esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Verification
### Cross-Validation of the Results of the Ascending and Descending Orbit Datasets  
Since ground leveling data and Global Navigation Satellite System (GNSS) measurements were unavailable in the study area, this paper analyzed the reliability of the results via cross-validation of the Sentinel-1 ascending and descending orbit datasets [6]. Surface deformation in urban areas is mainly caused by vertical subsidence. No obvious horizontal deformation has been reported in the main urban area of Kunming [27]. Therefore, it can be considered that the deformation along the LOS direction is caused by vertical surface subsidence. Here, we use incidence angle information to convert the LOS displacements of the ascending and descending datasets into corresponding vertical displacements, as shown in Figure 5.  
Figure 4: Annual mean surface deformation rate on the north bank of Dianchi Lake in the Kunming in the LOS direction. (**a**) Results of the ascending dataset; (**b**) results of the descending dataset.  
Figure 5 shows the annual mean deformation rate maps plotted with the vertical deformation rate results of the ascending and descending datasets. The land surface settlement area in the study area remained constant. The maximum vertical deformation rates are approximately \(-18.7\) mm/a and \(-18.3\) mm/a for the ascending and descending datasets, respectively. To better compare the results of the ascending and descending datasets, we plotted probability density histograms of the results from the two datasets in Figure 6. A total of 168,516 and 247,287 coherent points were extracted in the ascending and descending datasets, respectively. The provability distributions of the ascending and descending results are consistent. The vertical deformation rate of most points is near zero; that is, it remains stable. The standard deviation (STD) is 0.36 for the ascending dataset and 0.54 for the descending dataset. The results in descending orbit are noisier.  
In this study, profiles of the ascending and descending results are generated along the profile line P-P' in Figure 5 and are compared in Figure 7. This profile crosses the S1 and S2 areas with the most serious deformation on the north shore of Dianchi Lake in Kunming. The figure shows that the vertical deformation results of the two datasets suitably agreed, with a Pearson correlation coefficient (R2) of approximately 0.98 and a root mean square error (RMSE) of approximately 1.26 mm/a [15]. The results indicate that in the absence of ground leveling data and GNSS measurement data, InSAR results could be cross-validated with results from different orbits or different satellites.  
Figure 5: Annual mean rate of vertical deformation based on the ascending and descending orbit data: (**a**) ascending orbit and (**b**) descending orbit.  
Figure 6: Histogram of the vertical annual mean deformation rate in the ascending orbit and descending orbit.
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
## 2 Metal-Organic Frameworks for Cathode Applications in Lithium-Sulfur Batteries
### Metal-Organic Framework Composites
#### 2.2.4 Derivatives of Metal-Organic Frameworks
High entropy oxides (HEOs) represent a novel category of crystalline solid solutions, distinct from conventional derivatives, offering a unique combination of randomly dispersed active metal sites, superior electronic conductivity, and synergistic multicomponent interactions that effectively anchor polysulfides. [PERSON] et al. [71] prepared high entropy oxide (Ni\({}_{0.2}\)Co\({}_{0.2}\)Cu\({}_{0.2}\)Mg\({}_{0.2}\)Zn\({}_{0.2}\)) O (HEO850) from self-sacrificial multi-metal organic frameworks (M-MOFs) via a novel low temperature templating strategy. The battery has an initial specific discharge capacity of 1244 mAh g\({}^{-1}\) at 0.5 C and maintains a capacity of 784 mAh g\({}^{-1}\) even after 800 redox cycles, with a decay rate as low as 0.043% per cycle. [PERSON] et al. [72] prepared titanium-containing high entropy oxides (Ti-HEO) with enhanced electrical conductivity in the following year by employing the same facile synthesis technique as low temperature calcination of metal-organic framework (MOF) templates. The Ti-HEO/S/KB cathode demonstrates superior performance compared to the HEO850/S/KB, particularly under high sulfur loading conditions, owing to its enhanced catalytic activity for lithium polysulfide (LPS) conversion and improved electrical conductivity. Typically, it has an initial discharge capacity of up to about 1246 mAh g\({}^{-1}\) at a sulfur loading of about 3.4 mg cm\({}^{-2}\), and after 1000 cycles at 0.5 C, the capacity is maintained at about 542 mAh g\({}^{-1}\), with a capacity decay rate of only 0.056% per cycle. The MOF derivatives exhibit robust adsorption and catalytic capabilities towards poly-sulfides, facilitating rapid catalytic conversion of these species and significantly enhancing the cycling stability and capacity retention of lithium-sulfur batteries. While the material demonstrates a certain level of reversible capacity, substantial efforts are still required to further elevate its performance, particularly in terms of capacity and cycle stability, relative to the anode.
