esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 4 Electromagnetic Induction Imaging with Atomic Magnetometers  
### Motivations  
Conventional EMI/MIT systems rely on a set of coils to detect the secondary field's contributions. In this way, the performance and the imaging capability can be severely limited by the performance of the coils. Consequently, coil-based EMI has limited sensitivity below 50 MHz. This prevented the successful implementation of EMI for biological tissues, which produce very small secondary fields due to their very low conductivity (\(\sigma\simeq 1\,\mathrm{S/m}\)) and also in security and surveillance, where it is often necessary to operate the EMI systems at a frequency well below 1 MHz to achieve penetration through metallic shields or significant depths of water.  
To overcome these limitations in sensitivity, the use of atomic magnetometers [23] for the detection of the secondary field was proposed and demonstrated in 2013 [24]. AMs have extreme sensitivity (\(\leq\)10\({}^{-15}\) T\(/\sqrt{\mathrm{Hz}}\)), comparable to the performances of super-conductive quantum interference devices (SQUIDs) [23]. In contrast to SQUIDs, AMs are capable of room temperature and unshielded operation. Of particular importance for EMI applications, AMs retain their sensitivity also at very low frequencies, and notably below 50 MHz their sensitivity can be up to 10\({}^{7}\) times larger than that of a standard pick-up coil of the same size [25]. This was the main motivation behind the work which ultimately led to the implementation of EMI with atomic magnetometers.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 5 Experiment and Evaluation
### Performance Evaluation
#### 5.2.2 Performance Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weather Condition** & **Level of Weather Conditions** & **Training Error (\%)** & **Testing Error (\%)** \\ \hline \multirow{3}{*}{Rain (\(R_{min}\))} & Light (1 mm/h) & 0.13 & 0.21 \\ \cline{2-4}  & Moderate (2.5 mm/h) & 0.47 & 0.49 \\ \cline{2-4}  & Heavy (10 mm/h) & 0.52 & 0.57 \\ \hline \multirow{3}{*}{Wet snow (\(R_{c}\))} & Light (1 mm/h) & 0.12 & 0.23 \\ \cline{2-4}  & Moderate (1.5 mm/h) & 0.41 & 0.43 \\ \cline{2-4}  & Heavy (2.5 mm/h) & 0.46 & 0.52 \\ \hline \multirow{3}{*}{Fog (\(\mathcal{Z}\))} & Light (0.01 g/m\({}^{3}\)) & 0.08 & 0.12 \\ \cline{2-4}  & Medium (0.05 g/m\({}^{3}\)) & 0.19 & 0.24 \\ \cline{2-4}  & Thick (0.5 g/m\({}^{3}\)) & 0.32 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Surveying the SCAE with the different levels of weather conditions using the PM 2.5 dataset. Figure 8: Training error comparison with different noise variance using the PM 2.5 dataset. Figure 9: Testing error comparison with different noise variance using the PM 2.5 dataset.
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 3 Results and Discussion  
### Characterization of the GO  
GO has multiple layers of structural material with oxygen functional groups on the basal plane and at the edges [22; 23]. Most researchers believe that epoxy and hydroxyl groups are on the basal plane, whereas carbonyl and carboxylic acid groups are at the edges [24; 25; 26; 27]. These oxygen functional groups allow the GO to be easily and evenly dispersed because they decrease the van der Waals forces of the GO molecules [21]. The microstructure of the GO was studied by SEM, Figure 1a,b are SEM images of the GO magnified \(500\) and \(1000\) times, respectively. From Figure 1, the GO particles are mostly independent states and exhibit lots of shared edges, and the high face-to-face interaction stability or the face-to-edge interactions are not observed in the SEM pictures. Therefore, the GO may show a good compatibility with asphalt. On the other hand, these oxygen functional groups cause some defects on its basal plane and decrease its barrier performance [28; 29]. Thus, to improve the barrier property, in this paper, multilayered (5-10) GO was selected as the anti-aging modifier for asphalt.  
For its excellent gas-barrier property, GO can also improve the blocking performance of the modified materials to block the gas and the vapor diffusion. The barrier property can extend the travelling pathway of the O\({}_{2}\) when it diffuses to the internal asphalt, and also prolongs the travelling pathway of the volatile organic compounds (VOCs) when released from the asphalt [30; 31; 32]. Asphalt aging is mainly caused by oxidation, so the GO may be able to retard the aging of asphalt by decreasing the O\({}_{2}\) diffusion rate.
