esa-sceva/satcom-chunk-collection huggingface dataset:
# Siberian Pine and Larch Response to Warming-Drying Climate in the Southern Boundary of Their Range
###### Abstract  
Trees' growth and areal responses to changing climate are primarily expected within the edges of the species range. Here, we compared the responses of Siberian pine (_Pinus sibirica_ [PERSON]), a moisture-sensitive species, and drought-resistant larch (_Larix sibirica_ Ledeb.) at the southern part of their ranges in the Siberian Mountains (the Tannu-Ola Ridge). We study the species' growth and proportion in the forests from forest-steppe to treeline ecotone along the elevation gradient. These studies are based on radial growth index (GI) analysis and GI dependence on the climate variables. We used satellite time series to detect the land cover changes (areas of larch and Siberian pine, as well as shrubs and birch). We compared trees' GI before and after warming \"restart\" in the late 1990s. Generally, GI dependence on the air temperature was negative at elevations below c. 1600 m a.s.l, whereas GI dependence on the moisture variables (precipitation, vapor pressure deficit, and soil moisture) was positive for both species. Above 1600 m, increasing air temperatures stimulated species growth, whereas the influence of moisture variables was negative (for larch) or neutral (for Siberian pine). After the warming restart, the GI of both conifers increased in moisture-sufficient high elevations and treeline ecotone, whereas within low elevations (<1300 m), the GI was stagnant or suppressed. Both species', especially Siberian pine, negative growth dependence on air temperature and positive dependence on the moisture variables strongly increased since the warming restart. We found a risen growth dependence of both species on the soil-stored water during the previous year (September-October), which smoothed moisture stress at the beginning of the growing season. Yet both species' growth also suffered as a result of early spring warms. We found that larch is migrating in both uphill and downhill directions, while Siberian pine is migrating uphill only. Forests loss occurred at low elevations (<1300 m), whereas forest and shrub gain occurred at high (>2000 m) ones. The upper boundary of the forests and shrubs is migrating uphill at rates of about 0.8 and 0.3 m/y, respectively. We observed a decrease in Siberian pine proportion in the forests, whereas areas of larch and birch strongly increased (by 150% and 100%, respectively), which indicates the retreat of Siberian pine from its southern habitat. We suggested afforestation of the areas of Siberian pine mortality by the drought-tolerant larch species.  
larch growth; Siberian pine growth; mountain taiga; treeline; water stress; ranges shift; timberlinecolor=black]0.075
## 3 Results and Discussion
### Antioxidant Capability  
The results are expressed as an antioxidant capability percentage. In general, strains from pork fermented sausages presented higher values than those isolated from game meat products, as observed in Figure 3.  
Figure 3: Antioxidant capability (%) of yeast strains isolated from pork meat (**A**) and game meat (**B**) products, according to the DPPH assay.  
The percentages varied between 4% (GE112) and 79% (PA3, PB72, and PB84). Strains from curing rooms A and B showed a similar behavior, except for C. _zeylanoides_ PA45 and _D. hansenii_ PB86, which presented the lowest antioxidant activities (14% and 36%, respectively). Nevertheless, the strains from game meat did not surpass a 20% antioxidant capacity, except H. valbyensis GE115, with a value of 78%.  
Different activities were observed in strains belonging to the same species such as _D. hansenii_ and _H. valbyensis_, which indicated that the antioxidant activity is strain dependent, according to [PERSON] and [PERSON] (2018).  
This capability has not been studied previously in some of the tested species. Nevertheless, certain W. anomalous strains have shown a good antioxidant activity (49-51%) and have proven to be effective in avoiding olive oil oxidation, as well as some Candida sp. but not as good as the data provided by the strains tested in this study (2018).  
Few studies have been carried out on the antioxidant activity of yeast cultures in fermented meat products. However, it is known that yeasts involved in the fermentation process have an increment on this activity after the fermentation process, as [PERSON] et al. (2018) documented in the wine yeast.
