esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For example, [PERSON] and [PERSON] (2018) conducted a one-year measurement of CO\({}_{2}\) flux in a residential area of Tokyo with a significant fraction of vegetation cover, and reported representative seasonal and diurnal variation patterns of anthropogenic CO\({}_{2}\) emission in the city. In addition, [PERSON] et al. (2018) clarified the diurnal variation of CO\({}_{2}\) flux in a less vegetated residential area of Tokyo that was close to arterial roads. [PERSON] and [PERSON] (2018) summarized more than ten years of ground-based CO\({}_{2}\) concentration data measurements at five sites in the Greater Tokyo Area, finding different sub-daily and seasonal patterns of CO\({}_{2}\) concentrations from urban sites in Tokyo to rural ones in Saitama prefecture. In other efforts, GOSAT-1 and GOSAT-2 enhanced target mode observation, particularly addressing the Kanto Plain and provide an unprecedented amount of high-resolution atmospheric CO\({}_{2}\) concentration data to elucidate anthropogenic CO\({}_{2}\) emissions in Tokyo and to update its CO\({}_{2}\) inventory. To support analyses of these data in a top-down approach (i.e., inversion modeling system) for the megacity Tokyo, disaggregating anthropogenic CO\({}_{2}\) emissions from biogenic CO\({}_{2}\) flux at the order of a half-hour to several hours is necessary. To reduce biogenic CO\({}_{2}\) flux interference for city-scale CO\({}_{2}\) emissions estimation in particular, both photosynthetic CO\({}_{2}\) uptake and CO\({}_{2}\) emissions attributable to vegetation respiration, along with soil microbial and passive organic carbon decomposition within urban areas and surrounding vegetated land, should be explicitly considered. This study was conducted to develop a terrestrial biosphere model calculating sub-daily variation of \(NEE\) based on satellite data and meteorological conditions, to be coupled with a regional CO\({}_{2}\) transport model for better estimation of anthropogenic CO\({}_{2}\) emissions from megacities, with Tokyo and the Kanto Plain (139\({}^{\circ}\)E-141\({}^{\circ}\)E, 35\({}^{\circ}\)N-36.33\({}^{\circ}\)N) as the target area of study. To set up boundary conditions of CO\({}_{2}\) in the transport model AIST-MM [62; 63], the calculation of biogenic CO\({}_{2}\) fluxes was expanded further to the central area of Japan (134.5\({}^{\circ}\)E-142.5\({}^{\circ}\)E, 32.5\({}^{\circ}\)N-38.5\({}^{\circ}\)N). Moreover, the _NEE_ results provide reference information that is useful to estimate net vegetation photosynthetic CO\({}_{2}\) removal from the atmosphere in the region, which itself is part of the CO\({}_{2}\) inventory.
## 2 Structural Complexity and Composition of Native Lignin
### Applications of Lignin: Industrial Startups and Bio-Based Materials Innovations  
The global plastics industry, which currently accounts for approximately 4% of global oil consumption, is undergoing a transformative shift toward sustainability by increasingly adopting lignin-based bioplastics as a renewable alternative to petroleum-derived plastics (Figure 7) [69]. Lignin is emerging as a key feedstock for the development of bio-based materials that not only reduce reliance on fossil fuels but also offer enhanced functional properties [70]. One of the most promising applications of lignin is its incorporation into biopolymers such as polylactic acid (PLA) and Polyurethane (PU), a widely used biodegradable plastic [71; 72]. Studies have shown that the addition of lignin to PLA can improve tensile strength by up to 20% and reduce water permeability by 35%, making it a viable and competitive alternative to conventional plastics in terms of both performance and environmental impact [73]. These improvements are particularly significant for applications in packaging, agriculture, and consumer goods, where mechanical strength and barrier properties are critical. TEA and LCA studies have provided valuable insights into the economic and environmental viability of lignin-based bioplastics which can achieve cost parity with conventional plastics at production scales [74]. This cost competitiveness is further supported by advancements in processing technologies, such as solvent recovery systems, which are helping to mitigate challenges associated with lignin's inherent hydrophobicity. However, innovative solutions, including chemical modification and the development of efficient solvent recovery processes, are reducing these costs and improving the economic  
Figure 6: Lignin electrospinning schematic diagram.  
