esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antibacterial Activity  
The Kirby-Bauer test was performed on the glass hybrid materials to evaluate their possible use in preventing the proliferation of pathogenic bacteria. _Escherichia coli_ (ATCC 25922) as Gram-negative bacteria (\(-\)) and _Staphylococcus aureus_ (ATCC 25923) as Gram-positive bacteria (\(+\)) were grown in the absence and presence of specimens. A total of 100 mg of each of the glasses were finely ground to a powder and irradiated with UV light for 1 h for sterilization. Bacterial suspensions of 10\({}^{9}\) CFU/mL were obtained by diluting pellet strains in distilled saline water (0.9% NaCl). _E. coli_ was plated on TBX medium (Tryptone Bile X-Gluc) (Liofilchem, Italy), while _S. aureus_ was plated on Baird-Parker agar (Liofilchem, Italy). Both media were sterilized at 120 \({}^{\circ}\)C for 15 min. After autoclaving, Baird-Parker agar was cooled to 50 \({}^{\circ}\)C, and an emulsion of egg yolk containing potassium tellurite was added. After placing the materials on the centre of Petri plates, _E. coli_ plates were grown at 44 \({}^{\circ}\)C for 24 h, while _S. aureus_ plates were grown at 36 \({}^{\circ}\)C for 24 h. The diameter of inhibition halos (IDs) in relation to the Petri dish diameter (PPD) (6 cm) was calculated. Four measurements were carried out for each specimen to obtain the mean standard deviation. Data are expressed as bacterial viability (BV) (%).  
\[\text{BV}=(\text{PPD}-\text{IDs})/\text{PPD}\times 100 \tag{2}\]  
where 100% BV represents the bacteria viability without samples. The mean standard deviation is expressed as a relative standard deviation (RSD).  
A flow chart of antibacterial detection is represented in Figure S1.
## 2 Materials and Methods
### Methods
#### 2.2.4 Hydroymethylfurfural (HMF) Content  
Hydroymethylfurfural (HMF) content in honey samples was determined with the use of UV-Vis spectrophotometer (Shimadzu UV mini-1240) by absorbance measured at 550 nm wavelength. Before spectrophotometric analysis, each honey sample (10 g) was diluted in 50 mL of distilled water. An amount of 2 mL of the honey samples solution was transferred to four glass tubes and 5 mL of p-toluidine solution was added to each sample. Afterwards, 1 mL of barbituric acid solution was added to the honey samples and 1 mL of water to the blank sample, and all samples were gently shaken for 1-2 min. The absorbance measurement of samples was performed in relation to the blank sample as soon as the color intensity has reached a maximum (3-4 min after addition of the barbituric acid solution). HMF content in the honey sample was expressed in mg/kg of honey, and was calculated as:  
\[\text{HMF}=\frac{192\times\text{ABS}\times 10}{\text{weight of th honey in grams}}\]  
where ABS is the absorbance of the sample, 192 is the extinction coefficient, and 10 is the factor for dilution.
