esa-sceva/satcom-chunk-collection huggingface dataset:
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 3 Data Hiding Model
### Security Model  
The Hadamard matrix is easily generated as described in (11). Anyone can attempt with the Hadamard matrix to reconstruct the dictionary to detect the hidden data and also to reconstruct the audio. This leads to insecure watermark bits hidden in the host audio, and accordingly, we apply a procedure to secure the Hadamard matrix as also discussed in [19; 20; 21]. The Hadamard matrix is multiplied by \(-1\) at the row and the column of the matrix in a random manner. Denote \(l_{i}\in\{1,r\}\) as an integer random permutation value where \(i=1,2,...,N_{l}\), and \(N_{l}\) is the number of the generated integer random permutation value. Denote \(\mathbf{H}_{s}\) as a secured Hadamard matrix, \(\mathbf{H}_{s}(j)\) as a vector from the \(j^{\text{th}}\) row of \(\mathbf{H}_{s}\), and \(\mathbf{H}_{s}^{T}(j)\) as a vector from the \(j^{\text{th}}\) column of \(\mathbf{H}_{s}\), then the security model of the Hadamard matrix after initial definition \(\mathbf{H}_{s}=\mathbf{H}_{r}\) is defined as:  
\[\begin{split}\mathbf{H}_{s}(l_{i})&=-\mathbf{H}_{r} (l_{i})\\ \mathbf{H}_{s}^{T}(l_{i})&=-\mathbf{H}_{r}^{T}(l_{i}) \end{split} \tag{35}\]  
The above procedure is repeated \(N_{l}\) times from \(l_{1}\) to \(l_{N_{l}}\). Thus, with the secured Hadamard matrix, (12) is replaced by:  
\[\mathbf{p}_{j}=\mathbf{H}_{s}(j). \tag{36}\]  
Note that \(\mathbf{H}_{s}\) is not only needed in the embedding process, but also in the detection/extraction process. However, it is not needed to pass \(\mathbf{H}_{s}\) to the detector directly. We only pass \(l_{i}\) as the integer random permutation value to the detector as the security key. By the procedure (35), \(\mathbf{H}_{s}\) can be generated in the detector using \(l_{i}\) as the key. According to [19; 20], the modified Hadamard matrix combination using (35) has \((r12^{r})^{2}\) possibilities. For example, if \(r=16\), the number of modified Hadamard matrix is \(1.88\times 10^{36}\) possibilities. If the simulation needs one second to run the detection and reconstruction process using one Hadamard matrix, then it needs \(1.88\times 10^{36}\) seconds or \(5.962\times 10^{28}\) years using all Hadamard matrix possibilities. This confirms that this proposed security model is appropriate and meets the security requirement for the embedding and compression process.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.7.2 Tree Canopy Delineation  
The validation for delineation of objects can be made by different methods. In this study more than one approach was adopted for comparison with the existing literature and to suggest future works. The validation included the evaluation of the IoU value itself, the metrics precision_del (Pdel), recall_del (Rdel), F1-score_del (F1 del) and the statistical analysis of the canopy area value by linear regression.  
The Pdel and Rdel metrics were calculated according to an increment of the approach proposed by [29], who analyzed the relationship between the areas of the bounding boxes of the inferred, the reference segments, and their intersections. The modification proposed in relation to the work mentioned is that the delimiting masks themselves were considered instead of the bounding boxes. Pdel is obtained by the ratio between the intersection area of each corresponding pair and the area of the inferred mask; Rdel is obtained by the ratio of the intersection area over the reference mask; F1-score_del is obtained by equation IV with the metrics Pdel and Rdel, respectively replacing Pdet and Rdet. Figure 7 below displays each metric graphically.  
A regression analysis was performed to evaluate the relation between area and perimeter of the reference canopies and those estimated by the proposed method. For each  
Figure 7: Schematic illustration of delineation metrics.  
analysis, the coefficient of determination (R\({}^{2}\)) was computed, which indicates the degree of correlation between the two variables, the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE) and the Mean Absolute Percent Error (MAPE). The higher the R\({}^{2}\), the greater the correlation between the variables and the lower the RMSE, MAE, and MAPE, the closer. The four metrics were used for the analysis.
