esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Physicochemical Properties
#### 4.1.3 Water Absorption  
One of the most important physical features in determining the aggregate's quality is its ability to absorb water. The amount of water a rock can absorb in one day is commonly called the water absorption [18]. It is used to adjust the weight of aggregates as a result of water collected in pore spaces. When it exceeds the acceptable limit, the material's characteristics may alter. The investigated samples' average absorption of water falls between 0.03 and 0.22, shown in Figure 11 and Table 3. The weight of the rock specimen in the water and its oven-dried weight measured in the laboratory were the two fundamental prerequisites for figuring out how much water it absorbs. The specimens' absorption capacity was calculated using Formula (3). The maximum amount of water that rock or aggregate can absorb before being used in construction is 2%. Since the water absorption values for these samples are well below this threshold, the rocks are suitable for use as construction aggregate. \[\textit{Percentage Absorption}=\frac{\textit{Absorption}}{w_{3}\times 100} \tag{3}\]  
where  
_Absorption = \(w_{1}-w_{3}\)._  
_w\({}_{1}\)_= weight in the air._  
_w\({}_{3}\)_= oven-dried weight. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **Cube** & **Weight in** & **Weight in** & **Oven-Dried** & **Porosity** \\ \cline{2-6}
**Number** & **Sample** & **Air (\(W_{1}\))** & **Water (\(W_{2}\))** & **Weight (\(W_{3}\))** & **Porosity** \\ \hline Pi1 & Pi1 (a) & 370.26 & 232.27 & 368.75 & 1.323 \\ \hline \multirow{4}{*}{Pi2} & Pi2 (a) & 905.80 & 569.24 & 903.95 & 1.000 \\ \cline{2-6}  & Pi2 (b) & 961.49 & 604.15 & 960.21 & 0.719 \\ \cline{2-6}  & Pi2 (c) & 931.97 & 585.99 & 929.89 & 0.891 \\ \hline \multirow{4}{*}{Pi3} & Pi3 (a) & 942.35 & 592.35 & 941.24 & 0.553 \\ \cline{2-6}  & Pi3 (b) & 959.33 & 603.47 & 957.89 & 0.542 \\ \cline{2-6}  & Pi3 (c) & 963.07 & 605.55 & 961.88 & 0.472 \\ \hline \multirow{4}{*}{Pi4} & Pi4 (a) & 961.24 & 605.09 & 960.67 & 0.278 \\ \cline{2-6}  & Pi4 (b) & 961.92 & 604.79 & 959.72 & 0.541 \\ \cline{2-6}  & Pi4 (c) & 944.91 & 593.34 & 943.13 & 0.758 \\ \hline \multirow{4}{*}{Pi5} & Pi5 (a) & 395.1 & 247.94 & 394.24 & 0.867 \\ \cline{2-6}  & Pi5 (b) & 940.53 & 590.23 & 938.52 & 0.888 \\ \cline{2-6}  & Pi5 (c) & 923.13 & 580.71 & 921.59 & 0.927 \\ \hline \multirow{4}{*}{Pi6} & Pi6 (a) & 939.93 & 585.34 & 938.19 & 0.929 \\ \cline{2-6}  & Pi6 (b) & 910.19 & 567.51 & 909.56 & 0.921 \\ \cline{1-1} \cline{2-6}  & Pi6 (c) & 923.15 & 593.15 & 922.78 & 0.549 \\ \hline \end{tabular}
\end{table} 
Table 2: Porosity of the studied samples. Figure 11: Water absorption test results for the Inzari Formation rock samples. This figure shows the water absorption capacity of the samples, measured as a percentage increase in weight after submersion. The data are essential for assessing the durability of the rocks, as higher water absorption can lead to weakening over time.
