esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Polar Surface Area (PSA) and LogP  
Polar surface area (PSA) and octanol-water partition coefficient (logP) are essential parameters used in the prediction of ligands bioavailability. PSA is a useful parameter that has been shown to provide excellent correlation with intestinal absorption, blood-brain barrier penetration, and several other drug transport properties [64]. The PSA is obtained with the sum of the surface corresponding to all the polar atoms, specifically oxygen, nitrogen, and hydrogen attached to the molecules [64]. Molecules with PSA values greater than 140 A\({}^{2}\) are poorly absorbed in cell membranes, while those with values less than 90 A\({}^{2}\) are completely absorbed [64]. The SMILE codes for each ligands are ACA: C1 OC(OC2C(O)C(O)C(OC2 CO)OC3C(O)C(O)C(O)C3 CO)C(O)C(O)C1 NC4, MAY: CC1 OC(OC2C(O)C(=O)C(C)OC2C3C(O)CC4=C(C3O)C(=O)C(O)C(O)C(=C5)O)C(O)C1O and LUT: CC1=CC(=C2C(=O)C(OC2=C1)C3=CC(=C(O)C3)O) O. PSA value for the studied ligands were 321.16 A\({}^{2}\) for ACA, 236.81 A\({}^{2}\) for MAY, and 111.12 A\({}^{2}\) for LUT. The analysis of PSA values allows us to predict good intestinal absorption for the LUT, on the opposite, for ACA and MAY, we can suggest that intestinal absorption could be limited according to the PSA values obtained.  
Additionally, theoretically predicted values of octanol/water partition coefficient (logP) have been used for the estimation of molecular transport properties, molecular hydrophobicity, intestinal absorption, and blood-brain barrier penetration. The logP is one of the key factors that accelerates the process of drug discovery and development [65; 66]. It has been reported that molecules with logP values between \(-2<\)logP \(<5\) have proper intestinal absorption, bioavailability, and hydrophobicity [67]. The results of logP for ACA, MAY, and LUT were \(-5.51\), \(-2.84\), and \(1.97\), respectively. Based on these results, only LUT presented a logP value within the established range; which indicates that this molecule could present a major intestinal absorption when compared with ACA and MAY.
# Tuning Guidelines for an Adaptive-Gain Parabolic Sliding Mode Filter
## 1 Introduction  
The sliding mode filter [1, 2] employing a certain kind of parabolic-shaped sliding surface has been studied for effectively removing noise in feedback control of mechatronic systems. One of major advantages of the filter is that its output converges to the input when a constant input is provided. In addition, the filter does not require a dynamic model. The sliding mode filter has been recognized one of the most effective and robust filters, and its evaluation results [3, 4, 5] and applications [6, 7, 8, 9, 10, 11, 12, 13, 14] have been reported in the literature. However, the filter is prone to overshooting.  
In response to the drawbacks of the sliding mode filter [1, 2], [PERSON] et al. [15] presented a new parabolic sliding mode filter, which is referred to as PSMF. It is reported [16, 17] that PSMF is advantageous over the filter referred to in [1, 2] and linear filters because it is less prone to overshooting and produces smaller phase lag. After that, [PERSON] et al. [18] presented an adaptive-gain parabolic sliding mode filter, referred to as AG-PSMF, by extending PMSF. In that work, [PERSON] et al. stated that AG-PSMF effectively removes noise by balancing the tradeoff between the filtering smoothness and the suppression of delay [18]. However, due to the strong nonlinearity, theoretical evaluation and corresponding result-based parameter tuning guidelines remained as open problems for future study.  
To respond to these problems, in an alternative manner this paper provides insight into the issues of quantitative performance evaluation and parameter tuning of AG-PSMF through numerical analysis. Specifically, the contribution of the paper is two-fold. It (1) quantitatively evaluates the performance of AG-PSMF under different parameter values and input signals; and (2) based on the results of the quantitative evaluation, the paper presents practical tuning guidelines for AG-PSMF.  
The rest of this paper is organized as follows: Section 2 provides an overview of parabolic sliding mode filters; Section 3 quantitatively evaluates the performance of AG-PSMF; Section 4 presents tuningguidelines of AG-PSMF based on the evaluation results; Section 5 validates the effectiveness of the presented guidelines through numerical examples; and Section 6 covers concluding remarks.