# Assessing the Effect of Drivers' Gender on Their Intention to Use Fully Automated Vehicles
## 1 Introduction
### Greater Connectivity: Networking Mobility  
Among all the social and mobility needs that are expected to be, at least partly, fulfilled through technological developments, recent studies have highlighted mobility networking as one of the core features to be offered by automated vehicles [7]. Instead of operating in a standalone mode, automated vehicles (whose functional basis largely lies on connectivity) are expected to compose cooperative networks useful for different tasks such as traffic control, flow and density monitoring, alerts on critical events and dynamic (real-time) accident prevention [8]. In other words, and apart from maximizing the capacity of urban roads to a substantial extent [9], automated cars are expected to improve \"connected mobility\" through different resources, including collaborative driving systems (CDS) and vehicle-to-vehicle communication (V2V) systems, thus improving the information available both for our car and for the others' vehicles to make accurately safe decisions [10, 11].  
Notwithstanding, and although the forecasts about safety, efficiency and technical improvements that having a \"more connected\" mobility through data sharing would entail, some studies have questioned the growing concerns of drivers in relation to key issues such as data privacy, network stability and reliability, and the possibility that their vehicles (or the networks to which they are connected) could be \"hacked\" by third parties and, consequently, their privacy and security would get threatened [1, 12, 13, 14]. Precisely, a recent systematic review found that the behavioral intention to shift to an automated car can be substantially affected by technological fears which are becoming relatively common in current times [15].
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 2 Materials and Methods
### Methods
#### 2.3.2 Z-Score Method  
Z-score is a method often used for data standardization. It standardizes the data based on the mean and standard deviation of the original data to ensure the reliability of the results. SIF has been standardized, and the calculation formula is as follows:  
\[A_{b,a}=\frac{SIF_{b,a}-\overline{SIF_{b}}}{\sigma} \tag{6}\]  
where \(A_{b,a}\) indicates SIF abnormality in month \(b_{\text{th}}\) of year \(a_{\text{th}}\). \(\overline{SIF_{b}}\) represents the average SIF of \(b_{\text{th}}\) month from 2001 to 2020. \(\sigma\) is the standard deviation.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 3 Our Approach
### Second Stage  
At the second stage, we remove the square root normalization because, although the efficiency has already been improved, [PERSON]'s iteration still has an expensive computational complexity of \(O(D^{3})\), where \(D\) is the number of feature dimensions.  
However, as Equation (3) shows, square root normalization square-roots eigenvalues in the matrix \(\mathbf{A}\), and rejecting the normalization will change both the magnitude and distribution of eigenvalues. To quantify this change, we define the magnitude \(M_{e}\) as the sum of eigenvalues:  
\[M_{e}=\text{tr}(\mathbf{A}) \tag{7}\] \[\text{tr}(\mathbf{A})=\sum_{d=1}^{D}\lambda_{d}\]  
where \(M_{e}\) can be obtained via the trace of \(\mathbf{A}\). Subsequently, \(\mathbf{A}\) is divided by \(M_{e}\), and we have \(\mathbf{\hat{A}}\). The sum of eigenvalues \(\sum_{d=1}^{D}\hat{\lambda}_{d}\) in the matrix \(\mathbf{\hat{A}}\) is always 1. Then, the distribution of eigenvalues can be defined as the Frobenius norm of \(\mathbf{\hat{A}}\):  
\[D_{e}=\|\mathbf{\hat{A}}\|_{F} \tag{8}\] \[\|\mathbf{\hat{A}}\|_{F}=\sqrt{\sum_{d=1}^{D}\hat{\lambda}_{d}^{2}}\]  
The value of \(D_{e}\) can enable us to decide the flatness of the distribution of eigenvalues. For example, \(D_{e}=1\) represents the most peaky distribution where only one eigenvalue is equal to 1 and the others are zero. On the contrary, \(D_{e}=1/sqrt(D)\) represents the most flat distribution where all the eigenvalues are equal to \(1/D\). Within the interval \(D_{e}\in[1/sqrt(D),1]\), a smaller value means a flatter distribution and vice versa.  
For our approach RegCov, when the first stage terminates, we record the average distribution and magnitude values \(\bar{D_{e}}\) and \(\bar{M_{e}}\) for all the training samples. After the removal of square root normalization, we add two regularization terms in the loss function during training and the network is encouraged to output the bilinear representation whose \(D_{e}\) and \(M_{e}\) approach to the target \(\bar{D_{e}}\) and \(\bar{M_{e}}\):  
\[\mathcal{L} =\mathcal{L}_{CE}+\lambda_{d}\mathcal{L}_{d}+\lambda_{i}\mathcal{ L}_{m} \tag{9}\] \[\mathcal{L}_{d} =\frac{1}{S}\sum_{s}^{S}(D_{e}^{\text{s}}-\bar{D_{e}})^{2}\] \[\mathcal{L}_{m} =\frac{1}{S}\sum_{s}^{S}(M_{e}^{\text{s}}-\bar{M_{e}})^{2}\]  
Here, \(\mathcal{L}_{d}\) and \(\mathcal{L}_{m}\) push current \(D_{e}\) and \(M_{e}\) of the \(s\)-th training sample towards \(\bar{D_{e}}\) and \(\bar{M_{e}}\), \(\lambda_{d}\) and \(\lambda_{i}\) are loss weights whose values will be further studied in Section 4.6.
