esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
AEOSSP involves scientific generation of an observation plan for remote sensing tasks based on the users' demands and complex constraints to optimize overall profits. The improvement in satellite quality and imaging payload technology has resulted in enhanced application of AEOSs; thus, AEOS scheduling technology can effectively utilize AEOS resources and provide faster and enhanced remote sensing services. The optimization algorithm is crucial for AEOS scheduling. Considering the quality and speed of solution, this study proposed a DRL-based ADN, requiring the validation of both training process convergence and generalization ability. The convergence of the loss functions (Equations (16)-(19)) suggests that the network model has the ability to fit stably. However, in combinatorial optimization problems, the trend in the episode reward is the key indicator that reflects the stability of the training process. As the model parameters are updated, episode reward shows a gradual increase and approaches convergence, indicating that the model has acquired a stable optimization strategy. The generalization ability of the trained model is then tested by applying it to the testing sets, demonstrating its application prospects under untrained scenarios. Based on the training results (Section 4.2), the proposed ADN model successfully acquired a stable optimization strategy under each training scenario. Moreover, comparison results over a range of testing scenarios suggest that the proposed ADN has a strong generalization ability and high time efficiency. We also proposed ADN_LS, which is a combination of the ADN and the proposed start-time-shift-based local search. Based on the testing results (Section 4.3), ADN_LS improved the observation plan, although it did so with a prolonged running time. Although ADN_LS requires more time than ADN, it exhibited considerable time efficiency for the AEOSSP. For application, decision-makers have the option to select between the two methods (ADN and ADN_LS) according to the tradeoff between quality and runtime.
## 4 Problem Formulation and Brief System Architecture
### Operating Layer
#### 4.2.2 Architecture of the Data Warehouse  
Our proposed interactive healthcare system holds the structured clinical database in the data warehouse and enables more functionality. After analysis of the data in HPC storage in HDFS, the database with metadata is sent to the data warehouse. The data warehouse keeps the clinical database with the modification of metadata and sends the database to the cloud server for fast computation facility with higher accuracy and privacy. There is two-way communication between the HPC and the cloud server. The primary purpose of a data warehouse is to keep track of all the data in the database with the metadata along with log files. It will make an interactive environment of the doctor, patient, and system developer. The user can search and get the information quickly from the data warehouse, even if they are not familiar with any programming languages. This warehouse can take the user question and then convert it as the query language using natural language processing (NLP).  
The architecture of data warehouse holds two units: metadata with log files and structured clinical database. Nodes work with metadata to do faster searches and access the clinical database. For the system developer, it is essential to provide a developing interactive environment and provide a facility to omit a complex query. Thus, metadata offers exploratory information extraction through its log file system and distributed database. The top unit of metadata holds the knowledge graph, which maps with the information base and log file. In this unit, we apply NLP to extract the information of a given query interactively. The log file contains some intent keywords such as blood pressure, respiratory rate, etc., and the knowledge of database table is attributed, establishing the relationship of every table to one another. The other part of a data warehouse is a clinical database which makesthe communication between the cloud server and HPC. When a user wants to search something, the data warehouse takes the question and converts it into a query with a convenient word, and makes a map from the information base and knowledge graph and sends it to the cluster node for parallel computing to provide a fast searching facility. In Figure 2, we show the architecture of the data warehouse for an interactive healthcare system.
