esa-sceva/satcom-chunk-collection huggingface dataset:
# Turbulent Drag Reduction Characteristics of Bionic Nonsmooth Surfaces with Jets
## 3 Results and Discussion
### Drag Reduction Mechanism
#### 3.4.2 Pressure Distribution  
Figure 17a shows the pressure contour of the nonsmooth surface. Static pressure changes occured on the wall due to the existence of the nonsmooth unit cell. A high-pressure zone was formed on the windward side. An abnormal point was also found in the preceding discussion on turbulent kinetic energy at this same location (region C). The main cause of the high-pressure zone was the stagnation of the mainstream flow on the windward side of the unit cell (Figure 18). When the mainstream flow reached the windward side, some fluid continued to move forward, whereas the rest of the fluid entered the unit and formed a recirculation. Thus, the velocity at this position was greatly reduced, and a high pressure area was formed on the windward side. Moreover, the stagnation of the flow also lead to an increase in momentum exchange. This phenomenon was the reason for the increase in turbulent kinetic energy intensity in this area (discussed in Section 3.4.1).  
Figure 16: Recirculation region.  
Figure 17: Pressure contour of proposed surfaces. (**a**) Nonsmooth surface. (**b**) Active surface.  
When the fluid entered the inner part of the unit cell, a recirculation was formed within the unit cell, and a low-pressure zone was formed on the slip side. The rapid increase in pressure drag on the nonsmooth surface came from the alternating high- and low-pressure regions.  
The pressure contour of the active surface is shown in Figure 17b. The pressure concentration on the windward side was effectively alleviated. Instead of depending on the mainstream, the jets supplemented the fluid required for the recirculation formation in the unit cell, which eased the flow stagnation on the windward side and effectively reduced the positive pressure, thereby reducing the pressure drag. Furthermore, the flow stagnation solution also reduced the momentum exchange in the windward region, which reduced the energy loss and the skin friction.
## 2 Discussion
### Human Leukocyte Antigen (HLA)  
Human leukocyte antigens (HLA) are genes in major histocompatibility complexes (MHCs) that help the body differentiate between proteins that are self and non-self. These antigens are highly variable, especially class I HLA antigens: HLA-A, HLA-B, and HLA-C. These antigens are located on all nucleated cells and function to present identifying proteins to cytotoxic T-cells for subsequent immune reactions [13]. The two genes relevant to this category associated with psoriasis are HLA-C and ERAP1. HLA-C plays a central role in the immune system by presenting peptides derived from endoplasmic reticulum lumen. ERAP1 encodes an aminopeptidase involved in trimming HLA class I-binding precursors [4].  
HLA-C*6:02 is the major psoriasis risk allele that is located in psoriasis susceptibility locus 1 (PSORS1) within the short arm of chromosome 6p21 [10]. The PSORS1 locus encodes genes that play a role in antigen presentation to CD8+ T lymphocytes. Among the HLA types, HLA-C*6:02 has emerged as the strongest genetic risk factor for psoriasis, with the rs10484554 SNP within the gene showing a significant association. Additionally, the rs4349859 SNP in the HLA-B gene, specifically associated with HLA-B27, was implicated in an increased risk of psoriasis and psoriatic arthritis. Another risk allele, rs11652075, found in HLA-B13, was linked to an increased susceptibility to psoriasis. Furthermore, the rs60895 SNP within the HLA-DRB1 gene has shown an association with an increased risk of psoriasis, particularly in early-onset cases. Conversely, the rs3099844 SNP in HLA-B57 was associated with a decreased risk of psoriasis. These SNP variants in HLA genes highlight the important role of HLA-mediated immune responses in psoriasis pathogenesis, although further research is required to fully elucidate the precise mechanisms underlying these associations [3; 4; 9; 10; 13].
