esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation
### Constraint Functions Set for Achieving Complete Decoupling  
A completely decoupled MIMO compliant mechanism implies that each input load produces only one directional output motion without affecting the motions at other ports. However, one input load usually leads to undesired output displacement at non-corresponding ports, which is known as coupling. If the motion appears at the non-corresponding output port, we will call it output coupling, as depicted in Figure 3a. If the motion exists in any other input port, we will call it input coupling, as depicted in Figure 3b. Coupling goes against the precision operation of mechanisms and must be prevented.  
In Figure 3, a load \(F_{li}\) is applied at the \(i\)th input port \(li\) and produces a desired displacement \(u_{OI}\) at the output port \(OI\), whereas in the meantime, the coupled displacements at other ports are unavoidably generated, which are denoted by \(\tilde{u}_{OI,i}\) and \(\tilde{u}_{  
Figure 3: Schematic of the Output coupling (**a**) and the input coupling (**b**) produced by \(F_{li}\).  
the displacement at the output point \(Oj\) (\(j\
eq i\)) produced by \(F_{li}\), and the latter represents the displacement at the input point \(Ij\) (\(j\
eq i\)) produced by \(F_{li}\). To overcome the coupling issue, the following extra constraints are introduced into the optimization formulation:  
\[\delta_{j,i}=\left(\frac{\widetilde{u}_{Oj,i}}{u_{Oi}}\right)^{2}\leq[\delta] \tag{11}\]  
\[\gamma_{j,i}=\left(\frac{\widetilde{u}_{Ij,i}}{u_{Oi}}\right)^{2}\leq[\gamma] \tag{12}\]  
where \(\delta_{j,i}\) and \(\gamma_{j,i}\) denote the output coupling index at \(Oj\) (\(j\
eq i\)) and the input coupling index at \(Ij\) (\(j\
eq i\)) due to \(F_{li}\) at \(Ii\), respectively; both \([\delta]\) and \([\gamma]\) take very small positive numbers in order to suppress the movement coupling.
## 5 Summary and Analysis of Some Simplified Estimation Schemes
### Simplified Estimation--Estimation C  
A complexity efficient detector was also deeply considered in [8]. Actually, \(Y\) is quantized into 1, \(-1\), \(j\) and \(-j\) in this detector, a result later used in [2]. It follows from this that its estimator can be depicted by [10]  
\[\widehat{\varphi}\approx\begin{cases}0,&\text{ if }\quad Re(Y)>0\text{ and }|Re(Y)|\geq|Im(Y)|,\\ \frac{\pi}{2},&\text{ if }\quad Im(Y)>0\text{ and }|Re(Y)|<|Im(Y)|,\\ -\pi,&\text{ if }\quad Re(Y)<0\text{ and }|Re(Y)|\geq|Im(Y)|,\\ -\frac{\pi}{2},&\text{ if }\quad Im(Y)<0\text{ and }|Re(Y)|<|Im(Y)|,\end{cases} \tag{13}\]  
Obviously, Equation (13) can be derived from Equation (11) on condition that the approximation \(\tan^{-1}\left(x\right)\approx 0\) is involved. This avenue achieves an estimator with perfectly acceptable complexity. The detector with it works only for the CFOE approach to four fixed values, i.e., 0, \(\pi/2\), \(-\pi\) and \(-\pi/2\); otherwise, a large error similar to Equation (10) will be introduced. Not surprisingly, this is expected to limit the receiver performance. A quick comparison of Equations (12) and (13) reveals that the estimation error in the latter can be corrected by the former, where two types of adaptive offset terms \(\frac{Im(Y)}{Re(Y)}\) and \(\frac{Re(Y)}{Im(Y)}\) are additionally provided to improve the estimated accuracy of the time-variant undesired parameter \(\varphi\).
