esa-sceva/satcom-chunk-collection huggingface dataset:
# Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation
## 3 Results and Discussion  
### Pore Solution Analysis  
#### 3.1.1 Pore Solution Chemistry  
The ion concentrations detected in the leaching solution are shown in Table 3. The main ions in OPC and CSA are similar, including Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), SO\({}_{4}\)\({}^{2-}\), and OH\({}^{-}\). However, it should be noted that a type of ion containing the element of aluminum was detected in CSA. The ion was identified as Al(OH)\({}_{4}\)\({}^{-}\). Details on this ion will be discussed in Section 3.1.3. The ion concentration of these two cements are totally different. The contents of alkalis are similar, but the concentration of OH\({}^{-}\) reached from CSA was much lower than that of OPC, which leads to the low alkalinity in CSA. Both cements had a high initial concentration of SO\({}_{4}\)\({}^{2-}\), but OPC showed a rapid depletion of sulfate. After one day, the concentrations of SO\({}_{4}\)\({}^{2-}\) were negligible. In the case of CSA, the amount of SO\({}_{4}\)\({}^{2-}\) decreased initially from 5 min to 1 d, then the content rose again. Finally, the value stayed at 8.09 mmol/L at day 28. Al(OH)\({}_{4}\)\({}^{-}\) plays a significant role in the CSA leaching solution, with the second highest concentration. There is a potential relationship between Al(OH)\({}_{4}\)\({}^{-}\) and the solution alkalinity. When the concentration of Al(OH)\({}_{4}\)\({}^{-}\) increased after 3 h, the concentration of hydroxide also significantly higher. This kind of relationship was also found in previous research works [17; 23]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Composition**} & \multicolumn{3}{c}{**Concentration of Compounds (mol/L)**} \\ \cline{3-5}  & & **NaOH** & **Na\({}_{2}\)SO\({}_{4}\)** & **NaAlO\({}_{2}\)** \\ \hline
**Alkalinity** & \(x\) M NaOH & 0.1, 0.01, 0.001 & - & - \\ \hline \multirow{4}{*}{**Salfate**} & 0.1 M NaOH + \(x\) M Na\({}_{2}\)SO\({}_{4}\) & - & 0.01, 0.02, 0.03, & - \\ \cline{2-5}  & 0.01 M NaOH + \(x\) M Na\({}_{2}\)SO\({}_{4}\) & & 0.04, 0.05 & \\ \hline \multirow{4}{*}{**Aluminate**} & 0.1 M NaOH + 0.05 M Na\({}_{2}\)SO\({}_{4}\) & - & - & 0, 0.05, 0.1, 0.15, 0.2 \\  & + \(x\) M NaAlO\({}_{2}\) & & - & 0.01, 0.005, 0.01, \\ \cline{2-5}  & 0.01 M NaOH + 0.05 M NaAlO\({}_{2}\) & & - & 0.015, 0.02 \\ \cline{2-5}  & 0.001 M NaOH + 0.05 M NaAlO\({}_{2}\) & & - & 0, 0.0005, 0.001, \\ \cline{2-5}  & 0.001 M NaOH + 0.05 M NaAlO\({}_{2}\) & - & - & 0, 0.0005, 0.001, \\ \cline{2-5}  & Na\({}_{2}\)SO\({}_{4}\) + \(x\) M NaAlO\({}_{2}\) & & - & 0.0015, 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Constitutions of simulated pore solutions.
# Optical Boundaries for LED-Based Indoor Positioning System
## 2 System Model
### Problem Description  
In this section, the problems with indoor positioning at the boundaries of the LED footprints are identified. Given that the distance between the transmitter and receiver plane \(h\) is 3 m, the plots of the normalized received optical power of two LEDs (OSRAM SFH 4554 and VISHAY TSFF 5510 called LED\({}_{1}\) and LED\({}_{2}\)) with the properties given in Table 2 are shown in Figure 3. The normalized received  
Figure 2: Optical positioning system with LED transmitter and photo-detector (PD) receiver.  
