esa-sceva/satcom-chunk-collection huggingface dataset:
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 3 Results and Discussion
### The Contributions of Different Cloud Types to Discrepancies between MODIS and CALIOP  
The proportion of consistent and non-consistent observations for different cloud types between MODIS and CALIOP showed that low clouds (Cu and Sc) had a relatively lower consistent proportion and higher non-consistency proportion than other cloud types (Figures 7 and 8), suggesting that they contributed a great deal to the discrepancies between MODIS and CALIOP. The non-consistent proportion for Cu and Sc was much larger at high AOD (\(>\)2) and high SZA (\(>\)60\({}^{\circ}\)). For example, over dark surfaces, the consistent proportion values between MODIS and CALIOP for most cloud types agreed mostly with \(>\) 80% at low AOD (\(<\)2), whereas at high AOD the consistent proportion decreased to 62% and 55% for Cu and Sc, respectively (Figure 7a). As another example, over dark surfaces, the consistent proportion values between MODIS and CALIOP for Cu and Sc were mostly \(<\) 90% at low SZA (\(<\)65\({}^{\circ}\)) and decreased to 43% and 69% for Cu and Sc at high SZA (\(>\)65\({}^{\circ}\)), respectively, while the consistent proportions for middle and high clouds were \(>\) 90% at all SZA values (Figure 8a). The same is true for all other underlying surfaces (Figure 7b,c and Figure 8b,c). Similarly, for Cu and Sc clouds, the r_MODcloud_CALfree increased from 0.006 to 0.02 at low AOD to 0.04 to 0.12 at high AOD (\(-\)1-9 times, Figure 7d-f), and the r_MODcloud_CALfree increased from 0 to 0.02 at low SZA to 0.04 to 0.08 at high SZA (\(-\)2-165 times, Figure 8d-f), suggesting that high AOD and high SZA increase the discrepancies between MODIS and CALIOP. This is consistent with the results of all cloud types together (see Figure 4).  
Figure 7: The variation of the proportion of consistent (solid lines) and non-consistent (dashed lines) MODIS and CALIOP cloud detection data with AOD for different cloud types over (**a**) dark, (**b**) bright, and (**c**) water surfaces, respectively, and the variation of r_MODcloud_CALfree for each cloud type with AOD over (**d**) dark, (**e**) bright, and (**f**) water surfaces, respectively.  
In addition, the high clouds Ci and Dc also contributed to discrepancies at high AOD over dark and particularly water surfaces. The r_MODcloud_CALfree for Ci increased from 0.01 at low AOD to 0.10 at high AOD, and that for Dc increased from 0.002 to 0.01 at low AOD to 0.02-0.25 at high AOD. Over the North China Plain, the difference between MODIS and the ground-based cloud fraction would increase with an increase in AOD, which is the most obvious for Cu, Ac, Ci, and Dc that did not cover the whole sky [35]. It seems that misjudging the aerosol layer as clouds by MODIS may be the reason for the large discrepancies between MODIS and CALIOP occurring at high AOD for Ci and Dc.
# Artificial Neural Networks Applied in Civil Engineering
## 2 Contributions
### Material Properties and Performance
Despite adjusting the longitudinal reinforcement ratio, slab thickness, and addition of asphalt interlayer, the narrowly spaced cracks could not be effectively removed. The application of transverse partial surface saw-cuts significantly reduced the probability of randomly occurring cracks in the reconstruction project of the Motorway E313 in Herentals, Belgium. The field investigation has also indicated that the early-age crack induction in CRCP is quite susceptible to the saw-cut depth. The study by [PERSON] et al. [17] aimed toevaluate the effect of different depths and lengths of the partial surface saw-cut on the effectiveness of crack induction in CRCP under an external varying temperature field. The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and to analyze the characteristics of the cracks objectively. In the study by [PERSON] et al. [18], an image analysis technique using deep learning was developed to detect cracks and to analyze characteristics (e.g., length and width) in images for small-scale facilities. Three stages of an image processing pipeline were proposed for crack detection and to obtain its characteristics. In the first and second stages, two-dimensional convolutional neural networks were used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture was applied into our deep learning network. The fluid seepage in the saturated zone of the subgrade promotes the migration of fine particles in the filler, resulting in a change in pore structure and morphology of the filler and the deformation of the solid skeleton, which affects the fluid seepage characteristics. The muddy interlayer, mud pumping, and other diseases are then finally repeatedly formed. The research by [PERSON] et al. [19] shows that the volume fraction of fine particles first increases, then decreases, and finally becomes stable with an increase in time due to the continuous erosion and migration of fine particles in the saturated zone of the subgrade. The volume fraction of fine particles for the upper part of the subgrade is larger than that for the lower part of the subgrade. The porosity, the velocity of fluid, the velocity of fine particles, and the permeability show a trend of increasing first and then stabilizing with time; the pore water pressure has no significant changes with time. The vertical displacement increases first and then decreases slightly with the increase in time, and finally tends to be stable.
