esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Surface Treatment of Titanium  
Results of different surface treatments of samples were determined by FE-SEM (Figure 2). The untreated titanium sheet had a smooth, flat surface. After anodization (TNTs) treatment, a TNTs structure with a diameter of about \(18\pm 1.1\) nm appeared on the surface. The formation of the nanotube structure could be divided into the formation of titanium dioxide and the chemical attack causedby fluoride ions in the electrolyte. In past research, the diameter of TNTs could be controlled by the voltage and electrolytic solution. In this study, the conditions of the electrolysis reaction were not adjusted, so the hole sizes were not changed. After both alkali treatment and acid treatment of the titanium sheet, a uniform nanoporous network structure was observed on the surface. Calculating the large pores of the titanium sheet after different surface treatments showed that acid treatment produced the largest pore structure followed by alkali treatment and anodization (Figure 3). The results of the surface elemental analysis showed that the pure titanium sheet had only titanium and a little carbon, indicating that the sample surface was not contaminated (Table 2). A small amount of the fluorine element remained on the surface of TNTs due to erosion of fluoride ions in the electrolyte. After alkali treatment, the surface contained sodium because of the phenomenon of sodium titanate (Na\({}_{2}\)Ti\({}_{5}\)O\({}_{11}\)) being formed on the surface of titanium metal after alkali treatment [26]. Finally, only titanium was observed on the surface after acid treatment. This may have been due to corrosion of the entire surface oxide layer by hydrochloric acid [27].  
Figure 3: The holes size of the titanium sheet after different surface treatments. Means with different letters were significantly different (\(p<0.05\), mean \(\pm\) SD, \(n=10\)).  
Figure 2: Surface topography of the titanium sheet with different surface treatments and samples after LAB treatments (calcium-to-phosphorus (CaP) solution and cobalt-substituted hydroxyapatite (CoHA) solution) by SEM.
## 3 Dynamics Modeling and Parameter Optimization of Double Motors Driving System
### Dynamics Simulation of Dual Drive Motors with Gear Clearance  
This section will analyze the influence of gear gap on C-axis drive system of super-heavy vertical milling lathe based on the established double-motor dynamics model. A backlash module was added between the corresponding two drive gears and large gear in Matlab Simulink to study the actual situation of dual motor drive under the influence of backlash. The control block diagram is shown in Figure 3.  
Figure 4 shows the response curve of the system under the input of unit step signal and sinusoidal signal when the PID parameter of the system is not adjusted. The output response of the normal unit step signal (Figure 4a) is compared with that of the unit step signal obtained under the condition of backlash (Figure 4c). It is found that the introduction of the non-linear link of the gear gap reduces the overruns of the dual motor master-slave control system, but also increases the response time of the system. In addition, the increase of the backlash parameter will lead to the instability of the control system and cause continuous oscillation. Under the condition that the integral time constant remains unchanged, the oscillation frequency of the step response of the system increases with the increase of the proportionality coefficient \(K\), and the oscillation amplitude increases gradually. If the proportionality coefficient \(K\) is too small, then the oscillation frequency of the system is very low,  
but the adjustment time is too long, which cannot meet the actual demand of the high followability of the C-axis control system. While the proportion coefficient \(K\) is set too large, the system has a short adjustment time, but the overshoot and oscillation amplitude are large, the oscillation frequency is high, and the control is unstable. By comparing the response results of the sinusoidal signals in Figure 4b,d, it can be seen that the presence of the non-linear link of the backlash prolongs the response time of the system, reduces the amplitude, and reduces the followability of the C-axis control.  
After PID parameter tuning, the response of the system to unit step signal and unit sinusoidal signal is shown in Figure 5a,b. It can be seen that, after parameter setting, the dual motor drive control  
Figure 4: Tracking responses of unit step signal and sinusoidal signal considering gear clearance.  
Figure 3: The control system diagram of dual motor considering the gear clearance.  
system can get better time domain control index. As can be seen from Figure 5a, after tuning PID parameters, the signal's rising time \(t_{r}\) = 1.557 s, delay time \(t_{d}\) = 1.243 s, adjustment time \(t_{s}\) = 3.1 s, overshooting quantity \(p\) = 17%, and the number of oscillations \(N\) = 2.
