esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design, Simulation and Implementation for Scattering Measurement System
### Influence of Design Parameters on Measurement Results
The main reason for the small fluctuation on the simulated results was that the light source used in TracePro model was a grid point light source. When the beam diameter was changed, the grid point distribution of the light source would also change. Therefore, the position of each beam incident into the integrating sphere would change, which would have a certain fluctuation effect on the simulation results. In the actual processing of integrating sphere, considering that too large beam diameter would increase the difficulty for adjusting the incident beam position, and the too-small beam diameter would not cover the region to be measured on the coating surface, the diameter of 2 mm was selected as the parameter of the incident beam opening. Finally, the influence of the scattering rate for the inner wall of the integrating sphere on the received luminous flux had been investigated. The simulated results are shown in Figure 7d, where the scattering rate of the inner wall on the integrating sphere ranged from 90% to 99%. Because it was assumed that the inner coating of the integrating sphere followed an ideal Lambert's law scattering the simulated results would follow an exponential growth trend, with the increase of the inner coating reflectivity in integrating sphere. The results indicated that the enhancement of the coating scattering rate on the inner wall of the integrating sphere could effectively reduce the light energy loss, which had a significant impact on the accuracy improvement for the measurement results. Therefore, one should choose the integrating sphere with the largest scattering rate. In this paper, the inner surface of the integrating sphere had processed by gold-plating and sand-blasting technology. The highest internal reflectivity could be reached at 96%.
## 3 Proposed Methodology
### Parallel Genetic Algorithm to Determine the Community Structure of the Network
#### 3.2.4 Evaluation of the Effectiveness of the Detected Communities  
The effectiveness of the detected communities can be evaluated using mainly two methods: normalised mutual information (NMI) and modularity. Normalized mutual information [61] is an evaluation measure to determine the quality of the clusters formed and to determine how accurately the community detection algorithm has performed. Normalized mutual information determines the similarity of the detected communities to the existing communities in the network. It requires the groundtruth information to evaluate a community. It is predicated on a theory known as mutual information (MI), which attempts to quantify the amount of information that is shared between two distinct random variables. The NMI is a normalised variant of the MI that takes into consideration both the overall number of data points as well as the size of the clusters. Calculating the NMI requires first calculating the mutual information that exists between the two clusters and then normalising that value by the entropy that exists in each cluster:  
\[\text{NMI}(\text{X},\text{Y})=\frac{2\times\text{MI}(\text{X},\text{Y})}{\text{ H}(\text{X})+\text{H}(\text{Y})} \tag{8}\]  
where  
* X and Y are the two different clusters.
* MI(X, Y) is the mutual information between X and Y, which measures the amount of shared information.
* H(X) and H(Y) are the entropies of clusters X and Y, respectively, which measure the uncertainty or randomness in each cluster. The mutual information is given by \[\text{MI}(\text{X},\text{Y})=\sum_{i=1}^{|\text{X}|}\sum_{j=1}^{|\text{Y}|} \text{P}(i,j)\log\frac{\text{P}(i,j)}{\text{P}(i)+\text{P}^{\prime}(j)}\] (9) where
* P(i,j) is the probability of data occurring in cluster i (actual) and cluster j (predicted);
* P(i) is the probability of data occurring in cluster i (actual);
* P(j) is the probability of data occurring in cluster j (predicted). The entropy is given by \[\text{H}(\text{X})=\sum_{i=1}^{|\text{X}|}\text{P}(i)\log\text{P}(i)\] (10)\[\mathrm{H(Y)}=\sum\
olimits_{j=1}^{|Y|}\mathrm{P(j)logP(j)} \tag{11}\]  
where  
* \(\mathrm{H(X)}\) is the actual cluster assignments.
* \(\mathrm{H(Y)}\) is the actual cluster assignments.  
Another measure used to evaluate the communities detected is modularity. Modularity gives the strength of the partitions in the network. In modularity, the groundtruth information is not needed, which means that the modularity-based community detection algorithms do not require information about the real communities in order to detect communities in a network. The equation for modularity is given in Equation (4).
