esa-sceva/satcom-chunk-collection huggingface dataset:
# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 3 Proposed GazeGuide  
This section describes the design of GazeGuide in detail (Figure 2 shows an overview of GazeGuide). Raw gaze data are inherently noisy and thus not suitable for direct use [20; 42] in GazeGuide as the input for interaction. Therefore, to maneuver the camera, it is more suitable to use filtered data, particularly fixations that are smoother than saccades to avoid the jerky movements of the camera. Hence, the raw gaze data from the eye tracker were filtered using the I-DT algorithm [36] into fixations in real time (we used a time interval of 200 ms). The I-DT algorithm worked with the \(x\) and \(y\) data, and two fixed thresholds. It filtered the raw gaze data from the eye tracker into fixations. The filtered gaze data \(Gaze(eye_{x},eye_{y})\) were then used as the input modality to maneuver the camera in a 3D space, as detailed in Algorithm 1.  
GazeGuide includes two major parts (Figure 2): User-End and UAV-End; these two parts were connected over a network.  
**User-End:** User-End contains real-time video streaming on the HMD, which is integrated with the eye tracker. The eye tracker provides the eye-gaze data as a normalized 2D coordinate, which varies between 0 and 1. These normalized 2D gaze data are mapped onto the pitch and yaw control inputs for the UAV camera.  
When the user is ideally looking at the center of the field of view, the gaze value will be \((0.5,0.5)\). The implemented GazeMapper receives these \(Gaze(eye_{x},eye_{y})\) eye-tracker values to map to the corresponding camera pitch \((Pitch_{x}^{map})\) and yaw \((Yaw_{y}^{map})\) movements, as shown in Figure 3.  
**UAV-End:** UAV-End contains the UAV camera and receives the corresponding mapped (\(Pitch_{x}^{map}\), \(Yaw_{y}^{map}\)) values over the network from User-End. The UAV camera maneuvers according to the user's eye movements at a constant angular speed (0.1\({}^{\circ}\)/s).  
1. Retrieve RawGazeData \(\leftarrow\) Eye tracker.
2. Subjected to Fixation algorithm \(\leftarrow\) RawGazeData.
3. \(Gaze(eye_{x},eye_{y})\)\(\leftarrow\) Fixation data.
4. GazeMapper \(\gets Gaze(eye_{x},eye_{y})\). \(FoV=(\Theta,\Phi)\) (1) \[Pitch_{x}^{map}=(eye_{x}(C\Theta))-((C\Theta)/2))\] (2) \[Yaw_{y}^{map}=(eye_{y}(C\Phi))-((C\Phi)/2))\] (3) where \(FoV\) is the field of view (\(\Theta=120^{o}\), \(\phi=60^{o}\)) and C is a multiplicative constant (here, 10).
5. Camera Controller \(\leftarrow\) (\(Pitch_{x}^{map}\), \(Yaw_{y}^{map}\)) \[UAV_{input}^{cam}=(Yaw_{y}^{map},Roll,Pitch_{x}^{map})\] (4) where \(UAV_{input}^{cam}\) is the camera controller input to maneuver the UAV camera and \(Roll\) is the constant value (\(Roll=0\)) throughout the experiments.  
Figure 3: GazeMapper: Mapping of eye-gaze values to camera-controlled input.  
Figure 2: Overview of the proposed GazeGuide.
# Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme
## 4 Experimental Results and Analyses
### Analyses of the Methods Exploiting Both Low Rankness and Sparsity  
We analyzed the recovery results of the methods exploiting low rankness and sparsity. Figures 7-9 show visual comparisons of the eleven methods for recovering the House, Lena, and Baboon images, respectively. Table 4 shows the PSNR (dB)/SSIM results of the nine methods for recovering different color images under different missing patterns. Figure 10 depicts the PSNR curves of the inpainting results using the different methods; the missing ratio ranges from 10% to 70% under a random missing pattern. Figure 6: Comparison of KA and QA schemes under the corresponding TMac-TTKA method [23] and our TTLR method. The first row lists the painted images with a random missing pattern, and the missing ratio is 80%. The second row lists the recovered images by the TMac-TTKA method. The last row lists the recovered images by the LRTT method. Figure 8: The missing patterns and inpainting results of Lena image solved by different methods. Figure 7: The missing patterns and inpainting results of House image solved by different methods. Figure 9: The missing patterns and inpainting results of Baboon image solved by different methods. As shown in Figures 7-10 and Table 4, compared to the numerous close STDC, HaLRTC, FBCP, TMac-TTKA, SPCTV, and LRTV methods, the UnfoldingLRTV, tSVDLRTV, and TTLRTV methods had superior performance for both visual and quantity results. The SPCTV and LRTV methods also enhanced the low rankness and sparsity simultaneously, but the results were worse than our methods. Table 5 shows the PSNR (dB)/SSIM results of the eight methods. The MatrixLR method only constrains the low matrix rank; the TV method only exploits the TV prior; the UnfoldingLR, tSVDLR, and TTLR methods only constrain the low unfolding matrix rank, low tubal rank, and low TT rank, respectively; and the UnfoldingLRTV, tSVDLRTV, and TTLRTV methods combine both sparsity and low tensor rankness. As shown in Table 5, the combination of sparsity and low tensor rankness constraints could yield better inpainting results than enforcing sparsity or low rankness alone. The TTLR method was more efficient than the MatrixLR and TV methods. The results of the tSVDLR method and the TTLR method were comparable. The UnfoldingLR method provided the best results among the TTLR, tSVDLR, TuckerLR, MatrixLR, and TV methods. UnfoldingLRTV, tSVDLRTV, and TTLRTV methods had improved numerical results compared to the corresponding UnfoldingLR, tSVDLR, and TTLR methods, which demonstrates that TV prior is efficient in improving the accuracy of low rank-based inpainting methods.
## 4 Experimental Results
### Experiment 1: Noise Reduction Experiment to Evaluate the Multichannel Feedforward ANC System
The experimental arrangement is the same as that in Figure 7 and the simulation conditions are shown in Table 4. The secondary path model is also the same as that shown in Figure 9. From Figure 9d, the minimum group delay is 0.86 ms, which is used as the delay of the secondary path \(D_{\text{S}}\). The delay due to the calculation to generate the anti-noise \(D_{\text{C}}\) is zero because this experiment was conducted numerically. The noise was generated from noise source 1 from 0 to 100 s, from noise source 2 from 100 to 200 s, from noise source 3 from 200 to 300 s, and from noise source 4 from 300 to 400 s. The reference microphones were reselected when the reduction given by Equation (22) was smaller than 1 dB; in other words, the threshold for the reduction was set to 1 dB. This is because a reduction of less than 1 dB means that the noise level is higher than that in the case of no control. The simulation results are shown in Figure 13. From Figure 13a, the unwanted noises are reduced by the proposed ANC system. Moreover, from Table 1 and Figure 13b, the reference microphones that satisfy the causality constraint are selected in each region. From all of the experimental results, the proposed multi-channel ANC system is effective for noise reduction under the condition that the direction of the noise changes. \begin{table}
\begin{tabular}{l l} \hline \hline Noise & White noise \\ Sampling frequency & 8000 Hz \\ Cutoff frequency of LPF & 2000 Hz \\ Tap length of noise control filter \(N\) & 400 \\ Tap length of secondary path model \(L\) & 100 \\ Step size parameter \(\alpha\) & 0.2 \\ Regularization parameter \(\beta\) & 1.0 \(\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation conditions for experiment 2. Figure 13: _Cont._
