esa-sceva/satcom-chunk-collection huggingface dataset:
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 3 Results
### Classification Results
#### 3.3.2 Results of the Second Evaluation Scenario  
Similar to the experiment performed in the office environment, we only used the dataset collected in the hallway environment to train and test the classification model. A LOSO-CV procedure was adopted to test the effectiveness of our model. The dataset size used for this experiment is equal to 3000 observations acquired from all 10 subjects. Each time we train the SVM classifier, we used 2700 observations, and the remaining 300 observations were left to test the developed SVM classifier. An overall accuracy of 86.53% was obtained, with the walking activity achieving the highest true-positive rate of 96.5% and picking up the pen from the ground achieving the lowest true-positive rate of 54%. By examining the results, one can observe a reduction in the system's performance compared to the experiments performed for the data collected in the office environment. This degradation in the performance can be traced back to the following reasons: First, the distance between the transmitter and the receiver in the hallway environment is longer than the distance between the transmitter and receiver in the office environment. This in turn can cause the received signals to be a little weaker due to fading effects. Secondly, we had no control over the environment since the experiment was performed in a public university hallway. The confusion matrix generated based on using the dataset of the hallway environment is shown in Table 8, and the detailed results are provided in Table 9. In this new environment, falling can be detected with a TPR of 87.75%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Predicted Activity**} \\ \hline  & A1 & A2 & A3 & A4 & A5 & A6 \\ \cline{2-7}  & A1 & 3456 & 24 & 3 & 33 & 78 & 6 \\ \cline{2-7}  & A2 & 18 & 1056 & 0 & 39 & 54 & 33 \\ \cline{2-7}
**Performed Activity** & A3 & 0 & 6 & 1194 & 0 & 0 & 0 \\ \cline{2-7}  & A4 & 9 & 90 & 0 & 996 & 51 & 54 \\ \cline{2-7}  & A5 & 30 & 42 & 0 & 99 & 1020 & 9 \\ \cline{2-7}  & A6 & 9 & 39 & 0 & 54 & 6 & 492 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The confusion matrix obtained for the first evaluation scenario.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Activity** & **A1** & **A2** & **A3** & **A4** & **A5** & **A6** \\ \hline True Positive Rate & 96.00\% & 88.00\% & 99.50\% & 83.00\% & 85.00\% & 82.00\% \\ False Positive Rate & 01.37\% & 02.73\% & 00.04\% & 03.02\% & 02.56\% & 01.30\% \\ Precision & 98.13\% & 84. 01\% & 99.75\% & 81.57\% & 84.37\% & 82.83\% \\ F1 Score & 97.05\% & 85.96\% & 99.62\% & 82.28\% & 84.68\% & 82.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of our proposed approach obtained for the first evaluation scenario.
# A Novel Iterative Thresholding Algorithm Based on Plug-and-Play Priors for Compressive Sampling
###### Abstract  
We propose a novel fast iterative thresholding algorithm for image compressive sampling (CS) recovery using three existing denoisers--i.e., TV (total variation), wavelet, and BM3D (block-matching and 3D filtering) denoisers. Through the use of the recently introduced plug-and-play prior approach, we turn these denoisers into CS solvers. Thus, our method can jointly utilize the global and nonlocal sparsity of images. The former is captured by TV and wavelet denoisers for maintaining the entire consistency; while the latter is characterized by the BM3D denoiser to preserve details by exploiting image self-similarity. This composite constraint problem is then solved with the fast composite splitting technique. Experimental results show that our algorithm outperforms several excellent CS techniques.  
Keywords:compressive sampling; plug-and-play prior; BM3D; composite splitting +
Footnote †: journal:  
_Future Internet_**2017**, 9, 24; doi:10.3390/f9030024
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 3 Results and Discussion
### Grad-CAM Results Obtained for Revisited Dataset  
For the purpose of evaluating the model trained on the modified dataset, the approach described in Section 3.2 was replicated. The results of the Grad-CAM analysis for the sample images of the constructed dataset are presented in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Anomaly** & **Predicted Normal** \\ \hline True anomaly & 8 & 2 \\ \hline True normal & 17 & 73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix for the evaluated system.  
