esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 4 Conclusions  
This paper presented three different frequency-based data-embedding mechanisms for a D2C system. By selecting the robust frequency coefficients within the DCT image, data were embedded by using predetermined embedding factors that varied according to the embedding mechanism. To preserve the original purpose of display, all embedding mechanisms were carefully designed so that none of them compromised the viewing experience of the user. The effects of the signal processing properties of transmission channel on D2C system performance were also analyzed through extensive real-world experiments, with several performance metrics that include transmission distances and angles, and data transmission in an ambient lighting environment. The experimental results presented the maximum ADR of approximately 460 bps and a robust PSNR and BER performance for all data-embedding mechanisms within the MF regions of the spectral domain image. Therefore, the proposed scheme can be a viable solution to the D2C system requiring good communication performance in a display-to-camera link while preserving the visual quality of the original image.  
Conceptualization, L.D.T., and B.W.K.; methodology, L.D.T., and B.W.K.; software, L.D.T.; validation, L.D.T.; writing--original draft preparation, L.D.T., and B.W.K.; writing--review and editing, L.D.T., and B.W.K.; supervision, B.W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2019R1A2C4069822).  
The authors declare no conflict of interests.
## 3 Security Attacks on AV Sensors: A Closer Examination of GPS Systems
### The Significance and Applicability of Sensor Security in Autonomous Vehicles  
Security breaches targeted at sensor systems in autonomous vehicles (AVs) have emerged as a significant cause for concern due to their potential to compromise the vehicle's safety and the well-being of its passengers. AVs deploy a complex array of sensors, including but not limited to cameras, LiDAR, and radar. These sensors are essential for interpreting the surrounding environment and facilitating crucial decision-making processes. The compromise of these sensor systems could result in a distorted understanding of the environment, which could, in turn, elevate the risk of accidents and safety hazards.  
Historically, there have been various demonstrations highlighting the potential of such security attacks on AV sensors. These include sophisticated spoofing attacks capable of deceiving sensors into acknowledging non-existent obstacles and jamming attacks that effectively interrupt the sensors' capabilities in accurately detecting objects. Worryingly, the equipment needed to execute these attacks can often be simple and easy to procure, such as a laser pointer or a radio transmitter, thus increasing the potential pool of attackers.  
As AVs become more commonplace in our transportation landscape, the importance of addressing the security vulnerabilities of AV sensors becomes increasingly clear. This recognition underpins the need for the development and implementation of rigorous security measures tailored to detect and neutralize attacks on AV sensors. Alongside these security provisions, it is critical to raise public awareness regarding the potential risks associated with security attacks on AVs.  
Additionally, the creation and enforcement of comprehensive standards play a critical role in ensuring the safety and security of AVs. These standards would serve to reinforce the safety framework for autonomous vehicles, managing and mitigating the risks associated with potential security breaches of their sensor systems.  
In conclusion, the potential consequences of security intrusions on AV sensors are dangerous and global, threatening the protection of both the vehicle and its occupants. As the community moves towards a future characterized by the increasing quantity of autonomous vehicles (AVs), it becomes vital to develop and implement robust security measures supported by comprehensive and extensive standards in order to ensure the ongoing safety and security of these advanced vehicles.
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 2 Materials and Methods  
In the cloud image segmentation process, shallow features have higher resolution and mainly represent information such as texture, position, and spectrum. Deep features have lower resolution and contain richer semantic information. Due to the strong feature extraction capability of CNNs, they are widely used in image segmentation tasks, such as fully convolutional networks (FCNs) [18], U-Net [14], SegNet [19], etc. This paper proposes a deep convolutional neural network based on an improved U-Net, which has an overall encoder-decoder structure. Its main architecture and key components are shown in Figure 1.  
Figure 1: The overall architecture and key components of the model proposed in this paper.
# Novelty in Intelligent Controlled Oscillations in Smart Structures
## 3 Results  
### H\({}_{2}\) Norm  
In the control theory, the H\({}_{2}\) norm or H\({}_{2}\) performance is used to evaluate the performance of a system [6; 7; 25; 26; 27; 29; 30]. Significantly, it quantifies a system's ability to reduce noise and disturbances by measuring its energy in response to white noise. The H\({}_{2}\) norm of the system is defined by taking the square root of the sum of the squares of the impulse responses. The total system power response to a unit impulse input can be represented by the H\({}_{2}\) norm. It is an indicator of the amount of energy from the input signals that are attenuated or amplified across all frequency intervals by the system. In principle, a lower H\({}_{2}\)-norm indicates higher disturbance rejection and noise attenuation capacity. Minimizing the H\({}_{2}\) norm is often a goal in the control system design, particularly in optimal control frameworks. The use of the H\({}_{2}\) criterion for controllers that perform well under different operational circumstances is widespread in practice because it combines performance with robustness [16; 17; 18; 19; 20; 21; 28].  
