esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Based Prediction of the Displacement of DCFP Bearings
## 6 Proposed Prediction Method
### Application of the Prediction Method in Design  
In order to give a general prediction of the maximum response displacement change and input energy of an isolation layer under a group of earthquakes with a certain intensity, the specific earthquake characteristics, such as \(\Delta vg_{i}\) and \(kg_{i}\), should be replaced. Therefore, Equation (29) is proposed, where \(\zeta\) is the generalization parameter of the ground velocity. As shown in Figure 25, based on the earthquake records, \(|\Delta vg_{i}|\) was assumed to be 1.5 times the peak ground velocity (PGV) in this study.  
\(kg_{i}\) was assumed based on the average value of \(kg_{i}\) of the eight selected earthquakes with PGVs amplified to 250 mm/s, which was 930 mm/s:  
\[kg_{i}=930\times\frac{PGV}{250} \tag{30}\]  
Therefore, the prediction method can be generalized as:  
\[V_{\Delta E_{i}}-pre=\sqrt{1-\frac{\mu_{n}\times g}{930}}\times\zeta\times PGV \tag{31}\]  
\[V_{\Delta Ehr_{i}}-pre=V_{\Delta E_{i}}-pre\times\sqrt{\frac{(1+\varepsilon) \times\mu_{n}\times g}{\mu_{n}\times g+\varepsilon\times 930\times\frac{PGV}{250}}} \tag{32}\]  
\[\Delta y_{i}-pre=\frac{V_{\Delta Ehr_{i}}-pre^{2}}{2\times\mu_{n}\times g} \tag{33}\]  
where \(\zeta=1.5\) and \(\varepsilon=0.25\).  
The accuracies are shown in Figures 26 and 27:  
Figure 25: Relationship between \(|\Delta vg|\) and PGV.  
In Figures 26 and 27, the data points show the analysis results for the maximum \(V_{\Delta Ei}\), \(V_{\Delta Elnri}\) and \(\Delta y_{i}\) under various earthquake inputs with their PGVs amplified to 250, 500, 750 and 1000 mm/s; the dashed line shows the prediction result under earthquake groups with different PGVs. The prediction results are a little above the average because of the selected value of the generalization parameter \(\zeta\) and \(\varepsilon\).  
It should be mentioned that the prediction results would be made more conservative by enlarging the generalization parameter \(\zeta\) in Equation (31) and by decreasing the generalization parameter \(\varepsilon\) in Equation (32). By doing this, the maximum response displacement (the maximum \(\Delta y_{i}\) can be considered as the conservative value of \(y_{max}\), as shown in Figures 11 and 27) and the maximum input energy of the isolation layer under all the earthquake inputs with same intensity could also be roughly predicted.
## 3 Results and Methodology
Yet, it might be known in a simulated environment. Figure 5 shows the evolution of the mean squared error of the predictions over training steps measured in units of \(\lambda_{0}^{2}\). A straight line is plotted at the threshold \(rms=50\,\mathrm{nm}\). Below this value, the intersection is known to have a Strehl ratio greater than 0.8 [21]. Eventually, the agent gets to align the intersection with an accuracy of \(0,00082\lambda_{0}^{2}\) on both capture ranges \(\pm\lambda_{0}/2\) and \(\pm\lambda_{0}/4\). The two graphs shown in Figures 4 and 5 are somehow related. Getting higher reward means in general a better estimation of the piston misalignment. The graphs have been smoothed out with a moving average over the last five steps. Figure 4: Normalized maximum of the PSF reached by the algorithm during training. Capture range of \([-\lambda_{0}/2,+\lambda_{0}/2]\) in continuous line and \([-\lambda_{0}/4,+\lambda_{0}/4]\) in dashed line. Every step requires a real piston movement in the telescope, so the number of them needed by the agent to learn the task is an important aspect to consider. In that sense, the agent takes fewer steps to learn the task for the capture range \(\pm\lambda_{0}/4\). A long exposure image is the result of a large number of atmospheric perturbation realizations. It is necessary to use an exposure time much larger than \(\tau_{c}=10\,\mathrm{ms}\), depending on wind velocity, in order to capture the time averaging effect of the atmosphere. An exposition time of 2 s per image sets a lower bound duration of \(11.1\,\mathrm{h}\) to reach \(rms=50\,\mathrm{nm}\) in capture range \([-\lambda_{0}/2,+\lambda_{0}/2]\). Additionally, a lower bound duration of \(1.1\,\mathrm{h}\) is set for the agent to reach the same rms value in capture range \([-\lambda_{0}/4,+\lambda_{0}/4]\). The training though can be carried out in parallel at several intersections simultaneously. There are 10 of them to train on in a 36 segment mirror telescope. It makes the previous training times decrease to \(1.1\,\mathrm{h}\) and \(0.1\,\mathrm{h}\) respectively.