esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Workflow
#### 2.1.4 Input File Elaboration for GATE and Simulation  
A new version of the MCID tool, designed for the GATE/Geant4 10.4 code, was used in this study. MCID is a Windows Executable that can also be run on Windows virtual machines. MCID can generate input files for GATE starting from a morphological image (CT) and a coregistered functional image (SPECT or PET). First, the CT image is converted into a density image, assigning to each voxel density and atomic composition. In our case, the CT image was the resampled one, obtained from SIMIND. Tissue segmentation was realized assigning a constant density value for each HU (Hounsfield Units) range we defined on MCID. The materials selected for the segmentation are reported in the description of the simulated scenarios (SS 2.2). The Spectrum source description was set: \({}^{90}\)Y \(\beta\)' spectrum was discretized in steps of 10 keV [37]. \(10^{8}\) primaries were also selected for the simulations. All the files generated by MCID, used as input for GATE, are automatically stored in two folders, named _data_ and _mac_. Each.mac file contains a group of GATE commands which define the settings of the simulation, such as the geometry and the density of the system, the characteristics of the sources, the physics of the simulation, the features of the output, the level of verbosity, and the visualization settings. Moreover, some.mac files recall specific files stored in the data folder (such as the patient density and activity maps). A list of some.mac files, along with set parameters of most interest, is reported in Table 2.  
The file _Y90_spectrum.mac recalls the other files, therefore the user can start the simulation just running the command gate _Y90_spectrum.mac on the terminal. The simulations were performed on a desktop computer with Intel Core i7 5960X and 3 GHz, 16 GB RAM, and Linux Ubuntu 18.04 LTS.
# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## 5 DNN-Inspired Atmospheric Turbulence Characterization: Cn^2 Net Scintillometer
Note that under these conditions the scintillometer recordings exhibit strong fluctuations (see Figure 7a,c) caused by non-stationarity of the turbulence dynamics at the time scale of the \(C_{n}^{2}\) measurements. The machine learning experiments with the ATM datasets described raise a question about the \"right\" dataset for DNN training, which could provide full advantage of the Cn^2 Net scintillometer capabilities for real-time turbulence characterization. Such new capabilities in \(C_{n}^{2}\) sensing are important for the development of a wide range of atmospheric optics systems including free-space optical communications, adaptive optics, directed energy, lidars etc.
# Improved Detection and Response via Optimized Alerts: Usability Study
## 3 Results
### Hypothesis Testing  
Given that the data collected for this study did not meet the standard for normality, both parametric and non-parametric tests were performed. Parametric statistical procedures depend on assumptions about the shape of the distribution (assume a normal distribution) in the population and the form or parameters (means and standard deviations) of the assumed distribution [26]. On the other hand, nonparametric statistical procedures depend on few or no assumptions about the shape (normality) or parameters of the population distribution from which the sample was taken [26]. Nonparametric tests include the Mann-Whitney U test and the Friedman test. Parametric tests can be conducted via a mixed analysis of variance (ANOVA) with a Bonferroni correction. The mixed ANOVA tests included an approach for treatment of the dependent variable: security analysts' level of acceptance of the alert output. First, mixed ANOVA was performed across the TAM-based questionnaire categories, namely perceived usefulness (PU), perceived ease of use (PEU), attitude towards using (AU), and intent to use (IU), where the scores for all sub-scales were summed. Second, mixed ANOVA was performed on each sub-scale. For the individual sub-scales, statistical significance was set at \(\alpha/4\), or 0.0125.
# Static Structures in Leaky Mode Waveguides +
###### Abstract  
In this work, we suggest a new method of expanding the field of view in bottom-exit, leaky mode devices for transparent, monolithic, holographic, near-eye display. In this approach, we propose the use of static, laser-induced, grating structures within the device substrate to break the leaky mode light into diffracted orders. We then propose to use carefully timed illumination pulses to select which diffracted order is visible to the eye at every display refresh interval (up to 100 kHz). Each of these orders becomes a view for a different image point. To describe this new method, we use K-vector analysis. We give the relevant equations and a list of parameters which lead to a near-eye geometry with little or no overlap in higher-order view zones. We conclude that it should be possible to increase the field of view of our bottom-exit, leaky mode devices by as much as one order of magnitude by simply adding a laser-induced grating structure to the substrate and by carefully timing the device illumination. If successful, this method would make possible a transparent, holographic, near-eye display that is simple to fabricate, relative to pixelated approaches, and which has a wide field-of-view relative to our current bottom-exit displays.  