# Terahertz-Wave Absorption Gas Sensing for Dimethyl Sulfoxide
## 4 Conclusions  
THz-wave absorption spectroscopy for gas sensing of dimethyl sulfoxide (DMSO) was demonstrated using an electronic THz wave source and detector in a frequency range of 220-330 GHz at 297 K and 0.4 Torr (53 Pa). The measurements illustrate a distinctive DMSO absorption spectrum that exists as a series of repeating absorption bands. The current measurements agree with spectral simulations based on rotational constants derived in the prior work of [PERSON] et al. [26] and [PERSON] et al. [27]. The intensities of the rotational absorption lines are experimentally observed and reported for DMSO between 220-330 GHz. Measured line intensities for major absorption lines are in very good agreement with relative line intensities estimated by quantum mechanical calculations. Detection limits for DMSO in the current frequency range are estimated to be in the range of 10\({}^{14}\) to 10\({}^{15}\) molecules/cm\({}^{3}\) per meter pathlength. Improvements to the sensor detection limits could be made through additional time averaging of signals, through either slower scan rates or multi-spectra averaging or frequency modulation methods. Remote gas sensing for DMSO has relevance to industrial, chemical, and medical applications where certain DMSO use conditions may lead to possible human health and environmental hazards. The present work shows the potential for remote gas sensing using THz-wave electronics for volatile sulfur compounds and other polar industrial chemicals.  
Conceptualization, A.P., T.E.R., M.M.H., I.W. and M.A.O.; methodology, A.P., T.E.R. and M.W.M.; investigation, A.P., T.E.R., M.A.Z.C. and M.N.P.; writing--original draft preparation, A.P.; writing--review and editing, I.W., M.M.H. and M.A.O.; supervision, I.W., M.M.H. and M.A.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation, grant number CBET-185129.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Characterization of Encapsulated Pumpkin Oil in Carbohydrate Shells
#### 3.3.3 Oxidative Stability of Encapsulated Pumpkin Oil
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Compound**} & \multicolumn{6}{c}{**Encapsulated Pumpkin Oil in Carbohydrate Shell**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Maltodextrin**} & \multicolumn{6}{c}{**Wheat Starch**} & \multicolumn{3}{c}{**Lactose**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Surface Oil**} & \multicolumn{1}{c}{**Core Oil**} & \multirow{2}{*}{\%} & \multicolumn{1}{c}{**Surface Oil**} & \multicolumn{1}{c}{**Core Oil**} & \multirow{2}{*}{\%} & \multicolumn{1}{c}{**Core Oil**} & \multirow{2}{*}{\%} & \multirow{2}{*}{\%} \\  & **(mg/Oil)** & & & & & & & & \\ \hline \(\alpha\)-tocopherol & 3.42 (0.48) a & 3.46 (0.05) a & 70.3 & 3.31 (0.04) a & 3.34 (0.77) a & 68.1 & 2.10 (0.18) b & 3.69 (0.68) a & 81.2 \\ \(\gamma\)-tocopherol & 33.8 (1.7) a & 35.6 (4.3) a & 71.1 & 33.6 (0.6) a & 26.2 (2.1) b & 62.4 & 31.9 (0.3) b & 25.4 (0.9) a & 66.3 \\ Total Icebornos & 37.2 (1.2) a & 39.0 (4.3) a & 71.0 & 36.9 (0.6) a & 29.6 (2.2) b & 63.0 & 34.0 (0.5) b & 29.1 (1.5) a & 67.8 \\ Squalene & 215 (6.3) b & 239 (0.6) a & 72.3 & 251 (12.7) a & 374 (2.9) b & 24.0 & 158 (28.2) b & 91.8 (1.14) a & 58.9 \\ Spinastroid + & 132 (14.3) b & 173 (14.4) a & 75.3 & 149 (5.0) a & 153 (1.6) a & 68.6 & 93.4 (1.4) b & 24.8 (0.3) a & 39.6 \\ \(\Delta\)7,22,25- & & & & & & & & & \\ \(\Delta\)7,22,25- & & & & & & & & & \\ stigmatstringalenol & 63.2 (5.1) a & 61.4 (2.5) a & 69.4 & 57.1 (3.2) a & 31.5 (1.8) b & 53.9 & 36 (0.7) b & 10.5 (2.7) a & 41.8 \\ \(\Delta\)7-glomerated & 13.1 (2.06) a & 13.1 (2.8) a & 70.0 & 12.1 (1.5) a & traces (0.00) b & 0.00 & 6.70 (0.00) b & 1.50 (0.03) a & 35.5 \\ \(\Delta\)7,25- & & & & & & & & & \\ stigmatadienol & 68.0 (8.7) a & 60.2 (7.6) b & 67.5 & 54.3 (7.3) a & 18.9 (1.1) b & 42.4 & 29.8 (0.0) b & 13.4 (1.1) a & 52.5 \\ Total sterols & 276 (21.0) b & 307 (17.8) a & 72.2 & 272 (12.0) a & 204 (0.4) b & 61.3 & 166 (2.0) b & 50.2 (2.4) a & 42.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of bioactive unsaponifiable compounds contents in surface and total oil of capsules with the greatest encapsulation efficiency.
