esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 3 BGP Anomalies and Attacks
### MED Modification Attacks  
MED modification attacks focus on manipulating the BGP attribute known as the Multi-Exit Discriminator (MED), which is utilized by ASs to convey to their neighbors a more preferred path for incoming traffic [37,38]. The MED is a crucial attribute in influencing route selection, especially in scenarios where multiple paths exist between two As. By maliciously modifying the MED values in BGP announcements, an attacker can influence the path selection process, causing traffic to be routed through specific, potentially malicious, paths. This could be leveraged to facilitate traffic interception, create network congestion, or simply degrade network performance by forcing traffic through suboptimal paths. The subtlety of MED modification attacks lies in their ability to manipulate routing decisions without violating the BGP's path selection rules or causing overt routing conflicts.  
A MED modification attack is illustrated in Figure 7, within the context of a Tier 3 ISP. ASB has two eBGP sessions toward the same upstream provider ASA. Initially, ASB advertises its prefix 172.22.0.2/24 on both eBGP sessions--one ending on R3 with a MED value of 100, and the other on R4 with a MED value of 200. Initially, ASB advertises the prefix 172.22.0.2/24 to ASA through two eBGP sessions, with different MED values assigned to each advertisement for traffic engineering purposes. In a malicious act, the MED value for the advertisement from R2 (ASB) to R4 (ASA) is altered from 200 to 50, making this path artificially more attractive to ASA. In contrast to an attack such as AS path forgery, which falsifies the sequence of ASs to manipulate the perceived path of a route, a MED modification attack influences route selection by altering the MED value without changing the AS path itself.
## 3 Numerical Examples
### Blast Wave Problem  
This example was proposed by [PERSON] and [PERSON] [50]. The initial conditions are  
\[(\rho,u,p)=\left\{\begin{array}{ll}(1,0,1000),\ 0\leq x<0.1,\\ (1,0,0.01),\ 0.1\leq x<0.9,\\ (1,0,100),\ 0.9\leq x\leq 1.\end{array}\right. \tag{50}\]  
The simulation uses N = 301 uniform grid points in the area [0, 1], with reflective boundary conditions at all sides. A fixed-time step size \(DT=1\times 10^{-5}\) and the ultimate evolution time set to \(t=0.038\). Since there is no exact solution, the ROE scheme obtains the reference solution with N = 4001.  
Figure 12 shows the comparison results of several numerical schemes and approximate exact solutions. As can be seen from the figure, the other schemes, especially the URLBFS scheme, show better shock wave capture capabilities than the LF scheme. When using the WENO7 reconstruction technique, the URLBFS scheme achieves a resolution similar to that of the ROE scheme. However, when using the WENO5 method, the URLBFS scheme  
Figure 11: Density distributions of the Shu-Osher problem at t = 1.8 were obtained using the LF, ROE, and URLBFS schemes compared to the reference solution. The results illustrate the differences in accuracy and resolution between these schemes. Panels (**a**,**c**) show the results using the WENO5 and WENO7 reconstruction methods, respectively, while panels (**b**,**d**) provide partial zoom-ins of critical areas to visualize the differences better.  
shows a clear advantage in accurately resolving density peaks. These improvements show that the URLBFS scheme provides a more effective solution to complex fluid dynamics problems involving strong discontinuities than the LF and ROE schemes with enhanced resolution and shock wave capture capabilities.
## 3 Results
### Study Selection  
Following a comprehensive search across four databases (PubMed, Web of Science, Embase, and Cochrane), a total of 753 articles were imported into Covidence. Among these, 179 duplicates were promptly removed, resulting in 564 unique articles. Both researchers independently screened the titles and abstracts of each article, maintaining a blind review process to ensure unbiased decision-making. Collaboratively, any conflicting choices were resolved through discussion between the researchers, and no third-party involvement was necessary.  
