esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Artificial Neural Network (ANN) is a valuable and well-established inversion technique for the estimation of geophysical parameters from satellite images. After training, ANNs are able to generate very fast products for several types of applications. Satellite remote sensing is an efficient way to detect and map strong earthquake damage for contributing to post-disaster activities during emergency phases. This work aims at presenting an application of the ANN inversion technique addressed to the evaluation of building collapse ratio (CR), defined as the number of collapsed buildings with respect to the total number of buildings in a city block, by employing optical and SAR satellite data. This is done in order to directly relate changes in images with damage that has occurred during strong earthquakes. Furthermore, once they have been trained, neural networks can be used rapidly at application stage. The goal was to obtain a general tool suitable for re-use in different scenarios. An ANN has been implemented in order to emulate a regression model and to estimate the CR as a continuous function. The adopted ANN has been trained using some features obtained from optical and Synthetic Aperture Radar (SAR) images, as inputs, and the corresponding values of collapse ratio obtained from the survey of the 2010 M7 Haiti Earthquake, i.e., as target output. As regards the optical data, we selected three change parameters: the Normalized Difference Index (NDI), the Kullback-Leibler divergence (KLD), and Mutual Information (MI). Concerning the SAR images, the Intensity Correlation Difference (ICD) and the KLD parameters have been considered. Exploiting an object-oriented approach, a segmentation of the study area into several regions has been performed. In particular, damage maps have been generated by considering a set of polygons (in which satellite parameters have been calculated) extracted from the open source Open Street Map (OSM) geo-database. The trained ANN has been proposed for the M6.0 Amatrice earthquake that occurred on 24 August 2016, in central Italy, by using the features extracted from Sentinel-2 and COSMO-SkyMed images as input. The results show that the ANN is able to retrieve a building collapse ratio with good accuracy. In particular, the fusion approach modelled the collapse ratio characterized by high values of CR (more than 0.5) over the historical center that agrees with observed damages. Since the technique is independent from different typologies of input data (i.e., for radiometric or spatial resolution characteristics), the study demonstrated the strength of the proposed approach for estimating damaged areas and its importance in near real time monitoring activities, owing to its fast application.  
earthquake; damage assessment; neural networks; satellite data; SAR; Sentinel-2 2017; 7, 781; doi:10.3390/app7080781
# A Force Measurement Method Based on Flexible PDMS Grating
## 3 Force measurement  
### Measurement Principle  
The force measurement method is based on the diffraction property of the grating where the diffraction spot position can be calculated by the grating equation [22]. As shown in Figure 4, the laser light is normally incident to the PDMS grating, and the diffraction light spots are projected on to a screen with coordinate mark. The PDMS grating is stretched with an external force perpendicular to grating line. When the stretching force increases, the grating line-spacing simultaneously increases and the diffraction light spot shifts. By capturing the light spot shift using a camera, the relationship between light spot shift and stretching force can be established.  
First, according to the grating equation, the relationship of the grating line-spacing and laser wavelength \(\lambda\) is denoted as,  
\[d\sin\theta=m\lambda=\lambda \tag{1}\]  
where \(d\): grating line-spacing; \(\theta\): diffraction angle; m: diffraction order--here the 1\({}^{\text{st}}\) order is considered (m=1); \(\lambda\): laser wavelength.  
Second, from Figure 4, the geometric relationship is shown as follows,  
\[s=L\tan\theta \tag{2}\]  
Figure 4: Force measurement principle based on PDMS grating.  
Figure 3: The PDMS grating profile: (a) camera photo (b) micro pattern observed by SEM  
where \(L\): the distance between the PDMS grating and the screen; \(s\): the position of the 1 st order light spot on the screen.  
Third, the deformation of the PDMS grating follows Hooke's law as it is regarded as an ideal elastic material,  
\[E=\sigma/\varepsilon \tag{3}\]  
where E: the elastic modulus; \(\sigma\): the stress; \(\varepsilon\): the strain.  
The stress \(\sigma\) is calculated by,  
\[\sigma=F/A \tag{4}\]  
where \(A\): cross-sectional area of the sample; \(F\): force in the direction of tension.  
As the grating is homogeneous, the strain \(\varepsilon\) of the sample is equal to the extension ratio of each grating line,  
\[\varepsilon=\Delta d/d \tag{5}\]  
where \(\Delta d\) denotes the variation of the grating line-spacing.  
