esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Migratory shorebirds are notable consumers of benthic invertebrates on intertidal sediments. The distribution and abundance of shorebirds will strongly depend on their prey and on landscape and sediment features such as mud and surface water content, topography, and the presence of ecosystem engineers. An understanding of shorebird distribution and ecology thus requires knowledge of the various habitat types which may be distinguished in intertidal areas. Here, we combine Sentinel-1 and Sentinel-2 imagery and a digital elevation model (DEM), using machine learning techniques to map intertidal habitat types of importance to migratory shorebirds and their benthic prey. We do this on the third most important non-breeding area for migratory shorebirds in the East Atlantic Flyway, in the Bijagos Archipelago in West Africa. Using pixel-level random forests, we successfully mapped rocks, shell beds, and macroalgae and distinguished between areas of bare sediment and areas occupied by fiddler crabs, an ecosystem engineer that promotes significant bioturbation on intertidal flats. We also classified two sediment types (sandy and mixed) within the bare sediment and fiddler crab areas, according to their mud content. The overall classification accuracy was 82%, and the Kappa Coefficient was 73%. The most important predictors were elevation, the Sentinel-2-derived water and moisture indexes, and Sentinel-1 VH band. The association of Sentinel-2 with Sentinel-1 and a DEM produced the best results compared to the models without these variables. This map provides an overall picture of the composition of the intertidal habitats in a site of international importance for migratory shorebirds. Most of the intertidal flats of the Bijagos Archipelago are covered by bare sandy sediments (59%), and ca. 22% is occupied by fiddler crabs. This likely has significant implications for the spatial arrangement of the shorebird and benthic invertebrate communities due to the ecosystem engineering by the fiddler crabs, which promotes two vastly different intertidal species assemblages. This large-scale mapping provides an important product for the future monitoring of this high biodiversity area, particularly for ecological research related to the distribution and feeding ecology of the shorebirds and their prey. Such information is key from a conservation and management perspective. By delivering a successful and comprehensive mapping workflow, we contribute to the filling of the current knowledge gap on the application of remote sensing and machine learning techniques within intertidal areas, which are among the most challenging environments to map using remote sensing techniques.  
keywords: intertidal flats; Sentinel-1; Sentinel-2; remote sensing; intertidal sediments; random forest
## 3 Results on ML/DL-Based Channel Estimation
### Discussion on ML/DL-Based Channel Estimation in MC-UWA Communication Systems
It achieves lower prediction errors in both one-step and multi-step forecasting. While it reduces computational complexity compared to single-task learning, advanced models like transformers still require significant resources. The work reported in [30] addresses large Doppler spread, low SNR, and complex multipath propagation in UWA OFDM communication with DenseNet-based channel estimation. Due to extensive connection and feature reuse, the proposed DenseNet estimator efficiently captures complicated channel characteristics. The model is trained on WATEMARK BCH, KAU1, and KAU2 channel data [26]. Even with fewer pilot symbols, DenseNet surpasses conventional estimators in BER, MSE, and channel estimation accuracy. The study also shows the model's flexibility to BPSK, QPSK, and environmental circumstances. DenseNet's BER improvements (up to 96.3% over LS and 94.2% over MMSE) make it a reliable UWA channel estimate solution. The model reduces pilot overhead and errors in amplitude and phase estimation. A model-driven DL approach in [31] employs WATEMARK datasets [26] to train and test the model. The superiority of the model in terms of achieved BER over Least Squares, MMSE, CNN-MLP (Convolutional Neural Network-Multi-Layer Perceptron), DNN, and ComNet (Communication Network) is proved through simulations. However, scalability, Doppler resilience, and hardware deployment remain unexplored. An iteration-based design in [33] achieves channel estimation with a 0.001 BER within 0.5 dB of the ideal case. However, the model requires extensive offline training. It involves high computational complexity in iterative processing. Finally, a stacked CNN ResNet (S-CNN-ResNet) receiver for OTFS communications is presented in [34]. It integrates CNN for channel feature extraction from pilot data and an enhanced ResNet for symbol recovery to improve feature learning. While the model achieves better BER performance, it requires substantial training data and processing resources, which may limit its feasibility for real-time applications. To summarize, studies discussed in this section offer useful solutions for MC-UWA systems. Deep learning models like CNN-LSTM, DAE, BiLSTM, DenseNet, and ResNet show strong improvements in BER and MSE. Many models perform well under low SNR and complex channel conditions. However, most rely on limited datasets like WATEMARK and need heavy offline training. This affects real-time use and adaptability to new environments. Some approaches also have high computational costs. To move forward, future research should focus on simpler, scalable models. Testing under diverse underwater conditions is essential to build reliable systems.
