esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Identification of Phytochemical Compounds of Artichoke By-Products by HPLC-ESI-TOF-MS
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Peak** & \begin{tabular}{c} **RT** \\ **(min)** \\ \end{tabular} & **Proposed Compound** & \begin{tabular}{c} **Theoretical** \\ \(m\)/z** \\ \end{tabular} & \begin{tabular}{c} **Experimental** \\ **(\(m\)/z** \\ \end{tabular} & \begin{tabular}{c} **Molecular** \\ **Formula** \\ \end{tabular} & \
\begin{tabular}{c} **Error** \\ **(ppm)** \\ \end{tabular} &
\begin{tabular}{c} **PLE** \\ **Experiment** \\ \end{tabular} \\ \hline
1 & 2.14 & Quinic acid & 191.0561 & 191.0567 & C\({}_{7}\)H\({}_{12}\)O\({}_{6}\) & \(-\)3.2 & * \\ \hline
2 & 6.14 & Chlorogenic acid & 353.0878 & 353.0883 & C\({}_{16}\)H\({}_{18}\)O\({}_{9}\) & \(-\)1.5 & * \\ \hline
3 & 6.51 & UK 1 & 375.0663 & 375.067 & C\({}_{25}\)H\({}_{12}\)O\({}_{4}\) & \(-\)5.4 & 1,2,4,5,6,7,9 \\ \hline
4 & 6.62 & Rosamarinic acid & 359.0772 & 359.0735 & C\({}_{18}\)H\({}_{16}\)O\({}_{8}\) & 10.5 & 1,4,5,6,7,8,9 \\ \hline
5 & 7.36 & Cynarin isomer 1 & 515.1195 & 515.1211 & C\({}_{25}\)H\({}_{24}\)O\({}_{12}\) & \(-\)4.5 & * \\ \hline
6 & 7.65 & Luteolin-rutinoside & 593.1512 & 593.1514 & C\({}_{27}\)H\({}_{30}\)O\({}_{15}\) & \(-\)0.3 & * \\ \hline
7 & 7.95 & Luteolin-glucose & 447.0933 & 447.0919 & C\({}_{21}\)H\({}_{20}\)O\({}_{11}\) & \(-\)4 & * \\ \hline
8 & 8.08 & Cynarin isomer 2 & 515.1195 & 515.1207 & C\({}_{25}\)H\({}_{24}\)O\({}_{12}\) & \(-\)2.2 & * \\ \hline
9 & 8.58 & Apigenin-rutinoside & 577.1563 & 577.1571 & C\({}_{27}\)H\({}_{30}\)O\({}_{14}\) & \(-\)1.4 & * \\ \hline
10 & 8.98 & Apigenin-glucoside & 431.0984 & 431.0965 & C\({}_{21}\)H\({}_{20}\)O\({}_{10}\) & \(-\)3.1 & * \\ \hline
11 & 10.27 & UK 2 & 345.0405 & 345.0374 & C\({}_{20}\)H\({}_{10}\)O\({}_{6}\) & 9 & * \\ \hline
12 & 11.62 & UK 3 & 207.0663 & 207.0657 & C\({}_{11}\)H\({}_{12}\)O\({}_{4}\) & 2.7 & 1,2,3,5,6 \\ \hline
13 & 13.40 & Luteolin & 285.0405 & 285.0412 & C\({}_{15}\)H\({}_{10}\)O\({}_{6}\) & \(-\)2.5 & 1,3,4,5,6,7,8,9 \\ \hline
14 & 15.95 & Apigenin & 269.0455 & 269.0459 & C\({}_{15}\)H\({}_{10}\)O\({}_{5}\) & \(-\)6.8 & * \\ \hline
15 & 20.65 & Trihydroxy-octadecenoic acid & 329.2333 & 329.2331 & C\({}_{18}\)H\({}_{34}\)O\({}_{5}\) & 0.6 & * \\ \hline
16 & 22.51 & Dihydroxy-hexadecanoic acid & 287.2228 & 287.2229 & C\({}_{16}\)H\({}_{32}\)O\({}_{4}\) & \(-\)0.3 & 1,3,4,5,6,7,8,9 \\ \hline
17 & 23.15 & Methylapigenin & 283.0612 & 283.0607 & C\({}_{16}\)H\({}_{12}\)O\({}_{5}\) & 1.7 & 1,2,3,4,6 \\ \hline
18 & 26.62 &
\begin{tabular}{c} Cynarasaponin A/H isomer \\ \end{tabular} & 925.4802 & 925.476 & C\({}_{47}\)H\({}_{74}\)O\({}_{18}\) & 4.6 & 1,6,7,9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Phenolic and other polar compounds characterized in _Cynana scolymus_ L. PLE extracts analyzed by HPLC-time-of-flight mass spectrometry with an electrospray interface (ESI-TOF/MS).