optical power is the ratio of the received optical power to the peak received optical power. Taking the region beyond which the optical power is not detectable as the optical boundary. Peak optical power is received at the 0\({}^{\circ}\) angle of incidence point for both LEDs. The received optical power starts to reduce, as the mobile receiver moves towards the half angle. At the half angle, the optical power is still sufficiently high to give accurate positioning. Therefore, this angle is not suitable in defining the optical boundary for indoor positioning. At the full angle, which is twice the half angle (20\({}^{\circ}\) for LED\({}_{1}\) and 76\({}^{\circ}\) for LED\({}_{2}\)), the normalized optical power for LED\({}_{1}\) is 0.05, while that for LED\({}_{2}\) is almost 0. These inconsistencies around the half- or full-angle-based boundaries of the LED cause a mobile receiver to perform inconsistently when it is in the boundary region. In addition, wearable mobile receivers are subject to tilting. The received optical power as the PD moves along the horizontal plane is presented in Figure 4 for when the PD in Figure 2 is tilted at 0\({}^{\circ}\), 20\({}^{\circ}\), 40\({}^{\circ}\), and 60\({}^{\circ}\) to the right of LED\({}_{2}\). The boundary for positioning is seen to vary with the angle of tilt for a receiver. Consequently, neither the half angle nor full angle is enough to determine the boundary of proximity-based IPS. In view of this, a threshold angle, based on the receiver design, which suffices in determining the boundaries for positioning is defined in this work.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Light Emitting Diode (LED)** & **SFH 4554** & **TSFF 5510** \\ \hline Half angle \(\Phi_{1/2}\) & \(\pm 10^{\circ}\) & \(\pm 38^{\circ}\) \\ Peak wavelength \(\lambda_{p}\) & 860 nm & 870 nm \\ Total radiant power \(P_{t}\) & 70 mW & 55 mW \\ Rise and fall time \(t_{r}\),\(t_{f}\) & 12 ns & 15 ns \\ \hline Photodetector (PD) & \multicolumn{2}{c}{TSOP 38238} \\ Peak wavelength \(\lambda_{p}\) & \multicolumn{2}{c}{950 nm} \\ Minimum irradiance \(E_{(emin)}\) & 0.12 mW/m\({}^{2}\) \\ Detector physical area \(A\) & 1 cm\({}^{2}\) \\ Refractive index \(n\) & 1.5 \\ Field of View \(\varphi_{c}\) & 90\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for simulation  
Figure 3: Normalized received optical power for LEDs with a half angle of 10 and 38\({}^{\circ}\) and a horizontally moving receiver on a plane at a distance 3 m from the transmitter.
## 4 Results
### Global Explainability Results  
#### 4.1.1 Accuracy and Fidelity Trade-Off  
Table 1 presents the accuracy (AUC) and fidelity scores of our pre hoc and co hoc frameworks compared to the baseline white-box (WB) and black-box (BB) models. We observe that both of our proposed frameworks achieve higher fidelity scores than the original black-box model while maintaining comparable accuracy. The co hoc framework consistently outperforms the pre hoc framework in terms of fidelity across all datasets, demonstrating the effectiveness of joint optimization.  
The results confirm that our proposed approaches successfully maintain high accuracy while significantly improving fidelity. Notably, the co hoc framework achieves fidelity improvements of 10.9%, 6.8%, and 10.9% on ML-100k, ML-1M, and HELOC datasets, respectively, compared to the original black-box model. This demonstrates the effectiveness of our joint optimization approach in aligning the behaviors of the black-box predictor and white-box explainer models.
## 1 Introduction
### Notation  
\[\vec{n}=\{1,2,\cdots,n\},\]\[\mathbf{R}_{+}=\left\{z\in\mathbf{R}\::z>0\right\}\,;\,\mathbf{R}_{0+}=\left\{z\in\mathbf{R}\::z\geq 0\right\},\]  
\(sp(A)\) and \(\det(A)\) are the spectrum and determinant of \(A\in\mathbf{R}^{n\times n}\), respectively. For \(A\in\mathbf{R}^{n\times t}\), being in general rectangular, \(\|A\|\) denotes any unspecified norm of \(A\), \(\|A\|_{2}\) denotes the \(\ell_{2}\) or spectral norm of a matrix \(A\), \(\rho(A)\) denotes its spectral radius, and \(\|.\|_{\infty}\) denotes the \(H_{\infty}\)-norm of a stable real rational transfer matrix or function, \(I_{q}\) denotes the \(q\)th identity matrix, and \(\mathbf{i}=\sqrt{-1}\) is the complex imaginary unit.  
