esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Limitations and Future Directions  
This study presents several limitations that should be considered when interpreting the results. First, the small sample size limits the external validity of the findings, especially given that the sample consisted specifically of players from only two teams. This constraint also prevented the analysis of subgroups based on field position, age, experience, or minutes played, which could have allowed for the identification of distinct patterns in emotional profiles or injury vulnerability.  
Second, the lack of control over external variables (such as training load, previous injury history, specific playing position, or competitive context--e.g., match importance or outcome) may have influenced emotional states and anxiety levels, introducing potential confounding variables into the analyses. Moreover, the observational and cross-sectional nature of the design does not allow for definitive causal conclusions regarding the relationship between the psychological factors analyzed and the occurrence of injuries. To address this limitation, longitudinal studies and multilevel or mixed-methods designs are needed to examine how changes in anxiety and mood over time relate to injury incidence.  
Another relevant limitation is the reliance on self-report measures as the primary method of psychological data collection. This approach is vulnerable to potential biases, such as social desirability or limited athlete introspection, which may affect the validity of the responses. Incorporating qualitative methods (such as in-depth interviews or emotional diaries) could enrich the understanding of athletes' subjective experiences and provide a more comprehensive perspective.  
Additionally, the inclusion of objective data (such as physiological biomarkers, GPS-based external load tracking, and recovery indicators) would complement psychological assessments and improve the identification of injury risk profiles. For instance, monitoring biomarkers such as salivary cortisol levels could offer valuable insights into athletes' stress responses and their potential link to injury risk. It would also be relevant to explore the role of emerging psychological constructs (such as resilience, perceived stress, and self-efficacy) in the relationship between emotional states and injuries.
## 3 Illustrative Numerical Results
### Roundtrip Loss of Ring Resonators (a)  
The effect of roundtrip loss on the RR transfer function is to increase the drop insertion loss and, when various rings are cascaded, also to widen the bandwidth (_i.e._, the filter becomes less selective). Table 2 reports the values of the investigated parameters. The impact of the paths through the PSC is dramatic. \(B_{M}\) and \(B_{m}\) remarkably increase, causing an increase of \(\mathrm{XT}\). It is worth noting that loss also makes the variability of _IL_ across different channels to increase due to the low selectivity of filters and high _IL_ at the drop port.
## 6 Results and Discussion
### Inference Evaluation on Raspberry Pi
* For Low-Rank Parameterization, the decomposition rank affects the model size and performance of RNNs. Higher ranks improve performance but increase the model size proportionally. * The inference time on a Raspberry Pi was directly dependent on the time steps, the input feature size the RNN was processing, and the complexity of the RNN cell and architecture. * The complex workflows for application to device mapping have forced developers to be inventive. Dealing with frameworks and third-party packages and libraries is complex.
# A Computational Framework for 2D Crack Growth Based on the Adaptive Finite Element Method
## 2 Procedure of the Developed Program
### Displacement Extrapolation Method (DET)  
The SIFs for LEFM can be computed from the nodal displacement of finite elements using the displacement extrapolation technique. Three quarter-point singular elements surround the crack extremity in this technique, while six-node isoperimetric elements cover the remaining regions [42; 43]. These partial nodes at the tip and along crack lines have their displacement components evaluated during extrapolation. The components' SIFs are predicted using the necessary formulae. Using the displacement extrapolation technique, the rosette triangle components generated around the crack point are displayed in Figure 4.  
The SIFs have been calculated using the following formulas [36]:  
\[K_{I}=\frac{E}{3(1+v)(1+\kappa)}\sqrt{\frac{2\pi}{L}}\bigg{[}4({v^{\prime}}_{b}-{ v^{\prime}}_{d})-\frac{({v^{\prime}}_{c}-{v^{\prime}}_{e})}{2}\bigg{]} \tag{13}\]  
\[K_{II}=\frac{E}{3(1+v)(1+\kappa)}\sqrt{\frac{2\pi}{L}}\bigg{[}4({u^{\prime}}_{ b}-{u^{\prime}}_{d})-\frac{({u^{\prime}}_{c}-{u^{\prime}}_{e})}{2}\bigg{]} \tag{14}\]  
where \(E\) is the elasticity modulus, \(v\) is the Poisson's ratio, \(\kappa\) is the elasticity factor represented as:  
\[\kappa\ =\ \left\{\begin{array}{ll}3-4v\ for\ plane\ strain\\ \frac{(3-v)}{(1+v)}\quad for\ plane\ stress\end{array}\right. \tag{15}\]  
and \(L\) represents the quarter-point element length. Here, \(u^{\prime}\) and \(v^{\prime}\) are the two parameters of the displacement in x\({}^{\prime}\) and y\({}^{\prime}\), respectively, as shown in Figure 4.
