esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Investigating the carbon flux in orchard ecosystems is crucial for assessing agroecosystem productivity and optimizing management practices. We measured and estimated carbon fluxes (gross primary productivity, GPP; ecosystem respiration, R\({}_{\rm e}\); and net ecosystem exchange, NEE) and environmental variables in a seven-year-old kiwifruit Orchard over two years. Our results showed that diurnal carbon fluxes exhibited bell-shaped patterns, peaking between 12:30 and 15:30. Daily carbon fluxes exhibited a seasonal trend, characterized by an increase followed by a decrease. The average daily GPP, R\({}_{\rm e}\), and NEE values were 6.77, 4.99, and \(-\)1.79 g C m\({}^{-2}\) d\({}^{-1}\) in 2018, and 5.88, 4.78, and \(-\)1.10 g C m\({}^{-2}\) d\({}^{-1}\) in 2019, respectively. The orchard sequestered \(-\)444.25 g C m\({}^{-2}\) in 2018 and \(-\)285.77 g C m\({}^{-2}\) in 2019, which accounted for 26.4% and 18.6% of GPP, respectively. Diurnal GPP and NEE were significantly influenced by photosynthetically active radiation (PAR), with direct path coefficients of 0.75 and 0.88 (\(p<\) 0.01), while air temperature (T\({}_{\rm a}\)) significantly affected GPP and NEE through PAR, with an indirect path coefficient of 1.12 for both. PAR had a similar effect on daily GPP and NEE, while both were indirectly influenced by soil temperature (T\({}_{\rm s}\)) at a 5 cm depth and vapor pressure deficit (VPD), R\({}_{\rm e}\) was primarily impacted by VPD, with a direct path coefficient of 0.64 (\(p<\) 0.01), while T\({}_{\rm a}\) and the concentration of air carbon dioxide (CCO\({}_{2}\)) significantly affected GPP through VPD, with indirect path coefficients of 0.82 and \(-\)0.80. The leaf area index (LAI) and soil water content (SWC) at a 20 cm depth exhibited a significant correlation with carbon fluxes during the vigorous growing period.  
carbon flux; controlling factors; path analysis; kiwifruit Orchard ecosystem
## 2 Background
### IoFV  
The goal of the Internet of Flying Vehicles (IoFV) is, similarly to the IoV, the integration of the IoT and vehicles. Such integration could support an even wider adoption of UAVs by enhancing their applicability similarly as it was demonstrated with the IoV. The relationship between FANETs and the IoFV is comparable to the one observed in VANETs and the IoV. Therefore, both networks engage in similar communication models, especially UAV-to-UAV and UAV-to-base station. Differences between those two networks in connection and communication are less significant than in their ground vehicle counterparts due to the development of FANETs later in time. Like its counterpart of the IoV, the IoFV adopts cloud, edge, and fog computing to analyze data and accomplish complex tasks requiring the coordination, control, and analysis of collected data, which are then normally transmitted to the base stations for processing [33]. Eventually, when edge computing is integrated into the network, the computing load is reduced as a result of processing parts of the data locally, thus also reducing time latency and enabling real-time applications.  
The applications of the IoFV remain similar to those observed in FANETs. A specific example is the usage of smart agriculture to improve the efficiency of production and optimize crop quality while simultaneously minimizing the negative impact on the environment. Other applications include disaster recovery, in which UAVs help manage natural disasters such as fires, floods, earthquakes, or storms. This application domain utilizes UAVs to collect environmental data from sensors in disaster-prone locations. The collected data describes critical environmental parameters, such as temperature, humidity, luminosity, strain, and stress, which enables undertaking and appropriate actions even before the disaster occurs [33].
