esa-sceva/satcom-chunk-collection huggingface dataset:
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 4 Experimental Analysis
### Limitations and Future Works  
SATFuzz can be strengthened in certain aspects.  
First is the effective test case classification false positive rate problem. The disadvantage of the method in this paper is the high false positive rate of effective test case classification. In the future, we will study how to optimize the structure of the test case filtering model to improve its ability to correctly classify effective test cases and further improve the efficiency of network protocol fuzzing.  
Second is state prioritization issues. This article prioritizes the states with reference to the meaning of the first two digits of the status code and does not fully consider the inherent meaning of the status code. In the future, the state prioritization will be refined to further improve the efficiency of stateful protocol fuzzing.  
Third is no status code issues. This paper requires that the stateful protocol entity must meet the condition that the response message contains the status code; thus, for the stateful protocol entity that does not contain the status code in the response message, the method in this paper is no longer applicable. In the future, we will study how to fuzz test stateful protocol entities whose response messages do not contain status codes to expand the scope of application of this method.
## 3 Synthesis of Crown Ethers and Their Derivatives
### Synthesis of (\(\gamma\)-Arylpyridino)-dibenzoza-14-crown-4 Ether  
A fruitful procedure of domino-type condensation was carried out to synthesize (\(\gamma\)-arylpyridino)-dibenzoaza-14-crown-4 ether. The reaction temperature of 120 \({}^{\circ}\)C, reaction time of 6 h and three major components used for this type of condensation were: an aromatic aldehyde, ammonium acetate, and 1,5-bis-(2-acetylphenoxy)-3-oxapentane [59]. As \(\gamma\)-arylpyridine plays a significant role as a pharmacophoric fragment prompting an effective impact on the bioactivities of various drug molecules, which became a motivation for the synthesis of (\(\gamma\)-arylpyridino)-dibenzoaza-14-crown-4 ether (Scheme 7) [60; 61].  
**Scheme 5.** Synthesis of aza-crown ether-squaramide conjugate compounds (**8**-**11**).  
**Scheme 6.** Dibenzothiazolydibenzo-18-crown-6 ether reaction mechanism for synthesis.  
**Scheme 7.** Formation of (\(\gamma\)-arylpyridino)-dibenzoaza-14-crown-4 ether.  
**4. Biological and Pharmacological Analysis of Crown Ethers**  
Crown ethers are found to have synergistic biological and pharmacological potential, they are widely used in drug delivery. The activities include anti-cancer, anti-microbial, anti-inflammatory and drug delivery.
## 2 Electric Propulsion Shaft System Model and Static Characteristic Analysis
### High Elasticity Coupling Model and Characteristic Analysis
#### 2.1.1 Elastic Coupling Rubber Material Stress-Strain Relationship  
The rubber material of high-elastic couplings possesses superior elastic and damping properties. The constitutive relationship of its composite material is nonlinear, typically addressed using strain energy density functions in large nonlinear commercial software such as ABAQUS and ANSYS. In finite element simulation analysis, commonly used strain energy density functions include the Mooney-Rivlin model, the Yeoh model, and the Neo-Hookean model, etc. [43; 44; 45; 46]. From a mathematical perspective, this has driven the in-depth study of strain energy density functions. Based on the incompressibility and isotropy assumptions of rubber materials, the strain energy function W can be represented as a function composed of three stretch ratios, \(I_{1}\), \(I_{2}\), \(I_{3}\). The strain energy function W remains unchanged when two of these variables change their signs. That is, the Rivlin strain energy density function model as follows:  
\[I_{1}=\lambda_{1}^{2}+\lambda_{2}^{2}+\lambda_{3}^{2} \tag{1}\]  
\[I_{2}=\lambda_{1}^{2}\lambda_{2}^{2}+\lambda_{2}^{2}\lambda_{3}^{2}+\lambda_{ 1}^{2}\lambda_{3}^{2} \tag{2}\]  
\[I_{3}=\lambda_{1}^{2}\lambda_{2}^{2}\lambda_{  
\[W=\sum_{i+j=1}^{N}C_{i\bar{\mathrm{i}}}(I_{1}-3)^{i}(I_{2}-3)^{j} \tag{4}\]  
Equation (4) is the Rivlin model in rubber theory analysis, where \(W\) represents the strain energy density function; and \(C_{ij}\) are rubber material constants. Simplifying this model leads to the commonly used two-term Mooney-Rivlin model, with its strain energy density function W expressed as follows:  
\[W=C_{10}(I_{1}-3)+C_{01}(I_{2}-3) \tag{5}\]  
where \(C_{10}\), \(C_{01}\) are Mooney-Rivlin constants. Many researchers in related fields have made various modifications to the Mooney-Rivlin model, resulting in many higher-order forms of strain energy density functions. Due to the convenience of use and the fact that the constants of these models can be obtained through tensile and shear tests on standard specimens, the Mooney-Rivlin model and its derivatives are widely applied in finite element calculations for rubber material models.
