esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Ototoxic Effects of Cisplatin in a Rat Model: A Dose-Response Study
###### Abstract  
Cisplatin (cis-diclorodiaminoplatin, CDDP) is a common chemotherapeutic agent for solid tumors, but its use is limited by severe side effects such as ototoxicity and nephrotoxicity. Variability in CDDP dosing and administration, along with high mortality and sensitivity in animal models, complicates experimental approaches. This study aimed to evaluate ototoxic damage in rats by comparing a single bolus versus three divided CDDP injections, also considering nephrotoxic effects. Twenty-four Sprague-Dawley rats were divided into three groups: eight received a single intraperitoneal injection of CDDP (14 mg/kg), eight received three injections (4.6 mg/kg/day), and eight were untreated controls. All CDDP-treated rats showed significant high-frequency hearing loss and morphological damage, including cochlear outer hair cell loss and renal glomerular atrophy with proximal tubule necrosis. Oxidative stress markers (nitrotyrosine and SOD1 expression) confirmed cochlear and renal alterations. Notably, the single bolus group had a 25% mortality rate and significant weight loss among survivors, unlike the other groups. This study introduces the novel finding that cumulative administration of three doses reduces mortality and weight loss while maintaining similar ototoxic and nephrotoxic effects. Therefore, cumulative administration is recommended for future studies to mitigate ototoxic and nephrotoxic damage, offering a potentially improved protocol for the administration of cisplatin.  
auditory brainstem response; drug toxicity; acute kidney injury; ototoxicity; oxidative stress; hair cells; spiral ganglion +
Footnote †: journal: Applied sciences  
article  
[FOOTNO
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 2 Literature Review
### Recovery Strategy  
The network recovery mechanism focuses on strategies to repair failed components following a network failure and restore normal network functions as quickly as possible [14]. This is essential for ensuring the continuous operation of critical network infrastructure. The network recovery mechanism can be classified into two types: spontaneous recovery and targeted recovery [14]. Spontaneous recovery is characterized by each node or link in the network having the ability to return to normal working conditions after a failure through local decision-making and independent repair. However, spontaneous recovery lacks specificity, leading to lower network-recovery efficiency. Targeted recovery aims to efficiently restore the overall structure and functionality of the network. This approach involves prioritizing failed network components based on a specific strategy and then restoring them in sequence. By taking a global view of the network and allocating resources for prioritized restoration, directed recovery achieves higher efficiency.  
In their investigation of recovery strategies based on the Motter-Lai model, [PERSON] and [PERSON] simulated traffic networks constructed from two-dimensional Random Geometric Graphs (RGGs) to examine how localized disruptions--such as the closure of specific road segments--can trigger cascading congestion throughout the network [28]. Building on this foundation, [PERSON] et al. extended the Motter-Lai model to encompass various spatial network structures, including isotropic and anisotropic topologies, and systematically explored the critical conditions under which localized overloads escalate into large-scale cascading failures, as well as the spatiotemporal characteristics of their propagation [29]. However, real-time monitoring and adjustment of load distribution require substantial computational power, leading to high costs in practical applications. It is essential to explore repair mechanisms based on network characteristics and evaluate the impact of various factors on recovery propagation.  
Although previous studies have examined various recovery strategies, most approaches overlook the operational complexity and heterogeneity of real transportation networks. They rarely incorporate real-time traffic data or behavioral aspects such as route choice, limiting their ability to design effective and adaptive recovery plans. Therefore, there is a pressing need for recovery strategies that better reflect actual network dynamics, resource constraints, and the practical challenges of post-failure restoration.
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
###### Abstract  
Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but relies on access to a substantial number of AEs, a prerequisite that entails significant computational resources and the inherent limitation of poor performance on clean data. To address these problems, this study proposes a novel approach to improve the robustness of ML-based network traffic classification models by integrating derived variables (DVars) into training. Unlike adversarial training, our approach focuses on enhancing training using DVars, introducing randomness into the input data. DVars are generated from the baseline dataset and significantly improve the resilience of the model to AEs. To evaluate the effectiveness of DVars, experiments were conducted using the CSE-CIC-IDS2018 dataset and three state-of-the-art ML-based models: decision tree (DT), random forest (RF), and k-neighbors (KNN). The results show that DVars can improve the accuracy of KNN under attack from 0.45% to 0.84% for low-intensity attacks and from 0.32% to 0.66% for high-intensity attacks. Furthermore, both DT and RF achieve a significant increase in accuracy when subjected to attack of different intensity. Moreover, DVars are computationally efficient, scalable, and do not require access to AEs.  
machine learning; adversarial attack; network traffic classification; derived variables; robustness +
Footnote †: journal:  
00 000-0001-9907-2231][PERSON]  
1 School of Electronics & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; [EMAIL_ADDRESS] (J.M.A.); [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (H.M.B.)
