esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Simulation of CSG Dam upon Overburden  
### Project Overview and Establishment of Finite Element Model  
#### 2.1.1 Basic Assumptions  
The construction of CSG dams upon overburden is influenced by various factors and is a very complex engineering problem, so it is very difficult to simulate it completely and accurately. Starting from practical engineering, appropriate assumptions can be used to  
Figure 1: Roadmap of the study’s flow.  
improve calculation efficiency. Finite element software ANSYS 19.0 can be used to simulate the CSG dam upon overburden. The basic assumptions are as follows:  
1. The model is simplified into four parts: dam body, cut-off wall, overburden, and bedrock.
2. The material of the cut-off wall and the bedrock is uniform, isotropic, and linearly elastic.
3. The material of the overburden matches the Mohr Coulomb constitutive model.
4. The material of the dam body matches the Duncan Zhang constitutive model modified by rigid spring method.
5. The contact surface between the dam body and the covering layer is in complete contact.
6. Not considering the impact of groundwater.
7. Not considering changes in ambient temperature.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### Pulse Width
Wide swath mode in RADARSAT-1 offers extensive coverage for applications like agriculture and forestry, and it is approximately 100 \(\upmu\)s [6]. These are just a selection of pulse width values from various SAR missions, highlighting the diversity of applications and resolution requirements. The selection of pulse width is often tailored to the mission's specific objectives and the trade-offs between resolution and coverage.
###### Abstract
The validation of this model's efficacy was performed using a dataset with 15,165 student assessment needs across various academic institutions. The goal was to showcase the model's unparalleled accuracy in classifying academically at-risk students compared to other educational recommendation systems, thus providing a valuable resource for enhancing the educational trajectories of students through the strategic analysis of their academic history. Based on the aim of the paper to evaluate the effectiveness of a Gated Recurrent Neural Network (GRU) model on identifying and classifying academically underperforming students, here are the proposed research hypotheses:  
**Hypothesis 1 (H1):**_The GRU-based deep learning model significantly outperforms traditional educational recommendation systems in accurately identifying academically underperforming students within educational institutions._  
**Hypothesis 2 (H2):**_The use of dense layers, max-pooling layers, and the ADAM optimization algorithm within the GRUI model contributes to a higher accuracy rate in classifying student performance compared to models that do not utilize these advanced neural network features._  
**Hypothesis 3 (H3):**_The GRUI model's performance, as measured in terms of accuracy, precision, recall, and F1-score, is robust across diverse datasets comprising student assessment records from various academic institutions._  
The structure of this research paper is organized into various sections. Section 2 outlines the related works. This is followed by Section 3, which details the methods used in this research. Section 4 delves into the datasets utilized and the classification methods employed. Section 5 is dedicated to presenting the experimental results. Finally, Section 6 concludes the paper and discusses potential future work.
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 3 Hardware-Based Mitigation Techniques
### Exploiting the NX Bit Mitigation Technique  
The use of NX bit has prevented a considerable portion of BOF attacks by preventing code injection. Therefore, adversaries have adapted another strategy named code-reuse attacks [28] in which, instead of injecting malicious code, the attacker uses the pre-existing code in the process address space. Return-to-libc is one of the well-known code-reuse attack technique, in which the attacker exploits the program vulnerability to overwrite the return address with a pointer to the libc function. The standard C library is always linked to programs (written in C language) in almost all operating systems. The basic idea behind a return-to-libc attack is to change the target application's control flow to system() library function, which internally calls the execve() system call. The system() function is invoked with attacker-supplied arguments such as \"/bin/sh\" for getting a shell  
Figure 7: NX bit entry in the 64-bit page table for x86-64 Architecture.  
on the victim machine [29]. Although quite successful in many environments, however, return-to-libc attacks do suffer with following limitations:  
1. On x86 32-bit machines, arguments can be controlled because they are push on the stack. However, on 64-bit machines, since the function arguments are passed via registers, therefore, return-to-libc attacks would not work.
2. Attacker can only use those functions present in the code segment or the library's code, limiting the attack functionality.
3. The arguments that are passed by the attacker might need to contain NULL bytes. However, If the cause of buffer overflow is a function like strcpy() that terminates when encounters NULL bytes. Then return-to-libc attack payload can't carry NULL bytes in the middle of the payload.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 5 Experiments
### Results and Discussion
On the other side, although the Zernike radial polynomials are orthogonal, they are in the continuous domain of image coordinate space. Their implementation involves numerical approximations, which has an impact on the orthogonal property. Besides, the Zernike radial polynomials are defined inside the unit circle. Their application also requires an appropriate transformation of the image coordinate space, increasing the computational complexity. On the contrary, the Tchebichef polynomials are orthogonal in the discrete domain of the image coordinate space, defining the corresponding moments directly on the image coordinate space and involving no numerical approximation. These advantages make them superior to the Hu moments and Zernike moments, and thus they present the best performance in terms of image description. In a word, the proposed algorithm based on TMIs and the PSO-SVM model can achieve fire image detection accurately, which has advantages over algorithms based on HMIs and ZMIs. The effectiveness and feasibility of the proposed algorithm are also confirmed.
