esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Features of Decomposed Time Series from Tidal River Water Level
## 4 Chaotic Features Analysis of Decomposed Time Series  
### Estimation of Delay Time and Attractor  
4.1.1. Delay Times for the Decomposed Time Series  
To identify the chaotic characteristics of each component, phase space for each time series is reconstructed. For the reconstruction of the phase space, the delay time for each time series is calculated using the autocorrelation function (ACF) and average mutual information. The ACF for each component is shown in Figure 5.  
As shown in Figure 5a, the ACF of the water level time series is decreased exponentially until 200 days since the beginning. This exponential decrease indicates the presence of chaotic characteristics in the water level time series [33]. In addition, the small variability in the water level time series can be explained by the autocorrelation coefficients of the tide and wave components, which shows periodicity under tidal influence. For the noise and rainfall-runoff components, the delay time can be chosen from the first minimum and zero crossing points.  
On the other hand, Figure 6 shows the variability of average mutual information. Similar to the results of the ACF, the water level time series, tide, and wave components have a pattern showing regular fluctuation which shows the periodicities, and the noise and rainfall-runoff components show a little fluctuation around zero value. From the ACF and average mutual information, the delay time can be estimated (i) at the point where the value of each component becomes 0, (ii) at the first minimum point that the ACF has before passing 0, or (iii) at the point corresponding to 1/e, if the graph shows an exponential  
Figure 5: Autocorrelation function for each component: (**a**) water level, (**b**) tide component, (**c**) wave component, (**d**) noise component, (**e**) rainfall-runoff component.  
decrease. Considering the type of each ACF, the delay times are estimated as shown in Table 3.  
As mentioned, the results of the two methods are almost identical for the original water level time series, the tide component, and the wave component with periodicity. However, for the rainfall-runoff component, the results were different from each method. Such difference was caused by the ACF, which does not reflect the nonlinear characteristics and the average mutual information requiring a large amount of data [34].
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 3 Proposed DL-Based Detection Scheme
### One Hot Encoding per Antenna (OHA)  
The OH labeling represents the approach of the highest dimension because it requires an exponential number of possible values. However, when the OH encoded input is divided into the \(N_{t}\) antennas, this dimensionality is reduced to \(N_{c}=M\times N_{t}\) different class values.  
Table 5 shows an example of the OHA method. In this case, \(N_{t}=2\) transmission antennas and 4-QAM modulation are considered. The first \(M\) bits correspond to the OH vector for the first antenna, where the symbol index is indicated as the position of the binary value of \"1\" in each vector. For example, when both antennas \(a_{1}\) and \(a_{2}\) transmit the symbol \(s_{1}\), the corresponding bit label is \([10001000]\). We propose this labeling encoding procedure when the hardware requirements are higher than in the case of symbol encoding but lower than OH encoding. Although OH fits better with softmax, this labeling sigmoid activation is a better option since the encoded vectors contain \(N_{t}\) entries encoded as \"1\", having a pattern better recognized for this activation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  &
\begin{tabular}{c} **Symbol** \\ **Transmitted** \\ \end{tabular} & \multicolumn{2}{c}{**Bit Label**} \\  & \multicolumn{2}{c}{**Mapping**} \\ \hline \(s_{1}\) & \(s_{1}\) & \(1\,0\,0\,0\,0\,0\,0\,0\,0\,0\,0\,0\,0\,0\,0\,0\,0\,0\) \\ \(s_{1}\) & \(s_{2}\) & \(0
## 5 Results and Discussion
### Monthly Evaluation  
To evaluate monthly precipitation, we derived IMERG from half-hourly, 3B42 from 3-hourly and ERA-Interim from daily precipitation. As a reference we compared rain-gauge observations with their corresponding grid points on satellites and models for all days of the corresponding months. Figure 8 and Table 8 show underestimation for monthly scale in all three products but very slightly for IMERG with Bias, Rbias and Mbias of \(-29.18\) mm, \(0.98\) and \(-0.02\), respectively, which shows a better result in comparison with the other two products. Also, IMERG yields MAE and RMSE of 52.75 mm and 81.43 mm, respectively, in comparison to 59 mm and 102.76 mm for 3B42 while the same for ERA-Interim are 62.18 mm and 95.41 mm. On the other hand, CC of ERA-Interim indicates better agreement than others in the Guilan region.  
