esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load
## 4 Results and Discussion
### Experimental Implementation and Results  
Recently, it has been made possible to simulate electrical networks using real-time digital simulator (RTDS) to verify the theoretical simulation [38; 39]. The simulation could be performed faster in RTDS since it works continuously in sustained real-time. Microgrid elements such as loads, converter bridges, filters, and lines can be modeled inside the RTDS environmental using their physical representation built in a standard library blocks. Additionally, each power system component has its detailed model, which is already built in RTDS library. This model can resemble the real system. The converter (inverter/rectifier) bridge is ideally modeled. The inverters controllers and active load controllers are implemented in the RTDS using the optimal parameters obtained from PSO. The performance of the proposed controllers has been comprehensively tested. RTDS is used to analyze the microgrid described previously in Section 2. The microgrid setup including the inverter-based DGs and active load circuits is simulated using the RTDS. In real time, the controller performance of the autonomous microgrid is assessed using the optimal controller parameters. The effectiveness of the proposed approach for stability enhancement is investigated. With step up and down changes of the DG1 reference voltage, Figure 13a,b shows the DG1 output voltage and its reference respectively. The results show the controller effectiveness under the sever disturbances. Similarly, step up and down changes for the reference voltage of DG3 is applied to investigate the controller effectiveness. Figure 14a,b depict the output voltage of the DG3 and its reference due to this change. As shown in the RTDS results, the capability of the controller to track the reference during the step change is satisfactory without significant overshoot and delay time. Figure 10: Microgrid response when active load voltage stepped down to 0.5 pu. Figure 11: Microgrid response when the reference dc voltage stepped up by 5%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Microgrid Parameters**} & \multicolumn{6}{c}{**Active Load Parameters**} \\ \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(f_{s}\) & 8 kHz & \(V_{n}\) & 381 V & \(L_{f}\) & 2.3 mH & \(L_{c}\) & 0.93 mH \\ \(L_{f}\) & 1.35 mH & \(L_{c}\) & 0.35 mH & \(C_{f}\) & \(8.8\times 10^{-6}\) F & \(r_{c}\) & 0.03 \(\Omega\) \\ \(C_{f}\) & \(50\times 10^{-6}\) F & \(C_{b}\) & \(50\times 10^{-6}\) F & \(f_{f}\) & 0.1 \(\Omega\) & \\ \(r_{f}\) & 0.1 \(\Omega\) & \(r_{c}\) & 0.03 \(\Omega\) & \(R_{dc}\) & 67.123 \(\Omega\) & \(C_{dc}\) & 2040 \(\times 10^{-6}\) F \\ \(\omega_{n}\) & 314.16 rad/s & \(\omega_{c}\) & 31.416 rad/s & & & & \\ \(r_{1}+\dot{\mu}_{1}\) & \((0.23+\dot{\mu}_{2})\) & \(r_{2}+\dot{\mu}_{2}\) & \((0.35+\dot{\mu}_{0.58})\) \(\Omega\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System parameters.
# Extraction of Novel Bioactive Peptides from Fish Protein Hydrolysates by Enzymatic Reactions
## 3 Application of Enzymatic Hydrolysis for Fish Protein Isolation
### Endogenous Enzymes  
Enzymes that are naturally present or can be isolated from the digestive system of a fish are called endogenous enzymes, and they play a major role in digestion by breaking down carbohydrates [42]. After harvest, various biochemical changes occur in fish muscle, including the enzymatic hydrolysis of proteins. This breaks down muscle tissue and other connective tissue structures, resulting in decreased muscle integrity and changes in rheological properties [43]. In addition, autolytic processes in fish depend on the location of enzymes in the muscle, seasonal changes in enzyme concentration, and the synergistic effect of the enzymes in proteolysis [44]. Furthermore, endogenous proteases play a key role in the deterioration of most fin fishes [45; 46; 47]. Currently, studies are being developed to further assess the mechanism of action of these enzymes in various marine and aquatic fermented products [48].  
