esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Simulation Framework  
A low fidelity (Lo-Fi) simulation framework illustrated in [5] was used to evaluate the energy expenditure for fleets of vehicles and impact on the electricity grid for a given mission profile in this research, with modifications in the framework to accept measurement data as the scenario input. An offline scenario input process was developed for the framework in this research, meaning that the simulation is not occurring parallelly in real-time using the measurement data taken during the bus demonstrations. Rather, the measurement data from the sensors are stored and later input to the simulation.  
The simulation framework is based on basic electrical, mechanical, kinematic, and thermal equations needed to represent the charging infrastructure and forward-facingelectric bus (e-bus) powertrain model, as shown in Figure 1. Unlike a high fidelity (Hi-Fi) simulation model, where the simulation model is based on detailed physical equations of the actual system and uses small timesteps to ensure very high accuracy of the simulation output, the Lo-Fi framework uses look-up tables (LuTs) to define the efficiency maps of the various electronic, mechanical, electromechanical, and electrochemical devices integrated within the powertrain; and basic equations that model the overall energy transfer behavior of each component. The timestep in a Lo-Fi model is large to ensure high simulation speed at the cost of accuracy. Thus, even though a Lo-Fi model cannot simulate transient behaviors, they can be used to get a rapid estimate of the steady-state behavior. Therefore, Lo-Fi models can be used to simulate large time ranges covering the lifetime of the e-bus or large fleets of e-buses within a reasonable timeframe. Furthermore, a Lo-Fi model can be used to perform a fleet-level energy management and charging strategy (EM&CS) optimization, which require very fast simulation speeds. The Simulink framework was designed to use the measurement data from the demonstrations as inputs: the design of the energy storage system (ESS) block allowed comparison to be made between the simulated and measured battery SoC values for validation purposes, while the energy management system (EMS) block was designed to allow the model to be tuned to minimize the difference between the simulated and measured values. More details of the tuning process are provided in Section 4.
## 2 Substrate-Integrated Waveguide (SIW)  
As mentioned in the introduction, an SIW structure can be used to miniaturize a planar antenna. Figure 3 shows a section of SIW [9], which has two rows of vias at the edges connecting two parallel metal plates separated by a dielectric substrate. An SIW exhibits a  
Figure 1: Pictures of whip antennas mounted on vehicles [2,3]: (**a**) straight and (**b**) bent.  
Figure 2: A simulated 3D radiation pattern of a rectangular patch antenna on a vehicle’s roof.  
propagation characteristic similar to a rectangular metallic waveguide if the vias are closely spaced, thus no leakage radiation occurs.  
The design parameters of an SIW are the height of substrate (\(h\)), via's diameter (\(d\)), spacing (\(s\)), and width (\(w\)) as depicted in Figure 3. There are empirical design equations for an SIW derived from the rectangular waveguide theory [11]. The SIW width \(w\) to guide the fundamental transverse electric mode (TE\({}_{10}\) mode) can be calculated by  
\[w=w_{r}-1.08\frac{d^{2}}{s}+0.1\frac{d^{2}}{w_{r}}\;\;, \tag{1}\]  
where \(w_{r}\) is the width of a rectangular waveguide retaining the equivalent propagation characteristic as the SIW. Moreover, \(d\) and \(s\) are the diameter of the via and the spacing between vias, respectively. In order to avoid radiation leakages, the following requirements for \(d\) and \(s\) should be met [12]:  
\[d<\frac{\lambda_{\rm g}}{5}\;\;\;{\rm and}\;\;s<2d \tag{2}\]  
where \(\lambda_{\rm g}\) is the guided wavelength. As long as Equation (2) is met, the TE\({}_{10}\) mode will be tightly guided inside the SIW since the densely lined-up via fences cut off the leaking waves.
