esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Volatile Aroma Compounds
#### 3.2.5 Alcohols  
The concentration of the majority of alcohols with the highest \(F\)-ratio was significantly lower in \(LEV\) than in \(SCE\) wine, with the exception of _cis_-6-nonen-1-ol, 2-methyl-5-nonanol, 3-nonanol, 2-ethyl-2-(hydroxymethyl)-1,3-propanediol, and 6-methyl-5-hepten-2-ol (Table 6). \(LEV\) fermentation showed a tendency towards higher concentrations of some other minor alcohols, although without a significant difference. Among major alcohols, methanol and isobutanol were found in higher concentrations in \(LEV\) wine. Such a result for isobutanol was in line with previous findings by [PERSON] et al. [22], while [PERSON] et al. [8] reported variable concentrations of isobutanol produced by different \(L\). _thermotolerans_ strains under various inoculation regimes, although not significantly different from that found in control \(S\). _cerevisiae_ fermentation. 1-Propanol and isoamyl alcohol were found in lower concentrations in \(LEV\) than in \(SCE\) wine. The same trend for 1-propanol was reported by [PERSON] et al. [22]. 1-Propanol, isobutanol, and isoamyl alcohol are known contributors to the aroma of all fermented alcoholic beverages. In total concentrations above 300 mg/L, they may have a negative influence with their medicinal and solvent-like odors [44]. 2-Phenylethanol, a carrier of a pleasant odor reminiscent of roses, was also found in lower concentrations in \(LEV\) than in \(SCE\) wine. The same was reported by [PERSON] et al. [45], while [PERSON] et al. [16] noticed an increased concentration in fermentation with \(L\). _thermotolerans_. [PERSON] et al. [8] observed variable concentrations of major higher alcohols in wines produced under sequential and co-inoculation regimes with different strains of \(L\). _thermotolerans_; in some cases they were higher and in others lower than those found in control wine obtained via \(S\). _cerevisiae_ monoculture fermentation. The effects observed in this study suggest a different metabolism of higher alcohol amino acid precursors between \(L\). _thermotolerans_ and \(S\). _cerevisiae_ yeasts, while the discrepancies between different studies reveal apparent strain-specific effects, probably in interaction with other compositional characteristics and production conditions depending on the study. The concentrations of C\({}_{6}\)-alcohols, which are mainly formed via the degradation of lipids catalyzed by hydroperoxide lyase and lipoxygenase enzymes in pre-fermentation steps did not differ between the treatments (Table 6).
# Low-Complexity Hybrid Optical OFDM with High Spectrum Efficiency for Dimming Compatible VLC System
## Appendix B  
Upon taking the \(N\)-point FFT of \(z_{n}\), we obtain the frequency-domain sample \(Z_{K}\) for \(k=0,1,\cdots,N-1\) as:  
\[\begin{split} Z_{k}&=\sum_{n=0}^{N-1}z_{n}\exp \left(-j\frac{2\pi nk
## 7 Discussion  
Maintaining the ampacity of overhead transmission lines requires careful consideration of factors like conductor temperature, mechanical integrity, and adherence to safety standards. Overloading and exceeding maximum sag pose risks to both performance and safety, emphasizing the need for strict adherence to design limits and operational guidelines to ensure reliable power transmission.  
CIGRE and IEEE establish standards for estimating ampacity and conductor temperature, both based on heat balance equations considering heat gained and lost in the conductor, influenced by loading and environmental factors. Determining ampacity involves thermal assessment, divided into static and dynamic approaches. Static assessment assumes constant ampacity under various conditions, whereas dynamic methods adjust ampacity in real-time based on changing environmental factors. These approaches are crucial for ensuring safe and efficient operation of overhead transmission lines.  
The adoption of HTLS conductors presents a promising solution to meet escalating energy consumption challenges, particularly in addressing unplanned large-scale energy transfers caused by renewable energy fluctuations. These conductors, engineered for operation at elevated temperatures exceeding 100 \({}^{\circ}\)C and emergency conditions exceeding 150 \({}^{\circ}\)C, offer enhanced transmission capacity without necessitating significant structural modifications to existing lines, despite their higher costs.  
The application of Slovak standards and CIGRE Technical Brochure 601 provides insights into the ampacity of overhead transmission lines in Slovakia, considering factors like ambient temperature, wind speed, and solar radiation intensity. Analysis reveals preferences for certain conductor types under specific conditions, with DLR often proving advantageous due to its performance across various meteorological parameters.  
