esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis
### Gas Pressure Load  
Figure 6 illustrates that, due to the inherent symmetry of scroll compressors, compression chambers typically occur in pairs, allowing for the presumption of equal volumes within each pair. Under conditions where the air pressure inside and outside the tooth surfaces remains the same, it is reasonable to posit that their pressures negate each other, thus rendering their loads negligible. Consequently, calculations focus solely on internal pressure loads within regions with pressure differentials.  
Figure 8(a) illustrates that the central portion of the scroll tooth undergoes significant deformation, with the highest magnitude observed at the tooth head, measuring 4.5 \(\upmu\)m. Deformation diminishes progressively toward the outer regions of the scroll tooth. This deformation pattern can be attributed to the vertical fixation of the scroll teeth on the end plate, similar to the loading configuration of a cantilever beam, resulting in such deformation distribution.  
Analysis of the stress distribution depicted in Figure 8(b) reveals a predominant concentration in the vicinity of the exhaust chamber. The highest stress level, at 35 MPa, occurs at the root of the tooth head, while stress elsewhere is relatively minimal. This stress concentration phenomenon arises from the substantial difference in pressure between the inner and outer sides of the vortex tooth near the exhaust chamber. In addition, vertical fixation between the tooth root and the end plate, coupled with abrupt changes in cross-sectional area, contributes to stress concentration at the tooth root.
# Statistical Analysis of Acoustic Emission in Uniaxial Compression of Tectonic and Non-Tectonic Coal
## 5 Conclusions  
The AEs of the uniaxial compression experiment for tectonic coal and non-tectonic coal have been captured and studied statistically. For both kinds of coal, the probability density function of AE energy follows the power law well, but with different power law exponent values. The AE energy distributions show the AE signals of non-tectonic coal mainly comes from the fracture source, while the probability density function distribution of tectonic coal is a combination of fracture and friction effects. Only the waiting time distribution of non-tectonic coal follows the typical brittle fracture's double power law; the waiting time distribution of tectonic coal shows the single power law with smaller exponent value, which is associated with its granular like microstructure. The distribution of aftershock and Bath's law are not influenced by the different microstructures between two kinds of coal. At last, the correlation dimension results for the distribution of AE hypocenters indicate that the rough continuous decrease in dimension might be a precursor to devastating destruction. This study just considers the uniaxial compression condition; the future research direction is to study more realistic underground environmental conditions, such as triaxial and multi-fields coupling, and carry field experiment. Moreover, the coal samples in this study come from Permian; further related research can consider samples from different geological periods and with different coalification degrees.  
Conceptualization, R.L. and X.J.; methodology, R.L. and Y.H.; investigation, Y.Z. and S.R.; writing--original draft preparation, R.L. and X.J.; writing--review and editing, R.L. and X.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51908088, and Natural Science Foundation of Chongqing (cstc2019 jcyj-xfkxX0001).  
The authors declare no conflict of interest.
## 3 Factors Governing Survival and Occurrence of Opportunistic Pathogens in Premise Plumbing Systems
### Disinfection
#### 4.3.1 Chlorine  
Laboratory and full-scale studies have been conducted to assess the effectiveness of chlorine against _legionella_ across a range of physical and chemical conditions such as dose and residual levels, temperature, and pH [44; 160; 161]. In general, it was found that higher doses of chlorine in the order of 2-6 mg/L were needed for continuous control. Chlorine was proven to be more effective on control at higher temperatures; however, it also decayed faster. The fact that higher temperatures cause decay of chlorine residual further reinforces the need to ensure regular replenishment of water within premise plumbing systems to ensure that the disinfect can reach the system extremities (end-of-line fittings).  
The bactericidal action of the chlorine was also found to be enhanced at lower pH levels. Turbidity is suspected to interfere with the disinfection process and may need to be addressed prior to disinfection. One study found that the linkage between _Legionella pneumophila_ and protozoa including amoebae required much higher doses of chlorine for inactivation.
