esa-sceva/satcom-chunk-collection huggingface dataset:
# An Inspection of IFC Models from Practice
## 2 Industry Foundation Classes
### IFC Georeferencing
\begin{tabular}{l l l} \hline \hline
**LoGeoRef** & **Supported CRS** & **Storing Entities** \\ \hline _LoGeoRef10_ & No CRS, approximate location by means & _IfcPostalAddress_ referenced by either _IfcSite_ or _IfcBuilding_. \\ \hline _LoGeoRef20_ & WGS84 EPSG:4326 & Attributes _RefLattitude_, _RefLongitude_, _RefElevation_ within _IfcSite_ \\ \hline _LoGeoRef30_ & Any Cartesian CRS, including projected coordinates (CRS not specified in the file) & _IfcCartesianPoint_ referenced within _IfcSite_ (defining the projected coordinates of the model reference point); _IfcDirection_ attribute of _IfcSite_. 1 \\ \hline _LoGeoRef40_ & Any Cartesian CRS, including projected coordinates (CRS not specified in the file) & Attribute _WorldCoordinateSystem_ storing the coordinates of the reference point in any Cartesian CRS (including the projected ones) and direction _TrueNorth_. Both are stored within _IfcGeometricRepresentationContext_. 2 \\ \hline \multirow{4}{*}{_LoGeoRef50_} & \multirow{4}{*}{Specific projected CRS, specified by means of the EPSG code} & IFC v.4 only 3. Coordinates of the reference point stored in _IfcMapConversion_ using the attributes _Eastings_, _Northings_ and _OrthogonalHeight_ for global elevation. Rotation for the XY-plane stored using the attributes _XAxisAbscissa_ and _XAxisOrdinate_. The coordinate reference system (CRS) used is specified by _IfcProjectedCRS_ in the attribute _Name_ by means of the proper EPSG code. \\ \hline \multicolumn{2}{l}{Ad-hoc solution used by several tools. 2 Most official IFC2x3-way to store the reference system. 3 The IFC4 way of documenting the used CRS and other georeferencing parameters was proposed to be backported to property sets also for version 2x3 of IFC. (https://forums.b utildingsmart.org/t/geolocation-standards-in-ifc2x3-and-ifc4/2329, accessed on 1 March 2021). This could enable the achievement of _LoGeoRef50_ even for IFC2x3 files. \\ \hline \hline \end{tabular}   
Footnote 2: [[https://github.com](https://github.com)]([https://github.com](https://github.com))the two cases. This must be considered when developing software applications reading the data in the two cases. In the specifications, it is established that, whether omitted, the reference should be the geometric coordinate system (used in _LoGeoRef40_). It is therefore necessary to be careful in the priority given to the information stored in the two systems: _LoGeoRef30_ should be read first, if absent, then go to _LoGeoRef40_.
## 4 Experiments
### Implementation Details  
An experimental platform is established based on the Windows 10 operating system, leveraging a Nvidia GeForce RTX 3060 GPU, with 12 GB of memory, and an Intel Core i512400F CPU, for computational tasks. The framework utilizes PyTorch version 1.13+cu116. The SCA dataset is employed for testing purposes, with the relevant training parameters detailed in Table 4. Considering the limited size of the SCA dataset, transfer learning is adopted in each experiment to mitigate the risk of model overfitting. Specifically, the feature extraction networks ResNet and EfficientNet are utilized, with the weights pretrained based on the ImageNet [35] dataset, followed by fine tuning based on the SC Action dataset. This methodology ensures the enhanced generalization capabilities of the model, despite the constrained dataset size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameters** & **Value** \\ \hline \multirow{2}{*}{Multi-object Tracking} & Conf\_thres & 0.1–0.7 \\  & IOU\_thres & 0.1–0.7 \\ \hline \multirow{4}{*}{Behavior Recognition} & Input\_size & 448 px \(\times\) 448 px \\  & Num\_segment & 8 \\ \cline{1-1}  & Learning\_rate & \(2\times 10^{-5}\) \\ \cline{1-1}  & Batch\_size & 1 \\ \cline{1-1}  & Dropout & 0.8 \\ \cline{1-1}  & Epoch & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings.  
