esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Segmentation of the Air Region with Threshold Method (TLSM Approach)  
In the following tables, the highest values for the F-measure median are reported. Table 2 contains information of transverse images and Table 3 contains information of sagittal images. The maximum is shown in bold at the top of the second column. Only the groups with different values for \(T\_air\) and no significant difference (\(p>0.05\)) are reported in the subsequent lines. The values for the F-measure medians are shown along with the values for quartile 1 (Q1), quartile 3 (Q3) and Interquartile Range (IQR).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Region of Interest**} & \multirow{2}{*}{**Segmentation**} & **Parameter to** & **Name of the** & **Values Used for the** \\  & & **Method** & **Involve in the** & **Parameter in the** & **Parameter in the Parametric** \\  & & **Parametric Study** & **Parametric Study** & **Study (Nominal Variables)** \\ \hline Air & Threshold & \(T\) (threshold) & \(T\_air\) & 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, \\  & & & & 0.09 and 0.095 (in a grayscale of 256 levels between 0 and 1). \\  & Geodesic Active & \(c\) (constant & \(c\_gelpad\) & 0, \(-0.15\), \(-0.3\), \(-0.6\), \(-0.9\), \(-1.2\), \\ Gel-Pad & Contours (GAC) & velocity) & \(-1.5\), \(-1.8\), \(-2.1\) and \(-2.4\) \\  & & \(u_{0}\) (initial contour) & \(u\_0\) & \(W-i\) = \(i\) where \\  & Distance Regularized & \(\alpha\) (weighted area & \(0\), \(-0.5\), \(-1\), \(-2\), \(-3\), \(-4\), \(-5\), \(-6\), \\  & Level Set Evolution & coefficient) & \(a\_gelpad\) & \(-7\) and \(-8\) \\  & (DRISE) & \(\phi_{0}\) (initial contour) & \(phi\_0\) & \(W-i\) and \(W_{i}-i\) where \\ Tissue & - & - & - & \(i\) \(\in\mathbb{Z}:i\in[25,32]\) \\  & Geodesic Active & \(c\) (constant & \(c\_transducer\) & \(0\), \(-0.15\), \(-0.3\), \(-0.6\), \(-0.9\), \(-1.2\), \\ Transducer and & Contours (GAC) & velocity) & \(c\_transducer & \(-1.5\), \(-1.8\), \(-2.1\) and \(-2.4\) \\ Water 1 & Distance Regularized & \(\alpha\) (weighted area & \(0\), \(-0.5\), \(-1\), \(-2\), \(-3\), \(-4\), \(-5\), \(-6\), \\  & Level Set Evolution & coefficient) & \(a\_transducer & \(-7\) and \(-8\) \\  & (DRISE) & Threshold & \(T\) (threshold) 2 & \(T_{H\_transducer}\) & \(2,3,4,5,6,7,8\), and \(9\) \\ \hline \hline \end{tabular}  
* The initial contour, for both the GAC and DRISE methods, was given by the binary step function defined in Equation (10), so this parameter was not varied. Therefore, it was not considered in the parametric study. 2 The threshold used for the segmentation of the transducer region can be chosen between nine defined options between the two maximal peaks in the histogram of the Figure 6b. Each defined option is a threshold number as shown in Figure 10a. The selected threshold number to perform segmentation is Tn_transducer. The background of the image with the segmented transducer is the segmented water region.  
\end{table}
Table 1: Segmentation of the regions of interest with threshold and level set methods and parameters to involve in the parametric study along with their values.
# Assessment of Mechanical Properties of Corroded Prestressing Strands
## 3 Tensile Test of Corroded Strands
### Tensile Test of Corroded Strands  
A tensile test was conducted as shown in Figure 10a. The specimens were loaded to failure using the displacement control method with a speed of 5 mm/min. The two ends of the strand were held by the grip. Among the strands extracted from Bridges A and B, three non-corroded strands were defined as reference specimens for the 15.2 and 12.7 types. Table 1 lists the mechanical properties of the non-corroded strands as averages of the test results from the three specimens. Where, \(f_{py}\) is 0.2% proof stress of non-corroded strands, \(\varepsilon_{py}\) is strain corresponding to the 0.2% proof stress, \(f_{pu}\) and \(\varepsilon_{pu}\) are ultimate strength and strain of non-corroded strands, respectively.  
