esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Emerging Patterns from Existing Studies
### Spatial resolution requirements
[PERSON] _et al._[21] exploited this feature of irrigated areas in a two-dimensional near infrared versus red band spectral reflectivity plots of different land  
Figure 3: Sensitivity of four vegetation indices, namely NDVI, EVI, GI, and WDRVI, to irrigation presence as measured by Relative Sensitivity Index (RSI) in Nebraska for 2002. Each index was generated from an average response using a sample of approximately five individual sites. Please see the text for details on the RSI. Adapted from [PERSON] and [PERSON] [20]. cover types including that of irrigation (Figure 4). Typically, classes that have high near-infrared reflectivity and low red reflectivity are forests, agricultural lands and natural vegetation. Classes that occupy bright areas with high near-infrared and high red reflectivity are likely to be open/barren areas, sparse vegetation, dry vegetation, clouds and built-up areas. Classes that occupy wet areas have low near-infrared and low red reflectivity and are likely to be wetlands, moist lands, water bodies, cloud shadows and swamp forests. From irrigation perspective, these two dimensional spectral time curves provide very useful information on class behavior. For example, irrigated areas occupy the largest territory and the two dimensional plots provide very good indications of class temporal and spectral characteristics. Another approach to spectral identification of irrigation is to compare each pixel's greenness value to its regional mean as measured by an index. Here region is loosely defined and can include a  
Figure 4: Space-time spiral curves (ST-SCs) in class identification and labeling. The ST-SCs track changes of time series over time and across space. The numbers seen in each class represent Julian date and each class moves around a ‘territory’ in 2D feature space over time. Adapted from [PERSON] _et al._[57]. command area or an agro-ecological zone, and applies only to the same land cover type (e.g., agriculture). The assumption is that the pixels representing irrigated fields will have greater greenness than their non-irrigated counterparts within any region. Although it's examples are rare and far in-between in irrigation mapping studies, there is also significant value in considering hyperspectral narrow band indices for further improving our understanding and accurate identification of irrigated areas. Also known as imaging spectroscopy, hyperspectral imaging has been used under laboratory conditions for over 100 years for identification of materials and their composition. Spectroscopy can be used to detect individual absorption features due to specific chemical bonds in a solid, liquid, gas including vegetation. The key to a successful use of narrow band spectrometers in identifying irrigation status of vegetation canopies will be through detection of moisture stress (which is often non-existent in irrigated areas) and through leaf chemical properties that are unique under artifical watering conditions.
## 7 Discussion and Future Work
### The Android Platform for Research  
Sensor logging and measurement quality assessment on Android come with a set of challenges. During our research, we discovered that while there are numerous apps existing today for sensor logging, there were none suitable to log every sensor available on smartphones/smartwatches. Apps related to GNSS logging (e.g., GNSSlogger) only concentrate on motion-related sensors and do not allow customization on the sensor parameters. Therefore, we had to develop a new application to fit our research needs, which required a certain knowledge of Android development. As this app is open-source, it can be re-used and enhanced by the research community to be used in new applications. Alternatively, the dataset produced through this research can be used as also provided under open-source license.  
While the Android platform is not easy to access, it does allow common and easy access to raw measurements from sensors, which would require heavier hardware developments otherwise. Power management inside the device can prove challenging, as the system itself might perform automatic duty-cycling strategies which can impact sensor logging, which can be even trickier on smartwatches. An easy workaround for Mimir was to prevent screen lock and keep the app awake to avoid duty-cycling by the system. Yet, the assessment of power consumption is part of the future work for this research to review the impact of advanced positioning techniques on the battery life of a device.  
Regarding the smartwatch, developments have proven to be quite similar to the smartphone case. The common API made the development of a common Android library possible, avoiding the need for redundant code developments specific to each platform. Data extraction was, however, a bit more challenging, as the smartwatch did not have any USB port. Consequently, the data had to be extracted through WiFi and with the help of the Android development software. Future developments on the Mimir app will try to improve the interactivity between smartphone and smartwatch so that the data can be uploaded through a companion phone first before being extracted by USB.  
