esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 4 Proposed Model
### Real Traffic Implemented in Twelve Queues Experimental  
This experiment was implemented with a real traffic dataset from a real telecommunication network to validate the efficiency of the proposed WFQ continual-DRL for real traffic applications. In this paper, the data set was collected from the Universidad Del Cauca, Popayan, Colombia network. Packet captures were performed at different morning and afternoon hours between April and June 2019. A total of 2,704,839 flow instances were gathered and stored [32].  
Processing Real Traffic  
In the first step of processing, a column of categories that consists of 24 categories and a column of their interarrival times are selected. Then, the category is split into multiple files.  
After that, twelve categories from the total categories selected represent twelve sources, as differential arrival flows arrive at each queue independently. The selection is based on the most common and rich categories (chat, media, web, VoIP, software update, email, music, collaboration, cloud, network, download file transfer sharing, system). The interarrival rate for each category meeting is selected. According to the filtered data, removing rows indicates that invalid or missing data is present. The total dataset after processing is 1,265,319. Then, the arrival rate \(\lambda_{i}\) for each category is computed as in equation [33]:  
\[\lambda=\frac{1}{\overline{T_{A}}} \tag{17}\]  
In addition to the initially selected twelve categories, the analysis was expanded to include the diversity of network traffic types. The 12 recent categories (Streaming, PRC, VPN, shopping, social media, data transfer, mining, game, remote access, database, video, unspecified) were also processed. The total dataset after processing is 128,458. Computing the arrival rate for each category, as in Equation (14), enables a more accurate characterization of traffic patterns, which is essential for simulating realistic queueing behavior. This measure enables us to compare performance metrics across a broader range of traffic resources, thereby improving the validation of the proposed adaptive algorithm, WFQ continual-DRL.
## 2 Deep Neural Networks for Inverse Kinematics
### Supervised Learning
#### 2.1.2 Convolutional Neural Network  
Convolutional Neural Networks (CNN) are conventionally composed by convolutional and pooling layers [46]. Fully-connected layers (also called dense layers) are typically also placed towards the output of the network. Convolutional layers are composed of input data, a filter, and a feature map. This allows the CNN to extract significant features from the input database. Pooling layers conduct downsampling operations, reducing the number of parameters in the input. Finally, any fully-connected layer assures each node in the next layer is directly connected to a node in the previous layer. CNNs are typically used for image processing and recognition tasks [47], but they can also serve other purposes such as solving IK.  
In [48], a network composed by a CNN connected to shallow neural network of dense layers was tested against a simpler MLP, providing better accuracy and highlighting the importance of data scaling. Similar results are obtained in [49] for a 6-DOF industrial manipulator. A similar manipulator is used in [50], improving the precision of the CNN by tuning its hyperparameters. The network is then compared to recurrent neural networks and analytical methods with different types of noise. In general, it obtains better results than the analytical method and recurrent neural networks (RNNs) with Gated Recurrent Units but performs worse than RNN with Long-Short Term Memory units and its bidirectional version. The effect of the manipulator's number of DOF is also studied, proving that precision is reduced when the number of DOF grows for every network. To reduce the pose error, ref. [51] proposes the use of a CNN combined with a Bilateral Long-Short Term Memory and a squeeze-and-excitation network [52].
# Experimental Investigation on Self-Excited Thermoacoustic Instability in a Rijke Tube
## 1 Introduction
The primary reason is that the time response of traditional tools for measuring temperature, such as thermocouple, is too slow, typically lower than 100 Hz, to capture the dynamics of temperature in thermoacoustic instability, which usually varies at the rate of hundreds of Hz. Temperature is a crucial parameter for characterizing the thermoacoustic instability in the Rijke tube. As such, the accurate and fast measurement of temperature is of considerable importance. Numerous optical measurement techniques have emerged and have made abundant contributions, one of which is the tunable diode laser absorption spectroscopy (TDLAS). Benefiting from its high resolution, high accuracy, non-intrusiveness and fast response, TDLAS has been widely applied in numerous fields, such as combustion diagnostics [17; 18], environmental monitoring [19], biomass energy [20] and even human health [21] and so forth [22; 23]. Figure 1: Thermoacoustic feedback cycle adopted from [2]. In this paper, the instantaneous temperature of thermoacoustic instability in a Rijke tube was measured by a single-ended TDLAS technique. The measurement rate was 5 kHz. Meanwhile, the flame chemiluminescence intensity was recorded by a high-speed camera, demonstrating flame response to the thermoacoustic instability. Additionally, the pressure at the exit of the Rijke tube was monitored by a microphone. The analysis of frequency of temperature, flame response and pressure was presented. Furthermore, the etalon effects on TDLAS raw signals were mitigated by a lowpass filter to enhance measurement accuracy.