## 4 Discussion
Icarin possesses the osteoprotective property through the stimulating osteogenic differentiation of mesenchymal stem cells (MSCs), which related to the bone formation, and inhibiting the osteoclastogenic differentiation along with the bone resorption by osteoclasts [62; 63; 64]. Interestingly, icarin showed 64% inhibition toward Cathepsin K at the concentration of 1 mM [65]. Icarin dramatically increased the ATP level which was related to the inhibition of mitochondria apoptosis [66]. Insight into the structure, the presence of a prenyl moiety at the position 8 suggested the relevance to osteogenic activity [67], and enhanced the anti-osteoclastic activity [68]. Sagittatoside A (or icarin A) is a phytochemical compound from _Epimedii_ extract. Even a small amount recovered from extract, but sagittatoside A possess the potential effects on several tissues [61]. Treatment with sagittatoside A reported a strong osteoprotective effect, as well as icarin, in terms of increasing the proliferation and differentiation of osteoblast and suppressing osteoclastogenesis. Kaempferrin or lespedin is a glycosyllavone, constituted by two rhamn molecules bound to kaempfer at positions 3 and 7 through glycosidic linkage. This compound can be isolated from several plants and fungus. In the OVX rat, the given kaempferrin considerably improved the bone mass and microarchitecture and inhibited the osteoclastic function from in vitro assay [69]. [PERSON] et al. investigated the administration of _Podocarpium_ extract consisted of kaempferrin, the most abundant phytochemical, demonstrated the decreasing of bone resorption markers, acid phosphatase and Cathepsin K, in osteogenic cells [69; 70]. Our predictive model indicated that rutin, sagittatoside A, icarin, and kaempferrin were chosen on the basis of their high energy-based docking scores against osteoclasts on specific Cathepsin K, V-ATPase, and \(\alpha\
u\beta_{3}\) integrin receptors. We assume that these compounds might show a positive effect on the bone mechanism, particularly anti-osteoclastic activity. In particular, rutin demonstrated the highest energetically favorable interactions against three osteoclastic targets, according to molecular docking and clustering studies. Further investigations are needed to clarify the effects of each promising candidate on individual targets, especially molecular dynamics (MD), and the evaluation of anti-osteoclastic activity from in vitro and in vivo assays should be performed.
## 2 PPM Reception  
### Receiver Model  
The considered setup is shown in Figure 1. \(L\) identical synchronized branches are used to detect and demodulate the optical signal and optical amplifiers are introduced prior to detection to improve the receiver sensitivity. Each amplifier provides a gain equal to \(G\) and adds a noise component of spectral density \(N_{0}=n_{sp}\,h\,f\,(G-1)\), and optical filters at the amplifier outputs reject the out-of-band spontaneous emission noise. The optical signals are converted into electrical on photodiodes, and the electrical signals from all branches are added prior to demodulation. The demodulator estimates the optical signal energy at each respective PPM slot [6] and decides upon the transmitted symbol via soft-decision decoding.  
We now focus on acquiring the PER for one of the receiving branches, leaving the treatment of multiple branches for Section 3. Due to square-law detection on the photodiodes, the electrical signal at the photodiode output is a central \(\chi^{2}_{k,0}\) random variable (RV) with a pdf equal to [6]  
\[p_{0}\,(x;k)=\frac{x^{k-1}}{(k-1)!}\,e^{-x}\,, \tag{1}\]  
in slots where the optical signal is absent. Similarly, in the single slot that the optical signal is present, the electrical signal is a non-central \(\chi^{2}_{k,\lambda}\) RV with a pdf equal to [6]  
Figure 1: Amplification and diversity with equal-gain-combining. PPM: Pulse position modulation.  
\[p_{1}\left(x;k,\lambda\right)=e^{-\left(x+\lambda\right)}\,\left(\frac{x}{\lambda} \right)^{\frac{k-1}{2}}\,\,I_{k-1}\left(2\sqrt{x\,\lambda}\right)\,, \tag{2}\]  
where \(I_{n}(\cdot)\) denotes the modified Bessel function of the first kind. In the previous equations, \(\lambda=E/N_{0}=E_{b}/N_{0}\,\log_{2}Q\) is the optical signal-to-noise-ratio (OSNR) at the output of the amplifier and \(E=E_{b}\,\log_{2}Q\) is the symbol energy after amplification. \(k\) corresponds to the degrees of freedom of the distribution and is determined by the optical filter and the signal bandwidths. For the purposes of this work \(k\) is considered to be an integer.
