esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Mobile Edge Computing for Vehicular Networks +
## 3 Comparison of Task Delegation Scheme with Experimental Results
### Intersection Discovery  
Another task we employed to evaluate our architecture is the discovery of which intersections were encountered by the driver. We accomplished this by creating a database of all intersections in the region in which the vehicle operated. The coordinates of each intersection were found with OpenStreetMap and simple script that leverages the Overpass-turbo API. Figure 5 shows an example of the method we used to retrieve the coordinates of intersections, along with a graphical view of markers denoting intersections in a portion of our target area. The first step in discovering intersection encounters was to define the distance \(r\) from an intersection that the user must be within for a particular intersection to be considered visited. The parameter \(r\) represents the radius of a circle centered around the geographical coordinates of an intersection. If a vehicle passes in this area, the intersection was said to have been visited. As previously mentioned, we utilized a Haversine-based formula that takes  
Figure 4: Example of parsed data in comma separated values (CSV) format.  
Figure 3: Example of raw data in original JavaScript object notation (JSON) format.  
into account the spherical shape of Earth when measuring distance between two locations. Haversine assumes a perfect sphere, so we use Earth's equatorial radius of 6378 km. The distance \(d\) between the vehicle coordinates and the intersection can then be found as:  
\[d=2e\arcsin\left(\sqrt{\sin^{2}(\frac{\phi_{2}-\phi_{1}}{2})+\cos(\phi_{1})\cos( \phi_{2})\sin^{2}(\frac{\lambda_{2}-\lambda_{1}}{2})}\right),\]  
where \(\phi_{1}\), \(\phi_{2}\) are the latitudes of the intersection and the vehicle location, \(\lambda_{1}\), \(\lambda_{2}\) are longitudes, and \(e\) is the radius of Earth.
# Ad Hoc Data Foraging in a Life Sciences Community Ecosystem Using SoDa +
## 3 SoDa Architecture
### Workflow Meta-Language
#### 3.6.1 Process Descriptions  
The process description of a resource captures _how_ the resource functions behaviorally, i.e., the structure of the table it returns, what it needs to compute a table, and how the resource can be accessed. Typically, a workflow consists of a series of resources in which information from one resource moves to the next, each computed using an extract statement in BioFlow. To aid in the automatic construction of such a workflow node, we introduced the create process statement along the lines of Needle's create wetable statements as follows.  
The create process statement below describes the functionalities of the browser-based access to the DisGeNET database. It outlines how this database, when invoked, will return a gene-disease association (GDA). The four required components are the process identifier, the resource address and its features, and the input and output table schemes.  
% process identifier  
create process DisGeNETb  
% access protocol at _[[https://www.disgenet.org/_](https://www.disgenet.org/_)]([https://www.disgenet.org/_](https://www.disgenet.org/_))  
access browser  
postfix /browser/1/1/0/$Genes  
% input table scheme  
accepts table (  
_Genes_ EntrezID) (  
% output table scheme  
_DisGeNETKey_ EntrezID primary key,  
_Disease_ DisID,  
_Type_ string,  
_Disease_class_ string,  
_Score_gda_ decimal (4,2),  
...  
);  
This statement helps to programmatically construct the endpoint (such as [[https://www.disgenet.org/browser/1/1/0/29974/](https://www.disgenet.org/browser/1/1/0/29974/)]([https://www.disgenet.org/browser/1/1/0/29974/](https://www.disgenet.org/browser/1/1/0/29974/)) (accessed on 1 December 2024) for a gene, e.g., A1 CF, using its Entrez ID 29974. We would like to note that the DisGeNET database recently changed their access protocol and now this end-point must be constructed as [[https://disgenet.com/search?view=GENES&idents=29974&source=ALL&tab=GDA](https://disgenet.com/search?view=GENES&idents=29974&source=ALL&tab=GDA)]([https://disgenet.com/search?view=GENES&idents=29974&source=ALL&tab=GDA](https://disgenet.com/search?view=GENES&idents=29974&source=ALL&tab=GDA)) (accessed on 1 December 2024)), which is needed to access the gene-disease association of a given gene from the DisGeNET database. The $ sign in the postfix clause provides possible substitutions in the accepts clause. On the other hand, the process description for API access by genes, is constructed as follows in a similar but distinct way. Depending on the access type we wish to adopt, an algorithmic construction of retrieval protocol is now possible from either of these statements.  
create process _DisGeNETa_  
at _[[https://www.disgenet.org/api/_](https://www.disgenet.org/api/_)]([https://www.disgenet.org/api/_](https://www.disgenet.org/api/_))  
access API  
postfix /gda/gene/  
authorization _keyK_  
accepts table (  
_Genes_ EntrezID) (  
_DisGeNETKey_ EntrezID primary key,  
_Disease_ DisID,  
_Type_ string,  
_Disease_class_ string,  
_Score_gda_ decimal (4,2),  
...  
