esa-sceva/satcom-chunk-collection huggingface dataset:
# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
## 4 Results and Discussion
### Results and Analysis  
Using the proposed method, aesthetically pleasing 3D holographic images were generated from historical monochromatic photographs. While this approach shows promising performance in colorization and depth reconstruction, further research is required to address the limitations related to depth-map accuracy, half-parallax generation, and the processing of low-quality input images. One notable constraint is that the holograms were created with half-parallax only--that is, horizontal parallax--due to the lack of multiangle input data. This results in the loss of vertical parallax, which can reduce the overall realism and limits the 3D effect when the viewer moves vertically. Additionally, when applying AI-based colorization to historical portraits, challenges arise in accurately reconstructing clothing colors, as the model relies on learned patterns that may not align with authentic historical garments. To improve the reliability of such reconstructions, incorporating expert knowledge or applying constraints based on historical clothing data could enhance the fidelity of colorization, particularly for portraits of historical figures. Such uncertainties in both depth and color may lead to perceptual inconsistencies or historical misinterpretations, underscoring the importance of cross-disciplinary validation in future developments. Despite these challenges, our study makes a significant contribution to digital holography by enabling 3D visualization from easily accessible historical sources. Our method achieves a horizontal viewing angle of 85-100\({}^{\circ}\) for the holographic portrait, providing a noticeable parallax effect and a comfortable viewing experience for a single observer, and crucially, it requires only a single black-and-white image as input, whereas the methods discussed in the related work section typically rely on multiple color images. In contrast to high-fidelity frameworks such as MetaHuman, which require extensive data and computational power for detailed 3D modeling, our approach focuses on lightweight, image-based holographic stereogram synthesis. This contrast highlights the accessibility and practicality of our method, particularly in scenarios where historical data or computational capacity are limited. To provide a quantitative comparison between the generated holograms and the original perspective images, we conducted a detailed evaluation based on key visual metrics such as resolution, brightness, contrast, and edge sharpness. This comparison objectively assesses the consistency between the hologram and the perspective view synthesized from the original monochromatic image [2; 6; 27]. Table 2 summarizes these metrics, offering a comprehensive understanding of the visual fidelity achieved by the proposed method.  
Figure 11: Images of the final digital hologram portrait of [PERSON].
## 5 Module I: Anonymity
### Communication Phase
#### 5.3.6 Fake Broadcast Message  
The sensors need to send fake messages to prevent _time correlation_, _rate analysis_ and _statistical analysis_. A fake message is technically a one-hop broadcast message. However, to prevent correlation, the message needs to behave similar to real messages. Therefore, the message needs to be encrypted and have similar size as the real message to make it completely _indistinguishable_. Since it has to carry a dummy data, it will contain the _residual energy_ (\(\Delta\)) of the issuing sensor. This information will be extracted by the recipient neighbors and saved in the related tuple in the table (T). The fake broadcast message sent by SNi is as in Equation (20):  
\[\mathrm{M_{f}=Padding\parallel FPID_{i}\parallel E_{kfi}(\Delta_{i})} \tag{20}\]  
All the neighbors will receive the fake broadcast message from SNi. SNi and the recipients will then update FPIDi according to Equation (21):  
\[\mathrm{FPID_{i}=H_{1}(FPID_{i}\oplus c_{i})} \tag{21}\]  
There is no need to worry about the pseudonyms synchronization since the main purpose of the fake messages is to show activity in idle sensors to obfuscate real messages.
## 2 NOAA Ocean Satellite Course
### Data Access Tools  
The course employs three data access tools: ERDDAP, a data server developed by NOAA's ERD; the rerddapXtracto package [17] for accessing ERDDAP through R; and the Environmental Data Connector [18], a plug-in module for accessing ERDDAP through ArcGIS. Each of these tools in outlined in more detail below.
