esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
From this study, we can conclude that:  
(1) It was evident from analysis of BSE-EDX that the mineral phases of converter steel slag mainly contain silicate mineral phase, RO phase, calcium ferrite phase, and a small amount of metallic iron phase, among others.  
(2) The oversize substance after screening can be considered as the hardly grinding phases (HGP), which provides a simple method of determining the HGP in steel slag. The HGP proportion which  
Figure 12: Particle morphologies of steel slag powder with and without GA: (**a**) without GA; (**b**) with GA.  
was determined by a 0.9 mm square-hole screen is about 1.5%. After the initial 20 min grinding, the RO phase, calcium ferrite, and metallic iron phase made up most of the proportion in the HGP, while the metallic iron made up the most component after 70 min grinding.  
(3) For steel slag powder with about 360 m\({}^{2}\)/kg specific surface area (SSA), 20 min of grinding time can be saved with pretreatment. The relationships of SSA with the logarithm of grinding time show a good linear relationship, but pretreated steel slag has a higher grinding efficiency. In addition, the D50 of untreated steel slag can only reach 32.89 \(\upmu\)m after 50 min grinding, but that of pretreated steel slag can reach 18.16 \(\upmu\)m after the same grinding time.  
(4) Organic grinding aids (GA) can obviously improve the grinding property of steel slag, and the action effects of 0.05% dosage of GA on the grinding efficiency and particle characteristics were the best during 40-60 min grinding time. especially the proportions of particles in the range of 3-32 \(\upmu\)m, which were obviously increased by 7.24%, 7.22%, and 10.63% after 40 min, 50 min and 60 min grinding, respectively. This is mainly because of the reduction of agglomeration after the use of GA, as evidenced by SEM images.  
This work was financially supported by the China's Post-doctoral Science Fund (No. 2016M591170), the Open Fund of Key Laboratory of Advanced Civil Engineering Materials (Tongji University), Ministry of Education (No. 201602).  
[PERSON] conceived of, designed and performed the experiments. [PERSON] and [PERSON] analyzed the data and discussed the results. [PERSON] participated in writing this paper.  
The authors declare no conflict of interest.
## 3 Mudstone Electrochemical Modification Tests Program
### Test Setup and Procedure  
Experimental equipment: The experimental setup included an MS602D DC power supply (maximum voltage of 60 V, maximum current of 2A, real-time display of voltage and current), lead wires, three shear sample cylinders (inner diameter 61 mm, length 10 cm, round tube shape), three uniaxial compression sample cylinders (inner diameter 50 mm, length 20 cm, round tube shape), sealed rubber gaskets (diameter 6 cm \(\times\) 5 cm), a digital scale, a measuring cup, and a drying box.  
Experimental materials: Mudstone specimens, electrolytes (CaCl\({}_{2}\) and AlCl\({}_{3}\)), electrode sheets (graphite, aluminum, and iron), and pure water.  
Preparation process of mudstone specimens: Select the mudstone specimens for weighing, crush the specimens, and place them in an oven for drying. Weigh the required amount of mudstone for one specimen, and add electrolyte solution to achieve a 25% moisture content remoulded specimen. The 25% moisture content is higher than the plastic limit of the sample, meeting the requirements for both size and morphology.  
Experimental setup: Figure 2 depicts the experimental setup, in which a parting funnel containing the electrolyte solution is placed above the specimen barrel so that the electrolyte solution can flow into the mudstone specimen by gravity. A water collection dish is placed under the sample barrel to catch the solution that drains from the sample cathode.  
After assembling the experimental device [31], connect the power to start the electrochemical modification test. Continuously inject electrochemical solution from the solution conduit at the upper end, and determine the experimental progress by monitoring the current changes during the electrochemical modification process. When the current stabilizes for more than 5 h, the electrochemical modification of mudstone is considered complete.  
