esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 3 The Proposed Joint Data Hiding System
### Procedures of Proposed Method
#### 3.1.2 Data Hiding
After that, let \(C\) be a union of the positions of pixels in \(B_{1}\) and \(W_{1}\), and the number in \(C\) is \(QG\). The set \(C\) can be divided into \(Q\) groups: \(Q_{1}\), \(Q_{2}\),..., \(Q_{Q}\), sequentially. Then, the \(d\) th group, \(Q_{d}\), can be written as \(\{(i_{1,d},j_{1,d}),(i_{2,d},j_{2,d}),\)..., \((i_{G,d},j_{G,d})\}\), where \(d\) = 1, 2,..., \(Q\). The \(d\) th group can embed the \(d\) th permuted information bit \(A(d)\). If an information bit to be embedded is 1, that is \(A(d)\) = 1, one bit of \(G\) pixels where positions are in \(Q_{d}\) will be flipped. It is assumed that the \(t\) th bit can be flipped, where \(t\) is 4, 5, or 6. Here, the \(t\) th bit is flipped instead of the three LSBs to make the pixels more fluctuated. If an information bit to be embedded is 0, that is \(A(d)\) = 0, the \(t\) th bit of \(G\) pixels where the positions are in \(Q_{d}\) will remain the same. When all the bits, \(A(1)\), \(A(2)\),..., \(A(Q)\) are embedded, an embedded encrypted image, \(I^{\prime\prime}\), will be constructed and sent to the receiver.
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 3 Strategies for Analyzing Image DNA Techniques
### Experimental Data Setup  
To compare the performance of image DNA techniques, experiments were conducted using two datasets: a small-scale Kaggle dataset [27] and the COCO dataset (2014 version) [31]. The Kaggle dataset contains 1752 images, excluding exact duplicates, of which 34 image pairs deemed equivalent were used for experimentation. This dataset includes images that appear visually similar but are inherently different, as well as transformed versions of the same image, making it ideal for testing equivalence detection while ensuring sufficient diversity.  
The COCO dataset comprises 164,062 images with no equivalent pairs. For equivalence experiments, 20 types of transformations were performed as detailed in Table 2. For performance evaluation, only the large-scale COCO dataset was used, considering that the smaller Kaggle dataset exhibited significant speed fluctuations and that extraction and search time generally scale with dataset size.
## 3 Results and Discussion
However, for mechanistic investigations and thermal inactivation modeling, the use of a single, well-characterized strain allows for more controlled comparisons and reproducible kinetic analyses. The integration of FIV and PEO into food processing offers a promising strategy to control _L. monocytogenes_, particularly in RTE foods where the pathogen's ability to proliferate at refrigeration temperatures presents a major food safety concern [78]. Applying predictive microbiology models allows for the fine-tuning of processing parameters to optimize safety while minimizing thermal damage. This is particularly relevant in regions with limited regulatory oversight of listeriosis, where evidence-based decision-making is essential [79]. Moreover, integrating these predictive tools into user-friendly software could facilitate broader adoption by food processors and regulators, enhancing both safety and operational efficiency. For instance, results of previous research have shown that combining nanoemulsions of D-limonene with heat treatments has shown a decrease in _L. monocytogenes_ heat resistance by about one hundred times [80]. The inactivation parameters estimated by these authors were further integrated into the _D database_ ([[https://foodmicrowur.shinyapps.io/Ddatabase/](https://foodmicrowur.shinyapps.io/Ddatabase/)]([https://foodmicrowur.shinyapps.io/Ddatabase/](https://foodmicrowur.shinyapps.io/Ddatabase/)), accessed on 1 April 2025), a resource that supports benchmarking, model development and the sharing of validated data. Similarly, the inactivation parameters estimated in our study will also be submitted to the _D database_ to encourage their practical application in food safety interventions. FIV enhances antimicrobial efficacy through its synergistic thermal effects and could reduce the sensory impact commonly associated with essential oils, which is critical for consumer acceptance [27]. At the applied concentration of 0.06% \(v/v\), the levels of carvacrol and thymol were below thresholds reported to impact sensory attributes in food. Specifically, the resulting concentrations were approximately 319 ug/mL carvacrol and 112 ug/mL thymol for FIV and 159 ug/mL carvacrol and 63 ug/mL thymol for PEO. These values correspond to less than 100 mg/kg when extrapolated to food matrices, which is within the range of natural occurrence in herbs and spices. According to U.S. toxicological data, thymol has been detected in foods at up to 100 mg/kg, with the lowest level at which adverse effects were observed being 640 mg/kg [81]. Additionally, the Official Journal of the European Union has set a safe use level for carvacrol and thymol in animal feed at 125 mg/kg [82], suggesting a broad safety margin. Importantly, a previous study reported that the addition of 0.5% OEO in meatballs was considered sensory acceptable, further supporting the potential of lower concentrations, such as 0.06%, for use in RTE foods without compromising sensory quality [83]. Therefore, further studies in real food systems are needed to confirm sensory acceptability and regulatory compliance.
