esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellular Automata Approach to Topology Optimization of Graded Multi-Material Structures
## 2 Topology Optimization Basics  
While generating structural topologies, the material is redistributed within the design domain. The idea is to utilize the material in the best possible way, according to the assumed design criteria. Finally, it leads to removing material unnecessary from these criteria point of view. The classical and widely recognized formulation of the structural topology optimization problem is the minimization of the structure compliance \(c\), Equation (1) while satisfying the constraint imposed on the assumed volume fraction \(\kappa\)[37], Equation (2). Using the finite element approach, one calculates the following:  
\[\text{minimize}\ c(\mathbf{d})=\mathbf{u}^{T}\mathbf{ku}=\sum_{n=1}^{N}d_{n}^{p} \mathbf{u}_{n}^{T}\mathbf{k}_{n}\mathbf{u}_{n}, \tag{1}\]  
\[\text{subject to}\ V(\mathbf{d})=\kappa V_{0}, \tag{2}\]  
\[\mathbf{k}\ \mathbf{u}=\mathbf{f} \tag{3}\]  
\[0<d_{min}\leq d_{n}\leq 1 \tag{4}\]  
The quantity \(\mathbf{u}_{n}\) denotes displacement vector, \(\mathbf{k}_{n}\) stands for the stiffness matrix and both are defined for \(N\) elements. The design variable \(d_{n}\) represents the relative material density assigned to each element. In Equation (3) \(\mathbf{k}\) represents the global stiffness matrix, \(\mathbf{u}\) stands for the global displacement vector and \(\mathbf{f}\) is the vector of forces. Due to the simple bounds imposed on the design variables in Equation (4), with \(d_{min}\) as a non-zero minimal value (e.g., \(10^{-9}\)) of relative density, singularity is avoided.  
The quoted formulation requires relating the design variables to the mechanical properties of the material so that the modulus of elasticity \(E_{i}\) of each finite element has to be a function of the design variable \(d_{i}\). The most common approach the material representation is SIMP, i.e., Solid Isotropic Material with Penalization [38; 39] in the form of a power law, see Equation (5):  
\[E_{n}=d_{n}^{p}E_{0} \tag{5}\]  
In Equation (5) the quantity \(E_{0}\) stands for modulus of elasticity, defined for solid material, whereas \(p\) (typically \(p=3\)) is responsible for the penalization of intermediate densities. This allows controlling the design process and leads to obtaining black-and-white resulting structures. An alternative interpolation scheme for minimum compliance topology optimization problem was proposed in [40] as RAMP interpolation (Rational Approximation of Material Properties), see Equation (6):  
\[E_{n}=d_{n}/(1+q(1-d_{n}))E_{0}. \tag{6}\]  
The effect of penalization induced by the parameter \(q\) in RAMP is analogous to that of \(p\) in the SIMP scheme. The extended studies on material interpolation schemes in topology optimization are delivered by papers [41; 42; 43].  
In the present paper, both SIMP and RAMP schemes defined above are utilized and the results of topology optimization of graded multi-material structures for both of them are presented.
## 3 Results
### Ground--GEO Satellite Link
To estimate the required Eb/No we use the Eb/No, variation diagrams in relation to the bit error rate (BER). The reference we choose for BER is \(1\times 10^{-6}\), a common value in satellite communication systems. Starting from the theoretical results for each type of modulation, we see what coding gain we obtain when we improve the BER performance by considering \(1/2\) rate hard convolutional encoding with a constraint length of 7. This coding is typical for a scenario like this [24]. There are many ways to improve performance, but the purpose of our analysis consists of the discussions about the types of modulations. The results obtained for analyzed modulations are shown in Figures 4 and 5. We considered several levels for each type of modulation. In each of these figures, the gain coding due to performance optimization is obvious. For example, 16 DBRSK modulations have an Eb/No value of 21 dB for a BER of \(1\times 10^{-6}\  
Based on these considerations, for the designed link we obtain the performances described in Figure 6.  
Analyzing the figure, we notice that the OFDM modulation with PSK subcarriers offers the largest link margin. All 4 types of DPSK modulation offer a higher margin than any other type of conventional modulation. For PSK and QAM modulations, it is observed that as the modulation level increases, the margin decreases, but each analyzed modulation offers a margin of at least 7 dB for this particular case. The obtained results show us that the link was designed properly. Considering that the parameters used as properties are values encountered in practice, the analysis performed can be used as a starting point in other similar studies.
