esa-sceva/satcom-chunk-collection huggingface dataset:
# _Bacillus_ sp. GLN Laccase Characterization: Industry and Biotechnology Implications
## 3 Results  
### Bacteria Isolation, Lacase Production, and Identity Confirmation  
Bacterial strains with distinct morphological characteristics were selected from the cultivation plates. The isolated strains coded as SP-2, WP-3, WP-1, WP-4, SP-1, WP-2, WP-5, and SP-3 were qualitatively examined for potential lacase production (Table 1 and Figure S1). Six (SP-2, WP-3, WP-1, WP-4, SP-1, WP-2) out of the eight isolates exhibited reddish-brown color zones on guaiacol amended plates. Three isolates (SP-2, SP-1, WP-2) showed a deep purple color on \(\alpha\)-naphthol supplemented media due to oxidation by the secreted extracellular lactose. Additionally, three (SP-2, SP-1, WP-2) bacterial isolates oxidized both substrates by showing reddish-brown and deep purple color on guaiacol and \(\alpha\)-naphthol plates, respectively. However, WP-5 and SP-3 could neither oxidize guaiacol nor \(\alpha\)-naphthol on the plate assay. The quantitative assessment of the three isolates SP-2, SP-1, and WP-2 that oxidized both substrates on the plate assay showed respective extracellular lactose activity of 7.0 \(\pm\) 0.01, 6.67 \(\pm\) 0.02, and 7.40 \(\pm\) 0.04 (U/mL), as shown in Figure 1. Considering the potent lacase activity shown by WP-2, it was designated for further analysis.  
The microscopic characterization indicated that WP-2 is a bacilli (Figure 2A). Analysis of the 16S rRNA gene nucleotide sequence showed that WP-2 displayed high sequence homology (99%) with the _Bacillus cereus_ sensu late group (Figure 2B). Since it did not show complete discrimination among the species of this group, it was reasonably identified as _Bacillus_ sp. and further assigned a unique strain name GLN. The nucleotide sequence of _Bacillus_ sp. GLN was deposited in the nucleotide collection database with the accession number MK290989.  
\begin{table}
\begin{tabular}{c c c c} \hline
**S/N** & **Isolate** & **Guaiacol** & **\(\alpha\)-Naphthol** \\ \hline
1. & SP-2 & + & + \\
2. & WP-3 & + & \(-\) \\
3. & WP-1 & + & \(-\) \\
4. & WP-4 & + & \(-\) \\
5. & SP-1 & + & \(+\) \\
6. & WP-2 & + & \(+\) \\
7. & WP-5 & \(-\) & \(-\) \\
8. & SP-3 & \(-\) & \(-\) \\ \hline \end{tabular}
\end{table}
Table 1: The qualitative evaluation of lacase production by decaying wood-associated isolates.  
Figure 1: The quantitative evaluation of lacase production by decaying wood-associated isolates. Isolate WP-2 enzyme activity was significantly higher (**) than others at \(p<0.05\).
Remote Sens._**2014**, \(6\), 8945-8965; doi:10.3390/rs6098945  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 2,3, [PERSON] 4, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1**  
1 State Key Laboratory of Earth Surface Processes and Resource Ecology,  
Beijing Normal University, Beijing 100875, China;  
E-Mails: [EMAIL_ADDRESS] (W.C.); [EMAIL_ADDRESS] (W.D.);  
[EMAIL_ADDRESS] (Y.C.); [EMAIL_ADDRESS] (D.L.); [EMAIL_ADDRESS] (H.Z.)
2 State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; E-Mail: [EMAIL_ADDRESS]
3 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
4 Geospatial Science Center of Excellence, South Dakota State University, 1021 Medary Ave,  
Wecota Hall 115, Box 506B, Brookings, SD 57007, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-10-58-807-715; Fax: +86-10-58-803-002.  