# Candidate Waveforms for ARoF in Beyond 5G
## 2 Requirements to Waveforms for Beyond 5G
### Requirements in MmWave Wireless Communications  
MmWave signals are seriously affected by FSPL due to their inherent high frequencies. This fact plays an important role in determining the mmWave range. Furthermore, this type of signals are highly sensitive to attenuation. It turns out that atmospheric attenuation, rain-induced fading, snow, fog, foliage attenuation, and material penetration considerably adds more limitations to the maximum range of mmWave link [18]. Accordingly, mmWave signals reach shorter distances than the signals used in LTE. Fortunately, the reflected multipath components suffer a considerable attenuation so that their number is reduced [18]. Therefore, as the multipath effect in mmWave scenarios is less intense (except in special scenarios characterized by sand and/or dust atmosphere), it is more difficult to establish a non-line-of-sight (NLOS) communication [18]. Considering the limitation in distance that mmWave signals presents, a recommendable requirement for waveform formats is described below.  
* Efficient MIMO integration: Multiple-input multiple-output (MIMO) systems are a suitable technique to overcome the aforementioned significant low attenuation of mmWave wireless communications. Massive MIMO is an extended solution to form very directive lobes in a certain direction. However, this technique demands high signal processing requirements to manage its associated beamforming matrix [19]. Therefore, a modulation format with efficient MIMO integration is required to reduce the complexity of the beamforming system.
## 1 Introduction  
Lacases (benzenediol: oxygen oxidoreductase; EC 1.10.3.2) are multi-copper metallo-oxidases that catalyze one electron oxidation and reduce molecular oxygen to water. These enzymes exhibit broad substrate specificity, and operate in the pH range of pH 3-5 and temperatures range from 40-60 \({}^{\circ}\)C [1]. Laccases are found in a variety of organisms, including bacteria, fungi, insects, higher plants, and animals. Laccases are secreted during secondary metabolism, especially under limited nutrient levels (mostly nitrogen sources) [2]. These enzymes are biotechnology important due to their ability to oxidize both phenolic and non-phenolic lignin-related compounds [3] and highly recalcitrant environmental pollutants [4]. The type of degradation caused by lacases is expected to be very useful for the treatment of lignocellulosic biomass, waste water, and dye degradation [5]. Laccases are also increasingly being used in (i) the production of value-added chemicals from lignin; (ii) paper and pulp bleaching; (iii) prevention of wine discoloration; (iv) waste bleaching and detoxification; (v) bioremediation; and (vi) biosensors. Given the applications, lacases have been a subject of research since the 19 th century and the search for potent lacases still continues [6].  
Lignocellulosic biomass plays an important role in biofuel production as well as the paper and pulp industry. Effective degradation of lignocellulosic biomass has been a subject of research, especially in the area of biofuels production, for a long time, because it is the limiting factor in recovery of fermentable sugars from lignocellulosic biomass. Among the lignin-modifying enzymes, lacases constitute a major group of enzymes responsible for delignification. Genetic manipulation of plants to reduce lignin content in plants, which would lead to improved sugar yields from lignocellulosic and, thus, increased ethanol production, has also been reported. Such genetic manipulations have also shown that lignin reduction below a certain threshold can cause large changes in wood chemistry and plant metabolism, ultimately resulting in low ethanol yields [7]. Given the above, the use of lacase(s) to effectively pretreat lignocellulosic biomass appears to be more efficient in recovering fermentable sugars. The latter necessitates finding novel lacase with higher stability and optimal enzyme activity for industrial application. However, a serious problem often encountered in industrial exploitation of lacases reported so far is that their secretion into the broth by microorganisms are low/limited. Therefore, it is important to identify organisms, especially fungi that secrete high levels of lacases. With the latter as a goal, reported herein is the screening and identification of a fungus that can produce significantly higher levels of extracellular lactose with desirable properties.
