esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed RCSA Method  
### Overview  
Our proposed RCSA method aims to reduce the blocking probability of lightpath establishment by preventing the occurrence of spectrum fragmentation while suppressing  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(b_{r}\) & 10 [cm] \\ \(\alpha\) & \(4\times 10^{6}\) [m\({}^{-1}\)] \\ \(\Delta\) & 0.35 [\%] \\ \(o_{r}\) & 3.8 [\(\mu\)m] \\ \(V\) & 2.2 \\ \(W\) & \(1.1428V-0.996\) \\ \(X\) & \(\sqrt{V^{2}-W^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of parameters.  
Figure 3: Example of inter-core crosstalk.  
the impact of inter-core crosstalk in multi-core fiber environments. Whenever a new lightpath-setup request is generated, the proposed method selects a routing path, cores, and FSs for the lightpath establishment based on the following three policies:  
1. We expect to prevent the blocking of lightpath establishment by balancing the usage of FSs in each routing path. In the case where lightpaths are established intensively on a certain routing path and no available FSs remain in any cores of the routing path, the establishment of new lightpaths is blocked. Therefore, the proposed method selects a routing path so as not to use cores where many FSs are already assigned to existing lightpaths.
2. In order to further reduce the blocking probability, the proposed method prevents spectrum fragmentation by assigning lightpath-setup requests to prioritized cores according to their required number of FSs. Establishing lightpaths with the same number of FSs on the same core can prevent the generation of many small fragmented bandwidth gaps, i.e., spectrum fragmentation, because the utilization of FSs is smoothed [25]. However, the required number of FSs takes various values depending on the traffic demand and the modulation level given by (1), while the number of cores is limited, e.g., 3 or 7. Therefore, the proposed method groups lightpath-setup requests based on their required number of FSs and assigns the request groups to cores according to their priority. Furthermore, it utilizes frequency-spectrum resources by allocating preferred frequency bandwidths to lightpath-setup requests in each group, thereby reducing both the blocking probability and inter-core crosstalk.
3. As mentioned earlier, using the same FSs in adjacent cores degrades the quality of established lightpaths due to inter-core crosstalk. Therefore, to avoid such a situation, for a newly established lightpath, our proposed RCSA method utilizes FSs so as not to use the same FSs in adjacent cores.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 3 Materials
### Surface Radiation Budget Monitoring: SURFRAD  
The SURFRAD network [49] is a network of sensors that performs the monitoring of surface solar radiation in the USA.  
Currently, the SURFRAD network is composed of seven stations: Montana, Colorado, Illinois, Mississippi, Pennsylvania, Nevada, and South Dakota. These areas were selected with the aim of being representative of the main climate zones in the USA. From the stations available, 4 were selected for being used in this study (Table 2), because of their coincident position within the area covered by the ABOVE mission (Figure 1). The measurements of these stations in the SURFRAD network are used as reference values for the evaluation and comparison of point values obtained by the methodologies under study for surface albedo computation. The data records from these stations can be accessed through the National Oceanic and Atmospheric Administration (NOAA) (Silver Spring, MD, USA) download platform of the USA [50].  
The primary measurements in SURFRAD are solar and infrared (short-wave) radiations, regardless of whether they are upwelling or downwelling, and the complementary measurements include direct and diffuse solar radiation, Ultraviolet B (UVB), and meteorological parameters such as air temperature and atmospheric pressure.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Bands** & **Wavelength (\(\mu\)m)** \\ \hline Band 1—Coastal aerosol & 0.43–0.45 \\ Band 2—Blue & 0.45–0.51 \\ Band 3—Green & 0.53–0.59 \\ Band 4—Red & 0.64–0.67 \\ Band 5—Near Infrared (NIR) & 0.85–0.88 \\ Band 6—Short-wave Infrared (SWIR) 1 & 1.57–1.65 \\ Band 7—Short-wave Infrared (SWIR) 2 & 2.11–2.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reflectance bands in the OLI sensor of Landsat 8 satellite used in the computation of surface albedo.  
Each station is formed by three platforms where the measurement sensors are installed. Global solar radiation is measured on the main platform with a wide-band pyranometer oriented upwards. The direct component of the radiation is measured with a pyrheliometer with normal incidence installed on an automatic solar tracker and the diffuse solar radiation is measured with a shaded pyranometer mounted on the solar tracker. Last, a third pyranometer is installed downwards on a crosshead next to the upper part of the tower, measuring radiation reflected by the surface. Data measured from direct and reflected radiation allow the computation of instant surface albedo values. The accuracies obtained for the short-wave measurements are 2% for the pyrheliometer and 5% for the pyranometer.
