esa-sceva/satcom-chunk-collection huggingface dataset:
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
### Stability and Overshoot of GS-ARC0 Controller  
When analyzing the stability of the closed loop, which is described with an ultralocal model resulting in the P-controller (32) and the actual value of the process model parameter \"\(a\)\" (17), the characteristic polynomial of the closed-loop results is  
\[A_{KP}(s)=s+K_{P00}K_{s}+a. \tag{35}\]The stability condition follows from  
\[1/T_{00}+a>0. \tag{36}\]  
For any \(T_{00}>0\), the stability is always satisfied if \(a\geq 0\). For unstable systems with \(a<0\), \(T_{00}\) has to be chosen sufficiently small to fulfill  
\[T_{00}>-1/a. \tag{37}\]  
In the proportional band of ARC0 control, the simplified DOB  
\[d_{f}=-\frac{\Delta u}{T_{f00}s+1}, \tag{38}\]  
accomplishing the positive ARC0 feedback with \(T_{f}=T_{f00}>0\), provides the PI controller transfer function (33). The resulting transfer function of the closed-loop is then given as  
\[\begin{split}& F_{wy}^{00}(s)=\frac{C^{00}(s)S(s)}{1+C^{00}(s)S(s)}= \frac{(1+T_{f00}s)K_{p0}K_{s}}{T_{f00}s(s+a)+K_{p0}K_{s}(1+T_{f00}s)}\\ &=\frac{(1+T_{f00}s)}{T_{f00}s^{2}+T_{f00}s(a+K_{p}K_{s})+K_{p}K_ {s}},\end{split} \tag{39}\]  
with the characteristic polynomial  
\[A(s)=T_{f00}T_{00}s^{2}+T_{f00}(aT_{00}+1)s+1. \tag{40}\]  
The stability requirement can now be expressed by the inequality  
\[aT_{00}+1>0. \tag{41}\]  
If the proposed gain scheduling is restricted to systems with \(a>0\), all coefficients of the characteristic polynomial are positive and the system remains stable for arbitrary positive values of \(T_{f00}\) and \(T_{00}\). For unstable systems with \(a<0\), the stability of ARC0 will be fulfilled only for a sufficiently large  
\[T_{00}>-1/a. \tag{42}\]  
The stability limit is the same as for a simple P controller (37). By moving value \(a\) along the real axis to the left, the required critical value of \(T_{00}\) decreases, the corresponding gain of the controller (32) increases, and for \(T_{00}>0\), the stability limit is reached at  
\[a=-1/T_{00}. \tag{43}\]  
Due to the zero in the numerator \((1+T_{f00}s)\) of \(F_{wy}^{00}\) a certain overshoot in the setpoint step response is expected. The easiest way to eliminate this is to use a pre-filter at the controller input \(w\) or \(\Delta w\) with a time constant \(T_{p}=T_{f00}\)  
\[F_{p}^{00}(s)=1/(1+T_{f00}s). \tag{44}\]
## 5 Conclusions  
In this paper, we present an unsupervised learning-based neural network model that predicts the 3D depth of a monocular image using a stereo image as training data. Our model integrates a simple ResNet-based network with several loss functions. The network reconstructs the input image into images from different viewpoints, outputting an inverse depth or disparity map. As training progresses, it synthesizes the input image as closely as possible to the target image, proving that the model's ability to accurately predict depth will evolve progressively. Therefore, the image-reconstruction loss function should give an exact figure for the difference between the reconstructed image and the target image. We hypothesize that using a perceptual IQA model that effectively mimics the human visual system as an image-reconstruction loss function can significantly improve image-synthesis performance. As a result, we select PieAPP, which achieved a high ranking in the IQA algorithm-performance competition, as the image-reconstruction loss. Our model uses stereo images from the KITTI 2015 driving data set for training. For a fair comparison with existing studies, training is performed according to the Eigen split. Although our model adopts a simple ResNet50-based network structure, it shows surprising results that outperform other models in various evaluation indicators thanks to its effective loss function. In addition, the generalization performance of the model was verified through testing on the CityScapes dataset. In particular, our model shows better results than Monodepth [6], while having a similar network structure. Through ablation studies, we specifically found that using a perceptual image-error-evaluation algorithm as an image-reconstruction loss function effectively improves the performance of unsupervised learning-based monocular depth prediction. Although the proposed perceptual IQA model mimics the human visual system, there is still room for improvement, and computer vision-based IQA methods are superior in some areas. In future research, it is necessary to analyze the effect of various  
Figure 3: Qualitative results on CityScapes dataset for verifying our model’s generalization performance.  