###### Abstract  
The m-method is a commonly used method to calculate the internal force and deformation of pile foundations under lateral loads. However, for squeezed branch piles, the increase in the load-bearing plate leads to changes in the pile section and the generation of a resistance bending moment under loading, which means the load-displacement relationship at the load-bearing plate will no longer satisfy the linear relationship. In this paper, a hyperbolic load transfer model is established to describe the nonlinear relationship between the soil resistance and lateral displacement at the branch of the pile, and the m-method is used for the straight section of the pile. Laboratory model tests are used to verify the correlation between theory and experimentation. The results show that the theory is consistent with the measured curve. On the basis of the theoretical calculation, the influence of the bearing plate and pile body parameters on the force of the squeezed branch pile is analyzed. The research shows that that the bearing capacity of the squeezed branch pile is improved by increasing the plate's diameter, placing the plate closer to the ground, and ensuring that the pile top is embedded. The theoretical calculation method established in this paper can correctly and accurately reflect the bearing capacity characteristics of squeezed branch piles under horizontal loads, and it is more safe than performing measurements. Additionally, it can be applied to squeezed branch piles with different plate diameters, plate positions, plate section forms, and plate quantities as well as for piles with different boundary conditions and soil conditions. Moreover, it can also be applied to other pile shapes. This method is of significance for the analysis of the bearing characteristics of piles with variable sections under lateral loads.  
squeezed branch pile; horizontal load; theoretical analysis; influencing factors Article
## 3 Results and Discussion
### Digestion of Reference Materials and Procedural Blank  
Because a natural reference material for low-content metal sulfides for Re-Os dating is unavailable, we used the molybden references GBW 04435 (JDC) and GBW 04436 (HLP). The references were mixed with 3 g of pyrite for monitoring the low-content metal sulfides of the reference (Table 1). The uncertainties of the ages for both reference material samples (Table 2) included the spike calibration uncertainties for both Re and Os [30], the weighing errors for spikes and samples, the ICP-MS measurement errors, the errors caused by mass bias, as well as the 1.02% uncertainty in the decay constant \(1.666\times 10^{-10}\) a\({}^{-1}\) for \({}^{187}\)Re [2]. The total uncertainty at the 95% confidence level was calculated using the error propagation formula. The uncertainties for the weighted mean ages were calculated using ISOPLOT [31]. The results for JDC and HLP are in fairly good agreement with the certified values [32], which demonstrate that the proposed method is a reliable approach for determining Re and Os in metal sulfides. To keep a low procedural blank, all components in the setup of in situ distillation and Carius tube were only used once. This procedure was implemented to decrease the Os blank signal and avoid Os memory effect and cross contamination. The total procedural blanks are shown in Table 6. The blank values of Os were 1.0 pg for common Os and 0.1 pg for \({}^{187}\)Os while the blank value of Re was 4.4 pg.  
Figure 2: Os (**A**) and Re (**B**) signal for 3 g of pyrite with different volumes of digestion medium obtained using the HClO4-inverse aqua regia method.
## 1 Introduction
However, limitations lie in the current literature as time-frames of only up to 5 min prior to the onset of SCA have been thoroughly investigated, which, when applied to the clinical setting, does not provide enough time for a patient to respond with sufficient time to reach the hospital. The overall survival rate decreases by 10% each minute the patient remains in VF, thus, high importance is placed on detection as early identification and management is critical. Since the early detection of SCA is highly important yet not well established, the aim of this study is to, first, statistically compare the importance of various ECG features, such as heart rate and R-R interval related markers, between normal and patients at risk of SCA and, secondly, and most importantly, to determine the ideal time-frame spanning up to three hours prior to the onset of VF in which electrocardiographic changes alluding to SCA can be detected and accurately classified.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology
### Power Metric  
The power parameter used for narrowband transmission is the power measured by the SA. For 5G transmission, the SS-RSRP parameter measured by the SA is used as the power parameter. For LoRa and Zigbee transmissions, the RSSI measured by the transceiver modules is used. In order to make the ranges of each parameter comparable, a calibration will be made for each one, which will be explained in later sections.
