esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### MS of Coal Processed with VESFFs
The ratios of the peak areas of the D\({}_{1}\) and G peaks (A\({}_{\text{D1}}\)/A\({}_{\text{G}}\)) for the TS coal processed with VESFFs B, B\({}_{2}\), and C were all greater than those for the coal processed with deionized water, decreasing in the following order: B\({}_{2}>\) C \(>\) B. This result indicates that these three VESFFs destroyed the MS of coal, and the VESFFs composed of VESs with a longer hydrocarbon chain length and cationic-zwitterionic VESs more severely damaged the MS of the coal. As illustrated in Figure 11, the peak area ratios of the D\({}_{1}\) and G peaks (A\({}_{\text{D1}}\)/A\({}_{\text{G}}\)) in the NC coal processed with VESFFs C and D were notably lower than those of the coal processed with deionized water, with VESFF D demonstrating the most pronounced impact. This result suggests that VESFFs C and D enhance the aromatic layer extension (\(L_{a}\)) within the aromatic crystal nuclei of NC coal, thereby enlarging the micropore volume of the coal. Conversely, VESFFs B and B\({}_{2}\) were detrimental to the increase in aromatic layer extension (\(L_{a}\)) within the aromatic crystal nuclei of the NC coal. In Raman spectroscopy, the full width at half maximum of the D peak (FWHM-D) and the full width at half maximum of the G peak (FWHM-G) are defined as the spectral line widths at half the maximum peak heights of the D\({}_{1}\) and G peaks, respectively [48; 49]. Greater sharpness of the D and G peaks, particularly the G peak, indicates an increase in the size and order of the graphite microcrystals in the coal [50]. As illustrated in Figure 12, the FWHM-G of VESFF D acting on the coal from two regions exhibited a reduction, indicating that the spacing (\(d_{002}\)) between the aromatic ring layers of the macromolecules in the coal processed with VESFF D diminished, and the micropore volume increased. With regard to the TS coal, the sensitivity of the VESFF to the FWHM-G was found to decrease in the following order: D \(>\) C \(>\) B\({}_{2}>\) B. Furthermore, in comparison with the coal processed with deionized water, the interlayer spacing (\(d_{002}\)) of the macro-molecular aromatic rings exhibited a reduction. This result indicates that VESFFs B, B\({}_{2}\), C, and D increased the micropore volume of the TS coal. A comparison of the interlayer spacing (\(d_{002}\)) of the macromolecular aromatic rings in the NC coal processed with VESFF B with that of the coal processed with deionized water revealed a slight reduction in the former. The interlayer spacing (\(d_{002}\)) of the macromolecular aromatic rings in the coal processed with VESFFs B\({}_{2}\) and C was increased, indicating that the VESs containing longer hydrocarbon chains were not conducive to increasing the micropores in the NC coal. Figure 11: Peak surface ratio of coal samples processed with FFs.
# Stochastic Game Analysis of Cooperation and Selfishness in a Random Access Mechanism
## 2 Related Work  
Slotted ALOHA is one of the most widely used random access schemes. Nowadays, it is implemented in many technologies, e.g., satellite networks [15], LoRaWAN networks [19; 20; 21], IoT applications [2], Machine-to-Machine (M2M) communications [3], and NOMA for the Next-Generation IoT [22].