# 25 Years of Bluetooth Technology
## 6 Recent Research Results on Bluetooth
### Security  
As the number of Bluetooth devices keeps increasing, there is expanding growing research focus on analyzing the _security_ of BLE devices. [PERSON] et al. [55] have shown that Bluetooth Low Energy's most severe vulnerability is the _temporary key_ used in the pairing process. The pin is 6-digits long and therefore too small to prevent a modern computer from generating that key using the brute-force approach. Therefore, recent research recommends that Bluetooth Low Energy should not yet be used in mission critical systems or those with sensitive data.  
Studies on BLE attacks [56] have suggested the development of a protocol that provides an extra layer of security before the generation of the temporary key, and to generate a large and more random key which is difficult to determine easily via brute force methods. Apple's Bluetooth Low Energy (BLE) Continuity protocol [57] was designed to support interoperability and communication between iOS and macOS devices. Although the protocol provides a seamless communication experience, it leaks device and behavioral data to nearby listeners. A small amount of information is leaked at a time. However, this can be used to identify and track devices over long periods of time. BLE technology has privacy challenges that need to be addressed before its secure deployment becomes possible.
## 3 Data Generation
### Losses Analysis  
The computation of losses is an important issue in the design part of an electrical motor. The losses in different parts of the analysed system could affect the machine performances (e.g., demagnetisation, winding resistivity modification) and, in the end, decrease the efficiency [24]. A correct estimation of electrical machine losses allows, together with the information regarding material thermal properties and heat dissipation method, the computation of the temperature distribution in different parts of electrical machine [25].  
The machine losses can be computed either by using analytical methods or Finite Element Analysis Method [26]. In this paper, the motor losses are quantified and analysed using FEA for the nominal operating point of the system. Therefore, the electromagnetic parametric model is used to generate the losses for imposed input geometry conditions. The biggest source of losses for the machine under study comes from the windings [27]. Besides that, the iron losses, composed of the sum of hysteresis loss, eddy-current loss and excess loss is taken into account. Iron losses are computed a posteriori according to the Steinmetz model and separated into hysteresis and (lamination) eddy current. Because different parts of the electrical machines (e.g., stator teeth and stator back iron) are exposed at distinct flux density values, the losses are computed individually [25]. Hence, losses coming from different parts of the machine are analysed: the total machine losses, the winding, the iron, stator back iron, stator teeth, rotor back iron and the magnet losses, all expressed in kilowatts.  
The automatic motor losses extraction process starts by imposing the values of the geometric parameters generated with the LHS method to the parameterised electromagnetic model. Afterwards, the nominal operating point is set by imposing the corresponding direct and quadrature currents, together with the nominal speed. At the end, the electromagnetic model is solved and motor losses are computed and extracted.
