esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Data Analysis  
For our study, data were acquired from the CBS EC tower and the EO-1 Hyperion sensor. A semi-empirical statistical modeling approach was used to correlate hyperspectral remotely sensed surface characteristics indicated by bio-indicators to measure forest C fluxes data across different satellite overpass times. Partial least square regression (PLSR) analysis was used to explore sensitive bands and indices of observed GPP and Re. Ordinary linear regression (OLR) fitting was employed to build models. The optimal model was selected by comparing their correlation coefficients to GPP and Re. Using the optimal model, we then derived the regional maps of forest C fluxes that depict the daily C flux values for the temperate broad-leaved Korean pine forest landscapes. We validated inversed GPP by comparing our regional GPP map to the simulated regional GPP with the VPM and BEPS models, and MODIS MOD17A2 GPP products in different seasons, and explored the potential of utilizing narrowband bio-indicators. The detailed methods of key processing are described below. The study flowchart is presented in Figure 2.  
The processing of Hyperion images was conducted using Interactive Data Language (IDL8.2), ENVI5.0 (Exelis, 2014) and ArcGIS 9.3 software. PLSR analyses were performed utilizing the SIMCA-P 11.5 statistical software (Umetrics Inc., Kinnelon, NJ, USA) and additional statistical analyses were conducted using IBM SPSS Statistics 19 and SigmaPlot 10.0.
# Line of Sight and Image Motion Compensation for Step and Stare Imaging System
## 3 LOS and Motion Compensation Kinematics and Algorithm  
### Coordinate Frames  
Four principal coordinate frames are used in the system. These are the geographical coordinate frame, the aircraft body coordinate frame, the gimbal coordinate frame and the mirror coordinate frame, and all of the coordinate frames follow the \"right-hand\" coordinate system.  
Geographical coordinate frame: this frame is the reference coordinates, in which GPS/IMU carries out navigation computations. Its center of this frame is at the intersection of the IMU axes, and this coordinate can use either **north-east-down** (NED) or **east-north-up** (ENU) Cartesian coordinate forms. As interesting objects are below the aircraft, it is a more intuitive selection of the NED coordinate system. This coordinate frame is denoted by the symbol N.  
Aircraft body coordinate frame: this coordinate frame also has its origin at the GPS/IMU location. Its axes are consistent with that of GPS/IMU. When the GPS/IMU is in parallel with the aircraft, its x-axis points to the flight direction, and they-axis is orthogonal with the x-axis and refers to the right, then the z-axis completes the orthogonal right-handed set. This coordinate frame is denoted by the symbol b.  
Gimbal coordinate frame: gimbal axes are aligned to the aircraft body axes at zero gimbal angles. The outer gimbal axis is coincident with the aircraft pitch axis at zero roll gimbal angle. The inner gimbal axis is coincident with the aircraft roll axis at zero pitch gimbal angle. This coordinate frame is denoted by the symbol G.  
Mirror coordinate frame: all the mirrors are mounted on the inner gimbal. M1 mirror is a roll mirror and rotates about the x-axis. When its angle is zero, its normal vector is tilted 45\({}^{\circ}\) with the x-axis of the gimbal. M2 mirror is a pitch mirror and rotates about the z-axis. When its angle is zero, its normal vector is tilted through 45\({}^{\circ}\) with the y-axis of the gimbal. M3 and M4 mirrors are fixed mirrors. Their normal vector is respectively tilted through \(-\)45\({}^{\circ}\) and \(-\)135\({}^{\circ}\) with the z-axis of the gimbal. This coordinate frame is denoted by the symbol M [15; 16].
# Model Parallelism Optimization for CNN FPGA Accelerator
## 5 Experiment  
### Experimental Setup  
The FPGA chips selected for this experiment were two Intel Arria10 GX1150 FPGAs embedded in an Inspur F10A board. The software environment was Intel FPGA SDK for OpenCL pro19.1 with a BSP provided by the Wave FPGA development team, using an Intel(r) Xeon(r) Gold6128 CPU as the OpenCL host and DMA for data transmission between devices. The board support package (BSP) was a layer between the motherboard hardware and the driver in the operating system. It provided a functional package for the upper layer drivers to access the hardware device registers and make them work better on the hardware motherboard.  
