esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Vulnerabilities That Can Occur When Generating One-Time Password
## 1 Introduction  
In general, fixed passwords are vulnerable to re-use attacks due to malicious attacker's password collection. One-Time Password (OTP), on the other hand, is a password that is used differently each time for a login session or transaction [1]. Thus, this makes it impossible to reuse previously collected things. It also has the property of randomness, which makes it mathematically impossible to infer the next password from the current one. It is known to be highly secure because of these features, and it can be used as an authentication service in Information & Communication Technology (ICT) [2] fields such as mobile-payment [3, 4] and mobile-cloud computing [5, 6].  
OTP mechanism generates passwords using secrets shared in advance between the certification authority and the user, and time or event occurrence information. So the attacker can not infer this by himself. Currently, it is provided as a token, card type device (Hardware), and mobile phone application type (Software) [7, 8]. Hardware type is frequently used in the financial sector, and the number of issuances and transactions of this is continuously increasing. Software in mobile phones is widely used in online games and IT industries, but financial institutions are developing mobile OTP based on USIM.
## 2 Materials and Methods
### Statistical Analysis  
The sample size for this study was computed using the effect size that we expected to see (0.85). We considered \(\alpha\) = 0.05, a confidence interval of 95%, and power of 80%, obtaining a sample size per group of 22 patients. Finally, assuming a 10% dropout rate, 24 patients were considered for each group. The data analysis was conducted using Stata MP18 software. Continuous variables were expressed as mean \(\pm\) standard deviation and range, whereas categorical variables were presented as proportions. The normality of continuous variables was assessed using skewness and kurtosis tests, and, where applicable, a normalization model was constructed for the variables that were not normally distributed. The continuous variables were compared between groups using either the independent samples t-test (parametric) or the Wilcoxon rank-sum test (non-parametric), and between times and groups using repeated measures ANOVA. The categorical variables were compared between groups using the Chi-square test. To assess the relationship between the difference between T1 and T0 for several outcomes and the determinants of group (B vs. A), age, gender (male vs. female), the pathological foot side, the radiological grade of deformity, and the number of days of adherence to the therapy, multivariate linear regression was employed. Correlation coefficients were calculated with a 95% confidence interval (95% CI). For all tests, a \(p\)-value < 0.05 was considered statistically significant.
## 3 Results and Discussion
### Structure Analysis
#### 3.1.3 Pore Structure of PVDF Membrane  
Figure 4a shows the porosity of the PVDF composite ultrafiltration membrane with different GO or CGO content. It can be seen that the porosity of the composite membrane was significantly higher with CGO than with GO and the highest porosity was 64.6%. Comparing the two membranes, it can be seen that maximum porosity of the composite ultrafiltration membrane with GO is reached when the GO content is 0.75 wt%, while for the membrane with CGO, the value is 1.0 wt%. This result reflects that CGO has more oxygen-containing groups than GO, so it has better dispersion in PVDF, so it can be added in higher amounts without agglomeration.  
Figure 4b shows the relationship between the average pore size of PVDF composite ultrafiltration membrane and the amount of GO or CGO. It can be seen that the average pore size first increased and then decreased. The maximum was reached with the addition of 1.0 wt% GO and CGO, then it decreased. The reason for this result is similar to that for the effect of the added amount on porosity. In addition, when the amount of GO and CGO is too large, they will agglomerate, resulting in decreased pore size. The average pore size of the ultrafiltration membrane with GO increased by about 3 nm, while that with CGO increased by about 5 nm. This is due to more active groups in the CGO structure. Comparing the pore size of the two samples, it can be seen that after reaching the maximum  
Figure 4: (**a**) Porosity and (**b**) average diameter of PVDF composite ultrafiltration membrane.  
Figure 3: SEM images of (**a**) PVDF, (**b**) GO/PVDF and (**c.d**) CGO/PVDF. (**e**) TEM images of CGO/PVDF.  
value, the pore size of the membrane with GO decreased rapidly, while that with CGO decreased relatively slowly. The reason is that when the content of GO is too high, it cannot be evenly dispersed in the PVDF matrix and will agglomerate. However, the pore size of the composite membrane containing CGO decreased slowly, indicating that CGO had better dispersion in the matrix than GO. This is due to the fact that CGO contains more active functional groups and is does not agglomerate easily.
