esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 2 Study Area and Datasets  
### Study Area  
The study site is located in a typical urban agricultural region in Gaochun District of Nanjing, the capital of Jiangsu province, China, with central coordinates of 118\({}^{\circ}\)52\({}^{\prime}\) E and 31\({}^{\circ}\)19\({}^{\prime}\) N (see Figure 1). The area, covering a total area of 802 km\({}^{2}\), has a subtropical monsoon climate with an annual average temperature of 16.0 \({}^{\circ}\)C and abundant rainfall (the annual average rainfall is 1157 mm). The area has a complex topography, being low in the west and high in the east; the west is a plain, and the east is a low hilly area. The main winter crops are winter wheat and winter rapeseed. The winter wheat growth period is late October to the following June. Winter rapeseed is sown in early October and harvested in mid-May of the following year. Although the study area is not the main crop production area in China, it is a representative urban agricultural region in Southern China due to its abundant rainfall and complex crop planting structure.
## 3 Results
### Levodopa Effect  
Levodopa effects on each of the analyzed measurements can be seen in Figure 5. The correlation coefficient of spectral entropy is similar among PD subjects in resting-state pre- and post- levodopa administration (\(p\) = 0.52). During motor activation, levodopa produces significant changes in the correlation coefficient in the left-hand activation (\(p\) = 0.016); meanwhile, it produces no changes in the correlation coefficient in the right hand (\(p\) = 0.25). Although in the left hand, levodopa tends to restrain the increase of the coefficient observed in PD subjects, its values remain significantly different from controls (\(p\) < 0.001).  
In contrast with what is seen in pre-levodopa, resting-state coherence values of PD are significantly higher than those of control (\(p\) = 0.02) in all bands. During motor activation, coherence in both hands in PD subjects tends to remain similar to resting-state (right hand \(p\) = 0.18; left hand \(p\) = 0.25), contrary to the changes seen in pre levodopa motor activation (right hand \(p\)= 0.008; left hand \(p\) = 0.01).  
The administration of levodopa did not produce changes in the interhemispheric divergence in PD resting-state compared to pre-levodopa (\(p\) = 0.40). However, levodopa administration produced a significant increase in the interhemispheric divergence during motor activation in PD subjects (right \(p\) = 0.06 and left \(p\)= 0.015) with no significant differences when both hands are compared (\(p\) = 0.41).  
Figure 4: Distribution of Kullback–Leibler Divergences of the SpEn among electrodes in both hemispheres (left and right). Results are shown for the resting-state, right and left finger tapping. FT: Finger tapping. ***\(p\) < 0.001.
## Abstract  
Bottom ash (BA) is the main residue left by municipal solid waste incineration (MSWI). As the circular economy is strengthened, the use of BA in civil engineering is increasing, but its successful use is hampered by heavy metal leaching. In this study, we investigated the influence of natural weathering (6 months) on the stabilization of heavy metals (Cu, Zn, and Pb) with different particle sizes in MSWI BA. Natural weathering is the most popular and cost-effective treatment method for BA. During this process, calcium carbonate (CaCO\({}_{3}\)) is produced, which causes a reduction in heavy metal leaching. We used the following methods in the analysis: The fractionation of BA, XRF, and XRD; an extraction test (LST EN 12457-2:2003); and AAS. The results showed that the concentrations of all elements in BA decreased during natural weathering. An analysis of the mineral composition showed a very high (>20%) content of calcium carbonate (CaCO\({}_{3}\)). The calcium carbonate content increased by 3.2% during weathering because the Ca(OH)\({}_{2}\) in fractions <5.6 mm and <40.0 mm was hydrolyzed to CaCO\({}_{3}\). Our analysis showed that the metal concentrations (Cu and Pb) in untreated MSWI bottom ash eluate exceeded the limit values, and thus it cannot be used in civil engineering. After three months of stabilization, the heavy metal concentrations were less than the limit values.  
A Article  
Impact of Natural Weathering on Stabilization of Heavy Metals (Cu, Zn, and Pb) in MSWI Bottom Ash  
[PERSON] *\({}^{\copyright}\), [PERSON] and [PERSON] *\({}^{\copyright}\)  
Department of Environment Protection and Water Engineering, Vilnius Geodiminas Technical University,  
LT-10223 Vilnius, Lithuania; [EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] (V.V.)  
