esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Distribution Network Flexibility Indicators
#### 2.3.1 Traditional Flexibility Indicators  
(1) Net Load Adaptation Ratio  
Net Load Adaptation Ratio (NLAR) is the ratio of the dispatchable margin of the nodal flexibility resources to the net load change, which reflects the time attribute characteristic of flexibility. The larger value of the net load adaptation rate indicates that the flexibility resources can satisfy the flexibility demand, and the overall flexibility of ADN is better.  
\[H_{net}=\frac{1}{T}\sum_{t=1}^{T}\frac{s_{t}^{+}P_{t,t+}^{FS}+s_{t}^{-}P_{t,- }^{FS}}{\left|P_{t+1}^{DL}-P_{t}^{DL}\right|}\times 100\% \tag{11}\]  
where \(H_{net}\) is the net load adaptation rate; \(s_{t}^{+}\) and \(s_{t}^{-}\) denote positive and negative net load changes; \(P_{t,+}^{FS}\) and \(P_{t,-}^{FS}\) are the upward and downward adjustable margins of nodal flexible resources; \(P_{t}^{DL}\) is the net load;  
(2) Line Capacity Margin  
The line capacity margin indicator, which considers both the structural and the actual state of the line, is defined as the difference between the maximum and actual value of line transmission capacity, expressed as a percentage of the upper maximum value. This indicator reflects the spatial attribute that characterizes flexibility. A greater line capacity margin value indicates that the distribution branch is more resilient to uncertain changes in flexibility demand.  
\[H_{ij}=\frac{1}{T}\times\frac{1}{N_{E}}\sum_{t=1}^{T}\sum_{ij\in\Omega_{E}} \left(\frac{I_{ij,\max}-I_{ij}^{t}}{I_{ij,\max}}\right)\times 100\% \tag{12}\]where \(H_{ij}\) is the line capacity margin; \(\Omega_{E}\) is the set of branch circuits of ADN; \(N_{E}\) is the number of branch circuits of ADN; \(I_{ij}^{t}\) is the branch transmission current; \(I_{ij,\max}\) is the maximum value of transmission current.  
The above traditional flexibility indicators are established from the spatiotemporal attributes of flexibility resources, but these two indicators cannot adequately reflect the effect of access to DG on the flexibility of ADN. Consequently, this paper builds indicators from the power side of ADN, whose flexibility is mainly indicated by the admission of DG.
# Performance Assessment of NAMI DANCE in Tsunami Evolution and Currents Using a Benchmark Problem
## 3 Results
### Velocity Time Series  
The velocity time series are measured via ADVs. The effect of the island on the direction and magnitude of wave currents is also recorded and analyzed. The comparison of the results regarding the wave currents is given in Figure 4.  
The results showed that depth-averaged NSW equations are able to satisfactorily predict the velocity components in \(x\)-direction. On the other hand, the maximum velocity values in \(y\)-direction are underestimated even though the predicted and measured velocity components show a similar trend.
## 2 Materials and Methods
### Feature Extractors
#### 2.5.3 Comparison of MLP and CNN  
MLPs are flexible and straightforward to implement, making them applicable to a broad range of problems. However, they require a large number of parameters, which can lead to overfitting, particularly when the input data exhibit strong local correlations, as is often the case with time-series data.  
In contrast, CNNs excel in recognizing patterns in structured data, such as the relationships between consecutive time steps in financial data. Through convolutional layers, CNNs reduce the number of trainable parameters, which enhances generalization and decreases the risk of overfitting. However, CNNs can be more computationally intensive during training and inference, especially as the input data size increases.  
Overall, MLPs offer greater flexibility at the expense of parameter efficiency, while CNNs are more scalable and effective for structured data but may require more sophisticated architectures and tuning.  
In this paper, the MLPs in the different experiments are the vanilla implementations for each agent from the StableBaselines3 [40] library. Table 2 provides a detailed view of each architecture, including the activation functions, number of layers, and neurons used for each agent for the MLP variant.  
For the CNN, a custom model has been implemented with the architecture in Table 3.
## 3 Experimental Phenomena and Their Analysis
### Experimental Results
#### 3.3.2 Skeleton Curves  
The skeleton curves of J1 and J2 are shown in Figure 16, from which it can be seen that:  
1. The two joints in the experimental state go through: the elastic phase, the yield phase, the strengthening phase, and the damage phase. Both maintain good stiffness in the early stages of loading, and the skeleton curves of the specimens all tend to be approximately straight lines, indicating that the components are in the elastic stage with little change in stiffness and displacement. As the load continues to increase, the reinforcement of the precast beam starts to deform and yield and the bolt deformation of specimens J1 and J2 increases. The specimens begin to become elastoplastic, the specimen skeleton curves gradually shift towards the displacement axis and no longer change linearly, and the stiffness of the specimens gradually degrades.  