IQA methods on depth-prediction performance in the same network or to consider network structures and technologies that can complement them.  
Conceptualization, H.P. and S.P.; methodology, H.P.; software, H.P.; validation, H.P. and S.P.; formal analysis, H.P.; investigation, H.P.; resources, H.P.; data curation, H.P.; writing--original draft preparation, H.P.; writing--review and editing, S.P.; visualization, H.P.; supervision, S.P.; project administration, S.P.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the Education and Research Promotion Program of KOREAT-ECH in 2021.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
IQAImage Quality Assessment  
PieAPPPerceptual Image-Error Assessment Through Pairwise Preference
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 4 Engineering Background and Numerical Simulation Model
### Numerical Simulation Model
#### 4.2.2 Material Parameters  
The model comprises of five types of materials: soil, crown beam, waist beam, pile, and anchor cable. Solid elements are used to simulate the soil, crown beam, and pile. The soil follows the Mohr-Coulomb yield criterion, while the crown beam and pile follow the linear elastic constitutive equation. The waist beam and anchor cable are simulated using structural elements in the software. The waist beam uses beam elements, while the anchor cable uses cable elements, both following the linear elastic constitutive equation [27; 28]. The soil layer parameters of this model, as per the design report, are listed in Table 1, while other material parameters are displayed in Table 2.
# A Survey of the Applications of Text Mining for the Food Domain
## 5 Text Mining in Food Quality Control  
### Sentiment Analysis of Product Reviews  
Product reviews are valuable collections of user opinions, reflecting personal perspectives and consumer experiences. The use of NLP techniques for sentiment analysis, theme mining, and relationship extraction of food reviews, combined with comprehensive analysis of user data, enables the creation of consumer profiles and personalized quality control. For example, [PERSON] et al. [79] proposed an innovative model that combines sentiment analysis with BERT, a pre-trained text model, to enhance the understanding of online food reviews.  
However, these analyses face challenges, such as the complexity and diversity of consumer language and the need to verify review authenticity. Researchers are actively improving text representation and sentiment understanding to uncover subtle quality concerns. Meaningful categorization, as demonstrated by [PERSON] et al. [33], can contribute to healthier eating practices and assist policymakers and food companies in developing effective programs and products. work, empowers developers and marketers to better understand consumer needs and preferences, ultimately boosting the quality and sales of food products.  
In the effort to analyze what consumers say about the taste, smell, and overall characteristics of food in online discussions, one challenge arises from the lack of specialized words related to the senses. [PERSON] et al. [58] used a technique called word embedding, based on skip-word modeling, to study how people express their opinions about food on social media. This method predicts whether consumers will like a food without relying on specialized sensory words or expensive lab tests. By adopting this approach, food developers and marketers can better understand what consumers want, improving the quality and sales of food products. Similarly, [PERSON] et al. [57] introduced a method based on rough set theory to analyze clusters of Korean food and the adjectives people use to describe its taste. This method helps reveal the patterns and characteristics of flavors in Korean cuisine, making it easier for individuals to understand and describe the taste of Korean food. It also provides a new tool for researching and promoting Korean cuisine.  
In summary, examining and understanding consumer reviews are crucial for achieving user-focused quality control in the food industry. They are often used in text classification and sentiment analysis techniques, thus allowing food companies to really listen to the voice of the customer, thus potentially transforming and advancing quality management philosophies and approaches. This not only opens the door to utilizing the capabilities of emerging technology, but also represents a vital path for the food industry to enhance its quality management capabilities [30].
