esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Application Layer
### Application Layer Interoperability  
Another opportunity for the application layer is to provide service-level adaptations via middleware [123; 124; 125]. One aspect of the application layer adaptation is the service-level interoperability. Different from the network-level interoperability discussed in Section 5, which is concerned with the physical connectivity, interoperations at the service-level are context-oriented, _i.e._, it takes into account the content of information and attributes of services [124]. Middleware implemented at the application layer can discover, manage and exchange application program interface (API) information, focusing on the interoperability between diverse services instead of devices. The awareness of context is another important part of _IoT_ middleware [125]. Information regarding the context can be collected by devices such as wireless sensors. Middleware, in turn, manages these data and converts them into context knowledge meta-data. Various _things_ can obtain context awareness via the middleware and adjust their behaviors accordingly.  
An example of _IoT_ middleware that facilitates interoperability can be found in [123]. Specifically, after the detection of devices, semantics of available services are generated. By invoking service through APIs, operation details are hidden from users and the tasks are conducted using necessary services with no restrictions on the locality of service providers.  
A semantic architecture for highly heterogeneous smart homes is prsented in [126]. Making use of the Java-based Open Services Gateway Initiative (OSGI) framework, it provides the interoperation and contextual knowledge retrieval among various devices. Additionally, the abstraction of APIs not only facilitates the integration, but also development of future services.  
The UbiRoad [127] is a middleware devised for smart road environments. Due to the nature of the application, the awareness of context poses a vital requirement. Through the middleware, the traffic context is collected using wireless sensors. Additionally. artificial intelligence (AI) is employed to deduce contextual knowledge about driver behaviors. Utilizing this context information, the system can act accordingly to exert traffic safeguarding measures.
## 5 Conclusions  
This research investigated the applicability of space-borne multi-frequency passive microwave observations for improving weather forecasting by retrieving soil moisture and cloud information using two data assimilation systems (_i.e._, LDAS-A and CALDAS) over a mesoscale domain of Niger, Africa.  
The results showed that the assimilated soil moisture was reasonably comparable with satellite retrievals obtained from NASA and JAXA, whereas the no-assimilation case produced higher soil moisture contents and completely different spatial distributions compared with the assimilated case. However, assimilating soil moisture alone produced only marginal improvements in the model forecasts of atmospheric profiles (_i.e._, potential temperature and specific humidity).  
The assimilated cloud distributions from CALDAS were coherent and they compared well with the observed IR cloud cell distributions. The seeding of cloud information from high-frequency passive microwave radiance enhanced the self-development of simulated cloud activities over the model domain. Consequently, simulated cloud activities exhibited positive correlations for a period of about 12 h following the assimilation, whereas the no-assimilation case demonstrated mostly negative correlations with the IR observations during this period.  
The simulated soundings of both potential temperature and specific humidity from CALDAS showed significant improvements from the surface to the middle atmospheric layers (\(\sim\)650), which resulted from improvements in the representations of land and cloud conditions in the model. Furthermore, the rainfall predicted by CALDAS correlated better with the TRMM retrievals than the no-cloud assimilation cases. In particular, the locations of the heavy rainfall were better predicted by CALDAS, whereas the no-assimilation case produced rainfall in completely different locations compared with the TRMM rainfall retrievals.  
These results are encouraging in terms of producing reliable land and atmospheric states as well as regional forecasts, especially in poorly gauged or un-gauged regions, because CALDAS requires only satellite data as input. The consideration of using other satellite information such as the Global Precipitation Measurement mission, AMSU, and AIRS within the system will be researched in future studies to improve the model's robustness and performance further.  
Figure 8: Comparison of 6 hours (from 03 UTC to 09 UTC) accumulated rainfall (mm) obtained from (**a**) ARPS; (**b**) CALDAS; and (**c**) TRMM, respectively.
