esa-sceva/satcom-chunk-collection huggingface dataset:
# Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
## 3 Results
### Basic Navigation  
Most of the solutions examined implement features in the category of basic navigation, which we describe as core navigation features for autonomous drones. The Basic Navigation features outlined below are tabulated in Table 2.  
* **Autonomous Movement (AM):** The drone has a navigation policy that allows it to fly without direct control from an operator; this policy can be represented in forms as simple as navigation commands such as \"go forward\" or as complex as a vector of steering angle and velocity in two dimensions that lie on the x-z plane.
* **Collision Avoidance (CA):** The drone's navigation policy includes learned or sensed logic to assist in avoiding collision with non-distinct obstacles.
* **Auto Take-off/Landing (ATL):** The drone is able to enact self-land and take-off routines based on information from its awareness of the environment; this includes determining a safe spot to land and a safe thrust vector to take off from.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Year** & **Citations** & **SE** & **ODE** & **ODi** \\ \hline [PERSON] et al. [9] & 2020 & 34 & No & No & Yes \\ [PERSON] et al. [10] & 2020 & 11 & No & No & No \\ [PERSON] et al. [11] & 2020 & 8 & No & No & Yes \\ [PERSON] et al. [12] & 2020 & 6 & No & No & No \\ [PERSON] et al. [13] & 2020 & 6 & No & No & No \\ [PERSON] et al. [14] & 2020 & 5 & No & No & No \\ [PERSON] et al. [15] & 2020 & 4 & No & No & Yes \\ [PERSON] et al. [16] & 2019 & 55 & Yes & No & No \\ [PERSON] et al. [17] & 2019 & 50 & No & No & Yes \\ [PERSON] et al. [7] & 2019 & 43 & Yes & Yes & No \\ [PERSON] et al. [18] & 2019 & 19 & No & No & Yes \\ [PERSON] et al. [19] & 2019 & 11 & No & No & Yes \\ [PERSON] et al. [20] & 2019 & 9 & No & No & No \\ [PERSON] et al. [21] & 2019 & 8 & No & No & Yes \\ [PERSON] et al. [22] & 2018 & 158 & Yes & Yes & No \\ [PERSON] et al. [23] & 2018 & 60 & No & No & Yes \\ [PERSON] et al. [24] & 2018 & 58 & No & No & Yes \\ [PERSON] et al. [25] & 2018 & 57 & No & No & Yes \\ [PERSON] et al. [26] & 2018 & 23 & Yes & No & No \\ [PERSON] et al. [27] & 2018 & 14 & No & No & Yes \\ [PERSON] et al. [28] & 2018 & 14 & No & Yes & No \\ [PERSON] et al. [29] & 2017 & 165 & No & Yes & No \\ [PERSON] et al. [30] & 2017 & 98 & No & No & No \\ [PERSON] et al. [31] & 2017 & 88 & Yes & No & No \\ [PERSON] et al. [32] & 2017 & 43 & No & No & Yes \\ [PERSON] et al. [33] & 2017 & 31 & No & No & No \\ [PERSON] et al. [34] & 2017 & 25 & Yes & Yes & No \\ [PERSON] et al. [35] & 2017 & 11 & No & No & Yes \\ [PERSON] et al. [36] & 2016 & 424 & No & No & Yes \\ [PERSON] et al. [37] & 2016 & 263 & No & No & No \\ [PERSON] et al. [38] & 2016 & 26 & Yes & No & No \\ [PERSON] et al. [39] & 2016 & 3 & No & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: The most cited research pool entries as of 18 March 2021 in the context of awareness features.
# Optical Boundaries for LED-Based Indoor Positioning System
## 4 Investigations Showing the Effect of Defined Optical Boundaries
### Positioning Delay Due to Tilt  
The study of the effect of tilt plays a vital role in positioning as it covers practical scenarios encountered when the IPS is used in real life. The method used to analyze the effect of tilt is discussed in this section. Tilt is considered as being a direction away from the incident ray of the LED as illustrated in Figure 1 Therefore, when the receiver is tilted, the new angle of incidence at the receiver is \(\varphi~{}+~{}\varphi_{t}\). By substituting this value into (13), \(\phi_{th}\) is computed as  
\[\phi_{th}=\cos^{-1}\left\{\frac{2\pi h^{2}\sqrt{\sigma_{t}^{2}}\mathcal{Q}^{-1 }(1-\mathcal{P}^{\frac{1}{Np}})}{\mathcal{R}P_{t}A(m+1)g(\varphi+\varphi_{t}) \cos(\varphi+\varphi_{t})}\right\}^{\frac{1}{m+2}} \tag{22}\]  
within the limits \(0\leq\varphi+\varphi_{t}\leq\varphi_{c}\) because the incident rays fall outside the field of view of the receiver for \(\varphi+\varphi_{t}>\varphi_{c}\). In order to determine the positioning delay when tilt occurs, the difference in positioning times using \(\Phi\) and \(\phi_{th}\) is computed using a similar analysis as presented in Section 4.2. To observe the effect of an increasing amount of tilt, \(\varphi_{t}\) is increased and the positioning delay recomputed as explained in Section 5.6.
# Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning
## 2 Materials and Methods
### Segmentation of Signal  
Heart sound segmentation, which segmented the heart sounds into 4 periods (including S1, systolic, S2, and diastolic) based on the features obtained through preprocessing, was the focus of this paper. This paper adopted U-net implementation based on the deep CNN, as is shown in Figure 4. Additionally, the network structure is shown in Figure 5.  
Figure 4: Diagram of the segmentation method: the division of training set and test set, model training.  
Figure 5: U-net architecture with a depth of 5, which was used for heart sound segmentation. The number in the boxes indicates the feature dimension of the corresponding layer. The number on the arrow of the diagram represents the filter size. The number on the right side of the diagram represents the length of the input and output of each convolutional layer.  
The mathematical model of the convolution of each layer of the network in this architecture can be expressed in Equation (3):  
\[Z_{i,j}^{\ell}=\sum_{i=1}^{N^{\ell}}\sum_{j=1}^{k^{\ell}}A_{i,j}^{\ell}\text{W}_{ i,j}^{\ell} \tag{3}\]  
where \(A_{i,j}\) represents the element in the \(i\)-th row and \(j\)-th column of the matrix, the elements of matrix A correspond to the weights of different filters corresponding to different feature inputs of the \(\ell\)-th layer, and \(N^{\ell}\) represents the dimension of the input feature space of the \(\ell\)-th layer, \(k^{\ell}\) represents the output feature space dimension of the \(\ell\)-th layer, and \(W_{i,j}^{\ell}\) and \(Z_{i,j}^{\ell}\) represent the input and output of the \(\ell\)-th convolutional layer, respectively.  
The network can be understood as down-sampling, up-sampling, splicing, and fusion. Specifically, down-sampling implemented different degrees of convolution operations and learnt features at different levels. As the depth of the network increases, the learned features were also converted from low-dimension to high-dimension; Up-sampling implemented the deconvolution. After learning the deep features, the data length were gradually restored to the size of the original inputs; The splicing fusion combined the different dimensional features learned by down-sampling with the recovered data from up-sampling to realize the fusion of features at different scales. Finally, the network output was followed by a softmax activation function, and the output was each state sequence corresponding to the heart sound.  
It should be noted that the output of all convolutional layers of the network needed to be zero-filled to ensure that the data space dimension does not change after convolution, as shown in Figure 6.
# Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage
## 1 Introduction
The effectiveness of the adopted approach is demonstrated by a series of experiments involving comparisons with the hysteresis compensation controller based on the inverse BW model and the model reference adaptive controller (MRAC). The main contribution of this research is the raised HAC method for the precise positioning of P-AS. The remainder of this work is organized as follows. The Hammerstein model for P-AS is presented in Section 2. The design program of a hybrid adaptive controller based on the hysteresis compensator and the stability proof are presented in Section 3. Experimental results confirming the efficiency of the raised control strategy are given in Section 4. Conclusions are presented in Section 5.
