esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The terrestrial gross primary productivity (GPP) has increased over the past two decades. However, the climatic attribution and the physiological and phenological processes that control the trends in the GPP are still unclear. Here, we used remote-sensing-based vegetation GPP and phenology datasets, analyzed the spatial and temporal variation in the GPP, investigated the influence of the growing season length (GSL) and the maximum value of gross primary productivity (GPPmax) on the annual GPP, and quantified the effect of climate variables on the annual GPP. Our results identified a significant increase in the annual GPP (11.97 gC/m\({}^{2}\)/yr) during 2001-2020 in China's deciduous forest. The GPPmax trend dominated the trends in the GPP, when compared with the GSL. Moreover, climate warming in summer contributes to the increase in the GPP and the GPPmax, while the extension of the GSL is primarily due to the temperature rise in spring. The annual GPP of the planted forest showed a higher increasing rate than the natural forest, due to the significant enhancement of the GPPmax and the high sensitivity of the GSL to climatic factors in the planted forest. Our findings provide a new perspective on the phenological and physiological causes of the trends in the GPP, and emphasize the importance of capturing the variability in the GPPmax when modeling the GPP.  
g rss primary productivity; growing season length; maximum value of gross primary productivity; natural and planted forests; deciduous forest ecosystems; China 2023  
41880
# Filter Media-Packed Bed Reactor Fortification with Biochar to Enhance Wastewater Quality
## 2 Materials and Methods
### Sampling and Performance Analysis  
Sampling of both the influent and effluents was made at least once per week. The samples were analyzed for pH, turbidity, orthophosphate (PO\({}_{4}\)\({}^{3-}\)), total phosphorus (P\({}_{\rm tot}\)), and dissolved organic carbon (DOC). Using the SensION\({}^{\rm TM}\) PH31 (Hach\({}^{\rm\circledR}\)) and 2100P ISO Turbidity meter (Hach\({}^{\rm\circledR}\)), measurements of pH and turbidity, respectively, were made just after the sampling. The samples were frozen for later analyses of PO\({}_{4}\)\({}^{3-}\) and P\({}_{\rm tot}\) with the AutoAnalyzer 3 instrument (Seal Analytical Ltd.) and DOC (Shimadzu TOC-L Series, Shimadzu Corporation). Prior to P\({}_{\rm tot}\) and DOC analysis, the samples were pooled together to make representative monthly samples into acid-washed plastic bottles. In total, 13 samples of the inlet and effluents were obtained. The P\({}_{\rm tot}\) analysis followed a day after a two-hour autoclave treatment of the mixture of samples with H\({}_{2}\)SO\({}_{4}\) (aq) and K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) (aq). The results obtained from triplicate columns were averaged. A mass balance method was used to estimate the amount sorbed and the removal efficiencies (E%) of each filter bed. \(E\) was calculated using the relation:  
\[E=\left(1-\frac{C_{o}}{C_{i}}\right)*100 \tag{1}\]  
where \(C_{i}\) and \(C_{o}\) were the inlet and outlet concentration, respectively.
## 3 Methodology  
### Sampling and Partitioning Strategies  
#### 3.1.1 SVM  
The negative samples used in previous studies were usually obtained randomly, which might have incorrectly introduced some positive samples [13]. The introduction of a small number of erroneous samples would affect the performance of the model to some extent, but, in general, the model can still assign a value proportional to the actual probability to each sample. In this paper, an SVM model was built based on the positive samples and randomly obtained negative samples. Reliable negative samples could be obtained from samples with lower predicted values by the model.  
An SVM is a typical supervised learning model that can project complex, nonlinear combinations of sample features into a higher-dimensional feature space. The goal of an SVM is to find a hyperplane in the high-dimensional feature space that separates the positive and negative samples as correctly as possible while maximizing the spacing of the positive and negative samples from the hyperplane. In addition, the building process of the SVM not only considers the problem of classification accuracy, but also restricts the model structure to avoid the over-complexity of the hyperplane decision process, which is an excellent algorithm that integrates empirical risk and structural risk [27]. In practice, the SVM algorithm solves the arithmetic problem in high-dimensional spaces by introducing kernel functions. In this paper, the radial basis function (RBF) was selected. The parameter involved in this function was determined to be 0.1.
