esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 4 Discussions  
The long initialization time is still a serious problem of PPP, which limits the wider application of PPP in some time-critical applications. The fast motion of LEO satellites contributes to geometry diversity, allowing for the rapid convergence of PPP [21; 22; 23]. Since the previous studies focused on the contribution of LEO constellation to dual-frequency GNSS float solutions, in this contribution, we investigated the PPP rapid ambiguity resolution with the augmentation of LEO constellations and the triple-frequency observation data was fully exploited to further improve the performance of ambiguity resolution. The results demonstrated that the TTFF of the GREC fixed solution can be shortened from 7.1 to 4.8, 1.1, and 0.7 min and the positioning accuracy can be improved by about 60%, 80%, and 90% with the augmentation of 60, 192, and 288 LEO satellites, respectively. Moreover, the TTFF of 288-LEO augmented GREC solutions can be shortened to 33 s with triple-frequency  
Figure 12: TTFF of LEO-only PPP AR for each user station.  
Figure 13: Positioning accuracy of 1-min static LEO-only float and fixed solutions.  
observations. The joint of the third frequency brings more combinations to assist the narrow-lane ambiguity resolution, which can further improve the performance of the PPP AR. The 288-LEO constellation has the capability of achieving high-precision positioning independently, with the average TTFF of 71.8 s and 55.2 s in dual-frequency and triple-frequency modes, respectively. Since the LEO constellation is still in the demonstration stage or under construction, the contribution of multiple LEO-constellations and signals to PPP AR was investigated based on the simulation data. With more and more LEO satellites available, it is necessary to assess the augmentation performance of LEO based on the measured data.  
The fusion of GNSS and LEO constellation can significantly increase the number of observed satellites; optimize the spatial geometry; and improve the convergence, accuracy, continuity, and reliability of precise positioning. Especially, the multi-frequency signals of LEO constellation bring new opportunities for rapid ambiguity resolution. In the sequential studies, the observations of different LEO constellations with different orbit altitudes and types (such as a combination of equatorial and polar circular orbits) should be fully exploited to further improve the multi-GNSS performance. In addition, the LEO constellation not only enhances precise positioning applications, but can also provide a high-resolution (time and space) observable for monitoring of the neutral atmosphere, which might be highly valuable for meteorological applications such as now-casting of severe weather events or regional short-term forecast systems.
# Methods for Detecting Picric Acid--A Review of Recent Progress
## 3 Sensors for Detecting and Measuring PA  
### Metal-Organic Frameworks Sensor  
Metal-organic frameworks (MOFs) have attracted significant research interest due to their versatile catalytic [43], adsorption [44] and gas separation properties [45; 46].  
MOFs are composed of metal ions or clusters and organic ligands (\"linkers\"). Based on the geometry of one-, two- or three-dimensional coordination networks, organic linkers and coordination modes of inorganic metal ions, their structures can be modified depending on the desired properties.  
MOFs containing \(\pi\)-rich conjugated bonds have been shown to possess strong luminescence properties. The luminescent character of the materials is determined by various properties and phenomena, including ligand-based luminescence, ligand-to-metal charge transfer, metal-to-ligand charge transfer, antenna effects, sensitivity, excimer/exciplex emission and surface activity [47].  
This type of MOF luminescence has shown great potential for the production of fluorescent sensors.  
Some MOFs exhibit higher porosity than conventional porous materials, such as zeolites or carbon materials, because the pores in MOFs can be tuned by various combinations of metal ions and organic linkers, while functional sites can be readily immobilised on pore surfaces for their specific molecular recognition. The ultra-high porosity of MOFs, reaching up to 90% of the free volume [48] and large internal surfaces, exceeding the Langmuir area of 10,000 m\({}^{2}\)/g [49; 50] is another reason for using MOFs as selective sensors. MOFs as fluorescent sensors work on the principle of luminescence quenching. It is usually induced by the collapse of the MOF backbone, ion exchange between the central metal ion of the sensor and the compound to be analysed, or competitive absorption of light of the same wavelength between the sensor and the molecule [51; 52; 53; 54]. In dynamic quenching, electrons are directly transferred between the excited fluorescent material and the quenching agent in the form of a collision. Electron or energy transfer can be the main cause of quenching [55; 56]. The main factor determining the efficiency of electron or energy transfer is the distance between the quenching agent and the MOF [57].