Controller design in control systems aims to reduce the H\({}_{2}\) norm, particularly in optimal control frameworks. Because it strikes a good balance between robustness and performance, the H\({}_{2}\) standard is a popular choice for controller design that must operate effectively across various operating conditions [16; 17; 18; 19; 20; 21; 28]. In summary, the H\({}_{2}\) norm of control theory is useful for building systems that work well on average, even with noise and disturbances. Engineers can then reduce the H\({}_{2}\) norm to develop controllers that enhance the overall robustness and energy efficiency of a system [1; 6; 7; 25; 26; 27; 29; 30; 31; 35; 36].
# A Compact Thevenin Model for a Rectenna and Its Application to an RF Harvester with MPPT
## 2 Rectenna and Its Thevenin Model
\begin{array}{c}\eta_{\rm{rect}}=\frac{P_{\rm{o}}}{P_{\rm{av}}}=\frac{V_{ \rm{oc}}V_{\rm{o}}-V_{\rm{o}}^{2}}{P_{\rm{av}}R_{\rm{T}}}.\end{array}  \tag{19}\]  
Applying the maximum power transfer theorem, maximum power is extracted from the rectenna for \(V_{\rm{o}}=0.5V_{\rm{oc}}\), which is known as the maximum power point (MPP) voltage (\(V_{\rm{MPP}}\)). From (19), the resulting efficiency is as:  
\[ \begin{array}{c}\eta_{\rm{rect,max}}=\frac{V_{\rm{oc}}^{2}}{4P_{\rm{av}}R_{ \rm{T}}}.\end{array}  \tag{20}\]  
Thus, using (16) in (20), we arrive at:  
\[ \begin{array}{c}\eta_{\rm{rect,max}}=\frac{R_{\rm{p}}}{4G_{\rm{t}}^{2}R_{ \rm{a}}+R_{\rm{p}}}\Bigg{(}1-\frac{V_{\gamma}}{\sqrt{2R_{\rm{a}}P_{\rm{av}}}} \frac{4G_{\rm{t}}^{2}R_{\rm{a}}+R_{\rm{p}}}{4G_{\rm{t}}R_{\rm{p}}}\Bigg{)}^{2}.\end{array}  \tag{21}\]  
As can be seen from (21), \(\eta_{\rm{rect,max}}\) increases with increasing \(P_{\rm{av}}\). Obviously, with no losses (\(R_{\rm{p}}=\infty\) and \(V_{\gamma}=0\)) \(\eta_{\rm{rect,max}}=1\) is obtained. On the other hand, the dependence of \(\eta_{\rm{rect,max}}\) on \(G_{\rm{t}}\) is rather more complex. In [33], an optimum value of \(G_{\rm{t}}\) was derived arising from the trade-off between the losses introduced by the coil and that due to the voltage drop of the diode. This optimum gain leads, from (16), to a particular value of \(R_{\rm{T}}\).
## 2 Theoretical Background for the Nonlinear Analysis of End-Plate Connections
### Elasto-Plastic Material Behavior of Steel  
In this study, the elasto-plastic constitutive behavior of steel is considered. This material is widely used in civil engineering applications, particularly in semi-rigid joints such as end-plate connections. Under high-intensity loading, steel components may exhibit inelastic deformations that persist after unloading. Therefore, a safe and reliable design requires the evaluation and control of such inelastic behavior. To this end, the equivalent plastic strain is employed as a scalar measure to assess the inelastic response of the connection configuration. This parameter is incorporated as a constraint in the optimization procedure to ensure that plastic deformations remain within acceptable limits.  