Figure 4: Examples from the SCA dataset: (**a**) the left side shows the ship bridge, and the right side displays samples of driver behavior; and (**b**) a sequence of frames from a walking sample.
## 5 Experiment
### Jamming Array Reconstruction After Attack  
When UAVs conduct jamming, they are also susceptible to attacks. Therefore, it is essential to deploy redundant UAVs in the rear. This paper investigates scenarios where UAVs are attacked, the selection of new UAVs to join the jamming array, and the time required to restore jamming. It is assumed that there are also 16 redundant UAVs located 500 m from the jamming array. When the jamming array is attacked, the nearest redundant UAV is selected to join the array. The positions of the UAVs and the target are shown in the figure. When UAVs in the jamming array are attacked, the nearest redundant UAV is  
Figure 12: The constellation diagrams of the target receiver under different conditions. (**a**) The constellation diagram of the target receiver under ideal conditions. (**b**) The constellation diagram of the target receiver under jamming conditions.  
Figure 13: The relative errors between the array-based estimation algorithm and the waveform superposition algorithm under different modulation types.  
selected to reconstruct the jamming array. Notably, the new UAV does not need to fly to the position of the damaged UAV but instead chooses a location that is equidistant from the target as the damaged UAV. In Figure 14, distances \(d_{1}\) and \(d_{2}\) are the same, so the new UAV selects a closer position, thereby saving time.  
The relationship between the number of damaged UAVs and the bit error rate (BER) is simulated. As shown in Figure 15a, when the attack intensifies, the jamming effectiveness gradually decreases, and the trends for BPSK and QPSK are almost identical. Figure 15b illustrates the time required for jamming array reconstruction. Assuming a UAV flight speed of 30 m/s, the more UAVs that are reinforced, the more complex the mission decision-making and trajectory planning become, resulting in longer time consumption. In addition, the BER after the reconstruction is higher than that before the reconstruction. As shown in Table 1, the effective jamming is re-implemented after 4 UAVs are damaged, and the BER returns to normal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modulation Type** & **BPSK** & **QPSK** & 8 PSK & **QAM** \\ \hline
**BER1** & 0.1158 & 0.1162 & 0.2182 & 0.3360 \\
**BER2** & 0.1160 & 0.1170 & 0.2189 & 0.3364 \\ \hline \hline \end{tabular} Note: BER1 is the result based on the wave superposition algorithm, and BER2 is the result based on the array-based estimation algorithm.  
\end{table}
Table 1: The bit error ratio after the jamming array reconstruction.  
Figure 14: The positions of the UAVs and the target. The selected redundant UAVs fly to the given position to join the jamming task, and the unattacked UAVs continue to maintain the formation.  
Figure 15: The impact of UAV attack and the time required to reconstruct the jamming array. (**a**) The bit error rate versus the number of damaged UAVs. (**b**) The reconstruction time versus the number of damaged UAVs.
## 4 Performance Evaluation
### Overhead  
The overhead shows the ratio of total control packets sent to total received data packets at the destination. The overhead is calculated during dynamic vehicular mobility and by varying the size of the cluster. The cluster-based technique and multicast communication approach result in low overhead because of stable IVC links. Moreover, the overhead is distributed among several clusters, which does not affect the overall performance of the protocol, as depicted in Figure 4. The overhead may rise because of cluster maintenance at dynamic mobility. However, it is still low compared to other competing protocols, as shown in Figure 4b. The overhead is less than 4%, even at the cluster size of 18 cm, because of the LBCHE and CMN schemes, as noticed in Figure 4b.  
