esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 3 Proposed Algorithm
### Face Detection
#### 3.2.1 The Coarse-to-Fine CNN  
The coarse-to-fine CNN includes three networks: P-Net, R-Net and O-Net. The CNN architectures are shown in Figure 4.  
1. P-Net  
P-Net, a fully convolutional network, is the first layer, which roughly filters out nonface regions. The main task of P-Net is to obtain the bounding box regression vectors, calibrate the position of the face candidate regions, and then merge the overlapping face candidate regions with the nonmaximum suppression (NMS) method.  
2. R-Net  
The results of P-Net are resized to \(24\times 24\), which are the inputs of R-Net. R-Net is not a fully convolutional network, whose final layer has two full connection layers. Compared with P-Net, R-Net outputs more precise face classification and bounding box regression.  
3. O-Net  
O-Net will further screen candidate regions to obtain more accurate results. The outputs of R-Net are inputted to the final stage with the size of \(48\times 48\). O-Net processes images more accurately because it has an extra convolutional layer. O-Net obtains the final face position.  
From P-Net to O-Net, the network has increasingly larger input image sizes and deeper structures, and the features extracted from the network become more expressive.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 4 Evaluation and Limitations
### Additional Limitations
Due to the fact that the prediction model tries to find driving paths based on the detections from all the previous frames such objects will also be considered. Hence, this results in wrong driving path estimations. At the end, a proper tracking and counting of vehicles cannot be guaranteed. Figure 14: Known limitations in different environmental situations. Rainy conditions can smudge the camera (**a**). However, if there is not too much spray from the vehicles tracking is possible. Night situations with artificial light (**b**) or complete darkness (**c**) decrease the performance of the system rapidly. In some cases it is not even possible to detect a vehicle. Highways with multiple lanes and exits lead to difficulties estimating driving paths (**d**), rapid congestions, e.g., due to traffic lights impose complications in terms of tracking (**e**). Furthermore, parked vehicles in towns are also considered as active objects resulting in wrong driving paths (**f**). Due to the fact that the restricted bandwidth regarding remote camera accesses often results in a low image quality or resolution it can lead to problems detecting vehicles. The lower the resolution of the image the smaller the vehicles. The detection and classification framework has difficulties identifying such objects. For the reason that every other module relies on the initial detections low resolution or quality decreases the overall performance of the system.
# Wavefront Aberration Measurement Deflectometry for Imaging Lens Tests
###### Abstract  
Lenses play an important role in imaging systems. Having an effective way to test the aberrations of imaging lenses is important. However, the existing methods cannot satisfy the requirements in some conditions. To overcome these difficulties, wavefront aberration measurement deflectometry (WAMD) is proposed in this paper, which can reconstruct the wavefront aberrations of imaging lenses by measuring the angular aberrations. The principle of WAMD is analyzed in detail, and the correctness and feasibility of the proposed method are verified by both a simulation and an experiment. A telephoto lens and a single imaging lens were tested in an experiment, and the RMS errors were 166.8 nm (5.71%) and 58.9 nm (4.74%), respectively, as compared with the interferometer's results. This method is widely applicable with relatively reasonable accuracy. It has potential to be applied in the lens manufacturing and alignment process.  