* Correspondence: [EMAIL_ADDRESS]
# Temperature Fields of the Droplets and Gases Mixture
## 2 Experimental Set-Up and Procedure
### LIP Measurement Technique
In particular, the results from [32; 33] show that the effects of absorption and, on the contrary, dissipation of laser ultraviolet signal can lead to additional errors when determining the flow temperature. Thus, e.g., [PERSON], [PERSON] et al. [32] show that a phosphorescent signal can be generated even in the areas that are not seeded with BAM:Eu particles. This happens due to multiple dissipations in the areas in front of or behind the jet under study [32]. Therefore, it was necessary to conduct additional experiments. First, the coefficients of water and water vapor absorption were analyzed. According to the results from the work in [34], the water absorption coefficient in the range of wavelengths 355 to 465 nm varies from 0.00442 to 0.01137 m\({}^{-1}\). Given that the maximum distance, covered by the laser sheet in the measurement area, is comparable to the diameter of the quartz channel (0.1 m), the maximum absorption will not exceed 0.12%, even when the droplet concentration is at maximum. According to the data from HITRAN website, water vapors, forming as a result of droplet evaporation, absorb light mainly in the near-IR range (over 900 nm). Thus, the main reason for error, caused by aerosol droplets, can be the dissipation of laser radiation by them. To evaluate the influence of aerosol droplets on the results of LIP measurement, we conducted an experiment, using an air flow generation setup (Figure 1) at \(T_{\text{a}}\approx 25\)\({}^{\circ}\)C. At the first stage, the air temperature was measured without aerosol flow injection, at the second stage, with aerosol droplets in the recording area. The spray event duration in the experiment was \(t_{\text{imp}}\approx 3\) s. We calculated temperature fields and compared them. [PERSON], [PERSON] et al. [25] established that the intensity ratio (420/455 nm) in the recording area in two of the cases under consideration differs by no more than 1.5%, which does not exceed the systematic error of LIP for 25 \({}^{\circ}\)C The experimental results also indicate that the local areas, corresponding to water droplets, have much lower (2 to 3 times) intensity than the areas containing BAM:Eu particles. These areas were masked before the temperature field plotting. When analyzing and comparing the final temperature fields, it was established that at a volume concentration of droplets \(\gamma_{\text{d}}\)\(\approx\)3.8\(\cdot\)10\({}^{-2}\) l/m\({}^{3}\), their presence does not considerably deviate the temperature field. The difference in the mean temperature recorded with and without aerosol droplets did not exceed the error of LIP technique for 25 \({}^{\circ}\)C (\(\sim\)1.7% [25]). Moreover, the moment of aerosol injection, just as the whole process of its movement in the reversed gas flow, takes 1-5 s, which is much less than the duration of the experiment (15-20 s). These estimates make it possible to conclude that it is reasonable to use the newly developed measurement technique.
## 4 Discussion
However, TKR patients had further decreased joint position sense after surgery than before the surgery. AR training produces a dynamic motion that can increase muscle strength due to trunk and lower-extremity motion control and weight shifting through upper-extremity motion. Contrary to therapist-based training, during AR-based training, patients have to follow a certain rhythm of motion at an altered speed. Likewise, the speed of weight shifting increased with self-activity which induces an increase in joint proprioception in addition to muscle strength and an increase in corresponding balance control capability in the patients. Furthermore, the results of these studies showed that joint range of motion, muscle strength, and balance increased through augmented-reality-based training and increased the ability to support the motion of weight falling on the affected leg during training. Moreover, AR-based training is reported to be beneficial for increasing somatosensory input and proprioception [22]. Sometimes described as the \"sixth sense\", proprioception is the sense of self-movement and body position, and it is a key to knee rehabilitation as it influences pain and especially balance ability. This evidence supports the findings of our study, showing the effectiveness of AR-based training in decreasing pain and increasing balance ability, leading to an increase in ROM and strength. In our study, we can also notice that significant improvements in ROM and strength were only on the right side, regardless of the affected side. This is due to the fact that all the participants in this study were right-handed. A previous study on 16 aged women comparing the muscle strength of dominant and non-dominant sides and one-leg standing ability after 4 weeks of training showed a better improvement in the outcomes on the dominant side [13]. Sufficient weight bearing on the dominant side compared to the non-dominant side explains this result, which is similar to the result of the present study. Although the present study presents some evidence for the non-superiority of AR-based training over conventional physical therapy in patients post-TKR, it has some limitations that need to be acknowledged. First, the number of participants was small (12 participants in each group), making it difficult to generalize the findings. Second, despite the fact that gender may affect the results, we could not conduct a gender comparison since the majority of the participants were female. However, it would be interesting to include a large sample with an equal number of participants from both genders in further research to minimize the potential gender bias during the interpretation of the results. Moreover, a long period of exercise would provide evidence for the long-term effects.
# Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters
## 3 Results
### Comparison of Q/I and U/I Differences  
Rather than calculating \(DOLP\), we could also show the difference between the model result for \(Q/I\) and \(U/I\) versus the data, shown in Figure 10 for the Ligurian Sea case. This case is shown because it was neither the best result, which was for Hawaii, nor the worse, which was New York Bight. The advantage of \(DOLP\), and the reason most of the results are presented for this parameter, is that it is independent of the frame of reference, thus small errors in locating the solar plane in each image cause smaller differences in the comparison. \(Q/I\) and \(U/I\) comparisons are with respect to the frame of reference and depend on correctly locating the solar plane in the image, hence the comparison is not quite as good between the model and data result as with the DOLP. The line fits had a slope of 0.66 (\(\pm\)0.010, \(r^{2}\) = 0.70) for \(Q/I\) and 0.71 (\(\pm\)0.013, \(r^{2}\) = 0.67) for \(U/I\). The fact that the scatter is qualitatively the same in these graphs as in the DOLP figure can be simply understood since \(Q/I\) and \(U/I\) can be derived from a combination of the \(DOLP\) and the plane of polarization, \(\chi\). As mentioned earlier, \(\chi\) is a very stable parameter in the upwelling light field. The spread in the data is slightly larger for \(U/I\) versus \(Q/I\) because \(U/I\) has stronger spatial gradients, and small rotations affect this parameter more than \(Q/I\).  
Figure 8: \(DOLP_{diff}\) as a function of \(c_{t}\) and \(c_{t}/a_{t}\). Color code of data is given in Figure 7. (**a**) shows \(DOLP_{diff}\) vs \(c_{t}\), while (**b**) is \(DOLP_{diff}\) vs \(c_{t}/a_{t}\).  
Figure 9: \(DOLP_{diff}\) as a function of \(b_{bt}/b_{t}\). There were no \(b_{bt}\) measurements during the Hawaii data collection.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 3 Environments for Communication in Oceans
### Communication Medium
#### 3.5.1 Communication over Water Surface  
Although 3G and beyond cellular networks are very popular in terrestrial environments, their applications in marine environments still need to be investigated since radio signals can be easily absorbed by seawater. As mentioned above, the density of marine Internet users is sparse while many users are crowded in small places such as cruise ships. In this case, point-to-point communication is more effective than broadcast communication. However, the line-of-sight (LOS) propagation of radio waves for point-to-point communication is affected by the curvature of the sea surface in long distances. Atmospheric duct under investigations can change such situation since the near-surface wave propagation at microwave frequencies is trapped between the ducting layer and the sea surface [28] so that the signal can propogate beyond LOS distances, as illustrated in Figure 3. This phenomenon, which is nearly permanent in oceans and coastal environments, is caused by a region of rapidly decreasing humidity above the ocean that makes the refractivity to drop quickly with height [29].  
The effect of the evaporation duct on microwave signal propagation depends on many factors including atmospheric conditions, polarization, antenna's positioning and wave frequencies [30]. An experimental link for a range of about 78 km using antennas located 7 m above the mean sea level was built in [31], where the link operates at a frequency of 10.6 GHz and provides a data rate of 10 Mbps over 80% of time. The state-of-the art military system can enable 500-1000 km point-to-point communication in the ducting layer [28]. Therefore, it is interesting to study communication in atmospheric ducts to provide long range point-to-point high speed maritime communication, and particularly for communication between coastlines and ships in remote water areas.