## 2 Materials and Methods
### Radar Polarimetric Parameters and Optical Spectral Vegetation Indices Selection  
Based on published literature and the sensitivity of optical and polarization features to LAI and biomass, radar polarimetric parameters and optical spectral vegetation indices were used to estimate LAI and biomass. In this study, radar polarimetric parameters (HH, HV, VV, HH/VV, HH/HV,VV/HV, Vol/Span, Dbl/Span, Odd/Span, and radar vegetation index (RVI)) [48] and optical vegetation indices (Table 5[18, 49, 50, 51, 52, 53]) (ratio vegetation index (RVI1), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), optimized soil adjusted vegetation index (OSAVI), enhanced vegetation index (EVI), and modified triangular vegetation index (MTVI2)) were selected to analyze the relationships between vegetation indices and LAI, biomass.
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
## 3 Routing Protocols for Low-Power and Lossy Networks
### Initial Approaches
Aiming to avoid loops, DYMO-low uses a simplified sequence number with 16-bits of length (DYMO sequence numbers are 32-bit). RREP must only be sent by the destination node of an RREQ. In DYMO-low, RERR messages indicate just one unreachable destination and are generated when: (i) an entry of the route table expires; (ii) a data packet cannot be successfully delivered to the next hop; or (iii) a broken link is detected. A Hybrid Routing protocol for LLNs (HYDRO) was proposed in [47; 48]. Hydrois a routing protocol for LLN, created to attend collection-based and point-to-point traffic. The hybrid approach combines local agility with centralized control. The network nodes create Directed Acyclic Graphs (DAGs) as default routes to the border router. On the other hand, the border router aggregates information about the nodes to create a global view of the network. This information is sent by the nodes through specific control messages or through opportunistic piggybacks in data packets. By creating a triangle between two nodes, the border router is responsible for forwarding the point-to-point traffic in the network. When a node wants to send a data message to another node in the network (point-to-point traffic), the packet is sent to the border router that should forward the packet to the desired destination. When an active point-to-point flow is detected, Hydro uses a special control message to optimize the message exchange between these two nodes. Thus, the data packets can be forwarded through the shortest paths without the use of a border router. Each node maintains statistical information about its neighbors. This information is used to measure link quality and assist in route selection. These routing protocols were defined before the specification of the routing requirements of LLN (presented in Section 2). As they are only simplifications of already existent protocols, they were unable to meet the requirements identified by the RoLL working group. As already mentioned, after identifying that the existent routing protocols defined by IETF were unable to supply the demands of LLN, the RoLL working group started the development of RPL. A description of RPL is provided in the next subsection.
## 3 Results  
### Overview of the Data Coding and Analysis  
A coding scheme was developed based on the grounded theory [47] to explain the acceptance of AI technology for emotion regulation amongst young people. In this study, 584 quotations were coded and classified according to the following coding scheme. Table 1 shows the coding scheme and the proportion of each category/subcategory. Of the 584 quotations, 16.25% were coded as attitudes toward using AI technology for emotion regulation, 56.07% as reasons for using AI technology for emotion regulation and 27.68% as reasons not to use AI technology for emotion regulation.