esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
In the following of this Appendix A, as an example of the application of the proposed calculation model to the presented case study of Section 4, the design evaluation of the first compressor (C1-1 in Figure 2d) is presented in detail with reference to Equations (3)-(8).  
Considering a design mass flow rate and a density of the fluid at the inlet of each compressor respectively equal to 0.031 kg/s (necessary to produce 0.013 kg/s of LNG) and to 2.175 kg/m\({}^{3}\) (fluid density at 3 bar and \(-5\)\({}^{\circ}\)C), the suction volumetric flow rate is:  
\[\dot{V}_{ssc,des}=\frac{0.031}{2.175}=0.0143\;\text{m}^{3}/\text{s}\]  
Furthermore, the volumetric efficiency (\(\eta_{V,des}\)) can be calculated as:  
\[\eta_{V,des}=0.779-0.036\left(\left(\frac{24.5}{3}\right)^{\frac{1}{14}}\right) =0.618\]  
being \(p_{dis}=24.5\;bar\) and \(p_{suc}=3\;bar\) as a result of the application of the thermodynamic optimization analysis presented in Section 2. Furthermore, a value of the isentropic coefficient equal to \(k=1.4\) has been set.  
As it relates the compressor speed, the compressor of the analyzed case study is characterized by a value equal to 1500 rpm, corresponding to \(N_{des}=\)25 Hz. As a consequence the displacement results equal to:  
\[V_{disp}=\frac{0.0143}{25\cdot 0.618}=926\;\text{cm}^{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Units** \\ \hline b\({}_{1}\) & 0.779 & [–] \\ b\({}_{2}\) & \(-\)0.036 & [–] \\ dp & 0.000 & [–] \\ a\({}_{1}\) & 1.140 & [–] \\ a\({}_{2}\) & 0.295 & [–] \\ a\({}_{3}\) & 447.050 & [kPa] \\ c\({}_{1}\) & 293.487 & [K] \\ c\({}_{2}\) & 34.940 & [K] \\ c\({}_{3}\) & \(-\)24,202.367 & [K\(\cdot\)kPa] \\ US\({}_{\text{shell}}\) & 2.786 & [W/K] \\ d\({}_{1}\) & 1.223 & [–] \\ d\({}_{2}\) & \(-\)0.206 & [–] \\ d\({}_{3}\) & \(-\)0.017 & [–] \\ e\({}_{1}\) & 0.507 & [–] \\ e\({}_{2}\) & 0.606 & [–] \\ e\({}_{3}\) & \(-\)0.113 & [–] \\ \hline \end{tabular}
\end{table}
Table 1: Off-design calculation model parameters for a dataset of variable speed reciprocating compressor.  
Thus, the compressor shaft power (\(W_{des}\), [KW]) can be calculated as:  
\[W_{des}=\frac{1}{0.98}\cdot 300\cdot 0.0143\cdot 1.140\left[\left(\frac{2450}{300 }\right)^{0.295+\frac{14.3}{14}}+\frac{447.050}{2450}\right]=17.81\text{ kW}\]  
Once evaluated the compressor shaft power, the isentropic efficiency (\(\eta_{ls,des}\)) can be evaluated:  
\[\eta_{ls,des}=\frac{0.0143\cdot(1213-842)}{17.81}\cdot 2.175=0.643\]  
where \(h_{ls}=1213\) kJ/kg and \(h_{suc}=842\) kJ/kg are known from the previously presented thermodynamic analysis.
