esa-sceva/satcom-chunk-collection huggingface dataset:
Most bare electrodes used for the electrochemical determination of analyte have multiple restrictions (e.g., low selectivity, high overpotential, and negligible stability in a broad spectrum of solution compositions). Hence, electrode modification can bypass these barriers [26; 27; 28; 29; 30]. The mass transfer kinetics can be improved by chemically modified electrodes (CMEs) at low overpotential, which reduces the impacts of interferants and inhibits surface fouling [31; 32; 33; 34; 35; 36].  
In recent years, nanomaterials have been investigated extensively in various fields, owing to their small size, impressive porosity, large specific surface area, and special physicochemical traits [37; 38; 39; 40; 41]. Several advances in the field of nanomaterials have been made to improve the performance of the sensors [42; 43; 44; 45].  
Conducting polymer nanomaterials have many applications in the fields of optoelectronics, electronics, (bio)sensing systems, and energy storage [46]. Polypyrrole (PPy) nanomaterials are popular polymers because of their admirable electronic conductivity, high stability, ease of construction, and appreciable biocompatibility. In addition, their outstanding electrical properties are related to the resonance of the delocalized \(\pi\) electrons present in the entire carbon chain of the polymer structure [47; 48].  
It should be noted that conductive polymers such as the PPy nanomaterials can be employed in various types of electrochemical equipment, such as voltammetric and corrosion sensors, because of their great electrical and electrochemical windows. In addition, the morphology of polypyrrole nanomaterials affects the catalytic properties. In particular, the PPy NTs are of unparalleled utility for sensing systems owing to their appreciable electron transduction, large surface areas, huge surface functionalities and porous structure [49; 50; 51].  
In this paper, a graphite screen-printed electrode modified with polypyrrole nanotubes was fabricated for the determination of hydroxylamine. Such an electrode has not been tested for this purpose before. The fabricated sensor exhibited an enhanced catalytic performance for hydroxylamine oxidation.
## 3 Methods
### Feature Selection
#### 3.2.2 Variance Threshold Method  
The variance threshold method involves feature selection through removing features with low variance in the dataset. When constructing a model, if a feature has low variance, it indicates limited variability, and its contribution to the model is minimal. This suggests that the impact on model performance can be negligible. Therefore, low-variance filtering methods can be used to eliminate such features. [PERSON] et al. [47] applied the low-variance filtering feature selection method to choose 30 features from a multidimensional feature library as auxiliary data for spatializing population.
## 5 Conclusions  
* The observed spikes in the Z and F components is reduced by eight times in the lockdown period (in the absence of vehicular and metro rail traffic), with amplitudes between 3 and 4 nT, when compared to before lockdown.
* There was no obvious effect of lockdown on the D and I absolute measurements.
* The differences between the average of variable amplitudes and standard deviation of D\({}_{\text{B}}\), H\({}_{\text{B}}\), and Z\({}_{\text{B}}\) before and during lockdown were very small, 0.02 min, 0.05 nT and 0.05 nT, and 8 D \(\leq\) 0.03 min, \(\delta\) H \(\leq\) 0.1 nT and \(\delta\) Z \(\leq\) 0.05 nT, respectively. There was not much difference in D\({}_{\text{B}}\), H\({}_{\text{B}}\) and Z\({}_{\text{B}}\) during lockdown.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12052730/s1](https://www.mdpi.com/article/10.3390/app12052730/s1)]([https://www.mdpi.com/article/10.3390/app12052730/s1](https://www.mdpi.com/article/10.3390/app12052730/s1)), Figure S1: Durinal variation of HDZ & F along with K-index values on 07, 10, 14 March (left) and 17, 19, 29 April 2020 (right) are best quiet days of the respective months. For plotting convenience dH, dZ and dF are subtracted by 50 nT and 80 nT and 43620 nT; Figure S2: Shows first difference of 1-second measurements of Z and F. (a) First difference of Z-component on 07, 10, 14 March, 2020 (before lockdown) (b) First difference of Z-component on 17, 19, 21 April, 2020 (during lockdown) (c) First difference of F-component on 07, 10, 14 March, 2020 (before lockdown) (d) First difference of F-component on 17, 19, 21 April, 2020 (during lockdown).  
Conceptualization and Methodology: M.L., P.C.N. and K.A.; Writing--original draft preparation: M.L. and P.C.N.; Writing--review and editing and overall supervision: K.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
HYB Observatory 1 Hz data used for this study are available at Institutional repository of CSIR-NGRI [[http://ngri.org.in/upload/kusumita/NGRI_respiratory_MDPLzip](http://ngri.org.in/upload/kusumita/NGRI_respiratory_MDPLzip)]([http://ngri.org.in/upload/kusumita/NGRI_respiratory_MDPLzip](http://ngri.org.in/upload/kusumita/NGRI_respiratory_MDPLzip)) (accessed on 2 November 2021).  
