esa-sceva/satcom-chunk-collection huggingface dataset:
## Numerical Example
### Results  
Table 3 and Figure 5 show the routing and scheduling plan of the feeder buses. They travel a total distance of 5 km, 4.4 km, and 4.5 km, and their total travel times are 19.9 min, 17.8 min, and 17.5 min, respectively. Taking route 1 as an example, an empty vehicle leaves depot D1 at 6:09. The vehicle firstly arrives at C3 to pick up one passenger at 6:12; then, this vehicle visits C12, at which three passengers are loaded at 6:16; then this vehicle visits C9, C11, and C14, where one passenger, two passengers, and three passengers at 6:18, 6:23, and 6:26 are loaded, respectively; finally, this vehicle visits M to deliver all passengers at 6:29. All passengers would spend 3 min walking from the drop-off point of the rail station to the subway platform. Since they chose a subway schedule at about 6:36, all of them will wait for the train at the subway platform from 6:32 to 6:36. In this case, the time of the vehicle visiting each demand point is shown in Table 4. It can be seen from Tables 2-4 that the total ride-times and wait-times of the three routes have been obtained.  
Furthermore, our DRT model has unique features compared to the traditional ones. Figure 6 reveals the difference between our model and the traditional one with no synchronous coordination of rail transit (DRTNSCRT). Compared with DRTNSCRT, the total ride-time of this DRT model is significantly increased by 9.36%, but the total wait-time is significantly reduced by 67.5%, which results in the total travel time of proposed model being reduced by 2.46%. The reason is that the strong constraint (Equation (14)) that takes the synchronous coordination of rail transit into account may result in the vehicle spending more ride-time visiting all demand points, but passengers are saved from waiting at the rail station for their subway schedules. Therefore, it is very important to design feeder bus routing with the synchronous coordination of rail transit in practical applications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Vehicle**} & **Sequence of Demand Points Visited by Vehicle** & **Travel Distance (km)** & **Travel Time (min)** & **Number of Passengers** & **Wait-Time (min)** \\ \hline \multirow{2}{*}{V1} & D1(6:09)-C3(6:12)-C2(6:16)-C9(6:18) & \multirow{2}{*}{5.0} & \multirow{2}{*}{19.9} & \multirow{2}{*}{10} & \multirow{2}{*}{4} \\  & C11(6:23)-C14(6:26)-M(6:29) & & & & \\ \multirow{2}{*}{V2} & D3(6:07)-C13(6:09)-C5(6:15)-C6(6:16) & & & & \\ \multirow{2}{*}{V3} & D5(6:11)-C15(6:14)-C4(6:19)-C2(6:21) & & & & \\ \multirow{2}{*}{V1} & -C7(6:24)-C8(6:26)-M(6:29) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Routing and scheduling plan of each feeder bus.  
Figure 5: The result of three feeder bus routes.
## 3 Results and Discussion  
### Hydrochar Production  
The yields and characteristics of H-180, H-215, and H-250 were reported in a previous study, which addressed hydrochar production and characterization [12]. Table 1 summarizes the H-180, H-215, and H-250 yield, volatile matter, ashes, fixed carbon, O/C and H/C atomic ratios, pH, zeta potential, and surface area.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Hydrochar** & **Yield (\%)** & \begin{tabular}{c} **Volatile Matter** \\ **(\%) \\ \end{tabular} & **Ashes (\%) *** & \
\begin{tabular}{c} **Fixed Carbon** \\ **(\%) *** \\ \end{tabular} & **O/C** & **H/C** & **pH** &
\begin{tabular}{c} **BET Surface Area** \\ **(m\({}^{2}\)**.g\({}^{-1}\))** \\ \end{tabular} \\ \hline H-180 & 87.3 & 78.5 & 10.4 & 11.1 & 0.5 & 1.3 & 5.4 & 14.8 \\ H-215 & 72.7 & 74.4 & 8.0 & 17.6 & 0.4 & 1.2 & 5.2 & 9.6 \\ H-250 & 64.1 & 59.3 & 18.4 & 22.3 & 0.2 & 0.9 & 5.8 & 22.7 \\ \hline \hline \end{tabular}  
* dry basis.  