## 4 Application
### Non-Clinical Application
#### 4.2.6 Security  
In security systems, the biometric information of individuals is mostly face biometrics and is used for authentication. However, such biometric information can be stolen or duplicated by attackers, which is called biometric presentation attack (BPA). [PERSON] et al. [135] employed iPPG to differentiate authentic users based on deep Convolutional Neural Network (CNN) considering CASIA and Replay-Attack. [PERSON] et al. [136] and [PERSON] et al. [137] also reported that the iPPG technique can be exploited in biometric authentication.
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
###### Abstract  
This paper proposes a path-tracking method for agricultural vehicles based on the 4 WIS-4 WID structure and fuzzy control to improve the operation performance of agricultural machinery in the greenhouse. The influential model of two critical parameters, \(\alpha\) and \(R\), on the position correction is obtained based on the relationship analysis between the traditional pure pursuit model and the vehicle structure. Based on aiming pursuit, the relationship equation between the position deviation, lateral deviation \(d\), and heading deviation \(\theta\) is established. A two-input and two-output fuzzy controller is designed, and the lateral deviation \(d\) and heading deviation \(\theta\) are the input variables. Values of \(\alpha\) and \(R\) are obtained after fuzzification, fuzzy inference, and defuzzification, and a validated MATLAB model is used to simulate different scenarios. Results of the tests show that the steady-state error of path tracking based on fuzzy control pursuit is between 35 and 51 mm, and the stability distance is between 1661 and 3052 mm for straight path tracking in four initial states. The rectangular corners have the highest inaccuracy. The results of fuzzy control pursuit show a significant improvement in path-tracking performance that can influence vehicle navigation capability in the greenhouse.  
intelligent agricultural machinery; pure pursuit; fuzzy control; path tracking; 4 WIS-4 WID; position correction +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Support Vector Machine Regression
#### 2.4.1 SVR for Linear Regression  
In a regression problem, given a finite data set \(F=\left\{(xk,yk)\right\}_{k=1}^{n}\) derived from an unknown function \(y=g(x)\) with noise, we need to determine a function \(y=f(x)\) solely based on \(F\) and to minimize the difference between \(f\) and the unknown function \(g\). For linear regression, \(g\) is assumed to be a linear relationship between \(x\) and \(y\)  
\[y=g(x,w,b)=w\cdot x+b=\sum_{j=1}^{m}w_{j}x_{j}+b \tag{15}\]  
where \(x\) is called feature vector and the space \(X\) it lives in is named as feature space. \(m\) is the dimension of the feature vector x and the feature space \(X\). \(y\) is referred to as the label for each (\(x\),). Now that the relationship to be determined is assumed linear, our goal is to find a hyperplane \(y=f(x)\) in the \(m+1\) dimension space, where are \(\{(xk,yk)\}_{k=1}^{n}\) plotted and to minimize the fitting errors by adjusting the parameters. As is proven by Vapnik, the hyperplane is given as  
\[y=f(x,\alpha,b)=\sum_{k}a_{k}y_{k}x_{k}\cdot x+b \tag{16}\]  
where \(x_{k}\)'s are support vectors in the given data set \(F\) and \(y_{k}\)'s are the corresponding labels. \".\" represents the inner product in the feature space \(X\). Finding the support vectors and determining the parameters \(a\) and \(b\) turn out to be \(\alpha\) linearly constrained quadratic programming problem that can be solved in multiple ways (e.g., the sequential minimal optimization algorithm [24]. Such a process conducted on the given data set \(F\) is called learning. Once the learning phase is done, the model built can be used to predict the corresponding label \(y\) from any feature vector \(x\) in the feature space \(X\).
