esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
Roads and highway networks have made the world more connected than ever before. In recent decades, increasing traffic volume has led to an undeniable demand for expanding and rehabilitating existing roads and streets. However, the road construction industry's environmental impacts cannot be ignored. Asphalt mix production has been on the rise in the United States (US) [5], and its production and application are associated with natural resource use, energy consumption, and air pollution. Reducing global greenhouse gas (GHG) emissions is crucial for preventing climate change. \"To stabilize atmospheric GHG concentrations at 400 ppmv CO\({}_{2}\)e (the carbon dioxide equivalent), industrialized countries must reduce emissions by 25-30% between 1990 and 2020 and by 85-90% between 1990 and 2050\" [6]. Consequently, researchers have been investigating new additives and technologies that are more friendly from an environmental perspective. Among these approaches, the application of waste materials as a modifier or extender of bitumen in asphalt mix has gained popularity. Waste materials such as lignin, crumb rubber, used oil, crushed concrete, waste plastic bottles, recycled glass, and steel slag have all been investigated [7]. Using waste materials, coming from municipal, urban, agricultural, or forestry sources, as a bitumen modifier is a promising approach that could significantly reduce the environmental impact of road construction. Furthermore, bio-additives have shown potential for reducing the environmental impact of asphalt production with enhanced asphalt performance. These include bio-oils derived from biomass; biopolymers, such as natural fibers and starch; and natural rubber obtained from the Hevea tree; as well as waves and nanomaterials, such as biochar and nanoclays [8]. However, due to the complex structure of bitumen and the reactions between modifiers or extenders, further research is needed for a better understanding of the characteristics of modified bitumen [9; 10]. In addition, lignin is known as the second most abundant biopolymer and naturally exists in lignocellulosic plants [11]. Technical lignin refers to a form of lignin obtained from the processing of biomass. The molecular weight and structure of technical lignins can vary significantly, which influences the possible applications of lignin [12]. There are different types of technical lignins, including lignocellulosiconates, Kraft lignin, soda lignin, hydrolysis lignin, and Organosolv lignin [13].
## 2 Materials and Methods
### Signal Processing Features
#### 2.1.2 Frequency-Domain Features  
The frequency-domain features explored in this work are summarized in Table 3. For the ratio of the spectral peak feature, different discrete wavelength transform (DFT) methods were trialed to generate this feature in order to find which of the DFT methods could most improve the performance of frequency-domain features. In this study, the effect of different time-frequency transform methods on a feature were also investigated. In total, 3 alternative methods (default Welch transform, FFT, and DCT) were implemented to calculate this feature. Of the 3 approaches, FFT performed best. Welch, which refers to the Welch's overlapped segment averaging estimator, is usually a good approach under many conditions. However, due to segmentation in preprocessing and the limit of data length, FFT performed better. The performance of these three approaches are reported in Section 3. For our study, the following features were applied to the gyroscope data only: energy in band 0.5 to 3 Hz, windowed energy in band 0.5 to 3 Hz, regression line for windowed energy.  
The frequency-domain features listed in Table 3 are derived using spectral analysis techniques such as the Fourier transform, [PERSON]'s method, and DCT. These features capture the periodic and harmonic properties of walking patterns. For instance, average power measures the energy distribution over the frequency spectrum, while the ratio of spectral peaks (RSP) reflects dominant frequency components. SNR and THD quantify the strength and purity of the signal. Energy in specific bands (e.g., 0.5-3 Hz) and windowed band energy reflect the frequency content of gait cycles. The spectral centroid and bandwidth describe how power is spread across frequencies, and regression analysis on windowed energy reveals trends in signal power over time. In the formulas, \(X(f)\) denotes the spectral representation of the signal, \(P(f)\) indicates power at frequency \(f\), and \(X_{i}(f)\) corresponds to the spectrum in the \(i\)th window.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature** & **Description** & **Formula** \\ \hline Standard Deviation (\(\sigma\)) & \begin{tabular}{l} Measure for signal spread, square \\ root of variance [53; 54] \\ \end{tabular} & \(\sigma=\sqrt{\frac{1}{N}\sum(x_{i}-\mu)^{2}}\) \\ \hline Root Mean Square (RMS) & \begin{tabular}{l} Quadratic mean, statistical \\ measure [54] \\ \end{tabular} & \(RMS=\sqrt{\frac{1}{N}\sum x_{i}^{2}}\) \\ \hline Entropy Rate (H) & \
