esa-sceva/satcom-chunk-collection huggingface dataset:
## Case Study
### The Integrated System
Therefore, all components which come in contact with condensed water must be in compliance with food and beverage norms. Moreover, particular attention must be paid to those families of chemical substances that are subject to Fick's law of water-gas diffusion. In addition, biological hazards must be avoided with continuous sterilization. It is also important to highlight that water coming from air contains, due to the nature of its source, molecules of nitrogenous substances as the most relevant chemical pollutants. Several studies have been conducted to identify the most important pollutants that affect water coming from such a source as air. A detailed description of these is not the subject of the present research, and will be provided in a dedicated paper. However, it can be anticipated that nitrogenous substances and microorganisms can be mentioned as common pollutants in condensed water coming from air. For water treatment in the current application, the plant design was composed of:  
* adsorption resin filters;
* activated carbon filter;
* microfiltration stage;
* UV lamps; and
* reverse osmosis and a mineralization stage. With this configuration, it is possible to obtain, first, sterilized demineralized water, and second, safe mineralized water. The plant was equipped with probes for pressure, pH and electric conductivity monitoring, allowing continuous monitoring of the plant's behaviour. During employment of the system at the hotel, water tests were carried out before the mineralization stage. Results demonstrated that the produced water was suitable for human consumption, both from a chemical and from a microbiological point of view, not only in compliance with Mexican laws, but also with Italian laws (which have some of the strictest global limits). The main components of the integrated machine can be therefore briefly summarized as follows:  
* an air handling unit, incorporating a heat recovery system and a coil for direct evaporation, pocket filters and pre-filters;
* a screw compressor, with cooling power of 100 kW;
* a plate fin heat exchanger for domestic water heating;
* a condenser coil, cooled by external air;
* temperature and humidity probes on the air flux, in order to monitor system behaviour;
* pressure probes;
* a water treatment unit, complete with microfiltration, activated carbon, adsorption resin, reverse osmosis, salt cartridges, UV lamps, manometers, and pH and conductivity probes; and,* a PLC (Programmable Logic Controller) that regulates the machine behaviour, providing overall control, from the water treatment unit to the shift between the air condenser coil and the plate fin heat exchanger.
# Speaker Diarization: A Review of Objectives and Methods
## 6 Speech Activity Detection (SAD)  
A major task in SD is to determine which portions of an input audio signal contain speech, versus having silence or non-speech sounds. This is called speech activity detection (SAD) or voice activity detection (VAD) [57]. Classical approaches used a combination of energy estimation and spectral analysis to carry out SAD. When audio contains speech with only a small amount of background or channel noise, SAD could be as simple as applying an energy threshold, i.e., judge any strong signal as speech. In many practical circumstances, however, a microphone could receive many different sound sources. Since speech has several acoustic features that distinguish it from other sounds (Section 2), energy should be augmented by applying other useful data features, to accomplish more accurate SAD.  
SD research often reports results for two tasks: (1) with \"oracle SAD,\" which indicates that the classification system knows a priori the actual time stamps (_ground truth_), or (2) the system uses a speech activity detector. A classical approach to speaker-turn segmentation is to use hypothesis testing on acoustic segments with sliding (possibly overlapping) successive windows. Such speaker-change detection often requires a threshold that is empirically tuned for major changes in audio, e.g., movement of the sound sources or the microphone.  
As noted in Section 2, speech can be voiced or unvoiced. Unvoiced sounds are weaker and more easily confused with other audio, especially background noise; as such, speech derives from noise generated at a VT constriction but shaped by the upper VT (forward of the constriction). This shortness of the oral cavity of the VT for obstruent sounds generally renders the speech noise as high-pass in nature, as opposed to most environmental noise.  
In the majority of speech that is voiced, the resonances of the VT impose a formant structure to the spectrum, being roughly spaced (e.g., 1 kHz apart, on average, for the typical 17 cm long VT of men). In addition, the quasi-periodic glottal excitation of the VT imposes a rough line spectrum, spaced at F0 Hz. Both of these acoustic features make voiced speech unlike many other non-speech sounds. Most SD exploits these features very indirectly, via Mel spectrograms, rather than any direct feature extraction. Most modern SD, based on ANNs, does not explicitly utilize the speech production features noted in this paper, but allows the network models to learn distinctions via automatic training. The general assumption is that it is best to leave processing to the automatic training, but training with simple gradient descent on generic losses imposes a heavy burden on such methods.
