esa-sceva/satcom-chunk-collection huggingface dataset:
# Biochar Improved Sugarcane Growth and Physiology under Salinity Stress
## 4 Discussion
Biochar addition decreased the transpiration rate but increased the net photosynthetic rate during all growth stages and the uptake of macronutrient elements including nitrogen, phosphorus, and potassium at the elongating stage [32]. [PERSON] et al. [31] reported that biochar application reduced soil dry density and increased available moisture, resulting in higher yield and sugar content. Similarly, [PERSON] revealed that, by applying biochar, crop yield increased in both light- and heavy-texture soil and reduced nutrient losses in subtropical sugarcane production [49]. Under saline stress conditions, [PERSON] and [PERSON] [50] also indicated that biochar modifies the oxidative stress in plant tissues of bean seedlings and thereby enhances plant growth. Biochar improved soil nutrient availability, the growth of tomatoes [26], and the growth, yield, and nutrient uptake of wheat [27] under saline water irrigation. In sugarcane, this is the first report on the efficacy of biochar as a practical approach for mitigating the impacts of salinity. In addition to the above-ground parts, the application of biochar here also increased the root volume of sugarcane plants under both saline and non-saline conditions. Since root volume is a key root trait and was reported to be highly correlated with other root system architecture traits including root length, root surface, and root tip number [51], this suggests that the growth and architecture of the root was improved with biochar. This result was in agreement with previous studies on several crops including maize [52], rice [53], and cotton [54]. The induced improvement of root growth and architecture by biochar can be associated with the improved root-soil interaction environment caused by the changes in soil physicochemical properties. Biochar enriches the soils with nutrients, increases soil water-holding capacity, and promotes soil microbial activity [55]. The positive effects of biochar on sugarcane under salinity can be attributed firstly to the ability of this material to transiently absorb Na\({}^{+}\) in the soil [21], thereby lowering the Na\({}^{+}\) equilibrium in the soil [56]. This prevents the excessive uptake of Na\({}^{+}\) in the soil in sugarcane plants which could lead to ion disequilibrium and ion toxicity. Additionally, biochar helps to dilute the salt content in the soil as it increases the water-holding capacity and soil moisture [21; 57]. Last but not least, biochar contains a high amount of plant essential elements such as K, P, and Ca (depending on organic materials and thermal conditions in biochar manufacturing). The increase in the content of these elements in the soil possibly leads to the reduction of Na\({}^{+}\) in plants [21]. One of the limitations of our study is that we were unable to evaluate the effects of biochar at the latter growth stages in sugarcane. Thus, future studies aiming at investigating the mitigation effects of biochar for sugarcane under salinity stress are highly recommended, especially in field conditions.
## 2 Materials and Methods
### Mortar Formulation and Samples Preparation  
The mortar components were mixed according to the EN 196-1 standard for mortars. In the present study, the WSA and GGBS contents are 72 wt.% and 28 wt.%, respectively. Several studies have shown that is the optimum proportion for the mixture which leads to higher compressive resistance, [33]. The mortar mixtures have been made with this binder matrix added with standard sand. These mixtures contain a mass binder of m\({}_{\text{b}}\) (WSA+GGBS), a mass of sand, m\({}_{\text{s}}\) = 3 m\({}_{\text{b}}\) and a mass of water m\({}_{\text{w}}\). Ratios of water/binder (w/b) were fixed to 0.5 and 0.6 (Table 3).  
