esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunistic Weather Sensing by Smart City Wireless Communication Networks
## 3 OISAC in Smart Cities
In each of these figures, on the left, one can see the 24 h accumulated rainfall (in mm) as was reported by the Israeli Meteorological Services (IMS) radar, with a zoomed-in section of the map--of the city of Rehovot--depicted on the top right of the figure. At the bottom right corner of the figure, one can see the same map of accumulated rain--as was estimated by the proposed approach from signal level measurements in the SCWN. As one can see, the spatial resolution reported from the SCWN was higher than the resolution achieved by the radar by an order of magnitude: the radar spatial resolution in this scenario was 1 km by 1 km (based on the IMS radar data), whereas the SCWN produced a snapshot with a spatial resolution of roughly 300 m by 300 m. This high resolution will help smart cities' management systems in the future to better operate their cities--including water management, risk assessment, public and private transportation needs during and after weather events, electricity distribution in a smart-grid, etc. It is also worth mentioning that whereas the radar produces a single map once every 5 to 15 min (a standard value range for weather radars world-wide), the links produce a channel attenuation value every 30 s. That is, the temporal resolution of the SCWN-based rain products are also higher by an order of magnitude when compared to the standard weather radar, opening the options for actual real-time weather-influenced city management. Figure 3: (**a**) A map of the city of Rehovot, Central District, Israel, with 66 SCWN’s links (marked by colored lines and numbering 1 through 66) operating at a frequency range of 70–80 GHz. The shortest link is 53 m, and the longest is 2.42 km [53]. (**b**) The 66 SCWNs’ path length histograms [53]. (**c**) An image of an installation of two Tx/Rx terminals which form the nodes of the SCWNs’ networks. Each of the terminals is marked via a red circle [54]. Figure 4: Accumulated rain during a 24 h period of heavy rain (total rainfall: 59.8 mm). **Left**: IMS weather radar rain map. **Top right** zoomed-in radar map, covering the city of Rehovot (of Figure 3). **Bottom right**: a map generated by the available SCWN’s links [53]. Figure 5: Accumulated rain during 24 h period of moderate rain (total rainfall: 28.6 mm). **Left**: IMS weather radar rain map. **Top right** zoomed-in radar map, covering the city of Rehovot (of Figure 3). **Bottom right**: a map generated by the available SCWN’s links [53].
# SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
## 2 Phase Filtering of Shearlet Domain Based on Interferometric Phase Statistics  
### Interferometric Phase Statistics  
For the InSAR Interferometry, the coherence of two SAR images directly affects the accuracy of elevation measurement. Moreover, the coherence itself is affected by factors like system noise, baseline decoherence, time decoherence and image registration errors [25]. The complex correlation coefficient is defined as \[r=|r|\exp(j\phi_{x})=\frac{E\left\{S_{i}S_{j}^{*}\right\}}{\sqrt{E\left\{|S_{i}|^{2 }\right\}}E\left\{|S_{j}|^{2}\right\}},\,i,j=1,2,...,m \tag{1}\]  
where \(|r|\) denotes the coherence, \(\phi_{x}\) represents the average phase information, \(S_{i}\) and \(S_{j}\) are the two interferometric SAR images [26].  
The interference phase of a pair of SAR images is described by the _pdf_[27].  
\[pdf_{\phi}(\phi)=\frac{\Gamma(L+1/2)(1-|r|^{2})^{n}\beta}{2\sqrt{\pi}\Gamma(L) (1-\beta^{2})^{n+1/2}}+\frac{(1-|r|^{2})^{n}}{2\pi}{}_{2}F_{1}(L,1;\frac{1}{2} ;\beta^{2}) \tag{2}\]  
where \(\beta=|r|\cos(\phi-\phi_{x})\), representing the Gauss hypergeometric function [28], and \(\Gamma\) is the Gamma function.  
The _pdf_ of (2) depends only on the number of looks \(L\) and the complex correlation coefficient \(|r|\). The peak of the distribution is located at \(\phi=\phi_{x}\). Figure 1a shows distributions of correlation coefficient \(|r|=0.1\sim 0.9\), \(L=1\) and \(\phi_{x}=0\). Figure 1b shows a standard deviation versus \(L=1,2,5,10,15,20\). As Figure 1b shows, the multi-look processing improves the phase accuracy, i.e., the variance of the phase decreases for a larger number of looks. When the coherence is 0.5, the theoretical phase noise standard deviation for a single-look (\(L=1\)) interferogram is about three times larger than that of a twenty-look (\(L=20\)) interferogram [29]. Additionally, for a given look number, phase standard deviation is inversely proportional to the coherence, i.e., bigger coherence corresponds to smaller phase noise standard deviation and weaker noise, and vice versa. This means the standard deviation of the noise phase is closely correlated with the coherence and the multi-look number. The phase standard deviation can reflect the interference phase noise more comprehensively than coherence or multi-look number.
