esa-sceva/satcom-chunk-collection huggingface dataset:
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
###### Abstract  
Wireless sensor networks (WSNs) represent one of the most promising technologies for precision farming. Over the next few years, a significant increase in the use of such systems on commercial farms is expected. WSNs present a number of problems, regarding scalability, interoperability, communications, connectivity with databases and data processing. Different Internet of Things middleware is appearing to overcome these challenges. This paper checks whether one of these middleware, FIWARE, is suitable for the development of agricultural applications. To the authors' knowledge, there are no works that show how to use FIWARE in precision agriculture and study its appropriateness, its scalability and its efficiency for this kind of applications. To do this, a testbed has been designed and implemented to simulate different deployments and load conditions. The testbed is a typical FIWARE application, complete, yet simple and comprehensible enough to show the main features and components of FIWARE, as well as the complexity of using this technology. Although the testbed has been deployed in a laboratory environment, its design is based on the analysis of an Internet of Things use case scenario in the domain of precision agriculture.  
precision agriculture; wireless sensor networks; Internet of Things; FIWARE +
Footnote †: journal: Sensors
## 4 Discussion
### Interpretation and Clinical Relevance  
Only a few common variables were observed between the top three models, suggesting that different algorithms predict different variables as predictors for the model. In the RF model, high BMI, cataracts, lipid metabolism, and abnormal glucose levels are significant predominant predictors of glaucoma, whereas in the LR model, gender, gentamicin, and obesity have higher coefficients with glaucoma (Appendix B). The OR analysis shows that cataracts, atherosclerosis, and Type 2 diabetes led to a higher risk of developing glaucoma. Many studies have reported that African-American race is a significant risk factor for glaucoma. Analysis of the risk factors in the underlying genetic makeup of African Americans, as [PERSON] et al. have reported, shows that genetic polymorphisms in the mitochondrial cytochrome c oxidase subunit 1, which plays a role in trabecular meshwork function, are higher among African Americans, which may have a role in the increased prevalence of primary open angle glaucoma [44]. Furthermore, obesity, hypertension, cataracts, atherosclerosis, and Type 2 diabetes could be good predictors for the development of glaucoma before its clinical onset. [PERSON] et al. previously conducted a meta-analysis to better understand the relationship between hypertension and POAG, using IOP as endpoints. They identified 60 studies, including seven longitudinal cohort analyses, from various parts of the world. Virtually all studies showed a positive association between hypertension and POAG. However, when the RF model was compared to the logistic regression model in the current study, hypertension was found to be the most significant risk factor for glaucoma. The RF model predicted hypertension as a top-five critical glaucoma risk factor with the highest accuracy (Appendix B).  
Observing only a few common variables between the models suggests that each model uses a different set of variables for prediction. It is critical to understand the clinical relevance of the variables and their uses in the clinical application as an evolution measure of ML-predicted features. Integrating the clinically relevant predictive model into the EHR data would enable healthcare providers to target suitable patients for proper early intervention. The application of an evidence-driven ML predictive model in the clinical setting could be a significant step forward in preventing glaucoma-related vision loss by identifying patients' susceptibility to the onset of glaucoma and allowing for early intervention.
## 3 Results and Discussion
### The Effect of the Ratio of Spin Probe Concentration in Solution to Cell Amount on the ESR Spectrum  
The parameters of ESR spectra recorded at 240 K, reflecting endocytosis, were analyzed depending on the ratio of the spin probe amount in the solution to the number of yeast cells. The initial studies were conducted to determine the appropriate ratio of TEMPO (TEMPO) concentration to cell amount to observe differences in ESR spectra. The solutions studied were prepared for the ratio: TEMPO/TEMPO molecule amount/yeast cell number ranging from 0.7 \(\times\) 10\({}^{9}\) to 1.7 \(\times\) 10\({}^{9}\). The differences in endocytosis cannot be observed by ESR measurements for ratios below 1.7 \(\times\) 10\({}^{9}\). The concentration of spin probes used in current studies (0.5 g/L, which corresponds to 3.2 mM for TEMPO and 2.9 mM for TEMPO) does not affect the multiplication of yeast cells and even slightly stimulates proliferation [27]. The volume of yeast cell suspension (162 \(\times\) 10\({}^{9}\)/mL) was varied to obtain a specific ratio of the spin probe amount to cell number.  
