esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
## 2 Numerical Analysis  
Modern numerical methods are commonly used to study a wide range of civil engineering problems [17; 18; 19; 20], as they are able to a burdensome and, in most cases financially unfeasible, experimental methods. One of the greatest advantages of numerical analysis is that it allows the isolation of the governing parameters. Therefore, the commercial finite element software ANSYS [21] was used for comparison of the equivalent column method and the unified method according to Eurocode 9. Because the objective of this paper is to compare these two approaches, the same members and material properties of steel were used to determine the analytical member resistances according to these approaches. Members with IPE 100 and RHS 200/100/10 cross-sections were used in this analysis to simulate the behaviour of sections susceptible and not susceptible to fail in lateral-torsional buckling mode. Both sections belong to the 1.class of cross-section classification.  
The members were subjected to a combination of axial compressive force and uniaxial bending moment about both cross-sectional axes separately (\(N_{Ed}+M_{y,Ed}\), \(N_{Ed}+M_{z,Ed}\)). The definition of the cross-sectional axes for the appropriate type of cross-sections can be found in in Figure 1. Five different load cases were studied, four on simply supported member and one on a pinned-fixed beam-column, as seen in Figure 2. These load cases represent some of the elementary load cases, which are used by the equivalent column method to transform the buckling case of members with different boundary conditions to the standard case of a simply supported member using an appropriate beam-column model. The transformation of a pinned-fixed beam column to a simply supported member with linear bending moment gradient is also presented using this method.  
The ultimate resistances for these load cases were investigated for both in-plane (\(y\)-\(y\), \(z\)-\(z\)) and also for out-of-plane buckling modes. To represent a wide range of results, four nondimensional slenderness ratios \(\overline{\lambda}=0.5\); 1.0; 1.5; 2.0 were observed.
# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
## 2 Materials and Methods
### Evaluation Criteria  
This experiment uses MAE, RMSE, MAPE, and \(\mathbf{R^{2}}\) as evaluation indicators to measure the accuracy of the model, and the specific formulas for each evaluation indicator are as follows [29].  
MAE (Mean Absolute Error) is another commonly used indicator to measure the difference between the predicted model and the actual results. MAE calculates the average of the absolute values of the differences between predicted and actual values.  
\[\mathbf{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert\mathbf{y}_{i}-\overset{\alpha}{y}_{i}\rvert \tag{11}\]  
The smaller the MAE, the higher the prediction accuracy of the model.  
RMSE (Root Mean Square Error) is a commonly used indicator to measure the difference between the model's predicted results and the actual data. Its calculation method is to first calculate the square of the difference between each predicted value and the actual value, then take the average of all the squared differences, and finally take the square root of this average value.  
\[\mathbf{RMSE}=\sqrt{\frac{1}{n}\overset{\alpha}{\underset{i=1}{\sum}}\left(\mathbf{y} _{i}-\overset{\alpha}{y}_{i}\right)^{2}} \tag{12}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Site Name** & **Number of Missing Data** & **Number of Total Data** & **Missing Rate (\%)** \\ \hline
41048 & 63 & 8596 & 0.73 \\
46047 & 99 & 8757 & 1.13 \\
41040 & 112 & 8710 & 1.28 \\
42020 & 146 & 8751 & 1.66 \\
42057 & 110 & 8252 & 1.33 \\
46025 & 128 & 8748 & 1.46 \\
42002 & 103 & 8756 & 1.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of missing data in each site.  
The smaller the RMSE value, the closer the model's predicted results are to the actual data, and the better the model's prediction performance.  
MAPE (Mean Absolute Percentage Error) is an indicator used to measure the accuracy of the prediction model, which expresses the error between the predicted value and the actual value in percentage form.  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{|\hat{y_{i}}-\hat{y_{i}}}{y_{i}}\mid \tag{13}\]  
The smaller the MAPE value, the higher the prediction accuracy of the model.  
The coefficient of determination, also known as the goodness of fit, reflects the quality of the model's fit. Its result is between 0 and 1.  
\[R^{2}(y,\hat{y})=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n} (y_{i}-\overline{y}_{i})^{2}} \tag{14}\]  
The closer the coefficient of determination is to 1, the higher the prediction accuracy of the model.  