# Covert Surveillance Video Transmission with QAM Constellations Modulation
## 3 Performance Analysis of Cover Surveillance Video Transmission
### Theorem of the Necessary Conditions for Cover Surveillance Video Transmission  
In Sections 3.1 and 3.2, we characterize transmission rates based on mutual information and covertness performance based on KL divergence. Through derivation based on mutual information, we can obtain the maximum theoretical rate of the covert transmission of surveillance videos, providing theoretical support for the selection and setting of transmission protocols. Through derivation based on KL divergence, we can determine the covertness indicator of the covert transmission of surveillance videos, thus characterizing the covertness and security of transmission. The rate, covertness, and security of the covert transmission of surveillance videos are important parameters and necessary conditions for transmission. Specifically, through the optimization of the probability distributions of QAM modulation constellation points, we can achieve optimal covertness performance while maximizing covert transmission rates.  
In the preceding subsection, we presented covert transmission rate and covertness analyses for two different cases. It can be seen in (49) and (51) that when the necessary conditions (i.e., \(\sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}\mathbf{d}[j]=0\) and \(\sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}\mathbf{d}[k]=0\)) are satisfied, a surveillance video can be transmitted with a rate of \(\mathcal{O}(\sqrt{n})\) and a covertness parameter of \(\mathcal{O}(\beta^{4})\). As shown in (42) and (43), if the necessary conditions are not satisfied, the transmission rate deteriorates to \(\mathcal{O}(1)\) and covertness deteriorates to \(\mathcal{O}(\beta^{2})\). In previous research, the covert transmission rate is \(\mathcal{O}(\sqrt{n})\) and the covertness performance measured by KL divergence is \(\mathcal{O}(\beta^{4})\). Thus, we obtain the necessary conditions for surveillance video transmission based on QAM modulation in the following theorem.  
For any surveillance video transmitted using the QAM modulation method, the prerequisite for achieving optimal covert transmission is that \(\sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}\mathbf{d}[j]=0\) and \(\sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}\mathbf{d}[k]=0\).  
Building upon the theorem presented, we can design the modulation probabilities of QAM constellation points to improve transmission rates and covertness. This optimization facilitates the attainment of accelerated transmission rates while maintaining commensurate levels of covertness. It is imperative to emphasize that these fundamental conditions, anchored in QAM modulation, are not limited to conventional sets, such as 16\(-\)QAM, 64\(-\)QAM, and 256\(-\)QAM. Instead, they establish a broad-reaching conclusion that holds applicability across a diverse spectrum of constellation point modulation methodologies.
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 5 Conclusions  
This paper presents a model compression method using ant colony evolutionary pruning which can be widely applied to DETR-like neural networks for pruning. Our research aims to address the issue of inefficient detection caused by the large size and high computational cost of DETR-like neural networks when applied to remote sensing tasks. Based on the structural characteristics of DETR-like neural networks, we have employed channel sparsity operators, twin-channel sparsity operators, and attention head sparsity operators to handle different structures. Subsequently, considering the characteristics of remote sensing targets, we introduced Bayesian optimization as an environmental constraint and set different pruning ratios for different modules of the neural network. Finally, through extensive experiments, we have demonstrated that our method has excellent compression effects when applied to DETR-like models for remote sensing tasks.  
In future work, we plan to introduce this method into models of other types and tasks and further improve the performance of pruned models by using methods such as knowledge distillation. Considering the high redundancy of the Transformer layers, we anticipate adopting a finer-grained pruning strategy, such as pruning the entire Encoder or Decoder layers.  
Conceptualization, H.S. ([PERSON]) and Y.Z.; data curation, H.S. ([PERSON]); formal analysis, H.S. ([PERSON]); funding acquisition, H.S. ([PERSON]); investigation, H.S. ([PERSON]) and Y.Z.; methodology, H.S. ([PERSON]) and H.S. ([PERSON]); project administration, H.S. ([PERSON]); resources, H.S. ([PERSON]); software, H.S. ([PERSON]); supervision, H.S. ([PERSON]); validation, H.S. ([PERSON]) and Y.Z.; visualization, H.S. ([PERSON]); writing--original draft, H.S. ([PERSON]); writing--review and editing, H.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the National Natural Science Foundation of China under grants 62401538, 62401539, and 62475255.  
