esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Packet Length Effect on Sparse Signal Transmission
#### 6.2.1 Performance under Varying BER  
BER is a fundamental parameter that can be determined by transmitting power, modulation scheme and transmission distance. In practical applications, transmission distance is fixed once sensor nodes are deployed, as well as the modulation scheme with chosen hardware chips, for example chips that offer the IEEE 802.15.4 standard communication adopt the O-QPSK modulation scheme. Without considering transmitting power control in this paper, BER is almost settled. What is more, BER is the key parameter that determines the quality of the wireless channel. Therefore, next, we consider the sparse signal transmission performance under varying BER.  
Figure 17: Signal reconstruction error comparison with noise corruption.  
If the BER is too high, for example BER = 0.1, the packet length is 10 bytes, even without the payload. According to Equation (15), PRR = \(2.1847\times 10^{-4}\). With such a small measurement number, CS cannot recover the sparse signal. Both the data and signal transmission efficiency are zero.  
As BER increases, for example \(BER=0.005\), using a signal with sparsity \(K=5\), comparison between data and signal transmission efficiency is shown in Figure 18.  
Note that, when the payload length equals 10 bytes, we have the best data transmission efficiency as being around \(0.24\). However, for a signal with sparsity \(K=5\), it can achieve almost the total signal transmission efficiency with recovery error \(\varepsilon=3.6191\times 10^{-15}\), since it meets the need for measurement number used for signal reconstruction. Additionally, in this case, the optimal packet length for this sparse signal transmission is around 35 bytes, other than 10 bytes.  
What is more, when \(BER\) decreases, for example \(BER=0.001\), even using the longest payload length specified in IEEE 802.15.4, it can still meet the measurement number threshold, which means that signal transmission efficiency is always one, even when the payload length varies, as shown in Figure 18. Based on the above discussion, the optimal packet length is determined by both the specific application requirements and channel condition.  
Figure 19 shows the signal transmission efficiency comparison between using RCS and RCS with data interleaving when BER = 0.005. We can see that signal transmission efficiency varies as the application threshold for signal recovery changes. When the threshold is larger, a larger packet length can be chosen under the same channel condition (same BER). While data interleaving mitigates the bursty loss, RCS with data interleaving also allows one to use a larger packet length.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 3 Results and Discussion
### ET Validation
Moreover, using an automatic method but applied  
Figure 2: Monthly temporal distribution of ET in the areas of DC and SC in 2013.  
to MODIS images, the automated surface energy balance algorithm for land (ASEBAL), proposed by [26], estimated the ET for 282 images in the Caatinga area in the Ipanema River basin, with values ranging from 1.6 mm\(\cdot\)day\({}^{-1}\) to 6.2 mm\(\cdot\)day\({}^{-1}\) and a mean ET of 3.9 mm\(\cdot\)day\({}^{-1}\), which is closer to our results. The best performance for the ET of the MOD16A2 is related to the results obtained for the two control points within the study areas. This is a coherent result, as the eastern portion is closer to the Zona da Mata, which has a more humid climate with a predominance of Atlantic Forest vegetation. The analysis of ET values is critically related to understanding the characteristics of the vegetation at the satellite overpass time [6]. This understanding may be essential in the management of water resources and in encouraging the preservation of native vegetation. Furthermore, ET estimation can assess ecosystem losses at climatic extremes, which is vital for decision-making policy reports [25; 62].
