esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging
###### Abstract  
This paper presents a sparse superresolution approach for high cross-range resolution imaging of forward-looking scanning radar based on the Bayesian criterion. First, a novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more accurate than the conventional convolution model. Then, based on the Bayesian criterion, the widely-used sparse regularization is considered as the penalty term to recover the target distribution. The derivation of the cost function is described, and finally, an iterative expression for minimizing this function is presented. Alternatively, this paper discusses how to estimate the single parameter of Gaussian noise. With the advantage of a more accurate model, the proposed sparse Bayesian approach enjoys a lower model error. Meanwhile, when compared with the conventional superresolution methods, the proposed approach shows high cross-range resolution and small location error. The superresolution results for the simulated point target, scene data, and real measured data are presented to demonstrate the superior performance of the proposed approach.  
forward-looking imaging; Bayesian criterion; sparse regularization +
Footnote †: journal: Sensors
## 5 Conclusions  
In this work, a novel general definition of connectivity factor was proposed to measure the connection degree of the inner voids of a porous structure. Specifically, the connectivity factor was defined as the inverse of tortuosity, which was evaluated as a function of porosity for several regular isotropic structures of spherical particles in both non-overlapping and overlapping cases by computational fluid dynamics techniques. The considered structures are as follows: Simple Cubic (SC), Body-Centred Cubic (BCC), Face-Centred Cubic (FCC), and Tetragonal structure (i.e., mono-disperse CaF\({}_{2}\)-type), whose morphology was deeply analysed.  
A novel morphological parameter, the here-called _Limit Porosity Value_ (\(\epsilon_{Lim}\)), was identified and evaluated numerically for each structure considered. Such a parameter represents the porosity value below which all the inner voids are completely disconnected from each other, which corresponds to a condition of infinite tortuosity and, thus, zero connectivity.  
After providing a comparison between the tortuosity trends calculated in the present work and some correlations of literature, the connectivity of all of the considered structures was calculated as a function of porosity. Such a trend has revealed that the connectivity of the BCC structure is the highest among the analysed structures, followed by FCC and SC, which show similar trends in the extreme limit values of porosity close to unity and close to zero. Within these limits, the connectivity of the FCC structure is found to be higher than SC. We highlight that, interestingly, they show a very similar value of \(\epsilon_{Lim}\). As for the tetragonal structure, it was found to show the lowest connectivity with respect to the others.  
The proposed approach, which is applicable also to studies where tortuosity is evaluated experimentally, represents a systematic and general methodology to easily define and measure the connectivity degree of whatever structure, providing a powerful tool for morphological investigation on porous media membranes.  
Figure 14: Connectivity Factor \(\varphi\) as a function of porosity for all the considered structures.  
**Acknowledgments:** [PERSON] has received funding for this research through the \"Programma Per Giovanni Ricercatori \"Rita [PERSON]\" granted by the \"Ministero dell'Istruzione, dell'Universita e della Ricerca, MIUR\" (Grant no. PGR12 BV33A), which is gratefully acknowledged.  
**Author Contributions:** [PERSON] performed the simulations on the BCC, FCC and Tetragonal structures and wrote the paper with input from all Authors. [PERSON], [PERSON] and [PERSON] carried out the data analysis. [PERSON] performed the simulations on the SC structures. [PERSON] contributed to structure construction and data analysis. [PERSON] contributed to the computational settings for the solution of the diffusion problem. [PERSON] devised the study, supervised the manuscript progress and introduced the presented novel morphological parameters (connectivity factor and limit porosity value).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Background
### From BIM Models to Digital Twins  
Building Information Modeling (BIM) can be positioned in a central position in the digital transformation of the Architecture, Engineering, and Construction (AEC) industry and connects different information technologies working together throughout the whole infrastructure's life cycle [57]. BIM is also considered to be related to DTs [58]. The concept of DTs has been described by the National Aeronautics and Space Administration (NASA) as \"a comprehensive, multi-physics, multi-scale, probabilistic simulation of a vehicle or system that leverages state-of-the-art physical models, sensor data, fleet history, and more to replicate the entire lifespan of its airborne counterpart. This realistic simulation considers one or multiple critical and interconnected vehicle systems\" [59]. Over time, the concept of DTs has been extended to different sectors, such as manufacturing [60; 61], healthcare [62; 63], aerospace and defense [64; 65], energy [66; 67], transportation [68; 69], mining [70; 71], smart cities [72; 73], and environmental sciences. In this last application, DTs are used for simulating and monitoring ecosystems [74], weather patterns [75], and climate change [76]. The main output of these applications is related to understanding and mitigating environmental impacts [77].  
