esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Terminal Deoxynucleotidyl Transferase dUTP Nick End Labeling Apoptose Assay  
Twenty thousand (72 h incubation), 40,000 (48 h incubation), and 80,000 (24 h incubation) MCF-7 cells and 15,000 (72 h incubation), 30,000 (48 h incubation), and 60,000 (24 h incubation) MDA-MB-231 cells were treated with NIPP for 20 s and then incubated in 96-well scale for 24, 48, and 72 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). According to the manufacturer's instructions, the TiterTACS in situ apoptosis detection kit (Trevigen, Gaithersburg, MD, USA) was used to determine DNA double-strand breaks induced by apoptosis. Absorbance at 630 nm was measured every minute in the Infinite M200 Pro microplate reader (Tecan). When the linear range was left, the reaction was stopped by adding 100 uL of 0.2 M hydrochloric acid (Merck, Darmstadt, Germany) and an absorbance measurement (450 nm) was performed. The signal intensity of the 450 nm absorbance was normalized to the cell number. For this purpose, a parallel cell culture plate was prepared and the number of cells in each sample was examined. Values of NIPP-treated cells were normalized to argon-treated control cells (control = 1.0). Samples without the transferase enzyme (negative control) and samples with DNA nuclease (positive control) were included as controls.  
mRNA Analysis of the Pro-Apoptotic Regulators: Bcl-2 Antagonist of Cell Death (BAD) and Caspase-9 (Casp-9)  
One hundred thousand (MCF-7) and 70,000 (MDA-MB-231) cells were treated with NIPP for 20, 60, and 120 s and then incubated in a 24-well plate for 48 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). Total RNA was prepared (RNA Extraction Kit, Qiagen, Hilden, Germany) and reverse transcribed (RT) into cDNA using an iScript Select cDNA Synthesis Kit (Bio-Rad Laboratories, Munich, Germany). cDNA in the amount of 1.0 uL was used as a template in the polymerase chain reaction (PCR; SsoAdvanced Universal SYBR Green Supermix, Bio-Rad Laboratories, with BAD and Casp-9-specific commercial primers, QuantiTectPrimer Assay, Qiagen). The PCR protocol included initial denaturation (95 \({}^{\circ}\)C, 5 min) and 40 cycles of denaturation (95 \({}^{\circ}\)C, 10 s) and combined annealing/elongation (60 \({}^{\circ}\)C, 30 s). The mRNA expression of BAD and CASP9 was detected in an iCyler iQ5\({}^{\mathrm{TM}}\) system (Bio-Rad Laboratories) and evaluated by the comparative threshold cycling method. The reference gene was glyceraldehyde-3-phosphate dehydrogenase (GAPDH).
# Impact of Cubic Symmetry on Optical Activity of Dielectric 8-srs Networks
## 2 Materials and Methods
### Experimental Characterization  
For optical characterization a commercial Fourier-transformed infrared spectrometer (Vertex 70, Bruker, Billerica, Massachusetts, USA) coupled with an infrared microscope (Hyperion 2000, Bruker, Bruker, Billerica, Massachusetts, USA) was used to measure transmission spectra of 8-srs PhCs placed between both parallel and crossed linear polarizers. For the parallel condition, both input and output polarizers were aligned to the \(x\)-axis and we denote transmission T\({}_{\mathrm{xx}}\). For the crossed condition, input and output polarizers were aligned to the \(x\)- and \(y\)-axis respectively and we denote transmission T\({}_{\mathrm{xy}}\). For each measurement, transmission spectra were normalized relative to transmission through the silica substrate and illumination angle was limited to 8\({}^{\circ}\) using a pinhole in the light source plane of the optical path, such that light was incident mostly (but not completely) along the [001] axis of the network.  
