esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Groundtrack Pattern of a Single Pair Mission on the Gravity Recovery Quality
###### Abstract  
For future gravity satellite missions, aliasing of high frequency geophysical signals into the lower frequencies is one of the most challenging obstacles to recovering true gravity signals, i.e., to recover the truth. Several studies have investigated the impact of satellite groundtrack pattern on the quality of gravity recovery. Among those works, the concept of sub-cycle has been discussed as well. However, most of that research has focused on the impact of sampling patterns on global solutions up to a fixed maximum spherical harmonic coefficient, rather than the associated coefficient defined by the Colombo-Nyquist and modified Colombo-Nyquist rules. This work tries to look more closely into the influence of sampling patterns on the gravity recovery quality for global and regional studies when the Colombo-Nyquist and modified Colombo-Nyquist rules apply. For the regional study, the impact of groundtrack patterns of different satellite constellation scenarios are investigated for a hydrological basin in central Africa. The quality of the gravity products are assessed by different metrics, e.g., by spatial covariance representation. The potential meaning of the sub-cycle concept in terms of global and local impacts is also investigated by different repeat-orbit scenarios with even and odd parities. Different solution scenarios in terms of the original and modified Colombo-Nyquist rules will be discussed. The results of our study emphasize the impact of maximum harmonics of the recovery, the influence of sub-cycle on the local gravity recovery and the mission formation impact on the recovery error.  
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# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 4 Discussion
### Platform Strengths
Due to travel restrictions and physical distancing, clinical-trial investigators have difficulties recruiting participants, and a significant number of clinical trials have been delayed [33]. This platform can be an alternative in these circumstances. The self-reporting function and communication between clinicians and participants are unique features of this platform. Participants who receive trial drugs or medical devices in advance can communicate with medical staff and take part in clinical trials in the places where they live by using this platform. These features can make it easier to recruit a large number of participants and enhance their participation rates.
# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 3 Methods
### Principle of Ant Colony Algorithm  
#### 3.1.1 Heuristic Function  
The ant colony algorithm is a heuristic algorithm. While selecting the next node, the ant will preferentially select the node that is closest to itself. The distance from the current node \(i\) to the node \(j\) is \(d_{ij}(i,j=1,2,\cdots,n)\), and \(n\) is the number of destination nodes. The heuristic function between two nodes is \(\eta_{ij}\).  
\[\eta_{ij}=\frac{1}{d_{ij}} \tag{12}\]  
\[d_{ij}=\left(\left(i_{x}-j_{x}\right)^{2}+\left(i_{y}-j_{y}\right)^{2}\right) ^{\frac{1}{2}} \tag{13}\]  
where \(d_{ij}\) is the Euclidean distance between node \(i\) and node \(j\). \(i_{x}\) and \(i_{y}\) are the abscissa and ordinate of node \(i\). \(j_{x}\) and \(j_{y}\) are the abscissa and ordinate of node \(j\). The pheromone concentration between two nodes is \(\tau_{ij}\). The probability that ant \(k\) transits from node \(i\) to node \(j\) is \(pk_{ij}\).
# Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
## 2 Materials and Methods
### Bivariate Correlation Analysis  
A bivariate correlation analysis has been performed in R software framework [35]. Results are reported in Figure 3, in which traffic volume (Q), heavy vehicle percentage (P), total honking occurrences (H) and measured equivalent continuous sound pressure level (\(L_{eq}\)) are reported with bivariate scatter plots below the diagonal, histograms on the diagonal and the Pearson correlation above the diagonal. A high correlation (0.90) is found between \(L_{eq}\) and traffic volume, as expected since the main noise sources in the measurement sites are the vehicles. A significant (0.65) correlation is found between \(L_{eq}\) and P, as well as between \(L_{eq}\) and H.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** \\ \hline Traffic volume in 15 min (Q) [veh] & 633.8 & 341.2 & 583 & 216 & 1377 \\ Percentage of heavy vehicles (P) [\%] & 1.36 & 1.42 & 0.5 & 0 & 3.8 \\ Total honking occurrences in 15 min (H) [counts] & 218.0 & 88.9 & 227 & 90 & 406 \\ Equivalent continuous sound level (\(L_{eq}\)) [dBA] & 74.7 & 3.4 & 75 & 68 & 79.7 \\ \hline \end{tabular}
\end{table}
Table 1: Resume of the main statistics of data and parameters used in the calibration and testing of the models. All the features have been measured with 15 min time span.  