The authors would like to thank the Director of the CSIR-National Geophysical Research Institute for the permission to publish this work; contribution no. NGRI/Lib/2021/Pub-161. We thank the Editor and anonymous reviewers for the careful reading of our manuscript and insightful comments and suggestions, which have improved the structure and the standard of the manuscript.  
The authors declare no conflict of interest.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 4 Numerical Results
### Effect of Packet Length \(n\) on PER and Throughput  
One can observe the effect of increasing the packet length while keeping the rate \(R=k/n\) fixed. Figure 6 shows the PER performance variation of the proposed D-NOMA-HARQ and S-NOMA-HARQ with the increasing packet length. We use two different packet lengths as \(n=100\) and \(n=500\). It is clear from the figure that the reliability performance of both schemes increases with the packet length. This performance gain with increasing length follows the normal approximation and finite packet length assumption [10]. When the packet length is short, i.e., \(n=100\), the reliability gain with SNR is small due to poor SIC decoding capability. When the packet length increases, the receiver can utilize longer codewords to decode the packets better and remove the successfully decoded packet from non-orthogonal packets using SIC. Therefore, when the packet length is large, i.e., \(n=500\), the PER performance of D-NOMA-HARQ steadily improves with SNR.  
Figure 7 shows throughput performance of the proposed D-NOMA-HARQ and baseline S-NOMA-HARQ with packet lengths \(n=100\) and \(n=500\). As shown in this figure, the throughput performance of both schemes improves with increasing packet length,  
Figure 6: Packet error rate performance comparison between D-NOMA-HARQ and S-NOMA-HARQ at setting \(w_{1}=0.6\), (\(w_{2}=1-w_{1}\)) with CC-HARQ when \(m=1\), \(\alpha_{1}=0.5\), \(\alpha_{2}=0.4\), for various packet lengths \(n\).  
especially in the low SNR regime. When packet length is large, S-NOMA-HARQ performance starts to saturate earlier in the medium SNR range. This saturating performance is because, with S-NOMA-HARQ, the excessive retransmission penalty is larger when \(n\) is large. Since D-NOMA-HARQ conducts its retransmission by shared power and time resource, it does not incur retransmission overhead. Therefore, D-NOMA-HARQ achieves steady throughput performance gain even when the packet length is large. Nevertheless, we see user 1's saturating throughput performance trend with D-NOMA-HARQ. This is mainly because of excessive retransmission power fraction value for user 1, i.e., \(\alpha_{1}\), when \(n\) is larger. In practice, the user needs to adopt the power of retransmission with packet length. Therefore, when the packet length is larger, smaller values of retransmission powers \(\alpha_{j}\) are sufficient and vice versa. The effect of power-sharing parameters and their optimization are discussed in detail in the following
###### Abstract  
Two large earthquakes, the Maduo \(M_{\rm S}\) 7.4 earthquake and the Menyuan \(M_{\rm S}\) 6.9 earthquake, have been successfully recorded on the Chinese mainland, since the data of the CSES satellite were put into service for earthquake prediction work on the Chinese mainland at the end of April 2020. Obvious variations in O\({}^{+}\) density and electron density were found during our weekly data processing work during 5-11 May 2021 and 28 December 2021-2 January 2022, respectively. Two warnings of impending events around the anomalous areas within two weeks had been reported immediately after the anomaly appearance. The Maduo \(M_{\rm S}\) 7.4 earthquake occurred on 22 May 2021 and the Menyuan \(M_{\rm S}\) 6.9 earthquake on 8 January 2022, during these two warning periods. More details were revealed after these two large shocks occurred. Ionospheric enhancement took place on 8 May 2021, with a magnitude of 41.6% for O\({}^{+}\) density and 22.2% for electron density, a distance of 680 km from the Maduo epicenter, 14 days prior to the event. Before the Menyuan earthquake, ionospheric enhancement took place on 28 December 2021, as well as during its revisiting orbit on 2 January 2022, with a magnitude of 47.3% for O\({}^{+}\) density and 38.4% for electron density, an epicentral distance of 120 km, 11 and 6 days prior to this event. The Kp index was also examined to avoid the influence from solar activities. Despite this, accurate earthquake prediction is not possible due to much uncertainty, such as the correct location and magnitude of an impending event. Thus, long-term practice and comprehensive investigation of the seismo-ionospheric influence are necessary in the future.  
