esa-sceva/satcom-chunk-collection huggingface dataset:
# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
## 7 Discussion  
The Antiference project has been a challenging project. Below, we summarize some of the most important lessons learned.  
The detection and classification model has been successful and performs well. However, the system is very sensitive, and picks up very low-power RFI and small perturbations from the environment as well. This has led to a lack of distinguishing between harmless and dangerous RFI. The detection of RFI is made based on small, 10 ms chunks of data. While this solves the problem of detecting pulsed RFI, for the most part, and assures short detection delays, it also leads to some false negatives/positives as 100 detections and classification decisions are made every second. There are many ways this problem can be addressed, such as looking at a window of detections and employing majority voting, or having a secondary model in place that decides on the detection/classification results based on the detection-result streams.  
Antiference focused on ML-based methods for the detection and classification model and the fingerprint database. However, the estimation of filter parameters can also be delegated to ML models and might lead to improvements in performance.  
There is no standardized way to inform the user of the existence or detected type of RFI. Further developments are needed for meaningful interfacing with GNSS receivers and their operators.  
The proper training of ML models requires a massive amount of data. Unfortunately, currently there are not enough free or publicly available recorded datasets that can be used for the training of models. This led to recording campaigns internally, in addition to processing and labelling the data samples manually. However, the amount of data was insufficient for training the models to perform the interference mitigation. In our view, a significantly increased number of GNSS datasets (both clean and containing RFI) is needed for the future widespread development of ML-based RFI detection, classification, and mitigation.  
Figure 4: Antiference RFI classification outputs for the first recording from Figure 4. FM interference is hard to distinguish from AM interference and is sometimes mistaken for CW interference. As the mitigation of these types of interference is the same, this does not impact mitigation performance.  
Digital data were used for the training and testing of models. While this was appropriate for detection and classification, the integration of mitigation could not be carried out in this domain. It should be noted that the effective mitigation (that can lead to increased CN/0 in all cases) requires the real-time mitigation of signals in the analogue domain and the inclusion of the effects in the loop. It was suspected that the Antiference system would not be functional in real-time, so the decision was made to keep all processing (barring the COTS receivers) in the digital domain to reduce complexity and keep tests realistic. Until more performant detection and classification models are developed, this will remain a shortcoming of the system.
# Free Vibration of a Taut Cable with Two Discrete Inertial Mass Dampers
## 4 Two IMDs at the Same End
### Asymptotic Solution  
Similarly to the case of two opposite IMDs, assuming two IMDs locations \(x_{1},x_{2}\ll L\) and the wave number \(\beta_{n}\) of the damped cable to be small perturbations from \(\beta_{n}^{0}\), Equation (16) can be simplified as:  
\[\beta_{n}L\cong n\pi+\beta_{n}^{0}\frac{E_{2}+iF_{2}}{G_{2}+iH_{2}}, \tag{17}\]  
\[E_{2}=-\overline{b_{1}}x_{1}-\overline{b_{2}}x_{2}+(\overline{b_{1}b_{2}}- \overline{c_{1}c_{2}})(x_{2}-x_{1}), \tag{17a}\]  
\[F_{2}=\overline{c_{1}}x_{1}+\overline{c_{2}}x_{2}-(\overline{b_{1}c_{2}}+ \overline{b_{2}c_{1}})(x_{2}-x_{1}), \tag{17b}\]  
\[G_{2}=1-\overline{b_{1}}-\overline{b_{2}}+(\overline{b_{1}b_{2}}-\overline{c _{1}c_{2}})(1-x_{1}/x_{2}), \tag{17c}\]  
\[H_{2}=\overline{c_{1}}+\overline{c_{2}}-(\overline{b_{1}c_{2}}+\overline{b_{2 }c_{1}})(1-x_{1}/x_{2}). \tag{17d}\]  
Figure 5: The maximum achievable supplemental modal damping ratio of a cable equipped with a single IMD or two symmetric identical IMDs (\(x=x_{1}=x_{2}^{*}=1\%L\)).  