## 3 Relevant Research in Wind Farm Design and Optimization
### Electromagnetic Interference (EMI)
#### 3.2.5 Uncertainty and Risk Modeling
In addition, the discretized wind direction was assumed to change in the range (\(-45^{\circ}\), \(45^{\circ}\)) with a uniform probability distribution to emulate its stochastic nature. The wind speed probability density function was assumed lognormal. The approach maximized the NPV probability distribution of the project. However, one of the shortcomings of the proposed approach is that the model requires a large number of Monte Carlo-based samples to evaluate the objective function, which in turn is evaluated several times during the optimization process, thus being a very time consuming approach. A major contribution, including three different sources of uncertainty within the scope of the WFDO problem, was proposed by [PERSON] and [PERSON][54, 138] in which uncertainties in land availability due to landowner participation, wind shear, and economies of scale costs-reduction were studied. Landowner participation was represented with an uncertain utility model of willingness-to-accept monetary compensation (_i.e._, a minimum annual compensation per MW installed in their lands). An uncertain wind shear parameter, based on a wind shear power law, and a cost model incorporating uncertainty with an economies-of-scale cost reduction parameter (representing a cost reduction for purchasing multiple WT) were introduced and developed. The goal of the work was to propose a comprehensive framework, in which both the normalized mean and normalized standard deviation of the CoE were minimized during the WFDO process. The study considered a Latin Hypercube method for the quantification of the uncertainty propagation during the different stages of the CoE calculation while considering different scenarios with different probabilities of occurrence, as equivalently proposed by [PERSON] _et al._[135]. The results of the work demonstrated that even in uncertain environments, wind farm designers can predict the viability of the project with an estimated CoE while providing measures of CoE deviations. Models and methods describing and quantifying additional sources of uncertainty are presented in [133, 137]. However, several sources of uncertainty, as pointed out by [PERSON] [55], are still not studied/considered: technical availability, Rotor Nacelle Assembly (RNA) cost, foundations cost, HVAC and MVAC cable cost, tower cost, installation cost, decommissioning cost, wake losses, HVAC, MCAC, and substation electrical losses, among others. In this regard, it is recognized that significant research is needed to encompass all sources of uncertainty during the wind farm design.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 4 Simulations
### Simulation Results
By eliminating this problem, MICA significantly improves the network performance for multicasting.
# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 2 Methodology  
Three subsections are included: Section 2.1 derives the formula for displacements reconstructed from accelerations. The idea behind the formula is also provided. Section 2.2 provides MEMS and NB-IoT information that is needed in our approach. Section 2.3 describes the proposed damage  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Methods** & **Descriptions** \\ \hline Time-series damage index [13; 14] & Damage index can be extracted from measured accelerations for two different states under identical loading conditions. \\ \hline Static influence line deflection [15; 16; 17] & Dynamic properties are not needed. The influencing factors are loading, stiffness, and mass of bridges. \\ \hline Acceleration sensor attached on passing vehicle [20] & Six damage features are extracted from measured accelerations on the passing vehicle data without using equipment on bridges. \\ \hline Strain fiber optic sensor [19] & Damage could be distinguished from the increasing recorded strain embedded in concrete. \\ \hline Tilt-angle sensor [12] & Only accelerations in the gravity direction are measured, and damage status can be distinguished from the constructed rotational influence line. \\ \hline Global positioning system [20] & Measurement without any contact with bridges. \\ \hline The proposed method & Reference-free displacements are constructed from measured accelerations for Two different states without interrupting bridge traffic. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of methods in the literature.  
identification method of Levels 1 and Level 2. Basically, the proposed method is not able to distinguish different types of bridge damages. The proposed Levels 1 and 2 indicators only reveal the information of vertical deflections for a damaged bridge due to varied causes, such as foundation settlement or shear failure. To be specific, the Level 1 indicator detects damage occurrence and the Level 2 indicator localizes bridge damage. However, the performance of both indicators depends on the number of sensors mounted in the bridge. The more sensors, the more detailed the damaged location.
Future Internet_**2010**, \(2\), 388-416; doi:10.3390/fi2030388  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Review_  
**A Survey of QoS Multicast in Ad Hoc Networks**  
**[PERSON] *, [PERSON] and [PERSON]**  
Centre for Quantifiable Quality of Service in Communication Systems (Q2S), Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway; E-Mails: [PERSON]_ADDRESS] (L.L.); [PERSON]L_ADDRESS] (O.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +47-735-927-78; Fax: +47-735-927-90.  
_Received: 27 July 2010; in revised form: 26 August 2010 / Accepted: 1 September 2010 / Published: 14 September 2010_  
**Abstract:** This survey on Quality of Service (QoS) in multicast ad hoc networks uses a framework based on the mechanisms in three important elements: resource estimations, multicast tree/mesh administration, and multicast routing. Our contribution is an exploration of the design space and an identification of areas that have not been fully explored. We discuss the design space of central mechanisms and classify proposed QoS multicast schemes according to the mechanisms they used. In addition, we summarize the scenarios used for evaluating their performance. Furthermore, we identify issues, mechanisms, and scenarios that have not been fully investigated in existing works. The paper provides a coherent understanding of design principles, conceptual operation, and evaluated scenarios of schemes designed for QoS multicast application in mobile ad hoc networks (MANETs). It also outlines new areas for future research in this field.  
**Keywords:** multicast; Quality of Service; ad hoc networks; MANETs
## 3 Experimental Section  
An experiment was designed and implemented to investigate the weak absorption condition of a diffuse integrating cavity. Oxygen was chosen as the sample gas due to its non-toxicity and easy accessibility. To achieve enough absorption for observing the non-linearity, the absorbance of oxygen was estimated using the HITRAN-2016 database [22]. A \(35\times 35\times 35\) cm diffuse integrating cavity was fabricated for this experiment. The diameters of the input and output light apertures were both 2 mm. The inner surface of the cavity was coated with a 0.4 mm thickness of Avian-D paint (Avian Technologies LLC, New London, NH, USA) and the painted surface was functioned as an ideal Lambertian diffuse scatter. The reflectivity of the coating was 98.3-98.4% at 750-800 nm,according to the supplier [23]. To reduce the port fraction of the cavity, the input light aperture and the gas inlet/outlet holes were combined with a T-junction. The light apertures were glued with a wedge-shaped glass lens to eliminate interference effects. The structure of a cubic integrating cavity is shown in Figure 2a, the in/out light aperture is labeled and an enlarged view of the T-junction is shown. The cavity could be sealed after screwing and gluing the head cover.  
