esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methodology
### Framework Design  
We designed the framework for our proposal according to Figure 4.  
Figure 4: The framework designed using Hyperledger Fabric.  
Figure 3: Sequence diagram for accessing the health record of a patient by health personnel, such as a doctor.  
The following illustration describes how Hyperledger Fabric leverages a blockchain to preserve privacy in patient health record access:  
* A patient sends a request through a subscriber node, which handles access control by verifying user details.
* If the request is valid, the subscriber node issues a digital certificate and forwards the request to the endorsement node through a smart contract.
* This access control intervention increases security by preventing unauthorized access to the private blockchain.
* Consequently, the endorsement node executes a chaincode to access the ledger and determines whether the transaction is valid.
* The endorsement node signs the proposal and sends its response back to the patient through a smart contract application.
* The transaction is then broadcasted to the ordering node, which creates a block and forwards it to the committing peer (consensus node).
* The committing peer then updates the block to the ledger.
* Invalid transactions are flagged and filtered out during the consensus process, which is handled by the orderer node.
* All information logs are supervised by the patient, who receives feedback responses at the end of each transaction.
## 1 Introduction
In this structure, the forwarding node and neighboring node verify the data computed by the monitoring node in the same group, and detect false data when it appears immediately. Thereby, this structure reduces the data transmission in the network with compromised nodes. * We apply MACs to protect data integrity confidentially and conveniently. In our scheme, monitoring nodes compute MACs for the aggregated data, so that the group members can verify the integrity of data through computing and comparing the MACs directly. Therefore, we do not have to forward the plaintext for the verification. If the aggregator is captured, the attacker will not know the data. The rest of the article is organized as follows: in Section 2, we overview some related works on secure data aggregation. Section 3 introduces the network model and background knowledge. In Section 4, we give the detailed descriptions of our scheme FESA. Section 5 analyzes the performance of security properties. The simulation results of the proposed schemes are presented in Section 6. Finally, we summarize our conclusions in Section 7.
# On the Link between Diesel Spray Asymmetry and Off-Axis Needle Displacement
## Materials and Methods
### Modeling Approach
#### 2.3.2 Near Nozzle Eulerian Spray Modeling  
The Eulerian spray modeling is based on a multiphase method [40]. The gas and the liquid are treated as interpenetrating continuous phases, characterized by their volume fraction in the control volume. A number \(n\) of phases is considered. The first (\(n=1\)) is the gaseous one, and it represents the gas and the fuel vapor in the spray ambient. The other phases (\(n=2,\,\dots,\,n-1\)) represent the droplet size classes, whereas the phase \(n\) is the bulk liquid phase preceding the break-up process. For each phase, the set of conservation equations is solved separately. Mass--Equation (4), momentum--Equation (5) and enthalpy--Equation (6) conservation equations for the phase \(k\) are reported in the following. On the right hand side of each equation, there are the exchange-terms \(\Gamma_{kl},\,\,M_{kl},\,H_{kl}\) (mass, momentum, enthalpy) between the phases \(k\) and \(l\); these are the terms that contain the physics of the spray. The left-hand side of each equation is made of two terms, the rate of change and the convective transport of the phase flow property. The volume fraction of each phase must fulfill the compatibility condition, Equation (7):  
\[\frac{\partial\alpha_{k}\rho_{k}}{\partial t}+\
abla\cdot(\alpha_{k}\rho_{k} \tau_{k})=\sum_{l=1,\,\,l\
eq k}^{n}\Gamma_{kl}, \tag{4}\]  
\[\frac{\partial\alpha_{k}\rho_{k}\tau_{k}}{\partial t}+\
abla\cdot(\alpha_{k} \rho_{k}\tau_{k}\tau_{k})=-\alpha_{k}\
abla p+\,\,\
abla\cdot\alpha_{k}\big{(} \tau_{k}+\tau_{k}^{t}\big{)}+\alpha_{k}\rho_{k}f+\sum_{l=1,\,\,l\
eq k}^{n} \mathrm{M}_{kl}+\tau_{k}\sum_{l=1,\,\,l\
eq k}^{n}\Gamma_{kl}, \tag{5}\]  
\[\frac{\alpha_{k}\rho_{k}}{\partial t}+\
abla\cdot(\alpha_{k}\rho_{k}\tau_{k} \tau_{k})=\
abla\cdot\alpha_{k}(q_{k}+q_{k}^{t})+\alpha_{k}\rho_{k}f\cdot \tau_{k}+\alpha_{k}\rho_{k}\rho_{k}+\alpha_{k}\tau_{k}:\
abla v_{k}+\alpha_{k} \frac{dp}{dt}\sum_{l=1,\,\,l\
eq k}^{n}\mathrm{H}_{kl}+h_{k}\sum_{l=1,\,\,l \
eq k}^{n}\Gamma_{kl}, \tag{6}\]  
\[\sum_{k=1}^{n}\alpha_{k}=1. \tag{7}\]  
The Eulerian spray model is framed in the RANS (Reynolds-averaged Navier-Stokes) approach, with \(k\)-epsilon closure. The Eulerian-Eulerian spray model here adopted has been extensively validated against experimental data, as reported in [39], concerning atomization and evaporation process, and in [23] concerning reactive cases.