Figure 2: Localization of the identified sites, respectively site 1 (**a**), site 2 (**b**), site 3 and 4 (**c**), site 5 (**d**).  
It is interesting to notice that the heavy vehicle percentage, that is usually implemented in the most common road traffic noise models, being recognized as an important parameter of the phenomenon, correlates with the noise levels similarly to the total honking occurrences, that is practically always neglected. In the authors' opinions, this means that, at least in the five test sites and in all the sites with similar features, the honking events cannot be ignored when designing and calibrating an effective traffic noise model.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 2 The DED-RL Algorithm
### Discrete RL  
The advantage of a discrete algorithm is subtle, but very substantial. RL algorithms work by launching ray tubes of given cross section (e.g., triangular or rectangular) from the current Tx location into the surrounding space: when a ray tube hits an obstacle, the original direction of propagation is obstructed and, at the same time, the ray is \"bounced\" backward by the obstacle, where bouncing is a general term that means reflection, diffraction, and/or diffuse scattering. One big problem is that only part of the ray tube might hit an obstacle and a part might keep-on propagating in the same direction: in this case, the cross-section of the tube must be properly split using \"polygon clipping\" algorithms that are computationally intensive. If the obstacle is divided into small-enough tiles and ray tubes (or rays) are specifically launched toward the tiles that are visible from the Tx, ray splitting is naturally accomplished to the best of the chosen pixelization resolution without applying polygon clipping (see Figure 1). The notion of \"visibility\" refers to the existence of direct propagation path between two pixels, i.e., such a path is not blocked by any opaque object, and is very simple to verify: it only requires the verification of the intersection between a segment and a polygon.
## 2 Materials and Methods
### Agriculture Operations Performed and Machines Employed Within the Technical Itineraries
Fertilization involved the application through drip fertigation of 38.7 kg ha\({}^{-1}\) and 36.3 kg ha\({}^{-1}\) of N in the first and second year, respectively, along with 21.8 kg ha\({}^{-1}\) of K\({}_{2}\)O in the second year (NUTRIGREEN, Green Has Italia, Canale, Italy, 8-0-0; VIT-ORG, Green Has Italia, 3-0-6). Additionally, in the second year, 8 kg ha\({}^{-1}\) of CaO (NEWCAL, Green Has Italia, 16.8%) was applied via foliar spraying. During the first growing season, phytosanitary treatments consisted of four interventions to control the tomato late blight pathogen (_Pluyophthora infestans_), three of which were performed using only 755 g ha\({}^{-1}\) of tetraramic oxychloride each (Pasta Caffaro Blu, Sumitomo Chemical Italia S.r.l., Milan, Italy), while in the fourth intervention, 190 g ha\({}^{-1}\) of tribasic copper sulfate (Cuproxat SDI, Nufarm, Bologna, Italy) were also applied. In addition to controlling late blight pathogens during the second growing season, _Tuta absoluta_ (Meyrick) was also controlled. To control _Pluyophthora infestans_, five treatments with 400 g ha\({}^{-1}\) of copper sulfate (Poltiglia bordolese Disperss blu, Upl, Cesena, Italy) were performed. Six treatments with 74.18 kg ha\({}^{-1}\) of _Bacillus thuringiensis_ Berliner var. [PERSON] each (Delfin, Dupont, Wilmington, DE, USA) and five treatments with 30.13 g ha\({}^{-1}\) of azadirachtin each (Oikos, Sipcam, Durham, USA) were carried out to control _T. absoluta_. A sprayer with a working width of 12 m was employed. The dates on which each agricultural operation was carried out are reported in [22]. For operations requiring greater power, such as using the combined cultivator, a 4 WD tractor equipped with a 154.5 kW diesel engine was used. A 2 WD tractor equipped with a 51.5 kW diesel engine was used for all other operations.