# Environmental Impact of Massive Food Services and Homemade Meals: A Case Study on Steved Chickpeas
## 2 Materials and Methods
### Life Cycle Inventory
#### 2.2.2 Inventory Analysis  
In this research, the environmental impact associated with the previously described scenarios is evaluated using ReCiPe 2016 midpoint (H) to identify direct environmental impacts and ReCiPe 2016 endpoint (H/H) to assess the final consequences of these impacts on human health, biodiversity, and natural resources. The quantification and analysis of the data are supported by the databases Ecoinvent 3, Agribalsye V3.01, and Agri-footprint 5. The environmental impact assessment was conducted following various analyses, detailed below:  
* Environmental impact by process for each scenario: Different processes involved in preparing the selected dish were identified and defined. The considered processes include ingredient production, meal preparation, utensil washing, waste disposal, waste transport, and water treatment. This analysis enables a detailed assessment of the environmental impact generated by each phase of the preparation process.
* Environmental impact of ingredients for each scenario: An analysis of the ingredients used in preparing the selected dish. This analysis aims to quantify and compare the environmental impact of each ingredient.
* Environmental impact of resources and waste: Analysis of the consumption of essential resources (electricity, water, and gas), as well as waste management practices (watertreatment, disposal, and waste transport). This analysis enables the identification of areas for improvement in culinary preparation.  
To contextualize the findings, the study compares its results with existing LCA literature on food systems, focusing on the environmental impacts of massive food services versus homemade meals. Key processes, such as refrigeration, reheating, and electricity use, were benchmarked against studies [5, 27], which highlight the role of energy-intensive stages in food preparation. While the focus on stewed chickpeas ensures a culturally relevant case study, the methodology can be extended to other plant-based dishes (e.g., lentil stew bean soup) to explore diverse dietary patterns. Additionally, although not directly included in the LCA, consumer behavior (e.g., portion control, efficient appliance use) and institutional policies (e.g., organic sourcing, energy-efficient equipment) were considered to provide broader insights into sustainability. These factors, discussed in Section 3.3, highlight opportunities for reducing environmental impacts through optimized practices in both massive food services and home cooking, guiding future interdisciplinary research.
# Intelligent TCP Congestion Control Policy Optimization
## 4 Experiment  
### Experimental Environment  
#### 4.1.1 Hardware and Software Environment  
The test was performed on a powerful server, and the specific configuration is as follows: 1 CPU, AMD Ryzen 7 5800H with Radeon Graphics @3.19 GHz; 2 Memory, 16 GB DDR4; 3 GPU, NVIDIA GeForce RTX 3060 Laptop 6 GB; and 4 Operating System, Windows 11.  
The data space topology is simulated by the Mininet simulator, and the TCP-PPO\({}_{2}\) and TCP-CUBIC algorithms are implemented, which are compared with the traditional TCP congestion control algorithms.
## 4 Chitosan in Nanoscience  
Nanobiomaterials have been recently used to transport and release the drugs in the target site owing to their possible degradation in the biological system. They are accustomed to switch and handle drugs that are critical for the furtherance of human health and developing the quality of life. In recent years, chitosan-based nanomaterials have been paid weighty attention and used in different biomedical arenas. For example, chitosan-based nanoparticles, scaffolds, microfluidics, lab on chip, and organ on chip, have been established and widely used for various biomedical applications [15].  
