esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Effect of Direct Immersion on SPME Extraction in Solvent  
This research reported a novel comprehensive DI-SPME method for the extraction of lipids such as fatty acids and hydrocarbon waves from the cuticular surface and the whole body of insects (Figures 1 and 2). Since fiber coating was reported as a vital factor in the development of an appropriate SPME method [22; 23; 24], a three phase combination DVB/CAR/PDMS fiber was used in this study to extract the lipid compounds from the samples. The selection was based on previous research [25; 26] and also because the fiber coating of DVB/CAR/PDMS covers wide range of polarities from non-polar to polar compounds, which enables it to extract a wide range of compounds; as such, the extraction was strongly affected by the polarity of the SPME [24]. The lipid profile of _T. castaneum_ showed the most GC peaks of the hydrocarbon wax components in the low boiling point waxes (LBWs) range. In contrast, for _R. dominica_, a limited level of hydrocarbon wax peaks were seen in the same zone; however, most of the hydrocarbon wax peaks were shown in the region of high boiling point waxes (HBWs) (>300 \({}^{\circ}\)C of the GC-MS oven program). [PERSON], 1978 [27] reported that various classes of n-alkanes and branched alkanes in _T. castaneum_ were in the region of C25-C32 using conventional solvent method for lipid extraction, followed by an analysis with GC-MS. However, a similar result was simply achieved using DI-SPME fibers, e.g., alkanes and branched alkanes ranging between C25 to C32 from _T. castaneum_ and C26-C34 from _R. dominica_. The DI-SPME method developed from this research preserved all the extracted insect lipids, as the extraction procedure was conducted at room temperature without the application of external heat. Since the method was direct immersion in the extract solution, there was no need to reduce the extract volume for pre-concentration, which significantly reduced the loss of some volatiles during vaporization. Moreover, an important innovation is that the DI-SPME enabled a lipid analysis without introducing additional reactions and chemicals for derivatizing lipids. This significantly led to reduced time consumption, cost, loss of lipids, and soaping. Soaping is a problem if the reaction is not carried out in very restrict conditions [28]. The DI-SPME allowed for the conduction of longer periods such as a 14 h extraction at room temperature, which resulted in absorbing most of the analytes from the sample matrixes on the SPME fiber coating. However, a previous study used SPME to extract the lipids in the headspace of _Bagrada hilaris_ at high extraction temperatures of 130 and 150 \({}^{\circ}\)C [19], which significantly affected the distribution constant of the volatiles between the headspace and the matrix [29] and can also lead to the degradation of the long chains to small chains compounds. [PERSON] et al., 2002 [18], used SPME to directly rub the insect cuticle layer, but rubbing cannot extract all the compounds in insect bodies.
## Appendix A Specific Measures of the Radar Data Quality
### Constant Quality Index  
The quality factor in this case is the radar's position itself, its hardware, and settings. Measurements of reliable and properly set radars are evaluated as good quality (\(qi_{9,i}=1.0\)), while the data of less reliable or less precise radar sites are labelled with lower QI values. This quality index is also useful when other quality indices are not computed (due to missing data, etc.). In this case, the missing quality index (indices) can be replaced with a constant number.  
The definition of constant QI was motivated by the experience where the radars of a given composite are of a different overall quality; for instance, some are more influenced by wi-fi signals than others, or radars from a given country are set to generally lower temporal resolution than those from the neighboring country. Constant QI is, therefore, aimed at expressing the degree of the relative quality of the individual radars contributing to a composite, and is often assessed on the basis of subjective experience.