## 3 Materials and Methods
### Image Pre-Processing
#### 3.4.1 Contrast Enhancement Using CLAHE  
Contrast Limited Adaptive Histogram Equalization (CLAHE) [22] was applied to improve the visibility of pest-induced damage on maize leaves. CLAHE is an advanced version of Adaptive Histogram Equalization (AHE) that enhances local contrast while preventing over-amplification of noise. Unlike standard histogram equalization, which applies a uniform contrast enhancement across the entire image, CLAHE operates on small, localized regions (tiles), redistributing lightness values to enhance details without causing artificial distortions. It was chosen in this study due to its suitability for natural, outdoor images where illumination may vary across regions of the leaf. Based on its demonstrated effectiveness in prior studies, CLAHE was considered a robust choice for highlighting subtle pest-related symptoms without introducing noise artifacts.  
Figure 6 illustrates an example of a maize leaf before and after applying CLAHE, demonstrating the improved contrast that facilitates more accurate segmentation and feature extraction.
## 1 Introduction
### Continuous-Discrete System Model  
In a continuous-discrete target tracking system, the target state model is continuous time, and the measurement model is discrete time. Considering the random disturbance, the continuous-time target state model can be described as the following stochastic differential equation form [1]:  
\[d\mathbf{x}(t)=\mathbf{f}(\mathbf{x}(t),t)dt+\sqrt{Q}d\mathbf{w}(t), \tag{1}\]  
where \(\mathbf{x}(t)\) is the \(n\)-dimensional state vector, \(\mathbf{f}\) is known as the drift function, \(\mathbf{w}(t)\) is the zero-mean Gaussian white-noise process, and \(\mathbf{Q}\) represents the covariance matrix of the zero-mean Gaussian white-noise process.  
In order to deal with the continuous time model, the target model of the continuous-discrete system can be described by the moment differential equations (MDEs) [9], which are shown as  
\[\frac{d\mathbf{x}(t)}{dt}=\mathbf{F}(\mathbf{\hat{x}}(t)), \tag{2}\]  
\[\frac{d\mathbf{P}(t)}{dt}=\mathbf{J}(\mathbf{\hat{x}}(t))\mathbf{P}(t)+\mathbf{P}(t)\mathbf{J}^{\rm T} (\mathbf{\hat{x}}(t))+\mathbf{Q}, \tag{3}\]  
where \(\mathbf{\hat{x}}(t)\) is the expectation of \(\mathbf{x}(t)\) at time \(t\), \(\mathbf{P}(t)\) represents the error covariance matrix at time \(t\), \(\mathbf{F}(\mathbf{\hat{x}}(t))\) is the state equation of the system, and the Jacobian matrix of \(\mathbf{F}(\mathbf{\hat{x}}(t))\) is \(\mathbf{J}(\mathbf{\hat{x}}(t))\), that is, \(\mathbf{J}(\mathbf{\hat{x}}(t))=\partial\mathbf{F}(\mathbf{\hat{x}}(t))/\partial\mathbf{\hat{x}}(t)\).  
