esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review
## 3 Stimuli Dependent Techniques
### Physical Stimuli-Based Self-Assembly of Nanomaterials
#### 3.2.2 Magnetic-Induced Self-Assembly  
The precise control and guide of magnetic forces over the arrangement of tiny NPs into organized nanostructures under an applied magnetic field is known as magnetic field-induced SA. In this process, the core material of tiny particles that tend to assemble in the presence of a magnetic field should be ferromagnetic such as iron, nickel, and cobalt [101]. Magnetic forces provide an effective stimuli strategy that initiate easy and fast assembly, and they disassemble immediately when the field is dismissed [102; 103]. This technique improves the magnetic nature of the material without physical manipulation or damaging the delicate samples. However, various parameters such as NPs size, NPsloading concentration, solvent, and the strength of the magnetic field influence these arrangements of nanostructures [104].  
In a typical example, the SA of a cubic Fe\({}_{3}\)O\({}_{4}\) particle was experimentally investigated at various parameters particularly by changing the orientation of the magnetic field and NPs at the loading concentration (Figure 8a). The author confirmed the formation of a 1D filament of short chains formed when 13.4 nm sized Fe\({}_{3}\)O\({}_{4}\) cubes were placed under the magnetic field in non-miscible hexane solvents. Various superstructures such as helical, 1D filaments, and C-shaped SA have been developed by changing the concentration of Fe\({}_{3}\)O\({}_{4}\) NPs [105]. In addition, relatively low magnetized materials such as cobalt or ferrite exhibit super ferromagnetic behaviors on self-assembled nanostructures. In a typical example, authors have prepared the FeCo/CoFe\({}_{2}\)O\({}_{4}\) core shell structures and quantitatively evaluated the influence of the magnetic alignment and inter-intraparticle relation on improved collective magnetic characteristics. So far, most of the researchers focused on the magnetic field-induced SA of NPs using ferromagnetic/paramagnetic inorganic material or its composites [101]. However, developing a magnetic response in organic material is highly challenging due to their insensitivity to magnetic fields. In the year 2024, [PERSON], [PERSON] and co-workers developed a magnetic response in organic materials by incorporating multiple magnetic field-sensitive materials in a single molecule. Utilizing the specific helix protein combined with the peptide bonds in a rod-coil block-like superstructure alters the magnetic response in organic materials [106]. However, in the utilization of only ferromagnetic/paramagnetic material, unwanted interactions limit this technique.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 2 Fixed Wireless Access
### Planning Tools for FWA Designs  
Network modelling and planning is a continuous process of evaluating a network to support the incremental need of users. It started in the pre-internet era when telecom operators needed to dimension their telephony networks. Now, with the evolution of technology and data services, planning a dynamic wireless network has become an enormous task. It includes the definition of the topology and architecture that models the infrastructure the closest to reality as possible. In addition, users' traffic behaviour must be known in advance to be able to assign resources to users in the most optimal way. However, this optimal solution depends on the particular objectives of the network designer or business case that is evaluated. Additionally, based on the requirements of diverse government bodies, there is a need for network planning that accounts for all the aspects of the 5G networks, in particular, the ones that include power consumption and exposure analysis in their solutions.  
Nowadays, several network planners can be used to investigate the performance of 5G networks and plan them. There are comprehensive studies that provide the latest achievements in simulation techniques and platforms for 5G networks [24; 25]. However, none of them can evaluate FWA, energy consumption and the impact of exposure in the same tool. In the following subsections, the network planner tools are grouped within two focuses: (i) the industry-focused ones that aim to evaluate the performance of already deployed networks and the performance of future improvements, and (ii) the research-focused ones which aim to evaluate future technologies and methods for 5G networks. Table 1 summarises relevant 5G network planners and the aspects that they are capable to evaluate, compared with the GRAND Tool used in this paper.