## 1 Introduction
The boom's pump-controlled hydraulic cylinder system employs a control approach that combines displacement adjustment with motor speed regulation to ensure effective load-power matching. An experimental platform has been established for analyzing energy consumption in a variable-displacement hydraulic pump driven by a variable-speed motor. The experimental results indicate that optimizing speed under varying load conditions maximizes the energy conversion efficiency of the motor-hydraulic pump system while ensuring smooth boom operation. This method demonstrates significantly enhanced energy-saving effects compared with traditional valve-control systems and those utilizing only variable-speed pumps. Working Principle of the Variable-Speed Motor--Variable-Displacement Hydraulic Pump-Controlled Hydraulic Cylinder  
As illustrated in Figure 1, the variable-speed motor drives a variable-displacement hydraulic pump to regulate the hydraulic cylinder system. Serving as the power source for this system, motor 1 converts electrical energy into mechanical energy and transmits it to the hydraulic pump via a coupling mechanism. The hydraulic pump transforms this mechanical energy into hydraulic energy, delivering high-pressure oil to the system. Variable-displacement hydraulic pump 2 employs an electric proportional variable piston design. The system modulates working flow by adjusting the motor speed and hydraulic pump displacement. Accumulator 3 utilizes an air-bag accumulator that is sensitive, compact, and lightweight, and that operates at low pre-charge pressure; it supplements leakage from the hydraulic pump and cylinder while balancing asymmetric flow rates within the system. Relief valve 4-1 limits the maximum pressure within the accumulator; additionally, pre-filled high-pressure oil in this accumulator compensates for unbalanced flow resulting from area discrepancies between the two chambers of the hydraulic cylinder when reversing open check valves 6-1 and 6-2. Relief valves 4-2 and 4-3 act as safety valves designed to restrict maximum pressure across both chambers of the hydraulic cylinder. The control principle of the hydraulic cylinder system is driven by a variable-speed motor and a fixed-displacement pump, as illustrated in Figure 2. The position of the hydraulic cylinder is regulated in a closed-loop manner solely through adjustments to the motor speed. However, certain operational conditions present challenges, such as inadequate matching between system power and load requirements and low energy utilization efficiency. Figure 3 illustrates the integrated-control principle of speed and displacement. The primary controller regulates the position of the hydraulic cylinder within a closed-loop system by modulating the displacement of the hydraulic pump. The system continuously monitors external load forces and the operational speed of the hydraulic cylinder via force sensors and speed sensors, computes the real-time load power exerted on the hydraulic  
cylinder, and adjusts the motor speed accordingly to enhance the system-load power compatibility while minimizing energy consumption for both the motor and the pump.
## 2 Results
### Chemical Composition of Initial Wine and Retentates  
Reducing sugars, free and total SO2, total and volatile acids, alcohol content and total extract in initial Cabernet Sauvignon wine and retentates obtained by reverse osmosis and nanofiltration are presented in Tables 1 and 2, respectively.  
Figure 1: Influence of process duration (min) on retentate temperature (\({}^{\circ}\)C) during concentration of Cabernet Sauvignon red wine by reverse osmosis (**a**) and nanofiltration (**b**). Abbreviations: R—reverse osmosis retentate; N—nanofliltration retentate; 1–2.5 MPa with cooling; 2–3.5 MPa with cooling; 3–4.5 MPa with cooling; 4–5.5 MPa with cooling; 5–2.5 MPa without cooling; 6–3.5 MPa without cooling; 7–4.5 MPa without cooling; 8–5.5 MPa without cooling.  
Figure 2: Influence of transmembrane pressure (2.5, 3.5, 4.5 and 5.5 MPa) on: (**a**) final retentate temperature (\({}^{\circ}\)C) and (**b**) average permeate flux (L/m\({}^{2}\)h) during reverse osmosis (RO) and nanofiltration (NF) of Cabernet Sauvignon red wine with (C) and without cooling (WC).  