The discrete time measurement model is  
\[\mathbf{z}_{k}=\mathbf{h}(\mathbf{x}_{k},k)+\mathbf{r}_{k}, \tag{4}\]  
where \(\mathbf{z}_{k}\) is the actual observation value, \(\mathbf{h}\) is the observation function, and \(\mathbf{r}_{k}\) is the observation noise, which refers to Gaussian or non-Gaussian noise. The specific noise form of \(\mathbf{r}_{k}\) is defined by the measurement scenario, and its covariance matrix is \(\mathbf{R}_{k}\). Here, \(k\) refers to discrete time measurement points.
## 2 Operating Principle  
As shown in Figure 1, the proposed MFI scheme was placed after the modulation format-independent algorithms. \(E_{x}\) and \(E_{y}\) denote the received X-polarization and Y-polarization digital signals after coherent detection and analog to digital convention (ADC). The CD compensation algorithm, timing recovery algorithm, and CMA are applied to compensate for the CD impairments, timing-jitters, and polarization mixing. It should be noted that the CMA can only achieve complete polarization demultiplexing for constant-modulus signal. By using the proposed MFI scheme, the identified modulation format information is provided to the subsequent modulation format-dependent algorithms. For \(m\)QAM (\(m>4\)) signals, further polarization demultiplexing is achieved by multi-modulus algorithm (MMA). After that, the frequency offset is estimated and compensated. Finally, the carrier phase noise of incoming PDM signals is compensated by the carrier phase recovery algorithm.  
As shown in Figure 1, there are three steps in the proposed MFI scheme. Firstly, in order to utilize more detailed phase information, 4 QAM-BPS is introduced to roughly recover the phase of the CMA-equalized signal, regardless of the modulation format. The constellation diagrams in the rectangular coordinate system before or after 4 QAM-BPS are depicted in Figure 2, where the linewidth (LW) of the laser is assumed to be 100 kHz. After that, the signal is then converted into the polar coordinate system for further improving feature extraction. Secondly, after normalization and quantization, a three-dimensional polar coordinate graph is generated based on the quantized amplitude, phase, and number of symbols in each grid. According to the notable amplitude and phase features of different modulation formats present in the polar coordinate system, 20 specific zones are selected, and the number of symbols in these zones are grouped into six-dimensional features. Thirdly, the six-dimensional features are identified by the Gaussian weighted KNN algorithm to obtain the modulation format information.  
Figure 1: The proposed MFI scheme embedded in the DSP chain of the digital coherent receiver.  
Figure 2: The constellation diagrams in the rectangular coordinate system before or after 4 QAM-BPS.
## 2 Materials and Methods
### Study Selection  
Articles not meeting inclusion and exclusion criteria were excluded from the review. Of the articles initially screened, a total of 24 articles met the inclusion criteria and were incorporated into the study.  
Inclusion criteria were as follows: (1) prospective cohort studies, randomized controlled trials, and non-randomized controlled trials conducted within the last 20 years and (2) studies reporting clinically significant outcomes. Exclusion criteria were as follows: (1) non-clinical studies; (2) retrospective studies; (3) case reports; and (4) articles lacking information on parameters such as the distalization pattern in molar teeth, treatment duration, applied force, and anchorage loss.  
Maxillary molar distalization methods are generally divided into two categories: extraoral and intraoral mechanics. In this narrative review, extraoral appliances are briefly discussed, while the primary focus is on intraoral distalization methods. These intraoral approaches are categorized into two main types: tooth tissue-supported appliances and skeletal anchorage-supported appliances, each reviewed in detail under specific headings and subheadings.
