esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Atrial Fibrillation Detection
#### 3.3.2 Feature-Based Method  
The proposed privacy-preserving machine learning-based method is inspired by the solution of [PERSON] et al. [29], which secured the top position in the Physionet 2017 Challenge. Atrial fibrillation is identified by first extracting relevant features from the ECG and then feeding the resulting features to a classifier. To deal with the largely imbalanced training dataset that could lead to poor detection performance, [PERSON] et al. [29], chose to classify ECG recordings using a cascade of binary classifiers.  
The first module classifies an ECG signal into one of two intermediate classes (\"Normal\" + \"Others\" and \"AF\" + \"Noisy\"). Depending on the outcome of the first classifier, target-specific features are derived from the recording and fed to the corresponding binary classifier (i.e., \"Normal\" vs. \"Others\" and \"AF\" vs. \"Noisy\") for the final classification. Hence, the first binary classifier is responsible for identifying the intermediate class, while the two binary classifiers from the second stage of the pipeline have to recognize the specific type of rhythm. In each of the three binary classifiers, a distinct set of features is used. In a privacy-preserving scenario, such an approach would require access to the intermediate detection results. To avoid the disclosure of intermediate classification results, we have opted for an end-to-end classifier based on neural network. Hence, a single multi-class classifier is employed instead of a multi-layer cascaded binary classifier, and all features are used regardless of their type. The final input feature set is obtained by combining the three feature sets considered in [29].  
The proposed computer-aided detection system consists of three main components: noise removal, feature extraction, and classification. The feature extraction step relies heavily on the characteristics of the data and cannot be performed in the encrypted format. Therefore, the data preprocessing step must be performed on the device. The proposed workflow is outlined in Figure 2. Before the ECG data are analyzed by the machine learning algorithm, it is preprocessed on the device. The preprocessing step includes noise removal, feature engineering, and data normalization. The resulting information is then homomorphically encrypted with a secret key. Thereafter, the cloud-based machine learning classifier receives only the encrypted form of the data (ciphertext), while the raw data (plaintext) remains private on the device (at the user). Finally, since CipherML provides direct support for floating-point arithmetic and all operations performed within the neural network are formulated by exploiting the homomorphic properties of MORE,the network can be further used for detection. This network produces encrypted results that can only be interpreted after decryption with the secret key.  
Figure 1: Examples of different classes of ECG recordings.
# Study on Filling Capacity of Optical Glass in a Novel Rapid Hot Embossing Process
## 1 Introduction  
Hot embossing is a well-established technology for transferring micro/nano structures from a master mold onto a polymer or glass substrate, which has advantages of high replication fidelity, high-efficiency, low-cost and flexible process modification. Therefore, it has been widely used in the fabrication of various optical components (e.g., microlens arrays [1], raster lens arrays [2], Fresnel lenses [3], and anti-reflection films [4]), micro-electro-mechanical-systems (MEMS) [5, 6], biomedical products [7], and so on. As a result, over the past several years, many scholars conducted researches related to hot embossing technology, such as deformation behavior of glass [8], material modeling [9], and process optimization [10].  
It is found that hot embossing processes assisted by the ultrasonic vibration [11, 12], the gas [13], and the rubber pad [14] allow enhancement of the replication performance. However, these modified hot embossing approaches require complicated apparatus and high cost. Some simplified and low-cost hot embossing devices were developed and their usability had been demonstrated [6, 15]. However, they were only applicable to forming low-Tg materials, due to the limitation of heating performance and the lack of inert atmosphere.  
When hot embossing of high-Tg amorphous materials, like optical glass, the preform needs to be heated up to a temperature over its transition temperature, usually higher than 500 \({}^{\circ}\)C, so that it could be deformed under certain compression force during the molding stage. Electrical cartridge tubes heating [16] and infrared heating [17] are commonly usedmethods to achieve high temperature for glass hot embossing. However, they exhibit shortages of high energy consumption and long thermal cycle time, which limit the throughput. Therefore, it is non-trivial to develop a rapid hot embossing device for forming optical glass with high cost-efficiency and loading accuracy.  
This study aims to propose a novel configuration of hot embossing device, which consists of a rapid heating module and a precision loading module. By using the novel rapid hot embossing device, effects of process parameters (i.e., embossing force, embossing temperature, soaking time and annealing rate) on the filling behavior of N-BK7 glass in a microhole of silicon carbide mold are evaluated. The research findings can provide useful information for selection of appropriate process parameters for glass hot embossing with equivalent loading and heating methods.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 2 Theoretical Foundations
### Sampling  
Text decoding methods that solely focus on high probability outputs lead to very generic and repetitive outputs. That is why models like the GPT model utilize sampling to generate more human-like outputs [1]. Sampling is a method of text generation in which the probability of the next token is being calculated. The token with the highest probability in the probability distribution is chosen as the next word in the output [17]. There are two different sampling methods that can be utilized for the decoding process. These two methods are the deterministic method and the stochastic method.  
