esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 3 Microalgae-Assisted Aquaculture
### Microalgae-Based Wastewater Remediation  
#### Mechanisms of Wastes Assimilation  
Carbon sources in aquaculture wastewater for microalgae growth include inorganic carbon (CO\({}_{2}\) and HCO\({}_{3}\)\({}^{-}\)) and organic carbon (saccharides and volatile fatty acids). As the assimilation of CO\({}_{2}\) or HCO\({}_{3}\)\({}^{-}\) is driven by photosynthesis, it is a feasible way to promote the fixation of inorganic carbon by creating favorable conditions, particularly light and temperature, for photosynthesis [35]. With the fixation of CO\({}_{2}\) and the release of O\({}_{2}\), content of dissolved oxygen (DO) in a water body will be increased, constructing an oxygen-rich environment for aquatic animals. Compared with inorganic carbon assimilation, organic carbon assimilation might be more complicated and time-consuming in some cases since some forms of organic carbon could be utilized efficiently by microalgal cells [36]. For example, owning to the large size, some insoluble solids rich in carbon could not be absorbed by microalgal cells directly [14].  
To promote the carbon assimilation in aquaculture wastewater, the construction of algal-bacterial consortia has been widely studied [37; 38]. In consortia, CO\({}_{2}\) released by bacteria could be utilized by microalgae for photosynthesis, which generates O\({}_{2}\) for heterotrophic metabolisms in bacterial cells. As described by [PERSON] et al. (2013), in wastewater remediation, bacteria convert indigestible carbon forms to digestible carbon forms, such as volatile fatty acids, amino acids, and glucose, by secreting extracellular enzymes. Meanwhile, digestible carbon forms could be assimilated by microalgae in an efficient way. Therefore, compared with pure microalgae system, algal-bacterial consortia have a much better performance in nutrients recovery.
# A Novel Image Encryption Algorithm Based on Voice Key and Chaotic Map
### Combination of Image Encryption and Identity Recognition Technologies  
The image encryption utilized in the present study mainly includes encryption technology based on frequency and spatial domains. The encryption technology based on the frequency domain is mainly used for image compression, such as JPEG compression and compression technology based on the wavelet transform. The encryption technology based on the spatial domain is often used in security applications. The use of chaotic maps is common for spatial image encryption. Various existing image encryption algorithms can encrypt face images to ensure the security and privacy of the images, but they cannot be used to improve the reliability of facial image authentication. In order to deal with this issue, an image encryption algorithm based on chaotic maps and an algorithm for extracting human voice features into data are proposed in this article. The time-domain and frequency-domain features from a human voice are extracted to generate keys, which are used for image encryption. For identity recognition, the user's specific voice information is used to decrypt the encrypted image, and then the face recognition can be performed. This encryption and decryption method increases the difficulty of cracking the authentication scheme. The solution can also be applied in various other face recognition authentication situations to improve authentication security.
## 2 Burned Area Mapping and Vegetation Indices  
### Burned Area Indices  
Fire changes the landscape and disrupts the existing vegetation pattern, therefore, in order to assess the extent of this impact, a valuable solution is provided by the chlorophyll pigment absorption analysis [58]. This pigment electromagnetic spectrum absorption peaks for the blue and red corresponding wavelengths, while being highly reflective in the green portion [59]. Furthermore, the spongy structure of the mesophyll tissue and cavities within the leaf determines high NIR reflectance [60]. As soil's main absorption is in the blue region of the spectrum, this facilitates its distinction with respect to vegetation, allowing for the combination of these different bands in newly computed indices [60]. To assess postfire scar extent and vegetation recovery of the fire affected area, a multitemporal approach can be adopted [22]. Multitemporal observations gather better results when compared to single temporal observations as they rely on the estimation of changing vegetation cover. This technique is based on the computation operated via specifically developed indices: e.g., the delta normalized burn ratio (dNBR) [59], the relativized burn ratio (RBR) [61], the relative differenced normalized burn ratio (RdNBR) [62], or the burn area index for Sentinel-2 (BAIS2) [22], and all can be profitably used for burned area severity assessment.  
Although no agreement exists on which index performs best in the detection of fire-affected areas and for fire severity assessment, according to [PERSON], [PERSON], and [PERSON] [61], when averaged among different scenarios, RBR proved to have the highest classification accuracy in comparison to RdNBR and dNBR. Additionally, as with RdNBR, RBR has the capability to detect change in canopy cover over multitemporal images also for low vegetated areas. When compared to the other indices, the improved performance of RBR coupled with the detailed process implementation from ESA Research and User Support (RUS) [63] provides solid ground for the data processing and the index selection.
