esa-sceva/satcom-chunk-collection huggingface dataset:
# Screening of Scaffolds for the Design of G-Quadruplex Ligands
## 2 Materials and Methods
### Chemical Synthesis
#### 2.1.2 Synthesis of Naphthalene Derivatives  
The naphthalene derivatives ligands were prepared according to methods described previously, with slight modifications [16]. Briefly, to a solution of the desired polyamine (1.0 g, 9.6 mmol) in a mixture (40 mL) of absolute CH\({}_{3}\)CN:EtOH (\(v/v\); 1.7:1) at room temperature, was added dropwise one solution of 1-naphthaldehyde (0.5 g, 3.2 mmol) in dried CH\({}_{3}\)CN (10 mL). The solution was stirred at room temperature over 2 h and the solvent was evaporated to dryness. The resulting residue was dissolved in dried EtOH (15 mL) and NaBH\({}_{4}\) (1.1 g, 29.5 mmol) was added portion wise. The mixture was stirred at room temperature for 24 h. After that, the mixture was filtered, and the solvent was evaporated under reduced pressure. The residue obtained was extracted with chloroform (3 \(\times\) 50 mL) and evaporated to dryness obtaining one oil which was dissolved in a mixture of MeOHEtOH (\(v/v\); 1:1) and precipitated with some drops of HCl 37% to give the different naphthalene derivatives:  
N\({}^{1}\)-(2-aminoethyl)-N\({}^{2}\)-(naphthalen-1-ylmethyl)etane-1,2-diamine (Napht-3)  
From diethylenetriamine (1.0 g, 9.6 mmol) and 1-naphthaldeyde (0.5 g, 3.2 mmol). Yield: 79% (1.2 g, 4.9 mmol). White solid. mp 203 \({}^{\circ}\)C. \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) 8 9.99-9.35 (m, 3H), 8.18 (t, \(J\) = 10.1 Hz, 1H), 7.98-7.93 (m, 1H), 7.72-7.46 (m, 2H), 4.67 (s, 1H), 3.44 (s, 4H), 3.11 (s, 4H). \({}^{13}\)C NMR (101 MHz, DMSO-\(d_{6}\)) 8 133.7, 131.4, 130.2, 129.3, 129.2, 128.3, 127.4, 126.9, 125.8, 124.2, 47.6, 44.4, 43.7, 43.2, 35.6.  
N\({}^{1}\)-(2-aminoethyl)-N\({}^{2}\)-(2-((2-(((naphalen-1-ylmethyl) amino) ethyl) amino) ethyl) amino) ethyl)  
etane-1,2-diamine (Napht-5)  
From tetraethylenetamine (1.8 g, 9.6 mmol) and 1-naphthaldeyde (0.5 g, 3.2 mmol). Yield: 11% (0.2 g, 0.7 mmol). White solid. mp 310 \({}^{\circ}\)C. \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) & 10.67-8.64 (m, 2H), 8.50-8.26 (m, 1H), 8.19-7.97 (m, 1H), 7.96-7.44 (m, 2H), 4.83 (d, \(J\) = 8.0 Hz, 1H), 4.19 (s, 1H), 1.32 (s, 1H). \({}^{13}\)C NMR (101 MHz, DMSO-\(d_{6}\)) 133.8, 131.4, 130.2, 129.3, 129.2, 128.3, 127.4, 126.9, 125.8, 124.2, 70.3, 47.7, 44.6, 44.0, 43.4, 36.0, 31.8, 29.5, 29.2.
