esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To overcome these drawbacks, some heuristic algorithms have been used for training the network [41; 42; 43; 44; 45]. One among them is the Invasive Weed Optimization (IWO) algorithm. In 2016, [PERSON] et al. presented the Invasive Weed Optimization (IWO) algorithm [46]. This method is motivated by the spreading of weeds to find a suitable place for expanding and breeding. The technique is characterized by fast convergence for global optimization. Based on the properties of IWO, this algorithm is employed for optimizing the parameters of a neural network to reduce its cons such as a high dependence on input data. Furthermore, the IWO prevents the neural network from falling to local minima. This work proposed a new calibration method that includes the model-based calibration technique and unmodeled calibration method. At first, the kinematic and compliance parameters are simultaneously determined by a model-based calibration method [5]. Then, the method needs to compensate for some unmodeled errors that cannot be ignored such as friction, mechanical transmission error, and thermal expansion. A suggested neural network optimizing by IWO is used to compensate the residual positional errors. It should be noted here that the proposed method is a combination of model-based and artificial neural network (ANN) methods that used the IWO technique to determine the weight and bias. Meanwhile, most of the ANN-based technique is applied after the kinematic calibration. This calibration method simultaneously calibrates both the kinematic errors and joint compliances. After the simultaneous calibration, the IWO-NN-based compensation is accomplished for the unmodeled nongeometric errors. By using the IWO neural network, the proposed calibration method seems to reach the global minima easily. Therefore, the IWO neural network can be said to have better convergence capability than the traditional backpropagation NN. Finally, a HH800 robot is employed for the experimental study of the proposed algorithm to compare with four other calibration methods, including the conventional kinematic calibration method (KM), as well as the simultaneous identification of joint compliance and kinematic parameters method (SKCM), and the combination of an NN compensator and SKCM method (NN-SKCM). The advantages of the method have been shown: the enhanced position accuracy of the manipulator after the calibration confirms the feasibility and greater positional accuracy over the other calibration methods. Additionally, the adopted IWO neural network has better convergence capability than the back-propagation neural network in this calibration process. This advantage makes the proposed method more feasible in real offline programming environments.
## 5 Safety Analysis
### Failure Hazard Analysis  
As the first step of the FHA, the functional failure conditions shall be identified. Considering the simplex solution, the undetected and detected loss of ADF (Table 2) are considered. For example, a detected loss of ADF is considered following a unsuccessful BIT and the corresponding loss of ADF can be annualicated to the crew. The latter event is less critical than the undetected loss of ADF when wrong data are communicated to the crew, or autopilot, without any alert or warning about wrong or degraded data.  
The classification is defined by the A/C integrator according to the airworthiness regulations for the aircraft type [30].  
As far as the ADS is concerned, it is possible to define FHA tables for all ADFs (as collected in Tables 11-15).  
The worst cases (both for annunciate and unannuciated loss) are collected in Table 5. From the worst case analysis, the unannuciated loss emerges, of course, as the worst possible event for a simplex ADS. In fact, for all ADFs, the unannuciated loss (or erroneous failure condition) has the lowest probability of occurrence (extremely improbable). The quantification of probability occurrence is reported in Table 5 for the most critical conditions, and they are used for the PSSA as input for the FTA with the _top-down_ approach of Section 5.2.  
