esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Extraction and Quantification of Endotoxins  
To extract endotoxins, portions of the original filters were cut (half of the A filters and 1/4 or 1/8 of the B filters), further sectioned into small strips, and placed in a sterile 50 mL tube with 15 mL of RPMI-1640 supplemented with 0.025% Tween-20 (Sigma-Aldrich, St. Louis, MI, USA). The tubes were then subjected to ultrasonic bath treatment for 10 min and vortexed for 30 s. These steps were repeated three times. Subsequently, the samples were centrifuged at 5000 rpm for 10 min to remove glass fibers. The supernatant was then collected and then used for endotoxin determination [20; 21].  
For endotoxin quantification, the chromogenic endpoint LAL (Limulus Amebocyte Lysate) assay (QCL-1000(tm) 50-648 U, Lonza, Walkersville, MD, USA) was used, along with 96-well transparent polystyrene microplates (Costar(r) 3596, Corning Incorporated, Corning, NY, USA). This method is based on the activation of an enzymatic cascade in Limulus triggered by endotoxins, leading to the activation of a coagulation enzyme. When mixed with a chromogenic substrate and incubated, the activated enzyme cleaves the substrate, releasing p-nitroaniline, which produces a yellow coloration.  
The intensity of the yellow color was measured by spectrophotometric reading at 407 nm and 37 \({}^{\circ}\)C (TECAN Infinite(r) M200 Pro, Tecan, Seestrasse, Switzerland, I-Control(tm) software 1.10) and was directly proportional to the amount of endotoxin in the sample. Absolute quantitative evaluation was performed, following the supplier's instructions, using a standard curve (five points: 1, 0.5, 0.25, 0.1, and 0.05 EU/mL) obtained from a stock solution of lyophilized endotoxins reconstituted in 1 mL of LAL Reagent Water.  
The endotoxin concentration was expressed as EU (Endotoxin Unit) per 1 m\({}^{3}\) of sampled air for each of the six PM sub-fractions. For A filters, corresponding to five sub-fractions, results were aggregated by summing the determinations from each sub-fraction.
## 2 Materials and Methods
### Immunofluorescence of Cell Clusters  
After exposing cells to inhibitors for 48 h, cells were harvested and fixed with ice-cold 50% (v/v) acetone/methanol solution for 40 min at 4 \({}^{\circ}\)C. After fixing, ascitic fluid-derived cell clusters and OVCAR-5 cell clusters were washed twice with ice-cold PBS, pH 7.4, and re-suspended in 200 uL cold PBS. Clusters of cells were then mounted on poly-lysine-coated microscope slides and left to dry at 37 \({}^{\circ}\)C. Ascitic-derived cell compact aggregates and SKOV-3 aggregates were washed with PBS, pH 7.4, and stained with an aniline blue dye for 20 min Cells were washed with PBS, pH 7.4, twice before they were embedded in optimal cutting temperature (OCT) compound, an embedding medium used for the sectioning of frozen tissue samples in a cryostat (Thermofisher Scientific, New Zealand). The liquid OCT blocks were frozen at minus 20 \({}^{\circ}\)C for at least 24 h. Sections (7 um thick) from the block of frozen OCT were cut using a CM186 UV Cryostat (Lieca BIOSYSTEM, Wetzlar, Germany). Then, cells on slides were blocked with 4% BSA in 1 XPBS for 60 min. Cells were stained overnight with a 1:200 dilution of primary antibody at 4 \({}^{\circ}\)C. Cells were then washed with ice-cold PBS, pH 7.4. A 1:500 dilution of secondary antibody, conjugated with FITC or Atto-594 nm, was added to cells and further incubated for 60 min. The secondary antibody solution was removed, and 500 uL of 10 ug/mL DAPI was added to the cells and left for 20 min in the dark. Cells were then washed with 0.1% Tween-20 in PBS, pH 7.4, and were mounted with ice-cold anti-fading solution (2 mg/mL p-phenylenediamine in 80% glycerol, pH 7.8). Immunofluorescence staining was carried out using primary antibodies from Santa Cruz Biotechnology (Santa Cruz, CA, USA). The primary antibodies used in this study were anti-PCNA (sc-25280), anti-GAPDH (sc-25778), anti-EGFR (sc-03), anti-p-EGFR (sc-101668), anti-pHER2 (sc-12352-R), anti-ERK (sc-94), anti-pERK1/2 (sc-7383), anti-AKT (sc-8312), anti-p-AKT (sc-101629), anti-CA125, anti-cytokeratin-18, anti-E-cadherin, anti-N-cadherin and anti-vimentin. The anti-HER-2 was purchased from BD Biosciences (Auckland, New Zealand). Fluorescent images were captured using an epifluorescence microscope with a 40x/1.3 N.A. oil/DIC objective lens (AxioVision 4.5. Apotome software, Carl Zeiss, Oberkochen, Germany). During the capturing of fluorescent images of each protein, the exposure times of immunofluorescence intensity of proteins were similar in both control and drug-treated samples. The intensity profile of each protein was then analysed by a ZEN-blue software, and the profile was expressed as an arbitrary unit (au), which was then normalised to controls of each protein, and the final number was represented as a percentage (%).
