esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Antiglioma Activity of Bearberry Extracts  
The results of the multi-way ANOVA confirmed the significant impact of all the studied parameters, namely, the site (SI), solvent (SO), and concentration (C), and their interactions on apoptosis (Ap) in the human anaplastic astrocytoma MOGGCCM cell line and the human glioblastoma LN229 cell line in both the Et and Aq variants (Table 2). A similar result was obtained in the necrosis (Nec) analysis. The analyzed extracts had no impact on the initiation of cell death in normal fibroblasts. Our observation showed that the studied Et and Aq extracts had no influence on autophagy initiation in all the MOGGCCM and LN229 cell lines; therefore, these data are not presented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cell Line** & \multicolumn{2}{c}{**MOGGCCM**} & \multicolumn{2}{c}{**LN229**} \\ \hline  & **Ap** & **Nec** & **Ap** & **Nec** \\ \hline SI & 7.31 & 8.26 & 15.91 & 51.76 \\  & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) \\ SO & 540.50 & 1069.24 & 799.29 & 2494.59 \\  & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) \\ C & 884.55 & 2580.79 & 822.44 & 3622.77 \\  & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) \\ SI \(\times\) SO & 18.17 & 4.30 & 14.51 & 48.97 \\  & \(p<0.001\) & \(p<0.01\) & \(p<0.001\) & \(p<0.001\) \\ SI \(\times\) C & 10.60 & 7.63 & 20.78 & 48.03 \\  & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) \\ SO \(\times\) C & 917.05 & 845.03 & 688.96 & 2602.87 \\  & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) \\ SI \(\times\) SO \(\times\) C & 11.03 & 3.88 & 18.96 & 46.10 \\  & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of multi-way ANOVA. Effects of SI—site, SO—solvent, and C—concentration and their interactions on the levels of apoptosis (Ap) and necrosis (Nec) in MOGGCCM—human anaplastic astrocytoma cell line and LN229—human glioblastoma cell line.  
Figure 1: Cluster analysis based on the mean content of phytochemicals in bearberry leaf extracts. Ethanolic extracts: 1–6 and aqueous extracts: 7–12.
# Geotechnical Behaviour of Fly Ash-Bentonite Used in Layers
## 5 Conclusions  
The following conclusions are drawn based on the results of the experimental investigation:  
1. Fly ash is a nearly cohesionless substance that has a binding effect at the fly ash-bentonite layered system interfaces.
2. For F:B = 1:1, 1:2, 1:3, and 1:4, the value of cohesion has been varied in the ranges of 76.8-107 kPa, 75-110 kPa, 66.6-105 kPa, and 55-78.2 kPa, respectively, for different layer arrangements. As the ratio of fly ash to bentonite is increased, the value of cohesion is decreased, while at the same ratio, the value of cohesion is increased by increasing the number of interfaces.
3. For F:B = 1:1, 1:2, 1:3, and 1:4, the value of angle of shearing resistance has been varied in the range of 17-22.8\({}^{\circ}\) for different layer arrangements. The value of the angle of shearing resistance has been found to decreases with the increase in the number of interfaces for a specified ratio of fly ash to bentonite.
4. The stress-strain behaviour of the fly ash-bentonite layered system is initially elastic at low stress levels and becomes non-linear at higher stress levels.
5. As the number of interfaces and confining pressures grow, the shear strength and net safe bearing capacity of the fly ash-bentonite layered system increases.
6. Based on laboratory model tests, it has been suggested that the embankment may provide greater stability if it is constructed for a fly ash to bentonite ratio of 3:1 (75% of fly ash and 25% of bentonite in layers) by keeping the number of interfaces as N = 3.
7. It has been found that a 3:1 ratio of fly ash to bentonite with three interfaces can be successfully used for filling low-lying areas and in many other engineering constructions, while also providing a means of using the material without negatively impacting the environment. However, the authors recommend that large-scale studies be conducted in the future.  
M.H.: drafting--data collection and preparation of the manuscript, writing-review and editing, M.A.K. ([PERSON]): drafting--preparation of the manuscript, revision, and correction; A.H.A.: composing--reviewing and modifying; A.A.A.: composing--reviewing and modifying; A.A.K. ([PERSON]): composing--reviewing and modifying., T.B. reviewing and modifying.J.P.: reviewing and modifying. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support provided by Researchers Supporting Project Number (RSP2022R473), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
Figure 11: Effective angle of shear resistance values against fly ash–bentonite ratios.
