esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation
## 2 Interferometer Measurement Model
### Single-Axis Interferometer Laser Path Model  
Since the multi-axis interferometer consists of multiple regular single-axis interferometers, the single interferometer model is proposed first.  
The measurement principle of the single interferometer is shown in Figure 1; the laser emits from the interferometer, reflects off the smooth surface of the stage (or the retroreflector fixed on the stage), and returns to the interferometer. Let a fixed position of the stage be the reference position, as shown in Figure 1a, and the reading of the interferometer at the reference position is set to 0. Then, the reading of the interferometer at the measurement position in Figure 1b is set as \(V\) and is equal to the variation of the laser path length after the stage is moved from the reference position to the measurement position, that is  
\[V=L_{1}-L_{0} \tag{1}\]  
where \(L_{0},L_{1}\) are, respectively, the laser path length at the reference and measurement position.  
Moreover, it can be obtained from Figure 1 that  
\[\left\{\begin{array}{l}L_{0}=\frac{2\cos\theta_{0}}{\cos 2\theta_{1}}[(x_{0}-x_ {d})\cos\theta_{0}+(y_{0}-y_{d})\sin\theta_{0}-b/2]\\ L_{1}=\frac{2\cos\theta_{1}}{\cos 2\theta_{1}}[(x_{1}-x_{d})\cos\theta_{1}+(y_{1}-y_ {d})\sin\theta_{1}-b/2]\end{array}\right. \tag{2}\]  
where \((x_{d},y_{d})\) is the coordinate of the laser emission point, \((x_{0},y_{0})\) and \((x_{1},y_{1})\) are, respectively, the coordinate of the stage center at the reference and measurement position, and \(\theta_{0},\theta_{1}\) are, respectively, the angle of the stage at the reference and measurement position. \(b\) and \(h\) are the length and width of the stage.  
The single interferometer model proposed in Equations (1) and (2) shows the mathematical relation between the interferometer readings and the position of the stage. This model is still used in the following multi-axis interferometers.
## 3 Results and Discussion
### Determination of Total Phenolics, Phenolic Acids and Flavonoids  
The LC-MS analysis revealed that the natural extracts of the study have a rich antioxidant phytochemical content in phenolic compounds including phenolic acids, flavonoids, and conjugated tannins. Specifically, the three natural extracts contain pyrogallic acid (m/z 125'), catechol (m/z 109'), 3,5-dimethoxy-4-hydroxy tannic acid (m/z 224.05'), quercetin (m/z 300.5'), curcumin (m/z 366.8'), catechchin (m/z 289'), deosmin (m/z 607'), kampferol (m/z 285), coumaric acid (m/z 163'), and gallic acid (m/z 169.17') (Table 1). Moreover, we observed that the liquid mastic extract was richer in phenolic acids such as tartaric acid, ascorbic acid, and cinnamic acid (Table 1). This antioxidant activity is quite important in the food industry, where antioxidants can be used to prevent or stop the autoxidation of food items or to shield food from nutritional degradation during storage [51; 52; 53]. Plant-based food ingredients exhibit antioxidant protection through their content of phenolic compounds (carotenoids, phenolic acids, anthocyanins, flavonoids, tannins, hydroxytyrosol, and their derivatives) due to their ability to scavenge free radicals, donate free electrons, and chelate metals [27; 28].  
Figure 3: (**A–C**) The growth curves of the three _S. cerevisiae_ strains at 37 \({}^{\circ}\)C in the absence of natural extracts (yeast only, red line) and in the presence of: hypericum (_H. perforatum_), liquid mastic (_P. lentiscus var. Chia), and rosemary (_R. officinalis_) extracts. An extended lag phase and a slower growth rate indicates antifungal activity. (**D**) The growth rates of the three _S. cerevisiae_ strains at 37 \({}^{\circ}\)C. The average values and SD were compared with ANOVA followed by Duncan post hoc test. * Indicates significantly lower (_p_ < 0.05) than the “yeast only” in each yeast cell model (baker, wine, beer).  
