esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Composite Control Strategy of Quasi-Z-Source DC-DC Converter for Fuel Cell Vehicles
## 3 Component Parameters and Controller Design  
### Component Parameters Design  
The converter parameters are shown in Table 1. According to Equation (9), the inductor currents of \(L_{1}\) and \(L_{2}\) are consistent. The inductor \(L_{1}\) is taken as an example for calculation. The calculation Equation of inductor \(L_{1}\) can be obtained as shown in Equation (21).  
\[L_{1}=U_{L1}\frac{dt}{di_{L1}} \tag{21}\]  
where \(di_{L1}=\Delta I\mathrm{L}_{1}\), \(u_{L1}=u_{\mathrm{in}}+u_{\mathrm{C2}}\), \(dt=d\times T=d/f\) when the power switch \(Q\) is turned on. \(f\) is the switching frequency.  
According to Equations (1), (3), (5) and (21), the inductors \(L_{1}\) and \(L_{2}\) can be obtained as in Equation (22).  
\[L_{1}=L_{2}=\frac{d(1-d)u_{\mathrm{in}}}{(1-2d)r_{L1}f} \tag{22}\]  
where \(r\) is the ripple rate of the inductor current. \(r=\Delta I_{\mathrm{L1}}/I_{\mathrm{L1}}\).  
The calculation Equation of capacitor can be obtained as in Equation (23).  
\[C=i_{\mathrm{C}}\frac{dt}{du_{\mathrm{C}}} \tag{23}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Units** \\ \hline Rated Power \(P\) & 400 & W \\ Input voltage \(u_{\mathrm{in}}\) & 40\(\sim\)120 & V \\ Output voltage \(u_{\mathrm{O}}\) & 400 & V \\ Load resistance \(R\) & 400 & \(\Omega\) \\ Switching frequency \(f\) & 20 & kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Converter parameters table.  
where \(i_{\rm C}\) is the capacitor current and \(u_{\rm C}\) is the capacitor voltage. \(du_{\rm C}=\Delta u_{\rm C}\). \(\Delta u_{\rm C}\) is the capacitor ripple voltage.  
According to Equations (1)-(6) and (23), the calculation equations of capacitors are obtained as in Equation (24).  
\[\left\{\begin{array}{l}C_{1}=\frac{2d_{\rm O}}{(1-2d)\Delta u_{\rm C}l}\\ C_{2}=\frac{2d^{2}I_{\rm O}}{(1-2d)\Delta u_{\rm C}2f}\\ C_{3}=\frac{2d_{\rm O}}{\Delta u_{\rm C}l_{\rm O}}\\ C_{4}=\frac{4d_{\rm O}}{(1-2d)^{2}\Delta u_{\rm C}4f}\\ C_{5}=\frac{(1+d)I_{\rm O}}{\Delta u_{\rm C}5f}\end{array}\right. \tag{24}\]  
The inductor and capacitor parameters are finally obtained as shown in Table 2.
# Calibration of a Quartz Tuning Fork as a Sound Detector
## 2 Materials and Methods
### Methods  
The main goal of this investigation is to relate the QTF signal to sound pressure. For this, it is essential to know the sound pressure at the location of the QTF. This measuring task is carried out with the above-mentioned microphone. The first step, therefore, is to determine the exact sensitivity of the microphone used at the resonance frequency of the QTF.  
Figure 2: Electrical circuit of the homemade QTF transimpedance amplifier.  
In the next step, the QTF signal as a function of pressure was measured. To do so, medium and high sound levels were generated with the ultrasonic loudspeaker in front of an aperture diameter of 1 mm (see Figure 1). The microphone and the QTF were placed (one after the other) at a 2.00 mm distance from the aperture. The lateral distance to the aperture was controlled using a high-precision vernier caliper from Mauser Inox (Isony, Germany), with an accuracy of 0.05 mm. The microphone was positioned using an adjustable holder. The alignment was ensured by a marking on a mechanical guide. The sound wave hit the QTF approximately 2 mm below the tip, corresponding to approximately one-third of its total length, where the largest deflection is to be expected [15]. The microphone was placed with its most sensitive spot, the midpoint of its membrane, centered in front of the aperture opening. The setup was placed in a controlled environment with sound-absorbing walls to minimize external noise and environmental variability. The sensor signals were then recorded while the sound level was linearly increased. The measurements were averaged over several repetitions to ensure reliability and reduce random errors.  
