esa-sceva/satcom-chunk-collection huggingface dataset:
# The Volatile Compounds Change during Fermentation of _Saccharina japonica_ Seedling
## 5 Conclusions  
The new food material _S. japonica_ seedling was sensorily evaluated and found to have a strong fishy odor. Fermentation with _S. cerevisiae_ is effective in eliminating the fishy note and enhancing the fruity, sweet, green, and floral notes of _S. japonica_. After fermentation, the number of volatile compounds decreased from 43 to 26; compounds such as 1-octet-3-ol, 2-octet-1-ol, hexanal, 2-nonenal, and 1-octet-3-one disappeared, while 1-nonanal decreased from 47.4 to 11.6 \(\upmu\)g/g. On the contrary, some volatile compounds representing aroma increased after fermentation: \(\upbeta\)-ionone increased from 42.8 to 108.4 \(\upmu\)g/g, \(\upbeta\)-cyclocitral from 3.8 to 20.5 \(\upmu\)g/g, D-limonene from 0 to 8.3 \(\upmu\)g/g, dodecyl aldehyde from 2.3 to 4.1 \(\upmu\)g/g, and decyl aldehyde from 3.0 to 7.1 \(\upmu\)g/g. The OAV revealed 14 major odor compounds (OAV \(\geq\) 1), including 1-octet-3-ol, 1-nonanal, and trans-2,4-decadienal, which contribute to the strong fishy malodot; and \(\upbeta\)-ionone, \(\upbeta\)-cyclocitral, dodecyl aldehyde, and decyl aldehyde, which contribute to the floral and fruity aroma. After fermentation, the OAV of these fishy odorants decreases to 0, while the floral and fruity aroma increases. Fermentation with _S. cerevisiae_ is a practical method to enhance the flavor quality of the new food material _S. japonica_ seedling.  
J.G.: Conceptualization, data curation, formal analysis, investigation, methodology, validation, visualization, writing--original draft, writing--review and editing. H.N.: Conceptualization, methodology, project administration, writing--review and editing. X.W.: Data curation, investigation. Y.W.: Funding acquisition, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fujian Provincial Project for Promoting High-Quality Development of Marine and Fishery Industries, funding number: FJHYF-L-2023-3.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Related work
### Overview of IEEE 802.15.4g
#### 2.1.2 Sun-Oqpsk  
The OQPSK modulation was introduced in the first version of the IEEE 802.15.4 standard, in 2003, only for the 2.4 GHz band, and providing a bit rate of 250 kbps. In the IEEE 802.15.4g, other modes of use were defined, allowing the use of OQPSK modulation also in other frequency bands, and for bitrates varying from 6.25 to 500 kbps. The SUN-OQPSK employs DSSS (Direct Sequence Spread Spectrum), and different spreading factors may be used. The use of DSSS allows a better resistance to interference. For some bands, it is possible to use an alternative spreading mode, called MDSSS (Multiplexed DSSS).  
The PPDU of the SUN-OQPSK frame is composed by a SHR, a PHR, and the PSDU. The symbol rate is defined as the bit rate of the SHR. Other important parameters are defined in the PHR, such as the spreading mode and the rate mode. Up to four rate modes are supported for each frequency band, but only Rate Mode 0 is mandatory. The bit-to-chip mapping used for the SHR and PHR fields depends on the frequency band, and the mapping used for the payload depends on the rate mode. In Mode 3 of the sub-GHz bands, DSSS is not used for the payload, and it is possible to achieve a higher data rate. For the 2.4 GHz band, DSSS is used for the payload in all rate modes.  
The use of FEC is mandatory for the PHR field. When using DSSS as the spreading mode, FEC shall also be applied to the PSDU bits, with a 1/2-rate convolutional coding, with constraint length \(K=7\). For spreading mode set to MDSSS, FEC is optional, depending on the rate mode. Interleaving shall be employed in conjunction with FEC, to improve robustness against burst errors and to break the correlation of consecutive bits.
## 2 Background
### Patient Privacy within EPR  
As a result of these limitations, patient privacy within EPR systems is typically enforced through corrective mechanisms, such as two-factor authentication, training and confidentiality agreements [17]. Common approaches for detecting illegitimate access to EPRs therefore include: (i) restricting access control, (ii) applying patient-user matching algorithms, (iii) applying scenario-based rule extraction, and (iv) information gathering from EPR and non-EPR systems using a secure protocol. This is in addition to commonly used security mechanisms, such as secure networks with firewalls, encrypted devices and messages, strong user passwords, auditing and device timeouts.  