Subsequently, 22 studies met the criteria for full-text screening. During this stage, each researcher independently assessed the full texts of the articles to identify potential inclusion or exclusion indicators. To avoid biased opinions, both researchers worked blindly without knowing each other's assessments. Conflicts arising from their independent evaluations were resolved through direct discussion. Finally, five of the initial 22 studies were deemed suitable for data extraction in this systematic review. 17 Studies were excluded as they did not compare HRV and posture or due to the unavailability of the full text. Each of these studies employed a prospective case series design. The results are summarized in the form of a Prisma flow chart (Figure 1).
# Predicting Compressive Strength of
## 1 Introduction  
### Aim and Scope of the Research  
This article aims to present that machine learning can predict the compressive strength of building materials with a high degree of accuracy based on scanning electron microscope (SEM) images. The methodology of predicting the compressive strength of cement-stabilized rammed earth (CSRE) is described and the results achieved  
CSRE is a material used to build construction walls from locally available, inorganic soil found under the layer of humus. Most importantly, CSRE is a highly sustainable construction material. In the study [1], resulting from an analysis using the Building Research Establishment Environmental Assessment Method (BREAM), external building partitions containing a load-bearing rammed earth layer obtained the highest A+ rating. According to [PERSON] and [PERSON] [2], such a rating would also be obtained by a partition with a load-bearing layer of rammed earth stabilized with cement. However,designing CSRE of sufficiently high compressive strength is difficult because this feature depends on several mixture properties and technological issues, such as a compaction method [3], density of elements [4; 5; 6], porosity of elements [6], particle size distribution of the soil [4; 5; 7], mineral composition of the soil [8], content and type of cement used as a stabilizer [6; 9; 10], moisture content of the soil-cement mixture during construction [4; 11; 12; 13], moisture content of element during service [11; 12], exposure conditions of the construction elements [5; 14], and age of the construction elements [6; 15; 16].  
A multitude of factors hampers the practical application of rammed earth technology. Even if a method of predicting the compressive strength of CSRE based on these factors is developed, assembling a structure using locally sourced soil would require detailed laboratory tests on a case-by-case basis, to determine the basic parameters of that soil--e.g the mineral composition and particle size distribution of the soil. Other properties of the soil-cement mixture, such as the moisture content, may change unexpectedly due to changes in atmospheric conditions prevailing at the construction site. It is proposed to model the compressive strength of CSRE partitions based on SEM images using machine learning and deep learning techniques instead of the whole process involving laboratory testing and the creation of models of relationships between mixture properties and parameters. The proposed method is presented in the article.
## 1 Introduction
Baseline-free methods are based on advanced signal processing techniques to identify damage without the need to refer to prior response of the healthy state bridge [31], where damage is often identified as irregularities or peaks at the damage location. In fact, signal processing techniques have been employed for baseline-based and baseline-free damage detection. For example, wavelet transform (WT) techniques have been widely used to detect damage while relying on prior information of the intact bridge [32; 33; 34]. Furthermore, [PERSON], [PERSON] [35] and [PERSON], [PERSON] [36] used the empirical mode decomposition (EMD) technique for a baseline-based damage detection method. [PERSON], [PERSON] [37] and [PERSON], [PERSON] [38] proposed the use of instantaneous amplitude squared (IAS) for damage detection using a moving vehicle. [PERSON], [PERSON] [39] proposed the use of contact point displacement difference (CPDD) for a baseline-free method. They found out that the proposed method was not able to detect damage near the boundary. However, when using a baseline-based method, such as coordinate modal assurance criterion (COMAC), damage at the boundary could be detected. This paper proposes the use of a series of advanced signal processing techniques to detect damage using a stationary vehicle, without relying on previous information of the intact bridge. The following section explains the proposed method in detail. Thereafter, a numerical simulation of a damaged bridge is presented. The proposed method is implemented to demonstrate how a vehicle in a stationary position can be used for bridge damage detection.