## 5 Characterization of Isolated-Aperture Surfaces
### Example of Metascreen Characterizations  
Take the zero-thickness PEC metascreen shown in Figure 15a as an example, which is composed of periodic square apertures. The period \(p\) of its unit cells is equal to 3 mm and side length of the aperture is denoted as \(a\). At 10 GHz, this surface is a single-layer isotropic metascreen, which ensures that the characterization method derived above is valid.  
Firstly, two simulations of the metascreen's unit cell at 10 GHz are done using ANSYS HFSS, with one normal incidence and another TM-polarized \(\theta_{0}=60^{\circ}\) oblique incidence. Based on the simulated transmission coefficients, surface susceptibilities of its complementary PMC metafilm (Figure 15b) are extracted through Equation (39), which are shown in Figure 16 with variable \(a\).  
Then scattering coefficients of the given metascreen for arbitrary incident angles and polarizations can be analytically computed using Equations (37) and (38). The computed transmission coefficients with \(\theta=45^{\circ}\) at 10 GHz for both TE and TM polarizations are shown in Figure 17, which are also compared with full-wave simulated results. It can be observed that the computed results agree well with the simulated ones.  
Figure 16: Surface susceptibilities of the zero-thickness PMC metafilm shown in Figure 15b, which are extracted from simulated scattering coefficients of the metascreen shown in Figure 15a.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
### Aim of the Paper  
The key aim of this paper is to analyse and compare the energy efficiency of standard pre- and post-quantum NIST cryptographic key generation algorithms, as implemented in the OpenSSL 3.5 library released in April 2025 [10]. It differs from other evaluations, which have typically concentrated on time performance rather than energy performance, e.g., 'openssl speed' to give tables such as in [11] and from other work in the energy recording area such as [PERSON] et al. [12]. It creates a framework for the readily available Raspberry Pi and makes use of accurate, commodity USB energy meters.  
The Raspberry Pi is a popular single-board computer platform increasingly utilised in industrial process applications [13], where controllers are typically embedded with long operational lifetimes and where energy constraints can be a defining factor in design and implementation.
## Materials and Methods
### Methodologies
#### 2.2.3 Fourier Transform Infrared (FTIR) Spectrometer  
FTIR is a common method used by organic chemistry to identify the various functional groups, areas, and levels of the peak in a single chemical compound. The FTIR graph illustrates absorbance/transmittance versus the wave number. In this respect, the \(y\)-axis shows absorbance ranging from 0-100% and the \(x\)-axis represents a wave number ranging from 500 cm\({}^{-1}\)-4000 cm\({}^{-1}\) (\(>\)1500 cm\({}^{-1}\) called functional group region and \(<\)1500 cm\({}^{-1}\) fingerprint region). The peak is classified as strong, moderate, and weak [34, 35].  
FTIR spectrometer analysis was carried out on the BB, molasses, and MMBs using Spectrum Two (Waltham, MA, USA) equipment. The functional group and area of the peak were determined, and the fully absorbed and decreased indexes were detected.  
The FTIR spectrometer test was performed to evaluate the chemical structure of the aged binder samples before and after the addition of molasses. This study uses parameters such as the carbonyl index(I\({}_{\text{C=O}}\)) and the sulphoxide index (I\({}_{\text{S=O}}\)) to characterize the oxidation of the binder, and quantitative analysis can be performed for the two indexes using Equations(1) and (2) [34, 35, 36].  
\[I_{\text{C=O}}=\frac{Areaofthecarbonylbondaround1700\ cm^{-1}}{Areaofthespectralbandbetween2000\ cm^{-1}} \tag{1}\]  
\[I_{\text{S=O}}=\frac{Areaofthesulfonyloxidebondaround1030\ cm^{-1}}{Areaofthespectralbandbetween2000\ cm^{-1}and600\ cm^{-1}} \tag{2}\]  
Figure 3: Modified bio-binder (MMB) preparation.
