esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analytical Methods
### LA-ICP-MS Analysis of Phlogopite  
In situ trace element analysis of the phlogopite was carried out using thin sections on epoxy mounts using a laser ablation inductively coupled mass spectrometer (LA-ICP-MS) at Nanjing Hongchuang Geological Exploration Technology Service Co., Ltd. The Resolution SE model laser ablation system (Applied Spectra, West Sacramento, USA) was equipped with an ATL (ATLEX 300) excimer laser and a Two Volume S155 ablation cell. The laser ablation system was coupled to an Agilent 7900 ICPMS (Agilent, Santa Clara, CA, USA).  
LA-ICP-MS tuning was performed using a 50-micron diameter line scan at 3 \(\upmu\)m/s on NIST 612 at \(\sim\)3.5 J/cm\({}^{2}\) with a repetition rate of 10 Hz. The gas flow was adjusted to obtain the highest sensitivity (\({}^{238}\)U\(\sim\)6 \(\times\) 105 cps) and the lowest oxide ratio (ThO/Th \(<\) 0.2%). P/A calibration was conducted on NIST 610 using a 100-micron diameter line scan. The other laser parameters are the same as those used for tuning. The masses analyzed were \({}^{7}\)Li, \({}^{9}\)Be, \({}^{11}\)B, \({}^{23}\)Na, \({}^{25}\)Mg, \({}^{27}\)Al, \({}^{29}\)Si, \({}^{31}\)P, \({}^{34}\)S, \({}^{39}\)K, \({}^{43}\)Ca, \({}^{45}\)Sc, \({}^{49}\)Ti, \({}^{51}\)V, \({}^{53}\)Cr, \({}^{55}\)Mn, \({}^{57}\)Fe, \({}^{59}\)Co, \({}^{60}\)Ni, \({}^{63}\)Cu, \({}^{66}\)Zn, \({}^{71}\)Ga, \({}^{73}\)Ge, \({}^{75}\)As, \({}^{85}\)Rb, \({}^{88}\)Sr, \({}^{89}\)Y, \({}^{90}\)Zr, \({}^{93}\)Nb, \({}^{95}\)Mo, \({}^{11}\)Cd, \({}^{115}\)In, \({}^{118}\)Sn, \({}^{121}\)Sb, \({}^{133}\)Cs, \({}^{137}\)Ba, \({}^{139}\)La, \({}^{140}\)Ce, \({}^{141}\)Pr, \({}^{146}\)Nd, \({}^{147}\)Sm, \({}^{151}\)Eu, \({}^{157}\)Gd, \({}^{159}\)Tb, \({}^{163}\)Dy, \({}^{165}\)Ho, \({}^{166}\)Er, \({}^{169}\)Tm, \({}^{173}\)Yb, \({}^{175}\)Lu, \({}^{178}\)Hf, \({}^{181}\)Ta, \({}^{182}\)W, \({}^{197}\)Au, \({}^{205}\)Ti, \({}^{208}\)Pb, \({}^{209}\)Bi, \({}^{232}\)Th and \({}^{238}\)U, with a total sweep time of \(\sim\)0.34 s. Preamblation was conducted for each spot analysis using five laser shots (\(\sim\)0.3 \(\upmu\)m in depth) to remove potential surface contamination. The analysis was performed using a 38 \(\upmu\)m diameter spot at 5 Hz with a fluence of 3 J/cm\({}^{2}\)[56].  
The Iolite software package was used for data reduction [57]. NIST 610 and \({}^{29}\)Si were used to calibrate the trace element concentrations as an external reference material and internal standard element, respectively. The internal standard content of the sample was the value obtained from the EPMA data.