## 3 Results
### Landing Cushioning Phase
#### 3.2.2 Kinetics  
At the peak of VGRF, significant differences were observed in the VGRF across knee braces with different stiffness levels (\(p=0.004\)) (Figure 3). Furthermore, significant differences were found in the knee flexion moment (\(p=0.048\)), adduction moment (\(p=0.001\)), and external rotation moment (\(p=0.001\)) at the peak of the ground reaction force (GRF) (Figure 4). Significant differences were observed in the peak loading rate of the GRF across knee braces with different stiffness levels (\(p=0.006\)). When comparing the negative work performed by the knee joint under different stiffness conditions, significant differences were found between the no-stiffness knee brace and both the low-stiffness and high-stiffness knee braces. However, no significant difference was observed between the low-stiffness and high-stiffness knee braces. For positive work, significant differences were found between the no-stiffness and high-stiffness knee braces (\(p=0.004\)) (Table 3). The analysis revealed that the knee joint contact force was significantly higher under the no-stiffness knee brace condition compared to both the low-stiffness and high-stiffness knee braces. No significant differences in knee joint contact force were found between the low-stiffness and high-stiffness knee braces (Table 4).  
Figure 2: The knee joint angle in the sagittal, frontal, and horizontal planes during the pull-off phase and landing cushioning phase. The black dashed line represents a value of 0.  
Figure 4: The knee joint moment in the sagittal, frontal, and transverse planes during the pull-off phase and landing cushioning phase. The black dashed line represents a value of 0.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables (N)** & **No Stiffness** & **Low Stiffness** & **High Stiffness** & \(p\) & **F** \\ \hline Pop & & & & & \\ Knee joint contact force (BW) & 1.89 (0.61) & 2.78 (0.36) \# & 2.53 (0.54) \({}^{\star}\) & **0.001** & 6.180 \\ \hline Lcp & & & & & \\ VGRF (BW) & 2.15 (0.73) & 2.08 (0.79) & 1.95 (0.63) & **0.004** & 5.892 \\ VLR (BW) & 0.74 (0.43) & 0.71 (0.37) & 0.58 (0.43) & **0.006** & 6.293 \\ Knee joint contact force (BW) & 9.16 (1.36) & 11.21 (2.27) \# & 11.93 (2.09) \({}^{\star}\) & **<0.01** & 11.408 \\ \hline \hline \end{tabular} Note: The bold represent significant differences, with \(p<0.05\). \"#\" represents the interaction effect between no and low. \"*\" represents the interaction effect between no and high.  
\end{table}
Table 4: Knee joint contact force in two phases and VGRF and VLR in Lcp.  
Figure 3: VGRF in two phases. And knee sagittal contact force and knee joint work power in the sagittal plane. The black dashed line represents a value of 0.
# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 3 Experimentation and Analysis
### Subjective Evaluation  
The enhanced results of the UW-AAE model that we proposed are compared with six existing traditional algorithms and deep learning-based underwater image enhancement  
Figure 8: The variation of the loss function during the training process.  
methods, namely UDCP [10], HE [29], Fusion-based [27], ULAP [12], Ucolor [15], and Funie-GAN [27], respectively. Representative images with greenish, bluish tints, as well as cloudy and dark lighting conditions are selected for analyzing the contrast and color restoration performances of each algorithm from a subjective visual perspective. Figures 9 and 10 present a comparison of the aforementioned algorithms on the unreferenced underwater image test set A and the fully referenced underwater image test set B, respectively. While the UDCP algorithm exhibits a favorable performance in handling more turbid images, it tends to reduce the brightness of the image and is less effective in restoring images with greenish and bluish tones. The HE algorithm exhibits some ability to restore color in different types of images, but it tends to produce oversaturation. On the other hand, Ucolor enhances the contrast and brightness of the image but falls short in effectively removing turbidity. FunieGAN and ULAP do not perform well in recovering the original color of heavily bluish-greenish images, and in low-light situations, they introduce other colors. The Fusion-based method effectively recovers the color distortion of the image, but it may occasionally suffer from color deviation. In contrast, the algorithm we proposed in this study successfully enhances the image's brightness and contrast while preserving the structural information and accurately recovering the color of degraded underwater images.
# Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data
## 4 Discussion
The classification performances with [\(l\), \(q\), \(u\), \(v\)] and [\(l\), \(q\), \(u\)] were compared and the result is shown in Table 5, which indicates that the SOP of R and L can retrieve a close classification accuracy and the contributions of the feature, \(v\) or \(M_{44}\), are about 0.12 in both cases. This analysis indicates that the SOP of incident light L and R has a similar performance in the classification task of the dataset, and this result corresponds to the theoretical explanation with the Mueller matrix. With the analysis above, the information of \(M_{44}\) is suggested to be contained during the polarized light scattering measurement and the circular SOP of incident light is suggested to be included in the further measurement, as an addition to the measurement with linear SOP of the incident light, such as 150\({}^{\circ}\) linear SOP. There may only be one incident SOP allowed when the conceptual prototype is deployed to classify the suspended microalgae in the aquatic field, so an optimal incident SOP should be necessarily designed based on these considerations. Note that most microalgal cells are not uniform spheres in the morphology and structure; previous literature demonstrates that the top right and bottom left elements of their Mueller matrices are approximately zero [30]. Therefore, the discussion based on Equation (5) is still valid. During the experiments in this work, we measured 10 polarization states, which is time-consuming. The discussion gives clues to reducing the modulated polarization states; 150\({}^{\circ}\) linear SOP and the circular SOP are suggested to be included in future applications. The results in this work indicate that, due to the diversity and complexity, polarization data equipped by the machine learning algorithm are a feasible way to effectively classify marine microalgae, and more comprehensive methods and an abundant number of data would achieve a better classification performance. The fast-developing machine learning method will provide tools for us [31], and the Mueller matrix polarimetry of the individual microalgae may be expected in near future, and they both promote classification ability. Moreover, we notice that the optical microscopy is still the standard method to identify the species of the microalgae [4] and the morphological information and the internal structure play vital roles in the classification of diverse microalgae to which polarization parameters are sensitive [32,33]. In addition, an in situ prototype based on polarized light scattering was easily built and demonstrated to be powerful in classifying particles in seawater [34]. As such, the combination of the microscope and polarized light scattering is a promising tendency and may provide a way to accurately and rapidly classify the microalgae in water.
## 2 Materials and Methods
### Gamma Spectrometry Analysis and Evaluation of Radiological Hazard Effects  
In the laboratory, for the gamma spectrometry analysis, samples (packed in a 20 mL polyethylene plastic vial to reach geometric homogeneity around the detector) were counted for 70,000 s and spectra were analyzed in order to obtain the activity concentration of \({}^{137}\)Cs and \({}^{40}\)K through the evaluation of their \(\gamma\)-lines at 661.66 keV and 1460.81 keV, respectively.  
The experimental setup was composed by two Ortec HPGe detectors, shielded from environmental background by using lead shields with copper, and tin lining and integrated digital electronics. The first was a negative biased detector (GMX) with FWHM of 1.94 keV, peak to Compton ratio of 65:1 and relative efficiency of 37.5% at 1.33 MeV (\({}^{60}\)Co). The second one was a positive biased detector (GEM) with FWHM of 1.85 keV, peak to Compton ratio of 64:1 and relative efficiency of 40% at 1.33 MeV (\({}^{60}\)Co). The ANGLE 4 code was employed for the efficiency transfer factors calculations to the 20 mL vial sample holder geometry [16].  
