esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Note that the proposed solution methodology addressed in this research to locate, size, and operate D-STATCOMs in electrical distribution networks is different from recently published literature reports, since the GNDO approach allows reaching better objective function values with respect to the literature in the case of fixed reactive power injection, and also, with the improvement stage based on the ORPF solution, we find additional profits \begin{table}
\begin{tabular}{l l l} \hline \hline
**Solution Method** & **Objective Function** & **Ref.** & **Year** \\ \hline Evolutionary programming & Power loss minimization and voltage profile improvement & [16] & 2011 \\ \hline Particle swarm optimization & Power loss minimization and voltage profile improvement & [17] & 2014 \\ \hline Sensitivity index & Power loss minimization and voltage profile improvement & [18] & 2015 \\ \hline Gravitational search algorithm & Power loss minimization, voltage profile improvement, and investment & [19] & 2016 \\ \hline Adaptive particle swarm optimization & Energy loss and investment cost minimization & [20] & 2018 \\ \hline Particle swarm optimization & Energy loss minimization & [21] & 2018 \\ \hline Salp swarm algorithm & Energy loss and investment cost minimization & [22] & 2022 \\ \hline Two-stage convex optimization model & Energy loss and investment cost minimization & [23] & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main literature reports regarding the optimal placement and sizing of D-STATCOMs in distribution grids. for the utility company that have not been previously presented by solution methodologies based on combinatorial optimizers available in the current literature. It is important to mention that this work focuses on the optimal design and operation of D-STATCOMS in distribution networks under normal operating conditions and upon the basis of the demand curves provided by utilities. However, as future research, an interesting development could consider the probabilistic nature of the loads and new paradigms regarding the transformation of energy consumption habits with the massive integration of electric vehicles into medium- and low-voltage distribution networks [27]. The remainder of this work has the following structure presented: Section 2 presents the mathematical formulation of the D-STATCOM optimal integration problem in power distribution systems via mixed-integer nonlinear programming models. Section 3 presents the proposed solution method, which is based on the GNDO and the successive approximations power flow approach, with the aim of determining the nodes where the D-STATCOMs must be located as well as their sizes. These nodes are also used as inputs for the ORPF formulation to reach additional reductions in the objective function value. Section 4 outlines the main characteristics of the test system, which is a 33-node IEEE system that includes demand scenarios for residential, industrial, and commercial users. Section 5 presents the results of the simulation as well as its analysis and discussion. Finally, Section 6 describes the main conclusions derived from this study.
## 1 Introduction  
Headrace tunnels have various functions, such as flood control, securing urban and industrial water, and water purification. However, in recent years, similar to other types of infrastructure such as bridges, headrace tunnels have steadily deteriorated, and there have been concerns about safety, water quality, and adverse effects on the surrounding ground. Therefore, considerable effort is directed toward extending the life of headrace tunnels, including periodic inspections and repairs. Since 2014, the Ministry of Land, Infrastructure, Transport and Tourism of Japan has implemented a periodic inspection guideline for all bridges and tunnels longer than 2 m based on a detailed visual inspection once every five years, and inspections are currently conducted at this frequency for the subject infrastructure throughout Japan [1]. However, currently, inspections of headrace tunnels mainly involve visual inspection by inspection engineers, which requires a large amount of labor. There are also concerns that such visual inspections by inspection engineers may lead to individual differences in judging the state of deterioration or the deterioration being overlooked. Additionally, the following situation is unique to headrace tunnels: the water supply needs to be interrupted for surveys; however, the water supply cannot be interrupted frequently, leading to the problem that it is difficult to confirm the inspection results onsite. Therefore, the current approach is limited to low-cost and simple inspections during normal times, and in cases of severe deformities or progressing deterioration, the process is shifted to more accurate inspections and investigations in stages. Given these issues, for inspecting headrace tunnels, it is desirable to use photography devices that are easy to operate at various sites for acquiring high-quality wall images in a short period for highly accurate inspections even in normal times and to conduct damage detection using artificial intelligence (AI) with the captured images so that the results are not influenced by the individual differences of inspection engineers. Currently, the information is managed via inspection reports after visual inspections and investigations. The locations of deteriorated areas, occurrence of deterioration phenomena, and conditions are recorded in the report as text and listed in a table. There are typically no photographs of deteriorated areas, and even when there are photographs, they are stored as individual data; thus, they must be searched for and confirmed by the number in the inspection report. Hence, it is impossible to comprehensively grasp the deterioration status and its distribution from the previous survey. Therefore, in this study, we developed a photography device and damage detection method for a headrace tunnel whose walls are made of steel, and we conducted an experiment involving an actual headrace tunnel to validate the proposed method.