## 2 System Modeling
### Path Loss  
The large-scale path-loss model for the communication link between Base Station (BS) and user equipment (UE) according to Annex A of 3 GPP standard [5] for the deployment scenario of Cellular IoT is as follows:  
\[L_{\text{BS-UE}}=\ 120.9+37.6\log_{10}(d)(\text{dB}) \tag{2}\]  
where: \(d\) is the separation distance (km) between the base station and the user equipment and has been studied in [3]. In continue, we studied different standardized path-loss models in the international telecommunication union (ITU) and 3 GPP documents to find the most suitable path-loss model, including the critical factors that affect signal attenuation. Finally, we chose the path-loss models shown in Equations (3) and (4) respectively from the ITU [11], and 3 GPP [12] as both models provide an appropriate mathematical representation indoor pico-cell of radio propagation. The ITU basic path-loss model is:  
\[L_{\text{pico}}=20\log_{10}f+N\log_{10}d+L_{f}(n)-28(\text{dB}) \tag{3}\]  
where: \(d\) is the separation distance (m) between the base station and the user equipment, \(N\) is distance power loss coefficient, \(f\) is the frequency (MHz), \(n\) is the number of floors between base and portable and \(L_{f}\) is the floor penetration loss factor (dB). The values of \(N\) and \(L_{f}\) for different frequencies has been given in [11]. The microcell propagation model has been obtained from 3 GPP 36.814 standard [12], and it has the following form:  
\[L_{\text{micro}}=\max(38.46+20\log_{10}R_{2}+0.7d_{2D,\text{ indoor}}+L_{\text{ow}},15.3+37.6\log_{10}R_{2})(\text{dB}) \tag{4}\]  
where \(d_{2D,\text{indoor}}\) is the distance inside the house, \(R_{2}\) is the distance between receiver and transmitter, and finally \(L_{\text{ow}}\) is the penetration loss of one outdoor wall which is 10 dB.  
In Figure 1 three possible propagation scenarios exist which are described as follows:  
Figure 1: Small Cell Deployment Scenarios.  
1. The user Equipment (UE) such as a smart meter is inside the same house as a small cell (Femto cell or Pico-cell) Base Station (BS);
2. The UE is outside of the building;
3. The UE is inside a different house which will add \(L_{\text{ow,1}}\) and \(L_{\text{ow,2}}\) to the path-loss model for the wall attenuation in buildings one and two, respectively.  
The distances of UEs from different BSs is calculated on based on the scenario in Figure 1 for each building (20 m) and street (10 m) and the number of walls applied in the path-loss model. In this scenario we have assumed that femto cells (range of \(<\)30 m) are only installed inside building but for pico-cells (range of \(<\)100 m) we have possibility to install them both inside and outside.
## 2 Simulation Model
### Mesh Independence Analysis  
The size and number of the mesh affect the results of calculations in the CFD model. This study analyzed the effect of the mesh size on the results with 3 cases including coarse, medium, and fine mesh. Figure 3 shows 3 surveys' meshing results with parameters and calculation time at the top dead center (TDC) position. The mesh values and the calculation time are shown in Table 5 in the case where the combustion chamber diameter is 100 (mm).  
Figure 3: \(Cont.\)  
Figure 2: Fuel injection characteristics and the selection mode (\(p_{\bar{y}}\) = 300 MPa, \(Q\) = 60 mg).  
The comparison results between mean pressure, mean temperature, mean NO mass fraction, and mean CO\({}_{2}\) mass fraction in three cases are presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Resolutions** & **Coarse** & **Medium** & **Fine** \\ \hline Average cell size & 1 mm & 0.5 mm & 0.2 mm \\ \hline Number of faces & \(1518\) & 4512 & 25,159 \\ \hline Number of triangle faces & 0 & 0 & 0 \\ \hline Number of boundary faces & 183 & 329 & 807 \\ \hline Total number of cells & 105,768 & 363,452 & 3,027,552 \\ \hline Calculation time with computer & & & \\
64 bit, Intel® CoreTMi5-3210M & (12–16) h & (32–36) h & (80–82) h \\ CPU @ 2.50 GHz & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The mesh values and the calculation time.  
Figure 4: The CFD results of the simulations with three different meshes: (**a**) mean pressure; (**b**) mean temperature; (**c**) mean NO mass fraction; (**d**) mean CO\({}_{2}\) mass fraction.  
Figure 3: The cases of the computational mesh: (**a**) coarse; (**b**) medium; (**c**) fine.  
It can be seen that the simulation results gave higher accuracy with the fine mesh, especially at the position where the graph is bent, but the calculation time was extended for each case, while the results were relatively accurate and showed a reduced calculation time with the medium mesh. Therefore, the median mesh was selected in this study. The details of the chosen mesh for each of the combustion chambers is given in Table 6.  
Figure 5 shows the meshing of the model at the TDC position in the case of 67 mm and 100 mm diameters.