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 5 Results
### Macro-\(F_{1}\) Score as an Evaluation Metric  
It is worth highlighting the role of the macro-\(F_{1}\) metric in our evaluation, along with its strengths and limitations. We employ macro-averaged \(F_{1}\) to fairly assess performance under class imbalance (as present in both UNSW-NB15 and PneumoniaMNIST) and client imbalance (varying class distributions across different clients) conditions. Macro-\(F_{1}\) computes the \(F_{1}\) score independently for each class and then averages them, giving equal importance to all classes regardless of their frequency. This makes it a fair metric when some attack types or medical classes are under-represented: a high macro-\(F_{1}\) score indicates that the model performs well across all classes, not just the majority. In our results, the substantial gains in macro-\(F_{1}\) achieved by ERFO (compared to accuracy gains) confirm that our method is improving minority-class predictions--a critical aspect in both security and healthcare contexts.  
In summary, ERFO demonstrates consistently strong performance across both the UNSW-NB15 intrusion detection and the PneumoniaMNIST pneumonia classification tasks. It achieves superior or on-par accuracy and macro-\(F_{1}\) scores in comparison to state-of-the-art baselines, indicating that our approach generalizes well to different domains. This consistency across a network security dataset and a medical imaging dataset underlines ERFO's effectiveness and versatility in tackling diverse federated learning challenges.
## 2 Related Work
### Text Line Detection  
The goal of text line detection is to extract individual instances of text elements or lines from the document images. This task can be approached from different angles, e.g., as a pixel-labeling task with additional postprocessing or as an object detection task featuring deep neural networks such as Mask-RCNN.  
In general, the use of deep neural network techniques has reported outstanding results in many areas related to computer vision, including text line recognition. [PERSON] et al. treated this problem as a segmentation task [14]. Their pipeline used a spatial-attention-based network architecture to achieve a pixel-level segmentation of the image containing the baselines of the text lines. Afterward, they applied a complex postprocessing algorithm to generate text lines from the pixel-level segmentation. In [15], the baseline detection was extended by introducing baseline primitives and applying a relation network to further improve the results.  
Alternatively, morphological and visual features can be utilized to detect text lines.  
[PERSON] et al. [16] present a non-ML approach using a connected component analysis and significant postprocessing of the detected contours to recognize text lines in handwritten and printed historical documents. [PERSON] et al. [17] presented a method utilizing an FCN to detect text elements in a document image. In addition, they utilized a connected-component-based postprocessing to merge text lines, based on the output of the FCN.  
Alternatively, the challenge can be approached as an object detection task. [PERSON] et al. [18] used a Mask-RCNN [19] to detect the text line instances in historical documents. Theydivided the page into patches and, afterward, used a postprocessing algorithm, which combined the output. Afterwards, the text lines were extracted.  