## 7 Conclusions  
In this study, a novel cascaded Walsh-Hadamard Transform and Lifting Wavelet Transform-based GFDM waveform design is proposed as an alternative to the OFDM for 5G and beyond. The proposed waveform is obtained as a union of WHT and LWT with GFDM waveform. Computer simulations are carried out to evaluate the performance of the proposed waveform and to realize comparisons with conventional OFDM, GFDM, and WHT-GFDM waveforms. The gain in performance obtained using the proposed waveform is substantial. The proposed waveform has a SNR gain of 10 dB on flat Rayleigh fading channel and a SNR gain of 5 dB on AWGN channel. By using the WHT-LWT-GFDM, the proposed waveform has the additional benefit of improving PAPR performance. Without compromising BER performance, satisfactory gains in PAPR performances have also been obtained with the proposed waveform. Moreover, this waveform can be generated by systems with controlled complexity and it can be easily applied in the next generation of wireless telecommunication systems.  
Conceptualization, A.O. and A.S.; methodology, A.O. and A.S.; software, all authors; validation, A.O. and A.S.; formal analysis, A.O. and A.S.; investigation, all authors; resources, M.M., E.N.A. and M.G.; data curation, A.O.; writing--original draft preparation, A.O.; writing--review and editing, A.O. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 4 Experiment and Analysis
### Parameter Analysis  
To analyze the effect of different meta-paths on the recommendation performance, meta-paths are gradually added to the proposed model. From Figure 4, it can be observed that the overall performance of the model gradually improves with the addition of meta-paths. Also, different meta-paths have different weights on the recommendation performance improvement: adding UUUM and UMMM is more important for MovieLens.  
The meta-paths have different weights for different users and items. In this paper, the MovieLens dataset is taken as an example, and it can be found in Figure 5 that UMMM contributes the most to the interaction. By looking at the dataset, it is found that the movie genre of \"The Eighth Day\" is opera, which happens to be the favorite movie genre of user \"u782\". This partly explains why the meta-path UMGM contributes the most to the interaction and proves the interpretability of the model.  
This experiment investigates the output layer dimension size, setting different output layer dimensions of 8, 16, 32, 64, as shown in Figure 6. The performance gradually improves as the output layer dimension grows, reaching the best around dimension 32, and then starts to decline. The reason may be that the model needs a suitable dimension to encode the semantic information, and larger dimensions may introduce additional redundancy.  
Figure 4: Stepwise addition of meta-paths affects model performance for MovieLens.  
Figure 5: Attention scores for different meta-paths for MovieLens.  
The effect of the number of negative samples on the model performance was investigated by varying the number of negative samples in the set of 1, 3, 5, 7, 9. As shown in Figure 7, the model achieves the optimal performance when the number of negative samples is five. The experiment proves that the number of negative samples should not be set too large or too small because it may lead to overfitting and underfitting.
## 1 Introduction  
As wearable devices are becoming prevalent, data transfer between devices is becoming frequent and important. Previous work on personal area networks (PAN) pointed out the importance of a proper communication method between devices by describing that multiple devices without communication capability are prone to raise the issues of functional I/O redundancy [1]. For example, a person might carry multiple displays such as a smartwatch, mobile phone, and laptop, among others, if each device operates only for itself. In the case of recent smart devices, e.g., smartwatch and smartphone, each of these devices might need to be equipped with biometric sensors for the purpose of user authentication on each device. A variety of wireless technologies, such as near-field communication (NFC), Bluetooth, and infrared data association (IrDA) communications, have been proposed to resolve such redundancy issues. In the research area, many kinds of PAN, a small body-surrounding area where a set of specific devices can communicate with each other, have been proposed. Many of these systems establish connections by transmitting radio signals through the air. Although the wireless approach gives users great convenience, there can be issues involving message interception and vulnerabilities, including content spoofing [2]. This issue of data confidentiality may be mitigated by establishing communication channels without revealing signals to the air - _wired_ communication. In the context of PAN, wired communication is often implemented by regarding the human body as an electrical wire by utilizing the fact that the human body is electrically conductive (see Section 2.1.1). However, some users are concerned about using their bodies as a part of an electrical circuit all the time, even though passing a small amount of time-varying electric current through the body is known to be safe [3; 4; 5]. These psychological concerns are attributed to the fact that wearable devices continuously maintain physical contact with the user's body parts.  
