esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Trace Elements Concentrations in Plant Species
Similar results were found in abandoned red mud dumps (alumina refinery) in India [40], where high contents of Ni, Cd, and Cr (9.60, 1.50, and 2.05 mg kg\({}^{-1}\), respectively) were observed in plants from _Argemone mexicanu_, and in other studies carried out in an industrial area of Riyadh City, Saudi Arabia, where _A. mexicanu_ was also highlighted as a good bioaccumulator of Cr and Cd [41; 42]. A native plant from the genus _Sphaeralca_ has been described in studies in the United States of America, associated with desert environments in southeastern California [43] and an old uranium mine in Arizona [44]; _Sphaeralca ambigua_ showed concentrations of Br, Ca, Ce, Hf, U, and Zn above the regional geometric mean value [43] and a bioconcentration factor for U of 0.051 [44]. The results of our work indicated that _O. picensis_ (OP) and _S. velutina_ (SV) species exhibited medium (200-600 mg kg\({}^{-1}\)) accumulation of Cu, in LM and LG for OP and in LM for SV, respectively. This is in agreement with the above-reported studies and the work developed initially in the IC [12], which identified OP species as the plants with the highest capacity for Cu accumulation. However, in our work, other results about additional trace elements and their concentrations in plants at different industrial-affected locations are provided, which underline the higher ability of OP species for elemental accumulation.
## 2 Materials and Methods
### Shielding Materials
The energy loss rate of protons and electrons per unit length of material is approximately proportional to Z/A, but electrons are also subject to the elastic scattering of nuclei, equivalent to increasing shielding thickness. The energy loss rate of elastic scattering is proportional to Z\({}^{2}\)/A. Table 1 shows the three commonly used material properties. Al has a greater Z/A than Ta, which means that Al shields protons better than Ta, while for electrons, Ta shields electrons better than Al due to elastic scattering. As shown in Formula (9), bremsstrahlung increases rapidly with an increase in incident electron energy. A material with a high atomic number, such as Ta, can attenuate more energy of high-energy electrons, but the proportion of X-ray energy brought by bremsstrahlung is also significant. Secondary radiation decreases the initial attenuation effect. HDPE has a lower atomic number than aluminum, which means it interacts less strongly with electrons and protons. This can be beneficial in certain situations, as HDPE can more effectively attenuate the energy of electrons and protons and scatter electrons away from the protected area. This also means that less mass is required for HDPE to achieve the same shielding capability as aluminum. Additionally, HDPE is a good insulator, which can help prevent electrical discharge in the high-voltage environments of photodetectors. Because the energy distribution of charged particles in space is continuous, a single-layer material is not sufficient for effectively shielding the charged particles of all energies. To achieve better shielding results for different types and energies of charged particles, it is necessary to use combinations of multi-layer materials.
## 4 Research on Vibration Reduction of Shafting with Function Control Force Exerted by Smart Spring
### Analysis of Simulation Results of Supercritical  
#### 4.4.1 Analysis of Simulation Results of Over-Critical with Fixed Control Force  
The midpoint of the central line between support I and support II was selected as the measuring point, and the displacement response at this point was measured. Figure 21 shows the critical bending vibration response of a three-support shafting with Smart Spring support under different fixed control forces. The fixed control forces applied were 50 N, 100 N, 150 N, 200 N, 250 N, 300 N, 600 N, and 1000 N, respectively. It can be seen from the figure that the application of fixed control force significantly inhibited the peak value of critical response, and the critical speed obviously shifted to the right; when  
Figure 20: Control force fitting curve for 10 s \(\leq t\leq\) 14 s.  
the fixed control force was not greater than 100 N, the vibration response in the whole speed range of 0-8400 r/min (i.e., 0 s \(<t<\) 14 s) decreased; when the speed was lower than 7200 r/min (i.e., \(t<\) 12 s), the vibration was attenuated by applying 0-1000 N fixed control force, but when the rotating speed was less than 7200 r/min (when the speed was higher than 7800 r/min (i.e., \(t>\) 13 s)) and the fixed control force was greater than 150 N, the vibration increased when the fixed control force was applied.  
Figure 21: Response of shaft system under (**a–h**) fixed control forces, respectively 50 N, 100 N, 150 N, 200 N, 250 N, 300 N, 600 N and 1000 N.  
Figure 22 shows the relationship between the peak displacement response of the measuring point and the fixed control force when the shafting was over-critical. It can be seen from the figure that with the increase in fixed control force, the peak value of critical response first decreased and then increased gradually. When the control force was 0 N, the peak value was 0.1749 mm; when the control force was 200 N, the peak value was the minimum, which was 0.0777 mm, and the maximum vibration reduction rate was 55.6%.  
