esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ranging Error Source Analysis and Modeling
### Satellite Ephemeris Error Analysis and Modeling  
Since we assume that the clock error has been eliminated, we do not need to consider the clock error parameter. The broadcast ephemeris error three-dimensional root mean square (RMS) model is [25]:  
\[\text{RMS}_{Eph}=\sqrt{\sum_{k=1}^{n}\frac{R_{k}^{2}+T_{k}^{2}+N_{k}^{2}}{n}} \tag{1}\]  
where \(R\), \(T\) and \(N\) represent the orbital component errors in the radial, tangential and normal directions, respectively; the radial component represents the direction of the connection between the satellite and the receiver and the tangential component points to the velocity of the satellite; and the normal component points in a direction perpendicular to the orbital plane of the satellite. The signal-in-space range error (SISRE) calculation equation is often used to measure the accuracy of broadcast ephemeris and clock error parameters. Since the clock bias has been eliminated by default, we only consider the influence of the orbit, then the broadcast ephemeris orbit error and SISRE can be expressed by the following equations [26]:  
\[\text{URE}_{orb}=\sqrt{\left[A_{R}\text{RMS}(R)\right]^{2}+A_{T,N}^{2}[\text {RMS}^{2}(T)+\text{RMS}^{2}(N)]} \tag{2}\]  
\[\text{SISRE}_{orb}=\sqrt{\left[\text{RMS}(A_{R}R)\right]^{2}+A_{T,N}^{2}[ \text{RMS}^{2}(T)+\text{RMS}^{2}(N)]} \tag{3}\]  
where \(T\) is the trace error, \(N\) is the normal error, \(R\) is the radial error and \(A_{R}\) and \(A_{T,N}^{2}\) are the contribution factors of the SISRE, which are calculated from the projection of each error in the direction of the average pseudorange. Table 1 shows the contribution values of different types of systems [25; 27].  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**System Name** & **Orbit Inclination (deg)** & **Orbit Height (km)** & \(A_{R}\) & \(A_{T,N}\) & **Attrib-Utes** \\ \hline Telesat & 99.5 & 1000 & 0.6007 & 0.5653 & LEO \\ Hongyan & null & 1100 & 0.6007 & 0.5653 & LEO \\ SpaceX & 53 & 1150 & 0.6091 & 0.5607 & LEO \\ OneWeb/Telesat & 87.9/37.4 & 1200 & 0.6176 & 0.5562 & LEO \\ GLONASS & 64.8 & 19,100 & 0.9775 & 0.1491 & MEO \\ GPS & 55 & 20,200 & 0.9794 & 0.1427 & MEO \\ BDS-3 & 55 & 21,500 & 0.9810 & 0.1360 & MEO \\ Galileo & 56 & 23,222 & 0.9800 & 0.1210 & MEO \\ BDS-GEO/IGSO & 0/55 & 35,786 & 0.9920 & 0.0880 & GEO/IGSO \\ \hline \hline \end{tabular}
\end{table}
Table 1: SISRE contribution factors of different constellation types.
## 3 Results and Discussion
### Zero Test Using Filtered Clean Air
Changing power sources from one auxiliary battery to another one created sudden changes of ATN, resulting in spikes of BC concentrations. It took about 30 min to 2 h for the sudden spikes in BC concentration to disappear. It is recommended that users should start measurements with fully charged status and keep operating until the internal battery is drained without connecting to any external power source. For longer measurement, the auxiliary battery needs to be plugged to the fully charged device. Careful selection of power sources leads to the exclusion of unwanted noise. Figure 9: (**a**) Noise level increases when the sampling time is short. (**b**) Noise level is fitted to power law. Figure 10: Black carbon (BC) concentrations and variation of attenuation (ATN) for filter-cleaned particle free air at the sampling time of **(a)** 300 s; **(b)** 60 s; **(c)** 30 s; **(d)** 10 s; **(e)** 1 s.  
