esa-sceva/satcom-chunk-collection huggingface dataset:
# Representing Deep Neural Networks Latent Space Geometries with Graphs
## 3 Methodology
### Proposed Methodology  
In this section, we describe how to construct and exploit latent geometry graphs (LGGs) and illustrate the key ideas with a toy example. Given a batch \(\mathbf{X}\), each LGG vertex corresponds to a sample in \(\mathbf{X}\), and each edge weight measures similarity between the corresponding data points. More specifically, LGGs are constructed as follows:  
1. Generate a symmetric square matrix \(\mathcal{A}\in\mathbb{R}^{|\mathbb{V}|\times|\mathbb{V}|}\) using a similarity measure between intermediate representations, at a given depth \(\ell\), of data points in \(\mathbf{X}\). In this work, we choose the cosine similarity when data is non-negative, and an RBF similarity kernel based on the L2 distance otherwise.
2. Threshold \(\mathcal{A}\) so that each vertex is connected only to its \(k\)-nearest neighbors.
3. Symmetrize the resulting thresholded matrix: two vertices \(i\) and \(j\) are connected with edge weights \(w_{ij}=w_{ji}\) as long one of the nodes was a \(k\) nearest neighbor of the other.
4. (Optional) Normalize \(\mathcal{A}\) using its degree diagonal matrix \(\mathbf{D}\): \(\mathcal{A}=D^{-\frac{1}{2}}\mathcal{A}D^{-\frac{1}{2}}\).  
Given the LGG associated to some intermediate representation, we are able to quantify how well this representation matches the classification task under consideration by usingthe concept of label variation, a measure of graph signal variation for a signal formed as a concatenation of all label indicator vectors:  
**Definition 6** (Label variation).: _Consider a similarity graph for a given batch \(\mathbf{X}\) (obtained from some intermediate layer), represented by an adjacency matrix \(\mathcal{A}\), and define a label indicator matrix \(\mathbf{V}\) obtained by concatenating label indicator vectors \(\mathbf{v}^{c}\) of each class. Label variation is defined as:_  
\[\sigma=\mathit{tr}\Big{(}\mathbf{V}^{\top}\mathbf{LV}\Big{)}=\underbrace{\sum_{c}\sum_{ \begin{subarray}{c}i,j\\ v_{i}^{c}\
eq v_{j}^{c}\end{subarray}}}\mathcal{A}_{i,j}. \tag{5}\]  
**Remark 1**.: _Label variation has the advantage of being independent of the choice of labels for each class, which can be verified by noticing that for any permutation matrix \(\mathbf{P}\), it holds that:_  
\[\mathit{tr}\Big{(}(\mathbf{VP})^{\top}\mathbf{LVP}\Big{)}=\mathit{tr}\Big{(}\mathbf{P}^{ \top}\mathbf{V}^{\top}\mathbf{LVP}\Big{)}=\mathit{tr}\Big{(}\mathbf{V}^{\top}\mathbf{LVP}\mathbf{P} ^{\top}\Big{)}=\sigma. \tag{6}\]  
If the graph is well suited for classification, then most nodes will have immediate neighbors in the same class. Indeed, label variation is 0 if and only if data points that belong to distinct classes are not connected in the graph. Therefore, smaller label variation is indicative of an easier classification task (well separated classes).
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 4 Discussion
### Perceived Benefits  
Perceived benefits were one of the main positive elements in adopting contact-tracing apps, as highlighted in various studies across different countries [40; 44; 47]. These perceived benefits include personal health, protection of family and friends, contribution to community safety, aiding epidemic control [33], and providing peace of mind to individuals [24]. Moreover, the perceived usefulness of contact-tracing apps was a significant factor influencing people's intention to use them. In addition, the perceived effectiveness of the application was positively associated with the use of contact-tracing apps [80]. Therefore, it is evident that perceived benefit, usefulness and effectiveness are key elements contributing to the decision to use digital contact-tracing.  
