esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Communication Dependability
### Cybersecurity for IEC61850 Multicast Communication in Wide-Area Applications
#### 5.2.1 Secured Messages
There are other improvements. For example, IEC 61850-90-5 and IEC 62351-9 extend the original GDOI model to enhance its security for R-SV/R-GOOSE communication. IEC 61850-90-5 extends the original GDOI model in which security keys are specific to IP addresses only [30]. The extension allows security keys to be associated not only with IP addresses but also with an IEC61850 Dataset or delivery service. This extension enables key management for situations when a device with a single IP address contains multiple subscribers that support different services. In such cases, the security keys can be managed at the Dataset level (Dataset specific security key) or at the delivery service (R-SV or R-GOOSE) level, even if the IP address and the Dataset values are the same. Additionally, IEC 62351-9 uses a GDOI extension [40] that defines the support for IEC 62351 security services such as the standardized authentication and confidentiality algorithms used in TEK. This extension also enables KDC to distribute two sets of policies and keys (Policy1/Key1 and Policy2/Key2) in one message to the GMs. Each policy includes the remaining lifetime and the activation delay values. The first set (Policy1/Key1) is regarded as the current set and is activated immediately while the second set (Policy2/Key2) is regarded as the next set, in which the remaining lifetime and activation delay can be defined so as to overlap between the two sets. Allowing an overlap between the remaining lifetimes of the two sets provides a resilient security solution if a GM is disconnected from a KDC because the GM can still communicate securely, at least for the remaining lifetime defined in Policy 2.
# Blockchain Integrated IoT for Food Supply Chain: A Grey Based Delphi-DEMATEL Approach
###### Abstract
Food industry professionals are seeking to adopt the B-IoT in FSC to manage real-time information related to food origin and other handling-related information with stakeholders. While B-IoT offers many benefits to the FSC, its effective implementation seems quite challenging for the numerous FSC partners. Most of the studies focus on a single technology's adoption, either IoT or Blockchain. For example, [PERSON] et al. [19] found that there are few IoT implementation studies in FSCs. Similarly, [PERSON] et al. [21] focus on the acquiring of blockchain adoption in the FSC under the circumstances in India. In cases like this of large countries such as India, studies related to the integration of the adoption of blockchain and IoT are limited. Although B-IoT in FSC in emerging marketplaces have received increased attention, many FSC partners have expressed their concerns about the adoption of B-IoT in their FSC. To address this issue, this study attempted to answer the following questions:  
* What CSFs facilitate the successful adoption of B-IoT in the FSC of emerging countries? * How are these CSFs interrelated in the case of the adoption of B-IoT in the FSC? * What is the significance of each CSF in the adoption of B-IoT for FSC? As a result of the above-mentioned research questions, the following objectives will be pursued as part of this study:  
* To identify the significant CSFs of B-IoT in the FSC
* To investigate the causal relationships among the CSFs of B-IoT in the FSC
* To provide recommendations for the adoption of B-IoT in the FSC. In order to fulfill the research objectives, the literature review is conducted for the identification of the CSFs of B-IoT in the FSC. Further, these CSFs are assessed in the context of developing countries with the help of the grey Delphi method. After finalizing the CSFs, the causal relationship among them is explored with the grey DEMATEL. By achieving the mentioned objectives, the major contributions of this study are as follows:  
* The identification of CSFs that influence the adoption of B-IoT for FSCs in emerging economies helps the supply chain managers to adopt the B-IoT for achieving operational excellence. * The causal relationships among the CSFs are helpful for the policy planner and supply chain managers in making the adoption decision of B-IoT. * The proposed framework highlights the application of the Muti Criteria Decision Making (MCDM) method in the area of FSC that could easily be adopted by the practitioners. The remainder of the paper is organized as follows: Section 2 is devoted to a literature review about the B-IoT and its application in FSC; Section 3 provides the proposed research framework for analysis; data analysis and results are reported in Section 4; Section 5 discusses the research findings for valuable insights; Section 6 highlights the implications of the study; Section 7 provides the conclusions, limitations, and future scope.
