esa-sceva/satcom-chunk-collection huggingface dataset:
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 3 Adaptive Time-Triggered Systems  
The architecture of an adaptive time-triggered system, featuring a time-triggered network and multiple node computers, is depicted in Figure 1. The system is semi-static, where at any particular point in time, an _active time-triggered schedule_ determines the points in time for executing tasks and transmitting messages with respect to a global time base. This active schedule is used by a task/message dispatcher, which provides control signals for the operating system and the network interface.  
It is assumed that a time-triggered operating system or hypervisor (e.g., XtratuM [26], PikeOS [27]) and a time-triggered network (e.g., TSN, TTEthernet) are used, which manage access to resources based on TDMA [28] with system-wide synchronization. The active time-triggered schedule is taken from a multi-schedule graph, which contains schedules for all combinations of context events.  
Context events are events that require different schedules to improve energy efficiency, dependability, and adaptability. Contextual events, such as task slack times or warnings about low battery charges, can be used for energy management. Failures represent context events that enable fault recovery and degradation for improving dependability. Context events for adaptability include changes in the environmental conditions or the constitution of the computer system, which need to be reflected in the system's configuration. For example, task slack times can be used for an earlier transmission of output messages and an earlier execution of subsequent tasks. The resulting idle time at the end of the system cycle enables power/clock gating to reduce energy consumption.  
Figure 1: Adaptive Time-Triggered System.  
Key requirements for adaptation and changing schedules in a time-triggered system are the correctness of the offline precomputed schedules and the system-wide consistency in switching between schedules.
* **Correctness of schedules:** Time-triggered schedules need to prevent collisions on shared resources, guarantee deadlines, and ensure implicit synchronization. These requirements are fulfilled at development time by suitable offline scheduling algorithms.
* **Consistency of switching schedules:** When a node computer switches to another schedule due to a context event, then all other node computers need to switch accordingly. Collision and loss of synchronization would occur if subgroups of the system's node computers were on different schedules at any point in time.  
To satisfy the requirement for consistency, observed context events are subject to _context agreement_. The node computers execute a fault-tolerant agreement protocol to ensure that information about context events is consistent at all nodes where it is used for switching between schedules.
# A Survey of GIS and IoT Integration: Applications and Architecture
###### Abstract  
IoT, as an emerging technology along with GIS, can result in advanced and user-friendly features in Smart Cities. In order to investigate the capabilities offered by these technologies, this paper provides an overview of GIS and IoT integration focusing on applications and architecture. Specifically, this paper starts with investigating the role of GIS and IoT separately and jointly in different domains. Then, a review of GIS and IoT integration studies is provided to examine how GIS could be used in IoT architecture. The results showed that the capabilities of GIS in dealing with geospatial data and attributes along with offering visualization and analyzing tools make it possible to develop an integrated system benefiting from real-time data collection and real-time monitoring provided by IoT. The presented details would assist researchers in future studies on utilizing GIS and IoT at the same time.  
i 1 2021 11 10365 17 October 2021 29 October  
entity by spatial data through its location and shape. GIS functionalities cover a wide range of applications [16; 17]. To be more specific, this framework is helpful in dealing with the users' queries which can be divided into three types of naming: Factual, Graphical, and Spatial [11]. The four capabilities of GIS are mentioned at the beginning of Section 1.2. (Capturing, Managing, Analyzing, Visualizing) are involved in the process of answering users' queries. That is to say after the data is captured, according to the type of users' queries, GIS can perform some analysis in order to provide appropriate outputs. Presenting the storage capacities of all reservoirs located in parcels of a city, visualizing the distribution of shopping malls at the city center, locating parcels within 2 km of a power plant are respectively some examples of Factual, Graphical, and Spatial queries.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 3 System Model and Problem Formulation
### Key Challenges  
The focus of our study is on the issue of multi-constrained routing from sensors to sinks and BRs. Given that the RPL is a recognized standard, our investigation primarily revolves around this protocol. The RPL establishes a structured, tree-like, Destination-Oriented Directed Acyclic Graph (DODAG) for routing purposes. The paths from sensors to the sink/BR constitute a directed incast route. It is evident that, under multiple QoS constraints, a singular directed tree may not always suffice [17].  
