esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents an Acoustic Emission (AE)-based monitoring method tailored for monitoring the Abrasive Waterjet Cutting process, specifically focusing on the precision processing of composite materials. Through a comprehensive experimental investigation and mathematical modeling, several key findings and contributions have emerged:  
* The development of an in situ energy-based monitoring method represents a significant advancement in AWJC process monitoring. By correlating the AWJ energy with the AE signals, this method offers real-time insights into the process dynamics, enabling the detection of anomalous events and potential equipment malfunctions. Mathematical models were developed for calculating the _root mean square_ of the AE signal at the cutting head and at the workpiece. These models provide a normal level of \(AE_{RMS}\) signal for different values of process parameters.
* The proposed monitoring technique enables the early detection of equipment malfunctions, such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. By promptly identifying and addressing these issues, manufacturers can minimize the risk of part rejection, material wastage, and production delays. This study examined the issue of an accidental reduction in the abrasive flow rate, outlining the pattern of this malfunction.
* The findings of this study have practical implications for industries utilizing AWJC for precision machining of composite materials. By implementing the proposed monitoring method, companies can improve the process reliability, quality assurance, and operational efficiency, ultimately enhancing the competitiveness in high-value sectors.
* Future developments in this monitoring approach could involve the integration of anomaly detection algorithms for automated fault diagnosis and process adjustment. Additionally, advancements in Industry 4.0 integration may lead to the development of smart software systems capable of real-time monitoring and analysis, further enhancing process efficiency and overall equipment effectiveness.  
Figure 10: The kerf dimensions and the surface topography obtained in this scenario: (**a**) the normal AWJC process, (**b**) the AWJC with equipment malfunction.  
**Author Contributions:** Conceptualization, methodology, investigation: I.A.P., V.I.B., A.I.P. and C.C.; validation: I.A.P. and A.I.P.; supervision: N.B.; writing--original draft: I.A.P., V.I.B. and C.C.; research funding: N.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the HORIZON 2020--DiCoMI Project \"Directional Composites through Manufacturing Innovation\", GA Nr. 778068.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
###### Abstract  
In this article, a method of error source analysis and detection to improve the angle measurement accuracy of rotary encoders in precision measuring instruments is proposed. The angle measurement error caused by the installation eccentricity of the grating disk and the radial error motion of the rotating shaft is analyzed, and the error model is built. The method of measuring the radial error motion is introduced, and the visual system and image processing technology is proposed to detect the eccentricity. The verification experiment by the use of an autocollimator and a polygon is carried out. The residual error after comparison within \(\pm 6^{\prime}\) accounts for 9% of the angle measurement error. The proposed error model is verified, and the angle measurement error can be predicted if the installation eccentricity and radial error motion are known.  
Keywords:angle measurement error; eccentricity; radial error motion; image processing; rotary encoder; instrument +
Footnote †: journal: Applied Sciences
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## 2 Materials and Methods
### Preliminaries of Adding Doubling (AD) Algorithm
#### 2.1.4 Initializing Starting Layer  
A very thin layer must be initially simulated with optical thickness \(\mathrm{d}\tau\), albedo \(a\), and scattering matrices \(\mathbf{P^{+}}\) and \(\mathbf{P^{-}}\). The optical thickness is calculated as \(\tau=(\mu_{a}+\mu_{s})\cdot d\) and albedo as \(a=\mu_{s}/(\mu_{a}+\mu_{s})\), where \(d\) represents the thickness of the entire layer, and \(\mu_{s}\) and \(\mu_{a}\) are the scattering and absorption coefficients of the layer. Different approaches for initializing the layer exist, with diamond initialization being derived directly from the time-independent, one-dimensional, azimuthally averaged RTE; the details of derivation can be found elsewhere [15]. The initialization involves determining initial matrices using the following matrix relations:  
\[\begin{split}\mathbf{c}&=\mathrm{diag}(w)\\ \mathbf{M}&=\mathrm{diag}\Big{(}\frac{1}{v}\\ \mathbf{A}&=\mathbf{M}\big{(}\mathbf{I}-\frac{a}{2} \mathbf{P^{+}}\mathbf{c}\big{)}\frac{\mathrm{d}\tau}{2}\\ \mathbf{B}&=\frac{a}{2}\mathbf{M}\mathbf{P^{-}} \mathbf{c}\frac{\mathrm{d}\tau}{2}\\ \mathbf{G}&=\Big{(}\mathbf{I}+\mathbf{A}-\mathbf{B} (\mathbf{I}+\mathbf{A})^{-1}\mathbf{B}\Big{)}^{-1}\\ \mathbf{R}_{0}&=\mathbf{2}\mathbf{G}\mathbf{B}( \mathbf{I}+\mathbf{A})^{-1}\\ \mathbf{T}_{0}&=\mathbf{2}\mathbf{G}-\mathbf{I}\end{split} \tag{17}\]
## 2 Methodologies
### Bayesian Inference with \(\text{DREAM}_{(\text{ZS})}\)  
In the inversion process, suppose that the model to compute is \(f(u)\), which contains a series of continuously distributed parameters to be solved, \(u\), and a series of observations, \(Y_{\text{obs}}\). According to the Bayesian formula, we can obtain Equation (11).  