# Novel Common-Mode Current Suppression Method in Transformerless PV Grid-Connected System
###### Abstract  
The existence of high-frequency components in common-mode (CM) current would reduce the stability of a non-isolated PV grid-connected system. It has great impacts on the output power quality when the generated power from PV is low. The method of single CM inner loop in suppressing the high-frequency components has poor effect. Based on the CM equivalent circuit model and the cause analysis of the CM current, a novel dual CM inner loops method is proposed to restrain the high-frequency components in CM current. This method not only meets the grid connection demand of CM current, but also overcomes the high frequency resonant problem. Furthermore, the high-frequency components in CM current from the outer parasitic circuit are substantially reduced. Finally, the proposed method is verified by simulation and experimental results.  
Non-isolated; PV grid-connected system; common-mode current; inner loop; high-frequency component +
Footnote †: journal: Applied Sciences  
_Article_
# Application of Genetic Algorithms for Periodicity Recognition and Finite Sequences Sorting
## 4 Genetic Algorithm
### The Concept of Error for Nonperiodic Sequences  
After we have received a certain amount of variation to transform nonperiodic sequences into periodic ones, we need to choose one of them for further research. Thus, the concept of error was defined for such sequences. We will call an error the deviation of a nonperiodic sequence from its considered periodic transformation. The error in converting a nonperiodic sequence into a periodic sequence can be defined as the difference between the original nonperiodic sequence and the best periodic approximation of the same sequence. This error is measured by a value that can be called an \"error\" between the original and approximated sequences. For each resulting solution (variation), we will compare it with the input periodic sequence and the number of its modified elements:  
\[E=\sum_{i=1}^{N}\frac{\delta(a_{i},t_{i})}{2^{i}} \tag{4}\]  
Figure 10: Algorithm operation scheme.  
where \(N=\) the size of the sequence in question \(a_{i}=\) an element of the input nonperiodic sequence at the position \(i\) \(t_{i}=\) the element of the periodic sequence at the position \(i\) \(\delta(a_{i},t_{i})=\begin{cases}1,&\text{if $a_{i}$ is not equal to $t_{i}$}\\ 0,&\text{otherwise}\end{cases}\)  
This formula is the sum of the values of the function \(\delta\) for each pair of elements of two sequences, \(a_{i}\) and \(t_{i}\), which will calculate the number of modified elements between them. In the end, we divide by the value \(2^{i}\), since this is a convergent series, i.e., the contribution to the error of those elements that are far away is small. In the beginning, the error values will be relatively significant. After calculating all the error values, the one that best approximates the original nonperiodic sequence to its periodic approximation is selected. Choosing the option with the smallest error is standard practice when optimizing and approximating data. It allows us to find the best solution for the problem of converting a nonperiodic sequence into a periodic form in terms of minimizing the differences between them.
## 3 Numerical Simulation  
Due to the bidirectional coupling effect of the seepage field and temperature field, a complete freezing process is formed. Only by combining the joint action of the seepage field and temperature field can the variation law of the temperature field under seepage conditions be fully and comprehensively understood. This study simplifies the calculation of water-heat coupling and makes the following assumptions.  
1. Assuming an initial soil layer temperature of 20 \({}^{\circ}\)C, the soil is considered to exhibit groundwater flow characteristics, and it is assumed to be uniformly distributed within the model, with the material properties varying uniformly with the temperature and with the change process solely influenced by the temperature.