\begin{tabular}{l} Measures signal uncertainty and \\ regularity [53; 54; 55; 56] \\ \end{tabular} & \(H=-\sum\limits_{i=1}^{N}P(f_{i})\log_{2}P(f_{i})\) \\ \hline Regression Line for Local Maxima &
\begin{tabular}{l} Regression line of local extrema in \\ signal sequence [54] \\ \end{tabular} & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
# HS-SPME Combined with GC-MS/O to Analyze the Flavor of Strong Aroma Baijiu _Daqu_
## 3 Results
### Quantitative Analysis of Aroma Components in Daqu by MHS-SPME  
The quantitative results of 21 compounds in _Daqu_ have been reported in a previous article [21]. Quantitative Results showed the data of the standard curve obtained by MHS-SPME: the correlation coefficient (R\({}^{2}\)) is between 0.9717 and 0.9983, and the fitting is good. The LOD of all compounds was less than 68.4 ppb, which showed high sensitivity. The RSD of the compounds was calculated using the results of three replicates. The RSD of each
# Illuminant Adaptive Wideband Image Synthesis Using Separated Base-Detail Layer Fusion Maps
## 3 Proposed method
### Base-Detail Layer Decomposition  
For image processing of separate layers for the acquired images, first the luminance component to be synthesized is separated from the two input images through LAB color space conversion. An image in the LAB color space has an advantage in that the color information and luminance information of the image can be independently processed because the color difference and luminance channels are separated. Next, a bilateral filter is applied to decompose the two images into a base layer and a detail layer. The bilateral filter preserves the strong boundary components and blurs the detail to obtain a base layer image. In addition, the detail layer is obtained from the difference between the resulting  
Figure 4: Implementation of broadband imaging module: (**a**) camera module, (**b**) IR flash on, (**c**) IR flash off.  
Figure 3: Image acquisition device operation flowchart.  
image and the luminance channel image [13]. The image decomposition using a bilateral filter is shown in Figure 5.  
\[L_{B}(i,j)=B[L(i,j)] \tag{4}\]  
\[L_{D}(i,j)=L(i,j)-B[L(i,j)] \tag{5}\]  
where \(L_{B}\) is a base layer image obtained by applying a bilateral filter to the LAB color space luminance image \(L\), and \(L_{D}\). is a sublayer image obtained from the luminance image and base layer image. The kernel size of the bilateral filter for base-detail decomposition acts as the most critical factor that distinguishes the base and detail in the image. The larger the kernel size, the bigger the details that are included in the detail layer. In this study, the (21, 21) kernel was applied for decomposition.
## 2 Materials and Methods
### SEM Characterization  
The resulting material was investigated by SEM using a Zeiss (Oberkochen, Germany) Merlin microscope equipped with a Schottky electron source, working with an acceleration voltage of 5 kV and an electron current of 150 pA at a short working distance (high-resolution mode). Secondary electrons (SE) were collected to provide fine details on the surface morphology, while backscattered electrons (BSE) were collected to detect differences in local composition. SE and BSE were gathered using an in-lens detector and a multiple-sector detector placed on the bottom of the SEM objective lens, respectively. SEM-Energy Dispersive Spectroscopy (EDS) measurements were performed with an Oxford silicon drift detector (SDD) (Oxford Instruments, Abingdon, UK) with a detection area of 60 mm\({}^{2}\), collecting EDS spectra from 6 diverse, rectangular-shaped cross-sectional areas placed at increasing depths in the sample, aiming at finally covering the entire 224 um-thick sample. In order to properly compare the results obtained, rectangular zones with the same surface area were scanned by the electron beam, and an EDS spectrum from each of them was collected keeping the same SEM working conditions: acceleration voltage (30 kV), electron probe current (1 nA), and collection real-time (5 min per spectrum).  
SEM images were subjected to image analysis (IA) using AutoCad(r) (Version 24.0) to identify pores. To account for their irregular shapes, different characteristic lengths were roughly estimated per each pore, and average pore size was eventually calculated.