While the smartwatch offers a new playground for health-related sensors, they have not been reviewed here as outside the scope of this study. It has to be noted, however, that extraction of these sensor data is implemented in the Mimir app, and future research will also focus on reviewing their quality. As smartwatches can be very interesting for long-term study of patients, focus will be given on enhancing the energy-consumption of the app for extending the battery-life of the device.
# Convolutional Neural Network (CNN)-Based Frame Synchronization Method
###### Abstract
The proposed technique does not require any prior information except the correlator output. We examine the false detection probability (FDP) of the proposed CNN-based and the convolutional correlation-based methods through computer simulation. Without retraining the CNN, the FDP performances are evaluated under AWGN and fading channels. According to the results, it is verified that the proposed CNN-based technique outperforms the conventional method by 2 dB in both AWGN and fading channels. The main contribution of this study is summarized as follows. * CNN-based techniques to the frame synchronization problem. * 1D correlator output is transformed to a 2D signals for better training of the designed CNN. * The designed CNN is evaluated under various channel environments, namely, AWGN and fading channels. * The proposed CNN-based method improves approximately 2 dB SNR for the frame synchronization.
## 3 Results and Discussion
### Upv  
To validate the density results obtained from our experiment, we compared them with the ultrasonic pulse velocity (UPV) measurements and found that the two sets of data were in good agreement, confirming the accuracy of our density measurements. Figure 7 illustrates the effect of incorporating powder glass and PA fibers on the UPV of various cubes at different curing ages. The results indicate that the addition of 10% G without PA fibers leads to a slight increase in UPV at all curing ages. However, the UPV gradually decreases with the addition of 0.5, 1, and 1.5% PA fibers. This phenomenon can be attributed to the generation of voids, which is promoted by the presence of PA fibers, resulting in a decrease in pulse velocity. The voids or spaces in the material act as an obstacle to the passage of the mechanical impulse, thus decelerating the velocity of the pulse [69]. As the proportion of PA fibers in the mix increases, the number of voids in the material also increases, leading to a decrease in pulse velocity. Additionally, the type and size of fibers, the orientation and distribution of fibers within the material, and the curing conditions of the material also affect the pulse velocity [70]. It should be noted that all UPV values at 28 days exceed 4 km/s, indicating excellent to very good concrete quality [65].
# Encapsulation of Fennel Essential Oil in Calcium Alginate Microbeads via Electrostatic Extrusion
## 3 Results and Discussion
### Sphericity and Shrinkage of the Microbeads
As for the effect of drying, the freeze-dried microbeads had a significantly lower shrinkage factor, which was likely a result of minimized structural rearrangement by removing water without melting, as opposed to the air-drying which involves slow evaporation and, therefore, may result in the collapse of the structure and subsequent shrinkage of the beads. Similar results were observed by [PERSON] et al. [32], where freeze-drying of alginate beads resulted in over 20% lesser size reduction compared to vacuum- and air-drying. \begin{table}
\begin{tabular}{c c c c c} \hline
**Source of Variation** & **N** & **Sphericity Factor (Wet)** & **Sphericity Factor (Dry)** & **Shrinkage Factor** \\ \hline Alginate (\%, \(w/v\)) & & \(p\) = 0.93 & \(p\) = 0.03 * & \(p\) < 0.01 * \\
1.0 & 360 & 0.05 \(\pm\) 0.00 * & 0.13 \(\pm\) 0.00 * & 0.30 \(\pm\) 0.00 * \\