# Volatile, Microbial, and Sensory Profiles and Consumer Acceptance of Coffee Cascara Kombuchas
## 3 Results and Discussion
### Microbial Taxonomy
Many different positive and negative attributes, including apple, floral, tropical fruit, citrus and/or spicy, cracker, biscuit, clove, mousy, barnyard, smoky, plastic, phenolic, medical, \"band-aid\", metallic, humid leather, sweaty, and goat-like are used to describe the (often pungent) aroma profile of these strains [63]. In kombucha, _Brettanomyces bruxelensis_ can produce high amounts of alcohols and acids, such as isoamyl alcohol, phenylehtyl alcohol, isovaleric acid, hexanoic acid, octanoic acid, and lauric acid [64]. _Saccharomyces_ sp. is the major yeast genus involved in the production of alcoholic beverages [65]. _Saccharomyces_ sp. strains have been previously identified in the liquid and pellicle of kombuchas [43; 45; 51]. During fermentation, _Saccharomyces cerevisiae_ (0.4-1% in our kombuchas) produces a broad range of aroma-active substances that are vital for the complex flavor of fermented beverages, with esters being the most important compounds with industrial purposes, since they are responsible for fruity, candy, and perfume-like aromas of alcoholic fermented beverages [66]. _Saccharomyces ludwigii_ is considered detrimental to the winemaking process and contaminates ciders because it can produce in fermented beverages the volatiles ethyl acetate, isoamyl acetate, and acetaldehyde, which may confer negative undertones to wine when exceeding their respective thresholds of perception [67; 68]. It is worth noting that some bacteria and genera identified in CC K in this study (_Acetobacter_, _Pediococcus pentosaceus_, Enterobacteria, and _Saccharomyces_ sp.) are common to coffee fruit and seeds since coffee cascara is a postharvest by-product [69; 70]. However, as mentioned previously, these microorganisms were probably not viable for taking part in the fermentation process given that coffee cascara was subjected to near-boiling temperatures during the infusion preparation.
## Abbreviations  
* [1]Applied Remote Sensing Training
* [2]COMET no longer an acronym
* [3]EDC Environmental Data Connector
* [4]ERD & Environmental Research Division (part of NOAA/NMFS/SWFSC)
* [5]ERDDAP no longer an acronym
* [6]EUMETSAT European Organisation for the Exploitation of Meteorological Satellites
* [7]ESA European Space Agency
* [8]GHRSST Group for High Resolution Sea Surface Temperature
* [9]HDF Hierarchical Data Format
* [10]JPL Jet Propulsion Laboratory (part of NASA)
* [11]JPSS Joint Polar Satellite System (a NOAA/NASA collaboration)
* [12]MPA Marine Protected Area
* [13]NASA National Aeronautics and Space Agency
* [14]NESDIS National Environmental Satellite, Data, and Information Service (part of NOAA) netCDF Network Common Data Form
* [15]NMFS National Marine Fisheries Service (part of NOAA)
* [16]NOAA National Oceanic and Atmospheric Administration
* [17]NOS National Ocean Service (part of NOAA)
* [18]OPeNDAP Open-source Project for a Network Data Access Protocol
* [19]PBL Problem-Based Learning
* [20]PORSEC Pan Ocean Remote Sensing Conference
* [21]SOS Sensor Observation Service
* [22]SWFSC SouthWest Fisheries Science Center (part of NOAA/NMFS)
* [23]THREBDS Thematic Real-time Environmental Distributed Data Services
* [24]VIIRS Visible Infrared Imaging Radiometer Suite
* [25]WCN West Coast Node of NOAA CoastWatch Program
# Micro-Satellite Systems Design, Integration, and Flight
## 2 Systems Modeling of the CubeSat
### Network Models  
Figures 2-4 show models that focus on functions; a model that shows subsystems at the individual level is also needed. A network model would be a better way to analyze a system at the component level because it reveals the interface between the subsystems.  
Using network theory made it possible to see more clearly how the components interact. There are different network models that could model the CubeSat system. Even though the subsystems could be modeled as clusters, they all connect to the processor in a broader sense. So, for this manuscript, a central network model is enough.  
To make a network diagram, the context of each component is recorded. A common way to make a network diagram is to list all of the components of a subsystem and make an interface matrix. A more general system interface matrix was made for simplicity. See Figure 6 below.  
The relationships in Figure 6 also have a direction. Each cell with an X marks an interaction between two subsystems. The side of the diagonal that an  
Figure 5: Process diagram ([PERSON] [PERSON], p. 609 [33]).  
Figure 6: Interface matrix.  
the direction of the interaction. An X means that a flow starts from the subsystem in the row and ends at the subsystem in the matching column.  
Based on the information in Figure 6, the network diagram can be derived from the interface matrix. Figure 6 shows the basic structure of the network in CubeSat. The network in CubeSat can show how subcomponents that need an interface are connected. See Figure 7.  
The system becomes clearer when the subsystems are separated and the directions are added to Figure 7. Figure 8 displays the five sensors, the torque coils, solar panels, and RF transceiver that the CubeSat has. This helps to see how data move in the system. Refer to Figure 8 below.  
Figure 8: Full network diagram.  
Figure 7: Network diagram.  