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 2 Materials and Methods
### Statistics and Software
After model selection, the best model applicable to each ecoregion was coded with the response variable as a Gaussian function of \"Surface Temperature\", \"Soil Moisture\" (and their interaction), with \"Month\" and \"Year\" as independent random factors. All data used in LMMs were rescaled for compatibility and precipitation was not included in our models after initial data exploration showed Variance Inflation Factor (VIF) and correlation favouring Soil Moisture [72]. Model diagnostics was performed according to [PERSON] and Ieno [71] and [PERSON] et al. [73], and model selection carried out by examining diagnostic plots and Bayesian Information Criterion (BIC) analysis [71]. Holm-Bonferroni _post-hoc_ tests were also applied to adjust LMM outputs. Root Mean Square Error (RMSE) and R\({}^{2}\) (marginal and conditional) were calculated for all LMMs [74]. Aiming to aid the visualization of our results in general, but particularly those concerning the previously identified 2012 drought, we conducted diverse spatial analysis of SIF, SVIs and environmental indicators, calculating spatial averages and spatial correlation between the variables of interest. For this analysis we subset our data into three periods identified in preliminary time-series analysis: 2007 to 2011, 2012 and 2013 to 2017. Beyond the spatial analysis, we also used this subset scheme to present and discuss other results relating to the 2012 drought. Some data processing including format conversions, raster re-sampling and re-projection, were done using Qgis [75]. Remaining data exploration and analysis was done using R 3.6.1 [76] and the following R packages updated to the latest contemporary versions: stlplus [77], lme4 [78], car [79], ggplot2 [80], merTools [81], multcomp [82], MuMIn [83], raster [84], rasterVis [85] and rgdal [86]. All produced scripts are available at Zenodo [87]. Figure 2: Flowchart of the general study design. All data is represented within shaded parallelograms, while data groups, major procedures and tests are in rectangles. Please see Materials and Methods section for details.
# Realistic Simulation for Body Area and Body-To-Body Networks
### Contributions  
There are great opportunities and possibilities to develop a realistic BAN and BBN simulator, which can provide a realistic simulation environment and platform. To meet the requirements of the emerging new applications, this work presents a complete all-in-one package for modeling and evaluating body area networks and body-to-body networks. This includes realistic channel models based on a measurement campaign for both BAN and BBN, IEEE 802.15.6 standard-complaint modulation, path loss, radio link models at the physical layer, carrier sense multiple access collision avoidance (CSMA/CA), scheduled access MAC protocols, as well as random channel and time-shared co-channel interference strategies, which are evaluated and are available at the MAC layer. All of these models are standard specific and provide a benchmark for BAN- and BBN-specific future algorithms', strategies' and protocols' realistic performance evaluations. Further, the performance of IEEE 802.15.4 under co-located BANs is also a part of the package.  
In this paper, we present full deterministic and semi-deterministic (statistical) mobility approaches for BAN and BBN. The full deterministic approach is an enhanced version of the IEEE 802.15.6 standard proposed channel models by providing dynamic, space and time channel variations. The model relies on motion capture systems and biomechanical mobility modeling to have diverse mobility patterns. On the other hand, the semi-deterministic approach relies on a real-time measurement campaign and statistical modeling, which provides more realistic channel characterizations (_i.e._, link correlated and time-varying on-body and body-to-body models). We compare these approaches against the IEEE 802.15.6 standard proposed channel models. The performance is evaluated through several metrics, including the packet delivery ratio (PDR), energy consumption and packet delay. The results show that the semi-deterministic approach is more realistic under a constraint network topology and simulation setting, which is a limitation for a fair comparison. However, for the diversity of the mobility patterns and scenarios, biomechanical modeling and the deterministic approach are better choices.  
The rest of this paper is organized as follows. After this Introduction, related works about existing simulators, their potential benefits and shortcomings are highlighted. This is followed by the proposed simulator, for which, first, the architecture is introduced, then the two mobility and channel modeling approaches are proposed. Finally, a case-study is presented that compares the two approaches, and the results are presented.
## 2 Background  
### PID Controller  
The PID controller has been widely used since its invention in 1910 in both academia and industry [16]. There are many PID control configurations, but the most common implementation is shown in Figure 1.  
In general, the synthesis of the PID controller can be mathematically described by  
\[u(t)=k_{p}e(t)+k_{i}\int\limits_{0}^{t}e(t)dt+k_{d}\frac{d}{dt}e(t) \tag{1}\]  
where \(k_{p}\) is the proportional gain, \(k_{d}\) is the derivative gain, \(k_{i}\) is the integral gain, and the error \(e(t)\) is the difference between the set-point and the plant output \(y(t)\). In addition, Equation (1) can be rewritten in Laplace form as  
\[u(s)=e(s)[k_{p}+\frac{k_{i}}{s}+k_{d}s] \tag{2}\]  
Finally, under the above scheme the transfer function of the PID controller or the control law is specified by  
\[C(s)=\frac{u(s)}{e(s)}=k_{p}+\frac{k_{i}}{s}+k_{d}s \tag{3}\]  
For digital PID controllers, Equation (3) can be transformed via the z-domain into a difference equation for a recursive digital filter with an infinite impulse response (IIR) [16]  
\[\frac{U(z)}{E(z)}=k_{p}+\frac{k_{i}T_{s}z}{z-1}+\frac{K_{d}N(z-1)}{(1+NT_{s})z -1} \tag{4}\]  
where \(T_{s}\) is sampling period, \(N\) is the filter value.  
