esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
## 7 Measurements
### Template Based Solution  
The idea of anticipative flux rise given in Section 3.3 was verified by a measurement. The results obtained were then compared to the results of the suboptimal procedure from [16] to illustrate the benefit of increasing the flux prior to the torque. Again, the motor described by Table 13 was used. The template was generated offline using the procedure presented in Section 4.1 and stored in a lookup table.  
Figure 17 illustrates the optimal flux trajectory corresponding to the speed ramp. It can be observed that in the anticipative method, the flux rises prior to the ramp start time of \(t_{1}=0.54\) s. The anticipative flux rise interval \(t\in[0.4\,\mathrm{s}\quad 0.54\,\mathrm{s}]\) is 2.5 times the rotor time  
Figure 16: Comparison of measured energy and power Losses.  
Figure 15: Measurement results for the optimization procedure.  
constant \(t_{\text{R}}\). The flux trajectory corresponding to the method specified in [16] rises at the ramp start time, as illustrated in Figure 17. Thus, the flux at the moment of the torque step will be lower, requiring a higher q-current. For the anticipative method, higher flux at the beginning of the ramp results in a lower q-current demand for the commanded torque.  
Figure 17 illustrates the power loss comparison using the anticipative method and the method from [16]. It can be observed that the losses during interval \(t\in[0.54\,\text{s}\quad 0.6\,\text{s}]\) are less for the anticipative method compared to the suboptimal method of [16]. The saved energy \(\Delta E_{L}=E_{2}-E_{1}\), where \(E_{2}\) is the loss energy obtained with the method of [16] and \(E_{1}\) is the loss energy obtained with the anticipative flux rise method given in Figure 17. Although at the beginning, the energy losses of the new approach are higher due to the anticipative flux increase, the overall losses are lower compared to the method of [16]. This result illustrates the benefit of increasing the flux prior to the torque step.
## 3 Results and Discussion
### Synthesis and Characterization of Plx-g-Cmp
Most importantly, the peak at 1735 cm\({}^{-1}\) illustrates the formation of an ester bond and the absorption bands observed at 1618 and 1423 cm\({}^{-1}\) highlight the stretching and bending vibrations of the carbonyl (C=O) in the carboxymethyl groups. The other bands were assigned as follows: 3435 cm\({}^{-1}\) (O-H stretching vibration), 2924 and 2853 cm\({}^{-1}\) (C-H stretching vibration of methylene groups), 1328 cm\({}^{-1}\) (C-O stretching), 1157 cm\({}^{-1}\) and 1095 cm\({}^{-1}\) (C-O-C vibration and C-O stretching bands, respectively) [55], 858 cm\({}^{-1}\) corresponding to \(\alpha\)-configuration of \(\alpha\)-D-glucopyranoside units [56] and those at 765 and 930 cm\({}^{-1}\) indicated the \(\alpha\)(1-4) and \(\alpha\)(1-6) linkages, respectively [57]. The peaks at around 850 and 765 cm\({}^{-1}\) show the pullulan's \({}^{4}\)C  
Figure 1: \({}^{1}\)H NMR spectra of CMP and Plx-\(g\)-CMP. Figure 3: FT-IR spectra of pullulan (**a**), CMP (**b**) and Plx-\(g\)-CMP (**c**); for comparison, the FT-IR spectra of Plx and MAPlx are presented (**d**). Figure 2: \({}^{13}\)C NMR spectra of CMP (**a**) and Plx-\(g\)-CMP (**b**). The FT-IR spectrum of Plx-\(g\)-CMP (Figure 3c) shows both the specific absorption bands of CMP and Plx (2890 cm\({}^{-1}\) (C-H stretching aliphatic), 1385 cm\({}^{-1}\) (in-plane O-H bend) and 1112 cm\({}^{-1}\) (C-O-stretch) [58]) (see Figure 3d). In addition, a shift of the ester absorption band from 1735 to 1739 cm\({}^{-1}\) is observed, as well as the appearance of absorption bands specific to the amide bond (1635 cm\({}^{-1}\) (C=O, amide I), 1554 cm\({}^{-1}\) (coupling NH and CN, amide II) and 1450 cm\({}^{-1}\) (C-N stretch, amide III)), which demonstrates the coupling of poloxamer to the carboxymethyl pullulan chain through an amide bond. The density of the grafted polymer measured by the pycnometry method [59] in tetrahydrofuran (THF) at 25 \({}^{\circ}\)C, is 1.336 g/cm\({}^{3}\) and it remains soluble in water and polar solvents (dimethylfuran (DMF), dimethyl sulfoxide (DMSO), etc. ), and insoluble in THF, ethyl ether, and chloroform.
