esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Unlike the gravity model, which can only handle the interaction between two spaces, space syntax based on graph theory is a spatial interaction model that can calculate the interrelationship between all spaces in a system [7; 8]. To understand various features of the urban structure, urban space should be conceptualized as a system of networks and flows, representing the relationships among urban components [9]. Space syntax has more benefits than the methods mentioned above, as it can uncover functional configurations hidden in a simplified street network as a graph of nodes and links [10] and follow easy and simple analytical and interpretational procedures to reveal its configurations [11]. Retail types can be categorized by this spatial configuration and presented as diagrammatic descriptions to illuminate the interrelation of the sociality, spatiality, and economy of retailing [12]. Space syntax methodology offers objective and quantitative descriptions of the spatial configuration in addition to the diagrammatic descriptions [7; 8]. In this study, through the spatial analysis of Yukgeori Market as a representative market whose evolution has taken place in a non-metropolitan area operating under different conditions from markets in Seoul--a case not covered in previous studies--we identified the spatial configurations to maintain and promote social and commercial activities in this traditional market and, thereby, supporting its revitalization. [PERSON] and [PERSON] (2011) found that despite the global economic crisis during the 2000s, adaptively resilient retail centers have thrived due to the complementary relationship between urban components [13]. In this study, the spatial configuration of an urban district market is regarded as a network that causes interactions between the market and the surrounding urban components. Given that space syntax investigates aspects of flow, transport, and potential movement of pedestrians in cities through spatial configuration analysis [8], it can identify the interaction of retail activity with the spatial configuration of the street network [14]. Therefore, this study has adopted space syntax as a methodology to quantitatively analyze the relationships among the components based on the spatial configuration. How does spatial configuration support the survival and rehabilitation of a traditional market? To answer this question, this study has identified the measures to achieve the sustainability of traditional markets, based on the identification of the spatial compositional characteristics of the traditional Korean market that diverge from those of large discount stores and the western commercial architectural style, and to find out a spatial configuration applicable to market revitalization in terms of the network analysis. To do so, this study derives the characteristics of the spatial configurations that support the rehabilitation of traditional markets by calculating syntactic measures of nodes in the street network of the traditional market and its vicinity using an analytical model based on space syntax.
## 4 Results and Discussion
### Identification and Categorization of Factors
#### 4.2.4 Institutional Factors  
The roles that institutions such as private entities, collectives, and government agencies play are essential for the uptake of agricultural innovation. Several studies have shownthat government subsidies can significantly increase farmers' willingness to adopt digital technologies [30; 35; 37]. Availability of financial and extension services are also influential factors for technology adoption, especially among small-scale farmers. [PERSON] and [PERSON]'s research [39] indicates that contract farming can stimulate farmers' use of smart agriculture technologies. These findings imply that reliable support from the government or collectives is effective for farmers to take up new farming practices.
## 3 Results and Discussion
### Occurrence of Carbonyl Compounds in Adult Formulas
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Adult** & **Malondialdehydedehyde** & **Formaldehyde** & **Acetaldehyde** & **Acrolein** & **Methylglyoxal** & **Diacetyl** \\