## 3 Results and Discussion  
### Comparative Determination of VOCs Profile from \"Sabrina\" and \"Elide\" Strawberry Samples  
HS-SPME/GC-MS analysis of strawberry samples cultivated with three different mulch types (PE, N5, and N18) allowed to detect overall 69 and 65 VOCs in Elide and Sabrina cvs., respectively. In particular, the two strawberry cvs. contained VOCs of the same chemical classes, but only a few of these compounds, including alcohols (5), furans (3), lactones (3), and others (1), were common to the two cvs. Elide, in fact, presented 35 esters, 6 aldehydes, 8 acids, and 8 terpen, while Sabrina showed 32 esters, 5 aldehydes, 10 acids, and 6 terpen. At this regard, Table 1 lists the VOCs abbreviation code, the experimental and literature-reported Kovats index, and the identification methods for the two cvs. Tables S1 and S2 report the VOCs analysis of Elide and Sabrina, respectively, showing that the number and the content of aroma compounds varied between the two cvs. According to previous studies, esters were the most represented chemical class in both the cvs. accounting for about 71.2%, 59.8%, and 58.1% for PE, N5, and N18, respectively, in Elide and 50.4%, 55.0%, and 57.0% for PE, N5, and N18, respectively, in Sabrina (Tables S1 and S2). More in detail, methyl and ethyl esters appeared the most important contributors to the strawberry flavour, imparting fruity and floral aromas [14; 15]. In Elide strawberries, the predominant esters were trans-2-hexen-1-ol acetate (E20), ethyl hexanoate (E15), and ethyl butyrate (E6) for PE and N5 mulching (30.2%, 10.7%, and 8.9% for PE, respectively, and 18.3%, 12.2%, and 9.2% for N5, respectively), while ethyl cinnamate (E35) followed by E15, E20, E6, and methyl hexanoate (E14) were the most abundant esters detected in fruits grown on N18 (14.0%, 12.3%, 9.2%, 5.6%, and 5.5%, respectively) (Table S1). Specifically, E6 and E15 were identified among the principal odorants in strawberries [14; 15].
# An Overview of Nanofiltration and Nanoadsorption Technologies to Emerging Pollutants Treatment
## 3 Nanotechnological Approaches to the Treatment of Emerging Pollutants
### Nanoadsorbents
#### 3.2.3 Other Nanoadsorbents  
Dendrimers are polymeric nanostructures with a three-dimensional configuration that have appropriate characteristics for the adsorption of contaminants. They have a highly branched structure with different functional groups on the surface. Its high specific internal and external area improves the binding capacity to the pollutants of interest [58; 83]. Poly(propylene time) dendrimer was used to modify multi-walled carbon nanotubes for the removal of dyes from textile wastewater. The dye removal efficiency increased from 17.7% to 99.9% after modifying the nanotubes with dendrimer [87].  
Studies have developed nanocomposites from nanoscale materials associated with other matrices (Table 2) to improve the adsorbent potential [75; 83; 88]. A polymer that has been used in the synthesis of adsorbent nanocomposites is the polypyrrole [75; 83]. This conductive polymer tends to aggregate. However, its application in nanocomposites may reduce the aggregation and increase the surface area [83]. Nanocomposite from polypyrrole doped with graphene oxide and covalent organic structure was produced to remove indomethacin and diclofenac drugs. The nanocomposite showed higher adsorption capacity than graphene oxide and the combination of covalent organic structure with graphene oxide [75]. Thus, the high surface area of nanomaterials combined with the high number of functional groups of polymers improves the capacity for removing contaminants [83].  
Furthermore, recent studies have investigated combining adsorbent materials such as carbon-based nanomaterials and metal-based nanoparticles to increase the adsorption capacity [70; 74; 78]. For instance, [PERSON] et al. [78] synthesized magnetic compounds from carbon nanotubes, manganese ferrite nanoparticles, and \(\beta\)-cyclodextrintrin and evaluated the capacity to remove the tetracycline antibiotic. The adsorption capacity of this adsorbent (89.53 mg g\({}^{-1}\)) was higher than that of carbon nanotubes (82.66 mg g\({}^{-1}\)).