Subsequently, the background noise levels of the QTF and microphone were measured using the setup described in Figure 1. The signal of the QTF and the microphone were measured without a loudspeaker signal using their respective preamplifiers and the LIA. The random ambient noise levels were then compared in terms of distributed deviations.  
In a final step, the acoustic detection limits of the microphone and the QTF were determined. To do so, the QTF sensitivity measurements were repeated at extremely low sound levels.
# A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
## 5 Simulation Results
### The Performance in Dynamic Channel with Doppler Spread 20 Hz  
The BER performances in CDT1 and CDT6 channels with Doppler spread 20 Hz are presented in Figures 10 and 11, respectively. The LS channel estimation method is simple to implement, but it is susceptible to the AWGN of the channel. The DFT channel estimation method just reduces the channel estimation noise in time-domain, but it cannot adapt to the time-varying channel. The DF and proposed Haar wavelet methods both use the previous one OFDM symbol's CFR as the current OFDM symbol's CFR. Hence, these two methods have large estimation errors when the channel is time variant. Therefore, under the condition of the Doppler spread equivalent to 20 Hz, the BER and NMSE performance are worse than that in static channel.  
As can be seen from Figure 10, different channel estimation methods have the same trend in terms of BER. With the increase of SNR, the simulation results show a trend of overall decreasing. The proposed Haar wavelet method has the lowest BER. The simulation results show that the proposed Haar wavelet method is significantly better than other conventional methods in the CDT1 channel with Doppler spread 20 Hz. At the BER of \(10^{-2}\), compared with the DFT, LS, and DF channel estimation methods, the proposed Haar wavelet method has about 0.45 dB, 0.7 dB, and 2.35 dB BER gains, respectively.  
As can be seen from Figure 11, the proposed Haar wavelet method has the best performance among the four channel estimation methods under CDT6 channel with Doppler spread 20 Hz. When the BER is \(2\times 10^{-2}\), compared with the DFT, LS, and DF channel estimation methods, the proposed Haar wavelet method has about 0.95 dB, 2.2 dB, and 3.5 dB gains, respectively. Hence, the proposed Haar wavelet method has the best performance among the four channel estimation methods.  
Figure 10: BER performance of QPSK modulated CP-OFDM and wavelet-OFDM systems under CDT1 channel with Doppler spread 20 Hz.
# Traits and Transports: The Effects of Personality on the Choice of Urban Transport Modes
## 6 Discussion
### Discussion on the Findings
#### 6.1.1 Personality Traits and Car Driving  
Car driving is positively related to Conscientiousness, and negatively related to Openness and Environmental personality. This indicates that more conservative people drive car often, since conservatism is primarily related to a low degree of Openness, but also to a high degree of Conscientiousness [12; 13; 17]. The car is probably perceived as a more time-efficient travel mode compared to public transport, and therefore preferred by people high on Conscientiousness. At least in Sweden, car drivers perceive the car as an efficient mode to carry out everyday activities [40].  
Our finding regarding Openness is consistent with a representative study of the Swedish population [23]. However, there are also contradicting findings in some respects. The previous study found car driving positively related to Extraversion and negatively related to Neuroticism, while the present study did not find such relations. The difference between the two Swedish studies may be that the previous sample is population-representative in contrast to the sample in this study. This indicates some limitations in generalizing our findings outside Gothenburg, or perhaps outside urban areas. We suggest replications that investigate the relation between the Big Five personality traits and car driving, both inside and outside Sweden, in and outside urban areas  
Our negative relation between Environmental personality and car driving is consistent with previous research, it shows that a low degree of Environmental personality increases the likelihood of choosing a mode with more carbon emissions [18].
## 3 Methodology and Mathematical Formulations
### Airflow Physics and Turbulence Modeling
#### 3.3.2 Species Transport Equations  
Humidity is the concentration of water vapor in the air depending on the temperature and pressure of the system of interest. The transport of moisture occurs via convection and binary diffusion in the air. The governing transport equation was:  
\[\
abla\cdot\left(\rho\overset{\rightarrow}{\mathbf{U}}Y_{i}\right)=\
abla\cdot \left(\rho D_{i,m}+1.428\mu_{t}\right)\
abla Y_{i}-D_{T,i}\frac{\
abla T}{T_{i} }+R_{i} \tag{6}\]  
where \(R_{i}\) is the net rate of production species \(i\), \(\overset{\rightarrow}{\underset{i}{\int}}\) is the diffusion flux of species \(i\).  