## 2 Main Result
### Lyapunov Stability for the Estimated State Error  
Due to the presence of residual terms \(\{Bw_{x},v\}\) in the error equations, \(\{e_{x},\dot{e}_{y}\}\xrightarrow[t\to\infty]{}0\) cannot be guaranteed. To address this, Lyapunov stability analysis will be employed to minimize and possibly remove all of the error state. In this analysis, the internal error state \((e_{x})\) will be minimized by considering an energy-like function for the error system with assumed real scalars:  
\[V_{e}(e_{x})=\frac{1}{2}e_{x}^{\dagger}P_{x}e_{x};P_{x}>0, \tag{12}\]  
where \((\cdot)^{\dagger}\) is the conjugate-transpose operator. Following, \(P_{x}\) is a positive-definite \((\text{Re}(\sigma\{P_{x}\})>0)\) and self-adjoint matrix \((P_{x}=P_{x}^{\dagger})\), alternatively written as \(P_{x}>0\). To understand how the error state in the energy-like function evolves with time, take the time derivative of \(V_{e}\) and substitute error dynamics:  
\[ \begin{split} 2\dot{V}_{e}&=\dot{e}_{x}^{\dagger}P_{x}e_{x }+e_{x}^{\dagger}P_{x}e_{x}\\ &=(A_{m}e_{x}+Bw_{x}+v)^{\dagger}P_{x}e_{x}+e_{x}^{\dagger}P_{x} (A_{m}e_{x}+Bw_{x}+v)\\ &=e_{x}^{\dagger}(A_{m}^{\dagger}P_{x}+P_{x}A_{m})e_{x}+2\underbrace {(Bw_{x})^{\dagger}P_{x}e_{x}}_{=\dot{e}_{x}^{\dagger}P_{x}Bu_{x}}+2\underbrace {v^{\dagger}P_{x}e_{x}}_{=\dot{e}_{x}^{\dagger}P_{x}v}\\ &=e_{x}^{\dagger}(A_{m}^{\dagger}P_{x}+P_{x}A_{m})e_{x}+2e_{x}^{ \dagger}P_{x}Bw_{x}+2\underbrace{v^{\dagger}P_{x}e_{x}}_{=(v,P_{x}e)}.\end{split}  \tag{13}\]  
The inner product operator is detonated as \((\cdot,\cdot)\). Consider the SPR condition modified for the reference model:  
\[ \begin{cases}A_{m}^{\dagger}P_{x}+P_{x}A_{m}<-Q_{x}\\ P_{x}B=C^{\dagger},\end{cases}  \tag{14}\]  
where \(Q_{x}>0\). Applying the reference model SPR condition on \(\dot{V}_{e}\),  
\[ \begin{split}\dot{V}_{e}&=-\frac{1}{2}e_{x}^{\dagger}Q_{ x}e_{x}+\underbrace{e_{x}^{\dagger}C^{\dagger}}_{=\dot{e}_{y}^{\dagger}}w_{x}+ \underbrace{(v,P_{x}e_{x})}_{=(P_{x}e_{x},v)}\\ &=-\frac{1}{2}e_{x}^{\dagger}Q_{x}e_{x}+\underbrace{(\dot{e}_{y},w_{x} )}_{=(w_{x},\dot{e}_{y})}+(P_{x}e_{x},v).\end{split}  \tag{15}\]  
Removing the residual terms \(\{(\dot{e}_{y},w_{x}),(P_{x}e_{x},v)\}\) in Equation (15), results in \(\dot{V}_{e}\leq 0\). In order to remove or compensate the two residual terms in Equation (15), consider another Lyapunov energy-like function:  
\[V_{L}(\Delta L)=\frac{1}{2}\operatorname{tr}(\Delta L\gamma_{y}^{-1}\Delta L^{ \dagger});\gamma_{y}>0. \tag{16}\]  
To determine the energy-like time rate of change of \(V_{L}\), take the time derivative of \(V_{L}\):  
\[\dot{V}_{L}=\underbrace{\operatorname{tr}(\Delta L\gamma_{y}^{-1}\Delta L^{ \dagger})}_{=\operatorname{tr}(\Delta L\gamma_{y}^{-1}\Delta L^{\dagger})}. \tag{17}\]  
A control law is defined for the time rate of change of plant correction variance \((\Delta L)\):  
\[\Delta L=-\dot{\varrho}y^{\dagger}\gamma_{y}-aL, \tag{18}\]  
where \(a\) is a scalar. Additional constraints for \(a\) will be derived later. The term \(aL\) in Equation (18) functions as a _feedback filter_.
