esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Animals as Mobile Biological Sensors for Harsh Terrains
### Proposed System Structure
#### Sample HVD  
The steps of the process of detecting voice by the software used for recognizing human voice and location detection are as follows:  
1. Firstly, the intervals at which reading is taken are determined according to the infrastructure and the number of animals used in the system. This interval shows how often any particular type of animal will provide data. In this example, the interval has been determined as 1 minute.
2. Any sensor carrier animal is chosen using Animal ID (previously assigned to the animals). In this example, a vote with the ID V-001 was selected.
3. The next stage is determining the absence ratio, which in this example is used for checking (data), as the voice signals. This ratio varies according to the animal used; the absence ratio and reading data interval should be smaller for the fast and shuffling animals. It has been taken as 5 minutes for vote in this example.  
4. Noise level is a variable which is used in the scanning of human voice. It is used to discriminate the different levels of human voice from sensored animals. \"Pessimistic\" selection in this field is used for human voices at very low volumes in critical zones, whereas \"Optimistic\" is a parameter to filter low frequency human voices in probable places. In this example, \"pessimistic\" has been chosen in order to be used in the critical regions.
5. The movement of the animal with the coordinates and time spent is shown in the location grid available in the software. A different color is used if there is no signal for more than 5 minutes in the absence ratio. The distance is checked when the signal is reachable again, and a reading of 0 to one meter is considered to indicate that the animal remained in a location that did not allow a signal for that duration of time (assuming that it is not less than the absence ratio).
6. The voice sensors implanted into the animal are filtered to determine whether they contain human voices or not. Any human voice signals are transmitted to the software by the signal towers and shown on a spectrometer. These signals can also be listened to by the software user. If any significant and characteristic human voice is recognized, the position of the animal is determined and checked.
7. While this happening, it is possible to identify and confirm the location of the animal disappearance (the point where the signal was lost) and reappearance (the point where the signal resumes), as well as features of the voice being tracked.  
Some predictions can be made about the hiding places of the animal used in this sample, a vote. Since its hiding places are likely to be very small, uncertainty arisen as to whether these are suitable places for humans to shelter, and thus there is a need to consult someone familiar with the region. In addition, consultation with zoologists and guides with relevant expertise are necessary to select parameters according to animal and regional features.
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 2 Materials and Methods
### Algorithms Design
#### Anomaly Detection  
After establishing reference curves, the anomaly detection algorithm comes into play. The chosen approach involves an examination of deviations from reference characteristics. The process of finding the point-to-curve distance (PTCD) is described in [3]. The approach assumes the curve as a closed-form expression, which is the case presented in Section 2.2.2. Firstly, the distance from the curve to new measurements put on characteristics is calculated. Secondly, a histogram of distances from the curve is established. The last step is to identify outliers. To identify them, it was decided to use the Interquartile Range (IQR) Criterion [2].  
Concise mathematical use of the IQR Criterion could be presented as follows. Firstly, IQR is calculated as presented in Equation (1).  
\[IQR=Q_{3}-Q_{1} \tag{1}\]  
where \(Q_{3}\) denotes the third quartile (75 th percentile) and \(Q_{1}\) denotes the first quartile (25 th percentile) of all acquired PTCD values for each characteristic separately. For each characteristic separately, any PTCD value denoted as \(PTCD_{i}\) that meets the condition presented in Equation (2) is considered as abnormal.  
\[PTCD_{i}>Q_{3}+1.5 IQR \tag{2}\]  
While using IQR Criterion, values that also meet the condition presented in Equation (3) are considered abnormal. However, for the case analyzed in this study, the wider the point is from the reference curve, the more suspicious it is to be considered as an  
Figure 5: Scatter plot with fitted curve—power due to fuel consumption.  
Figure 6: Scatter plot with fitted curve—pressure due to rotation.  
anomaly. Therefore, to determine if a point on characteristics is considered an anomaly, only the condition from Equation (2) is applied in the article.  
\[PTCD_{i}<Q_{1}-1.5 IQR \tag{3}\]  
There are more anomaly detection algorithms available, such as those based on autoencoders [49] or SVM [50]. However, PTCD with IQR Criterion was applied in this study because it was considered as easy enough to interpret by domain experts from PONAR Wadowice. For now, it is not possible to change anomaly detection algorithms because the solution was deployed into production and HPU works with the client.  