# Cystic Fibrosis: Overview of the Current Development Trends and Innovative Therapeutic Strategies
## 3 Non-Gene Therapy for CF
### Targeting Bacterial Biofilms
The antibiofilm activity of AMPs functions via different mechanisms of action [132]. Under stressful conditions, bacteria produce guanosine tetraphosphate and guanosine pentaphosphate [(p)ppGpp], which are essential regulators of biofilm homeostasis [135]. The IDR-1018 peptide directly attaches to (p)ppGpp and thus, affects biofilm formation [136]. However, bacterial biofilms are typically embedded in EPS, which protects them from harsh environments and antimicrobial agents [137]. DNase I and glycoside hydrolase dispersin B peptides cause inactivation of eDNAAs and cleavage of primary EPS components, respectively, and thus, have potent activities against dispersed bacterial biofilms [72]. Moreover, certain bacteria synthesize amyloids, which promote surface adhesion, aggregation, and biofilm formation [138]. Translocation-dependent antimicrobial spore component (TasA), a significant biofilm component, forms amyloid-like fibers that help in biofilm formation [139]. This amyloid formation can be inhibited by AA-861 and parthenolide AMPs, leading to the attenuation of bacterial virulence and biofilm destruction [139]. As pyocyanin produced by the bacteria promotes the continuous generation of eDNAAs that are essential for biofilm formation [140], another antibiofilm strategy is to crosslink these compounds with EPS273 AMP, thereby inhibiting the release of eDNAAs [141]. In this review, we have described the antimicrobial action of some AMPs. However, further investigations are required to circumvent the challenges associated with these AMPs. Thus, this review facilitates the development of novel AMPs, thereby providing new opportunities in the fields of drug discovery and antibiofilm therapy.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 9 Analysis and Discussion of Cyber Risks and Countermeasures in HE
### Financial Fraud  
The findings document _cyber-crime_ as the main threat actor for academia. Primarily motivated by financial gain, this actor infiltrate systems and routines looking for financial and transaction data for exploitation. Fraud is conducted mainly through social engineering, looking to exploit weak security routines or low-security awareness. Criminals can also leverage extortion techniques to coerce universities and employees into paying money. A popular attack vector is hijacking acquisition processes through social engineering and sending false invoices to the target. This attack technique succeeded tricking, for example, the University of Tromso into paying 1.2 million Euro ([[https://uit.no/nyheter/artikkel?p_document_id=659434](https://uit.no/nyheter/artikkel?p_document_id=659434)]([https://uit.no/nyheter/artikkel?p_document_id=659434](https://uit.no/nyheter/artikkel?p_document_id=659434)) (Visited 1 November 2020)).  
_Countermeasures:_ A potential countermeasure for financial fraud is implementing sufficient security policies and awareness training in the organization. The paper from [PERSON] and [PERSON] [46] highlight several recommendations to implement information security awareness training in higher education. These recommendations include information security awareness campaigning, training users on information system security best practices, practice reward and punishment and continuous evaluate and readjust, to name a few.
## 3 Proposed Scheme
### Scrambling and Shuffling of Image Pixels  
The rearranging, shuffling, and shifting operations in steps 1-3 of the proposed image encryption scheme are used to increase the complexity and randomness of the encryption process. The motivation behind these operations is to make it difficult for an attacker to decipher the encrypted image without knowledge of the secret keys. The scrambling of the image pixels in step 1 ensures that the image data are not in their original form, making it difficult for an attacker to recognize the image. The shuffling of the rows in step 2 and the shifting of the columns in step 3 further increase the complexity of the encryption process by introducing additional randomness. Without these operations, the algorithm may be less effective in ensuring secure transmission of information.  
Step 1: Take a grayscale plain image \(\beta\) of size \(256\times 256\) and divide it into four different equal blocks of \(\beta_{1},\beta_{2},\beta_{3},\beta_{4}\) such that \((\beta_{1},\beta_{2},\beta_{3},\beta_{4})\in\beta\) and rearrange those blocks as in Figure 1.  
Step 2: Each block is scrambled by sorting odd columns only and shift them to left side, odd rows and shift them to the top of the image; for example, as illustrated in Figure 2a,
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
## 4 Results and Discussion
### Scour Prediction Equations  
Various models used for predicting the scour depth around piers, along with their applicability range and limitation, are listed in Appendix B. These models are commonly adopted for piles, even though the scouring behavior around piles is different from that around piers. Based on simplicity and their use of influencing factors consistent with scouring around piles, the empirical equations proposed by [PERSON], [PERSON] (2018) and [PERSON] (2018) are compared with the experimental results. The empirical models are stated as.  