## 3 Overview of Physical Unclonable Functions
### PUF Implementation
#### 3.2.2 Ring Oscillator (RO)  
Ring oscillator (RO) PUFs, as shown in Figure 5, are built on frequency deviation, while switch-based/arbiter PUFs are based on propagation delay. There are odd numbers of NOT gates in an RO. The output of the oscillator varies between two levels of voltage, and these two levels can be classified as true and false. Such an oscillator operates in a feedback manner where the output of last NOT gate is the feedback to the input to the RO. The advantage of the RO is the ease of implementation.  
Figure 4: Optical PUF [12].  
Figure 3: Challenges and response of PUF [11].
# Advances in Engineering and Application of Optogenetic Indicators for Neuroscience
## 3 Potassium Indicators  
Sodium and potassium are important physiological ions crucial in maintaining and regulating membrane potentials in neurons. In neurons at rest, cytoplasmic sodium levels are an order of magnitude lower than extracellular (\(\sim\)10 mM intracellular vs \(\sim\)150 mM extracellular) [73]. During an action potential, intracellular sodium levels transiently increase [74; 75], and because sodium is the major current carrier of glutamate-gated voltage changes, noninvasive optical imaging of sodium would enable the visualization of a key correlate of excitatory synaptic transmission. Potassium is also a crucial regulator of cell excitability [76; 77]. At rest, cytoplasmic potassium levels are high, but during an action potential, there is a potassium efflux, the change in the absolute number of ions is relatively modest [76; 77]. Noninvasively recording potassium at single cell resolution would offer an important proxy to neural activity. Unfortunately, there are no genetically-encoded sodium sensors developed to date, and genetically-encoded potassium sensors are only an emerging class of optical probes. We note that an important part of the difficulty in engineering potassium and sodium sensors--compared to calcium sensors--is that while calcium concentrations during an action potential change by more than four orders of magnitude, changes in potassium and sodium concentrations during an action potential are only one order of magnitude. Thus, imaging potassium and sodium is inherently more difficult than imaging calcium transients owing to the more subtle changes in ion concentration.  
Genetically-encoded potassium indicators (GEPI) are currently represented mainly by three Forster (fluorescence) resonance energy transfer (FRET)-based indicators (Figure 1c; Table 1). All three GEPIs share the same Kbp protein from _E. coli_ as a potassium binding domain and utilize CFP/YFP fluorescent FRET-pairs to register conformational changes in the Kbp part [47; 48]. Since GEPI dissociation kinetics from potassium ions is limited by hundred milliseconds, they are not appropriate for the detection of fast potassium dynamics during neuronal activity. Meanwhile, GEPIs were successfully applied to monitor slower potassium transients in different cellular compartments in cultured cells. Thus, further development of faster and non-FRET-based variants of GEPIs with higher dynamic range are in high demand.
###### Abstract  
In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.  
clustering; routing; energy efficiency; underwater wireless sensor network; metaheuristics; fitness function +
Footnote †: journal: Sensors  
1
## 2 Materials and Methods
The first and last 1-2 measurements of the day were omitted to avoid erroneous measurements due to mismatch of the lamp timer and HH2 internal clock/auto-timer setting or any drift in either timer. A daily average CF spectrum and standard deviation at each wavelength was calculated. The resulting CF spectra were smoothed (central-moving average, window = 5). In addition to the full CF spectrum, CF values at 685 nm and 740 nm were extracted (Fred, Ffar, respectively) and then the Fred/Ffar ratio calculated [21; 51; 52; 53]. Daily NDVI [54] was calculated by calculating reflectance from the target plant spectrum and the IRL reference spectrum taken once each day and using:  
\[\mathrm{NDVI}=\frac{R_{800}-R_{680}}{R_{800}+R_{680}} \tag{1}\]  
and PRI [55] as  
\[\mathrm{PRI}=\frac{R_{531}-R_{570}}{R_{531}+R_{570}} \tag{2}\]  
where \(R_{\lambda}\) is the reflectance and \(\lambda\) is the indicated wavelength in nm. To examine the relative capabilities of the measurements to capture the dynamics of photosynthetic function, we also calculated the proportion of maximum NDVI, PRI, Fred, and Ffar as a time series. Custom programs to read, write, and batch process the spectra from their proprietary format as well as specifically process and make figures from the experiment were written in Interactive Data Language (IDL; L3 Harris Geospatial, Boulder, CO, USA).
