esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Input Data
### Twilight Model and Coefficients  
The RSF during twilight conditions is derived with an empirical model in which it linearly depends on the solar zenith angle (\(\theta_{0}\)). A regression model (Equation (1)) mimics the average flux-\(\theta_{0}\) relation in the instantaneous CERES Single Scanner Footprint (SSF) TOA shortwave fluxes (in Wm\({}^{-2}\)), with \(\theta_{0}\) ranging between 84-86.5\({}^{\circ}\) for (parts of) years 2004, 2005, 2007, 2008, 2011, 2012. The regression model is written as follows:  
\[F_{twilight}=A+(\theta_{0}-84^{\circ})\cdot B, \tag{1}\]  
where \(F_{twilight}\) stands for Flux during twilight conditions (Wm\({}^{-2}\)), \(A\) and \(B\) are the twilight coefficients, and \(\theta_{0}\) is the Solar Zenith Angle (degrees). The models are created for five different Twilight (TWL) surface types separately for clear-sky and overcast conditions. Clear-sky is defined as an SSF footprint with 0% cloud cover and overcast as 100% cloud cover. The sample size (\(n\)) for each scene type is given in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Data Type** & **Data Source** & **Data Accessibility** \\ \hline FDR AVHRR radiances \(\rho_{0.6}\), \(\rho_{0.8}\) & EUMETSAT * & PPS (v2018-patch5) L1c output \\ Cloud products & CNASF CLARA-A3 & PPS (v2018-patch5) L2 output \\ Snow depth & ERA5 reanalysis [7] & C3S Climate Data Store ** \\ Wind speed & ERA5 reanalysis [7] & C3S Climate Data Store ** \\ Sea ice conc. & OSI SAF [30] & Archive HL FTF server ** \\ TSI & C3S/RMIB [2,31] & C3S Climate Data Store \\ COT climatology & CERES EBAF [32] & [[https://ceres.larc.nasa.gov/](https://ceres.larc.nasa.gov/)]([https://ceres.larc.nasa.gov/](https://ceres.larc.nasa.gov/)) (accessed on 1 August 2021) \\ \hline Land cover map (30\({}^{\circ}\) res.) & IGBP classification applied & USGS Earth Explorer \\  & on GLCC database [33,34] & \\ Land cover map (10\({}^{\circ}\) res.) & CERES surface type map & pers.comm. NLoeb [21] \\ ADMs & CERES [35,36] & [[https://ceres.larc.nasa.gov/](https://ceres.larc.nasa.gov/)]([https://ceres.larc.nasa.gov/](https://ceres.larc.nasa.gov/)) (accessed on 1 August 2021) \\ \hline \end{tabular}
* * Processed by EUMETSAT using the CMSAF PyGAC tool [22]. ** These variables are in theory accessible on those locations, but were in practice regrided to the orbit grid and made available to the authors in that format (together with the PPS output).  
\end{table}
Table 1: External input data: overview.  
The models are shown in Figure 3 for clear-sky conditions (left) and overcast conditions (right): the solid lines represent the observations as \(\theta_{0}\) bin averages, demonstrating the linear nature of the flux-\(\theta_{0}\) relation, and its scene type dependency. From these observations, linear regressions were derived to extrapolate the flux for \(\theta_{0}\) higher than 86.5\({}^{\circ}\) (dotted lines). The intercept (\(A\)) and slope (\(B\)) of these regressions are listed in Table 2.
## 4 Full Dimensioning of V2G Energy Resource
### Technical-Economic Dimension
Considering these values for the USA, it would take just 10.5 years for a homeowner to recover that value, thanks to the revenues generated by the V2G (disregarding other benefits of ownership of an EV, such as reduced fuel costs). At the same time, note that frequency regulation prices are highly variable over time and for electricity networks. * Monitoring and control equipment: As the technology (V2G charging unit) already has all the monitoring and control equipment built-in, the cost, in this case, will be zero. * Operation and Maintenance: This is around 5% of the cost of implementation [20]. * Lifetime: There are no conclusive studies regarding the impact of V2G on battery life. A question that remains is whether participation in the V2G also affects the battery life. In [21], it is argued that the use of vehicle batteries for V2G energy incurs approximately half the loss of capacity compared to the fast cycle found while driving. The percentage of loss of capacity (by normalized Wh or processed Ah) is quite low: 0.006% for conduction support and 0.0027% for V2G support. The analysis shows that several thousand driving days/V2G incur substantially less than 10% loss of capacity, regardless of the amount of V2G support used. The extent of the impacts can be less, zero, or even improve the health of the battery [20; 22]. Disregarding the battery degradation due to the V2G and only taking into account the useful life of this battery due to the use of the car, the value is about 8 years (or according to the recharge cycle indicated by the manufacturer).
