esa-sceva/satcom-chunk-collection huggingface dataset:
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 2 Speech Enhancement  
Let \(y(n)\) denote the observed noisy speech signal given by \(y(n)=x(n)+d(n)\) with \(x(n)\) the clean speech, \(d(n)\) the additive noise, and \(n\) the discrete-time index. The pre-processing stage for performing speech enhancement in the spectral domain starts with a short-term speech analysis of the segmented \(y(n)\) into overlapping frames through the application of a window function. Then, a short-term Fourier transform (STFT) is used to obtain the spectral representation:  
\[Y(k,l)=\sum_{n=0}^{N-1}y(n+lM)h(n)e^{-j(2\pi/N)nk}, \tag{1}\]  
where \(l\) is the time frame index, \(k\) is the frequency bin index, \(h(n)\) is the analysis window of size \(N\), and \(M\) is the number of samples between two frames.  
Figure 1 depicts the spectral-domain speech estimator algorithm. The power spectrum \(|Y(k,l)|^{2}\) is used as input of the noise reduction block, while the spectral phase is kept apart for the last block of post-processing for speech reconstruction. The output of the system, \(\hat{x}(n)\), is an enhanced version of the noisy signal as similar as possible to clean speech.  
Figure 1: Spectral-domain speech estimator algorithm.  
The core of the enhancement method is depicted in the central block. This approach is the paradigm followed by the family of spectral-domain speech estimator algorithms. However, notice that this is the case where it is separately considered the hypothesis of speech presence and absence [24; 25]. The spectral-domain speech estimator algorithm uses \(|Y(k,l)|^{2}\) for computing the gain of the MMSE estimator (\(G_{MMSE}\)), which is used for obtaining the filter gain function that modifies the \(|Y(k,l)|^{2}\) according to the speech presence probability (see Section II in [5]).
# Development of Automatic Berthing Support Program for Autonomous Ships
## 2 Methods
### Forces Acting on the Hull During Berthing
#### 2.1.3. Frictional Force  
When current acts laterally on the hull, it generates lateral current pressure, whereas when it acts longitudinally, it applies frictional force on the underwater surface area of the hull. Frictional force (\(F_{F}\)) is calculated using Equation (7), and the frictional resistance coefficient is obtained from the Schoenherr curve and the ITTC 1957 model-ship correlation curve based on the Reynolds number [26]. Additionally, the wetted surface area (\(S\)) is calculated using Equation (8) and applied in Equation (7).  
\[F_{F}=\frac{1}{2}\rho_{w}C_{f}SV_{c}^{2}\frac{1}{1000}, \tag{7}\]  
\[S=(1.7d+C_{b}B)L_{BP}, \tag{8}\]  
where \(F_{F}\) represents the frictional force, \(\rho_{w}\) represents the seawater density, \(C_{f}\) represents the frictional resistance coefficient, \(S\) represents the wetted surface area, \(V_{c}\) represents the current speed, \(d\) represents the draft, \(C_{b}\) represents the block coefficient, \(B\) represents the beam, and \(L_{BP}\) represents the waterline length.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 2 Background
### Reconstruction of the State-Space Attractor  
Usually, a dynamic system can be described by a first-order differential equation:  
\[\dot{x}=F(x,t), \tag{1}\]  
where \(F\) is a function of the variables \(x\) and \(t\). Variable \(x\) represents the state variable and \(t\) represents time.  
If we plot \(x_{1}(t)\) against \(x_{2}(t)\), the resulted space is called to be'state space'. If the solution of the equation is visualized as a trajectory in state space, for a given set of initial conditions, \(x_{i}(0)\), the trajectories representing the dynamic properties of the system in the state space will trace out a unique path. After a certain period of disturbance, the path will gradually migrate towards an invariant geometric figure in state space. This geometric figure is called to be 'attractor' [22].This attractor in state space represents the steady state point of a dynamic system and exhibits sensitive dependence on the initial conditions of the system. This is the reason why the state space predictive model can be used for identifying the dynamic change (usually caused by damage) of a system [22; 24; 25; 26]. Usually, an initial baseline attractor is reconstructed for an intact system and compared with the attractor reconstructed for a damaged one. Damage has been shown to change the geometry of the attractor. Hence, by identifying a change in the geometric properties, damage can be detected and even quantified. This is the basic damage detection principle of this paper.  