## 4 Model Verification and Comparison Based on 1 DCNN-BiLSTM-AdaBoost
### Evaluation Indicators
#### 4.1.1 Confusion Matrix  
The confusion matrix is a tool for measuring the performance of the category model. For the actual label set \(y_{\text{true}}\) and the predicted label set \(y_{\text{pred}}\), the elements of the confusion matrix C are calculated as follows:  
\[C_{i,j}=\sum_{k=1}^{N}\delta\Big{(}y_{\text{true}}^{(k)}\,i\Big{)}\cdot\delta \Big{(}y_{\text{pred}}^{(k)}\,j\Big{)} \tag{12}\]  
where \(N\) is the total number of test samples; \(y_{\text{true}}^{(k)}\) is the true category of the \(k\)-th sample; \(y_{\text{pred}}^{(k)}\) is the predicted category of the \(k\)-th sample; and \(\delta(a,b)\) is the Kronecker delta function, which takes the value 1 when \(\text{a}=\text{b}\), otherwise it is 0.
## 4 Conclusions  
In this paper, a new method to skip the phase unwrapping process in DHI based on CNNs is proposed. The CNN and the Regressor were used to estimate the concentrations of liquid samples in a cylindrical glass, using the images obtained with digital holographic interferometry. Using DHI to measure difference concentration values in liquid samples, large differences created phase map differences with high-frequency fringes, and the fringes appeared as noise. In addition, the liquid samples needed to be fragmented to create minor concentration liquid values, which created phase difference maps that could be correctly mapped by the sensor, and could be unwrapped with common methods.  
Using a CNN, the concentration liquid samples were estimated using the phase map difference, and the unwrapping process was omitted. This method directly estimated the concentration values in liquid samples associated with difference phases, without the necessity of common phase unwrapping processes due to the CNN that was trained to quantify the phase wrapped images. In other words, the proposed method was able to estimate the concentration values from an input image based on the spatial distribution of the phase wrapped image without using the conventional phase equations. Although the differences among the image samples for each class could be obvious to the eye, their classification and quantification is not, and the CNN must be trained to estimate the sample concentrations that are different from those it was trained with.  
In other words, our proposed method was able to estimate the concentration values for classes that were unknown based on the central region of the input image. The results showed a performance with high accuracy and precision. Once the CNN was trained and the Regressor was fitted, the proposed method was able to calculate the concentration values directly, with classes that were not used in the training process, as long as the values were in the operational range. As further work, a range extension could be performed, and a different form of the unwrapping phase based on CNNs could be analyzed.  
Conceptualization, C.G.-M., T.S.-A. and D.L.-B.; methodology, C.G.-M. and D.L.-B.; software, C.G.-M.; validation, M.A.-E., and C.O.-O.; formal analysis, I.M.; investigation, C.G.-M. and D.L.-B.; resources, T.S.-A.; data curation, C.G.-M. and T.S.-A.; writing--original draft preparation, I.M., D.L.-B. and C.G.-M.; writing--review and editing, I.M., D.L.-B. and C.G.-M.; visualization, C.G.-M. and T.S.-A.; supervision, T.S.-A., M.A.-E. and I.M.; project administration, C.G.-M. and D.L.-B.; funding acquisition, T.S.-A. and C.O.-O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Structurally Modified Bioactive Peptide Inhibits SARS-CoV-2 Lentiviral Particles Expression
## 3 Results  
### SARS-CoV-2 Spike S1 RBD-ACE2 Interaction Inhibition  
The SARS-CoV-2 spike S1 RBD-ACE2 interaction assay showed that structural modification of the parent molecule, IRW (Figure 1), leads to inhibition of Spike S1 RBD and ACE interaction in vitro (Figure 2A). Compared to the IRW, its five analogs, A8, A9, A10, A11, and A14 (50 \(\upmu\)M) exhibited significant inhibition of SARS-CoV-2 spike S1 RBD-ACE2 interaction (Figure 2B). The 50 \(\upmu\)M concentration was selected as per our recent reports showing impact of IRW on ACE2 and other pharmacological targets [29; 48; 49]. Among the five pharmacologically active analogs, the SARS-CoV-2 spike S1 RBD-ACE2 interaction was mitigated by A9 (55%, Acetyl-Ile-Arg-Trp-Amide) and A14 (63%, Formyl-Ile-Arg-Trp-Amide) were the strongest in vitro (\(p<0.01\)). However, constituent dipeptide combinations of ARW, IAW, and IRA failed to inhibit the SARS-CoV-2 spike S1 RBD-ACE2 interaction in vitro (Figure S1). Based on the inhibition activity results, two analogs of IRW, A9 and A14 were selected for further experimentation. Additionally, their toxicity analysis showed that all cells treated with both peptides (100 \(\upmu\)M) exhibited 92-97% cell viability (Figure 3C), thus exhibiting a safe profile for further analysis.  
