esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 4 Results
### Simulation
#### 4.2.1 Two Locations Relationship  
Such as in the modeling stage, in the first analysis performed, the behavior of wind speeds in two locations is evaluated. The calculation time of the algorithm until the tolerance is reached is 22 h and 3.35 min in 2,599,096 iterations in the case of Fragavella and Labrada and 16 h and 36.59 min in 2,231,562 iterations in the case of Fragavella and Lanzos, existing significant differences in the computation times and iterations between both studies. On the other hand, it can be verified that the calculation times are higher in the modeling stage than in simulation one, corresponding to some hours in the simulation concerning a few seconds in the modeling.  
As can be seen in Figure 8, the initial data deviation concerning the tolerance marked in the study with Fragavella and Labrada is higher than that with Fragavella and Lanzos and, consequently, the calculation time period is higher, unlike in the case of the modeling, where the times were practically identical. However, the behavior of the data deviation between the required solution and the calculated one varies in a similar way along the time, so in both cases, there is a period with a rapid decrease, up to 4 h in the case of Fragavella and Labrada and up to 3 h in the case of Fragavella and Lanzos, after which the refinement to the required solution slows down.  
Figure 7: Evolution of the calculation time required to carry out the modeling stage considering a different number of locations.  
Tables 9 and 10 show the results obtained for the correlations of the simulation phase in each of the analyses performed. Observing their values and comparing them with Tables 3 and 4, it can be deduced that the method used to correlate the random series given by Equation (16) has a result that is very close to the solution considering just the spatial correlations. This is mainly because the method used to correlate the series is the Cholesky decomposition of the spatial correlation matrix. However, the values of the temporal correlations are close to 0 because the series was randomly generated. On the other hand, it can be verified that there is a very good accuracy of the iterative algorithm, so the solutions are very close to the objective, with differences lower than 5% in each of the cases.
# Experimental Analysis of Plantar Fascia Mechanical Properties in Subjects with Foot Pathologies
## 2 Materials and Methods
### Mechanical Testing  
Each end of the sample was glued between two patches of balsa wood to prevent any slippage [30; 31], clamped by the grips and then hydrated by dropping the PBS on the sample surface for the entire duration of the test (Figure 1b). A gauge length of 15 mm was selected to ensure a mean aspect ratio (length/width) of about 3, in accordance with other test protocols [8; 32]. The samples were stiff enough to be placed flat between grips with almost null force acting (<0.01 N). Mechanical tests were carried out by means of Bose ElectroForce(r) Planar Biaxial Test Bench instrument (TA Instruments, New Castle, DE, USA) under displacement control with precision of \(\pm\) 0.001 mm and adopting a load cell of 22 N with a precision of \(\pm\)0.02 N. All the tests were carried out at room temperature (\(25\pm 1\)\(\;{}^{\circ}\)C). Uniaxial tensile tests consisted of five loading-unloading cycles at a constant strain rate of 10% s\({}^{-1}\) by varying motor displacement up to a maximum nominal strain of 4%, 6%, 8% and 10%, separated by a rest period of 60 s. Stress-relaxation tests were carried out by elongating the sample at a fast strain rate of 100% s\({}^{-1}\) and maintaining a constant strain for 300 s at each different level of applied strain (4%, 6%, 8% and 10%). The samples were then brought back to initial length and kept in this condition for 300 s. Nominal strain \(\varepsilon\) was evaluated by measuring the displacement of the grips and dividing it by the initial gauge length of the sample. The nominal stress P was calculated as the ratio between the force, measured by the load cell, and the initial transversal area of the sample.  
The loading protocol of mechanical tests in terms of nominal strain vs. time and typical response of a PF sample in terms of nominal stress P vs. time are shown in Figure 2. For each stress-relaxation test, normalized stress P\({}_{\rm norm}\) was calculated as the ratio of stress at the current time t and the maximum stress measured at the initial time of the relaxation process. Mean values of nominal stress P vs. \(\varepsilon\) in tensile tests and mean values of normalized stress P\({}_{\rm norm}\) vs. t in stress-relaxation tests were calculated for each subject, with associated standard error of the mean (SE). The secant elastic modulus Es was calculated from the mean of tensile data of each subject, as the slope of the straight line drawn from the origin of the stress-strain diagram and intersecting the mean curve at 8% strain.  
