esa-sceva/satcom-chunk-collection huggingface dataset:
# Congestion-Free Ant Traffic: Jam Absorption Mechanism in Multiple Platoons
## 2 Model and Simulation Scenario
### Model
#### 2.1.2 Stage II: Pheromone Updating  
The second stage of the update represents changes in the environment (trail) over time due to the interactions of agents with it. At each time step, the pheromone concentration on the trail changes for two reasons: (i) evaporation due to environmental factors (normally, the evaporation rate \((er)\) remains constant if the surroundings remain approximately unchanged) and (ii) pheromone accumulation due to further discharge of the pheromone by the ants (in one time step, an ant can release an amount \(\tau\) of pheromone, referred to as a pheromone unit). At the end of Stage II, we obtain the subset \(\sigma_{i}(t+1)\) using the subsets \(S_{i}(t+1)\) and \(\sigma_{i}(t)\), and the velocity of the ant in the cell as follows:  
* Evaporation: \[\sigma_{i}^{\prime}(t+1)=\sigma_{i}(t)-(\sigma_{i}(t)\times er),\quad if\quad \sigma_{i}(t)>0.\] (3)
* Accumulation: \[\sigma_{i}(t+1)=\begin{cases}(\sigma_{i}^{\prime}(t+1)+\tau),&\quad if\quad s _{i}(t)=1\quad and\quad\sigma_{i}^{\prime}(t+1)<\sigma_{sat}\quad and\quad v _{j}(t)>0\\ \sigma_{sat},&\quad if\quad s_{i}(t)=1\quad and\quad\sigma_{i}^{\prime}(t+1)> \sigma_{sat}.\end{cases}\] (4)  
Herein, as we discussed, \(\sigma_{sat}=80\). As given by Equation (3) for evaporation, a certain fraction of the pheromone concentration evaporates from each cell at each time step depending on \(er\). After evaporation, the remaining pheromone concentration on the cell is further affected by the addition of pheromone emitted by the ant in the cell at the same time step. As shown in Equation (4), for accumulation, there are two possibilities.  
In the first case, a cell is occupied by an ant \((s_{i}(t)=1)\) that moved forward in the previous time step \((v_{j}(t)>0)\). Moreover, the pheromone concentration in the cell after evaporation (\(\sigma_{i}^{\prime}(t+1)\)) is below saturation. In that case, the ant releases a unit volume of pheromone that is added to the current pheromone concentration in the given cell.  
In the second case, a cell is occupied by an ant (\(s_{i}(t)=1\)), but \(\sigma_{i}^{\prime}(t+1)\) is above saturation. As such, there is no need to add any further pheromone because it would go undetected. In this case, the concentration remains at the saturation level \(\sigma_{sat}\).
# Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR
## 4 Results  
### Raw Data  
The strain monitoring data on the entire optical fiber was obtained during the static load test. Since the optical fiber on the pile is only a part of the entire fiber, the monitoring data belonging to the pile body should first be judged and extracted. The monitoring data of the three levels of loading are taken as an example, and the extraction data is shown in Figure 3.  
It can be seen from Figure 3 that there is a large fluctuation in the original data and it is difficult to see the specific trend. This is mainly because a large amount of noise signals is present in the original data, thus it is necessary to deal with certain denoising and smoothing on the original data. It should be noted that because part of the pile head is exposed to the ground and the shallow depth of the pile head is highly susceptible to environmental influence. Additionally, the data trend of the first 1-2 m part of pile head is unreasonable, and attention should be paid to the data analysis.
## 3 Simulations and Discussion
### PV Budget Analysis  
For understanding the track deflection, potential vorticity (PV) tendency budget is useful for providing an explanation for the typhoon track responses to different physical processes involved in the PV tendency equation as already illustrated in [PERSON] et al. (2020). The typhoon track at 0000 UTC 7 July for BND_H is more southward than that for BND as shown in Figure 13a. Figure 16 shows the wavenumber-one (WN-1) PV tendency budget averaged at 1-8 km height at 0000 UTC 7 July 2016 for BND and BND_H. For BND, the west-northwestward translation of the typhoon indicated by the net PV budget (Figure 16a) essentially follows the translation velocity induced by horizontal PV advection (Figure 16b) that is slightly offset by differential diabatic heating (Figure 16c). For BND_H, the induced typhoon movement is faster and becomes northwestward and is closely related to the net PV budget (Figure 16d) and horizontal PV advection (Figure 16e), while the differential  
Figure 14: The differences in (**a**) potential temperature (shaded in K) and (**b**) specific humidity (shaded in g kg\({}^{-1}\)) at 850 hPa between BND_RVH (with the higher resolution of 60-15-3 km) and the ERA-Interim at 0000 UTC 7 July 2016.  
diabatic heating is much weaker (Figure 16f). The induced typhoon translations for both experiments are in agreement with the WN-1 flow in Figure 16 and the actual typhoon movement shown in Figure 13. Thus, the more northward movement of the typhoon after 0000 UTC 7 July for BND_H is primarily controlled by the horizontal PV advection as a consequence of flow steering.  
