esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Datasets
#### 3.1.2 Test Datasets  
Four datasets (ADC04, MIREX05 ([[http://labrosa.ee.columbia.edu/projects/melody/](http://labrosa.ee.columbia.edu/projects/melody/)]([http://labrosa.ee.columbia.edu/projects/melody/](http://labrosa.ee.columbia.edu/projects/melody/))), MedleyDB, and RWC) to evaluate the performance of melody extraction were used. Non-vocal audio clips were excluded to focus on vocal melody extraction. To compare the voice detection performance, our models were also evaluated using Jameto [40], a public dataset that is mainly used for singing voice detection.  
* **ADC04**: 20 excerpts of 20 s that contain pop, jazz, and opera songs, as well as synthesized singing and audio from MIDI files. Jazz and MIDI songs were excluded from the evaluation.  
* **MIREX05**: 13 excerpts that contain rock, R&B, pop, and jazz songs, as well as audio generated from a MIDI file. We used 12 songs out of a total of 20, excluding jazz and MIDI files for evaluation.
* **MedleyDB**: 12 songs not included in the training set. For a comparison of results under the same conditions, we selected the test sets according to [18].
* **RWC**: 15 songs not included in the training set. This was used internally to evaluate the performance of the proposed models.
* **Jamendo**: 93 songs designed for the evaluation of singing voice detection. Among them, only 16 songs designated as a test set to measure the performance of singing voice detection were used.
## 2 Specimen Preparation and Test
### Mode-I Fracture Toughness Test  
The SCB samples were loaded into an Instron materials tester. In the displacement loading mode, the loading rate is 0.0002 mm/s, and the span of the two loading ends is 30.5 mm (\(S\) = 30.5 mm). The test results are automatically recorded by computer. The test loading system and assembly are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Diameter (D)** & **Thickness (B)** & **Artificial-Notch Length (_a_)** & **Span Length (S)** \\ \hline
50 mm & 20 mm & 12.5 mm & 30.5 mm \\ \hline \end{tabular}
\end{table}
Table 1: Geometrical dimensions of SCB specimens  
Figure 2: Processing and instruments. (**a**) Cylindrical samples; (**b**) Brazilian disc samples; (**c**) Micro milling; (**d**) Semi-circular bend sample.  
The experimental results were calculated according to the method recommended by the ISRM [6], and the fracture toughness was calculated with the following formula.  
\[K_{IC}=\frac{P_{\max}\sqrt{\pi a}}{2 RB}\gamma_{I} \tag{2}\]  
\[Yt=-1.297+9.516(S/2R)-(0.47+16.457(S/2R))\beta+(1.071+34.401(S/2R))\beta^{2} \tag{3}\]  
\[\beta=a/R \tag{4}\]  
where \(P_{max}\) is the peak load at sample failure and \(\gamma^{\prime}\) is the dimensionless stress intensity factor. The standards used in this paper are: \(S/(2R)=0.61\), \(a/R=0.5\).
## 4 Simulation Experiments
### Analysis and Results  
#### 4.2.1 Analysis of Downstream Intersection Performance Intersection Capacity  
The comparison of control effects on the downstream intersection for the two transition types under low-inflow traffic conditions is shown in Figure 8. The optimal capacity ratio of the downstream intersection fluctuates around 0%, indicating that the application of the control strategies has a relatively mild impact on capacity under these conditions. As the inflow traffic flow increases, the optimal ratio remains negative, indicating that applying the control strategies under these conditions leads to some decrease in the capacity of the downstream intersection but within an acceptable range.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Influencing Factor** & **Range** & **Adjustment Step Size** \\ \hline Inflow traffic volume (pcu/h) & [1000, 3000] & 500 \\ Transition segment length (m) & [440, 1240] & 200 \\ Desired speed (km/h) & [30, 70] & 10 \\ Difference in cycle lengths (s) a & [10, 50] & 10 \\ \hline \hline \end{tabular}  
* Difference in cycle lengths of the upstream and downstream intersections.  
\end{table}
Table 1: Ranges and adjustment step sizes of the influencing factors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Name** & **Value** \\ \hline Simulation duration (s) & 3600 \\ Maximum acceleration (m/s2) & 2.5 \\ Minimum acceleration (m/s2) & \(-\)2.5 \\ Response time (s) & 1.5 \\ Vehicle type & Car, HGV a, Bus \\ Percentage of vehicles & 0.7, 0.1, 0.2 \\ Lane width (m) & 3.5 \\ simulation.RandomSeed & 40 \\ \hline \hline \end{tabular}  
* Heavy goods vehicle.  
\end{table}
Table 2: VISSIM traffic simulation parameter setting.  
