esa-sceva/satcom-chunk-collection huggingface dataset:
# A Momentum-Based Local Face Adversarial Example Generation Algorithm
## 4 Our Method
### Loss Functions  
As above-mentioned, this algorithm optimizes the \(C\Big{(}x,x^{adv}\Big{)}\) function in the local region. For the targeted attack and non-targeted attack, the relationship between the clean face image \(x\) and the three target images \(x^{tar}\) and the adversarial example image \(x^{adv}\) was compared.  
1. For the non-targeted attack, an adversarial example \(x^{adv}\) was generated for the input image \(x\) so that the difference between them was as large as possible. When the difference was larger than the threshold value calculated by the deep detection model, the attack was successful; on the other hand, for the targeted attack, the generated adversarial example \(x^{adv}\) needed to be as similar as possible to the target image \(x^{tar}\). The loss function \(\mathcal{Loss}_{1}\) is shown as Equation (12). \[\mathcal{Loss}_{1}=\alpha\cdot\cos\Big{(}f(x),f\Big{(}x^{adv}\Big{)}\Big{)}-(1 -\alpha)\cdot\cos\Big{(}f\Big{(}x^{adv}\Big{)},f\big{(}x^{tar}\Big{)}\Big{)}\] (12) where \(cos(\cdot,\cdot)\) is the cosine similarity of the feature vector calculated by Equation (10); \(\alpha\) takes the value of 0 or 1, representing the non-targeted attack and targeted attack, respectively.
2. The perturbation size is constrained by the \(L_{2}\) norm, thus ensuring that the visibility of the perturbation is kept within a certain range when an effective attack is implemented. The loss function in this section constrains the perturbation after the restriction as follows. \[\mathcal{Loss}_{2}=L_{2}(mask\odot r)\] (13) where \(r\) is the perturbation. The mask is that generated from the first face image of the face pair to restrict the perturbation region. It is a [0, 1] matrix scaled to the same size as the image. The \(\odot\) symbol indicates the dot product operation between the elements.
3. The \(TV\) is used to improve the smoothness of the perturbation through Equation (14), and the loss function of this part also deals with the perturbation after restriction, as follows. \[\mathcal{Loss}_{3}=TV(mask\odot r)\] (14) In summary, for the above targeted and non-targeted attacks, the loss function is minimized by solving the following optimization problem of Equation (15), which can generate the final adversarial perturbation \(r\) : \[\underset{r}{min}\mathcal{Loss}=\underset{r}{min}(\lambda_{1}\mathcal{Loss}_{1 }+\lambda_{2}\mathcal{Loss}_{2}+\lambda_{3}\mathcal{Loss}_{3})\] (15)  
The hyperparameters \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are used to control the relative weights of the perturbation losses. The correlation coefficients of the two regular term loss functions \(\mathcal{Loss}_{2}\) and \(\mathcal{Loss}_{3}\) are gradually reduced as the number of iterations increases.
## 3 Results and Discussion
### Characteristics of Pollution Episodes during Summer and Winter
Figure 8 shows the MODIS images for the summer episodes (14, 15, and 21 August) and winter episodes (December 19 and 26), and the transport pathways of the air masses for 14-15 August, 22-24 August, 19-20 December, and 26 December, when the concentrations of PM\({}_{2.5}\) and its OC and secondary inorganic species were highly enhanced. The 72 h backward trajectories of air masses arriving at the site were calculated with the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model [75] for the heights of 500, 1000, and 1500 m above ground level at 0900 UTC on each day. The MODIS images taken on those days show layers of haze lingering over regions of eastern China and just west (over the Yellow Sea) of the Korean peninsula. Additionally, the air masses for the pollution periods passed over layers of haze distributed in eastern China and the Yellow Sea prior to reaching the sampling site. As a result, the MODIS images and the backward trajectories of air masses support the notion that the pollution episodes that occurred at the site during the summer and winter periods were largely affected by regionally transported haze pollution. Therefore, we conclude that increases in the PM\({}_{2.5}\) during summer were greatly affected by long-range transported SO\({}_{4}\)[2], while the PM\({}_{2.5}\) increases during the winter were associated with local pollution as well as the regional transport of aerosols. Figure 8: MODIS images and transport pathways of air masses arriving at the sampling site during the summer and winter pollution episodes. Red, blue, and green lines on air-parcel back-trajectories represent the pathways of air masses at heights of 500, 1000, and 1500 m AGL (above ground level).