Figure 3: The binding modes of peptides A9 and A14. (**A**,**B**) A9 and A14 are shown as balls and sticks, the amino acids that are within 4Å of the peptides are labeled and shown as sticks (**C**) the surface cavity of the RBD is shown as molecular surface and (**D**) The 2D interaction diagram of A9 and A14. The surface is colored with the YRB scheme, yellow for the carbon atoms with high potential to form hydrophobic interactions, blue for the sidechain nitrogen atoms of arginine and lysine, and red for the sidechain oxygen atoms of glutamate and aspartate.  
Figure 2: Impact of IRW and its analogs on SARS-CoV-2 S1 RBD-ACE2 interaction. (**A**) An in vitro assay was conducted to assess the possible (**B**) inhibitory impact of IRW and its analog peptides (50 uM) on SARS-CoV-2 S1 RBD-ACE2 interaction and (**C**) toxicity analysis of the selected peptides. The results are expressed as percentage inhibition with respect vehicle control (nuclease free water). Data expressed as mean \(\pm\) SEM of n = 3–6. Experiments were reproduced three times with independent tips and samples. \(p\) values were determined by Analysis by one-way ANOVA followed by [PERSON]’s post hoc test for the vehicle. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\) versus vehicle and ns: nonsignificant.
# A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing
## 3 Results
### Features
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fault Detection** & \begin{tabular}{c} **Predictive Maintenance** \\ \end{tabular} & **Communication** & **Virtualization** & \begin{tabular}{c} **Human-Machine Interference** \\ \end{tabular} \\ \hline Anomaly detection & Big data & Blockchain & Augmented reality & Adaptability, flexibility \\ \hline \multirow{2}{*}{High accuracy} & \begin{tabular}{c} Condition-based maintenance \\ \end{tabular} & Cloud computing & Cost minimization & Agility \\ \hline Improved performance & Equipment reliability & Cloud-assistance & Mixed reality & \begin{tabular}{c} Cobot programming by \\ demonstration \\ \end{tabular} \\ \hline Quality improvement & \begin{tabular}{c} Labor activity \\ monitoring \\ \end{tabular} & \begin{tabular}{c} Computational \\ self-awareness \\ \end{tabular} & Task placement & Digital twin \\ \hline \begin{tabular}{c} Real-time stress \\ prediction \\ \end{tabular} & Production control & Decentralized & Virtual training & High scalability \\ \hline \begin{tabular}{c} Reduction of \\ breakdown risk \\ \end{tabular} & Reinforcement learning & Edge computing & Virtual reality & \
\begin{tabular}{c} Human capability \\ enhancement \\ \end{tabular} \\ \hline Root cause diagnosis & Scrap reduction & Energy efficiency & & Immersive analytics \\ \hline \multirow{4}{*}{Uncertainty reduction} & \multirow{2}{*}{Fog computing} & \multirow{2}{*}{
\begin{tabular}{c} Improved ergonomic \\ conditions \\ \end{tabular} } \\ \cline{1-1} \cline{3-5}  & Usage prediction & IoT & & Process planning \\ \hline \multicolumn{3}{c}{What-if analysis} & Less downtime & Remote control \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Features identified in articles. In the following subsections, the findings of the articles included in the SLR are discussed in further detail to give a better understanding of the ways in which various information technology methodologies can be used to move towards Industry 4.0 principles in medical device manufacturing. For this, general smart manufacturing, CPS, IoT, data-driven decision making, digital twin and (Human-Robot Collaboration) HRC concepts are specifically elaborated on in more detail.