The overhead of the DSR and MAODV rises upon varying the mobility of the vehicles. This behavior shows that the DSR and MAODV are not adapting to dynamic changes in the network. Hence, the number of control packets increases to maintain the network connections, resulting in a sudden increase in overhead.
## 1 Introduction
This approach is novel in the scientific literature, because the applications of data clustering and non-linear multivariate power curves are at their early stages, and it is interesting for the industry because the use of data-driven models for custom wind turbine performance monitoring has been gradually becoming a necessity. The general structure of the method is summarized in the workflow of Figure 1.  
The study is organized as a test case discussion, based on the analysis of the SCADA data of a Vestas V52 wind turbine sited in Italy (850 kW rated power): the data were provided by the Lucky Wind company. Practically, the goodness of the proposed approach is discussed through the analysis of the most common performance metrics (MAE, MAPE, RMSE) for the validation of the data-driven model. Furthermore, a method for analyzing the accumulated performance change is proposed: it is based on the analysis of how the MAPE changes in two subsets of the target data set. This procedure is useful in the applications for individuating small performance changes accumulated along a relatively long period: this can happen in the form of performance improvement due to technology optimizations [37] or, vice versa, in the form of performance worsening [38; 39], which can occur due to the aging of the machine. The work is therefore organized as follows: In Section 2, the test case and the data set are described. In Section 3, the method is described. The results are collected in Section 4, and the conclusions are drawn in Section 5.
## 3 Results
Therefore, this model could potentially be used as a valuable tool for predicting pipeline damage. In addition to the KNN algorithm, four other machine learning algorithms were implemented to classify the pipeline damage data: the support vector machine (SVM), random forest, XGBoost, and multilayer perceptron neural network (MLPN) algorithms. The comparison results of these five algorithms are shown in Figure 5.  
The grid search technique was also used to identify the optimal hyperparameters for each algorithm, and the optimal hyperparameters varied between algorithms. For SVM, the optimal values tested were kernel type, gamma, and C. For XGBoost, the objective type, maximum depth, and the number of estimators were tested. Lastly, for MLPN, the number of neurons per layer, learning rate, and activation function were tested. The results from the grid search revealed that the optimal hyperparameters for each algorithm were as follows: for SVM, kernel type = 'rbf', gamma = 0.01, and C = 1000; for XGBoost, maximum depth = 3, objective = multisoftmax, and the number of estimators = 100; and for MLPN, the number of neurons per layer = 100, learning rate = 0.01, and activation function = ReLU. The random forest algorithm's hyperparameter was not tuned since the model employs randomization when creating trees to avoid increasing the complexity of the algorithm. The algorithms were trained using the optimal hyperparameters and evaluated using testing data. The results indicated that the XGBoost algorithm had the highest accuracy of 92.3% on the validation dataset. The random forest was the second-best-performing algorithm with an accuracy of 92.0%, followed by SVM with 89.74% and MLPN with 87.18%. Precision  
Figure 5: Performance of algorithms used to model natural gas pipeline. and recall values were calculated for each class, and the results showed that XGBoost had the highest precision and recall values for all the classes, as shown in Figure 5.  
Overall, XGBoost was found to be the most suitable algorithm for classifying pipeline damage instances with high accuracy. However, the random forest and SVM algorithms also showed promising results and can be considered alternative options. The results demonstrate the effectiveness of machine learning algorithms in classifying pipeline damage instances and suggest that these models can be used as valuable tools for predicting pipeline damage.
## 3 Results
### Extracted Ion Data for Carpet Substrates
#### 3.2.2 Carpet #2 Stain Master Essential Carpet  
To investigate the effect of the background interferences of carpet substrates made from polyester in argon analysis, the Stain Master Essential Carpet (carpet #2) was burned in the absence and presence of ILs (Figure 8). The pyrolysis products identified in the chromatogram of carpet #2 burned without ILs, were benzene (RT = 1.81 min, RSI = 951), toluene (RT = 3.17 min, RSI = 957), styrene (RT = 6.30 min, RSI = 944), benzaldehyde (RT = 8.46 min, RSI = 945), and naphthalene (RT = 15.19 min, RSI = 925), which were consistent with those reported in the literature using the activated charcoal strip (ACS) method for polyester fibers [11].  