[PERSON] [20] proposed a segmentation-free hand writing recognition system based on multi-dimensional long short-term memory recurrent neural networks. The method requires a rough layout analysis in advance, as it was applied to paragraph images. A separate line segmentation step was then no longer necessary, because this was implicitly performed within the network, and the network then directly provided an OCR output.  
Most-commonly used OCR systems, such as [21], require images for individual text lines. Therefore, it is either relevant for further processing of the document images to perform postprocessing on a segmentation or to detect text lines directly in the images. Baseline methods such as [14] deliver good results, but need additional postprocessing steps to generate the required polygons describing individual lines from the baselines.
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 4 Datasets
### Tide Gauge Data
In other words, in \(DT_{TG\in SA\ cycle}\)- and \(DT_{SA\in\mathbb{GT}}\)-based sea-level trend estimates, different DT values may participate; such discrepancies are addressed later, in Section 5.3.2. It is important to note that there might be a gap in the TG precisely at \(t_{cycle}\). However, employing \(DT_{TG\in SA\ cycle}\) allows us to effectively represent the DT derived from TG, considering the relatively low fluctuations in DT within the \(w\) time frame. Figure 5: TG stations used in this study: (**a**) VLM rate [mm/year] over the Baltic Sea region according to the NKG2016 LU model [75]. The geoid changes (rise over the Baltic Sea) [mm/yr] are also shown with red isolines. The numbered triangles denote the location of TG stations used in this study. (**b**) TG (blue) and SA (green) monthly data availability during the study period at each TG station (TG monthly averaged data are used for illustration). Gaps of data are illustrated in different colours (TG data gaps in light blue and SA data gaps in red). The black dots represent the TG sensor installation events (full details in Table 1). Note that, during the study period, four TG stations had significant data gaps (cf. Table 2) which may negatively affect the TG-derived sea-level trend estimates. For instance, for the Dirhami (#1) station, sea-level data were visually captured twice a day up to end of 2009; for the entire year 2010, there were no data record at all, whereas the hourly TG data records started from 2011. The monthly averaged TG data enable the detection of longer data gaps in the TG and SA time series; see Figure 5b, which may be different from the instantaneous data gap (mentioned in Table 1). In this study, the longest data gap (Dirhami) reaches up to one year.
## 4 Meta-Modeling
### Analysis of Analytical Results  
Analysis of variance (ANOVA) was conducted for the analytical results according to the variable conditions. Secondary interactions for the inner diameter were confirmed, and terms with low significance were pooled as error terms. \(\mathbf{b}_{2}\), \(d_{0}\), and \(d_{1}\) were identified as geometry variables with a significant impact; among these, the inner diameter demonstrated the largest influence. The regression Equation (10) was derived from this. Table 6 shows the ANOVA results.  
Excluding terms with low significance, the regression equation was derived, as in Equation (10). The R-sq and R-sq (adj) of the model were 98.01% and 97.21%, respectively, showing its high explanatory power. Figure 15 shows the work results through the response surface. Figure 15 shows the change in work for the length (\(b_{2}\)) and outer diameter (\(d_{0}\)) geometry variables; Figure 15, the length (\(b_{2}\)) and outer diameter (\(d_{1}\)); and Figure 15, the change in work according to changes in outer diameter (\(d_{0}\)) and inner diameter (\(d_{1}\)). The response surface results demonstrate that work is influenced most by \(d_{1}\).  