Strands with no or little corrosion showed failures of all the wires fracturing at the same moment, as shown in Figure 10b. For the corroded strands, shown in Figure 10c), the most corroded wire fractured earlier than the other wires. Owing to the fracture of the wire, the strand's strength suddenly decreased; however, the remaining wires maintained their structural capacity until the next wire was fractured, as shown in Figure 11a. Furthermore, the amount of load reduction between the moments of the first and second fractures was \(\frac{1}{f_{pu}}\), where \(f_{pu}\) is ultimate strength of a corroded strand. This means that the load was evenly carried by the seven wires, and the most corroded wire fractured earlier than the others. In this study, therefore, the failure event of the strand was defined as the moment at which even one wire was broken, and the corresponding strength and strain were used to define the ultimate strength and strain.  
Figure 9: Relation between wire-unit section loss of the most corroded wire and strand-unit section loss, considering the number of corroded wires.  
Figure 11: (**a**) Load–strain curve of a corroded strand and (**b**) samples from specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Strand**} & **Nominal** & **0.2\% Proof** & **Strain Corresponding** & **Ultimate** & **Ultimate** \\  & **Sectional Area** & **Stress,** & **to 0.2\% Proof Stress,** & **Strength,\(f_{pu}\)** & **Ultimate** \\  & **(mm\({}^{2}\))** & \(f_{py}\) **(MPa)** & \(\varepsilon_{py}\) & **(MPa)** & **Strain, \(\varepsilon_{pu}\)** \\ \hline
15.2 type & 138.7 & 1726 & 0.0099 & 1865 & 0.075 \\
12.7 type & 98.71 & 1135 & 0.0090 & 1867 & 0.083 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of the non-corroded strands.  
Figure 10: (**a**) Test setup, (**b**) failure of non-corroded strand, and (**c**) failure of corroded strand.  
Figure 11b shows samples of the test results from the test specimens. The elongation and maximum load due to the fracture showed significant differences among the specimens; however, the yield performances were similar. This is because even if one wire yields first due to a cross-sectional loss, the remaining wires carry the load until they yield.
# GPS Multipath Analysis Using Fresnel Zones
## 4 Multipath Analysis from a Spatially-Limited Reflector Using Fresnel Zones  
Theoretically, multipath effects can only occur if the reflecting surface meets the following two prerequisites (cf. Section 2.5):  
1. The reflecting surface needs to be smooth enough. That means, the Rayleigh criterion defined by Equation (19) is fulfilled.
2. The reflecting surface needs to be large enough. That means, the first Fresnel zone should completely be located on the reflecting surface.  
The aim of this section is to empirically analyze and assess these theoretical assumptions using the data from an appropriate field test in a controlled environment. The measurement concept and the data preparation are described in Section 4.1. Afterwards, in Section 4.2, the multipath analysis is presented, which is performed by comparing real SNR observations to simulations that are derived from the multipath propagation theory (cf. Section 2) and by relating the results to the location and size of the respective Fresnel zones.