## 4 Discussion  
### Active, Passive or Hybrid Exoskeletons?  
Safety concerns on the use of active exoskeletons primarily focused on increased leg muscle activity required to support the weight of the exoskeleton, suggesting the need for more frequent breaks to prevent muscle fatigue and potential effects on coordination, particularly in the shoulder, which could increase the risk of WMSDs and injuries. In terms of performance, in most cases active exoskeletons did not contribute to increased productivity. Regarding user acceptance of active exoskeletons, the results are encouraging. In positive evaluations, users reported reduced discomfort when performing tasks with the exoskeleton compared to without it. In neutral evaluations, users did not perceive any difference in discomfort with or without the active exoskeleton.  
Regarding the safety of passive exoskeletons, the majority of cases (87%) showed a deterioration in safety conditions, while a small percentage (10%) demonstrated an improvement, and a minor portion (3%) reported no change. Concerns regarding the deterioration of safety conditions were mainly related to weakened trunk muscles, restricted movements due to the rigid structure of the exoskeleton, and an increased risk of balance loss and falls. Passive exoskeletons are generally not recommended for tasks involving potential collisions with objects or individuals due to the increased risk of injuries. However, passive exoskeletons have largely positive effects on health, including supporting the targeted body part without imposing excessive strain on other areas, reducing peak loads, and decreasing metabolic consumption during lifting. Negative health effects primarily involve shoulder muscles' imbalance and increased body temperature in hot environments. In terms of performance, passive exoskeletons generally show a tendency towards deterioration, although the data is better compared to active exoskeletons. Overall, the acceptance of passive exoskeletons appears to be favorable, with fewer criticisms compared to active exoskeletons. The inclusion of hybrid exoskeletons in the analysis in this study is based on a limited number of instances found in scientific articles. However, the insufficient analysis of these instances hinders the provision of positive or negative evaluations regarding safety, health, performance, and subjective opinions of users.
## 4 Results and Discussion
### Rock Physics Modeling  
The various modeling scenarios were perturbed using [PERSON]'s fluid substitution method. Utilizing rock physics analyses from the well data, we successfully identified the reservoirs and distinguished between various fluid and lithology effects within the study area. This information is succinctly presented in Figure 6a, which showcases a log display summarizing the outcomes of the fluid and synthetic modeling at in situ reservoir conditions. Notably, the AVA Pick domain, highlighted in magenta, represents the location of the AVA (amplitude versus angle) analysis.  
The observations from Figure 6a reveal that the hydrocarbon-bearing sands exhibit lower \(V_{p}/V_{s}\) (compressional wave velocity to shear wave velocity ratio) and P-wave impedance (AI) values compared to the water-saturated sands. Additionally, the synthetic modeling results indicate that, at around 2.42 s, the in situ and hydrocarbon scenarios exhibit brighter amplitude responses than the wet scenario; moreover, there is a significant difference in the amplitude between the wet \(V_{s}\) hydrocarbon synthetic seismograms, with the difference reaching as high as 10%. The AVA analyses conducted at approximately 2.42 s further substantiate these findings, as follows: the hydrocarbon and in situ scenarios display a class III AVA response, characterized by an AVA gradient of approximately \(-0.025\) and a reflectivity of around \(-0.04\), while the wet scenario demonstrates a class IV AVA response, with an AVA gradient of about 0.01.  
In Figure 6b, which depicts the cross plot results of the in situ conditions, we can observe a clear separation in the \(V_{p}/V_{s}\) and P-impedance domains, allowing for the differentiation of lithology and fluid effects. The distinction between the reservoir and the background trend is notably well defined. Furthermore, there is a marked sensitivity to fluid variations among the different scenarios, enabling the discrimination of hydrocarbon-rich conditions from water-saturated ones.  
