esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Water-Reducing Agents on the Mechanical Properties of Foamed Phosphogypsum
###### Abstract  
In the present experiment, two types of water-reducing agents, naphthalene (FDN) and polycarboxylic acid (PCE), were selected, and their effects on the mechanical properties of foamed phosphogypsum were evaluated. It was shown that when the water-reducing agent contents were increased, the strength of the foamed phosphogypsum first increased and then gradually decreased, and that the dry density of the foamed phosphogypsum first decreased and then gradually increased. The FDN samples had better mechanical properties and a lower dry density than the PCE samples. The effect of the water-reducing agent dose on the apparent viscosity and shear stress of the phosphogypsum slurries was in the order of 0% > 0.4% > 0.3% > 0.5% > 0.2% > 0.1%. The apparent viscosity and shear stress of the gypsum slurry mixed with 0.4% FDN or PCE were the highest. FDN and PCE both enlarged the pore size distribution range, increased the size and proportion of large pores, and decreased the total pore content of foamed phosphogypsum; however, the effect of PCE was more significant. The foamed phosphogypsum slurry mixed with 0.4% FDN had the highest total pore content. Among the samples, the total pore content of foamed phosphogypsum A was able to reach 91% and the total pore content of foamed phosphogypsum B reached 77%; the lowest proportion of large pores for foamed phosphogypsum A and B separately reached 17% and 7%, respectively. The water-reducing agents mainly reduced the water consumption of the phosphogypsum slurries; improved the viscosity and shear stress of the slurries; affected the stability of the foam in the gypsum slurries; influenced the pore size and distribution in the foamed phosphogypsum samples; and caused a difference in the strength and dry density of the foamed phosphogypsum samples. The viscosity of the gypsum slurry doped with 0.4% FDN better matched that of the foam; therefore, it had the highest macro-strength and the lowest dry density.  
foamed phosphogypsum; water-reducing agent; rheology; pore structure distribution Article
## 5 WebGIS-Based Violation Monitoring Network Information Management System
### System Construction
#### 5.2.3 System Development and Implementation  
(1) Building the server environment: Establishing a server environment mainly involves the installation of OpenGeo Suite, a PostGIS database, and QGIS. All the software needs to be installed in the same directory. After the database is constructed, users need to load the functions of the extension module provided by PostGIS, which are mainly a series of processing functions based on the OpenGIS standard. After connection to the database, the server environment is realized by opening the \"console\" box in the PgAdmin menu and then executing the following SQL statement:  
* Enable PostGIS (includes raster) CREATE EXTENSION postures;
* Enable Topology CREATE EXTENSION postgis_topology;
* Fuzzy matching needed for Tiger CREATE EXTENSION fuzzysrtmatch;
* Enable US Tiger Geocoder CREATE EXTENSION postgis_tiger_geocoder;  
(2) Building the map server: GeoServer structures mainly use the default Jetty application server of OpenGeo. Because GeoServer is built on Java 2 Platform Enterprise Edition (J2 EE), users need to install and configure the Java environment before installing OpenGeo. They also need to start Geoserver-related services after installation, then open OpenGeo's Dashboard console through a browser.  
At the same time the needed vector data are imported and connected with GeoServer and PostGIS database, the user selects Import Data-\(>\) postGIS and enters the correct parameters, including the database name, port number, user name, password, and so on. After the data import is completed, the user can employ GeoExplorer to modify the layer style, including color, symbol, and transparency. Multiple layers can further build the layer group to improve computational efficiency, as shown in Figure 10.  
For importing image data, GeoServer provides an extended model that can directly import the data into the remote sensing images and automatically generate a mosaic. So, to acquire UAV aerial data, the user can go directly along the path (Import Data-\(>\) Mosaic) to select the appropriate storage path for completing the data import. After the import is completed, the user can employ the GeoWebCache tool to set the appropriate parameters, including hierarchy levels and image formats. After an image pyramid is built, the user can preview it, as shown in Figure 11.  
(3) Front-end access page development: In this study, we used the OpenLayers 3 and LeafLet frameworks to develop the front page. When the remote sensing data for the demonstration of research target cases is introduced, the page results show that the core part of the Map display is the definition of the Map element. Then, the remote sensing data can define the current map needed to load the layer. In this method, requisite points of interest, roads, and other layers can be added to order. The display of different layers can be controlled through the layer's panel, and the final effect is shown in Figure 12.  