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Categories** & **Subcategories** & **Definition** & **Percentage** \\ \hline Attitudes toward using AI & Positive attitudes & A person’s favourable & Regulating emotional \\ technology for emotion & (47.24\%) & evaluation of using AI & states (44.15\%) \\ regulation (16.25\%) & & technology for emotion & Functional values (38.96\%) \\  & & regulation & Raising emotional \\  & & & awareness (12.99\%) \\  & & & Addressing the source \\  & & & problem (3.90\%) \\ \hline Neutral attitudes & A person’s neither favourable & Usage environment \\ (12.88\%) & nor unfavourable evaluation & (52.38\%) \\  & of using AI technology for & No disadvantages (19.04\%) \\  & emotion regulation & Low positive impact \\  & & (14.29\%) \\  & & Single form (14.29\%) \\ \hline Negative attitudes & A person’s unfavourable & Negative effects (55.39\%) \\ (39.88\%) & evaluation of using AI & Unresolved issues (15.38\%) \\  & technology for emotion & Inconvenience (12.31\%) \\  & regulation & Poor quality-price ratio \\  & & (7.69\%) \\  & & Complexity (4.62\%) \\  & & Addiction (3.07\%) \\  & & Security and privacy risks (1.54\%) \\ \hline Reasons for the use of AI & Functional outcomes & The extent to which using AI & Usefulness (23.57\%) \\ technology for emotion & (76.48\%) & technology for emotion & Privacy (21.66\%) \\ regulation (56.07\%) & & regulation is perceived to be & Trust (15.29\%) \\  & & instrumental in achieving & Convenience (12.74\%) \\  & & valued outcomes & Intelligent (12.74\%) \\  & & High value (5.1\%) \\  & & Accessibility (4.44\%) \\  & & Functionality (2.55\%) \\  & & Interactive feedback \\  & & (1.91\%) \\ \hline Hedonic outcomes & The individual’s level of & Interest and fun (93.75\%) \\ (22.08\%) & curiosity during the & Beautiful interface (4.17\%) \\  & interaction and the perception & Emotional experience (2.08\%) \\  & that the interaction is & (2.08\%) \\  & intrinsically enjoyable & \\ \hline Social influence (1.44\%) & The extent to which social & Peer influence (66.67\%) \\  & values and members of a & Surrounding environment \\  & social network influence the & influence (33.33\%) \\  & user behaviour & \\ \hline \end{tabular}
\end{table}
Table 1: The coding scheme and proportion of the code (n = 584).
# A High Signal-Noise Ratio UWB Radar for Buried Pipe Location Using Golay Complementary Sequences
## 1 Introduction
Then two-dimensional imaging of buried pipes is achieved by utilizing the back projection (BP) algorithm, which is beneficial for target recognition. The experiments are carried out to locate the underground plastic pipe and metallic pipe. Furthermore, we compare the performance of different UWB radar systems. This paper is organized as follows: Section 2 provides the Golay-based radar system and its measurement principle. Section 3 introduces the generation and characteristics of the Golay complementary sequences. Section 4 shows the experimental results of underground pipe location. Section 5 shows the comparison results among proposed radar, chaotic radar and stepped frequency signal radar. Section 6 discusses the effect of the frequency dispersion on the approach and gives the conclusions.
## 3 Results and Discussion
### Thermal Gravimetric Analysis  
TGA was conducted to investigate the weight changes in certain ranges for the dehydration and decomposition of hydration products. Three \"PC-\" mixtures, PC00, PC02, and PC05, after curing for 28 days were used as samples. The weight losses from the ambient temperature to 1000 \({}^{\circ}\)C are plotted in Figure 9.  
The weight loss rate for PC00 was the most gradual at 150 \({}^{\circ}\)C, and otherwise, a stiff slope was observed for PC05. Usually, the dehydration of C-S-H continued till 200 \({}^{\circ}\)C, and water was obtained from Ca(OH)\({}_{2}\) between 400 and 600 \({}^{\circ}\)C [30]. This implies that PC05 contained more water owing to the characteristics of CNTs that absorb more water compared with other mixtures.  
To evaluate the phase changes after the thermal conductivity experiments, TGA results for cement composites as references, where heat experiments were not conducted, were indicated by solid lines, \"PC-before\" in Figure 9. After the thermal conductivity experiments, samples near wire meshes were taken out to conduct TGA to observe the phase changes.  
Weight losses between references and samples after heat experiments were not significant; less than one percent difference was observed for all mixtures. This is because heat conductivity experiment was not critically influenced by the deterioration of microstructures in CNT-cement composites, and the maximum temperature difference between ambient temperature and peak temperatures during the test was less than 75 \({}^{\circ}\)C in the sample. In addition, it can be considered that dehydration of Ca(OH)\({}_{2}\) and decomposition of CaCO\({}_{3}\) between references and tested samples was very similar; therefore, the heat experiments did not result in significant phase changes.
# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## 3 Mathematical Statement and Solution Methods of the Optimization Problem
### Dynamic Optimization
#### 3.2.2 Solution Methods of the Dynamic Optimization Problem  
The methods developed for the solution of the dynamic optimization problem are classified in two main categories: _indirect methods_ and _direct methods_.  
The indirect methods include calculus of variations (COV) [24; 25] and dynamic programming (DP) [26]. As explained in [2; 23], they are not particularly useful for optimization of energy systems, as they are considered here.  
The direct methods convert the original problem, which is continuous in time, to a discrete one. They are divided in two subcategories: _sequential methods_ and _simultaneous methods_. Detailed description is avoided here, but it can be found in related publications [21; 22; 27; 28], while application examples on energy systems on land, airplanes, or ships appear in [8; 23; 29; 30; 31; 32].
# Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
## 3 The Proposed Closed Form \(N\) Expression  
One of the proposed solutions to improve the performance of IRS-NOMA is to find the optimal number of reflective elements. Optimizing the number of the reflective elements could also boost the spectral efficiency and help in reducing the required transmission power, which in turn, enhances the overall energy efficiency. The derivative of (6) with respect to \(N\) could be used as  
\[\frac{dP_{IRS-NOMA}^{u}}{dN}=P_{E}-\left(2\pi I_{IRS}^{u}\left(2^{SE_ {IRS-NOMA}^{u}}-1\right)\right)*\] \[\left(\frac{\sum_{\gamma=u+1}^{U}P_{IRS-NOMA}^{v}}{\eta_{PA}\left(I _{SD}^{u}+N\alpha I_{IRS}^{u}\right)^{2}}+\right.\] \[\left.\frac{\left(z^{u}+\sum_{\gamma=u+1}^{U}P_{IRS-NOMA}^{v} \left(I_{SD}^{u}+N\alpha I_{IRS}^{u}\right)^{2}\right)}{\eta_{PA}\left(I_{SD}^{ u}+N\alpha I_{IRS}^{u}\right)^{3}}\right). \tag{8}\]  
On the other hand, to investigate the nature of \(P_{IRS-NOMA}^{u}\) as a function of \(N\), the second derivative is necessary, which is obtained to be  
\[\frac{d^{2}P_{IRS-NOMA}^{u}}{dN^{2}}=6\alpha^{2}(I_{IRS}^{u})^{2} \left(2^{SE_{IRS-NOMA}^{u}}-1\right)*\] \[\left(\frac{\left(z^{u}+\sum_{\gamma=u+1}^{U}P_{IRS-NOMA}^{v} \left(I_{SD}^{u}+N\alpha I_{IRS}^{u}\right)^{2}\right)}{\eta_{PA}\left(I_{SD} ^{u}+N\alpha I_{IRS}^{u}\right)^{4}}-\right.\] \[\left.\frac{\sum_{\gamma=u+1}^{U}P_{IRS-NOMA}^{v}\left(I_{SD}^{u} +N\alpha I_{IRS}^{u}\right)^{2}}{\eta_{PA}\left(I_{SD}^{u}+N\alpha I_{IRS}^{ u}\right)^{2}}\right], \tag{9}\]  
It is clear from (9) that \(\frac{d^{2}P_{IRS-NOMA}^{u}}{dN^{2}}\)>0; hence, the function has a convex nature. Obtaining the optimal \(N\) is possible by setting \(\frac{dP_{IRS-NOMA}^{v}}{dN}=0\) and solving for \(N\) to obtain  
\[\frac{N_{IRS-NOMA}}{\sqrt{2\left(\left(\alpha I_{IRS}^{u}\right)^{ 7}P_{E}^{2}u^{\eta}_{PA}\left(2^{SE_{IRS-NOMA}^{u}}-1\right)\right)}}{\left( \alpha I_{IRS}^{u}\right)^{3}P_{E}\eta_{PA}}, \tag{10}\]  
the subscript \(()_{IRS-NOMA}\) is included for recognition between the IRS-NOMA and IRS-OMA parameters.  