A wide conception of DTs can be identified, encompassing a digital representation of a real-world system implemented through software or models mirroring a physical object, process, organization, person, or another abstraction [78]. Developing a DT imposes a set of requirements to achieve a complete result confirming the previous definitions of the DT. A main requirement of DT developing is achieving a fully integrated data flow between the physical and digital objects in both directions [79]. The seamless integration of processes results in an automated cycle, enabling interoperability with the physical system.  
BIM models are foundational to the development of DTs. They provide a detailed digital blueprint that, when integrated with IoT and real-time data, evolves into a dynamic DT. This evolution enhances the value of the information model by extending its utility from the planning and construction phases into the entire lifecycle of the asset, including operation, maintenance, and eventual decommissioning.
## 4 Results
### Quantitative Acceptability Study
#### 4.2.2 Analysis at D + 21  
After 21 days (D + 21), users still condition their intention to use the technology on sufficient facilitating conditions and effort expectations. However, they add to these two elements an attribution of utility to the technology, since it is observed that performance expectations determine the intention to use 21 days later, which was not the case 21 days earlier (Figure 6 and Table 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Performance** & **Effort** & **Social** & **Facilitating** & **Behavioral** \\  & **Expectancy** & **Expectancy** & **Influence** & **Conditions** & **Intention** \\ \hline Performance & — & & & & \\ Expectancy & 0.555 * & — & & & \\ Effort Expectancy & 0.555 * & — & & & \\ Social Influence & & & — & & \\ Facilitating & 0.735 *** & 0.523 * & — & & \\ Conditions & & & & 0.473 * & — \\ Behavioral & & & & & \\ Intention & & & & & \\ \hline \hline \end{tabular} Note.* \(p<0.05\), *** \(p<0.001\).  
\end{table}
Table 4: Correlation Matrix UTAUT dimensions—SHERPAM project D0.  
Figure 5: Correlation between the different dimensions of the UTAUT at D0—SHERPAM project. At D0, two variables are related to Behavioral Intention: Effort Expectancy and Facilitating Conditions. There was no effect of moderators on the results at D0.  
Figure 6: Correlation between the different dimensions of the UTAUT at D + 21—SHERPAM project. The main difference is related to expected performance: this relationship is significant at D + 21, whereas it was not at D0. Moreover, at D + 21 the same correlation between Effort Expectancy/Behavioral Intention and Facilitating Conditions/Behavioral Intention as at D0 was found. There was no effect of moderators on the results at D + 21.  
Despite prior user testing, the technology offered to users for 21 days did not work optimally. As a result, the participants' performance expectations were disappointed. As a result, they rated the technology as less useful compared to their initial judgment.  
The analysis of comparisons of each of these variables provides insight into the pre-used judgment versus the post-used judgment (Table 6). The Student's \(t\)-statistics confirm this point of view. It is observed that judgments about expected performance, as well as facilitating conditions and intention to use, become less important in people's judgments. Thus, the judgment about the expected performance of the technological device loses one average point after use compared to the judgment made before use.  
Overall, the intention to use the SHERPAM platform decreased between D0 and D + 21, an evolution strongly linked to the important decrease in judgments of the expected performances of the device. This decrease is the result of the numerous dysfunctions during this 21-day test period.
## 3 Results
### GVF to NDVI Trend Changes

Table 4: Comparison of 4 datasets (g-NDVIg, 3g-NDVIg, ST-STAR, V-VIP) in the trends of the interannual maximum annual green vegetation fraction (MGVF), GVF greening period duration, start date, end date for all IGBP land cover classes and all land pixels (Total) from 1982 to 2008. Significant negative trends (italics) and positive trends (bold) were estimated using a t-test (with a 95% confidence level). In order to compare to the NDVI-based phenology, Table 4 shows how GVF-based phenology is changing per land cover class for each product. The differences between the NDVI- and GVF-based phenology trends (Tables 3 and 4) are large and similar in magnitude to the mean trend values. However, the overall results towards increased duration of season due to a delayed end of season over most vegetation classes remain the same for VIP, STAR, and NDVIIg. For MGVF, deciduous broadleaf forests, mixed forests, woody savannah and mixed cropland-vegetation areas all have agreement on an increase in MGVF with at least 3 significant datasets (Table 4). While our study shows general agreement on positive trends in most vegetation classifications, the study of [PERSON] _et al._[14] showed negative trends over evergreen forests, mixed forests and urban classes and positive and generally larger trends were seen over deciduous needleleaf, open shrubland, savannah, grassland, wetland and cropland classes. However, a direct comparison cannot be made since our study region only includes a small subset of the global analysis they completed.