## 5 Experiment
### Experiment Results
#### 5.2.2 Validation Experiment of Alignment Autonomous Control Method  
Under the same experimental conditions, the initial alignment target is the same, and the alignment autonomous control method is used to control the device. The alignment control process is shown in Figure 6. Since the initial target range of air pressure and vertical displacement is already very large, it is not desirable to adjust it, so it is acceptable to adjust the target range of the self-rotation angle and horizontal offset. \(H_{p_{\min}}\), \(H_{p_{\max}}\), and \(H_{z}\) are set to larger values, while \(H_{x_{\text{c}}}\) and \(H_{\beta}\) are set to smaller values.  
It can be seen from Figure 6 that the air spring vibration isolation device can normally complete the alignment control not only under medium and low output torque conditions but also in high output torque conditions. The main experimental results are:  
(1) From the trend of Figure 6a, with the increase of output torque, horizontal offset and self-rotation angle sharply decrease in the S2 state, in which the self-rotation angle goes beyond the given control precision range. In the S3 state, the self-rotation angle is controlled within the given control precision range immediately. As the output torque increases further (S4 state), the self-rotation angle goes beyond the given control precision range greatly.  
(2) In the S5 state, the system is judged uncontrollable under hard constraints. The system state is classed as an **II** alignment control fault state under soft constraints. The smallest adjustment of self-rotation angle boundary and horizontal offset boundary is calculated, in which the boundary of self-rotation angle is adjusted from \(\pm\)0.7 mm/m to \(\pm\)0.8 mm/m and the boundary of horizontal is adjusted from \(\pm\)0.5 mm to \(\pm\)0.55 mm. The alignment state can then be controlled to the given accuracy range under soft constraints. Meanwhile, all vertical displacements are within the given control precision range in Figure 6b all the time.  
The experiment result confirms that the autonomous control is effective in HPDME-ASVIS.
# Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation
## 2 Materials and Methods
### Statistical Testing  
We tested how DT and ER changed at 1-4 intervals using additional controls of stimulus orientation and ambiguity. We performed repeated measures ANOVA with 1-4 intervals, ambiguity (HA and LA), and orientation (Left and Right) as within-subject factors. In general, ANOVA requires the homogeneity assumption: the population variances of the dependent variable must be equal for all groups. At the same time, this assumption may be ignored if the sample size is equal for each group. In this study, we used a within-subject design with repeated measures. Therefore, the sample size for each condition was equal, and we did not control for variance homogeneity. If the tested samples did not obey the normality condition, we applied Greenhouse-Geisser correction to ANOVA results. For significant main effects, we performed a post hoc analysis using parametric or nonparametric tests, depending on sample normality, which was determined using the Shapiro-Wilk test. All test types are specified in the Result section and in the figures captions. A statistical analysis was performed in IBM SPSS Statistics.  
Statistical analyses of brain activity were carried out based on the subject-level wavelet power, averaged over trials and over TOI1. Contrasts between the four intervals were tested for statistical significance using a permutation test combined with the cluster-basedcorrection for multiple comparisons. Specifically, the \(F\)-tests compared four wavelet power sets for all pairs (channel, frequency). Items that passed the threshold corresponding to a \(p\)-value of 0.001 (one-tailed) were labeled along with their adjacent items and collected in separate negative and positive clusters. The minimum required number of neighbors was set to 2. The \(F\)-values in each cluster were summarized and corrected. The maximum amount was entered into the permutation structure as a test statistic. A cluster was considered significant if its \(p\)-value was below 0.01. The number of permutations was 2000.  
A similar procedure was followed on the source level results. We performed a cluster-corrected statistical intra-subject permutation test on the test-averaged and TOII-averaged source power distributions to determine significant differences between four intervals [30; 31]. The threshold for paired comparisons with \(F\)-test was \(p=0.005\). The p-threshold for the cluster was 0.025. The number of permutations was 2000. Finally, we calculated, for each subject, the average power of the source activity in the region of the identified cluster for each of the four intervals.  
All described operations were performed in Matlab using the Fieldtrip toolbox [21; 32].
