esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Data Sovereignty in Public Cloud--A Comparison of Existing Solutions
## 4 Models and Computational Experiments
### Data Sovereignty Model  
In this section, we present a data sovereignty model Figure 4 that is generalized from various reference architectures and implementations, e.g., three architectures presented above but also the model presented in paper [1]. Our contribution is the analysis and generalization of various models that group common concepts and divide functionality into corresponding layers. The data sovereignty model is divided into four logical layers: hardware, platform, data protocols, and distributed applications. These distinct layers group technologies and functions that enable data sovereignty.  
The lowest layer comprises heterogeneous hardware components, including servers and devices of various sizes and physical locations. This foundational layer provides essential computational, storage, and network resources that support all upper layers. The second layer, known as the trusted infrastructure, is designed to implement trusted platform components that enforce policies, manage certificates, and handle other security measures. It establishes a secure foundation for the data exchange and application layers by integrating robust security features. The subsequent layer encompasses data exchange protocols, such as ODRL [24], NGSI-LD [25], and IDS usage control language, which enable  
Figure 4: Data sovereignty model.  
Figure 3: FIWARE reference architecture (Source: fiware.org).  
standardized data exchange. This layer integrates international standards and common data models that are interoperable across diverse solutions and systems. Finally, the top layer consists of application frameworks, such as Hadoop, Spark, and Flink, which support distributed processing with data sovereignty enforcement. This layer represents the highest level of the stack, where the complete business logic of applications is implemented. Connectors span the top three layers, as they implement functionalities that belong to the platform, data protocols, and application layers.  
Connectors represent a critical component of the data sovereignty model, as they integrate the three primary layers and enable the hosting of essential security and privacy features. An example of an implementation of the abstract connector component is the IDS connector. In the following chapter, we will discuss computational experiments conducted to evaluate the performance of the connector.
## 4 Applications of 3D PFFT-BEM
### Model Comparisons of the PFFT-BEM with the 3D Parabolic Equation Method
#### 4.1.3 Backscattering Effects: 3D Seamount  
In the second numerical example, we show a comparison of the 3D PFFT-BEM and a 3D PE method for an idealized seamout. The water column is homogeneous with a sound speed of 1500 m/s, a density of 1.0 g/cm\({}^{3}\), and no medium absorption for sound waves. The bottom is also homogeneous with a sound speed of 1700 m/s, a density of 1.5 g/cm\({}^{3}\), and medium attenuation at 0.5 dB/\(\lambda\). The origin  
Figure 11: Sketch of the ASA wedge.  
Figure 12: **(a)** TL in the far field along the x-axis at \(z=30\) m for ASA wedge: comparison among 3D PFFT-BEM, benchmark results [25] and a 3D PE model results; **(b)** phase differences between 3D PFFT-BEM direct simulation and a 3D PE model results as a function of distance from the source.  
Figure 13: TL contour at z = 30 m obtained by PFFT-BEM.  
of the coordinate system is located above the seamount at the sea surface with z pointing down towards the ocean. The 100 Hz sound source in this example is placed away from the tip of the seamount at \(x=-2000\) m, \(y=850\) m, and \(z=250\) m. The summit of the seamount is at a depth of 200 m, and its slope is \(7^{\circ}\). The radius of the seamount is 2500 m, and the water depth outside the seamount area is 512.5 m. Figure 14a compares the 3D PFFT-BEM numerical results of TL to the 3D PE results by [19] along the \(x-\)axis at \(z=250\)\(m\) in front of the seamount (2 km away from the source). First, as shown in Figure 14a, convergence is observed with an increase in the element numbers, which demonstrates the accuracy of PFFT-BEM. The difference between 3D PE and PFFT-BEM is more significant in front of the seamount. This difference is mainly due to the backscattering effects, which are not included in the 3D PE. We zoom in on the range from 750 to 1100 m, which is in front of the seamount. As shown in Figure 14b, significant differences between the PFFT-BEM and 3D PE results (3 \(\sim\) 5 dB) are observed. In Figure 14b, we compare the TL along the x-axis for two cases with different slopes (1/6 and 1/8). As expected, the TL is smaller (i.e., larger acoustic pressure) in the larger slope case (1/6), which indicates stronger backscattering effects. Under such conditions, we expect that the errors due to neglecting the backscattering effects are more significant, and the use of a 3D full field method such as PFFT-BEM is more necessary. To demonstrate the 3D scattering effects, we plot Figure 15 to show the TL contour computed by 3D PFFT-BEM.  