Experimental design: Experiments were conducted to preferentially select electrochemical modification agents for mudstone based on two variable factors: electrolyte (CaCl2, AlCl3) and electrode materials (Fe, Al, graphite). As shown in Table 1, the orthogonal experimental method was used to design six sets of electrochemical modification tests. Only graphite electrodes were used in the tests for the preferential selection of the electrolyte solution concentration, and the concentrations (mass fractions) of the two electrolytes were set according to Table 1.
## 4 Energy Consumption Model and Power Control
### Power Control
Thus, based on satisfying a certain packet error rate, its receiving signal sensitivity, _i.e._, minimum transmission power, can be calculated as \(P_{R\rm min}=\gamma_{SNR}^{\prime}N_{0}R_{b}\), where \(R_{b}\) is the bit rate and \(\gamma_{SNR}^{\prime}\) is the SNR needed to detect a packet, which is typically set as 3 dB. We now consider the transmission power adapted in the different phases of the PEAFC-CL algorithm. When the BS launches a new round at network initialization, it uses the maximum transmission power to ensure that all nodes in the network can receive the message:  
\[P_{T\rm max}(d_{\rm max})=P_{R\rm min}PL(d_{\rm max})F_{L} \tag{16}\]  
\(d_{\rm max}\) refers to the distance between the BS and the node in the scenario farthest from it. \(F_{L}\) is the signal fade margin, which is introduced to keep the probability of packet failure due to the random fluctuations of the channel under control:  
\[F_{L}=\sqrt{2}\delta/erfc(2p_{out})=\frac{2\sqrt{2}\delta}{\sqrt{\pi}}\int_{2p _{out}}^{\infty}e^{-t^{2}}dt \tag{17}\]  
where \(p_{out}\) is the maximum outage probability, which depends on the SNR of the current link and its channel fading distribution characteristics [38], and \(p_{out}=p_{out_{start}}\) in the network initialization stage. In the clustering stage, the transmitting power of the CH broadcasting the message of becoming a CH to surrounding nodes is \(P_{Br}=P_{R\rm min}PL(d_{R_{CH}})F_{L}\), where \(p_{out}=p_{out_{B}}\). In the \(T_{CF}\) period, the power of the receiving data and the CH broadcasting message is:  
\[P_{R_{CF}}(dB)=P_{R\rm min}(dB)+10\rm{log}_{10}F_{L}(dB)-X_{\sigma} \tag{18}\]  
In each round of communication, we adopt the same communication frequency band, and based on the node deployment region and density, the distance between a CH and the BS is considerablysmaller compared to the distance between the CH and the member nodes; therefore, the communication between a CH and its member nodes mainly refers to visual range communication with little communication loss. Here, the transmitting power of member nodes in a cluster sending data to the CH in the \(T_{IC}\) period is set as \(P_{T_{IC}}=P_{\text{Rmin}}PL(d_{i-CH})\). During the \(T_{TS}\) stage, the transmitting power of all CH nodes directly communicating with the BS (including the nodes not participating in the cluster set but directly communicating with the BS) can be calculated using the formula \(P_{T_{-}TS}=P_{\text{Rmin}}\cdot PL(d_{i-BS})\) if the truth value of the path loss is successfully calculated; otherwise its transmitting power will be set as in Equation (16), where \(p_{out}=p_{out_{TS}}\).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Maturity Groups (13.45)**} & \multicolumn{3}{c}{**Effect of Maturity (13.45)**} \\ \hline