## 3 Results and Analysis  
### Accumulated Rainfall  
Figure 2 shows the daily-accumulated rainfall (00:00 UTC on 21 July 2012 to 00:00 UTC on 22 July 2012). The 3-hourly rainfall rates at 0000 UTC and 2400 UTC were accumulated for 1.5 h while rainfall rates at other times (_i.e._, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 UTC) were  
accumulated for 3 h to compute the daily rainfall from the TMPA rainfall rate. The gauge accumulation based on Kriging interpolation indicates that the intensive rainfall area extends from southwest of Beijing northeastward to the adjacent areas of Beijing and Hebei province and eastward to the center of Tianjin (Figure 2a). Kriging interpolation embedded in Interactive Data Language (IDL, Version 8.2) was used in this study. The 3B42 RT captures the intensive rainfall areas well in southwestern and northeastern Beijing as shown in Figure 2b. The 3B42V7 (Figure 2c) demonstrates the most intensive rainfall area in northwestern Tianjin and northeastern Hebei province excluding the rainfall in the center of the region (indicated by the red \"A\" in Figure 2a).  
For quantitative comparison, only the grids that cover two and more rain gauges have been used to compute the statistics metrics. Such a grid-based comparison technique has been reported in many previous studies [1, 27, 28, 29, 30]. A total of 5086 gauge abnormal measurements were removed during the quality control. The total 345 grids were available for the direct comparison in the scale of spatial resolution \(0.25^{\circ}\times 0.25^{\circ}\) for the entire domain with 30 grids for. Figure 2d-e shows that both 3B42 RT and 3B42V7 have moderate CC (\(\sim\)0.6) with gauge-based observation, indicating 3B42 RT and 3B42V7 cannot capture the spatial pattern of rainfall very well in the entire HBT region. The RB result shows  
Figure 2: **(a)** Gauge accumulation based on Kriging interpolation (Guage_KRIG); **(b)** 3B42 RT accumulated rainfall; **(c)** 3B42V7 accumulated rainfall; **(d–g)** Scatter plot of gauge _vs._ 3B42 RT/3B42V7 accumulated rainfall over Hebei, Beijing and Tianjin.  
that 3B42 RT (3B42V7) overestimates the daily-accumulated rainfall by 21.95%( 11.92%) in the HBT region and by 56.04% (38.08%) in the Beijing Region.  
Figure 3 shows the time series cloud top brightness temperature maps derived from the MTSAT-2 satellite. It is noted that the spatial pattern of 3B42 RT is highly correlated to the cloud top temperature patterns, especially the patterns shown in Figure 2e. The research product 3B42V7 still showed the effect of the IR sensor that was shown in 3B42 RT, even though 3B42V7 was corrected by rescaling the 3B42 RT to the monthly rain gauge data based on a limited number of gauges. This is likely due to the inherent shortfall of the IR technique: the IR brightness temperature to estimate precipitation produces large errors [38,39].