One of interesting results regarding ATN is that ATN is not constant for particle-free air. Rather, ATN fluctuates up and down. The increase in ATN for particle-free air was reported for some AE31s during the monitoring for 13 days [7]. The decrease in ATN for particle-free air toward microAeth\({}^{\circledR}\) AE51 during 10 days of monitoring was observed in the present study for the first time, as far as the author knows. The unsteady variation of ATN might be due to the deposition of light-absorbing particles caused by leaks at flow channel or the deposition of light-scattering particles which might penetrate through the particle-removal filter. The decrease in ATN shown around 22:00 to 02:00 in Figure 10a matches well with the negative BC concentrations shown in the same time frame. In fact, the ATN readings do correspond to the BC concentrations though the non-uniform ATN appears to have nothing to do with the BC concentrations. The decrease in ATN consequently results in negative BC concentration, which is physically meaningless, as already mentioned. This is an additional issue other than the noise amplification. The negative concentration seems partly due to a compensation scheme which forcedly displays zero concentration under nearly zero concentration condition. One of these schemes is probably an averaging scheme, which is simple to apply to the instrument circuit.
# Impact Features Extracting Method for a Reciprocating Compressor Based on the ABC-SGMD Model
## 2 Theoretical Algorithms
### Adaptive Differential Evolution Extreme Learning Machine  
The Extreme Learning Machine (ELM) is a single hidden layer feedforward neural network model [25]. Its basic idea is to randomly initialize the weights of the input layer and the hidden layer and then directly obtain the weights from the output layer to the hidden layer. The method brings about fast training for the basis of the idea of minimizing the mean square error, which principle is to accelerate training by randomly initializing the parameters of the hidden layer nodes. Under certain conditions, it can achieve comparable or even better performance in a shorter training time.  
Self-Adaptive Evolutionary Differential Evolution (SaDE) is an evolutionary algorithm designed for global optimization problems. The SaDE algorithm simulates the evolutionary process in nature, utilizing differential mutation and crossover operations to search for the optimal solution. It is characterized by fast convergence and strong global search capabilities.  
The SaDE-ELM method combines the ELM and SaDE algorithms, using the Adaptive Differential Evolution algorithm to optimize the parameters of the ELM network, including the weights and biases of the hidden layer nodes, as well as the weights of the output layer. During the optimization process, the network parameters are gradually optimized through continuous iterative evolution of the population through selection, crossover, and mutation operations, resulting in a better model fitting ability and generalization ability [26].  
As the core highlight of the SaDE-ELM method, it employs the Adaptive Differential Evolution algorithm to search for better parameter combinations, which improves the performance of the Extreme Learning Machine. Compared to the traditional ELM method, the SaDE-ELM model is more likely to avoid local optimal solutions and has stronger global search capabilities. In addition, the training speed of the SaDE-ELM method is faster, making it suitable for handling large-scale datasets and complex tasks.
applied sciences  
Comparative Study of Potato (_Solanum tuberosum_ L.) and Sweet Potato (_Ipomoea batatas_ L.): Evaluation of Proximate Composition, Polyphenol Content, Mineral and Antioxidant Activities  
**[PERSON] \({}^{1}\)\({}^{\copyright}\)**, [PERSON] \({}^{2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{4}\), [PERSON] \({}^{5}\), [PERSON] \({}^{6}\),  
**[PERSON] \({}^{7}\)**, [PERSON] \({}^{8,*}\)\({}^{\copyright}\), [PERSON] \({}^{9,*}\)\({}^{\copyright}\), [PERSON] \({}^{10,11}\)\({}^{\copyright}\) and  
**[PERSON] \({}^{10,*}\)\({}^{\copyright}\)**  
\begin{tabular}{p{34.1 pt}}
1 & National Institute of Food Science and Technology (NIFSAT), Faculty of Food Nutrition and Home Sciences (FFNHS), University of Agriculture, Faisalabad 38040, Pakistan; [EMAIL_ADDRESS] \\