## 3 Results
### Correlation Analysis for Hyperspectral Waveband and AGB  
As Figure 3 displays the correlation coefficient between AGB and canopy spectral reflectance at four different stages, at seeding stage, the correlation coefficient in the range of 400-706 nm showed a negative correlation, and the maximum negative correlation was about \(-\)0.37, being located at 661 nm. In the waveband range of 690 to 750 nm, the correlation coefficient changed rapidly from negative correlation to significant positive correlation, with the increase of wavelength. In the NIR plateau (750-1300 nm), the correlation coefficient was relatively stable and close to 0.6.  
Figure 2: Canopy spectral reflectance and field pictures at four different stages. (**a**) Canopy spectral reflectance at seeding, bolting, flowering and pod stages; (**b**) Field picture at seeding stage; (**c**) Field picture at bolting stage; (**d**) Field picture at flowering stage; (**e**) Field picture at pod stage.  
At bolting stage, the correlation coefficient analysis was analogous to that of the seeding stage; in the NIR plateau (750-1300 nm), the extremely significant positive correlation coefficient was also relatively stable, being close to 0.8.  
At the flowering stage, the correlation coefficient changed rapidly from negative correlation to positive correlation with the increase of wavelength (480-551 nm). In the waveband range of 550 to 700 nm, the positive correlation coefficient slightly decreased, while the correlation coefficient increased rapidly with the increase of wavelength (710-750 nm). In the NIR plateau (750-1300 nm), the positive correlation coefficient was always higher than 0.4, but there were two big low near 970 nm and 1230 nm.  
At the pod stage, the correlation coefficient in the range of 400-1300 nm was insignificant, except for in the range of 570-710 nm, and the negative correlation remained in the almost waveband range of 400 to 1300 nm.  
The correlation coefficient analysis indicated that the relationship between NIR bands (750-1300 nm) and AGB was relatively stable at four different stages and it might optimize NIR bands combination to improve AGB estimation accuracy.
## 3 Results and Discussion
### Economic and Resilience Analysis of Microgrid Systems
#### 3.7.1 Investment Analysis and Financial Viability  
Investment planning for microgrid systems requires detailed evaluation of both initial capital costs and long-term operational benefits [13; 24]. Although advanced technologies such as ESSs and intelligent control platforms require considerable upfront investment, these costs can be balanced by lower operating expenses and improved energy management over time [19; 55].  
Economic analyses increasingly apply life cycle cost assessments to compare technology options and their expected financial outcomes over extended periods [17; 37]. Policy incentives and government subsidies also influence the financial attractiveness of microgrid initiatives, particularly in remote areas and developing regions [30; 36].  
However, regulatory uncertainty and fluctuating energy market prices introduce financial risks that complicate investment planning [32; 39]. These challenges highlight the importance of developing economic models that incorporate sensitivity analyses and risk evaluations to support informed decision-making.
## 2 Related Work  
Despite rapid growth in computer vision technologies, accurate monitoring of crowd flow in congested scenes remains a challenging task of deep learning. Different automated deep-learning solutions are constantly being developed based on computer vision techniquesapplied to surveillance videos. In this section, we review crowd-monitoring methods with deep learning and computer vision technologies.  