## 3 Results
As a result, the total annual electricity demand to propel the LDV fleet is estimated to be 1.71 TWhe (half of the current overall grid demand in the island), similar to the 1.5 TWhe figure projected by the government in [45]. The dump charging profile (applied in previous works [17]) was extracted from the mobility strategy supporting PTECan [45]. * The PTECan hypothesis relies on the H\({}_{2}\) use as a primary fuel for heavy-duty (HD) transportation through fuel cells. Unlike LDVs, the technology selection for HD transportation is challenging due to the longer distance, power demands, and the wide range of business cases. Therefore, current OEMs' roadmaps are considering BEVs, FCEVs, or ICEs fuelled with renewable fuels such as H\({}_{2}\), biofuels, or e-fuels [65]. In this work, a combination of BEV and FCEV technologies to propel the HDV fleet was considered. Different shares were evaluated with the aim of providing trends of the energy system response to cover the resulted demand in terms of electricity and H\({}_{2}\) (produced internally with electrolysers). Similar to the LDV fleet, the fossil fuel demand was shifted to electricity and H\({}_{2}\), applying the following expressions: \[Elec_{HDV}=(E_{D}\eta_{ICE_{D}})\chi_{EV}\frac{1}{\eta_{Char}\eta_{EV}\eta_{Trans}}\] (2) \[H_{2 HDV}=(E_{D}\eta_{ICE_{D}})\chi_{H_{2}}\frac{1}{\eta_{FCEV}}\] (3) where \(Elec_{HDV}\) and \(H_{2 HDV}\) are the electric and H\({}_{2}\) demand of the island's heavy-duty fleet, \(E_{D}\) is the current diesel energy demand, and \(\eta_{ICE_{HD}}\) is the average HD diesel ICE efficiency. \(\eta_{EV}\), \(\eta_{Charging}\), \(\eta_{trans}\), and \(\eta_{FCEV}\) correspond to the average electric motor efficiency, the charging efficiency, the grid transportation efficiency, and fuel cell efficiency, respectively. Finally, \(\chi_{EV}\) and \(\chi_{H_{2}}\) correspond to the demand share of electricity and H\({}_{2}\), respectively. Due to the nature of the HD service, the majority of the fleet will be charged overnight. Therefore, only a dump charging strategy was used. * The required H\({}_{2}\) for both power-generation and transport would be generated by electrolysers. Rather than imposing a predefined operation by the user, EnergyPLAN switches on the electrolysers based on the H\({}_{2}\) demand and the availability of renewable energy. The electrolyser capacity required to cover the H\({}_{2}\) demand will be a simulation output rather than imposed by the user. The previously mentioned hypotheses required several efficiency parameters to perform the different energy conversions that are fed later to EnergyPLAN. Table 5 includes the efficiencies of the H\({}_{2}\) route (Power-to-H\({}_{2}\)-to-Power) and the energy storage charging/discharging processes. The information is completed with the efficiency figures applied to shift from the internal combustion engine-based propulsion demand to both electricity (BEVs) and H\({}_{2}\) (FCEVs).
## 3 Results and Discussion  
### Difference of Overall Flavor in Six Citrus EOs Based on Sensory Analysis and e-Nose Analysis  
A sensory panel evaluated the flavor of six citrus EOs, which were described as fruity, sweet, floral, green, fatty and woody (Figure 1A,B). Notably, significant differences were observed between two varieties of citrus EOs, with HY EO presenting a stronger fatty, green and woody odor, while GF EO having a stronger fruity, sweet and floral aroma. The comparison of different extraction methods showed that the EOs extracted by CP and MADH had a more intense flavor than those extracted by SCC. This was explained by the fact that the first two were pure essential oils, whereas the latter was hydrate containing little oil. Additionally, the characteristic aroma of the CP EO was more prominent among three methods. Therefore, for subsequent GC-O experiments, CP EOs were chosen for further analysis.  
Figure 1: Difference of overall flavor in six citrus EOs. Sensory analysis of HY EOs (**A**) and GF EOs (**B**); the e-nose radar map of HY EOs (**C**) and GF EOs (**D**); PCA (**E**) and DFA (**F**) of six citrus EOs based on e-nose data. EOs, essential oils; HY, Huyou; GF, grapefruit; CP, cold pressing; MADH, microwave-assisted hydrodistillation; SCC, spinning cone column; PCA, principal component analysis; DFA, discriminant factor analysis.  
