esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Breed and Finishing Diet on Volatile Compounds
#### 3.2.5 Ketones
However, it could mark the aromatic profile of this group, since several studies [23; 82; 83] reported that ketones, especially 2-ketones (as 2-heptanone), had a relevant effect/influence on the flavor of meat, owing to their low detection odor threshold and peculiar aroma (as spicy, butter, cheese notes, and ethereal). As regards butylactone, researchers found that this lactone is normally formed in ruminants from the corresponding hydroxyl-fatty acids, which are formed in the rumen by the oxidation of oleic (C18:1\(n\)-9) and linoleic acid (C18:2\(n\)-6) supplied in the diet [54; 79]. In this respect, the abundance of butylactone in the D1 group could be especially related to the higher contents in C18:2\(n\)-6 detected in D1 feeds (Table 1) and reflected also in the fatty acid profile of D1 foals (Table 2). Our data are consistent with those shown by other researchers [64], who reported a higher quantity of this ketone in animals finished with commercial concentrates. Therefore, according to the finishing diet, this family of volatile compounds did not contribute significantly to the total volatile compounds of our samples; in fact, it simply represented the seventh most abundant percentage. Among the four groups of study, BU-D1 recorded the highest amounts in total ketones (25.26 AU \(\times\) 10\({}^{4}\)/g fresh meat), followed by JN-D1 (21.10 AU \(\times\) 10\({}^{4}\)/g fresh meat), BU-D2 (18.80 AU \(\times\) 10\({}^{4}\)/g fresh meat) and JN-D2 (16.86 AU \(\times\) 10\({}^{4}\)/g fresh meat). These outcomes reflected what commented above, confirming that the BU breed and D1 treatment favored a major production of ketones compounds. Thus, typical ketone aroma could be detected in BU-D1 foals. Nevertheless, significant interactions between the main fixed factors (breed \(\times\) finishing diet) were not detected, except for 2,3-pentanedione, acetoin and 5-hexene-3-one.
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
###### Abstract  
The design of a Phase-Locked Loop (PLL) to generate the clock reference for the new Spacefibre standard is presented in this paper. Spacefibre has been recently released by the European Space Agency (ESA) and supports up to 6.25 Gbps for on-board satellite communications. Taking as a starting point a rad-hard 6.25 GHz Voltage Controlled Oscillator in 65 nm technology, this work presents the design of the key blocks for an integrated PLL: a Triple Modular Redundancy Phase/Frequency Detector, a Charge Pump, and a passive Loop Filter. The modeling activities carried out in an Advanced Design System have proven that the proposed PLL can be completely integrated on-chip, with a Loop Filter area consumption of only 6000 \(\upmu\)m\({}^{2}\) (considering the 65 nm technology). The design of active circuits has been carried out at the transistor level in a Cadence Virtuoso environment, implementing both system and layout rad-hard techniques, and different solutions are discussed in this paper. As a result, a compact (0.09 mm\({}^{2}\)), low power (10.24 mW), dead zone free and rad-hard PLL is obtained with a Phase Noise below \(-\)80 dB/Hz @ 1 MHz. A preliminary block view and floor plan of the test chip is also proposed.  
rad-hard; PLL (phase-locked loop); SEE (single event effects); Spacefibre; TID (total ionization dose); charge pump; phase/frequency detector; frequency divider +
Footnote †: journal: Sensors
## 4 Experimental Setup
### Performance Measurement  
Since our work is a binary classification problem, aiming to predict if a crashing instance is inside or outside the stack trace, we employed four metrics, also referred to as indicators, including F-measure, g-mean, Matthews Correlation Coefficient (MCC), and Area Under the Receiver Operating Characteristic Curve (AUC) to evaluate the effectiveness of our proposed SCM feature selection framework. The four concepts are defined as follows: the quantity of crashing fault samples whose true labels and predicted labels are both 'InTrace' is referred to as True Positive (TP), the quantity of samples whose true labels are 'OutTrace' but predicted labels are 'InTrace' is referred to as False Positive (FP), the quantity of samples whose true labels are 'InTrace' but predicted label are 'OutTrace' is referred to as False Negative (FN), and the quantity of samples whose true labels and predicted labels are both 'OutTrace' is referred to as True Negative (TN). The F-measure for crashing fault samples is a trade-off between Precision (\(Precision=\frac{TP}{TP+FP}\)) and Recall (\(Recall=\frac{TP}{TP+FN}\)); its formula is provided in Equation (7).  
