esa-sceva/satcom-chunk-collection huggingface dataset:
# Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins
## 2 Materials and Methods
### Attenuated Total Reflectance (ATR) FTIR Spectroscopy  
A Vertex 70 series spectrophotometer (Bruker Optics, Billerica, MA, USA) was used to study secondary structure of the proteins. This spectrometer was equipped with a Bio-ATR Cell II measurement accessory (to study reference protein solutions) and a horizontal multi-reflectance ZnSe crystal accessory (to study hydrated zein and gluten). The instrument housed a deuterated triglycine sulfate (DTgS) detector and a KBr beam splitter. The spectra of proteins were collected in the 800-3000 cm\({}^{-1}\) region at room temperature using 4 cm\({}^{-1}\) resolution. An aperture diameter of 8 and 6 mm, and number of scans of 128 and 32 were used for reference proteins, and hydrated zein and gluten, respectively. A common problem associated with the accurate measurement of protein infrared spectra is the occurrence of liquid water and water vapor bands in the amide I region due to the presence of water vapor in the radiation path [5]. In order to minimize the presence of water vapor in the system, the internal humidity of the instrument was brought to zero by replacing the desiccants the night before measurement and purging the system with N\({}_{2}\) gas during the measurement. The humidity in the instrument was zero during the measurements. The FTIR spectra of the protein solutions and hydrated proteins (sample measurement) and continuous phase (reference measurement) were collected at least in triplicate. All the measured spectra were subjected to a two-step normalization process, vector and offset. The vector normalization is used to correct for the differences in the depth of penetration and calculates the average of the y-value of the spectrum, which then is subtracted from the spectrum. The spectrum is then divided by the square root of the sum of the squares of all y-values. As a result, the spectra are scaled such, that the sum squared deviation over the indicated wavelengths equals one [35]. The offset normalization is performed to correct for the baseline and it shifts the spectrum intensities in such way that the minimum absorbance will equal 0. All the original FTIR spectra shown in the paper are background corrected and normalized. The average of normalized reference spectra (Milli-Q water) was then subtracted from each normalized sample measurement. OPUS software (Bruker Optics, Billerica, MA, USA) was used for spectra acquisition, normalization, and subtraction of the reference spectrum [5]. The data are reported as mean values and standard deviations.
## 2 Machine Learning Applications in Big Data
### Healthcare  
ML techniques, especially DL, reinforcement learning (RL), and ensemble methods, have demonstrated transformative potential in the healthcare sector, leveraging big data to revolutionize patient care, diagnostics, and treatment planning. One of the most prominent applications is predictive analytics. ML models forecast disease progression, patient outcomes, and potential complications by analyzing large datasets, including patient history, genetic information, and lifestyle factors. These models enable personalized medicine, allowing healthcare providers to tailor treatments to individual patients based on their unique data profiles [20, 21, 22, 23].  
Furthermore, ML is extensively used in medical imaging, where convolutional neural networks (CNNs) and other DL techniques have achieved state-of-the-art performance in detecting and diagnosing conditions from images such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans. For instance, ML models can detect early signs of diseases like cancer, often more accurately than human radiologists, by training on vast datasets of labeled medical images. ML algorithms accelerate drug discovery by analyzing genomic data, chemical structures, and protein-ligand interactions to predict viable drug candidates efficiently. These techniques also enable virtual screening, molecular optimization, and drug repurposing, significantly reducing time and cost [24, 25, 26, 27].  
Additionally, natural language processing (NLP) techniques are employed to extract valuable insights from unstructured data such as clinical notes, research papers, and patient-reported outcomes. This enables the development of more comprehensive and context-aware healthcare models. Exploiting electronic health records (EHRs) with ML models facilitates real-time monitoring and decision support, enhancing the ability of clinicians to make informed decisions quickly. ML algorithms process diverse EHR data, including medical histories, imaging results, and lab reports, to predict patient outcomes, recommend treatments, and identify potential complications. For instance, NLP can extract critical information from clinical notes, while DL techniques analyze imaging data for timely diagnoses. By enabling real-time decision support, these applications not only improve patient outcomes but also reduce costs and enhance the overall efficiency of healthcare delivery [28, 29, 30, 31].  