Further, the Heracles Neo e-nose was also applied to distinguish the flavor profiles of six EOs. The radar map (Figure 1C,D) of e-nose analysis reflected the intensity and abundance of odor, which was plotted based on the peak area and retention time. The results showed that the chromatograms of CP and MADH EOs were similar, but differentiated from those of SCC extracts greatly. For a better visualization of the data, the three-dimensional plots of PCA (Figure 1E) and DFA (Figure 1F) were created to differentiate between different EO samples. PCA is an unsupervised method for reducing data dimensionality while retaining as much original information as possible [27]. The cumulative variance contribution rate of PC1, PC2 and PC3 exceeded 99.999%, indicating that the PCA model was strongly effective to explain the total variance. The samples distribution in each group were closely clustered, indicating good repeatability. The distance between the GF_SCC and HY_SCC groups was relatively close, suggesting the minor aroma difference between the two samples. Apart from that, the region division of other EO samples was relatively obvious.  
In contrast to PCA, DFA is a supervised pattern recognition method, which increases the between-class variance and reduces the within-class variance, resulting in better discrimination in different sample groups [27]. The total variance was 98.809% (PC1 = 91.503%, PC2 = 4.687% and PC3 = 2.619%), revealing that the EOs from different citrus varieties and extraction methods clearly had distinct regional distribution characteristics. The DFA results were consistent with those of PCA, which further verified the reliability of PCA. In conclusion, the Heracles NEO e-nose can be an excellent discriminator to distinguish the flavor of citrus EOs.
# Carbon Mineralization in a Soil Amended with Sewage Sludge-Derived Biochar
## 3 Results and Discussion
### Relationship between C Mineralization and Available N in Soil Amended with Biochar  
A principal component analysis (PCA) was conducted to determine the relationships between the SS physicochemical properties and the SS-derived biochar's CCE and N-NO3\({}^{-}\), N-NH4\({}^{+}\) contents after applying it to soil. The following properties were used for PCA: total C (C) and total N (N), H, C/N ratio, H/C ratio, pH, N-NO3\({}^{-}\), N-NH4\({}^{+}\), specific surface area (SSA), pore volume (PV), and CCE (Figure 5). In general, along the first PC axis (PC1), a clear separation between two groups was observed: SS and BC300 (group 1) and BC400 and BC500 (group 2), as indicated in Figure 5. By means of PCA, it was possible to demonstrate that the higher values of CCE presented by the SS and the SS-derived biochar produced at 300 \({}^{\circ}\)C (BC300) were related to their following properties: N, C, H, and H/C ratio. Therefore, these materials increased soil N-NO3\({}^{-}\) and N-NH4\({}^{+}\) availability compared to BC400 and BC500. On the other hand, increasing pyrolysis temperatures were associated with losses of organic matter and N, a decrease of H/C, and higher pH, C/N, PV, and SSA. Thus, BC400 and BC500, after 127 days of incubation, showed low soil N (NO3\({}^{-}\), NH4\({}^{+}\)) availability. In general, the greatest contrast (highest PC1 score variations) was observed among SS and BC300 compared to BC500.  
Overall, multivariate analysis (PCA) enabled a combined evaluation of all the studied variables, thus showing the properties of SS-derived biochars that were mostly influenced at different pyrolysis temperatures. Among such properties, five variables presented a correlation coefficient higher than 0.9 with PC1 (Table 4). Total N, C/N, H, PV, and SSA were the variables that were mostly affected by the increase in pyrolysis temperature. Therefore, these properties were the ones underpinning the changes in CCE and N availability observed when different materials were added to the soil. Our work can  
Figure 4: Soil ammonium (N-NH4\({}^{+}\)) contents after incubation (127 days) with sewage sludge (S + SS), sewage sludge-derived biochars produced under different pyrolysis temperatures of 300 \({}^{\circ}\)C (S + BC300), 400 \({}^{\circ}\)C (S + BC400), and 500 \({}^{\circ}\)C (S + BC500) and soil (S - control). Means followed by the same letters do not differ by the Tukey test (\(P<0.05\)). Vertical bars represent the standard errors.  
provide guidance to select the most appropriate pyrolysis temperature and to future studies on SS biochar production for agro-environmental purposes.