Figure 14: **(a)** TL along the x-axis at \(z=250\)\(m\) for the conical seamount: comparison between 3D direct simulations by PFFT-BEM and 3D PE by [19]; **(b)** TL along the x-axis at \(z=250\)\(m\) for the conical seamount: comparison between cases with seamount slopes of 1/6 and 1/8 from PFFT-BEM and a seamount slope of 1/8 from 3D PE.  
Figure 15: TL contour at \(z=250\) m obtained by 3D PFFT-BEM.
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 2 System Model and Assessment Formulation
### Information System and Attributes
#### 2.1.3 Activation Mechanism  
Common activation mechanisms of existing HTs include _Time-based_, _Physical-Condition-based_, and _User Input - based_, while other HTs are _Always On_. Always-on HTs are activated as soon as their hosting designs are powered on. In the case of an FPGA, they are usually inserted with the attack effect of degraded performance, which is difficult to detect. In addition, they can carry out long-term attacks on ICs; thus, they pose the greatest threat. HTs activated by _user input_ perform specific functions based on specific user input. They can achieve a precision strike and are highly threatening. _Time-based_ HTs have a long latency period and pose a low threat. HTs activated by physical conditions pose the lowest threat, as the attacker must know the specific application of the IC in advance. An FPGA, for example, can be used in aerospace or automobiles. In different application scenarios, the physical conditions of the IC are quite different. Under extreme conditions, the _PhysicalCondition-Based_ HTs may not be triggered in the IC life cycle. For example, an HT activated by the operating frequency of a high-speed communication system cannot be triggered in a low-speed communication system.
## 3 Design of Composite Control Method
### Optimized Design of Disturbance Observer
#### 3.2.1 Analysis of the Two-Degrees-of-Freedom System with DOB  
The feedback control system with the DOB system is shown in Figure 12, which consists of the DOB inner loop and the outer loop of the general feedback controller \(K(s)\). In the figure, \(r\), \(d\), \(\xi\), and \(y\) are the reference input, disturbance, and DWS measurement noise and output signals, respectively.  
Figure 11: Robust stability analysis of the system.  
Figure 10: Frequency response of weighting function and sensitivity function.  
The purpose of DOB is to eliminate the impact of external disturbance and model mismatch on the system. The low-pass filter \(Q(s)\) not only makes the inverse model physically realizable but also suppresses measurement noise and ensures the system's robust stability to model mismatch. When matching the model (\(P_{\text{n}}=P\)), the output equation of the two-degree-of-freedom system is  
\[y=\frac{P_{\text{n}}(s)K(s)}{1+P_{\text{n}}(s)K(s)}r+\frac{P_{\text{n}}(s)(1-Q( s))}{1+P_{\text{n}}(s)K(s)}d+\frac{P_{\text{n}}(s)K(s)+Q(s)}{1+P_{\text{n}}(s)K(s)}\xi. \tag{19}\]  
From Equation (19), it can be inferred that the influence of system disturbances and measurement noise on the system output is not only related to the filter \(Q(s)\) but also to the external feedback system of the system. The sensitivity function and complementary sensitivity function of a two-degrees-of-freedom system are defined as follows:  
\[\left\{\begin{array}{l}S(s)=\frac{1-Q(s)}{1+P_{\text{n}}(s)K(s)}=S_{0}(s) \cdot S_{\text{DOB}}(s)\\ T(s)=\frac{P_{\text{n}}(s)K(s)+Q(s)}{1+P_{\text{n}}(s)K(s)}=T_{0}(s)+S_{0}(s) \cdot T_{\text{DOB}}(s)\end{array}\right., \tag{20}\]  
In Equation (20), \(S_{\text{DOB}}(s)=1-Q(s))\) and \(T_{\text{DOB}}(s)=Q(s)\) represent the sensitivity and complementary sensitivity functions of the inner-loop return DOB system, respectively.