For structural characterization, scanning electron microscopy (SEM) and focused ion beam (FIB) milling (FEI Scios Dualbeam FIBSEM, Thermo Fischer Scientific, Waltham, MA, USA) were utilized to image 8-srs PhCs along both the [001] and [011] directions. SEM images were used to determine lateral and axial line widths of the network segments respectively and FIB milling was utilized to enable observation of the interior 8-srs network morphology.
## 2 System Arrangement  
### Application Scope  
Power outages caused by protection, especially its communication systems, have been reported all over the world. The North American Electric Reliability Council has reported that 11 out of 58 incidents were caused by protection and communication facilities in 2000 [19]. During the 2003 blackout of the Italian power grid, the result of a cascading failure is due to the inter-dependency of the power grid and the communication network that it relies on [20]. Similarly, because of the severest ice and snow disaster, the power system in south central China had 6.209 billion kWh power loss and 261.82 million people were affected. In these events, communication channel disruption has been regarded as an aggravating factor in this disaster [21; 22].  
The application scope of this work includes differential protection of transmission lines whose communication channels are easily interrupted. Pilot projects and experiments with RCCs have been made in Hunan and northern Guangdong Province. These specific regions have still been in the midst of icing risks over the past few years.
## 4 Evaluation Methods
### Bleu  
The BLEU score, also known as the Bilingual Evaluation Understudy score, is a metric used to assess machine-translated text and evaluate how accurate it is compared to a set of references. More specifically, it is the product of n-gram precision and a brevity penalty (BP). The brevity penalty refers to the penalty applied to the BLEU score when the translated text is much shorter than the reference text, and it restitutes for the BLEU score not having a recall term [29]. The n-gram precision is calculated by counting the total number of word sequences from the MT system output that are also in the set of references [30]. An n-gram, put simply, is the set of n consecutive words within a given sentence [31]. For example, considering the sentence \"the wall is white\", a 1-gram or unigram is a set that consists of \"The\", \"wall\", \"is\", and \"white\", and a 2-gram or bigram consists of \"The wall\", \"wall is\", and \"is white\". It is important to note that the words within an n-gram must be taken in consecutive order [31]. The product between the geometric mean of the n-gram precision scores and the BP returns a BLEU score that falls in a range of 0 and 1 (or 0-100), where 0 indicates no overlap between the machine-translated text and the reference text [30]. A score of 1 indicates that the machine-translated text perfectly matches the reference text. Since even linguistic consultants or human translators do not achieve a perfect translation, a BLEU score of 1.0 is almost impossible. However, as a rough guideline, a score between  
Figure 1: Number of Patent Applications Submitted Each Year From 2006 to 2020 [22].  
60 and 70 is generally the best a model can achieve. The n-gram precision of BLEU depends on exact word matches between the output and references. However, since a specific reference may not be the only correctly translated option, a good translation may be scored lower [32]. Despite the transparency of the flaws associated with using the BLEU score, it continues to be widely used in MT research mainly due to its high correlation with human judgment of accuracy [32].
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Non-Isolated Topologies  
This section reviews the various non-isolated DC-DC converters for satellite applications. The classification is shown in Figure 8. Non-isolated converters provide benefits such as design simplicity, reliability, and a reduced component count [69]. Nevertheless, they suffer from problems such as a lack of controllability and increased losses due to extreme duty-cycle operation [70]. They find applications in small satellites for converting and regulating power.  
Figure 8: Classification of non-isolated DC-DC converters for satellite applications.  
Figure 7: Desirable features of DC-DC converters for satellite applications.
## 2 Methods
### Stochastic Model  
In a GNSS positioning solution, along with the functional model describing the mathematical relationship between the unknown parameters and GNSS observations, a stochastic model is also required to describe the statistical properties of the observations. The stochastic model is usually described by a variance-covariance matrix [35]. The variance component primarily reflects the accuracy information of the observations themselves, while the covariance component describes the correlation between the observations. The stochastic model affects not only the estimation of the unknown parameters but also the estimation of the ambiguity and tropospheric parameters. Hence, an accurate and appropriate stochastic model plays an important role in the quality control of the GNSS solution, solution accuracy, and other aspects.  
