esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In recent years, the problem of DOT is becoming more attractive since it presents many advantages. It is a non-invasive, non-ionizing, and an inexpensive technique compared to other imaging modalities such as Magnetic Resonance Imaging (MRI) and X-ray [1; 2; 3]. DOT has been applied to detect breast tumors [4; 5; 6; 7], brain injuries [8; 9], imaging newborn infants' heads [10], and providing some important information about tissue metabolism. Solving the DOT problem involves addressing the radiative transfer equation (RTE) that describes the light propagation in biological tissues [11; 12]. However, the RTE does not have an analytical close form solution for complex geometries, and its numerical alternative is computationally expensive. Since the diffusion approximation (DA) of the RTE is easy to implement, we will use it as the forward model throughout this work.  
It is a well known fact that the inverse problem in DOT is nonlinear and severely ill-posed. Gradient-based methods are commonly used to solve minimization problems in optical tomography [11].  
In recent years, a number of new optimizers have been proposed to tackle the problem of convergence when there is insufficient prior knowledge to elect a good learning rate. One of the most popular and practical techniques used to control the distance of each step. The Adaptive moment estimation (Adam) is one of the first adaptive moment optimizers proposed in literature and was presented by [PERSON] and [PERSON] [13]. It is a combination of adaptive gradient algorithm (AdaGrad) [14] and Root Mean Square propagation with momentum (RMS prop) [15]. Adam is an efficient optimizer that only requires first order gradients and uses square gradients to scale the learning rate implementing momentum by using the moving average of the gradient rather than the gradient itself. To cope with the shortcoming of Adam, mainly the lack of convergence guarantees, a number of variants of Adam algorithm have been derived lately such as Nesterov-accelerated Adaptive Moment Estimation (Nadam) [16] and the AmsGrad optimizer [17]. For more details, we refer the reader to [18; 19].  
In this work, we will examine the convergence behavior of Adam, Nadam, and AmsGrad optimizers when applied to the problem of DOT. A comparison between these optimizers will be investigated and discussed. We will characterize the performance of these algorithms with respect to the choice of some hyperparameters and the initial guess error. To evaluate the quality of reconstructed images by the algorithms in quantitative manner, we use quality metrics, such as the structural similarity index (SSIM) and the peak signal-to-noise ratio (PSNR) on the reconstructed images.  
The structure of this paper is as follows: In Section 2, we give an overview of the mathematical formulation of the diffusion approximation in continuous wave (CW) cases. In Section 3, we describe the inverse problem and the algorithms we use to reconstruct the absorption coefficient of DOT. In Section 4, we show the results of our statistical analysis of the simulation data. We present conclusions in Section 5.
# A Sustainability Analysis Based on the LCA-Energy-Carbon Emission Approach in the Building System
## 2 Material and Methods
### LCA-Carbon Emission Calculation Model
The specific calculation equation is as follows:  
\[E_{o}=\sum_{j}^{m}P_{i,j}\times N_{i}\times H_{i}\times F_{j}\times t+\sum_{r= 0}^{n}Q_{r}\times\beta_{r}\times F_{r}\times t \tag{13}\]  
where \(E_{o}\) is the carbon emission calculation of operational use stage; m is the total types of energy; n is the material renewal quantity; t is the life of the building (year); \(P_{i,j}\) is the energy expended per hour; \(N_{i}\) is the total number of equipment; \(H_{i}\) is the average operating hours of the device; \(F_{j}\) is the carbon emission factor of equipment; \(Q_{r}\) is the maintenance update consumption; \(\beta_{r}\) is the annual renewal rate; \(F_{r}\) is the carbon emission factor of alternate material. Carbon emission calculation of building demolition stage  
The carbon emission at the stage of building demolition consists of two parts: the carbon emission of mechanical equipment and the carbon emission of waste transportation. The specific equation is shown in (14). \[E_{d}=E_{de}+E_{dw} \tag{14}\]  
where \(E_{d}\) is the carbon emission at the stage of building demolition; \(E_{de}\) is the carbon emission of mechanical equipment; \(E_{dw}\) is the carbon emission of waste transportation. Carbon dioxide emissions  
In the whole life cycle of the building, the most carbon dioxide is emitted in the building material production stage, at approximately 85%. In this paper, the carbon dioxide can be computed by Equation (15). \[A_{CO_{2}}=\sum_{j=1}^{n}S_{CO_{2}}\times L_{CO_{2}} \tag{15}\]  