# The Nuclear Receptor PXR in Chronic Liver Disease
## 4 PXR and Liver Steatosis
Additionally, the activation of PXR regulates specific transcription factors involved in the metabolic homeostasis of the liver, in particular the sterol regulatory element binding protein ( SREBP1) and PPAR\(\alpha\)[71]. Hence, PPAR\(\alpha\) is involved in lipid metabolism and translocation, fatty acid (FA) oxidation, and glucose synthesis [72; 73]. Patients and mice with NAFLD showed reduced levels of hepatic PPAR\(\alpha\) with respect to those of PPAR\(\delta\), leading to an increased lipogenesis and decreased fatty acid oxidation [71]. In conclusion, according to these studies, PXR activation induces steatosis in the liver by a complex mechanism leading to both an increase in lipogenesis and a decrease in the \(\beta\)-oxidation of FAs [71; 74]. However, PPAR\(\alpha\) activation is also responsible for the production of fibroblast growth factor 21 (FGF21) [75], whose signaling plays a key role in the progression of NAFLD, due to a direct crosstalk with PXR and CYP3A4-related pathways [63; 71]. Many studies indicated that the expression and activity of CYP3A4 decrease with the progression of steatosis, probably following the FGF21-PXR-CYP3A4 axis [36; 63; 76]. The relevance of CYP3A4 modulation is linked to its prominent role in the metabolism of drugs since more than 50% of the drugs currently used are metabolized by this enzyme [77; 78]. As demonstrated by the interesting findings of [PERSON] and collaborators, NAFLD is characterized by an excess of endoplasmic reticulum (ER) stress that induces the expression of FGF21. This growth factor might enhance mitogen-activated protein kinase (MAPK), which phosphorylates PXR, modulating its intracellular localization. This process results in a reduction in PXR translocation in the nucleus, subsequently leading to a decreased PXR-mediated activation of CYP3A4 transcription [76]. Additionally, in vitro experiments demonstrated that changes in CYP3A4 expression might be associated with free FA-induced steatosis [79]. One of the reasons for the controversial data that have been obtained could reside in the difference in the mechanisms observed in preclinical models and humans. For example, it has been demonstrated that the activation of PXR transcriptional activity leads to opposite effects on gluconeogenesis in rodents and humans [64]. Moreover, the possible influence of disease severity on PXR activation and role can be an explanation of the observed inconsistencies. In conclusion, more studies are needed to unravel whether PXR plays a role in the development and the progression of NAFLD/NASH and to understand the timing of its function in the different stages of this complex disease.
## 2 Theory  
Surface textures represent intentionally engineered topographical features with precisely controlled geometric parameters, standing in stark contrast to the random surface roughness that emerges unpredictably from manufacturing processes. These designed microstructures exhibit repeatable patterns of cavities and protrusions--including but not limited to cubic depressions, cylindrical pillars, spherical dimples, and complex hybrid configurations--each carefully dimensioned and spatially organized to achieve specific functional outcomes. In contrast to the stochastic nature of surface roughness, textured surfaces are precision-engineered to enhance the tribological performance of dynamic interfaces--as exemplified by the tread patterns of automotive tires that improve road adhesion and the dimpled topography of golf balls that strategically reduce aerodynamic drag [25; 33; 41].  
Textured surfaces demonstrate beneficial effects in lubrication applications, including mechanical seals, thrust bearing pads, and journal bearing inner surfaces. These surfaces are primarily employed in rotating systems to improve both static and dynamic performance parameters of bearing supports. Micro-textured surfaces (with dimensions ranging approximately from 1 to 10 \(\upmu\)m) can control lubrication properties in mechanical seals and fluid film bearings, modifying load capacity, frictional torque, and hydrodynamic stiffness and damping parameters. Consequently, proper selection of texture geometry and placement significantly improves bearing system performance. These micro-texturedured surfaces function as lubrication reservoirs during startup, capture particulate contaminants, and protect contacting surfaces while improving hydrodynamic efficiency. Contemporary fabrication methods enable complex 3D surface architectures through deposition, energy beam processing, chemical etching, and mechanical texturing techniques [26; 42].  
Engineered surface features on bearing shell interiors designed to improve system performance typically divide into two principal forms. Protruding microstructures or positive textures with carefully specified dimensions and shapes are manufactured on the inner bearing surface while corresponding indentations or negative textures with precise depth characteristics are produced within the same surface area [33; 42]. Figure 1 illustrates examples of positive and negative textures with different geometric shapes.  
Surface textures created on journal bearing shells, due to variations in the thickness of the lubricating oil film trapped in the bearing clearance space, can contribute to improving the static performance parameters and dynamic stability of the rotor-bearing systems. In Figure 2, a textured two-lobe journal bearing is shown.  
Figure 1: A sample of positive and negative textures created on a smooth surface.