## 4 Test Scenarios
### Scenario I: Nominal Maritime Navigation without the Presence of Jamming  
In all scenarios, GNSS data were collected in an open sea environment between the Helsinki-Tallinn ferry route. The nominal situation refers to the dataset that is free of any intentional jamming. Average C/N\({}_{0}\) is computed for all the tracked satellites per GNSS signal. Figure 6 illustrates the computed average C/N\({}_{0}\) of all tracked GNSS signals, ranging between 43 and 50 dB-Hz depending on the GNSS signals.
## 3 Results and Discussion
### Ceruloplasmin and Antiprotease Activity  
Fourteen days after treatment, all groups showed relatively higher ceruloplasmin activity compared with the control. T2 group showed the highest ceruloplasmin activity (\(p<0.05\)) among all groups. However, on the 7 th day after treatment, ceruloplasmin activity was higher in T1 and T2 and lower in T3 (Figure 4). In another similar study, methanolic extract of _C. antenmina_ also significantly increased ceruloplasmin activity in treatment groups of _L. rohita_ after induction of _E. arada_ infection [21]. In a previous study, a significantly positive (0.51) correlation was observed with all the studied immune parameters and survival in _L rohita_ against aeromoniasis [49]. The plasma concentrations of several acute phase proteins including ceruloplasmin show a rapid and sharp rise against infections induced by bacterial pathogens. Such non-specific acute phase reactions are considered first-line defenses against pathogenic attack [49]. Ceruloplasmin performs its function in different ways depending on its activities like revamping tissue damage, preventing spread of infectious microbes, and invading pathogens [40].  
All groups of fish exposed to _C. aerea_ extract showed significantly higher (\(p<0.05\)) antiprotease activity relative to controls on the 14 th day after treatment. Antiprotease activity was highest in T2 compared with controls (Figure 5). Similarly, in another study, highest level of antiprotease activity, such as trypsin inhibition, was observed in T2 group (75 mg/kg of body weight) of _L. rohita_ compared to controls after treatment with methanolic  
Figure 3: Serum myeloperoxidase activity under different treatments administered with various levels of _C. aerea_ extract in _L. rohita_ during post treatment days (values are mean \(\pm\) SE). Mean values with different superscript within a column for a parameter are significantly different (\(p<0.05\)).  
extract of _C. antenna_[21]. Several antiproteases, including a1 antiprotease, a2 antiplasmin, and a2 macroglobulin, have been documented in fish plasma. These antiproteases have been shown to restrict bacterial invasion and growth in vivo [50]. We found significantly higher antiprotease activity in treatment groups. A higher inhibition (%) due to antiprotease activity was observed in the susceptible line compared with a resistant line, although this difference was not statistically significant [51]. In another study, serum antiprotease activity varied widely in several species of rohu. A negative correlation was found with survival against Edwardsiellosis [52].
## 5 Performance Evaluation of Our Routing Protocol
### Comparison with Different Number of Channels  
Figure 11 shows the performance comparison with the variation in the number of channels. For these set of figures, we assume 10 active sources with a transmission rate of 185 KBps. As expected, increasing the number of NICs improves the network performance because of reduced channel contention and network interference. With 4 NICs, the network throughput increases by \(\sim\)20%, whereas the delivery ratio improves by \(\sim\)10%, compared to the scenario with 2 NICs. The traffic delay also reduced by \(\sim\)3 times with higher number of NICs.  
The results show that _the JRCA improves the network throughput up to three times, while reduces the traffic delay by six times, in presence of 12 channels and four NICs._ This establishes the effectiveness of our scheme in a multi-gateway, multi-channel WOBAN, with only a small increase in the complexity.  
Figure 10: Comparison of (**a**) throughput (**b**) delivery ratio (**c**) delay (**d**) jitter with different loads for multi-channel WOBAN with multiple gateways.
# High-Fidelity Steganography: A Covert Parity Bit Model-Based Approach
## 3 The DCT Transform  
The Discrete Cosine Transform (DCT) is a widely used mathematical transform in signal processing and image compression. It converts a sequence of data points into a sum of cosine functions that oscillate at different frequencies, allowing data to be represented in the frequency domain rather than the time or spatial domain. Among the various types of DCT, the most common are DCT-I, DCT-II, DCT-III, and DCT-IV, with DCT-II being the most widely utilized, particularly for image compression applications.  