**Recommendations for government.** Governments should highlight genuine success stories where contact-tracing apps contributed to controlling outbreaks, protecting communities, or preventing the spread of disease. This is because demonstrating tangible benefits reinforces an app's perceived value, thereby motivating users to adopt the app through the prospect of personal and societal gains. Governments should also work with app developers to integrate additional app features, such as local health updates or access to testing facilities, to enhance perceived usefulness. This is because governments can increase the appeal of apps by demonstrating various benefits beyond contact-tracing, aligning with the public's desire for utility and effectiveness.  
**Recommendations for app developers.** App developers should add personalised features, such as risk alerts based on exposure, and integrate health resources like symptom checkers or nearby testing sites. This is because enhancing perceived benefits aligns with users' desire for usefulness and demonstrates the app's value beyond contact-tracing, motivating adoption. App developers should also include gamification elements, such as rewards for consistent app usage or community milestones, to incentivise engagement. This is because gamification makes app use engaging and reinforces positive behaviours, amplifying the perceived value of the technology.
## 4 Tests and Results
### Comparison with Other Methodologies  
A comparison of the optimization model developed in this paper with other methodologies presented in the specialized literature is presented in this section. Table 7 shows, for different optimization techniques, the computational time and whether or not the global optimum was reached in the restoration process of the IEEE-39 RST test system with conventional generation. It can be observed that the methodology developed in this paper allows achieving a global optimal solution with a satisfactory computational time. It is worth mentioning that a comparison of the complete methodology integrating NCRES and BESS is not possible to carry out since to the best of the authors knowledge there are no other methodologies that simultaneously integrate this two resources within the optimal restoration process (see Table 1). On the other hand, a comparison regarding the execution time would not be fare, since the results were obtained with different computers. The enumeration algorithm was processed on a Core i3 computer @ 2.53 GHz. The two-step algorithm does not refer to the characteristics of the test computer, and the proposed methodology was performed on a computer with Intel(R) core(TM) i5-4200U @ 1.6 GHz 2.3 GHz.
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
## 3 Analysis of Coupling Factors
### Analysis of Coupling Factors
#### 3.2.1 Coupling Factors of Electromagnetic Field to Temperature Field  
From the above analysis, it can be observed that during the operation of the motor, due to the effect of the electromagnetic field, loss will be generated, and most of these losses will be converted into heat, thus affecting the temperature of the motor, which is the main factor affecting the temperature to the electromagnetic characteristics [18]. The main losses of the motor are iron loss in the stator and rotor core, copper loss in the winding and eddy current loss of the PM.  
* Iron loss  
Iron loss includes hysteresis loss, eddy current loss, and remanence loss. The proportion of remanence loss is very small, about 0.1% to 0.2%. Therefore, it is generally ignored in analysis. [PERSON]'s model of iron loss separation can be used to calculate the iron loss of silicon steel sheets. This method is widely used and has certain accuracy. The core loss of the stator and rotor can be calculated by the following Equation [19]:  
\[P_{iron}=P_{h}+P_{c}+P_{e}=K_{h}fB_{m}^{x}+K_{c}(fB_{m})^{2}+K_{e}(fB_{m})^{1. 5}, \tag{3}\]  
\[K_{c}=\pi^{2}\cdot\sigma\cdot d^{2}/6. \tag{4}\]  
Figure 2: Schematic diagram of coupling relationship.  