Keywords:near-eye display; leaky mode; lithium niobate; holographic video; augmented reality; 3D display; acousto-optic modulator; laser-induced structures +
Footnote †: journal: Applied Physics Letters B
## 4 Secrecy Outage Performance of MISO-CR-NOMA Network
### The Maximum Channel Capacity Scheme  
In general, the CSI of the links between the BS and secondary users can be relayed to the former through a combination of signaling and channel feedback mechanisms [24]. In contrast, E remains in a passive operational state, such that no exchange of information exists between it and the BS. Thus, it is considered that the CSI of BS and secondary user links are available at BS, while the CSI of the BS to E link is not available. To this end, the MCC scheme is proposed to maximize the SNR, or alternatively, SINR of the secondary users while concurrently ensuring that the QoS requirements of PD are upheld. Specifically, the optimal transmit antenna for the signal of BS transmitted to the secondary is identified as the one that maximizes the channel capacity. Assuming no loss of generality, we contemplate that the \(i\)-th (\(1\leq i\leq M\)) transmit antenna of BS is selected as the best antenna to transmit signal with power \(P_{s}\). Next, we analyze three situations, called max-\(U_{1}\), max-\(U_{2}\) and max-\(U_{3}\), respectively.
## 2 Materials and Methods
### Methodology  
The research methodology compared ten LULC classification approaches (PB and OB, all based on the RF algorithm) applied to various dataset combinations (Table 2). In addition to five classifications of dataset combinations including PL data, five additional classifications based on S2 and S1 composite datasets were considered to extend the analysis and assess the thematic accuracy improvements of integrating PL data with S2 and S1 data. The general workflow of the research includes four main steps (Figure 2): (1) composition of the base datasets; (2) textural analysis and image segmentation; (3) LULC classification; and (4) accuracy assessment. The first step is to compose the available S2, S1, and PL images for the selected period and in the selected study area. The second is aimed at the textural analysis applying the GLCM algorithm, the image segmentation using SNIC, and the data composition to obtain the final feature dataset for the classification. The third step includes the RF classifications (PB or OB) using for training a subset of the sample  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Land Cover Classes** & **Number of Sample Points** \\ \hline Woodlands & 341 \\ Shrublands & 457 \\ Grasslands & 424 \\ Croplands & 422 \\ Built-up & 225 \\ Bare soil & 286 \\ Water bodies & 245 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Land cover classes and number of sample points used in the research.  
Figure 1: Study area location in Brazil (**a**,**b**). Visible and infra-red composite images of PlanetScope (**c**,**d**) and Sentinel 2 (**e**,**f**). VV (**g**) and VH (**h**) composite images of Sentinel 1.  
points and the dataset combinations obtained in the previous steps. The last step is the accuracy assessment through a confusion matrix calculated using a validation subset of the sample points classified using the same RF classifiers of the previous step. A statistical assessment of the accuracy differences between the various classifications was performed through multiple McNemar tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach Type** & **Approach Code** & \begin{tabular}{c} **Multispectral** \\ **Information** \\ \end{tabular} & \
\begin{tabular}{c} **Textural** \\ **Information** \\ \end{tabular} &
\begin{tabular}{c} **Object** \\ **Recognition** \\ \end{tabular} \\ \hline PB & PB\_S2 & S2 & - & - \\ PB & PB\_S2S1 & S2, S1 & - & - \\ PB & PB\_PL & PL & - & - \\ PB\_S2S1 PL & S2, S1, PL & - & - \\  & OB\_S2S1 & S2, S1 & S2 & S2 \\ OB & OB\_S2S1T & S2, S1 & S2, S1 & S2 \\ OB & OB\_PL & PL & PL & PL \\ OB\_S2S1 PL & S2, S1 & PL & PL \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sources of multispectral and textural information and datasets used for object recognition in the various approaches compared in the research. PB: pixel-based, OB: object-based, S2: Sentinel 2, S1: Sentinel 1, PL: PlanetScope.  
