esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Challenges and Trends in Wireless Sensor Nodes
### Acceleration of Data Compression Techniques  
According to [21], 80% of the energy consumed by a sensor node is due to data transmission. Hence, by minimizing the data size, a sensor node can save energy. As noted elsewhere, a typical strategy is to perform compression on the acquired data. [PERSON] _et al_. argued that the main problem of implementing compression algorithms on sensor nodes was the complexity and required time of execution [21]. Hence, FPGAs can be employed in this respect, for instance, to accelerate arithmetic applications via fast multipliers and adders [52].  
Adaptive sampling techniques have also been proposed on WSNs in order to reduce the data size using prediction systems [53]. Hence, the coordinator node or gateway can create a model based on the last received measurements. This model is used to correlate new data with the aim of demanding the nodes to stop sending data or to reduce the frequency if values do not change [54]. Since a sensor network can consist of hundreds of nodes, the correlation algorithm can be accelerated via FPGA. In this respect,such adaptive sampling techniques could be applied to either subgroups or clusters in the networks in order to reduce the number of nodes required to be coupled with an FPGA, thus minimizing costs.  
In [55], [PERSON] _et al_. presented the implementation of a data prediction mechanism in order to reduce the data transmission in WSNs. Such technique is based on the least-mean-square (LMS) algorithm and was implemented on a Spartan-3E FPGA (XC3S100E). According to the authors, it was possible to reduce the communication with 90% of the time with an error of 0.5 degrees using a temperature sensor.  
Since Wireless Multimedia Sensor Networks (WMSNs) are based on transmission of audio and video data, performing compression on the sensor nodes plays a key role [56]. These networks capture, process and transmit audio and video signals and operate on traffic enforcement, health care and ambient monitoring applications among others. In those fields, reducing the bandwidth is important since the size of data being transmitted is considerable. Thus, compression is either performed intraframe or interframe. In the latter, since predictive coding requires strong computation, FPGAs are used in order to implement hardware compressors based on MPEG, H.263, H.264 or even via distributed compression in the network [56].  
We give in Section 4.2 an overview of existing implementations of FPGA-based sensor platforms in the context of multimedia.
# Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference
## 2 Methodology  
### Characteristic Frequencies of Rolling Bearing  
Rolling element bearings principally comprise four key components: the inner race, the outer race, the rolling elements, and the cage. During their operation, these bearings may exhibit various fault modes, each leading to distinct failure mechanisms. When any part of the bearing becomes locally damaged during operation, the machinery produces corresponding vibration frequencies, known as the fault characteristic frequencies of the rolling bearing.  
When faults and defects occur in the bearings, unsmooth vibration and noise will happen. Chattering and rattled noise can be heard. Different components of bearings correspond to different characteristic frequencies during vibration and noise. The characteristic fault frequencies of the primary components of rolling bearings are outlined in Table 1[13].  
The variables are defined as follows: \(n\) denotes the rotational speed of the rolling bearing; \(Z\) denotes the number of rolling elements; \(d\) denotes the diameter of the rolling elements; \(D\) denotes the diameter of the circle on which the centers of the rolling elements lie; \(\alpha\) denotes the contact angle of the rolling elements.  
Sliding window techniques are indispensable tools for processing and analyzing time series data, serving as a common method for dataset augmentation. By effectively aggregating and analyzing data over a defined period, the window is incrementally moved over time, enabling the continuous analysis and processing of new datasets.
