esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Unified Calculation of Four Statistical Moments of Epistemic Uncertainties  
### Four Statistical Moments  
By using similar methods for discrete random variables with determinate PDF, the first-order moment (mean) \(M_{1}\), second-order moment (variance) \(M_{2}\), third-order moment (skewness) \(M_{3}\), and the fourth-order moment (kurtosis) \(M_{4}\) of epistemic uncertainty \(x\) can be calculated through Equations (8)-(11):  
\[M_{1}=E(x), \tag{8}\]  
\[M_{2}=E\big{(}x^{2}\big{)}-(E(x))^{2}, \tag{9}\]  
\[M_{3}=E\big{(}x^{3}\big{)}-3E\big{(}x^{2}\big{)}E(x)+2(E(x))^{3}, \tag{10}\]  
\[M_{4}=E\big{(}x^{4}\big{)}-4E\big{(}x^{3}\big{)}E(x)+6E\big{(}x^{2}\big{)}(E(x ))^{2}-3(E(x))^{4}, \tag{11}\]  
where \(E(x)=\sum\limits_{i=1}^{ms}\widetilde{x}_{i}p(\widetilde{x}_{i})\), \(E\big{(}x^{2}\big{)}=\sum\limits_{i=1}^{ms}\widetilde{x}_{i}^{2}p(\widetilde{ x}_{i})\), \(E\big{(}x^{3}\big{)}=\sum\limits_{i=1}^{ms}\widetilde{x}_{i}^{3}p(\widetilde{x}_{i })\), \(E\big{(}x^{4}\big{)}=\sum\limits_{i=1}^{ms}\widetilde{x}_{i}^{4}p(\widetilde{ x}_{i})\), \(\widetilde{x}_{i}\), and \(p(\widetilde{x}_{i})\) are the random sampling points and corresponding probability density values, and \(ns\) is the number of sampling points.  
For epistemic uncertainty \(x\), it is impossible to obtain an accurate PDF of \(x\), as the values of \(E\big{(}x^{l}\big{)}\) change with the sampling points and their probability density values. Therefore, the calculated four statistical moments \(M_{1}\sim M_{4}\) are variational and are expressed as intervals \(\big{[}M_{l}^{L},M_{l}^{U}\big{]},l=1,2,3,4\). The bounds of \(M_{1}\sim M_{4}\) are estimated using the optimization methods presented in [48]:  
\[M_{l}^{L}/M_{l}^{U}=\underset{p(\widetilde{x}_{i})}{\text{min}}\text{max}M_{l}, \tag{12}\]  
where \(ns\) sampling points \(\widetilde{x}_{i}(i=1,\cdots,ns)\) are determinate, and are uniformly distributed in the uncertain design space of epistemic uncertainty. The probability density values of these sampling points \(p(\widetilde{x}_{i})\) are optimization variables that are constrained by the specific uncertainty types of epistemic uncertainty. For example, if the epistemic uncertainty \(x\) is represented using a single interval, the probability density values \(p(\widetilde{x}_{i})\) are random values in \([0,1]\), and the total probability density values of \(ns\) sampling points \(\widetilde{x}_{i}(i=1,\cdots,ns)\) are equal to 1. The optimization formulations presented in Equation (12) are solved using the MATLAB function _fmincon_, which is implemented using the sequential quadratic programming algorithm with random initial points. The optimization algorithm is repeatedly implemented several times to reduce the random error due to the initial points. If the epistemic uncertainty is represented using the interval approach, regardless of whether the available uncertainty information is a single interval or multiple intervals, the four statistical moments are calculated using the method of Zaman [35] based on the theory presented in Equations (8)-(12)
###### Abstract  
As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.  