If the ratio is about 1.7 \(\times\) 10\({}^{9}\), endocytosis occurs very quickly. Only 30 s after injection of TEMPO solution with incubated yeast cells (310 K), the ESR spectrum at 240 K was recorded as a narrow triplet, NT (Figure 3, spectrum 3 and Figure 5a). It confirmed the penetration of all TEMPO molecules into the cells.  
However, TEMPO molecules are characterized by a slower penetration rate into yeast cells. Therefore, endocytosis proceeds relatively slowly. Complex ESR spectra are recorded, i.e., wide WT and narrow NT triplets (Figure 5b), if their concentration is significantly larger than the number of cells. Additionally, further spectrum modifications depend on endocytosis rate and/or recombination processes over incubation time. The spectrum turns into a narrow NT triplet (Figure 3, spectrum 3), or a narrow triplet disappears, and only/mostly a broad WT remains (Figure 3, spectrum 4). The latter is observed when the recombination process is faster than endocytosis. There are evidently some differences in spectroscopic parameters for observed spectra. For Figure 5a--narrow triplet (NT)--a total hyperfine splitting is 3.3 mT and line widths are \(\Delta\)H\({}_{1}\) = 0.6 mT, \(\Delta\)H\({}_{2}\) = 0.6 mT, and \(\Delta\)H\({}_{3}\) = 0.8 mT. For Figure 5b, it is difficult to calculate the line widths. However, the total  
Figure 5: ESR spectra of spin probes with yeast cells at 240 K: (**a**) TEMPO with the narrow triplet; (**b**) TEMPOL with wide (WT) and narrow (NT) triplets.  
hyperfine splitting for a wide triplet (WT) is 7.5 mT, and for a narrow one (NT) it is also 3.3 mT.
# Network Visualization of Cooperative Games
## 6 Examples  
This paper explores ten canonical examples to illustrate how their network representation displays relevant information contained in different types of cooperative games. Among these, the Airport Game example appears in 103 reviewed references, including a seminal discussion in [44]. The Bankruptcy Game example is noted in 129 references, with one prominent citation in [45]. The Voting Game example, referenced in 593 studies, is similarly acknowledged in [44]. Although the Shoe Game example is mentioned in only 40 references, it serves as an important illustration drawn from [46]. Additionally, Myerson Game examples are recognized in 163 studies, as discussed in [47]. Together, these examples substantiate this paper's value proposition by demonstrating how network representations can capture essential insights into cooperative game dynamics.  
Let us note that among many other things, the visualization of the interaction between players in a cooperative game allows us to identify clusters of players highly related by the need for cooperation. From the examples in this section, we can see that this visual representation simplifies the detection of interconnected players, revealing underlying patterns and structures within the cooperative game. By observing these clusters, researchers can better understand the relationships and dependencies among players, leading to more informed hypotheses and enhanced analytical insights.  
This fact can be seen especially in the example in Section 6.1 (and also in the example in Section 6.2), where players LG1 and LG2 form a cluster with players MG1, MG2, and MG3. It is clear that LG1 and LG2 have a real need to be together with MG1, MG2, and MG3, which cannot be easily identified in other ways. Nodes SG1, SG2, SG3, and SG4 do not present the same interest or necessity in the large coalition as the cluster of LG1, LG2, MG1, MG2, and MG3.  
Please be aware that the values of \(\psi_{i}\) and \(r_{i,j}\) shown in the \(\Psi\) and \(R\) vectors and matrices of the following examples are truncated to four decimal positions. This means that summing their column values may not result in the exact expected value.  
We will use the weighted degree \(s_{i}\) of the nodes in \(R=I^{+}\), \(R=I^{-}\), \(R=I\), and \(R=I^{|\cdot|}\) to determine the statistical characteristics of each network. Please take the measures shown in each case as examples of the analytical potential offered by the proposed representation method, with the reader realizing that they could make use of other centrality measures depending on the needs they have.  
