esa-sceva/satcom-chunk-collection huggingface dataset:
# Pharmacological and Cosmeceutical Potential of Seaweed Beach-Casts of Macaronesia
## 2 Materials and Methods
### Antibacterial Activity  
The antibiotic activity of all extracts and fractions was individually determined employing four bacterial species, two Gram-positive i.e., _Bacillus subtilis_ and _Staphylococcus aureus_, and two Gram-negative i.e., _Escherichia coli_ and _Salmonella enterica_.  
The different bacterial strains were individually grown in 125 mL Erlenmeyer flasks containing 10 mL of sterile liquid nutrient medium TSB (Tryptic Soy Broth) (Scharlab, Barcelona, Spain), to which an inoculum of 5 \(\upmu\)L from a previous fresh overnight culture was added. Finally, the bacteria were incubated at 37 \({}^{\circ}\)C with orbital shaking at 100 rpm in the dark for 24 h.  
For conducting the bioassays and to get a range of 1-5 \(\times\)\(10^{5}\) CFU/mL two single dilutions of 10\({}^{-4}\) and 10\({}^{-5}\) of the overnight cultures were freshly prepared and individually used.  
All samples were tested at two final concentrations of 50 \(\upmu\)g/mL and 100 \(\upmu\)g/mL, freshly prepared in sterile TSB medium under aseptic conditions from sample stocks stored at \(-20\)\({}^{\circ}\)C.  
Bioassays following the microdilution method similar to published reports (e.g., [PERSON] et al. (2017) were performed in standard 96-well flat bottom plates (VWR, Barcelona, Spain). Each well was loaded individually with 100 \(\upmu\)L of the prepared bacterial culture diluted to obtain 1-5 \(10^{5}\) CFU/mL. To this, a further 100 \(\upmu\)L of the freshly prepared doubled concentrated samples in TSB was added.  
Three replicate wells for each sample and concentration were seeded, and the microplates were incubated for 24 h at 37 \({}^{\circ}\)C.  
A vehicle containing the same amount of DMSO used to dissolve the extracts, a positive control containing 50-100 units of the antibiotic mixture streptomycin/tetracycline (Sigma-Aldrich, Madrid, Spain), and a blank were also set up in triplicates, and incubated under the same conditions.  
Finally, the absorbance of each well was measured at 600 nm using a MultiSkan Go spectrophotometer (Thermo Fisher Scientific, MA, US).  
The blank reading value was subtracted from all mean values to obtain the normalized values. Cell survival rate of the bacterial population following treatment was calculated using the normalized values and the following formula:  
\[\text{Survival (\%)}=[\text{(sample absorbance value}\times 100\text{)/(normalized value of positive control)}] \tag{1}\]
## 4 Discussion
### Evaluation  
Much research based on non-invasive micro-test technology (NMT) such as plant stress [41], plant heavy metal [42], growth and development [43], plant nutrition [44], plants and microbes interaction [45], and plant defense [46] are mainly conducted on a two-dimensional microscope imaging system. In all of these above, the measurement operations are performed by manually manipulating the electric motor under the microscope to further control the robot arm. The proposed method can facilitate the establishment of an automated micromanipulation system that can keep the distance between the selective microelectrode and the measured object safe, stable, and reliable and avoid their contact. The mean square error, the root mean square error, the max distance error, and the related error of the proposed automatic distance measurement algorithm is 3.15 \(\upmu\)m, 3.54 \(\upmu\)m. 6.10 \(\upmu\)m, and 1.32%, respectively. Normally, the distance between the ISME tip and the objects to be measured is 30 \(\upmu\)m in ion flux measurements. Thus, the proposed automatic distance-measurement algorithm can meet this requirement. The proposed method can not only ensure the accuracy and repeatability of measured data but also lighten the burden on researchers.  