Variables used within the anomaly detection algorithm were proposed by domain experts from PONAR Wadowice based on their experience. The choice of the variables was also supported by correlation analysis performed in Section 2.2.1. These variables are power, pressure, fuel consumption, and rotation speed. Other variables, such as temperature or fluid level, can help increase the accuracy of anomaly detection; however, other variables were taken into the final algorithm design based on domain experts' experience. The range of variables that are used by the anomaly detection algorithm could not be performed and implemented because the solution is currently deployed in production. The use of other variables than those chosen so far for the anomaly detection algorithm could be carried out while deploying the algorithm solution on the next HPU.
## 2 Results and Discussion
### Volatile Compounds in the Wines
#### 2.2.3 Esters  
Esters, including acetate esters and ethyl esters, are the primary source of fruity aromas and positively contribute to the desired fruit aroma characters of wine [7; 26]. Three acetate esters were detected in this study, including isoamyl acetate, hexyl acetate, and phenethyl acetate, and only isoamyl acetate (banana note) exceeded its threshold (Table S1, Figure 2). Adding 12, 60 mg/L OA, and 12 mg/L LA increased the productions of acetate esters, and the highest total content was observed in OA_12 wine (107.6%, 30.5%, and 144.6%) higher than those of the control, LA_12 and ALA_12, respectively). Surprisingly, the promotion was not found in ALA added wine. Similar to MCFAs, increasing UFAs concentration deprived the stimulation on acetate esters and conversely became the inhibition. The highest inhibition was found in ALA_240 wine, followed by OA_240, (91.5% and 83.3% reduction as compared to the control, respectively). Exogenous UFAs can repress _ATF1_ transcription to inhibit the biosynthesis of acetate esters [27]. [PERSON] et al. found that adding 132 mg/L LA into Sauvignon Blanc reduced acetate esters formation, which was consistent with our results [14]. However, we found that the impact of UFAs on acetate esters is the concentration and type dependent. The low amount of OA and LA in must (below 60 mg/L in this work) showed the promotion (especially isoamyl acetate), with the exception of ALA, while high concentrations of UFAs (above 120 mg/L) caused the inhibitions, irrespective of UFA types.  
Ethyl ester is another important group of esters in wine, which is produced through ethanolysis of acyl-CoA during yeast fermentation [7; 8]. Eight ethyl esters were detected in all samples. Ethyl butanoate (banana, pineapple, and strawberry note), ethyl hexanoate (banana and green apple note), ethyl octanoate (sweet, floral, fruity, banana, and pear note), and ethyl decanoate (fruity and fatty note) exceeded their individual threshold (Table S1, Figure 2). Consistent with the results of previous literatures [14; 22; 26], except for ethyl hexanoate in OA_12 wine, adding UFAs reduced the formations of ethyl esters. Besides concentration, the extent of inhibition is varied with the type of UFAs. ALA had highest inhibition among three UFAs with the same levels of treatments. The lowest total content of ethyl esters observed in ALA_240 wine showed 81.9%, 45.1%, and 54.3% reduction when compared to the control, OA_240, and LA_240, respectively (Figure 3). The biosynthesis of ethyl esters is usually believed to be substrates (MCFAs) dependent [28]. In this study, high MCFAs (OA_12 and LA_12 wine) cannot correspondingly guarantee the higher production of ethyl esters, implying that regulations of aroma-related genes might be involved in ethyl esters formations, as stated by [PERSON] et al. [29].
## The Contribution of Various Partial Waves in Each Lamb Wave
### Using Opposing Phases to Sort Symmetric and Anti-Symmetric Lamb Wave Modes  
Another advantage of using the partial-wave method is that the phase difference between the partial waves at the top and bottom of the plate can be different depending on the type of Lamb wave. That is, in symmetric modes, the top partial wave will be about 180\({}^{\circ}\) out-of-phase with the bottom partial wave. Anti-symmetric modes will typically have the top and bottom partial waves in-phase. This can be demonstrated by calculating \(q\) from Equation (45):  
\[q=\frac{B_{Top}^{(L)}}{B_{Bottom}^{(L)}} \tag{49}\]  
Figure 8: Amplitude of the (**a**) longitudinal (i.e., \(B_{avg}^{(L)}\)) and (**b**) shear vertical (i.e., \(B_{avg}^{(SV)}\)) partial waves after normalizing and averaging the magnitude for various points on the Lamb wave dispersion curves for a 1-mm thick aluminum plate.  