In theory, all the vector variables of the system are needed to reconstruct the attractor. Unfortunately, it is difficult to obtain every vector variable in practice. Luckily, according to the embedding algorithm proposed by [PERSON] [27], the unknown variables can be reconstructed by the measured ones. Assuming \(x(n)\) is the measured time series, the attractor \(X(n)\) can be expressed as follows:  
\[X(n)=[x(n),x(n+T),\ldots,x(n+(m-1)T)], \tag{2}\]  
where \(n\) is the discrete time index of the sampled value; \(m\) is the embedding dimension, it is the dimension of the reconstructed state space; \(T\) is the time delay, and all of the reconstructed vectors are simply time-delayed versions of the measured signal with a lag of \(T\). The choice of parameters \(T\) and \(m\) influences the reconstruction result. In this study, the commonly acknowledged methods for the choice of the time delay \(T\) and embedding dimension \(m\), namely, the average mutual information function (AMI) [28] and Cao's function [29], are proposed to calculate the embedding parameters.  
Figure 2: The illustration of laser-generated ultrasonics modulations.
# The _E. coli_ Global Regulator DksA Reduces Transcription during T4 Infection
## 3 Results
### DksA Does Not Inhibit T4 Pe Transcription In Vitro  
Our results suggested that host protein DksA might directly down-regulate the activity of T4 Pe's. Although DksA regulated promoters often have AT- or GC-rich sequences within the \(-4\) to \(-6\) region (called the discriminator) that are needed for DksA action [(61; 62)], a comparison of the sequences of down-regulated and unchanged Pe's revealed no obvious sequence motif that typically correlates with DksA modulation of transcription ([PERSON] NIDDK, Bethesda, MD. Comparison of promoter sequences, 2017.). To ask whether DksA inhibited Pe transcription directly, we first purified DksA and tested its previously characterized activity to inhibit the ribosomal promoter Pr_rnB1_. As seen in Figure S9, addition of DksA specifically inhibited Pr_rnB1_ but did not affect transcription from a control \(\sigma^{\text{70}}\)-dependent promoter (_PS17_). As expected, the presence of ppGpp improved this inhibition. Next, we performed in vitro transcription reactions using T4 DNA under the same buffer conditions. We assayed for transcription from specific Pe's using primer extension (Figure 5). DksA did not inhibit transcription from any of the tested Pe's that we previously showed were affected by the presence of DksA in vivo: Pe40.4 or Pe41 (Figure 5a), Pe35.3 (Figure 5b), or Pe148.6 (Figure 5c). The presence of DksA also did not affect transcription from the middle promoters Pm_dsbA_ (Figure 5c) or Pm_46_ (Figure 5b). It is important to note that the overall level of MotA/AsiA-activated transcription in this in vitro system was low because of the use of buffer conditions suited for DksA. We speculate that DksA reduction of specific Pe activity is either indirect or requires additional factors/conditions that were not present in our in vitro transcription system.
## 3 Methods
### Daily 100-m Reflectance Dataset Generation
(2016) and [PERSON] et al. (2017). To derive parameters for the crop identification and yield estimation, the widely used NDVI and land surface water index (LSWI) were employed. NDVI is calculated based on the surface reflectance data of the red (RED) and near infrared (NIR) bands, and LSWI is based on the reflectance of the NIR and short-wave infrared (SWIR) bands ([PERSON] et al., 2012; [PERSON] et al., 2013; [PERSON] et al., 2014). To avoid spatial coverage gaps resulting from negative atmosphere conditions such as clouds and haze, the values of the pixels that were labeled as \"good\" during 10 days (10-day syntheses, the starting days were the 1 st, 11 th, or 21 st day of a month) were averaged to composite the NDVI and LSWI. Finally, Savitzky-Golay (S-G) filtering was applied to remove the noise resulting from atmospheric variability and sun zenith angle changes from the two indices that ([PERSON] et al., 2013; [PERSON] et al., 2013). \[\text{NDVI}=\ \left(\rho_{\text{NIR}}-\rho_{\text{RED}}\right)/ \left(\rho_{\text{NIR}}+\rho_{\text{RED}}\right) \tag{3}\] \[\text{LSWI}=\ \left(\rho_{\text{NIR}}-\rho_{\text{SWIR}}\right)/ \left(\rho_{\text{NIR}}+\rho_{\text{SWIR}}\right) \tag{4}\]  
where \(\rho_{\text{RED}}\), \(\rho_{\text{NIR}}\) and \(\rho_{\text{SWIR}}\) represent the reflectance of the red, near-infrared and short-wave infrared bands.