where \(A_{CO_{2}}\) is the amount of carbon dioxide emissions; \(S_{CO_{2}}\) is the mass amount; \(L_{CO_{2}}\) is the emission factors of different building materials.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ASAR & Advanced Synthetic Aperture Radar \\ ASDC & Atmospheric Science Data Center \\ ATSR & Along-Track Scanning Radiometer \\ CEOS & Committee on Earth Observation Satellites \\ CSIR & Council for Scientific and Industrial Research \\ EC & Eddy Covariance \\ ECMWF & European Centre for Medium-Range Weather Forecasts \\ ECV & Essential Climate Variable \\ EOS & End Of (vegetative) Season \\ EO & Earth Observation \\ EPSG & European Petroleum Survey Group \\ ERA & ECMWF Re-Analysis \\ FAPAR & Fraction of Absorbed Photosynthetically Active Radiation \\ GCOS & Global Climate Observing System \\ IPCC & Intergovernmental Panel on Climate Change \\ JRC & Joint Research Centre \\ JRC-TIP & Joint Research Centre Two-stream Inversion Package \\ LAI & Leaf Area Index \\ LiDAR & Light Detection And Ranging \\ LOS & Length Of (vegetative) Season \\ MISR & Multi-angle Imaging SpectroRadiometer \\ MISR-HR & Multi-angle Imaging SpectroRadiometer High Resolution \\ NASA & National Aeronautics and Space Administration \\ NDVI & Normalized Difference Vegetation Index \\ PDGF & Parametric Double Gaussian Function \\ PDHyTgF & Parametric Double Hyperbolic Tangent Function \\ PDLF & Parametric Double Logistic Function \\ PDSF & Parametric Double Sine Function \\ POLDER & Polarization and Directionality of Earth's Reflectances \\ RPV & [PERSON], Pinty, Verstraete \\ SOM & Space Oblique Mercator \\ SOS & Start Of (vegetative) Season \\ SRES & Special Report on Emissions Scenarios \\ \end{tabular}
## 4 Materials and Methods
### RNA-Seq Analysis  
Total RNA was isolated using TRIzol (Invitrogen), depleted of DNA by DNase treatment (TURBO DNase, Invitrogen). Libraries were prepared with the TruSeq Stranded mRNA kit (Illumina, San Diego, CA, USA) and sequencing was performed with a Novaseq system (Illumina) with 75 bp single-end reads with the Genomic Unit of CABIMER (Sevilla, Spain). Two biological replicates for each condition were sequenced. Reads were aligned to mouse genomes (GRCm38/mm10 and NCBI37/mm9) using the _subjunc_ function from the Rsubread package. The downstream analysis was performed on.bam files with duplicates removed using the samtools (v0.1.19) rrmdup command. Differential gene expression analysis and statistics were performed using the DESeq2 Bioconductor. Differentially expressed genes with \(p\)-adjusted values \(<0.05\) and Log\({}_{2}\)FC \(>0.5\) (upregulated genes) or Log\({}_{2}\)FC \(<-0.5\)(downregulated genes) were selected for further analysis. Gene ontology (GO) functional categories were analyzed using DAVID Bioinformatics Resources. Non-adjusted \(p\) values were used to determine the enrichment significance.
## 6 Discussion of Results
### Experiment 3  
With two thirds of attacks being over the 100% power increase over baseline, the results are very one-sided with a majority of attacks posing a great danger to RPL-based networks.  
Figure 13: Version number attack.  
All attacks within the range have been identified as being level 5, and technically a lot over the threshold to be level 5. This proves to be an issue as these results do not really mean much as not much comparison can be made between them. However, this is the reason why the percentage increases are also provided within the study, and these are going to be very important to make meaningful comparisons between the results. A way around this would have been to modify the scale and allow for more levels of attack, but this would likely detract from the other results that use a smaller scale. The results from this section were, however, not anticipated to be this large. The attacks within the study all reacted differently to the scaling up of networks. The black hole attack reacted best to a slight up-scaling of the network peaking at 13 nodes, providing a 14% power consumption increase over the baseline power consumption. This result was likely due to the way the DODAG graph of the network formed around the malicious node, making the network lose efficiency. The DIS flooding attack also worked the most efficiently with the 13-sensor network model, providing a 679% increase in power draw; losing efficiency within a 16-node network was likely down to the ratio between attacking nodes to malicious nodes. Finally, the version number attack reported similar findings to the two attacks before with an increased power usage by 538%. Using the data presented, there is inconclusive evidence to support the idea that all attacks increase efficiency when more devices are present in a network; however, there is data to support the attacks that have a specific node to malicious device ratio that allow them to function as efficiently as possible. Making conclusions from the data, it is likely that the peak network saturation is achieved at a ratio of 13 sensor nodes to one for most RPL-based attacks; this may directly be comparable to other attacks.