## 2 Materials and Methods
### Characterization  
The composites were characterized by UV-visible spectroscopy using a UV-vis spectrophotometer (Thermo Scientific, Evolution 220, Waltham, MA, USA), in the wavelength range from 190 nm to 800 nm. For the analysis of natural zeolite, zeolite was sonicated for 15 min in ultrapure water, then the supernatant was analyzed over a wavelength range from 190 nm to 800 nm [39]. Subsequently, an aqueous solution of GO was prepared at a 1 mg mL\({}^{-1}\) concentration and further diluted for the UV-visible analysis. Finally, all nanocomposites (ZGO2.5, ZGO5, and ZGO10) were sonicated in the same way as zeolite, and then their UV-visible spectra were obtained. The FTIR analysis was carried out using the Fourier transform infrared spectrometer Jasco FT/IR-4000 (Easton, MD, USA). The structure and surface morphology were determined on a scanning electron microscope (SEM, JSM-IT100 InTouchScope, JEOL, Musashino, Akishima, Tokyo, Japan) equipped with a JEOL-made electron-dispersive X-ray spectrometer (EDS) with accelerating voltage of 9 kV and 12 kV.
# Tailoring the V-Model for Optics: A Methodology for Optomechatronic Systems
## 1 Introduction  
### Motivation  
Based on mechatronics, optomechatronics describes the engineering discipline in which optical, mechanical, electronic and information-processing components interact by means of optical radiation to achieve a specific realization of different functions [1, 2, 3]. According to [4], optomechatronic systems can have the main functions shown in Figure 1 or combine several of these functions. For example, the main function \"Measure\" is used in the field of biomedical engineering in optical coherence tomography (OCT) and dermoscopy [5, 6]. In systems used for visible light communication, for example, the main functions \"Illuminate\" and \"Data transfer\" are combined [7, 8].  
For successful integration of optical, mechanical, electronic and information-processing components, a methodical approach is recommended, as with mechatronic systems, in which the specific challenges of the technology and product context are taken into account. For this purpose, [PERSON] presents in [9] a generic process model based on the V-Model of VDI guideline 2206 [10]. With regard to optomechatronic systems, particularly challenging developments are those in which the basic functions shown in Figure 1 interact.  
Competing and partly contradictory requirements have an influence on the development in these cases and require detailing of the generic process from [PERSON].  
Therefore, the motivation of this contribution is to elaborate this detailing and to show how it can be performed for the specific application area of high-resolution lighting systems. Such systems are, for instance, used in video projectors and recently also in vehicle headlamps [11].  
In several innovation projects, the technological potentials of high-resolution lighting systems have been analyzed at the Institute of Product Development at Leibniz University Hanover by means of prototypes [12; 13; 14; 15]. Based on these innovation projects, [PERSON] derived in [16; 17] specific design methods for the preliminary and detailed design of high-resolution lighting systems. With these practical development experiences and elaborated design methods, the authors contribute to this field and show how the generic process to develop optomechatronic systems of [9] can be detailed.  
We show how the specific methods of [16; 17] can be linked with the general framework of [9] to obtain a methodical development approach for high-resolution lighting systems. To illustrate the presented approach, the development of a high-resolution headlamp is described as an application example. In the case of high-resolution lighting systems, the specific requirements arising from the basic functions \"Display information\" and \"Illuminate\" lead to a conflict of objectives between high optical efficiency and high imaging quality of the system [12].
# Application of Internet of Things in a Kitchen Fire Prevention System
## 2 Literature Review
### Current Research on Kitchen Fire Prevention
#### 2.4.3 Related Patents  
This study searched patents related to kitchen fire prevention on the Global Patent Search System and found the following two patents:  
1. TW M501536 (Fire detection and extinguishing equipment) [65]  
Name: Fire detection and extinguishing equipment. This kitchen fire detection and extinguishing equipment consists of natural gas pipelines, a gas stove, an exhaust hood, a flame detector, and an extinguishing device. The extinguishing device consists of a gas cylinder connected to a branch pipe through a solenoid valve; the branch pipe and its nozzle are installed on the exhaust hood. When the flame detector detects flames, it outputs a signal to the driver through a signal transmission device. After receiving the signal, the driver turns off the gas supply and opens the solenoid valve to discharge the fire extinguishing gas from the cylinder to the solenoid valve, branch pipe, and then the nozzle to extinguish the fire on the gas stove. The drive motor of the exhaust hood is automatically turned on, which activates the exhaust fan to clear the air. Thus, the equipment effectively suppresses fire accidents and improves safety in kitchens.  