In the present study, we propose a novel inter-device communication system that transmits data through internal body structures using mechanical wave propagation. With the proposed approach, a smartwatch wrapped on the wrist can send signals to the fingertip such that the signals can be detected by a touch smartphone. The main contributions of the present study are:  
1. Use of mechanical waves through the human body parts for inter-device communication.
2. Measurement and analysis of body-transmitted high-frequency signals.
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**MOCVD** & **MBE** & **MSE** \\ \hline Chemical vapor deposition & Physical vapor deposition & Physical vapor deposition \\ \hline Toxic precursors, require special handling & Metal and inert gases as precursors & Metal and inert gases as precursors \\ \hline Up to atmospheric working pressure & Ultra-high vacuum. Base pressure is \(<1\times 10^{-8}\) Torr. Operation is in \(\sim\)10\({}^{-5}\) Torr range & Ultra-high vacuum. Base pressure is \(<1\times 10^{-8}\) Torr. Operation is in \(\sim\)10\({}^{-3}\) Torr range \\ \hline Growth happens near thermodynamic equilibrium & Able to grow thermodynamically forbidden materials & Able to grow thermodynamically forbidden materials \\ \hline Can produce interfaces with precision down to several nanometers & Can produce very sharp interfaces with monolayer precision & Can produce sharp interfaces \\ \hline Suitable for mass production & Mostly limited to lab research & Potential for very large-scale production \\ \hline Growth at high temperature (\(>\)100 \({}^{\circ}\)C). & Growth at moderate temperature (\(\sim\)800 \({}^{\circ}\)C) & Growth at moderate temperature (\(\sim\)700 \({}^{\circ}\)C). High-quality film growth at lower temperature is possible. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of GaN film growth technology using metal–organic chemical vapor deposition (MOCVD), molecular beam epitaxy (MBE), and magnetron sputter epitaxy (MSE). This review paper is organized as follows. Section 2 provides an introduction to the reactive sputtering mechanism and gives a brief review of GaN sputtering using alternative targets. Afterwards, a detailed explanation of GaN MSE system configuration using liquid Ga target is given. Section 3 focuses on the growth mechanism of GaN thin film, discussing how the various growth parameters of the MSE system influence the properties of the GaN film. We also discuss the use of MSE system based on pulsed sputter deposition (PSD) and HiPIMS for GaN growth. Finally, we review the possibility of using MSE for both n-type and p-type GaN, which are essential for device application. Section 4 focuses on the growth of both self-assembled and selective-area growth (SAG) of GaN nanorods using MSE in terms of process conditions and patterning methods.
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 1 Introduction
### SinGAN  
The original SinGAN architecture, as referenced in [13], is notable for its ability to generate synthetic data using a GAN model trained on a single image. The training process in SinGAN employs an image pyramid, which consists of differently scaled versions of the same input image, progressing from low to high resolution. In this hierarchical structure, SinGAN creates a GAN pyramid, where each level corresponds to an image from the image pyramid and undergoes training. SinGAN can input a three-channel RGB image and generate a three-channel RGB output. The use of multiple scales enables the creation of various synthetic versions of the input image across different resolutions. The number of scales is determined by the dimensions of the input image. This approach allows SinGAN to produce multiple synthetic variations from a single real image, illustrated in Figure 1 as a 1:\(N\) generation, where \(N\) represents the number of layers in the GAN pyramid.  
Additionally, the intrinsic design and training mechanism of the SinGAN model provide it with versatility, which has been utilized and modified in studies such as SinGAN-Seg [9]. While SinGAN-Seg introduces modifications such as expanding to four channels  
Figure 1: TopoSinGAN’s multiresolution architecture.  
(RGB plus a segmentation mask), the core architecture and the image pyramid-based training of the original SinGAN remain fundamental to its operation. SinGAN relies on two central loss functions. The adversarial loss \(L_{\text{adv}}\) measures the differences between patch distributions in the original image and the generated images. It uses a Markovian discriminator, \(D_{N}\), that classifies patches as real or fake, similar to patch-based GANs. This adversarial method employs the WGAN-GP loss, known for its training stability, and focuses on the entire image, facilitating the learning of boundary conditions [16]. Meanwhile, the reconstruction loss \(L_{\text{rec}}\) ensures the model can recreate the original image using specific noise maps, with variations based on the scale to emphasize the pixel-wise similarity between the original and generated images. The reconstructed image further aids in determining the noise's standard deviation across scales. While \(L_{\text{adv}}\) and \(L_{\text{rec}}\) are effective at capturing visual details, they may neglect topological nuances, which can present challenges in applications that require precise topological accuracy.