A study conducted by [PERSON] et al. [49] on the assessment of the endogenous proteases in fermented fish sauce revealed that four proteases, namely, serine protease inhibitor, trypsin inhibitor, aspartic protease inhibitor, cysteine protease inhibitor, and metalloproteinase inhibitor, were present in the medium. The authors further explained a positive correlation of the endogenous protease activity on the antioxidant activities of fermented fish sauce. The fermentation of low-salt fish paste ka-pi-plaa, studied by [PERSON] et al. [50], revealed high concentrations of endogenous proteases (mainly cathepsin-D) in the fermented product. The authors added that cathepsin-D is naturally occurring and a predominant proteinase participating in the autolysis process of beardless barbs. Fermented fish products are commonly consumed as conditions and are also utilized as flavor enhancers that add nutrition to the final product [51]. Thus, the action of endogenous enzymes in proteolysis during fermentation results in the production of peptides and amino acids, which are somehow responsible for the development of taste and aroma unique to most fermented fish products.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 4 End-Edge Computation Offloading Based on Lyapunov-Guided DRL
### MDP Modeling  
However, the transformed problem \(P_{2}\) is still difficult to solve as the variables are still coupled with each other. Thus, we first transform it into a MDP and then employ DRL to approximate the optimal solution.  
For MDP modeling, there are state space, action space and reward space for DRL.  
* **State space**: In \(t\)-th time slot, the state \(s(t)\) consisting of the channel gain and the system queue is defined as \[s(t)=\{\mathbf{h}(t),\mathbf{Q}(t),\mathbf{Y}(t)\},\] (20) where \(\mathbf{h}(t)=\{h_{n,m}(t)\}_{N\times M}\) is the channel gain among industrial base stations and industrial terminals, \(\mathbf{Q}(t)=\{Q_{n}(t)\}_{N}\) is the set of data queue length, and \(\mathbf{Y}(t)=\{Y_{n,m}(t)\}_{N\times M}\) is the set of virtual energy queue length.
* **Action space**: In \(t\)-th time slot, the action \(a(t)\) consists of an optimal offloading action, the computation offloading time ratio, the local CPU frequency and the total energy consumption, defined as \[a(t)=\{\mathbf{d}(t),\mathbf{\tau}(t),\mathbf{f}(t),\mathbf{e}(t)\},\] (21) where \(\mathbf{d}(t)=\{d_{n,m}(t)\}_{N\times M}\) is the set of optimal offloading action, and \(\mathbf{\tau}(t)=\{\tau_{n}(t)\}_{N}\) is the set of computation offloading time ratio, \(\mathbf{f}(t)=\{f_{n}(t)\}_{N}\) is the set of local CPU frequency, \(\mathbf{e}(t)=\{e_{n,m}(t)\}_{N\times M}\) is the set of energy consumption.
* **Reward space**: The reward \(r(t)\) denotes the action reward generated when taking action \(a(t)\) in the current state \(s(t)\). According to the objective function, the reward \(r_{n}(t)\) for \(n\)-th industrial terminal and \(m\)-th industrial base station in \(t\)-th time slot is defined as \[r_{n,m}(t)=(Q_{n}(t)+V\beta_{n})z_{n,m}-Y_{n,m}(t)e_{n,m}(t).\] (22) Based on this, the sum of incentives received by all industrial terminals is calculated as \[r(t)=\sum_{n=1}^{N}\sum_{m=1}^{M}r_{n,m}(t),\] (23) Obviously, the larger the reward, the larger the network computation rate of the industrial terminal. Furthermore, the cumulative reward is defined as \[R(t)=\sum_{t=0}^{K}\gamma^{t}r(t),\] (24) where \(\gamma\in[0,1]\) is the discount factor indicating how the previous reward impacts the current reward.  
Through the above process, we model the transformed problem into an MDP. As such, we can maximize the network computation rate by maximizing the long-term accumulative reward while satisfying all constraints, during which an effective policy can be obtained.