## 4 Statistical Results
### Short-Period Region  
Figure 4 illustrates the comparison between the benchmark model and scaled model for the EPP system with a radius (\(R\)) of 4 and a time period (\(T\)) of 0.2 s. The aftershock records are scaled using two different scaling factors, denoted as \(\
abla S_{\text{a}}\) and \(\
abla PGA\). Additionally, the values of the IMs derived from the scaled ground motions, which serve as the basis for the scaled model, are depicted in Figure 4. Furthermore, Figure 4 presents the outcomes of the bias introduced by the scaling of ground motions. It can be observed that using \(\
abla S_{\text{a}}\) as the scale indicator produces a lower bias than \(\
abla PGA\) for this system after scaling aftershock records six times (i.e., \(SF\) = 6.0). For example, the bias corresponding to \(\
abla S_{\text{a}}\) and \(SF\) = 6.0 is 11% (as shown in Figure 4b), while the bias for \(\
abla PGA\) and \(SF\) = 6.0 is 33% (as shown in Figure 4d). The platform section disappears in the scaled model, indicating the minimum \(\
abla IM\) in the scaled database exceeds the intensity threshold and aftershock start to induce additional damage.  
The study computes the mean biases that arise from the adjustment of aftershock ground motions in EPP systems within the short-period range of 0.1 to 0.5 s. The results are depicted in Figure 5, which showcases the biases for eight distinct scale indicators. The results demonstrate a gradual increase in biases with higher SF. For the biases corresponding to \(\
abla S_{\text{a}}\) and \(\
abla PGA\), the results of different \(R\)s are close to each other, and no clear trend can  
Figure 4: Comparison between the benchmark model and scaled model for the EPP system with \(R\) = 4 and \(T\) = 0.2 s: (**a**) \(\
abla S_{\text{a}}\), \(SF\) = 1.0; (**b**) \(\
abla S_{\text{a}}\), \(SF\) = 6.0; (**c**) \(\
abla PGA\), \(SF\) = 1.0; (**d**) \(\
abla PGA\), \(SF\) = 6.0.  
be observed among the different \(R\)s. For the biases corresponding to the other six scale indicators, the biases increase with the decrease in \(R\), indicating that using the other six scale indicators produces higher biases for stronger systems (e.g., \(R=2\)). After incrementally scaling aftershock record up to ten times, the biases corresponding to \(\
abla S_{\text{a}}\) and \(\
abla PGA\) are generally within 30% and 50%, respectively, while the biases corresponding the other six scale indicators can exceed 50%, especially for stronger systems (e.g., \(R=2\)). It should be noted that using \(\
abla SI\) and \(\
abla PGV\) as scale indicators produces biases within 30% for systems with \(R\) being 4 or even larger, indicating that \(\
abla SI\) and \(\
abla PGV\) can be used as alternative scale indicators for systems with short periods and larger \(R\) (e.g., \(R>4\)). However, although \(\
abla I_{\text{A}}\) and \(\
abla I_{\text{C}}\) are acceleration-related intensity measures, using them as scale indicators has no advantage of introducing lower biases.
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 4 Experiments
### Dataset  
Our experiments utilized the CTU-13 botnet dataset, which is a collection of botnet traffic that was assembled by CTU University, Czech Republic, in 2011. This is a publicly available dataset which contains 13 botnet scenarios or captures of different botnet samples summarized in 13 bidirectional NetFlow files, which comprises actual botnet traffic, offering a valuable resource for scrutinizing real botnet behavior and gaining insights into the diverse strategies employed by botnet operators [39]. Out of these 13 bidirectional NetFlow files, we have selected the 12 th scenario for our experiments because only the 12 th scenario contains P2P bot nodes' communication as well as benign nodes' communication. The first reason for selecting this dataset is that it is publicly available, whereas there are some datasets which are not publicly accessible. Another reason is that there are certain datasets that comprise bot flows originating from a single bot node, while others feature multiple bot nodes without inter-node communication. However, the CTU-13 (Scenario 12) dataset contains three P2P bot nodes along with benign nodes and also incorporates the communication flow among bot nodes.  
While the CTU-13 Scenario 12 dataset originates from 2011, it continues to serve as a valuable benchmark for botnet research--particularly in the context of P2P botnetdetection. This scenario is among the few publicly available datasets that include real-world P2P botnet communication with multiple infected hosts and inter-bot interactions. These characteristics are essential for studying decentralized botnet behavior. Nevertheless, we acknowledge that botnet communication patterns evolve, and future work will involve validating the proposed feature selection and classification framework on newer datasets or real-time traffic to further assess generalizability and resilience to emerging threats.  
The features related to the network traffic for Scenario 12 are shown in Table 2. Table 2 represents information like size, duration, number of packets, number of flows, number of bots, and bot family [35].  
This dataset is labelled and comprises bidirectional NetFlows. NetFlow is a network protocol developed by Cisco that facilitates the collection and monitoring of IP traffic information. The CTU-13 (Scenario 12) dataset is specifically a bidirectional NetFlow file and is characterized by 15 features [6]. Table 3 describes various features of the dataset used in our experiments.