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 3 Results
### Sprayed LM Patch Antenna  
A coaxially-fed rectangular patch antenna was designed to radiate at 3.8 GHz using dimensions of 31.5 mm \(\times\) 25.4 mm. First, the antenna was implemented and measured using copper (Cu), then by a sprayed LM patch using a polyimide mask; both were fabricated on Rogers Duroid 5880 substrate. Figure 13 shows a good match between the reflection coefficients of the Cu and LM patch antennas, as both resonate around 3.8 GHz. Similar performance of LM and Cu antennas has been reported in literature. An unstretched LM loop antenna had a radiation efficiency of 98% [23]. The gain of an LM monopole antenna was 5.10 to 5.26 dBi [24], matching the gain of a standard monopole (5.17 dBi). An embedded vascular LM dipole antenna had nearly identical radiation patterns as a Cu dipole antenna [25], and a helical LM antenna had the same gain as the Cu version of the antenna [26].  
Figure 11: Set-up for scraping sprayed LM: (**a**) Schematic of the set-up, (**b**) photo of the Inkredible 3D Bioprinter, and 30-gauge needle for physically scraping the liquid metal film.  
Figure 12: Characterization of scraped LM. (**a**) Images of the scraped LM with different gap widths. (**b**) Gap width in the sprayed LM vs. the \(z\)-position of the needle. (**c**) Resistance measured across the gap vs. gap width.
## 5 Conclusions  
The chemical investigation of _Aquimarina_ sp. MC085 led to the isolation of compounds **1-3**, which suppressed TGF-\(\beta\)-induced EMT of A549 human lung cancer cells. _Cyclo_(L-Pro-L-Leu) (**1**) and N-phenethylacamide (**3**) inhibited TGF-\(\beta\)/Smad pathways and suppressed the metastasis of A549 cells by affecting TGF-\(\beta\)-induced EMT. However, _cyclo_ (L-Pro-L-Ile) (**2**) downregulated mesenchymal factors via a non-Smad-mediated signaling pathway.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11198866/s1](https://www.mdpi.com/article/10.3390/app11198866/s1)]([https://www.mdpi.com/article/10.3390/app11198866/s1](https://www.mdpi.com/article/10.3390/app11198866/s1)), Figure S1: LR-ESI-MS data of **1**; Figure S2: \({}^{1}\)H NMR spectrum (250 MHz) of **1** in DMSO-\(d_{6}\); Figure S3: \({}^{13}\)C NMR (63 MHz) spectrum of **1** in DMSO-\(d_{6}\); Figure S4: HMQC spectrum of **1** in DMSO-\(d_{6}\); Figure S5: HMBC spectrum of **1** in DMSO-\(d_{6}\); Figure S6: \({}^{1}\)H NMR spectrum (600 MHz) of **1** in CD\({}_{3}\)OD; Figure S7: \({}^{13}\)C NMR (150 MHz) spectrum of **1** in CD\({}_{3}\)OD; Figure S8: [PERSON]'s analysis of **1**; Figure S9: LR-ESI-MS data of **2**; Figure S10: \({}^{1}\)H NMR spectrum (250 MHz) of **2** in CD\({}_{3}\)OD; Figure S11: \({}^{3}\)C NMR (63 MHz) spectrum of **2** in CD\({}_{3}\)OD; Figure S12: [PERSON]'s analysis of **2**; Figure S13: LR-ESI-MS data of **3**; Figure S14: \({}^{1}\)H NMR spectrum (250 MHz) of **3** in CDCl\({}_{3}\); Figure S15: \({}^{13}\)C NMR (63 MHz) spectrum of **3** in CDCl\({}_{3}\).  
**Author Contributions:** Conceptualization, K.S.K. and H.C.; formal analysis, M.J.L., G.J.K., M.-S.S., J.M. and S.K.; investigation, M.J.L. and J.-WN.; writing--original draft preparation, M.J.L., G.J.K. and H.C.; writing--review and editing, K.S.K.; visualization, M.J.L. and G.J.K.; supervision, K.S.K. and H.C.; project administration, K.S.K. and H.C.; funding acquisition, K.S.K. and H.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the 2020 Yeungnam University Research Grants and the research grants from the Basic Science Research Program through the National Research Foundation of Korea (NRF) (grant number: 2019R1F1A1059173 and 2020R1A6A1A03044512).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Proposed mechanism of action of compounds **1–3** on TGF-\(\beta\)-induced EMT of A549 cells.