Figure 8 shows each value that each of the sensors recorded. This breakdown shows how the data points are recorded by the Teensy 3.5 microcontroller in real time. Each data packet in the network has 17 different data points
# Can Self-Similarity Processes Be Reflected by the Power-Law Dependencies?
## 2 Confirmation of the SSP for the Weierstrass-Mandelbrot Function and in Other Models
### Description of Real Data  
To verify the efficiency of the proposed algorithms, two sets of data were generated. The first, subsequently designated Signal 1, comes from the simulation of a first-order system with the transfer function  
\[H_{l}(z)=\frac{1}{1+z} \tag{38}\]  
with a uniform noise as input. For the simulation, the sample time is chosen equal to \(T_{e}=0.624\) ms. Thus, these data do not initially exhibit a power-law behavior.  
Conversely, the second data set denoted Signal 2 was obtained under the same simulation conditions, but this time simulating a fractional system of the transfer function:  
\[H_{f}(z)=\left(\frac{1+z/\omega_{h}}{1+z/\omega_{l}}\right)^{0.5}\omega_{l}=0. 01 rd/|z|,\ \omega_{h}=1000 rd/|z| \tag{39}\]  
For the simulation, the transfer function is approximated by a 10 poles and zeros distribution [9; 13]. Thus, these data do exhibit a power-law behavior.
# Sentiment Analysis of Twitter Data
## 6 The Approaches for Twitter Sentiment Analysis
### Machine Learning-Based Approach
#### 6.1.2 Linear Classifier  
The linear classifier is generally used to determine which class a feature belongs to. The classification decision is made based on linear predictor functions, which linearly combine feature values. Support Vector Machine (SVM) and Neural Network (NN) are another two widely used implementation methodologies.
## 1 Introduction  
Virtual reality (VR) is a contemporary technology that is being used in medical education and simulations during surgery. It is important for the surgeons to understand the patient's anatomy and practice accurate surgical skills. This is particularly important in maxillofacial surgery, which greatly influences patients' appearance and masticatory ability [1]. The first use of VR in the medical field was reported in the 1990s, which involved visualization of complex medical data during surgery and establishing surgical plans preoperatively with the assistance of VR [2]. A VR medical simulation is usually composed of a virtual patient in a medical three-dimensional (3D) model, with a reconstructed pathological lesion, surgical instruments, and tools for a VR interface [3].  
With the improvement in computer graphics and sensors, augmented reality (AR) has created new possibilities for developing diagnostics and surgical techniques in maxillofacial surgery [4]. [PERSON] et al. [5] reported a conceptual description of AR application in a three-dimensional stereoscopic overlay of the operating field. AR can provide the operator with visualization of a preplanned 3D model by overlaying the surgical field in real time during the surgical procedure [6].  
Computer-assisted surgery includes surgical planning with simulation software and fabrication of customized surgical guides. However, the process of manufacturing surgical guides is time-consuming and involves additional costs. Furthermore, surgical guides cannot respond to various unexpected situations during surgery. These disadvantages can be overcome by replacing this process with VR simulation and AR-guided surgery [7].  
This study describes the application of VR planning and AR-guided surgery for total maxillectomy in a patient with malignant fibrous histiocytoma in the maxilla.
# Towards Adaptive Multipath Managing:
## 6 Conclusions and Future Work  
Mobile computing devices attached to more than one wireless network interface can improve their transmission performance by making use of the MPTCP technology. However, the path management mechanism in MPTCP is very simple and vulnerable to the quality differences of multiple paths in heterogeneous wireless environments. As a remedy, this paper presented a lightweight multipath management mechanism for MPTCP (MPTCP-LM\({}^{3}\)) necessitating the following aims: (i) optimizing MPTCP path management mechanism and possibly preventing an underperforming path from multipathing, (ii) reducing the out-of-order data reception and alleviating the receiver buffer blocking problems in MPTCP, (iii) improving the throughput performance and quality of service of multipath transmission. The simulation results demonstrated that MPTCP-LM\({}^{3}\) outperformed the baseline MPTCP and MPTCP + (PU)\({}^{2}\)M\({}^{2}\) in terms of data transmission service quality.  
By carrying out simulations on top of NS-2, we noted a limitation that the throughput performance of all the three MPTCP variants was far from satisfactory, as we discussed in the Performance Evaluation Section. Recent research argues that there is an open-source implementation of multipath TCP that exists and is well maintained in the Linux kernel [35]. Our future work will develop a Linux kernel based hardware test-bed, then implement the proposed MPTCP-LM\({}^{3}\) solution in the test-bed, and provide real experimental results.  