As shown in Figure 23, the first critical speed increased gradually and tended to be stable after the control force was greater than 600 N, which was close to 7800 r/min, that is, the damping control gradually transited to the stiffness control.  
It can be concluded from Figures 22 and 23 that the Smart Spring support has good vibration reduction performance. By applying fixed control force, the bending vibration response of three-support shafting accelerating through critical can be greatly attenuated, but the greater the control force, the better. The optimal fixed control force is 200 N, and the vibration response of shafting is the minimum.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 3 State of the Art, Regulation, Opportunities, and Challenges
### Opportunities and Challenges  
The choice of a 77 GHz band offers roughly a nine times reduction in antenna size and three times better velocity resolution owing to the small wavelength and the dependence of the carrier phase on the wavelength, respectively. Lastly, a twenty-fold improvement in range resolution is possible due to the 4 GHz of available BW from 77 to 81 GHz in contrast to only 250 MHz in the 24 GHz band. Nevertheless, there are challenges associated with this migration such as the increased noise figure and the daunting free space propagation loss (FSPL) for such a small wavelength [45]. These impending challenges are likely to exacerbate the existing issue of the low transmit power yielding an even lower signal-to-noise ratio (SNR), causing a compromised maximum range. It is, therefore, important to evaluate the feasibility of using these radars for the intended application from an SNR perspective. The challenge of a low SNR for altitude estimation at high altitudes sets the stage for exploring new avenues. Conventionally, increasing the transmitter power and antenna directivity are potential methods to address the issue at hand. However, these approaches are not without their challenges. Increasing the transmitter power, for instance, compromises the cost-effectiveness and SWaP considerations. Simultaneously, antenna modification entails an increased volume and the conformity to airframes. It must be highlighted here that these methods are beyond the scope of this article as the rationale of this study has been categorically stated in the introductory section. This study remains steadfast in its premise of harnessing the potential of existing automotive radars for UAS altimetry. It is crucial to realize that this study does not dismiss the promise inherent in conventional methods, but, beyond a certain point, increasing the SNR may not harness further benefit since the maximum range of the radar is a function of SNR, as well as the low-pass intermediate frequency (IF) filter [46]. Nevertheless, the use of 77 GHz allows for a higher gain antenna with an even reduced volume. Moreover, Section 4 elucidates that backscattering from the ground surface increases significantly with a reduction in wavelength due to the reduced penetration ability.
###### Abstract  
Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage-Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.  
dynamic state estimation (DSE); synchronous machine; ensemble square root filter (EnSRF); Sage-Husa algorithm +
Footnote †: journal: Applied Sciences
## 1 Introduction
The U.S. Department of Agriculture (USDA) defines a food hub as \"a centrally located facility with a business management structure facilitating the aggregation, storage, processing, distribution, and/or marketing of locally/regionally produced food products\" [13]. Hubs act both as supply consolidators and distributors. As such, hubs can also facilitate access to healthy foods to vulnerable populations by redirecting food flows to underserved areas that could hardly be reached out to with direct links [11]. In Colombia, the CONPES 3982--National Policy of Logistics of January 2020 document, establishes as one of its strategic pillars, the adoption of specialized logistics infrastructures--(ILEs in Spanish), which can play a prominent role as articulators between cargo and transport modes and providing value-added logistics services. Food hub location problems have attracted researchers from different disciplines such as operations research [9], economics [14,15], and social sciences [16]. This paper focuses on the hub location problem adapted to the food systems in the central region of Colombia. Accordingly, we propose the following research questions:  
* RQ1: \"How rural food hubs in emerging economies can become a sustainable strategy to improve farmer's access to urban markets?\"
* RQ2: \"Where should a network of food hubs be located with the goal of both maximizing farmer coverage and minimize logistics costs?\"RQ3: \"How the transportation and emissions costs are impacted by the hub strategy?\" This paper addresses the above three questions. The main contributions of this work can be summarized as follows:  
* A new mathematical model of competitive hub locations that incorporates constraints specific of the situation of farmers in emerging countries with provisions for a sustainable operation. * A combination of heuristics with the mathematical model aimed at solving realistic size problems. * An extensive analysis of scenarios with recommendations that can be used for public policies.
