esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Mathematical Modeling
#### 2.2.2 Battery System Design  
The solar system can generate the energy required to operate the machine by converting energy from the sunlight into a flow of electrons using the photovoltaic effect. However, this energy is generated only when there is solar irradiation. This section describes the design of the battery bank, which is the essential component for BS. The total energy required for the refrigeration machine from Equation (2) is used as a design criterion for the battery bank. This amount of energy is to be supplied by the battery system in a single day to operate the system efficiently. This battery storage system is designed independently from the photovoltaic array. To size the battery bank, the total electrical load is converted from watt-hours to amp-hours. Battery capacity in amp-hours is calculated by using Equation (13).  
\[\text{Total amp-hour demand per day }=\frac{E_{total\text{ }required}\times 1000}{ Battery_{bus\text{ }voltage}} \tag{13}\]  
This battery bus voltage provides the nominal operating DC for the system. The battery bus voltage for this application is picked as 600 V, which also corresponds to the DC input voltage for the inverter. The maximum fraction of capacity that can be withdrawn from the battery, also known as the allowable depth of discharge, is considered in designing the battery system. For this application, 0.8 depth-of-discharge is considered. This gives the actual required battery capacity, as shown in Equation (14).  
\[\text{Actual battery capclity }=\frac{Total\text{ }amp-hour\text{ }demand\text{ }per\text{ }day}{ depth-of-discharge} \tag{14}\]  
Once the total required capacity of the battery bank is estimated, the battery to be used in the system is selected. The Tesla Powerball battery specification data sheet is used for the selection of the battery. Accordingly, the battery selected in this case has a usable energy storage capacity of 13.5 kWh. The total number of batteries in series and parallel is given by the Equations (15) and (16).  
\[N_{parallel}=\frac{\text{Actual battery capacity}}{\text{amp-hour capacity of selected battery}} \tag{15}\]  
\[N_{series}=\frac{\text{Battery bus voltage}}{\text{voltage of selected battery}} \tag{16}\]  
Batteries in series are arranged in such a way that the DC voltage provided by the battery system corresponds to the inverter DC input voltage. Additionally, the number of batteries in parallel is fixed to provide the battery required capacity. The overall idea is to make sure that the required amp-hour is supplied with the required voltage supply. Equations (17) and (18) provide the total number of batteries required and total battery capacity, respectively.  
\[N_{total}=N_{series}\times N_{parallel} \tag{17}\]  
\[\text{Total battery capacity }=N_{total}\times\text{Usable energy of }\sin\text{gle battery} \tag{18}\]
## 6 Conclusions and Future Works  
This paper addressed the optimal integration of FACTS in electrical distribution grids, considering the minimization of the annual costs related to energy losses and the investments made in installing these devices. This integration was solved using a mixed-integer convex model, which was obtained by transforming the hyperbolic constraints of the MINLP model into second-order conic constraints. The effectiveness of our proposal was evaluated in three IEEE test systems, and it was compared to the BWO and VSA and some GAMS software solvers. The results showed that the proposed convex model found the global optimum of the studied problem. Fixed and variable operations were considered for the FACTS. For the fixed operation, the annual operating costs were reduced by 12.63%, 13.97%, and 26.53% for the IEEE 33-, 69-, and 85-bus test systems, respectively. These results were achieved by the proposed convex model and the BWO algorithm; the other solvers sometimes failed to converge or reached a worse solution. For the variable operation of SVC devices, the annual equivalent operating costs were reduced by 14.24%, 15.79%, and 30.31% for the IEEE 33-, 69-, and 85-bus test systems, respectively. These results were also achieved by the proposed convex model and the BWO algorithm.  
The variable operation of SVC devices allowed for a more significant reduction in the annual energy losses costs, although the total size of the SVCs increased by 61.17%, 24.35%, and 27.71% for the IEEE 33-, 69-, and 85-bus test systems, respectively. However, this saved USD 6693.3, 3778.4, and 3778.4/year more than the fixed operation.  