**Challenge 1: A Unique DODAG Cannot Satisfy the QoS Requirements of All Sensors.**  
A single DODAG may not adequately address the multi-constrained QoS routing needs in a specific WSN and its QoS specifications. The illustration in Figure 2 demonstrates that a DODAG cannot accommodate all nodes, even within a limited domain. For instance, consider two additive QoS parameters (e.g., delay and cost). The maximum tolerated values, delineated as \(\vec{L}^{T}=(13,13)\), indicate the QoS limits at the BR, with corresponding link values depicted in Figure 2. Only the specified blue and red QoS paths from nodes E, F, and G to the BR can be utilized. Given that these paths share a common prefix, the routes from G and F combine to form a single DODAG. However, it is crucial to note that no single QoS-compatible DODAG can incorporate the path from E to the BR. The figure presents two distinct DODAGs (red and blue) that cannot be unified. Nodes B, C, and D are present in both QoS paths and can, therefore, choose between the blue and red paths to transmit messages to the BR. The routing decisions for messages originating from these nodeslack clarity, rendering the unambiguous configuration of the routing table challenging. For example, at node C, messages from node E must be routed to node D, while messages from F must be directed to node B.  
**Challenge 2. Paths from sensors far from the BR may not meet QoS demands.**  
Let \(L^{T}=(11,11)\) represent the vector of the QoS requirements in the network shown in Figure 2. None of the routes from nodes E, F, and G to the BR fulfill this QoS requirement. The routes from nodes E, F, and G to the BR satisfy this QoS requirement.  
**Challenge 3: The optimal DODAG under QoS constraints is NP-hard.**  
Assuming that distances are calculated using a non-linear length function based on multiple additive metrics, the DODAG can be viewed as a minimum-cost, diameter-constrained arborescence directed from its non-root nodes (sources) to the root node (BR). The undirected version of this problem, which involves only one additive hop distance, is known to be NP-hard [54]. Furthermore, the minimum cost r-arborescence problem, where \(r\) refers to the root node, is also NP-hard. We conjecture that the problem of directed diameter-constrained r-arborescence using non-linear weights is also NP-hard.
# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
## 2 Materials and Methods  
### Satellite Data and Vegetation Index  
The Sentinel-2 satellite images used in this study were pre-processed and made available by DGT and correspond to the NDVI obtained from Sentinel-2 Level-2A located in the T29 TNE sector with cloud coverage less than 60% processed through the MAJA algorithm [32] and obtained through the Theia portal [33]. The set of images date from 1 October 2019 to 30 December 2020, totalling 80 images used for the model calibration and testing.  
The Level-2A of Sentinel-2 images corresponds to the reflectance at the lower part of the atmosphere (bottom-of-atmosphere--BOA) and it is derived from a Sentinel-2 Level-1C image processing to minimise effects from the top of the atmosphere.  
The MAJA algorithm detects the presence of clouds using three tests: (i) a cirrus band test available on some satellites (1.380 nm); (ii) multitemporal analysis to detect increased blue band reflectance (0.490 nm), which indicates the presence of clouds; and (iii) the correlation with neighbouring pixels, as it is understood that it is unlikely that two different clouds on successive dates will have the same shape.  
At the end of this stage, all the Sentinel-2 Level-2A images were geometric corrected (i.e., orthorectified), radiometric corrected to BOA, and processed with the cloud, earth, and water masks available in Sentinel-2 Level-1C images.  
The literature consulted revealed that most studies adopted the NDVI, being as the main index of analysis [28,29,34], or for comparison with other indices [31,35]. Because of this, we opted for the use of this index in this work.
## 5 Technologies Stack
### Infrastructure
#### 5.3.2 Inter-Service Communication--Apache Kafka  
By now, the described system design offered a detailed overview of the communication layers available in the cluster. There is an obvious need for a decoupled asynchronous messaging service simply because the core functionality cannot be provided in a near-real-time fashion using blocking or synchronous communication methods.  
To supply this need, Apache Kafka was used as a messaging service. Apache Kafka is a messaging system that allows for the scaling and distribution of work to facilitate the flow of data from producers to consumers. Essentially, Kafka is a very sophisticated and distributed queue-like structure where messages are pushed.  