In the stochastic model, \(\sigma^{2}\) mainly represents the statistical accuracy of the observed quantity itself, while the variance element is mainly determined by various empirical models. The appropriate variance element should satisfy the following: more precise observations have less variance and larger weights and contribute more to the parameter estimates. However, in the actual GNSS solution, the satellite signal is affected by the hardware characteristics of the receiver's antenna, multipath effect, atmospheric effect, and other factors. Accurate power fixing is a difficult, complex task. The commonly used empirical models are the equal-weight model and height-angle-based and signal-to-noise ratio-based stochastic models.  
Due to the disparity between the equal-weighting model assumption, which assumes that observation data from all GNSS satellites in each constellation and orientation have the same level of accuracy, and the actual scenario, this model finds limited application in practical algorithms. Instead, the altitude angle model is commonly employed in practical solutions because of its simplicity and reliability. The main errors in the double-difference post-GNSS observations include tropospheric, ionospheric, and multipath errors. As the altitude angle increases, the tropospheric and ionospheric error decreases, and the likelihood of multipath errors diminishes, leading to a higher accuracy in the corresponding observations. The height angle stochastic model used in this system is as follows:  
\[\sigma^{2}=a^{2}+b^{2}/\Big{(}\sin(Elev))^{2} \tag{13}\]  
where \(Elev\) denotes the altitude angle of the satellite, and \(a\) and \(b\) are empirical values, generally chosen as \(a=4\) mm and \(b=3\) mm.
## 6 System Architecture
The trust disseminator module serves as a critical link between the fog layer, where trust evaluations occur and the BRGTL is updated, and the device layer, where trust information is required for network operations and decision making. By efficiently distributing the updated trust values, this module enhances the trust-management system's effectiveness. It contributes to the successful detection and mitigation of blackhole and greyhole attacks in the GBG-RPL framework. * Eliminator: The eliminator module is a crucial component in the GBG-RPL methodology for taking action based on the BRGTL received from the trust disseminator module. This module ensures the containment of malicious nodes and maintains the integrity of the CPS network. Upon receipt of the BRGTL from the disseminator, the sink node forwards the same to the parent nodes. The parent node initiates action to isolate or deny access to the network to the malicious node. After attacker containment, the RPL is rebuilt, and subsequently, routine CPS network operations are started. Similarly, if a new node is joining the network, the RPL is rebuilt, followed by routine CPS network operations. The eliminator module's functionality is critical in maintaining a secure and reliable CPS network. By promptly isolating malicious nodes and incorporating new nodes through RPL rebuilding, this module ensures that the network remains resilient to blackhole and greyhole attacks. \begin{table}
\begin{tabular}{c c} \hline
**Trust Value** & **Trust Status** \\ \hline
0.7–1 & Poor Trust \\
0.5–0.6 & Less Fair Trust \\
0.2–0.4 & Fair Trust \\
0.0–0.2 & Good Trust \\ \hline \end{tabular}
\end{table} 
Table 6: Trust rating for CPS nodes. Figure 4: System architecture.
## 2 System Principle
### Analysis of HDOP for Dual V-Shaped and Combined LCX Cable Layouts  
As shown in Figure 9, the signal propagation path in the dual V-shaped LCX cable layout includes two parallel LCX cables, each with a length of L meters and separated by a distance of H meters. The B1I signal from the BDS system drives the A1 port of LCX_1, radiating to the receiver at point \(\mathrm{C_{i}}\) (\(\mathrm{i}=1,2,\ldots,\mathrm{N}\)) with a radiation angle of \(\alpha_{1}\). The G1 signal from the GLO system excites the B1 port of LCX_1, radiating to the receiver at point \(\mathrm{D_{i}}\) (\(\mathrm{i}=1,2,\ldots,\mathrm{N}\)) with a radiation angle of \(\alpha_{2}\). The L1 signal from the GPS system excites the A2 port of LCX_2 and radiates to the receiver at point \(\mathrm{E_{i}}\) (\(\mathrm{i}=1,2,\ldots,\mathrm{N}\)) with a radiation angle of \(\alpha_{3}\).  
