esa-sceva/satcom-chunk-collection huggingface dataset:
# Light Propagation in Confined Nematic Liquid Crystals and Device Applications
## 4 Device Applications
### Nematic LC Electrically Tunable Bragg Reflectors
The nematic LC molecules lie with their director aligned along the propagation direction when no voltage is applied, while the field arising when a voltage is applied induces the LC molecular reorientation in the longitudinal plane, resulting in a pure twist deformation that finally leads to a bidimensional optical confinement of the propagating TE polarized beam in the _xy_ plane, as shown in Figure 19b. In Figure 20a, the sinusoidal behaviour of the refractive index modulation achieved along the direction propagation is plotted, corresponding to a phase grating for distributed feedback. Figure 19: Tunable Bragg reflector with coplanar electrode configuration and planarly anchored nematic LC: (**a**) three-dimensional view; (**b**) molecular orientation above the electrodes without (V = 0) and with (V > 0) applied electric field. Figure 18: Tuning characteristics of the nematic LC tunable Bragg reflector with top–bottom electrode configuration: (**a**) refractive index profile modulation along propagation direction for applied voltages between 2 V (bottom line) and 3 V (top line) in 0.2 V steps; (**b**) normalized grating reflectivity for 2.5 V (red), 2.8 V (green) and 3 V (blue). Reprinted with permission from Ref. [66]. Copyright 2009 OSA. The resulting index contrast versus voltage between 0 and 13 V is reported in Figure 20b. The nematic LC reorientation effect is almost negligible up to 2.4 V, leading to little index modulation, but this changes rapidly after this threshold: at \(V=5\) V, the nematic LC molecules are completely reoriented, reaching their maximum twist \(\phi=90^{\circ}\). Further voltage increase allows LC reorientation which also occurs in the regions that have not yet been completely reoriented, yielding a decrease in modulation. Above 12.4 V, almost all the nematic LC molecules are reoriented parallel to y, and the index contrast is zero. The back-reflected power (spectral reflectivity) as a function of the resonant wavelength for a device length of 1.5 mm, corresponding to 3000 periods, is reported in Figure 20c. A wide tuning range of 104 nm (1521-1625 nm) can be obtained by varying the applied voltage between 2.9 and 10.2 V, keeping the back-reflection above 50%, with a good spectral selectivity.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
###### Abstract  
The present study proposes a novel proportional-integral-derivative (PID) control design method in discrete time. In the proposed method, a PID controller is designed for first-order plus dead-time (FOPDT) systems so that the prescribed robust stability is accomplished. Furthermore, based on the control performance, the relationship between the servo performance and the regulator performance is a trade-off relationship, and hence, these items are not simultaneously optimized. Therefore, the proposed method provides an optimal design method of the PID parameters for optimizing the reference tracking and disturbance rejection performances, respectively. Even though such a trade-off design method is being actively researched for continuous time, few studies have examined such a method for discrete time. In conventional discrete time methods, the robust stability is not directly prescribed or available systems are restricted to systems for which the dead-time in the continuous time model is an integer multiple of the sampling interval. On the other hand, in the proposed method, even when a discrete time zero is included in the controlled plant, the optimal PID parameters are obtained. In the present study, as well as the other plant parameters, a zero in the FOPDT system is newly normalized, and then, a universal design method is obtained for the FOPDT system with the zero. Finally, the effectiveness of the proposed method is demonstrated through numerical examples.  