\[R=3760+463b_{2}+359d_{0}-2183d_{1}+442d_{1}^{2} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DF** & **Adj SS** & **Adj MS** & **F-Value** & **\(p\)-Value** \\ \hline Model & 4 & 41,614,451 & 10,403,613 & 122.90 & 0.000 \\ Linear & 3 & 40,884,482 & 13,628,161 & 160.99 & 0.000 \\ \(b_{2}\) & 1 & 1,712,25
# Optical Camera Communication as an Enabling Technology for Microalgae Cultivation +
## 4 Methodology
### Experiments
#### 4.2.2 Outdoor Experiment  
This experiment aimed to evaluate how the presence of external light sources interferes with the signal received by the camera, considering both the light that is reflected on the container's transmission surface and the light that enters through the sides of the container. In this experiment, the camera node faced just one photobioreactor, which repeatedly transmitted a beacon packet through different biomass concentrations (from low to high). The selected microalgae species was _BEA 1286B Rhodosorus marinus_ (_R. marinus_) (brown algae) to evaluate the communication restrictions that imply the use of microorganisms with different absorption curves. The beacon signal is comprised five sequential pulses (green, red, blue, cold white, warm white) followed by a dark guard. The key parameters of this experiment are summarised in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & _Value_ \\ \hline Horizontal view angle (\(\theta\)) & 0\({}^{\circ}\) to 80\({}^{\circ}\) in steps of 10 degrees \\ Distance & 1.5 m \\ LEDs & Warm and cold white LEDs \\ Camera & PiCamera version 2 \\ \hline \hline \multicolumn{3}{c}{Microalgae} \\ \hline Genus & Arthospira \\ Species & _BEA 0007B Arthospira platensis_ \\ Biomass concentration (mg1\({}^{-1}\)) & 0 to 100 in steps of 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indoor experiment key parameters.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & _Value_ \\ \hline Optical signal & Beacon \\ Chip duration \(T_{\text{chip}}\) & 1/8400 \\ Distance (m) & 2 \\ \hline \multicolumn{3}{c}{Microalgae} \\ \hline Genus & Rhodosorus \\ Species & _BEA 1286B Rhodosorus marinus_ \\ Biomass concentration (mg/L) & (estimated) 75, 195, 430 \\ \hline \hline \multicolumn{3}{c}{Camera} \\ \hline Model & Mi 9T Pro \\ Image sensor & Sony IMX586 \\ Image resolution (px) & 4000 \(\times\) 3000 \\ Focal length (mm) & 4.8 mm \\ Aperture value & F/1.7 \\ ISO speed rating & 450, 490, 670 \\ Exposure time (\(\upmu\)s) \(t_{\text{exp}}\) & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Outdoor experiment key parameters.
# Defining Breast Tumor Location Using a Four-Element Wearable Circular UWB MIMO Antenna Array
## 2 The Proposed Antenna Design
### Antenna Elements
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Antenna Shape & & & & \\ \cline{2-5}  & 2 & 3.19 & 2.72 & 2.84 \\ \cline{2-5}  & 5.7 & 5.27 & 3.55 & 3.87 \\ \cline{2-5}  & - & 7.37 & 5.48 & 5.83 \\ \cline{2-5}  & 9.55 & 8.7 & 8.12 & 8.24 \\ \hline Bandwidth (GHz) & 1.68–2.5, 8.9–10.2 & 2.8–3.9, 4.54–11 & 2.4–10.6 & 2.57–12.6 \\ \hline Gain (dBi) & 3.7 & 7.06 & 4.74 & 5.49 \\ \cline{2-5}  & 11.8 & 11.8 & 9.04 & 9.87 \\ \cline{2-5}  & - & 13.3 & 11.14 & 11.9 \\ \cline{2-5}  & 12.4 & 14.55 & 14.5 & 14.7 \\ \hline \multirow{4}{*}{Directivity (dBi)} & 4.14 & 7.47 & 6.8 & 7.3 \\ \cline{2-5}  & 12.2 & 12.34 & 9.48 & 10.3 \\ \cline{2-5}  & - & 14.1 & 11.7 & 12.3 \\ \cline{2-5}  & 13.3 & 15.6 & 15.5 & 15.4 \\ \cline{2-5}  & 80.9 & 76.7 & 62.6 & 67.5 \\ \cline{2-5}  & & 68.3 & 76.8 & 76 \\ \cline{2-5}  & 58.7 & 51.7 & 87.6 & 79.2 \\ \cline{2-5}  & 70.2 & 46.8 & 43 & 43.3 \\ \hline \multirow{4}{*}{Radiation Efficiency (\%)} & 90.43 & 91 & 62.17 & 65.9 \\ \cline{2-5}  & 90 & 88.9 & 90.37 & 91.3 \\ \cline{2-5}  & - & 83.9 & 88.52 & 89.6 \\ \cline{2-5}  & 80.9 & 79.3 & 80.53 & 85.4 \\ \hline \multirow{4}{*}{Total Efficiency (\%)} & 85.7 & 90.12 & 61.89 & 65.8 \\ \cline{2-5}  & 81.7 & 88.4 & 90.19 & 90.2 \\ \cline{1-1} \cline{2-5}  & - & 83.8 & 88.47 & 89.6 \\ \cline{1-1} \cline{2-5}  & 80.8 & 79.3 & 80.37 & 85.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of the planned wearable UWB antenna’s developmental stages.