The favorable combination of substantial porosities and thickness in the Hugin Formation window sands makes them highly suitable for distinguishing between lithology and fluid properties under in situ rock property conditions.  
Figure 6: _Cont._
# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 4 Discussion
In summary, the successful implementation of the proposed model can significantly improve the performance and lifespan of wireless sensor networks, providing a new avenue for research in this field. A comparison with other WSN algorithms is shown below to Table 7.  
As shown in Figure 33, as the data transmission rate gradually increases, the network survival time also decreases. However, it can be seen that in all three cases, the improved algorithm has made some progress compared to the original algorithm. When the average network rate is 1 Mbps, the WOA-BP algorithm has the same effect as the LEACH algorithm and the EEUC algorithm. From the simulation experiment results, it can be seen that with a network speed greater than 1.5 Mbps, the WOA-BP algorithm has a short transmission time and a fast transmission rate. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Parameter Values** \\ \hline Network area & 600 m \(\times\) 600 m \\ \hline Number of nodes & 360 \\ \hline Packet length & 256 byte \\ \hline Node initial energy & 10 J \\ \hline Energy consumption of data fusion & 5 nJ/bit \\ \hline Communication channel bandwidth & 0.5 Mb/S, 1 Mb/S, 1.5 Mb/S, 2 Mb/S, \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Simulation of main parameters. Figure 33: Comparison of performance of three algorithms. From the above Figure 34, it can be seen that at a runtime of 900, the LEACH algorithm and the EEUC algorithm have a wireless sensor node death rate of 80-100, while the optimized WOA-BP algorithm has a node death rate of 10.
## 5 Information Modulation and Demodulation  
The previous section analyzes the information transfer channel of the proposed WPT system, which verifies the possibility of full-duplex communication of information. This section introduce signal modulation and demodulation model design.  
The schematic in Figure 11a is the structure of information modulation. \(V_{d}\) is the DC voltage source. \(Q_{H}\) and \(Q_{L}\) make up high bridge inverter. \(C_{d}\) is the compensation capacitor. \(V_{\text{T}}\) represents a modulated high-frequency voltage source, which is amplified by \(LC\) resonance through capacitor \(C_{d}\) and transformer inductor. Through transformer coupling, the high-frequency carrier signal is injected into the power loop through a high-frequency transformer. The receiver circuit in the pick-up side is coincident with the information transmission resonant point to realize the information transmission. After receiving the high-frequency carrier signal, the sampling resistor \(V_{r}\) on the receiving module sends it  
Figure 10: Bode plot of crosstalk interference between forward and backward information transfer. (**a**) Crosstalk from “Backward TX” to “Forward RX”. (**b**) Crosstalk from “Forward TX” to “Backward RX”.  
to the demodulation circuit for signal processing. The structure of demodulation cell is shown in Figure 11b,c.  
The forward information transmission carrier frequency is set to 1.7 MHz, and the backward transmission carrier is set to 1 MHz. In order to prevent mutual interference between information carriers, the structures of forward and backward information demodulation circuits are different. Therefore, for the forward transmission carrier frequency, the secondary side information demodulation circuit uses a high pass filter (HPF) to filter out the low-frequency power wave and the 1 MHz backward information carrier. And, for backward information transmission, the primary side information processing circuit uses a band-pass filter (BPF), whose band-pass frequencies range from 900 kHz to 1.2 MHz. After passing through the envelop detection (ED) circuit, the voltage peak envelope voltage is obtained. The output waveform is obtained after the signal is sent to the amplifier and comparator circuit.
## 1 Introduction
### Scope  
Given the complexity of the problem, the proposed design procedure and the results of the numerical analysis rely solely on the redesigned structural systems, with all research data derived from finite element simulations. Additionally, only the concrete casting stage of the OMSS was simulated and analysed numerically under various simplified scenarios that do not account for the kinematic behaviours of advancement between execution spans. The bearing capacity of the supports (brackets) and piers is assumed to be sufficient, and the extension and movement of the supports are not covered in this paper. The span of the erected bridge deck is assumed to be straight, meaning the minimum curvature radius and gradient are both zero. The manufacturing and installation of metal structures, including surface treatments, welding, bolted connections, and camber design, are not discussed, and all structural components are assumed to be in an ideal state. Furthermore, electrical devices, hydraulic cylinders, and other auxiliary equipment are excluded from this project. Fatigue failure, temperature effect, geometric imperfections, and equipment reuse only serve as references to guide the design of numerical elements and joints. Moreover, only a simplified analysis of dynamic wind loads was conducted, as rigorous design analysis would require further studies using Computational Fluid Dynamics (CFD) tools and wind tunnel experiments, which are beyond the scope of this project. Such aspects should be addressed in future studies.