# Translating Workflow Nets to Process Trees: An Algorithmic Approach
## 6 Discussion
### Relation to Refined Process Structure Tree
As the RPST decomposition ignores the semantics of a WF-net, i.e., contributing to its more general applicability with respect to the algorithm presented in this paper (only applicable to process models that can be transformed into a WF-net), it also exists for WF-nets that are _unsound_. Due to the generic nature of the RPST, i.e., it defines a graph-theoretical property of a workflow graph, it (largely) ignores the semantics and graph-theoretical properties of Petri nets. In particular, as an activity in a BPMN model only consists of a single entry and exit arc, within the underlying workflow graphs these activities are simply presented as a single edge. Since transitions in Petri nets represent process activities, and transitions are able to have multiple incoming and outgoing arcs, transitions in a WF-net cannot be represented as a single arc in the corresponding WF graph. For example, consider Figure 18, in which we show the RPST of a WF-net that is not _well-handled_, i.e., \(t_{1}\) generates concurrent behavior, place \(p_{3}\) merges both branches of concurrent behavior. Since the RPST decomposition is not aware of the conceptual difference between places and transitions, the RPST subdivides the graph into several fragments. However, this is rather inconvenient, since the WF-net is not sound at all. Hence, from the RPST decomposition itself, one cannot judge whether a corresponding process tree exists. Figure 17: Example of an RPST decomposition (Figure 17c) based on the workflow graph (Figure 17b) of a simple sound WF-net \(W_{2}\) (Figure 17a). (**a**) Simple WF-net \(W_{2}\). (**b**) The workflow graph of \(W_{2}\), including its canonical fragments. (**c**) The RPST of \(W_{2}\). Figure 18: Example of an RPST decomposition (Figure 18c) based on the workflow graph (Figure 18b) of a simple sound WF-net \(W_{2}\) (Figure 18a). (**a**) Simple _unsound_ (not _well-handled_) WF-net \(W_{3}\). (**b**) The workflow graph of \(W_{3}\), including its canonical fragments. (**c**) The RPST of \(W_{3}\). The reverse is also possible, i.e., given a WF-net with a corresponding process tree representation, finding an RPST can be challenging. For example, when computing the RPST of the running example used in this paper, i.e., Figure 3, the behavior formed by the loop transition \(t_{6}\) cannot be decomposed into smaller chunks (in terms of the RPST, the loop structure is generating a _rigid fragment_). Observe that the algorithm proposed in this paper is able to find the loop behavior due to the simplification of the reduction steps executed prior to the loop detection in Figure 6c. Note that the aforementioned examples are not intended to disqualify the application of the RPST decomposition for the purpose of transforming WF-nets into process trees. However, they merely indicate that using the RPST decomposition as a basis for such translation is not a trivial adoption.
## 2 Materials
### Airborne LiDAR Data and Preprocess  
We conducted aerial campaigns over the study area from March 2020 to February 2021 using the National Forest and Grassland Inventory Airborne Observatory Platform that is an integrated observation equipment with a small-footprint LiDAR (RIEGL VQ-1560i (Riegl GmbH, Horn, Austria) with a laser wavelength of 1064 nm), a digital imager (Phase One iXU-RS 1000 (Phase One Inc, Copenhagen, Denmark)), and a position and orientation system (Applanix AP-60 (Trimble Inc., Sunnyvale, CA, USA)). The LiDAR uses an analogue detector to record discrete, time-stamped trigger pulses from the received waveform in real time, i.e., the discrete-return system. The airplane (Cessna 208B-9617) flew at a relative flight altitude of about 1800 m relative to the take-off point and with flight speed of around 260 km/h, resulting in a LiDAR beam diameter of about 0.45 m. The laser emission frequency is 2000 kHz and the lateral overlap of LiDAR was set as 22%.  