# Effects of Pretreatment on the Volatile Composition, Amino Acid, and Fatty Acid Content of Oat Bran
## 3 Results and Discussion
### HS-SPME-GC-MS Analysis of Volatile Compounds of Oat Bran
Alcohols, aldehydes, and ketones have been reported to play an important role in the flavor profiles of foods [42; 43]. It has been reported that ketones are mainly obtained by the Maillard reaction and oxidative degradation of unsaturated fatty acids [44]. The content of typical Maillard reaction products, such as pyrazine and furan, increases after heat treatment. The S-OB and M-OB contained two kinds of pyrazines, whereas the HA-OB did not contain pyrazines. The content of 2-pentylfuran in S-OB, M-OB, and HA-OB was higher than that in OB. This 2-Pentyfuran is the oxidation product of linoleic acid. Those substances had the lowest odor thresholds, which suggested that compounds, such as 2,5-dimethylpyrazine, 2-ethyl-3,5-dimethylpyrazine, and 2-pentylfuran, as the degradation reaction products of the Strecker reaction, were important to the nutty flavor developed in pretreated oat bran. Pyrazines have a toast or nut-like taste. They are formed by the interaction of amino acids and carbohydrates, with the highest proportion of the three types of heterocycles. The flavor of oat bran was mainly formed during heat treatment, and the precursors and enzymes in natural oat bran were helpful for flavor formation. Pretreated oat bran was progressively more oat-like and nutty. Two classes of volatile components were detected in heated oats: (1) compounds derived from the oxidative decomposition of unsaturated fatty acids, resulting in saturated and unsaturated aldehydes and ketones, (2) Maillard reaction products, including heterocyclic pyrazine, pyrrole, furan, and sulfur-containing compounds, which are rich in high temperature, low moisture, extruded oats that produce toasted, roasted flavors [20]. [PERSON] and [PERSON] found that typical Maillard-derived compounds such as pyrazine and furanones were increased after heat treatment in oat [23; 24]. Compared with microwaving and hot air drying, steamed oat bran has a better flavor, probably because the steaming treatment temperature is lower, but the heating time is longer (100 \({}^{\circ}\)C, 20 min). This processing method results in a higher moisture content of the sample. The starch in the oat bran expands and gelatinizes with water absorption, partially affecting the flavor [45].
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
## 3 Time-Delay ML Estimation (MLE) and Cramer-Rao Bound
\right], \tag{7}\] \[\mathrm{CRB}_{\tau|\underline{c}} = \frac{1}{2\mathrm{SNR}_{\mathrm{out}}}\frac{1}{F_{s}^{2}}\frac{ \left(\frac{\epsilon^{H}\mathbf{V}\mathbf{c}}{\epsilon^{H}\mathbf{c}}-\mathrm{ Im}\left\{\frac{\epsilon^{H}\mathbf{A}\mathbf{c}}{\epsilon^{H}\mathbf{c}}\right\}^{2} \right)}{\underset{\text{real signal}}{=}}\frac{1}{2\mathrm{SNR}_{\mathrm{ out}}}\frac{1}{F_{s}^{2}}\frac{\epsilon^{H}\mathbf{V}\mathbf{c}}{ \epsilon^{H}\mathbf{c}},\] (8) \[\mathrm{CRB}_{\varphi|\underline{c}} = \frac{1}{2\mathrm{SNR}_{\mathrm{out}}}\left(1+\frac{\mathrm{Im} \left\{\frac{\epsilon^{H}\mathbf{A}\mathbf{c}}{\epsilon^{H}\mathbf{c}}\right\} ^{2}}{\frac{\epsilon^{H}\mathbf{V}\mathbf{c}}{\epsilon^{H}\mathbf{c}}-\mathrm{ Im}\left\{\frac{\epsilon^{H}\mathbf{A}\mathbf{c}}{\epsilon^{H}\mathbf{c}}\right\}^{2}} \right)}\underset{\text{real signal}}{=}\frac{1}{2\mathrm{SNR}_{\mathrm{ out}}},\] (9) \[\mathrm{CRB}_{\tau,\varphi|\underline{c}} = \frac{1}{2\mathrm{SNR}_{\mathrm{out}}}\frac{\mathrm{Im}\left\{ \frac{\epsilon^{H}\mathbf{A}\mathbf{c}}{\epsilon^{H}\mathbf{c}}\right\}}{F_{s }\left(\frac{\epsilon^{H}\mathbf{V}\mathbf{c}}{\epsilon^{H}\mathbf{c}}-\mathrm{ Im}\left\{\frac{\epsilon^{H}\mathbf{A}\mathbf{c}}{\epsilon^{H}\mathbf{c}}\right\}^{2} \right)}\underset{\text{real signal}}{=}0. \tag{10}\]  
Then, we can see that, for a real signal, there is no impact on the time-delay estimation if we consider or not the signal phase, i.e., \(\mathrm{CRB}_{\tau,\varphi|\underline{c}}=0\), the time-delay CRB is the same as the one derived from Equation (4), and the performance on the phase estimation does not depend on the signal, \(\mathrm{CRB}_{\varphi|\underline{c}}=\frac{1}{2\mathrm{SNR}_{\mathrm{out}}}\).