# Technology Mapping of FSM Oriented to LUT-Based FPGA
## 4 A Separate Implementation of Transition and Output Block of FSM  
A typical method of carrying out FSM is based on separate synthesis of two independent blocks: a transition block described in the form of the function \(\delta\): \(B^{N+K}\to B^{K}\) and an output block whose form  
Figure 5: Modified triangle tables (**a**) for card(Xb) = 4, (**b**) for card(Xb) = 5 and obtained mappings (**c**) for card(Xb) = 4, (**d**) for card(Xb) = 5 (sharing blocks is taken into account).  
depends on a type of used automaton. In the case of [PERSON]'s FSM, a synthesis is based on mapping in LUT-based blocks of multi-output function \(\lambda\): \(B^{K}\to B^{M}\). In the case of [PERSON]'s FSM--multi-output function \(\lambda\): \(B^{N+K}\to B^{M}\) is synthesized.  
The result of a logic synthesis includes independent blocks that have no common elements. Thus, there is no sharing of logic resources that would enable to use common logic resources to carry out a transition and on output blocks.  
Figure 6 illustrates the example of a typical carrying out of FSM. A symbolic description of FSM, in the format kiss2, includes four states s0, s1, s2, s3, and 10 symbolic multi-output implicants. After having associated the states s0-s3 with two-bit words (s0: = 00; s1: = 01; s2: = 10; s3: = 11), a coded FSM, which includes 5 bits in an output part, is the result. The first two bits describe a transition function (\(\delta\): \(\mathrm{B}^{5}\to\mathrm{B}^{2}\)) and the other three bits determine the states of FSM's outputs (\(\lambda\): \(\mathrm{B}^{5}\to\mathrm{B}^{3}\)). The function description \(\delta\): \(\mathrm{B}^{5}\to\mathrm{B}^{2}\) and \(\lambda\): \(\mathrm{B}^{5}\to\mathrm{B}^{3}\) is presented in the form of files in a pla format. An independent logic synthesis of separate functions leads to the structure with a clearly split transition block marked on Figure 6 with the symbol \(\delta\) and an output block marked with the symbol \(\lambda\).  
In the case of carrying out FSM in LUT-based FPGA, an especially vital element of a synthesis turns out to be the process of multi-output decomposition of the function \(\delta\): \(\mathrm{B}^{5}\to\mathrm{B}^{2}\) and \(\lambda\): \(\mathrm{B}^{5}\to\mathrm{B}^{3}\). In a standard approach of carrying out FSM, decomposition is conducted separately for a transition block and an output block. Logic synthesis of multi-output function, in which the DECOMP system [49] was used and is presented in Figure 6, led to the solution illustrated in Figure 7.  
Figure 6: The result of separate implementation of transition and output blocks of FSM.
## 3. Strategical Approach
### Two-Phase Approach for Estimation  
Based on the degradation path and the acceleration model, a two-phase approach has been developed to provide a strategic solution for obtaining the V&V test result and the reliability information simultaneously, as well as to address the obstacle of obtaining prior reliability information on the model parameters. The basic idea behind this two-phase approach is simple: to maximize the utilization of the fixed settings of the V&V tests.  
