esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Antenna for Application in UAVs
## 6 Conclusions  
The Portuguese Air Force (FAP) is the branch of Army Forces with the most knowledge on UAVs in Portugal. The algorithm integrated in the Arduino was based on the orientation methodology used in UAVs by the FAP. The developed algorithm also uses procedures to change the UAV route, taking into account the limit conditions given by experienced officers in these aircraft. The implemented solution changes the route of the UAV for 10 s, whenever line-of-sight communication is not possible, i.e., the control system, in this case controlled by an Arduino, changes the flight path of the UAV, if necessary, during the time of the communication.  
After completing and validating the algorithm of the control system, the P-ESPAR antenna and the electronic circuit were built to integrate the control system.  
P-ESPAR antenna tests were performed in the anechoic chamber of the Telecommunications Institute at IST. Through the analysis presented in a previous section, it was possible to conclude that the results were not the same as those in the simulator, but rather close. These tests allow to verify the possibility for variation of the radiation lobe, as well as adaptation for the dimensioned frequency.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Plane E** & **Plane H** & **1S11** & **Polarization** & \(f_{\text{rc}}\) \\ \hline \multirow{2}{*}{\(CT_{1}\)} & \multirow{2}{*}{\(CT_{2}\)} & **Maximum** & **Maximum** & **HPBW** & & \\  & & **Direction** & **Direction** & **Direction** & & \\ \hline
1.9 pF & 2.7 pF & \(-11^{\circ}\) & \(0^{\circ}\) & \(360^{\circ}\) & \(-8.0\) dB & Linear & 1.41 \\
2.6 pF & 2.6 pF & \(-6^{\circ}\) & \(0^{\circ}\) & \(360^{\circ}\) & \(-8.3\) dB & Vertical & GHz \\
2.7 pF & 1.9 pF & \(-2^{\circ}\) & \(0^{\circ}\) & \(360^{\circ}\) & \(-8.0\) dB & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Conclusions drawn from the tests carried out in the anechoic chamber.  
It was also possible to conclude which factors were detrimental to the measurements, and how to provide the necessary changes.  
As previously mentioned, the construction of the antenna with two substrates of FR-4 and an air layer was an economical option that allowed the dimensioning and development of a prototype. The built antenna is not a final version, but an intermediate step, to demonstrate the potential of this solution to the FAP.  
Conceptualization, A.B.; data curation, A.F.A.C.; investigation, J.P.N.T. and M.J.M.M.  
This work was supported by FCT through IT, under the project UID/EEA/50008/2013.  
We thank [PERSON] and [PERSON] who accompanied the entire construction process of the antenna.  
The authors declare no conflict of interest.
## 3 Validation of Proposed CEDM
### Results and Analysis  
Figure 5 shows a comparison of testing and simulated crater depths. It can be seen that crater depth increases with damping times, so that a cylindrical crater is formed whose shape is similar to that of the tamper. In addition, the simulated crater depths under different tamping times are consistent with the testing results. Figure 6 shows testing and simulated dynamic stresses in vertical and horizontal directions under the first tamping. It can be seen from the figure that the peak dynamic stress decreases with increases in distance from the tamping point. However, the attenuation laws for the peak dynamic stresses in the vertical and horizontal directions are different. By comparing the experimental and simulated results, it may be verified that the numerical simulation method and soil model can reflect the deformation and dynamic stress propagation characteristics of soil foundation induced by DC. Thus, the numerical simulated improvement range for soil foundation can be used to verify the applicability of the proposed CEDM.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\omega\) (\%) & \(\rho\) (kg/m\({}^{3}\)) & \(E\) (kPa) & \(c\) (kPa) & \(\varphi\) & \(\chi\) & \(A\) (kPa) & \(B\) \\ \hline
32.1 & 1850 & 5000 & 8 & 12.5 & 0.8 & 0.0002 & 0.4 \\ \hline \end{tabular} \(\omega\)—moisture content; \(\rho\)—density; \(E\)—elastic modulus; \(c\)—chesion; \(\varphi\)—friction angle; \(\chi\)—shape parameter of cap; \(A\), \(B\)—volumetric hardening coefficients.  