# RNase L Induces Expression of A Novel Serine/Threonine Protein Kinase, DRAK1, to Promote Apoptosis
## 4 Materials and Methods
### Cell Culture and Transfections  
Prostate cancer cells PC3 and DU145 were grown in RPMI 1640 supplemented with streptomycin (100 ug/mL), penicillin (100 units/mL), 2 mmol/L glutamine, and 10% fetal bovine serum (Sigma-Aldrich). The human fibrosarcoma cell line, HT1080 (a gift from [PERSON], Cleveland Clinic, Cleveland, OH, USA), U3A (STAT1-defective, derived from parental 2 fTGH cells, which were derived from HT1080 cells (a gift from [PERSON], Cleveland Clinic, Cleveland, OH, USA) and 293T, were cultured in Dulbecco's modified minimal essential medium with 10% fetal bovine serum (Sigma-Aldrich), 100 ug/mL penicillin/streptomycin, 2 mM L-glutamine, and non-essential amino acids. Cells were maintained in 95% air, 5% CO\({}_{2}\) at 37\({}^{\circ}\)C. Transfection of 2-5A (10 uM) was performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer's protocol. Briefly, cells were plated one day before transfection, so that the cells are 80-90% confluent at the time of transfection. 2-5A was diluted into serum-free media and then mixed with Lipofectamine 2000 for 20 min before being added to cells in growth media. Poly I:C (2 ug/mL), plasmids were transfected into cells using Polyjet reagent (SigmaGen Laboratories, Gaithersburg, MD, USA). For experiments involving inhibitors, cells were transfected with DRAK1-GFP plasmid in 10 cm culture dishes, then six hours later the inhibitors were added. Transfection of siRNA in PC3 cells were performed using Lipofectamine 2000 reagent (Invitrogen, Carlsbad, CA, USA), according to the manufacturer's protocols. DRAK1 and DRAK2 targeting siRNA and control siRNA were obtained from Santa Cruz Biotechnology. Knock-down of endogenous DRAK1 or DRAK2 was determined by western blotting.  
Construction of RNase L gene knockout cells: The HT1080 RNase L KO cells were generated by using the CRISPR Cas9 system (Available online: [[https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design](https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design)]([https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design](https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design))). The sgRNA sequences were: RNL F CACCGTTATCCTCGCAGCG ATTGCG and RNL R AAACCGCAATCGCTGCAGGAATAC targeting exon 1. The guide RNA sequences were synthesized as DNA oligonucleotides and annealed, phosphorylated and ligated into the vector pSpCas9(BB)-2A-Puro (PX459) V2.0 (a gift from [PERSON] (Addgene plasmid # 62988) that was prepared by digestion with BsmBI [42]). The resulting plasmids were used to transfect HT1080 cells and selected in puromycin. Clones were obtained by limiting dilution and screened for knockout of RNase L expression by immunoblotting. Knockout cell lines were expanded and tested for loss of RNase L enzyme activity following 2-5A transfection and analysis of RNA cleavage using total RNA as previously described [15]
_applied sciences_  
_Article_  
Effects of Minimalist Footwear and Foot Strike Pattern on Plantar Pressure during a Prolonged Running  
**[PERSON] 1**, [PERSON] 2,3(r), [PERSON] 2(r),  
**[PERSON] 2(r)**, [PERSON] 1(r) and [PERSON] 2,*(r)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/article/pdf/](https://www.mdpi.com/journals/article/pdf/)]([https://www.mdpi.com/journals/article/pdf/](https://www.mdpi.com/journals/article/pdf/))  
Footnote 3: [[https://www.mdpi.com/journals/article/pdf/](https://www.mdpi.com/journals/article/pdf/)]([https://www.mdpi.com/journals/article/pdf/](https://www.mdpi.com/journals/article/pdf/))  
Footnote 4: [[https://www.mdpi.com/journals/article/pdf/](https://www.mdpi.com/journals/article/pdf/)]([https://www.mdpi.com/journals/article/pdf/](https://www.mdpi.com/journals/article/pdf/))  
Footnote  
and in most cases forcing the runner to change his or her running pattern [22]. Despite the observed changes in muscle demand, the use of MS does not affect dynamic stability [23]. There exists some controversy as to whether the use of MS can reduce the risk of injury in running [24; 25]. In this sense, the most frequent injuries reported in the literature during MR are located in the posterior leg musculature, such as Achilles tendonitis [25], and in the foot, such as plantar fascia rupture, bone marrow oedema or metatarsal stress fracture due to repetitive microtrauma and increased plantar loading and pressures [26; 27; 28].  
