esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of LoRaWAN for IoT: From Technology to Application
## 2 Introduction to LoRa and LoRaWAN
### LoRa
Optionally, the end of preamble can include another two unmodulated base chirps that will be used for fine time and frequency synchronization [1]. The structure of the preamble is shown in Figure 0(a), while Figure 0(b) shows the spectrum capture of a LoRa packet where upchirps at the beginning of preamble are easily noticeable. Note that transmitter and receiver should know the SF in advance in order to detect the preamble, as preamble size scales with SF and there is no single preamble for all SFs [1]. Optionally, the preamble is followed by a physical header. In such a case, the header contains the payload length in bytes, the FEC code rate of the payload and the header CRC. The header is always protected with FEC with highest code rate of 4/8. If these three parameters are known in  
Figure 1: LoRa preamble. advance, the header can be removed completely. This decreases the time on air of the packet. In this case, the implicit header mechanism is applied, where the header parameters are fixed beforehand at both ends, the receiver and the transmitter side. The payload contains either LoRaWAN MAC layer control packets or data packets. Optionally, the payload can be followed by a payload CRC. The frame structure is shown in Figure 2.  
As previously said, the LoRa modulation mechanism has been patented and its implementation is never made public. The first mathematical description on how LoRa modulation works is presented in [4]. The authors described the LoRa modulation as Frequency Shift Chirp Modulation (FSCM) and give the mathematical model for it. The LoRa physical layer operates in the 433-, 868- or 915-MHz frequency bands. In Europe, only the 868- and 433-MHz bands can be used. In the 868-MHz band, there are three 125-kHz channels that are mandatory to be implemented in every end device. There are another five 125-kHz channels in the 867-MHz sub-band that can be optionally used for LoRa communication [5]. In Europe due to transmission regulations [6], each transmission in any of the 868 MHz and 867 MHz sub-bands should comply with a 1% radio duty cycle or implement a listen-before-talk or adaptive frequency agility mechanism. When the duty cycle regulation is followed, it means that, if the radio transmitted for 1 s, it cannot transmit for the next 99 s.
# Genetic Algorithm as a Tool for the Determination of the Self-Stress States of Tensegrity Domes
## 1 Introduction
In recent years, in the process of searching for a stable configuration of tensegrity structures, evolutionary optimization techniques have been increasingly used. These methods look for better solutions, modeling their operation on mechanisms occurring during natural evolution. One of the optimization techniques is the genetic algorithm. This algorithm, inspired by the process of natural selection and genetics, was first presented by [PERSON] [40]. In the case of tensegrity, the genetic algorithm is mostly used as a form-finding method for regular [27; 41; 42; 43] and irregular [44; 45; 46] structures. The methods mentioned above are mainly used to shape the structure, but they can also be used to search for the self-balanced forces (force finding) of the existing structure [47; 48; 49; 50]. In [49], the genetic algorithm is used to determine the self-stress states. In this paper, flat structures and a three-dimensional double quadruplex module were considered. In the case of cable domes, the literature lacks a study using the genetic algorithm to search for the self-stress states. The authors of this study intend to fill that gap. In this paper, the genetic algorithm and the spectral analysis of compatibility matrix are used. The obtained results are compared with those documented in the literature in order to verify the effectiveness of the present method. Three selected tensegrity domes are considered, i.e., Geiger, Levy and Kiewitt. The rest of the paper is organized as follows: Section 2 describes two approaches used for the identification of the self-stress states, i.e., the spectral analysis of compatibility matrix and the genetic algorithm. In Section 3, in order to compare the proposed methods, the solution procedures are presented on the example of a simple two-dimensional truss. Furthermore, the results for three tensegrity domes are provided in Section 4. The calculation procedure was written in the Python language. The genetic algorithm was implemented using pyGAD (Python library) [51]. Finally, in Section 5, some conclusions are drawn.