# Noise Impact Analysis of School Environments Based on the Deployment of IoT Sensor Nodes
## 2 Methods  
During the execution of this research, we followed the methodological steps depicted in Figure 1. Specifically, as a first step, we identified the school units where recent renovation activities had been performed to isolate the buildings and transform them into constructions that are environmentally friendly. Although this is the main governmental guideline regulation for old buildings built during the last 50 years, noise immunity is something that is not always considered throughout the renovations or reconstructions.  
Within this scope, we have selected a high school and a senior high school complex that reside nearby and have been energy upgraded. Specifically, these buildings have been isolated with double-glass windows and shell thermal insulation of facades, which translates to higher noise immunity than relevant infrastructures of other schools. Therefore, the key point that requires further exploration is if these building improvements have considerably mitigated noise impacts. As a second step, we have selected the suitable acoustic sensors and constructed the IoT sensor nodes to transmit data to an IoT platform and store it in a time-series database. The next step was to perform tests and calibrations on the noise sensors and further adjustments on the selected locations where the IoT sensor nodes had been placed. The sensor outputs are collected and visualized in IoT dashboards  
Figure 1: Methodology steps.  
that adequately monitor the sensor levels. Data export, limitation concerns, and statistical analysis are performed to evaluate the measurements and examine the sensor levels. The Kruskal-Wallis H test and the Mann-Whitney U test have been facilitated to analyze the received sensor data and assess the noise during the class breaks.
# Tomographic Diffractive Microscopy: A Review of Methods and Recent Developments
## 2 Theoretical Background
### The Principle of Tomographic Diffractive Microscopy  
An incident electromagnetic wave of \([\mathbf{E}_{\text{inc}},\mathbf{H}_{\text{inc}}]\) interacts with a sample in vacuum that occupies a bounded region \(\mathbf{V}\) in three-dimensional space and a relative permittivity \(\varepsilon(\mathbf{r})\) for \(\mathbf{r}\in\mathbf{V}\), \(\varepsilon(\mathbf{r})\)\(=1\) for \(\mathbf{r}\
otin\mathbf{V}\). By deducing the Maxwell equations, the total scalar field satisfies:  
\[\
abla\times\
abla\times\mathbf{E}(\mathbf{r})-\varepsilon(\mathbf{r})\mathrm{ k}_{0}^{2}\mathrm{E}(\mathbf{r})=0 \tag{1}\]  
where \(\mathrm{k}_{0}=2\pi/\lambda\) is the wave number, where \(\lambda\) is the wavelength in vacuum. We transform Equation (1) as:  
\[\
abla\times\
abla\times\mathbf{E}(\mathbf{r})-\mathrm{k}_{0}^{2}\mathrm{E}( \mathbf{r})=\mathrm{k}_{0}^{2}\chi(\mathbf{r})\mathrm{E}(\mathbf{r}) \tag{2}\]  
where \(\chi(\mathbf{r})=\varepsilon(\mathbf{r})-1\) is the contrast of permittivity. The method for solving Equation (2) is to find the Green's function, i.e., to find the solution of the corresponding differential equation with a Dirac delta inhomogeneity:  
\[\
abla\times\
abla\times\mathbf{G}(\mathbf{r},\mathbf{r}^{{}^{\prime}})- \mathrm{k}_{0}^{2}\mathbf{G}(\mathbf{r},\mathbf{r}^{{}^{\prime}})=\mathbf{I} \delta(\mathbf{r}-\mathbf{r}^{{}^{\prime}}) \tag{3}\]  
where \(\mathbf{I}\) is the identity matrix. This Green's function is known as the free-space dyadic [PERSON]'s function:  
\[\mathbf{G}(\mathbf{r},\mathbf{r}^{{}^{\prime}})=\left[\mathbf{I}+\frac{1}{ \mathrm{k}_{0}^{2}}\
abla\
abla\right]\frac{\mathrm{e}^{\mathrm{i}\mathrm{k}_{0 }|\mathbf{r}-\mathbf{r}^{{}^{\prime}}|}}{4\pi\left|\mathbf{r}-\mathbf{r}^{{}^ {\prime}}\right|} \tag{4}\]  