These V&V tests can be categorized by the stress level they apply, such as the normal use condition and the harsh condition. These stress levels and the number of test units for each stress level are predetermined, because these tests are related to the performance and quality of the product either before or during the development period. As a result, the normal use condition and the highest harsh condition can be regarded as \(s_{0}\) and \(s_{M}\), respectively. That is, during the V&V tests, the additionally observed degradation data and the loaded stress levels are used to analyze the reliability information. The test durations for these two stress levels are extended, because the duration of V&V tests are generally shorter than the required duration for the reliability estimation. This constitutes the first phase. In addition, based on the D-optimality criteria, the small number of test units are additionally added to the harsh condition, because, generally, the number of tests under the normal use condition is greater than the number of tests under the harsh condition.  
In consideration of the number of test units used in the first phase, the second phase is designed. The main purpose of the reliability test in the second phase is to perform compromise tests to minimize the asymptotic variance and to verify the presence of possible curvature effects in the relationship between the parameters and stress variables. In ATs, the number of test units at the low stress level is greater than the number of test units at the maximum stress level. However, in the first phase, due to the D-optimality condition, the number of test units under the normal use condition and the maximum stress level is the same. Therefore, based on the first-phase reliability information, a different number of test units are added to each stress level, and the new stress level and the number of test units for the compromise test are determined to minimize the asymptotic variance. That is, the additional number of test units for each stress level and the new stress level are the decision variables in conducting this strategic approach.
## 4 Results Analysis
### Verification of the Accuracy of the Standard Curve Model  
The standard curve in Figure 4 is used to test the 4% SBS-modified asphalt samples without additives, and the test results are shown in Table 3.  
It can be seen from Table 3 that the errors of the four groups are \(-\)0.05%, \(-\)0.07%, \(-\)0.06%, and \(+\)0.08%, all within the required error range of \(\pm\)0.1%, which meets the test requirements of SBS content detection. It proves that the standard curve models are all correct and can be used for SBS content determination after adding additives.  
Figure 4: SBS standard curve model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Sample**} & **SBS Modifier** & **Additives** & \multirow{2}{*}{**A966/(A966 + A1377)**} & **Test Results** \\  & **Content** & **Amount** & & \\ \hline First group & 4\% & 0 & 0.1245 & 3.95\% \\ \hline Second group & 4\% & 0 & 0.0765 & 3.93\% \\ \hline Third group & 4\% & 0 & 0.1152 & 3.94\% \\ \hline Fourth group & 4\% & 0 & 0.0822 & 4.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of 4% SBS-modified asphalt without additives.
## 3 Results
### DNJ Alleviates High-Glucose-Induced Oxidative DNA Damage  
High glucose leads to increased cellular oxidative stress. To investigate this, we examined whether DNJ treatment protected cells against oxidative DNA damage. The levels of 8-OHdG were used to assess DNA oxidative damage levels. HUVEC were cultured in medium containing 11 mmol/L glucose for 24 h. Then, the cells were further cultured in medium containing 11 mmol/L or 50 mmol/L glucose for 48 h. The levels of 8-OHdG were detected via immunofluorescence staining. As shown in Figure 2A, the level of 8-OHdG in the cells cultured in medium containing 50 mmol/L glucose showed an elevation of 48% as compared to that in the cells cultured in medium containing 11 mmol/L glucose, showing that high glucose resulted in exacerbated oxidative DNA damage. The addition of 5 \(\upmu\)mol/L DNJ diminished the elevation of 8-OHdG caused by 50 mmol/L glucose, indicating that DNJ plays a role in mitigating oxidative DNA damage. OGG1 is an enzyme responsible for the repair of oxidative DNA damage. As shown in Figure 2B, its expression levels were enhanced by the addition of DNJ. In the 50 mmol/L glucose treatment group, a 30% increase in OGG1 expression was observed with the addition of DNJ. This result indicated that DNJ relieved oxidative DNA damage via enhancing the repair thereof.  