compressed sensing; moving measurement data; multi-hop network; total variation; coal mine +
Footnote †: journal: Sensors  
1
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 3 Results  
### Inversion Model of Soil Relative Humidity and Classification  
The quadratic curve model to inverse the soil relative humidity is defined as follows:  
\[RH=3049.6\times\text{TVDI}^{2}-4936.5\times\text{TVDI}+2078 \tag{10}\]  
where \(RH\) is the soil relative humidity, TVDI is the temperature vegetation drought index. The accuracy assessment results combined the soil moisture derived from Landsat-8 OLI image with the field gauged soil moisture showed that the RMSE was 13.76 which proved that the method is suitable to extract the soil moisture of the large-scale floodplain. Figure 4 shows the spatial distribution of the soil relative humidity, which have been divided into five drought levels according to the drought standard provided by the Ministry of Water Resources of the People's Republic of China. The soil moisture of the study area is gradually drying from northeast to southwest. The regions with low soil moisture often occurred in the southwest of the plain, and the regions with high soil moisture are concentrated in the middle of the plain.  
Figure 4: TVDI retrieval of soil relative humidity.  
Figure 3: Maps of the normalized vegetation index (NDVI) (**a**), canopy response salinity index (CRSI) (**b**), and automatic water extraction index (AWEI) (**c**) obtained from the Landsat-8 images.
###### Abstract  
The aim of this paper is to study the main areas in which artificial intelligence (AI) will impact the field of project management in relation to cost, risk and scheduling. The research model was based on a previous study of the ten project management knowledge areas presented in PMI's _PMBOK_ 6 th edition, where project schedule, cost and risk management knowledge areas were identified as being the ones most likely to be affected by the development of AI. A group of graduates from a Master of Project Management program were assessed in an online questionnaire, reflecting the _PMBOK_'s elements of best practices and how AI will affect the project management profession in the future. Different elements of the three knowledge areas were considered to be affected more by AI than others. The schedule baseline is the element believed to be affected the most out of the project schedule management elements. For project cost management, the estimation of resource costs is believed to be affected the most. In the case of project risk management, the application of AI will have the strongest impact on the probability and impact formats.  
project management; artificial intelligence (AI); project schedule management; project cost management; project risk management Article  
A Qualitative Study on Artificial Intelligence and Its Impact on the Project Schedule, Cost and Risk Management Knowledge Areas as Presented in _PMBOK_\({}^{\text{\textregistered}}\)
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### Cut Problems
#### 4.5.3 \(k\)-Cut  
Given an undirected graph \(G=(V,E)\) and an integer \(k\in\mathbb{N}\), the \(k\)-Cut problem asks to remove the smallest number of edges such that \(G\) is partitioned into at least \(k\) non-empty connected components. The edge contraction algorithm by Karger and Stein [237] yields a randomized exact XP algorithm running in time \(O(n^{2k})\), which was made deterministic by Thorup [238]. There were recent improvements to the running time [154; 239]. There is an exact parameterized algorithm with parameter Opt[221; 240]. For general \(k\), it admits a \((2-2/k)\)-approximation algorithm [241], and is NP-hard to approximate within a factor \((2-\varepsilon)\) for any \(\varepsilon>0\) under the Small Set Expansion Hypothesis [74].  
A simple reduction shows that \(k\textsc{-Cut}\) captures \((k-1)\textsc{-Clique}\), so an exact FPT algorithm with parameter \(k\) is unlikely to exist. Gupta et al. [153] gave an \((2-\delta)\)-approximation algorithm for a small universal constant \(\delta>0\) that runs in time \(f(k)\cdot n^{O(1)}\). The approximation ratio was improved to 1.81 in [154], and further to 1.66 [242]. Very recently, Lokshtanov et al. [243] gave a PAS that runs in time \((k/\varepsilon)^{O(k)}n^{O(1)}\), thereby (essentially) resolving the parameterized approximability of \(k\textsc{-Cut}\).