The total strain \(\varepsilon\) in an elasto-plastic material can be decomposed into elastic and plastic components:  
\[\varepsilon=\varepsilon^{el}+\varepsilon^{pl} \tag{4}\]  
where \(\varepsilon^{el}\) represents the elastic strain, which is associated with the stress \(\sigma^{el}\) that would occur under purely elastic conditions. The relationship between stress and elastic strain is defined through the elastic stiffness matrix \(C^{el}\) as given in [41]:  
\[\varepsilon^{el}=C^{el}\sigma^{el} \tag{5}\]  
The evolution of the plastic strain is governed by the associated flow rule, which determines the plastic strain rate \(\dot{\varepsilon}^{pl}\) as follows [42]:  
\[\dot{\varepsilon}^{pl}=\lambda\frac{\partial f}{\partial\sigma},\ \dot{\lambda}\geq 0 \ \text{if}\ f=0\ and\ \dot{f}=0,\ otherwise\ \dot{\lambda}=0 \tag{6}\]  
In this context, \(f(\sigma)\) is the yield function, which defines the boundary of admissible stress states and forms a convex surface in stress space when \(f(\sigma)=0\).  
In the finite element simulations, the equivalent plastic strain was computed and employed to assess the plastic response of the structure. This scalar measure reflects the accumulated plastic deformation within the material and is defined by the following expression [43]:  
\[\overline{\varepsilon}^{pl}=\int_{0}^{t}\sqrt{\frac{2}{3}\dot{\varepsilon}^{ pl}_{ij}\dot{\varepsilon}^{pl}_{ij}}dt \tag{7}\]  
where \(\dot{\varepsilon}^{pl}_{ij}\) is the plastic strain rate tensor, and \(t\) denotes time.  
As recommended in EN 1993-1-5 [35], a strain limit of 5% is prescribed for regions under stress. In alignment with this provision, a corresponding constraint was introduced into the optimization procedure within this study.  
Figure 2: Illustration of the joint rotation mechanism.
# StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
###### Abstract  
Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.  
convolutional neural network; image steganography; steganography capacity +
Footnote †: journal: Future Internet
# A Federated Generalized Linear Model for Privacy-Preserving Analysis
## 2 Materials and Methods
### Algorithm for a Federated GLM  
Here, we describe the algorithm of a federated version of the GLM model. The main idea behind it is that the components of Equation (10) can be (partially) computed at each party \(k\) and put together afterwards without ever bringing the data together.  
Let us consider \(M\geq 2\) parties (e.g., data registries, hospitals, banks, etc.) holding an exclusive partition of the full dataset. Let us denote by \(n_{m}\) the number of observations in the \(m\)-th data source such that the total sample size of the study is given by \(n=n_{1}+\cdots+n_{M}\)The first and the expected second derivatives of Equations (5) and (6) can be rewritten as in Equations (11) and (12), respectively:  
\[l^{\prime}(\beta^{(t)})=\sum_{m=1}^{M}\sum_{i=1}^{n_{m}}\frac{y_{i}-\mu}{Var(y_{i })}\left(\frac{\delta\mu_{i}}{\delta\eta_{i}}\right)x_{ij} \tag{11}\]  
\[\mathbb{E}\left[l^{\prime\prime}\big{(}\beta^{(t)}\big{)}\right]=-\sum_{m=1}^{ M}\sum_{i=1}^{n_{m}}\frac{1}{Var(y_{i})}\left(\frac{\delta\mu_{i}}{\delta \eta_{i}}\right)^{2}x_{ij}x_{ik} \tag{12}\]  
Therefore, using the matrix form, we can rewrite Equation (10) as Equation (13):  
\[\beta^{(t+1)}=\Bigg{[}\sum_{m=1}^{M}\left(X_{(m)}^{T}W_{(m)}X_{(m)}\right) \Bigg{]}^{-1}\Bigg{[}\sum_{m=1}^{M}\left(X_{(m)}^{T}W_{(m)}z_{(m)}\right) \Bigg{]} \tag{13}\]  
where \(X_{(m)}\) is the \((n_{m}\times K)\)-matrix of covariates for party \(m\), \(W_{(m)}\) is the correspondent \(K\)-dimensional diagonal matrix of weights, and \(z_{m}\) is the \(n_{m}\)-vector of adjusted dependent variable of the \(m\)-partition.  
The federated algorithm is described in Algorithm 2. Note that the proposed algorithm is mathematically equivalent to the one used for centralized data (Section 1.2.1), but it does _not_ require the data to be pooled together.  
The federated GLM was implemented in Rv. 4.1.3 [25]. Its code as well as the code used for its validation (Section 2.3) is publicly available in our GitHub repository (accessed 14 June 2022).
## 4 Security Attacks
### Consensus and Incentive Layer
#### 4.3.1 51% Majority Attacks  
In a 51% majority attack [18; 27; 29; 32; 42; 45], an adversary gains control of more than half of the network's mining power. With this majority control, the attacker can determine which transactions are included in new blocks and which are excluded. This control enables the adversary to reverse previously confirmed transactions (facilitating double-spending) or prevent other miners from validating transactions and creating new blocks, effectively halting network operations.  