2 & Nutrition Division, National Institute of Health, Islamabad 44000, Pakistan; [EMAIL_ADDRESS] \\
3 & Department of Food Science and Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan; [EMAIL_ADDRESS] \\
4 & Department of Botany, University of Agriculture, Faisalabad 38040, Pakistan; [EMAIL_ADDRESS] \\
5 & College of Food Science \& Technology, Huazhong Agricultural University, Wuhan 430070, China; [EMAIL_ADDRESS] \\
6 & Institute of Food Science \& Nutrition, Bahauddin Zakariya University, Multan 60000, Pakistan; [EMAIL_ADDRESS] \\
7 & School of Life Sciences, Northeast Normal University, Changchun 130024, China; [EMAIL_ADDRESS] \\
8 & Department of Biochemistry, Faculty of Sciences, University of Agriculture, Faisalabad 38040, Pakistan \\ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. \\ Comparative Study of Potato (_Solanum tuberosum_ L.) and Sweet Potato (_Ipomoea batatas_ L.): Evaluation of Proximate Composition, Polyphenol Content, Mineral and Antioxidant Activities. _Appl. Sci._ **2021**, _11_, 11844. [[https://doi.org/10.3390/app112411844](https://doi.org/10.3390/app112411844)]([https://doi.org/10.3390/app112411844](https://doi.org/10.3390/app112411844)) \\ \end{tabular}  
**Abstract:** The objective of the present study was to differentiate and compare the proximate composition, minerals, flesh colour, phenolic composition, and antioxidant activities of varieties of white-fleshed sweet potato (WFSP) and potato (WFP) locally grown in Pakistan. The results showed that WFP presented higher moisture and crude fat content, while WFSP offered better ash, crude protein, and crude fibre contents. Colour analysis revealed that WFSP and WFP showed the highest L* (lightness) values and exhibited the maximum total phenolic content and total flavonoids content of 9.27 \(\pm\) 0.88 mg GAE/g and 19.01 \(\pm\) 0.66 mg QE/g. _In vitro_, results demonstrated that WFSP possessed better antioxidant activity with the highest ferric reducing antioxidant power of 58.67 \(\pm\) 0.22 mM Fe\({}^{2+}\)/g and DPPH scavenging activity of 39.12 \(\pm\) 0.33% compared to WFP. It is concluded that WFSP possesses a better proximate and mineral profile followed by higher antioxidant activity.  
**Keywords:** white-fleshed sweet potato; white-fleshed potato; proximate composition; antioxidant; DPPH; phenolic composition
# Sign Language Interpreting System Using Recursive Neural Networks
## 4 Materials and Methods
### Performance Assessment  
We assessed the performance considering the Accuracy, Sensitivity, Specificity, and F1 Score since these are typical for evaluating Neural Network-Based systems.  
We consider that it is mandatory to evaluate the performance of the proposed system in an online scenario considering the number of errors the system makes during the time the signaler is performing a signal, this is because it will result in a sequence of non-sense words, which surely will confuse users. To the best of our knowledge, there is no metric to evaluate this particular behavior, and thus, we propose a metric to assess the performance in this setup. We call this metric the _\"Average error in live video during translation\"_ (AELVT); this metric measures the average of number of times the system mistranslated a sign before the system is able to translate the sign correctly. We define the AELV as follows:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Layer** & **Model 1A** & **Model 1B** & **Model 1C** & **Model 2** & **Model 3** & **Model 4** \\ \hline
1 & 64 & 32 & 128 & 64 & 128 & 64 \\ \hline
2 & 128 & 64 & 256 & 128 & 64 & 128 \\ \hline
3 & 64 & 32 & 128 & 64 & 64 & 64 \\ \hline
4 & – & – & – & 32 & 32 & – \\ \hline
5 & 64 & 64 & 64 & – & – & 64 \\ \hline
6 & 32 & 32 & 32 & 64 & 20 & 32 \\ \hline
7 & – & – & – & 32 & – & – \\ \hline
8 & 20 & 20 & 20 & – & – & 20 \\ \hline
9 & – & – & – & 20 & – & – \\ \hline
10 & – & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 4: Layer sizes for each model listed in Table 1. The rows in each column specify the number of neurons in each layer of the corresponding model.  
\[AELV=1-\frac{N_{W}-M_{BT}}{N_{W}}. \tag{4}\]
* \(AELV\) : Average error in live video translations.