## 4 Results: Threat Analysis
### Threat 1: Transaction Manipulation by \(t_{M}\)  
A major threat in permissioned blockchain platforms is the possibility that the operator \(t_{M}\) could exert discretionary control over transaction processing. This includes selectively rejecting, delaying, or reordering transactions for arbitrary or self-serving purposes. The proposed method systematically eliminates these threats through a combination of transaction hash commitment and dual-chain disclosure. We explain each aspect below:  
* **Rejection and Delay:** Users submit only the hash value of their transaction for initial registration in \(\mathbb{P}\mathbb{C}_{M}\), which includes a random number \(r\) that conceals the transaction's content. As \(t_{M}\) cannot know what the transaction contains, it cannot selectively reject or delay it for content-based reasons. Any delay without justification becomes auditable once the segment is anchored to \(\mathbb{R}\mathbb{C}\).  
* **Reordering:** Since all transaction hashes appear opaque at the time of registration, \(t_{M}\) cannot prioritize or rearrange them meaningfully. For batch-registered transactions, their positions within the Merkle tree can be verified later, preventing order manipulation.
* **Modification:** In conventional blockchains, transaction content is safeguarded against tampering through the use of digital signatures. In the proposed method, an additional concern arises from the requirement to disclose the results of smart contract executions for the sake of user convenience and transparency. This introduces the risk that \(t_{M}\) could manipulate the published results to distort the intended execution outcome. To counter this, the framework allows the auditor \(A\) to independently recompute the outputs of all contract executions based on the original transaction and the associated smart contract logic. By comparing the actual disclosures on the platform chain with the expected outputs, \(A\) can detect and report any inconsistencies. This verification process ensures the integrity of execution results and deters manipulation by \(t_{M}\).  
In summary, the proposed method mitigates operator manipulation through a combination of transaction hash commitment and dual-chain disclosure. By hiding transaction content during registration while ensuring cryptographic verifiability, it prevents content-based manipulation by the operator. This reduces discretionary control and reinforces trust in the fairness of transaction processing.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 4 Case Studies of Multi-Channel and Multi-Hop QoS  
The following case studies will provide detail as to how to calculate the probability of confidentiality and the probability of integrity for various network configurations. Several intermediary steps may be necessary to reach the equivalent single-channel, point-to-point network, which provides the most direct method of comparison.
## 4 Discussion
These might explain the empirical GPP model we presented in this study using both PRI and SIF showed good agreement with tower-based fluxes with low uncertainties. Furthermore, we achieved significant improvement by using both PRI and SIF together for GPP estimates, rather than using either PRI or SIF alone to attain a more comprehensive description of the entire carbon fixation mechanism, and hence, to deliver more accurate GPP estimates. Cross-validation approaches are designed to evaluate the robustness and consistency of statistical models. One crucial factor about cross-validation is the strategy to divide the entire dataset into training and validation data, using a random sampling and a systematic partitioning to test our results. We demonstrate that the GPP model based on 2008 data was able to predict GPP using PRI and SIF (red) in the three other years--2010, 2011, and 2012. This highlights the robustness of the integration of PRI and SIF (red) to capture important information about physiological status of this cornfield, thus providing accurate estimates of carbon assimilation with small uncertainties. The k-fold cross-validation approach was also applied to the model with PRI and SIF (far-red), with only a slightly lower performance (\(\text{r}^{2}_{\text{cv}}=0.77\), \(\text{RMSE}_{\text{cv}}=0.205\) mg CO\({}_{2}\)/m\({}^{2}\)/s). It can be difficult to retrieve SIF (red) from space [108] since it is a narrower and weaker feature than SIF (far-red), and this capability has yet to be attained. Even though our results suggested better performance of SIF (red) to model photosynthetic processes in ground level studies, we also demonstrated the potential for using SIF (far-red) for carbon monitoring using satellite measurements at global level.
# Method of Planning Repairs of the Installation including Building Waste
## 3 Analysis of the Results Obtained and Discussion  
The most favorable refurbishment strategy is the option with the highest sustainability factor, _S_s. This coefficient is the result of a multi-criteria analysis in which two equivalent criteria were used:  
Figure 3: Prediction of changes in technical condition of a building refurbished according to S3 strategy and a building without refurbishment.  
Figure 2: Prediction of changes in technical condition of a building refurbished according to S2 strategy and a building without refurbishment.  