It has been suggested that the mechanism of antimicrobial action of various flavonoids varies depending on the group [54]. In a recent work, [PERSON] et al. (2023) [55] studied the composition of nine _Hypericum_ species from Greece and found high concentration of flavonoids. Regarding the non-polar extract of _R. officinalis_ plants from the Greek territory, [PERSON] and [PERSON] (2013) found high concentrations of flavonoids [56]. Therefore, it may be assumed that the mode of antimicrobial action of the extracts is linked to the mechanisms of antimicrobial action of the flavonoids. Regarding the antifungal activity of flavonoids, in a review paper, [PERSON] and [PERSON] (2020) summarized the main inhibitory mechanisms that include plasma membrane disruption, induction of mitochondrial dysfunction, inhibition of cell wall formation, cell division, RNA and protein synthesis, as well as dysfunction of the cell membrane pump system [54].
# Modeling the Energy Performance of LoRaWAN
## 3 LoRaWAN Overview
### LoRaWAN MAC Layer  
#### 3.3.1 MAC Message Format  
LoRaWAN defines a set of MAC message types that are transmitted as payload of a physical layer message (i.e., are carried in the PHY Payload field). Three basic types of MAC messages are defined: (i) the Join message; (ii) the Confirmed Data message and (iii) the Unconfirmed Data message. The format for a MAC message is shown in Figure 4. The MAC message comprises: (i) the MAC Header (MHDR), which indicates the type of MAC message; (ii) the MAC payload, which can carry application data or a Join message; and (iii) the Message Integrity Code (MIC), which allows a receiver to check the integrity of a MAC message received. On the other hand, data messages can carry MAC commands in the frame payload (FRM Payload), which can also be carried in the frame header (FHDR), depending on the FPort value. MAC commands are intended to configure radio and MAC layer parameters. An important subfield in the FHDR is a bit that allows acknowledging the last Confirmed Data message received.
# Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code
## 2 Preliminaries
### Hamming Code  
The Hamming Code (HC) [38] is a single error-correcting linear block code with a minimum distance of three for all the codewords. In HC(\(n,k\)), \(n\) is the length of the codeword, \(k\) is the number of information bits, and (\(n-k\)) is the number of parity bits.  
Let \(x\) be a \(k\) bit information word. The \(n\) bit codeword \(y\) is created by using \(y=xG\), where \(G\) is the \(k\times n\) generator matrix. Let \(e=y-\tilde{y}\) be the error vector that determines whether an error occurred while sending \(y\). If \(e=0\), no error occurs, and \(\tilde{y}=y\).  
Otherwise, the weight of \(e\) represents the number of errors. Let \(H\) be a \((n-k)\times n\) parity matrix with the relation of \(G\cdot H^{T}=[0]_{k\times(n-k)}\). Let us assume that the codeword \(\tilde{y}\) has an error like \(e=(y-\tilde{y})\). In this case, we could correct one error (\(e=y\oplus\tilde{y}\)) from the codeword \(\tilde{y}\) by using the syndrome \(S=\tilde{y}\cdot H^{T}\), where the syndrome denotes the position of the error in the codeword. As show in Equation (6), the error \(e\) can be obtained.  
\[\left\{\begin{aligned} \tilde{y}\cdot H^{T}&=(e \oplus y)\cdot H^{T}=e\cdot H^{T}+y\cdot H^{T}\\ (y\cdot H^{T})&=(x\cdot G)\cdot H^{T}=x\cdot(G\cdot H ^{T})=0\\ &=e\cdot H^{T}+0=e\cdot H^{T}\end{aligned}\right. \tag{6}\]  
Consider HC(\(7A\)) with the following parity matrix.  
\[H=\begin{bmatrix}0&0&0&1&1&1&1\\ 0&1&1&0&0&1&1\\ 1&0&1&0&1&0&1\end{bmatrix} \tag{7}\]  
For example, assuming that one error bit occurred in \(y\) (e.g., the second bit from the left in \(e=(e_{1},e_{2},\ldots,e_{7})=(010000)\), we may obtain the error position and recover the one bit error from the codeword \(y\) by calculating the syndrome \(S(=y\cdot H^{T}=(010))\).
