esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Challenge-Based CIDNs
### Interactions  
To improve the detection accuracy of a separate IDS node, collaborative networks enable many IDS nodes to exchange data with other nodes; that is, several nodes can exchange alarm information to obtain a high-level view of the network status. In a challenge-based CIDN, two types of messages would be used during node interactions.  
* _Challenges._ This type of message contains several IDS alarms requesting the target node to rank the severity. For instance, a testing node can send a challenge periodically to one or several tested  
Figure 1: The high-level architecture of a typical challenge-based collaborative intrusion detection network (CIDN).  
nodes and then obtain their answers. Because the testing node extracts IDS alarms from its own database, it can know the alarm severity in advance. Accordingly, it can evaluate the tested nodes' trustworthiness by identifying the deviation between the expected and the received feedback. For the satisfaction mapping, we refer to Section 5.
* _Normal requests_. This type of message is sent by a detector to collect data for alarm aggregation. In a CIDN, if a node starts to aggregate alarms, it can send a normal request to other IDS nodes. Then, other trusted nodes can give alarm information on the basis of their own experience. Intuitively, alarm aggregation is a very important step to improve the detection accuracy of a separate intrusion detector. It is worth noting that the alarm aggregation process only considers the information from trusted nodes.
# Ontology Modeling Using Fractal and Fuzzy Concepts to Optimize Metadata Management
## 4 Case Study: Mold Assembly
### FFOM Visualization and Analysis
The  
Figure 9: Visualization of FFOM’s multi-level fractal pattern in MoldAssembly domain: (**a**) Top-level fractal; (**b**) Recursive subfractals; (**c**) Fully expanded structure. architectural structure in Figure 9b is intentionally identical to that of Figure 9a, thus visually reinforcing FFOM's core principle of fractal self-similarity. Every fractal, whether it is a top-level assembly or a nested process, is instantiated based on a unified 'Fractal Pattern Template' that governs its internal and external relationships. Figure 9c presents the fully expanded fractal view, integrating all subfractals and their nested subfractals into a single unified model. This complete structure shows how recursively applying the same pattern results in a scalable and semantically coherent ontology hierarchy. For example, CoreAssembly is decomposed into Polishing and HeatTreatment, with their respective data properties (e.g., PolishType, TreatmentTemperature) and fuzzy-temporal annotations. The self-similar structure of FFOM is not only conceptually elegant, but also practically advantageous. Because each level reuses the same modeling schema, ontology extension becomes modular, predictable, and easier to maintain. Furthermore, this recursive pattern allows for scalable reasoning and querying: systems can infer or retrieve all instances of a recurring process (e.g., Drilling) across multiple fractal layers, without the explicit traversal of every path. This modeling framework also captures nuanced uncertainty using fuzzy sets. For example, HeatTreatment temperatures are represented as fuzzy ranges (e.g., fuzzyRange (280 \({}^{\circ}\)C, 290 \({}^{\circ}\)C, 310 \({}^{\circ}\)C, 320 \({}^{\circ}\)C)), rather than fixed intervals, enabling the system to better reflect real-world variability. Scalar fuzzy values, such as alignment confidence or bolt torque, can also be used where appropriate. All such data remain updateable at the assertion level, without modifying the schema-level definitions. Altogether, the progressive decomposition shown in Figure 9 illustrates how FFOM integrates fractal hierarchy, fuzzy logic, and temporal semantics within a standard OWL framework. This design enables dynamic, scalable, and uncertainty-aware modeling, ultimately enhancing semantic reasoning, SPARQL-based querying, and real-time decision support in regard to complex manufacturing systems.
## 2 Trust Concepts  
In the realm of 6G networks, trust represents the belief or conception held by entities, such as mobile devices (MDs), regarding the reliability and integrity of the network environment. It extends to encompass the networkwide perception of an entity's trustworthiness, termed as reputation [1]. Within the context of information technology, trust denotes the confidence placed in a dependable source, which is crucial for maintaining the integrity and security of the system. The existence of trust hinges upon the participation of entities and both quantifiable and non-measurable factors [8; 9].  
Trust serves as a foundational concept for the development and operation of 6G networks, which are envisaged to prioritize human-centric interactions over machine-centric processes [10]. Consequently, trust assumes paramount importance in fostering acceptance and reliance on network automation. The transition to 6G underscores the need for a holistic approach to trust establishment, integrating trust as an integral component of the network architecture [11]. Moreover, the identified security, privacy, and trust challenges within 6G networks underscore the critical role of trust in ensuring network reliability and integrity [12].  