## 4 Biological Applications
### Diagnostic Applications
#### 4.1.1 Biosensors
reported the capture of Gram-negative and Gram-positive bacteria by FePt nanoparticles with polyvalent ligands at an ultra-low concentration (15 cfu/mL) [170; 171]. The polyvalent ligand, vancomycin, provided a specific biofunctional receptor to capture the target bacteria. Moreover, PtCo bimetallic nanoparticles, displaying a unique combination of magnetism and oxidase-like catalytic activity, have been reported to enhance the colorimetric biosensing of MCF-7 cells [117]. The magnetic PtCo nanoparticles were conjugated with aptamers that can selectively capture cancer cells via their magnetic properties. Further, the magnetic nanoparticles show oxidase-like catalytic activity toward the oxidation of chromogenicsubstrates, which further enhances the quantitative detection of cancer cell lines through a colorimetric assay. These results highlight a detection method that can be observed with the naked eye without requiring H\({}_{2}\)O\({}_{2}\) as an oxidant or illumination by light [117]. The fabricated bimetallic nanoparticles represent a facile, rapid, and highly selective detection method.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 3 Materials and Methods
### DualCT Module  
CNN and Transformer have their own advantages in feature learning. In order to effectively represent the coupling of spatial and spectral features of HISIs, our DualCT module deploys the self-attention mechanism of the Transformer and localized convolution as two parallel branches, and uses bidirectional interactions to provide the fusion of complementary features in both spectral and spatial dimensions. At the same time, motivated by the mask guidance mechanism proposed in [25], we designed mask attention and added it to the DualCT module. According to the imaging characteristics of the CASSI system, mask attention can guide the network to focus on regions with high-fidelity HSI representation, enhancing the representation capability of spatial-spectral structural details. Specifically, as shown in Figure 1b, the DualCT module mainly consists of layer normalization (LN) [42], a parallel complementary fusion structure, and mask attention. The parallel complementary fusion structure contains a spatial domain CNN branch and a spectral domain Transformer branch. The two branches achieve effective feature fusion through bidirectional cross-linking interactions. The calculation process of DualCT can be expressed as follows:  
\[\begin{split}\hat{X}^{l}&=CT(LN(X^{l}))+X^{l},\\ \hat{X}^{l+1}&=CONV(LN(\hat{X}^{l}))\odot M^{{}^{ \prime}}+\hat{X}^{l},\end{split} \tag{4}\]  
where \(X^{l}\) represents the feature input to the DualCT module, LN stands the layer normalization, and \(CT(\cdot)\) represents the complementary fusion of dual branches. \(M^{{}^{\prime}}\in R^{H\times W\times C}\) is the mask attention mapping, \(\odot\) represents element-wise multiplication, and \(\hat{X}^{l+1}\) represents the output feature of the DualCT module. We denote \(CONV(LN(\hat{X}^{l}))\) as \(X^{l+1}\). The specific design of the complementary fusion structure will be introduced in detail below.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 6 Experimental Result
### ATS Function Comparison  
By enabling and disabling the ATS function, we can demonstrate the impact of ATS on network performance. Figure 10 shows the results of multiple experiments, clearly indicating that the function of ATS significantly reduces the delay and packet loss probability of TS traffic, which leads to a more stable network operation.  
Figure 8: Link load with TS and BE flow.  
Figure 9: Link load with TS flow in one cycle.
## 7 Impact Assessment of EVs
### Impact of EV Integration on Grid
#### 7.3.2 Challenges of EV Integration in terms of Power Quality  
The integration of EVs into the power system has the potential to affect the power quality of the system. The effects of EV integration have been extensively studied, with a particular emphasis on power quality characteristics. These characteristics include voltage profile, voltage imbalance, power losses, and harmonics. The impact of power quality on the grid integration of EVs is influenced by various factors, including charging characteristics, network characteristics, and the overall number of EVs [145; 146; 147; 148]. The issues pertaining to power quality resulting from the integration of EVs have been concisely summarized in Table 23.  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline \multicolumn{1}{c}{**Impact of EV Integration**} \\ \hline Voltage Stability [140] & EV charging has peculiar load properties when compared to conventional loads. EV integration may have a negative effect on the stability of the grid’s voltage depending on the area, level of penetration, and EV charging time. \\ \hline Frequency Stability [141] & The level of load demand is raised by the unknowns around the EV connection site, penetration level, and connection and disconnection timeframes. As a result, the grid’s frequency stability can be compromised. EVs can function as controlled loads and take part in frequency regulation of the grid with a faster ramp rate and ancillary services. \\ \hline Oscillatory Stability [142] & When compared to traditional loads, an EV load has quite distinct properties. The properties of negative exponential EV loads affect the power system’s oscillatory stability more than those of normal system loads. \\ \hline Increase in Peak Load [143] & EVs can considerably increase grid demand, especially during peak charging hours. Peak load rise is affected by the number of EVs, charging behavior, and charging infrastructure. The widespread deployment of EVs is expected to increase peak electricity demand. Some reports studied the implications of EV charging on the US electricity system. EV adoption might increase nighttime peak electricity demand by 30\%. \\ \hline Transformer Aging [144] & Transformers are vital to electrical infrastructure, and EV charging can hasten their aging. If EV charging demand rises, transformer maintenance or replacements may cost more. A case study in a city with widespread EV use examined how EV charging affected transformer aging. Compared to sites with low EV charging demand, locations with more EVs increased transformer aging by up to 15\%. \\ \hline \hline \end{tabular}
\end{table}
Table 22: Impact of EV Integration on Grid Stability.