\[\frac{\mathrm{d_{s}}}{\mathrm{b}}=1.5\bigg{(}\frac{\mathrm{d_{o}}}{\mathrm{b} }\bigg{)}^{0.3} \tag{13}\]  
\[\frac{\mathrm{d_{s}}}{\mathrm{b}}=3.3\bigg{(}\frac{\mathrm{D_{50}}}{\mathrm{b} }\bigg{)}^{0.2}\bigg{(}\frac{\mathrm{d_{o}}}{\mathrm{b}}\bigg{)}^{0.13} \tag{14}\]  
where \(\mathrm{d_{s}}\) represents the scouring depth. \(\mathrm{b}\) defines the diameter of breath or diameter of the piles, \(\mathrm{D_{50}}\) is the sediment diameter and \(\mathrm{d_{o}}\) represents the depth of the approach flow. A comparison of the experimental and the estimated scour holes is given in Table 3 and Figure 9. Accordingly, those pile positions have been selected that experience the maximum scouring, i.e., position A in the square and circular piles and position B in the lenticular piles. It is anticipated that these empirical equations cannot estimate the scour hole around the circular and square piles. However, the empirical estimation is nearly equal to the experimental results obtained from the lenticular piles.
## 3 Experimental Validation of the Mechanical Amplifier  
### Manufacturing of the Mechanical Amplifier  
Following the theoretical design and modeling of the mechanical amplification mechanism, its manufacturing was carried out at the National Research and Development Institute for Electrical Engineering ICPE-CA in Bucharest. The Wire Electrical Discharge Machining (WEDM) process was performed using a Smart Dem 320 A machine ([PERSON], Konigsberg, Germany). The WEDM uses a metallic wire to cut or shape a workpiece (that is often a conductive material) with a thin electrode wire that follows a precisely programmed path. In our case, the diameter of the chosen electrode was 0.4 mm. The developed 3D  
Figure 7: Compliant mechanical amplification device with two stages of amplification (**a**) and the proof-of-concept developed system (**b**), with its actual dimensions (subject to future miniaturization).  
model presented in the previous subsection was utilized in order to obtain the necessary parts. Four mechanical amplifiers were fabricated as a single stack in order to ensure a uniform geometry across all units. Following this fabrication, a slicing operation was carried out in order to separate the individual amplifiers. Thus, each required component was obtained.  
The mechanical properties of the utilized steel were determined experimentally using the nanoindentation method. The dedicated tool was a Vickers FM 700 Microhardness Tester (Future-Tech Corp. FM-700, Tokyo, Japan), while the Oliver and Pharr calculation method was employed. The material properties have been introduced in the FEA (i.e., they were declared as parameters of a new material in the performed simulations), as presented in Figure 11 (Appendix A).  
The manufacturing stage was followed by the metrological inspection of the components in order to assess eventual deviations from their intended geometry (Figure 8). All the components were examined using a Zeiss Stemi2000-C microscope, equipped with an Axiocam Icc 1 video camera and a Zeiss 038-05436 tunable light source (ZEISS AG, Oberkochen, Germany). In order to measure the geometry of the components, the image analysis was conducted using the AxioVision software 4.8, which is associated with the above video camera (and manufacturer).  
Upon completing the geometric verification of the parts, the cut components exhibited dimensional deviations with a maximum error of 0.01 mm. This corresponds to a deviation of up to 3% from the nominal dimensions. All spherical joints, as well as the fillet radius in the right-angle areas, were carefully inspected. The most significant measurements are illustrated in Figure 9. All four mechanical amplifiers were verified, thus confirming that the geometric deviations from the designed model were within the 0.01 mm tolerance. The next step involved conducting a static analysis of the mechanical components.  
Figure 8: Experimental setup utilized for the metrological inspection of the manufactured components of the developed device.
## 3 Results and Discussion
### Characteristics of MnCe-GAC  
The BET-specific surface areas for GAC and MnCe-GAC were approximately 811 and 590 m\({}^{2}\)/g, respectively. The lower surface area of MnCe-GAC indicated that some of the pores of GAC might be blocked by the impregnation of Mn and Ce on the GAC surface. Surface characteristics of GAC and MnCe-GAC were further examined by SEM as shown in Figure 2a and b, respectively. The amount of Mn and Ce on MnCe-GAC surfaces was approximately 65.1 mg Mn/g (SD = 1.5 mg Mn/g) and 27.0 mg Ce/g (SD = 1.1 mg Ce/g), respectively. The loss of Mn and Ce on MnCe-GAC was determined by comparing the amount of Mn and Ce on MnCe-GAC before and after the treatment processes. In these tests, the total loss of Mn and Ce was less than 6%. This result indicated that MnCe-GAC has the potential to be reused.