Figure 11: Image release.  
Figure 10: Layer modification of (**a**) the vector layer group and (**b**) the label layer.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 2 Motivations
Therefore, V2V communication is a pivotal element in the future of transportation. Hence, it enables a boost in safety, an increase in traffic flow, a decrease in impact on the environment, and the implementation of a strong functional ITS infrastructure, which makes it crucial in the process of transportation systems modernization [30]. Therefore, with a steadily progressing world in terms of technology, its application toward the implementation and further development of V2V communication will be crucial in overcoming the challenges of urbanization and achieving the best and safest means of transport in society [31].
## 5 Discussion
### Major Areas of Discussion in UR Publications
#### 5.1.5 Decision Making and Disaster Management  
It can be argued that some important safety procedures against man-made disasters are not performed today due to a lack of theoretical knowledge and, as a result, incorrect policy actions. Some authors advocate that there a common misunderstanding about complex systems is to consider that these can be adequately governed or that socioeconomic systems self-correct without significant threats to society. Due to the systemic character of man-made catastrophes, it is difficult to make someone accountable for the harm inflicted. As a result, traditional self-adjustment and feedback processes fail to assure responsible behavior to prevent potential tragedies [56]. Because the world's interconnect assets and risk management strategies are too complicated to be optimized by top-down management in real time, the notion of a sole dictator would not work efficiently. Decentralized cooperation with impacted system components can produce better results that are tailored to local requirements. This implies that a participative strategy that makes use of local resources might be more effective. This method is also more resilient to disruptions.  
The Sendai Framework for Disaster Risk Reduction applies to the risk of small-scale and large-scale, frequent, and rare, unexpected and gradual disasters caused by natural or manmade disasters, and environment related, technological, and biological associated risks, with the goal of significantly reducing disaster risk and risks in lives, livelihoods, and health, and economic, physical, social, cultural, and environmental assets of individuals, organizations, societies, and governments [72]. It aims to \"prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience\".  
There are various frameworks that can be support decision making and enhance UR, namely, action plans for future vigilance to lessen the increasing effects of risks on cities. These have been devised as a road map for establishing an UR knowledge system for practitioners, decision-makers, and local authorities [122].  
UR decision-making tools are built in response to the needs of the urban environment, considering many dimensions and indicators, functioning alone or in conjunction, both with and without weighting of MCDM approaches, and can be subjective (expert-based) or objective (data-driven/stochastic). Choosing the appropriate mix of techniques is context-dependent and is a challenge in itself. This is something that needs further exploration and future research work.
## 3 Stability Analysis of Equilibria
### The Incommensurate Case
-(1-\gamma^{50})\mathcal{J}^{*}\Biggr{)}=0, \tag{45}\]  
_equivalent to_  
\[\frac{6699}{50000}\gamma^{100}-\frac{303}{500}\gamma^{93}+\gamma^{77}-\frac{6 699}{25000}\gamma^{50}+\frac{303}{500}\gamma^{43}+\frac{6699}{50000}=0. \tag{46}\]  
_Solving Equation (46) yields 100 solutions, \(\gamma_{j}(j=1,\ldots,100)\). All solutions remain in \(\mathbb{C}\setminus\mathbb{L}^{\frac{1}{50}}\). Theorem 3 demonstrates the local asymptotic stability of \(E_{*}\)._  
**Example 6**. : _Consider System (36) with parameters \((b,\lambda,d,e)=(20,0.04,0.2,0.13)\), it follows that_  
\[\mathcal{J}_{0}= \begin{pmatrix}-0.2&-4\\ 0&3.67\end{pmatrix} , \tag{47}\]  
_Let \((\eta_{1},\eta_{2})=(0.68,0.86)\), which implies that \(M=50\),_  
\[\det\Biggl{(} \begin{pmatrix}\gamma^{34}&0\\ 0&\gamma^{43}\end{pmatrix} -(1-\gamma^{50})\mathcal{J}_{0}\Biggr{)}=0, \tag{48}\]  