The importance of trust in the 6G ecosystem is underscored by the critical need for security and reliability, which are essential for realizing the full potential and benefits of 6G networks [13]. This emphasis on trust is further highlighted by the integration of blockchain technology, which facilitates efficient resource sharing, secure data interactions, and privacy protection [14]. Furthermore, the adoption of a software-defined zero-trustarchitecture underscores the need for trust in establishing a secure and scalable security framework [15]. Early specifications of 6G concepts prioritized trust, security, and privacy, highlighting the foundational role of trust in the development of 6G networks [16].  
In conclusion, trust is integral to the reliability, security, and privacy of 6G networks. Integrating trust-building mechanisms, robust security frameworks, and privacy-aware technologies is essential for the successful deployment and operation of 6G networks.
###### Abstract  
This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012-2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, were derived: (1) a narrow-band NDVI (NDVI\({}_{680,800}\), derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO\({}_{2}\) flux, and NDVI values were compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tower flux data. All three NDVIs provided good estimates of dry green biomass and were able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods were considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods were also calibrated against/APAR\({}_{\text{green}}\) (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APAR\({}_{\text{green}}\) (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships between fluxes and optical measurements, suggesting a slight change in efficiency between the first and second half of the growing season. Consequently, the best results were obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APAR\({}_{\text{green}}\) rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations between these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tower to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems.  
grassland; NDVI; CO\({}_{2}\) flux; optical remote sensing; LUE model; gap filling 20168260103390/rsp030260
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results  
In this section, we evaluated the performance of TBRL routing protocol through simulations. To highlight the outstanding qualities of the TBRL, we combined the 2N-RM with the TBRL and compared the performance of TBRL with DBR and Reliable Energy-efficient Routing Protocol (R-ER\(P^{2}\)R). The simulations' setting and results are discussed in the given section.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 2 Literature Review
In this paper, our previous work, the Two-level Clustering Approach via Timer (TCAT) [13] scheme, is used to build two-level clustering for the network architecture. Consequently, combining the Bayesian particle filter [14] and local neighboring information, the proposed CTCI scheme is able to efficiently complete the tracking task. Here, we compare the information processing model of TCAT, CTCI and DCTTP for target tracking (shown in Figure 2). Observe that there is only a single hierarchy in DCTTP, but two hierarchies in TCAT. Thus, TCAT focuses on organizing the active sensors triggered by the target. Consequently, the system of TCAT is more stable and efficient than that of DCTTP, and TCAT can instantly acquire the result of target tracking at Level 2 clustering. Although there are many protocols proposed for  
Figure 1: The result area of the Data Compressing-based Target Tracking Protocol (DCTTP). target tracking, the network architecture and information processing problems are not considered simultaneously [7; 8; 9; 10; 11; 15; 16; 17]. Hence, we tackle the tracking task and the data processing problem based on the network architecture in TCAT. Even though the energy consumption and the data size of CTCI are slightly higher than that of TCAT and DCTTP in some cases, the system of CTCI is able to adaptively provide the measurement distribution via information compression, not just describing the target position with a single estimate. Notice that the TCAT scheme can be considered as a special case of CTCI with a large compression bound, which compresses the samples to the mean. Moreover, the estimation accuracy and the integrality of the tracking result of CTCI are better than that of TCAT (improved by 20%) and are much better than that of DCTTP (improved by 100%). We investigate the trade-off between the tracking quality and the energy consumption in Section 5.
## 2 Related Work  
### Traditional MVS  
Early MVS methods can be categorized based on their technical characteristics into point-based methods [13; 14], voxel-based methods [15], depth map-based methods [16; 17; 18], and polygon mesh-based methods [19]. Point-based approaches extend from initial matching points to surrounding pixels, iteratively refining feature points to achieve dense reconstruction. However, this method limits the capability of parallel data processing. In certain scenarios, such as those with uneven texture distribution, this approach heavily relies on accurate feature extraction, resulting in less-than-ideal outcomes. Voxel-based methods initially calculate the scene's bounding box and then identify voxels near irregular grids in 3D space. [PERSON] et al. [20] proposed a method that partitions 3D space into \"object\" and \"no-object\" regions, enforcing photometric consistency between adjacent areas and expanding the \"object\" region. However, discrete spatial partitioning increases memory usage for improved accuracy, making this method suitable only for low-resolution small scenes. Depth map-based approaches decompose these steps into two parts, starting with multiple single-view depth estimations. This approach can be combined with the previous two methods, merging depth maps to obtain the final predicted point cloud. Compared to the methods mentioned earlier, this approach offers greater flexibility. Polygon mesh-based methods initialize the evolution of the scene surface and iteratively enhance multi-view photometric consistency while evolving the scene surface. These early MVS methods have their advantages, but they also face limitations such as parallel processing capability, robustness in specific scenarios, and applicability to different scene sizes.