## 4 Generic Context Aware Cross-Layer Framework
### Ontology-Based AmI Context Model
#### 4.3.1 Generality of the Model  
While the context classes and attributes of the ontology model can be used in different AmI scenarios, each instance of this ontology will be populated by context data that is scenario-specific. Therefore, the ontology model can be scenario-dependent, but only for its context data. Apart from the ontology model, the two other components of the proposed framework: communication mechanism and node architecture described in Sections 4.1 and 4.2, respectively, are scenario-independent, _i.e._, the same communication mechanism and node architecture can be applied to different AmI scenarios.  
To illustrate the generality of the ontology model itself (not its context data), we followed a design evaluation method called _scenarios_[17], which constructs detailed scenarios around the artefact (in this case our ontology model) to demonstrate its utility. Table 1 shows how contexts from four different AmI scenarios in literature: smart home [18], smart office [19], outdoor smart lighting [20], and elderly care [21], can be expressed by the same ontology model.
## 5 Conclusions  
In this study, we fabricated a simple circadian light meter that can simultaneously measure visual effects (VIL) and non-visual effects (melanopic EDI and CIL) of light using optical filters that selectively transmit light. The circadian light meter was verified and calibrated in a natural light environment with a wide illuminance range. Finally, the light environment to which the participants were exposed was recorded for 24 h and compared with a natural light environment. The conclusions are as follows:  
* To directly measure the three types of illuminance values, optical filters that mimic the corresponding functions were simulated and fabricated. The percentage spectral mismatches of the V-, Mel-, and C-filters were 7.27%, 7.67%, and 7.74%, respectively. Because the transmittance spectra of the optical filters mimic the respective action spectrum, an identical method can be applied to measure other types of illuminance not mentioned in this study. * The circadian light meter was manufactured using a simple circuit with a single gain resistor and a log-scale amplifier, and can measure illuminance over a wide range from approximately 0.1 to 50,000 lx. However, the f1\({}^{\prime}\) of the VIL measurement channel recorded 13.1%. In particular, the f2 of the VIL, melanopic EDI, and CIL channel were 24.4%, 22.2%, and 25.9%, respectively, so further improvement of off-axis irradiance is required. * The measured melanopic EDI and CIL values differed only slightly depending on the optical filter used. While a non-standard CIL was used in this study to allow a simple comparison with the CIE standard melanopic EDI, but it was not clear whether the spectral resolution of the filters used could distinguish the difference between the two illuminances. In other words, the circadian light meter can be further simplified and miniaturized to measure only the melanopic EDI. * Using the circadian light meter, it was quantitatively confirmed that the participants lived in a light environment with very low illuminance levels compared to natural light during the daytime: the maximum VIL and melanopic EDI values of natural light were 53,609 \(\pm\) 1280 lx and 52,511 \(\pm\) 888 lx, respectively, while those values of the light exposed to the participants were 1580 \(\pm\) 702 lx and 1342 \(\pm\) 567 lx, respectively. These considerable differences have the potential to disturb the circadian rhythm. * Although additional tightly controlled clinical and physiological studies on the non-visual effects of light and circadian rhythm are necessary, nevertheless, the circadian light meter has a potential to be a useful tool to enhance and maintain an individual's circadian rhythm. In addition, if the circadian light meter is commercialized at a reasonable price, it may be valuable in large-scale field studies considering the individual's sensitivity to blue light and its effects on melatonin levels and changes.