## 3 Results and Discussion
### Small-Signal Amplification Measurements  
The strongest amplification was achieved when both signal and pump beams had TM (\(\sigma\)) polarization, which correlates with the lower losses for this polarization. In Figure 6a, the small-signal gain is plotted as a function of the pump power. As expected for small-signal gain measurements, the internal gain increases with the pump power up to 6.5 dB/cm and 5 dB/cm for the 607 nm and 639 nm signal wavelengths, respectively. The maximum achieved gain was limited by the available pump power, but the onset of gain saturation is already visible in Figure 6a. The gain dependence on the signal beam power at a fixed pump power of 55 mW is shown in Figure 6b. As expected, the gain increases with decreasing signal power. For comparison, in proton-implanted Nd\({}^{3+}\):YAG channel waveguides, a 24 dB/cm gain was achieved, and in C\({}^{5+}\)-implanted Er\({}^{3+}\):YAG waveguides, a 4 dB/cm gain was achieved [5]. In several Nd\({}^{3+}\)-doped ion-implanted waveguides, even laser operation in the infrared range was achieved [5]. However, as far as we know, the amplification of visible light in a waveguide induced by ion implantation has not been demonstrated until now. Overall, the measured gain in our ion-implanted Pr:YLF ridge waveguides exceeds the measured loss by far, which is promising for generating laser radiation in these waveguides using suitable resonator geometry, e.g., with mirrors deposited directly onto the waveguide's end facets.  
Figure 6: (**a**) Small-signal internal gain vs. pump power for wavelengths of 607 nm and 639 nm in a 22 \(\upmu\)m wide ridge waveguide at fixed signal powers of 0.5 \(\upmu\)W (607 nm) and 0.7 \(\upmu\)W (639 nm), respectively. (**b**) Small-signal gain as a function of the signal power at a fixed incident pump power of 55 mW.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 2 Material
### Other Geo-Referenced Datasets  
A digital elevation model from Land Survey of Finland was used in orthorectification and radiometric normalization of SAR images, as well as to calculate the average elevation, slope and aspect for each stand and pixel. The original pixel size is 10 m \(\times\) 10 m and elevation resolution 10 cm [39].  
The reason for also using these datasets as explanatory variables in the models is our assumption that the windstorm damages vary also by the aspect and steepness of the slopes of the hills.
# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 2 Literature Survey
Three different DQN-based approaches, namely centralized, multi-agent, and transfer learning, were developed for confirming the capability of QoS and power-efficient modification in each transmission link. The centralized DQN obtained higher overhead while collecting wide data from the environment. [PERSON] et al. [29] presented the Predictive Power Allocation (PPA) using Deep Neural Network (DNN) for Vehicle-to-Everything (V2X) communication. The QoS of each route is guaranteed, and the throughput of V2X is increased because of the PPA. The developed DNN-PPA uses the CSI feature of earlier time slots, which does not require any CSI feedback. However, the increment in the transmitted power causes interference. [PERSON] [30] developed the multi-user Intelligent reflecting surface (IRS)-aided Multiple-Input Multiple-Output (MIMO) system. In IRS-assisted multi-user MIMO, the Base Station (BS), used the chain architecture of sparse RF to minimize circuit power consumption. Here, an Equal Power Allocation Scheme (EPAS) is used to assign the power for all streams on each subcarrier. The weighted sum rate is improved using the joint optimization of the hybrid beamforming in BS. However, inappropriate power allocation to the users of multi-user MIMO affects the performance. [PERSON] et al. [31] presented a central-based dynamic RRM algorithm that employs the radio interference maps from local and global Radio Environment Maps (REM), and traffic demands in certain eNB. The radio resources are assigned based on QoS requirements by considering the traffic demands and radio interference maps. Then, the signal strength is calculated by using the REM. This central-based dynamic RRM algorithm is used to mitigate intra- and inter-network interference. [PERSON] et al. [32] introduced the multi-cell reception technique for improving the capacity of Ultra-Reliable Low-Latency Communications (URLLC) in 5G scenarios, where the multi-cell reception is a joint reception. The integration of multiple Base Stations (BS) is a method from the LTE which is utilized for improving the reliability. Additionally, two multi-cell reception-aware RRM schemes are introduced for allocating the optimal resources to the network, and this developed RRM is depends on the closed-loop power control/modulation and the selection of coding scheme. The substantial gains obtained by the multi-cell reception is less when compared to the single-cell reception. The disadvantages of existing pieces of research are higher interferences and inappropriate power allocation to the users of the network. In the proposed LSTM-RRM method, the burst errors and ISI are mitigated using frequency interleaving and guard level insertion while broadcasting the data. Subsequently, the LSTM-based RRM is used to allocate an adequate power and bandwidth to the users of the 5G network. Here, the hyperparameter tuning of LSTM is done using grid search optimization.
# On a Novel Approach to Correcting Temperature Dependencies in Magnetic Observatory Data
## 3 Temperature Dependence in Data from the Saint Petersburg Observatory
### Temperature Dependence in June 2021 when the Spline Breaks  
In early June 2021, there was a jump in the SPG data due to technical reasons, which led to a jump in the G plot as well. This plot, together with the variometer sensor temperature plot, are shown in Figure 3. The jumps in G in this case were usually corrected with a \"break\" of the baseline spline.  
In this case, however, there was a visually evident similarity between G and the ascending segment of the temperature plot at the time of the data jump. Moreover, the absolute observations, marked by the second left vertical line in Figure 3, were made after the jump in the data. Taken together, this combination of factors led to the fact that, without the special temperature correction described in the next section, it was only possible to eliminate this jump by artificially inserting additional baseline values intraday. Such an approach was not stipulated by the INTERMAGNET standards, which required absolute measurements not more than once a day, so special justification should be provided when transmitting the definitive data to the INTERMAGNET. Such an insertion is also very time consuming to implement and, therefore, in general is highly undesirable. The need for a special temperature correction is also instigated due to the apparent temperature dependence G observed in the second half of June (Figure 3), during which the temperature plot, just as in February, had ascending and descending branches.
# Self-Service Data Science in Healthcare with Automated Machine Learning
###### Abstract  
(1) Background: This work investigates whether and how researcher-physicians can be supported in their knowledge discovery process by employing Automated Machine Learning (AutoML). (2) Methods: We take a design science research approach and select the Tree-based Pipeline Optimization Tool (TPOT) as the AutoML method based on a benchmark test and requirements from researcher-physicians. We then integrate TPOT into two artefacts: a web application and a notebook. We evaluate these artefacts with researcher-physicians to examine which approach suits researcher-physicians best. Both artefacts have a similar workflow, but different user interfaces because of a conflict in requirements. (3) Results: Artefact A, a web application, was perceived as better for uploading a dataset and comparing results. Artefact B, a Jupyter notebook, was perceived as better regarding the workflow and being in control of model construction. (4) Conclusions: Thus, a hybrid artefact would be best for researcher-physicians. However, both artefacts missed model explainability and an explanation of variable importance for their created models. Hence, deployment of AutoML technologies in healthcare remains currently limited to the exploratory data analysis phase.  
automated machine learning; applied data science; self-service data science; healthcare analytics +
Footnote †: journal: Applied Sciences
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 2 Methods
### Change Detection Based on Random Forest  
After the optimal feature combination is obtained, the feature vector of pixels in the image is cleaned, and only the vector information of the optimal feature combination is retained. In this way, the random forest classification model is reconstructed. The final binary classification result is the object-oriented change detection result of the remote sensing images.