The examination of the TIC of carpet #2 burned without ILs shows potential interferences with the identification of gasoline. However, comparing the alkylbenzene profiles of carpet #2 burned without ILs and neat gasoline shows that the abundance and the peak ratios are not consistent with the pattern observed for gasoline. The comparison of polynuclear aromatic profiles of carpet #2 burned without ILs with that of neat gasoline also shows an interference at retention time of 15.19 min, which was identified as naphthalene (RSI = 925). Although some indane derivatives were identified in carpet #2 burned without ILs (Figure 9a), the retention times and the peak abundance relative ratios were not consistent with the indane compounds identified in neat gasoline (Figure 9c) and carpet #2 burned with gasoline (Figure 9b). The indane profile for carpet #2 burned with gasoline shows the characteristic indane profile observed in neat gasoline, which makes the indane profile a suitable profile for the identification of gasoline in the charred debris of carpet #2.  
Figure 8: Total ion chromatograms (\(m\)/z 50–500) of carpet #2 (a) burned without ILs; (b) burned with gasoline; and (c) burned with kerosene.  
Potential Interference with the Identification of Kerosene  
The TIC and extracted ion profiles for carpet #2 burned without IL do not show interferences with kerosene. The TIC and the extracted ion profiles obtained for carpet #2 burned with kerosene match very well with those obtained with neat kerosene. The obtained profiles are distinguishable from carpet burned without ILs, indicating that this method can be reliably used for the identification of kerosene in the charred debris of carpet #2.  
Comparison of the TICs of the SPME extracts of carpet #2 burned without ILs and in the presence of gasoline and kerosene (Figure 8), indicates that the chromatograms are easily distinguishable and that this method can successfully be used to identify gasoline and kerosene in charred Stain Master Essential (polyester) carpet.
# Battery Characterization via Eddy-Current Imaging with Nitrogen-Vacancy Centers in Diamond
## 4 Battery Measurement
The ceramic solid battery consists of external aluminum electrodes, Li-based ceramic oxide inner electrodes (noted as 2), a Li-based ceramic oxide electrolyte (noted as 3), and a copper charge collector (indicated as 1). **(b)** A photograph of the battery with dimension \(4\times 3\times 1\) mm\({}^{3}\). Distinctly visible is the defect on the external electrode. **(c–f)** Maps of the \(R\) of LIA for modulation frequencies of \(1\,\mathrm{kHz}\), \(5\,\mathrm{kHz}\), \(10\,\mathrm{kHz}\) and \(40\,\mathrm{kHz}\). **(g–j)** Maps of the \(\theta\) of LIA for modulation frequencies of \(1\,\mathrm{kHz}\), \(5\,\mathrm{kHz}\), \(10\,\mathrm{kHz}\) and \(40\,\mathrm{kHz}\), respectively. where \(\omega=2\pi f\) denotes the angular frequency, \(\sigma\) is the conductivity of the material and \(\mu\) is the mutual permeability of the material. In order to estimate the AC magnetic field generated by the battery, we adopted the calibration formula in reference [8],  
\[\delta\phi =\arctan{(\frac{R_{0}\cos{(\phi_{0})}-R_{m}\cos{(\phi_{m})}}{R_{m} \sin{(\phi_{m})}})}, \tag{4}\] \[\delta R =\sqrt{R_{m}^{2}\sin{(\phi_{m})}^{2}+(R_{0}\cos{(\phi_{0})}-R_{m} \cos{(\phi_{m})})^{2}},\]  
where \(\delta\phi\) indicates the phase shifts generated by the battery, \(\delta R\) is the amplitude contrast generated by the battery, \(R_{0}\) and \(\phi_{0}\) are the background-corresponding amplitude and phase of the LIA, respectively, and \(R_{m}\) and \(\phi_{m}\) are the measured lock-in amplitude and the phase, respectively. The calibrated results are shown in Figure 6. We calibrated the  
Figure 4: Imaging pictures of solid batteries with the impurity. (**a–d**) are amplitudes and phases of LIA for the battery with an impurity at frequencies of 5 kHz and 20 kHz. Figure 5: Imaging pictures of solid batteries with the crevice. (**a–d**) are amplitudes and phases of LIA for the battery with a crevice at frequencies of 5 kHz and 20 kHz. magnetic field and phase shifts generated by the battery (the same as the one used in Figure 3) at a modulation frequency of 5 kHz. The external electrode without the defect induces the maximum magnetic field of 0.04 mT and phase shift of 0.03 rad.
