esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sentinel-2 Images Preprocessing and Variable Calculation  
To obtain a temporal match between the investigated sample plot data and the satellite data for the whole of Hunan Province, the GEE filtering options were used to select the Sentinel-2 Level-1C (TOA, top-of-atmosphere) satellite images between 1 May 2017, and 31 October 2017, and between 1 May 2018, and 31 October 2018. The Level-1C product is an ortho-image in UTM/WGS84 projection. Level-1C processing includes radiometric and geometric corrections, including orthorectification-Digital Elevation Model (DEM, processed from Shuttle Radar Topography Mission 90 m Digital Elevation Data Version 4) to project the images in cartographic geometry. Per-pixel radiometric measurements are provided in TOA reflectances along with the parameters to transform them into radiances, and spatial registration was processed on a global reference system with subpixel accuracy. In addition, the Level-1C products provide a bitmask band (QA60) to show cloud information. More details about the Level-1C products could be found in the Sentinel-2 User Handbook [79]. The Level-1C product stored in the GEE has been shown to be suitable for direct use for land mapping by [PERSON] et al. [57]. The cloud mask steps were processed in this study. The QA60 band was first used to identify and mask out the flagged cloud and cirrus pixels. Then, the two thresholds of Band 1 \(\geq\) 0.15 and Band 2 \(\leq\) 0.25 were used to mask the remaining clouds and noise [57; 80]. A total of 3150 images covering the entire study area over the study period were processed. After that, the Level-1C TOA reflectance data were resampled with a resolution of 25 m to match the spatial resolution of survey plots. Then, we extracted the median values of the characteristic variables using the GEE cloud computing platform [58]. The characteristic variables included the multispectral bands (B2, B3, B4, B5, B6, B7, B8, B8A, B10, B11, and B12) and the vegetation indices selected according to the research of [PERSON] et al. [4], [PERSON] et al. [81], and [PERSON] et al. [82]. The vegetation indices were calculated as follows (Table 3).  
\begin{table}
\begin{tabular}{c c} \hline
**Tree Specie** & **Formula** \\ \hline _Cunningham lanccolata_ & V = 0.000058777042D\({}^{1.9699831}\)H\({}^{0.89646157}\) \\ _Pitus massoniana_ & V = 0.000062341803D\({}^{1.8551497}\)H\({}^{0.95682492}\) \\ _Quercus_ sp. & V = 0.000050479055D\({}^{1.9085054}\)H\({}^{0.99076507}\) \\ _Pitus elliotili_ & V = 0.000086791543D\({}^{1.663800575+0.0094299757}\)(D+10 fI)H\({}^{0.9693040868}\)-0.0292030826(D+2.5H)) \\ _Populus_ sp. & V = 0.000041028005D\({}^{1.8006303}\)H\({}^{1.13059897}\) \\ _Cinnamomum camplora_ & V = 0.000050479055D\({}^{1.9085054}\)H\({}^{0.99076507}\) \\ _Cupressus funcbris_ & V = 0.000058777042D\({}^{1.9699831}\)H\({}^{0.89646157}\) \\ \hline \end{tabular} Note: The FSV calculation formula of the other tree species are supplied in supplementary 1.  
\end{table}
Table 2: The FSV calculation formula of the seven major tree species in Hunan Province [78].
## 3 Jitter Modeling
### Jitter Components
#### 3.2.2. Duty Cycle Distortion Jitter (DCD)  
DCD is an important deterministic jitter. It is caused by non-idealities such as asymmetric rising and falling edges of the path, and it is half of data rate, which can be modeled as [25]  
\[\begin{array}{ll}\Delta t_{\text{DCD}}[n]&=J_{\text{DCD}}\times\cos(n\pi)\\ &=[-J_{\text{DCD}},J_{\text{DCD}},-J_{\text{DCD}},J_{\text{DCD}},\ldots]\end{array} \tag{9}\]  
where \(\Delta t_{\text{DCD}}[n]\) is the DCD of the sampling time \(nT\), and \(J_{\text{DCD}}\) is the amplitude of the DCD.