**Formula** & \(\upmu\)g/mL** & \(\pm\)SD** & \(\upmu\)g/mL** & \(\pm\)SD** & \(\upmu\)g/mL** & \(\pm\)SD** & \(\upmu\)g/mL** & \(\pm\)SD & \(\upmu\)g/mL & \(\pm\)SD \\ \hline HP1 & 2.38 & 0.39 & 2.86 & 0.90 & 2.16 & 0.31 & 2.29 & 0.73 & 1.50 & 0.28 & ND & - \\ HP2 & 0.98 & 0.05 & 0.94 & 0.03 & 1.90 & 0.12 & 0.24 & 0.00 & ND & - & ND & - \\ HP3 & ND & - & ND & - & ND & - & ND & - & ND & - & ND & - \\ HP4 & 0.53 & 0.02 & 1.20 & 0.50 & 1.08 & 0.67 & 2.39 & 0.94 & 1.89 & 0.77 & 2.84 & 0.98 \\ HP5 & 2.89 & 0.56 & 2.60 & 0.70 & ND & - & 2.80 & 0.17 & 0.61 & 0.08 & ND & - \\ \hline SAF1 & 2.65 & 0.82 & 0.90 & 0.11 & 1.63 & 0.45 & 1.27 & 0.70 & 1.65 & 0.77 & ND & - \\ SAF2 & 1.73 & 0.54 & 1.56 & 0.31 & 2.91 & 0.23 & 1.43 & 0.35 & 2.69 & 0.82 & 0.97 & 0.27 \\ SAF3 & ND & - & ND & - & ND & - & ND & - & ND & - & ND & - \\ SAF4 & ND & - & ND & - & ND & - & ND & - & ND & - & ND & - \\ \hline SP1 & 2.09 & 0.75 & 1.65 & 0.75 & 1.16 & 0.24 & 2.49 & 0.76 & 2.02 & 0.52 & 2.11 & 0.49 \\ SP2 & 2.00 & 0.80 & 1.48 & 0.51 & 0.82 & 0.53 & 1.89 & 0.42 & 2.46 & 0.93 & 1.67 & 0.96 \\ SP3 & ND & - & 2.39 & 0.31 & 0.79 & 0.24 & 2.53 & 0.56 & 1.16 & 0.14 & 1.88 & 0.30 \\ \hline \hline \end{tabular} SD, standard deviation; HP, high-protein adult formula; SAF, standard adult formula; SP, special formulated adult formula; ND, non-determined as below limit of quantification.  
\end{table} 
Table 2: Occurrence of carbonyl compounds in adult formula. These findings suggest that MDA, FCHO, ACRL, ACE, MGO, and DA concentrations vary significantly across adult formula samples. The data suggest that high-protein and specialized formulations may be more susceptible to carbonyl compound formation than standard formulas. This variability highlights the importance of monitoring carbonyl compound levels in these products as potential indicators of oxidation which could affect product quality and safety.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 3 Principle and Method of Polar Code Parameter Recognition  
As the first step of channel model simulation, the simulation of polar codes in the AWGN channel has already attracted sufficient attention. According to the polar code coding structure detailed in the preceding section, the parameters that must be determined for polar code recognition under Gaussian channels include polar code length, code rate (or the number of information bits), and index of information-bit-positions. When the code length and rate are determined and the channel information known, the information-bit positions can be determined by the GA method [21], so that the generator matrix can be determined, and the channel parameters required by the GA construction method can be easily estimated by the received signal [22; 23].
## 3 Numerical Example #1: Two-Link Network  
A graphical illustration of the relationships between each of the extremes of range (67) and parameter \(\theta\) is provided in Figures 2 and 3 for fixed values of \(b\) and \(Q\). Note that the upper bound \(\gamma\_max\) is independent of the inertia and memory depth parameters \(\alpha\) and \(\beta\), and that the latter can be interchanged without affecting the value of the lower bound \(\gamma\_min\). We also observe that, according to condition (69), any negative value of \(\gamma\_min\) is replaced by \(0\), which explains the null portions of the curves shown in Figure 3. For the same reason, the curves corresponding to low values of \(\alpha\) and \(\beta\) (0.1, 0.2, and 0.3), which would be entirely replaced by null functions, are not shown in the graph.  
Figure 2: Maximum value of \(\gamma\) ensuring fixed-point stability as a function of the Logit route choice parameter \(\theta\) (\(b=2;\ Q=1\)).  
The joint analysis of Figures 2 and 3 reveals that the range of \(\gamma\) values ensuring fixed-point stability tends to shrink as \(\theta\) increases, and that this effect becomes more and more pronounced as \(\alpha\) and \(\beta\) grow and tend to unity. From a policy standpoint (recalling that \(\theta\) increases as drivers' knowledge of actual travel costs improves), this amounts to a progressive reduction of the signal timing options available to the traffic manager to offset the destabilizing effects of drivers' accurate cost perception, low inertia, and shallow memory.  
The effect of the model parameters on the trajectory of the dynamical system (46) and (47) is illustrated by means of the examples displayed in Figures 4-8, which show the time evolution of \(F\) over 2  
Figure 5: Time evolution of \(F\) for \(\alpha=0.9;\beta=0.8;\gamma=1.05;\theta=1.5;b=2.5;Q=1;F(0)=0.2\).