## 3 Results and Discussion
### Load-Crack Width Response  
Figure 12 shows the load-crack width responses for the UHPC beams. This figure illustrated that the introduction of tensile reinforcement bars in the beam notably increased the beam's cracking capacity before failure, as the maximum crack widths of the bar-reinforced concrete beams (10.8-18.4 mm) were significantly less than those of the control beam (7.4-8.0 mm), as outlined in Table 7. As an established fact, the maximum crack width increased as the reinforcement effective depth decreased. After the beam reached its maximum load, as the effective depth increased, the rate of crack propagation decreased (shown in Figure 12). This preferable behavior indicated that the efficient design of bar reinforcements in UHPC beams could help control the development of cracking and thus play an important role in beam serviceability and durability.
## 4 Implementation
### Operating System Deployment  
The multi-agent framework is deployed on a dedicated system running Ubuntu 24.04.7. This environment is configured with our custom multi-agent program, its dependencies, a collection of CTF tools, and the Chromium browser with its WebDriver for web interaction. All operational commands from LLM agents are executed directly on the host machine.  
A critical design decision regarding deployment concerned the choice between containerization and virtualization. Given that browser interaction is a core capability of our agent and inherently requires a desktop environment, and considering the known compatibility challenges and overheads associated with running Linux desktop environments reliably within Docker containers, we opted to package the entire system as a virtual machine rather than a Docker image for deployment. This VM-based approach provides a complete, isolated desktop environment, mitigating potential display server or graphics issues often encountered in containerized desktop setups.
###### Abstract  
Many studies have demonstrated the usefulness of some optical coherence tomography (OCT) parameters, like total macular volume (TMV) and retinal nerve fiber layer thickness (RNFL-T), for monitoring patients with multiple sclerosis (MS). However, there are no real-world, long-term studies on patients with relapsing-remitting MS (RR-MS) treated with fingolimod. Therefore, the purpose of this study was to describe retinal changes associated with fingolimod therapy during a two-year follow-up while considering previous episodes of optic neuritis (ON). Patients diagnosed with RR-MS and treated with fingolimod (46 in total) underwent a two-year follow-up. Based on previous ON history, we identified 16 ON+ and 30 ON- patients. The ophthalmological evaluations, including visual field (VF) examination and OCT, were performed at a baseline at 3-6, 12 and 24 months to evaluate the progression rate for each parameter. When analyzing the whole sample, OCT showed no cases of macular edema. Instead, we observed a significant reduction rate in the central retinal thickness (CRT) (\(p<0.001\)), TMV (\(p<0.001\)) and RNFL (\(p<0.05\)). Moreover, we observed a significant difference in the progression rate between ON+ and ON- patients, relative to the VF and RNFL (\(p<0.05\)) examinations. OCT highlighted a significant progression rate of retinal damage in MS patients despite fingolimod therapy, especially in MS ON+ patients.  
Keywords:optical coherence tomography; fingolimod; multiple sclerosis; total macular volume; central retinal thickness; peripapillary retinal nerve fiber layer; annual progression rate +
Footnote †: journal: Medical and Surgical Sciences
# You Only Attack Once: Single-Step DeepFool Algorithm
## 2 Related Works
### Adversarial Attacks
While this algorithm can generate adversarial examples with minimal perturbations, its high computational cost arises from the need for multiple iterations. Building upon the concept of DeepFool, [PERSON] et al. [10] proposed a universal perturbation method that calculates the sum of vectors representing the distances from multiple original inputs to the decision boundary to generate a universal perturbation. This algorithm successfully perturbs a majority of images in the dataset, demonstrating a certain level of generalization. By iteratively finding the minimal perturbation and summing the found minimal perturbations, the final universal perturbation is obtained. To address the shortcomings of the DeepFool algorithm, [PERSON] et al. [11] proposed a targeted version called Targeted DeepFool, which can misdirect misclassification towards a specific target class, realizing a targeted attack approach for DeepFool. [PERSON] et al. [12] introduced an enhanced version of Targeted DeepFool, which increases the ability to misclassify specific classes and introduces a minimum confidence score requirement hyperparameter to enhance flexibility, albeit at the cost of increased algorithm complexity. In the latest research on adversarial sample transferability, [PERSON] et al. [13] proposed a simple channel switching method called CS-MI-FGSM, which obtains variants of the input image and mixes them during momentum updating to improve the transferability of adversarial samples. In our study comparing the strengths and limitations of existing algorithms, we propose the YOAO (You Only Attack Once) algorithm, which not only rapidly generates adversarial examples but also ensures their potent attack capability. This algorithm is a gradient-based, fast DeepFool method that calculates the maximum gradient disparity across different classes to more swiftly identify the minimal perturbation. By generating adversarial samples in a single-step iteration, it addresses the slow attack speed issue of the original DeepFool algorithm, significantly enhancing the algorithm's attack efficiency.