Criterion 1--improvement of the technical condition obtained as a result of refurbishment strategy S1, S2 or S3.  
Criterion 2--generation of as little construction waste as possible during refurbishment works according to strategy S1, S2 or S3.  
The sustainability coefficient was determined by the formula:  
\[S_{S}=w1k1+w2k2 \tag{4}\]  
where  
\(w1\), \(w2\)--criteria weights;  
\(k1\), \(k2\)--criteria measures.  
The method adopts the two most important criteria, but other criteria, such as inconvenience of use during refurbishment, can also be used.  
The results obtained are presented in the Table 3. Strategy S1 proved to be the most beneficial. In strategy S1, the increase in changes in performance may not be very impressive, but the small amount of construction waste makes this option the more beneficial than the others. The least favorable is of course variant S3, where, due to the lack of refurbishment of the installation, a failure occurred, and as a result, many additional damages increased the amount of refurbishment works and thus construction waste.  
As a result of the repairs done in the 30 th year of the building, the building condition improved from 0.787 to 0.823, an increase of only 3.6%. There is also a small amount of construction waste. Waste includes listed plumbing elements (pipes), plumbing fixtures and waste associated with the renovation of these elements--floor fragments (at 20%) and wall and ceiling plaster (at 20%), totaling 18.48% of building elements.  
For refurbishment works carried out in year 40, the building condition improved from 0.682 to 0.754, there was an increase of 7.2%. Compared to the previous strategy, the technical condition improvement is small, while there is twice as much building waste, i.e., eight elements, which is 32% of all building elements.  
The repair work carried out in the 50 th year of operation resulted in an improvement in the technical condition of the building from 0.592 to 0.744, i.e., a significant increase in performance of 15.2%. The improvement in technical condition is the largest compared to previous strategies, which is due to the extensive renovation work. Unfortunately, the amount of building waste is incomparably higher than in previous strategies, as many as 12 building elements are waste (48% of building elements).  
The most favorable strategy is strategy number S1, where there is an improvement in technical condition and there is a small amount of construction waste.
## 2 PV Model of the Power System
### Calculation of GMM-HMM Parameters  
When the observed historical data are \(O=\{O_{1}\), \(O_{2}\),..., \(O_{T}\}\) in the training phase of the HMM model, the corresponding latent variable is denoted as \(I=\{I_{1}\), \(I_{2}\),...,\(J_{T}\}\), and \(\lambda=(\pi,A,\mu,\Sigma)\) is the model parameter that needs to be acquired. By using the maximum likelihood estimation method, the logarithmic likelihood function \(L(\lambda)\) of O with respect to parameter \(\lambda\) is maximised.  
\[\hat{\lambda}=\underset{\lambda}{\operatorname{argmax}}\log L(\lambda) \tag{9}\]  
\[L(\lambda)=\log P(O\mid\lambda)=\log\sum\limits_{I}P(O,I\mid\lambda)=\log \left(\sum\limits_{I}P(O\mid I,\lambda)P(I\mid\lambda)\right) \tag{10}\]  
Since the Equation (10) contains hidden variables, which makes it difficult to solve directly, the Baum-Welch algorithm is primarily used for the learning of HMM parameters [14]. The calculation process of the algorithm is as follows:  
1. Set the initial value of the model parameter \(\lambda\);
2. Step E, construct the Q function so that \(L(\lambda)\geq Q(\lambda,\lambda^{(i)})\). Among them, \(Q(\lambda,\lambda^{(i)})\) is the expectation of the log-likelihood function \(\log P(O,I\mid\lambda)\) on the conditional probability distribution \(P(I\mid O,\lambda^{(i)})\) of the hidden variable \(I\) under the known observation data \(O\) and the current parameter \(\lambda^{(i)}\): \[Q(\lambda,\lambda^{(i)})=E_{I}\Big{[}\log P(O,I\mid\lambda)\Big{|}O,\lambda^{ (i)}\Big{]}=\sum\limits_{I}\log P(O,I\mid\lambda)P(I\mid O,\lambda^{(i)})\] (11) \[Q(\lambda,\lambda^{(i)})\text{ can be represented by }\eta_{j,k}(t)\text{ and }\zeta_{i,j}(t)\text{, where: }\] \[\eta_{j,k}(t)=P\Big{(}I_{t}=j,\phi_{j}(t)=\psi_{j,k}\mid O,\lambda\Big{)}\] (12) \[\zeta_{i,j}(t)=P(I_{t}=i,I_{t+1}=j\mid O,\lambda)\] (13)Here, \(\eta_{j,k}(t)\) represents the probability that \(I_{t}=j\), given \(\lambda\) and \(O_{t};\zeta_{i,j}(t)\) is the probability that the concealed state at time \(t\) is \(i\), and the concealed state at time \(t+1\) is \(j\). \(\eta_{j,k}(t)\) and \(\zeta_{i,j}(t)\) can be calculated using the forward-backward algorithm to facilitate the solution.
