esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit
### Health  
The contribution of satellite remote sensing on human health may rely on monitoring infectious diseases, air quality, and water quality. With experience from remote sensing, a climatology from CDR may help with long-range prevention and protection.  
Infectious diseases such as malaria, yellow fever, diarrhea and tuberculosis, are a heavy burden on human populations, killing millions of people each year, a major cause of work loss [117]. Even though it is not possible to directly identify mosquitoes in satellite data, one can locate mosquitoes and their effects, such as malaria, with high accuracy by identifying their habitat (commonly soil, water, rocks, flora, fauna, and air). Many successful examples have been reported, e.g., [PERSON] _et al._[118] used Landsat data to identify mosquito larval habitats in Nebraska and South Dakota, USA, based on their association with freshwater plant communities. By cumulating the digital database with excellent temporal and spatial references, it is possible to analyze the relationships of vectors and diseases at landscape-level, and set alerts from season to season.  
Satellite remote sensing of trace gases and aerosols for air quality applications has a rich history, and it has evolved dramatically over the last decade [119] by employing varieties of passive and active techniques. Aerosols, tropospheric O\({}_{3}\), tropospheric NO\({}_{2}\), CO, HCHO, and SO\({}_{2}\) are not directly observed, but retrieved through calculating the atmospheric composition that best reproduces the observed radiation. Major applications of retrieved trace gases and aerosols fall into three categories:analyses and forecasts of events that affect air quality, inference of surface air quality, and estimates of emissions. As an example of the first category, [PERSON] [120] presented a case study in which MODIS satellite data show pollution transport that led to 150 health advisories in Texas. Also, [PERSON] [51] studied air quality changes in coastal megacities by examining the long-term trends of aerosol optical thickness using AVHRR AOT CDR product.  
To analyze water quality, suspended sediments, algae, aquatic vascular plants, and temperature need to be monitored, as these are the key pollutants listed by the Environmental Protection Agency, which estimated that approximately 40 percent of U.S. waters do not meet minimum water quality standards [121]. For example, using Landsat Thematic Mapper Data, suspended sediment in Lake Chicot can be mapped [121]. Higher spatial and spectral resolution may achieve better performance.
# Dynamic Viscosity and Transverse Ultrasonic Attenuation of Engineering Materials
## 3 Results and Discussion
### Attenuation Behavior of Fiber-Reinforced Plastics
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & **C\({}_{11}\)** & **C\({}_{33}\)** & **C\({}_{12}\)** & **C\({}_{44}\)** & **C\({}_{66}\)** & **\(\eta_{11}\)** & **\(\eta_{13}\)** & **\(\eta_{14}\)** & **\(\eta_{166}\)** & **Density** & **Fiber** & **Notes** \\ \hline  & **GPa** & **GPa** & **GPa** & **GPa** & **GPa** & & & & & **Mg/m\({}^{3}\)** & **Content \%** & \\ \hline GRP rod & 18.57 & 48.47 & 7.97 & 6.10 & 5.76 & 0.019 & 0.014 & 0.061 & 0.030 & 1.97 & 56.5 & \\ GRP pultraded & & 53.00 & & 6.71 & 0.022 & & 0.043 & 2.07 & 61.5 & & \\ CFRP AS4 UD & 15.28 & 132.9 & 7.44 & 6.68 & 7.07 & 0.029 & 0.031 & 0.044 & 0.032 & 1.53 & 50.0 & Heasel 3501-6 \\ CFRP G50 UD & 12.30 & 201.8 & 6.96 & 5.35 & 6.88 & 0.105 & 0.046 & 0.067 & 0.050 & 1.58 & 60.0 & Heasel F584 \\ CFRP T700 UD & 10.47 & 125.0 & 2.51 & 5.29 & 5.63 & 0.169 & 0.023 & 0.097 & 0.038 & 1.48 & 41.0 & Toray 2510 \\ GRP XP & 14.60 & 31.17 & & 4.93 & 7.17 & 0.046 & 0.031 & 0.183 & 0.127 & 1.81 & 38.0 & woven \\ C50 XP & 11.35 & & 4.15 & & 0.021 & & 0.042 & & 1.58 & 60.0 & Heasel F584 \\ KFRP & 9.91 & & 2.33 & & 0.108 & & 0.079 & & 1.32 & 32.0 & & Woven \\ Epoxy & 10.51 & & 2.65 & & 0.028 & 0.028 & 0.052 & 0.052 & 1.39 & & \\ PMMA & 5.47 & & 1.86 & & 0.008 & 0.008 & 0.013 & 0.013 & 0.95 & & \\ \hline \end{tabular}
\end{table} 
Table 6: Elastic moduli and damping factors of fiber-reinforced plastics. prepreg system and appeared to result in strong damping. Relatively high \(\eta_{ij}\) values of G50 CFRP were surprising as its matrix of F584 was a standard aerospace grade. In addition, this CFRP has the most metallic sounds when hit with a metal rod. Woven FRP produced high damping factors as fiber volumes were low and continuous resin layers were present. Damping factors of nine GRP and CFRP samples from six studies were compiled in [12]. All of them used the double through-transmission method [24; 30; 31; 32; 33; 34; 35]. The ranges of damping factors are generally comparable to those in Table 6. However, the details of these composite samples were unavailable. Without the knowledge of fiber types, volumes, as well as matrix resin types, further comparison adds little.