# Brewing with 10% and 20% Malted Lentils--Trials on Laboratory and Pilot Scales
## Results and Discussion
### Laboratory-Scale Brewing Trial
#### 3.1.7 Extract and Apparent Extract  
The Congress wort produced from all-barley malt had the highest extract content with 8.87 \(\pm\) 0.06 \({}^{\circ}\)Plato (\({}^{\circ}\)P). This value was expected, as the malt extract in %, as given by the supplier, is 81% or more. As the amount of the lentil malt adjunct increased, the extract of the Congress wort samples decreased to 8.60 \(\pm\) 0.02 (BM + 10% LM) and 8.30 \(\pm\) 0.04 \({}^{\circ}\)P (BM + 20% LM). As the lentil malt used showed an extract content of 59.9% in previous research, this drop in extract content was expected [34]. The measured extract contents are still in the range of all-barley malt Congress wort samples and even higher than Congress wort samples produced with some other raw materials, such as spelt or oats [7; 49]. When applying 30% lentil malt that had been germinated for 6 days, [PERSON] et al. [43] reported an extract content of 6.69 \({}^{\circ}\)P. Such low values can be due to too long of a germination period, as this causes the seed to consume large amounts of starch compounds required for rootlet growth. Additionally, the lower extract reported by [PERSON] et al. [43] can be caused by the use of barley malt with low extract content.  
The apparent extract, which is the extract measured after fermentation, was similar between all-barley malt beer (1.90 \(\pm\) 0.04 \({}^{\circ}\)P) and beer with the addition of 10% lentil malt (1.89 \(\pm\) 0.07 \({}^{\circ}\)P). With the addition of 20% lentil malt, a slightly higher apparent extract of 1.98 \(\pm\) 0.04 \({}^{\circ}\)P was observed. As the initial extracts of the Congress wort samples with lentil malt adjuncts were lower than those with all-barley malt, the apparent extract was expected to be lower. The higher apparent extract left in the beer with 20% lentil malt adjunct is presumably due to different protein fractions transferred to the wort sample. These are also detected by the density meter and can therefore slightly change the extract value.
## 2 Methodology: Computation of the Altimetry Performance
### Platform Error Sources in GNSS-R Altimetry
#### 2.4.2 Impact of ISS Shadowing and Multiple Scattering
Doppler effects can be neglected because of the short distances involved, and the static geometry of the ISS itself. For illustration purposes, assuming that \((\theta_{1}^{dir},\ \phi_{1}^{dir})=(124^{\circ},\ 80^{\circ})\) for the direct signal, and \((\theta_{1}^{scat},\ \phi_{1}^{scat})=(56^{\circ},\ 84^{\circ})\) for the far-field direction of the scattered signals. In this case, there are first and third order reflections from the scattered signals, and first and second order reflections from the direct signals. For the scattered signal, the direction of arrival of the third order reflection is \((\theta_{3}^{scat},\ \phi_{3}^{scat})=(76^{\circ},\ 38^{\circ})\), and the delay \(\tau_{3}^{scat}=0.188\ \upmu\)s (56 m = 0.19 coarse/acquisition chips or C/A chips). For the direct signal, the direction of arrival of the second order reflection is \((\theta_{2}^{dir},\ \theta_{2}^{dir})=(79^{\circ},\ 36^{\circ})\) and the delay \(\tau_{2}^{dir}=0.188\ \upmu\)s (24 m 0.08 C/A chips). Note that \(\theta_{2}^{dir}<90^{\circ}\), i.e., the second order rays (single reflected) of the direct signal can be collected via down-looking antenna. Taking into account this multiple reflection, the simulation results are show in Figure 12. The waveform affected by multipath (light blue dash-dot line) \(W_{f}(\tau)\) is calculated as the convolution of the waveform \(W_{1}(\tau)\) (dark blue solid line) and \(I_{m}(\tau)\) (red peaks). Because of the multipath, the waveform peak increases by a factor of 1.7, and it is delayed by 0.06 C/A chips (=18 m), which is not negligible at all for altimetry purposes. The altimetric delay is found by the peak of the waveform derivative, and not by the waveform peak itself. Despite this, multi-path can be mitigated by computing the waveforms not as the incoherent average of the individual coherent waveforms, but as the variance of the measured coherent waveforms instead of performing the incoherent average, as demonstrated in [18].