Figure 2: Loading protocol of mechanical tests in terms of nominal strain \(\varepsilon\) vs. time (**a**) and typical response of a PF sample in terms of nominal stress P vs. time (**b**).
## 3 Results  
A total of 36 patients (17 women and 19 men) with a mean age of 51.7 years (range, 38-65 years) were enrolled in the study. Demographic data recording the age and gender of patients are shown in Table 1. It is noteworthy that GBR augmentation was carried out in the anterior and posterior regions, involving 9 patients for each region, whereas CS augmentation was carried out in the anterior and posterior regions, involving 8 and 10 patients, respectively. All the patients completed their follow-up and all bone augmented regions healed well without complications, both in CS and GBR sites. Neither exposure of the collagen membrane nor any clinical sign of infection or inflammation were observed in any of the 36 patients.  
During implant application, the augmented site had bone quality of Type II or III and a torque value of \(\geq\)25 Ncm was achieved in all cases (Table 2). Primary stability was obtained during dental implantation. At the second-stage surgery, neither implant mobility nor bone loss were detected. Moreover, no pain or deep periodontal probing depths were encountered. The survival rate value at second-stage surgery was recorded at 100%.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Demographic Variables** & \(p\) & **CSA Group (n = 18)** & **GBR Group (n = 18)** \\ \hline Age & 0.09 & 49.89 \(\pm\) 8.80 & 53.5 \(\pm\) 6.91 \\ Gender (Female/Male) & 0.73 & 0.8 & 1.0 \\ \hline \end{tabular} Note: Data are presented as Mean \(\pm\) Standard Deviation.  
\end{table}
Table 1: Patient demographics.  
Figure 3: Volumetric evaluation of a grafted site.  
The mean overall bone volume loss in the test group and control groups was 8.26 \(\pm\) 1.60% and 14.36 \(\pm\) 3.55%, respectively. Statistical significance was observed between the two groups (Table 3).
## 3 Results and Discussion
### Scientific Mapping
#### 3.2.10 Latency and Registration Issues  
Another issue that is commonly cited is the time between head movements and viewing the scene.  
The authors of [46] identified registration issues between virtual and physical elements of the environment, which they linked to hand and finger optical tracking controller calibration or tracking errors. The authors sought to improve tracking by instructing participants on how to adjust the head-mounted display before beginning the trial; however, the adjustment cannot be controlled from the outside by the experimenter. The resolution of the head-mounted display, according to the authors, might play a role in this scenario.  
When interacting with mixed physical-virtual prototypes, several study participants mentioned a dimensional discrepancy between the virtual and physical aspects of the prototypes (for example, the cockpit's physical wheel and the virtual air conditioning control).  
The necessity of calibrating the position and visibility of virtual objects with physical prototypes was identified as a barrier throughout the research. The main difficulty is the amount of time it takes to calibrate the system, given that calibration modifications are dependent on the user's participation.
## 2 Related Work
### Academic Solutions
#### 2.2.3 Protocol Compatibility Approaches
The previously mentioned messaging protocols are probably the most popular, but there are also other relevant MOMs that have achieved a certain degree of popularity in specific scenarios, like OpenWire, Amazon SQS, the traditional IRC (Internet Relay Chat), PSYC, Beanstalk, ZeroMQ, Peafowl, Gearman, Sparrow, Kestrel or Apache Kafka. Among all the different messaging systems, MQTT is arguably the most used by IoT and WSN applications because of being so lightweight that it can be implemented in resource-constrained devices like the ones usually found in HAS. Specifically, MQTT can be found in applications based on low-power sensors for smartwatches [77], in robotics [78], healthcare [79] or optical camera communications [80]. In the field of home automation, MQTT has been used in different applications to control lights [81], to monitor ambient assisted living parameters [82], for home energy monitoring [83], or for the general management of a smart home [46; 84]. In academic research, the major rival of MQTT is XMPP, but most XMPP-based home automation developments [85; 86; 44; 87; 88] were published before MQTT became an ISO standard [89]. Other messaging protocols have not had relevant success in home automation, although, for instance, some researchers optimized JMS performance [90] or proposed using AMQP through RabbitMQ [91].