_equivalent to_  
\[\frac{1}{500}(-\gamma^{50}+5\gamma^{34}+1)(367\gamma^{50}+100\gamma^{43}-367) =0. \tag{49}\]  
_Solving Equation (49) yields 100 solutions, \(\gamma_{j}(j=1,\ldots,100)\). Specifically, \(\gamma_{1}=0.99504\in\mathbb{L}^{\frac{1}{50}}\), satisfying \(|\gamma_{1}|\leq(2\cos(50|\arg(\gamma_{1})|))^{\frac{1}{50}}\), and \(|\arg(\gamma_{1})|\leq\frac{\pi}{100}\). Theorem 3 provides a sufficient condition for the instability of \(E_{0}\). And we have_  
\[\mathcal{J}_{*}= \begin{pmatrix}-2.4242&-0.33\\ 2.22424&0\end{pmatrix} , \tag{50}\]thus,_  
\[\det\Biggl{(} \begin{pmatrix}\gamma^{34}&0\\ 0&\gamma^{43}\end{pmatrix} -(1-\gamma^{50})\mathcal{J}_{*}\Biggr{)}=0, \tag{51}\]  
_equivalent to_  
\[\frac{917499}{1250000}\gamma^{100}-\frac{12121}{5000}\gamma^{93}+\gamma^{77}- \frac{917499}{625000}\gamma^{50}+\frac{12121}{5000}\gamma^{43}+\frac{917499}{1 250000}=0. \tag{52}\]  
_Solving Equation (52) yields 100 solutions, \(\gamma_{j}(j=1,\ldots,100)\). There's \(\gamma_{1}=1.010355\in\mathrm{L}^{\frac{1}{50}}\), satisfying \(|\gamma_{1}|\leq(2\cos(50|\arg(\gamma_{1})|))^{\frac{1}{50}}\), and \(|\arg(\gamma_{1})|\leq\frac{\pi}{100}\). By Theorem 3, \(E_{0}\) is unstable._  
Based on the above, the computer virus incommensurate Model (36) is shown to satisfy a requirement for a chaotic attractor. Figure 2 shows the state evolution for the incommensurate Model (36) of the three previous examples with \((\eta_{1},\eta_{2})=(0.68,0.86)\). In the following section, advanced analytical and numerical tools, such as Lyapunov exponents, bifurcation analysis, or phase space reconstruction, are necessary to fully understand and characterize its dynamics.
## 4 Experimental Results
### Time Consumption Results  
The comparison of the results regarding localization time between our method and the comparison methods is presented in Table 7. In the industrial park's underground parking scene, our method achieved an average localization time ranging from 102 ms to 155 ms for each group of experiments, while [PERSON]'s method resulted in an average localization time ranging from 1469 ms to 1993 ms. PAN-SLAM resulted in an average localization time ranging from 98 ms to 121 ms.  
In the small's underground parking scene, our method's average localization time for each group of experiments fell within the range of 122 ms to 165 ms, while the Tao meth-od's average localization time ranged from 1731 ms to 1965 ms. PAN-SLAM resulted in an average localization time ranging from 105 ms to 125 ms.  
Figure 14: Comparison of the local feature matching results under different lighting conditions. (**a**–**d**) Depict the lighting conditions for Group 1, Group 2, Group 3, and Group 4, respectively.  
Based on the experimental results regarding the localization time, our method achieved a localization time of no more than 155 ms in all scenes, meeting the real-time requirements. In contrast, [PERSON]'s method exhibited a localization cycle of no less than 1731 ms across all scenes, indicating a more extended localization period. Compared with [PERSON]'s method, our method significantly enhanced the localization efficiency, which was primarily attributable to utilizing the holistic features. On one hand, encoding the images directly into a holistic feature drastically reduced the feature extraction time. On the other hand, representing the similarity between the nodes with the distances between the holistic features reduced the feature matching time. [PERSON]'s method relied on the number of matched local feature pairs to represent the node similarity, and since there are hundreds of local features on each image, both the feature extraction and matching required a considerable time to complete. Furthermore, the [PERSON] method's map nodes were densely distributed, resulting in more map nodes in the localization candidate set \(M_{\text{candi}}\), further extending the localization time.  
PAN-SLAM relied primarily on feature matching between the adjacent panoramic images to achieve localization, thereby achieving a high localization frequency. Our method accelerated the map node retrieval efficiency by employing holistic feature matching, maintaining a localization frequency comparable with PAN-SLAM. Consequently, both our method and PAN-SLAM met the requirements for the positioning timeliness.