As seen in Figure 9 and Table 9, IMERG and 3B42 tend to overestimate while ERA-Interim underestimates in Bushehr. The 3B42 yields for all indices better results than the other two data sets while ERA-Interim shows significant agreement with a correlation coefficient of 0.95.  
Regarding to monthly precipitation over the Kermanshah area, based on Figure 10 and Table 10, IMERG and 3B42 overestimate and ERA-Interim underestimates the observed values. 3B42 presents lower Bias, MAE and RMSE in comparison with the other two data sets. Furthermore, based on Figure 10, ERA-Interim shows a very slight bias during the January, February and March (winter season).  
As seen in Figure 11 and Table 11, 3B42 tends to overestimate Tehran monthly precipitation whereas IMERG and ERA-Interim tend to underestimate. IMERG shows strong overestimates during October while ERA-Interim is almost unbiased in this month. Generally, IMERG represents better value of Bias, Mbias and Ribias while Era-Interim yields a better value for RMSE and MAE.
## 4 Characteristics of the BDS Satellite Clock
### Frequency Accuracy  
The frequency accuracy sequence was calculated by Equation (7), with days as the sampling time interval, and the average RMS value of the frequency accuracy was determined.  
It can be seen from Figure 9 that the calculation result of frequency accuracy of atomic clock of each satellite is close to the output frequency value, but in fact, the influence of individual small fluctuations is excluded, which highlights the overall change trend. According to the calculation results, the frequency accuracy of the BDS-2 satellite clock is generally in the order of \(10^{-11}\), and the frequency accuracy of C04 is the worst, with an RMS of \(1.66\times 10^{-10}\). The frequency accuracy of the BDS-3 satellite atomic clock is mainly in the order of \(10^{-12}\), or even reaching the order of \(10^{-13}\). The PHM has better continuity and a smaller fluctuation range than the RAFS, and its change tends to be linear.  
Figure 9: _Cont._
# Concept for Generating Energy Demand in Electric Vehicles with a Model Based Approach
## 5 Simulation and Result  
### Approach  
For the simulation of energy consumption, the created vehicle model (see Section 4.1) drives on a defined route, and the progression of the battery state of charge is recorded. The average consumption is then calculated from these results. For the validation of the simulation results, a standardized test cycle is at first chosen, since there are already real measured results for such an energy consumption measurement [39]. In this work, the simulation results are validated with the literature values of the NEDC [40]. In the second step, a real test drive is simulated, which is compared with the recorded results of the real test drive. Additionally, for the validation, the modeled supercapacitor is deactivated, since it is not provided in the real BMW i3. The vehicle model is therefore structurally equivalent to the real model. For the comprehensibility of the simulations, the tests are carried out under the same boundary conditions as taken from the literature data. The simulation-dependent results are compared with the respective literature data given by the test cycle. In order for an evaluation of the simulation to take place, relevant measured variables from the simulation are recorded. A simulation always starts with a state of charge (SOC) of 100%, which corresponds to a fully charged battery. All auxiliary consumers were deactivated for this simulation, and the reference temperature in the simulations was set to 20 \({}^{\circ}\)C. Monitoring the simulated speed profile (see Figure 11) provides the background for comparable measurement results.  
In [20; 41], a similar approach was used for the validation of the simulation-based energy consumption. While [20; 41] created the predicted energy consumption values using algorithms and simplified vehicle models sufficient for the needs, in this study, a whole vehicle model (see Section 4.1) in combination with white box models of the energy storage  
Figure 11: Speed profile of the simulated NEDC consisting of four equal ECE-15 urban segments and one extra-urban EUDC segment.  
system and engine model are simulated to produce the energy consumption values in a simulation environment.  
The duration and distance of the simulation exhibit only minor deviations (0.73% and 0.00%) compared to the literature data (see Table 5). This results in a simulated energy consumption which deviates from the literature value by only 1.21%.
