esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Elaborating the Models in MATLAB-Simulink DC-DC Buck Converter
#### 2.2.3 Incremental Conductance Algorithm  
The incremental conductance algorithm is directly based on the power variations. The output voltage and current of the photovoltaic system FV are used in order to calculate the conductance and incremental conductance. The ratio between the power derivate and the voltage derivate is given by the relation [19; 23]:  
\[\frac{dP}{dV}=\frac{d(VI)}{dV}=I\frac{dV}{dV}+V\frac{dI}{dV}=I+V\frac{dI}{dV} \tag{8}\]  
To find the maximum power point (MPP), the following conditions need to be fulfilled [19; 23]:  
Figure 5: Organigram of Perturb and Observe (P&O MPPT algorithm).  
\[\frac{dP}{dV}=0\to I+V\frac{dI}{dV}=0\rightarrow-\frac{I}{V}=\frac{dI}{dV} \tag{9}\]  
where the instant conductance is given by the ratio \(I/V\), and the incremental conductance (instantaneous change in conductance) is given by the ratio \(dI/dV\). The comparison of these two amounts shows on which side of the maximum power point MPP the photovoltaic system FV is currently running. The analysis of the derivates presented below will allow us to determine whether the photovoltaic system FV is running at the maximum power point MPP or far away from this [19].  
\[\begin{array}{l}\frac{dI}{dV}>0\ for\ V<V_{mpp}\\ \frac{dI}{dV}=0\ for\ V=V_{mpp}\\ \frac{dI}{dV}<0\ for\ V>V_{mpp}\end{array} \tag{10}\]  
The diagram in Figure 6 presents the running principle of the incremental conductance algorithm. This algorithm is simple, easy to implement, and features a very high follow-up efficiency. In the case of several ideal conditions, it is able to establish whether the current running point is on the maximum power point MPP or not, but when measured, the running point might oscillate around the maximum power point MPP. The incremental conductance algorithm provides a better guidance on the direction of the maximum power point motion, and lower oscillations result than in the case of the Perturb and Observe (P&O) algorithm; instead, it requires more intense calculations. Nevertheless, a micro-controller such as the Atmega 328 was included on the development board. Arduino Uno can easily provide the needed calculations for a photovoltaic system FV by using the incremental conductance algorithm and may supply the synchronizing signals for the high resolution pulse-width modulation (PWM) needed to control the DC-DC converter.  
Figure 6: Organigram of the incremental conductance algorithm.
## 5 Results and Discussion
### Effects of Dual-Rotor Rotation Direction on Sealing Performance
Therefore, the presence of a pressure differential increases gas film pressure and improves the gas film's force. These effects are evident from the sealing gas film force results under the four rotation modes (Figure 9). Figure 8: Mode 4 (only the inner rotor rotates): (**a**) without inlet pressure difference; (**b**) with inlet pressure difference. Figure 7: Mode 3 (counter-rotation): (**a**) without inlet pressure difference; (**b**) with inlet pressure difference. Although the bearing capacity of the gas film is maximised when the two rotors rotate in the same direction biaxially and minimised when the two rotors are in counter-rotation biaxially with/without differential pressure, gas film forces varied during each rotation mode, with significantly reduced differential pressure. Compared with Mode 1 (Figure 5), the gas film force in Mode 3 (Figure 7) was reduced by about 48.67% without differential pressure and 25.86% with differential pressure. Thus, inlet differential pressure mitigates the adverse dynamic pressure effects of the inversion interface and improves the gas film's bearing capacity. As shown in Figure 9, friction power consumption is maximised when the two rotors rotate in the same direction biaxially with/without differential pressure. Meanwhile, friction power consumption is minimised during counter-rotation. Measures of the friction power consumption when the two rotors rotate uniaxially exist between these two extremes since friction power consumption is mainly associated with fluid velocity in the gas film gap. The fluid velocity is maximised during same-direction rotation and minimised during counter-rotation. As can be seen from the comparison of the friction power consumption with/without differential pressure in different rotation modes, there are fewer changes in friction power consumption at different pressures. For instance, compared with Mode 1 (Figure 5), the friction power consumption without differential pressure in Mode 3 (Figure 7) decreases by about 32.14%; meanwhile, the friction power consumption with differential pressure decreases by about 25.78%. During counter-rotation, the gas film's dynamic pressure effects are weak, the bearing capacity is small, and contact wear is likely between the rotor and the sealing ring. Thus, sealing shafts in counter-rotation was a challenge for analysis and design. Therefore, the following will focus on the sealing performance when the inner and outer rotors rotate in reverse. Figure 9: The influence of four rotating modes on sealing performance.
# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
###### Abstract  
Xerostomia, commonly known as dry mouth, is characterized by reduced salivary secretion, which can lead to various oral health issues and discomfort. In this paper, we propose a novel, non-invasive method for predicting xerostomia through the analysis of tongue images. To predict salivary gland secretion from tongue images, we collected images from patients who visited the hospital with complaints of dry mouth and measured their saliva secretion. Features were extracted from these tongue images, and correlation analysis was performed using machine learning techniques to assess the relationship between the extracted features and measured saliva secretion. We obtained tongue images and saliva secretion measurements from 176 patients. Images were cropped to 100 \(\times\) 100 pixels, resulting in 462 features. The dataset was divided into training and test sets, consisting of 160 and 16 samples, respectively. The correlation coefficients for the training and test datasets were 0.9496 and 0.9415, respectively, while the correlation coefficient for the entire dataset was 0.9482. The estimated linear equation was y = 0.9244x + 2.1664. This study aimed to predict salivary gland secretion based on tongue images. By extracting features from color images and employing a neural network machine learning model, we estimated salivary gland secretion. With a sufficiently large dataset of tongue images, further advancements in regression analysis using deep learning techniques could enhance the accuracy of these predictions.  
d Article  
Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image. _Appl. Sci._**2024**, _14_, 10118. [[https://doi.org/10.3390/app142210118](https://doi.org/10.3390/app142210118)]([https://doi.org/10.3390/app142210118](https://doi.org/10.3390/app142210118))
# A Preliminary Study of Model-Generated Speech
## 1 Introduction
### The Acquisition of Vowels, Consonants, and Tones by Children in Terms of Their Order of Emergence
In addition, quantitatively speaking, the number of sounds with unmarked features is always more than or equal to those with marked features. For example, ref. [17] documented sound distributions in more than 300 languages. They found that when compared to unmarked sounds, infrequent sounds are marked, and the domain could be either within one language, usually referring to the phoneme level, or exist across languages. Furthermore, simple-articulated sounds are more frequent than complex-articulated sounds. The vowel \([\mathrm{a}]\) offers a good example, as it appears in all languages, whereas the vowel \([\mathrm{\ddot{e}}]\) is relatively rare. It is well known that \([\mathrm{i},\,\mathrm{a},\) and \(\mathrm{u}]\) are canonical vowels, meaning all languages have them. As such, it is no wonder that these canonical vowels are easier models to learn. As for consonants, with respect to the MOA and POA, it is clear that stops are more consonant-like compared to other consonants (and fricatives are more easily articulated than affricates), while coronals represent the default/unmarked POA. As for tones, previous studies on the unchecked tones acquired by children were reviewed because the checked tones are seldom reported. For Mandarin Chinese, ref. [18] proposed that the high-level and falling tones are acquired before the rising and dipping tones, meaning the level tones are likely easier than the contour tones. In this case, the former maintains a consistent frequency in pitch, while the latter needs to change the pitch level at least once. [PERSON] (2001) postulated a similar argument. In addition to contour vs. level tones, she also proposed that rising tones are more marked than falling tones, while high-level tones are more marked than low-level tones. Her theory of tone thus predicts that all the unmarked tones are acquired earlier than the marked tones.