The experimental setup, including a gas-sampling system and a gas-measuring system, is shown in Figure 2b. Different oxygen concentrations were achieved by mixing different proportions of oxygen and nitrogen with mass flow controllers (MFC D07-19C, Sevenstar, Beijing, China). Based on TDLAS, a vertical-cavity surface-emitting tunable diode laser (Laser Components: Single Mode VCSEL 763 nm TO46), with a free running output power of 0.3 mW, was used as the light source. The laser temperature was controlled at 22.47 \({}^{\circ}\)C (Temperature Controller: TED 200C, Thorlabs, Newton, MA, USA), and the laser injection current was 1.29 mA (VCSEL Laser Diode Controller: LDC 200, Thorlabs, Newton, MA, USA). The injection current was modulated using a sawtooth wave (10 Hz, 200 mV), corresponding to a current varying from 1.16 to 1.40 mA. The output light from the integrating cavity was detected with a photomultiplier tube (PMTH-S1-1P28, Hamamatsu, Japan). Data were recorded using a data acquisition card (DAQ card: National Instrument Co., NI PCI-6133, Austin, TX, USA). The sawtooth wave was simultaneously put into the DAQ card as the trigger signal. The ambient temperature was controlled at 25 \({}^{\circ}\)C for the experiments. The center frequency of the laser was used to scan over the oxygen-absorption P9 line in the A-band at 763.84 nm by comparing the measured laser center wavelength with the calculated oxygen absorption line based on the HITRAN data. Each oxygen concentration was measured 15 times to obtain the detection error. Each signal was averaged 500 times per minute.
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art  
As shown in Figure 3, an important number of NI&M-based IR methods have been proposed in the last decade. Nevertheless, an updated revision of the SoTA is needed due to new and novel enhancements having been contributed, and none of them having been reviewed in previous surveys on the topic [3; 35].  
Figure 3: Scientific production regarding nature-inspired and the metaheuristic (NI&M)-based IR.  
In this contribution, our revision of the SoTA regarding the application of this kind of optimization strategy is based on the selection of twenty-four papers coming from more than one hundred and fifty papers plotted in Figure 3. The considered selection criteria was three-fold and inspired by the following:  
* Novelty: Does the contribution make use of a new NI&M-based optimization algorithm which has not been tested for the IR problem yet?
* Technology: Is parallel computing taken into account to improve optimization in any way?
* Continuity: Does the contribution provide a new advancement based on any previous research?  
Next, we analyze in deep detail and chronologically the resultant twenty-four selected contributions.
## 2 Materials and Methods
### Satellite Precipitation Products (SPP)  
We used the Frequent Rainfall Observations on GridS (FROGS) dataset to evaluate the performance of SPPs in detecting extreme rainfall in different regions. These data consist of daily precipitation gridded products, including satellites, ground-based, and reanalysis products adjusted to a common \(1^{\circ}\times 1^{\circ}\) grid resolution. Data were downloaded from the Institut Pierre Laplace Simon (IPSL)'s repository. More information about this product can be found in [44]. We used eleven SPPs with one-degree daily resolution (1 DD): 3B42g, 3B42,  
Figure 1: The five regions of Brazil according to climatological rainfall regime. Southern Brazil (R1-blue), central region (R2-green), northeastern (R3-black), northeast coast (R4-yellow), and north Brazil (R5-red), adapted from [29].  
CMORPH, CMORPHg, GSMAP, GSMAPg, CHIRP, CHIRPSg, PERSIANNg, COSCHg, and TAPEER. The sub-index 'g' indicates which products were available with gauge correction. The full name and version are detailed in Table 1.