## 2 Robot Manipulator Modeling  
A robot manipulator is a highly nonlinear, uncertain, complex, multi-degree-of-freedom (DOF) system. Therefore, modeling this system is a major challenge in the design of a model-based technique for FDC. The mathematical model of a robot manipulator based on the Lagrange equation is represented as follows:  
\[\tau-\tau_{f}=\alpha(q)\ddot{q}+\beta\big{(}q,\dot{q}\big{)}\dot{q}+\gamma(q)+\Theta. \tag{1}\]  
Here, \(\tau,\tau_{f},\alpha(q),\beta\big{(}q,\dot{q}\big{)},\gamma(q),\Theta\), and \(q\) are the torque, fault torque, inertial matrix, Coriolis and centrifugal (nonlinear) matrix, gravity vector, robot manipulator fault and manipulator joint vector, respectively.Therefore, based on Equation (1), the expression for \(\big{(}\tau-\tau_{f}\big{)}\) requires the acceleration of the robot manipulator. To rule out the need for acceleration, a low pass filter (LPF) and the integral term of Equation (1) are used to derive the following equation:  
\[\tau_{LPF}=\psi\int(\text{T}-\tau_{LPF})dt, \tag{2}\]  
where \(\tau_{LPF},\text{T}\) and \(\psi\) are the torque LPF, the difference between the torque and fault torque, and the coefficient to find the cut-off frequency, respectively. Based on Equations (1) and (2), the acceleration matrix is represented as follows:  
\[\frac{d}{dt}\big{(}\alpha(q)\dot{q}\big{)}=\dot{\alpha}(q)\dot{q}+\alpha(q) \ddot{q}, \tag{3}\]  
Based on the skew-symmetry of \(\dot{\alpha}(q)-2\gamma\big{(}q,\dot{q}\big{)}\), and according to [28], it follows that:  
\[\dot{\alpha}(q)=\beta^{T}\big{(}q,\dot{q}\big{)}+\beta\big{(}q,\dot{q}\big{)}+ \gamma(q), \tag{4}\]  
Thus, the LPF torque is represented by Equation (5):  
\[\tau_{LPF}=\psi\bigg{\{}\int(\gamma(q)-\beta^{T}\big{(}q,\dot{q}\big{)}\dot{q }-\tau_{LPF}+\Theta)\bigg{\}}dt+\alpha(q)\dot{q}. \tag{5}\]  
In a real application, the LPF torque can be calculated using the following equation:  
\[\dot{\tau}_{LPF}=\psi\bigg{\{}\int(\hat{\gamma}(q)-\hat{\beta}^{T}\big{(}q, \dot{q}\big{)}\dot{q}-\tau_{LPF}+\hat{\Theta})\bigg{\}}dt+\dot{\alpha}(q) \dot{q}. \tag{6}\]  
Here, \(\dot{\tau}_{LPF},\dot{\alpha}(q),-\hat{\beta}\big{(}q,\dot{q}\big{)},\hat{ \Theta}\), and \(\hat{\gamma}(q)\) are the estimated LPF torque, the estimated inertial matrix, the estimated Coriolis and centrifugal (nonlinear) matrix, the fault estimation based on the system model, and the estimated gravity vector, respectively. Therefore, the actual and estimated low pass filter torque can be used to find the torque in the robot manipulator without any need to find the acceleration. Based on Equation (6), the mathematical modeling of the robot manipulator includes uncertainties and unknown conditions. To address this drawback, and to increase the accuracy of the signal estimation, an adaptive neuro structure variable observer (ANSVO) can as described in the following section.
## 2 Materials and Methods  
### Cloning and Transfection  
The cloning and transfection procedure has been described earlier [10]. Briefly, total RNA was extracted using a High Pure RNA Isolation Kit (Roche Diagnostics, Monza, Italy) from primary cultures of human keratinocytes stimulated with the LPS of _P. aeruginosa_ and TNF-\(\alpha\) in order to obtain a high production of antimicrobial peptides. It was subsequently transcribed into complementary Cdna, and two pairs of degenerate primers [10] designed on their specific amino acid were used to amplify, by RT-PCR, genes coding HBD-2 and HBD-3 with FastStart High Fidelity (Roche Diagnostics). The amplified DNA fragments were subjected to restriction and sequencing analysis and cloned into the pEF/V5-HIS TOPO (Invitrogen, Carlsbad, NM, USA) vector using the T4 DNA ligase (Invitrogen, USA), in accordance with the manufacturer's protocol and then transformed into _Escherichia coli_ TOP 10 (Invitrogen, USA). The cloning vectors, pEF/V5-HIS TOPO-HBD-2 and pEF/V5-HIS TOPO-HBD-3, were extracted from the bacterial culture and amplified using a QIAprep Spin Midiprep Kit (QIAGEN, Germantown, MD, USA). Caco-2 cells were transfected using the IBAfect reagent (IBA), according to the manufacturer's manuals. After incubation, the success of the experiment was verified by the extraction of mRNA from treated cells and by the amplification of HBD-2 and HBD-3 genes by PCR [10]. Cell-free supernatants of the transfected cells were recovered by centrifugation and assayed for the HBD-2 and HBD-3 concentration by an enzyme-linked immunosorbent assay (Phoenix Pharmaceuticals, Burlingame, CA, USA). For blasticidin selection, untransfected and transfected cells were cultured for 14 days in the presence of the following increasing concentrations of blasticidin S (Sigma-Aldrich, Darmstadt, Germany): 5, 10, 20, 50, 100 and 250 \(\mu\)g/mL. Thereafter, MTT labelling reagent was added at a final concentration of 0.5 mg/mL [10].