From Equations (3), (4), and (5), the following relationship can be derived,  
\[\frac{\Delta d}{d}=\frac{F}{EA} \tag{6}\]  
Fourth, infinitesimal analysis of Equation (1) and (2) is done,  
\[\cos\Delta\theta=-\frac{\lambda}{d^{2}}\Delta d \tag{7}\]  
\[\Delta s=\frac{L}{\cos^{2}\theta}\Delta\theta \tag{8}\]  
where \(\Delta\theta\): the variation of the diffraction angle; \(\Delta s\): the light spot shift.  
Finally, the relationship of F and \(\Delta s\) can be derived as follows,  
\[F=-EA\frac{d\cos^{3}\theta}{\lambda L}\Delta s \tag{9}\]  
It is easy to find that the tension force \(F\) is proportional to the light spot shift \(\Delta s\), as long as \(\Delta s\) is relatively small. In other words, \(F\) can be linearly derived by measuring the light spot position.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 5 Our Privacy-Preserving Swarm Learning
### System Initialization  
#### 5.1.1 Blockchain P2P Network Construction  
\(\mathbf{MC}s=\{\mathbf{MC}_{1},\mathbf{MC}_{2},\ldots,\mathbf{MC}_{K}\}\) form blockchain-based peer-to-peer network. \(\mathbf{MCs}\) have pseudonymity, i.e., pseudo public keys \(pk_{1}^{psu},pk_{2}^{psu},\ldots,p_{K}^{psu}\), and their corresponding secret keys \(sk_{1},sk_{2},\ldots,sk_{K}\) are privately kept, respectively.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Implications** \\ \hline \(K\) & the number of medical centers \\ \(m\) & the threshold of Sharmir secret sharing \\ \(\mathbf{MC}_{1},\ldots,\mathbf{MC}_{K}\) & a set of \(K\) medical centers \\ \(pk_{k}^{psu},sk_{k}\) & pseudo public key and secret key of medical center \(\mathbf{MC}_{k}\) \\ \(\alpha\) & the security parameter \\ \(pk_{FHE},sk_{FHE}\) & the keys of CKKS cryptosystem \\ \(sk_{FHE}^{k}\) & the distributed secret keys splited from \(sk_{FHE}\) \\ \(key_{kl}\) & the symmetric encryption key between medical center \(\mathbf{MC}_{k}\) and \(\mathbf{MC}_{l}\) \\ \(\mathit{Enc}_{key_{l}}(\cdot)\), \(\mathit{Dec}_{key_{l}}(\cdot)\) & the symmetric key encryption or decryption algorithm using \(key_{kl}\) \\ \(\mathbf{w}_{t}\) & the weight of the global model after the \(t-1\)-th iteration of training \\ \([\![\cdot]\!]_{pk_{FHE}}\) & the cipher-text generated by CKKS algorithm \\ \(v\) & the size of decryption set \(DS\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of notations in PBSL.
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 1 Introduction
### Organization  
In Section 2, we present the necessary preliminaries for this work, including the foundational security assumption (the LWE problem), Shamir secret sharing, and fully homomorphic encryption, as well as the definitions and properties of commitment schemes and non-interactive zero-knowledge (NIZK) proofs. Section 3 introduces the core building blocks of our scheme, including the basic structure of TMFHE, the share resharing technique, and the oracle query. Section 4 provides a detailed description of the complete construction of the proposed VTMFHE scheme, covering the verifiable distributed key generation protocol, the distributed threshold decryption protocol, and the NP relations of NIZK. Section 5 offers a systematic analysis of the scheme's correctness, security, and performance, and presents a simulation-based hybrid experiment proof of active security. Finally, Section 6 concludes the paper by summarizing the main contributions and discussing potential directions for future research.
## 3 Results and Discussion
### Pollution Identification Based on ERT
At the intersection of ML-III and ML-IV, the position of ML-III was about 10 m; the resistivities of the two were similar, which proved the accuracy of the ERT. The surface resistivity on the east side of the ML-IV profile was relatively low and the resistivity in the area below 2 m underground increased again. At 50-55 m, there was a high-resistance anomaly area where the resistivity exceeded 200 \(\Omega\)-m. The abnormal resistivity of this area could be caused by a high concentration of pollution.
