esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work presents a process to measure CF using relatively straightforward methods and interpretation compared to other approaches aiming to capture fluorescence responses. Additionally, the technique can be applied to a single plant or larger canopies using LED, in dark conditions. The ability to collect data at leaf, plant, and canopy levels reliably and scale between these levels effectively with measurable uncertainties could also be applied, for instance, using manned or unmanned vehicles in agricultural or military applications. This scaling cannot be accomplished as easily or at all with other photosynthesis measurement techniques, such as pulse amplitude modulated fluorimetry (PAM). These procedures can be effectively employed as-is or with modifications in vegetation research and applications in multiple ways. For instance, plant function on multiple timescales could be further investigated by employing calibration for LEDs and examining timescales where signals would be statistically significantly different. In addition, CF measurements with our technique could be used in conjunction with PRI and NDVI measurements to inform plant studies along multiple timescales, i.e., CF is sensitive on the finer scale to track photosynthetic function and PRI on longer timescales to inform changes in leaf pigment, and add NDVI for seasonal structure changes. Therefore, the HH2 and LED system presented has broad appeal to multiple vegetation research areas.  
**Author Contributions:** Conceptualization, T.M.; methodology, T.M., R.B., M.L.S.P.J. and J.R.; data curation and analysis T.M., R.B., J.R. and S.P.; writing--original draft preparation, T.M.; writing--review and editing, T.M., R.B., S.P., M.L.S.P.J. and J.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Goetz Instrument Loan Program (2015) through ASD (Analytical Spectral Devices), as well as a partnership between the Sao Paulo Research Foundation (Fundacao de Amparo A Pesquisa do Estado de Sao Paulo, FAPESP), Vanderbilt University, Nashville, TN, USA and Universidade Estadual Paulista (UNESP), Rio Claro, Brazil (FAPESP #2013/50421-2 and 20599-00-5).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data and code are freely available. Location and access provided upon request to the primary author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lithium Metal: The Key to Green Transportation
### Properties of Lithium towards Lithium-Ion Battery  
Lithium, like other metals, oxidizes in the air to form Li\({}_{2}\)O, Li\({}_{3}\)N, Li\({}_{2}\)CO\({}_{3}\), and LiOH. Among them, Li\({}_{2}\)CO\({}_{3}\) is the favorable product of weathering lithium metal. However, the oxidation rate remains relatively low, resulting in the inefficiency of the process [44]. Not only does the air influence the oxidation of lithium, but also other parameters such as humidity, the atmosphere composition, duration of exposition to the air, temperature, and thickness of lithium-sheets are involved [45]. The study by [PERSON] et al. [44] revealed that it requires two days to complete the oxidation of 1 mm of lithium-sheet thickness at constant ambient temperature. The oxidation speed and efficiency can be accelerated by higher temperatures [19]. Those properties of resisting oxidation in ambient conditions make lithium a selective element to be used as an anode for batteries. However, once the reaction is accelerated by a high temperature, lithium creates a high affinity with oxygen and nitrogen. Even efficient or fast oxidation is successful at higher temperatures [44,45], resulting in the non-recycling of lithium-anode production residues. Table 1 lists the types of LIB based on the applications.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Applications** & **Estimated Global Market Share (\%)** \\ \hline Primary Lithium & They are single-use LIB for electronics, that range from button cells to car batteries. & n/a \\ \hline Lithium cobalt oxide (LiCoO\({}_{2}\)) & They have the high energy storage density required for portable electronics. Thus, used in portable electronic devices (e.g., phones, laptops, tablets, cameras, etc.). & 37.2 \\ Lithium nickel manganese cobalt oxide (NMC) (LiNiMnCoO\({}_{2}\)) & They are used in power tools, EVs, energy storage, and medical devices. & 29 \\ \hline Lithium manganese oxide (LiMn\({}_{2}\)O\({}_{4}\)) & It has a shorter life than others and has a high discharge or recharge with better thermal stability. It is used in power tools, EVs, and medical devices. & 21.4 \\ \hline Lithium nickel oxide (LiNiO\({}_{2}\)) & They are used in EVs. & 7.2 \\ \hline Lithium iron phosphate (LiFePO\({}_{4}\)) & Not thermally stable as other cathodes. They are used in energy storage tools, EVs, and medical devices & 5.2 \\ \hline \end{tabular}
\end{table}
Table 1: Applications of different Lithium-ion batteries. Adapted from [43,46].  