# A Lightweight Forest Test Image Recognition Model Based on Improved YOLOv8
## 2 YOLOv8 Object Detection Algorithm
### Backbone  
YOLOv8 incorporates an enhanced CSPDarknet53 [4] as its backbone network for efficient feature extraction. Upon image input, YOLOv8 employs Mosaic data augmentation at the input layer. During the final 10 epochs of training, Mosaic data augmentation is deactivated. This strategic adjustment significantly enhances the diversity of backgrounds for detected objects, thereby effectively boosting the model's performance and robustness. Within the backbone network, the CBS module conducts convolutional operations on input data, followed by batch normalization and SiLU activation, as depicted in Figure 1--the CBS module. YOLOv8 replaces the original Cross-Stage Partial (CSP) module with the C2f module in its structure, as illustrated in Figure 1--the C2f module. The C2f module, adopting gradient-concatenated connections, enriches the information flow of the feature extraction network while ensuring a lightweight design. The Spatial Pyramid Pooling Fusion (SPPF) module transforms feature maps of arbitrary sizes into adaptive-sized feature vectors. Initially, channel-balanced operations (CBSs) are applied to the input feature map to extract feature information. Subsequently, max-pooling operations with sizes of \(1\times 1\), \(5\times 5\), \(5\times 5\) and \(5\times 5\) are separately performed on the feature map. Following each pooling operation, the obtained features are sequentially output to obtain multi-scale features.  
Figure 1: YOLOv8n model architecture encompassing the feature extraction network, SPPF architecture, feature fusion, and prediction network. The w (width) and r (ratio) in Figure 1 are parameters used to represent the size of the feature map.  
These multi-scale feature maps are then fused, followed by another round of channel-balanced operations (CBSs), resulting in the final feature map output. In comparison to the conventional SPP [22] module, the SPPF module, as illustrated in Figure 1-SPPF module, reduces computational complexity and exhibits lower latency by sequentially connecting three max-pooling operations.
## 2 Methodology
### Tunnel Axis Segmentation
#### 2.2.1 Constructing MST of Point Clouds along the Tunnel Axis  
The initial extracted axis points in Section 2.1 are unorganized and without topology. We need to restore the topological relationships, i.e., clock-wise or counterclockwise order of these points. To this end, we first construct the MST, from which the weight on any edge is the Euclidean distance between two points. After the completion of the MST, the maximum depth of a subtree is traversed via the depth-first searching (DFS) algorithm. This subtree implicitly includes the topological relationship among axis points, i.e., clockwise or counterclockwise sequence of axis points. As shown in Figure 3a, the central axis of a tunnel can be well recognized via undirected graph MST although it has a certain degree of noises along the central axis. After DFS traversing, in Figure 3b, the sequence of the central axis points connected by the green lines is well maintained. Apart from restoring topology of axis points, it can be observed in Figure 3b, our MST-based traversing strategy has the capability to remove a certain degree of noises which deviates far from the tunnel axis to some extent, thereby refining the axis points.  
Figure 3: Restoring the topological relationship among axis points via MST (Minimum Spanning Tree). (**a**) the axis point clouds are organized by an undirected graph MST; (**b**) the maximum depth of a subtree is recognized and overlaid on the MST. The blue points present the initial axis points. The red and green lines represent the established MST and the extracted axis, respectively.
## 5 Discussion
### Technical Analysis and Comparative Effectiveness of ML and DL Models
#### 5.2.5 SARIMA-LSTM-BP Combination Model  
Technical Details: This new model combines the strengths of SARIMA, LSTM, and backpropagation. SARIMA captures seasonal trends, LSTM learns and predicts temporal patterns, and backpropagation (BP) adjusts the model to improve its reliability and accuracy.  
Performance: The SARIMA-LSTM-BP model, as discussed by [PERSON] et al. [11], surpasses traditional econometric models and standalone LSTM models in predicting new energy vehicle demand, offering higher reliability. The performance of this hybrid model in prediction is also improved, with an 18% reduction in error compared to standalone LSTM models.