To be implemented on an FPGA platform, a digital PID controller must be transformed to a difference equation, as shown below  
\[\frac{U(z)}{E(z)}=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}}{a_{0}+a_{1}z^{-1}+a_{2} z^{-2}} \tag{5}\]  
Figure 1: Block diagram of typical PID control structure.  
A differential equation can be derived from Equation (5) as follows  
\[u[n]=k_{1}u[n-1]+k_{2}u[n-2]-k_{3}e[n]-k_{4}e[n-1]-k_{5}e[n-2] \tag{6}\]  
Therefore, the relationship between the PID gains and its implementation in a digital system is shown in Table 1.  
where the differential equation variables equal  
\[\begin{array}{l}a_{0}=(1+N*T_{s})\\ a_{1}=-(2+N*T_{s});a_{2}=1\\ b_{0}=k_{p}*(1+N*T_{s})+k_{i}*T_{s}*(1+N*T_{s})+k_{d}*N\\ b_{1}=-(k_{p}*(2+N*T_{s})+k_{i}*T_{s}+2*k_{d}*N)\\ b_{2}=k_{p}+k_{d}*N\end{array} \tag{7}\]
## 2 Materials and Methods
### Various Performance Measures  
Table 2, with the dimensions \"actual\" and \"predicted\", as well as the scales \"True Positives (TP)\", \"True Negatives (TN)\", \"False Positives (FP)\", and \"False Negatives (FN)\", makes up the confusion matrix. The following performance measures assess predictions using ML and DL models for specified categorization problems [5; 30; 31]. The range of correct predictions is created as a quantitative relation of all made predictions; therefore, classification accuracy should be described. It is equal to (TP + TN)/(TP + FP + FN + TN). Precision, used in document retrievals, is also explained by the diversity of accurate documents that ML and DL models deliver. Precision is equal to (TP + FP)/TP. Because it indicates how successfully a model can recognize positive samples, recall is also examined. The recall equals (TP + FN)/TP.  
Specificity is also reviewed to assess a model's capacity to forecast true negatives for each available category. Specificity is defined as (TN + FP)/TN. The F1 score is calculated using the recall and precision weighted average. The F1 score is influenced equally by recall and precision. It equals 2 \(\times\) (recall \(\times\) precision)/(recall + precision). The AUC-ROC statistic measures a model's ability to identify the categories. Balanced accuracy deals with unbalanced datasets in binary and multiclass classification problems. It is outlined because the average recall was found in each category.
## 3 Results and Discussion
### Assessment of Phytotoxicity
#### 3.2.2 Soil Application
Both the total chlorophyll and carotenoids decreased significantly with an increasing concentration of SPs recording respective contents of 3.57 and 0.34 mg/g fresh weight after 14 days of treatment with 0.06 mg/mL of SPs. An opposite trend can be seen upon an application of Cu NPs for 14 days, since they both increased with an increasing Cu NPs concentration to 0.06 mg/mL, recording a 9.39 and 1.02 mg/g fresh weight, respectively. This corresponds to a 49.76 and 70% increase in the contents of total chlorophyll and carotenoids, respectively. However, no effect on either the total chlorophyll or carotenoids content was observed after 7 days of application, as was previously encountered with chlorophyll (b). Previous studies reported an increase in the carotenoids content of _Dracocephalum moldavica_ when exposed to Ag NPs (10 nm) at a concentration of 80 mg/L after 21 days from the application of Ag NPs [50], as well as an increase in chlorophyll (a) and the total chlorophyll contents of rocket (_Eruca sativa_) upon exposure to Fe\({}_{3}\)O\({}_{4}\) nanoparticles (25 nm) at concentrations of 1 and 2 mg/L after 5 weeks from the application [54]. The stimulating effect of Cu NPs in soil applications is contradictory to the inhibitory effect encountered in foliar applications. This can be owed to the different pathways by which Cu NPs uptake or absorption onto plants can occur. Foliar uptake involves adsorption and internalization via the cuticle and then penetration of metals via stomatal pores. The metals are thus transported inside the plants from the aerial parts towards the different plant organs (roots, flowers fruits, etc.) via the phloem. However, soil uptake entails metal transport from the roots to the aerial parts via the xylem system. Therefore, the type of application may have different effects on the metal distribution at the tissue and cellular levels in the plants [55].