Figure 2: Main applications of lithium.
## 4 Results and Discussions
### Physicochemical Properties
#### 4.1.4 Specific Gravity  
Specific gravity is the ratio of the density of a material (mass per unit volume) to the density of water, indicating its relative density compared to water. Compressive softening tests on various engineering materials have shown that the origin of rock samples influences the degree of softening experienced when submerged in water. Over time, these rocks swell gradually, resulting in a loss of both strength and density [19]. For rocks to be considered suitable as building materials for large structures, their specific gravity typically needs to be greater than 2.55. The specific gravity values of the tested samples are presented in Table 4 and Figure 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Number** & **Cube Sample** & **Weight in Water (\(W_{2}\))** & **Oven-Dried Weight (\(W_{s}\))** & **Specific Gravity** \\ \hline Pi1 & Pi1 (a) & 232.27 & 368.75 & 2.666 \\ \hline \multirow{3}{*}{P2} & Pi2 (a) & 569.24 & 903.95 & 2.674 \\ \cline{2-5}  & Pi2 (b) & 604.15 & 960.21 & 2.677 \\ \cline{2-5}  & Pi2 (c) & 585.99 & 929.89 & 2.680 \\ \hline \multirow{3}{*}{P3} & Pi3 (a) & 592.35 & 941.24 & 2.683 \\ \cline{2-5}  & Pi3 (b) & 603.47 & 957.89 & 2.688 \\ \cline{2-5}  & Pi3 (c) & 605.55 & 961.88 & 2.687 \\ \hline \multirow{3}{*}{Pi4} & Pi4 (a) & 605.09 & 960.67 & 2.694 \\ \cline{2-5}  & Pi4 (b) & 604.79 & 959.72 & 2.689 \\ \cline{2-5}  & Pi4 (c) & 593.34 & 943.13 & 2.676 \\ \hline \multirow{3}{*}{P5} & Pi5 (a) & 247.94 & 394.24 & 2.671 \\ \cline{2-5}  & Pi5 (b) & 590.23 & 938.52 & 2.671 \\ \cline{2-5}  & Pi5 (c) & 580.71 & 921.59 & 2.678 \\ \hline \multirow{3}{*}{Pi6} & Pi6 (a) & 585.34 & 938.19 & 2.634 \\ \cline{2-5}  & Pi6 (b) & 567.51 & 909.56 & 2.635 \\ \cline{2-5}  & Pi6 (c) & 593.15 & 922.78 & 2.784 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specific gravity values for the studied samples.  
Figure 12: Specific gravity results for the Inzari Formation rock samples. The specific gravity values, which range between 2.81 and 2.86, are shown here. Higher specific gravity values indicate denser rock material, supporting the conclusion that these rocks are suitable for large-scale construction projects.  
From Table 4 and Figure 12, it is evident that the specific gravity values of the samples range between 2.81 and 2.86, suggesting that they are well suited for intensive construction tasks. The specific gravity of rock samples is calculated using two key measurements: the weight of the specimen in water and its weight after drying in the oven. These measurements were used to determine the specific gravity using Formula (4). Additionally, the specific gravity values of the Inzari Formation rocks, which range from 2.81 to 2.86, are comparable to those of the NikanaiGhar limestones, which were reported to have values between 2.71 and 2.81, further supporting the suitability of Inzari Formation rocks for construction purposes.  
