esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Cell Culture  
The BRAFV600E-mutated, human-derived, metastatic melanoma cell line 501 mel (CVCL_4633) was obtained from the University of California, San Francisco (a generous gift from Dr. [PERSON]). It was obtained under ethical guidelines and informed consent was signed [26]. As all information regarding the donating patient has been de-identified, the use of the line for this study is not considered Human Subjects Research by the National Institutes of Health. Cells were cultured in growth media consisting of 10% Fetal Bovine Serum (VWR, Radnor, PA, USA), 1% L-Glutamine (Gibco Thermo Fisher Scientific, Gaithersburg, MD, United States), 1% Non-essential Amino Acid supplement (Gibco Thermo Fisher Scientific, Gaithersburg, MD, United States), and 1% Penicillin-Streptomycin (Gibco Thermo Fisher Scientific, Gaithersburg, MD, United States) in RPMI-1640 medium (Gibco Thermo Fisher Scientific, Gaithersburg, MD, United States). The cells were passaged twice a week at a ratio of 1 to 10. The cells were kept in a standard 37 \({}^{\circ}\)C humidified mammalian cell incubator with 5% CO\({}_{2}\) (Thermo Fisher Scientific, Waltham, MA, United States).  
Preliminary dose-response experiments were conducted to determine the IC50 of each compound. Briefly, the cells were seeded in 6-well plates (Sarstedt, Numbrecht, Germany) at a density of 60 kcells per well in 3 mL of growth media. The cells were incubated for approximately 24 h after being seeded. Growth media was then aspirated and 3 mL fresh growth media supplemented with indicated concentrations of dimethyl sulfoxide (DMSO, MilliporeSigma, St. Louis, MO, United States), staurosporine (Tocris Bioscience, Minneapolis, MN, United States, Cat. No. 1285, 0.001-10.0 \(\upmu\)m in DMSO), erastin (Selleckchem, Houston, TX, United States, Cat. No. S7242, 0.1-30 \(\upmu\)m in DMSO), or shikonin (Selleckchem, Houston, TX, United States, Cat. No. S8279, 0.1-10.0 \(\upmu\)m in DMSO) was added. The standard plate lid was replaced with an optically clear Hololid (Phase Holographic Imaging, Lund, Sweden). After addition of the Hololid, plates were incubated for 45 min to eliminate condensation. The dose-response application in AppSuite (v2.1.2.0, Phase Holographic Imaging, Lund, Sweden) was then used to determine the IC50.  
For classification experiments, cells were cultured and treated as above, except that each plate contained a single well of each compound at the calculated IC50. The 6-well plate was then loaded onto the DHC platform and imaged via AppSuite. The parameters were set to 48 h, with images taken every 1 h from 20 random fields. The training and test sets were generated using independent passages, plates of cells, and freshly generated working solutions of each compound, and were conducted at different times. In two test set experiments, the erastin-treated well was lost due to technical reasons resulting in a sample size of two instead of four for this condition.
# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 1 Introduction
By using them as feature extractors, we generate embeddings that capture high-level representations of speech, allowing the identification of acoustic and paralinguistic features essential for emotion recognition. The objective is to systematically benchmark the performance of these PTMs using their hidden layers as feature extractors, specifically for emotion recognition in Spanish speech, thereby, a layer-wise and LOSO evaluation are introduced, contributing to a detailed understanding of the performance of various PTMs and their correlation with fine-tuning to the target language, while comparing with the SOTA of Spanish SER. The main contributions of this work are as follows:  
1. We present the first comprehensive benchmarking of PTMs for Spanish SER, covering six diverse databases and addressing the lack of evaluations in underrepresented languages. 2. We devise a robust experimental framework combining LOSO validation with layer-wise feature extraction, enabling accurate and interpretable comparisons across models. 3. Our proposed approach achieves superior results on multiple Spanish datasets, outperforming existing state-of-the-art baselines while highlighting the benefits of Spanish-focused fine-tuning. 4. We reveal how architectural depth and pretraining strategies influence emotional representations, guiding model selection in Spanish SER tasks through a detailed layer-wise and LOSO analysis. 5. We establish a reproducible benchmarking pipeline that advances research on feature extraction, generalization, and fine-tuning in Spanish SER, offering consistent metrics across diverse scenarios. To guide the reader through our methods and results, this paper is structured as follows: Section 2 describes related work, emphasizing previous studies in the Spanish language and recent approaches in SER with PTMs. Section 3 presents the databases used and the methodology applied, including feature extraction from different layers of PTMs, the classifiers used, and the validation schemes, both cross-validation and LOSO. Section 4 shows and analyzes the results obtained for each model and database. Section 5 then compares these findings with the SOTA, discussing strengths, limitations, and notable trends. Finally, Section 6 presents the main conclusions, limitations, and points out possible directions for future work.