## 3 Results and Discussion
### Leachability of manure Blends through the Soil  
#### 3.3.1 Physicochemical Properties before and after Incubation  
The physicochemical properties of manure and cotton stems/bio-solid biochar, with different combinations before and after the incubation period, are represented in Table 4. The most important properties that affect the leachability of nutrients and heavy metals from soil, in addition to the retention of moisture by soil and, consequently, plant growth, are the pH and the water holding capacity of the solid materials, respectively. As can be noted, before incubation all solid mixtures were slightly alkaline (pH = 7.6-7.8) with small electrical conductivity values (1.05-1.1 mS/cm), properties which positively influence soil amelioration [4; 32]. After incubation, there was a small drop in pH and a marked  
\begin{table}
\begin{tabular}{c c c c} \hline
**Wavenumber (cm\({}^{-1}\))/Functional Groups** & **N\({}_{2}\)** & **H\({}_{2}\)O** & **CO\({}_{2}\)** \\ \hline
500–600/C-X halo compounds & & 556 & 564 \\  & & 592 & 592 \\
650–1000/C=C alkenes & 666 & & \\
1000–1400/C-O alcohols, ethers & 1046 & 1030 & 1036 \\  & 1114 & 1090 & \\  & 1146 & & \\  & 1156 & & \\
1234 & & & \\
1300–1600/C-H alkanes, C=C aromatic compounds & 1454 & 1550 & 1440 \\
1600–1670/C = C alkenes & 1650 & & 1602 \\
1650–1750/C = O carboxylic acids & 1714 & & \\
2000–2400/O = C = O carbon dioxide & 2318 & & \\
2350–3000/C-H aldehydes, alkanes & 2848 & & \\  & 2918 & & \\
3000–4000/-OH alcohols, carboxylic acids & 3292 & & 3436 \\ \hline \end{tabular}
\end{table}
Table 3: FTIR analysis of manure biochar produced under nitrogen, steam, or carbon dioxide.  
decrease in electrical conductivity, revealing that some alkaline salts were released, leaving a solid material with a higher potential for uptake of nutrients or water by plants upon soil application [4]. This is strengthened by the fact that after incubation the volume (density decrease) and the water holding capacity of all blends, especially those containing the biochar, were increased (d was decreased up to 42% and WHC was increased up to 30%), improving absorption of water by soil.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 3 Field Experiment of the Path Planning Techniques on the Fish Farm
### Control Software Development  
To produce the mission plan, the custom flight control software takes in a text file of the list of pond coordinates produced by the three routing techniques and generates a set of waypoints with appropriate landing, takeoff, and payload activation commands to successfully and reliably land in the water, activate the sensor payload to lower and return, take off and report the data to the base station. The payload was activated through the camera activation pin in the SplashDrone momentarily before it lands in the water, whereupon receiving the signal would lower the winch and begin sensing, then after a set time would retract the winch and stop sensing. The payload was then activated again once the drone reaches its maximum altitude to send the data to the base station. The software used to transmit missions to the SplashDrones, along with the GLOP and HPP methods, are available at [[https://github.com/tonydavis629/HAUCS](https://github.com/tonydavis629/HAUCS)]([https://github.com/tonydavis629/HAUCS](https://github.com/tonydavis629/HAUCS)), accessed on 6 February 2023.  
A satellite image of the Southern Illinois University Aquaculture Research Center is seen in Figure 8. This facility has the capacity for 90 ponds, however at the time of testing only 26 ponds were safe to land in. Issues such as lack of water, excessive overgrowth, and power line obstacles prevented the use of 64 ponds. These 26 available ponds were loaded into the three path planning methods: GLOP, GAM and HPP.  
Figure 7: System diagram of the HAUCS path planning software and drone hardware.
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 2 Materials and Methods
### Methods  
The general workflow consists of three main steps, as illustrated in Figure 2. The first step was preprocessing for building the annual Landsat-NDVI time series of the forested areas and the second step comprised the developed OASB method with two procedures: (1) simple non-iterative clustering (SNIC) to generate the forest objects and (2) shapelet-based time-series classification (SBTSC) to distinguish the planted forests from the natural forests and estimate the planting year. The forest maps were validated in the final step.  
Figure 2: Workflow of the procedures developed in this study to map the planted forest dynamics. The major module is the OASB method, comprised two components: (1) image segmentation to yield forest objects and (2) a time-series classification to identify planted forests and estimate the planting year.