Furthermore, due to the strict layering principles, MPTCP-LM\({}^{3}\) can only use the networking parameters at the transport layer to estimate each path's transmission quality and thereby make a \"best-effort\" transmission behavior. Considering the fact that variations of the wireless channel, such as channel fading and co-channel interference, are the extremely influencing factors in the wireless transmission, our future work will be devoted to the optimization of our MPTCP-LM\({}^{3}\) solution by jointly considering the cross-layer design and \"smart collaborative networking\" method [36; 37; 38; 39]. Then, a comprehensive wireless network environment including many and promising wireless technologies (i.e., Bluetooth, LoRaWAN, SigFox, etc.) will be considered, and the comparison with the state-of-the-art MPTCP schemes will be investigated in the performance evaluations.  
**Author Contributions:** Proposed the ideas, Y.C., M.C., S.X., L.H., X.T., and Z.Z.; writing, original draft preparation, Y.C., M.C., and Z.Z.; writing, reviews and editing, S.X.; investigation and validations, M.C. All authors read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61962026, by the National Key R&D Program of China under Grant No. 2017 YFC0806404, and by the Natural Science Foundation of Jiangxi Province under Grant Nos. 20192 ACBL21031 and 2017 IBAB212014.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 2 Literature Review of AAS
### Prediction of AAS by Machine Learning Techniques
Evaluation metrics further reinforced XGBoost's superiority, showing high accuracy at 0.918 and robust values for sensitivity, specificity, positive predictive value, and negative predictive value. In [PERSON] et al. 's [17] study, continuous variables were presented as mean \(\pm\) standard deviation, and categorical variables as frequencies and percentages. A decision tree, utilizing demographic and clinical data from the training set, reveals distinct risk profiles for various aortic dissection types. A nomogram based on logistic regression analysis established a scoring system using 23 selected variables. The data were randomly divided into training (80%) and testing (20%) sets, with missing values imputed using the median of recorded measurements. This pragmatic strategy ensured effective model training and testing, offering insights into model performance on unseen data while addressing missing values. The study explored various machine learning techniques, with the XGBoost model demonstrating the highest accuracy and robustness. Shapley Additive explanations analysis highlighted factors such as Stanford type A, maximum aortic diameter > 5.5 cm, high variability in HR, high variability in diastolic BP, and involvement of the aortic arch impacting in-hospital deaths before surgery. [PERSON] et al. 's study [18] showcased the impressive performance of a Convolutional Neural Network (CNN) model in predicting acute type A aortic dissection rupture. The CNN achieved a remarkable AUC of 0.99, along with notable sensitivity (93%) and specificity (90%). The investigation identified age, gender, specific biomarkers, and aortic morphological parameters as independent predictors for acute type A aortic dissection rupture, emphasizing the CNN's efficacy in accurately predicting and identifying potential risk factors. Notably, both random forests and CNN outperformed logistic regression (LR) in predicting acute type A aortic dissection risk, while the Support Vector Machine (SVM) demonstrated inferior performance compared to LR.
## 7 New Technologies and Future Perspectives
### Optical Coherence Tomography  
Optical coherence tomography (OCT) is a non-invasive imaging technique used to capture high-resolution images of biological tissues, such as the retina in the eye. It works by measuring the echoes of light waves as they reflect off different layers of tissue. OCT uses a low-power light source, typically in the near-infrared range, to produce images that have a resolution of a few micrometers. The light is directed onto the tissue, and then the echoes of the light waves that bounce back are analyzed to create a detailed image of the tissue. OCT generates images by measuring the backscattered light from the tissue using a Michelson interferometer. The light source emits near-infrared light, which penetrates the tissue and is backscattered by the tissue structures. Finally, the backscattered light is collected by the probe and then processed by the interferometer to generate a 2D or 3D image of the tissue [167].  
A recent study [168] used OCT images to evaluate the characteristics of plaque and create a scale together with clinical indicators and digital subtraction angiography to pinpoint symptomatic plaque features.