);
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 6 Resource Utilization Analysis
### Dataset Allocation in PSRAM  
The training dataset is stored in external PSRAM due to its larger capacity and faster access compared to internal Flash or SD cards, as demonstrated in our previous work [57]. Because of limited memory, we used a reduced MNIST dataset containing 1000 images, each resized to 196 pixels. Stored as normalized 32-bit floats, the image data occupies approximately 784 KB (\(1000\times 196\times 4\) bytes). Dataset labels, used here solely for experimental evaluation, add about 1 KB (\(1000\times 1\) byte). In practical deployment, these would not be stored as data is collected in an unsupervised manner.
## 4 Materials and Methods
### Feature Factor Selection
Since MEA is highly correlated with the \(-\)Y wing output current, only the \(-\)Y wing output current is retained to avoid information redundancy. 3. Additionally, although temperature-related parameters (PC1, PC2, PC7, PC8) are highly correlated with the \(+\)Y wing output current prediction, their correlation with the \(-\)Y wing load current is equally significant, so temperature factors are excluded. Regarding the exclusion of MEA as a feature factor, we conducted an experiment. We incorporated both load current and MEA voltage as feature factors in the LSTM model for Satellite A, and compared its performance with the model using only load current. The differences were quantified using three metrics: RMSE, MSE, and MAE (see specific data in Table 8). The experimental results showed that the prediction accuracies of the two feature combinations are generally comparable, while the model including MEA voltage slightly underperformed the model using only load current in all metrics. These results indicate that although MEA voltage exhibits a high Pearson correlation with the solar array output current, the information it contains can be indirectly reflected through the load current (because the MEA voltage also has an extremely high correlation with the load current). Moreover, the additional inclusion of MEA may slightly reduce the model's generalization ability due to feature redundancy or noise interference. The results of LSTM prediction for solar arrays using load current and MEA as characteristic factors are shown in Figure 8 below. Through this ablation experiment, we further confirmed that in short-period prediction scenarios, load current, as a core feature, can already capture the dynamic fluctuation characteristics of the solar array output current, and its sole use is sufficient to meet the requirements of prediction accuracy. The final feature sets are determined as follows:  
Normal satellites: \(X=[I_{load}]\to Predict_{Y+},I_{Y-}\); \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Load** & **Load + Mea** \\ \hline MSE & 0.000970 & 0.001078 \\ RMSE & 0.031142 & 0.032826 \\ MAE & 0.024311 & 0.025985 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of LSTM prediction with and without MEA as a feature factor on Satellite A.
## 2 Theoretical Background
* Logic and Proof: an (automated) reasoning system built on top of the ontology structure with the purpose of inferring new relationships. Therefore, a software agent can deduce whether a certain resource fits its needs by utilizing such a system (and vice versa). Trust: This component is the final layer of the stack, and it refers to the issues of trust and trustworthiness of the information [16]. There are two main approaches regarding trust, the one is based on policy and the second on reputation [17]. Nowadays technology, trust and proof are regarded as the most emerging research areas of the Semantic Web [17]. Semantic Web technologies are regarded as an approach to manage knowledge by utilizing ontologies and semantic web standards, allow individuals to establish data repositories on the Web, create vocabularies, and write rules for data processing. Linked data are assisted by technologies such as RDF, SPARQL, OWL and SKOS [18]. Additionally, a very classic but perhaps outdated framework is the Ranking Semantic Search framework (RSS). This framework enables ranking of the search results on the Semantic Web and, through the use of novel ranking strategies, avoids returning disordered search results [19]. Semantic Web technologies can be applied to a website so it will be more easily readable and accessible by search engines for better Search Engine Optimization (SEO). For instance, website owners or content managers can enhance their text descriptions with semantic annotations and check if this leads to a more satisfying user experience. Towards this end, [PERSON] et al. [20] investigated e-commerce websites and whether there is a correlation between the enhancement of product text descriptions with semantic annotations and the perceived consumers' satisfaction. Their study concluded that the inclusion of semantic web elements in the products descriptions is important for a more pleasant customer experience. In fact, one of the most interesting findings was that the consumer regards knowledge graphs as having high significance in an e-commerce website [20]. A way to add information that is machine-understandable to Web pages that is processed by the major search engines to improve search performance is schema.org [21]. Schema.org's wide adoption is related to its promotion by major search engines as a standard for marking up structured data in HTML web pages [22]. This adoption addresses a fundamental issue for the Web, by making it easy to annotate data within websites, at least for the most common types of Web content [23]. Although using Semantic Web technologies will lead to a more pleasant and usable user experience, it is not certain that this automatically means an improvement in terms of popularity. It is a fact that SEO techniques are used in order to improve a website searchability and consequently popularity, but it is not certain that utilizing Semantic Web technologies will automatically result in increased popularity. This is what this research tries to shed light on.