## 3 System Model
### Problem Formulation  
We aim to maximize the processing time gain harvested from the task offloading. Hereafter, the term revenue and reward are used interchangeably to denote this objective. The joint task offloading and resource allocation problem at the edge with constrained bandwidth and computing resources is formulated as a mixed-integer nonlinear-programming (MINLP) problem, which is denoted as \[\underset{\mathbf{x},f,\mathbf{p},\mathbf{\alpha},\mathbf{\beta}}{Maximize}: \sum_{m=1}^{M}x_{m}e_{m}^{f}(T_{m}^{f}-t_{m}^{up}-t_{m}^{com})+(1-x_{m })e_{m}^{f}(T_{m}^{f}-t_{m}^{loc})\] (8) s.t. \[\quad\text{C1}: \quad\sum_{m=1}^{M}x_{m}\alpha_{m}\leq 1\] \[\quad\text{C2}: \quad\sum_{m=1}^{M}x_{m}\beta_{m}\leq 1\] \[\quad\text{C3}: \quad f_{m}\leq Fc_{m}\quad,\forall\text{m}\in\mathcal{M}\] \[\quad\text{C4}: \quad p_{m}\leq p_{m}^{max}\quad,\forall\text{m}\in\mathcal{M}\] \[\quad\text{C5}: \quad x_{m}\in\{0,1\}\] (P0)  
\(C1\) is the bandwidth allocation constraint, and \(C2\) is the computing resource allocation constraint. \(C3\) reveals the maximum local computing speed, while \(C4\) shows a UD's maximal transmit power.  
The formulated problem (P0) is intractable due to the coupling of variables \(\mathbf{x}=(x_{1},x_{2},\cdots,x_{m},\cdots,x_{M})\), \(\mathbf{\alpha}=(\alpha_{1},\alpha_{2},\cdots,\alpha_{m},\cdots,\alpha_{M})\) and \(\mathbf{\beta}=(\beta_{1},\beta_{2},\cdots,\beta_{m},\cdots,\beta_{M})\). However, once \(\mathbf{x}=\{x_{m}\}^{M}\) is determined, (P0) is reduced to a convex optimization problem.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Test in Hardware Platform
#### 4.2.1 Test of Link Management Protocol  
When the protocol stack runs, the link management protocol firstly builds control channels and collects local resources.  
As shown in Figure 19, we could see that each node has collected its local resources by the link management protocol.  
Figure 17: Hardware platform architecture. LVDS: low-voltage differential signaling.  
Figure 18: Hardware Platform.
## 3 Theoretical Results and Discussion
On the other hand, if the inter-band absorption of Ge is typically used for detecting the wavelength of 1.55 \(\upmu\)m for telecommunications applications, graphene/n-Ge Schottky PDs could allow the detection of wavelengths longer than 1.55 \(\upmu\)m, where the the Ge inter-band absorption suddenly decreases. Indeed, graphene/n-Ge Schottky PDs with optimized \(R/NEP\) ratio show a responsivity and \(NEP\) of 227 mA/W and 31 \(\text{pW}/\sqrt{\text{Hz}}\) at 2 \(\upmu\)m, respectively, enabling their employment in environment monitoring applications. The predicted responsivity of graphene/n-Ge PDs is higher than that reported for NIR Ge PDs based on the introduction of tin (Sn) atoms in the Ge lattice. Indeed, with a substitutional Sn concentration of 6.5%, Ge-based PDs are able to absorb optical radiation at 2 \(\upmu\)m but provide a limited responsivity of only 20 mA/W [39]. Note that NEP depends on many parameters, such as graphene's optical absorbance, the graphene area in contact with the semiconductor, and the temperature. Among these, particular attention should be paid to the temperature, which appears in the exponential argument of the dark current (Equation (4)), which in turn affects the NEP (Equation (12)). As an example, in a graphene/n-Si Schottky PD we evaluated this by increasing the temperature of 1 \({}^{\circ}\)C with respect to the room temperature, and an increase in optimized NEP below 5% could be achieved at any wavelength in the range of interest for this junction. As reported in Figure 4a,b, semiconductors such as n-Si, n-Ge, and n-GaAs can be exploited at room temperature for the realization of optimized graphene-based Schottky PDs in the spectral range from 1955 to 2080 nm with a responsivity from 219 to 245 mA/W (Figure 1b); however, while n-GaAs would be characterized by a lower NEP, n-Si and n-Ge would have the advantage of a better compatibility with CMOS technology. Figure 4: (**a**) The optimized \(R/NEP\) and (**b**) the optimized \(NEP\) of the Schottky graphene-based PDs for various semiconductors as function of the wavelength range individuated in Table 3. All figures were obtained at room temperature and by considering a graphene circular area in touch with the semiconductor with radius of 500 \(\upmu\)m and a load resistance of 10 M\(\Omega\). The arrows indicate the validity regions of the proposed optimization procedure.