**Variables** & **Early (_n = 12_)** & **On-Time (_n = 154_)** & **Late (_n = 6_)** & **\(H\)** & **\(p\)** & \(\eta_{P}^{2}\)** \\ \hline Practice experience (years) & 4.6 \(\pm\) 2.5 & 5.4 \(\pm\) 2.6 & 6.5 \(\pm\) 2.3 & 2.42 & 0.298 & 0.014 \\ \hline Training load (h/week) & 6.9 \(\pm\) 2.0 & 6.2 \(\pm\) 1.5 & 5.3 \(\pm\) 0.8 & 4.20 & 0.122 & 0.024 \\ \hline CA (years) & 13.8 \(\pm\) 0.4 & 13.8 \(\pm\) 0.5 & 13.8 \(\pm\) 0.5 & 0.47 & 0.789 & 0.003 \\ Maturity offset (years) & 1.6 \(\pm\) 0.4 *\# & 0.4 \(\pm\) 0.7 \(\infty\) & \(-\)0.8 \(\pm\) 0.4 & 36.95 & \(<\)0.001 & 0.179 \\ APHV (years) & 12.2 \(\pm\) 0.1 *\# & 13.3 \(\pm\) 0.5 \(\infty\) & 14.6 \(\pm\) 0.1 & 48.20 & \(<\)0.001 & 0.222 \\ \hline MORPHOLOGY & & & & & & \\ Body Mass (kg) & 74.5 \(\pm\) 9.8 *\# & 59.3 \(\pm\) 8.8 \(\infty\) & 39.8 \(\pm\) 3.3 & 34.60 & \(<\)0.001 & 0.170 \\ Stature (cm) & 186.3 \(\pm\) 4.6 *\# & 172.1 \(\pm\) 8.0 \(\infty\) & 159.8 \(\pm\) 5.2 & 36.97 & \(<\)0.001 & 0.180 \\ Arm span (cm) & 189.0 \(\pm\) 6.8 *\# & 175.4 \(\pm\) 9.3 \(\infty\) & 158.7 \(\pm\) 3.6 & 33.34 & \(<\)0.001 & 0.165 \\ Hand span (cm) & 23.1 \(\pm\) 1.3 * & 22.2 \(\pm\) 1.6 \(\infty\) & 19.9 \(\pm\) 1.3 & 15.17 & 0.001 & 0.082 \\ BMI (kg/m\({}^{2}\)) & 21.5 \(\pm\) 2.9 \# & 19.9 \(\pm\) 2.1 \(\infty\) & 15.6 \(\pm\) 1.1 & 18.54 & \(<\)0.001 & 0.099 \\ \%FM & 20.8 \(\pm\) 6.6 * \# & 16.8 \(\pm\) 5.4 \(\infty\) & 9.4 \(\pm\) 2.9 & 16.77 & \(<\)0.001 & 0.090 \\ FFT (kg) & 58.5 \(\pm\) 3.5 *\# & 49.0 \(\pm\) 6.2 \(\infty\) & 36.1\(\pm\)3.0 & 36.60 & \(<\)0.001 & 0.178 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics (mean and standard deviation) and results of Kruskal-Wallis tests to evaluate the effect of maturity groups (APHV = 13.45) in U14 years basketball players’ characteristics.
## 2 Numerical Model Development
### Boundary Condition  
The body boundary condition should be applied differently depending on the hydrodynamic problem. In the radiation problem, the body boundary condition was described as the velocity of the rigid body (\(V_{j}\)) times the normal vector (\(n_{j}\)) in Equation (8).  
\[\frac{\text{\texti}f_{j}}{\text{\texti}n}=V_{j}\ n_{j}\ on\ S_{B}\ \text{for the radiation problem only} \tag{8}\]  
where \(j\) is modes of rigid body motion, e.g., \(i=1\)-\(3\) for translational motion and \(i=4\)-\(6\) for the rotational motion. Equation (9) expresses the body boundary condition for the diffraction problem.  
\[\frac{\text{\texti}f_{D}}{\text{\texti}n}=\quad\frac{\text{\texti}f_{j}}{ \text{\texti}n}\ on\ S_{B}\ \text{for the diffraction problem only} \tag{9}\]  
The linearized free-surface boundary condition, which is a combination form of the kinematic and dynamic free-surface boundary conditions, is expressed as Equation (10) [25].  