2. The model is set up with the inflow and outflow at the left and right sides as groundwater flow zones, while the other surfaces are impermeable. The flow within the model is a unidirectional horizontal groundwater flow, with fixed head conditions upstream and downstream, in line with [PERSON]'s law.
3. The temperature load is directly applied to the freezing walls of the circular and irregular freezing pipes in the model. Heat loss during heat exchange between the refrigerant and the soil, as well as water migration during the freezing process, is neglected in the calculation.
4. The soil is assumed to start freezing when its temperature drops below \(-1\)\({}^{\circ}\)C, with heat transfer during the freezing process considered only through conduction and convection.  
Figure 1: Gongbei Tunnel section and pipe roof freezing method diagram (modified from [PERSON] et al. [21]).  
Figure 2: The arrangement of the temperature measurement points in a temperature measurement section.  
(5) The computational model developed in this research is a fully coupled water-heat model, disregarding the influence of stress fields on the temperature field in practical engineering scenarios.
## 4 Network Layer: The Main Building Block of Fixed Networks
### F1G PONs
#### 4.1.5 F3g  
2005-2010 (Broadband Era)  
2005-2010 (2009) was inevitable, as network operators had already developed EPON and no replacement of legacy equipment was required. The 10G-EPON provides strong confidence in the viability of the EPON investment and is easy to manage. On the other hand, end users could enjoy exciting applications over the Internet [10].  
_DS/US Rates and wavelengths_: The 10G-EPON keeps backwards compatibility with EPON, while the transmission rate both in the downlink and uplink increases at 10 Gb/s. An asymmetric transmission at 10.3125 \(\cong\) 10 Gb/s in the downlink and 1.25 Gb/s (with line encoding 8B/10B) in the uplink is offered. In the downlink, the wavelength ranges of 1480-1500 nm and 1575-1580 nm are used for 1.25 Gb/s and 10 Gb/s transmission, respectively. In the uplink, the wavelength ranges of 1260-1360 nm and 1260-1280 nm are used for 1.25 Gb/s and 10 Gb/s transmission, respectively.  
_QoS:_ Error control is supported by Forward Error Correction (FEC) with Reed-Solomon (RS) coding [6]. In 10G-EPON (as in EPON), QoS support is based on MPCP and the DBA algorithm (inherently offers QoS support), which arbitrates the transmission of different users (eight priority levels are considered according to the IEEE 802.1Q standard--six priority levels out of the eight levels are devoted to user traffic) [26].  
**XG(S)PON--ITU-T Rec. G.987.x:** Support for new bandwidth-demanding applications, connecting a larger number of users, better QoS and security were the motivations for 10 Gb/s GPON (10G-GPON).  
_DS/US Rates and Wavelengths:_ The 10G-GPON, Romanized as XGPON, was standardized (2010) to offer the transmission rate both in the downlink and uplink at 9.953 \(\cong\) 10 Gb/s (XGS-PON) and asymmetric transmission at 10 Gb/s in the downlink and 2.488 \(\cong\) 2.5 Gb/s in the uplink (XG-PON) [10]. In the downlink and the uplink, the wavelength ranges of 1575-1580 nm and 1260-1280 nm are used, respectively [12]. The XGS-PON was standardized in the year 2016. As in 10G-EPON, error control is supported by FEC with RS coding [26].  
_QoS:_ Like GPON, XG(S)PON supports both ATM and Ethernet traffic via XGEM (XG(S)PON encapsulation method). For QoS, the ONU associates different traffic flows with a specific XGEM Port ID (the identifier of the XGEM port to which the frame belongs) [10]. QoS can be granted according to demand and based on the SLA. Moreover, DBA is based on SLA and strictly follows the four priority levels, like GPON.  
_Security:_ Security (of the XGEM payload) is provided by the AES-CTR (CounTeR mode) algorithm, which applies a forward cipher with a secret key known only to the OLT and ONU [6,10].
