esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Heat and Drought on Gross Primary Productivity in China
## 2 Study Region and Data
### Data  
Three main types of data were utilized in this study. First, monthly potential evapotranspiration, near-surface average daily maximum and mean temperature, and precipitation data from 1979 to 2017 were derived from the University of East Anglia Climatic Research Unit (CRU) Time-Series (TS) version 4.04 dataset ([[https://catalog.ceda.ac.uk/uuid/89e1e34](https://catalog.ceda.ac.uk/uuid/89e1e34) ec3554 dc98594a5732622 bce9]([https://catalog.ceda.ac.uk/uuid/89e1e34](https://catalog.ceda.ac.uk/uuid/89e1e34) ec3554 dc98594a5732622 bce9)) with a 0.5\({}^{\circ}\) resolution [56]. To be consistent with the resolution of the GPP dataset, the nearest neighbor method was used to resample the climate data from a resolution of 0.5\({}^{\circ}\) to a resolution of 0.05\({}^{\circ}\). CRU TS data have been widely used to explore the possible impacts of climate change in different fields of study. Many studies have also verified the accuracy of the CRU TS dataset, including studies on potential evapotranspiration [57], precipitation [57; 58; 59] and temperature [58], and proved the efficiency of the dataset in climate change studies and extreme climate event analyses. Second, GPP data were obtained from the National Science and Technology Basic Conditions Platform-National Earth System Science Data Sharing Service Platform ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn))), which provides GPP data with a resolution of 0.05\({}^{\circ}\) from 1982 to 2017 on a time scale of 8 d. This data set was estimated by the EC-LUEC-LUE (Eddy Covariance Light Use Efficiency) model developed by [PERSON] et al. [60], and the results of a verification of the dataset showed that its simulation capability exceeded that of the MODIS (Moderate Resolution Imaging Spectroradiometer) GPP product. MODIS GPP was produced by National Aeronautics and Space Administration (NASA), Washington, DC, USA. Third, China's vegetation zoning vector boundary data were obtained from the 1:1 million vegetation coverage map of China from the Data Sharing Center of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/))). The zoning data reflect the detailed regional distribution and zonal differentiation of vegetation in 36 subareas of the 8 vegetation regions of China.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 3 Sensor Node Design
### Sensor Unit  
The sensor unit can detect and respond to different inputs from the environment. These specific inputs can be light, heat, pressure, acceleration, etc. The input signal is generally an analog signal that is converted to digital form with an Analog to Digital Converter (ADC). The ADC unit is integrated into the sensor unit in this study. Both sensing and conversion processes take time and consume power [11].
# _In Silico_ Development of SARS-CoV-2 Non-Covalent Mpro Inhibitors: A Review
## 2 Mpro Inhibitors for SARS-CoV-2
### Discovery Strategies for Non-Covalent Inhibitors of Mpro
In natural products, compounds like baicalein and selenoquercetin illustrate that modest starting activity can be significantly improved through targeted derivatization, for instance, introducing selenium to enhance hydrogen bonding with Gln189. Together, these examples highlight the power of _in silico_ approaches in identifying effective inhibitors of SARS-CoV-2 Mpro, utilising both natural and synthetic compounds. \begin{table} \begin{tabular}{c c c c} \hline \hline
**Inhibitor** & **Structure** & **Strategies for the Discovery of Non-Covalent** & **Mpro IC\({}_{50}\) (\(\mu\)m)** \\  & & **Mpro Inhibitors** & \\ \hline Shikonin & & High-throughput screening. & 0.397 \\ \hline Quercetin & & & 7.4 \\ \hline Selenoquercetin analogues compound 2d & & & 192 \\ \hline Baicalein & & & \\ \hline Calycosin-7-O-\(\beta\)-glucopyranoside & & & \\ \hline \hline \end{tabular} 
\begin{tabular}{c c} \hline \hline  
Computational tools play a crucial role in antiviral drug development by streamlining hit identification and optimisation. Ultimately, bridging the gap between the discovery and clinical application of non-covalent Mpro inhibitors relies on ongoing collaboration among computational modelling, rational drug design, and biological validation.