\[\frac{\text{\texti}f_{F}}{\text{\texti}n}=\quad\frac{w^{2}}{\text{g}}\,\hat{F} +C=0 \tag{10}\]  
where \(g\) and \(C\) denote the gravitational acceleration (\(=9.81\) m/s\({}^{2}\)) and an artificial damping term if ADZ is applied, respectively. Section 3.2 provides details of the damping terms. There are three options for the side- and end-wall boundary condition in the frequency domain: the Sommerfeld radiation boundary condition, an impermeable boundary condition, and the wave-adapted wall condition. Conventionally, FR-NWT uses the Sommerfeld boundary condition (Equation (11)) for the open sea condition, as reported elsewhere [22]. \(K\) is the wave number in infinite water depth, and \(R\) denotes the distance from any fixed point. In the case of adopting the ADZ method, the wall is expressed by the impermeable  
Figure 1: Computational fluid domain of a 3D FR-NWT.  
condition (\(\fint\)/\(\fint n=0\), second option). The last is the same as the body boundary condition for the diffraction problem in Equation (12). This condition is used as the boundary condition for a fixed breakwater in the frequency-domain diffraction problem. Moreover, the image method is used to express the flat bottom:  
\[\lim_{R\uparrow\;\;\&}\;^{D}\overline{R}\bigg{(}\frac{\fint\!F}{\fint\!R}+ikf \bigg{)}=0\;\textit{on}\;S_{w} \tag{11}\]  
\[\frac{\fint\!F_{D}}{\fint\!n}=\;\;\;\frac{\fint\!F_{I}}{\fint\!n}\;\textit{on} \;S_{w} \tag{12}\]  
Finally, the boundary-integral equations for the radiation and diffraction problems can be solved with those given boundary conditions. The radiated wave potential and diffracted wave potential can be obtained from those hydrodynamic problems. The HG matrix (influence matrix) was evaluated using the Hess and Smith [26] method and the multipole expansion method [1]. This study used the LU decomposition method as a matrix solver, as well as the back-substitution method.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 5 Forensic Evidence in Windows 10 IoT Core
### Pagefile and Hiberfil  
The exchange of data between the physical memory and the persistent storage leaves very useful sources of forensic information, such as the hiberfil and pagefile files, both of them available in Windows 10 IoT Core. They are stored in the root directory of the \"MainOS\" partition. In order to be created, the option has to be enabled in the system registry, which is not the case of the hiberfil file, as the hibernation option is not active. They contain the following information:  
* Hiberfil: file used to save the state of the device when the system is put in hibernation mode. It contains data that, instead of being stored in RAM memory, is saved temporarily in the storage before shutting down the system, and then recovered when it restarts, such as user passwords, deleted files, connections established or information about running processes, among other data. As can be seen in Figure 14, after enabling the hibernation mode and putting the system in that state, the file is created.
* Pagefile: contains data temporarily exchanged between RAM memory and persistent storage. This occurs when the system needs more space available in physical memory, so virtual memory is created through paging, therefore storing a piece of information about RAM memory in the pagefile file. In order to analyze it, a tool such as Volatility [53] must be used to interpret the file content.  
Figure 12: Entry in the registry for the known WiFi networks.  
Figure 13: Registry entry logging the Bluetooth devices paired.
## 4 Results and Discussion
### The 3 DCP-SIT Technique  
As discussed previously, the staple improvement was lower than those reinforced with meshes, wires, or cables, because the staples were scattered and functioned separately. However, meshes should be cut before putting one printed layer, and embedded wire/cable feeding should sync with the motion speed; otherwise, there will be cable-knot [13] and cable-stretch within the mortar, creating voids and fractures. The staple inserting technique is more straightforward than the mesh/wire/cable reinforcing techniques.  
SIA was a prototype in this study. The mortar-printing and staple-inserting were conducted in turns, as shown in Figure 9. Thus, the staple inserting operation extended the time interval between two adjacent layers. We tried to increase the staple density to achieve high staple overlaps. However, the extended time interval weakened the composite bonding between layers. Then, the flexural strengths were not improved either.  