# Conformational Transitions of Double-Stranded DNA in Thin Films
## 5 Conclusions  
In this study, we utilized FTIR spectroscopy to monitor the structural organization of dsDNA in thin films obtained in carefully controlled and monitored hydration conditions. Analysis based on a large number of spectra obtained in the 1350-750 cm\({}^{-1}\) range proved that by controlling the time of desiccation (from 3 min to 40 min) it is possible to (a) track transition of DNA in different hydrated states of thin films and (b) obtain DNA in thin films in different conformation with great reproducibility. Additionally, we were able to almost completely remove spectral variations caused by ambient conditions by increasing the time of desiccation without inducing significant conformational changes in thin films. Specifically, 3-min and 5-min films yielded in A-like dsDNA conformation, while 10-40 min films reflected B-like conformation. Utilizing band shape analysis (peak fitting) of two distinct regions, the conformation-related region from 900-750 cm\({}^{-1}\) and the backbone region from 1150-1000 cm\({}^{-1}\), we were able to resolve between the bands sensitive to conformation changes and the ones sensitive to hydration changes. Phosphate vibrations at \(\approx\)1230 cm\({}^{-1}\) and 1089 cm\({}^{-1}\) and backbone vibrations at \(\approx\)1030 cm\({}^{-1}\) and 765 cm\({}^{-1}\) were found to be sensitive to changes in hydration rather than conformation due to their continuous changes in intensity even after changes on the conformation markers saturate. Conformational changes were ascribed to bands at \(\approx\)890, 860, 837, and 805 cm\({}^{-1}\). Our results consistently demonstrate the ability of asymmetric and symmetric phosphate vibrations in thin films to show significant band reshaping and intensity changes without conformational transition. Consequently, the importance of extending the spectral range to often overlooked backbone regions below 900 cm\({}^{-1}\) is stressed, especially in the case of utilizing FTIR spectroscopy for clinical applications.  
Conceptualization, K.S. and S.D.B.; methodology, K.S. and S.D.B.; formal analysis, K.S.; investigation, K.S., N.S., and M.K.; data curation, K.S., N.S., M.K.; writing--original draft preparation, K.S.; writing--review and editing, K.S. and S.D.B.; supervision, S.D.B.; funding acquisition, S.D.B. All authors have read and agreed to the published version of the manuscript.  
This research and publication was co-financed by the European Union through the Europe Regional Development Fund, Operational Program Competitiveness, and Cohesion, under grant agreement No. KK.01.1.1.01.0008, Reproductive and Regenerative Medicine--Exploring New Platforms and Potentials.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 The 3Q Protocol  
The ack-QKD protocol is a two-quantum-flow based protocol (2Q for simplicity), but it can be extended to a three-quantum-flow protocol (3Q). In the 3Q protocol, [PERSON] chooses randomly three non-orthogonal states from the set: \(\{\left(\left|0_{X}\right\rangle,\left|0_{Y}\right\rangle,\left|0_{Z}\right\rangle \right)\},\left(\left|0_{X}\right\rangle,\left|0_{Y}\right\rangle,\left|1_{Z} \right\rangle),\left(\left|0_{X}\right\rangle,\left|1_{Y}\right\rangle,\left|0 _{Z}\right\rangle\right),\)\(\left(\left|0_{X}\right\rangle,\left|1_{Y}\right\rangle,\left|1_{Z}\right\rangle \right)\},\left(\left|1_{X}\right\rangle,\left|0_{Y}\right\rangle,\left|1_{Z} \right\rangle\right),\left(\left|1_{X}\right\rangle,\left|1_{Y}\right\rangle, \left|1_{Z}\right\rangle)\}.\)  
The corresponding parallel states are: \(\{\left(\left|0_{X}\right\rangle,\left|0_{X}\right\rangle,\left|0_{X}\right\rangle \right)\},\left(\left|0_{Y}\right\rangle,\left|0_{Y}\right\rangle,\left|0_{Y} \right\rangle\right),\left(\left|0_{Z}\right\rangle,\left|0_{Z}\right\rangle,\left|0_{Z}\right\rangle\right),\)\(\left(\left|1_{X}\right\rangle,\left|1_{X}\right\rangle,\left|1_{X}\right\rangle \right),\left(\left|1_{Y}\right\rangle,\left|1_{Y}\right\rangle,\left|1_{Y} \right\rangle),\left(\left|1_{Z}\right\rangle,\left|1_{Z}\right\rangle\right)\}\). Figure 5 shows an example of the non-orthogonal states, e.g., \(\left(\left|0_{X}\right\rangle,\left|0_{Y}\right\rangle,\left|0_{Z}\right\rangle\right)\).  