The purpose of the proposed system is to measure the two-dimensional and relative distance of two micro-objects in a working plane; thus, two advantages of this system are that we do not need to get the world coordinates and camera coordinates of the micro-objects and we do not need not to calibrate the microscope imaging system. The proposed method is simple to implement, but there are some disadvantages of the proposed method, such as this system not providing the real-world location of the world and camera coordinates of the micro-objects. This system is two-dimensional and does not provide three-dimensional information of the micro-objects. If researchers need the real-world location of the world coordinates, as well as the camera coordinates and the three-dimensional information  
Figure 23: Corner detection using different methods.  
of the micro-objects, the microscope imaging system should be additionally calibrated. Among the calibrations, there is both a strong anti-/interference ability and a good real-time performance of the calibration based on structured light [20]. [PERSON] and [PERSON] [21] proposed a three-dimensional microscope vision system based on micro laser line scanning and adaptive genetic algorithms for retrieving metallic surface, and their system has a better performance compared with traditional systems, though its implementation is more complex. To sum up, the proposed method is simple and easy to implement, and it can be used in many applications based on micromanipulation and facilitate automated micromanipulation research. To address the limitation, researchers could add additional functions to this system based on their requirements.
# The IoT/IoE Integrated Security & Safety System of Pompeii Archeological Park
## 6 Development Methodology  
The development methodology was structured into the following phases:  
1. Preliminary analysis, consisting of the following:  
* Analysis and definition of the most suitable Integrated Multidisciplinary Model for Security and Safety Management (IMMSSM) for the specific context.  
Risk analysis, aimed at identifying and assessing all present risks.
* Needs analysis, including the identification of desired functionalities and services.
* Analysis, identification, and definition of subsystems and technologies to be adopted in accordance with the outcomes of the previous points.
* Analysis and identification of the most suitable network infrastructure to meet the requirements and needs emerging from the preceding steps.
* Analysis, identification, and definition of security and safety policies and procedures, taking into account the previous points, including the design of a security procedure management system.
* Analysis, identification, and definition of a training program for the involved personnel.
* Analysis and definition of residual risks based on the previous assessments.
* Analysis, identification, and definition of an emergency management system, considering the results of the previous analyses.
* Analysis, identification, and definition of a service continuity system, in line with the outcomes of the previous phases.
* Analysis, identification, and definition of a disaster recovery system, based on the prior evaluations.
* Design phase, consisting of the following:
* Design of the telecommunication infrastructure.
* Design of the subsystems.
* Implementation phase, consisting of the following:
* Implementation of the telecommunication infrastructure.
* Implementation of the subsystems.
* Testing phase, consisting of the following:
* Testing of the telecommunication infrastructure.
* Testing of the subsystems.
* Operation, maintenance, and evolution of the systems, technologies, and the IMMSSM.  
As for the implementation phase, the corresponding timeline was carefully designed to account for the specific requirements of the subsystems' deployment. In this regard, the telecommunications infrastructure--which required considerable time due to the site's complexity and extent--was organized in such a way that each of its parts could be promptly activated to support the operation of subsystems as they were completed according to varying timelines.  
The authors carried out all activities related to the preliminary analysis, developed the design of the system along with all its subsystems and components, supervised and coordinated the implementation phases, conducted system testing, and continue to oversee and coordinate the management, maintenance, and evolution of the systems, technologies, and the IMMSSM, in line with a continuous improvement cycle based on the Plan-Do-Check-Act (PDCA) model.