# failures < - 0 while(indx <= N) #loop checked all elements of vector data {
#start checking vector
#100% inspection currentrun = 0
#while(currentrun < n) {
#if(indx > N) {
#break
#else
#checking value of spot/slot/element if(data[indx]==0) { currentrun=currentrun + 1 } else { effective = rbinom(1,1,theta) if(effective == 1) { failures=failures+1 #keep currentrun=0 in case currentrun!= 0, but needs to be reset currentrun=0 } else {
#sampled = sampled +1 indx = indx +1 }
#break out of loop to reduced sampling stopreduced = 0 while(stopreduced == 0) { indx = indx + skip #if index is still outside vector if(indx > N) { stopreduced = 1 break
#if index is inside vector else {
#if element of position index is equal to 1 if(data[indx]==1) { effective = rbinom(1,1,theta) if(effective == 1) { failures = failures + 1 stopreduced = 1 else} } } } } ```
sampled=sampled+1  indx=indx+1 } } } } totalsamples[k] = sampled residualfailures[k]= (F - failures) k = k+1 }
```

#average outgoing quality per simulation AOQ <- (mean(residualfailures))/N * 100 #percent of entire simulation vector sampled pctsampled <- (mean(totalsamples)/N) * 100 cat(\"Average Outgoing Quality:\", AOQ, \"n\") cat(\"Average Percent Sampled:\", pctsampled, \"n\") } ```

## References

* (1) [PERSON] A Sampling Inspection Plan for Continuous Production. _Ann. Math. Stat._**1943**, _14_, 264-279. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON] The Dodge CSP-1 Continuous Sampling Plan under Inspection Error. _AIIE Trans._**1973**, \(5\), 193-202. [CrossRef]
* (3) [PERSON]. The Effect of Short Production Runs on CSP-1. _Technometrics_**1977**, _19_, 259-262. [CrossRef]
* (4) [PERSON] Average Outgoing Quality Limit for Short-Run CSP-1 Plan. _Tamkang J. Sci. Eng._**2005**, \(8\), 81-85.
* (5) [PERSON]; [PERSON] Minimum Average Fraction Inspected for Short-Run CSP-1 Plan. _J. Appl. Stat._**1998**, _25_, 733-738. [CrossRef]
* (6) [PERSON], [PERSON] A Renewal-Process Approach to Continuous Sampling Plans. _Technometrics_**1993**, _25_, 59-67. [CrossRef]
* (7) [PERSON]; [PERSON] Computerized Continuous Sampling Plans with Finite Production. _Comput. Ind. Eng._**1993**, _25_, 4345-4448. [CrossRef]
* (8) [PERSON]; [PERSON] Additional Continuous Sampling Inspection Plans. _Ind. Qual. Control_**1951**, \(7\), 7-12. [CrossRef]
* (9) [PERSON]; [PERSON] Comparison of Certain Types of Continuous Sampling Plans (CSPs) and its Operating Procedures--A Review. _Int. J. Sci. Res._**2015**, \(4\), 455-459.

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# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 4 Traditional and Modern Processes of Architectural Design
### GAI-Driven Architectural Design Workflow
#### 4.2.4 Three Key Elements of Architectural Design  
The three key elements of architectural design include environmental information collection, plan generation, and design evaluation.  
Building environment information extraction is a foundation in the design process, which involves collecting and screening the information that is essential to building design from the environment. Buildings act as interfaces between internal and external environments, requiring environmental data to shape the design's initial state. The combination of modern sensor technology [82] and AI algorithms makes data collection and analysis more efficient. For example, AI can work with sensors to collect information about human activity patterns, brain wave signals, and social media sentiment, understanding public concerns and preferences.  
Plan generation is the creative phase of the design process that generates alternatives based on environmental information and evaluation feedback. AI is increasingly used in this process, including planning algorithms, reinforcement learning, swarm intelligence, and combinatorial variation-based algorithms, helping architects optimize designs. For example, genetic algorithms can optimize the layout of photovoltaic panels on building surfaces to enhance solar energy utilization.  
The goal and evaluation of architectural design is the key to ensure that the design meets human needs and expectations. AI aids by optimizing designs for diverse user groups, extracting preferences implicitly, and accelerating evaluations. For example, the use of feedforward neural networks to assess environmental changes and the qualitative assessment of wind environments in architectural buildings through deep neural networks are effective applications of AI in architectural design evaluation.