## 3 Dataset and Data Pre-Processing
### Data Pre-Processing  
Before we employ classification algorithms on the data, some data pre-processing steps are required.  
The initial step involved renaming the dataset's features, which were originally long and incomprehensible. This was done to ensure more comprehensive and descriptive references. Subsequently, we conducted a thorough assessment and determined that the feature indicating musculoskeletal discomfort among recreational runners was redundant as each recreational runner invariably reported such discomfort. In the event that this condition was not met, specifically, if there existed at least one instance of a recreational runner who did not experience musculoskeletal discomfort, it would render the responses they provided to the subsequent questions invalid. This is because, according to the premise, all runners are expected to report some degree of musculoskeletal discomfort. Thus, it was excluded from the dataset. Furthermore, we identified three samples with missing values, which were subsequently removed. Among all the features in the dataset, only the \"Experience\" feature contained missing values. While our classification process primarily relied on a subset of features, it is worth noting that our analysis encompassed several tests involving various features within the dataset. Consequently, it became imperative to exclude those samples that contained incomplete values to ensure the integrity of our findings. Last but not least, many of the features represented categorical string values, making them unsuitable for classification, as the classification algorithms are only compatible with categorical numerical feature values. To address this issue, we mapped every categorical string value of a feature to a numerical value. Finally, by counting the number of samples in each category, their inherent imbalance was revealed.  
Table 1 demonstrates that not only is the data imbalanced but the ratio of the total number of samples to the number of categories is relatively low, implying that there are very few samples per category, thereby making the classification process a challenge.
## 2 Research Aims  
A method for analyzing the weakly expressed inelastic behavior of HSC using compressed NSC subjected to relatively high loading rates is investigated in this study. Since longitudinal inelastic deformations in compressed HSC specimens are practically not manifested, transverse deformations can be used for concrete inelastic behavior analysis [6]. The value of these deformations is approximately five times less than that of the longitudinal ones (Poisson's ratio is approximately equal to 0.2). However, it also does not provide a good solution for the HSC problem since the initiation of longitudinal cracks caused by transverse deformations practically coincides with specimen failure.  
For this reason, another solution is considered in this paper. As mentioned above, NSC behaves elastically almost until failure at rather high loading rates, as does HSC. However, maximal longitudinal deformations in NSC are almost two times higher than in HSC. At the same time, concrete tensile strength practically does not grow when the concrete class increases to over C70. Therefore, transverse deformation development in NSC is much more significant than in HSC, which leads, in turn, to the longitudinal crack development that determines inelastic concrete behavior in the transverse direction of a specimen. Thus, NSC subjected to relatively high loading rates can be considered as a model of HSC behavior at normal loading rates based on transverse deformation analysis.  
The main purpose of this research is to investigate transverse deformation development in a concrete specimen under axial compression throughout the entire concrete behavior range until failure. The influence of transverse deformations on the nature of crack development and the specimen failure pattern is also considered based on the structural phenomenon [13]. For this purpose, transverse deformation dependence on longitudinal deformations is investigated throughout the entire loading process, from the beginning of loading until failure.  
The objectives of this study are:  
* Detailed analysis of the elastic-plastic behavior of cylindrical NSC specimens and revealing the advantages of using transverse deformations as one of the main features of compressed concrete behavior;
* Analysis of the nature of specimen failure based on transverse crack development;
* Verification of a theoretical model of the specimen's upper surface failure pattern [6] based on the structural phenomenon;
* Confirming that the unbroken cylindrical core area is twice as small as the initial one;
* Checking the possibility of interpreting HSC behavior using relatively high NSC specimen loading rates.