\end{table}
Table 1: Physical and mechanical parameters of soil.  
Figure 5: Comparison of testing and simulated crater depths.
# Multiple Facies Metamorphic Rocks: A Rare Example from the East Qinling Orogen, China
## 2 Geology of the Rutile Deposits in the Baishugang-Wujianfang District  
The East Qinling Orogen and adjacent areas include the southern part of the North China Block, the North Qinling Domain and South Qinling Domain (Figure 1). The Shangdan Suture separates the North and South Qinling domains, and the Kuanping Suture separates the North Qinling Domain and the southern part of the North China Block (Figure 1). Both of the North and South Qinling domains host rutile deposits (Figure 1). From north to south, the North Qinling Domain includes the Neoproterozoic Kuanping, Paleozoic Erlangping, Paleoproterozoic Qinling and Meso- to Neoproterozoic Danfeng groups, and Meso- to Neoproterozoic Songshugou Complex. The Kuanping Group consists of greenschist- to amphibolite-facies rocks with tholeiititic basalt or ophiolitic protoliths, mica-schist, geniss, meta-clastic rocks and marble.  
The Baishugang-Wujianfang rutile deposits are in the East Qinling Orogen, central China [15]. The Kuanping Group hosts these rutile deposits, which metamorphosed at greenschist- to amphibolite-facies [15]. The estimated total reserve is 600 Mt with a grade of 2.2 wt% TiO\({}_{2}\)[16].  
The mineralisation in the area is predominantly hosted by gneiss, and to a lesser extent, by biotite schist and gneissic amphibolite (Figure 2). Individual orebodies are several thousand metres long, and several tens to hundreds of metres thick. The mineralisation includes rutile and rutile-bearing silicate minerals as gangue including biotite, hornblende, and diopside. The rutile is translucent and brownish red to dark brown in colour, coexists with magnetite, and forms 0.2 mm long subhedral and euhedral crystals, aggregates, chains or thin layers parallel to the curved foliation (Figures 3A,B and 4A-C).  
Figure 1: Geological map of the Eastern Qinling Orogen showing major rutile deposits. Reprinted with permission from ref. [9]. 2017 Elsevier. Abbreviations: LLWF = Lingbao–Lushan–Wuyang Fault, LLF = Luoman–Luanchuan Fault, SDS = Shangdan Suture, SNF = Shangzhou–Nanzhao Fault, NSF = Ningshan–Shanyang Fault, ZF = Zhuyangguan Fault, NQD = North Qinling Domain, SQD = South Qinling Domain, and S-NCB = Southern North China Block.  
Figure 3: Representative photomicrographs under plane-polarized light. (**A**) Dense rutile clusters along the curved foliation formed predominantly by biotite; (**B**) isolated rutile grain in biotite. Abbreviations: Bt = biotite, Cal = calcite, Di = diopside, Mag = magnetite, Qzt = quartz, Rt = rutile.  
Figure 2: Hand specimen photographs at the Bamiao–Qingshan district. (**A**) Rutile-bearing amphibole-biotite schist; (**B**) rutile-bearing gneiss.
## 3 Data and Method
### Methodology
#### 3.2.1 Logistic Regression (LR)  
The Logistic Regression model (LR) is a regression analysis method, in which the dependent variable is a binary categorical one, belonging to the nonlinear multivariate statistical model [15; 40]. A LR model describes the relationship between a dichotomous dependent variable \(P\), coded to take the values 1 (presence of landslide) or 0 (absence of landslide), and j independent variables \(x_{1}\), \(x_{2}\),......, \(x_{j}\). It predicts a dependent variable on the basis of continuous or categorical explanatory variables. The selection of the logistic regression model in this study mainly is for the following three reasons: (1) The independent variables do not necessarily have normal distributions. (2) The LR model carries out a comprehensive evaluation between various control factors and landslide samples, and can better solve the problem of interdependence between factors. (3) It accepts both binary and scalar values as the independent variables, which allows for the use of independent variables that are not continuous or qualitatively derived.  