Cell senescence is closely associated with both elevated DNA damage and high glucose exposure. Since DNJ treatment mitigated high-glucose-induced oxidative DNA damage, we next detected whether DNJ prevents high-glucose-induced senescence using a SA-\(\upbeta\)-gal assay. As shown in Figure 2C, the ratio of senescent HUVEC increased from around 10% in the 11 mmol/L glucose exposure group to around 25% in the 50 mmol/L glucose exposure group. Treatment with DNJ decreased 50 mmol/L glucose-induced senescence to a ratio of 12%. In addition, cell senescence marker p21 was detected via Western blotting. As shown in Figure 2D, compared with 11 mmol/L glucose-treated cells, the expression of p21 in 50 mmol/L glucose-treated cells increased by 33%, while the addition of DNJ restored the expression of p21. These results indicate that DNJ treatment mitigates high-glucose-induced oxidative DNA damage and senescence.  
Figure 1: (**A**) Cytotoxicity test of DNJ at different concentrations using a CCK\(-\)8 assay. Three replicates were set for each concentration of DNJ, and the experiment was performed four times. (**B**) Measurement of FMOC-derived DNJ via high-performance liquid chromatography, representative picture, and the standard curve. Each sample was analyzed three times. # means the \(p\) value < 0.05. One-way ANOVA with Tukey test was used to analyze the significance.
# From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning
## 2 Materials and Methods
### Features
They propose a different information measure:  
\[\mathrm{info}_{0}(t)=\left|\mathrm{info}_{0}(\mathrm{t})-\mathrm{info}_{1}( \mathrm{t})\right| \tag{23}\]and choose the threshold that minimizes this measure. They define  
\[ \begin{split}\text{info}_{0}(t)=-\frac{1}{W_{0}(t)}\sum\limits_{i=1 }^{t}p(i)\text{log}\,P_{0,t}(i)\\ \text{info}_{1}(t)=-\frac{1}{W_{1}(t)}\sum\limits_{i=t+1}^{t}p(i) \text{log}\,P_{1,t}(i),\end{split}  \tag{24}\]  
where  
\[ \begin{split} P_{0,t}(t-i)&=0.5+\frac{1}{2W_{0}(t)} \sum\limits_{j=0}^{i}p(t-i+j)\\ P_{1,t}(t+i)&=0.5+\frac{1}{2W_{1}(t)}\sum\limits_{j= 0}^{i}p(t+i-j).\end{split}  \tag{25}\]  
[PERSON] et al. [26] proposed a maximum entropy criterion based on the discrepancy between the binarized image and the original image and the number of bits required to represent the binarized image. They define entropy as  
\[\text{E}(t)=-\sum\limits_{i=1}^{t}\frac{p(i)\text{ln}\,p(i)}{W_{0}(t)}-\sum \limits_{i=t+1}^{t_{max}}\frac{p(i)\text{ln}\,p(i)}{W_{1}(t)}. \tag{26}\]  
They choose the threshold with the maximum entropy. [PERSON] [26] proposed an algorithm based on preserving the moments of the original image in the binarized image. They use the first 4 raw moments and obtain the following system:  
\[ \begin{split} W_{0}(t)+W_{1}(t)=m_{0}\\ W_{0}(t)z_{0}+W_{1}(t)z_{1}=m_{1}\\ W_{0}(t)z_{0}^{2}+W_{1}(t)z_{1}^{2}=m_{2}\\ W_{0}(t)z_{0}^{3}+W_{1}(t)z_{1}^{3}=m_{3},\end{split}  \tag{27}\]  
where \(m_{i}\) is the \(i\)-th raw moment, and \(z_{0},z_{1}\) are representative gray values for the two classes of pixels. Considering that  
\[W_{0}(t)+W_{1}(t)=1,\ \ \forall t\in\overline{1,I_{max}}, \tag{28}\]  
solving the system is trivial, and the threshold can be determined from the resulting \(W_{0}(t)\) as the \(W_{0}(t)\)-th percentile. [PERSON]'s bimodality coefficient (BC) [28] uses skewness and kurtosis to ascertain how separable two populations that form a bimodal distribution are. It can also be generalized (GBC) [29] by using standardized moments of higher orders. For both BC and GBC, values range from 0 to 1, with 1 indicating no overlap between the two populations and lower values indicating more overlap. The formula for the \(k\)-th GBC is  
\[\text{GBC}_{k}=\left\{ \begin{array}{rl}0,&\text{for distr.~{}concentrated on one point}\\ &\frac{\widetilde{\mu}_{2k+1}^{2}+1}{\mu_{2k+2}\mu_{2k}},&\text{otherwise} \end{array} \right. \tag{29}\]  
where \(\widetilde{\mu}_{n}\) is the \(n\)-th standardized moment. [PERSON]'s bimodality coefficient is GBC\({}_{1}\).