While there have been different approaches that monitor crowd movements and behaviors, vision-based monitoring with images and videos using CNN is the most widely used method. Among them, density estimation with CNN is a popular approach. Density estimation CNN models take an input image containing crowds and generate a density map that represents the distribution of people in the image. The first known CNN model for density estimation and counting is CrowdCNN [10], a model consisting of three convolution layers followed by three fully connected layers. Further down the road, multi-column CNNs that have filters of different sizes in the columns are developed to handle the variation of pedestrian head and body sizes in images [11; 12; 13]. Subsequently, single-column counting networks are proposed for learning size-relevant without relying on multi-column networks using modules containing multiple filters of different receptive field sizes [14; 15]. The density map outputted is useful for identifying where crowds gather and can be integrated to obtain an estimate of the crowd count [2]. The density estimation approach is particularly effective when crowds are extremely dense, and occluded and individuals are small.  
A limitation of density estimation, however, is that the approach can only compute the spatial distribution of the macroscopic crowd count, rather than the monitoring of individuals. An alternative approach is detection-based, where a CNN model first performs pedestrian detection, generating a set of bounding boxes around any individuals in an image. For instance, variants of a one-stage detector, You Only Look Once (YOLO) have been used for video-based crowd counting in specific regions to enforce social distancing [16; 17]. Detection-based methods tend to perform well when the crowd is sparse. However, when the crowd becomes dense, occlusion and small person-size tend to reduce performance significantly [18]. On the other hand, since detection-based methods yield bounding boxes around individual pedestrians, microscopic monitoring of each individual is possible. In this work, we show that when detection methods are integrated with tracking and information on the surrounding space, spatiotemporal mapping of individual trajectories can be obtained. The spatiotemporal mapping can then be leveraged to provide useful visualization and even spatiotemporal forecasting to assist the process of crowd congestion monitoring.
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 4 Methods
### Kernel Size  
The pixel intensity values of the preretinal space are similar to those of the vitreous. Therefore, the network may not have enough information about the surroundings to correctly assign a given pixel to a class. Furthermore, the area and shape of the preretinal space differ from B-scan to B-scan.  
Another way of providing the network with the information of where a given pixel belongs within the image is using a bigger convolution kernel. In contrast to works described in the literature review in Section 2, we propose the use of a non-typical convolutional kernel. It has been reported that by utilizing a vertical convolutional kernel of the size \(7\times 3\) px for ReLayNet [35], the network captures the entire retina in the lowest convolution level. Nevertheless, this approach has not been sufficiently discussed or analyzed in retina layers segmentation to explain the selected kernel size.  
Within the retina scan pixel intensities vary significantly in the vertical direction, therefore it can be beneficial to utilize a bigger kernel to detect those changes. In our experiments we check the influence of:  
* square kernels: \(3\times 3\), \(5\times 5\), and \(7\times 7\) px,
* vertical kernels: \(5\times 3\), \(7\times 3\), and \(9\times 3\) px,
* horizontal kernels: \(3\times 5\), \(3\times 7\), and \(3\times 9\) px.  
Bearing in mind the computational cost of incorporating a bigger convolutional kernel, we pose that even a non-uniform filter will significantly improve the accuracy of pixel-wise segmentation.  
Figure 4: Visualization of used distance maps for an example B-scan: “2 NetR”, a map based on previous retina segmentation with a neural network, “2 NetPR”, a map based on previous segmentation of retina and preretinal space, “CumSum”, a Cumulative Sum Map, and “BasicOrient”, a Basic Map with Orientation.