## 2 Research Scope, Methodology, Bibliometric, and Citation Analysis
### Connectivity  
An IoT-based composting system demands a robust and dependable network architecture to facilitate a seamless transmission of composting data from different sensors to a centralized monitoring system [40]. These mandates utilize reliable connectivity methods such as Wi-Fi networks, cellular networks, or any other mode of IoT connectivity. The effective transmission of the real-time composting sensor data to a central monitoring network depends on uninterrupted connectivity [41]. Depending on the requirements in composting facilities and environmental conditions in which the IoT-based composting system is deployed, the type of connectivity needed will differ [42]. For example, Wi-Fi networks provide a stable connection to a small-scale composting facility but might need additional access points for large-scale facilities. On the other hand, cellular networks might incur recurring subscription costs for connectivity services [43]. Therefore, operators and planners of composting facilities can use connectivity technologies such as Long-Range Wide Area Network (LoRaWAN) to gain long-range communications with relatively low power consumption [44]. The selection of suitable connectivity assures the facility's timely and accurate transmission and communication.
## 3 Environment Characterization
### Proposed Methodology  
To ensure a reliable link between the SM and DC in a well-defined area, the choice of the communication technology, either RF or NB-PLC, should be based on several parameters. These parameters can be set to evaluate and compare the performance of each technology. Figure 8 presents the proposed methodology to study the environmental impacts of RF and PLC for AMI. The solution design in this paper focuses on the definition of the characteristics of parameters of RF and NB-PLC channels for distinct geographical areas. In fact, the design concerns the distance between the SM and DC, the number of paths for wireless links, the length of the wired link, and the noise model of each link.  
The methodology is summarized in four steps:  
* The choice of RF and PLC standards in compliance with the AMI-NAN system: The authors study the choice of Wi-SUN for RF and G3-PLC for NB-PLC. In fact, for the RF link, Wi-SUN allows utilities to monitor and control the electrical grid more efficiently, leading to improved energy management and reliability. Moreover, it supports mesh networking, allowing devices to communicate with each other via multiple paths. For the PLC link, IEEE 1901.2 is a standard for narrowband orthogonal frequency division multiplexing (NB-OFDM) PLC technology. The use of OFDM, which is known for its ability to handle multipath propagation and mitigate the effects of noise and interference, makes communication robust in challenging environments.
* The choice of models for RF and PLC channels: The authors outline the key characteristics of wireless and narrowband power line systems. A bottom-up approach is used for the NB-PLC channel. For the attenuation of the RF channel, the Rayleigh fading model is adopted as it shows the effects of small-scale fading of non-line-of-sight reproduction in wireless communications.
* The environment model design: The authors focus on the environment model design to characterize the RF and NB-PLC channels in distinct geographical environmentsrural, urban, and industrial. These characteristics play a crucial role in shaping the design and effectiveness of the AMI system.
* Analysis of performances regarding the BER and link selection for each environment: The authors compare NB-PLC and RF performances for the following three environments, rural, urban, and industrial.
# Fast 3D Analytical Affine Transformation for Polygon-Based Computer-Generated Holograms
## 1 Introduction
They have defined a pseudo inverse matrix to map the spatial triangle with a three-dimensional right primitive triangle (which has an analytical spectrum expression). The major issue of the technique is the inaccuracy caused by the inversion of the pseudo matrix. [PERSON] et al. [28; 29] have derived a correct analytical expression in the context of 2D affine transformation [23] and proposed a method to achieve 3D transformation from an arbitrary triangle to a primitive triangle through a 3D rotation of the arbitrary triangle and the use of a 2D affine matrix [29]. Although this method avoids the time-consuming calculation of the pseudo inverse matrix, the process is rather complex. [PERSON] et al. [8] also have proposed a method called the fast 3D affine transformation (F3 DAT) method by translating the primitive triangle to avoid the use of the pseudo inverse matrix to improve the computational efficiency. In the wave-optics based approach, it can provide accurate depth cues. However, view-dependent properties' rendering requires additional calculations. Rendering technology of computer graphics makes the reconstructed 3D scene more realistic [36; 37; 38]. [PERSON] et al., have discussed the methods of shading and texturing [20; 21; 22] and created a large-scale full-color CGH successfully [39]. Subsequently, many methods have been proposed to improve the quality of reconstructed images through texturing [36; 37], shading [22; 38], and resolution improvement [40]. Additionally, the use of the silhouette method for hidden surface removal has been described [41]. In this study, based on the three-dimensional affine theory [25; 29], we present a fast 3D analytical affine transformation (F3 DAAT) method to obtain a full-analytical spectrum of a spatial triangle. We obtain the analytical expression of a 3D affine matrix algebraically, and the spectrum of tilted triangles can be obtained directly. Compared with previous methods, we show improved computational efficiency. In addition, in order to improve the image quality, we add flat shading to make the reconstructed image more realistic. We also demonstrate reconstructed 3D objects composed of tens of thousands of polygons numerically as well as the use of a spatial light modulator (SLM) for optical reconstruction. In Section 2, we briefly introduce the basic principle of the polygon-based method. In Section 3, we present the theory of F3 DAAT. In Section 4, we demonstrate the reconstruction of 3D objects numerically and optically. The computational efficiency is also compared with previous methods. The results of adding flat shading are also illustrated.