\(y_{i}\) is the true value of the i-th prediction sample; \(\hat{y}_{i}\) is the predicted value of the i-th prediction sample; \(\overline{y}_{i}\) is the mean value of the actual values of the prediction samples; and N is the total number of prediction samples.
## 7 Application on Engineering Design and QoS-CSC Problem  
This section applies MIARO to three engineering design problems, with well-structured experiments designed to verify its efficacy in solving the QoS-CSC problem. Three of the classical engineering design problems are pressure vessel design, compression spring design, and welded beam design [59; 60]. The experimental setup, including all parameters and environmental conditions in this section, mirrors that of Section 6 to ensure consistency across experiments.
# Experimental Study on the Features of Infrasonic Waves of Sandstone under Shear Load
## 4 Analysis of Experiment Results
### Impact of Sample Size on Infrasound Features in Shear Destruction  
From the perspective of mechanics, during shear destruction, with an increase in sample size, the shear resistance strength of the rock sample increases logarithmically (as shown in Figure 7). We found that the total infrasound energy of rock sample a4-1 is 6 times higher than that of rock sample a4-2. However, from the photos of the test results, the failure of the a4-1 rock sample produced fewer, longer cracks, and the sounds created by the failure of the rock sample are mainly concentrated in the low-frequency range. In contrast, the failure of sample a4-2 produced fractures larger in number but shorter in length, and the sounds generated by the rock sample failure are mainly concentrated in the high-frequency range. When damaged by the same load, the two rock samples produced similar total acoustic emission energy. Therefore, the acoustic emission energy generated by the failure of rock sample a4-1 in the infrasound frequency range must be higher than that of rock sample a4-2. Therefore, when the sandstone is damaged under the same shear load under the same size condition, the factor that has the greatest influence on the infrasound energy of the rock sample is the integrity of the sandstone damage.  
From the photos of the experiment results (Figure 8), when the sample is small (as shown in Figure 8a,b), the failure surface is flat and low in roughness, the damage of the shear plane is mainly slip, and it appears as mostly surface wear. When the sample increases in size (as shown in Figure 8c,d), the damage surfaces become jagged, including two types: the serrated type and the flat type. Meanwhile, the sample have cracks which appear and the damaged surface increases in roughness. Destruction in this stage is mainly shear destruction.  
Figure 7: Relationship of shear resistance strength and infrasound energy amount.  
size, the cracks caused by loading increased in length, and the corresponding excellent frequency of infrasound became lower and lower.
## 4 Experimental Results  
### Experimental Setup  
The experiment based on the setup of Figure 1 is shown in Figure 5. Center wavelength and output optical power of LD are set at 1550.12 nm and 10 dBm, respectively, to satisfy requirements of the link. The laser source is connected to the commercial modulator (ixblue, MXAN-LN-20) so that the optical carrier can be coupled to the MZM. The HF and LO signals are combined by an electrical combiner and drive the arms of the MZM which has a half-wave voltage of 5 V and optical insertion loss of 3.5 dB. An EDFA is connected after the modulated optical path for optical power compensation. The working current of the EDFA is set to 100 mA under an amplification ability of 25 dB. Next, the photoelectric conversion is completed by a PD, and the responsivity is 0.8 A/W, and the bandwidth is 20 GHz to achieve spurious suppression of out-band. In the beat-frequency process, the frequency of HF and LO signals is determined by IF and RF signals. We should note that the IF/RF signal in this experiment is assumed to have been upconverted to HF signal via a 22 GHz signal in advance, because the commercial active microwave upconverters have a spurious suppression ratio of over 60 dBc and a conversion gain of over 3 dB [25; 26], and this is good enough to meet our experimental needs.  