# Integration Between Serious Games and EEG Signals:
## 2 Literature Review Process
### Identification Phase  
The search was performed in the following databases or bibliographic tools: Web of Science (WoS) ([[https://clarivate.com/academia-government/scientific-and-academic-research/research-discovery-and-referencing/web-of-science/web-of-science-core-collection/](https://clarivate.com/academia-government/scientific-and-academic-research/research-discovery-and-referencing/web-of-science/web-of-science-core-collection/)]([https://clarivate.com/academia-government/scientific-and-academic-research/research-discovery-and-referencing/web-of-science/web-of-science-core-collection/](https://clarivate.com/academia-government/scientific-and-academic-research/research-discovery-and-referencing/web-of-science/web-of-science-core-collection/))), IEEE Xplore ([[https://ieeexplore.ieee.org/Xplore/home.jsp](https://ieeexplore.ieee.org/Xplore/home.jsp)]([https://ieeexplore.ieee.org/Xplore/home.jsp](https://ieeexplore.ieee.org/Xplore/home.jsp))), Science Direct ([[https://www.sciencedirect.com/](https://www.sciencedirect.com/)]([https://www.sciencedirect.com/](https://www.sciencedirect.com/))) and Springer ([[https://link.springer.com/](https://link.springer.com/)]([https://link.springer.com/](https://link.springer.com/))), using the following keywords and operators: \"BCI\" AND \"EEG\" AND \"serious game\". All the resources were collected over the course of one week to ensure the consistency and dependability of our inquiries. First, on 2 September 2024, we assembled the list of references in a single day. We then obtained specific documents from online libraries over the next several days to 7 September 2024. This staged strategy was put in place to avoid an overwhelming number of queries that would compromise our authorized access to those digital databases. A total  
Figure 2: The PRISMA flow diagram for our systematic literature review examination.  
of 473 references were identified through the database search. The process of removing duplicated registers excluded 118 references. Another 250 references were removed before screening, either for lacking full-text availability or for not being related to the search topics after the initial abstract inspection.
## 4 The Proposed NSGA-II-MOEA/D-CIS Algorithm
### Design of the Upper-Level NSGA-II Algorithm  
#### 4.2.1 Encoding and Decoding  
Considering the characteristics of the upper-level order splitting problem and the properties of the NSGA-II algorithm, this section proposes an integer-based matrix encoding method. In this encoding scheme, each chromosome represents an order splitting result, and the chromosome is divided into multiple segments based on the number of product specifications in the order. Each segment of the chromosome includes three key pieces of information: a splitting flag (for quickly distinguishing whether the order has been split), the number of splitting levels (to facilitate the retrieval of sub-orders based on the BoM), and the number of splitting batches (indicating how many batches the order has been split into). The splitting level in this study refers to the recursive depth of order decomposition. A finished product assigned to Level 1 indicates that no further splitting is required. If the order is split once (e.g., into assembly or processing sub-tasks), its components are assigned to Level 2. Further splitting leads to Level 3, and so on. To ensure that all chromosomes have equal lengths, the segments corresponding to missing product specifications are assigned values of 0 for the splitting flag, splitting levels, and splitting batches.  
To illustrate the encoding and decoding process of the matrix encoding method more clearly, this paper selects three order splitting instances as examples, where Order 1 contains four product specifications, Order 2 contains four product specifications, and Order 3 contains three product specifications. Figure 3 presents a numeric matrix that encodes the splitting strategy for multiple orders across different product specifications. Each cell is formatted as batch count/splitting level/splitting flag, representing the production quantity division, hierarchical allocation, and activation status of each specification, respectively.  