Figure 2: The methodological workflow of the research.
# Light-Controlled Microbots in Biomedical Application: A Review
## 2 Prospective Applications
### Micro Force Measurements and Mechanical Characterization of Bio-Cells  
Understanding the impact of mechanical characteristics on cells is very important for studying cell mechanotransduction. In biology, a new field of study has evolved termed cell mechanics and mechanobiology, focusing on the evaluation of the mechanical properties of living cells [77]. Mechanical properties of cells, such as elastic modulus or shear modulus, can help direct many cell functions, i.e., cell motion, cell division, cell adhesion, as well as cell growth [78]. The mechanobiological forces have important implications on cell characteristics [79] and a change in mechanical stress in cells can lead to tissue rebuilding, e.g., an increase in bone material strength index (BMSi) [80; 81], variation in the construction of blood vessel tissue, etc. [82]. These mechanobiological forces can help with cancer diagnosis, drug monitoring, immune status, etc. [83]. Research shows that increased stiffness in tissues may be associated with cancer development due to the high content of stromal collagen [84] and fibrotic lesions [85]. Mechanical characterization of biocells has recently been measured using optical tweezers [86]. By making a nano-indentation in the cell membrane, elastic properties of the cells, e.g., Young's modulus, can be measured using optical tweezers and custom-made microtools. Figure 4 depicts the characterization of the cell membrane performed by [PERSON] et al. in hCMEC/D3 human microvascular cerebral endothelial cells. Many studies have reported using microbeads with different diameters and pushing them towards the cell membrane in the axial direction to determine [PERSON]'s modulus [87; 88; 89; 90]. Elastic properties can usually be determined by measuring the radius of curvature of the probe of an atomic force microscope (AFM) and the measured force is then calculated [87; 91]. However, unlike AFM, optical tweezers can apply a much smaller force with a greater surface area, which increases the accuracy of measurement in the softer cells. [PERSON] et al. were able to apply force in the range of 1-5 pN, which is significantly less than that of an AFM [86]. In a dense medium, it often occurs that multiple particles are captured in a single laser trap. Measurement errors are unavoidably present when many particles are captured with a single laser source. In biomedical applications, there are many instances where the medium is very dense or colloidal [92].
###### Abstract  
Ghost imaging (GI) is an indirect imaging approach that can retrieve an object's image even in a harsh environment through measuring the fourth-order correlation function (FOCF) between the signal and idle optical paths. In this paper, we study lensless GI with a partially coherent beam carrying twist phase, i.e., twisted Gaussian Schell-model (TGSM) beam, in the presence of oceanic turbulence. Explicit expression of the FOCF is derived based on the optical coherence theory and Rytov approximation, and the effects of the twist phase and the oceanic turbulence on the quality and visibility of image are investigated in detail through numerical examples. Our results show that the simulated oceanic turbulence strongly affects the GI. The quality of image decreases monotonously with an increase of the strength of turbulence whereas the visibility increases. When the illumination light carries a twist phase, the visibility of the image is improved while the quality of the image is reduced in contrast to those without a twist phase. By properly selecting the strength of the twist phase, the image can still be maintained at an acceptable level of quality with high visibility. Furthermore, it is found that the quality and visibility of the ghost image are less affected by the oceanic turbulence using a TGSM beam with larger twist factor. Our findings will be useful for the application of GI in an oceanic turbulent environment.  
