esa-sceva/satcom-chunk-collection huggingface dataset:
# Metasurface-Based Polarimeters
## 3 Metasurface-Based Polarimeters
### Metacoupler-Based Polarimeter  
Inspired by the polarization-controlled unidirectional excitation of surface plasmon polaritons (SPPs) [33], [PERSON] et al. suggested a metasurface-based polarimeter that can effectively couple normally incident light to in-plane plasmonic waveguide modes. By calculating the relative efficiency of excitation between predefined propagation directions, the incident SOP can be directly retrieved. Similar to the work in Reference [68], three properly designed GSPMs made up of metal-insulator-metal (MIM) nanoantennae embedded in a polymer layer were incorporated to make the so-called waveguide metacoupler, as depicted in Figure 4a,b. This metacoupler unidirectionally excites the plasmonic waveguide modes instead of far-field reflection propagating in six different directions for the three polarization basis sets--(\(|x\rangle\),\(|y\rangle\)), (\(|a\rangle\),\(|b\rangle\)), and (\(|r\rangle\),\(|l\rangle\))--dictated by the definition of the Stokes parameters. Figure 4c-e displays the intensity distributions in an area of \(30\times 30\,\upmu\)m\({}^{2}\) for three extreme SOPs, namely, \(|x\rangle\), \(|a\rangle\) and \(|r\rangle\) polarizations, thereby illustrating all-polarization sensitivity. It is obvious that the metacoupler can launch the waveguide modes propagating in the six designed directions. Additionally, the power distribution in the six channels are strongly polarization-dependent and one of the six channels is suppressed depending on the input SOP. For example, port #1 is greatly suppressed for \(|x\rangle\) polarization (Figure 4c) while port #4 becomes weak when the polarization is switched to \(|a\rangle\) (Figure 4d). By integrating the power flows of the waveguide modes at the ports marked in Figure 4c, all the coupling efficiencies can be calculated, through which the normalized contrast denoted \(D_{1}\)-\(D_{3}\) is determined. Here it should be noted that, unlike the previous work [67], there is no direct mathematical equivalence between \(D_{1}\)-\(D_{3}\) and \(s_{1}\)/\(s_{0}\)-\(s_{3}\)/\(s_{0}\), thus the retrieved contrasts can only roughly represent the input Stokes parameters. In order to achieve the best performance of the polarimeter, one needs to properly relate the six coupling efficiencies to the three Stokes parameters with calibration. After calibration, the proposed in-plane polarimeter shows excellent performance, with the retrieved results perfectly overlapping with the Stokes parameters, as displayed in Figure 4f.
## 2 Materials and Methods
### Statistical Analyses  
We followed the data analysis criteria used by [PERSON] et al. (2021) [16] to obtain the A\({}_{0}\) and S\({}_{0}\) values per player per period (Figure 2). The team and player average values and standard deviation (SD) for A\({}_{0}\) and S\({}_{0}\) were calculated per each period and for the entire season. Then, an exploratory analysis of the data was performed to check the normal distribution of the values and the variance homogeneity. To study differences between team averages values over periods, the repeated measures one-way ANOVA test was calculated. Additionally, a one-way ANOVA was used to study players individually over the period. The effect size (ES) (Eta-squared, \(\eta^{2}\)) was calculated for each player, using the following scale: non-existent (<0.1), small (0.1-0.3), medium (0.3-0.5), large effects (>0.5) [25]. Post-hoc pairwise comparisons were made (Tukey's test), in which case significant differences were found (\(p<0.05\)). Additionally, SDs were used to assess magnitudes of differences between periods for each player. The thresholds for assessing the observed intra-player differences in the SD of mean were 0.2, 0.6, and 1.2 for small, moderate, and large variations, respectively [26]. All statistical analyses were developed using statistical packages (openxlsx to edit worksheets; dply to manipulate data; ggplot2 to create graphs; statix to check assumptions for ANOVA test; ez to apply ANOVA test; andeffectsize to calculate effect size) from RStudio software (R Core Team, 2020). Additionally, Prism software (GraphPad Software, San Diego, CA, USA) was also used to create graphs.