## 3 Results
### Effects of Low Temperature on the GPP/SIF Ratios for Different PFTs
#### 3.3.1 Global Satellite Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**PFTs**} & \multirow{2}{*}{**Pearson Correlation**} & \multicolumn{4}{c}{**Partial Correlation Coefficient**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**PAR**} & \multicolumn{2}{c}{**VPD**} \\ \cline{2-6}  & **A** & **B** & **A** & **B** & **A** & **B** \\ \hline TrEBF & — & \(-0.08\) & — & \(-0.08\) & — & \(-0.15\) \\ TrDBF & — & \(-0.05\) & — & \(-0.09\) & — & \(-0.16\) \\ TeENF & — & \(-0.002\) & — & \(-0.06\) & — & \(-0.08\) \\ TeEBF & — & \(-0.43\) & — & \(-0.41\) & — & \(-0.44\) \\ TeDBF & \(0.40\) & — & \(0.38\) & — & \(0.31\) & — \\ BoENF & \(0.77\) & — & \(0.74\) & — & \(0.62\) & — \\ BoDBF & \(0.66\) & — & \(0.64\) & — & \(0.53\) & — \\ BoDNF & \(0.41\) & — & \(0.36\) & — & \(0.32\) & — \\ TeX3 GRA & \(0.17\) & \(-0.27\) & \(0.15\) & \(-0.23\) & \(0.17\) & \(-0.20\) \\ TrC3 GRA & — & \(-0.26\) & — & \(-0.24\) & — & \(-0.24\) \\ BoC3 GRA & \(0.26\) & — & \(0.25\) & — & \(0.24\) & — \\ C4 GRA & \(0.10\) & \(-0.06\) & \(0.12\) & \(-0.06\) & \(0.21\) & \(-0.06\) \\ C3 CRO & \(0.40\) & \(-0.24\) & \(0.29\) & \(-0.26\) & \(0.27\) & \(-0.30\) \\ C4 CRO & — & \(-0.25\) & — & \(-0.25\) & — & \(-0.30\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Pearson correlation and partial correlation coefficients between the GPP/SIF ratio to temperature and other environmental parameters (PAR and VPD). Horizontal bars denote no available values. By separately controlling PAR and VPD, the partial correlation coefficients of the GPP/SIF ratio with temperature also demonstrated that the GPP/SIF ratio varied with temperature (Table 3). The GPP/SIF ratio in group A was found to be strongly positively correlated with temperature in the boreal region, where low-temperature stress conditions were more likely. To better understand the differences between SIF and GPP in response to temperature, we further analyzed the correlation between the GPP/SIF ratio with temperature in groups A and B of TeC3 GRA, C4 GRA, and C3 CRO, which had samples in both groups. From Figure 12, we can see that the slopes between the GPP/SIF ratio and temperature in group A were positive, while those in group B were negative. The distribution of the GPP/SIF ratio for the data when the temperature was above 10 \({}^{\circ}\)C differed from that obtained when temperature was below 10 \({}^{\circ}\)C (Figure 13). Therefore, the satellite data indicated that the GPP/SIF ratio was largely influenced by temperature conditions for different PFIs, and generally decreased in response to low-temperature conditions. Figure 11: Relationship between the GPP/SIF ratio and temperature for different plant functional types (2018–2020). A and B represent different groups. The slopes and determination coefficients of linear regressions are listed in each panel. The color bar represents the fraction of point density, normalized by the maximum point density.