# Rapid Molecular Identification of Scolytinae (_Coleopterara: Curculionidae_)
###### Abstract  
Routine identification of bark and ambrosia beetles is done using morphology. For people lacking the necessary taxonomic knowledge, proper identification of a novel specimen can be challenging and time consuming. This study compares the usefulness of four genetic markers (28S, EF-1a, ITS2, and COI) and five primer pairs (D2F1/D3R2, eflafor1/eflarev1, ets149/efa754, ITS2F/ITS2R, and LCO1490/HCO2198) to identify Scolytinae beetles, and outlines a molecular identification strategy, with results possible in two days. Markers COI and EF-1a were selected based on the ability of the respective primers to amplify DNA from multiple genera (_Coptoborus_, _Xyleborus_, _Hypothenemus_, _Theoborus_, and _Araptus_) and the ability of the resulting sequences to provide accurate and unambiguous matches in GenBank. BLASTn analysis of EF-1a sequences (both primer pairs) correctly identified four out of the five genera and COI sequences identified at least one sample of every genus tested and was the only primer pair to correctly identify _Araptus_ specimens. Further, 28S sequences successfully identified _Coptoborus_, _Xyleborus_, and _Theoborus_ but not _Hypothenemus_ or _Araptus_. The low number of EF-1a (1), 28S (7), and ITS2 (0) sequences from _Araptus_ individuals present in GenBank compared with COI (137) is likely the reason that only the latter marker was capable of identifying members of this genus. ITS2 sequences were insufficient to identify any of the samples tested. This study also determined the minimum quantity of DNA that could be used for molecular identification. Primers D2F1 and D3R2, which had the highest rate of amplification in all genera tested, could yield an informative sequence with as little as 0.00048 ng of DNA, however, at least 0.0024 ng was needed for reliable amplification.  
DNA barcoding; _Coptoborus_; _Xyleborus_; _Theoborus_; _Hypothenemus_; _Araptus_; molecular markers; ambrosia beetles; bark beetles; Scolytinae
# Statistical Modeling of Cultural Differences in Adopting Autonomous Vehicles
## 2 Models for Public Opinion about AVs
### Results
#### 2.4.2 Regression Model  
Regression models were developed with the survey data from the collected literature and the values of the dimensions in the 6-D model.  
At first, the regression models for average concern about AVs were expressed as Equation (2). Countries with a higher PDI, lower UAI, and higher INDU had more concerns about AVs. The \(p\)-values of the variables included in the model were all less than 0.05, and the adjusted R-square was 0.9446, confirming that the model well represented the concern about AVs. Additionally, when the observed data from the survey and the value predicted by the regression model were compared through the paired t test, it was confirmed that the model could accurately predict the concern about AVs because there was no significant difference between the two groups (t(5) = 0.2629, \(p\)-value = 0.80).  
\[\text{Concern}=38.24+0.6097\times\text{PDI}-0.1278\times\text{UAI}+0.2340\times \text{INDU}. \tag{2}\]  
Furthermore, the regression model for willingness to pay is shown in Equation (3). This showed that people in countries with lower MAS and INDU were more willing to pay extra for automated vehicles. The \(p\)-values of the variables included in the model were all less than 0.05, and the adjusted R-square was 0.9981, confirming that the model well represented willingness to pay for purchasing AVs. Additionally, when the observed data from the survey and the value predicted by the regression model were compared through the paired \(t\) test, it was confirmed that the model could accurately predict the WTP because there was no significant difference between the two groups (t(5) = \(-\)0.2451, \(p\)-value = 0.82).  
\[\text{WTP}=-122.526-0.8241\times\text{MAS}-0.6547\times\text{INDU}. \tag{3}\]
## 4 Coordinate System Conversion Algorithm-based Depth Estimation
### Construction and Surveying of Environment for Image Mapping (World Coordinate System Setting)  
Coordinate systems in a 3D space can be divided into two major categories. A camera coordinate system is based on the camera that acquired the image, whereas a world coordinate system is based on an arbitrary point in the real world. The camera coordinate system has the center of the lens as the origin, the horizontal and vertical directions of the image are the X and Y axes, respectively, and the direction to the principal point is the Z axis. It is automatically determined according to the camera position and direction. In contrast, the world coordinate system can be arbitrarily set as X, Y, and Z axes as well as the origin, and it is commonly set inside the space where the camera image was acquired.  
Figure 7 shows the world coordinate system set in the test environment. The bottom of the ground was set as the X-Y plane using a flat point. A license plate with black numbers written on a white background consisting of actual measurement information was placed on the floor to facilitate the image coordinate calculation.  
Figure 8 shows the actual measurement information of the floor plan world coordinate system and the CCTV camera. The height of the camera was 1 m 30 cm, and the distance between the camera and the number plate in the first row was 5 m 60 cm. The number plates were 3 \(\times\) 3 in size, 5 m 60 cm wide, and 21 m 40 cm long, with a total of nine plates placed at intervals.  
Figure 6: Estimated map of the standard grid for underground utility tunnels.