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 5 The Majority Hash Rate Problem
### The Double-Spending Issue  
Double-spending is a technique that is initiated to respond the same currency [44]. A weakness in the consensus mechanism uplifts the likelihood of a majority hash rate attack, and successful exploitation leads to double-spending by allowing attackers to cancel transactions and spend the same coin again. In order to carry out double-spending, attackers first spend their coins in the legit chain. After that, they start building another chain privately where the attackers' coins are not spent. Once the privately mined chain is long, the attackers present the new chain to the network. Since the chain is longer than the one being used, the new chain will be used by the network as a legit chain discarding the blocks where attackers spend their coins.  
The probability of double-spending on PoW consensus varies depending upon the number of blocks an adversary is able to generate [12]. However, the more confirmations introduced, the less the chance of double-spending. If an attacker is in possession of a 10% hash rate, then double-spending is still possible but with a smaller possibility of success. Various exchanges grant a transaction approval after six confirmations to mitigate the double-spending issue; nevertheless, attackers with 51% hashing power can keep building blocks secretly at a faster pace and carry out double-spend regardless of the number of confirmations set by the exchanges.
# Dual Stimuli-Responsive Polysaccharide Hydrogels Manufactured by Radiation Technique
## 4 Conclusions  
The formation of networks from natural or semi-synthetic polymers is not straightforward, particularly when chemical initiation may be unacceptable, e.g., due to restrictions in the use of initiators that may limit biomedical applications, or the use of high temperatures that may cause the decomposition of thermolabie drugs or, more generally, biologically active compounds to be incorporated in situ into the matrix. In this work, it was demonstrated that the use of ionizing radiation to induce crosslinking reactions in polysaccharide derivatives in order to produce functional materials is possible with some prerequisites. Since polysaccharide-only gels (alone) are weak with extensive swelling, the efficient compensation of polysaccharide degradation reactions and increased network density were achieved with a PEGDA crosslinker, the use of which is known to be acceptable in biomaterials.  
The combination of temperature and pH-stimuli-responsive polymers resulted in hydrogels of dual-stimuli-responsive properties, the extent of which depends on the HPC/CMC ratio in the gel. In general, despite the almost equal degree of substitution in HPC and CMC, gels with the same content of both cellulose derivatives are significantly more sensitive to pH changes than to temperature changes. Electrostatic repulsion causes  
Figure 11: pH-dependent swelling of 5% CMC hydrogels formed with 5% PEGDA, 25 kGy; HPC% content: (**a**) 0, (**b**) 5, and (**c**) 10.  
the ionized CMC to swell significantly, however, with the neutralization of the carboxylate groups, the elastic recoil of the chains is no longer counterbalanced and the network contracts. If temperature-responsiveness is also desired for the envisaged application, it is necessary to use a higher concentration of the temperature-sensitive component in the blend or to use HPC with a substantially higher degree of substitution than that of CMC to achieve the same level of responsiveness as to the change in pH. Last but not least, the radiation doses applicable for the synthesis of HPC/CMC with PEGDA are within the typical sterilization range of 20-40 kGy, which may be beneficial for the designing of a technological process in which network synthesis and sterilization are accomplished in one step.  
Conceptualization, R.A.W.; methodology, R.A.W.; investigation, G.P., A.A.-W., B.R., A.K.O. and R.A.W.; writing--original draft preparation, R.A.W.; writing--review and editing, R.A.W., A.A.-W., C.D. and P.U.; visualization, R.A.W. and A.A.-W.; supervision, R.A.W., C.D. and P.U.; funding acquisition, P.U. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the International Atomic Energy Agency (IAEA) CRP F23035, Radiation Effect on Polymer Materials Commonly Used in Medical Devices.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 2 System and Channel Model  
In this section, we present our suggested system model, along with a comprehensive explanation of the HHT and HDT protocols.