Comparison between patent TW M501536 and this study: The patented system and this study both use the same fire detection module and gas shutoff device. However, the patented system does not have an alarm device, an automatic door unlocking function, or a Line message reporting mechanism.  
1. TW 201709157 (Kitchen fire prevention system and kitchen monitoring device) [66]  
Name: Kitchen fire prevention system and kitchen monitoring device. The system can be installed on the gas stove and gas pipelines. When a fire accident occurs, it can send a message to a portable warning device to wirelessly notify users. The kitchen monitoring device starts to count time as it detects the user moving a certain distance away from the gas stove. The device sends warning messages to the portable warning device wirelessly at certain time intervals. As the device detects that the user has left the gas stove for a certain time period, it controls the solenoid valve and shuts off the gas supply. The kitchen monitoring device and portable warning device can repeatedly remind the user to check the gas stove after leaving the kitchen. Once the user leaves the gas stove for a particularly long time or particularly long distance, the device automatically shuts off the gas supply, thereby effectively preventing fire accidents. Thus, this is a convenient and feasible fire prevention system.  
Comparison between TW 201709157 and this study: The patented kitchen monitoring system encompasses an automatic shutoff device, a wireless reporting mechanism, and an alarm system, all ofwhich are also proposed in this study. However, the patented system does not have flame, temperature, and gas detection and automatic door unlocking functions.
# Ontology-Based Spatial Data Quality Assessment Framework
###### Abstract  
Spatial data play a critical role in various domains such as cadastre, environment, navigation, and transportation. Therefore, ensuring the quality of geospatial data is essential to obtain reliable results and make accurate decisions. Typically, data are generated by institutions according to specifications including application schemas and can be shared through the National Spatial Data Infrastructure. The compliance of the produced data to the specifications must be assessed by institutions. Quality assessment is typically performed manually by domain experts or with proprietary software. The lack of a standards-based method for institutions to evaluate data quality leads to software dependency and hinders interoperability. The diversity in application domains makes an interoperable, reusable, extensible, and web-based quality assessment method necessary for institutions. Current solutions do not offer such a method to institutions. This results in high costs, including labor, time, and software expenses. This paper presents a novel framework that employs an ontology-based approach to overcome these drawbacks. The framework is primarily based on two types of ontologies and comprises several components. The ontology development component is responsible for formalizing rules for specifications by using a GUI. The ontology mapping component incorporates a Specification Ontology containing domain-specific concepts and a Spatial Data Quality Ontology with generic quality concepts including rules equipped with Semantic Web Rule Language. These rules are not included in the existing data quality ontologies. This integration completes the framework, allowing the quality assessment component to effectively identify inconsistent data. Domain experts can create Specification Ontologies through the GUI, and the framework assesses spatial data against the Spatial Data Quality Ontology, generating quality reports and classifying errors. The framework was tested on a 1/1000-scale base map of a province and effectively identified inconsistencies.  
spatial data quality; quality ontology; web-based quality assessment; specification ontology; spatial data quality ontology; Semantic Web Rule Language (SWRL); rules-based spatial data quality assessment
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Position Aware Routing Protocols
#### 4.2.3. Jamming-Resilient Multipath Routing (JarmRout)  
[PERSON] et al. [45] proposed a new protocol called JarmRout. The protocol proposes three solutions for link quality, network traffic, and network environment, and details the criteria for judging the link quality of neighboring nodes. Chebyshev inequality is used to estimate the link. In probability theory, Chebyshev inequality guarantees that in any data sample or probability distribution, the strictly positive expectation \(E(X)\) and the variance \(var(X)\) have the following inequality with the discrete variable \(X\),  
\[P\{|X-E(X)|<\varepsilon\}\geq 1-\frac{var(X)}{\varepsilon^{2}}. \tag{4}\]  
when \(var(X)\) is close to zero; it reflects the value of \(X\) are always tend to or equal to its expected value, which means \(X\) is relatively stable.  