\[\text{F-measure}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{7}\]  
F-measure ranges from 0 to 1, with higher values indicating better performance.  
Additionally, the g-mean is another important metric used to evaluate model performance on imbalanced data, and it is defined in Equation (8).  
\[\text{g-mean}=\sqrt{\frac{\text{TP}}{\text{TP}+\text{FN}}\times\frac{\text{ TN}}{\text{TN}+\text{FP}}} \tag{8}\]  
g-mean also ranges from 0 to 1, where higher values indicate better performance.  
The indicator MCC is defined in Equation (9).  
\[\text{MCC}=\frac{\text{TP}\times\text{TN}-\text{FP}\times\text{FN}}{\sqrt{( \text{T}\text{ P}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+ \text{FN})}} \tag{9}\]  
MCC ranges from \(-1\) to 1, with higher values indicating better performance.  
AUC is defined as the area formed under the receiver operating characteristic (ROC) curve with the coordinate axes. The ROC curve can easily detect the effect of an arbitrary threshold on the generalization performance of the model. The probabilistic significance of AUC is that, when a positive instance and a negative instance are randomly taken out and put into the classifier to discriminate the corresponding probability of positive output, then AUC is the probability that the positive instance is positive is greater than the probability that the negative instance is positive. In general, an AUC greater than 0.5 indicates that the classifier achieves performance exceeding random guessing and effectively distinguishes between classes.
# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 4 Design of the Flexible Wearable Heart Rate Signal Preprocessing Module  
In modern physiological signal monitoring, heart rate signal preprocessing [86] plays a critical role. Following signal acquisition, preprocessing operations are essential to effectively eliminate noise interference, motion artifacts [87], baseline drift, and achieve data normalization. Proper preprocessing not only establishes a reliable foundation for subsequent heart rate calculations but also significantly reduces system processing complexity, thereby contributing to lower overall power consumption.  
This section will explore the design methods of low-power heart rate signal preprocessing modules from three key dimensions: hardware design, algorithm design, and hardware-algorithm co-design. Each part will demonstrate how researchers achieve system power consumption optimization while ensuring the integrity of preprocessing functions.
# A Concise Review of Silver Diamine Fluoride on Oral Biofilm
###### Abstract  
Studies have shown that silver diamine fluoride (SDF) is an effective agent to arrest and prevent dental caries due to its mineralizing and antibacterial properties. While plenty of studies have investigated the mineralizing properties, there are few papers that have examined its antibacterial effect on oral biofilm. The objective of this study was to identify the effect of silver diamine fluoride on oral biofilm. Method: The keywords used were (silver diamine fluoride OR silver diamine fluoride OR SDF OR silver fluoride OR AgF AND biofilm OR plaque). Two reviewers screened the titles and abstracts and then retrieved the full text of the potentially eligible publications. Publications of original research investigating the effect of SDF on oral biofilm were selected for this review. Results: This review included 15 laboratory studies and six clinical studies among the 540 papers identified. The laboratory studies found that SDF could prevent bacterial adhesion to the tooth surface. SDF also inhibited the growth of cariogenic bacteria, including _Streptococcus mutans_, _Lactobacillus acidophilus_, _Streptococcus sobrinus_, _Lactobacillus rhamnosus_, _Actinomyces naslundii_, and _Enterococcus faecalis_, thus contributing to its success in caries arrest. One clinical study reported a decrease in _Streptococcus mutans_ and _Lactobacillus_ sp. in arrested caries after SDF treatment, and another clinical study found that SDF inhibited the growth of periodontitis microbiota, including _Porphyomonas gingicialis_, _Tammella forsythia_, and _Prerotella intermedia/nigrescens_. However, three clinical studies reported no significant change in the microbial diversity of the plaque on the tooth after SDF treatment. Moreover, one laboratory study and one clinical research study reported that SDF inhibited the growth of _Candida albicans_. Conclusion: Not many research studies have investigated the effects of SDF on oral biofilm, although SDF has been used as a caries-arresting agent with antibacterial properties. However, a few publications have reported that SDF prevented bacterial adhesion to the teeth, inhibited the growth of cariogenic and periodontal bacteria, and possessed antifungal properties.  