## 2 Model and Methodology
### Methodology  
To find the 12 parameters in the model is a nonlinear constrained optimization problem. There are several common optimization methods including the penalty function method, augmented multiplier method, and the sequence quadratic programming (SQP) method. SQP has a wide range of applications and good development. When compared with other optimization algorithms, its most prominent advantages are good convergence, high computational efficiency, and strong edge search ability. Therefore, this research chooses the SQP method to find the 12 parameters.  
A typical constrained nonlinear programming problem can be expressed as:  
\[\begin{array}{ll}\text{min}f(X)&\\ &h_{i}(X)=0\quad i=1,2,\cdots,m\\ s.t.&\\ &g_{j}(X)\geq 0\quad j=1,2,\cdots,n\end{array}. \tag{11}\]  
where the objective function \(f(X)\), equality constraint \(h_{i}(X)\) and inequality constraint \(g_{i}(X)\) are all second order, continuous and differentiable. The Lagrange function of this problem is given in Equation (12).  
\[L(X,\gamma)=f(X)+\sum_{i=1}^{m}\gamma_{i}h_{i}(X)+\sum_{j=m+1}^{n}\gamma_{j}g_{ j}(X). \tag{12}\]  
where \(\gamma_{i}\) and \(\gamma_{j}\) are Lagrange operators.  
According to the idea of SQP, this problem can be transformed into a series of quadratic programming (QP) problems. The QP problem of \(X_{k+1}\) can be further changed into the QP problem of search direction \(d\) in order to ensure that \(X_{k}\) is close to the feasible solution in each iteration and the objective function also has a downward trend. Then, the following form of the QP subproblem can be obtained:  
\[\begin{array}{ll}\min_{d\in R^{n}}&\frac{1}{2}d^{T}H_{k}d+(\
abla f(X_{k}))^ {T}d\\ &(\
abla h_{i}(X))^{T}d+h_{i}(X)=0\\ s.t.&\\ &(\
abla g_{j}(X))^{T}d+g_{j}(X)=0\\ &X_{k+1}=X_{k}+\alpha_{k}d_{k}.\end{array}. \tag{13}\]  
where \(\alpha_{k}\) is the step size. The iterative process of the SQP method can be expressed as follows:  
Step 1: Determine the initial value of \(X^{0}\) and \(H^{0}\).  
Step 2: Solve the QP subproblem and determine the search direction \(d^{k}\) and \(\gamma_{i,j}^{k+1}\).  
Step 3: \(X_{k+1}=X_{k}+\alpha_{k}d_{k}\), if the convergence condition is satisfied, \((X_{k+1},\gamma^{k+1})\) is taken as the optimal solution of the original problem; if not, return to Step 2 and continue to iterate.  
The problem in this study has variable constraint conditions and objective function. Also, the problem is not suitable for iteration with traditional Lagrange operators. This study adopts intelligent method, the monitoring technology combining constraint condition and objective function, to select new iteration points. In addition, the possibility of failure in optimization iteration is dealt with by temporary expansion of the feasible domain method. These changes improve the effectiveness and computational efficiency of the optimization algorithm. In consequence, the overview of the model and methodology is shown in Figure 1.