Figure 15: Comparison of experimental and simulated curves.  
After J1 and J2 reach the maximum load-bearing capacity, the load-bearing capacity of the beam end continue to increase when the load continues to increase. This indicates that the new joints have stronger structural stiffness under displacement loading and their stiffness degradation trend is smoother, showing that increasing the strength of the bolts can improve the ductility of the joints, which in turn can increase the seismic energy dissipation capacity.
3. It can be seen that the J2 yield load is 39.4% higher, the ultimate load is 11.5% higher, and the damage load is 13.2% higher than J1 for the same working conditions. The reason for this is that J2 used stronger bolts, which allowed the fractures of the beam to crack and move back, increasing the stiffness and load-bearing capacity of the expanded end of the precast beam, which increased load-bearing capacity. Therefore, an appropriate increase in bolt strength can improve the energy dissipation capability of the core area of the joint, which can effectively improve the bearing capacity of the specimens. It is suggested that in actual engineering, an appropriate increase in connection bolt strength can be used to improve the bearing capacity of the joints.
4. Specimen J1 has a positive peak load of 151 kN and a negative peak load of 113 kN, a difference of 25.2%. Specimen J2 has a positive peak load of 178 kN and a negative peak load of 121 kN, a difference of 32.02%. The corbel acts as a support for the beam and was not subjected to forces, the load capacity being determined by the structure of the beam itself. The final damage to both J1 and J2 was due to the bolts yielding and cracks in the beam end surfaces, but the load-bearing reinforcement did not yield. It can be seen that the energy consumption of the new joints is primarily based on bolts and cracks produced in the core area of the joints; no damage occurred to the corbel and columns, as intended by the new joint design concept.
# Indole-3-Acetic Acid Action in Outdoor and Indoor Cultures of _Spirulina_ in Open Raceway Reactors
## 3 Results  
### Biomass Growth and Productivity Evaluation  
When evaluating the final biomass concentration (X\({}_{\text{final}}\)) and productivity (P\({}_{\text{final}}\)), both cultivation conditions supplemented with indole-3-acetic acid (IAA), specifically indoor cultivation supplemented with IAA (IAA-I) and outdoor cultivation supplemented with IAA (IAA-O), demonstrated superior performance compared to experiments conducted without supplementation, represented by indoor control culture (C-I) and outdoor control culture (C-O) (Table 1). A 13.9% increase in biomass concentration was observed when comparing the indoor conditions C-I with IAA-I, and an 18.0% increase was observed between the outdoor conditions control (C-O) and supplemented (IAA-O). The productivity was enhanced by 24% when comparing C-I with IAA-I and improved by 18.5% from C-O to IAA-O.  
During the outdoor experimental period, environmental conditions were monitored, revealing a wide natural range with a minimum temperature of 4.2 \({}^{\circ}\)C and a maximum of 39.7 \({}^{\circ}\)C. The minimum and maximum illuminance levels were 160 and 1200 umol photons m\({}^{-2}\) s\({}^{-1}\), respectively. The average minimum and maximum temperature values were 11 \({}^{\circ}\)C and 32 \({}^{\circ}\)C, respectively, and the average illuminance hovered at approximately 700 umol photons m\({}^{-2}\) s\({}^{-1}\). Under these conditions, phytohormone supplementation (IAA-O) resulted in an approximately twofold increase in biomass generation compared to indoor experiments with the addition of the phytohormone (IAA-I) (Table 1).  
During day zero of the cultures, the maximum initial photochemical efficiency (Fv/Fm initial) consistently showed similar values across all conditions (Table 1). However, a reduction in the final maximum photochemical efficiency (Fv/Fm final) was observed after 30 days of cultivation under indoor conditions (C-I and IAA-I). In contrast, the outdoor conditions (C-O and IAA-O) did not exhibit a statistically significant difference in the final response compared to the initial measurement. At the end of the experimental period, higher pH values were observed in outdoor cultures (C-O and IAA-O) compared to indoor experiments (C-I and IAA-I) (Table 1). Notably, control and supplemented conditions within the same environment exhibited no significant difference in pH values.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 3 Proposed Watermarking Scheme
### Watermark Embedding  
#### 3.2.1 Selection of Embedding Operations  
As discussed in Section 3.1, there is an inevitable shift in the corresponding positions between the corrected image and the original image. Fortunately, due to the nature of DFT coefficients in image translation, the coefficients of captured images can be corrected to correspond to the original image, if the four corners are carefully selected to avoid too much rotation and scaling distortions after perspective rectification. Therefore, it is advantageous to select the DFT domain for embedding a watermark message.  