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 2 Parameter Estimation with Kalman Filter  
### Kalman Filter  
Consider the following linear discrete-time stochastic system represented by  
\[\begin{array}{l}x_{k+1}=Ax_{k}+Bu_{k}+w_{k}\\ z_{k}=Hx_{k}+v_{k}\end{array} \tag{3}\]  
where \(x_{k}\in R^{n}\), \(z_{k}\in P\) and \(u_{k}\in R^{m}\) are state, measurement and input vectors, respectively. \(A\in R^{n\tau n}\) is the state transition matrix, \(B\in R^{n\tau m}\) is the input matrix and \(H\in R^{p\tau n}\) is the measurement matrix. The noise sequence \(w_{k}\), \(v_{k}\) are zero mean uncorrelated gaussian random sequences with  
\[E\left[\begin{array}{c}w_{k}\\ v_{k}\end{array}\right]\ \ \left[\begin{array}{c}w_{j}\\ v_{j}\end{array}\right]^{T}\ \right]=\left[\begin{array}{cc}Q_{k}&0\\ 0&R_{k}\end{array}\right]\delta_{kj} \tag{4}\]  
where \(Q_{k}>0\), \(R_{k}>0\), and \(\delta_{kj}\) is the Kronecker delta function. \(E[\cdot]\) is the statistical expectation, and the superscript \(T\) denotes transpose. \(Q_{k}\) is the process noise covariance matrix, and \(R_{k}\) is the measurement noise covariance matrix.  
The Kalman filter algorithm consists of two groups of equations,  
* Time update (or prediction) equations: \[\begin{array}{l}\hat{x}_{k}^{-}=A\hat{x}_{k-1}+Bu_{k-1}\\ P_{k}^{-}=AP_{k-1}A^{T}+Q_{k-1}\end{array}\] (5)  
These equations project, from time step \(k-1\) to step \(k\), the current state and the error covariance estimate to obtain _a priori_ estimates for the next time step.
* Measurement update (or correction) equations: \[\begin{array}{l}K_{k}=P_{k}H^{T}[HP_{k}^{-}H^{T}+R_{k}]^{-1}\\ \hat{x}_{k}=\hat{x}_{k}^{-}+K_{k}[z_{k}-Hx_{k}^{-}]\\ P_{k}=[I-K_{k}H]P_{k}^{-}\end{array}\] (6)  
These equations incorporate a new measurement with _a priori_ estimates to obtain an improved _a posteriori_ estimate. Here \(\hat{x}_{k}\) is an estimate of the system state vector, \(K_{k}\) is commonly referred as the filter gain or Kalman gain matrix, and \(P_{k}\) is the covariance matrix of estimation error, which is defined by  
\[P_{k}=E[(x_{k}-\hat{x}_{k})(x_{k}-\hat{x}_{k})^{T}]. \tag{7}\]
## 4 Case Studies  
To demonstrate the compliance of the proposed methods, we used two protocols: the glucose management protocol and the weaning protocol of the intensive care unit (ICU) as currently used in the Maastricht University Medical Centre+, Maastricht, The Netherlands. The Maastricht University Medical Center ICU consists of three different wards. Two of them, denoted as \(A\) and \(B\), treat mixed medical/surgical patients, where most patients (90%) are acutely ill patients (requiring immediate assessment or treatment). Ward \(C\) treats mostly elective cardiac patients after planned surgery.  
The purpose of the glucose management protocol is to control and stabilize the patients' glucose levels so that it remains within safe intervals. Hyperglycemia (glucose level above 11.1 mmol/L) is common in critically ill patients, both with and without diabetes, e.g., due to the stress factor [72; 73; 74]. Both hyperglycemia and hypoglycemia (glucose level below 2.2 mmol/L) are associated with a higher risk of mortality and morbidity [75; 76].  
The weaning protocol describes the process of decreasing the level of ventilator support, including extubation (removal of the tube from the patient's airway). The aim of the protocol is to safely reduce the level of ventilator assistance as fast as possible because of the patients' risk increase with prolonged duration mechanical ventilation. This protocol is used for postoperative cardiothoracic surgery patients in the ICU in ward \(C\).