## 4 Experiments and Results Discussion
### Models Evaluation
#### 4.2.2 Comparison of Performance of Different Models
However, the proposed MDISE model shows similar prediction accuracy to the PSS-PLS method proposed by [PERSON] et al. and the D-CNN model proposed by [PERSON] et al., demonstrating its ability to effectively overcome the issue of poor spectral consistency and achieve high-precision concentration inversion. Notably, the MDISE model outperforms the CARS-BAS-SVR method proposed by [PERSON] et al. in 2023 ([PERSON] et al., 2024), in terms of both high-concentration _MAE_ (2.244%) and overall average _MAE_ (0.646%), proving that the MDISE model offers higher prediction accuracy and stability across all concentration intervals, highlighting its novelty.
## 3 Fibre Design Approaches  
### Conventional Approach  
For normal strength steel fibre concrete, usually macro steel fibres are used, whose slenderness, i.e., the ratio of length to diameter, is about 60 [-]. The diameter of the common macro steel fibres varies between 0.50 mm and 1.20 mm and the length between 30.0 mm and 70.0 mm. The tensile strength of these macro steel fibres is about 1000 N/mm\({}^{2}\).  
The tensile strength of the fibre material has roughly tripled in recent years, which makes them increasingly appropriate for high and ultra-high-performance concrete [14]. Figure 1 shows some steel fibre types for normal-, high- and ultra-high-strength concretes.  
Due to the comparatively low bond stress of plain steel fibres in normal strength concrete, macro steel fibres usually have end hooks (anchorages) increasing the pullout resistance. The geometry (especially with regard to the end anchorage) and tensile strength of the fibres should be matched in such a way that continuing pullout occurs in steel fibre reinforced concrete components under tensile stress.  
Macro steel fibres with mechanical anchorage are normally not suitable for high and ultra-high performance concrete. The high bond stress in addition to the anchorage force of the end hook would exceed the material strength of the fibres and thus causing an unwanted fibre rupture [7]. Therefore, plain high-strength micro steel fibres with diameter between 0.10 mm and 0.25 mm and length between 6.0 mm and 25 mm suit much better for UHPC. The tensile strength of such micro steel fibres is usually significantly more than 2000 N/mm\({}^{2}\).  
Modern steel fibre types have an optimized anchorage (e.g., a second hook, cf. 5D-fibre in Figure 1c), leading to an intended fibre rupture under quasi-static loading. In that case, the elongation capacity of the steel fibre material defines the post-cracking tensile behaviour of the fibre concrete.
# Isothiocyanates: An Overview of Their Antimicrobial Activity against Human Infections
## 4 Isothiocyanates Antimicrobial Properties against Human Pathogens
### Staphylococcus Aureus and Methicillin-Resistant S. aureus (MRSA)
Results indicated that synthetic BITC and PEITC induced a total inhibition of the _S. aureus_ strain CECT 976 (standard MRSA strain) growth showing the best antimicrobial activity. Regarding the other ITC included in the study, PEITC and ERN showed better antimicrobial activity than IBN which in turn was more effective than SFN. No effect was observed for AITC. In addition all the effective ITC showed a better antimicrobial activity compared to vancomycin [46]. Finally, the synergic effect of a combined treatment of each ITCs with vancomycin against _S. aureus_ CECT 976 was also evaluated [46]. As expected, both BITC and PEITC retained their strong antibacterial activity indicating that the combined treatment improved the antibacterial effect of vancomycin against _S. aureus_ CECT 976. Moreover the association of ERN and vancomycin improved the antimicrobial activity of each one replace similar results to BITC and PEITC. Finally the association of IBN with the antibiotic had no effect [46]. Although AITC has appeared not to have antimicrobial activity against _S. aureus_, [PERSON], et al. [55] recently showed that AITC-rich _Wasabia japonica_ powder was able to induce a significant reduction of _S. aureus_ B0031 growth in a dose dependent manner [55] and similar effects were observed by using the synthetic analog AITC (10 \(\upmu\)g/mL < MIC < 100 \(\upmu\)g/mL), which suggests that AITC is the most active _Wasabia japonica_ component to inhibit _S. aureus_ B0031 growth. In addition [PERSON] et al. [49] indicated that AITC and PEITC showed antibacterial activity against _S. aureus_ CECT 976 with similar effect (MIC = 100 \(\upmu\)g/mL). The author also reported that AITC and PEITC treatment altered the bacterial cell membrane integrity lead to bacterial death [49]. A few years ago, [PERSON] et al. [51] evaluated the antibacterial properties of synthetic AITC, BITC and PEITC on resistant phenotypes (MRSA). The experimental work involved 15 clinical isolates of methicillin-resistant _S. aureus_ and one standard strain (_S. aureus_ CECT 976). Results indicated that BITC and PEITC showed higher inhibition activity compared to vancomycin and to AITC. More specifically, 10 isolates were more susceptible to PEITC, two isolates were more susceptible to BITC and two were highly susceptible to both BITC and 2-PEITC. Furthermore, AITC and PEITC were essentially bacteriostatic while BITC was bactericidal in 11 MRSA isolates (representing 69% of isolates tested). Based on these results, BITC was more effective than PEITC in suppressing MRSA strains. The authors hypothesized that variations in the chemical structure of ITCs might explain the different grade and type of antibacterial activity, and they postulated that phenyl rings present in BITC and PEITC structure could be a predisposing factor for antibacterial efficacy.
## 1 Introduction
In addition to the absorption refrigeration cycle, vapour compression cycle refrigerator is another technology that requires mechanical power/electricity to drive the compressor. Therefore, the concept of combining ORC and vapour compression cycle (VCC) was proposed as an alternative heat-driven refrigeration technology by [PERSON] and [PERSON] [21]. Compared to the thermally powered absorption cooling technologies, the ORC-VCC has some potential advantages in terms of performance and simplicity. Furthermore, the VCC powered by an ORC can make use of the heat source throughout the year [22] to provide either cooling or electricity when cooling is not required [23], increasing the operational flexibility and improving the economic profitability. Great efforts have been devoted to the development of ORC-VCC systems since such a concept was proposed. [PERSON] [24; 25] compared the performance of solar powered ORC-VCC systems for building cooling applications with five different working fluids. R113 and FC88 were considered as the best working fluids. To reduce the system complexity, [PERSON] and [PERSON] [26] proposed an ORC-VCC concept, of which the compressor and expander are integrated in the same unit, use the same working fluid, and share the same condenser. [PERSON] et al. [27; 28; 29] carried out a series of investigations on the working fluid of ORC-VCC ice makers and found that R600 is the most suitable working fluid. An experimental test on the ORC-VCC system was conducted by [PERSON] [23], and reported a coefficient of performance (COP) of 0.48. [PERSON] et al. [30] introduced the design, fabrication, and test of a solar-powered organic Rankine cycle heat pump and chiller system capable of delivering 63.3 kW of heating and cooling. When R11 was used as the working fluid, the heating COP of the system ranged from 1.6 to 2.25, while the cooling COP varied between 0.5 and 0.75. In addition to the above studies, several other researchers [31; 32; 33; 34; 35] have also reported the performance of the ORC-driven VCC for heating purposes. Despite numerous studies on individual WHR technologies for marine diesel engines, the effect of transmission ratio on the performance of the thermodynamic WHR has not been tackled thus far In this paper, a marine engine waste heat recovery and cooling system with combined ORC and VCC is proposed. A comprehensive energy and exergy analysis was carried out and the potential of the proposed thermodynamic system was demonstrated.