For example, supposing there are three specifications in Order 3, then all cells of specification 4 are set to 0. In addition, for specification 1, the value \(1/4/1\) indicates that the specification needs to be split to the fourth level and the quantity divided into a total of one batch. For specification 2, the value \(0/1/1\) indicates that no split is needed in this specification, so both the splitting level and batch count are set to 1. For specification 3, the value \(1/5/2\) indicates that the specification needs to be split to the fifth level and the quantity divided into a total of two batches.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 6 Conclusions  
1. The simulation model of a jet-generation system was constructed, and the motion laws of the piston and reversing spool and the evolution characteristics of the inlet pressure and jet pressure were analyzed. The piston and valve core move reciprocally in a limited stroke. There is a short pause when the reversal of the valve core is completed, and the displacement curve is approximately trapezoidal. There is no pause during the movement of the piston, and the displacement curve is approximately sawtooth-shaped. The pressure state of the jet corresponds to the motion state of the piston one by one. The low-pressure stage and boosting stage of the jet correspond to the backward stage and stroke stage of the piston, respectively.
2. The effect of the key structural parameters of the device on jet characteristics was analyzed. When the nozzle diameter or boost ratio exceeds a certain value, the pulse pressure drops and the pulse duration and pulse frequency no longer change; there is an optimal nozzle diameter or boost ratio to maximize the pulse pressure and pulse frequency. In addition, with the increase in piston maximum displacement or piston diameter \(d_{\rm p2}\), the pulse duration increases and pulse frequency decreases. When the piston maximum displacement or piston diameter \(d_{\rm p2}\) is lower than a certain value, the pulse pressure decreases. In addition, pulse pressure is not affected by piston diameter \(d_{\rm p3}\); the increase in piston diameter \(d_{\rm p3}\) results in the increase in pulse duration and decrease in pulse frequency.
3. The design criterion of the key structural parameters of the generator was established, and the optimal parameters were obtained based on the design criterion and simulation results. Compared with the original device, both the pulse frequency and output energy of the jet generated by the optimized device are significantly improved without reducing the pulse pressure. The rock breaking test results show that the diameter and volume of the granite erosion pit generated by the optimized device are 1.4 and 1.5 times those generated by the original device, respectively.  
Conceptualization, investigation, methodology, writing--original draft, Y.L.; Funding acquisition, supervision, software, visualization, X.W.; Data curation, formal analysis, project administration, resources, validation, J.T. All authors have read and agreed to the published version of the manuscript.  
The work is jointly supported by the National Natural Science Foundation of China (U1804145) and the Scientific and Technological Breakthroughs Project of Henan Province (NO. t242102220062).  
Figure 14: Granite erosion pit by the jet generated by (**a**) original device [35], (**b**) optimized device.
## 3 Results
### Characterization of hSMN S290 Phospho-Mutant and Phospho-Mimetic
To avoid the pleiotropic effects of _smn-1_ depletion, we used a transgenic model with silencing of _smn-1_ specifically in D-type motoneurons [_smn-1(MNs RNAi)_]. These animals are viable and fertile but present impairment in locomotion and motoneuron death [35]. Thus, we generated and injected three constructs expressing _hSMN WT_, _hSMN S290A_ and _hSMN S290D_ under the control of a pan-neuronal promoter in a _smn-1(MNs RNAi)_ background. The expression of _hSMN WT_ partially rescued the neuronal death shown by _smn-1(MNs RNAi)_, reducing the average number of dying motor neurons from 9.32 to 5.65 (Figure 3A,B). We also observed that neither the non-phosphorylatedable mutant (_hSMN S290A_) nor the phospho-mimetic (_hSMN S290D_) were able to rescue the neuronal death of _smn-1(MNs RNAi)_,  
Figure 3: Phosphorylation state of serine residue 290 (S290) affects motoneuron survival in a _C. elegans_ Spinal Muscular Atrophy (SMA) model. (**A**) Representative fluorescence images show dying MNs. _smn-1(MNs RNAi)_; _hSMN WT_ animals (upper right panel) presents less dying neurons (arrows) than _smn-1(MNs RNAi)_ (upper left panel) and _smn-1(MNs RNAi)_; _hSMN S290A_ and _smn-1(MNs RNAi)_; _hSMN S290D_ animals (lower left and right panels, respectively). * indicates the vulva localized at mid-body. In all panels, anterior is left and ventral is down. Scale bar represents 10 μm. (**B**) Quantification of the effects of _hSMN WT_, _hSMN S290D_, and _hSMN S290D_ on motoneuron survival in a _C. elegans_ SMA model. Each dot represents the number of dying motor neurons in a single animal. The mean number of dying motoneurons and the SEM is also shown. The number of animals scored is annotated (n). One-way ANOVA with Kruskal-Wallis test, ****\(p<0.0001\), ns = not significant. with 9.69 and 9.70 dying MNs, respectively (Figure 3A,B). These results demonstrate that the serine residue in position 290 is essential for SMN function in _C. elegans_ motoneneurons.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 2 Intelligent Transportation Systems (ITS)