1.5 & 360 & 0.05 \(\pm\) 0.00 * & 0.14 \(\pm\) 0.00 * & 0.38 \(\pm\) 0.01 * \\
2.0 & 360 & 0.05 \(\pm\) 0.00 * & 0.11 \(\pm\) 0.00 * & 0.42 \(\pm\) 0.02 * \\ \hline Whey protein (\%, \(w/v\)) & & \(p\) < 0.01 * & \(p\) = 0.65 & \(p\) = 0.39 \\
0 & 360 & 0.06 \(\pm\) 0.00 * & 0.13 \(\pm\) 0.00 * & 0.35 \(\pm\) 0.00 * \\
0.75 & 360 & 0.05 \(\pm\) 0.00 * & 0.13 \(\pm\) 0.00 * & 0.36 \(\pm\) 0.01 * \\
1.5 & 360 & 0.04 \(\pm\) 0.00 * & 0.12 \(\pm\) 0.00 * & 0.39 \(\pm\) 0.02 * \\ \hline Alginate (\%, \(w/v\))/Whe protein (\%, \(w/v\)) & & \(p\) = 0.02 * & \(p\) = 0.30 & \(p\) < 0.01 * \\
1/0 & 120 & 0.07 \(\pm\) 0.01 * & 0.14 \(\pm\) 0.03 * & 0.25 \(\pm\) 0.02 * \\
1/0.75 & 120 & 0.04 \(\pm\) 0.01 * & 0.15 \(\pm\) 0.01 * & 0.35 \(\pm\) 0.01 * \\
1/1.5 & 120 & 0.03 \(\pm\) 0.01 * & 0.12 \(\pm\) 0.03 * & 0.21 \(\pm\) 0.02 * \\  & & \(p
## 3 Results and Discussion
### Effect of Adsorption Conditions
#### 3.3.7 Effect of Solution pH  
An important parameter, solution pH value, could affect the surface charge of adsorbent/adsorbate and interfacial transport phenomena [49]. The removal of MB by CPP200 at different pHs was studied (Figure 7E). Meanwhile, the zeta potentials of CPP200 in the pH range of 3.0-10.0 were measured in order to provide information about the surface charge of the adsorbent (Figure 7F). In the pH value range of 3-6, the zeta potential decreased from 13.167 to \(-\)38.833, respectively, indicating that an increase in surface electronegativity of BC would facilitate its adsorption capacity for cationic species. Under strong acidic conditions, CPP200 became protonated, which would repel cationic MB, resulting in lower adsorption capacities [50]. In addition, H\({}^{+}\) and cationic MB would compete for the active sites on the surface of CPP200 [51]. Therefore, the adsorption capacities of CPP200 were relatively lower at lower pH. The electronegativity of CPP200 reached its lowest value at pH = 6.30, while its maximum adsorption capacity could be obtained. In the pH range of 6-8, the zeta potential of CPP200 increased from \(-\)38.833 to \(-\)15.233, respectively, and the electrostatic attraction between CPP200 and MB gradually weakened, resulting in a gradual decrease in the substrate's adsorption capacity. At higher pH (>8.34), the surface electronegativity of CPP200 increased again due to the introduced OH\({}^{-}\), and interactions with cationic MB through electrostatic attraction became stronger. Therefore, the removal rate of MB by CPP200 increased again at high pH [52]. It is worth noting that the pH of aqueous MB solution is 7.32, and CPP200 exhibited slightly lower adsorption capacity for MB at this basicity than the maximum value obtained for it at a pH of 6.30 (Figure 7E). Considering the convenience of experimental operation and the reduced use of toxic reagents, aqueous MB solutions prepared by dissolving MB in deionized water without adjusting pH were directly used for the following adsorption experiments.
## 3 Results and Discussion
### Effect on Product Yield and Composition
#### 3.3.2 Effect on Shale Oil Yield  
Figure 7 displays the oil yields of various samples at different temperatures. The results show that the addition of AlCl3-6H2O to the OS by both mixing methods can increase the production of shale oil, particularly at higher temperatures. Based on the above analysis, this could be attributed to the addition of AlCl3-6H2O for carbonate removal, improvement of pore space, and catalytic effect on kerogen cracking. It is noteworthy that this promoting effect was more pronounced in the dry-mixed samples. This may be due to the fact that more carbon dioxide is produced by decomposition during pyrolysis in the dry-mixed samples, and its carryover effect during the release process also promotes the release of hydrocarbon products. In contrast, much less carbon dioxide is produced from carbonate decomposition in wet-mixed samples, thus weakening the promoting effect.