wavefront aberration test; imaging lens; phase measuring deflectometry +
Footnote †: journal: Medical Imaging  
**2022**, _12_, 7857. [[https://doi.org/10.3390/app12157857](https://doi.org/10.3390/app12157857)]([https://doi.org/10.3390/app12157857](https://doi.org/10.3390/app12157857))
## Appendix C  
Proof of Lemma 2.: By definition, we have following outage probability:  
\[\begin{split}& D_{22}=\Pr\left(\gamma_{SD2,k*}^{PDMA}<\varepsilon_{2} \cup\max\left\{\gamma_{SD1\gets 2,k*}^{PDMA}\chi_{D2}^{PDMA}\right\}< \varepsilon_{1}\right)\\ &=\underbrace{\left(1-\Pr\left(\gamma_{SD2,k}^{PDMA}\geq \varepsilon_{2}\right)\right)}_{\Xi_{1}}\underbrace{\left(1-\Pr\left(\gamma_{ SD1\gets 2,k}^{PDMA}\geq\varepsilon_{1}\right)\right)}_{\Xi_{2}}\\ &\times\underbrace{\left(1-\Pr\left(\chi_{D2}^{PDMA}\geq \varepsilon_{1}\right)\right)}_{\Xi_{3}}.\end{split} \tag{13}\]  
Firstly, \(\Xi_{1}\) can be calculated as  
\[\begin{split}&\Xi_{1}=1-\Pr\left(\gamma_{SD2,k}^{PDMA}\geq \varepsilon_{2}\right)\\ &=1-\Pr\left(\left|g_{2,k}\right|^{2}\geq\frac{\varepsilon_{2} \oplus\rho\left|f_{2}\right|^{2}+\varepsilon_{2}}{a_{2}\rho}\right)\\ &=1-\frac{1}{\lambda_{f_{2}}}\int_{0}^{\infty}\sum\limits_{k=1}^{ K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\exp\left(-\frac{(\varepsilon_{2}\oplus\
u y+ \varepsilon_{2})k}{a_{2}\rho\lambda_{2}}-\frac{y}{\lambda_{f_{2}}}\right)dy \\ &=1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{a_{2}\rho\lambda_{2}}{a_{2}\rho\lambda_{2}+ \varepsilon_{2}\oplus\rho k\lambda_{f_{2}}}\exp\left(-\frac{\varepsilon_{2}k} {a_{2}\rho\lambda_{2}}\right).\end{split} \tag{14}\]  
Similarly, \(\Xi_{2}\) can be calculated as  
\[\begin{split}&\Xi_{2}=1-\Pr\left(\gamma_{SD1\gets 2,k}^{PDMA} \geq\varepsilon_{1}\right)\\ &=1-\Pr\left(\left|g_{2,k}\right|^{2}\geq\frac{\varepsilon_{1} \oplus\rho\left|f_{2}\right|^{2}+\varepsilon_{1}}{a_{1}\rho-\varepsilon_{1}a_{ 2}\rho}\right)\\ &=1-\frac{1}{\lambda_{f_{2}}}\int_{0}^{\infty}\sum\limits_{k=1}^{ K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\exp\left(-\frac{(\varepsilon_{1}\oplus\
u y+ \varepsilon_{1})k}{(a_{1}\rho-\varepsilon_{1}a_{2}\rho)\lambda_{2}}-\frac{y} {\lambda_{f_{2}}}\right)dy\\ &=1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\hat{\varrho}_{2}.\end{split} \tag{15}\]  
It is noted that \(\Xi_{3}\) can be formulated as  
\[\begin{split}&\Xi_{3}=1-\Pr\left(\chi_{D2}^{PDMA}\geq \varepsilon_{1}\right)\\ &=1-\Pr\left(\left|h_{2}\right|^{2}\geq\frac{\varepsilon_{1} \oplus\rho\left|f_{2}\right|^{2}+\varepsilon_{1}}{\rho}\right)\\ &=1-\frac{1}{\lambda_{f_{2}}}\int_{0}^{\infty}\exp\left(-\frac{ \varepsilon_{1}\oplus\
u y+\varepsilon_{1}}{\rho\lambda_{h_{2}}}-\frac{y}{ \lambda_{f_{2}}}\right)dy\\ &=1-\frac{\rho\lambda_{h_{2}}}{\rho\lambda_{h_{2}}+\varepsilon_{1} \oplus\rho\lambda_{f_{2}}}\exp\left(-\frac{\varepsilon_{1}}{\rho\lambda_{h_{2 }}}\right).\end{split} \tag{16}\]  
This completes the proof of Lemma 2.
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 2 Materials and Methods
### Model Evaluation  
The performance of the models was evaluated using the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), mean absolute error (MAE), and ratio of percent deviation (RPD). These metrics were calculated using the following equations:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(y_{ i}-\bar{y})^{2}} \tag{13}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{n}} \tag{14}\]  
\[\text{MAE}=\frac{\sum_{i=1}^{n}|\hat{y}_{i}-y_{i}|}{n} \tag{15}\]  
\[\text{SD}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}} \tag{16}\]  
\[\text{RPD}=\frac{\text{SD}}{\text{RMSE}} \tag{17}\]  
where \(y_{i}\), \(\bar{y}_{i}\), \(\bar{y}\), and \(n\) represent the measured value, predicted value, mean of the measured values, and number of samples, respectively. The R\({}^{2}\) metric measures the goodness of fit between predicted and actual values, with values closer to 1 indicating higher accuracy. RMSE quantifies the average deviation between predicted and actual values, while MAE captures the average magnitude of errors. Lower values for both RMSE and MAE reflect better model performance.  