_Received: 20 May 2014; in revised form: 28 August 2014 / Accepted: 10 September 2014 / Published: 22 September 2014_  
+++ ==WARNING: Truncated because of repetitions==  
++  
keywords:gross primary production (GPP); light use efficiency (LUE); CASA; CFix; CFlux; EC-LUE; MODIS; VPM; VPRM
# Point-Kernel Code Development for Gamma-Ray Shielding Applications
## 3 Gamma Flux Solution for Simple Source Geometries
### Source Slab with a Parallel Slab Shield
#### 3.4.4 Method of Bowman-Trubey  
[PERSON] and [PERSON] proposed this formula for energy absorption (heating) in a 2-layer slab shield and proved to be adequate in comparison with their MC calculations:  
\[B(\mu_{1}x_{1},\mu_{2}x_{2})=B_{1}(\mu_{1}x_{1})B_{2}(\mu_{2}x_{2})e^{-\mu_{2} x_{2}}+B_{2}(\mu_{1}x_{1}+\mu_{2}x_{2})\big{(}1-e^{-\mu_{2}x_{2}}\big{)}. \tag{31}\]  
All of these methods are based on fundamental exposure \(B\)-factor data of point gamma source in infinite homogeneous media [3]. For the first two methods, the \(B\)-factor is expressed as a linear function of composite shield thickness, gamma-ray energy and \(Z_{\textit{eff}}\) as follows:  
\[B(Z_{\textit{eff}},E,\sum\limits_{i=1}^{N}\mu_{i}x_{i})=\alpha_{\textit{eff}}( Z_{\textit{eff}},E)\sum\limits_{i=1}^{N}\mu_{i}x_{i}. \tag{32}\]  
The fitting parameter \(\alpha_{\textit{eff}}\) is found using the bilinear interpolation of gamma-ray energy \(E\) over eight discrete energies (0.5, 1, 2, 3, 4, 6, 8 and 10 MeV) and of \(Z_{\textit{eff}}\) atomic number over seven typical shielding materials (H\({}_{2}\)O, Al, Fe, Sn, W, Pb and U) [23].
# 3D DNA Nanostructures: The Nanoscale Architect
## 2 3D DNA Nanostructure Design  
### History  
#### 2.1.1 DNA Tiling Lattices  
[PERSON] first published the seminal observation that DNA could serve a structural purpose in 1982, where it was envisioned that repeating units of branching DNA junctions could hybridize to form a stiff 3-dimensional lattice to facilitate crystallographic study of other molecules [17]. This became the inspiration for _DNA tiles_, the earliest general technique for structural DNA design. More generally, a DNA tile is a DNA nanostructure that is self-assembled from a small number of single-stranded DNA (ssDNA) that hybridize together with stabilizing Holliday junctions known as crossovers. Once assembled, the DNA tiles have ssDNA (termed _sticky ends_) which allow the DNA tile to further self-assemble into DNA nanostructures known as _DNA lattices_, which have been used to build 1D and 2D DNA tiling assemblies and lattices for molecular-scale computation. [PERSON] also demonstrated the first 3D DNA cube using 8 branching junctions [18]. However, yields, sizes, and shapes of early DNA nanostructures self-assembled by DNA tiles were limited. Persistent effort [19; 20] since then has yielded micron-scale DNA crystals [21], which to date are the largest DNA nanostructures to be synthesized, but those are so far limited to rhombohedron shapes. The self-assembly of patterns by DNA tiles has proved to be quite complex, as shown in Sierpinski triangle assemblies by [PERSON], [PERSON], and [PERSON] [22], which computed mod 2 sums within the assembly, forming a fractal-like 2D pattern. However, the class of possible geometries of DNA tiling assemblies still remains restricted to algorithmic patterns.