Figure 10: Results of applying Grad-CAM method for samples taken from additional dataset (**on the right**), together with original images (**on the left**) for comparison.  
Figure 9: Learning process for proposed model.  
The figure presents the model's output activation heatmaps for both the anomalous (bottom right corner) and normal samples. By looking at the original counterparts, we can see that the model correctly detects novelty changes in the environment. However, looking at the normal representatives, it can be seen that the output of regular and consistent images appears to be indeterministic, as the model attempts to create an internal representation of regular data with irregular shapes.
## 3 Landing Trajectory Data Correction
### Landing Interval Extraction  
The landing phase only accounts for approximately 1% of the entire flight process. It is necessary to extract the landing interval from the QAR data for more efficient analysis. Therefore, we first identify the touchdown moment (i.e., when one of the landing gears first touches the ground) during the aircraft landing process. Then, a 120 s interval is selected by taking 20 s before the touchdown moment and 100 s after it, which serves as the landing interval, as shown in Figure 1.
## 3 Results and Discussion
### Recovery Section  
For a single pore, the results are encouraging, and the next question that needs to be answered is how fast the concentration profile re-establishes after the pore, since it is disturbed by the porous section, see Figure 10 for an illustration. The results are shown in Figure 11; the method with which the recovery was characterized can be found in Appendix C. The center of the channel is taken as a reference point. Please note, the reference point chosen and the extent to which the SID profile is disturbed will have an influence on the results, therefore detailed analysis of membrane design are part of ongoing research.  
Figure 11 shows the relative development length after the pore when compared to the entrance section for different volume fractions extracted, and for bulk particle volume fractions of 10 and 30 percent. The values for reaching 95% of the fully developed profile for a bulk particle volume fraction of 50 percent were zero, indicating that the profiles re-establish extremely fast, which is essential for process design.  
For lower bulk particle volume fractions, a higher development length is needed but it never exceeded 42% of that of the closed channel, even if as much as 10% of the liquid was removed. The trends that we see are as expected, with higher concentrations re-establishing faster, and this is also the case if less liquid is removed. In brief, extraction of more volume gives a greater disturbance of the SID profile, which therefore needs longer to recover.  
The channel height does not have a great effect on the result; the narrower channels need, relative to the development length of the entrance section, slightly longer to re-establish but this effect is not as much as expected from Figure 7. This is caused by the fact that the profile is still rather established after the pore (and near the wall), while it needs to form from scratch at the entrance. When looking in the center of the channel, the effect of channel height is much clearer, since more particles need to be moved to re-establish the entire profile, as shown in Figure 12. For process design this is not very relevant since liquid is removed near to the wall, but we show the results here to illustrate that the extent to which the profile is distorted and recovers is a strong function of the position in the channel.  
Figure 10: Particle volume fraction versus the relative channel height. The graph gives an indication of the shift in the SID profile of a suspension with a 50% bulk particle volume fraction: fully developed SID profile before the pore at 9.5 cm (black line, 1); disturbed SID profile just after the pore at \(\sim\)10 cm (light grey line, 2) and the restored SID profile after the pore at 19.5 cm (dark grey line, 3).
## 4 Experimental Results And Analysis
### Comparison with Related Works  
In this section, we compare the proposed method with others of the state-of-the art. The following parameters are selected: Volume adjustment (attenuation), re-quantization (up), downsampling and ER. Tables 8 and 9 show the results.  
According to the results of Table 8, the proposed method is the most effective in handling re-quantization; it has an intermediate position for volume adjustment, as well as for down-sampling. The previous works do not provide information regarding conversion to FLAC and MPEG-4 AAC. In our case, FLAC compression does not change the hash value, whereas for MPEG-4 AAC, the mean BER value is \(47\times 10^{-3}\). Here it is important to take into account both the mean and the standard deviation of the BER values. A high \(\sigma\) value may imply the appearance of FAR or FRR.  