# Unveiling the Core Patterns of High-Latitude Electron Density Distribution at Swarm Altitude
## 4 Results
The modes (labeled hereinafter by Roman numerical notation) and the residue are among them. The first mode (I mode), which is essentially noise, is the sum of the initial two modes. The second mode (II mode), a combination of the third to sixth initial modes, is associated with the satellite's orbit. The third (III) and fourth (IV) modes are direct results of the initial  
Figure 3: (**Left**) the MLT period of each mode reported in Figure 2 and (**Right**) the energy content of each mode shown in Figure 2 evaluated as the normalized variance. The horizontal dashed line indicates the energy value of the first mode, which is associated with noise. decomposition, and given their higher energy values represent the primary modes through which the original signal is decomposed. The same identical procedure was used for the electron density distributions during summer and winter seasons when the geomagnetic activity was low, and was repeated for the geomagnetically disturbed period. In all the analyzed cases, regardless of the season and the geomagnetic activity level, the original electron density distribution is consistently decomposed into eight modes along with the residue. In addition, during all the analyzed configuration, which are not shown here for brevity, the modes' average periods and energy distributions follow the same behaviour as the one obtained in the first study case, i.e., during the equinoctial period and low geomagnetic activity, as reported in Figure 3. This makes it clear that even in the other five configurations under analysis it was possible to distinguish three different classes of modes which contribute different amounts of energy  
Figure 4: Polar view of the fundamental modes and residue obtained from the Multivariate Empirical Mode Decomposition (MEMD) of electron density distribution during the equinoctial period under low geomagnetic activity in the Northern Hemisphere. Imode corresponds to the sum of the \(1^{\circ}\) and \(2^{\circ}\) modes reported in Figure 2, II mode corresponds to the sum of the \(3^{\circ}\), \(4^{\circ}\), \(5^{\circ}\), and \(6^{\circ}\) modes in Figure 2, and III and IV modes are equal to the \(7^{\circ}\) and \(8^{\circ}\) modes in Figure 2, respectively. Maps are represented in the QD–magnetic latitude (MLat) and MLT reference system. The concentric circles are plotted in \(10^{\circ}\) intervals, with the outermost circle corresponding to \(50^{\circ}\). to the original signal. This observation led us to use the same mode grouping approach described in detail previously during the equinoctial period of low geomagnetic activity. In this way, in each case we obtained four decomposed modes accompanied by their residue. The third and fourth modes and the residue obtained for each configuration of season and geomagnetic activity level are the ones that provide the more relevant contributions to the electron density description, and are shown and discussed in the following Section 5.
## 3 Result and Discussion
### Initial Detection Characteristics of Temperature and Mass Fraction of Combustion Products  
The mass fraction of the combustion products is defined as the mass of combustion products (kg) per unit mass (1 kg) of the mixture of fuel, air, and combustion products within a controlled volume [33]. In this study, the effects of the location of the ignition along the X-, Y-, and Z-axes on the initial detection timing of combustion products and the temperature were evaluated. The detection threshold was defined as the time at which the mass fraction reached 0.01 kg/kg and the temperature reached 351.15 K. This corresponded to the thermal detector activation criterion established by the FSS Code [43].  
Figure 10 presents the visualization results of the initial detection times. Along the X-axis, the ignition at the centre position (6.1 m) resulted in the shortest detection time for combustion products (recorded as 1.11 s at monitoring point MP11). In contrast, ignition near the sidewalls (0.1 m and 9.9 m) resulted in significantly delayed detection times of  
Figure 9: Flammable gas reduction rate base on ignition position. **(a)** Flammable gas reduction rate along the X-axis. **(b)** Flammable gas reduction rate along the Y-axis. **(c)** Flammable gas reduction rate along the Z-axis.  
2.03 s and 1.94 s at MP9 and MP10, respectively. The temperature detection exhibited a similar pattern, with the shortest response time being observed at the central ignition point and increased delays being seen as the ignition approached the walls. The Y-axis results demonstrate a comparable trend. Ignition at the centre (7.5 m) produced the shortest detection times of 0.36 s and 0.41 s for the combustion product mass and temperature at MP11, respectively. The slowest detection occurred when ignition occurred near the forward wall (14.9 m), with both indicators being recorded at 2.09 s at MP10. This implies that the distance from the spatial centre significantly affects the time required for heat and product diffusion to reach the monitoring points. For the Z-axis, the ignition near the ceiling (4.9 m) resulted in the earliest detection, with both the mass fraction and temperature reaching the threshold at 0.91 s at MP9. Conversely, ignition at the intermediate height (3.1 m) resulted in the longest detection times of 1.16 s and 1.14 s, respectively, at MP10. These results imply that ignition in the upper regions promotes upward diffusion through buoyant flow, and thus enables early detection. In contrast, mid-level ignition causes an asymmetrical spread owing to interference from the surrounding structures. This, in turn, resulted in detection delays.  