## 2 Materials and Methods
### Identification by UPLC-Q-Orbitrap MS  
The extract was identified by ultra-high-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry (UPLC-Q-Orbitrap MS) and compared with reference standards. The 200 mg SCHE powder was dissolved with a 1 mL methanol (ThermoFisher, Waltham, USA) solution (methanol:water, 8:2, _v:v_), centrifuged at 20,000\(\times\)_g_ for 10 min, and filtered through a 0.22 \(\upmu\)m PES membrane. Scopoletin (HPLC \(\geq\) 98%, Solarbio, BJ, CHN) and chlorogenic acid (3-Caffeoylquinic acid, HPLC \(\geq\) 98%, Aladdin, SHH, CHN) were dissolved in methanol and sonicated for 5 min to obtain a standard stock solution with a concentration of 20 \(\upmu\)g/mL.  
For this analysis, the phase A consisted of 0.1% (_v:v_) formic acid (ThermoFisher, Waltham, USA) and water; phase B consisted of 0.1% (_v:v_) formic acid and acetonitrile (ThermoFisher, Waltham, USA). The injection volume of SCHE and standard solutions were set at 5 uL; meanwhile, a flow rate of 0.300 mL/min and a 5 min gradient at 10 \({}^{\circ}\)C were fixed (RP-C18, 150 \(\times\) 2.1 mm i.d., 1.8 \(\upmu\)m, Welch, NYC, USA). The gradient elution was performed under the following conditions: 98% phase A (0-1 min), 98-80% phase A (1-5 min), 80-50% phase A (5-10 min), 50-20% phase A (10-15 min), 20-5% phase A (15-20 min), 5% phase A (20-25 min), 5-98% phase A (25-26 min), and 98% phase A (26-30 min). The mass spectrometry apparatus was equipped with a Q Exactive (ThermoFisher, Waltham, USA) ESI source and operated in a positive and negative ion mode within a scan range of 150-2000 \(m/z\) for 30 min. The conditions were performed as follows: spray voltage, 3.8 kV (positive); capillary temperature, 300 \({}^{\circ}\)C; collision gas, argon (\(\geq\) 99.999% purity); sheath gas and auxiliary gas (350 \({}^{\circ}\)C), nitrogen (\(\geq\)99.999% purity).
## 3 Results and Discussion
### Descriptive Analysis  
#### 3.1.1 Evolution of Number of Documents  
Since the first article identified in these databases was published in 1995, the time horizon used in this study was from 1995 to 2020. In order to analyze the trends and patterns in the publications, three periods were identified (1995-2001, 2002-2010, and 2011-2020) on the basis of the main turning points and milestones in the evolution of cool surfaces. The results of the contributions for the three periods identified are summarized below.  
The first period (1995-2001): In this period, the contribution of the academic literature to the topic was very poor, with only five publications found. However, the period was marked by promoting cool roofs through building codes. In fact, starting in 1999, several energy-building standards adopted cool roof credits or requirements, such as ASHRAE 90.1, ASHRAE 90.2, the International Energy Conservation Code, and California's Title 24.  