## 3 Experiments
### Used Databases for GMM-Based Stress Detection and Evaluation in the Plonation Signal  
Three different audio corpora were used to create and train the GMM models for the classes of the normal and stressed speech. Our first corpus (further called DB1) was taken from the International Affective Digitized Sounds (IADS-2) [40] comprising 167 sound and noise records with duration of 6 s. The database is standardized and rated using Pleasure and Arousal (P-A) parameters in the range of <1-9>. The second created corpus (DB2) was extracted from the emotional speech database Emo-DB [41]. It contains sentences of the same content with six acted emotions and a neutral state by five male and five female German speakers with time durations from 1.5 s to 8.5 s. We used sentences in a neutral state and a surprise for the C1N class; a fear, an anger, and a disgust for the C2S stress class, and a sadness with a joy for the C3O class--separately for both genders (234 + 306 in total). The third audio corpus (DB3) was extracted from the audiovisual database MSP-IMPROV [42] recorded in English. This database has sentences also evaluated in the P-A scale but in the range from 1 to 5. For compatibility with the DB1, all the applied speech records were resampled at 16 kHz and the mean P-A values were recalculated to fit the range from 1 to 9 of the DB1. We have used only declarative sentences with acted speech in a neutral state by three males and three females, in total 2 \(\times\) 250 sentences (separately for male and female voices) with duration from 0.5 to 6.5 s.  
Applied P-A ranges and mean values for basic emotions are shown in Table 4. For the class C1N, the records with P = {3.5-5.5}, A = {4-6} corresponding to the neutral state and joy were finally used. The sound/noise records with P \(\leq\) 3, A \(\geq\) 6 corresponding to the anger, disgust, and fear emotions were used for the stressed class C2S. The class C3O represented negative emotions of sadness (with both P and A parameters low) and a positive emotion joy (both P and A parameters high)--compare the 4 th and the 7 th line in Table 4. These three described audio databases were used because their records are freely accessible without any fee or other restrictions.  
Figure 8: An arrangement of the phonation recording and PPG signal measurement in the laboratory conditions (for \(MF_{1}\) and \(MF_{4}\) phases): (1) a pick-up microphone Mic2, (2) the analogue mixer XENYX Q802, (3) a control and recording device, (4) body of the wearable PPG sensor with BT data transfer, (5) a reflection optical pulse sensor mounted on the forefinger of the left hand.
# Image Noise Reduction by Means of Bootstrapping-Based Fuzzy Numbers
## 5 Filtering Images  
Since the intensity of the darkness or lightness of the color can be fuzzy in nature, in this section, we want to have a fuzzy look at the color of the pixels. To use this method, which will be described below, we converted the crisp numbers for each pixel of the image to a triangular fuzzy number. On the other hand, to calculate the distance and similarity of these fuzzy pixels using a fuzzy metric, we need a metric that calculates the distance between two fuzzy numbers (as introduced in Section 3). In the following, we explain the required definitions and describe the approach.  
The fuzzy metric filter developed by [PERSON] et al. [15] uses two operators to reduce image noise, which is denoted by \(CFM\). It measures the similarity of the pixels with the help of an operator and uses the second operator to measure the spatial closeness of the pixels in detecting the replacement value of the noisy pixel. The algorithm introduced in this section has the same functionality as the previous method, except that our innovation is to use the fuzzy metric for fuzzy sets and bootstrapping. Hence, the similarity operator is the fuzzy metric for fuzzy sets, but the second operator, which measures the distance or proximity of pixels, is unchanged and similar to the previous filter.
# Improving the Solving of Optimization Problems:
## 4 Quantum Annealer
### Recent Evolutions  
Classical and quantum solver performance is strongly dependent on the energy profile. For example, SA and other local search-based methods are effective in exploring wide and smooth barriers in the energy landscape, but they cannot efficiently overcome high and narrow barriers. In contrast, QA is particularly well suited for problems where the energy landscape is characterized by high and narrow peaks, leveraging quantum tunneling to bypass these barriers. However, QA is expected to be less effective in wide and flat regions [52]. Real-world problems, however, often exhibit heterogeneous energy profiles, as shown in Figure 10 of [46]. A solver effectiveness depends on the size and features of the region in the energy profile that matches its exploration mechanism. For instance, in a heterogeneous landscape, significant improvements can be achieved by combining a local approach for exploring wide and smooth regions with QA to handle rough, peak-filled regions.  
For these reasons, interest in the development of hybrid solvers, which are designed to effectively alternate between local and global search strategies, arises. Several proposals for hybrid approaches have been introduced in recent works [53]. Nowadays, D-Wave has also proposed hybrid solvers [54], which divide the problem, assigning different parts to classical solvers and quantum annealers, and then reconstructing a global solution from the local ones. These solvers support constrained problems in addition to QUBO.  
In addition, the recently proposed reverse annealing [9] begins from a known candidate solution (classical solution), gradually reintroducing quantum fluctuations (superposition) to explore nearby states in the solution space, allowing for refinement and local search within a specific region of the energy landscape.