## 3 Results and Discussion
### Effects of Abiotic Variables (P. regina)
The following trends are clear:  
* Pupae reared on chicken liver have lipid profiles with higher quantities of palimitic acid, palimitoleic acid, unknown alkane B, and cholesterol. * Pupae reared on ground beef have lipid profiles with higher quantities of lauric acid, myristic acid, palmitelaidic acid, linoleic acid, and stearic acid. * All other compounds appear in greatest abundance in feeder rats. Figure 5: Discriminant analysis of _P. regina_ pupae reared on various diets. The most meaningful takeaway from this series of experiments is that, which the exception of diet, changes in any given abiotic factors did result in reliably differentiable classes for _P. regina_. However, each factor had a larger capacity to obfuscate accurate species determinations when comparing to a population of _P. regina_ reared under normal conditions. When each chemical profile under every single differing abiotic condition was validated using discriminant analysis with the four species (G10) as the training set, most species classifications were less than 50% correct (Table 8). However, when comparing the control conditions (these conditions are identical to the G10 conditions) with expected classification accuracies of >82% (see Table 5), only 37.7% classification accuracy was recovered. This is indicative that conditions changed between the G10 rearing conditions and that of the abiotic factor experiments conducted sometime later. Nevertheless, it should be noted that classification accuracy using discriminant analysis is only as good as the similarity between the training and validation data sets. In this case, the data sets differ, thus obscuring an accurate species identification. Our data indicate that diet has the greatest impact (with classification accuracies less than 2%), whereas temperature and pupation substrate appear to be most like the control profiles (Table 8). This is not surprising; given that pupae produce lipids in their cuticles for the protection against desiccation and predation, temperature and pupation substrate (our substrates were all dry) should not have much of an effect. However, humidity (different lipids/lipid ratios would be needed to protect the pupa), and diet (the lipids would be obtained in different ratios) would significantly influence the profiles.
## 3 Major Routes of Xylose Transport and Metabolism in Bacteria  
Xylose is a very common sugar in residual lignocellulosic biomass, being the second major sugar found in most lignocellulosic hydrolysates and the major sugar in hemicellulose hydrolysates. For that reason, xylose is a very promising carbon source, and it makes sense to understand the fundamentals of the mechanism used by bacteria to metabolize xylose into high-value by-products.  
This review focuses on the metabolism of xylose to xylitol and xylonic acid by native xylose-consuming bacteria. Other valuable compounds, such as 1,2,4-butanetriol, butane-diol, glutaric acid, \(\gamma\)-aminobutyric acid, 3,4-dihydroxybutyric acid, glycolic acid, ethylene glycol, and acetoin have also been produced from xylose using genetically engineered bacteria (see Section 3.2) [41]. Biodegradable polyesters such as polyhydroxyalkanoateates (PHAs) have also been synthesized from xylose by native xylose-consumers such as _Paraburkholderia_ sacchari (formerly classified as _Burkholderia sacchari_), _Pseudomonas pseudoflava_, and _Burkholderia cepacia_. In this case, PHA synthesis is promoted by manipulating culture conditions, i.e., by limiting the concentration of N or P nutrients and by supplying excess carbon [17; 42]. Xylitol and xylonic acid have been ranked within the top 30 high-value chemicals from biomass by the U.S. Department of Energy [43]. In spite of its biotechnological importance, xylose metabolic pathways are far less studied than the ones of other fermentable sugars, namely glucose. This section aims to present a review of the major pathways involved in xylose transport and metabolism in bacteria and its conversion into xylitol and xylonic acid. Additionally, the main metabolic engineering efforts to increase titers and productivity of these metabolites using genetically modified bacteria are described.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 5 MA with Code Separation
### Implementing Belch Sequences in NOMA Models
As illustrated in Table 1.  