## 4 Experimental Design and Setup
### Datasets
** We convert the total number of refactoring changes in each class (Refact_Sum) to a binary variable (Change_Prone) that indicates whether a change has taken place in each class during the maintenance period. Refact_Sum contains the total number of source code refactoring operations that have been applied in each class in a certain observation period. The reason for this is that the number of refactoring is very low, ranging from 0 to 8 with a median of zero in all datasets (see Table 4). Change_Prone is set to TRUE if any refactoring changes were made on the class (regardless of the number) or FALSE if no refactoring changes were made. * **Step 3. ** We remove 23 refactoring metrics from the independent variables in the refactoring datasets. These are related to specific refactoring changes that have taken place and are not relevant for the prediction of change proneness and would also prejudice the outcome of the study as they identify when a change has been made. After applying these steps, we are left with (Supplementary C in the reproduction package) a new version of the refactoring datasets, referred to as the change-proneness datasets, which contain 102 metrics as independent variables and Change_Prone metric as dependent variable. The explanation of independent and dependent variables will be presented in the next two sections.
## 4 Forest Fire Emissions Estimation in China
### Quantifying Emission of Forest Fire in China using Satellite Data and Emission Model  
Forest fire is a major process and contributes to an increasing concentration of pollutants in the atmosphere, such as CO\({}_{2}\) CO, CH\({}_{4}\), and aerosols in China [35,36]. [PERSON] _et al._ estimated the amount of carbon emissions directly from forest fires in China during 1991 to 2000 using satellite data and an emission model [35]. It was concluded that during this period China's forest fires emitted CO\({}_{2}\) 74.2-104.7 Tg, CH\({}_{4}\) 1.797-2.536 Tg, and smoke aerosols 0.999-1.410 Tg. During 1991-2000, the average CO\({}_{2}\) and CH\({}_{4}\) emissions of forest fire accounted for 2.7%-3.9% and 3.3%-4.7% of the total CO\({}_{2}\) and CH\({}_{4}\) emissions, respectively.  
For examining the spatial and temporal patterns of the fire-induced carbon emissions, [PERSON] _et al._[36] estimated the emission of carbon (C) and carbon-containing trace gases including CO\({}_{2}\), CO, CH\({}_{4}\), and nonmethane hydrocarbons (NMHC) from forest fires in China from 1950 to 2000 by using a combination of remote Sensing (Landsat TM/ETM images) [92], the forest fire inventory, and a terrestrial ecosystem model. The results showed that mean annual carbon emission was about 11.31 Tg from forest fires in China, ranging from a minimum of 8.55 Tg to a maximum of 13.9 Tg. This amount of carbon emission resulted from the atmospheric emissions of four trace gases, _i.e._, 40.66 Tg CO\({}_{2}\) with a range from 29.21 to 47.53 Tg; 2.71 Tg CO with a range from 1.48 to 4.30 Tg; 0.112 Tg CH\({}_{4}\) with a range from 0.06 to 0.2 Tg, and 0.113 Tg NMHC with a range from 0.05 to 0.19 Tg [36]. [PERSON] _et al._[93] studied the influence of fire on carbon distribution and net primary production of boreal forests in north-eastern China, the result showed that the 1987 conflagration in north-east China released 25-49 Tg C to the atmosphere. Information on the amount of emission by forest fires should help the investigation on the transportation and dispersion studies of the plumes, and also provide important input data to the climate models.