* \(N_{W}\): Number of words that the system can translate.
* \(M_{BT}\): Number of bad translations before reaching the correct one.  
Next, we calculate the Average Error in the entire Live Video using the next formula:  
\[AELVT=\frac{\sum AELV}{N_{W}}. \tag{5}\]  
The above formula measures the AELVT for an entire class.
## 1 Introduction
A gap was found between the efforts to accurately develop and measure performance indicators and the efforts to ease their interpretation and use on a strategic level to support the operational level regarding scheduling strategies. To address this gap, a reporting tool was developed considering the design of precise and accurate indicators embedded into graphical interpretation tools. In order to achieve the mentioned objective, in this paper, we develop three aspects of research methodology. First, an exploratory study is performed to analyse and establish the features and capabilities of PBI. Second, a descriptive study is conducted on maintenance indicators, leading to a final correlating study between maintenance indicators and the opportunistic grouping strategy proposed in [9]. Ultimately, the development of this article is based on a methodology framework divided into four stages, namely:  
1. Definition and formulation of KPI;
2. Analysis of capabilities and limitation of Power BI;
3. Python script analysis, measurement, and integration with PBI;
4. Reporting tool design and implementation. Although no significant research has been conducted regarding measurement of the performance of grouping strategies, in this article, we introduce a methodology to accurately measure the most important aspects and features of a system related to the maintenance planning. Furthermore, the generic nature of the input data and the precision of the defined metric enables the reporting tool to be implemented in almost any case where the input data are available.
## 2 Metamaterial Sensor for eNDE and Theory
\[I\left(x^{{}^{\prime}},y^{{}^{\prime}}\right)=\frac{1}{\lambda^{2}d_{ 1}d_{2}}\int\limits_{\rightarrow}^{\infty}\exp\left[i\frac{k\left(\left(x^{{}^{ \prime}}-x_{{}_{1}}\right)^{2}+\left(y^{{}^{\prime}}-y_{{}_{1}}\right)^{2} \right)}{2d_{{}_{1}}}\right]P\left(x,y\right)\exp\left[i\frac{k\left(x_{{}_{1 }}^{2}+y_{{}_{1}}^{2}\right)}{2f}\right]\times \tag{5}\] \[\left(\int\limits_{\rightarrow}^{\infty}\int\limits_{\rightarrow \infty}^{\infty}O\left(x,y\right)\exp\left[i\frac{k\left(\left(x_{{}_{1}}-x \right)^{2}+\left(y_{{}_{1}}-y\right)^{2}\right)}{2d_{{}_{1}}}\right]\mathrm{ dx}\mathrm{dy}\right)\mathrm{dx}_{{}_{1}}\mathrm{dy}_{{}_{1}}\]  
where \(P\left(x,y\right)\) is the pupil function defined as [73**Error! Bookmark not defined. **]:  
\[P\left(x,y\right)= \begin{cases}1&x^{2}+y^{2}\leq d^{2}\\ 0&\mathrm{otherwise}\end{cases}  \tag{6}\]  
\(O\left(x,y\right)\) is the object defined as:  
\[O\left(x,y\right)= \begin{cases}e_{{}_{v}}\left(x,y\right)&\mathrm{for}\quad \mathrm{TEz}\quad\mathrm{polarized}\quad\mathrm{incident}\quad\mathrm{waves}\\ h_{{}_{v}}\left(x,y\right)&\mathrm{for}\quad\mathrm{TMz}\quad\mathrm{polarized}\quad\mathrm{incident}\quad\mathrm{waves}\end{cases}  \tag{7}\]  
\(f\) is the focal distance of the lens equal with the height of CSR; \(\lambda\) is the wavelength in a vacuum; \(k=2\pi/\lambda\) is the wave number; \(d_{{}_{1}}=R+l\) is the distance from the object to the center of the lens; and \(d_{{}_{2}}=l\) is the distance from the center of the lens to the detecting coil. The most convenient method from an experimental point of view is to measure \(S\) parameters for the MM that fill a waveguide or in free space, using an emission and reception antennas. The relation between \(S_{11}\) and \(S_{21}\), applying the effective method [74] using a 4395A Network/Spectrum/Impedance Analyzer Agilent (Agilent Technologies, Santa Clara, CA, USA) coupled with an Agilent 87511A S Parameters Test kit and effective refractive index \(n\), is given by [75,76]:  