However, authorised users can access EPR data from virtually anywhere, which allows for increased productivity compared with paper-only records but increases security risks. Due to the risk of unauthorised use, access and disclosure of patient information, patient privacy and confidentiality concerns need to be addressed [18]. The patient privacy perspective is operationalised through using privacy concerns as the most common measure [11]. Leakage or modification of patient data can be intentional or unintentional. It also derives from both external attackers and internal staff [19]. The intrinsic value of stolen healthcare data on the black market is well-recognised [20]. Additionally,the healthcare sector mandates public disclosure of data breaches, increasing public awareness and concern over privacy. EPR-based privacy is clearly a major concern and, as such, is the motivation for the research presented in this paper.
# Integration of Freeplay-Induced Limit Cycles Based On a State Space Iterating Scheme
## 3 State Space Iterating (SSI) Scheme
### Preliminary Concepts
#### 3.1.2 Attractor and Basin of Attraction  
The attractor is a special set where mapping \(F\) shows the attracting features and is helpful for analyzing nonlinear behaviors. For a system with \(n_{x}\) states \(\mathbf{x}(t)\), [PERSON] defined an attractor as a set \(U_{\text{min}}\subset\mathbb{R}^{n_{x}}\) of the mapping \(F:\mathbf{x}_{n+1}=\)\(F(\mathbf{x}_{n})\) as follows (see Section 9.3 in [22]): (1) Any \(\mathbf{x}(t)\) that starts in \(U_{\text{min}}\) stays in \(U_{\text{min}}\) for all time; (2) there exists an open set \(U\) containing \(U_{\text{min}}\) such that if \(\mathbf{x}_{0}\in U\), then the distance from \(\mathbf{x}(t)\) to \(U\) tends toward zero as \(t\rightarrow\infty\); and (3) there is no proper subset of \(U_{\text{min}}\) that satisfies both (1) and (2).  
In summary, a set \(U_{\text{min}}\) is an attractor of a mapping \(F:\mathbf{x}_{n+1}=\)\(F(\mathbf{x}_{n})\) if  
\[\left\{\begin{array}{ll}1.&\forall\mathbf{x}_{0}\in U_{\text{min}}:\mathbf{ x}_{1}\in U_{\text{min}}\\ 2.&\exists U\supseteq U_{\text{min}}\text{ and }\forall\mathbf{x}_{0}\in U: \lim_{n\rightarrow\infty}F^{n}(\mathbf{x}_{0})=\mathbf{x}_{n}\in U_{\text{ min}}\\ 3.&\text{ there is no }U_{\text{sub}}\subset U_{\text{min}}\text{ satisfies }1. \text{ and }2.\end{array}\right. \tag{6}\]  
where \(U\) is called the \"basin of attraction\" for the attractor \(U_{\text{min}}\).  
An LCO is actually a special attractor that contains only one point, a fixed point \(\mathbf{x}_{L}\), which allows  
\[\mathbf{x}_{L}-F(\mathbf{x}_{L})=0 \tag{7}\]  
Note that the mapping \(F\) could be a Poincare map, Lorenz map, or any other carefully designed type of mapping which reflects the periodicity of the system when any LCO occurs.
## 4 Case Study Results
### The Core Elements of AeroLogic's Business Model
#### 4.4.4 AerLogic's Product  
AeroLogic's product is the airport-to-airport transportation of the joint venture partners' air cargo traffic. This is quite a different strategy to other all-cargo airlines, for example, Luxembourg-based Cargolux Airlines offers a range of branded products that are targeted at discrete air cargo market segments: 'alive', 'classic', 'jumbo', 'power', 'hazmat', 'pharma', 'fresh', 'precious','select', and'select' [170]. Japan-based Nippon Cargo Airlines (NCA) has pursued a similar strategy to Cargolux and also offers a range of branded products targeted at discrete air cargo market segments. These products are 'NCA Express', 'NCA Freight', 'NCA Super Sensitive', NCA Sensitive', 'NCA Space', 'NCA Cool', 'NCA Art', 'NCA Vehicle', and 'NCA Pharmacare' [171].
# Status of Pure Electric Vehicle Power Train Technology and Future Prospects
## 4 Electric Propulsion
### Electric Machines and Drives  
Electric machines are used for converting the energy from electrical to mechanical and vice versa. In electric vehicles, electric machines are used to provide power and torque to the transaxle for propulsion. The electric motor provides propulsive power in electric vehicles. The efficiency of energy conversion by electric machine is higher compared to internal combustion engine, in between 80-95% [70; 71]. An electric motor provides high torque and high-power density with better torque characteristics at lower speed and the instantaneous power rating with two or three times the rated power of the motor [64]. The electric machines process the power in the reverse direction when turning the electric motors as generators. The braking mode can be termed as regenerative braking.  