# A Survey on Dynamic Corrective Control of Asynchronous Sequential Machines
## 3 Stable Reachability and Detectability
### Stable Reachability of Input/State ASMs  
To address the stable reachability of \(\Sigma\), denote the state set by \(X:=\{x_{1},\ldots,x_{n}\}\) whenever necessary hereafter.  
**Definition 1**.: _Given \(\Sigma=(A,X,\bar{x},f)\), \(R(\Sigma)\in(P(A^{+}))^{n\times n}\) is the matrix of stable transitions of which \((i,j)\) entry is defined as \((i,j\in\{1,\ldots,n\})\)_  
\[R_{i,j}(\Sigma):=\{t\in A^{+}|s(x_{i},t)=x_{j},1\leq|t|\leq n-1\}.\]  
\(K(\Sigma)\in\{0,1\}^{n\times n}\) _is the skeleton matrix of \(\Sigma\) of which \((i,j)\) entry is defined as_  
\[K_{i,j}(\Sigma):=\begin{cases}1&R_{i,j}(\Sigma)\
eq\emptyset\\ 0&R_{i,j}(\Sigma)=\emptyset\end{cases}\]  
_where '\(\emptyset\)' denotes the empty set._  
\(R_{i,j}(\Sigma)\) contains all the input strings in response to which \(\Sigma\) moves from \(x_{i}\) to \(x_{j}\) via a sequence of stable transitions. Since every state of \(\Sigma\) is reachable in at most \(n-1\) steps of stable transitions (in the absence of infinite cycles), the length of \(t\in A^{+}\) in \(R_{i,j}(\Sigma)\) does not exceed \(n-1\). On the other hand, \(K_{i,j}(\Sigma)\) serves a compact representation of the stable reachability from \(x_{i}\) to \(x_{j}\).  
Polynomial algorithms for computing \(R(\Sigma)\) and \(K(\Sigma)\) are presented in the previous work [27; 28]. Recently, an interesting and promising branch of corrective control has been developed based on the semi-tensor product (STP) of matrices [36; 37; 38; 39; 40; 41; 42]. Among relevant results, [43; 44] propose numerical methods of deriving \(R(\Sigma)\) and \(K(\Sigma)\) in which tedious symbolic computations used in [27; 28] are avoided by presenting a numerical approach in the framework of STP.
# Evaluation of the Performance of Previous Concrete Inspired by CO\({}_{2}\)-Curing Technology
## 2 Materials and Methods  
### Sample Preparation  
Portland cement of CEM I 52.5R grade, according to EN 197-1 [30], was used in the preparation of the pervious concrete samples in this study. The cement sample was tested using the Cilas 1090 model particle size analyzer. Figure 1 shows the size distribution of the cement particles, and the average size was calculated to be 128 \(\upmu\)m. The cement samples were passed through a 45 \(\upmu\)m sieve, and the passed material was analyzed using Bruker D8 XRD (Billerica, MA, USA) and JEOL JSM-6610A SEM (Tokyo, Japan) to obtain information on morphology and mineralogy. XRD was performed with a Cu K\(\alpha\) source, and the sample was scanned at a rate of 0.02\({}^{\circ}\) 20 per min. The sample was mounted on a steel stub using a carbon adhesive, coated with gold for 60 s, and scanned with the SEM operated at 20 kV to obtain the secondary electron (SE) image. These SEM and XRD results are also illustrated in Figure 1.  