# Health Project Prioritization in Meta, Colombia, Using Anp-Dematel
## 5 Discussion
The ANP-DEMATEL framework provided a robust structure for modeling complex interdependencies among criteria and alternatives. Grounded in Participatory Process Theory, these tools integrate diverse perspectives, fostering inclusive decision-making; however, their implementation faces challenges, including the need for decision-maker training and reliance on high-quality data. To maximize utility, technical analyses should be paired with participatory processes that enhance decision legitimacy. Our findings correlate with the Situational Health Analysis (ASIS) of Meta, where prioritized projects (e.g., A05 and A04) address critical infrastructure gaps. Projects A02 and A01 target geographic dispersion and access barriers in rural and Indigenous communities, reinforcing the need for context-specific strategies to reduce health inequities [45]. This article contributes to administrative discourse by demonstrating how advanced analytical methods (ANP-DEMATEL) can be combined with structured stakeholder participation to improve decision-making in decentralized health systems. Specifically, it provides a practical framework for local authorities to prioritize health investment projects in a way that balances technical criteria with social legitimacy. The article offers actionable insights for policymakers in the Meta Department, including (1) the adoption of integrated multicriteria methodologies (ANP-DEMATEL) to align technical decisions with stakeholder engagement; (2) the use of structured surveys to map interest and influence in territorial health planning; and (3) the prioritization of infrastructure and accessibility interventions as high-impact criteria in underserved contexts. In doing so, it illustrates how expert judgment and participatory processes can be integrated into formal planning tools, reinforcing transparency and institutional coordination. Future research should explore additional decision criteria, such as financial sustainability and implementation capacity, which were not explicitly addressed in this analysis. Longitudinal studies assessing the impact of prioritized projects on health indicators are also recommended. Policymakers are encouraged to adopt collaborative and adaptive strategies that align decisions with the evolving dynamics of the health system [46; 47]. In addition, the prioritization results were shared with participating stakeholders, reinforcing transparency and encouraging institutional appropriation of the findings.
## 3 Results
Unlike SDBR and BCP, because BNST does not depend on four neighboring locations and nodes degrees, BNST performed better and had a higher efficiency at detecting nodes whose transmission ranges intersected with the jammer's transmission range. As shown in Figure 9, at the time step of 10 s, 32 nodes were present around the jammer field, and they could catch JRSS. Please note that BNST recognized 30 nodes, SDBR recognized 12 nodes, and BCP recognized 45 nodes, including those nodes that were incorrectly detected. Because SDBR is based on nodes degree and limited to be equal to or larger than six nodes, it fails to recognize boundary nodes that have less than six nodes on the second hop. Because of the random deployment, location of nodes, transmission range, and neighboring nodes, BCP defined any nodes that had less than four nodes on each side as boundary nodes. BCP was successful in detecting all boundary nodes. Note that nodes that are located far away from the jamming region with their transmission range not intersecting with the jammer transmission range and having less than four neighbors on each side are defined as boundary nodes. Therefore, the number of boundary nodes detected by BCP is always larger than that by BNST and SDBR. When the jammer is closer to the network center, SDBR failed to locate any boundary nodes because the jammer is positioned around the center. Moreover, because of the jamming effect, all nodes deployed around the edge of the network lost most of their neighbors. Figure 10 shows the percentage error of the selected boundary nodes when the jammer's transmission range was adjusted to 15 m, 20 m, 30 m, and 35 m. Note that BNST has a higher efficiency and fewer boundary nodes recognition errors compared to SDBR and BCP. As shown in Figure 10, when the jammer's transmission range was adjusted to 35 m, which indicates more jammed nodes and a longer jamming region was covered by the jammer, SDBR failed to detect the boundary nodes. However, EKF is designed to receive tracking data at each time step by all the detected boundary nodes. As shown in Figure 11, EKF stopped tracking the jammer at t = 32 s because SDBR failed to detect the boundary nodes and EKF did not receive any data. When applied to BCP, EKF failed to track the enemy at all times because there are certain boundaries that were wrongly selected and there was no data feed provided to EKF. However, BNST detected most boundary nodes, and when this data was provided to EKF, it could track the jammer and detect its location at each time step. Figure 9: Boundary nodes recognized by BNST, SDBR, and BCP.
