esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Reduction on Circular Disks with Vibroacoustic Metamaterials
## 3 Experimental Characterization
### Measurement Setup  
The experimental investigation of vibrations in a rotating system with a stationary observer is a complex measurement task. It necessitates both a suitable excitation mechanism of the rotating system and a preferably non-contact sensor for measuring the resulting system response. Since the stop band is expected in the frequency range between 2000 Hz and 3000 Hz, an excitation mechanism capable of inducing vibrations up to at least 3000 Hz is required. Due to rotation, high levels of noise amplitudes occur depending on the measurement method used. Hence, it is necessary to excite sufficiently large amplitudes to measure the system behavior above the noise level.  
Preliminary investigations by [PERSON] [34] revealed that a measurement setup consisting of an automated impulse hammer for excitation and an LDV for measuring the structural vibrations is most suitable for the underlying measurement problem. The impact force of the impulse hammer is measured with a piezoelectric force sensor at the hammer's tip. Figure 10 shows the experimental setup together with the utilized rotor dynamics test bench. The circular disk is mounted on an electric motor via a flange with a diameter of 61 mm. The rotational speed of the motor can be adjusted via a frequency converter. The disk is excited with the automated impulse hammer on the side of the disk facing the motor. The automation of the impulse hammer enables a reproducible and remotely controllable excitation. The surface velocities of the disk are measured using an LDV (Polytec PSV-300-H, Germany-76337 Waldbronn). To realize a diffuse reflection behavior, the circular disk is sprayed with chalk spray, which enhances the signal quality.
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### SRAM Device Characteristics
#### 4.2.2 Patterns  
In an SRAM device, even if the responses have an equal number of '1's and '0's, there may still be unexpected patterns present in the SRAM cells that can impact the security and randomness of the PUF. The SRAM chips used in this study, specifically the ISSI IS64 WV6416 BLL SRAM chips, are organized into 65,536 words, each containing 16 bits. To ensure the absence of repeating patterns, a heatmap was generated to visualize the state of each SRAM cell for each SRAM device.  
Upon examining the heatmaps, patterns can be seen occurring with temperature; as such, each SRAM needs to be independently verified to avoid any temperature-dependent patterns. A heatmap of several SRAM chips can be seen in Figure 10. A likely cause of the vertical striations is internal cell read-out circuity that causes biasing at different temperatures.
## 3 Results and Analysis
### Performance Evaluation of Modified Nano-SiO\({}_{2}\)
#### 3.2.4. Depressurization and Injection Enhancement Performance  
During the start-up process of water injection, given that the low-permeability core needed to overcome a certain start-up pressure, the pressure initially increased to the maximum value and then decreased to the equilibrium value. The low-permeability core surface had a layer of hydration boundary, and fluid flow was not easy. Smaller core permeability corresponded with a greater resistance to overcome the fluid flow and a greater starting pressure gradient. As shown in Figure 11, the initial injection pressure of the core was 0.141 MPa. After the injection of 0.5 PV nano-SiO\({}_{2}\) dispersion solution with a concentration of 300 mg/L, the pressure of the subsequent water flooding was stable at 0.071 MPa, and the pressure reduction rate reached 49.6%. After flushing 20 PV water at  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solution Type** & **Expansion of the Volume (mL)** & **The Expansion Rate (\%)** \\ \hline Kerosene & 1 & \\ Modified nano-SiO\({}_{2}\) & 1.8 & 42.9 \\ Water & 2.4 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Swelling volume of bentonite in different solutions.  
Figure 10: Zeta potential of surface active aqueous dispersion after NaOH adjustment.  