CSES satellite; Maduo \(M_{\rm S}\) 7.4 earthquake; Menyuan \(M_{\rm S}\) 6.9 earthquake; ionospheric anomaly +
Footnote †: journal: _Atmosphere_  
1
Footnote 1: [[https://www.mdpi.com/journal/atmosphere](https://www.mdpi.com/journal/atmosphere)]([https://www.mdpi.com/journal/atmosphere](https://www.mdpi.com/journal/atmosphere))
# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 4 Discussion
### Accuracy of Prediction  
From the results in Table 3 (precision, recall, and F1 score for each model), it can be observed that both the syncope and simple falls models (overall accuracy of 0.83 and 0.79 in the Kaggle outputs, respectively) had a better prediction performance, while the all falls and syncope and complex falls models had a more modest performance (overall accuracy of 0.60 in the Kaggle outputs).  
The performance of our XAI algorithms to predict future falls in TILDA can be compared to that of a recent TILDA study which utilised conditional inference forests and included additional input features that were not available in the public TILDA dataset. Compared to the present study, their study showed a lower overall accuracy for future syncope (0.62), and similarly low predictive accuracy for future recurrent, injurious, and unexplained falls [25]. However, it is possible that our achievement of higher accuracy for syncope may be explained by the higher degree of class imbalance in the model, thereby resulting in a larger number of times that the data belonging to the smaller class was duplicated. This could also apply to our simple falls model, which also had higher overall accuracy. By being trained on more duplicates, a model could be more likely to correctly predict the classes of the duplicates in the test dataset.  
However, it is of contextual importance that in community-dwelling older adults, the prediction of falls remains elusive, and a recent systematic review showed that existing  
Figure 8: Syncope SHAP bar plot.  
prognostic models had high risk of presenting a bias, rendering them unreliable for prediction applications in clinical practice, and in the few validated models that have been reviewed, the area under the curve ranged from 0.62 to 0.69 [26]. This low predictability could be related to the fact that in community-dwelling older people, the occurrence of falls is, overall, infrequent. In contrast, a study that was looking at various AI models, consisting of calculations of the bagging, random forest, adaptive boosting and classification trees for inpatient fall risk prediction using electronic health records, showed that they had more accurate predictions [27].
## 1 Introduction
At present, the two most widely used methods for theoretical analysis of ground settlement calculations are the following: [PERSON]'s empirical equation method [25] and random media the theory method [26]. Based on the Peck curve, [PERSON] [27], studied the law of ground settlement in the excavation of the two-lane subway tunnel and proposed a formula for calculating the lateral surface settlement. The key stage in the construction technology of the pile-beam-arch method is the construction of the pilot tunnel, so it has similarities with the excavation of the two-lane tunnel. With the development of computers, finite elements can quickly solve approximate solutions to complex differential equations using the powerful computing power of computers to derive numerical values, making it a reliable tool for engineering design and analysis [28]. Finite elements can be used to predict geotechnical stability problems such as deep excavations, tunnels, vibrations, geological restoration, and other complex problems in civil engineering [29; 30; 31]. [PERSON] [32] used the smooth finite element method to solve the stability problem of two circular tunnels at different depths in cohesive soils. [PERSON] [33] solved the deformation problem of a very common discontinuity in rock engineering by the multi-body finite element method. [PERSON] [34] optimised the finite element mesh to simplify the method of calculating stresses in the soil. The above studies show that the finite element method was applicable to the field of civil engineering and helps to solve difficult problems in geotechnical engineering and structural fields. Among them, because in the pile-beam-arch method, the ultimate load was transmitted through the arch structure and the longitudinal beam to the pile foundation, and finally the load was compensated by the pile side frictional resistance and the pile end resistance to ensure the stability of the structure. The pile and soil have obvious tangential slip and normal separation (or compression) tendency, so the pile-soil-structure interaction cannot be ignored [35; 36]. In the actual finite element modelling analysis, the contact interface element can usually be set to simulate the pile-soil interaction. In this paper, a three-dimensional finite element model of the pile-soil interaction interface was established to analyse the influence of different rise-span ratios on surface settlement to achieve the goal of settlement control. The mechanical characteristics of steel pipe piles and side piles are analysed by changing the arch rise-span ratio, and a reasonable rise-span ratio is proposed to reasonably optimize the building height of the station and improve the construction space of the station.