By solving Equation (2), we obtain the integral equation as:  
\[\mathbf{E}(\mathbf{r})=\mathbf{E}_{\text{ref}}(\mathbf{r})+\mathrm{k}_{0}^{2 }\int\limits_{\mathrm{V}}\mathbf{G}(\mathbf{r},\mathbf{r}^{{}^{\prime}})\chi( \mathbf{r}^{{}^{\prime}})\mathbf{E}(\mathbf{r}^{{}^{\prime}})\mathrm{d}\mathrm{V} \tag{5}\]  
where the reference field \(\mathbf{E}_{\text{ref}}(\mathbf{r})\) consists of the field without a research object and is a special solution to the homogeneous equation obtained by setting \(\varepsilon(\mathbf{r})\) to the homogeneous background, i.e., \(\varepsilon(\mathbf{r})-1\). The scattered field \(\mathbf{E}_{\text{sca}}(\mathbf{r})\) is the difference between the total field and the reference field:  
\[\mathbf{E}_{\text{sca}}(\mathbf{r})=\mathrm{k}_{0}^{2}\int\limits_{\mathrm{V} }\mathbf{G}(\mathbf{r},\mathbf{r}^{{}^{\prime}})\chi(\mathbf{r}^{{}^{\prime}}) \mathbf{E}(\mathbf{r}^{{}^{\prime}})\mathrm{d}\mathrm{V} \tag{6}\]  
Notice that the conventional tomographic diffractive microscopy approach neglects the polarization effects induced by the object and the setup, so that the scalar approximation used for the field and Equation (6) can be rewritten as a scalar propagation equation in an inhomogeneous medium.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 4 Experiments
### Experimental Environments  
We conducted experiments in the convention center environment expressed in Figure 10 to apply the proposed framework. The specified working environment was a size of 5000 m\({}^{2}\) with dynamic pedestrians. In Figure 9(a), a red circle represents the robot's home position, and a translucent red polygon represents the area where the robot performed the mission. Additionally, Figure 9(b),c respectively represent the home position and the working area expressed in Figure 9(a). The robot used Stereolabs' ZED camera and Velodyne's VLP16 to recognize the environment. The SIP and SAN modules of the framework were operated in a ROS Kinetic with Ubuntu 16.04 environment on Intel Zotac NUC En1070 with [EMAIL_ADDRESS] GHz.  
The environmental database, illustrated in Figure 11, for the first floor in the convention center consisted of 23 instances from three subclasses of _Object_ and 16 instances from nine subclasses of _Place_. Although there were many objects on the first floor, we selected only static objects that can be worked as dominant features for recognizing environments; We defined this property as _isKeyObject_ in the implicit  
Figure 10: Experimental environment.  
Figure 9: The Deep Deterministic Policy Gradients (DDPG) structure used to approximate an autonomous navigation policy.  
model for objects. In the case of objects which were not key objects (e.g., pedestrian), we considered the objects only in the short term memory for the efficiency of memory consuming. By doing so, the database consumed memory less than 0.1 MB in size, although it covered the large environments.
# Quantum Modular Adder over \(GF(2^{n}-1)\) without Saving the Final Carry
## 2 Related Works
### Residue Number System  
The Residue Number System (RNS) has been widely applied to efficient carry-free arithmetic operations in classical computers without the carry propagation problem [28; 29; 30]. RNS-based arithmetic modulo \(2^{n}-1\) computation is one of the most common RNS operations that is used in pseudorandom number generation and various cryptographic algorithms [31; 32]. The basic idea for modulo \(2^{n}-1\) operation is to use the constant value \(-(2^{n}-1)\) to be added by the sum of two numbers \(A\) and \(B\), where the two inputs \(A\) and \(B\) are to be in the range \(\{0,2^{n}-2\}\).  