Figure 9: Zeta potential of surfactant aqueous dispersion before NaOH conditioning.  
a flow rate of 0.1 mL/min, the displacement pressure was 0.076 MPa, and the pressure reduction rate remained as high as 46%. This result indicated that the modified nano-SiO\({}_{2}\) adsorbed onto the surface of the rock passage and can maintain a low injection pressure for a long time even after long-term water erosion, indicating long-term effective performance.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 3 Proposed Statistical Dimensioning Model
### Expressions of \(m_{N}\) and \(v_{N}\)
#### 3.3.2 Multiuser Case  
In the multiuser case, we assume that each RR is shared by \(n_{u}\) users simultaneously as depicted in Figure 2. To cancel the interuser interference, the collectors uses a Zero-Forcing (ZF) decoder. The equivalent fading observed by each sensor \(j\) device is denoted by \(A_{f}^{(j)}\) having a distribution that will be specified in the next section.  
Distance based multiuser scheduling scheme: In this scheme, the active users are sorted according to their proximity to the collector into different groups of \(n_{u}\) users, each _independently_ of their fading. As shown in Figure 3, the first group contains the \(n_{u}\) nearest neighbor to the typical collector, the second group the \((n_{u}+1)^{\text{th}}\) to \(2n_{u}^{\text{th}}\) collector neighbor and so on. We adjust the number of allocated RRs to the furthest user from the collector in each group independently of the fading coefficient experienced by other users. The furthest node \(x\) in each group \(i\) is the \(i\times n_{u}^{\text{th}}\) neighbor of the collector and it corresponds to the radius of the ball \(\mathcal{B}(|x|)\) with radius \(|x|\) containing \(i\times n_{u}\) nodes.  
**Proposition 3** (Multiuser case).: _The expressions of \(m_{N}\) and \(v_{N}\) are_  
\[m_{N} = N_{s}\times\sum_{k=1}^{N_{\max}}k\times\mathbb{E}_{r}\ \mathbb{E}_{I,P_{n}}\ \mathbb{E}_{A_{f}}\left[\mathbf{1}_{\{A_{fj}\leq A_{f}<A_{fj-1}\}}\sum_{i\in \mathbb{N}^{*}}\text{Prob}\left\{|\mathcal{B}(r)|=in_{u}-1\right\}\right] \tag{24}\] \[v_{N} = N_{s}\times\sum_{k=1}^{N_{\max}}k^{2}\times\mathbb{E}_{r}\ \mathbb{E}_{I,P_{n}}\ \mathbb{E}_{A_{f}}\left[\mathbf{1}_{\{A_{fj}\leq A_{f}<A_{fj-1}\}}\sum_{i\in \mathbb{N}^{*}}\text{Prob}\left\{|\mathcal{B}(r)|=in_{u}-1\right\}\right] \tag{25}\]  
Figure 3: Illustration of the scheduling with \(n_{u}=4\). The number of RR is adjusted with respect to the furthest node in each group, to say \(i\), situated at distance \(r_{i}\) corresponding to the radius of the ball containing \(in_{u}-1\) nodes.  
Figure 2: MU-MIMO: \(n_{u}\) users are simultaneously scheduled on the same radio-resource.  
_where \(\mathcal{B}(r)\) is the ball with radius \(r\), and_  
\[\sum_{i\in\mathbb{N}^{*}}\mathrm{Prob}\left\{\left|\mathcal{B}(r)\right|=in_{u}-1 \right\}=\exp(-\lambda_{a}\pi r^{2})\sum_{i\in\mathbb{N}^{*}}\frac{(\pi\lambda _{a}r^{2})^{(in_{u}-1)}}{(in_{u}-1)!}. \tag{26}\]  
_with \(\mathbb{N}^{*}\) being the set of nonzero positive integers. The distribution of \(r\) in the typical cell is given by \(f(r)\) defined in (7)._  
Proof.: Please refer to Appendix C.  
Note that Proposition 3 is a generalization of Proposition 2 with \(n_{u}=1\). For \(n_{u}=1\),  
\[\sum_{i\in\mathbb{N}^{*}}\frac{(\pi\lambda_{a}r^{2})^{(i-1)}}{(i-1)!}=\exp(- \lambda_{a}\pi r^{2}) \tag{27}\]  
and the expressions of \(m_{N}\) in (24) becomes equal to (22).