## 5 Implementation and Results
### Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Classification** & & **Existing System** & & **Proposed System** \\ \hline \multirow{4}{*}{Related Technology} & \(\bullet\) & BAS (Building Automation System) & \(\bullet\) & Smart IoT based sensor \\  & & based sprinkler, alarm, evacuation & & clustering technology \\  & & speaker guide system & \(\bullet\) & AR based simulation technology \\ \hline \multirow{4}{*}{Total extinguishing and evacuation Time} & \(\bullet\) & BAS-based simply operation system sprinkler operation, Fire alarm and evacuation announcement & \(\bullet\) & IoT-based clustering and LTE \\  & & A total 6 step delay until the reporting to the safety center & \(\bullet\) & A total 4 step delay until the reporting to the safety center from smart sensor to occupants, administrator and rescuer through LTE modem \\ \hline CPU control signal transfer rate from server to sensors & x*a & i*a & \\ \hline \multirow{4}{*}{Speed of the fire situation detection} & \(\bullet\) & Low speed for determining the fire situation because of 1:1 connection between sensor and central server & \(\bullet\) & High speed for determining the fire situation because of IoT based sensor clustering \\  & & & & Intelligent control by interconnection between sensors and offer a refuge/rescue guideline \\ \hline \multirow{4}{*}{Speed of the actuator control} & \(\bullet\) & The sprinkler is operated by the fire detection sensor, and the primary evolution is supported & \(\bullet\) & The sprinkler supports the primary evolution from current system and additionally provides real-time monitoring/guidance to occupants, building managers, rescue teams, and control centers with fast web data transfer via smart sensor-gateway-LTE modem \\  & & & & \\ \cline{1-1}  & & It is hard to rapidly and accurately rescue & \(\bullet\) & Rapid and accurate rescue is possible through AR based real-time monitoring/guidance system \\ \hline \multirow{4}{*}{Ease of use about rescue} & \(\bullet\) & The rescue is delayed when the building managers or occupants are late to report or misjudge & \(\bullet\) & Reporting and responding quickly to the rescue team through real-time fire detection monitoring and linking with the control center \\ \cline{1-1}  & & & & the rescue team through real-time fire detection monitoring and linking with the control center \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Difference of existing system and proposed system.
## 3 Results  
### Facial Expressoin Scores among Universities  
Results about analysis of one-way ANOVA on facial expression scores across university campuses are shown in Table 2. Happy expression scores were higher in Shandong University, Xi'an Jiaotong University, and South China University than those in most of other universities (Figure 1). Harbin Engineering University had the lowest happy score. On the other hand, Ocean University of China, Inner Mongolia University, and Harbin Engineering University tended to have the highest sad expression scores, while sad scores appeared to be lower in Shandong University, Zhejiang University, and Xi'an Jiaotong University (Figure 1). The PRI generally showed the similar trend of happy score but had some distinctive inverses (Figure 1). For example, PRI in China Pharmaceutical University, Xinjiang University, and Ocean University of China were reduced by the relatively higher level of sad scores. In contrast, the sudden increase of PRI, such as that in Northwestern Polytechnical University, was caused by the relatively lower sad scores.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variables** & **Unit** & **Parameter** \\ \hline \multirow{4}{*}{Environment} & \(\times 10^{4}\) Tons & Discharge of industrial sewage \\ \cline{2-3}  & Tons & Emission of industrial SO\({}_{2}\) \\ \cline{2-3}  & Tons & Emission of industrial dust \\ \cline{2-3}  & Percent & Centralized disposal of sewage \\ \cline{2-3}  & Percent & Disposal of household garbage \\ \hline \end{tabular} Note: \({}^{1}\) Enterprises above designated scale, annual revenue over 20 million Yuan from the primary business of industrial enterprises; \({}^{2}\) C\&T, city and town; \({}^{3}\) institutions of higher education, educational institutions that can grant degrees that are higher than high-school education, including university, college, vocational technique university/college, etc.  