Given the two \(n\)-bit inputs \(A=a_{n-1},...,a_{0}\) and \(B=b_{n-1},...,b_{0}\), where \(0\leq A,B<2^{n}-1\), the modulo \(2^{n}-1\) of \(A+B\) can be represented as follows:  
\[(A+B)\;mod\;(2^{n}-1)=\begin{cases}A+B-(2^{n}-1),&if(A+B)>2^{n}\\ A+B,&otherwise.\end{cases} \tag{1}\]  
The sum of A and B over binary system-based computation can be an \(n+1\)-bit output \(C=c_{n},c_{n-1},...,c_{0}\) because of the carry. The most significant bit, \(c_{n}\), saves the final carry of \(a_{n-1}\) and \(b_{n-1}\), where \((A+B)>2^{n}\), such as the first case in Equation (1). \(A+B-2^{n}\) is equivalent to removing the most significant bit, \(c_{n}\), from the bit sequence \(C=c_{n},c_{n-1},...,c_{0}\), where \(A+B-(2^{n}-1)=(A+B-2^{n})+1\).
# Adaptable and Automated Small UAV Deployments via Virtualization
## 4 Validation of the Solution
### Validation Scenario
The configuration operations for each lightweight VNF, as well as the virtual resources required for their execution, are summarized in Figure 4.  
Once the deployment and the configuration of the lightweight VNFs was completed, we connected a wireless VoIP phone ZyxEL Prestige 2000W to each of the access points offered by our NS, with the objective of carrying out a VoIP call. In addition, a commodity laptop was connected to the access point involved in the operations related with the exchange of telemetry information, emulating an equipment at the GCS. Figure 4(a),b illustrates the traffic exchanged during the VoIP call established by the VoIP terminals, including the DNS and the SIP signalling messages needed to execute the call. The figure also reflects the voice traffic transmitted and received by one of the wireless phones. The call was made without errors and with appropriate sound quality. On the other hand, Figure 4(c) represents the control traffic generated and received periodically by the MANO system, which is exchanged in order to monitor and synchronize the information of the available/consumed resources at each compute node. This traffic was measured to get an insight on how control traffic operates in our platform once an NS is being executed, and to verify how this traffic may affect the provided service. As expected, the measurements show that this traffic is negligible (with an average of 20.65 kbps) compared with the traffic exchanged during the execution of an NS, e.g., during the VoIP call. Finally, Figure 4(d) shows the bandwidth consumption of a telemetry stream received at the ground control station (i.e., at the laptop), originated by the Telemetry VNF of one of the SUAVs.
## 2 Methodologies
### The Principles of Phase Space Reconstruction  
The basic concept of phase space reconstruction is to treat the value of a specific variable at a certain moment and those values after \(\tau\), \(2\tau\),..., \((m-1)\tau\) time intervals as coordinates of a special point in \(m\)-dimension phase space [35]. A graphical representation of a nonlinear system can be obtained through describing a sequence of data points in the above phase space. An \(m\)-dimensional phase space can be constructed from a single time series variable \(x_{1},\ x_{2},\ \ldots,\ x_{N}\) by introducing the embedding dimension parameter \(m\) and delay time \(\tau\):  
\[X_{i}=[x_{i},\ x_{i+\tau},\ \ldots,\ x_{i+(m-1)\tau}]^{T} \tag{1}\]  
where \(i=1,\ 2,\ \ldots,\ L,\ L=N-(m-1)\tau,N\) is the number of sample points. Then, a phase space matrix can be obtained, which represents the coordinates of the signal trajectory:  
\[\mathrm{X}=\left[\begin{array}{c}X_{1}\\ X_{2}\\ \vdots\\ X_{L}\end{array}\right]=\left[\begin{array}{c}x_{1},\ x_{1+\tau},\ \ldots,\ x_{1+(m-1)\tau}\\ x_{2},\ x_{2+\tau},\ \ldots,\ x_{2+(m-1)\tau}\\ \vdots\\ x_{L},\ x_{L+\tau},\ \ldots,\ x_{L+(m-1)\tau}\end{array}\right]^{T} \tag{2}\]  
In essence, the phase space reconstruction trajectory is the strange attractor of a time series data in a chaotic system [36], e.g., electroencephalogram, short time power load, and stock exchange. In this paper, the disturbance components carried by periodical voltage signals are considered and a \(x(t)-x(t+\tau)\) phase space is constructed to describe the signal trajectory. Hence, the 1D time series data of PQDs are mapped into 2D images, which serve as the input data to the CNN model for PQD classification. By comparing the traditional time-frequency-based feature extraction methods, the PQDs can be identified from a graphic perspective in the time domain, which overcomes the spectrum aliasing problem in the time-frequency transforming process.