The optimum ratio of the intersection average vehicle delay time of the downstream intersection exhibits significant fluctuations for the two transition types, as shown in Figure 9. Combinations of strategies were observed that yielded better results and others that yielded worse results. This indicates that under the combined influence of the four relevant factors, applying coordinated control strategies to the downstream intersection may have either a positive or negative impact on the average vehicle delay time.
## 2 Results and Discussion  
### Isolation and Identification of B. deamine kh3  
A novel _Brettanomyces_ species, _B. deamine_ kh3 was successfully isolated from apple vinegar and registered as a novel _Brettanomyces_ genus and granted accession numbers from the Korean Collection for Type Cultures (KCTC; 14262 BP) and the National Center for Biotechnology Information (NCBI; MT755882). _Brettanomyces_ has recently been applied in the production of craft, specialty beers, and wines [23]. Subsequently, we investigated the possibility of off-flavor removal, using the novel isolated _Brettanomyces_ species, _B. deamine_ kh3. The morphological characteristics of _B. deamine_ kh3 were observed using scanning electron microscopy (SEM). The SEM images indicated that the morphology of _B. deamine_kh3 exhibited a rod-shaped and round, short length of approximately 4.8 \(\upmu\)m (Figure 1), which is commonly observed in yeast strains.  
The physiological properties of the novel _B. deamine_ kh3 strain were evaluated in terms of optimal growth conditions (temperature, pH, NaCl concentration, and growth medium) and characteristics (enzymatic activity and tolerance test) (Supplementary Table S2). Results indicated that the strain could grow at temperatures of 20-40 \({}^{\circ}\)C (optimum 30 \({}^{\circ}\)C), pH of 4.0-10.0 (optimum 7.0), NaCl concentration under 7%, and ethanol concentration less than 8%. Upon monitoring the substrate degradation activity, the strain could hydrolyze seculin, but it did not work for gelatin, casein, DMA, Tween 20, and Tween 80. The strain was found to have growth capability under certain growth media, such as yeast malt (YM) agar, yeast extract peptone dextrose (YPD) agar, trypticase soy agar (TSA), nutrient agar (NA), Luria-Bertani (LB) agar, potato dextrose agar (PDA), De Man, Rogosa, and Sharpe (MRS) agar, Czapek yeast extract agar (CYA), Reasoner's 2A (R2A) agar, and MacConkey (MAC) agar (data not shown). Furthermore, the enzymatic activity profiles of our novel strains were obtained using a commercial API-ZYM kit assay. The results showed that _B. deamine_ kh3 showed positive activity against 11 enzymes. Among them, the strongest activity (3 to 5+) was observed for alkaline phosphatase, leucine arylamidase, valine arylamidase, acid phosphatase, naphthol-AS-BI-phosphohydrolase, \(\alpha\)-glucosidase, and \(\beta\)-glucosidase. However, a relatively lower activity (1 to +2) was observed for esterase (C4), esterase lipase (C8), cystine arylamidase, and \(\alpha\)-mannosidase. No visible activity was observed for lipase (C14), trypsin, \(\alpha\)-chymotrypsin, \(\alpha\)-galactosidase, \(\beta\)-galactosidase, \(\beta\)-glucuronidase, \(N\)-acetyl-\(\beta\)-glucosaminidase, and \(\alpha\)-fucosidase (Figure 2).  
As can be observed in the ITS phylogenetic tree (Figure 3), the closest relative of _B. deamine_ kh3 was _B. bruxellensis_ with 95.5% identity, which was formed in the same clade.