## 3 Problem Formulation
### Vectorization  
In the vectorization step, the matrices \(\mathbf{S}\) and \(\mathbf{X}\) are vectorized by stacking all of their respective column vectors into single column vectors. Correspondingly, matrices \(\mathbf{A}(\Theta)\) and \(\mathbf{A}\Big{(}\widehat{\Theta}\Big{)}\) are also updated. This is given by  
\[\begin{split}&\mathbf{\bar{s}}=\text{vec}(\mathbf{S})\\ &\mathbf{\bar{x}}=\text{vec}(\mathbf{X})\\ &\mathbf{\overline{A}}(\Theta)=\mathbf{I}_{N}\otimes\mathbf{A}( \Theta)\\ &\mathbf{\overline{A}}\Big{(}\widehat{\Theta}\Big{)}=\mathbf{I}_ {N}\otimes\mathbf{A}\Big{(}\widehat{\Theta}\Big{)}.\end{split} \tag{8}\]  
The CM constraint is given in terms of \(\mathbf{\bar{s}}\) as  
\[|\mathbf{\bar{s}}(i)|=1,i=1,2,\cdots,MN \tag{9}\]  
which is, equivalently, given by  
\[\mathbf{\bar{s}}^{T}\mathbf{E}_{i}\mathbf{\bar{s}}=1,i=1,2,\cdots,MN \tag{10}\]  
where  
\[\mathbf{E}_{i}(m,n)=\left\{\begin{array}{ll}1,&m=n=i\\ 0,&otherwise\end{array}\right. \tag{11}\]  
where \(0\leq m,n,i\leq MN\). At the end of the vectorization step, the problem Equation (7) can be expressed as  
\[\begin{split}\underset{\mathbf{\bar{s}}}{\text{minimize}}& \mathbf{\bar{s}}^{H}\mathbf{\overline{A}}\Big{(}\widehat{\Theta}\Big{)} \mathbf{\overline{A}}^{H}\Big{(}\widehat{\Theta}\Big{)}\mathbf{\bar{s}}\\ \text{subject to}&\mathbf{\overline{A}}^{H}(\Theta)\mathbf{\bar{s}}= \mathbf{\bar{x}}\\ &\mathbf{\bar{s}}^{T}\mathbf{E}_{i}\mathbf{\bar{s}}=1,i=1,2, \cdots,MN.\end{split} \tag{12}\]
## 4 Discussion
### Effects of Grazing Management  
The relationship between ANHP and iAPAR was insensitive to grazing management treatments applied at our semi-arid shortgrass steppe study site. Importantly, we investigated differences in spatio-temporal grazing patterns (continuous grazing vs. pulse grazing) at the same stocking rate, as well as differences in within-year grazing intensity (none, moderate, and heavy). In all cases, a given plant community type produced roughly the same amount of ANHP per unit of iAPAR. This result is powerful because it means managers can feasibly use satellites to estimate herbaceous production across broad regions of the shortgrass steppe, even if short-term grazing management details are unknown or changing for many pastures.  
We note that our analysis excluded sites for which grazing management differences have been in place long enough to actually shift plant community type. In the shortgrass steppe, this type of plant community shift in response to sustained grazing intensity treatments can take over 30 years to manifest [5; 40]. Our ecosite results suggest that the ANHP-iAPAR relationship is indeed sensitive to plant community type. Thus, we would hypothesize that long-term, sustained grazing treatments may eventually lead to shifts in the ANHP-iAPAR predictive relationship via their effects on the plant community.  
We expect that the ANHP-iAPAR relationship might be more sensitive to grazing in mesic systems with higher production and more regrowth capacity for at least two reasons. First, it is harder to estimate production using traditional ground-based methods in the presence of ample regrowth. Unless production is estimated multiple times within the growing season via movable cages (e.g., [6; 41; 42]), within-season over-compensation can alter the relationship between caged (ungrazed) and uncaged (grazed) productivity, and this bias could affect efforts to estimate caged production using satellite imagery of uncaged areas. Second, and more importantly, grazing may have larger within-season effects on vegetation structure and reflectance in more productive systems. For example, in systems with dense, overlapping canopies, it is possible that the removal of upper canopy layers via grazing could expose lower canopy layers with different APAR properties. Our analysis was unable to examine the effects of season-long heavy grazing on the more productive Sandy Plains or Salt Flats ecosites, which could shed some light on this question. Overall, we recommend that future work focus on evaluating empirical relationships between ANHP and iAPAR for a broader variety of management, ecological, and climatic conditions.