## 1 Introduction
Studies carried out for liquids indicate the existence of the influence of various mechanisms of ultrasound on convective heat exchange. All authors consider acoustic cavitation to be the most important mechanism. The formation, growth, and then violent implosion of the cavitation bubbles cause strange mechanical and thermal effects. The pressure locally could reach more than \(10^{8}\) Pa and a temperature more than 5000 K. This results in shock waves, microjets, and microscale turbulence [24] and finally gives heat transfer enhancement. Another phenomenon caused by ultrasound is acoustic streaming. It could induce rapid mixing of fluid inside the boundary layer. As a result, an increase of the heat transfer through the layer could be obtained [36]. Reviews of the literature on the effects of ultrasound application during convective drying [25; 37] also list pressure pulsation and fluid oscillation close to the solid, reducing the boundary layer, the sponge effect inside the porous material, atomization of the liquid on the boundary, and the heating effect due to acoustic energy absorption as further reasons for the improvement of convective heat transfer by the use of ultrasound. The latter is usually weak and, therefore, is not taken into account by many authors [25]. So far, only four articles have presented the influence of ultrasonic waves on convective heat exchange in the air [36; 38; 39; 40]. Two of them analyze acoustic streaming in the gap between a hot plate and a vibrating beam [36; 38]. The third deals with free convection to the small cylinder [39]. Ultrasound was generated by a pair of oscillating transmitters. The last article [40] presents the results of heating various metal samples in a hybrid dryer. In this case, forced convection with a low-flow velocity was used, while ultrasound was generated using a system equipped with a vibrating disk. All of these works show a high intensification of heat transfer because of the use of ultrasound. However, there is still no extensive research on the enhancement of free convective heat exchange in the air through the acoustic field (ultrasound). This paper aims to fill this gap. It investigates the cooling of metal samples of various shapes with air. The free airflow is due to the high temperature of the samples. The ultrasonic field is generated by a vibrating disk placed at a distance from the samples. As a result of the research, a high intensification of the process was demonstrated through the acoustic field.
# COMS Visible Channel Calibration Using Moon Observation Data
## 3 Results
### Uncertainty Assessments  
COMS-visible channel calibration showed clear temporal trends in spite of seasonal variations and time series scatters. However, several error sources affected the absolute values of the retrieved annual drift values. Therefore, before interpreting long-term calibration results from the visible channel with lunar observations, a sensitivity analysis was required to quantify their individual contributions to the degradation trend.  
The magnitudes of uncertainties and their influence on long-term calibration stability were investigated with a predominant focus on the ratio between measured and modeled lunar irradiances. However, the selection of on-Moon pixels required a threshold value to discriminate between Moon and space pixels. This was not the same value as that of the space counter, as the value was retrieved near Moon disk pixels (see Appendix A.1.1). According to the sensitivity test, the uncertainty caused by different threshold values was within 0.01% per year of its annual drift. However, these conditions cannot be ignored. In addition, common criteria cannot be employed to calculate measured irradiances with respect to lunar calibration, as each detector has unique characteristics. Therefore, the uncertainty caused by moon pixel selection needed to be considered to determine the accuracy of lunar calibration.  
Figure 4: Comparison of visible channel degradation of COMS imager among several calibration methods: Moon (orange dot), DCC (using RTM) (blue asterisk) and integrated method (green diamond) from April 2011 to December 2017. The shades represent the 95% confidence interval of the linear regression for moon (orange shade) and DCC (light blue shade).  
The selection of a higher threshold value increased the magnitude of annual drift, while that of lower value decreased the magnitude of sensor performance degradation.  
Another uncertainty was that of phase angle dependence when retrieving irradiances. When i the phase angle was increased among the Sun, satellite, and Moon, the RMSEs of retrieved irradiance ratios (\(\Delta\)Irr) were increased. For example, the retrieved annual drift between a phase angle of 0 and 30 was \(-\)1.26 \(\pm\) 0.12% per year, while the value between 60\({}^{\circ}\) and 90\({}^{\circ}\) was \(-\)1.25 \(\pm\) 0.24% per year. However, according to the perturbation test for the GIRO model, GIRO retrieved values are very stable. Irradiance variation was within 0.1% for a 10 \(\times\) 10 km position perturbation. The large uncertainty in the irradiance ratio within increased phase angles was considered to be related to a combination of on-Moon pixel selection and model performance. There was a small total number of Moon pixels and the boundary between the Moon and space was not clear enough at large phase angles.