###### Abstract  
Super-resolution enhances the spatial resolution of remote sensing images, yielding clearer data for diverse satellite applications. However, existing methods often lose true detail and produce pseudo-detail in reconstructed images due to an insufficient number of ground truth images for supervision. To address this issue, a prediction-to-prediction super-resolution (P2P-SR) network under a multi-level supervision paradigm was proposed. First, a multi-level supervision network structure was proposed to increase the number of supervisions by introducing more ground truth images, which made the network always predict the next level based on the super-resolution reconstruction results of the previous level. Second, a super-resolution component combining a convolutional neural network and Transformer was designed with a flexible super-resolution scale factor to facilitate the construction of multi-level supervision networks. Finally, a method of dividing the super-resolution overall scale factor was proposed, enabling an investigation into the impact of diverse numbers of components and different scale factors of components on the performance of the multi-level supervision network. Additionally, a new remote sensing dataset containing worldwide scenes was also constructed for the super-resolution task in this paper. The experiment results on three datasets demonstrated that our P2P-SR network outperformed the state-of-the-art (SOTA) methods.  
multi-level supervision; remote sensing; super resolution; deep learning 20232003131827. [[https://doi.org/10.3390/app132111827](https://doi.org/10.3390/app132111827)]([https://doi.org/10.3390/app132111827](https://doi.org/10.3390/app132111827))
## 3 Results and Discussion
### Land Use Changes
In total, 51 percent of people's income and 38 percent of Bali's employment opportunities are directly linked with expenditures by tourists and tourism investment [77]. On average, the tourism sector has accounted for 30% of the gross domestic product (GDP) of Bali Province over the past two decades, where economic growth is largely contributed by the southern regions as centres of tourism activity, including Denpasar city [14]. The growth in economic income, living standards, and population are common causes of land use change in urban areas [78; 79]. The relationship between population growth and regional GDP and land use change was also described by [PERSON] et al. [80] in the Yangtze River Basin, China. Moreover, an increase in the number of tourists can affect the land needs associated with their activities [72], and similar results were presented by [PERSON] et al. [81] in South Carolina. As previously described, the Inna Grand Bali Beach Hotel is the tallest building in Bali today with a height of 10 floors. In Bali, the Decree of the Governor of Bali Province No. 13/Perbang.1614/II/a/1971, which was made in 1971, sets the maximum building height to be 15 metres, typically only four floors. This regulation stems from local wisdom of the Hindu religion and Balinese culture on the urban and tourism development processes. This rule ensures that the construction of buildings such as residences, government offices, businesses, multi-functional buildings for tourism, and others do not spread skyward. Consequently, sideways expansion is a logical choice for obtaining large buildings with the largest volumes but directly and indirectly sacrifices greenspaces for settlement areas. This regulation may be one of the causes of the rapid change in land use in Denpasar, although government regulation can control and block the acceleration of land use change [82; 83; 84]. As mentioned earlier, Jakarta and Beijing did not experience any rapid land use change compared with Denpasar. Although both cities are capital cities, they allow the existence of taller buildings. Therefore, further studies related to the impacts of this regulation need to be undertaken to identify their linkage with declining greenspaces and the expansion of settlement.
# The Effect of Silica-Filler on Polyurethane Adhesives Based on Renewable Resource for Wood Bonding
## 1 Introduction
It is well studied that silica filler in a polymeric matrix may contribute to increase in mechanical strength and fracture toughness by the mechanism of crack tip blunting or crack pinning [29; 30; 31; 32]. It was shown that doping of polymer composites with nanosilica may be an efficient way to enhancement of their strength and toughness [33]. Silica particles have also been proved to increase adhesion and surface energy of a polyurethane as well as to modify its rheological behavior [27; 34]. Improvement of properties like Young's modulus [35], storage, and loss moduli of a polyurethanes have also been reported [34]. In the present work, the effect of using the colloidal silica as the filler on the reactivity, viscosity, thermal stability, and adhesive interactions were examined and discussed.
technology for advancing chatbots and enhancing the real-world applicability of LLMs. By retrieving relevant information in real time, RAG mitigates the problem of knowledge cutoff and helps overcome the inherent limitations of pre-trained language models that rely solely on the static data they were trained on [8].  