\[S_{{}_{11}}=\frac{R_{{}_{0}}\left(1-\mathrm{e}^{{}^{\prime}2n\mathrm{d}\cdot d }\right)}{1-R_{{}_{0}}^{2}\mathrm{e}^{{}^{\prime}2n\mathrm{d}\cdot d}}\ \ S_{{}_{21}}=\frac{\left(1-R_{{}_{0}}^{2}\right)\mathrm{
## 3 Opportunistic Transmission Scheme for Integrated UAV-ICV Networks Enabled by Node Cooperation  
Based on the node cooperation types elaborated in Section 2 and the constructed encounter probability analysis model, this paper evaluates the forwarding capabilities of each relay node. Specifically, the node cooperation type of a relay node can be determined by whether the value of \(H_{\text{max}}\) changes, where \(H_{\text{max}}\) changes, and \(H_{\text{max}}\) is the maximum hop for information to reach the destination node or fail. Based on this, a decision can be made on whether to forward the information. Additionally, to prolong the network lifetime, the opportunistic transmission scheme should consider both the number of copies and transmission reliability. Taking into account the described practical factors, an opportunistic transmission scheme for integrated UAV-ICV networks enabled by node cooperation is proposed. The information transmission process in this scheme consists of two main parts: initial information flooding and UAV-assisted information transmission. The detailed flow chart of the proposed solution is shown in Figure 2.
## 3 End-to-End SNR Statistics  
Under DF relaying, the end-to-end instantaneous SNR is denoted by  
\[\gamma_{S_{G}RD}=\frac{\gamma_{S_{G}R}\gamma_{RD}}{\gamma_{S_{G}R}+\gamma_{RD}+1} \cong\min(\gamma_{S_{G}R},\gamma_{RD}) \tag{18}\]  
The mixed channel's CDF is denoted by [29]  
\[F_{\gamma_{S_{G}RD}}=P_{r}\big{[}\min(\gamma_{S_{G}R},\gamma_{RD})<\gamma\big{]} =F_{\gamma_{S_{G}R}}(\gamma)+F_{\gamma_{RD}}(\gamma)-F_{\gamma_{S_{G}R}}( \gamma)F_{\gamma_{RD}}(\gamma) \tag{19}\]  
Substituting (9) and (17) into (19) results in the following equation  
\[F_{\gamma_{S_{G}RD}}(\gamma)=1-\exp\biggl{(}\frac{-\gamma}{\Omega_{S_{G}R}} \biggr{)}\frac{\xi_{\text{mod}}^{2}}{r\Gamma(a)\Gamma(b)}H_{3,3}^{3,1}\Biggl{[} \begin{array}{c}a\phi\gamma^{1}\\ (b-1)\mu_{r}^{\frac{1}{b}}\end{array}\Biggr{]}\begin{array}{c}(1-b,1),\bigl{(} \xi_{\text{mod}}^{2}+1,1\bigr{)},\bigl{(}1,\frac{1}{r}\bigr{)}\\ \Bigl{(}0,\frac{1}{r}\bigr{)},\bigl{(}a,1\bigr{)},\bigl{(}\xi_{\text{mod}}^{2}, 1\bigr{)}\end{array}\Biggr{]} \tag{20}\]
# Urban CO2 Budget: Spatial and Seasonal Variability of CO2 Emissions in Krakow, Poland
## 3 Results and Discussion
### Net \(CO_{2}\) Flux from an Urban Area  
Under unstable stratification, 90% of the measured net flux originates within average distance of 363 m, corresponding to source area of 0.41 km\({}^{2}\). The area for which the normalized footprint function is less than 1%, (virtually no flux is detected) extends to 18-22 m from the measuring station, depending on the direction, and contains the building on which the mast is installed. Footprint function maximum is located on average at 133 m from the site, corresponding to an area of 0.055 km\({}^{2}\). Within this source area, all of the main local CO\({}_{2}\) budget components are present: traffic, vegetation and individual households with their inhabitants (Figure 3).  