Figure 16: Wavenumber-one (WN-1) PV tendency budget (shaped colors in \(10^{-5}\) PVU s\({}^{-1}\)) averaged at 1-8 km height at 0000 UTC 7 July 2016 for BND for (**a**) net PV budget, (**b**) PV horizontal advection, and (**c**) differential diabatic heating, with the overlaid WN-1 horizontal flow (vectors in m s\({}^{-1}\)). (**d–f**) are the same as (**a–c**), respectively, but for BND_H. The regressed typhoon translation velocity (m s\({}^{-1}\)) is indicated by the vector at the typhoon center with the magnitude given at the lower right of each panel. A reference wind vector (m s\({}^{-1}\)) is given in the upper right for the WN-1 flow in each panel.  
Figure 15: The RMS forecast errors for BND_RVH (green), BND_RPH (blue), and BND_H (red) verified against the ERA-Interim for (**a**) potential temperature (in K) and (**b**) specific humidity (g kg\({}^{-1}\)), at 0000 UTC 7 July 2016.
## 3 Results and Discussion
### Exposure Levels for the Monitored Tasks
It is noted that the results of the present study have been considered for the revision of the Greenhouse Agricultural Operator Exposure Model [26] included in the updated EFSA Guidance on the assessment of exposure of operators, workers, residents and bystanders in risk assessment of plant protection products [27]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pesticide A**} & \multicolumn{6}{c}{**Bupirimate mg a.s./kg a.s. Handled**} \\ \cline{2-7}  & **AP1** & **AP2** & **AP3** & **AP4** & **AP5** & **AP6** \\ \hline PDE body & 120 & 101 & 126 & 16.6 & 10.0 & 19.6 \\ PDE hands & 6.81 & 5.52 & 4.21 & 1.45 & 0.49 & 0.75 \\ PDE head & 0.05 & 0.41 & 0.21 & 0.02 & 004 & 0.04 \\ Total PDE & 127 & 107 & 130 & 18.0 & 10.5 & 20.4 \\ ADE body & 046 & 0.71 & 0.30 & 0.20 & 0.097 & 0.51 \\ ADE hands & 0.007 & 0.019 & 0.022 & 0.115 & \textless{LCO} & 0.044 \\ ADE head & 0.05 & 0.41 & 0.21 & 0.018 & 0.04 & 0.04 \\ Total ADE & 0.52 & 1.13 & 0.53 & 0.34 & 0.14 & 0.60 \\ \hline \hline \multirow{2}{*}{**Pesticide B**} & \multicolumn{6}{c}{**Tebufenoxide mg a.s./kg a.s. handled**} \\ \cline{2-7}  & **n/a \({}^{*}\)** & **n/a \({}^{*}\)** & **n/a \({}^{*}\)** & **AP4** & **AP5** & **AP6** \\ \hline PDE body & - & - & - & 50.7 & 51.1 & 107 \\ PDE hands & - & - & - & 4.39 & 4.92 & 7.11 \\ PDE head & - & - & - & 0.15 & 1.24 & 0.52 \\ Total PDE & - & - & - & 55.3 & 57.2 & 114 \\ ADE body & - & - & - & 0.32 & 0.39 & 0.60 \\ ADE hands & - & - & - & 0.063 & 0.023 & 0.45 \\ ADE head & - & - & - & 0.15 & 1.24 & 0.52 \\ Total ADE & - & - & - & 0.53 & 1.65 & 1.57 \\ \hline \hline \end{tabular}  
* AP1, AP2, AP3 handled pesticide A (morning), while AP4, AP5, AP6 handled pesticide B (afternoon) on the same crop, and thus have been exposed to pesticide A existing deposits.  
\end{table} 
Table 7: PDE and ADE values for applicators (mg a.s./kg a.s. handled).