## 5 Results and Discussion
### Qualitative Performance
Specifically, the higher MSSIM values indicate that Hyper-CycleGAN excelled at maintaining structural consistency in the images, including accurately reproducing brightness, contrast, and local texture details. Furthermore, the higher MPSNR values highlight its effectiveness in reducing noise interference and preserving pixel-level image quality. Beyond individual metrics, Hyper-CycleGAN demonstrated comprehensive advantages in reproducing spectral-spatial distributions with high accuracy. This capability ensured that the generated hyperspectral images closely aligned with real data in terms of visual quality and structural fidelity, thereby setting a new benchmark in the field of hyperspectral image synthesis.
## 2 Concept
Thus, for positive chirping, where shorter wavelengths have less delays than longer ones, the dependence of the delay on the center frequency \(\omega_{0}\) of a pulse propagating through the LC-FBG is given by:  
\[V_{in}(f)\longrightarrow\exp\Bigl{[}-jT(f-f_{0})^{2}/2B\Bigr{]}\cdot\text{rect }[(f-f_{0})/B] \tag{2}\]0  
where:  
\[V_{in}(f)\longrightarrow\exp\Bigl{[}-jT(f-f_{0})^{2}/2B\Bigr{]}\cdot\text{rect }[(f-f_{0})/B] \tag{2}\]1  
and while ignoring \(h(\bullet)\), this yields:  
\[V_{in}(f)\longrightarrow\exp\Bigl{[}-jT(f-f_{0})^{2}/2B\Bigr{]}\cdot\text{rect }[(f-f_{0})/B] \tag{2}\]2  
The fact that the device has frequency dependence delay means it has dispersion, so its \(\beta_{2,LC-FBG}\
eq 0\) gives rise to pulse distortion. On the other hand, a chirped-and-sampled FBG (CS-FBG) dispersion compensator is a short device that offers the same amount of constant delay to the angular frequencies at the center of the CS-FBG channels \(\omega_{0}=\omega_{k}=\omega_{initial}+k.\Omega_{CS-FBG}\), where \(\omega_{initial}\) is the first central angular frequency carrier in the ITU grid, \(k\in[1,2,\dots,K]\), and \(\Omega_{CS-FBG}\) is the ITU grid frequency spacing. Thus, the CS-FBG effect on the group delay at the frequencies \(\omega_{k}\)'s could be neglected as it is independent of \(\omega_{k}\). Also, the device has a pre-designed dispersion at the frequencies belonging to the ITU grid opposing those of the LC-FBG:  
\[V_{in}(f)\longrightarrow\exp\Bigl{[}-jT(f-f_{0})^{2}/2B\Bigr{]}\cdot\text{rect }[(f-f_{0})/B] \tag{2}\]3  
Therefore, the overall optical transfer function of the concatenation of the two FBGs at the \(\omega_{k}\)'s is \(H_{total}(\omega)|_{\omega_{k}}=\exp[-j\omega_{k}\tau_{LC-FBG}(\omega_{k})]\), which is simply a delay-only element with no dispersion for carrier frequencies populating the ITU grid. After passing through the amplifier with the gain (G) and selecting the frequency of the desired delayed signal in the filter, the output selected signal becomes:  
\[V_{in}(f)\longrightarrow\exp\Bigl{[}-jT(f-f_{0})^{2}/2B\Bigr{]}\cdot\text{rect }[(f-f_{0})/B] \tag{2}\]4  
So by selecting the signal on the frequency \(\omega_{k}\) modulated on the \(mth\) comb carrier, while \(\Omega_{R}=\Omega_{CS-FBG}\) holds, one can define the amount of added delay using the system using Equation (13). And the selected optical signal in the time domain becomes the inverse Fourier transform of \(E_{out}(t)\simeq\mathcal{F}^{-1}\{E_{out}(\omega)|_{\omega=\omega_{k}}\}\).