The installation of SVC devices was the best option to reduce the final objective function value in all test systems. The second best option was TCSC devices. The total size of the SVCs was greater in all test systems than the other FACTS, thus enabling a greater reserve of reactive power as demand grows.  
Conceptualization, methodology, software, and writing (review and editing): W.G.-G., O.D.M. and C.L.T.-R. All authors have read and agreed to the published version of the manuscript.  
This research received support from the Ibero-American Science and Technology for Development Program (CYTED) through thematic network 723 RT0150, \"Red para la integracion a granescala de energias renovables en sistemas electrics (RIBIERSE-CYTED)\". This research was also supported by project no. 6-23-7, titled Desarrollo de una metodologia para la compensacion optima de potencia reactiva en sistemas electricos de distribucion and project no. 6-23-5, titled Analisis de estrategias de control Jeraquico para la operacion estable de microrredes ac y dc considerando energias renovables from at Universidad Tecnologica de Pereira.  
Not applicable.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing does not apply to this article.  
The authors also want to thank Vicerrectoria de Investigacion, Innovacion y Extension from Universidad Tecnologica de Pereira for the support provided in this investigation.  
The authors declare no conflict of interest.
## 1 Introduction
In addition, an automatic method for identifying registration subregions is proposed on the basis of the region partition of printed images and a subregional-associated searching method. The proposed method can cope with the misregistration of partial areas caused by paper deformation or slight rotation. Moreover, we propose a method of how to represent a good shape and give an effective feature region searching method. To the best of our knowledge, no previous research has explored this topic. The main contributions of this work are fourfold. The details are as follows. 1. An automatic feature region searching algorithm based on a combination of contour point distribution information and edge gradient direction information for image registration in printing defect detection systems was proposed for the first time. Despite the real-time requirements, the proposed algorithm is not complicated, and it solves the problems in printing defect inspection systems, such as low efficiency and inconsistent standards in the manual selection of registration feature  
have stable registration feature regions. The proposed feature region searching method associated with neighborhood subregions can address this problem. The paper is organized as follows. Section 2 presents the methodology, including the problem description, shape feature analysis, proposed shape searching method based on contour point distribution information, shape searching method based on edge gradient direction, and the improved algorithm based on the combination of contour point distribution information and edge gradient direction information. Section 3 discusses the experimental results of the automatic searching of entire image registration feature regions and subregions. Section 4 provides the conclusions.
# Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties
## 3 Controller Design
### Controller Design  
The main purpose of the controller was to track the reference command \(r(t)\) with the existence of the uncertainties \(\Lambda\), \(\theta^{T}\), and \(d\). A traditional method is to combine MRAC with a linear quadratic regulator (LQR) to control the nominal system. However, standard MRAC can only deal with parametric uncertainties, and the system may become unstable when unmatched disturbance uncertainty exists. Therefore, a robust adaptive controller was proposed that consists of three parts: baseline controller \(u_{bl}(t)\), adaptive controller \(u_{ad}(t)\), and robust controller \(u_{rc}(t)\), which can be written as  
\[u(t)=u_{bl}(t)+u_{ad}(t)+u_{rc}(t) \tag{6}\]The control design procedure is presented step by step as follows.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 2 Structure of CFRP and Its Electrical Conductivity
### Conductivity of CFRP and Its Characteristics
The resistivity in both directions could indicate the longitudinal strain since the strain caused the degree of fiber alignment to increase, thereby decreasing the longitudinal resistivity and increasing that in the through-thickness direction [63]. However, the volume of the composite as the sensor is less amenable to spatial distribution sensing [53]. The interlaminar interface acted as a sensor, whereas the interlaminar interface acted as a thermocouple junction or a thermistor. The thermistor function was based on the mechanism that the contact electrical resistivity of the interlaminar interface decreased with the increase in temperature [53]. The self-sensing technique is a valuable, non-destructive detection method and has been extensively explored [64]. The electrical conductivity of CFRPs has been explored for different applications involving carbon fibers [65; 66]. [PERSON] et al. investigated mechanical damages during fatigue tests by measuring the electrical resistance in specimens [20]. The self-sensing ability of these composites is realized through the piezoresistivity phenomenon [46]. [PERSON] and his co-workers concentrate on identifying the interlaminar's damage shape and size in multi-ply CFRP laminates. They adopted reinforcement carbon fibers as sensors to identify the delamination crack in thin CFRP laminates. The change of electrical resistance is a main parameter to indicate the location and dimension of the delamination [67]. The authors also used the time difference between the measured signals reflected from a transmission line end and from damage respectively to determine the location of the damage [68]. The time difference reflectometry (TDR) method would be used to detect damage caused by light strike at a fastener as well [69]. Several similar studies used silver paste utilized with the circumferential lead wires to detect damage in unidirectional CFRP [70].