There are a series of functionalities that will generate events that need to travel in a near real-time fashion between the client and the service. It is common for microservices to interact with one another. In the designed system, the document content manipulation API might produce an event that needs to be transported toward the client via a real-time child service [51]. Kafka can facilitate this low-latency communication [52].  
Besides the fast processing of data, Kafka has another major advantage: its distributed nature. The messaging service can run on multiple systems over the network thus being able to deliver messages even when one or more systems go offline.
## 2 Flat Panel Antenna Design  
The configuration of the flat panel antenna terminal is presented in Figure 2a, while the detailed structure is shown in Figure 2b,c. The antenna is stacked up with three double-sided Rogers _RO_-4003\(C\) substrates with a thickness (_t_) of 1.52 mm and a dielectric constant (\(\epsilon_{r}\)) of 3.55 (see Figure 2b,c). To achieve SIW feeding, the bottom substrate (D\({}_{1}\)) is in the shape of a rectangle and has two metallic layers on both sides (M\({}_{1}\) and M\({}_{2}\)) to configure the parabolic structure of the pillbox. The middle substrate (D\({}_{2}\)), on the other hand, has a ground plane for metallic patterns (M\({}_{2}\)), as well as a coupling slot on the metallic structure (M\({}_{3}\)) on the top of the substrate. The top substrate (D3) is shaped like a disc and contains metallic strip patterns that work as an antenna array (M4). In principle, the antenna has two main parts: (a) the radiating part (printed on the top layer (D3)), which consists of a rotating disc containing metallic patterns, and (b) the feeding part (a pillbox feeding system), which includes SIW feeding, a coupling slot (etched in the middle layer), and a parabolic reflector (etched through metallic vias between the bottom and middle layers). The disc has a radius of 150 mm, while the overall size of the antenna is \(340\times 335\times 2\) mm\({}^{3}\). The working principle of the antenna is discussed in Section 2.1.  
Figure 2: (a) The geometry of the proposed antenna and (b) its detailed configuration showing multiple layers and (c) side view of the layers, where M and D refer to metal and dielectric layers, respectively.
## 5 Comparison with Other Known Methods
### Optimal Settings  
A particle swarm optimization PSO algorithm [28] was used to determine the optimal settings of a PID controller with a parallel structure controlling the plant of Example 2. The population of the candidate solution was assumed to be 16 particles. The objective function to be minimized was taken as follows:  
\[J_{c}=\frac{1}{T}\int\limits_{0}^{T}(w_{1}\cdot|e(t)|+w_{2}\cdot|u(t)|)dt \tag{62}\]  
where \(w_{1}\), \(w_{2}\)--weight factors, \(e\)--error, \(u\)--control signal, \(T\)--time of the measurement.  
The cost function takes a candidate solution as an argument in the form of a vector of real numbers [\(K_{P}\)\(K_{i}\)\(K_{d}\)] and produces a real number as the output, which indicates the objective function value of the given candidate solution \(J_{c}\).  
The constraints of the search space and the velocity \(\text{v}_{\text{i}}\in\mathbb{R}^{n}\) were adopted arbitrarily, while the initial values were chosen randomly. The weight coefficients \(w_{1}\), \(w_{2}\) were assumed to be equal to 1 and 0.075, respectively. Parameters of the optimal, with respect to (62), controller were tuned, and after 90 iterations of the PSO algorithm, a vector [1.3653, 0.1132, 3.6896] was obtained, which minimizes the adopted objective function, taking into account control costs. The tuning process of optimal controller settings is shown in the Figure 12.  
Figure 11: Responses of the control system with a PI regulator controlling the G7 plant designed using the proposed method and [PERSON]’s method.  
Figure 14 shows the responses of a system controlled by a PID controller with the settings determined by the proposed method and those determined by the PSO algorithm.  
In comparison, the measured value of the objective function \(J_{\rm c}\) during control in the indicated time interval was 0.0013 higher than the one achieved during the last iterations  
Figure 14: Comparison with the optimally tuned PID controller.  
Figure 12: Tuning process of optimal controller settings.  
Figure 13: The convergence curve of the designed controller on the PSO.  
(Figure 13). However, the value of \(J_{\rm c}\) in the case of tuning the PID controller according to the proposed method was equal to 0.1826.
