esa-sceva/satcom-chunk-collection huggingface dataset:
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 3 Stream-DBSCAN Water Quality Detection Clustering Model
### Water Quality Data Sampling  
The Menlou Reservoir's water quality parameters are gathered through a wireless sensor network. The sensors measure various parameters, such as NH4N, pH, temperature, and flow rate, and transmit this data to a computer system through an aggregation gateway [26]. Unlike traditional manual sampling methods, the wireless sensor network increases sampling frequency, accelerates information transmission speed, and enhances the real-time value of data [27]. This paper utilizes a high-density dataset, generated by numerous sensors in the Menlou Reservoir, with small time granularity and coherence.
# Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting
## 4 Conclusions  
In this paper, we compared three methods--ARIMA models, Elman, and LSTM networks--to perform out-of-sample forecasting of the EUR/USD exchange rate dataset. The time series did not present a trend, seasonality, or stationarity; therefore, the time series was determined to be a random walk. An ARIMA (0, 1, 0) model was selected to analyze a first-order differentiated series and its ACF. Elman and LSTM networks were modeled using systematic simulations by adjusting parameters until we obtained the best results. These three models were then used to forecast and compare six windows for the validation datasets. Through this comparison, we determined that ARIMA generated constant numbers, while LSTM provided the best forecasts up to a 22-day window in the short term. With Elman, we obtained better results in the long term. The selected window was then evaluated in detail to identify the observations with the lowest errors,  
Figure 8: MAPE of the 22 predictions.  
finding only four observations among the 22 windows where LSTM best approximated the validation dataset.  
The average accuracy for the 22-day window was 71.76%. By comparing our results with the results of previous studies, we can conclude the following. First, it is difficult to make a direct comparison since there is no standardized method for selecting training and validation datasets. Second, previous studies reported an average accuracy lower than 70% for different approaches [34; 35; 36] and achieved greater than 70% average accuracy when using NN, combining models, or introducing a hybrid model [37; 38; 39; 40]. Based on the comparison of the three forecasting models in this work, LSTM fit better in the short term, although the results were not entirely desirable, as LSTM only coincided in four observations with 95.99% average accuracy.  
The advantage of using an RNN of the Elman or LSTM type is its efficiency when working with a time series. Both techniques have similar characteristics in their networks; the only difference lies in their memory capacity. The limitation of ARIMA is that it represents a general univariate model in which the assumptions must be fulfilled to succeed. A combination of these methods as a type of hybrid model could be an aim of future studies, similar to the model reported in [41; 42].  
Programming, figures formatting, and draft the process, W.A.; first draft preparation; P.E.; supervision, conceptualization, P.E., J.P.; writing--review and editing, W.A., P.E., J.P.; funding acquisition, P.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Indaumerican Technological University and Chimborazo's Superior Polytechnic School.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
The wrapper-based technique is the most frequently employed feature selection method as compared to the less computationally expensive filter-based approach due to its higher performance accuracy [22, 23]. In this work, the ML model is to be developed to predict the SGT's defect in terms of length, width, and depth of crack from the given gray level co-occurrence matrix feature from the MFL image. The feature selection method and the number of selected features will affect the performance of the ML models. The R\({}^{2}\) and root mean square error (RMSE) values are considered metrics to measure the performance of the ML models. Multiple contradictory performance measures require the conversion of multiple objectives into a single objective, which initiated the implementation of a multi-criterion decision-making method, namely [PERSON]'s method. The dynamic population gray wolf optimization (DPGWO) algorithm is implemented to select the number of features and their combinations to minimize the prediction error. The effectiveness of the DPGWO algorithm is proved by comparing its performance with the gray wolf optimization (GWO) Algorithm [24] and moth flame optimization algorithm (MFO) [25] by implementing a non-parametric Friedman test [25] and performance indicators. The paper is organized as follows. Section 2 describes the problem statement, and Section 3 describes the proposed methodology and the various stages, namely prioritizing the features, Taguchi orthogonal array experimental design [25], analysis of Variance(ANOVA) Test, and meta-heuristic algorithms for feature selection involved in solving the problem. Section 4 deals with the results and discussion, including a comparison of the performance of the DPGWO algorithm with the MFO and GWO algorithms. Finally, the conclusion part of the paper gives future scope.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 2 Signal Design Requirements  
Compared to GNSS satellites located in MEO, LEO satellites have a lower orbit height, usually around 1000 km. The design of LEO navigation augmentation signals has unique requirements. This section discusses the requirements of signal design, including dual-frequency broadcasting requirements, high-precision measurement requirements, high data rate requirements and compatibility requirements.