\begin{table}
\begin{tabular}{c c l l l} \hline \hline \multirow{2}{*}{**MCC ID**} & \multirow{2}{*}{**ADF**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**FHA Ref.**} & \multicolumn{2}{c}{**Classification [30]**} \\  & & & & **(Probability of Occurrence Per Flight Hour)** \\ \hline MCC1.1-E & & \begin{tabular}{l} Erroneous static pressure measure during \\ in-flight phases \\ Loss of the static pressure measure during \\ in-flight phases \\ \end{tabular} & ADS1.1-1.a, c, e, g & Catastrophic (\(1\times 10^{-6}\)) \\ MCC1.1-L & & \
\begin{tabular}{l} Lagardous (\(1\times 10^{-5}\)) \\ \end{tabular} \\ \hline MCC1.2-E & \multirow{2}{*}{ADF1.2} & \multirow{2}{*}{ADF1.2} & \multirow{2}{*}{ADF1.2} & \multirow{2}{*}{ADS1.2-1.a, c, e, g} & Catastrophic (\(1\times 10^{-6}\)) \\ MCC1.2-L & & & &
\begin{tabular}{l} Lagardous (\(1\times 10^{-5}\)) \\ \end{tabular} \\ \hline MCC2-E & \multirow{2}{*}{ADF2} & \multirow{2}{*}{ADF1.2} & \multirow{2}{*}{ADF1.2} & \multirow{2}{*}{ADS2.1.b, d} & \multirow{2}{*}{Hazardous (\(1\times 10^{-5}\))} \\ MCC2-L & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: ADS most critical conditions (MCCs) for ADF loss or annunciated failure.
## Materials and Methods
### Penetration Depth  
When SAR microwaves reach the playa lake saline soil, the electromagnetic wave penetrates through while interacts with the soil bulk constituents. Solid rock particles, air and brines scatter the incoming power. The depth at which the SAR power transmitted and attenuated is referred to as penetration depth (\(\delta\)p) and it is defined as the depth at which the power of a propagating wave is equal to exp (\(-1\)) = 0.3679 of its power at the medium's surface. If scattering losses are ignored, \(\delta\)p can be calculated according to [28].  
\[\delta p=\frac{\lambda 0}{4\pi}\left\{\frac{\varepsilon^{\prime}}{2}\left[\left(1+ \left(\frac{\varepsilon^{\prime\prime}}{\varepsilon^{\prime}}\right)^{2} \right)^{\frac{1}{2}}-1\right]\right\}^{\frac{-1}{2}} \tag{1}\]  
where \(\lambda 0\) is the wavelength in free space and \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) are the dielectric permittivity and loss, respectively, for the medium. For soil particles typically \(\varepsilon^{\prime\prime}\)\(<\) 0.05 [29] and the losses are therefore dominated by the salinity of the brines. If scattering losses are accounted for, the penetration depth is even smaller. \(\delta\)p computed as in (1) is, therefore, an upper limit for wave penetration and subsurface scattering. Simple dielectric mixing models compute \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) using the dielectric values for dry soil and saline water, each weighted by its respective proportion of the combined mixture, being the last given by the soil porosity when soil is saturated. Figure 7 illustrates the \(\delta\)p for 5.55 cm (C-band), and 23.4 cm (L-band) calculated using a soil porosity of 34% and a soil temperature of 20 \({}^{\circ}\)C based on the measurements reported by [12] for a salar. Over the study area, subsurface brine salinity ranges between 2 and 45 psu as reported in 2013 by [20]. A salinity of 66 psu was measured recently in the northern lake near the site S2. The salinity has a significant impact on radar backscatter in terms of microwave penetration depth. Power losses at C-band result in surface scattering as the dominant backscattering mechanism, which implies a more considerable sensitivity to crust roughness than L-band. Moreover, L-band might have some components from subsurface scattering.  
Figure 7: C- and L-band penetration depth \(\delta\)p through a saturated saline soil of varying brine salinity. The dielectric constant is computed by means of a simple dielectric mixing model.