## 3 Evaluation of Experimental Results  
### Macrographic Examinations  
Although the welding deposition operation ensures material continuity, the welded area does not exhibit a homogeneous structure. As a result of heating, melting of the filler material, partial melting of the base material (BM), and subsequent cooling, various structural transformations occur.  
Macroscopic examinations of the samples hardfaced under the specified technological conditions aimed the geometry of the depositions and any defects such as porosity and cracks. For these investigations, samples were sectioned perpendicular to the deposition. After grinding with metallographic paper CarbiPro Silicon Carbide Abrasive Paper from 60 grit to 1200 grit and further polished with diamond suspension from 9 to 1 \(\upmu\)m, the samples were chemically etched with the Villela reagent.  
In Figure 2, macroscopic images of samples hardfaced with one, two, and three layers are presented. These images highlight the corresponding geometry of the deposited metal (layer) zone and the absence of material continuity defects. Since austenitic steel does not exhibit polymorphic transformations, it is not possible to achieve a fine-grain finish in the heat-affected zone (HAZ), and the large grains resulting from each pass seem to impede each other's growth.
# Could the 'Spinner Innovation' and 'Triple Helix' Models Improve System Innovation?
## 2 Theoretical Background
### Triple Helix and Spinner Models
#### 2.2.2 Spinner Innovation Model  
The Spinner Innovation Model (Figure 1) set out a current and dynamic model for analyzing the propensity for innovation in SMEs. This model has already undergone application in different scientific fields, including management, information system and economics, and has both bolstered the understanding of and contributed to the European Commission in terms of regional development, performance and economic growth [46].  
The model's main contribution stems from analyzing and understanding the propensity to innovate in SMEs based on three variables, knowledge creation, knowledge transfer and knowledge intensive-solutions [47]. The model incorporates the dimensions of sector, SME and business to support the analytical approach. Firstly, the model identifies the sector, including the first, second and third economic sectors. Subsequently, the model analyses the variables in order to grasp the propensity to innovate, including private and public  
Figure 1: Spinner Innovation Model.  
knowledge. The knowledge intensive solutions provided may derive from professional, technological or cultural knowledge and the typologies of innovation span the product, its marketing, the business model and processes [2].  
In terms of an example, the technology service sector strives to improve innovation as a driver of the strategy to obtain greater competitiveness. However, this clearly requires understanding the propensity to innovate and identifying any shortcomings in the variables supporting future business innovation processes. Furthermore, the model returns different results as per the individual variables and/or all the variables integrated into the model. In addition, this contributes further value in terms of the knowledge needed for firms integrating into partnerships involving private and public knowledge [48]. The Spinner Model was supported by the Spinner Flow concept that deploys several technological tools for structuring innovation processes [46].