ML-driven predictive analytics empowers healthcare providers to move from reactive to proactive care by forecasting disease risks, identifying complications early, and tailoring treatments to individual patients. This transformative capability not only enhances patient outcomes but also reduces operational costs, making ML a cornerstone of modern healthcare strategies [32, 33, 34].
## 3 Data and Methods
### Curvature Dependence on Matching Algorithms  
Since most geographic studies are not concerned with flat surfaces, such as our salar, we use curvature as a proxy for examining DEM quality compared to a correlation algorithm and parameters on the off-salar (i.e., steeper) parts of our study area. Nevertheless, the DEM variance vs. PAN variance relationship from the salar does provide information on where higher and lower-quality results may be expected on the entire DEM. Aside from image texture issues, low-quality DEM results can also be caused by geometric issues with the satellite view angles compared to an object's orientation, local processing blunders in the correlation, and systematic errors or biases caused by the sensor. We are not able to untangle or investigate all of these factors from our available data, but we can provide results to suggest optimal processing parameters for stereo matching to improve SPOT6 DEMs for geometric application.  
To explore the DEM quality for geomorphic application, we calculate the landscape curvature in the mountainous parts of our study area. As the second derivative of elevation, curvature is highly sensitive to DEM noise. Since our study area contains diffusive and smooth, vegetation-free hillslopes, with only small, localized rocky outcrops (Figure 1C), spiky curvature across the landscape should be the result of lower-quality matches and not a true topographic signal.  
Curvature was calculated in a \(3\times 3\) pixel window using the free and open-source LSDTopoTools software [47] on all DEMs projected to UTM coordinates with 3 m resolution. Many options exist for curvature [48], but here we choose the tangential curvature as output by LSDTopoTools via a quadratic-fitting approach [49]. This was selected since tangential curvature (measured perpendicular to the steepest topographic slope direction) is important for the identification and analysis of sharp features in the landscape (i.e., ridge crests, valley bottoms, channel heads), and we seek the DEM processing parameters that best preserve these features.
## 4 Results and Discussion
### Model Performance Evaluation
To more intuitively compare the predictive performance of the MC-TCGRU model with other time-series decomposition algorithms and deep learning networks, we used bar charts to display the model's performance across the five points based on different AEMs. The comparison results are shown in Figure 14a-e. In summary, time-series decomposition methods effectively handle data with complex features and a poor stability by breaking down the original sequence into its components, allowing for the more accurate modeling of each component and thereby improving the prediction accuracy of deep learning models. The proposed MC-TCGRU model decomposes the InSAR subsidence time series into multiple components, capturing both long-term and cyclical trends within the sequence. Each component is typically easier to model and interpret than the original undecomposed subsidence time series. When modeling each component individually, the data variation characteristics within each component are clearer, allowing for more precise parameter settings for the model. This approach also increases the flexibility in adjusting the model parameters, thereby reducing the risk of overfitting. The CEEMDAN decomposition method used in the MC-TCGRU model offers superior data feature decomposition capabilities compared to the EMD method and provides more detailed decomposition than the STL method, resulting in an overall superior predictive performance compared to models based on these other decomposition methods. Figure 14: _Cont._
## 4 Experimental Verification
### Pattern Recognition  
The original dataset is segmented into the first 10% of the three working conditions (labeled 1, 2, and 3) for training; meanwhile, the remaining 90% are for testing. The test set and training set samples are imported into the PSO-SVM model. The number of particles is set to 30, the variable dimension is set to 1, the maximum iterations is set to 100, the inertia  
Figure 9: Comparison of RCMDE with parameter optimization and RCMDE without parameter optimization of each working condition with scale factors 1-11: (**a**) RCMDE with parameter optimization; (**b**) RCMDE without parameter optimization; (**c**) Comparison of attribute value under scale factor 1 condition.  