# Non-Plaque Induced Diffuse Gingival Overgrowth: An Overview
## 3 Drug-Influenced Gingival Overgrowth (DIGO)
\begin{table}
\begin{tabular}{l l} \hline \hline
**Involved Drug** & \multicolumn{2}{c}{**Clinical Characteristics**} \\ \hline  & - & First sign of enlargement affects the interdental papilla. \\  & - & Gingival lobulations extend labiolingually and coronally to cover the entire anatomic crowns. \\  & - & Tissue is dense, resilient, and may be stippled. \\ Antieipletics & - & Presence of secondary inflammation may occur. \\ (sodium valproate, phenobarbital, vigabitrin, primidone, mephenytoin and ethosuximide) & - & Teeth may be displaced, whereas generally the dedentulous areas \\  & - & In patients treated with phenobarbital, the gingiva grows globally and uniformly without lobulation of the papillae, and gingival lesions may be more severe in the posterior areas than in the anterior areas [16]. \\  & - & Gingiva is markedly more inflammatory and the gingival bleeding \\  & - & Hyperplasia appears to begin in the interdental papilae and, more commonly, on the labial surface of anterior regions. \\ Immunosuppressants (cyclosporine and tacrolimus) & - & Lesions are generally limited to the keratinized gingiva that appears firm, with focal lobulations, having a stippled and pink surface. \\  & - & Risk of chronic mycosis is possible [16; 17; 18]. \\  & - & Gingival enlargement may appear as a firm nodular enlargement of the interdental papillae. \\  & - & Anterior teeth are more affected than the posterior ones. \\  & - & More pronounced on the facial/buccal than the palatal/lingual surfaces. \\ Calcium antagonist drugs & - & In severe cases the entire papillae and the surrounding tissues are enlarged, giving the gingival tissues a lobulated appearance. \\  & - & The overgrown tissue creates pockets that harbor pathogenic bacteria \\  & - & that are beyond the reach of a toothbrush or dental fossa. Can lead to an \\  & - & increased host susceptibility to oral infection, caries and periodontal \\  & - & Hemorrhagic and hyperemic gums. \\ Oral Contraceptives & - & Localized or diffuse increased volume. \\  & - & Loss of periodontal attachment, possibly tooth mobility [23; 24]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Clinical characteristics of Drug Induced Gingival Overgrowth.
# A Model-Based Intelligent Adjustment Method of Toolface for Bent-Housing Motor
## 3 Model-Based Intelligent Method
### BP Neural Network Algorithm  
A BP neural network is a multilayer feedforward neural network trained according to the error back propagation algorithm. In terms of structure, a BP network has an input layer, hidden layer, and output layer. Figure 2 shows the structure of a three-layer BP neural network with \(l\) input neuron, \(m\) hidden neurons, and \(n\) output neurons. The number of hidden layer nodes of the neural network can be determined using the following formula.  
\[m=\sqrt{l+n}+a \tag{16}\]  
where \(a\) is a constant between 1 and 10.  
In essence, the BP algorithm takes the MSE (mean square error) of the network as the objective function and uses the gradient descent method to calculate the minimum value of the MSE. Assuming that the output data after BP neural network training are \(Y_{P}(\widehat{y_{1}},\widehat{y_{2}},\widehat{y_{3}},\ldots,\widehat{y_{n}})\), and the actual data are \(Y_{R}(y_{1},y_{2},y_{3},\ldots,y_{n})\), MSE is calculated according to the following formula.  
\[MSE=\frac{\sum\limits_{i=1}^{n}(y_{i}-\widehat{y_{i}})^{2}}{n} \tag{17}\]  
The BP algorithm includes the forward propagation of a signal and back propagation of error. During forward propagation, the input value acts on the output node through  
Figure 1: Toolface of actual value and the drill string dynamics model calculated value.  
the hidden layer, and generates the output value through nonlinear transformation. If the actual output value does not match the expected output value, the back propagation process of error will be started. The error back propagation is a process of transmitting the output error back to the input layer through the hidden layer and distributing the error to all units of each layer. The error value obtained from each layer is used as the basis for adjusting the weight of each unit. By adjusting the weights and thresholds of input nodes and hidden nodes, as well as hidden nodes and output nodes, the error can be reduced along the gradient direction. After repeated training, the weights and thresholds corresponding to the minimum error are finally determined, and the training is completed.  
The advantage of a BP neural network is its strong nonlinear mapping ability. It has been proven that the three-layer BP neural network model can approximate any nonlinear problem [23]. However, the BP neural network also has some defects; the main defect is that it is easy to fall into the local optimal solution in the training process [11; 14].
## 3 Materials and Methods
### Feature Selection
#### 3.2.1 FOS Features  
FOS texture features are the most straightforward and most commonly used to represent the image textural characteristics [37]. They are intensity based and mainly computed from a histogram of gray-level values of the image. They include mean, variance, skewness, and kurtosis which specify some of the image's textural characteristics and are obtained from the distribution of intensity values and individual pixel values in an image, as follows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **HRF dataset** & **Dataset\_2** \\ \hline DR & 15 & 96 \\ Glaucoma & 15 & 96 \\ Normal & 15 & 96 \\ \hline Total & **45** & **288** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of images in each dataset.  
Figure 3: Sample images from the two datasets: (**a**) DR image from HRF dataset; (**b**) glaucoma image from HRF dataset; (**c**) normal (healthy) image form HRF dataset; (**d**) DR image from Dataset\_2; (**e**) glaucoma image from Dataset\_2; (**f**) normal image from Dataset\_2.  