Mixtures have been compacted and cast into steel molds (40 \(\times\) 40 \(\times\) 40 mm\({}^{3}\) prisms). Four different levels of energy per unit of volume of sample have been selected for the reference samples (Mix 1), and the compaction energies are as follows: 400, 600, 800 and 1000 kN\(\cdot\)m/m\({}^{3}\), see Table 3 and Figure 1. According to the mechanical strength results obtained on reference samples, it has been found that energy E = 600 kN\(\cdot\)m/m\({}^{3}\) is the optimum level of energy needed, which is also related to normal Proctor energy [34]. All the activated samples (Mix 2, Mix 3 and Mix 4) have been achieved using this level of energy (see Figure 1). In laboratory, different proposed compaction procedures were tamping, dynamic compaction using hammering shocks, instead of hydraulic pressure used in industrial manufacturing, but a compaction process is necessary for manufacturing dry mortars (see [PERSON] et al. [23]).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Oxide** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **K\({}_{2}\)O** & **TiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **Na\({}_{2}\)O** & **MnO** & **SO\({}_{3}\)** & **LO.I** \\ \hline Cement & 64.50 & 19.40 & 4.80 & 3.90 & 1.20 & 0.81 & 0.30 & 0.30 & 0.11 & 0.00 & 2.60 & 2.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of cement.  
With reference to soil compaction using the normal Proctor test (E = 600 kN\(\cdot\)m/m\({}^{3}\)), which makes it possible to determine the optimum dry density as a function of moisture content, four levels of compaction framing this reference E value were selected. These range from 400 to 1000 kN\(\cdot\)m/m\({}^{3}\). As mentioned above, the energy corresponding to a better densification, i.e., high strength, of dry mortars is close to the reference E value. This value has therefore been accepted for the different mixes of activated dry mortars. The samples preparation, compaction protocol and the other details on each mixture are reported in [PERSON] et al.'s work [23].
## 2 Materials and Methods
### Applied PLA Materials (Filaments)
Table 1 represents the materials used and the recommended and applied printing parameters of the specimens. The specimens were printed using the Creality Ender 3--v2 (Creality 3D Technology Co, Ltd., Shenzhen, China) 3D printer. The molds were printed with a Bambu Lab A1 (Bambu Lab, Shenzhen, China) mini printer. However, the nozzle and bed temperature parameters applied differed according to the material; the other parameters, such as cooling rate, nozzle size, layer height, and printing speed, were the same. The applied identical printing parameters were as follows: nozzle size 0.4 mm, cooling fun 100%, layer height 0.2 mm, print speed 50 mm/s, and a skirt was also used. Table 2 summarizes the materials tested (PLA, ABS, HI, ENG, and ESD) and compares their properties with each other and with other relevant materials (PETG); it also includes symbols for easy comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Task** & **Manufacturer** & **Printing Parameters** & **Recommended [\({}^{\circ}\)C]** & **Applied [\({}^{\circ}\)C]** \\ \hline \multirow{2}{*}{PLA} & \multirow{2}{*}{none} & Filaticum Kft., & Nozzle temperature & 190–215 & 200 \\  & & Miskolc, Hungary & Bed temperature & 55–70 & 60 \\ \hline \multirow{2}{*}{High-impact} & \multirow{2}{*}{HI} & Filaticum Kft., & Nozzle temperature & 190–210 & 200 \\  & & Miskolc, Hungary & Bed temperature & 45–65 & 60 \\ \hline \multirow{2}{*}{Engineering PLA} & \multirow{2}{*}{ENG} & Filaticum Kft., & Nozzle temperature & 215–240 & 225 \\  & & Miskolc, Hungary & Bed temperature & 65–75 & 75 \\ \hline \multirow{2}{*}{Electrostatic} & \multirow{2}{*}{ESD} & Filaticum Kft., & Nozzle temperature & 195–215 & 200 \\  & & Miskolc, Hungary & Bed temperature & 55–70 & 65 \\ \hline \multirow{2}{*}{Gypsum PLA} & \multirow{2}{*}{GYP} & Filaticum Kft., & Nozzle temperature & 190–215 & 200 \\  & & Miskolc, Hungary & Bed temperature & 55–70 & 60 \\ \hline \multirow{2}{*}{ABS} & \multirow{2}{*}{ABS} & Filaticum Kft., & Nozzle temperature & 260–290 & 260 \\  & & Miskolc, Hungary & Bed temperature & 80–95 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recommended and used printing parameters of the specimens and molds. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Tensile Strength** & **Flexibility** & **Heat Resistance [\({}^{\circ}\)C]** & **Printability** & **Eco-Friendliness** \\ \hline \multirow{2}{*}{PLA} & Moderate & Brittle & 55–70 & Easy & Biodegradable, \\ \cline{2-5}  & \(\bullet\bullet\) & \(\bullet\bullet\) & \(\bullet\bullet\bullet\) & non-recyclable \\ \hline \multirow{2}{*}{HI} & Moderate & Flexible & 65–70 & Easy & Biodegradable, \\ \cline{2-5}  & \(\bullet\bullet\) & \(\bullet\bullet\) & \(\bullet\bullet\) & \(\bullet\bullet\bullet\) & non-recyclable \\ \hline \multirow{2}{*}{ENG} & High & Flexible & 65-70 & Easy & Biodegradable, \\ \cline{2-5}  & \(\bullet\bullet\bullet\) & \(\bullet\bullet\) & \(\bullet\bullet\bullet\) & \(\bullet\bullet\bullet\) & non-recyclable \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative table of material properties.