## 3 Results and Discussion
### Development of Map for Soil Amplification Factor  
This phase covers the determination of the soil amplification factor and map-plotting for the selected sites under six seismic events for five return periods. The existence of soil amplification at any location is affected by a combination of soil layers in terms of soil type and thickness. It is known that offshore areas normally consist of soft soil that has high potential in experiencing an amplification effect. In the Malay Basin, many offshore facilities are already aging but are still operating; this type of structure has potential in experiencing additional lateral loads if an amplification effect exists. However, the existence of the amplification effect in the Malay Basin is still questionable. Thus, the second objective of this study was to develop a map of soil amplification factors for the Malay Basin. The soil amplification factor presented in this phase could be used in the analysis after obtaining PGa values of any location, which was predicted using the attenuation equation.
## 2 Materials and Methods
### Features Extraction and Classification Methods
#### 2.3.1 Short-Time Fourier Transform (STFT)  
STFT was proposed based on Fourier transform, which has been widely adopted to the analysis signals jointly in time and frequency [36]. As shown in Figure 7, in order to compromise the computational complexity and consistency between adjacent window signals, we choose partial overlap between consecutive windows. The sliding window length is Ws while the movement distance of each window is L, and L is equal to Ws/2 typically.  
In detail, given sample \(\mathbf{s}\), \(\mathbf{s}\) is multiplied by a window function which is non-zero only for a short time. When the window slides along the time axis, Fourier Transform of the sample is obtained, resulting in a two-dimensional representation of the signal. In mathematics, this is written as:  
\[STFT(m,n)=\mathbf{X}(m,n)=\sum_{n=-\infty}^{\infty}\mathbf{s}(n)\mathbf{w}(n-m)e^{-jun} \tag{1}\]  
where, \(\mathbf{s}(n)\) is the sample and \(\mathbf{w}(n)\) is the window function. Thus, the one-dimensional sample \(\mathbf{s}\) is transformed into a two-dimensional signal \(\mathbf{X}\) containing the time and frequency characteristics. Since \(\mathbf{X}\) is a two-dimensional complex number, we convert \(\mathbf{X}\) into a two-dimensional features image using the square of the magnitude.  
\[\textit{spectrogram}[\mathbf{X}(m,n)]=\big{|}\mathbf{X}(m,n)\big{|}^{2} \tag{2}\]  
In this study, different kinds of window function and window sizes have been applied to extract time-frequency features. Detailed parameter settings and corresponding experimental results are presented in Section 3.
## 5 Mathematical Analysis of the Proposed Mechanism  
### Energy Consumption  
Energy consumed by each sensor node depends on the sensing of event, image compression, GPS, exchange of control packets for path reservation, and number of packets processed by each node. Total energy consumed by the sensor node for data transmission \((e_{t}^{d})\) is given in Equation (7).  
\[e_{t}^{d}=N_{dp}\times(e_{r}(k)+e_{t}(k,d)) \tag{7}\]  
where \(N_{dp}\) is the number of data packets processed. Total energy consumed by the sensor node for exchanging the control packets \((e_{t}^{e})\) is given in Equation (8).  
\[e_{t}^{c}=(N_{PRM}+N_{ACK})\times(e_{r}(k)+e_{t}(k,d)) \tag{8}\]  
Figure 5: Example showing the fault-tolerance.  
where \(N_{PRM}\) and \(N_{ACK}\) are number of PRM and ACK processed by each node. The energy consumption for image processing per bits (\(e_{cb}\)) [44] is given in Equation (9).  
\[e_{cb}=2(e_{pre}+e_{dct})+e_{code} \tag{9}\]  
where \(e_{pre}\) is the energy dissipation for LBT preprocessing, \(e_{dct}\) is the energy dissipation for binary DCT, and \(e_{code}\) is the energy consumption for coding.  
Noting r as the compression ratio, it's better for camera sensor to compress the raw data before sending than to directly send the raw data, when \(\frac{e_{cb}+e_{t}}{r}<e_{t}\).  
Total energy consumed by the total image size (\(e_{t}^{ip}\)) is given in Equation (10)  
\[e_{t}^{ip}=I_{s}\times e_{cb} \tag{10}\]  
where \(I_{s}\) is the size of the image. Total energy consumed by each sensor node (\(e_{t}^{sn}\)) is given in Equation (11)  
\[e_{t}^{sn}=e_{t}^{d}+e_{s}+e_{gps}+e_{t}^{ip}+e_{t}^{c} \tag{11}\]  
where \(e_{s}\) is the energy consumption for sensing and \(e_{gps}\) is the energy consumed by the GPS.
# Linear and Nonlinear Modelling of the Usable Area of Buildings with Multi-Pitched Roofs
## 3 Results
### Linear and Nonlinear Models Based on the Existing Buildings in Koszalin
#### 3.3.2 Nonlinear Regression Based on Dataset of Existing Buildings  
Table 6 presents the results of estimation of the parameters of the best fit among multivariate models of nonlinear multiple regression without five outliers, of which four were classified as outliers in the linear model.  
