esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation Metrics
#### 2.4.1 Precision Metric  
This evaluation metric calculated the ratio between the correct case predicted as positive over all predicted as positive cases.  
\[\text{Precision}=\frac{\text{True Positive}}{\text{True Positive}+\text{ False Positive}}\]  
The precision metric provides us with the intuition of the ability to find all cases relevant to the dataset. The precision metric relates to direct costs; if there is a large false positive, then there is a higher true positive cost.
## 2 Architecture
### M2M Gateway Resource Control and Management
#### 2.2.2 Resource Mapping Mechanism  
The PLC stores the data collected by the sensor in real time within its storage area. Because different sensors measure different physical variables with different definitions, in the PLC, the measurement of the different sensors is stored in different registers. Therefore, the operation of reading and writing sensor data is implemented on the register specific to each sensor. In the field of industrial control, hardware companies divide the storage area in PLCs into seven domains (_i.e._, S, X, Y, T, M, C, and D). Storage domains S and M store the switch data (such as the open and closed states of valves and parameter correction on and off states), and the data stored in storage domain D are the analog data (such as temperature, pressure, and wind velocity). Therefore, the form address of the resource must be converted to the physical address. In Table 10, Device is the storage area region (such as region S or region M). Range is the range of address numbers on the application level, e.g., the range of address numbers for region S is 0-1023. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station ID** & **Plcid** & **Sensorid** & **Type** & **High** & **Low** \\ \hline
1 & 1 & D2000 & 1 & & \\
1 & 1 & D2002 & 2 & 58.5 & 98.5 \\
1 & 2 & D1000 & 1 & & \\
2 & 1 & D1500 & 2 & 24.7 & 36.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Simple description of a resource operation table. Type is the value represented by the storage area (bit indicates the switch data and word indicates the analog data). The communication address Hex is the specific register address of this storage area in the PLC. For example, the register address of S512 in the storage area of this PLC is 0 \(\times\) 0200. Therefore, the address information constituted by any Device and Range must be converted to the form in the communication address Hex column. This then facilitates the operation of reading and writing on a specific register in the communication with the sensor. Because different hardware devices have different storage area divisions, the design of a mapping rule table for a specific hardware device can be used to map the abstract address of that device.
## 3 Description of Research Methods
### Optimization-Based Attacks  
These attacks solve optimization problems to find the least perturbation required to misclassify a given sample.  
C&W Attack: The C&W attack, proposed by [PERSON] and [PERSON] [34], crafts adversarial examples across different distance metrics. While it predominantly uses the \(L_{2}\) norm, the method's optimization ensures that the perturbed input remains within a valid input space, leading to high-confidence adversarial examples.  
Deepfool: Deepfool [35] is an optimization-based attack that operates under the \(L_{2}\) norm. Its goal is to determine the minimal perturbation required to have an input sample misclassified by crossing the decision boundary to another class. In comparison to FGSM and BIM, Deepfool is particularly efficient in achieving high misclassification rates with minimal perturbations.
# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 2 Materials and Methods
### Seamless Weather Integration for TBO
#### 2.2.2 Technological Framework
RDD operations are broadly divided into four steps: input, transformation, and action. In this system, we implemented the following RDD workflow:  
Figure 3: Technological framework for the 4 DT-Wx prototype system. 1. Generate an initial RDD from ADS-B files and NWP files using sc:parallelize;
2. Dynamically calculate the optimal number of partitions;
3. Perform transformations such as Comma-Separated Values (CSV) parsing, 4D data extraction, and interpolations using a map, removing unwanted results using a filter;
4. Collect results through the collect() method, which triggers an actual calculation. By leveraging Spark's Lazy Evaluation characteristic, transformation operations are delayed until an action is called, providing optimization opportunities for the overall operation and streamlining memory usage by preventing the unnecessary storage of intermediate results. Based on this highly optimized technical framework, the 4 DT-Wx system can efficiently process large amounts of weather data and aircraft trajectory information, providing accurate and rapid weather information to users. This has the potential to significantly improve the safety and efficiency of aviation operations in TBO environments, providing a solid foundation to respond to more complex weather conditions and flight scenarios in the future.
