esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### \(Co_{2}\) Variability  
The average maximum and minimum CO\({}_{2}\) concentrations outside the greenhouses were 504 ppm (10:00 h) and 387 ppm (7:00 h), respectively. The average maximum and minimum CO\({}_{2}\) concentration inside the greenhouse without crops was 710 ppm (09:00 h) and 510 ppm (07:00 h) in the top layer, respectively. The average maximum and minimum CO\({}_{2}\) concentrations inside the greenhouse with crops were 959 ppm (01:00 h) and 508 ppm (07:00 h) in the middle and top layers, respectively. Plants require CO\({}_{2}\) for photosynthesis. The optimal level of CO\({}_{2}\) varies depending on the plant and light conditions in the CSG. When the light intensity decreases in the greenhouse, photosynthesis and CO\({}_{2}\) consumption decrease. Figure 11 illustrates the daily variation in the CO\({}_{2}\) concentration at various layers within and outside of both greenhouses. The CSG with crops (Figure 11a) demonstrated a considerable daily fluctuation in CO\({}_{2}\) levels. The cotton blanket was closed throughout the night, and the CO\({}_{2}\) content grew monotonically as a result of plant respiration, which slowed down with time and reached 700 to 959 ppm. As soon as the cotton blanket was opened, crop photosynthesis caused the CO\({}_{2}\) levels to decrease significantly to 500-600 ppm. Light intensity was in opposition to the trend, which showed a significant connection between the two variables. A similar pattern was observed by [PERSON] et al. [44] in tomato plants and by [PERSON] et al. [45] in their study. In the CSG without crops (Figure 11b), there were incredibly small fluctuations throughout the day.  
Figure 11: The ambient CO\({}_{2}\) concentrations inside the solar greenhouses: (**a**) with crops and (**b**) without crops.  
CO\({}_{2}\) levels in ambient air are typically about 340 ppm by volume, and all plants grow well at this level [46]. However, when CO\({}_{2}\) levels are increased by 1000 ppm, photosynthesis rises correspondingly, resulting in an increase in available sugars and carbohydrates for plant development [47] and inducing a favorable effect on crop yield [48]. According to Figure 11, the CSG with crops demonstrated optimal growing conditions between 600 and 1000 ppm CO\({}_{2}\). The maximum CO\({}_{2}\) concentration was found at 2:00-3:00 h, and the minimum CO\({}_{2}\) concentration was found at 17:00-18:00 h in the CSG with crops.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 4 Nanocellulose-Based Composites  
Nanocellulose possesses strong reinforcing qualities and is frequently utilized to create composites. Composites are made up of two or more phases (matrix and reinforcing) that have been combined to form a single new entity. There is a defined interface between these phases, which differ in their physicochemical characteristics. If a biopolymer is employed in manufacturing, the composite is termed a biocomposite. In nanocellulose-based composites, polymer serves as the matrix phase and nanocellulose as the reinforcing phase. In contrast to the matrix phase, the reinforcement phase makes up a more significant proportion of the composite [83].  
Various composites, including sheets, paper, and film, have been produced differently. Interestingly, the inclusion of a modest percentage of nanocellulose contributes to a notable improvement in the properties of the composite due to the considerable surface area of the nanocellulose filler [84].