The second period (2002-2010): This period is characterized by the rising concern with regard to the topic, which coincided with the third assessment report of the Intergovernmental Panel on Climate Change, which highlights the increase in greenhouse emissions and the global average surface temperature in the 20 th century by 0.6 \({}^{\circ}\)C. The number of publications increased considerably (to 58) during this period (Figure 3).  
The third period (2011-2020): The cool surface strategy was a rapidly growing research topic during this period. In fact, this period is by far the most prolific in terms of publications, with 87.74% of the records published during this time (Figure 3). This period coincides with the foundation of the European Cool Roofs Council, which seeks to develop knowledge and research regarding cool roof technology and promotes the use and implementation of this technology in Europe.  
Considering the significant evolution of the number of academic documents in this field, an increasing interest in this topic has been explicitly detected (Figure 3).  
Figure 3: Number of documents per year.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 4 Experiments and Results
### Twin Dataset
#### 4.1.2 Construction Process of Twin Data  
The experimental data were generated using a high-fidelity digital twin model to ensure accuracy and reliability in the simulation. During the fault analysis of the electric screw press, several typical fault modes were identified, including circuit aging, hydraulic failure, and rail aging. To investigate these fault modes in depth, we selected seven representative fault conditions and their corresponding key input variables based on the multi-physics simulation model of the screw press digital twin. These input variables include motor stator resistance, rail friction, and hydraulic valve push-out force.  
During the simulation process, the parameters of the relevant input variables were adjusted according to different fault modes. For example, in the cases of rail aging and normal operation, the friction coefficient was set to 0.30 and 0.05, respectively. MATLAB2023b software was then used to simulate the operational state of the screw press. The simulation yielded 40 key output variables, including flux, rotational speed (Nr), and electromagnetic torque (Te). Finally, these data were uniformly stored in.csv file format for subsequent training and evaluation of the fault diagnosis model. These output variables comprehensively reflect the operational status of the spiral compressor under different operating conditions:  
\[Y_{it}=[X_{1t},X_{2t},X_{3t},X_{4t},X_{5t},X_{6t},X_{7t}], \tag{12}\]  
Table 14 provides a detailed description of various output variables, including characteristics and parameters of various output signals. Table 15 comprehensively documents the output signal characteristics of the spiral compressor under different operating conditions, specifically covering seven fault states and one normal operating state. Each operating condition consists of 332,196 samples, resulting in a total of 8 different operating states with 8 \(\times\) 332,196 = 2,657,568 original samples.
# Teleolocation--Remotely Operated Legged Robots
## 1 Introduction
### Dynamic Locomotion
#### 1.3.2 Bio-inspired Locomotion  
Legged natural organisms are adept at leveraging contact forces to negotiate complex obstacles [45]. Challenges remain for walking robotic platforms. However, examining the dynamic forms of locomotion from nature can lead to sophisticated solutions for robotic locomotion. For example, central pattern generators (CPGs) are biological networks that produce coordinated rhythmic output signals under the control of rudimentary input signals [46]. [PERSON] et al. presented a control architecture constructed around a CPG implemented as a system of coupled non-linear oscillators for the BoxyBot, a novel fish robot capable of both swimming and crawling [47].  
Bipedal, quadrupedal, hexapedal and rotating tri-legged platforms are popular modes of robot locomotion [45]. Examples include BigDog, RHex, Whegs, and iSprawl [48; 49; 50; 51]. [PERSON] et al. used a fast, anytime footstep planner guided by a cost map for Boston Dynamics' LittleDog [52]. Arthropod locomotion, specifically cockroach movement dynamics, has been a source of much inspiration for hexapedal robots. Cockroaches make use of passive dynamics in order to achieve a high degree of stability, speed and maneuverability. [PERSON] et al. presented the DynaRoACH, an under-actuated cockroach-inspired hexapedal robot capable of running at 14 body lengths per second and executing dynamic turning actions [53].