## 3 Results and Discussion
### UCS of MSWCs after Uniaxial Cyclic Loading and Unloading
Under a low mudstone proportion and low cyclic damage, the cementitious material reacts sufficiently, the specimen damage degree is low, the uniaxial compressive strength of the MSWC is high, the crushing degree is high, and it shows obvious brittle failure; under a high mudstone proportion and high cyclic damage, the cementitious material reacts sufficiently, the specimen damage degree is low, the uniaxial compressive strength of the MSWC is low, the crushing degree is low, the specimen has more fine cracks, the axial deformation after loading increases, and it shows obvious plastic failure. With increases of mudstone proportion and the number of uniaxial cyclic loading and unloading, the MSWC gradually changes from brittle failure to plastic failure. To explore the failure modes of the MSWC under different mudstone proportions and uniaxial cyclic loading and unloading times, Figure 9 was obtained. Visible cracks depicted by red line, and small cracks depicted by yellow line. Under a low mudstone proportion and low cyclic damage, the slag cementitious material reacts sufficiently, and the specimen damage degree is low, the MSWC uniaxial compressive strength is high, the crushing degree is high, and it shows obvious brittle failure; under a high mudstone proportion and high  
Figure 8: Effect of the number of uniaxial cyclic loading and unloading on the UCS of the MSWC. Figure 7: Effect of mudstone proportion on the UCS of MSWC. cyclic damage, the mudstone cementitious material reacts sufficiently, the specimen damage degree is low, the MSWC uniaxial compressive strength is low, the crushing degree is low, the specimen has more fine cracks, the axial deformation increases after loading, and it shows obvious plastic failure. With an increase of mudstone proportion and uniaxial cyclic loading and unloading times, the MSWC gradually changes from brittle failure to plastic failure. Figure 9: Effect of the number of cycles (\(n\)) and mudstone proportion (\(r\)) on the MSWC fracture mode. By analysing the coupling effect of the uniaxial cyclic loading-unloading times and the mudstone proportions, a three-dimensional surface fitting is performed to obtain the uniaxial compressive strength of the MSWC under different uniaxial cyclic loading-unloading times and mudstone proportions. The results are shown in Figure 10.
## 2 Study Area and Materials
### Auxiliary Geo-Data  
Related geo-data can offer significant information to support disentangling different crop phenology and other specific agricultural practices [38]. For this reason, the digital elevation model (DEM) dataset was involved in this work in order to retrieve elevation and slope variables. DEM was obtained for free from the Land Processes Distributed Active Archive Center (LP DAAC) (United States) at 30 m of spatial resolution [39].
# Integrating Sustainable Performance into the Digital Maturity Models for SMEs in Manufacturing
## 4 Discussion
### Contribution
Their results show that the combined adoption of a circular economy and I4.0 has a positive effect on a company's social performance. Indeed, one of the differences between the conceptual framework of [34] and that presented in this article is that the authors use a combination of the circular economy and I4.0 as variables affecting the sustainable development of SMMEs. These two approaches are very similar, but the conceptual framework in Figure 5 suggests that it is not necessary to combine the circular economy with I4.0 to measure its benefits on a company's sustainable performance. One additional contribution of the framework presented in this article is the thoroughness of the measured subcategories. For example, ref. [34] does not consider durability in the measurement of the environmental performance of surveyed companies. This is, in fact, the most recurrent  
Figure 5: Contribution to Achieving the Objective of Establishing the Link Between Maturity Measurement and Sustainable Performance [26]. difference across the literature. Similarly, ref. [33] also does not use product durability to measure environmental gains. Furthermore, the model presented by [33] uses I4.0 facilitators and barriers in relation to sustainable development to quantify an institution's financial gains. Finally, ref. [70] proposes a model very similar to the conceptual framework proposed in this paper, specifically a model measuring the impact of I4.0 technologies on sustainable performance within the supply chain. The two articles are aligned in the same direction, but the contribution of [70] is more specific. Additionally, one aspect aligning the findings of this article with those of others is that the authors do not break down the measurement of each category. They directly use economic, social, and environmental performance as indicators; however, this type of measurement is highly subjective and may lead to biases among responding companies. While this contribution provides a significant and unique advancement, it also raises several discussion points and considerations that must be addressed. These aspects deserve in-depth exploration to identify the inherent limitations of this research and highlight opportunities for future improvements. The following sections outline key limitations of this study, including the specificities of SMMEs, the selection process for articles and databases, the choice of the digital maturity model, the lack of empirical validation of the conceptual framework, the weighting of performance indicators, and the need for further empirical research. These discussions provide a comprehensive perspective on the study's scope and open avenues for future research on digital transformation and sustainable performance in SMMEs.