## 2 Materials and Methods
### Data Processing  
Optical marker trajectories obtained by OTS were filtered by 4 th-order, low-pass Butterworth filter and cut-off frequency was set at 5 Hz [12]. Kinematic data of the lower extremities and L5/S1 moments were calculated using Euler angle and numerical differentiation methods. Based on the kinematic data, the ground reaction force data of 6 axes, and the subject's anthropometric information, the lower extremities and L5/S1 moments of sagittal plane were calculated. These calculations were performed using kinematic and kinetic models developed in the Biomedical Engineering Laboratory of Sungkyunkwan University [8; 30]. During the lifting task, pressure data obtained from the insole sensor were filtered with 3 rd Butterworth, zero-lag low-pass filter and the frequency was set at 7 Hz [31]. The event of the lifting task was segmented based on the optical marker attached to the box. The time when the box was moved from the ground was marked as the starting point and the time when the box reached the highest point was marked as end of task. The amplitude of moment obtained from the OTS and 99 pressure data collected from the insole system were normalized by dividing the subject's body weight (BW). In addition, the length of moment and pressure data were normalized to 100% for each trial [27]. In the proposed model, each posture was labeled for motion classification where the squat posture was labeled as 0 and the stoop posture was labeled as 1. The total insole pressure, lower extremities, and L5/L1 moments dataset was 480.
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 5 Simulations and Performance Comparison
### Results and Analysis
#### 5.2.3 The Influence Analysis of Maximum Communication Radius  
Figure 9 shows the change curve of average localization error with maximum communication radius. The average localization error of all the algorithms decreases with the increase of the maximum communication radius, and the average localization accuracy of methods in references [23; 24; 25] is better than that of basic DV_Distance algorithm and [PERSON]'s method, and their advantages are even more significant when the maximum communication radius is relative small. Although [PERSON] uses the ratio of the actual Euclidean distance to the accumulated hop distance, the localization accuracy is slightly better than that of methods in references [23; 24; 25] with the increase of the maximum communication radius, and the main reason is that the correction model based on unknown nodes are made full use of in [PERSON]'s method. The localization accuracy of the proposed algorithm is obviously better than that of other algorithms no matter what the maximum communication radius is. Figure 10 shows the change curve of mean square deviation with maximum communication radius. When the maximum communication radius is relatively small, the localization stability of methods in references [23; 24; 25]  
Figure 8: The change curve of mean square deviation with anchors density.  
Figure 7: The change curve of average localization error with anchors density.  
is superior to that of basic DV_Distance algorithm and [PERSON]'s method. However, the localization stability of references [23; 24; 25] is gradually consistent with that of [PERSON]'s method and basic DV_Distance algorithm with the maximum communication radius increasing. Besides that, when the maximum communication radius is relatively small, the localization stability of [PERSON]'s method is the same as that of methods in references [23; 24; 25], but the localization stability of [PERSON]'s method is better than them when the maximum communication radius is becoming large. Finally, the localization stability of the proposed algorithm is better than that of other algorithms no matter what the maximum communication radius is.  
To sum up, the average localization accuracy and stability of the proposed algorithm are better than those of other algorithms in each situation.
## 4 Fast Automatic Adjustment Algorithm of Imaging Waveform  
On the basis of the characteristics of the static and dynamic actual imaging waveforms, and according to the centroid position shift of two adjacent frames of imaging waveforms, an automatic fast waveform adjustment algorithm was proposed. This method could satisfy the dynamic response of the dispensing robot during its high-speed motion, and could also ensure its static measurement accuracy. The algorithm flow is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Centroid Shift (Pixels)**} & \multicolumn{2}{c}{**Intensity Difference (a.u.)**} \\ \cline{2-5}  & **Static** & **Dynamic** & **Static** & **Dynamic** \\ \hline White diffuse reflector & 0.0259 & 0.5312 & 4 & 9 \\ Black frosted reflector & 0.0352 & 0.8641 & 4 & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Change of centroid position and peak intensity of static and dynamic imaging waveforms.  
Figure 6: Flow chart of fast waveform adjustment algorithm based on centroid difference.  
The proposed algorithm mainly included three main parts, i.e., waveform preprocessing, measurement mode judgment, and peak intensity adjustment which are described as follows: (1) The waveform preprocessing filtered out the noise interference and obtained the effective waveform information. (2) The measurement mode judgment determined whether the measurement mode was static or dynamic by setting the centroid difference threshold of two waveforms, and different peak intensity adjustment intervals were selected for these two measurement modes. (3) During the peak intensity adjustment part, a linear approximation prediction method was presented to quickly adjust the imaging waveform.