Figure 10: _Cont._  
Overall, the ignition positions closer to the spatial centre in the X- and Y-directions and to the upper boundary in the Z-direction facilitated the earliest detection of combustion indicators. These results verify that the spatial relationship between the ignition source and monitoring locations closely governs the initial detection characteristics of the combustion products and temperature.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 3
#### 3.3.5 Case 3: Analysis of 1,000,000-2,000,000 Frequency Range (Hz)  
The upper high-frequency range (1,000,000-2,000,000 Hz) is heavily affected by floating conductive parts, partial discharges, and loose clamps or connections (Table 7). These  
Figure 20: Case 3, 100,000–1,000,000 frequency range (Hz). (**a**) Six Sigma control chart and process capability. (**b**) Six Sigma range variation and upper control limits.  
defects cause severe capacitive instability, which manifests as sharp, erratic variations in the FRA signature.  
In Figure 21a, a high deviation appears between 1.6 MHz and 1.9 MHz, characterized by abrupt oscillations. This is a strong indication of floating metallic parts or unstable grounding paths.  
The \(C_{p}\) and \(C_{pk}\) indices, observed in Figure 21b, fall below 1.0, signaling that the process is now out of control (Table 4). Additionally, the range chart reflects irregular spikes, further reinforcing the likelihood of floating conductive elements disrupting capacitive stability.  
Figure 21: Case 3, 1,000,000–2,000,000 frequency range (Hz). (**a**) Six Sigma control chart and process capability. (**b**) Six Sigma range variation and upper control limits.  
Remarks: It should be noted that the transformer is at high risk of failure due to floating conductive parts and dielectric instability. Immediate corrective actions are needed to inspect and secure all internal conductive elements.  
The analysis of Case 3 reveals a generally stable transformer response with localized deviations across all frequency bands, suggesting progressive structural and dielectric deterioration. At low frequencies (10-1000 Hz), deviations indicate core movement or looseness, necessitating structural reinforcement to prevent further degradation. Moving into the mid-low frequency range (1000-10,000 Hz), the response highlights winding displacement and clamping loss, suggesting mechanical instability that requires close monitoring. In the mid-frequency band (10,000-100,000 Hz), significant axial and radial deformations are observed, likely due to insulation compression failure, necessitating corrective measures to restore stability. The high-frequency range (100,000-1,000,000 Hz) reveals early-stage insulation breakdown and grounding concerns, signaling potential dielectric integrity issues that demand urgent testing. At upper high frequencies (1,000,000-2,000,000 Hz), erratic variations strongly suggest floating conductive parts and capacitive instability, posing an immediate failure risk. Overall, while the transformer is not yet in catastrophic failure, the presence of progressive winding drift, insulation breakdown, and mechanical deformations necessitates immediate diagnostic assessment, corrective maintenance, and reinforced structural clamping to ensure long-term operational reliability.
## 3 Basic Theory
### Time Domain Analysis  
#### 3.2.1 Description  
The vibration signal generated during the clamping and unclamping of the toggle was collected by the accelerometer in the time domain. Time domain is defined as a collection of time-indexed data points collected over a historical time [19]. In practice, time domain signals are generally complex, as they capture a wide range of vibrational responses from other moving components in machine paired with Gaussian noise [20]. This high complexity makes it difficult for the raw signal to be directly used for condition monitoring. In order to derive a useful health indicator from the complex time signal, statistical features capable of describing the essence of the signal can be computed. Past literature has supported the root mean square (RMS) algorithm as a suitable approach for extracting signatures indicative of progressive failure [21].
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
###### Abstract  
Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.  
remote sensing; multispectral imaging; hyperspectral imaging; vegetation reflectance indices; water reflectance indices; pigment reflectance indices; photochemical reflectance index +
Footnote †: journal: _Photomics_
## 3 Results and Discussions
### UV-Visible Spectroscopy  
The absorption spectra of TiO\({}_{2}\) nanoparticles were measured by using UV-Visible spectroscopy. Figure 4 displays the UV-Vis absorption spectra of the TiO\({}_{2}\) nanoparticles in the range of 200-800 nm. The optical band gap was calculated by the Tauc plot method using Equation (2) [27]:  
\[ahv=A\big{(}hv-E_{g}\big{)}^{1/2} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **TiO\({}_{2}\)** \\ \hline Symmetry & Tetragonal Anatase \\ Lattice constants & a = 3.775 Å \\  & b = 9.581 Å \\   Cell volume & 136.54 Å\({}^{3}\) \\   Crystalline size D & 20 nm \\ \hline \end{tabular}
\end{table}
Table 1: Microstructural parameters of TiO\({}_{2}\) calculated from XRD pattern.  