## 1 Introduction
Despite focusing on SELs, this study includes the investigation of Single-Event Upsets (SEUs) and Single-Event Functional Interrupts (SEFIs). Here, we present the instrumentation applicability through a case study focusing on an SRAM memory, but the instrumentation and methodology can be applied to different types of memories and even extended to other complex systems (e.g., processors, systems-on-chip). The studied memory is a Commercial Off-The-Shelf (COTS) 4 Mib SRAM with a 16-bit parallel interface: Samsung's K6R4016V1D-TI10 [12], from the year 2001 (Rev. D). It was employed on-board PROBA-V [13], a satellite mission from the European Space Agency (ESA). We investigate this memory based on in-flight behavior related to SEL events and through two experimental campaigns with proton and heavy-ion accelerated beams, in which the proposed instrumentation and experiments are discussed in this article, and the characterization results are subject to a complementary publication [14]. For that, we introduce relevant instrumentation enhancements, such as improved test modes, log synchronization, and measurements for achieving more assertive results in comparison to baseline SEL characterization. The remainder of the paper is structured as follows. Section 2 introduces related work and key aspects of the testing methodology targeting in-depth analysis. Section 3 presents the proposed experimental setup. Section 4 provides results from the performed irradiation campaigns and examples of analysis exploiting the instrumentation capabilities. Section 5 discusses the capabilities, limitations, and potential extension of the proposed approach. Finally, Section 6 gives conclusions on this work.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 2 State-of-the-Art in Non-Uniform and Low Latency Resampling
### Kriging  
Application of the Kriging methods starts with estimating an experimental variogram from recorded data. Then, the variogram is modelled by a positive semidefinite function, e.g., by a Gaussian variogram \(\gamma(t)\), where \(r\) is the variogram range. The sill gives the value for \(s=\gamma(t\rightarrow\infty)\), equivalent to the signal's variance. For zero distance, the variogram is always zero \(\gamma(0)=0\) by definition. A bandlimited signal overlaid with white noise results for infinitesimal small distances in a variogram value equivalent to the noise variance,which is called the nugget value \(n_{g}=\gamma(t\to 0)\). For noise-free processes, the nugget is zero [22]:  
\[\gamma(t)=\big{(}s-n_{g}\big{)}\bigg{(}1-e^{-\frac{3t^{2}}{r^{2}}}\bigg{)}+n_{g}, \text{ except for }\gamma(0)=0. \tag{8}\]  
The value at a target position \(t\) is estimated by the weighted sum of \(n\) input samples:  
\[\hat{y}(t)=\sum_{j=1}^{n}w_{j}(t)y_{j}. \tag{9}\]  
The following short forms are as follows  
\[\gamma_{i,j}=\gamma\big{(}t_{i}-t_{j}\big{)}\gamma_{i,t}=\gamma(t_{i}-t). \tag{10}\]  
Ordinary Kriging calculates the weights by solving the matrix equation [23]  
\[\hat{y}(t)\begin{bmatrix}\gamma_{1,1}&\cdots&\gamma_{1,n}&1\\ \vdots&\ddots&\vdots\\ \gamma_{n,1}&\ldots&\gamma_{n,n}&1\\ 1&&1&0\end{bmatrix}\begin{bmatrix}w_{1}(t)\\ \vdots\\ w_{n}(t)\\ \mu\end{bmatrix}=\begin{bmatrix}\gamma_{1,t}\\ \vdots\\ \gamma_{n,t}\\ 1\end{bmatrix}. \tag{11}\]  
The auxiliary variable \(\mu\) can be ignored. Kriging requires a matrix inversion of order \(n+1\), with the matrix in (11) representing the statistical distance between the input samples. The weights and the matrix depend only on the location of the input samples, not on their values. Kriging efficiently handles the problem of clustered input samples. If multiple input points are in close proximity, their weight is reduced as they offer minimal additional information.  
A more detailed description of the Kriging methods can be found in our earlier publications, e.g., [24] and standard textbooks [23]. Gaussian Process Regression provides an alternative way to formulate the same interpolation principle [25], which is based on the covariance of the input samples instead of describing the statistical properties using the variogram.