One of the primary applications of the DCT is in image compression standards such as JPEG. By transforming images into the frequency domain, the DCT helps to reduce the amount of data required to represent an image, concentrating visually significant information into just a few coefficients. In addition to image compression, the DCT is also employed in audio codecs such as MP3, where it aids in compressing audio signals by identifying and preserving essential frequencies. Video codecs such as H.264 utilize the DCT as part of their compression algorithms, enabling efficient storage and transmission of video data.  
The DCT possesses several important properties that contribute to its effectiveness. One notable feature is its strong energy compaction, which means that a significant portion of the signal energy is concentrated within a small number of coefficients, helping to facilitate data compression. Additionally, the cosine functions used in the DCT are orthogonal, allowing for the efficient reconstruction of signals. The DCT also produces real-valued coefficients, which simplifies computation and reduces storage requirements.  
Mathematically, the DCT of a one-dimensional signal \(x[n]\) is defined as follows:  
\[X[k]=\sum_{n=0}^{N-1}x[n]\cos\left[\frac{\pi}{N}\left(n+\frac{1}{2}\right)k \right],\quad k=0,1,\ldots,N-1 \tag{1}\]  
where \(N\) is the number of samples, \(n\) is the index of the input signal, and \(k\) is the index of the DCT coefficient. The resulting DCT coefficients \(X[k]\) represent the frequency components of the original signal.  
The DCT is particularly effective in image compression, as it tends to produce a significant number of zero or near-zero coefficients in the high-frequency region. This allows these insignificant coefficients to be replaced with other data, such as secret information in data hiding applications, while maintaining acceptable quality of the stego signal.  
In practical applications, the two-dimensional DCT is often employed, particularly for image processing applications. The two-dimensional DCT is defined as follows:  
\[X[u,v]=\sum_{x=0}^{M-1}\sum_{y=0}^{N-1}x[x,y]\cos\left[\frac{\pi}{M}\left(x+ \frac{1}{2}\right)u\right]\cos\left[\frac{\pi}{N}\left(y+\frac{1}{2}\right)v\right] \tag{2}\]  
where \(M\) and \(N\) are the dimensions of the input image and \((u,v)\) are the frequency indices.
# Thermoelastic Investigation of Carbon-Fiber-Reinforced Composites Using a Drop-Weight Impact Test
## 2 Methodology  
In this study, the thermoelastic response of carbon-fiber-reinforced polymers (CFRP) subjected to high strain rates was investigated using a novel DWIT experimental setup. Fiber-reinforced composites are usually manufactured by stacking laminae of fiber sheets pre-impregnated with resin. The structure is therefore heterogeneous, and so the stress discontinuities between the fibers and matrix should be considered for the purpose of calculating the overall temperature field [24; 25]. It would be expected that the fibers and matrix would experience different temperature fluctuations due to their dissimilar thermoelastic constants [14]. After the drop impact, viscous (frictional) energy is converted into thermal energy. This energy propagates from the point of impact toward the boundaries due to the temperature gradient. The goal of this study was to investigate the thermoelastic phenomenon in the CFRP material that produces spatial variations in the surface temperatures and allows correlation between measured time-averaged IR images, heat flow due to pure diffusion, and the strain waves on the surface.  
The study was divided into two phases. The first phase was the experimental study, which involved performing the DWIT with a specialized rosette strain gauge, amplifiers, a high-speed oscilloscope, and an IR thermographic camera. This study yielded useful insight into the oscillatory elastic deformation (Video S1: Drop-Weight Impact Testing(DWIT) video) and internal friction phenomenon captured via IR thermography within the CFRP (Video S2: IR thermographic recording) when subjected to drop-weight impacts.  
The second phase was the development of numerical models to describe the physical problem that was predicted from the experimental findings. The heat and wave equations in polar coordinate systems were coupled and solved using the forward-time central-space (FTCS) method and finite-difference method (FDM) using MATLAB(r) software. The derived numerical analysis solutions were validated using experimental findings to optimize the models.
## 3 Experiments and Discussions
### Measure Respiratory Variations for Human Subjects by the Previous Methods  
The respiratory splits estimated by the HVD method and the STFT method are shown in Figures 12 and 13, respectively. It can be found that the respiratory splits of all subjects estimated by the HVD method can reflect respiratory modulation. However, the respiratory splits of subject 2, 5 and 10 by the STFT method are somewhat widely disperse, as seen in Figure 13. It concludes that the STFT method is less robust than the HVD method. To evaluate the performance of the proposed method and the two previous methods quantitatively, the scatters are fitted by a second-order polynomial, as seen the black solid lines in Figures 11-13.  