# Time-Specified Adaptive Robust Control Framework for Managing Nonlinear System Uncertainties
## 3 Method
* Sensitivity to unstructured uncertainties: While it enhances robustness against parametric uncertainties, it may not be as effective if the uncertainty has high temporal variability. * Practical implementation challenges: In real-world applications, backstepping design requires precise state and parameter estimation, which can be challenging in noisy systems or those with delays. Our robust enhancement mitigates several of these limitations, particularly through the introduction of the composite saturation-tanh function that balances robustness and control smoothness.
# A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands +
## 2 Related Works
Conversely, in the approach proposed here, the evaluation is performed by resorting to simulation. Petri nets have been also extensively used for modeling overall telecommunication networks as well as specific portions of them [36]. Recently, a Petri net extension, generalized nets (GNs), have been used to model part of a more complex telecommunication system [37]. In [37], the authors proposed a GN model based on the classical conceptual model of an overall telecommunication system working both in packet and circuit switch ing mode and aimed at quality of service estimation. The system was evaluated under stable conditions and focused on the traffic intensity. In this work, our contribution is related to the use of GSM/EDGE channels by devices acting as SUs in different ways:  
* We consider a synchronized access to the channel in order to reduce the probability of collision with PUs;
* We propose a novel model of the GSM/EDGE PU traffic based on a Petri net, which takes into account the transponder load condition (so that it can be tuned in terms of expected load) in terms of the usage of the slotted periodical TDMA by both typical switching circuit and packet switching circuits;
* We also capture specific characteristics of mobile phone calls, such as the discontinuous transmissions obtained in the PU, thanks to the use of a voice activity detector;
* Taking into account a more realistic traffic model, we evaluate the service time and jitter of SUs, and we can validate the results while considering a more accurate distribution of the busy period.
## 4 Verification by Simulations
Figure 8: Experimental results of proposed method from forward 50 r/min to reverse 50 r/min: (**a**) Actual rotor position; (**b**) Estimated rotor position; (**c**) Rotor position error. From the above simulation results, it can be seen that the rotor position error obtained by the two methods has little difference when the motor is operating in a steady state. However, the rotor position error obtained by the proposed method is obviously smaller than that obtained by the traditional method when the motor speed changes or the load changes. Figure 11: The error comparison of the proposed method in different resistance parameters. Figure 10: Error comparison during load change: (**a**) Given load; (**b**) Rotor position error. Figure 9: Rotor position error comparison during speed change from forward 50 r/min to reverse 50 r/min. Therefore, the speed-tracking performance and the anti-load disturbance performance of the proposed sensorless control algorithm are better than those of the traditional method.
## 5 Conclusions  
A commercial polarization-sensitive optical coherence tomography (PS-OCT) setup was used to investigate ex-vivo human brain samples of patients diagnosed with Alzheimer's disease (AD). The performance of multicontrast OCT for the imaging of amyloid-beta plaques, one hallmark of the disease, was investigated. Following OCT imaging, histological staining was performed for a comparison to the gold-standard technique for AD diagnosis. Plaques were segmented in the OCTintensity and retardation data sets and the plaque contrast was analyzed. Notably, complementary distinct plaque patterns were observed in both contrast channels, only 5% of plaques were visualized in both intensity- and retardation-based contrast. The segmentation data were further used to evaluate the plaque diameter, area and the number of plaques per mm\({}^{3}\). The same parameters were evaluated for histology data, showing that significantly fewer plaques are visualized by OCT and that they appeared significantly bigger (\(p<0.01\)). In the OCT intensity contrast, a similar amount of plaques was counted compared to the retardation data. Plaques in retardation images appeared significantly larger. In conclusion, the results suggest that a multicontrast OCT is a promising tissue-preserving imaging technique to investigate amyloid-beta plaques.  
Conceptualization, B.B., A.L., J.G., C.W.M. and M.A.; C.K.H.; methodology, J.G., A.L., T.R. and A.W.; software, A.L., M.A. and P.E.; validation, J.G., A.L. and D.J.H.; formal analysis, J.G. and A.L.; investigation, J.G., A.L. and D.J.H.; resources, A.L., J.G. and C.K.H.; data curation, A.L., J.G., T.R. and P.E.; writing--original draft preparation, J.G., A.L. and B.B.; writing--review and editing, A.L., J.G., B.B., D.J.H., C.K.H., M.A., C.W.M., P.E. and T.R.; visualization, A.L. and J.G.; supervision, B.B. and A.W.; project administration, B.B.  
