esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Degradation and Life Prediction of Polyethylene
## 5 Prediction Techniques for Polyethylene Materials
### Equal Conversion Rate Method
#### 5.3.1 Ozawa-Flynn-Wall (OFW) Method  
Based on the mass loss and temperature data of the polyethylene material at various heating rates, the Ozawa-Flynn-Wall (OFW) approach calculates the activation energy \(E_{a}\) of the thermal degradation process [124]. The complexity of the decomposition mechanism can be ascertained using this method, which does not necessitate prior knowledge of the steps of the degradation mechanism used by the material. Instead, the activation energy for various conversion rates can be assessed. The conversion rate \(\alpha\) and the reaction model \(g(\alpha)\), which are integrated based on the Doyle approximation, are considered constants in this method despite variations in the heating rate [125] as shown in Equation (19) [126; 127]:  
\[\ln\beta=\ln\frac{AE_{a}}{Rg(\alpha)}-5.331-1.052\frac{E_{a}}{RT_{p}} \tag{19}\]  
where \(\beta\) is the heating rate, \(E_{a}\) is the activation energy, \(R\) is the gas constant, and \(T_{p}\) is the peak temperature. The experimental thermal spectra captured at the heating rate can be used to derive a linear regression of \(\ln\beta=f(1/T)\), and the slope of the straight line can be used to calculate the activation energy \(E_{a}\) at a constant conversion rate \(\alpha\)[126].  
The Ozawa-Flynn-Wall (OFW) method can be used in reaction systems where the activation energy changes over time; however, it may not work when various reaction types with various activation energies coexist. Additionally, competitive responses involving a range of different products cannot be studied using the Ozawa-Flynn-Wall (OFW) Method [128].
## 2 Materials and Methods
### Search Strategy  
An electronic database search was performed relating to the effects of laser PBM application associated with surgical tooth removal, TMJDS or orthodontic treatment. The data platforms used were ScienceDirect, PubMed, Google Scholar, Cochrane, Scopus and EBSCO, using the following MeSH terms and Boolean operators: (Photobiomodulation OR PBM OR LLLT OR Low level Laser) AND (soft tissue OR oral surgery OR buccal mucosa) for oral surgery, (Photobiomodulation OR PBM OR LLLT OR Low level Laser) AND (orthodontic OR maxillary expansion) for orthodontic tooth movement, (Photobiomodulation OR PBM OR LLLT OR Low level Laser) AND (TMJ) for TMJ, published after 2018. The last search for recently published papers was carried out in February 2024.  
The initial article scanning delivered a total of 4491 for oral surgery, 5104 for orthodontic tooth movement and 5337 for TMJ.  
After removing ineligible trial articles and duplicated reports, the remaining articles were 82 for oral surgery, 87 for orthodontic tooth movement and 92 for TMJ.  
Subsequently, the titles and abstracts of these articles were independently screened by three reviewers (SP, EA, VM), using the inclusion and exclusion criteria listed below. Any disagreements that arose during this process were resolved through discussion between the researchers involved.  
The inclusion criteria were as follows:  
* Randomised clinical trials;
* Laser PBM therapy associated with wavelengths in the range of 445-1064 nm;
* Articles were written in the English language;
* Control group and appropriate conventional non-PBM therapy;
* A minimum of 10 patients/samples per group;
* An adequate and appropriate protocol description. The exclusion criteria were as follows:
* Laser wavelength outside the range 445-1064 nm;
* Case studies;
* Narrative review papers;
* Languages other than English;
* Pilot studies and/or case series;
* Experimental studies;
* Animal studies;
* Conference presentation papers or book chapters;
* Editorial articles or opinions;
* Short notes or comments in erratum;
* Press articles in the press;* Non-retrievable studies.  
After the implementation of these criteria, 54 studies were included in this systematic review, spread across oral surgery (n = 19), orthodontic tooth movement (n = 15) and TMJ (n = 20).  
In accordance with the PRISMA 2020 statement [22], the details of the selection criteria are presented in Figures 1-3.  
Figure 1: PRISMA flow-chart of selected criteria for the included oral surgery studies.  
Figure 2: PRISMA flow-chart of selected criteria for the included orthodontic studies.