## 4 Conclusions  
In this paper, two kinds of algorithms, SDA and TODA, are proposed to identify rotor unbalances in multi-disc and multi-span rotors from unbalance responses. Rotors are modeled based on the continuous rotor dynamic analysis method. By using the unbalance responses as inputs and considering the unbalance responses at the bearings and the eccentric discs as unknown variables, an inverse problem model is developed based on the matrix method to eliminate the coupling between bearing coefficients and rotor unbalances. Four kinds of numerical simulations considering sensor resolutions and measurement errors were conducted to study the algorithms. Compared with existing methods, the proposed algorithms have the flexibility to incorporate any number of bearings and discs. Moreover, test runs and external exciters are not required. The conclusions are summarized as follows. 1. The proposed algorithms provide a technique with which to monitor all the rotor unbalances on-line under operational conditions. For SDA, the unbalance responses in only one direction are needed. For a rotor with \(m\) discs and \(n\) bearings, the number of required unbalance responses is \(m+n+1\). While for TODA, the unbalance responses in both the \(x\) and \(y\) directions are required. The necessary measuring position of the two methods should be at the disc whose unbalance is to be monitored. For monitoring the unbalance of all discs, there should be a sensor mounted at each disc, while the other measuring positions can be at or around the bearings or discs. Moreover, numerical simulations indicate that there should be one measuring point called the adjustment point to achieve a high identification accuracy. The proposed adjustment point should be near the disc whose unbalance is to be monitored. In order to identify all the discs' unbalances accurately and simultaneously, there should be \(m+n+n\) measuring points, among which \(n\) adjustment points near each disc are necessary. 2. The identification accuracy of the proposed algorithms requires a high performance of the unbalance response measurement system. Numerical simulations indicate that if the measuring errors of all the required unbalance responses are zero, the identification error will be zero, too. When the measuring errors are the same, the identification error will be equal to the measuring errors. It is indicated that the consistency of each channel's measurement errors plays a critical role in identifying rotor unbalance when using the proposed algorithms. In addition, SDA has a better identification accuracy than TODA when considering sensor resolution from the perspective of engineering applications. The identification error of SDA is only high at a very low frequency and the critical frequencies when sensor resolutions are considered. Hence, SDA is suitable for medium-speed and high-speed rotors. Moreover, identification accuracy is strongly related to sensor resolution. Sensors with resolutions of 1 um should be avoided and sensors with 1 nm and 0.1 nm resolutions are recommended.
## 4 Discussion
### Patterns, Trends, and Insights
#### 4.2.2 Integration of AI with IoT and Big Data  
The convergence of AI with IoT devices and Big Data analytics facilitates real-time data collection, analysis, and decision-making. This integration enhances the speed and accuracy of safety interventions, leading to more efficient and effective safety management practices.
# Modeling and Simulation Tools for Fog Computing--A Comprehensive Survey from a Cost Perspective
## 2 Basic Concepts  
Since 2009, [PERSON] et al. [21] have supported that cloud computing suffers from large scale and increasing latency, and have pointed out the need for new technologies. Among the newer developments, fog computing is a groundbreaking technology that bolsters and extends the cloud. The term \"fog computing\" was initially introduced by Cisco, in 2012, as a _\"cloud-to-thing continuum\"_[1]. It is defined in different ways, which encompass the cooperation of a huge number of distributed devices and the placement of storage, computational and networking services at the edges, the network or the cloud [2]. Fog computing is not a standalone paradigm, it is located between the cloud and the network edges, supporting virtualization and the cooperation of end devices. It arose as a result of the efforts of academia and industry in order to provide low latency, high scalability while reducing costs and energy. It is considered as a promising technology, capable of handling extraordinary amounts of data that are generated by IoT devices. The features of fog computing include heterogeneity support, geographical distribution, location awareness, ultra-low latency, support of real time and large-scale applications, and virtualization [4].
## 2 Material and Methods
### Adsorption Kinetics  
An initial concentration (\(C_{i}\)) of 100 mg L\({}^{-1}\) was used in adsorption kinetic experiments in order to find out the equilibrium time (\(t_{e}\)). After stirring during pre-defined times, the residual concentration of paracetamol was determined (\(C_{t}\)) as described above. In the case of _Synechocystis_ sp., 0.05 g of biomass were used as biosorbent, whereas 0.005 g of activated carbon were used as adsorbent. Experimental conditions were controlled and kept at 250 rpm stirring and 25 \(\pm\) 1 \({}^{\circ}\)C of temperature. The pH of solutions was not fixed at any initial value neither buffered, but stability in the values was observed along the kinetic experiments (7.0 \(\pm\) 0.5).  
Three replicates were run for each considered adsorbent and time. In parallel, for each adsorbent, controls and blanks were also run in triplicate. Controls were used to verify that the concentration of paracetamol remained stable throughout the duration of adsorption experiments and consisted of 50 mL of aqueous solution of paracetamol at the corresponding experimental \(C_{i}\). Meanwhile, blanks consisted of 50 mL of distilled water together with 0.05 g of microalgal biomass or together with 0.005 g of activated carbon. Blanks were used as reference for the spectrophotometric determination of the residual paracetamol concentration after stirring.  