## 3 Results
### Construction of the Nitrogen Content Model Based on the Wavelet Coefficients  
The original spectral reflectance was converted into wavelet coefficients corresponding to 10 scales using CWT. Sensitivity analysis was conducted on the wavelet coefficient and nitrogen content of leaves at different leaf positions, and the correlation matrix diagrams were drawn. The results are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sot**} & \multirow{2}{*}{**Flag Leaf**} & \multicolumn{2}{c}{**Upper 1 Leaf**} & \multicolumn{2}{c}{**Upper 2 Leaf**} & \multicolumn{2}{c}{**Upper 3 Leaf**} & \multicolumn{2}{c}{**Upper 4 Leaf**} \\ \cline{3-8}  & & & \(R^{2}\) & **RMSE** & **NRMSE** & \(R^{2}\) & **RMSE** & **NRMSE** \\ \hline \multirow{4}{*}{**Flag leaf**} & PLSR & 0.68 **\({}^{**}\)** & 0.07 & 0.06 & 0.58 ** & 0.10 & 0.09 \\  & SVM & 0.51 ** & 0.08 & 0.16 & 0.58 ** & 0.12 & 0.22 \\  & RF & 0.49 ** & 009 & 0.07 & 0.45 ** & 0.12 & 0.10 \\  & MLR & 0.64 ** & 0.08 & 0.06 & 0.54 ** & 0.09 & 0.08 \\ \hline \multirow{4}{*}{Upper 1 leaf} & PLSR & 0.60 ** & 0.14 & 0.08 & 0.57 ** & 0.18 & 0.11 \\  & SVM & 0.59 ** & 0.13 & 0.15 & 0.52 ** & 0.17 & 0.19 \\  & RF & 0.45 ** & 0.15 & 0.09 & 0.43 ** & 0.19 & 0.11 \\  & MLR & 0.55 ** & 0.13 & 0.08 & 0.51 ** & 0.17 & 0.10 \\ \hline \multirow{4}{*}{Upper 2 leaf} & PLSR & 0.33 ** & 0.16 & 0.12 & 0.28 * & 0.12 & 0.09 \\  & SVM & 0.53 ** & 0.09 & 0.08 & 0.49 ** & 0.21 & 0.19 \\  & RF & 0.38 ** & 0.10 & 0.07 & 0.48 ** & 0.23 & 0.17 \\  & MLR & 0.54 ** & 0.14 & 0.10 & 0.53 ** & 0.11 & 0.09 \\ \hline \multirow{4}{*}{Upper 3 leaf} & PLSR & 0.44 ** & 0.10 & 0.10 & 0.52 ** & 0.10 & 0.10 \\  & SVM & 0.42 ** & 0.11 & 0.16 & 0.32 * & 0.13 & 0.22 \\ \cline{1-1}  & RF & 0.24 ** & 0.10 & 0.10 & 0.44 ** & 0.15 & 0.14 \\ \cline{1-1}  & MLR & 0.50 ** & 0.09 & 0.08 & 0.52 ** & 0.11 & 0.11 \\ \hline \multirow{4}{*}{Upper 4 leaf} & PLSR & 0.31 ** & 0.08 & 0.10 & 0.28 * & 0.13 & 0.17 \\ \cline{1-1}  & SVM & 0.33 ** & 0.10 & 0.16 & 0.31 * & 0.11 & 0.16 \\ \cline{1-1}  & RF & 0.16 * & 0.10 & 0.12 & 0.12 & 0.13 & 0.16 \\ \cline{1-1}  & MLR & 0.37 ** & 0.08 & 0.10 & 0.35 * & 0.13 & 0.16 \\ \hline \hline \end{tabular} Note. \({}^{*}\) indicates a significant correlation at the 0.05 level and ** indicates a significant correlation at the 0.01 level.  
\end{table} 
Table 4: Correlation between vegetation indices and nitrogen content from different leaf positions in winter wheat.
## 1 Introduction
Regarding research on stress-strain curves of PPF concrete, studies related to PPF concrete subjected to high temperatures mainly demonstrate the characteristic changes in compressive strength and modulus of elasticity of PPF concrete after exposure to high temperatures. However, experimental data show a large dispersion, and the conclusions of related studies differ significantly, indicating a need for further research [38; 39; 40; 41]. On actual construction sites, the slump test is a simpler and more direct performance indicator that can quickly provide a general understanding of the concrete mix. Modern concrete buildings are typically constructed using concrete batching plants for batching, concrete mixers for mixing and transportation, and concrete pumps for pouring into the specified location. This also requires testing for the concrete's slump. Studies have shown [42] that the degree of slump has a correlation with the mechanical properties of concrete. The determination of the slump degree is measured according to the relevant provisions of the specification GB/T50080-2002 Standard for Test Methods for the Properties of Ordinary Concrete Mixes [43]. According to the provisions of GB50164-92 \"Concrete Quality Control Standard\" [44], the slump degree grade is divided into four levels, and low-slump concrete refers to concrete with a slump degree range of 10-40 mm. In the actual practice of production, concrete segregation and segregation should be avoided as much as possible, and the working properties of low-slump concrete happen to meet the requirements. As the stability and integrity of low-slump concrete are better than normal concrete, higher strength can still be obtained with reduced cement dosage [45]. In environments such as pavement construction and bridge construction, low-slump concrete is used in large quantities due to its advantages [46]. Most of the concrete studied in the literature has a slump greater than 50 mm and is generally concentrated above 100 mm, resulting in few studies on low-slump concrete and almost a void in the study of low-slump PPF concrete. In actual manufacturing, transportation, and pumping of low-slump concrete, adequate vibration and the addition of fibers are needed to ensure that the low-slump concrete meets the commonly used acceptance standards for slump concrete. With increasing attention to fire hazards, the mechanical properties of low-slump PPF concrete after high temperature need to be urgently addressed, and the principal structural relationship of low-slump PPF concrete after high temperature has more value for practical engineering applications and theoretical analysis. S tests of low-slump polypropylene fiber (PPF) concrete at room temperature and after high temperature with different admixtures and lengths were conducted, and the physical and mechanical properties of PPF concrete were compared and analyzed, based on the test results. Finally, the principal structural relationship of low-slump PPF concrete after high temperature was conclusively identified, based on the test data.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 6 Research Challenges