# Performance of Satellite-Based Evapotranspiration
## 5 Discussion and Conclusions
In addition, these data would facilitate the implementation of ET\({}_{\mathrm{a}}\) monitoring aimed at reducing risk of water stress during the increasingly recurrent summer drought periods. Taken together, our combined analysis and validation of the four remote sensing evapotranspiration models and the two global evapotranspiration products showed that the SEBS and SSEBop models can provide an accurate (R\({}^{2}\): 74-79 and RMSE \(<\) 1 [mm day\({}^{-1}\)]) field-scale estimate of grassland evapotranspiration in the region, which can be complemented by readily available weekly estimates at 500 m from the global PML_V2 product. This finding is encouraging for the implementation of a near-real time monitoring tool for evapotranspiration in the region for the timely application of effective irrigation programs during the recurrent summer droughts. However, widespread irrigationmight increase the pressure on already limited water resources, and thus the practice has to be properly regulated and considered within a framework of sustainable agriculture and adaptation to climate change. The following are available online at [[http://www.mdpi.com/2073-4441/12/12/3587/s1](http://www.mdpi.com/2073-4441/12/12/3587/s1)]([http://www.mdpi.com/2073-4441/12/12/3587/s1](http://www.mdpi.com/2073-4441/12/12/3587/s1)): Figure S1. Meteorological data for the study area. (A) is the wind speed [m s\({}^{-1}\)], (B) air temperature [K], (C) instantaneous global radiation [W m\({}^{-2}\)] y (D) relative humidity [%]. The meteorological information is on a topography layer of the region represented in gray. Figure S2. Validation of relative humidity (RH), global radiation (Rx), air temperature (Ta) and wind speed (u). (A) Scatterplot of RH, Rx, Ta and u in Oromo Calibration Site (x-axis) and spatially interpolated variables (y-axis). (B) Evaluation of bias, sigma and RMSE at pixel scale over OCS. Figure S3. Spatial correlation between global models (GLEAM and PML) and SSEBop model. [PERSON] and [PERSON] conceived and designed the research. [PERSON], [PERSON] and [PERSON] performed the data processing and analysis. All authors contributed to the manuscript write-up and editing. All authors have read and agreed to the published version of the manuscript  
This work was funded by the PYT-2016-0265 program of the \"Fundacion para la Innovacion Agraria (FIA)\" of the Chilean Ministry of Agriculture and the Regional Government of the Los Lagos Region, Chile. The authors also thank the USGS, LP DAAC for provide ASTER GED, MODIS and Landsat Data. JB acknowledges support from the FONDECYT grant number 1171496. Finally, the authors acknowledge the useful support of [PERSON] for editing the manuscript. The authors declare no conflict of interest.
## 5 Performance Evaluation  
The edge-based real-time patient management system proposed in this paper is intended to evaluate performance from two perspectives. First, the integrity of the transmitted data is measured by the IoT pulse oximeter. Second, the persistence of the real-time chart is also measured.  
In IoMT, which transmits a large number of bio signals, it is important that the original data is not damaged. Five IoT pulse oximeters connect to the edge node and measure the loss rate of the transmitted data. The measurement method compares the packets transmitted from the IoT pulse oximeter and the packets received from the edge node. As shown in Table 4, as a result of repeating the analysis for 5 times for 24 h, a loss rate of 0% was confirmed.  
The continuity of the real-time chart means that the biosignal raw data shown in real time on the UI is continuously visualized. In this paper, in order to continuously visualize  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IoT Device** & **Number of** & **Number of** & **Measurement** & & **Loss** \\
**ID** & **Packets Sent** & **Received Packets** & **Time (Hours)** & & **Rate (\%)** \\ \hline IoT A & 86, 400 & 86, 400 & 24 & 5 & 0.00 \\ IoT B & 86, 400 & 86, 400 & 24 & 5 & 0.00 \\ IoT C & 86, 400 & 86, 400 & 24 & 5 & 0.00 \\ IoT D & 86, 400 & 86, 400 & 24 & 5 & 0.00 \\ IoT E & 86, 400 & 86, 400 & 24 & 5 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Loss rate of data transmitted from IoT devices over 24 h.  
Figure 8: Cloud GUI screen that shows PR and SpO2 transmitted from the edge node in real time.  
real-time charts, the monitoring system collects raw data while buffering for 3 s and then displays them sequentially. In order to check the continuity of the five real-time charts, the amount of data collected during buffering used by each chart is analyzed. The analysis method determines that the chart is seamless if there is at least one raw data sample in the buffer. Table 5 shows an average of 126.6 data samples collected under buffering for visualization of the raw data transmitted from the five IoT pulse oximeters. Moreover, the minimum number of buffer data samples was confirmed to be 87.5.  
