esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Automated Satellite-Based Surface Water Mapping
#### 2.3.1 Image Preprocessing  
Image preprocessing is usually required for the raw satellite images to correct the atmospheric and terrain effects for practical applications. For the optical images, we used the quality assurance bits in \"QA\" generated using the C function of mask (CFMask) [54] algorithm to remove the pixels that are affected by snow/ice, cirrus, cloud, and cloud shadow. The processed images are used to generate mNDWI products based on the Green and SWIR bands. The mNDWI is calculated using the equation [46]:  
\[\mathrm{mNDWI}=(\mathrm{Green}-\mathrm{SWIR})/(\mathrm{Green}+\mathrm{SWIR}), \tag{1}\]  
For SAR images, since the data collection on the GEE was already preprocessed, we will focus on the algorithms for SAR speckle filtering and slope correction, respectively. Speckle appears in SAR images like granular noise that is caused by the interference of radar waves reflected from the elementary scatterers on the ground. Because speckle typically lowers the accuracy of image segmentation and classification in SAR images, adding speckle filters is a standard preprocessing step to alleviate the effects of speckle. The scientific community has developed multiple algorithms (e.g., Lee Sigma [55], Gamma Map [56], and Refined Lee [57]) which are also incorporated into HYDRAFFloods toolsets. In this study, we used the Gamma Map algorithm, as suggested by the tool.  
Meanwhile, the SAR images are also sensitive to the terrain effects. In this study, we applied an angular-based radiometric slope correction routine using ALOS data to reduce the impact of terrain on the SAR data. More details regarding the algorithm and the input arguments can be found in [PERSON] et al. [58].
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 2 Proposed Methodology  
The proposed methodology for land-cover classification is discussed in this section, which mainly includes data preprocessing and model training and evaluations, as shown in Figure 1. In this study, we use different deep learning models for fine-tuning and transfer learning, optimizing critical hyper-parameters like batch size, activation function, and learning rate. The utilization of optimizers assists in adjusting the learning rate within the neural models to enhance performance. All these steps are briefly explained in the subsequent sections.
## 1 Introduction
In particular, based on the brief literature review and identified research gaps, the study specifically aims to make the following contributions:  
* The introduction of a dimensionless parameter, called overlapping rate (\(r_{0}\)), which is a quantitative predictor of \(r_{d}\) as a function of \(\overrightarrow{v}\) and \(fps\), for a specific camera field of view (\(S\));
* A set of Python scripts containing equations and algorithms to model a virtual field, simulate the motion of the virtual camera, and perform plant hill detection;
* An evaluation of \(r_{d}\) based on different combinations of published \(fps\) and \(\overrightarrow{v}\) from existing systems through the proposed theoretical approach and simulation;
* A detailed analysis through simulation of the concept of increasing \(S\) or using several adjacent synchronous cameras (\(n_{vis}\)) to prevent missed detections in MVS with low \(fps\) at high \(\overrightarrow{v}\); and
* A reusable and scalable CNN-based vision module for plant detection based on Robot Operating System (ROS) and the Jetson Nano platform.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 4 Datasets
### HisarMod2019.1  
This dataset contains 26 different modulation signals belonging to the five modulation groups and is affected by five types of fading noise. The dataset consists of five main modulation groups. Each modulation type comprises 1500 signals with a length of 1024 I/Q samples. The number of samples is 780,000. The SNR of each signal sample ranges from \(-\)20 dB to 18 dB. When generating signals, the oversampling rate is selected as 2 and the raised cosine pulse shaping filter with a roll off factor of 0.35 is used. The display parameters of the dataset are described in Table 10.  
MATLAB 2017a is used to create random bit sequences, symbols and wireless fading channels. In addition, the dataset consists of signals passing through five different wireless communication channels, which are ideal, static, Rayleigh, Rician (k = 3) and Nakagami-m (m = 2). These channels may also be distributed on the dataset; therefore, there are 300 signals for each modulation type and each SNR level. An ideal channel is one in which there is no fading but additive white Gaussian noise (AWGN). In a static channel,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **RadioML2018.01a** \\ \hline Number of modulation mode & 24 \\ \hline \multirow{4}{*}{Modulation mode} & 32 PSK, 16 APSK, 32 QAM, FM, GMSK, 32 APSK, OQPSK, 8 ASK, \\  & BPSK, 8 PSK, AM-SSB-SC, 4 ASK, 16 PSK, 64 APSK, 128 QAM, \\  & 128 APSK, AM-DSB-SC, AM-SSB-WC, 64 QAM, QPSK, \\  & AM-DSB-WC, 256 QAM, OOK, 16 QAM \\ \hline Format of each sample & \(2\times 1024\) \\ \hline Number of samples & 5,234,491,392 \\ \hline SNR(dB) & \(-\)20:2:30 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Description of display parameters for RadioML2018.01a dataset.  