## 5 Results on ML/DL-Based Modulation Recognition
### Discussion on ML/DL-Based MR Techniques in UWA Communication Systems
The computational intensity of the transformer architecture, particularly the multihead self-attention, might pose challenges for real-time implementation on resource-constrained UWA platforms. To automate and optimize model design, authors in [44] propose neural architecture search (NAS). It introduces a feature fusion method combining time-frequency and cyclic spectrum features with an attention mechanism to enhance phase-modulated signal recognition. Focusing on enhancing representational diversity through feature fusion, the work in [9] proposes a multi-scale feature fusion hybrid model (HM) for UWA communication signal modulation recognition. Their primary contribution is the integration of Gram angle field (GAF), Markov transition field (MTF), and recurrence plot to fuse time and frequency domain features into low-dimensional representations. This approach demonstrates superior recognition accuracy (94.31% in lake trials) as validated by comparative experiments. A potential shortcoming could be the computational overhead associated with generating and fusing these multiple feature representations which might impact real-time deployment in resource-constrained UWA systems. Finally, complementing deep learning with signal processing and channel estimation, [PERSON] et al. (2024) [45] propose a modulation classification method for non-cooperative UWA communication by integrating channel estimation to mitigate signal distortion caused by multipath fading and environmental noise. It leverages higher-order cumulants as features and employs various classifiers (e.g., SVM, GBDT, and XGBoost) to validate the approach, demonstrating improved recognition accuracy after signal restoration. The method is tested on both simulated and real-world datasets, showing robustness across different SNRconditions. The method's performance depends on the accuracy of channel estimation, which can be compromised by measurement errors. The reviewed studies present DL models like SCNet, R&CNN, and UMC-SCL to show high accuracy and low latency but often require large datasets and have complexity concerns. Signal-processing techniques using cumulants and bispectrum offer precision, though they may struggle under noisy conditions. Edge-focused approaches balance speed and efficiency but face limitations at low SNR. Advanced architectures like DCNs, transformer-based models, and NAS improve feature handling but add computational overhead. Hybrid models combining feature fusion and attention mechanisms perform well in real data. Overall, these methods reflect progress toward accurate adaptive modulation recognition, though real-time deployment and environmental generalization remain key challenges.
# Topic Scaling: A Joint Document Scaling-Topic Model Approach to Learn Time-Specific Topics
## 2 Method
### Measurement Model  
The measurement model of _Topic Scaling_ consists of a parametric estimation of document positions that infers a scale to cluster documents based on word frequency similarities over time. _Wordfish_[3] assumes word frequencies \(w_{ik}\) are drawn from a Poisson distribution, to estimate word effects \(\beta_{k}\) and a latent scale \(\psi_{i}\) as proxy of document positions.  
\[w_{ik}=\text{Poisson}(\lambda_{ik}) \tag{1}\]  
\[log(\lambda_{ik})=\alpha_{i}+\
u_{k}+\beta_{k}\times\psi_{i} \tag{2}\]  
where: \(w_{ik}\) is the count of the word \(k\) in document \(i\), \(\lambda_{ik}\) is the parameter of the Poisson distribution denoting its mean and variance. Parameters to be learned are: \(\alpha_{i}\) as a document-specific fixed effect, \(\
u_{k}\) a word-specific fixed effect, \(\beta_{k}\) is the relationship of word \(k\) to the latent document position and \(\psi_{i}\) is the latent position of document \(i\) or the measurement scale. The model is estimated via the Expectation Maximization algorithm, consisting of estimating word parameters (\(\
u\), \(\beta\)) and document parameters (\(\alpha\), \(\psi\)) alternatively, until reaching a convergence [3]. Variational inference (Monte Carlo-Markov Chains) with Bayesian priors could be used as well to estimate _Wordfish_ parameters. We notice that the main result of _Wordfish_(\(\psi\)) has an undetermined scale (direction) and needs to be restricted to identify the _Wordfish_ equation given in (2) [3].  
This parametric measurement allows a time series scale to be estimated solely based on the observed word frequencies. Although the model is prone to potential departures from the [PERSON] hypothesis of conditional independence (expected value being equal to the variance) [29], it remains a robust method for outliers in word usage [4], compared to Correspondence Analysis (CA) [6].