3. Step M: Find the \(\lambda\) value that maximises \(Q(\lambda,\lambda^{(i)})\) and update the model parameter \(\lambda\) as: \[\hat{\pi}_{j}=\sum\limits_{k=1}^{K}\eta_{j,k}(1)\] (14) \[\hat{a}_{i,j}=\frac{\sum\limits_{t=1}^{T-1}\zeta_{i,j}(t)}{\sum\limits_{t=1 }^{T-1}\sum\limits_{k=1}^{K}\eta_{j,k}(t)}\] (15) \[\hat{w}_{j,k}=\frac{\sum\limits_{i=1}^{T}\eta_{j,k}(t)}{\sum\limits_{j=1}^{T }\sum\limits_{k=1}^{K}\zeta_{i,j}(t)}\] (16) \[\hat{\eta}_{j,k}=\frac{\sum\limits_{t=1}^{T}\zeta_{i,j}(t)O_{t}}{\sum\limits_{t =1}^{T}\zeta_{i,j}(t)}\] (17) \[\sum\limits_{j,k}=\frac{\sum\limits_{t=1}^{T}\eta_{j,k}(t)\Big{(}O_{t}-\mu_{ j,k}\Big{)}\Big{(}O_{t}-\mu_{j,k}\Big{)}^{T}}{\sum\limits_{t=1}^{T}\eta_{j,k}(t)}\] (18)
4. After obtaining the updated model parameters, it is determined whether the parameters satisfy the convergence conditions. If convergence is met, the algorithm terminates. If not, the second step is turned around.
## 4 Perspectives on Embedded AIoT Devices for SMEs Based on Identified Attributes
### Verification of the Designed Devices Considering the Identified Essential Attributes
#### 4.2.1 Jetson Nano  
The Jetson Nano is built with a 64-bit quad-core Arm Cortex-A57 CPU running at 1.43 GHz alongside an NVIDIA Maxwell GPU with 128 CUDA cores capable of 472 GFLOPs (16 bits floating point) and has 4 GB of 64-bit RAM onboard along with 16 GB of eMMC storage and runs Linux for Tegra. The board size is 70 \(\times\) 45 mm and has a 260-pin SODIMM connector supporting many interfaces, such as video, audio, USB. Jetson Nano is powered by micro-USB and consumes only 5 watts, and has extensive I/O ranging from GPIO to CSI.  
NVIDIA also provides a Software Developer Kit (SDK) named Jetpack. Jetpack comprises many features: a board support package (BSP), Linux OS, NVIDIA CUDA and TensorRT software libraries for deep learning, computer vision libraries and GPU computing and multimedia processing. These capabilities enable multi-sensor Autonomous Robots, IoT devices with intelligent edge analytics and advanced AI systems. The SDK also includes the ability to natively install popular open source Machine Learning (ML) frameworks such as TensorFlow, PyTorch, Caffe, Keras and MXNet, along with frameworks for computer vision and robotics development such as OpenCV and ROS [68].  
Figure 8: Jetpack Software ecosystem.
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 4 Polarization Correction and Verification  
Based on the above influence analysis of error source, this section describes the polarization correction procedure of the GF-3 satellite. The point-target method and distributed-target method are used to verify the effectiveness of the correction method by assessing the quality of corrected data.