# A Graph-Based Approach for Modelling Quantum Circuits
## 5 Supporting Tools and Model Transformations
### Identification of the Strategy Followed in a Model  
It is possible to detect which kind of modelling strategy has been employed in a model by executing the OCL query operation kindOfStrategy, defined in Listing 2. This operation is needed to avoid mixing strategies in the same model, as this can confuse the users of the model and hinder the development of further tools. Also, some tools in the toolchain may require that the input models follow a particular strategy.  
```
contextNode::totalEdgesCount():Integerbody:self.incoming.size()+self.outgoing.size() contextNode::hasControlledGate():Booleanbody:self.gates.size()>1 contextNode::hasMultipleNonControlGates():Booleanbody:(self.gates->select(g|not.isTypeOf(Control))).size()>1 //-1:error //1:slice,2:linear,3:swim-lane,4:mixedswimlane,5:mixedlinear contextCircuit::kindOfStrategy():Integerbody:
```  
```
if(self.nodes->forAll(n|n.totalEdgesCount()<=2))then if(self.nodes->exists(n|n.hasMultipleNonControlGates()))then1 else if(self.nodes->exists(n|n.hasControlledGate()))then5 else2  if(self.nodes->exists(n|n.hasControlledGate()))then5 else2  sendif endif else2  if(self.nodes->exists(n|n.hasMultipleNonControlGates()))then-1 else3  elif(self.nodes->exists(n|n.hasControlledGate()))then4 else3  endif endif
```  
Listing 2: OCL query operation that detects the kind of strategy employed in a model.  
This operation relies on identifying the differentiating properties of each strategy. To do this, we first determine whether the graph is a line, that is, if the maximum value of the sum of incoming and outgoing Edges to/from a Node is 2. In this case, if at least one Node contains more than one gate that is not a Control gate, then the model follows the strategy \"slice\". If not, but at least one Node contains more than one gate, then it follows the strategy \"mixed linear\". Otherwise, it follows the strategy \"linear\". In the case of the strategies \"swim-lane\" and \"mixed swim-lane\", Nodes can be the source or destination of more than one Edge, but at least one Node contains more than one gate in the case of the \"mixed swim-lane\" strategy. This is not the case for the strategy \"swim-lane\".
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 2 Existing Surveys
### Surveys on Image Encryption  
Image files are increasingly distributed across the Internet. This distribution requires security techniques that are different from traditional practices to manage confidentiality. The reason is that images can be vulnerable to several attacks, particularly if these files are sent through insecure channels. Medical images, for example, contain highly sensitive data, and thus, sending these images over the network requires a strong encryption algorithm that protects against these attacks [16].  
In recent years, reviewing the literature of image encryption has been of interest to researchers [17; 18]. Moreover, different related topics have been reviewed. For example, some researchers have conducted surveys on the techniques for encrypting plaintext into images through an algorithm that calculates the RGB value [19]. Furthermore, some related techniques such as image steganography have been studied along with image encryption [16]. As another topic of interest, some surveys have focused on the applications of image encryption in specific areas [20].
