esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative Image Steganography via Encoding Pose Keypoints
## 4 Evaluation and Comparisons
### Practical Application Case  
We designed a visualization scenario to demonstrate the proposed method's practical application by embedding stego-images into real-world contexts. Specifically, these stego-images were integrated into promotional tweets, akin to those used by fashion brands for marketing campaigns.  
For instance, a tweet announcing a new fall/winter collection could feature a series of images where a model showcases the same outfit from different angles, providing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Number**} \\ \cline{2-4}  & **500** & **2000** & **5000** \\ \hline Original Data & 3.03 s & 5.12 s & 6.71 s \\ Processed data & 0.77 s & 0.93 s & 1.32 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of time spent in the sequence generation process.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline  & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Payload**} \\ \cline{3-5}  & & **0.5 \(\times\) 256** & **1 \(\times\) 256** & **2 \(\times\) 256** \\ \hline \multirow{4}{*}{XuNet} & S-UNIWARD [4] & 46.93 & 46.72 & 46.14 \\  & HiNet [37] & 36.36 & 35.33 & 34.71 \\  & IDEAS [15] & 48.78 & 48.63 & 47.03 \\  & Proposed Method & 49.57 & 49.41 & 49.01 \\ \hline \multirow{4}{*}{YeNet} & S-UNIWARD [4] & 44.29 & 44.06 & 44.03 \\  & HiNet [37] & 31.45 & 30.74 & 27.45 \\ \cline{1-1}  & IDEAS [15] & 48.28 & 47.78 & 46.14 \\ \cline{1-1}  & Proposed Method & 49.27 & 49.23 & 48.87 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection results \(P_{E}\) of the XuNet and YeNet.  
a detailed view of its design and fit. Figure 11 illustrates such a tweet, highlighting how the stego-images blend seamlessly into promotional content while preserving their covert functionality.  
By disguising stego-images as authentic marketing material, the method integrates naturally into daily communication formats. This demonstrates its capacity to produce visually consistent and contextually appropriate image sets that align with real-world marketing strategies. Furthermore, the method enables the covert transmission of hidden information while satisfying aesthetic and functional requirements, showcasing its practicality for dynamic and public communication platforms like social media.
# Extraction of Dietary Fibers from Plant-Based Industry Waste: A Comprehensive Review
## 3 Extraction Methods
### Microwave-Assisted Extraction  
Microwave frequencies range from 300 MHz to 300 GHz and belong to non-ionizing radiation. In the food matrix, microwaves produce heat due to their interactions with the cellular compounds of polar nature. The produced heat causes the ionic conduction and rotation of dipoles [4; 36]. Over the last three decades, microwaves have been increasingly used for the preservation of food, extraction, inactivation and inhibition of enzymes, and inactivation of microorganisms. As an innovative method, microwave-assisted extraction combines the application of microwaves with the conventional extraction using solvents. The advantages of microwave-assisted extraction over conventional methods are the reduced use of applied solvents, increased yields, shorter process time, and a reduction in energy consumption [4; 35].
## 3 Experimental  
First, Ag and Au targets (purity 99.99%) were used to generate nanoparticles. LAL was done using 1 mM Na\({}_{2}\)PdCl\({}_{4}\) solution; the control was 2 mM NaCl (both in deionized water). Precursor Pd is ionic in Na\({}_{2}\)PdCl\({}_{4}\) solution and interacts with the laser to inhibit the growth of certain nanoparticles, but also to form AgPd and AuPd nanoparticles. The solid targets were fixed in the solution; laser ablation induced fragmentation and growth of heterogeneous nanoparticles. We used two laser beams. The femtosecond laser pulse width was 170 fs; after generation of a Ti:sapphire seed beam, the pulse energy was amplified. The nanosecond (fiber) laser pulse width was 5 ns. Both beam wavelengths were in the near-infrared region (1064 and 1070 nm, respectively). The pulse energy was 20 or 40 \(\upmu\)J for both lasers. The beams were delivered to metal targets fixed in the solution via a scanner with a focal length of 100 mm. The laser spot size on the solid surface was 50 \(\upmu\)m and the laser fluence was approximately 1.0-2.1 J/cm\({}^{2}\).  