###### Abstract  
High-rate multi-constellation global navigation satellite system (GNSS) precise point positioning (PPP) has been recognized as an efficient and reliable technique for large earthquake monitoring. However, the displacements derived from PPP are often overwhelmed by the centimeter-level noise, therefore they are usually unable to detect slight deformations which could provide new findings for geophysics. In this paper, Global Positioning System (GPS), GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS), and BeiDou navigation satellite system (BDS) data collected during the 2017 Mw 6.5 Juzhaigou earthquake were used to further exploit the capability of BDS-only and multi-GNSS PPP in deformation monitoring by applying sidereal filtering (SF) in the observation domain. The equation that unifies the residuals for the uncombined and undifferenced (UCUD) PPP solution on different frequencies was derived, which could greatly reduce the complexity of data processing. An unanticipated long-term periodic error term of up to \(\pm\) 3 cm was found in the phase residuals associated with BDS satellites in geostationary Earth orbit (GEO), which is not due to multipath originated from the ground but is in fact satellite dependent. The period of this error is mainly longer than 2000 s and cannot be alleviated by using multi-GNSS. Compared with solutions without sidereal filtering, the application of the SF approach dramatically improves the positioning precision with respect to the weekly averaged positioning solution, by 75.2%, 42.8%, and 56.7% to 2.00, 2.23, and 5.58 cm in the case of BDS-only PPP in the east, north, and up components, respectively, and 71.2%, 27.7%, and 37.9% to 1.25, 0.81, and 3.79 cm in the case of GPS/GLONASS/BDS combined PPP, respectively. The GPS/GLONASS/BDS combined solutions augmented by the SF successfully suppress the GNSS noise, which contributes to the detection of the true seismic signal and is beneficial to the pre- and post-seismic signal analysis.  
BDS GEO; multi-GNSS; uncombined and undifferenced PPP; sidereal filtering; earthquake monitoring 2019
## 1 Introduction
### Energy Numerical Model Approaches  
Recently, a variety of applications have become accessible for assessing the energy efficiency of buildings. These devices fall into three categories: static, semi-dynamic, and dynamic. Simple methods that just take a few things into account include stationary and semi-dynamic approaches. They are mainly concerned with the assessment of a building's performance under ordinary circumstances of usage, and often, the datasets are standard references from national databases. Outcomes using static tools are limited because they ignore both the thermal inertia of the materials and the cyclical trend of temperature. Semi-dynamic systems are capable of doing this, but they call for streamlined inputs for building descriptions and meteorological information. On the other hand, dynamic simulation tools can precisely assess all variables, but they require specific datasets for weather parameters and building characteristics. Due to their sophistication, these technologies can generate more reliable analyses, and they are more flexible, making them more suitable for simulating old buildings. According to a recent study [49], the dynamic modeling of ancient structures yields superior findings to other techniques when evaluating their energy performance.  
An important factor in using the sophisticated capabilities of the tool in order to reflect the real scenario as exactly as possible and to be a trustworthy way to recommend retrofitting solutions is to utilize such software in combination with climatic measurements to achieve proper calibration of the simulation model. A calibration process can be particularly difficult for ancient buildings, especially when construction materials are not exactly understood, and structures of various dates are layered on top of one another. Therefore, precise research of the building's construction history is necessary for the use of dynamic simulations of historic buildings.
## 5 Conclusions  
To explore the use of CO\({}_{2}\) as a heat-absorbing fluid for power plant cooling applications, thermophysical and transport properties of CO\({}_{2}\) were evaluated and compared with those of air. The following conclusions can be deduced:  
* With its high heat capacity and excellent Joule-Thomson cooling effect, CO\({}_{2}\) is a better and more effective fluid for removing heat from a thermal power plant than air.