## 3 Results and Discussion
### Key Volatile Compounds in Yellow Wine Fermentation  
Figure 4 shows the ROAV of the characteristic flavor compounds. Aldehydes with lower threshold values contributed more to the flavor of yellow wine throughout the fermentation process. Nonanal and decanal were the key flavor substances at each stage. Although there is only one acid in the fermentation process, its influence on the flavor of yellow wine cannot be ignored. Acetic acid is a common volatile organic acid. It is one of the main flavor substances in the F3 and F4.  
Figure 4: The ROAV for the 10 characteristic volatiles (H27, H30, and H31 were not queried for thresholds). The higher the ROAV, the greater its contribution to the overall flavor.  
Alcohols were also more abundant in yellow wine, and their content increased significantly at the early stage of fermentation. The threshold of alcohols is relatively higher than aldehyde, but it plays an important role in enhancing the overall flavor of yellow wine. The four characteristic esters screened were ROAV > 1, indicating that they are key volatile flavor substances in the yellow wine fermentation process. One of the most frequent alcohols in yellow wine is phenylethyl alcohol, which is mostly created via an amino-acid-based metabolic pathway [37]. Phenylalanine is decarboxylated to produce an aldehyde, which is then converted to alcohol. In addition, the 1-Hexanol, [R,R]-2,3-butanediol, and 1-phenylethanol found in this study have rarely been recorded, and more research is needed to evaluate their effect on the flavor of yellow wine.  
Esters, as the most common volatile substances in yellow wine, were detected throughout the fermentation. In red yeast yellow wine, ethyl butyrate and ethyl laurate contribute significantly to the flavor; in sorghum yellow wine, the more abundant esters are mostly ethyl lactate and ethyl phenylacetate [38]. These esters were discovered in this study as well, although they were not key flavor compounds and contributed less to the overall flavor. The key esters we found in the pair were ethyl tetradecanoic and ethyl hexadecanoic, which contribute significantly to the flavor of yellow wine at all five stages. In addition, ethyl acetate, a common ester, has a relatively high content in F3, but does not contribute as much to flavor as other compounds due to its high threshold.
## 2 Overview of Smart Water Grid Technologies and Systems
### Technological Description  
#### 2.2.1 Sensing Devices  
Sensing devices that collect and transmit data about the water system on a real-time basis is the foundation of any smart water grid. At the municipal level, the most common way to monitor the water delivery system has been to manually read flow and pressure meters, while water contamination is commonly monitored by collecting water samples that are analyzed in a laboratory environment. In a smart water grid system, these parameters would be collected, stored, and transmitted to a computer by the meter itself, or from a sensor to detect contamination. This increases the amount and frequency of information about the system and decreases the need for field work. Smart sensors for municipalities include smart water meters for flow, smart water meters for pressure, and contaminant sensors and biosensors for contamination detection [16; 17].  
Smart water meters have additional advantages over manual meters. One of these advantages is increased sensitivity to low water flows, which increases data collection accuracy. Other advantages of these more sensitive meters include the ability to measure backflow, which can indicate a problem in the system. They are also less susceptible to corrosion from particles in the system [18].  
Whether in a residential, commercial or industrial setting, the typical situation for water use detection is a single flow meter measuring total water consumption of a facility. How total water consumption breaks down for different uses is generally not measured. Only measuring total flow has two disadvantages: leaks are difficult to detect by metering and users lack information on potential inefficiencies in the system. One option is to install additional meters within a facility. With current technology, installing meters for every fixture would be prohibitively expensive for most end-users.  
An alternative to installing additional flow meters is to use a device that measure pressure waves. Each fixture has a pressure \"signature\" that propagates through the piping system, and a sensitive pressure-gauge can distinguish between these signatures. The HydroSense technology developed by [PERSON] and others needs only one sensor to determine the disaggregated use of all fixtures (e.g., faucets, toilets, and dishwashers) in a single family home [19]. If a fixture starts to leak, the end-use sensing device will pick up this flow as \"noise\" in the system. For larger end-users, multiple smart meters and end-use sensing devices would be more appropriate. The key point is that a combined flow meter and pressure sensor system requires fewer devices, substantially reducing costs.  
Figure 1: A simplified diagram of an example urban smart water grid.  