The algorithm of logistic regression applies maximum likelihood estimation after transforming the dependent variable into a logit variable. It means that the LR coefficients that make the observed results most \"likely\" are selected. The relationship between the explanatory variables and the dependent variable \(P\) is nonlinear in the LR model. The relationship between the probability of landslide occurrence and the independent variables can be written as follows:  
\[Z=a+\sum_{j}b_{j}x_{j}\;\mathrm{j=1,2,\ldots m} \tag{1}\]  
\[P=\frac{1}{1+\mathrm{e}^{-(z)}} \tag{2}\]  
where \(P\) is the probability of a landslide occurrence, varying from 0 to 1 on a S-shaped curve; \(Z\) is the linear combination, varying from \(-\infty\) to \(+\infty\); \(a\) is the intercept of the model; j represents the number of independent variables; \(b_{j}\) (\(j\) = 1, 2, 3,...,m) is the slope coefficient of the model; and \(x_{j}\) (\(j\) = 1, 2, 3,..., m) represents the independent variable.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 6 Trust Model and Calculation Techniques
### Data Pre-Processing  
Many models that follow the basic structure of Figure 1 calculate trust scores for various parameters in phase two of the diagram. These values are calculated directly from the trust parameters and can be calculated in a number of ways, depending on the data type. The values can be comprised of things including the direct measurement of parameters, such as bandwidth, message frequency, resource usage, etc., or something calculated through an equation, such as ratios of good to bad interactions, the sum of previous good/bad interactions, the error between the data given and the average data or data expected values, or a QoD score, to name just a few. This stage simply involves putting a numeric value on the parameters measured. We should note that these values are sometimes normalized in order to prevent having to deal with large numbers in trust calculations. From the research conducted, it appears that the way in which this pre-processing is carried out is not hugely important, as long as the numeric value is representative and correlates in some form (linearly, inversely, exponentially, etc.) with the way in which the trust is affected by the parameter.  
Data may not always be transposed to a numeric value for further use. In some cases, the discretization of data may be carried out using fuzzy levels to split the data into a number of predefined levels, e.g., untrustworthy, neutral and trustworthy. Fuzzy trust levels are used in [18; 27; 28] to discretize trust values.
###### Abstract  
Potassium concentration in grape berries can affect acidity and pH in must and wines. Under the current warming scenario, where preserving equilibrated value for these grape parameters is increasingly challenging, K fertilization could represent a tool to manage grape composition. In this study, the effect of potassium fertilization was investigated over 4 years (2013-2016) in field-grown grapevines (_Vitis vinifera_ cv. Glera). Four different potassium rates (0, 15, 30, 60 kg K\({}_{2}\)O ha\({}^{-1}\) year\({}^{-1}\)) were tested and agronomic responses, grape quality as well as K concentration in the berry were recorded over the four years. At harvest, yield parameters and total soluble solids were unaffected by potassium fertilization. On the contrary, the tittatable acidity of the muss was increased by the higher rate of potassium (K60), and both tartaric and malic acids showed higher values when the K rate was higher. K fertilization did not affect the pH, as all the treatments displayed comparable pH values and in an optimal range for winemaking. Overall, in our experimental conditions, medium potassium inputs showed better results on Glera grape quality compared to low K rates, by promoting higher tittatable acidity levels without altering the pH in muss.  
fertilization; potassium; grape acidity; grape pH; climate change +
Footnote †: journal: Applied Sciences
## 3 Results
### Correlation between \(c^{*}\) and State Conditions  
We find that the wide difference of \(c^{*}\) among the 50 states is partially related to a state's economic and social conditions. Specifically, we consider population demographics, air traffic flow and the economic development level, which can be quantitively characterized by the following proxies. A state's population size as of 2019 is estimated by the U.S. Census Bureau, along with the population density measured by the number of residents per square mile. The air traffic flow is measured by enplanement (i.e., the number of passengers boarding) in 2017 and 2018 (see details in the Methods Section). Besides, we collected each state's gross domestic product (_GDP_) as well as the GDP per capita as of the 2019 4 th quarter to measure economic output.  