# _remote sensing_
## 3 Related Data and Correction Models in Cal/Val of Altimeters  
The datum of altimeter calibration was defined using the four PGSs in WSCS. The height benchmarks of the ATGs were defined using the DGBs with supports of the PGSs. Moreover, the ATGs has a distance of 15-30 km from the altimeter comparison point. Therefore, the MSS and tidal differences must be taken into consideration in such distance [12]. The global/regional tide models were compared using four in situ tide gauge data (Figure 2). A GPS towing-body and a highly accurate ship-based SSH measurement system (HASMS) have been used to measure the sea surface of this area and validate the MSS models [18].
## 2 Methods
### Flux Data, LUE and GPP Modeling  
The cornfield is equipped with a 10 m high eddy-covariance-instrumented tower that measures fluxes from a field approximately 16 hectares in area. Our sampling was approximately 65 m away from the flux tower and within the footprint. Flux data are reported at 30 min intervals throughout all 24-h periods of the full growing season. Net Ecosystem Production (NEP) is determined from the measured net CO\({}_{2}\) exchange using the eddy covariance method, supported by the tower's micro-meteorology measurements (e.g., PAR, temperature, wind speed and direction, precipitation). Data were processed following the FluxNet/AmeriFlux guidelines [87], and the NEP was partitioned into respiration and GPP [88], compatible with AmeriFlux standards. Air temperature was measured at four meters above the ground on the flux tower and reported at half-hourly intervals. Precipitation was measured with a tipping rain gauge mounted one meter off the ground at a USDA/BARC meteorological station.  
Corresponding flux data were extracted when spectral observations were made on each field day. A combined flux and optical dataset was used to examine the potential of modeling LUE and GPP as the product of PRI, SIF (red and far-red), or both optical variables. This goal was accomplished through linear regression analyses with the R language and software by JMP (SAS Institute Inc., Cary, NC, USA). The general model formulations are listed in Table 2.
# Speckle Noise Reduction in Digital Holography Using a DMD and Multi-Hologram Resampling
## 7 Conclusions  
We presented an alternative methodology to reduce speckle on digital holographic imaging based on a DMD device. The simplicity and robustness of the setup allow us to test different strategies to obtain multiple registers with uncorrelated speckle. We determined that simple plane waves of varying tilt can provide enough variance to improve the quality when multiple reconstructed intensities are averaged. We presented a complementary numerical processing of the registers that allows improving the final reconstruction. A random 3D resampling of the original hologram stack allows obtaining newly composed holograms. The new synthetic holograms are produced from the superposition of nonoverlapping squared patches selectively extracted from the original holograms using binary masks. The parameters of the resampling provide control over the randomness of the synthetic holograms, particularly the patch size being the most important, as it requires to balance the desired increased uncorrelation with the loss of resolution. We determined that the best patch size responds to a factor of 1:1/4 with respect to the original hologram size. Further improvements in our speckle reduction technique can be obtained if the angular variation of the plane waves is increased. With a larger angular range, the recorded holograms will present a larger speckle variance, thus producing a smoother averaged reconstructed intensity. Due to the fast switching nature of the DMD and the computational simplicity of our resampling algorithm, our methodology has potential applicability to fast dynamic scenarios.  
Conceptualization, J.R., N.C.-R. and J.H.-R.; methodology, J.R., N.C.-R. and J.H.-R.; validation, J.R. and J.H.-R.; formal analysis, J.R. and J.H.-R.; investigation J.R. and N.C.-R.; data curation, J.R.; writing--original draft preparation, J.R.; writing--review and editing, J.R. and J.H.-R.; visualization, J.R.; project administration, N.C.-R.; funding acquisition, N.C.-R. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Instituto Tecnologico Metropolitano (Grant number P17217).  
The authors declare no conflicts 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.