A technological addition to the water system that is useful for water storage is the flood sensor. Flood sensors detect strain on water infrastructure, such as dams, and detect early flooding over flood embankments [20].
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 6 Conclusions  
A new multiple-access scheme based on the trellis tone modulation, TTMMA, was proposed. Unlike the conventional FDMA-based scheme, TTMMA performs the discovery of multiple UEs on the same resource. This eliminates the need for strict collision avoidance and collision detection techniques, so that the discovery procedure can be designed concisely. The proposed message-passing demodulation scheme effectively discovers multiple UEs from a single DRU and the discovery capacity is resultantly increased. In addition, the TTMMA scheme generates a single-tone discovery signal to concentrate the signal energy and solve the PAPR problem of OFDM transmission, thereby increasing the transmission signal power and greatly improving the discovery range. The proposed TTMMA scheme significantly outperforms the conventional FDMA-based scheme at lower computational complexity.  
The proposed TTMMA scheme can be used without being limited to the D2D discovery, but to more general multiple-access environments. In particular, the fact that PAPR is set to 1 by transmitting signal generation in a single-tone manner can be a solution to the persistent problem of uplink communication in conventional mobile communication systems. In addition, the capacity of the uplink can be increased by improving the amount of information that can be transmitted with the same resource.  
**Acknowledgments:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2018R1A2B6004195) and (NRF-2015R1D1A1A01058975).  
**Author Contributions:** [PERSON] and [PERSON] coinvented the proposed scheme: the TTMMA scheme and the demodulation algorithm and analyzed its complexity. [PERSON] mainly wrote the manuscript. [PERSON] ran the simulations. [PERSON] designed the evaluation and comparison method and provided the complexity analysis of the conventional scheme. All the authors have put much effort on the writing of the manuscript and they have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Demodulation** & **Decoding** & **Total** \\ \hline FDMA-based (w/o SIC) & 3456 (for 6 DRUs ) & 221664 (for 6 DRUs) & 225120 (for 6 DRUs) \\ FDMA-based (w/ SIC) & 3456 \(\sim\)8064 (for 14 UEs) & 221664 \(\sim\)517216 (for 14 UEs) & 225120 \(\sim\)525280 (for 14 UEs) \\ TTMMA & 175824 & N/A & 175824 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of computational complexity in number of additions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(N_{modsym}\) & **144** & \(N_{mc}\) & **512** \\ \(N_{constel}\) & 4 & \(N\) & 75 \\ \(N_{DRU1}\) & 6 & \(I_{max}\) & 3 \\ \(N_{UE}\) & 1 \(\sim\)14 & \(M\) & 12 \\ \(L\) & 8 & \(d\) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in performance evaluation.
# Characterization of Sorghum Processed through Dry Heat Treatment and Milling
## 3 Results and Discussion
### Functional Properties and Proximate Composition
#### 3.1.3 Optimization and Model Validation  
The optimization process for each sorghum flour fraction revealed that dry treatment at 121.00 \({}^{\circ}\)C would be recommended for S fraction, 132.11 \({}^{\circ}\)C would be appropriate for the M fraction, while for L fraction a temperature of 139.47 \({}^{\circ}\)C (Table 5) can be applied to obtain the desired functional and nutritional properties.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Variables** & **Protein** & **Fat** & **Ash** & **Moisture** & **Carbohydrates** & **OAC** & **WRC** & **HC** & **SP** & **EA** & **ES** \\ \hline