To determine the residual concentration (\(C_{t}\)), at the end of each prefixed stirring time (\(t\)), the absorbance in the filtered (MF Millipore, 0.45 \(\mu\)m) aqueous phase was measured. The amount of paracetamol adsorbed in the adsorbent (\(q_{t}\)) was determined by a mass balance at each time, and in function of the initial paracetamol concentration (\(C_{i}\)), \(C_{t}\), the volume of the paracetamol solution (\(V\)) and the mass of the adsorbent (\(m_{ads}\)).  
\[q_{t}=\frac{(C_{i}-C_{t})}{m_{ads}}\times V \tag{1}\]  
Paracetamol adsorption kinetics were analyzed by the determination of the fittings to pseudo-first (Lagergren equation) and pseudo-second order equations. The Equation (2) was obtained by the integration of \(q_{e}\), \(q_{t}\) and pseudo-first order kinetic constant (\(k_{1}\)) of the Lagergren equation.  
\[q_{t}=q_{e}\Big{(}1-e^{-k_{1}t}\Big{)} \tag{2}\]  
The Equation (3) was obtained after the integration of the pseudo-second order equation, and it depends on \(q_{e}\), \(q_{t}\) and second order kinetic constant (\(k_{2}\)) parameters.  
\[q_{t}=\frac{q_{e}^{2}k_{2}t}{1+k_{2}q_{e}t} \tag{3}\]  
In order to establish the fittings to these equations to kinetic experimental results, non-linear regression was carried out using the software GraphPad Prism (demo, version 6, GraphPad Software, La Jolla, CA, USA).
###### Abstract  
Over the last 50 years, countries across North Africa and the Middle East have seen a significant increase in dam construction which, notwithstanding their benefits, have endangered archaeological heritage. Archaeological surveys and salvage excavations have been carried out in threatened areas in the past, but the formation of reservoirs often resulted in the permanent loss of archaeological data. However, in 2018, a sharp fall in the water level of the Mosul Dam reservoir led to the emersion of the archaeological site of Kemune and allowed for its brief and targeted investigation. Reservoir water level change is not unique to the Mosul Dam, but it is a phenomenon affecting most of the artificial lakes of present-day Iraq. However, to know in advance which sites will be exposed due to a decrease in water level can be a challenging task, especially without any previous knowledge, field investigation, or high-resolution satellite image. Nonetheless, by using time-series medium-resolution satellite images, combined to obtain spectral indexes for different years, it is possible to monitor \"patterns\" of emerging archaeological sites from three major Iraqi reservoirs: Mosul, Haditha and Hamrin lake. The Normalised Difference Water Index (NDWI), generated from annual composites of Landsat and Sentinel-2 images, allow us to distinguish between water bodies and other land surfaces. When coupled with a pixel analysis of each image, the index can provide a mean for highlighting whether an archaeological site is submerged or not. Moreover, using a zonal histogram algorithm in QGIS over polygon shapefiles that represent a site surface, it is possible to assess the area of a site that has been exposed over time. The same analyses were carried out on monthly composites for the year 2018, to assess the impact of monthly variation of the water level on the archaeological sites. The results from both analyses have been visually evaluated using medium-resolution true colour images for specific years and locations and with 3 m resolution Planescope images for 2018. Understanding emersion \"patterns\" of known archaeological sites provides a useful tool for targeted rescue excavation, while also expanding the knowledge of the post-flooding impact on cultural heritage in the regions under study.  
remote sensing; NDWI; cultural heritage; reservoirs; archaeology Article  
Use of Time-Series NDWI to Monitor Emerging Archaeological Sites: Case Studies from Iraqi Artificial Reservoirs
# A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs
## 3 A Hybrid Multi-Objective Optimization Method
### Adaptive Operators
#### 3.3.3 Selection of the Operators  
Based on the characteristics of the two kinds of crossover operators, namely SBX and two-point crossover, and the two kinds of mutation operators, namely Gaussian and polynomial mutations, it can be drawn that the SBX crossover possesses better local search ability than the two-point crossover, and that the Gaussian mutation is more suitable for local exploitations than the polynomial mutation. In this regard, SBX crossover and the Gaussian mutation are chosen as the set to be used to endow the proposed hybrid algorithm with better local search ability. Accordingly, two-point crossover and polynomial mutation are chosen as another set to further enhance the global search ability.  