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 4 Results
### Similarity of the FOM  
We expect that \(\Delta FOM\) decreases as the number of iteration increases; however, this is not the case for the GA in the SCL band depicted in Figure 4 (top). From this figure, we make two key observations. First, GA has the most dissimilar FOM trends among its three executions. Each GA execution finds a different best parametrization multiple times through its 100 generations. Second, CMA-ES and PSO have a clear stabilization of \(\Delta FOM\). This is especially true for CMA-ES after the 40 th generation. The similarity of the FOM is down to 0.05, and this result constitutes the best one for the SCL band analysis.  
From Figure 4 (bottom), we make the following observations. First, the PSO has the best similarity of FOM in the OESCL analysis, with \(\Delta FOM\leq\) 0.1 after convergence at the 18 th generation. Second, GA and CMA-ES algorithms have around the same similarities as FOM in the convergence region. However, CMA-ES keeps the same pattern as the SCL analysis, while the GA cannot be directly compared. We conclude that PSO and CMA-ES results are more congruent in their pattern of convergence than the GA, even after evaluating them with different FOM integrals.  
Figure 3: History of the FOM for each algorithm (GA, PSO and CMA-ES) in every optimization process. (**Top**) SCL band. (**Bottom**) OESCL band.
# Throughput Capacity of Selfish Wireless _Ad Hoc_ Networks with General Node Density
## 3 Achievable Rate
### Routing Protocol  
We now elaborate on the detailed operation in each phase of routing scheme. As shown in Figure 3, the routing scheme involves three phases: _Draining phase_, _Highway phase_, and _Delivery phase_.  
1. _Draining phase:_ In the draining phase, source node \(s\) drops packets to an entry point on the nearest horizontal crossing highway.
2. _Highway phase:_ In the highway phase, packets are first moved along horizontal highway, and then along the vertical highway until they arrive at an exit point that is close to the destination node \(D\).
3. _Delivery phase:_ In this phase, packets are delivered to the destination node \(D\) from the exit point located on the highway.
###### Abstract  
This study investigated the conversion of kitchen waste into environmentally friendly fertilizers and soil improvers using five various treatments: (1) inoculation with effective microorganisms followed by anaerobic fermentation, (2) 12 days of decay before adding a double dose of effective microbes, (3) sterilization at 70 \({}^{\circ}\)C for 1 h after 12 days of decay, and (4) sterilization followed by effective microbes addition. In (5), waste was decayed, sterilized, and digested without effective microbes. For comparison, a commercial NPK fertilizer and pelleted cow manure were also applied. These treatments were tested in a ryegrass growth experiment in a glasshouse under warm conditions in northern Poland, with four successive harvests. Measurements included dry matter yield, N uptake, N utilization per hectare, and soil properties. Results showed that granulated cow manure and (5) were the most effective fertilizers during the first two months, while other kitchen waste treatments performed better in months 3 and 4, functioning as slow-release fertilizers. The relative agronomic effectiveness after four months was 88%, 67.8%, 60.2%, 48.6%, and 48.6% for (5), (4), (2), (3), and (1), respectively, compared to cow manure, and 36.3%, 27.9%, 24.8%, 20%, and 20% compared to NPK fertilizer. The study indicated that using a double dose of effective microbes produced yields comparable to sterilized waste, suggesting that microbial treatments could replace energy-intensive sterilization, reducing costs. Soil properties were largely unaffected, but residual soil N levels increased from 0.5 to 1.1 g/kg.  
agronomic effectiveness; food waste; kitchen waste; nitrogen use; ryegrass growth; waste pre-treatment 2025
# Compatibility Analysis Between RedCap Non-Public Networks and 5G NR in TDD FR1 and FR2 Bands
## 8 Conclusions  
The widespread adoption of 5G NR has created opportunities for diverse applications, ranging from enhanced mobile broadband (eMBB) to ultra-reliable low-latency communication (uRLLC) and massive machine-type communication (mMTC). RedCap technology, as an advanced IoT solution, bridges the gap between low-complexity 4G-based solutions and the more demanding 5G NR infrastructure. By delivering cost-effective, simplified, and energy-efficient devices, RedCap is well suited for applications such as wearables, industrial automation, and asset tracking.  
However, the integration of RedCap into non-public networks raises interference concerns, particularly in urban areas using the TDD FR1 and FR2 frequency bands. Our analysis shows that while downlink throughput losses remain within acceptable thresholds, uplink losses in FR1 exceed the tolerable limits, potentially affecting 5G NR's quality of service (QoS). Conversely, FR2 bands demonstrate minimal throughput loss due to advanced beamforming techniques and higher attenuation levels.  