This research was funded by the European Research Council Starting Grant 640396 OPTIMALZ.  
The authors want to thank [PERSON] and their PS-OCT team for providing the PS-OCT setup and support during imaging and post-processing. We want to thank [PERSON] for her expertise in neurodegeneration and the evaluation of histologic stainings and the whole neuropathology lab of the Medical University of Vienna. We particularly acknowledge [PERSON] for his support while setting up the OCT system and performing the measurements. This work was funded by the European Research Council (ERC StG 640396 OPTIMALZ).  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Verification of Potential Sliding Surface in Dry Season without Subsurface Drainage  
Figure 15a displays the total incremental displacement field calculated by SRM slope stability analysis which directs to southeast and is similar to the main direction of ground movement estimated by the directions of tension cracks, slumps, and subsidence lines as shown in Figure 15b. In addition, the calculated potential sliding area as shown in Figure 15c also approximates the measured scope of ground movement delineated by the ellipse in Figure 15b. Further on, comparing Figure 15d with Figure 6, it can be seen the calculated (\(-11.4\) m for \(B12\)-W and \(-35.6\) m for \(B09\)-\(W\)) and measured (\(-12.0\) m for \(B12\)-\(W\) and \(-36.0\) m for \(B09\)-\(W\)) depth of potential sliding surface along \(B\)-\(B\)' profile (see Figure 4) are in an excellent coincidence. As a result, the effectiveness of the numerical model can be verified once more.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Drainage** & **Groundwater** & **Groundwater Drawdown** & **Variation of Factor** & **Increasing Percentage** \\
**Duration** & **Depth of \(B12\)-W** & **Percentage** & **of Safety** & **of Factor of Safety** \\
**t (day)** & \(H_{w}(t)\) **(m)** & **\(P_{drawdown}\) (\%)** & **FS(t)** & **\(P_{FS}\) (\%)** \\ \hline \(0\to 1\) & \(-12.97\rightarrow-15.27\) & 17.73 & 1.383 \(\to 1.443\) & 4.34 \\ \hline \(1\to 2\) & \(-15.27\rightarrow-16.97\) & 30.84 & 1.443 \(\to 1.479\) & 6.94 \\ \hline \(2\to 3\) & \(-16.97\rightarrow-17.47\) & 34.70 & 1.479 \(\to 1.489\) & 7.66 \\ \hline \(3\to 4\) & \(-17.47\rightarrow-18.17\) & 40.09 & 1.489 \(\to 1.499\) & 8.39 \\ \hline \(4\to 5\) & \(-18.17\rightarrow-18.67\) & 43.95 & 1.499 \(\to 1.501\) & 8.53 \\ \hline \(9\to 10\) & \(-19.97\rightarrow-20.07\) & 54.74 & 1.501 \(\to 1.510\) & 9.18 \\ \hline \(19\to 20\) & \(-20.57\rightarrow-20.62\) & 58.98 & 1.520 \(\to 1.521\) & 9.98 \\ \hline \end{tabular}
\begin{tabular}{c} Remarks: (1) The groundwater depth measured from the ground surface; (2) \(P_{drawdown}\) (\%) = \([\Delta H_{w}(t)]\times 100\%\) / \(H_{w}(0\;\text{day})\) = \([H_{w}(t)-H_{w}(0\;\text{day})]\times 100\%\)/(\(-12.97\)); (3) \(P_{FS}\) (\%) = \([\Delta FS(t)]\times 100\%\)/\(FS(0\;\text{day})\) = \([FS(t)-FS(0\;\text{day})]\times 100\%\)/ (\(1.383\)). \\ \end{tabular}
\end{table}
Table 6: Variations of groundwater drawdown and factor of safety with elapsed time during subsurface drainage (K\({}_{\text{S}}\) =\(1.0\times 10^{-4}\) cm/sec).  
Figure 14: Comparison of groundwater drawdown of observation well B12-W with and without subsurface drainage for different hydraulic conductivity of colluviums.