Therefore, the 3 DCP-SIT system still needs hardware upgrades to increase the insertion speed, reducing the time interval between the printed layers. Furthermore, it can insert staples while the mortar is being extruded, as illustrated in Figure 17, especially for large-scale printing, which needs a large staple size. It is technically feasible that the SIA is the second printing nozzle, and the dual- or multiple-printheads have been successfully utilized in thermoplastic [54], food, or drugs. More techniques could be utilized to achieve further possibilities, such as vibrating the staple, brushing glue on the staple, rotating the insertion angle, or controlling the staple density. The upgraded corresponding software can handle the printing and insertion operations by reading the CAD geometry sketch with staple information, including the locations and directions.  
Compared to the meshes/wires/cables, short staples are the short fibers to reinforce printed concrete. Hooked steel fibers can also be used in this inserting technique. Eventually, the 3 DCP-SIT fabricates a printed fiber-reinforced concrete structure; the cast steel-fiber reinforced concrete attracted much research attention in past decades and was applied widely in construction [55], including tunneling [56].
## 2 The Applied Methodology
### Analysis: Output Current and Power  
The rectifier's input current is the difference between the secondary current, \(i_{2}(t)\), and the magnetizing current, \(i_{m}(t)\); see Figure 4c. Therefore, the charge delivered to the CVL during a half cycle is  
\[q_{b}=\int_{t_{1}}^{t_{2}}{(i_{2}(t)-i_{m}(t))dt}, \tag{22}\]  
Examining the waveforms in Figure 2 reveals that in the steady-state, the magnetizing current is symmetrical. This means that the charge drawn by the magnetizing current is zero:  
\[\int_{t_{1}}^{t_{2}}{i_{m}(t)dt}=0, \tag{23}\]  
This also complies with the principle that for the lossless system in the steady state, the energy captured by an ideal magnetizing inductance is fully recycled.  
Combining (22) and (23), the average output current can be found as:  
\[I_{b}=\frac{2}{T}\int_{t_{1}}^{t_{2}}{I_{2m}\text{sin}(\omega t)dt}=\frac{I_{ 2m}}{\pi}(\cos{(\omega t_{1})}-\cos{(\omega t_{2})}), \tag{24}\]  
whence the average output power delivered to the constant voltage load is  
\[P_{o}=V_{b}I_{b}=\frac{2\sqrt{2}}{\pi N}V_{b}I_{1 rms}(\cos{(\omega t_{1})}- \cos{(\omega t_{2})}), \tag{25}\]
# Thrust Measurements on the High Efficient and Reliable Vacuum Arc Thruster (HERVAT)
## 3 Experimental Results
### Thrust Measurements  
In Figure 7, the measured average thrust levels \(\hat{T}\) for all configurations at the given pulse frequencies are shown. If only the measurement without magnetic field are considered, it can be observed that the increase of \(C_{1}\) leads to an increase of thrust. This is also the case for the group of measurements with an additional magnetic field. Yet, the growth rate is here bigger compared to the case without magnetic field. If all the data are inspected independent of presence of magnetic field, it can be stated that the thrust increases with increasing arc energy with exception of \(E\) = 1.1869 J. When taking into account the data presented in Table 1, it is evident that the use of magnetic nozzle increases the thrust significantly with respect to the same \(C_{1}\) value used in the PPU with magnetic nozzle switched off.  