**Deterministic sampling:** In deterministic sampling, also known as greedy sampling, the token with the highest probability to be the next one, based on the probability distribution, is generally chosen. This leads to a very predictable output, which can cause repetitive and generic outputs by virtue of the deterministic approach [18; 19].  
**Stochastic sampling:** In stochastic sampling, tokens are generally chosen based on the probability distribution. However, in contrast to deterministic sampling, the next token is chosen arbitrarily from a vocabulary subset. The problem with this approach can be the loss of semantic meaning caused by the randomness of the approach [19]. Stochastic samplingmethods like nucleus sampling [17] try to avoid this problem by generating the text based on a dynamic nucleus, which is a small subset of the whole vocabulary containing a varying amount of potential words. This method stands in contrast to other stochastic sampling methods that rely on a fixed candidate pool.
## 3 Results
### Best Hearing Prostheses versus Hearing Loss Types  
Figures 2 and 3 show bar charts reporting numbers of subjects with regard to their individually predicted best hearing prosthesis option, as well as the type of hearing loss. For each subject, the best prosthesis option is the one with the minimum LAP. Figures 2 and 3 report numbers of subjects without and with CS, respectively. For NH and sole CGL (first row of Figure 2), all subjects were predicted to have minimum LAP for unaided hearing and sole acoustical amplification, respectively. This provides a sanity check for NH subjects, as well as suggests that sole CGL is most appropriately compensated using sole acoustical amplification. For most subjects with IHCL (with or without additional CGL), electrical stimulation with and without additional acoustical amplification appear to be the best options for approximately equally many subjects, followed by sole acoustical amplification. None of the subjects with IHCL were suggested to remain unaided.  
Figure 3: Numbers of subjects with regard to the individually predicted best hearing prosthesis, i.e., the prosthesis corresponding to the minimum cost-adjusted loss of action potentials (LAP) score. The types of hearing loss are as follows: (**a**) cochlear synaptopathy (CS), (**b**) cochlear gain loss (CGL) + CS, (**c**) loss of IHCs (IHCL) + CGL + CS, (**d**) IHCL + CGL + CS. The types of hearing prosthesis are as follows: unaided hearing (ua), acoustical amplification (ac), electrical stimulation (el), and el combined with ac (co).  
Figure 2: Numbers of subjects with regard to the individually predicted best hearing prosthesis, i.e., the prosthesis corresponding to the minimum cost-adjusted loss of action potentials (LAP) score. The types of hearing loss are as follows: (**a**) normal hearing (NH), (**b**) cochlear gain loss (CGL), (**c**) loss of IHCs (IHCL), (**d**) IHCL + CGL. The types of hearing prosthesis are as follows: unaided hearing (ua), acoustical amplification (ac), electrical stimulation (el), and el combined with ac (co).  
For sole CS (Figure 3, top left), most subjects are suggested to stay without a prosthesis, followed by sole acoustical amplification. Some subjects, presumably the ones with the most severe CS, are suggested to be implanted. For CS with CGL (top right), the majority of subjects are suggested to get a hearing aid, while some subjects, presumably the ones with severe CS, are suggested to receive a combined prosthesis. For subjects with IHCL and CS (bottom row), the results are comparable to the results for subjects with IHCL and no CS (bottom row of Figure 2).
## 4 Results and Analysis
### Characteristics of Spatiotemporal Variation in NPP
#### 4.3.1 Trends in Climate Change and their Impact on Vegetation NPP
The relative role of temperature and precipitation on NPP was further explored (Figure 17), with climatic factors affecting up to 64.18% of the area. The highest percentage of regions with a weak common influence was 42.23%. This was followed by precipitation and temperature, with 16.67% and 4.02%, respectively. The strong common influence saw the lowest percentage of regions, with only 1.25%.
## 5 Potential 6G Applications in the Maritime Sector
### Remote Maritime Surveillance and Border Security  
Securing territorial waters and detecting unauthorized activity (e.g., illegal fishing trafficking) is a top priority for many nations [83]. With 6G, autonomous UAV swarms, connected to LEO satellites and intelligent edge controllers, can perform coordinated surveillance over wide maritime zones. These UAVs share data through multi-hop aerial relaying and use AI-based anomaly detection to flag suspicious patterns in real-time, as shown in Figure 10[84]. RIS elements on buoys and sea BSs can extend detection coverage, while digital twin integration allows mission operators to simulate threats and test response protocols.
