esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ways to Assess Psoriasis Severity and Therapeutic Strategies for Psoriasis Treatment
### Methods for Determining the Severity of Psoriasis  
According to the recommendations of the Polish Society of Dermatology [23] and the British Society of Dermatology [24], the so-called Rule of Tens is used to assess the severity of psoriasis, which is particularly applicable to psoriasis vulgaris. According to this rule, mild psoriasis is considered to be cases for which the skin lesions do not exceed 10% of the total body surface area (BSA < 10%--body surface area index), their severity index does not exceed 10 points (PASI < 10 points--Psoriasis Area and Severity Index), and the quality of life expressed by the DLQI is less than 10 points (DLQI < 10 points--Dermatology Life Quality Index). Other psoriasis cases, for which BSA \(\geq\) 10% or PASI \(\geq\) 10 points or DLQI \(\geq\) 10 points, fall into the category of psoriasis of at least moderate severity (Figure 1). Currently, insufficient data have been collected to clearly delineate moderate from severe psoriasis [23]. Established delineation of psoriasis severity enables the selection of an appropriate and most beneficial treatment strategy for the patient [23].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**SCALE** & **PASI** & **BSA** & **DLQI** & \\ \hline \multirow{2}{*}{Parameter Evaluated} & Extent Severity & Body surface area occupied by lesions & Quality of life—the impact of the disease on the patient’s life \\ \cline{3-6}  & Each parameter evaluated separately. The score is the sum of the evaluation points of both parameters. & Calculation using the “rule of nines”. & The patient answers 10 questions. Each question can be scored from 0 to 3 points. The score is the sum of points. 0-1 pts—no impact of the disease on quality of life, 2-5 pts—slightly reduced quality of life, 6-10 pts—moderately reduced quality of life, 11-20 pts—severely reduced quality of life, 21-30 pts—very severely reduced quality of life \\ \hline \hline Max. score & 72 PTS & 100\% & 30 PTS \\ \hline Mini. score & 0 PTS & 0\% & 0 PTS \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scoring scheme according to the three clinically most important scales based on [22; 23].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Local Treatment** & \multicolumn{2}{c}{**Phototherapy and Photochemotherapy**} & \multicolumn{2}{c}{**Systemic Treatment**} & \multicolumn{2}{c}{**Supportive Methods**} \\ \hline \(\bullet\) & Keratinolytic agents & & & & \\ \(\bullet\) & Vitamin D3 analogs & \(\bullet\) & PUVA & \(\bullet\) & Retinoids & \\ \(\bullet\) & Glucocorticoids & \(\bullet\) & NB-UVB-311 & \(\bullet\) & Immunosuppressants & \(\bullet\) & Nutrition and diet \\ \(\bullet\) & Reducing agents & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Methods used to treat psoriatic skin [19].
## 2 Materials and Methods
### Classification Phase
#### 2.2.1 Modified Salp Swarm Algorithm
\begin{array}{l}F_{d}+c_{1}((ub-lb)\oplus\text{Levy}(\lambda )+lb),c_{3}\geq 0.5\\ F_{d}-c_{1}((ub-lb)\oplus\text{Levy}(\lambda)+lb),c_{3}<0.5\end{array} \right.\] (13) where \(F_{d}\) is the base target position, Levy\((\lambda)\) is the [PERSON] flight path, \(c_{1}\) and \(c_{3}\) are control parameters, \(c_{3}\) is a random number between [0,1], which determines the direction and step size of the leader's position update, and \(c_{1}\) is the convergence factor, which is used to balance the convergence speed of the algorithm in the iterative process, as shown in Equation (14):  
\[c_{1}=2e^{-(4\times l/l_{\max})^{2}} \tag{14}\]  
where \(l\) is the number of iterations and \(l_{max}\) is the maximum number of iterations. * **Step 3:** Improve update strategy for follower positions  