For the above reasons, the values of \(p\) and ER are also taken into account. The closer \(\sigma_{e}\) is to \(\sigma_{t}\), the better the performance of the hash function, then \(p_{e}\) is more similar to \(p_{t}\), i.e., 0.5, and as a consequence, the ER value is closer to 1 (ideal). In other words, if the dispersion of BER values is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Perceptual Modification** & **Ours** & [37] & [23] & [2] & [39] & [40] \\ \hline Volume adjustment & \(98\times 10^{-4}\) & \(78\times 10^{-3}\) & \(98\times 10^{-3}\) & \(46\times 10^{-6}\) & 0 & \(78\times 10^{-5}\) \\ Down-sampling & \(27\times 10^{-3}\) & 0 & \(27\times 10^{-3}\) & \(11\times 10^{-4}\) & \(52\times 10^{-2}\) & \(37\times 10^{-4}\) \\ Re-quantization & 0 & NP & \(11\times 10^{-5}\) & 0 & \(3\times 10^{-2}\) & NP \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with state-of-the art approaches.  
Figure 8: EER for re-sampling manipulation. The X-axis represents the threshold (0–1), and the y-axis represents the error rate (0–100).  
significantly greater than the theoretical value, the system does not work with an adequate transition probability between two hash values nor does it distinguish between ability and compression rate. Table 9 shows the results for some selected state-of-the-art methods.  
According to the results of Table 9, the proposed method has a high entropy rate value, and therefore, the proposed hash function follows the suggested characteristics for perceptual applications.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 1 Introduction  
Digital video broadcasting-second generation terrestrial (DVB-T2), DVB-second generation cable (DVB-C2), and DVB-second generation satellite (DVB-S2) are standards for terrestrial, cable, and satellite digital television (DTV) services, respectively [1; 2; 3]. While DVB-S2 uses a single carrier transmission scheme, DVB-T2 and DVB-C2 use orthogonal frequency division multiplexing (OFDM). Each standard is based on a different technology, but they adopt the same baseband (BB) frame structure and low density parity check (LDPC)/Bose Chaudhuri Hocquenghem (BCH) coding scheme. Furthermore, DVB-C2 uses 4K-point fast Fourier transform (FFT) and scattered pilots from the OFDM modes used in DVB-T2. For this reason, DVB-T2, DVB-C2, and DVB-S2 are referred to as the same family of standards, i.e., DVB-X2 [4].  
Although there are many broadcasting receivers that support the individual standards of DVB-X2, developing an integrated DVB-X2 receiver to support multi-mode broadcasting standards in a single platform is still challenging. There are many similarities among DVB-T2, DVB-C2 and DVB-S2. Each of the standard technologies consists of similar, or the same, functional blocks. Therefore, the integrated DVB-X2 receiver can be implemented by minimizing additional hardware (H/W) logic resources. If one receiver is able to receive different types of broadcasts regardless of the transmission media, manufacturing and production costs can be reduced, rather than developing receivers for each consumer broadcasting system separately. The integrated receiver allows low-cost and small-sizeconsumer set-top boxes to be designed for DVB-T2, DVB-C2, and DVB-S2 networks. For these reasons, various commercial chips were developed to support multi-mode broadcasting standards.  
This paper proposes an integrated DVB-X2 receiver architecture with the common accelerator. The remainder of this paper is organized as follows. After detailed system descriptions in Section 2, architectures of the proposed receiver and DVB-T2/C2 demodulator are described in Sections 3 and 4, respectively. Implementation results and conclusions are presented in Sections 5 and 6, respectively.
# New Approaches of Stochastic Models to Examine the Vibration Features in Roller Bearings
## 3 Spectral Analysis  
The SKF 6209 test bearing underwent various loadings as outlined in Table 1. Throughout the initial 20 h of operation, vibration levels remained stable, and no significant defect frequencies were observed. Subsequently, after approximately 10 more hours of bearing operation under a radial load of 45 N, along with variable axial loading, defect frequencies associated with inner race damage were detected, accompanied by a slight increase in vibration levels. As the bearing progressed further in its failure state, outer race defects became evident. At this point, the vibration signal exhibited a distinct periodicity due to impacts generated by metal-to-metal contact. However, in the later stages of the fault, the periodic impulses became obscured by the noise energy resulting from the propagated defect across the bearing's outer race. Spectral results for the SKF 6209 bearing under a 45 N load at different stages are depicted in Figure 3a-c for reference.  
