esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Malware Analysis Methods  
The process of malware analysis is an important topic, as the method used to study malware may cause the software to activate and spread across the machine where the study is conducted. For this reason, malware analysis is divided into two broad categories, which include the following techniques:  
* Static Analysis: This involves identifying the structure of the malware without needing to execute its code. Various techniques can be employed to extract information that defines the type of software or file, such as source code analysis, string extraction, and behavioral pattern identification. The goal is to extract characteristics of the file being studied to determine whether it is malicious or benign.
* Dynamic Analysis: This involves executing the malware in a controlled environment to observe its behavior [1]. A dynamic environment example for feature extraction and malware detection is cloud-based virtual machines (VMs). This consists of a highly controlled environment where multiple VMs are run to simulate different operating systems and user environments. These VMs are used to analyze malware samples and extract features that can aid in the early and accurate detection of threats. With cloud infrastructure and resources, researchers can conduct thorough and comparative malware analyses, identifying behavioral patterns and malicious signatures to develop better protection measures and risk mitigation strategies [40].
# Digital Twin Conceptual Model within the Context of Internet of Things
## 6 Data Analytics and Visualization Layer (DAVL)
### Complex Tasks that Include Data Cleaning and Processing  
Some of the data wrangling and pre-processing techniques subjected to a multi-variate sensor data set involve outlier detection, smoothing/discretization, feature extraction, data reduction, and imputation (omitting missing values) from the raw sensor data. Smoothing on sensor data is crucial for eliminating outliers before fitting the data into a machine learning model. Many discretization approaches have been discussed in the context of sensor data. Discretization is based on defining sensor values into nominal values based on a predefined interval of their respective threshold values. For instance, equal frequency, equal width binning, entropy minimization, and Boolean reasoning are some examples to perform discretization on sensory data. Discretization is important because most of the data mining algorithms, such as k-means clustering or naive Bayes classification, require nominal or categorical data values instead of continuous data for training the model [21].  
Attribute reduction methods such as principle component analysis (PCA) can be instrumental in data transformation into an independent set of attributes. Additionally, it is useful for selecting sensor attributes that reflect the most variation, thereby reducing complexity in a strongly dimensional dataset while still preserving the trends and patterns.
# Forward and Inverse Studies on Scattering
## 3 Inverse Problem by Modified BEM
### Numerical Examples
#### 3.2.1 Choice of Parameters  
The radius r, maximum depth \(d_{\rm max}\) in Figure 4a, as well as the distance l in Figure Figure 4b for the double-flaw case, are the key geometric parameters for arc-shaped anyons; the maximum frequency \(\Omega_{\rm max}\) is an important parameter of input data.  
For generality, we perform dimensionless analysis in this paper. The normalized radius \(R=r/r\) is kept constant as 1; the depth is normalized as \(D=d/r\); and frequency \(\Omega=\Omega r/c_{\rm T}\), where \(c_{\rm T}\) is the shear wave velocity in the half-space. Meanwhile, the normalized coordinates are written as \(X_{i}=x_{i}/r\) (\(i=1,2\).)  
The readers can readily verify that, in dimensionless analysis, the model (i) with radius \(r\), maximum depth \(d_{\rm max}\), and frequency range \(0\sim\Omega_{\rm max}\), is equivalent to model (ii) with radius \(2r\), maximum depth \(2d_{\rm max}\), and frequency range \(0\sim 0.5\Omega_{\rm max}\). Thus, the absolute value of radius is of little importance. Instead, the depth-radius ratio \(d_{\rm max}/r\) (or \(D_{\rm max}/R\)) is a more essential parameter showing the \"steepness\" of the canyon. Another important factor is the normalized frequency range, which reflects the incident Rayleigh wavelength in comparison with the geometric flaw scale.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 4 Design of the INC-Based System
### Design of the Data Plane
If not, the origin of the  
Figure 5: Flux diagram for the parser phase. Figure 6: Flux diagram and code for the ingress phase. packet is checked as in the ingress pipeline, so if it comes from one of the brokers, it is sent to the IIoT device (P0). However, if it comes from that IIoT device, it is sent to the cloud broker (P1). Finally, before rebuilding the packet in the deparser, it is mandatory to update checksum values in the headers that had fields modified, such as IP and TCP. To do so, an extern is used, which is a type of action or function that is specific to the architecture. Specifically, the extern used is _Checksum16_, which receives input parameters that are necessary for the checksum value calculation and updates it in the corresponding packet. This is crucial, because if the IP address and MQTT topic are modified in a packet and the checksum is not updated, that packet would be discarded upon receiving it in the destination due to an incorrect checksum value.