\end{table}
Table 1: _Cont._Figure 1: Ranked scores of happy and sad expressions and the positive response index (PRI) among selected 211-Project university campuses across mainland China. Universities are ordered by the growing score of happy expression. Different letters marked for significant difference are shown in Tables S2–S4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & \multicolumn{2}{c}{**Degree of Freedom**} & \multicolumn{2}{c}{**Sum of**} & \multicolumn{1}{c}{**Squares**} & \multicolumn{1}{c}{**\(F\) Value**} & \multicolumn{1}{c}{**\(p\) Value**} \\ \hline \hline \multirow{2}{*}{Happy} & \multirow{2}{*}{95} & \multirow{2}{*}{4231} & \multirow{2}{*}{4326} & \multirow{2}{*}{354,720,095} & \multirow{2}{*}{2.49} & \multirow{2}{*}{\(<\)0.0001} \\ \cline{1-1} \cline{5-5} \cline{5-
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
## 3 Derivation of Closed Form Average BER Expression
### Average BER for Log-Normal Channel  
The average BER of the proposed scheme is defined as in [13]:  
\[ABER=\int\limits_{0}^{\infty}P(e/L,h)f_{h}(h)dh \tag{13}\]where the \(P(e/L,h)\) is defined as in [13]:  
\[P(e/L,h)\approx\frac{1}{2^{L}}\left(1+\sum_{M=1}^{L-1}\left(\begin{array}{c}L\\ M\end{array}\right)\frac{M}{L}P(e/L,M,h)\right) \tag{14}\]  
where \(\left(\begin{array}{c}L\\ M\end{array}\right)\) is the number of combinations of \(M\) items out of \(L\).  
\(P(e/L,M,h)\) is defined as in [13]:  
\[P(e/L,M,h)=Q\left(\frac{h}{\sqrt{2N_{0}+\frac{N_{0}}{2M}+\frac{N_{0}}{2(L-M)}}}\right) \tag{15}\]  
where \(M=\sum\limits_{K=1}^{L}s[k]\) is the number of ones in the observation window with length \(L\).  