## 4 Simulation Results
### Simulation Results of the Smart Inverter Control
#### 4.1.3 Per-Unit Value of Mains Voltage Was between 0.97 p.u. and 1 p.u.  
Environmental factors, such as poor weather or shades from objects, would cause reduced power generation of the photovoltaic power generation system, which leads to the voltage of the grid-connected point less than 1 p.u. and easily causes appliance damage at the load end. Therefore, the mains voltage must be increased. Hence, when the per-unit value of mains voltage detected was between 0.97 p.u.-1 p.u., the photovoltaic smart inverter would start the control for supplying reactive power. From Figure 15, it could be observed that in the case of a sudden reduction in mains power fed from the photovoltaic system, the mains voltage \(V_{grid}\) would reduce gradually if not regulated. However, should regulation be conducted with the voltage-power control technique of the photovoltaic smart inverter mentioned, the mains voltage \(V_{grid}\) would increase to the root-mean-square value of 220 V (1 p.u.), which would maintain the mains voltage at a stable value. From Figure 15, it could be also observed that the PF of the smart inverter was adjusted to 0.9 (leading); thus, the reactive power could be provided to the mains. Figure 15, by contrast, also displayed that the output power of the smart inverter when the per-unit value of mains voltage was between 0.97 p.u. and 1 p.u. From Figure 15, it could be observed that the inverter provided reactive power of 774 VAr (Q \(<\) 0) to the mains, the output apparent power S for the inverter still maintained output capacity of the 160 A as for the PVMA, and the output real power P of the smart inverter reduced to 1440 W. Figure 16 displayed the three-phase output voltage and current waveforms of the smart inverter when the per-unit value of mains voltage was between 0.97 p.u. and 1 p.u. From Figure 16, it could be observed that the three-phase balance was still maintained, and then, the current was leading ahead of the voltage; thus, the PF was leading.  
Figure 14: The three-phase output voltage and current waveforms of the smart inverter when the per-unit value of mains voltage was between 1 p.u. and 1.03 p.u.
## 5 ESEDG Comparison with Existing Routing Protocol
### Performing Assessment
#### 5.3.1 Analysis of Average E2E Delay and PDR  
The AUV is the medium from source to destination for any AUV-oriented routing protocols. The delay cycle is normal and natural when the data collection process is initiated through AUVs. The delay occurs when data moves from the source node and GNs to the AUV for further processing and destination nodes. The time of the AUV trajectory also plays a role in unnecessary delays. For 50 nodes, the proposed protocol ESEDG has an elliptical circuit of the AUV from source to destination; therefore, it remains 0.3 s until the nodes reach 150. Similarly, the E2E delay decreased from 0.3 s to 0.2 s for 200 nodes compared to ALP.  
DSG-DSA is closer to the proposed approach with the working mechanism of the AUV cycle but has a 0.35 s and a 0.38 s delay, which shows it is not outperformed in the comparison stage. Meanwhile, E2E delay is the fundamental concern of AEDG. Due to the non-elliptical path in AEDG (only typical trajectory of the AUV is being examined) and because of the large rounds of the AUVs that unnecessarily delay the traffic burden of the intermediate nodes, the delay goes to 0.44 s. Furthermore, AEDCO and AEDCO-A are both used for the AUVs reduction in travel time, but despite the path planning, it resulted in an exceedingly average E2E delay. The primary comparison of the proposed approach ESEDG with other benchmarks is 11% increased for AEDG, 9% for SEDG, and 6% for ALP. In contrast, other techniques such asAEDCO and AEDCO-A have almost the same number of E2E delays which are 0.41 and 0.40 s, respectively, as represented in Figure 5.  