## 1 Introduction
The local signal amplitude can be determined using the so-called analytical signal, i.e., the Hilbert transformation. However, the algorithms should determine local features associated with a narrow frequency range. Band-pass quadrature filters have such properties [8]. Generally, signal enhancement methods are based on either fundamental domain or frequency domain signal processing. The use of appropriately selected filters allows us to obtain a more useful form of signal. As is well known, Gabor filters achieve optimal resolution in the time and frequency domains. Therefore, it seems that their application to TFBG spectra will allow the synthesis of an effective demodulation algorithm. Later in this article, the applications of Gabor filters and their mathematical description will be explained. Following this, the TFBG spectra will be analysed using the Gabor and log-Gabor filters, and the adaptive algorithms will be applied to the previously differentiated TFBG spectrum. The results will be compared based on the shapes of the envelope derivatives determined for each of the algorithms. The proposed algorithm produces accurate results and allows us to predict its practical usefulness. It should be added that the TFBG optical spectrum has a multi-scale nature, which has not been used so far. To analyse the optical spectrum, we use band-pass Gabor filters, and their selection is based on Fourier analysis.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### IRSs for Cellular Networks
#### 2.2.4 Phase Shifts and Real-Time Adaptation  
Another critical feature of IRS technology is its ability to introduce phase shifts to the reflected signals. By controlling the phase of each element in the array, IRSs can adjust the wavefront of the reflected signals, directing them toward the desired receiver with minimal loss. This allows for real-time adaptation to changes in the environment, such as user mobility and shifting interference patterns, ensuring that the communication link remains stable and secure [103].  
The IRS is particularly advantageous for future communication systems, such as 5G and beyond, where the ability to control the wireless propagation environment will become increasingly important. The use of IRSs in these systems promises enhanced data rates, improved energy efficiency, and higher security, making it a critical component of next-generation wireless infrastructure.
# Decimal Multiplication in FPGA with a Novel Decimal Adder/Subtractor
## 4 Decimal Multiplier
### Partial Product Generator--Method 1
Therefore, each partial product output consists of N+1 BCD/excess-6 digits, \(\{PP_{0},PP_{1},\dots,PP_{N-1}\}\), and N+1 bits, \(\{ISbcd_{0},ISbcd_{1},\dots,ISbcd_{N-1}\}\), one for each digit, indicating if the digit is represented in BCD or excess-6. The partial product generator produces all N partial products in parallel using N (single digit) partial product generators (see Figure 4). Each partial product generator receives one digit of operand B.
# Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications
## 4 Trusted Computing
### Chain of Trust  
The identification of the software components is performed through a Chain of Trust, i.e., a hierarchy of trust rooted into a Trust Anchor (TA). The TA is an authoritative entity for which trust is assumed and not derived, i.e., usually a small and carefully designed piece of firmware, executed by the system at power up or hardware reset. The TA is responsible for starting to build the Chain of Trust during bootstrap of the node. Each element of the chain is a software component that when executed performs its tasks and then identifies, loads and finally executes the next software component (see Figure 3).  
The identification of a component is made of two steps: the measurement, i.e., a digest, calculated over that component and the storage of such measurement in the TPM, i.e., the accumulation of the measurement via PCR extension operation.  