We calculated the Spearman correlation coefficient between these six variables and the \(c^{*}\) of Twitter volume and Google Trends index, finding a significantly positive correlation, as shown in Table 1. In particular, the more people, the higher population density, the higher air traffic and wealth a state has, the higher the correlation of the Twitter and Google Trends with the COVID-19 pandemic is. This makes intuitive sense, as higher income is correlated with higher education, and higher geographic mobility leads to higher information exchange, both raising early awareness of the pandemic. There is no significant correlation between \(l^{*}\) and the states' demographic variables.  
Figure 2: Illustrations of the lagged correlations between new confirmed COVID-19 infections and data from Google Trends and Twitter in the selected 4 states: (**a**) Oregon, (**b**) Massachusetts, (**c**) California (**d**) Iowa.
## 3 Analysis of Crack-Characteristic Stress
### Analysis of Stress-Strain Curve  
According to the above calculation method, each strain of the rock in the process of unloading confining pressure is calculated, and the stress-strain curve is drawn. Due to space limitations, taking the confining pressure of 10 MPa as an example, the stress-strain curves and variation trends of each strain of rock samples were analyzed. Figure 2 represents the stress-strain curves of sandstone samples. The stress-strain change during confining pressure unloading is divided into five stages:  
1. Compaction stage (o-\(\sigma_{cc}\) stage): the axial strain increases slowly and presents a concave growth. The growth of circumferential strain is small, almost zero. The volume strain trend and value are consistent with the axial strain. The volume strain of the crack gradually decreases to zero, and the primitive micro-cracks gradually close at this stage.
2. Linear elastic deformation stage (\(\sigma_{cc}\)-\(\sigma_{ci}\) stage): the starting and ending positions correspond to the crack closure stress \(\sigma_{cc}\) and crack initiation stress \(\sigma_{ci}\), respectively. The axial strain increases linearly, the circumferential strain increases slightly, and the volumetric strain increases approximately linearly. The volumetric strain of the crack is zero. At this stage, the rock sample gradually compacts and begins to crack.
3. Steady crack growth stage (\(\sigma_{ci}\)-\(\sigma_{cd}\) stage): when the crack initiation stress \(\sigma_{ci}\) is exceeded, the axial strain still keeps a large growth rate, the growth rate of circumferential strain increases significantly, the volume strain increases slightly, reaching the maximum, and the volume strain of crack begins to increase. At this stage, rock sample cracks develop steadily and increase continuously.
4. Expansion cracking development stage (\(\sigma_{cd}\)-\(\sigma_{p}\) stage): when the dilatancy stress \(\sigma_{cd}\) is exceeded, the axial strain and circumferential strain increase stably, the volume strain deflects to the negative direction, the volume strain decreases rapidly, and the crack volume strain increases obviously. At this stage, the volumetric strain is reversed, the volume compression decreases obviously, and the rock samples enter the plastic yield stage.
5. Post peak stage (the stage after \(\sigma_{p}\)): when the peak stress \(\sigma_{p}\) is exceeded, almost axial strain, circumferential strain, and volume strain increase rapidly, crack volume strain also increases significantly, and the sample volume expands. The reason is the rapid development of internal cracks in rock samples, forming a large area of penetration, resulting in significant volume expansion; rock samples also undergo failure.
