esa-sceva/satcom-chunk-collection huggingface dataset:
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 5 Experimental Results
### Experiments with Smooth Changes in Irradiance  
The results of the experiment with smooth changes in irradiance and using a P&O controller are shown in Figure 20. Irradiance started decreasing from 545 W/m\({}^{2}\) to 370 W/m\({}^{2}\) in the first minute, and then it increased with slight fluctuations until it reached a maximum of 700 W/m\({}^{2}\). Meanwhile, the cell temperature moderately decreased from 44 \({}^{\circ}\)C to 42 \({}^{\circ}\)C, but this drop was negligible for the outcome of the experiment. The experiment showed, in the first part, how the MPP, due to the initial conditions of the scenario, was close to a 0.5 duty cycle, which corresponded with the arbitrary initial duty cycle start point coded in the controller. This implies that the only time needed for reaching the MPP was the time coming from the dynamics of the system. This casualty was exceptional and should not be expected. Usually, there will be a transient time when the algorithm searches for the MPP point of the system. For the rest of the experiment, the power curve mimicked the irradiance curve, both in the decreasing part and in the ascending part, achieving a maximum power of 200 W. This experiment confirmed that the P&O controller performs well in scenarios of slightly decaying and increasing irradiance.  
Figure 21 contains the graphical results of another experiment, where a smooth change in irradiance took place, albeit with the SMC controller this time. The irradiance started at 750 W/m\({}^{2}\) and decreased to 500 W/m\({}^{2}\) at around 30 s. Then, it increased again fairly steadily up to 885 W/m\({}^{2}\). The temperature remained practically constant between 40 \({}^{\circ}\)C and 41 \({}^{\circ}\)C. It can be seen from the duty cycle curve that the controller needed about 8 s to find the MPP. From that moment on, the controller managed to replicate the slight fluctuations of the irradiance curve. The power dropped to 140 W and then rose again to 250 W when the irradiance reached the maximum value of 885 W/m\({}^{2}\). This experiment confirmed that the SMC controller is also a good candidate in situations of a smooth decrease and increase in irradiance. This behavior is to be expected as, without abrupt  
Figure 19: The experiment results of the constant irradiance and temperature conditions for the SMC controller. (**a**) Cell temperature. (**b**) Solar irradiance. (**c**) PV power. (**d**) Measured PV voltage and reference PV voltage. (**e**) Duty cycle.  
external changes, the SMC controller has enough time to react and adjust the duty cycle to reach the MPP.  
Figure 21: The experiment results of the smooth irradiance change conditions for the SMC controller. (**a**) Cell temperature. (**b**) Solar irradiance. (**c**) PV power. (**d**) Measured PV voltage and reference PV voltage. (**e**) Duty cycle.  
Figure 20: The experiment results of the smooth irradiance change conditions for the P&O controller. (**a**) Cell temperature. (**b**) Solar irradiance. (**c**) PV power. (**d**) Duty cycle.
## 3 Numerical Model  
### Governing Equation  
The governing equations include the equation describing the continuity of various particles, the Poisson equation describing the distribution of the electric field, and the equation for the conservation of the electron energy density. In addition, charged particles are subjected to an electric field force in an electric field, the force equation of which is shown in Equation (1).  
\[M\frac{dv}{dt}=eE \tag{1}\]  
where \(M\) is the electron mass, \(v\) is the electron velocity, and \(eE\) is the force on the electron in the electric field \(E\).  
The initial position equation of the stressed particle is shown in Equation (2).  
\[\begin{array}{l}z=z_{0}+(z_{1}-z_{0})R_{f}\\ r=r_{0}+(r_{1}-r_{0})R_{f}\end{array} \tag{2}\]  
where \(R_{f}\) is a random number in [0, 1].  
The force, initial position, and initial velocity of the particle are known and can be found for any moment and any position. The velocity and relative position of the ion at the nth time step are shown in Equations (3) and (4). The chemical reaction equations used in this paper are referenced in the literature.  
\[\
u_{n}=\
u_{n-1}+(em/M)\Delta t \tag{3}\]  
\[x_{n}=x_{n-1}+\
u_{n-1}\Delta t+(\frac{em}{2M})(\Delta t)^{2} \tag{4}\]  
where \(\
u_{n}\) is the particle velocity of the layer at time \(n\); \(\
u_{n-1}\) is the particle velocity of the layer at time \(n-1\); \(x_{n}\) is the particle position of the layer at time \(n\); and \(x_{n-1}\) is the particle position of the layer at time \(n-1\).