which should be equivalent to:  
\[q=\frac{B_{Top}^{(SV)}}{B_{Bottom}^{(SV)}} \tag{50}\]  
However, since the frequency-wavenumber pairs are approximations, \(q\) will vary from \(-1\) to \(+1\) for most Lamb waves. The values of \(q\) are overlaid on the dispersion curves in Figure 9. It is worth emphasizing that the phase difference between the top and bottom partial waves applies to both the longitudinal and shear vertical partial waves, as shown in Equation (45).  
Figure 9 demonstrates the phase difference characteristics of symmetric and anti-symmetric Lamb waves, but it also shows how those characteristics break down at high frequencies where dispersion curves intersect. At these intersection points, a phase difference (either \(+1\) or \(-1\)) will dominate the surrounding points of both dispersion curves. The clearest examples of this phenomenon are circled in red in Figure 9.
## 4 Materials and Methods
### RNA Extraction and Transcriptome Sequencing  
The RNA was extracted from the _A. sinensis_ samples using the TRIzol Kit (Thermo Fisher Scientific, Waltham, MA, USA) in accordance with the manufacturer's instructions. Subsequently, the quality of the extracted RNA was assessed utilizing a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). First-strand cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase(RNase H-). Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3' ends of DNA fragments, adaptors with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 370-420 bp in length, the library fragments were purified with the AMPure XP system. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and index (X) primers. At last, PCR products were purified (AMPure XP system), and library quality was assessed on the Agilent Bioanalyzer 2100 system. Finally, the sequencing was conducted on the Illumina NovaSeq 6000. Gene expression was quantified by FPKM [74] using featurests (1.5.0-p3). Differential analysis of the genes was carried out using edgeR [75] with a negative binomial generalized log-linear model. For GO and KEGG annotations, custom datasets were constructed and used for enrichment analysis by clusterProfiler [76].
## 5 Production of Carotenoids by Haloarchae
### Bacterioruberin Is One of the Major Carotenoids Produced by Haloarchae
reported an analysis of pH and other environmental factors through response surface methodology on the total carotenoid production of extremely halophilic archaeon _Halorubrum_ sp. TBZ126 [116]. They have found that optimum conditions for biomass and total carotenoid production occurred between pH 7 and 10 and biomass and total carotenoid production in pH 10 was about 93% and 90% respectively, compared to data reached from optimum conditions. More recently, bacterioruberin has been used for the detection of extremely halophilic archaea embedded in halite in terrestrial and possibly extra-terrestrial samples [117]. Figure 3: Absorption spectra of acetone extracts of _Haloferax mediterranean_ cells grown in complex medium **(a)** pH 5; **(b)** pH 7 and **(c)** pH 9. _Hfx. mediterranean_ was grown in complex medium pH 7 (0.5% yeast extract and 25% salted water) until shortly before the culture entered the stationary phase, after which cells were transferred to fresh complex medium pH 5, 7 or 9. Although bacterioruberin and its derivatives possess extraordinary biological functions, the research regarding the biosynthesis regulation or practical applications of C\({}_{50}\) carotenoids produced by halophilic archaea is still scarce.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 2 Signal Representations  
Datasets for the tasks of ASC or AED contain digitized audio recordings. The resulting acoustic signals are commonly represented as waveforms that denote the amplitude of the recorded signal over discrete time samples. In most cases, ASC or AED systems perform the tasks of interest on derived signal representations, which will be introduced in the following section.