Optimization of the chord distance \(d_{cord}\) makes it possible to obtain a level of mutual correlation between several code sequences close to zero, while the optimization task is to pack the subspace \(G\)[29]. The x-Nordic distance between two sequences is determined by the formula  
\[d_{cord}=min_{G}\bigg{(}max_{1\leq i\leq j\leq k}\sqrt{1-\big{|}S_{i}^{\prime} \cdot S_{j}\big{|}^{2}}\bigg{)} \tag{4}\]  
Table 2 explained various changes of the Gram \(|\)S'S \(|\) that demonstrate the correlation features of WBE-sequences for the WSMA system. For a system with \(K=4\) active subscribers per one orthogonal resource, \(S\) sets of these sequences were constructed by optimizing different indicators for the situation \(K=\) L with L-length WBE-sequences (100% utilization). In this way, the following structure of Gram determinants is obtained for various values of the optimal parameters of TSC and \(d_{cord}\):
# A Review of Femtosecond Laser-Induced Emission Techniques for Combustion and Flow Field Diagnostics
## 6 Conclusions  
In summary, the development and application of a number of fs laser-based techniques for combustion and flow field diagnostics are reviewed. These novel techniques include fs two-photon laser-induced fluorescence (fs-TPLIF), fs laser-induced breakdown spectroscopy (fs-LIBS), fs laser electronic excitation tagging (FLEET), filament-induced nonlinear spectroscopy (FINS) and fs laser-induced plasma spectroscopy (FLIPS). In spite of their relatively short history, significant impacts of these techniques have been demonstrated in measuring velocity, temperature and intermediates in combustion and flow field.  
However, there is still plenty of space for further development of these techniques, as all the techniques mentioned above have the temporal-resolved measurement capability, and the time resolution is in the range of nanosecond to sub-microsecond level. The fundamental mechanisms governing the chemical and physical processes introduced by the ultra-short laser pulse and ultra-high peak power are still to be investigated to form a knowledge-based understanding of the interaction between the fs laser and the combustion flow field and the mechanisms of filamentary electric discharge guided by fs lasers. It is predicted that the fs laser-based technologies will play an important role in combustion and flow field diagnostics in the near future.  
Conceptualization, Z.L.; Resources, D.Z., J.L. and Y.T.; Data curation, D.Z.; Writing-original draft preparation, B.L., D.Z. and Z.L.; Writing-review and editing, Z.L., B.L. and Q.G.; Supervision, Q.G. and Z.L.  
Figure 12: Emission spectra from the filamentary column of different plasmas collected with spectrometer slit, vertically orientated perpendicular to the thin plasma columns. (**a**) Imaging spectra of FALED with a resistance of 0 k\(\Omega\); (**b**) Imaging spectra of FALED along the jet flow with a resistance of 1 k\(\Omega\); (**c**) Spectral curve of emission from the filamentary plasma with the resistances of 0 k\(\Omega\) (black line) and 1 k\(\Omega\) (red line).  
**Funding:** This work was financially supported by the National Natural Science Foundation of China (NSFC) (91741205, 51776137).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Radio link budgets  
XBEE XSC PRO  
Transmit power 20 dBm.  
Receiver sensitivity \(-\)106 dBm.  
Spread spectrum FHSS.  
Outdoor/line-of-sight range 6 mi.  
Urban range 1200 ft.  
SIM5320A  
Receive sensitivity \(-\)106 dBm.  
Transmission power 33 dBm.  
The weather station will be an important part of the forecasting model in the future, adding the variables it measures to be able to predict floods with more time.  
It is intended to study the variables and compare them with the historical data of past hurricanes that have caused the overflow of rivers in the city. They are presented as part of the early warning system solution so that it is fully known.  
This node (diagram shown in Figure 3) integrates a series of electronic components that the ATMOS 41 (all-in-one weather station [20]) weather station employs. Its high-performance, low-power Microchip 8-bit AVR RISC-based microcontroller handles the data of the 12 weather sensors included on ATMOS 41. The data are packed and sent to the platform via a wired internet connection. They also get stored with a timestamp on a Micro SD card as a backup.  
Figure 2: RiverCore hardware block diagram.
## 4 Spatial and Temporal Variation Characteristics of TEC in the Polar Regions
### Case Study of Tracking Ionization Patches  
At present, the SuperDARN is often used to track ionization patches [30], but the radar network is sparse and cannot completely cover the polar regions in spatial domain. In addition, based on the SCH model, the evolution of ionization patches in the Arctic has been tracked [12]. In this section, a high-precision regional ionospheric model based on GNSS tracking stations was used to track such patches in the Antarctic during a geomagnetic storm, which provided a significant basis for further automatic identification and tracking of these kind of phenomena.  