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Sensing Layer
#### 4.1.5 CS-Based Routing Protocols  
Routing and CS have been jointly addressed in [90] in which the routing path is iteratively built through a greedy choice to minimize the intermediate coherence of the measurement matrix with the sparse matrix. Moreover, based on the results obtained in [81; 90], where the authors proved the benefit of their approaches in terms of good reconstruction quality with fewer transmitted samples, [PERSON] et al. [91] introduced and quantified a new data routing approach. The authors proposed a DCS-based data gathering in large-scale WSNs based on CS acquisition, where they have investigated the performance from a node lifetime point of view. The approach considered in [91] provides each node with the ability to take the proper decision about the optimum value of its CF. After compression, the data are routed by exploring a hybrid approach based on DCS and pack and forward (PF) technique. The aim of this hybrid is to reduce the overall number of transmitted packets. The obtained results reveal an extension in the network lifetime compared to the use of conventional DCS even with a large number of sensors in the network. Moreover, [PERSON] et al. [92] built up a probabilistic model for the ensemble of the signal using a Bayesian approach and adopted belief propagation (BP) algorithm to recover the ensemble measurements. Their results show an improved reconstruction quality as well as a reduction in the energy consumption up to 20% compared to Multichannel BP associated with DCS.
## 7 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Movement Model**} \\ \hline
**Routing** & **Network Size** & \multicolumn{2}{c}{**Random Way**} & \multicolumn{2}{c}{**Bus Model**} & \multicolumn{2}{c}{**Working Day Model**} \\ \cline{3-8}
**Algorithm** & **Privacy Mode** & **Small** & **Large** & **Small** & **Large** & **Small** & **Large** \\ \hline \multirow{3}{*}{Epidemic} & 30\% & 45.71 & 44.94 & 46.74 & 41.89 & 40 & 21.73 \\  & 50\% & 94.72 & 102.96 & 96.23 & 90.52 & 133.33 & 133.18 \\  & 80\% & 334.11 & 403.23 & 303.17 & 387.8 & 233.33 & 2300 \\ \hline \multirow{3}{*}{Prophet} & 30\% & 40.50 & 40.96 & 50.54 & 46.67 & 19.99 & 40.0 \\  & 50\% & 94.20 & 93.18 & 104.49 & 102.59 & 33.33 & 104.54 \\  & 80\% & 310.47 & 389.18 & 355.02 & 388.82 & 150 & 400 \\ \hline \multirow{3}{*}{Spray and Wait} & 30\% & 45.20 & 43.94 & 46.79 & 40.47 & 38.02 & 20.68 \\  & 50\% & 96.38 & 102.18 & 98.45 & 99.5 & 108.51 & 133.33 \\  & 80\% & 356.30 & 368.44 & 318.15 & 402.78 & 308.33 & 289.99 \\ \hline \multirow{3}{*}{Epidemic} & Stranger then Familiar & 34.18 & 15.17 & 17.015 & 17.64 & 70.83 & 75 \\  & Familiar then Stranger & 32.26 & 8.011 & 28.07 & 6.9 & 25.75 & 21.73 \\ \hline \multirow{3}{*}{Prophet} & Stranger then Familiar & 49.24 & 23.71 & 17.56 & 20.44 & 62.5 & 50.9 \\  & Familiar then Stranger & 27.07 & 9.12 & 23.63 & 12.04 & 38 & 40 \\ \hline Spray and Wait & Stranger then Familiar & 59.74 & 58.46 & 50.9 & 57.76 & 66.53 & 71.85 \\  & Familiar then Stranger & 26.73 & 22.06 & 22.75 & 18.22 & 28.64 & 20.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PER of the trust-threshold-based mode. routing can further enhance privacy. Thirdly, designing proper incentives to encourage participation in privacy-preserving message propagation can improve adoption. Fourthly, equipping the privacy layer with machine learning capabilities to automatically learn user preferences can make the system more adaptive.