# 3D Photoacoustic Tomography System Based on Full-View Illumination and Ultrasound Detection
###### Abstract  
A 3D photoacoustic computed tomography (3D-PACT) system based on full-view illumination and ultrasound detection was developed and applied to 3D photoacoustic imaging of several phantoms. The system utilized an optics cage design to achieve full-view uniform laser illumination and completed 3D scanning with the rotation of a dual-element transducer (5 MHz) and the vertical motion of imaging target, which obtains the best solution in the mutual restriction relation between cost and performance. The 3D-PACT system exhibits a spatial resolution on the order of 300, and the imaging area can be up to 52 mm in diameter. The transducers used in the system provides tomography imaging with large fields of view. In addition, the coplanar uniform illumination and acoustic detection configuration based on a quartz bowl greatly enhances the efficiency of laser illumination and signal detection, making it available for use on samples with irregular surfaces. Performance testing and 3D photoacoustic experiments on various phantoms verify that the system can perform 3D photoacoustic imaging on targets with complex surfaces or large sizes. In future, efforts will be made to achieve full-body 3D tomography of small animals and a multimodal 3D imaging system.  
Keywords:3D photoacoustic tomography; full-view illumination and ultrasound detection; photoacoustic coplanar; quartz bowl +
Footnote †: journal: Applied Physics Letters
## 3 The Proposed Scheme  
In this study, the WSN model contains three assumptions. First, the locations of the BS and the sensor nodes are fixed. Second, each sensor node has energy awareness and location awareness with a GPS [22]. Finally, sensor nodes are energy constraints and have energy-monitoring capabilities. Additionally, the proposed scheme contains three phases: grid construction, tree construction, and data transmission.
## 2 Materials and Methods
### Mono-Metal and Multi-Metal Kinetic and Adsorption Experiments  
For both kinetic and adsorption experiments, the reagents used of analytical grade, purchased by Sigma-Aldrich Chemical Co. (Darmstadt, Germany), were Sr(NO\({}_{3}\))\({}_{2}\), MnSO\({}_{4}\).H\({}_{2}\)O, Pb(NO\({}_{3}\))\({}_{2}\) and Na\({}_{2}\)HAsO\({}_{4}\).7H\({}_{2}\)O. A stock solution of 1000 mg/L was prepared for each heavy metal in de-ionized water. A series of concentrations of 1, 5, 10, 25, 50, 75 and 100 mg/L was obtained for each metal by dilution with de-ionized water. The initial pH of the solutions was adjusted to Ph = 7 by adding either 0.1 M NaOH or 0.1 M HCl. A bench pH meter, model Metter Toledo, was used.  
For the kinetic tests, a biochar sample of 4 g/L was mixed with a 10 mg/L standard metal solution in a sealed quartz beaker and agitated on a mechanical shaker at 200 rpm, for 24 h, at room temperature. Liquid aliquots were withdrawn at time intervals of 0.5 h, 1 h, 2 h, 3 h, 6 h, 12 h and 24 h, filtered through microporous Whatman filters, measured for pH and stored in the refrigerator for further analyses. The concentration of each metal in the filtrates was determined by an inductively coupled plasma mass spectrometer, model ICP-MS 7000 cx of Agilent Technologies, assisted by an Anton Paar Multiwave 3000 oven for the digestion of the samples. The amount of each metal adsorbed by the solid material was calculated by the difference from the initial amount.  
Following the determination of equilibrium contact time from the above tests, the experiments for both mono-metal or multi-metal adsorption were carried out, for the same range of concentrations for each metal, under the same experimental conditions. The data reported below are the average values of duplicate tests. The experiments were also repeated for an adsorption dose of 2 g/L.