Electric vehicles have different electric machines and drives compared to electric machines and drives developed for industrial applications [72; 73]. The electric propulsion system is the heart of pure electric vehicles, where electric machines and drives are the core technology for pure electric vehicle power train system that converts the electrical energy to the desired mechanical linear motion. The most electric vehicle comes with single speed reducer and most transmission systems are kept optional to drive the wheels. The stationary part stator and rotating part rotor of the electric motor play an important role in the overall performance of the motor technology [42; 74].  
Figure 4: (a) Mahindra E20 with rear wheel drive with single motor using single speed transmission (b) all-wheel drive Nissan IMX.  
The choice of an electric vehicle motor depends on the conditions defined by the three variables as shown in Figure 5. From Figure 5, we can realize that the three variables are vehicle requirement, vehicle restriction, and power source [63]. The vehicle requirements are defined by a drive cycle schedule. The vehicle restriction includes the type of vehicle, weight of vehicle, payload, and battery weight. Considering the above variables, we can choose a motor that satisfies the performance requirements of the vehicle.  
Different electric motor exists. Two broad classes of electric machines for electric vehicles applications are: direct current (dc) and alternating current (ac) motors. The requirements for a motor to be used for an EV use include higher power and torque, variable range of speed e, higher efficiency, high reliability, and affordability. Direct current (DC) motor drives used to be earlier electric vehicles choice for the propulsion but inefficient unreliability made them less attractive [62] induction and permanent magnet (PM) types have become most favored ones with the advance development of their power electronic systems [75].
# Signal Detection for GSTFIM Systems with Carrier Frequency Offset +
## 3 Proposed MMSE Detectors for GSTFIM Systems
### SC-MMSE Detector  
The detail of the SC-MMSE detector is described as follows. For the \(l\)-th subcarrier, we find the index of the maximum value of the GSTSK symbol in the probability vector \(P(\mathbf{X}_{l}=\mathbf{f}_{i})\), which is denoted by \(p_{l}\). Repeating the same process for all the \(N\) subcarriers, the indices can be expressed as  
\[\mathbf{P}_{SC}=\{p_{1},p_{2},\ldots,p_{N}\}. \tag{22}\]  
Then, \(\mathbf{P}_{SC}\) is mapped to \(K\) blocks to reconstruct the GSTFIM symbol. For the \(k\)-th block subcarriers, the corresponding carrier indices are \(\{1+N_{b}(k-1),2+N_{b}(k-1),\ldots,N_{b}k\}\), where \(k=(1,2,\ldots,K)\). Thus, for the \(k\)-th block subcarriers, the indices \(\mathbf{P}_{SC}^{k}\) of the maximum value of the GSTSK symbol are expressed as  
\[\mathbf{P}_{SC}^{k}=\{p_{\{1+N_{b}(k-1)\}},P_{\{2+N_{b}(k-1)\}},\ldots,p_{N_{b }k}\}. \tag{23}\]  
Let \(\mathbf{I}^{k}\) be the corresponding probability set of \(\mathbf{P}_{SC}^{k}\) and \(\Delta_{k}\) be the indices of the maximum \(N_{a}\) values in \(\mathbf{I}^{k}\). That is, \(\Delta_{k}\) records the indices of the activated subcarriers in the \(k\)-th block.  
In general, the estimated GSTFIM symbol \(\hat{\mathbf{X}}^{k}=[\hat{\mathbf{X}}_{1}^{k},\hat{\mathbf{X}}_{2}^{k}, \ldots,\hat{\mathbf{X}}_{N_{b}}^{k}]^{T}\) in the \(k\)-th block can be reconstructed as follows. For \(\hat{\mathbf{X}}_{n_{b}}^{k}(n_{b}=1,\ldots,N_{b})\), if \(n_{b}\) belongs to set \(\Delta_{k}\), then \(\hat{\mathbf{X}}_{n_{b}}^{k}\) is equal to the GSTSK symbol. Moreover, this GSTSK symbol is obtained by the probability in \(\mathbf{I}^{k}\) corresponding to \(\Delta_{k}\). However, if \(n_{b}\) does not belong to set \(\Delta_{k}\), \(\hat{\mathbf{X}}_{n_{b}}^{k}\) is estimated to be an all-zero matrix. Traversing \(n_{b}\) from \(1\) to \(N_{b}\), we can obtain the estimated GSTFIM symbol \(\hat{\mathbf{X}}^{k}\).