# Changes of Gait Patterns after Correction of Refractive Error in the Elderly with Cataracts
## 2 Materials and Methods  
### Subjects  
A total of 30 elderly subjects with a mean age of 78.77 \(\pm\) 7.27 years, who had not worn corrective lenses during the passing year (in order to exclude the effects of adaptation and habituation due to the experience of wearing glasses) and were able to walk independently without the aid of assistive devices, were selected for inclusion in this study. A questionnaire was administered in order to confirm that the subjects had no histories of frequent falls or balance-related conditions such as neuro-musculoskeletal conditions, systemic diseases, or related medication. All participants had received a diagnosis of age-related cataracts in one or both eyes from an ophthalmologist; however, our selection was limited to those who had not undergone cataract surgery. The average of spherical equivalent refractive powers in the binocular was S + 0.13 \(\pm\) 0.77 D (ranging from S \(-\) 1.50 D to + 2.19 D), and the mean astigmatism was C \(-\) 0.72 \(\pm\) 0.56 D (ranging from C \(-\) 0.00 to \(-\) 2.00 D). The average visual acuity in the binocular without glasses was 0.64 \(\pm\) 0.23, and the average visual acuity in the binocular with glasses was 0.82 \(\pm\) 0.19. All participants received appropriate verbal and written explanations of the purpose and methods of the study and their consent was obtained prior to the conduction of the experiment.
## 1 Introduction
To accurately calculate the TE and backlash of a cycloid-pin gear pair, a novel calculation method based on error tooth surfaces is presented that has the ability to consider the influence of some abnormal meshing phenomena caused by PMs, MEs, and AEs on the TE and backlash, such as the instantaneous mesh-apart of tooth pairs and the eccentric load on the tooth surface. In this paper, the first section reviews the research on the TE, backlash, and other meshing characteristics of cycloid-pin drives. An error tooth surface model of a cycloid gear and pin considering the PMs, MEs, and AEs is established in Section 2. Section 3 describes in detail the principle of the novel calculation method of TE and backlash. The presented method is verified by comparing the results with the theoretical analysis results and the literature, as delineated in Section 4. Section 5 describes the investigation of the effects of the PMs, MEs, and AEs on the TE and backlash of the cycloid-pin gear pair through a series of simulation cases, and the analysis of the abnormal meshing phenomena caused by these errors in the transmission process. Section 6 presents the conclusions of this study, and the significance of the proposed method is summarized.
###### Abstract  
It is critically necessary to maximize the efficiency of agricultural methods while concurrently reducing the cost of production. Varieties, types, and fruit classification grades are crucial to fruit production. High expenditure, inconsistent subjectivity, and tedious labor characterize traditional and manual varieties classification. This study developed machine learning (ML) models to classify ten apple varieties, extracting the histogram of oriented gradient (HOG) and color moments from RGB apple images. Support vector machine (SVM), random forest classifier (RFC), multilayer perceptron (MLP), and K-nearest neighbor (KNN) classification models were trained with 10-fold stratified cross-validation (Skfold) by using the textural and color features, and a GridSearch was implemented to fine-tune the hyperparameters. The trained models, SVM, RFC, MLP, and KNN were tested with separate test data and performed well, having an accuracy of 98.17%, 96.67%, 98.62%, and 91.28%, respectively. Having the top results, the MLP and SVM models demonstrated the potential of applying HOG and color moments to train ML models for classifying apple varieties. This study suggests conducting further research to thoroughly examine additional image features and determine the impact of combining features and utilizing different classifiers.  
apple; machine learning; classification; histogram of oriented gradient; color moments +
Footnote †: journal: Applied Sciences
# Remote Sensing and Machine Learning Tools to Support
## 2 Methods  
### Bibliographic Base and Search Query Definition  
In preparation for this meta-analysis and systematic review, the Science Citation Index (Web of Science (WoS) Core Collection) bibliographic database was used on and up to 30 June 2022 to retrieve scientific documents, including papers published in journals and conference proceedings constrained to the time span from 1990 to 2022. For this purpose, three sets of keywords were systematically defined, providing a logical literature search query to locate highly relevant references in the database (see Figure 2). To retrieve papers that incorporated RS data and ML tools to address a wetland application, the search was conducted in the topic field (i.e., title/abstract/keyword) using the keywords  
Figure 1: A conceptual overview of the use of ML and RS tools to derive productions.  
listed in the second and third columns. However, to narrow down the search results and make them more specific, the first column keywords were only searched in the title field. Consequently, the predefined search query retrieved 528 journal and conference papers in the primary search in the WoS database. Afterward, the PRISMA checklist, known as the methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses, was followed [35; 36] to delineate the papers that fit within the scope of the topic and are eligible to be included in this meta-analysis. An overview of the paper selection methodology as per the PRISMA statement and review process is outlined in Figure 3.  