## 3 Results and Discussions  
### Output Characteristics under High Temperature  
The output characteristic of the pressure sensor was measured by a digital pressure test system consisting of a high-precision piston pressure meter, a hot air gun, and a control system. A constant DC voltage of 3.3 V was applied as the incentive power of the Wheatstone bridge, and the output voltage was captured by a digital multimeter (VC8246B).  
To test the output characteristics under different temperature conditions, a piezoresistive pressure sensor with a 50 \(\upmu\)m sensing diaphragm was pressured from 0 to 3 MPa and the output voltage of the Wheatstone bridge was recorded for every 0.2 MPa. The temperature test points were set from 25 to 125 \({}^{\circ}\)C for every 10 \({}^{\circ}\)C. The V-P characteristic curve of the pressure sensor under different temperatures is depicted in Figure 5. As shown in Figure 5a, the output voltage exhibits an expected linear correlation in a wide effective pressure range from 0 to 3 MPa and a temperature range from 25 to 125 \({}^{\circ}\)C. Sensitivity of the pressure sensor is the ratio of response change to excitation pressure change, and the nonlinearity is the maximum deviation between the actual output of the device and the ideal output curve that changes linearly with the input pressure. Fitting the experimental data with a linear least-square technique, the sensitivity of the pressure sensor was 22.99 mV/MPa and the nonlinearity was 0.25% F.S. at 25 \({}^{\circ}\)C. Due to the application of SOI wafer, the pressure sensor showed a stable performance with the nonlinearity of 0.31% F.S. under 125 \({}^{\circ}\)C.  
Figure 4: (**a**) Micro pressure sensor after bonding without a protection layer. (**b**) Micro pressure sensor after bonding with a 10 \(\upmu\)m PI protection layer.  
Figure 5: Output characteristics of the silicon piezoresistive pressure sensor: (**a**) output voltage versus pressure and (**b**) sensitivity versus temperature.  
The ambient temperature would greatly influence the output characteristics of the pressure sensor. The curve of the sensitivity variation versus the temperature is shown in Figure 5b. To depict the relation between sensitivity and temperature more accurately, the experimental data is fitted by a two-degree polynomial. The results show that the increase of ambient temperature can still lead to a distinct decline of the sensitivity. This is because the buried oxide layer can only reduce the leakage current of the piezojunction in the handle layer, but not in the device layer of the SOI wafer.
Combining these signatures with the archaeology and gas and oil efforts in this region allows new discoveries. Each density variation under the surface (of the causative body) generates gravity variations and an anomalous gravity signal (gravity aspect changes). We were able to detect, among others, oil and gas localities [1,2] and/or paleolakes in the Sahara [3,4]. Thus far, archaeological sites have been studied by exploration on the ground (field archaeology) or from the air (aerial or satellite images) [5,6]. In [1], we review discoveries of candidates of subglacial volcanoes, lakes and a lake basin, as well as the impact craters on the surface, beneath the ice cover or on the bottom of the ocean (references in Sections 2.1 and 3.2). We work with the gravity anomalies \(\Delta g\) but also with the Marussi tensor of the second derivatives of the disturbing geopotential, namely with its radial component \(T_{zz}\), with the gravity aspects \(I_{1}\) and \(I_{2}\) and their specific ratio \(I\) (a 2D indicator causative body), with the virtual deformations \(vd\) (they stand for an alternative and concise representation of the Marussi tensor) and with places with more or less one-way oriented (\"combed\") vectors of the strike angles \(\theta\). All these gravity aspects should be treated together to provide the most representative signature of the gravity sources; this approach moves beyond the use of the traditional gravity anomaly. For definitions of the gravity aspects, explanation of their (geo)physical meaning and examples of various geological features, see [1]--from theory to graphical samples. The layers of oil, gas and water in the subsurface correlate with the combed strike angles and thus we assume that, gravitationally, this material mixture generates strike angle signatures. Moreover, deeper or wider river valleys are indicated by the gravity aspects, including combed \(\theta\). When the river water is filled with sediment (sand, silt, clay), the density of the combined body of fluid (pool) and sediment still contrasts with respect to surrounding rocks due to the significant porosity contrast; this is what we detect (if the contrast is sufficiently large). Then, the pre-existing body of water shows negative \(\Delta g\) and \(T_{zz}\) and specific values of the other gravity aspects, such as those observed, for example, at the Libyan-Egyptian border in the Great Sand Sea (GSS), investigated in [4] or between Kharga in the Western Desert of Egypt and Toshka at the Nile river (investigated here). These signals appear to be preserved for a long time, but endogenous and exogenous forces are transporting masses and, in the course of time, change the local gravity signal. Locally, for this paper, we use the abbreviation \(TD\) for the \"target deposit\", meaning here hydrocarbons (oil and gas) and/or groundwater/paleolake. In Section 2, we outline the theory, method and data that we work with. For additional details, see [1]. In Section 3, we focus on oil and gas deposits. In Section 4, we deal with groundwater and hypothetical paleolakes in relation to archaeological sites.