# Effects of Glyphosate and Its Metabolite AMPA on Aquatic Organisms
## 2 Glyphosate (N-(phosphonomethyl)glycine)
### Toxic Effects
#### 2.3.1 Fish
For example, [PERSON] (2018) have studied chronic exposure to glyphosate with a concentration of 1 ug/L on rainbow trout for 10 months. No significant changes in reproduction, metabolism, nor even oxidative response were observed. However, some occasional impacts on immune response have occurred. Other chronic effects have been studied with different concentrations of glyphosate (0.2, 0.8, 4 and 16 mg/L) in _Oreochromis niloticus_ for 80 days (2018). It was found that glyphosate exposure reduces antioxidative ability, disturbs liver metabolism, promote inflammation, and suppresses immunity.
# Simulation of Self-Consumption Photovoltaic Installations: Profitability Thresholds
## 2 Materials and Methods
### Characterisation of the Power Produced by the Collectors  
To evaluate self-consumption systems, in this work the behaviour of a self-consumption photovoltaic plant was simulated in 12 synoptic days of the installation (one for each month of the year). In this way, the simulation method provides a detailed analysis of energy balances every hour, estimating every 10 min the power generated by the PV system and the power demanded/injected into the electrical network.  
\[P^{PV}=\eta\bigg{\{}\frac{I(t)}{I_{STC}}PP[1-k(T_{cell}-T_{STC})]\bigg{\}}, \tag{1}\]  
where:  
\(I(t)\) is the irradiance on the plane of the inclined collector (expressed in \(\text{Wm}^{-2}\)) in the instant of time \(t\),  
\(I_{STC}\) is the irradiance under standard conditions (1000 \(\text{Wm}^{-2}\)),  
\(PP\) is the maximum power generated under standard conditions by the photovoltaic modules (in W),\(k\) is the coefficient of decrease in performance due to the increase in cell temperature, which for silicon cells, is set at 0.004 \({}^{\circ}\)C\({}^{-1}\),  
\(\eta\) is a performance factor that groups the investment return and the possible losses in generation and distribution,  
\(T_{STC}\) is the cell temperature in the standardised assay (25 \({}^{\circ}\)C), and  
\(T_{cell}\) is the actual working temperature of the cell that can be evaluated by means of Equation (2), where \(T_{a}\) is the ambient temperature and \(T_{NOC}\) is a parameter of the cell that must be provided by the manufacturer and that represents the temperature of the photovoltaic cells under nominal operating conditions.  
\[T_{cell}=T_{a}+\frac{T_{NOC}-20}{800}I(t), \tag{2}\]  
In order to estimate the solar irradiance in the collector plane, \(I(t)\), the use of the model of [PERSON] et al. [37] is proposed, which estimates the global solar irradiance on inclined surfaces as the sum of the direct, reflected and diffuse components, which in turn is decomposed into three terms: diffuse isotropic, diffuse circumsolar and diffuse from the horizon. Thus, the solar irradiance is given by Equation (3) where \(\rho\) is the albedo and \(F_{1}\), \(F_{1}\), \(a_{p}\) and \(b_{p}\) are the weighting parameters of the model that allow the diffuse irradiance to be calculated as the sum of the three subcomponents defined above.  