## 2 Materials and Methods
### Data Description
#### 2.1.3 Work Orders Dataset  
All the maintenance tasks that have been carried on in the windfarm, including inspections, regular checks as well as extraordinary interventions, are registered in the work order logs. An example of the available work order logs is provided in Table 3. This information has been used for labeling turbines' SCADA data. Records preceding critical interventions to the turbines have been removed from the training dataset. Work orders have also been used for the prediction evaluation. The information of the work orders is not provided in any form to the predicting algorithm, it is uniquely utilized to process data, assigning labels, and finally, evaluate the predictions, thus being the ground truth for the algorithm.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 4 Physical Layer Tests Analysis
### Previous Work
The wideband sounding has also been analyzed in order to determine the maximum delay spread and Doppler spread [6; 7; 11]. These two channel measures give light to the most suitable modulation for each time of the day and its parameter selection. During all the campaigns, maximum delay spreads observed measure 3.5 ms, this value forces us a minimum symbol time to minimize Intersymbol Interference (ISI). The maximum Doppler spread observed is around 2.5 Hz, thus the coherence time of the channel is around 150 ms, because we defined the coherence time as \(T_{e}=\frac{0.423}{f_{D}}\)[25]. The reader is referred to [7] for more details about the wideband sounding analysis conclusions and results. Using the aforementioned frequency transmissions just detailed, and the Doppler and delay spread wideband results, several modulation tests were already conducted in the past. Deumal started with the spread spectrum tests [26], that study let them define the Signaling concept in order to increase the bitrate of the transmission. Also the comparison between Direct-Sequence Spread Spectrum (DS-SS) [27] and OFDM was tested in [9]. The preliminary results obtained by all these tests allowed us to define a modulation testbench including the modulations with best performance and including new modulation schemes to be tested in the channel. The results of this complete analysis will be shown in Section 4.4.
# A Digital Twin-Based State Monitoring Method of Gear Test Bench
## 3 Physical Information Acquisition of Gear Test Bench
### Temperature Acquisition of Gear Test Bench  
During the running process of the gear test bench equipment, factors such as frictional heat and changes of laboratory temperature will result in the change of temperature of parts of the gear test bench, and the normal operation process can be disturbed. Therefore, it is necessary to measure the temperature of the gear test bench, especially the gearbox. In the gear transmission process, the generated friction heat will transfer to the gearbox and lubricating oil, causing a temperature rise of the oil. During the testing process, the lubricant temperature in the main gearbox after thermal balance shall not be higher than 80 \({}^{\circ}\)C, and the temperature of the main test gearbox shall not be higher than 75 \({}^{\circ}\)C, which is taken as the judging criteria of the gear test bench's normal operation. During the temperature acquisition testing, an infrared temperature gun is used to measure the temperature of the main test level gearbox, and the normal temperature is 54.8 \({}^{\circ}\)C after the thermal equilibrium.
# Calibration of Rotary Encoders Using a Shift-Angle Method
## 3 Experiments
### Shift-Angle Method
The curve fitting function can be expressed as the second periodic function as \(\alpha(\theta)=A^{*}\)sin(\(2^{\circ}\theta+B\)) + \(U\)(fitting), as illustrated in Figure 9. For a shift-angle \(S\) of \(0^{\circ}\), the measured values are fitted as \(A=1.0314^{*}\), \(B=-2.4233\), and \(U_{0}\)(fitting) is \(0.9596^{*}\). For a shift-angle \(S\) of \(5^{\circ}\), the \(A\) and \(B\) values are fixed to check the setup error \(U_{5}\)(fitting). The \(U_{5}\)(fitting) value is \(-1.5927^{*}\). For the shift-angle \(S\)\(10^{\circ}\), the \(U_{10}\) (fitting) value is \(1.1545^{*}\) using the same procedure. As shown in Equation (7), the setup  
Figure 8: Shift-angle method for SelfA rotary encoder at different shift-angles. (**a**) \(S=5^{\circ}\). (**b**) \(S=10^{\circ}\). Figure 3:0 Calibration results at three shift-angles. \(S=0^{\circ}\), blue solid line and circle label; \(S=5^{\circ}\), light blue solid line and star label; and \(S=10^{\circ}\), red solid line and rectangular label. error \(U_{0}\) is 0.9596\", \(U_{5}\) is \(-1.5927\)\", and \(U_{10}\) is 0.1545\". The difference in the setup error is less \(10^{-4}\)\"using two different methods, Equation (7) and curve fitting.