## 5 Issues in Data Anomaly Classification
### Image-Resolution-Related Issue  
The conversion of time-series data into images can impair data anomaly classification because the information in the data is partially lost during the conversion process. The converted image, which was used as the input of the CNN model, could not contain the full information of the measured dynamic signal. The image size used in this study was only 100 \(\times\) 100 pixels, whereas the acceleration data contained 60,000 data points digitized with a 24-bit analog-to-digital converter (ADC). Indeed, lost information can lead to the misclassification of relevant data.  
One of the adverse effects of image conversion is the loss of information on the amplitude axis, similar to quantization error. A vertical length of 100 pixels, which is less than 2\({}^{7}\), is equivalent to applying a 6- or 7-bit ADC to the original acceleration signal.  
Figure 17: CNN-based classification results for _Trend-ambiguous_ data samples.  
Figure 18: CNN-based classification results for _Drift-ambiguous_ data samples.  
The resulting images after the low-bit ADC are often seen as data of the _Minor_ class, the main reason for which is the quantization phenomenon. Figure 19 shows examples of misclassification (i.e., data in the _Normal_ class were classified as _Minor_).  
Image conversion also causes information loss on the time axis, which behaves like resampling. In this dataset, one pixel corresponded to 600 data points. Thus, fewer than 600 missing data points could not be included in the converted image. For example, the images at the top of Figure 20 exhibit missing data, whereas the converted images shown at the bottom are classified as _Normal_.
## 5 Conclusions  
This study conducts a preliminary comparison of the Thomson algorithm, the adaptive sine multi-window power spectral density estimation algorithm, and the LPSD algorithm, identifying the LPSD algorithm, the LISA-LPSD algorithm, and the adaptive sine multi-window power spectral density estimation algorithm as the focal points of this research. Through a combination of simulation and experimental validation, an improved adaptive sine multi-window algorithm is proposed, dynamically adjusting based on low-frequency cone numbers, with performance assessed by mean square error and computational time. Experimental results indicate that the improved algorithm shows significant enhancement in mean square error metrics compared to both the original algorithm and the LISA-LPSD algorithm, achieving a maximum error reduction of up to 80% under ideal simulation conditions, while maintaining approximately 40% error reduction in actual complex noise scenarios. Additionally, the computational time is reduced to 65-70% of that required by traditional methods, demonstrating excellent real-time performance and robustness.  
It is noteworthy that the optimization strategy for low-frequency cone numbers has been confirmed to play a critical role in the amplitude spectrum analysis of non-stationary signals, particularly for steep sine noise. This provides a new technical pathway for high-precision noise spectrum estimation in the context of performance evaluation for space gravitational-wave-detection systems. However, further adaptation is still necessary in practical applications to address noise complexity and non-ideal environments. Future research could focus on refined modeling of noise characteristics, enhancing algorithm adaptability in extreme spectral aliasing scenarios, and integrating optimization with other time-frequency analysis methods to expand its universality in engineering practice.
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 2 Materials and Methods
### Internal Quality Attributes Assessment  
Destructive analysis of tangerines was performed at room temperature of approximately 23 \({}^{\circ}\)C. The tangerine samples were peeled after spectra collection. First, to measure the firmness of tangerines, the probe of the GY-4 fruit firmness tester (HANDPI, Wenzhou, China) was inserted into the pulp of eight different parts of each tangerine and the mean value is taken as the final firmness of the tangerine. Next, each tangerine was squeezed individually in an automatic squeezer. The juice was then filtered and the total soluble solid content (SSC), total acidity (TA), and acid-sugar ratio (A/S) were measured using a digital refractometer (Atago Model PAL-BX\(\,\)ACID F5, Tokyo, Japan). The determination of Vitamin C (VC) was conducted using the 2,6-dichlorophenol (DCPIP) titration method. This method relies on the oxidation-reduction titration of the acidic extract of the sample containing L(+)-ascorbic acid with a standard solution of 2,6-dichlorophenol. The reaction between ascorbic acid and DCPIP results in a color change, allowing for the quantitative determination of Vitamin C content (mg/100 g) [25]. The statistical information of the tangerine samples is listed in Table 1 and the histograms for the five quality parameters are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target** & **Min** & **Max** & **Mean** & **STD** & **Measurement Accuracy** \\ \hline SSC (\({}^{\circ}\)Brix) & 10.2 & 15.9 & 12.697 & 1.3615 & \(\pm\)0.2\% \\ TA (\%) & 0.42 & 1.28 & 0.8037 & 0.1866 & \(\pm\)0.1\% \\ A/S & 10.3 & 36.05 & 16.974 & 5.7591 & \(\pm\)0.3\% \\ Firmness (kg) & 0.2802 & 0.8304 & 0.4958 & 0.1003 & \(\pm\)1\% \\ VC (mg/100 g) & 425.5319 & 884.6154 & 608.9775 & 81.9140 & \(\pm\)2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistical information of the tangerine samples.  