## 3 Numerical Method
### Approximation of the Integral Operators  
Let \(S\) be one of the surfaces \(\Sigma_{n}\), \(n=1,2\), and \(\tilde{S}\) is its approximation. We approximate the direct values of the integral operators \(W_{\lambda_{m}}[\tilde{S},\tilde{h}]\), \(U_{\lambda_{m}}[\tilde{S},\tilde{g}]\), \(\
abla W_{\lambda_{m}}[\tilde{S},\tilde{h}]\) in collocation points \(x_{i}\in\Sigma_{n}\), \(n=1,2\), and the following formulas:  
\[W_{\lambda_{m}}[\tilde{S},\tilde{h}](x_{i})=\sum_{\sigma_{k}\in \tilde{S}}h_{k}W_{ik}^{\lambda_{m}},\quad W_{ik}^{\lambda_{m}}=\frac{1}{4\pi} \int_{\sigma_{k}}\frac{e^{-\lambda_{m}\pi_{\tilde{i}}}}{r_{i}}d\sigma_{y},\] \[U_{\lambda_{m}}[\tilde{S},\tilde{g}](x_{i})=\sum_{\sigma_{k}\in \tilde{S}}g_{k}U_{ik}^{\lambda_{m}},\quad U_{ik}^{\lambda_{m}}=\frac{1}{4\pi} \int_{\sigma_{k}}(x_{i}-y,\mathbf{n_{k}})\frac{e^{-\lambda_{m}\pi_{\tilde{i}}}( \lambda_{m}r_{i}+1)}{r_{i}^{3}}d\sigma_{y},\] \[\
abla W_{\lambda_{m}}[\tilde{S},\tilde{h}](x_{i})=\sum_{\sigma_ {k}\in\tilde{S}}h_{k}\mathbf{w}_{ik}^{\lambda_{m}},\quad\mathbf{w}_{ik}^{\lambda_{m} }=\frac{-1}{4\pi}\int_{\sigma_{k}}(x_{i}-y)\frac{e^{-\lambda_{m}\pi_{\tilde{i }}}(\lambda_{m}r_{i}+1)}{r_{i}^{3}}d\sigma_{y}.\]  
Here, \(r_{i}=|x_{i}-y|\), and \(m\) is the number of subdomains where the direct value of the potentials are calculated. Note that if \(i=k\), the integrals \(W_{ik}^{\lambda_{m}}\) are improper absolutely convergent, and \(U_{ik}^{\lambda_{m}}\), \(\mathbf{w}_{ik}^{\lambda_{m}}\) are singular considered as the principal value.  
The direct values of the double layer potential gradient \(\
abla U_{\lambda}[S,g]\) in collocation points \(x_{i}\in\Sigma^{m}\) are approximated the following way:  
\[\
abla U_{\lambda_{m}}[S,g]=\sum_{\sigma_{k}\in\tilde{S}}g_{k}\mathbf{u}_{ik}^{ \lambda_{m}},\quad\mathbf{u}_{ik}^{\lambda_{m}}=\
abla\int_{\sigma_{k}}\frac{ \partial F_{\lambda_{m}}(x_{i}-y)}{\partial n_{y}}\]  
To calulate the integral \(\mathbf{u}_{ik}^{\lambda}\), we separate the singularity and integrate it analytically. We decompose the function \(F_{\lambda}(x-y)\) into  
\[F_{\lambda}(x-y)=F_{0}(x-y)+\tilde{F}_{\lambda}(x-y),\quad F_{0}(r)=\frac{1}{4 \pi r},\quad\tilde{F}_{\lambda}(r)=\frac{e^{-\lambda r}-1}{4\pi r}.\]  
with \(r=|x-y|\). So, the integral is decomposed the following way:  
\[\mathbf{u}_{ik}^{\lambda}=\mathbf{u}_{ik}^{0}+\tilde{\mathbf{u}}_{ik}^{\lambda},\quad\mathbf{ u}_{ik}^{0}=\
abla\int_{\sigma_{k}}\frac{\partial F_{0}(x_{i}-y)}{\partial n _{y}}d\sigma_{y},\quad\tilde{\mathbf{u}}_{ik}^{\lambda}=\
abla\int_{\sigma_{k}} \frac{\partial\tilde{F}_{\lambda}(x_{i}-y)}{\partial n_{y}}d\sigma_{y}.\]  
To calulate \(\mathbf{u}_{ik}^{0}\), we use the Biot-Savart law [27; 28], and integral \(\tilde{\mathbf{u}}_{ik}^{\lambda}\) is improper convergent.  
The integrals in expressions \(W_{ik}^{\lambda}\), \(U_{ik}^{1}\), \(\mathbf{w}_{ik}^{0}\), and \(\tilde{\mathbf{u}}_{ik}^{\lambda}\) are calculated approximately by the rectangular method. Such a scheme for improper and singular integrals was described in the paper [24].
## 3 Results
### Color-Tunable Vortices  
Chromatic effects in white-light vortices can be troublesome [21]. One option is filtering, whereby the broad spectrum exiting the supercontinuum fiber may be spectrally filtered to pick out one particular color. In this example, we used a variable wavelength filter to isolate red or green light. The SLM is easily calibrated for either of these wavelengths, and independent vortices are produced to demonstrate interference, as shown in Figure 10. The SLM is AR coated for 800 nm, which means that at the green wavelengths, the amount of zeroth order reflection substantially increases. But since we typically use the first order diffraction, this only affects the brightness of the vortices and not their quality. In this result, the entire SLM is illuminated with either red or green light. The SLM generates two vortices of opposite charge that interfere in the image plane of the CCD. The experimental results are in excellent agreement with the numerical model from Figure 3b.  