\[Specific\;gravity=\frac{w_{3}-w_{2}}{w_{3}}\times 100 \tag{4}\]
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 3 Methods for Real-Time Food Monitoring
### CO\({}_{2}\) Detectors  
Two main groups of optical sensors have been mainly reported for CO\({}_{2}\) monitoring in food packages. The first group is based on luminescent dyes which modify their fluorescence upon CO\({}_{2}\) exposure, while the second is based on colorimetric indicators sensitive to the pH changes induced by CO\({}_{2}\) hydrolysis in the sensor matrix [47, 85, 86, 87, 88, 89]. Luminescent dyes are characterized by performing extreme sensing capabilities, but they require sophisticated instruments, both for excitation and sensing, and are not suitable for consumer use. One of the first devices of this type was developed by embedding 8-hydroxypyrene-1,3,5-trisulfonic acid within a polymeric film, obtaining a response and a recovery of less than 10 s [90]. Such a system was further developed by first immobilizing the fluorophore molecule on a silica matrix [88] and then adding a dual fluorophore referencing system [47]. However, since the chemicals required for the sensor production are not food-grade, it is, hence, of fundamental importance that they do not become in contact with the products. To develop CO\({}_{2}\) sensors with actual applicability in large-scale packages, recent efforts have been devoted to the exploitation of colorimetric indicators. These generally have a lower sensitivity with respect to luminescent dyes but are much cheaper, safer, and provide a response that is discernible by the human eye without the need for any instrumentation [47, 91, 92, 93]. For example, the m-cresol purple dye has been broadly employed to fabricate colored polymer films sensible to protonation and deprotonation [94, 95, 96, 97, 98], as schematically shown in Figure 1b, which reports a colorimetric detector based on a mixture of bromothymol blue, bromocresol green and phenol red [85]. The absorbance spectrum of the mixture in a CO\({}_{2}\)-free environment (top panel of Figure 1a,  
Figure 1: (**a**) O\({}_{2}\) sensors based on a colorimetric redox dye. Reproduced with permission from [7]. Copyright © 2019 American Chemical Society. (**b**) Label color changes in response to CO\({}_{2}\). Adapted with permission from [85]. Copyright © 2014 Elsevier B.V. All rights reserved.  
purple line) shows a maximum in the green region, corresponding to a purple color, as depicted in the bottom panel of the same figure. Upon CO\({}_{2}\) exposure, the previously mentioned peak is quenched and a second one arises in the blue and violet spectral regions. Thereby, such a variation allows the color of the system to visibly turn from purple to green upon CO\({}_{2}\) exposure. Additionally, both a good shelf-life and functional design have been demonstrated by using food-grade formulations comprised of L-lysine, poly L-lysine, and anthocyanins [47].
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 3 Proposed Power Control for SC-GFDMA  
Based on the maximization problem in (22), the basic strategy of the proposed algorithm is to secure as many QoS-satisfied users as possible by adjusting the transmit powers of the users. Because it is difficult to solve the maximization problem in (22) explicitly, with respect to the transmit powers, we consider an intuitive approach that helps the scheduler secure more users after power control. Specifically, in each chunk, as a larger transmit power of a user causes a larger interference to other users, decreasing the transmit power of a user can increase the probability that a different user is selected in the same chunk; however, allocating excessively low transmit power to a user may result in excluding that user from scheduling because of the QoS constraint. Thus, the transmit power of  
Figure 2: Control plane procedure for power control and scheduling.  
each user should be carefully adjusted to be as small as possible while potentially satisfying the QoS constraint. The primary objective of this study is to analyze the condition to be satisfied by the transmit powers and to propose a power control algorithm based on the analyzed condition.  
Accordingly, we observe how the SINR \(\Gamma_{u,m}^{(i)}\) of each user behaves as the number of iterations of the IMD, \(i\), increases. Subsequently, based on the observation, a sufficient condition for the successive detection of consecutive users is proposed. The transmit powers of the users are then determined by the smallest values that satisfy both the sufficient condition and the QoS constraint \(\Gamma_{u,m}^{(I)}>\Gamma_{th}\).
## 3 Proposed Methods
### Reasoning-Based Scheduling Method
#### 3.2.3 Targets Selecting and Scheduling  
The target selection and scheduling process primarily involves four steps: (1) select the targets based on the weight calculation results; (2) determine whether the constraints are met; (3) obtain the scheduling results including the selection and order of observation targets; and (4) calculate the other decision variables.  