Figure 3: The absorbance of 118 tangerines.
## 1 Introduction
The data showed that long-term rSS used in patients with chronic brain injury can improve tactile and sensorimotor functions, which, however, in some cases only developed after weeks of stimulation and continued to improve over the following months [32]. On the other hand, [PERSON] et al. using rSS in post-stroke patients found that it was well tolerated and appeared to offer additional benefits compared to usual care. At the same time, they emphasized that, apart from examining the effectiveness, further work is necessary to investigate the effect of different doses of rSS on the arm function and the mechanism by which rSS induces sensorimotor regeneration in the acute post-stroke period [33]. The tipstim(r) device (manufacturer BOSANA, Medizintechnik GmbH, Dorsten, Germany) (Figure 1) also stimulates the brain through the patient's fingertips [34; 35; 36; 37]. The therapy is completely painless, has no side effects, and is very easy to use. The tipstim(r) device has been designed for sensitive electrical stimulation of the fingertips and for tension regulation in the treatment of upper limb spasticity [38]. The area of medical applications tipstim(r) was intended for the treatment of patients with incomplete sensorimotor paresis (paralysis) after stroke or cranicerebral trauma, as well as for the treatment of CRPS (Complex Regional Pain Syndrome Type [38]). The literature presents studies conducted with the use of the tipstim(r) device on patients in the acute phase of the disease (up to two weeks after a stroke). The effectiveness of the glove was also assessed depending on the time of its use and the parameters used [32]. In our work, we tested the handgrip strength in a sitting position [39]. Similarly, we examined motor coordination in a sitting position [40]. However, we also decided to evaluate the results of handgrip strength and coordination in a stable lying position. There are reports of other possibilities of testing the handgrip strength and coordination of the movement of the distal part of the upper limb, emphasizing the effectiveness of the results obtained in the supine position [41]. In the search for the best ways to improve the coordination of distal upper limb movement and handgrip strength, the authors decided to analyze the impact of tipstim(r) interventions and whether the testing position has any relevance to the improvement of the studied variables. Therefore, the aim of this study was to investigate the effect of using the tipstim(r) device and the tested body position and the affected upper limb on selected parameters of motor coordination and grip strength in patients after ischemic stroke. Moreover, our study was also aimed at examining whether the magnitude of radial and medial sensation is of importance on the parameters of motor coordination and handgrip strength.
# Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic--[PERSON]
## 2 Materials and Methods
### Sample Preparation  
2.1 Balb/3T3 Mouse Fibroblast Cells Exposed to CoFe\({}_{2}\)O\({}_{4}\) NanoParticles (NPs) and Met5A Cells Exposed to Crocidolite Fibres  
The Balb/3T3 mouse fibroblasts and Met5A cells were seeded on 100 nm thick silicon nitride (Si\({}_{3}\)N\({}_{4}\)) membranes contained in a 6-well plate (Corning, Costar) at the density of \(10^{5}\) cells/well and covered with 3 mL of standard culture medium. After 24 h, the medium was replaced with a treatment medium containing 1000 uM of CoFe\({}_{2}\)O\({}_{4}\) NPs in the case of fibroblasts [43] or containing 5 ug/mL of crocidolite [33; 76] in the case of Met5A cells.  
CoFe\({}_{2}\)O\({}_{4}\) NPs were supplied by Colorobia S.p.A. (Vinci-Firenze, Italy) as a suspension in 100% diethylene glycol (DEG). The particles show a diameter of 36 nm in the stock suspension but clustering up to 600 nm in the culture cell medium [77]. Crocidolite Asbestos UICC Standard fibres (SPI#02704-AB) were supplied by SPI Supplies Division, Structure Probe, Inc. (West Chester, PA 19381-0656, USA). Dried fibres were suspended in sterile phosphate-buffered saline (PBS) at a concentration of 10 mg/mL. The fibres were sonicated for 1 h and dispersed by a 5-10 mL syringe with a 21-gauge needle [33].  