In this study, we considered the flow to be fully turbulent with interaction of the species. The air was set to the primary species, with moisture as the secondary species, coupled with turbulence interaction during air movement. The net rate of production of species and the diffusion flux of species can be written as:  
\[\overset{\rightarrow}{\underset{i}{\int}}_{i}=-(\rho D_{i,m}+1.428\mu_{t}) \
abla Y_{i}-D_{T,i}\frac{\
abla T}{T} \tag{7}\]  
\[\overset{\rightarrow}{H}=\
abla\cdot\left(\sum_{i=1}^{n}h_{i}\overset{ \rightarrow}{\underset{i}{\int}}_{i}\right) \tag{8}\]  
where \(D_{i,m}\) is the mass diffusion coefficient for species \(i\) in the mixture, \(Y_{i}\) is the mass fraction of species \(i\), \(D_{T,i}\) is the thermal diffusion coefficient, \(T\) is the temperature (K), and \(\mu_{t}\) is the turbulent viscosity.
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 3 Experiment and Results  
### Preparation of Experiments  
To conduct this algorithm verification experiment, we selected Visual Studio 2019 as the development environment and PCL 1.11.1 as the dependent library, as shown in Table 1. This experimental platform has the following hardware configurations: an Intel i5 processor, 32 GB of memory, and a Windows 11 operating system. We selected three samples from the point cloud of the National Alpine Sking Center of the Beijing Winter Olympics to verify the cableway segmentation and extraction algorithm proposed in this study. The basic conditions of the three sample point clouds are shown in Table 2.  
Three sets of ropeway sample data are segmented and extracted using the proposed method during the experiment. The original sample point cloud, the preprocessed point cloud, and the final extracted cableway point cloud are presented from both front and side angles for convenience of presentation. As well, the number of cableway point clouds extracted in this paper is counted, and the cableway point clouds are manually extracted with CloudCompare, then the two results are compared to determine the accuracy rate.
# Ensure we have enough candidates to encode bits_per_word  if len(refined_candidates) < 2'bits_per_word:  refined_candidates = candidates[:2'bits_per_word]
#### 3.2.2 Adaptive Embedding Algorithm  
The adaptive embedding algorithm dynamically adjusts its encoding strategy based on text characteristics and message properties. The algorithm employs the following methodology:  
1. Message preparation and optimization: - Convert the input message to binary representation - Segment the binary message into chunks of size (bits_per_word + additional_bits)
2. Adaptive bit allocation: - Analyze available synonym space for each token - Dynamically adjust bit allocation based on synonym availability - Apply binary mapping that optimizes for both security and capacity
3. Context-aware replacement strategy: - Evaluate both local context (surrounding words) and global context (document theme) - Select replacements that maintain natural language patterns- Preserve statistical properties to resist detection  
The algorithm's core process is described by the following pseudocode:  
Pseudocode for adaptive embedding algorithm function adaptive_embed(binary_message, container, secret_key, bits_ per_word, additional_bits): encoded_message = [] current_bit_index = 0  
for token, replacement_options in zip(container.split(), secret_key):  
Check if token can be replaced and there are bits left to encode if is_replaceable(token, replacement_options) and current_bit_index < len(binary_message):  
Dynamic bit allocation available_bits = log2(len(replacement_options [token])) bits_to_encode = min(available_bits, remaining_ bits(binary_message, current_bit_index))  
Extract message segment message_segment = binary_message[current_bit_ind-ex:current_bit_index + bits_to_encode]
# P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images
## 4 Performance Validation and Discussion
### Automatically Annotated QT Database Results  
The detection performance of the automatically annotated QT database obtained by the proposed P-wave detector is shown in Table 3. 9450 images were divided into 6300 images for training and 3150 images for testing. When comparing the output to the automatic annotation, the detection performance over the automatically annotated QT database obtained an average SE of 98.97%, a +P of 99.45%, a training accuracy of 99.7% and a test accuracy of 98.74%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Record** & **TP** & **FP** & **FN** & **SE (\%)** & **+P (\%)** \\ \hline  
& 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: P-wave detection performance over the automatically annotated QT database.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Record** & **TP** & **FP** & **FN** & **SE (\%)** & **+P (\%)** \\ \hline  
& 100 \\ \hline  & 31,511 & 153 & 334 & 98.97 & 99.45 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._Table 4 summarizes the performance comparison of the proposed P-wave detection algorithm over the automatically annotated QT database along with [45]. The performance comparison is in terms of the number of beats, methodology, SE, and accuracy. Using five-fold validation, the proposed algorithm achieved an average sensitivity of 98.4% and an accuracy of 98.74%. The proposed algorithm scored slightly higher performances on SE and accuracy overall when compared to [45]. It is clear that the proposed algorithm can handle ECG recordings with high performance over the 111,201 automatically annotated heartbeats.