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
## 4 Results  
As already mentioned, one of the main characteristics that an IoT device must have is low consumption. Specifically, in the agriculture sector, it is essential to design a very low consumption device with which a great autonomy is achieved [37]. In this way, the farmer does not have to worry about the maintenance of the device during the months of maximum work, being able to devote his time to other tasks. Given the importance of a high autonomy of the device in agriculture, in this study we focused on quantifying the consumption of our BoXmote, performing a consumption test in the laboratory. Then, from the data obtained in the said test, the life of the battery in a real crop was estimated. Finally, a comparison of the results obtained with other existing devices was made.  
For the experiments, we followed the methodology used in [1; 6; 13; 38]. The node used to perform the experiment consisted of a Moteino microcontroller and a LoRa module with the RN2483 chip. To measure volumetric water content, soil temperature, and bulk electrical conductivity data, it was decided to use ECHOH2O 5 TE and ECHOH2O 10 HS sensors, which are widely used in agriculture. As regards the battery, a lithium battery with a nominal voltage of 3.7 V and a capacity of 2000 mAh was available.  
To perform the experiment, the node was configured to take measurements every 93 s. It was decided to perform the test with such a short interval between measurements in order to decrease the total duration of the experiment. In order to save the battery as much as possible in the intervals in which the node is not working, the different components go to sleep mode, drastically lowering the consumption and preserving even more the battery charge.  
The node was located in the laboratory at a temperature of approximately 20 \({}^{\circ}\)C with the meter sensors in the air, and at an approximate distance between the node and the Gateway of 600 m.  
In order to correctly evaluate the consumption of the device, its process was divided into three states:  
* Active mode: in this mode, the node configures the microcontroller and initializes the RN2483 chip. Once these actions have been carried out, it proceeds to read the data provided by the sensors and performs the subsequent processing of the information.
* Send mode: this mode of operation is the one that consumes the most energy, since it is the moment in which the information is sent to the nearest gateway.
* \"Sleep\" mode: Unlike in the previous case, this mode saves the maximum battery charge as it is not necessary to take or transmit measurements. To do this, both the microcontroller and the RN2483 chip \"fall asleep.\"  
Figure 3: Flow chart of the node sensors.  
Figure 4 shows the BoXmote with which the battery consumption test was carried out. As previously mentioned, the 5 TE and 10 HS sensors were incorporated, since they are currently the most widely used in agriculture.
## 3 Iterative Learning Observer-Based Thruster Fault Reconstruction Approach  
### Design of the Iterative Learning Observer  
For reconstructing the thruster bias faults, the new continuous-time ILO was constructed for (3) in the following form,  
\[\left\{\begin{array}{l}\dot{\mathbf{q}}(t)=\mathbf{A}\hat{\mathbf{q}}(t)+\mathbf{B}\mathbf{u}(t)+ \mathbf{H}\hat{\mathbf{F}}(t)+\mathbf{L}_{1}(\mathbf{y}(t)-\hat{\mathbf{y}}(t))+\gamma\text{sgn}( \mathbf{y}(t)-\hat{\mathbf{y}}(t))\\ \dot{\mathbf{y}}(t)=\mathbf{C}\hat{\mathbf{q}}(t)\\ \dot{\mathbf{F}}(t)=\mathbf{Q}_{1}\ddot{\mathbf{F}}(t-\tau)+Q_{2}\mathbf{L}_{2}(\mathbf{y}(t)-\hat {\mathbf{y}}(t))\end{array}\right. \tag{5}\]  
where \(\hat{\mathbf{q}}\in\mathbb{R}^{6}\) and \(\hat{\mathbf{y}}\in\mathbb{R}^{6}\) denote the estimated state vector and measurement output vector, respectively. Variable \(\hat{\mathbf{F}}(t)\) denotes the reconstructed thruster bias fault, which was updated by both its previous information at \(t-\tau\) and current output estimation error, where \(\tau\) was the learning interval. It is worth noting that the learning interval \(\tau\) could be taken as the sampling interval, or as an integer multiple of the sampling interval in sampled-data control systems. The diagonal matrix \(\mathbf{Q}_{1}=\text{diag}\{\sigma_{1}\), \(\sigma_{2}\), \(\sigma_{3}\}\) with \(\sigma_{i}\in(0,1]\), \(\mathbf{L}_{1}\), and \(\mathbf{L}_{2}\) are gain matrices with appropriate dimensions that are determined later. \(Q_{2}\) is a positive constant.  