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.1 DBR [45]  
The authors in [45] suggested a depth-based routing (DBR) protocol handle dynamic networks while maintaining high energy efficiency. The DBR protocol employs a greedy method to transmit data packets from the source to the sink node. The node with the most significant depth difference is the best data-forwarding node. The depth of the forwarding node is continually decreased during the data transmission process to send the data to the sea surface (assuming no \"void\" zone is present). As a result, the DBR protocol's central principle is that the node determines packet forwarding based on its depth and the depth of the preceding sender. DBR uses the flooding technique for forwarding, it is not energy efficient in a dense network, and it would not work in sparse networks as it is hard for nodes to communicate as it can outside the communication zone.
# River Water Quality Monitoring Using LoRa-Based IoT
## 6 Conclusions and Future Work  
The developed prototype offers a reliable and affordable solution, providing real-time river water parameters. This enables timely action to address any irregularities.  
Other LPWAN networks were studied, but it was concluded that for this prototype, in a point-to-point connection, LoRa is the best option.  
Using Node-RED, an application was developed to display all information in real time and record data in daily files for future reference. Additionally, with MQTT, these data can be published externally to the system, such as in cloud applications.  
Radio coverage tests were conducted in an NLOS scenario within a suburban area. The results further validate the effectiveness of using LoRa technology in IoT applications.  
Emphasis was placed on reducing energy consumption, particularly for the IoT Node. Consumption measurements were conducted to determine the optimal energy supply solution for this subsystem, considering any LoRa modulation configuration for future use.  
A river was monitored for a few hours to demonstrate the prototype's functionality in a real-world scenario.  
One challenge is coverage, as riverbeds are typically located in valleys, which are obstructed and lack a direct line of sight to the reception Gateway.  
Integrating this system into the LoRaWAN network would be a valuable future enhancement. Additionally, upgrading the sensors to those designed for continuous  
Figure 33: River water parameter variation: (a) temperature, (b) pH, (c) conductivity and (d) turbidity.  
use and incorporating additional sensors to measure parameters such as dissolved oxygen levels or dissolved organic matter would further improve the system.  
Conceptualization, L.M.P. and J.G.; methodology, L.M.P. and J.G.; software, L.M.P. and J.G.; validation, L.M.P. and J.G.; formal analysis, L.M.P. and J.G.; investigation, [PERSON] and J.G.; resources, [PERSON] and [PERSON]; data curation, [PERSON] and J.G.; writing--original draft preparation, [PERSON] and J.G.; writing--review and editing, [PERSON] and J.G.; visualization, [PERSON] and J.G.; supervision, [PERSON] and J.G.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflicts of interest.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Simulation of Formation Health Optimization Management
#### 4.2.1 All Robots Are Healthy
In the above four cases, the health optimization management greatly increases the lifetime of the weakest robot, and the adjustment of the existing framework is very small. Health optimization management is very important for multi-robot formations to ensure that the formation can operate for a long period, to reduce health loss, and to ensure the reliability of robots in the task. It can be seen from Table 1 that by applying intelligent health balancing to each robot, the overall health value can be improved by about 20-30%, and the whole robot formation is more robust to health changes caused by many problems that may reasonably be expected. The system can deal better with accidental damage caused by collision, mechanical failure, or insufficient maintenance. Figure 13: Comparison before and after adding HOM in the third scenario: (**a**) \(h_{i}(t)\) of robots under normal conditions; (**b**) \(h_{i}(t)\) of robots under HOM; (**c**) \(e_{i}(t)\) under normal situations; (**d**) \(e_{i}(t)\) under HOM.
