esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing Infrastructure-as-a-Service Public Clouds Using Security Onion
## 2 Background
### Cloud Computing
#### 2.2.6 Automated Cloud Security Using Intrusion Detection/Prevention  
The continuous evolvement of Cloud computing architecture means that traditional firewalls are unable to secure the Cloud effectively. In disparity to traditional firewalls which secures a network by probing network packets passing through network ports, IDS monitors and analyses the headers and payloads of network packets and compares them with normal/benign traffic to detect any vulnerability exploits against a target application or system. IDS primarily try to identify patterns in known attacks and alerts the security administrator. Equally, intrusion prevention systems (IPS) is similar to IDS, though it also has the capability to reject malicious traffic and to terminate a suspicious connection.  
As we know that the backbone of Cloud computing is a high-speed network, therefore this calls for the use of an automated or faster IDS/IPS. IDSs have a variety of flavors and approaches used to detect suspicious traffic (see Figure 2). A network-based intrusion detection/prevention system (NIDS/NIPS) attempts to secure all devices on a network by analyzing network traffic for signs of malicious behaviors, while a host-based intrusion detection/prevention system (HIDS/HIPS) attempts to secure one particular host [9]. HIDS/HIPS is particularly used to secure valuable or private information on a specific server. As well as HIDS/HIPS and NIDS/NIPS, hypervisor-based IDS provide another type IDS used to ensure the security of data and information in the Cloud. This type of IDS/IPS runs inside the hypervisor, making it possible to monitor and analyze communication between VMs, hypervisors, and the virtual network.  
Typically, IDS have two types of detection approaches: Anomaly detection and signature detection [24]. The anomaly detection approach maintains a pool of information regarding user profile, hosts, application and connection (ports, devices, and protocols) specified by a network administrator. Anomalies are detected by comparing current activity with the rules defined by the administrator. In disparity, the signature detection approach compares signatures of activities on the system against an array of malicious signatures or threat patterns.  
Figure 2: Types of intrusion detection system (IDS) in Cloud Computing.
# Characterization of the Aroma Profile of Commercial Prosecco Sparkling Wines
## Results and Discussion
### Effect of the PDO on Volatile Profile
Six VSC were significantly different, showing, in general, slightly higher but significantly different concentrations in Asolo wines (Figure 1). The presence of these compounds can negatively contribute to wine aroma with the smell of onions, rotten egg, and truffle. Many factors promote the formation of VSCs, such as residual sulfur-containing pesticides, yeast strain used for fermentation, development of microorganisms, and temperature of fermentation [35,46,47]. With regard to the fermentative compounds, ethyl octanoate, hexanoic acid and phenylethyl alcohol showed statistically significant differences. Wine samples from Treviso DOC showed a lower amount of hexanoic acid and phenylethyl alcohol. Ethyl octanoate was found to be more concentrated in Asolo wines. It should be noted that, although not significant, the content of ethyl hexanoate also tended to be higher in Asolo samples, while no significant differences were observed for acetic esters and branched ethyl esters. The benzenoids 4-vinylguaiacol and benzaldehyde were found at higher levels in Valdobbiadene wines. However, the concentrations detected did not appear to be such as to involve a real olfactory contribution.
# Prediction of Blood Pressure after Induction of Anesthesia Using Deep Learning: A Feasibility Study
###### Abstract  
Anesthesia induction is associated with frequent blood pressure fluctuation such as hypotension and hypertension. If it is possible to precisely predict blood pressure a few minutes ahead, anesthesiologists can proactively give anesthetic management before patients develop hemodynamic problem. The objective of this study is to develop a real-time model for predicting 3-min-ahead blood pressure from the start of anesthesia induction to surgical incision. We used only vital signs and anesthesia-related data obtained during anesthesia-induction phase and designed a bidirectional recurrent neural network followed by fully connected layers. We conducted experiments on our collected data of 102 patients, and obtained mean absolute errors between 8.2 mmHg and 11.1 mmHg and standard deviation between 8.7 mmHg and 12.7 mmHg. The average elapsed time for prediction of a batch of 100 unseen data was about 26.56 milliseconds. We believe that this study shows feasibility of real-time prediction of future blood pressures, and the performance will be improved by collecting more data and finding better model structures.  