## 3 Effective Medium Parameters Extraction Method  
The simulation approach of the proposed modified Coptic cross shape-based unit cell was executed using a time domain solver with hexahedral mesh in the frequency span of 1 to 16 GHz. The NRW technique was used to obtain the relative permittivity, relative permeability, refractive index, and impedance [34]. The equations were as follows to determine the effective parameters:  
\[\epsilon_{r}\sim\frac{2}{jk_{0}d}\times\frac{(1-V_{1})}{(1+V_{1})} \tag{1}\]  
\[\mu_{r}\sim\frac{2}{jk_{0}d}\times\frac{(1-V_{2})}{(1+V_{2})} \tag{2}\]  
\[\eta_{r}=\frac{2}{jk_{0}d}\times\sqrt{\left\{\frac{(S_{21}-1)^{2}-S_{11}{}^{2} }{(S_{21}+1)^{2}-S_{11}{}^{2}}\right\}} \tag{3}\]  
\[Z=\sqrt{\frac{(1+S_{11})^{2}-S_{21}^{2}}{(1-S_{11})^{2}-S_{21}^{2}}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Dimension (mm)** & **Parameter** & **Dimension (mm)** & **Parameter** & **Dimension (mm)** \\ \hline W & 11 & S & 0.4 & V & 0.8 \\ L & 11 & T & 1.2 & X & 0.3 \\ P & 0.8 & U & 1.0 & Y & 10.4 \\ Q & 2.7 & M & 10.4 & Z & 9.4 \\ R & 0.6 & N & 9.4 & O & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The design specification of modified Coptic cross shape-based metamaterial.  
Figure 2: Simulation geometry of the proposed unit cell.  
where, \(V_{1}\) and \(V_{2}\) are the composite terms, which is derived using transmission and reflection coefficient of the unit cell; \(k_{0}=\frac{2\pi f}{c}\), \(V_{1}=\left\lvert S_{21}\right\rvert+\left\lvert S_{11}\right\rvert\), \(V_{2}=\left\lvert S_{21}\right\rvert-\left\lvert S_{11}\right\rvert\), and \(\mathrm{d}\) is the substrate thickness; and \(\varepsilon_{r}\), \(\mu_{r}\), \(\eta_{r}\), and \(Z\) are the relative permittivity, relative permeability, refractive index, and normalized impedance, respectively.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 4 Sensitivity Analysis of Smoothing Probability
### Initial Parameter Variation  
In this subsection, the sensitivity of the backward probability to initial parameter variation is derived based on SMF. The sensitivity function \(p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{\gamma})\) for initial parameter variation \(\theta_{\gamma}=\gamma_{t}\), as shown in the recursive backward probability sensitivity function in Equation (10), can be represented in SMF as  
\[p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{\gamma})=AO^{t+1}p( \mathbf{y}_{\mathbf{e}}^{t+2:T}|x_{z}^{t+1})(\theta_{\gamma})\]  
For \(t=T-1\), \(p(\mathbf{y}_{\mathbf{e}}^{T+1:T}|x_{z}^{T})(\theta_{\gamma})=1\) and the recursive probability expression reduces to  
\[p(\mathbf{y}_{\mathbf{e}}^{T:T}|x_{v}^{T-1})(\theta_{\gamma})=AO^{t+1}\]  
The procedure to compute the coefficients of the function \(p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{\gamma})\) in backward matrices \(B^{k}\) for \(k=T,\cdots,t\) using SMF is summarized in Algorithm 5.  