The load balancing factor in the link is also considered, and the channel is selected according to the current traffic load, which is calculated by  
\[Q_{i}^{buf}=\beta\cdot Q_{i}^{buf}+(1-\beta)\cdot B_{i}^{k-1}. \tag{5}\]  
where \(B_{i}^{k-1}\) is the latest measured number of waiting packets stored in the buffer.  
Secondly, the disjoint path from the source node to the target node is found by calculating the distance of the node in the flight space. The author proposed a new spatial distance scheme along with a new distance metrics to measure the physical distance between multiple paths. The design of this protocol effectively solves the problem of network interference, which allowing for intentional interference and interruption, or isolation and localization, and will not affect the links of the overall network.  
**Advantages:** (1) It can improve the delivery rate of packets and reduce the interruption probability of end-to-end communication. (2) If interference occurs suddenly in the network, JarmRout can take some measures to protect the network and improve network resilience without consuming network resources, which is applicable to FANET because it is resource-constrained.  
**Disadvantages:** (1) JarmRout selects a path where two nodes do not intersect to send packets. These two paths have no public nodes except the source node and the destination node. However, if the target node has only one neighbor, it is difficult to find multiple paths. (2) This article implicitly assumes a bidirectional link, and the proposed JarmRout may not work properly on a unidirectional link.
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 3 Experiments and Results
### Synthetic Data
#### 3.1.2 Numerical Stability  
In this section, the performance of the numerical stability is tested. Here, 5000 independent trials are performed for our proposed method, P3P solver, and RPnP solver, individually, using synthetic data. No noise is added. The results of the numerical stability are reported in Figure 4.  
From Figure 4, the log\({}^{10}\) of the total rotation error, and the rotation error around the \(X\)-axis and \(Z\)-axis are observed clearly. It can be seen that our proposed method has the best performance for numerical stability, and P3P solver and RPnP solver perform similarly, but both are worse than our proposed method.  
Figure 3: Robustness to roll angle noise: (**a**) the total rotation error; (**b**) the rotation error around the \(X\)-axis; (**c**) the rotation error around the \(Z\)-axis.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 3 Proposed Machine Learning Models
### Artificial Neural Network  
The artificial neural network (ANN), also called a neural network, originated from simulating biological neural networks. Generally, it consists of many neurons in layers including one input layer, one or several hidden layers, and an output layer [60]. The neurons are fully interconnected between the neighboring layers by the weight, and typically, there are no inter-connections between neurons within the same layer [61].  
There are many possible network structures available. BPNN was utilized in this study because the back-propagation (BP) algorithms are the most widely used and effective learning algorithms for training an ANN. A preliminary architecture of the BPNN was determined to be 4-\(n\)-1, where 4 input neurons represent the input features standing for FA, LP, SF, and crack width before self-healing, \(n=5\) indicates the number of neurons in the hidden layer, with 1 target neuron in the output layer for the predicted crack width after self-healing. This is a three-layer network with one hidden layer capable of approximating most continuous functions, of which the complex nonlinear relationship could be approximated in accuracy [31]. The architecture of the BPNN model for predicting self-healing is demonstrated in Figure 6.  