# TEG Design for Waste Heat Recovery at an Aviation Jet Engine Nozzle
###### Abstract  
Finite element model (FEM)-based simulations are conducted for the application of a thermoelectric generator (TEG) between the hot core stream and the cool bypass flow at the nozzle of an aviation turbofan engine. This work reports the resulting requirements on the TEG design with respect to applied thermoelectric (TE) element lengths and filling factors (\(F\)) of the TE modules in order to achieve a positive effect on the specific fuel consumption. Assuming a virtual optimized TE material and varying the convective heat transfer coefficients (HTC) between the nozzle surfaces and the gas flows, this work reports the achievable power output. System-level requirement on the gravimetric power density (>100 Wkg\({}^{-1}\)) can only be met for \(F\leq 21\%\). When extrapolating TEG coverage to the full nozzle surface, the power output reaches 1.65 kW per engine. The assessment of further potential for power generation is demonstrated by a parametric study on \(F\), convective HTC, and materials performance. This study confirms a feasible design range for TEG installation on the aircraft nozzle with a positive impact on the fuel consumption. This application translates into a reduction of operational costs, allowing for an economically efficient TEG-installation with respect to the cost-specific power output of modern thermoelectric materials.  
Keywords:thermoelectric generator; energy conversion; energy harvesting; aviation; jet engine; specific fuel consumption; module design; filling factor; FEM; simulation +
Footnote †: journal: Applied Physics Letters
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 2 Short-Circuit Fault Level Estimation for Active Distribution Networks
### Direction Identification of Disturbance  
It has been confirmed that the direction of fault on a transmission line can be distinguished on the basis of the angle difference between voltage and current phasors [28]. Similarly, the change of the voltage and current can be used for the identification of disturbance direction.  
Take the simplified equivalent circuit of a general radial distribution network as an example (as shown in Figure 3). Suppose the disturbances occurs on the downstream, which is to say \(\dot{E}_{eq}\) and \(Z_{eq}\) are constant while \(Z_{L}\) changes from \(Z_{L1}\) to \(Z_{L2}\). Consequently, the voltage on CB8 and the current through it change from \(\dot{U}_{1}\) and \(\dot{I}_{1}\) to \(\dot{U}_{2}\) and \(\dot{I}_{2}\), respectively. Define the reference directions of the current and voltage phasors as indicated in Figure 3, then the following equations hold.  
\[\dot{E}_{eq} =\dot{U}_{1}+\dot{I}_{1}Z_{eq}, \tag{3}\] \[\dot{E}_{eq} =\dot{U}_{2}+\dot{I}_{2}Z_{eq}, \tag{4}\]  
The following can be derived:  
\[Z_{eq}=-\frac{\dot{U}_{2}-\dot{U}_{1}}{\dot{I}_{2}-\dot{I}_{1}}=-\frac{\Delta \dot{U}}{\Delta\dot{I}}, \tag{5}\]  
Figure 3: The equivalent circuit for direction identification of disturbance.  
On the contrary, if the disturbance occurs on the upstream or grid side, the equivalent load impedance \(Z_{L}\) and voltage \(\dot{E}_{DG}\) keep constant, while \(\dot{E}_{eq}\) and \(Z_{\alpha q}\) vary. Then the following two equations yield.  
\[\dot{E}_{DG}=\dot{U}_{1}-\dot{I}_{1}Z_{L}, \tag{6}\]  
\[\dot{E}_{DG}=\dot{U}_{2}-\dot{I}_{2}Z_{L} \tag{7}\]  
The following holds:  
\[Z_{L}=\frac{\dot{U}_{2}-\dot{U}_{1}}{\dot{I}_{2}-\dot{I}_{1}}=\frac{\Delta\dot {U}}{\Delta\dot{I}} \tag{8}\]  
Equations (5) and (8) show the relationship between the impedance and the variations of voltage and current in two different situations. In terms of the practical power systems, the real parts of \(Z_{eq}\) and \(Z_{L}\) are usually greater than zero [14]. With regard to \(Z_{\alpha q}\), it implies that the transfer conductance of power grid cannot be ignored. The non-zero real part of \(Z_{L}\) represents some active loads on the feeder. Therefore, the following index is designed to distinguish the direction of the disturbance.  
\[Dir=-real\left(\frac{\Delta\dot{U}}{\Delta\dot{I}}\right), \tag{9}\]  
Specifically it means the disturbance occurs downstream to the circuit breaker if \(Dir>0\); otherwise, it is supposed to be the event upstream.  