# Femtosecond Pulse Laser Near-Field Ablation of Ag Nanorods
## 3 Results and Discussion
### Controlling the Morphology of Ag NRs by Tuning the Laser Polarization and Wavelength
Off-resonant excitation resulted from multipolar oscillation, which was related to the wavelength of the laser and size of the nanostructures [38]. By adjusting the laser wavelength, the enhanced electronic field distribution could be shifted along the NR. Thus, the ablation morphology of the NRs was controllable. Although the enhancement factor was small, the enhanced field could yield intense ablation under the effect of multiple femtosecond laser pulses. With perpendicular polarization and 800 nm laser wavelength, which is off-transverse resonant excitation, small clusters formed on the two sides of the Ag NRs, as indicated in Figure 4e. By analyzing the calculation results in Figure 4f, the electronic field at the two sides was found to be stronger than that at the ends of the NRs. Thus, the as-prepared NRs became elongated NRs under these processing conditions. Especially at the near-transverse resonance band of 400 nm in Figure 4g,h, smaller and thinner Ag NRs were obtained because of the higher enhancement factor of 4.3 than that at 800 nm wavelength. This suggests that some useful nanostructures can be obtained by adjusting the electronic field distribution. Figure 4: TEM images and corresponding calculated electric field distributions of Ag NRs after irradiation with lasers of different polarizations and wavelengths. (**a**,**b**) Ablation was observed at the ends of a Ag NR when parallel polarization and 800 nm laser wavelength were employed. (**c**,**d**) Ablation occurred at both the ends and sides of a Ag NR when parallel polarization and 400 nm laser wavelength were used. (**e**,**f**) Ablation was observed on the two sides when perpendicular polarization and 800 nm laser wavelength were employed. (**g**,**h**) Ablation occurred at the two sides when perpendicular polarization and 400 nm laser wavelength were used, and the electric field intensity was higher than that when 800 nm wavelength was used. The arrows indicate the laser polarization direction. The scale bar is 50 nm.
# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 3 Results  
The data used in this study were obtained from the ADNI open database, for a total of 69 acquisitions of AD and HC subjects. The goal of this paper was to compare AD patients with a group of HC to evaluate their morphological and functional differences, both through the correlation analysis and through the SVM classification. First, a Mann-Whitney U-test was performed to explore the statistical differences of the normalized whole brain volume between the AD patient group and the control group [71]. In Figure 4, the results show that there was a significant difference between the two groups (\(p\) = 0.0011, \(z\) = \(-\)3.26). Moreover, AD patients presented a lower volume when compared with HCs. The findings from [5; 20] supported our results and suggested that AD is related to a lower brain volume with quantifiable differences with respect to the HC subjects. Furthermore, 36 ROIs presented a lower GMV (\(p<0.05\)) in AD patients compared with HC. Even in this case, a Mann-Whitney U-test was performed. After [PERSON]'s statistical correction, only two ROIs survived, the right rolandic operculum and the left superior temporal gyrus. Given the AAL atlas, some of the regions that did not survive after [PERSON]'s correction (the discarded 34 ROIs) belonged to frontal lobe, as is reported in Table 2, which is involved in cognitive function. Supposedly, these ROIs are affected by the pathology state [72]. Moreover, many of these regions were found in other structural studies that used the ADNI database, such as insula [38], frontal cortex [38,73], posterior cingulate cortex [23,73,74], hippocampus [73,75], fusiform [76], temporal lobe [38,73], and, in particular, left middle temporal gyrus [45,76]. These confirm and validate our results. As performed in other studies [76,77], the possible correlation between GMV and the cognitive status, i.e., MMSE, across groups was studied. The AD patients had a MMSE score [78] of \(12\div 28\), whereas HC had an MMSE score of \(24\div 30\). This analysis step was necessary to evaluate if cognitive status was associated with structural status. [PERSON]'s correlation coefficient was used [79]. Across groups, GMV was correlated to the MMSE score in 10 ROIs, as we can see in Table 3, but after [PERSON]'s correction, no one survived. Even in this small-sized database, AD patients were associated with lower values of MMSE, with respect to the HC subjects. The lower score of MMSE was associated with a lower GMV in the left superior temporal gyrus (\(\rho=0.3602\), \(p=0.0024\)), as shown in Figure 5. This result shows that in AD subjects, the cognitive status was positively correlated with GM atrophy. Thus, for the atlas and the dataset analyzed in this study, this finding probably implies that a lower cognitive capacity is linked to a lower brain volume, which is the first consequence of pathology in AD [80]. Figure 4: Statistical differences in normalized whole brain volume between HC and AD groups (\(p<0.05\)).
# A Hybrid Watermarking Approach for DICOM Images Security
## 3 Proposed Method  
### General Description  
The proposed method combines a zero-watermarking in the anatomical region of the image (ROI) and a non-zero-watermarking (insertion of a watermark) in the black background region of the image (RONI).  