Each PCR can contain one digest value, which is the result of one or more PCR extension operations: \(PCR_{i+1}=PCR_{i}||hash(component)\). For instance, the hash function can be sha1, sha256, or sha384. The Infineon TPM used in our prototype [48] implements sha1 and sha256 thus, for the sake of simplicity, we selected sha1. It is not possible to store a value directly in a PCR, but the only way to access a PCR for writing is through  
Figure 3: Basic element of a Chain of Trust.  
the PCR extend operation: this guarantees that all measurements are accumulated. By building on the fact that the Trust Anchor is trusted by definition and each software component is considered trusted at least to identify the next component, the identification of all components is trusted as well due to the transitive property of trust. The bootstrap process enhanced to build the Chain of Trust, i.e., the capability of each software component to identify and load the next one, is called Authenticated Boot(strap). The Authenticated Boot goes from the CRTM up to the applications in userspace through bootloader and Linux kernel.
## 2 Related Work  
### Modeling and Applications of Artificial Neural Networks  
Artificial neural networks (ANNs) are a type of machine learning algorithm inspired by biological neurons. They simulate the mechanism of brain structure and external stimulus response, can capture any nonlinear relationship with high precision, and fit and estimate functions, thus forming mathematical mapping between any input-output relationship. Once the ANN model is trained, it can quickly and accurately complete calculations with almost negligible overhead. Therefore, artificial neural network technology is considered a possible solution, training the ANN model with the simulation results of relevant elecromagnetic simulation software in order to reduce the cost of simulation calculations by replacing expensive EM models with ANN models [11; 12].  
Artificial neural network technology has been widely applied in the modeling and design of microwave components or circuits, including the modeling of on-chip square spiral inductors [13], the development of efficient and accurate models of CPW-related components [14], and the modeling of small-signal and large-signal models of field-effect transistors combined with equivalent circuit modeling and ANN modeling technology [15]. It also involves research directions such as the optimization of filter design [16; 17; 18], power amplifiers [19], and microstrip antennas [20]. Another design idea based on ANN modeling is called \"neural network inverse modeling\" [21], from which there have been some research results [22; 23; 24]. Unlike the forward model of a microwave device, where the input is the geometric design parameters, the input of the neural network inverse model is electrical parameters and the output is the geometric design parameters of the device. Although the inverse model of the neural network can produce design solutions faster than traditional optimization methods, the multivalued problem that the input and output are not uniquely mapped makes the inverse modeling much more difficult than the forward one [25].  
Neural network modeling technology is not only used in the field of microwave device modeling; ANN modeling technology is also employed to predict the boundary conditions of the concentration distribution of a microfluidic concentration gradient generator (a device used to generate gradient concentrations in microfluidic chips [26]), which is widely used in chemistry, biology, medicine, and other fields. Compared to traditional methods, this approach achieved an acceleration of over 300 times while maintaining an accuracy rate of 93.71%.  
In this work, we utilize neural network modeling techniques to establish an ANN surrogate model for hairpin filters, replacing expensive EM simulations.
# A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks
## 7 Conclusions and Future Work  
SDN-MNs provide many advantages for 5G mobile networks, but they also introduce new security challenges into the networks. The main problem is how to measure the security level of SDN-MNs. In this paper, we have proposed a novel security assessment mechanism for SDN-MNs. We analyze the new characteristics of SDN-MNs and find that SDN-MNs introduce a dynamic nature and complexity into mobile networks. The traditional network security assessment mechanisms cannot be directly applied to SDN-MNs. To solve the new security problems, we propose a security assessment methodology based on attack graphs and an analytic hierarchy process. A novel definition and attack graph generation algorithm are proposed to address the complexity of SDN-MNs. Also, we define NME as the index value of a security assessment to capture the quantitative dynamic property influence of SDN-MNs. By evaluating our scheme, we prove that  
Figure 11: Shortest attack paths: (**a**) “Network Administrator” scheme; (**b**) “Application User” scheme.  
our security assessment mechanism can deal precisely with the distinctive features of SDN-MNs and is very effective for security assessment for SDN-MNs. However, we use the classical AHP in our mechanism, so there are still margins for improvement. The classical AHP has the property of unbalanced scale of judgment. In the pair-wise comparison process, it is not precise to solve the inherent uncertainty and vagueness. In the future, it would be useful to employ a fuzzy AHP approach [31]. With a fuzzy AHP approach, quantifications of attack costs would become more precise.  