The analysis outlines strategies for enhancing permissible load current by transitioning from ACSR to ACCC conductors. The selection process prioritizes maintaining a similar conductor diameter and weight to mitigate impacts on wind and icing loads, ensuring compatibility with existing infrastructure. Utilizing standard STN EN 50341-2-23 [23] for ACCC conductors reveals substantial increases in ampacity compared to traditional ACSR conductors, highlighting the considerable advantages of adopting advanced conductor technologies.  
Utilizing dynamic steady-state ampacity analysis on the transmission line V427 Rimavska Sobota--Moldava, replacing the ACSR conductor with a compatible ACCC conductor (LUBBOCK), significantly increased ampacity. With an average increase of 778.22  
Figure 15: ACCC conductor replacement impact on dynamic steady-state ampacity \(I_{\text{max}}\) analysis for transmission line V427.  
A, ranging from 694.26 A to 973.29 A, this highlights the efficacy of adopting advanced conductor technologies for enhancing transmission line performance.
## 3 Methodology
### Data Collocation  
As far as the collocation of GPM data (providing rain rate in mm/h unit) with SEVIRI data is concerned, this is achieved in 5 steps.  
1. A timestamp is associated to each SEVIRI pixel. It is calculated starting with the nominal date and time provided in the SEVIRI data file-name and adding the scans time at the latitude of the pixel (about 12 min at 45\({}^{\circ}\) N).
2. Geographical area and the temporal window are defined. The spatial and the temporal window should be large enough to obtain a statistically significant dataset but not too wide in order to avoid climatologically different zones, for example the Mediterranean area and tropics, being processed at the same time.
3. Using the corresponding EUMETSAT cloud mask, all the cloud-free SEVIRI pixels are filtered out from the dataset.
4. For each GPM pixel belonging to the chosen area and temporal window, all the SEVIRI pixels at a distance less than 2.5 km (half SEVIRI pixel resolution at mid-latitude) and with a temporal distance less than 7.5 min (half SEVIRI repeat cycle) are selected.
5. Among the SEVIRI pixels from step 4, the nearest in time is selected and collocated with the GPM pixel.
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
## 4 The Performance Evaluation of MIMO Antenna Systems
### Envelope Correlation Coefficient and Diversity Gain  
In addition, gain, the envelope correlation coefficient, and diversity gain are also considered to be valuable parameters to evaluate the antenna system performance. ECC represents the degree of correlation between the antenna elements of a multiple antenna system. The calculation formulas of ECC calculated from the S-parameters or far-field radiation characteristics are shown in Equations (2) and (3) [16], respectively. However, only when the antenna efficiencies are nearly 100%, is Equation (2) accurate. Therefore, ECC is calculated using Equation (3) in this paper.  
\[\text{ECC}=\frac{|\overset{*}{S_{11}}\overset{*}{S_{12}}\overset{*}{S_{22}} \overset{*}{S_{21}}|^{2}}{(1\ -|\overset{*}{S_{11}}|^{2}+|\overset{*}{S_{21}}|^{2})(1\ -|\overset{*}{S_{22}}|^{2}+|\overset{*}{S_{12}}|^{2})} \tag{2}\]  
\[\rho_{\text{e}}=\iint\frac{\big{[}\overset{*}{\overset{*}{\Gamma}}(\theta, \varphi)\times\overset{*}{\overset{*}{\Gamma}}(\theta,\varphi)\big{]}\text{d} \theta^{2}}{\iint\overset{*}{\overset{*}{\Gamma}}(\theta,\varphi)\big{|}^{2} \text{d}\theta\iint\overset{*}{\overset{*}{\Gamma}}(\theta,\varphi)\big{|}^{2} \text{d}\theta} \tag{3}\]  
Figure 8: Gain and radiation efficiency of the proposed MIMO antenna.  
Figure 7: Two-dimensional radiation patterns at E-plane and H-plane for the intended two-port MIMO antenna at port-1: (**a**) 2.5 GHz and (**b**) 5.7 GHz.  
where 'F\({}_{i}\)(\(\theta\),\(\varphi\))' is the radiated field of the first antenna.  
On the other hand, the DG acts as the other index to evaluate the MIMO antenna isolation performance. The diversity gain is calculated through the following Equation (4):  
\[\text{DG}=10\sqrt{1\ -(\text{ECC})^{2}} \tag{4}\]  
Figure 9 depicts the measured values and simulated values of ECC and DG for the proposed MIMO antenna, respectively. It can be seen that the ECC is below 0.005, and the DG is greater than 9.65 dBi within the operating bands, which meets the engineering standard. A performance comparison of the proposed antenna with previous dual-band antennas is provided in Table 2. In this table, it can be observed that the presented dual-band MIMO antenna has achieved a more compact size, a lower ECC, and a higher isolation compared to those of the other referenced antennas.