# Seismic Behavior of RC Beam Column Joints with 600 MPa High Strength Steel Bars
## 2 Experimental Investigation  
### Description of Specimens  
In the experimental investigations, joint specimens originated from five-story frame structures with different site categories, seismic fortification intensities, and anti-seismic grades, designed with Chinese Codes GB50010-2010 [23] and GB50011-2010 [29]. Moreover, the sizes of beam and column sections are kept as close as possible to the ones in the actual frame to avoid the influence of size effects. The dimensions of beams and columns are \(250\times 500\times 1500\) mm and \(450\times 450\times 2500\) mm, respectively.  
In the beams, the main longitudinal reinforcements whose nominal yield strength is 600 MPa are 20 mm and 16 mm in diameter with a 25 mm concrete cover. HRB400 grade bars are used as stirrupsand longitudinal reinforcement in the columns. Considering that the resistance of cracking and shear behaviors can be enhanced by closed stirrups in reinforced concrete frame beams and columns, they are put in place with the spacing of 200 mm. Differently, for the joint region, the spacing is reduced to 50 mm and 100 mm for columns and beams, respectively. The grades of the concrete compressive strength in every specimen are C30, and the average cubic compressive strength and elastic modulus of concrete are 24.1 MPa and 27,400 MPa, respectively. The specimens were labeled with QD1, QD2, QD3, BD1, BD2, and BD3 in Table 1, and the details of QD1 and BD1 are shown in Figure 1. The steel bars are denoted by D and F, which represent grade HRB400 and HRB600 steel bars, respectively. Also, six specimens that are tested under reversed cyclic loading are designed with different axial compression ratios and longitudinal reinforcement ratios. The different parameter variations are described in Table 2.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
### Related Work  
This subsection provides a concise summary of each study's key findings and contributions, offering a broad overview of the current state of smart grid cybersecurity research. Our work contributes to the evolving field of smart grid cybersecurity, building upon the foundational insights provided by recent surveys and reviews. While [PERSON] et al. [2] categorize cyber threats and propose future research directions, [PERSON] et al. [3] explore machine-learning-based intrusion detection. In contrast to [PERSON] et al. [4], who focus on the integration of advanced metering infrastructure (AMI), our study also focuses on novel security protocols for real-time data protection. Addressing gaps identified by [PERSON] et al. [5] regarding communication vulnerabilities, we propose adaptive encryption techniques to enhance system resilience. Moreover, unlike [PERSON] et al. [6], who emphasize data privacy challenges, our research integrates robust encryption algorithms to safeguard sensitive information. Building on [PERSON] et al.'s [7] mitigation strategies, our approach leverages machine learning for anomaly detection, enhancing proactive defense mechanisms. Finally, similar to [PERSON] et al. [8], we emphasize cyber-physical situational awareness, innovating in response strategies to combat sophisticated operational technology attacks. In summary, our study presents an updated and advanced version of smart grid cybersecurity, integrating cutting-edge technologies and methodologies to address emerging threats and vulnerabilities. It distinguishes itself by offering a targeted synthesis of the most pressing threats in contrast to broader surveys, which often dilute technical specificity.
## 1 Introduction  
In recent years, urban air mobility (UAM) has significantly gained the attention of smart citizens for providing daily life services such as package delivery, surveillance, agriculture monitoring, passenger flying cars, and rescue management. With the increasing usage of civilian UAM, the risk of cyberattack and theft is also growing exponentially due to the utilization of public network infrastructure [1, 2, 3, 4, 5, 6, 7]. However, UAM safety can be increased by proactively monitoring UAM behavior from their operational control messages and employing the capability of analyzing such behavior. In particular, the UAM service provider must require an analytical tool that can intuit the cause of safety threats and visualize the difference between normal and abnormal activities from UAM operational messages in real time. One suitable way of developing this is by unitizing the notion of lightweight artificial intelligence (AI) and inferring the outcomes using statistical analysis so that UAM service providers can take necessary safety measures in advance for protecting UAM from cyberattacks, losses, and crashes.
## 2 Materials and Methods
### Characterization of the Starting Materials
#### 2.2.2 X-ray Diffraction Analysis  
The as-poured materials, after being ground in powders, were investigated from a structural viewpoint through wide-angle (2\(\theta\) within 10-70\({}^{\circ}\)) X-ray diffraction analysis (XRD); a \(\chi\)' Pert Philips diffractometer (PANalytical, Eindhoven, The Netherlands; Cu anode, K\(\alpha\) radiation with wavelength \(\lambda\) = 15.405 nm, \(\Delta\)(2\(\theta\)) = 0.02\({}^{\circ}\), fixed counting time of 1 s per step) operating at 40 kV and 30 mA was used. Crystalline phases were identified by making use of \(\chi\)'Pert HighScore software (2.2b) equipped with the PCPDFWIN database ([[http://pcpdfwin.updatestar.com/](http://pcpdfwin.updatestar.com/)]([http://pcpdfwin.updatestar.com/](http://pcpdfwin.updatestar.com/))).