Figure 4 depicts the sequence of actions in a 51% attack, also known as a majority attack, which occurs when a single entity or group gains control of more than half of the total computational power in a blockchain network. The diagram starts at the top with the blockchain network, which typically consists of many participants, including honest miners who follow the rules. However, if a majority attacker (shown on the right)gains control of over 50% of the mining power, they can create new blocks faster than the honest miners. This allows them to initiate a chain reorganization, replacing the legitimate blockchain history with their own version. As shown in the lower part of the diagram, this manipulation can lead to serious consequences: the attacker can exclude certain transactions from being recorded (left path) or perform double spending (right path), where the same cryptocurrency is spent multiple times. The image highlights how control over block creation enables manipulation of the ledger, undermining the core trust model of blockchain. A notable real-world example is the Ethereum Classic (ETC) attack in August 2020, where approximately $5.6 million worth of ETC was double-spent during a chain reorganization event. To mitigate such attacks, increasing the overall network hash rate makes it more difficult for an adversary to accumulate the required mining power. Improvements in consensus algorithms and continuous network monitoring also serve as effective countermeasures.
## 3 Results
### Deriving Midday Stem Water Potential for Mevo Beitar Vineyard
Thus, for irrigation management, either approach could be used to monitor relative difference (spatial variability) within the field at Mevo Beitar, to the extent that such information could be used to improve the efficiency of ground operations. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{4}{c}{\(r\)} & \multicolumn{4}{c}{RMSE (MPa)} & \multicolumn{4}{c}{\(\Psi_{\rm stem}\) (MPa)} \\ \cline{2-10}
**DOY** & **GNDVI** & **NDVI** & **EVI** & **SAVI** & **GNDVI** & **NDVI** & **EVI** & **SAVI** & **Average** & **Std** \\ \hline
126 & 0.45 & 0.56  & 0.56  & 0.60  & 0.091 & 0.084 & 0.084 & 0.081 & \(-\)1.008 & 0.126 \\
140 & 0.51  & 0.48  & 0.47  & 0.53  & 0.049 & 0.050 & 0.050 & 0.048 & \(-\)0.633 & 0.103 \\
154 & 0.72  & 0.76  & 0.76  & 0.78  & 0.78  & 0.094 & 0.088 & 0.088 & 0.086 & \(-\)0.728 & 0.149 \\
168 & 0.32 & 0.32 & 0.30 & 0.28 & 0.129 & 0.129 & 0.129 & 0.130 & \(-\)0.990 & 0.226 \\
175 & 0.46 & 0.56  & 0.56  & 0.55  & 0.061 & 0.061 & \(-\)1.089 & 0.130 \\
189 & 0.78  & 0.76  & 0.78  & 0.086 & 0.090 & 0.090 & 0.086 & \(-\)1.056 & 0.178 \\
196 & 0.76  & 0.77  & 0.77  & 0.087 & 0.085 & 0.085 & 0.082 & \(-\)1.250 & 0.145 \\
203 & 0.76  & 0.74  & 0.73  & 0.75  & 0.124 & 0.128 & 0.128 & 0.125 & \(-\)1.408 & 0.198 \\
217 & 0.84  & 0.81  & 0.81  & 0.82  & 0.108 & 0.116 & 0.116 & 0.115 & \(-\)1.196 & 0.447 \\
245 & 0.84  & 0.79  & 0.79  & 0.81  & 0.147 & 0.166 & 0.166 & 0.160 & \(-\)1.364 & 0.285 \\ \hline Average & 0.64 & 0.65 & 0.65 & 0.67 & 0.098 & 0.099 & 0.100 & 0.097 & \(-\)1.072 & 0.199 \\ \hline \end{tabular}  
Statistically significant models are marked: \({}^{1}\)\(p<\) 0.05; \({}^{2}\)\(p<\) 0.01; \({}^{3}\)\(p<\) 0.001.  
\end{table} 
Table 2: Statistics of the per-week multivariable linear models (**MB-Mult**) using the four Planet-derived VIs (GNDVI, NDVI, EVI and SAVI). The model with the highest Pearson’s-\(r\) and lowest RMSE (in MPa) scores is indicated in bold for each date. Low/high \(r\) and RMSE scores along the season are denoted with blue/red colors per VI. Figure 4: High-resolution \(\Psi_{\rm stem}\) maps of Mevo Beitar vineyard derived from **MB-Mult** (the multivariable regression model) using SAVI for selected weeks along the season. Plots \(\Psi_{\rm stem}\) measured values are also shown in circles. Note that all figures in this panel use the same legend and scale.