## 2 Transmission Mode Analysis and System Modeling
### Simulation Model Theoretical Modeling  
One way to build an engine model is to perform a theoretical analysis of the engine at different times and use mathematical models to model and analyze it, but the engine is a complex nonlinear system, so the difficulty of the modeling and calculation is significant [14]. Another approach leverages bench test data to construct an interpolation model, providing a relatively simple and intuitive method for implementation [15].  
An interpolation modeling method, grounded in bench test data, is employed to facilitate the development of an accurate engine model in this research. A series of dynamometer bench tests were performed on a Weichai WP6.180E40 engine, with the primary objective of recording engine output speed, torque, and throttle opening data. A 3D map correlating engine speed, torque, and throttle opening was then generated using data interpolation, as depicted in Figure 7.  
Engine output torque can be numerically modeled as:  
\[T_{e}=f_{1}(\alpha,n_{e}) \tag{6}\]  
where \(\alpha\) represents throttle opening; \(T_{e}\) represents engine torque; \(n_{e}\) represents engine speed.  
The starting resistance moment is added to simulate the cold starting process of the engine, as shown in Figure 8. With the increase in the starting speed, the average backward towing torque generated also increases, and the reverse towing torque eventually presents an approximate linear relationship with the increase in engine speed [16].  
Figure 6: Speed regulation characteristic curve.  
Figure 7: Engine theoretical model.  
The hydraulic system consists of a variable pump and a quantitative motor. This article sets the closed circuit of the hydraulic system in an ideal state, ignoring the circuit volume and mechanical losses. The pump and motor are set to have no friction loss and leakage during operation. Therefore, the output flow rate \(Q_{P}\) of the pump is equal to the input flow rate \(Q_{M}\) of the motor.  
\[Q_{P}=Q_{M} \tag{7}\]  
The quantitative motor displacement is shown in Formula (8):  
\[D_{M}=\frac{2\pi\cdot T_{M}}{\Delta p\cdot\eta_{M}} \tag{8}\]  
where \(D_{M}\) is the quantitative motor displacement; \(T_{M}\) is the output torque of the quantitative motor; \(\Delta p\) is the quantitative motor working pressure; \(\eta_{M}\) is the quantitative motor mechanical efficiency.  
The quantitative motor flow rate is presented in Equation (9):  
\[Q_{M}=\frac{D_{M}\cdot n_{m}}{1000\cdot\eta_{v1}} \tag{9}\]  
where \(n_{m}\) is the output speed of the quantitative motor; \(\eta_{v1}\) is the volumetric efficiency of the motor.  
Variable pump displacement is Formula (9):  
\[D_{P}=\frac{1000\cdot Q_{P}}{n_{P}\cdot\eta_{v2}} \tag{10}\]  
where \(D_{P}\) is variable pump displacement; \(n_{P}\) is variable pump input speed; \(\eta_{v2}\) is the variable pump volumetric efficiency.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 2 Related Work
### Technical Analysis Models  
Traditional financial market prediction methods primarily rely on technical analysis, which uses historical price and volume data. Models such as ARIMA, GARCH, and hybrid approaches incorporating LSTM have been extensively applied. For example, [PERSON] and [PERSON] (2018) [2] integrated LSTM with GARCH-type models to predict stock index volatility. While these models are effective for time series analysis, they often overlook the potential integration of exogenous variables, such as market sentiment or macroeconomic indicators, which could enhance predictive accuracy by capturing additional dimensions of market behavior.  
These models are not inherently limited from including external variables. For example, methods like ARIMAX (ARIMA with exogenous variables) and GARCH-X have shown that it is possible to integrate outside factors into statistical models, thereby improving their ability to explain market behavior. Likewise, LSTM architectures can easily handle multiple inputs, making them ideally suited for frameworks incorporating elements such as GARCH, VIX, or sentiment indices. The research by [PERSON] and [PERSON] (2018) [5] emphasizes the adaptability of LSTMs in managing multivariate time series; they effectively capture temporal relationships while considering the significance of external influences using an attention mechanism. This capability positions LSTM architectures as a natural choice for financial settings demanding integration of diverse data sources.  
Additionally, the work of [PERSON] et al. (2024) [6] demonstrates the advantages of combining LSTM with techniques like CEEMDAN to decompose and integrate exogenous features, further improving the accuracy of financial time series predictions. The results indicate that LSTM models, when coupled with carefully selected exogenous variables, outperform traditional methods by capturing complex, nonlinear dependencies in market dynamics.  
