esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Threat Model and Security Assumptions
#### 3.2.2 Component Trust Assumptions  
We categorize the architectural components according to their trust levels as follows:  
* Trusted Components: We consider both the input agent and output agent as trusted entities within our security model. This assumption is justified by their functional simplicity, uncomplicated structure, and amenability to hardened implementation through specialized hardware or formal verification methodologies. Additionally, these components can be subjected to rigorous physical isolation and strict access control mechanisms, further reinforcing their trustworthiness. The operational simplicity of these agents permits exhaustive security verification, significantly reducing the attack surface.
* Untrusted Components: Each executor within the heterogeneous executor set is considered potentially compromised. We assume that executors may contain unknown vulnerabilities or undocumented backdoors that have not yet been identified or patched. However, we establish that executors operate within strict isolation boundaries, preventing lateral movement or cross-contamination between execution environments.
* Semi-trusted Components: The arbitration and scheduling module is classified as semi-trusted. While we assume it executes its designated algorithms faithfully and performs arbitration functions according to specification, we acknowledge inherent privacy vulnerabilities in its operation. Since this module necessarily processes outputs from all executors and makes critical system decisions, its complexity exceeds that of the input/output agents, making it difficult to implement through pure hardware solutions or to verify exhaustively.
# Analysis of Lightweight Cryptographic Algorithms on IoT Hardware Platform +
###### Abstract
The motivation for analyzing the performance of lightweight cryptography algorithms is to identify which algorithms are the most efficient and secure for use in resource-constrained devices, such as IoT devices, mobile devices, and embedded systems. This allows for the selection of the best algorithm for a given application and for the optimization of the implementation of the chosen algorithm to minimize resource usage and increase security. Additionally, it also helps in identifying any weaknesses or vulnerabilities in the algorithm that could be exploited by attackers. The rest of this paper is structured as follows: Section 2 provides a literature review about the work conducted related to the implementation of an LWC using different hardware platforms. Section 3 details the software and hardware setup performed, the measuring metrics used, and the methodology applied to evaluate the communication and computation cost of implementing such schemes. Section 4 presents the results of the experimentation, and Section 5 analyzes and discusses the results achieved. Finally, Section 6 concludes the paper and presents areas of interest for further research.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 5 Conclusions  
This paper presents an anti-recompression video watermarking algorithm based on H.264/AVC. The algorithm optimizes subblock selection and watermark embedding with the proposed modulation method to minimize the number of subblock modifications. In experiments, the SSIM of the algorithms are above 0.99, the NC values are greater than 0.9, BER is 0.0271--lower than the comparison algorithms. The experimental results show that the proposed algorithm can resist the recompression and requantification attacks well, and the video quality changes little. Compared with other similar methods, it has more advantages.  
The algorithm in this paper can be used to solve the security and copyright problems in the current video compression domain, and can be used to identify and protect the copyright of images in videos. At present, the information such as subblock position and modulation method is also needed in the watermark extraction. It is a non-blind watermark. Future research continues on blind extraction of watermarks and examination of algorithm performance under noise, clipping and other image attacks.  
[PERSON] the first author, proposed and completed the algorithm to guide the experiment and data analysis. Revising and improving the paper. [PERSON] the second author, proposed and improved algorithms, and was responsible for experiments and data sorting. Wrote this paper. [PERSON] the third author, assisted in experiments and data processing, and was responsible for drawing tables, etc. [PERSON] the forth author, assisted in the collaborative paper, improved the chart formula, and checked the whole paper. [PERSON] the corresponding author, supported the proposal and improvement of algorithms, and guided the ideas and schemes of experiments and data analysis. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the following projects: Scientific research project of National Language Commission (YB135-125).  