The architecture of RAG is composed of two key components: a retriever model and a generator model. The retriever selects relevant pieces of external knowledge in response to a query, while the generator combines the retrieved information with the language model's internal knowledge to generate more accurate and contextually appropriate responses. This architecture enables LLMs to better handle tasks that require up-to-date knowledge or specialized expertise that may not be fully captured during training [9].  
RAG integrates external knowledge to mitigate the hallucination phenomenon in LLMs, effectively reducing the generation of inaccurate outputs. In QA tasks, queries can be categorized as either single-hop or multi-hop [10]. Early research on retrieval-augmented LLMs predominantly addresses single-hop queries [11; 12]. In these cases, answers are generally located within a single document; thus, the retrieval process involves identifying a relevant document based on the query, which is then incorporated into question-answering models to generate a response. However, unlike single-hop question answering, certain queries require linking and combining information from multiple documents. This task t cannot be accomplished through a straightforward, single-step retrieval and response process. For instance, a query such as \"What type of fertilizer should I use at the moment for my crop located in Hubei Province?\" requires three distinct reasoning steps to answer (what fertilizer, what crop, and what time). Applying a compatible knowledge-retrieving strategy according to the complexity of user queries is a feasible approach. However, issues regarding retrieving efficiency and accuracy still hinder the complex queries using RAG in such a fashion. The following research question arises: how do we maintain fast knowledge retrieval and high accuracy for queries of various complexity using RAG, especially in multi-turn interactions?  
This study contributes the following to address the above issues:  
1. This study proposes a Layered Query Retrieval framework that adaptively handles both simple and complex queries, balancing both efficiency and accuracy in responses.
2. We designed a relevance classifier to filter out irrelevant documents from the retrieved ones, preventing the large model's responses from being influenced by too much irrelevant information.
3. We conducted experiments on multiple public datasets, and the results show that our model improves accuracy and F1 score by over 10% compared to state-of-the-art methods.
## 1 Introduction
### Intelligent Products and Communicating Materials  
Many different definitions of \"Intelligent Products\" are available in the literature, and numerous works are reported. Some frameworks are proposed to classify them. As an example, Ref. [1] presents a three-dimensional classification model, whose dimensions are related to the level of intelligence, the location of intelligence, and the aggregation level of intelligence of the product. The level of intelligence refers to the decision-making capabilities of the product. The location of the intelligence indicates the place where the decision algorithms are executed. The last one, the aggregation of intelligence, distinguishes products capable of managing not only themselves but their components as well.  
Communicating materials are raw materials where IoT devices are spread [2]. As a subclass of Intelligent Products, they can also exhibit smart features and they keep these smart functions, even when undergoing physical transformations such as milling, cutting, or sawing. Diverse early prototypes were designed for the manufacturing and the construction industry, by using micro-electronics devices into a material. The material could be either wood, textile, or concrete and the inserted devices could be either RFID tags or self-powered wireless sensor networks (WSNs) embedded into the material. In the framework of the French ANR McBIM project [2], a prototype of a communicating concrete has been proposed and presented in Figure 1. This last architecture is the one considered in this paper.  
This communicating concrete is in fact a normal concrete beam in which wireless sensor nodes are inserted, all forming a global wireless sensor network (WSN). This network is then used to endow the concrete with additional sensing capabilities, e.g., for applications in Structural Health Monitoring. It is composed of two types of nodes, communicating nodes (CN) and sensing nodes (SN), whose roles are described in Figure 1.