# Long-Term Effects in Structures: Background and Recent Developments
## 3 Long-Term Effects in Concrete
### The Models for Prediction of Creep and Shrinkage  
Over the past three decades, numerous models have been proposed to estimate the creep and shrinkage. In the majority of cases, those two phenomena are treated together. These models aim to strike a balance between reliability and ease of use, and they mainly differ in the number and type of parameters they take into account. The reliance of the engineering community, which typically possesses limited knowledge of concrete creep and shrinkage, on simplified models underscores the importance of such models in the field.  
Despite a level of uncertainty such simplifications bring into the model, the right choice of parameters can much accelerate the calculations while preserving model predictions.  
Some of the most commonly used models in engineering practice are as follows:  
* [noitemsep]  
by Recommendation of RILEM (The International Union of Laboratories and Experts in Construction Materials, Systems and Structures).  
With the exception of the last three models (GL2000, B3 and B4), all others are based on norms for structures design and express creep strain as the product of the concrete elastic deformation and the creep coefficient. The creep coefficient can be calculated according to several intrinsic and/or extrinsic variables [55]. Some of the mentioned codes with factors which were accounted for in calculations are listed below (Table 1) [55].  
All mentioned models would give significant differences in determining the creep coefficient and the relative shrinkage, so one has to be very careful in which cases to use them. A comparative study about model usage can be found in [55].
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 2 The Proposed Image Compensation-Based R-D Model  
The ICRD model combines the R-D model with a low-order polynomial image compensation model. This section first introduces the R-D model and the image compensation model, followed by the technique for calculating the parameters of the image compensation functions. Subsequently, the method for image geolocation and image correction using the ICRD model is presented. The overall technical roadmap of this paper is shown in Figure 1.  
Figure 1: Technical roadmap of the ICRD model positioning.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 2 Methodology
### Probability Estimation Methods for REs
#### 2.2.2 Fuzzy Set Theory-Based Expert Judgment Method  
Since there exist REs for which failure sample data are scarce or non-existent, a fuzzy-based method that incorporates expert judgments is proposed to address the data uncertainties and quantify their occurrence probabilities. This methodology, which has been previously introduced in our work [10], is distinguished by its utilization of two crucial indicators: the relative agreement degree (RAD) and the importance degree (ID). These indicators facilitate the aggregation of expert opinions, leading to a more rational and comprehensive assessment. The fundamental steps of this approach are outlined below:  
* Evaluate the likelihood of occurrence for each RE using natural language terms.
* Convert the experts' linguistic assessments into fuzzy numbers to quantify their subjective evaluations.
* Aggregate the individual experts' assessments into a unified, comprehensive fuzzy possibility score.  
Transform the fuzzy possibility score into a probability value for subsequent analysis or application.
## 2 Materials and Methods
### Model Generation
#### 2.5.2 Net Ecosystem Exchange (NEE)  
Considering the footprint of the eddy covariance tower, yearly cumulative NEE [20; 37; 47] was related to the yearly average of a modified water index (MWI) calculated for the Landsat TM5 scenes in Table 3 over a period of 10 years (1999-2011) through OLS regression. The MWI from [48] was calculated as (Equation (2)):  
\[\mathrm{MWI}_{\mathrm{TM5}}=\frac{\rho 830-\rho 660}{\rho 560} \tag{2}\]  
For the 2013-2016 period for which Landsat TM5 imagery was no longer available due to the sensor having been decommissioned in January 2013 [49], Landsat 8 OLI imagery (Table 3) was used to determine the relationship through OLS regression between the MWI and yearly cumulative NEE following Equation (3) [50]:  
\[\mathrm{MWI}_{\mathrm{OLB}}=\frac{\rho 864.7-\rho 654.6}{\rho 561.4} \tag{3}\]  
Due to the differences in the spectral responses of the NIR, red, and green bands (Figure 2) used for the MWI as well as radiometric resolution differences between Landsat TM5 (8 bit) and Landsat 8 OLI (12 bit), data from the two sensors were considered separately. Eleven Landsat 8 OLI scenes from 2016 (April-September) were used to determine a third NEE OLS regression model between individual \(\mathrm{MWI}_{\mathrm{OLB}}\) surfaces and 30 min NEE measurements (3 h average around the time of image acquisition).  