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
## 3 Results
### Changes in Sensory Pasta Properties  
Sensory evaluation of the pasta samples by German panelists revealed a significant decrease in the typical pasta odor for all samples except samples dG, AF and 3% CV + dG, in comparison with the control (Table 4). In contrast, the fishy/off odor and the after/off taste increased for all samples except dG (odor and taste), AF (taste) and 3% CV + dG (taste). The sample 5% CV + AF was perceived as significantly saltier and all samples were perceived to have a higher bite resistance whereby the difference was only significant for samples 3% CV, 5% CV + dG, 3% CV + AF, 5% CV + AF, and 3% CV + dG + AF due to large SDs. The stickiness of all samples was perceived as comparable and only the dG sample did not differ in color from the control. A PCA, describing 88.97% of the variance in the first two principal components, shows a similar result (Figure 3). Typical odor is in the opposite direction to all taste and smell attributes, as well as bite resistance, and the control, dG, and AF samples are closest to it. With regard to the question of acceptance as a newly developed commercial product, the addition of 3 and 5% CV alone led to a decrease in acceptance from 91% to 27 and 36%, respectively, whereas the addition of dG and AF caused no or only slight change in acceptance, respectively. Simple mixtures of CV + dG or CV + AF increased the acceptance compared with CV samples. Mixtures of all the ingredients were the least acceptable. A multiple logistic regression analysis (MLRA, Supplementary Data, Table S4) showed no correlation between acceptance and any attribute. According to the PCA (Figure 3), reduced stickiness (although not significant) seems to be the main reason for the low acceptance of 3%CV + dG + AF besides the changes in taste, odor, and bite resistance. Figure 3: Principal component analysis of the sensory results of trained German panelists. The first two principal components explain 88.97% of the variance. AF, apple fiber; CV, _C. vulgaris_; dG, denatured gluten.
# Gene Editing with CRISPR/Cas Methodology and Thyroid Cancer: Where Are We?
## 4 Gene Editing with CRISPR/Cas in Thyroid Cancer Research
### Targeting Coding Genes in Thyroid Cancer
#### 4.1.4 Epigenetics  
Chromatin accessibility changes due to posttranscriptional modification in histones and DNA per se may lead to changes in gene expression. At the histone levels, the effect of methylation and acetylation depends on the histone tail position with transcription activation output for acetylation at H3K4 or methylation at H3K36, and transcriptional inhibition for trimethylation at H3K27, for example [61].  
High levels of histone deacetylases (HDAC) were detected in PTC and ATC, and the blockage of HDAC function with specific inhibitors induced apoptosis in the transformed thyroid cells [62] and redifferentiation by restoring genes, such as _NIS_ (_SLC5A5_), in ATC cells [63]. Moreover, the double-knock out of _HDAC1_ and _HDAC2_ using CRISPR/Cas9 showed a strong induction of histone acetylation, cell cycle arrest, and apoptosis in ATC cells [64]. Another protein involved in histone acetylation and chromatin accessibility is Bromodomain containing 4 (BRD4) [65]. The CRISPR/Cas9-mediated _BRD4_ gene editing resulted in reduced cell viability and proliferation by downregulating c-Myc and cyclin D1 in PTC cells [66]. Moreover, this effect was mimicked by ARV-825, a specific inhibitor of BRD4, in vitro and resulted in tumor growth inhibition in vivo.  
Histone methylation is another mechanism involved in gene regulation. For example, ASH1, such as histone lysine methyltransferase (ASH1L), a member of Trithorax group proteins that usually activates gene expression, dimethylates histone H3 at lysine 36, forming H3K36 me2. Furthermore, ASH1L is highly expressed in ATC and is regulated by _miR-200b-3p_, a miRNA poorly expressed in this type of cancer. _ASH1L_ knockout with CRISPR/Cas9 caused reduction of ATC cell growth in vitro and significantly reduced tumor growth in vivo, but it caused extensive loss of H3K36 dimethylation, especially at _CCAT1_ lncRNA [67]. Indeed, _CCAT1_ lncRNA knockdown mimicked the _ASH1L_ knockout effects in cells, indicating a role of this lncRNA in ATC biology.  
Importantly, the CRISPR/dCas9 system can modulate epigenetics (explored in Section 2.4 New Applications for CRISPR/Cas: Tailoring Cas Activity) when dCas9 is fused with chromatin activators (e.g., VPR) or inhibitors (e.g., KRAB) that modify chromatin accessibility in the sgRNA-targeted region, and result in the induction or repression of gene expression, respectively.
