esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Effect of Cutting Frozen Soils on the Supports of Above-Ground Trunk Pipelines
###### Abstract  
The transportation of oil and gas in Russia's northern and Arctic regions has seen significant growth in recent years. However, the presence of permafrost in these areas can cause malfunctions in the main pipelines due to soil frost heaving. The operational pipelines also often suffer from various defects in their body and surface. To mitigate these issues, above-ground trunkline supports are utilized to protect the pipelines from cryogenic processes. Nevertheless, these supports are subjected to ground loads caused by cryogenic frost heaving, which poses a threat to the pipeline's integrity and the environment. In response to these challenges, this study presents a design for pipeline support to maintain the pipeline's stability in the face of soil displacement caused by unequal frost-heaving forces. A numerical model was created to evaluate the fracture of frozen rock and the resulting stresses in the soil and support structure. The input data for the model includes coefficients that describe the soil's state during the cryogenic process and the proposed support's parameters. The experimental results showed the proposed design to be effective in protecting the pipeline from soil frost heaving. The paper also provides the results of numerical and experimental studies on soil fracture stresses depending on the rock type and temperature. This design promises to increase both the safety of above-ground trunk pipelines and their technological efficiency.  
above-ground trunk pipeline; support design; sharp edge of support slab; permafrost soil; frost heave forces; soil fracture stress +
Footnote †: journal: Applied Sciences
###### Abstract  
With the development of the economy and society, the environmental problems caused by heavy metals have always been the focus of attention. Strong concern has been recently shown for the heavy metal pollution of soils in southwestern China. The heavy metals of surface soils in the Xinping area of Yunnan province, China are surveyed along with some crop samples. There are 3312 surface soils and 95 crop samples collected in about 370 square kilometers. Heavy metals including As, Cd, Cr, Hg, and Pb and pH are analyzed. New single and integrated pollution indices of heavy metals for soils (_PI_ and _PI_a) and crops (_PI_a) based on Chinese criteria (GB15618-2018 and GB2762-2022) are described and presented here and used to assess the pollution status of heavy metals. The results indicate that the background level of surface soils is about 62.1%, the screening level is about 33.4%, and the intervention level is about 4.5%, which is mainly a result from Cr and spatially coincides with the peridotite rock, indicating a geogenic pollution source. Most crop samples are not contaminated with heavy metals. Comparing the results of the two integrated pollution indices between soils and crops, two inconsistent assessments are observed. One is that some contaminated crops are growing in unpolluted areas (or Type I) and the other is that some uncontaminated crops are growing in polluted areas (or Type II). This indicates a new challenge between the assessment criteria on soils and crops.  
heavy metals; surface soil; crops; pollution index; Xinping area +
Footnote †: journal: Applied Sciences
## 6 Model Building and Analysis
### Fuzzy Logic Modelling
#### 6.3.2 Assigning Membership Function
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Main**} & \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Sub-Factor**} & \multicolumn{1}{c}{**Sub-Factor**} & \multicolumn{1}{c}{**Std Dev**} & \multicolumn{1}{c}{**Global**} & \multicolumn{1}{c}{**Normalized**} \\