blood pressure prediction; deep learning; recurrent neural network; real-time sequence prediction +
Footnote †: journal: Applied Sciences
## 3 Agent-Based Model for Co-Traveling
### Problem Layer
#### 3.3.1 Exploration  
Each non-carpooling agent explores the carpooling social network for carpool partners to cooperate with while performing their daily trips. The presented framework is established on the movement of vehicles between Traffic Analysis Zones (TAZ) as different to agents' street or home addresses. The model considers that people board at home and alight at work TAZ locations. People agree to choose carpool companions from the group of individuals segmented on the basis of similar home and work TAZ locations.  
The agents, once they decide to carpool, may start searching for carpool partners from the carpooling social network. In case they do not want to carpool, they continue traveling solo by using  
Figure 1: The agent-based framework for co-traveling based on organizational concepts.  
their own car. The agents who reside in the same carpooling social group (grouped on the basis of same home and work TAZs) may interact by exchanging text messages. Through the interaction process, the agents may search for the ideal candidate for carpooling and may reach a coordinated solution by negotiating on the departure times of both the trips, on the selection of the driver, and hence on the choice of vehicle. Agents may continue looking for participants in the exploration process throughout the simulated period (in case they are unable to find a carpool partner).  
For a carpooling agent, the time intervals, path, and personal profile are considered as the relationship information to compare with other agents. In general, every agent has a basic set of properties for interaction such as mutual interests and requirements. The common interest (intention to invite someone for carpooling) and requirements (the similarity values: path, route, time intervals, and some personal attributes) for the corresponding individuals need to match satisfactorily well for the interaction process.  
Each agent (the sender) may look for a carpooling partner by sending him a carpool invitation message. This emission of a carpool invitation depends on the personal intension to invite someone with similar attributes i.e., the similar home and work TAZ. Some people do not like to carpool so they will not emit any carpool invitation. Within a day, an individual can search for carpool partners multiple times in the carpooling social network. The receiver agent may accept the sender or inviter as a carpooling partner when the negotiation succeeds. The negotiation process is described in the following section.
## 3 The Algorithm
### Complexity Analysis  
From Figure 7, we conclude that, for N subcarriers, the time-domain NN models' complexity in both QPSK and DCM, in terms of number of integer multiplications and integer additions is 14 \(\times\) N and 12 \(\times\) N, respectively. The frequency-domain NN models complexity, in terms of number of integer multiplications and integer additions is 14 \(\times\) 4 \(\times\) N and 12 \(\times\) 4 \(\times\) N for QPSK, and 14 \(\times\) 12 \(\times\) N and 12 \(\times\) 12 \(\times\) N for DCM. In the proposed frequency-domain NN models, the complexity is relative to the  
Figure 6: New time-domain and frequency-domain NN for DCM.  
Figure 7: NN model design.  
type of modulation used, so the number of integer multiplications and integer additions is 14 \(\times\) N and 12 \(\times\) N for QPSK, and 14 \(\times\) 3 \(\times\) N and 12 \(\times\) 3 \(\times\) N for DCM, respectively (Table 2).
# CuInSe\({}_{2}\)-Based Near-Infrared Photodetector
## 2 Materials and Methods  
### Fabrication of CISe Thin Film Photodetectors  
CISe thin film photodetectors were fabricated on top of an SLG substrate. The SLG substrates were cleaned to remove organic contamination using acetic acid and deionized water prior to depositing the back contact layer. Molybdenum with a thickness of 1.0 \(\upmu\)m was sputtered on SLG as a back contact layer with sputtering power of 300 W under low Ar pressure of 2 mTorr. The CISe layer was deposited using a three-stage co-evaporation process, which is shown in Figure 1a. The In\({}_{2}\)Se\({}_{3}\) precursor layer was deposited during the first stage with a substrate temperature of 350 \({}^{\circ}\)C, followed by Cu and Se evaporation during the second stage with a substrate temperature of 550 \({}^{\circ}\)C. In the third stage, In and Se were co-deposited. Then the films were cooled within a flux of Se until the substrate temperature dropped to 350 \({}^{\circ}\)C. The 60 nm-thick CdS layer was subsequently deposited using a chemical bath deposition method. After RF magnetron sputtering of a transparent conducted oxide (TCO) layer consisting of a 70 nm thick i-ZnO layer and a 350 nm thick aluminum-doped ZnO layer (ZnO), a metal electrode consisting of a 50 nm thick Ni adhesion layer and 1 \(\upmu\)m thick aluminum was deposited by e-beam evaporation to complete fabrication of the CISe based photodetectors. The 65 nm thick magnesium fluoride (MgF\({}_{2}\)) thin film was used for the antireflection coating to minimize the surface reflection. Schematic designs of the CISe thin film photodetectors are shown in Figure 1b. The diameter of the exposed area of the CISe-based photodetectors was 2 mm.  