###### Abstract  
The measurement of the cesium (Cs) \(5p^{6}7d\)\({}^{2}\)\(D_{5/2}\) excited state's hyperfine splitting intervals and hyperfine interaction constants was experimentally investigated using a ladder-type (852 nm + 698 nm) three-level Cs system (\(5p^{6}6s\)\({}^{2}\)S\({}_{1/2}\)-\(5p^{6}6p\)\({}^{2}\)\(P_{3/2}\)-\(5p^{6}7d\)\({}^{2}\)D\({}_{5/2}\)) with a room-temperature Cs atomic vapor cell. By scanning the 698 nm coupling laser's frequency, the Doppler-free high-resolution electromagnetically-induced transparency (EIT)-assisted double-resonance optical pumping (DROP) spectra were demonstrated via transmission enhancement of the locked 852 nm probe laser. The EIT-assisted DROP spectra were employed to study the hyperfine splitting intervals for the Cs \(5p^{6}7d\)\({}^{2}\)\(D_{5/2}\) excited state with a room-temperature Cs atomic vapor cell, and the radio-frequency modulation sideband of a waveguide-type electro-optic phase modulator (EOPM) was introduced for frequency calibration to improve the accuracy of frequency interval measurement. The existence of EIT makes the DROP spectral linewidth much narrower, and it is very helpful to significantly improve the spectroscopic resolution. Benefiting from the higher signal-to-noise ratio (SNR) and much better resolution of the EIT-assisted DROP spectra, the hyperfine splitting intervals between the hyperfine folds of (\(F^{\prime\prime}=6\)), (\(F^{\prime\prime}=5\)), and (\(F^{\prime\prime}=4\)) of the Cs \(5p^{6}7d\)\({}^{2}\)D\({}_{5/2}\) state (\(\text{HFS}_{6^{-}5^{-}}=-10.60(17)\) MHz and \(\text{HFS}_{5^{-}4^{-}}=-8.54(15)\) MHz) were measured and, therefore, the magnetic dipole hyperfine interaction constant (\(A\)= -1.70(03) MHz) and the electrical quadrupole hyperfine interaction constant (\(B\)= -0.77(58) MHz) were derived for the Cs \(5p^{6}7d\)\({}^{2}\)\(D_{5/2}\) state. These constants constitute an important reference value for an improvement of the precise measurement and determination of basic physical constants.  
Keywords:hyperfine splitting interval; hyperfine interaction constant; double-resonance optical pumping (DROP); electromagnetically-induced transparency (EIT); signal-to-noise ratio (SNR) +
Footnote †: journal: Applied Sci. 2020, 10, 8178; doi:10.3390/app10228178
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 2 Overview of V2X Protocols
### IEEE 802.11p  
The IEEE 802.11p protocol is an evolution of the IEEE 802.11 protocol [19]. It supports traffic shifting in vehicular networks because the MAC sub-layer is based on the prioritization and contention established in the Enhanced Distributed Channel Access (EDCA) scheme and the multichannel operation of the wireless access in vehicular environments (WAVE) system. Within the context of IEEE 802.11p, WAVE refers to the vehicular communication system that utilizes the 802.11p amendment to enable wireless communication between vehicles and traffic infrastructure. This technology is particularly relevant for road safety applications, such as collision alerts, traffic management, and communication among autonomous vehicles.  
The 802.11p protocol supports speeds of up to 200 km/h, response times of around 100 ms, and a communication range of up to 1000 m. The physical layer (PHY) of 802.11p is based on orthogonal frequency division multiplexing (OFDM). The Modulation and Coding Schemes (MCSs) of the 802.11p standard are described in [20].  
The main difference between IEEE 802.11p, compared with the IEEE 802.11a protocol, is that the carrier spacing and bandwidth are reduced by a factor of two, resulting in a symbol duration that is twice as long [21]. Moreover, the cyclic prefix (CP) duration is also doubled, which makes it possible to compensate for longer delays and makes it more suitable for outdoor environments. However, the throughput and end-to-end delay degrade rapidly as the data rate increases, which is why the protocol is only suitable for transferring low bit-rate data streams in vehicular environments and cannot be used for V2X applications where reliability is required and throughput is high.