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 3 NDN for V2X Communications
### NDN Basics
More advanced policies have been proposed for data caching and replacement [14], with decisions performed according to a variety of parameters such as content popularity, freshness, and geographical validity of information.
# Experimental Investigation into the Thermal Performance of Personal Cooling Mechanisms
## 1 Introduction
### Personal Cooling Systems  
From 2001 until the end of 2024, around 315 scientific publications have been dedicated to personal cooling systems, including vests and garments. This total includes 136 publications on cooling vests, 138 on cooling clothes in general, and 41 on personal cooling systems. Notably, 2023 and 2024 recorded the highest number of publications within this timeframe. Various cooling garments are available on the market, but most scientific journal studies have focused on cooling vests. This is because the torso is the most sensitive part of the body to temperature [15], and in terms of ergonomics, vests are more accessible to wear than a full suit.  
Selecting the most appropriate cooling vest for a particular activity and environment can be challenging, given the variety of cooling mechanisms available and the often limited information regarding their performance for end users. Different cooling garments employ diverse heat-dissipating mechanisms, and literature suggests they are effective when their cooling capacity ranges from 165 to 540 W [16]. However, the effectiveness of many of these products has not been thoroughly verified. As a result, some items are labelled as cooling garments without a comprehensive understanding of the level of thermal comfort they provide to the wearer. Each cooling mechanism presents its advantages and disadvantages, and the choice of which to use ultimately depends on factors such as the intended application, ergonomics, and weight. The market offers various cooling vests that differ in their cooling mechanism to dissipate body heat [17]:  
* Cooling Water Circulation Vest: This cooling vest uses cool water to dissipate body heat by the physical parameter of conduction of the cold water.
* Air cooling vest: This cooling technique uses convection and sweat evaporation due to air circulation generated by fans.
* Phase-change material cooling vest: This cooling mechanism utilises microcapsules that can change from solid to liquid to remove heat from the user's body.
* Water evaporation vest: This vest utilises a simple cooling technique, which is the evaporation of water to reduce body heat.
* Hybrid cooling vest: This innovative vest integrates multiple cooling techniques into a single design. For instance, it may feature a combination of phase change materials (PCMs) alongside fans to enhance air circulation or blend water evaporation methods with PCMs for effective cooling.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 3 Reliability Analysis Process of Gas Monitoring System Based on Fuzzy-FTA
### Comparison of FTA and Fuzzy-FTA  
In FTA, the probability of occurrence of various accidents X1-X19 at the gas monitoring system site comes from the expert questionnaire filled out by the on-site monitoring personnel. Each probability data required to fill in the expert questionnaire is specific and accurate, but this requirement is difficult for the on-site monitoring personnel, who are more inclined to fill in a probability interval for failure [28]. In Fuzzy-FTA, they only need to fill in the probability interval of failure.  
In the process of data calculation, FTA is calculated according to simple rules, and the result obtained is a specific probability value. As an example, take the data in Table 1, then take \(\lambda\) = 1, and take the probability of occurrence of various accidents X1-X19 as the intermediate value, the probability of occurrence of the top event is 0.00011. The Fuzzy-FTA is calculated according to the fuzzy mathematics rule, taking \(\lambda\) = 0.5, we can get the probability of the top event in the interval: [0.000105, 0.000125]. From this, we can know that the minimum probability of the top event is 0.000105, the maximum probability of the top event is 0.000125, and the high probability of the occurrence of the event belongs to the interval: [0.000105, 0.000125].  
From the comparison of FTA and Fuzzy-FTA, it can be seen that Fuzzy-FTA can more easily obtain data close to the actual on-site, and obtain calculation results that are more in line with the actual on-site. However, compared with FTA, Fuzzy-FTA requires a large amount of data and more complex calculations [29].
## 4 Discussion of the Fault-Diagnosis Results
### Validation Setup  
The training parameters of the model are as follows: batch size is 64, training rounds are 100, the optimization algorithm is Adam, the initial learning rate is 0.001, and learning rate attenuation is set. In addition, the initialization of network weight follows the Glorot normal distribution initialization method.  
The value of loss function and accuracy obtained by single training is shown in Figure 11. As can be seen, no matter the training set or the verification set, the MSFFCNN algorithm converges at about 20 epochs. In this case, the accuracy of the training set is stable at 99.9%, while the accuracy of the test set can reach about 99.8%.  