The continuity of the real-time chart means that the biosignal raw data shown in real-time on the UI is continuously visualized. In this paper, in order to continuously visualize real-time charts, the monitoring system collects raw data under buffering for 3s and then displays them sequentially. In order to check the continuity of the five real-time charts, the number of data samples collected in the buffer used by each chart is analyzed. The analysis method determines that the chart is seamless if there is at least one raw data sample in the buffer. Table 5 shows that there is an average of 126.6 data samples collected in the buffer for visualization of the raw data transmitted from the five IoT pulse oximeters. Moreover, the minimum number of buffer data samples was confirmed to be 87.5.
## 2 Experimental Section
### Data
#### 2.2.2 Landsat ETM+  
We used the web enabled Landsat database (WELD) web service [42] to download time series of Landsat ETM+ data from 2009 to 2011 for 36 pixels within the four hectare area measured by each flux tower. Each pixel time series was cleaned (_i.e._, filtered to meet the following conditions) using the QA/QC data associated with WELD products [42]; only the pixels classified as containing no saturated bands and not cloudy (determined by the automatic cloud cover assessment algorithm, ACCA) [43] were retained. To remove effects of snow cover on the VI, the normalized difference snow index (NDSI) was used to remove snow covered pixels from the analysis. If more than 25% of the total analysis region for either the control or girdled site was covered by snow, the entire acquisition date was removed from the analysis. [PERSON] _et al._[44] suggest mapping snowy pixels as NDSI >0.4. However, we used a lower threshold of 0.2 in this study, which we validated with ground based imagery and field technician reports. The majority (82%) of the Landsat ETM+ data were acquired within \(\leqslant\)3 days of a RapidEye acquisition (see Section 2.2.3), with the greatest time separation being 10 days. The Landsat ETM+ pixel time series was then linearly interpolated to daily intervals for comparison with the RapidEye data. In this manner, the same number of Landsat ETM+ VI and RapidEye VI were used in each of the models tested. Geolocation error for the Landsat ETM+ imagery was generally <0.5 pixel according to the WELD documentation.
# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
## Appendix A Detailed Explanation of the Schrodinger Equation in the Five Algorithms
4. Security Analysis of this approach  
Strengths  
Quantum Unpredictability: Since eigenvalues and wavefunctions are based on true quantum properties, keys are inherently random. Resistant to Classical Attacks: Unlike classical PRNGs, these methods do not depend on deterministic number generation. High Entropy: Eigenvalues and quantum measurements generate high entropy, ensuring strong cryptographic security. Potential Weakness  
Numerical Approximation Risks: Reducing grid size may introduce biases in eigenvalues. Side-Channel Attacks: Practical QKD systems are vulnerable to eavesdropping through hardware imperfections. Classical Simulation Limitations: True quantum randomness requires physical quantum devices; a simulated Schrodinger equation may not provide the same level of security. Countermeasures  
Use larger quantum systems to maintain security. Introduce randomised potential to increase unpredictability. Implement hardware-based quantum randomness sources for stronger cryptographic guarantees. In conclusion by discretising the Schrodinger equation, we efficiently extract quantum randomness from wavefunctions, eigenvalues, and QFT-based phase distributions. The ultra-optimised versions balance speed and security, making them suitable for high-security cryptographic applications. However, real-world quantum hardware implementations remain necessary for true unbreakable randomness.
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 1 Introduction
In addition to this disadvantage, these protocols require knowing the maximum delays of the messages in the communication channels, which is not always feasible [13]. In this paper, we present the design of a protocol that ensures causal ordering of the messages in the overlay networks with free scale topology without using the global time reference and maintaining the low overhead in the communication channels and in the peers. To achieve this, our causal protocol works on two communication groups according to their connection type. An internal group consists of peers that are connected to a super peer. This group uses a bit vector to represent message dependencies producing overhead on a bit level. Another group is an external group consisting of interconnected peers. This group uses an idea of hierarchical clocks [15] to represent message dependencies. The results obtained via a simulation demonstrate that the overhead in an internal group is 3.5 times lower than the overhead from the Immediate Dependency Relation protocol [14] while the overhead in the external group can be compared to the overhead in the Immediate Dependency Relationship protocol, but it is half the overhead of the Dependency Sequences protocol.