After airborne LiDAR (ALS) data acquisition, the aircraft GPS trajectories for each airborne survey were firstly corrected to the ground-based GPS base station using the POSpac version 8. 3 software (Trimble Inc., Sunnyvale, CA, USA). The scan angles and laser pulse return were then integrated with the trajectories using the RiProcess version 1.8.5 software (Riegl GmbH, Horn, Austria). The point cloud files were finally exported and geographic coordinates, elevation, and intensity information were included. Based on 263 ground control points, the horizontal and vertical errors were less than 0.15 m and 0.08 m, respectively.  
The ALS point clouds were classified into ground return and other returns using the cloth simulation filter (CSF) algorithm [32]. The digital terrain model (DTM) with spatial resolution of 1 m was created using the ground points with the k-nearest neighbor with inverse distance weighting interpolation algorithm. The produced ALS-based DTM (DTM\({}_{\text{ALS}}\)) was used to normalize the ALS point clouds and subsequently used in the process of airborne image-based point clouds. After normalization, all elevations of points were relative to the same ground level datum. The ALS point clouds were divided into canopy returns and below-canopy returns using a height threshold of 2.0 m.
## 2 Research Background Overview
### Research Problem and Methods
With a functional approach, the forms of organizations and institutions are taken as given, and the emphasis is on the way they function both individually and in a relation to each other, whereas structural approach explores the deeper structures and forces which create these particular forms of organizations [6]. The functional analysis of the city is a statement based on the results of the empirical observation, which has limited explanatory possibilities and no prognostic potential. This is the simplest collection of data on the current situation \"who, where, when, how\" and so on. A functional approach (for example, a comprehensive plan developed in the territorial planning process) zones territory (CSS) according to its functions, when the structural approach (e.g., SPDIAM) evaluates CSS connections and explores a structure that can perform a variety of functions. More advanced approaches provide a functional and structural analysis of a city modeled as a composition of several networks [32]. SPDIAM with the help of Space Syntax is essentially a structural approach because it evaluates the functional potential of a city through the network configuration (e.g., structure) measures that show the availability, transit of the location, and so on. Space Syntax creates a simulation model based on the structure, connectivity, etc., and it corresponds to the real processes of the city. The functions themselves are shown by statistic data (or indicators), such as building density, points of interest, flows, and so on. Thus, in the first case, we have only the essentially observational data (statistical indicators) without a model, and, in the second case, we have the model and the observational data to check the accuracy and obtain the additional information. For example, a transport model, if presented only as a result of the flows measurement, can be classified as a functional approach, but, if it is based on a model, such as network analysis, it becomes a structural approach. In addition to Space Syntax, there are other methods that belong to the group of structural approaches: Agent-based modeling, Cellular Automata, and Fractal analysis. The main problem of the existing methods of CSS analysis, and, commonly, even spatial analysis, is that these methods only explore the statistics and functions of CSS, but they lack a structural approach [16] and use quantitative spatial analysis methods instead of qualitative ones. Summarizing all the problems described above, it can be concluded that there is a great need for the methods and tools for a structural approach to CSS and quantitative spatial analysis methods of spatial phenomena (or SP).
## 3 Results and Discussion
### Flavonols  
In this current study, compounds **17-23** were identified in basil, fenugreek, coriander, and mint leaves, which are considered to fall under flavonoids in negative as well as positive ionization mode ([M\(-\)H]\({}^{-}\)/[M\(+\)H]\({}^{+}\)). Compound **17** with [M\(+\)H]\({}^{+}\) at \(m/z\) 479.0801 was putatively designated as quercetin 3\({}^{\prime}\)-O-glucuron, which was detected in only basil leaves. Quercetin 3\({}^{\prime}\)-O-glucuron is a metabolite of quercetin, a flavonoid that is found in many plant-based foods. Quercetin 3\({}^{\prime}\)-O-glucuron has potent antioxidant and anti-inflammatory properties, immune-boosting properties, and potential therapeutic effects in the treatment of diabetes [71]. It may also have unique benefits in improving exercise performance and recovery and protecting the liver against damage caused by toxins [72]. Compound **18** was characterized as rutin (C\({}_{27}\)H\({}_{30}\)O\({}_{16}\)) at \(m/z\) 609.1466 in fenugreek, basil, and mint leaves. Compounds 19 was identified in basil, coriander, and mint leave with a precursor ion at [M\(-\)H]\({}^{-}\)\(m/z\) 463.0887 and 483.0882 was designated as myricetin 3-\(O\)-thamoside (C\({}_{21}\)H\({}_{20}\)O\({}_{12}\)). Prior reports noted the remarkable antioxidant properties of mint and lemon. Compound **20** was identified at \(m/z\) 433.0756 in the negative mode which was tentatively categorized as quercetin 3-\(O\)-arabinoside (C\({}_{20}\)H\({}_{18}\)O\({}_{11}\)) in basil, mint and fenugreek leaves only. Compound **21** was characterized as Isorhamnetin (C\({}_{16}\)H\({}_{12}\)O\({}_{7}\)) at \(m/z\) 609.1466 in mint leaves. Isorhamnetin has potent anti-cancer and anti-inflammatory properties, cardiovascular benefits, reduced inflammation and oxidative stress, and improved blood pressure and cholesterol levels. Isorhamnetin may also have neuroprotective effects and may help in the treatment of neurological disorders, for instance, Alzheimer's disease. Additionally, It has been discovered to control blood sugar levels and has skin-protective properties, potentially preventing premature aging and protecting against UV damage [73]. Compound **22** (quercetin, \(m/z\) 301.0332) was identified in mint, basil, and fenugreek leaves. Compound **23** (\(m/z\) 329.0658--3,7-dimethylquercetin) was identified in basil leaves only in the negative mode of ionization.