For experiments relying on the suppression of interference, we developed an approach for generating two independent vortices of different color. Spectral transmission filters are placed in front of the SLM to section off two halves (one for red, one for green). Each vortex is then independently controllable, and the color is determined by the particular filter. This system allows us to generate perfect integral vortices with high efficiency, since each half of the SLM can be addressed with a different gray level. For instance, on an 8-bit grayscale (0 to 255), the red vortex with a 630 nm center wavelength has a gray level that goes from 0 to 135, which corresponds to 0 to 2\(\pi\). The green vortex with a 540 nm center wavelength has a gray level that goes from 0 to 97, which for this wavelength corresponds to 0 to 2\(\pi\). The red and green vortices are shown in the lower panels of Figure 11. In the upper panels, a red vortex is held stationary while a second red vortex passes through it. Not surprisingly, interference fringes develop as the red vortices overlap. In the lower panels, the green vortex is held stationary while the red vortex passes through it. Note that when the green and red vortices overlap there is, of course, no interference pattern, but merely a superposition of red and green, making yellow. Furthermore, since the vortex diameter may be controlled independently of the charge, it is possible to generate two or more distinct vortices that can have equal or unequal charge but still have the same size. This could be a huge asset for two-particle trapping experiments that require true independent control, with no crosstalk between trapping regions.  
The interference of a pair of vortices of same color, like the one seen above in Figure 11, is tested when using the same and opposite topological charges. This sequence is seen in Supplementary Material Figure S1.  
Figure 10: Interference from oppositely charged vortices (\(m=10\) and \(m=-10\)), (**a**) red vortex interference; (**b**) green vortex interference; and (**c**) numerical prediction from the IDL code.
# Infrared Spectroscopy for the Analysis of Bioactive Analytes in Wheat: A Proof-of-Concept Study
## 4 Discussion
Work on other grain crops (faba beans, chickpeas, and mungbeans) has also failed to produce accurate NIRS or MIRS models for TMA content [38], confirming that this challenge is not unique to the wheat matrix. In general, the MIRS models performed more poorly compared to the NIRS models in both the calibration and test sets. The only exception to this trend was for the prediction of moisture content, which showed a higher R\({}^{2}\)\({}_{\text{test}}\) and similar RMSEP in the MIRS model. MIRS was able to predict the protein content of wheat with acceptable accuracy (R\({}^{2}\)\({}_{\text{test}}\) = 0.93; RMSEP = 0.62%). However, its accuracy was approximately three times lower compared to the use of NIRS for the same analyte (R\({}^{2}\)\({}_{\text{test}}\) = 0.991; RMSEP = 0.22%), reflecting the challenge in obtaining reproducible spectra when using MIRS instruments with an attenuated total reflectance sampling module. Similarly, the linearity (R\({}^{2}\)) and RMSEP were slightly poorer for the MIRS prediction of TPC and FRAP compared to their respective NIRS models. However, the slope of the FRAP MIRS model (1.20) was closer to the ideal value of 1. In contrast to the similar loadings seen in the NIRS models for TPC and FRAP prediction, the MIRS models for these analytes were noticeably different in their loadings. While both included the carbonyl region (\(\sim\)1700 cm\({}^{-1}\)), the OH (\(\sim\)3400 cm\({}^{-1}\)) and cellulose (\(\sim\)1000 cm\({}^{-1}\)) regions showedgreater influence in the TPC model, while the FRAP model included more influence from the CH stretch (\(\sim\)3000 cm\({}^{-1}\)) and amide regions (1600-1400 cm\({}^{-1}\)). This indicated that the FRAP model was relatively specific for the prediction of antioxidant compounds (potentially including other non-phenolic antioxidant compounds such as tocoperbols and or carotenoids), rather than just relying on the correlation between FRAP and TPC values to predict the former analyte. On average, wavelength optimisation via moving window analysis improved the RMSECV values by 18 \(\pm\) 6% for NIRS, and by 20 \(\pm\) 8% for MIRS. Consequently, moving window PLSR appears to be an easy and readily implementable method for improving model performance via wavelength selection, although it can be computationally expensive for large datasets.
## 1 Introduction
### Paper Contribution and Organization  
The solutions presented in the papers referred to above increase the possibilities of DL-based solutions for CSI; however, there is a lack of studies focusing on the characteristics of a system based on 5G NR and, therefore, the channel model and performance in terms of the BLER of the data channel. In this article, we extend our findings in [32] and propose a DL-based technique for channel state information feedback reporting in FDD transmission mode, based on the implementation of a convolutional neural network, called NR-CsiNet. The improvements in our approach with respect to [12] can be summarized in terms of the following new features:  
1. provision of MIMO capabilities through the use of antenna arrays at both the transmitter and receiver sides;
2. addressing the estimation of the downlink channel affected by noise;
3. testing the system with a 5G NR fully compliant simulator with a clustered delay line channel model.  