## 2 Results and Discussion
The relationships between volatile esters and linoleic acid and linolenic acid, induced by increases in linoleic acid and linolenic acid, or measures promoting their formation, could enhance the volatile esters biosynthesis in fruits. Volatile esters are formed by esterification of alcohols and acyl from acyl-CoAs in a reaction catalyzed by alcohol acyltransferase (AAT) [14,15]. The concentration of substrates, including alcohols, acyl-CoAs and the precursors of these compounds in fruits, may be a limiting factor for ester production. As demonstrated in the present study, addition of linoleic acid and linolenic acid, which were precursors of many straight-chain alcohols and aldehydes, increased ester production in pear fruits. Previous experiments in apples have shown that off-flavor fruits, such as those harvested prior to reaching maturity or those stored for an extend period of time in low O\({}_{2}\) and/or high CO\({}_{2}\) environments, immediately produced high amounts of aroma volatiles after feeding on various volatile precursors [5]. These results validated the hypothesis that enhancing levels of linoleic acid and/or linolenic acid in fruits could increase the ester content. As intermediate of fatty acid metabolism, the direct precursors of ester formation are also affected by the key enzymes catalyzing their production. The activities of these key enzymes may be affected by cultivar, maturity stage, and environmental conditions, either on or off of the plant [16, 17, 18, 19, 20, 21]. The greater increase of esters in \"Nanguoli\" fruits than in \"Dangshansuli\" fruits fed on the same quantities of linoleic acid and linolenic acid may have been caused by differing enzyme activity in the cultivars. Additionally, (_Z_)-2-hexenyl acetate was detected only in \"Dangshansuli\" fruits fed on linolenic acid, ethyl (_Z_)-4-octenoate was detected only in \"Nanguolii\" fruits fed on linolenic acid, methyl tetradecanoate was the only ester detected in \"Dangshansuli\" and \"Nanguolii\" fruits fed on linolenic acid. The result may due to the specific recognition of key enzymes.
## 5 Experimental Evaluation and Results
### Experimental Setup
This approach ensured a fair comparison with Dash, which also utilized a hashing index. To thoroughly test the performance and robustness of our hashing scheme, we employed both uniform and skewed/zipfian distributions with varying sizes. These distributions were used to stress test the scheme and assess its performance under different scenarios. The experimental results demonstrated that our scheme outperformed others, primarily due to the cross-bucket record storage technique that reduced the frequency of costly directory doubling operations. Additionally, the metadata incorporated in our scheme helped minimize unnecessary operations and contentions, further enhancing its performance. For the experiments, a fixed-size key-value structure consisting of 8-byte integers was used. Furthermore, the experiment included variable-length keys generated by Dash [18] using their benchmark's pre-generated variable keys, ensuring consistency and comparability with previous studies.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 4 Discussion
### Radiometric Comparability between S-1A and S-1B over a Wide Backscatter Range  
Summarizing all target types and polarization channels used to cover a wide backscatter range, the radiometric comparability between S-1A and S-1B is investigated by a direct backscatter comparison between both SAR instruments. Figure 11 depicts the radar backscatter difference as a function of SNR for distributed targets used. The lake results (dark blue) have the lowest SNR at about 0.8 dB, the ice area (blue) observed with cross-polarization channel shows low SNR below 10 dB; for the ice area with copolarized channel and the rainforest targets (green), medium SNR between 10 dB and 20 dB are visible.  
In addition to distributed targets, results derived from point targets (black plus and cross signs) are also depicted for the differences between S-1A and S-1B within Figure 11. Due to the high RCS of the point targets, the related SNRs are significantly higher compared to the distributed targets. Therefore, the point target results are plotted at the right side of the graph. For point targets a low difference between the RCS derived from S-1A and S-1B is found: between \(-\)0.3 dB and \(+\)0.1 dB for the VV channel (plus symbols) and between \(+\)0.1 dB and \(+\)0.5 dB for the VH channel (cross symbols).  
Figure 10: Standard deviation from derived RCS (point targets) and radar brightness \(\beta^{0}\) for different distributed target types for S-1A (blue) and S-1B (red) using copolarized (squares) and cross-polarized (crosses) data.  
The radiometric intercomparability between S-1A and S-1B is expressed by the radar brightness differences for distributed targets and RCS differences for point targets as depicted in Figure 11. In addition to the observed image backscatter differences (full symbols), the noise subtracted backscatter differences (open symbols) are also plotted. For low SNRs (below about 10 dB) the noise impact is no more negligible; therefore, the noise should be subtracted for estimating and comparing radar brightness values. In all analyzed cases the noise subtraction improves the \(\beta^{0}\) differences between S-1A and S-1B. Except for the lake case, the differences are between \(-0.3\) dB and \(+0.5\) dB and in a good agreement with the point target results. Note that for very low SNR values noise subtracted backscatter results are very sensitive to the noise and image power derived from the data.