The time series of the disturbance storm time (Dst) index is shown in Figure 16. The Dst varied within 0-50 nT during DOY 075, and it dropped down to \(-225\) nT during DOY 076. A geomagnetic storm occurred on DOY 076 (corresponding to Mar 17) in 2015 and it was accompanied by ionization patches.  
Figure 14: The time series of the C0,0 coefficient with 2 h resolution in March, June, September, and December under low (**top**) and high (**bottom**) solar activity year in the Arctic.  
Figure 15: The time series of the C0,0 coefficient with 2 h resolution in March, June, September, and December under low (**top**) and high (**bottom**) solar activity year in the Antarctic.  
Figure 17 shows the variation of the Antarctic ionospheric TEC based on the SCH model at UTC from 19:30 to 22:00 during DOY 075 in 2015. The initial condition of the TEC distribution was exhibited at 19:30. At 20:00, a local TEC enhancement (ringed in black) was observed, which appeared in the west close to the geographical South Pole near noon, indicating the formation of an ionization patch. From 20:30 to 21:30, the ionization patch was constantly growing and moving toward low latitude. At 22:00, the ionization patch continued to move toward low latitude. Figure 17 clearly reveals the formation and evolution of an ionization patch in the Antarctic, indicating that the ionospheric model based on the GNSS data is an effective method for monitoring the ionization patches.  
Figure 16: Time series of disturbance storm time (Dst) index on days of year (DOYs) 075-077 in 2015.  
Figure 17: Antarctic ionospheric TEC mapping at UTC (Universal Time Coordinated) from 19:30 to 22:00 on the DOY 076 in 2015. The black ellipses in each subplot highlight the ionization patches.
## 3 Inversion Experiments and Results
### Inversion of Survey Data
Each survey point contained 45 channels of electromagnetic response data after preprocessing. As with the survey data extracted from the Leach Lake Basin, we needed to make simple adjustments to the input and output vector dimensions of the CNN structure, as well as the number of convolutional layers of the two-part convolution, to accommodate for the inversion of the second survey data. After the adjustment, the input of the network structure was a one-dimensional vector with a length value of 46 (46 \(\times\) 1), and the output was a one-dimensional vector with a length value of 30 (30 \(\times\) 1). Due to the long sampling time of the secondary field of the VTEM(tm) system, it can reflect the medium properties in the deeper subsurface. Therefore, when constructing the geoelectric model for the training samples, we reset the maximum depth of the model to 450 m and kept other parameters unchanged before re-interpolating the 1 million geoelectric models used during the synthetic data experiments to form new geoelectric models. Then, the VTEM(tm) system parameters were used for forward computation to obtain new training sample data. The ATEM survey of Gregory in Queensland, Australia, led to the collection of a total of 1646 km of ATEM data, and we selected the data of Flight Line 5060, with 8922 survey points from 7960 km to 7980 km in northing for the CNN inversion, as shown in Figure 7. For comparative analysis, we still performed regularized inversion on these data, and the results of the two inversion methods are shown in Figure 8. According to the reference information provided by [PERSON] [30], the topography of the selected data area is relatively gentle, and the subsurface medium exhibits a layered structure, which is consistent with the two inversion results in Figure 8. The inversion results in Figure 8 show that the  
Figure 6: The inversion results of Flight Line 10,430. (**a**) CNN inversion results; (**b**) regularization inversion results. two inversion results are consistent, and both present a three-layer medium structure; the middle layer is a low-resistance layer. The thickness of the middle layer obtained via CNN inversion is smaller than that of regularized inversion, and the demarcation between the layers is clearer, indicating that the CNN inversion is also effective for the simple layered structure in the survey data. In addition, the conductivity prediction of CNN inversion for these 8922 survey points only took 29 s, which still indicates a very big advantage in time efficiency. Based on the above analysis, the CNN inversion strategy proposed in this paper is effective for the inversion of ATEM survey data. By simply adjusting the input and output of the CNN structure, as well as the number of convolutional layers, this CNN inversion method can be applied to ATEM data measured by different observation systems. In addition, this CNN inversion method can obtain better inversion results for both simple and complex stratigraphic structures, and the computation time required is much shorter than that for the conventional optimal inversion method.