# Characterization of Volatile Substances in Pu-erh Tea (Raw Tea) at Different Storage Times
###### Abstract  
There is a lack of theoretical evidence regarding the transformation of the aroma of Pu-erh tea (raw tea) during long-term storage. In this study, we comprehensively investigate the aroma characteristics of Pu-erh tea (raw tea) from the same manufacturer, stored for different storage times (7-21 years). Sensory evaluation and qualitative and quantitative analysis of volatile substances were performed on the experimental samples. The results showed that the aroma of Pu-erh tea (raw tea) changed from fruity/floral to smoky and fragrance during the storage process. A total of 290 volatiles were identified by HS-SPME/GC\(\times\)GC-Q-TOF-MS. The key substances for the fruity/floral aroma are fenchene, (E)-1,2,3-trimethyl-4-propenyl-Naphthalene, (+/--theaspirane, and decanal, and the key substances for the smoky aroma were 2-ethyl-furan, camphene, 1-methyl-4-(1-methylethhenyl)-Benzene, and cis-\(\beta\)-Ocimene. The key aroma substances for the fragrance aroma are 1-methyl-4-(1-methylethylidene)-Cyclohexene, \(\alpha\)-Terpinene, trans-\(\beta\)-Ocimene, (E,E)-2,4-Heptadien, octanal, 2,5-Dimethoxyethylbenzene, 2,4-Dimethylanisole, 1,2,3-trimethoxybenzene, and 3,4-Dimethoxytoluene. This study helps us to understand further the aroma changes of Pu-erh tea (raw tea) during long-term storage.  
Pu-erh tea; fruity/floral aroma; smoky aroma; storage time
# Morphological and Mechanical Symmetry of the Quadriceps and Hamstrings in Athletes
## 2 Materials and Methods
### Statistical Analysis  
The normality and homogeneity of the data were assessed through Shapiro-Wilk, and Levene tests, respectively. Dependent sample t-test was used to compare all variables between sides. Simple linear regressions were performed in order to verify the relationship between sides for all the variables investigated. [PERSON]'s effect sizes (ES) were computed for the analysis of the magnitude of the differences and rated as trivial (<0.25), small (0.25-0.49), moderate (0.5-1.0), and large (>1.0) [36]. Backward multiple linear regressions were used to estimate the relationship of dependent variables (torque at 60\({}^{\circ}\)/s and 300\({}^{\circ}\)/s) with the independent variables of (CSA and EI) of both sides of the quadriceps and ham-string muscle groups for DO and ND limbs. Our collinearity diagnostic analysis revealed variance inflation factors (VIF) below 2.0, indicating acceptable levels of multicollinearity among the independent variables [37]. In addition, the post-hoc power of the multiple linear regression was calculated according to [PERSON] et al. [38]. The ES of the multiple linear regression was calculated and classified as small (>0.02); moderate (>0.13); and large (>0.26) a priori using G*Power 3.1.9.7 as described by [PERSON] et al. [39]. The correlation matrix showed the magnitude of the Pearson product-moment correlation coefficient between the dependent variables (knee extensors and flexors maximal torque) with the independent muscle variables (CSA and EI of quadriceps and hamstrings, respectively). The correlation was classified as negligible (r = 0.0-0.3); low (r = 0.3-0.5); moderate (r = 0.5-0.7); high (r = 0.7-0.9); and very high (r = 0.9-1.0) according to the thresholds recommended by [PERSON] et al. [40]. All statistical analyses were carried out using SPSS 22.0 for Windows (IBM SPSS Inc, Armonk, NY, USA), with a significance level of \(\alpha\) = 0.05. All dataset is in Supplementary Material (Table S1).  
Figure 1: Illustration of the muscular architecture of the quadriceps (Q) and hamstrings (H). All the images show the cross-sectional area, with the Q indicating the vastus lateralis (VL), vastus intermedius (VI) indicated by the blue line, rectus femoris (RF) indicated by the yellow line, vastus intermedius (VI) indicated by the green line, and vastus medialis (VM) indicated by the red line. For H, we the biceps femoris (BF) with the green line, semitendinosus (ST) with the blue line, and semimembranosus (SM) with the red line.