From Equations (12) and (17), the asymptotic modal damping ratio of a cable with two IMDs at the same end can be obtained as:  
\[\xi_{n}\cong\frac{\text{Im}[\Delta\beta_{n}]}{\left|\beta_{n}^{0}\right|}=\frac{ F_{2}G_{2}-E_{2}H_{2}}{\left(G_{2}\right)^{2}+\left(H_{2}\right)^{2}}. \tag{18}\]
# The Optimization Algorithm of the Forced Current Catholic Protection Base on Simulated Annealing +
## 4 Simulation Experiments and Results Analysis  
Taking the grounding grid 200 m \(\times\) 200 m as an example, the buried depth is 0.8 m, the grounding trunk is 70 mm \(\times\) 8 mm galvanized flat steel, the electrical conductivity is \(1.0\times 10^{-7}\) S/m, the soil conductivity is 340 \(\upmu\)s/cm, the optimal potential is \(\phi_{p}=-0.9\) V, the weighting coefficient \(\alpha_{1}=0.6\), \(\alpha_{2}=0.4\), according to the above SAOA algorithm, the schematic diagram of the surface of the grounding grid after the cathodic protection model is divided as shown in Figure 3.  
The SAOA algorithm is used to optimize the calculation and the optimal solution for obtaining the optimal protection effect of the grounding grid cathodic protection system is: anode potential \(\phi_{e}=93.6\) V; number of anodes \(n_{e}=3\); positions of three anodes are A (0, 156.8, 83.7), B (132.6, 0, 78.6), C (200, 200, 64.2). At this time, the distribution of the surface potential of the grounding grid is as shown in Figure 4. The maximum potential of the grounding grid surface is \(-0.85\) V and the minimum potential is \(-1.25\) V, which meets the engineering requirements. Figure 4 shows the distribution of the surface potential of the grounding grid in a three-dimensional space, which is represented by a different color, from red to blue, indicating that the potential gradually becomes smaller.  
According to the SAOA algorithm, when the anode potential \(\phi_{e}=90\) V, the anode is placed 5 to 50 m below the grounding grid and the grounding grid potential distribution caused by different positions is shown in Figure 5.  
Figure 4: Surface potential distribution map of grounding grid based on simulated annealing optimization.  
Figure 3: Finite element splitting diagram of the grounding grid surface.  
It can be seen from Figure 5 that as the depth of the anode is increased, the average potential and variance of the surface of the grounding grid become smaller.  
The finite element simulated annealing algorithm (SAOA), variable scale method (DFP) and genetic algorithm (GA) are used to solve the above grounding grid, respectively. The optimization calculation results of various algorithms are shown in Table 1.  
It can be seen from Table 1 that in the optimization method of the grounding grid cathodic protection system, the design accuracy based on the simulated annealing algorithm is 1.12% higher than that based on the genetic algorithm and the design accuracy is 1.64% higher than that based on the variable scale method.