\[p(u|Y_{\text{obs}})=\frac{p(u)p(Y_{\text{obs}}|u)}{p(Y_{\text{obs}})} \tag{11}\]  
where \(p(u|Y_{\text{obs}})\) is the posterior density probability function of \(u\), \(p(Y_{\text{obs}}|u)\) is the likelihood function, and \(p(Y_{\text{obs}})\) is the probability of the observation arising from the value of the observation, which is a constant independent of \(u\).  
In the usual inversion process of MCMC sampling, if the proposed distribution is poorly correlated with the target distribution, a large number of proposed samples are rejected because they fall outside the posterior distribution, which leads to the need for a much higher number of iterations and greatly prolongs the overall inversion computation time. Therefore, an effective parametric inversion algorithm, \(\text{DREAM}_{(\text{ZS})}\), is used in the inversion process of this paper.  
\(\text{DREAM}_{(\text{ZS})}\) creates the jump vector in DREAM from the past states of the joint chains [19; 20]. Instead of generating candidate samples \(\zeta^{i}\) based only on the samples in the current row of the matrix X, all the samples in the current and past state matrices Z of the chain are used, the equation for obtaining the \(c\) candidate sample \(\zeta^{i}\) in the \(i\) chain is Equation (12).  
Figure 1: Variation in eigenvalues’ size with order in KL expansion.  
\[\zeta^{i}=\chi^{i}_{c-1}+(1+e_{\eta})g\Big{(}\psi,\eta^{\prime}\Big{)}\Bigg{[} \sum_{j=1}^{\psi}Z^{r_{1}(j)}-\sum_{k=1}^{\psi}Z^{r_{2}(k)}\Bigg{]}+e_{\eta} \tag{12}\]  
where: \(\chi^{i}_{c-1}\) is the \(c-1\) sample on the \(i\) Markov chain; \(\eta\) is a normally distributed random number with mean 0 and standard deviation \(b^{*}\); \(e_{\eta}\) is a uniformly distributed random number in the interval \([-b,b]\); \(g(\psi,\eta^{\prime})\) is a scale factor, the value of which is determined by the parameters \(\psi\) and \(\eta^{\prime}\), \(\psi\) is the number of parallel chains for generating candidate samples, and \(\eta^{\prime}\) is the number of parameters being updated in candidate sample \(\zeta^{i}\); \(Z^{r_{1}(j)}\) and \(Z^{r_{2}(k)}\) are the \(r_{1}(j)\) and \(r_{2}(k)\) samples randomly selected from the matrix \(Z\), \(r_{1}(j)\
eq r_{2}(k)\), \(j,k\in{1,2,\cdots,\psi}\).  
In this way, the inversion problem at high latitudes can be solved with a smaller number of parallel Markov chains and shorter chain lengths compared to the traditional MCMC algorithm.  
The DREAM\({}_{\rm(ZS)}\) V1.0 software can be downloaded from the [[https://faculty.sites.uci.edu/jasper/](https://faculty.sites.uci.edu/jasper/)]([https://faculty.sites.uci.edu/jasper/](https://faculty.sites.uci.edu/jasper/)) (accessed on 15 May 2024).