# Carrier Dynamics in InGaN/GaN on the Basis of Different In Concentrations
## 3 Results and Discussions
### XRD Results  
Figure 3 shows the \(\omega\)-20 line scan with different In\({}_{\rm x}\)Ga\({}_{1-x}\)N layers grown on sapphire (Al\({}_{2}\)O\({}_{3}\)) substrate. The peak of Bragg reflection from the GaN buffer layer showed the greatest intensity, whereas the arrows in the figure represent the In\({}_{\rm x}\)Ga\({}_{1-x}\)N peaks of four samples (i.e., SI27, CSI27, PIN15, and PIN27). SI27 exhibited a diffraction signal of In\({}_{0.27}\)Ga\({}_{0.73}\)N at 33.56\({}^{\circ}\). Sample CSI27 showed a diffraction signal of In\({}_{0.48}\)Ga\({}_{0.52}\)N and In\({}_{0.19}\)Ga\({}_{0.81}\)N at 32.92\({}^{\circ}\) and 33.80\({}^{\circ}\), respectively. PIN27 presented the diffraction signals of In\({}_{0.47}\)Ga\({}_{0.53}\)N and In\({}_{0.2}\)Ga\({}_{0.8}\)N at 32.94\({}^{\circ}\) and 33.78\({}^{\circ}\), respectively. PIN15 exhibited the diffraction signal of In\({}_{0.15}\)Ga\({}_{0.85}\)N at 34.05\({}^{\circ}\), whereas CSI27 and PIN27 produced two different crystal phases of In\({}_{\rm x}\)Ga\({}_{1-x}\)N. These findings are possibly attributable to the extremely high concentration of doped In. Hence, the concentration of a part of In was diffused into the uppermost layer of GaN, thereby resulting in two different crystal phases of In\({}_{\rm x}\)Ga\({}_{1-x}\)N. Table 1 presents the XRD measurement results of SI 27, CSI27, PIN27, and PIN15 according to equation (2). The InGaN peak shown in Figure 3 exhibited an increased distance from the GaN peak. All InGaN layers were characterized by pseudomorphicity on the GaN buffer layer. CSI27 and PIN27 exhibited two separate InxGa1-xN phase peaks detected from the InGaN/GaN scan in Figure 3. The tilt of the InGaN layer to the GaN buffer layer was absent in the four samples studied.
# Cooperative Terrestrial-Underwater FSO System: Design and Performance Analysis
## 3 Channel Model
### Underwater Link
Here, assuming \(E[h_{u}]=1\) and according to [35]\(\Lambda=\frac{1}{\Gamma(1+1/K)}\), whereas the scintillation index is provided by  
\[\sigma_{u}^{2}=\frac{\Gamma(1+2/K)}{\Gamma(1+1/K)^{2}}-1\approx K^{-11/6}. \tag{19}\]  
Thus, from this equation, the value of \(K\) is computed as \(K\simeq(\sigma_{u}^{2})^{-6/11}\) where the scintillation index can be obtained using a simple analytic approximation proposed in Appendix A in [33], which is given by the following equation:  
\[\sigma_{u}^{2}=\Lambda_{1}z^{2}+\Lambda_{2}z+\Lambda_{3}, \tag{20}\]where \(z\) is the link distance (depth) for \(z<100\) m, and parameters \(\Lambda_{1}\), \(\Lambda_{2}\) and \(\Lambda_{3}\) can be obtained after a curve-fitting approach for different values of the relative strength of temperature and salinity fluctuations. We note that from the integration of (18) we can obtain the CDF of the underwater channel, which is key to the derivation of the expression for the error probability in Section 3.3, from obtaining the joint CDF of the FSO-TU system.
## 5 Connected Vehicles
### Technological Developments
#### 5.3.4 Hong Kong  
In the Smart City Blueprint Consultancy Study Report published in June 2017, the Hong Kong Government proposed the following strategic measures for smart mobility involving CVs [112]:  
* Developing \"in-vehicle units\" (IVUs) that enable drivers to access traffic information in real-time and pay for their driving remotely via it instead of using toll booths;
* Installing approximately 1200 traffic detectors on all critical links by 2020 to provide instant and accurate traffic information;
* Public participation in developing a specific Electronic Road Pricing (ERP) trial program to be implemented in the middands and nearby regions in 2019;
* Gradually introducing intelligent traffic signal systems with pedestrian and vehicle detectors at intersections starting in 2021;
* Implementing the auto toll system in the new Tseung Kwan O-Lam Tin Tunnel for trial by 2021, upon approval of the supporting legislation approved by the Legislative Council;
* Taking forward the trial implementation of self-driving vehicles in the West Kowloon Cultural District and other regions;
* Encouraging transportation service providers to adopt e-payment for transportation and focusing on system reliability, client orientation, and operational efficiency;
* Encouraging transportation service providers to share their service data with relevant authorities;* Developing technologies to deter inappropriate use of load/unload zones and illegal parking starting in 2018;
* Experimenting with crowd control systems to enable the detection of pedestrian and vehicular traffic during large-scale activities;
* Publishing instant traffic information about licensed buses via mobile devices by 2018 and installing real-time traffic screens at transportation interchanges and over 1300 bus stations by 2021;
* Establishing new types of on-street parking meters that can provide available space information in real-time and allow for multiple forms of payment, including app-based remote payment, from 2019 or 2021;
* Encouraging existing public parking operators to upgrade infrastructure that can provide available parking space information in real-time and enacting feasible measures that appeal to new public parking lots to be equipped with this capability.