## 3 Background
### Modified Density Peak Clustering Algorithm
#### 3.1.1 The Kernel-Based Similarity Measure  
Since Euclidean distances are difficult to cluster on complex, linearly inseparable datasets, we adopt a kernel function to map two data points into high-dimensional feature space to make the data easier to cluster. Given a dataset \(S=\{\vec{x}_{i}\}_{i=1}^{N}\), where \(\vec{x}_{i}\in\mathbb{R}^{d}\), we use a nonlinear kernel function to map the raw data from the input space \(\mathbb{R}^{d}\) to the high-dimensional feature space \(\mathbb{H}\), as follows:  
\[\varphi:\mathbb{R}^{d}\rightarrow\mathbb{H},\ \vec{x}_{i}\rightarrow\varphi( \vec{x}_{i}), \tag{1}\]  
where  
\[\vec{x}_{i}=[x_{i,1},x_{i,2},\cdots,x_{i,d}],\]  
and  
\[\varphi(\vec{x}_{i})=[\varphi_{1}(\vec{x}_{i}),\varphi_{2}(\vec{x}_{i}),\cdots,\varphi_{H}(\vec{x}_{i})].\]  
Hence, the kernel distance between two data points, \(\vec{x}_{i}\) and \(\vec{x}_{j}\), is calculated as follows:  
\[\begin{split}&||\varphi(\vec{x}_{i})-\varphi(\vec{x}_{j})||^{2}=( \varphi(\vec{x}_{i})-\varphi(\vec{x}_{j}))^{T}(\varphi(\vec{x}_{i})-\varphi( \vec{x}_{j}))\\ &=\varphi^{T}(\vec{x}_{i}).\varphi(\vec{x}_{i})-2\varphi^{T}( \vec{x}_{i}).\varphi(\vec{x}_{j})+\varphi^{T}(\vec{x}_{j}).\varphi(\vec{x}_{ j})\\ &=K(\vec{x}_{i},\vec{x}_{i})-2K(\vec{x}_{i},\vec{x}_{j})+K(\vec{ x}_{j},\vec{x}_{j}).\end{split} \tag{2}\]  
The Gaussian kernel can be expressed as follows:  
\[K(\vec{x}_{i},\vec{x}_{j})=\exp(-\frac{||\vec{x}_{i}-\vec{x}_{j}||^{2}}{2 \sigma^{2}})\,,\sigma>0. \tag{3}\]  
Clearly, \(K(\vec{x}_{i},\vec{x}_{i})=K(\vec{x}_{j},\vec{x}_{j})=1\), thus Equation (2) reduces to:  
\[||\varphi(\vec{x}_{i})-\varphi(\vec{x}_{j})||^{2}=2(1-K(\vec{x}_{i},\vec{x}_{ j})). \tag{4}\]  
In fact, we use this measurement in MDPCA to compute the distance between two data points \(\vec{x}_{i}\) and \(\vec{x}_{j}\), as follows:  
\[d_{i,j}=||\varphi(\vec{x}_{i})-\varphi(\vec{x}_{j})||=\sqrt{2(1-K(\vec{x}_{i},\vec{x}_{j}))}. \tag{5}\]
## 4 Results
### Impact of the Model's Parameters on \(t_{0A}\) and \(t_{A}\)
#### 4.2.2 Induction Time with AS tA  
The induction time with AS, tA, depends on two independent variables: the induction time without AS, t0A, and the AS concentration, Ca as derived in Equation (19) for tA. However, since Equation (19) is an implicit function of the three variables, t0A, Ca, and tA, it is useful to analyze the impact of t0A and Ca on tA through the partial derivatives via Equation (19). The derivation results are presented in Equations (23) and (24) and show that tA increases monotonously with the increase in both t0A and Ca.  
The main parameter that contributes to the increment of tA is \(\mathrm{k_{a}}\) in both Equations (23) and (24). This means that an AS with a better adsorption rate constant yields a further increased rate of tA with both t0A and Ca. As expected, \(\mathrm{k_{d}}\), the desorption rate constant, has the opposite effect to that of \(\mathrm{k_{a}}\) in both equations.  