## 3 Results and Discussion
### Experiment Design: Optimal Operating Conditions  
A statistical design of experiments was carried out to study the optimum operating conditions of the factors involved in the biosorption process. Table 2 shows the experimental results obtained for the biosorption capacity (q) under equilibrium conditions, which ranged from 2.96 to 24.50 mg Cu(II)/g dry biomass.  
Subsequent statistical adjustment (Design-Expert(r) v.12 software, Minneapolis, MN, USA) yielded Equation (3).  
\[\begin{array}{c}q=-43.3077+23.1506\ pH+5.11094\ B-10.5387\ pH\ B-\\ -1.58606\ pH^{2}+33.2120\ B^{2}\pm 0.9733\end{array} \tag{3}\]  
where \(B\) is the biosorbent dose (g/L).  
Figure 3: _Cont._  
Figure 4: _Cont._  
Figure 3: (**a,b**): FE-SEM images obtained before and after the Cu(II) biosorption, respectively, showing an overview of the state of the bacteria and the copper precipitates on them. (**c,d**): Detail of the bacterial cell surface before and after the biosorption step, respectively. (**e,f**): EDX spectra obtained before and after the Cu(II) biosorption by _Staphylococcus epidermidis_ CECT 4183, respectively.  
Based on Equation (3), the optimal operating conditions for Cu(II) biosorption were obtained: pH 5.5 and a biosorbent dose of 0.2 g/L.  
Figure 5 shows the response surface (a) and the perturbation diagram (b) of the biosorption process, and in both diagrams, it can be seen that the most determining factor in the biosorption of Cu(II) is the pH.  
\begin{table}
\begin{tabular}{c c c} \hline
**pH** & **B, g/L** & **q, mg/g** \\ \hline
3.0 & 0.20 & 7.05 \\
3.0 & 0.76 & 10.93 \\
4.3 & 0.50 & 16.22 \\
4.3 & 0.50 & 14.74 \\
4.3 & 0.85 & 15.91 \\
2.5 & 0.50 & 2.96 \\
5.5 & 0.20 & 42.28 \({}^{*}\) \\
4.3 & 0.50 & 14.44 \\
4.3 & 0.50 & 15.06 \\
4.3 & 0.08 & 24.50 \\
6.0 & 0.50 & 17.00 \\
4.3 & 0.50 & 15.24 \\
5.5 & 0.76 & 16.14 \\ \hline \end{tabular} B: biosorbent dose; q: biosorption capacity; \({}^{*}\) The values were not considered in the statistical adjustment.  
\end{table}
Table 2: Rotatable Central Composite Design (RCCD) for the optimization of biosorption of Cu(II) by _S. epidermidis_ CECT 4183.  
Figure 4: Images obtained by two electron microscopy techniques, TEM (**a**) and FE-SEM (**b**), showing the CuO-NPs obtained after the synthesis stage from the cellular extract of _Staphylococcus epidermidis_ CECT 4183.
## 3 Results and Discussion
### Thermal analysis  
DSC analysis of Ga-5 HF and Al-5 HF revealed a flat stage between 25 and 300 \({}^{\circ}\)C and a sharp small endothermic peak visible at around 300 \({}^{\circ}\)C. When metal complexes are included in DPCNS and PMDACD, the DSC curves have a shape like the DSC curve of the polymer, revealing a wide endothermic region at 100-120 \({}^{\circ}\)C, corresponding to the loss of retained water. The small endothermic peak at 300 \({}^{\circ}\)C disappears, indicating the inclusion of Ga-5 HF and Al-5 HF in the cyclodextrin cavities and polymer pores (Figure 4). On the DSC curves of Ga-5 HF-PMDACD1 and Al-5 HF PMDACD1, a small endothermic peak appears at 150-200 \({}^{\circ}\)C, corresponding to the melting point of traces of 5-hydroxyflavone (probably formed during complex preparation).  