The application of PMUs can enhance the accuracy on the location identification of the disturbances. If there are several PMUs on the feeder, the disturbance directions obtained from each PMU can be compared with each other, and the results without consistency can be eliminated.
## 2 Materials and Methods  
### Structure-from-Motion  
Structure-from-Motion (SfM) is a computer vision method that enables the calculation of 3D space coordinates from 2D images captured from different angles [32]. Conventional photogrammetry requires knowledge of the 3D position of a set of control points and external orientation elements (the coordinates of the image capture point and the rotation values of the image) to determine the 3D coordinates of points in a scene. On the other hand, the SfM approach does not require any of these to be known before the scene is reconstructed. Additionally, the camera pose and scene geometry are simultaneously reconstructed by automatically detecting similar features in various images.  
The first step of the algorithm is the detection of matching points from multiple images by using feature extraction methods such as SIFT [33]. Feature-based methods have been widely used for the image matching step in SfM [34]. These methods are robust to homography transformations, perspective transformations, and illumination variations [35]. Matching features in two images are evaluated by the epipolar geometry, which is the intrinsic projective geometry between the two views. The epipolar geometry can be expressed using a \(3\times 3\) matrix which is known as the fundamental matrix (\(F\)).  
\[x^{\prime}Fx=0 \tag{1}\]  
where \(x^{\prime}\) and \(x\) are the projected points of the 3D point onto the camera image plane [36].  
To preliminarily calculate the fundamental matrix, matched features are used to apply the normalized eight-point algorithm. Thus, the coordinates of a 3D point in the scene can be calculated by triangulation with calibration, rotation, and translation matrices as follows:  
\[\lambda_{j}\mathbf{x}_{j}=\mathbf{M}_{j}\mathbf{X}_{j}=1,\cdots,n \tag{2}\]  
where \(\lambda\) is a scale factor and \(\mathbf{M}\) is the projection matrix, which is expressed as  
\[\mathbf{M}=\begin{bmatrix}\mathbf{K}&0\end{bmatrix}\begin{bmatrix}\mathbf{R}& \mathbf{t}\\ 0^{\intercal}&1\end{bmatrix} \tag{3}\]  
Bundle adjustment aims to iteratively minimize the distance between the projected and observed points. This method is used on the collective set of camera and scene parameters:  
\[g(C,X)=\sum_{i=1}^{n}\sum_{j=1}^{m}\omega_{ij}\parallel\mathbf{x}_{ij}-\mathbf{P} \big{(}\mathbf{C}_{i},\mathbf{X}_{j}\big{)}\parallel^{2} \tag{4}\]  
where \(\omega_{ij}\) refers to an indicator variable: \(\omega_{ij}
# A Trust-Influenced Smart Grid: A Survey and a Proposal
## 2 NIST Priority Areas On Smart Grid
### Advanced Metering Infrastructure (AMI)  
Power usage by consumers is a key parameter in observing demand within the Smart Grid. In the traditional grid, meters were manually read and recorded before being computed to know the actual utilization within a given period. The introduction of the Smart Grid assures the near-real-time monitoring of power usage with AMI. AMI creates a dual-channel network between the smart meters and business systems of utility providers. This enables the collection and distribution of meaningful data to customers and utility providers as well as competitive retail suppliers. Such information can be used to implement residential demand responses. Even though there are many different designs of AMI, it consists of communications software and hardware and their associated system and data management software.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) provides a new and direct way of monitoring photosynthetic activity. However, current SIF products are limited by low spatial resolution or sparse sampling. In this paper, we present a data-driven method of generating a global, spatially continuous TanSat SIF product. Firstly, the key explanatory variables for modelling canopy SIF were investigated using in-situ and satellite observations. According to theoretical and experimental analysis, the solar radiation intensity was found to be a dominant driving environmental variable for the SIF yield at both the canopy and global scales; this has, however, been neglected in previous research. The cosine value of the solar zenith angle at noon (cos (SZA\({}_{0}\))), a proxy for solar radiation intensity, was found to be a dominant abiotic factor for the SIF yield. Next, a Random Forest (RF) approach was employed for SIF prediction based on Moderate Resolution Imaging Spectroradiometer (MODIS) visible-to-NIR reflectance data, the normalized difference vegetation (NDVI), cos (SZA\({}_{0}\)), and air temperature. The machine learning model performed well at predicting SIF, giving R\({}^{2}\) values of 0.73, an RMSE of 0.30 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) and a bias of 0.22 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) for 2018. If cos (SZA\({}_{0}\)) was not included, the accuracy of the RF model decreased: the R\({}^{2}\) value was then 0.65, the RMSE 0.34 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) and an bias of 0.26 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\), further verifying the importance of cos (SZA\({}_{0}\)). Finally, the globally continuous TanSat SIF product was developed and compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIF data. The results showed that the globally continuous TanSat SIF product agreed well with the TROPOMI SIF data, with an R\({}^{2}\) value of 0.73. Thus, this paper presents an improved approach to modelling satellite SIF that has a better accuracy, and the study also generated a global, spatially continuous TanSat SIF product with a spatial resolution of 0.05\({}^{\circ}\).  