## 4 Results  
The results of using Python and 28 regressors supported by the Scikit-learn package are shown in Table 2. In the case of estimating the solar panel output voltage based on joint BH1750 and UV sensor readings and considering the R\({}^{2}\) score, Random Forest, MLP, and KNN regressors are identified as the most efficient regressors with a score of 0.95 and higher. The XGBoost, Gradient Boosting, Decision Tree, SVR, and Extra Trees are similarly efficient with a score of 0.90 or higher. The impact of the accuracy of the single-sensor-based estimation is slightly different. For example, for BH1750 only, the R\({}^{2}\) scores are 0.97894 for Random Forest, 0.96298 for K-Neighbors, 0.89068 for XGBoost, 0.61396 for SVR, 0.90218 for Gradient Boosting, 0.88959 for Decision Tree, and 0.95015 for Ex Trees. For UV single-sensor reading, the R\({}^{2}\) results for various regressors are 0.90521 for XGBoost, 0.95892 for Random Forest, 0.90604 for SVR, 0.90652 for Gradient Boosting, 0.95037 for MLP, 0.94961 for K-Neighbors, 0.89174 for AdaBoost, 0.90451 for Decision Tree, 0.90952 for Extra Trees, and 0.88147 for Voting. In both cases, the single-sensor impact is less accurate than the two-sensor estimation but still usable.  
The R\({}^{2}\) score comparison for two sensors simultaneously is shown in Figure 3. The comparison of regressors is evaluated further with MSE and MAE metrics, as shown in the following figures.  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Sc. No.** & **Input Data** & **R\({}^{2}\)** & **RMSE** & **MAE** & **EVS** & **Max. Err** \\ \hline
1 & Lux, UV\_Raw, Temp\_DHT22, Hum\_DHT22, Temp\_TMP36 & 0.9521 & 0.0904 & 0.2705 & 0.9529 & 0.2078 \\
2 & Lux, UV\_Raw, Temp\_DHT22, Temp\_TMP36 & 0.8926 & 0.1354 & 0.3156 & 0.8942 & 0.4926 \\
3 & Lux, UV\_Raw, Temp\_TMP36 & 0.9535 & 0.0891 & 0.2662 & 0.9556 & 0.2113 \\
4 & UV\_Raw, Temp\_TMP36 & 0.9581 & 0.0845 & 0.2583 & 0.9604 & 0.2122 \\
5 & Lux, UV\_IFeadRaw & 0.9618 & 0.0807 & 0.2401 & 0.9654 & 0.2460 \\
6 & Lux & 0.9650 & 0.0772 & 0.2278 & 0.9661 & 0.3188 \\
7 & Temp\_DHT22 & 0.3548 & 0.3318 & 0.3843 & 0.3749 & 2.1943 \\
8 & Hum\_DHT22 & \(-\)0.3656 & 0.4827 & 0.4820 & 0.3109 & 2.6242 \\
9 & Temp\_TMT36 & 0.4444 & 0.3079 & 0.3475 & 0.4565 & 2.1791 \\
10 & UV\_Raw & 0.9424 & 0.0991 & 0.2895 & 0.9478 & 0.1995 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various sensor combinations with MLP regressor in the estimation of solar panel output.  
Figure 3: Comparison of R\({}^{2}\) score for different Scikit-learn regressors.  
The comparison of MAE metrics is given in Figure 4.  
The comparison of MSE metrics is given in Figure 5.
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## 3 Results
### Performance of Multiple VIs at Bungoma  
For all VIs tested, the agreement (as measured by adjusted R\({}^{2}\)) between ground-measured and satellite-derived calibrated yield estimates was consistently better on larger fields, especially when field size was greater than 0.5 acre (Figure 4). Almost all VIs tested can explain more than 40% of the variation in measured yields when field size was greater than 0.75 acre, although the value should be interpreted with caution because the sample size was small (i.e., 10 fields). Not surprisingly, purestand fields outperformed estimates for intercropped fields when field size was greater than 0.5 acre. However, purestand fields only account for about 20% of the total surveyed fields (Figure 4). Both GCVI and MTCI outperformed other indices (although differences were not statistically significant due to small sample sizes) for fields between 0.5 and 0.75 acre, among which MTCI captured about 35% and 45% of the yield variation when minimal field size was 0.5 and 0.625 acre, respectively. We therefore selected GCVI and MTCI for further yield estimation on a regional scale, and designated the regression model for purestand fields with minimal size of 0.5 acre as the preferred calibrated model for the B-K sub-region (Table S1). Among the independent variables, both the Skysat imagery on 17 May and the RapidEye imagery on 1 June and 4 June contributed to the model performance as the adjusted R\({}^{2}\) was lower when either of them were removed from the regression. The usefulness of the early season Skysat imagery was likely because it helped capture differences in sowing dates.