PID control; model-based design; discrete time system; sensitivity function; robust stability; FOPDT system +
Footnote †: journal: Applied
## 4 Experiments
### Ablation Studies
#### 4.3.2 Self-Attention Family vs. MVI-Attention  
To investigate the difference between the multi-view attention mechanism proposed in the DESTformer and the traditional attention mechanism, a second ablation experiment is set up, in which the multi-view attention mechanism of the DESTformer for seasonal term information is replaced with a common attention mechanism, and this version of the model is denoted as DESTformer-s. As shown in Table 4, according to the experimental results, it can be seen that our multi-view attention mechanism outperforms the traditional attention mechanism in terms of model performance. Notably, when the prediction step length is short, the multi-view attention mechanism can reduce the MSE of the model by more (6.4%). Therefore, it is believed that the multi-view attention mechanism can better extract seasonal term information from sequence data and retain as much information as possible for model prediction.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Input Length I**} & \multicolumn{2}{c}{**96**} & \multicolumn{2}{c}{**192**} & \multicolumn{2}{c}{**336**} \\ \cline{2-10} \multicolumn{2}{c}{**Prediction Length O**} & \multicolumn{1}{c}{**336**} & **720** & **1440** & **336** & **720** & **1440** & **336** & **720** & **1440** \\ \hline \multirow{2}{*}{DESTformer} & MSE & **0.291** & **0.398** & **0.503** & **0.302** & **0.397** & **0.467** & **0.312** & **0.388** & **0.552** \\  & MAE & **0.355** & **0.393** & **0.488** & **0.327** & **0.402** & **0.455** & **0.366** & **0.407** & **0.533** \\ \hline \multirow{2}{*}{DESTformer-f} & MSE & 0.318 & 0.416 & 0.522 & 0.320 & 0.408 & 0.472 & 0.325 & 0.401 & 0.568 \\  & MAE & 0.366 & 0.401 & 0.508 & 0.335 & 0.418 & 0.472 & 0.379 & 0.425 & 0.549 \\ \hline \multirow{2}{*}{DESTformer-s} & MSE & 0.362 & 0.457 & 0.544 & 0.377 & 0.458 & 0.505 & 0.358 & 0.433 & 0.598 \\  & MAE & 0.398 & 0.453 & 0.532 & 0.365 & 0.452 & 0.497 & 0.346 & 0.425 & 0.587 \\ \hline \multirow{2}{*}{DESTformer-t} & MSE & 0.327 & 0.453 & 0.563 & 0.355 & 0.446 & 0.544 & 0.346 & 0.435 & 0.604 \\  & MAE & 0.325 & 0.447 & 0.556 & 0.346 & 0.435 & 0.537 & 0.334 & 0.424 & 0.597 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results on the ETTh1 dataset.  
Figure 2: Visualization analysis of the predicted sequences (red) and true sequences (blue) of DESTformer on six different datasets.
# Analysis of Nucleation and Glass Formation by Chip Calorimetry
## 3 Results and Discussions
### Glass Formation
#### 3.2.2 In-Situ Formation of Glass in Liquid Crystals
Even at the low heating rates from 10 to 100 K/min under conventional DSC, the endothermic hump signal of \(T_{S}\) shows the obvious tendency to merge with the peak of \(T_{SmN}\) while the distance between the peaks of \(T_{SmN}\) and \(T_{N/l}\) stays almost the same. Meanwhile, the profile of the peaks gradually smoothes with increasing heating rate. Therefore, for Flash DSC measurements seen in Figure 8d, the reheating curves upon slow cooling (\(R_{c}<100\) K/s) show two apparent peaks related to the overlapped \(T_{g}\) with \(T_{SmN}\) and the small one about \(T_{N/l}\). Consistent with the relaxation process in Figure 8b [5; 34], upon cooling at \(R_{c}>500\) K/s in Figure 8d, there is only the glass transition without the signal of the LC transitions, which indicates that the isotropic to nematic transition was avoided during this fast cooling process to form the isotropic ITZ glass. Since the highly disordered isotropic glass can be formed upon \(10^{3}\) K/s quenching, to obtain more kinds of ITZ glass, we conducted annealing treatments on the as-quenched sample according to the protocol shown in Figure 8e. The sample was annealed at 323 K (\(T_{a}>T_{g}\)), 338 K (\(T_{g}<T_{a}<T_{SmN}\)) and 348 K (\(T_{SmN}<T_{a}<T_{N/l}\)) for sufficient time according to the relaxation process [5; 34], respectively. As a result, in Figure 8f the reheating curve of the sample annealed below \(T_{g}\) only shows one deep endothermic peak due to the aging effect of the glass, which indicates that there was no apparent smectic or nematic order formed during annealing and sample remained in the isotropic glassy state. For the higher \(T_{a}\) annealed samples, the red curve shows both endothermic peaks of the overlapped \(T_{g}\) with \(T_{SmN}\) and then \(T_{N/l}\), indicating the formation of smectic layers during annealing. The blue curve only shows the obvious \(T_{N/l}\) and the weaker signal of \(T_{g}\) with \(T_{SmN}\) as a shoulder, indicating that the nematic to isotropic transition dominates in this heating process. Therefore, upon in-situ annealing the various LC orders can be effectively trapped in a glass at a \(10^{3}\) K/s cooling rate.