## 3 Dynamic Reliability Framework  
The reliability function \(\mathrm{R(t)}\), also referred to as the survival function, is defined as the probability that the device is still functioning at time t. and it is expressed as follows:  
\[\mathrm{R(t)=P(S(t)>T_{M})=1-\int_{0}^{T_{m}}f_{s}(t)dt} \tag{1}\]where S represents the stochastic variable describing the time t of surviving of a system while f\({}_{\rm s}\)(t) is the probability density function of the failure component. In reliability modelling, Dynamic Reliability method has been conceived as an extension of the traditional theory in order to relax the hypotheses of the classical approach. The main goal is to formulate mathematical models able to reflect numerous characteristics of complex systems and, in particular, the effects of the physics of a process into the system. The implementation of the Dynamic Reliability model allows assessing the aging process of a system overcoming the traditional reliability theory restriction. Indeed, in the traditional approach, the aging effect is simplified assuming a linear dependency with the mission time (e.g., at time t = T\({}_{\rm m}\) the system has operated uninterruptedly and its working condition is changed in terms of functionality, availability, reliability, and safety as it operated for T\({}_{\rm m}\) unit time). This assumption results in a reduced quality of the model. To this end, [PERSON] et al. [44] adopt a Piecewise Markov Process to consider the actual aging L(t) of a component; thus, its effects are accounted for only when such a component is operating. Accordingly, the nonlinear aging L(t) can be introduced in place of linear variable t within the Equation (1) as reported:  
\[\rm R(t)=P(S(L)>T_{M}) \tag{2}\]  
Dynamic reliability models have to consider the influence of the working and operational conditions on the system operations. The generic formulation of such influence can be written as follows:  
\[\rm W=(t,\ X(t),\ e) \tag{3}\]  
where \(\Gamma\) is a nonlinear discrete function that depends on three different variables: the time \(t\), the system dynamic X(t) and the vector \(e\). The latter consists of stochastic variables that allow mapping the function W into a set of discrete states aimed at identifying the possible operational conditions of the system. Therefore, Equation (2) should be rewritten taking into consideration all these effects, as follows:  
\[\rm R(t)=\ P(S(L)>T_{M},\ W,\ X(t)) \tag{4}\]  
The dynamic reliability formulation as reported in Equation (4) reveals the dependencies with respect to the aging L, the working conditions W and the system evolution X. As it can be seen, this equation is a conditional and joint probability that belongs to the class of Stochastic Partial Differential Equations (SPDEs). The complexity of such problems requires a wide variety of mathematical techniques and high computational efforts. In this context, the simulation-based approach represents a key aspect of solving SPDEs; thus, the main concern of researchers shifts from the resolution algorithms to the modelling tools that must be powerful, intuitive, and user-friendly.
## 4 Methodology
### Moran's I  
Global spatial autocorrelation analysis mainly uses the global Moran's I index to reflect whether the pattern expressed is clustered, dispersed, or random in the whole study area. Moran's I is expressed as Formula (3) [41; 42]:  
\[I\ =\ \frac{n\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}(x_{i}\ -\ \overline{x})\Big{(}x_{j}\ -\ \overline{x}\Big{)}}{\Big{(}\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij} \Big{)}\sum_{i=1}^{n}(x_{i}\ -\ \overline{x})^{2}} \tag{3}\]  
where \(n\) is equal to the total number of regions of the study object space; \(x_{i}\) and \(x_{j}\) represent the attribute value in the \(i\)-th and the \(j\)-th region, respectively; \(\overline{x}\) is the average value of the attribute value of the studied region; \(w_{ij}\) represents the spatial weight between the attribute values in the \(i\)-th and the \(j\)-th region, and \(i\) is not equal to \(j\). Moran's I is [\(-\)1, 1], in general, a Moran's I value near 1.0 indicates clustering or a stronger spatial positive autocorrelation while a value approaching \(-\)1.0 indicates dispersion or a stronger spatial negative autocorrelation. Random distribution exists when the value is closer to zero.  