weight is set to 0.5, the individual learning factor is set to 1.5, and the global learning factor is set to 1.5. The final classification result of RCMDE optimized by GA is compared with RCMDE without parameter optimization. The group size is set to 50, maximum iterations is set to 100, the cross probability is set to 0.5, and the mutation probability is set to 0.0001. The recognition accuracy of RCMDE with parameter optimization reaches 100%, as shown in Figure 9(a). The recognition result of RCMDE without parameter optimization is shown in Figure 9(b). It can be evidently seen that the identification accuracy of the reciprocating compressor valve wear state is relatively low, and the overall accuracy is 87.037%. The recognition accuracy of MSE and MPE are 88.889% and 85.185%, as shown in Figure 9(c),d.  
This study shows that the RCMDE with optimized GA has a better recognition effect and can better distinguish different features of the reciprocating compressors in normal and fault modes.
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 4 Data
### Basic Statistics
#### 4.3.2 Daily Data
\begin{table}
\begin{tabular}{r r r r r r r r r r} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{3}{c}{**Daily Input**} & \multicolumn{3}{c}{**CNN**} \\ \hline
**Model** & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**LSTM**} & \multicolumn{3}{c}{**CNN**} \\ \hline
**Temperature** & **Average** & **Minimum** & **Maximum** & **Average** & **Minimum** & **Maximum** & **Average** & **Minimum** & **Maximum** \\ \hline
1 day & **1.412 (0.031)** & 1.530 (0.015) & 2.052 (0.062) & 1.567 (0.117) & 1.616 (0.148) & 2.453 (0.405) & - & - & - \\
3 days & 1.417 (0.021) & 1.504 (0.022) & 1.972 (0.027) & 1.494 (0.091) & 1.536 (0.111) & 2.225 (0.334) & 1.368 (0.018) & **1.452 (0.011)** & 1.953 (0.041) \\
5 days & 1.438 (0.031) & **1.497 (0.017)** & **1.967 (0.030)** & **1.451 (0.071)** & **1.439 (0.054)** & **2.171 (0.155)** & **1.350 (0.023)** & **1.426 (0.021)** & 1.940 (0.032) \\
10 days & 1.484 (0.050) & 1.588 (0.052) & 1.974 (0.040) & 1.598 (0.482) & 1.564 (0.198) & 2.179 (0.199) & 1.372 (0.023) & 1.462 (0.020) & **1.921 (0.025)** \\
30 days & 1.828 (0.072) & 1.904 (0.060) & 2.268 (0.070) & 1.570 (0.240) & 1.581 (0.235) & 2.196 (0.247) & 1.453 (0.163) & 1.699 (0.335) & 1.940 (0.040) \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{3}{c}{**Holurly input**} & \multicolumn{3}{c}{**LSTM**} & \multicolumn{3}{c}{**CNN**} \\ \hline
**Temperature** & **average** & **minimum** & **maximum** & **average** & **minimum** & **maximum** & **average** & **minimum** & **maximum** \\ \hline
1 day & **1.269 (0.022)** & **1.365 (0.028)** & 1.874 (0.031) & **1.366 (0.305)** & **1.273 (0.088)** & **2.131 (0.140)** & 1.224 (0.026) & 1.287 (0.028) & 1.926 (0.082) \\
3 days & 1.286 (0.012) & 1.457 (0.020) & **1.862 (0.014)** & 1.566 (0.871) & 1.433 (0.556) & 2.199 (0.356) & **1.210 (0.025)** & 1.238 (0.035) & 1.900 (0.080) \\
5 days & 1.301 (0.020) & 1.409 (0.021) & 1.910 (0.017) & 1.446 (0.833) & 1.606 (0.969) & 2.134 (0.239) & 1.230 (0.036) & **1.234 (0.033)** & 1.945 (0.235) \\
10 days & 1.321 (0.018) & 1.429 (0.022) & 1.922 (0.022) & 1.495 (0.401) & 1.496 (0.541) & 2.304 (0.384) & 1.231 (0.031) & 1.251 (0.042) & **1.900 (0.086)** \\
30 days & 1.768 (0.120) & 1.906 (0.098) & 2.207 (0.053) & 1.666 (0.796) & 1.989 (1.410) & 0.420 (1.265) & 1.337 (0.139) & 1.366 (0.093) & 1.930 (0.133) \\ \hline \end{tabular}
\end{table} 
Table 17: Forecasting results, represented using the MAE, for coastal area (Seongsan).