## 2 Experiments  
### Experiment System  
The experimental arrangement for the laser-ablation plume observation system is shown in Figure 2. The targets were mounted on a disk-shaped holder in a cylindrical vacuum chamber measuring 2.4 m in diameter and 3 m in length with a pumping system. A fiber laser was employed to generate a 5 ms pulsed laser with a wavelength of 1064 nm and an energy output of 5 J. The laser beam entered the vacuum chamber through a quartz glass window and was focused on the target surface by a convex lens with a focal length of 400 mm, resulting in a laser spot of approximately 0.8 mm in diameter. The dynamics of the plume were captured in the experiment using an ultra-high-speed intensified charge-coupled device (ICCD) camera (PCO DIMAX S4) with a fixed exposure time of 5.236 \(\upmu\)s. The timing of the ICCD camera and the laser was regulated by an industrial computer. Furthermore, all test groups in our experiments were conducted at a background pressure of 5 \(\times\) 10\({}^{-4}\) Pa with other conditions being kept consistent.
## 2 Materials and Methods
### Modelling Framework  
Figure 2 shows the modelling framework used in this study and the model calibration and validation steps. As mentioned in the introduction, the main objective of this study is to find a combination of indices to be used with LUE models for GPP estimation at peatland sites. Additionally, a site-explicit light use efficiency parameter (LUE) for this site was derived. Initially, GPP was modelled using Equation (1), with six satellite-derived indices (Table 1), literature-based LUE parameters values (Table 2), and measured PAR for 2018. The results were then validated against field-based EC-derived GPP for 2018 to obtain the potential indices for the hybrid models. Overall, six hybrid models were developed on the basis of regression analysis, and modelled GPP were validated against EC-derived GPP for 2019 and 2020. Site-specific LUE parameters (\(\epsilon_{\text{site}}\)) for drier (2018) and wetter years (2019-2020) for the near-natural peatland were established on the basis of the analysis of light response curves at the site.  
Figure 2: The modelling framework showing calibration and validation steps to derive hybrid models and to derive site-specific LUE parameters. Light orange boxes show objectives of this study; blue and green boxes show time frame for calibration and validation.
## 4 Results
### Technological Transition and Service Adoption
* Emergence of fiber optics as a leader: By 2023, in almost all states, fiber optic technology emerges as a leading telecommunication service, especially in economically advanced regions such as Ciudad de Mexico and Nuevo Leon, where fiber optic has surpassed other technologies. These trends suggest that fiber optics will become the backbone of telecommunications infrastructure in Mexico's top economic states, essential for supporting future 5G networks, smart cities, and digital services that are critical for continued economic growth and global competitiveness.
# Aerospace
## 6 Mars Small Satellite Constellation
### B-Plane Targeting and Approach Trajectories  
The approach trajectories for the five small satellites are defined by \(r_{p}=(3389.5+52)\) km and \(\psi=[3\pi/2,\,11\pi/8,\,5\pi/4,\,9\pi/8,\,\pi]\), whose aim points on the Mars B-plane are shown in Figure 26. These approach trajectories target orbits with inclinations of 1.65 deg., 22.5 deg., 45 deg., 67.5 deg., and 90 deg. for the small satellites. Since all the small satellite trajectories have the same \(r_{p}\), the aim points have the same \(|\)B\(|\) and lie on the SmallSat \(|B|\) circle. Hence, all the five SmallSat approach trajectories result in the same EFPA \(=-9.25\) deg. (effect of planet rotation causes the atm. relative EFPA to be slightly different and must be accounted for). Hence, the aerocapture trajectory results for the single satellite from Section 4 are valid for each of the five SmallSats. The approach trajectory of the host orbiter is the same as that defined in Section 4.8, \(r_{p}=(3389.5+250)\) km, and \(\psi=\pi\).