###### Abstract  
Post-fabrication heat treatment (PFHT) is one of the most applied strategies for achieving the desired microstructure and mechanical resistance on additive manufactured components because of the non-equilibrium microstructural state of the material in the as-built condition. In particular, during PFHT, 17-4 PH martensitic stainless steel is mainly strengthened by the precipitation of Cu-rich nanometric particles and Nb carbides into the metal matrix. In this work, the influence of specifically designed PFHTs on the microstructural and mechanical properties of 17-4 PH single tracks fabricated via direct energy deposition was studied. Different solubilization and aging times, as well as a direct aging strategy, were considered. Optical microscopy, X-ray diffractometry, and transmission electron microscopy were used to investigate the microstructure evolution induced by the PFHTs. Moreover, Vickers microhardness measurements were performed to evaluate the increase in mechanical strength. In all cases, the heat-treated single tracks showed a mean microhardness higher than that of the depositions in the as-built condition. In the single tracks subjected to solution treatment, followed by aging for about 100 h, the presence of both Cu-rich precipitates and Nb carbides was assessed; conversely, when directly aged from the as-built condition, only Nb carbides were detected. In the latter case, the carbides were finer and closer to each other than those in the single tracks aged after the solution treatment.  
Keywords:17-4 PH steel; laser-direct energy deposition; heat treatment; transmission electron microscopy; hardness +
Footnote †: journal: Applied sciences
## 4 Ground Motion Parameters
Since the JMA seismic intensity scale of 0-VII is quite different than the MSI I-XII in China, the JMA equivalent peak acceleration (\(A_{0.3}\)) rather than JMA seismic intensity is used to characterize ground motion:  
\[A_{0.3}=a_{0}\big{|}\tau(a_{0})\geq 0.3 \tag{5}\]  
where \(a_{0}\) is the vector composite acceleration, and \(\tau(a_{0})\) is the duration of composite acceleration larger than \(a_{0}\). The destructive index (DI) has been proposed by [PERSON] [42] to estimate the damage potential of ground motion by calculating the logarithm of the product of vertical acceleration and velocity, and is given by  
\[\text{DI}=\text{max}\Big{(}\lg\Big{(}\big{|}a(t)v(t)\big{|}\Big{)}\Big{)} \tag{6}\]  
The acceleration spectrum intensity (ASI) is proposed to analyze ground motion effect on short period structures like concrete dams, and is given by  
\[\text{ASI}=\int_{0.1}^{0.5}S_{a}(\xi=0.05,T)dT \tag{7}\]  
where \(S_{a}(\xi=0.05,T)\) is the acceleration response spectrum with damping ratio \(\xi=0.05\). The velocity spectrum intensity (VSI) is proposed to indicate ground motion damage potential on most structures whose fundamental periods are between 0.1 and 2.5 s:  
\[\text{VSI}=\int_{0.1}^{2.5}\text{PSV}(\xi=0.05,T)dT \tag{8}\]  
where \(\text{PSV}(\xi=0.05,T)\) is the pseudo-velocity response spectrum with damping ratio \(\xi=0.05\). The Housner intensity (HI) is quite similar to velocity spectrum intensity, except that the damping ratio is selected as 0.2, since the damping ratio will become larger when the structure is damaged by an earthquake:  
\[\text{HI}=\frac{1}{2.4}\int_{0.1}^{2.5}\text{PSV}(\xi=0.2,T)dT \tag{9}\]  
Three component accelerations are used to calculate JMA equivalent peak acceleration, while only up-down (UD) component acceleration is used for the destructive index. For the remaining ground motion parameters, the geometric means of the two horizontal component accelerations are used. The natural frequency of middle- and high-rise buildings is mainly within 0.1-2.0 Hz, and in low-rise buildings is within 5.0-10.0 Hz. The corrected acceleration is filtered by a second-order Butterworth bandpass filter with a passing band of 0.1-10.0 Hz. A complete list of the 16 calculated ground motion parameters from the set of 125 ground motion records is shown in Supplementary Materials Table S1. The scatter plots of MSI versus ground motion parameter are shown in Figure 2, and the corresponding absolute values of Pearson correlation coefficients are shown in Figure 3. It can be seen that ASI, \(A_{0.3}\), \(Ic\), PGA, DI, HI, PGV, VSI, AI, \(a_{\text{RMS}}\), and CAV have higher linear correlations with MSI, and duration parameters \(Td_{\text{b},5},\ Td_{\text{s}},Td_{\text{b},5\%}\) and frequency parameters central frequency (CF) and \(v_{\text{max}}/a_{\text{max}}\) almost have no linear correlation.