# A Survey of Internet of Things (IoT) Authentication Schemes +
## 7 Conclusions  
This paper presents a taxonomy and a literature review of authentication in the IoT context. The analysis of a large spectrum of authentication protocols/schemes leads to identify a number of requirements and open issues that should be taken into consideration by researchers and developers while developing new authentication schemes for IoT networks and applications. In sensor-based applications, where sensors (constrained in terms of memory, processing power, battery, etc.) are the main end-devices, the proposed protocols must be lightweight, making a trade-off between power consumption and security. * The robustness of authentication protocols against potential attacks, e.g., sybil, node capture, replay, password guessing, message forgery, brute force, man-in-the-middle, DoS, collision, chosen-plaintext, etc., should be considered and analyzed. In particular, it is important to to consider Distributed Denial of Service attacks (second attack against IoT in 2017 as per [6]). * There is a need to consider location and identity privacy in certain IoT applications especially smart grids and VANETs. * The communication overhead of authentication protocols is a key factor, especially when dealing with power-limited devices; the number of messages exchanged between authentication parties should be kept as low as possible. In the same context, the size of the messages should be as small as possible due to the restricted bandwidth of the wireless communication protocols used. * Low computation cost should be considered while designing IoT authentication schemes especially for power-constrained and processing-limited IoT environment. This emphasizes the need to adopt lightweight cryptographic algorithms and protocols while designing authentication solutions. * IoT authentication scheme should be scalable in the sense that it should manage large number of nodes s well as have the ability to add new nodes without any further setup or configuration. * Authentication service should be ensured for the three layers of IoT architecture (application, network and perception layer). * Heterogeneity of devices in IoT networks must be taken into consideration while designing IoT authentication schemes. * Hardware security using \"PUF\" is the current trend due to its advantages over software security approaches. A combination between software solutions (lower cost) and hardware solutions (more secure) should be considered.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 3 V2I Communication
### Communication Technologies
#### 3.2.1 Dedicated Short-Range Communications  
Dedicated short-range communications (DSRC) are a wireless communication technology used for V2I communication, namely between the On-board Unit (OBU) installed in the vehicle and the Road Site Unit (RSU) located in the road infrastructure equipment [13]. Communication works based on WLAN technology. When individual vehicles and transport infrastructure equipment are within range, they create an ad-hoc network. The messages sent include, for example:  
* Cooperative Awareness Message (CAM),
* Basic Safety Message (BSM),
* In Vehicle Information Message (IVI),
* Service Request Message (SRM) [13].  
The technology is part of WLAN IEEE 802.11 standard. It is known as ITS-G5 in Europe and as Wireless Access in Vehicular Environments (WAVE) in the USA [13]. The DSRC range was determined to be 300 m by testing [24]. The delivery of messages over a longer distance is possible using multi-hop. A communication node equipped with a DSRC module routes the received messages to another communication node. However, there is a delay in the transmission of messages, which is particularly a problem for the transmission of safety messages that should reach the target vehicle as quickly as possible. Another problem is the use of the same radio channels by an increased number of connected devices, for example, at peak times when the capacity of the network equipment is maximized. This results in an increase in signal interference, transmission latency and a reduction in data transfer rates. Data latency normally reaches 4-5 ms. However, the delay is even greater during peak times. The advantage of the DSRC is that it does not need a supporting communication infrastructure and the communication takes place on free frequency bands [13].