To provide a complete description of the RS algorithm, the target selection and scheduling were combined with the content in the previous two sections. The flowchart of the RS algorithm is shown in Figure 6 while the calculation steps are as follows:  
1. Predict the satellite states according to the method mentioned in Section 3.1, and calculate the preferred flag of targets using rule-based reasoning according to the method mentioned in Section 3.2;
2. Unselected targets form a set \(U_{u}\). Determine whether the sequence satisfies the attitude maneuver constraint after each unselected target is inserted at the position of the maximum sequence's backward time slack. Subsequently, select targets that satisfy the attitude maneuver constraint and form a target set that satisfy the attitude constraint \(U_{a}\). The attitude maneuver constraint is stated in Equation (3), and the calculation of the insertion position of the maximum backward time slack is included in Appendix C;
3. Calculate the weights of targets in \(U_{a}\) according to Equation (22);
4. If the number of targets in \(U_{a}\) is more than 0, go to step 5, otherwise, go to step 9;
5. Take out the target with the highest weight in \(U_{a}\) and insert it into the current observation sequence at the maximum sequence's backward time slack position. Then, update the target observation sequence and remove the selected target from \(U_{u}\);
6. Based on the target observation sequence, calculate the satellite's attitude, data transmission time windows, the data downloading time, occupied memory capacity, and available electrical energy, the calculation method of the satellite state can be found in Appendix B;Determine whether the data transmission constraints and electricity constraint are satisfied according to Equations (4)-(9). If the observation sequence does not satisfy the constraints after inserting the target, remove the target from the observation sequence and go to step 4, otherwise, go to step 8;
* If the number of unselected targets in \(U_{u}\) is more than 0, return to step 2, else, go to step 9;
* The observation sequence contains the information of \(x_{i}\) and \(g_{ij}\). According to \(x_{i}\) and \(g_{ij}\), calculate other decision variables and parameters including \(y_{i}\), \(h_{ij}\), \(t_{osi}\), and \(t_{disi}\) using the calculation method mentioned in Appendix B.  
Figure 6: Flowchart of RS.
## 3 Material and Methods
### Basin Prioritization Model (BPM)  
The basin prioritization model has been widely applied in soil erosion studies [6; 7; 30]. The prioritization of the sub-watersheds model was applied based on the distribution of several morphometric features to assess the detection of soil erosion spots [31; 32]. This  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Morphometric Parameters**} & \multicolumn{1}{c}{**Formula**} & \multicolumn{1}{c}{**References**} \\ \hline Basin area (\(A\)) & Projected area enclosed by sub-basin boundary & [20] \\  & Horizontal projection of length of basin divide & [20] \\ Basin perimeter (\(P\)) & Distance between outlet and farthest point on basin boundary & [20] \\ Basin length (\(Lb\)) & Hierarchical order & [14] \\ Stream orders (\(Lu\)) & \(Nu=N1+N2+\ldots Nn\) & [21] \\ Stream numbers (\(Nu\)) & \(Rb=Nu/Nu+1\), where Nu is the number of streams of any & [22] \\ Bifurcation ratio (\(Rb\)) & given order, and \(Nu+1\) represents the number for the next & \\  & higher order & \\ Channel length (\(Lc\)) & \(Lc=\)longest water path length in a given basin & [9] \\ Fitness ratio (\(Rf\)) & \(Rf=L/P\) & [23] \\ Form factor (\(F\bar{f}\)) & \(Ff=A/Lb^{2}\) & [24] \\ Shape factor (\(Sh\)-\(f\)) & \(Sh\)-\(f=1/Ff\) & [14] \\ Relative perimeter (\(Rp\)) & \(Rp=A/P\) & [20] \\ Length area relation (\(Lr\)) & \(Lr=1.4\times A^{0.6}\) & [17] \\ Rotundity coefficient (\(Rc\)) & \(Rc=Lb^{2}\times\pi/4A\) & [14] \\ Basin width (\(Wb\)) & \(Wh=A/Lb\) & [25] \\ Drainage texture (\(Dl\)) & \(Dl=Nu/P\) & [21] \\ Compactness coefficient (\(Cc\)) & \(Cc=0.282\times P/\surd A\) & [21] \\ Elongation ratio (\(Re\)) & \(Re=Dc/Lb\) & [20] \\ Circularity ratio (\(Rc\)) & \(Rc=4\pi\)\(A/P^{2}\) & [26] \\ Drainage density (\(Dl\)) & \(Dd=Lu/A\) & [14] \\ Stream frequency (\(F\)) & \(F=Nu/A\) & [25] \\ Channel maintenance constant (\(C\)-\(cm\)) & \(C\)-\(cm=1/Dd\) & [20] \\ Infiltration number (\(Inf\)) & \(Inf=F\times Dd\) & [27] \\ Drainage intensity (\(Dl\)) & \(Di=F/Dd\) & [28] \\ Basin overland flow length (\(Lg\)) & \(Di=F/Dd\) & [28] \\ Basin relief (\(H\)) & \(Lg=0.5\times Dd\) & [21] \\  & \(H=Z\)–\(Z\), where \(Z\) and z are the highest elevation and lowest & [29] \\ Relief ratio (\(Rhl\)) & elevation, respectively & \\ Ruggedness number (\(Rn\)) & \(Rhl=H/Lb\) & [28] \\ Melton ruggedness number (\(MRn\)) & \(Rn=Dd\times(H/1000)\) & [14] \\  & \(MRn=H/0.5\)\(A\) & [9] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphometric parameters applied in this study.  
model was designed based on the weighted sum approach (WSA), which was used for identifying the final priority of soil erosion impact at the scale of sub-basins. In this technique, the relative value for every single parameter was recognized, assessing the weightage of each single parameters regarding its significance in terms of the soil erodibility potential [6].