In this paper, TBVI at the street spot location is used as the attribute value of the point to calculate the global Moran's I, which measures the degree of spatial autocorrelation of the Chinese traditional-style buildings in the study area.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 5 Performance Evaluation
### Simulation Setup  
In the simulated scenario, we produced a Walker constellation of 64 satellites, each with an orbital inclination of 90 degrees. The constellation had eight orbits with eight satellites in each orbit. Each satellite had four intersatellite links, i.e., connecting two satellites in the same orbit and two satellites in adjacent orbits. The buffer size of each queue in the ISL was 16 KB. Considering the limited onboard resources, the scheduling time slot of TSN was set to 500 \(\upmu\)s. The traffic flows were generated using the ON/OFF model, and adjusted by controlling the OFF period of the flows [27]. The packet size followed bimodal distribution, while the flow size followed Pareto distribution. The ratio of regular traffic to time-sensitive traffic was 3:1. We divided the time-sensitive traffic into four levels, and the deadline of TSFs was [2; 4; 6; 8] ms. This time-sensitivity requirement did not consider the propagation delay because the ISL's propagation delay was more significant than that of the terrestrial network.
## 4 Results and Discussion
### Carbonation Depth  
In Figure 3, the measured values of carbonation depth (\(x\), in mm) were reported vs. days of air exposure (\(t\)). Experimental data can be quite accurately described by a linear relationship between the carbonation depth and square root of time (following the law \(x=k\cdot\sqrt{t}\)). The higher carbonation depth, equal to about 7 mm after 1 year of exposure, was found for the mixture with natural aggregates (Nat-0.6), while the lower carbonation depth, equal to 2.8 mm, was found in the concrete mixture realized with natural aggregates and fly ash. These results confirm that the addition of high volumes of fly ash is able to significantly reduce the carbonation process, even when a porous aggregate (such as the recycled aggregate) is used, thanks to the refinement of the pores and a consequent improved microstructure.
# Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
## 3 Methodology
### Machine Learning  
SVMs are accurate as they contain appropriate kernels that work well even if the data is not linearly separable in the base future space. By using the kernel functions of SVM [28], one can perform a non-linear classification more accurately by mapping its input to high-dimensional feature spaces [29]. There are various hyperplanes that separate the classes; however, it is important to select the best one which has the largest distance to the nearest data point of two classes. Grid search [30] is the conventional method of performing the optimization of hyper parameter utilizing parameter sweep or grid search through a manually specified subset of the hyper parameter of a learning algorithm. This algorithm must be guided by some performance metric, normally measured by evaluation on a held-out validation set or by cross-validation of the training dataset. For this work, we are using an SVM classifier with an RBF kernel having at least two parameters (regularization constant \(C\) and kernel hyper parameter \(\gamma\)) that need to be tuned to achieve high performance on the testing data. The mathematical descriptor is shown below for a binary classification problem: \(\{(x_{1},\,y_{1}),\,(x_{2},\,y_{2}),\,\ldots\,,\,(x_{k},\,y_{k})\}\), where \(x_{i}\in\mathbb{R}_{n}\) represents the n-dimensional feature vectors, and \(y_{i}\in\{1,\,-1\}\) is the corresponding class label. The SVM requires the solution of the following optimizing problem:  
\[\begin{split}\min(\,\frac{1}{2}\omega^{T}\omega\ +\ C\ \sum\limits_{i=1}^{k}\varepsilon_{i})\\ \text{Subjected to}\ y_{i}\ (\omega^{T}\varphi(x_{i})+b)\geq 1 -\varepsilon_{i},\ \varepsilon_{i}\geq 0,\ i=1,\ \ldots\,,k\end{split} \tag{2}\]  
Here, \(\varepsilon_{i}\) is the slack variable for misclassified examples, and C is the penalty parameter of error term. In addition, \(K(x_{i},\,x_{j})=\varphi(x_{i})^{T}\varphi(x_{j})\) is the kernel function. Basically, there are four kernel functions used for pattern recognition and classification: linear kernel, polynomial kernel, radial basis function (RBF), and sigmoid kernel. We have adopted the RBF [28] kernel in this paper:  
\[K\big{(}x_{i},\ x_{j}\big{)}=\exp\ (-\gamma\parallel x_{i}-x_{j}\parallel^{2}), \ \gamma\ >\ 0 \tag{3}\]  
Here, \(\gamma\) is the parameter which must be carefully selected in the experiment. The optimum values for parameter \(C\) and \(\log_{2}\gamma\) were selected from the range: \((-8,7,6,\ldots,6,7,8)\). The grid method [29] was adopted as the searching procedure (a 0.8 step was used). Each \(\gamma\) and \(C\) value pair was used in the training data with ten-fold cross-validation in order to evaluate the model performance. Once the optimal values of \(\gamma\) and \(C\) were found, they were adopted to train a new SVM model.