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 3 Proposed Superframe Structures
### Proposed Scheme
#### 3.1.1 Handshaking Period  
Each session in a communication round starts with a handshaking period. During the handshaking period, the \(RSU\) broadcasts the announcement message (\(RSU_{Amm}\)) to all vehicles in its communication range. All vehicles must keep their radios ON to receive the announcement message. Vehicular nodes select the \(RSU\), whose announcement message is heard with the largest signal strength. In the case of a tie, the next \(RSU\) is selected. After that, vehicles reply with a join request (\(Join_{Req}\)) message. This message consists of the control message, vehicle's address, \(RSU\) address and frame check sequence. After that, \(MN\) waits for some time to receive all the \(Join_{Req}\) and then calculates the total number of vehicle requests. \(RSU\) computes a unique 1-byte short address for all the associated member nodes and for itself. Therefore, a maximum of 255 member nodes can be associated with a single \(RSU\).  
After receiving all the \(Join_{Req}\), \(RSU\) assigns the separate control slot to each member node and sends the control slot allocation (\(Alloc_{CS}\)) message to all members nodes using CSMA. This message consists of the control byte, \(RSU\)'s short address, nodes' extended and short address, nodes' assigned control slot number, start time of data slot announcement period, and frame control sequence.
## 3 Test Results and Discussion
### Strength Characteristics
#### 3.1.2 Effect of Dynamic Loading Rate under the Same Static Pre-Stress
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Case**} & \multirow{2}{*}{\begin{tabular}{c} **Static** \\ **Stress/MPa** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Dynamic** \\ **Stress/MPa** \\ \end{tabular} } & \multirow{2}{*}{**SUCS/MPa**} & \multirow{2}{*}{
\begin{tabular}{c} **Elastic Mod-** \\ **values/GPa** \\ \end{tabular} } & \multirow{2}{*}{**Ue/((kJ/m\({}^{3}\))**)**} & \multirow{2}{*}{**Ud/(kJ/m\({}^{3}\))**} & \multirow{2}{*}{**U/(kJ/m\({}^{3}\))**} \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline D1 & 30.84 & \(2\times 10^{-4}\) & 78.20 & 15.59 & 196.2 & 88.9 & 285.1 \\ D2 & 38.55 & \(2\times 10^{-4}\) & 82.10 & 18.74 & 179.8 & 40.3 & 220.1 \\ D3 & 46.26 & \(2\times 10^{-4}\) & 77.13 & 17.62 & 168.8 & 78.1 & 246.9 \\ D4 & 53.97 & \(2\times 10^{-4}\) & 80.57 & 23.05 & 140.8 & 101.3 & 242.1 \\ D5 & 30.84 & \(2\times 10^{-3}\) & 92.53 & 19.00 & 225.3 & 43.3 & 268.6 \\ D6 & 38.55 & \(2\times 10^{-3}\) & 87.88 & 20.72 & 186.4 & 32.3 & 218.7 \\ D7 & 46.26 & \(2\times 10^{-3}\) & 83.26 & 20.07 & 172.7 & 86.6 & 259.3 \\ D8 & 53.97 & \(2\times 10^{-3}\) & 77.62 & 17.61 & 171.1 & 156.4 & 327.5 \\ D9 & 30.84 & \(1\times 10^{-2}\) & 103.25 & 21.63 & 246.4 & 73.6 & 320.0 \\ D10 & 38.55 & \(1\times 10^{-2}\) & 89.89 & 22.2 & 182.0 & 60.9 & 242.9 \\ D11 & 46.26 & \(1\times 10^{-2}\) & 85.63 & 21.71 & 168.8 & 162.6 & 331.4 \\ D12 & 53.97 & \(1\times 10^{-2}\) & 77.00 & 21.24 & 139.6 & 101.2 & 240.8 \\ D13 & 30.84 & \(2\times 10^{-2}\) & 107.82 & 22.62 & 257.0 & 37.5 & 294.5 \\ D14 & 38.55 & \(2\times 10^{-2}\) & 96.05 & 23.69 & 194.7 & 59.9 & 254.6 \\ D15 & 46.26 & \(2\times 10^{-2}\) & 90.00 & 22.53 & 179.7 & 86.3 & 266.0 \\ D16 & 53.97 & \(2\times 10^{-2}\) & 81.90 & 21.41 & 156.7 & 61.8 & 218.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental results of superimposed static and dynamic loading rates. Under the same static pre-stress, the variation of elastic modulus with the dynamic loading rate shows a similar trend with that of the SUCS, as shown in Figure 7b. The relationship between the elastic modulus and the dynamic loading rate can be expressed as:  
\[E_{1}=28.89+1.6\ln\dot{\epsilon} \tag{8}\]  
\[E_{2}=28.80+1.4\ln\dot{\epsilon} \tag{9}\]  
\[E_{3}=26.65+1.1\ln\dot{\epsilon} \tag{10}\]  
where \(E_{1}\), \(E_{2}\) and \(E_{3}\) are the elastic modulus when the static pre-stresses are 40% of the UCS, 50% of the UCS and 60% of the UCS, respectively, and \(\dot{\epsilon}\) is the dynamic loading rate.