In the original SSA, the follower blindly followed the previous salp, making it miss the better fitness position. In the improved algorithm MSSA, a conditional \"piecewise\" location update is adopted: firstly, the fitness of the previous salp is compared with the current fitness to select the new location so that the new location is more inclined to the side with better fitness. Compared with blind random updating, this mechanism can approach the optimal solution faster, thus speeding up the convergence speed of the algorithm. Therefore, the position of followers is updated in the improved way shown in Equation (15):  
\[X_{d}^{n}=\varepsilon\Big{(}X_{d}^{n-1}+X_{d}^{n}\Big{)} \tag{15}\]  
where \(\varepsilon\) is the coefficient of position offset, and its calculation equation is as follows:  
\[\varepsilon=\left\{ \begin{array}{ll}0.5\times\text{rand}(0,1)&f\Big{(}X_{d}^ {n-1}\Big{)}<f\big{(}X_{d}^{n}\big{)}\\ 0.5&f\Big{(}X_{d}^{n-1}\Big{)}=f\big{(}X_{d}^{n}\big{)}\\ 1-0.5\times\text{rand}(0,1)&f\big{(}X_{d}^{n-1}\big{)}>f\big{(}X_{d}^{n}\big{)} \end{array} \right. \tag{16}\]  
where \(f\) is the fitness function. According to the update method of population individuals described above, MSSA can be obtained, as shown in Algorithm 1. ```
1: Initialization parameters: population size \(N\), dimension \(D\), maximum number of iterations \(l_{\max}\). 2: Generate the initial population \(X_{N\times D}\) by Equation (11);
3: Calculate the fitness value for each individual search agent. 4: While \(l\leq l_{\max}+1\) do
5: Update \(c_{1}\) by Equation (13);
6: for \(i=1:N\) do
7: if \(X_{i=1}\)(leader) then
8: Update random numbers \(c_{3}\) and \(\beta\);
9: Update the position of the leader salp as in Equation (19);
10: else
11: Update \(\varepsilon\) by Equation (16);
12: Update the position of the follower salp as in Equation (20);
13: end if
14: end for
15: set \(l=l+1\);
16: end while Output: Best classification and predication results. ```  
**Algorithm 1** Modified salp swarm algorithm (Pseudo-code)
# Yeast Fermentation for Production of Neutral Distilled Spirits
## 2 Overview of Neutral Distilled Spirits Production
### Alcoholic Fermentation  
Yeasts ferment sugars into alcohol, carbon dioxide, and secondary metabolites, as they reproduce and grow under anaerobic conditions. The reproduction of _S. cerevisiae_ is by a mechanism known as budding. Figure 4 summarises the main pathways adopted by yeast when converting sugars into alcohol under anaerobic conditions. Glycolysis, also known as the Embden-Meyerhof-Parnas pathway, involves the action of 10 cytosolic enzymes in yeast that degrades glucose (and other fermentable sugars) into pyruvate with the production of energy (2 ATP) for yeast cell growth. Full details of the glycolysis pathway, including the enzymes and the substrates, can be found in any biochemistry and distilled spirits textbooks (e.g., [19; 26; 27]). The final pathway is fermentation, which involves two yeast enzymes that convert pyruvate into ethanol and carbon dioxide as the yeast regenerates NAD to control the redox balance. The key fermentative enzymes in question are the pyruvate decarboxylase and the alcohol dehydrogenase.  
The principal fermentable sugars found in cereal wort are glucose, maltose, and maltotriose, whilst those found in molasses are predominantly sucrose. The initial concentration of these sugars dictates the final yield of ethanol at the end of fermentation [9]. Theoretical yields can be calculated utilising the Gay-Lussac equation, which states that 1 g glucose yields 0.511 g of ethanol. In a 100% efficient system, 1 tonne of starch liberates 1.111 tonnes of glucose (due to the addition of 1 molecule of water per 1 molecule of glucose  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Yeast Format** & **\% Dry Matter** & **Shelf Life** & **Storage Conditions** \\ \hline Cream (Liquid) & \(\sim\)18\% & 9–10 days & \(<\)5 \({}^{\circ}\)C \\ \hline Cake (Pressed) & 27–33\% & 16–30 days & \(<\)7 \({}^{\circ}\)C \\ \hline Dried & \(\sim\)96\% & 2 years or more &
\begin{tabular}{c} Stable in vacuum packs at room temperature \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Distilling yeast formats.  
formed) which, in turn, produces 567.7 kg of ethanol or 719 L of pure ethanol. In practice, yields are typically lower than these estimates as a result of processing and equipment inefficiencies.
# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 4 Experiments  
### Experimental Settings  
The model is trained on the PINet dataset [19], which is a large-scale natural dataset consisting of 7549 images taken by the light field camera named Lytro Illum. These images contain 178 categories, such as cats, camels, fans, bottles, etc. Each image in the PINet dataset is randomly cropped into patches of size 832 \(\times\) 832 for training. The MFLFIC-Net is implemented using PyTorch and optimized with the Adam optimizer [32]. The training of the model requires approximately 21 days on a PC equipped with 4 NVIDIA GeForce RTX 3090 GPUs. In addition, it is worth noting that model training is divided into three stages, and the initial learning rate for each stage is different. The initial learning rates for the three stages are set to 1 \(\times\) 10\({}^{-4}\), 1 \(\times\) 10\({}^{-5}\), and 1 \(\times\) 10\({}^{-6}\), respectively. To ensure the fairness and impartiality of the experimental comparison, all methods are tested on the commonly used EPFL dataset [33] with 12 light field images using an NVIDIA GeForce GTX 1080 Ti GPU.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 4 Results
### Assimilation  
The effect of the assimilation was shown for the Montpellier region. In this region, the summer of 2019 experienced a pronounced drought [61], while the summer of 2018 was relatively wet and the summer of 2017 was moderately dry [62]. When using Equation (2), 2017 was used as the reference year. The effect of the assimilation on the simulated RZSM was more apparent when contrasting years, such as 2018 and 2019, were considered.
# A Study of Regular and Irregular Neutrosophic Graphs with Real Life Applications
## 8 Conclusions  
The main contribution of this manuscript is to introduce the idea of regularity in neutrosophic graph theory. In this paper, we have described the notion of the \(d_{m}\)-degree and \(td_{m}\)-degree of nodes in a neutrosophic graph. Some different types of neutrosophic graphs such as the regular, regular strong, \(d_{m}\)-regular, \(td_{m}\)-regular, and complete bipartite neutrosophic graph were introduced here. We have also provided some sufficient criteria for which the \(td_{m}\)-regular neutrosophic graph and \(d_{m}\)-regular neutrosophic graph are equivalent. We have introduced the definition of the \(\mu\)-complement and \(h\)-morphism of a neutrosophic graph. Some properties of the complement and isomorphic regular neutrosophic graph were also presented here. Finally, some real-life applications of the regular neutrosophic graph and complete bipartite neutrosophic graph were described for the assignment problem, road transportation network, and social networks. In the future, we will focus on the study of neutrosophic intersection graphs, neutrosophic interval graphs, neutrosophic hyper graphs, and so on. The idea of the neutrosophic graph can be used in several areas of expert systems, image processing, computer networks, and social systems.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], and [PERSON] conceived of the presented idea. [PERSON], [PERSON], [PERSON], and [PERSON] developed the theory and performed the computations. [PERSON] and [PERSON] verified the analytical methods. [PERSON] contributed reagents/materials/analysis tools. [PERSON], [PERSON], and [PERSON] wrote the paper.  
**Funding:** The study was supported by the \"Major scientific and technological innovation project of Shandong Province, China (Grant No. 2017 CXGC0901)\" and the \"Major basic research project of Shandong natural science foundation, China (Grant No. ZR018 ZC0436)\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Computational Test for Conditional Independence
## 2 Computational Testing with Categorical Variables  
Our proposed computational procedure for testing CI, which we call the computational conditional independence test (CCI test), is, in principle, quite flexible. Still, we focus on the case where the aim is to test CI between two categorical variables.  
Let \(Y\) and \(X\) represent categorical variables with \(j=1\ldots J\) and \(k=1\ldots K\) categories, respectively. Under the null hypothesis of CI, a classification model for \(Y\), \(P(Y=k\mid X,Z)\)should perform equivalently to a model using a permuted \(X^{*}\) instead of \(X\). Hence, we posit the null and alternative hypotheses as follows  
\[H_{0}: M(P(Y=k\mid X^{*},Z))=M(P(Y=k\mid X,Z))\] \[\Leftrightarrow M(P(X=j\mid Y^{*},Z))=M(P(X=j\mid Y,Z)), \tag{2}\] \[H_{1}: M(P(Y=k\mid X^{*},Z))\
eq M(P(Y=k\mid X,Z))\] \[\Leftrightarrow M(Pr(X=j\mid Y^{*},Z))\
eq M(Pr(X=j\mid Y,Z)), \tag{3}\]  
where \(M\) denotes a suitable performance metric whose distribution is (usually) unknown. A performance metric, is a quantified evaluation of an ML model's performance; how effective an ML model is on a given task. The type of performance metric in ML depends on the problem being solved (classification, regression, clustering). Within a specific problem, different metrics assess various aspects of an ML model [15]. The CCI test approximates the distributions of \(M()\) under \(H_{0}\) and \(H_{1}\) by computing over many Monte Carlo samples.