The data in the experimental images used to support the findings of this research are available from the corresponding author upon reasonable request.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pruning Rates** & **DETR** & **Deformable DETR** & **DINO** \\ \hline
0 & 81.2\% & 90.6\% & 90.5\% \\
0.1 & **83.3\%** & **92.3\%** & **92.6\%** \\
0.2 & 82.5\% & 91.7\% & 91.9\% \\
0.3 & 81.6\% & 91.2\% & 91.3\% \\
0.4 & 80.9\% & 90.1\% & 90.2\% \\
0.5 & 79.8\% & 89.7\% & 89.4\% \\
0.6 & 77.4\% & 87.5\% & 85.8\% \\
0.7 & 72.2\% & **84.3\%** & 82.8\% \\
0.8 & 66.1\% & 77.2\% & 75.5\% \\
0.9 & 53.5\% & 66.7\% & 61.0\% \\ \hline \hline \end{tabular} Note: The bolded data represents the optimal value of accuracy for each network.  
\end{table}
Table 3: Manual setting of accuracy changes with different pruning rates.  
**Acknowledgments:** This research was supported by the Jilin Provincial Key Laboratory of Machine Vision Intelligent Equipment.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Creating Honeypots to Prevent Online Child Exploitation
## 2 Background
### Honeypots  
Honeypots represent a lure for bad actors and enable the identification of those intending to engage in prohibited behaviour. [PERSON] published the first definition of a honeypot as \"a resource who's [sic] value is in being attacked or compromised\" [8]. The concept of a honeypot was developed through the late 1980s and early 1990s with the first recorded use (although not using that name) by [PERSON] in his widely read book _The Cuckoo's Egg_, which popularised the importance of computer security to a broad audience [12]. Through the 1990s, other authors, and indeed software makers, created tools for producing a honeypot within a network, such as the Deception Toolkit [9].  
Simply put, the role of a honeypot is to discover the presence of a bad actor and to distract them away from production systems. Discovery then enables steps to be taken to prevent actions against the production system, increasing their protection. While honeypots do not reduce the number of attacks that occur, they do lower their effectiveness and enable the identity (IP address) and attack methodologies to be discovered. This means that honeypots are not limited to a particular threat but can be deployed in a broad range of contexts [13].  
Honeypots present themselves as legitimate objects that are then targeted. They can be created physically, or they can be any part of our digital infrastructure, from servers or routers to entire fake networks [11; 13]. They commonly have features that make them of interest to a bad actor, possibly through some clear security flaw or higher availability than typical systems. Ideally, they should be discovered rapidly to enable monitoring systems to discover the attack before they have carried out any actions against a production system.  
[PERSON] describes the purpose of computer security as prevention, detection, and response [14]. A honeypot does not focus on prevention in the first instance, as it is premised on allowing an attack to occur, albeit on a false target. The value of the honeypot in this context lies in detection and enabling an appropriate response to be made. Ideally,detection and response should occur without the bad actor realising that they were not interacting with a true target. This is quite different to the other forms of defensive cyber security infrastructure described above, which aim to prevent attacks or stop them in process as quickly as possible. This is an infrastructure that can be quite valuable for cyber crime research [15].  