# Scale-Free Features in Collective Robot Foraging
## 3 Results and Discussion
### Correlation with Swarm Performance
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Correlation with \(N_{coll}\)** & **Correlation with \(\omega_{ca}\)** & **Correlation with \(\omega_{e}\)** \\ \hline \multicolumn{4}{c}{_Discontinuous communication mode (DCM)_} \\ \hline \(d\) & \(0.11\) & \(0.17\) & \(-0.03\) \\ \(\Delta d\) & \(-0.19\) & \(-0.33\) & \(0.64\) \\ \(d_{c,sent}\) & \(0.03\) & \(-0.05\) & \(-0.03\) \\ \(d_{c,rec}\) & \(0.14\) & \(0.29\) & \(-0.51\) \\ \(\tau_{ca}\) & \(0.76\) & \(0.80\) & \(0.06\) \\ \(\tau_{r}\) & \(0.74\) & \(0.77\) & \(0.08\) \\ \(\tau_{f}\) & \(0.27\) & \(0.26\) & \(-0.20\) \\ \(\tau_{h}\) & \(0.22\) & \(0.28\) & \(-0.44\) \\ all & \(0.40\) & \(0.42\) & \(0.02\) \\ \hline \multicolumn{4}{c}{_Continuous communication mode (CCM)_} \\ \hline \(d\) & \(0.50\) & \(0.56\) & \(0.10\) \\ \(\Delta d\) & \(-0.02\) & \(-0.16\) & \(0.51\) \\ \(d_{c,sent}\) & \(-0.15\) & \(-0.39\) & \(0.54\) \\ \(d_{c,rec}\) & \(0.44\) & \(0.44\) & \(0.15\) \\ \(\tau_{ca}\) & \(0.75\) & \(0.80\) & \(-0.13\) \\ \(\tau_{r}\) & \(0.59\) & \(0.67\) & \(-0.02\) \\ \(\tau_{f}\) & \(0.26\) & \(0.29\) & \(-0.24\) \\ \(\tau_{h}\) & \(0.11\) & \(0.17\) & \(-0.27\) \\ all & \(0.40\) & \(0.41\) & \(0.05\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation coefficients quantifying correlations of \(\rho_{q}\) with \(N_{coll}\), \(\omega_{ca}\) or \(\omega_{e}\).
## 2 Methods of Eliminating Hormones and Pharmaceuticals  
Conventional wastewater treatment plants are generally not designed to remove pharmaceuticals and hormones. Wastewater treatment plants (WWTPs) are designed mainly to remove organic carbon compounds, phosphorus or nitrogenous substances [23]. As a result, basic processes such as straining, sedimentation, flocculation, and biological treatment are not sufficient to remove micropollutants [24]. Mechanical wastewater treatment processes are ineffective due to their hydrophilic nature [23]. Most pharmaceuticals have low logKow [mol\(\cdot\)dm\({}^{-3}\)] values, indicating their high solubility in water, and the probability of adsorbing on settling particles is very low.  
The amount of hormones and pharmaceuticals eliminated using the wastewater treatment process in Korea was 28% [25]. According to a study in a wastewater treatment plant in Korea, the removal of pharmaceuticals active compounds (PhAC) by nanofiltration was shown to be 97%, and that for naproxen and diclofenac was about 60% [26; 27]. The disadvantage of this system is the incomplete removal of salts and monovalent ions. Reverse osmosis removed 99% of PhAC. However, this process has a number of disadvantages that are not conducive to its use, including high pressure, and the membranes have a short life span and sensitivity. In terms of energy consumption, studies show that the reverse osmosis process requires between 3 and 8 kWh/m [28].  