# A New Chaotic-Based Approach for Multi-Objective Optimization
## 4 Computational Experiments
### Impact of the Tchebychev Scalarization Strategies
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**XTornado-TS**} & \multicolumn{2}{c}{**XTornado-TM**} & \multicolumn{2}{c}{**XTornado-ATS**} \\ \hline
**Problem** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline \(\text{ine}\;F_{1}\) & \(1.27\times 10^{-3}\) & \(4.50\times 10^{-7}\) & \(2.38\times 10^{-3}\) & \(7.10\times 10^{-7}\) & \(1.27\times 10^{-3}\) & \(1.70\times 10^{-6}\) \\ \(F_{2}\) & \(5.24\times 10^{-4}\) & \(6.09\times 10^{-4}\) & \(6.61\times 10^{-4}\) & \(4.42\times 10^{-4}\) & \(8.68\times 10^{-4}\) & \(1.08\times 10^{-3}\) \\ \(F_{3}\) & \(2.91\times 10^{-3}\) & \(1.24\times 10^{-4}\) & \(2.45\times 10^{-3}\) & \(1.68\times 10^{-4}\) & \(2.52\times 10^{-3}\) & \(5.36\times 10^{-4}\) \\ \(F_{4}\) & \(1.51\times 10^{-3}\) & \(3.81\times 10^{-4}\) & \(1.72\times 10^{-3}\) & \(3.61\times 10^{-4}\) & \(1.34\times 10^{-3}\) & \(1.88\times 10^{-6}\) \\ \(F_{5}\) & \(5.76\times 10^{-3}\) & \(2.23\times 10^{-3}\) & \(3.88\times 10^{-3}\) & \(1.97\times 10^{-3}\) & \(9.57\times 10^{-4}\) & \(1.26\times 10^{-4}\) \\ \(F_{6}\) & \(1.45\times 10^{-3}\) & \(1.08\times 10^{-4}\) & \(1.39\times 10^{-3}\) & \(3.41\times 10^{-5}\) & \(1.40\times 10^{-3}\) & \(8.18\times 10^{-5}\) \\ \(F_{7}\) & \(1.27\times 10^{-3}\) & \(1.13\times 10^{-6}\) & \(2.38\times 10^{-3}\) & \(5.41\times 10^{-7}\) & \(1.27\times 10^{-3}\) & \(1.62\times 10^{-6}\) \\ \(F_{8}\) & \(2.38\times 10^{-3}\) & \(1.01\times 10^{-4}\) & \(2.38\times 10^{-3}\) & \(1.23\times 10^{-6}\) & \(2.50\times 10^{-3}\) & \(2.41\times 10^{-4}\) \\ \(F_{9}\) & \(3.06\times 10^{-3}\) & \(1.32\times 10^{-4}\) & \(2.16\times 10^{-3}\) & \(4.11\times 10^{-4}\) & \(2.67\times 10^{-3}\) & \(1.87\times 10^{-4}\) \\ \(F_{10}\) & \(5.95\times 10^{-4}\) & \(1.17\times 10^{-5}\) & \(6.41\times 10^{-4}\) & \(5.50\times 10^{-6}\) & \(5.86\times 10^{-4}\) & \(6.20\times 10^{-6}\) \\ \(F_{11}\) & \(2.07\times 10^{-3}\) & \(4.15\times 10^{-10}\) & \(2.38\times 10^{-3}\) & \(4.30\times 10^{-10}\) & \(2.07\times 10^{-3}\) & \(1.09\times 10^{-9}\) \\ \(F_{12}\) & \(9.76\times 10^{-4}\) & \(9.33\times 10^{-7}\) & \(8.42\times 10^{-4}\) & \(1.43\times 10^{-6}\) & \(9.76\times 10^{-4}\) & \(5.03\times 10^{-7}\) \\ \(F_{13}\) & \(8.76\times 10^{-4}\) & \(8.60\times 10^{-11}\) & \(7.80\times 10^{-4}\) & \(7.50\times 10^{-3}\) & \(8.76\times 10^{-4}\) & \(2.65\times 10^{-13}\) \\ \(F_{14}\) & \(4.24\times 10^{-4}\) & \(1.37\times 10^{-11}\) & \(5.66\times 10^{-4}\) & \(1.02\times 10^{-11}\) & \(4.24\times 10^{-4}\) & \(3.47\times 10^{-11}\) \\ \hline \hline \end{tabular}  
* TS: Standard Tchebycheff, TM: Tchebycheff with multiple references points, ATS: Augmented Tchebycheff.  
\end{table} 
Table 2: The Generationnal distance metric result for the three versions of X-Tornado on the 14 selected problems.
# Detecting Rug-Pull: Analyzing Smart Contract Backdoor Codes in Ethereum
## 2 Related Work
### Backdoor Code Detection Models  
Backdoor code detection models aim to identify and detect backdoor codes hidden in smart contracts. These detection models are divided into static detection models and dynamic detection models, depending on the backdoor code detection method [22; 23].  