the channel coefficients are randomly determined at the beginning and remain constant during the propagation time. The signal passing through the Rayleigh channel is used to make the system resist the non-line-of-sight (NLOS) condition. On the other hand, since the dataset covers mild fading, Rician fading with shape parameter k of 3 is used. In addition to these channel models, for the rest of the signals in the dataset, the distribution of received power is selected as Nakagami-m and the shape parameter m is 2. Therefore, the dataset includes signals with different fading models. Note that the number of multi-path channel taps may also be 4 and 6 and ITU-R M1225 [109] is adopted for these two taps.
## 1 Introduction  
From the beginning of aerial photography, which has been used for different tasks [1; 2; 3; 4; 5], archaeologists realized the potential of this technique to study and discover new sites, because large areas of land could be analyzed more quickly, allowing them to understand the spatial organization of archaeological structures [6]. Today, the range of image acquisition techniques in aerial archaeological research is vast; from satellites to small unmanned aerial vehicles (UAVs), these images can be produced in different visible light spectra (e.g., [7]). This data source has opened new research perspectives for cultural heritage and archaeology at much vaster scales [8; 9; 10]. The digital era has nevertheless produced a bottleneck. The use of large amounts of images is a problem since the images are often labeled manually or semi-automatically; this is a problem for humans due to the time required to label each archaeological structure [11]. Fortunately, this repetitive task can now be tackled using methods based on machine learning [12; 13]. The problem, in a nutshell, consists of the binary classification of pixels (structure versus other types of objects); we focus on object detection through supervised machine learning, particularly the detection of archaeological structures from satellite images with abundant vegetation [14; 15; 16; 17]. Mesoamerican civilizations developed in Mexico, Guatema, Belize, western Honduras, and El Salvador covering more than 300,000 km\({}^{2}\)[18; 19]. There are still several unexplored and unstudied areas in this territory with hidden archaeological structures among the vegetation [20].  
Nowadays, digital image processing techniques, as well as computational learning algorithms, have been increasingly used by archaeologists for the discovery of new archaeological structures. Recently, [PERSON] et al. [21] proposed a method to detect from orthomosaics the enormous amount of dry stones used by past societies to build funerary complexes in the steppes of Mongolia. The problem with this method in sites with large amounts of vegetation, is obtaining reliable DEM maps, so, as [PERSON]. [PERSON] et al. commented, their method does not give good results in the jungle or wooded areas. In this work, we propose a method that allows the detection of archaeological structures in areas with a large amount of vegetation, from satellite images obtained with SASPlanet. For detection in these types of images, two color spaces (RGB and HLS) and different filters (Canny, Sobel, and Laplacian) were used; also, different supervised classifiers were tested. The experiments with different images taken from the archaeological sites of Calakmul, Comalcalco, Tikal, Zaculeu, Xambo, Mayapan, Ake, and Palenque show that the proposed method achieves good results.  
The work presented is organized as follows: Section 2 contains the related work; in Section 3 our proposed method is described; in Section 4, some experiments carried out at different archaeological sites are shown; in Section 5, we analyze our method and its limitations; and in Section 6 our conclusions and future work are presented.
## 7 Conclusions  
In order to improve the pointing accuracy of MPEOT, a new optimized parameter model was proposed in this paper to compensate for the systematic errors, based on a comprehensive analysis of geometric error sources and correlation among parameters. Several star-gazing experiments with a shipboard electro-optical telescope have verified the effectiveness of the proposal.  
This study has found that pointing errors involve equipment and platform navigation systems, such as manufacturing errors, misalignment, and environmental disturbance, among others. Moreover, the study found that these factors have mutually coupled effects on pointing. The second major finding was that the optimized parameter model established in this paper through stepwise regression outperformed the mount model in terms of accuracy and valid duration. Concretely, the corrected pointing accuracy was better than 50 arcsec for approximately 23 h. The presented method is also applicable to analogous two-degrees-of-freedom equipment.  