In this layout, after applying the VEM correction, the geometric paths of signals from different GNSS systems transmitted from the LCX ports to the receiver are given by Equation (22).  
\[\left\{\begin{array}{l}S_{BDS}=\mathbb{C}\cdot x+\left(\frac{1}{sina_{1}}- \frac{C}{V\cdot tana_{1}}\right)\cdot y\\ S_{BDS}=\mathbb{C}\cdot(L-x)+\left(\frac{1}{sina_{2}}-\frac{C}{V\cdot tana_{2} }\right)\cdot y\\ S_{BDS}=\mathbb{C}\cdot x+\left(\frac{1}{sina_{3}}-\frac{C}{V\cdot tana_{3}} \right)\cdot(H-y)\end{array}\right. \tag{22}\]  
Substituting Equation (22) into Equation (8) and rearranging terms, the coefficient matrix \(\mathrm{G}\) for the receiver coordinates in this layout is obtained, as shown in Equation (23).  
\[\mathrm{G}=\left[\begin{matrix}\mathbb{C}&\left(\frac{1}{sina_{1}}-\frac{C}{V \cdot tana_{1}}\right)\\ -\frac{C}{V}&\left(\frac{1}{sina_{2}}-\frac{C}{V\cdot tana_{2}}\right)\\ \mathbb{C}&-\left(\frac{1}{sina_{3}}-\frac{C}{V\cdot tana_{3}}\right)\end{matrix}\right] \tag{23}\]  
Figure 9: Signal propagation path in the dual V-type LCX cables layout.  
The signal propagation path diagram for the combined LCX layout is shown in Figure 10, where LCX_1 is a V-shaped LCX and LCX_2 is a T-shaped LCX. Both LCX cables have a length of L meters and are separated by a distance of H meters. The B11 signal from the BDS system drives the A1 port of LCX_1 and radiates to the receiver at point \(\text{C}_{\text{i}}\ (\text{i}=1,2,\dots,\text{N})\) with a radiation angle of \(\alpha_{1}\). The G1 signal from the GLO system excites the B1 port of LCX_1 and radiates to the receiver at point \(\text{D}_{\text{i}}\ (\text{i}=1,2,\dots,\text{
## 5 Methodology
### Data Collection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistic** & **N** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Min.** & **Max.** \\ \hline
**Policies** & & & & & \\ \hline Total no. of policies in China & 18 & 28.83 & 34.32 & 0 & 96 \\ Total no. of policies in the USA & 18 & 46.5 & 16.22 & 18 & 63 \\ Total no. of policies in Brazil & 18 & 7.94 & 4.09 & 2 & 13 \\ Total no. of EI\&RD\&D policies in China & 18 & 11 & 11.44 & 0 & 31 \\ Total no. of EI\&RD\&D policies in the USA & 18 & 26.39 & 9.68 & 11 & 36 \\ Total no. of EI\&RD\&D policies in Brazil & 18 & 3 & 1.64 & 0 & 5 \\ Total no. of IE\&VA policies in China & 18 & 1.444 & 1.423 & 0 & 5 \\ Total no. of IE\&VA policies in the USA & 18 & 6.67 & 1.78 & 3 & 8 \\ Total no. of IE\&VA policies in Brazil & 18 & 1.28 & 0.46 & 1 & 2 \\ Total no. of PS\&RI policies in China & 18 & 16.39 & 21.82 & 0 & 60 \\ Total no. of PS\&RI policies in the USA & 18 & 13.44 & 4.99 & 4 & 19 \\ Total no. of PS\&RI policies in Brazil & 18 & 3.67 & 2.2 & 1 & 6 \\ \hline
**RE installed capacity (GW)** & & & & & \\ \hline Total RE installed capacity in China & 18 & 258.73 & 179.44 & 80.83 & 647.14 \\ Total RE installed capacity in the USA & 18 & 154.419 & 44.46 & 110.18 & 246.61 \\ Total RE installed capacity in Brazil & 18 & 88.16 & 19.39 & 63.74 & 128.29 \\ Total hydro installed capacity in China & 18 & 193.87 & 93.52 & 79.35 & 341.2 \\ Total hydro installed capacity in the USA & 18 & 100.44 & 1.48 & 98.41 & 102.97 \\ Total hydro installed capacity in Brazil & 18 & 78.56 & 11.53 & 61.06 & 100.32 \\ Total