In relation to plantar pressure, although the literature is scarce in the analysis of MR, some studies have reported an increase in pressure in the forefoot area [9; 29; 30; 31; 32]. Another factor to consider with an impact on plantar pressure is the influence of prolonged running, which can lead to pressure redistribution under CS conditions [33; 34]. Other significant effects of prolonged running include increased contact time, reduced running economy [35], increased frequency and decreased stride length [36].  
There are few studies that analyse the effect of plantar pressure combining both factors: minimalist shoes and prolonged running [36; 37]. In addition, most research with MS does not consider other important factors such as the type and/or pattern of foot strike (forefoot, rearfoot, midfoot), or the runners' previous experience with this type of shoe [8; 9; 29; 30; 31; 32; 36; 37; 38; 39]. Therefore, the aim of this study was to analyse the changes produced using MS on plantar pressure during a prolonged running, as well as its interaction with the time and foot strike pattern. The hypotheses posed were: (I) the use of MS will induce higher pressure values (mean and peak) compared to CS, regardless of the foot strike pattern; (II) the use of MS will lead to increased pressure during forefoot support; (III) at the end of the run, pressure values will be higher with MS compared to CS.
## 2 Materials and Methods  
### Samples Collection  
Table 1 reports the Identification (IDs) and the Global Positioning System (GPS) coordinates of the sampling sites (fifty samples of pyroclastic products in total, five for each site).  
Figure 1 shows the map of the sampling area, with the site IDs (1-10) indicated.  
Pyroclastic products were collected according to [22]. In particular, in the ID 1, 4, 5, 7 and 9 sites, freshly erupted air-fall pyroclastic products were sampled, while in the remaining ID 2, 3, 6, 8 and 10 sites, samples from heaps of pyroclastic wastes from previous volcanic activities were collected [22]. The samples were stored into labeled plastic vials, with adequate caution taken in order to prevent their contamination.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Site ID**} & \multicolumn{2}{c}{**GPS Coordinates**} \\ \cline{2-3}  & **Latitude** & **Longitude** \\ \hline
1 & 37.68864 & 15.09499 \\
2 & 37.64975 & 15.05663 \\
3 & 37.71456 & 15.11615 \\
4 & 37.68809 & 15.13123 \\
5 & 37.61574 & 15.09846 \\
6 & 37.57984 & 15.0326 \\
7 & 37.61873 & 15.05600 \\
8 & 37.69899 & 15.00120 \\
9 & 37.65249 & 15.09540 \\
10 & 37.69900 & 15.05389 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Identification (IDs) and the Global Positioning System (GPS) latitude and longitude coordinates of the investigated pyroclastic products sampling sites.