In addition to the Shapley value, there are other values for measuring the relevance of the different players in cooperative games. Among these, we should highlight those calculated as weighted averages of the marginal contributions, such as the Banzhaf value [41] or the Owen value [48,49], and those arising from the concept of the core, such as the nucleolus [43]. If, by the definition of a game, a measure other than the Shapley value is considered to be the most appropriate, the graphs should be represented with that measure.
## 2 Material and Methods
### Location Determination Coordinates and GCP  
The model required the determination of location coordinates, the installation and measurement of Ground Control Points (GCP), aerial photos, aerial photo processing, tsunami distribution point formulation, tsunami distribution point visualization, and tsunami distribution point distance measurement (start point distance and endpoint distance). Table 1 below is the location of the coordinates for determining the location to fly the drone.  
To fly the drone at the research location, used the DroneDeploy software to establish the autopilot setting, as illustrated in Figure 4.  
Before flying the drone, ten Ground Control Points (GCPs) were installed on the ground for the global positioning of object points (Premark). The coordinate point parameters used as control points in the coordinate transformation are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Object Type** & **Latitude** & **Longitude** \\ \hline Point 1 & 7°43′34.81′′ S & 109°12.67′′ E \\ Point 2 & 7°43′50.25′′ S & 109°11.77′′ E \\ Point 3 & 7°43′50.39′′ S & 109°126.60′′ E \\ Point 4 & 7°43′35.17′′ S & 109°132.46′′ E \\ Point 5 & 7°43′29.21′′ S & 109°123.71′′ E \\ Point 6 & 7°43′28.87′′ S & 109°118.27′′ E \\ Point 7 & 7°43′34.73′′ S & 109°114.32′′ E \\ \hline \hline \end{tabular}
\end{table}
Table 1: Determination of Location Coordinates.  
Figure 3: Maximum tsunami height along the south coast of Java, the \(x\)-axis presents longitude information and the \(y\)-axis is models tsunami height. Taken from [PERSON] et al., 2020.  
Figure 2: Map of Cilacap, Source: www.google.com/maps (accessed on 15 September 2021).
## 3 SPME Process
### Quantitation  
Contrary to traditional sample preparation methods such as liquid-liquid extraction, solid-phase extraction or Soxleth, SPME is a non-exhaustive technique where only a fraction of the target analyte is extracted from the sample [22]. Consequently SPME must be calibrated carefully for quantitative analysis.  
Quantification of analytes in complex food and environmental matrices is currently one of the major challenges of SPME procedures. Nevertheless, several international standards applying the SPME methodology have recently been implemented, proving that SPME has been accepted as a state-of-the-art analytical technique at least in some fields of application (e.g., environmental). Firstly, the International Standard ISO 27108 [159] \"Water quality-Determination of selected plant treatment agents and biocide products-Method using solid-phase microextraction (SPME) followed by gas chromatography-mass spectrometry (GC-MS)\" has been established. The described procedure is based on the German Standard DIN 38407-34 [160]. Another standard based on DIN 38407-41 [161] has been published as a Draft International Standard in 2014: \"Water quality - Determination of volatile organic compounds in water-Method using headspace solid-phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) (ISO/DIS 17943) [162]. In addition, two other standards applying SPME methods have been published: the Austrian OENORM A 1117 [163] (Determination of volatile compounds in cellulose based materials by SPME) and the American ASTM D 7363a [164] (Standard test method for determination of parent and alkyl polycyclic aromatics in sediment pore water using solid-phase microextraction and gas chromatography/mass spectrometry in selected ion monitoring mode).  
Essential to developing quantitative SPME methods is the knowledge of fundamental principles governing the mass transfer of analytes in multiphase systems, such as thermodynamics and mass-transfer kinetics [5, 43, 44]. The current available SPME calibration methods can be categorized into traditional calibration methods including external standard, internal standard and standard addition as well as newer methods such as equilibrium extraction, exhaustive extraction and diffusion-based calibration (see Table 2). [PERSON] and [PERSON] [18, 22] published a review giving more detailed information about the mentioned individual methods.