## 2 Methodology
### Generating TLS and Coloring  
#### 2.2.1 Laser Scanning Point Cloud Acquisition  
The working principle of the TLS is similar to the traditional total station. TLS scan surrounding objects by transmitting a large number of laser beams at high speed, and calculating the distance, horizontal angle, and vertical angle of the corresponding position according to laser reflection. These parameter are stored in the formatted point clouds data. Taking the Trimble TX8 TLS as an example, it can obtain information of objects within the range of 340 m at the furthest in the scanning process with a field of view of 360\({}^{\circ}\)\(\times\) 317\({}^{\circ}\) (Figure 7). Table 2 shows the relationship between the main accuracy levels and the obtained data.  
Figure 6: UAV photogrammetric point clouds and PMVS densification point clouds. (**a**,**c**) are UAV photogrammetric point clouds. (**b**) is PMVS densification point clouds based on (**a**). (**d**) is PMVS densification point clouds based on (**c**).  
Figure 7: Field of view of Trimble TX8 TLS.  
In the process of collecting TLS point clouds, it is necessary to pay attention to the location of the scanner and the site environment. TLS is an optical device, it is affected by perspective relationship. Therefore, when the slope is high, the lower slope may partly block the upper slope due to perspective relationship. The point clouds of this part can be supplemented by point cloud generated by UAV photography. In addition, if there is fog or dust on site, small particles scattered in the air will also affect the process of point clouds acquisition. Therefore, data collection should be carried out as far as possible under clear weather and good sight conditions.  
Point clouds includes the measured object's reflection intensity, the times of reflection and its \(x\), \(y\) and \(z\) coordinates relative to the set position of the scanner. However, it does not include the corresponding color and texture information of the measured object. Scanned point clouds without RGB color cannot be used to calculate the slope surface displacement by the method of identifying the monitoring points based on the color. In addition, since point clouds coordinates generated by TLS are generated relative to the position set by scanner. Generated point clouds need to be converted to same coordinate system as point clouds generated by UAV for registration [34].
## 2 Results
### Assessing if Selecting Rif\({}^{\text{R}}\) CFU co-Selects for P\({}^{\text{R}}\) Cells  
Cellular sensitivity to \(P\) expression from pclpR-\(P\)-timm was demonstrated [35] by showing that sensitive cells are transformable at 25-30 \({}^{\circ}\)C, where the CI[Ts] repressor is active, but not at 37 \({}^{\circ}\)C where the CI[Ts] repressor activity is reduced, permitting some \(P\) expression. Two categories of Rif\({}^{\text{R}}\) mutants were screened in Table 6, those from cells where P had never been introduced into the cell (Table S2), and survivors from FA#1 that were exposed to P, had lost pclpR-\(P\)-timm during their growth on plates at 37 \({}^{\circ}\)C, and had survived a 82-fold toxic effect of P-exposure. Two of the 11 Rif\({}^{\text{R}}\) CFU selected without cell exposure to P showed weak P\({}^{\text{R}}\) and the remainder were P\({}^{\text{S}}\). Six of the 11 Rif\({}^{\text{R}}\) CFU arising from the FA#1 incubation at 37 \({}^{\circ}\)C varied from being high to moderately P\({}^{\text{R}}\), revealing that some Rif\({}^{\text{R}}\) mutations confer P\({}^{\text{R}}\), while most others are P\({}^{\text{S}}\) (Figure 4). Table S3 shows 109 characterized mutations within _rpoB_ conferring Rif\({}^{\text{R}}\). Figure 4 summarizes 53 Rif\({}^{\text{R}}\) mutations we localized to 34 independent sites within _rpoB_, representing 25 mutations that were previously identified, and 9 unique mutations including base pair sites (designated by ##, Table S3) 433(I145F), 1351(R451S), 1532(L511R), 1595(A532E), 1600(G534S), 1609(G537C) and the deletions 1319-1324(\(\Delta\)GEV440-442V), 1604-1612(\(\Delta\)PGGL535-538P), and 1605-1613(\(\Delta\)PGGL535-538P). One previous deletion in _rpoB_ conferring Rif\({}^{\text{R}}\), \(\Delta\)1589-97 base pairs, was described [78]. The 109 Rif\({}^{\text{R}}\) mutations localize to five general regions of _rpoB_ (expanded from Figure 1 of [79]): region I, codons 139-153(181); II, codons 395-451; III, codons 507-574 (major group with 79 of 109 mutations; previously subdivided into three groups [79]); IV, codons 631-687; and V, codons 1244-1260. Structural studies of _Thermus aquaticus_ RNA polymerase with Rif revealed that Rif binds and is inhibited through the same biochemical mechanism as for _E. coli_ RNAP [79]. These authors identified deep within the