**Factors** & \multicolumn{1}{c}{**Weight**} & & **Weight** & **Weight** & **Weight** & **Global Weights** \\ \hline \multirow{8}{*}{Non-financial} & \multirow{8}{*}{64.84} & Clear Vision Mission and Goals & 15.21 & 4.14 & 986.41 & 0.904 \\  & & Competitive Strategy & 15.30 & 4.31 & 991.92 & 0.910 \\  & & Availability of Knowledge & 15.21 & 3.29 & 986.41 & 0.904 \\  & & Business Experience (no. of years) & 8.35 & 4.93 & 541.60 & 0.475 \\  & & Employee Culture Environment & 16.49 & 4.19 & 1069.07 & 0.984 \\  & & Training for employees & 12.69 & 4.68 & 822.92 & 0.747 \\  & & Market Conditions/Customer Eng & 16.74 & 3.79 & 1085.60 & 1.000 \\ \hline \multirow{8}{*}{Financial} & \multirow{8}{*}{30.10} & Growth Rate 1 year 2 & 9.37 & 5.22 & 282.11 & 0.225 \\  & & CAGR 3 year 3 & 18.37 & 9.34 & 552.88 & 0.486 \\  & & General Building 4 & 28.54 & 18.35 & 858.96 & 0.781 \\  & & Manufacturing 5 & 2.90 & 6.44 & 87.33 & 0.037 \\  & & Power 6 & 4.21 & 8.95 & 126.62 & 0.075 \\  & & WWS 7 & 2.16 & 5.46 & 64.90 & 0.016 \\  & & Industrial/Petroleum 8 & 7.97 & 17.28 & 239.97 & 0.184 \\  & & Transportation 9 & 3.13 & 6.54 & 94.32 & 0.044 \\  & & Hazardous Waste 10 & 1.62 & 6.31 & 48.79 & 0.000 \\  & & Telecom & 2.33 & 5.93 & 70.13 & 0.021 \\  & & \% CM at Risk 12 & 10.77 & 13.59 & 324.10 & 0.266 \\  & & Revenue per employee & 8.63 & 7.13 & 259.90 & 0.204 \\ \hline Market & 5.06 & Diversification Entropy & 100 & 0 & 506.07 & 0.441 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Factor and sub-factor weights, globalized weights and normalized weights. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Impact on Organizational Performance**} \\ \hline If Clear Vision Mission and Goals is & Very High & then & Impact of non-financial factor is & Very High \\ If Clear Vision Mission and Goals is & High & then & Impact of non-financial factor is & High \\ If Clear Vision Mission and Goals is & Moderately low & then & Impact of non-financial factor is & Moderately low \\ If Clear Vision Mission and Goals is & Low & then & Impact of non-financial factor is & Low \\ If Clear Vision Mission and Goals is & Very Low & then & Impact of non-financial factor is & Very Low \\ \hline \hline \end{tabular}
\end{table} 
Table 7: If-then rules for sub-factors.
# Numerical and Experimental Analysis of Pressure Pulsation Attenuator Based on Helmholtz Resonator
###### Abstract
[PERSON] [30] analyzed and discussed the existing pulsation attenuation methods and proposed a new evaluation method: the inlet insertion loss and outlet insertion loss were used to express the actual attenuation effect after the pulsation attenuator was installed into the hydraulic system. [PERSON] et al. [31] use the lattice Boltzmann method to study the damping effect of circular holes in pipes in the time domain. This method reduces the computational cost compared to traditional methods. The traditional pulsation attenuator has the disadvantages of large volume and mass, restricted installation space, poor effect, and narrow attenuation frequency range. The space and weight limitations of the aero-engine hydraulic system put forward higher requirements for the pressure pulsation attenuator: (1) It effectively reduces pressure pulsations and covers the main pulsation frequencies in hydraulic systems. (2) It is required to have a small volume mass, compact, and easy to install. In this paper, a pressure pulsation attenuator is designed by using the Helmholtz resonant chamber. The attenuation characteristics of the HTPA are studied from three aspects: theoretical model, simulation, and experiment, as shown in Figure 1. It is compact, small in volume and can effectively reduce the pressure pulsation at the pump source. Figure 1: Research process.
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 3 Results  
In this section, we discuss the UL duty cycles calculated from the parameters reported from the drive test tool QualiPoc, as well as other relevant parameters that offer enhanced insights for evaluating exposure: the use of bandwidth, the PUSCH transmit power, the transport block size, the number of bits per resource block (RB), and the reference signal received power (RSRP). We conclude this section with a summary of the duty cycles per application and communication technology.