Figure 1: XRD spectra of prepared TiO\({}_{2}\) nanoparticles.  
where \(h\) is [PERSON]'s constant, \(\upsilon\) is the incident light frequency, \(A\) is a constant, and \(E_{g}\) represents the band gap energy, respectively. The band gap energy was calculated by plotting \((\Delta h\upsilon)^{2}\) vs. photon energy (\(h\upsilon\)) and extrapolating the value from the linear portion to \((\Delta h\upsilon)^{2}\) = 0 as shown in the inset of Figure 4. The calculated band gap of the TiO\({}_{2}\) photocatalyst was 3.12 eV [28].  
Figure 3: FTIR spectra of TiO\({}_{2}\) photocatalyst: (**a**) entire spectrum; (**b**) enlargement in the range of 2500–4000 cm\({}^{-1}\).  
Figure 2: SEM images of prepared TiO\({}_{2}\) photocatalyst at different magnifications scale bars: (**a**) 1 \(\upmu\)m, (**b**) 500 nm and (**c**) 200 nm.
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## Materials and Methods  
### Datasets  
The change detection analysis and validation were performed on point data representing archaeological sites from the EAMENA database [4]. Verifiable users can obtain a registration for the database to access the exact location of the sites. The sites are named according to codes assigned during their initial identification, either through remote recording or field survey. For example, prefixed with 'EMENA' for remote recording (Jufra), and with location-related codes for the AKAP field-identified sites, e.g. 'WT'. Satellite imagery accessed through the Google Earth Engine catalogue was used to perform and validate the script (Table 1). For the change detection, we selected the Sentinel-2 L2A product (available back to 2017) which is provided already corrected to surface reflectance usingthe Dense Dark Vegetation algorithm (DDV) and Atmospheric Pre-corrected Differential Absorption (APDA) algorithm [22].  
PlanetScope imagery offers exciting possibilities for regular monitoring of sites, but has not yet attracted significant attention in the archaeological remote sensing literature; few projects have adopted it. For example, [PERSON] and [PERSON] recent review [7] neglects to mention it. It is not completely open-source, however a free license for a limited quantity of data per month is available for many types of stakeholder. With over 130 small satellites in the constellation, it is possible to obtain near-daily imagery for most locations at a resolution of c. 3.5-3.9 m (depending on how close to nadir the imaged location is). We have used it to verify specific instances of change detected from the Sentinel-2 imagery.  
High-resolution imagery via free sources, such as Google Earth, has been used extensively by the EAMENA project [2]. It comprises imagery from commercial sensors, such as WorldView (resolution as high as 0.3 m in the panchromatic) and Pleiades (0.5 m). This has allowed rapid image interpretation comprising an identification of new sites and recording of known ones. In some cases, there is sufficient imagery on the Google Earth Pro platform to also identify changes, or validate changes detected by the change algorithm. Google and Bing imagery can also be obtained as a tile layer within QGIS. Unfortunately, it is not possible to query the original spectral properties of the free high-resolution imagery so it cannot be used for automated change detection, and purchasing the original data to do so is very expensive. We used only open-source satellite imagery because with prices of high-resolution data at \(\pm\)10 per km\({}^{2}\), or higher, commercial imagery is prohibitively costly for monitoring archaeological sites. We are committed to making our workflows and data as available as possible to heritage professionals.
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Exploratory Data Analysis  
Principal component analysis (PCA) was employed for the data exploration. PCA is a common dimensionality reduction technique widely applied in chemometrics to study the similarity among the samples characterized by multiple variables [14].  
\begin{table}
\begin{tabular}{c c c} \hline
**Element** & **Concentration in Steel Samples, \%** & **Concentration in Ore Samples, \%** \\ \hline Si & 0.03–2.74 & 0.27–35.94 \\ Al & 0.17–2.65 & 0.17–11.38 \\ Ti & 0.20–3.05 & 0.01–23.62 \\ Mn & 0.14–8.68 & 0.01–2.15 \\ \hline \end{tabular}
\end{table}
Table 1: Concentration of elements of interest in steel and ore samples.