## 4 Drug Delivery Systems--Applications in Infectious Diseases
### Ultrasound- and Light-Sensitive DDS  
A combination of prolonged antibiotic elution with on-demand release of ceftaxone when triggered with therapeutic ultrasounds was demonstrated for \"smart\" polylactic acid films with ceftaxone-containing microchamber arrays [26]. Thiol chitosan-wrapped gold nanoshells, triggered by near-infrared laser, have been tested as photothermal antibacterial agents for Gram-positive (_S. aureus_) and Gram-negative (_P. aeruginosa_ and _E. coli_) antibiotic-resistant pathogens [27]. A photodynamic nano-assembly of chlorin e6 covalently conjugated with chitosan exhibited a bactericidal action against methicillin-resistant _S. aureus_ (MRSA) and _Acinetobacter baumanni_, with therapeutic efficacy comparable to that of vancomycin in MRSA infection murine models [28]. Gold nanoshells functionalized with carboxylate-terminated organosulfur ligands have been shown to be effective in killing _E. faecalis_ on silicone-based catheters [29].  
Furthermore, photothermal destruction of bacteria has also been tested in combination with triggered antibiotic release. Specifically, an injectable hydrogel-based drug reservoir containing ciprofloxacin-loaded photothermal polydopamine nanoparticles mixed with glycol chitosan was tested in a mouse skin defect _S. aureus_ infection model. The hydrogel combines the trapping of bacteria on its surface and the release of the antibiotic while also inducing local hyperthermia when triggered with near-infrared light irradiation [30].
## 3 Results
### Performances of H-LUE in Different PFTs  
We tested the performances of the H-LUE model in different PFTs on the test dataset, and the results are shown in Figure 6. The H-LUE showed significant differences in performance among different PFTs. The best model performance was observed in C3 crops, C4 crops, and deciduous broadleaf forests, with an R\({}^{2}\) value of 0.92. However, for evergreen broadleaf forests, savannahs, and shrubs, the H-LUE showed low performance and captured only small variations in GPP with R\({}^{2}\) values of 0.43, 0.37, and 0.51, respectively. Additionally, the H-LUE showed intermediate performance in the remaining four PFTs (i.e., evergreen needleleaf forests, grasslands, mixed forests, and wetlands). In general, the H-LUE showed satisfactory performance across PFTs. However, poor performance in a few vegetation types should also be noted.  
Figure 4: Modeled light use efficiency (LUE from MLP, VPM, and VPRM) vs. LUE obtained by EnKF for the test dataset. The color bars represent the density of points within a circle area with a radius of 0.25 gC M\({}^{-1}\).  
Figure 5: Evaluation of three models (H-LUE, VPM, and VPRM) when estimating daily gross primary productivity (GPP) on the test dataset. The color bars represent the density of points within a circle area with a radius of 1.0 gC m\({}^{-2}\) d\({}^{-1}\).