# Experimental Study on the Effect of Different Parameters on Rotor Blade Icing in a Cold Chamber
## 3 Experimental Results and Analysis
### Basic Rules of Rotor Blade Icing  
In this paper, icing experiments under various conditions were carried out. Although the different experimental conditions led to change of the ice shapes, a large number of experimental results showed that there were some general rules for the icing of rotor blades. As an example, in Figure 6 it can be seen that icing mainly occurred in the leading edge of the rotor blade, which was similar to that of the fixed wing. However, the distribution of ice thickness on the surface of the rotor blade was different from the fixed wing. The ice shapes of the fixed wing with no swept angle were almost the same, while that of rotor blades varied with different section positions. The ice thickness increased with the increase of distance from the rotor hub. When the icing position was close to the blade root, the amount of ice was small, and sometimes even no icing occurred. This was because the relationship between the linear velocity and the blade spanwise length is linear. The closer the position to the blade tip, the more liquid water collects on the surface per unit time, and the greater the icing content. Therefore, in the design of anti-icing and de-icing systems, the heat load demand increases with the increase of blade spanwise position.  
At the same time, it can also be seen that there was ice formation at the blade tip beyond the blade span. This was because the surface liquid water flowed along the spanwise direction under the action of centrifugal force in the icing process, resulting in ice formation along the spanwise direction. There were more droplets impinging on the icing surface, which led to further ice formation. There was a part of pinnate ice behind the blade, and its direction was pointing to the blade tip, which showed the effect of centrifugal force on icing process during the rotation. Although the ice behind the blade  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Case**} & **Rotation** & \multirow{2}{*}{**LWC**} & **Icing** & \multirow{2}{*}{**MVD**} & **Angle of** & \multirow{2}{*}{**Icing**} & **Number of** & **Blade** \\  & **Speed** & **(g/m\({}^{3}\))** & & **Temperature** & **(\(\mu\)m)** & **Attack** & **Time (s)** & **Blades on** & **Material 1** \\ \hline
1 & 450 & 3.69 & \(-\)10 & 35 & 0 & 180 & 4 & (s) \\
2 & 450 & 3.36 & \(-\)5 & 35 & 0 & 180 & 4 & (s) \\
3 & 450 & 3.69 & \(-\)15 & 35 & 0 & 180 & 4 & (s) \\
4 & 450 & 1.45 & \(-\)10 & 35 & 0 & 180 & 4 & (s) \\
5 & 600 & 1.38 & \(-\)10 & 35 & 0 & 180 & 4 & (s) \\
6 & 750 & 1.42 & \(-\)10 & 35 & 0 & 180 & 4 & (s) \\
7 & 450 & 3.40 & \(-\)10 & 35 & 0 & 360 & 4 & (s) \\
8 & 450 & 3.61 & \(-\)10 & 35 & 0 & 180 & 2 & (s) \\
9 & 450 & 2.15 & \(-\)10 & 35 & 0 & 180 & 4 & (s) + (c) \\ \hline \end{tabular} \({}^{1}\) (s) is steel and (c) is composite.  
\end{table}
Table 1: icing experimental conditions.  
had little effect on the aerodynamic shape, the increase of surface roughness would also lead to the decrease of aerodynamic characteristics.
# Versatile Video Coding-Post Processing Feature Fusion:
## 3 Proposed Method
### Insight of Data Ingestion
Figure 3: Illustration of video-to-image conversion process: (**a**) original videos converted to original images using FFmpeg, and (**b**) reconstructed videos converted to reconstructed images using FFmpeg. 4:2:0 format. This involves discarding every other chroma sample, both horizontally and vertically, resulting in a 2:1 reduction in chroma resolution. The luminance component remains unchanged, while the chroma blue and chroma red components are subsampled, reducing the overall data size and preparing the output for efficient storage, transmission, and display.
# Designing an Interactive Visual Analytics System for
## 3 Comprehensive Hourly Precipitation Dataset
### Managing HPD Data  
We implemented a multi-table database structure to efficiently handle a large number of HPD (approximately 163,779,579 hourly precipitation records and continuously growing) and to enhance data access performance and maintainability. Specifically, a per-state structure was adopted to store and handle HPD for each U.S. state. This approach enhances query performance because state-wide data retrieval operations and precipitation data analyses occur within isolated tables. The structure ensures faster data fetching and more efficient index utilization. It also provides significant advantages when inserting new HPD records while addressing unexpected data format issues or missing values. As each table's indices handle only the HPD recorded in a U.S. state, their size remains relatively small compared to managing all HPD in one table.  