wind installed capacity in China & 18 & 43.98 & 56.42 & 0.341 & 164.1 \\ Total wind installed capacity in the USA & 18 & 34.9 & 29.54 & 2.38 & 87.54 \\ Total wind installed capacity in Brazil & 18 & 2.39 & 3.8 & 0.02 & 12.29 \\ Total solar installed capacity in China & 18 & 16.66 & 35.03 & 0.034 & 130.65 \\ Total solar installed capacity in the USA & 18 & 8.81 & 12.82 & 0.457 & 43.03 \\ Total solar installed capacity in Brazil & 18 & 0.07 & 0.26 & 0 & 1.1 \\ Total bioenergy installed capacity in China & 18 & 4.23 & 2.936 & 1.1 & 11.24 \\ Total bioenergy installed capacity in the USA & 18 & 10.28 & 1.78 & 8.25 & 13.07 \\ Total bioenergy installed capacity in Brazil & 18 & 7.15 & 4.47 & 2.66 & 14.58 \\ \hline \hline \end{tabular} Data source: [9; 25].  
\end{table} 
Table 9: Descriptive statistics: policies and RE installed capacity (2000–2017).
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface
### Use of the Infrared (IR)  
Oil of thicknesses greater than about 10 um, absorbs light in the visible region and re-radiates a portion of this in the infrared spectrum, mostly in the 8-14 um wavelengths [12; 13; 14]. Solar-heated oil will emit infrared radiation as oil shows greater infrared emissivity than water. Thick oil appears heated or \"hot\" compared to the surrounding water in infrared images, intermediate thickness layers of oil appear \"cool\", and thinner layers of oil or sheens are not distinguished. The transition thicknesses are not known, but indications are that the difference between the \"cold\" and \"hot\" layers lies between 50 and 150 um and the least detectable thickness is in the range of 10-70 um [3]. The reason for the \"cool\" slick occurrence may be that a 20-50 um layer of oil on the water causes destructive interference of the IR wave fronts, thereby reducing the thermal radiation emitted. This destructive interference was shown to explain the \"rainbow sheet\" in the visible [3]. The cool slick then implies thicknesses between about 16 and 40 um, because the minimum thickness resulting in destructive interference is 2 times the wavelength that is between 8 and 10 um. This results in a destructive interference onset from 16-20 um to 4 wavelengths or 32-40 um. The \"cool\" portion is observed only on fresh or test spills, because only early phases of oil spreading yield the correct thickness to exhibit the wavelength destruction. Slicks that have resided on the sea for longer times are thicker or thinner than 16-40 um. The threshold of the \"hot\" phenomenon is at thicknesses of about 50 um that is greater than shown by the destructive layer of 16-40 um [3].  
Oil spill infrared sensing uses thermal infrared of wavelengths of 8-14 um. Research on the 8-14 um infrared shows that there are no spectral differences with thickness or other conditions [15]. Nighttime IR camera tests of IR cameras indicate that there is occasional oil detection (oil appears cool on a less-cool ocean); however, the definition is not as clear as in daytime [16]. Daytime images of oil using visible and infrared are shown in Figures 3 and 4.  
Figure 3: A visible photo of a small test slick. The sun glint on the left confounds the interpretation of slick edges (photo from Environment Canada).  