This work was supported in part by National Natural Science Foundation of China under Grant No. 61431008, 61401273. Additionally, this work was supported in part by the JSPS KAKENHI Grant No. 26730056, 15K15976 and JSPS A3 Froesight Program..  
**Author Contributions**: [PERSON], [PERSON] and [PERSON] contributed to the methodology, attack graph and TOPSIS of security assessment, [PERSON] and [PERSON] contributed to verification of the experiments.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Controller Design
### Output Constraints Tracking Control
#### 3.3.2 Adaptive NN Impedance Control  
When the system has uncertain parameters, the controller (40) is no longer applicable. Therefore, the NN-based controller is given in the following.  
We define \[W^{*T}\Delta(Z)+\sigma=M(\zeta)\hat{\chi}+C(\zeta,\hat{\zeta})\chi+G(\zeta) \tag{42}\]  
where \(\Delta_{i}(\cdot)\) is the basis function of the NN, \(Z=[\zeta^{T},\zeta^{T},\chi^{T},\hat{\chi}^{T}]\), \(\sigma\) is the estimation error, which satisfies \(\sigma\leq\tilde{\sigma}(\tilde{\sigma}>0)\), and \(\hat{W}^{T}\) is the estimation of \(W^{*T}\).  
We design the NN adaptive law as  
\[\hat{W}_{i}=-\Gamma_{i}(\Delta_{i}(Z)z_{2,j}+\delta_{i}|z_{2,j}|\hat{W}_{i}),i =1,2,3,\cdots n \tag{43}\]  
where \(\Gamma_{i}=\Gamma_{i}^{T}\) denotes the positive gain matrix, and \(\delta_{i}\) is a small positive constant.  
We design the adaptive NN controller \(\tau\) as  
\[\tau=\hat{W}^{T}\Delta(Z)-f-\sigma z_{1}-\text{K}z_{2}-\text{sgn}(z_{2}^{T}) \odot\tau_{dis} \tag{44}\]  
where \(\odot\) is defined as \(a\odot b=[a_{1}b_{1},a_{2}b_{2},\ldots,a_{n}b_{n}]^{T}\) with n-dimensional vectors \(a\) and \(b\).  
We choose the Lyapunov candidate function as  
\[V_{3}=V_{2}+\frac{1}{2}\tilde{W}_{i}^{T}\Gamma_{i}^{-1}\tilde{W}_{i} \tag{45}\]  
where \(\tilde{W}_{i}=\hat{W}_{i}-W_{i}^{*}\).  
Differentiating \(V_{3}\) yields  
\[\dot{V}_{3} \leq-\sum_{i=1}^{n}\varsigma_{i}\frac{\xi_{i}^{2}}{1-\xi_{i}^{2}} +\sum_{i=1}^{n}\varphi_{i}z_{1,i}z_{2,j}\] \[+z_{2}^{T}(\tau+f-\tau^{dis}-M(\zeta)\hat{\chi}-C(\zeta,\hat{ \zeta})  
with \(\lambda_{\text{min}}(\cdot)\) and \(\lambda_{\text{max}}(\cdot)\) referring to the minimum and maximum eigenvalues of the matrix, respectively. And K satisfies  
\[\lambda_{\text{min}}(\text{K}-I)>0 \tag{52}\]  
to ensure \(\gamma>0\).  