However, this approach has the limitation of increasing query complexity when analyzing precipitation data across multiple states. To support multi-station analysis, accessing cross-state data in different tables requires additional logic and more sophisticated application codes. It may also increase storage overhead and memory usage for table metadata because separate table structures and indexes are used to handle each state's precipitation data. Despite these limitations, we adopted the multi-table database structure because it is particularly beneficial for designing an interactive visual analytics system where quick response time is crucial for real-time data analysis. Additionally, the per-state structure enables faster updates or corrections of HPD records because of utilizing isolated operations on specific tables.
## 2 Materials and Methods  
This review is based on a curated set of 185 scientific articles selected from journals indexed in MDPI and Elsevier, as well as through the Google Scholar search engine. The selection was carried out between January and April 2025. The search process employed combinations of keywords such as \"fault diagnosis\", \"power plant defects\", \"turbine monitoring\", \"deep learning in thermal systems\", and \"predictive maintenance\". Boolean operators and specific field filters (e.g., engineering, energy systems) were applied to refine search results. Articles included range from original research papers to relevant reviews, published between 2019 and 2025. The initial list was compiled manually, followed by relevance screening and structured annotation in Microsoft Excel 2019 (San Francisco, CA, USA) format.  
Although Google Scholar was included to ensure coverage of emerging research and gray literature, care was taken to exclude sources lacking methodological rigor. Specifically, theses, preprints, or conference papers were only retained if they presented concrete diagnostic frameworks with algorithmic validation. This filtering step was essential to reduce the impact of quality variability and to minimize potential biases introduced by heterogeneous publication formats.
## 2 Numerical Experiment
### Overview of the Simulation  
The simulated track, minimum sea level pressure, and maximum 10-m wind speed of Lekima are compared with the best-track data of JMA at 6-h resolution in Figure 1. It can be found that the simulated track of Lekima agrees well with the observation throughout the simulation (Figure 1a). The model reproduces well the intensification of Lekima during the simulated period, and achieves a lowest minimum sea level pressure of 938.8 hPa and highest 10-m maximum wind speed of 48.5 m s\({}^{-1}\) (Figure 1b).  
Figure 1: **(a)** Typhoon track and **(b)** minimum sea level pressure (MSLP; dotted line) and maximum 10-m wind speed (MWS; solid line) in the JMA observation (black) and simulation (blue).  
Figure 2a shows the vertical profile of the basic-state Brunt-Vaisala frequency during the simulation, which is defined as the inner domain-averaged value and reflects the static stability of the background atmosphere. The frequency is almost constant below \(z=5\) km, with a mean value of \(1.26\times 10^{-2}\) s\({}^{-1}\). It then decreases slowly from \(1.28\times 10^{-2}\) s\({}^{-1}\) at \(z=5\) km to \(6.48\times 10^{-3}\) s\({}^{-1}\) at \(z=14\) km, while experiences a sharp growth from \(z=14\) km and achieves a maximum value of \(2.91\times 10^{-2}\) s\({}^{-1}\) at \(z=18\) km. Thus, the heights from 14 to 17 km can be roughly treated as a transition zone from troposphere to stratosphere.  
The diabatic heating released by the microphysical processes can be output by the WRF model directly. The time-height distribution of the heating rate averaged over a \(200\times 200\) m\({}^{2}\) square horizontal area being centered in the Typhoon is shown in Figure 2b. During the simulation, the powerful heating always penetrates the whole troposphere, implying the vigorous deep convection throughout the intensification of Lekima. In addition, the heating does not distribute uniformly in the troposphere, but exhibits two relatively separated vertical subranges, with heights of 2-5 and 6-12 km, respectively. The lower subrange stems mainly from the release of heating by the condensation process; the upper subrange stems mainly from the release of heating by the condensation, freezing, and sublimation processes; and the gap of heating between these two subranges comes from the absorption of heating induced by the melting process (not shown).