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 4 Discussion
### Differences in the Impact of Aerosol DFE on Different Vegetation Types  
Our results show that aerosol DFE increased GPP for most broad-leaved forests. However, in coniferous forests, the aerosol greatly reduced the total PAR and reduced the \(\Delta\text{GPP}\) by 3.36%. One possible explanation is that the aerosol DEF of coniferous forests is not as obvious as that of broad-leaved forests [64], which may be caused by the sparse canopy and lower leaf area index (LAI) of coniferous forests [12; 65]. Another possible reason is that the negative effect by the reduction of total PAR more than offset the DEF. As a result of the increase in the proportion of leaves exposed to moderate light levels, terrestrial ecosystems with high LAI were more sensitive to diffuse radiation [66]. A modeling study by [PERSON] et al. [67] showed that under the condition of an increased diffuse fraction, the actual LUE increased by 33% for a broad-leaved forest with an LAI of 5.05 but by only 6% for a Scottish coniferous forest with an LAI of 2. The simulations by [PERSON] and [PERSON] [15] using a multilayer canopy model also indicated that the aerosol DFE decreased with a decrease in LAI.  
In addition, previous studies have suggested that GPP in some ecosystems with fewer leaves or open canopies, such as grasslands and wetlands, is also less sensitive to diffuse radiation [17; 50]. However, in our research, we found that in some drought-tolerant grasslands (such as WSA) with low LAI, the aerosol DFE caused a significant increase in GPP (Figure 7). [PERSON] et al. [29] found that CO\({}_{2}\) absorption increased significantly due to aerosols and thick clouds in grasslands with extremely low LAI (0.37). There is a significant relationship between canopy structure and the aerosol DFE for the multilayer arctic shrub system with low LAI (1.5) [68]. Some previous studies also have suggested that the LAI of temperate coniferous forests and temperate deciduous forests was similar, and LAI alone cannot explain the differences in the response of GPP between plant functional types (PFIs) to the aerosol DFE [15; 69]. Therefore, our results support [PERSON] et al.'s [11] view that canopy structure may be more important in determining the aerosol DFE than LAI.
###### Abstract  
With the increase of soil consolidation degree, the pore water pressure induced by thermal loading drops dramatically. To conveniently and quickly calculate the thermal pore water pressure inside the soil under different overconsolidation states and quantify overconsolidation effect on thermal pore water pressure, a calculation method of thermal pore water pressure considering overconsolidation effect for saturated clay is proposed. The method is verified by the relevant experimental data, and good agreements were achieved. Through analyzing the influence mechanism of \(OCR\) on the thermal pore water pressure, three important findings were captured. (1) For overconsolidated clay, thermal pore water pressure decreases nonlinearly with the increase of \(OCR\). (2) There is a critical threshold of \(OCR\) 4.3; when \(1<OCR\leq 4.3\) (slightly overconsolidated state), the ratio of compression line slope to recompression line slope (\(\Lambda\)) of overconsolidated clay is consistent with that of the normally consolidated clay, while when \(OCR>4.3\) (highly overconsolidated state), the value of \(\Lambda\) is smaller than that of normally consolidated clay. (3) For highly overconsolidated clay (\(OCR>4.3\)), considering the reducing of \(\Lambda\) with \(OCR\), the prediction accuracy of the thermal pore pressure calculation method has been greatly improved; especially when \(OCR\) equals 30, the prediction accuracy improves by 92.7% as temperature change achieves 35 \({}^{\circ}\)C.  
overconsolidation effect; thermal pore water pressure; calculation method; saturated clay +
Footnote †: journal: Applied Physics Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
[PERSON]; [PERSON] A Calculation Method of Thermal Pore Water Pressure Considering Overconsolidation Effect for Saturated Clay.  
1
## 2 Training Datasets Generation through Finite Element Simulation
### Type-2 Datasets Creation: Reflection Wave Propagation Simulation  
For the type-2 training datasets generation, we have created three different geometrical 2D CAD models, as shown in Figure 1b. The overall physical dimension of each model is 40 mm in length and 40 mm in height. A total of 1500 CAD models are created, of which 500 are of the same geometrical shape. The excitation load is applied on the top-edge nodes of the CAD models, as shown in Figure 1b, and on the remaining edges, a traction-free boundary condition is applied to allow the sidewall and back wall reflections. After performing each FE simulation, the displacement plots are saved as a sequence of images, with a time interval of 0.0125 us.  