## 3 Results  
### Particle Size and Morphology  
The size of the CS microparticles were \(798.15\pm 18.60\)\(\mathrm{\SIUnitSymbolMicro m}\) in wet state and \(522.30\pm 16.26\)\(\mathrm{\SIUnitSymbolMicro m}\) in dry state. The loading of the active substances was associated with increasing of the particles size. As a result, the size of CS-Gly microparticles was \(822.00\pm 23.11\)\(\mathrm{\SIUnitSymbolMicro m}\) in wet state and \(532.98\pm 40.08\)\(\mathrm{\SIUnitSymbolMicro m}\) in dry state, while the size of CS-LA microparticles was \(1130.00\pm 31.25\)\(\mathrm{\SIUnitSymbolMicro m}\) in wet state and \(916.73\pm 39.91\)\(\mathrm{\SIUnitSymbolMicro m}\) in dry state. For CS-Gly-LA, the size of microparticles ranged from \(1079.00\pm 25.48\)\(\mathrm{\SIUnitSymbolMicro m}\) in wet state to \(863.74\pm 32.19\)\(\mathrm{\SIUnitSymbolMicro m}\) in dry state. SEM images of the CS, CS-Gly, CS-LA, and CS-Gly-LA are shown in Figure 1. It was noted that CS microparticles (Figure 1A) are spherical, with regular shape, and with a well-organized polymeric network. Encapsulation of active substances (Gly, LA) into CS matrix was associated with a slight (for CS-Gly, Figure 1B)) and intense (CS-LA, Figure 1C)) shape deformation. For these polymeric systems a slightlyrough surface was also observed. For the new polymeric systems, CS-Gly-LA, a spherical shape with a slightly rough surface was also noted (Figure 1D).
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Modify
#### 3.3.1 Data Cleaning  
Data cleaning is a crucial step in data analysis, ensuring the data's quality and integrity. Anomalies such as missing values and outliers can exist within datasets, and addressing them helps improve the analysis's reliability. According to [PERSON] et al. [34], there are three types of missing values: Missing Completely At Random (MCAR), where values have a high likelihood of missing in the dataset across various instances and attributes; Missing At Random (MAR), where missing values are observed in any of the attributes; and Not Missing At Random (NMAR), where values are absent because they depend on another value that is also missing. During data inspection and visualization, we encountered four MAR-type missing values for the \"Flow Bytes/s\" feature. The present work utilized Python as the primary programming language to address the issue by employing the Pandas library's mean() and fillna() functions. These functions allowed us to replace the null values in the dataset with the corresponding means.  
In a dataset, there are outliers, that is, data points that deviate from others, either in terms of the data's location, distinct behavior, or uncommon values within the same feature. As indicated by [PERSON] et al. [35], there are various methods for detecting these values, including those based on statistical techniques, distance between observations, density, clustering, and machine learning. We identified six records with infinite values and subsequently removed them during data inspection.
## 1 Introduction  
The ASI begins with the North American power system collapse of 14 August 2003 which emphasized the fact that there was great pressure to operate power systems at elevated stress levels sometimes with inadequate understanding of the consequences. The resulting analysis carried out by the Canada-US task force classified four groups of issues mainly related to reliability and prepared the 46 recommendations to avoid such blackouts in the future [1].  
Among these, recommendation 22 states that real-time tools be adopted and recommendation 30 states that criteria for identification of operationally critical facilities be clarified and improved.  
It is in this context that the ASI is proposed as a response to these recommendations.  
The ASI is a device that acts as an early warning system to alert the System Operator of possible system violations that may lead to system collapse. The idea behind the ASI is that if sufficient warning time has been given to the System Operator, remedial actions can be initiated to avert the possibility of a system collapse. Everything depends, therefore, on the detection of system stresses and the prediction of their degree of severity as seen from the ASI location.  
Thus the ASI acts as a local intelligent system predictor that records the on-line system parameters at its location, detects system stresses and predicts their severity. All these operations are carried out in a selected location termed an Area of Vulnerability. Deployment of several ASIs in identified areas of vulnerability forms a system of autonomous distributed predictors to alert the System Operator of possible system stresses and their severity so that required remedial actions can be taken. Thus the ASI is in fact a stress-alert device.  