We selected two datasets (MNIST and CIFAR-10) to verify the accuracy and performance of the inside shuffle algorithm. These two datasets comprised 10,000 images for testing, and the rest of the images were used for training. On this basis, we used the _ShuffleNet_ model to verify the effectiveness of the model parallelism, evaluating it in terms of resource utilization, speed-up ratio, and energy consumption.
## 3 Method  
### Overview  
Considering the hierarchical properties of speech, we propose HierTTS, which deeply couples the hierarchical properties of speech into VAEs. The overall architecture is shown in Figure 1, which contains a hierarchical audioencoder (HAE), a hierarchical context encoder (HCE), and a hierarchical audiodecoder (HAD). HierTTS introduces five latent variables at different temporal resolutions--the sentence, word, subword, phoneme, and frame levels. First, HAE extracts the hierarchical latent variables from the linear spectrogram in a fine-to-coarse manner, and then the HAD reconstructs the speech waveform from coarse-to-fine leveraging-extracted hierarchical latent variables. To introduce text information, HCE obtains linguistic and phonological information at different scales from phoneme and character sequences and then injects it into each hierarchy of HAE and HAD. For the modeling of duration, we inject phoneme-scale durations at the phoneme-level encoder and reconstruct the durations using the phoneme decoder. Thus, the duration and waveform reconstruction share some of the hierarchical hidden variables, which facilitates learning more consistent prosody. The training goal of HierTTS is to maximize the lower bound of evidence (ELBO) for the marginal log-likelihood log \(p_{\theta}(x,\mathcal{D}\mid c)\).  
\[\begin{split} ELBO=&\mathbb{E}_{q_{\phi}(z|x)}\log p _{\theta}(x,\mathcal{D}\mid z,c)-\mathbb{E}_{q_{\phi}(z_{<}|x,\mathcal{D},c)} \big{[}KL\big{[}q_{\phi}(z_{5}\mid x,z_{<5})\mid p_{\theta}(z_{5}\mid z_{<5}) \big{]}\\ &-\sum_{\ell=1}^{4}\mathbb{E}_{q_{\phi}(z_{<\ell}|x,\mathcal{D},c )}\big{[}KL\big{[}q_{\phi}(z_{\ell}\mid x,\mathcal{D},z_{<\ell},c_{\ell})\mid p _{\theta}(z_{\ell}\mid z_{<\ell},c_{\ell})\big{]}\end{split} \tag{4}\]  
where \(z_{\ell}\) and \(c_{\ell}\) denote the latent variables and linguistic features located at the \(\ell\)-th level, respectively. The first term represents the reconstruction loss, and the second and third terms denote the Kullback-Leibler (KL) divergence. Since the second term models the frame-level latent variable, it does not conditionally depend on linguistic features. In the training phase, HAE learns the posterior distribution of the latent variables to guide the prior distribution in HAD. However, HAD directly generates a speech waveform using predicted prior at the inference. The specific structure of HierTTS is described in detail in Section 3.5.
###### Abstract  
The wildland-urban interface (WUI)--the area where wildland vegetation and urban buildings intermix--is at a greater risk of fire occurrence because of extensive human activity in that area. Although satellite remote sensing has become a major tool for assessing fire damage in wildlands, it is unsuitable for WUI fire monitoring due to the low spatial resolution of the images from satellites that provide frequent information which is relevant for timely fire monitoring in WUI. Here, we take advantage of frequent (i.e., ca. daily), high-spatial-resolution (3 m) imagery acquired from a constellation of nano-satellites operated by Planet Labs (\"Planet\") to assess fire damage to urban trees in the WUI of a Mediterranean city in Israel (Haifa). The fire occurred at the end of 2016, consuming ca. 17,000 of the trees (152 trees ha\({}^{-1}\)) within the near-by wildland and urban parts of the city. Three vegetation indices (GNDVI, NDVI and GCC) from Planet satellite images were used to derive a burn severity map for the WUI area after applying a subpixel discrimination method to distinguish between woody and herbaceous vegetation. The produced burn severity map was successfully validated with information acquired from an extensive field survey in the WUI burnt area (overall accuracy and kappa: 87% and 0.75, respectively). Planet's vegetation indices were calibrated using in-field tree measurements to obtain high spatial resolution maps of burned trees and consumed woody biomass in the WUI. These were used in conjunction with an ecosystem services valuation model (_i-Tree_) to estimate spatially-distributed and total economic loss due to damage to urban trees caused by the fire. Results show that nearly half of the urban trees were moderately and severely burned (26% and 22%, respectively). The total damage to the urban forest was estimated at ca. 41 \(\pm\) 10 M USD. We conclude that using the method developed in this study with high-spatial-resolution Planet images has a great potential for WUI fire economic assessment.  