The results showed that reverse osmosis and nanofiltration processes resulted in higher reducing sugars content in retentate than in initial wine. Application of RO membranes resulted in slightly higher concentrations of sugars than the application of NF membranes. Pressure increase and cooling regime were more favourable for sugars retention in NF retentates than lower pressure (2.5 and 3.5 MPa) and higher temperatures. However, RO membranes retained same amount of sugar at 4.5 and 5.5 MPa with and without cooling (no significant difference among concentrations). Similar trend was observed for total acids retention: higher temperatures and lower pressure resulted in lower retention. RO membranes retained slightly higher concentrations of total acids than NF ones at same operating conditions. Volatile acids content decreased during both processes comparing to the initial content in wine (1.44 g/L). The decrease of volatile acids was a result of membrane permeability to acetic acid that is the volatile acids representative. The highest concentrations of volatile acids were retained in RO retentates at 5.5 MPa with cooling (1.40 g/L).  
Alcohol content (mostly ethanol) in retentates followed the same trend as volatile acids. Retentates contained lower content of alcohol than initial wine (13.62 vol.%), with highest concentrations at 5.5 MPa with cooling (11.01 vol.% in RO retentate and 10.38 vol.% in NF retentate). NF membranes showed slightly higher permeability to ethanol. The lowest concentrations of free and total SO2 were found in RO and NF retentates at 2.5 MPa at both temperature regimes. Slightly higher retention of SO2 was observed with pressure increase and retentate cooling, as with RO membranes application.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **Reducing** & **Free SO2** & **Total SO2** & **Total Acids** & **Volatile Acids** & **Alcohol** \\  & **Sugars (g/L)** & **(mg/L)** & **(mg/L)** & **(g/L)** & **(g/L)** & **(vol.\%)** \\ \hline W & 3.66 \(\pm\) 0.2 \({}^{\rm a}\) & 40.1 \(\pm\) 0.1 \({}^{\rm e}\) & 64.0 \(\pm\) 0.1 \({}^{
## 5 The Simulation Results and Discussion
### Simulation Setup
#### 5.1.4 Genetic Algorithm Setup  
In this study, the Python language has been chosen to implement the optimization process of the FLC by using the GA. For the Python language, there is the Pymoo library that has already implemented a number of optimization algorithms, thus the GA will be implemented in Google Colab(r) with the help of the Pymoo library so that the suboptimal FLC can be found. For the optimization of the FLC, the GA was implemented in the Pymoo library with the size of the population set to 50 chromosomes and the generation count was set to 200. The size of the population and the number of the generation have been found as a good trade-off between the fast optimization process and the behavior of the resulting FLC.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 2 System Model
### Compressive Sampling  
As illustrated in Figure 2, each primary user (PU) typically occupies a continuous segment of the spectrum in practice [27; 28]. Furthermore, the sampled spectrum consists of blocks that are either occupied by continuous non-zero elements or occupied by continuous zero elements, thereby forming a block sparsity structure.  
Figure 1: Block diagram of CSS.  
Wideband spectrum signals \(\mathbf{x}_{f}\) can be divided into \(J\) blocks in the frequency domain because of BSF and can be expressed as  
\[\mathbf{x}_{f}=\underbrace{[x_{1,1}\cdots x_{I,1}]}_{\mathbf{x}_{1}^{\top}}\underbrace{x _{1,2}\cdots x_{I,2}}_{\mathbf{x}_{2}^{\top}}\cdots\underbrace{x_{I,J}\cdots x_{I,J} ]}_{\mathbf{x}_{j}^{\top}}^{\top}, \tag{1}\]  
where each block consists of \(I\) elements, and \(x_{i,j}\in\mathbb{C}\).  
This work employs a multi-coset sampler (MCS) for sampling wideband spectrum signals. The MCS consists of \(P\) parallel cosets, each uniformly sampling at a rate of \(\frac{\ell_{s}}{L}=\frac{1}{L^{\prime}}\), with a sampling time offset of \(c_{p}T\), and \(L\gg P\). Here, \(c_{p}\) is chosen from the integer set \(\{0,1,\ldots,L-1\}\), and \(f_{\mathrm{s}}=\frac{1}{\mathbf{\tau}}\) is the nyquist sampling rate. The compression ratio \(\beta\) is defined as \(P/L\).  