## 4 Simulation Results and Discussion
However, with the increase of transmission power budge, our strategy's energy efficiency performance keeps at a very high level while the location-based user clustering strategy declines. When the transmission power budget becomes higher, more power will be allocated to the clusters with low channel gain users, and thus lower energy efficiency that the location-based user clustering strategy can achieve. This can be supported by scenario 2, where the location-based user clustering strategy can not satisfy the energy requirements of low channel gain users under the low transmission power budget. As all low channel gain users are paired with high channel gain users in our strategy, lower energy is needed to ensure their minimum data transmission rate requirements, and more energy can be allocated to the users with high channel gain to improve the global energy efficiency. From Figure 5(b), when the transmission power budget is meager, allocating equal power to each cluster is challenging to satisfy all constraints. At the same time, our Lagrangian multiplier based dynamic programming model can still get a possible result. Figure 6: Energy efficiency versus maximum transmission power of BS for NOMA systems equipped with the proposed user clustering and power allocation methods, location-based user clustering strategy, and equal power allocation scheme. Figure 7: Energy efficiency versus minimum data transmission rate requirements of each user for our proposed algorithm, location-based user clustering strategy, and equal power allocation scheme. As the transmission power budget grows, our algorithms can always allocate energy appropriately. However, the equal power allocation scheme will allocate more redundant energy, which leads to lower energy efficiency. It is worth mentioning that NOMA systems' energy efficiency performance increases slightly with the average cluster size growth. More users are in the same cluster, there are more combinations for power allocation, and better energy efficiency that the algorithm can obtain. However, for the location-based user clustering strategy and equal power allocation scheme, an opposite result is observed. This is because inappropriate user clustering and power allocation will cause tremendous energy waste, and its influence increases with the cluster size. Therefore, it is more difficult for them to achieve good energy efficiency when the cluster size is more prominent. Finally, we compare the curves of scenario 1 and scenario 2 in Figures 4-7. We can see that the energy efficiency of scenario 2 is much lower than that of scenario 1. When the user's channel condition worsens, more energy should be consumed to meet the same transmission requirements. On the other hand, our solution performs better than other benchmarks in both scenarios, proving that it is insensitive to the user's distribution and universal to some extent.
# A Social Multi-Agent Cooperation System Based on Planning and Distributed Task Allocation
## 5 Distributed Task Allocation
### The Model Checking of Distributed Task Allocation  
The model checking of distributed task allocation consists of two steps. The first step is to represent the distributed task allocation with the Petri net, which permits the representation of a concurrent process. The second step is the verification of some properties using computation tree logic (CTL) with the model checker _its-ctl_[36].  
As explained in the previous section, we distinguish two kinds of participating agents, which are the initiator and the participant agent (Figure 5). For further explanation of the Petri net diagram (especially the transition meanings), refer to Table 1.  
Figure 5: Formal distributed task allocation with Petri net.  
**CTL properties**: To verify that our Petri net model is correct, we present some properties expressed with the CTL formalism. These properties are checked by the ITS-CTL model checker [36]  
* Property 1: Whenever an initiator agent needs to ensure a task but does not have all the necessary resources, the initiator agent informs the participant agent,. AG (P1 = > EF P9).
* Property 2: During the negotiation, the initiator agent receives a positive response from the participant agent, or receives a disapproval message. EF P1 AND EF (P11 OR P15).
* Property 3: The participant agent, could not receive two different decisions from the initiator agent at the same time (i.e., either the initiator agent accepts or refuses the new distributed allocation). NOT EF (P5 AND P6). All these properties are proven to be true with the ITS-CTL model checker. **eCTL properties**: The following eCTL property is proven to be true: Property 4: Whenever the participant agent, accepts the new distributed task allocation, it awaits the final decision from the initiator agent, which can be a confirmation to apply a new distributed allocation or to cancel the new distributed allocation. AGA t11 XAGA (t5 OR t7) X p14.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 2 Materials and Methods  
### Data  
In rapid mapping, it is always important to have access to various sensor types, resolutions, and satellites. Indeed, SAR sensors detect through clouds, while optical sensors benefit from of multi spectral bands to better classify the crisis information.  
The wide range of available resolutions means the operator can select the best one for the assigned task, which include: Medium Resolution (MR) sensors, with a pixel size greater than 30 m, which are perfect for a rough, but quickly generated (2 h for the First Estimate Product in the CEMS-RM portfolio) crisis map; High Resolution (HR) sensors, with a pixel size between 30 and 4 m (such as Sentinel-1/2 and Landsat-8), which are very pertinent in delineating large to medium sized floods and fires; and Very High Resolution (VHR) sensors, with a pixel size below 4 m, which are more suitable for accurate event delineation and damage assessment mapping.  