## 4 Research Challenges and Directions
### Transformer-Based Encoder-Decoder Multi-Agent Actor-Critic Frameworks  
Actor neural networks require self-attention on their long observation sequences to predict the dynamic topology state by generating fixed-size state embedding and mapping it into the optimal action. In contrast, critic networks require cross-attention to pay attention to not only its own observation-action spaces but also to the neighboring agents in a multi-agent cooperative-competitive environment to precisely estimate the value function. This type of framework can deal with joint trajectory control, task offloading, and resource allocation problems that require hybrid action spaces in UAVSNs. Additionally, in space-air-ground integrated networks, computationally intensive and delay-critical tasks of GUs are offloaded based on binary or partially only to the hierarchical locations (UAVs, HAPs, or low earth orbit (LEO) satellites). Here, neighboring UAVs might have ideal resources that can be traced by paying attention to their observations to introduce both vertical and horizontal offloading [62]. Thus, low-complexity transformer-based self-attention encoder-decoder in actor and cross-attention-based encoder-decoder in critic networks require further studies to deal with these problems [57; 63].  
The high mobility of LEO satellites causes dynamic coverage toward UAVSNs or HAPs, which creates challenges for maintaining stable satellite-to-UAV or H2S links. Accurate prediction of LEO satellite mobility and pairing with accurate LEO satellite based on link quality [17], considering satellite constellations in orbit, is critical to ensure consistent coverage and reliability for these backhaul connections. To tackle this situation, the potential of generative adversarial learning within an actor-critic framework could be explored to enhance the adaptability and robustness of link prediction and resource allocation [64]. Additionally, the pre-trained large language models (LLMs) powered by transformer modules in the form of multi-agent actor-critic networks can be utilized for optimal trajectory control, task offloading, and resource allocation, which requires further investigations [65]. It is because LLMs are promising, owing to their capability to handle and understand vast amounts of data, capture complex time series patterns, and generalize across diverse tasks.
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 7 Conclusions  
The mobility of UAVs is a powerful function and a simple way to enhance the SINR of a wireless backhaul link in the URN. Simultaneous transmissions from mBS and sBS in the URN with an optimal time allocation is proposed to maximize the throughput of the UE and guarantee a seamless relay transmission without an outage of \(U_{o}\). The outage probability of \(U_{o}\) is analyzed, and an optimal time allocation algorithm is presented. Simulation results demonstrate that simultaneous transmissions from mBS and sBS perform better than a single transmission from mBS or sBS with respect to the overall throughput, and the optimal time allocation is necessary for throughput maximization and the extension of the operation range.  
This research received no external funding.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & unmanned aerial vehicle \\ URN & UAV relay network \\ CRN & conventional relay network \\ LOS & line-of-sight \\ SINR & signal-to-interference-plus-noise-ratio \\ UE & user equipment \\ mBS & macrocell base station \\ sBS & small cell base station \\ SIC & successive interference cancellation \\ AWGN & additive white Gaussian noise \\ MRC & maximal ratio combination \\ CDF & cumulative distribution function \\ MGF & moment-generating function \\ \end{tabular}
## 1 Introduction  
The demand for healthier dietary products among the public is rising. Cereal products high in functional ingredients (FI) such as dietary fibres (DF) are of particular interest. This is due to the fact that dietary fibres can have several benefits [1], including reducing risks of cardiovascular diseases, diabetes mellitus and different cancers [2]. Nevertheless, consumers expect acceptable shelf life and reasonable sensory properties, and these characteristics might be affected by the addition of FI. Crumb quality and texture are especially affected by FI additional to breads [3, 4].  
Several FI are incorporated in cereal products such as breads to improve the nutritional content and sensory characteristics. Different types of fibre also have differing effects on post-prandial blood glucose levels. Insoluble dietary fibres have less of an influence on the glycaemic index (GI), but soluble dietary fibres were found to reduce the glycaemic response and increase satiety [5].  
Several studies used FI independently, including inulin (IN) [5] guar gum (GG) [6] and Waxy wheat flour (WWF) [7] in order to impart functional properties. These were used for different purposes, including improving glycaemic and satiety indices, extending shelf life and improving the sensory characteristics of cereal products such as breads. To the researchers' best knowledge, research has not been performed on the use of these FI independently and/or in combination on Pita and Tandoori breads.  
Therefore the aim of this work was to assess the post-prandial glycaemic and satiety indices of Pita and Tandoori breads supplemented with FI; inulin (IN), guar gum (GG) and wavy wheat flour (WWF). A secondary aim was to assess the sensory characteristics and shelf-life of the supplemented breads.