Figure 4: Simulated electrical spectrum of upconversion (**a**), downconversion (**b**), and output power of RF (**c**), IF (**d**) signal when RF signal is tuned from 0.8 to 18 GHz.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 2 Study Area and Data Sets
### SMAP Soil Moisture  
The methodology used in this study to correct the GPM products is based on the assimilation of soil moisture data from the SMAP satellite. SMAP is a unique mission that combines passive (radiometer) and active (radar) observations to provide global mapping of soil moisture and freeze/thaw state with unprecedented accuracy, resolution, and coverage. The SMAP instrument incorporates an L-band radar (1.26 and 1.29 GHz) and an L-band radiometer (1.41 GHz) that share a single feed horn and parabolic mesh reflector. Unfortunately, the SMAP radar stopped operating on July 7, 2015, leaving the SMAP radiometer as the only operating instrument on the spacecraft. The SMAP products represent four levels of data processing. The enhanced SMAP L3_SM_P_E product is a daily global composite of the enhanced SMAP L2_SM_P_E product, which contains the gridded data of 6:00 a.m. (descending) and 6:00 p.m. (ascending) SMAP radiometer-based soil moisture retrieval, ancillary data, and quality-assessment flags on the global 9-km Equal-Area Scalable Earth (EASE 2.0) Grid. This product only provides data from March 31, 2015 to present, which sets the limit for our study period. In addition, to avoid the impacts of snowfall and frozen soil on the simulations of the API model, we choose May to September of 2015 to 2017 as the study period. The SMAP data can be downloaded from the National Snow and Ice Data Center (NSIDC) website ([[https://nsidc.org/data/SPL3](https://nsidc.org/data/SPL3) SMP_E/versions/2]([https://nsidc.org/data/SPL3](https://nsidc.org/data/SPL3) SMP_E/versions/2)).  
Figure 1: Mean daily precipitation (mm/day) over China for May to September in 2017 from (**a**) AMPA and (**b**) GPM-IMERG-EARLY. The four letters in (**a**) indicate the 0.5\({}^{\circ}\) boxes for which the monthly precipitation time series are shown in Figure 5.
## 5 Computer Simulation  
### Wireless Transceiver  
IEEE802.11a [47] specification is assumed as a radio transceiver in each node. Transmission power is assumed to be 20 dBm. Packet reception failure happens when signal-to-interference-plus-noise power ratio (SINR) level becomes lower than 10 dB. The combination of CSMA/CA with IPT may reduce forwarding efficiency because of the overhead due to the handshakes relating to the CSMA/CA operations. Even though such an unfavorable fact exists, this combination should remain for immunity to unexpected packet forwarding failures. Otherwise, nodes may not be able to exit from collision state forever. In order to reach a compromise between possibility of unexpected packet reception failures and the performance loss of IPT forwarding, MinCW is set shorter than the original value: 7 slots for nodes in IPT forwarding while the original 15 slots for ones in the normal mode. The maximum CW, on the other hand, is set to 1023 slots based on the specification of IEEE802.11a irrespective of forwarding modes.  
Packet buffer is established as per adjoining relay node. Packet transmission for each buffer is managed in first-in first-out (FIFO) principle; when a relay node receives a packet to be forwarded, the packet is put into an adequate buffer at first and then it is sent out from the oldest packet in the buffer that was added most recently among all existing buffers. In case of RR-IPT, buffers are scheduled in round robin manner while it also follows FIFO manner in the conventional scheme. All buffers are assumed to be large enough so that the packet loss due to buffer overflow is not considered.
## Optical Setup and Image Formation Model
### Multiwavelength Object and Image Modeling
In reality, the spectral characteristics of the sensor must be measured [55] and taken into account. In particular, the cross-talk between the color channels exist on both sides in sources and sensor color filters [38]. As a model of this cross-talk the following linear convolution equation can be used:  
\[u_{s,c}=\sum_{\lambda\in\Lambda}w_{c,\lambda}u_{s,\lambda},c\in(R,G,B), \tag{9}\]  
where \(u_{s,\lambda}\) are the source wavefronts and \(u_{s,c}\) are the sensor wavefronts as they are registered by the sensor pixels. In general, the number of the spectral components \(n_{\Lambda}\) can be different from the number of the color filters in the sensor. Here \(w_{c,\lambda}\) are the cross-talk spectral weights formalizing the sensitivity of \((R,G,B)\)-filter outputs to the wavelength of the sources with the index \(\lambda\). Despite the importance of the cross-talk effects, in this paper for simplicity of presentation we will assume that the color filters of CFA are nearly ideal and the cross-talk problem can be omitted.