The schematics of the process is shown in Figure 2. The process involved four steps (Figure 2a-d). A metal target was placed in the center of the container with the liquid, and pulsed laser beams were used to irradiate the target. The laser interacted with the target surface to cause ablation, and the plasma thus generated removed metal nanoparticles from the target. The detached nanoparticles were either further fragmented by laser pulses or reacted with elements in the plasma to form novel fused nanoparticles. As this sequence repeated, homogeneous and heterogeneous nanoparticles were produced.  
The heterogeneous nanoparticles were collected and analyzed. Transmission electron microscope (TEM: JEM-2010; JEOL, Tokyo, Japan) images were acquired and the nanoparticle shapes were analyzed. The TEM operated at 200 kV; we also used an LaB\({}_{6}\) electrode. About 500-800 nanoparticles on an image were analyzed in terms of the size distribution. Component analysis was performed using a TEM/energy-dispersive X-ray spectroscopy (TEM/EDS) instrument (INCA; Oxford Instruments, Abingdon, UK); the detector was 50 mm\({}^{2}\) in area and the solid angle was 0.28 sr. We correlated the laser parameters with the levels of constituent elements in the nanoparticles.
## 3 Results
### Correlating the SSI with Hydrological Variables  
To further emphasis the role of the coupled vegetation-runoff system in simulating the SSI, a spatial correlation map (Figure 4) between the SSI, hydrological variables (infiltration rate and soil moisture) and meteorological variables (relative humidity) is plotted. From Figure 4, it can be observed that there is a high negative spatial correlation between the SSI, hydrological (\(-0.4\) to \(-0.8\)) and meteorological variables (\(-0.6\) to \(-1\)) variables. Such high negative spatial correlation confirms the inverse relationship between the SSI, hydrological and meteorological variables as suggested by [12; 13]. This result supports the fact that the land-surface process (runoff in this study) plays a critical role in constraining the SSI using a regional climate model. In addition, ref. [14] reported that there is a strong negative correlation between SSI and gross primary production (GPP; correlation ranges from \(-0.6\) to \(-1\)). This strong inverse relationship ensures the favorable conditions for maximizing the GPP [15].  
The RegCM4 performance can be further quantified using a box-and-whisker plot following [16]. From Figure 5, it can be noticed that the RegCM4 model performance varies both spatially and temporarily. For instance, the CN-VIC is closer to the SRB than the CN-TOP during the summer season of area 1. In the winter season, the CN-VIC overestimates the SSI more than the CN-TOP in comparison with the SRB; such behaviour is in agreement with the results reported in Section 3.1. Over area 2, the CN-VIC severely overestimates the results more than the CN-TOP with respect to the SRB. In addition, the CN-VIC has a  
Figure 4: The figure shows the average spatial correlation between SSI and different hydrological and meteorological variables. (**a**) correlation between SSI and infiltration rate (QINFL_Corr), (**b**) correlation between SSI and Soil moisture of depth 10 cm (SM10_Corr), (**c**) correlation between SSI and 2-m relative humidity (RH2M_Corr). Areas with black dots are statistically significant at 95% confidence level of Student’s \(t\)-test.  
Figure 3: The figure shows a simple chart describes the influence of the land-surface hydrology scheme (TOP/VIC) on the simulated SSI.  
higher spread than the CN-TOP and SRB. On the other hand, the CN-VIC shows a better agreement than the CN-TOP when it is compared with the SRB. Lastly over area 3, both simulations are close to the SRB during the summer season, while both simulations severely underestimate the SSI during the winter season.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 2 Background
### SDN and Bloom-Filter-Based Forwarding  
Software-defined networking (SDN) [13] is a technology that enables the use of programmable network switches, which can be configured by a centralized entity, known as the \"network controller\" (or simply controller). A controller can use a protocol, such as OpenFlow [14], to configure switches with \"rules\" that are used for making switching decisions.  