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### GPS-Based Systems Integration with Other Sensory Data
#### 4.1.3 Sa-Gps
This can provide more automation in the construction activities which can enhance the productivity and safety of the activities as a long-term objective in the construction industry [46]. Integrating GPS data with aided sensory data can compensate for the limitations in utilizing the individual GPS information. In fact, it would make it possible to capture the big picture of the construction operations by accurately distinguishing between productive, idle and out of service times during construction activity. Furthermore, the idea for using integrated sensors on a mobile phone device can mitigate the difficulties associated with mounting sensors on equipment body (e.g., attachment and detachment of various sensors for every data collection session), dust in a construction site, data storage issues, and unstructured arrangement of resources which makes calibrating of sensors difficult. Furthermore, it can improve inaccurate and unrealistic simulation models which are usually working by static input data and are built upon historical information during the pre-construction phase [47]. Having said that, the process of fusing the data acquired from various sources is an obstacle for achieving a real-time and automated procedure [26,44].
# Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels +
## 1 Introduction
### Related Work  
Among various approaches used to solve the BSS problem of FH sources is time-frequency analysis (TFA) [20; 21; 22; 23]. TFA methods are applied to study representations of the received FH signal in both time and frequency domains. However, as captured by the uncertainty principle, it is not possible to reach good time and frequency resolutions simultaneously [24]. TFA methods also suffer from cross-term interference and spectral leakage, resulting in high SNR requirements [25].  
TFA-based methods have been used as exploratory tools towards more refined solutions to blind estimation of hop timings and frequencies. When only one FH signal is present, reference [26] proposes to first apply TFA to estimate the hopping pattern, and, subsequently, a particle filter operates on the initial estimation. The initial estimation of hopping patterns in [26] depends on TFA-based methods, and therefore has high signal-to-noise (SNR) requirements. A blind maximum-likelihood (ML)-based iterative algorithm is proposed in [27] that estimates hop timing and frequency hops for a single user. The ML-based algorithm has been shown to have lower SNR requirements than TFA-based approaches in [27]. However, the formulation in [27] cannot be generalized to multiple FH signals.  
For multiple FH signals, the method proposed in [28] implements a dynamic programming-based ML estimator that yields estimates of joint hop timings and frequencies. An approach based on sparse linear regression is introduced to estimate the hop timings and frequencies of multiple FH signals in [29]. Each hop in [28; 29] is treated as a distinct source. This method is not suitable for grouping frequency hops according to physical sources. To associate frequency hops to a source and label FH signals, the DOAs of the sources are estimated as they are source specific extraneous to the FH pattern.  
In reference [30], a two-step approach is introduced to estimate DOA, hop timings, and frequency hops for multiple sources. A TFA method is applied to signals received by a uniform linear array (ULA) to identify a hop-free duration for DOA estimation. After the DOAs are recovered, joint estimation of hop timings and frequencies is performed for each signal originating from the same DOA. However, as mentioned earlier, references like [30] that rely on TFA-based approaches for initial estimations suffer from cross-term interference and high SNR requirements. The joint estimation of FH parameters and DOA for multiple sources is studied in [31; 32; 33; 34] under the assumption that all sources are active throughout the entire observation interval. Additionally, in reference [34], the hop periods are assumed uniform. None of the approaches in [31; 32; 33; 34] are able to incorporate sources that are sparse spatially and have intermittent activity, and probabilistic source models, such as Markov models. A comprehensive summary of the approaches in the current literature and their limitations can be found in Table 1.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 2 System Model and Signal Processing Methods
### Antenna Model  
The proposed method is applicable to any array antenna operating in the navigation frequency band, and it is recommended to utilize the \(6+1\) array antenna model described in reference [27]. This particular model has been widely adopted to design GNSS anti-jamming nulling antennas, as illustrated in Figure 2. The design effectively achieves a trade-off between signal measurement accuracy and antenna availability by integrating relatively more elements within a compact space. The model consists of 7 antenna elements, where element 0 serves as the reference and is located at the center of the antenna structure, while elements 1-6 are uniformly distributed along a circular path of a radius, denoted by \(R\). To ensure that there is no spatial ambiguity between adjacent array elements, the baseline length between elements is equal to half of the carrier wavelength.  
As depicted in Figure 2, we build a Cartesian coordinate system with the position of antenna element 0 as the origin. The \(x\)-axis is oriented positively from element 0 towards element 1, while the \(y\)-axis is oriented positively from element 0 towards the midpoint between elements 2 and 3. Moreover, the \(z\)-axis is perpendicular to the plane of the antenna model. The angle \(\gamma_{i}=2\pi(i-1)/6,\ i=1,\ 2,\cdots,6\) represents the inclination of element \(i\) with respect to the positive \(x\)-axis, thus allowing us to express the position vector of the array elements as \(\mathbf{p}_{i}=[Rcos\gamma_{i},Rsin\gamma_{i},0]^{T}\). This diagram provides a schematic depiction of \(i=2\) positioned at an angle \(\gamma_{2}\).