# Additive Manufactured Waveguide for E-Band Using Ceramic Materials
###### Abstract  
Ceramic materials are chemical- and temperature-resistant and, therefore, enable novel application fields ranging from automotive to aerospace. With this in mind, this contribution focuses on developing an additive manufacturing approach for 3D-printed waveguides made of ceramic materials. In particular, a special design approach for ceramic waveguides, which introduces non-radiating slots into the waveguides sidewalls, and a customized metallization process, are presented. The developed process allows for using conventional stereolithographic desktop-grade 3D-printers. The proposed approach has, therefore, benefits such as low-cost fabrication, moderate handling effort and independence of the concrete waveguide geometry. The performance of a manufactured ceramic WR12 waveguide is compared to a commercial waveguide and a conventionally printed counterpart. For that reason, relevant properties, such as surface roughness and waveguide geometry, are characterized. Parsing the electrical measurements, the ceramic waveguide specimen features an attenuation coefficient of 30-60 dB/m within the E-Band. The measured attenuation coefficient is 200% and 300% higher compared to the epoxy resin and the commercial waveguide and is attributed to the increased surface roughness of the ceramic substrate.  
printing; additive manufacturing (AM); ceramics; rectangular waveguides; rapid prototyping +
Footnote †: journal: Applied  
_Acticle_  
Additive Manufacturing (AM); ceramics; rectangular waveguides; rapid prototyping
## 4 Experiment and Results
### Experiment Method  
The mobile robot conducted experiments with the following scenarios:  
1. The mobile robot moves to find the location of the object to be transported.
2. When the mobile robot arrives at the location of the object, the operator picks up the object and places it on the transport tray.  
Figure 10: Warehouse Layout and Environment for Experiment [22].  
3. The mobile robot moves to the final destination with the transport tray and delivers the object to the final destination. The operator gets the object from the transport tray.
4. The mobile robot returns the transport tray to its initial position.  
In this test method, the final destination position is fixed and does not change, but the starting position and picking position of the mobile robot are continuously changed at random, so various starting positions and picking positions of objects can be considered for learning.
# Status of BIPV and BAPV System for Less Energy-Hungry Building in India--A Review
###### Abstract  
The photovoltaic (PV) system is one of the most promising technologies that generate benevolent electricity. Therefore, fossil fuel-generated electric power plants, that emit an enormous amount of greenhouse gases, can be replaced by the PV power plant. However, due to its lower efficiency than a traditional power plant, and to generate equal amount of power, a large land area is required for the PV power plant. Also, transmission and distribution losses are intricate issues for PV power plants. Therefore, the inclusion of PV into a building is one of the holistic approaches which reduce the necessity for such large land areas. Building-integrated and building attached/applied are the two types where PV can be included in the building. Building applied/attached PV(BAPV) indicates that the PV system is added/attached or applied to a building, whereas, building integrated PV (BIPV) illustrates the concept of replacing the traditional building envelop, such as window, wall, roof by PV. In India, applying PV on a building is growing due to India's solar mission target for 2022. In 2015, through Jawaharlal Nehru National Solar Mission, India targeted to achieve 100 GW PV power of which 40 GW will be acquired from roof-integrated PV by 2022. By the end of December 2019, India achieved 33.7 GW total installed PV power. Also, green/zero energy/and sustainable buildings are gaining significance in India due to rapid urbanization. However, BIPV system is rarely used in India which is likely due to a lack of government support and public awareness. This work reviewed the status of BIPV/BAPV system in India. The BIPV window system can probably be the suitable BIPV product for Indian context to reduce the building's HVAC load.  