## 3 Results
### Efficiency of Automated Approach vs. Manual  
To compare the data processing speed of our automated software tool with the manual approach, we have monitored the timings of data analysis for both automated and manual approaches. On average, the observed timing of the fully automated software tool was 3.4 seconds per sample, running under the entire data set (see Supplementary Video). This performance was about 30-fold faster in comparison with the observed times that a trained technician took to process and analyze the same data (\(\sim\)2 min per sample). In general, mass spectral data obtain by MALDI-ToF showed good quality for diagnostic purpose using both manual and automated approaches for quality control assessment (Table 2, see also Supplementary Table S1 in a file for further details). Our data also shows that quality drops slightly with dilution between 1:500 and 1:2000 but still keeping a reasonable acceptance rate. Except for 17 cases, the quality control decision using automated software and manual assessment were in agreement (Table 2). In most cases, spectra had a good quality, where the \(\alpha\)-globin and \(\beta\)-globin peaks were identified by both approaches and no spectral issues observed (see example in Figure 2a). For most of the rejected samples, there was not enough signal in the range of \(\alpha\)-globin and/or \(\beta\)-globin peaks, showing only noise in the mass spectrum (see example in Figure 2b). For the cases where automated and manual approaches were in disagreement, mass spectra showed lower signal to noise ratios for \(\alpha\)-globin and/or \(\beta\)-globin peaks close to quality threshold (see example in Figure 2c).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Automated** & **Manual** & **Dilution 1:500** & **Dilution 1:1000** & **Dilution 1:2000** \\ \hline Accepted & Accepted & 94.7\% (54 cases) & 82.5\% (47 cases) & 66.7\% (36 cases) \\ Rejected & Rejected & 3.5\% (2 cases) & 10.5\% (6 cases) & 11.1\% (6 cases) \\ Accepted & Rejected & 1.8\% (1 case) & 7.0\% (4 cases) & 22.2\% (12 cases) \\ Rejected & Accepted & 0.0\% (0 cases) & 0.0\% (0 cases) & 0.0\% (0 cases) \\ \hline \end{tabular}
\end{table}
Table 2: Quality control decision comparison between the automated and manual approach.
# Three-Dimensional Inversion of the Time-Lapse Resistivity Method on the MPI Parallel Algorithm
## 3 Synthetic Example Tests
### MPI Parallel Calculation Efficiency  
We evaluate the computational efficiency of the three-dimensional MPI parallel inversion via the time-lapse resistivity method. Taking the above synthetic data as an example, with consistent grid division, inversion of the initial model, and observation data errors, the program was executed using 5, 10, 20, 30, and 40 processes, respectively, and the times of the serial program were compared. Table 1 shows the running times of the serial program and the MPI parallel program.  
As shown in Table 3, the relative acceleration ratio between the MPI parallel program and serial programs is not an integer multiple relationship. When the number of processes  
Figure 4: The sections of time-lapse model. The three columns from left to right represent the models at three times. The top panels (**a**) are schematic diagrams in the X-Z direction at T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\), and the bottom panels (**b**) are schematic diagrams in the X-Y direction at T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\). The resistivity value of background A is 100 \(\Omega\)-m, and the resistivity of abnormal bodies B\({}_{1}\) and B\({}_{2}\) is 10 \(\Omega\)-m. The direction of the red arrow represents the change direction of B\({}_{2}\).  
Figure 5: Schematic diagram of the observation system. The red circle triangles denote transmitters, the blue dots denote receivers, and the black rectangle denotes the projection of the prism on the surface.  
is 5, the acceleration ratio increases greatly. As the number of processes increases, the growth rate of the acceleration ratio decreases. When the number of processes is 40, the acceleration ratio is almost unchanged compared with when the number of processes 30. This has two causes. First, during the execution of parallel programs, data transfer between processes takes up time, decreasing efficiency. Second, only processes with high computational complexity such as forward response, objective functions, and gradients need to use MPI parallel computing; the other processes do not need to use MPI parallel programs. Overall, a reasonable number of processes can not only ensure the improvement of computing efficiency but also avoid the waste of computer processes. In practice, we should set the number of processes reasonably according to the specific number of sources.
## 3 Services Layer: From Telephony to Virtual Reality
### Main Achievements, Key Services and Requirements of Each Generation  
A list with the most representative services for each fixed network generation is shown in Table 1. The requirements of this table have been set using a degree of arbitrariness, as a strict definition of their specific values is a very difficult procedure since a service may include: (a) different types of user participation, such as passive or active, e.g., in online learning; (b) different video definitions, such as high or low, e.g., in videoconferencing; and (c) a different number of employed features, e.g., in smart infrastructure. In the rest of this section, we elaborate on the main services and their requirements based on Table 1.  