## 3 Results and Discussion
### Aroma Reconstitution Test  
To verify the aroma contribution of odorants with high OAVs to peach spirits, aroma reconstitution tests were carried out. Fourteen odorants with OAV \(\geq\) 1 quantified in distilled peach spirit were dissolved at natural concentrations (Table 2) in 40% ethanol aqueous solution to simulate the original spirit. Similarly, 20 odorants with OAV \(\geq\) 1 quantified in pervaporation produced peach spirit were also dissolved at natural concentrations (Table 2) in another 40% ethanol aqueous solution. Then the reconstituted solutions were evaluated by 16 trained panelists. As shown in Figure 3a, the results of ANOVA indicated that no significant difference existed between the reconstituted solution and distilled peach spirit. The aroma profile of reconstitution was similar to the distilled peach spirit, especially in the honey, malty, fruity, and cooked-apple aroma properties, while the intensities of the alcoholic, floral, and acidic aroma were a little lower than those in the original spirit. As shown in Figure 3b, the aroma profiles of pervaporation produced peach spirit and corresponding reconstitution were nearly overlapped with high similarity of alcoholic, malty, floral, and honey sensory. The intensities of grass and acidic aroma were slightly lower, and the intensities of cooked-apple and fruity were slightly stronger in reconstituted solution compared to the original spirit. There were 5 unknown compounds and several unquantified compounds being unconsidered in the aroma reconstitution test, especially some of them with high FD factors. In addition, the interactions among aroma compounds also influence the aroma profile. These might be the reasons for differentiations presented between the reconstitutions and original spirits.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 3 Unary Graph Operations and Graph Transformations
### Edge Contraction  
In the case that the two vertices \(v,w\in V(G)\) of a vertex identification \(\operatorname{Ident}(G,v,w)\) are adjacent, i.e., \(\{v,w\}\in E(G)\), we call the operation an _edge contraction_.  
Theorem 3.2: _For a graph \(G\) and two different vertices \(v,w\in V(G)\) with \(\{v,w\}\in E(G)\), it holds that_  
\[\operatorname{tw}(G)-1 \leq\operatorname{tw}(\operatorname{Ident}(G,v,w))\leq \operatorname{tw}(G)\quad\text{and}\] \[\operatorname{pw}(G)-1 \leq\operatorname{pw}(\operatorname{Ident}(G,v,w))\leq \operatorname{pw}(G).\]  
Proof.: Let \((\mathcal{X},T)\) be a tree-decomposition (\(\mathcal{X}\) be a path-decomposition) for \(G\) of width \(\operatorname{tw}(G)\) (\(\operatorname{pw}(G)\)). We replace all occurrences of \(v\) and \(w\) in all bags of \(\mathcal{X}\) with \(u\) and denote the resulting decomposition by \(\mathcal{X}^{\prime}\). Since \(v\) and \(w\) are adjacent in \(G\), we know by (tw-2) ((pw-2)) that there is at least one bag in \(\mathcal{X}\) that contains \(v\) and \(w\). Consequently, (tw-3) ((pw-3)) must hold for \(\mathcal{X}^{\prime}\), and it follows that \((\mathcal{X}^{\prime},T)\) is a valid tree-decomposition (\(\mathcal{X}^{\prime}\) is a valid path-decomposition) for \(\operatorname{Ident}(G,v,w)\) of width at most \(\operatorname{tw}(G)\) (\(\operatorname{pw}(G)\)). Because \(\{v,w\}\in E(G)\) is true, we can argue that (tw-3) ((pw-3)) holds for \(\mathcal{X}^{\prime}\). This argument is not valid for an arbitrary vertex identification, as \(v\) and \(w\) are not guaranteed to be adjacent (see Theorem 3.2).  