In order to use DFT coefficients as a watermark carrier, we need to analyze their variation rules in the screen-cam process first. The variations of the DFT magnitude coefficients with different shooting conditions were analyzed in detail. As mid-frequency coefficients are commonly employed as watermark carriers, we take the mid-frequency spectrum of \(512\times 512\) sized Lena image and the variation after screen-cam as an example to illustrate the details of their variation rules, as shown in Figure 6. The axis scale value is the coordinate in the spectrum of the original image.  
We find that most of the magnitude coefficients with high values are well preserved. For example, (301, 299), (301, 300), (302, 304), and other points of deep warm color in Figure 6. Furthermore, the magnitude coefficients with low values commonly vary to become higher values. In general, the more blurred the image is, the higher the magnitude coefficients with low values will increase. Examples of this are the points (297, 305), (300, 296), and (302, 303) in Figure 6.  
The changes can be summarized as blew. In the mid-frequency bands, the magnitude coefficients with high values are well preserved during the screen-cam process, while those with low values commonly become higher values to approximate their adjacent magnitude values. Therefore, we choose mid-frequency bands and embed the message by modifying the selected magnitude coefficients to higher values.
## 3 Results and Discussion
### Effect of Different Amplitudes  
#### 3.2.1 Z-Direction Specimens  
According to the same process, the stress-strain relationship of \(Z_{0}^{0}\_i\), \(Z_{700}^{0,1}\_i\), \(Z_{700}^{0,2}\_i\), and \(Z_{700}^{0,3}\_i\) (\(i\) = 1-9) is compared and analyzed in Figure 7. The influence of the vibration field with the same frequency (700 Hz) but different amplitudes (0.1-0.3 g) on the tensile properties of Z-direction specimens can be determined. Details are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Specimens (\(i\) = 1–9)** & \(X_{0}^{0}\_i\) & \(X_{100}^{0,1}\_i\) & \(X_{200}^{0,1}\_i\) & \(X_{300}^{0,1}\_i\) & \(X_{400}^{0,1}\_i\) & \(X_{500}^{0,1}\_i\) & \(X_{600}^{0,1}\_i\) & \(X_{700}^{0,1}\_i\) & \(X_{800}^{0,1}\_i\) & \(X_{900}^{0,1}\_i\) \\ \hline Average tensile strength (MPa) & 53.85 & 50.08 & 51.47 & 52.36 & 52.58 & 52.93 & 54.36 & 55.45 & 55.22 & 55.99 \\ Standard deviation & 1.99 & 1.72 & 1.63 & 1.86 & 1.62 & 1.83 & 1.58 & 1.92 & 1.84 & 1.76 \\ Growth (\%) & - & \(-7\) & \(-4.42\) & \(-2.85\) & \(-2.36\) & \(-1.71\) & 0.95 & 2.97 & 2.54 & 3.97 \\ Average plasticity (\%) & 10.1 & 9.65 & 9.8 & 10 & 10.4 & 10.3 & 10.7 & 10.5 & 10.6 & 10.2 \\ Standard deviation & 0.53 & 0.51 & 0.38 & 0.33 & 0.41 & 0.36 & 0.47 & 0.43 & 0.50 & 0.39 \\ Growth (\%) & - & \(-4.46\) & \(-3.06\) & \(-0.99\) & 10.6 & 2.97 & 5.94 & 3.96 & 4.95 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The tensile properties of the X-direction specimens processed without applied vibration and with different applied vibrations.  
Figure 6: The effect of applied vibration (0.1 g amplitude with different frequencies) on the tensile properties of X-direction specimens.  
The average tensile strength of \(Z^{0.1}_{700\_i}\), \(Z^{0.2}_{700\_i}\), and \(Z^{0.3}_{700\_i}\) are, respectively, 34.65, 36.54, and 38.25 MPa, and they are, correspondingly, increased by 46.3, 54.3, and 61.5% compared with \(Z^{0}_{0\_i}\). In terms of plasticity, the corresponding values are 7.14, 7.35, and 7.7%, increased by 25.3, 28.9, and 35.1%, respectively. Therefore, increasing the amplitude of the applied vibration is also able to further improve the tensile properties of ME specimens built in the Z direction. This is due to a similar reason as explained in Section 3.1.1. Figure 8 shows the SEM micrographs of the specimens' cross sections. It shows that with the increase in the vibration amplitude, the bonding area is increased and the bonding gap becomes smaller. There are fewer defects of layer separation, distortion, and deformation.