# Microseismic Location Error Due to Eikonal Traveltime Calculation
## 3 Numerical Results  
In this section, we compare the performance of regular FSM, factored FSM, and the PINN eikonal solver in microseismic source localization. We begin by highlighting the problem with regular FSM using a homogeneous model and then study the three approaches on a heterogeneous model. The PINN model is implemented using the SciANN package [12]--a high level Tensorflow wrapper for scientific computations. We use a neural network with 9 layers having 20 neurons in each hidden layer and train on 10,000 randomly selected points within the computational domain. Choosing a fraction of the total grid points, allow us to implement the method efficiently as the training cost is directly proportional to the number of training points. Previous studies have found random selection of these points to be the optimal approach [13]. We start with a randomly initialized network for the first source position and then use transfer learning for the following sources, i.e., use the trained network from the previous source as the initial weights for the neural network.
## 4 Implementation and Analysis
### Customized Emotional Analysis
#### 4.7.1 Building the Dictionary  
A dictionary of the word is needed for both of the libraries used. Like [22], we identified as many words (verbs, adjectives, and adverbs) as possible related to each emotional class. To reduce this hand-made work, we kept only the infinitive form of verbs. The result was a list of \"word: class\" pairs in a text file. Figure 3 shows a summary of the customized dictionary developed for this application. In most lexicon-based works [8; 22], the dictionary is complemented by including synonyms and antonyms in the WordNet library. We tried to do it, but unfortunately, we observed that the synonyms we found were often not adapted to education's vocabulary. As a result, when we began to remove the incorrect synonyms manually, it led to removing too many words. So, we concluded that this time-consuming work poorly improved the initial lexicon, and we decided to abandon this idea and keep only our hand-made dictionary. We also found the text2 emotion's codes interesting in handling negations. For example, we managed negation with a first function that was to delete any negation's contraction, which means replacing any \"n't\" with \"not\".  
Figure 3: Customised hand-made emotions dictionary.
# Osteoarthritis--The Role of Mesenchymal Stem Cells in Cartilage Regeneration
## 4 MSC-Based Therapy for OA
### Sources of MSCs
#### 4.2.3 UC-MSCs for OA Therapy  
Recent research has shown that umbilical cord mesenchymal stem cells (UC-MSCs) can be helpful source of therapeutic stem cells due to their advantages over BM-MSCs in terms of their capacity for proliferation, functional differentiation, and immunomodulatory effects [127; 233].  
UC-MSCs have rapid self-renewal and differentiation capabilities, which are beneficial for the modulation of immune responses and repairing damaged tissue. These cells are very easy to obtain and do not cause any discomfort during extraction [234]. UC-MSCs multiply at a pace approximately three to four times faster than that of AD-MSCs. In addition, it is common knowledge that UC-MSCs are capable of secreting several growth factors that contribute to skin renewal. These growth factors include HGF, epithelial growth factor (EGF), collagen type 1, and growth differentiation factor-11 (GDF-11) [230; 235; 236].  
MSCs can be extracted from the UC compartments such as [PERSON]'s jelly, perivascular tissue, and cord blood [237]. MSCs derived from UC can be obtained painlessly and with fewer ethical considerations.  
Prior research has demonstrated that UC-MSCs exhibit undesirable features, such as early morphological changes and a faster loss of amplification capability and diminished attachment efficiency [238; 239].  
UC-MSCs have been evaluated in several clinical trials for their efficacy in OA treatment.  
A 6-month safety and efficacy trial of UC-MSC transplantation was conducted on 36 patients with moderate or severe knee OA. On the Lysholm, WOMAC, and short form-36 (SF-36) measures, the group receiving cell therapy did better than the control group. The therapeutic potential of UC-MSCs in joint improvement was demonstrated by the fact that there was no recurrence of knee soreness throughout the follow-up period [240].  
In a separate clinical trial, three patients with knee OA were injected intra-articularly with 5-7 UC-MSCs. During a 3-month follow-up period, biochemical parameters did not alter between pre- and post-treatment in any three patients [241].  
[PERSON] et al. discovered that a UC-MSCs product was safe and effective for the regeneration of hyaline-like cartilage in knee OA after seven years of follow-up. UCB-MSCs were employed to treat knee OA with excellent clinical results and highly qualified regeneration [242].