In the proposed framework, the exogenous variables incorporated are specifically limited to GARCH, representing historical volatility, and VIX, reflecting implied market volatility. These variables are directly integrated into the LSTM, providing a targeted yet robust approach to capturing the external dynamics influencing market states. This focused selection ensures that the framework remains computationally efficient while addressing critical aspects of market variability. By leveraging the strengths of LSTM for multivariate analysis, the framework achieves a balance between model complexity and predictive performance.
# Feasibility of Obtaining Surface Layer Moisture Flux Using an IR Thermometer
## 3 Discussion of Statistical Analysis and Measurement Results
### Single Factor ANOVA for Three Devices for Multiple Means Comparisons  
This section largely deals with analysis of variance for comparison of the controls (i.e., Kestrel and Sling Psychrometer) and test (i.e., IR Thermometer) groups for dry and wet-bulp temperatures measured above grass, concrete and sand surfaces, the details of which are shown in Table 2a,b. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Dry-Bulb Temperature (\({}^{\circ}\)C)** & **Wet-Bulb Temperature (\({}^{\circ}\)C)** \\ \hline
**Surface Type** & **Control Group** & **Kestrel** & **Sling Psych** & **Kestrel** & **Sling Psych** \\ \hline \multirow{8}{*}{Grass} & Mean & 25.816 & 24.521 & 18.910 & 19.128 \\  & Sdev (\(\pm\)) & 2.930 & 3.320 & 2.710 & 2.800 \\  & t-stat & 1.575 & & \(-\)0.302 & \\  & \(p\) (T \(\leq\) t) one-tail & 0.060 & & 0.382 & \\  & t-critical one-tail & 1.673 & & 1.673 & \\ \hline \multirow{8}{*}{Concrete} & Mean & 23.668 & 22.770 & 18.378 & 18.333 \\  & Sdev (\(\pm\)) & 4.640 & 4.930 & 3.500 & 3.470 \\ \cline{1-1}  & t-stat & 0.797 & & 0.055 & \\ \cline{1-1}  & \(p\) (T \(\leq\) t) one-tail & 0.214 & & 0.478 & \\ \cline{1-1}  & t-critical one-tail & 1.670 & & 1.667 & \\ \hline \multirow{8}{*}{Sand} & Mean & 25.364 & 24.747 & 18.944 & 18.763 \\ \cline{1-1}  & Sdev (\(\pm\)) & 3.240 & 3.750 & 2.740 & 2.960 \\ \cline{1-1}  & t-stat & 0.412 & & 0.1469 & \\ \cline{1-1}  & \(p\) (T \(\leq\) t) one-tail & 0.342 & & 0.441 & \\ \cline{1-1}  & t-critical one-tail & 1.725 & & 1.725 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Paired _t_-test for a sample assuming equal variances between two control groups (Kestrel and Sling Psychrometer) measured for three different surfaces (\(\alpha\)\(\leq\) 0.05). Table 2a,b show dry-bulb (DB) and wet-bulb (WB) temperature measurements by the control instruments and test instrument groups over grass, concrete and sand surface areas. The means of the dry-bulb temperatures measured by the Kestrel and Sling Psychrometer (control groups) and by the IR Thermometer (test group) for grass, concrete or sand are not statistically different at \(p\leq 0.05\), primarily for two reasons: (i) the calculated \(p\)-values (0.419 for grass) appear to be much larger than the probability level at 5%; and (ii) the F-ratio between groups and within groups (0.879) is much smaller than the F-critical (3.101) for the grass area. The same trends can be observed for the Concrete and Sand surfaces in Table 2a. The means of the wet-bulb temperatures measured by the Kestrel and Sling Psychrometer (control groups) and by the IR Thermometer (test group) for grass, concrete or sand shown in Table 2b are also not statistically different at \(p\leq 0.05\) for two reasons similar to those of the dry-bulb cases: (i) the calculated \(p\)-values (0.367 for Grass) appear to be
# Coaxial Fibres Incorporated with Phase Change Materials for Thermoregulation Applications
## 3 Results  
### Degree of Substitution for CAt  
The process of verifying the degree of substitution for the CAt was carried out by initially separating 0.1 g of the recycled CAt in a flask and adding 5 mL of NaOH solution (0.25 mol/L) and 5 mL of absolute ethanol. After the samples had rested for 24 h, 10 mL of HCl solution (0.25 mol/L) was added and left to rest for 30 min. Finally, three drops of phenolphthalein solution were added to each of the samples, and the acid-base titration was carried out by shaking the samples and using the standardised NaOH (0.25 mol/L) solution until a pink colour was achieved. [PERSON] et al. (1989) stated that by calculating the degree of acetylation (%\(GA\)), it is possible to determine the degree of substitution of the CAt, since a %\(GA\) of 43.5% is associated with a degree of substitution (DS) of 2.88 ([PERSON] et al., 1989). Thus, the DS of the CAt samples was calculated, and the results are shown in Table 1.  