* The condenser is an important component in power plants. Its primary function is to produce saturated liquid water before pumping it back into the boiler while maintaining the back pressure on the exhaust side of the turbine. Sample calculations carried out for a simple steam-condensing device shown in Figure 5 indicated that CO\({}_{2}\) is a better heat-removing fluid than air for a condenser to meet these functions.
* The condensing surface area was also estimated, and the results show that when CO\({}_{2}\) is used, the condensing surface is 50% to 60% less than the case if air is used. This leads to significant reductions in the condenser size and capital costs.
* We roughly estimated the amount of CO\({}_{2}\) that can be stored and utilized for a steam power plant that operates with steam of 540 \({}^{\circ}\)C (813 K) and 10 MPa at the turbine inlet and saturated-vapor steam at 0.008 MPa at the turbine outlet. The results indicate that if CO\({}_{2}\) is used as a cooling fluid, the CO\({}_{2}\) emitted from a 1000 MW power plant during a period of 250 days can be stored and utilized.  
[PERSON] thought of this problem and performed most of the derivations and all the calculations. [PERSON] helped in the derivation and the formulation of the problem. Both authors contributed to the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
## 2 Navigation Sensors and Techniques for Drones
### Gnss  
GNSS receivers are often used onboard drones to satisfy the absolute positioning requirements. They use signals transmitted from GNSS satellites to determine the user PVT. For a standalone GPS L1 receiver, it has horizontal accuracies of 10 m and vertical accuracies of 15 m [11]. However, its accuracy has improved significantly to a decimeter level with the addition of differential GNSS (DGNSS), precise point positioning (PPP), real time kinematics (RTK), triple frequency on L1, L2, and L5 bands, and multi-constellation, including GPS, Galileo, GLONASS, and BeiDou, and geostationary systems, such as GAGAN and QZSS [12]. Furthermore, both the accuracy and the integrity of the GNSS PVT has increased with the addition of fault monitoring systems, such as the ground based augmentation system (GBAS), space-based augmentation system (SBAS), and local area DGNSS (LADGNSS) [13]. Therefore, GNSS is able to provide the necessary high accuracy and integrity information to the drones. However, GNSS has two significant limitations for drone applications. One of these is indoor navigation or operation in urban canyon environments where there are many high-rise buildings around the receiver. When there are obstacles blocking the line-of-sight (LOS) signal between the receiver and the user, GNSS signals may have large amounts of multipath due to signals bouncing off structures, which leads to inaccurate ranging measurements, or may be significantly attenuated or unavailable [14]. The second and more troubling limitation is operation in the presence of RFI, such as spoofing or jamming. In the case of spoofing, false GNSS signals are transmitted to the receiver to fool the receiver into thinking that it is at the wrong location or time. The concept of GNSS spoofing was introduced by [PERSON], and demonstrated on drone navigation by [PERSON] [15; 16]. Furthermore, GNSS spoofing has been proven fatal for commercial GNSS receivers, such as u-Blox, which is often used by most drones, Apple smartphones, and the latest Android smartphone GNSS chipsets produced by Qualcomm, Broadcom, and Huawei [17]. In the case of jamming, the entire GNSS signal spectrum is flooded with noise, causing there to be no signals available for processing. Although there exist multiple methods to identify potential spoofing or jamming using GNSS raw measurements, GNSS NMEA messages, INS, network positioning, or automatic gain control (AGC), methods to mitigate spoofing are limited [17; 18; 19; 20]. Consequently, another source of absolute positioning is required to check for GNSS positioning integrity, and serve as an alternative source of PVT when there is no GNSS availability or if GNSS integrity is compromised.
## 3 Field Measurements and Model Verification  
### Experimental Outline  
In order to verify the TLM-VM-PML method to study the dynamic interaction between the tunnel and soil foundation, a vibration excitation test was carried out in the tunnel. The ground acceleration responses at different horizontal distances under the action of the underground hammer force in the tunnel excitation test were calculated. The calculated results were compared with the measured ones.  