The medical image is initially divided into two parts: the anatomical part (ROI) and the black background part (RONI). Then from the header of the DICOM image, the first letter of the given name and the first letter of the family name of the patient are extracted, and pertinent features are extracted from the anatomical part. These pertinent features are skewness, entropy, and median. The patient name initials and pertinent features are then used as input to a Jacobian Model to build the zero-watermarking secret share as well as an encoded watermark. Several copies of the watermark are then embedded in the RONI, using a linear interpolation technique [22]. In this embedding process, the watermark and the RONI are split into several parts with a mapping between the watermark parts and RONI parts, and each watermark part is inserted in the corresponding RONI part. Finally, the anatomical region and the watermarked black background region (RONI) are merged to obtain the watermarked image.  
On the receiver side, the process is similar to that used on the sender's side. The image is divided into two parts (the anatomical part and the black background part), the watermark is extracted from the black background region using the extraction procedure of the linear interpolation technique, and the original watermark is compared to the extracted watermark. On the other hand, the zero-watermarking secret share is rebuilt from the ROI of the received image and compared to the original secret share.  
Figure 1 shows the framework of the proposed watermarking method.  
The proposed model is based on three phases on both the sender and receiver sides: the anatomical region and the black background region separation, zero-watermarking secret share generation from the anatomical region, and watermark embedding/extraction in/from the black background region. These three phases are followed by a last step on the receiver's side, consisting of the comparison of the original and recovered secret shares and watermarks, to authenticate the medical image.
# Performance Prediction and Design of Stratospheric Propeller
## 3 Propeller Design  
According to the propulsion system requirements of the stratospheric airship design, the design indexes of the propeller were determined and are listed in Table 1. The required advance velocity appeared to be 20 m/s; the required thrust resulted to be larger than 100 N, and the efficiency larger than 70%.  
A two-blade propeller was selected due to its low speed and large diameter, and the FX63 airfoil was selected for the propeller. The Mach number of the propeller tips was restricted as follows:  
\[\frac{\sqrt{\left(\pi Dn_{s}\right)^{2}+V_{0}^{2}}}{c}\leq 0.7 \tag{19}\]  
where \(c\) is the local sound velocity.  
The propeller geometry is defined by the pitch angle \(\theta\) and the chord length \(b\) along the radius of the blade from the hub to the tip, which is determined as follows:  
\[\theta=\theta_{char}+0.4387+0.3040\overline{r}-3.9616\overline{r}^{2}+5.1180 \overline{r}^{3}-1.6284\overline{r}^{4}-0.3244\overline{r}^{5} \tag{20}\]  
\[b=\left(0.084241-0.85789\overline{r}+4.7176\overline{r}^{2}-9.6225\overline{r} ^{3}+8.5004\overline{r}^{4}-2.7959\overline{r}^{5}\right)D \tag{21}\]  
where \(\theta_{char}\) is the characteristic pitch angle of the propeller, \(D\) is the diameter of the propeller, and \(\overline{r}\) = 0.1, 0.2,..., 1.
# Complex Network Analytics for Structural-Functional Decoding of Neural Networks
## 3 Structural-Functional Decoding Model
### Pooling Layer  
(1) Node description  
The pooling layer of complex network modelling is obtained through the spatial compression and feature selection mechanism to achieve hierarchical abstraction; each pooling region is used to generate an aggregation node, the core of which lies in the establishment of the mapping relationship between the input feature region and the pooling node [2]. We let the precursor layer contain \(N\) neuron nodes; for the pooling operation of size \(k\times k\) and step size \(s\), the number of nodes in the pooling layer \(N^{\prime}=N/(k/s)^{2}\). Each pooling node corresponds to the local receptive field of \(k\times k\) in the precursor layer.  
(2) Connected edge modelling  
The pooling layer constructs spatially contracted connections from precursor nodes to compression nodes, which is essentially feature selection or aggregation of local regions, and the connection mode is determined by the pooling type. Maximum pooling forms a star-radial topology, where only the precursor node \(v_{h}\) with the largest activation value establishes a valid connection to the pooled node. In complex networks, only the edge from the input node to the output node with the largest initial weight is activated, and the remaining edges are removed; that is \(E=\big{\{}e_{hj}|\forall j\in[1,N^{\prime}]\big{\}}\). This connection filtering strategy reproduces the feature selection behaviour of pooling at the topology level while avoiding the network size inflation caused by the introduction of pooled nodes. Average pooling creates a fully connected topology, where all predecessor nodes \(\Omega_{h}\) within the sensory field are involved in the connection. Input nodes are connected to the same output node, and the weights of each edge are set to the mean of the original values, a treatment that makes the activation strength of the output node equal to the average response of the input region, which is \(E=\big{\{}e_{hj}|\forall h\in\Omega_{h},j\in[1,N^{\prime}]\big{\}}\).  