_FIG: 1970-2000 (the narrowband-to-wideband era)_  
The FIG era is dedicated mainly to telephony and fax, which required 64 kb/s data rates and latency of tens of ms. Generic internet browsing also became possible mainly at the end of this era, as significant technological achievements such as IPv4, Ethernet and the OSI reference model were standardized during the 1980s. At first, access to the PSTN infrastructure, along with the use of a modem, allowed the essential elements to connect to the Internet. Later, the introduction of the Integrated Services Digital Network (ISDN) standard, depending on its narrowband or broadband variant, increased the overall data rate, decreasing in this way the overall user's downloading time. _F2G: 2000-2005 (the wideband era)_  
The second era provided Mb/s data rates and allowed the users to enter the broadband era (as it was perceived at that time). This generation comprised Internet-centered services, such as web browsing, search engines, e-mail, image downloading and low-resolution video. Overall, a F2G network provider in order to efficiently serve the end users had to offer data rates larger than 1 Mb/s, medium latency at the order of a fraction of a second and high availability, especially in companies that used the World Wide Web for their business purposes. This became possible with the introduction of Asymmetric Digital
# Textile-Based Flexible Coils for Wireless Inductive Power Transmission
## 4 Fabrication
### Flexibility of Coils  
A nominal DC resistance of 1 \(\Omega\) has been achieved by increasing the thickness of the conductive layer. The DC resistances of the printed coils are measured after bending round a mandrel with a radius of 150 mm to demonstrate the functionality of the coils after deformation. The DC resistance is increased to 1.15 \(\Omega\pm 7\%\) in 6 samples which is an increase of 15% over an un-deformed coil. This demonstrates an acceptable degree of flexibility.
## 4 Discussion
### Data Distribution (Statistical Data Heterogeneity) Challenge
#### 4.3.2 Non-IID Mitigation Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Number of** & **Non-IID** & **Non-IID Mitigation** & **Studies/Year** \\ \cline{3-5}
**Partition** & **Nodes** & **Characteristics** & **Non-IID Mitigation** & **Studies/Year** \\ \hline \hline \multirow{8}{*}{HFL} & 10 & Quantity Skew & Balancing the training dataset & [PERSON] et al., 2018 [52] \\ \cline{2-5}  & 50 & Quantity Skew & Not Available & [PERSON] et al., 2019 [19] \\ \cline{2-5}  & 20 & Quantity Skew & Balancing the training dataset & [PERSON] et al., 2020 [45] \\ \cline{2-5}  & 90 & Quantity Skew & Adaptive Hyperparameters: & \\ \cline{2-5}  & & & Adaptive Loss Function & [PERSON] et al., 2020 [46] \\ \hline \multirow{8}{*}{HFL} & \multirow{8}{*}{HFL} & \multirow{8}{*}{HFL} & Domain Adaptation: Mixture & \\  & & & of Expert and Domain & [PERSON] et al., 2020 [18] \\ \cline{2-5}  & & & Adversarial & \\ \cline{2-5}  & 5 & Quantity Skew & Not Available & [PERSON] et al., 2020 [47] \\ \cline{2-5}  & 10 & Quantity Skew & Not Available & [PERSON] et al. [38] \\ \cline{2-5}  & 5 & Quantity Skew & Balancing the training dataset: & \\ \cline{2-5}  & & & SMOTE Algorithm & [PERSON] et al., 2020 [21] \\ \cline{2-5}  & 10 & Not Available & Not Available & [PERSON] et al., 2021 [54] \\ \cline{2-5}  & 4 & Quantity Skew & Balancing the training dataset: & \\ \cline{2-5}  & 8 & Quantity Skew & Not Available & [PERSON] et al., 2021 [39] \\ \cline{2-5}  & 3 & Quantity Skew & Balancing the training dataset: & \\ \cline{2-5}  & 4 & Quantity Skew & Balancing the training dataset: & \\ \cline{2-5}  & 6 & Quantity Skew & Not Available & [PERSON] et al., 2021 [41] \\ \cline{2-5}  & 10 & Quantity Skew & Balancing the training dataset & [PERSON] et al., 2021 [42] \\ \hline \multirow{2}{*}{HFL} & \multirow{2}{*}{HFL} & \multirow{2}{*}{HFL} & Balancing the training dataset: & \\  & & & SMOTE Algorithm & [PERSON] et al. [49] \\ \cline{2-5}  & 3 & Not Available & Not Available & [PERSON] et al. [50] \\ \cline{2-5}  & 5 & Quantity Skew & Not Available & [PERSON] et al., 2021 [55] \\ \cline{2-5}  & 8 & Not Available & Not Available & [PERSON] et al., 2021 [51] \\ \cline{2-5}  & 8 & Not Available & Not Available & [PERSON] et al., 2021 [43] \\ \hline \multirow{2}{*}{HFL} & \multirow{2}{*}{HFL} & \multirow{2}{*}{HFL} & Balancing the training dataset: & \\  & & & Geometric Transformation & [PERSON] et al., 2021 [53] \\ \hline \multirow{2}{*}{HFL} & \multirow{2}{*}{HFL} & \multirow{2}{*}{HFL} & Balancing the training dataset: & \\  & & & Generative Adversarial & [PERSON] et al., 2021 [44] \\ \cline{2-5}  & & & Network (GAN) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of different data partition methods, number of nodes, non-IID characteristics, and non-IID mitigation employed in the published federated learning for healthcare applications.