As expected, the area surrounding the REA-AGH measurement site is a net source of CO\({}_{2}\), with a seasonal variation of the observed emissions. The minimum values have been observed during the summer, with an average flux density of 4.28 \(\pm\) 0.15 umol m\({}^{-2}\) s\({}^{-1}\); in the remaining seasons the observed flux was significantly higher, with the average values of 9.35 \(\pm\) 0.19 umol m\({}^{-2}\) s\({}^{-1}\), 9.40 \(\pm\) 0.30 umol m\({}^{-2}\) s\({}^{-1}\), and 7.51 \(\pm\) 0.14 umol m\({}^{-2}\) s\({}^{-1}\) representing winter, spring and autumn, respectively.  
The observed flux values varied significantly with the direction. The highest observed values occurred from the traffic land-use sector, with a median of 7.68 umol m\({}^{-2}\) s\({}^{-1}\). The lowest values were observed from the direction of green land-use sector, with a median of 2.65 umol m\({}^{-2}\) s\({}^{-1}\) and 24% of the measured fluxes being negative. Negative values indicate net CO\({}_{2}\) uptake. Although, under favorable conditions, i.e., in a growing season and under sufficient sunlight, urban area may become a net sink of CO\({}_{2}\), most of the time this is not the case. Spatial inhomogeneity of the net flux was most evident in autumn and winter, when the observed CO\({}_{2}\) influx from the traffic sector was more than twice as high as from the other directions (Figure 6).  
The results obtained at the REA-AGH site are comparable with data available in the literature. In Helsinki, the seasonal average CO\({}_{2}\) flux from various land use sectors ranged from -1.2 to 11 umol m\({}^{-2}\) s\({}^{-1}\)[40]. In Lodz, one of the largest Polish cities, the most commonly occurring values were in the range of 0-15 umol m\({}^{-2}\) s\({}^{-1}\), with some negative values during summer and exceeding 30 umol m\({}^{-2}\) s\({}^{-1}\) during winter [41]. In London, the range of the measured net CO\({}_{2}\) flux range was even wider: from 7 to 47 umol m\({}^{-2}\) s\({}^{-1}\)[8], and in Basel from 5 to 25 umol m\({}^{-2}\) s\({}^{-1}\)[42].  
Figure 5: The annual mean CO\({}_{2}\) flux density from the Vistula river measured at three sites located along the river (Figure 1). Error bars denote standard deviation of the mean values.
## 3 Results  
### SQI Variations with Distances to Roads  
The calculated integrated \(SQI\) reflects the relative soil quality degree of different soil points away from the roads. Using Equations (1) and (2), the membership value \(Q(x_{i})\) of each soil quality factor was calculated and the \(SQI\) for soil nutrients and soil heavy metals (\(SQI_{nutrients}\) and \(SQI_{n metals}\)) could be compared. The higher values of \(SQI\) mean higher soil fertility and less soil contamination.  
Figure 2 shows the trends of \(SQI\) index with distance to roads. We classified the points to the natural lands and farmlands. It is very clear that the \(SQI\) values varied greatly in the natural soils regardless of soil nutrients or soil heavy metals (Figure 2a). No obvious trends were found for the points in natural soils. However, in farm dry lands, it is clear that the \(SQI_{nutrients}\) had a general decreasing trend and \(SQI_{n metals}\) had an increasing trend. Both changing trends with the distances in farmlands could be fitted by logarithm models with significant levels (\(p\) < 0.01) (Figure 2b).