## 3 Monitoring and Analysis of Subsidence in Shield Tunneling Under-Passing the Bridge and Culvert
### Monitoring and Analysis of Ground Subsidence
#### 3.2.1 Monitoring and Analysis of Surface Subsidence at Transverse Measuring Points  
Figure 11 presents the transverse surface subsidence curves. The transverse surface subsidence curves of the five monitoring sections show that the surface above the tunnel vault has the largest settlement value. As the transverse monitoring point on the surface is far away from the tunnel vault, the surface settlement value gradually decreases. The surface settlement value near the tunnel vault changes faster than that far away from the tunnel vault. The measured surface settlement curve of the transverse monitoring point has an obvious settlement groove, which is consistent with the predicted curve of Peck [23]. In addition, owing to the influence of the soft-rock height ratio, the surface settlement value of the transverse measurement point increases with an increase in the soft-rock height ratio, and the settlement trough becomes deeper. When the soft-rock height ratio is 1, the settlement value reaches the maximum value of 7.97 mm. When the soft-rock height ratio is within 0.5-1.0 and 0-0.2, the settlement value of the monitoring point far away from the tunnel vault is close. Because the soil on the right side of the left tunnel axis has experienced two tunneling disturbances of the leading tunnel and the following tunnel, the surface settlement value of the soil in this area is slightly larger than that of the soil on the left. In the range of 7-12 m from the left and right sides of the tunnel axis, the settlement of the soil surface increases at different degrees, and the settlement amplitude increases with an increase in the height ratio of the soft rock.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods
### CNN-BiLSTM-RF Anomaly Detection Model  
This paper introduces a novel ensemble model for anomaly detection in electric loads, named CNN-BiLSTM-RF. The architecture of this model is illustrated in Figure 1. The CNN-BiLSTM-RF model consists of three layers: the convolutional neural network layer, the bidirectional long short-term memory network layer, and the random forest layer. The convolutional neural network layer primarily utilizes CNN to extract features from the load data, capturing temporal and spatial patterns. The bidirectional long short-term memory network layer employs BiLSTM to model the extracted features, aiming to capture the temporal dependencies in the data more effectively. Lastly, the random forest layer uses an RF classifier to detect anomalies in the electric load data.
## 3 System Performance Analysis
### Outage Probability
On the other hand, if IoT device \(D_{n}\) cannot decode at least a message \(x_{i}\) (\(x_{i}\in\{x_{N},\ldots,x_{n}\}\)) in the received signal as (20) for \(t=\) odd or (21) for \(t=\) even._  
_In other words, the OP at IoT device \(D_{n}\) in transmission block \(t\) is expressed as follows:_  
\[OP_{D_{n}}(t)=1-\Pr\biggl{\{}\min\limits_{i=\{N,\ldots,n\}}\left\{R_{R_{r}-x_{ i}}^{T_{1}^{(i)}}\right\}\geq\mathcal{R},\min\limits_{i=\{N,\ldots,n\}}\left\{R_{2 _{D_{n}-x_{i}}}^{T_{2}^{(i)}}\right\}\geq\mathcal{R}\biggr{\}}. \tag{44}\]By applying CDF, expression (44) has been solved and obtained in closed-form as follows:  
\[OP_{D_{n}}(t)=1-\exp\left(-\frac{\gamma}{\Omega_{n}}\right), \tag{45}\] \[s.t. \omega_{n}=\min_{i=\{N,\dots,n\}}\{(1-\lambda_{R_{r}})\beta_{i}\rho \sigma_{S,R_{r}}\},\] (46) \[\omega_{n}=\min_{i=\{N,\dots,n\}}\{\beta_{i}\eta\lambda_{R_{r}} \rho\sigma_{S,R_{r}}\sigma_{R_{r},D_{n}}\},\] (47) \[\Omega_{n}=\min\{\omega_{n},\omega_{n}\}. \tag{48}\]  
Lemma 2: _Assuming IoT hub \(S\), relay \(R_{r}\) and device \(D_{n}\) have been equipped by number of antennas \(A_{S}>1\), \(A_{R}>1\) and \(A_{D}>1\), respectively. TAS/SC techniques have been employed at transmitter and receiver to select the best signal. As a result, OP at IoT device \(D_{n}\) in MIMO scenario is expressed as shown in (A13)._  