## 2 Theoretical Analysis and Simulations
### Simulations  
Based on the above theoretical analysis, the fiber-optic sensor was numerically simulated using the beam propagation method (BPM). The simulate conditions were based on a 2D model with a mesh size in the X and Z directions of 0.1 \(\upmu\)m and 1 \(\upmu\)m, respectively; the boundary condition adopted a perfectly matched layer (PML) condition in the model. The SMF core and cladding diameters were 8.2 \(\upmu\)m and 125 \(\upmu\)m, and the corresponding RIs were 1.4682 and 1.4628, respectively. The length (\(l_{1}=l_{2}\)) and taper diameter (\(D_{1}=D_{2}\)) of the micro taper fiber were set as 500 \(\upmu\)m and 80 \(\upmu\)m, respectively. The simulated transmission spectra had lengths of \(L=1\), 2, and 3 cm and a wavelength range of 1540-1600 nm, as shown in Figure 2a. The corresponding spatial spectrogram calculated with fast Fourier transform (FFT) is shown in Figure 2b. Figure 2c,d show distributions of the optical field propagating along the MZI and the corresponding normalized optical intensity change at dip A and peak B, respectively. The spatial spectrum \(\varepsilon\) relationship is [34]:  
\[\varepsilon=\Delta m_{eff}L/\lambda^{2},\ \ \Delta m_{eff}=\Delta n_{eff}- \lambda_{0}\frac{\partial}{\partial\lambda}\Delta n_{eff} \tag{6}\]  
where \(\Delta n_{eff}\) is the effective RI difference due to the inter-mode dispersion, \(\Delta m_{eff}\) indicates the effective RI difference caused by inter-mode dispersion, and \(\lambda\) is input wavelength, when it is constant, the spatial spectrum is proportional to the effective RI difference \(\Delta m_{eff}\) and the interference length. As can be seen from Figure 2b, for all the three \(L\), there are several peaks in the MZI spatial spectrum, indicating that there are multiple modes participating and interfering with each other. A main peak (exclude the frequency 0) whose amplitude is much larger than other small peaks can be observed, and the spatial frequency increases as \(L\) increases. Since the high-order cladding mode corresponds to a higher spatial frequency, the low-order cladding mode dominates the inter-mode interference, as shown in Figure 2b. Therefore, it is reasonable to assume that the MZI is mainly caused by interference between the excited low-order cladding mode and core mode.
## 2 Research Background
### Application of POI Data and Night Lights Remote Sensing Data
From the perspective of POI data, with the continuous expansion of their application, their analysis methods have also been enriched [46], including the nearest neighbor index [43], kernel density analysis [43], spatial autocorrelation analysis [47], standard deviation ellipse analysis [48], spatial clustering analysis [49], accessibility analysis [50], and other techniques. For this study, we referred to existing research and built a nighttime economic vitality index (NEVI), based on which--including NTL RSD and POI data--we analyzed the features of nighttime economic development and the spatial distribution of the YRD urbanagglomeration from the standpoint of multi-source data. We also explored the supply and demand balance of NSFs.
# Biohydrogel Based on Dynamic Covalent Bonds for Wound Healing Applications
## 3 Results and Discussion
### Formulation and Relevant Characterization of Hydrogels
#### 3.1.6 Wound Size Reduction  
Wound size measurements on each evaluation day (Figure 2) showed significantly smaller wound size, higher wound size reduction, and faster healing in the group treated with 3% \(w/w\) hydrogel alone compared to the other groups. Generally, there was no significant difference (\(p>0.05\)) in wound size among the other groups although the group treated with a combination of 3% hydrogel and Savlon antiseptic showed significantly lower healing at the later days (8 and 9). The wound length of groups treated with 3% hydrogel was reduced from 4 cm at day 0 to 1.82 cm at day 6, equivalent to a 54.5% reduction in wound size.  