## 2 Materials and Methods
### Sampling Strategy  
Areas with different levels of bottom trawl fishing effort were selected by using the information collected through the Vessel Monitoring System (VMS) in the area. Two periods were considered, 2014-2015 and 2017-2018, before and after the declaration of two Fishing Protected Zones (FPZ) in the Menorca Channel in 2016 (BOE A-2016-8512 Orden AAA/1479/2016). VMS data consist of position and instantaneous velocity that each fishing vessel sends automatically via satellite communications every two hours. Data generated during navigation were excluded from the analyses, by considering only VMS signals with instantaneous velocities ranging from 2 to 3.6 knots, which is the towing speed used by trawlers in the area [30]. A map with filtered VMS signals was created in order to estimate the fishing effort in the study area before and after the declaration of the FPZ (Figure 1).  
Considering differences in bottom trawl fishing effort, four sampling areas were identified: (i) Non-Impacted: area that has never been subject to bottom trawl fishing activity; (ii) Low effort history: area placed within the FPZ previously subjected to low level of fishing effort; (iii) Medium effort history: area placed within the FPZ previously subjected to a high level of fishing effort; and (iv) High effort: area subjected to a high level of fishing effort until nowadays (not included in the FPZ). The mean number of fishing trips in each of these areas during the period 2014-2015, estimated from VMS data were 0, 77, 125, and 148 for Non-impacted, Low effort history, Medium effort history, and High effort, respectively. During 2017 and 2018, the High effort area registered 218 fishing trips, whereas the effort was null in the remaining areas.
## 4 Discussion
### Discrepancies between VPP Estimates from the Regression Models and MODIS-LSP
The majority of agricultural land in temperate Asia has multiple cropping patterns [54], while the majority of agricultural land in temperate North America and European Mediterranean region has a single cropping pattern [75]. Therefore, the complex phenology for CRO with multiple cropping patterns in temperate Asia results in large random differences between VPP and MODIS-LSP.
## 3 Results
### Feature Wavelength Selection
#### 3.2.2 Methodology for Feature Wavelength Point Screening
Initially, with an increase in sample size, the quantity of variables diminishes rapidly, but the rate of variable elimination gradually slackens over time, as shown in Figure 9(a1), demonstrating the trend of variable selection count. During the sampling process, cross-validation is carried out on each variable subset, and RMSECV is computed to identify the subset with the lowest RMSECV. Irrelevant variables are systematically eliminated as the sample size increases, causing a slow decline in RMSECV. However, if critical variables are removed, RMSECV spikes significantly, signaling a substantial deterioration in model performance. Based on the RMSECV trend portrayed in Figure 9(a2), the sample size corresponding to the minimum RMSECV value is chosen. In this instance, a sample size of 24 delivered the smallest RMSECV value, and the variable subset attained at this sample size is designated as the final variable combination. The variation of regression coefficients for each wavelength variable across different sample sizes is presented in Figure 9(a3) as the regression coefficient path. These paths represent the regression coefficients for all wavelength variables and provide insights into the correlation between different wavelength variable subsets and their regression coefficients. Variables with higher regression coefficients contribute more significantly to the model. The vertical dashed line indicated by a blue asterisk represents the position where the minimum RMSECV is achieved, signifying the optimal sample size and variable subset. Across 24 sampling iterations, the CARS algorithm selected a total of 80 variables. The distribution of the selected feature wavelengths is portrayed in Figure 9b. Figure 9: Feature wavelength selection process and distribution using CARS algorithm. (**a**) Variable selection process. (**b**) CARS feature variable selection. The germination rate SVR prediction model was constructed utilizing Near Infrared (NIR) spectral data from rapeseed plant seeds, undergoing SG convolution smoothing preprocessing and feature variable selection. Table 5 summarizes SVR modeling outcomes obtained via different variable selection algorithms. Notably, the SVR model, built with 80 selected feature wavelength points using the CARS algorithm, exhibited superior performance. It registered a training set RMSEC of 14.57% and a determination coefficient R\({}^{2}\)c of 0.8266. The testing set RMSEP was 19.10%, accompanied by a determination coefficient R\({}^{2}\)p of 0.8149. Comparison of the modeling results, using the selected data and the original spectra, revealed significant improvements. R\({}^{2}\)p increased from 0.4196 to 0.8149, while RMSEP decreased from 26.63% to 19.10%. This data signifies a substantial improvement in the predictive capability of the germination rate model, with a 94.21% improvement in R\({}^{2}\)p and a 28.28% reduction in RMSEP. The training set and testing set prediction results for the optimal model are visually represented in Figure 10.