# Power Dissipation and Wear Modeling in Wheel-Rail Contact
## 2 Formulation of Wheel-Rail Contact Problem
### Wear Evaluation Models
#### 2.4.1 Dissipated Energy Wear Model  
In the literature, there are many proposed wear laws based on the evaluation of the dissipated energy due to the work of the friction force. These formulations include, among others, Zobory, Krause-Poll, BRR and USFD wear laws [1; 8]. In this approach, the removed material volume \(Vol\) is estimated. The amount of removed material directly depends on the frictional work and is calculated using the formula:  
\[Vol=\begin{cases}\alpha_{f}E_{f}&P_{f}/A\leq\pi_{lim}\\ k_{sm}\alpha_{f}E_{f}&\text{ \ \ \ otherwise}\end{cases} \tag{11}\]  
where \(k_{sm}>0\), \(A>0\) and \(\pi_{lim}>0\) denote a proportionality constant, contact patch area and a threshold value, respectively. In (11), the estimated experimental wear constants \(\alpha_{f}>0\) and \(\alpha_{s}=k_{sm}\alpha_{f}>\) are associated, respectively, with mild and severe wear regimes.  
The frictional work \(E_{f}\) may be estimated from the frictional power \(P_{f}\) and time integration step \(\triangle t\) as follows:  
\[E_{f}=P_{f}\bigtriangleup t\ \ \ \text{and}\ \ \ P_{f}=T\gamma\cdot V=(\mid F_{x} \zeta\mid+\mid F_{y}\eta\mid+\mid M_{z}\omega\mid)\cdot V \tag{12}\]  
The power \(P_{f}\) is calculated as the product of velocity \(V\) as well as force \(F=[F_{x},F_{y}]\) and creepage \(\zeta=[\zeta_{x},\zeta_{y}]\) consisting of longitudinal and lateral components. Moreover, in the 3D case, the product of spin moment \(M_{z}\) and spin creepage \(\omega\) is added. The energy wear coefficient \(\alpha_{f}\) is determined experimentally [22] and belongs to rail material parameters. Its range of applicability is large [22]. In order to evaluate the total dissipated energy \(E_{f}\), let us first consider one cycle of the movement of the frictional force \(F_{1}\) from point \(x_{1 min}\) to point \(x_{1 max}\) of the contact interface during time interval \((t-\triangle t,t)\). Using Formula (8), the energy \(E_{f}\) dissipated in one slip cycle \(i\) is equal to the product of friction force and sliding distance according to the formula:  
\[E_{f^{i}}=\int_{t-\triangle t}^{t}\mid F_{1}\mid dx_{1}=\int_{t-\triangle t}^{ t}\mu\mid F_{2}\mid dx_{1}=\mu\mid F_{2}\mid(x_{1}(t)-x_{1}(t-\triangle t)), \tag{13}\]  
Since in Formula (13) the history of sliding displacement is used, it makes it very useful in energy modeling for bodies in contact subject to plastic deformation. Summing energies dissipated in each cycle, we obtain the formula describing the total dissipated energy \(E_{f}\) as equal to  
\[E_{f}=\alpha_{f}\sum_{i}E_{f^{i}} \tag{14}\]  
We shall refer to the system (13) and (14) as the description of the global dissipated energy method [17; 22].
## 5 Conclusions  
Emblica has been ubiquitously used in Ayurvedic medicine for nourishing hair since ancient times. The results of this experiment show that emblica extract solution promoted hair strength (tensile strength and extensibility). Among all treatments (DI water, 3%EXS, 6%EXS, and 9%EXS), the results showed that tensile strength/extensibility of VH and BH and concentration of emblica extract solution are directly related; 9% EXS was the best concentration that could achieve the maximum tensile strength (\(1.124\pm 0.123\) N) and extensibility (\(37.14\pm 5.95\%\)) for VH. Only the effect of 3% EXS on VH led to a statistically insignificant increase, which means that the emblica extract solution can increase the tensile strength of VH when the concentration is more than 3%. All concentrations of emblica extract solution increased the tensile strength of BH and TH in a statistically insignificant manner except for the increase of the tensile strength of BH by 9% EXS, which was statistically significant; this means that emblica extract solution can increase the tensile strength on BH when the concentration is more than 9%.  