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 4 DVB-S2X System Operation with Coded FTN Signaling and IDD  
Figure 5 depicts the DVB-S2X system with the coded FTN signaling. The input data are modulated through the encoder, interleaver and modulator. The modulated data symbols are filtered by the FTN pulse-shaping filter, which uses the \(\boldsymbol{\tau}_{\text{code}}\) for FTN signaling. The received signal is filtered and down converted by the matched filter. For downsampling, the symbol rate information \(\boldsymbol{\tau}_{\text{code}}\) is required. For interference cancellation and signal detection, the IDD scheme is considered. In the IDD scheme, to regenerate the transmitted signal, an FTN filter is used, and the FTN filter requires information about the FTN symbol rate. Therefore, for the sampling and interference cancellation, the \(\boldsymbol{\tau}_{\text{code}}\) is used in the receiver. In Figure 5, the gray dotted block represents IDD. As shown in the figure, the signal decoded through the low-density parity check (LDPC) decoder is remodulated, and FTN signaling is applied to regenerate the transmission signal. After that, the error value between the regenerated FTN signal and the remodulated signal is calculated, and then removed from the received signal iteratively.  
As shown in Equation (4), the transmitted physical layer security signal \(z_{\text{code}}\) is received after additive noise, and filtered by the matched filter. After the log-likelihood ratio (LLR) of the received signal is calculated, the deinterleaver and LDPC decoder are operated. Then, the regeneration process of the decoded signal is executed. In the signal modulation process of the transmitter, the decoded information is interleaved and modulated. The coded FTN signal is regenerated by using the coded FTN, filtering with the \(\boldsymbol{\tau}_{\text{code}}\), as follows:  
\[\hat{g}(t)=\left(\sum_{k}\hat{w}_{k}\cdot f\left(t-\sum_{m=0}^{k}\boldsymbol{ \tau}_{m}T\right)\right)*f^{*}(-t) \tag{8}\]  
Figure 4: Block/flow diagram of the proposed technique.  
Figure 5: DVB-S2X System with Coded FTN signaling.  
where \(\hat{w}_{k}\) is the \(k\)-th remodulated symbol. The interference value sampled at \(k\)-th time is calculated by using the regenerated coded FTN signal \(\hat{y}_{k}\) and the remodulated signal \(\hat{w}_{k}\), as follows:  
\[e_{k}=\hat{y}_{k}-\hat{w}_{k} \tag{9}\]  
In the second iterative process, the interference value \(e_{k}\) is removed from the received signal as follows:  
\[\widetilde{y}_{k}=y_{k}-e_{k} \tag{10}\]  
where \(y_{k}\) is the received signal sampled at \(k\)-th time. After the interference cancellation, the identical demodulation/ decoding and interference calculation processes are operated.  
The IDD process is repeated up to a predetermined repetition number in consideration of the detection performance and the processing time.
## 4 Conclusions  
Based on the MWNT content, we analyzed the effect of the hybrid structure, produced by sealing the pure NR layers on the MWNT/NR composite, on the hysteresis and the sensing range of the strain sensor. The adhesion between the pure NR layer and the MWNT/NR composite was excellent, as can be observed from the SEM images. Electrical conductivity, based on the number of MWNTs, was measured using the electrical percolation threshold. The tensile stress test was conducted to set the strain-sensing range, and it was observed that the elongation of the hybrid structure composite increased compared to that of the single composite. Hysteresis analysis was performed at 50% and 100% CNT content, and the hysteresis reduction effect of the hybrid structure was maximized in the case of low CNT content and large tensile range. In addition, the inevitable plastic deformation in a single composite can be reduced in the hybrid structure. Because the pure NR layer holds sufficient surface roughness on the single composite, a strain sensing of approximately 150%, which was not possible in the 1 wt.% MWNT/NR single composite, was possible. Repeatability and durability were excellent in the strain range of 130% in the 5 wt.% hybrid composite. This study confirmed that the pure NR layer can control the structural surface roughness to lower the hysteresis and increase the sensing range. The results of this study show that the characteristics of a strain sensor can be modified through structural deformation. Our conclusions constitute a significant milestone for the future development of strain sensors.  