\end{table}
Table 1: Yields and characteristics of hydrochar (H-180, H-215, and H-250) from Co-hydrothermal carbonization (Co-HTC) of sawdust and non-dewatered sewage sludge. Source: [12].  
The yield of hydrochar was observed to decrease in proportion to the increase in temperature, while the fixed carbon content was enhanced. The O/C and H/C atomic ratios exhibited a reduction when Co-HTC was conducted at 250 \({}^{\circ}\)C, indicating an improved carbonization at elevated temperatures. This condition also resulted in a hydrochar with a superior surface area compared to those obtained at 180 and 215 \({}^{\circ}\)C. All hydrochars had a natural pH of around 5.5 [12]. The zeta potentials of H-180, H-215, and H-250 were \(-\)16, \(-\)17, and \(-\)21 mV, respectively [25].  
The pH\({}_{\rm{PZC}}\) values of H-180, H-215, and H-250 were around 4. It means hydrochar is positively charged at a pH solution lower than 4 and negatively charged at a pH solution higher than 4. Therefore, at its natural pH, hydrochar presents a negatively charged surface, which could be useful for adsorbing cationic molecules, such as methylene blue.
## 7 DDM Incorporation into Other Advanced Biomaterials  
DDM alone showed promising effects in bone and tooth regeneration. In addition, the dentin matrix could also be incorporated into other biomaterials [67; 68; 69; 40; 43]. For example, TDM, aligned PLGA/Gelatin electrospun sheet (APES), and native dental pulp extracellular matrix (DPEM) were combined into DPEM/TDM construct and APES/TDM construct for dental pulp and periodontitis regeneration. Both APES/TDM and DPEM/TDM prompted odontogenic differentiation of dental stem cells, while the groups without TDM did not (Figure 5). Then the combinations were implanted into tooth pockets in swine. After 12 weeks, new pulp/dentin complex-like tissues were formed on the DPEM/TDM surface, and cementum and periodontal ligament-like tissues were formed on the APES/TDM surface [69]. Therefore, the APES/TDM and DPEM/TDM complexes offered an inspiration for complex tissue or organ regeneration. In a 3D-bioprinting study, DDM was mixed with alginate hydrogels as a bioink, which was blended with cells and then bioprinted (Figure 6). The bioinks modified by the dentin matrix demonstrated good cytocompatibility and natural odontogenic capacity [70]. In another study, the TDM combined with Mg-based bioceramic scaffolds and dentinogenesis-related morphogens, were developed and applied to induce dentin-like tissue formation [67]. In another study, the root dentin matrix was demineralized by EDTA to obtain TDM roots, and then the TDM roots were combined with platelet rich fibrin (PRF). The combination of TDM/PRF were placed into the tooth extraction pockets in canines, which achieved functional regeneration of the cementum and the periodontal ligament fibers [68]. Therefore, the dentin matrix represents an important bioactive and nano-structured material that can be incorporated into the development of other advanced biomaterials to enhance tissue repair and regeneration.  
Figure 4: Biochemical changes after implantation in DDM/rhBMP-2 group and DDM group. (**A**) Dry weight (mg/pellet). (**B**) ALP activity (IU/pellet). (**C**) Ca content (mg/pellet). (From Murata [62]_Journal of Biomedical Materials Research_ Part A, with permission.)  
Figure 5: The combination of APES/TDM and DPEM/TDM on odontoblastic differentiation of DPSCs. (**A**,**B**) The APES group up-regulated DSPP and DMP-1, two classic odontogenic markers. Meanwhile, the expression of vasculogenesis and angiogenesis marker VEGF was up-regulated, but early osteogenic/odontogenic markers ALP, BSP and RUNX-2, were down-regulated. (**C**,**D**) In DPEM group, the expression of ALP, BSP and RUNX-2 expression were inhibited. The expression of DMP-1 and VEGF did not change. (**E**,**F**) In TDM group, DMP-1 and DSPP were up-regulated, but ALP, BSP and VEGF were not. (**G**,**H**) In the group of DPEM/TDM or APES/TDM, the expression of DMP-1 and DSPP were up-regulated, while ALP and RUNX-2 were down-regulated. Interestingly, DPEM/TDM not only up-regulated BSP, but also kept the expression level of VEGF. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\), NS: no significance. (From [PERSON] [69], _Biomaterials_, with permission.)