# Shafting Torsional Vibration Analysis of 1000 MW Unit under Electrical Short-Circuit Fault
## 4 Electrical Short-Circuit Fault Simulation Analysis  
### Simulation Analysis of Generator Terminals Two-Phase Short-Circuit  
The generator outlet short-circuit fault is realized by the three-phase fault module in the SIMULINK Toolbox, and the position is shown in Figure 1. The generator terminals short-circuit fault 1. The generator is set to impart 80% load before the fault occurs, and the system's simulation time domain is 2 s. The short-circuit fault between phases A and B occurs at 0.2 s, and it is removed at 0.25 s. The short-circuit time is 0.05 s. The algorithm ode23 tb is used to solve the unit value of electromagnetic torque, fault current and velocity difference at the connection of the low-voltage generator under the transient fault, as shown in Figure 6[55,56].  
Figure 6a shows that the transient electromagnetic torque of the generator undergoing a short-circuit fault is about 7 times that under stable conditions, and the short-circuit current of the fault phase circuit also changes instantaneously, as shown in Figure 6b. In the fault time domain, speed fluctuation appears at the connection between the low-voltage rotor and the generator rotor, and the speed oscillation reaches its maximum value at the moment of fault removal, as shown in Figure 6c.  
Since the impulse excitation obtained by the SIMULINK simulation is a per unit value, it is not beneficial to the subsequent quantitative analysis of transient dynamics. The nominal values of torque and current calculated by the simplified load flow module in the SIMULINK Power GUI module are shown in Table 4[57]. The calculation of the given reference parameters shows that the maximum value of transient alternating electromagnetic torque generated by a two-phase short-circuit reaches 2.14 \(\times\) 10\({}^{7}\) Nm.
# Adversarial Data Augmentation on Breast MRI Segmentation
## 4 Discussion  
Noticeably, the versions of the models we report in Section 2 generally contain less layers and use inputs of lower resolutions than in the original papers. In part, this is related  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multicolumn{2}{c}{**Synthesis**} & \multicolumn{5}{c}{**DICE**} \\ \cline{3-8} \multicolumn{1}{c}{**Generator**} & \multicolumn{1}{c}{**Best \({}^{*}\)\(p\)**} & \multicolumn{1}{c}{**Bgd**} & \multicolumn{1}{c}{**Breast**} & \multicolumn{1}{c}{**Pleura**} & \multicolumn{1}{c}{**Pect.M.**} & \multicolumn{1}{c}{**Sterum**} & \multicolumn{1}{c}{**Clav.**} \\ \hline \multirow{2}{*}{No Aug} & \(\mu\uparrow\) & \multirow{2}{*}{0} & 0.976 & 0.946 & 0.637 & **0.669** & 0.777 & 0.497 \\  & \(\sigma\downarrow\) & & 0.011 & 0.042 & 0.218 & **0.173** & 0.228 & **0.282** \\ \hline \multirow{2}{*}{Pix2 PixHD} & \(\mu\uparrow\) & \multirow{2}{*}{0.333} & 0.976 & 0.945 & 0.652 & 0.625 & **0.808** & 0.440 \\  & \(\sigma\downarrow\) & & **0.009** & 0.049 & 0.215 & 0.238 & **0.154** & **0.293** \\ \hline \multirow{2}{*}{ResGAN} & \(\mu\uparrow\) & \multirow{2}{*}{0.333} & 0.976 & 0.941 & 0.657 & 0.672 & 0.799 & **0.540** \\  & \(\sigma\downarrow\) & & 0.010 & 0.061 & **0.199** & 0.198 & 0.187 & 0.330 \\ \hline \multirow{2}{*}{CycleGAN} & \(\mu\uparrow\) & \multirow{2}{*}{0.15} & 0.976 & **0.950** & **0.659** & 0.667 & 0.791 & 0.502 \\  & \(\sigma\downarrow\) & & 0.011 & **0.040** & 0.204 & 0.198 & 0.230 & 0.284 \\ \hline \hline \end{tabular} \(p\)–augmentation factor: 0–only original set, 0.5—original and synth sets in 50/50; \({}^{*}\)Best during validation phase.  