solar-induced chlorophyll fluorescence (SIF); cosine value of the solar zenith angle at noon; MODIS +
Footnote †: journal: remote sensing
## 4 Simulation and Discussion
### Synthetic Data and Real-World Data  
We evaluate the reconstruction performance of the proposed method using synthetic sparse data and real-world sparse data, respectively. Real-world data are non-negative voltage disturbance signals sampled from the vehicle power system, which are extraordinarily common impulse signals in this system. They are sparse in time domain, with approximately 10% of total elements nonzero. In order to give convenient explanation, values of all non-zero elements are normalized to [0, 1]. Therefore, the voltage disturbance signals can be represented as one-dimensional vectors.  
Given the characteristics of real disturbance signals, we further generate Gaussian sparse data in a similar way. Specifically, suppose the one-dimensional sparse signal contains \(K\) nonzero coefficients (\(K\) is the sparsity level) with random locations. The nonzero coefficients are randomly generated with each entry independently drawn from a normalized Gaussian distribution. Synthetic sparse signals can be utilized as an essential supplement to real sparse signals to verify the completeness of FRSVssMP. Figure 3a,b depict examples of one-dimensional synthetic signals and the original real signals, respectively, where we set the signal length equal to 128.  
Figure 2: The implementation of vehicle health monitoring with FRSVssMP.  
In this paper, essential experiments were conducted to illustrate superior performance of FRSVssMP against the state of the art algorithms including StOMP, CoSaMP, SAMP, AStMP [20] and CBMP [25]. Typical synthetic and real-world voltage disturbance signals from the vehicle power system were selected as trial data. Performance evaluation metrics includes reconstruction time and success rate. Reconstruction time \(T\) in seconds refers to average time implemented for reconstruction. Reconstruction success rate is defined as the ratio of the number of successful trials to the total number of trials. To be specific, suppose \(N\) is the signal length and \(M\) denotes the length of measurement vector, then the root-mean-square (RMS) can be expressed as:  
\[\text{RMS}=\sqrt{\sum_{i=0}^{N-1}\left[S_{i}-\hat{\mathbf{S}}_{i}\right]^{2} /\sum_{i=0}^{N-1}\mathbf{S}_{i}^{2}}\times 100\%. \tag{18}\]  
where \(\mathbf{S}\in\mathbf{R}^{N\times 1}\) is the \(N\times 1\) originalsignal and \(\hat{\mathbf{S}}\in\mathbf{R}^{N\times 1}\) denotes the reconstructed signal. The successful trial refers to a trial with RMS less than \(10^{-6}\). In our simulations, each experiment was repeated for 5000 trials, and the sensing matrix \(\mathbf{A}\in\mathbf{R}^{M\times N}\) was generated as a zero mean random Gaussian matrix with columns normalized to unit \(l_{2}\) norm. Experiments were implemented in MATLAB R2020a on Windows 10 (published by Microsoft, Redmond, America) with Intel Core i7 processor (3.6 GHz) with 4-GB RAM.