[PERSON] et al. [6] leverage this property of SDN and implement source-routing using Bloom filters [15]. From a high-level perspective, this solution is implemented as follows. Firstly, it assumes that each outgoing interface of an SDN switch is identified by a bitstring identifier. Then, each sender can create a \"forwarding identifier\" using a Bloom filter constructed by ORing, the identifier of all interfaces through which a packet should be forwarded; this forwarding identifier is stored in the IPv6 address field of the transmitted packet. SDN switches are pre-configured with rules that allow them to forward these packets through the interfaces whose identifier is included in the forwarding identifier. An interesting property of this solution is that it can implement stateless multicast since a forwarding identifier may include a complete multicast delivery tree. Another useful property of this system is that given a forwarding identifier \(X\) for a path from a node \(A\) to a node \(B\), and another forwarding identifier \(Y\) from the same node \(A\) to a node \(C\), by ORing \(X\) and \(Y\), we obtain a forwarding identifier that corresponds to a multicast delivery tree from \(A\) to \(B\) and \(C\).
## 3 Summary of the Artificial Neural Network (ANN) for the CO2 Emission Prediction Model
### Long Short-Term Memory (LSTM)  
LSTM is a type of recurrent neural network (RNN) designed to handle sequential data by considering temporal dependencies. As shown in Figure 4, unlike traditional RNNs, LSTM networks incorporate three specialized gates--input gate, output gate, and forget gate--which regulate the flow of information and mitigate the vanishing gradient problem [43]. The memory cell structure of LSTM networks enables the retention of both short-term and long-term dependencies through the linear transfer of cell states and controlled gate interactions. This architecture makes LSTM networks particularly effective for processing and predicting time-series data, especially when learning from sequences with long time steps [41].  
Figure 3: Structure of the one-dimensional (1D) convolutional layer.
## Nomenclature  
_Symbols_  
\(t\)  time (s)  
\(m\)  mass (kg)  
\(p\)  pressure (Pa)  
\(A\)  area (m\({}^{2}\))  
\(\rho\)  density (kg: m\({}^{-3}\))  
\(y\)  valve displacement (m)  
\(Q\)  heat in the control volume (J)  
\(h\)  specific enthalpy (J: kg\({}^{-1}\).K\({}^{-1}\))  
\(q_{f}\)  acoustic source power-flux per unit volume  
\(V\)  volume (m\({}^{3}\))  
\(T\)  temperature (K)  
\(u\)  characteristic velocity (m: s\({}^{-1}\))  
\(D\)  diameter (m)  
\(r_{1}\)  crank radius (m)  
\(r_{2}\)  length of connecting rod (m)  
\(kk\)  spring stiffness (N: m\({}^{-1}\))  
\(A_{D}\)  valve plate area (m\({}^{2}\))  
\(G_{init}\)  pre-load force (N)  
\(c_{eq}\)  damping coefficient (N: s\({}^{-}\)m\({}^{-1}\))  
\(C_{D}\)  drag coefficient  
\(\mathbf{U}\)  solution vector  
\begin{tabular}{l l}
**F** & flux vector \\
**B** & source term vector \\ \(G_{f}\) & friction term \\ \(rc\) & reflection coefficient \\ \(tc\) & transmission coefficient \\ \(Y\) & acoustic impedance \\ \(a\) & sound speed (m\(\cdot\)s\({}^{-1}\)) \\ \(\sigma\) & wave number \\ \(e\) & specific internal energy (J\(\cdot\)kg\({}^{-1}\)) \\ \(a_{A}\) & entropy level (m\(\cdot\)s\({}^{-1}\)) \\ \(q\) & heat transferred between gas and \\  & walls per unit of mass (W\(\cdot\)kg\({}^{-1}\)) \\ \(Y\) & Acoustic impedance (kg\(\cdot\)m\({}^{-2}\).s\({}^{-1}\)) \\ _Abbreviations_ & \\ _CFD_ & computational fluid dynamics \\ _3D_ & three dimensional \\ _1D_ & one dimensional \\ _0D_ & zero dimensional \\ _FEM_ & finite element method \\ _MOC_ & method of characteristics \\ _ESD_ & Equivalent Short Duct \\ _Greek symbols_ & \\ \(\varphi\) & flow coefficient \\ \(\alpha\) & heat transfer coefficient (W\(\cdot\)m\({}^{-2}\).K\({}^{-1}\)) \\ \(\omega\) & angular speed (rad\(\cdot\)s\({}^{-1}\)) \\ \(\xi\) & damping factor \\ \(\gamma\) & specific heat ratio \\ \(\lambda\), \(\beta\) & riemann variables \\ \(\Delta\) & change quantity \\ _Superscripts_ & \\ \(*\) & acoustic properties \\ \(k\) & period step \\ _Subscripts_ & \\ _cv_ & control volume \\ \(s\) & suction \\ \(d\) & discharge \\ \(p\) & piston \\ _cl_ & clearance \\ _eq_ & equivalent \\ _reb_ & rebound \\ _imp_ & impact \\ \(0\) & stagnation state \\ _FW_ & forward \\ _BW_ & backward \\ _ref_ & reference condition \\ \(i\), \(j\) & port number of chamber \\ \end{tabular}
# Automatic Blob Detection for Dental Caries
## 2 Methods and Techniques
### Feature Extraction
#### 2.3.3. Caries Candidate Selection  
From setting the blobness sensitivity parameters, many redundant non-caries candidates and actual caries candidates were detected. If all the caries candidates were used to extract features and estimate the likelihood of caries, processing would take a long time. Additionally, a long processing time leads to poor performance of the CAD system, and this is because of the high frequency of false positives (FPs). Therefore, caries candidate selection is adopted to eliminate FPs from carious candidates. High blobness values indicate the candidate has caries, while those with lower blobness values are non-caries candidates. Therefore, maximum and mean blobness values of caries candidates were used to eliminate false positives. The formulas are defined as:  
\[Blobness_{mean}(T)=\frac{\Sigma_{p\text{\tiny{T}}}Blobness(\lambda_{p})}{N_ {T}} \tag{7}\]  
\[Blobness_{max}(T)=max_{p\text{\tiny{T}}}Blobness(\lambda_{p}) \tag{8}\]  
where \(T\) refers to a caries candidate with \(N_{T}\) voxels and \(p\) a voxel that belongs to the caries candidate. The size of blobs was also used as a feature in selecting caries candidates. Size feature is used because the caries candidate detection based on Hessian analysis is sensitive to intensity variations and leads it to detect many small regions including edge spaces of teeth as caries candidates. These edge spaces and small regions are not part of true caries lesions and their size can be used to eliminate many false positives from among the caries candidates as seen in Figure 3. The linear regression model [20] is applied to eliminate false positives from among caries candidates. The caries selection function \(L_{s}(T)\) is defined as:  
\[Z(T)=\beta_{0}+\sum_{i=1}^{N_{f}}\beta_{i}x_{i} \tag{9}\]\[L_{s}(T)=\frac{1}{1+e^{-Z(T)}} \tag{10}\]  
where \(N_{f}\) is the number of features, \(x_{i}\) the feature value, \(\beta_{0}\) is the constant coefficient, and \(\beta_{i}\) is the corresponding coefficient estimated by the linear regression model. Lastly, the criteria used for caries selection is expressed as:  
\[L_{s}(T)\geq THresh \tag{11}\]  
where \(THresh\) is the threshold used to determine the remaining caries candidates after eliminating false positives. The detected regions remain as dental caries when values estimated by the caries selection model was equal or greater than threshold. Those values less than the threshold given were removed. In our experiments, the convexity threshold value was set at 0.9 to detect dental caries. After the caries candidate selection, false positives were eliminated, and only carious candidates remain, which are now classified as \"Dental Caries Detected\". ```
1:procedureEncoder(Y)\(\triangleright\)\(Y:y_{i}\) is an input image with dimension (L, H).
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
###### Abstract  
Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems.  