## Nomenclature  
### A. Acronyms  
UPI  
Coal-fired Unit with Plasma Ignition  
UOI  
Coal-fired Unit with Oil Injection  
RPS  
Regular Peak Shaving  
DPSW  
Deep Peak Shaving Without combustion supporting  
DPSC  
Deep Peak Shaving with Combustion supporting  
SHWC  
Safety of Hydrodynamic Work Conditions  
_B. Indices and Sets_  
\(i,N\)  
Index and total number of coal-fired units  
\(j\)  
Index for power-flow interface  
\(t,T\)  
Index and total number of time interval  
\(k,\Omega_{j}\)  
Index and Set of tie line in power-flow interface \(j\)  
_C. Parameters_  
\(a,b\) and \(c\)  
Coefficient of coal consumption cost  
\(\tau\)  
Coefficient of low-cycle fatigue life  
\(P_{i,t}^{b}\)  
Operating power of the burner  
\(W_{i,t}^{u-c}\)  
Anode- cathode cost of unit \(i\) during period \(t\)  
\(\delta_{s},\delta_{N}\)  
Sulfur content and nitrogen content of the coal  
\(W_{s}\), \(W_{N}\)  
Charge for SO\({}_{2}\) and NOx emissions  
coefficient  
Penalty coefficient of wind power curtailment  
\(S_{oil}\), \(S_{coal}\)  
Oil price and coal price  
Electricity price of power plants;  
\(S_{inp}\), \(S_{idown}\)  
Shut-up and shut-down cost of unit \(i\)  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Unit number** & \(\mathbf{a\) (\$/MWh^{2})}\) & \(\mathbf{b\) (\$/MWh)}\) & \(\mathbf{c\) (\$) (\$) & \(\mathbf{P_{a}\) (MW)}\) & \(S_{ipp}\) (\$) & \(S_{iddewn}\) (\$) & \(\mathbf{P_{a}\) (MW)}\) & \(\mathbf{P_{b}\) (MW)}\) & \(\mathbf{P_{c}\) (MW)}\) \\ \hline U 1 & 0.00048 & 16.19 & 1000 & 455 & 4500 & 4500 & 227.5 & 204.75 & 136.5 \\ U 2 & 0.00031 & 17.26 & 970 & 455 & 5000 & 5000 & 227.5 & 204.75 & 136.5 \\ U 3 & 0.002 & 16.6 & 700 & 130 & 550 & 550 & 65 & 58.5 & 39 \\ U 4 & 0.00211 & 16.5 & 680 & 130 & 560 & 560 & 65 & 58.5 & 39 \\ U 5 & 0.00398 & 19.7 & 450 & 162 & 900 & 900 & 81 & 72.9 & 48.6 \\ U 6 & 0.00712 & 22.26 & 370 & 80 & 170 & 170 & 40 & 36 & 24 \\ U 7 & 0.00079 & 27.74 & 480 & 85 & 260 & 260 & 42.5 & 38.25 & 25.5 \\ U 8 & 0.00413 & 25.92 & 660 & 55 & 30 & 30 & 27.5 & 24.75 & 16.5 \\ U 9 & 0.00222 & 27.27 & 665 & 55 & 30 & 30 & 27.5 & 24.75 & 16.5 \\ U 10 & 0.00173 & 27.79 & 670 & 55 & 30 & 30 & 27.5 & 24.75 & 16.5 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Quantily oil consumption of UOI during deep peak shaving with combustion supporting (DPSC).
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 2 Introduction to Performance Assessment and Fault Warning Methods  
### Steady-State Interval Identification Algorithm  
Ships operate under various navigation states, such as acceleration, deceleration, and stoppage, resulting in the collection of substantial non-steady-state data by sensors. These transient datasets introduce significant interference in the performance evaluation of marine diesel main engines. The SSD (slope sign detection) steady-state detection algorithm, which requires minimal manual parameter configuration and demonstrates strong capability in multi-variable steady-state detection, is particularly suitable for identifying the stable operating ranges of marine diesel main engines [11]. The fundamental principle of the SSD algorithm is as follows:The SSD algorithm is based on the assumption that the system exhibits dynamic drift. The univariate process signal \(y(t)\) of the system is constructed by multiplying the relative time by a non-zero slope, with the mathematical expression formulated as follows:  
\[y_{t}=mt+\mu+a_{t} \tag{1}\]  
In the equation, \(mt\) represents the dynamic drift component; \(t\) denotes the cycle index; \(\mu\) is the mean under the assumed steady-state condition, whose value equals the arithmetic mean or sample mean of the window when the slope is zero; \(a_{t}\) corresponds to a white noise or random error sequence following a normal distribution (0,\(\sigma_{a}\)).  