economic model; ecosystem services; fire; GCC; GNDVI; Haifa; _i-Tree_; Mt. Carmel; NDVI; Planet; satellite; WUI; urban forest; vegetation +
Footnote †: journal: Remote Sens. 10, 1479; doi:10.3390/rs10091479
## 3 Circuit Design and Implementation  
For validation, an X-band Class-J MMIC PA is designed using a 0.25 \(\upmu\)m commercial GaN HEMT process. The schematic of the fabricated PA is shown in Figure 4 and its corresponding photograph is presented in Figure 5. The drain DC bias voltage (\(V_{\text{d}}\)) of all three stages is 28 V, while the gate DC bias voltage (\(V_{\text{g}}\)) is set to \(-2.2\) V. The transistor sizes  
Figure 3: (**a**) Consistent power circle of the load-line theory; (**b**) 0.3 dB and 0.5 dB power contours within the impedance space of CCBJ.  
Figure 2: (**a**) The impedance of the load, \(Z_{\text{L}}\); (**b**) variations of \(V_{\text{ds}}\) with \(Z_{\text{L}}\).  
for the three stages are 2 \(\times\) 6 \(\times\) 80 \(\upmu\)m, 8 \(\times\) 6 \(\times\) 80 \(\upmu\)m, and 16 \(\times\) 6 \(\times\) 150 \(\upmu\)m, respectively. To ensure stability, a balancing resistor is added between every two pairs of transistors in the second and third stages, and resistors are also placed on the gates of all transistors, as shown in Figure 4. The layout adopts a fully symmetrical structure, with a top-and-bottom symmetric power supply configuration. All capacitors without labeled values in Figure 4 are bypass capacitors. As shown in the blue box in Figure 4, the matching circuit is divided into four parts: the output matching network, two intermediate matching networks, and the input matching network. The details of each part are described below.  
Figure 4: Schematic of the designed PA.  
Figure 5: Photograph of the fabricated PA.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Cross-Site Scripting (XSS) Attacks: Characteristics and Analysis  
XSS (cross-site scripting) vulnerabilities represent a growing and critical threat to the security of modern web applications, with a demonstrated impact in critical sectors such as finance, healthcare, and education [1]. These attacks allow the injection of malicious scripts into legitimate web pages, compromising the data integrity and privacy of millions of users through techniques such as session cookie theft, account hijacking, and sophisticated phishing.  
Cross-site scripting (XSS) has been a well-documented web application security vulnerability for over two decades. This overview will delineate the key characteristics of XSS attacks, supported by relevant academic research. XSS attacks occur when malicious scripts are injected into trusted websites and executed in users' browsers. Their fundamental characteristic is that they violate the same-origin policy, which prevents websites from accessing each other's data [2].
## 4 Experimental Setup and Results
### Illegal Graffiti Detector
#### 4.2.1 Bounding Box Predictions Post-Processing  
After the identification of the graffiti coordinates in the image, in the cases where more than one bounding box is identified in the same image, it is necessary to check if the bounding boxes need to be grouped.  
It was found that the model often separates graffiti into more than one bounding box, thus decreasing the intersection area with the coordinates of the actual bounding boxes, even if the prediction is correct. To tackle this, the bounding boxes are grouped when the intersection area between two bounding boxes is greater than or equal to 0.35 times the area of one of the two bounding boxes in question. For example, Figure 18 exemplifies a case where it was necessary to combine two bounding boxes because the model detected two different bounding boxes for the same graffiti. As these overlapped, they were merged.
# Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
## 6 Discussion
Overall, the DQN algorithm outperformed the DDPG scheme for the docking of the AUV onto a fixed platform in 3 DOF by presenting smoother control action signals and a lower final velocity. These features enable a realistic implementation that abides by the systems physical limits and lower the risk of damage due to high collision forces, respectively. Furthermore, DQN results in a shorter docking time as compared with PID control at the expense of an inversion in the spinning direction of the motor and propeller. The success of DQN is mainly due to the bespoke state and action signals that have been selected here, with the action space being represented by the step changes in the control input. Conversely, a bang-bang control type would be hardly achievable with real hardware. The results from the DDPG algorithm can be improved, but ultimate docking is still achieved. In the future, penalties should be imposed on changes in the motor torque and sternaplane angle. Additionally, alternative RL algorithms with continuous state and action spaces can be investigated, e.g., soft-actor critic, which present much higher performance than DDPG in complex control tasks [47]. In Table 5, it is interesting to compare the performance of RL and classical control strategies. PID control is most efficient during deployment and requires no training. However, the algorithm is more likely to suffer in more complex scenarios, e.g., under strong disturbances due to waves or currents or an AUV attempting to dock onto a moving platform. Parametric optimal control does not require training either as compared with RL strategies, which typically take up to 24 h per seed with the selected implementation on a stand-alone desktop. However, during deployment, both RL schemes are five orders of magnitude faster than parametric optimal control. In fact, optimal control would be replaced by nonlinear model predictive control with a moving window. Hence, although the computation of the control signal would be expensive for the first time step, the calculations for the subsequent time steps would take fewer resources because they can be initialised from the previous time step. Additionally, a pure C implementation is likely to be faster than the prototype code developed here. However, the point stands that RL strategies achieve comparable performance to parametric optimal control (in fact, shorter docking time), whilst presenting a much lower computational cost at deployment. Fundamentally, the computational time at deployment for both DQN and DDPG enables a realistic on-line implementation of these control schemes considering the selected time step used by the controller (0.2 s).
## 3 System Function  
The overall scheme of the 3D spectral/temporal/spatial OCDMA system is shown in Figure 1. Initially, there are \(K_{1}\cdot K_{2}\cdot K_{3}\) transmitter-receiver pairs and \(L_{3}\) star couplers (SCs), where each one of them is assigned a 3D-SWZCC code word from \(A_{g,h,l}\). As shown in Figure 2, the transmitter structure for each user is composed of a pseudorandom bit sequence (PRBS) generator, a non-coherent light source, such as a light emission diode (LED), an electrical/optical modulator (EOM), a delay line, a power splitter (PS), and a single set of fiber Bragg gratings (FBGs).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{\(R^{(1)}(g,h,l)\)} & \multicolumn{3}{c}{\(R^{(2)}(g,h,l)\)} & \multicolumn{3}{c}{\(R^{(3)}(g,h,l)\)} & \multicolumn{3}{c}{\(R^{(4)}(g,h,l)\)} \\ \hline \(g=0\cap h=0\cap l=0\) & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\  
First, the binary data are generated with help from the PRBS generator, which outputs the data in the form of an electrical signal. These data are delivered to the EOM, which modulates it depending on the ON-OFF keying (OOK) format and converts it from an electrical form to an optical form. Second, the FBG receives the modulated optical pulses from the EOM and encodes them spectrally in accordance with the wavelength corresponding to '1' of the \(X_{g}\) code sequence; meanwhile, the other wavelengths, which do not correspond to '1' of the \(X_{g}\) code sequence, are removed and filtered out.  
After that, these optical and encoded pulses are reflected to compensate for the run-trip delay, and they are then delivered to the delay line, which aims to encode them temporally in accordance with the moment corresponding to '1' of the \(Y_{b}\) code sequence. The output of the delay line is delivered to the specified SC, i.e., the SC corresponding to '1' of the \(Z_{l}\) code sequence to perform the spatial encoding. Thus, the three-dimensional encodings for the optical signal are complete.  
The receiver structure of each user is shown in Figure 3. It presents the operation opposite to that of the transmitting structure, and it is composed of a power combiner (PC), a delay line, a single FBG, low-pass filter, and a single PIN-photodiode (PIN-PD), because the spectral direct detection (SDD) technique is being applied.  
The combiners work to sum the incoming signals from the SCs in accordance with the corresponding \(Z_{l}\) code sequence to decode the signals spatially. Then, they are transmitted to the delay line to decode them temporally, in accordance with the corresponding moment of the \(Y_{b}\) code sequence.  
Finally, the delay line output is sent to the FBG to decode the data spectrally; it reflects back the components of spectral code sequence \(X_{g}\) that correspond to '1'. Additionally, the reflection helps to compensate for the run-trip delay. Lastly, the PD detects the reflection and converts the optical and decoded signal into an electrical signal, and the low pass filter (LPF) filters it.  