Let \(A\in\mathbf{R}^{n\times n}\) be symmetric. Then, \(\lambda_{\max}(A)\) and \(\lambda_{\min}(A)\) are, respectively, the maximum and minimum eigenvalues of \(A\).  
\(M_{n}^{n\times n}\) is the set of Metzler matrices (any off-diagonal entry is non-negative) of \(n\)th order.  
\(Z^{n\times n}\) is the set of \(Z\)-matrices (any off-diagonal entry is non-positive) of \(n\)th order.  
\(M^{n\times n}\) is the set of \(M\)-matrices (\(Z\)-matrices which are stable or critically stable) of \(n\)th order.  
Assume that \(A=\left(A_{ij}\right),B=\left(B_{ij}\right)\in\mathbf{R}^{n\times n}\). Then, the notations \(A\)\(\geq\)\(B\), \(A\)\(>\)\(B\) and \(A\)\(>>\)\(B\), are, respectively, equivalent to \(B\)\(\leq\)\(A\), \(B\)\(<\)\(A\) and \(B\)\(<<\)\(A\), meaning that \(A_{ij}\geq B_{ij}\), \(A_{ij}\geq B_{ij}\) (and \(B\
eq A\)) and \(A_{ij}>B_{ij}\); \(\forall i,j\in\overline{n}\), respectively. In particular, \(A\)\(\geq\)\(0\), \(A\)\(>\)\(0\) and \(A\)\(>>\)\(0\) are reworded as \(A\) is non-negative, positive and strictly positive, respectively, and \(A\)\(\leq\)\(0\), \(A\)\(<\)\(0\) and \(A\)\(<<\)\(0\) are reworded as \(A\) is non-positive, negative and strictly negative, respectively.
# Discrete Time Model for Process Meta Language with Fictitious-Clock
## 2 Related Work
Each timer is represented with a variable of the short data type. Value of the variable defines the number of ticks before the timer expires at value zero. Expired timer becomes inactive with the variable set to \(-1\). Extension introduces six macros, that can be used to manipulate the timers (Listing 1):  
1. set(tmr,value) is used to activate a timer with the number of ticks set by the value,
2. expire(tmr) evaluates if the timer has expired,
3. reset(tmr) deactivates the selected timer,
4. delay(tmr,value) activates the timer and blocks execution of the process until its expiration,
5. udalay(tmr) non-deterministically delays execution for an unbounded number of ticks or continues with execution without delay,
6. bd delay(tmr,val,auxtmr) implements bounded delay with the use of two timer variables. ```
1#defineOFF(-1)
2#defineTOVAL(0)
3
4 typedeftimer
5{
6 shortval=OFF;
7}
8
9#defineset(tmr,value)tmr.val=value
10#defineexpire(tmr)(tmr.val=TOVAL)
11#definereset(tmr)tmr.val=OFF
12
13#definedelay(tmr,value)set(tmr,value);expire(tmr)
14#defineundelay(tmr)do::delay(tmr,1)::break;od
15
16#definebdelay(tmr,val,auttmr)\
17 set(tmr,val);{
18 do
19::expire(tmr)->break\
20::else->if::break;::delay(auttmr,1);fi\
21
22
23 activeproctype_Timers()
24{
25 end_Timers:do::timeoutod;
26}
```  
**Listing 1** DT Spin macros. Timers of a simple SDL system could be modeled in DT Promela only with the macros set, reset, and expire. In SDL, expired timer appears as a signal in the input port of the process instance. In [12] the authors describe how to use DT Promela timers (variables) to model timers in SDL (signals). For each timer, expiration is modeled with an additional guard for the timer expiration at choice statements that correspond to the states with no outgoing transitions. If the timer should be discarded, transition is made to the same state, otherwise, transition is taken upon timer expiration. In SDL, time advances only when there is no possible transition, therefore this presents an equivalent model. However, to implement support for timer parameters, save construct, asterisk state, asterisk input, implicit transition, and enabling condition fully, our framework models timers as signals. A timer can be associated with parameters that are defined with expressions at the Set-node. The value of evaluated expression is received as a signal parameter at the expiration of the timer. The latest update of DT Spin at our disposal is an extension of the Spin tool version 4.1.1 that was kindly provided by the authors during the initial development of our Promela framework for SDL systems. DT Promela is supported by the _sdl2 pml_ tool, but the expiration of a timer is modeled with signals, e.g., ::expire(tmr) -> process_input_queue!tmr( _pid, parameter1, parameter2,...). To the best of our abilities, we did not find research papers that would include real-life SDL systems. Most of the research is based on simplified SDL models.