Let an image \(I\), of size \(H\times W\) pixels, where \(H\) and \(W\) are the height and width, respectively, of \(I\) in pixels. Then,  
1. The mean, \(\mu\), gives information concerning the central tendency of the pixel intensities in the image. If \((h,w)\), \(h=0,1,\ldots,H-1\), \(w=0,1,\ldots,W-1\) is a given pixel and \(I(h,w)\) is the intensity of that pixel, then the mean of \(I\) is given by \[\mu=\frac{\sum_{h=0}^{H-1}\sum_{w=0}^{W-1}I(h,w)}{H\times W}.\] (1)
2. The variance \(\sigma^{2}\), where \(\sigma\) is the standard deviation, determines how the intensities of pixels are scattered around the mean intensity \(\mu\) and is given by \[\sigma^{2}=\frac{\sum_{h=0}^{H-1}\sum_{w=0}^{W-1}(I(h,w)-\mu)^{2}}{H\times W}.\] (2)
3. The skewness \(\mu^{3}\) gives knowledge regarding the symmetry of the gray-level values around the mean. A given distribution of data is symmetric if it looks the same to the left and right side of the mean, as in the normal distribution; otherwise, it is skewed left or right, and the skewness is given by \[\mu^{3}=\frac{\sum_{h=0}^{H-1}\sum_{w=0}^{W-1}(I(h,w)-\mu)^{3}}{H\times W\times\mu}.\] (3)
4. The kurtosis, \(\mu^{4}\), concerns the peakedness of the pixel intensity distribution in the image's histogram. It is a measure of the extreme values in the tail of the distribution, with a large kurtosis indicating that the tail peakedness exceeds that of the normal distribution. Data distributions with low kurtosis exhibit tail data that are generally less extreme than the normal distribution tails. The kurtosis is given by \[\mu^{4}=\frac{\sum_{h=0}^{H-1}\sum_{w=0}^{W-1}(I(h,w)-\mu)^{4}}{H\times W\times \mu}-3.\] (4)  
Though simple to compute, FOS features ignore the spatial relationship between pixels and their surrounding neighbors. So, this group is not sufficient to quantify or discriminate between changes in retinal images [38].
###### Abstract  
Hospital intensive care units (ICUs) frequently experience inadequate lighting conditions, with low daytime and excessive nighttime illuminance, which can negatively affect patient recovery and the work performance of health personnel. This study examines the impact of window design parameters--specifically, window-to-wall ratio (WWR) and window position--and interior surface reflectance on visual comfort, lighting performance, energy consumption, and human well-being in intensive care units (ICUs) in Mediterranean climates, according to orientation. Using dynamic lighting metrics, such as daylight autonomy (_DA_) and circadian stimulus autonomy (_CSA_), this research quantifies the influence of these design factors. The results suggest that a WWR of 25% is optimal for achieving sufficient _DA_ and _CSA_ values, with centered window configurations preferred for uniform daylight distribution and circadian stimulus. This study further emphasizes the significance of interior reflectance, recommending bright coatings to maximize outcomes, while advising against dark finishes, particularly in north-facing rooms or with smaller WWRs. Although Seville shows slightly better performance than Barcelona, the proposed configurations are effective across both locations, highlighting the prioritization of window sizing, positioning, and reflectance over Mediterranean geographical differences. These findings offer practical guidance for ICU design to enhance natural lighting, supporting patient recovery and overall well-being through improved circadian alignment.  