Conceptualization, Y.Z.; methodology, Y.H.; software, Y.H.; validation, Y.H., Y.Z. and X.F.; formal analysis, Y.H.; investigation, Y.H.; resources, Y.Z., S.D. and Z.W.; data curation, Y.Z., S.D. and Z.W.; writing--original draft preparation, Y.H.; writing--review and editing, Y.Z., X.F., S.D. and Z.W.; visualization, Y.H.; supervision, Y.Z. and X.F.; project administration, Y.Z. and X.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
We thank the Key Laboratory of Optical Engineering, Chinese Academy of Sciences for supporting this work.  
The authors declare no conflict of interest.
_applied_  
_sciences_  
_Article_  
Natural Phenolic Acids as Effective Bulk Oil Antioxidants: Oxidative Stability Modeling Using Olive Kernel Oil as a Case Study  
**[PERSON] 1**, **[PERSON] 1**, **[PERSON] 1(c)** and **[PERSON] 2,*(c)**  
1 Department of Food Quality & Chemistry of Natural Products, Mediterranean Aeronomic Institute of Chania (M. A. I. Ch.), International Centre for Advanced Mediterranean Aeronomic Studies (CHEAM), P.O. Box 85, 73100 Chania, Greece; [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (F.D.); [EMAIL_ADDRESS] (S.G.)  
2 Green Processes & Biofnernery Group, Department of Food Science & Nutrition, School of Agricultural Sciences, University of Tussaly, N. Tempone Street, 43100 Karditas, Greece  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +30-24410-64792  
**Abstract:** Natural polyphenols may be very effective lipid antioxidants; however, the studies pertaining to specific commodities, such as refined olive kernel oil (OKO), are extremely limited, and thus there is a lack of relevant information. To provide coverage for this gap in the literature, this study was performed with the view of testing structurally related natural hydroxycinnamates, namely caffeic acid (CA), ferulic acid (FA), and \(p\)-coumaric acid (CouA), as effective OKO antioxidants, and establishing concentration-activity relationships. First, a ranking concerning the antioxidant potency of the three polyphenols was attempted using the radical probe DPPH, and then the compounds were assayed using OKO as the lipid substrate. Employing a concentration-activity model, the potency of the three polyphenols to stabilize OKO, based on Rancimat measurements, was CA \(>\) CouA \(\approx\) FA. A further simulated long-term trial revealed that all three polyphenols may be equally effective in inhibiting peroxide onset over a period of 40 days, but the determination of thiobarbituric acid reactive substances (TBARS) formation evidenced that FA may be a more efficient antioxidant. The outcome was that all three polyphenols tested may behave as very effective agents against oxidative OKO rancidity, and the prospect of producing such compounds from agri-food wastes may provide unprecedented opportunities for replacing the controversial synthetic antioxidants.  
**Keywords:** hydroxycinnamates; lipid antioxidants; oil oxidation; polyphenols
## 3 Multiplier-Free Modified Godard Timing Recovery
### Modified Godard Algorithm for Simplified Clock Tone Calculation
(**c**) Shows the overlap of \(\widetilde{r}_{k}\) and \(\widetilde{r}^{*}{}_{k+N/2}\) and in blue their multiplication. It can be seen that elements of the multiplication are zero and so negligible. The second step takes the amount of oversampling of the processed signal into account. This is illustrated in Figure 5, where the oversampling \(\eta\) is reduced from 2 to 4/3. The original Godard technique would fail in this situation because it requires two samples per symbol. However, when overlapping the red and green half of the spectrum as displayed in Figure 5c, the estimate for the timing error can still be calculated. Figure 4: Simulation results for the jitter over roll-off factor. The results of the Godard algorithm (red) and modified Godard algorithm (green) are shown. (M = 16 QAM quadrature amplitude modulation, amount of evaluated symbols per roll-off factor equals 51.2 million, random data, DFT size 1024, oversampling of 2, SNR = 16.5 dB); QAM: quadrature amplitude modulation; SNR: signal-to-noise ratio. Figure 5: Second simplification towards the modified Godard algorithm by adapting the shift to the amount of oversampling. (**a**) Shows the coefficients \(\widetilde{r}_{k}\) of an 4/3 oversampled, raised-cosine (RC) spectrum with roll-off factor 1/3. (**b**) Shows the spectrum shifted by \(N/4\). (**c**) Shows the overlap of \(|\widetilde{r}_{k}|\) and \(|\widetilde{r}^{*}_{k+N/4}|\). The multiplication is only calculated in the area of \(N/4\) to \(N/2-1\). Even though the oversampling is reduced to below two, the estimate can still be calculated. This adaption brings us to:  
\[\hat{\tau}_{\epsilon}=\sum_{k=\frac{1-\beta}{2\eta}N}^{\frac{1+\beta}{2\eta}N-1} \text{Im}\{\widetilde{r}_{k}\widetilde{r}_{k+(1-1/\eta)N}^{\ast}\},\text{ with }\eta\geq\beta+1\text{ and }0<\beta\leq 1. \tag{5}\]  
\(\beta\), \(\eta\), and \(N\) should be chosen in a way such that the lower and upper bound of the summation results in an integer number. Without oversampling (\(\eta=1\)), the roll-off factor would have to be zero and so the sum would no longer contain any elements. Therefore, \(\eta>1\) and \(\beta>0\) is required for timing error estimation in the frequency domain. The same limitation occurs for a duobinary signal [16]; here too, no elements would be left in the summation.