# Using Statistical Shape Models to Optimize TKA Implant Design
###### Abstract  
(1) TKA implants should well fit on each patient's anatomy. Statistical Shape Models (SSM) statistically represent the anatomy of a given population. The aims of this study were to assess how to generate a valid SSM for implant design and provide guidelines and examples on how to use the SSMs to evaluate the anatomic fit of TKA components. (2) Methods: A Caucasian SSM was built from 120 anatomies (65 female, 55 male) and an Asian SSM was based on 112 patients (75 female, 37 male). These SSMs were used to generate a database of 20 bone models. The AP/ML dimensions of the bone models were compared to those of the input population. Design input parameters, such as the tibial contour, trochlea, and femur curvature were extracted from the SSMs. Femur and patella components were virtually implanted on the bone models. (3) Results: the dimensions of the generated bone models well represented the population. The overhang of the femoral component as well as the coverage and peak restoration of the patella component were visualized. (4) Conclusions: SSMs can be used to efficiently gain input into TKA design and evaluate the implant fit on the studied population.  
statistical shape models; morphology; implant fit; TKA implant design Article Using Statistical Shape Models to Optimize TKA Implant Design  
[PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Using Statistical Shape Models to Optimize TKA Implant Design. _Appl. Sci._**2022**, 12, 1020. [[https://doi.org/10.3390/app12031020](https://doi.org/10.3390/app12031020)]([https://doi.org/10.3390/app12031020](https://doi.org/10.3390/app12031020))
# Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans
## 8 Conclusions  
This paper has described the field navigation system of the mobile robot Andabata at low speeds with fuzzy elevation maps (FEMs). For this purpose, the robot obtains leveled three-dimensional (3D) laser scans in motion by integrating ranges from a 360\({}^{\circ}\) field of view 3D laser scanner with odometry, inclinometers and gyroscopes. Each leveled 3D scan is employed to produce an FEM and a fuzzy reliability mask (FRM) of the surroundings, which are processed to choose the best motion direction to reach a distant goal point while crossing through traversable areas. This has been implemented with different ROS (Robot Operating System) nodes distributed among the cores of the onboard processor.  
It is the first time that a mobile robot navigates with FEMs obtained from 3D laser scans in outdoors. To do so, a complex navigation scheme has been developed by integrating numerous tasks, where it has been necessary to employ 3D point cloud filtering and multithreaded execution to increase the building frequency of FEMs and FRMs. Experimental results of Andabata navigating autonomously in an urban park have been presented.  
Work in progress includes navigation in more complicated natural environments and quantitative comparison with state-of-the-art outdoor navigation methods.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 5 Evaluation
### Energy Model Comparison  
In order to indicate the accuracy gain of using the proposed packet size-aware model over the model introduced by [PERSON] et al., these two models are compared in Figure 9. The two models are compared for packet sizes going from 58 bytes, which is the minimum 6 TiSCH packet size without additional payload, up to the maximum packet size, i.e., 127 bytes (125 bytes and 2 CRC bytes). The consumption of both models is compared to the packet size-aware model using the exact duration measurements for the packet sizes of 75, 100 and 125 bytes. The device state current values of Table 15 are used for this comparison. In the states where the CPU is sleeping and the radio is sleeping or in idle mode, the PM2 values were preferred over the PM_NOACTION values.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Mode Type**} & \multicolumn{2}{c}{**Measured (\(\mu\)C)**} & \multicolumn{2}{c}{**Calculated (\(\mu\)C)**} \\ \cline{2-5}  & **CC2538** & **CC1200** & **CC2538** & **CC1200** \\ \hline Leaf (Sleep) & 7833.6 & 8698.05 & 7752.35 & 8816.48 \\ Leaf (TxDataRxck) & 7910.1 & 8942.85 & 7852.17 & 9052.78 \\ Relay & 8086.05 & 9348.3 & 8002.81 & 9442.96 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Measured and calculated charge drawn during a slot frame.  
