esa-sceva/satcom-chunk-collection huggingface dataset:
# Poly(vinyl alcohol)/Gelatin Scaffolds Allow Regeneration of Nasal Tissues
## 2 Materials and Methods
### Morphology and Surface Analysis of PVA/G Sponge  
Scanning electron microscopy (SEM) was used to analyze the architectural features of the plain scaffolds. Samples were observed as obtained, post-dried (using either lyophilization or vacuum heating and after mechanical compression). The specimens were cross-sectioned, mounted on aluminum stubs, sputter-coated with gold (Edwards Sputter Coater S150B, Edwards, NY, USA) and examined under a scanning electron microscope JEOL JSM-5600 LV (Jeol Ltd., Tokyo, Japan) with an accelerating voltage of 12 kV.  
The surface roughness analysis of lyophilized sponges was carried out with an atomic force microscope (AFM) (Molecular Imaging, TX, USA) equipped with silicon tips (nominal radii 10-20 nm) in air at 20 \({}^{\circ}\)C and 40% relative humidity. The analysis was performed working on tapping or contact mode and consisted of topographic data recording of square areas of approximately 40 \(\times\) 40 \(\upmu\)m\({}^{2}\) (1024 \(\times\) 1024 pixels). The roughness data were processed as the average among 15 different areas randomly selected upon the investigated surface.
## 3 Results and Discussion
### Single-Cell Oil (SCO)
#### 3.4.4 Biodiesel Properties
curvatus_, both when acetic was used among the other VFAs where the SCO had the lowest amounts of PUFAs. To predict the biodiesel's performance in low temperatures, the CFPP (\({}^{\circ}\)C) is an important parameter, which is a filterability test that specifies the lowest temperature at which the fuel can pass through the standardized filter [16; 64]. The limits of the CFPP are not specified by the international standards, since this parameter is country and season specified. As can be seen in Table 4, the CFPP point for _R. toruloides_ ranges from 18.7 \({}^{\circ}\)C to 32.8 \({}^{\circ}\)C while for _C. curvatus_, ranges from 14.1 \({}^{\circ}\)C to 46.6 \({}^{\circ}\)C. It is noteworthy that the lowest CFPP values for both yeasts are achieved when propionic acid was used, in which the SCO contained the highest SFAs amount, but the lowest LCSF values. It has been indicated that a low LCSF benefits the CFPP [12]. For the case of the SCO produced when BSG VFAs were used as a substrate, the CFPP was lower for _R. toruloides_ (25.2 \({}^{\circ}\)C) than for _C. curvatus_ (44.9 \({}^{\circ}\)C). Furthermore, for both yeasts, a slight decrease in the CFPP was noted (29.4% for CC and 18.3% for _RT_) when the yeasts were grown in BSG VFAs (containing acetic acid as the dominant fatty acid) than in acetic acid alone. We can thus assume that this decrease was due to the combination of acetic acid with propionic acid, which results in lower CFPP values, indicating that tuning the composition of effluent VFAs can result in improved biodiesel properties. The CFPP values are within the ranges expected for yeast SCOs, with few exceptions [12]. The poor cold flow behavior is the biggest problem associated with the production of biodiesel by oleaginous yeasts. However, several approaches have been suggested to overcome this, such as blending with different biodiesel's or petrodiesel, winterization, fractionation, use of additives, and structural modifications of alcohol moieties [13; 14; 68]. Another approach to improve the CFPP is to genetically engineer the microorganisms to regulate the production of certain fatty acids so that biodiesel with desirable physicochemical properties will be produced. It should be noted, however, that it is challenging to produce biodiesel with ideal properties since the targeted requirements can have a contradictory effect on the fatty acid composition [68]. Finally, it is also noteworthy that higher amounts of saturated fatty acids, and especially the LCSF ones in the produced biodiesel, are responsible to lower the nitrogen oxides (NO\({}_{\rm x}\)) emissions [64; 68]. NO\({}_{\rm x}\) is formed due to the high combustion temperature and is composed of NO and NO\({}_{2}\)[62]. Therefore, since the LCSF values are high in the SCOs produced in the current work, it can be assumed that the produced biodiesel by the oleaginous yeasts will occur in low NO\({}_{\rm x}\) emissions.