## 5 Results
### Case 1: Optimal Placement Evaluation  
We consider a sensor node \(s_{i}\) located at \((x_{i},y_{i},z_{i})\). Node \(s_{i+1}\) is located at \((x_{i}\pm j.r,y_{i}\pm j.r,z_{i+1}),j\in N\), \(r=4,\!000\) m for shallow water, \(r=10,\!000\) m for deep water, \(z_{i+1}\!<\!z_{i}\). The acoustic link \(l_{i\,i+1}\) is affected by a shadow zone with height \((z_{\textit{sc}-end}-z_{\textit{sc}-start})=9\) m, where \(z_{i}>z_{\textit{sc}-end}\) and \(z_{i+1}<z_{\textit{sc}-start}\). Node \(s_{i}\) is uncoupled into sensor nodes \(s_{i\to 1}\) and \(s_{i\to 2}\) and the optimal location of node \(s_{i\to 2}\) has been found using NLP solving the optimization problem **P1**.  
Figure 16 shows the average transmission loss as a function of frequency for shallow and deep water under different propagation phenomena. We have computed the transmission loss for the acoustic link \(l_{i\,i+1}\) (between \(s_{i}\) and \(s_{i+1}\)) affected by the shadow zone (shallow water \(+\) shadow zone or deep water \(+\) shadow zone). \(s_{i}\) is uncoupled and the optimal location for node \(s_{i\to 2}\) is found outside the shadow zone using our mathematical model; the transmission loss for the acoustic link \(l_{i\to 2\,\,i+1}\) (between \(s_{i\to 2}\) and \(s_{i+1}\)) (shallow water (optimal), deep water \(+\) convergence zone (optimal) or deep water \(+\) deep sound channel (optimal) has also been computed. The average transmission loss is increased when the frequency is increased and is higher for deep water \(+\) shadow zone than for shallow  
Figure 15: Energy consumption in the sensor displacement.  
water + shadow zone. It is also higher for deep than shallow water. The transmission loss values are higher than the transmission loss threshold for deep water+shadow zone, which means that the transmitted signal will not be properly received. The average transmission loss values are reduced significantly when the optimal locations are computed and the communication between sensor nodes is again restablished outside the shadow zone. The maximum transmission loss improvement is of 4.9 dB for shallow water, of 45.1 dB for deep water + convergence zone and of 42.9 dB for deep water + deep sound channel. Therefore, we can conclude that using our mathematical model deep water + convergence zone shows the best improvement in the diminishment of the transmission loss.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 5 Results
### Complexity
In addition, the _taskset_ command is used to bind the application process to one single core [48]. Three parameters are shown in Table 3. First, the total time complexity \(T_{\text{C}}\) in seconds. Second, the average time complexity at each iteration \(T_{\text{Iter}}\) in nanoseconds:  
\[T_{\text{Iter}}=\frac{T_{\text{C}}}{3\times 10^{8}}\times 10^{9}\qquad\text{[ns]} \tag{59}\]  
Finally, the added time complexity \(T_{\text{Added}}\) compared to the standard loop filter \(T_{\text{C}}^{\text{Standard}}\):  
\[T_{\text{Added}}=\frac{T_{\text{C}}}{T_{\text{C}}^{\text{Standard}}}\qquad \text{[times]} \tag{60}\]  
While this approach depends on how exemplary the algorithm's software implementation is, the results show a correlation between the number of operations and the time complexity.
## 3 System Overview
### Global SLAM Loop Closure  
The process to estimate pose in local SLAM always generates errors caused by the presence of grid map resolutions, sensor noises, and different map features which affect the accuracy. Although the error is small, after some prolonged distance of travel, it accumulates and creates a bigger error because the calculation is based on the total sum of every previous translation and rotation. The drift error becomes bigger as the travel distance is accumulated. By comparison, the global SLAM process tries to reduce this error using the optimization method called sparse pose adjustment (SPA) technique [26]. In this optimization method, a similar scan matcher in local SLAM is used but in a more extensive map range to cover the accumulated submaps (global map) for pose correction.