(3) Connected edge weights  
The edge weights encode the contribution of the input features to the pooling results, which are differentially distributed according to the pooling mechanism [28]. Maximum pooling uses 0-1 hard selection weights and average pooling uses uniform diffusion weights to directly encode the global average pooling value \(s_{k}=\frac{1}{H\times W}\sum_{i,j}F_{i,j}^{k}\) of the original feature graph \(F^{k}\), where \(H\) and \(W\) are, respectively, the height and width of the feature map. Quantifying the channel significance through the functional-structural correspondence enables topological analysis in complex networks to directly reflect the feature extraction mechanism of the original model.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 3 System Models
### Channel Model  
We assume that the radio channel experiences Rayleigh fading and path loss. The received power of a receiver (i.e., a legitimate user or an eavesdropper) at a distance \(r\) from its nearest transmitter (legitimate user or friendly jammer) is \(hr^{-\alpha}\), where \(h\) is a random variable following an exponential distribution with mean \(\frac{1}{\mu}\) and \(\alpha\) is the path loss factor. More specifically, we denote \(h\sim\exp(\mu)\).  
We then consider the _Signal to Interference plus Noise Ratio_ (SINR) model. The SINR of the receiver at a random distance \(r\) from its transmitter is expressed as  
\[\mathrm{SINR}=\frac{P_{t}hr^{-\alpha}}{\sigma^{2}+I_{t}+I_{j}}, \tag{1}\]  
where \(\sigma^{2}\) is the noise power, \(I_{t}=\sum\limits_{i\in\Phi/t_{0}}P_{t}h_{i}R_{i}^{-\alpha}\) denotes the cumulative interference from all the legitimate users except for the transmitter denoted by \(t_{0}\), \(\Phi\) denotes the set of legitimate users, \(P_{t}\) denotes the transmitting power of the legitimate transmitter and \(I_{j}\) denotes the cumulative interference generated by friendly jammers. The value of \(I_{j}\) heavily depends on the placements of friendly jammers, which will be analyzed in Section 4.  
We then define the _eavesdropping condition_ to determine whether the transmission from a certain legitimate user can be wiretapped by an eavesdropper.  
Figure 1: FJ-Reg Scheme: every jammer is placed at a gray square. Note that we only show a part of the whole network.  
Figure 2: FJ-Ran Scheme: every jammer is randomly placed according to homogeneous Poisson Point Process (PPP). Note that we only show a part of the whole network.  
**Definition 1**.: _Eavesdropping Condition. A confidential transmission can be snooped by an eavesdropper if and only if \(\mathrm{SINR}>T\), where \(T\) is the received power threshold that an eavesdropper can successfully decode the transmission._
## 2 Multi-Constellation PPP Model
### Stochastic Models  
Error corrections affect the performance of PPP. Some corrections can be eliminated by using function models, which are discussed in Section 3. However, function models are not sufficient to improve the accuracy and convergence time of PPP. Consequently, an appropriate stochastic model must be applied. Different stochastic models reflect different PPP results. Generally, three common stochastic models exist: the equal-weight stochastic model, the carrier-noise rate-based stochastic model and the elevation angle-based stochastic model. In this study, the last model was applied.  
Most of the GNSS observation errors (i.e., the troposphere refraction delay, ionosphere refraction delay, and multipath effect) are related to the elevation angles of satellites. To decrease these errors, stochastic models based on the elevation angles of satellites can be established. Elevation angle-based stochastic models mainly include trigonometric function models and exponential function models [29]. In this study, we used the sine function-based elevation angle stochastic model, which is described by Equation (30):  
\[\sigma^{2}=\frac{\sigma_{0}^{2}}{\sin^{2}\theta} \tag{30}\]  
where \(\theta\) is the elevation angle of the satellite, and \(\sigma_{0}^{2}\) is the prior variance of observations.  