# An Effective Hybrid Routing Algorithm in WSN:
## 4 ACOHCM Routing Algorithm
### Dynamic Energy Threshold Strategy  
In order to balance the energy consumption of the entire network, this paper proposes a dynamic energy threshold strategy. The basic idea can be stated as follows. Assumed that the initial energy value of each node is \(C\). The energy threshold \(\theta\) and the lower bound energy threshold \(\theta_{min}\) are set  
Figure 1: Hop count-classification network topology in an area of 100 m \(\times\) 100 m.  
for all nodes during the initialization. They are both less than \(C\), and \(\theta_{min}\leq\theta\). When an ant searches for the next hop node, \(\theta_{c}\), a copy of the energy threshold \(\theta\), is created, then the algorithm seeks the nodes whose current energy are greater than \(\theta_{c}\) as the alternative next hop nodes according to the priority order of parent, sibling, child nodes in the set of neighbor nodes of the node \(i\) where the ant is located. Those nodes whose current energy are less than or equal to \(\theta_{c}\) are only responsible for sensing data and sending data collected by themselves. They do not transfer the data from the other nodes. If the current energy of every neighbor node of the node \(i\) is less than or equal to \(\theta_{c}\), \(\theta_{c}\) will be updated dynamically according to the Formula (3) as follows.  
\[\theta_{c}=\begin{cases}\lambda\times\theta_{c},&\lambda\times\theta_{c}> \theta_{min}\\ \theta_{min},&\lambda\times\theta_{c}\leq\theta_{min}\end{cases} \tag{3}\]  
\(\lambda\) represents the extent of decrease of the threshold, and \(0<\lambda<1\). If the current energy of every neighbor node of the node \(i\) reaches the lower bound energy threshold \(\theta_{min}\), the power amplifier is started and the alternative next hop nodes are selected from the nodes with \(h\pm 2\) hops, here \(h\) is the hop count of the node \(i\).  
During the running of the network, the energy of the nodes is constantly consumed. If the remaining energy of 85 percent of the nodes in the network have reached the energy threshold \(\theta\). \(\theta\) is updated dynamically according to the Formula (4) as follows. Then \(Set_{reach\_th}\) is emptied. Here \(Set_{reach\_th}\) is a set used to store the nodes whose remaining energy have reached or lower than the energy threshold.  
\[\theta=\begin{cases}\lambda\times\theta,&\lambda\times\theta>\theta_{min}\\ \theta_{min},&\lambda\times\theta\leq\theta_{min}\end{cases} \tag{4}\]  
When the remaining energy of a node \(i\) reaches the energy threshold of \(\theta\) and \(i\
ot\in Set_{reach\_th}\), the route update is triggered. That is, the sink node will initiate a new route discovery process, and the node \(i\) joins into \(Set_{reach\_th}\).
## 2 Materials and Methods
### Description of the XMed-Dry Network and the Multiplatform Observations
#### 2.1.4 MODIS/MAIAC AOD  
The MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) is a recent advanced algorithm which uses time series analysis and a combination of pixel- and image-based processing to improve accuracy of cloud detection, aerosol retrievals and atmospheric correction [34]. MAIAC provides a suite of atmospheric and surface products in Hierarchical Data Format for the NASA Earth Observing System (HDF4_EOS), including daily atmospheric properties (MCD19A2). For each Terra/Aqua orbit, the MAIAC daily MCD19A2 file includes, among other things, the: (i) over land AOD and type; and (ii) the over water AOD outside of glint area and Fine Mode Fraction (FMF). Data access is granted through the NASA/USGS Earthdata search portal [35] from which AOD-HDF4 data may be downloaded for the selected domain and time interval of interest. The current MODIS Collection 6 MAIAC algorithm has changed considerably since its first release [36] to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data [37].