The cement particles were found to have an angular structure and consist mainly of calcium silicates. Granite-derived crushed aggregates, potable water, and a polycarboxylate-ether-based superplasticizer were used in addition to cement to prepare the concrete samples. The water absorption and specific gravity of these aggregates were determined to be 0.42% and 2.77 and were determined according to the European testing standard [31]. The quantities of liquid admixture, water, cement, and aggregate used to prepare 1 m\({}^{3}\) of concrete mixture were 0.3, 34.7, 100, and 450 kg. Aggregates up to 8 mm in size were used to prepare the concrete mixture and were not well graded, as shown in Table 1, to obtain an improved rate of water percolation. The raw materials were mixed in a 60 l tilt-type concrete mixer in a laboratory environment for 4 min. The fresh sample was filled in three equal layers in an iron mold sized 100 mm in diameter and 200 mm in length. A Marshall hammer was used to compact each concrete layer. The diameter, weight of the drop, and free fall of this hammer were 98.4 mm, 4.54 kg, and 457 mm. Twenty-five hammer blows were applied after filling each layer. The upper surface of the fresh sample was smoothed with a tapping knife and then left undisturbed for 24 h.  
Figure 1: (**A**) SEM image, (**B**) XRD pattern, and (**C**) size distribution of cement particles.  
Fresh concrete samples were demolded after 24 h of casting and cured in water for up to 28 days. The other set of demolded samples was dried in the laboratory environment for up to 7 days and stored in a 1200 L capacity concrete carbonation chamber of the ThermoScientific model for up to 28 days. The CO2 concentration, temperature, and relative humidity inside the chamber were maintained at 2%, 25 \({}^{\circ}\)C, and 65% during this storage period. This storage experiment was conducted at normal atmospheric pressure. Figure 2 shows the various stages involved in the preparation of these concrete samples.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 4 Design of the Framework
### PDoS Severity Score Based on Combined Criteria
#### 4.9.4 Class 4 (Severe)  
Class 4 has the following characteristics:  
* Impact Effect: Fast or Instant;
* DoS Effect Duration: Permanent;
* Recovery Action: Involves extensive recovery efforts, such as major hardware overhauls.  
Malware such as Mamba and StoneDrill can be classified as Class 4, where the extent of harm requires extensive recovery efforts, including major hardware overhauls or complete system restorations. The swift nature of these attacks, coupled with their irreversible damage, categorizes them as highly critical, demanding immediate and comprehensive response strategies.  
At a technical level, a service scan of Siemens ET200S PLC can be classified as Class 4 PDoS attack, as it is instant, but temporary, and can be restored by a physical restart of the device.  
Class 5 (Critical)  
Class 5 has the following characteristics:  
Impact Effect: Instant;  
DoS Effect Duration: Permanent;  
Recovery Action: Full-device or full-system replacement.  
Class 5 shows the most critical and destructive category of attacks. This class is characterized by its instantaneous impact effect and permanent damage, leading to dire consequences such as full system or device replacement. The profound severity of these attacks, exemplified by malware like USB Killer and PaperW8, showcases their capability to inflict irreversible damage to critical infrastructure or hardware. Such attacks often exploit deeply ingrained system vulnerabilities or deploy sophisticated techniques, leading to substantial financial and operational burdens. The recovery from Class 5 attacks is not only costly, but also complex, involving extensive resource allocation and potentially long-term operational downtime. The classification of an attack as Class 5 serves as a stark indicator of the highest level of threat, necessitating urgent attention and the mobilization of significant cybersecurity resources.  
The benefits of using such an approach include comprehensive analysis, clear response guidelines, and strategic planning. Using this methodology, this classification becomes a tool for cybersecurity professionals and academic researchers to evaluate threats.