## 3 Results and Discussion
### Characterization of BCA-Mag/AB
The second decomposition stage is observed at 200-380 \({}^{\circ}\)C, with a mass loss of =10.5%, indicating the thermal decomposition of sodium alginate from the surface of the BCA-Mag/AB. The third decomposition stage is related to further decomposition of sodium alginate, with a weight loss of =24.8% occurring between 380 and \(-\)520 \({}^{\circ}\)C. The final decomposition stage, marked by a lower mass loss of =8.2%, is due to the transition phase of the nanocomposite, occurring at 520-680 \({}^{\circ}\)C [23]. Thus, the TGA study confirms the stability of the BCA-Mag/AB nanocomposite below 200 \({}^{\circ}\)C, indicating that alginate on the surface decomposes above this temperature. SEM images were used to evaluate the changes in surface morphology of PP, BCA, BCA-Mag/AB, and BCA-Mag/AB after the MB dye adsorption process (Figure 2a-d). PP exhibited a hard amorphous structure with partial porosity (Figure 3a). Following the activation of PP with KOH, the structure transformed into a completely porous form \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Unit** & \\ \hline S\({}_{\text{BET}}\) & (m\({}^{2}\) g\({}^{-1}\)) & 455.4 \\ V\({}_{\text{Total}}\) & (cm\({}^{3}\) g\({}^{-1}\)) & 0.268 \\ V\({}_{\text{micro}}\) & (cm\({}^{3}\) g\({}^{-1}\)) & 0.204 \\ V\({}_{\text{meso}}\) & (cm\({}^{3}\) g\({}^{-1}\)) & 0.064 \\ Pore Size (4 V/A by BET) & nm & 2.3 \\ \hline \end{tabular}
\end{table} 
Table 3: BET analysis of BCA-Mag/AB. (Figure 3b). The BCA-Mag/AB structure displayed roughness, cracks, voids, and pores (Figure 3c), which facilitated the effective adsorption of dye molecules. After the MB adsorption, it was observed that the upper part of BCA-Mag/AB was completely covered (Figure 3d). The EDX analysis is presented in Table 4. It showed that all samples generally contained elements such as C, O, Fe, Ca, Al, and K. The high amount of carbon atoms in PP powders is due to the cellulose structure. After activation, the structure changed completely, and the carbon content increased. The presence of iron atoms in the BCA-Mag/AB structure indicates that it has been significantly altered and provides evidence for the addition of Fe\({}_{3}\)O\({}_{4}\) to the structure. Following the MB dye adsorption, the surface of BCA-Mag/AB (Figure 3d) became smoother and denser, with a reduction in roughness and particles. This indicates that the MB molecules were captured by the surface functional groups of BCA-Mag/AB, filling the irregular surface areas and pores to create a smoother surface. The EDX analysis after the adsorption of MB confirmed the effective adsorption of MB dye onto the BCA-Mag/AB surface by showing an increase in carbon content. Figure 2: SEM images of (**a**) PP, (**b**) BCA, (**c**) BCA-Mag/AB, and (**d**) after MB adsorption of BCA-Mag/AB.
Four primary risk factors consistently linked to DF are the use of fluoridated drinking water, F supplements, F toothpaste, and the consumption of infant formulas before the age of 6 years [8; 10]. Thereby, children under the age of 4 are susceptible to DF affecting their permanent incisors and first molars. This is because the calcification and maturation of these teeth take place during this age range [8; 10]. Similarly, premolars and second molars undergo calcification and maturation between the ages of 4 and 6. However, the risk of DF becomes less significant after 6 years of age [4; 8; 10]. Drinking water is one of the primary sources of F for the human body, supplying approximately 2/3-4/5 of the daily F requirement [17; 18]. The F content in streams and lakes typically ranges between 0.1 and 0.3 mg/L, in atmospheric precipitation between 0.1 and 0.2 mg/L, in groundwater between 0.1 and 10 mg/L, and in oceans and seawater between 1.2 and 1.4 mg/L [19]. In the European Union (EU), the F concentration in bottled mineral waters can vary significantly. For example, in Portugal, the concentration can reach as high as 14 mg/L, while in Bulgaria, it can be as high as 5 mg/L, and in France, it reaches approximately 8.5 mg/L [20]. Regular consumption of water with high F concentrations, as mentioned earlier, can significantly increase the risk of DF in children [10; 21]. The European Academy of Paediatric Dentistry (EAPD) states that bottled drinking water is widely consumed in many countries, even in those with community water fluoridation, potentially resulting in decreased consumption of fluoridated tap water. Whenappropriately fluoridated, bottled water could present an extra alternative for preventing dental caries in the population. Nonetheless, additional research is needed to explore the impact of bottled waters containing F on dental caries and DF [8]. The objective of our study was to optimise preventive measures for DF by evaluating one of the primary sources of F, namely natural mineral or spring waters, and assessing the accessibility of the obtained data at the community level. Specifically, our study aimed to determine the concentration of F in bottled water available in the Romanian market using the direct potentiometric technique with a F ion-selective electrode (ISE). Additionally, we aimed to evaluate the level of accuracy and grade of labelling of F values on the bottle labels.