## 4 Fault Detection  
Model-based fault detection method is discussed in this section. With the implementation of observers, fault alarms could be generated and its residual provides the mechanism for the flight controller switch to the FTC.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process
### Types of Adsorbent
#### 6.6.3. Chitosan and Seafood Processing Wastes
The molecular imprinting method was used to modify the chitosan by the use of methacrylic acid. This modified adsorbent was used for the extraction of Cr\({}^{6+}\) ions from the contaminated Xiangjiang river water. It was found that imprinted polymer has more vacant sites available and adsorption value increased after modification. The adsorption equilibrium was obtained in eight hours within pH value 4.5\(-\)7.5. The maximal sorption at equilibrium shown by imprinted chitosan was 98.3%. The highest adsorption value obtained from Freundlich and [PERSON] adsorption isotherms was 15.784 mg/g [136]. [PERSON] et al., have synthesised chitosan-magnetite nanocomposite strips for the removal of the heavy metal solution containing Cr (VI) ions. They have proposed a mechanism of adsorption and chromium removal as shown in Figure 5 [137]. The time to attain equilibrium by chitin was six hours (5.33 g) for the Zn\({}^{2+}\), four hours for Cd\({}^{2+}\) (13.37 mg/g), and three hours for Cu\({}^{2+}\) (20.5 mg/g). In case of chitosan, equilibrium was achieved within five hours for Zn\({}^{2+}\) (19.31 mg/g), four hours for Cd\({}^{2+}\) (37.7 mg/g) and two hours for Cu\({}^{2+}\) (44.7 mg/g) [137]. [PERSON] et al., synthesised xanthate crosslinked with chitosan/PVA for the extraction of Pb\({}^{2+}\), Cu\({}^{2+}\) from an aqueous solution. This adsorbent can be used several times and effectively because of the presence of nitrogen, sulphur atoms. These atoms present on xanthate crosslinked with chitosan/PVA (XCMCP) can effectively coordinate with toxic metal ions Figure 6. The batch method was used to study adsorption phenomena by using 150 mg of dried XCMCP for different concentrations of metal ions solutions [138]. Figure 5: Schematic representation of removal mechanism of chromium ions by chitosan-magnetite nanocomposite strip. Reproduced with permission from [137].
# The Effects of Plasma-Activated Water on Heavy Metals Accumulation in Water Spinach
## 3 Results and Discussion
### BCF of Vegetables Planted in Contaminated Soils
At the lethal concentration of Pb, the barriers in the root endodermis will be broken and cause lead to enter the vascular tissues. This point may further affect the mineral nutrition and water balance, inactivate the enzyme activities, change the hormonal status, and affect the membrane structure and permeability [46]. [PERSON] et al. [47] reported that once Pb enters the root system, approximately 95% or more of Pb is accumulated in the roots and only a small fraction is translocated to the aerial plant section. This phenomenon might be different for each kind of heavy metal, and is specific to Pb. Pb is blocked in the endodermis by the Casparian strip, which is followed by symplastic transport. In addition, the majority of the Pb entering the root is removed by the plant's detoxification systems. Some plant species are able to tolerate higher concentrations of Pb ions due to various detoxification mechanisms, including selective metal uptake, excretion, complexation by specific ligands, and compartmentalization. According to a report by [PERSON] et al. [48], lower pH influences the metal uptake of plants because Cd and Zn are more soluble at lower pH; this may be an effective strategy for enhancing the phyto-extraction of metals from soil. [PERSON] et al. [49] also reported that soil pH and organic matter content are important in affecting the Cd availability in soil. Both of these elements determined the adsorption of Cd at soil binding sites, as well as its solubility and mobility in soil. The determination of soil pH was not performed in this study; hence, the mechanisms of low pH of PAW are expected to increase the metal solubility, while increased uptake by plants needs to be determined in the future. In this study, the accumulation of Cd in water spinach was reduced, but the Pb concentration in water spinach in the PTS + PAW treated group was enhanced. Our results for Pb concentration increment in water spinach were similar to those of [PERSON] et al. [24]. Meanwhile, our study reported a reduction in Cd concentration in water spinach through the inhibition of expression of Cd transporter in the root by plasma treatment, but an increase in Pb concentration may be related to the activation of Pb transporter in the root of water spinach by plasma treatment on seeds and PAW irrigation.
## 2 Description of the Data  
### Description of ERA5 Re-Analyses  
The ERA5 numerical model is a weather and climate model [25] developed by the Copernicus Climate Change Service of the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 is a reanalysis dataset combining various observations (ground, weather balloon, satellite) and a mathematical model constructed around primitive dynamic equations [26] governing atmospheric motions with equations of moist processes and convection, ground interactions, and physico-chemistry equations. The model is therefore constantly adjusted according to the arrival of observations. This method makes it possible to best approach the reality of the phenomena and to make forecasts [27]. This model simulates many atmospheric, oceanic, and terrestrial variables, including dynamic parameters (pressure, temperature, wind) and density of atmospheric species, including radiatively active ones for tides like ozone, water vapor, and oxygen. In addition to providing global forecasts, these analyses are carried out regularly over a long period with the same model in order to monitor changes, particularly in relation to anthropogenic forcing.  