## 2 Methods
### Installation  
The SATH(r) platform considers a pre-laid mooring system of six catenary lines in three groups of two lines oriented at 120\({}^{\circ}\) to each other [20]. It considers a single point mooring, which makes an effective maritime system to handle anchors and lines of lower dimensions, which are less expensive [20]. For the installation of the mooring lines, an Anchor Handling Vessel (AHV) is required [20; 30]. Anchors and chains are lifted by an onshore crane into the AHV deck area where they are fastened for the transport [20].  
The installation of the electric cable (inter array and export cable) can be developed considering cable-laying vessels specialized in this type of work: load the cable manufacturer, storage in carousels, burial of the cable into seabed, etc.; This is because its installation is similar to other offshore wind turbines [20].  
The installation of the SATH(r) platform is carried out using a main tug vessel that will tow the platform and two auxiliary tugboats that will assist the towing operation if necessary.
# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
###### Abstract  
A high-spatial resolution aerosol optical depth (AOD) dataset is critically important for regional meteorology and climate studies. Chinese Huanjing-1 (HJ-1) A/B charge-coupled diode (CCD) data are a suitable data source for retrieving AODs. However, AOD cannot be retrieved based on the dark target method due to the absence of a shortwave infrared band. In this study, an AOD estimation method based on the relationships between visible bands of HJ-1 A/B CCDs is proposed. The Polarization and Directionality of the Earth's Reflectances (POLLER) Bidirectional Reflectance Distribution Function (BRDF) dataset was used to construct a lookup table for interband regression coefficients that varied by solar/view angle and land cover type. Finally, high-spatial resolution AODs could be retrieved with the aerosol lookup table and constraints. The results showed that the AODs retrieved from the HJ-1 A/B CCD data had the same range of distribution and trends as a visual interpretation of the images and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products did. The validation results using four sites of the Aerosol Robotic Network (AERONET) in Beijing showed that the value of the correlation coefficient \(R\) was 0.866, the root mean square error (RMSE) was 0.167, the mean absolute error (MAE) was 0.131, and the expected error (EE) was 53.9%. If the measurements of an AERONET site were used as prior knowledge, AOD retrieval results could be much more accurately obtained by this method (\(R\) is 0.989, RMSE is 0.052, MAE is 0.042, and EE is 96.7%).  
a 201911 832 10.3390/rs110/70832
## 2 Cost Model of Predictive Maintenance
### Maintenance Costs
#### 2.2.2 Cost of Corrective Maintenance  
The CM cost for the \(i\)th inspection is paid only when there is no predictive replacement and if a failure occurs before inspection _i_+1. Thus, the CM expected cost during the time cycle \(D_{T}\), called \(C_{c}\), can be described by the following equation:  
\[C_{c}=\sum_{i=1}^{N_{in}-1}c_{c}\cdot(1-N_{i})\cdot\int_{t_{i}}^{t_{i+1}}f_{i }(t\backslash T\geq t_{i})\cdot dt+c_{c}\cdot(1-N_{N_{in}})\cdot\int_{t_{N_{in }}}^{D_{T}}f_{N_{in}}(t\backslash T\geq t_{N_{in}})\cdot dt \tag{2}\]  
where \(c_{c}\) is the constant cost of a single CM action.  
The first term in Equation (2) corresponds to the expected cost of CM from the 1 st inspection till the \(N_{in}^{th}\) inspection, while the second term in Equation (2) corresponds to the expected cost of CM from the \(N_{in}^{th}\) inspection till \(D_{T}\).