Generally, the multipath and large observation noise exist at low elevation angles. We defined the weight segment to reduce the weight of the observations at lower elevation angles. The corresponding code and carrier phase variance matrices are:  
\[\sigma_{P}^{2}=\left\{\begin{array}{cc}\frac{\sigma_{P,0}^{2}}{\sin\theta}& \theta>\alpha\\ \frac{\sigma_{P,0}^{2}}{\sin^{2}\theta}&\theta<\alpha\end{array}\right.,\sigma_ {\phi}^{2}=\left\{\begin{array}{cc}\frac{\sigma_{P,0}^{2}}{\sin\theta}& \theta>\alpha\\ \frac{\sigma_{\phi,0}^{2}}{\sin^{2}\theta}&\theta<\alpha\end{array}\right. \tag{31}\]  
where \(\sigma_{P,0}^{2}\) and \(\sigma_{\phi,0}^{2}\) are the prior variances of code and carrier phase observations, respectively. \(\alpha\) is the elevation angle threshold and is typically set to 30\({}^{\circ}\). When adopting the code and carrier phase observations simultaneously, the variance-covariance expression is:  
\[\sigma_{i}^{2}=\left[\begin{array}{cc}\sigma_{P,i}^{2}&0\\ 0&\sigma_{\phi,i}^{2}\end{array}\right] \tag{32}\]  
It should be noted that different GNSSs have different prior observation variances. For the GPS and GLONASS code and carrier phase observations, the precisions are set to 0.3 m and 0.002 m, respectively. Because the BDS satellite orbit and clock have relatively lower accuracies [30; 31], their measurements are down-weighted. That is, the phase observation precision is set to 0.004 m, and the code observation precision is set to 0.6 m for the BDS [25].
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 3 Double-Step Deep-Learning Framework (DSF)
### Severity Prediction Task (Second Step)  
**Regression backbone.** This second-step network in the pipeline is intended to derive the actual severity map within the pixels in the region identified as burned by the first-step network. The desired output is a map associating each pixel with a damage intensity level between 0 and 4. This goal is performed by providing the satellite images masked by the first backbone output binary mask as input. Predicted unburnt areas are suppressed to reduce the data variability, obtaining a second backbone focusing mainly on the prediction on the severity levels greater than 1 (i.e., above threshold).  
An excessively permitting binary mask (i.e., a small threshold) would pass to the second network a greater portion of the satellite image, reducing the benefits of the DFS. On the other hand, with a too strict mask (i.e., a higher threshold), the second backbone would be definitely unable to see and so identify relevant portion of the input images. Then, accurate binary masks are fundamental to provide only the information related to regions affected by wildfires as, in this way, the variability of the input data to learn is reduced. For this reason, false-positive predictions (i.e., unburnt areas classified as burnt) result in a reduction of network performance.  
Figure 1: Double-Step deep learning Framework architecture.  
**Regression loss.** After the training process of the binary-class backbone, an extra loss function is used to train the regression backbone. During this second step, the weights of the previous backbone are kept constant.  
As this second task can both be viewed as a classification and a regression problem, our decision in selecting optimal loss functions aims at minimizing the relevant KPIs of the two. In this sense, the experiments of the following sections inspect the results obtained with the Mean Squared Error (MSE), a generalization of the sIoU to a multiclass case, and a combination of the MSE and the F\({}_{1}\) score. The last two proposed alternatives intend to give relevance to the network ability to exactly find the target severity class instead of just minimize the distance.  
The complete list of configurations tested in the experiment is summed up in Figure 2. Each column specifies the identifier name, the binary loss, and the regression loss of the referring configuration, respectively.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 7 Algorithms and Protocols
### Fault Detection with Artificial Intelligence  
An AI algorithm for fault detection is developed for autonomous cooperative drones ensuring continuous monitoring of the bridges, railways, and high-voltage power lines. To make the computer vision-based inspection algorithm more robust, a deep learning  
Figure 11: Protocol for formation flying.  
framework is used. Considering that autonomous cooperative drones should perform continuous inspection in a real-time environment, mounting heavy devices on drones results in high energy consumption. Therefore, for a real-time inspection, we inherited the YOLOv4-based deep learning architecture [51]. The YOLOv4 algorithm can run on low-power devices in real-time as well as perform object detection with high accuracy. The proposed fault detection algorithm consists of three main steps (Figure 12):  
* Step-1: Data collection, labeling, and training.
* Step-2: Drone-based fault detection by running real-time onboard deep learning algorithm for inspections.
* Step-3: Cloud communication and sending data of faults to the cloud database.  
For communication and integration, the AI algorithm is developed as an ROS node that runs on the high-level control part of the drone processing hardware.