## 3 Machine Learning and Fault Diagnosis
### Ensemble Learning Algorithms  
#### 3.6.1 Decision Algorithm Tree (DT)  
The decision tree algorithm is a hierarchical representation of the data structure in the form of decision sequences in order to predict a result or a class [78; 113; 114]. Indeed, it allows the prediction of a target variable from other so-called explanatory variables (model). The principle of the DT algorithm consists of determining the best possible characteristic for a set of data; separating the data into subsets containing the values of the best characteristic [115]. It also allows you to recursively generate new decision trees using the subset of data created and make the decision when you can no longer classify the data [111]. In other words, the DT model has decision nodes that have several branches and are used to make decisions and leaf nodes that represent the result or class of these decisions [75]. Each node tests a condition on a variable and each of its child nodes corresponds to a possible outcome for that condition. The label or class of an observation is predicted by following the test results from root to leaf as shown in Figure 15. This model is simple to visualize and understand, requires little data preparation, uses categorical and numerical data and handles multi-class problems with robustness and resistance to noisy data [74]. However, it is a lazy algorithm with generally low modeling capacity and instability.  
The start node is called the root of the tree and the path from the root to the terminal node represents the classification rule [113]. Classification and decision tree regression trees are non-parametric algorithms whose regression output is numerical, while that of classification is a function of the input data used for training and testing [110]. This model is proposed to predict the output powers of photovoltaic and wind systems [113]. The simulation results demonstrate that the model is capable of correctly predicting dam classes. An approach using the decision tree algorithm made it possible to detect short-circuit faults and string faults in a photovoltaic installation connected to the grid at an accuracy rate of 99% [33].  
Figure 15: Structure of a DT classification model with six labeled classes (N, S, O, L, A, R).
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 2 Materials and Methods
### Alignment Camera  
Reference  
In order to compute evaluation metrics, the SpO\({}_{2}\) values from the camera and reference finger-probes needed to be aligned in time. The delay between both is a combination of nonlinear processing (reference values from black box algorithms with unknown (post-)processing and heuristics) and physiological delays (face versus finger). Therefore, we performed an individual synchronization of both data streams.  
Figure 6: The experimental setup comprises two armatures with incandescent light bulbs (blue) and four identical monochrome cameras (red) equipped with different optical filters (black) to obtain spectral selectivity. This information is used to calculate the values for the pulse signature vector (Equation (4)).  
Figure 7: The breathing protocol used for the creation of our dataset, 3.5 min deep breathing followed by 3.5 min shallow breathing, leads to an average SpO\({}_{2}\) reduction of 3% with short-term desaturations as low as 86%.  
First, the data from the finger-probes were resampled at the sampling rate of the camera. Hereafter, we subtracted the mean from both the camera and reference traces to be less susceptible to a possible bias, and determined the delay using cross-correlation resulting in \(22.6\pm 3.47\) s (mean \(\pm\) SD) for the dataset.
# COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
## 1 Introduction  
Coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has developed into a global pandemic that, as of 4 May 2020, is still in progress [1, 2]. Since first being identified in December 2019 in Wuhan, China [3], the ongoing outbreak has caused serious global socioeconomic turmoil [4]. As of 4 May 2020, more than 3.5 million cases of COVID-19 have been recorded in a total of 187 countries and regions resulting in more than 248,000 deaths, while more than 1.13 million people have recovered [5].  
In response, many countries have implemented measures such as self-isolation and social distancing in order to prevent further spread [6], consequently flattening the epidemic curve, which could prove crucial in maintaining health services to patients most in need of care either for COVID-19 or for other serious conditions [7].  
The ability to identify the rate at which the disease is spreading is crucial in the fight against the pandemic. Being aware of the level of spread at any given point in time has the potential to help governments in public health planning and policy-shaping in order to address the consequences of the pandemic [7, 8]. One way to achieve this is through accurate testing at a large scale. While testing methods for COVID-19 differ from country to country [9] with varying volume of people tested [10], as of 23 April 2020, no country had tested more than 13.4% of their population with the average over all affected countries being 1.3%. Meanwhile, antibody tests, although in development as of 6 April 2020, were not approved to be widely adopted [11, 12, 13]. Another way to be aware of the scale of thespread of the disease and therefore the timing of its peak is to be able to accurately estimate the the number of active cases at any given point in time.  