## 2 Literature Review
### Modeling and Simulation of Lattice Structures  
The numerical analysis of the mechanical behavior of large lattice structures is challenging due to the high computing (memory and time) requirements [4]. Large lattice structures demand heavy FEA meshes formed by solid elements. Sometimes solid FEA meshes can be simplified using simpler and lighter FEA elements (beams or shells). This approach has allowed for simulations of relatively large domains of a few hundred of cells [4; 15; 16; 17]. However, this technique cannot be applied to surface-type lattice structures like Schwarz primitive lattices, since this kind of architecture cannot be approximated by long struts or thin plates.  
Regarding the joint use of FEA and DOE, we found that they have been combined in several applications in non-lattice structures. The current work can be divided into three groups: (i) evaluation of material and mechanical properties, including metals [18], resins [19] and composite materials [20]; (ii) shape optimization of mechanical parts, including medical devices [21] and automobile parts [22]; and (iii) generation of meta-models to estimate the stress-strain responses in small lattice domains using DOE [5]. However, the produced meta-models are not used for any further analysis with large lattice domains. To the best of our knowledge, the works in the literature do not implement a methodology that integrates systematically material homogenization and DOE for stress-strain estimation in the field of lattice materials.  
Monte Carlo methods [23; 24] use random samples in the domains of input variables for an experiment. The experiment is run under the prescribed combination of input variables, and the resulting output values recorded. The model mathematical model for the system or cause/effect is computed on the basis of maximal likeness. In our case, the expenses of running each test are significant since each test requires the preparation and setup of the FEA experiment; the execution itself and its post-processing; and the analysis of results. This high cost is common to almost all experiments, and leads to choosing a minimal (and as possible orthogonal) set of samples of the input set, leading to DOE. This DOE, more economical than the Monte Carlo trials, was chosen for the present work.
## 3 Result and Discussion
### Vegetation Indices: Clustering Analysis Results  
We perform a cluster analysis, based on CLARA, on the vegetation index (NDVI, EVI, and SAVI) and burn index (NBR) to determine the pixel value range according to the cluster of vegetation regeneration areas for each index.  
An internal assessment of the vegetation indices showed that it was most appropriate to divide all three vegetation indices into seven clusters. The results of the internal assessment are shown in Table 5 and Figure 6.  
The optimal clusters by index are derived based on the internal evaluation results shown in Table 6. The results of the clustering analysis are shown in Figure 7.  
Figure 7a presents a cluster analysis according to NDVI. The green area that appears to be a reproduction zone corresponds to cluster number 1 of NDVI in Table 6 and is mainly consistent with the region at the enhanced regrowth level of dNBR. Figure 7b depicts the results obtained using EVI, which show the vegetation in the areas corresponding to cluster numbers 1 and 2 of EVI; further details are given in Table 6. As in the NDVI results, the observed regeneration zone matches most areas at the enhanced growth level of dNBR. Figure 7c presents the result of SAVI analysis and, like in the EVI results, regrowth of vegetation is evident in the areas corresponding to cluster numbers 1 and 2 of SAVI in Table 6. These regions match the regions at the enhanced growth level of dNBR. EVI was more limited in identifying regeneration areas than NDVI, with SAVI being intermediate between the two other indices.When visually compared using EVI or SAVI, it is evident that regrowth sites were sporadically distributed.
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 2 System Architecture
### Key Parameters Analysis  
The aforementioned structures can be compared using several benchmarks, such as system attenuation in the optical domain, the number of switch and splitter elements, and the average number of working switch elements. The optimal structure will be proven by comparing within these parameters.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 4 Rain Detection  
In the previous section, we have outlined the large variability of the rain signatures on C-band SAR co- and cross-polarized images with respect to incidence angle and to some extent to the underlying sea state. Due to the variety of rain-induced features, a simple methodology solely based on thresholds and NRCS intensities values may not be satisfactory enough. In the following, we thus consider textural information to attempt the detection and segmentation of rain on sea surface SAR images.