## 5 Activity Recognition
### Hierarchical Classification Study
#### 5.2.1 LoD1--Work vs. Idle  
Table 3 displays each class's average F1 and F1 scores when the models are applied to the test set. The baseline MLP and the two hybrid model variants have a similar average F1 score of about 0.83. Among the individual labels, the work label consistently has the highest F1 score of around 0.86 in all three cases. The F1 score for the idle label is 0.80 for the MLP and DeepConvBiLSTM and 0.82 for the DeepConvLSTM.  
Figure 6 shows the confusion plot for the DeepConvBiLSTM model (the confusion plots for the MLP and the DeepConvLSTM are shown in Appendix A). It reveals differences in performance between the models. The MLP has an accuracy of 73% for the idle class and 70% for the work class, while the two hybrid models show an increase in accuracy. The work class accuracy improves to 90% in the hybrid models, with only a slight increase for the idle class.  
The hybrid models perform well in identifying the work class, with only 10% of samples that belong to the work class classified as idle. However, the main challenge lies in identifying the idle class as 25% of the samples idle class classified as work. One reason is the limited number of samples in the dataset, i.e., less than a quarter of the total dataset. Data augmentation methods can solve this imbalance of the datasets. Another reason is the similarity of the classes, which makes classifying difficult.
## 2 Method and Data
### Main Data for GLOPEM-CEVSA Model Input and Validations
#### 2.2.3 Validation and Comparison of the Derived GPPs
Annual GPPs are from the references listed for the eight sites. fluxes have been measured there since November 2001 using the eddy covariance technique with a sonic anemometer-thermometer (CSAT3; Campbell Scientific Inc., Logan, UT, USA) and an open-path CO2/H2O analyzer (LI7500; Li-Cor Inc., Lincoln, NE, USA) at 41.3 m height, about 15 m above the forest canopy. More details are given in Reference [80]. (2) Methods of Validation and Their Comparison  
The GPP estimations from the three FPAR products (GPP\({}_{\rm NDVI3g}\), GPP\({}_{\rm NDVI1g}\), and GPP\({}_{\rm MOD15}\)) were validated and evaluated with the GPP from the eddy towers. They were also compared with the MODIS GPP product MOD17A2 (GPP\({}_{\rm MOD17}\)) [25,49]. All three GPP products were simulated using the same climatic data input and different FPAR datasets from the GIMMS NDVI3g (FPAR\({}_{\rm NDVI2g}\)), the previous version of GIMMS NDVI1g (FPAR\({}_{\rm NDVI1g}\)) and the MODIS FPAR product (FPAR\({}_{\rm MOD15A2}\)). This allowed comprehensive exploration of the reasons for GPP differences. Linear regression analysis was applied to evaluate the performance of the remote sensed GPP and FPAR with reference to the on-ground observed GPP. Correlation coefficient \(r\), adjusted coefficient of determination \(R^{2}\), \(P\)-value of a standard \(T\)-test statistic \(p\), and sample size were used to define accuracy statistically. We also applied Root-mean-square error (RMSE) and relative RMSE to quantify the errors of the remote sensing based GPPs.
## 3 Hypothesis Development
### Enjoyment and Engagement  
Even though enjoyment and engagement are two different concepts [48], some visualization studies use them interchangeably. In this case, they are related but not identical. Enjovable activities are also usually very engaging, but it is clear that it is possible to be deeply engaged in an activity even if it is not necessarily exciting and enjoyable. Engagement is a complex process that combines multiple influences to encourage a sense of flow and fluid interaction, fulfilling arousal and the pleasant emotions of curiosity, surprise, and happiness [49]. Furthermore, [PERSON] and [PERSON] [50] consider engagement as a category of user experience characterized by challenge, positive affect, endurability, aesthetic and sensory appeal, attention, feedback, variety/novelty, interactivity, and perceived user control. A study he conducted on user engagement found that participants (web users, shoppers, webcast viewers, and video game players) all expressed a range of positive emotions, including enjoyment, satisfaction, and fun, during the engagement period [50]. These positive emotions were considered to be a form of engagement. In summary, we propose the following hypothesis:  