## 2 General Overview of the Proposed Method  
The proposed method to tune power-system controllers considering stochastic power fluctuations due to RES integration is shown in Figure 1, and is a combination of two main parts: Part 1, which calculates the statistical SSS information, and Part 2, which utilizes this information to enhance oscillatory stability by coordinating different power-system controllers, such as PSS and BESS controllers, with the help of the optimization technique. The overall proposed methodology can be described as follows:1 Probabilistic modeling of input random variables (RES output power) using power-forecast error. The so-obtained probabilistic models are efficient and convenient to use with analytical methods, and are described in detail in Section 3.1.
2 Input PDFs are then used with an analytical method based on cumulants [2; 26] to calculate the PDF of output random variables. Output random variables in this paper are the real eigenvalue part (also called the damping constant) and damping factor. The cumulant method was chosen over other analytical methods, such as point estimate and probabilistic collocation, due to its higher accuracy as well as computational efficiency [27].
3 Next, the Gram-Charlier expansion method [26] was used with output random variables to calculate statistical indices that provide information about the small-signal stability margin. More details for Steps 2 and 3 can be found in Section 3.2.
4 If the system is found to have a critical value of indices with respect to oscillatory stability, the power-system controllers are then tuned in a coordinated manner. This coordination is achieved by formulating it as an optimization problem. The objective function of this optimization problem consists of probabilistic stability indices (obtained from Part 1) and is solved using the firefly algorithm. The detailed description of this process can be found in Section 4.2.  
Moreover, constraints of this optimization problem are the different parameters of power-system controllers, and solving the optimization problem provides optimized value for the controller parameters. All controllers, such as PSS and BESS controllers, were modeled in great detail and can be found in Section 4.1.
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
## 3 Results and Discussion
### Substrate Specificity and Kinetic Parameters

Table 1: Comparison of biochemical properties of reported DAEases and DTEases. sp. [15], _Clostridium_ sp. [41], and _Corynebacterium glutamicum_[46], all of which showed a similar catalytic bias toward D-allulose. These findings indicate a conserved substrate recognition pattern among DAEases that likely underpins a shared catalytic mechanism [49]. The kinetic parameters of TcDAEase toward D-fructose were further determined by Michaelis-Menten analysis. The enzyme exhibited a \(K_{m}\) of 260.9 \(\pm\) 7.4 mM, a \(k_{cat}\) of 96.6 \(\pm\) 0.5 s\({}^{-1}\), and a catalytic efficiency \(k_{cat}/K_{mt}\) of 0.38 \(\pm\) 0.02 s\({}^{-1}\).mM\({}^{-1}\). A comparison with previously reported DAEases (Table 2) shows that while the kinetic performance of TcDAEase is comparable to other characterized enzymes, its combination of high activity and exceptional thermostability renders it a promising biocatalyst for large-scale, continuous D-allulose production in industrial settings. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Enzyme Source (Strain)** & \(k_{cat}\) (s\({}^{-1}\)) & \(K_{m}\) (mM) & \(k_{cat}\)/\(K_{m}\) (s\({}^{-1}\).mM\({}^{-1}\)) & **Conversion Rate** (\%) & **Reference** \\ \hline _Agrobacteriumium tumefaciens_ & 34.46 \(\pm\) 0.46 & 24 \(\pm\) 0.15 & 1.41 \(\pm\) 0.02 & 32.9 & [37] \\ \hline _Clostridium cellulolyticum_ & 55.91 \(\pm\) 0.787 & 53.5 \(\pm\) 1.8 & 1.045 \(\pm\) 0.025 & 29 & [40] \\ \hline _Ruminococcus_ sp. & 59.4 \(\pm\) 0.483 & 216 \(\pm\) 2 & 0.267 \(\pm\) 0.017 & 28 & [15] \\ \hline _Clostridium_ sp. & 272.87 \(\pm\) 1.37 & 279 \(\pm\) 10.8 & 0.978 \(\pm\) 0.065 & 33 & [41] \\ \hline _Clostridium scindens_ & 5.83 \(\pm\) 0.43 & 40.1 \(\pm\) 2.5 & 0.145 \(\pm\) 0.02 & 30 & [42] \\ \hline _Clostridium boleae_ & 59 \(\pm\) 1 & 59.8 \(\pm\) 8.2 & 0.99 \(\pm\) 0.05 & 28.8 & [14] \\ \hline _Dorea_ sp. & 5507.45 & 153 & 3.31 & 30 & [44] \\ \hline _Treponema primitia_ & 292.88 \(\pm\) 6.48 & 279 \(\pm\) 20.7 & 1.05 \(\pm\) 0.097 & 28 & [31] \\ \hline _Flavorinfractor plautii_ & 225,280 & 162 & 156 & 31 & [32] \\ \hline _Arthrobacter globiformis_ & 41.8 & 37.5 & 1.12 & 26 & [13] \\ \hline _Agrobacteriumium_ sp. & 106.7 & 110 & 1.06 & 30 & [45] \\ \hline _Corynebacterium glutamicum_ & 40.92 \(\pm\) 0.175 & 366.7 \(\pm\) 10.5 & 0.11 \(\pm\) 0.077 & 30 & [46] \\ \hline _Bacillus subtilis_ & 41.99 \(\pm\) 2.3 & 141.43 \(\pm\) 7.5 & 0.29 \(\pm\) 0.09 & 31 & [16] \\ \hline _Caballeronia fortuita_ & 157.21 \(\pm\) 0.53 & 81.9 \(\pm\) 4.3 & 1.30 \(\pm\) 0.12 & 29.4 & [33] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the kinetic parameters of reported DAEases and DPEases. Figure 5: (**a**) Relative activity of TcDAEase toward different D-ketohexoses, with D-allulose set as 100%; (**b**) Michaelis–[PERSON] kinetic curve of TcDAEase for D-fructose.
# Bacterial Aerosol in Ambient Air--A Review Study
## 2 Sources of Bacterial Aerosol in Ambient Air  
Bacteria enter the near-surface atmosphere through aerosolization from various surfaces exposed to air currents. [PERSON] and [PERSON] [34] propose that bacteria from soil and plant surfaces are released into the air through the resuspension of particles, a concept supported by numerous studies. For example, research by [PERSON] et al. [35], [PERSON] and [PERSON] [36], and [PERSON] and [PERSON] [37] has demonstrated a correlation between land cover types and bacterial concentrations in near-surface air.  
Additionally, the composition of airborne bacterial communities is shaped by geographical factors such as landscape and land use. For instance, crop harvesting has been shown to significantly increase the number of airborne bacteria [20]. Identifying the sources of bacteria in outdoor environments presents challenges due to the diverse range of organisms and events that contribute to the release and transport of microorganisms.  
Global research reveals significant variability in bacterial concentrations across different outdoor environments, with notable seasonal fluctuations [38; 39; 40]. The World Health Organization (WHO) recommends a maximum concentration of 300 CFU/m\({}^{3}\) for airborne bacteria in environments occupied by immunocompromised individuals. In contrast, several countries have established higher thresholds: Finland (4500 CFU/m\({}^{3}\)), Germany,and the Netherlands (10,000 CFU/m\({}^{3}\)). The UK and Ireland have set specific exposure limits for green waste composting sites with \"sensitive receptors\" (i.e., locations where individuals are present for more than six hours at a time). The ambient environmental compliance parameter at 250 m downwind is set at 1000 (+30%) CFU/m\({}^{3}\) for total bacteria, as outlined in the \"Technical Guidance Note M9: Monitoring Methods for Ambient Air\" by the UK Environment Agency [41]. Among the studies reviewed (Table S1), the lowest average concentrations of ambient bacteria (<100 CFU/m\({}^{3}\)) were reported in Mexico [42], during summer in Iran [43], and during winter in Poland [4,44] and France (Porquerolles Island) [45]. In contrast, the highest average concentrations (>1000 CFU/m\({}^{3}\)) were found in Vietnam [46], China [47,48], India [49], Pakistan [50], and Korea [40].  
Geographic and climatic factors play a crucial role in shaping outdoor microbial concentrations. The dispersion of bioaerosols is primarily governed by hydrodynamic and kinetic mechanisms, while their persistence and fate are influenced by their chemical composition and the meteorological conditions they encounter [51].  
Furthermore, bioaerosols can function as typical atmospheric aerosols, exerting a significant influence on the climate system by serving as cloud condensation nuclei (CCN) and ice nuclei, thereby participating in cloud formation and the cloud water cycle [11,22,52]. Due to their aerodynamic buoyancy, bioaerosols can be transported over vast distances [53], facilitating the dissemination of trace elements and pathogens far from their original sources [54].