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
###### Abstract  
High-accuracy and dependable prediction of the bias of space-borne atomic clocks is extremely crucial for the normal operation of the satellites in the case of interrupted communication. Currently, the clock bias prediction for the Chinese BeiDou Navigation Satellite System (BDS) remains still a huge challenge. To develop a high-precision approach for forecasting satellite clock bias (SCB) in allusion to analyze the shortcomings of the exponential smoothing (ES) model, a modified ES model is proposed hereof, especially for BDS-2 satellites. Firstly, the basic ES models and their prediction mechanism are introduced. As the smoothing coefficient is difficult to determine, this leads to increasing fitting errors and poor forecast results. This issue is addressed by introducing a dynamic \"thick near thin far (TNTF)\" principle based on the sliding windows (SW) to optimize the best smoothing coefficient. Furthermore, to enhance the short-term forecasted accuracy of the ES model, the gray model (GM) is adopted to learn the fitting residuals of the ES model and combine the forecasted results of the ES model with the predicted results of the GM model from error learning (ES + GM). Compared with the single ES models, the experimental results show that the short-term forecast based on the ES + GM models is improved remarkably, especially for the combination of the three ES model and GM model (ES3 + GM). To further improve the medium-term prediction accuracy of the ES model, the new algorithms in ES with GM error learning based on the SW (ES + GM + SW) are presented. Through examples analysis, compared with the single ES2 (ES3) model, results indicate that (1) the average forecast precision of the new algorithms ES2 + GM + SW (ES3 + GM + SW) can be dramatically enhanced by 49.10% (56.40%) from 5.56 ns (6.77 ns) to 2.83 ns (2.95 ns); (2) the average forecast stability of the new algorithms ES2 + GM + SW (ES3 + GM + SW) is also observably boosted by 53.40% (49.60%) from 8.99 ns (16.13 ns) to 4.19 ns (8.13 ns). These new coupling forecast models proposed in this contribution are more effective in clock bias prediction both forecast accuracy and forecast stability.  
BeiDou satellite navigation system (BDS); satellite clock bias (SCB) prediction; exponential smoothing (ES) method; smoothing coefficient; thick near thin far (TNTF) principle; sliding windows (SW) +
Footnote †: journal: Applied Sciences
## 3 Vertical Displacement of Cantilever Beam by IHAM
### Vertical Displacement at Free end of Cantilever Beam
#### 3.3.4 30 th-Order Approximation Solution of Vertical Displacement
The relative error in Table 4 is calculated by  
\[\Delta=\frac{\left|V_{b}^{[n=30]}-V_{b}\right|}{V_{b}}\times 100\% \tag{3.45}\]  
When the relative error \(\Delta\leq 1\%\), the convergent regions of the 30 th-order approximation by HAM (\(\varepsilon_{1}=0\)) are  
\[\alpha\in[0,4.6],\ V_{b}\in[0,0.70],\ 0\leq\delta_{v}\leq 0.70L \tag{3.46}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(\kappa\)} & \multirow{2}{*}{**Exact Solution**} & \multicolumn{2}{c}{\(\varepsilon_{1}=0\)} & \multicolumn{2}{c}{\(\varepsilon_{1}=24(\pi-2)/\pi^{3}\)} \\  & & **(HAM)** & & **(IHAM)** & \\ \cline{2-6}  & \(V_{b}\) & \(V_{b}\) & \(\Delta\) & \(V_{b}\) & \(\Delta\) \\ \hline
0 & 0 & 0 & 0 & 0 & 0 \\ \hline  
\(\infty\) & 1 & 1 & 0 & 1 & 0 \\ \hline \end{tabular}
\end{table} 
Table 4: 30 th-order numerical solutions \(V_{b}^{[n=30]}\) by IHAM (\(\varepsilon=8/\pi^{2}\), \(h=-0.1\), \(h_{1}=-0.3\)) compared with exact solutions \(V_{b}\). When \(\varepsilon_{1}=24(\pi-2)/\pi^{3}\), \(h_{1}=-0.3\), the 30 th-order approximation of \(V_{b}\) by IHAM is  