feasibility of lignin-based materials [75]. The growing interest in lignin-based bioplastics is also reflected in the activities of industrial startups, which are playing a pivotal role in driving innovation and commercialization. These startups are leveraging lignin's unique properties to develop a wide range of products, from biodegradable packaging films and agricultural mulches to high-performance composites for automotive and construction applications. Bioplastics based on lignin represent a transformative opportunity for the plastics industry to transition toward a more sustainable and circular bioeconomy. By replacing petroleum-derived plastics with renewable alternatives, lignin-based materials can significantly reduce greenhouse gas emissions, decrease reliance on fossil fuels, and contribute to the development of a low-carbon industrial landscape.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 2 Materials and Methods
### GPS Observables
#### 2.1.1 Pseudorange  
For a satellite-receiver pair, let \(r^{i}\) be the true range from the receiver to the \(i\)-\(th\) satellite. This range can be calculated from the transit time of the GPS signal; however, both receiver and satellite present some errors in their clocks, \(\delta t_{r}\), and \(\delta t^{i}\), respectively, which means that the transit time calculation yields a range different from the true range. Other errors, such as the tropospheric and ionospheric delays, \(T^{i}\) and \(I^{i}\), respectively, and multipath errors, \(MP^{i}\), also add to this difference. This new distance is called the pseudorange and is given by [13]  
\[\rho^{i}=R^{i}+c\delta t_{r}-c\delta t^{i}+T^{i}+I^{i}+MP^{i}+\epsilon_{\rho} \tag{1}\]  
where all terms are in units of meters and \(\epsilon_{\rho}\) corresponds to other unmodeled errors. The true range \(R^{i}\) can be given by [2]:  
\[R^{i}=\sqrt{\left(x^{i}-x\right)^{2}+\left(y^{i}-y\right)^{2}+\left(z^{i}-z \right)^{2}} \tag{2}\]  
where \(\left(x,y,z\right)\) are the receiver coordinates, and \(\left(x^{i},y^{i},z^{i}\right)\) are the \(i\)-\(th\) satellite coordinates.
# Investigation of a Precise Control Scheme for Rice Quality
## 2 Materials and Methods  
### Sample Preparation  
A freshly harvested high-quality early indica rice variety, MeiXiangZhan NO.2 (Guangdong, China), was selected for the experiments after screening to remove impurities and immature seeds. Prior to testing, the samples were naturally dried in a shaded and ventilated environment to reduce moisture content. A moisture analyzer (HS153, Mettler-Toledo, Zurich, Switzerland) was utilized for real-time measurement of the moisture content to ensure the experiments' required level was achieved. The samples were hermetically sealed in polyethylene bags and stored under refrigeration at an ambient temperature of 4 \(\pm\) 1 \({}^{\circ}\)C and a relative humidity of 60 \(\pm\) 2.5% prior to testing. Furthermore, the initial moisture content of the rice at harvest, as determined by drying for 24 h at 105 \({}^{\circ}\)C, was found to be 30% (wet basis, w.b.). The initial percentage of cracked grains was measured to be 1%, and natural drying had a negligible impact on this. The accuracy of the moisture analyzer had been calibrated using an oven method [16].
## 3 Materials and Methods
### Dataset  
A renowned dataset sourced from ISIC2018 [31] serves as the foundation for evaluating the proposed model. The ISIC2018 Task 3 dataset encompasses 7 distinct categories: melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AKIEC), benign keratosis (BKL), dermatofibroma (DF), and vasculopathy (VASC). Comprising a total of 10,015 samples, the dataset exhibits an inherent imbalance in the distribution across various skin lesion categories. Notably, the images maintain a uniform color depth of 24 bits and possess dimensions of 600 \(\times\) 450 pixels. The dataset distribution is delineated as follows: MEL (1113 samples), NV (6705 samples), BCC (514 samples), AKIEC (327 samples), BKL (1099 samples), DF (115 samples), and VASC (142 samples). Refer to Figure 1 for illustrative samples showcasing different manifestations of skin lesions.  
In this experiment, the ISIC2018 dataset undergoes partitioning into distinct training and testing subsets, adhering to an 80% allocation for training and a 20% allocation for testing, as shown in Table 1.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 2 PLC Transmission via Overhead MV Power Lines  
Signal transmission via MV power lines differs considerably from signal transmission via twisted-pair, coaxial, or fiber-optic cables due to specific characteristics related to the topology and the physical properties of power lines [8]. In typical overhead MV installations, power lines hang above lossy ground at a height ranging from 8 to 10 m. They normally consist of the three phases, separated by 0.3 to 1 m, and a ground return. To determine the transfer function of multi-conductor transmission lines (MTL) employed for PLC transmission, the well-known two-conductor analysis is extended employing appropriate matrix analysis [8].