## 5 A Group Decision-Making Approach for Proportional Dual Hesitant Fuzzy Information
### Topsis  
To solve decision-making problems with PDHFS, our choice is to design a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method that uses PDHFS to express the opinions of DMs. The TOPSIS was constructed to address problems of multi-attribute decision-making with uncertain and complex data. It is a practical and effective technique for ranking and choosing of the best alternative in a group decision environment. For better visibility, in this technique, the given data of alternatives judged against each criteria provided by DMs are placed in the evaluation matrices and then these preferences of DMs are aggregated into the overall evaluation matrix. Normalization process is done, if needed. After that, positive ideal solution and negative ideal solution are computed by applying specific formulae. Then, the separation of each alternative from the positive ideal solution and the negative ideal solution are calculated by using distance formula. In the end of TOPSIS, a closeness coefficient is computed in order to find a scenario that is closest to the positive ideal solution and farthest from the negative ideal solution to classify the alternatives from best to worst. Unlike other developments, this scheme is indeed a unified mechanism and is easily applicable to many real life decision-making problems without enlarging the computational burden. Motivated by [14; 22], in the following, we design the TOPSIS in the PDHFS regard. This decision raking procedure involves the following steps.  
Step no 1: Analyze the given MAGDM problem and determine a set of alternatives \(x=\left\{x_{1},x_{2},0...,x_{n}\right\}\), a set of attributes \(c=\left\{c_{1},c_{2},0...,c_{n}\right\}\) and DMs provide evaluation matrices \(R_{k}=\left[\left\langle h_{ij}^{k},v_{ij}^{k}\right\rangle\right]_{m\times n};k =1,2,0..,l\).  
Step no 2: Calculate the overall evaluation information matrix  
\(R=\left[pd_{ij}\right]_{m\times n};pd_{ij}\left(i=1,2,0...,m,j=1,2,0...,n\right)\) is a PDHFE.  
Step no 3: Since all the values in the overall evaluation matrix \(R\) are PDHFEs, there is no need to normalize them.  
Step no 4: Compute the PIS \(R^{+}\) and the NIS \(R^{-}\) of all the alternatives by using Definitions 17 and 18, respectively.  
Step no 5: Definitions 19 and 20 are utilized to find \(s_{i}^{+}\) and \(s_{i}^{-}\) of each alternative.  
Step no 6: Obtain the closeness coefficient \(cl\) of each alternative by solving Equation (15).  
Step no 7: Rank all alternatives and pick the best one according to the closeness coefficient \(cl\).  
Step no 8: End.  
The Figure 1 presents the flowchart of proposed decision methodology.
## 2 Materials and Methods
### Study Selection and Data Extraction  
Titles were assessed for potential eligibility by two authors (F.M. and F.B.) separately, and complete texts were screened for final eligibility. Country of origin, year of publication, patients, etiology of chronic kidney disease, technical failures, renal biopsy used for the evaluation of fibrosis in CKD, the quality of specimen obtained in renal biopsy, the correlation coefficient between kidney SWS and eGFR, the area under the curve (AUC) (if provided) and cut-off values for predicting CKD were all extracted. We found 37 kidney elastography studies, which are presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Nr.** & **Study** & **Elastography Method** & **CKD Patients** & **Healthy Subjects** & **Kidney SWS in CKD Group** & **CKD vs. Cowf Coefficient Kidney** & **Correlation Coefficient Kidney** & **AUROC/Cut-Of Values** \\ \hline
1. & [PERSON] et al., 2010 [11] & TE &
\begin{tabular}{c} 57 trans- \\ planted \\ patients \\ \end{tabular} & - & High & - & Correlated & - \\ \hline
2. & [PERSON] et al., 2014 [12] & TE &
\begin{tabular}{c} 23 trans- \\ planted \\ patients \\ \end{tabular} & - & High & - & Correlated & - \\ \hline
3. & [PERSON] et al., 2015 [13] & TE &
\begin{tabular}{c} 27 trans- \\ planted \\ patients \\ \end{tabular} & - & High & - & Correlated & - \\ \hline
4. & [PERSON] et al., 2013 [14] & TE &
\begin{tabular}{c} 164 trans- \\ planted \\ patients \\ \end{tabular} & - & High & - & - & - \\ \hline
5. &
\begin{tabular}{c} [PERSON] et al., 2015 [15] \\ \end{tabular} & SE & 58 & 40 & High & Yes & - & Performed \\ \hline
6. &
\begin{tabular}{c} [PERSON] et al., 2016 [16] \\ \end{tabular} & SE & 121 & 40 & High & Yes & - & Performed \\ \hline
7. &
\begin{tabular}{c} [PERSON] et al., \\ 2017 [17] \\ \end{tabular} & RTE & 148 & 277 & Low & Yes & Correlated & - \\ \hline
8. &
\begin{tabular}{c} [PERSON] et al., \\ 2013 [18] \\ \end{tabular} & VTQ & 64 & 327 & Low & Yes & Correlated & Performed \\ \hline
9. & [PERSON] et al., 2011, [19] & VTQ &
\begin{tabular}{c} 18 trans- \\ planted \\ patients \\ \end{tabular} & - & High & - & - & - \\ \hline
10. & [PERSON] et al., 2011, [20] & VTQ & \
\begin{tabular}{c} 30 trans- \\ planted \\ patients \\ \end{tabular} & - &
\begin{tabular}{c} No rela- \\ township \\ \end{tabular} & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Primary elastography research in kidneys that have been published.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 8 Results  
In this section, we first empirically establish the presence of a nontrivial domain gap in the multispectral cloud detection task (Section 8.1). Then, we evaluate the performance of the proposed algorithms for offline and online domain adaptation (Section 8.2).