###### Abstract  
This research article describes a technique for realizing wideband dual notched functionality in an ultra-wideband (UWB) antenna array based on metamaterial and electromagnetic bandgap (EBG) techniques. For comparison purposes, a reference antenna array was initially designed comprising hexagonal patches that are interconnected to each other. The array was fabricated on standard FR-4 substrate with thickness of 0.8 mm. The reference antenna exhibited an average gain of 1.5 dBi across 5.25-10.1 GHz. To improve the array's impedance bandwidth for application in UWB systems metamaterial (MTM) characteristics were applied it. This involved embedding hexagonal slots in patch and shorting the patch to the ground-plane with metallic via. This essentially transformed the antenna to a composite right/left-handed structure that behaved like series left-handed capacitance and shunt left-handed inductance. The proposed MTM antenna array now operated over a much wider frequency range (2-12 GHz) with average gain of 5 dBi. Noched band functionality was incorporated in the proposed array to eliminate unwanted interference signals from other wireless communications systems that coexist inside the UWB spectrum. This was achieved by introducing electromagnetic bandgap in the array by etching circular slots on the ground-plane that are aligned underneath each patch and interconnecting microstrip-line in the array. The proposed techniques had no effect on the dimensions of the antenna array (20 mm \(\times\) 20 mm \(\times\) 0.87 mm). The results presented confirm dual-band rejection at the wireless local area network (WLAN) band (5.15-5.825 GHz) and X-band satellite downlink communication band (7.10-7.76 GHz). Compared to other dual notched band designs previously published the footprint of the proposed technique is smaller and its rejection notches completely cover the bandwidth of interfering signals.  
bandgap rejection; electromagnetic bandgap (EBG); metamaterials (MTM); composite right/left-handed structures (CRLH); ultra-wide band (UWB); antennas +
Footnote †: journal: _Micromachines_
## 4 Discussion and Conclusions
In summary, the ambient noise analysis implemented in this study for the city of Durres can be considered efficient and cost effective and confirms its ability to provide reliable results for seismic microzonation purposes in extended metropolitan areas. However, some assumptions made in this work (e.g., 1D consideration of subsoil profile, linear soil behavior) may constitute limitations of the research carried out and consequently additional effort is needed to improve our findings. Finally, the 1D ambient noise synthetics HVSRs, in comparison to actual HVSRs at the same sites, can effectively validate 1D geophysical profiles for use as seismic response input to any earthquake excitation scenario. In the case of Durres, the 1D approximation seems to be satisfactory. However, seismic site response corrections due to 2D/3D effects and the attenuation factor (Q) of the upper geologic layers must be carefully investigated to realistically assess future ground shaking in Durres. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122211309/s1](https://www.mdpi.com/article/10.3390/app122211309/s1)]([https://www.mdpi.com/article/10.3390/app122211309/s1](https://www.mdpi.com/article/10.3390/app122211309/s1)). Conceptualization, N.T., E.D., L.D. and N.K. ; Data curation, E.D., L.D., I.G., A.P. and A.H.; Investigation, N.T., E.D., L.D., I.G., A.H., N.K. and R.K.; Methodology, N.T., L.D. ; Software, A.P. ; Supervision, N.T. ; Writing--original draft, N.T. and E. D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data availability StatementData used in this study are available upon request. Processed data can be found in the Supplementary Material attached to this study. The Institute of Geosciences of Polytechnic University of Tirana and the Institute of Engineering Seismology & Earthquake Engineering, ISTAK-EPPO, Thessaloniki, Greece are greatly acknowledged for providing the equipment for field-data acquisition. [PERSON] substantially improved the English of the manuscript. The remarks and suggestions of two anonymous reviewers and of the Academic Editor have also significantly improved the manuscript. The authors declare no conflict of interest.
# A High-Resolution Texture Mapping Technique for 3D Textured Model
## 1 Introduction
[32] further applied mean-value weights for the barycentric map to preserve the shape of 2D meshes. For all above-mentioned barycentric mapping, the boundary is fixed and the numerical complexity is linear, which is not suitable for texture editing. For texture mapping, a method of free boundary is more appropriate as it can ensure that the boundary of each island of 2D meshes is close to the real profile, making the texture editing easy. Some other approaches have focused on minimizing the area distortion [33] and distance [34]. For texture map generation, the main idea is to deal with the texture transferring problem. [PERSON] et al. [35] proposed a texture transferring method by identifying the most appropriate image source for a group of meshes. They also minimized the color inconsistency at the transition of two different groups and synthesized the invisible meshes using the color of neighboring pixels. [PERSON] et al. [36] proposed a method to extract and render the texture dynamically. The extraction was implemented by horizontally scanning the pixels and rendering every color onto the meshes. [PERSON] [37] proposed a blending method to handle the color difference between two different images. The images were separated into high and low bands; the low band images were averaged to minimize the color difference, whereas the high band images were kept to preserve the outline profile. In addition, texture synthesizing is commonly used to improve the transition between different textures. [PERSON] et al. [38] proposed an image quilting method to quit together different texture patterns. They extended the boundary of each original pattern and calculated the minimum color difference on the overlapping area to find the new boundary between two patterns. [PERSON] et al. [39] proposed an algorithm to synthesize the texture pattern based on deterministic searching and use tree-structured vector quantization to improve the efficiency. These two approaches focus mainly on the transition synthesis between two texture patterns.