Denote state estimation error, output estimation error, and bias fault reconstruction error as \(\mathbf{e_{q}}=\mathbf{q}-\hat{\mathbf{q}}\), \(\mathbf{e_{y}}=\mathbf{y}-\hat{\mathbf{y}}\), and \(\mathbf{e_{F}}=\mathbf{F}(t)-\ddot{\mathbf{F}}(t)\), respectively. Therefore, it followed that  
\[\dot{\mathbf{e_{q}}}(t)=(\mathbf{A}-\mathbf{L}_{1}\mathbf{C})\mathbf{e_{q}}+\mathbf{H}\mathbf{e_{F}}+\mathbf{E} \mathbf{d}(t)-\gamma\text{Csgn}(\mathbf{e_{q}}) \tag{6}\]  
\[\mathbf{e_{y}}(t)=\mathbf{C}\mathbf{e_{q}}(t) \tag{7}\]
# Deep Learning-Based Portable Image Analysis System for Real-Time Detection of _Vespa velutina_
## 5 Conclusions  
A system was developed to minimize the damage to honeybees that inhabit beehives by sending notifications to the user when _V. velutina_ are detected in beekeeping farms. For _V. velutina_ detection, we trained YOLOv5s using a training set of 1960 images and evaluated its detection performance using a test set of 396 images. The detection performance was evaluated in terms of accuracy, precision, recall, F1 score, and mAP_0.5, which were found to be 92.3%, 98.0%, 94.0%, 0.959, and 0.959, respectively, at a confidence threshold of \(\geq\)0.6 and IoU of \(\geq\)0.5. Additionally, we conducted distance-based experiments to set the optimal detection range and confirmed that a high detection rate could be achieved at a wider range, showing a detection accuracy of 83.3% when monitoring three beehives. Furthermore, when _V. velutina_ were detected twice in a row, the system successfully transmitted the image data to the server, enabling immediate notifications to be sent to the user.  
The proposed system sends alarms to the user when _V. velutina_ appears in an apiary site, allowing them to monitor the site in real time, and it also enables immediate response to unexpected weather conditions and events. In addition, the object tracking function can be added by increasing the detection frequency and accuracy through the augmentation of training data, and better image analysis devices can be used to improve the system.  
In Korea, the damage caused by _V. velutina_ and the expenses for their control amount to over 137 million dollars annually [34]. If this system is commercialized and widely adopted in beekeeping farms, it is expected to contribute to minimizing damage to honeybees and help reduce the scale of damage caused by _V. velutina_, leading to cost-saving effects as well.  
Conceptualization, I.C. and K.-C.K.; data curation, M.-S.J. and Y.J.; formal analysis I.C., J.L., C.-W.L., M.-S.J. and Y.J.; investigation, I.C., S.-H.Y., S.-b.K., D.K., K.-C.K., M.-S.J. and Y.J.; Methodology, I.C., J.L., K.-C.K., S.L. and C.-W.L.; resources, S.-H.Y., S.-b.K., D.K. and S.L.; software, M.-S.J. and Y.J.; supervision, I.C.; validation, I.C., J.L., M.-S.J. and Y.J.; writing--original draft preparation, I.C., M.-S.J. and Y.J.; writing--review and editing, I.C., M.-S.J. and Y.J. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was carried out with the support of the Research Program for Agricultural Sciences (PJ01497002), National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Quantitative Real Time-Polymerase Chain Reaction  
Cells were disrupted by QIAzol lysis reagent (Qiagen, Hilden, Germany) and total RNA was extracted via RNeasy plus kit (Qiagen) according to the manufacturer's protocol. The quality and quantity of isolated RNA samples were analyzed on 1% agarose gels and using a Nanodrop spectrophotometer (Thermo Fisher Scientific), respectively. Possible genomic DNA contaminations were removed using the DNA-free(tm) DNA Removal Kit (Ambion, Life Technologies, Carlsbad, CA, USA). DNase-treated RNA was transcribed into complementary DNA (cDNA) using the ImProm-II(tm) reverse transcription system (Promega, Madison, WI, USA). Quantitative real-time reverse transcriptase chain reaction (qRT-PCR) was performed using SYBR Green reagent (Life Technologies). Primer sequences are listed in Supplementary Table S3. The 2\({}^{-\Delta\text{Ct}}\) method was employed for estimating the relative mRNA expression levels. _Hprt1_ was used as a housekeeping gene.
# PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning
## 2 Background
### Features and Feature Engineering as Analogues to the Scientific Method  
A feature is a number or collection of numerical values that describes some direct or processed experimental observable. Any form of data can be converted into a feature if it can be assigned a numerical value. For example, the crystal structure of a protein consists of a set of datapoints (atoms) with several attributes: Cartesian position \((x,y,z)\), element (N, C, O, H), atom type (C\({}_{\text{alpha}}\), C=O), B-factor, and more. Each of these attributes is a feature of that molecule and the experimental conditions under which the data were collected. At first glance, attributes such as element and atom type are categorical non-numeric values, but they can be converted into numbers using several strategies. These strategies might include using the isotopically averaged mass of the atom (14.007, 12.011, 15.999, and 1.008 au for each element, respectively) or using one-hot encoding (for example, 1000, 0100, 0010,0001 for each element, respectively). While the strategy one chooses to encode their data is often subjective, algorithmically the results based on the encoding chosen is objective, and thus one must think carefully about how their data can best be represented.  
Generic features can describe all types of objects, but only a subset of documented mathematical features have analogues in biomolecular representations. Furthermore, biomolecules have some features that other objects lack. For example, for an iris classification task, a certain flower might have the sepal length of 5.1 cm. Other objects, such as people, trees, or buildings, also have associated lengths. However, consider a particular protein-ligand pair that has the kinetic dissociation constant of \(K_{d}=0.14\) millimolar (Figure 2). Flowers, people, trees, and buildings do not have well-defined notions of kinetic dissociation constants because they are not molecules and cannot be characterized in this manner.  
However, just because a feature exists does not mean it is useful for a task of interest. Indeed, scientists routinely evaluate what the features they derive from their data suggest about the systems they study, which has led to the calculation of feature importance [28] and guidance for feature selection [29; 30]. Often new features (like new hypotheses) are needed to explain a biochemical phenotype, so they are developed through feature engineering. In this process, features are equivalent to hypotheses of relevance.
## 3 Results and Discussion
### Optical Characterization
However, an important reduction in light transmission for the whole wavelength range can be observed in Figure 10b for the Sf-NPAM/TiO\({}_{2}\)(f) sample when compared with the Sf-NPAM/TiO\({}_{2}\) sample; in particular, a reduction of around 18.0% at 600 nm and of 7.0% in the NIR (at 1600 nm) were obtained. This reduction in transmission through the fouled membrane could be an indication of the presence of a BSA layer on the membrane surface, which would modify the high transparency of membrane material indicated above; this fact would be in agreement with membrane potential analysis previously performed. Consequently, light transmission measurements for membranes made of highly transparent materials could be used as a way to determine fouling mechanisms by comparing spectra for clean and fouled samples. Figure 9: Light transmission spectra for (**a**) Sf-NPAM/Al\({}_{2}\)O\({}_{3}\) (solid gray line), Sf-NPAM+Fe\({}_{2}\)O\({}_{3}\) (solid red line), and Ox-NPAM/SiO2 (solid blue line) samples; (**b**) Sf-NPAM/TiO\({}_{2}\) (solid black line) and Ox-NPAM/TiO\({}_{2}\) (dashed black line) samples. Reflection spectra (R%) for clean and fouled membranes were also measured, and the corresponding absorption curve (A%) was obtained: A (%) = 100 \(-\) T (%) \(-\) R (%). Figure 11 shows a comparison of wavelength dependence of absorption spectra for both BSA fouled samples; for comparison, absorption spectra for both clean samples are shown in the inlet (for wavelengths between 500 nm and 2000 nm). Differences between both fouled samples can be observed in the visible region, with a significant adsorption reduction, but more closely similar and constant values were obtained for both clean samples.