## 3 Results and Discussion  
### Single-Factor Experimental Analysis  
The effects of extraction pressure on extraction yield of RES are depicted in Figure 2a. The other extraction variables were set as follows: extraction time of 20 min, extraction temperature at 150 \({}^{\circ}\)C and solid/solvent ratio of 1:15 g/mL. The results showed that RES yield increased from that of 0.5 MPa: in 1.0 MPa, the yield was highest (6.90 \(\upmu\)g/g); from 1.0 MPa to 2.5 MPa, it decreased and intensively decreased during 1.5 MPa to 2.5 MPa. This was the result of the rupture speed of cell wall accelerating with the increasing of pressure and hence the enhanced extraction efficiency. However, the structure of RES would be damaged if the pressure was overly high [24]. The effects of extraction time on extraction yield of RES is depicted in Figure 2b. Extraction time was from 10 to 30 min, while other extraction variables were set as follows: extraction temperature at 150 \({}^{\circ}\)C, extraction pressure at 1.0 MPa, and solid/solvent ratio of 1:15 g/mL. Results demonstrated that maximum RES yield (5.96 \(\upmu\)g/g) was reached during a 25 min extraction interval, from which point yield began to decrease. At the beginning of the extraction, solvent kept the diffusion from internal of grape seed to dissolve RES, then it reduced due to partial degradation of RES over time and high temperature [25]. In Figure 2c, we found that from 110 \({}^{\circ}\)C to 150 \({}^{\circ}\)C, the yield of RES changed significantly. An increase in temperature favored the extraction of phenolics by enhancing the diffusion coefficient of solvent, solubility of solutes, diffusion rate of analytes, and reducing solvent viscosity and surface tension [26]. At this point, the otherconditions were set as follows: solid/solvent ratio, 1:15 g/mL; pressure, 1.0 MPa; and extraction time, 25 min. Compared with pressure, time, and temperature, we could clearly see in Figure 2d that the influence of solid/solvent ratio on the extraction rate was small. In the range from 1:5 to 1:25 g/mL, the yield hardly changed. Therefore, in the following optimization of the process, the solid/solvent ratio might be considered a constant value.
## 3 System Model
### Energy Model  
We adopt the same radio model applied in [14; 25; 28] to calculate the energy consumption. As shown in Figure 3, the energy consumption for transmission contains two parts: transmitter and receiver. Once the signal is generated by the transmitter, the amplifier will strengthen it using two different powers according to the transmission distance. Therefore, the energy model for transmission is divided into the free space model for short-haul communication and the multipath fading model for long-distance communication.  
The energy \(E_{Tx}\) used for each node to transmit an \(l\) bits package over communication distance of \(d\) is given as follows [14]:  
\[E_{Tx}(l,d)=\left\{\begin{array}{ll}l\cdot E_{elec}+l\cdot\varepsilon_{fs} \cdot d^{2},&if~{}~{}~{}d<d_{0}\\ l\cdot E_{elec}+l\cdot\varepsilon_{mp}\cdot d^{4},&if~{}~{}~{}d\geq d_{0}\end{array}\right. \tag{2}\]  
where \(E_{elec}\) denotes the energy consumption to run the transmitter or receiver circuit, \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) denote the amplification coefficient for the free space model and the multi-path fading model, respectively, and \(d_{0}\) is a threshold distance calculated by [28]:  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}}~{}. \tag{3}\]  
The energy \(E_{Rx}\) used for receiving an \(l\) bits package can be calculated by [14]:  
\[E_{Rx}(l)=l\cdot E_{elec}~{}. \tag{4}\]  
Figure 3: Energy model.