The test was conducted in a laboratory with an underground tunnel. The tunnel was a double-layer horseshoe-shaped tunnel. The underground first layer was a curved tunnel with a buried vault depth of 6 m, and the underground second layer was a straight tunnel with a buried vault depth of 14 m. The height and width of both layers were 4 m, and the wall thickness of the tunnel was 0.55 m. The plan and cross-section of the tunnel laboratory are shown in Figure 9. The underground excitation test was carried out in the underground first layer. In order to minimize the interference of external background noise, the test was carried out on a quiet night.  
Figure 9: Laboratory diagram.  
In the test, automatic drop hammer excitation equipment was used for exciting the vibration source. As shown in Figure 10, the impact force can be changed by adjusting the weight of the drop hammer and the lifting height of the drop hammer. The falling process of the drop hammer can be approximately regarded as a free-falling motion. The hammer head was equipped with a piezoelectric stress sensor, and the hammer force signal can be collected with high-precision data acquisition instrument. The equipment can excite a large impact energy in the tunnel in order to generate an effective vibration response at ground level.  
The INV3062S 24-bit high-precision data acquisition instrument was used for automatic acquisition in the test. The acceleration sensor LC0130 with a measuring range of 0.12 g was used (Figure 11). The sampling frequency of the acceleration signal was 2048 Hz. The vertical vibration acceleration was collected.  
The automatic drop hammer equipment was used to impact the tunnel track bed in the underground layer of the laboratory. In order to ensure that each sensor on the ground can pick up a clear acceleration signal, five mass counterweight blocks were selected in the automatic drop hammer equipment, each with a lifting height of 15 cm. A total of 20 useful signals were used for average processing.  
The sensors were also arranged on the ground. The sensor just above the impact location in the tunnel (0 m) was defined as point P0. From this point to the north along the road, points P1-P11 were defined, as illustrated in Figure 12. Points P1-P11 were located on the ground surface.  
Figure 11: Data acquisition instrument and acceleration transducer.  
Figure 10: Automatic drop hammer equipment.
## 4 Mathematical Description of the Principles of ED Using SLC in MIMO-OFDM Systems
### Mathematical Representation of the Detection and False Alarm Probability in MIMO Systems  
With respect to Equation (6), the mathematical formulation of the detection and false alarm probability is represented as follows:  
\[P_{d}[\text{Pr}(\Lambda_{SLC}<\lambda)\ \approx H_{1}] \approx Q\Bigg{(}\frac{\lambda_{d}-N\big{(}2\sigma_{w}^{2}\big{)}(R+ \gamma_{SLC})}{\sqrt{N(R+2\gamma_{SLC})}\ \big{(}2\sigma_{w}^{2}\big{)}}\Bigg{)}\approx Q \Bigg{(}\frac{\lambda_{d}-RN\big{(}2\sigma_{w}^{2}\big{)}(1+\overline{\gamma_ {SLC}})}{\sqrt{RN(1+2\overline{\gamma_{SLC}})}\ \big{(}2\sigma_{w}^{2}\big{)}}\Bigg{)} \tag{7}\] \[\approx Q\Bigg{(}\frac{\lambda_{d}-RN\big{(}2\sigma_{w}^{2}\big{)} \Big{(}1+\frac{P}{2R\sigma_{w}^{2}}\Big{)}}{\sqrt{RN\left(1+\frac{P}{R\sigma _{w}^{2}}\right)}\ \big{(}2\sigma_{w}^{2}\big{)}}\Bigg{)},\]  
\[P_{fa}[\text{Pr}(\Lambda_{SLC}>\lambda)\ \approx H_{0}]\approx Q\Bigg{(}\frac{ \lambda_{fa}-RN\big{(}2\sigma_{w}^{2}\big{)}}{\sqrt{RN}\ (2\sigma_{w}^{2}\big{)}}\Bigg{)}, \tag{8}\]  
where Q (.) denotes the Gaussian Q-function expressed as \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-\frac{x^{2}}{2}}\). By analysing (7) and (8), it can be noted that \(P_{d}\) and \(P_{fa}\) probabilities depend on the number of Tx-Rx chains on each side, as well as the total number of detection samples, the total noise variance (\(\sigma_{w}^{2}\)), and the levels of false alarm and detection thresholds. Equation (7) also shows that the \(P_{d}\) values depend on PU Tx power.  