## 3 Proposed Work
### Fibonacci Transform Function  
The Fibonacci function receives a block \(E_{i}\), a data unit \(I_{i}\) as input, and, depending upon the data unit \(I_{i}\), the block gets scrambled. The image block is taken as a two-dimensional matrix. When the size of the block is \(A\), then the block has \(A\times A\) elements. Let \(x\), \(y\) stand for the position of the pixel. Then, \(x\), \(y\in\{0,1,2\ldots,A-1\}\). Here for each pair \((x,y)\), after Fibonacci scrambling, becomes \((x^{\prime},y^{\prime})\). Hence, the pixels from \((x,y)\) move to the location \((x^{\prime},y^{\prime})\) after the scrambling. Similarly, the Fibonacci scrambling function traverses all the pixel points of the block to complete one round of Fibonacci scrambling.  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\end{array}\right]=\left[\begin{array}{cc}1&1\\ 0&1\end{array}\right]\cdot\left[\begin{array}{c}x\\ y\end{array}\right]\text{mod A}\]  
Here, if the data unit that has to be embedded is 0, then the Fibonacci transform will scramble the block once. If the data unit is 1, then the block will be scrambled twice. Similarly, if the data needed to be embedded is \(n\), then the block will be scrambled \(n+1\) times. Thus, the Fibonacci transform function will return a scrambled block.  
```
Input: An encrypted image \(E\) of size \(M\times N\) pixels and the secret information \(I\). Output: An image \(E^{\prime}\) which is encrypted and embedded with the secret information \(I\). \(r=1;\ s=1\);  Let \(E^{\prime}\) be an image of size \(M\times N\) pixels, initialized with zeros.  Divide the image \(E\) into non-overlapping blocks \(NB\) of fixed size \(A\times A\) pixels. while\((r\leq M-A+1)\)do  while\((s\leq N-A+1)\)do  Get each block \(NB_{i}\) of size \(A\times A\) pixels from \(E\). /* \(i\) is the index of the block \(NB\) in \(E\). */  Get the data unit \(I_{i}\) from the secret information stream \(I\). /* \(i\) is the index of the data processed from \(I\). */  Call the FibonacciTransfer function by passing the block \(NB_{i}\) and the data unit \(I_{i}\) as the actual parameters. The /* \(FibonacciTransfer\) function returns a block \(NB_{i}^{\prime}\) after embedding the data unit \(I_{i}\) in \(NB_{i}\). */  Place the block \(NB_{i}^{\prime}\) at the index \(i\) of the output image \(E^{\prime}\). Return the image \(E^{\prime}\) to the receiver.
```  
**Algorithm 1**Securing the secret data.  
An illustrative example of the data flow while hiding the secret information is shown in Figure 2.
# Power Modeling and Experiment Study of Large Flapping-Wing Flying Robot during Forward Flight
## 2 Materials and Methods  
### Structure of the Flapping-Wing Air Vehicle  
The HIT-Hawk is designed to mimic large birds that can produce lift and thrust by flapping movements. As shown in Figure 1, the HIT-Hawk is basically composed of five components.  
1. The fuselage skeleton is made of a high-strength carbon fiber frame with a thickness of 2 mm. It is utilized to mount and connect all structural components and provide structural support for all functional components. As with the skeletal structure of birds, the fuselage skeleton of the HIT-Hawk is constructed with a large number of hollow holes to provide strength while minimizing weight.
2. The wing unit is manufactured entirely of high-strength, lightweight carbon fiber. The single-segment wing is designed to imitate the size and shape of a large bird's wing, and nylon polyester fabric with exceptional toughness qualities is used to create HIT-Hawk's feathers. Through the reciprocal flapping of the wings, the lift and thrust required for flight can be generated.
3. The tail unit is utilized to maintain the balance of the vehicle and modify its attitude during flight. The tail unit of HIT-Hawk contains two servos, which are used for pitch control and yaw control, and is designed with an outer circular tail shape, mimicking the tail of an eagle.
4. The drive unit of HIT-Hawk consists of a high-speed brushless motor and a reduction gear box with a gear ratio of 81:1. The reduction gear box converts the high speed and low torque motor rotation into a low speed and high torque circular rotation, which is used to drive the reciprocating flapping of the wing unit.
5. Space linkage unit--the linkage unit of HIT Hawk is a four-link RSSR space mechanism coupled to the drive unit and the wing unit. Lift and thrust can be generated by the wing through converting the circular motion of the drive unit into the reciprocating flapping of the wing unit by the space linkage unit.