## 2 System Model of Downlink FDD MMIMO
The received signal at the \(k\)th user, which has been defined previously in Equation (1), can be written as  
\[z_{k}=\sqrt{\rho_{\mathrm{d}}\varpi}\mathbb{E}[\mathbf{h}_{k}^{\mathrm{H}} \mathbf{v}_{k}]a_{k}+\sqrt{\rho_{\mathrm{d}}\varpi}\big{(}\mathbf{h}_{k}^{ \mathrm{H}}\mathbf{v}_{k}-\mathbb{E}[\mathbf{h}_{k}^{\mathrm{H}}\mathbf{v}_{k }]\big{)}a_{k}+\sqrt{\rho_{\mathrm{d}}\varpi}\mathbf{h}_{k}^{\mathrm{H}}\sum _{i\
eq k}^{K}\mathbf{v}_{i}a_{i}+n_{k}. \tag{7}\]  
The DASR can be expressed as  
\[\mathrm{ASR}=\left(\frac{\tau_{\mathrm{c}}-\tau_{\mathrm{tr}}}{\tau_{\mathrm{c }}}\right)\sum_{k=1}^{K}\log_{2}\!\left(1+\mathrm{SINR}_{k}\right), \tag{8}\]  
where the received signal \(\mathrm{SINR}_{k}\), by taking into account (7), can be written as [11, 62]  
\[z_{k}=\sqrt{\rho_{\mathrm{d}}}\,\mathbf{h}_{k}^{\mathrm{H}}\mathbf{x}+n_{k}, \tag{1}\]0  
where the nominator denotes the desired signal power, and the denominator represents the interference introduced by other users and the noise introduced at the user side that affects the received signal. Further details about the derivation of expression Equation (9) can be found in [62]. Furthermore, various channel realizations are used to calculate the expectations in Equation (9); these realizations are performed independently using massive Monte Carlo simulations. This is often considered to be a computationally intensive operation, because it is necessary to evaluate the SINR and the DASR for a range of values of \(N\), where \(N\gg 1\). However, the theory of the random matrix yields computationally realistic solutions for the SINR and the DASR, which are developed here to find an analytical solution for the SINR with RZFP. The downlink channel estimation, power allocation optimization procedure, and the problem formulation of the DASR in MMIMO systems with the FDD mode are provided in the following section.
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Reinforcement Learning Integration (Same as Previous Work)
#### 3.5.3 Iterative Learning Process  
The RL algorithm proceeds iteratively, adapting the broadcasting strategy over time to optimize performance metrics such as Coverage, Overhead, and Delay. The steps are as follows:  
1. Initialization : Assign initial values to \(Q(s,a)\) for all state-action pairs.
2. Simulation loop: 1. Observe the current network state \(s_{t}\), which is determined by the fuzzy state representation under the active profile. 2. Select an action \(a_{t}\in\{\texttt{BC},\texttt{DNB}\}\) using the \(\epsilon\)-greedy policy. 3. Execute \(a_{t}\), observe the new state \(s_{t+1}\), and compute the reward \(R_{t+1}\) using (4). 4. Update \(Q(s_{t},a_{t})\) using the selected RL method: * TD learning: \[Q(s_{t},a_{t})\gets Q(s_{t},a_{t})+\alpha\Big{[}R_{t+1}+\gamma\max_{a^{ \prime}}Q(s_{t+1},a^{\prime})-Q(s_{t},a_{t})\Big{]},\] where \(\alpha\) is the learning rate and \(\gamma\) the discount factor. * MC methods: \[Q(s,a)\gets Q(s,a)+\alpha\Big{[}G_{t}-Q(s,a)\Big{]},\] where \(G_{t}\) is the total return computed as the sum of future rewards: \[G_{t}=\sum_{k=0}^{T-t-1}\gamma^{k}R_{t+k+1}.\]
3. Convergence: The agent continues iterating until the Q-values stabilize, indicating that an optimal BC/DNB policy has been learned. This policy minimizes redundant broadcasts while ensuring sufficient network coverage.  
The iterative process ensures that the RL agent dynamically adjusts the BC/DNB ratio based on real-time network conditions. By considering multiple parameter profiles, the fuzzy logic system enables the RL agent to operate across a broader range of scenarios, while the core logic for policy derivation remains consistent across all profiles.