On the other hand, based on (2) and (7), the same procedure could be followed to obtain the optimized \(N\) in IRS-OMA case as  
\[N_{IRS-OMA}=\frac{\sqrt[3]{\varphi^{u}}-\Gamma_{SD}^{u}\sqrt[3]{P_{E}\eta_{PA} }}{\alpha\Gamma_{IRS}^{u}\sqrt[3]{P_{E}\eta_{PA}}}, \tag{11}\]  
where \(\varphi^{u}=\left(2^{SE_{IRS-OMA}^{u}}-1\right)z^{u}\alpha\Gamma_{IRS}^{u}\).
# Anthracene Absorption and Concentration Dynamics in Radishes [PERSON]
## 1 Introduction  
Anthracene (hereinafter referred to as \"AN\") is a polycyclic aromatic hydrocarbon that is used as an insecticide, gasoline stabilizer, triplet sensitizer, and quenching agent. As AN is generated unintentionally by the burning of the fossil fuels such as petroleum and coal, it is discharged into the environment from numerous emission sources, including automobiles, incinerators, and factories [1; 2; 3; 4; 5]. Accordingly, AN has been detected in air, water, and other environmental media around the world [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. In an evaluation by the International Agency for Research on Cancer (IARC), AN was categorized as a compound that cannot be evaluated with respect to carcinogenicity in humans (Group 3) [20]. However, due to its persistence, accumulation in ecosystems, and toxicity, it was included in the first list of substances of very high concern (SVHC) released by the European Chemicals Agency in 2008, and it can be considered a harmful chemical substance of note [21]. The accurate calculation of the health risk of AN requires the collection of information on the exposure level; the accumulation of data on its concentration in environmental media including water, air, and food; and the consideration of the substance's environmental dynamics.  
The present study focused on the radish vegetable (_Raphanus sativus_). This member of the Brassicaaceae family is native to Europe. Radishes, of which _R. sativus_ is one type, are among the most popular vegetables in Japan. The plant can be harvested approximately 1 month after sowing when grown at a temperature of approximately 20 \({}^{\circ}\)C. In large specimens, the leaves grow to a height of approximately 30 cm, and the root develops into a red, spherical, tuberous root with a diameter of approximately 3 cm. The radish was selected for this study, because the time until harvest is short, and its growth management is relatively straightforward. Previous studies have investigated the uptake anddistribution of micropollutants, as well as pesticides, heavy metals, and pharmaceuticals in radiishes [22; 23; 24; 25; 26; 27; 28; 29; 30]. However, limited information is available on the uptake pathways and accumulation of AN by vegetables. To construct an AN exposure assessment system, this study aimed to evaluate its absorption and concentration properties in radiishes grown in contaminated soil and specifically the temporal variation in its absorption and the dynamics of its accumulation in radiish tissue.
## 2 Use of Agricultural Residues as Low-Cost Sorbents in Colour Removal
### Optimisation of System Using Agricultural Residues  
#### 2.1.1 Pretreatment of Lignocellulosic Residues  
The agricultural residues are lignocellulosic in nature; their 20-25% lignin content is a barrier in the effective use of cellulosic content. These residues can be effectively used for the sorption process after removing the lignin content from the substrates, making their cellulosic content freely-available for dye-sorption. The pretreatment of three agricultural residues, including barley husk, corncob, and wheat straw, were performed mainly for their delignification and to increase the surface area for the effective adsorption of coloured molecules by treated substrates [7]. A few commonly used pretreatment methods, mainly milling of large size material, steaming, alkali treatment, and ammonia-steeping, were tried and the performance of untreated and pretreated substrates was checked for their colour adsorption capabilities. The milled (powdered) samples removed a higher percentage of dye at a faster rate; therefore, milling of substrates to reduce particle size proved to be a better option for pretreatment of solid agricultural residual materials. Milling is also cost-effective and eco-friendly treatment process compared to steaming, alkali, or ammonia-steeping. The authors recommended the milling or grinding of substrates so no cost was calculated for chemical pretreatment methods. Three milled residues have been used in studies of adsorption process for colour removal from a solution of five commonly used dyes, which were obtained for experiments from a textile company (Remazol Black B, Cibacron Yellow C-2R, Cibacron Blue C-R, Cibacron Red C-2G, and Remazol Red RB) [7].
# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 3 The Proposed Method
### Divergence Distance  
To address the influence of the above shortcomings, based on traditional Euclidean distance, the divergence distance is refined. Inspired by the importance ranking of data points, the definitions of divergence and divergence distance are shown as follows [46].  
**Definition 1. (Divergence).**_For a dataset \(D\) containing \(n\) points, where \(m\) points \((x_{1},x_{2},\cdots,x_{i},\cdots,x_{m})\) appear in the same circle. Point \(x_{i}\) can be represented by the Euclidean distance from point \(x_{k}\) (\(1\leq k\
eq i\leq m<n\)) to it. The divergence of \(x_{i}\), denoted as DV \((x_{i})\), is computed by:_  
\[DV(x_{i})=\frac{E_{u}(x_{1},x_{2})+E_{u}(x_{2},x_{i})+\cdots+E_{u}(x_{m},x_{i} )}{6\times d}=\frac{\sum_{i=1,k\
eq i}^{m}E_{u}(x_{k},x_{i})}{6\times d} \tag{5}\]  
_where \(E_{u}(x_{k},x_{i})\) is the Euclidean distance between \(x_{k}\) and \(x_{i}\); the diameter of the circle where these points are located is \(d\), the amount of accumulation is \(\theta,\theta=m-1\). \(E_{u}(x_{k},x_{i})\) ranges between 0 and \(d\), so there is a range of 0 to 1 for DV \((x_{i})\)._  
**Definition 2. (Divergence distance).**_The divergence distance between point \(x_{i}\) and point \(x_{j}\) under the divergence defined in Definition 1 (tagged as DVdis(\(x_{i}\), \(x_{j}\)) is:_  
\[DVdis\big{(}x_{i},x_{j}\big{)}=E_{u}\big{(}x_{i},x_{j}\big{)}-e^{-(DV(x_{i})- DV(x_{j}))^{2}} \tag{6}\]  
According to Definition 2, there are two components to the divergence distance between points \(x_{i}\) and \(x_{j}\). There are two parts to the distance between them: the Euclidean distance and the divergence difference.
## 4 RFF Extraction and Identification Methods
### Preamble Field-Based RFF Extraction Method
 \tag{21}\]  
where \(X(k)\) is the spectrum of the ideal signal and is a fixed value, the linear channel part and linear fingerprint part can be cancelled, and the nonlinear fingerprint part can still be retained. Therefore, the above Equation (21) can be further simplified to  
\[RFF(k)=A\frac{F_{STF}^{\dagger}(k)}{F_{LTF}^{\dagger}(k)} \tag{22}\]  
where \(A\) denotes a constant value. It can be seen from the above Equation (22) that only the transmitter fingerprint feature is included, and the RFF expression \(RFF(k)\) is only related to the characteristics of the transmitter itself and has no relationship with the channel. Therefore, this expression can be used as the RFF feature expression in the mobile multipath environment. The above-mentioned method of removing noise by superimposing the short preamble and long preamble in the time domain, and then removing the channel by division in the frequency domain has basically realized that the multipath effect and environmental noise interference of the wireless channel are eliminated only through the preamble field of the collected signal of one frame. At the same time, the pure RFF information of the device is preserved to the greatest extent.