The efficiency of micropollutant decomposition during ozonation and photolysis depends on both the operating conditions of the process, the type of compound (BPA, E2, or EE2), and the water matrix (deionised water solution, model drain, and actual drain). In this respect, in the case involving the need to eliminate micropollutants that are not very susceptible to decomposition (e.g., bisphenol A) from water, the use of complex systems (UV/O\({}_{3}\)) is more favourable. Their efficiency can reach up to ca. 94%. Before ozonation, a filtration process should be performed for better results and multistage contact columns can be used, allowing for the precise contact time of wastewater with ozone to be determined [29]. Table 1 provides a summary of the processes in terms of the elimination of hormones and pharmaceuticals from wastewater, along with their disadvantages, advantages, and by-products.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
## Appendix A
Where (**a**–**i**) contains 12 terrestrial biomes: CRO (croplands), CSH (closed Shrublands), DBF (deciduous broadleaf forest), DNF (deciduous needleleaf forest), EBF (evergreen broadleaf forest), ENF (evergreen needleleaf forest), GRA (grasslands), MF (mixed forests), OSH (open shrublands), SAV (savannas), WET (permanent wetlands), WSA (woody savannas). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Slope** & **Intercept** & **R2** & **Slope** & **Intercept** & **R2** & **Slope** & **Intercept** & **R2** \\ \hline  & & site\_daily & & & site\_year & & & site\_years & \\ \cline{2-10} EC-LUE & 0.76 ** & 0.25 ** & 0.61 & 0.83 * & 0.87 * & 0.74 & 0.95 * & 0.79 * & 0.81 \\ VPM & 0.8 ** & \(-\)0.18 ** & 0.60 & 0.93 * & \(-\)0.27 ** & 0.71 & 0.98 ** & \(-\)0.67 * & 0.81 \\ CFIX & 0.71 & 0.77 & 0.48 & 0.77 & 0.53 & 0.69 & 0.84 & 0.17 & 0.79 \\ MODIS & 0.69 * & 0.83 * & 0.57 & 0.76 * & 0.58 & 0.78 & 0.82 & 0.23 & 0.82 \\ GR & 0.73 & 0.89 & 0.54 & 0.81 * & 0.55 & 0.73 & 0.89 & 0.18 & 0.82 \\ VI & 0.77 * & 0.25 ** & 0.53 & 0.76 & 0.32 & 0.72 & 0.8 * & 0.13 * & 0.85 \\ TG & 0.73 * & 0.58 * & 0.48 & 0.87 ** & \(-\)0.01 ** & 0.73 & 0.96 & \(-\)0.39 & 0.8 \\ AVM & 0.75 & 0.44 & 0.46 & 0.87 * & \(-\)0.03 ** & 0.72 & 0.94 * & \(-\)0.38 * & 0.79 \\ LUE-NDWI & 0.79 ** & 0.94 * & 0.68 & 1 ** & 0.3 ** & 0.75 & 1 ** & 0.38 * & 0.82 \\ LUE-EF & 0.82 ** & 0.84 * & 0.74 & 1 ** & 0.32 ** & 0.85 & 1.06 ** & 0.06 ** & 0.97 \\ \hline  & \multicolumn{3}{c}{biome\_daily} & \multicolumn{3}{c}{biome\_year} & \multicolumn{3}{c}{biome\_years} \\ \cline{2-10} EC-LUE & 0.84 ** & \(-\)0.05 ** & 0.73 & 0.84 * & \(-\)0.07 * & 0.83 & 0.97 ** & 0.72 * & 0.94 \\ VPM & 0.86 ** & \(-\)0.58 * & 0.77 & 0.96 ** & \(-\)0.68 * & 0.77 & 1.03 * & \(-\)0.87 * & 0.96 \\ CFIX & 0.76 * & 0.45 * & 0.76 & 0.79 * & 0.45 * & 0.76 & 0.83 * & 0.2 * & 0.92 \\ MODIS & 0.75 * & 0.49 * & 0.75 & 0.79 * & 0.49 * & 0.75 & 0.82 * & 0.23 ** & 0.91 \\ GR & 0.79 * & 0.57 * & 0.81 & 0.79 * & 0.58 * & 0.81 & 0.87 * & 0.27 ** & 0.89 \\ VI & 0.82 & 0.03 & 0.8 & 0.88 & 0.33 & 0.8 & 0.79 & 0.17 & 0.87 \\ TG & 0.82 ** & 0.19 ** & 0.78 & 0.88 * & 0.29 ** & 0.78 & 0.93 ** & \(-\)0.15 ** & 0.89 \\ AWM & 0.85 * & 0.06 * & 0.79 & 0.9 * & 0.26 ** & 0.79 & 0.91 * & \(-\)0.14 * & 0.84 \\ LUE-NDWI & 0.98 ** & 0.38 ** & 0.76 & 1.04 ** & 0.11 ** & 0.84 & 1.09 ** & 0.1 ** & 0.94 \\ LUE-EF & 0.96 * & 0.49 * & 0.84 & 1.03 * & 0.15 ** & 0.88 & 1.08 ** & 0.02 ** & 0.97 \\ \hline \end{tabular}  