## 5 Results and Discussion  
### Phase Advance by IP  
We verified the phase-advance tendency of IPs as a function of \(\varepsilon_{r,sub}\) by using a 3-D electromagnetic (EM) simulation tool (HFSS). The simulation was run using SIW structures by regulating the number of IPs, 1 (Figure 4a) and 2 (Figure 4b), at 28 GHz; \(\varepsilon_{r,sub}\) was varied from 1.5 to 3.5, with \(r\) = 0.1 mm and \(p\) = 0.5 mm, respectively. The phase advance decreased as \(\varepsilon_{r,sub}\) increased, as depicted in Figure 7. As we showed with Equations (3)-(9), it is remarkable that even though the permittivity increased, the phase advance term rather showed a tendency to decrease, which showed a tendency to coincide with the formula. The slight discrepancy between theoretical calculations and simulation results could be due to the use of equivalent circuit assumptions for the T-network high-pass filter. Nonetheless, we have confirmed that the trend of our simulation results is consistent with the calculated results, demonstrating the effectiveness of the LC-based SIW concept of utilizing inductive posts for phase shifting.
## 3 Modelling and Control
### Approaching Flow Stage (AFS)
\begin{array}{c}G_{p}=STF(s)=\frac{\theta_{1}(s)}{\theta(s)}=\frac{\frac{b_{ 2}}{\Omega}s^{2}+\frac{\xi}{\Omega}}{s^{2}+\frac{\xi}{\Omega}},\\ b_{2}=\left(\frac{4}{2}-L_{1}-l_{1}\right).\end{array}  \tag{14}\]  
A control action proposed here is based on a proportional-integral-derivative (PID) controller using the system transfer function (STF) given by Equation (14) as plant gain (\(G_{p}\)). This STF comes from Equation (9) where the input is the angle of vessel 1, \(\theta\), and the output, the angle of the free surface \(\theta_{1}\). PID control is well known for the ability to remove a steady state error, which via Equation (14), turned to be one half. It was also deduced by Equation (14) that roots exist only in the imaginary plane for an undamped time response system, so, a trained proportional-integral-derivative (PID) controller/compensator in a closed loop with \(G_{p}\) can add the additional zeros to dissolve the pure undamped system and turn it into a stable damped one. Using the manual tuning root locus in _Matlab(r)_, PID constants that better suit the damping needed to the particular system were built. Recall that the STF equation depends on the vessel dimensions, so further experimentation would require a range of PID constants for a range of vessel dimensions. In contrast, this STF will work for any filling height (\(A\)). Figure 8 shows close loop PID control block diagram. \(G_{p}\) is populated with the vessel dimension data used in our simulations: \(A=0.159,\ L_{1}=0.0207,\ l_{1}=0.0615\). Those values are in meters. The compensator gain, \(G_{c}\), described by the PID controller, will add two zeros and one pole to a second order \(G_{p}\). The idea is to add a dominant pole to the close loop transfer function so that the system becomes stable, damped and with minimum steady state error. The new two additional zeros will affect the amplitude of the system gain, but not the nature of the response. Equation (15) shows the PID compensator gain formula. \[G_{c}=\frac{K_{d}s^{2}+K_{p}s+K_{i}}{s}. \tag{15}\]  
In Section 4, the results of running simulations are presented, including the steady state error from the close loop system and the way PID control tuning reverts the undamped response. Additionally, the numerical constants (\(K_{p}\), \(K_{i}\), and \(K_{d}\)) of the PID control were tested and are discussed in next Section. Figure 8: Simulink® block diagram of the close-loop proportional-integral-derivative (PID) control. \(G_{p}\) is the plant gain with populated values, and \(G_{c}\) is the controller gain properly tuned to reduce undamping.
## 3 Simulation Results and Interpretation
### Scenario 2: Effect on the Number of Targets
#### 3.2.2 Simulation with Clutter Model 2  
We realized the simulation of the three targets with clutter model 2 (targets embedded in Rayleigh distribution clutter) and a scale parameter b = 200, considering the temporal and frequency values of the Gabor transform to be M = 64 and N = 16, respectively.  
In Figure 32, the simulation of the targets with clutter model 2 and a scale parameter b = 200 reveals that the proposed detector was able to detect only two targets, instead of all the three targets used for the simulation, at M = 48 and M = 58.  
We maintained the scale parameter b = 200 but modified the temporal and frequency values of the Gabor transform to M = 128 and N = 8 for the first simulation then increased the scale parameter to b = 250 for the second simulation.  
We observe in Figures 33 and 34 that the proposed detector was able to distinguish the three targets at M = 81, M = 102, and M = 120 when we increased the temporal and frequency values of the Gabor transform to M = 128 and N = 8. The proposed detector successfully distinguishes the position of the targets while the OS-CFAR detector was able to just distinguish one target at M = 83 with low false alarms.  