Next, this paper describes a honeypot that has been deployed in two studies which aimed to detect and educate naive internet users who demonstrate an interest in 'deviant' but legal pornography with close parallels to CEM. The next section begins by describing the CEM prevention context, before a description of the honeypot used in this study is provided and key findings are discussed.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 3 Proposed Technique
### Error Rate from Simulation  
We defer description of the details of simulation settings to Section 5. Here, we describe the methodology for obtaining error rate from simulation. We simulate a noisy PUF cell in SPICE using 45 nm technology [27] by randomly varying supply noise. The power supply noise distribution is varied to control the error rate of the SRAM cell. One million samples were collected from the cell. The outputs were fed into a 4-bit UP/DOWN counter, and the new, more reliable output bits were obtained. The new error rate was calculated for these bits to obtain the reliability improvement. As SPICE simulations are expensive, for an initial error rate of less than 0.24, we generated \(10^{8}\) bits with required initial error rate from a Python script and fed the bits to the UP/ DOWN counter. As shown in Figure 5, the error rate of the proposed technique obtained through simulation is less than \(10^{-6}\) for an initial error rate of \(\leq\)0.16. We reserve the reliability improvement over traditional TMV for the next section. As the order of magnitude of the error rate obtained through simulation is in the range of \(10^{-6}\), a large number of simulations may be required to get an accurate estimate. In order to estimate the error rate with higher accuracy, analysis of the proposed technique is presented in the next section.  
Figure 3: UP/DOWN counter (UDC) based voter scheme.
## 4 Discussion
In fact, the majority of studies included in this review reported on patients aged 65 years and older. Although kinematic studies found advantages of BCR-TKA, its design does not achieve native knee function, as forces to the ACL may be increased and posterior femoral translation and internal rotation decreased. In addition, sagittal plane motion and tiboferomal articular contact characteristics including pivoting patterns were not fully restored [25; 48; 49; 50]. Nevertheless, kinematic analyses suggest superior static balance ability on a single-leg test of BCR-TKA compared to PS-TKA and UKA designs [51], and that compared to the CRTKA design, ACL retention may lead to improved muscle recruitment during (downhill) walking and increased neuromuscular control  
Recently, a BCS-TKA design was introduced which uses a dual-post-cam mechanism as a substitute for the ACL and PCL, with due to the design provides guidance for axial rotation and posterior translation. One kinematic analysis that compared this system to BCR-TKA found that BCR-TKA (n = 10) improved gait ability more than BCS-TKA (Journey II XR) (n = 15) at a short-term follow-up of 6 weeks [53]. As BCS-TKA may be less prone to native ACL strain, aseptic loosening and surgeon experience, future (randomized) controlled trials comparing these designs would be of great interest [56]. Moreover, as retention of the native ACL seems to have kinematic advantages over the other designs, third-generation BCR-TKA designs with an optimal (robotic or navigated) placement may prove to achieve the unmet need for a TKA design for the younger and active patient. In conclusion, the current systematic review underlines the low methodological quality of clinical trials investigating BCR-TKA. Currently, there are insufficient data to support broad (\"state of the art\") implantation of BCR-TKA, and its utility remains to be assessed [57]. Preclinical and clinical (kinematic) research suggests, however, that the design has a potential benefit to achieve improved kinematics in the young and active arthroplasty patient and warrants future research for new-generation designs with optimal (tibial) fixation and reproducible (robot or navigated) surgical placement. T.S.D.W. : Systematic search, analysis of manuscripts, manuscript writing. S.N.V.L. : Manuscript writing. G.G.V.H. : Manuscript writing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 4 Vibration Analysis of Subway Tunnel Construction
### Vibration Speed Analysis of the Top Plate of the Subway Tunnel  
During subway tunnel excavation, vibration velocities at the diaphragm wall and center pillar are measurable, but direct monitoring on the roof is not feasible. Therefore, FLAC3D was used to simulate and predict roof particle velocities. Prediction points are shown in Figure 13, with the maximum roof vibration speed predicted at 1.537 cm/s.  
Figure 11: Main vibration frequency of the monitoring points.  
Figure 12: Metro tunnel vibration speed cloud map.  
Comparing roof measurements to center pillar data, it is evident that roof velocities are generally higher, while both roof and pillar velocities exceed those of the diaphragm wall.  