India; BIPV/BAPV; BIPV-Glazing; JNNSM; MNRE; Zero energy-building 25 February 2020; 24 March 2020; Published: 29 March 2020
## 3 Results
### Evaluation and Application of the EMRe Models under all sky Conditions during 2013-2016
#### 3.2.4 Daytime and Nighttime Re Simulations Using Re (LDR) Method  
In view of the systemic underestimations of Re using the Re (LDR) method, dark respiration (Re (DR)) was considered in daytime Re simulation, i.e., using Re (LR) and Re (DR) methods to calculate daytime Re, respectively, and Re (DR) to calculate night Re. This method is named as Re (LDDR). The calculated results for the averages of the hourly sum of Re are displayed in Tables 13 and 14 and Figure 11. The ratios of the calculated to the observed Re values for daily, monthly, and annual sums of Re were also similar to that of hourly sum and not shown. In general, the simulated Re displayed evident seasonal variations, much higher in summer and lower in winter than the observations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Time** & \multirow{2}{*}{**\(\delta\)avg**} & \multirow{2}{*}{**NMSE**} & \multirow{2}{*}{**\(\sigma_{\text{cal}}\)**} & \multirow{2}{*}{**\(\sigma_{\text{obs}}\)**} & \multirow{2}{*}{**cal/obs**} & \multirow{2}{*}{**\(n\)**} & \multicolumn{2}{c}{**MAD**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{1-1} \cline{6-10} \multicolumn{1}{c}{**Period**} & & & & & & **(mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\))** & **(\%)** & **(mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\))** & **(\%)** \\ \hline
2013–2014 & 103.31 & 1.555 & 0.214 & 0.072 & 0.90 & 13,893 & 0.111 & 72.37 & 0.182 & 118.44 \\ \hline
2015 & 77.37 & 1.165 & 0.202 & 0.062 & 0.97 & 8754 & 0.110 & 67.33 & 0.174 & 106.38 \\ \hline
2016 & 78.60 & 1.401 & 0.208 & 0.083 & 0.73 & 8724 & 0.121 & 64.07 & 0.191 & 101.11 \\ \hline
2013–2016 & 88.91 & 1.375 & 0.209 & 0.074 & 0.87 & 31,611 & 0.113 & 68.19 & 0.182 & 109.40 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Same as Table 8, but for mean hourly daytime and nighttime Re during each year and 2013–2016 using 3-factor Re (LR). (Re (LDR) method).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Time** & \multirow{2}{*}{**\(\sigma_{\text{cal}}\)**} & \multirow{2}{*}{**\(\sigma_{\text{obs}}\)**} & \multirow{2}{*}{**cal/obs**} & \multirow{2}{*}{**\(n\)**} & \multicolumn{2}{c}{**MAD**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{1-1} \cline{6-10} \multicolumn{1}{c}{**Period**} & & & & & & **(mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\))** & **(\%)** & **(mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\))** & **(\%)** \\ \hline
2013–2014 & 104.24 & 1.530 & 0.210 & 0.072 & 0.86 & 13,893 & 0.106 & 69.30 & 0.176 & 114.70 \\ \hline
2015 & 76.57 & 1.083 & 0.194 & 0.062 & 0.91 & 8754 & 0.103 & 63.09 & 0.162 & 99.32 \\ \hline
2016 & 77.31 & 1.300 & 0.200 & 0.083 & 0.69 & 8724 & 0.112 & 59.17 & 0.179 & 94.  
Several studies report the annual respiration biases of \(\sim\)6% [62], 9.7% [31], and 20% [63; 64], respectively. In general, the EMRe exhibited reasonable simulations of respiration.
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 2 Literature Review
### The Application Prospects and Potential Limitations of AI in Cultural Restoration  
The development of modern science and technology has significant implications for contemporary art history. \"Science\" and \"art\" may seem like parallel lines with little intersection, except for the recent development of new media art. However, looking back at art history, science has profoundly influenced the development of art. During the Renaissance, artists used \"optical instruments\" to aid in painting, and spectral analysis research played a crucial role in the development of Impressionism [15]. The application of artificial intelligence (AI) in the restoration of art collections has various advantages and disadvantages. On the positive side, AI can provide high-precision restoration techniques. Through image recognition and machine learning technologies, AI can accurately analyze and repair damaged parts of artworks. For example, AI can automatically detect cracks and color changes in paintings and offer optimal restoration solutions. Additionally, AI enables remote collaboration, allowing experts worldwide to participate in restoration work, thereby improving efficiency and quality [16].  
However, the application of technology also faces several challenges and drawbacks. Firstly, the high costs and maintenance requirements of technology can be burdensome for some institutions. Secondly, data privacy and security issues pose significant challenges, especially when large amounts of environmental data are collected and processed. Furthermore, over-reliance on technology may lead to a degradation of personnel skills, reducing sensitivity to environmental changes and the ability to respond effectively [17]. Additionally, there are still many challenges to overcome, including data collection and quality, ethical concerns and cultural sensitivity, expert involvement and interpretation, etc. [14; 18].