## 5 Experimental Verification
### Reconstructed Displacements Calculated from Accelerations  
Environmental stochastic effects were also considered in the experiment, and an electric vibration table was used to generate noise excitations with an amplitude of 10 V. Figure 13 presents the time-domain signal and time-frequency distribution of acc1 calculated by short-time Fourier (STFT) transformation, with a sampling frequency of 1024 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Carbon Fiber Composite** & & \\ \hline Young’s modulus (GPa) & E\({}_{\text{x}}\) & E\({}_{\text{y}}\) & E\({}_{\text{z}}\) \\  & 47.45 & 60.3 & 3.9 \\ Shear modulus (GPa) & G\({}_{\text{xy}}\) & G\({}_{\text{yz}}\) & G\({}_{\text{xz}}\) \\  & 72.9 & 1.5 & 62.35 \\ Poisson’s ratio & \(\gamma_{\text{xy}}\) & \(\gamma_{\text{yz}}\) & \(\gamma_{\text{xz}}\) \\ Density (Kg/m\({}^{3}\)) & & 1800 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the carbon fiber-reinforced composite plate.  
Figure 12: (**a**) Experimental platform and (**b**) experimental process. 1: Signal generator, 2: Power amplifier, 3: Electric shaker, 4: Carbon fiber-reinforced composite cantilever plate, 5: Fixture, 6: Accelerometer, 7: Signal acquisition instrument, 8: Computer.  
The spectrum diagram in Figure 13b was calculated by a Hamming window with a segment length of 210 and a step size of 1. It is noticeable that excited frequencies were the same in the entire time domain with white noise excitations. Acceleration signals in the interval of 20-30 s were locally amplified to analyze the characteristics of system responses (Figure 14, where Figure 14a,b present the time-domain signal and the spectrum diagram, respectively). The proposed frequency-domain integration algorithm was then applied to obtain the dynamic displacement responses of the structure in the interval of 20-30 s. The target frequency for reconstruction was selected as 1 Hz. Figure 15a compares the time histories of measured accelerations and reconstructed displacements. It is clear from Figure 15b that higher-order modal components for reconstructed displacement were dramatically reduced as compared to those for measured accelerations. Therefore, reconstructed displacements could eliminate peak responses in the high-frequency range of measured accelerations and also reduce the non-stationarity of tested data.  
Moreover, the modal parameters of the cantilever plate were obtained through a sinusoidal frequency sweep experiment. The frequency range was set as 1-100 Hz, and the excitation amplitude was 500 mV. Figure 16 displays the spectrum of measured accelerations obtained from the sinusoidal frequency sweep experiment. The first four frequencies were detected as 4.304, 20.839, 30.518, and 55.839 Hz respectively, and the corresponding damping ratios were calculated by the half-power points of resonant peaks as 2.10%, 3.68%, 2.30%, and 3.88%, respectively.  
Figure 14: (**a**) Locally amplified acceleration signals at 20–30 s and (**b**) spectrum diagram of measured accelerations.  
Figure 13: (**a**) Time-domain signal of acc1 and (**b**) time-frequency distribution of acc1.
## 7 Implementation and Evaluation
### Multimedia Workflows  
The stream data is not sent through SOAP messages; therefore, we used a separate socket based channel (cf. Figure 9). This means that each service instance has a direct Transmission Control Protocol (TCP) connection to his predecessor and successor. In both ways we are able to provide several services like scaling and transcoding. We also developed a display service which is responsible to show the adapted content on the client side. Furthermore, we developed a content provider which provides a multiplicity of different media files, and a publisher service that is responsible for delivering the processed multimedia data via HTTP to the client.  
The instantiation of a suitable service chain for an individual user is done by a Monitoring Service. This service also monitors the workflow and is responsible for service replacements. The client device only interacts with the Monitoring Service using Web service calls, as visualized in Figure 9. Thus, we are able to use different services in a chain to change different aspects of the stream and display it on a client device.
# On the Finite Element Modeling of the Lumbar Spine: A Schematic Review
## 3 Results
### Modelling of the Different Components
#### 3.3.3 Ligaments  
The seven major ligaments can be treated as connector, truss, or beam elements. The constitutive models used are usually nonlinear models or isotropic elastic response models with a varying Young's modulus according to the strain rate. Other approaches exploit non-linear \(\sigma-\epsilon\) curves based on experimental data.