## 1 Introduction
Systems using higher-order modulation formats can enhance the AIRs and spectral efficiencies, but will be more seriously deteriorated by EEPN. It is also found that the selection of a better-quality LO laser can greatly reduce the bandwidth requirement and the computational complexity in the MC-DBP. This paper is organized as follows. In Section 2, the numerical transmission system, the principle of EEPN, the mutual information (MI), and the modulation error ratio (MER) are introduced. In Section 3, simulations and results under different scenarios are analyzed. In Section 4, additional insights and discussions are provided. The conclusions are summarized in Section 5.
# Modeling and Resolving Uncertainty in DIKWP Model
## 4 Problem Processing
### Concept Space
#### 4.1.4 Operation  
The operation of Concept Space involves a series of actions performed within the Concept Space to query, add, or modify concepts and their relations.  
* **Query operation:** The querying operation involves retrieving a relevant set of concepts within the Concept Space based on query conditions \(q\) (such as specific attributes or relations). It can be expressed as follows: \[Query(V_{ConC},E_{ConC},q)\rightarrow\{v_{1},v_{2},\ldots,v_{m}\}\] (18) where \(V_{ConC}\) and \(E_{ConC}\) were defined in Section 4.1.1. We can utilize the aforementioned equation to query all concepts related to HFT, for instance, retrieving all companies employing HFT within a certain order-to-trade ratio range.
* **Add operation:** We can add a new concept \(v\) to the concept set \(V_{c}\) using the following equation: \[Add(V_{ConC},v)\] (19) For example, due to the addition of a new regulation, we need to add the interpretation of this regulation to the corresponding concept set.
* **Modify operation:** Furthermore, we can maintain the relevant attributes of existing concepts through the following operation: \[Update(V_{ConC},v,A(v)).\] (20) For example, due to changes in the thresholds for HFT stipulated in the regulations, we need to modify the threshold attribute in the HFT definition clause to update the Concept Space.  
Through the above formal representation, HFT firms can more clearly identify and understand the specific impacts of the Dodd-Frank Act on their businesses, particularly in terms of definition misunderstandings, regulatory ambiguity, inconsistent enforcement, and missing details in the act's description. This aids companies in formulating more effective strategies to ensure compliance while optimizing their trading strategies and operational efficiency.
## 5 Conclusions  
In this research, we introduce a novel RGB-to-RGB image steganography algorithm that incorporates the Josephus permutation into the LSB 3-3-2 technique. While the Josephus permutation has been extensively utilized in image cryptography, its application in image steganography is novel. Our study differentiates itself by leveraging the Josephus permutation to enhance the randomness and security of the embedding process. Previous studies have primarily focused on embedding text or grayscale images using various LSB schemes. However, the use of basic logistic maps in LSB techniques has shown vulnerabilities to steganalysis due to their predictable patterns, highlighting the need for increased randomness to ensure better security.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cover Image** & **Secret Image** & **Embed Time (s)** & **Extract Time (s)** \\ \hline lenna & rainbow & 0.8172 & 0.7864 \\ malamute & rainbow & 0.7946 & 0.7634 \\ malamute & monarch & 22.8363 & 22.7879 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Average insertion and extraction times.  
Our implementation initially employed the chaotic logistic map function within the range \(3.5688<r<4\) to generate the keystream. The effectiveness of this keystream generation was assessed using the Lyapunov exponent and bifurcation diagram, ensuring the parameters induced chaotic behavior. The generated keystream was then scrambled using the Josephus permutation, providing enhanced randomness. This step significantly mitigated the risk of detection by steganalysis, as the Josephus permutation introduced non-repetitive sequences, improving the scrambling effects and permutation efficiency.  
Our results demonstrate that even a slight difference between the key used for embedding and the one used for extraction results in a completely different extracted image compared to the original secret image. This property enhances the security of the technique against brute force attacks. The randomly shuffled key sequence from the Josephus permutation was employed to determine pixel distribution locations in both the embedding and extraction processes using the LSB 3-3-2 embedding technique.  
For RGB secret image schemes, the LSB 3-3-2 technique yields a payload approximately 2.67 times larger than LSB-1 and around 1.33 times larger than LSB-2. Although LSB 3-3-2 has a payload size 1.5 times smaller than that of LSB-4, this reduced payload is compensated for by the higher quality of the stego image. Our analysis of the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) between the stego and cover images reveals satisfactory results (MSE below 10.0 and PSNR above 40 dB). For the secret versus extracted images, the MSE is 0.0 and the PSNR is infinite, indicating successful extraction with high fidelity.  