# A Spectrum Correction Method Based on Optimizing Turbulence Intensity
## 4 Conclusions  
* The truncation standard deviation's bias \(\varepsilon\) that is generated due to the simulated frequency range truncating from the fully defined range affects the simulated turbulent intensity \(\hat{I}_{T}\) of the fluctuating wind speed. Reducing the error between the truncation standard deviation \(\sigma_{theory}\) and the defined standard deviation \(\sigma\) can effectively reduce the effect and take the simulated turbulence intensity \(\hat{I}_{T}\) closer to the defined turbulence intensity \(I_{T}\);
* Under the same simulation conditions, the influence of the initial frequency \(F_{min}\) on the truncation standard deviation's bias \(\varepsilon\) is significantly greater than that of the cutoff frequency \(F_{max}\). When \(F_{min}<10^{-4}\), the ratio of truncation standard deviation \(\sigma_{theory}\) to defined standard deviation \(\sigma\) has fallen within the confidence interval of \(2\sigma\). Under this value, the value range of cutoff frequency \(F_{max}\) can be within [5; 10], and the truncation deviation is small enough;
* The turbulence intensity of the turbulent wind field simulated by the Kaimal spectrum of IEC61400-1 and IEC referenced the exponential coherent function is more conservative and more consistent with the defined turbulence intensity;
* The calculation method of the compensation coefficient \(\beta\) proposed in this paper is not that precise, for its value would change with the simulated height and distance. Nevertheless, the correction methodology can be employed to any wind speed PSD model for wind speed time-history simulation in uniform space.  
Conceptualization, W.Y., Z.L.; Methodology, W.Y.; Data curation, W.Y., J.H.; Formal analysis, W.Y.; Investigation, W.Y., X.Z.; Project administration, Y.C.; Software, W.Y.; Supervision, Z.H.; Validation, W.Y.; Visualization, W.Y., J.H.; Writing--original draft, W.Y., Z.L.; Writing--Review and editing, W.Y., X.Z., Z.L., J.H.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51976113.  
Not Applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{l}{Dependent Variable: lnGHG} \\ \hline Method: Generalized linear model (Newton–Raphson/Marquardt steps) \\ \hline Sample: 2010 2022 \\ \hline Family: Normal \\ \hline Link: Identity \\ \hline Dispersion computed using Pearson Chi-Square \\ \hline Convergence achieved after 0 iterations \\ \hline Coefficient covariance computed using observed Hessian \\ \hline Variable & Coefficient & Std. Error & z-Statistic & Prob. \\ \hline AFFORD & \(-\)0.029424 & 0.009841 & \(-\)2.990033 & 0.0028 \\ \hline INFR & \(-\)0.032348 & 0.010679 & \(-\)3.029205 & 0.0025 \\ \hline USAD & 0.021947 & 0.006896 & 3.182481 & 0.0015 \\ \hline C & 20.80956 & 1.064793 & 19.54329 & 0.0000 \\ \hline Mean dependent var & 18.45490 & S.D. dependent var & & 0.778523 \\ \hline Sum squared resid & 17.99738 & Root MSE & & 0.588306 \\ \hline Log-likelihood & \(-\)46.27946 & Akaike info criterion & & 1.933825 \\ \hline Schwarz criterion & 2.083921 & Hannan–Quinn criter. & & 1.991368 \\ \hline Deviance & 17.99738 & Deviance statistic & & 0.374945 \\ \hline Restr. deviance & 30.91103 & LR statistic & & 34.44142 \\ \hline Prob (LR statistic) & 0.000000 & Pearson SSR & & 17.99738 \\ \hline Pearson statistic & 0.374945 & Dispersion & & 0.374945 \\ \hline \hline \end{tabular} Source: Own processing, 2024.  
\end{table} 
Table 5: GLM estimations for M1. Taken together, the results from Tables 5 and 6 provide empirical evidence that digital transformation has a measurable and significant impact on both GHG emissions and renewable energy adoption. While digital infrastructure improvements contribute to emissions reduction and facilitate renewable energy integration, the effects of affordability and user adoption remain complex. These findings emphasize that the environmental benefits of digitalization are not automatic and depend on the energy composition of the economy, regulatory frameworks, and parallel investments in clean energy technologies. To ensure that digital transformation supports climate objectives, policymakers should prioritize sustainable digital infrastructure, promote energy-efficient technologies, and accelerate the transition toward renewable energy sources. The results also suggest that future research should further explore the interplay between digitalization, energy policies, and emissions reduction strategies in different economic contexts. The results of the two models, M1 and M2, provide valuable insights into how digital infrastructure, affordability, and user adoption of digital technologies impact greenhouse gas emissions and the use of renewable energy in emerging economies. Figure 2 presents how these models respond to the research questions provided in this study.