# Aerospace
###### Abstract  
Small satellite constellations in multiple-inclination, low-circular orbits around Mars and Venus have the potential to perform a range of high-value science investigations within cost-constrained missions. A major challenge for small satellites is that they require large \(\Delta V\) to enter low-circular orbits, which can drive up both spacecraft mass and cost. Compared to chemical propulsion, which requires large amounts of propellant, and electric propulsion, which requires large solar arrays and comes with long flight times, aerocapture enables direct access to low-circular orbits at Mars and Venus with minimal \(\Delta V\). The study shows how drag-modulation aerocapture, when combined with small B-plane targeting maneuvers, allows the delivery of multiple small satellites to various-inclination, low-circular orbits to establish a constellation. Preliminary cost estimates indicate that by reducing the required \(\Delta V\) for orbit insertion, aerocapture can potentially reduce the cost of a small satellite going to a low-circular Mars orbit compared to propulsive insertion. The ability of low-cost spacecraft to enter planetary orbits will enable a new paradigm of interplanetary missions using small dedicated launch vehicles and planetary constellations at Mars and Venus.  
aerocapture; drag modulation; small satellite; orbit insertion; planetary constellations 2023202103390/aerospace10030271
# _Applied_
## 2 Materials and Methods
### Expression Analysis of Riboflavin Biosynthesis Genes  
The expression of the riboflavin operon genes required for riboflavin biosynthesis, including ribA, ribB, ribC, and ribG, was confirmed by real-time Polymerase chain reaction (PCR). LAB was cultured in riboflavin-free CDM or MRS broth at 37 \({}^{\circ}\)C for 24 h. Cultured LAB were centrifuged at 13,000\(\times\)\(g\) for 5 min, and total RNA was isolated using the AllPrep bacterial RNA kit (QIAGEN Sciences Inc, Germantown, MD, USA) according to the manufacturer's instructions. USA cDNA was synthesized by Reverse Transcription System (QIAGEN Sciences Inc, Germantown, MD, USA). The synthesized cDNA was subjected to real-time PCR using PowerTrack SYBR Green Master Mix (Applied Biosystems, Foster City, CA, USA) and QuantStudio 6 Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). The primer sequences of the riboflavin biosynthesis genes were designed using Primer-BLAST (www.ncbi.nlm.nih.gov/tools/primer-blast/, accessed on 28 December 2020) based on the genomic sequence of _Lactobacillus (L.) plantarum_ JDM1 (NC_012984.1), which is known to have a complete riboflavin operon (Table 2). Bacteria universal 16S rRNA primer [31] and _Lactobacillus_ specific 16S rRNA primer [32] were used as controls to normalize the expression level of each gene.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound** & **Concentration (g/L)** & **Compound** & **Concentration (g/L)** \\ \hline Glucose & 20 & L-glutamine & 0.6 \\ KH\({}_{2}\)PO\({}_{4}\) & 3.1 & L-leucine & 0.4 \\ K\({}_{2}\)HPO\({}_{4}\) & 1.5 & L-alanine & 0.2 \\ MgSO\({}_{4}\) & 0.5 & L-lysine & 0.2 \\ MgSO\({}_{4}\) & 0.02 & L-phenylalanine & 0.2 \\ FeO\({}_{4}\) & 0.02 & L-proline & 0.2 \\ Tween 80 & 1 & L-serine & 0.2 \\ Inosine & 0.1 & L-cysteine & 0.2 \\ Xanthine & 0.02 & L-arginine & 0.2 \\ Bition & 0.0005 & L-asparagine & 0.2 \\ Cyanocobalamin & 0.0001 & L-tryptophan & 0.2 \\ Folic acid & 0.0001 & L-valine & 0.2 \\ \(p\)-Aminobenzic acid & 0.0005 & L-histidine & 0.2 \\ Nicotinic acid & 0.0005 & L-glycine & 0.2 \\ Calcium pentotheanate & 0.0005 & L-threonine & 0.2 \\ Pyridoxine & 0.0005 & L-isooleucine & 0.2 \\ Myo-inositol & 0.0005 & L-methionine & 0.1 \\ L-ascorbate & 0.0001 & L-tyrosine & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the chemically defined medium (CDM).