# Cold-Adapted Fungi: Goldmine of Biomolecules Applicable in Industry
## 4 Biotechnological Application of Psychrophilic Fungi in Food and Agriculture  
Among extremophiles that thrive at extreme biological temperatures, thermophiles have traditionally been seen as the most promising source of biotechnological innovations. However, recent advancements involving cold-adapted organisms and their biomolecules have highlighted the immense potential of psychrophiles. To date, due to their unique properties and high practical value, cold-active enzymes have garnered the greatest interest.
## 8 Conclusions  
Connectivity is an essential enabler for future autonomous and remote-controlled ships. Since the ship is equipped with numerous sensors, actuators, and radio technologies, gateway devices and connectivity management are needed to efficiently use the resources and to ensure the safety and integrity of the operations. This paper provides a survey covering related terrestrial and satellite technologies and standardization aspects, and describes the connectivity architecture for global operations. The role of people in the development of future communication architecture in addition to autonomous systems is emphasized. Thus, connectivity development should take both people and machine requirements into account. With selected examples, we show how ship simulators can be used to enhance connectivity research, enabling the development of autonomous systems in a safe way while taking into account practical limitations of existing wireless networks. The use of ship simulators in connectivity research helps in better understanding the effects of imperfections in connectivity on the efficiency and operational safety of the ships. Finally, we defined research directions for the future including the need for secure communication to avoid outsiders from interfering with the communication, or even starting to control the ships.  
**Author Contributions:** Conceptualization, M.H.; methodology, M.H.; software, J.M.; validation, M.H. and J.M.; investigation, M.H.; writing--original draft preparation, M.H.; writing--review and editing, J.M.; visualization, M.H.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the VTT New Space Program.  
**Acknowledgments:** The authors would like to acknowledge discussions with and feedback from ABB Marine, Rolls-Royce, Kongsberg, Tieto, Nokia, Ericsson, and SyncTechno during the course of work. The authors would also like to acknowledge [PERSON] and [PERSON] for simulator development. The views of this paper, however, are views of the authors, and do not represent the views of the mentioned organizations.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### Far Infrared Emissivity and Anion Amounts of Elastic Warp-Knitted Fabrics  
Warp-knitted fabrics contain bamboo charcoal powders. When fabric is heated, bamboo charcoal releases far-infrared radiation with a wavelength of 8-14 \(\upmu\)m. In accordance with radiation laws of Kirchhoff and Wien's displacement, the far infrared wavelength coordinated with that released by the human body, thereby easily absorbed by the body and was thus effective to physical therapy and health care [19; 31].  
Figure 9 shows the far-infrared emissivity of three kinds of fabrics made by BC, PC and BPC ring-spun yarns. The far-infrared emissivity of fabrics made by BC yarns was slightly higher than that of PC yarns but slightly lower than that of BPC yarns. This finding can be explained by the fact that fiber assembles plied in a more compact manner had better far-infrared emissivity than those plied in a fluffier manner [32]. Combined by Figure 8, the BPC fabrics had more compact structure and higher far-infrared emissivity than the others. In addition, the front and rear faces of Fabric A had no significant difference because of the similar structure between two faces. The far-infrared emissivity of elastic warp-knitted fabrics reached \(>\)0.9 \(\varepsilon\), which was the level of far infrared health-care effects [33].  