### Methods to Calculate SUHI Intensity  
While various methods have been used to quantify SUHI, it is challenging to compare SUHIs across cities or studies due to the highly diverse SUHI estimates. For example, [PERSON] et al. [68] compared 11 methods for quantifying SUHI in 263 European cities and found weak and even negative correlations between the calculated SUHI intensities in most cases, especially during the daytime. [PERSON] et al. [233] indicated that the SUHI value averaged for 31 Chinese cities differed by a factor of two among the five commonly used methods. [PERSON] et al. [234] showed that different methods would lead not only to contrasting intensities but also to opposite signs (from a heat island to a cool island) of the SUHI estimates, particularly in arid cities and during the daytime.  
The varying results from different methods can be attributed primarily to the large uncertainties associated with the definitions of urban and reference areas [235]. For example, the SUHI intensity would most likely be underestimated if referenced to nearby suburban areas, since the SUHI footprint usually extends far beyond the physical boundaries of urban areas [234]. The SUHI intensity, defined as the urban-rural LST difference, also varied considerably across cities and studies due to the strong control of agricultural activities (e.g., irrigation) on rural LST [186]. In highly urbanized metropolitan areas, nearby reference areas will very likely be influenced by the SUHI of neighboring cities [120]. Some methods such as the linear regression slope of LST with regionalized ISA [131] and the LST difference relative to natural vegetation condition [120] have been developed to avoid the bias caused by urban-rural definitions, but they are largely limited by data quality [131] or the natural vegetation coverage [120].
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 2 Underwater Photon Propagation Model
### Channel Propagator  
The photons were propagated using the Monte Carlo algorithm presented in Section 2.1. To be able to simulate an architecture of sensors in a plane rotated with regard to the emitter, the condition of termination of each photon had to be changed. Instead of finishing the photons in a given finished plane \(z=z_{f}\), they were finished in a general plane given by its normal vector \(p_{n}\) (\(p_{nx},p_{ny},p_{nz}\)). Thus, the photons' new position \((x^{\prime},y^{\prime},z^{\prime})\) was evaluated  
Figure 5: Rotation of the emitter direction from the default \(\sharp\) direction to any desired vector \(\mathbf{w}\).  
Figure 6: AUV communicating with fixed receiver. Both emitter and receiver are rotated in relation to the \(z\) axis by the angles \(\gamma_{e}\) and \(\gamma_{s}\), respectively.  
after every displacement. In the case where the photon was past the plane, the following condition  
\[p_{nz}x^{\prime}+p_{ny}y^{\prime}+p_{nz}z^{\prime}>cz_{f}, \tag{17}\]  
would be verified. Then, the intersection with the plane was calculated, and the photons' data (i.e., the position in the last plane, the vector of propagation, and the weight) would be saved in a file for later analysis.
## 5 Impact of Color Conflicts Caused by Late Arrivals, Node Mobility or Topology Changes
### Dysfunction Detected at the MAC Layer
#### 5.3.2 By Deduction  
At the medium access level a real conflict happens when frames are lost due to collisions. But in the wireless network domain, collisions cannot be easily detected (in the sense of CSMA/CD), however they can possibly be deduced. Due to the conditions of power threshold that have been taken especially concerning \(Rule\)\(R3\) of this contribution, our coloring process applied to a stable topology reduces considerably the risk of frame losses due to bad link quality. Hence the following dysfunctions can be deduced:  
If a frame sent by \(N_{i}\) is not acknowledged by the target node \(N_{nix}\), it is reasonable to assume that this frame (or its acknowledgement) was subject to a collision. In that case TDMA/CA switches to the CAmode and a backoff value is randomly chosen before retransmitting. Thus TDMA/CA compensates this dysfunction but at the cost of a bandwidth reduction and an increase in medium access delay.  
