esa-sceva/satcom-chunk-collection huggingface dataset:
# A Physical Approach to Simulate the Corrosion of Ceramic-Coated Magnesium Implants
## 2 Materials and Methods
### In Vitro Immersion Test  
The immersion test in m-SBF was used to collect mass loss (ML) data of high purity magnesium (HP-Mg) due to corrosion for both the bare and coated sample groups. Round coupons of 10 mm in diameter and 3 mm thickness were cut, dry sanded using SiC sandpapers of 400-2000 grit size, cleaned in an ethanol bath, and then dried using hot air for 2 min. The weight measurements for both groups were recorded using a 1 mg accuracy precision scale. The samples were then immersed in 50 mL/cm\({}^{2}\) of m-SBF to eliminate the solution volume effect [20]. The buffering reagent is HEPES in the m-SBF to hold the pH around the physiological value of 7.4. An automatic pH controller (Bluelab, New Zealand) was also used to adjust the rising pH by dosing 1M HCl. The test was conducted inside a thermal incubator (Thermo Scientific Herather) to hold the temperature at 37 \({}^{\circ}\)C. The test was conducted for 7, 14, 21, and 28 days (\(n=3\)) for both groups. By the end of each period, the samples were collected and cleaned in chromic acid (150 g/L chromium trioxide, 10 g/L silver chromate, and ultrapure water) according to the ASTM G1-03 standard to remove the corrosion products [23]. Samples were then rinsed in distilled water followed by ethanol cleaning and drying before recording the new weight. In order to guarantee symmetric exposure of the round samples in the test solution, the samples were mounted floating by two concentric nylon screws along the sample axis. The mounting setup is shown in Figure 4 for two samples. The jigs and screws were made of inert nylon to eliminate alteration of the solution chemicals and galvanic corrosion with the test samples.  
Figure 3: A schematic of the MAO setup.  
Figure 2: Schematic of the saturation test setup. The temperature is controlled by means of a heated plate, and the added salt is being stirred using a magnetic stirrer. The temperature is monitored by an external thermometer as a feedback measurement.
## 2 Hardware Selection--Commercial off the Shelf or Custom?
For example the TEMPO3 system mentioned in Section 1 may be reprogrammed to operate in a clinical environment in which continuous data streaming is a requirement, or in a more longitudinal study in which data may be stored on node and offloaded after an extended measurement session. Additionally, COTS devices have steadily been improving in computing performance. When TEMPO1 was introduced in 2006, the processor employed had 48 kB of flash memory, 2 kB of RAM, and operated at a maximum clock frequency of 8 MHz. There are now available pin compatible drop-in devices from the same family that have over 100 kB of flash, 8 kB of RAM, and are capable of operating at 20 MHz within similar power budgets. This clearly leads to an expanded application space for a given sensing technology, with little or no non-recurring engineering (NRE) costs for hardware design. High volume, single purpose, mass market devices favor ASIC approaches in which the NRE costs are amortized over many units. Even if COTS systems provide a weaker solution (e.g., by limiting lifetime) than ASICs, simple economics will make COTS the better choice for many BSN applications that cannot provide the volume required to justify an ASIC solution.
## Concept and Kinematic Model Formulation
### The Embedded Distributed Sensors
As defined, under temperature or stress influence, the grating dimension changes. The Bragg length shift of \(\Delta\lambda\) is modulated by axial deformation change \(\Delta\varepsilon\) and temperature range \(\Delta T\) in the form:  
\[\frac{\Delta\lambda}{\lambda}=\left(\alpha_{f}+\xi\right)\cdot\Delta T+\left(1 -p_{e}\right)\cdot\Delta\varepsilon; \tag{2}\]  
where \(\lambda\) is the FBG wavelength, and \(p_{e}\), and \(\alpha_{f}\) are the photoelastic coefficient and the thermal-expansion value, respectively. The relationship between the temperature fluctuation and the FBG sensor is expressed by the following equation:  
\[\Delta\lambda=\left(\alpha_{f}+\xi\right)\cdot\lambda\cdot\Delta T; \tag{3}\]  
By using Equations (2) and (3) the temperature effect to be compensated may be extrapolated.