# Novel Fault Injection Attack without Artificial Trigger
## 2 Background
### PGO DFA Method  
[PERSON] et al. performed their formulated DFA on AES [3] so that one byte was affected by a fault between last MixColumns and penultimate MixColumns due to the fault spreading across exactly four bytes of a ciphertext. Figure 2 represents a propagation process of the fault induced into the first byte of the 9 th round of SubBytes. Here, what \(K_{i,j}\) means is the j-th byte round key of the i-th round of AES. At the first of a ciphertext, we formulate the equations for the normal and faulty cases as follows:  
\[S(2A\oplus 3B\oplus C\oplus D\oplus K_{9,1})\oplus K_{10,1}=O_{1}, \tag{1}\]  
\[S(2X\oplus 3B\oplus C\oplus D\oplus K_{9,1})\oplus K_{10,1}=O_{1}^{\prime}. \tag{2}\]  
By computing XOR equation between Equations (1) and (2), \(K_{11,1}\) is neutralized, and the resulting \(U_{1}\) can be expressed as follows:  
\[S(2A\oplus 3B\oplus C\oplus D\oplus K_{9,1})\oplus S(2X\oplus 3B\oplus C \oplus D\oplus K_{9,1})=O_{1}\oplus O_{1}^{\prime}=U_{1}. \tag{3}\]  
Equation (3) can be rewritten as Equation (4):  
\[S(Y_{0})\oplus S(2Z\oplus Y_{0})=U_{1}(Z=A\oplus X,Y_{0}=2A\oplus 3B\oplus C \oplus D\oplus K_{9,1}). \tag{4}\]  
Similarly, equations corresponding to the other bytes affected by the injected fault can be expressed according to Equations (5)-(7) as follows:  
\[S(Y_{1})\oplus S(Z\oplus Y_{1})=U_{14}(Z=A\oplus X,Y_{1}=A\oplus 2B\oplus 3C \oplus D\oplus K_{9,2}), \tag{5}\]  
\[S(Y_{2})\oplus S(Z\oplus Y_{2})=U_{11}(Z=A\oplus X,Y_{2}=A\oplus B\oplus 2C \oplus 3D\oplus K_{9,3}), \tag{6}\]  
\[S(Y_{3})\oplus S(3Z\oplus Y_{3})=U_{8}(Z=A\oplus X,Y_{3}=3A\oplus B\oplus C \oplus 2D\oplus K_{9,4}). \tag{7}\]  
Figure 1: Principle of electromagnetic fault injection.  