Hence, the difference between the scatters and the fitted values can be used to evaluate the performance. In this paper, the performance is evaluated in two ways. In the first way, the root mean square (RMS) of the difference is considered for this purpose, see Figure 14. It can be found that the proposed method has the lowest RMS for each subject. Meanwhile, the HVD has a greater RMS for subject No. 3, No. 5 and No. 10; the STFT method has a greater RMS for subject No. 2, No. 5, No. 10 and No. 12. In the second way, the authors investigate the difference distribution for all subjects. Hypothesis testing shows that the difference is like a Gaussian distribution. The confidence interval for the mean and standard deviation of the difference can be estimated by stati  
Figure 11: Measured respiratory splits of the 12 subjects by the proposed method.  
confidence level is set as 0.95. Table 2 gives the confidence intervals of the two indicators. It can be seen that the intervals of both mean and standard deviation of the proposed method are narrowest in the three methods. That is to say, the proposed method has much higher consistency and much higher accuracy than the two previous methods.  
Figure 12: Measured respiratory splits of the 12 subjects by the HVD method.  
Figure 13: Measured respiratory splits of the 12 subjects by the STFT method.
## 3 Spectral Library and Dataset
### Extracting Spectral and Spatial Features from HSI Data  
Convolutional layers are the key operations of DenseNet, which use a stack of kernels to learn the feature representations from the input HSI data. The proposed hybrid DenseNet is composed of a 1D spectral branch and a 2D spatial branch. The 1D spectral branch mainly contains 1D convolutional layers, which can extract spectral features; while the 2D spatial branch contains 2D convolutional layers extracting spatial features of adjacent pixels.  
In the 1D convolutional operation, input data is convolved with 1D convolutional kernels, before going through the activation function to form the output data. Formally, in the \(l\)th 1D convolutional layer, the output \(v^{x}_{lj}\) of a neuron at position \(x\) of the \(j\)th feature map is denoted as:  
\[v^{x}_{lj}=f\Bigg{(}\sum_{m}\sum_{d=0}^{D_{l}-1}k^{d}_{ljm}v^{x+d}_{(l-1)m}+b_ {lj}\Bigg{)} \tag{3}\]  
here, \(m\) indexes the \(m\)th feature map in the \((l\)-\(1)\) layer connected to the current feature map, \(k^{d}_{ljm}\) is the value at position \(d\) of the 1D kernel connected to the \(m\)th feature map, \(D_{l}\) is the width of the kernel along the spectral dimension, and \(b_{lj}\) is the bias of \(j\)th feature map in the \(l\)th layer. \(f(\cdot)\) is the activation function, which introduces nonlinearity into the model. Similarly, in the 2D convolution operation, the value of the neuron \(v^{xy}_{lj}\) at position \((x,y)\) of the \(j\)th feature map is denoted as:  
\[v^{xy}_{lj}=f\Bigg{(}\sum_{m}\sum_{h=0}^{H_{l}-1}\sum_{w=0}^{W_{l}-1}k^{hw}_{ jm}v^{(x+h)(y+w)}_{(l-1)m}+b_{lj}\Bigg{)} \tag{4}\]  
where \(H_{l}\) and \(W_{l}\) is the height and width of the 2D convolutional kernel, respectively. For the activation function \(f(\cdot)\), we adopt the Mish function whose formula is [25]:  
\[f(x)=x\times\tanh(\ln(1+e^{x})) \tag{5}\]
## 2 Sampling and Methods
### Analytical Methods
#### 2.2.3 EDXRF  
Energy dispersive X-ray fluorescence (EDXRF) was employed to measure major and trace elements for all 105 samples using an Epsilon 4 spectrometer (Malvern PANalytical, Malvern, UK, max 50 kV/2 mA/10 W). High-temperature coal ash samples were used for this analysis. The procedures followed [31], and the equipment's reliability has been discussed by [32].  
XRF was selected as a rapid and routine method to process a large number of samples. After obtaining full data, further investigations are planned using long-term digestion experiments and advanced techniques to analyze individual elements from promising sample units.