highly unbalanced rhythmic patterns and boundary noises. Figure 5b illustrates that the segmentation networks were well-trained and successfully recognized the rhythmic patterns when it has sufficient training samples. The output masks can be of different widths (as shown in Figure 5) based on the length of the audio.
## 4 Methodology
### Time Series Generation
#### 4.3.1 Encoder  
The input of the encoder is the historical trajectory H. In order to better extract its features, a linear layer is used to embed the historical trajectory, mapping it to a higher-dimensional hidden space.  
\[H^{h}=\text{Linear}(H) \tag{14}\]  
\(H^{h}\) is the output of the linear layer.  
In spatiotemporal trajectories, the spatiotemporal relationship is not only related to the state of the trajectory but also related to its time period, and the spatiotemporal relationship is highly related to the total time through this path. Therefore, this paper uses a multi-head attention mechanism to capture the spatiotemporal relationship. First, the input sequence \(H^{h}\) is mapped to three subspaces: \(Q^{h}\), \(K^{h}\), and \(V^{h}\).  
\[Q^{H}=W_{Q}H^{h} \tag{15}\]  
\[K^{H}=W_{K}H^{h} \tag{16}\]  
\[V^{H}=W_{V}H^{h} \tag{17}\]  
where \(W_{Q}\),\(W_{K}\), and \(W_{V}\) are the weight matrices of Q, K, and V, respectively, mapping \(H^{h}\) to the three hidden spaces of Q, K, and V. The operation of multi-head self-attention is defined as  
\[Attention(Q,K,V)=soft\max(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{18}\]  
\[MultiHead(Q,K,V)=[Head_{1},Head_{2},...,Head_{h}]W^{O} \tag{19}\]  
\[Head_{i}=Attention(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}) \tag{20}\]  
where h is the number of attention heads and \(W^{O}\) is the weight matrix of the final output. Multiple attention heads create multiple subspaces, mapping the input data to multiple subspaces and then jointly focusing on the representation information of these subspaces, effectively capturing the spatiotemporal relationship information in the trajectory.  
After the multi-head self-attention, the model uses residual connections and layer normalization layers to solve the problem of multi-layer network training and speed up the convergence. The specific formulas are as follows:  
\[LayerNorm(X+MultiHeadAttention(X)) \tag{21}\]  
where the residual connection refers to \(X+MultiHeadAttention(X)\), where X is the input of the multi-head self-attention. This approach is often used in residual networks; it allows the network to focus only on the current differences and is usually used to solve the problem of multi-layer network training. LayerNorm refers to layer normalization, which converts the mean and variance of the input of each layer's neuron to ensure uniformity; this can speed up convergence.  
The multi-head self-attention and residual connection & layer normalization module in the encoder part are repeated L times, where the output of the previous layer is used as the input of the next layer, until the output of the last layer of the encoder. Then, we use this to map the output of the last layer to the K and V hidden spaces for the decoder to query.
## 1 Introduction
Technologies such as the guitar AR-based Fretlight(r) reduce cognitive load and the effort that must be made to learn [6]. The learning system used in Fretlight(r) provides direct and overlapping information directly on the neck of the guitar indicating where and when to place fingers. It is important to note that both students and teachers experience difficulties with the traditional Master-Apprentice model. Trainers need to design a teaching strategy that organizes time based on: student experience; face-to-face training; and periods of autonomous learning during which the student's progress will very much depend on his or her own personal skill set [17]. In reviewing the main obstacles faced by students receiving musical training, it was found that students typically encounter two main problems: developing their musical ear [18], and understanding the instructional material used for learning (e.g., diagrams or drawings). In terms of the latter, AR can be used to address this challenge as it can be used to facilitate the visualisation of elements such as finger positioning [19]. A good example of this is the AR system proposed by [PERSON] et al. [20], who designed and implemented an AR model that uses mobile projector mounted on the guitar's headstock to project instructions that facilitate correct finger placement. Another recent proposal for learning the guitar suggests using coloured finger markers and then projecting the corresponding colours onto the strings and frets using AR to instruct learners how to perform a chord [21]. The aforementioned studies do not display hand movements involved in the chord transitions of short melodies. Building on the aforementioned work, this paper describes a new AR system that has been developed by the authors that allows learners to see the correct position and movement of fingers when playing chords and chord transitions. The AR system developed by the authors facilitates visualization and, consequently, imitation by learners who are complete beginners. This is achieved by means of colour-coded finger markers, and a semi-transparent 3D model of a hand. Both elements are superimposed onto the neck and frets of the physical guitar displayed onscreen. For the purpose of this study, research has focused on individuals who are right-handed. The focus of the research has been to develop and test the usability of a system that provides user-centred training (to individuals learning the guitar). An informal learning method has been proposed that has a low cognitive load, and which is capable of teaching musical chords in a short period of time.