# Diffuse Optical Tomography Using Bayesian Filtering in the Human Brain
###### Abstract  
The present work describes noninvasive diffuse optical tomography (DOT), a technology for measuring hemodynamic changes in the brain. These changes provide relevant information that helps us to understand the basis of neurophysiology in the human brain. Advantages, such as portability, direct measurements of hemoglobin state, temporal resolution, and the lack of need to restrict movements, as is necessary in magnetic resonance imaging (MRI) devices, means that DOT technology can be used both in research and clinically. Here, we describe the use of Bayesian methods to filter raw DOT data as an alternative to the linear filters widely used in signal processing. Common problems, such as filter selection or a false interpretation of the results, which is sometimes caused by the interference of background physiological noise with neural activity, can be avoided with this new method.  
Keywords:diffuse optical imaging; image reconstruction algorithms; Bayesian filtering +
Footnote †: journal: Medical Imaging
## 2 Materials and Methods
### Methodology to Study Multi-Pass Welds  
The base material plates were V-60 bevelled and tacked along the 500 mm edge to be welded. The root pass filled the complete groove length (500 mm), but every subsequent weld pass was shortened 50-70 mm in length from the previous weld pass length, as shown in Figure 2. The aim was to have one coupon containing the last (as-deposited) pass and the previous ones. Therefore, it was possible to obtain, for each weld pass, a section to characterize the weld and HAZ microstructure and to follow the evolution of the microstructure after each thermal cycle experienced by the material.  
The microstructural inspection included the evaluation of the general microstructure and the presence of secondary phases: nitrides, secondary austenite (\(\mathrm{v_{2}}\)), and \(\sigma\)-phase in the weld metal and the HAZ.  
The measurement and recording of the temperatures was conducted by successive instrumentation of the multi-pass welds, pass by pass, by addition of thermocouples in each weld  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Welding Process**} & \multicolumn{3}{c}{**Arc Energy (\& \(\mathrm{d}/\mathrm{mm}\))**} & \multicolumn{3}{c}{**Recorded Inter-Pass**} & \multirow{2}{*}{**Joint Prep.**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Weld Passes** \\ \end{tabular} } \\ \cline{3-3} \cline{5-10}  & & **Root Pass (\(\mathrm{p1}\))** & & & & & & & & & & \\ \hline GR & GMAW & 1.5 & 1.2–1.3 & 80–100 & V with GTAW root & 10 \\ FR & FCAW & 1.5 & 1.1–1.3 & 80–100 & V with ceramic backing & 10 \\ GH & GMAW & 2.0 & 2.2 & 174-196 & V with ceramic backing & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental settings.  
Figure 1: Welding sequence layout. (**Left**) FR weld; (**Center**) GR weld; (**Right**) \(\mathrm{GH}\) weld.  
Figure 2: Welding configuration used to extract coupons to study the evolution of the microstructure caused by the subsequent weld passes. The numbers from 1 to 7 correspond to the locations where coupons with passes from 1 to 7 were extracted and contained that number of weld passes, i.e., 1 means one weld pass, 2 means two weld passes, and so forth until 7, including the seven weld passes in the GH weldment.  
pass. A combination of K-type thermocouples, (1) harpooned in the melt pool and (2) inserted from the backside through 2 mm drilled holes from previously deposited passes and spot welded on the surface of the joint, was used. Figure 3 shows two inserted thermocouples after drilling--the plate and previous weld passes--and spot welding them to the surface of the joint. Figure 4 illustrates the layout of the inserted thermocouples at the back side of the joint and the harpooned thermocouples at the top side of the joint. At each weld pass, 2-4 thermocouples were added to record the thermal cycles. Up to 32 thermocouples were used to record the thermal cycles simultaneously in one weld.