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 2 Literature Review
[18] developed a model that jointly examines assortment planning and store-wide shelf space allocation decisions. The model is then solved by a heuristic approach that constructs a high-quality initial solution that is further enhanced using a large-scale neighborhood local search procedure. Recently, network analysis has been applied to transaction data to provide a better understanding of product relationships from various points of view [19; 20]. [PERSON] and [PERSON] [21] investigated co-purchase networks and demand levels associated with over 250,000 interconnected books over a year. Their results showed that the visibility of co-purchase networks amplifies shared purchasing of complementary products. [PERSON] and [PERSON] [22] used market-basket analysis to evaluate associations between two products, establishing a product network based on history transaction data, followed by community detection. [PERSON] et al. [23] used product network analysis to extend the market basket analysis with social network analysis. They integrated the centrality concept into product networks and tried to explain the result through social network analysis in actual retail stores. [PERSON] et al. [24] proposed a novel shopping behavior prediction system which considers both the quantity and utility of purchased products. A set of frequent shopping patterns, called high-utility mobile sequential patterns (UMSPs), is generated using the UMSPL algorithm. However, the product-to-shelf problem is not addressed in this study. [PERSON] et al. [25] introduced a mathematical model to optimize the retail revenue while considering products' cross-elasticity on demand. The space allocated to each product is considered as a continuous variable that has a lower boundary of one product face. The resulting model, a NP-hard one, is solved using an adaptive meta-heuristic algorithm based on artificial bee colony (ABC). [PERSON] et al. [26] presented a novel mixed integer programming formulation for the shelf space allocation problem considering two innovative features emerging from merchandising rules:Hierarchical product families and display directions. The formulation uses single commodity flow constraints to model product sequencing and explores the product families' hierarchy to reduce the combinatorial nature of the problem. [PERSON] et al. [27] provided a closed-form expression for the approximate solution to the lower-level problem determining the retail prices and the allocated shelf spaces. This solution then is incorporated into the manufacturers' profit resulting in a single-level optimization problem which is easier to solve.
## 2 Material and Methods
### Peel-Out Test Method and Calculation of Measurement Results  
The calculation method for the peel-out test measurement results, as specified in KS F 4934, is described in Figure 5. The test specimen is passed through a sliding jig, and one end that is not adhered to the test substrate is fixed to the tension testing device, ensuring a peel angle of 90 \(\pm\) 5\({}^{\circ}\). The sheet is then peeled by 20 mm, excluding the initial peel length, and gradually pulled at a tensile speed of 100 mm/min.  
For the peel-out test, the load-peel length curve is divided into four equal sections beyond the initial peel length of 20 mm, as shown in Equation (1). The peel load values (P1, P2, P3, P4, and P5) at the intersection points (\(P_{i}\)) of the division lines and the load curve are read as the measurement values of the test specimen. The result is based on the average value that is calculated from measurements taken on five specimens [4].  
\[F=\frac{\sum_{i=1}^{5}P_{i}}{5\times 50}, \tag{1}\]  
where  
\(F=\) Adhesive Strength (N/mm);  
\(P_{i}=\) Force (N).  
Figure 4: Peel-out test types and methods; (a) Roller drum peel method, (b) 180\({}^{\circ}\) peel-off method, (c) 90\({}^{\circ}\) peel-off method.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## 4 Model Evaluation: Setting the Stage
### Synthetic Data Set  
The anomaly detection task is performed on \(N=20\) stocks simultaneously. The stock prices are diffused according to the Black and Scholes model. Each stock has its own drift and volatility and the 20 stocks are correlated, as described in Section 3. Each time series represents \(T=1500\) the daily stock prices, split into two sets. The first 1000 observations corresponding to the training set are used to learn the model parameters, i.e., the PCA transfer matrix and the NN weights and biases. The last 500 observations corresponding to the test set are used to assess the quality of the estimated parameters when applied to unseen samples. For the application of the sliding window technique, we set the length of the resulting time series \(p\) to be 206. Table 2 sums up the composition of each data set before and after data augmentation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Nb of Time Series** & **Nb of Observed Values per Time Series** & **Nb of Anomalies** \\ \hline Train set & 20 & 1000 & \(4\times 20\) \\ Test set & 20 & 500 & \(2\times 20\) \\ \hline  & **Nb of Time Series** & **Nb of Observed Values per Time Series** & **Nb of Anomalies** \\ \hline Train set & 12,000 & 206 & 6000 \\ Test set & 2500 & 206 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data set composition (**top**) before and (**bottom**) after data augmentation.  