silver diamine fluoride; SDF; silver fluoride; AgF; biofilm; plaque
###### Abstract  
A metamaterial aperture antenna (MAA) that generates frequency-diverse radiation field patterns has been introduced in the context of microwave wave imaging to perform compressive image reconstruction. This paper presents a new metamaterial aperture design, which includes two kinds of metamaterial elements with random distribution. One is a high-Q resonant element whose resonant frequency is agile, and the other one is a low-Q element that has a high radiation efficiency across frequency band. Numerical simulations and measurements show that the radiation efficiency of up to 60% can be achieved for the MAA and the far-field patterns owns good orthogonality, when using the complementary electric-field-coupled (CELC) element and the complementary Jerusalem cross (CJC) element with a random distribution ratio of 4 to 1, which could be effectively used to reconstruct the target scattering scene.  
Keywords:agile frequency response; compressive image reconstruction; metamaterial aperture antenna +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Intact Rock Properties  
#### 3.1.1 Limestone  
A total of 19 saturated specimens of 31 mm of \(d\) and an \(H/d\) ratio of 2 were tested, whose results for uniaxial compressive strength, density, and porosity are presented in Table 4. The compressive strength test was performed with saturated specimens, although the results did not differ from those with dry specimens.  
The average value of \(\rho_{sat}\) was 2.63 g\(\cdot\)cm\({}^{-3}\), close to the 2.7 g\(\cdot\)cm\({}^{-3}\) found by [PERSON] [52] and within the range between 2.37 and 2.7 g\(\cdot\)cm\({}^{-3}\), collected by [PERSON] and [PERSON] [53]. The average \(n_{\mathit{eff}}\) (2.55%) was considered low, according to IAEG [54], and the 68.99 MPa of \(\sigma_{c}\) was within the range of 24-290 MPa collected by [PERSON] [55], and that of [PERSON] and [PERSON] [56], from 50 to 200 MPa. Regardless of the range, neither the \(H/d\) ratio of the specimen nor \(d\) is specified in the case of being cylindrical, so these historical data can be collected from different geometries and degrees of weathering.  
The results of the point load test are presented in Table 5, where the values of \(I_{s(50)}\) were determined from Equation (27), with a \(k_{2}\) of \(-0.3126\), obtained experimentally from Figure 2. This suggests that the model is ascending, although it cannot be confirmed whether the decrease in \(I_{s}\) begins in the range of 50-60 mm of \(d_{e}\), due to a lack of data, as occurs with the limestone of Hawkins [44].  
Figure 2a shows the \(P\)-(\(d_{e}\))\({}^{2}\) curve, the one designated to determine \(I_{s(50)}\), but since data that were part of the same line cannot be discarded [3; 22], it was decided to use the \(I_{s}\)-\(d_{e}\) curve (Figure 2b) as a reference, obtaining values of 4.09 and 3.98 MPa, respectively. The average value of these two results (4.03 MPa) was used to determine the curves of the scale effect, \(I_{s}/I_{s(50)}\)-50/\(d_{e}\) (Figure 2c), according to Equation (27), and the most common \(I_{s}/I_{s(50)}\)-\(d_{e}\) (Figure 2d).