# A Software-Defined Radio Platform for Teaching Beamforming Principles
## 1 Introduction
The proposed system is based on hybrid architecture, allowing students to ex  
Figure 1: 5G and Wi-Fi beamforming networks deployed in indoor and outdoor environments. plore and implement beamforming techniques on customizable antenna platforms. By providing this hands-on experience with real-world wireless communication, the students can delve into a deeper understanding of modern RF and signal processing concepts. The contributions of this work include the development and validation of a hybrid SDR-based beamforming system aimed at providing telecom engineering students with an agile, user friendly, educational platform for observing, testing, and evaluating beamforming. This paper also documents the identified challenges associated with the use of an SDR for beamforming along with testing and validation on an array of patch antennas operating in the 3.5 GHz frequency band. The beamforming platform was deployed in both indoor and outdoor environments in order to observe the differences in the system behavior in both environments. The remainder of the paper is structured into four sections, with Section 2 detailing the system hardware and software architecture. Section 3 describes the system parametrization, while Section 4 is focused on the implementation and testing of the beamforming system on a custom-designed antenna. The final section presents the conclusions of the paper.
## 1 Introduction
It should be noted that the experiments were performed with shorter pipe length. Nevertheless, the detectability of the defects is mostly dependent on the characteristics of the received signal. The algorithm can easily detect a perfect axisymmetric feature such as a weld in long-range. Nonetheless, for defects which are generally smaller in size and asymmetric, the hypothesis is that the feature can be detected if the characteristic of the torsional wave is still detectable in the received signal. It should also be noted, that as the inspection pipe length increases the energy of the flexural waves reduce due to their dispersion in time and the effect of attenuation on them. Therefore, false alarms using this method would be expected to be less in long-range inspections. Figure 1: (**a**) Example dispersion curve of T(0,1) wave mode in an 8” schedule 40 steel pipe. (**b**) The effect of dispersion on a simulated flexural wave for two propagation distances [20]. The paper is organised as follows. Section 2 explains the proposed method for detection of defects using torsional waves. Section 3 demonstrates the results of the method on both synthesised and real experimental data and, finally, Section 4 concludes the paper.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 3 Material and Methods
### TOA Based Distance Determination  
Generally, the time required for a signal to travel from one node to another is used to estimate the distance between the nodes. According to our proposed method, a device would request another device to exchange their position information, TOA of the same communication can be utilized to measure the distance between them. A brief process of two-way time of arrival is illustrated in Figure 3.  
Two-way time of arrival is a ranging protocol [48], based on the reception of timestamp packets distance between two devices can be estimated in the absence of time synchronization. Assuming the propagation delays of the request and response frames are the same, the TOA and physical distance between the two devices can be calculated as follows:  
\[T_{t}=\frac{(T_{x2}-T_{x1})-(T_{y2}-T_{y1})}{2} \tag{2}\]  
\[d_{xy}=T_{t}\times c \tag{3}\]  
Here \(c\) is the speed of light. [PERSON] et al. [49] proposed a method using a smartphone to measure the TOA, employ modulated light with a signal for the time-of-flight short measurement, they achieved positioning error of less than 10 mm. However, in an indoor environment, most connections between devices are NLOS as the LOS paths are blocked by wall, ceiling, or furniture. The LOS path is often mixed with multiple time-delayed NLOS paths. NLOS can degrade the D2D ranging since they have significant positive biases which make the measured pair-wise distances much larger than their actual values [50]. Some studies have focused on distinguishing NLOS connections from LOS connections. An identification technique based on maximum likelihood estimation is described in [51]. By exploring physical layer information 95% LOS was identified in [52]. [PERSON] et al. [34] showed how to detected actual TOA from an NLOS / multipath channel from received signal energy. According to them in a multipath channel with a low signal to noise ratio, some low energy level value may appear before the maximum energy value. By setting a threshold value, actual TOA can be determined as shown in Figure 4.  
Figure 3: Two-way TOA diagram.