Future Internet_**2013**, \(5\), 611-630; doi:10.3390/fi5040611  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**A Low Energy Consumption DOA Estimation Approach for**  
**Conformal Array in Ultra-Wideband**  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 2 and [PERSON] 3**  
\({}^{1}\) College of Information and Communication Engineering, Harbin Engineering University,  
Harbin 150001, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Information and Communication Systems, Hohai University, Changzhou 213022, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Instituto de Telecomunicacoes, University of Beira Interior, Covilha 6201-001, Portugal;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-130-3008-2029.  
_Received: 1 November 2013; in revised form: 6 December 2013 / Accepted: 10 December 2013 / Published: 16 December 2013_  
**Abstract:** Most direction-of-arrival (DOA) estimation approaches for conformal array suffer from high computational complexity, which cause high energy loss for the direction finding system. Thus, a low energy consumption DOA estimation algorithm for conformal array antenna is proposed in this paper. The arbitrary baseline direction finding algorithm is extended to estimate DOA for a conformal array in ultra-wideband. The rotation comparison method is adopted to solve the ambiguity of direction finding. The virtual baseline approach is used to construct the virtual elements. Theoretically, the virtual elements can be extended in the space flexibility. Four elements (both actual and virtual elements) can be used to obtain a group of solutions. The space angle estimation can be obtained by using sub-array divided technique and matrix inversion method. The stability of the proposed algorithm can be guaranteed by averaging the angles obtained by different sub-arrays. Finally, the simulation results verify the effectiveness of the proposed method with high DOA estimation accuracy and relatively low computational complexity.  
**Keywords:** direction-of-arrival (DOA) estimation; conformal array; arbitrary baseline algorithm
## 4 Selection of Materials for Each Layer
### Selection of Interlayer Materials  
Commonly used fibers in laminated composite materials include glass fiber, ultra-high-molecular-weight polyethylene fiber (UHMWPE), armid fiber, and carbon fiber. Table 3 presents the key performance indicators of several commonly used fiber composite materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Alumina** & **Silicon Carbide** & **Titanium Boride** & **Boron Carbide** \\ \hline Density (g/cm\({}^{3}\)) & 3.6–3.9 & 3.12–3.28 & 4.5 & 2.5 \\ Elastic modulus (GPa) & 340 & 408–451 & 570 & 400 \\ Vickers hardness (HK) & 1800 & 2500 & 2600 & 2900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the main performance data for commonly used ceramic materials in armor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Content Ratio of Al\({}_{2}\)O\({}_{3}\) (\%)**} \\ \cline{2-5}
**Performance Parameters** & **85** & **90** & **96** & **99.5** \\ \hline Density (g/cm\({}^{3}\)) & 3.68 & 3.76 & 3.80 & 3.82 \\ Elastic modulus (GPa) & 200 & 275 & 344 & 370 \\ Compressive strength (GPa) & 1930 & 2150 & 2260 & 2600 \\ Fracture toughness (MPa\(\cdot\)m\({}^{1/2}\)) & 3.1 & 3.3 & 3.7 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the performance parameters of ceramics with different Al\({}_{2}\)O\({}_{3}\) content levels.  
The table clearly indicates that glass fiber possesses the highest density and the lowest specific energy absorption rate. On the other hand, UHMWPE demonstrates outstanding energy absorption capabilities owing to its well-connected main chain, high degree of molecular alignment, and high crystallinity. This enables it to absorb a significant amount of energy during plastic deformation, resulting in the highest specific energy absorption rate, which characterizes the energy absorption characteristics of materials due to deformation in armor material or structure. UHMWPE exhibits exceptional resistance to high-speed impacts. Aramid fiber has a relatively lower tensile strength, and carbon fiber T700 exhibits the highest tensile strength, enabling it to withstand higher tensile stresses. Furthermore, carbon fiber T700 has a specific energy absorption rate that is only surpassed by UHMWPE. It retains its strength at temperatures above 2000 \({}^{\circ}\)C in an oxygen-free environment and exhibits excellent high-temperature resistance. With a low coefficient of thermal expansion (0\(\sim\)1.1 \(\times\) 10\({}^{-6}\) K\({}^{-1}\)), carbon fiber T700 exhibits fatigue and corrosion resistance properties. Therefore, it was chosen as the interlayer material.
# A Review on Distribution System State Estimation Algorithms
## 3 DSSE Fundamentals and Main Algorithms  
The purpose of this Section is to analyze the foundations of DSSE, from the state vector options up to the main DSSE algorithms.