daylighting; hospital; daylight autonomy; energy efficiency; visual comfort; circadian stimulus +
Footnote †: journal: Medical Imaging
## Appendix A Mutual Coherence of an Incoherent Source  
The expression (1) of the complex visibility for a pair of separated antennas observing the same brightness-temperature distribution is established in this annex with the approach and the reasoning found in radio astronomy [13] but with the symbols and the notations used in this note to facilitate understanding for remote sensing readers. Furthermore, the voltage pattern of the two elementary antennas is here taken into account, whereas it is not in chapter 15 of [13] nor in chapter 10 of [12], which are, however, the most cited references for applications of the Van Cittert-Zernike theorem in the radio domain and in that of optics, respectively. The quasi-monochromatic electromagnetic fields at the elementary antennas \(\mathcal{A}_{p}\) and \(\mathcal{A}_{q}\) coming from a source element \(\mathrm{d}S\) in the direction \((\theta,\phi)\) are given by:  
\[E_{p}(\theta,\phi,t)=\mathcal{E}(\theta,\phi,t-\frac{r_{p}}{c})\frac{\mathrm{e }^{-2j\pi v_{o}(t-\frac{r_{p}}{c})}}{r_{p}}, \tag{1}\]  
and  
\[E_{q}(\theta,\phi,t)=\mathcal{E}(\theta,\phi,t-\frac{r_{q}}{c})\frac{\mathrm{ e}^{-2j\pi v_{o}(t-\frac{r_{q}}{c})}}{r_{q}}, \tag{2}\]  
where \(\mathcal{E}(\theta,\phi,t)\) is the complex amplitude of the electromagnetic field at the level of the source \(\mathrm{d}S\) found in the direction \((\theta,\phi)\): \(|\mathcal{E}|\) is the strength and \(\arg(\mathcal{E})\) is the phase of the radiation, \(r_{p}\equiv\|\mathbf{r}_{p}\|=\|\mathbf{SA}_{p}\|\) and \(r_{q}\equiv\|\mathbf{r}_{q}\|=\|\mathbf{SA}_{q}\|\) are the distances from \(\mathrm{d}S\) to \(\mathcal{A}_{p}\) and \(\mathcal{A}_{q}\), respectively. The exponential term in Equations (1) and (2) represents the phase change when traveling from \(\mathrm{d}S\) to \(\mathcal{A}_{p}\) and \(\mathcal{A}_{q}\), whereas \(1/r_{p}\) and \(1/r_{q}\) represents the attenuation of the amplitude during the same paths. In the words of [PERSON] in chapter 20 of [54], each expression (1) and (2) _\"...represents a general spherical wave travelling outward from the origin at the speed \(c\).\"_ There is a similar expression with \((t+r/c)\) instead of \((t-r/c)\), which _\"...also represents a spherical wave, but one which travels inward from large \(r\) toward the origin,\"_ but it is here obviously without any interest.
## 3 Proposed Methodology
### Data Enrichment
#### 3.2.1 Correlation between Social Media and Market Data  
At this step, we started from the intuition that the information extracted from social media has a strong correlation with the price fluctuations of cryptocurrencies. For each considered cryptocurrency, we calculated some social engagement metrics (number of tweets, followers, likes, and retweets) to determine their correlation with the daily closing prices. In particular, we used both Pearson and Spearman correlation tests, which are statistical measures used to assess the relationships between variables. The Pearson correlation evaluates linear relationships between normally distributed time series data, while Spearman evaluates monotonic relationships between variables, which may not necessarily be linear. Given the nature of cryptocurrency prices and social data, both correlation measures were considered to provide a comprehensive understanding of the relationship among time series. For example, Figure 2 provides a view of the social metrics and price fluctuations of Shiba Inu, where the values have been normalized to make the chart more understandable and comparable.  
For all the cryptocurrencies considered, it has been verified that there is a strong correlation between the social metrics introduced above and the daily closing price. For example, Table 3 reports the Pearson and Spearman correlation coefficients of three meme coins (i.e., Shiba Inu, Floki, and CateCoin). As shown, the results indicate strong positive correlations between the tweet volume and cryptocurrency prices (ranging from 0.723 to 0.868 for Pearson and 0.841 to 0.909 for Spearman). The follower count exhibits moderate correlations, while likes and retweets show variable values, with Floki showing a strong correlation.  
Figure 2: A five-month view of daily Twitter metrics and a closing price associated with Shiba Inu.  
Finally, these correlation values are added as features to the dataset in order to use them for training a machine learning model.
## 2 Geometry of the Presented Antenna  
The geometry of the proposed design is shown in Figure 1. It consists of an L-shaped rectangular dielectric resonator (DR) excited by two symmetrical slots of equal dimensions coupled through microstrip feed lines. DR is made up of ceramic material (\(\varepsilon_{\tau}\) = 10) and has a loss tan\(\delta\) = 0.002, and is placed on a ground plane of size 100 \(\times\) 100 mm\({}^{2}\). FR4 substrate (\(\varepsilon_{\tau}\) = 4.6) of the size of the ground plane with height 1.6 mm is the base layer of the design. Figure 1a shows a cylindrical air-gap of 22 mm in height with a hole of 6 mm radius drilled in the DR along with two metallic strips (6 \(\times\) 20 mm\({}^{2}\)) at the optimized position to improve isolation. A top view of the design is shown in Figure 1b, in which \(pl\) and \(pw\) is the length and width of both the coupling slots, whereas \(s1+s2+s3\) is the stub length to improve impedance matching and \(m\_w\) is the width of the microstrip feed lines (placed at the bottom surface of the substrate). Both the coupling slots have the dimensions \(pw\times pl\) etched on the ground plane and are fed by two 50 \(\Omega\) microstrip feed lines. Table 1 lists the optimized dimensions of the proposed design after a thorough parametric analysis.  
Figure 1: Presented antenna Geometry (**a**) 3-D view; (**b**) Top view.