The remainder of this paper is organized as follows: Section 2 introduces CSI-RS, which are reference signals used for downlink channel sounding. Section 3 provides a description of the New Radio link simulator software developed for all our experiments in order to outline the simulation environment. Section 4 presents the NR-CsiNet DL model, describing its internal structure and the data-processing steps required. The simulation results are presented in Section 5. Section 6 concludes the article with some observations and suggestions for potential future research directions.
## Non-Invasive Method for In-Service Induction Motor Efficiency Estimation Based on Sound Acquisition
###### Abstract
[PERSON] and [PERSON] [25] used an algorithm based on the wavelet packet to detect faults in IM bearings, and [PERSON] et al. [26] used a multiple-signal classification algorithm to get the diagnostic. The method proposed in this study is the first to use sound to estimate efficiency of IM and performs a completely non-invasive approach that does not require any setup or contact with the motor. Considering that the literature does not present any similar method, this is the main contribution of this study. The Arduino-based system performs the acquisition of the sound signal emitted by the in-service motor, which allows the estimation of the shaft rotational speed. By using a mechanism that is the opposite of the mechanism used by [PERSON] et al. [12], the torque and output power are estimated based on the shaft rotational speed and the data from the motor manufacturers' manuals. The dispositive that captures the sound signal is portable and the motor efficiency determination can be carried out by bringing it closer to the motor that is going to be monitored. Being portable allows the same dispositive to be utilized to monitor different motors in an industrial environment, drastically decreasing the cost when compared to the methods that need permanent installations. It is worth noting that even though the purpose of the system is not to detect the fault directly, motors working at a relatively low efficiency can be indicative of a fault, which can be investigated using specific methods for this purpose. The proposed technique was implemented, tested, and validated with laboratory experiments and was compared to the air gap torque technique, which is considered the technique with low invasiveness and good reliability.
## 3 Experiments
### Parametric Experiments for the Number of Segments \(m\)
(**a**), (**d**), and (**g**) are bubble plots for different map sizes with SR-GP, the time for calculation and the number of segments as their x-axis, y-axis, and bubble size, respectively; (**b**), (**e**), and (**h**) are scatter plots for different map sizes with the number of segments and \(f_{1}\) as their x-axis and y-axis, respectively; (**c**), (**f**), and (**i**) are scatter plots for different map sizes with the number of segments and \(f_{2}\) as their x-axis and y-axis, respectively. Additionally, note that SPA* does not have a large range of runtime changes when the number of segments \(m\) is adjusted significantly. For 100 \(\times\) 100, the running time change is about 0.01 s; for 200 \(\times\) 200, the running time change is about 0.04 s; for 400 \(\times\) 400, the running time change is about 1.8 s. Conversely, observe that the SPA* on \(f_{1}\) and \(f_{2}\) are more variable and are more important indicators in route planning. Therefore, we need to consider the value of the number of segments \(m\) in different cases, so that the performance of SPA* is guaranteed. Based on the above analysis, the optimal intervals of \(m\) under different map sizes are given. For 100 \(\times\) 100, the lower the number of segments, the better, but not less than 6; for 200 \(\times\) 200, the number of segments should be 10-20; for 400 \(\times\) 400, the number of segments should be 15-30. The results show that the optimal performance is near 15 grids per segment for various map sizes. After limiting the range of \(m\), SPA * also shows strong control ability on \(f_{1}\) and \(f_{2}\). Therefore, we believe that SPA * has the advantages of fewer parameters, strong compatibility, and robustness.
## 3 Results and Discussion
### Spectral Shape Sensitivity of EAP Size-Based PFT Detection
The B/G ratio shows a strong biomass dependency and a small sensitivity to size at large sizes, for 0.5 \(\leq\) Chl \(a\)\(\leq\) 4.5 mg/m\({}^{3}\). The b\({}_{b\phi}\) ratios all display a strong size signal at 2-4 μm, and the G/R ratio shows a corresponding size-related feature. opportunities for identifying higher resolution size classes than the 2 to 20 \(\upmu\)m and \(>\)20 \(\upmu\)m categories currently frequently employed [6; 8; 10; 84]. The ability to achieve better resolution within the 2-20 \(\upmu\)m size class is particularly desirable for marine ecosystem modelling [6].