In contrast, the Packet Delivery Ratio (PDR) has an inversely proportional relationship with E2E; as PDR increased, E2E delay decreased and vice versa (this condition only hold when other networks' condition remains stable). A PDR is only used when a large number of nodes is running and impacting the network. Initially, no impact is recorded until the nodes reach 35. When all the nodes are in a working state, the impact of the PDR for the proposed approach and EDG is about 46% and 48%, respectively. Meanwhile, the rest of the baseline schemes such as SEDG have 4% lower than ESEDG, and AEEDCO-A has almost the same values as ESEDG when it reaches 46%.
## 3 Results
### Grain-Flour Classification
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**MA** & **PP** & **WF** & **MF1** & **MF2** & **SF** & **TS** & **SS** & **CS** & **MS** & **A (\%)** & **K** \\ \hline \multirow{8}{*}{LDA} & NoP & 1.00 & 0.68 & 0.66 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 92.19 & 0.91 \\  & D1 & 1.00 & 0.67 & 0.68 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 86.74 & 0.91 \\  & D2 & 1.00 & 0.67 & 0.68 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 88.49 & 0.91 \\  & MSC & 1.00 & 0.41 & 0.90 & 0.99 & 1.00 & 1.00 & 1.00 & 1.00 & 87.33 & 0.90 \\  & MA & 1.00 & 0.67 & 0.68 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 87.29 & 0.91 \\  & NM & 1.00 & 0.58 & 0.80 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 92.28 & 0.91 \\ \hline \multirow{8}{*}{PLSDA} & NoP & 1.00 & 0.31 & 0.65 & 1.00 & 1.00 & 1.00 & 1.00 & 0.95 & 92.25 & 0.85 \\  & D1 & 0.98 & 0.00 & 0.97 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 87.30 & 0.85 \\  & D2 & 0.92 & 0.26 & 0.70 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 88.24 & 0.84 \\  & MSC & 0.95 & 0.05 & 0.66 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 87.37 & 0.82 \\  & MA & 0.95 & 0.31 & 0.65 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 86.64 & 0.85 \\  & NM & 0.96 & 0.09 & 0.84 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 93.22 & 0.85 \\ \hline \multirow{8}{*}{SVM} & NoP & 0.99 & 0.60 & 0.77 & 1.00 & 0.92 & 0.89 & 0.89 & 1.00 & 92.20 & 0.87 \\  & D1 & 0.99 & 0.54 & 0.82 & 1.00 & 0.92 & 0.89 & 0.89 & 1.00 & 86.33 & 0.87 \\  & D2 & 0.99 & 0.58 & 0.79 & 1.00 & 0.92 & 0.89 & 0.89 & 1.00 & 88.48 & 0.87 \\  & MSC & 0.99 & 0.39 & 0.90 & 1.00 & 0.92 & 0.89 & 0.89 & 1.00 & 87.14 & 0.86 \\  & MA & 0.99 & 0.49 & 0.83 & 1.00 & 0.92 & 0.89 & 0.89 & 1.00 & 86.80 & 0.86 \\  & NM & 0.99 & 0.41 & 0.88 & 1.00 & 0.91 & 0.89 & 0.89 & 1.00 & 92.98 & 0.86 \\ \hline \hline \end{tabular}  
* MA: multivariate analysis, PP: preprocessing method, NoP: no-preprocessing, A: Accuracy, K: kappa coefficient, WF: weak rice flour, MF1: mimyeon1 (128 \(\mu\)m), MF2: mimyeon2 (76 \(\mu\)m), SF: strong rice flour, TS: tapioca starch flour, SS: sweat potato starch flour, CS: chestnut starch flour, MS: mungbean starch flour, LDA: linear discriminant analysis, PLSDA: partial least square discriminant analysis, SVM: support vector machine, CART: classification and regression tree.  
\end{table} 
Table 4: Result of fluorescence discriminant accuracy and kappa coefficient of eight samples.