The proposed ASI is a device that monitors the contents of the protection relays on a suspect weak bus and generates a performance level related to the degree of system performance degradation. This gives the system operators some time (minutes) to take corrective action. In a given operating area there would not likely be a need for an ASI on every bus. Note that the ASI does not trip any breakers; it is an INFORMATION ONLY device. An important feature is that the system operator can subsequently interrogate the ASI to determine the factor(s) that led to the performance level that has been initially annunciated, thereby leading to a course of action.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 2 Hydrogel Materials
### Natural Hydrogels
#### 2.2.4 Matrigel  
Matrigel is a natural hydrogel derived from the extracellular matrix of Engelbreth-Holm-Swarm (EHS) mouse sarcoma cells. It is rich in structural proteins such as laminin, collagen IV, entactin, and growth factors as well as cytokines, enabling the most native ECM environment [107]. Benefiting from its unique composition, Matrigel serves as a robust scaffold for cell adhesion, differentiation, and proliferation in in vitro cell culture models [108; 13; 109].  
Matrigel intrinsically exhibits thermosensitive properties and undergoes phase transition from a liquid state to a gel state over about 10 \({}^{\circ}\)C temperature increase. Once it becomes a gel, it provides a three-dimensional environment conducive to the growth and maintenance of various cell types, including stem cells and cancer cells. It is very easy to handle and can be directly applied to in vivo applications as well [91; 110].  
Applications of Matrigel are extensive in the biomedical field and are most widely used in cancer and neural cell research for studying cell differentiation and growth, metastasis of tumor cells, etc. (Table 2). Matrigel also largely contributed to the development of organoid and 3D cell culture systems, making it indispensable for modeling organ development and disease progression [111; 112]. Recently, Matrigel has also been widely utilized in vascular cells and angiogenesis studies [113; 112; 113; 114]. While Matrigel has been transformative in biomedical research, its predefined composition and possible heterogeneity inherited from the fact that it originated from a biological source remains a major challenge.
## Recent Changes in Terrestrial Gross Primary Productivity in Asia from 1982 to 2011  
**[PERSON] \({}^{1,2,\star}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{3}\), [PERSON] \({}^{4}\), [PERSON] \({}^{5}\), [PERSON] \({}^{2}\), [PERSON] \({}^{6,7,8}\) and [PERSON] \({}^{8}\)**  
* Faculty of Symbiotic Systems Science, Fukushima University, Kanayagawa, Fukushima 960-1296, Japan; E-Mails: [EMAIL_ADDRESS] (M.K.); [EMAIL_ADDRESS] (Y.O.)
* Center for Global Environmental Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba 305-8506, Japan; E-Mail: [EMAIL_ADDRESS]
* Graduate School of Life and Environmental Sciences, Osaka Prefecture University, 1-1, Gakuen-cho, Naka-ku, Sakai 599-8531, Japan; E-Mail: [EMAIL_ADDRESS]
* Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan; E-Mail: [EMAIL_ADDRESS]
* National Center for Agro-Meteorology, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 151-921, Korea; E-Mail: [EMAIL_ADDRESS]
* State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; E-Mail: [EMAIL_ADDRESS]
* Center for Applications of Spatial Information Technologies in Public Health, Beijing 100101, China
* Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +81-24-548-5256; Fax: +81-24-548-5256.  
_Received: 30 September 2013; in revised form: 25 October 2013 / Accepted: 11 November 2013 / Published: 15 November 2013_  
**Abstract:** Past changes in gross primary productivity (GPP) were assessed using historical satellite observations based on the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series and four terrestrial biosphere models to identify the trends and driving mechanisms related to GPP and NDVI in Asia. A satellite-based time-series data analysis showed that approximately 40% of the area has experienced a significant increase in the NDVI, while only a few areas have experienced a significant decreasing trend over the last 30 years. The increases in the NDVI are dominant in the sub-continental regions of Siberia, East Asia, and India. Simulations using the terrestrial biosphere models also showed significant increases in GPP, similar to the results for the NDVI, in boreal and temperate regions. A modeled sensitivity analysis showed that the increases in GPP are explained by increased temperature and precipitation in Siberia. Precipitation, solar radiation and CO\({}_{2}\) fertilization are important factors in the tropical regions. However, the relative contributions of each factor to GPP changes are different among the models.  