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 3 Experiments
### Parameter Selection  
Table 1 shows the experiments conducted on the DNS Challenge 2020 dataset for selecting a suitable parameter \(N\), which is the number of paired Repair and HFO blocks. From Table 1, we see that as \(N\) starts to increase from 2, all metrics in the table show a significant improvement, with PESQ being the most obvious. When \(N\) exceeds 4, STOI does not show a noticeable increase, while other metrics begin to decline to different extents and contribute to an increase in the model parameters. Based on these results, in our following experiments, we set \(N\) to be 4.
## 5 Experimental Results  
As a practical demonstration of the proposed dual-mode control strategy, a prototype DAB converter with a power rating of 1 kW was built and tested. The image of the actual circuit experimental platform is presented in Figure 11.  
In this experiment, the prototype converter uses a DSP TMS320F28335 produced by Texas Instruments (Dallas, TX, USA) as a digital controller to implement the proposeddual-mode control strategy. The proposed dual-mode DAB converter prototype has the specifications shown in Table 3:  
Experimental data describing the bidirectional power conversion of the proposed DAB converter are divided into two categories: the measured waveforms of the forward power conversion mode (power flow from the high-voltage side to the low-voltage side) and the measured waveforms of the backward power conversion mode (power flow from the low-voltage side to the high-voltage side).
## 2 Methods
### Experiment Tasks and Procedure  
#### 2.3.1 Experiment Tasks  
A participant judges the four accident situations for the experiment through six indicators (Rx power, Rv water level, PZR Pr., CTMT Rad., CTMT Pr., Sec. Rad.) on simulator screens. The changes of the six indicators in four accident situations are shown in Table 1.  
In the four accident situations, the screen's reactor power (RX Power) starts to drop when an accident occurs. At this time, if the reactor water level (Rv water level) decreases, the containment building radiation (CTMT Rad.) increases, the containment pressure (CTMT Pr.) increases, and the LOCA is judged to have occurred. On the other hand, if the Rv water level decreases and the radioactivity of the secondary system (Sec. Rad) increases, it is judged that SGTR has occurred. If the containment pressure rises, it is judged as an SLB situation. Finally, if there is no change in the other variables other than a drop in the pressure of the primary system, it is considered an LOV situation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LOCA** & **SGTR** & **SLB** & **LOV** \\ \hline Rx Power & \(\downarrow\) & \(\downarrow\) & \(\downarrow\) & \(\downarrow\) \\ RV water level & \(\downarrow\) & \(\downarrow\) & - & - \\ PZR Pr. & \(\downarrow\) & \(\downarrow\) & \(\downarrow\) & \(\downarrow\) \\ CTMT Rad. & \(\uparrow\) & - & - & - \\ CTMT Pr. & \(\uparrow\) & - & \(\uparrow\) & - \\ Sec. Rad. & - & \(\uparrow\) & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Changes of six indicators in four accident situations.
## 2 Materials and Methods
### Row Unit Force Analysis
In addition, although it can ensure that the gauge wheel is close to the surface and can reach the sowing depth set by the user when providing enough downforce, in actual operation too much downforce will cause the deflection of the gauge wheel arm with a very small angle \(\theta_{b}\), i.e., the tendency of the gauge wheel to move to state II, as shown in Figure 1. This phenomenon will cause excessive soil compaction, i.e., the compaction of the sidewall of the groove will increase, the groove environment will be damaged and it will be difficult for the seed to penetrate the sidewall and germinate. In this paper, by introducing the hydraulic system as the external pressure source, according to the working principle of the profiling mechanism, as shown in Figure 1, to make the gauge wheel and the coulter maintain the sowing depth \(H\) and so there is no soil compaction phenomenon, at this time the hydraulic force \(F_{Y}\) needs to meet  
\[\cos\theta_{a}F_{Y}=F_{XY}\frac{L_{f1}}{L_{f}} \tag{3}\]  
where \(F_{Y}\) is the hydraulic force, i.e., the current output hydraulic force of the cylinder, N; \(L_{f1}\) is the hydraulic force arm, mm; \(L_{f}\) is the effective length of the profiling lower rod, mm; and \(\theta_{a}\) is the cylinder tilt angle, the angle between cylinder axis and plumb line, \({}^{\circ}\). The above Equations (1)-(3) are combined to obtain the relationship between the output hydraulic force \(F_{Y}\) of the cylinder and the applied downforce \(F_{XY}\). It can be seen that  
Figure 1: Row unit force diagram: (1) integrated pressure-reducing cylinder (IPRC), (2) four-bar linkage, (3) seed box, (4) closing wheel, (5) sowing depth adjustment mechanism, (6) state II position of gauge wheel, (7) state I position of gauge wheel, (8) double-disc coulter, (9) ripple disc. the hydraulic force \(F_{Y}\) as the source of external pressure, after the transfer of the mechanical structure, should meet the required downforce \(F_{XY}\) changes. In the process of sowing depth control, the downforce \(F_{XY}\) changes constantly with internal and external factors. It is necessary to ensure the premise of good contact between the gauge wheel and soil, and to improve the level of downforce as much as possible, so as to avoid the excessive compaction of soil on the sidewall of the seed groove, in order to reduce the influence of the soil resistance and machine weight on the consistency of the sowing depth. From the force analysis, it is known that the contact force \(F_{JC}\) indirectly reflects the change in the soil compaction force \(F_{Sy}\). Therefore, the sowing depth can be measured and controlled by applying variable hydraulic pressure to the profiling mechanism.