## 3 Quantitative Analyses of Disruptive Effects of Large Frequency Errors
### Effects on the Code Tracking Error Estimation  
When a large frequency tracking error occurs, if the coherent integration time is limited so as to not exceed the data bit interval, Equation (1) can be rewritten as follows:  
\[\begin{split} I_{\alpha}(n)&=AR(\tau_{\alpha}) \mathrm{sinc}(\pi f_{1}n_{1}T_{1})\cos(\phi_{1})+AR(\tau_{\alpha})\mathrm{sinc} (\pi f_{2}n_{2}T_{1})\cos(\phi_{2})+w_{\Omega\alpha,n}\\ Q_{\alpha}(n)&=AR(\tau_{\alpha})\mathrm{sin}\, \mathrm{c}(\pi f_{1}n_{1}T_{1})\sin(\phi_{1})+AR(\tau_{\alpha})\mathrm{sinc} (\pi f_{2}n_{2}T_{1})\sin(\phi_{2})+w_{\Omega\alpha,n}\end{split} \tag{22}\]  
where \(f_{1}\) is the trivial frequency error; \(f_{2}\) is the large frequency error; \(T_{1}\) denotes the pseudo-code period, where for the GPS L1 C/A, \(T_{1}\) is 1 ms; \(n_{1}\) and \(n_{2}\) are the number of pseudocode periods corresponding to \(f_{1}\) and \(f_{2}\), respectively; and \(\phi_{1}\) and \(\phi_{2}\) are phase errors. In Equation (22), for simplicity, it is assumed that large frequency errors are aligned with the pseudocode periods. Phase errors can be expressed as  
\[\begin{split}\phi_{1}&=\pi f_{1}n_{1}T_{1}+\theta_ {e}\\ \phi_{2}&=\pi f_{2}n_{2}T_{1}+2\pi f_{1}n_{1}T_{1}+ \theta_{e}\end{split} \tag{23}\]  
where \(\theta_{e}\) is the initial phase error.  
The autocorrelation amplitudes \(E\) and \(L\) in Equation (7) are attenuated by the \(\mathrm{sinc}(\cdot)\) term in the case of a large frequency error, resulting in an increase in the code discriminator error. Since the  
Figure 2: Estimated \(C\)/\(N_{0}\) attenuation caused by frequency tracking errors. The solid lines represent the theoretical analysis results, and Monte Carlo simulation results are represented by discrete points.  
probability distributions of \(E\) and \(L\) follow the complex Rice distribution, it is difficult to obtain the analytical expression of the effect of frequency error on the code discriminator. The Monte Carlo method was applied to numerical simulation analyses. For the code discriminator, the coherent integration time was set to 20 ms. The time range of large frequency errors was 0-20 ms, that is, in Equation (22), \(1\leq n_{2}\leq 20\) The true \(C\)/\(N_{0}\) was set to 40 dB-Hz.  
Figure 3 shows the root mean square (RMS) errors of the code discriminator in Equation (6) for 1000 Monte Carlo simulations. When the frequency error is close to 0, the RMS errors of the code discriminator is less than 0.04 chips. The maximum code discriminator output error caused by a frequency error is close to 0.2 chips, which is about five times that without a frequency tracking error.