\[\frac{\partial\mathrm{t_{A}}}{\partial\mathrm{t_{0A}}}=\frac{\mathrm{b}}{ \mathrm{k_{d}+a}\mathrm{e^{-bt_{A}}}}>0 \tag{23}\]  
\[\frac{\partial t_{A}}{\partial\mathrm{C_{a}}}=\frac{k_{a}\Big{[}b^{2}t_{0A}-k _{d}+(k_{d}-abt_{A})e^{-bt_{A}}\Big{]}}{b^{2}(k_{d}+ae^{-bt_{A}})}>0 \tag{24}\]  
An explicit relationship between tA and t0A may be approximated in Equation (19) when \(\mathrm{e^{-btA}}<<1\). In this case, tA increases linearly with t0A, as shown in Equation (25). As a numerical example, the conditions \(\mathrm{e^{-btA}}<<1\), calculated using Equation (25) for tA with b = 2.65 \(\times\) 10\({}^{-4}\) 1/s derived from the data of [25], resulted in tA > 10\({}^{4}\) s.  
\[\mathrm{t_{A}}\approx(1/(\mathrm{b-a}))(\mathrm{bt_{0A}-a/b}) \tag{25}\]
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Architecture
#### 3.1.3 Data Plane  
The data plane serves to emulate network scenarios directly, and contains SDN switches, servers, emulation nodes, etc. These underlying network resources are connected with each other in a hierarchical star network structure. Emulation nodes are connected to the virtual switch on the server and represent SN nodes at the logical plane. Diverse software and applications are installed in the emulation node. Servers and external devices or networks are linked to each other by the SDN hardware switch.  
Figure 1: Architecture of the proposed platform.
###### Abstract  
As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes.  
agile Earth observation satellite; knowledge-guide search; hybrid local search algorithm +
Footnote †: journal: _Symmetry_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Knowledge-Guided Parallel Hybrid Local Search Algorithm for Solving Time-Dependent Agile Satellite Scheduling Problems. _Symmetry_**2024**, _16_, 813. [[https://doi.org/10.3390/sym16078013](https://doi.org/10.3390/sym16078013)]([https://doi.org/10.3390/sym16078013](https://doi.org/10.3390/sym16078013))
## 2 Experimental Section
### Instrumentation and Synthesis
#### 2.2.3 1-Fluoro-2-(Nitroprop-1-Enyl)Benzene **10**  
2-Fluoro-benzaldehyde (4.81 g, 38.8 mmol) was dissolved in nitroethane (4.01 g, 53.5 mmol, 20% excess), ammonium acetate (4.00 g, 52 mmol) and glacial acetic acid (5 mL) added and the mixture refluxed for 2 h in an oil bath at 100 \({}^{\circ}\)C. The orange mixture was then chilled and de-ionized water (6 mL) was then added. A small portion of the crude orange crystalline product obtained by filtration was taken for determination of melting point. The rest of the product was dissolved in hot ethanol (95%, 2 mL) and chilled for an hour to obtain the recrystallized product. The recrystallization procedure was repeated and light yellow crystals were obtained (2.38 g, 34% yield, mp 45-47 \({}^{\circ}\)C. \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)): \(\delta_{\rm H}\) (ppm): 7.98 (s, 1H, H-C=C), 7.39-7.33 (t, \(J\) = 8.70 Hz, 1H, Ar-H), 7.19 (d, \(J\) = 8.67 Hz, 1H, Ar-H), 7.12 (d, \(J\) = 8.57 Hz, 1H, Ar-H), 7.04 (d, J = 8.76 Hz, 1H, Ar-H), 2.36 (s. 3H,C-H, \(E\)), 1.59 (s, 3H, C-H, \(Z\)). \({}^{13}\)C NMR (300 MHz, CDCl\({}_{3}\)) \(\delta_{\rm C}\) (ppm): 165.1 (d, \(J\) = 252.21 Hz, C-F), 147.5 (C=C, \(\beta\) carbon), 132.5 (C=C, \(\alpha\) carbon), 132.2 (Ar), 132.0 (Ar), 128.7 (Ar), 116.4 (Ar), 116.2 (Ar), 14.0 (CH\({}_{3}\)); GC/MS \(m\)/\(z\) (M\({}^{+}\)) 181.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 4 Results and Discussions
### Comparisons with State-of-the-Art Methods in the WHU-RS19 Dataset  
Considering that the amount of data in this data set is small, we use all the images in the dataset as a test set. For our approach and other compared SR models, we all use the model that has been trained with the Kaggle dataset to do the test. The result is shown in Table 7. Our proposed approach still has the best scores in PSNR and SSIM, except FSIM. Figure 11 shows the output SR result and Figure 12 is an enlarged partial view of the SR result of each method in the WHU-RS19 dataset. It can be observed that our approach has the best visual results compared with the other four models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **SRGAN [3]** & **ESRGAN [4]** & **EEGAN [5]** & **ESRGAN [6]** & **Ours** \\ \hline PSNR & 31.703 & 31.786 & 31.827 & 31.839 & **32.010** \\ SSIM & 0.862 & 0.855 & 0.871 & 0.844 & **0.881** \\ FSIM & 0.980 & 0.983 & 0.983 & **0.984** & 0.980 \\ \hline \end{tabular}
\end{table}
Table 7: SR result performance comparison in the WHU-RS19 dataset, where the best results are shown in boldface.  
