esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Some Key Issues on the Use of SLNs and NLCs as Oral Nanovehicles for Nutraceuticals or Bioactives
### Liposoluble Nntraceuticalticals
#### 4.1.3 Omega-3 Polyunsaturated Fatty Acids (\(\omega\)-3 PUFAs)
The co-loading of quercetin and linseed oil not only improved the in vitro antioxidant capacities of quercetin-loaded NLCs, but also increased the oxidation stabilities of lipids. It also increased physical stability upon long-term storage [89]. [PERSON] et al. [30] indicated that the oxidative and physical stability of fish oil-loaded NLCs stabilized by lecithin was highly dependent on the type of lecithin used (high- or low-melting). They observed that the NLCs stabilized with a high-melting lecithin exhibited much greater stability against oxidation of \(\omega\)-3 PUFAs in fish oil than those with low-melting lecithin. This improved stability was attributed to the formation of solidified shell layer, induced by the crystallized high-melting lecithin as the interfacial heterogeneous nuclei [30]. To better illustrate this, they proposed a mechanistic explanation for the formation of core-shell lipid nanoparticles stabilized by the high-melting surfactant (lecithin), as displayed in Figure 6. Upon cooling to 35 \({}^{\circ}\)C, the adsorbed high-melting lecithin (80H) on the interface of emulsified droplets (consisting of a melted mixture of tristearin and fish oil) crystallized (solidified) before the high-melting lipid (tristearin). The crystallized 80H was subsequently able to act as the heterogenous nuclei and induce crystallization of tristearin close to the 80H solid surface. When the temperature was further cooled to 20 \({}^{\circ}\)C, interfacial heterogenous nucleation led to the formation of an ordered crystal structure, with lipid liquid (fish oil) incorporated within the solid lipid matrix (Figure 6). Meanwhile, in the case of low-melting lecithin (PC 75), PC 75, at the interface of emulsion droplets, remained liquid upon cooling to 35 \({}^{\circ}\)C, and as a result, no crystallization of tristearin was induced. When the temperature was decreased to 20 \({}^{\circ}\)C, the melted tristearin crystallized to form a solid lipid matrix, leading to the occurrence of expulsion of \(\omega\)-3 PUFAs from the crystallized lipid matrix (Figure 6). The excluded \(\omega\)-3 PUFAs from the solid matrix seemed to be prone to chemical oxidation. It should be noted that, aside from the use of a high-melting surfactant, core-shell NLCs can also be formed by additional coating with a charged polymer on the NLCs. [PERSON] et al. [103] successfully prepared chitosan-coated NLCs, consisting of stearic and oleic acids as the solid and liquid lipids, respectively, by electrostatic deposition, and confirmed that the coating greatly increased the physical stability of the as-fabricated NLCs. However, at the time of this writing, whether the oxidative stability of liquid lipids in NLCs can be improved remains to be seen.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Proposed Architecture  
To handle the EAP authentication involved during the bootstrapping service, we have designed a new EAP lower-layer based on CoAP. Basically the idea consists of transporting EAP packets in the payload of the CoAP messages involved during the service execution. In general, the Smart Object performs the _CoAP server role_ and the Controller the _CoAP client role_. This decision is further explained in Section 3.6.1. To save system resources, it is assumed that the Smart Object will have only a single ongoing bootstrapping exchange and will not process simultaneous EAP authentications in parallel with the same Controller.  
Figure 3 shows the architecture of our bootstrapping service using CoAP as a transport for EAP packets between the Smart Object and the Controller. We assume that a Controller manages a security domain and, therefore, the bootstrapping process. In this way, any smart object or entity wanting to join the security domain will have to engage with the Controller.  
The Smart Object acts as _end user_ in the AAA framework, and it will act as EAP peer and perform the client-side of a particular EAP method. The Controller acts as the EAP authenticator for the bootstrapping service and acts as the EAP authenticator (typically in pass-through mode for big scale deployments) and ships a AAA client (RADIUS or Diameter) to interact with the backend AAA infrastructure. The EAP server is typically placed in the IdP's AAA server where the Smart Object is registered. Several intermediate AAA proxies can be placed between the Controller and the IdP's AAA server, especially in federated environments (although it will depend on the specific deployment) [20].  