## 6 Adsorption Thermodynamic Study  
Adsorption thermodynamics are discussed in detail in order to demonstrate the thermodynamic viability of any adsorbent for metal ion adsorption. Thermodynamic parameters help to describe the energy changes involved in the adsorption process. It helps to determine whether adsorption phenomena are physical or chemical, spontaneous, or non-spontaneous, and exothermic or endothermic. Thermodynamic factors such as change in enthalpy (\(\mathrm{\Delta H^{\circ}}\)), change in entropy (\(\mathrm{\Delta S^{\circ}}\)), and change in Gibbs free energy (\(\mathrm{\Delta G^{0}}\)) can be calculated through the Van 't Hoff equation. The value of \(\mathrm{\Delta G^{0}}\) is negative at a particular temperature, which indicates the adsorption process is spontaneous, and the positive change in value of \(\mathrm{\Delta G^{0}}\) suggests that desorption may take place at high temperatures. The positive value of \(\mathrm{\Delta H^{\circ}}\) suggests that the reaction is endothermic, and the negative value of \(\mathrm{\Delta H^{\circ}}\) suggests that the process is exothermic. A positive \(\mathrm{\Delta S^{\circ}}\) implies enhanced randomness at the adsorbent/adsorbate interface.  
\[\mathrm{d}(\mathrm{In}\mathrm{K}_{\mathrm{eq}})/\mathrm{d}\mathrm{T}=\mathrm{ \Delta H}/\mathrm{RT}^{2} \tag{12}\]  
\[\mathrm{\{\backslash}displaystyle\;K\_eq\}|\mathrm{K}_{\mathrm{eq}}=\mathrm{q}_{ e}/\mathrm{C} \tag{13}\]\(\mathrm{K_{eq}}\) is the thermodynamic equilibrium constant; \(\mathrm{R}\) is the ideal gas constant; \(\mathrm{\Delta H}\) is the reaction enthalpy; and \(\mathrm{T}\) is the temperature (\(\mathrm{K}\)).  
The free energy change in the reaction can be calculated by the following equation:  
\[\mathrm{\Delta G^{0}=\mathrm{\Delta H^{0}-T\Delta S^{0}}} \tag{14}\]  
The Gibbs free energy change is related to the equilibrium constant by the equation:  
\[\mathrm{\Delta G^{0}=-RT\;InK_{eq}} \tag{15}\]  
By using Equations (14) and (15), Equation (16) is obtained that shows a relationship between the equilibrium constant of thermodynamic, change in enthalpy, and entropy as explained by the Van't Hoff equation given below.  
\[\mathrm{In\;K_{eq}=-\;\mathrm{\Delta H^{0}/RT+\Delta S^{0}/R}} \tag{16}\]  
The values of \(\mathrm{\Delta H^{\circ}}\) and \(\mathrm{\Delta S^{\circ}}\) are calculated from the slope and intercept of a straight-line graph between \(\mathrm{InK_{eq}}\) versus \(1/\mathrm{T}\) as shown in Figure 17[62].  
[PERSON] et al. investigated the effect of temperature on the adsorption of malachite green dye by thiourea-modified poly(acrylonitrile-co-acrylic acid) (TU-poly(AN-co-AA)) at various temperatures of 298 K, 308 K, 318 K, and 328 K [62]. The negative value of change in enthalpy (H\({}^{0}\) = \(-17.06\) kJ/mol) confirmed that the adsorption of the malachite green dye on TU-poly(AN-co-AA) was not supported at elevated temperatures. Further, the negative value of \(\mathrm{\Delta G^{0}}\) indicated that the process was spontaneous.
