esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16234474/s1](https://www.mdpi.com/article/10.3390/rs16234474/s1)]([https://www.mdpi.com/article/10.3390/rs16234474/s1](https://www.mdpi.com/article/10.3390/rs16234474/s1)), Table S1: Average climatological data during the trial; Figure S1: PlanetScope Superdove and Micasense RedEdge-M bands from SuperDove sensor; Table S2: PlanetScope & Micasense bands with their respective spectrum; Figure S2: RGB and NDVI from PlanetScope and Micasense-M during drone acquisitions; Figure S3: MicaSense and PlanetScope: bands comparisons with Pearson R\({}^{2}\) and RMSE (per date and overall); Table S3: Comparison of MicaSense and PlanetScope NDVI Values and Rankings Across Multiple Dates for Different Treatments; Figure S4: Time Series of fCover Modeled Using PlanetScope EVI Across Different Fertilization Treatments; Table S4: fCover prediction models for PlanetScope and Micasense RedEdge-M.  
Conceptualization, L.O. and R.M. ; methodology, I.M.-L., B.F. and N.K. ; software, I.M.-L. and K.C. ; validation, S.S.-M. and I.M.-L.; formal analysis, L.O., I.M.-L. and K.C. ; investigation, S.S.-M., M.A.M-U., F.J.-A.-R. and J.A.S.-T.; resources, F.J.A.-R. and J.A.S.-T.; data curation, L.O. and R.M. ; writing--original draft preparation, I.M.-L., K.C., L.O. and S.S.-M.; writing--review and editing, K.C., P.C.G. and N.K. ; visualization, F.J.-A.-R. and J.A.S.-T.; supervision, B.F. and N.K. ; project administration, B.F. and R.M. ; funding acquisition, B.F. and R.M. All authors have read and agreed to the published version of the manuscript. This study forms part of the AGROALNEXT/2022/016 and COFRUT-MONITOR project (AGROALNEXT/2022/046) program and was supported by MCIN with funding from European Union NextGenerationEU (PRTR-C17.I1) and by Generalitat Valenciana. The study was funded by the program Generacio Talent of Generalitat Valenciana (CIDERENT/2018/009) and Santiago Grisolia program (CIGRIS 2021/161), supported by the Conselleria d'Educacio, Cultura, Universitats i Coupacio from Generalitat Valenciana. The data presented in this study are available on request from the corresponding authors. The authors thank the NASA \"Private-Sector Small Constellation Satellite Data Product Pilot Project\" for providing access to the full Planetscope archive. Conflicts of Interest: Author [PERSON] was employed by the company Planet Labs Germany GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
## 3 Results and Discussion
### Properties of the Biowaste
#### 3.1.4 Summary of Biowaste Properties  
Among the biowaste types analyzed, kitchen biowaste exhibited the most favorable characteristics for biochar production via slow pyrolysis. It recorded the highest valuesfor volatile matter (76.55%), carbon content (43.36%), hydrogen content (7.03%), and the lowest ash content (6.81%). These factors positively influence biochar yield and quality. Additionally, it demonstrated the highest higher heating value (HHV) at 17.24 MJ/kg. However, kitchen biowaste also contained the highest levels of nitrogen (3.12%), chlorine (0.71%), and sulfur (0.10%) among all samples. These elements can negatively affect combustion by increasing emissions of nitrogen oxides (NO\({}_{\text{x}}\)), sulfur oxides (SO\({}_{\text{x}}\)), and hydrogen chloride (HCl), and may contribute to corrosion in thermal processing equipment. Despite these concerns, the physicochemical properties of the kitchen biowaste were consistent with the literature values reported globally for food waste [31; 49]. These findings support the conclusions of [PERSON] et al. [55], who noted that the composition of household food waste is relatively uniform across different countries.  