**H3:**_In addition to direct effects, perceived interactivity may influence user engagement through enjoyment, which, in turn, affects user attitudes toward the news._  
The relationship between the variables is illustrated in Figure 2. In summary, the influence of perceived interactivity on users' attitudes can be mediated by three possible mechanisms, including two parallel mediating relationships and one chain mediating relationship:  
\[\begin{split}\text{Perceived Interactivity}\rightarrow\text{Enjoyment} \rightarrow\text{Attitude}\\ \text{Perceived Interactivity}\rightarrow\text{Engagement} \rightarrow\text{Attitude}\\ \text{Perception}\end{split}\]
# On Finding and Enumerating Maximal and Maximum \(k\)-Partite Cliques in \(k\)-Partite Graphs
## 3 The Asymptotic Optimality of MMCE
In the \(k\geq 6\) case, we generalize the construction of the last two cases, with \(n\) evenly divisible by \(3k\) and \(p=n/3k\). Let \(G\) denote an edgeless \(k\)-partite graph whose vertices are arranged in partite sets \(S_{1}\), \(S_{2}\),..., \(S_{k}\), each set of cardinality \(3p=n/k\), with \(S_{1}=\{s_{1,1}\), \(s_{1,2}\),..., \(s_{1,3p}\}\), \(S_{2}=\{s_{2,1}\), \(s_{2,2}\),..., \(s_{2,3p}\}\),..., \(S_{k}=\{s_{1,k}\), \(s_{1,2}\),..., \(s_{k,3p}\}\). For \(1\leq i\leq k\), we partition \(S_{i}\) into \(S_{i,1}=\{s_{i,1}\), \(s_{i,2}\),..., \(s_{i,p}\}\), \(S_{i,2}=\{s_{i,p+1}\), \(s_{i,p+2}\),..., \(s_{i,2p}\}\) and \(S_{i,3}=\{s_{i,2p+1}\), \(s_{i,2p+2}\),..., \(s_{i,3p}\}\). We now place in \(G\) every interpartite edge except those in the \(kp\) disjoint triangles \(\{s_{1,i},s_{2,i+p},s_{3,i+2p}\}\), \(\{s_{2,i},s_{3,i+p},s_{4,i+2p}\}\), \(\{s_{3,i},s_{4,i+p},s_{5,i+2p}\}\), \(\dots\), \(\{s_{k-2,i},s_{k-1,i+p},s_{k+2p}\}\), \(\{s_{k-1,i},s_{k+p},s_{1+2p}\}\), \(\{s_{k,i},s_{1+p},s_{2+i,2p}\}\), where \(1\leq i\leq p\). Let \(M\) denote the set of all  
Figure 1: An illustration of the construction used in the \(k=4\) case of Theorem 2. Lines denote bundles of edges that are absent from a balanced complete \(4\)-partite graph of order \(n\). maximal \(k\)-partite cliques in \(G\) containing at least one vertex from each of \(S_{1,i}\), \(S_{2,i}\),..., \(S_{k,i}\), \(1\leq i\leq 3\). Again following the reasoning of the \(k=3\) case, there are \(3^{n/3k}-3(2^{n/3k}-1)\) ways to choose clique candidate vertices from each of \(\{S_{1,1},S_{2,2},S_{3,3}\}\), \(\{S_{2,1},S_{3,2},S_{4,3}\}\), \(\{S_{3,1},S_{4,2},S_{5,3}\}\),..., \(\{S_{k-2,1},S_{k-1,2},S_{k,3}\}\), \(\{S_{k-1,1},S_{k,2},S_{1,3}\}\), \(\{S_{k,1},S_{1,2},S_{2,3}\}\). Since these \(k\) sets do not overlap, we apply the product rule and conclude that \(M\) contains \((3^{n/3k}-3(2^{n/3k}-1))^{k}\) elements. Assigning this function to \(f(n)\) and \(3^{n/3}\) to \(g(n)\), it follows that \(\lim\limits_{n\to\infty}\frac{f(n)}{g(n)}=1\), which we address in Appendix B. Letting \(F(n)\) denote the number of maximal \(k\)-partite cliques in \(G\), we have \(f(n)\leq F(n)\leq g(n)\). By the squeeze theorem of calculus, \(\lim\limits_{n\to\infty}\frac{F(n)}{g(n)}=1\), from which we conclude that the number of maximal \(k\)-partite cliques in the family of graphs described by \(G\) is \(\sim\)\(3^{n/3}\). In summary, for every \(k\geq 3\), we have shown that an infinite set of \(k\)-partite graphs exists with \(\sim\)\(3^{n/3}\) maximal \(k\)-partite cliques.