The extensibility of VH and BH were statistically significantly increased by all concentrations of EXS except for the increase in the extensibility of BH caused by an increase from 6% EXS to 9% EXS, which was statistically insignificant; this means that 6% EXS was the minimum concentration that could cause the maximum extensibility for BH (\(0.40\pm 0.05\%\)).  
The extensibility of TH fluctuated with statistically insignificant changes. The microscopical surface and cross-sectional images of SEM showed the morphological attachment of emblica on VH, BH, and TH. As an extract solution, emblica has potential as a hair strengthening treatment for virgin hair and 12% of hydrogen peroxide on BH.  
Conceptualization, and methodology, P.T., T.T., C.C., and D.N.; formal analysis, data curation, P.T.; writing--original draft preparation, P.T.; writing--review and editing, B.S.S., C.C., D.N.; supervision, project investigator, C.C., D.N. All authors have read and agreed to the published version of the manuscript.  
This project was partially supported by Suan Sunandha Rajabhat University, Bangkok, Thailand. This project was partially supported by Chiang Mai University, Thailand.  
BSS would like to acknowledge postdoctoral grant of Chiang Mai University.  
The authors declare no conflict of interest in this work.  
Figure 9: Schematic depicting some of the fracture patterns of human hair modify from Chemical and Physical Behavior of Human Hair [46].
## 5 Experiments and Analysis of Results
### Evaluation Indicators  
Average transmission delay, throughput, and acceleration ratio are the core metrics to measure the efficiency of data transmission and system performance. The average transmission delay reflects the time from the sender to the receiver, which is the key to evaluate the response speed and real-time performance of the system, especially for real-time data exchange scenarios, such as efficient scheduling between smart devices in ports. Throughput represents the amount of data transmitted per unit time, which directly affects the bandwidth utilization and data processing capacity of the system. In large-scale data transmission scenarios, improving throughput can accelerate information transmission, avoid network bottlenecks, and ensure efficient communication between devices. Acceleration ratio measures the degree of system performance improvement. A high acceleration ratio means that the optimization scheme significantly improves the processing efficiency, which is of great significance for improving the overall performance of the system, especially in large-scale data transmission and complex computing environments. These three metrics together determine the efficiency, stability and scalability of the system in practical applications.  
1. The average transmission delay is the average time it takes for data to be sent from send to receive and is calculated as Equation (8):  
\[T_{d}=L/R \tag{8}\]  
where \(T_{d}\) represents the average transmission delay, \(L\) represents the length of the packet, and \(R\) represents the transmission rate.  
2. Acceleration ratio is a measure of how many times a system's performance improves after the introduction of some improvement and can help evaluate the effectiveness of system optimization measures. It is calculated by Equation (9):  
\[S=T_{old}/T_{new} \tag{9}\]  
Among them, \(S\) is the acceleration ratio, \(T_{old}\) is the execution time before optimization, and \(T_{new}\) is the execution time after optimization.  
3. Throughput is the amount of data successfully transmitted per unit of time and reflects the ability of the system to process data per unit of time. Its calculation formula is Equation (10):  
\[T=N/T_{total} \tag{10}\]  
where \(T\) is the throughput, \(N\) is the amount of data passing through the system and \(T_{total}\) is the total transmission time.  
These above metrics can help evaluate and optimize the performance of a data transmission system to improve the efficiency and reliability of data transmission.