If such a dysfunction persists, it is necessary to signal it to SERENA in order to expect that a solution will be given. In this purpose, the sender node is able to specify the couple (source address and destination address of the frame as well as their respective color of the two conflicting nodes).  
An indication to the SERENA is sent when the sliding average of the value of Backoff exceeds a given threshold or when \(n\) consecutive collisions occur. It means that dysfunction is persistent.  
If \(N_{i}\) has already acknowledged a given data frame from \(N_{mix}\) and yet it receives another copy of the same frame, it is possible to deduce that the acknowledgement was corrupted by a collision. In this case, TDMA/CA reacts at the level of \(N_{mix}\). Thus, if for \(N_{mix}\) this situation becomes persistent, SERENA will be notified.  
If TDMA/CA succeeds in desynchronizing the sending of the two competing frames, and if these two frames in competition can be sent in the same colored slot, any receiver will be able to detect the conflict that can be treated as stated before.
# Kinetics of Ions in Post-Lithium Batteries
## 4 Oxygen Ion Batteries
### Oxygen Battery  
In the seminal study by [PERSON] et al. [50] an oxide ion battery was constructed with a La\({}_{0.6}\)Sr\({}_{0.4}\)FeO\({}_{3-\delta}\) (LSF) cathode and a La\({}_{0.5}\)Sr\({}_{0.5}\)Cr\({}_{0.2}\)Mn\({}_{0.8}\)O\({}_{3-\delta}\) (LSCrMn) anode. Figure 5 shows schematically how the oxygen ion batteries compare with respect to other related electrochemical technologies with respect to their potential power and energy density [50]. The cycling performance was excellent, whereas DC measurements determined capacities up to 120 of A h cm\({}^{-3}\) at a cell voltage of 0.6 V and a temperature of 350-400 \({}^{\circ}\)C [50]. The  
Figure 4: Schematic representation of (**a**) the Mg ion diffusion paths constructed in MgV\({}_{2}\)O\({}_{4}\) and (**b**) the energy profile diagram for the Mg migration [45].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Activation Energy/eV** & **Comments** \\ \hline La\({}_{2}\)NiO\({}_{4+\delta}\) & 0.56 & [121], anisotropic mechanism \\ GdBaCo\({}_{2}\)O\({}_{5+\delta}\) & 0.60 & [122] \\ Sr\({}_{0.75}\)V\({}_{0.25}\)CoO\({}_{2.625}\) & 1.56 & [123], isotropic mechanism, MD \\ La\({}_{0.8}\)Sr\({}_{0.2}\)MnO\({}_{3-\delta}\) & 2.80 & [120] \\ \hline \hline \end{tabular}
\end{table}
Table 3: The activation energies of oxygen diffusion in representative oxides.  
study of [PERSON] et al. [50] showed that mixed ionic electronic conducting-based oxide ion batteries are a possible electrochemical energy storage solution. In a subsequent study, [PERSON] et al. [124] prepared porous La\({}_{0.6}\)Sr\({}_{0.4}\)CoO\({}_{3-\delta}\) (LSC) thin film electrodes on yttria-stabilized zirconia. They determined that rechargeable oxygen ion batteries efficiently store oxygen by changing the oxygen non-stoichiometry and by the encapsulation of high-pressure oxygen in the closed pores of the cathode [124].  
The high operating temperatures required (typically 200-500 \({}^{\circ}\)C) exclude this technology from mobile applications where Li-ion batteries dominate (such as laptops, phones, and cars). Nevertheless, it is a promising technology to store energy where there can be a supply of heat and off-peak electricity production, for example near a civil nuclear reactor. Such an energy storage solution with provide added benefit for the use of centralized nuclear energy production.
# Influence of Atmospheric Pollutants and Weather Conditions on Agricultural Productivity in Italy
###### Abstract  
The quantity of the main pollutants present in the atmosphere (particulate matter, ozone, sulfur dioxide, nitrogen dioxide) and meteorological events (rain) can cause permanent or catastrophic damage to plant growth. This study was conducted for the environmental assessment of the Italian territory, in the cities of Palermo, Bari, Ferrara, Padua, and Venice, which respectively represent southern, central, and northern Italy, in order to have a territory global environmental view. The aim of this research is to analyze the relationship between air pollution (PM10, O\({}_{3}\), NO\({}_{2}\), SO\({}_{2}\)) and crops (durum and common wheat, corn) as a basis for the subsequent definition of an agronomic model. Later, meteorological events were also added to the analysis, to have a complete overview for the evaluation, since meteorological events contribute to the pollutants' behavior and favor the deposition of the latter on the vegetation, increasing the negative and toxic effect on crops. The analyses showed that pollutants have a significant effect on crops and in particular ozone appears to be the most influential parameter.  