\[I=\frac{\cos\theta}{\cos\theta_{z}}I_{B}+\left[(1-F_{1})\frac{1+\cos\beta}{2}+ F_{1}\frac{a_{p}}{b_{p}}+F_{2}sin\beta\right]I_{D}+\rho\frac{1-\cos\beta}{2} \ (I_{B}+I_{D}), \tag{3}\]  
According to this model, the solar irradiance in the collector plane depends on the direct irradiance, \(I_{B}\), and diffuse, \(I_{D}\), on the horizontal plane, which are estimated from the monthly average radiation on the twelve representative days of each month of the year, using, for this, the model of [PERSON] et al. [38]. The dependency at each instant of the irradiance in the collector plane with respect to its geometry is characterised by the variables \(\theta\), \(\theta_{z}\) and \(\beta\) that are defined below.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 4 Simulation Results and Analysis
### Simulation of Multi-Goal Path Planning  
#### 4.5.1 Multi-Goal Path Planning in Static Environment  
In this section, we improve the D* Lite to support multi-goal path planning and apply it to a static environment. We set one start node and three goal nodes in the static environment. Figure 15 shows the static environment and the planned path.  
First, the improved D* Lite plans a path from the goal node (\(Goal\_1\)) to the start node. Affected by the obstacles and safe distance, the algorithm plans a straight line between  
Figure 14: Efficiency evaluation of minimum binary heap.  
Figure 15: Multi-goal path planning in a static environment.  
_Goal_1_ and position \(c\), and then turns left to position \(b\). Influenced by the two obstacles above, there is a curve between positions \(b\) and \(c\). Constrained by the maximum steering angle, the path slightly turns left and moves towards the start node. After planning, the USV starts to move from the start node to node _Goal_1_ along the planned path. When the USV reaches _Goal_1_, the algorithm starts to plan a new path from _Goal_2_ to _Goal_1_. Finally, the USV reaches the destination of _Goal_3_ according to the planned path.
## 2 Modeling and Formulation
### Reporting and Failure Detection  
Three types of messages exist between \(BS\) and nodes in the sensing field: the first is the \(AHM\) that all nodes send to inform the \(BS\) about their own status for the first time; the second is from the \(BS\) to the nodes, informing the nodes about their location area \(SN_{i}^{j}\) and \(CH\) after dividing the field; and the third is the recharging request from the nodes to the \(BS\), as shown in Equation (7).  
\[Eq_{k}=\left(n_{k},SN_{i}^{j},ER_{k},rt_{k}\right) \tag{7}\]  
\[Qc=\left\{Eq_{k},Eq_{k+1},\ldots\right\},\;k\geq 1 \tag{8}\]  
The \(BS\) receives the sensor-recharging requests (\(Eq_{k}\)) and adds these requests to the queue (\(Qc\)), as shown in Equation (8). The \(BS\) collects incoming messages from the nodes and processes these data to solve node problems by either recharging or replacing the nodes. The \(BS\) receives various abnormal recharging requests from the same node. Even after being recharged shortly (i.e., after recharging or during the charging process), the same node still sends a request for recharging. In this study, we assume that the maximum battery lifetime is the given battery material breakthroughs for ultra-fast charging [27]. Therefore, depending on \(Eq_{k}\), the \(BS\) decides which sectors and the supposed failed nodes that should be replaced first. The \(BS\) then organizes the number of requests for each sector in the sector replacement sequence (\(SRs\)), as shown in the following equation:  
\[SRs=\left\{\left(SN_{i}^{j},NR_{i}\right),\left(SN_{i}^{j}+1,NR_{i+1}\right), \ldots\right\} \tag{9}\]  
where \(NR_{i}\) is the number of nodes in the sectors that must be replaced. Before the Air-R starts a new round trip (\(RT\)) to replace the nodes in the sectors, the \(BS\) calculates and analyzes the information based on the _priority number_ (\(PN_{\max}\)), which is the number of abnormal recharging requests \(Eq_{k}\) for each node (i.e., if some nodes have a large number of abnormal recharging requests in a sector, then they are failed nodes and have high priority to send the Air-R to replace them). The \(BS\) sends the node replacement sequences (\(NRs\)) to the Air-R with the \(PN_{\max}\), as shown as follows:  