###### Abstract  
Wireless technologies that are based on radio frequencies are currently widely used, with numerous applications around the world. However, they pose some disadvantages to human health. High frequencies can have potentially harmful effects on children, hospital patients, and even healthy people if the signal power exceeds the permitted standard. Conversely, the use of visible light for data transmission is a trend that presents new options, including optical wireless communication, optical camera communication, and visible light communication. This paper proposes a modulation scheme based on on-off keying in the time domain, which is applied to a monitoring system using optical camera communication. This scheme has various compatible supports for the global-shutter camera and rolling-shutter camera, which are popular commercially available cameras. Furthermore, this scheme facilitates a low-cost monitoring system. By using small light-emitting diodes (LEDs) and controlling the exposure time in a single camera, the camera, as a receiver, can simultaneously detect signals from up to 10 sensor devices in different positions at a maximum distance of up to 50 m, with a low error rate.  
monitoring system: optical camera communication; on-off keying; OOK: Multiple Input Multiple Output On-Off Keying: MIMO-OOK
## 2 Study Area and Data
### Geographical Data  
Geographical data, such as underlying surface information required by the Variable Infiltration Capacity (VIC) model, including soil texture, topography, and vegetation types, were also used in this study. The global Digital Elevation Model (DEM) data (GTOPO30) with a resolution of thirty arcseconds were downloaded from the website of the US Geological Survey (USGS; [[https://lta.cr.usgs.gov/GTOPO30](https://lta.cr.usgs.gov/GTOPO30)]([https://lta.cr.usgs.gov/GTOPO30](https://lta.cr.usgs.gov/GTOPO30))). The soil texture information was obtained from the Food and Agriculture Organization dataset [58]. The vegetation data were taken from the global vegetation classifications database developed by the University of Maryland, which has 14 types of land use/cover with a spatial resolution of 1 km \(\times\) 1 km [59].
## 3 Results  
### Short-Term Correlation between MODIS Products and Flux Tower GPP  
Short-term GPP was more highly correlated with EVI than with NDVI, LAI, or FPAR (Table 3), in agreement with other studies that have shown high correlation between EVI and GPP [21, 22, 23, 24]. The correlations with NDVI and EVI were minimally affected by changing the compositing period from 16-days to 32-days, demonstrating that the auto-correction feature of vegetation indices to non-vegetation signals through normalization of the reflectances is effective. On the other hand, LAI and FPAR showed improved fitting when they were composited to 32-days. Because of their inability of capturing short-term changes in GPP (_i.e._, temporary reductions in GPP due to adverse weather conditions), both MOD13 and MOD15 products showed weaker correlation for evergreen forests than for deciduous forests. For all the products, no correlation was found with GPP of tropical forests (Table 3). This can be attributed to the small seasonal changes observed in tropical forest leaves, which are smaller than the interannual variation in leaf phenology (e.g., [PERSON] _et al._[24]). EVI generally showed smaller RMSE than NDVI, LAI, and FPAR.  
For all analyzed sites, the reference data of MOD17 GPP was most highly correlated with short-term flux tower GPP, both for deciduous and evergreen forests (Table 3). This result can be explained by the fact that MOD17 GPP parameters have been well calibrated and tested at many FLUXNET sites [31, 39, 67]. This result also supports the need for meteorological input data in addition to the satellite metrics for better capturing the seasonal variations in photosynthesis ([PERSON] _et al._[9]).  
We plotted 32-day composites of MODIS products against flux tower GPP estimates for all the sites (Figure 3). NDVI and FPAR clearly showed saturation at values of 0.75 and above. EVI showed the highest correlation with flux tower GPP, but the modest \(R^{2}\) of 0.62 implied low accuracy in the estimation of short-term GPP through one equation using satellite metrics only. Theoretically, the four metrics should be correlated with each other. Indeed, Figure 3(a,b) shows a similar scatter even though NDVI and FPAR were calculated through very different approaches. This similarity can be explained by the well-known linear relationship between NDVI and FPAR [68]. FPAR and LAI are correlated through an exponential relationship usually described by Beer's law, which explains the differences between the scatters of Figure 3(c) and Figure 3(d). However, even correlating the logarithmic transformation of GPP with MODIS NDVI and FPAR did not significantly increase the \(R^{2}\).