## 2 Materials and Methods
### Animals and Experimental Design
#### 2.3.7. Heavy Metals in Rats' Serum and Liver  
The serum samples were diluted 20\(\times\) with a 1% HNO\({}_{3}\) solution and were then used to detect Cu, Mn, and Zn with ICP-MS (CETAC Varian 720-ES, Omaha, NE, USA). The concentrations of the three HMs in the liver tissues were also measured by ICP-MS after acid digestion by microwave. A 0.5 g organic tissue was pre-digested with 5.5 mL of HNO\({}_{3}\) (68%) in a PTFE vessel at 110 \({}^{\circ}\)C for half an hour and then transported into a microwave digestion system (MARS 6 classic, CEM, Matthews, NC, USA) for additional mineralization. Three blank samples were similarly examined. The conditions for the digestion of MARS 6 classic were as follows: up to 180 \({}^{\circ}\)C for 25 min and then constant for 20 min, followed by a cooling stage (30 min) at 80 \({}^{\circ}\)C. Digested samples were diluted with ultrapure water after cooling to room temperature. These samples were used for the final heavy-metal element analysis, performed with an ICP-MS equipped with a concentric Nebulizer, a quartz torch with a quartz injector tube, and a cyclonic spray chamber [55].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Forward Primer** & **Reverse Primer** \\  & **(\({}^{\circ}\)5-\({}^{\circ}\)3)** & **(\({}^{\circ}\)5-\({}^{\circ}\)3)** \\ \hline _APP_ & AACGAAGACCACTGTGAGC & CGTCGACAGGCCTAACTTCA \\ _MAPT_ & TGGCTAAAAGCTGAAAGCA & GCCCTTGGCTTCTTCTTCG \\ _Caspase 3_ & GGTATTGAGACAGACGTGG & CATGGGATCTGTTCTTGC \\ _\(\beta\)-actin_ & AACTCCTCACCCTCCCAAAA & AACGAATGCGTGTACACCTTCCC \\ \hline \hline \end{tabular}
\end{table}
Table 2: Forward and reverse primers sequence used in qPCR.
## 1 Introduction  
The model of Mealy finite state machine (FSM) is used very often in the process of designing control units of modern digital systems [1; 2; 3]. There are many problems connected with optimization of characteristics of control units [4; 5]. One of the most important problems is a problem of hardware reduction [6; 7].  
Solution of this problem allows reducing the power consumption and increasing the performance (maximizing operating frequency) [8; 9]. To solve this problem, it is necessary to take into account the specific features of both an FSM model and logic elements used to implement the circuit of FSM [3; 10].  
The main specific of Mealy FSM is a dependence of input memory functions and output functions on both input variables and state variables [1; 8]. Our investigation of standard benchmarks [11] shows that it could be up to 17 arguments in Boolean functions representing FSM circuits.  
Presently, the field-programmable gate array (FPGA) chips are widely used for implementing different digital systems [8; 12; 13]. Of course, FPGAs also are used to implement control units of these systems. There are three main elements of FPGA which could be used to implement FSM circuits. They are: look-up table (LUT) elements, embedded memory blocks (EMB) and tools of programmable interconnections [14; 15; 16]. LUTs fit for implementing Boolean functions represented as sum-of-products (SOP) [8]. EMBs implement large truth tables representing systems of Boolean functions (SBF).  
A LUT is an array of SRAM cells with \(S_{L}\) inputs (\(S_{L}\leq 6\)) [13; 14]. Outputs of LUTs are connected with programmable flip-flops which could be bypassed. Therefore, it is possible to implement distributed registers keeping state codes [3].  
An EMB is a RAM with \(S_{A}\) address inputs and \(t_{F}\) outputs. The main specific of EMBs is their reconfigurability [3]. It means that the values of \(S_{A}\) and \(t_{F}\) could be changed. Of course, the number of bits (the volume of EMB) is constant. It is determined as  
\[V_{0}=2^{S_{A}}\cdot t_{F}. \tag{1}\]  
Due to the reconfigurability, it is possible to tune EMBs to meet the requirements of a particular design. There are the following pairs \(<S_{A}\), \(t_{F}>\)[13]: \(<15\), \(1>\), \(<14\), \(2>\), \(<13\), \(4>\), \(<12\), \(8>\), \(<11\), \(16>\), \(<10\), \(32>\) and \(<9\), \(64>\). It gives \(V_{0}=32\,K\), bits.  
In this article, we propose a method of synthesis leading an FSM circuit to implemented as a network of EMBs and LUTs. The method is based on the structural decomposition [17] of FSM circuit.
# Impact of Intracavity Power Variations toward Ultrashort Pulse Generation
## 4 Experimental Setup  
Figure 6 depicts the experimental setup of a mode-locked EDFL using the homemade GNP-SA. A 975 nm pump laser diode (LD) with a maximum power of 99 mW was employed as the excitation source. This pump light is coupled to a 5 m long erbium-doped fiber (EDF) via a 980/1550 nm wavelength division multiplexer (WDM). The EDF has a 5.2 dB/m absorption coefficient at 1530 nm, 0.19 numerical aperture, and 8.5 mm mode field diameter. The GNP-SA is positioned between two isolators (ISOs), namely ISO-1 and ISO-2. This arrangement is critical to ensure unidirectional light propagation. In addition, by having two isolators, back-reflections from the GNP-SA can be avoided. The presence of back-reflected light is generated at the interface between fiber ferrule and GNP material due to the refractive index difference. By using this arrangement, multiple reflections that degrade laser stabilities can be completely rectified. A polarization controller (PC) is inserted in the laser cavity to regulate the polarization state and optimize cavity birefringence impacts.  