## 2 Simulation Basics  
### NACA0012 Models  
Airfoil Tools (UIUC Airfoil Coordinates Database), the NACA 4-digit airfoil generator, and the definition formula of the NACA0012 airfoil are the three standard modeling methods. Airfoil Tools provides 132 data points and the trailing edge of the designed airfoil is not closed. Therefore, it needs to be connected manually to form a blunt trailing edge. If the two points of the trailing edge are not connected manually, after being imported into modeling tools such as DesignModeler, a blunt elliptic shape will be formed at the trailing edge of the generated two-dimensional airfoil, which would fail the mesh generation. The NACA 4-digit airfoil generator supports a maximum of 200 data points and provides the close trailing edge option to design the sharp trailing edge. The definition formula of the NACA0012 is as Equation (1), where x is the \(X\)-axis location.  
\[y=\pm 0.60\times[0.30\times x^{1/2}-0.13\times x-0.36\times x^{2}+0.30\times x ^{3}-0.11\times x^{4}] \tag{1}\]  
To compare and analyze the influence of the number of data points on the accuracy of the numerical simulation, we doubled the 132 points provided by Airfoil Tools to 264 points by adding the average value between two points. Similarly, the 200 points supplied by the NACA 4-digit airfoil generator were doubled to 400 points. Then we substituted the above four different data points into Equation (1) to establish six NACA0012 models. The chord length of the airfoil \(L\) is 1 m and the corresponding characteristic length \(c\) is 1 m as well. The six NACA0012 models are shown in Figure 1. From Figure 1b, we can see that there are apparent differences between distinct models.  
Therefore, in this paper, we establish six two-dimensional NACA0012 models, including two trailing edge shapes. For the blunt trailing edge, we applied Airfoil Tools to build one NACA0012 model including 132 points. For the sharp trailing edge, five NACA0012 models were made using the following five methods: (1) NACA4-digital generator applying 200 points; (2) definition formula of NACA0012 applying 132 points provided by Airfoil Tools; (3) definition formula of NACA0012 applying 200 points provided by NACA4-digital generator; (4) definition formula of NACA0012 applying 264 points; (5) definition formula of NACA0012 applying 400 points.
# Olive Paste-Enriched Cookies Exert Increased Antioxidant Activities
## 1 Introduction  
The idea of functional foods dates back thousands of years. In 1984, Japan was the first country to promote the idea of functional food [1]. According to one of the most commonly used definitions, functional foods are foods that have been enriched with essential and physiologically useful effects [1].  
It is widely acknowledged that functional foods have a positive impact on human health and the lifestyle that people follow has moved toward a healthier one [2], where functional food intake is a new way for consumers to communicate their well-being [3, 4]  
Cookies are a worldwide popular bakery snack for consumers of all ages. They have high nutritional value, affordable cost, long shelf life, and they are ready to eat in different tastes. The above characteristics make cookies an ideal snack that can be fortified with extra ingredients to suffice health-promoting human dietary demands [5].  
As a result of growing interest in functional foods, bioactive components are being used to create innovative cookies with enhanced nutritional value [6]. Antioxidants are a major category of foodstuff molecules through which functional foods have been shown to exert positive effects on human health. Therefore, the enrichment with antioxidants is one of the most interesting concepts in functional food development [7; 8; 9].  
Olive paste is a product from olives and characteristic of the Mediterranean diet. It contains various phenolic compounds, with hydroxytyrosol being the most abundant that exert health preventive activities associated with various chronic diseases where oxidative stress plays a major role in their onset [10; 11; 12; 13].  