# Stabilization of Pickering Emulsions by Hairy Nanoparticles Bearing Polyanions
## 3 Results and Discussion
### Standing Time  
For Pickering emulsions prepared under optimal ultrasonic conditions, tiny changes could exist after standing for a while at ambient temperature (25 \({}^{\circ}\)C). Thus, we monitored the Pickering emulsions over time. Figure 5a shows microscope images of hexadecane/H\({}_{2}\)O Pickering emulsions stabilized by PS@PSS for one hour. Immediately after emulsion formation, droplets were rather homogeneous and the droplet size was normally distributed around 7.1 \(\upmu\)m. As time went on, the droplets were not as homogeneous as the beginning, and average size became larger. This was up to 10.0 \(\upmu\)m. Within 30 min, the mean size of the Pickering emulsion particles increased relatively rapidly (Figure 5b,c). During this period, the size of small droplets decreased and the large ones became larger, indicating polarization in droplets size. From 30 min to 1 h, no obvious change in emulsion particle size was observed. However, as shown in Figure 5c, the small droplets became smaller, and their number increased; the large droplet became larger, and their amount also increased. This indicated that Pickering emulsions are thermodynamically unstable; there was a ripening trend in this system. For hairy nanoparticles with partial wettability like PS@PSS, they thermodynamically preferred to anchor at the water/oil interface rather than stay in one of the bulk phases [21]. However, [PERSON] et al. [38] reported that the tension of particle-covered interfaces changed with time; there were various sizes of droplets. If small droplets lost oil, then compression of the adsorbed PS@PSS layer would occur with a concomitant lowering of tension. Large droplets swelled with additional oil, which may induce adsorption of PS@PSS from the bulk to cover the new interfaces. This could explain the droplet changes in one hour.  
The emulsion droplet size was normally distributed, and changes were subtle indicating that polymer chains of PS@PSS nanoparticles were still relatively stable in water and PS core closely touched with hexadecane molecule with longer time. Thus, Pickering emulsions stabilized by PS@PSS nanoparticles could be conserved for a long time.  
Figure 5: (a) Microscope images of hexadecane/H\({}_{2}\)O Pickering emulsion stabilized by PS@PSS in 1 h. Here the water:oil ratio is 1:1, salt concentration is 0.1 mM, ultrasonic power is 150 W, and ultrasonic time is 3 min. The bar is 10 \(\upmu\)m. (b) Droplets size distribution in number corresponding to microscope images. (c) Droplets volume distribution corresponding to microscope images.
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 2 Related Work
### Recent Studies on Anomaly Detection in V2X Communications
This approach underscores the importance of a layered defense but differs from our focus on detecting semantic anomalies in potentially already authenticated message content. Moreover, the broader context of V2X security highlights unique challenges, including the dynamic nature of vehicular networks, the need for real-time detection, scalability, and adaptability ([PERSON] et al., 2019). Lightweight ML solutions play a critical role in a robust, adaptive, and secure vehicular network against emerging threats ([PERSON] et al., 2019). Challenges such as data privacy, computational constraints on OBUs/RSUs, and the need for interpretable models are also widely acknowledged.