## 3 Methods
### Data Source and Data Quality  
Clinica Universitaria Bolivariana (CUB) is a university general hospital with an important focus in obstetrics. CUB has provided more than 80,000 deliveries in the city of Medellin, with a mean of 5200 deliveries per year which was approximately 13.5% of the city's total in 2020. The Hospital Information System and EHR system used by the clinic is Servinte Clinical Suite (Carvajal SA, Medellin, Colombia, v1.3, 2019). According with de Data Dictionary, we queried the EHR system to extract clinical records of patients who gave birth in the institution during the period 2015 to 2019. CUB did not provide demographic data; due to privacy constraints, the dataset was limited to extracted unstructured patient notes. The data were obtained using Oracle SQL Developer and stored in binary format for processing and analysis. We used tables containing information about progress notes corresponding on SOAP (Subjective-Objective-Analysis-Plan) forms performed by clinicians in each encounter. Patients' data were assigned an internal identification number to match the progress notes and their corresponding ICD-10 code at discharge.  
To assess the document quality, we conducted an Exploratory Data Analysis (EDA) on the progress notes. Different aspects were examined, including note length, vocabulary size, word frequency distribution, structural patterns, sparsity, punctuation usage, and formatting conventions. Subsequently, we decide the steps for preprocessing.  
The content of the progress notes is separated into various fields according to the design of the form in the EHR system. As a consequence, we found duplicate information in the fields of the same progress notes. Then, before processing the text we eliminated the duplicated data and joined all the content of each field into a single document. Lastly, episodes without progress notes or notes with fewer than 10 words were discarded.  
To assess the performance of our model, we divided the dataset into two independent subsets: a training set and a test set. Specifically, we allocated 20% of the total dataset as the test set. To ensure that both sets maintained a proportional representation of classes, we employed a stratified sampling. Stratification helps preserve the distribution of target labels in each subset; this methodology ensures that our model is subjected to a representative test set.
# A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines
## 3 Results
### Experimental Results  
Training of ASAM is conducted on an RTX 4090 and uses OpenMMLab's MMSegmentation. It is compared with classic semantic segmentation algorithms, including UNet, SegFormer, PSPNet, K-Net, Fast-SCNN, and DeepLabV3. To ensure fairness, the same loss function is used, and the epoch is set to 50. Among them, UNet, SegFormer, PSPNet, Fast-SCNN, and DeepLabV3+ fail to segment the fire detection line mask on the custom dataset. We qualitatively analyze the reason, as the custom dataset images have relatively elongated features, and the dataset is small, which prevents these models from learning effective features. Among these models, only K-Net was able to generate valid segmentation masks on this task, making it the only operational baseline model in our experiments. We compare the Dice coefficients and the accuracy of the minimum distance between the fire detection lines for ASAM and K-Net, both when trained with only Dice Loss and when trained with the fusion loss function. ASAM uses the sam_vit_b weights and r = 512. K-Net uses the swin_large weights.  
The accuracy of the minimum distance between the fire detection lines for the target task, denoted as A, is defined by Equation (10). The experimental results are detailed in Table 1.  
\[A=1-\frac{D-D_{record}}{D_{record}} \tag{10}\]  
As shown in Table 1, the use of a fused loss function that includes the target task loss function has a minimal impact on the model's segmentation performance. Still, it significantly improves the accuracy of the minimum distance measurement between the fire detection lines. This provides insights for applying machine vision models to various domain tasks, emphasizing not only the model's segmentation performance but also considering the application effects on the final target task during the training stage.