Several drugs and polyphenols encapsulated inside chitosan moieties were synthesized such as catechin and epigallocatechin [16], tamoxifen [17], alendronate sodium [18], insulin [19] and peptide [20]. These nanoparticles are delivered in severalapproachesincluding injection drug delivery [21], topical drug delivery [22], colon-targeted drug delivery [23], carcinoma therapy [24] and gene delivery [25]. They are further administrated as oral delivery, injectable delivery, or in cream like form (See Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Morphology** & **The Role Chitosan Played** & **Preparation Method** & **Application** \\ \hline \multirow{6}{*}{Nanogels} & Chitosan-carbon dot hybrid nanogels & Covalent cross-linking & Photothermal–chemo therapy \\ \cline{2-4}  & pH responsive encalyptus oil coated & & \\  & double walled biodegradable nanogels & & \\ \cline{2-4}  & PEGylated and fluorinated chitosan & & \\  & nanogel & & \\ \cline{2-4}  & Reversible swelling-shrinking nanogel & Covalent modification/cross-linking & Character of deep tumor \\ \cline{2-4}  & Chitosan-based pH-sensitive polymeric micelles & & \\  & pH-responsive aerobic micelles & & \\ \cline{2-4}  & Chitosan-pluronic micelles & & \\ \cline{2-4}  & Multifunctional nanoparticles & Covalent modification/self-assembly & Targeted photothermal \\ \cline{2-4}  & Chitosan grafted methoxy & & \\  & poly(ethylene glycol)-poly(\(\epsilon\)-caprolactone) & & \\ \hline \multirow{2}{*}{Nanofibers} & Biomimetic mineralization of carboxymethyl chitosan nanofibers & Electrospinning process & Improve osteogenic activity \\ \cline{2-4}  & Arginine-modified nanostructured lipid carriers & Covalent modification/self-assembly & Anticancer drug delivery \\ \cline{2-4}  & Glycosaminoglycan modified chitosan & & \\  & liposome & & \\ \cline{2-4}  & Aptamer-modified liposomal & Covalent modification/other & Reverse drug resistance in \\  & complexes & processing & lung cancer \\ \cline{2-4}  & Gold nanoshell-coated liposomes & Covalent modification/electrostatic & Photothermal and \\  & & adsorption & chemotherapy \\ \cline{2-4}  & Glycol chitosan-coated liposomes & Covalent modification/self-assembly & pH-responsive drug-delivery \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chitosan assembling structure and its role in drug delivery system (adapted from [10]).
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
## 6 Modes of Fermentation Process
### Batch Mode  
Batch operation mode is the simplest and easiest process since it requires low cost, low labor work, less control, as well as easy sterilization and preparation of feedstock [103]. Thus, it is usually preferred due to the practicability of recovery, which needs no remaining substrate in the fermentation broth [115]. Many works on terpenoid biosynthesis by engineered _S. cerevisiae_ strains were performed in batch fermentation, mainly in flask assays. For instance, monoterpenes, such as limonene [80; 81], geraniol [88], and sabinene [70] were produced in shake flask fermentations by engineered _S. cerevisiae_ strains in the range between 0.49 and 17.5 mg/L (Table 3). Moreover, a biosynthetic alternative to D2 diesel (gasoil), sesquiterpene bisabolone, was accumulated by more than 900 mg/L in shake flask fermentation [66]. More recently, other sesquiterpenes, nerolidol [19] and the sedative valerenic acid [82], and polpunonic acid [20], the precursor of the anti-obesity agent diterpene celastrol, have been produced in the range between 1.4 and 336.5 mg/L by highly engineered strains in shake flask fermentations. Concerning batch fermentations in a bioreactor, one example was the production of (S)-linalool, which was accumulated in a tank at 0.26 mg/L [68].  
Interestingly, immobilized cell method (ICM) was employed for improving production of some terpenes in flask assays. Immobilized cells prepared by agarose entrapment and free cells (FCM) of _S. cerevisiae_ were compared in shake flask fermentations for citronellol production [116]. It was observed that ICM yielded the main product, citronellol (48.5%), whereas FCM accumulated more products along with citronellol, which just yielded 24.2% and 27.2% at incubation time of 6 and 15 days, respectively [116]. Higher citronellol production by ICM was explained by possible improvement of specific metabolic activities [117].  
Although batch fermentation has several advantages, as explained above, some limitations also exist. The main disadvantage is the growth inhibition of microorganism due to product toxicity, especially in the production of monoterenes. This problem can only be overcome by continuous removal of the inhibitor product (e.g., limonene) from culture during fermentation, for example, by a two-phase system or by headspace removal [54]. Alternatively, fed-batch fermentation with daily broth draw can be employed.
## 3 Results and Discussion
### Volatile Aroma in Wine  
Chemical characterization of wine volatiles was carried out by HS-SPME coupled with GC-MS analysis. The optimization of the two variables, extraction time and extraction temperature, for the fractionation of headspace wine volatile compounds was performed. For this purpose, a multivariate approach (fractional factorial design) was carried out (see supporting information, Table S1 and Figure S1), and a set of 13 extraction experiments performed. The ranges considered for the experiments were 40-60 \({}^{\circ}\)C for the temperature and 30-40 min for the extraction time, in accordance with the literature data [40; 41]. The ratio between the areas of target compound and the area of internal standard was considered. The sum of these ratios for each class of compound (esters, alcohols, acids, and terpenes), analyzed using a linear model, generated four response surface areas. The analysis of these four models (Figure S1) revealed that the extraction time in the range considered had less effect with respect to the temperature for three of the four classes. This evidence led us to select an extraction time of 30 min and an extraction temperature of 60 \({}^{\circ}\)C.  