## 4 Description of MRP  
MRP is divided into three phases: cluster formation, multipath construction and data transmission. The first phase is executed when an event happens. Its objective is to realize dynamic clustering. In the second phase, the CH use ACO to search for multiple paths. The last phase dynamically chooses one path to transmit data according to an evaluation function.  
To start the operation of the routing scheme, nodes having information for the sink initialize the routing task by transmitting an ADV message to neighbor nodes. Each node then broadcasts the ADV message to its neighbor nodes, and so on. At the end of the initiation stage of the network, each node constructs a table containing neighbor information, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Neighbor** & **Pheromone** & **Residual** & **Distance** & **Hop** & **Tag** \\
**ID** & **Value** & **Energy** & **to Sink** & **count** & \\ \hline \(i\) & \({}^{r_{i}}\) & \(E_{i}\) & \(D_{ix}\) & \(h_{i}\) & 0 \\ \(j\) & \({}^{r_{ij}}\) & \(E_{j}\) & \(D_{ji}\) & \(h_{j}\) & 0 \\... &... &... &... &... & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Neighbor Information.  
The information in Table 1 will be used to help an ant search for routing. ID indicates the identification number of a node. \(\tau_{ci}\) is the pheromone value on link _(c,i)_, which represents the local link situation and quality. At the beginning, the pheromone in the network is a constant; then, it varies with ant routing. \(D_{is}\) is the distance between node \(i\) and sink, and it may be estimated by the received signal strength (_RSS_) [28]. _Hop count_ is the number of hops from a node to sink. _Tag_ indicates the instance of being visited by an ant. \(Tag=1\) indicates that the current node has been visited by an ant, otherwise, \(Tag=0\).
## 3 STRP-DBSCAN  
### Background Knowledge  
DBSCAN finds the directly density-reachable relationship from a data point p, located at the core of a dense region, to a data point q to form a cluster of the maximal set of data points connected by this relationship. To implement parallel DBSCAN, a cell-based grid structure is used to split the entire dataset into regions for parallel computing.  
According to [5; 10], there are some preliminaries to understand. There are two parameters in DBSCAN that need to be defined in advance, i.e., the radius of a neighborhood \(\varepsilon\) and the minimal points number _MinPts_ within \(\varepsilon\). The data space of the entire \(d\)-dimensional data points is portioned into a grid consisting of many equal-sized cells, and each cell is a \(d\)-dimensional hypercube with diagonal length \(\varepsilon\). If the number of neighborhood points of data point \(p\) is not less than _MinPts_, \(p\) is a core point and it can represent a dense region. A point \(q\) is directly density-reachable from a core point \(p\) if the Euclidean distance between \(p\) and \(q\) is less than or equal to \(\varepsilon\). A point \(q\) is density-reachable from a point \(p\) if a sequence of points \(p_{1}\), \(p_{2}\),..., \(p_{n}\) exists such that \(p=p_{1}\ \land\ q=p_{n}\ \land\ \forall i\in[1\), \(n\)-\(1]\): \(p_{i+1}\) is directly density-reachable from \(p_{i}\). A cell \(C\) is considered as a core cell if at least one core point exists within \(C\). When \(C_{1}\) and \(C_{2}\) are both core cells, \(p\) and \(q\) are the core points of \(C_{1}\) and \(C_{2}\), respectively, if the Euclidean distance between \(p\) and \(q\) is less than or equal to \(\varepsilon\), then \(C_{2}\) (\(C_{1}\)) is fully directly reachable from \(C_{1}\) (\(C_{2}\)). When \(C_{1}\) is a core cell and \(C_{2}\) is not a core cell, if the Euclidean distance between the core point \(p\) of \(C_{1}\) and a data point \(q\) of \(C_{2}\) is less than or equal to \(\varepsilon\), then \(C_{2}\) is partially directly reachable from \(C_{1}\). Figure 1 shows the density reachability between cells.