# Modal Processor Effects Inspired by [PERSON] Tonewheel Organs
## 4 Hammondizer Modal Processor Implementation
### Frequency Range  
The first step of adapting the modal reverberator to create the Hammondizer effect is to pick the mode frequencies which specify the heterodyning and modulating sinusoids \(\boldsymbol{\varphi}(t)\) and \(\boldsymbol{\psi}(t)\). The unique sound of the [PERSON] organ is largely due to the tonewheels being tuned to the 12-tone equal tempered scale. Here we discuss how to preserve this feature in the context of the Hammondizer audio effect.  
Since each mode of the modal reverberator is a narrow bandpass filter, a sufficient frequency density of modes is required to support typical wideband musical signals. In particular, unless each frequency component of the input is sufficiently close to a mode center, it may not contribute audibly to the output. For this reason, tuning the modal reverberator's frequencies to the 12-tone equal tempered scale used by the [PERSON] organ heavily attenuates the frequencies \"in the cracks\", producing an artificial sound (compare to composer [PERSON]'s \"Talking Piano\" [50]).  
To avoid this effect, we use many exponentially-spaced mode frequencies per semitone. Denoting the number of modes per semitone as \(S\), the tuning of each mode is  
\[f_{w}=f_{1}\,2^{w/(12\,S)}\,\mathrm{Hz} \tag{26}\]  
(cf. Equation (2)). \(S\) is chosen to satisfy two subjective constraints. As \(S\) gets larger, the computational cost of the modal processor grows. As \(S\) becomes small, the modal density decreases and produces an artificial sound. We found by experimentation that \(S=14\) is a good setting that balances these two constraints.  
Heterodyning and modulating sinusoids at constant frequencies are given by  
\[\varphi_{w}(t) =\exp\{-j\omega_{w}t\} \tag{27}\] \[\psi_{w}(t) =\exp\{+j\omega_{w}t\} \tag{28}\]  
Figure 6: One tonewheel processor in the Hammondizer.  
(cf. Equation (18)).  
The next step of adapting the modal reverberator to create the Hammondizer effect is to choose the range of mode frequencies. The range of the Hammond organ is C1 (\(\approx\)32.7 Hz) to F#7 (\(\approx\)5919.9 Hz). For simplicity, we set \(f_{1}=40\) Hz and let the modes range up seven octaves, up to \(f_{1177}=5120\) Hz; these modes are indexed by a tonewheel index \(w\in[1\cdots 1177]\). These round numbers correspond very closely to the range of the Hammond organ. Forty Hz corresponds to \(k\approx 3.5\) and 5120 Hz to \(k\approx 87.5\); therefore, this range technically cuts off \(\approx\)3 semitones from the top and bottom of the range of the [PERSON] organ tonewheel range. Nonetheless, it does not negatively affect the qualitative effect of the Hammondizer.