After finishing the bootstrapping service, the Controller becomes a trusted third party in the security domain for the Smart Object so that it can interact in a secure fashion with the rest of the entities within the domain.  
Figure 3: The constrained application protocol (CoAP)-EAP architecture.
## 3 Attacks on HWMP
### External Attacks
#### 3.1.2 Path Diversion Attacks  
An attacker can increase the originator sequence number of a PREQ frame to trick other mesh STAs into processing the forwarding information as new information. In this case, the routing table for each mesh STA will be updated based on the assumption that the PREQ frame contains new information. The attacker can also lower the path metric value to trick other mesh STAs into believing that a path is a better one when, in fact, it is inferior to the previous paths.  
Figure 8: Example of a path diversion attack.  
Information from the transmitted frames is processed according to Figure 8 as follows:  
_Case 1: Fake Path Metric_  
S: S \(\rightarrow\) Broadcast: PREQ[S - D][Metric = 0]  
A: A \(\rightarrow\) Broadcast: PREQ[S - D][Metric = 100]  
M: M \(\rightarrow\) Broadcast: PREQ[S - D][Metric = 10]  
Note that S: S \(\rightarrow\) Broadcast: PREQ[S - D][Metric = 0] indicates that the sender of the frame is S; the frame is broadcasted; the type of frame is PREQ; the source of the frame is S; the destination is D; and the current metric is 0.  
D calculates the metric of path {S\(\leftrightarrow\)A\(\leftrightarrow\)D} to be 550, while {S\(\leftrightarrow\)M\(\leftrightarrow\)D} is calculated to be 460 because of the fake metric field. Therefore, path {S\(\leftrightarrow\)M\(\leftrightarrow\)D} will be chosen as the better path, and D will thus transmit the PREP frame to M.  
_Case 2: Fake Originator Sequence Number_  
S: S \(\rightarrow\) Broadcast: PREQ[S - D][Metric=0][Originator Sequence Number = 100]  
A: A \(\rightarrow\) Broadcast: PREQ[S - D][Metric=100][Originator Sequence Number = 100]  
M: M \(\rightarrow\) Broadcast: PREQ[S - D][Metric=500][Originator Sequence Number = 101]  
This time, D knows that the path {S\(\leftrightarrow\)A\(\leftrightarrow\)D} has a lower path metric, but D determines the path {S\(\leftrightarrow\)M\(\leftrightarrow\)D} to be a new path and thus chooses the new path. D will thus transmit the PREP frame to M.  
Figure 8 shows an example of a path diversion attack. Path {S\(\leftrightarrow\)A\(\leftrightarrow\)D} is the better path with a metric of 550, while the metric for {S\(\leftrightarrow\)M\(\leftrightarrow\)D} is 950. However, the malicious mesh STA M can divert a path to go through the malicious mesh STA itself by reporting a higher sequence number or a lower metric value.
# In Vitro Activity of Caffe Acid Phenethyl Ester against Different Oral Microorganisms  
[PERSON]  
1 Department of Restorative Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammann 31441, Saudi Arabia 1  
[PERSON]  
2 Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh 11545, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Oral Basic and Clinical Sciences, College of Dentistry, Taibah University, Al Madinah Al Mur  
conditions [7]. Evidence has identified _Streptococcus mutans_ as the predominant cariogenic. Other factors that contribute to the development of caries are poor oral hygiene, decreased salivary flow and composition, and insufficient exposure to fluoride [7,8].  
Due to public concern about the side effects of synthetic medications, organic products have recently gained popularity and attention [8,9]. Organic therapy is undergoing extensive research and has proven to be effective in treating and preventing a variety of illnesses and medical disorders [10, 11, 12]. Propolis, often known as bee glue, has traditionally been applied in a wide variety of cultural herbal remedies. It has been used to cure a variety of ailments, including cold sores, burns, diabetes, and skin conditions. It has been proven that propolis extract improves oral health and reduces dental cavities, gingivitis, and periodontitis [12]. One of the medicinal propolis extracts, caffeic acid phenethyl ester (CAPE), is a combination of bee-released and plant-derived substances. Generally, pure propolis is made up of approximately 50% resins, 30% paraffin, 10% essential oils, 5% pollen, and 5% other natural substances. Some of the chemicals contained in propolis are amino acids; terpenes; flavonoids including flavones, flavonoids and flavanones; vanillin; isalpinin pinocombrin chrysin galangin; tectochromis; caffeic acid; ferulic acid; caffeic acid phenethyl ester; cinnamic acid, and cinnamyl alcohol [12].  