* Static detection model  
Static detection models detect backdoor codes by analyzing the source code or EVM code without executing the smart contract. Since this model does not require execution, it detects backdoor codes by inspecting only the code itself without input data. For example, DEFIDE-FENDER tracks the execution path of the smart contract function and collects execution conditions, then compares the code patterns to detect backdoor codes [26]. First, an Abstract Syntax Tree (AST) is constructed in Ethereum bytecode to trace the execution path of the function. Then, static execution is performed based on the execution path of the function to collect the execution conditions of the backdoor code, and branch-by-branch execution conditions of the function are collected. Finally, the backdoor code is detected by comparing the collected execution conditions with the pattern of the function. This detection model is effective when the execution conditions and the pattern of the code match the known backdoor code patterns. However, this model has a limitation in that it cannot detect backdoor codes hidden in atypical parts through modification.  
* Dynamic detection model  
Dynamic detection models detect backdoor codes while actually executing a smart contract. This model generates the input data required for execution and detects backdoor codes by analyzing transactions and state changes that occur through repeated executions. For example, Pied-Piper deploys EVM code to a local environment and detects backdoor codes by inducing the behavior of the backdoor code through repeated function calls [27].  
First, it operates a local node and deploys EVM code. Then, it randomly generates input values for function calls. Finally, it repeatedly executes the function based on the generated input values and compares the changes in state to detect backdoor codes. However, this model has a limitation in that it takes a long time to detect backdoor codes since it repeatedly executes based on randomly entered input values and cannot detect backdoor codes if it fails to execute them. To solve this problem, this study proposes a balance-tracking-based backdoor code detection model. Since the proposed model operates based on a static detection model, not a dynamic detection model, it shortens the detection time and allows it to detect hidden backdoor codes by tracking the balance changes that occur within the function.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 4 Downstream Effectors of RHO GTPases
### RHO GTPase-Mediated Effector Signaling  
The fact that effectors commonly contact distinct residues within the highly conserved switch I and II regions of RHO GTPases [3; 5] strongly suggests that other domains bind cooperatively to sites outside the switch regions [3; 156]. This possibility might explain the cellular specificity of RHO GTPase-effector interactions. Pioneering experiments by [PERSON] and colleagues showed that the reorganization of the actin cytoskeleton is regulated by proteins in the RHO family including CDC42, RAC1, and RHOA (Figure 5) [157]. CDC42 and RAC1 activation, in turn, activates the ARP2/3 complex indirectly via WASP and WAVE to induce branched actin filament networks and the formation of tight bundles of parallel filaments that form the core in filopodia and the formation of a network of diagonally oriented actin filaments that give rise to thin sheets of lamellipodia. RHOA activation leads to the activation of ROCK and DIA and the organization of actomyosin bundles into stress fibers as well as the formation of focal adhesions [1; 5]. Coordination of the distinct roles of these GTPases is crucial for regulating cell migration, as demonstrated by wound closure in a fibroblast monolayer: CDC42 regulates cell polarity, RAC1 regulates the protrusion of lamellipodia at the leading edge, and RHO regulates the turnover of highly organized structures termed focal adhesions (reviewed in [7; 158; 159; 160; 161; 162; 163; 164; 165]).  
Moreover, RHO GTPases control signal transduction pathways that influence gene expression including the serum response factor (SRF), nuclear factor \(\kappa\)B (NF\(\kappa\)B) transcription factor, c-JUN N-terminal kinase (JNK), and p38 mitogen-activated protein kinase pathways [19; 166]. It has been reported that several enzyme activities can be altered by RHO GTPases. RAC1 can bind directly to p67\({}^{\text{phox}}\), a component of the NADPH oxidase complex, and activate NADPH oxidase activity to generate reactive oxygen species (ROS) (Table 1) [167]. The BCR gene produces a 160 kDa product called p160\({}^{\text{br}}\), which encompasses several distinct domains. p160 exhibited GAP activity toward RAC1, RAC2, and CDC42 GTPases. An early study has shown that BCR regulates RAC-mediated superoxide production by the NADPH-oxidase system of leukocytes [168].
# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
## 5 Conclusions  
In this paper, a robust digital image watermarking algorithm based on SIFT, SVD, and APBT is proposed for copyright protection. Detailed introductions about the SIFT algorithm are made, and by combining the SIFT algorithm with the local digital watermark, the watermarking algorithm is proposed. Then, the neighborhood of feature points for embedding can be obtained by filtering beyond the image boundary and in the overlapping neighborhood. The block-based APBT is carried out on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Combination of Attacks** & **BER** & **NC** \\ \hline
1 & 0.0008 & 0.9994 \\
2 and 4 & 0.0061 & 0.9945 \\
2 and 5 & 0.0039 & 0.9974 \\
2 and 6 & 0.0049 & 0.9991 \\
3 and 4 & 0.0046 & 0.9949 \\
3 and 5 & 0.0049 & 0.9949 \\
3 and 6 & 0.0081 & 0.9915 \\
4 and 5 & 0.0088 & 0.9838 \\
4 and 6 & 0.0120 & 0.9847 \\
2, 4, and 5 & 0.0046 & 0.9991 \\
2, 4, and 6 & 0.0046 & 0.9993 \\
3, 4, and 5 & 0.0061 & 0.9889 \\
3, 4, and 6 & 0.0066 & 0.9915 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of the watermarking algorithms based on APDCBT under different combinations of attacks.  