Figure 1: (**a**) Schematic of the three-stage co-evaporation process. (**b**) Schematic designing of CISe-based photodetectors.
## 6 Simulation Results
### Simulated Time Offset Estimation Performance  
In Figure 5, we plotted the time offset estimates \(\hat{T}\) for the duration of the simulated flight path. In Figure 5a, we plotted the true time offset \(T\) in red and the clock drift \(\hat{T}\) in blue. In Figure 5b,c, we plotted the estimates generated by the optimal first- and second-order one-shot estimators described in [9], labeled STP1 and STP2, respectively. Both of these estimators successfully estimated the time offset to a precision of about \(\sigma=0.05\) ns and the clock drift to a precision of about \(\sigma=1\) ns/s. In Figure 5d,e, we plotted the estimates produced by the proposed first- and second-order EKF tracking solutions, labeled EKF1 and EKF2, respectively. The first-order method depicted in Figure 4d achieved comparable performance and converged to a solution after only a single interaction cycle, while the second-order method depicted in Figure 5e again took several cycles to converge, displaying the same tradeoff between the computational complexity and convergence speed as the previous results.  
Figure 5: (**a**) True time offset and drift, (**b**,**c**) existing first- and second-order one-shot estimators [9] labeled STP1 and STP2, respectively, and (**d**,**e**) our proposed first- and second-order extended Kalman filter estimators labeled EKF1 and EKF2, respectively.
## 3 Geometry and Numerical Method
### Grid Independence Validation  
Figure 7 presents the structured mesh, which is generated by ANSYS ICEM 18.0. It is noted that the mesh is denser near stationary and rotational walls compared to the center since the flow is complex in boundary layer. To satisfy the requirements of the turbulence model, the first cell height near walls is 5.0 \(\times\) 10\({}^{-7}\) m with a growth rate of 1.1, ensuring that \(Y^{+}\) is appropriately controlled.  
The method recommended by [PERSON] et al. [38] was used to perform grid independence validation with the three different grid numbers, consisting of 1.6 million, 3.2 million, and 6.4 million grids, to ensure that CFD simulations were independent of the number of grids. The non-dimensional velocity distributions under different axial position conditions for each mesh are shown in Figure 8a, where \(u\) denotes fluid velocity, \(V\) represents the linear velocity of rotor, and \(Z\) is the axial coordinate. It exhibits a negligible variation as the number of grids increases from 3.2 million to 6.4 million. Furthermore, the maximum relative deviation between their results is only 2.32%. Figure 8b describes the 3.2 million grid solution with a discretization error. The maximum relative deviation between the non-dimensional velocity and the extrapolated solution for 3.2 million grids is merely 1.66%, indicating that mesh refinement can enhance reliability in CFD results. Therefore, adopting the 3.2 million grids allows for the performance of CFD simulations in this study.  
Figure 8: Grid-independence validation. (**a**) Non-dimension velocity distribution, (**b**) 3.2 million grid solution.  
Figure 7: Mesh of fluid domain.
## 3 Modelling of Wave Propagation in Wooden Poles  
To simulate the wood pole embedded in the soil and demonstrate the wave propagation behaviour in the embedded poles, three issues should be considered in numerical modelling: wood material, soil and soil-pole interaction. Adopting the proper modelling approach, the wave behaviour in the embedded timber pole will be investigated.