\[ \begin{array}{l}V_{b}=\frac{1}{3}\alpha-3.6198\times 10^{-2}\alpha^{3}+6.6118 \times 10^{-3}\alpha^{5}-1.2700\times 10^{-3}\alpha^{7}\\ +2.2683\times 10^{-4}\alpha^{9}-3.5802\times 10^{-5}\alpha^{11}+4.8726\times 10^{-6} \alpha^{13}\\ -5.6396\times 10^{-7}\alpha^{15}+5.5006\times 10^{-8}\alpha^{17}-4.4899\times 10^{-9} \alpha^{19}\\ +3.0512\times 10^{-10}\alpha^{21}-1.7202\times 10^{-11}\alpha^{23}+8.0374 \times 10^{-13}\alpha^{25}\\ -3.1168\times 10^{-14}\alpha^{27}+1.0073\times 10^{-15}\alpha^{29}-2.7301 \times 10^{-17}\alpha^{31}\\ +6.2517\times 10^{-19}\alpha^{33}-1.2190\times 10^{-20}\alpha^{35}+2.0389 \times 10^{-22}\alpha^{37}\\ -2.9432\times 10^{-24}\alpha^{39}+3.6841\times 10^{-26}\alpha^{41}-4.0095 \times 10^{-28}\alpha^{43}\\ +3.7952\times 10^{-30}\alpha^{45}-3.1164\times 10^{-32}\alpha^{47}+2.2056 \times 10^{-34}\alpha^{49}\\ -1.3297\times 10^{-36}\alpha^{51}+6.6933\times 10^{-39}\alpha^{53}-2.7186 \times 10^{-41}\alpha^{55}\\ +8.3645\times 10^{-44}\alpha^{57}-1.7196\times 10^{-46}\alpha^{59}+1.7421 \times 10^{-49}\alpha^{61}\end{array}  \tag{3.47}\]  
When the relative error \(\Delta\leq 1\%\), the convergent regions of the 30 th-order approximation by IHAM (\(\varepsilon_{1}=24(\pi-2)/\pi^{3}\)) are  
\[\alpha\in[0,5.1],\ V_{b}\in[0,0.72],\ 0\leq\delta_{v}\leq 0.72L \tag{3.48}\]  
From Table 4, the improved homotopy analysis method extends the convergence region from \(\alpha\in[0,4.6]\) by the homotopy analysis method [32] to \(\alpha\in[0,5.1]\), which is an 11% increase. From the above comparison, it can be seen that the expansion rate of the convergence region for vertical displacement by IHAM against that by HAM increases with the increasing iterations.
# A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm
## 3 New Proposed Cascade-Correlation Growing Deep Learning Neural Network Algorithm (CCG-DLNN)
### The Convergence Proof  
**Theorem 1**.: _There exists a weight setting for neurons within the newly added layer 1 and the output neurons in the CCG-DLNN algorithm such that the number of patterns misclassified by the network after the addition of the N neurons to the l layer and the retraining of the output weights is less than the number of patterns misclassified before that (i.e., \(E_{t}<E_{t-1}\))._  
Proof.: At iteration t, the loss function \(L(\varphi_{t})\) will be:  
\[L(\varphi_{t})=\frac{1}{S}\sum_{m=1}^{S}(E^{m}(\varphi_{t}))^{2} \tag{8}\]  
where:  
\[E^{m}(\varphi_{t})=y_{t}^{m}-o^{m} \tag{9}\]  
For hidden layer \(l\):  
\[g_{t}^{l,m}=\varphi_{t}h_{t}^{l-1,m} \tag{10}\]  
if \(l=N_{HL}+1\), then:  
\[y_{t}^{m}=\varphi_{t}h_{t}^{N_{HL},m} \tag{11}\]  
from [31], we have:  
\[\lim_{t\rightarrow\infty}\|\
abla L(\varphi_{t})\|=0 \tag{12}\]  
So:  
\[L(\varphi_{t})\leq L(\varphi_{t-1}) \tag{13}\]  
from Equation (8):  
\[\frac{1}{S}\sum_{m=1}^{S}(E^{m}(\varphi_{t}))^{2}\leq\frac{1}{S}\sum_{m=1}^{S }(E^{m}(\varphi_{t-1}))^{2} \tag{14}\]  
Let us dispose of the summing symbol for simplicity. The summation symbol is important, particularly for the definition of the stochastic gradient descent (SGD) versus batch gradient descent. In the batch gradient descent, we focus only on the error of all the training examples at once when we look at each error at a time in the SGD. However, just to keep things easy, we are going to assume that we are referring to each error one at a time:  
\[\frac{1}{S}(E^{m}(\varphi_{t}))^{2} \leq\frac{1}{S}(E^{m}(\varphi_{t-1}))^{2} \tag{15}\] \[(E^{m}(\varphi_{t}))^{2} \leq(E^{m}(\varphi_{t-1}))^{2}\] (16) \[E^{m}(\varphi_{t}) \leq E^{m}(\varphi_{t-1}) \tag{17}\]  
from Equation (9):  
\[y_{t}^{m}-o^{m} \leq y_{t-1}^{m}-o^{m} \tag{18}\] \[y_{t}^{m} \leq y_{t-1}^{m} \tag{19}\]  
from Equation (11):  
\[\varphi_{t}h_{t}^{N_{HL},m} \leq\varphi_{t-1}h_{t-1}^{N_{HL},m} \tag{20}\]  
If we add new one neuron to the last hidden layer, then \(y_{t}^{m}\) will be:  
\[y_{t}^{m}=y_{t-1}^{m}+\varphi_{new}h_{new} \tag{21}\]  
where \(\varphi_{new}\) is the weight from the newly added neuron to the output neuron and \(h_{new}\) is the output of the newly added neuron.  