By addressing these gaps, our proposed method advances the field of image steganography by providing a more secure and efficient technique for hiding color images. This research offers practical implications for its application in secure communications, ensuring that the embedded data remain well protected against various types of attacks.
## 2 Related Work  
### Recreating Real-World Scenes from Photographs  
#### 2.1.1 Issues with Single-Viewpoint Capture  
To recreate real-world scenes as photorealistic background environments for VR, many authors today rely on the use of 360\({}^{\circ}\) capture devices [7; 11; 12; 13]. Indeed, consumer-grade 360\({}^{\circ}\) cameras are light, easy to use, cheap, and capable of acquiring visual data coming from all directions in a single shot. However, 360\({}^{\circ}\) visuals alone can only render the scene as perceived from a single viewpoint: moving one's head in any direction will immediately reveal the absence of geometry through the lack of motion parallax. By comparing users' perception of 360\({}^{\circ}\) videos rendered with or without depth data, [PERSON] et al. [7] thus show that users are effectively more likely to feel discomfort and VR-induced sickness when there is no recovered geometry, thereby justifying the need for adding depth when recreating scenes for VR based on input photographs. Several recent works have thus been dedicated to finding efficient ways of recovering this geometry from 360\({}^{\circ}\) photographs to provide motion parallax in VR [7; 12].
## 5 Applications and Discussion
### Planar Sedimentary Microfacies  
Through the integrated application of geological understanding with SOM multi-attribute optimization and waveform-indicated inversion techniques, a detailed characterization of beach-bar sedimentary microfacies in the Lower E3d2 Sub-member of the study area was conducted, identifying three microfacies types: bar body, beach body, and beach edge. Two distinct patterns emerge in microfacies boundary delineation: a sparsely drilled area in the western sector and a relatively well-drilled region in the eastern sector of the focus area.  
Based on geological characteristics and depositional system analysis, the eastern sector primarily employs well-controlled waveform-indicated inversion for microfacies boundary characterization. This approach involves: determining microfacies types at well locations through single-well facies analysis combined with logging facies characteristics, supported by sandstone core samples and lithological analysis; delineating sandbody extension boundaries using inversion profiles to objectively represent sedimentary microfacies distribution.  
For the western low-well-density area, a comprehensive methodology was implemented. Geological-seismic attribute analysis guided the creation of SOM-processed attribute hollow maps for the Lower E3d2 Sub-member. Seismic characteristics from drilled areas were analogously applied to undrilled zones. Integrated analysis incorporating geological data, acoustic impedance inversion, and SOM multi-attribute optimization established micro-facies assemblage relationships of beach-bar sandbodies, enabling effective sedimentary microfacies characterization in the undrilled western sector.  
The planar distribution characteristics of the beach-bar sedimentary microfacies are as follows:  
Eastern Beach-bar: Near the B8-2-a well at the northern boundary, the sandbody is relatively thick and the bar body is well-developed, with the same extension direction as the western beach-bar. The outer beach body extends in the SW-NE direction along the direction of lake wave reworking. One large-scale and two small-scale beach bodies  
Figure 13: Composite SW-NE well sections and inversion sections for the Lower E3d2 Submember in HHK Depression (Selection of well logging curves: SP-Spontaneous Potential Log (unit mV); GR-Gamma Ray Log (unit API). (**a**) Logging Interpretation Profile; (**b**) Seismic Inversion Profile.  
develop to the south, with the beach edge surrounding the beach body in a south-north strip-like distribution (Figure 14).  
Western Beach-bar: Near the C3-1-a well, the sandbody is relatively thick and the bar body is well-developed, showing an enhanced northward extension trend. A large-scale beach body is present in the form of a wide strip, and a smaller-scale beach body is developed in the north in the form of a narrow strip. The beach edge develops around the beach body, showing a near-NS strip-like distribution (Figure 14).
# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 4 Experimental Studies
### Aggregate Classification  
In the PHQ-8 scale [38], the scoring is as follows: 0-4 points indicate no depression, 5-9 points suggest mild depression, 10-14 points indicate moderate depression, and a score of 15 or higher indicates moderate to severe depression. The four types of pseudo-labels (1, 2, 3, and 4) correspond to different PHQ-8 scores, denoted as \(\text{PHQ}_{\text{s}}\). The general depression consultation data will only provide tags corresponding to the entire duration of the long video. To transform the depression classification task of long videos into the score classification task of segmented short time series units, a new label must be created for each unit. Based on depression-related research findings, the short-sequence units corresponding to pseudo-label 4 have been found to be more significant than those corresponding to pseudo-labels 1, 2, and 3 in assessing depression. However, the frequency of facial changes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input** & **Operator** & **\#Exp** & **\#Out** & **SE** & **Stride** \\ \hline \(64^{2}\times 20\) & Conv2d 3 \(\times\) 3 & - & 84 & - & 2 \\ \(32^{2}\times 84\) & G-bneck & 120 & 84 & 1 & 1 \\ \(32^{2}\times 84\) & G-bneck & 240 & 84 & 0 & 2 \\ \(16^{2}\times 84\) & G-bneck & 200 & 84 & 0 & 1 \\ \(16^{2}\times 84\) & G-bneck & 184 & 84 & 0 & 1 \\ \(16^{2}\times 84\) & G-bneck & 184 & 84 & 0 & 1 \\ \(16^{2}\times 84\) & G-bneck & 480 & 112 & 1 & 1 \\ \(16^{2}\times 112\) & G-bneck & 672 & 112 & 1 & 1 \\ \(16^{2}\times 112\) & G-bneck & 672 & 160 & 1 & 2 \\ \(8^{2}\times 160\) & G-bneck & 960 & 160 & 0 & 1 \\ \(8^{2}\times 160\) & G-bneck & 960 & 160 & 1 & 1 \\ \(8^{2}\times 160\) & G-bneck & 960 & 160 & 0 & 1 \\ \(8^{2}\times 160\) & G-bneck & 960 & 160 & 1 & 1 \\ \(8^{2}\times 160\) & Conv2d 1 \(\times\) 1 & - & 960 & - & 1 \\ \(1^{2}\times 960\) & AvgPool 8 \(\times\) 8 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of GhostNet network.  
corresponding to label 4 is lower throughout the entire video. Thus, we developed a score aggregation function for adjusting the weights of various pseudo-labels during the aggregation process.  
Assuming that video \(V\) has \(l\) short time series units, the model output label of the \(i\)th (\(0<i\leq l\)) short time series unit is denoted as \(v_{n}\). All short sequential units can be expressed as \(V=\{v_{1},v_{2},v_{3},...,v_{l}\}\). Then, the expression of the score aggregation function is as follows:  
\[\hat{s}_{V}=\frac{1}{l}\sum_{i=1}^{i=l}\begin{cases}\rho_{1}\times v_{i}&(if\ v_{i}=1)\\ \rho_{2}\times v_{i}&(if\ v_{i}=2)\\ \rho_{3}\times v_{i}&(if\ v_{i}=3)\\ \rho_{4}\times v_{i}&(if\ v_{i}=4)\end{cases} \tag{22}\]  
For the weight parameters \(\rho_{1}\), \(\rho_{2}\), \(\rho_{3}\), and \(\rho_{4}\), we make selections via the differential evolution algorithm [39]. Finally, we determine whether the corresponding patient suffers from depression by judging whether the aggregated score \(\hat{s}\) is greater than the set threshold.
## 2 Theoretical Foundation and Hypotheses Development  
Central bank digital currencies are a type of digital currency issued by a country's central bank and linked to its fiat currency [20]. CBDCs can be implemented using both centralized and decentralized architecture [21]. Several nations are currently investigating the prospective applications of CBDCs, their risks, and the impacts of these advances on their economies, monetary systems, and stability. One potential application of CBDCs is in facilitating cross-border settlement [7]. Some international payment challenges stem from the currency exchange process, differences in various countries' legal and technological infrastructure, problems related to time zones, and coordination issues among intermediaries [22]. The literature on central bank digital currencies addresses various aspects. Some studies have focused on understanding the drivers and inhibitors of the adoption of CBDCs by users. Studies have been conducted to understand the privacy perceptions of consumers [20], the role of trust in users' CBDC adoption [14; 23], the public perception of CBDCs [16], and performance expectations [23]. The other portion of the literature focuses on the relationship between CBDCs and cryptocurrencies. Various studies have investigated the impact of central bank digital currency news on stock and cryptocurrency markets and the directional predictability between CBDCs and major cryptocurrencies [10; 11; 12].  