Figure 9: Comparison between the proposed packet size aware model and the model introduced by [PERSON] et al., which linearly scales the charge consumption based on the packet size. (**a**) TxData time slot; (**b**) TxDataRxAck time slot; (**c**) RxData time slot; (**d**) RxDataTxAck time slot; (**e**) TxDataRxNoAck time slot.  
As can be seen in the graphs, the proposed model accurately represents the charge consumption for all packet sizes. The model of [PERSON] et al., however, becomes highly inaccurate especially when the packet size decreases. Looking at a packet size of 75 bytes, the average errors of the [PERSON] et al. model are \(32.03\,\mathrm{\SIUnitSymbolMicro C}\) (\(\sigma=21.05\,\mathrm{\SIUnitSymbolMicro C}\)) and \(17.05\,\mathrm{\SIUnitSymbolMicro C}\) (\(\sigma=12.41\,\mathrm{\SIUnitSymbolMicro C}\)) for the CC1200 and CC2538 radio, respectively. Of course, for the maximum packet size, both models estimate the consumption correctly.  
The reason for this large inaccuracy introduced by the model of [PERSON] et al. is that their approach linearly scales the entire slot consumption. This is not the correct approach, as only the states in which data are transmitted over the radio or copied between the radio and the CPU can be scaled. Since a time slot consists of many more states than only data processing states, those states should not be scaled. Because the proposed model differentiates between the state durations that depend on the packet size and the durations that are independent of the size, as can be seen in Tables 8-14, it accurately models the slot consumption for different packet sizes.
## 3 Optimizing Setting Base on CBR
### Improved Case-Based Reasoning
#### 3.2.5 Weight Optimization for CBR  
\(\omega_{i}\) and \(\beta\) are key parameters in the case matching and case retrieval [15]. The value of them directly affects the value of \(SIM(F,F_{(k)})\), which in turn affects the final optimization result of CBR. Usually, the value of \(\omega_{i}\) and \(\beta\) are distributed by expertise. However, experts can only give \(\omega_{i}\) and \(\beta\) qualitative indicators based on experience, and it is difficult for them to determine the exact value. In the paper, we use the NSGA-II intelligent optimization algorithm to calculate \(\omega_{i}\) and \(\beta\) based on the historical cases of the case base. The algorithm is summarized in Figure 4. The algorithm steps are as follows:  
1. Organize the case base, select 70% original cases and all generated cases as the training samples, and the remaining 30% original cases as the testing samples. Regard the training samples as the new base database. Regard the testing samples as the new bases.
2. Set \(\omega_{i}(i=1,\cdots,7)\) and \(\beta\) as the decision variables. Generate initial random father populations of \(\omega_{i}\) and \(\beta\). Use the method CBR mentioned above to calculate the \(J\) of every new case.
3. Build the mathematical model. 1. Objective functions: * The MAE (mean absolute error) between the optimized amount of ore feeding and the actual value of the test set is the smallest. * The MAE (mean absolute error) between the optimized amount of water feeding and the actual value of the test set is the smallest.  
2. Constraint conditions: \[\left\{\begin{aligned} \sum_{i=1}^{7}\omega_{i}=1\\ SIM(F_{\text{F}}F_{(k)})\leq 1\\ 0\leq\omega_{1}\leq 0.1\\ 0.2\leq\omega_{2}\leq 0.4\\ 0\leq\omega_{3}\leq 0.2\\ 0\leq\omega_{4}\leq 0.2\\ 0\leq\omega_{5}\leq 0.2\\ 0\leq\omega_{6}\leq 0.1\\ 0.1\leq\omega_{7}\leq 0.3\\ 0.7\leq\beta\leq 0.9\\ \end{aligned}\right.\] (5) \[\text{Adopt the NSGA-II multi-objective optimization algorithm, obtaining the non-inferior solution set through populations, across and mutual.}\] (5) \[\text{Adopt the TOPSIS algorithm to obtain the optimal solution of the non-inferior solution set as the optimized feature weight, based on the Euclidean distance.}\] (6) \[\text{When the new case comes, adopt the optimized feature weight to case retrieve from the whole case base. Then, solve the ore and water feeding as the typical steps of CBR as introduced above.