As can be seen, the recycled CAt showed a DS of \(\approx\)2.83 (very similar to the theoretical value) and a %\(GA\) of 42.80%. These results show that the acetyl groups partially replaced the hydroxyl groups through an acetylation reaction, making it possible to obtain the recycled CAt. These results are in line with those of the commercial samples. CA is the most important ester of cellulose, with the OH groups of cellulose being replaced with acetyl groups at a DS of approximately 2.50 ([PERSON] et al., 1989).  
\begin{table}
\begin{tabular}{c c c} \hline
**Samples** & **\%\(GA\)** & **DS** \\ \hline CAt & 42.70 & 2.83 \\ CAt\({}_{1}\) & 42.74 & 2.83 \\ CAt\({}_{2}\) & 42.96 & 2.84 \\ Average & 42.80 \(\pm\) 0.14 1  & 2.83 \(\pm\) 0.01  \\ \hline \end{tabular}
\end{table}
Table 1: Degree of acetylation and degree of substitution for synthesized CAt.  
During the testing of the DS of recycled CAt, it was shown that there can be a large amount of intermolecular interaction between the two hydroxyl groups, between two acetyl groups, and between the hydroxyl and an acetyl. These intermolecular interactions contribute to the packing and movement of the cellulose acetate chains. Since all positions are approximately uniformly acetylated, the type and number of intermolecular interactions are also distributed uniformly throughout the polymer [38]. Therefore, due to the results obtained, recycled CAt can be tested in the manufacturing of fibres.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 5 Issues and Challenges in 5G and Edge-Based V2X
### Low-Latency  
The network providers need to offer low-latency network slicing services for enterprise or vehicle users in order to satisfy the MEC application specifications for very low latency, high security, and high reliability. The MEC sliced network consists of 5G base stations (gNBs), a vehicle network (between gNBs and MEC) and User Plane Function (UPF). The network service flows from all of the networkingdevices and the vehicular network to reach MEC. The fewer networking devices the packet goes through, the faster the slicing, and the shorter the transmission delay.
# Unveiling the Core Patterns of High-Latitude Electron Density Distribution at Swarm Altitude
## 1 Introduction
It is critical to identify and differentiate the various physical mechanisms influencing electron density dynamics in order to build more accurate ionospheric models of the middle and high latitude region and to improve existing ones. In this context, it is critical to understand the scales at which these mechanisms operate, their relative importance, and their reliance on external parameters. In this paper, we use data from the Swarm mission to apply the Multivariate Empirical Mode Decomposition (MEMD) method to the electron density distribution in the middle and high latitude ionospheric region. Our goal is to identify and isolate the various contributors and spatial scales that have an impact within this area. The MEMD technique allows the main intrinsic modes in the ionospheric density distribution to be identified; these are associated with different spatial scales, and influence the plasma density dynamics in the selected region. Having extracted these modes, their relative contributions to the original distribution can be assessed, allowing the most significant modes to be identified along with the spatial scales at which they operate. We have concentrated our research on the period from 1 January 2016 to 31 December 2021, which was characterized by a low level of solar activity. This choice allows the effect of other parameters on the electron density distribution to be investigated in greater depth under similar solar activity conditions. Specifically, we looked at how the resulting modes change with seasonal and geomagnetic activity conditions.
## Results
### Ultrastructural Effects  
SEM (Figure 1) and TEM (Figure 2) imaging were used to assess the occurrence of ultrastructural changes in _L. infantum_ promastigotes after 7 h incubation in presence or absence of _L. luisieri_ EO.  