Therefore, this study aimed to enrich cookies with olive paste and evaluate their antioxidant activities compared to non-enriched cookies to assess the olive paste effectiveness as an ingredient that confers to dietary added value in enriched cookies.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 5 Application Analysis of Passenger Flow Prediction  
Taking the passenger flow data of Chongqing Rail Transit Line 3 in April 2021 as an example, based on the above TCN-LSTM model, the first 80% of the data is taken as the training sample and the last 20% as the test sample. The short-term passenger flow of rail traffic is predicted in a short time to verify the prediction accuracy and validity of the model.
# Low Kinetic Energy Oxygen Ion Irradiation of Vertically Aligned Carbon Nanotubes
###### Abstract  
Vertically aligned multiwalled carbon nanotubes (v-CNTs) were functionalized with oxygen groups using low kinetic energy oxygen ion irradiation. X-ray photoelectron spectroscopy (XPS) analysis indicates that oxygen ion irradiation produces three different types of oxygen functional groups at the CNTs surface: epoxide, carbonyl and carboxyl groups. The relative concentration of these groups depends on the parameters used for oxygen ion irradiation. Scanning electron microscopy (SEM) shows that the macroscopic structure and alignment of v-CNTs are not affected by the ion irradiation and transmission electron microscopy (TEM) proves tip functionalization of v-CNTs. We observed that in comparison to oxygen plasma treatment, oxygen ion irradiation shows higher functionalization efficiency and versatility. Ion irradiation leads to higher amount of oxygen grafting at the v-CNTs surface, besides different functional groups and their relative concentration can be tuned varying the irradiation parameters.  
ion irradiation; functionalization; carbon nanotubes +
Footnote †: journal: Applied Physics
## 4 Discussion  
This study successfully demonstrated the efficiency and applicability of the HONO collection system using an ultrasonic nozzle and spray chamber for ambient air measurements. The system attained an efficiency of 97.7% and MDLs of 0.15 ppbv for HONO across two serial stages. Interference checks revealed negligible impacts from NO2 or OH radicals. From 11 December to 25 December 2023, the collected data showed that HONO concentrations exhibited typical diurnal variations, with peaks during the night and troughs around midday. This pattern corresponds with established photolysis dynamics and previous studies. Diurnal variations in O3 and HNO3 also occurred as expected, with O3 reaching its maximum during daylight and HNO3 escalating due to photochemical responses. Throughout the study, we observed specific instances of inverse correlations between HONO and O3 concentrations, particularly during episodes of ozone depletion. These correlations, with r2 values of 0.94, 0.81, and 0.52 across different timeframes, highlighted the interplay between ozone titration and HONO formation. Moreover, variations in the HONO/NOx ratio indicated the influence of heterogeneous reactions beyond simple NOx chemistry. Overall, these findings confirm that the HONO collection system is capable of reliably capturing atmospheric chemical reactions involving HONO, corroborating data from real-time measurement devices (O3, NOx). This validation promotes the system's utility in ambient air quality monitoring and research, offering valuable insights into the atmospheric mechanisms regulating HONO dynamics.  
[PERSON]-[PERSON] contributed to this work via experimental measurements, data analysis, and manuscript preparation. [PERSON], [PERSON], D.-H.K. and S.C. contributed to the research sample analyses. [PERSON]-S.B. contributed to the experimental planning and data analysis. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Vertical Structure of Rainfall
#### 5.1.1 Precipitation Height of Rainfall Systems  
Studies found that there was a difference of rainfall height between convective and stratiform precipitation and between precipitation over land and over oceans. Most of them concluded that the height of convective precipitation was higher than stratiform precipitation [105; 106; 107], and the precipitation height was higher over land than over oceans [105; 107; 108; 109; 110; 111]. These conclusions implied that stratiform and warm rain were more common over oceans than over land, while ice-based microphysics was more apparent over land than over oceans, especially for continental convection (Figure 6) [110; 112]. A few studies noticed no significant height difference between stratiform precipitation over land and over oceans [106; 108], and even that the height of stratiform precipitation over oceans was higher than that over land [113]. There was also precipitation height difference over different oceans or different land [114; 115].