RPD is the ratio of the standard deviation (SD) of the measured data to the standard error of prediction, with larger RPD values indicating better predictive performance of the model. According to [PERSON] et al. (2017), models can be categorized based on their RPD values, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting** \\ \hline Number of LSTM layers & 2 \\ LSTM units/hidden neurons & 16 \\ Cov1D * filters & 64/128/256/384/512 \\ Cov1D kernels & 3 \\ Cov1D strides & 2 \\ Dropout & 0.02 \\ Activation & ReLU \\ Dense layer parameters & 128/1 \\ Epoch & 100 \\ Learning rate & 0.0005 \\ Optimizer & Adam \\ Batch size & 128 \\ Loss function & Mean absolute error \\ \hline \hline \end{tabular}  
* Cov1D: 1D convolution.  
\end{table}
Table 1: Model framework parameters.
# Research on Dynamic Modeling and Transition Flight Strategy of VTOL UAV
## 1 Introduction  
### Background  
The induced VTOL UAV is a new type of UAV. It combines performances of both helicopter and fixed-wing aircraft, such as vertical take-off/landing, air hovering and low altitude and low speed of helicopter, and high-altitude cruise of fixed-wing aircraft. With excellent performance and good economy, it plays an important role in both military and civil fields and has broad development prospects.
# Cross-Lingual Cross-Domain Transfer Learning for
## 2 Related Work  
### Rumor Detection  
Initially, models were based on the features of messages. Among the features considered were attributes based on the text, user, and network [6, 32, 33, 34], where the use of classic machine learning was predominant, such as in SVM [32, 35, 36, 37], Decision Trees [32], Bayesian networks [32], Logistic regression [35, 37, 38], and Random Forest [35, 36, 38]. Subsequently, patterns associated with the credibility of messages were identified [6], and the relationship between user stance and rumor veracity was explored [12, 34, 39].  
Then, with the rise of neural networks, these began to be used for the task of rumor detection, with one of the pioneering works of [PERSON] et al. [40] using tanh-Recurrent Neural Network (tanh-RNN), LSTM, and GRU. This was followed by works using CNN [41, 42], LSTM [42, 43], Adversarial Networks [44], Feedforward Networks [43], and Ensembles [45, 46]. In the work of [PERSON] and [PERSON] [47], the performance of ten neural network models was compared, including LSTM, Stacked LSTM, GRU, Stacked GRU, Bidirectional LSTM, Bidirectional Stacked GRU, CNN1D, and Recurrent CNN, for rumor detection in English.  
[PERSON] et al. [48] studied the existence of patterns that incorporate message propagation and message replies over time. [PERSON] et al. [49] explored the use of contextual learning during an event, analyzing the dynamics of the messages considering the message to classify and previous messages. Following this line, [PERSON] et al. [50] used the structure of the conversation, representing it as a tree, where the first message of the conversation corresponds to the root, and all the replies and reposts are represented by the nodes of the tree. This topic was also studied by [PERSON] et al. [13].  
In the work of [PERSON] et al. [51], a model considers the network of friends, viewed as global information, and the structure of conversations on social networks, as local information, demonstrating that these two sources of information are useful for the rumor detection task. Similarly, [PERSON] and [PERSON] [52], in their work, capture contextual relationships and patterns of truthfulness in conversational structures. Along the same lines, [PERSON] et al. [53] proposed a web-based system that contains two modules: fact-checking and rumor detection, using a bidirectional convolutional network model based on bidirectional graphs.  
A topic that is similar to rumor detection, but in the context of reviews on e-commerce and social media platforms, is the Fake Review Detection problem. Several techniques and methodologies have been proposed, such as graph-based neural networks [54]. A review on this topic is given by the work of [PERSON] et al. [55].  
For a detailed study of the techniques of rumor detection, stance classification, bot detection, and the combinations thereof, we refer the reader to our previous review [16] that considered 97 articles and serves as the theoretical background for our current research.