We propose to consider more complete aspects of this technology in future work. To mitigate interference, we recommend prioritizing RedCap deployment in frequency division duplex (FDD) bands within FR1 and leveraging FR2's inherent strengths. Strategic deployment planning will allow RedCap and 5G NR networks to coexist effectively, supporting diverse use cases without compromising network performance.  
Conceptualization, A.P. and V.T.; methodology, A.P.; software, A.P.; validation, V.T., A.P., S.D. and O.V.; formal analysis, V.T., S.D. and O.V.; investigation V.T. and S.D.; resources, V.T. and A.P.; data curation, A.P. and S.D.; writing--original draft preparation, A.P.; writing--review and editing, V.T., S.D. and O.V.; visualization, A.P. and S.D.; supervision, V.T.; project administration, V.T.; funding acquisition, V.T. and S.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available upon request from the corresponding authors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Link Direction** & **FR1** & **FR2** \\ \hline Downlink & 4.5\% & 1.5\% \\ Uplink & 21\% & 0.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average throughput losses of the 5G public networks in the FR1 and FR2 bands.  
**Acknowledgments:** A.P. would like express special thanks to the Radio Research and Development Institute (NIIR) for its dedicated support and guidance.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Contact Angles Measurement  
The contact angle measurement was used to measure the wettability of the pipeline's surface [28; 29]. In this study, the detailed experiment schematic diagram is shown in Figure 1. The experiment procedures were as follows [29]: before measuring the static contact angles, the pipeline's surface was cleaned and dried, and then the water drops were dripped onto the pipeline surface, and the contact angles of the water onto the surface were measured [30], and the original contact angle was the static contact angle, as shown in Figure 1a. In other words, the vibration would be used to alter the static contact angles, and the contact angles followed the rule described above. It was difficult to measure the water/gas/solid dynamic contact angles, because the water drops were evaporating. Therefore, the water/oil/solid three-phase contact angles were measured (Figure 1b). The dynamic contact angle altered over time. For dynamic contact angles, the oil drops were dripped onto the pipeline's surface, and the system was placed into the aqueous environment (clean water, sewage water).  
The surface wettability influenced the fluid flow, which has been widely studied in previous studies [31; 32; 33; 34]. The results indicated that as the surface became more hydrophilic, the fluid flow became faster [35; 36]. [PERSON] [23] et al. studied the impact of wettability on pore-scale flow regimes. The result showed that the pore scale wettability influenced the fluid flow property. The wettability had an impact on the fluid phase connectivity, and the fluid flow property was influenced. [PERSON] et. al. [37] explored the surface wettability by molecular dynamics, and the results indicated that there are three contact states on the surface under the vibration field: wetting, wetting/dewetting, and dewetting. In addition, the results also indicated that the surface wettability influenced the fluid flow process.  
Figure 1: The (**a**) static, and (**b**) dynamic contact angles’ measurements.
# Perfect Roman Domination: Aspects of Enumeration and Parameterization +
## 1 Introduction
\(B^{A}\) is the set of all total functions \(f:A\to B\). Likewise, \(\binom{A}{2}\) is the set of all \(2\)-element subsets of \(A\). For \(A\subseteq B\), \(\chi_{A}:B\to\mathbb{N}\) denotes the characteristic function (\(\chi_{A}(a)=1\) if and only if \(a\in A\); \(\chi_{A}(a)=0\), otherwise). Let \(G=(V,E)\) be a graph. For a set \(A\subseteq V\), the open neighborhood is defined as \(N_{G}(A)\coloneqq(\bigcup_{v\in A}N_{G}(v))\). The closed neighborhood of \(A\) is given by \(N_{G}[A]\coloneqq N_{G}(A)\cup A\). The _private neighborhood_ of a vertex \(v\in A\) with respect to \(G\) and \(A\) is denoted by \(P_{G,A}(v)\coloneqq N_{G}[v]\setminus N_{G}[A\setminus\{v\}]\). Let \(G=(V,E)\) be a graph and \(f:V\to\{0,1,2\}\). We define \(V_{i}(f)\coloneqq\{v\in V\mid f(v)=i\}\) for \(i\in\{0,1,2\}\). If \(v\in V\) obeys \(f(v)=2\), then \(u\in N_{G}(v)\) with \(f(u)=0\) is called a _private neighbor_ of \(v\) (with respect to \(f\)) if \(|N_{G}(u)\cap V_{2}(f)|=1\). In the previous notation, we dropped the subscript \(G\) if it was clear from the context. **Observation 2**. : _Let \(G=(V,E)\) be a graph and \(f:V\to\{0,1,2\}\). (1) If \(f\) is a perfect Roman dominating function, then for all \(v\in V_{2}(f)\) and \(u\in N(v)\cap V_{0}(f)\), \(u\) is a private neighbor of \(v\) \begin{table}