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 5 Discussion  
Greenhouse has a complex topology as shown in the Figure 1. Most of the common inherent challenges in the greenhouses are captured in Table 1 along with their respective impact. We proposed the sequential steps to be followed, for the deployment of WSNs in the greenhouse and the benefits it brings at each stage. Deployment of different types of sensor boxes helped to study the impact of the box enclosure on the sensor readings. This is very important and critical consideration while any WSN deployment for the precision agriculture. The evolution of sensor boxes from simple fruit boxes to the new airflow boxes is shown in Figure 4. The airflow boxes help in airflow circulation which concisely contributes to removing the error element in sensor readings. A LoRaWAN setup is deployed in a greenhouse which is shown in Figure 2, along with different components used at different parts of the deployment. This setup helps in monitoring tomato crop in PCH and build the WSN platform based on LoRaWAN for data collection and analysis. The dashboard assists different stakeholders to do the analysis and take necessary action for the growth of plant. Through the data generated from the sensors (interval of 5 min) as in Figure 9 and in Table 5, we see that there is a huge variation of different climatic conditions such as temperature, humidity, CO\({}_{2}\) and likewise throughout the greenhouse over a period of one month. Therefore, regular monitoring is crucial for the proper growth of the plant. Also, we see the frequent change in the received signal strength indicator (RSSI) for each message in Figure 12, in-spite of the static location of the sensor boxes. It shows the implication of plant growth and other movement inside the greenhouse over signal strength.  
Figure 11: Overview of sensor box and its location in the greenhouse.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sensor** & **Minimum** & **Maximum** & **Average** \\ \hline Temperature (°C) & 18.63 & 33.22 & 24.8 \\ Relative Humidity (\%) & 48 & 91 & 67.5 \\ Illuminance (lux) & 274 & 12452 & 4034.17 \\ CO\({}_{2}\) (ppm) & 596 & 1092 & 781 \\ Conductivity (\(\mu\)5 cm\({}^{-1}\)) & 91 & 301 & 182 \\ \hline \hline \end{tabular}
\end{table}
Table 5: One-month (August) sensor data for tomato crop at PCH, Belgium.
## 4 Conclusions  
Based on the performed experiments, it is possible to draw the following conclusions:  
* The moisture parameter of individual samples was statistically evaluated and a significant dependence of the type of technological operation on dust moisture was confirmed;
* Spruce wood dust obtained from selected wood processing plants had a different particle size distribution, where it was not possible to statistically confirm due to the difference in the used sieves. Wood dust from sawing has the largest proportion--69% of the 250 \(\upmu\)m fraction--and the saw sample (WD_Saw) did not ignite on the hot-plate. Sawdust represents the particle size distribution with the largest proportion of sieve sizes with a cut-off value of <90 \(\upmu\)m. Grinding, as a fine technological operation, produces the smallest dimensions of dust particles, with the most numerous fraction of 63 \(\upmu\)m (87%);
* The monitoring of the ignition of settled dust samples of spruce (5 mm thickness) obtained through selected technologies on a hot surface was realized. The monitored parameter of the minimum ignition temperature of settled dust \(t_{uMIN}\) was identified for WD_Briq, WD_ExtrD and WD_Grin. These values were comparable. However, time of ignition was different. The time of ignition increased with the decrease in the average value of particle size in the samples (Table 3).