## 5 Conclusions  
The vibration signal of rotary machinery with impact faults is generally a mixture of multiple sources, in which the characteristic component revealing the latent impulses, is often weak and difficult to detect. Although many feature extraction techniques have been put forward to accurately identify fault feature hidden in the signals, such as SVD, MSK, MCKD, etc., those methods only serve in the context of two-dimensional representation. Their applications are limited when confronted with naturally high-order data. Another issue is that the increasing complexity of modern machinery has led to the growing difficulty for fault diagnosis due to multi-excitations and compound interferences. With the purpose of handling these issues, we propose a novel strategy of fault feature extraction for vibration signal based on sparse nonnegative tensor factorization.  
SNTF is endowed with the ability of decomposing the multi-TFD tensor into meaningful low-rank representations, which comprise vibrations of different characteristics. The utilization of multiple TFDs allows comprehensive observations of a single vibration signal despite the time-varying interferences. As we impose the sparseness constraint to the NTF model, the sparsity level of the factors is improved, and the reconstructed components are less interfered by noise. The implementation of this strategy on the experimental signals of the bearing dataset has indicated the effectiveness of the SNTF-based feature extraction. The results of comparisons to other representative methods also suggest that the proposed method possesses better performance of feature extraction. Pragmatically, we carried out an experiment on a swashplate piston pump test rig, in which the impulse feature of the vibration signal was successfully extracted. Therefore, the proposed method is verified feasible in practice and can be identified as of great importance for vibration signal analysis in the field of mechanical health management.  
Further research might further explore a more adaptive approach for the determination of window length in STFT. Also, it would be interesting to see the effect of applying NTF to fault diagnosis for a more complex rotary machine.  
Conceptualization, L.L. and L.F.; methodology, G.L.; software, H.W.; validation, M.L., H.W., and G.L.  
This research was supported by the National Natural Science Foundation of China (Grant No. 51575438), the China Postdoctoral Science Foundation (Grants 2017M623159 and 2018T111046), the Postdoctoral Science Foundation of Shaanxi Province (Grant 2017 BSHEDZZ68), and the Fundamental Research Funds for the Xi'an Jiaotong University (Grant XJJ2018047).  
The authors declare no conflict of interest.
## 3 System Description
### Performance Evaluation Metrics
#### 3.5.1 RMS Delay Spread and Achievable Bit Rates  
Apart from the LoS components, the power arriving at the PD also takes contributions from rays that are reflected off multiple paths. However, these multipath reflections lead to time spreading which limits the maximum achievable symbol rate without ISI. This time spreading is defined by the root mean square delay spread (\(D_{rms}\)), and it is given by [5]:  
\[D_{rms}=\sqrt[]{\frac{\int{(t-\mu)^{2}h^{2}(t)dt}}{\int{h^{2}(t)dt}}} \tag{22}\]  
where \(\mu\) is the mean delay spread and \(h(t)\) is the total channel impulse response (CIR) that can be computed, as described in [5]. Using \(D_{rms}\), the maximum supported data rate (\(R_{b}\)) for OOK-NRZ modulation format, without an equalizer, can be calculated as follows:  
\[R_{b}\leq\frac{1}{10D_{rms}} \tag{23}\]