The Gamma Vision (Ortec) software was used for data acquisition and analysis [17]. The quality of the measurements was also demonstrated through the participation in intercomparison exercises organized by international organizations [18]. The measurement result uncertainty, coverage factor k = 2, was calculated taking into account the following components: uncertainty of the counting estimation, of the calibration source, of the efficiency calibration, of the background subtraction and of the \(\gamma\)-branching ratio [17].  
The evaluation of radiological hazard effects was made in terms of the annual effective dose for wine ingestion. It was calculated by the following [19]:  
\[D_{ing}\Bigg{(}\frac{Sv}{y}\Bigg{)}=h_{ing,K-40}xJ_{ing,K-40} \tag{1}\]  
where \(h_{ing,K-40}\) is the coefficient of effective dose for the insertion unit, for the ingestion of \({}^{40}\)K (Sv/Bq) and \(J_{ing,K-40}\) is the intake of \({}^{40}\)K (Bq/year) [19]. The latter value was obtained by multiplying the mean annual wine consumption for the activity concentration of the investigated radionuclide experimentally measured.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 5 Future Opportunities
### Biological Samples  
With the extension of time scales, the dynamics of biological samples will become accessible. These samples will need to be studied in their native aqueous environments. The relevant length scales, e.g., the size of a protein, is usually in the range of few nm, thus, diffusion dynamics as fastest relevant process will extend to the sub-\(\upmu\)s regime. Radiation-induced damage has to be considered for such samples, leading to a modified signal-to-noise ratio [37; 64]. First XPCS studies on protein diffusion have been demonstrated recently using fast detectors with access to \(\upmu\)s time scales [36; 199; 200]. In addition to storage-ring based XPCS, application of XSVS schemes will be applied to limit the radiation damage [65; 140], as well as studies at FEL sources are foreseen using different pulse schemes [37]. Besides protein dynamics, XPCS will become possible for nanomedicine studies, such as probing nanoparticle-protein interaction which is important for drug release in cellular environments [201].
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 3 Implementation of the Proposed Approach
### Prediction and Inspection Method Based on Radiation Fusion  
After assimilating, more data can be obtained. By using neural network for data training, it can better fit the real changes of the data.  
Suppose the original data is \(D\), and the data after assimilation is \(D^{\prime}\). Divide the original data and the assimilated data into a training set and a test set according to the same time and space. The original data are divided into \(trainX\), \(trainY\), \(testX\) and \(testY\), the assimilated data are divide into \(trainX^{\prime}\), \(trainY^{\prime}\), \(testX^{\prime}\), and \(testY^{\prime}\).  
By training the neural network model on the original dataset we have the following:  
\[D^{\mathit{trainX,trainY}}f(D).\]  
Figure 3: The red circle represents the measured data, the blue circle represents the remote sensing data, and the connected edges represent the fusion calculation of the remote sensing data with the measured data with the same cluster.  
The data training network model after assimilation is  
\[D^{\prime\ trainX^{\prime}trainY^{\prime\prime}}f^{\prime}(D^{\prime}).\]  
After the training is completed, the assimilated data is tested through two models, so the absolute error value can be obtained by using the original data model.  
\[MAE=f(testX^{\prime},trainY^{\prime})\]  
After assimilation, the data model can be obtained as follows:  
\[MAE^{\prime}=f^{\prime}(testX^{\prime},trainY^{\prime}).\]  
By comparing \(MAE\) and \(MAE^{\prime}\), the effect of the assimilation model and the prediction model can be seen.  
The root mean square error (\(RMSE\)) and mean absolute error (\(MAE\)) are mainly used as the criteria for evaluating the quality of the model. The root mean square error can measure the deviation between the observed value and the true value; the root mean square error represents the average value of the absolute error. The formula is as follows:  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{(X_{i}^{p}-X_{i}^{r})}^{2}}}}} \tag{8}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{{{\left|{X_{i}^{p}-X_{i}^{r}}\right|}}}}. \tag{9}\]