The lower bounds follow from the same argument as for the lower bounds in the proof of Theorem 3.2.  
Contracting any edge of a clique \(K_{n}\) of size \(n\) results in a clique \(K_{n-1}\) of size \(n-1\). By Lemma 3 (Lemma 3.2), we know that \(\operatorname{tw}(K_{n})=n-1\) (\(\operatorname{pw}(K_{n})=n-1\)) holds. Consequently, the lower bounds specified in the previous theorem are tight.
## 5 Morse Code Decoding
### Dot-Dash Recognition  
Considering that both dots and dashes are represented by \"1\" (high level) according to Morse encoding rules, the only difference between them is the time duration. The dot duration is the basic unit of time measurement in code transmission, while the dash duration is three units. As the time duration of the voltage level changes after pretreatment, it is necessary to recalculate the dot, as well as the  
Figure 8: Error correction cases.  
Figure 9: Pretreatment of Morse signal interpreting “SOS”.  
dash, duration thresholds. Thus, the mk-means clustering algorithm is performed to accomplish the classification of different high-level samples and dot-dash recognition. Figure 10a shows a flowchart of our dot-dash recognition algorithm:  
1. Extract the high-level samples output after signal pretreatment, stored in an array;
2. Perform the mk-means method to partition these data samples into two clusters, and store the classification results in a new array _HA_;
3. Sort the two cluster centroids in ascending order, stored in an array _CH_, and ensure that _CH_(0) is less than _CH_(1). If the order of the cluster centroids changes, then update the centroid index stored in _HA_;
4. Assume the basic dot duration computed above is \(D_{0}\) and \(s=CH\)(1)/\(CH\)(0). If \(s<2\), it indicates that the two clusters belong to one common category. At this point, if _CH_(1)/\(D_{0}\leq 2\), then it means all the samples in _HA_ denote \"dot,\" and all indexes in the array need to be updated to 0; else, if _CH_(1)/\(D_{0}>2\), then it means _HA_ represents \"dash,\" and the indexes should be updated to 1.
## 2 Background
### Types of Authentication  
There are five types of authentication, which are presented in Figure 2:  
One of the most well-known methods, single sign-on (SSO), allows a valid user to efficiently access a wide range of service providers by employing a unique key [1; 34].  
1. Factors: An authentication factor is a kind of security identification that is used to authenticate the identity and authorization of a user who is seeking to obtain access, transmit messages, or request data from a secure network, system, or application. There are two types: multifactor and single-factor [35; 36].
2. Three-way authentication: There are three different stages of authentication in the general transaction between server and client.
3. Mutual authentication: Multifactor authentication is slightly more complicated since it requires an additional step from the user, such as the well-known out-of-band (OOB) management or zero-knowledge password proof (ZPP) [37].
4. Biometrics: Biometric authentication is the technique of identifying persons based on physiological and behavioral characteristics. There are two types (multi-modal biometrics and single-mode biometrics) [13].
## 1 Introduction
The trajectory planning process for a vehicle with Ackermann steering can be divided into several steps, which may include the following:  
* Specify the initial and final conditions, such as the vehicle's starting position, orientation, and velocity, and the desired final position, orientation, and velocity. * Definition of constraints and objectives, such as the maximum steering angle of the wheels, the allowable acceleration and deceleration, and the optimization criteria. * Generation of a candidate trajectory that satisfies the constraints and objectives, using techniques such as model predictive control, polynomial curve fitting, or optimization algorithms. * Evaluation and refinement of the trajectory, considering factors such as tire slip, lateral and longitudinal forces, and vehicle dynamics. * Execution of the trajectory, using a feedback control system to track the planned trajectory and adjust for any deviations or disturbances. * In trajectory planning with Ackermann steering, it is important to consider the steering system's limitations and constraints, such as the maximum steering angle and the turning radius. The trajectory planning algorithm must ensure that the vehicle can follow the planned trajectory without exceeding these limits or causing any instability or loss of control. * Overall, trajectory planning with Ackermann steering is a complex process that requires a deep understanding of vehicle dynamics, control theory, and optimization algorithms. It is an important tool for achieving accurate and efficient motion control in autonomous vehicles, robotic systems, and other applications requiring precise and agile motion.
# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
###### Abstract
In the model, the computational problem of drawing geographic vector data is transformed into a spatial analysis problem of judging the distribution of geographic features in a spatial range mapped by the pixel unit. Furthermore, the computational complexity dependent on vector feature size is transformed such that it is instead dependent on the viewport display pixel size. When the pixel size of the viewport is much smaller than that of the geographic vector data, the visualization efficiency is greatly improved. * The index structure supporting the viewport generalization visualization model--viewport pixel quadtree (VPQ)--is designed. The structure takes viewports and their display pixels as index objects, records the distribution of geographic vector data in the pixel-mapping space, and accelerates the analysis and calculation of the display values of each pixel in the viewport region by a pruning strategy. The structure takes the viewport display pixel containing vector elements in the mapping spatial range as the index object, records the distribution of geographic vector data in the mapping spatial range of the pixel, and accelerates the analysis and calculation of the display value of each pixel in the viewport region by a pruning strategy. * Based on the computational characteristics of the viewport generalization model, a parallel algorithm for the online interactive visualization of geographic vector data based on the VPQ index is proposed. Compared with the existing methods, it has an obvious speed advantage and can better support the needs of BGVD interactive visualization in the network environment. At the same time, due to the high computational speed of the proposed method, the visualization results of geographic vector data at all scales can be calculated in real time, without the need to generate a cache picture through predictive calculation. Therefore, compared with the existing visualization methods based on the physical tile cache, it has the obvious advantage of saving cache storage space.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Design Guidelines
#### 8.3.5 Knowledge Plane Design  
It is better to implement knowledge-generating models that can detect errors in data and correct themselves. It is better to use explainable machine learning instead of conventional machine learning to interpret how machine learning models make decisions and diagnose problems, in order to make knowledge generation models accountable for their decisions. The knowledge generation models must be transparent, meaning that their inner workings and decision-making processes are visible to the users. Furthermore, knowledge generation models must be robust and perform accurately and reliably under a range ofconditions and input data. The knowledge generating models must be thoroughly assessed and validated to ensure that they fulfill explainability, transparency, robustness, and other requirements. They must also be trained on a variety of representative data to guarantee that they can make appropriate judgments under a variety of circumstances. The subject, predicate, and object triplet (RDF), the ontology (OWL), the logic-based representation (KIF), or knowledge graphs (RDFS), which are organized representations of knowledge for effective querying and reasoning, can all be used to describe knowledge. A machine learning model that has been pre-trained on a big dataset and then fine-tuned on a small task-specific dataset can be improved via transfer learning. Additionally, active learning may be used to enhance the process by which knowledge-generating models are taught, allowing the models to choose the most instructive instances to utilize as training data. Additionally, attention techniques can be employed to narrowly concentrate attention on the portions of the input data that are most crucial to the activity at hand. It is required to define a clear knowledge ontology using an ontology editor for the knowledge composition plane. An ontology is a set of concepts and categories that define the relationship between different pieces of knowledge. By defining a clear knowledge ontology, different pieces of knowledge can be clearly analyzed and combined based on their relationships in the knowledge composition plane. Human expert input can be assisted in knowledge composition where possible to increase the accuracy and effectiveness of the knowledge combination process. A physically distributed knowledge plane is desired in order to distribute the workload of data among multiple knowledge generation models instead of a single model.
## 4 System Design and Goals
### Message Sequence  
The proposed message sequence is designed to decongest OLSRv2 message broadcasts and enhance information base maintenance. The new message sequence can achieve the dual goals of network decongestion and optimum information base maintenance by ensuring nodes refrain from responding to OLSRv2 message broadcasts while writing to OLSRv2 tables. Although it is called a message sequence, the actual messages are preceded by either a variant of DB_SELF or LISTEN. The proposed message sequence is DB_SELF, LISTEN, HELLO, DB_NIEGHBORS_RCVR, HI, DB_NIEGHBORS_BOTH, TC, DB_ROUTES.
## 3 Results  
Table 1 shows some characteristics of FEE enhancements that were selected for analysis. The data set was acquired from the POES--15,16,19 satellites, which are designated as P5, P6, P9. Enhanced flux in the \(>\)300 keV channel is marked by an asterisk (*). The characteristics include time, local time, longitude, \(L\)-shell, and flux intensity, which correspond approximately to the moment the satellite crosses the equator. The last column contains information on the solar wind (UT corresponds to the time of the ACE/Wind monitor measurements, i.e., without applying a time lag) and storm phase.  
During super storms caused by strong impact of coronal mass ejections on the Earth's magnetosphere, solar wind plasma and IMF parameters are characterized by extreme values and change dramatically. The first response of the geomagnetic field is often a sudden storm commencement (SSC) or sudden impulse (SI), observed by low-latitude ground-based magnetic stations. SSC is associated with the arrival of an interplanetary shock wave at the magnetopause. Extreme negative values of the SYM-H index at the storm main phase indicate intensification of the ring current due to the effect of a large value of southward \(Bz\) IMF component. Following the CME-driven shock, other large-scale solar wind structures may impact the magnetosphere with a time lag that may differ slightly from the propagation time of the shock at the solar wind plasma velocity [48]. We use measurements of the geomagnetic field on the ground in order to determine the time of the disturbance impact more accurately.  