To examine the transferability of the MWI:NEE models to other sensors, MWI was calculated from individual Sentinel-2A scenes with Equation (4) (similar bands to Landsat) and Equation (5) substituting the red edge band (\(\rho\)740) for the red band (\(\rho\)665) (Figure 2).  
\[\mathrm{MWI}_{\mathrm{S2}}=\frac{\rho 865-\rho 665}{\rho 560} \tag{4}\]  
\[\mathrm{MWI}_{\mathrm{S2 Re}}=\frac{\rho 865-\rho 740}{\rho 560} \tag{5}\]  
The CASI HSI (Table 2) were resampled spectrally and spatially to Sentinel-2A specifications, resulting in images at 20 m spatial resolution with the same band set as Sentinel-2A. The MWI was  
Figure 2: Spectral responses of Landsat TM5 (solid), Landsat 8 OLI (dashed), and Sentinel-2A (dotted) bands used for the calculation of the modified water index (MWI). Bands 2 (green), 3 (red), and 4 (NIR) are shown for Landsat TM5. Bands 3 (green), 4 (red), and 5 (NIR) are shown for Landsat 8 OLI. Bands 3 (green), 4 (red), 6 (red edge), and 8A (NIR) are shown for Sentinel-2A (Tables A1 and A2).  
also calculated from the resampled CASI HSI (Equations (4) and (5)). Eddy covariance tower daily average (daytime) and 30 min NEE averaged over a 3 h window around the time of satellite and CASI HSI image acquisition were used to validate the MWI:NEE models.
# Assessing the Security and Privacy of Baby Monitor Apps
## 6 Discussion & Conclusions
### Conclusions  
In addition to offering information on network communication and security measures of proprietary, and therefore, hard-to-assess video surveillance apps, we conducted a security analysis of the network protocols used to monitor video transmission over the Internet, as well as their implementation. For privacy analysis, we evaluated privacy policies and investigated the transfer of personal data regarding privacy leaks. We examined the data practices and compared them to the statements of the respective privacy policies. Although we expected a high level of security and privacy protection due to the privacy-sensitive domain of baby monitoring, this study discloses several security and privacy issues in the most popular baby monitor apps on the Google Play Store. Consistent with previous research results [7; 8; 9; 11], the identified issues may be considered alarming, as there are no essential security mechanisms in some of the mobile apps examined, threatening user privacy. We responsibly disclosed the issues to the vendors and sent a complete report of our findings to each company, waiting for responses to discuss possible fixes. When evaluating the results of the related work and this study, it becomes evident that further research is needed to improve the state of mobile app privacy. The broad applicability of privacy and security assessments to a wide range of mobile applications would benefit consumers and developers, thus improving the overall level of privacy protection. Future research is required to develop automated security and privacy analysis tools that address the same issues as those examined during this study, focusing on scalability and ease of integration. In the future, additional laborious tasks that currently hinder privacy analysis should be automated, including interpreting encoded or obfuscated data transmissions without the need to reverse engineer proprietary formats manually. We expect that investigations that interpret these data will potentially uncover even more privacy leaks, making any advances in this area fruitful for future studies.  