One FE simulation generation executed using Dual Intel Xeon Platinum 8168 processor with 48 cores machine (Figure 1b) took 3600 s, using time-domain FE analysis, which is a time-consuming process. We have introduced the SDL model to overcome this limitation, which generates wave propagation simulations in minimal time once trained.
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 5 Evaluation of UWB Data
### Analysis of Range Measurements
These clocks have both have their own clock offset error and clock drift and, therefore, the resulting time of flight estimate has an error that increase as the replay time increase. One should note that the time replay include also the message length which also affect the TOF. In our experience the ultra-wideband was tested with several settings but the results of the present work are obtained using channel 5, preamble length 1024, prf 64 MH, and bitrate of 110 kbps. With these parameters the clock induced error is about 1 ns. The range between all the anchors and the moving tag for all five test has been compared with the geometric range obtained by the TS direct measurements and the statistical parameters of the errors are reported in Table 3.  
Figure 6: Position estimation time drift. This allows also the effect of a multipath signal in TWR to be evaluated observing the distance errors. Note that the UWB signal overcomes the multipath effects compared to other narrow-band signals. However, signal disturbance because of multipath effects in UWB is still noticeable in distance error estimation, as shown by the different behavior of anchors 0x6726 and 0x6765 visible also in Figure 7.  
In Table 4 the Root Mean Square Error (RMSE) and the % of outlier for each anchor is reported. The outlier is defined as the values belonging to the first and fourth quartiles of the weighted average distribution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Anchor** & **RMSE (m)** & **Ranges** & **Outlier** & **Outlier \%** \\ \hline
0x617e & 0.43 & 2562 & 145 & 6\% \\
0x6119 & 0.48 & 1476 & 48 & 3\% \\
0x6735 & 0.58 & 2043 & 30 & 1\% \\
0x6726 & 1.73 & 2373 & 402 & 17\% \\
0x672d & 0.47 & 1129 & 18 & 2\% \\
0x6765 & 0.82 & 1346 & 125 & 9\% \\
0x6840 & 0.5 & 4268 & 197 & 5\% \\
0x617c & 0.45 & 4068 & 134 & 3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ranges RMSE and % of outliers for each anchor. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **0x617e** & **0x6119** & **0x6735** & **0x6726** & **0x672d** & **0x6765** & **0x6840** & **0x617c** \\ \hline max (m) & 1.33 & 1.33 & 1.38 & 1.36 & 1.31 & 0.82 & 3.78 & 1.89 \\ min (m) & \(-1.79\) & \(-4.31\) & \(-5.96\) & \(-8.09\) & \(-0.66\) & \(-4.19\) & \(-0.95\) & \(-1.40\) \\ mean (m) & 0.30 & 0.20 & 0.25 & \(-0.62\) & 0.34 & \(-0.65\) & 0.28 & 0.30 \\ std (m) & 0.31 & 0.43 & 0.52 & 1.61 & 0.33 & 0.50 & 0.41 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical parameter of the errors in ranging measurement for 8 anchors during all 5 tests performed. Figure 7: Ranging error of Pozyx UWB system for each anchor.
## 2 Results and Discussion
### Sweet Paprika  
A total of 50 VOMs were identified in sweet paprika (C. _annuum_ L.), being mainly characterized by terpenoids (54.8%), followed by carbonyl compounds (13.7%) and volatile phenols (11.8%) (Figure 1A; Table 1). The principal VOMs identified in sweet paprika include \(p\)-cymene (#42), \(\gamma\)-terpinene (#36), eugenol (#155), limonene (#31), thymol (#156), cuminaldehyde (#130), cinnamaldehyde (#150) and geraniol (#135), representing over 41.7% of its total volatile profile. In this sense, the sweet paprika sample is richer than the other spices, since many VOMs contribute to its volatile composition with very similar relative peak areas. On the other hand, VOMs such as benzaldehyde (#67), \(\beta\)-pinene (#22), hexanal (#20), 1-methyl pyrrole (#26) and caryophyllene isomer (#87) were also identified, although in lower amounts (relative peak areas of 2.92%, 2.64%, 2.59%, 2.52% and 2.13%, respectively) (Table 1). The VOMs identified only in sweet paprika are listed in Table S1. [PERSON] et al. [31] confirm the presence of some VOMs identified in the sweet paprika samples analyzed in this investigation. The chemical structures of the most representative VOMs identified in each investigated spice and the corresponding odor descriptors are presented in Figure 2.  
Figure 2: Chemical structures and characteristic odor associated with the major volatile organic metabolites (VOMs) identified in the investigated spices.