# Recent Advances of the BIRALET System about Space Debris Detection
## 2 The BIRALET System
Figure 3: Simulated received power at the antenna (SRT) as a function of the radar cross section (RCS) and range of the object for the Bistatic Radar for Low Earth Orbit (LEO) Tracking (BIRALET) system. noise calibration and antenna unit injection, for the calibration of the entire system. Then, the signal is sent to the polarizer, which allows the retrieval of the signal in both linear and circular polarizations. The last stage is the choice of a suitable filter depending on the type of measurement. More details on the P-band receiver can be found in [23]. The half-power beamwidth (HPBW) of the SRT in P-band (410 MHz) is about 0.8 deg, as shown in the radiation pattern reported in Figure 4.  
The P-band receiver block is the first part of the space debris dedicated channel of the SRT. A complete schematic of the channel is reported in Figure 5. After the P-band receiver block, the signal goes in input to the focus selector, which allows the choice of the focus for the observation (the primary focus in our case). This section is characterized by a set of filters, amplifiers, and attenuators, providing an overall gain of about 20 dB. From the top of the parabolic reflector, the signal is then carried down to a shielded room at ground level, by means of a 500 m optical link. Inside the shielded room, downstream to the optical link, the signal passes through the IF distributor, which allocates the signals coming from the antenna to the different back-ends, and is characterized by an overall gain of about 20 dB. The dedicated back-end for space debris observations is composed of the down-conversion block, and, finally, the Red Pitaya. The down-conversion block lowers the 410 MHz frequency of the incoming signal to match the working frequency of the back-end (0-50 MHz). The down-converted signal is filtered and amplified. A digital step attenuator, controlled by the Red Pitaya, is located upstream to the final amplifier in order to avoid saturation of the system. The setting of the digital attenuator is calibrated upon the power level of the carrier, as explained in [13]. The last block of the dedicated channel is the Red Pitaya, a commercial system-on-a-chip FPGA board provided with a dual-core 800 MHz ARM9 processor, a FPGA with 28,000 logic cells, 512 MB of DDR3 RAM system memory,  
Figure 4: Radiation pattern of the SRT in P-band (410 MHz). Figure 5: Schematic of the space debris dedicated channel of the SRT. RF, radio frequency. 2 RF inputs and 2 RF outputs, an overall 3 dB-bandwidth of 50 MHz, a sample rate of 125 M. Sample/s, and 14 bit of ADC resolution. A digital signal processing chain was built inside the board, allowing the conversion of the input signal in a base-band complex signal. This raw signal can be stored and post-processed using an FFT engine and, finally, displayed on a computer.
# 3D Printed Antibacterial Prostheses
###### Abstract  
The purpose of the current investigation was two-fold: (i) to describe the development of 3D printed prostheses using antibacterial filaments and (ii) to verify the antibacterial properties of the 3D printed prostheses. Three-dimensional printed finger prostheses were manufactured using PLACTIVETM antibacterial 3D printing filaments. Two adults with left index finger amputations at the proximal phalanx were fitted with a customized 3D printed finger prosthesis manufactured with an antibacterial filament. The manual gross dexterity was assessed during the Box and Block Test. Patient satisfaction was assessed using the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST 2.0). Bacterial analysis of the 3D printed prostheses was performed by two independent laboratories against _Staphylococcus aureus_ and _Escherichia coli_ (ISO 22196). Two customized 3D printed partial finger prostheses were manufactured using a 3D printed antibacterial filament. The bacterial analysis showed that PLACTIVETM with 1% antibacterial nanoparticles additives was up to 99.99% effective against _Staphylococcus aureus_ and _Escherichia coli_. The manual gross dexterity assessed was improved after using the 3D printed partial finger prosthesis. The research subjects indicated that they were \"quite satisfied\" to \"very satisfied\" with the 3D printed partial finger prosthesis. The present investigation showed that the antibacterial 3D printed filament can be used for the development of functional and effective antibacterial finger prostheses.  