## 2 Materials and Methods  
### Hypercolorimetric Multispectral Imaging (HMI) and Ultraviolet Fluorescence Photography (UVF)  
Multispectral images and UVF photographs were acquired through Hypercolorimetric Multispectral Imaging (HMI), a portable system developed by the Italian company Profilocolore(r) (Rome, Italy) consisting of a Nikon (Nital SpA, Moncalieri Torino, Italy) D800 36 Mpx camera, modified to obtain full-range light (300 to 1000 nm), 3 filters named A (UV-Vis acquisition), B (Vis-NIR acquisition) and UV-IR cut (sandwiched with filter A for UVF acquisition), standard white patches and a color-checker positioned in the scene [21; 24; 25]. The illuminating system for multispectral imaging is based on modified flashes. The UVF image was acquired using two CR230B-HP 10W UV LED projectors, with peak emission at 365 nm, mounted at 45\({}^{\circ}\) with respect to the camera [26].  
The two acquired images, one with filter A and the other with filter B, are then calibrated through SpectraPick(r) (Version 1.2, created by Profilocolore, Rome, Italy) software, which produces seven high resolution spectral images in.tiff format from UV to NIR at 350, 450, 550, 650, 750, 850 and 950 nm and the RGB image. The calibration procedure allows for achieving a precision higher than 95% on the spectral reflectance images and a color error CIE \(\Delta\)E2000 less than 2. After the calibration step, the images are uploaded in the processing software, PickViewer(r) (Version 1.0, created by Profilocolore, Rome, Italy), that provides several diagnostic imaging and statistical tools to produce false color images, apply principal component analysis (PCA) to different spectral bands to enhance hidden information and map materials based on their spectral reflectance or colorimetric values.
## 5 Results and Discussion
### Feature ranking test  
Boxplots were estimated from data, with the aim of verifying both the asymmetric and the outlier values. Figure 12 shows that the boxplots from the data are very symmetric. The outliers are not representing a problem because they are an outcome of the variability from data acquisition as seen in the variance of Table 2. Therefore, the chi-square test used to determine feature ranking can be applied using Equation (9).  
Figure 13 shows the feature ranking tests, using descriptive statistics with its predictors such as variance, standard deviation, mean, and median; and Figure 14 shows the feature ranking tests using statistical modeling, across the generalized extreme value distribution, respectively, across its predictors such as shape, scale, and location. Please observe these interesting results that can be corroborated with Tables 2 and 3. Figure 13 shows that the median has a significant rank for all the diseases and defects studied and therefore can be used as a strong feature, while Figure 14 shows that the \(\mu\) parameter, related to the location, and the \(\kappa\) parameter, related to the shape, are strong features. Please note the interesting central tendency and location in both figures. These features are determinants for the learning stages of clas  
Figure 12: Boxplots of each class show a generalized symmetry, and no significant outliers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Albinism** & **Fungus-Rust** &
\begin{tabular}{c} **Over-** \\ **Riemens** \\ \end{tabular} & **Peduncle** & **Healthy** \\ \hline \multirow{2}{*}{Shape (\(\kappa\))} & 0.73 & 0.76 & 0.69 & 0.52 & 0.53 \\  & [0.71, 0.75] & [0.70, 0.82] & [0.65, 0.73] & [0.49, 0.55] & [0.51, 0.55] \\ \hline \multirow{2}{*}{Scale (\(\sigma\))} & 54.09 & 52.94 & 59.09 & 57.49 & 59.26 \\  & [52.77, 55.44] & [48.97, 57.24] & [56.45, 61.84] & [54.20, 60.99] & [57.61, 60.96] \\ \hline \multirow{2}{*}{Location (\(\mu\))} & 58.22 & 56.25 & 77.16 & 64.32 & 72.69 \\  & [57.07, 59.37] & [52.77, 59.73] & [74.47, 79.85] & [61.31, 67.32] & [71.02, 74.36] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis using the generalized extreme value distribution. The table shows the average thresholds and the learning bounds of each class.