## 6 Numerical Examples
### Validation of Results  
A preliminary validation of the proposed mathematical formulation is performed against some results available in literature, see Tables 2 and 3. More specifically, Table 2 summarizes the results in terms of dimensionless natural frequencies for a simply-supported square graphene sheet, as computed by the present method, against predictions by [PERSON] [40] based on SSDT, and or those ones by [PERSON] and [PERSON] [41] according to the higher-order refined plate theory and the DQM approach. A parametric investigation analyzes the variation of the NPs and foundation constants. The outcomes in Table 2 clearly show that the natural frequencies gradually decrease for an increased value of \(\mu\) and increased for increasing values of \(\mathrm{K_{W}}\) and \(\mathrm{K_{P}}\). The very good agreement between our results  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline (**K**) & \(\boldsymbol{E_{1}}\) (**TPa**) & \(\boldsymbol{E_{2}}\) (**TPa**) & \(\boldsymbol{G_{12}}\) (**TPa**) & \(\boldsymbol{\
u_{12}}\) & \(\boldsymbol{\alpha_{11}(10^{-6}/\text{K})}\) & \(\boldsymbol{\alpha_{22}(10^{-6}/\text{K})}\) \\ \hline \multicolumn{7}{c}{_Zigzag sheet I:_\(a=4.855\) nm, \(b=4.888\) nm, \(h=0.154\) nm} \\
300 & 1.987 & 1.974 & 0.857 & 0.205 & 2.1 & 1.9 \\
500 & 1.974 & 1.968 & 0.870 & 0.205 & 2.3 & 2.0 \\
700 & 1.961 & 1.948 & 0.870 & 0.205 & 2.4 & 2.1 \\ \multicolumn{7}{c}{_Zigzag sheet II:_\(a=9.496\) nm, \(b=4.877\) nm, \(h=0.145\) nm} \\
300 & 2.145 & 2.097 & 0.938 & 0.223 & 1.7 & 1.5 \\
500 & 2.103 & 2.055 & 0.959 & 0.223 & 2.0 & 1.7 \\
700 & 2.069 & 2.014 & 0.959 & 0.223 & 2.1 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermo-mechanical properties of the material [39].  
and predictions from literature, clearly confirms the reliability of the present approach to capture the response of the problem, where few negligible differences are only related to the different selected solution methodology and plate theory.  
As another attempt for validation, we compute the critical buckling loads for isotropic square graphene sheet under biaxial load with different lengths, whose results are summarized and compared in Table 3 with the ones based on an MD approach [42], a DQM approach [43], or a refined strip method (RFSM) [19]. The gradual increase of the geometrical dimension \(a\) yields a meaningful reduction of the critical loading, whereas the very good agreement between our results and those reported in literature, confirms once again the accuracy of the proposed method.
## 3 Electron Acceleration with Radially Polarized Laser Beams
### Free-Space Electron Acceleration in Laser Beams
This can be explained by the fact that an electron moving longitudinally can possibly stay in phase with the longitudinal electric field over longer distances. During RPS the electrons leave the focal region of an ultra-intense Gaussian beam with a large transverse momentum, and very few of them remain close to the beam propagation axis [51; 58; 59]. To limit the transverse excursion, it was proposed using a combination of a Gaussian beam and higher-order modes to create an intensity minimum at beam center that acts as a confining potential [60]. It was later shown that the use of RPLBs improves even further upon longitudinal acceleration and electron beam confinement [61; 62]. The particular geometry of RPLBs forces a significant proportion of the particles to move toward the beam propagation axis and remain there while they are accelerated by the longitudinal electric field component. Authors usually refer to this phenomenon as to relativistic ponderomotive trapping (RPT) [61], in analogy to RPS where electrons are pushed toward the beam periphery.
## 6 Simulation Results
### Performance Evaluation  
Referring to the settings in Section 6.1, this subsection investigates the performance with regard to the group size, the number of in/out group members (_i.e._, the link up/down dynamics), the connection time  
Figure 10: Examples of the HDTC scheme and information routing: Scenario 1 (**Top Left**); Scenario 2, forward propagation (**Top Right**); Scenario 3 (**Middle Left**); Scenario 4, backward propagation (**Middle Right**); and Scenario 5 (**Bottom**).  
and the number of hops for information routing, with varying \(\theta\), the transmission range and the vehicle speed, respectively.
## 2 Theory
### Telecommunication Indicators on the Framework  
While a profile of the telecommunication network is required to provide information on the current access position, the socioeconomic profile is required to provide information to the investor in the telecommunication infrastructure, such as infrastructure readiness (road), population, purchasing power, etc. ITU DataHub provides 11 connectivity concerns, where our three indicators are included in it, as shown in Table 1. Moreover, the quantity of mobile cellular (2G/3G/4G) sites gives cross-validation into coverage prediction. This does not necessarily mean the districts with cellular transmitting antennas, where residents are served by telecommunications signals. It could be that the signal is on the roof of the house but does not penetrate the house.