## 6 ANN Applications in Agricultural and Biological Engineering
Figure 12 shows the ANN method distribution of applications in agricultural and biological engineering. Fuzzy-neural systems have been developed and used in agricultural and biological engineering. Table 1 listed papers and reports on fuzzy-neural methods and systems in applications in agricultural and biological engineering. [PERSON] _et al._[109] applied neural network modeling for fuzzy food extrusion control. [PERSON] and [PERSON] [110] used the BP algorithm in training ANN models for prediction of volume, browning and bread temperatures. The outputs of the ANN models were used for fuzzy control simulation of the oven used in the baking process. [PERSON] _et al._[111] proposed and then applied a new fuzzy control technique, which efficiently selects optimal membership functions and control rules by using neural networks and genetic algorithms to the control of relative humidity in a fruit-storage house. The response of relative humidity, as affected by ventilation, was first identified using neural networks, and then optimal membership functions and control rules were sought through simulation of the identified model using GAs. [PERSON] _et al._[112] developed a strategy consisting of fusing the fuzzy logic and ANN on a conceptual and structural basis for an easy and efficient means of tuning fuzzy ET (Evapotranspiration) models. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input-output examples selected to cover the data hyperspace of the sites evaluated. [PERSON] _et al._[113] developed a novel segmentation approach that partitions color images into two uniform regions is described. This unsupervised procedure is based on a SOM neural network and fuzzy c-means clustering (FCM). The SOM allows the mapping of a color image related to edible beans into a consistent two-dimensional table through a non-linear projection. Fuzzy clustering is then applied to the Kohonen map to determine the two cluster centers. [PERSON] _et al._[114] conducted a digital camera operation study for classifying uniform images of grass, bare soil, corn stalks residue, wheat straw residue, and a barium sulfate reference panel, based on color. The classifications were conducted with fuzzy inference systems, built with subtractive clustering, an ANFIS. [PERSON] _et al._[115] applied a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profiles using ground-penetrating radar (GPR) imagery. [PERSON] and [PERSON] [116] developed an on-line hydraulic vane pump fault detection system. This fault detection system decomposed vertical pump vibration signals using wavelet packet analysis. Packets containing signal features distinguishing normal and failed pump operation were entered into an ANFIS for pump health classification. [PERSON] and [PERSON] [117] developed an artificially intelligent controller based on an ANN
## 4 Design of the Proposed Structures
### Series Inductive Compensation Structure (SICS)  
Figure 5 shows the modified \(\pi\)-type attenuator with a series inductive compensation structure and its equivalent circuits for the reference and attenuation states.  
For the amplitude, when the resistance \(Rs\) is connected in series with two inductors \(Ls\), the resultant total impedance becomes \(Rs+2j\omega Ls\). Notably, the total impedance value escalates in correlation with the frequency, thereby compensating for the attenuation values at high frequencies.  
For the phase, the transmission phases \(\varphi_{R}\) and \(\varphi_{A}\) in the reference and attenuation states of SICS can be written as (1) and (2), respectively.  
Figure 5: (**a**) Schematic diagram of the modified \(\pi-\)type attenuator and its equivalent circuits. (**b**) Reference state. (**c**) Attenuation state.  
\[\varphi_{R}=-\tan^{-1}(\frac{2((R_{s,on}{}^{2}+4\omega^{2}L_{s}C_{p,off}Z_{0}(Z_{0 }+R_{s,on}))L_{s}+(Z_{0}R_{s,on}{}^{2}+R_{s}R_{s,on}(R_{s,on}+4Z_{0})+R_{s}{}^{2}( Z_{0}+R_{s,on}))Z_{0}C_{p,off})\omega}{(2Z_{0}+R_{s})R_{s,on}{}^{2}+4R_{s,on}{}R_{s}{}^{2}+2(R_{s} {}^{2}+2R_{s}R_{s,on})Z_{0}-2\omega^{2}L_{s}(2C_{p,off}R_{s,on}{}^{2}Z_{0}-2L_{ s}R_{s,on}-4L_{s}Z_{0})}) \tag{19}\]  
\[\varphi_{A}=\tan^{-1}(\frac{(C_{s,off}{}R_{s}{}^{2}+12\omega^{2}L_{s}{}^{2}C_{ s,off}-2L_{s})(R_{p}+Z_{0})\omega}{(R_{s}R_{p}+Z_{0}R_{s}+4Z_{0}R_{p})(1-4\omega^{2}L _{s}C_{s,off})}) \tag{20}\]  
The inductor required to realize (17) is derived as follows:  
\[L_{S}\cong\frac{1-\sqrt{1-16\omega^{2}R_{s}{}^{2}C_{s,off}{}^{2}}}{2\omega^{2 }C_{s,off}} \tag{21}\]  
Figure 6 shows the simulated relative attenuation and additional phase shift versus frequency for different \(Ls\) values of the 8 dB modified \(\pi\)-type attenuator. As the value of compensation inductance \(Ls\) increases, both the relative attenuation value and additional phase shift progressively approach ideal values. Selecting the appropriate \(Ls\), the attenuation bandwidth can be broadened, and the additional phase shift can be optimized. This paper uses microstrip lines instead of series inductors to achieve compensation functions.  
It is worth noting that \(Ls\) has little effect on the low-frequency performance, which is critical for designing ultra-wideband attenuators.