## 3 Establishment of Water Rut Safety Model and Analysis of Braking and Side Deflection  
### Simulation Model Establishment  
#### 3.1.1 Simulation Steps  
In order to carry out the simulation analysis of driving safety in waterlogged ruts, according to the data characteristics and research requirements of this paper, a waterlogged-rut driving safety simulation model is established. First, we build the full-car model in CarSim. The Vehicle Module in CarSim contains different types of vehicles, which can be selected according to the research needs. Secondly, we establish a road surface model and input road surface point cloud data through the Geometry Module. The adhesion coefficient of each road grid is input to CarSim through the Road Friction Map module. Finally, based on the analysis of vehicle braking and handling stability, the braking distance and side  
Figure 6: Adhesion coefficient varies with vehicle speed and water film thickness.  
deflection distance are selected as the output variables of water rut safety simulation to analyze the impact of water-filled ruts on driving safety.  
To verify that the CarSim simulation model established in this article can be used for the accurate and comprehensive evaluation of driving safety in waterlogged ruts, the simulation model established in this article is first validated. The difference between the model input used in this article and the previous scholars' simulation analysis of waterlogged ruts is that the previous scholars used a single adhesion coefficient converted from the maximum waterlogged depth of the ruts, while the adhesion coefficient input in this article is a matrix library based on road grid partitioning. For comparative verification, this article conducts simulation analysis on braking distance and offset distance based on the same waterlogged rutting under the two types of adhesion coefficient inputs mentioned above. In addition, braking and lateral deviation simulation analysis is performed on the waterlogged ruts at speeds of 60 km/h, 80 km/h, 100 km/h, 110 km/h, and 120 km/h. Figure 7 shows the variation curves of braking distance and lateral deviation distance with speed.  
From Figure 7, it can be seen that under the condition of water accumulation in the ruts, the braking distance and lateral deviation distance of both simulation models increase with the increase in speed, which is consistent with previous research conclusions. The braking distance and lateral deviation distance output by the model in this article are both smaller than the output values of previous models. This is because previous models used the maximum water depth to represent the water depth of the entire road section, while the grid adhesion coefficient input in this article is a gradually changing adhesion coefficient converted based on the actual water depth in each cell, which is closer to the actual state of the road surface.  
The flowchart for the establishment of the water rut safety model and analysis of braking and side deflection is shown in Figure 8.  
Figure 7: Simulation results of two models.
## 5 Results and Discussion
The profiles acquired in Croatia show an overall increase in the V\({}_{\rm P}\)/V\({}_{\rm S}\) ratio in the eastern lines (especially Line 3) compared to the western line. This indicates a more fractured medium that is possibly saturated with water. As for the Italian site, the V\({}_{\rm P}\)/V\({}_{\rm S}\) ratio is very high in both profiles, reaching a maximum in Line 2. The sharp increase in both lines at a depth of approximately 3 m confirms the position of the water table. Furthermore, such high V\({}_{\rm P}\)/V\({}_{\rm S}\) probably indicate the presence of water-saturated unconsolidated clay sediments. In Figure 7, we show profiles of the V\({}_{\rm SV}\)/V\({}_{\rm SH}\). In the Croatian site, shown in Figure 7a-c, the presence of anisotropy is due to the layering of the Eocene flysch [14]. From the geometrical considerations applied to the raypaths, it is possible to estimate the bedding angles (dip and strike), as shown in [14]. As for the Italian site, shown in Figure 7d,e the presence of anisotropy confirms that the sediments are composed of horizontal layers of clay and silt, which are known for producing anisotropy [33; 34]  
Figure 6: V\({}_{\rm P}\)/V\({}_{\rm SH}\) (**a**) KaStela Line 1. (**b**) KaStela Line 2. (**c**) KaStela Line 3. (**d**) Ferrara Line 1. (**e**) Ferrara Line 2.