## 1 Introduction
In addition, different works are currently suggesting that CHG may be ineffective upon long treatment and is not free from the occurrence of resistance mechanisms [14]. For these reasons, in recent years, other antiseptic agents have been considered for the control of oral infections, in particular, quaternary ammonium compounds such as cetylpyridinium chloride (CPC) [14; 15]. Experimental data concerning the efficacy of these molecules against dental plaque microorganisms, especially in biofilm matrixes, are still controversial and not fully consistent [7; 16; 17]. CHG is, thus, still regarded as the reference compound among oral antiseptics, although undesirable side effects are reported to occur with its long-term use, including teeth yellowing staining and burning sensations [18]. Therefore, different studies in recent years investigated the antibacterial and antibiofilm activity of several natural extracts and essential oils alone and in combination with CHG, suggesting a potential beneficial synergistic effect [19; 20; 21; 22]. Although several studies have been undertaken, results still show high variability and lack of consistency [19; 20]. In the literature, a limited number of studies are reported that investigate and compare the efficacy of different CHG-containing mouthwash formulations. In these cases, the antimicrobial efficacy seems to vary extensively when comparable CHG concentrations are combined in different pharmaceutical formulations [23; 24; 25]. The purpose of this study was to compare and evaluate the antimicrobial and antibioticality of _S. mutans_ of CHG and three different CHG-containing mouthwash commercial formulations. Minimal Inhibitory Concentration (MIC) and Minimal Bacterial-dal concentration (MBC) were used to evaluate the antimicrobial activity of mouthwashes along with biofilm prevention concentration (BPC) and CHG formulation activity towards mature biofilm.
## 3 Results
### Mechanical Properties  
For CPC, the mechanical strength of the membrane should be able to withstand the damage caused by the strenuous action and washing. Figure 9 is the stress-strain curve of M1-M6 membranes. The elongation at break decreases with the increase in the content of GO-SSS, that is, as the amount of added GO-SSS increases, the flexibility of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **IEC (mmol/g)** & **WU (\%)** & **LSR (\%)** & **CA of Water (\({}^{\circ}\))** \\ \hline M1 & 1.97 & 53.6 & 16.8 & 40.1 \(\pm\) 2.2 \\ M2 & 1.95 & 53.2 & 16.3 & 44.9 \(\pm\) 2.6 \\ M3 & 1.90 & 53.1 & 16.1 & 50.2 \(\pm\) 2.3 \\ M4 & 1.88 & 52.8 & 15.8 & 55.0 \(\pm\) 2.4 \\ M5 & 1.83 & 52.4 & 15.6 & 58.3 \(\pm\) 2.1 \\ M6 & 1.80 & 51.6 & 15.2 & 60.6 \(\pm\) 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: WU, ICE, LSR, and WCA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Thickness (\(\mu\)m)** & **Strength (MPa)** & **Elongation at Break (\%)** \\ \hline M1 & 120 & \(17.5\pm 2.3\) & 235.3 \(\pm\) 18.8 \\ M2 & 117 & \(13.2\pm 2.2\) & 113.7 \(\pm\) 14.2 \\ M3 & 113 & \(15.3\pm 2.3\) & 102.4 \(\pm\) 19.5 \\ M4 & 109 & \(13.7\pm 1.5\) & 60.6 \(\pm\) 15.4 \\ M5 & 106 & \(14.1\pm 1.3\) & 46.4 \(\pm\) 8.4 \\ M6 & 103 & \(12.9\pm 1.6\) & 26.8 \(\pm\) 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thickness and mechanical properties of PVDF-g-SSS membranes.  
Figure 8: WCA of M1–M6 membranes.  
Figure 7: SEM image of cross section of (**a**) bare PVDF-SSS membrane, (**b**) composite membrane.  
the film decreases. The strength of the membrane has no obvious trend. The overall strength of the membrane doped with GO-SSS is not as strong as the original PVDF-g-SSS membrane. Among the GO-SSS-doped membranes, M3 has relatively higher tensile force and elongation at break and better comprehensive mechanical properties. As seen from Figure 9, the slope of the linear part (elastic strain) of the tension curve increases with the increase in GO-SSS dosage, indicating that the Young's modulus of the membrane increases with higher content of GO-SSS. With the increase, the rigidity of the membrane becomes stronger, that is, it is less prone to suffer deformation, which is detrimental for the application of protective clothing. The tensile strength and elongation at break of the film are summarized in Table 2. In comparison to M1 (the original membrane without GO-SSS), the overall membrane material becomes more rigid and easier to break with the increase in GO-SSS dosage.