## Appendix B Correlator Output Variance Derivations
### Data Bits Aligned  
In this case, the variance at the output of correlator will be:  
\[E\Big{\{}|y_{k}|^{2}\Big{\}} \tag{14}\] \[=\int_{kT_{b}}^{(k+1)T_{b}}\int_{kT_{b}}^{(k+1)T_{b}}\sum\limits_{ p=-\infty}^{\infty}p_{T_{c}}(t-2046 kT_{c}-vT_{c})e^{2\pi fs}e^{-2\pi ft}dsdt\] \[=\int_{kT_{b}}^{(k+1)T_{b}}e^{2\pi fs}\int_{kT_{b}}^{(k+1)T_{b}} \sum\limits_{p=-\infty}^{\infty}p_{T_{c}}(t-2046 kT_{c}-vT_{c})e^{-2\pi ft}dtds\]  
the inner part of which is a Fourier transform:  
\[\mathcal{F}[p_{T_{c}}(t)]=T_{c}\sin c(fT_{c})e^{-j\pi fT_{c}} \tag{15}\]  
and therefore we have:\[E\Big{\{}\left|y_{k}\right|^{2}\Big{\}} \tag{122}\] \[=T_{c}\sin c(fT_{c})e^{-j\pi fT_{c}}\sum\limits_{k=0}^{19}e^{-j2\pi fkT }\int_{kT_{b}}^{(k+1)T_{b}}e^{j2\pi f(s-vT_{c})}dtds\]  
where:  
\[\sum\limits_{k=0}^{19}e^{-j2\pi fkT}=\frac{1-e^{-j2\pi fT_{b}}}{1-e^{-j2\pi fT}} \tag{123}\]  
where exponential sum formula is applied. Without loss of generality, we focus on the first data bit:  
\[\begin{array}{l}\int_{0}^{T_{b}}e^{j2\pi f(s-vT_{c})}ds\\ =\sum\limits_{k=0}^{19}\int_{kT}^{(k+1)T}e^{j2\pi f(s-(\lfloor s/T_{c}\rfloor- 2046\lfloor s/T\rfloor)T_{c})}ds\\ =\sum\limits_{k=0}^{19}e^{j2\pi fkT}\sum\limits_{m=0}^{2045}\int_{mT_{c}}^{(m+ 1)T_{c}}e^{j2\pi f(s-\lfloor s/T_{c}\rfloor T_{c})}ds\\ =\sum\limits_{k=0}^{19}e^{j2\pi fkT}\sum\limits_{m=0}^{2045}\int_{0}^{T_{c}}e ^{j2\pi f_{s}}ds\\ =2046T_{c}\sin c(fT_{c})e^{j\pi f_{c}}\sum\limits_{k=0}^{19}e^{j2\pi fkT}\end{array} \tag{124}\]  
which assumes \(k=0\) in Equation (122), and finally we have:  
\[\begin{array}{l}E\Big{\{}\left|y_{k}\right|^{2}\Big{\}}\\ =T_{c}[\sin c(fT_{c})]^{2}\cdot\frac{1-e^{-j2\pi fT_{b}}}{1-e^{-j2\pi fT}}\cdot \frac{1-e^{j2\pi fT_{b}}}{1-e^{j2\pi fT}}\\ =T\cdot T_{c}\cdot\frac{\sin^{2}(\pi fT_{c})}{(\pi fT_{c})^{2}}\cdot\frac{\sin ^{2}(\pi fT_{b})}{\sin^{2}(\pi fT)}\end{array} \tag{125}\]  
which is the variance at the output of correlator. Next, we will derive analytical form of correlator output variance when the data bits are misaligned by an integer number of code chips.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 4 Property Analysis of Moving Target in the Complex Image Domain
### Along-Track Interferometry Phase
One can find out that the ATI phase is proportional to \(v_{tr}\), while the variation of the ATI phase with \(v_{ta}\) is very slight. In addition, the variation of ATI phase with the target's distance and radial acceleration are shown in Figure 6b,c. One can see that the ATI phase is inversely proportional to \(R_{0}\), and that the ATI phase is proportional to \(a_{tr}\) when there is Doppler ambiguity.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 3 Results
### Routine Daily SSH Products from iGNSS-R Altimeter  
Unlike conventional radar altimeters that provide continuous along-track measurements, GNSS-R interferometric altimetry produces segmented observations with variable durations dependent on the elevation angles of visible GNSS satellites. Tracking of individual satellites terminates when their incidence angles fall below 35\({}^{\circ}\), at which point the system switches to alternate satellites. Figure 10 demonstrates the typical daily distribution pattern of these GNSS-R interferometric altimetry products. The system's capability to simultaneously track up to four GNSS satellites enables concurrent observation of up to four distinct sea surface height swaths, representing a key advantage over traditional single-beam altimeters.