Caffeic acid has been significantly utilized as an alternative remedy to battle microbial pathogenesis and chronic infections caused by microbes such as bacteria, viruses, and fungi [13,14].  
Most published experiments concentrate on CAPE's antimicrobial efficacy against a particular bacteria species or on a certain type of analysis. There has been no comprehensive research on the antibacterial impact of CAPE on a variety of dental bacteria. Therefore, the goal of this study was to assess the antibacterial effect of CAPE (concentration and time of exposure) against different oral microbes.
## 5 Results and Discussions
Figure 11: Comparison of the fill factor obtained from simulations and experiments for the bare PV cells at STCs. Figure 10: Comparison of the fill factor obtained from simulations and experiments for the RADTIRC-PV structure at STCs. The difference between the simulation and experimental values can be contributed by several reasons. One of the most important one is the effect of temperature on the solar cell. During the experiments, the RADTIRC-PV and the bare PV cells were exposed for a period of 5 s to obtained one set of I and V readings. This short exposure increases the cell's temperature. The temperature has minimal effect on the short circuit current [63], but noticeable effect on the open circuit voltage and subsequently the maximum power output [64]. The effect of temperature was more apparent on CPV design when compared with the bare cell due to an increase in radiation concentration at the exit aperture [65; 66]. This leads to much higher errors on the RADTIRC-PV readings. Other source of errors include (i) errors during the manufacturing processes which made the physical dimensions of the RADTIRC to deviate from the dimensions of the CAD design dimensions, the entrance aperture's irregular surfaces and over-polishing on the RADTIRC's side profile; (ii) errors that occurred during the assembly processes where the effective area of the PV cell was reduced due to soldering of the tabbing wire on the solar PV cells, misalignment between the exit aperture of the RADTIRC and the solar cells, as well as losses at the lower part of the RADTIRC profile due to the index matching gel; and (iii) errors attributed to the rays of the sun such as reflection on the front surface of the concentrator and scattering which reduces the total rays arriving at the PV cell attached to exit aperture of the RADTIRC.
## 3 Wettability Alteration
### Injection of Low-Salinity Water
demonstrated that reducing water salinity from 85,000 to 4000 ppm led to an increase in the USBM index from approximately 0.26 to 0.38 on average (see Figure 14) [40]. Thus, the studies by [PERSON] et al. provide insights into the impact of low-salinity waterflooding on wettability alteration and enhanced oil recovery in clean sandstone formations. The modeling of rock wettability is another important aspect of research. [PERSON] et al. (2023) have demonstrated progress in understanding the mechanisms of wettability alteration, including the impact of pore space structure on this process [45]. It is also important to consider that different research models show mixed results in predicting the effectiveness of wettability alteration and its impact on enhanced oil recovery. For instance, the model developed by [PERSON] et al. (2023) incorporates several mechanisms of wettability alteration, such as surface charge, calcite dissolution, and ion exchange, and successfully reproduces experimental data. This model enables the prediction of oil recovery efficiency using low-salinity solutions, which is crucial for the practical implementation of such methods [46]. The study by [PERSON] et al. provides insights into the effect of diethylenetriamine penta-acetic acid seawater (DTPA-SW) on wettability alteration in sandstone and enhanced oil recovery [47]. The introduction of a 5% DTPA-SW solution resulted in a significant reduction in the contact angle between the rock and oil phases, as well as a decrease in potential, shifting the rock wettability from an oil-wet state to a strongly water-wet state (see Figure 15). The tests also demonstrated a 39.6% increase in oil recovery with the use of the DTPA-SW solution. These results enhance the understanding of the effectiveness of DTPA in altering rock wettability and its impact on improving the oil recovery process [47]. Figure 14: Average USBM index values obtained for rock samples I, II, and III as a function of water mineralization level [40].