* In terms of the risk of initiating a settled dust layer, the spruce dust sample emerges from the briquetting as the worst. Although WD_Briq has a minimum ignition temperature of 360 \({}^{\circ}\)C and a grinding sample of 10 \({}^{\circ}\)C, the ignition time of the WD_Briq sample is 7 min. The sample taken from the grinding ignited after 12 min.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dust**} & **Layer** & \multicolumn{3}{c}{**MIT (\({}^{\circ}\)C) by**} \\ \cline{3-5}  & **(mm)** & [49] & [64] & [36] \\ \hline \multirow{3}{*}{spruce} & 5 & & 320 & 340 \\  & 12 & & 290 & \\ \cline{1-1}  & 15 & 300 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Overview of minimum ignition temperatures of flammable dusts according to EN 50281-2-1 [48].  
writing--review and editing, E.M., I.T. and M.H.; project administration, J.J. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant number K-026 UMB-4/2021 Demonstration laboratory of work safety for manual devoices in human--machine interaction.  
Not applicable.  
This article was supported by the Cultural and Education Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic on the basic of the project No. K-026 UMB-4/2021 Demonstration laboratory of work safety for manual devoices in human--machine interaction.  
The authors declare no conflict of interest.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches
### Variational Mode Decomposition
#### 2.1.2 Wavelet Transform  
The wavelet transform (WT), which employs the wavelet basis function, was proposed by [PERSON] in 1974 [41]. WT uses the basis function to match a specific fault symptom. Meanwhile, WT is a kind of inner product transform that analyzes the non-stationary contents of the signal through a pre-determined wavelet basis. WT is suitable to analyze the transient signal from the measured vibration signal [42; 43; 44; 45]. WT is categorized as continuous wavelet transform (CWT), discrete wavelet transform (DWT) and wavelet packet transform (WPT). The detail decerebrations are provided as follows.  
The CWT of any signal \(x(t)\) can be computed by integrating the signal with a family of wavelet. CWT can be defined in Equation (8):  
\[CWT_{x}(\tau,a)=\frac{1}{\sqrt{a}}\int\limits_{-\infty}^{+\infty}x(t)\varphi( \frac{t-\tau}{a})dt \tag{8}\]  
where wavelet \(\varphi[(t-\tau)/a]\) is derived by dilating and translating the wavelet basis \(\varphi(t)\), \(a\) is the scale parameter (\(a>0\)), and \(t\) is the time shift.  
DWT is the discretization of the scale and translation variables in the CWT. DWT can be described as follows:  
\[DWT_{x}(r,c)=\frac{1}{\sqrt{2^{r}}}\int x(t)\varphi(\frac{t-c2^{r}}{2^{r}})dt \tag{9}\]  
where \(2^{r}\) is the scale parameter, and \(c2^{r}\) is the shift parameter. WPT is a substitute for CWT. DWT decomposes one signal in the low region, while WPT decomposes one signal in the high region.
# Similarity-Based Malware Classification Using Graph Neural Networks
## 4 Performance Analysis
### Similarity Model  
The similarity model predicts whether the pair-wise data is in the same family and thus it is unnecessary to calculate the average of metrics. Figure 11 and Table 4 show the performance result of the similarity model using a confusion matrix, an ROC curve, and evaluation metrics. The similarity model has an accuracy of 0.92, a precision of 0.929, a recall of 0.91, a f1-score is 0.919 and an AUC of 0.92 on a test dataset.  
A Siamese network uses contrastive loss to cluster data in the same class and keeps data in distinct classes far from each other. Hence, samples are mapped into vector space using the similarity model, and principal component analysis (PCA) is used to reduce the dimensionality of the embedding vector of the samples. Each sample is transformed into a 32-dimensional vector by the model. This 32-dimensional vector is mapped to a two-dimensional space using PCA, and a scatterplot is generated, as shown in Figure 12. The ten families of samples are clustered by family and distributed among various locations. Accordingly, the similarity model has effectively learned the relationships between samples.