\end{table}
Table 4: Class-wise segmentation test scores. Presents the best DICE mean (\(\mu\)) and standard deviation (\(\sigma\)) results for the labels: Background, Breast, Pleura, Pectoral Muscle, Sternum and Clavicules, respectively, for the augmentation experimental setups of each generator. The no augmentation baseline (_No Aug_) is also shown.  
Figure 7: Best visual test results for the segmentation U-Net, trained with the respective synthsets ResGAN and Pix2 PixHD with BS8. CycleGAN with BS1 and Experience Replay. Further results on Appendix A Figures A3 and A4.  
with the lower native resolution of our data. On the other hand, the equipment listed in Section 3.1 limited the total number of parameters a model can leverage. These limitations, as it turns out, happen to not hamper substantially and still enable to conduct this study.
## 6 Quantization and Deployment of Deep Neural Networks with MicroAI
### Quantization of Deep Neural Networks  
After the initial training phase, the trained model can be quantized to perform inference using a fixed-point data format instead of floating point. Quantization is done after freezing the weights of the model as a post-training quantization step. Optionally, before freezing the weights, the model can be fine-tuned while taking into account the quantization error as a quantization-aware training step. While the values are quantized, a floating-point data type is still used during quantization-aware training. The data type conversion from floating-point to integers using fixed-point representation happens during the generation of the C inference library, both for quantization-aware training and for post-training quantization.  
The quantization scheme of MicroAI does not make use of advanced quantization techniques such as non-power-of-two scale factors or asymmetric ranges [23]. Instead, a less complex quantization scheme is used: uniform quantization, per-layer power-of-two scale factor and symmetric ranges. Additionally, biases are quantized the same way as weights. Activations are quantized using a separate scale factor.  
As it will be shown in Section 7, post-training quantization with 16-bit integers has no impact on accuracy. Moreover, the same fixed-point coding, set to Q7.9 [24] in our case, is used for all layers.  
On the other hand, quantizing over 8-bit integers does negatively affect the accuracy. To mitigate the quantization loss, the fixed-point coding can be different between layers and is chosen considering the range of the training set values. In practice, this conversion method starts by finding \(m\), the number of bits required to represent the largest unsigned integer part. In the fixed-point representation, one bit is used for the sign, \(m\) bits are used for the integer part and the remaining bits are used for the fractional part. Each floating-point value is then multiplied by \(2^{m}\) and cast to an integer, truncating the fractional part. In the following experiments, quantization-aware training is not used since it did not bring a significant improvement over post-training quantization.
# The Use of Correlation Features in the Problem of Speech Recognition
## 5 Conclusions  
Algorithms for the preprocessing of speech messages were proposed to extract features in the problem of recognizing phraseological radio exchange messages. Preprocessing was based on spectral analysis and obtaining correlation functions from the signal spectrum. Building an autocorrelation portrait of a speech message was also proposed. Then, the data were processed by convolution neural networks. This made it possible to increase the recognition accuracy compared to recurrent networks by about 2%. However, in noisy conditions it is not possible to maintain maximum accuracy. Performance approaches 50% at the signal-to-noise ratio q = 0.1. Quantization also ensures the speed of the two-dimensional convolutional network at the level of the recurrent network. The main results of the work are data acquisition and the proposed methods for their preprocessing. Based on the recordings of speech messages and their spectra, it is proposed that correlation functions are obtained so that all classes become increasingly separable. This is confirmed by the results of the study, the increase in accuracy after preprocessing reaches 1-2%. An additional improvement is provided by the use of autocorrelation portraits. In the future, an investigation into how the size of such portraits affects the accuracy is planned. On data in the absence of noise, LSTM networks are preferable, but they turned out to be unstable under the influence of noise. In this regard, the plans for further research include signal filtering.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications
## 1 Introduction
We now focus our study on how recently developed smartphone contact tracing solutions can impact the spread of infectious diseases, particularly for COVID-19. Firstly, we describe how these applications work by characterizing their effectiveness in detecting contacts based on their utilization (i.e., the percentage of users using the application) and the users' willingness to check their exposure. We then compare the application model (centralized vs. decentralized), and how this model impacts the tracing process (mainly on the tracing speed). Then, using an epidemic deterministic model, we evaluate how these issues impact the spread and management of the COVID-19 epidemics. The main conclusions of this paper are the following: smartphone contact tracing, when no other measures are taken, can only be effective using a centralized model and when a very significant proportion (more than 80%) of the population uses the smartphone application. Under these conditions, smartphone-based contact tracing is an unfeasible solution. Nevertheless, when combined with other mild measures that can slightly reduce the reproductive number \(R_{0}\) (for example, social distancing), smartphone contact tracing can be feasible and very effective. This efficiency clearly depends on the implementation model. A centralized model is much more effective, requiring a percentage of utilization of about 50% to control an outbreak, which can be considered a realistic objective. On the contrary, a decentralized model would require a higher utilization, in the range of 60% to 80% depending on the tracing speed. This paper is organized as follows: the related research works are discussed in the following section. Section 3 describes how mobile tracing works and details how its precision and utilization can be parametrized, which are then used in the deterministic model detailed in Section 4. In particular, Section 4 describes the models and their precision, while Section 5 evaluates how the infection is controlled (or not), and the required effort to control it, that is, the number of people quarantined. Finally, in Section 6 we present the conclusions.
# COVID-Related Misinformation Migration to BitChute and Odysee
## 4 BitChute Analysis
### Temporal Distribution  
We first study the temporal distribution of the BitChute videos. Figure 4 illustrates a timeline showing the BitChute videos per the corresponding YouTube video. Each line corresponds to one YouTube video and its BitChute duplicates. The horizontal axis corresponds to the time between posting the YouTube video and its BitChute duplicates. We plotted the BitChute videos posted at most within a day of the posting of the YouTube video. Due to missing metadata, we examined 297 YouTube videos and their 2335 BitChute duplicates. In total, 12% of the BitChute duplicates were posted minutes to a day after posting the YouTube video, while 50% of the BitChute duplicates appeared on the platform between one day and a week after posting the YouTube video. The remaining 34% of videos were shared more than a week after the original YouTube video. Therefore, more than half of the videos appeared in BitChute at most within a week after the original YouTube video.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Texual**} & \multicolumn{2}{c}{**Visual**} & \multicolumn{2}{c}{**Manual**} & \multirow{2}{*}{**Total/Unique**} & \multirow{2}{*}{**Total \#yt**} \\ \cline{2-2} \cline{6-7}  & \#dup1 & \#yt & \#dup1 & \#yt & \#dup1 & \#yt & \#dup1 \\ \hline Bitchute & 3860 & 893 & 4256 & 372 & 1293 & 182 & 9409/4096 & 1048 \\ \hline Odysee & 2088 & 487 & 487 & 119 & 968 & 222 & 3543/1810 & 591 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of BitChute and Odysee videos retrieved by each filtering process and the number of YT videos they correspond to.
## 2 Experiments and Methods
### Characterization Method  
Here, the thickness of OFHC substrates used for thermal conductivity measurements was 2.5 mm, with diameters of 12.7 mm. The specific heat and thermal diffusivity were measured by a NETZSCH LFA 467 Hyper Flash system from 25 \({}^{\circ}\)C to 300 \({}^{\circ}\)C, with the acquisition rates of the temperature sensor at 2 MHz. To increase the absorptivity and emissivity of the sample, graphite was coated on both sides of the sample before thermal conductivity measurements. Thermal conductivity (W m\({}^{-1}\) K\({}^{-1}\)), \(\lambda\), of the sample can be calculated via the following equation:  
\[\lambda(T)=\alpha(T)\cdot c_{p}\cdot\rho(T) \tag{1}\]  
where \(\alpha(T)\) is the thermal diffusivity (mm\({}^{2}\) s\({}^{-1}\)), \(c_{p}\) is the specific heat capacity (J g\({}^{-1}\) K\({}^{-1}\)), and \(\rho(T)\) is the bulk density (g cm\({}^{-3}\)). The density of the sample was measured by Archimedes' method. The thermal diffusivity accuracy of NETZSCH LFA was about \(\pm\)3%. The accuracy of thermal conductivity was around \(\pm\)5%, which was influenced by the accuracy of thermal diffusivity, specific heat capacity, and bulk density.  