The spring garden biowaste differed significantly from kitchen biowaste. Compared to the literature values for garden waste, it contained higher levels of moisture (81.53%), ash (17.75%), and fixed carbon (39.82%), while showing lower levels of volatile matter (42.43%), carbon (31.94%), hydrogen (4.53%), and chlorine (0.05%). The contents of oxygen (42.82%), nitrogen (2.84%), and sulfur (0.07%) were similar to the values reported in the literature. However, its HHV was lower, at 10.60 MJ/kg, indicating reduced energy potential [31; 50; 51].  
In contrast, autumn garden biowaste had a moisture content of 63.51% and a notably high ash content of 43.83%, both higher than the typical literature values [50]. It also had elevated nitrogen content (1.19%), but lower levels of volatile matter (43.75%), carbon (29.99%), hydrogen (2.02%), oxygen (20.92%), and chlorine (0.01%). Its HHV was also relatively low at 11.16 MJ/kg. The fixed carbon (12.43%) and sulfur (0.03%) content in autumn garden biowaste were in line with the data from the literature for similar materials [51; 52].  
For further comparison, the properties of the tested biowaste were evaluated against commonly used biomass materials for pyrolysis-based biochar production, such as wood [9], almond shells [12], and rice husks [56]. It was shown that among the biowaste analyzed, kitchen biowaste has the most similar properties, and thus would be best suited as a feedstock to produce biochar through the slow pyrolysis process.
# ScienceIoT: Evolution of the Wireless Infrastructure of KREONET
## 2 Background  
The necessity of building wireless infrastructure in research fields has been actively indicated by NRENs such as ESNet, SURFnet, and SINET. They have raised several points regarding research activities conducted without wireless infrastructure. First, last-mile connectivity should be provided to researchers, experimental equipment, and research facilities. In this context, wireless infrastructure built for research fields would support the use of special experimental devices, such as mobile data collectors and sensors located in hostile areas, without physical or economic difficulties (such as installing cables between a monitoring server in the laboratory and a sensor station located at the top of a mountain). Second, sneakernet refers to the transfer of digital data via physical media such as magnetic tapes, floppy disks, optical discs, flash drives, or even external hard drives. This data collection scheme is quite simple but also time-consuming. Data transfer through wireless links would reduce the time required for data collection and enhance the efficiency of research activities. Finally, cable salad refers to untangling knots or the entanglements of cables. Repetitive experiments involving the relocation of experimental equipment, rewiring, and the movement of wired devices might result in unintentional cable salad. This phenomenon disrupts future experiments and exhausts researchers, as it necessitates the recovery of a well-arranged research environment.  
In this section, we briefly review the wireless communication technologies used for the IoT and introduce KREONET, a representative research and education network in Korea. Among the various wireless technologies, we mainly focus on LPWAN technology, which enables scientific data transmission even under hostile conditions owing to its wide coverage and robustness. By contrast, KREONET provides high-performance, dedicated network services to scientists and researchers. Owing to advances in proliferation of the data-oriented research paradigm, KREONET is aiming to build wireless IoT infrastructures for accommodating the requirements of various research areas such as environment, energy, agriculture, forestry, and oceanology.