## 2 Materials and Methods
### Statistical Analysis  
We estimated the change in mean understory height and biomass before the fire, immediately after the fire, and five and twelve months after the fire for the field-plot measurements. A linear mixed-effect model with a nested ANOVA was used (the 'lme' package in RStudio) to examine how understory heights and biomass differed among different periods with plots nested within the wet and dry soil moisture groups. The TukeyHSD test was used to test for differences in height/biomass between different periods. Similarly, for LiDAR, we used the same model to contrast the changes in mean understory height before the fire and five months after the fire. A multiple regression model was used to assess the relationship between field height and biomass using soil moisture as a fixed variable. We compared the slopes (using 'pairs' function in 'lsmeans' package in RStudio) between May 2018 and November 2018 understory heights in order to determine whether there were any statistically significant differences between the relationship for the two time periods. For statistical evaluations, we calculated the correlation coefficient (r) to show the deviation between the two estimated height variables. We used absolute and relative root mean square errors (RMSE) (%) to explain the deviation of the predicted and measured heights.  
To assess the difference in height vs. biomass for field and LiDAR measurements, we used a simple linear regression model to compare the relationships (Figure S1). Models (linear or quadratic) were investigated (May 2018, and November 2018, and combined heights) that best described the relationships between the understory field biomass and understory height estimated with LiDAR. For each model, regression assumptions were tested (using 'autoplot' function in 'ggfortify' package in RStudio). Slopes and intercepts comparisons were used to determine the significant difference between the two methods. We summarized the LiDAR-derived plot data using basic statistics for each plot and then used a paired \(t\)-test to compare before and after fire height and imputed biomass values across the plot. Finally, we used power analysis and the variance from LiDAR to estimate the required number of sample plots to detect the change in biomass after the fire.  
Figure 3: Pre- (Green) and Post-fire (Red) LiDAR point clouds for a 6 m sample profile along the transect presented in (**Right**) the overview map as a yellow dash line (**Left**).
# Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning
## 2 Literature Review
### Temporal Hierarchies
(2018) provided different estimators for the \(\mathbf{W}\) matrix in an attempt to bypass the above mentioned limitation, as summarized below:  
* **Hierarchy variance scaling (\(\Lambda_{H}\))** A diagonal matrix that consists of the estimates of the in-sample one-step-ahead error variances of the series that make up the hierarchy. In our example, that would be \(\Lambda_{H}=diag(\hat{\sigma}_{A},\hat{\sigma}_{SA1},\hat{\sigma}_{SA2},\hat{ \sigma}_{Q1},\hat{\sigma}_{Q2},\hat{\sigma}_{Q3},\hat{\sigma}_{Q4})^{2}\). * **Series variance scaling (\(\Lambda_{V}\))** A diagonal matrix that consists of estimates of the in-sample one-step-ahead error variances at each series. In our example, that would be \(\Lambda_{V}=diag(\hat{\sigma}^{[4]},\hat{\sigma}^{[2]},\hat{\sigma}^{[2]},\hat {\sigma}^{[1]},\hat{\sigma}^{[1]},\hat{\sigma}^{[1]})^{2}\). * **Structural scaling (\(\Lambda_{S}\))** A diagonal matrix containing the number of forecast errors contributing to the bottom aggregation level. In our example, that would be \(\Lambda_{S}=diag(4,2,2,1,1,1,1)\). In conclusion, (multiple) temporal aggregation has been widely explored in the literature, but additional research is still required to effectively select the most appropriate temporal aggregation level or define the weights to be used for combining the forecasts produced at multiple levels. The selection of the \"best\" aggregation level is typically done using heuristic rules, while the combination of the forecasts is conducted in a linear fashion, with the combination weights being estimated based on frequency-related information or in-sample forecast errors. We argue that ML methods can be used to tackle these issues, enabling nonlinear estimations of combination weights that focus on post-sample accuracy. Moreover, we claim that the parameters of the combination scheme provided by such methods can be optimized across multiple series, thus allowing for more robust and generalized estimations.