# Itarare Group Sandstone as a Sustainable Alternative Material for Photon Radiation Shielding
## 2 Materials and Methods  
### Study Area, Site Selection, and Rocky Sampling  
The study area consists of sandstones and diamictites from the Itarare Group, which dates back to the Carboniferous-Permian period. This formation is located in the Parana Sedimentary Basin in southern Brazil [21]. At the research site, identified by UTM coordinates 7,181,700 and 629,590 (Figure 1), we observed diamictites, which are a mixture of sand and silt, along with layers of fine to medium sandstone. These sandstone layers are part of the Lagoa Azul Formation, also included in the Itarare Group. Both the sandstones and the diamictites show signs of weathering, indicated by the presence of iron oxide veins. The rocks exhibit vertical and sub-vertical fractures, while the sandstones are characterized by massive structures [22]. Additionally, the slope features a layer of young residual soil at the top and has an incline of approximately 90 degrees.  
Rock samples were collected at various depths using specialized hammers and chisels designed for obtaining geological material. The samples were taken from the following depths, measured from the top of the rock (Figure 2): 13.97 m (AR4), 14.02 m (RMPS), 14.12 m (RMPI), 14.23 m (RMPS), 14.32 m (AR2), and 15.50 m (AR1).  
To determine the density of the samples, 15 mL volumetric flasks were utilized. The samples were initially sieved using a 2 mm mesh, which is standard for dry fine soil, and then ground to a particle size of approximately 45 \(\upmu\)m using a mortar and pestle. This particle size was chosen for analyzing the chemical composition of the samples. The internal volume of the volumetric flask (Equation (1)) was calculated by filling it with water and applying the known density of water at the measured temperature.  
\[\mathrm{V_{bv}}=\frac{\mathrm{m_{bv+H2O}-m_{bv}}}{\mathrm{\rho_{H2O}}}\quad, \tag{1}\]  
where \(\mathrm{V_{bv}}\) is the internal volume of the volumetric flask, \(\mathrm{\rho_{H2O}}\) is the density of the water at room temperature, and \(\mathrm{m_{bv}}\) is the mass of the dry and empty volumetric flask.  
Figure 1: Map showing the region where the samples were collected in Parán State, Brazil.  
After drying the volumetric flask, it was filled with rock samples until it reached the same volume previously occupied by water during the internal volume measurement step. This allowed for the determination of density using the relation provided in Equation (2):  
\[\rho_{\mathrm{s}}=\frac{\mathrm{m}_{\mathrm{bv+rock}}-\mathrm{m}_{\mathrm{bv}}}{ \mathrm{V}_{\mathrm{bv}}}\ \ \, \tag{2}\]  
where \(\rho_{\mathrm{s}}\) represents the sandstone's density.
# End-Effectors Developed for Citrus and Other Spherical Crops
## 2 Materials and Methods
### Clamping Mechanism Design
#### 2.2.3 Mathematical Model of Citrus Picking Finger Length  
Due to the great differences in the growth characteristics of citrus fruits, in order to determine the effective grip of the picking finger on the target fruit, according to the measurement results of the physical characteristics of the actual citrus fruit, it is known that the horizontal diameter of mature citrus fruit is 48.25-80.85 mm, the average citrus fruit horizontal diameter is 62.64 mm; the vertical diameter range is 41.83-72.62 mm, the average citrus fruit vertical diameter is 55.85 mm; the diameter range of citrus fruit stalks is  
Figure 1: Structure diagram of the end-effector.  
1.25 to 3.03 mm, with an average citrus fruit stalk diameter of 2.03 mm. Different diameters of citrus-picking schematic citrus fruit can be defined as spheres [42]. When the gripping fingers of the end-effector have gripped and wrapped the citrus fruit, the large diameter fruit was gripped as shown in Figure 2a, and the small diameter fruit was gripped as shown in Figure 2b. In order to determine the length of the clamping finger, a clamping mechanism model with the equatorial plane of the citrus fruit as the base plane was established with the range of the citrus fruit's transverse diameter as the index. In order to ensure the effective clamping of the clamping finger on the citrus fruit, the clamping coverage area of the clamping finger must be greater than 55% when picking large diameter fruit. According to the analysis in Figure 2, the length of the clamping finger is 70 mm.