## 2 Problem Formulation
### Risk Averse Stochastic Unit Commitment with Transmission Reserves  
In this section, we present a risk-averse SUC model with a predictive method for transmission reserves to enhance the utilization of the available wind power and alleviate the transmission congestion in the grid. The proposed framework is formed as a bi-level problem on the typical two-stage market basis, i.e., the day-ahead and real-time market. The upper-level problem is the day-ahead UC, where the objective is to decide the day-ahead status of the thermal generation units, including unit on/off status and amount of power generated at each time period, while the unit and system constraints involving the transmission congestion limit are satisfied. The objective function of UC \(f_{1}(x_{UC})\) is to minimize the total operation cost, which consists of the no-load cost, start-up cost, spinning reserve cost and variable cost of all generators. The lower level problem is the real-time economic dispatch (ED) process with remedial measures under the wind power uncertainty, and its objective function \(f_{2}(x_{ED})\) is the expected redispatch cost which includes the cost of real-time dispatch and remedial measures (wind curtailment and load shedding). The main purpose of the ED sub-problem is to obtain the statistical features of line flows for the estimation of the upper bound of transmission reserve in the upper level. Since the two objectives in both levels are of the same type (the operation cost of generators), we can simply combine them as a unified one, which is written as  
\[\text{Min}\;f_{1}(x_{UC})+f_{2}(x_{ED}) \tag{3}\]  
The two items of the objective function are detailed as  
\[ \begin{array}{l}f_{1}(x_{UC})=\sum\limits_{t\in\mathcal{T}}\sum\limits_{g\in \mathcal{G}}\Big{(}C_{g}P_{g}^{t}+SUC_{g}u_{g}^{t}+NC_{g}x_{g}^{t}+RC_{g}r_{g} ^{t}\Big{)},\\ f_{2}(x_{ED})=\sum\limits_{t\in\mathcal{T}}\sum\limits_{g\in\mathcal{G}}C_{g} \hat{P}_{g}^{t}+\sum\limits_{t\in\mathcal{T}}\sum\limits_{n\in\mathcal{N}} \Big{(}C_{cr}W_{n,cr}^{t}+C_{ls}D_{n,ls}^{t}\Big{)}\end{array}  \tag{4}\]  
where \(P_{g}^{t}\), \(u_{g}^{t}\), \(x_{g}^{t}\), and \(r_{g}^{t}\) are, respectively, the dispatch, start-up, on/off and generation spinning reserve decisions of generator \(i\) at time \(t\), and \(C_{g}\), \(SUC_{g}\), \(NC_{g}\) and \(RC_{g}\) are their associated cost coefficients, respectively. \(\hat{P}_{g}^{t}\) is the actual power production at time \(t\) in the real-time dispatch. The penalty cost terms of wind curtailment \(W_{n,cr}^{t}\) and load shedding \(D_{n,ls}^{t}\) are proportional to the Value of Wind Curtailment (VOWC) \(C_{cr}\) and Value of Lost Load (VOLL)\(C_{ls}\), respectively.
## 3 Wavelet Coefficient and Feature Extraction
### Feature Extraction from PSNR  
PSNR values were evaluated through Equation (9) for the measured responses. For the adhesive wear, PSNR increased as the contact pressure increased, as was observed in the measured sound pressure in Figure 7a, but there was no tendency for level 1 as expected. The surface irregularity was maintained due to adhesion phenomenon, even if the relative motion occurred continuously. In addition, as the contact pressure increased, the adhesion surface area increased, which caused more acoustic noise. On the other hand, in the case of abrasive wear, the PSNR tended to decrease as the contact pressure increased. The PSNR evaluated from the detail coefficient in abrasive wear is shown in Figure 7b. The calculated value from level 1 did not show any consistent variation. For the abrasive wear, the surface was evenly distributed due to friction by the impurity particles. The contact pressure increased the distribution. Under the adhesive wear, the fracture and crack on the surface of the metals influenced the dynamic responses. With the increasing pressure, the crack and fracture progress increased. This induced a larger noise ratio in the acoustic responses. On the other hand, there should be much more impurities on the surface under abrasive wear. The flat surface did not radiate a larger noise. With the increasing pressure, the degree of the abrasive wear progressed faster and caused smaller cracking of the surface. It was confirmed that the frequency component variation from scratching affected the period of the wavelet coefficients. It can be seen that the emission period of the acoustic energy varied depending on the contact pressure and was successfully classified as the PSNR. Figure 8 shows the results of the tendency and reproducibility of the PSNR values according to the  
Figure 5: Wavelet coefficient for the detail responses and influence of the contact pressure for adhesive wear: (**a**) detail wavelet coefficients in overall range of 1 s and (**b**) detail wavelet coefficients in level 2.  
Figure 6: Wavelet coefficient for the detail responses and influence of the contact pressure for abrasive wear: (**a**) detail wavelet coefficients in overall range of 1 s and (**b**) detail wavelet coefficients in level 2.  
contact pressure at level 2 for adhesive and abrasive wear. In the case of both adhesive and abrasive wear, it was confirmed that the PSNR values formed a group, and the tendency was reversed. This group formation can be used as a monitoring factor in the system self-diagnosis using acoustic sensors.
# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 4 Results and Discussion
### Wind Direction  
#### 4.2.1 Wind-Direction Distribution  
During the measurement period, the data of a certain day with a higher data effective ratio was selected to analyze. The spatio-temporal distribution of wind direction at the locations of the 5 virtual wind towers was shown in Figure 13. Overall, the wind direction ranges from 120\({}^{\circ}\) to 300\({}^{\circ}\), with southerly wind dominating, which is consistent with the annual main wind direction at the bridge site. Wind direction changes with time, height, and position in a complex way, and it is difficult to find a uniform rule.  
Taking VWT-3 as an example, 6 specific heights were selected. Figure 14 shows the time-history change of wind direction at this position. It can be roughly seen that wind-direction fluctuation decreases with the increase in height, reflecting that the interference of a U-shaped valley to incoming flow gradually decreases with height.  
Figure 13: _Cont._  
Figure 14: Time history of wind direction.  
Figure 13: Spatio-temporal distribution of wind direction, (**a**) VWT-1; (**b**) VWT-2; (**c**) VWT-3; (**d**) VWT-4; (**e**) VWT-5.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 1 Introduction  
Optimal transport (OT) is an efficient and flexible tool to compare two probability distributions, which has been popularized in the computer vision community in the context of discrete histograms [1]. The introduction of entropic regularization of the optimal transport problem in [2] has made possible the use of the fast Sinkhorn-Knopp algorithm [3], scaling with high-dimensional data. Regularized optimal transport has thus been intensively used in machine learning with applications such as geodesic PCA [4], domain adaptation [5], data fitting [6], training of a Boltzmann machine [7], or dictionary learning [8, 9].  
The computation of optimal transport between two data relies on the estimation of an optimal transport matrix, the entries of which represent the quantity of mass transported between data locations. Regularization of optimal transport with strictly convex regularization [2, 10], nevertheless, involves a spreading of the mass. Hence, for particular purposes such as color interpolation [11] or gradient flow [12], it is necessary to consider small parameters \(\varepsilon\) for the entropic regularization term. The Sinkhorn-Knopp (SK) algorithm is a state-of-the-art algorithm to solve the regularized transport problem. The SK algorithm performs alternated projections, and the sequence of generated iterates converges to a solution of the regularized transport problem. Unfortunately, the lower \(\varepsilon\) is, the slower the SK algorithm converges. To improve the convergence rate of the SK algorithm, several acceleration strategies have been proposed in the literature, based for example on mixing or overrelaxation.
## 4 Simulation and Analysis
### Simulations
#### 4.1.3 KF Effectiveness of Smoothing Error  
Meanwhile, there is still a large frequency error in adjusting the carrier loop directly using the signal parameters estimated by MLE for weak TC-OFDM signals. KF is adopted to smoothing the frequency error, so it is necessary to demonstrate the validity of KF smoothing error, and the MLE combined with KF loop is compared with MLE loop. The SNR is \(-25\) dB and other parameters are same as Table 1. Figure 9 shows that the error of frequency estimation after KF is obviously decreased in contrast to using MLE only, which verified the validity of KF smooth frequency error. The MLE combined with KE denotes as MLE&KF and the other denotes as MLE in Figure 9, respectively.
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 3 Proposed Method
### Detailed Design  
After a comprehensive analysis of the algorithm's overall flow, the detailed design of the algorithm is presented below. The distributed frequent subgraph mining algorithm, FSM-BC-BSP, based on BC-BSP, is primarily divided into four major phases: data preparation, local computing, messaging, and synchronization.  
The data preparation phase involves reading the graph database, data division, obtaining edge frequent subgraphs, and generating static data. The local calculation phase focuses on generating candidate subgraphs and performing isomorphism judgment. The messaging phase facilitates data sharing. In addition to fulfilling the super step synchronization tasks mandated by the BC-BSP architecture, the synchronization phase is primarily dedicated to frequent counting, support calculation, and tasks related to writing frequent subgraphs to the HDFS.  