The difference between the signal \(y_{t-1}\) at the previous time step and the signal \(y_{t}\) at the current time step is  
\[m=\frac{1}{n}\sum_{i=1}^{n}\left(y_{t}-y_{t-1}\right) \tag{2}\]  
The steady-state judgment criterion is constructed using Student's \(t\)-test, with the mathematical expression formulated as follows:  
\[\left|y_{t}-\mu\right|\leq t_{crit}\sigma_{a} \tag{3}\]  
In the equation, \(t_{crit}\) is the critical value of Student's \(t\)-test, determined from statistical tables based on the significance level \(a\) and degrees of freedom v; when the significance level is set to 0.05, it implies a 5% Type I error rate (i.e., 5 non-steady-state data points out of 100 tested); \(\sigma_{a}\) represents the standard deviation of the window sample, calculated as follows:  
\[\sigma_{a}=\sqrt{\frac{1}{n-2}\sum_{t=1}^{n}\left(x_{t}-mt-\mu\right)^{2}} \tag{4}\]
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 6 Conclusions  
This systematic review and meta-analysis indicate that machine learning models, particularly Random Forest and Logistic Regression, hold considerable potential for predicting mortality risk in hemodialysis patients. While some studies reported high discriminatory power, notably Random Forest achieving AUCs up to 0.89 in long-term follow-up, our meta-analysis focused on one-year mortality found no statistically significant difference in pooled AUC between Random Forest and Logistic Regression.  
Clinically, these findings suggest that ML tools can supplement existing methods for risk stratification, potentially enabling earlier identification of high-risk individuals who may benefit from tailored interventions and enhanced monitoring. However, the significant heterogeneity observed across studies--stemming from diverse populations, time periods, included features, ML methodologies, and reporting standards--currently limits definitive conclusions about the superiority of any single model and hinders the generalizability of findings.  
To advance the field and facilitate reliable clinical implementation, future research should prioritize conducting large-scale, multi-center prospective studies using standardized data collection protocols, including common data elements, harmonized laboratory techniques, and consistent follow-up schedules, to minimize temporal and methodological biases. In addition, performing rigorous head-to-head comparisons of different ML models trained and validated on these shared, standardized datasets.  
Conceptualization, A.C.M. and A.M.; methodology, A.C.M. and N.O.; software, A.C.; validation, A.C.M., N.O. and L.M.; formal analysis, N.O.; investigation, L.M. and L.C.; resources, G.N.P.; data curation, V.M.I.; writing--original draft preparation, A.C.M.; writing--review and editing, A.M., A.C.M.C.J. and A.A.; visualization, A.C.; supervision, A.S.; project administration, A.M.; funding acquisition, G.N.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** We would like to acknowledge \"Victor Babes\" University of Medicine and Pharmacy Timisoara for their support in covering the costs of publication for this research paper.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 2 Methodology of the Multi-Classification Multi-Leak Identification (MC-MLI) Scheme
### The Adaptive Kernel Design for MC-MLI
The feature vectors lying on the hyperplanes are defined as support vectors. The gap between the support vectors is defined as a margin. A larger separation distance of the margin will increase classification accuracy because the data for other classes are less likely to cross the large-distance margin. The high leak identification accuracy will be achieved by solving the maximizing margin problem. The customized optimization problem is formulated as follows:  
\[ \begin{split} L(\alpha,w_{n})=\text{argmax}\left\{\sum\limits_{i= 1}^{L}\alpha_{i}-\frac{1}{2}\sum\limits_{i=1}^{L}\sum\limits_{j=1}^{L}\alpha_ {i}\alpha_{j}y_{i}y_{j}K_{a}\left(x_{i},x_{j}\right)\right\}\\ s.t.\left\{\begin{array}{c}\alpha_{i}\geq 0\\ \sum\limits_{i=1}^{L}\alpha_{i}y_{i}=1\\ \sum\limits_{n=1}^{M}w_{n}=1\end{array}\right.\quad\forall i=1,2,\ldots,L\\ \end{split}  \tag{6}\]  
where \(\alpha\) denotes the Lagrange multiplier and \(K_{a}\left(x_{i},x_{j}\right)\) is the designed adaptive kernel.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 1 Introduction
### Research Gap and Objective  
This paper closes the research gap by embedding both tooth-resolved flank measurements and the full pitch-deviation map into a single MBD model built in MSC Adams View, as shown in Figure 1. The following three simulation scenarios are compared:  