### Data Pre-Processing  
Data pre-processing, when necessary, is conducted right after the sensing task. It can address data quality issues that are hard to be addressed previously. Noisy data and missing data are two major problems in traffic data pre-processing and cleaning. While data denoising can be done with satisfactory performance using traditional methods, such as wavelet filter, moving average model, and Butterworth filter [26], missing data imputation is much harder since it adds information properly. Another commonly applied traffic data denoising task is trajectory data map matching. The most popular models for this task that denoises the map matching errors are often based on the Hidden Markov Model [27; 28; 29]. There have been quite some efforts in deep learning-based missing data imputation lately. These state-of-the-art methods often focus on learning spatial-temporal features using deep learning models so that that are able to inference the missing values using the existing values [30; 31; 32; 33; 34; 35]. Given the spatial-temporal property of traffic data, the Convolutional Neural Network (CNN) is a natural choice due to its ability to learn image-like patches. [PERSON] et al. designed a CNN-based method for loop traffic flow data imputation and demonstrated its improved performance over the state-of-the-art methods [32]. Generative adversarial network (GAN) is another deep learning method that is appropriate for traffic data imputation, given its recent advances in image-like data generation. [PERSON] et al. proposed a GAN algorithm to generate time-dependent traffic flow data. They made two modifications to the standard GAN on using the real data and introducing a representation loss [31]. GAN is also experimented for travel time imputation using probe vehicle trajectory data. [PERSON] et al. developed a travel times imputation GAN (TTI-GAN) considering the network-wide spatial-temporal correlations [35].
# Mapping Phonation Types by Clustering of Multiple Metrics
### Classifying Phonation Types  
To classify phonation into various types, many metrics and methods have been tested. Those time domain or frequency domain metrics reflect attributes of airborne voice signals. Glottal inverse filtering (GIF) [7] is the estimate of flow waveforms; H1-H2 is the amplitude difference between the first and second harmonic proposed by [PERSON] [8]; [PERSON] et al. [9] use low-frequency spectral density (LFSD) to distinguish breathy, modal, and pressed phonation; and so on.  
The electroglottographic signal (EGG) is a method for representing the contacting of the vocal folds that is relatively low-cost and non-invasive, as compared to endoscopic imaging. Since the EGG signal can represent the changing contact area of the oscillating vocal folds, while neither impeding nor being much affected by the articulation, the EGG is often used to characterize phonatory behavior, sometimes as an ancillary diagnostic tool in clinical practice. A few EGG metrics have been widely used, such as the open quotient (OQ), the contact quotient (CQ), and the speed quotient (SQ). Additionally, some researchers used other signals such as neck surface acceleration [10]. However, the physiological interpretation of these metrics [11], that is, in what manner these artificially selected and defined metrics are correlated with the physiological structures or movements, is still incompletely described.  
Some approaches successfully classify phonation types using multiple metrics combined with signal processing methods [12] and algorithms such as the Gaussian mixture model, random forests, DNN [7], or using classification on the EGG waveform itself [7; 13]. Those models have shown better discrimination compared to the attempts that use one particular metric for detecting phonation types. However, the interpretation of some EGG metrics remains to be elaborated upon.
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 7 Proposed 1D CNN Regression for Cutter Wear Prediction
### Mean Absolute Error (MAE)  
The mean absolute error (_MAE_) function was used as a metric of the output layer in the proposed convolutional neural network. The _MAE_ shows the percentage of the estimated variables. Equation (7) expresses the _MAE_ formula [55].  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i}) \tag{7}\]