Conceptualization, S.-H.P.; methodology, I.-Y.J. and B.-H.K.; formal analysis, S.-H.P.; resources, B.-H.K.; writing--original draft preparation, B.-H.K.; writing--review and editing, S.-H.P.; supervision, S.-H.P.; project administration, S.-H.P.; funding acquisition, S.-H.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT). (No. 2020R1A2C1013489).  
Not applicable.  
The authors declare no conflict of interest.
## 5 ACTOR Design
### Initialization of Action Values  
RSSI is not always correlated with PDR due to variations in local link quality [35] and, thus, is not the best candidate for the long-term selection of transmission power. To take external interference and link asymmetry into consideration, ACTOR uses the reception/loss of the packets as the reward. However, for the initialization of the action values, RSSI can be advantageous, as nodes can estimate their distance.  
This mechanism requires the DIO packets to be sent with maximum transmission power (0 dBm) or a constant transmission power level noted as \(P_{DIO}\). Upon receiving a DIO, nodes measure the loss in the signal strength, \(\Delta P\), which is simply the difference between \(P_{DIO}\) and the received power, (\(P_{RX}\)). If we assume the link is symmetric, and the transmission power is configured to \(a\), then the received power at the receiver would be \(a-\Delta P\). Based on the empirical results from Section 4, we estimate the \(\theta_{-}\) and \(\theta^{+}\), which are the minimum and maximum RSSI measurements that put the link in the transitional region.  
ACTOR initializes the Action Values in three classes, as in Equation (5). Simply put, \(X_{0}(a)\) is the estimated value of the PDR of the link if the transmission power is set to \(a\). If a certain transmission power puts the link in the transitional region, the PDR is set using a linear function of the power loss and the difference between \(\theta^{+}\) and \(\theta_{-}\)[34]. This initialization is performed quickly, but it needs to be augmented later with the probing mechanism since it cannot capture noise or link asymmetry.  
\[X_{0}(a)=\begin{cases}1,&\text{if }\theta^{+}<a-\Delta P\\ \frac{\theta-\Delta P}{\theta^{+}-\theta_{-}},&\text{if }\theta_{-}<a-\Delta P< \theta^{+}\\ 0,&\text{if }a-\Delta P<\theta_{-}\end{cases} \tag{5}\]
## 4 Results and Discussion
### Drug Release Mechanism and Kinetics  
The drug release data were fitted to all mathematical models considered here over two different time ranges (0-6 and after 6 h) [73]. The highest value of the regression coefficients was assumed to determine the best mathematical model of drug release kinetics [44]. Table 4 presents the results of n and k values for both films, which showed excellent linearity (r\({}^{2}\) = 0.91-1.00). The values of n for both PEC films in the first 6 h were 0.5 indicating that both films follow a Fickian model. After 6 h, however, there was a statistically significant (\(p\) < 0.05) decrease (0.06 and 0.08 for TM-PEC %1 and TM-PEC %0.4, respectively), where the mechanism of drug release would be considered as a Pseudo-Fickian diffusion mechanism based on the Korsmeyer-Peppas equation [74]. By increasing the concentration of PEC from 0.4 to 1%, the kinetic constant k increased showing an increased drug release.  
Figure 9: Drug releases of TM-PEC films.  
In the first-order model a constant fraction of drug in the body is eliminated per unit of time, while zero-order kinetics describes the process of constant drug release from a drug delivery system, and drug level in the blood remains constant during the delivery [38]. In both time ranges, the released drug data fitted the first-order model indicating that the release rates are directly related to the concentration of drug and do not follow the zero-order release kinetics due to their lower values of regression coefficient in comparison with that of first-order model (Table 4).  
[PERSON] describes the drug release as a diffusion process based on [PERSON]'s law with a square root time dependence and hypothesizes that the swelling of matrix and dissolution are small or negligible [75]. The Hixson-Crowell cube root law describes the release from systems where there is a change in surface area and diameter of particles or tablets [76]. This model assumes that the release rate is limited by the dissolution rate of drug particles and not by drug diffusion [77] During the initial 0-6 h, the release data followed the Hixson-Crowell model closely, with high values of regression coefficients 0.93-0.97. This supports drug dissolution as the main kinetic barrier to drug release due to changing polyelectrolyte structure. After 6 h, the drug release became stable, and by comparison between the fitted release data, it can be observed that there is little difference between the Higuchi and Hixson-Crowell models (0.83-0.87 in comparison with 0.84-0.89). Actually, after 6 h, drug diffusion through the PEC matrices becomes more effective (Table 4).