Given a set of inputs \(\{x_{1},x_{2},x_{3},\cdots,x_{n}\}\), while information is passed through the input layer to the hidden layer, each neuron in the input layer is multiplied by the respective weights added by a bias and summed together. After that, an activation function \(f\) is applied to form the output \(z\), which can be expressed in the following equation [25]:  
\[z=f(\sum_{i=1}^{n}w_{ij}x_{i}+b_{j}) \tag{10}\]  
where \(w_{ij}\) is the connection weights between the \(i\)th neuron of the input and the \(j\)th neuron of the hidden layer and \(b_{j}\) is the bias of the \(j\)th neuron. The sigmoid function was applied as the activation function between the input, hidden, and output neurons to form the output.  
\[f(x)=\frac{1}{1+e^{-x}} \tag{11}\]  
The goal of training a neural network is to determine the values of the connection weights and the biases of the neurons. The back-propagation indicates an iterated method to adjust the weights from the output layer to the input layer. At first, the outputs were calculated in a feed-forward from the input layer via the hidden layer to the output layer. Then, an error was generated by comparing the output with the target output. After that, the error was back-propagated to the hidden layer and input layer. By adjusting the connection weights and biases, the error was further reduced. The process was repeated until the error was minimized or reaching the termination to avoid over-fitting [62].
# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 4 Network Selection
### Practicability  
Since the NetSel-RF model needs information from devices (e.g., battery consumption) and APs (e.g., occupation), it requires a network global view for a real deployment. In this sense, NetSel-RF is envisioned to be deployed on WiFi networks following the SDN paradigm. SDN offers programmability, a global view, logically centralized control, and the decoupling of network control and packet forwarding. It is noteworthy that our training and validation datasets were collected by using the Mininet-WiFi emulator, which is SDN-oriented.  
We analyzed the most common parameters used in the literature to build the final dataset used to train and validate NetSel-RF. The use of parameters other than those used in the data interpretation and data preparation phases is out of the scope of this paper. It is also worthy of note that we use the parameters available in the device and AP because, in practice, NetSel-RF is geared towards Software-Defined Wireless Networks [39; 40; 41], which is implicit from the data interpretation phase to the evaluation phase. For actual deployment, we suggest installing the NetSel-RF learning agent in the management plane [42] or in the cognitive plane [43].
###### Abstract
For example, combining SUMO with the Lattice Boltzmann method (OpenLB) allows for dynamic simulation of traffic-air quality interactions in highly detailed urban scenarios, while Gaussian process models integrated with CFD are used to optimize traffic flow and reduce CO emissions [30]. CFD-RANS models have also proven effective in reproducing pollution hotspots at the street and neighborhood scales, offering high spatial resolution in simulating the dispersion of PM, NO\({}_{\rm x}\), and CO under real-world conditions [31; 32]. Simultaneously, solar radiation absorbed by dark surfaces and heat retention in densely built areas intensify the UHI effect, especially under increasingly frequent and intense heatwaves caused by climate change [33; 34]. Urban materials and design play a central role in modulating thermal loads: the strategic use of high-albedo surfaces, vegetative shading, and traffic reorganization has been shown to reduce thermal stress and improve outdoor comfort [35; 36; 37; 38; 39; 40; 41]. Microscale modeling tools such as ENVI-met enable high-resolution simulation of key microclimatic parameters, including air temperature, Mean Radiant Temperature (MRT), and Physiological Equivalent Temperature (PET), by accounting for detailed urban morphology, surface materials, and vegetation properties. ENVI-met is widely used in urban climate studies due to its capability to represent small-scale interactions between the built environment and atmospheric processes, making it particularly suitable for scenario-based assessments in pedestrian-oriented areas [42; 43; 44; 45; 46; 47; 48; 49; 50]. Other models, such as SOLWEIG and OpenFOAM, provide complementary approaches: the former focuses on solar radiation and shading patterns in urban canyons, while the latter offers CFD-based simulations of airflow and pollutant dispersion. However, these tools typically operate at lower spatial and temporal resolution and may require more complex calibration [50; 51]. A comparative overview of key studies is presented in Table 1, which summarizes modeling methods, spatial scales, and mitigation strategies. The table highlights how many contributions focus on isolated aspects--either microclimatic or air quality-related--often neglecting the systemic interaction between them. It also clearly shows the limited attention devoted to university campuses, and especially to those located in Mediterranean regions.