## 2 Materials and Methods
### The Autoregressive Moving Average (ARMA)  
ARMA models are used for modeling stationary data and are a combination of \(AR\) and \(MA\) models. In these models, the observation value of any time series is expressed as a linear combination of a certain number of previous observation values and the error term. If the ARMA model is a combination of \(AR(p)\) and \(MA(q)\) models, it contains \((p+q)\) terms and is expressed as \(ARMA(p,q)\)[46].  
\[x_{t}=\sum\limits_{i=1}^{p}\phi_{i}x_{t-1}-\sum\limits_{j=1}^{q}\theta_{j}a_{t -j}+a_{t}\quad\ t=1,2,\ldots,T \tag{9}\]Here, \(a_{t}\) is known as the normal white noise process. It has a zero mean and variance \(\sigma^{2}\). \(T\) is the amount of data in the time series [47]. \(AR\) parameters should satisfy the condition for stationarity, and \(MA\) parameters should satisfy the condition for inevitability [48].  
The ARMA model was applied to the height component of GNSS stations in the same region by using the daily average temperature, pressure, relative humidity, wind speed, and precipitation values measured at 37 meteorology stations in the study area. The model results for four GNSS stations are given in Figure 4 as an example, and the model results for all stations subject to the study are given in Table 1.  
The ARMA model results are generally consistent with the time series of all stations. The RMS values are generally less than 5 mm in the vertical directions, except for the ARTV, ERZ2, GIRS, MUUS, and SIRT1 stations. The RMS values of the vertical component were computed with all meteorological data.  
Figure 5 compares the height components obtained from the ARMA model and GPS measurements. Histograms are particularly useful for understanding the shape of the data distribution. In a histogram, the type of data being measured is represented on the horizontal axis, and the vertical axis represents how many observations are in each bin. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & **RMSE** & **Station** & **RMSE** & **Station** & **RMSE** \\ \hline ARPK & 4.3 & FASA & 3.7 & RHY & 4.0 \\ ARTV & 6.0 & GEME & 3.6 & RZE1 & 4.6 \\ BAYB & 3.8 & GIRS & 5.6 & SAM1 & 5.0 \\ BING & 4.5 & GUMU & 4.5 & SIRT1 & 5.8 \\ BTMN & 4.6 & GURU & 4.0 & SIV1 & 4.6 \\ DYB & 4.6 & HINI & 4.7 & SIVS & 3.6 \\ EKIZ & 4.3 & HORS & 4.7 & SSE1 & 4.5 \\ ELAZ & 4.1 & MALY & 4.6 & TNCE & 4.4 \\ ERGN & 4.3 & MAR1 & 5.0 & TOK1 & 4.1 \\ ERZ2 & 5.9 & MUUS & 5.8 & TRBN & 5.2 \\ ERZR & 4.3 & RDY & 4.3 & TUF1 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ARMA model results of all GNSS stations (mm).  
Figure 4: ARMA model results for RZE1, DIYB, GEME, and HINI stations, respectively.  
histograms of all stations had a bell-shaped distribution. The model is quite compatible and makes predictions close to the true values.
## 3 Verification and Validation of Condensation Heat Transfer Model  
After the construction of the condensation model, it is necessary to carry out further verification and validation of the model. For verification, this paper uses the code-to-code method to model and calculate the same example by using the PISAA and traditional thermal hydraulic codes, respectively, and then compares the calculated results from the different codes. For validation, this paper creates a calculation example by referring to the Wisconsin condensation experiment, and then compares the calculated results with the experimental data.