Figure 3: FT-IR spectra of (**a**) Al-5 HF-PMDACD1 and (**b**) Ga-5 HF-PMDACD1.
## 4 Numerical Results
Figure 7: The comparison between the performance of the SGPA algorithm and two benchmark schemes, i.e., FH and FP. affects the covert communication when the \(\varepsilon\) is large enough and the optimal flying altitude of the UAV reaches \(H_{\text{min}}\). By comparison, the proposed SGPA scheme can obtain a higher covert rate than the benchmark schemes, which verifies the outperforming of the joint optimization of the SGPA algorithm. In addition, we observe that the \(\varepsilon\) is larger, and FH can obtain better performance than FP, due to the relaxation of the covertness constraint.
## 5 Synthetic Data Generation for Network-Based Analyses
### Base Case: Local (One-Hop) Interactions  
In the local interaction scenario, the synthetic network generation process begins by sampling an undirected random graph \(\hat{G}\) with \(n\) nodes according to the chosen random model (Erdos-Renyi, Watts-Strogatz, or Barabasi-Albert). Each undirected network acts as the structural backbone for the subsequent directed network construction.  
Let \(G=(V,E)\) be an undirected network and \(G^{\prime}=(V^{\prime},E^{\prime})\) be the new directed network. Here, \(V=V^{\prime}\). We assign a direction to each edge \((u,v)\in E\), creating an initial directed edge \((u,v)\in E^{\prime}\). Additionally, the reverse edge \(((v,u))\) is introduced probabilistically (e.g., with probability 0.3), thus resulting in a directed network \(G^{\prime}\). The rest of the calculations are performed over \(G^{\prime}\). We compute its adjacency matrix \(A\) and the diagonal out-degree matrix \(D^{\text{out}}\), from which the directed Laplacian matrix is derived as  
\[L=D^{\text{out}}-A. \tag{16}\]  
Consistent with the previously described consensus dynamics, we introduce the auxiliary matrix \(K=L+A\) and determine the iterative update matrix \(P\) as:  
\[P=I-\epsilon L,\quad\text{where}\quad\epsilon=\frac{1}{c\ \text{max}(K)}, \tag{17}\]  
with \(c\) typically chosen around 100 to ensure numerical stability. The initial state vector \(\phi(0)\) is constructed from the diagonal elements of \(K\) (i.e., \(\phi_{i}(0)=K_{ii}\)). The consensus evolution then iterates according to:  
\[\phi(t+1)=P\,\phi(t), \tag{18}\]  
until the state convergence reaches a predefined tolerance \(\tau\) or until the maximum allowed iterations are performed. Only the first ten state vectors and the final convergent mean state are retained to ensure computational efficiency and manageable data volume.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
###### Abstract  
Crowd counting has attracted much attention in computer vision owing to its fundamental contribution in public security. But due to occlusions, perspective distortions, scale variations, and background interference it faces a great challenge. In this paper we propose a novel model to count crowds, named FDCNet: frontend-backend fusion dilated network through channel-attention mechanism. It merges the frontend feature map with the backend feature map, achieving a fusion of various scale features without additional branches or extra subtasks. The fusion is fed into the channel-attention block to optimize the procedure and to conduct feature recalibration to use global and spatial information. Furthermore, we utilize dilated layers to obtain a high-quality density map, and the SSIM-based loss function is added to compare the local correlation between the estimated density map and the ground truth. Our FDCNet is verified in four common datasets and gets a brilliant estimation.  