## 2 Results and Discussion
### The Headspace Chemical Composition of the Residue (HS-SPME)
In addition, the headspace composition contained a variety of low-molecular weight aliphatic carboxylic acids and esters [acetic acid (0.1-2.0%), ethyl acetate (0.0-0.4%), propionic acid (0.1-2.1%), ethyl propionate (0.0-0.4%), butanoic acid (0.0-1.6%), isovaleric acid (0.0-0.4%), valeric acid (0.0-1.2%), hexanoic acid (0.1-4.6%), heptanoic acid (0.0-0.8%), octanoic acid (0.5-1.4%) and nonanoic acid (0.0-0.5%)]; and aliphatic alcohols and carbonyl compounds [acetone (0.3-2.8%), butan-2-one (0.0-0.6%), isoamyl alcohol (0.3-0.9%), heptan-2-one (0.0-1.0%), heptanal (0.0-0.4%), octanal (0.0-0.7%), nonan-2-one (0.0-1.3%)]. In addition, several volatile benzene derivatives were found only in the headspace such as benzaldehyde (0.6-20.4%), 2-hydroxybenzaldehyde (0.0-0.2%), 1-phenylethanone (0.0-0.7%), 2-phenylethanol (1.2-3.3%) and others (Table 1). The presence of a variety of highly-volatile aliphatic compounds with low abundance is an interesting observation since they are not present in the headspace of natural pine resin (Table 2) as well as in natural resins of other origins [5,6]. It is more likely that they were preserved within the organic residue structure as the consequence of the stored wine - the amphore's originally content. Several hundred compounds have been identified as important contributors to wine aroma [13]. These components comprise several classes of organic compounds, including esters, alcohols, organic acids, ketones, aldehydes, and monoterepne alcohols. Good results for wine esters, alcohols, and terpenes were obtained by HS-SMPME, but organic acids were not effectively analyzed [14,15]. Isobutanol, isoamyl alcohol, 2-phenylethanol and propan-1-ol are known secondary products of yeast metabolism. These compounds could be synthesized by yeast through either the anabolic pathway from glucose, or the catabolic pathway from their corresponding amino acids (valine, leucine, isoleucine and phenylalanine). Acetate esters are the result of the reaction of acetyl-CoA with alcohols formed by degradation of amino acids or carbohydrates. Any ancient ethanol would have been metabolized by  
Figure 2: Total ion chromatogram of the organic residue headspace composition obtained by HS-SPME (PDMS/DVB fiber) on HP-5 MS column. Numbers refer to Table 1.  
microorganisms. Tartaric acid, a grape-wine marker of archaeological samples [16,17], was not found, nor in the CH\({}_{2}\)Cl\({}_{2}\) solution of the sample. As an extremely water soluble compound it is thus prone to leaching and the precise method for its GC determination includes a silylation procedure [16]. Syringic acid, which is a slightly less specific biomarker for wine [18], was not detected either, but syringaldehyde was found (0.2-0.4%). Constituents of grape wine were found in other ancient samples [19] including alcohols, acids, esters, aldehydes, fatty acid derivatives, and terpenes.
## 2 Materials and Methods
### Data Collection
#### 2.2.4 Sentinel Images  
To characterize the factor \"o\"--organisms--we also used long-term Sentinel-1/2 time series and compared their effectiveness with crop rotation. Sentinel-2 carries a multispectral imager (MSI) and has been increasingly used in DSM. Besides optical images, radar data such as synthetic aperture radar (SAR) sensitive to soil moisture and ground-surface conditions may also be useful for soil pH mapping, yet it remains unexplored [42; 43]. In this study, we took advantage of the full collection of Sentinel-1/2 since the satellite constellations consistently provided dense time series. The time frames for accessing Sentinel-1 and Sentinel-2 were May 2015-December 2021 and July 2017-February 2022, respectively, and they finally produced 40 bimonthly radar and 14 seasonal optical composites. The Sentine-2 images were seasonally composited because they were frequently blocked by clouds.  