## 1 Introduction
The observation methods for fault slip and deformation can be divided into contact type (such as resistance strain gages or displacement gages) and non-contact type (e.g., the digital image correlation method). The contact type observation requires the sensors to be in contact with the sample, while the non-contact type observation allows the sensor to be separated from the sample. The contact type sensor occupies a certain area, which limits the number of sensors used in measurement. For instance, only dozens of strain gages can be used to cover a fault of tens of centimeters long, which were usually used in previous experimental studies [18; 19; 21; 22; 23; 24]. However, via the digital image correlation (DIC) method, thousands of pixels can be used by camera photography to observe the slip and deformation along a fault of equivalent length [25; 26; 27; 28; 29; 30]. Therefore, compared with the contact type observation, the spatial sampling rate of the DIC method is dramatically higher. On the other hand, the measurement precision of the contact type sensor is usually higher than that of the non-contact type sensor. For example, the measurement precision of the strain gage can easily reach 1 \(\mu\epsilon\) (micro-strain) [23], which is difficult to achieve by the DIC method. Therefore, it will be effective and economical for measurements to comprehensively utilize the two types of observations via making their respective advantages and verifying the results of each other. Thus, the DIC method combined with strain gages are used in this study to observe the detailed spatiotemporal evolution of ruptures along a bent fault.
# A Novel Approach for the Definition of an Integrated Visual Quality Index for Residential Buildings
###### Abstract  
Visual quality is an important component of indoor environment quality and greatly affects inhabitants' perception of the living space. Nevertheless, the lighting design of residential buildings is usually underestimated by both designers and standards. This paper presents an integrated index for evaluating the visual quality of an indoor environment in residential buildings. The main parameters considered are daylight access, probability of glare from daylight and electric illumination, maintained illuminance, and color temperature. These aspects are evaluated throughout a whole year using a fixed set of metrics and are combined using relative weights. It was decided to also consider qualitative parameters in order to give more importance to some psychological aspects of visual comfort. Finally, the building visual quality index is obtained by a weighted average of the results of each room with the addition of the qualitative parameters. The applicability of the index was tested through the simulation of a dwelling that will be built and monitored in the context of the Comfort for Sustainable Buildings in the Alps (CASA) research project. The building visual quality index proved to be useful during the design process, allowing for improvements in both electric illumination and daylight access through a comparison of different solutions. This study could represent a step towards the holistic evaluation of indoor environment quality.  
visual comfort; visual quality assessment; residential buildings; classification index; building performance simulation +
Footnote †: journal: Applied Sciences
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 4 Network Flow Programming Models  
This section serves as a foundation for many types of optimization problems in WSNs, specifically, routing problems. Many of the routing design problems in WSNs problems are formulated as network flow problems. The examples that we provide later in part III of this paper, also include non-linear network flow problems. In order to understand the basics of this class of optimization problems, We believe it is very important to provide the linear network flow problems and their special cases, since nonlinear network flow problems extend the concepts in linear ones.  
A network is a set of nodes joined by a set of arcs which carry flow, where the total flow entering each node must equal the total flow leaving each node. One optimization problem that is defined on a network is to determine the assignment of flows to the arcs such that the total cost of the flow is minimized. This is called the network flow programming (NFP) problem or, alternatively, the minimum-cost flow problem. The cost in WSNs could be the average end to end delay from a sensing node to a sink node when considering a delay sensitive design problem. Another possibility can be the amount of battery energy consumed in all sensor nodes while performing RF transmissions for routing data to a sink node. Also, the network flow problem can be a maximization problem in which we desire the maximization of the throughput for all sink nodes.  
NFP can be considered a special case of LP, where an equivalent graphical model is more appropriate and suitable in modeling the LP network problem due to its special structure. Problems that can be modeled with NFP include some of the classical problems of optimization such as the assignment problem, the shortest path problem, the maximum flow problem, the pure minimum-costflow problem, and the generalized minimum-cost flow problem. The network is a compact graphical representation that allows the analyst to quickly visualize the essence of the problem as shown in Figure 2 for example. When a situation can be modeled entirely with NFP, very efficient algorithms are available for solving the associated optimization problem, many times more efficient than the standard simplex method with respect to computational effort and storage requirements. Figure 3 shows a taxonomy of the different NFP subclasses as well as a popular special structure problem known as the _minimum spanning tree problem_. The figure shows their inter-relations and the corresponding solution algorithms. Further explanation is provided on those later in this section.