## 5 Technology Trends and Barriers to Large Scale Deployment
### Fermentation  
Simultaneous utilization of pentose sugars by highly effective industrial yeast strains is still a challenge in developing continuous fermentation, which is expected to increase the yield and reduce the cost of the final product. The tolerance of ethanol producing bacteria for high substrate, inhibitor, and product concentration still needs to be improved; its progress on developments of co-fermenting microbes have been slower than projected. Review on \"present technical issues\" hindering cellulosic ethanol production indicated microbial species had been engineered to ferment both C5 and C6 sugars, but ethanol yields were low, and the microbes had low tolerance to high ethanol contents compared to C5 fermenting microbes [46]. [PERSON] et al. [14] further reported that there has been considerable work trying to improve co-fermenting bacteria for at least four decades to date, but still today, it's possible to recognize familiar difficulties amongst various projects.  
Other ethanol producing organisms such as yeasts, Escherichia coli, Klebsiellaiella oxytoca, Lactobacillus sp., Clostridium sp., and others are developed for the simultaneous utilization of pentose and hexose sugars but their practical application has still to be proven. Alcohol producing strains with the ability to hydrolyze polymeric substrates have been in development for some time; although high end product concentration and selectivity (toward greater production of the desired alcohol), and insensitivity to inherent and generated inhibitors and process conditions remain major goals.  
High dry matter concentration in a fermentation batch is also desirable as this will give high product concentration and help product recovery from fermentation broth. This could be achieved by developing novel process layouts involving systems aiming at immobilization of the fermenting organisms by the advanced use of non-fouling membrane systems, encapsulation of the organisms in polymer beads, etc. Efficient product separation is another advantage of advanced fermentation set-ups. This could be a critical step in the direction of a transfer from the current batch-wise into continuous fermentation processes, which would represent a more effective conversion.
## 4 Results  
### Dynamic Load Analysis  
Figure 7 shows the dynamic load profiles corresponding to the four field operations. Each task was divided into a working and steering period, where the dynamic load profile for each period was different. The torque of the spiral bevel gear in the working period during moldboard plow tillage was approximately 500-700 Nm, while the torque in the steering period showed large variation (Figure 7a). The maximum torque was temporarily displayed. The torque during subsoiler tillage gradually increased with an increase of the tillage depth in the working period, showing a torque of about 900-1300 Nm in the subsoiler period (Figure 7b). In the steering period during subsoiler tillage, the torque range was lower than that of the working section; however, the torque temporarily increased. This temporary torque increase was determined to be due to the tractor stopping for steering, according to the working pattern of the tractor: Driving (Tillage)--Stop--Three-point hitch ascending--Driving (Steering). During rotary tillage, the torque of the spiral bevel gear showed a maximum value when starting to drive the tractor from a standstill, with a torque of less than 100 Nm in the tillage section (Figure 7c). Unlike moldboard and subsoiler tillage, rotary tillage drives a power take-off (PTO) blade and, so, it is considered that low torque is required, as the soil is pushed through the PTO blade and driven by the reaction force in the forward direction of the tractor. The torque in the steering period during rotary tillage was higher than that of the working period, due to the influence of the steering force required for steering. The baler operation, similar to rotary tillage, shows the maximum torque value when driving a tractor stopped at the starting point of the tractor (Figure 7d). In the working period, the tractor performs operations such as baling for collecting Italian ryegrass, and binding for bale compression, for 10-50 s, showing a torque range of 0-150 Nm. From 50 to 75 s, it showed almost constant torque as it was the process of discharging the bale after stopping. In the turning section, the torque range was about 0-300 Nm. Table 6 provides a statistical description of the spiral bevel gear torque for each operation, during both the working period and steering period. The average torque data during the working period of moldboard plow and subsoiler tillage was higher than that in the steering period, while the average torque data during the steering period in rotary tillage and baler operation was higher than the working period. In the working period, the average torque was highest in the order of subsoiler tillage, moldboard plow tillage, baler operation, and rotary tillage, while the average torque in the steering period was highest in the order of subsoiler tillage, rotary tillage, moldboard plow tillage, and baler operation. As the required time for the working period accounts for more than about 80% of the total torque profile, the torque of the spiral bevel gear in the working section is considered to have a greater influence on the fatigue life.