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 2 Materials and Methods
### Methods  
#### 2.3.1 Downscaling of MODIS GPP Products Based on the EBK Interpolation  
The EBK was used to downscale the MODIS GPP products from a 500 m spatial resolution to 30 m [29,30]. The EBK is superior compared with the conventional spatial downscaling methods that rely solely on the spectral data of images and do not take into account image texture and structure, as well as with the classical kriging methods that ignore the explanation of the error introduced by modeling semivariograms [31]. The EBK is a powerful non-stationary algorithm and divides an image into subsets and uses simulation to makes the process automatic for spatial interpolation [32]. In this method, the following Kriging Equation (2) was utilized to predict GPP values [30,33,34]:  
\[Z_{(x_{0})}=\sum_{i=1}^{n}\lambda_{i}\cdot Z_{(x_{i})}+\sum_{i=1}^{n}s_{i} \cdot U_{(x_{i})} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Growth Stage** & **Tiltering Stage** & **Jointing Stage** & **Heading Stage** & **Maturity Stage** \\ \hline \multirow{4}{*}{Acquisition date (m/d/y)} & 8/20/2011–8/27/2011 & 9/13/2011–9/20/2011 & 10/15/2011–10/22/2011 & 11/8/2011–11/15/2011 \\  & 8/19/2012–8/26/2012 & 9/12/2012–9/2012 & 10/14/2012–10/21/2012 & 11/7/2012–11/14/2012 \\  & 8/20/2013–8/27/2013 & 9/13/2013–9/20/2013 & 10/15/2013–10/22/2013 & 11/8/2013–11/15/2013 \\  & 8/20/2014–8/27/2014 & 9/13/2014–9/20/2014 & 10/15/2014–10/22/2014 & 11/8/2014–11/15/2014 \\  & 8/20/2015–8/27/2015 & 9/13/2015–9/20/2015 & 10/15/2015–10/22/2015 & 11/8/2015–11/15/2015 \\ \hline \end{tabular}
\end{table}
Table 1: Acquisition dates of MODIS-GPPs and corresponding with rice growth stages.  
where \(Z_{i},\ (i=1,\ \ldots,\ n)\) is the GPP value at location \(x_{i}\), \(\lambda_{i}\), (i = 1,2,3 \(\ldots\ n\)), represents the kriging weight generated using the parameters of cross-variograms, and \(s_{i}\), (i = 1,2,3 \(\ldots\ n\)) is the kriging weight estimated on the basis of a cross-variogram between \(Z_{(x_{i})}\) and \(U_{(x_{i})}\). The \(n\) denotes the total number of observations. The variable \(U_{(x)}\) was a standardized rank that was calculated as [30]:  
\[U_{(x_{i})}=\frac{R}{n} \tag{3}\]  
where \(R\) represents a rank of the \(R\)th order statistic of GPP on the land surface at location \(x_{i}\). Downscaling the 500 m MODIS GPP products to the 30 m spatial resolution using EBK was performed using ArcGIS 10.3 Geostatistical Analyst (Environmental Systems Research Institute, Inc., Redlands, CA, USA).
## 2 Motivating Scenario  
The supply chain attack on SolarWinds' Orion software in 2019 and 2020 was one of the most impactful for many organizations [7]. The attack was noticed by the cyber-security company FireEye. FireEye, like many other organizations, monitored its own network with the Orion platform. The attack went unnoticed for months. The attackers then proceeded to connect to the FireEye Virtual Private Network (VPN) by adding a cell phone as an approved device for two-factor authentication on an employee's account. When a new device is added to an employee's account at FireEye, it triggers an alert to the system administration, which was followed up, and, thereby, it was discovered that a second phone was registered and the newly added phone was not the employee's phone. In order to register another factor, the employee's username and password had to be already known to the attackers.  
In further investigation, the hack became visible. The attackers were able to access and modify the source code for Orion on SolarWind's internal repositories. SolarWinds used the weak password \"solarwinds123\" for File Transfer Protocol (FTP), which was additionally checked into a public repository at GitHub. After the malware was inserted in Orion's source code, a backdoor was planted onto FireEye's network. The backdoor was then used to access domain credentials, such as user accounts and passphrases. In stage three, token-signing certificates were used to access Office 365; for example, specific email accounts. This was the first occurrence of a Golden Security Assertion Markup Language (SAML) attack in the wild. At this stage, trusted domains were also modified or added and credentials were compromised. Last, but not least, the red team tools were stolen from FireEye. As a consequence, FireEye had to publish its internal tools and signatures for them to be recognized to avoid their misuse [8].  
Crawlers can search for sensitive information in public repositories and use the information after its discovery. Consequently, such information should never be published. Even without publishing, a weak password could, similarly to default passwords, easily be brute-forced; for example, by wordlists and dictionary-based attacks. If passwords are reused, attackers, who know one of them, can try to guess further credentials based on their knowledge. This shows that passwords with a low entropy pose a threat to organizations and should be avoided by, for example, enforcing quality-ensuring password policies. Depending on the criticality and the possible methods, further factors might be required for using a specific service. Even though a self-service portal for users improves usability and reduces the hassle in contacting the service desk, further checks during the authentication lifecycle, for example, when enrolling additional factors, are required.