Regarding SEM findings, untreated cells (control) displayed elongated body shape and anterior flagella (Figure 1A,B), while EO-treated ones (Figure 1C-F) displayed round (Figure 1D,E) and aberrant forms (Figure 1C), with cell body septation (Figure 1 D). Note the uneven bubble-forming surface of all treated parasites. On the other hand, considering TEM imaging, control parasites evidenced normal nucleus, kinetoplast, mitochondria, and flagellar pocket (Figure 2A). Cytoplasmic organelles disorganization (Figure 2B,C,F), in addition, an increase in cytoplasmic clearing (Figure 2B,F) was the most prominent ultrastructural effect observed in _L. luisieri_-treated cells. A marked raise was also found in a number of autophagosomal structures, featured by intense cytoplasmic vacuolization (Figure 2F). EO-treated parasites also evidenced cell body (Figure 2F) and mitochondria (Figure 3B,D,F) swelling. Indeed, single and highly branched mitochondria swelling led to inner mitochondrial membrane disorganization, as shown by the presence of complex invaginations and concentric membranous structures formation (Figure 2B,D,F), and, lastly, mitochondria clearing (Figure 2B,D). Another common alteration was kinetoplast swelling and clearing (Figure 3B,D,E).  
Figure 1: Scanning electron micrographs of _Leishman infantum_ promastigotes exposed to _Lavandula luisieri_ essential oil. **(A,B)** Untreated cells showing the typical elongated shape, parasite body and anterior flagella; **(C–F)**, Treated promastigotes. Note round forms and aberrant forms **(C–F)** with cell body septation **(D)**. Note the irregular surface. **(A–F)** Bars = 5 \(\upmu\)m.
## 3 EQPD-MAC Protocol Development
### Castalia Implementation and Simulation
#### 3.4.2 Simulation Process  
The simulation runs for 10,000 s in a 200-by-200-m area and comprises 46 nodes, 45 of which are randomly distributed. The SINK node, which has the ordinal number of 0, is located at the center of the sensor field.  
1. First, navigate into the directory Castalia/Simulations/radioTest, where the omnetpp.ini file is located. Modules such as Routing, Application, and MAC protocol must all be included in the simulation's omnetpp.ini file. The file allows the simulation to be executed in Castalia. The simulation parameters are recorded in this file. For example, the simulation area, the total number of sensor nodes, the simulation period, the packet rate, the initial energy, and so on.  
sim-time-limit = 10,000 s  
SN.field_x = 200  
SN.field_y = 200  
SN.numNodes = 46  
SN.deployment = \"[0]->center;[1..45]>uniform\"
2. In order to start the simulation, open a terminal window in Ubuntu and change the directory to the -/Castalia/Simulations/radioTest$ and enter the following commands:../../bin/Castalia -c EQPD, AODV
3. Castalia creates two files once the simulation is complete: a text file (e.g., 220117-030436.txt) and a CastaliaTrace file. The text file provides information on the overall energy consumption of each sensor node, the number of packets received, the initial energy, and so on. CastaliaTrace file contains all the events of interest that was specified in the code to be viewed, mainly for verification and validation purposes. The EQPD.cc file is the main source of the program's code from which trace instructions can be included. CastaliaTrace, for example, may contain whether the Tx-beacon or Data packet is transmitted or, if the Rx-beacon is received from the sink node, or to see the computation of the average end-to-end latency of the packets, etc.
4. To observe the packet delivery ratio and how much energy each node consumed, the following instructions are typed into the terminal window in Ubuntu.  
../../bin/CastaliaResults -i 220117-030436.txt -s energy  
../../bin/CastaliaResults -i 220117-030436.txt -s packetsAfter each simulation run, the results are verified in this manner.
# A Simple Analysis Method of Specific Anammox Activity Using a Respirometer
## 2 Materials and Methods  
### Anammox Granules  
Anammox granules of the laboratory-scale anammox cultivation reactor (working volume 36 L), which has been operating with 200 mgNH\({}_{4}{}^{+}\)-N and 200 mgNO\({}_{2}{}^{-}\)-N/L were used in this study. This reactor was operated in a continuous stirred mode under 8-10 h of HRT. A nitrogen removal efficiency of approximately 85% and nitrogen removal rate of 0.82-1.02 kg/m\({}^{3}\)/day were achieved. Moreover, the granules were enriched with uncultured anammox bacterium (68.1%), Ca. _Procedia_ (8.6%), Ca. _Jettenia_ (1.9%), and Ca. _Kuenenia_ (2.6%).