## 4 Results: Rank-1 IR  
The Rank-1 IR performance for synthetic Band 8, for 10 features, evaluated at various gallery sizes (1000, 2000, 4000, 8000, 16,000, 32,000, 64,000) is presented in Figure 5A. Every dot in this figure represents the mean across 30 random repetitions (random subset of features and subjects). The Rank-1 IR for 1000 subjects was approximately 58%. This rate declines steadily as the gallery size increases. At a gallery size of 64000 the Rank-1 IR has dropped below 15%. The decrease is described by a linear function of log(Gallery Size) (Figure 5B).  
This drop can be prevented if additional information is added in the form of additional features. We understand that, in any real world application, investigators would likely use all of the information available in the first instance. Nonetheless, we believe this additional analysis provides some insight and guidance for improving Rank-1 IR performance. The red dots in Figure 5A represent the Rank-1 IR that can be achieved by adding additional  
Figure 5: **(A)** Rank-1 IR as a function of gallery size for synthetic Band 8 features. Each dot represents the mean of 30 repetitions. The black dots are the Rank-1 IR for 10 features for the following gallery sizes: 1000, 2000, 4000, 8000, 16,000, 32,000, 64,000 subjects. The red dots represent the Rank-1 IR for feature numbers greater than 10 which are chosen to produce a Rank-1 IR most similar to that for 10 features and 1000 subjects. **(B)** Same data as **(A)** plotted on a log(Gallery size) scale. Note the fit of the decline to a linear function of log(Gallery Size). **(C)** Plot of the number of features required for gallery sizes greater than 1000 to match the Rank-1 IR for 10 features, 1000 subjects. **(D)** Same data as **(C)** plotted on a log(Gallery size) scale. Note the fit of the increase to a linear function of log(Gallery Size). In this case a linear equation in log(Gallery Size) was able to match the results perfectly (r-squared = 1.0).  
features. The number of features needed to achieve this performance was also indicated. Note that this analysis is limited in accuracy by the discrete nature features.  
In Figure 5C, we plot the number of features required to stabilize the Rank-1 IR as gallery size increases. In this case, the best fitting function for these feature numbers was also a linear function in terms of log(Gallery Size) (Figure 5D).  
In Figure 6, we present a comparable analysis for our face recognition data set. In this case the Rank-1 IR was calculated for the first 10 PCA components, as a function of gallery size from 1000 to 9000 in steps of 1000 (Figure 6A). Once again, we see a decrease in Rank-1 IR as gallery size increases. The decrease was linear in log(gallery size) (Figure 6B). The number of PCA components required to stabilize the Rank-1 IR performance was also a linear function of log(gallery size) (Figure 6D).
## 3 Results
### Intake Recommendations for Total and Specific Fatty Acids
#### 3.1.5 Comparison between FAO/WHO and EFSA Fat Recommendations  
The DRIs for fats do not differ much between the two authorities. Nevertheless, compared to FAO/WHO, EFSA has set higher minimum values for LA (4%E vs. 2 or 3%E) and for EPA + DHA for children older than 2 years (250 mg/day vs. 100 mg/day [2-3 years], 125 mg/day [3-5 years] or 175 mg/day [5-7 years]). EFSA has made age-specific recommendations for total fat (until the age of 4 years) and DHA (until the age of 2 years) while FAO/WHO values were age-specific for LA also and evolved until the age of 18 years. Most recommendations are expressed as %E, so the recommended absolute amounts of fat and PUFA increase with age, according to the increasing energy intake. The FAO/WHO also set an upper AMDR for total PUFA (15%E for the age 1-2 years and 11% for 2-18 years) and an AMDR for MUFA (obtained by difference between total fat and all the other FA types). EFSA did not set any DRV for total PUFA or total MUFA. Despite its presence inhuman milk and its role in building structures of all body cells, no recommendation has been set for the intake of ARA.