High values of \(P_{fa}\) mean that opportunities for reusing the shared frequency spectrum are missed. Thus, a minimum false alarm probability (\(P_{fa}\)) must be kept to maximize the spectrum efficiency. Furthermore, good detection performance necessitates maximizing the detection probability (\(P_{d}\)). High \(P_{d}\) and low \(P_{fa}\) probabilities lead to the suitable protection of PU and a suitable quality of service (QoS) for the SU in terms of ED precision.  
Based on Equations (7) and (8), and considering that the DT in the equations is essentially the same (\(\lambda_{d}=\lambda_{fa}=\lambda\)), the \(P_{d}\) can be formulated as a function of \(P_{fa}\) as follows:\[P_{d}=Q\left(\frac{Q^{-1}\Big{(}P_{fa}\Big{)}-\sqrt{\frac{N}{K}}\,\gamma_{SLC}}{ \sqrt{\left(1+2\,\frac{\gamma_{SLC}}{K}\,\right)}}\right)=Q\left(\frac{Q^{-1} \Big{(}P_{fa}\Big{)}-\sqrt{RN}\,\overline{\gamma_{SLC}}}{\sqrt{\left(1+2\, \overline{\gamma_{SLC}}\,\right)}}\right)=Q\left(\frac{Q^{-1}\Big{(}P_{fa} \Big{)}-\frac{\sqrt{N}P}{2\sqrt{R}\sigma_{w}^{2}}}{\sqrt{\left(1+\frac{P}{R \sigma_{w}^{2}}\right)}}\right), \tag{9}\]  
where \(Q^{-1}(.)\) represents the inverse standard Gaussian Q- function. From Equation (9), it can be concluded that if a specified level of false alarm probability is well known, then the PU signal detection can be obtained without the exact knowledge of the level of DT.
# Modeling Wicking Fabric Inhibition Effect on Frost Heave
## 5 Results and Discussions
### Parametric Analysis
#### 5.2.2 Effect of Groundwater Level  
Frost heave and the effects of WF are also influenced by groundwater availability. Groundwater provides the mass necessary for ice formation. Based on [PERSON]'s pioneering study [55] of frost heave, reducing the GWL reduces frost heave. [PERSON] et al. [56] also demonstrated that the higher the water level, the greater the amount of heave.  
Figure 12 illustrates the simulated results of frost heave strain with time at different GWLs for a fixed installation position for the WF in the Tomakomai soil. Figure 10 illustrates how the WF was very effective in suppressing frost heave in both the Touryo and Tomakomai soils. However, the WF reduced 26.58% of the deformation in the Touryo soil, while it decreased 46.2% of the deformation in the Tomakomai soil. Therefore, in the simulation, the Tomakomai soil was found to be more effective in inhibiting frost heave in the freezing process. Hence, the Tomakomai soil was chosen as the subject of investigation in order to more clearly observe the variation in the effect of WF on frost heave inhibition in response to variances in the GWL. Moreover, the saturation (\(S\)) variation during the freezing process in the NWF model and WWF model is shown in Figure 13. The frost heave strain increased in both the NWF model and the WWF model as the GWL increased. It should be noted that the difference in frost heave strain between the NWF and WWF models at different GWLs was 8.448% and 6.68%, respectively, where 8.448% corresponds to a GWL at 0 mm, and 6.68% corresponds to a GWL at 60 mm. In other words, the inhibition effect of frost heave by WF decreased as the GWL increased. The following reasons may account for the impact of the GWL on frost heave strain. When the GWL increases, the soil becomes more saturated. Consequently, soil permeability increases in such circumstances. This results in a significant increase in the water flow rate and frost heave. Further, the higher the saturation, the greater the latent heat amount available in the soil, making it more difficult for the soil to freeze. In the WWF model, a higher GWL lowers the distance between the WF and GWL, therefore making it significantly more difficult to drain all the excess water immediately--in other words, reducing drain water efficiency. Additionally, a higher GWL will also lead to greater soil saturation, which in turn creates a more significant frost heave strain during the freezing process.  
Figure 11: Contour plot of Effect of soil types on frost heave and WF inhibiting effect.