## 1 Introduction
### Time-to-Event Data  
Survival analyses typically require availability of sufficiently large datasets. In this research, the National Bridge Inventory (NBI) database was selected as the source of data for the analyses. NBI provides a comprehensive database of information on bridges from all across the U.S. The NBI data include geometric information, bridge type, conditions ratings of bridge components, traffic information, and inspection data. Bridge inspections include assignments of numerical ratings for the condition of major bridge components given by inspectors at two-year intervals. The process of extracting the NBI data used in this study is summarized in a following section.  
The numerical ratings range from 0 to 9 and are given to individual components by bridge inspectors based on the Federal Highway Administration (FHWA) guidelines [29]. A rating of 0 for a bridge component indicates a failed condition, while other ratings are described as follows: imminent failure condition (1), critical condition (2), serious condition (3), poor condition (4), fair condition (5), satisfactory condition (6), good condition (7), very good condition (8), and excellent condition (9).  
A typical slab-girder bridge cross section commonly used in Wisconsin is shown in Figure 1. The usual types of bridge girders are prestressed concrete I-shaped, prestressed concrete adjacent box-girders, steel I-shaped girders, and steel box girders.  
Figure 1: Typical bridge cross section used in Wisconsin.  
It is realized that many factors could influence the probability of survival of bridges. The NBI records provide substantial prior data that are needed for survival analyses. However, the number of independent parameters available in the NBI records that are relevant to superstructure deterioration are limited. For example, the NBI data do not include all parameters that can influence deterioration of concrete bridges such as compressive strength of concrete, cover depth, diffusion coefficient, deicing salt usage, reinforcing bar layout, etc. Although the number of independent and relevant parameters available from the NBI database is somewhat limited, the results can nonetheless serve as field performance benchmarks for probabilistic assessment and verification of analytical deterioration models and their statistical simulations.  
The NBI data provide a valuable source of observation data, but only provide specific parametric data that are part of the dataset. This does not negate the fact that valuable survival information can be gathered using the available observation data, while the effects of various parameters on survival can be explicitly assessed when data on those parameters are available.
## 3 Results
### Chemical Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Compound** & **KI\({}^{\mathbf{a}}\)** & \begin{tabular}{c} **L. _nobilis leaf_** \\ **EOL (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **L. _nobilis fruit_** \\ **EOF (\%)** \\ \end{tabular} &
\begin{tabular}{c} **L. _nobilis fruit_** \\ **fatty oil (\%)** \\ \end{tabular} \\ \hline
59. & \(\alpha\)-cubebene & 1345 & & 0.1 & 0.1 \\
60. & \(\alpha\)-terpinyl acetate & 1346 & 10.4 & 7.0 & 1.0 \\
61. & eugenol & 1356 & 2.0 & 0.6 & 0.7 \\
62. & hydroxycinnamyl acetate & 1366 & & 0.1 & \\
63. & \(\alpha\)-ylangene & 1373 & & 0.2 & t \\
64. & \(\alpha\)-copaene & 1374 & - & 0.2 & t \\
65. & \(\beta\)-bourbonene & 1387 & & 0.2 & t \\
66. & \(\beta\)-elemene & 1389 & 1.1 & 5.7 & 2.8 \\
67. & cyperone & 1398 & 0.1 & & \\
68. & methyl eugenol & 1403 & 3.1 & & \\
69. & \(\alpha\)-gurjunene & 1409 & & 0.1 & \\
70. & trans-(E)-caryophyllene & 1417 & 1.4 & 0.9 & 0.5 \\
71. & \(\alpha\)-guaiene & 1437 & 0.1 & 0.4 & 0.2 \\
72. & 6,9-guaiadinen & 1442 & & 0.2 & \\
73. & cinnamyl acetate & 1443 & 0.1 & & \\
74. & (E)-isoeugenol & 1448 & & 0.2 & t \\
75. & cis-muurola-3,5-diene & 1448 & & 0.2 & t \\
76. & \(\alpha\)-humulene & 1452 & 0.9 & 0.4 & 0.2 \\
77. & allo-aromadendrene & 1458 & & 0.2 & t \\