## 5 Findings and Analysis  
This portion emphasizes a comparative evaluation of the examined hybrid systolic multiplier in relation to various notable systolic and semi-systolic multiplier configurations from existing research [19; 20; 43; 44; 50]. The evaluation is structured into two portions to provide a comprehensive understanding of the proposed design's performance. The first portion explores the resource consumption and execution time of the proposed framework in relation to competing designs, highlighting the qualitative aspects. Through an in-depth analysis of these factors, we seek to offer valuable observations regarding resource consumption and performance speed, which are critical factors in the design of efficient computational systems.  
In the following section, we will validate our complexity assessment through practical implementation. By executing the proposed design in a real-world environment, we can evaluate its actual efficiency and contrast it with the anticipated features derived from our theoretical analysis, highlighting the quantitative aspects. This execution will ensure our analysis accurately represents the function of the multiplier in practical scenarios, allowing us to identify any discrepancies between expected and observed outcomes. Moreover, the results from this implementation will serve to inform future enhancements to the design, potentially leading to improved efficiency and effectiveness in IoT applications tailored for individuals with disabilities.
## 2 Materials and Methods
### Pig Behavior Monitoring
#### 2.3.2 Sensor Activity Detection  
Sow and piglet activities within each pen were monitored using an SRN-2000 PIR Detector (Visonic Inc., Bloomfield, CT, USA). The sensor has a maximum detection coverage of 18 m (L) \(\times\) 18 m (W) at a 90\({}^{\circ}\) horizontal angle. It is the only commercially available PID sensor that provides analog output signals, which range from 0 to 4 VDC with an offset at 2 VDC. The sensor analog output signals were acquired and processed by the OSCS [17].  
The sensor lens consists of 36 sub-lenses or beams and covers a 90\({}^{\circ}\) wide angle. To restrict the sensor detection range (length and width) to the dimension of a single pen of 2.6 m (L) \(\times\) 1.5 m (W), the sensor was strategically mounted on the ceiling (2.2 m above the floor) at the sow's tail-end in each pen and tilted downward at 50\({}^{\circ}\). This setup constrained the sensor detection length to 2.6 m (Figure 2).  
\begin{table}
\begin{tabular}{c c c} \hline
**Behavior** & **Sow * & **Piglets ** \\ \hline Eating & Head in the feeder and munching & NA \\ Standing & Erect on all four limbs (drinking) & NA \\ Drinking & Actively gulping water with the mouth through the water nozzle & NA \\ Siting & Dog-sitting position with the hind limbs lateral to the floor (nursing) & NA \\ Lying & Flat on the floor, either on the side or the abdomen (nursing) & NA \\ Nursing & All-to-most piglets on the teats, actively suckling (sleeping) & \(\geq\)1 suckling \\ Sleeping & Lying with the eyes closed (lying or nursing) & \(\geq\)1 lying with the eyes closed \\ Walking & NA & \(\geq\)1 moving on all four limbs \\ \hline \end{tabular} Note: * Parentheses in the sow columns show possible simultaneous behaviors recorded during the same observation for sows. ** NA = Not applicable. Observation for piglets could involve the recording of any combination of the three behaviors.  
\end{table}
Table 1: Ethogram of behavioral observation for sows and piglets.  
Figure 2: Sensor mounted on the ceiling at a 50\({}^{\circ}\) angle downward facing the single pen (**left**). Inside view of the sensor front cover (**middle**). Outside view of the sensor front cover (**right**).  
The mounting angle was calculated based on the specifications in the sensor manual. To further refine the detection width to match the pen width of 1.5 m, the sensor lens was partially obscured with black tape, leaving only a 0.6 cm wide center sub-lens exposed. This configuration was tested in a laboratory setting to ensure accuracy in matching the pen dimensions.