The screening of the resulted 528 potential papers was then started to filter them by title and abstract, providing 404 candidates. The meta-analysis did not consider documents categorized as review studies, book chapters, reports, and non-English papers. In the next stage, by assessing the full texts of those candidates, only publications utilizing RS and ML techniques in wetland monitoring were selected as the final items for review to comply with the predefined inclusion criteria and maintain a controllable workload while focusing on the lead questions and objectives of the current study. Finally, a total of 344 papers were included in our meta-analysis procedure (see Figure 3).
## 6 Security Analysis
### Energy Efficiency  
The HMPS scheme has been designed with energy efficiency as a core consideration, making it particularly suitable for IoT environments where devices often operate with limited power resources [22]. Energy consumption was assessed during critical cryptographic operations, including signature generation, verification, and aggregation.  
The results demonstrated that HMPS consumes significantly less energy compared to alternative techniques due to its lightweight cryptographic design. By optimizing computational requirements and reducing communication overhead, the scheme ensures minimal power usage without compromising security. This makes HMPS ideal for battery-powered devices in resource-constrained scenarios.
## Materials and Methods
### FE Analysis Methods  
#### 2.3.1 Validation of Finite Element Analysis  
To ensure the reliability of the FE analysis, the expansion force results of the G1N and S1N groups were compared with those obtained from an FE analysis under the same conditions. The grooved and smooth ball models for the G1N and S1N groups were modeled using Rhino3D (Robert McNeel & Associates, Seattle, WA, USA) (Figure 2). A hexahedron with eight nodes was used for the Al tube model mesh, and a total of 22,523 nodes and 19,000 elements were produced. For FE analyses, a 1/20 cyclic symmetry condition was applied based on the x-axis to decrease the analysis time (DEFORM-3D, Scientific Forming Technologies Corporation, Columbus, OH, USA). The expansion ball was assumed to be rigid, and the expansion speeds were 1 m/s and 0.5 m/s.  
To select the proper friction coefficient for the FE analysis, the expansion force results from FE analyses using three friction coefficients that were selected based on previous studies (i.e., 0.2, 0.3 and 0.45) [18; 19] were compared with those from expansion experiments. The steady-state values of the expansion force profiles from FE analyses were similar to those from the experiments when the friction coefficient between the expansion ball and Al tube was 0.3 (Figure 3a). The maximum expansion force of S1N was 245.1 N in the FE analysis and 267.0 N in the experiment (an 8.2% difference). The maximum expansion force of G1N was 247.7 N in the FE analysis and 274.8 N in the experiment (a 9.9% difference). However, the transient response of the expansion force profiles was different between the FE analyses and experiments due to the viscoelastic nature of the Al, which was not considered in the FE analysis. For the steady-state response, however, the experimental expansion force profiles showed a little oscillation, which could have been possibly due to a change in the friction coefficient between the expansion ball and tube during expansion. There was negligible oscillation in the analytical force profiles when using a fixed friction coefficient, but the average force values of the steady-state response were quite comparable between the experiments and simulations. The change in the friction coefficient during the tube expansion in the experiments could have occurred for various reasons, including changes in the expansion-driving actuator speeds and adhesion of the tube's inner grooves (Figure 3b).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experimental Group** & **Expansion Speed (m/s)** & **Lubricant** & **Expansion Ball Shape** \\ \hline G1N & 1 & Non-lubricated & Grooved \\ S1N & 1 & Non-lubricated & Smooth \\ S1A & 1 & DH173A & Smooth \\ S1H & 1 & DH23H & Smooth \\ S05H & 0.5 & DH23H & Smooth \\ \hline \end{tabular}
\end{table}
Table 1: Experimental groups for Al tube expansion experiments.  
Figure 2: Three-dimensional models of: (**a**) a smooth expansion ball; and (**b**) grooved ball with a lubricant groove on its surface for Al tube expansion experiments.