The literature also addresses the technological risks and benefits of CBDCs. Several technical advantages have been identified, such as the potential of the CBDCs to stabilize the economic fluctuations caused by the negative interest rate policy [24], aiding tax officials in keeping track of citizens' tax payments, and in the battle against tax evasion and fraud [25]. Furthermore, CBDC design attributes such as budgeting usefulness, anonymity, the bundling of bank services, and rate of return [17] are believed to drive demand. However, a digital currency must first comply with state regulations to be adopted in any state. Studies have noted several concerns such as data protection, digital divide [25], and cybersecurity risks [15]. Table 1 summarizes a few noteworthy studies.  
Whether information technologies have distinct penetration and speed trajectories in different countries is still open to investigation and no unified framework currently exists. Central bank digital currencies are gaining traction worldwide because of their potential to address issues such as trust, verification, and cross-border transaction costs [20]. In this regard, we investigated the technological, legal, environmental, and economic factors that determine the CBDC adoption status in nations, along with corresponding hypotheses.
## 3 Results
### Comparison between Jump Tests  
Results from ANOVA showed no significant differences across all metrics were present between two test sessions during CMJ (\(p\geq 0.129\)), DJ (\(p\geq 0.079\)), and CMRJ (\(p\geq 0.161\)) with the ES values presented in Table 1. The jump height was not significantly different between CMJ and CMRJ1 (\(p\geq 0.118\)) and DJ and CMRJ2 (\(p\geq 0.812\)) during both test sessions 1 and 2. For the strategy-based metrics, the TTTO from the CMJ was significantly longer than CMRJ1 by 0.07 s in test session 1 (\(p=0.022\), \(g=0.62\)) and 0.06 s in session 2 (\(p=0.027\), \(g=0.49\)). Moreover, CM depth from the CMJ was not significantly deeper by 0.02 m in both sessions 1 (\(p=0.304\), \(g=0.27\)) and 2 (\(p=0.192\), \(g=0.32\)) compared to the CMRJ1. The nonsignificant small differences in the GCT between the DJ and CMRJ2 were observed in both sessions 1 (\(p\leq 0.308\), \(g=-0.24\)) and 2 (\(p\leq 0.324\), \(g=-0.24\)). The comparisons of kinetic variables from different jump actions were also presented in Table 1, and Figures 2-4. In both test sessions, the negative hip (\(p\leq 0.019\), \(g\leq 0.62\)) and knee (\(p\leq 0.035\), \(g\leq 0.58\)) joint works and positive hip joint works (\(p\leq 0.029\), \(g\leq 0.54\)) were found to be significantly larger in the CMJ than CMRJ1. No significant differences were revealed in other joint work and individual joint contributions between CMJ and CMRJ1 (\(p\geq 0.137\)). Compared to the CMRJ2, the positive (\(p\leq 0.034\), \(g\leq 0.54\)) and negative knee work (\(p=0.005\), \(g\leq 0.71\)) from the DJ were significantly smaller in both sessions. A significantly larger ankle contribution to the total positive work was observed in DJ than CMRJ2 in both sessions 1 (\(p=0.048\), \(g=0.53\)) and 2 (\(p=0.042\), \(g=0.43\)).
## 2 Experimental Section
### Data
#### 2.2.4 Spectral Vegetation Indices  
NDVI (Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge), and NDWI (Normalized Difference Wetness Index), were calculated to inform our simple models of GPP and to test our hypotheses. NDRE and NDWI are only available using RapidEye and Landsat ETM+, respectively, and indicated by their subscripts in Table 1. The red edge here refers to a waveband on the RapidEye constellation of sensors that spans 690 to 730 nm.  
\[NDVI=\frac{NIR-RED}{NIR+RED} \tag{1}\]  
\[NDRE=\frac{NIR-Red\;Edge}{NIR+Red\;Edge} \tag{2}\]  
\[NDWI=\frac{NIR-SWIR}{NIR+SWIR} \tag{3}\]  
Figure 1: Time series of tower-measured gross primary production (**A**) and precipitation (**B**) at both the control (grey) and girdled (black) sites. RapidEye acquisition dates are indicated by the rug plot above the top plot.