## 3 PDM and Parameter Identification
### Servo System Model  
The permanent magnet synchronous motors (PMSMs) are always employed in IRs. The associated servo system can be streamlined, as depicted in Figure 8, revealing three nested control loops within the servo driver. The innermost loop, known as the current loop, directly drives the motor and comprises a current controller along with various functional modules, including coordinate transformation modules, a Space Vector Pulse Width Modulation (SVPWM) generator, current detection mechanisms, and feedback systems. The outer loops, namely, the velocity and position loops, are relatively simpler and incorporate a velocity controller and a position controller, respectively, to ensure precise motor control and positioning, respectively. \(L_{q}\) is the stator inductance, \(R_{m}\) is the stator phase resistance, \(K_{T}\) is the torque coefficient, \(\tau_{L}\) is the load torque, \(\lambda_{PM}\) is the number of magnetic linkage, \(P\) is the pole-pair number, and \(K_{ppm}\) and \(T_{ppm}\) are the amplification factor and time constant of the simplified SPWM inverter.  
The PID algorithm is extensively utilized in servo systems due to its effectiveness in achieving stable and accurate control. In such systems, the output current from the current controller is converted into the driving torque for the PMSM. Typically, a PI controller, which is a simplified version of PID without the derivative term, is employed within the current loop. This PI controller uses two key parameters, \(K_{cp}\) and \(K_{ci}\), to modify the regulatory response in accordance with the discrepancy between the target and actual current readings. The output of the velocity loop, which is responsible for maintaining the desired rotational speed of the motor, serves as the input to the current loop, ensuring that the motor operates within the specified speed range while delivering the required torque. The PI controller is also adopted using parameters \(K_{up}\) and \(K_{vi}\). The outermost position loop is always allocated a proportional controller with parameter \(K_{qp}\), which ensures tracking accuracy for the given reference position. The aforementioned torque translation parameters can be queried from the product manuals, and the parameters in the PID algorithm can be obtained from the servo drivers.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
###### Abstract  
A cognitive networking approach to the anycast routing problem for delay-tolerant networking (DTN) is proposed. The method is suitable for the space-ground and other domains where communications are recurrently challenged by diverse link impairments, including long propagation delays, communication asymmetry, and lengthy disruptions. The proposed method delivers data bundles achieving low delays by avoiding, whenever possible, link congestion and long wait times for contacts to become active, and without the need of duplicating data bundles. Network gateways use a spiking neural network (SNN) to decide the optimal outbound link for each bundle. The SNN is regularly updated to reflect the expected cost of the routing decisions, which helps to fine-tune future decisions. The method is decentralized and selects both the anycast group member to be used as the sink and the path to reach that node. A series of experiments were carried out on a network testbed to evaluate the method. The results demonstrate its performance advantage over unicast routing, as anycast routing is not yet supported by the current DTN standard (Contact Graph Routing). The proposed approach yields improved performance for space applications that require as-fast-as-possible data returns.  