Figure 2: Drift period \(Td\) (hr) of energetic electrons at low \(L\)-shells calculated by Equation (1).
## 2 Monin-Obukhov Similarity Theory  
According to MOST [19], any dimensionless characteristics of the turbulence depends only on the dimensionless stability parameter \(\zeta=z/L\), where \(z\) is the height above the surface and the Obukhov length \(L\) that is defined by  
\[L\equiv-\frac{u_{*}^{3}}{\kappa\frac{g}{\delta}\overline{w^{\prime}\theta^{ \prime}}} \tag{1}\]  
in which \(\kappa=0.4\), \(g\) is the gravity acceleration, \(\Theta\) is the time-averaged potential temperature, \(\theta^{\prime}\) is the fluctuation of the potential temperature, and \(u_{*}=\sqrt{-u^{\prime}w^{\prime}}\) is the friction speed, where \(u^{\prime}\) and \(w^{\prime}\) are the fluctuations of the longitudinal and vertical velocity components.  
The flow similarity functions of the gradient of velocity, the gradient of potential temperature, the turbulence kinetic energy, and its dissipation can thus be defined as  
\[\phi_{m}(\zeta) \equiv \frac{\kappa z}{u_{*}}\frac{\partial U}{\partial z} \tag{2}\] \[\phi_{h}(\zeta) \equiv \frac{\kappa z}{\theta_{*}}\frac{\partial\Theta}{\partial z}\] (3) \[\phi_{k}(\zeta) \equiv \frac{\sqrt{C_{\mu}}}{u_{*}^{2}}k\] (4) \[\phi_{\varepsilon}(\zeta) \equiv \frac{\kappa z}{u_{*}^{3}}\varepsilon \tag{5}\]  
where \(U\) is the mean streamwise speed, \(\theta_{*}=-\overline{w^{\prime}\theta^{\prime}}/u_{*}\) is the scaling temperature, \(C_{\mu}=0.033\), \(k\) is the turbulence kinetic energy (TKE), and \(\varepsilon\) is the TKE dissipation. According to the eddy viscosity hypothesis by [PERSON] [20], we have  
\[-\rho\overline{u^{\prime}w^{\prime}}=\mu_{t}\frac{\partial U}{\partial z} \tag{6}\]  
where \(\rho\) is the air density and the eddy viscosity is modeled in the \(k-\varepsilon\) model as [21]  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\varepsilon} \tag{7}\]  
Combining the definitions of \(\phi_{m},\phi_{k}\), and \(\phi_{\varepsilon}\) with Equations (6) and (7) leads to  
\[\phi_{k}(\zeta)=\sqrt{(\phi_{\varepsilon}(\zeta)/\phi_{m}(\zeta)} \tag{8}\]  
Based on measurements of flows over flat terrain in the atmospheric surface layer, the classical similarity functions commonly used in literature are given as [22; 23]  
\[\phi_{m,\text{cls}}(\zeta)=\begin{cases}(1-\gamma_{m}\zeta)^{-1/4}&-2<\zeta<0 \\ 1+\beta_{m}\zeta&0<\zeta<1\end{cases} \tag{9}\]  
\[\phi_{h,\text{cls}}(\zeta)=\begin{cases}\chi(1-\gamma_{h}\zeta)^{-1/2}&-2< \zeta<0\\ \chi+\beta_{h}\zeta&0<\zeta<1\end{cases} \tag{10}\]  
\[\phi_{\varepsilon,\text{cls}}(\zeta)=\begin{cases}1-\zeta&\zeta<0\\ \phi_{m,\text{cls}}-\zeta&\zeta>0\end{cases} \tag{11}\]where \(\gamma_{m}=\gamma_{h}=16\), \(\beta_{m}=\beta_{h}=5\)[23], and \(\chi\) will be determined based on field measurements in Section 4. The classical similarity functions \(\phi_{m}\) and \(\phi_{h}\) are, however, only valid for the flux Richardson number \(R_{f}\) below 0.25 [17]. We thus proposed new functions, noted as \(\phi_{m,\text{exp}}\) and \(\phi_{h,\text{exp}}\), in the full range of \(R_{f}\) based on a field experiment in a wind farm.