## 3 Results
### Helicity-Dependent Domain Wall Motion (HD-DWM)  
Light-matter interaction has been also observed in high PMA ferromagnetic thin films by performing domain wall (DW) motion experiments. When focusing a circularly polarized train of laser pulses close to a DW, its dynamics can be promoted [11]. In order to ensure that the displacement of the DW is caused by the motion and not by the local switching, the fluence in this kind of experiments must be within the no-switching regime discussed in the previous sections. Therefore, for the following results, the fluence is maintained bellow the switching threshold.  
Depending on the laser position with respect to the DW location and on the laser helicity, several experiments have been performed. In some of them, the laser beam is focused in the middle of a DW [7]. The DW moves to the right or to the left depending on the laser helicity. If the DW is an up to down transition (up-down DW) and the laser has right-handed circular polarization (\(\sigma^{+}\)), the DW is displaced to the right. The same DW is displaced to the left for left-handed polarization (\(\sigma^{-}\)). The direction of the DW motion reverses for a down-up DW configuration [8]. The HD-DWM was also experimentally observed when the laser is slightly focused to the left or to the right with respect to the initial position of the DW [11]. For up-down DW and a right-handed (left-handed) helicity, the DW moves to the right (left) when the laser beam is focused at the right side (left side) of the DW position. In the following subsections, we numerically study the HD-DWM as caused by the IFE (Section 3.2.1) and the MCD (Section 3.2.2).
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### FTS Computation Cost  
To evaluate the scalability of the proposed trust framework, we conducted experiments measuring the computational cost of calculating the FTS across increasing network sizes. Figure 4 presents the average time taken to compute the FTS per node as the number of nodes increases from 50 to 250. This trend indicates that while computation time increases as more nodes contribute to trust aggregation, the cost remains well below 25 ms, even for 250 nodes.  
Figure 3: The cost of generating and reading blocks on IOTA for different payload sizes.  
The increase in cost is attributed primarily to the higher number of neighbor interactions and trust score inputs and additional local consensus operations required to ensure robustness in larger peer sets. Despite this growth, FTS computation remains lightweight in the context of IoT operations. Since trust is evaluated at intervals (every 60 s), even the highest recorded cost (23.7 ms) represents less than 0.04% of a trust cycle.  
These results affirm that the proposed framework offers excellent scalability in terms of computational overhead, even as the network scales toward hundreds of devices.
## 2 Materials and Methods
### Data Pre-Processing  
Finite impulse response (FIR) is a digital filter which was applied using the _filter_ function from the _scipy_ package in Python, with parameters \(n=10\), \(a=1\), and an additional parameter \(b\) calculated as shown in Equation (8). It is worth also noting that parameter \(b\) (numerator coefficient in a 1D sequence) must be an array of length \(n\), and so should be repeated \(n\) times, whereas \(a\) (denominator coefficient in a 1D sequence) remains constant.  
\[b=\ \ [(\tfrac{1}{n})_{1}\ \ \ (\tfrac{1}{n})_{2}\ \ \ \cdots\ \ \ (\tfrac{1}{n})_{n}] \tag{8}\]  
The filter helped avoid noisy signals being used to train the predictive model, applied before normalization to have cleaner data on each force (\(F_{x}\), \(F_{y}\), \(F_{z}\)).  
Min-max feature scaling used _MinMaxScaler_ class from the _scikit-learn_ package in Python to normalize data depending on both the subject and the feature (independently). Data normalization helps transform each subject's data into a shared space; moreover, the scaler transforms values into a domain \(0<x<1\), which works best when using a DL approach to avoid exploding gradients. Feature scaling would need two equations: Firstly, a \(X_{std}\) value calculated as shown in Equation (9).  
\[X_{std}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{9}\]  
Secondly, \(max\) and \(min\) for each subject and feature were also calculated; thus, for each \(X_{std}\) value, we transformed it into an \(X_{scaled}\) value, which was calculated as shown in Equation (10).  
\[X_{scaled}=X_{std}\cdot(max-min)+min \tag{10}\]