Bamboo charcoal fibers were formed by the addition of bamboo charcoal powders during spinning. Bamboo charcoal had a graphite structure in which each carbon atom formed three covalent bonds with other carbon atoms and retained one free electron to transfer charge. Therefore, it had high  
Figure 9: Far-infrared emissivity of elastic warp-knitted fabrics (Fabrics A, B, and C) made by BC (**a**); PC (**b**) and BPC (**c**) ring-spun complex yarns.  
electronic conductivity and provided a strong electric field energy to ionize the polar groups of water molecules into anions (OH\({}^{-}\)) [34].  
Figure 10 shows the anion amounts of elastic warp-knitted fabrics with different compositions of complex yarns and feeding methods of weft yarns. The control group showed the anion amount in the detecting environment. Single-faced Fabric C made by BC complex yarns had the highest anion amounts, _i.e._, up to 380-420 counts/cm\({}^{3}\), which was at least 80 counts/cm\({}^{3}\) higher than that of control group. This finding was due to the fact that Fabric C had a more compact structure than others, and closer bamboo charcoal easily accumulated higher electric energy to separate the polar groups from water. The anion amounts of the resulting warp-knitted fabric reached the level of metropolitan park [35], which benefits the physical and spiritual health to humans.
## 2 Materials and Methods
### Physicochemical Analysis  
_Temperature determination._ Before and after pulsed-light treatments, the meat temperature was measured by an IR-thermometer (Testo, Barcelona, Spain). The sample temperature variances were calculated. The average of three determinations was retained for further data analyses.  
_Colour evaluation._ Meat colour measures using the coordinates L\({}^{*}\), a\({}^{*}\), and b\({}^{*}\) of the CIELAB colour system just before the package opening were performed with a colourimeter (Minolta CR-300, Chromometer, Osaka, Japan). A white tile was used to calibrate the instrument according to the manufacturer's instructions. The average value of the three, L\({}^{*}\), a\({}^{*}\), and b\({}^{*}\) measurements, was used for statistical analysis.  
_Volatile aldehydes._ The volatile aldehydes in the meat samples were analyzed by solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS) adapted from [25]. Briefly, about 5 g of meat sample was minced, weighted to a glass vial, and closed with an aluminium cap with a PTFE-septum. Samples were then conditioned at 35 \({}^{\circ}\)C for 15 min, and afterwards, a divinylbenzene/carboxen/polydimethylsiloxane (DVB/Carbouxen/PDMS) Stable Flex SPME fiber (50/30 \(\upmu\)m; 2-cm long) (Supelco, Bellefonte, PA, USA) was exposed to headspace for 30 min at 35 \({}^{\circ}\)C. The fibre was inserted in the injector of the Shimadzu GC-MS QP2010 Plus (Shimadzu, Kyoto, Japan), set at 250 \({}^{\circ}\)C, and kept for 30 min to complete the fibre desorption.  
The GC-MS had an SPB-5 capillary column (30 m \(\times\) 0.25 mm internal diameter \(\times\) 0.25 \(\upmu\)m film thickness, Supelco Inc., Bellefonte, PA, USA), and the initial oven temperature was set at 40 \({}^{\circ}\)C, held for 8 min, and then increased to 220 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and held for 20 min. Helium was used as the carrier gas at a 1 mL/min flow rate. The MS conditions were as follows: ion source temperature, 220 \({}^{\circ}\)C; interface temperature, 220 \({}^{\circ}\)C; ionization energy, 70 eV; scan, 35-500 atomic mass units. Before each analysis, the SPME fiber was conditioned for 30 min at 250 \({}^{\circ}\)C in the GC injector. The identification of the aldehydes was performed by comparison with the mass spectra of the NIST/EPA/NIH Mass Spectral Database (Version 2008), by comparing with commercial standards and using the linear retention index (LRI) that was calculated using the retention times of a  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Voltage/V** & **Distance/cm** \\ \hline Control & 0 & 0 \\
1 & 2414 & 2.6 \\
2 & 2000 & 3 \\
3 & 2828 & 3 \\
4 & 1828 & 4 \\
5 & 2414 & 4 \\
6 & 2414 & 4 \\
7 & 2414 & 4 \\
8 & 3000 & 4 \\
9 & 2000 & 5 \\
10 & 2828 & 5 \\
11 & 2414 & 5.4 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental data obtained for the optimization of voltage (V) and distance (cm) to the light source applied to poultry samples.  
homologous series of n-alkanes C5-C25. Aldehydes were expressed as a percentage of total volatile compounds in the chromatograms.