## 1 Introduction  
For the successful deployment of residential smart grids, it is evident that interdisciplinary information about energy technologies, energy markets and the needs of various types of stakeholders must be identified, merged, and implemented in practice. Therefore, in this paper, the results of an in-depth literature study are presented, aimed at elaborating a framework for gathering knowledge and developing understanding about residential smart energy systems. This framework, called the three-layer model, comprises three layers: stakeholders, the market, and technologies [1]. Each layer is defined below. The framework originates from the European research program, ERA-Net Smart Grids Plus, in which it is considered to provide a common context for interdisciplinary smart grid research (see Figure 1) [2]. According to the International Energy Agency (IEA), a smart grid is defined as \"an electricity network that uses digital and other advanced technologies to monitor and manage the transport of electricity from all generation sources in that (local) network to meet the varying electricity demands of end-users\" [3].  
In this paper, we focused on the specific category of smart grid environment known as residential smart grids and their pilot projects for experimenting with diverse features. Residential smart grids are located in the low voltage grid, usually in the built environment, and involve tens to hundreds of households that are equipped with smart energy products and services.  
Our definition of smart energy products and services (SEPS) includes all the products and services that have the ability to support the active participation of end users by efficiently and reliably managing their energy systems and balancing the mismatch between electricity demand and supply [4].
## 1 Introduction
Due to the limitations observed in the use of remote sensing in Mexico, such as limited research using remote sensing and map course resolution, this study has integrated remote sensing, Geographic Information Systems (GIS) analysis, field sampling and image revision to assess the potential of the use of Landsat 8 satellite data to assess and monitor land use changes and coverage changes in the southeastern region of the state of Veracruz, Mexico, located in the southern coastal area of the Gulf of Mexico. Three widely used classification methods are explored to select the more appropriate algorithm for LULC classification under the same level of information previously extracted from field data, digital maps and ancillary data. This comparison allows for conclusions on the advantages and drawbacks of current classification algorithms. The analysis encompasses the definitions of macro-classes of land use and their derived classes. The Normalized Difference Vegetation Index, also known as NDVI, was used to identify and improve discrimination between macro-classes and classes within the study area. A supervised classification method, the MaximumLikelihood Classification (MLC), was applied in this study due to its availability within the GIS applications in addition to it not requiring an extensive training process [34; 35]. The most important advantage of the MLC as a parametric classifier is that it takes into account the variance within the selected classes and that, for normally distributed data, the MLC method performs a better qualification than other methods, namely, decision trees [34; 36]. Additionally, this study proposes a methodology to obtain updated temporal information of the change in land use and land cover for the Coatzacoolcos area using Landsat 8 satellite imagery, which has a higher resolution with the available MODIS maps. The mapping of land use and its changes are critical and important factors for sustainable development as well as the monitoring of environmental impacts. The final product of this research will help government agencies to make decisions on urban development and preservation of available natural resources, since one of the emblematic projects in Mexico known as the Interoceanic Corridor is being executed in the present year. Lastly, this piece of research is going to serve as the baseline for additional contributions on LULC change prediction with more complex analyses of the drivers forcing these landscape modifications.
# Country-Based COVID-19 DNA Sequence Classification in Relation with International Travel Policy
## 1 Introduction
As the home to more than 9% of the world's population and a region with a surge in infection cases since March 2020 [22], it is crucial to investigate the role of geographical proximity in shaping the genetic structure of the SARS-CoV-2 genome in the Association of Southeast Asian Nations (ASEAN) countries. Several strains that were isolated before the national implementation of border control exhibited a high degree of invariance, while others demonstrated approximately 80% of synonymous mutations, suggesting possible ongoing virus adaptation in the region [23]. Focused on DNA, research related to sequence profiling has been carried out regionally, involving five ASEAN countries with a relatively limited number of sequences [24]. To enhance the analysis with a larger dataset and provide clear information on how the virus' DNA differs between countries, particularly where the countries are geographically close to each other, this paper purposes to classify SARS-CoV-2 DNA sequences based on the country using a deep learning model. Concentrating on nine ASEAN countries, we also primarily explore the connection between the classification results and each country's international policy restrictions to gain insight into how the virus behaves in different places under different rules.
# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
## 2 Related Literature
### Junction-Oriented Vehicle Selection
The mechanism takes into consideration the interference of vehicles from the surroundings during the relay selection procedures. Nevertheless, the SSIM index of the video streams is not considered in measuring the quality of the video. Multiple path solutions with error correction for video streaming over VANETs (LIAITHON+) have been presented; the aim of this is to reduce collision and packet loss in high data rate networks Salkuyeh and Abolhassani (2018). LIAITHON+ employs a multipath approach to distribute high data rate traffic into a set of paths. However, the quality of the streaming video is not measured based on PSNR and SSIM index metrics. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) present a video stream routing QoS for the multi-rate mechanism in order to achieve congestion control and avoidance. The mechanism employs the Ant Colony Optimization (ACO) algorithm and Simple Network Management Protocol (SNMP) monitoring features. The mechanism is called QoRA; it decides on paths by considering applications that QoS needs and prevents transmission flow from entering congested nodes. Nevertheless, the mechanism is not adequately benchmarked. QoE-driven and link-quality receiver-based (QOALITE) transmission is proposed to imrpove the quality of video while considering a challenging VANET environment [PERSON] and [PERSON] (2018). A geographical receiver-based beaconless strategy has been proposed as a solution for transmitting video streams in VANETs. However, some parameters are considered in choosing the best relay node and building up reliable backbones to deliver video messages. In addition, the dynamic adjustment of the time window needs to be considered for tackling collisions. Therefore, proper vehicle selection in the junction area is not considered in some routing schemes. Consequently, an optimal next forwarding vehicle selection scheme is required that considers the junctions and neighbor vehicle mobility information and link quality to select an optimal next forwarder for video streaming in the multipath setup.
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## Materials and Methods
### Mathematical Model
#### 2.1.3 Mathematical Model  
Interpolation with radial basis function is used to solve the mapping relation. In order to guarantee positive definiteness and invertibility of interpolation matrix, the interpolation formula is improved as shown in Equation (2).  
\[s(x)=a_{0}+a_{x}^{T}x+\sum_{i=1}^{M}w_{i}\varnothing(\parallel x-c_{i}\parallel) \tag{2}\]  
In Equation (2) \(x\) represents an image coordinates \(I_{i}\), \(w_{i}\), \(a_{0}\), \(a_{x}\) are unknown parameters, \(\{c_{i}\}i=1,\cdots,\ M\) is a set of control points, \(\varnothing\) is the radial basis function.  
Rewriting Equation (2) in matrix form as  
\[s(x)=(\begin{array}{cc}\varnothing(x)&p(x)\end{array})\left(\begin{array}[] {c}w\\ a\end{array}\right) \tag{3}\]  
In Equation (3), \(w\) and \(a\) are unknown parameters, \(\varnothing(x)\) is the radial basis function expressed as \(\varnothing_{i}(x)=\varnothing(\parallel x-c_{i}\parallel)\), \(\{c_{i}\}i=1,\cdots,M\) is a set of control points, \(p(x)\) is homogeneous image coordinate expressed as \(p(x)=(\begin{array}{cc}1&u&v\end{array})\).  
Supposing \(h_{wa}=\left(\begin{array}{c}w\\ a\end{array}\right)\), rewriting Equation (3) as  
\[s(x)=\Big{(}\begin{array}{cc}\varnothing(x)&p(x)\end{array}\Big{)}h_{wa} \tag{4}\]  
Six independent interpolations based on radial basis function describe a vector-valued function \(f(x)\) as shown in Equation (5)  
\[f(x)=(s_{1}(x),s_{2}(x),s_{3}(x),s_{4}(x),s_{5}(x),s_{6}(x))^{T} \tag{5}\]  
Replacing \(s_{i}(x)\) in Equation (5) by Equation (4)  
\[f(x)=\left(\Big{(}\begin{array}{cc}\phi(x)&p(x)\end{array}\Big{)}\Big{(} \underbrace{h_{wa}^{(1)}&\ldots&h_{wa}^{(6)}}_{H_{cam}}\Big{)}\right)^{T} \tag{6}\]  
In Equation (6), \(H_{cam}\) is called camera matrix. Equation (6) can be simplified as  
\[f(x)=\Big{(}\Big{(}\begin{array}{cc}\varnothing(x)&p(x)\end{array}\Big{)}H_{cam} \Big{)}^{T} \tag{7}\]  
Based on the mathematical model, when the set of image points, the set of control points and camera matrix are known, the vector valued function can be solved for a given radial basis function. The set of image points and the set of control points can be gained by measuring, and camera matrix can be gained by calibrating.