See Appendix B for proof. From expression (44), the study provides an algorithm for OP Monte Carlo simulation at the IoT device \(D_{n}\) as Algorithm 3. ```
0: Initialize the parameters as in Algorithm 2;
0: Simulation (Sim) results of OP at the IoT device \(D_{n}\);
1: Calculate SINR at relay \(R_{r}\) by applying (12) and (13) for \(r=1\) or (14) and (15) for \(r=2\);
2: Calculate achievable bit-rate at relay \(R_{r}\) as (16) for \(r=1\) or (17) for \(r=2\);
3: Find the minimum of achievable data rate \(\min_{i=\{N,\dots,n\}}\left\{R_{R_{r}-x_{i}}^{T_{2}^{(t)}}\right\}\);
4: Calculate SINR at device \(D_{n}\) applying (22) and (23) for \(t=\textit{odd}\) or (24) and (25) for \(t=\textit{even}\);
5: Calculate achievable bit-rate at device \(D_{n}\) by applying (26) for \(t=\textit{odd}\) or (27) for \(t=\textit{even}\);
6: Find the minimum of achievable data rate \(\min_{i=\{N,\dots,n\}}\left\{R_{D_{n}-x_{i}}^{T_{2}^{(t)}}\right\}\);
7: Initialize variable \(\textit{count}\gets 0\);
8:for\(l=1\) to \(1\times 10^{6}\) samples do
9:if\(\left(\min\limits_{i=\{N,\dots,n\}}\left\{R_{R_{r}-x_{i}}^{T_{2}^{(t)}} \right\},\min\limits_{i=\{N,\dots,n\}}\left\{R_{D_{n}-x_{i}}^{T_{2}^{(t)}} \right\}\right)\geq\mathcal{R}\right)\)then
10:\(\textit{count}\gets\textit{count}++\);
11:endif
12:endfor
13:return OP at device \(D_{n}\) in transmission block \(t=\{\textit{odd},\textit{even}\}\) as given \(OP_{D_{n}}(t)=1-\textit{count}\Big{/}10^{6}\);
```  
**Algorithm 3** Algorithm for investigation OP at IoT device \(D_{n}\) in a transmission block \(t=\{\textit{odd},\textit{even}\}\)
# Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review
## 3 Experimental Techniques
### Experimental Techniques for Chatter Avoidance
[86] studied the implementation of variable turning speeds during the turning of a piece and adapted the Chebyshev collocation method and the Homotopy Perturbation Method (HPM) for chatter onset prediction (Figure 5b). For validation, speed functions of sine-wave type were generated and verified using a laser tachometer. Good agreements were obtained in A and B chatter types. However, thermal aspects in the spindle speed were not considered in their analysis. It is well known that the stability boundaries depend on the specific combination of spindle speed and uncut chip load, however, the single time-varying parameters (STVP) is an alternative technique that proved chatter suppression such as time-varying tool rake angle and time-varying federate. However, the authors pointed out that the robustness of this technique can be improved with the Multiple Time-Varying Parameter (MTVP) which offered chatter reductions close to 80% in some cases [87].
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 1 Introduction
### Research Contributions  
The key contributions of this research are:  
1. To develop a unified analytical framework showing that PPM demodulation fundamentally yields the derivative of the message signal.
2. To analyze and compare several carrierless PPM demodulation methods, confirming their inherent link to the derivative of the message signal.
3. To propose and implement a novel PPM modulation scheme where the differentiated message signal drives a VCO and monostable multivibrator, streamlining carrierless PPM generation.
4. To validate the proposed approach through simulations and experiments, demonstrating effective signal recovery and highlighting performance benefits over conventional methods in tested scenarios.  
This paper is organized as follows. Section 2 presents a signal component analysis of PWM and PPM, establishing their mathematical and spectral properties. Section 3 derives the proposed derivative-based carrierless PPM method--employing a VCO and monostable multivibrator--from a unified framework encompassing multiple carrierless PPM demodulation strategies. Section 4 analyzes the performance of the technique in comparison with existing approaches. Section 5 describes the experimental setup and results, while Section 6 discusses key considerations and future directions. Lastly, Section 7 concludes with a summary of contributions and directions for future work.