Figure 9: APDCBT-based watermarked images and extracted watermarks under combinations of different attacks: (**a**) 2 and 4; (**b**) 2 and 5; (**c**) 2 and 6; (**d**) 3 and 4; (**e**) 3 and 5; (**f**) 3 and 6; (**g**) 4 and 5; (**h**) 4 and 6; (**i**) 2, 4, and 5; (**j**) 2, 4, and 6; (**k**) 3, 4, and 5; (**l**) 3, 4, and 6.  
neighborhood, respectively, to obtain the coefficients matrix for SVD watermark embedding. To make the scheme more secure, the Arnold transform is applied to the watermark.  
Experimental results show that our proposed method has better performance in robustness and imperceptibility. For further work, we will continue to focus on the improved algorithm with small entropies host image and asymmetrical watermarks to achieve better performance on more types of images. Additionally, we will apply the proposed algorithm to color images.  
This work was supported by the National Natural Science Foundation of China (No. 61201371), the Research Award Fund for Outstanding Young and Middle-Aged Scientists of Shandong Province, China (No. BS2013 DX022), and the Natural Science Foundation of Shandong Province, China (No. ZR2015 FP004). [PERSON] and [PERSON] conceived the algorithm; [PERSON] and [PERSON] designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the results; [PERSON] drafted the manuscript; and [PERSON] revised the manuscript. All authors have read and approved the final manuscript. The authors declare no conflict of interest.
## 5 Proposed Model Architecture
### Error Metrics  
For our experiment, we employed the root mean square error (RMSE), mean absolute error (MAE), and standard deviation (STD) as evaluation metrics. The equations below illustrate the calculation process, where \(\bar{y_{i}}\) represents the estimated value, \(y_{i}\) represents the true value, and \(n\) represents the sample size. RMSE is utilized as a measure of how well the model fits the data, and it is computed through the mean square function. It quantifies the difference between predicted and actual values:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{1}^{n}(y_{i}-\bar{y_{i}})^{2}} \tag{25}\]  
Regression models are evaluated using MAE as a metric. This calculation involves taking the average of the absolute errors between the predicted and actual values. The MAE provides a measure of the average magnitude of the errors in the predictions:  
\[MAE=\frac{1}{n}\sum_{1}^{n}|y_{i}-\bar{y_{i}}|^{2} \tag{26}\]  
The R-squared (\(R^{2}\)) value, also known as the coefficient of determination, is utilized to assess the goodness of fit of a regression model. It measures how closely the data points align with the fitted line. The R-squared value is calculated as the ratio of the residual sum of squares (RSS) to the total sum of squares (TSS):  
\[R^{2}=1-\frac{RSS}{TSS} \tag{27}\]
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 2 System Description
### Approximation  
The fractional order transfer function has irrational terms and hence integer-order approximations are commonly used for the realization of fractional order systems. The term \(s^{\alpha}\) can be approximated using analog or digital approximation methods [43]. The Commande Robuste d'Orde Non-Entier (CRONE), also called the Oustaloup approximation method is a widely used approximation method. The CRONE approximation for the fractional order \(\alpha\) is given as:  
\[s^{\alpha}\approx C\prod_{i=1}^{N}\frac{1+(s/\omega_{z,i})}{1+(s/\omega_{p,i})} \tag{5}\]  
This method uses N poles and N zeros, alternately placed on the negative real axis, and lying in a limited frequency range \(\{\omega_{b},\omega_{h}\}\)[44]. \(N\) is the order of approximation.  
\[\omega_{z,i}=\omega_{b}\bigg{(}\frac{\omega_{h}}{\omega_{b}}\bigg{)}^{(2i-1- \alpha)/2N} \tag{6}\]  
\[\omega_{p,i}=\omega_{b}\bigg{(}\frac{\omega_{h}}{\omega_{b}}\bigg{)}^{(2i-1+ \alpha)/2N} \tag{7}\]