## 5 Conclusions  
This paper discusses how to reduce the language bias of the VQA model via self-knowledge distillation and proposes a new online learning framework, \"language bias-driven self-knowledge distillation (LBSD)\", for implicit learning of multi-view visual features. Moreover, in order to help student models learn unbiased visual knowledge, the authors of this paper propose generalization uncertainty to measure the learning results of student models and use KL divergence to reinforce the debiased mutual learning of student models. In this way, the student model can learn unbiased knowledge from  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **All** & **Yes/No** & **Num** & **Other** \\ \hline GVQA [67] & 39.23 & 64.72 & 11.87 & 24.86 \\ UpDn [56] & 37.87 & 42.58 & 14.16 & 42.71 \\ \hline \multicolumn{5}{l}{_Group 1_} \\ \hline Reweight* [32] & 41.46 & 61.52 & 13.02 & 32.94 \\
**LBSD-** & **43.66** & 66.63 & 12.23 & 33.45 \\
**Reweight** & & & & \\ \hline \multicolumn{5}{l}{_Group 2_} \\ \hline AReg [29] & 41.17 & 65.49 & 15.48 & 35.48 \\ RUBi [31] & 44.81 & 69.65 & 14.91 & 32.13 \\ LMH [32] & 55.27 & 76.47 & 26.66 & 45.68 \\
**LBSD-LMH** & **55.93** & 75.43 & 34.17 & 45.28 \\ \hline \multicolumn{5}{l}{_Group 3_} \\ \hline CSS [36] & 60.95 & 85.60 & 40.57 & 44.62 \\ CSS+GS [38] & 58.05 & 78.50 & 37.24 & 46.08 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison (Accuracies %) with the state-of-the-art model’s accuracy on the VQA-CP v1 test. * indicates the results from our reimplementation using officially released codes.  
Figure 5: Qualitative examples of VQA-CP v2 (test set). The wrong and right answers are highlighted in red and green.  
each other through the output of Top-K information entropy. In addition, the paper also discusses the effect of the heterogenous student models on the reduction of language bias. The experiment proves that even the heterogeneous student model can improve the unbiased learning ability through the LBSD method. Extensive experiments and ablation experiments on the VQA-CP v2, VQA-CP v1 and VQA v2 datasets verify the effectiveness of the proposed method. In the future, we will continue to explore how to better define the concept of unbiased knowledge, such as using multimodal knowledge graphs to help the model understand the type of knowledge in the dataset and how to optimize the loss function to enable the model to distinguish biased and unbiased knowledge, so as to reduce the experimental bias against language.  
Conceptualization, D.Y., L.W., Q.W., F.M., K.N.N. and L.X.; methodology, visualization, experiments and writing, D.Y. and L.W.; writing, review and editing, Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China; grant numbers 61831005 and 61971095.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experiments
### Training and Validation  
We perform repeated training and validation using rolling period samples. Our training set size is \(N=5000\) and each row in the training data comprises data from \(L=30\) days. After training our deep learning net, we then forecast the next \(F=\{10,30,1000\}\) days market direction. We then roll the sample \(F\) days forward and repeat this experiment. Altogether, depending on the value of \(F\), we have different numbers of experiments. For example, when \(F=10\), we conduct 422 experiments across the entire time series of the dataset (think of this as 422-fold cross validation). The general scheme is shown in Figure 7. Training for each experiment across all rolling cases takes about 3 h, with 6 CPU and 25 GB assigned to the h2o cluster. Therefore, each episode of training and test takes less than 30 s.  
Unlike cross-sectional data, our time series dataset lends itself better to rolling training and validation where the training period always must precede the validation period. Hence, there is no need to keep a hold-out test sample. For each experiment we report various test statistics: precision, recall, F1 score, area under the curve (AUC), and root mean squared error (RMSE). Because we have many experiments, we are in a position to report the distribution of these statistics generated from experiments spanning more than 5 decades (1963-2016). Therefore, we get a pretty good idea of whether deep learning neural nets reveal market inefficiency.