## 2 Beidou-Binocular Cooperative Navigation Model
### Beidou Navigation Positioning Module  
The Beidou navigation positioning module is one of the core modules of the Beidou-Binocular Cooperative Navigation Model (BBCN), responsible for providing the robot's global positioning information and providing initial position references for path planning and environmental perception. In complex industrial environments like substations, the accuracy and robustness of Beidou navigation positioning are vulnerable to electromagnetic interference, signal blockage, and multipath effects. Therefore, multiple optimization techniques are required to enhance the reliability and precision of positioning. This section provides a detailed description of the working principle of the Beidou navigation system, the main issues in complex environments, and the improvement strategies and algorithms addressing these issues.
## 4 Future Prospects for China's Weather Routing Services
### Intelligent Weather Routing Technology to Empower Low-Carbon Transition in Shipping  
The global shipping industry's commitment to carbon reduction policies, particularly under the IMO's greenhouse gas (GHG) emission strategy [4], has created multidimensional opportunities for advancing ship weather routing technologies. As vessel operators increasingly prioritize energy efficiency optimization, intelligent weather routing systems are emerging as a transformative solution. These systems directly contribute to meeting IMO targets (e.g., EEXI, CII) by enabling vessels to continuously optimize their route and speed based on real-time weather, currents, and vessel performance models, thereby minimizing fuel consumption and associated emissions [14]. Future systems may incorporate advanced algorithms, such as deep reinforcement learning (DRL), for dynamic route planning that balances fuel economy, carbon intensity, and safety.  
The convergence of intelligent weather routing with emerging low-carbon technologies is driving a paradigm shift in maritime decarbonization. Centered on real-time meteorological sensing, these systems synergize with wind-assisted propulsion systems and low-carbon fuel technologies to establish a closed-loop framework: environmental energy harvesting, power configuration optimization, and emission control. Leveraging precise marine weather predictions, weather routing algorithms dynamically adjust parameters such as the angle of attack of wind sails and the power output of clean energy engines, enabling vessels to achieve dual breakthroughs in speed optimization and energy efficiency enhancement under complex sea conditions [54]. This integration not only transforms meteorological factors into actionable navigational resources but also provides critical technical support for achieving IMO's decarbonization targets through the synergy of intelligent routing and clean energy systems. Barriers include the development of accurate digital twins for diverse vessels, reliable sensor integration for performance monitoring, and the cost associated with implementing compatible technologies onboard.
## 3 Results and Discussion
### Influence of the Interaction of Pulse Energy and Repetition Rate on CVD Diamond Coating Ablation  
In order to investigate the influence of the combination of single pulse energy and effective number of pulses on the ablation results of CVD diamond coatings, three conditions, namely, 500 Hz \(\times\) 200 \(\upmu\)J, 10 kHz \(\times\) 10 \(\upmu\)J, and 100 kHz \(\times\) 1 \(\upmu\)J, are adopted by adjusting the single pulse energy and the laser repetition rate simultaneously. The scanning speed is 1 mm/s. Since average laser power, \(P\), can be calculated by the following equation: \(P=E\!f\), it is easy to know that the average power of the three conditions is maintained at 100 mW. For the sake of clarity and comparability, the SEM photographs produced under the three conditions are combined into one picture (as seen in Figure 9). It is clear that morphologies produced under the three conditions exhibit uniform micro-structures, their LIPSS characteristics especially are almost identical.  
Furthermore, as displayed in Figure 10a-c, either the white-light interferometer obtained topographies or the measured x or y profiles resemble one another to a large extent. It is interesting to find that the morphology and topography present in Figure 9 have many features similar to those in Figures 5d and 6d, where the calculated average power is also 100 mW. Hence, we can reach a conclusion that regardless of how the single pulse energy or repetition rate changes (either separately or together), the final ablation results rely on the average power derived from these two parameters. This conclusion can also be evidenced by the Ra curve plotted in Figure 10d, where the roughness of the irradiated regions fluctuated around 0.4 \(\upmu\)m. On the contrary, the MRR decreased sharply under the given conditions, and the MRR at 500 Hz \(\times\) 200 \(\upmu\)J was almost 200 times higher than that of 100 kHz \(\times\) 1 \(\upmu\)J.  
Figure 8: Raman spectra of the diamond coatings irradiated under different pulse energies. (**a**) 50 \(\upmu\)J, (**b**) 70 \(\upmu\)J, (**c**) 80 \(\upmu\)J, and (**d**) 100 \(\upmu\)J.