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
## 2 MFC Power Supply for Sensors and Biosensors
### Effect of Organic Loading Rate  
Organic loading rate (OLR) can be defined as the rate of biological oxygen demand (BOD) per volume unit of solution. BOD is an indicative measure of the oxygen required by aerobic biological organisms to oxidize the organic matter (usually considered as pollutants) contained in a solution at isothermal conditions. Hence, OLR is conventionally used to quantify the incoming mass rate of organic matter in a reactor for wastewater or sludge treatment. At this point it must be noted that the potential use proposed for MFCs is as a self-powered reactor technology in a waste water/sludge treatment [22], regardless of the fact that inorganic substrates can be also used. In that sense, OLR applied to a reactor is also known as volumetric loading rate, expressed in kg of BOD per unit volume of the reactor per day. The organic loading rate can also be defined per unit weight of microbial mass, and be used to measure the potential for substrate conversion in an MFC.  
Regardless of the waste removal, operating at optimum OLR or sludge loading rate (SLR) is critical for an effective performance of MFCs, since both power density and current efficiency depend on the substrate conversion rate directly related to OLR [63; 71; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103]. Thus, the energy generation is generally boosted by increasing the OLR up to a maximum, from which higher loading rates decrease the power output of the MFC. The higher OLR translates into a higher availability of feedstock to be oxidized in the anode, which favors the metabolic activity of the electroactive biofilm and explains the enhanced power generation. On the contrary, an increased OLR has been related to lower efficiency for electron transfer processes from anode to cathode, hindering the adequate performance in an MFC [97].  
As a case in point, [PERSON] et al. studied an MFC for the treatment of wastewater. As shown in Figure 5[100], the decreasing of columbic efficiency while OLR increases is unambiguously clear.  
Eventually, the optimization of MFC BOD biosensors pass through the prevention of oxygen diffusion [104], suppression of methanogens [105], and overcoming cathode limitations [106].
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 2 Materials and Method
### Offshore Wave Data  
Wave data, for comparison with the GNSS buoy data, were obtained (via the Channel Coastal Observatory website [[https://coastalmonitoring.org/](https://coastalmonitoring.org/)]([https://coastalmonitoring.org/](https://coastalmonitoring.org/)) (accessed on 4 June 2021)), from the Cleveleys wave buoy (WGS: 53 deg53.70' N, 03 deg11.78'' W) owned by Sefton Council and situated 9.5 km to the west (Figure 2) of the GNSS deployment site at Rossall. The wave data were recorded as thirty-minute values by the Datawell Directional Haverider Mk III buoy deployed in approximately 10 m of water depth. For reference, and to contrast with the wave heights encountered during the experiment in August 2019, the mean significant wave height (Hs) was 1.0 m, and standard deviation was 0.60 m, with minimum value of 0.09 m and maximum value of 2.67 m.
# Near-Optimal Weather Routing by Using Improved A* Algorithm
## 4 Case Study  
### Prediction of Ship's Estimated Time of Arrival  
To find the optimal ship route, FOC should be used to evaluate the economics of ship routes. However, FOC data were not available in this research; thus, the SOG from AIS and maritime weather data was used to derive the ETA that will be used as economical index instead of FOC, since ETA and FOC parameters are linearly correlated [6]. Nevertheless, to ensure that the optimal route based on the ETA is equivalent to that based on the FOC, the following assumptions are needed [6].  
1. The revolutions per minute of the propeller should be maintained with a constant value throughout the operation of the vessel,
2. At the same time, with condition (a), the main engine should have a constant torque value,  
which means that engine power remains constant as follows.  
\[Engine\ Power=\ Engine\ RPM\times Engine\ Torque \tag{8}\]  
However, because these assumptions cannot be satisfied under real operational conditions, using an ETA optimal route as the most economical route would include prediction errors. Nevertheless, in general, shipping companies do not share engine data of ships, such as FOC, so ship route optimization with only with AIS and weather data may be necessary. In addition, even if engine data are used to optimize ship routes, because it is necessary to consider more parameters to predict the  
Figure 6: Northern hemisphere of the earth.  