channel-attention; crowd counting; density map; dilated layer; feature fusion +
Footnote †: journal: Applied Sciences
# Frechet-Derivative-Based Global Sensitivity Analysis and Its Physical Meanings in Structural Design
## 3 Review of Three Classical Global Sensitivity Indices
### Failure-Probability-Based GSI  
The failure probability is of paramount significance in measuring the safety of a system. However, it may be highly sensitive to basic input random variables and, in particular, may vary considerably with respect to different distribution parameters, e.g., mean values or standard deviations [18]. To this end, it is essential to evaluate the failure-probability-based GSI, which is commonly defined by [17]  
\[\mathcal{S}_{i,j}^{\text{F}}=\frac{\partial P_{\text{F}}(\xi)}{\partial_{i,j} ^{\alpha}},\ i=1,2,\cdots,s,\ j=1,2,\cdots,s_{i}, \tag{15}\]  
where \(\xi_{i,j}\) is the \(j\)-th distribution parameter for the \(i\)-th input random variable \(\Theta_{i}\), and \(P_{\text{F}}\) is the failure probability calculated by  
\[P_{\text{F}}(\xi)=\int_{\Omega_{\text{F}}}p_{X}(x;\xi)\text{d}x, \tag{16}\]  
where \(\Omega_{\text{F}}\) stands for the failure domain corresponding to the failure criteria based on strength, deformation, displacement, energy, etc. Specifically, if the function \(g(\cdot)\) in Equation (1) is the performance function [34], the failure domain can be defined as \(\Omega_{\text{F}}\subseteq\Omega_{X}:x=g(\theta)<0\).  
Alternatively, the failure-probability-based GSI can be expressed by combing Equations (15) and (16) and by exchanging the order of differentiation and integration in Assumption (a) in Section 2, i.e.,  
\[\mathcal{S}_{i,j}^{\text{F}}=\int_{\Omega_{\text{F}}}\frac{\partial p_{X}(x; \xi)}{\partial_{i,j}^{\alpha}}\text{d}x. \tag{17}\]  
An illustration of the failure-probability-based GSI is drawn in Figure 1c, with consideration given to the toy model in Equation (10), where the failure domain is defined by \(x>5\).  
Unlike variance-based GSI or moment-independent GSI, failure-probability-based GSI for each \(\Theta_{i}\) may have more than one value, i.e., \(\mathcal{S}_{i}^{\text{F}}=(\mathcal{S}_{i,1}^{\text{F}},\cdots,\mathcal{S}_{ i,s_{i}}^{\text{F}})^{\top}\), whose dimension is dependent on the number of distribution parameters of \(\Theta_{i}\). In addition, \(\mathcal{S}_{i,j}^{\text{F}}\) is not guaranteed to be positive. For convenience in future discussions, we define \(\|\mathcal{S}_{i}^{\text{F}}\|_{\infty}\) as a characteristic measure of the failure-probability-based GSI, where \(\|\cdot\|_{\infty}\) is the \(\infty\)-norm.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Multi-Scale Modeling (MSM)  
Multiscale modeling approaches are employed to capture the interaction between crack propagation and microstructural features in materials. These approaches bridge the length scales from the microscopic to the macroscopic level, enabling the investigation of crack growth behavior at different scales [166; 167; 168]. The microstructural features, such as grain boundaries, inclusions, and phase boundaries, can significantly influence crack propagation by affecting the local stress fields, energy dissipation, and crack path [169; 170]. MSM, such as concurrent coupling and homogenization, integrates information from the microstructure into the finite element analysis [171]. This allows for a more accurate representation of crack growth behavior in heterogeneous materials and provides insights into the effect of microstructure on fatigue crack initiation and propagation.