The Gaussian \(Q\) function is [21]:  
\[Q(y)=\frac{1}{\pi}\int\limits_{0}^{\pi/2}\exp\left(-\frac{y^{2}}{2\sin^{2} \theta}\right)d\theta \tag{16}\]  
The PDF of the fading coefficient, \(h\), which is given as \(h=h_{l}h_{a}\):  
\[f_{LN}(h)=\frac{1}{h\sqrt{8\pi\sigma_{x}^{2}}}\exp\left(-\frac{\left(\ln( \frac{h}{h_{l}})-2\mu_{x}\right)^{2}}{8\sigma_{x}^{2}}\right) \tag{17}\]  
Substituting (14) and (17) in (13), we get:  
\[\begin{array}{ll}ABER&=\frac{1}{2^{L}}\left(1+\sum_{M=1}^{L-1}\left( \begin{array}{c}L\\ M\end{array}\right)\frac{M}{L}\right)\\ &\times\int_{0}^{\infty}Q\left(\frac{h}{\sqrt{2N_{0}+\frac{N_{0}}{2M}+\frac{N_ {0}}{2(L-M)}}}\right)\frac{1}{h\sqrt{8\pi\sigma_{x}^{2}}}\exp\left(-\frac{ \left(\ln(\frac{h}{h_{l}})-2\mu_{x}\right)^{2}}{8\sigma x^{2}}\right)dh\end{array} \tag{18}\]  
Making the change of variables:  
\[x=\frac{\ln(\frac{h}{h_{l}})-2\mu_{x}}{\sqrt{8\sigma_{x}^{2}}} \tag{19}\]  
This results in:  
\[ABER=\int_{-\infty}^{\infty}\frac{1}{2^{L}\sqrt{\pi}}\left(\left(1+\sum_{M=1} ^{L-1}\left(\begin{array}{c}L\\ M\end{array}\right)\frac{M}{L}\right)Q\left(\frac{h_{l}\exp(x\sqrt{8\sigma_{x} ^{2}}+2\mu_{x})}{\sqrt{2N_{0}+\frac{N_{0}}{2M}+\frac{N_{0}}{2(L-M)}}}\right) \exp\left(-x^{2}\right)dx\right) \tag{20}\]  
This integration in (20) could be solved using Hermite polynomial approximation [22]:  
\[\int_{-\infty}^{\infty}\exp(-x^{2})f(x_{i})dx\approx\sum_{i=1}^{n}w_{i}.f(x_{i}) \tag{21}\]  
where \(w_{i}\) and \(x_{i}\) are the weights and the roots of the Hermite polynomial of order \(n\), respectively.  
Applying Equation (21) on Equation (20) yields:  
\[ABER\leq\frac{1}{2^{L}\sqrt{\pi}}\left(1+\sum_{M=1}^{L-1}\left(\begin{array}{c}L \\ M\end{array}\right)\frac{M}{L}Q\left(\frac{h_{l}\exp(x\sqrt{8\sigma_{x}{}^{2}}+2 \mu_{x})}{\sqrt{2N_{0}+\frac{N_{0}}{2M}+\frac{N_{0}}{2(L-M)}}}\right)\right) \tag{22}\]  
For the dual hop system proposed, an approximated average bit error rate (ABER) is calculated [23]:  
\[ABER\leq\frac{1}{2}\left[\begin{array}{c}1-(1-2 ABER_{1})(1-2 ABER_{2})\\ \end{array}\right] \tag{23}\]  
where \(ABER_{1}\) and \(ABER_{2}\) are the ABER for the first and second node, respectively.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 4 Resource Management Algorithm
### Time Complexity of DB-IPDD  
Since the number of the optimized variables is \(NMK\), the complexity of the inner loop procedure is \(\mathcal{O}(NMK)\). For the middle procedure, the complexity of the procedure is \(\mathcal{O}(2 NM)\). For the outer loop procedure, the complexity can be denoted by \(\mathcal{O}(2 NM)\). As a result, the total complexity of the DB-IPDD algorithm can be given as \(\mathcal{O}(INM(K+4))\), where \(I\) is the number of iterations. The DB-IPDD algorithm achieves low time complexity as polynomial for the data size and also low time complexity with respect to the number of satellites for large-scale satellite networks.
## 3 Results and Discussion
### Drought Trend
#### 3.2.3 Annual Time Series  
The 12-month SPI index up to December was used to study annual droughts. The annual average SPI of all study stations is given in Figure 8.  
Considering the values of 0.5 to \(-\)0.5 as the normal limits of SPI, the numbers of annual wet and dry periods were the same, and during 17 years of study, six dry periods and six wet periods were observed. The temporal distribution of these periods is at variance, so that there were high fluctuations of annual precipitation in the first few years with alternating repetition of positive and negative periods. From 2002 to 2006, wet conditions prevailed in most parts of study area. As of 2008, however, drought periods ensued in most areas of the study area and were prolonged seven years until reaching its utmost severity in 2014.  