The probability of selecting the corresponding set of operators is determined by  
\[\begin{cases}\quad P_{S}=\tau/\tau_{max}\\ P_{T}=1-\tau/\tau_{max}\end{cases} \tag{14}\]where \(\tau\) is the current iteration number, \(\tau_{max}\) is the maximum number of iterations, \(P_{S}\) is the probability of selecting the SBX crossover operator and Gaussian mutation operator, and \(P_{T}\) is the probability of selecting the two-point crossover operator and polynomial mutation operator.  
In the initial stage of the proposed hybrid algorithm, the two-point crossover and polynomial mutation are executed to obtain a larger searching space, which helps to jump out of local optima. As the iterations proceed, the SBX crossover and Gaussian mutation operator are gradually chosen, local exploitation is strengthened, and excellent individuals are more inclined to be persevered, which facilitates the convergence of the optimization procedure.
# A Secure and Efficient Group Key Management Scheme for Clusters of String Inverters
## 4 The Group Key Management Scheme
### The Join Operation  
Let us consider the situation in which K\({}_{\text{G}}\) is the current group key and a set of inverters has to join the group. We called the Joining Set that set and denoted it as \(JS\). The join operation consists of the following steps.  
1. Initially, the KDS selects a KDC and securely sends it a Join, JS; message that specifies the Joining Set \(JS\).
2. Upon receiving the Join, JS message, the KDC reads a timestamp \(\tau\) from its clock and securely unicasts every inverter in the joining set JS a \(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{\  
At the end of the protocol, all inverters in the group \(G\) but the leaving one can communicate using the group key \(K^{{}^{\prime}}_{G}\).  
The leave operation logically evicts a leaving inverter from the group by preventing it from communicating after the leave operation has completed. This eviction must be performed at two layers: at the application layer, by revoking the current group key and redistributing the new one to every inverter but the leaving one (step 2.c); and at the transport layer, by updating the certificate revocation list (step 2.b).
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 4 Results
### Unexplained Variability of Consumer Ad Engagement  
Based on the \(R^{2}\) values, the models presented explain only a small portion of the variability (up to 10%) in the consumer ad engagement scores collected from UES-SF. Figure 3 shows the scatter plots with the six vertical clusters bound to the feature values of each video and plotted on the \(x\)-axis.  
This unexplained variability is not uncommon, especially in human psychology studies [14]. Moreover, this was expected in the present study because of the nature and complexity of the engagement construct and the lack of experimental data from related work. Consequently, the proposed models based on low-level video features alone cannot achieve a higher value of explained variance (\(R^{2}\)).  
To put the reported \(R^{2}\) values into context, the question is what the contribution is of the evaluated models compared to the maximum achievable value of \(R^{2}_{m}\) for a given feature. To determine the maximum achievable \(R^{2}_{m}\) for an ideal model based on video features, the ad engagement scores were evaluated using categorical regression based on the ad videos themselves. Categorical data from the six videos were quantified by assigning numerical values to their respective categories with the goal of the model yielding the best possible linear regression fit. The mean value for consumer engagement in ads was used as a separate transformation of the categorical video value for each engagement dimension.  
An example of a scatter plot and the corresponding linear regression plot for overall User Engagement Score (UES) is shown in Figure 4. A high level of variability in consumer engagement can be observed for each video.  
A summary of maximum achievable \(R^{2}\) values for the case of categorical regression based on video advertisements themself can be found in Table 11, where values in the data column contain the estimated \(R^{2}_{m}\) values of the ideal model. Relatively low values agree with our initial assumption (see Equation (1)) that there are other factors affecting the consumer engagement apart from the content (video) itself.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Light** & **Color** & **Shot** & **Motion** & **Motion** \\  & **Key** & **Var** & **Length** & **Mean** & **Std** \\ \hline FA & & & & & \\ PU & & & & & \\ AE & & & & & \\ RW & & & & & \\ UES & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Model shapes of four O’Brien UES user engagement scale dimensions estimated from 5 low-level ad video features. The foreseen possible model shapes are labelled with symbols \(\cup\) representing U-shape, \(\cap\) indicating upside-down \(U\) shape, \(\
earrow\) showing increasing and \(\searrow\) for decreasing behaviour.