## 4 Security and Privacy of IoNT
### Security of IoNT  
The IoNT is subject to various security risks, similar to the IoT, owing to its inherent vulnerabilities. In terms of security, a potential attacker can compromise the security of the IoNT by concentrating on the following focal points, violating physical, network, and data security measures and exploiting software vulnerabilities:  
1. Physical/hardware security Because of their small size, IoNT devices are vulnerable to physical tampering and damage. This can make it difficult to secure them against unauthorized access or tampering [1; 4];
2. Network security IoNT devices often communicate over wireless networks, which can be vulnerable to hacking and other cyber-threats. Ensuring the security of these networks is essential to protect the data transmitted by IoNT devices [4; 17];Data privacy  
The data collected by IoNT devices may be sensitive and protecting these data from unauthorized access is deemed essential. This includes both the data transmitted by the devices and the data stored on them. These data can be vulnerable to unauthorized access or misuse [9];  
4. Software vulnerabilities  
As is the case for any software, the software running on IoNT devices is subject to vulnerabilities that can be exploited by hackers. Ensuring that these devices are kept up to date with the latest security patches is important to prevent such attacks [4; 16; 20].
# Design and Kinematic Analysis of Deployable Antenna for Bionic Bird Tail Feather
## 5 Expanding Process Motion Analysis  
Then, the motion analysis of the unfolding process focused on calculating the trajectory of the mechanism's unfolding. Given that the motion of the synchronous linkage is determined relative to the unfolding ribs and has no impact on the overall unfolding of the mechanism while maintaining synchronicity, the root hinge section of the mechanism was isolated for analysis.  
As illustrated in Figure 22, the mechanism comprises a total of 12 ribs, each separated by an angle of \(20^{\circ}\) in the fully expanded state. AB denotes the fixed rib end, BC signifies the first rib connected to the fixed rib, and CD represents the second rib connected to the fixed rib. Assuming that the angle between the first rib and the fixed rib at any given moment of motion is denoted as \(\theta\), and due to the presence of the synchronization mechanism, the angle between the second rib and the first rib is denoted as \(\theta_{2}=2\theta_{1}\), and so forth.  
Figure 21: Changes in angle, angular velocity, and angular acceleration. (**a**) Angular variation; (**b**) angular velocity variation; (**c**) angular acceleration variation; (**d**) simulation comparison of angular variation.  
Figure 22: Rib expansion analysis diagram.  
\(l_{AB}=l_{BC}=l_{CD}=\cdots=l=16\); let the independent variable vary in the range of \([0^{\circ},20^{\circ}]\), \(l_{g}=1500\), and calculate the coordinates of the final point (x\({}_{\text{e}}\),y\({}_{\text{e}}\)) and the outermost point of the outermost rib (x\({}_{\text{o}}\),y\({}_{\text{o}}\)).  
\[\left\{\begin{array}{l}x_{\text{e}}=l_{AB}+l_{BC}\cos(\theta_{1})+l_{CD}\cos( \theta_{2})\\ \quad\quad+\cdots l\cos(\theta_{5})+l\cos(\theta_{6})/2\\ y_{\text{e}}=l_{BC}\sin(\theta_{1})+l_{CD}\sin(\theta_{2})+\cdots\\ \quad\quad+l\sin(\theta_{5})+l\sin(\theta_{6})/2\\ x_{\text{o}}=x_{\text{e}}+l_{S}\sin(\theta_{6})\\ y_{\text{o}}=y_{\text{e}}+l_{S}\cos(\theta_{6})\end{array}\right. \tag{13}\]  
The horizontal unfolding trajectory of both ends of the outermost rib was calculated using MATLAB, as demonstrated in Figure 23, thereby defining the motion space within the plane. The entire mechanism can be unfolded within a horizontal space of 4 m \(\times\) 3.5 m, with no obstructions in the vertical direction of the space antenna. As such, the vertical unfolded trajectory is not a consideration.  
The next step is expected to be the creation of a product prototype for material selection and torque calculation of the drive source, followed by the control of the antenna's unfolding speed.
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 4 Numerical Simulations
### Single-Factor Numerical Simulation Analysis  
A single-factor numerical simulation was conducted focusing on three variables: depth of pit excavation, depth of pipeline burial, and the distance between the pipeline and the pit. Each factor was evaluated for five to six different levels. The water supply pipeline was analyzed as an example. The vertical and horizontal displacements of the pipelines served as assessment indicators, allowing for a comparative analysis of their impact on pipeline deformation. Based on the results of the numerical simulations, displacement curves of the pipelines were plotted, aligning with the coordinate axes established in Figure 1. Pipeline settlement was defined as negative, while heave was defined as positive. Horizontal displacement toward the inside of the pit was considered positive, and toward the outside was considered negative.