## 4 Experimental Validation
### Experimental Test Rig  
A 3 DOF VSL manipulator prototype was developed in our laboratory from a combination of silicone rubber structures, polypropylene plastic mesh, and fabrics (see Figure 7). VSLs have the ability to control their stiffness and are pneumatically actuated. By adjusting the pressure value within their structure, the links can be tuned to different stiffness levels. Our prototype is equipped with a pressure sensor (BD SENSORS 26.600 G-11002-R--1-5-100-300-1-000) to sense the robot's body possible collisions with a human. The base and elbow motors were serial servomotors with built-in encoders to measure the angle of rotation. The shoulder motor was a stepper motor with a gearbox and an encoder connected to the shaft. All experiments were carried out at a pressure of 2 bar in the links. This pressure was selected experimentally. The highest pressure for maximum stiffness and, at the same time, the pressure change at collision could be determined.  
Figure 6: Layout of the experiments. Dimensions are in mm.  
Figure 7: VSL manipulator test rig.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 2 Data and Methodology
### Numerical Model
#### 2.3.1 Governing Equations  
The following equations, the conservation of mass and momentum integrated over the vertical, describe the flow and water level variations:  
Continuity equation:  
\[\frac{\partial\zeta}{\partial\mathrm{t}}+\frac{\partial p}{\partial x}+\frac{ \partial q}{\partial y}=\frac{\partial d}{\partial\mathrm{t}} \tag{1}\]  
Momentum equation:  
\[\frac{\partial p}{\partial\mathrm{t}}+\frac{\partial}{\partial x}\left(\frac{ p^{2}}{\mathrm{h}}\right)+\frac{\partial}{\partial y}\left(\frac{pq}{\mathrm{h}} \right)+gh\frac{\partial\zeta}{\partial\mathrm{t}}+\frac{gp\sqrt{p^{2}+q^{2} }}{C^{2}h^{2}}-\frac{1}{\rho_{w}}\left[\frac{\partial}{\partial x}(h\tau_{xx })+\frac{\partial}{\partial y}\left(h\tau_{xy}\right)\right]-\Omega q-fVV_{x}+ \frac{h}{\rho_{w}}\frac{\partial}{\partial x}(p_{a})=0 \tag{2}\]  
\[\frac{\partial p}{\partial\mathrm{t}}+\frac{\partial}{\partial y}\left(\frac{ q^{2}}{\mathrm{h}}\right)+\frac{\partial}{\partial x}\left(\frac{pq}{\mathrm{h}} \right)+gh\frac{\partial\zeta}{\partial\mathrm{t}}+\frac{gp\sqrt{p^{2}+q^{2} }}{C^{2}h^{2}}-\frac{1}{\rho_{w}}\left[\frac{\partial}{\partial y}(h\tau_{yy} )+\frac{\partial}{\partial x}\left(h\tau_{xy}\right)\right]-\Omega q-fVV_{y}+ \frac{h}{\rho_{w}}\frac{\partial}{\partial y}(p_{a})=0 \tag{3}\]  
Symbol list: \(d\) is still water depth; \(\zeta\) is surface elevation (\(\mathrm{h}=\zeta+d\)); \(p\),\(q\) is unit-width discharge in the \(x\) and \(y\) directions (m/s); \(C\) is Chezy's coefficient; \(f\) is wind resistance coefficient; \(V\) is wind speed; \(V_{x}\), \(V_{y}\) is current velocity in the \(x\) and \(y\) directions (m/s); \(\Omega\) is Coriolis parameter; \(p_{a}\) is atmospheric pressure; \(\tau_{xx}\), \(\tau_{xy}\), \(\tau_{yy}\) is effective shear stress in the different directions [20].  