# Effects of Electron Correlation inside Disordered Crystals
## 2 Hamiltonian of an Electron-Phonon System of a Disordered Crystal
The basis orthogonalisation is carried out by means of the Lowdin approach [27]:  
\[\left|\widetilde{\psi}_{ni\delta}\right>=S^{-1/2}|\psi_{ni\delta} \rangle,S_{ni\delta,n^{\prime}i^{\prime}g^{\prime}}=\left<\psi_{ni\delta}|\psi_ {ni\delta\
u}\right> \tag{14}\]  
where \(S_{ni\delta,n^{\prime}i^{\prime}g^{\prime}}\) is the overlap of the integral matrix. Vanefunctions \(\phi_{ni\gamma}(\mathbf{r},\sigma^{\prime})\), where the system's Hamiltonian is described by Equation (1), are obtained from the following equation:  
\[\phi_{ni\gamma}\big{(}\mathbf{r},\sigma^{\prime}\big{)}=\widetilde{\psi}_{ni \delta}(\mathbf{r}-\mathbf{r}_{ni})\chi_{\sigma}\big{(}\sigma^{\prime}\big{)} \tag{15}\]  
where \(\chi_{\sigma}(\sigma^{\prime})\)--are the spin part of the wave function, \(\gamma=\delta\sigma\). The orthogonalised wave function may be written as follows:  
\[\widetilde{\psi}_{n_{1}i_{1}\delta_{1}}(r_{1},\theta_{1},\varphi_{1})=\sum_{ n_{2},j_{2}\delta_{2}}S^{-\frac{1}{2}}_{n_{2}\delta_{2},n_{1}i_{1}\delta_{1}}R_{ \tilde{\gamma}i_{2}l_{2}}(r_{2})Y_{l_{2}m_{2}}(\theta_{2},\varphi_{2}). \tag{16}\]  
In Expression (16):  
\[ \begin{array}{l}\mathbf{r}_{1}=\mathbf{r}-\mathbf{r}_{n_{1}i_{1}},\mathbf{r }_{2}=\mathbf{r}-\mathbf{r}_{n_{2}i_{2}}=\mathbf{r}_{1}-\mathbf{r}_{n_{2}i_{ 2}n_{1}i_{1}},\\ r_{2}=\left(\left(x^{1}-x^{1}_{n_{2}i_{1}i_{1}}\right)^{2}+\left(x^{2}-x^{2}_{ n_{2}i_{2}n_{1}i_{1}}\right)^{2}+\left(x^{3}-x^{3}_{n_{2}i_{2}n_{1}i_{1}}\right)^{2} \right)^{\frac{1}{2}},\\ x^{1}=r_{1}\sin\theta_{1}\cos\varphi_{1},x^{2}=r_{1}\sin\theta_{1}\sin\varphi _{1},x^{3}=r_{1}\cos\theta_{1},\\ x^{\alpha}_{n_{2}i_{1}i_{1}}=\sum\limits_{\
u}\left(l^{(2)}_{\
u}-l^{(1)}_{\
u }\right)a^{\
u}_{\
u}+\rho^{\alpha}_{i_{2}}-\rho^{\alpha}_{i_{1}},\\ \end{array}  \tag{17}\]  
\[ \begin{array}{l}\cos\theta_{2}=\frac{r_{1}\cos\theta_{1}-x^{3}_{n_{2}i_{2} n_{1}i_{1}}}{r_{2}},\\ \varphi_{2}=\arccos\frac{r_{1}\sin\theta_{1}\cos\varphi_{1}-x^{1}_{n_{2}i_{2} n_{1}i_{1}}}{r_{2}(1-\cos^{2}\theta_{2})^{\frac{1}{2}}}.\end{array}  \tag{19}\]  
Summation over \(n_{2}i_{2}\) in Expression (16) means summation over \(\mathbf{r}_{n_{2}i_{2}}\), in accordance with Formula (13). The overlap matrix \(S_{ni\delta,n^{\prime}i^{\prime}g^{\prime}}\) is defined by the following equation:  
\[ \begin{array}{l}S_{n_{1}i_{1}\delta_{1},n_{2}i_{2}\delta_{2}}=\\ \iint R_{\tilde{\gamma}i_{1}l_{1}}(r_{1})Y^{*}_{l_{1}m_{1}}(\theta_{1},\varphi _{1})R_{\tilde{\gamma}i_{2}l_{2}}(r_{2})Y_{l_{2}m_{2}}(\theta_{2},\varphi_{2} )r_{1}^{2}\sin\theta_{1}dr_{1}d\varphi_{1}\end{array}  \tag{20}\]  
where \(r_{2},\theta_{2}\), \(\varphi_{2}\) are expressed through \(r_{1},\theta_{1}\), \(\varphi_{1}\) in accordance with Formulas (17)-(19). The procedure for calculating the matrix elements of the Hamiltonian (1), based on the use of Formulas (17)-(19), is described in Refs. [28; 29].