# The Complex Relationship between Virulence and Antibiotic Resistance
###### Abstract  
Antibiotic resistance, prompted by the overuse of antimicrobial agents, may arise from a variety of mechanisms, particularly horizontal gene transfer of virulence and antibiotic resistance genes, which is often facilitated by biofilm formation. The importance of phenotypic changes seen in a biofilm, which lead to genotypic alterations, cannot be overstated. Irrespective of if the biofilm is single microbe or polymicrobial, bacteria, protected within a biofilm from the external environment, communicate through signal transduction pathways (e.g., quorum sensing or two-component systems), leading to global changes in gene expression, enhancing virulence, and expediting the acquisition of antibiotic resistance. Thus, one must examine a genetic change in virulence and resistance not only in the context of the biofilm but also as inextricably linked pathologies. Observationally, it is clear that increased virulence and the advent of antibiotic resistance often arise almost simultaneously; however, their genetic connection has been relatively ignored. Although the complexities of genetic regulation in a multispecies community may obscure a causative relationship, uncovering key genetic interactions between virulence and resistance in biofilm bacteria is essential to identifying new druggable targets, ultimately providing a drug discovery and development pathway to improve treatment options for chronic and recurring infection.  
antibiotic resistance; virulence genes; biofilms; microbial communication [1][PERSON]. [PERSON], [PERSON], A.  
resistance. Bacteria have found a host of mechanisms to circumvent antibiotic killing and transmit resistance [7; 8; 9; 10], that until recently were thought to impose a significant burden on overall evolutionary fitness [1], allowing susceptible organisms to ultimately outcompete their resistant counterparts. Generally, these mechanisms can be grouped as either innate resistance or, of more concern to modern clinical and agriculture practice, acquired resistance [11; 12; 13; 14] (Figure 1). Unfortunately, the once predominant dogma that described antibiotic resistance and fitness as an inverse relationship has not been borne out in the face of ever-increasing reports of antibiotic resistant bacteria, hinting at the possibility of compensating co-mutations.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 4 Communication
### Inter-Cell Routing and Load Balancing  
Here we adopt the inter-cell routing protocol between CHs presented in [24]. CHs at ring \(r_{p}\) deliver the information to the nearest CH(s) at ring \(r_{p}-1\). More specifically, a CH located on one of the axes \(A_{0}\) to \(A_{5}\) and ring \(r_{p}\) transfer the information to the CH located in the same axes but at ring \(r_{p}-1\), for example \(\langle 3,3\rangle_{h}\rightarrow\langle 2,2\rangle_{h}\), see Figure 4. In addition, non-axes CHs transfer the information to two inner-ring neighbors, for example \(\langle 2,3\rangle_{h}\rightarrow\langle 2,2\rangle_{h}\) with probability \(p_{\langle 2,3\rangle_{h}-\langle 2,2\rangle_{h}}\) and \(\langle 2,3\rangle_{h}\rightarrow\langle 1,2\rangle_{h}\) with probability \(p_{\langle 2,3\rangle_{h}-\langle 1,2\rangle_{h}}\), \((p_{\langle 2,3\rangle_{h}-\langle 2,2\rangle_{h}}+p_{\langle 2,3\rangle_{h}-\langle 1,2 \rangle_{h}}=1)\). The routing probabilities are chosen according to the load balancing principle between CHs of the same ring. For instance, let us consider the routing probabilities from ring 3 to ring 2, where the traffic load in each CH is \(\rho_{k}\), with \(k=3,2\) respectively (intuitively \(\rho_{3}<\rho_{2}\), this is formally discussed in Section 6). Apart from the local traffic, the exogenous load received at CH \(\langle 2,2\rangle_{h}\), \(e_{2}\), is equal to \(e_{2}=p_{\langle 2,3\rangle_{h}-\langle 2,2\rangle_{h}}\rho_{3}+\rho_{3}+p_{ \langle 3,2\rangle_{h}-\langle 2,2\rangle_{h}}\rho_{3}=(2p_{\langle 2,3\rangle_{h}- \langle 2,2\rangle_{h}}+1)\rho_{3})\) where the last equality follows from the symmetry of the load balancing, i.e., \(p_{\langle 2,3\rangle_{h}-\langle 2,2\rangle_{h}}=p_{\langle 3,2\rangle_{h}-\langle 2 \rangle_{h}}\). In addition, the exogenous load received at CH \(\langle 1,2\rangle_{h}\), also \(e_{2}\), is equal to \(e_{2}=p_{\langle 1,3\rangle_{h}-\langle 1,2\rangle_{h}}\rho_{3}+p_{ \langle 2,3\rangle_{h}-\langle 1,2\rangle_{h}}\rho_{3}=2p_{\langle 1,3\rangle_{h}- \langle 1,2\rangle_{h}}\rho_{3}\), since \(p_{\langle 1,3\rangle_{h}-\langle 1,2\rangle_{h}}=p_{\langle 2,3\rangle_{h}- \langle 1,2\rangle_{h}}\). Then, from the above equations we identify \(2p_{\langle 2,3\rangle_{h}-\langle 2,2\rangle_{h}}+1=2p_{\langle 2,3\rangle_{h}- \langle 1,2\rangle_{h}}\), that is, \(p_{\langle 2,3\rangle_{h}-\langle 1,2\rangle_{h}}=3/4\). Figure 4 shows the case of CHs located in the set \(A_{1}\cup S_{1}\cup A_{2}\) for rings \(2\leq r_{p}\leq 5\). The generalization to arbitrary rings and sectors can be found in Table II of [24].