Conceptualization, L.S. and H.H.; methodology, L.S. and H.H.; software, L.S.; validation, L.S. and H.H.; formal analysis, L.S. and H.H.; investigation, L.S. and H.H.; resources, L.S. and T.H.; data curation, L.S. and H.H.; writing--original draft preparation, L.S., H.H. and T.H.; writing--review and editing, L.S., H.H. and T.H.; visualization, L.S. and H.H.; supervision, T.H.; project administration, T.H.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 3 Sensing Matrix Optimization of Compression Acquisition
### Noise Power Analysis  
The influence of noise will be amplified in compressed sensing, which is also called noise folding phenomenon. Considering the existing noise, Equation (5) can be written as  
\[\mathbf{y}=\mathbf{\Theta}\cdot(\mathbf{X}+\mathbf{v}) \tag{25}\]  
where \(\mathbf{v}\) is the Gaussian noise, and \(\mathbf{v}\sim N(0,\sigma_{n}^{2})\), the noise variance of the measurements obtained with the sensing matrix can be written as  
\[\begin{split} D(\mathbf{\Theta v})&=E\Big{(}( \mathbf{\Theta v})^{2}\Big{)}-E^{2}(\mathbf{\Theta v})=E\Big{(}(\mathbf{\Theta v })^{2}\Big{)}-0\\ &=E\Big{(}\mathbf{\Theta v}(\mathbf{\Theta v})^{\mathrm{T}}\Big{)} =\mathbf{\Theta}E(\mathbf{vv}^{\mathrm{T}})\mathbf{\Theta}^{\mathrm{T}}\\ &=\sigma_{n}^{2}\mathbf{\Theta\Theta}^{\mathrm{T}}\end{split} \tag{26}\]  
A necessary and sufficient condition is based on the Welch lower bound, which is \(\mathbf{\Theta\Theta}^{\mathrm{T}}=\frac{N}{M}\mathbf{I}_{M}\)[21], so the variance can be expressed as \(D(\mathbf{\Theta v})=\sigma_{n}^{2}\frac{N}{M}\mathbf{I}_{M}\), and we can know that the noise power increases by \(N/M\) times. When the measurement length \(M\) is only half of the code length \(N\), the SNR will be reduced by 3 dB.  
After conjugate gradient optimization, the mutual coherence \(\mu(\mathbf{\Theta})\) of the sensing matrix \(\mathbf{\Theta}_{\mathrm{opt}}\) will become smaller, which is more conducive to signal recovery and reconstruction [35].
# Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester
## 2 Materials and Methods
### Design of Loss Detection and Counting System for Rapeseed Kernels  
As can be seen from Figure 9, when the carob shell impacts the inductive plate, the final output signal of the circuit is only one pulse signal; when the kernel impacts the inductive plate, the final output signal of the circuit has two pulse signals; and when the short stem impacts the inductive plate, the final output signal of the circuit has three pulse signals.  
The detection and counting system is composed of a ESP32-S3-B single-chip microcomputer produced by the Le Xin Technology Company in Nanjing, China, and a TAOJINGCHI intelligent serial port screen produced by Shenzhen TaoJINGCHI Electronics Co., LTD., located in Shenzhen Longhua District, Huaning Road 46, Donglongxing Science and Technology Park 3, floor 4. The ESP32-S3-B is a single-chip microcomputer developed by Le Xin Technology which has a low-power MCU system-on-chip (SoC). The chip integrates a high-performance Xtensa(r) 32-bit LX7 dual-core processor produced by Cadence, located on Floor 5-10, Qiantan World Trade Center (Phase III), No. 1, Lane 221, Dongyu Road, Pudong New Area, Shanghai, an ultra-low power coprocessor, a Wi-Fi baseband, a Bluetooth baseband, an RF module and peripherals. The single-chip microcomputer has a clock frequency up to 240 MHz, supports various interfaces such as UART, SPI, SDIO, I2C, IR, DAC, etc., and supports 2.4 GHz Wi-Fi and low-power Bluetooth(r) LE wireless communication. All categories of the output pulse signals of the circuit are detected and counted by the MCU. Then, the number of detected kernels and different impurities is displayed on the serial port screen, and information such as the thousand grain weight, yield, width, speed and proportional coefficient can also be input to display the cleaning loss in real time.  
The process of the loss detection and counting system for rapeseed kernels is shown in Figure 10 (Figure 10 was created using Microsoft Visio 2019). The output signals of U3, U7 and U8 of the triple voltage comparison of the signal processing circuit are read by the single-chip microcomputer and saved to the variables A, B, and C, respectively.  
Figure 9: The output waveform diagrams of the three different types of signals: (**a**) carob shell; (**b**) kernel; (**c**) short stem.
## 2 Stability of Columns, Mathematical Formulation for Determining the Critical Loads
### Buckling Resistance  
Elastic buckling theory linked with empirical parameters is converted into recommendations [21], explained in Eurocode 3, part 1-1 [23].  
Figure 1: Effective length in columns (from left to right): fixed-fixed, fixed-pinned, pinned-pinned, and fixed-free ends.  
According to Eurocode 3, part 1-1 [23], the buckling resistance of members in pure compression is calculated using the following simplified equations for different classes of steel cross-sections designed: class 1 (plastic section), class 2 (compact section), class 3 (semi-compact section), and class 4 (slender section). The classification of a cross-section depends on the width-to-thickness ratio and the yield strength of the material, which allows us to know how the resistance and rotation ability of a column cross-section can be affected by plasticity or local buckling phenomena [19; 22].  