pollution; particulate matter; agriculture; crops; ozone; Agriculture 4.0 +
Footnote †: journal: Applied Sciences
## 3 Results  
### Spatial Distribution Characteristics of Vegetation NPP  
The mean vegetation NPP during 2000-2022 was calculated to analyze its spatial distribution. Figure 5 showed that vegetation NPP in China ranged from 0 to 1933 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). By using ArcGIS 10.7, the vegetation NPP was divided into five grades: level I (<200 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), level II (200-400 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), level III (400-600 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), level IV (600-800 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), and level V (>800 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), as shown in Appendix A, Figure A1). The level V region accounted for the smallest proportion (only 12.54%), which was mostly located in the southern parts, including Guangdong Province, Guangxi, and Yunnan Province. The level IV region accounted for 13%, being mostly located in the central and southern regions, including Guizhou Province and Zhejiang Province. The level III region accounted for 25%, which was mostly located in the central parts, including Shaanxi Province and Anhui Province. The level II region accounted for 25.94%, being distributed in northeast Tibet. The level I region accounted for 25.00%, mainly distributed in the central and western regions, including the foothills of the Tianshan Mountains and Qinghai Province. Overall, the mean vegetation NPP gradually increased from north to south and from west to east.
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 3 Material and Methodology
### Environmental Influences on Measurements  
Due to external influences, measurements at different points in time or at different locations will never be exactly the same. One of the most significant influences is interference. In an indoor environment, many electrical devices are present and most of them emit some sort of Radio Frequency Interference (RFI). Additionally, the ionospheric Total Electron Content (TEC) changes over time and this results in different scintillation effects. Ionospheric scintillation results in rapid fluctuations of the amplitude and phase which is caused by electron density irregularities and has been observed the most on VHF frequencies [3].
## 3 System Overview  
A schematic diagram of the proposed system is shown in Figure 5, composed of a source computer, arbitrary waveform generator (AWG), LED driver, LED source, lens, receiver module, analog-digital converter (ADC), and MCU. First, the information bits are encoded by a PC into an unstable Morse code set and converted to an analog signal by AWG. Then, the signal is amplified and modulated to drive the LED via the driver circuit for transmitting visible light signals. At the receiver side, the incoming light signal is received by a photodetector (PD) for photoelectric conversion and amplified by a transimpedance amplifier (TIA). Then, the ADC is used to convert the electrical analog signal into a digital signal. Afterward, the digital signal is fed to the MCU for further processing.  
The Cortex M4-based MCU STM32 is the core part of this whole hardware system, which has a rich peripheral interface with high integration, composed of a serial peripheral interface (SPI), finite impulse response (FIR) filter, timers (TIM), universal synchronous/asynchronous receiver transmitters (USART), and a decode module. The control signal and sampled data stream are transmitted between the MCU chip and ADC using a SPI. After FIR denoising, the sampled signal is further processed by the decode module, where the recognition algorithm is implemented. Then, the USART module is used for the interaction between the automatic recognition system and the host computer, including the transmission of control commands and return of the decoding results. In addition, the TIM module provides a working clock for the entire system to ensure that each functional component can run in an orderly manner.  
As indicated in Figure 6, the implementation of our proposed recognition algorithm contains three stages: Signal pretreatment, Morse decoding, and accuracy analysis. The input sampled signal is filtered first by the FIR module to reduce the effects of noise on the subsequent process. Then, the threshold between the high level and low level is determined to classify all the voltage samples into two levels (0 and 1) in the stage of binarization. As there still exist some irregular hopping points after FIR denoising, which will affect the automatic decoding, resulting in the decline of recognition accuracy, the error correction stage is designed as a necessary complement to address this issue by eliminating the spikes in the signal. Afterward, a relatively pure and stable Morse code signal is obtained and classified according to the characteristics of the dot, dash, and space code. After utilizing the dot-dash and space recognition algorithm, the identified Morse code set is able to be interpreted  
Figure 5: Block diagram of hardware setup.  
into a string message sequence by table lookup decoding, and is then finally output to the computer for accuracy computation.