## 2 Passive Systems  
As previously defined, passive devices are characterized by the absence of battery and active radio. These remotely powered, fully passive transponders entirely rely on the power transmitted by the interrogator [17]. Due to these characteristics, these circuits are normally used simply as tag identifiers or, in some cases, as sensors, have a short range of operation and low processing capabilities. However, for a wide range of applications, its extended lifetime, placing flexibility, and low price are major advantages that justify the use of this technology. These types of IoT devices, generally associated with RFID tags, are based on technologies with a high maturity level and have been widely used in diverse applications fields, notably smart logistics, product traceability, and value chain optimization. In [18], the authors claim that RFID tags are \"the next big thing\" in management because these tags can optimize multiple business processes and, more importantly, the emergence of new intelligent or smart processes.  
Figure 2: IoT characterization considering energy and communication.  
Backscatter communication, WPT, EH, and chipless techniques are the technologies that have been shaping passive solutions.
###### Abstract  
Defect detection based on machine vision and machine learning techniques has drawn much attention in recent years. Deep learning is very suitable for such segmentation and detection tasks and has become a promising research area. Surface quality inspection is essentially important in the manufacturing of mobile phone back glass (MPBG). Different types of defects are produced because of the imperfection of the manufacturing technique. Unlike general transparent glass, screen printing glass has totally different reflection and scattering characteristics, which means the traditional dark-field imaging system is not suitable for this task. Meanwhile, the imaging system requires high resolution since the minimum defect size can be 0.005 mm\({}^{2}\). According to the imaging characteristics of screen printing glass, this paper proposes a coaxial bright-field (CBF) imaging system and low-angle bright-field (LABF) imaging system, and 8K line-scan complementary metal oxide semiconductor(CMOS) cameras are utilized to capture images with the resolution size of 16,000*8092. The CBF system is applied for the weak-scratch and discoloration defects while the LABF system is applied for the dent defect. A symmetric convolutional neural network composed of encoder and decoder structures is proposed based on U-net, which produces a semantic segmentation with the same size as the original input image. More than 10,000 original images were captured, and more than 30,000 defective and non-defective images were manually annotated in the glass surface defect dataset (GSDD). Verified by the experiments, the results showed that the average precision reaches more than 91% and the average recall rate reaches more than 95%. The method is very suitable for the surface defect inspection of screen printing mobile phone back glass.  
defect detection; deep learning; screen printing glass; machine vision +
Footnote †: journal: Medical Imaging
# Analysis of 'Pre-Fit' Datasets of gLAB by Robust Statistical Techniques
###### Abstract  
The GNSS LABoratory tool (gLAB) is an interactive educational suite of applications for processing data from the Global Navigation Satellite System (GNSS). gLAB is composed of several data analysis modules that compute the _solution_ of the problem of determining a position by means of GNSS measurements. The present work aimed to improve the _pre-fit outlier detection_ function of gLAB since _outliers_, if undetected, deteriorate the obtained position coordinates. The methodology exploits _robust statistical tools_ for regression provided by the Flexible Statistics and Data Analysis (FSDA) toolbox, an extension of MATLAB for the analysis of complex datasets. Our results show how the robust analysis FSDA technique improves the capability of detecting actual outliers in GNSS measurements, with respect to the present gLAB _pre-fit outlier detection_ function. This study concludes that robust statistical analysis techniques, when applied to the _pre-fit_ layer of gLAB, improve the overall reliability and accuracy of the positioning solution.  
GNSS positioning; robust statistics; GNSS LABoratory--gLAB; Flexible Statistics and Data Analysis toolbox--FSDA 2021 4, 400-418. [[https://doi.org/10.3390/stats4020026](https://doi.org/10.3390/stats4020026)]([https://doi.org/10.3390/stats4020026](https://doi.org/10.3390/stats4020026))
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 4 Hierarchical System Architecture
### Infrastructure Control System  
When a DSP system is deployed over a large-scale environment, managing resources is not a trivial task: the number of computing resources as well as the network latency among them can introduce prohibitively high delays in managing the DSP system. To address this management challenge, we exploit the concept of deployment region, considering that resources within a single region can be managed independently from other regions. Hence, we introduce, within the DSP system, a two-layered hierarchical control system that resorts on an _Infrastructure Manager_ (IM) and multiple Region Managers (RMs), one per each region.  