Figure 4: Nonlinear saturable absorbance of SA measurement setup.  
Figure 5: Nonlinear saturable absorbance properties of GNP-SA.  
The output of the PC was coupled to an optical coupler (OC) which was the variable parameter in this study. Five different sets of OCs were tested in the laser cavity, namely 10/90, 20/80, 30/70, 40/60, and 50/50. The variable output coupling ratio was determined by changing the output arm for all couplers. Therefore, there were two sets of output coupling ratios for each OC except the 50/50 OC. The laser cavity consisted of three types of optical fibers: 8.4 m length of Corning SMF-28, 1 m length of Corning Hi-1060 fiber, and 5 m length of Lucent Technologies HP980 EDF. The dispersion coefficient, \(\beta_{2}\) at 1550 nm for the SMF-28, Hi-1060 SMF, and EDF were \(-\)22, \(-\)7 and 23 ps\({}^{2}\)/km, respectively. The total group velocity dispersion of the laser setup was \(-\)0.077 ps\({}^{2}\), which confirms its operation in the anomalous dispersion regime.
## 3 Problem Description
### The Space-Time Network Design Problem  
In fact, the timetable can be envisioned as a space-time network, wherein nodes signify the arrival and departure events of trains at each station, and arcs depict the passenger waiting process on platforms alongside the transit processes of trains between stations. Passengers board and alight trains within these arrival and departure windows, thereby navigating their travel demands through various segments that constitute a path. This network may also be delineated as a station-indexed and train-indexed network, with stations and trains distinguishing spatial positions and timestamps, respectively. Optimizing timetables is equivalent to identifying the optimal arc costs, which denote the durations of the events represented by these arcs. The process of the integrated optimization of line planning and timetabling seeks to determine the optimal network topology and arc costs. Consequently, these problems can be viewed as a network design problem (NDP) or a space-time network design problem (STNDP), as illustrated in Figure 5. When passengers' boarding behaviors (also called travel path selection behaviors) satisfy the Wardrop's first principle, it is termed an equilibrium space-time network design problem (ESTNDP).  
Figure 4: Typical examples of different time–distance diagrams under the operation plan in Figure 3: (**a**) an example infeasible timetable because there are no overtaking facilities at station 5, and (**b**) an example feasible timetable because station 3 is an overtaking station.  
Figure 3: Exemplary line plan of the urban rail line in Figure 2.
# Bit Independence Criterion Extended to Stream Ciphers
## 3 BIC Algorithm in Stream Ciphers
### BIC Acceptance Test
Although generally \(\alpha_{1}\leq 0.1\) the binomial distribution \(B\left(n\cdot C_{2}^{m},\;\alpha_{1}\right)\) can be approximated by the normal distribution, with mean \(E\left(T|H_{0}\right)=\alpha_{1}\cdot n\cdot C_{2}^{m}\) and variance \(\sigma^{2}\left(T|H_{0}\right)=\alpha_{1}\cdot n\cdot C_{2}^{m}\) (\(1-\alpha_{1}\)), taking into account that \(n\cdot C_{2}^{m}\) grows very quickly with \(m\). Under \(H_{1}\), the variables \(t(v_{\cdot j}^{i},v_{\cdot k}^{i},\alpha_{1})\) that appear in the expression of \(T\) are not identically distributed, since the rejection of the BIC means that there are several matrices \(H^{i}\) for which the hypothesis \(H_{0}\) of independence between \(v_{\cdot j}^{i}\) and \(v_{\cdot k}^{i}\) is rejected. In this case, \(p_{jk}^{i}\
eq\alpha_{1}\) and may be different when \(i,j,k\) varies. For this reason, a binomial does not appear directly as the distribution of \(T\). However, it is still possible to approximate the distribution of \(T\) by the Normal distribution. For this it is sufficient to calculate the mean  
\[P_{n\cdot C_{2}^{m}}=\frac{\sum_{i}^{n}\sum_{j}^{m-1}\sum_{k>j}^{m}p_{jk}^{i} }{n\cdot C_{2}^{m}}, \tag{13}\]  