1. Ideal geometry;
2. Measured microgeometry only;
3. Pitch error only.
## 3 Performance Evaluation
### Acquisition Performance
#### 3.2.2 Acquisition Complexity
The results illustrate that the acquisition complexity of pulsed signals is significantly lower than that of continuous signals, but the decrease in complexity is not linear with \(N\) because the Doppler search range is fixed, and the number of Doppler frequency searches tends to be constant when \(N\) is large. Commonly, \(1/N\) is not greater than 0.1; then, substituting \(N=10\) into Table 2 yields the number of multiplications and additions of the pulsed signals. In this case, the number of multiplications and additions for Pulse-OTFS and Pulse-BPSK signal acquisition is reduced by at least 89.4% compared to OTFS and BPSK signals. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signals** & **Pulse-OTFS** & **Pulse-BPSK** & **OTFS** & **BPSK** \\ \hline Multiplication & \((160/N+1)\Delta_{m}\) & \((160/N+1)\Delta_{m}\) & \(161\Delta_{m}\) & \(161\Delta_{m}\) \\ Addition & \((160/N+1)\Delta_{a}\) & \((160/N+1)\Delta_{a}\) & \(161\Delta_{a}\) & \(161\Delta_{a}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acquisition complexity for different signals. Figure 7: Multiplication and addition complexity ratio of Pulse-OTFS/Pulse-BPSK to OTFS/BPSK.
## 5 Results and Discussion  
We trained the MLP classifier for 120 epochs by recording accuracy and loss at each step. The training process demonstrated strong convergence, with accuracy increasing from 90.55% in Epoch 1 to 99.12% in Epoch 120, as shown in Figure 2. The model learned efficiently, with rapid improvement in early epochs, and maintained stable optimization in later stages. The training loss, as represented in Figure 3, decreased consistently. It started at 0.6300 and reached 0.0318, which confirms that the model effectively minimizes errors. The smooth decline in loss and the low gap between training accuracy (99.12%) and test accuracy (97.36%) indicate that the model generalizes well without overfitting.  
To evaluate the model's robustness, three cybersecurity attacks were simulated, which included the adversarial FGSM with a perturbation magnitude of \(\epsilon=0.1\), data poisoning (random label flipping of 10% of training labels) and adversarial evasion using Gaussian noise addition with a variance of \(\sigma^{2}=0.05\). The model's performance was quantified using accuracy, confusion matrices and the PAVI to understand and mitigate vulnerabilities.  
Figure 1: A high-level workflow of the HAVA.  
Figure 2: Training accuracy over epochs.
## 6 Results and Discussion
### Other Methods Considered for Artifact Removal  
During the initial stage of our study, we also considered the use of other commonly applied EEG artifact removal methods, such as adaptive filters--least mean squares (LMS) [26] and recursive least squares (RLS) [27]--as well as artifact subspace reconstruction (ASR) [15].  
Adaptive filters such as LMS and RLS are classical approaches used to remove artifacts based on artifact indicator channels--in our case, additional EMG channels. Their main advantage is the ability to dynamically adjust filtering parameters in real time, which theoretically allows for the effective suppression of artifacts with minimal interference to the EEG signal. In our experiments, both LMS and RLS methods enabled a partial reduction of muscle-related artifacts. However, the performance of these filters was highly sensitive to the selection of adaptation parameters--such as the step size in LMS or the forgetting factor in RLS. In many cases, these parameters proved to be very sensitive: overly aggressive settings led to an excessive attenuation of relevant EEG components, while overly lenient settings failed to sufficiently remove the artifacts. Notably, the visual inspection of the EEG signals processed using these filters revealed that in many cases, the filters substantially altered the signal structure--not only in the artifact-contaminated segments but also in those that were potentially clean.  
In contrast, ASR is a statistical method that detects and suppresses short-term, atypical deviations from a reference EEG template, treating them as potential artifacts. It can operate in both data rejection and reconstruction modes, making it theoretically comparable to methods that aim to restore the original signal. Nevertheless, we decided not to include ASR in the comparative analysis for several reasons. First, ASR does not utilize direct information about the sources of muscle artifacts. Its operation is based on a statistical model of \"clean\" EEG and component variance analysis, without the ability to explicitly identify and separate muscle activity as an independent source. This contradicts our core assumption that effective EEG signal reconstruction should, as far as possible, leverage information from EMG channels. Moreover, we conducted tests using ASR on our dataset. As with the adaptive filters, the visual assessment of the EEG signals processed with ASR showed significant signal modifications, particularly in segments with artifacts. We observed waveform smoothing, amplitude changes, and waveform distortions, all of which could negatively affect subsequent analyses. Furthermore, ASR's effectiveness was clearly subject-dependent, and even within the same recording session, there were instances where some artifacts were either insufficiently removed or removed too aggressively, resulting in undesirable signal distortions. For these reasons, despite ASR's advantages in general EEG denoising applications, it was not included in our comparative analysis, which focused on reconstructing EEG signals contaminated with EMG artifacts while preserving their natural structure.