## 3 Results and Discussion
### Physical Characterization of CMSC
#### 3.8.3 Molecular Weight, Solubility, and pH  
Table 4 indicates the molecular weight (MW) of CMSC, which is 6.296 \(\times\) 10\({}^{4}\) g/mol. It is known that the molecular weight would increase due to the number of -OCH\({}_{2}\)COONa groups per glucose unit that also increase the hydrodynamic volume of CMSC. This was shown in another study done by [PERSON] _et al._[62] regarding about viscosity of CMC and carboxymethylation of dextran [63].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Degree of** & **Moisture** & & **NaOH** & \multirow{2}{*}{**Topped**} & \multirow{2}{*}{**Solubility/\%**} & **Molar Mass** \\  & **Substitution** & **Content** & **pH** & **Content** & & & **g/mol** \\  & **(DS)** & **(\%)** & & **(\%)** & & & **g/mol** \\ \hline
1 & 0.59 \(\pm\) 0.01 & 8.23 \(\pm\) 0.94 & 9.76 & 1.36 \(\pm\) 0.21 & 11.02 & 93.06 & 5.8 \\
2 & 0.61 \(\pm\) 0.03 & 7.78 \(\pm\) 0.66 & 9.63 & 1.43\(\pm\) 0.25 & 11.50 & 94.45 & 6.7 \\
3 & 0.63 \(\pm\) 0.02 & 8.32 \(\pm\) 0.77 & 9.70 & 1.27\(\pm\) 0.27 & 12.12 & 94.34 & 6.2 \\ Average & 0.61 \(\pm\) 0.02 & 8.11 \(\pm\) 0.79 & 9.70 & 1.35 \(\pm\) 0.24 & 11.55 & 93.95 & 6.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characterization of physical and chemical properties of CMSC (carboxymethyl sago cellulose).  
In Table 4, the solubility of CMSC is high (95% dissolved) is attributable to the carboxylate group appended to the cellulose backbone [64]. The pH value of the CMSC solution under the conditions tested is 9.7. The elevated alkaline pH value could be due to the presence of a certain amount of NaOH that could have been trapped in the swollen molecules of CMSC even after prolonged washing.
###### Abstract
Consequently, in this study, the smart building infrastructure and IoT device parameters were all derived from real-world data, informing and influencing the implementation and evaluation. Therefore, this paper's novelty and main contributions represent a significant contribution to the body of knowledge in Cloud and Fog computing data management in a smart building using real-world data. In addition, this study also aims to contribute to the field of sustainability and the importance and relevance of building knowledge and awareness in the area of energy efficiency and sustainability, such as the United Nations (UN) Sustainable Development Goals (SDG), particularly SDG 11 on sustainable cities and communities [19]. This study develops a Cloud and Fog computing architecture-based model using the iFogSim2 network simulation toolkit to simulate smart IoT device usage in a smart building. The smart office building presented in this study is a case study exhibiting a refined design for an intelligent office floor plan layout. The layout integrates state-of-the-art IoT devices, including advanced lighting sensors, sophisticated CCTV surveillance cameras, and sensitive CO2 sensors. These sensors collect data to analyse and determine various smart building functions, such as controlled air quality, person occupancy, a security system, and energy-saving operations. The major contributions of this study are as follows:  
* Evaluation of the critical placement of IoT smart devices in a smart building floor plan and the capture of real-world IoT smart device datasets generated within iFogSim2 for a smart building;
* Providing analysis and insight into the impact of fixed devices on the data management process of performance metrics;
* Adopting the iFogSim2 toolkit to evaluate Fog computing's impact and benefits on performance metrics using real-world data in a smart building environment;
* Demonstrating a comparative analysis of device scalability in Cloud/Fog computing scenarios concerning performance metrics during the data management simulation process. The rest of this paper is structured as follows: The Section 2 comprises the literature review, where the current and trending literature in IoT and Fog and Cloud computing was explicitly explored in this study. The Section 3 delves into the methodology and techniques adopted to collect smart IoT device sensor datasets for the benefit of this study. The Section 4 highlights the implementation of the simulation experiments using the iFogSim2 toolkit. The Sections 5 and 6 critically discuss the simulation output results, with the Section 7 concluding the research conducted in this study.