###### Abstract
However, it remains to be discussed how the spatial-temporal resolution of water vapor retrieved from microwave instruments affects the accuracy of AMVs. Understanding the relationship between them contributes to improved AMV tracking algorithms and the design of future microwave instrument platforms. [PERSON] et al. [27] and [PERSON] et al. [28] conducted AMVs experiments by tracking WRF Model water vapor features. However, the tracking algorithm they used is a traditional local area pattern matching method, which only calculates the regional average wind vectors [16], failing to meet the requirements of future high-resolution satellite observations [29]. Additionally, the traditional algorithms have high computational costs and large errors. In this study, the Farneback optical flow (OF) algorithm [30] is proposed to track the water vapor feature pixel by pixel and saves much calculation cost. This study aims to obtain pixel-wise 3D wind fields by tracking features of 3D water vapor fields using the Farneback OF method. The wind field directly simulated by the WRF model can be regarded as the \"truth\". The deviation between \"WRF wind field\" and \"OF wind field\" can be considered as the wind results\" \"error\". Analyzing the error structure and sensitivity can verify the feasibility of obtaining 3D wind fields by tracking 3D water vapor retrieved from microwave observations and will help to explore the sensitivity and requirements of the Farneback OF wind track algorithm to the satellite revisiting time and sensor's field-of-view (FOV) sizes. In Section 2, we introduce WRF simulated data and the Farneback OF wind track algorithm. In Section 3, the AMVs' error structure and sensitivity are compared and analyzed for different weather systems. The conclusions and suggestions for future work are described in Section 4.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 3 Methodology
### Machine Learning Algorithms  
We start by introducing two standard techniques from machine learning. Both these techniques generate, after analysing training data, a _classifier_: in our case, an algorithm to which we input the 13 features of the input graph and which then outputs which of the three treewidth solvers to run on that graph.  
Decision Trees are a widely-used model for solving classification problems [42]. The model works by partitioning the training dataset according to the features of each instance, with each partition being assigned one of the target labels. The partitioning is done in steps--first, the entire dataset is partitioned and then each of the resulting partitions may be further partitioned recursively, until a certain condition is met. The resulting model can be expressed hierarchically as a rooted tree, where every node represents a partition and every edge represents a partitioning rule based on instance features. Then, classifying an instance is as simple as starting at the root node and following the path that the instance takes according to its features and the partitioning rules it encounters. The leaf node in which the instance ends up determines the label that it should be assigned. A comprehensive introduction to decision trees is due to [PERSON] [42]. Decisions trees are popular due to their interpretability. Some of their disadvantages include a propensity for overfitting through building too-complex trees, as well as the possibility for small changes in the training dataset to produce radical differences in the resulting tree [43].  
Random Forest is also a widely-used model for solving classification problems, based on decision trees [44]. Essentially, a Random Forest is an ensemble model that uses a majority-voting system between multiple decision trees to classify instances. The trees themselves are also built according to special rules--a detailed explanation of those and Random Forests in general is due to [PERSON] et al. [44]. Random Forests exhibit strong predictive performance and resistance to over-fitting.  
Due to their strong predictive performance, our primary results use Random Forests to learn the mapping from graphs to algorithms. However, compared to a single decision tree, Random Forest classifiers can be difficult to interpret. For this reason we also consider classification based on a single decision tree, which in this article turns out to have marginally lower predictive power but allows us to analyse and interpret the sequence of decisions taken to map graphs to algorithms. Once built, the computational time required to execute our classifiers is negligible.