## 4 Results and Evaluation
### Assessing Operating Temperatures  
As ambient temperature conditions affect the performance of the soil instruments and of the assistive electronics/battery equipment (and their longevity), it is interesting to be able to inspect the performance properly. The proposed measurement platform is capable of such measurements. For better comprehension and verification purposes, Figure 11 illustrates the evolution of temperature as a function of time for a 5-day period, including wetting of the soil on the first day. The red curves correspond to the TEROS 12 built-in temperature sensor readings and the blue curves to the LM35 sensor reference values from inside each node enclosure box. Figure 11a corresponds to node #A1, while Figure 11b to node #A2. As node #A1 has all its components placed underground, the differences between the temperature readings from inside the box enclosure and from the soil sensor thermistor are not significant. Nevertheless, TEROS 12 temperatures have slightly smaller variation and larger hysteresis, as this instrument is deeper inside the ground, compared with the enclosure of node #A1 (i.e., at 15 cm depth instead of 10 cm). The deviations between the temperature reported by the TEROS 12 and the one from inside the box become greater on days 4 and 5 due to progressive soil cracking (a result of the drought following the initial wetting event) and to the presence of an ant colony close to #A1, which allowed the penetration of sunrays closer to the box enclosure of node #A1. This is proof of the impact of the numerous sources of inaccuracy that exist in real environmental conditions (Figure 12a,b). Periodic variation, deviations, and hysteresis phenomena are more intense in the case of node #A2, having its electronics (expect its battery) above the ground, thus reaching temperatures close to 50 \({}^{\circ}\)C during the day. This temperature variation behavior is in line with the findings of other research works [41].  
Figure 11: The evolution of temperature as a function of time for a 5-day period, including wetting of soil on the first day, according to TEROS 12 temperature readings (red curves) and to the LM35 sensor reference inside each node enclosure box (blue curves) for (**a**) node #A1, having all its components placed underground; (**b**) node #A2, being installed above the ground level.  
Figure 12: Sources of inaccuracies: (**a**) soil cracking due to the fast drought, allowing the penetration of sunrays; (**b**) additional presence of ant colonies close to the instrument installation area.
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
## 2 Magnetoelectric (ME) Effect
ME composites with 0-0 connectivity (Figure 1a) refer to a composite consisting of FM and PE particles embedded on a neutral matrix, while 0-3 composites (Figure 1b) consist of FM particles introduced to a PE matrix [23]. The connectivity type of 1-3 [24] indicate a rod, filament, tube or fiber type FM constituent in a PE matrix (Figure 1c). Planar and thin-film composites of the 2-2 type (Figure 1d) consist of alternating layers of FM and PE materials and are found in a variety of geometries ranging from the micro- to the nanoscale [25]. Advances in the realization of nanocomposites have also introduced more complex connectivity schemes, such as core-shell structures in nanoparticles (Figure 1e) and nanorods (Figure 1f) [26]. The material properties present a direct effect on the ME response since the ME coefficient depends both on the magnetostrictive and piezoelectric properties of the constituent phases, with dielectric constant, permeability, magnetostriction, coercive and anisotropy magnetic fields, remnant magnetization, electromechanical coupling and piezomagnetic and piezoelectric coefficients having a significant impact on the selection of an optimal material combination [21]. Regarding the PE phase, properties such as a high piezoelectric constant with low dielectric losses are favorable. For the FM phase, the increased piezoelectric coefficient and high electrometrical coupling, along with the large saturation magnetostriction and large magnetic susceptibility, combined with soft magnetic properties,are favorable, especially for sensor applications [27]. A wide range of ferromagnetic materials were employed in magnetoelectric composites ranging from ferromagnetic metals with appreciable magnetostriction, such as Fe, Ni and Co, to ferromagnetic alloys, including FeCo [28], FeGa [29], FeGaB [30], FeCoBSi [27], Metglas and Terfenol-D, with enhanced piezomagnetic coefficients [31]. Additionally, cobalt and nickel ferrites (CFO, NFO) have found application mainly in magnetoelectric nanocomposites [32; 33]. The piezoelectric constituents in ME composites consist commonly of PZT [34], AlN [35], ZnO [36], PVDF [37] and, more recently, AlScN [38] in thin-film form, 0-3 and 1-3 composites and nanostructures (nanoparticles, nanorods, etc. ).