## 3 Results and Discussion  
### Physico-Chemical Parameters  
The results of the physico-chemical analysis were shown in Table 1. Bottarga reported a pH value of 5.3 and 5.45 without significant difference between BWC and BEC samples. Also, water activity (W\({}_{\text{a}}\)) did not differ between the samples from the east and west coasts. The values of Aw were found to be 0.90 \(\pm\) 0.46 and 0.91 \(\pm\) 0.62 (mean \(\pm\) SD) for the samples of the west coast and east coast, respectively. There is no information in the literature about the Aw regarding bottarga samples; however, the growth of the bacteria is limited when this value is <0.91 [24]. The concentration of protein was 40.5% \(\pm\) 1.73 and 38% \(\pm\) 0.46 for the bottarga from the west and east coasts, without any difference between the two areas. The fat concentration was, on average, 18.6%. The bottarga also had a good concentration of calcium, equal to 455 and 413 mg/kg for the west and east coasts, respectively. The concentration of sodium in the samples, equal to 0.70%, was lower than reported by other authors (4.3% of dried weight) [25]. The cholesterol found in the samples was 417 and 389 mg/100 g of the edible part for bottarga from the west and east coasts, respectively. These values were lower than that found by [PERSON] et al. [25], who reported a concentration of cholesterol of 730 mg/100 g of the edible part.
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
## 5 Experimental Result  
Figure 29 shows the experimental setup of the four-cell converter system with the load to verify the proposed control algorithm. Each converter cell consists of a DSP controller, a driver circuit, and an IGBT power module. The topology of the power converter is presented in Figures 10a and 11a. Information on experimental components is given in Table 7. The DSP controller is the LaunchPad Development Kit using C2000 Delfino MCUs F28379D from Texas Instrument (TI). With this setup, the experimental results of the conventional method and the proposed method can be obtained by changing only the DSP program accordingly whilst keeping the system hardware unchanged.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multirow{2}{*}{**Actions**} & \multicolumn{2}{c}{**Conventional**} & \multirow{2}{*}{**Proposed DSA-PSC**} \\  & & **Modified Updating** & & **Rule** \\  & & **(K = 0.66; \(\varepsilon(\Delta\theta_{n,\,n+1})\) & \((\varepsilon(\Delta\theta_{n,\,n+1})=0^{0})\)** \\  & & \(\leq(10^{-4})^{0})\)** & \\ \hline \multirow{3}{*}{Number of time steps} & Removing cell 3 & 17 & 10 \\ \cline{2-3}  & Removing cell 5 & 24 & 8 \\ \cline{1-1} \cline{2-3}  & Reinserting cell 5 & 15 & 8 \\ \cline{1-1} \cline{2-3}  & Reinserting cell 3 & 24 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluate the system reconfiguration time for a six-cell converter.  
Figure 28: Multiple reconfigurations of six interleaved carriers using, (**a**) conventional phase-shifted carrier (CPSC) method (modified updating rule); (**b**) proposed DSA-PSC method.  
Further, for rapid programming, the DSP is coded using the Matlab/Simulink software combining with the Code Composer Studio (CCS) compiler from TI, which is proposed in the previous works [32; 33; 34]. The programming sequence is conducted in three steps: (1) the model of the control algorithm is built using Matlab/Simulink; (2) the Real-Time Workshop(r) in Matlab generates C code file from this model; (3) the C code file is downloaded into the DSP board using the CCS compiler.
# Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry
## 4 Results  
We set the minimum humidity at 20% cm\({}^{3}\)/cm\({}^{3}\) and increase it by 1% cm\({}^{3}\)/cm\({}^{3}\); the maximum humidity is 80% cm\({}^{3}\)/cm\({}^{3}\), and increases by 1% cm\({}^{3}\)/cm\({}^{3}\). Each group is simulated 1000 times, and the results are displayed in Figure 12. The reverse osmosis model is on the left, and the sink model is on the right; together they represent the scatter plot of set soil moisture and the results obtained by the model suggested in this study for the two modes. The results of the measurements are somewhat lower than the predetermined soil moisture, according to both models. Additionally, when SMC increased, the relationship between the set SMC and the retrieved results was less correlated. This is due to the fact that when SMC rises, the GNSS signal's attenuation when passing through soil rises and the accuracy of data processing reduces.  
The simulation results in this study demonstrate that although the layers set in the model have little impact on the variation of reflectance with the satellite elevation angle, the variable range of soil moisture from the surface to the bottom has a substantial impact. The entire transmission properties of vertically linearly polarized electromagnetic waves in GNSS-IR technology allow the three-layer soil model to estimate stratified moisture for conventional surface soil.  
The two soil moisture-layered models developed for this work generally yield satisfactory retrieval results, with a 0.92 correlation between the measured results and the set SMC. Ground-based experiments were not performed in this study to time the restrictions; however, they will be in subsequent studies.  
Figure 11: Spectrum of the two models with different soil moisture changes.  
Figure 10: EMD decomposition results and spectral characteristics.