between the probabilities of all the variables \(t(v_{\cdot j}^{i},v_{\cdot k}^{i},\alpha_{1})\) and the distribution of \(T\) can be approximated by the binomial distribution \(B(n\cdot C_{2}^{m},P_{n\cdot C_{2}^{m}})\). This distribution, in turn, can be approximated by the Normal distribution, taking into account high values of \(n\cdot C_{2}^{m}\). The precision of this approximation depends on the difference between the probabilities \(p^{i}_{jk}\) involved in \(P_{n\cdot\mathbb{C}^{n}}\), therefore the variance value between these probabilities can be a measure of the quality of the approximation. When comparing the distribution of \(T\) under \(H_{0}\) and \(H_{1}\), similarities and differences are observed. They are similar in that in both cases \(T\) follows a Normal distribution, but there are two differences, the first and most important is observed between the expected values of both distributions (it will be higher under \(H_{1}\)) and the second refers to the level of adjustment to this distribution (may be lower under \(H_{1}\)). In the rest of this work, the proposed method to evaluate the BIC in stream ciphers will be called the BIC test.
## 3 Results and Discussion
### UV-Visible Spectroscopy  
The UV-visible spectroscopic study measures the absorptivity of CDs, and it is a fundamental property of materials and nanomaterials. The CDs showed colour between indigo to blue under UV-light and emission wavelength between 430-450 nm. The specific emission wavelength is related to the colour absorbed by the substance producing the emission. Therefore, the measure of the absorptivity of CDs using a UV-Visible spectrophotometer, among other parameters, can be a quantitative and qualitative characterization technique for the CDs [1]. The UV-Visible spectroscopic measurement of the samples was obtained by scanning the wavelength between 200-700 nm (Figure 8). It shows prominent absorption peaks at 260-280 nm and 320-340 nm. The peak at 260-280 nm corresponds to \(\pi\)-\(\pi^{\ast}\) transition of aromatic C=C bonds from the core sp\({}^{2}\) hybridized carbon fragments and the conjugated \(\pi\) nanodomain within the carbon dots structure. The peak at 320-340 nm, were attributed to the n-\(\pi^{\ast}\) transition of non-bonding electrons in C=C and C=O, an attribute from the molecular state of the carbon dots [1,87,88].  
Figure 7: X-ray diffraction spectroscopy (XRD) for (**a**) CDs-EDA (**b**) CDs-L-Ph.
## 3 Results and Discussion  
### Identification and Determination of Volatile Compounds in Forty Apple Cultivars  
The identification of volatile compounds and studies of diversity among cultivars were performed based on the retention indices obtained from GC-MS. A total of 78 volatile compounds were identified and quantified in 40 apple cultivars, including 47 esters, 12 aldehydes, 5 alcohols, 3 ketones, 1 acid and 10 other compounds (Table 2). On average, 35 types of volatile compound were detected in each cultivar. Changfu No. 2 (CF2) contained the highest number of volatile compounds (47), while the Qinyue (QYE) contained contained the least (20) (Table 3). More than 40 types of volatile compound were identified in Ralls (RL, 45 types), Huayu (HY, 44 types), Modi (MI, 44 types) and Ruixianghong (RXH, 43 types). Fewer than 25 types of volatile compound were identified in Huashuo (HS, 21) and Cox Orange (COP, 24) (Table 3). Eight volatile compounds (E17 hexyl acetate, E26 butyl caproate, E27 hexyl butyrate, E28 hexyl 2-methylbutyrate, E40 hexyl hexanoate, A1 hexanal, A4 2-hexenal and O8 \(\alpha\)-farnesene) were present in peels of all apple cultivars (Tables S1 and S2). As shown in Table 2, hexyl butyrate (E27), hexyl 2-methylbutyrate (E28), hexyl hexanoate (E40), and 2-hexenal (A4) and \(\alpha\)-farnesene (O8) were the most abundant compounds (average content \(>\) 700 ug/kg FW) in the apple cultivars, which is in agreement with the results of previous studies [20; 21; 22].