Figure 10: An enlarged partial view of the SR result of each method in the AID dataset, where the best results are shown in red font.  
Figure 11: SR visual result comparison for all models in the WHU-RS19 dataset, where the best results are shown in red font.  
Figure 12: An enlarged partial view of the SR result of each method in the WHU-RS19 dataset, where the best results are shown in red font.
## 2 Materials and Methods
### Calibration  
NIR models for predicting the SSC of strawberry samples were developed using PLS regression. PLS regression is a statistical approach utilized to model the correlation between a set of independent variables, known as predictors, and dependent variables, known as responses. The PLS technique is especially beneficial when there is a high correlation among the predictors, and many predictors exist, making the construction of a conventional regression model challenging. In PLS, latent variables, also referred to as factors, are derived from the original data to represent the predictors and responses as linear combinations. These factors are selected to account for the maximum variance observed in both the predictors and responses.  
Before establishing the PLS model, the data set should be divided into calibration set and validation set. A calibration set and a validation set were created for the strawberry samples with a split ratio of 7:3 to build and assess models, respectively. Additionally, transfer sets were created for grape and apple, with 20 samples each, to transfer the strawberry model, while the remaining samples were used for validation. Preprocessing methods, including SG smoothing, SNV, and CWT, were applied to the NIR spectra, and the optimal number of latent variables (_n_LV) was determined using 10-fold cross-validation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fruits** & **SSC Range (\({}^{\circ}\)Brix)** & **Number of Samples** \\ \hline Strawberry & 6.40–13.20 & 94 \\ Grape & 6.15–18.20 & 80 \\ Apple & 9.35–17.55 & 125 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample information.  
based on the root mean square error of cross-validation (RMSECV). The final PLS model was constructed using the optimal \(n\)LV and the preprocessed spectra. The model performance was evaluated using the root mean square error and correlation coefficient in the calibration and validation sets (RMSEC, RMSEP, Rc, and Rp). Independent sets of grape and apple samples were utilized to test the generalization performance of the strawberry model on other fruits.