Then, we find \((Y_{0},Z)\) pair that satisfies Equation (4) corresponding to the known fixed value \(U_{1}\). As SubBytes is a nonlinear function, the \(Z\) range of guessing the values can be declined repeatedly by using formulas for \((Y_{1},Z),(Y_{2},Z)\), and \((Y_{3},Z)\) pairs. Only several values of \(Z\) can simultaneously satisfy Equation (4), then \(Y_{0},Y_{1},Y_{2}\), and \(Y_{3}\) guessed values corresponding to the \(Z\) range can be reduced. The step is repeated using the other fault-injected ciphertext by considering only the narrow \(Y_{0},Y_{1},Y_{2}\), and \(Y_{3}\) values. Then, we conduct this step repeatedly until the \(Y_{0},Y_{1},Y_{2}\), and \(Y_{3}\) values can be recovered. We assume that the other fault is injected at the same location. If we obtain three different fault-injected ciphertexts and the original one, we can recover the accurate \(Y_{0},Y_{1},Y_{2}\), and \(Y_{3}\) values accordingly. By recovering \(Y_{0},Y_{1},Y_{2}\), and \(Y_{3}\) values, \(K_{10,1},K_{10,14},K_{10,11}\), and \(K_{10,8}\) can be obtained according to Equation (8):  
\[S(Y_{0})\oplus K_{10,1}=O_{1},\ S(Y_{1})\oplus K_{10,14}=O_{14},\ S(Y_{2}) \oplus K_{10,11}=O_{11},\ S(Y_{3})\oplus K_{10,8}=O_{8}. \tag{8}\]
# Spinning of Endless Bioactive Silicate Glass Fibres for Fibre Reinforcement Applications
## 2 Materials and Methods
### Single-Fibre Tensile Test (SFTT)  
The tensile strength of single fibres after drawing, storing and silanisation was conducted in accordance to DIN EN ISO 5079 by using the Favimat+ (Textechno, Germany) automatic single-fibre test system. The fibres to be tested were stored at a standard climate for at least one hour before the measurement. The fibres were then separated with the help of a dissecting needle and a pretension weight with a mass of 150 mg was applied. Single fibres were fixed between two clamps with a gauge length of 50 mm. Before the tensile test, the diameter of the single filament can be determined by the vibroscopic method, in which the fibre is set in vibration and its resonance is registered by an optoelectric sensor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Glass**} & **Crucible Temperature** & **Nozzle Temperature** & **Drawing Speed** & **Fibre Diameter** \\  & \({}^{\circ}\)**C** & \({}^{\circ}\)**C** & **m/min** & **\(\upmu\)m** \\ \hline
**1-06** & 1270 & 1170 & 1413 & 15.3\(\pm\) 1.3 \\
**18-06** & 1200 & 1110 & 1413 & 14.9 \(\pm\) 1.5 \\
**13-93** & 1220 & 1120 & 848 & 15.5 \(\pm\) 2.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Drawing parameters of the produced fibres.  
Figure 1: Schematic illustration of the fibre drawing plant.  
The tensile test was performed at room temperature and a speed of 25 mm/min. A total of approximately 50 fibres were tested and the results are presented as mean value with standard deviation. Due to the brittle nature of the fibres, the Weibull statistic was used to describe the reliability of the strengths. The Weibull modulus (m) is not a material constant, but it gives a good indication of how homogeneous the material is. The Weibull modulus can therefore be considered as a defect frequency distribution factor. High values (>4) indicate that defects are evenly distributed in the material, whether they are plentiful or not, so the strength is almost independent of the length of the fibres. Low values indicate that the flaws are less evenly distributed. This leads to a greater scatter in the strength. The Weibull modulus is therefore a tool for evaluating the homogeneity of the fibre in terms of mechanical properties. The two-parameter Weibull distribution describes the probability of failure \(P\) by:  
\[P=1-e^{-\left(\frac{\sigma}{\pi_{0}}\right)^{m}} \tag{1}\]  
where \(m\) is the Weibull modulus, \(\sigma_{0}\) is the scale parameter (Weibull critical strength), and \(\sigma\)--the fracture strength. The characteristic strength \(\sigma_{0}\) is slightly higher than the average strength and corresponds to the value of the stress for which the probability of failure (\(P\)) is equal to 63.2 % [30; 35]. Figure 2 displays the Weibull assessment exemplarily for glass 18-06.
## 3 Experimental Program
### Test Setup  
The flexure tests were conducted to investigate the structural performance and splicing performance of the proposed PC modular beam system. The supports were installed 200 mm from both ends of the specimen for simply supported condition, and loading points were 1750 mm away from the center of each supports. Two-point loading was applied at a rate of 2 mm/min using a hydraulic universal testing machine (UTM) with a capacity of 500 tons. To measure the displacement of the test specimen, an LVDT was installed at the center and 300 mm from the center of the test specimen. The specimen setup is shown in Figure 9. To observe the strain of each specimen, a strain gauge was attached to the concrete, the reinforcement steel, and the steel plate inserted into the PC module. The PC modules were symmetrical; thus, the gauge was attached to only one module. The gauge attachment position is shown in Figure 10. The instruments installed on the specimens are summarized in Table 2, and they are used widely in technical studies when measuring test results such as strain and displacement [21; 22; 23].  
Figure 8: Construction process of proposed PC modular beam system: (**a**) PC modules; (**b**); assembling the steel plates; (**c**) connection with shear studs; (**d**) non-shrink mortar injection.  
Figure 9: Test setup for flexural test: (**a**) schematic test setup (mm); (**b**) view of test setup.