# Metal Nanozymes: New Horizons in Cellular Homeostasis Regulation
## 8 Metal Nanozymes in Cancer Treatment
Their study revealed that HepG2 cells treated with Fe\({}_{3}\)O\({}_{4}\) nanozymes exhibited the characteristic morphologic features of swelling and vacuolation of the mitochondria, disrupting the endoplasmic reticulum and chromatin condensation and margination, and nucleolus disintegration. Furthermore, nanozymes with significant peroxidase activity (such as Cu(OH)\({}_{2}\), V\({}_{2}\)O\({}_{5}\), or Ag NPs) activated cell death exhibiting common morphological features and nanozymes with little peroxidase activity (such as Fe\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), TiO\({}_{2}\), or ZnO) induced only limited cell death of HepG2. This observation based on transition electron microscopy showed that Fe\({}_{3}\)O\({}_{4}\) nanozyme induced unique cell lethality distinct from the currently well-defined apoptosis, necrosis, autophagy, pyprotosis (inflammation-triggered programmed cell death), and ferprotosis (iron-triggered programmed cell death). The authors checked the variations of protein profiles and gene expression in Fe\({}_{3}\)O\({}_{4}\) nanozyme-treated HepG2 cells and found that Kirsten rat sarcoma viral oncogene (KRAS) and ACLY played the central roles in Fe\({}_{3}\)O\({}_{4}\) nanozyme-induced cell death. Moreover, they join the fact that Fe\({}_{3}\)O\({}_{4}\) nanozyme-induced cell lethality was regulated by ACLY-dependent RAS signaling because ACLY is a cytosolic enzyme required for RAS farnesylation. The short review describing the translation of metal and metal oxide nanozymes in cancer treatment showed that regardless, cancer remains a disease with limited treatment approaches, researchers are developing more and more complex nanosomes to overcome these barriers. The cancer treatment can be spiked by combining nanozymes such as modified MOF with radiosensitizers or metal/metal oxide nanozymes with dual enzyme-like activity and irradiation or even molecules in newly discovered pathways. The created new class of nanozymes are candidates for enhancing the efficiency and safety of cancer diagnosis and therapy. However, more efforts should be taken into pharmacological safety, toxicological safety, and the illumination of EPR and protein corona (PC) mechanisms in the human body. Looking forward, the new complex nanozymes will be tailored to target not only cancer cells but also the TME environment, including the immune system.
## 5 Experimental Results
### Propagation Model Parameters  
Figure 18 shows the obtained PL and RSS of the Hata propagation model of the distance between the ground station and the drone at the cloud layer. Experimental predictions of the considered Hata propagation model show that the range of PL floats between \(-97\) dB to \(-127.5\) dB. Additionally, PL has a positive correlation with distance, which represents the drone altitude. Noticeably, the shadowing effect and reflection of signals from interfering obstacles were low, due to line-of-sight connectivity between the ground station and the drone. As RSS is linked to PL, results show RSS varying between \(-40\) dBm to \(-70\) dBm, which has again a positive correlation with distance (drone altitude). Unsurprisingly, increasing transmission power would enhance RSS, since RSS depends on transmitter power, antenna gains, and environmental factors. Having the results of the propagation model including PL and RSS is useful in monitoring system performance, network planning, and coverage to achieve perfect reception. In our scenario, this means communication between drone and ground station as well as wireless server gateway showing a high level of reception with the smallest attenuated signal, besides achieving stronger wireless connectivity. This also helps in delivering collected data from the drone to the cloud for storage and analysis in an efficient and timely manner.  
Figure 17: Sensing temperature, humidity, soil moisture, rain level, and solar radiation in 24 h on 15 September 2020: (**a**) sensing temperature in 24 h on 15 September 2020; (**b**) sensing humidity in 24 h on 15 September 2020; (**c**) sensing rain level in 24 h on 15 September 2020; (**d**) sensing solar radiation in 24 h on 15 September 2020; (**e**) sensing soil moisture in 24 h on 15 September 2020.
# ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
## 5 Conclusions  
The ENDURUNS project provides a major opportunity to develop new applications in seafloor exploration and surveying, thanks to highly extended autonomy of its AUV implementation in conjunction with the USV unit.  
The ENDURUNS system will contribute to the enrichment of our knowledge with respect to deep sea habitats but also geological and geophysical formations. With the capability and versatility of various types of sensor payloads combined with effective prolonged autonomy, deep ocean habitats and geophysical formations, such as ridges, volcanic sites can be mapped in high detail. Moreover, offshore infrastructures are critical for the global economy. Indeed, more and more facilities are built offshore in deeper waters and more remote areas. Such infrastructure needs to be carefully inspected for structural or other types of defects to avoid catastrophic failure. It is therefore important to have effective inspection means, which can be deployed for long-term evaluation. Leaking oil and gas pipelines, installed underwater, can pose extreme environmental hazards which need to be detected timely. The use of the ENDURUNS system can contribute decisively in the accurate and continuous evaluation of the structural health of critical infrastructure, helping protect the environment as well as improve the sustainability of valuable industrial sectors to the global economy.  