In this study, the use of statistical and machine learning-inspired time series methods was proposed to estimate the percentage of active cases with respect to the total population for the ten countries with the most active cases. More specifically, six different time series approaches, namely ARIMA [14], the Holt-Winters additive model (HWAAS) [15], TBAT [16], Facebook's Prophet [17], DeepAR [18] (as implemented in [19]) and N-Beats [20], were developed and then compared for each of the following countries: USA, UK, Italy, Spain, Russia, France, Turkey, Germany, Iran and Brazil. In terms of evaluation metrics, the root mean square error (RMSE) was employed to assess the performance of each time series model. The results indicate that, although there is not a one-size-fits-all approach when it comes to predicting active cases for different countries, ARIMA and TBAT demonstrate superior performance in seven out of ten countries, while achieving second best results in another two.
## 2 Energy Tracking Methodology
### Energy Tracking during Battery Charge/Discharge  
The energy used to charge the battery comes from the public grid and the PV power plant. As stated in the introduction, the possibility of monitoring and managing the origin of the energy to charge the EV has not been considered so far. The reason for this is the lack of the methodology of registering the origin of the energy coming out of the battery. Therefore, it is necessary to create a system that can monitor how much energy from renewable sources entered the battery, how much came out and finally, how much energy is charged to the car from renewable sources or the network.  
Figure 2 shows the principle of energy tracking and registration of energy origin. Blue circles indicate the energy of unknown origin (from the distribution network), and green indicates energy from renewable sources. When charging the battery (Figure 2a), the amounts of energy entering the battery are determined by the state of the network and the instantaneous power of both sources. It can be easily determined by solving power flows equations in the network. The maximal energy to which the battery can be charged is limited by the battery capacity.  
When discharging the battery, the situation is different (Figure 2b). The amount of  
Figure 1: Connection scheme of Photo-voltaic (PV)/battery energy system (BES)/electric vehicles (EV) charging system.  
energy emitted from a battery is determined by the relative ratio of available energy coming from different sources.  
Let us indicate the state of charge of the battery with \(SOC\), the amount of accumulated energy from renewable sources (solar) with \(SOC_{S}\), and the accumulated energy from the grid with \(SOC_{G}\). The battery power \(P_{B}\), the share of battery power from solar energy \(P_{BS}\) and the share of battery power from the grid \(P_{BG}\) will be positive if the battery is charging and negative if it is discharging. The charge and discharge behavior of the battery during the time step \(\Delta t\) can be generically modeled as follows:  
\[SOC_{t}=SOC_{t-1}+\Delta t\cdot k_{c/d}\cdot P_{B,t}, \tag{1}\]  
\[SOC_{S,t}=SOC_{S,t-1}+\Delta t\cdot k_{c/d}\cdot P_{BS,t}, \tag{2}\]  
\[SOC_{G,t}=SOC_{G,t-1}+\Delta t\cdot k_{c/d}\cdot P_{BG,t} \tag{3}\]  
\[SOC_{t}=SOC_{S,t}+SOC_{G,t}, \tag{4}\]  
In Equations (1)-(3), the charging/discharging efficiency factor \(k_{c/d}\) equals \(\eta_{c}\) when charging and \(1/\eta_{d}\) when discharging the battery. If the total power that discharges the battery is \(P_{B}\), then the power from renewable sources (\(P_{BS}\)) and the grid (\(P_{BG}\)) are given by Equations (5) and (6), respectively:  
\[P_{BS,t}=P_{B,t}\frac{SOC_{S,t}}{SOC_{t}}, \tag{5}\]  
\[P_{BG,t}=P_{B,t}\frac{SOC_{G,t}}{SOC_{t}}, \tag{6}\]