## 2 Materials and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specification**} & \multirow{2}{*}{**Treatments**} & \multicolumn{3}{c}{**Cold Storage Period (Days)**} & \multicolumn{1}{c}{**Standard Error**} & \multicolumn{1}{c}{**ANOVA**} \\ \cline{3-6}  & & **1** & **7** & **10** & **of the Mean** & **(Two Factor: T and Z)** \\ \hline \multirow{3}{*}{Hardness 1 (N)} & K & 111.47 \({}^{\text{xy}}\) & 135.93 \({}^{\text{xy}}\) & 124.05 \({}^{\text{asy}}\) & 7.70 & \\  & MS & 125.55 \({}^{\text{xy}}\) & 139.23 \({}^{\text{x}}\) & 85.88 \({}^{\text{by}}\) & 8.45 & \(\text{Z}\times\text{T}^{*}\) \\  & SEM & 9.41 & 8.08 & 6.75 & & \\ \hline \multirow{3}{*}{Hardness 2 (N)} & K & 70.08 & 86.46 & 81.13 \({}^{\text{a}}\) & 6.36 & \\  & MS & 76.31 & 87.53 \({}^{\text{x}}\) & 60.89 \({}^{\text{by}}\) & 6.94 & \(\text{Z}\times\text{T}^{*}\) \\  & SEM & 4.10 & 8.16 & 7.70 & & \\ \hline \multirow{3}{*}{Rigidity 5 (N)} & K & 14.73 & 20.40 & 9.39 \({}^{\text{a}}\) & 1.72 & \\  & MS & 28.97 & 21.21 & 5.55 \({}^{\text{b}}\) & 2.10 & \(\text{Z}^{*}\) \\  & SEM & 1.34 & 2.59 & 1.81 & & \\ \hline \multirow{3}{*}{Rigidity 8 (N)} & K & 66.31 & 82.01 & 63.95 \({}^{\text{a}}\) & 5.20 & \\  & MS & 83.96 & 84.33 & 33.01 \({}^{\text{b}}\) & 4.47 & \(\text{Z}^{*}\) \\  & SEM & 3.35 & 4.75 & 6.42 & & \\ \hline \multirow{3}{*}{Adhesiveness (mJ)} & K & 1.74 & 2.29 & 2.07 & 0.47 & \\  & MS & 1.54 & 1.74 & 2.09 & 0.38 & - \\  & SEM & 0.23 & 0.29 & 0.76 & & \\ \hline \multirow{3}{*}{Cohesiveness} & K & 0.27 & 0.25 & 0.28 & 0.05 & \\  & MS & 0.23 & 0.25 & 0.26 & 0.08 & - \\  & SEM & 0.06 & 0.04 & 0.10 & & \\ \hline \multirow{3}{*}{Springiness (mm)} & K & 3.39 & 3.41 & 4.10 & 0.88 & \\  & MS & 2.82 & 3.97 & 4.02 & 0.91 & - \\  & SEM & 0.92 & 0.91 & 0.86 & & \\ \hline \multirow{3}{*}{Resilience} & K & 0.22 & 0.20 & 0.21 & 0.09 & \\  & MS & 0.13 & 0.16 & 0.27 & 0.06 & - \\  & SEM & 0.08 & 0.09 & 0.07 & & \\ \hline \multirow{3}{*}{Chewiness (mJ)} & K & 100.99 \({}^{\text{xx}}\) & 106.96 \({}^{\text{ax}}\) & 146.58 \({}^{\text{ay}}\) & 10.0 & \\  & MS & 60.79 \({}^{\text{bx}}\) & 139.55 \({}^{\text{by}}\) & 94.63 \({}^{\text{bz}}\) & 11.33 & \(\text{Z}^{*}\); T \({}^{*}\) \\ \cline{1-1}  & SEM & 8.55 & 12.88 & 10.58 & & \\ \hline \hline \end{tabular}  
* K represents the control sample; MS refers to the combined treatments of both methods used; T denotes the cold storage duration; \(Z\) indicates the type of treatment; \({}^{*}\)_p_! 0.05. Statistically significant differences were observed between the groups (_p_! 0.05); \({}^{\text{ab}}\) refers to differences within the same column, while \({}^{\text{xy}}\)\({}^{\text{z}}\) indicates differences within the same line. The absence of letters or the use of identical letters signifies no statistically significant differences.  
\end{table} 
Table 4: Texture characteristics of pork loan meat stored under refrigerated conditions after application of the processing method.
# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 2 Overall Design
### System Overview  
As shown in Figure 1, the project designed an magnetotelluric signal acquisition and monitoring system based on an IoT cloud platform, connecting magnetotelluric monitoring stations, control terminals, and cloud servers via 4G/5G networks to achieve a real-time and efficient monitoring system.  
The interaction process between different roles in the IoT cloud platform monitoring system is shown in Figure 2. Operators can directly interact with the control terminal, sending control commands to the acquisition station through the control terminal. The acquisition station executes the corresponding operations and returns the results to the control terminal, which displays the results in real time on the interface. Operators can also interact directly with the cloud platform, viewing online device information and downloading data files uploaded by the acquisition station. Currently, we use a test server that can host up to 50 simultaneous instrument connections. In the future, we plan to expand the server to accommodate more instrument connections.