* Copper loss  
The copper loss in the windings is related to the current and resistance of the windings. The copper loss of the coils can be calculated by Equation (5)  
\[P_{copper}=ml^{2}R. \tag{5}\]  
* Eddy current loss of permanent magnet The main loss in permanent magnets is eddy current loss. The equation is as follow [20]:  
\[P_{magnet}=\int_{V}EJ_{s}dv. \tag{6}\]  
The losses mentioned above are eventually converted to heat, and the heat generation rate is used as the heat source load in the calculation of the temperature field. The relationship between loss and heat generation rate is as follows:  
\[q=P_{loss}/V. \tag{7}\]
# Post-Certification Quality Analysis of Traditional Indian Fried Snacks
## 2 Materials and Methods
### Physicochemical Analysis  
The analytical methods applied by the company followed the IS 12711 (1989): Bakery products--Methods of analysis [33]. Product samples were finely ground, and the ground powder was used for the estimation of physicochemical quality parameters, including moisture content, salt content, pH, and acid value of extracted fat. These parameters are the most frequently recommended tests to assess the quality of deep-fried snacks, and serve as essential indicators of product safety, shelf life, and sensory quality [34]. The moisture content of the samples was measured by the hot air oven method, where 10 g of the sample was dried in a 100 \(\pm\) 2 \({}^{\circ}\)C oven until constant weight was achieved. The moisture content of finished products is usually less than 2% [35]. The salt content was analyzed by ashing 5.0 g of the sample, dissolving the chloride residues, and titrating with 0.1N silver nitrate (AgNO\({}_{3}\)) using potassium chromate as an indicator until a permanent light brick red color was observed. The pH of the 10 g samples homogenized with 100 mL of distilled water was determined with a calibrated pH meter. The lower pH value (acidic) in food samples may result from the oxidation of fat content, resulting in the formation of fatty acids [36]. The acid value of extracted fat was determined by extracting fat using a Soxlet apparatus and then dissolving it in a solvent (isopropyl alcohol/toluene) and titrating it with a standard alkali (potassium hydroxide/sodium hydroxide) solution after adding phenolphthlein as an indicator. A lower acid value indicates better fat quality in food [37], values exceeding one are generally considered an indication of rancidity and reduced product safety [38].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Samples** & **Ingredients** \\ \hline Kerala Murukku & Rice Flour, Refined Palmolein Oil, Black Gram flour (Urud Dal), Chili Powder, Salt, Small Onion, Garlic, Sesame Seeds, Fennel seeds, Ajwain \\ Kerala Mixture & Besan (Chickpea) Flour, Peanuts, Refined Palmolein Oil, Pea Flour, Rice Flour, Salt, Chili Powder, Curry Leaves, Turmeric, Garman Masala (Spices), Asafoetida \\ Banana chips & Plantain, Refined Palmolein Oil, Turmeric, Salt \\ Tapioca chips & Tapioca, Refined Palmolein Oil, Chili Powder, Salt \\ Achappam & Rice Flour, Sugar, Refined Palmolein Oil, Eggs, Corn flour, Sesame Seeds \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ingredients of sample products.  
Figure 1: Images of the products used for this study: (**a**) Kerala Murukku, (**b**) Kerala Mixture, (**c**) Banana Chips, (**d**) Tapioca Chips, and (**e**) Achappam.
## 3 Method
### Symbol Definition and Standard Mesh Operations  
Given an input mesh \(M=(V,E,F)\) is a closed 2-manifold triangle mesh, where \(V,E,T\) are the sets of vertex, edge, and triangular element, respectively, for an edge \(e_{ij}\), vertices \((v_{i},v_{j})\) are the endpoints of it. The definition of the 1-ring neighborhood \(\Omega(v_{i})\) of \(v_{i}\in V\) is shown in Figure 2a:  
* For vertex \(v\), if the \(v\in\Omega(v_{i})\), the set \(\{v\}\) is all of the vertices adjacent to \(v_{i}\), as shown in red vertices in Figure 2a.
* For edge \(e_{ij}\), if \(e_{ij}\in\Omega(v_{i})\), the set \(\{e_{ij}|1\leq i,j\leq n\}\) is all of the edges whose endpoints are the pair of vertices \((v_{i},v_{j})\).
* For triangle \(t_{ijk}\), if \(t_{ijk}\in\Omega(v_{i})\), the set \(\{t_{ijk}|1\leq i,j,k\leq n\}\) is all of the triangles incident to vertex \(v_{i}\).
* In particular, for edge \(e_{jk}\), if \(e_{jk}\in t_{ijk}\) and \(t_{ijk}\in\Omega(v_{i})\), the set \(D(v_{i})\) could denote all \(e_{jk}\) which satisfy the condition, as indicated by the blue lines in Figure 2a.  
Figure 2b shows a series of standard local mesh operations. These operations were originally designed for the mesh simplification algorithm [2]. The effect on the local Delaunay properties of the mesh is not considered when the operations are executed. It is necessary to improve them to make these operations more suitable for Delaunay mesh construction and simplification algorithms.
# TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space
## 3 Proposed Framework
### Components  
In Figure 2, we illustrate a simple outline of the proposed 'TrackInk' framework. It is composed of the following components: (i) Sensors, (ii) servos, (iii) input controller, (iv) processing unit, (v) satellite server, and (vi) visualisation server.  
The sensors are used to collect physical parameters of the apparatus, and they help determine where the device is on Earth and its orientation. The servos are used to articulate the system and perform its desired action of indicating the location of satellites and other celestial bodies. The input controller allows user interaction with the system and determines what the device should be doing at any given time. The processing unit determines where the satellite or celestial body is relative to the sensor readings, determining which objects may be overhead, prioritising which to track, and commanding the servos to move to the desired position. The satellite server stores the corresponding satellite data that is used to determine where satellites are in orbit. Finally, the system status and satellite data are sent to a visualisation server, among other values.
# A Multiple and Multi-Level Substructure Method for the Dynamics of Complex Structures
## 4 Analysis of Dynamic Characteristics Based on Substructure
### Modal Experiment of Combustion Rotor and Verification of Substructure Method
#### 4.3.5 Verification of Substructure Method  
The tetrahedral and hexahedral meshes are used to model the combustion rotor. The finite element model has 16,530 nodes and 10,980 elements in total. Our assumption is a homogeneous, isotropic, linearly elastic material. The material parameters are shown in Table 9.  
Based on the analysis in Section 4.1, the center rod of the combustion rotor is selected as the residual structure, and 50% of the external nodes are selected, and then the substructure is divided. The finite element model and substructure division method of the combustion rotor are shown in Figure 15. Free mode analysis is performed on the substructure model and the finite element model, respectively. The first three-order modal shapes and natural frequencies are selected for comparison. From Table 10, it can be seen that the calculation deviation is small. According to the time-consuming comparison of the dynamic characteristic analysis in Table 11, the multi-level substructure model saves 24.37 s compared with the finite element model. Figure 16 is the diagram of modal assurance criteria. The diagonal element of the MAC is 1, and the rest is close to 0, which indicates that the modal shapes are consistent.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Modal Shape** & **Modal Frequency/Hz** & **Damping Ratio/\%** \\ \hline
1 & 1254.9 Hz & 0.166 \\
2 & 2525.2 Hz & 0.216 \\
3 & 3584.7 Hz & 0.067 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Free-mode experimental results of the combustion rotor (three-dimensional).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Elastic Modulus/GPa** & **Density/g-cm\({}^{-3}\)** & **Poisson’s Ratio** \\ \hline Steel & 196 & 7.8 & 0.3 \\ Titanium alloy & 121 & 4.48 & 0.3 \\ Nickel base alloy & 204 & 8.24 & 0.3 \\ Nickel base alloy & 214 & 8.3 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Material parameters of a combustion rotor.  
Figure 16: Modal assurance criteria for the experimental model and substructure model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Natural Frequency/Hz**} & \multicolumn{2}{c}{**Error/\%**} \\ \cline{2-4}
**Order** & **Experimental Model** & **Sub** & **Experimental Model and Sub** \\ \hline
1 & 1254.9 & 1335.7 & 6.4 \\
2 & 2525.2 & 2396.3 & \(-\)5.1 \\
3 & 3584.7 & 3816.4 & 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Frequency and error of each model.  
Figure 15: Finite element model and substructure division of the combustion rotor.
## 3 Results and Discussion
### Links with Heat Released in the Premixed Phase of Main Combustion  
Figure 7 presents the relationship between heat released in the premixed phase of the main combustion, maximum in-cylinder pressure (Figure 7a), soot emissions (Figure 7b) and NO\({}_{\text{X}}\) emissions (Figure 7c).  
A direct correlation between HR\({}_{\text{premix}}\) and p\({}_{\text{max}}\) is always apparent in Figure 7a. This was expected for reducing levels of EGR rate/CDI [1] but it was confirmed in all the tested modes.  
Figure 5: Influence of rail pressure control on pressure diagrams in operating condition No.2 (**a**) and No.3 (**b**).  
Figure 6: Influence of rail pressure control on the rate of heat release in operating condition No.2 (**a**) and No.3 (**b**).  