## 1 Introduction
This highlights the need for alternative approaches to corrosion monitoring that can overcome the challenges of sensors in dynamic and chemically aggressive seawater systems. To overcome these challenges, various studies are exploring advanced approaches for improving sensor reliability. Machine learning-based algorithms and regression models have gained attention, as they incorporate environmental variables to predict CRs more accurately. [PERSON] et al. [12; 13] applied ensemble learning and non-linear regression techniques to predict internal corrosion in oil and gas pipelines, while [PERSON] et al. [14] proposed a machine learning algorithm for multiphase pipelines. Similarly, [PERSON] et al. [15] developed a model using field data, achieving 93.22% accuracy in predicting damage from internal corrosion in gas pipelines. Although machine learning techniques have shown promise in oil and gas pipelines, utilizing corrosion sensors for real-time monitoring in complex environments such as seawater remains limited. In seawater systems, multiple factors--chloride, oxygen, flow velocity, and temperature--interact, making accurate monitoring and prediction challenging. Corrosion sensors typically use electrodes made from the same material as the monitored target, such as carbon steel. However, in seawater, carbon steel can experience thickness losses of up to 1 mm per year [16], with higher flow velocities and temperatures accelerating the process. This not only impacts the monitored material but also degrades sensor electrode, reducing the electrode's lifespan and necessitating more frequent maintenance. As a result, measurement inaccuracies occur, undermining the effectiveness of real-time monitoring by corrosion sensor. Developing predictive models or estimation equations based on key environmental variables, such as flow velocity, temperature, and pH, could provide a reliable method to assess corrosion rates without relying solely on sensors. These models would not only reduce the dependency on hardware prone to fouling and degradation but also offer a cost-effective and durable solution for long-term monitoring in harsh marine environments. In this study, we aim to predict the CR of carbon steel in seawater pipes by developing a regression model for Mils per Year (MPY) based on flow velocity and temperature, using data obtained from a commercial LPR sensor. This approach will allow us to validate the reliability of LPR sensors in monitoring corrosion and evaluate the potential to predict long-term corrosion damage based on instantaneous CR.
## 3 Results and Discussion
### Chemical Composition
#### 3.2.1 Chemical Composition of ESDD
[39] in which Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), Na\({}^{+}\), and Ca\({}^{2+}\) are the main ions of the soluble contamination; especially, SO\({}_{4}\)\({}^{2-}\) and Ca\({}^{2+}\) are higher than other ions. [PERSON] et al. [40] found analogous results, except for NH\({}_{4}\)\({}^{+}\), which was abundant because of the closeness to vast tracts of farmland of the sampling electric power line. The chemical composition of the solution was then expressed in terms of normality (equivalents for unit volume of solution) and related to the measurements of conductivity (expressed in \(\mu\)S cm\({}^{-1}\)) performed on each solution and reported at 20 \({}^{\circ}\)C (as detailed in Section 2.2). In this respect, Figure 6a shows an excellent linear correlation (R\({}^{2}\) > 0.9) between the chemical quantity of charges in solution due to the analyzed inorganic ions and the electrical conductivity measured immediately after the sponging is observed in the three campaigns. In addition to the aforementioned result, according to the Equations reported in the IEC/TS 60815-1 [4] (detailed in Section 2.2), the expected conductivity of each sample was also computed, starting from the chemical composition analyzed by ion chromatography and compared with the experimental conductivity measured on the same sample. The results are shown in Figure 6b, which reports the linear correlation between the measured and the estimated conductivity with a 1:1 ratio, highlighting that the whole ensemble of ionic inorganic fraction (i.e., the real salt deposit density, SDD) is responsible for the whole conductivity, and therefore explains the total equivalent ESDD mass deposit. Figure 6: (**a**) Linear correlation between measured electrical conductivity \(\sigma_{20}\) at 20 \({}^{\circ}\)C (\(\mu\)S cm\({}^{-1}\)) and the concentration of total equivalents in solution (neg mL\({}^{-1}\)) and (**b**) linear correlation between measured electrical conductivity \(\sigma_{20}\) (\(\mu\)S cm\({}^{-1}\)) and estimated conductivity \(\sigma_{20}\) (\(\mu\)S cm\({}^{-1}\)) for the collected samples in Italy for the 6 th (orange), 7 th (blue) and 8 th (green) campaigns. The linear correlation between the estimated ESDD (mg cm\({}^{-2}\)) and the calculated ESDD (mg cm\({}^{-2}\)) of the starting solution was computed, and it is shown in Supplementary Material Figure S5. Thus, the total measured ionic content represents the real salt deposit density (SDD) and was on average 94.5 \(\pm\) 18.8% in weight of the ESDD on the whole of Italy.