It is also very important to have several data sources for every resolution range, in order to maximize the chance of covering the monitored area, which are dependent on:  
* the availability of the satellites (they can be used for other purposes, such as military),
* the overflight feasibility analysis (which depends on satellite orbit and agility),
* the cloud cover over the scene at the acquisition time for optical data.  
The delay until the next acquisition is also optimized and reduced by multiplying potential data sources, thus increasing the chance of passing over the monitored area.  
The ESA Data Access Portfolio (DAP) [14] describes more than 25 sensors, and covers the main sensors used in SETIT's rapid-mapping service through CEMS (Copernicus missions or contributing missions) or the International Charter (members). The ESA DAP is quite complete with both SAR and optical sensor types, with resolutions from less than one meter to decametric sensors, which cover all the use cases encountered. The two following tables (Tables 1 and 2) describe all the constellations handled by EEO.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constellation** & **Instrument** & **Product Type/Processing Level** \\ \hline Sentinel-2 & MSI & L1C \\  & MSI & L2A \\  & Theia \\ \hline Sentinel-3 & OLCI & EFR \\  & SLSTR & RBT \\ \hline Landsat 1–5 & MSS & \\ Landsat 4–5 & TM & L1 \\ Landsat 7 & ETM & \\ Landsat 8–9 & OLI + TIRS & \\ \hline PlanetScope & PS2, PS2-SD, PSB-SD & L1B, L3B \\ SkySat & SkySat Camera & Collect Ortho \\ \hline Pleiades-Neo & Pleiades-Neo Imager & \\ Pleiades & HiRI & \\ SPOT-6/7 & NAOMI & \\ \hline Vision-1 & Vision-1 optical sensor & PRJ, ORTP \\ \hline WorldView-1 & WV60 & \\ WorldView-2 & WV110 & \\ WorldView-3 & WV110 & \\ WorldView-4 & SpaceView-110 & Standard, Ortho \\ QuickBird-2 & BGIS-2000 & \\ GeoEye-1 & GIS & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optical constellations handled in EEO as of June 2022.
# Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions
## 3 Multimedia Data Encryption
### Audio Cryptography  
Many sensor nodes may be equipped with audio units to retrieve relevant information for different scenarios. For many applications, audio may be used to detect events of interest, to track mobile targets, to perceive environment variables, to perform intrusion detection, among many other functions [62]. On the other hand, these media may also be used only to complement visual information in heterogeneous wireless multimedia sensor networks.  
In general terms, audio typically represents human voice or a relevant noise for the application, with the transmission rate usually lower than 64 kbps. Different codecs are available, with diverse compression ratio, resistance to packet errors and reproduction quality. Audio compression requirements are different than visual data compression, as well as the amount of produced data (usually lower than images and videos streams), with direct impact on how cryptography is performed. Moreover, cameras retrieve information in a different way from audio sensors, demanding proper solutions for coverage optimization and special concerns about privacy.  
Although audio streaming is less stringent than visual data sensing, cryptography may also be optimized. A reasonable approach is to perform selective encryption, only protecting the most relevant information [63]. For that, some transform should be applied to produce data with different importance for the reconstruction process, as the Modified Discrete Cosine Transform (MDCT) [64].  
A generic example of selective audio encryption is presented in Figure 3. In that example, compressed audio data are packetized, and only packets containing the most relevant data for the reconstruction process are encrypted. Doing so, even if unencrypted packets are captured, the original audio stream cannot be recovered.  
Audio streaming in wireless sensor networks can also be protected in different ways. Among the possibilities, watermarking can also be applied for authentication purposes [65], similarly as happens with images and videos. As an example, the work [66] optimizes audio watermarking in wireless multimedia sensor networks. An early study conducted in [67] proposed audio watermarking based on DWT for ad hoc networks.  
Whatever is the adopted protection mechanism for audio streams, the particularities of this media type should always be considered, as well as the potential source constraints of the employed sensor nodes.