###### Abstract
The transverse dimension, perpendicular to the direction of oscillating fluid, is limited by the thermal penetration depth \(\delta_{k}\), which is defined as:  
\[\delta_{k}=\sqrt{\frac{2k}{\omega\rho_{m}c_{p}}}, \tag{5}\]  
where \(\rho_{m}\) and \(c_{p}\) are the mean density and the isobaric specific heat of the fluid, respectively. [PERSON] [17] pointed out that the relative displacement amplitude has an important effect on the heat transfer performance. The relative displacement amplitude \(A_{r}\) is the ratio of the peak-to-peak displacement value and the channel length, which is defined as follows:  
\[A_{r}=\frac{2x_{A}}{l}=\frac{2u_{A}}{wl}, \tag{6}\]where \(x_{A}\) is the displacement amplitude of the flow. [PERSON] (1986) and [PERSON] (1991) suggested that the \(A_{r}\) value should be close to, or larger than, one. In fact, \(A_{r}\) is not an independent geometrical parameter and can be practically transformed into an expression, including the dynamic parameters, as follows:  
\[A_{r}=\left.\left(\frac{2 Re_{\max}}{Va}\right)\right/\left(\frac{l}{d_{h}}\right) \tag{7}\]  
Equation (6) indicates that the influence of geometrical parameters on heat transfer performance can be discussed in relation to the controlled working conditions. As a geometrical parameter of the heat exchanger, \(l/d_{h}\) can be correlated as a major factor when considering the heat transfer characteristics. Following the work presented in Reference (15), this paper presents an experimental study using six different geometrical dimensions of finned heat exchangers with parallel plates, in order to analyze the impacts of fin length, plate spacing, and corresponding relative fluid displacement amplitude, under various working conditions. This is completed in order to propose a comprehensive empirical correlation for the finned heat exchangers with parallel plates working in oscillating flow, displaying a wider range of application.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 4 Chitosan
### Parameters Influencing the Antibacterial Properties of Chitosan  
#### 4.1.1 pH of the Solution  
CS has a low solubility above pH 6.5, meaning the antibacterial effect is only developed at an acidic pH. This is due to the protonation of NH\({}_{2}\) groups, where CS becomes polycationic and interacts with negatively charged microbial cell membrane components such as phospholipids, proteins, anionic polysaccharides, fatty acids, and bile acids [65; 66; 67].  
Figure 4: Chemical structures of chitin and chitosan. Reprinted from [64], originally published under a CC-BY 4.0 license.  
The positive charge enhances the adsorption of chitosan on bacterial cells and increases the antibacterial activity at lower pH values [68]. At higher pH values (>6), chitosan precipitates out of solution as it loses its charge due to deprotonation of amino groups [69; 70; 71; 72].  
The antibacterial activity of chitosan against _Klebsiella pneumoniae_ depends on the degree of protonation of the chitosan amino groups, which depends on the degree of polymerization as well as the pH of the medium [73]. CS is not soluble in water. For the production of nanofibers, CS can be dissolved in acidic solutions with a pKa value of 6.3 or below, because at this pH value the glucosamine units will convert to the protonated soluble form (-NH3+). The solubility of CS depends on its precursor source, its molecular weight (MW), and the degree of deacetylation (DDA) [74].
## 3 Model Development
### Rollback Detection  
When the server receives estimates from logical process \(L_{i}\) it determines whether a rollback should be triggered for any _LPs_ based on rollback detection function \(Rollback(\cdot)\). The rollback detection function compares the flow rate estimates of each LP with the corresponding global instances in the Space-Time Memory and decides which LP needs to renew its estimates. Since _LPs_ model their own network portions of interest and their model networks overlap, each link can be simulated by multiple _LPs_. Also, the link can be an inbound, outbound, or internal link depending on the network configuration of each LP.  
Consider link \(j\), a link which some _LPs_ have as an inbound link, some as an outbound link, and some as an internal link of their own network simulations. Whenever there is an update on the global instance, \(G_{j,k}\) in the Space-Time Memory, the server checks the difference between \(G_{j,k}^{FlowRate}\) and estimates on link j as boundary links (inbound link or outbound link) for the individual _LPs_ that is \(g_{i,j,k}^{FlowRate,Inbound}\) or \(g_{i,j,k}^{FlowRate,Outbound}\). If the difference is greater than a given threshold \(RollbackThreshold\), then the estimates of the corresponding LP are considered invalid and a rollback is issued from the server.  