## 3 Results  
### Lens Doses for PET Nurses  
Table 1 shows the H\({}_{p}\)(10), H\({}_{p}\)(0.07), and H\({}_{p}\)(3) values for PET nurses, the numbers of procedures performed, and the amounts of drugs administered to patients during PET examinations. Over a 6-month period, the mean H\({}_{p}\)(10) and H\({}_{p}\)(0.07) (glass badge) values were 0.31 \(\pm\) 0.076 and 0.29 \(\pm\) 0.076 mSv, respectively; the mean monthly H\({}_{p}\)(3) (DOSIRIS) value was 0.47 \(\pm\) 0.064 mSv. The respective annual doses were 0.62 \(\pm\) 0.15, 0.60 \(\pm\) 0.15, and 0.93 \(\pm\) 0.13 mSv (Table 2). The annual dose estimates were all considerably less than 20 mSv, although the doses measured by DOSIRIS were higher than those measured by the glass badge. The estimated number of procedures that would need to be performed by a PET nurse for an annual exposure dose \(>\) 20 mSv was 12,216.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Nurses**} & **Neck Badge H\({}_{p}\)(10)** & **Neck Badge H\({}_{p}\)(0.07)** & **DOSIRIS H\({}_{p}\)(3)** & **Number of Procedures** & **Injected Dose** \\  & **(mSv/6 Month)** & **(mSv/6 Month)** & **(mSv/6 Month)** & **(/6 Months)** & **(GBq/6 Month)** \\ \hline Ns.1 & 0.34 & 0.32 & 0.57 & 321 & 104.07 \\ Ns.2 & 0.4 & 0.39 & 0.43 & 304 & 98.29 \\ Ns.3 & 0.19 & 0.18 & 0.4 & 240 & 77.34 \\ Ns.4 & 0.31 & 0.3 & 0.46 & 271 & 87.11 \\ mean \(\pm\) SD & 0.31 \(\pm\) 0.076 & 0.29 \(\pm\) 0.076 & 0.47 \(\pm\) 0.064 & 284 \(\pm\) 34.12 & 91.70 \(\pm\) 10.29 \\ median (IQR) & 0.33 (0.28–0.36) & 0.31 (0.27–0.34) & 0.45 (0.42–0.49) & 287.50 (263.25–308.25) & 92.70 (84.67–99.74) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Radiation doses of nurses, numbers of procedures performed, and amounts of PET drugs administered to patients by nurses over a 6-month period.
## 2 Design and Methods
### Engineering Design Process  
The standard five-step approach commonly employed in problem-solving is applicable to design problems as well. Design problems are generally defined more vaguely and have multiple correct solutions, due to the design process not being linear. Consequently, the design process often involves retracting steps and repeating iterations. Solving a design problem is a dynamic process, and the ultimate solution may encounter unexpected challenges and undergo changes during its development.  
The five steps utilized in the engineering design process are as follows [21]:  
1. Clearly define the problem.  
To commence the resolution of a design problem, it is crucial to have a clear unambiguous definition of the issue at hand. Design problems often begin with vague and abstract concepts in the mind of the designer. Crafting a precise definition for a design problem presents greater challenges compared to defining an analytical problem. As the design process unfolds, the problem's definition may evolve through a series of steps or processes, as a more comprehensive understanding of the issue is developed.  
2. Gather background information and data.  
To proceed effectively in the design process, it is essential to gather all available information that pertains to the problem. Novice designers often overlook this step and jump straight into generating alternative solutions. The process of gathering pertinent information can lead to the discovery of facts that may prompt a redefinition of the problem.  
3. Generate numerous potential solutions.  
In the design process, creativity involves going beyond set rules and theories to develop innovative solutions. This entails dissecting existing solutions, identifying weaknesses, and merging fresh ideas and techniques to formulate unique answers. Collaboration and brainstorming foster the generation of diverse solutions in the engineering design process.  
4. Analyze and choose the most suitable solution.  
After generating alternative solutions in the design process, the next step involves analyzing these solutions to determine the most suitable one for implementation. Analysis  
Figure 1: Design process/workflow steps.  
entails evaluating proposed designs using technical knowledge to make an informed choice. This stage becomes more comprehensive in advanced engineering courses. Considering the outcomes of design analysis is a subjective process that benefits from the input of experienced individuals. This section presents a systematic approach to evaluating alternative designs and aiding decision-making processes.  
5. Test and implement the selected solution.  
The last stage of the design process is implementation, encompassing the testing, construction, and production of the design problem's solution. Various implementation methods like prototyping and concurrent engineering should be considered, alongside specific activities like documenting the design solution and seeking patents.