# Using a Pair of Different-Sized Spheres to Calibrate Radar Data in the Microwave Anechoic Chamber
## 2 Materials and Methods
Figure 1: Environment of the anechoic chamber: (a) the spatial arrangement during the test, (b) a photo of the test. The difference between the RCS values of the two spheres was 9.5424 dB, which was calculated by  
\[\sigma_{\text{d}}=\sigma_{\text{a30}}-\sigma_{\text{a10}} \tag{8}\]  
We measured the radar data in three radar bands: the S-band (2-4 GHz), the X-band (8-12 GHz), and the Ku-band (14-18 GHz). The S-band had a radar bandwidth of 2 GHz, while the X-band and Ku-band both had radar bandwidth of 4 GHz. The number of sampling points was 1600. The range resolution was 0.075 m for the S-band and 0.0375 m for the X-band and Ku-band. We also measured the radar data at 12 angles ranging from 0\({}^{\circ}\) to 360\({}^{\circ}\) with a 30\({}^{\circ}\) interval in each radar band. The RCS value of any sphere in the Rayleigh region and Mie region is positively correlated with the radar frequency. When the spheres act as calibration objects for each other, the background radar data can be counterbalanced. The primary measurement error was caused by the equipment, including the network analyzer, the pair of antennas, and the cables. We measured for a considerable amount of time to collect the radar data of all of the spheres at each angle and in each radar band. Then, we calculated the mean RCS values of the spheres in the three radar bands. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Content** & \multicolumn{3}{c}{**Parameters**} \\ \hline
**Test Band** & **S** & **X** & **Ku** \\ \hline Frequency span (GHz) & 2–4 & 8–12 & 14–18 \\ Sampling point & 1601 & 1601 & 1601 \\ IF bandwidth (Hz) & 100,000 & 100,000 & 100,000 \\ Power level (dBm) & 12 & 12 & 12 \\ Time span (ns) & 70–85 & 70–85 & 70–85 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the network analyzer. Figure 2: Example from the background environment: (**a**) data of the background, (**b**) a photo of no object on the foam holder, and an image of a large sphere on the foam holder. The RCS value of a sphere depends on the scattering regions. We introduced the electric size, \(ka\), to calculate the RCS values of the spheres in the three scattering regions. The electric size, \(ka\), of a sphere is given by [6,9]  
\[ka=\frac{2\pi a}{\lambda} \tag{9}\]  
where \(\lambda\) is the radar wavelength and \(a\) is the radius of the sphere. In the optic region, when \(ka>10\), the sphere RCS calculated using Equation (2) becomes constant and does not change with the electric size. In the Mie region, when \(1<ka<10\), the scattering mechanism follows Mie scattering rules, which state that the RCS value of a sphere will be amplified if the frequency is resonant with the natural frequency of the sphere. We chose radii of 0.05 m and 0.15 m because of the rules in the scattering regions. The typical wavelength values of the S-band, X-band, and Ku-band are 10 cm, 3 cm, and 2 cm, respectively, and their radar data in different radar bands are in different scattering regions. The relationships are shown in Table 2.
## 2 Materials and Methods
### Experimental Design for Extraction Optimization  
A central composite rotatable design (CCRD) combining five-levels of the independent variables \(X_{1}\) (time, \(t\), 1-45 min), \(X_{2}\) (ultrasonic power, \(P\), 5-500 W) and \(X_{3}\) (ethanol concentration, EtOH, 0-100%, \(v\)/\(v\)) was implemented to optimize the extraction of flavonoids from kiwi peel using RSM (Supplementary Material Table S1). These variables and the respective range of values were selected based on previous optimization studies [11; 12]. Design-Expert software, Version 11 (Stat-Ease, Inc., Minneapolis, MN, USA) was used to generate the 20 experimental points of the CCRD design, which included eight factorial points, six axial or star points chosen to allow rotatability, and six replicated center points. The 20 runs were randomized to minimize the effects of unexpected variability.