In a normal and healthy state, the bearing displays a typical vibration pattern characterized by low amplitudes and energy. However, when an inner race defect is present, there is a noticeable increase in amplitude within the vibration pattern, accompanied by observable frequencies linked to inner race defects. Similarly, the presence of an outer race defect results in elevated amplitudes in the vibration pattern, along with frequencies that can be observed and linked to outer race defects.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S.No** & **Radial Loading (mm)** & **Defect Width (mm)** & **Defect Depth (mm)** \\ \hline
1 & 30 & 38 & 12 \\ \hline
2 & 45 & 38 & 12 \\ \hline
3 & 60 & 383 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental condition.  
Figure 3: Behavior of spectrum at different stages of bearing. (**a**) Healthy condition; (**b**) Inner race defect; (**c**) Outer race defect.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 2 Preliminary
### The OSD Decoding Algorithm  
As mentioned in the Introduction, the OSD method is an MRIP reprocessing decoding algorithm that performs bit-flips on the \(K\) MRIPs, where \(K\) is the information sequence length. These \(K\) MRIPs are determined using the generator matrix \(G\) together with the absolute value of the received symbols from the channel. The OSD decoding algorithm with parameter \(L\) (OSD-\(L\)) has three steps. First, sort the columns of \(G\) according to the absolute value of the vector \(Y\) which is received from the channel. Second, find the \(K\) most reliable independent columns of the column swapped version of \(G\). Third, test each codeword generated by flipping no more than \(L\) bits from first \(K\) bits of the hard decision codeword \(\overline{X}^{N}\) and select the one with minimum \(f\) as the decoded codeword. We refer readers to Section 10.8 of [PERSON] and [PERSON]'s book [13] for details of these OSD algorithms.
# RT Engine: An Efficient Hardware Architecture for Ray Tracing
## 3 Proposed RT Engine Architecture  
### Multiple Stacks  
As shown in Algorithm 1, each ray is input to the TRV unit, and TRV will send the information of nodes to the stack based on the results. If the ray intersects with two child nodes, the farther node will be pushed to the stack, and closer nodes will be sent to the next stage. If the ray does not interact with two child nodes, it will send a pop stack request. On the other hand, when the triangle intersection test is completed, the IST will request the pop stack. The stack features save the nodes and ensure the accuracy of the integrated process.  
However, only one ray can be processed when there is only one stack in the system. This design cannot achieve multiple ray parallel processing, resulting in a waste of hardware resources. In this case, we consider setting a group of stacks to save the nodes' information of multiple rays. This kind of design allows multiple rays to process at the same time.  
Stack Management is set in our system design to handle stack operations. To solve the system's accuracy when multiple stacks store nodes' information, we set up the LUT unit to manage stacks. The data stored in the stack are the nodes' address, and the nodes may be internal nodes or leaf nodes. As shown in Figure 2, the push request is from the TRV, and the pop request is from TRV and IST. After the MUX selects the request, the current corresponding ray's address is compared to the ray's address in the LUT. After matching the ray's address, the request will be sent into the corresponding stack. Such a design guarantees the parallelism of the system while ensuring accuracy. Less hardware resource overhead is used, achieving a relatively high performance improvement.  
Compared with a single stack, the performance of multiple stacks by LUT can improve by 9.99-20.78\(\times\).