## 3 The PID-CTOPC Approach
### Stabilization Model  
A new error state with PID structure is defined as follows:  
\[s(t)=K_{p}e_{1}+K_{i}\int_{0}^{t}e_{1}dt_{0}+K_{d}\dot{e_{1}} \tag{8}\]  
where, \(e_{1}=x_{1}-y_{d}\). \(K_{p}\), \(K_{i}\), and \(K_{d}\) are tuning parameters, representing proportional, integral, and differential coefficients, respectively. In the above, \(\int_{0}^{t}e_{1}dt_{0}\) represents the accumulative error and \(\dot{e_{1}}\) represents the derivative error.  
Differentiating \((n-1)\) times for the new state \(s(t)\) relative to \(t\) yields:  
\[\left\{\begin{array}{l}\dot{s}=K_{p}\dot{e_{1}}+K_{i}e_{1}+K_{d}\ddot{e_{1}} \\ \vdots\\ s^{[n-2]}=K_{p}e_{1}^{[n-2]}+K_{i}\dot{e_{1}}^{[n-3]}+K_{d}e_{1}^{[n-1]}\\ s^{[n-1]}=K_{p}e_{1}^{[n-1]}+K_{i}e_{1}^{[n-2]}+K_{d}e_{1}^{[n]}\end{array}\right. \tag{9}\]  
Then, by taking into account of Formula (1), \(s^{[n-1]}\) can be written into the following affine form:  
\[\begin{split} s^{[n-1]}&=\underbrace{K_{p}e_{1}^{[n-1]} +K_{i}e_{1}^{[n-2]}+K_{d}\big{[}f(x)-y_{d}^{[n]}\big{]}}_{\overline{f(x)}}+ \underbrace{K_{d}\cdot g(x)}_{\overline{g}(x)}\cdot u\\ &=\overline{f}(x)+\overline{g}(x)\cdot u\end{split} \tag{10}\]  
Since the objectives of controlling system (1) are \(x_{1}\to y_{d}\) and \(x_{i}\to y_{d}^{[i-1]}\), \(i=2,\ldots,n\), hence, the new error state given by Formula (8) is expected to converge to zero, namely, \(s\to 0\), which implies that the prescribed reference tracking problem of system (1) is transformed into a stabilization one.
## 2 Related Work
### Optimal Placement of Mobile UAVs  
In [23], a novel cyclical multiple access method using a base-station-equipped mobile UAV was proposed, to enhance wireless connectivity and communication quality for distributed GTs through scheduled interactions. In [24], the author presented a mobile relaying technique, using high-speed UAVs to enhance communication between a source and destination by optimizing the trajectory and power allocation of the UAV iteratively. However, the abovementioned studies considered a single UAV and designed a simple trajectory for it. The study in [25] focused on energy-efficient UAV-to-GT communications, proposing a circular trajectory with the center position at the GT, optimizing the UAV's trajectory to find a balance between communication throughput and energy consumption. However, the paper focused on optimizing the speed and flight radius of a single UAV, to achieve an optimal circular trajectory.
# Differential Expression of MicroRNAs in Dark-Cutting Meat from Beef Carcasses
## 3 Results
### Identification of Differentially Expressed miRNAs
\begin{table}
\begin{tabular}{c c c c c} \hline