The results of the nonlinear regression analysis indicate a very good (and slightly better than for a linear model) model fit. The coefficient of determination \(R^{2}\) reached 0.941 and its adjusted value was \(<\)1% lower (0.933), which indicates that the model reliably explains more than 93% of the dependence between variables. All independent variables left in the model were statistically significant. Each variable contributes to the explanation  
The multivariate nonlinear regression model shows that the following variables are important estimators of the usable area of a building: covered area (\(A_{C}\)), square of garage area (\(G_{A}\)2), number of storeys (\(S_{N}\)), square of the share of garage area in the building body (\(G_{P}\)2), and share of garage area in the building body (\(G_{P}\)).  
Detailed analysis revealed that the rejected observations were objectively atypical in the adopted dataset. Some of them had a very large usable area or did not have a garage. The empirical formula obtained from the estimation of the nonlinear multiple regression model takes the form:  
\[A_{U}=-42.084+0.587\cdot A_{C}+0.030\cdot G_{A}^{2}+65.001\cdot S_{N}+82.146 \cdot G_{P}^{2}-100.537\cdot G_{P} \tag{8}\]  
For a similar set of market data, also Formula (8), similarly to (7), may help property appraisers estimate the value of usable area on the local market.  
To visually compare the quality of forecasted models (linear and nonlinear) based on real data of existing building, plots of the dispersion of the observed values of the dependent variable in relation to its predicted values were created (Figure 4a,b).  
Figure 4a,b are very similar and the points in both plots are relatively close to the regression line. The red dashed lines indicate the 95% confidence interval. For the nonlinear model, there is a slightly greater overlap of the predicted values of the usable area to the observed values of this variable; the points are more concentrated around the regression line and the determined confidence area. This is consistent with the evaluation of both models based on the coefficient of determination.  
It can be stated that both types of models, linear and nonlinear, suitably reflect the dependence of the usable area on the selected characteristics of the building. Nevertheless, since the difference in the quality of fitting in favour of the nonlinear model is small (about 2%), the nonlinear model would probably be an easier tool in practical applications by property appraisers due to the simplicity of linear regression.
# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
## 3 Junction-Aware Vehicle Selection Scheme
### Video Data Forwarding Phase  
In this section, the Junction-Aware Multipath Video Streaming concept is discussed considering geographical routing; specifically, the greedy forwarding concept is adapted by modifying some of its concepts. As mentioned in the previous section, firstly, the SINR and VDR of each neighbor vehicle are evaluated based on Equation (1) and (2).  
Below mentioned Algorithm 1, presents the procedure involved in video data forwarding when a C-NFV is in the junction area. The vehicle mobility information is gathered considering line 2. Line 3 checks if the C-NFV is in the junction area; that is, whether the C-NFV is before the junction, inside the junction or after the junction. Then, the SINR is computed considering the three scenarios of the junction area in line 4. However, the vehicle with the highest SINR value is preferred if it has not exited the junction area, as for the remaining cases of the scenarios. Thus, the SINR value selection has a higher priority than the different positions in the junction area. Lines 5-7 check if the C-NFV's road-ID is ahead of the junction location and if it has a higher signal strength value, and the optimal C-NFV--that is, the RVN--is selected. Lines 8-11 choose the first two RVNs (\(P_{2}\) and \(P_{3}\)), whose I-frames are forwarded via \(P_{2}\), and the B and P frames are forwarded through \(P_{3}\). \(P_{2}\) and \(P_{3}\) are the first two relay nodes before the other intermediate nodes in the two paths. The suitable RVNs are the nodes with the maximum efficient link, selected using Equation 5. Lines 12 and 13 check if the C-NFV is at the end of the road, which is at the traffic light. This C-NFV is selected if there is no C-NFV after the junction location towards the DVN. Line 14 forwards the video data via the RVN as previously discussed. Otherwise, the C-NFV is checked if its location is inside the junction, meaning a C-NFV with no road-ID, and this is selected if there is no vehicle after the junction or inside the junction based on lines 16-18. Otherwise, the algorithm checks whether the DVN belongs to the set of the C-NFVs: if true, the video data is forwarded to the DVN and terminated; otherwise, it is forwarded to NFV based on lines 19-29. Line 30 terminates the whole procedure of the algorithm. Figure 3 shows the flow of the algorithm. As previously stated, the complete routing concept is based on the greedy-based geographic routing protocol. The implementation process, including the simulation setup and performance evaluation of the proposed JA-MVS scheme, has been presented in Section 4.  
Figure 2: Information packet format.