\[NRs=\left\{\left(SN_{i}^{j},n_{k},PN_{\max}\right),\left(SN_{i}^{j}+1,n_{k+1}, PN_{\max}\right),\ldots\right\} \tag{10}\]
## 4 Integration of Biosensors
### Node Configuration
#### 4.2.3. Routing Node Configuration  
* Routing algorithm: Routing nodes, a crucial part of multi-hop IoT networks, use dynamic algorithms such as AODV and DSR to efficiently route data through the network [82]. AODV dynamically establishes routes based on the current state of the network, which is crucial for dynamic IoT environments. In contrast, DSR focuses on real-time route discovery, optimizing transmissions through route caching. Both AODV and DSR are able to handle dynamic environments, adjust routing paths, and adapt to topology changes for reliable communication in IoT scenarios [83]. Following an on-demand routing approach, AODV and DSR establish routes only when needed, conserving network resources and promoting energy efficiency. AODV uses sequence numbers for route identification, and DSR efficiently manages routing through route caching, adopting a decentralized approach for scalability. In a multi-hop IoT network, AODV initiates route discovery when a device needs to send data. Using AODV, the source node broadcasts route request (RREQ) messages to its neighbor nodes when it needs a route. Neighboring nodes receiving the RREQ either forward it or respond with a route reply (RREP) if they have a valid route, while intermediate nodes record the route back to the source in the RREQ. In DSR, the source node determines the complete route to the destination as a network link, with each intermediate node acting as a routing node. However, depending on network conditions, energy efficiency, and application requirements, routing nodes in multi-hop IoT networks use AODV and DSR for efficient route discovery, maintenance, and forwarding, as shown in Figure 11.
* Memory and processing power: Memory resources are an important factor for effective routing-node configuration in multi-hop IoT networks, as they play a vital role in storing routing tables and network topology information [84]. In dynamic IoT environments, sufficient memory allows for rapid adaptation to frequent network  
Figure 11. A routing protocol diagram for AODV and DSR.  
topology changes, ensuring efficient routing decisions [85]. Insufficient memory can lead to increased latency and packet loss, impacting performance [86]. Processing power is also essential, especially for dynamic routing algorithms such as AODV and DSR, which require efficient execution to reduce latency and improve network responsiveness [87]. In resource-challenged IoT environments, it is a critical balancing act between adaptability and resource management. As networks grow, the demand for memory and processing power increases, requiring scalable routing node configurations to ensure continued reliability and performance. Memory and processing power are essential for maintaining efficient routing, adaptability, and overall network performance in multi-hop IoT networks.
## 5 Results and Discussion
### Significance of the CVROSS
#### 5.3.1 Smart Parking for Roadside Operations  
In the field of urban development, smart cities are thought to be a future trend and emerging technologies are applied to formulate different forms of decision support and intelligence, to improve efficiency and effectiveness. In the evolution of the smart city, smart mobility is specific to objects (including human beings), transportation and logistics. The ontology of smart parking is developed from smart transportation, which is an active research area. In this paper, smart parking for roadside operations was applied, to eliminate double-parking and enhance roadside occupancy. Via the adoption of IoT technologies, the new topic of smart parking has been explored to address the problem of double-parking at the roadside. Therefore, novel contributions relating to smart parking have been made in this paper.