## 3 Preclinical Evidence Evaluating the Effects of Phytochemicals on Immune Checkpoints
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors/Year** & **Pytochemical Group** & **Pytochemical Compound** & **Source Compound** & **Cancer Type** & **Cancer Model or Toxicity** & **Mechanism of Action** \\ \hline [PERSON] et al. (2018)[21] & Non-flavoodal polyphenols & Curcumin & Turmeric & HNSCC & 4-NQO induced C7 PLL/6 tongue squamous cell carcinoma mice & Decrease PD-L1 and p-STAT3706 protein expression \\ \hline [PERSON] et al. (2020)[44] & Non-flavoodal polyphenols & Curcumin & Turmeric & HCC & HepG2 hepatocellular tumor-bearing BALB/c female nude mice & Decreased the protein expression of PD-L1 inhibiting the TLR/NF-kB signaling pathway angiogenesis. \\ \hline [PERSON] et al. (2018)[43] & Non-flavoodal polyphenols & Resveratrol & Red wine, Grapes, Passion fruit & Breast cancer and SW620 colon cancer & Breast cancer & Increase the expression level of PD-L1 via HDA/3D-mediated nuclear factor (NF)-kB signaling \\ \hline [PERSON] et al. (2020)[45] & Non-flavoodal polyphenols & Resveratrol & Red wine, Grapes, Passion fruit & Breast Cancer cells & JIMT-1 and MDA-MB-231 breast cancer cells & Increased PD-L1 dysfunction \\ \hline [PERSON] et al. (2021)[46] & Non-flavoodal polyphenols & Resveratrol & Red wine, Grapes, Passion fruit & NSCLC & Human lung adenocarcinoma cells (A549 and H1299) & Activation of StrTi decaptivesle leads to cell lines (A549 and H1299) & disassembly of the destruction complex, thereby enhancing the binding of g-catenin/TCF to the PD-L1 promoter \\ \hline [PERSON] et al. (2016)[47] & Flavoodal & Apigenin & Parsley, onions, grapefruit, oranges, & Breast Cancer & Triple-negative MDA-MB-468 DC cells, HER(2+) SK-BB-3C cells, and 4T1 mouse mammary carcinoma cells & Inhibit IFN-induced PD-L1 upregulation cells, HER(2+) SK-BB-3C cells & Inhibit IFN-induced activation of STAT tumors-bearing C7 PLL/6 mice & Inhibition the IFN-\(\gamma\)-induced activation of STAT Decreased expression levels of PD-L1 \\ \hline [PERSON] et al. (2020)[48] & Flavoodal & Apigenin & _Saliva pllevia_ & CRC & hPD-L1 knock-in MC38 tumor-bearing humanized PD-1 mouse model & Blocking of PD-1/PD-L1 interaction mouse model \\ \hline [PERSON] et al. (2018)[49] & Flavoodal & Epigallocatechin gallate (EGCG) & Green tea & NSCLC & 4-(methylnitrosamino)-1-(3-pyridyl-1-butanone induced nonsmal-cell lung cancer A/J mice & Down-regulate IFN-\(\gamma\)- and EGF-induced PD-L1 expression \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of phytochemicals targeting PD-L1/PD-1 and CTLA-4 pathways in different types of cancer.
## 3 Results  
### Characterization and Selection of the Prototype MCs  
#### 3.1.1 Shape and Dimensions of the MCs  
More than 50 different MC prototypes were manufactured with different polymeric compositions, different gelatines, and peptides derived from gelatin hydrolyzation were tested in combination with different shapes, sizes, and porosities of microbeads. After founding the best combination that promotes cell attachment and proliferation, we focus on the characterization of the biochemical engineering properties of _BR44_. It consists of a mixture of poly lactic-co-glycolic acid (PLGA) and porcine gelatin. Scanning electron microscopy (SEM) of the _BR44_ MC showed good uniformity of shapes and sizes (Figure 1), comparable with standard commercial MCs composed of natural bio-polymers (see Supplementary Material Figures S2 and S3).  
Analytical characterization of MCs represents an important tool for the comparison of prototypes obtained during the screening phase. Figure 2 shows and Table 1 summarizes the biophysical properties of the _BR44_ MC. The results of the particle size distribution measurements demonstrate the reproducibility of the manufacturing process since the size distribution of the _BR44_ MCs varied only slightly between the different batches.