Consider _LPs_ which have link \(j\) as an outbound link. They only model the upstream area of link \(j\), not including downstream area of link \(j\) in their network. Since link \(j\) is the end link of the network, vehicles on link \(j\) exit the network at free flow speed unless there is an outflow constraint. In this case, they may not have a good estimate on link \(j\) when the downstream traffic condition outside the boundaries results in a spillback of congestion into the network being modeled. Thus, the traffic condition on link \(j\) needs to be adjusted to reflect the spillback traffic condition. On the other hand, _LPs_ which have link \(j\) as an inbound link and generate vehicles at a pre-determined flow rate may not represent traffic condition well when a sudden change in incoming traffic is predicted outside of the network boundaries. In this case, input rate on link j is adjusted based on \(G_{jk}^{FlowRate}\) which is included in rollback messages sent from the server.  
When the simulation time k is far ahead from the current wall-clock time, \(G_{jk}^{FlowRate}\) is calculated from the LP estimates which are available in the Space-Time Memory at the time period when the server checks \(G_{jk}^{FlowRate}\). Therefore, it is expected that rollback statistics would vary depending on how many _LPs_ are contributing to the aggregated global values. The number of _LPs_ contributing to the aggregated global values is determined by geographical distributions of LP locations.
# Impact of Nitric Oxide on the Surface Properties of Selected Polymers
## 2 Experimental Section
### Dynamic Contact-Angle Measurements  
Dynamic contact-angle measurements were conducted using a Datap physics OCA 35L (Databases, Filderstadt, Germany). The results were analyzed using the software SCA202. For MABS and PU, the sessile drop method was used. The polymer surface's wettability was quantified by depositing a liquid droplet. The contact-angle \(\theta\) between the droplet and the surface results from the interfacial tensions at the solid-vapor (\(\gamma_{\text{sv}}\)), solid-liquid (\(\gamma_{\text{sl}}\)), and liquid-vapor (\(\gamma_{\text{lv}}\)) interfaces. In ideal situations, the relationship between the surface tensions and the contact-angle is described by the Young equation [28]:  
\[\cos\theta_{\text{Y}}=\frac{\gamma_{\text{sv}}-\gamma_{\text{sl}}}{\gamma_{ \text{lv}}} \tag{2}\]  
In practical situations, factors like surface roughness and chemical heterogeneities lead to the pinning of the three-phase contact line. In the measurements, only the advancing \(\theta_{\text{A}}\) and receding contact-angle \(\theta_{\text{R}}\) can be determined. The Young contact-angle \(\theta_{\text{Y}}\) is between these two limits, such that \(\theta_{\text{R}}\leq\theta_{\text{Y}}\leq\theta_{\text{A}}\). Samples with a size of two by one centimeters were prepared for the measurements. Analogous to the samples for the zeta-potential measurements, care was taken to ensure the consistency of surface morphology, as factors like surface roughness can have pronounced impacts on the results [29,30]. Millipore water was used as the droplet fluid. An initial volume of 5 \(\mu\)L of water was placed on the sample's surface. During the measurement of the advancing angle \(\theta_{\text{A}}\), 15 \(\mu\)L of water was added to the surface. All 20 \(\mu\)L of water was removed again from the polymer surface while measuring the receding contact-angle \(\theta_{\text{R}}\). Water was added and removed at a rate of 0.1 \(\mu\)L/s.  
The Wilhelmy Method was employed to analyze the contact-angle of PMP. The advancing and receding contact-angles (\(\theta_{\text{A}}\), \(\theta_{\text{R}}\)) were calculated using the Wilhelmy Equation [31,32]:  
\[\text{F}_{\text{w}}=\pi\text{d}\gamma_{\text{lv}}\cos\theta \tag{3}\]  
\(\text{F}_{\text{w}}\) is the measured force, \(\text{d}\) is the diameter of the fiber, and \(\gamma_{\text{lv}}\) is the liquid-vapor surface tension. Measurements with PMP were conducted using single fibers, which were approximately 40 mm long. The fibers were compressed on the lower end to keep water from entering the inside of the fibers. The fiber was inserted into a cuvette filled with Millipore water to a maximum depth of 12 mm at a rate of 6 mm/min and then withdrawn at equal speed to measure both advancing and receding contact-angles. The contact-angles were recorded on both sides of the droplet or the fiber and averaged. Three repeat measurements were conducted for all three polymer materials.