## 2 Materials and Methods
### Statistical Analysis  
Shapiro-Wilk test was used to determine if parameters were normally distributed. To compare parameters within groups, 1-way analysis of variance or Kruskal-Wallis was used. Since AF prevalence increases with age, subjects were divided into three groups (<50 years, between 50 to 60 years, and >60 years) to assess age effect on LA stasis. Tukey's test or Mann-Whitney _U_-test were performed for multiple comparisons. Bonferroni correction was used to adjust for multiple comparisons and the differences were considered significant if \(p\) < 0.0166. Pearson correlation was calculated to investigate relationships between parameters. Statistical analysis was performed using SPSS 25 (SPSS, Chicago, IL, USA).  
Figure 1: Data processing and analysis workflow. Panel (**A**) shows an example of corrected 4D-flow images. Panel (**B**) illustrates a time-average phase-contrast (PC) magnetic resonance angiogram (MRA) with an optimal threshold for visualizing left atrial (LA) anatomy. The LA was manually segmented using slice-by-slice, isolating the LA volume. Panel (**C**) shows the calculated LA stasis maps within the LA volume using a sagittal and a top-view maximum intensity projection. Red areas represent high stasis; blue areas represent low stasis.
## 5 Discussion
### Security Analysis of Network Coding Chain  
At the relay node, multicast signals are aligned with the front and rear users of the communication, respectively, forming a hand-in-hand coding mode in the physical layer, \(s_{1}\oplus s_{2},s_{2}\oplus s_{3},s_{3}\oplus s_{4},\cdots s_{K-1}\oplus s_{K}\), where \"\(\oplus\)\" represents XOR.  
Superimposed signals in the coding mode are encoded by the PNC. Due to multicast groups, every coded signal in the coding mode has the desired reception signal. Therefore, interference suppression and interference cancellation are not required in BC stage relay forwarding. From the decoding process, due to the unique structure of the network coding chain, decoding can be started from any node in the coding mode. Only the user nodes participating in the communication can use the side information \(s_{i}\) sent by themselves as the key for physical layer network decoding. Even if an illegal user intercepts the relay broadcast signal, the attacker cannot obtain the desired signal of each user from the mixingsignal. The self-information of each node is regarded as the private key in the public key cryptography. Decode the expected signals in all mixing signals on the coding mode in turn. During the decoding procedure, users do not interchange private keys, and the private keys of nodes are different. The security of the system is improved by means of the encoding transmission of the encryption idea.