M +
Footnote †: journal: Applied Sciences  
Article  
MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions  
MANET routing protocols optimized link state routing (OLSR); destination-sequenced distance vector (DSDV); ad hoc on-demand distance vector (AODV); dynamic source routing (DSR); ad hoc on-demand multipath distance vector (AOMDV); temporally ordered routing algorithm (TORA); zone routing protocol (ZRP); geographic routing protocol (GRP); OMNeT++ 6.0.1; INET-4.5.0
# Fault Detection Algorithm Based on Dynamic Global-Local Preserving Projection
###### Abstract  
Industrial system operations usually have dynamic characteristics. If these characteristics are ignored, the performance of fault detection degrades. Herein, the fault-detection algorithm of dynamic global-local preserving projection (DGLPP) is employed to solve the problem mentioned. First, time-delay data are added to the sample to form an augmentation matrix and characterize the system dynamics. Second, the dimensionality of the augmented matrix is reduced using global-local preserving projection. The dimensionality-reduction method can preserve the data's global and local structures. Then, a DGLPP model is built using the dimensionality-reduced data. Moreover, [PERSON]'s T\({}^{2}\) and squared prediction error (SPE) statistics are used for fault detection. Finally, this method is used to detect the fault in the Tennessee Eastman (TE) process. The experimental results show that the DGLPP method has an enhanced fault detection rate. Moreover, the fault-detection effects of the DGLPP method are better than those of the principal component analysis (PCA), local preserving projection (LPP), and global-local preserving projection (GLPP) methods.  
dynamic; fault detection; global-local preservation projection +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))).
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 3 Scheduling Optimization by Genetic Algorithms
### Fitness Function  
The genetic algorithm employs a fitness function to assess the effectiveness of solutions. The fitness function outlined in Algorithm 3 evaluates the quality of a solution, denoted as \(s\), consisting of a machine id \(m\) and a task instance id \(t\). The fitness function verifies whether the machine associated with \(m\) can accommodate the task instance linked to \(t\). In order for task instance \(t\) to be accommodated by machine \(m\), the available resources of machine \(m\)--denoted as \(P\), \(Q\), and \(R\)--must meet the required resources of task instance \(t\), denoted as \(p\), \(q\), and \(r\). If this condition is met, the function yields the increased GPU utilization as the fitness value when task instance \(t\) is scheduled on machine \(m\). Conversely, if the accommodation is not feasible, the fitness value returned is \(-1\).  
```
1:Input:One of solution \(s:\left[m_{j},\ t_{i}\right]\)
2:\(Fitness\ value\ f\)
3:\(j\) th machine \(m_{j}\gets s[0]\)
4:\(i\) th task instance \(t_{i}\gets s[1]\)
5:if\(m_{j}\) can accommodate \(t_{i}\)then
6:\(f\ \leftarrow\ \Delta\) The increase of GPU utilization
7:else
8:\(f\ \leftarrow-1\)
9:endif
10:return\(f\)
```  
**Algorithm 3** Fitness Function
# Development of the Virtual Reality Application:
## 1 Introduction  
Positioned in the intersection of technological and other sectors, VR applications are often in the middle of multidisciplinary stakeholders, fulfilling various needs and addressing complicated issues. The VR experience \"The Ships of Navarino\" was developed for the Oculus Rift S and the Oculus Quest, to be presented as a part of a physical exhibition held in the Eugenides Foundation, Athens, Greece. More specifically, the VR experience \"The Ships of Navarino\" is a tour inside four significant ships that participated in the battle of Navarino: the British \"Asia\", with admiral [PERSON] in the lead, the French frigate \"Armide\", the Russian \"Azov\", and the Ottoman \"Kuh-I-Revan\". Considering that the VR experience was a part of a larger exhibition, it should have a limited duration and be accessible to all audiences.  
It is difficult to visualize a historical artifact when there are technological limitations. Optimization processes will often boost the performance based on the cost of visual fidelity and the risk of historical inaccuracies. This paper will highlight the development process of the recent VR project \"The Ships of Navarino\" and will provide an empirical contribution to the optimization methodology of a VR application that communicates intact historical information and opens up the possibilities of a broader dissemination range. During thisproject, based on historical sources, four ships involved in the historic Battle of Navarino were digitally recreated and developed into a VR application that became accessible to a wide audience as part of the anniversary exhibition \"Run onto the waves of the formidable sea. 1821, the War at Sea.\" in the Eugenides Foundation [1]. The optimization methodologies adopted targeted the VR platforms and resulted in 3D models also suitable for online hosting, enriching the dissemination of the project. Furthermore, based on the 3D models and 3D textures, accurate models of the ships were 3D-printed and painted for display in the exhibition.