## 3 Results and Discussion
### Effect of Extrusion Width on the Dynamic Property  
Figure 7 presents the average measured frequency response curves of a set of ME plates fabricated with different extrusion widths separately being 0.4, 0.3 and 0.2 mm. It is noted that no filtering was implemented as no specific frequency band needed to be filtered out. By analyzing and identifying the peaks of the curves, the natural frequencies of each sample could be preliminarily determined. The results are detailed in Table 3. It can be seen that Sample E\({}_{0.4}\) had the lowest values of resonant response (occurring due to the impulse excitation), meaning that the anti-vibration capability was the best compared with Sample E\({}_{0.3}\) and E\({}_{0.2}\). Therefore, the sample's anti-vibration capability would be improved if the extrusion width was increased.  
Table 4 summarizes the effect of different extrusion widths on the values of predicted and measured dynamic inherent characteristics. It can be seen from the comparison that the predictions agreed well with the measurements. The influencing trend of different extrusion width was predicted precisely. The discrepancy between predictions and measurements in natural frequency was in the range of 0.7-11%. The predicted mode shapes were the same as those of the measurements. The 1 st order mode shape was the 1 st order bending vibration, the 2 nd order one was the 1 st order torsional vibration and the 3 rd order one was the 2 nd order bending vibration. Therefore, the theoretical modeling based on the bidirectional beam function combination method could give reliable predictions on the inherent characteristics of ME plates. In addition, larger extrusion width mainly led to the increase in the material properties, i.e., elastic modulus, shear modulus and density, which would enhance the strain (or structural stiffness), potential and kinetic energy in the plates according to Equations (A12), (A14) and (A15). It eventually increased the natural frequency. For example, the values of material properties of Sample E\({}_{0.4}\) were larger than those of E\({}_{0.3}\) and E\({}_{0.2}\) as shown in Table 2, the corresponding natural frequency was the highest, with the first three measured natural frequencies being 18 Hz, 154.9 Hz and 384.8 Hz. The corresponding predicted natural frequencies were 19.3 Hz, 170.1 Hz and 396.4 Hz. Since the structural characteristics were not changed with various extrusion widths, the mode shapes were kept the same.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Average Resonant Response (m/s\({}^{2}\) N)**} \\ \cline{2-4}  & **1 st Order** & **2 nd Order** & **3 rd Order** \\ \hline E\({}_{0.4}\) & 4.3 & 4.4 & 3.0 \\ E\({}_{0.3}\) & 5.0 & 5.0 & 3.6 \\ E\({}_{0.2}\) & 5.2 & 5.8 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The resonant response values of the samples fabricated with different extrusion widths.  
Figure 7: The average measured frequency response of the samples fabricated with different extrusion width.