In the 3Q protocol, the eavesdropper cannot adjust simultaneously the three reference photonic gains, \(Q_{(+3)}\), \(Q_{(+2)}\) and \(Q_{(+1)}\), for the triple-, double- and single-detection events, respectively. We found for the 3Q protocol that the corresponding error rate is \(\frac{0.5(Q_{0}+Q_{1}+Q_{2})+0.5^{2}Q_{3}+0.5^{3}Q_{4}+\ldots}{Q_{0}+Q_{1}+Q_ {2}+\ldots}\).  
The security of the ack-QKD protocol is supported by Alice's capability to verify the photonic gain of single matching (\(S\)-\(i\)) and double matching \((2M)\) detection events, parallel and non-orthogonal, which cannot be controlled by the eavesdropper without introducing errors.  
The ack-QKD protocol preserves the general behavior of the BB84 protocol, which includes the usual optical equipment. However, as the rate of the double detection events is small (\(\sim\)10\({}^{-8}\)), the protocol requires that the amount of photonic pulses sent by the transmitter station to be large and fast, which can be achieved in practice with the current technology (it may reach the order of \(10^{8}\) pulses per second). Furthermore, the mean value of the photonic source (\(\mu\)) can be increased in order to augment the final gain of double-detection events, e.g., when \(\mu\sim 0.5\), the gain goes to \(10^{-7}\) and \(\mu\sim 1\) increases the gain to \(10^{-6}\) in the 2Q version of the protocol.
## 2 Data and Methods
### Study Methods
#### 2.2.5 Libya-4 PICS--Hyperion Time-Series Using Different Atmospheric Correction Models  
To test differences of various atmospheric correction techniques and potential impacts of using Hyperion for cross calibration, we used a time-series of surface reflectance data from 2004-2016 in the Libya-4 desert PIC site, which is commonly used as a calibration site for Earth observing sensors.  
Thirty-six images from WRS path 181, row 40 were co-registered. All data were nadir \(\pm\)10 degrees and collected between May and September to reduce seasonal SZA effects. These data were atmospherically corrected to surface reflectance using ATREM, ACORN, and FLAASH [30; 34; 47]. ATREM uses a radiation transport model based on 6S, whereas ACORN and FLAASH use a more complex radiation transport model that retrieves atmospheric properties from bands near absorption features [48; 49]. All three models account for differences in the measured upwelling radiance from differences in solar irradiance due to different acquisition dates and times, and all models had similar parameters applied for the Libya-4 PICS. Combined atmospheric model uncertainty was estimated using a quadrature statistic [50] expressed as the square root of the coefficient of variation of the sum of squares from each atmospheric correction approach.  
A digital terrain model was also generated for this site using same date cross-track 50 cm panchromatic WorldView-1 and WorldView-2 stereo imagery to characterize terrain slope impacts to reflectance products. Greater than 50 tie points were used and the RMSE for the product was less than 3.5 m with a resulting resolution of 2 m. This was done in order to see if SZA change from precession  
Figure 4: Mean reflectance and standard deviation for select wavelengths for the Rail Road Valley Playa (RRVP) site before late mission precession changes (2001–2008 data, \(n\) = 15 images, \(\sim\)10:05 a.m. mean local time acquisition).  
combined with large dune shadowing at the site effected the results. More information about how these data were processed is available in [51].