Certain factors may be responsible for the improved wound healing observed with the 3% \(w/w\) samples. Materials containing boronic acid have been reported to bind with the glycoproteins present in cell membranes that cause the proliferation of lymphocytes and other related cells, facilitating faster wound healing [29; 30]. Boronic acid units are able to covalently bond to biological macromolecules with cis-diol moieties, resulting in the formation of boronate ester rings [31; 32]. Indeed, prior reports have shown that boronic acid-functionalized platforms are useful for wound healing [33]. Furthermore, alginate-based hydrogels have structural similarities to the extracellular matrix, which may facilitate the process of wound healing and closure [26]. The higher wound size reduction observed in the 3% phenylboronic acid-alginate treatment compared to its corresponding sodium alginate treatment show that the attachment of phenylboronic acid to alginate is essential for its improved wound healing activity. We therefore suspect that the unique bonding characteristics of boronic acids may be contributory. The gross appearance of the wound on days 0 and 9 is presented in Figure 3. Typical wound dressings require replacement as surfaces in direct contact with the wound usually degenerate. Therefore, the capacity of the formulated hydrogels to continuously repair and self-heal may reduce the need for replacement by keeping interactive surfaces fresh. Conversely, the combination  
Figure 2: Wound size profile obtained in animal groups after various treatments. N = 5 groups of 5 rats per treatment and control. Treatments: 3% PBA-Alg hydrogel, 4% PBA-Alg hydrogel, 3% PBA-Alg hydrogel + antiseptic. Values are presented as mean \(\pm\) standard deviation. Data were analyzed using one-way analysis of variance (ANOVA). Significant difference at * \(p\) < 0.05 with 3% PBA-Alg hydrogels showing significantly smaller wound size compared to other treatments.  
of PBA-Alg and antiseptic may have caused reduced activity because of interference in the performance of the PBA-Alg. Moreover, delayed wound healing has been observed in the use of some antiseptics or antimicrobial agents [34] and this may be more pronounced where the antiseptic is persistently present in the wound area as is the case in our study.
# Learning Kernel Stein Discrepancy for Training Energy-Based Models
## 2 Relative Work  
### Score-Matching Generative Models  
This paper is about density estimation, where the general problem description is as below:  
**Problem 1**.: _Given a finite set of samples \(\left\{x_{i}\right\}_{i=1}^{n}\subset\chi\subset R^{d}\)(also named true samples) from an unknown theoretical density distribution \(q(x)\), which we approximate by \(p(\theta;z)\), where \(z\) is a random variable, which is sampled from a known simple distribution \(q(z)\), we optimize parameters \(\theta\)so that the output values (also named as false samples) of \(p(\theta;z)\)match the theoretical distribution \(q(x)\)._  
The energy model is a commonly used explicit density estimation algorithm. In the energy model, energy function \(e\) transforms every observation into a single scalar, named as \"energy\". Then, the Gibbs distribution can be expressed:  
\[q(x)=\frac{\exp(-e(x))}{Z} \tag{1}\]  
where \(e:R^{d}\to R\), and \(Z=\int_{x}\exp(-e(x))\) is the integral item, which is also known as the partition function. When using Formula (1) to calculate the explicit density distribution of samples, the partition function is intractable or even unsolvable. In order to solve the intractable problem, [PERSON] [26] proposed score matching to eliminate computation of the partition function. The score is the derivative of the log density of the Gibbs distribution in Formula (1):  
\[\mathbf{s}_{q}(x)=\
abla_{x}\log q(x)=\
abla_{x}\log\left(\frac{\exp(-e(x))}{Z }\right)=\
abla_{x}(-e(x))-\
abla_{x}\log(Z) \tag{2}\]  
The second term \(\
abla_{x}\log(Z)\) is a constant term, which can be ignored; we just need to calculate the first term \(\
abla_{x}\log(-e(x))\). Score matching minimizes the squared distance of two density distributions' scores, which can enable the fake distribution to approach the true theoretical distribution. Score-matching generative models [27; 28; 29] directly train the score network to approach the score of given samples. When the training is completed, the unnormalized density distribution is achieved, which Langevin dynamics can sample from. However, this generation model has some limitations on the density distribution. The Langevin dynamics sampler is not suitable for mixed distributions, and the results are not accurate in low-density regions.
## 5 Data Products, Processing and Distribution
### Data Processing System
#### 5.2.4 Thermal Data  
By selecting a single line of cross-track pixels from the thermal image array, the thermal data can be treated as a line imager in the same manner as the imaging spectrometer. However, the number of cross track pixels in a single line and frame rate limits the product to a coarse spatial resolution (\(\sim\)3 m) using this method. Since the field of view of the full array is much greater than the spacing between images, we can use spatial oversampling techniques to create a smoothed, fine resolution (1 m) temperature product. We might also use additional information from the LiDAR and imaging spectrometer (e.g., land cover) to resolve non-linear edge effects, such as abrupt land cover boundaries [31]. Both of these methods for deriving fine-resolution surface temperature products are currently under development.