## 3 Different Aspects of Ransomware Studies
### Deep Learning Ransomware Attacks  
The deep learning methods are more adaptive in inferring the data [61]. The use of deep learning techniques is increasing and providing good results in the ransomware detection [62]. Deep learning is a robust approach which provides good results in extraction of hidden patterns from the unsupervised data [16].  
A memory assisted stochastic dynamic fixed-point arithmetic-based ransomware detection system was proposed in [48]. Random bit streams hold by memory was used to train the deep belief network classifiers. This was an online detection system, presented for the hardware with limited memory and power resources, i.e., IoT and embedded systems. System focused on the accuracy and the speed of detection.  
An automated study utilizing the deep learning technique LSTM to detect the ransomware was presented in [63]. The system utilized API calls to observe the behavior of a process. Tensorflow was used to implement the LSTM network. The system was designed to deal with the obfuscation techniques of ransomware. Analysis was carried out for 20 min to deal with the obfuscation techniques.  
A detection system was presented in [64], which utilized machine learning classifier SVM and artificial neural network technology MLP. API calls were captured to detect the ransomware existence. MLP outperformed as compared to other shallow networks. MLP performed well in the detection and classification of ransomwares in comparison to the other classical machine learning classifiers while the deep network performed well in comparison to other traditional shallow networks.  
A machine and deep learning-based ransomware detection model was presented in [65] for the mobile devices. This preventive study was able to detect the ransomware attack in a minute. This model was able to detect unknown ransomware samples in the real time. Initially, the fed samples were analyzed statistically to check if these were benign or malicious, which can allow the obfuscating ransomware to bypass the detection. This system built the DNA profile for each ransomware family. It compared the fed sample DNA with available DNA profiles. However, due to statistical analyses of samples, model was prone to evasion techniques.  
A deep learning method to detect the ransomware was presented in [66]. This detection study utilized the machine and deep learning technologies to detect the ransomware. This detection system collected the payloads and http requests from real-world data. Model was trained on the features extracted from the TCP and UDP traffic. Neural network was built using TensorFlow. This detection model was able to detect the previously unknown ransomware variants. However, this system performed the static analysis, which could be by passed by the obfuscating ransomware.
## 4 Evaluation Method Design for Driver Risk Behaviors
### Safety Evaluation Indicators
#### 4.2.5 Load Stability  
Load stability is assessed by measuring the swing angle of the load during movement. A smaller swing angle indicates better stability.  
From the recorded position data, the spreader position \(d_{p}(x,y,z)\) and the trolley position \(d_{c}(x_{c},y_{c},z_{c})\) are identified. Let the swing angle of the load be \(\varphi\) and define the unit vector \(d_{v}(0,0,-1)\) pointing vertically downward. The difference vector between the two positions \(d\) is then  
\[d=d_{p}-d_{c}=(x-x_{c},y-y_{c},z-z_{c}) \tag{16}\]  
The swing angle \(\varphi\) is then computed as  
\[\varphi =\cos^{-1}\biggl{(}\frac{\mathbf{d}\cdot\mathbf{d_{v}}}{| \mathbf{d}||\mathbf{d_{v}}|}\biggr{)}\times\frac{180}{\pi}\] \[=\cos^{-1}\biggl{(}\frac{-(z-z_{c})}{\sqrt{(x-x_{c})^{2}+(y-y_{c} )^{2}+(z-z_{c})^{2}}}\biggr{)}\times\frac{180}{\pi} \tag{17}\]  
\[\varphi_{final}=\begin{cases}-\varphi,&\text{if }y-y_{c}<0\\ \varphi,&\text{if }y-y_{c}\geq 0\end{cases} \tag{18}\]  
In the formula, \(|d|\) is the length of the position difference vector between the spreader and the trolley, and \(\frac{180}{\pi}\) is the factor used to convert radians to degrees. Equation (18) adjusts the sign of the angle based on the sign of the \(y\) component of the difference vector \(d\) to ensure the correct direction of the final angle. If \(y-y_{c}\geq 0\), it indicates that the spreader is in front of the trolley (relative to the negative \(y\)-axis), and the angle is taken as positive; otherwise, it is negative.
# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 2 Background
### Cloud-Fog-Edge Computing  
Cloud-fog-edge computing is a paradigm that combines the power of cloud computing, fog computing, and edge computing to create a distributed and collaborative computing platform. At its core, cloud-fog-edge aims to optimise the processing and storage of data by leveraging the strengths of each computing model. Utilising cloud computing, this architecture can benefit from its vast resources and scalability for handling large-scale data processing and storage [30]. Fog computing, on the other hand, brings the computational power closer to the data source, reducing latency and improving real-time processing capabilities [31]. Edge computing enables processing and storage to be performed at or near the edge devices themselves, reducing the need for data transmission and thus improving efficiency. This combination of cloud, fog, and edge computing addresses the limitations and challenges of traditional centralised computing models.  
As the agriculture domain evolves with the integration of cloud, fog, and edge computing, a powerful architecture emerges that significantly impacts farming practices. Edge computing, situated closer to on-field devices, ensures fast data processing and minimal latency, which is essential for real-time decision-making [32]. However, fog computing plays a pivotal role in this integration, not only seamlessly connecting with edge devices but also conducting intelligent analysis [33; 34]. The unique capability of fog computing to extend its reach to both the edge and the cloud makes it an indispensable component of agricultural systems. The introduction of mobile fog zones, actively gathering data from the fields, and static fog zones, providing localised storage and computing capabilities for immediate data analysis, marks a crucial transformation [35]. This cloud-fog-edge combination architecture not only optimises data processing and decision support at the field level but also leverages cloud resources for comprehensive analysis. The importance of fog computing in agriculture is underscored by its role in bridging the gap between on-field operations and broader cloud-based insights, thereby enhancing the efficiency, scalability, and adaptability of agricultural practices.
## 2 Materials and Methods
### The Frequency of Recherge with the Grid  
The simulations consider two occurrences of recharge with the grid: every night before a driving day and the maximal recharge frequency (_maxChargeFrequency_) depending on battery size (_storageEnergy_) and on the consumption of the vehicle (_dailyConsumption_). For this specific case, we obtained five days using Equation (2). Charging every five nights is enough for driving this passenger car for five days.  
\[MaxChargeFrequency=\text{ROUNDDOWN}\bigg{(}\frac{80\%\times storageEnergy}{ dailyConsumption}\bigg{)}. \tag{2}\]  
Equation (3) gives the state of charge profile of the battery using the method described in our previous work [6] (p. 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Components** & **Via 12 V Battery** & **Via 48 V Battery** & **In Main Battery** \\ \hline DCDC (PV/LV battery) & 99.1\% & 98.7\% & NA \\ DCDC (LV/HV) & 95.5\% & 97.5\% & 97.7\% \\ MPPT & 99\% & 99\% & 99\% \\ LV battery & 97\% & 97\% & NA \\ DC/AC (battery/engine) & 98.7\% & 98.7\% & 98.7\% \\ HV battery & 97\% & 97\% & 97\% \\ System efficiency & 87\% & 88.5\% & 92.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: System efficiency for a VIPV passenger car (2030 cases) for the three architectures considered.  
\[SOC_{n}=\max\biggl{(}\min\biggl{(}\frac{SOC_{n-1}\times energyStorage-cons+PVprod+ gridCharge}{energyStorage},SOC_{max}\biggr{)},SOC_{min}\biggr{)}. \tag{3}\]  
\(SOC_{n}\) is the state of charge (SOC) at time step \(n\); \(SOC_{n-1}\) is the SOC at time step \(n-1\). \(SOC_{min}\) and \(SOC_{max}\) are the minimal and maximal SOC values, respectively. \(energyStorage\) is the battery's nominal energy or battery size in Wh. For the considered time step, \(cons\) is energy consumption (Wh), \(PVprod\) is PV energy net production (Wh) and \(gridCharge\) is the possible charge increment with the grid (Wh).  
Here \(SOC_{max}\) is 100% and \(SOC_{min}\) is 0%.  
The initial SOC is equal to \(SOC_{max}\).  
This calculation yields useful PV energy considering the influence of battery saturation.