Figure 1: 3D spectral/temporal/spatial OCDMA system.  
Figure 2: Proposed Spectral/Temporal/Spatial OCDMA Transmitter Architecture.
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 2 GNSS Compression Acquisition Algorithm  
### Sparsification of GNSS Signal  
The GNSS signal is not sparse, but becomes sparse when correlated with a local signal in the acquisition procedure [17]. The sampled GNSS intermediate frequency (IF) signal \(r(n)\) can be expressed as  
\[r(n)=Ad(n)c(n-\tau)\exp[j(2\pi\big{(}f_{\rm{IF}}+f_{\rm{d}}\big{)}nT_{\rm{s}} +\varphi)]+v(n) \tag{1}\]  
where \(A\) is the amplitude, \(d\) is the data, \(c\) is the PRN code with a code length of \(N\) and chip width of \(T_{c}\), \(f_{\rm{IF}}\) is the intermediate frequency, \(f_{\rm{d}}\) is the Doppler frequency, \(T_{\rm{s}}\) is the sampling interval, \(\tau\) is the code phase, \(\varphi\) is the unknown carrier phase, and \(v\) is the noise.  
GNSS signal acquisition consists of a 2D search in time (code phase) and frequency. The GNSS receiver replicates the PRN code that is transmitted by the satellite being acquired and shifts the phase of the replica code until it correlates with the satellite PRN code. The PRN code autocorrelation function can be expressed as [18]:  
\[R_{\rm{c}}(\tau)=\frac{1}{N}\sum\limits_{k=1}^{N}c(k)c(k-\tau)= \left\{\begin{array}{cc}1,&\tau=0,N,2N,\ldots\\ -\frac{1}{N},&\text{otherwise}\end{array}\right. \tag{2}\]  
As observed in Figure 1, the PRN code autocorrelation function is a series of correlation triangles with a period of \(N\) PRN code chips, and there is a low correlation level of \(1/N\) in the intervals. There is a maximum correlation when \(\tau=0\), which means the phase of the GNSS receiver replica code matches the phase of the incoming satellite code.  
Therefore, we can assume that the sampled correlation output is sparse with a few peaks and abundant small values.  
We assume local code vectors \(\mathbf{c}=[c_{0},c_{1},\cdots c_{N-1}]\in\mathbb{R}^{N\times 1}\) and circularly shifting to get the code matrix \(\mathbf{C}\)  
\[\mathbf{C}=\left(\begin{array}{cccc}c_{0}&c_{1}&\ldots&c_{N-1}\\ c_{1}&c_{2}&\cdots&c_{0}\\ \vdots&\vdots&\ddots&\vdots\\ c_{N-1}&c_{0}&\cdots&c_{N-2}\end{array}\right) \tag{3}\]Using the code matrix \(\mathbf{C}\) to make the GNSS signal sparse [15]  
\[\mathbf{X}{=}\mathbf{C}\cdot\mathbf{r}=\mathbf{C}\left[\begin{array}{c}r(0)\\ r(1)\\ \vdots\\ r(N-1)\end{array}\right] \tag{4}\]  
where \(\mathbf{X}\) is the correlation vector, \(\mathbf{r}=[r(0),r(1)\cdots,r(N-1)]^{\mathrm{T}}\) is the incoming signal vector.
## 3 Solar Arrays Pointing Algorithms  
The ADCS architecture described above is implemented in the onboard software as a sequential algorithm, which is dedicated to computing the desired torque level according to the time evolution of the Sun angle on the solar arrays. The starting point of the algorithm coincides with the activation of the system, and it is concluded only when the system is deactivated. The ADCS algorithm is divided into two well-defined states, the operative one and the error one. The error state output an error flag and no control torque whenever the input data from the solar arrays are numerically corrupted or not available. The operative state is composed of the determination and control sections. The former takes the input from the electric power subsystem and computes the input for the following control section, whose final output is the control torque to be commanded. These sequential operations are continuously carried out until the system is switched off, or a transition to the error state happens. The visual representation of the operative state algorithm is reported in Figure 2. The algorithm complexity is linear with the dimension of the batch of sampled current measurements, as will be described in the next section. The proposed algorithm has been entirely conceived and developed to achieve the pointing of the solar arrays to the Sun by only exploiting Sun angle and angular rates measurements. The theoretical foundations used for the implementation of these algorithms are those introduced in Section 2.