###### Abstract  
Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, it has been demonstrated that RS can perform an accurate classification of chondrosarcoma tissues. Chondrosarcoma is a cancer of bones, that can occur in the soft tissues near the bones. It is normally characterized by three different malignant degrees and a benign counterpart, knows as enchondroma. In line with these findings, in this paper, we exploited ML algorithms to distinguish, as well as possible, between the three grades of chondrosarcoma and to distinguish between chondrosarcoma and enchondroma. We obtained a high level of accuracy of classification by analyzing a dataset composed of a relatively small number of Raman spectra, collected in a previous study by one of the authors of this paper. Such spectra were acquired from micrometric tissue sections with a confocal Raman microscope. We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). These models, especially RFC and MLPC, showed excellent classification performances, with accuracy reaching 99.7%. This outcome makes the aforementioned models a promising route for future improvements of diagnostic devices focused on detecting cancerous bone tissues. Alongside the diagnostic purpose, the aforementioned approach allowed us to identify characteristic molecules, i.e., amino acids, nucleic acids, and bioapatites, relevant for obtaining the final diagnostic response, through the use of a tool named by us Raman Band Identification (RBI). The method to evaluate RBI is the most important contribution of this paper, because RBI could represent a relevant parameter for the identification of biochemical processes on the basis of the tumor progression and associated malignant degree. In turn, the spectral bands highlighted by RBI could provide precious indicators in an attempt to restrict the spectral acquisition to specific Raman bands. This last objective could help to reduce the amount of experimental data needed to obtain an accurate final grading outcome, with a consequent reduction in the computational cost.  
chondrosarcoma; enchondroma; confocal Raman microscopy; machine learning; deep learning; permutation feature importance; Raman Band Identification +
Footnote †: journal: applied sciences  
1
Footnote †: journal: applied sciences
# Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
## 2 State-of-the-Art Review of Consumer Attitudes Towards Ultra-Processed Foods  
UPFs are now deeply embedded in modern dietary patterns, influenced by their widespread availability, cost-effectiveness, and extended shelf life. While UPFs offer a convenient food option for many consumers, increasing concerns about their potential health risks have fuelled academic and public debates. Several studies indicate that the frequent consumption of UPFs is linked to negative health outcomes, including cardiovascular diseases, and metabolic disorders [11, 12]. Additionally, the environmental impact of large-scale UPF production raises further concerns about sustainability and food system resilience [13].  
Despite increasing awareness of these issues, consumer demand for UPFs remains strong, driven by aggressive marketing strategies, evolving food technologies, and shifts in lifestyle preferences. The rise of health-conscious consumers has prompted food manufacturers to reformulate UPFs by reducing unhealthy additives or incorporating functional ingredients, such as added probiotics, plant-based proteins, or fortified micronutrients [14]. However, public scepticism about the actual health benefits of these reformulated products persists. Consumers' attitudes towards UPFs are dependent on various factors, including health awareness, price sensitivity, trust in food labelling, and socio-economic conditions.
## 3 Pedestrian Detection and ROI Location  
Observation of a complete raw color image revealed that the background, which did not move, accounted for a large part of the image and would result in redundant computation during subsequent NN training. Furthermore, non-pedestrian regions or objects in the background would cause interference. To eliminate excessive input and focus the analysis on the pedestrians, a pretraining NN for object detection, namely YOLOv2, was introduced to detect pedestrian location. This NN captured PROIs from image sequences.  
Object detection is a common problem in the CNN framework. Object detection methods involving NNs are many in number. Better-known methods include the two-stage R-CNN series and YOLO, the NN with an end-to-end framework [12]. The logic and technology of YOLO and the improved YOLOv2 [10] are described as follows.  
The R-CNN series employs a region proposal as its logical basis. Initially, a high number of possible bounding boxes is generated. Then, a classifier is applied to discriminate the content of these bounding boxes. Finally, postprocessing is conducted to eliminate repeated bounding boxes and retain the optimal solution. The subsequently developed Fast R-CNN [13] and Faster R-CNN [14] inherited the same logic and integrate various network models used over multiple stages into one unified NN. Finally, two main NNs are generated, namely the region proposal network and classification network.  
Compared with the R-CNN series, YOLO regards the detection problem as a regression problem and combines the processes of predicting bounding and objects into a single NN. In addition, YOLO conducts image prediction based on the entire image. Therefore, compared with the R-CNN, the rate of mistaking the background as an object can be reduced by more than 50%. Moreover, evaluation of plural candidate frame areas is no longer required, and thus speed and generalizability are improved.