###### Abstract  
With its superior performance, the unmanned combat air vehicle (UCAV) will gradually become an important combat force in the future beyond-visual-range (BVR) air combat. For the problem of UCAV using the BVR air-to-air missile (AAM) to intercept the highly maneuvering aerial target, an autonomous attack guidance method with high aiming precision is proposed. In BVR air combat, the best launching conditions can be formed through the attack guidance and aiming of fighters, which can give full play to the combat effectiveness of BVR AAMs to the greatest extent. The mode of manned fighters aiming by manual control of pilots is inefficient and obviously not suitable for the autonomous UCAV. Existing attack guidance control methods have some defects such as low precision, poor timeliness, and too much reliance on manual experience when intercepting highly maneuvering targets. To address this problem, aiming error angle is calculated based on the motion model of UCAV and the aiming model of BVR attack fire control in this study, then target motion prediction information is introduced based on the designed model predictive control (MPC) framework, and the adaptive fuzzy guidance controller is designed to generate control variable. To reduce the predicted aiming error angle, the algorithm iteratively optimizes and updates the actual guidance control variable online. The simulation results show that the proposed method is very effective for solving the autonomous attack guidance problem, which has the characteristics of adaptivity, high timeliness, and high aiming precision.  
UCAV; beyond-visual-range (BVR) air combat; autonomous attack guidance; model predictive control (MPC); adaptive fuzzy guidance controller +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Handling the Missing Data  
Following image processing, landmarks with their corresponding coordinates for each frame were extracted into a single dataset (landmarks_data.csv). Each column represents coordinates of a value (x, y or z) for a specific landmark, and each row corresponds to a specific image. Moreover, a final column was added for classification labeling (1 marking the argumentative class and 0 the non-argumentative class).  
As mentioned in Section 2.1, in case of a failed detection, landmarks, and therefore coordinates, were left empty. KNN imputation method was replaced by simple imputation in order to avoid overfitting and underfitting issues [31]. Taking the small size of the datasets used into account, simple imputation provided a more consistent bias by replacing missing values with a constant, whereas the KNN imputer may have introduced noise due to the fact that it relies on nearest neighbors, and therefore not true to the actual values. The number of images was divided equally for each class during training in order to avoid data imbalance. A simple imputer can easily manage imbalanced data, whereas KNN relies solely on equal data distribution among classes. Further, simple imputation can be beneficial in the case of the Random Forest Classifier and Gradient Booster because they are inherently robust to noise thanks to their ensemble nature. Therefore, overfitting can be avoided, as the simple imputer minimally affects the bias of the model. This means that the model's performance remains unaffected, while the KNN imputer could introduce patterns to the Random Forest classifier and Gradient Booster leading to potential overfitting.  
Figure 2: Non-argumentative image dataset sample.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 7 Future Directions
### Scale Issues of SUHI  
The variety of scales and their potential impact on study findings have generated a challenging dilemma for researchers to \"determine the most appropriate scale and resolution of studying and assessing the effects of scale and resolution\" [259]. This certainly applies to SUHI studies in which scientists inherently engage with all the scales from a UHI perspective and from LST data used to measure the associated SUHI. On one hand, UHI is formed by multiple processes on a small scale such as human metabolism to meso-scale phenomena like synoptic weather patterns [24]. The significance of a process depends strongly on the resolution of observation. Nevertheless, there is no feasible way to consider all-scale processes simultaneously due to data and knowledge limitations in characterizing the phenomenon at each scale. On the other hand, LST data vary greatly by sensors in terms of spatial and temporal resolutions, which suggests that SUHI results might be sensitive to the sensor choice. Some studies proved the scaling effects of SUHI formulation in terms of spatial extent and pixel size [171; 173; 225; 260; 261; 262; 263], but they concentrated mainly on the relationship between SUHI and land surface properties (e.g., landscape features, ISA, and NDVI) at a local scale. Several key scale issues remain unclear. For example, what are the differences in the factors contributing to the SUHI at local, regional, and global scales? Are there predictable differences in the magnitudes and patterns of SUHI derived from the LST observation at different spatial and temporal resolutions? What is the extent of a metro city SUHI impact on the climate or SUHI of associated satellite cities? What is the optimal scale for conducting measures to mitigate SUHI? The previously mentioned discussion suggests caution with regard to the scale of study when interpreting the previous results, and thus warrants a significant need for a thorough investigation of SUHI from a scale perspective.