# Operator-Based Adaptive Tracking Capacity Control in Complex Manufacturing Processes
## 3 Operator-Based Adaptive Capacity Control
### Decoupling Control for MIMO Systems  
A multi-workstation, multi-product job shop manufacturing process is a typical MIMO system with various delays, uncertainties, and couplings. From the definitions in Section 3.1, RRCF was adopted in the capacity control of this system. Based on the mathematical model in (4), the input rate from the initial stage \(X_{0i}^{\mathrm{I}}(t)\) was assumed to randomly change with a Gaussian distribution, which was considered the uncertainty of the system. The disturbance \(X_{i}^{D}(t)\), e.g., the rush order, which was also from the initial stage, was included in the uncertainty \(X_{i}^{D}(t)\), which was represented by \(\triangle N_{i}\). Then, the right factorization of the complex MIMO system was \(Q=(N+\triangle N)\circ D^{-1}\), where the \(N\) and \(D\) were designed with  
\[w_{i}(t) =D_{i}^{-1}(u_{j}(t-\tau_{2}-\tau_{1}))(u_{i}(t-\tau_{2}))\] \[=X_{0i}^{\mathrm{I}}(t-\tau_{1})+\sum_{j=1}^{n}p_{ji}\cdot(n_{j}^{ D}\cdot v_{j}^{D}+u_{j}(t-\tau_{2}-\tau_{1})\cdot v_{j}^{R})\] \[\qquad-(n_{i}^{D}\cdot v_{i}^{D}+u_{i}(t-\tau_{2})\cdot v_{i}^{ R}) \tag{9}\] \[y_{i}(t)= N_{i}+\triangle N_{i}=y_{i}(0)+\int_{0}^{t}w_{i}(\tau)+X_{i}^{D}( \tau)d\tau. \tag{10}\]  
In (9), there are couplings among the input signals \(u_{i}\) for \(i\in\{1,\ldots,n\}\), which can be described as \(n\) linear equations. The solution is obtained with \(u_{i}(\cdot)=\sum_{j=1}^{n}D_{ij}(w_{j})(\cdot)\). In order to simplify the computation for the RRCF-based capacity control of the MIMO system, the following theorem is given for the decoupling controller design [5].  
**Theorem 2**.: _Consider the decoupling control system shown in Figure 4. If there exists a linear operator \(G\) and a stable and invertible operator \(Z\) with_  
\[\sum_{j=1,j\
eq i}^{n}[Z_{ij}(w_{i})](w_{j})+G_{i}\circ D_{ij}(w_{j}) =0 \tag{11}\] \[Z_{ii}(w_{i})+G_{i}\circ D_{ii}(w_{i}) =L_{i}(w_{i}) \tag{12}\]  
_to ensure that \(L_{i}\) is stable and invertible, then the MIMO system is decoupled, where \(\mathbf{L}=(L_{1},\ldots,L_{n})\) is the decoupled operator with \(v_{i}=L_{i}(w_{i})\)._  
With this theorem, the stability of the decoupled job shop system can be proved based on the following proposition (cf. Proposition 1 in [5] for a detailed proof).  
Figure 4: Decoupling structure of the MIMO system.  
Figure 3: Nonlinear feedback system with disturbances.  
**Proposition 1**.: _Consider the system (4). Let the decoupling parameters \(h_{i}\) be finite with \(|h_{i}|\
eq\frac{1}{v_{i}^{R}}\), let \(\mathbf{G}=(G_{1},\ldots,G_{n})\) be the identity operator, and let \(Z_{ii}\) be unimodular for \(i=1,\ldots,n\), such that_  
\[\sum_{j=1,j}^{n}[Z_{ij}(w_{i})](w_{j})=-\sum_{j=1,j\
eq i}^{n}G_{i}\circ D_{ij} (w_{j}),\]  
_holds; then,_  
\[L_{i}(w_{i})=(h_{i}-\frac{1}{v_{i}^{R}})\cdot w_{i}-\frac{v_{i}^{D}n_{i}^{D}}{ v_{i}^{R}} \tag{13}\]  
_holds. Furthermore, \(L_{i}(w_{i})\) is stable and invertible if \(n^{R}\) is sufficiently large._
# Study on the Flow Velocity of Safe and Energy-Saving Transportation of Light-Particle Slurry
###### Abstract  
In order to determine the recommended flow velocity for the safe and energy-saving transport of ice-slurry-type light particle slurries, it is necessary to study the flow characteristics of light particle slurries, especially the critical flow velocity. Therefore, in this paper, a numerical simulation method based on the mixed turbulence model with the RANS (Reynolds averaged Navier Stokes) equation is used, and a new concentration distribution method is proposed for the first time to derive the critical flow velocity, as follows: the flow velocity of the light particle slurry when the ratio of the solid volume fraction vf at the position of 0.08D above the bottom of the pipeline to that at the center of the pipeline, vf/vf(y) = 0.75, is taken as the critical flow velocity. The flow changes in the slurry (polyethylene particles with a density of 922 kg/m\({}^{3}\) and water) under 0.1-1.0 m/s (at intervals of 0.1 m/s) were investigated experimentally, and the pressure drop data obtained from the experiments were used to determine the recommended flow rate for safe and energy-saving transportation of the light particle slurry. The pipe diameter used for the experiments and simulations was 28 mm, and the solid-phase particle sizes were 0.3 mm, 0.4 mm, and 0.5 mm, with solid-phase contents of 5 vol%, 10 vol%, 15 vol%, and 20 vol%. In addition, the experimental and numerical simulation results show that an increase in solid-phase content and particle size leads to an increase in critical flow velocity.  