**Keywords:** terrestrial carbon cycle; NOAA AVHRR; Asia; terrestrial biosphere model
## 2 Materials and Methods
### Experimental Protocol  
At the first visit to the laboratory, a maximal voluntary isometric contraction (MVIC) test was performed to obtain the peak electromyogram (EMG) amplitude of the rectus femoris (RF), medial gastrocnemius (MG), and soleus (SOL) muscles using a CON-TREX motorized dynamometer (CON-TREX MJ) System, CMV, Dubendorf, Switzerland). For each joint, we used the setting recommended by the manufacturer [31].  
After the MVIC tests, participants completed the running trial in the lab. Participants were randomly assigned into two groups based on whether they wore the NN or EVA shoe during the trails and when running for 5 km. The participants warmed up with a 5-minute exercise of running at a self-selected speed on a motorized treadmill [32]. Next, as a preparation for the experiment, 34 reflective markers were attached and secured with tape to the participants' bodies according to Opensim's Gait-2392 model (see supplementary file). We excluded the head marker after considering the model's impact on the lower limbs [33]. The marker trajectories were recorded with an eight-camera VICON motion capture system (Oxford Metrics Ltd., Oxford, UK), which had a sampling frequency of 200 Hz during the running trials. Vertical ground reaction force was collected at a sampling frequency of 1000 Hz with a force plate (AMTI, Watertown, MA, USA) embedded in the floor of the lab, which was approximately located in the middle of the range of the track that could be captured using the cameras. In addition, EMG activity of the RF, GM, and SOL were recorded at 1000 Hz using a wireless Delsys EMG device (Delsys, Boston, MA, USA) [34]. All data collection was performed simultaneously. Participants were asked to run across a 20-meter runway at their self-selected running speed, which was measured using time gates placed 2 m before and after the force plate. A total of 6-8 trials were carried out by each participant, but only three trials that were the closest to the self-selected speed (established in the familiarization session) were included in the analysis.  
After the running trial, participants ran 5 km on a 400-meter track outdoors. Brower timing gates were installed at the outdoor track to monitor each participant's average running speed to ensure a relatively consistent running speed. To ensure that the markers and EMG sensors' locations remained the same place, the 5-kilometer running trial was executed with the markers and sensors remaining on their body. After the 5-kilometer running trial, a surface thermocouple portable thermometer (WRNM-01) was used to measure the shoe-sole temperature at the heel triangle of the shoe (Figure 1). Next, the running trial in the lab was repeated using the above-described conditions.
# Understanding Oxygen Concentrator Failures in Low Resource Settings: The Role of Dust and Humidity
## 2 Materials and Methods  
### Zeolite Samples  
Synthetic zeolite samples were collected from three different African regions: Ethiopia, Uganda, and South Africa. All the samples were recovered from the canisters of oxygen concentrators (OCs) employed in a relevant medical environment. In particular, the samples from Ethiopia, renamed E-1 and E-2, were provided by the Nekemte Specialized Hospital and Shashemene General Hospital, respectively. The samples from Uganda, identified as U-1 and U-2, were provided by the Mbarara Regional Referral Hospital and St. Francis Hospital Nsambya, respectively. The sample from South Africa (SA) was provided by the University of Cape Town. For each zeolite sample, the oxygen purity (% \(v/v\)) outgoing from the OC at the sampling time was also recorded and is reported in Table 1. Unfortunately, some other information, such as the amount of working operation time and the model of the OC, was not available for every sample. Table 1 summarizes all the main information available for the samples. Commercial 13X zeolite, identified as C-X, was purchased from Themo Scientific (CAS 63231-69-6) and constituted a benchmark to compare the characterization results of the zeolites used in the field. All the zeolite samples were ground into powder prior to analytical measurements. Additionally, special care was taken to minimize their exposure to open air: whenever possible, the canisters were shipped directly to the experimental lab, allowing the zeolites to be removed in situ just before analysis. In other cases, the samples were stored in sealed Falcon tubes and only opened immediately before testing.  
Figure 1: Schematic representation of an OC unit: (a) air gross particle filter; (b) air inlet filter; (c) air compressor; (d) adsorption molecular sieve bed; and (e) desorption molecular sieve bed.