# Reduction of Coupling Interface Degrees of Freedom in Mixed-Interface Component Mode Synthesis
## 3 Normal Mode Correction for the Master Substructure
### Partition of the Master Substructure  
Another way to correct normal modes is partitioning the master substructure by the fundamental concept of FCMS method [12]. For a free-interface substructure, the modal transformation consists of two groups of modes, including the normal modes \(\mathbf{\Phi}\) and the residual attachment mode \(\mathbf{\Psi}_{d}\). The normal mode \(\mathbf{\Phi}\) can also be partitioned into two categories: the truncated normal mode \(\mathbf{\Phi}_{k}\) that is kept for modal analysis and the higher abandoned mode \(\mathbf{\Phi}_{d}\) that will not involve in mode synthesis. Thus, the first coordinate transformation can be rewritten as  
\[\left\{\mathbf{u}_{f}\right\}=\left[\mathbf{\Phi}_{k}\quad\mathbf{\Psi}_{d}\right]\left\{ \begin{array}{c}\mathbf{q}_{k}\\ \mathbf{g}_{f}\end{array}\right\}=\left[\mathbf{T}_{f}^{1}\right]\left\{\begin{array} []{c}\mathbf{q}_{k}\\ \mathbf{g}_{f}\end{array}\right\} \tag{18}\]  
\[\left[\mathbf{\Psi}_{d}\right]=\left[\mathbf{\Phi}_{d}\right]\left[\mathbf{\Lambda_{d}}^{- 1}\right]\left[\mathbf{\Phi}_{d}^{B}\right]^{\mathrm{T}} \tag{19}\]  
where, \(\mathbf{\Phi}_{d}^{B}\) and \(\mathbf{\Lambda}_{d}\) are the abandoned normal mode corresponding to the coupling interface DOFs and the remaining eigenvalue, respectively.  
The revised generalized stiffness and mass matrices of the master substructure then can be obtained as follows  
\[\left[\mathbf{K}_{f}^{(1)}\right]=\left[\mathbf{T}_{f}^{1}\right]^{\mathrm{T}}\left[ \mathbf{K}_{f}\right]\left[\mathbf{T}_{f}^{1}\right]\quad\quad,\quad\left[\mathbf{M}_{f}^{ (1)}\right]=\left[\mathbf{T}_{f}^{1}\right]^{\mathrm{T}}\left[\mathbf{M}\right]\left[ \mathbf{T}_{f}^{1}\right] \tag{20}\]  
Assembling all the generalized stiffness and mass matrices of the master substructure Equation (20) and that of the slave substructure Equation (8), the eigenvalue problem for the reduced global system will be done. However, as the generalized coordinates here consist of four categories: \(\mathbf{q}_{k}\), \(\mathbf{q}_{m^{\prime}}\), \(\mathbf{g}_{f}\) and \(\mathbf{u}_{c}^{B}\), an extra coordinate transformation is needed to eliminate the independent ones in the truncated normal modes.
## 4 Multi-Electrode System Fabrication  
The multi-electrode system is designed to sit on top of standard, 24-well culture plates with dimensions of 127.6 mm \(\times\) 85.3 mm \(\times\) 23.1 mm. It has 24 groups of electrodes, with each group corresponding to one well. The connector pads on the main board are wired to external connector points at the end of the board. These connectors are used to apply stimulus from external circuitry. The main board is shown in Figure 5.  
Each standard well has a diameter of 16 mm and a depth of 16 mm. Two electrodes are fabricated at the end of a 16 mm strip, enabling the electrodes to reach the bottom of the well. Each electrode has a gold-plated surface area of 1.25 mm \(\times\) 0.5 mm that is in contact with the bottom of the well. The two electrodes on one strip are separated by 1 mm of insulation. The total width of each strip
## 3 Results
### The Influence of Preservative Systems on the Viability of Probiotics in cosmetic Formulations  
This research study was aimed at assessing the impact of preservative systems (1P-6P) protecting the cosmetic formulation on the viability of the _L. casei_ strain enclosed in selected microspheres over the considered time. The initial density of _L. casei_ cells in the microspheres was 9.60 log CFU/g. The tests were performed according to the methodology included in Section 2.4, and the results obtained are presented in Table 3.  