solid-liquid two-phase flow; critical flow velocity; flow characteristics; light particle slurry; ice slurry; energy-saving +
Footnote †: journal: Applied Sciences
## 4 Data Analysis and Results
### Validity and Applicability of the BTS Classification for Evaluating the Stone Durability
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Deterioration Process**} & \multicolumn{3}{c}{**BTS (MPa)**} \\ \cline{2-7}  & **Type** & **Cycle** & **Number** & **Initial** & **Conditions** & **Deteriorated** & \multicolumn{1}{c}{* **RL (\%)**} \\ \hline [PERSON] et al. [5] & Freezing–thawing & 55 & Ignimbrite & 1.25 & 0.71 & 43.2 \\ [PERSON] et al. [12] & Freezing–thawing & 30 & Ignimbrite & 12.9 & 10.9 & 15.5 \\  & & & Granite & 12.1 & 10.1 & 16.5 \\  & & & Granite & 14.5 & 13.9 & 4.1 \\  & & & Dacite & 18.4 & 17.8 & 3.3 \\  & & & Tuff & 11.2 & 8.8 & 21.4 \\ [PERSON] et al. [13] & Freezing–thawing & 300 & Monzogranite & 8.73 & 5.93 & 32.1 \\  & & & Tonalite & 11.72 & 8.6 & 26.6 \\  & & Granoidorite & 14.67 & 12.76 & 13.0 \\ [PERSON] et al. [14] & Heating–cooling & 1 & Granite & 14.78 & 11.81 & 20.1 \\  & & & Granite & 10.56 & 7.21 & 31.7 \\  & & & Granite & 11.83 & 8.84 & 25.3 \\ \hline \hline \end{tabular}  
* Calculated using Equation (6).  
\end{table} 
Table 6: Data of previous studies used to validity and applicability of BTS classification in evaluating the stone durability.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 3 Lightning Analysis of PR Data
### Lightning Activity over Land and Oceans  
Studies noted that lightning appeared more frequently over land than over oceans (Figure 4) [73; 74; 75; 76; 77; 78]. It is well known that the charge separation mechanism requires collisions between graupel and small ice particles in the presence of supercooled liquid water [77; 78]. The environmental differences over land and oceans could impact the precipitation height and thus the mixed-phase processes, leading to the significant difference of flash production. Although the difference of lightning occurrence over land and oceans was noticed, the reasons have not been firmly established. At present, there are two main theories accounting for the land-ocean contrast of lightning: ground-surface property and aerosol concentration [79; 80]. In our view, the influence of water vapor availability could be also considered, but there were few related studies. When the water vapor availability over land was not as great as that over oceans, it might result in higher condensation altitudes and a greater possibility for the occurrence of solid or mixed phase particles. However, the relative humidity might play opposite roles in wet or dry regions with different magnitudes of water vapor content [81]. It would be worthwhile to give further analysis on aspects of all impact factors.