The surface microstructures were investigated by A JEOL 7800F Schottky field scanning electron microscopy (SEM). Surface roughness characteristics of the samples were obtained using LEXT Olympus OLS4000-SU laser scanning confocal microscopy (LSCM). The Brunauer-Emmett-Teller (BET) surface areas of the laser treated and untreated OFHC before and after film deposition were analyzed by a BELSORP-Max instrument (MicrotracBEL, Osaka, Japan). The samples were outgassed at 383 K for 6 h in the vacuum. The surface chemical properties of the samples were determined via an AXIS Ultrabld X-ray photoelectron spectroscopy (XPS) with monochromatized Al Ka Xray source. The SEY properties were characterized under normal incidence using a dedicated SEY measurement device which was introduced in Reference [17]. The electron dose for SEY tests was about 7 \(\times\) 10\({}^{-6}\) C cm\({}^{-2}\), with an electron beam spot size of \(\sim\)1 mm.
# Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System
###### Abstract  
Miniaturized wearable Inertial Measurement Units (IMU) offer new opportunities for the functional assessment of motor functions for medicine, sport, and ergonomics. Sparse reliability validation studies have been conducted without a common specific approach and protocol. A set of guidelines to design validation protocol for these systems is proposed hereafter. They are based on the comparison between video analysis and the gold standard optoelectronic motion capture system for Gait Analysis (GA). A setup of the protocol has been applied to a wearable device implementing an inertial measurement unit and a dedicated harmonic oscillator kinematic model of the center of mass. In total, 10 healthy volunteers took part in the study, and four trials of walking at a self-selected speed and step length have been simultaneously recorded by the two systems, analyzed, and compared blindly (40 datasets). The model detects the steps and the foot which supports body weight. The stride time and the cadence have a mean absolute percentage error of 5.7% and 4.9%, respectively. The mean absolute percentage error in the measurement of step's length and step's speed is 5.6% and 13.5%, respectively. Results confirm that the proposed methodology is complete and effective. It is demonstrated that the developed wearable system allows for a reliable assessment of human gait spatio-temporal parameters. Therefore, the goal of this paper is threefold. The first goal is to present and define structured Protocol Design Guidelines, where the related setup is implemented for the validation of wearable IMU systems particularly dedicated to GA and gait monitoring. The second goal is to apply these Protocol Design Guidelines to a case study in order to verify their feasibility, user-friendliness, and efficacy. The third goal is the validation of our biomechanical kinematic model with the gold standard reference.  
Keywords:wearable sensors; gait analysis; human kinematics; system validation; reliability; assessment protocol; functional analysis; biomechanical modeling +
Footnote †: journal: Applied Sciences
## 4 Influence of the Scaling Factor  
In order to study the influence of SF on the numerical result, a case without using the density scaling method (i.e., SF = 1) was performed by the same high-performance computer. The computation time of the two cases was firstly compared. It took about 140.0 h for SF = 1 when the penetration depth reached \(8D\). Remarkably, it took only about 2.1 h for SF = \(10^{6}\) when the pile arrived at the same depth, proving the scaling factor has a significant impact on the computation time. The computation efficiency is increased approximately 67 times.  
Figure 5 compares the vertical distribution of excess pore pressure \(\Delta p\) for SF = 1 and SF = \(10^{6}\). One can see a slight difference that only occurs when the penetration depth \(L\) is \(4D\). Thus, it is feasible to believe that the scaling factor has little effect on the \(\Delta p\), and the density scaling method [6; 11] can increase the computation efficiency of the proposed model without decreasing accuracy.  
Figure 4: Relation between SF and \(\Delta t_{m}\) in the proposed model.  
Figure 5: Effect of scaling factor on the distribution of excess pore pressure.