We recall that both steps of the approach are preceded by a feature extraction step, for which the latent space dimension \(k\) needs to be calibrated. As shown by the numerical results provided in Appendix C, the feature extraction also guarantees the stationarity of the time series used for the anomaly detection task, \(\epsilon\).
## 2 Diversity of LPBSs and NRPSs from _Bacillus_ and _Pseudomonas_
### Bacillus
#### 2.1.2 Fengycin Synthetase  
Fengycin, also referred to as plipastatin when Tyr\({}_{3}\) and Tyr\({}_{9}\) is present as the L-and D-form, repectively. It is an anti-fungal antibiotic that inhibits filamentous fungi but is ineffective against yeast and bacteria. It is also capable of inhibiting phospholipase A\({}_{2}\) and biofilm formation of several bacteria [4, 37, 77, 78]. These types of lipodecapeptides are produced by various strains of _Bacillus_ spp. and exhibit moderate surfactant activities [4, 17, 79]. Fengycin is expected to form a lactone between the hydroxyl group of L-Tyr\({}_{3}\) and the _C_-terminal carboxyl group of L-Ile. Fengycin synthetase (Fen) contains five NRPS subunits: FenC (287 kDa), FenD (290 kDa), FenE (286 kDa), FenA (406 kDa), and FenB (146 kDa). Like SrfA and Lic, Fen is also composed of an _N_-acyl domain at the _N_-terminus of FenC, conventional E-domains, and a typical type I Te-domain. Fen assembles to form a co-linear chain ordered as FenC-FenD-FenE-FenA-FenB [80].
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Google Earth Engine  
Several cloud computing platforms provide remote sensing data [60; 61]: Sentinel Hub [61; 62], Open Data Cube [63; 64], SEPAL [65; 66], JEODPP [67; 68], pipsCloud [69; 70], OpenEO [71; 72], and Google Earth Engine (GEE) [73; 74], with GEE being among the most widely used [75].  
GEE [73; 74] is a powerful online platform widely used for Earth observation and environmental research. It provides an intuitive and user-friendly interface, including a code editor and graphical user tools, making it accessible to researchers across disciplines such as geography, geology, and remote sensing. It hosts an archival database of images from various satellites, such as Sentinel, Landsat, and MODIS, along with geospatial data. This facilitates the creation of diverse maps and offers users the ability to download data, tools for processing, capabilities to sort and filter, mask clouds and their shadows, and data analysis [73; 74]. Its popularity is attributed to key features such as being free to use and offering efficient parallel processing capabilities without the need to download data [76]. It enables easy progress using data from different spatial and temporal ranges [77; 78], thereby saving a significant amount of time and processing large volumes of data [79].  
GEE is widely used for tasks such as forest and deforestation mapping using machine learning algorithms, such as Random Forest [50; 80; 81]. A portion of this study was conducted using GEE. This included the collection of spectral and time series data from Sentinel-2, the calculation and addition of spectral indices, and the labeling of the data. Finally, the processed data were downloaded in tabular format for training the machine learning models, along with the images to which these models were applied [82].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Band Name**} & \multirow{2}{*}{**Description**} & \multicolumn{2}{c}{**Wavelength (nm)**} & \multirow{2}{*}{**Pixel Size (m)**} \\  & & **S2A/S2B** & & \\ \hline B1 & Aerosols & 443.9/442.3 & 60 \\ B2 & Blue & 496.6/492.1 & 10 \\ B3 & Green & 560/559 & 10 \\ B4 & Red & 664.5/665 & 10 \\ B5 & Red Edge 1 & 703.9/703.8 & 20 \\ B6 & Red Edge 2 & 740.2/739.1 & 20 \\ B7 & Red Edge 3 & 782.5/779.7 & 20 \\ B8 & NIR & 835.1/833 & 10 \\ B8A & Red Edge 4 & 864.8/864 & 20 \\ B9 & Water vapor & 945/943.2 & 60 \\ B10 & Cirrus & 1373.5/1376.9 & 60 \\ B11 & SWIR 1 & 1613.7/1610.4 & 20 \\ B12 & SWIR 2 & 2202.4/2185.7 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentinel-2 Level 1C spectral bands [59].