## 2 Materials and Methods
### Image Preprocessing  
Images were acquired using two whole-body Siemens 3T MR scanners (MAGNETOM and Skyra). Image acquisition details have been previously reported [27]. This dataset was collected and curated for research on prostate MRI CAD at the Prostate MR Reference Center, Radboud University Medical Center, Nijmegen, Netherlands. T2 WI was acquired using a turbo spin echo sequence and with a resolution of around 0.5 and 3.6 mm for plane and slice thickness, respectively. DWI was acquired using a single-shot echo planar imaging sequence comprising diffusion-encoding gradients in three directions with a resolution and slice thickness of 2.0 and 3.6 mm, respectively. Three b-values were acquired (50, 400, and 800), and scanner software calculated the ADCmap. All images were acquired without an endorectal coil.  
The T2 WI and ADCmap in the dataset had different fields of view, spatial image resolution, and slice numbers at imaging time. Each subject's slice number differed, with most ADCmaps having fewer slices than T2 WI. Therefore, T2 WI positions exceeding those in the ADCmap were deleted by referencing the ADCmap head and tail slice positional information retrieved from the digital imaging and communications in medicine tag information. The T2 WI was processed using the prostate extraction obtained from the semantic segmentation model, resulting in an image containing only the prostate. The same coordinate information was used to generate an ADCmap, which also contained only the prostate. The ADCmap and T2 WI with and without semantic segmentation were resized to 224 \(\times\) 224 for 3D-CNN input. The image data were saved in Neuroimaging Informatics Technology Initiative format (Figure 2). A segmentation volume was defined as a file group with segmentation, while a normal volume was defined as one without segmentation.  
Figure 2: Schematics of image preprocessing. T2 WI, T2-weighted images; ADCmap, apparent diffusion coefficient map; NIfTI, Neuroimaging Informatics Technology Initiative.
## 2 Nonlinear Dynamic Modeling of Spur Gear Systems
### Influence of Damping
#### 3.3.2 Analysis of Supporting Damping  
Supporting damping means the ratio of supporting damping to meshing damping. Let the load torque \(T_{c}\) = 10.8 Nm, the rotational speed be 960 rpm, the supporting stiffness be \(4\times 10^{6}\) N/mm, and the damping coefficient \(\xi\) = 0.12. When supporting damping, \(C_{\text{hyz}}\), takes different values, the system's bifurcation diagram, phase diagrams, Poincare maps, and time-domain diagrams can be obtained.  
As shown in Figure 31, the influence of supporting damping on the displacement along the LOA is complicated. When supporting damping falls into (1, 1.92), the system is in the four-band chaotic motion. When supporting damping is in (1.92, 10.82), the complexity of the chaos is significantly less than before, and multiple periodic motion and quasiperiodic motion occasionally arise, as shown in Figure 32. With increasing supporting damping, the system's dynamic response gradually becomes stable, changing from the four-band chaotic motion to the 4n periodic motion. As shown in Figure 33a,b, the system is in the stable period octuple-motion state when \(C_{\text{hyz}}\) = 12, indicated by eight closed curves in the phase diagram and eight isolated dots in the Poincare map. With an increase in supporting damping, the system motion changes from the periodic octuple motion to the period-quadrupling motion through inverse period-doubling bifurcations, as shown in Figure 34.  