## 2 Basic Theories and Principles
### Ranging Principle of ToF Cameras
It can be seen from the principle that the measurement accuracy of the time-of-flight camera does not change with the measurement distance, so the ToF depth camera is also more resistant to interference.
## 4 Empirical Study
### Experimental Results
#### 4.4.3 Model Validation and Model Testing  
Cross-validation is performed for model validation. Figures 8 and 9 respectively present the predictive error of cross-validation using Bayesian posterior probability estimation and Maximum Likelihood estimation.  
In the case of the structure learning algorithm with HC, the medians of the predictive errors with Aic, Bic, BDe and k2 are similar, and the range is relatively small. Nevertheless, there is an outlier in the predictive errors of HC.Aic, which indicates the volatility. Moreover, the predictive error of HC.k2 has the smallest range while also being most stable. In the case of the structure learning algorithm with TABU, the ranges of predictive error are obviously larger than with HC. More specifically, the TABU.BDe performs best when the structure learning algorithm is TABU.  
By comparing the medians of the predictive errors, we see that Bayesian posterior probability estimation performs better than Maximum Likelihood estimation as a whole. Moreover, HC performs better than TABU, and the HC.k2 with Bayesian posterior probability estimation performs the best.  
Then, Figure 10 presents the results of the model testing with various combinations of structure learning algorithms and score functions. All models except for TABU.Aic have an accuracy above 80%, which indicates that the risk prediction model based on Bayesian network has high reliability and accuracy. The predictive model of HC.k2 has the highest accuracy and recall, and the predictive model of HC.BDe has the highest specificity. Considering three indices of accuracy, recall, specificity, the predictive models using HC performs better than models using TABU.
## 8 Application Example: 2023 Bangladesh Flooding Season
### Affected Agriculture Areas  
As HydroSAR's AG30 agriculture extent product is not yet fully operational and implemented, we used Dynamic Land Cover Product version 3, distributed by the Copernicus Global Land Monitoring Service [36], to assess the impact of the 2023 flood season on Bangladesh's agriculture regions. This 100 m resolution product offers annual global land cover information derived from observations of the PROBA-V satellite [60]. The latest version of this product was assessed to achieve a classification accuracy of 80% on each continent [36]. We extracted the agriculture extent information from this product and resampled the extracted layer to match the posting of the HYDRO30 products for further joint analysis.  
We first derived the time series of agriculture inundation by intersecting the agriculture extent layer with the bi-weekly HYDRO30 surface water extent information. The results of this analysis are shown in Figure 18b. Similar to the water extent time series in Figure 16, we observed the minimum inundated agriculture in early April. Agriculture inundation started to increase with the onset of the monsoon and reached a maximum on 6 August 2023, where 13.5% of all agriculture lands were identified as water-covered. Inundation dropped rapidly after this date as monsoon waters began to recede.  
In flood situations, the extent of crop damage and therefore the impact on crop yield often depends on the duration of flooding. To study how long Bangladesh's flood-affected agriculture lands were inundated, we could intersect the Copernicus agriculture extent information with the TAFD layer, as shown in Figure 18a. Note that this analysis would reveal how long a certain agriculture patch was inundated but did not allow assessing when this inundation occurred.  
Figure 19 shows that 11.2% of Bangladesh's agriculture areas were inundated for 30 days or less, where 5.3% of the crop lands were inundated for between 30 and 100 days and 4.6% of Bangladesh's agriculture areas were inundated for more than 100 days. Combined with information about crop types, these data could help in the evaluation of flood impacts on food production.  
The examples shown in Section 8 provide some indication of the value of the HydroSAR service for flood analysis in the region. Beyond the retrospective studies shown here, HydroSAR data can be used a range of other applications, such as the analysis of individual flooding events or the statistical analysis of multiple years of data in support of flood mitigation.  
Figure 19: Agriculture areas covered in water as a function of flooding duration, calculated by intersecting AG with TAFD.