## 2 Methodologies
### Proposed Model  
The framework of the proposed model is illustrated in Figure 6, and mainly includes three parts, i.e., feature engineering, feature extraction and load forecasting. In the feature engineering stage, the load data are decomposed into multiple sub-series by VMD. A new feature matrix is constructed with these sub-series and external factors. In the feature extraction stage, the 1D-CNN network is used as a deep network to extract in-depth spatial features, and the TCN extracts the long-term temporal dependencies of the input matrix. Furthermore, the temporal features hidden in load series are further extracted using LSTM. Then, SAM can dynamically adjust the weight of spatiotemporal features to obtain an important feature matrix. Finally, load forecasting can be obtained through the fully connected layer. Table 1 lists the hyperparameters of each algorithm used in the proposed model. Moreover, the steps of the proposed model are described in the following:  
Step 1: VMD is employed to decompose the raw load data into eight IMFs. These IMFs are normalized along with temperature, while seasons, holidays and weekends are processed by using a one-hot encoder. The feature matrix can be reconstructed by combining IMFs and external factors in parallel.  
Step 2: The three-layer 1D-CNN is used as a deep network to extract deep spatial features between adjacent time points, and then the TCN is utilized to globally capture temporal features from the load data.  
Step 3: LSTM can further enhance the extraction of long-term dependencies. Furthermore, the SAM dynamically adjusts the correlations of different features, strengthens long-range dependencies and obtains the important spatiotemporal features.  
Step 4: The feature matrix processed from SAM is reshaped into time series that are used to predict load data through the fully connected layer.  
Figure 6: Flow chart of the proposed model.
# Investigation on the Effect of Femtosecond Laser Induced Spin Transfer Torque of GdFeCo Alloy
## 4 Conclusions  
We suggested a new physical mechanism undertaking the AOS process in GdFeCo amorphous alloy thin film: the spin transfer torque induced by femtosecond laser single pulse acts on the spins of Fe, leading to the reversal of Fe spins' orientation first; the elec  
Figure 7: Time-resolved variation process of the normalized spin transfer torque in the case of sublattice magnetization flip (**a**) The injection current density is \(10^{6}\) A/cm\({}^{2}\); (**b**) the injection current density is \(10^{7}\) A/cm\({}^{2}\).  
tronic temperature then quickly dropped below the Curie temperature, and the exchange interaction between Fe and Gd sublattices makes Gd spins switch. For this, we established the atomistic spin model to describe the magnetization dynamics of RE-TM materials. The results of the simulation show pretty good agreement with the experimental results carried out by [PERSON] et al., showing that spin transfer torque takes an important role in the process: (1) the higher the power density of the incident single laser pulse, the lower the threshold of the spin current density required by AOS for RE-TM materials, and when the power density of the single laser pulse is within 10-20 mJ/cm\({}^{2}\), the order of the threshold between 10\({}^{5}\)-10\({}^{7}\) A/cm\({}^{2}\) can be observed; (2) when the spin current density exceeds the threshold, the higher the current density is, and the faster the magnetization switching of Fe sublattice will be, the magnetization switching of Gd sublattice will lag behind that of Fe sublattice, and the lag time of Gd sublattices behind Fe relates to the magnitude of the spin current density.  
The combination of ultra-fast laser-induced spin current and spin-mediated magnetization dynamics will give rise to many interesting applications. Understanding the underlying mechanism will be a great help for these applications, including improving the performance of magnetic storage technology. Our attempt for this conclusion is a bold try for the construction of a complete theoretical system of ultrafast laser-induced spin current magnetization dynamics.  
Methodology, H.H.; Software, H.H.; Validation, Z.Z. and R.W.; Formal Analysis, X.Z.; Resources, W.C.; Writing-Original Draft Preparation, H.H.; Supervision, H.W.; Funding Acquisition, C.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by National Natural Science Foundation of China (NSFC) (61432007), the Creative Research Group Project of NSFC (61821003) and Science Technology Support Program of Hubei Province (2015 BCE054).  
Not applicable.  
The authors declare no conflict of interest.
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 4 Primary Innovation  
(1) In terms of research significance, the IRSL-FSR proposed in this paper represents a groundbreaking achievement in the integrated design of high-performance communication, radar stealth, and infrared stealth. This advancement effectively enhances the survivability of current flight communication platforms such as satellites/aircraft under the threat of ground-based radar + space-based infrared sensor multi-spectrum detection. As a result, it has significant potential applications in the military field.  
(2) In terms of research methods, this paper proposes an FSR design method based on the non-frequency variable parallel resonant structure. Compared with the current dense metal wire layout design scheme, this method can significantly reduce processing requirements and achieve broadband absorption while ensuring a wide transmission band. This reflects good engineering value and can provide references for the wideband transmission design of FSR.  