The maximum permissible concentrations of antimicrobial substances were used in the study. The use of the highest possible concentration of preservative should ensure, in addition to the survival of probiotic bacteria enclosed in microspheres, the microbiological stability of the product.  
Among the tested systems, preservative system 1P had the weakest impact on the viability of the _L. casei_ strain encapsulated in microspheres based on sodium benzoate combined with sodium salts of levulinic and anisic acid. Compared with the initial density of the _L. casei_ strain in the microspheres, the viability of the strain decreased by 0.26 log immediately after receiving the cosmetic formulation, by 3.42 log after 7 days and by 3.54 log after a one month. A slightly greater reduction in live _L. casei_ cells was observed after the use of preservative systems 2P (dehydroacetic acid and sodium benzoate) and 6P (sodium dehydroacetate) in the cosmetic. In the case of preservative system 2P, immediately after receiving the cosmetic, the decrease in the number of bacterial cells was 2.31 log; after 7 days, it was 6.51 log; and after 30 days, it was 7.3 log. Preservative system 6P, immediately after creating the formulation, reduced the number of _L. casei_ bacteria by 5.7 log. In the subsequent stages of the study, the decrease in cell number was even greater and amounted to 6.10 log, and after 30 days, it was 7.52 log. In the case of preservative systems 4P (phenoxyethanol and ethylhexylglicerin), 5P (methylparaben and propylparaben) and 3P (hydroxyacetophenone), the following probiotic cell reduction values were obtained immediately after receiving the cosmetic: 6.34 log, 5.66 log and 9.60 log. The cosmetic formulation containing preservative system 3 was not subjected to the assessment of the survival of _L. casei_ cells contained therein after 7 and 30 days of storage due to their complete elimination already in the first stage of testing. In cosmetic formulations protected with preservative systems 4 and 5, after 7 days of storage, complete elimination of _L. casei_ cells was found.  
Based on the obtained results, system 1P--sodium benzoate combined with sodium salts of levulinic and anisic acid--was selected for further research due to this variant ensuring the highest level of survival of probiotic bacteria. Additionally, this system was tested in previous studies for in vitro cytotoxicity, and the studies confirmed no negative impact on the viability of HaCaT cells [44].
# How Human Mobility Models Can Help to Deal with COVID-19
## 4 Generation of Scenarios
### Basic Synthetic Scenarios  
The first set of scenarios consist of 100 pedestrians moving freely on an open square area of 100 m \(\times\) 100 m. We consider three synthetic models: Random WayPoint Model (RWP), Small World in Motion (SWIM), and RWPa, which is the RWP model with attraction points.  
For generating the traces we used the BonnMotion tool [66]. BonnMotion is a free Java software which creates and analyses mobility scenarios and is most commonly used as a tool for evaluating OppNets and MANETs. It implements most of the synthetic models described in Section 3.1. Its main objective is to create mobility traces using mobility models.  
The main parameters of the three synthetic scenarios are in Table 1. The first scenario, RWP, is a pure random scenario where pedestrians move according to the Random Waypoint model, with a random speed between 0.5 m/s and 1.5 m/s and a maximum pause of 60 s. SWIM is a more complex synthetic model, where each node is assigned a so-called home, which is a randomly and uniformly chosen point over the square area. Then, the node itself assigns to each possible destination a weight that grows with the popularity of the place and decreases with the distance from home. The parameters used for the generation are shown in Table 1.  
Finally, the last scenario, RWPa, is an extension of the basic RWP model, where instead of choosing new destinations uniformly distributed from the simulation area, we can define attraction points. This way, we can set the locations where the pedestrians are more likely to move and stay (rather than moving randomly all over the defined area). Each attraction point is defined by five values: x and y coordinates, weight, and two standard deviations that are used to determine the nodes' distances to the attraction point on both dimensions. Attraction points with higher weights will attract nodes with higher probabilities. In this scenario, we define three attraction points located in the following coordinates: (25,25), (75,75), and (60,30), with weights 1.5, 1.2, and 1.3. In all the points the two standard deviations used by the RWPa model to determine the nodes' distance are set to 10.