All the authors contributed to the Investigation and Conceptualization; mission requirements design, S.M. and F.M. and F.F. and L.B.; Vehicles design, A.T. and J.P. and C.V.C. and E.C. and P.W. and K.N. and M.P. (Makthoum Peer) and T.G.; Energy management, M.A. and Y.S.M.C. and F.P.G.M. and P.J.B.S. and T.S. and G.C.; Mission planning S.G. and B.S. and A.B.; Sensing and data processing, N.G. and E.O. and S.M. and F.B. and F.M. and F.B.; Data communication, S.G. and B.S. and A.B.; writing--original draft preparation, S.M. and N.G. and F.P.G.M. and P.J.B.S. and J.P. and C.V.C. and F.B.; writing--review and editing, S.M. and F.B.; supervision, T.Y. and M.P. ([PERSON]); project administration, T.Y. and M.P. ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The ENDURUNS project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (Research Grant Agreement H2020-MG-2018-2019-2020 n.82438), and it is partly supported by the International Research & Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning(Grant number: 2018K1A3A7A03089832).  
The authors declare no conflict of interest.
# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods  
Compared with traditional methods that rely on low-level and manual features to extract alpha mattes, deep learning methods leverage extensive datasets to train neural networks that can automatically learn image features and predict high-quality alpha mattes without requiring manual intervention. This approach has significantly improved alpha matting performance and has become increasingly popular recently. Deep learning-based image matting algorithms can be categorized into two categories: (1) algorithms improving the shortcomings of traditional algorithms through deep learning; and (2) end-to-end deep learning algorithms that generally achieve better performance than the former. Therefore, this paper focuses on recent innovative and breakthrough end-to-end deep learning algorithms, which can be categorized into two categories: trimap-based methods, which use a trimap as an additional input; and trimap-free methods, which do not require a trimap. The relevant methods mentioned in this article and their major innovations will be presented in Table 1. \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Method** & **Major Innovations and Contributions** \\ \hline DIM [9] & The first model to implement end-to-end neural networks for image matting. \\ \hline AlphaGAN [12] & The first generative adversarial network in image matting. \\ \hline VDRNet [17] & The introduction of a residual structure in the encoder helps to address the issue of performance degradation that arises with increasing network depth. \\ \hline LSM [15] & The proposed method has improved the accuracy of alpha mattes by reducing unknowns through the calculation of foreground and background colors and by combining sampling methods with deep learning techniques. \\ \hline MatteFormer [39] & The first method that introduced a transformer into image matting. \\ \hline GCA [24] & The proposed model with an additional module named the guided contextual attention module effectively addresses the issue of poor matting performance in semi-transparent areas of images. \\ \hline IndexNet [16] & The proposed approach has enhanced image details by learning index functions and leveraging them to guide the down-sampling and up-sampling processes. \\ \hline SIM [25] & Expanding the traditional trimap to a semantic trimap that can provide various semantic information for the model. \\ \hline DisIM [13] & Proposed a network that consists of one encoder and two decoders to identify the real mixed pixels in the unknown regions and estimate their alpha values simultaneously. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The major innovations and contributions of relevant image matting methods.
## 4 Experimental Settings and Datasets  
### Dataset Description  
A platform is established to evaluate the performance of the proposed methodology using a three-axis accelerometer and gyroscope. Wearable sensors are used to acquire human activity data, and the data are uploaded to signal processing software. Our algorithm can be applied in real-time situations, especially for the health care assessments of children and elderly people.  
Three datasets were used in this experiment. The MOTIONSENSE dataset [38] is taken from the accelerometer and gyroscope sensors of an iPhone with a time constraint of 6 seconds. Six activities were performed by 24 participants in different manners, i.e., downstairs, jogging, going upstairs, sitting, walking, and standing.  