# Pretreatments Applied to Wheat Straw to Obtain Bioethanol
## 3 Pretreatments Applied to Wheat Straw
### Physical Pretreatment  
#### 3.1.1 Mechanical Pretreatment  
The physical pretreatment processes involve the transformation of the biomass into a fine powder (by shredding, grinding, or chopping) to facilitate subsequent chemical and biological treatments. For example, by chopping, particles of size 10-30 mm are obtained, and by grinding or shredding, particles of approximately 0.2-2 mm are obtained. Depending on the nature of the raw material, the pretreatment method is different, thus minimizing the degradation of the substrate and, at the same time, improving the hydrolysis yield. The better the biomass is shredded, the easier the access of the polysaccharides to the catalytic site of the enzymes becomes. The ideal shredding size varies from a few centimeters to 1-3 mm [43].  
Through the mechanical pretreatment of the straws, they are chopped, ground with a hammer mill, balls, a wet disc, or rollers in order to reduce the size of the straws, obtaining an improvement of enzymatic hydrolysis [8; 29]. The energy consumed for grinding wheat straw in the hammer mill is 42.57 \(\pm\) 2.04 kWh t\({}^{-1}\)[8]. The energy consumed by grinding straw depends on moisture, the initial size of the particles, the speed of feeding the material, the properties of the material, and the variables of the machine. It was found that the energy consumption was reduced when grinding wheat straw in the hammer mill to 53.0 kWh t\({}^{-1}\) from 232.0 kWh t\({}^{-1}\)[8; 32], together with the reduction in their humidity from 19.6% to 9.2% [8].  
When the degrees of the hammer and the speed of operation of the hammer mill increased, it was found that the specific energy decreased in the case of grinding wheat straw [8].  
Since the flow rate of the material decreased with the decrease in the size of the hammer mill sieve, it was found that the specific energy required for grinding agricultural biomass increased with the decrease in the size of the hammer mill sieve from 30 to 1.6 mm [33].  
The specific energy consumption for grinding wheat straw with the sieve mill with 0.8 and 3.2 mm sieves was 51.6 and 11.4 kWh t\({}^{-1}\)[32]. For sieve sizes of 1.6 mm, the total specific energy consumed for grinding wheat straw was double the case of the sieve of 3.2 mm [8].  
Through mechanical or physical pretreatment, the size of the lignocellulosic biomass was reduced to 10-30 mm [31].  
Through physical pretreatment, the biomass was heated to a temperature of 50-240 \({}^{\circ}\)C [33]. Wheat straw pretreated at 180 \({}^{\circ}\)C had a 53% higher yield compared to untreated materials, and the chemical bonds of the lignocellulosic structure of wheat straw were modified [35; 36; 37; 38; 39]. The significant elimination of lignin was achieved by thermally assisted alkaline pretreatment of the straws [40]. By ball-milling wheat straw at 80 \({}^{\circ}\)C for 30 min, water absorption at 400% (\(w/w\)) increased the glucose yield by up to 66.69% than at 100 \({}^{\circ}\)C; thus, the intake of water during the grinding process made the process efficient [29].