c pacs: 15 June 2021  
1
Footnote 1: institutetext: Department of Engineering Technology, University of Houston, Houston, TX 77204, USA; [EMAIL_ADDRESS]
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 5 Conclusions  
ShB is a common disease in rice, and the levels of ShB severity are correlated with grain yield loss. The symptoms of the disease are characterized by water-soaked, circular to oblong, ellipsoid to avoid or even irregularly elongated discolored lesions on the leaf sheaths and the leaf blades. This study used hyperspectral remote sensing technology to evaluate spectral response curves of leaf blade lesions to find specific characteristics for establishing the spectral feature library of rice ShB. We hope to apply these features to hyperspectral data at the leaf scale to explore the possibility of remote sensing monitoring of rice ShB at the regional or an even larger area scale. The spectrum curves of diseased leaves with lesions were significantly different from those of healthy leaves in original reflectance and derivative-transformed spectrum curves, especially in the blue edge, green peak, red valley, and red edge. \"Three-edge\" parameters and some narrow-band indices were selected and calculated based on spectral responses of the disease to construct the spectral feature library of rice ShB. These features closely linked to diseased leaf blade fractions with different development stages of symptoms by statistical analysis and the Relief algorithm, including (\(\text{Rg}-\text{Ro}\))/(\(\text{Rg}+\text{Ro}\)), (\(\text{SDy}-\text{SDb}\))/(\(\text{SDy}+\text{SDb}\)), Rg/Ro, NRI, RVSI, yellow edge area, SDy/SDb, PRI, NBNDVI, and blue edge amplitude. Then, the spectral feature library was examined in the disease data set at the leaf scale. The model with Rg/Ro had higher classification accuracy. Rice ShB is different from other diseases of rice in that it initially occurs in leaf sheaths. Therefore, this study also discussed spectral response differences between the leaf sheath lesions and the leaf blade lesions. The spectral curve shapes between them were similar, but the leaf sheath lesions had more obvious characteristics of absorption valley, reflection peak and reflecting steep slope in the blue edge, green peak and red edge than the leaf blade lesions. The most significant feature was SDr/SDg. Although they were based on analysis of hyperspectral images acquired under laboratory conditions, these results can provide the basis for the development of portable diagnostic equipment for rice ShB based on multispectral wavebands. All authors contributed to the study conception and design. Methodology, writing--original draft preparation, F.L. ; writing--review and editing, D.Z. ; data curation, S.G.; investigation, C.T. ; conceptualization and formal analysis, X.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 41301505. The authors declare no conflict of interest.
## 3 Results  
### Xrd  
Figure 1 shows the XRD patterns obtained for samples with different Co concentrations. These patterns reveal that all samples presented a tetragonal Nd\({}_{2}\)Fe\({}_{14}\)B hard phase (space group P42/mmm) and an Nd\({}_{1.1}\)Fe\({}_{4}\)B\({}_{4}\) phase (tetragonal structure and orthorhombic space group _Pccn_) [42; 43; 44; 45]. Additionally, a CoO phase (Wurzita structure and space group _P63 mc_) was detected for high concentrations of Co (x = 20 and 25). It can be noted that the intensity of the peaks corresponding to the Nd\({}_{2}\)Fe\({}_{14}\)B phase was slightly reduced and those of Nd\({}_{1.1}\)Fe\({}_{4}\)B\({}_{4}\) increased when the Co content increased.  
Rietveld refinement allowed us to determine the type of structures with these alloys. The parameter values for samples with a stoichiometry of Nd\({}_{16}\)Fe\({}_{76\to\)xCo\({}_{\rm{x}}\)B\({}_{8}\) with x = 0, 10, 20 and 25 are reported in Table 1. These data indicate that the crystallite size for Nd\({}_{1.1}\)Fe\({}_{4}\)B\({}_{4}\) and Nd\({}_{2}\)Fe\({}_{14}\)B phases was in the nanometer range, from 5 to 90 nm. The crystallite size parallel to the Nd\({}_{1.1}\)Fe\({}_{4}\)B\({}_{4}\) and Nd\({}_{2}\)Fe\({}_{14}\)B phases increases with increased concentrations of cobalt. It can be noted that the two phases of parallel crystallite sizes are greater than the perpendicular ones, indicating that the crystallite shape is not spherical but elongated in that direction. The refinement of the mean crystallite size of the hard phase is required in order to obtain higher remanence values [46; 47] and to increase the ferromagnetic exchange coupling between the soft and hard grains [10]. The obtained values of the crystallite size indicate that these melted alloys are nanostructured.  
Table 1 shows the quantitative evolution with the Co content of the weight fraction of the different obtained phases. It can be noted that the Co also decreases the stability of the hard phase, and the decomposition of this phase is conducive to the increase of the Nd\({}_{1.1}\)Fe\({}_{4}\)B\({}_{4}\) phase.  