The Region Manager (RM) is a distributed entity that controls computing resources within a single region. To perform these operations, the RM is organized according to the MAPE loop and realizes the lower level control loop of the ICS. The RM monitors the computing nodes used by E2 DF within the region through the _Resource Monitor_. The latter periodically queries the active nodes to retrieve the number of hosted application operators and the average utilization of its computing and memory resources. Then, through the _Local Reconfiguration Manager_, it analyzes the monitored data and determines if new resources should be acquired, or leased ones should be released (e.g., to reduce resource wastage). To this end, the Local Reconfiguration Manager is equipped with a local policy, whose details are presented in Section 6. When the RM local policy determines that some adaptation should occur, it issues an adaptation request to the higher layer.  
At the higher layer, the _Infrastructure Manager_ (IM) coordinates the resource adaptation among the different computing regions through a global MAPE loop. By means of the _Infrastructure Monitor_, it collects aggregated monitoring data from the different available regions. Then, through the _Global Reconfiguration Manager_, the IM analyzes the monitored data and the reconfiguration requests received by the multiple RMs to decide which reconfiguration should be granted. For example, the Global Reconfiguration Manager can decide that it is more convenient to acquire resources from a specific region, so it will inhibit scaling operations proposed for other regions. According to its internal policy (see Section 6.3), the Global Reconfiguration Manager can interact with the AM and accordingly adapt its behavior. For example, it may suggest the AM to consolidate the managed DSP operators on fewer computing nodes (the AM can conveniently accept or deny the request), or it may request to balance load among the deployment regions. Using the _Global Actuator_, the IM communicates its reconfiguration decisions to each RM, which can, finally, scale the computing infrastructure by means of the their local _Reconfiguration Actuators_.
# High Density RF-DC Plasma Nitriding under Optimized Conditions by Plasma-Diagnosis
## 2 Experimental Procedure
### RF-DC Plasma Nitriding System  
Figure 1 illustrates the plasma nitriding system with instrumentation of OES. This system consists of a stainless-steel chamber with a diameter of 500 mm and a height of 500 mm, mass flow control (MFC), dipole electrodes for ignition of RF-plasmas, a vacuum pump and a DC bias plate. Unlike conventional DC- or RF- plasma generators, there is no mechanical matching box. The input and output powers are automatically matched by frequency self-adjustment around 2 MHz. The plasma discharging is ignited by the dipole electrodes for RF-generation and by the DC bias for DC-application.  
In the nitriding experiments, the chamber was evacuated down to the base pressure of 0.1 Pa and the gas mixture was introduced inside the chamber. The flow ratio was adjusted by MFC. The hydrogen gas flow rate ratio in the mixture varied from 5% to 50%. The pressure (P) varied from 30 to 80 Pa. The RF-voltage (V\({}_{\text{RF}}\)) varied from 100 to 250 V. The DC bias (V\({}_{\text{DC}}\)) varied from \(-\)300 to \(-\)500 V. The nitrogen and hydrogen gases were mixed  
Figure 1: RF-DC plasma nitriding system with instrumentation of OES for monitoring the emissive light spectra from N\({}_{2}\) + H\({}_{2}\) plasmas.  
for generating the plasma for nitriding. In the screening studies before this experiment, the plasma parameter was selected to be 35 Pa for pressure, \(-\)600 V for DC-bias, and 250 V for RF-voltage, respectively. In the nitriding experiments of AISI304 stainless steels, the holding temperature varied between 673 K and 623 K and the duration time was constant by 14.4 ks. The optimized hydrogen flow rate was employed in both nitriding experiments.
###### Abstract  
While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a \"rational\" player who continuously updates its own \"action\" through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.  
EO satellite; distributed satellite system; distributed mission scheduling +
Footnote †: journal: Sensors  
1  
Zhenghong Dong  
2 School of Space Information, Space Engineering University, Beijing 101416, China; [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (D.Y.)  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Multivariate Time Series Data Prediction Based on ATT-LSTM Network
## 4 Experiment
### Comparison Model and Parameter Configuration  
To evaluate the effect of the ATT-LSTM model, we compare the model with the following models:  
**VAR:** The vector autoregressive model (VAR) adopts the simultaneous form of multiple equations and is not based on economic theory. It is an unstructured multiple equation model. This model is often used to predict relevant time series data.  
**LSTM:** Long Short-Term Memory Neural Network (LSTM) [32] is a variant of Recurrent Neural Network (RNN), which solves the long-term dependence problem of RNN. It is a classic time series data prediction network.  