## 7 Data Volume and Resources  
In this section, we perform a comparison analysis of using traditional detectors versus CS based detectors.  
1. Data volume storage Using a \(n\times n\) image, with a 14-bit ADC resolution, we would expect the total data volume to be:  
We would make the assumption that the photodetector is not saturated with the ADC bit resolution needed to sample. Without data compression, we will need to transfer \(14n^{2}\) bits/ FOV using a traditional detector. Using CS approach for 25% measurements, we can will need to transmit \(14\times 0.25\times n\times n=3.5n^{2}\) bits/ FOV.
2. Computational resources On-board computation will consist of programming the spatial modulator and storing the \(m\times n\) size acquired data for each \(n\times n\) spatial image. To compare this with a traditional detector system, we would require computational resources for compressing data on-board, in order to be accommodated in the data down-link bandwidth. In terms of on-board Field Programmable Gate Array (FPGA) resources for each of the modules listed in Table 13, we would expect a similar amount of logic gates, except for item 3. There are different methods for implementing data compression, including compression algorithms and pixel averaging [24, 25]. For CS detectors, spatial modulation implementation will depend on the spatial modulator used. In addition, we would require either storage or generation and transmission of the spatial modulation matrix (CS measurement matrix) on-board. The on-board storage needed for traditional detectors would be significantly higher than storage needed for CS architecture modules.
## 4 Experiments
### Experimental Setup
**Metrics and Parameters:** In this paper, Precision, Recall, and F1-score are calculated based on the following equations:  
\[Precision@k=\frac{\sum_{u\in U}|R(u)\cap T(u)|}{\sum_{u\in U}|R(u)|}, \tag{10}\]  
\[Recall@k=\frac{\sum_{u\in U}|R(u)\cap T(u)|}{\sum_{u\in U}|T(u)|}, \tag{11}\]  
\[F1-score@k=\frac{2 Recall@k\times Precision@k}{Recall@k+Precision@k}, \tag{12}\]  
where \(k\) is the length of recommendation list (i.e., top-\(k\) recommendation), \(R(u)\) is the set of POIs which are recommended to the user in the recommendation list, and \(T(u)\) is the set of POIs which are actually visited by the user in the test dataset. For a given user, the corresponding check-in records are sorted by the timestamps and the latest 15% check-ins are considered as the test dataset to evaluate the performance of models. The experimental setup and the used parameters are introduced in detail as below. The segmentation of datasets is 70%/15%/15% based on the temporal check-in records where the first 70% is the training set for learning the models, the second 15% is the validation set for optimizing the parameters, and the third 15% is the testing set for evaluating the performance. The sliding window, which is used to split the contextual POIs, is set as 3. The learning rate of discriminator and generator is set as 0.01. The weights of loss in the generator are set as \(\alpha=0.5\) and \(\beta=0.3\). The dimension of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistics** & **Foursquare** & **Gowalla** \\ \hline Count of Users & 1895 & 5951 \\ Count of POIs & 2109 & 7449 \\ Check-in records & 115,114 & 270,765 \\ Records per user & 60.76 & 45.50 \\ Records per POI & 54.60 & 36.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The overview of Foursquare and Gowalla. user embedding and POI embedding is 300, while the dimension of the hidden layer in LSTM is set as 100. The training epoch is defined as 20. The technique of simultaneous gradient descent is used to update the model. The rate of training epochs of the generator compared with the discriminator is 1:1. To evaluate the topn POI recommendation of baselines and proposed approach, the precision, recall, and F1-score are calculated at Top 5, 10, 15, and 20. In addition, the losses (i.e., \(loss_{pred}\), \(loss_{gen}\), and \(loss_{loc}\)) demonstrate the difference between predicted and actual ones. Therefore, the fluctuation of loss is considered as an effective way to determine that whether the model is converged or not in this domain.