# Mutational Chemotaxis Motion Driven MdpI for Engineering Applications
## 5 Engineering Design Problems
### I-Beam Design Problem  
The fourth engineering example aims to obtain the minimum vertical deflection in the I-beam structure. The model contains four parameters: the structure's length, two thicknesses, and height. The details of the model are shown below.  
\[\text{Consider}:\ \overset{\rightarrow}{x}=[x_{1}\ x_{2}\ x_{3}\ x_{4}]= \left[b\ h\ t_{w}\ t_{f}\right]\]  
\[\text{Minimize}:\ f\left(\overset{\rightarrow}{x}\right)=\frac{5000}{\frac{x _{3}\left(x_{2}-2x_{4}\right)^{3}}{12}+\frac{x_{1}x_{3}^{3}}{6}+2x_{1}x_{4} \left(\frac{x_{2}-x_{3}}{2}\right)^{2}}\]  
\[\text{Subject to}:\ g\left(\overset{\rightarrow}{x}\right)=2x_{1}x_{4}+x_{3} \left(x_{2}-2x_{4}\right)\leq 0,\]  
\[g_{1}\left(\overset{\rightarrow}{x}\right)=\frac{18x_{2}\times 10^{4}}{x_{3} \left(x_{2}-2x_{4}\right)^{3}+2x_{1}x_{4}\left(4x_{4}+3x_{2}\left(x_{2}-2x_{4} \right)\right)}+\frac{15x_{1}\times 10^{3}}{\left(x_{2}-2x_{4}\right)x_{3}^{3}+2x_{4}x_{ 1}x_{3}}-6\leq 0\]  
\[\text{Variable ranges}:\ 10\leq x_{1}\leq 50,\ 10\leq x_{2}\leq 80,\ 0.9\leq x_{3}\leq 5,\ 0.9\leq x_{4}\leq 5\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Optimal Values for Variables**} & \multirow{2}{*}{**Optimum Cost**} \\ \cline{2-2} \cline{4-5}  & \(T_{s}\) & \(T_{h}\) & \(R\) & \(L\) & \\ \hline HMCMMFO & 0.8125 & 0.4375 & 42.09845 & 176.6366 & 6059.714 \\ G-QPSO [95] & 0.8125 & 0.4375 & 42.09840 & 176.6372 & 6059.721 \\ CDE [96] & 0.8125 & 0.4375 & 42.09840 & 176.6376 & 6059.734 \\ IDARSOA [82] & 0.812500 & 0.4375 & 42.09711 & 177.1901 & 6072.430 \\ AGOA [89] & 0.812500 & 0.437500 & 41.73619 & 181.1778 & 6104.325 \\ SCADE [78] & 1.465138 & 0.679596 & 65.0882 & 13.36838 & 6126.139 \\ GWO [73] & 2.176906 & 0.614469 & 63.9998 & 15.78465 & 6199.028 \\ Improved HS [83] & 1.12500 & 0.625000 & 58.29015 & 43.69268 & 7197.730 \\ Branch-and-bound [97] & 1.12500 & 0.625000 & 47.70000 & 117.7100 & 8129.104 \\ GSA [86] & 1.12500 & 0.625000 & 55.98866 & 84.45420 & 8538.836 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison results of pressure-vessel-design problem.  
The minimum vertical deflection obtained by different algorithms in this model is shown in Table 12 below, where the result obtained by HMCMMFO is 0.013074; HMCMMFO obtains the same optimal cost as IDARSOA and SOS, and all three obtain the same value on the \(x_{1}\), \(x_{2}\), and \(x_{3}\), with only a small difference in \(x_{4}\). It can be seen that HMCMMFO can effectively solve this problem and achieve desired results.