Here we use the latest version, named ERA5 [28]. Data series extend from 1950 to today and present a temporal resolution of one hour with a horizontal resolution of 2.5\({}^{\circ}\), corresponding to 144 longitudes and 73 latitudes. Vertically, the data extend from the surface up to 0.01 hPa (about 80 km altitude). Vertically, the model is divided into 137 levels, each defined by a level of pressure. In order to develop the methodology, the data used here cover a full week from 24 January to 31 January 2021 (i.e., 192 h). This length was chosen to hold the mean temperature relatively constant, expecting stationary tides (Figure 1). Therefore, 29 levels are used, from 31 to 80 km in altitude. A short period was used to develop the methodology rather than to fully evaluate the model or the seasonality of tide characteristics. The winter period was selected because most of the tide information was retrieved through lidar observation over the northern hemisphere during the time when night is the longest [6; 7; 9; 21].
# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 4 Numerical Analysis Methods
### Mutation  
Gaussian mutation is adopted for mutation, with a mutation rate of 0.005. Random individuals are generated according to the Gaussian distribution, preserving the diversity of the individuals.  
In this paper, a real-coded genetic algorithm (RCGA) is used to search for the optimal hovering points. The primary purpose is to evaluate the feasibility of using a genetic algorithm, rather than to fine-tune the genetic operations and GA parameters. Therefore, we adopt commonly used algorithms for selection, crossover, and mutation. The process of searching for the optimal hovering point using RCGA is shown in Figure 9. From Figure 9, in contrast to the first generation, when the individuals are randomly distributed throughout the area, the individuals converge to where the UEs are densest after several generations. In this article, the maximum number of generations is set as 100. When it reaches the 100 th generation, the individual with the highest fitness function will be selected as the hovering point. Then, the UEs which are already covered will be removed from the search by GA, and this process will be repeated until all remaining UEs are covered. While the result obtained by GA is based on the maximum coverage radius of the UAV, i.e., the flight altitude is 150 m, it is necessary to apply the hill climbing algorithm to optimize the flight altitude.  
Figure 8: Sample of blend crossover.  
Figure 9: UE clustering by K-means: (**a**) 1 st generation, (**b**) 3 rd generation, (**c**) 5 th generation, (**d**) 10 th generation, (**e**) 15 th generation, (**f**) 20 th generation.
## 6 Exploration of Findings
### Case No. 1: RF-Based Feature Selection  
Following the same procedures, the first twenty-three features, as reported in Figure 7a, are employed to train the three classifiers. The results are reported in Table 3. As can be clearly seen, the performance of all three classifiers improved significantly. The accuracy of the SVC increased to 96.75%, with a fitting time of 5.30 s. This remarkable improvement highlights the crucial effect of removing irrelevant and noisy features, which allows the SVC to generalize much more effectively. Similarly, the ANN's average accuracy increased to 98.68%, with a remarkable reduction in fitting time of 8.30 s. The random forest classifier's  
Figure 7: Illustration of each feature’s contribution to the model’s predictive power. (**a**) Features sorted based on the random forest algorithm and (**b**) features sorted based on permutation analysis.  
average accuracy slightly improved to 99.24%, with a fitting time of 0.72 s. This minor improvement in accuracy, together with the slight reduction in fitting time, confirms the robustness and efficiency of the RFC.  
Furthermore, Table 3 demonstrates that the RFC consistently achieved the highest precision, recall, and F1-scores, particularly excelling in detecting complex faults, including those involving simultaneous defects in both the front and rear bearings. The ANN followed closely, offering competitive F1-scores but with slightly reduced precision and recall in some fault cases. In contrast, the SVC showed a noticeable drop in recall, especially for rear defects (as low as 0.920) and single-phase faults (0.936), which indicates limited sensitivity to these conditions. Average metrics further confirmed the RFC's robustness and generalization capabilities compared to those of other classifiers, with the macro and weighted F1-scores reaching 0.993 and 0.995, respectively. These results underscore the RFC's reliability for multiclass fault diagnosis in IMs, while the ANN remains a viable alternative. In contrast, the SVC appears less suitable without further optimization.