# Integration Between Serious Games and EEG Signals:
## 3 Foundations of Serious Games in EEG-Based BCI Systems  
### Serious Game  
Several attempts have been made to define serious game [24; 25; 26; 27; 28]. However, there is no universal or widely accepted definition to date. In this work, we use the definition proposed by [PERSON] [9], which specifies serious game in the following way: \"playful instruments to achieve a desired objective, that is, learning effect, training or a better state of health\". On the other hand, we unify the serious game concept by including related terms such as video games, computer games, neurogames, BCI games, and neuro-feedback games. Regarding the fields of application, [PERSON] et al. [29] propose five categories to classify serious games: educational training, advertising, edumarket, political games, and training and simulation games. For their part, [PERSON] and [PERSON] established a classification system based on three criteria: Gameplay or playability (G), Purpose (P), and Sector (S), forming \"the G/P/S model,\" which can be a guide to classify serious games taking into account their playful and serious dimension [27]. In contrast, [PERSON] [9] indicates that the fields of application may include training and simulation, digital education, vocational or workplace training, marketing and advertising, health (for prevention and rehabilitation), or awareness and social impact (involving issues of politics, security, religion, energy, or climate). The former works show that some taxonomies that have been proposed vary according to the principles, methods, and purposes of the classification. However, they are useful tools for classifying serious games. It depends on the criteria of the researcher and the specific field of their work to be guided by one classification system or another.  
On the other hand, it is important to highlight that applications in serious games combine concepts, principles, and methods of game design with information and communication technologies (ICT) and specific methods or technologies (involving the objective of the game). Typical ICTs include artificial intelligence for automatic game control; human-computer interaction (HCI) aspects for game and device control (I/O); sensor technology to retrieve context information (user/player); multimedia aspects; and usability and game experience features [9]. All these technologies can be found in several research works [30; 31; 32; 33; 34; 35]. This work focuses its study on Serious games under an approach based on brain-computer interfaces (BCI), specifically using electroencephalographic signals (EEG).
## 3 Results and Discussion
MO1—control beverage; MO2—beverage enriched with 10% raspberry; MO3—beverage enriched with 10% strawberry; MO4—beverage enriched with 10% apple; MO5—beverage enriched with 5% raspberry and 5% strawberry; MO6– beverage enriched with 5% raspberry and 5% apple; MO7—beverage enriched with 5% strawberry and 5% apple; MO8- beverage enriched with 4% apple, 3% raspberry, and 3% strawberry; P—proteins, FAA—free amino acids, FAA DC—free amino acids in digested beverages rehydrated at 20 \({}^{\circ}\)C, FAA DH—free amino acids in digested beverages rehydrated at 80 \({}^{\circ}\)C.  
antioxidant potential of the beverages rehydrated at both temperatures was significantly higher in the supplemented beverages. It was in the range of 8.5-10.4 mg TE/100 mL and 9.7-12.1 mg TE/100 mL in the beverages rehydrated at 20 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, respectively. Generally, the beverages reconstituted at the higher temperature showed a greater ability to quench radicals and corresponding reducing power. Furthermore, similar trends in the ability of the rehydrated beverages to reduce Fe\({}^{3+}\) ions (RP) were observed. Most importantly, the beverages obtained after the simulated digestion processes (both with the control and with the addition of functional powders) were characterized by significantly higher antioxidant potentials than the other ones. These results suggest higher bioavailability of these compounds. Between the two temperatures of rehydration, the bioaccessibility of antioxidants was surprisingly higher at 20 \({}^{\circ}\)C. The in vitro digestion significantly influenced reducing power and antioxidant capacity, while optimal results of overall bioaccessibility were obtained for MO4 and MO7. Generally, it was observed in our study that the bioaccessible fractions of antioxidants in the gastric medium were almost similar to or higher than those natively found in the reconstituted samples. The in vitro digestion released compounds with strong abilities to quench radicals. Such behavior was previously observed after in vitro digestion of bread enriched with onion skin [59], fruit and vegetable juices [60], and powdered seasonings [61]. The bioactivity of food ingredients depends primarily on their bioaccessibility in the gastrointestinal tract, and secondly on the food matrix. Previous in vitro studies confirm that the digestion process may promote the release of bioactive compounds from a food matrix responsible for antioxidant abilities and support reliable prediction of bioaccessibility [7; 62]