**Protein** & \(1.00\) & & & & & & & & & & \\
**Fat** & \(-0.77\)** & 1.00 & & & & & & & & & \\
**Ash** & \(-0.86\) & 0.93** & 1.00 & & & & & & & & \\
**Moisture** & \(-0.12\) & 0.19 & 0.23 & 1.00 & & & & & & & \\
**Carbohydrates** & \(0.51\)** & \(-0.89\)** & \(-0.82\)** & \(-0.53\)** & 1.00 & & & & & & \\
**OAC** & \(-0.52\)** & 0.82** & 0.84** & 0.26 & \(-0.85\)** & 1.00 & & & & & \\
**WRC** & \(0.50\)** & \(-0.69\)** & \(-0.66\)** & \(-0.28\) & 0.68** & \(-0.50\)** & 1.00 & & & & \\
**HC** & \(0.55\)** & \(-0.44\)** & \(-0.41\)** & \(-0.35\) & 0.38 & \(-0.21\) & \(-0.11\) & 1.00 & & & \\
**SP** & \(-0.75\)** & \(0.80\)** & \(0.84\)** & 0.16 & \(-0.68\)** & \(0.71\)** & \(-0.70\)** & \(-0.14\) & 1.00 & & & \\
**EA** & \(0.60\)** & \(-0.83\)** & \(-0.75\)** & \(-0.51\)** & 0.87** & \(-0.80\)** & 0.44** & 0.59** & \(-0.66\) & 1.00 & & \\
**ES** & \(0.20\) & \(-0.34\) & \(-0.11\) & \(-0.11\) & 0.28 & \(-0.15\) & \(-0.15\) & 0.69** & \(-0.07\) & 0.60** & 1.00 \\ \hline \hline \end{tabular}  
* \(p<0.05\), ** \(p<0.01\), HC—hydration capacity, OAC—oil-absorption capacity, SP—swelling power, WRC—water-retention capacity, EA—emulsion activity, ES—emulsion stability.  
\end{table}
Table 4: Pearson correlation coefficients.  
Mathematical model validation revealed that the differences between the predicted and experimental values were less than 10%. Compared to Control S, the Optimal S sample presented higher OAC, SP, ES, fat, and ash contents, while the WRC, EA, and carbohydrates were lower. The Optimal M sample exhibited higher EA, ES, protein, fat, and ash contents and lower SP, WRC, and carbohydrates than Control M. Raised HC and ash content and smaller WRC, EA, ES, protein, fat, and carbohydrates values were observed for the Optimal L sample than Control L. Fractionation can lead to nutrient structure changes, mainly due to photo-oxidation, and to the decrease of some compounds' bioavailability [12]. Previous research revealed that sorghum grain processing through thermal treatment, soaking, and steaming can significantly influence physical tissue structure, nutrient levels, and functional properties of grains [43].
## 5 Proposed Multi-EC Enabled Air-Ground VN Scenarios
### Cost Efficient Federated Learning Platform for Vehicular Applications
#### 5.1.2 Possible Challenges  
Here we describe the important challenges that need to be considered for implementing an efficient FL platform over a Multi-EC enabled VN.  
* **Proper FL-server selection** The FL process training cost can depend upon the number of users participating in the training process, channel environment, the transmission distance between FL devices and the server, type of ML model, types and quality of datasets, etc. Selecting a proper FL-server can limit the training cost. The FL server should have better coverage, high computation capacity, a short transmission distance, and better channel environments. However, most of these properties are contradictory and thus require tradeoffs while selecting the FL server node. For example, RSU EC nodes can be considered as an FL-server for reducing transmission distances and better quality channel environments but with coverage limitations, only a few VUs can participate in the training process. Similarly, considering the LAPs/UAVs as FL-server can be costly due to their limited coverage, shorter flight times, etc. On the other hand, HAP-EC nodes can have better coverage for allowing many VUs to participate in the training process; however, long transmission distance and poor quality channel environment can be challenging.
* **FL Device Selection** It is not always possible and advisable to consider all VUs data while performing the FL training process. It is often the case that some of the VUs might not be able to transmit their ML model parameters to the FL server because of poor transmission medium. Some VUs might have a poor quality dataset which results in poor training performance. Considering such VUs into the FL training process can degrade the overall training process performance. Therefore a suitable strategy is required to be implemented for selecting a proper set of VUs for the training process.
* **Training Cost Vs Convergence in FL** During FL, the training process can be continued till the loss function value converges to some predefined value. For having a reduced FL training process, the number of FL rounds performed by each VU can be limited based upon its available resources.