# Less Is More: Influence of Cross-Linking Agent Concentration on PFOS Adsorption in Chitosan
## 4 Conclusions  
We could show that cross-linking chitosan with ECH negatively affects its ability to adsorb PFOS at a neutral pH value. This decrease is accompanied by the loss of membranes, as revealed by scanning electron microscopy images of the inner scaffold structure of the chitosan polymers. However, the modification of chitosan is necessary because there are some drawbacks to common chitosan in practical use, such as low mechanical strength and solubility in acidic mediums. Therefore, to obtain acid-stable chitosan, the lowest necessary concentrations of ECH should be used. Compared to other materials in the literature, the herein-presented chitosan showed one of the highest adsorption capacities for PFOS. In the scope of this study, the rapid small-scale column tests showed that milled activated carbon was the best adsorber material for the decontamination of a highly concentrated PFOS solution. On the contrary, the least capable adsorption material was milled chitosan. The unmilled chitosan and activated carbon columns showed similar results with regard to their clean-up potential. However, comparing the PFOS loading capacity of the adsorption materials (which is calculated based on the dry mass of the adsorber) revealed that in this experiment, chitosan has a much higher PFOS adsorption per dry mass than activated carbon. Future studies should, therefore, look at ways to utilize this advantage of chitosan by finding a process to remove the water content from the PFAS-loaded chi tosan polymer without releasing the contaminants, thereby making it the economically advantageous method.
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 3 Haze Removal Using PCA and Haze-Relevant Features
### Estimation of the Atmospheric Light Using PCA
In experiments, we differently estimated the atmospheric light using existing and proposed methods, and the transmission was obtained using [PERSON]'s method [1] as  
\[t(x)=1-\omega D(x), \tag{6}\]  
where  
\[D(x)=\min_{c\in\{R,\mathcal{G},\mathcal{B}\}}\min_{y\in\Omega(x)}g_{c}(y). \tag{7}\]  
\(t(x)\) represents the transmission; \(\omega\) is the positive constant parameter; \(D(x)\) is the dark channel at the local window of size \(n\times n\) whose center is located at \(x\); and \(\Omega\) is the the region of the local window. Since the transmission is estimated by inverting the normalized and weighted DCPs, it can be regarded that the DCP approximately represents the quantity of haze components [1]. Figure 3a shows the ground-truth of an atmospheric light and input haze image. Since [PERSON]'s method estimated darker atmospheric light than the ground-truth, the resulting image shows brightness saturation in the sky region, as shown in Figure 3b. [PERSON]'s method produces color distortion because the color of the estimated atmospheric light is different from that of the ground-truth [15]. On the other hand, the proposed method accurately estimated atmospheric light similarly to the ground-truth, and as a result, it can remove the haze without brightness saturation or color distortion.
## 4 Comparison between Two Models
### Acceleration Responses of the Sites
#### 4.1.2 Acceleration Amplification Factors along the Rock Height  
The variation in the peak acceleration along the rock height can be expressed using the acceleration amplification factor (AAF). It is defined as the ratio of the peak acceleration at the measurement points to the peak acceleration input from the shaking table [34].  
In order to evaluate the acceleration amplification effect of the rock models along the vertical direction, three measurement points were selected: AS0, AS3, and AS2. The AAF under different frequency excitations was compared, as shown in Figure 7.  
Overall, the acceleration increased gradually from bottom to top and reached the maximum at the ground surface. Under the transverse excitation, the HRM model exhibited an increasing acceleration amplification effect with frequency, and reached the maximum value of 4.1 at 23 Hz of the input case. The AAF of the JRM model showed a tendency of increasing and then decreasing, and reached the maximum value of 4.0 at 17 Hz of the input case.  
Under longitudinal excitation, the AAF of the HRM model reached the peak at 21 Hz of the input case, with a maximum value of 2.9. The AAF of the JRM model reached the peak at 19 Hz of the input case, with a maximum value of 2.8.  
The difference between the amplification effect of the JRM model and the HRM model was due to the greater effect of the joints on the transverse modulus of the JRM model, which resulted in the transverse fundamental frequency of the JRM model being much  
Figure 6: The acceleration time histories of rock at AS1 and AS2: (**a**) transverse excitation; (**b**) longitudinal excitation.  
smaller than that of the HRM model, causing the JRM model to reach the peak at 17 Hz of the input case. The smaller effect of the joints on the longitudinal modulus resulted in the longitudinal fundamental frequency of the JRM model being slightly smaller than that of the HRM model, causing the JRM model to reach the peak at 19 Hz of the input case. This was analyzed in Section 2.4 by applying the elastic modulus equation for a jointed rock mass, and was verified by the white noise transfer function in Section 4.2.1.  
In summary, the seismic responses of the rock models showed an obvious amplification effect, which was correlated with the excitation frequency. Meanwhile, the maximum acceleration amplification factors of the two models were similar, but the peak response frequency of the JRM model was lower than that of the HRM model, which is attributed to the reduction of the model stiffness due to the joints and discontinuity planes in the JRM model.