**MicroRNA** & **Counts** & **Fold Difference** & **_p Value** & **_p_adj** \\  & **(Normalized) 1** & **(DFD/NON) 2** & **(Wald Test) 3** & **(FDR \(\leq\) 0.05) 4** \\ \hline _bta-miRR-2422_ & 5.54 & 1.72 & 9.98 \(\times\) 10\({}^{-5}\) & 0.054073752 \\ _bta-miRR-10174-5p_ & 2.68 & 1.93 & 0.0018698 & 0.253362794 \\ _bta-miRR-1260b_ & 12.13 & 1.58 & 0.0009795 & 0.253362794 \\ _bta-miRR-144_ & 25.26 & 0.56 & 0.001551818 & 0.253632794 \\ _bta-miRR-142-5p_ & 145.19 & 0.77 & 0.0045699 & 0.28605911 \\ _bta-miRR-2285 at_ & 2.18 & 1.77 & 0.0043885 & 0.28605911 \\ _bta-miRR-2285e_ & 3.14 & 1.61 & 0.0036349 & 0.286059911 \\ _bta-miRR-3613a_ & 375.89 & 0.79 & 0.0053443 & 0.289662932 \\ _bta-miRR-493_ & 20.74 & 1.26 & 0.006497 & 0.320127236 \\ _bta-miRR-27a-5p_ & 10.57 & 1.38 & 0.0074149 & 0.334904236 \\ _bta-miRR-136_ & 8.86 & 0.68 & 0.0100435 & 0.400069983 \\ _bta-miRR-146a_ & 246.05 & 0.75 & 0.0110384 & 0.400069983 \\ _bta-miRR-23b-5p_ & 1.56 & 2.12 & 0.011072 & 0.400069983 \\ _bta-miRR-10162-5p_ & 1.32 & 1.84 & 0.0233158 & 0.554531616 \\ _bta-miRR-1307_ & 12.53 & 1.29 & 0.0189402 & 0.554531616 \\ _bta-miRR-2285 aa_ & 1.32 & 0.45 & 0.0198697 & 0.554531616 \\ _bta-miRR-2285r_ & 1.56 & 0.50 & 0.0189285 & 0.554531616 \\ _bta-miRR-31_ & 4.48 & 0.63 & 0.0221755 & 0.554531616 \\ _bta-miRR-2284k_ & 1.44 & 0.54 & 0.0267659 & 0.580285276 \\ _bta-miRR-2299-5p_ & 1.89 & 0.56 & 0.0265474 & 0.580285276 \\ _bta-miRR-11994_ & 6.87 & 1.28 & 0.0468123 & 0.604508006 \\ _bta-miRR-18b_ & 0.47 & 0.37 & 0.0462686 & 0.604508006 \\ _bta-miRR-2285q_ & 3.66 & 0.66 & 0.0304856 & 0.604508006 \\ _bta-miRR-2478_ & 32.27 & 1.24 & 0.0307361 & 0.604508006 \\ _bta-miRR-431_ & 1.26 & 2.08 & 0.0323255 & 0.604508006 \\ _bta-miRR-543_ & 12.77 & 1.28 & 0.0328225 & 0.604508006 \\ _bta-miRR-6123_ & 5.08 & 0.73 & 0.047955 & 0.604508006 \\ _bta-miRR-671_ & 2.17 & 0.60 & 0.0348298 & 0.604508006 \\ \hline _MyomiRs 5_ & & & & \\ _bta-miRR-206_ & 10,196.55 & 1.12 & 0.004750072 & 0.286059911 \\ _bta-miRR-208b_ & 236.105 & 0.80 & 0.063240802 & 0.604508006 \\ _bta-miRR-1_ & 2,711,501.56 & 0.92 & 0.090611395 & 0.680638063 \\ _bta-miRR-499_ & 16,961.84 & 0.93 & 0.258200039 & 0.904697654 \\ _bta-miRR-486_ & 1758.97 & 1.06 & 0.333875388 & 0.973667764 \\ _bta-miRR-133a_ & 59,175.47 & 0.99 & 0.99297291693 & 0.987194328 \\ _bta-miRR-133b_ & 833.48 & 1.04 & 0.440191294 & 0.992229627 \\ _bta-miRR-208a_ & 2.25 & 0.94 & 0.769097948 & 0.992229627 \\ \hline \end{tabular}
\end{table} 
Table 2: MicroRNAs differentially expressed in muscle biopsies from normal beef carcasses (NON) and those exhibiting the dark, firm, dry phenotype (DFD). The threshold for statistical significance for differential expression is \(p_{\text{adj}}\leq 0.05\). MyomiR data are also included in the table.
## Abbreviations  
3 GPP 3 rd Generation Partnership Project  
4G  Fourth generation (mobile network)  
5G  Fifth generation (mobile network)  
5G-MANTRA 5G Multipath Aggregation for Network Traffic Routing and Adaptation  
5G-NR 5G New Radio  
8 PSK 8-phase-shift keying  
16 APSK 16-amplitude and phase-shift keying  
32 APSK 32-amplitude and phase-shift keying  
AI  Artificial intelligence  
AIM  AI module  
Adam  Adaptive moment estimation (optimization algorithm)  