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 3 The Four-Layer IoT Cyber Risk Management Framework
### IoT Cyber Risk Assessment Layer
#### 3.3.2 Risk Quantification  
At the risk quantification stage, the impact, frequencies, and defense probability of each IoT-asset-vulnerabilities-cyber threat grouping is estimated. The following techniques and tools are utilized to quantify risks.  
For risk quantification, the frequency of cyberattacks refers to the expected number of cyberattacks in a given period. The expected impact of a cybersecurity attack in a given period can be estimated with selected metrics such as the number of compromised data records and/or financial loss. A defense probability of each attack is also estimated (e.g., a defense probability of 0.9 for threat type A). As the first activity of the risk quantification, the frequency of cyberattacks can be modeled as a random process of arrival with a Poisson probability density function, which is commonly used for a variety of arrival applications, including cyberattacks [79].  
Secondly, a risk matrix is developed to understand the risk standing of all IoT asset-vulnerabilities-threats. The risk matrix has two dimensions. One dimension is the frequency of penetrated attacks of each IoT asset-vulnerabilities-threat and the other dimension is the expected financial loss per penetrated attack. Penetrated attacks are a failure of defense. Through the construction of the risk matrix, high priority IT asset-vulnerabilities-threats can be identified.  
The risk matrix in Figure 2 shows an example. For illustrative purposes, dotted lines are arbitrarily drawn to divide a high, medium, and low risk area. In the figure, IoT asset-vulnerabilities-threat-1 and IoT asset-vulnerabilities-threat-2 are high risk, IoT asset-vulnerabilities-threat-3 and IoT asset-vulnerabilities-threat-4 are medium risk. IoT asset-vulnerabilities-threat-5 is low risk. The risk matrix can help managers prioritize the asset-vulnerabilities-threats and allocate resources for proper risk management. Though it is not a trivial task, the risk matrix should be updated periodically to protect an organization from evolving attack patterns as much as possible.
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 3 Datasets and Experimental Settings
### Dataset Description
#### 3.1.3 Ground-Based Cloud Dataset (GCD)  
The images in this dataset were captured by camera sensors in nine Chinese provinces over a period of more than one year and have a great diversity. This dataset classifies images according to the classification criteria published by the World Meteorological Organization, specifically, altocumulus (Ac), cumulonimbus (Cb), cirrus (Ci), clear (CI), cumulus (Cu), mixed (Mi), and stratocumulus (Sc). All images are stored in a JPEG format with a resolution of 512 \(\times\) 512 pixels, and more details of the dataset are presented in [29]. In this paper, the image size is uniformly resized to 448 \(\times\) 448 pixels. An example of images in the dataset is shown in Figure 10. Table 4 describes the distribution of images in this dataset.
## 5 Experimental Results and Analysis
### Performance Analysis
However, each tweet in the Weibo dataset was composed of text and corresponding images, and this difference may have affected the information interaction between text and image objects. As a result, the training samples of the BERT and ResNet50 models were too similar, which made the generalization performance of the models not good enough and prone to overfitting on the Twitter dataset. However, the Weibo dataset did not have such an imbalance problem. In summary, the proposed TDEDA model could capture more abundant internal feature information of text objects and visual objects through its dual self-attention mechanism. In addition, the potential connection between text objects and image objects could be fully mined, the relevant features in visual and text objects could be effectively captured, the feature representation between more extensive modal could be obtained, and the accuracy of rumor detection could be improved through effective fusion.