## 3 Results and Discussion
### Analytical Performance of the Developed Voltammetric Method
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Electrode** & **Analyte** & **Method** & **Linear Range (mol L\({}^{-1}\))** & **LOD (mol L\({}^{-1}\))** & **Reference** \\ \hline HMDE & VK1 & DPP & \(2.2\times 10^{-8}\)-\(2.2\times 10^{-7}\) & & [36] \\ HMDE & VK1 & SWAdSV & \(1.0\times 10^{-9}\)-\(1.0\times 10^{-6}\) & & [37] \\ HMDE & VK3 & SWAdSV & \(2.0\times 10^{-10}\)-\(5.0\times 10^{-7}\) & \(1.3\times 10^{-10}\) & [33] \\ CPE & VK1 & LSAdSV & \(6.7\times 10^{-7}\)-\(4.4\times 10^{-6}\) & \(4.0\times 10^{-7}\) & [26] \\ GCE & VK1 & SWAdSV & \(5.0\times 10^{-6}\)-\(1.0\times 10^{-4}\) & \(5.1\times 10^{-8}\) & [2] \\ GCE & VK1 & SWAdSV & \(1.0\times 10^{-8}\)-\(1.0\times 10^{-6}\) & \(8.9\times 10^{-9}\) & [2] \\ GCE & VK1 & SWAdSV & \(7.7\times 10^{-8}\)-\(1.0\times 10^{-6}\) & \(2.5\times 10^{-8}\) & This work \\ GCPE & VE (\(\alpha\)-TOH) & SWASV & \(5.0\times 10^{-7}\)-\(4.0\times 10^{-5}\) & \(1.0\times 10^{-7}\) & [1] \\ GCE & VE (\(\alpha\)-TOH) & SWAdSV & \(2.9\times 10^{-8}\)-\(1.0\times 10^{-6}\) & \(1.0\times 10^{-8}\) & This work \\ \hline \end{tabular} Notes: CPE—carbon paste electrode; GCPE—glassy carbon paste electrode; DPP—differential pulse polarography; HMDE—hanging mercury drop electrode; LSAdSV—linear sweep adsorptive stripping voltammetry and VK3—me-mendoing LOD—detection limits.  
\end{table} 
Table 1: Overview of stripping voltammetric methods for the determination of VK and vitamin E (VE). Figure 4: SWadSV voltammograms of calibration curves of VK1 and \(\alpha\)-TOH at optimized working conditions: (**A**) 0 (blank), 2, 4, 6, 8 (dashed) and 10 (dotted line); (**B**) 0 (blank), 0.05, 0.1-, 0.2-, 0.4-, 0.6-, 0.8- and 1.0-\(\mu\)mol L\({}^{-1}\) VK1 and \(\alpha\)-TOH.
## _Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Application of Receiver Operating Characteristic (ROC) Curves for Explosives Detection Using Different Sampling and Detection Techniques  
**[PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Chemistry and Biochemistry and International Forensic Research Institute,  
Florida International University, 11200 SW 8 th St. OE 116A, Miami, FL 33199, USA;  
E-Mails: [EMAIL_ADDRESS] (M.Y.); [EMAIL_ADDRESS] (W.F.); [EMAIL_ADDRESS] (A.R.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-305-348-3917; Fax: +1-305-348-4485.  
_Received: 16 November 2013; in revised form: 26 November 2013 / Accepted: 3 December 2013 / Published: 6 December 2013_  
**Abstract:** Reported for the first time are receiver operating characteristic (ROC) curves constructed to describe the performance of a sorbent-coated disk, planar solid phase microextraction (PSPME) unit for non-contact sampling of a variety of volatiles. The PSPME is coupled to ion mobility spectrometers (IMSs) for the detection of volatile chemical markers associated with the presence of smokeless powders, model systems of explosives containing diphenylamine (DPA), 2,4-dinitrotoluene (2,4-DNT) and nitroglycerin (NG) as the target analytes. The performance of the PSPME-IMS was compared with the widely accepted solid-phase microextraction (SPME), coupled to a GC-MS. A set of optimized sampling conditions for different volume containers (1-45 L) with various sample amounts of explosives, were studied in replicates (\(n=30\)) to determine the true positive rates (TPR) and false positive detection rates (FPR) for the different scenarios. These studies were obtained in order to construct the ROC curves for two IMS instruments (a bench-top and field-portable system) and a bench top GC-MS system in low and high clutter environments. Both static and dynamic PSPME sampling were studied in which 10-500 mg quantities of smokeless powders were detected within 10 min of static sampling and 1 min of dynamic sampling.  
**Keywords:** planar solid phase microextraction (PSPME); solid phase microextraction (SPME); ion mobility spectrometer (IMS); military explosive; smokeless powder; receiver operating characteristic curve (ROC)