## 2 Positioning Model and Residual Analysis
### Observation Model
\begin{bmatrix}a^{r}+a^{1}&a^{r}&\dots&a^{r}\\ a^{r}&a^{r}+a^{2}&\dots&a^{r}\\ \vdots&\vdots&\vdots&\vdots\\ a^{r}&a^{r}&\dots&a^{r}+a^{n}\end{bmatrix}  \tag{6}\]  
where \(\sigma_{0}^{2}\) is the unit weight variance. \(a^{r}={\sigma_{\text{R}}^{r}}^{2}+{\sigma_{\text{S}}^{r}}^{2}\), \(a^{i}={\sigma_{\text{R}}^{i}}^{2}+{\sigma_{\text{S}}^{i}}^{2}\left(i=1,2, \dots\dots n,\ i\
eq r\right)\), \(r\), and \(i\) represent the reference satellite and non-reference satellite, respectively. \({\sigma_{\text{R}}^{i}}^{2}\) and \({\sigma_{\text{S}}^{i}}^{2}\) denote the variance in the observed values of each satellite for reference station R and smartphone S. Building upon the calculated \(X\) and incorporating the smartphone's approximate coordinates (\(x^{\circ}\), \(y^{\circ}\), \(z^{\circ}\)), one can ascertain the smartphone's final coordinates, as shown in Equation (7). \[ \begin{cases}x=x^{\circ}+\delta x\\ y=y^{\circ}+\delta y\\ z=z^{\circ}+\delta z\end{cases}  \tag{7}\]  
It can be seen that different variances in observations will result in different covariance matrices and different corresponding weights, and whether the weights are appropriate will directly affect the positioning performance. This paper aims to determine the weight of GNSS observations based on observational quality, minimizing the contribution of low-quality observations to enhance positioning accuracy.
## 3 Learning with Corrupted Data
### Observations
The fitted curves align well with the function defined in Equation (2). Figure 5: Signal-RL model performance under varying data corruption ratios. (**a**) Noise insertion: The \(x\)-axis represents the noise level. Inserting noise is more detrimental than missing data, often leading to unstable model performance. (**b**) Masked region: The \(x\)-axis denotes the masking-region ratio. A ratio of \(p\) indicates that the farthest \(400\times p\) meters of each lane are hidden from the model, simulating the limited visual range of video-based vehicle detectors. Model performance remains stable even when up to 70% of less critical data are removed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data-missing ratio** & 0.0 & 0.05 & 0.1 & 0.15 & 0.2 \\ \hline
**Model score** & 0.4669 & 0.4663 & 0.4588 & 0.4572 & 0.4455 \\ \hline
**Data-missing ratio** & 0.25 & 0.3 & 0.35 & 0.4 & 0.45 \\ \hline
**Model score** & 0.4359 & 0.4283 & 0.4312 & 0.4106 & 0.4012 \\ \hline
**Data-missing ratio** & 0.5 & 0.6 & 0.65 & 0.7 & 0.75 \\ \hline
**Model score** & 0.3906 & 0.3501 & 0.3357 & 0.3186 & 0.2619 \\ \hline
**Data-missing ratio** & 0.8 & 0.85 & 0.9 & 0.95 & 1.0 \\ \hline
**Model score** & 0.2375 & 0.2008 & 0.1654 & 0.0897 & 0.0081 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NLP-SL model scores at different data-missing ratios; plotted in Figure 3a.
## 2 Materials and Methods
### Online Monitoring of PD Using the UHF Method
#### 2.1.2 Parameters and Functions of the UHF PD Monitoring System  
The Qualitrol Portable PDM system (Qualitrol Company LLC, Fairport, NY, USA) was used for UHF partial discharge monitoring (Figure 1a). The system has four measurement channels, the first three of which are used to connect PD detectors operating in the VHF/UHF band, while the last channel is designed to connect a special antenna that reduces external electromagnetic interference. The dynamic sensitivity range of the measurement channels ranges from \(-\)35 to \(-\)75 dBm, and the amplitude of the recorded pulses is related to the maximum value of the measurement range and is given in a percentage. The system software records the number of pulses, amplitude, and PRPD pattern of UHF PD pulses, the image of which is used in the defect identification (PD-type) process. To detect UHF pulses, a Qualitrol CMD01591-type wideband rod antenna (Figure 1b) with a bandwidth of 200 to 1500 MHz (Figure 1c) was used, which was placed in the oil drain valve. The measurement system was additionally equipped with a gating antenna, which allowed filtering of unwanted external electromagnetic interference. Implementation of the gating antenna technique allowed for a more effective detection of UHF pulses from PDs by minimizing the influence of irrelevant or transient RF signals.  
Figure 1: Online PD monitoring using the UHF method. **(a)** Photograph of the Qualitrol Portable PDM system used in the tests. **(b)** Photograph showing the installation of the Qualitrol CMD01591 antenna in one of the tested power transformers: 1—oil drain valve; 2—antenna; 3—active part of the antenna (7.5 cm long rod with oil-resistant, rubber protective coating); 4—mounting flange with an  
internal O-ring; 5--potential equalization cable; 6--rod for adjusting the antenna installation depth; 7--mounting bracket with RF connector. (**c**) Frequency response of the Qualitrol CMD01591 antenna showing the S11 parameter.