## 4 Analysis of the Factors Affecting on the Gathering Process and Self-Preservation of the STACER
### Influence of Acceleration on the Gathered State of STACER during Launching Process
The friction force decreases and some of the layers exceed the innermost layer, and then it deploys from the storage barrel. In the other five acceleration conditions, during the movement between the inner layer and the layer of STACER, only limited local areas show intermittent gaps. The friction force of the whole spring reel is stable. The top rod is able to provide sufficient resistance force to avoid the large displacement of the top rod, which keeps the self-preservation and stability of the STACER. It is concluded that during the satellite launching process, the spring reel should be placed along the direction of the +X-axis so as to reduce the large gap between the STACER layers and maintain good self-preservation performance. Since the friction force is a favorable factor for blocking the relative movement between the layers of the spring reel during the launching process, the friction coefficient is suggested to be increased to a certain extent when the STACER is gathered. Figure 12: Force diagram of the top rod under different acceleration conditions.
## 4 Conclusions  
In summary, Ag nanoparticles in borate-bismuth glass were obtained via thermal annealing. Due to the existence of eutectic between Bi\({}_{2}\)O\({}_{3}\) and Ag, an influence of Bi\({}_{2}\)O\({}_{3}\) on the formation of Ag nanoparticles was observed, which was reflected in the color change of the glass with a high molar content of Bi\({}_{2}\)O\({}_{3}\) (B\({}_{2}\)O\({}_{3}\):Bi\({}_{2}\)O\({}_{3}\) molar ratios of 50:50) during the annealing of the glass, from slightly yellow to red. In this sample, a strong absorption maximum on the UV-Vis spectrum was observed at a wavelength of about 500 nm, caused by the localized plasmon resonance of Ag nanoparticles. However, at a high content of Ag nanoparticles in the glass, quenching of the luminescence of Eu\({}^{3+}\) ions took place, caused most probably by the energy transfer from Eu\({}^{3+}\) ions to Ag nanoparticles. Selecting the most optimal glass composition at which the intensity of rare-earth ion luminescence would be maximal requires further research. The obtained results show that B\({}_{2}\)O\({}_{3}\)-Bi\({}_{2}\)O\({}_{3}\) glass and glass ceramics, with Ag nanoparticles and rare-earth ions, could be considered as a new phosphor for light-emitting diodes (LEDs) [41,42].  
Conceptualization, B.K. and K.M.; methodology, B.K., K.M., M.M., M.L., W.S., M.N. and A.S.; formal analysis, K.M. and B.K.; data curation, K.M. and B.K.; writing--original draft preparation, K.M. and B.K.; writing--review and editing, B.K., K.M., M.M., M.L., W.S., M.N., A.S. and A.B.; supervision, B.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank [PERSON] for fruitful discussions.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
XRD X-Ray Diffraction FTIR Fourier Transform Infrared Spectroscopy DTA Differential Thermal Analysis TEM Transmission Electron Microscopy XPS Fourier Transform Infrared Spectroscopy XPS X-Ray Photoelectron Spectroscopy LEDs Light-Emitting Diodes LSPR Localized Surface Plasmon Resonance HT Heat Treatment NPs Nanoparticles