\begin{tabular}{c c c} \hline \hline
**Graph Class** & **PRD** & **URRD** \\ \hline chordal & NP-complete [20] & NP-complete [19] \\ interval & P Theorem 6 & P [19] \\ split & NP-complete Theorem 8 & P Theorem 7 \\ cobipartite & P Theorem 10 & P Lemma 2 \\ bipartite & NP-complete [20] & NP-complete [18] \\ chordal bipartite & NP-complete Theorem 13 & NP-complete Theorem 13 \\ distance-hereditary & P Theorem 14 & P [19] \\ cographs & P [20] & P [19] \\ bounded treewidth & P Theorem 15 & P Theorem 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Survey of basic complexity results of Perfect Roman Domination (PRD) and of Unique Response Roman Domination (URRD) on graph various classes. (2) If \(f\) is a Roman dominating function such that for all \(v\in V_{2}(f)\) and \(u\in N(v)\cap V_{0}(f)\), \(u\) is a private neighbor of \(v\), then \(f\) is a perfect Roman dominating function._  
**Observation 3**. : _Each unique response Roman dominating function on a graph without isolated vertices is a minimal Roman dominating function._  
A final note on parameterized complexity: If P is some problem and \(\kappa\) is some parameter, we write \(\kappa\)-P to denote the problem P parameterized by \(\kappa\).
## 4 The Monte Carlo Simulation and Experimental Results
### Discussion  
Another way of investigating into this simulation cases is to conduct the sensitively analysis of our approach. In Table 2, since the \(S_{t,i}\) in column 5 denote the initial time of the traffic light and \(SCW_{t,i}\) in column 8 denote the initial queued vehicles and the \(S_{t+1,i}\) in column 15 denote the adjusting time of the traffic light at next period. After our dynamic adjusting model was executed, we could observe heavy traffic behavior by whether the real-time queued vehicles, \(SCW_{t,i}\), increased or decreased. For example, in run 1 and \(i=1\), \(S_{t,i}\) is 40 and \(SCW_{t,i}\) is increased from 13 to 19 (i.e., in run 2 and \(i=1\)), the adjusting traffic light time \(S_{t+1,i}\) also is increased from 40 to 109. By contrast, in run 2 and \(i=1\), the queued vehicles are decreased from 19 to 13 (i.e., in run 3 and \(i=1\)), the adjusting traffic light time also is decreased from 109 to 73. It means that the proposed dynamic adjusting model could adapt to the constraints of real demand not merely related to each road capacity of vehicles \(RT_{t,i}\) linearly. This study infers that our proposed dynamic adjusting model can adapt to each length on intersection roads \(R_{t,i}\). It might be said that the minimum length can be less influence of adjusting degree. By contrast, while the road capacity of vehicles is maximum (i.e., \(i=3\) and 850 m) then the dynamic adjusting degree go speed up. This study infers that the maximum length the large influence of adjusting degree.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** & **(4)** \\  & **Reducing Number** & **The Queued** & **Traffic Jam** & **Traffic Jam Time** \\  & **of Vehicles** & **Vehicles** & **Amount** & **Traffic Jam Time** \\ \hline Average rate for FIFO (\%) & 9.79 & 24.68 & 1.35 & 1570.6 \\ Standard deviation for FIFO (\%) & 2.27 & 10.02 & 0.08 & 91.54 \\ Average rate for the dynamic adjusting queueing model (\%) & 15.56 & 20.27 & 0.34 & 1245.9 \\ Standard deviation for the dynamic adjusting queueing model (\%) & 7.96 & 10.79 & 0.09 & 88.43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics regarding Monte Carlo simulation.  
In addition, one can know that the IoT concept would enable the data to be collected automatically due to its convenience. Our solution procedure can be implemented once these data can be derived in an appropriate manner.