## 2 UAV Platform Evolution
### Multispectral Sensors
UAV chemical sensing shows immense promise for environmental research and regulation. Networked UAVs can provide rapid, widespread air-quality assessments and help enforce pollution standards [4; 101]. For climate change research, UAV methane mapping can improve our understanding of gas emission sources, such as permafrost thaw, and quantify carbon cycle feedbacks [102]. Equipping UAVs with radon sensors can also improve earthquake predictions by tracking underground gas seepages [103; 104]. As sensors become miniaturized, UAVs will likely grow into a standard airborne platform for environmental chemical sensing across scales from localized events to global processes. Figure 7 summarizes the different meteorological, chemical, and LiDAR sensors, resolution, size, and weight. Light-detection and ranging (LiDAR) sensors utilize laser radiation to survey environmental features by measuring reflected laser pulses. By precisely timing the flight duration between emission and reflected beam detection, known as time of flight, the range or distance between the scanner and object can be calculated based on the speed of light. The angles of the emitted and reflected beams are also measured to determine precise 3D point locations. Detailed 3D maps of the surrounding geometry and terrain can be rapidly constructed through millions of timed laser pulse reflections from the LiDAR sensors. LiDAR's active sensing approach using laser range determination enables the highly accurate mapping of physical environments. LiDAR sensors on UAV platforms (LiDAR-UAV) provide high- to very-high-spatial-resolution terrain and surface data. Unlike satellite data, LiDAR UAVs offer a superior resolution and are not limited by cloud cover conditions [109]. UAV-based LiDAR can also acquire accurate ground elevation data, even in areas with dense vegetation or restricted accessibility [110]. The studies show UAV LiDAR generates quality comparable to terrestrial laser scanning (TLS) for terrain mapping [111]. RGB photogrammetry from UAVs offers a lower-cost alternative for some vegetation surveys versus LiDAR [112; 113]. However, photogrammetry has limitations  
Figure 7: Meteorological, chemical, and LiDAR UAV sensors and their resolutions [105; 106; 107; 33]. in analyzing ground surfaces obscured by dense vegetation [114]. UAV LiDAR provides accessibility, ease of planning, and cost-effectiveness compared to airborne laser scanning (ALS) [115; 116].
# Emerging Non-Canonical Functions and Regulation by p53: p53 and Stemess
## Abbreviations  
5-FU 5-Fluorouracil ABC  ATP-binding cassette ADR  Adriamycin AKT/mTOR  Protein kinase B or serine/threonine-specific protein kinase/mechanistic target of rapamycin Ang-1  Angiopoietic 1  ATP Adenosine triphosphate Bcl-2  B-cell lymphoma 2 bHLH factor E47  Basic helix-loop-helix factor E47 BMP  Bone morphogenetic protein BBB  Blood brain barrier BRACA1  Breast cancer gene 1 CAF  Cancer-associated fibroblast CBL  Casitas B cell lymphoma CDKN1A  Cyclin-dependent kinase inhibitor 1A COX  Cyclooxygenase2 CSC  Cancer stem cell CTD  C-terminal regulatory domain DNA  Deoxynitonucleic acid H2 AK119 E3 ligase  Histone 2A at lysine 119 E3 ligase hESC  Human embryonic stem cell ECM  Extracellular matrix ERK  Extracellular signal-regulated kinase EMT  Epithelial-mesenchymal-transition FoxO  Forkhead box O FAK  Focal adhesion kinase Gfi-1  Growth factor independent 1 transcription repressor GOF  Gain-of-function GOX  Glucose oxidase HIF1  Hypoxia-inducible factor 1 HIF2  Hypoxia-inducible factor 2 HFSC  Hair follicle stem cell HMGA2  High mobility group at-hook 2 HSC  Hematopoietic stem cell ICM  Inner cell iPSC Induced pluripotent stem cells  
KLF4 Kruppel-like factor 4  
LIN28A Lin-28 homolog A  
MAPK
Mitogen-activated protein kinase  
mCSC  
Metastatic cancer stem cells  
Mdm2  
Mouse double minute 2 homolog  
MEFs  
Murine embryonic fibroblasts  
MEF/Elf4  
Myeloid Elf-1-like factor/ETS-related transcription factor 4  
kinase  
mESC  
Mouse embryonic stem cell  
MET
Mesenchymal-epithelial-transition  
MGMT  