Infrared oil detection is not interference-free, because natural objects may appear like oil, such as seaweeds, sediment, organic matter, shoreline, and oceanic fronts. On the other hand, infrared sensors are cheap and are remote sensing tools that are readily available to the oil spill response worker. IR is not capable of slick thickness measurement.
# The Potential of Visible and Far-Red to Near-Infrared Light in Glaucoma Neuroprotection
## 2 Photobiomodulation  
The utility of red light has been valued in the practice of medicine since ancient times, and it was \"re-discovered\" at the end of the 19 th century by the Nobel Prize in Medicine and Physiology winner [PERSON], who cured skin disorders with the use of red light [10]. The therapy consists of a series of brief illuminations with low energy photon irradiation in the visible (400-700 nm) and far-red (FR) to near-infrared (NIR) ranges of the electromagnetic spectrum (700-1000 nm); these methods are collectively termed photobiomodulation (PBM) and are carried out by different devices, including a laser, a light emitting diode (LED), or a broadband light. Notably, LEDs generate negligible amounts of heat, thus reducing the risk of thermal injury and tissue damage, which instead occurs during the use of lasers [11].  
The source and the pulse structure of the light, as well as the wavelength and the density of the energy and the duration of the laser application are important parameters for the effectiveness of PBM on the target tissue [12]. Regarding the type of wave, whether continuous or pulsed, there are still conflicting opinions as to which is the best and which factors should determine the pulse parameters [13].  
High tissue penetration is achievable by the long wavelengths. To accelerate wound healing, reduce neurologic pain, and potentiate healing after peripheral nerve injury, ischemic stroke and heart attack, PBM therapy is currently applied [14]. Thus, applications of PBM offer a promising innovative and non-invasive therapeutic approach to a host of challenging sight-threatening retinal conditions, including methanol-induced retinal damage, retinopathy of prematurity, diabetic retinopathy, [PERSON]'s hereditary optic neuropathy, age-related macular degeneration, amblyopia, and possibly others [10]. Indeed recent studies have demonstrated that diseased tissues, including the retina, are penetrated by FR/NIR photons, which are most effective at inducing beneficial effects in vivo in cells without apparent specialized photopigments [15].
## 2 Material and Data Collection
### Focal-Loss Function and Label-Smoothing
#### 2.4.2 Label-Smoothing Principle  
As a parameter, \(\gamma\) ranges from [0, 5]. When \(\gamma\) becomes 0, it becomes the initial cross-entropy-loss function. Label-smoothing, also called label smooth regularization (LSR), refers to a regular update of the binary label vector y of the sample:  
\[y=(1-\varepsilon)y+\varepsilon/n \tag{6}\]  
where \(\varepsilon\) is a small hyperparameter and n is the number of categories of classification (the number of bits of the label vector). \(y_{k}\) is defined as the binary label vector value of the K-th category, the target is the real label value of the current category, and the I-th bit value of the one-hot code of the label vector is I, then there is  
\[y_{k}=\left\{\begin{array}{cc}1-\varepsilon+\varepsilon/n&i=target\\ \varepsilon/n&i\
eq target\end{array}\right. \tag{7}\]  
Let \(\alpha=-\varepsilon+\varepsilon/n\), then there is  
\[y_{k}=\left\{\begin{array}{cc}1-\alpha&i=target\\ \alpha/(n-1)&i\
eq target\end{array}\right. \tag{8}\]  
To sum up, we can establish that the focal-loss function of a single sample is defined as  
\[LOSS=\frac{1}{n}\sum_{k=1}^{n}y_{k}\times(1-p_{t})^{7}\times log(p_{t}) \tag{9}\]At this time, the binary vector value \(y_{k}(i\
eq\textit{target})\) at the wrong label is no longer 0, so the process of minimizing L by Formula (9) of the loss function will no longer make the model learn in the direction that the difference between the correct prediction and the wrong label logits increases infinitely, thus solving the problem of overconfidence of the network.