###### Abstract  
In Multi-Line Transmission (MLT), high frame-rate ultrasound imaging is achieved by the simultaneous transmission of multiple focused beams along different directions, which unfortunately generates unwanted artifacts in the image due to inter-beam crosstalk. The Filtered-Delay Multiply and Sum (F-DMAS) beamformer, a non-linear spatial-coherence (SC)-based algorithm, was demonstrated to successfully reduce such artifacts, improving the spatial resolution at the same time. In this paper, we aim to provide further insights on the working principle and performance of F-DMAS beamforming in MLT imaging. First, we carry out an analytical study to analyze the behavior and trend of backscattered signals SC in MLT images, when the number of simultaneously transmitted beams and/or their angular spacing change. We then reconsider the F-DMAS algorithm proposing the \"short-lag F-DMAS\" formulation, in order to limit the maximum lag of signals used for the SC computation on which the beamformer is based. Therefore, we investigate in simulations how the performance of short-lag F-DMAS varies along with the maximum lag in the different MLT configurations considered. Finally, we establish a relation between the obtained results and the signals SC trend.  
filtered-delay multiply and sum beamforming; multi-line transmission; spatial coherence; ultrasound imaging +
Footnote †: journal: Applied Sciences
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 3 Methodology
### Analysis of GDP Temporal and Spatial Variation Trends  
For the changes in continuous data, the direction and trend in the data are often brought to people's attention. For the dynamic changes in GDP in different years, the Sen trend estimation method [34] is used, and the MK trend test method [35] is used for significance test. The combination of the two methods can improve the accuracy of spatial pixel trends to a certain extent and reduce noise interference [36]. This method does not require the time series to meet most of the assumptions of normal distribution and sequence autocorrelation, and is insensitive to outliers in the time series [37]. It is widely used in scientific research.  
Theil-Sen trend estimation method is also known as the Sen slope, which is a robust non-parametric statistical method. By calculating the slope between two pairs of data in the sequence, the median of the slopes of all data pairs is taken as the overall trend in the time series.  
significance. It can more effectively investigate the trend in change and test the significance of long time-series data. The calculation formula is as follows:  
\[Z=\begin{cases}\frac{S-1}{\sqrt{\text{var}(S)}},\ S>1\\ \begin{array}{c}0,\ S=0\\ \frac{S+1}{\sqrt{\text{var}(S)}},\ S<1\end{array}\end{cases} \tag{6}\]  
\[S=\sum\limits_{i=1}^{n-1}\sum\limits_{j=i+1}^{n}\text{sgn}(x_{j}-x_{i}) \tag{7}\]  
\[\text{sgn}(x_{j}-x_{i})=\begin{cases}1,\ x_{j}-x_{i}>0\\ 0,\ x_{j}-x_{i}=0\\ -1,\ x_{j}-x_{i}<0\end{cases} \tag{8}\]  
\[\text{var}(S)=\frac{n(n-1)(2n+5)-\sum\limits_{i=1}^{m}t_{i}(t_{i}-1)(2t_{i}+5) }{18} \tag{9}\]  
In the formula, \(Z\) is the standard normal test statistic; \(S\) is the test statistic; \(var\) is the variance function; \(n\) is the number of data in the time series; \(sgn\) represents the sign function; \(x_{j}\) and \(x_{i}\) are the time-series data of the \(j\) year and the \(i\) year, respectively; \(m\) is the number of data groups that appear repeatedly in the sequence; and \(t_{i}\) is the number of repeated data in the \(i\) repeated data group. In this study, a bilateral trend test is used. Under a given significance level \(\alpha\), the critical value \(Z_{1-\alpha/2}\) is found in the normal distribution table. When \(|Z|\leq Z_{1-\alpha/2}\), the null hypothesis is accepted; that is, the trend is not significant. When \(|Z|>Z_{1-\alpha/2}\), the null hypothesis is rejected; that is, the trend is considered significant. In this study, the significance level \(\alpha=0.05\), then the critical value \(Z_{1-\alpha/2}=\pm 1.96\); when the absolute value of \(Z\) is greater than 1.65, 1.96 and 2.58, it means that the trend has passed the significance test with a confidence level of 90%, 95%, and 99%, respectively.
# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 4 Results & Discuss
### Results  
As described in the previous section, Figure 3 can indicate the training process of the wavelet predictor after normalizing the data.  