###### Abstract  
The aim of the present study was to evaluate the acute effects of three different warm-up protocols on jump performance in elite male handball players. Warming up with a traditional (TR) set of exercise was compared with balance (BA) and unbalanced (UN) activation protocols. Thirty-two male handball players (age 24.2 \(\pm\) 4.7 (m \(\pm\) SD) y, weight 84.4 \(\pm\) 8.5 kg, height 183.9 \(\pm\) 7.3 cm) from two Italian first division National championship teams completed the three warm-up protocols in three different occasions and performed three countermovement jumps each with a 3 min rest. The jump height was evaluated from flight time. The players jumped an average of 40.7 \(\pm\) 4.8 cm, 42.4 \(\pm\) 4.9 cm, and 42.5 \(\pm\) 5.0 cm in TR, BA, and UN conditions, respectively. A significant main effect (\(p\) < 0.001; \(\eta^{2}\) = 0.812) between the warm-up protocols for jump height was detected. A post hoc analysis showed that athletes performed significantly higher jumps under BA (\(p\) < 0.001) and UN (\(p\) < 0.001) conditions compared to performing a TR warm-up protocol. In conclusion, both BA and UN warm-up protocols induced significantly higher jump heights in elite male Italian handball players, compared to the traditional warm-up routine. The use of dynamic activation exercises enhance the neuromuscular properties of the warm-up compared to traditional protocols.  
countermovement; activation; balance; core; boost Article Article
## 2 Theoretical Concepts
### Development of Hysteresis Models
On the other hand, if the trial bending values are within capacity function limits, the reloading on the unloading path case is considered and therefore \(k_{i}^{br}\) remains unmodified and the trial bending moment value is set back to the previous step value. \[ \begin{bmatrix}m_{1}^{trial}\\ m_{2}^{trial}\\ \vdots\\ m_{nph}^{trial}\end{bmatrix} _{k+1}\geq \begin{bmatrix}f_{1}(\theta^{{}^{\prime \prime}})\\ f_{2}(\theta^{{}^{\prime\prime}})\\ \vdots\\ f_{nph}(\theta^{{}^{\prime\prime}})\end{bmatrix} _{k+1} \tag{16}\]  
Further, using the updated values of \(\mathbf{m_{k+1}^{trial}}\) and \(\mathbf{K_{d}}\), the actual change in plastic rotation is calculated. \[\mathbf{\Delta\theta}^{{}^{\prime\prime}}=(\mathbf{K}_{R}+\mathbf{K_{d}})^{- 1}(\mathbf{K}_{P}^{T}\cdot\mathbf{u_{k+1}}-\mathbf{K}_{R}\cdot\mathbf{\theta} ^{{}^{\prime\prime}}-\mathbf{m_{k+1}^{trial}}) \tag{17}\]  
Moreover Equation (10) is used to determine the actual \(\mathbf{m_{k+1}}\). It is important to point out that in each time step, certain elements encounter plastic behavior while the rest exhibit elastic behavior. Therefore, it must be taken into account that, when determining \(\mathbf{m_{k+1}}\), it is possible, due to the bending moment redistribution, certain elements exhibit the transition from elastic to inelastic behavior. In this case, the \(\mathbf{K_{d}}\) corresponding values are updated while bending moments are capped to capacity. Consequently, the actual plastic rotation \(\mathbf{\Delta\theta}^{{}^{\prime\prime}}\) and bending moments are recalculated \(\mathbf{m_{k+1}}\). This simple routine provides the framework for the nonlinear dynamic analysis of the RC frame structures. In the next sections some well-known key concepts are brought to the reader's attention. The energy approach to structural response and cumulative damage indices are further used to demonstrate the capabilities of the new FAM 3D algorithm endowed with a hysteresis function that models the degrading cyclic behavior of the RC elements in a spatial frame structure.