Using (49), the stability of the closed-loop system is ensured, and the signals \(z_{1}\), \(z_{2}\), and \(\tilde{W}\) will be convergent to the compact sets \(\Omega_{z_{1}}\), \(\Omega_{z_{1}}\), and \(\Omega_{\tilde{W}}\) respectively, are which defined as  
\[\Omega_{z_{1}}:=\left\{z_{1}\in\Re^{n}\Big{|}-\underline{D}_{z_{ 1},i}\leq z_{1},i\leq\overline{D}_{z_{1},i}\right\} \tag{53}\] \[\Omega_{z_{2}}:=\left\{z_{2}\in\Re^{n}\Big{|}\|z_{2,j}\|\leq \sqrt{\frac{D}{\lambda_{\text{min}}(M)}}\right\}\] (54) \[\Omega_{\tilde{W}_{i}}:=\left\{\tilde{W}_{i}\in\Re^{l\times n} \Big{|}\Big{\|}\tilde{W}_{i}\Big{\|}\leq\sqrt{\frac{D}{\lambda_{\text{min}} \Big{(}\Gamma_{i}^{-1}\Big{)}}}\right\} \tag{55}\]  
where \(l\) is the node number of the neural network and satisfies \(l>1\), \(D=2(V_{3}(0)+\Psi/\gamma)\), \(\overline{D}_{z_{1},i}=\sqrt{\kappa_{b,i}^{2}(t)(1-\delta_{i}^{-D})}\), and \(\underline{D}_{z_{1},i}=\sqrt{\kappa_{a,i}^{2}(t)(1-\delta_{i}^{-D})}\). At this point, the system stability proof is complete. \(\diamond\)
## 3 Our Advanced Proposed Methodology
### Generation of Virtual Sensor Data
#### 3.3.1 Step 1: Polynomial Feature Transformation  
A polynomial feature transformation of degree 3 is applied to the first half of the physical sensor data for each reading. The polynomial degree is chosen to introduce non-linearities and model more complex interactions. The transformation includes the original data points raised to powers of up to three, expanding the feature space.  
\[X_{\text{poly}}=\text{PolynomialFeatures}(X,\text{degree}=3)\]  
This transformation allows the model to capture higher-order relationships between the physical sensor values.
## 2 Study Areas and Data
### Data  
Three kinds of satellite data, GF-1, Sentinel-2, and ICESat-2, were used in this study, among which the GF-1 remote sensing images were obtained from the China Center for Resources Satellite Data and Application (CRESDA). The GF-1 images acquired in this study are all Level-1A raw data, which need to be pre-processed to derive surface reflectance data. The satellite IDs and image information of the GF-1 data used in this study are shown in Table 1.  
Sentinel-2 is a high-resolution multispectral imaging satellite launched by ESA, which carries a multispectral instrument that provides free multispectral image data with 10 m resolution. Due to the need for multi-year Sentinel-2 satellite imagery data in this study, the Google Earth Engine (GEE) platform was chosen as the basis for data access and processing, which is a large-scale cloud computing platform launched by Google Inc. to process satellite remote sensing image data and other Earth observation data. It provides a number of publicly available satellite image datasets, including Sentinel-2, and can quickly and efficiently obtain a large number of Sentinel-2 image data and process them quickly.  
The ICESat-2 L2A data ATL03 dataset, which has been widely demonstrated to be applicable in shallow water bathymetry inversion [24; 25; 26; 27; 28; 29], is also used in this study. However, due to the raw photons being very noisy, not all photons carry the correct height information, and even though ATL03 encodes the confidence level of all photons, it has been shown that there will still be some obvious underwater topographic signals that will be labeled as noise photons. Therefore, the improved density-based spatial clustering of applications with noise (DBSCAN) method proposed by [PERSON] et al. [30] needs to be used for denoising before using the ICESat-2 data. In addition, since water depth is influenced by tidal levels, tidal correction is required for integrating ICESat-2 data from multiple dates. For this purpose, the Oregon State University Tidal Prediction Software (OTPS) with model TPXO7.2 was used to correct the tidal levels of each dataset to the same mean sea level and then merge them. Therefore, the vertical reference for water depth is based on the mean sea level.