## 5 Research on Slope Treatment Plan  
### Management Approach  
The bottom slopes of the open-pit mine are relatively steep. The slope stability evaluation results during the water impoundment process (Figure 7) indicate that, under different water levels, the bottom section of the northern slope--from the roof of the oil shale upward--remains in an unstable state. To ensure the stability of the bottom slopes of both the southern and northern walls, as well as the interbedded green mudstone and brown shale zone, and to prevent environmental contamination caused by coal-oil mixed pollutants originating from the coal seams and oil shale at the pit bottom, a comprehensive backfilling of the pit bottom is proposed, along with toe reinforcement of the slope in the green mudstone and brown shale interbedded zone [26].  
The preliminary design of the backfilling scheme is as follows: high-density fill materials (such as waste rock or concrete) will be used to backfill the pit bottom up to an elevation of \(-150\) m, forming a toe structure to increase the self-weight of the bottom slope and thereby enhance its sliding resistance. Below the elevation of \(-40\) m on the northern slope, stepped toe-reinforcement will be applied to mitigate the adverse effects of water on the stability of the interbedded green mudstone and brown shale slopes. Between \(-150\) m and \(-40\) m, three stepped internal benches will be constructed. The overall internal slope angle is designed as \(16^{\circ}\), with a single bench face angle of \(30^{\circ}\). Starting from \(-150\) m, the first and second benches are each 40 m high, while the third is 30 m high. The sperm width is 90 m. The upper slope section requires complete drainage, and the surface needs to be sealed with concrete or impermeabilized using an HDPE geomembrane. The detailed layout of the scheme is shown in Figure 8.  
Figure 7: Contour of slope stability coefficients at different water storage levels (SRF: Factor Of Safety), (**a**) current slope, (**b**) store water up to \(-50\) m level, (**c**) store water up to \(0\) m level, and (**d**) store water up to \(50\) m level.
# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 2 Materials and Methods
### Datasets  
The proposed approach is evaluated on two different datasets--still frames from colonoscopy and WCE--to solve the problem of polyp localization. The colonoscopy images are utilized from the MICCAI 2015 sub-challenge on automatic polyp detection [21] and GIAANA (Gastrointestinal Image ANAlysis) 2018, which was part of Endoscopic Vision Challenge [34]. The WCE videos are provided by the Mayo Clinic.  
The colonoscopy still frame analysis is performed using a publicly available polyp database from CVC-ClinicDB [35] for both training and tuning the proposed model and evaluating the CVC-ColonDB and CVC-PolyPHD databases [36; 37; 35] and the ETIS-Larib [22] polyp database. The CVC-ClinicDB contains 612 standard definition still images of 384 \(\times\) 288 resolution with 31 different polyps from 31 different sequences. The CVC-ColonDB database has 300 images of 500 \(\times\) 574 resolution. The frames are extracted from 13 video sequences from 13 patients. The CVC-PolyPHD contains 56 high-definition (1920 \(\times\) 1080) images. Each of these still frame images has the presence of polyps along with an accurately segmented annotated ground truth mask. The ETIS-Larib is a polyp database that has 196 high-definition still images with a resolution of 1225 \(\times\) 966 and 44 different polyps from 34 sequences. Table 1 gives a summary of all the colonoscopy databases used in the study.  
Figure 3: Flowchart depicting the proposed approach.  
The video database for wireless capsule endoscopy (WCE) is provided by the Mayo Clinic. The database has a total of 121 short videos from various patients. These are PillCam SB3 videos with 8:1 magnification and 30% higher resolution compared to SB2. PillCam SB3 and PillCam SB2 system allows direct visualization of small bowel. For the purposes of this study, a total of 1800 polyp-containing still frames were extracted from 18 different videos. Out of 1800 frames, 530 frames contained polyps. The ground truth segmentation masks were manually drawn and verified by expert clinicians. The proposed model was trained with 429 frames, and the rest were considered together as a validation dataset. The test dataset contained 55 frames decoded from WCE videos of various patients. There were a total of 67 polyps in 55 frames.