g 2019; 9 3023; doi:10.3390/app9153023
## 5 Privileged Account Management Subsystem
### Account-Password Management Module
#### 5.1.1 Encrypted Storage  
In this subsystem, the account passwords are stored with hierarchical encryption. The encrypted storage scheme is shown in Figure 5. In this scheme, each account password is encrypted with a Data-Encryption Key (DEK), for each application or cloud resource; additionally, all of the DEKs are encrypted with a Key-Encryption Key (KEK), and all of the KEKs are encrypted with the Root Key (RK). AES-256-GCM is used for all of the encryption processes in this part. The secret sharing algorithm [27] can divide the RK into N key blocks, and send them to N administrators of storage. The storage of the account passwords can be decrypted when at least K of N administrators provide their key blocks simultaneously. The secret sharing algorithm is implemented as follows:  
**Initialization Phase.** We define finite field GF(p). Here, p is a large prime number. We then randomly select N non-zero elements (\(x_{1}\), \(x_{2}\)... \(x_{N}\)) in the GF(p) and send them to the related administrator (\(A_{1}\), \(A_{2}\)... \(A_{N}\)) in public. Element \(x_{r}\) can represent the identity of administrator A\({}_{r}\).  
**RK Distribution Phase.** We select an integer s less than p as RK, and select (K \(-\) 1) elements in the GF(p) as \(a_{1}\), \(a_{2}\)... \(a_{K}\). Equation (9) is a polynomial function where \(a_{0}\) = s:  
\[f(x)=a_{0}+\cdots+a_{K-1}x^{K-1}\ \ \ (mod\ p) \tag{9}\]  
For each \(A\), in (\(A_{1}\), \(A_{2}\)... \(A_{N}\)), we generate a key blocks \(s_{r}\) by Equation (10):  
\[s_{r}=f(x_{r})=a_{0}+\cdots+a_{K-1}x_{r}^{K-1}\ \ (mod\ p) \tag{10}\]  
Then, we send key blocks \(s_{r}\) to \(A_{r}\) in secret.  
**RK Recovery Phase.** Any K administrators (\(A_{1}\), \(A_{2}\)... \(A_{K}\)) can recover RK by function (3), the Lagrange interpolation formula is used in Equation (11):  
\[s=f(0)=\sum_{i=1}^{K}f(x_{i})\prod_{j=1,j\
eq i}^{K}\frac{x_{j}}{x_{j}-x_{i}} \ (mod\ p) \tag{11}\]
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### Related Work
#### 2.7.2 Energy  
[PERSON] et al. [90] evaluate IoT communication technologies (802.154/e, BLE, 802.11, 802.11 ah, LoRa and Sigofx) for battery consumption with a realistic energy models. The considered network is limited with single hope and 802.1 ah and Bluetooth Low Energy (BLE) multi hope scenarios are ignored. The model accounts for frame retransmissions due to collisions or external effects on the network. Study accounts TX, RX, and sleep modes of IoT platforms while calculation of the devices. To keep calculation simple, the authors omit security and other MAC overheads. Results revile when BLE offers the best performance when high traffic applications and LoRa has better performance ultra-low traffic. [PERSON] et al. [91] compare ZigBee and LoRaWAN energy efficiency and it is highlighted that LoRaWAN networks are a higher advantage and can be run with solar energy since the presented network uses 6 Joules per day.  
LoRaWAN Class A devices optimize the energy with controlling the DL receive windows. In most cases selected TX parameter directly affects the battery usage of the end-devices. [PERSON] et al. [92] present a game theoretical approach to selecting the configuration to decrease energy consumption while maximizing the throughput.  
[PERSON] et al. [93] aim to calculate frame transmission cost in terms of Joule and present a model for channel based energy consumption. It also offers a calculation for a lifetime of a LoRa battery. The model is not validated with a real testbed.  
Researchers working on different aspects of energy efficiency such as amplifier design [94], on/off switches to save energy for remote systems [95], tracking systems [96], solar power monitoring systems [97] and running end-devices energy gained through bridge seismographic movements [98]. Spectrum usage, bandwidth and battery usage analysis are evaluated among LPWAN technologies; LoRa, Sigfox, Weightless, LTE-M, NB-IoT [99].  