Figure 31: Targets simulated with clutter model 1 (a = 25 and b = 0.9) distinguished with proposed detector (M = 32 and N = 16).  
Figure 32: Targets simulated with clutter model 2 (b = 200) distinguished with proposed detector (M = 64 and N = 16).
## 1 Introduction
A complement to \"a priori\" mechanism is \"a posteriori\" protection, which can be provided by watermarking. Apart from unauthorized access to sensitive content, another potential threat to medical multimedia content is possible manipulations. Existing, widely available, image editing software and image altering tools allow us to easily manipulate a digital image nowadays. Studies of various image manipulation detection techniques are available in the literature. Numerous image forgeries that can be performed on the image and different image manipulation detection and localization methods were presented in [23]. Image manipulation can also concern biomedical sciences where the use of images to depict laboratory results is widely disseminated. Results published in [24] have shown an alarming level of image manipulation in the published record. A dedicated tool was used to detect some of the most common misbehaviors, running tests on a random set of papers and the full publishing record of a journal. Currently, image tampering detection can be also realized with the use of Convolutional Neural Networks [25]. Image protection and manipulation detection are extremely relevant in all applications where the sensitive data is transmitted from the imaging sensor to a remote destination where it is further processed and analyzed [26]. Such protection can be realized in aerial photography, area monitoring, and satellite imagery [27]. The same applies to medical applications of remote sensing where electromagnetic radiation is most commonly the sensing medium and the sensors of diagnostic devices, which are exterior to the body of a patient, can detect various features of human tissues in a noninvasive way [28]. The paper is organized as follows. The next section is dedicated to Periodic Haar Piecewise-Linear Transform. Section 3 introduces a new method for data embedding. Section 4 presents the potential application of the proposed algorithm for the detection of image manipulations. In Section 5 the experimental results are presented and the comparison between the proposed solution and the DCT approach is discussed. Finally, Section 6 contains the conclusions and future work.
## 2 Materials and Methods
### Scaled Model Simulation
#### 3.2.2 Results of Scaled Model Simulation  
Table 4 shows the total amount and maximum values of surface runoff and infiltration. The cases of the mass error of 0.5% or less were used for the analysis. The rainwater drainage through surface runoff occurs predominantly for all cases. Figures 10-12 show the dimensionless runoff for the same rainfall time by dividing the infiltration and surface runoff by the total runoff on the \(y\)-axis, and dividing time by the total rainfall duration on the \(x\)-axis. They also show the drainage characteristics in the case of the same duration with dimensionless variables. In the case of the scaled model, 90% of the total runoff is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case No.** & **Rainfall Intensity (I, mm/h)** & **Duration (\(t\), Seconds)** & \(\Delta x\) **(mm)** \\ \hline Sc-I1D1 & & 100 & \\ \hline Sc-I1D2 & 10 & 200 & 5 \\ \hline Sc-I1D6 & & 600 & \\ \hline Sc-I3D1 & & 100 & \\ \hline Sc-I3D2 & 30 & 200 & 5 \\ \hline Sc-I3D6 & & 600 & \\ \hline  
surface runoff, because the volume of porous media is not enough to hold infiltrated water under surface. Figures 10-12 show that the dimensionless surface runoff remains constant over time and the dimensionless infiltration increases. Therefore, it was revealed that there is an effect of reducing surface runoff if sufficient porous media to hold infiltrated water.  
When the product of the total rainfall and the duration is the same, that is, when the rainfall depth (\(I\times t\), mm) from Table 3 (2.78 and 5.56 mm) is the same, the runoff characteristics according to the rainfall intensity of the IRD system were analyzed as shown in Figures 13 and 14. They represent infiltration and surface runoff with dimensionless variables, respectively.  
In the case of a rainfall depth of 2.78 mm, the infiltration scale increases by up to 16.8% and an average of 10.8% as the rainfall intensity increase. In case of rainfall depth of 5.56 mm, the surface runoff scale decreases by up to 23.7% and 43.4%. This reversal of the infiltration scale was analyzed to be caused by insufficient volume of porous media compared to rainfall. Therefore, installing the IRD system, it is necessary to design appropriately considering the amount of rainfall and the duration of rainfall.  
Figure 11: Results of scaled model simulation for 200 s: (**a**) surface runoff; (**b**) infiltration.  
Figure 12: Results of scaled model simulation for 600 s: (**a**) surface runoff; (**b**) infiltration.  