Figure 2a,b shows the behavior of the lattice parameters with respect to the cobalt concentration of the Nd\({}_{2}\)Fe\({}_{14}\)B and Nd\({}_{1.1}\)Fe\({}_{4}\)B\({}_{4}\) phases for all samples. It is observed that, for x = 0, 10, 20 and 25, the lattice parameters \(a\) and \(c\) of Nd\({}_{2}\)Fe\({}_{14}\)B and Nd\({}_{1.1}\)Fe\({}_{4}\)B\({}_{4}\) phases decrease considerably with the variation of the concentration of cobalt. This behavior is a consequence of the substitution of Fe by Co atoms, which has an atomic radius (1.25 A) lower than the atomic radius of Fe (1.26 A), causing the lattice to reduce. These parameters are consistent with those reported in the literature [48; 49].
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 5 Environmental Application of Biochar for Wastewater Treatment
### As a Filtration Support Media
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Feedback** & **Preparation Conditions** & \multicolumn{6}{c}{**Blockar Characteristics**} & \multicolumn{6}{c}{**Removal Efficiency**} \\ \cline{3-14}  & **SSA [air/g]** & **Possibly [Y=]** & **C [Y=]** & **H [Y=]** & **O [Y=]** & **COD [Y=]** & **Notions [Y=]** & **TSS/Tunkidity [Y=]** & **IM [Y=]** & **L.coli [Y=]** & **Ref.** \\ \hline \multirow{2}{*}{Waste wood pellets} & \multirow{2}{*}{Condition, 520\({}^{\circ}\)C} & - & - & - & - & - & NO\({}_{3}\)-S & 86 & 17-75 & 27 & [14] \\ \hline \multirow{2}{*}{_Moomettes_} & \multirow{2}{*}{Pyubyik, 580\({}^{\circ}\)C, 30 min} & 500 & - & 80.0 & 1.3 & 8.1 & 74 & N/N\({}_{4}\)-\(\lambda\) & 73 & 31 & - & 23 & [17] \\ \hline \multirow{2}{*}{_H_-based_} & \multirow{2}{*}{-} & \multirow{2}{*}{184} & \multirow{2}{*}{27-74} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{65} & \multirow{2}{*}{18-75} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{[90]} \\  & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{_Time-space_} & - & 170-300 & 72-74 & - & - & - & - & - & - & - & - & [91] \\  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{_F_-_} & \multirow{2}{*}{-} & \multirow{2}{*}{170-300} & \multirow{2}{*}{27-74} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{[90]} \\  & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{_F_-_} & \multirow{2}{*}{_Pyubyik, 580\({}^{\circ}\)C, 30 min} & 500 & - & 80.0 & 1.3 & 8.1 & 74 & N/N\({}_{4}\)-\(\lambda\) & 73 & 31 & - & 23 & [17] \\  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{_H_-_based_} & \multirow{2}{*}{-} & \multirow{2}{*}{184} & \multirow{2}{*}{27-74} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{[90]} \\  & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{_Time-space_} & - & 170-300 & 72-74 & - & - & - & - & - & - & - & - & [91] \\  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{_F_-_} & \multirow{2}{*}{_Pyubyik, 700\({}^{\circ}\)C, 15\({}^{\circ}\)N_} & 13.0 & - & - & - & - & - & - & - & - & - & 92-99 & [92] \\  & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{_Time-space_} & \multirow{2}{*}{_Pyubyik, 700\({}^{\circ}\)C, 15\({}^{\circ}\)N_} & 13.0 & - & - & - & - & - & - & - & - & - & 92-99 & [92] \\  & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{_Time-space_} & \multirow{2}{*}{_Pyubyik, 855\({}^{\circ}\)C, 15-3\({}^{\circ}\)N_} & - & - & - & - & - & - & - & - & - & - & - & 96 & [90] \\ \end{tabular}
\end{table} 
Table 4: Performance of different biochar filters used in the literature to remove pollutants from wastewater.