[PERSON] et al. [100] combine Multiple Radio Access Technologies (RAT)on a single IoT board to provide energy efficient IoT platform which can be used for general purpose applications. NB-IoT and LoRaWAN are selected and radio modules can be configured to transmit data sequentially or in parallel. Both single and combined use cases of the RAT evaluated in terms of energy consumption. The result indicates that Multi RAT platform is more efficient where, the total energy consumption of single RATs has 1005.56 mW, parallel Multi RAT has 941.58 mW and sequential Multi RAT has 738.5 mW.  
A Far-Field Wireless Power Transfer (WPT) sensor node is developed and integrated into a LoRa based system to measure temperature and humidity remotely [101]. It is shown that battery free sensor implementations will increase the use of cases of LPWAN. LoRaWAN fits well with real battery constrain applications both for UL and DL data transmissions.
## 3 The Epsom-Hyb Estimator
### Estimating Truncated Gaussian Probabilities
By running the EP algorithm to convergence, we can calculate the following mean and covariance parameters of the normal distribution \(q\),  
\[\mu=\Sigma\Bigg{(}K^{-1}m+\sum_{i=1}^{d}\frac{\tilde{\mu}_{i}}{\tilde{\sigma}_ {i}^{2}}e_{i}\Bigg{)},\quad\Sigma=\Bigg{(}K^{-1}+\sum_{i=1}^{d}\frac{1}{ \tilde{\sigma}_{i}^{2}}c_{i}c_{i}^{\prime}\Bigg{)}^{-1},\]  
where \(e_{i}\) is the \(i\)-th standard basis vector. With this, we also obtain a closed-form expression for the normalizing constant of \(q\),  
\[\log Z= -\frac{1}{2}\Big{(}m^{\prime}K^{-1}m+\log|K|\Big{)}+\sum_{i=1}^{ d}\Bigg{(}\log\tilde{Z}_{i}-\frac{1}{2}\Bigg{(}\frac{\tilde{\mu}_{i}^{2}}{ \tilde{\sigma}_{i}^{2}}+\log\tilde{\sigma}_{i}^{2}+\log(2\pi)\Bigg{)}\Bigg{)}\] \[+\frac{1}{2}\Big{(}m^{\prime}\Sigma^{-1}\mu+\log|\Sigma|\Big{)},\]  
which approximates the Gaussian probability \(F(\mathcal{A})\) in Equation (7). It is worth noting that the algorithm is still valid for arbitrary factors \(t_{i}(x)\), albeit with different parameter updates. Our simplistic choice of \(t_{i}(x)\) to be the indicator function defined over the constant lower and upper limits of integration reflects the rectangular nature of the partition sets used in the Hybrid-EP estimator. With this, recall the Gaussian integral in Equation (6),  
\[\int_{A_{k}}\mathcal{N}\Big{(}u\mid H_{k}^{-1}b_{k},H_{k}^{-1}\Big{)}\,du. \tag{8}\]  
Taking \(p_{0}(u)\equiv\mathcal{N}\Big{(}u\mid H_{k}^{-1}b_{k},H_{k}^{-1}\Big{)}\), we observe that the above integral is exactly the one given in Equation (7). Thus, we can directly use the EPMGP algorithm to obtain an estimate for the Gaussian probability in Equation (8). Another benefit of using the EPMGP algorithm is its exceptional accuracy when the constraint set is rectangular, which coincides with our setup, where \(A_{k}=\prod_{l=1}^{d}[a_{k}^{(l)},b_{k}^{(l)}]\) is a \(d\)-dimensional hyper-rectangle.
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 8 Conclusions  
In this work, we have presented a model for determining the wireless channel usage efficiency for the SCHC standard on LoRaWAN technology. We showed that the model is accurate when compared to efficiency measurements from experimental results. Furthermore, we showed theoretically and experimentally that the efficiency decreases when the probability of error increases, and that this is true for all spreading factors. According to the results, there is no linear relationship between the spreading factor and efficiency since spreading factor 10 presents an atypical behavior resulting in an efficiency reduction compared to a hypothetical linear behavior. In summary, the efficiency is a consequence of the transmission period for each SCHC Fragment, which itself depends on the spreading factor and the error probability, whereas the window size does not apparently have a notable influence.