These findings align with the stress cloud results: tunnel blasting produces a maximum predicted vibration velocity of 1.537 cm/s at the roof, followed by 1.482 cm/s at the base of the center pillar, and 1.424 cm/s at the diaphragm wall. According to China's Blasting Safety Regulations, allowable vibration for newly poured concrete in foundation pits is 2.0-2.5 cm/s, confirming structural safety within these limits.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 5 Simulations and Results  
### Simulations  
#### 5.1.1 Simulation Environment  
We mainly simulated three protocols: AODV, ND-AODV, and CND-AODV using the NS3.35 simulation platform. NS3 [24] is a powerful network simulation tool used for modeling and analyzing various network scenarios, including wired networks, wireless networks, mobile networks, satellite networks, and internet protocols, among others. NS3 is known for its stability, robust functionality, easy extensibility, and being a free network simulation software. NS2 [25] is a widely used discrete-event network simulator, primarily used for studying and evaluating network communication algorithms. However, NS2 has not been updated in recent years, and its user base has declined. NS3, on the other hand, offers better performance, greater flexibility, superior documentation, and strong community support, making it a more suitable choice for meeting users' simulation needs.  
In this experiment, we used a simulation time of 110 s, a simulation area of \(1000\times 1000\) m\({}^{2}\), and a TwoRayGroundPropagationLossModel as the propagation loss  
Figure 12: Flowchart depicting the route discovery process in the proposed CND-AODV protocol.  
model. The packet size for transmission was set to 512b, the transmission rate was 2 Mbps, and the transport layer protocol was UDP. The transmission model used was CBR (constant bit rate), and the node mobility model was set as RandomWaypointMobilityModel, see Table 3.  
The experiments cover two different environments. Environment 1: Nodes are randomly distributed, and various threshold values (\(z\)) are introduced for comparison. Simulations are conducted by altering the number of nodes to obtain the optimal threshold parameter (\(z\)). Environment 2: Scenario 1--varying node count; scenario 2--nodes are uniformly distributed to assess CND-AODV performance in extreme conditions; scenario 3--changing node movement speeds. In each scenario, the performance differences among the different protocols are compared.
# Investigation into the Yaw Control of a Twin-Rotor 10 MW Wind Turbine
## 4 Results and Discussion
### Stability Analysis of the System Dynamic Model  
To apply a position control law to the system Simulink model, first, the system state space model had to be obtained. The MATLAB system identification toolbox was used to capture the system dynamics using nonlinear ARX model estimators. The resultant system state space model and its state space matrices are shown in Equation (30):  
\[A=\begin{bmatrix}-0.554&-0.056\\ 0.0625&0\end{bmatrix},\quad B=\begin{bmatrix}128\\ 0\end{bmatrix},\quad C=\begin{bmatrix}186.8&2.232\end{bmatrix}\ and\quad D=[0] \tag{30}\]  
Then, the Lyapunov direct method was used to determine the system's stability. The Lyapunov equation is shown in Equation (31):  
\[-Q=A^{T}P+PA \tag{31}\]  
where \(Q\) is the positive definite matrix, \(A\) is the system matrix, and \(P\) is the solution matrix.  
To determine the system's stability, the eigenvalues of the \(P\) matrix and \(A\) matrix were calculated and found to be \(\begin{bmatrix}0.904\\ 0336.886\end{bmatrix}\) and \(\begin{bmatrix}-0.547\\ -0.006\end{bmatrix}\), respectively. This means that the \(P\) matrix is a positive definite matrix and the eigenvalues of the \(A\) matrix have negative real parts. Thus, the system is asymptotically stable.
## 3 Results and Discussion
### Comparative LCA of Hydrogen Production: Biogas Reforming vs. Agricultural Residue
#### 3.2.2 Ozone Depletion and Ionizing Radiation Impact  
Stratospheric ozone depletion potential (SOD) is found to be slightly higher in biogas reforming (2.72936 \(\times\) 10\({}^{-5}\) kg CFC11 eq) than in gasification (2.14344 \(\times\) 10\({}^{-5}\) kg CFC11 eq), though the difference is relatively minor. However, the ionizing radiation impact is notably negative (\(-\)0.0355 kBq Co-60 eq) for gasification, suggesting a potential benefit in certain scenarios. In contrast, biogas reforming has a positive impact value of 0.3729 kBq Co-60 eq, indicating higher environmental burdens in this category. biogas reforming contributed to higher levels of radioactive emissions due to the energy-intensive nature of the process and the reliance on fossil-derived methane. In contrast, gasification processes are associated with lower radiative impacts, particularly when using renewable feedstock.