In test Sets 1 and 2, EGR rate and CDI were controlled. As their level increased, a longer ignition delay with slower combustion was observed, leading to a reduction of HR\({}_{\text{premix}}\) and p\({}_{\text{MAX}}\), which is related to the dilution effect of EGR technique [35; 36]. When only the high-pressure EGR circuit was activated, this outcome was also related to the reduction of intake pressure and mass flow rate [20; 21]. In set 2, the activation of LP EGR loop and the effects of VNT control led to counteracting influence, with different trends of intake quantities, anyway the overall effects on HR\({}_{\text{premix}}\) and p\({}_{\text{MAX}}\) did not change.  
In Sets 3 and 4, rail pressure control led to higher levels of HR\({}_{\text{premix}}\) and p\({}_{\text{MAX}}\) (Figure 5), due to the enhancement of spray development and mixture formation already outlined in Section 1.2. As a consequence, the premixed phase of the combustion was improved.  
This effect had further consequences, as shown in Figure 7b referring to the inverse relationship between heat released in premixed phase and brake specific soot emissions. A lower soot emission is related to the enhanced premixed phase [37], as outlined for Sets 3 and 4 when applying higher P\({}_{\text{rail}}\) levels. Similar behaviour was anyway observed for Sets 1 and 2 as a consequence of EGR control. As already mentioned, the increase of EGR rate had an inverse effect on premixed phase, weakening it and slowing down the whole combustion process. For the same reasons, brake specific NO\({}_{\text{X}}\) emissions (bsNO\({}_{\text{X}}\)) presented a direct relationship with HR\({}_{\text{premix}}\), as depicted in Figure 7c. In this case, a higher sensitivity was observed in Set 1, due to the activation of HP EGR circuit, while lower changes were obtained when a higher EGR rate was considered (Set 2) or when rail pressure control was applied (Sets 3 and 4). By comparing lines referred to operating condition No.3 in Sets 3 and 4, the effectiveness of LP EGR circuit can be observed, as significant reductions were achieved by adding its contribution to the overall EGR rate, as widely discussed in References [20; 21].  
Figure 7: Relationship between HR\({}_{\text{premix}}\) and p\({}_{\text{MAX}}\) (**a**), bsS (**b**), bsNO\({}_{\text{X}}\) (**c**) in different test sets.
## 3 Methodology: Extending the Processing Toolbox
### Route Navigability Assessment Model  
The route navigability assessment model (Figure 5) was developed to assess road network feature completeness by comparing point-to-point distances with actual OSM routing distance based on the heuristic method. This method depends on a sanity check that the routing distance length along a road network should never be shorter than the shortest distance between the two points. Hence, this approach was used to compare the relative difference in the shortest map distances from direct distances between the given origin-destination point pair. The shortest routing function was designed using inbuilt functions of pgRouting ([[http://pgrouting.org](http://pgrouting.org)]([http://pgrouting.org](http://pgrouting.org)), accessed on 10 October 2016) (Version 2.2.3). In the current study, the geo-location of each random point was obtained by the geocoding plugin in QGIS. The origin-destination points table was used as the potential start and end points for calculating the shortest path routes. Further, it is possible that random points fetched may not be connected to the roads or on the roads. Hence, the KNN function from PostGIS ([[http://postgis.net](http://postgis.net)]([http://postgis.net](http://postgis.net)), accessed on 10 October 2016) was used to select the nearest navigable point on the road dataset.  
Figure 5: The route navigability assessment model contains two submodels: “point-to-point direct distance model” and “origin-destination routable shortest distance model”.  
This model (Figure 5) took two inputs: (1) routable graph; (2) points (origin-destination) data table. The first component created a matrix of origin-destination direct distances using the haversine algorithm; whereas, the second component created a matrix for the shortest routable distances between origin-destination points data using the Dijkstra algorithm. The outcomes of the point-to-point and shortest distance algorithm were given to the \"joining attribute table\" processing algorithm, for joining these tables based on the common parameter. Further, a field calculator was used to find the ratio of the shortest distance and point-to-point distance. The last component, based on the heuristic parameter, calculated the number of non-navigable routes due to the error of omission or incompleteness.