**GRU:** Gated Recurrent Unit (GRU) [33] is a very effective variant of the LSTM network. GRU has a more straightforward structure than LSTM.  
**LSTM+Zoneout:** Adding Zoneout technology [34] to a single-layer LSTM network can significantly improve the learning ability and improve the LSTM network's predictive ability.  
**LSTM-RNN:** This model was proposed by [PERSON]. [35] and others, which further reduces the prediction error and improves the accuracy of the prediction.  
**Attention-RNN:** This model was proposed by [PERSON]. [36] and others. It applies the attention mechanism to the Recurrent Neural Network (RNN) so that RNN can focus on different parts of input and output.  
Since the data sets are two realistic multivariate time series data sets and contain economic and financial series and time-temperature series, the stationarity of the data needs to be tested in subsequent experiments. Only a stationary series can establish a VAR model. We will perform an ADF test on the two data sets respectively to determine whether there is a unit root in the data set, and whether the data set is a stationary series. If the data set is a non-stationary sequence, the difference operation is performed until the differenced sequence reaches a plateau and a VAR model is established.  
For the LSTM and the ATT-LSTM model, many parameters (time step, number of hidden units, batch size, etc.) need to be set and debugged during model training to achieve the best prediction effect. The final parameter settings of this experiment are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Time Step** & **Units** & **Batch Size** & **Learning Rate** \\ \hline NASDAQ 100 & 10 & 128 & 256 & 0.001 \\ PM2.5 of Beijing & 10 & 64 & 128 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings.
## 2 MCDA Methods
### Analytic Hierarchy Process  
This method is one of the most common MCDA techniques [21]. Unlike the WSM and the WPM, this method evaluates the alternatives and weight criteria differently.  
The AHP is based on a hierarchic structure. The zero level of the hierarchy is a goal (in our study, we took the fuel cost as the zero level). The first level is represented by categories of criteria (in our case, these were environmental, energy performance, social, economic, and safety). The second level is composed of the criteria themselves (ignition delay times, NO\({}_{\text{x}}\) and SO\({}_{\text{x}}\) concentrations in combustion products, etc.) [22].  
The first step of the calculations is to normalize the criteria in each category in such a way that the values obtained add up to 1. Then weight coefficients are determined by comparing the criteria in pairs. Table 1 illustrates examples of pair-wise comparisons. If, for example, criterion b is twice as important as criterion d, this statement is written as a fraction 2/1.  
After that, common fractions are converted into decimal ones. Then, the sum of the values in each line and the total sum are calculated. At the next stage, the sums in lines are normalized such that they add up to 1. For that, the sum of the weight coefficients for each line should be divided by the total sum (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(d\) & \(e\) & \(f\) \\ \hline \(a\) & 4/1 & 1/1 & 2/1 \\ \(b\) & 2/1 & 3/1 & 1/1 \\ \(c\) & 1/3 & 1/4 & 1/1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of pairwise comparisons of criteria.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(d\) & \(e\) & \(f\) & Sum & Normalized Sum \\ \hline \(a\) & 4 & 1 & 2 & 7 & 0.480 \\ \(b\) & 2 & 3 & 1 & 6 & 0.412 \\ \(c\) & 0.33 & 0.25 & 1 & 1.58 & 0.108 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Normalized weight coefficients.  
Equation (3) is used to determine the weighted sum of criteria \(A_{n}\) by considering the values of the zeroth level of the hierarchy (normalized cost \(C\)), weight coefficients obtained for each category of criteria (first level of the hierarchy), and each criterion (second level of the hierarchy):  
\[A_{n}=C\sum w_{n}\sum(w_{j}\cdot x_{ij}). \tag{3}\]  
The best option is the one for which the integral indicator \(A_{n}\) is the greatest.  
The AHP can be employed using specialized software (such as Expert Choice [23]), or Microsoft Office. Specialized software is recommended when there are a lot of levels in the hierarchy. The main advantage of the AHP is that weight coefficients are not set directly, but are obtained using a pairwise comparison of criteria. Undoubtedly, all the MCDA methods are somewhat subjective. This cannot be regarded as a disadvantage, though, since the calculations are made in accordance with the existing interests and priorities. This aspect should be taken into account when analyzing the findings.