Reviewing the frequency percentage of various SPI categories on an annual scale acknowledged that the wetness severity was contained by mild to medium states in most stations, and more than half of the frequencies were devoted to these two categories. The same trend was held for the drought periods. In the severest wet year in 2002, 60% of the study area experienced extreme wetness and 40% underwent medium wetness. As to the severest drought occurred in 2014, more than 80% of the study area was stricken by very extreme drought. The mapping of drought in 2014 is shown in Figure 9. On the basis of this figure, the lowest level of drought was observed in the south of Ghasr-e-Shirin, with the SPI value of \(-\)1.4. In the other parts of the study area, the SPI lay below \(-\)2, which points to a very severe drought.  
Figure 8: 12\(-\)month SPI.  
Annual monitoring of drought showed that the severity of wet periods was much more than that of drought periods. Due to the fact that the majority of wet years fall in the medium category, and most of the droughts belong to the mild category.  
[PERSON] et al. [25] and [PERSON] [26], using SPI, showed that in the last decade, most cities of Kermanshah experienced severe and extreme droughts. [PERSON] et al. [27], using support vector machine algorithm, enhanced vegetation index (EVI), and SPI showed that in the duration of 1978-2008, the most severe drought was in 2008 in the study area, which is consistent with the results presented in Figure 8. [PERSON] [28] and [PERSON] et al. [29] showed that, in recent years, at most stations in Iran, including stations in the Kermanshah, the severity of the drought has become more severe with time, which is consistent with the results of this study.
# Understanding the Intricate Pathophysiology of Psoriasis and Related Skin Disorders
## 2 Genetic Background of Psoriasis: Key Loci and Advances in Genome-Wide Association Studies (GWAS)  
Genetic factors have long been acknowledged as critical components in the etiology of psoriasis. The prevalence of psoriasis is markedly higher among the affected individuals' first- and second-degree relatives than the general population. Furthermore, the condition exhibits greater concordance among monozygotic twins than dizygotic twins [2].  
Linkage studies have identified at least nine genomic regions (loci) associated with psoriasis, called PSORS1 through PSORS9. The PSORS1 region, which is located within the class I interval of the major histocompatibility complex (MHC), primarily encodes genes implicated in antigen presentation. This region also includes the gene corneodesmosin, which encodes a desmosomal protein that is essential for keratinocyte cohesion and desquamation. The PSORS1 locus accounts for approximately 35-50% of the heritability of the disease explained by known loci.  
Additionally, the PSORS2 and PSORS4 loci, situated on chromosomes 17q25 and 1q21, respectively, have been associated with the development of psoriasis. The CARD14 gene, located within the PSORS2 locus, encodes an activator of nuclear factor-kB (NF-kB) and harbors variants linked to rare and common psoriasis forms. Proteins involved in the terminal differentiation of the stratum corneum are encoded by genes linked to the late comified envelope found in the PSORS4 locus. Genome-wide association studies (GWAS) spanning Chinese and European populations have linked this region to psoriasis susceptibility.  
GWAS employ highly optimized microarrays that enable efficient genotyping of millions of genetic markers across the genome. Given sufficiently large sample sizes, GWAS facilitates the detection of even subtle differences in allele frequencies between affected individuals and unaffected controls, thereby proving to be a more potent method than linkage analysis. As a result, GWAS has profoundly influenced the genetic investigation of common complex diseases such as psoriasis [2].  
However, one intrinsic limitation of GWAS is that it primarily reveals statistical associations. The genetic variants identified may, due to linkage disequilibrium, merely serve as proxies for distinct \"causal\" variants that exert biological effects and influence disease risk. To enhance GWAS signals and identify potential causal susceptibility alleles, researchers have utilized genotyping arrays with dense coverage in relevant regions.  
Moreover, the integration of datasets from international collaborations through meta-analyses of GWAS has proven essential for augmenting statistical power and identifying novel loci associated with disease susceptibility [2].