Conceptualization, H.S.; methodology, H.S. and S.Z.; software, H.S. and Y.Z.; investigation, H.Z. and J.L. ([PERSON]); resources, S.Z.; writing--original draft preparation, H.S.; writing--review and editing, Y.Z. and S.Z.; supervision, J.L. ([PERSON]) and X.A.; funding acquisition, J.L. ([PERSON]), X.A. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 61890545, 61890540, 61571031.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & \begin{tabular}{c} **–10 dB Absorption** \\ **Band/Relative Bandwidth** \\ \end{tabular} & \begin{tabular}{c} **–1 dB Transmission** \\ **Band/Relative Bandwidth** \\ \end{tabular} & \
\begin{tabular}{c} **Thickness/\(\lambda_{L}\)** \\ \end{tabular} &
\begin{tabular}{c} **Low IR Emissivity** \\ \end{tabular} \\ \hline Ref. [17] & 2.80–8.30 GHz/99.0\% & 9.30–10.1 GHz/8.2\% & 0.112 & No \\ \hline Ref. [18] & 2.40–7.10 GHz/98.9\% & 8.30–11.07 GHz/34.0\% & 0.166 & No \\ \hline Ref. [19] & 3.00–8.90 GHz/100.1\% & 9.00–12.63 GHz/33.3\% & 0.105 & No \\ \hline Ref. [20] & 2.10–6.20 GHz/98.7\% & 7.90–12.80 GHz/47.3\% & 0.110 & No \\ \hline Ref. [21] & 3.20–8.70 GHz/92.4\% & 9.60–10.10 GHz/5.07\% & 0.121 & No \\ \hline Ref. [22] & 2.35–6.78 GHz/97.0\% & 9.30–13.45 GHz/36.4\% & 0.118 & No \\ \hline
**This paper (FSR)** & **2.65–8.80 GHz/107.4\%** & **9.15–17.71 GHz/63.7\%** & **0.099** & **No** \\ \hline
**This paper (IRSL-FSR)** & **2.66–8.01 GHz/100.3\%** & **9.16–17.94 GHz/64.8\%** & **0.100** & **Yes** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of this research to previously reported works.
## Materials and Methods
### Assimilation  
In general, and as, e.g., presented in [37; 38; 39; 40; 41; 42], data assimilation is likely to improve the accuracy of the modelled snow parameters in a way that the modelled snow parameter or run-off results agree better with the real situation. For the SnowSense data assimilation, we used information gathered by the SnowSense GNSS in situ stations and the already existing online SWE measurement sites, as well as information from EO data. Regarding the in situ locations, we used the SWE and LWC (were available), regarding the EO sources, we used binary information whether the snow is dry or wet from Sentinel-1. All information was then compared twice a week with the non-assimilated output of SWE, LWC and the snow cover extent simulated by the PROMET model. In case of significant differences between simulated and observed values, assimilation runs for the last calculation period had been triggered. To adjust the model output to the observed in situ and EO information, the following options were available [43]: Adjustments of (i) the precipitation gain for the amount of snow/solid precipitation, (ii) the critical temperature for the transition of liquid and solid precipitation, and (iii) the albedo parameter for the short wave energy fluxes controlling the ablation dynamic. The sequence of the assimilation steps is following a decision tree based work-flow. After adjusting the model parameterization, the model is re-calculating the required output parameters for an improved agreement between model and reality. The adjustment of i,ii and iii is performed spatially. During the winter 2017/2018 phase, the assimilation process was performed partly manual. For a future service application, we will establish a more automated process.
## 3 Quad-Port MIMO Antenna
### Diversity Analysis: ECC, DG, TARC and MEG  
The ECC between various port combinations is derived with the S-parameters utilizing Equation (2) [25; 26]. As depicted in Figure 20, both simulation and experimental results indicate that ECC remains under 0.05 throughout the UWB spectrum. Average ECC is 0.01432, which is far below the 0.5 limit, indicating excellent diversity. Figure 21 shows the DG values stay close to the optimal 10 dB across the full UWB band. The DG ranges from 9.31 to 9.99 dB with an average of 9.65 dB across the desired UWB band. This confirms effective signal enhancement using multiple antenna elements. TARC is evaluated based on Equation (4) [25; 26]. Figure 22 presents both simulated and measured outcomes, each indicating a TARC is under \(-10\) dB. This validates the antenna's effective diversity performance. The MEG and MEG difference between the antenna elements of the proposed design are computed with the help of Equations (5) and (6) [27]. For balanced diversity, the MEG difference should be within 3 dB. As shown in Figure 23, the calculated difference stays below 0.3 dB across the UWB band. This confirms the antenna's stable and well-balanced performance.