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Miniaturization
#### 2.2.7 Use of Metamaterial  
Metamaterials are defined as artificially engineered materials, which are designed to provide material properties that are not commercially available to satisfy any extraordinary conditions [41]. They can also be engineered to achieve materials with close-to-zero values of permittivity, negative permittivity or permeability, or simultaneous negative permittivity and permeability. Therefore, they can dramatically reduce the in-body antenna size and can also improve its bandwidth and gain [42]. In Ref. [43], a circularly polarized in-body antenna could achieve 84% size reduction by using a metamaterial design. Although the use of metamaterials has been effective in reducing antenna size, there is a substantial cost in terms of using a complex material, significantly narrow operating bandwidths and lower radiation efficiencies [32]. Additionally, in metamaterial-based miniaturization methods, substantial care must be taken with the analytical models, which are used for the analysis. These analytical models typically ignore the polarization of the field, which might cause different behaviors as compared with the regular incidence or non-polarized models commonly used for analysis to calculate the effective medium properties.
## 3 Results
### Ecosystem Optical Diversity and Productivity  
Ecosystem optical diversity showed linear positive correlations with the best NEE predictors (Figure 3), indicating evident optical diversity and productivity relationships. The collinearity test showed that there was no collinearity between SVIs and CVs as at the Variance Inflation Factor (VIF) values range was included between 1 and 2.61. The highest correlations between SVIs and CVs (CV calculated from full spectrum) were observed for MTCI (R\({}^{2}\) = 0.61) and NIDI1 (R\({}^{2}\) = 0.51), which were, in turn, the SVIs which were best correlated with NEE. Quite surprisingly, the widely-used SVIs NDVI and EVI, although they were good proxies of NEE (Table 3), showed very low correlations with CV. Across-scale observations indicated a clear scale-dependency of optical diversity and ecosystem productivity relationships, with an evident across-scale hump-shaped pattern of R\({}^{2}\) values for most SVIs. The best results were mostly obtained with 10 meters pixels (Figure 3), while the 20 meters spatial resolution (corresponding to the S2 resolution of the bands needed to calculate all the investigated SVIs) was still able to provide important information on ecosystem diversity and productivity relationships (MTCI R\({}^{2}\) = 0.54). Conversely, when the 30-meter spatial resolution was considered, a noticeable decrease in the coefficient of determination was observed (MTCI R\({}^{2}\) = 0.40).  
When optical diversity (CV) was calculated from S2 simulated bands (Figure 3), a similar across-scale pattern was observed, with only slightly lower R\({}^{2}\) values compared to the ones obtained when CV was calculated from full-range hyperspectral data.  
The MTCI and CV maps including the average optical diversity and the MTCI productivity proxy values within the 30 60 \(\times\) 60 m ROIs enabled the visualization of the ecosystem diversity-productivity patterns for the investigated grassland ecosystems (Figure 4).  
Figure 3: Across-scale optical diversity and productivity relationships (calculated for the 30 60 \(\times\) 60 m ROIs) expressed as the R\({}^{2}\) values of the linear regression between CV (calculated from continuum-removed reflectance) and spectral vegetation indices at increasing pixel sizes (0.9–30 m). Solid color bars indicate the R\({}^{2}\) values when the CV was calculated from the AisaEAGLE full spectrum continuum-removed reflectance, while the striped-line bars indicate the R\({}^{2}\) values when the CV was calculated only from the continuum-removed reflectance values of the Sentinel-2 simulated bands. The bars highlighted with a bold line are indicating the R\({}^{2}\) values obtained with the full simulation (both spectral and spatial resolution) of Sentinel-2 data.
## 6 Results and Discussion
### Surface Current  
The induced surface current at 27 GHz for Antenna-1 is shown in Figure 15. As it can be seen from Figure 15b, a large current is induced along the length of W and St on the ground plane. This is in line with our earlier discussion in Section 4, where it is noted that there is a significant change in the resonant frequency when the length of W and St is  
Figure 14: (**a**) TARC. (**b**) CCL.  
Figure 13: (**a**) Envelop Correlation coefficient. (**b**) Diversity Gain.  
changed. Additionally, as seen in Figure 15a, a large current is inducted along slits placed on both sides of the feedline. This is again in line with our previous discussion, where there is large change in the radiation characteristics when the length of Sh (the slit length) is altered. So, we can clearly infer that the length of the ground plane and the slit length have a significant role to play in achieving the antenna's response. The surface current for only one antenna element is shown, as other antennas also follow a similar pattern.