Therefore, the experimental results can be summarized in that the X-axis means the training iteration, and the Y-axis describes the error between the predicted and actual voltage. Since the wavelet network adaptively configures the appropriate number of hidden layers, it reaches a satisfactory accuracy of prediction at the 470 th iteration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\Delta d\)\(\mathcal{P}\)/\(d\)U & \(\mathcal{\Delta}\)**NB** & **NS** & **Z** & **PS** & **PB** \\ \hline NB & PB & PS & NS & NB & NB \\ NS & PB & PS & NS & NS & NB \\ Z & PB & PS & Z & NS & NB \\ PS & PB & PS & Z & Z & NB \\ PB & PB & PS & PS & Z & NB \\ \hline \end{tabular}
\end{table}
Table 2: Fuzzy Rules for IFL Controller.  
Figure 2: The membership functions of the IFL controller (e.g., NB refers to red triangular): (**a**) \(dP\)/\(d\)U membership function; (**b**) \(\Delta dP\)/\(d\)U membership function; (**c**) \(\Delta U\) membership function.  
The overlap between the predicted and actual values also proves that the wavelet predictor has the ability to predict MPP as long as the network is trained. In addition, the repeated training can, in general, demonstrate that the average error is around \(-3\) dB and the prediction success rate reaches 85-90%.  
Furthermore, the results of the proposed W-IFL are described in Figure 4, including the error curve (black) of the predictor and the output power (red) of the controller. Precisely, the error of the wavelet predictor is around 1 dB, and the output power of the IFL controller stabilizes at 260.9 W.
## 3 Spatial Enhanced Detection Algorithm
### Energy-Weighted Local Uncertainty Measure
These data are utilized to generate an uncertainty distribution map, which illustrates the strength of the signal consistency at each point, providing a foundation for subsequent filtering and target-confirmation processes. (4). Gaussian template matching filtering: A \(3\times 3\) Gaussian template matching filter is applied within the three-layer nested window. This step enhances the contrast between the target signal and the background by performing local weighting and smoothing on the signal, thereby rendering the signal characteristics of the target area more pronounced. The Gaussian template utilized is given by \([1/16,2/16,1/16;2/16,4/16,2/16;1/16,2/16,1/16]\), where each coefficient's denominator is a power of two. This configuration of the Gaussian template facilitates effective matched filtering while reducing computational overhead on the hardware. (5). Generation of the energy-weighted uncertainty map: The local energy-weighting factor is calculated based on the filtered data to produce the energy-weighted uncertainty map. This map reflects the likelihood of the presence of targets in various regions after signal enhancement, providing a quantitative basis for the final target detection and localization. (6). Threshold segmentation and target detection: Finally, threshold segmentation techniques are employed to process the energy-weighted uncertainty map, allowing for the separation of high-probability target areas. Through this stage of processing, the algorithm is capable of accurately identifying and localizing targets within the image.
## 2 Unit Cell Design Configurations
### Corner-Modified Square Loop (MJCL) FSS  
The CMSL-FSS is depicted in Figure 1b. Primarily, a conventional square loop is selected. Next, the square loop is split up into four segments. Then, rectangular strips are etched from each segment at the corners. In addition, each segment is truncated at the corner to obtain a split-ring resonator structure. Further, each split-ring resonator is joined together to form a truncated square loop element to obtain a miniaturized unit cell, as shown in Figure 2. Thus, the CMSL notches in the X-band. This illustrates better angular stability over the conventional square loop when incorporated with the MJCL-FSS in the co-planner arrangement.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 4 Proposed Image Compression and Encryption Algorithm  
Figure 5 illustrates the process of image compression and encryption by the proposed method. The whole process of compression and encryption was divided into three stages: At the first stage, compression and encryption were performed via lifting IWT and the SPIHT algorithm. In the second stage, compression of SPIHT output along with permutations and substitution operations was performed via a modified Kd-tree and a wavelet tree. Further compression was achieved through Huffman coding. Finally, at the third stage, encryption was performed on the output of the second stage by varying parameters in the logistic map and modified quadratic chaotic maps.