# Global Buckling Investigation of the Flanged Cruciform H-shapes Columns (FCHCs)
## 6 Conclusions  
The paper investigated seventy-two novel FCHCs with pin-ended supports by ABAQUS analysis. The buckling strength and deformation were determined by the verified FE Model that considers material and geometric nonlinearity and contact. Based on the ABAQUS analysis results, the conclusions are formed as follows:  
1. The representative values of comprehensive imperfections given in GB50017-2017 are suitable for global buckling analysis on FCHCs. The analysis results are a little bit conservative;
2. The overall instability form of FHCs with initial imperfections is flexural buckling around the axis of symmetry, and overall torsional buckling will not occur. Hot-rolled H-shapes selected according to Chinese standards generally do not undergo local buckling;
3. When \(\lambda_{\mathrm{n}}\leq\) 0.5 or \(\lambda_{\mathrm{n}}\geq\) 1.5, curve b in GB 50017-2017 and EN 1993-1-1 are suggested for the global buckling design of FCHCs. When the 0.5 \(<\lambda_{\mathrm{n}}<\) 1.5 \(\lambda_{\mathrm{n}}\), take curve c in both GB 50017-2017 and EN 1993-1-1;
4. New column curves proposed in the paper to predict the buckling strength of FCHCs are accurate, but they still need more investigation to confirm. The fitting imperfection factor is \(\alpha\) = 0.410 based on the expression in EN 1993-1-1, and the corresponding factors based on the expression in GB50017-2017 are \(\alpha_{1}\) = 0.60, \(\alpha_{2}\) = 0.970, and \(\alpha_{3}\) = 0.320.
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 1 Introduction
We introduce Graph Attention Networks (GAT) [13] into an Attribute Aggregator (AA) to effectively assign weights for attribute values according to their importance. Finally, a transformation matrix maps the embeddings of distinct KGs onto a unified space, and entity pairs that represent a real-world object are linked by calculating the similarity of their embeddings. The main contributions of this paper are as follows:  
* Considering that few relationship connections exist in domain-oriented embedded representation and the underuse of attribute values information, we employed a SA to generate high-order neighborhood-aware embeddings of entities through attribute values. Moreover, an AA utilizes the self-attention mechanism to dynamically calculate the weights between entities and attribute values and generates the attribute-aware embedding of entities. In addition, Multi-type Graph Neural Networks can enhance the aggregation of entity features. * In order to exclude unnecessary computations and improve the accuracy of the entity alignment, we designed the filtering mechanism with entity attributes (e.g., taste or cooking technique) on domain KGs, which select the candidate set by the blocker. The experiments show that this can achieve expectations. * Our approach only needs a few pre-aligned entities, and does not require any pre-aligned relationships or attributes between the KGs, which reduces the cost of manually annotating data in the early stages. * The experimental results on a real-world dataset show that compared to the six state-of-the-art methods, our approach has higher accuracy and better stability.
## 1 Introduction
### Review of Related Works
The paper identified five key domains important for the successful deployment of HAR in smart living: sensing technology, real-time processing, multimodality, resource-constrained processing, and interoperability [2]. It emphasised the importance of HAR in enabling a seamless integration of technology into our daily lives and enhancing our overall quality of life. [PERSON] et al. [4] discussed the advantages of Deep Reinforcement Learning (DRL) in HAR, such as its ability to adapt to different data modalities and learn without explicit supervision. The authors explained that DRL, a machine learning technique where agents learn through interaction with an environment, can improve the accuracy and efficiency of HAR systems by identifying the most informative parts of sensor data, such as video frames or body joint locations. The authors also acknowledged the challenges of DRL, like the need for large amounts of interaction data and the high computational cost. They also suggested future research directions such as multiagent reinforcement learning, unsupervised and few-shot activity recognition and computational cost optimisation [4]. While some survey papers point out the potential of HAR in healthcare [2; 6] and even the use of sensors to monitor health vitals [7], they do not discuss the importance of health vitals datasets nor the combination of other sensor data and health vital data in HAR. With the recent advances in technology, it is important to stay up to date, and some of the available surveys do not cover these recent research areas [3; 6; 7]. More recent survey papers are focused on specific areas like the application of HAR in smart homes [2] or the use of deep learning models [4; 8; 9]. This survey paper aims to bridge that gap.