## 3 Results  
### Spatiotemporal (ST) Parameters  
The step variables, i.e., swing times, stance times, and step lengths, were analyzed in pairs to facilitate comparison between the first and second steps. Results demonstrated that Step and the interaction between Step and Condition were significant for swing time, stance time, and step length (Table 3); interaction here means that the relationship between the first and second steps was different for PD than it was for CON. Post hoc \(t\)-tests (Table 4) of Condition within Step indicate that the second step length was statistically smaller in the PD group. The results in Table 4 indicate that the significance of the F-tests in Table 4 was primarily due to differences between the first and second steps.  
The ANOVA for stride variables, i.e., stride length, stride width, and gait velocity, had only Condition (i.e., CON and PD) as a two-level fixed effect. There were no differences between groups for these ST parameters. The post hoc interaction analysis for the CON and PD groups by Step during obstacle crossing is shown in Table 4. We did not assume symmetry between limbs during obstacle-crossing and compared the gait parameters of the limbs associated with the leading and trailing limbs. Results demonstrated that there were very small differences, i.e., CON-PD, for all of the parameters; however, a significant difference between groups was found for step length for the second step (\(p\) = 0.005), with the PD group demonstrating a shorter step, i.e., 0.08 m (\(\sim\)3.0 inches) (Figure 5).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measurement** & **Condition** & **Step** & **Interaction** \\ \hline Stance Time & 0.190 & 0.003 & \textless{}0.001 \\ \hline Swing Time & 0.502 & \textless{}0.001 & \textless{}0.001 \\ \hline Step Length & 0.089 & \textless{}0.001 & \textless{}0.001 \\ \hline \end{tabular}
\end{table}
Table 3: ANOVA F \(p\)-values for main effects of Condition and Step with interaction.  
The post hoc interaction analysis of the first step and second steps by Condition (not shown since we focused on the differences between CON and PD) revealed that all gait parameters, i.e., swing time, stance time, and step length, between step one and step two demonstrated significant differences. However, no differences between steps one and two were evident for stance time in the CON group. Therefore, these findings suggest that within Condition, there were significant differences in swing and stance times, and step length between steps one and two.
# Siamese Tracking with Adaptive Template-Updating Strategy
###### Abstract  
Recently, we combined a contour-detection network and a fully convolutional Siamese tracking network to initialize a new start-up of vehicle tracking by clicking on the target, generating a contour proposal template instead of using a fixed bounding box. Tests on the OTB100 and Defense Advanced Research Projects Agency (DARPA) datasets proved that our method outperformed the state of the art and effectively solved the partial-occlusion problem. However, the current Siamese tracking method uses the target in the first frame as a template during the whole tracking period, and leads to the failed tracking of target deformation. In this paper, we propose a new template-update method and reconstruct the whole tracking process with a template-updating module. To be specific, the innovative adaptive template-updating module is comprised of a neural contour-detection network and a target-detection network. Experiment results on the DARPA dataset prove that our new tracking algorithm with the template-updating strategy prominently improved tracking accuracy regarding the deformation condition.  
Keywords:object tracking; Siamese network; template-updating; deep learning +
Footnote †: journal: Applied
# Time Management in Wireless Sensor Networks for Industrial Process Control
## 2 Materials and Methods  
This section describes the hardware, software, and protocol-level elements used to implement and validate a custom time management algorithm for industrial wireless sensor networks (IWSNs). The system architecture is based on COTS STM32 microcontrollers from STMicroelectronics, Geneva, Switzerland and AT86 RF212B transceivers from Microchip, Chandler, AZ, USA, configured to operate in IEEE 802.15.4 TSCH mode. A slot frame structure with 10-millisecond timeslots was designed, allowing synchronized communication between wireless nodes using enhanced beacon frames for time alignment. Hardware-level time tracking was implemented using external 32.768 kHz crystals with \(\pm\)20-ppm tolerance. Development and debugging were conducted in STM32 CubeIDE, using CMSIS-compliant C code and a custom hardware abstraction layer (HAL) for micro-controller and transceiver control. System validation was performed with a logic analyzer, the Saleae Logic 8 from Saleae, San Francisco, CA, USA, to measure synchronization drift, confirming sub-millisecond accuracy. The hardware components and the development boards were acquired from Farnell and shipped from within the EU. The logic analyzer was acquired directly from the manufacturer's website and shipped from the US.