78. & 9-epi-(E)-caryophyllene & 1464 & 0.2 & 0.1 & \\
79. & \(\chi\)-muurolene & 1478 & 0.3 & 0.2 & 0.1 \\
80. & \(\gamma\)-curcumene & 1481 & & 0.1 & t \\
81. & germacren D & 1484 & 0.3 & 0.4 & 0.3 \\
82. & \(\beta\)-seline & 1489 & 0.1 & 0.4 & 0.4 \\
83. & acophyllene & 1499 & & 0.2 & t \\
84. & bicyclogermacrene & 1500 & 1.0 & 1.0 & 0.5 \\
85. & \(\beta\)-himachalene & 1500 & 0.3 & & \\
86. & germacren A & 1508 & 0.2 & & \\
87. & \(\gamma\)-cadinene & 1513 & t & 0.3 & 0.3 \\
88. & (Z)-\(\gamma\)-bisabolene & 1514 & & 0.5 & t \\
89. & trans-cubebol & 1514 & & 0.1 & \\
90. & \(\delta\)-cadinene & 1522 & 0.5 & 0.7 & 0.5 \\
91. & zonarene & 1528 & 0.1 & 0.6 & \\
92. & \(\gamma\)-(E)-bisabolene & 1529 & 2.3 & & \\
93. & trans-cadina-1,4-diene & 1533 & & 0.1 & 0.7 \\
94. & \(\alpha\)-cadinene & 1537 & & 0.1 & \\
95. & silphierfol-5-en-3-ol A & 1557 & & 0.1 & \\
96. & \(\beta\)-calacorene & 1564 & & 0.1 & \\
97. & dodecanoic acid/lauric acid & 1565 & & 1.4 & 13.8 \\
98. & (2E)-tridecanol & 1568 & & 0.1 & \\
99. & germacrene-D-4-ol & 1574 & 0.2 & 0.1 & \\
100. & fokienol & 1576 & & & 0.9 \\
101. & spathulenol & 1577 & 0.9 & 0.3 & 1.1 \\
102. & pygmaein & 1581 & & & 0.4 \\
103. & caryophyllene oxide & 1582 & 0.5 & & \\
104. & globulol & 1590 & 0.2 & 0.2 & 0.3 \\
105. & viridiflorol & 1592 & & 0.2 & \\
106. & ethyl dodecanoate & 1594 & & 0.1 & \\
107. & isoaromadendrene epoxide & 1594 & & & 0.5 & \\
108. & ledol & 1602 & 0.1 & 0.2 & 0.2 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Damage Diagnosis of Frame Structure Based on Convolutional Neural Network with SE-Res2 Net Module
## 4 Experiments
### Comparative Experiment of Diagnostic Accuracy  
In order to verify the superiority of the proposed model for damage diagnosis, the accuracy comparison experiments were carried out with TICNN, two-dimensional model LeNet-5, 1 DCNN and support vector machine (SVM). The convolution kernel, pooling layer, fully-connected layer and activation function between each layer of LeNet-5 were not changed. The structure and parameters of 1 DCNN were the same as those used by [PERSON] in reference [37], and the Gaussian kernel function was adopted in the SVM model. The accuracy and its average value of the above  
As can be seen from the data in Table 4, when the training epoch is equal to 1, the accuracy of the new model is 12.1%, the accuracy of TICNN is 10.5% and the accuracy of the other three models is all lower than 10%. When the training epoch is equal to 5, the accuracy of the new model is 91.9%, the accuracy of TICNN is 71.4% and the accuracy of the other three models is all lower than 35%. When the training epoch reaches 30, the accuracy of the new model is 99.6%, the accuracy of TICNN is 98.4%, the accuracy of LeNet-5 is 96.8%, the accuracy of 1 DCNN is 95.5% and the accuracy of SVM is 85.6%. From all of the eight sets of data, although the accuracy of the new model is slightly lower than that of TICNN for two sets, on the whole, the accuracy of the new model is significantly higher than that of TICNN and the convergence speed is faster. In addition, from the average values of accuracy, the accuracy of the new model is 77.3%, which is 3.8% higher than that of TICNN, and the accuracy of other models is below 65%. It can also be seen from Figures 12 and 13 that the diagnostic capability of the new model is superior to other models.
## 2 Sequential Modelling Strategy
### Net Dynamics
#### 2.2.1 Relative Reference System  
As it is a standard choice for in-orbit servicing activities, a Local-Vertical-Local-Horizontal (LVLH) coordinate system is employed, with the chaser spacecraft designated as the reference point, denoted as \(O\). The axis notations used in this investigation are as follows:  
* \(x\): Radial direction.
* \(y\): Along-track or flight direction.
* \(z\): Cross-track direction.  
For the purposes of this study, it is assumed that the net deployment direction aligns with the flight direction. A visual representation of these coordinates is provided in Figure 2.