## 6 Experiment
### Simulation Experiment \(D\)  
In this section, the feasibility of the proposed scheme in dynamical environments, which consisted of 2 dynamic obstacles represented by red blocks and other static obstacles represented by black blocks, was tested. The starting point of the mobile robot was (3.5, 2.5), and the end point was set to (28.5, 28.5). The dynamic obstacles moved vertically down and right at a rate of 2 m per time step and moved a total of 6 steps. Figure 12 shows the process of MuL-ACO generating the optimal trajectory. In the initial trajectory generation stage, the planned trajectory avoided steep regions and dynamic obstacles in the environment and contained several redundant trajectory nodes. In particular, it can be seen that the planned trajectory actively bypassed the vertical downward moving obstacles and adjusted the trajectory to reach the end point. In the trajectory optimization stage, the length and smoothness of the initial trajectory were minimized by a mutual learning-based trajectory optimization algorithm. The specific length and smoothness of the initial trajectory and the optimized trajectory are given in Table 6. Clearly, the proposed scheme can stably plan desired trajectories in dynamic environments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Map**} & \multirow{2}{*}{**Length**} & \multirow{2}{*}{**Iterations**} & \multirow{2}{*}{**Turns**} & \multicolumn{2}{c}{**Height**} & \multirow{2}{*}{**Time (s)**} \\  & **(with Different Start and End Points)** & & & & & \\ \hline \multirow{4}{*}{ACO} & a & 34.80 & 37 & 11 & 6.81 & 1.51 \\  & b & 35.80 & 25 & 16 & 7.44 & 1.40 \\  & c & 31.62 & 23 & 12 & 3.63 & 1.53 \\  & d & 38.60 & 15 & 21 & 11.02 & 1.52 \\ \hline \multirow{4}{*}{MH-ACO} & a & 41.80 & 23 & 5 & 0.34 & 1.63 \\  & b & 35.20 & 17 & 5 & 1.49 & 1.53 \\  & c & 32.80 & 14 & 8 & 2.65 & 1.55 \\  & d & 41.00 & 11 & 8 & 0.38 & 1.66 \\ \hline \multirow{4}{*}{MF-ACO} & a & 36.60 & 17 & 7 & 2.40 & 2.20 \\  & b & 36.20 & 13 & 6 & 2.07 & 2.10 \\  & c & 32.20 & 9 & 4 & 3.23 & 2.24 \\  & d & 40.20 & 10 & 4 & 4.33 & 2.13 \\ \hline \multirow{4}{*}{MuL-ACO} & a & 40.09 & 11 & 3 & 0.39 & 1.38 \\  & b & 34.84 & 9 & 3 & 1.49 & 1.12 \\ \cline{1-1}  & c & 30.89 & 10 & 3 & 3.04 & 1.24 \\ \cline{1-1}  & d & 39.42 & 6 & 4 & 0.38 & 1.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation results of ACO, MH-ACO, MF-ACO, and MuL-ACO on the map with different starting points and ending points.  
Figure 12: (**a–f**) Results of trajectory generation and optimization at different time steps (step = 1–6) in dynamic environments.
# Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions
###### Abstract  
Constellations of satellites are being proposed in large numbers; most of them are expected to be in orbit within the next decade. They will provide communication to unserved and underserved communities, enable global monitoring of Earth and enhance space observation. Mostly enabled by technology miniaturization, satellite constellations require a coordinated effort to face the technological limits in spacecraft operations and space traffic. At the moment in fact, no cost-effective infrastructure is available to withstand coordinated flight of large fleets of satellites. In order for large constellations to be sustainable, there is the need to efficiently integrate and use them in the current space framework. This review paper provides an overview of the available experience in constellation operations and statistical trends about upcoming constellations at the moment of writing. It highlights also the tools most often proposed in the analyzed works to overcome constellation management issues, such as applications of machine learning/artificial intelligence and resource/infrastructure sharing. As such, it is intended to be a useful resource for both identifying emerging trends in satellite constellations, and enabling technologies still requiring substantial development efforts.  