After 24 h, the treatment was removed and the samples were washed twice with PBS without calcium, magnesium, and sodium bicarbonate and fixed with 4% (\(v/v\)) of paraformaldehyde solution prepared in PBS buffer [43].
# An Experimental Strain-Based Study on the Working State of Husk Mortar Wallboards with Openings
## 4 Data Analysis
### MK Criterion  
To distinguish the stressing state jump of the structure via the \(F_{j}-E_{j,\text{norm}}\) curve, the MK (Mann-Kenddall) method is applied [24]. The function of \(F_{j}\) is monotonically increasing because the loading scheme is force controlled. The characteristic point defined by the MK method can be used to describe the structural stressing state.  
The sequence of \(E_{i}\) is statistically independent. Then, a statistical quantity \(d_{k}\) at the \(k\)th load can be defined as  
\[d_{k}=\sum_{i}^{k}m_{i}\ (2\leq k\leq n)\ \ \ \ m_{i}=\left\{\begin{array}{cc}+1&E( i)>E(j)\ (1\leq j\leq i)\\ 0&\text{otherwise}\end{array}\right. \tag{2}\]  
where \(m_{i}\) is the cumulative number of the samples and \(+1\) indicates adding one to the existing value if the inequality on the right side is satisfied for the \(j\)th comparison. Next, calculate the mean value \(E(d_{k})\) and \(Var(d_{k})\) of \(d_{k}\):  
\[E(d_{k})=k(k-1)/4\ \ \ (2\leq k\leq n)\] \[Var(d_{k})=k(k-1)(2k+5)/72\ \ \ (2\leq k\leq n) \tag{3}\]  
Figure 12: Crack load and failure load with different methods. Note: CL-O is the load corresponding to the first cracking, as based on observation; FL- O is the load corresponding to the formation of a complete cracking pattern, as based on observation; CL-I is the cracking load based on the strain analysis; and FL-I is the failure load based on the strain analysis.  
A new statistical quantity \(U\!F_{k}\) is defined by Equation (4), and the \(U\!F_{k}-F\) curve can be plotted.  
\[U\!F_{k}=\left\{\begin{array}{cc}0&k=1\\ d_{k}-E(d_{k})\sqrt{Var(d_{k})}&2\leq k\leq n\end{array}\right. \tag{4}\]  
For the inverse sequence of \(E_{i}\), the same steps are conducted to yield the \(U\!B_{k}-F\) curve. The intersection point of the \(U\!F_{k}-F\) and \(U\!B_{k}-F\) curves is the characteristic point of the structural stressing state. In this way, the failure point can be defined properly.  
For example, the characteristic point of specimen 1-3 was analyzed, as shown in Figure 13.  
The other six specimens were correspondingly analyzed with the MK method, and the loads at the MK point are recorded in Figure 14. When CL-O is considered as the standard value, then the load value derived from the MK method is closer than CL-I, as seen from Figure 14.
## 4 Energy Efficient Protocols, and Scheduling in WBSNs
### Energy Efficient Protocols in MAC Layer  
Power efficiency is the dominant factor in the design and implementation of protocols in WBSNs, see Table 3. MAC protocols in WSNs aim to reduce power consumption and delay, as well as to increase throughput. Looking for energy saving medium access techniques, researchers have established two main approaches; Asynchronous: Low Power Listening (LPL); or Synchronous: Scheduled Contention (SC) for TDMA allocation. Nonetheless, TDMA schemes have limitations as overhead, lack of flexibility, adaptability and scalability. LPL and SC are scalable, flexible and adaptive but they are energy inefficient in transceiver mechanisms and overhead cost due to scheduling, respectively. Several mechanisms to optimize energy consumption in MAC protocols have been developed for WBSNs; an energy-saving Distributed Queuing (DQBAN) MAC superframe is introduced in [40]. The proposed DQBAN eliminates collisions and back-off periods in data packet transmission and behaves as a random access mechanism for low traffic load and switches smoothly and automatically to a reservation scheme when traffic load grows. DQBAN uses a superframe for communication between sensors to BAN coordinator with Contention Access Period (CAP) for body sensor access requests and an access Contention Free Period (CFP), exclusively for free collision transmissions.