## 6 Fully-Distributed Clock Synchronization Algorithms
### Fully-Distributed Clock Synchronization Algorithms under Gaussian Delays
#### 6.2.2 Consensus-Based Synchronization Algorithms  
The fully-distributed algorithms reported in [49; 50; 51] rely on the average consensus principle. However, the message delays are not considered in these algorithms, and their performance degenerates significantly when message delays exist [46]. Other consensus-based distributed synchronization algorithms, such as [52; 53], typically suffer from slow convergence speed and require a high number of data packages [22]. To overcome this issue, [PERSON] _et al._[54] recently presented a fully-distributed low-complexity consensus-based synchronization algorithm for embedded WSNs. Another fully-distributed clock synchronization method based on a cascade of two consensus algorithms was proposed by [PERSON] _et al._[55], which exhibits the advantage of being asynchronous and being robust to packet losses, node failures and communication topology changes. The details of these consensus-based algorithms are omitted herein, since they are not based on the statistical framework described in Section 6.1. [PERSON] _et al._[56] generalized the consensus-based algorithm to estimate the clock offset by taking into account both fixed delays, as well as random Gaussian delays. In each iteration of the generalized consensus-based algorithm, each node processes and decodes the timestamps from its neighbors and then updates its local clock time using a weighted average of the time differences with its neighbor nodes. Such a strategy naturally leads to the introduction of a special network topology referredto as the time delay balanced network in which average timing consensus is achieved in the presence of Gaussian random delays.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 3 Results
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles
#### 3.2.2 FTIR Analysis of Zinc Oxide Nanoparticles  
FTIR measurements were performed to look into the possible biomolecules that could cap and stabilize the metal nanoparticles that the seaweeds produced. Additionally, it attests to the employed seaweeds' ability to reduce metal ions. Figure 3 shows that ZnO-NPs were produced by _U. lactuca_ at peaks of 410, 480, and 615 cm\({}^{-1}\) and by _U. intestinalis_ at approximately 412, 475, and 841 cm\({}^{-1}\). On the other hand, the stretch for the ZnO-NPs synthesized by _S. muticum_ was discovered to be approximately 425, 530, and 613 cm\({}^{-1}\). The peak around 1320-1000 cm\({}^{-1}\) were due to C-O stretching alcohols, carboxylic acids, esters, ethers. The peaks at 1500-1400 cm\({}^{-1}\) were due to C-C stretch (in-ring) aromatics. The peak at around 3450 cm\({}^{-1}\) indicates the O-H group. With the attachment of the various functional groups, proteins and metabolites such as terpenids surrounded the produced ZnO nanoparticles. The results of the FTIR study show that the reduction, capping, and stability of the biosynthesised ZnO-NPs were all influenced by the organic compounds in the seaweed extract.  
Figure 3: FTIR analysis of ZnO nanoparticles of the different seaweeds: _Ulva lactuca_ (**A**), _Ulva intestinalis_ (**B**), and _Sargassum muticum_ (**C**).
## 1 Introduction  
### 1.1 Background and Motivation  
With the development of smart grid technology and the proposal of decarbonization goals, the proportion of distributed sources and loads, mainly new energy, connected to the grid has been continuously increasing, bringing abundant social benefits and various accompanying challenges [1]. In particular, the massive influx of power electronic devices has led to a surge in monitoring data for power system health situational awareness (PSA), which is centered on power quality (PQ) monitoring. Meanwhile, due to the unpredictable changes in renewable energy sources, external environments, and actual working conditions, PQ issues are becoming more complex [2]. According to existing reports, PQ issues are estimated to cause annual economic losses amounting to several billion dollars worldwide [3, 4]. In addition to the financial impact, they adversely affect industrial operations, commercial activities, and residential life, and may even pose threatsto critical public infrastructure, such as hospitals and transportation systems. Equipment damage increases maintenance costs and prolongs recovery time, while large-scale power outages can lead to social disorder and result in immeasurable indirect losses [5], thereby drawing significant attention and concern toward PSA.  
The premise of effective PSA is to accurately and rapidly identify the types of power quality disturbances (PQDs) in monitoring data. The results of PQD classification can guide power grid companies in formulating targeted maintenance decisions and implementing precise maintenance to ensure the safe and stable operation of the power grid while reducing operation and maintenance costs. In modern smart grids, the increasing penetration of distributed sources and power electronic devices has significantly intensified the coupling and complexity of PQDs. In extreme cases, up to four different single-disturbance types may be superimposed on a single waveform, which presents additional challenges in overcoming abnormal changes in voltage waveforms. Furthermore, with the massive growth of monitoring data from PQ sensors, grid operation and maintenance departments have established higher demands for the efficiency of PQD situation awareness. Consequently, there is an urgent need to develop PQD classification algorithms that offer both high efficiency and high accuracy to meet the requirements of modern power systems for real-time and intelligent operation and maintenance.