large constellations; operations; traffic; regulation; spacecraft +
Footnote †: journal:
## 4 Conclusions  
In conclusion, discretely tunable multiwavelength visible lasers were demonstrated through the solid-state SRS technique, SHG and SFG processes. The 1 st, 2 nd, and even 3 rd-order Stokes lasers with seven wavelengths of 1158.9, 1177.1, 1197.7, 1272.1, 1316.7, 1369.5, and 1493.9 nm were acquired through an external Raman oscillator, which cascades one Ba(NO\({}_{3}\))\({}_{2}\) crystal with two axis-orthogonal KGW Raman crystals as the Raman gain medium. As 120 mJ of the 1064 nm laser was adopted, a maximum energy of multiorder Stokes lasers of 44 mJ was obtained. Moreover, the aforementioned Stokes lasers were injected as the fundamental lasers for SHG and SFG processes in a KDP crystal. A total of 16 wavelengths, varying from 579.5 to 658.4 nm, can be generated with tuning the angle of the KDP crystal. As the \(\theta\) angle of KDP increases, the spectra of output lasers shift from the yellow waveband to the red waveband. Furthermore, 11 of above-mentioned visible lasers can be produced simultaneously when the angle of the KDP crystal is tuned to 43.5\({}^{\circ}\). The maximum energy of the  
Figure 4: (**a**) All detected 16 visible wavelengths from 579.48 to 658.48 nm; (**b**) measured spectra and energy of output visible lasers at different angles of the KDP crystal.  
Figure 5: The energy of the visible lasers with different angles of the KDP crystal.  
visible lasers reached 8.87 mJ, corresponding to an optical conversion efficiency of approximately 7.4%. This technique of utilizing a multicrystal based SRS oscillator combined with SHG/SFG provides a novel method for the production of high-energy multiwavelength visible solid-state lasers, as useful for scientific and industrial applications.  
Conceptualization, X.L. and J.C.; Data curation, Y.L. ([PERSON]); Formal analysis, X.L. and X.W.; Funding acquisition, Y.L. ([PERSON]); Investigation, X.L. and J.C.; Methodology, J.C.; Project administration, Y.P. and Y.L. ([PERSON]); Resources, J.C. and Y.L. ([PERSON]); Supervision, Y.P. and Y.L. ([PERSON]); Validation, X.L. and J.C.; Visualization, X.L. and G.L.; Writing--original draft, X.L. and J.C.; Writing--review and editing, Y.P., Z.H. and Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Shanghai Sailing Program, grant number 18 YF1426200; National Key R&D Program of China, grant number 2017 YFE0123700; Strategic Priority Research Program of the Chinese Academy of Sciences, grant number XDB1603; National Natural Science Foundation of China, grant numbers 61925507 and 62075227; Program of Shanghai Academic/Technology Research Leader, grant number 18 XD1404200; Shanghai Municipal Science and Technology Major Project, grant number 2017 SHZDZX02; Science and Technology Commission of Shanghai Municipality, grant number 18511109702; Youth Innovation Promotion Association CAS, grant number 2020248.  
The authors declare no conflict of interest.
###### Abstract  
In this paper, we propose a finite alphabet iterative decoder (FAID) named rate-compatible mutual-information-maximizing quantized shuffled min-sum (RC-MIM-QSMS) decoder, for decoding quasi-cyclic low-density parity-check (QC-LDPC) codes with various code rates. Our proposed decoder exchanges the coarsely quantized messages represented by symbols from finite alphabets and adopts single-input lookup tables (LUTs) to implement the node updates. To construct the LUTs used for decoding, we first propose a modified density evolution by considering the shuffled schedule to generate the LUTs which vary with different layers and iterations. Furthermore, to reduce the memory requirement for storing the LUTs, we optimize the constructed LUTs into a unique set of LUTs that only change with different decoding iterations. To the best of our knowledge, the RC-MIM-QSMS decoder is the first one to integrate the rate compatibility of LDPC codes with the shuffled decoding schedule. Simulation results show that the proposed RC-MIM-QSMS decoder outperforms the floating-point shuffled belief propagation decoder in the high signal-to-noise region and achieves comparable convergence speed to other state-of-the-art FAIDs. Moreover, the RC-MIM-QSMS decoder is able to save up to 93.22% memory requirement compared to the benchmark MIM-FAIDs.  
low-density parity-check codes; finite alphabet iterative decoder; mutual-information-maximizing; quasi-cyclic; rate-compatible 2022113206. [[https://doi.org/10.3390/electronics11193206](https://doi.org/10.3390/electronics11193206)]([https://doi.org/10.3390/electronics11193206](https://doi.org/10.3390/electronics11193206))