```
0:\(A\), \(O\), \(\Gamma\), \(\mathbf{e}\), and \(\theta_{\gamma}\)
0:\(B^{T},\cdots,B^{t}\)
1:\(B^{T}\gets ones(n,1)\)
2:for\(k=T-1\) to \(t\)do
3:\(O^{k+1}\gets diag(O_{\cdot,\mathbf{e}\mathbf{k}\uparrow})\)
4:\(B^{k}\leftarrow\widehat{A}\ast O^{k+1}\ast B^{k+1}\)
5:Return\(B^{T},\cdots,B^{t}\)
```  
**Algorithm 5** This computes the coefficients of the backward probability sensitivity function \(p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{\gamma})\) with \(\theta_{\gamma}=\gamma_{t}\) in backward matrices \(B^{k}\) for \(k=T,\cdots,t\) using SMF.
## 3 Results and Discussion
### Evaluation of Antibacterialeral Effect of C. axillaris Dried Extracts
Out of the 32 samples (four different extracts individually tested against eight different bacterial strains), 23 samples demonstrated observable zones of inhibition (ZOI). These 23 samples were further analyzed to determine their minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values, which were measured in milligrams per milliliter (mg/mL). As shown in Table 8, the tested extracts showed a range of minimum inhibitory concentration (MIC) values, varying from 0.52 mg/mL to 8.33 mg/mL, as well as minimum bactericidal concentration (MBC) values ranging from 0.78 mg/mL to 16.66 mg/mL. Out of the extracts analyzed, the acetone extract from _C. axillaris_ demonstrated the highest level of effectiveness against _S. aureus_. It displayed a minimum inhibitory concentration (MIC) of 0.52 mg/mL and a minimum bactericidal concentration (MBC) of 0.78 mg/mL. Besides, ethyl acetate extract also depicted almost similar efficacy against _S. epidermidis_ with MIC and MBC value of 0.65 mg/mL and 1.30 mg/mL, respectively. Conversely, the aqueous extract displayed the highest MIC and MBC value of 8.33 mg/mL and 16.66 mg/mL, respectively, against _S. aureus_. According to the information presented in Table 8, it was observed that towards all four extracts, only the _E. coli_ bacteria exhibited sensitivity to both inhibitory and bactericidal effects, in examined concentration. Furthermore, concerning Gram-negative bacteria, the aqueous extract demonstrated the highest efficacy against _K. pneumonia_. It displayed a minimum inhibitory concentration (MIC) value of 1.4 mg/mL and a minimum bactericidal concentration (MBC) value of 2.6 mg/mL. In a study conducted in China, using the ethanolic extract of _C. axillaris_ fruit, the peel extract showed MIC values of 0.16 mg/mL and 5.00 mg/mL against _S. aureus_ and _E. coli_, respectively. Similarly, the MBC values against _S. aureus_ and _E. coli_ were 1.25 mg/mL and 10.00 mg/mL, respectively [56]. Here, we investigated the inhibitory and bactericidal potency of _C. axillaris_ dried fruit extracts against other six bacteria for the first time. Although bioassay guided antibacterial screening of _C. axillaris_ have not been conducted yet, numerous abundantly present bioactive constituents like ellagic acid, catechchin, ascorbic acid, beta-sitosterol, chrysin, gallic acid, quercetin, dihydroquercetin, epicatechin gallate, protocatechuic acid, octacosanol, quercetin, pinochemrin, sinapic acid, salicylic acid, triacentanoic acid, and daucosterol [25] might be responsible for observed antibacterial effect.