The different stages are interconnected through the BC-BSP system's super step mechanism until frequent subgraphs are no longer generated or until the predefined number of super steps is reached.  
Subsequently, this paper will elucidate the specific implementation details of each stage.
## 3 Algorithms
### Parcel-Receiving Station and Public Route Planning Based on GA-MMAS
#### 3.2.3. Parcel-Receiving Station and Public Air-Route Planning Strategy  
In this paper, the genetic algorithm and max-min ant system are nested to solve the logistics UAV parcel-receiving station and public air-route planning problem. First, the genetic algorithm is used to generate the location scheme, and the max-min ant system is used to carry out the public route-air planning for each location scheme in the population. The comprehensive fitness of each individual in the population to each cost is calculated and genetic operators such as crossing and mutation operators are performed on the population. We then iterate through the above operations until the end of the algorithm.  
This nested solution of GA-MMAS requires a great deal of computational power. When MMAS is used to solve the lower-layer model, the calculation amount of the route-planning sequence with the minimum public air-route operating cost is positively correlated with the number of stations to be planned. In addition, when the number of station location results is too large, the selected stations count cost of the upper-layer model is higher and the economy of the location scheme is poor, so this kind of solution is not the best solution to this problem. Therefore, to reduce the calculation amount and improve the calculation efficiency, the genetic algorithm can be used to solve only the upper-layer model and obtain the parcel-receiving station location scheme without considering the influence of the lower-layer model. When performing the GA-MMAS algorithm nesting solution, the number of parcel-receiving stations \(N_{S}\) is scaled. If the number of parcel-receiving stations \(N_{i}\) is greater than \((1+\chi)N_{S}\), it will not be transferred to the calculation of public air-route planning to avoid unnecessary waste of computing resources.
## 2 Results  
### Method Validation and Data Acquisition  
The untargeted analysis objective is to determine as many metabolites as possible; therefore, highly repeatable and reproducible data are required. However, it has been reported that it is not clear how exhaustive and reliable current raw data processing is [12]. Therefore, although complicated, it is clear that method validation and quality analysis are needed. In order to validate the method, guidelines for targeted methods were included (Repeatability, Reproducibility, Linearity, LOD and LOQ). Although these parameters had to be determined for each targeted metabolite, in untargeted methods it is suggested to select metabolites that are present in samples, have similar chemical properties and molecular mass and are distributed along the runtime of the acquisition method [10]. Based on this, the chemical standards \(\alpha\)-Pinene, \(\beta\)-Pinene, p-Cymene and 2-Undecanone were selected (Supplementary Materials Table S1). The repeatability and reproducibility of the extracted component area of each level and metabolite were <20.0% RSD (Supplementary Materials Table S1), complying with recommended criteria [33]. The retention-time (RT) reproducibility of all standards was <0.5% RSD, where maximum variation (SD) was of 0.09 min (5.4 s); this minimal variation enhances alignment across samples and identification by default. Hence, a match-factor penalty is applied in method identification if the RT variation is greater than 12 s. Match-factor reproducibility was <2.0% RSD, which can be related to mass-fragmentation spectrum stability which depends on mass-spectrum comparison and mass accuracy. Mass accuracy in the five most abundant fragments of each standard was <5 ppm (Figure 1e,f), which allowed match factors greater than 80 on all metabolite identifications. Figure 1: (**a**) Total ion chromatogram (TIC, black) and (**b**) extracted components (green) identified (see Table 1) by manual recursive analysis. (**c**) Enlarged ethyl nonanoate peak. (**d**) Ethyl nonanoate ion peaks; each color represents an ion, exact mass is represented in the same color. (**e**) Ethyl nonanoate mass-spectrum fragmentation-pattern comparison, orange: acquired spectrum, (**f**) black: library spectrum.