ATSSS  Access Traffic Steering, Switching and Splitting  
BPSK  Binary phase-shift keying  
FPGA  Field-programmable gate array  
Free5 GC  Open-source 5G core network  
GCS  GUI, Control, and Channel Switching Logic  
GUI  Graphical User Interface  
HUT  Hybrid User Terminal  
IoT  Internet of Things  
LDPC  Low-Density Parity Check  
LEQ  Low Earth Orbit
## 2 Electrical Resistivity/Impedance Measurement: Is It Really that Simple?
### Water Penetration  
Water penetration can be evaluated according to EN-12390-8 \"Testing hardened concrete--Part 8: Depth of penetration of water under pressure\" [170]. Water penetration test is used to evaluate the concrete permeability and, consequently, its performance and durability [171]. Water is applied on one face of a cubic specimen under a pressure of 0.5 MPa, kept constant for 72 h; then, the specimen is split into two halves and the maximum penetration depth is considered as an indicator of water penetration. When water penetrates, some soluble salts (e.g., chloride ions) can go through concrete, easing corrosion of embedded reinforcements and undermining its durability. Concrete electrical resistivity has proved to be correlated with concrete permeability and water penetration [100].  
[PERSON] et al. [100] highlighted a strong power relationship between concrete electrical resistivity (measured as surface resistivity--SR--with four-point Wenner array probe) and water penetration (measured in compliance to EN 12390-8) when the same type of cementitious materials are considered (y = 107.88x - 0.777, with \(\mathrm{R}^{2}\approx 0.87\)). On the other hand, when different cementitious materials are used, the correlation is slightly reduced (\(\mathrm{R}^{2}\approx 0.83\)), since the surface resistivity test depends on microstructure and pore solution, whereas water penetration only on pore solution. [PERSON] et al. [96] investigated the correlation between water permeability and electrical resistivity; they used a pressurized, uniaxial, steady-state flow permeameter [172] and the procedure reported in AASHTO T358 [95] (surface resistivity) and AASHTO TP119 [70] (bulk resistivity). They proved that electrical resistivity increases as water permeability decreases; different trends are observable with different designs and SCMs; in particular, electrical resistivity measurements in specimens containing sugarcane bagasse ashes and ground glass tend to overestimate the concrete penetrability. [PERSON] et al. [131] used non-contact electrical impedance measurements [129] for the development of a fractal permeability model to evaluate the permeability of young cement pastes (ring-shaped specimens); pores with a diameter \(<\) 6.2 nm were not considered, since the ions movement is constrained by pore walls [163]. Finally, [PERSON] et al. [28] found a power relationship between electrical resistivity and saturation degree (y = 24.848x\({}^{-3.262}\), with \(\mathrm{R}^{2}=0.9702\)), since resistivity increases while water content decreases.
## 4 SUI-PID Controller  
The SUI-PID implements the MDOF concept by utilizing two controllers. The first addresses large errors to achieve rapid error correction, while the second addresses small errors to achieve fine-tuning. The output can be calculated as [22; 23]  
\[Output=O/P_{1}(error)+O/P_{2}(1-error) \tag{9}\]  
where \(O/P_{1}\) is the first controller's output, which deals with the significant error, and \(O/P_{2}\) is the second controller's output, which deals with the fine-tuning error.  
Figure 6 illustrates the SUI-PID controller's construction, in which two adaptive weights were added to the outputs of the two controllers, (1-error) and (error), resulting in an adequate tracking response. The design for each controller can be simplified by a PID controller, as shown in Figure 7, which has the following parameters:  
\[K_{P}=Abs(e);K_{i}=Abs(\int e);K_{d}Abs(de/dt) \tag{10}\]  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Value** \\ \hline Capacitance & 4.7 uF \\ Inductance & 1 mH \\ Frequency & 25 KHz \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the converter.  
Figure 5: The single-switch buck-boost converter.  
Lastly, the output can be calculated as  
\[C_{m}=CKE+C(1/K)(1-E) \tag{11}\]  
\[C_{m}=CE[K-(1/K)]+(C/K) \tag{12}\]  
For K \(\gg 1\)  
\[C_{m}=CEK \tag{13}\]  
where \(C_{m}\): tuned controller output; \(E\): error signal; \(K\): controller gain; and \(C\): controller output.  
When the SUI-PID is used on a photovoltaic pumping system, the controller's input and output are the error signal and duty cycle, and the error can be calculated as [13]  
\[Error=\max.\ power-measured\ power \tag{14}\]  
The maximum output power can be estimated by evaluating the properties of the photovoltaic panel under various irradiation levels and temperatures, as shown in Figure 3. The system with the SUI-PID Simulink model is illustrated in Figure 8.  
Figure 6: The construction of the SUI-PID.  
Figure 7: Design of the simple PID controller.