Such a behaviour of the thrust-PPU system can be explained when observing the movement of the arc along the auxiliary anode. When no magnetic field is switched on, the arc movement towards the inside of the thruster head can be optically observed. This behaviour can be also confirmed by the increasing auxiliary anode currents at the end of the pulse. In this case, a significant lower ion and particle stream can move out of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(C_{1}\) & \multicolumn{2}{c}{**91.9 μF**} & \multicolumn{2}{c}{**198.2 μF**} & \multicolumn{2}{c}{**436.9 μF**} \\
**Magnetic Nozzle** & **Off** & **On** & **Off** & **on** & **Off** & **On** \\ \hline \(E_{PPUeff}\) [] & 0.5578 & 0.5079 & 0.9453 & 0.8361 & 1.7968 & 1.4980 \\ \(E_{ar,eff}\) [] & 0.3276 & 0.4209 & 0.5854 & 0.6922 & 1.1869 & 1.3747 \\ \(E_{ar,eff}/E_{PPUeff}\) & 0.5874 & 0.8288 & 0.6192 & 0.8279 & 0.6606 & 0.9177 \\ \hline \end{tabular}
\end{table}
Table 1: Calculated energies stored in the PPU and measured dissipated arc energies for different energy storage configurations and additional magnetic fields.  
thruster head, due to the shielding effect of the cathode. This effect is particularly strong for \(E=1.1869\) J. The presence of magnetic fields prevents the arc from moving inside the thruster head. Thus, less material is shielded by parts of the thruster head and a higher thrust level can be measured.
# A Robust Security Architecture for SDN-Based 5G Networks
## 5 Performance Evaluation
### Replay Attack  
Finally, we show the simulation results under Replay attack in Figures 12 and 13. In a replay attack, we control the attacker to eavesdrop on the communication channel and record the messages from devices, and then let the attack repeat the associated message to the SecE. We vary the number of attacks from 0 to 500 and record the associated disconnect rate. The overall simulation result is shown in Figure 12, as we can see that, when the number of attacks reaches 500, the disconnect rate is lower than 0.1%. Then, we compare the performance of _Robust_ with two state-of-the-art ones. For clarifying,  
Figure 11: Comparison under MITM attack. (**a**) sparse; (**b**) moderate; (**c**) dense.  
Figure 10: Performance under MITM attack.  
we take the case of _moderate_ as an example. As we can see, _Robust_ has the lowest disconnect rate under replay attack. In the worst case, our scheme has a disconnect lower than 0.01%, whose value is the lowest one among the three security architectures mentioned. For the two other architectures, this rate is about 75% for \(TFSv1\) and 8% for \(SDSecurity\). Although the disconnect rate of \(SDSecurity\) is low in moderate, its performance will decrease a lot with a disconnect rate of 73% in the _dense_ scene. These observations indicate our scheme can have better performance when used to detect replay attacks. Considering the outstanding performance of our scheme under the three security attack mentioned in SDN architecture, we say that our scheme can meet the requirement of commercial use.
## 3 Results
### The Impact of LUCs on ESV  
LA can measure the extent of land use within a specified area. A higher LA value indicates a greater degree of land use and higher intensity of human activities, while a lower value suggests a lesser degree of land use and lower intensity of human activities. As shown in Figure 7, the Moran's I index was negative for the years 2000-2020, with the majority of the scatter plots located in the second quadrant. The index increased year by year, indicating a significant local spatial negative correlation between LA and ESV in the study area, with the correlation strengthening over the years. The cluster map shown in Figure 8 reveals that the clustering patterns over the 20-year period were primarily low-high and high-low patterns. The low-high pattern was mainly distributed in water bodies, while the high-low pattern was predominantly found in built-up land and farmland, showing a distinct characteristic of ESV distribution based on land use types. Areas with lower ESV exhibit a spatial coupling relationship with regions of higher land use intensity, reflecting the erosion of natural ES functions by human development activities.  
Through the analysis of [PERSON]'s I index and cluster maps from 2000 to 2020, it is observed that with the advancement of urbanization, the spatial differentiation of land use  
Figure 7: Bivariate spatial autocorrelation scatter plot of LA and ESV.  
and ESV has gradually intensified, with a more distinct contrast between high and low ESV areas. The low-high cluster pattern shows that water areas remain the core of high ESV; however, they are increasingly threatened by human activities, which impact the ecological functions of aquatic systems. The high-low cluster pattern has strengthened year by year, reflecting the growing encroachment of built-up land on ES functions, particularly in the central cities and surrounding areas of the study area.