antimicrobial material; biocompatible material; 3D printing filament; additive manufacturing; orthoses; rapid prototyping; upper-limb prosthetics; prosthetic design +
Footnote †: journal: Applied Soft. 2018, 8, 1651; doi:10.3390/app8091651
## 4 Construction of Deep Learning Database
### Contamination Zone Analysis
Based on the aforementioned clustering results, the clusters of interest are extracted as contaminated areas to obtain a binary label map. The key steps involve creating a blank image with the same dimensions as the original data or inversion image and iterating through each pixel in the image--if the pixel (or data point) belongs to the target cluster, it is set to 1; otherwise, it is set to 0. This process yields a binary image of the contamination area labels, as shown in Figure 13, where the white areas (value 255) represent contaminated zones, and the black areas (value 0) denote non-contaminated zones. Following the aforementioned procedures, each original inversion image is processed to generate corresponding labels. Considering that ultra-high-resolution images impose extreme hardware requirements for CNN learning and inference whi  
Figure 11: The grayscale inversion image. Figure 12: Clustering results of inversion images. Figure 10: Resistivity profile image generated by inversion (The red dashed lines denote the resistivity anomaly zone). model training and testing speeds, the image-label pairs, composed of Figures 10 and 13, are partitioned using a sliding window approach. This process yields 204 pairs of 512 \(\times\) 512 original inversion images with their corresponding labels, ultimately forming the final deep learning database. Four representative samples are displayed in Figure 14. It should be noted that the original inversion images in the database were stored in 24-bit PNG format, while the label images maintain the previously described 8-bit PNG format. The dataset was further partitioned into training and testing sets for CNN application, following an 8:2 ratio. This division yielded 163 training samples and 41 testing samples. Throughout the training process, the testing samples remained completely isolated to ensure a comprehensive and unbiased evaluation of the model's contamination identification performance. It is important to note that the UNet model does not explicitly classify NAPL types. Instead, it identifies regions with anomalous resistivity values, which may result from either single-type or mixed-type NAPL contamination.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Monitoring Set-Up
#### 7.2.2 Monitoring System Hardware and Software  
Two piezoelectric transducers, forming an array and attached under the railhead, are employed to perform pulse-echo measurements [9]; the sandwich piezoelectric transducers have been designed to effectively excite the selected mode of propagation around 35 kHz [65]. The distance between the axes of the transducers has to be equal to one quarter of the wavelength of the excitation signal.  
Two specific technical problems have been faced in system design [56]. The first problem is represented by the slightly different resonant frequencies of the two transducers; this is due to the tolerances characterizing the manufacturing process. Moreover, the transducer performance may change differently over time and, in the presence of temperature variations, thus causing asymmetric changes in the measured reflections and replicas. A possible solution to this problem is to independently scale the reflections from the positive direction and from the negative one, so that the difference in their amplitudes can be compensated for; this requires the estimation of the attenuation in both directions. The second problem is represented by the presence of multiple replicas originating from reflections; phased array processing can be exploited to solve this problem.  
The experiments were performed on a section of tracks consisting of rails joined by aluminothermic welds. Transducers and the artificial defects mentioned in Section 7.2.3 have been placed far from the joints.
# A Simple Model to Predict Machined Depth and Surface Profile for Picosecond Laser Surface Texturing
## 4 Conclusions  
In this paper, a mathematical model was developed to predict the machined depth and surface profile for picosecond LST. Experiments of pulse trains with different pulse numbers were initially conducted to provide baseline values and to calculate the estimated simulation parameters. The depths and profiles of created micro-channels with various scanning speeds or adjacent intervals were simulated using the developed model and calculated parameters. An excellent agreement was obtained for the predicted and experimental depths, and the error was negligible. An increase in scanning speed or adjacent interval decreased the corresponding overlap, leading to the decrease of machined depth. Meanwhile, the experimental and simulated profiles were also in very close agreement within the test range. Generally, the model developed here combining basic experiments can be used for predicting machined depth and surface profile for LST.  
Conceptualization, X.W. and J.X.; methodology, J.X. and Z.Z.; validation, X.F.; and L.L.; data curation, J.X. and X.F.; writing--original draft preparation, J.X.; writing--review and editing, X.W. and M.K.; supervision, X.W.; project administration, X.W.; funding acquisition, X.W.  
This research was funded by National Natural Science Foundation of China, grant number 51705258, the Fundamental Research Funds for the Central Universities, grant number KJQN201843, Natural Science Foundation of Jiangsu Province, grant number BK20150685, Young Teachers Fund of Nanjing Agricultural University, grant number rcqdl16-05 and Foundation for Distinguished Young Talents, College of Engineering, Nanjing Agricultural University, grant number YQ201604.  
The authors declare no conflict of interest.