Figure 10: Results of scaled model simulation for 100 s: (**a**) surface runoff; (**b**) infiltration.
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 2 System Model  
### System Model  
This section describes the system model of this paper. This paper proposes the relay selection scheme for multi-hop transmission system of MU-MIMO. Figure 1 shows the multi-hop transmission system model of MU-MIMO.  
In the MU-MIMO multi-hop transmission system, the BS transmits a signal to the relay through the MIMO channel. The relay multiplies the received signal from the BS by the pre-coding matrix and transmits the signal to UEs. In Figure 1, \(\mathbf{H}_{S,i}\) means the MIMO channel matrix between the BS and relays, and \(\mathbf{H}_{Ri,j}\) refers to the channel matrix between the relays and UEs. In the above channel matrix, the \(i\) denotes the index of the relays, and \(j\) refers to the index of the UEs. When the number of transmit/receive antennas of the BS is \(N_{t}\), the relay is \(N_{r}\), and UE is \(N_{UE}\), the \(\mathbf{H}_{S,i}\) is \(\mathbf{H}_{S,i}\in\mathbb{C}^{N_{r}\times N_{t}}\), \(\mathbf{H}_{Ri,j}\) is \(\mathbf{H}_{Ri,j}\in\mathbb{C}^{N_{UE}\times N_{r}}\).
## 2 Grid-Connected PV Generation System  
### Structure of the Proposed System  
Irradiance, temperature, and other environmental information present nonlinear characteristics with respect to the PV output power. The output-voltage-power curves of the PV arrays exhibit complex multi-peak shapes when local shading occurs. Because the ANN has the capability to be used in nonlinear applications and can respond rapidly to changes in reference, its usage in different areas has been growing. An ANN does not require reprogramming as it is based on a learning process. Therefore, in this paper, the irradiances of different solar cells are detected to train the ANN controller to obtain an MPP reference.  
The power electronic converter model is a typical nonlinear system model. Conventional controllers such as the PI controllers based on small-signal modeling cannot track the reference rapidly and precisely when the converter quiescent operating point shifts over a wide dynamic range. The static operating point of the boost DC/DC converter will vary widely under PSC. This paper proposes an AFL controller based on the DC/DC converter to solve the problems that exist in conventional controllers.  
Figure 1 shows the structure of a grid-connected PV system in which a PV generator interacts with a DC/DC converter controlled by an MPPT algorithm using ANN. By detecting the irradiance and temperature information, the ANN provides maximum power with offline training. According to the theory of exact feedback nonlinear linearization, an AFL controller is utilized to track the MPP reference given by the ANN. The control of the DC/AC inverter can maintain the DC-link voltage at a constant reference value (700 V, in this paper).  
Figure 1: Structure of a PV (photovoltaic) system with a classical MPPT (maximum power point tracking)-based ANN (artificial neural network).
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 4 Comparison and Discussion
### Design Approaches to Reduce Area/Power  
To reduce the area and power overheads of the most conventional design, DICE-12 T, various designs have been proposed. Many structures with a reduced number of transistors--Quatro-10 T, NS-10 T, PS-10 T, RHBD-10 T, QUCCE-10 T--utilize the four storage nodes, while only two storage nodes are accessible through two access transistors. The radiation hardening is realized by causing each of the four storage nodes to be driven to V\({}_{\text{DD}}\) and 0 V by two FETs that are controlled by different nodes. To hold the data, two or more cross-coupled nFETs or pFETs are employed, so that the four storage nodes are configured in the positive feedback. With these structures, the number of required transistors is reduced so that the area and power consumption overhead can be minimized.  
However, in these structures, the internal storage nodes exposed to both types of SEU (falling and rising) are directly driven by the cross-coupled nFETs or pFETs that are used to hold the data. Thus, the positive feedback can be easily triggered even with small charge noise, which might lead to a data flip. As a result, these structures--NS-10 T, PS-10 T, and RHBD-10 T in particular--have small values of Q\({}_{\text{C}}\), meaning that they have limited robustness to SEU.  
In addition, these structures have limited write ability and enlarged write delays. This is because when the access transistor attempts to change the voltage of a storage node, the contention current is very significant. The reason for this is the configuration for the radiation hardening--the use of different signals for FETs driving one storage node. In addition, the cell designs have only partial resilience to SEU due to the use of the positive feedback structure to hold four storage nodes.