\(\mathcal{O}^{6}\)-methylguanine DNA methyltransferase  
MSC  
Mammary stem cell  
Mut p53  
Neutral precursor cell expressed developmentally down-regulated  
protein 4  
NOD/SCID  
Non-obese diabetic/severe combined immunodeficiency  
NOS2  
Nitric oxide synthase 2  
NOTCH  
Notch homolog 1, translocation-associated  
NSC  
Neural stem cells  
PCL1  
Polycomb like 1  
PHD  
Plant homeodomain  
PI3K/Akt  
Phosphatidylinositol-3-kinase  
PIGs  
p53-induce genes  
PRC2  
Polycomb repressor complex 2  
PTEN
Phosphatase and tension homolog  
pCSC  
Primary cancer stem cell  
rCSC  
Recurrence cancer stem cell  
RING  
Really interesting new gene  
Ring1B/RNF2  
Really interesting new gene 1B/ring finger protein 2  
RNAi  
Ribonucleic acid interference  
ROS  
Reaction oxygen species  
SCF  
SKP1-CUL1-F- box protein  
SLUG  
SNAIL  
SNAII or Snail family zinc finger 1  
SMUC  
SNAI3 or Snail family zinc finger 3  
SMWC  
Small molecular weight compounds  
SPARC  
Secreted protein acidic and rich cysteine  
TAD  
N-terminal transactivation domain  
TGF-B  
Transforming growth factor beta  
TIC  
Tumor-initiating cell  
TMZ  
Temozolomide  
TPO  
Thrombopoietin  
TRIM24  
Tripartite motif-containing 24  
TWIST1  
Twist family BHLH transcription factor 1  
TWIST2  
Twist family BHLH transcription factor 2  
USP7  
Ubiquitin-specific-processing protease 7  
VEGF  
Vascular endothelial growth factor  
VHL  
von Hippel-Lindau  
Wt p53  
Wild-type p53  
ZEB1  
Zinc finger E-box binding homeobox 1  
ZEB2  
Zinc finger E-box binding homeobox 2
# Recent Advances in Extraction and Stirring Integrated Techniques
## 3 Stir Membrane Extraction  
Stir membrane extraction (SME) was proposed by our research group in 2009 as a new stirring technique based on the use of polymeric membranes as sorptive phases [29]. Membranes present a high surface-to-volume ratio and permeability, especially if they are compared with classical SBSE coatings, which are advantageous characteristics from the kinetics point of view. To be applied, SME requires a special device that integrates stirring (a protected iron wire) and extraction (membrane) elements. The first unit, schematically shown in Figure 4A, was designed in polypropylene using commercial products as precursors. In its first application, aimed at determining selected polycyclic aromatic hydrocarbons (PAHs) in water samples, SME provided better results than stir bars fabricated by the same membrane polymer (PTFE). Although SME can be applied in the normal extraction/elution workflow, it can be directly combined with spectroscopic techniques that allow the in-membrane detection of the targets without a previous elution. This strategy was reported for the determination of the hydrocarbon index in water by infrared spectroscopy [30]. For this purpose, the SME unit was built in stainless steel (Figure 4B) to promote the retention of the analytes into the membrane which presents a low thickness (40 \(\upmu\)m), to permit the transmission of the infrared beam. The versatility of SME is based on the great availability of commercial and lab-made membranes. In fact, the use of fabric phases, a porous hybrid inorganic-organic sorbent material chemically bonded to the flexible and permeable substrate matrix, [31] has been also proposed as a sorptive phase in SME [32]. This combination allowed the extraction of several triazine herbicides from water samples with enrichment factors in the range from 444 to 1410.  
Figure 4: Stir membrane extraction device. (**A**) Diagram of the unit built in plastic; (**B**) picture of the unit built in stainless steel (reproduced with permission from [30], copyright Springer, 2010).  
Figure 3: Scheme of ice concentration linked with the extractive stirrer technique. The stirrer is located in the bottom of the vial (**A**) which is cooled down. As a consequence, ice is created (**B** and **C**) leaving a more concentrated solution than the original sample. The pictures show the extraction of methyl violet as the model analyte: (**a**) initial state, (**b**) extraction and (**c**) final state. Reproduced with permission from [28], copyright Elsevier, 2016.