Salinity variations were simulated by MIKE 21 AD (advection-dispersion module), which solves so-called advection-dispersion equation for dissolved or suspended substances in two dimensions (this is in fact the mass-conservation equations):  
Figure 3: Cumulative frequency curve of river discharges in the dry season at the Datong station from 1957 to 2015.  
\[\frac{\partial}{\partial t}(hc)+\frac{\partial}{\partial x}(uhC)+\frac{\partial}{ \partial y}(\eta hC)=\frac{\partial}{\partial x}\left(hD_{x}\frac{\partial c}{ \partial x}\right)+\frac{\partial}{\partial y}\left(hD_{y}\frac{\partial c}{ \partial y}\right)-FhC=S \tag{4}\]
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 5 Experimental Results
### Publish-Completion Delay  
In the second experiment, we focused on quantifying the publish-completion delay experienced by the publisher when no response from the subscriber was necessary. This delay is characterized by the duration between the moment the publisher sends a PUBLISH packet and the point at which it receives a corresponding PUBCOMP packet. By progressively increasing the number of subscribers from 10 to 80, we measured the publish-completion delay experienced by the publisher. The experiment's outcomes are visually depicted in Figure 15. The average publish-completion delay values of MQTT, asynchronous E-MQTT, and synchronous E-MQTT were 6.87, 7.53, and 8.20, respectively. Upon reviewing the graph, it is evident that the difference between MQTT and E-MQTT remains minor, manifesting as a mere difference of a few milliseconds. Despite E-MQTT incorporating supplementary logic at the broker's end, it maintains its efficiency and remains on par with MQTT even in cases where no interaction is required.  
Figure 15: Comparison of publish–completion delay results.
## 2 Materials and Methods
### Experimental Procedure  
#### 2.2.1 Lab-Scale Experiment  
Raw water was collected at the pilot plant site (Han River, South Korea). The raw water was stored at 4 \({}^{\circ}\)C immediately after collection. The jar test experiment was conducted at 22 to 25 \({}^{\circ}\)C.  
_Anabaena_ sp. (AG10279) was obtained from the Korean Type Culture Collection (KTCC). _Anabaena_ sp. was cultured in a 250 mL Erlenmeyer flask with a BG-11 media and under culture conditions of a constant temperature of 25 \({}^{\circ}\)C, light exposure for 12 h (150 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) and dark intervals for 12 h with a shaking speed of 150 rpm. To examine the algae removal efficiency of the PMC-based coagulant, a known amount of _Anabaena_ sp. culture was mixed with raw water collected from the Han River. A water sample for lab-scale tests was prepared by injecting _Anabaena_ sp., into the raw water collected from the influent of the pilot system. To ensure stable algae concentration, we monitored the optical density at 680 nm (OD\({}_{680}\)) of the test water using a spectrophotometer [14]. The OD\({}_{680}\) of the sample solution was stable after 6 h, and the initial concentration of chlorophyll-a was measured as 97.1 mg/m\({}^{3}\). Other parameters of the tested water are listed in Table 1.  
The coagulation process was conducted using a programmable jar tester to determine the optimum dose of PMC for the removal of algae, turbidity, and organic matters from the incident water of the pilot system. The PMC was added to 1 L of raw water in a 1 L beaker. The addition of PMC was followed by rapid mixing at 140 rpm for 1 min, then slow mixing at 40 rpm for 59 min. These conditions were determined to simulate the pilot-system. After allowing the contents in the beaker to settle for 30 min, samples for water quality measurements were collected from approximately 3 cm below the water surface. Varying PMC-based coagulant dose, with five levels (0, 5, 10, 20, 40, and 70 mg/L) were evaluated for algal cells, turbidity, dissolved organic carbon (DOC), and UV\({}_{254}\) removal. The zeta potentials of the coagulated samples with varying doses of coagulant were also measured.  
The photodegradation experiments were carried out using a bench-scale quasi-collimated beam apparatus [15] equipped with two 11-W low-pressure lamps (Philips, Amsterdam, The Netherland), which primarily emit light at 253.7 nm. A 50 mL geosmin and 2-MIB aliquot was placed in a Petri dish at a depth of about 0.786 cm [15; 16; 17]. The solution was stirred using a cross-shaped stirring bar with a diameter of 1 cm to ensure that it was homogenously exposed to UV light, and the solution is well mixed in the system. The UV irradiance (mW/cm\({}^{2}\)) was determined using the peroxide actinometry method with a calibrated radiometer equipped with a UV 254 detector (UVX Radiometer; UVP, Upland, CA, USA) at the height of the surface of the Petri dish [15; 17]. The average incident irradiance across the surface of the solution was determined using a Petri factor and a reflection factor [15].