## 2 Related Works
### Evacuation Route Planning  
[PERSON] analyzed the progress of current research on underground mine fire, constructed a priority-based network planning algorithm, and proposed a new method for an underground emergency evacuation management system, aiming to maximize the efficiency of evacuation exit utilization [14]. Using oblique photogrammetry technology, [PERSON] generated an accurate 3D model scene of emergency shelters, providing an effective basis, in terms of space, for positioning emergency facilities to help emergency command and decision makers [15]. [PERSON] proposed a disaster evacuation route guidance system based on map API, which can identify flood situations in forest areas, recreational forests, and parks. This system calculates an evacuation route based on map API and sends it to the nearest shelter, to users, helping them safely evacuate [16]. [PERSON], taking one subway station as an example, analyzed the fire development situation in different locations of the station, studied the safe evacuation of subway station personnel, and optimized the layout of the public area in the station based on the actual situation, thus improving evacuation efficiency [17].  
From the aforementioned literature, it can be observed that current research on safe emergency evacuation mainly focuses on detailed analyses of the on-site environment, with less attention given to individual differences (such as varying mobility speeds) and detailed self-organizing phenomena during the evacuation process (such as assisting behaviors among personnel); however, these factors are crucial and cannot be ignored in safe evacuation, hence the need for further research.
## 2 System and Mathematical Models  
### System Model  
In a double-node CCFD system, the near-end and far-end nodes can be defined so that both sides of the communication can simultaneously transmit and receive data in the same frequency band [30]. The transceiver model of the system is shown in Figure 1. An orthogonal dual-tap direct RFCC structure is adopted and uses a near-end transceiver as an example. At the transmitting end, the transmitted signal, through digital modulation, transforms to \(s(n)\), and then the RF output signal \(s(t)\) is obtained through digital-to-analog conversion (DAC), up-conversion, and power amplification (PA), and fed into the transmitting antenna. At the receiving end, the received signal \(r_{0}(t)\) includes not only the desired signal \(r_{u}(t)\) from the far-end nodes, but also the high-power SI signal \(r_{SI}(t)\) from the near-end nodes and the additive white Gaussian noise (AWGN) n(t). Therefore, the full-duplex receiver needs to suppress the SI signal in the RF domain after obtaining the received signal.  
For the transmitter of the near-end transceiver in the full duplex system, the output s(t) can be expressed as follows:  
\[s(t)=\sqrt{2P_{n}}s(n)\cos(2\pi f_{c}t+\varphi_{n}) \tag{1}\]  
where \(P_{n}\) is the near-end transmitted power, \(f_{c}\) is carrier frequency, and \(\varphi_{n}\) is the initial phase. According to the transceiver model of the system, the signal received by the full duplex communication node entering the near-end is obtained as  
\[r_{0}(t)=r_{SI}(t)+r_{u}(t)+n(t). \tag{2}\]  
The near-end SI signal can be specifically expressed as follows:  
\[r_{SI}(t)=h_{SI}\ (t)*s(t) \tag{3}\]  
\[h_{SI}\ (t)=k_{n}e^{-2 jp_{n}f_{c}t_{n}} \tag{4}\]  
Figure 1: Near-end transceiver model for the Co-frequency Co-time Full Duplex (CCFD) system.  
where \(h_{SI}\left(t\right)\) is the impulse response of the SI channel, \(k_{n}\) and \(t_{n}\) are the amplitude attenuation factor and time delay of the SI channel, separately. Therefore, the SI signal can be represented as  
\[r_{SI}(t)=h_{SI}\left(t\right)*s(t)=k_{n}s(t-t_{n}). \tag{5}\]  
In this paper, the SI signal in the RF domain of the near-end receiver of the full-duplex system is estimated using the DTV-LMS algorithm. The implementation process of this algorithm is described in detail in Section 3. The residual SI signal after SI cancellation in the RF domain (i.e., the error signal) can be expressed as  
\[e(t)=r_{0}(t)-r_{1}(t)=r_{SI}(t)+r_{\rm u}(t)+n(t)-r_{1}(t) \tag{6}\]  
where \(r_{1}(t)\) is the estimated SI signal (i.e., the reconstructed SI signal).