The constructed models were trained offline and then used for online fault diagnosis. Table 2 shows the comparison of the predicted time of a single sample between MSFFCNN and 1 DCNN under five tests. As can be seen, MSFFCNN takes longer to test a single sample than 1 DCNN. This makes sense because the MSFFCNN model has a deeper network structure and more parameters than 1 DCNN, so it inevitably consumes more time when processing the test data. However, in practical engineering applications, a single forecast cost of 0.5 ms is totally acceptable for real-time diagnosis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Test Time of Single Sample (ms)**} \\ \cline{2-6}  & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** \\ \hline
1 DCNN & 0.314 & 0.305 & 0.282 & 0.306 & 0.294 \\ MSFFCNN & 0.524 & 0.513 & 0.508 & 0.516 & 0.522 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time of 1 DCNN and MSFFCNN for single sample.  
Figure 11: Training and validation of the MSFFCNN: (**a**) loss on training and test data; (**b**) accuracy on training and test data.
## 2 Theoretical Background
### Acoustic Performance of EoLHM in the Literature
#### 2.3.4 Egg-Boxes and Trays  
Egg-boxes and trays can be made of different materials such as plastic, recycled paper, cardboard, but what distinguishes them is their shape. For a long time, egg-cartons have been considered good sound absorbing materials, and they have been widely used for this purpose since they are inexpensive, easy-to-install and easily available [50]. However, their acoustic performances have been recently questioned. It was pointed out that egg-boxes provide good sound absorption only at high frequencies, their \(NRC\) equal to 0.4 is too low for considering them sound absorbing elements, and the sound absorption coefficient profile is irregular [51]. The experimental tests presented in [52] showed that the sound absorption coefficient of egg-boxes and fruit trays is affected by the material, orientation of the boxes, and by if they are closed or open. Experimental tests showed that egg-cartons can reduce the reverberation time at mid-frequency [53]. In spite of this, researches have been looking for a way to improve their sound performance by coupling them with other materials. A sound absorber made of egg-boxes pulp showed an optimized \(NRC\) equal to 0.5 [54]. A non-standardized test method showed that filling egg-boxes with mineral wool blocks a percentage of sound ranging between 14.42% and 17.71% depending on the frequency. Egg-boxes were filled with shredded rice straw paper and textile waste [50], and with polyurethane foam [52]. The panels proposed in [50] showed higher sound absorption coefficients than common egg-boxes cartons at all frequencies, and those presented in [52] featured a \(NRC\) equal to 0.87.
# Analysis and Design of Single-Phase Unidirectional Transducers with High Directivity
## 3 Fabrication Technologies  
As shown in Figure 5, on the premise of limiting the width of the three electrode fingers to \(2\upmu\)m, \(3/32\lambda\), and \(3/32\lambda\), the reflection coefficient and unidirectional angle reach their optimal values \(|\kappa|\lambda=5.17\%\) and \(\theta=-90^{\circ}\), respectively, when the finger spaces are \(g_{1}=0.1069\lambda\) and \(g_{2}=0.4414\lambda\). Due to EWC-SPUDT being a kind of unidirectional transducer--which means that the characteristics of the SAW propagation along the forward and backward directions are different--it is necessary to study the frequency responses of acoustic waves in two directions.  
According to our analysis based on the COM theory, the transducers face-to-face, corresponding to the forward direction, shown in Figure 5(a) have a lower insertion loss near the center frequency than that of the transducers back-to-back, corresponding to the backward direction, as shown in Figure 5(b). To verify the conclusion above, transducers both face-to-face and back-to-back, based on optimized EWC-SPUDT cells, were developed and performance experiments were carried out.  
Figure 7 shows the processing procedure for developing SAW devices. In our experiments, the AZ5214 reversal photoresist was used for ultraviolet (UV) photolithography. After lithography, Al film of 300 nm thickness was deposited on \(128^{\circ}\) YX LiNbO\({}_{3}\) substrate by magnetron sputtering. Then, the conventional lift-off technique was utilized to remove the regions of photoresist [39; 40]. Each SAW delay line consists of input interdigital IDTs and output IDTs, each of them including 73 pairs of EWC-SPUDT cells with an aperture of 50 \(\lambda\). Figure 8 presents an optical microscope image of the SAW device fabricated by the conventional lift-off technique. Specifically, as mentioned above, transducers face-to  
Figure 5: Reflectivity and unidirectional angle dependence on the two gaps between three fingers.  
Figure 6: Test device configuration with (**a**) transducers face-to-face and (**b**) transducers back-to-back.  
face and back-to-back based on traditional EWC-SPUDT cells were also developed for comparative experiments.