* * represent that the slope and 1:1 line is significant within the 95% confidence interval, and ** is significant within the 99% confidence interval.  
\end{table} 
Table 2: The comparison between model GPP and EC-tower GPP at different scales (site scale, biome scale).
# SDN-Based Survivability Analysis for V2I Communications
## 1 Introduction
The contributions of this work can be summarized as follows:  
Because the research on the survivability focuses on the direct communication between vehicle to vehicle (V2V) and V2I networks, a MPTCP mechanism for SDN-based vehicle network survivability model is proposed. In the action of the SDN central control and MPTCP, the link between each V2I can be switched seamlessly, leading to that results in the vehicle network still have some survivability under abnormal conditions (i.e., software or hardware is destroyed), and the survivability of the network is improved rapidly for a timely repairing mechanism. Current survivability research methods normally adopt the mathematical theory derivation and simulation, which consume a lot of time and energy. Therefore, in this work, we propose time-based attributes by using the probability model detection. During analyses, by using probabilistic model detector PRISM [36; 37; 38], we can not only automatically search all the state space but also use continuous stochastic logic (CSL) to define the properties of the proposed model at various angles, verify the model, and obtain accurate numerical analysis results. The proposed method avoids the complicated mathematical derivation process and carries out the automatic mathematical accurate calculation to the complex survivability model. In the meanwhile, it has a simple and high efficiency characteristic and a certain reference value to the other network survivability research. SDN-based vehicle networks currently focus more on reliability and security research but not for survivability. This paper proposes a survivability study for a SDN-based vehicle network. Since that survivability pays attention to the overall security performance of the network system, considering whether the system can \"survive\" under the accident of attack and hardware and software failure. Therefore, this paper comprehensively considers the software and hardware failure problems that may be encountered in the whole V2I network, injects emerging MPTCP protocol into the survivability comprehensive model, and analyzes the survival performance of the proposed model. Due the use of single form Markov chain modeling for current survivability models, this paper considers two fault types that exist in a SDN-based vehicle network that lead to the failures of V2I link communications, these are, the vehicle node fault and the link fault. In what follows, the survivability model of these two fault types is established separately and the survivability models are considered. From the proposed survivability comprehensive model of the network, the analysis results show that the model satisfies the survivability property definitions. The remainder of this paper is organized as follows. Section 2 describes the proposed SDN-based V2I vehicle network. Section 3 proposes the survivability definition of the V2I vehicle network. Section 4 provides a probabilistic model checking approach to analyze the survivability of the V2I network. Section 5 concludes this paper and provides possible future works.