In conclusion, increasing the meshing damping and the supporting damping can improve system stability and decrease vibration response. Therefore, it is better to take measures to increase the gear system's damping, for example, adopting high-damping materials.
## 3 Results and Discussion
### Influence of Ambient Conditions--Surrounding Temperature and Ventilation (Parameter Sweep Study)
#### 3.3.3 Summary (Parameter Sweep)  
Both investigated factors, the ambient temperature and convection coefficient, have a strong influence on the performance of the cooling system. For designing an adequate cooling system, it is therefore strongly recommended to have a detailed look at the conditions in which the system shall be used. As the effect of both factors on the performance is contrary, a hot environment (a high ambient temperature) could be compensated for by high ventilation (e.g., forced ventilation by the charging unit), and low ventilation could be compensated for by placing the charging unit in cold or cooled surroundings. Comparing the two factors within the investigated ranges, the convection coefficient has a higher impact than the ambient temperature. The influence on the maximum temperature is small. The recommendation by the cell manufacturer to keep the cell pack at lower than 80 \({}^{\circ}\)C during the use phase (discharging) was fulfilled for all the configurations.  
Looking at the governing material properties, it can be noted that at low convection coefficients and high ambient temperatures, the heat dissipation by the surface is very limited; thus, the heat capacity of the cooling material is of high importance. At high convection coefficients and low ambient temperatures, by contrast, the heat transfer ability of the cooling material is the governing material property, and the heat capacity is of lower significance.
## 2 Background
### Capital Expenditure (CAPEX)  
CAPEX in CWPs usually covers the initial investments for infrastructure, equipment, and system setup. These are important for a project's success. Through detailed estimation and budgeting methods, including equipment and installation costs, CAPEX is critical in assessing long-term project feasibility [94; 95]. Capital or initial investment costs can impact operational expenditure; for example, purchasing high-quality equipment can reduce operating and maintenance expenses [96]. Additionally, these investments facilitate accurate cost estimation, aiding in realistic budgeting and financial planning.  
Various methods exist for calculating CAPEX. For instance, [PERSON] et al. [97] did not derive CAPEX through a specific equation but gathered it from current market values for individual cooling systems (ICSs). They obtained the capital costs for district cooling systems from the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) district cooling guide [81]. However, this approach has its drawbacks. Using market values for ICS can result in a lack of standardization and could affect estimation accuracy. Moreover, incorporating data from different sources, such as the ASHRAE district cooling guide, might present challenges in ensuring consistency and comparability in the assessment of capital costs.  
A few studies [52; 54; 98] have used a standard equation for CAPEX calculation depending on the number of chillers, as expressed in (1) and (2). The focus is calculating the initial costs (\(IC_{i}\)) of chillers and their accessories within the overall investment costs. The widely used calculation method for capital or initial cost is the six-tenth method, primarily based on the Williams method [98]. Thus, the initial cost (\(IC_{i}\)) (USD) of the \(i\)th chiller is expressed as (1).  
\[IC_{i}=IC_{0}\cdot\ \left(C_{i}/C_{0}\right)^{R} \tag{1}\]  
where \(IC_{0}\) (USD) is the initial cost of a reference chiller, \(C_{0}\) (kW) is the capacity of the reference chiller, \(C_{i}\) (kW) is the maximum cooling capacity of the \(i\)th chiller, and \(R\) is the scaling exponent [6], which represents the nonlinear rate at which costs increase with size and capacity, reflecting economies of scale in industrial equipment such as chillers.  
The initial cost of the multiple-chiller plant (\(IC\)) can then be derived as (2).  
\[IC=\sum_{i=1}^{n}IC_{i} \tag{2}\]  
The initial investment may include optional costs, such as hardware (e.g., servers and graphics processing units) and software (e.g., AI frameworks and licenses). However, it highly depends on the specific project requirements, the complexity of the AI models, and the availability of existing resources [99].