FOC, the routing process requires large calculation time. Thus, the route optimization only using AIS and weather data can provide the optimal ship route more efficiently than the one using engine data.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 5 Results and Comparative Analysis
### Scenarios with Homogeneous Nodes
#### 5.1.8 Packet Delivery Ratio  
In terms of packet delivery ratio (PDR), a thorough comparison is shown in Figure 8 below. A baseline for comparison is established at 20 nodes, where all techniques display different PDRs. Because proactive protocols continuously maintain their routes, they exhibit greater PDRs, which makes them more effective for smaller networks. For very large networks, however, the overhead of maintaining routing tables reduces their performance. Under moderate traffic levels, the overhead of route discovery causes reactive protocols to noticeably lose PDR as the network size grows to 80 nodes. PDR is decreased by more congestion, collisions, and interference between 20 and 80 nodes. Higher connectivity at 200 and 500 nodes, however, makes it possible for more dependable multi-hop pathways, which enhances protocol performance and boosts the number of successful packet deliveries.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Network Size** & **Proactive Protocols (OLSR,** & **Reactive Protocols (AODV,** & **Hybrid Protocols (ZRP, GRP)** \\
**Nodes)** & **DSDV)** & **DSR, AOMDV, TORA)** & **Hybrid Protocols (ZRP, GRP)** \\ \hline
20 Nodes & Moderate packet loss rate, efficient for smaller networks & High packet loss rate, generally higher than proactive protocols & Moderate packet loss rate, balanced performance \\
80 Nodes & Noticeable decrease, consistent performance & Significant decrease due to route discovery overhead & Balanced decrease, effective management of reduced packet loss rate \\
200 Nodes & Sharp improvement, low packet loss rate & Peak improvement, highlighting scalability benefits & Peak improvement, balanced performance, effective integration of strategies \\
500 Nodes & Stabilizes or decreases, more optimal due to efficiency & Significant decrease, indicating return to efficiency & Stabilizes, maintains low packet loss rate, most suitable for high-density networks \\ \hline \end{tabular}
\end{table}
Table 11: Protocol’s Suitability for Different Scenarios Considering the Packet Loss Rate.  
Figure 7: Packet Loss Rate across the Four Scenarios.  
The biggest PDR improvement is shown at 200 nodes, underscoring the difficulties with scalability and adaptability that come with greater node density. Because hybrid protocols exhibit balanced performance by combining proactive and reactive techniques, they are especially well-suited for very high-density networks (500 nodes), where they can effectively handle the elevated PDR. For the majority of protocols, PDR tends to stabilize or rise at 500 nodes, suggesting a return to efficiency at the highest network density. The effectiveness and scalability of each protocol category are highlighted in this analysis, which also offers important insights into their operational performance and possible limitations in a range of network scenarios. The protocol appropriateness for the four distinct scenarios considering PDR is summed up in Table 12 below.
# Measurement of Ultra-High Speed by Optical Multistage Cascade Frequency Reduction Technology
## 3 Discussion and Results
### Simulation
From Figure 7, the evolution characteristics of the three types of signals can be analyzed. Among them, the fundamental frequency line generated by the shift in the fundamental frequency signal with the sweep frequency point has obvious broken line characteristics. Ghost signals are characterized by linear changes, which will overlap across signal lines. The fundamental frequency line does not move with the shift in the sweep frequency point, but it also crosses the signal line. In the first-stage frequency conversion spectrum, it can be seen that the overlap between the ghost line and the fundamental frequency line and the signal line occurs in the area covered by the oscilloscope bandwidth (8 GHz), which can be recorded by an oscilloscope, so it has an impact on the actual signal line extraction. However, it can be seen from the second-stage frequency conversion spectrum that the overlap of the hatching line, fundamental frequency line, and signal line occurs outside the area covered by the oscilloscope bandwidth (8 GHz), which will not affect the extraction of the actual speed line, but the trend of the ghosting line is similar to that of the signal line after turning.