For Sentinel-1, the Level-1 ground range-detected (GRD) product was used. Besides the vertical-horizontal (VH) and vertical-vertical (VV) polarization backscattering coefficients, we also computed the cross-ratio index (VH/VV), which was more sensitive to the dynamics of land surface and the phenological development of crops [44]. For Sentinel-2, the Level-2A surface reflectance product was used. Multiple vegetation and water indices associated with soil pH were calculated, including the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), and the land surface water index (LSWI) [45]. We also incorporated three soil salinity indices (SSI, SSI2, and SSI3), considering that they can reflect the soil salinity which is closely related to the soil pH [46]. The  
Figure 2: Crop rotation main systems in 2020 (**a**) and 2021 (**b**) and the subsystems in 2020 (**c**) and 2021 (**d**). Figures (**a1**–**d1**) correspond to the zoomed-in maps for the black squares in (**a**–**d**).
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation
### Transceiver Design
#### 3.5.2 GMSK Receiver  
When the receiver receives the signal, it resamples it before passing it on to the low pass filter, and then on to demodulation, which is performed by the block \"GMSK Demod\". Its output is a bit stream (Figure 9).  
The AX.25 frame bits are encoded such that the ones represent a change in the actual data bit value, while the zeros denote that the data bit value is the same as the previous bit. Therefore, to derive the original data bits, it is necessary to use a Non-Return-to-Zero-Inverted (NRZI) decoder block. Subsequently, a descrambler is used same as shift registers in the hardware-based radio for the synchronization and clock recovery.  
In AX.25, the functions of high-level data link control (HDLC) are used. Thus, the \"HDLC Deframer\" block must discard corrupted frames via the frame check sequence (FCS).  
Finally, the resulting bit stream is framed in AX.25 by the \"HDLC to AX.25\" block before sending it to the dedicated TCP port.
# Chemicals from Brominated Flame Retardants: Analytical Methods, Occurrence, Transport and Risks
## 2 Analytical Methods for the Detection of BFRs in Environmental and Biological Samples
### Analytical Instruments  
A reliable, selective, sensitive, and rapid analytical method is vital for the accurate detection and quantification of trace environmental contaminants, including BFRs. To date, gas chromatography (GC) and liquid chromatography (LC)-based methods have been reported, and some representative analytical methods for BFR quantification are briefed in Table 1. Gas chromatography-mass spectrometry (GC-MS), gas chromatography-tandem mass spectrometry (GC-MS/MS), and liquid chromatography-tandem mass spectrometry (LC-MS/MS) are powerful instruments for structural identifications and play vital roles in BFR analysis in biological and other environmental samples. GC-MS is widely used for the analysis of BFRs due to its high sensitivity and selectivity. Sample preparation methods have been reported in previous sections following samples' collection and pretreatment, sample extraction, clean-up, and then GCMS analysis. Derivatization techniques may be employed to enhance the detection of certain BFRs. In addition, both GC and MS, combined with selective detectors, have been used for the determination of BFRs in environmental samples. Some examples include: a trace 1310 GC coupled to an ISQ(tm) single quadrupole mass spectrometer employed for the analysis of PBDEs and NBFRs ([PERSON] et al., 2020); GC-ECNI-MS ([PERSON] et al., 2020); GC-MS/MS; the GC-MS/MS (EI) method; and GC-ECD ([PERSON] et al., 2020). While various detectors have been attached to the GC during BFR analysis, most researches employing GC focused on GC-ECNI-MS combination. In the review, it was also observed that research on BFRs used GC to analyze PBDEs and NBFRs, while HBCDDs were analyzed using LC methods. This observation may be due to HBCDDs being relatively polar compounds which may degrade at high temperatures, a situation that may be observed during the GC-MS process.  
LC-MS has also been used in analyzing BFRs in biota samples. It is often preferred over gas chromatography-mass spectrometry (GC-MS) for the analysis of certain compounds like hexabromocyclododecanes (HBCDDs). A major reason as described above is the polarity and volatility of HBCDDs. Some reports have shown the application of LC and various detectors in the analysis of BFRs; some reports include LC-MS, LC-MS/MS (ESI), High-Performance Liquid Chromatography (HPLC) (HPLC-MS/MS ([PERSON] et al., 2020), and UHPLC-MS/MS. [PERSON] et al. (2017) analyze the effects of PE-MPs and TBBPA on _Rhiinella arenarum_ tadpoles at the laboratory scale. To verify the correct dosage, TBBPA concentration in the aquarium weekly were measured by direct UHPLC-MS/MS analysis. In general, these techniques offered low LOQs and good recoveries for most BFR congeners.