## 3 Results
### Fully Distributed Detection and Localization of Short Impacts on Pipes
Figure 4: Data evaluation for tracking of propagating acoustic waves using a fully distributed sensor configuration. Excitation took place on the front face (position A). (**a**) Temporal excerpt of the acquired raw data with marked windows of the sections represented in (**b,c**); (**b,c**) data filtered by differentiation using a temporal shift of 2 and 5, respectively, for improved edge detection. Automatically detected signal onset (white dots) as well as fitted (white solid line) and manually plotted (white dashed line) slopes used for determining the velocities are overlaid. Figure 5: Excerpts of data acquired when hitting the pipe in an arbitrary position on the side (position B) using a fully distributed sensor configuration. The upper row depicts raw data for different optical pulse lengths (100 ns, 50 ns, and 30 ns), while corresponding filtered data is presented in the lower row after differentiation with temporal shifts of 2 (100 ns) and 3 (50 ns, 30 ns). The fiber-optically identified position of impact in the filtered data with associated localization precision is represented by white bars. White dots depict automatically detected signal onsets and white lines show fitted (solid lines) and manually plotted (dashed lines) edges, respectively. However, signal onsets, which are the important signal feature for localization of the impacts, are still observable. For 100 ns pulse length, the onset may be determined automatically as in the case of impact in position A described above. Linear fitting results in a propagation velocity of (\(4620\pm 520\)) m/s in agreement with the above value within the scope of the measurement uncertainties. The observed greater uncertainty when hitting the side of the pipe may be a result of less effective excitation of the present acoustic modes than for excitation on the front face. For shorter pulse lengths (50 ns, 30 ns) and impacts in position B the signal-to-noise ratio is not sufficient for automatic edge detection and derivation of velocities. For these cases, acoustic propagation velocities are estimated manually from visual recognition of the edges. The derived values are approximately 5100 m/s for the main mode and 3300 m/s for the second mode using 50 ns pulses and 5100 m/s for the main mode using 30 ns pulses. All of these are in good agreement with the ones derived for position A.  
Localization of the source is readily obtained in all cases, as indicated in Figure 5, and localization precision is directly given by the spatial resolution of the measurement system. We find 8 m, 5 m, 3 m, for 100 ns, 50 ns, and 30 ns respectively.
## 4 Experimental Methods and Results
### Experimental Methods and Results of the Proposed Framework  
The proposed framework was used for the closing price of stock market prediction. This framework contained many processes in both the feature engineering and prediction model part. Therefore, this sub-section was used to verify the efficacy of the proposed model in each process. A set of sensitivity experiments was established using various combinations of the EMD, LSTM, PCA and financial news components validating the proposed model from several perspectives. The experimental design can be seen in Table 4 and the output data for all experiments are the closing price of the stocks market in Thailand.  
* Experiment 1 was a comparison result between the EMD-LSTM model and other prediction models. The purpose of this experiment is to apply the models to the original closing price directly without using additional input features. The comparison results between the proposed model and other models evaluate whether the EMD-LSTM model effectively improves the outcomes of prediction over state-of-the-art models in stock price time series modeling.
* Experiment 2 is a comparison of the effects of adding principal components and technical indicators to EMD-LSTM. This experiment applies an additional input feature to the proposed model, which is the original technical indicator and the principal component of PCA. The experiment aims to show whether the principal component from PCA can improve the prediction of EMD-LSTM. The comparison results comparing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Model Performance Indicators**} \\ \cline{2-6}  & \multicolumn{4}{c}{**F1-Score**} & \multirow{2}{*}{**Accuracy (\%)**} \\ \cline{2-6}  & **Positive** & **Neutral** & **Negative** & **Average** \\ \hline Textblob & 0.43716 & 0.48763 & 0.28358 & 0.40279 & 42.67 \\ Vader & 0.51777 & 0.49402 & 0.47368 & 0.49515 & 49.67 \\ Original FinBERT & 0.76724 & 0.73256 & 0.83673 & 0.77884 & 78.00 \\ FinBERT with Thai news fine-tuning & 0.80198 & 0.81319 & 0.84259 & 0.81925 & 82.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results between the FinBERT with the Thai news model and other models for sentiment analysis.  
using the principal components as input features and using the original technical indicators as input features examines whether the model, when applying PCA effectively, improves the outcomes of prediction due to the curse of dimension.
* Experiment 3 is a comparison of the effects of adding news sentiment score to prediction models. This experiment applied an additional input feature from FinBERT to the proposed model. The experiments are evaluated to identify whether applying a news sentiment score improves model performance.