So Equation (19) will be:  
\[y_{t-1}^{m}+\varphi_{new}h_{new}\leq y_{t-1}^{m} \tag{22}\]So:  
\[\varphi_{new}h_{new}\leq 0 \tag{23}\]  
After adding a new neuron and from Equation (9):  
\[E^{m}_{new}(\varphi_{t})=y^{m}_{new,t}-o^{m} \tag{24}\]  
from Equation (21):  
\[E^{m}_{new}(\varphi_{t})=y^{m}_{t-1}+\varphi_{new}h_{new}-o^{m} \tag{25}\]  
\[E^{m}_{new}(\varphi_{t})=E^{m}(\varphi_{t-1})+\varphi_{new}h_{new} \tag{26}\]  
from Equation (23):  
\[E^{m}_{new}(\varphi_{t})\leq E^{m}(\varphi_{t-1}) \tag{27}\]  
\(\square\)  
So the number of unclassified patterns by the network after adding the \(N\) neurons in layer \(l\) and the extra training of the output weights \(E^{m}new(\varphi t)\) is less than the amount of patterns unclassified before that.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Data Preprocessing
#### 3.2.1 Mask Preprocessing  
Mask preprocessing plays a crucial role in consolidating the left and right lung labels into a single mask for enhanced segmentation accuracy. The merging process involves equating the pixel values of the left lung mask and right lung mask, resulting in a unified lung mask that includes both lung regions. By incorporating this preprocessing step, the resulting lung and infection masks exhibit three distinct pixel values: 0 represents the background, 1 represents the lung, and 2 represents the infection. This merging process aims to assist the model in avoiding errors in distinguishing between the left and right lungs. The identification of the left lung and the right lung can be seen by projecting the prediction results of the model in 3D. The following in Table 3 is an example of a lung and infection mask before and after mask preprocessing.  
\begin{table}
\begin{tabular}{c c c} \hline
**3D Projection of** & **CT-Scan** & **CT-Scan Slice** & **Lung and Infection Mask** \\ \hline \end{tabular}
\end{table}
Table 1: Three samples from the used dataset [14].
## 3 Results
### Expression Analysis of ADGRG3 Gene in Various Cancers
#### 3.1.3 Expression of GPR97 in CHOL Based on Clinical Attributes  
In cholangiocarcinoma (CHOL), ADGRG3 (GPR97) expression was evaluated across various clinical and demographic factors. Based on the sample types (Figure 3A), GPR97 expression was found to be significantly higher in primary tumor samples (\(n=36\)) compared to normal samples (\(n=9\)), indicating an upregulation of ADGRG3 in CHOL tumor tissues. When examined across individual cancer stages (Figure 3B), GPR97 expression peaked in stage 2 (\(n=9\)), suggesting increased expression in early-to-mid stages of CHOL, with a decline observed in later stages.  
Gender-based analysis (Figure 3C) revealed that both male (\(n=16\)) and female (\(n=20\)) patients had elevated GPR97 expression compared to patients with normal samples, with a slightly higher median expression in males. Age-based analysis (Figure 3D) showed the highest expression levels in younger patients (21-40 years, \(n=2\)), with decreasing expression in older age groups, as follows: 41-60 years (\(n=12\)) and 61-80 years (\(n=20\)). The 81-100-year-old group (\(n=2\)) showed slightly elevated GPR97 expression compared to patients with normal samples but lower expression than the younger age groups.  
Figure 2: Expression of GPR97 in LAML based on (**A**) the French–American–British classification, (**B**) patients’ gender, and (**C**) patients’ age. The higher GPR97 expression was observed in subtypes M2 (\(n=39\)), M6 (\(n=2\)), and M7 (\(n=3\)), than other subtypes and the highest expression in the 21–40-year-old group (\(n=34\)).  
These findings indicate that GPR97 is upregulated in CHOL tumors, with variability across cancer stages, genders, and age groups. Elevated expression in early-to-mid stages and younger patients may point to the potential role of ADGRG3 in CHOL tumorigenesis and progression, warranting further investigation.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 3 Methods
### Algorithm  
In this section, we propose a new Hybrid Genetic Algorithm (HGA) to address the aforementioned problem. The HGA simulates the evolutionary process of \"survival of the fittest\" observed in nature, thereby aiding in obtaining the optimal solution.