The qualitative chemical characterization of wine volatiles (Table 5) showed that the headspace of Cannonau wines was homogenous among the samples analyzed. In fact, the general VOC fingerprint was very similar for all the samples studied. The GC-MS analysis revealed the presence of 37 main components and, in addition to several parietal compounds already detected in Cannonau grape samples, several volatiles that arise from yeast metabolism were identified. Limonene, hexanol, citroncellol, and other compounds can all be classified as varietal compounds, whereas the two isomers of methyl-butanol and the ethyl esters of fatty acids are commonly linked to yeast metabolism. The volatile esters produced by yeasts are commonly classified as acetate esters and medium-chain fatty acid esters. Both these categories are well represented in Cannonau wines. A comparison of our data with the results of [PERSON] et al. [24] revealed some similarities among the classical compounds detected in wines. Beside the typical ethyl esters of fatty acids, the varietal compounds limonene, 2-ethyl-1-hexanol, benzaldehyde and benzyl alcohol were also detected in Cannonau samples from Jerzu [24]. The quantitative data, obtained using calibration curves of pure standards (Table S2), showed a predominance of yeast-derived compounds in the headspace (Table 5). Of all the quantified compounds, the two isomers of methyl butanol (mean value 132,196 ug L\({}^{-1}\), obtained by summing all the samples) and 2-phenylethanol (mean value 56,402 ug L\({}^{-1}\), as above) were the main volatiles in the headspace. In the classical Erlich pathway, these components arise from amino acids leucine/isoleucine for methyl butanol isomers and from phenylalanine for 2-phenyl ethanol; their sensory attributes are banana/fruity and rose/flower, respectively.
## 4 Results and Discussions  
### Measured Flame Describing Function  
Figure 3 shows the gain and phase-lag of the FDF under different excitation amplitudes obtained through PMT. The gain and phase-lag exhibit distinct changes in response to different amplitudes of driving force, with respect to frequency. This indicates the nonlinear response of the flame. The gain exhibits a typical low-pass filter feature. When the frequency exceeds 80 Hz, the gain consistently remains smaller than 0.25. The gain at \(f_{e}=10\) Hz is  
Figure 2: Schematic diagram of flame length and the height of COM. \(L_{f}\) represents the flame length, while \(P_{fg}\) represents the height of COM.  
significantly larger than the gain at other frequencies. A local gain peak is observed near 30-35 Hz, and a local gain valley appears at 15-20 Hz under most excitation amplitudes. The saturation effects of the excitation amplitudes are not observed, and the gain increases trendily as the excitation amplitudes increase, especially for relatively higher frequencies of \(f_{e}\) = 45-80 Hz. The phase-lag evolution with frequency varies significantly under different excitation amplitudes, particularly when the frequency is below 25 Hz. Specifically, at an excitation frequency of 20 Hz, the phase-lag is close to 2\(\pi\) when the excitation amplitude is below 0.20. However, if the excitation amplitude is greater than 0.20, the phase-lag equals approximately 0.5\(\pi\). The overall trend of phase-lag variation with excitation frequency is almost linear when the excitation frequency exceeds 25 Hz, with a slight dip near 50 Hz, followed by an increase. Notably, the frequencies corresponding to the anomalous phase-lag change (15 Hz-20 Hz and 45 Hz-50 Hz) are close to the frequencies associated with the valleys of the gain. In previous studies [48; 49], this anomalous variation of phase-lag, which corresponds to the frequency band where the gain valley occurs, has been observed. In the following sections, we will elucidate these non-linear phenomena by analyzing the dynamic changes in flames.
## 3 Materials and Methods
### System Model and Problem Formulation
The MAC and physical layers of the DSRC layer for vehicle communication use the IEEE 802.11p standard. Additionally, the IEEE 802.11p standard is capable of supporting eight distinct data speeds based on the orthogonal frequency division multiplexing (OFDM) technique [31].