The compressive sampling of MCS can be expressed as  
\[\mathbf{\mathrm{Y}}=\mathbf{\Phi}\mathbf{\mathrm{X}}+\mathbf{\mathrm{E}}, \tag{2}\]  
where \(\mathbf{\mathrm{Y}}\in\mathbb{C}^{P\times M}\), \(\mathbf{\mathrm{X}}\in\mathbb{C}^{L\times M}\) is the sparse representation of the original signal in the frequency domain and \(\mathbf{\mathrm{E}}\in\mathbb{C}^{P\times M}\) is the noise matrix. \(\mathbf{\Phi}\in\mathbb{C}^{P\times L}\) is the observation matrix, with its elements denoted as \(\mathbf{\Phi}_{p,l}=\frac{1}{L^{\prime}L_{\mathrm{s}}}\exp(\frac{2\pi\cdot c_{p} \cdot l}{L})\). Please note that \(\mathbf{\mathrm{X}}\) is obtained by reshaping \(\mathbf{x}_{f}\).
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Technologies Involved in DTs
#### 3.1.4 Reliability, Integrity, and Credibility  
Reliability, integrity, and credibility are pivotal when DTs are entrusted with sensitive tasks involving transaction management with their real counterparts. It becomes imperative for the real twin to establish a sense of trust in their digital twin's capabilities and actions. Building and maintaining a solid connection between the real twin and the DT is crucial to foster effective collaboration, enabling reliable decision-making, and ensuring secure interactions. Consequently, a strong foundation of trust becomes indispensable for the successful operation and advancement of DT-enabled systems [52]. Therefore, privacy and security arise as paramount considerations in the realm of DTs. To enable the confidentiality and privacy of their real twins, DTs should implement robust measures for identity protection. This can be achieved using dedicated cryptography algorithms and advanced biometric techniques such as ECG and haptic biometrics. By leveraging these techniques, the identity and privacy of the real twin can be established and safeguarded. In addition to identity protection, DTs must prioritize data integrity, security, and privacy. Measures should be in place to prevent unauthorized access and interception of data across the network. Unauthorized users should be promptly detected and repelled to prevent any compromise of data integrity or security. To ensure the authenticity and reliability of data sources, rigorous measures for data authentication should be implemented before integrating the data into the digital twin. Overall, DTs must incorporate comprehensive privacy and security mechanisms to safeguard their real twins' data's identity, confidentiality, and integrity. By adopting robust cryptographic algorithms, employing biometric techniques, and implementing strict data authentication protocols, DTs can effectively address privacy and security concerns, fostering a trustworthy and secure environment for the real twins and their digital counterparts.
# Recent Developments in the Field of the Metal-Insulator Transition in Two Dimensions
## 2 Metal-Insulator Transition in Zero Magnetic Field  
The existence of a metallic state at zero magnetic field in 2D was first predicted by [PERSON] [11; 12] and [PERSON] et al. [13]. The combined effects of interactions and disorder were studied by perturbative renormalization group methods. The possibility of metallic behavior was confirmed later in [9; 10; 14].  
Typical experimental data showing the resistivity \(\rho\) as a function of temperature \(T\) for different electron densities in various systems are presented in Figure 1[8; 15; 16]. At the lowest temperatures, the resistivity exhibits a strong metallic temperature dependence (d\(\rho/\)d\(T>0\)) for electron densitiesabove a well-defined critical value, \(n_{\rm c}\), and insulating behavior (\({\rm d}\rho/{\rm d}T<0\)) for \(n_{\rm s}<n_{\rm c}\). One refers to the regime at \(n_{\rm s}>n_{\rm c}\) as metallic and to that at \(n_{\rm s}<n_{\rm c}\) as insulating [17]. The two regimes are separated by a separatrix, indicated in Figure 1a by a dotted red line. This curve is often tilted in agreement with [10], but becomes flat in the low-temperature limit or over an extended temperature range in the most uniform samples [18]. The \(\rho(T)\) dependences on the metallic side of the transition at \(n_{\rm s}\) just above \(n_{\rm c}\) are non-monotonic; while at temperatures exceeding a density-dependent value \(T_{\rm m}\), the derivative \({\rm d}\rho/{\rm d}T\) is negative; it changes sign at temperatures below \(T_{\rm m}\).  
However, to determine the critical density for the MIT, one cannot rely on the derivative criterion alone. In [17], an additional method was used based on vanishing activation energy and the vanishing nonlinearity of the current-voltage characteristics (looking from the insulating side of the transition). It was shown that in clean Si MOSFETs in zero magnetic field, the critical density \(n_{\rm c}^{*}\), determined by such a method, coincides with the critical density \(n_{\rm c}\) determined by the derivative criterion.