## 3 Materials and Methods  
DVB, technical grade (64% o-DVB, m-DVB, p-DVB, and 36% ethylstyrene, as determined by gas chromatography), was purchased from Purolite (Victoria, Romania), and used as received. Benzoyl peroxide (BPO) was obtained from Fluka, and employed as a radical polymerization initiator after recrystallization twice from methanol. Acrylonitrile (AN), purchased from Sigma-Aldrich (Burlington, MA, USA), was distilled at 76-77 \({}^{\circ}\)C under a pressure of 101.3 \(\times\) 10\({}^{3}\) Pa. 1,2-Diaminoethane (EDA), also purchased from Sigma-Aldrich, was distilled at 118 \({}^{\circ}\)C prior to use. Dimethylformamide (DMF), purchased from Sigma-Aldrich, was used as received. HCl and NaOH were purchased from Chemical Company (Iasi, Romania), and used as received. Dibenzylphosphite ([PERSON], Tehn \(\geq\) 85%), diethylphosphite Fluka, Tehn \(\geq\) 90%), 3-pyridylinecarboxylaldehyde (Aldrich, 98%), tetrahydrofuran (Sigma-Aldrich, anhydrous, \(>\)99.9%) and ethanol p. a. (Chimopar, Bucuresti, Romania) were used as materials for the preparation of polymeric antimicrobial agents.  
The antimicrobial activity was tested using standard bacterial strains: Gram-positive _Staphylococcus aureus_ (ATCC 25923), and Gram-negative _Escherichia coli_ (ATCC 25922), both obtained from Microbiogics Inc. (Saint Cloud, MN, USA). The bacterial cultures were grown on Mueller-Hinton Agar (MHA).  
FTIR spectroscopy (KBr pellets) was performed on a JASCO FTIR spectrophotometer (JASCO, Tokyo, Japan) in the range 4000-400 cm\({}^{-1}\). Products were characterized by thermogravimetric analysis on a TGA/SDTA 851-LF1100-Mettler (Switzerland) device at a temperature ranging from 25 to 800 \({}^{\circ}\)C and at a heating rate of 10 \({}^{\circ}\)C/min, under nitrogen atmosphere. The elemental composition of the copolymers was evaluated with an Energy-Dispersive X-ray (EDX) analyzer (Octane Elect Super SDD detector, Ametek, Berwyn, PA, USA) equipped on a Verios G4 UC (Thermo Scientific, Brno, Czech Republic) SEM device and Elemental Analyzer UNICUBE--Elementar Analysensysteme GmbH, Langenselbold (Berlin, Germany).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Antimicrobial Materials** & **Kinetic Parameters \({}^{\ast}\)** & _E. coli_ & _S. aureus_ \\ \hline  & \(\chi^{2}\) & 0.00006 & 0.0014 \\ Bz-DVB-AN & b & 0.01675 & 0.02811 \\  & n & 1.21009 & 1.0716 \\ \hline  & \(\chi^{2}\) & 0.00326 & 0.002188 \\ Et-DVB-AN & b & 0.06697 & 0.04805 \\  & n & 0.85784 & 1.14772 \\ \hline \hline \end{tabular} \({}^{\ast}\)b is Weibull scale parameter; n is Weibull dimensionless shape parameter.  
\end{table}
Table 5: Weibull model parameters for _E. coli_ and _S. aureus_ reduction by treatment with Bz-DVB-AN and Et-DVB-AN.
## 4 Verification by Experiment  
### Introduction of Fiber-Packaged Long-Gauge Fiber Bragg Grating (FBG) Sensors  
Basalt fiber-packaged FBG sensors are utilized in the experiment. The design and manufacturing of fiber-packaged FBG sensors are demonstrated in Figure 10. A single packaged sensor is mainly composed of two parts: sensing section and anchoring points. The plastic separation tube outside the bare FBG makes sure the average strain between two anchoring points can be measured, and the length of the separation tube indicates the sensing length. In addition, basalt fibers outside the separation tube can guarantee the durability of sensors. It is noteworthy that basalt fibers should be reinforced with epoxy resin to produce a certain degree of stiffness, and pre-strain of the bare FBG can be transferred by this reinforced tube for the measurement of compressive strain. Detailed sensing properties about this kind of sensor can be found in [26].  
Figure 9: Results of sensor optimization.