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 2 Case Study
### Geological Setting  
Figure 2 shows the typical soil profile of the excavation depth. There are seven main soil layers. The soil of the excavation depth at the foundation site is mainly powdery clay and fine powdery sand from the surface to the planned depth: 1 \(\lx@sectionsign\)miscellaneous fill; 2 plain fill; 3 \(\lx@sectionsign\)silty powdery clay; 4 \(\lx@sectionsign\)powdery fine sand; 5 \(\lx@sectionsign\)fine-medium sand; 6 \(\lx@sectionsign\)strongly weathered tuff; 7 \(\lx@sectionsign\)medium weathered tuff. According to the indoor test results and survey experience, 3 \(\lx@sectionsign\)silty powdery clay is a highly compressive soil with low bearing capacity, poor homogeneity, and high sensitivity, so is the main soil layer challenging the excavation. Representative parameters of different soil layers were obtained through laboratory tests and field measurements. The groundwater level at the site varies slightly depending on the weather and is between 1.2 and 1.7 m below the surface. For the foundation pit, layered dewatering and step-by-step excavation were adopted to control the groundwater level to 1-2 m below the excavation surface. A \"two-in-one\" underground diaphragm wall was set up around the supported excavation. As shown in Figure 2, the underground diaphragm wall + supports were the foundation pit enclosure. The underground diaphragm wall was 50 m deep and 1200 mm thick and was embedded in weathered rock formations. Reinforced concrete supports were placed 1.5 m, 7.0 m, 13.5 m, 18.5 m, and 25.0 m below the ground level to support the diaphragm wall. Supports for levels 1 to 4 had a cross-section of 1000 mm \(\times\) 1000 mm. Steel lattice columns, 500 mm \(\times\) 500 mm, were embedded into the column piles below to support the reinforced concrete supports.  
Figure 1: Schematic layout of pipeline settlement monitoring points (unit: m).
# Constraints of Using Conductive Screen-Printing for Chipless RFID Tags with Enhanced RCS Response
## 3 Tags with Enhanced RCS Response
### Re-Arranged Shorted U-Dipoles  
As shown in Figure 4, the geometry of the first 20-bit chipless RFID tags is composed of an array of U-shaped shorted strip scatterers. The geometry of the basic element is inspired by the structure used in [11]. To improve the robustness of the RCS response, the mutual coupling between scatterers with neighbouring resonance frequencies was reduced by the element re-arrangement proposed in [13].  
The size of the tag can be seen in Table 1. The incident excitation field is oriented vertically to the tag presented in Figure 4. The simulated RCS response of the tag is depicted in Figure 5.  
Figure 4: Sketch of 20-bit shorted re-arranged U-dipole tag.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline Re-arranged shorted U-dipoles [13] & 2.2–3.8 & 20 & 112.0 & 12.5 & 70.0 & –35 & No & 1 & 4 & - & - \\ \hline Re-arranged U-slots in the metallic plate [34] & 2.2–3.8 & 20 & 76.9 & 12.5 & 48.1 & –17 & No & 1 & 1 & - & - \\ \hline Array of detuned planar dipoles & & & & & & & & & & & \\ closely coupled to a rectangular metallic plate [33] & 2.2–3.5 & 20 & 61.6 & 15.4 & 47.4 & –15 & Yes & 2 & 2 & 0.5 & –1.5 \\ \hline Interdigital U-dipole coupled to the metallic loop sensor tag [35] & 2.6–3.6 & 1 & 5.3 & - & - & –17 & No & 1 & 17 & 4 & –13 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of frequency-domain chipless RFID tags.
# StreamflowVL: A Virtual Fieldwork Laboratory that
## 6 Comparison of the Innovative VL with the Traditional Approach  
The choice of an open-air site suitable for experiments is never easy, especially in winter months and bad weather conditions. Even during the course, the site can sometimes become inaccessible, which forces teachers to conduct field work activities in a shorter period of time and students to try and acquire knowledge and skills more quickly, inevitably leaving out some aspects of the whole measuring procedure.  
Following this teaching experience, the authors propose the use of an indoor virtual environment that is not subject to time or space limitations and is always accessible during the course. This way, the learning process takes just the right amount of time and allows all students to individually practise the entire measurement procedure, which is often impossible with traditional methods. Other limitations overcome by the StreamflowVL are: problems of transport and accommodation in the case of distant sites; considerable equipment and organisational costs; risks of accidents in field; and the wearing out and breaking of misused equipment and the subsequent interruption of the activities. In particular, from an educational point of view, traditional field lessons alone carried out with large groups of students have never been completely effective, since they increase the students' waiting time for their turn to perform the experiment, distracting them and preventing them from completing all the phases of the water discharge measurement procedure. These are the main reasons why StreamflowVL, being a one-to-one teaching method, allows students to be in control, use their initiative and master unfamiliar topics while being supported when needed, and to take pleasure in the learning process itself.  
The latter could also be a disadvantage in the future in the case of excessive enthusiasm in the \"gaming\" aspect of the VL, which can lead to distraction. SVL does not require the same level of discipline and caution that is necessary in field. However, this effect could be reduced if monitored by teachers, who can underline the seriousness of the activity, as they would do in any learning environment. The lack of hands-on contact with devices and equipment in SVL could be another limitation as it does not improve the manual skills that are acquirable by working with real-life equipment. Another disadvantage of StreamflowVL is that it must include complex mathematical models in order to take into account all the details of a real experiment.  
In view of this analysis, the proposed innovative approach, which combines virtual and field lessons, seems be the most effective learning process and it is able to fill the gaps in the traditional method.  
Table 3 shows a comparison between the approach proposed in this paper and the traditional one.