The MHEALTH dataset [39] is an accelerometer, gyroscope, and magnetometer dataset. In this paper, only the accelerometer and gyroscope sensor data were used to evaluate the proposed model. The sensors were placed on the subject's chest, right wrist, and left ankle. Twelve outdoor activities were performed, including walking, climbing stairs, standing still, sitting and relaxing, waist bending forward, cycling, jogging, running, jump forward and back, knees bending, frontal elevation of arms, and lying down.  
The IM-AccGyro dataset [40] is our self-annotated human-machine interactive dataset. Three accelerometer sensors were attached at three different locations, which were the arm, leg, and neck of the subject, as shown in Figure 10. The ages of the participants ranged from 15 to 30 years. Six different indoor and outdoor physical exercise activities were performed including boxing, walking, running, sitting-down, standing-up, and clapping.
## 3 Methodology
### Feature Extraction and Selection  
After framing and preprocessing the data, fourteen different features are extracted in each frame, including statistical features, Mel Frequency Cepstral Coefficients (MFCC) features, ECG features,  
Figure 1: Flow architecture of the proposed methodology for human activity recognition.  
Figure 2: Band-stop filtration of the accelerometer data in the preprocessing step. The red and blue signals represent the filtered data and the unfiltered data, respectively.  
and GMM features. The statistical features are computationally less intensive and can be easily extracted in real time [26; 27; 28]. The statistical features applied in this paper are the mean, median, harmonic mean, position vector, sine, and cosine. The MFCC features represent the frequency and amplitude of the sensor signals and are individually helpful in finding the pattern of each activity. The ECG features are commonly used to find the absolute pattern of a signal. In this paper, the ECG features include the autoregressive, waveform length, slope sign change, and Willison amplitude, which can efficiently detect the specific pattern of each activity. Furthermore, the GMM features include the weighting ratio, mean, and covariance for activity recognition. Figure 3 defines the flowchart of feature combinations as:
## 5 Performance Evaluation and Discussion  
### Simulation Environment  
We use a Matlab simulator to evaluate the performance of our proposed routing algorithm here. In a 100 \(\times\)100 m\({}^{2}\) rectangle sensor network, there are 100 sensor nodes randomly distributed. Initial energy \(E_{o}\) of each sensor node is 2 Joules, and each sensor will generate a data packet of 2,000 bits. In order to guarantee a certain level of network connectivity, the transmission range of each sensor node is set to 50 to 80 m. Besides, other simulation parameters are defined as the follows: the energy dissipation \(E_{elec}\) to run the radio device is set to 50 nJ/bit. The free space model of transmitter amplifier \(\varepsilon_{fs}\) and the multi-path model of transmitter amplifier \(\varepsilon_{mp}\) are respectively set to 10 pJ/bit/m\({}^{2}\) and 0.0013 pJ/bit/m\({}^{4}\).  
Figure 5: Flowchart to find a substitute forwarding sensor node.
## 4 Simulation and Experiment
### Experiment and Results
#### 4.2.2 Example Two  
The second set of experiments estimates the interval points for solving non-stationary signals given that the WSAW signals considered are non-stationary signals, and the above algorithm is suitable for stationary signals, the amplitude of the WSAW is inversely proportional to the frequencies of the underwater sounder source. Thus, a different sound source frequency corresponds to a different amplitude. After unwrapping, the phase is a piecewise stationary signal, and thus the interval segmentation of each stationary signal can be solved, i.e., the above-mentioned improved RELAX algorithm can be used to estimate the micro-amplitude frequency. In this example, we employ Equation (8) to solve the interval of strong and weak signals, where, the overall data length is 42,000 sampling points, the number of samples is set to 2000, and the data length of each sample is 21 sampling points. Figure 12a highlights that the number of interval points is 1111, and the interval point in the measured data can be calculated as 23,331, which is consistent with the interval point 23,275 obtained from the actual time-frequency analysis diagram of Figure 12b. Therefore, the interval points of different frequencies can be calculated and the WSAW signals are separated.