To calculate the design buckling resistance \(N_{b,~{}Rd}\), we need to apply Equation (3), depending on the reduction factor for the buckling mode \(\chi\) (Equation (4)), and the partial member resistance factor to instability \(\gamma_{M1}\) equivalent to 1, for buildings.  
\[N_{b,~{}Rd}=\frac{\chi}{\gamma_{M1}} \tag{3}\]  
\[\chi=\frac{1}{\Phi\sqrt{\Phi^{2}-\overline{\lambda}^{2}}}~{}~{}~{}~{}but~{}~ {}\chi\leq 1 \tag{4}\]  
where  
\[\Phi=0.5\Big{[}1+\alpha\left(\overline{\lambda}-0.2\right)+\overline{\lambda }^{2}\Big{]} \tag{5}\]  
\(\alpha\) is an imperfection factor, relating to the correct buckling curve in function to the cross-section [23], the fabrication process, the direction buckling appears, and the yield strength. The non-dimensional slenderness \(\overline{\lambda}\) is obtained according to Equation (6) for classes 1, 2, or 3.  
\[\overline{\lambda}=\sqrt{\frac{A\,f_{y}}{N_{cr}}} \tag{6}\]  
For tubular cross-sections and material S235, dependent on hot or cold finish, the constant quantity is equal to 0.21 or 0.49, correspondingly.  
For slenderness \(\overline{\lambda}\leq 0.2\) or for \(\frac{N_{tot}}{N_{cr}}\leq 0.04\), the buckling effects can be disregarded [23].
# Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoders
## 4 Implementation Results and Comparison  
Table 1 reports a resource breakdown of the proposed MCSN for 5G NR LDPC decoder. As can be seen from Table 1, the proposed MCSN requires 972,256 adders/subtractors, 483,092 comparators, 732,228 multiplexers and 482,448 logic shifters. The number of adders is about 85% of the number of subtractors. The greater comparator is the majority of the comparators.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & \multicolumn{2}{c}{**Resource Consumption**} \\ \hline \multirow{2}{*}{Adders/Subtractors} & Adders & 445,970 \\  & Subtractors & 526,286 \\ \hline \multirow{2}{*}{Comparators} & Lesesqual Comparators & 2070 \\  & Greater Comparators & 481,022 \\ \hline Multiplexers & & 732,228 \\ Logic Shifters & & 482,448 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resource Breakdown of Multi-Size Circular-Shift Network for 5G NR LDPC Decoders.  
To confirm the efficiency of our solution, we have compared the conventional RIP structure [23], the fine-coarse architecture with MUXs network [16], the fine-coarse structure with RIP network [24] with our proposed MCSN network for multi-size message passing network of the 5G LDPC decoder where the full lifting set \(Z\) is given by {2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 24, 26, 28, 30, 32, 36, 40, 44, 48, 52, 56, 60, 64, 72, 80, 88, 96, 104, 112, 120, 128, 144, 160, 176, 192, 208, 224, 240, 256, 288, 320, 352, 384]. The proposed MCSN network structures are synthesized using the TSMC 65-nm CMOS technology. The ASIC post synthesis implementation results on are reported in Table 2.  
As can be seen from Table 2, the proposed MCSN structure occupies an area of 0.301 mm\({}^{2}\) and works at a high frequency of 580 MHz. Since the proposed work and other architectures are implemented with different parameters such as maximum shift size \(P\), CMOS technology, and the quantization bits, it is necessary to apply performance normalization to 65-nm CMOS technology for a fair comparison. Specifically, the normalization method presented in [25] is used. We also consider the maximum shift size \(P\) and the quantization bits in the normalization. From the normalized results, it is confirmed that the proposed solution helps reduce the complexity in comparison to the works introduced in [16, 23, 24]. Specifically, the area reduction from the RIP structure [23], the fine-coarse architecture with MUXs network [16], and the fine-coarse structure with RIP network [24] are 56.75%, 18.87%, and 49.75%, respectively. The proposed MCSN can be well operated at the high frequency of 580 MHz. Therefore, the proposed MCSN structure is a promising solution to reduce the complexity of the QC-LDPC decoders for 5G NR.