## 3 Results and Discussion
### The Fluid Dynamics of the Cold Fluid
#### 3.2.1 The Velocity Distribution of the Ambient Air into the HE  
The velocity distributions corresponding to the different inlet ambient air \(RH_{0}\) values of 80%, 85% and 90% are depicted in Figure 6a-c, respectively. As can be seen from the figures, the maximum \(v_{air}\) for the \(RH_{0}\) of 80%, 85% and 90% is 8.70, 8.66 and 8.57 m\(\cdot\)s\({}^{-1}\), respectively, and the distributions of the \(v_{air}\) in the three nephograms are also similar. Accordingly, it can be concluded that the \(RH_{0}\) has a slight influence on the velocity distribution of the ambient air into the HE. In the initial stage of the ambient air flowing into the HE, the \(v_{air}\) is distributed evenly, the flow path changes a little and the fluid flow is relatively smooth. However, with the increase in the flow path, the flow direction of the air demonstrates a slight bending trend, which might owe to the fact that the alternative arrangement of the pipe rows leads to the reduction in the fluid flow path, and then to the gradual expansion of the flow path. In other words, the fluid path flowing from the front row is significantly affected by the later one, and further leads to the change in the fluid flow path. Moreover, it can be also concluded from all the nephograms that in the middle of the HE, the \(v_{air}\) decreases with the increase in the flow path owing to the fact that the resistance accumulation is also increased with the increase in the flow path. However, the \(v_{air}\) close to the wall increases rapidly after half of the flow path is completed, which might be caused by the fact that cold fluid flows to both sides under interference from the tube bundle. The phenomenon might result in the insufficient heat exchange between the cold fluid and the tube bundle. The reduction in the distance between the tube bundle column and the wall of the HE might be an effective method to enhance the heat exchange performance of the HE, while a higher resistance will be needed instead. Therefore, it is necessary to figure out a suitable distance for enhancing the heat exchange performance while retaining the low flow resistance of the cold fluid.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data
### Artificial Intelligence Methods  
Over the past few decades, researchers have progressively automated the anomaly detection process using Artificial Intelligence (AI) methods [20; 22; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. Several AI techniques, designed to learn and extract features from telemetry data, have been used widely to detect abnormalities in spacecraft. These methods mainly include machine and deep learning models.
## 4 Antimicrobial Agents
### Synthetic Small Organic Molecules
For example, Dihydro-pyrrol2-ones (DPO) derivatives such as diethyl1-(3-chlorophenyl)-4-((3-chlorophenyl) amino)-5-oxo-2,5-dihydro-1H-pyrrole-2,3-dicarboxylate exhibited inhibition in _P. aeruginosa_ growth and biofilm formation by inhibiting mannitol dehydrogenase (MDH) and eDNA. MDH is involved in the synthesis of alginate which is one of the EPS components of _P. aeruginosa_[90]. Brominated furanone derivatives are known to inhibit biofilms in different bacterial species. The synthetic (Z)-5-bromomethylene-2(5H)-furanone repressed microbial communication mediated by AI-2 in several Streptococcus such as _Streptococcus angionus_, _Streptococci intermedius_, and _Streptococcus mutans_. Similarly, bicyclic brominated furanones inhibited AI-2 mediated QS in _Tannerella forythia_, _Porphylyoromonas gingivalis_, and _Fusobacterium nucleatum_[91]. Halogenated phenazines showed powerful activity against Methicillin-resistant _S. epidermidis_ (MRSE), Methicillin-resistant _S. aureus_ (MRSA), and Vancomycin-resistant _Enterococci_ (VRE) by binding with iron (II) and copper (II) that exhibited antibiofilm activity [92]. In the past, several bacterial infections were treated with quinolones. However, using an in silico virtual screening method, it has been lately identified that quinolone compound Ia could reduce _P. aeruginosa_ biofilms by inhibiting PqsR (regulatory proteins). It also showed a synergistic effect with other antibiotics such as tobramycin [93]. [PERSON] et al. showed the 3,4-dimethoxyichamide derivative showed biofilm inhibition in _P. aeruginosa_ by inhibiting LecB [94]. Although several small molecules have proved to be efficient biofilm inhibitors, none of these agents have reached clinical use due to a lack of experiments in animal models. Therefore, new in vivo studies using small molecules are urgently needed to assess their therapeutic potential [78].