## 4 Field Application of GSCRF  
### Feasibility Determination of GSCRF to the 1510 Working Face  
According to its actual production, Xinyi Coal Mine plans to implement a GSCRF test along the 1510 track gateway in the upper part of the fifth mine. After determining the feasibility of pillarless mining of the 1510 working face by GSCRF, through calculation and demonstration, the 1510 working face of Xinyi Coal Mine was found to have the following characteristics:  
1. Immediate roof cavability The immediate roof of the 1510 working face of Xinyi Coal Mine is composed of mudstone and argillaceous sandstone, with low strength and well-developed fissures, which caves in upon excavation. The immediate roof has a tensile strength \(\sigma_{Tr}\) of 0.2 MPa, a thickness \(H_{ir}\) of 3 m, a density \(\rho\) of 2.5 t/m\({}^{3}\), and a gravitational acceleration \(g\) of 9.8 m/s\({}^{2}\). Accordingly, based on Table 1, the failure limit \(L_{c}\) of the cantilever beam is: \[\begin{array}{ll}L_{c}&=\sqrt{\frac{\sigma_{Tr}H_{ir}}{\beta g}}\\ &=\sqrt{\frac{\Omega_{2}\,\text{MPa}\times 3\,\text{m}}{3\times\frac{\Omega_{ 2}\,\text{m}}{m^{3}}\times\frac{2\,\text{m}}{m^{3}}}}\\ &=0.286\,\text{m}<0.3\end{array}\] (1)
2. Gagne wall formability The mining height of the 1510 working face of Xinyi Coal Mine is 1.5 m; the thickness of the immediate roof is 3 m; the rock dilatancy coefficient of the roof is 1.35 after the immediate roof has fallen, as can be calculated according to Table 2. The height of the fallen gangue is 4 m, which is much higher than the roadway. This way, a wall is formed at the gob side as a roadside.
3. Impact-free main roof subsidence For the 1510 working face, according to Table 3. \[\begin{array}{ll}\Delta H&=H+H_{ir}-H_{ir}K_{a}\\ &=1.5\,\text{m}+3\,\text{m}-3\,\text{m}\times 1.35\\ &=0.5\,\text{m}<1\,\text{m}\end{array}\] (2)  
After the immediate roof has fallen, the fallen gangue is already in contact with the main roof. While rotating and falling, the main roof will not release strong bending elastic energy. Its rotation, sinking and gangue touching will not impact the roadway. That is, it will make a \"soft landing\".  
The above results show that pillarless mining by GSCRF is feasible for the 1510 working face.
## 2 Method
### Label Switching  
The FMM as a probabilistic model could be used to represent the whole data into its related subpopulations by assigning individuals to the main subpopulations. A question might arise why the FMM was not implemented in the context of Bayesian, like the main model. There are few concerns with the use of the FMM in the context of Bayesian, which the new few paragraphs would outline.  
First there is an issue of the necessity to set prior information for the parameters. Although the problem might be resolved by setting non-informative priors like the main model, it might add an extra uncertainty. The problem actually has been discussed to be more challenging when using Bayesian technique for parameters estimation and clustering using mixture models (see for example [12]).  
The second main issue is due to label switching. For the Bayesian technique, the parameters' posterior distribution is expected to be highly symmetric and multimodal [13]. Although imposing artificial identifiability has been proposed in the literature for solving the label switching issue, it has been discussed that the method does not always result in a satisfactory solution [14]. Thus, the estimation of the FMM model was conducted in the context of maximum likelihood.
## 3 A Machine Learning Approach for Predicting Dementia
### Data Extraction  
The dataset used in this research is provided by The Alzheimer's Disease Prediction of Longitudinal Evolution [14]. This research has merged data from all phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI)* database (adni.loni.usc.edu) [33; 34; 35], hence the name ADNIMERGE dataset. *Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: [[http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf](http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf)]([http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf](http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf)) (accessed on 11 January 2023).  
The ADNI was launched in 2003 as a public-private partnership led by Principal Investigator [PERSON], MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. It contains both cognitive test scores and neuroimaging measurement values for 13,892 records of 2132 patients. Many patients have visited multiple times, and each visit is recorded as a new record because a cognitive test score changes on each visit. Cognitive Normal (CN) records in the dataset were 4911, while Alzheimer's Disease (AD) records were 8981, as shown in Figure 2. ADNIMERGE standard datasets contain some or all of the eight biomarkers, including (i) the main cognitive tests (ADAS, MMSE, and RAVLT), (ii) MRI ROIs (volumes, areas, and thicknesses), (iii) FDG PET ROI averages, (iv) AV45 PET ROI averages, (v) AV1451 PET ROI averages, (vi) DTI ROI measures (cell radial diffusivity, axonal diffusivity), (vii) CSF biomarkers, and (viii) some other features such as APOE status, demographic information, and diagnosis.