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 3 System Modeling
### Multi-Sensor Real-Time Routing Scheduling Problem
Constraint Condition (5) means that any sensor data can only be transmitted on one base station at any time. As shown in Figure 3, the network topology consists of \(I\) starting nodes, \(I^{\prime}\) routing nodes, and a single terminal node \(D\). For any \(i\in\{0,1,\ldots,I-1\}\) and \(i^{\prime}\in\{0,1,\ldots,I^{\prime}-1\}\), let \(i^{\prime}\in\text{nbr}(i)\). Moreover, for any \(i^{\prime}\in\{0,1,\ldots,I^{\prime}-1\}\), it must hold that \(D\in\text{nbr}(i^{\prime})\). Let any \(Q_{i}=\infty\). Clearly, the problem \(\mathcal{S}^{\prime}\) in Figure 3 is a subproblem of the problem \(\mathcal{S}\) discussed in this paper. We now prove that \(\mathcal{S}^{\prime}\) is an NP-hard problem. Proof. : Let \(W=I\) and \(C=I^{\prime}\); this allows us to describe this problem as a multi-task, multi-processor scheduling problem [23] with \(W\) tasks and \(C\) processors. Nodes 1 to \(n\) in the figure  
Figure 3: A special case of the multi-sensor real-time routing scheduling problem. are base stations with sensor data, and a column of nodes in the middle is a routable base station. Each sensor data can face the same n routing nodes after departure. It is evident that this transformation process has polynomial-time computational complexity. The problem proposed in [23] has been proven to be an NP-complete problem; therefore, the problem \(S^{\prime}\) is NP-hard. Since a simpler subproblem \(S^{\prime}\) is NP-hard, the more complex problem \(S\) must also be NP-hard.
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 2 System Model
### Channel Model  
Due to spatially sparse nature of the mmWave channels, because of relatively higher path-loss and low-scattering in comparison to sub-6 GHz frequencies, they effectively have \"quasi\"-optical propagation properties [1], where the line-of-sight (LoS) path is the dominant link that mainly contributes to the communication. For simplicity, we omit the potential NLoS components due to lack of multipath fading, and define the channel between a BS-user pair \((b,k)\) is simply as  
\[\mathbf{h}_{b,k}=g_{b,k}\mathbf{a}_{R}(\phi_{b,k})~{},~{}\forall b\in\mathcal{ B}~{},~{}\forall k\in\mathcal{K}~{}, \tag{1}\]  
where \(g_{b,k}~{}=~{}h_{b,k}d_{b,k}^{-\theta/2}\), in which \(h_{b,k}\) represents the complex gain of dominant LoS link with zero-mean and unit-variance, \(d_{b,k}\) is the distance between BS-user pair with path-loss factor of \(\vartheta\), and \(\mathbf{a}_{R}(\phi_{b,k})\in\mathbb{C}^{N_{R}}\) denotes receive antenna array response vector for angle-of-arrival (AoA) \(\phi_{b,k}\) with  
\[[\mathbf{a}_{R}(\phi_{b,k})]_{m}=e^{i\frac{2 md}{\lambda}(m-1)sin(\phi_{b,k})}~ {},~{}m\in\{1,2,...,N_{R}\}~{}. \tag{2}\]  
It should be noted, because of the higher penetration and path loss [16], mmWave channel between any BS-user pair can be categorized in two states: LoS or blockage. A channel is in blockage state, if the dominant link between a BS-user pair is not available because of some obstacle or resulting path loss is sufficiently high to establish communication. The blockage state is modeled as \(\mathbf{h}_{b,k}=0^{N_{\mathrm{R}}x1},\ \forall b,\ \forall k\). The LoS state is defined in Equation (1), assuming perfect beam-alignment.
###### Abstract
A field campaign was conducted in which photosynthesis measurements by CO\({}_{2}\) exchange were combined with fluorescence at the leaf and canopy levels (i.e., SIF). Leaf chlorophyll fluorescence data were collected using the pulse amplitude modulation (PAM) measuring principle, which allowed us to quantify the probability of each of the three alternative fates of absorbed photons in the pigment bed: photochemistry, heat dissipation, and fluorescence. The canopy SIF was obtained with a manual ground system. Other physiological parameters, including the leaf area index and pigment content, were also simultaneously collected. Based on these measurements, we aimed to address the following questions: (1) Does SIF show a linear relationship with GPP in senescent crops? (2) If the relationship is nonlinear, what caused the result? Answers to these questions can provide a further understanding of the application of SIF in the large-scale detection of GPP.