## 3 Results and Discussion
### Characteristic Time of Magnetic Particle Alignment  
The time evolution of \(\varphi(t)\) for the simplest model, the ODE model, involving the inertial, magnetic, quasi-steady viscous, and no stochastic Brownian torques, was used as the input data for the least-square minimization fit using a fitting function:  
\[\varphi(t)=\frac{\pi}{2}\exp\left(-\frac{t}{\tau_{\text{char}}}\right) \tag{18}\]  
with the founded parameter \(\tau_{\text{char}}\) for each \(B_{0}\). The obtained fits with estimated values of parameter \(\tau_{\text{char}}\) are shown in Figure 4a-g. This parameter \(\tau_{\text{char}}\) has the meaning of time \(t\) when the angle \(\varphi(t)\) reduces to the value of \(\frac{\pi}{2e}\) and, therefore, can be denoted as the characteristic time of magnetic particle alignment. The obtained fits from simulations are  
Figure 3: **(a)** Time evolution of \(\varphi(t)\) for \(N=10^{3}\) samples of the SDE model, i.e., inertial, magnetic, quasi-steady viscous, and stochastic Brownian torques in the weak \(B_{0}\) field limit (timestep \(h=10^{-5}\,\text{s}\)). **(b)** Comparison of time evolution of mean and std values of \(\varphi(t)\) averaged over \(N=10^{4}\) samples of the SDE model for different finite timesteps \(h\).  
in good agreement for the whole range of concerned \(B_{0}\), except for its strong limit case (\(B_{0}=5000\) mT case in Figure 4g).  
The estimated values of \(\tau_{\text{char}}\) for each \(B_{0}\) were further used to find the expression of their dependence. The following fitting function was used:  
\[\tau_{\text{char}}(B_{0})=\frac{C}{B_{0}}. \tag{19}\]  
Parameter \(C\) was found from the least-square minimization fit with the value \(C=0.169\) us T (see Figure 4h).
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### FHE System Setup and Performance  
The core of our FHE analysis is understanding the setup costs and performance trade-offs. For our evaluation, we used a standard 128-bit security parameter set, with a polynomial degree of 8192. The one-time setup costs are detailed in Table 2.  
The findings show that the client-side context initialization is a fast, one-time operation. The main overhead is the 33.83 MB of public key materials that must be sent to the server once per session. This cost is amortized, meaning that its impact becomes negligible in sessions where many images are processed.  
The 33.83 MB size of the public materials represents the primary bandwidth cost of the setup phase. In our model, this cost is incurred only once per session and is thus amortized over all subsequent image processing transactions. A naive, non-amortized approach, where these materials are transmitted with every image, would be prohibitively expensive for any interactive application. This amortization strategy is therefore crucial for the framework's practical viability, especially in batch processing scenarios.
## 3 Results
### Sem/edx  
The surfaces of both BC-HiFlow (Figure 3A) and BC-EndoSequence (Figure 3B) had a nearly similar structure before immersion in water. They showed a homogenous structure of granular mass. The surface of Adseal showed uniform, homogenous structure of irregular small particles (Figure 3C). The bright particles of radio-opacifiers (ZrO\({}_{2}\)) were distributed over the surface of all sealers. The intra-granular voids and pores were detected in all sealers where more prominent in Adseal surface.  
The elemental analysis determined that all tested sealers contain carbon (C), oxygen (O), silicon (Si), phosphorous (P) and calcium (Ca) peaks with different wt.% (Figure 3D-F). Before immersing (day 0), the surface of the set sealers contained high C peak and low O and Ca peaks (\(p<0.001\)). The significant greater C, P peaks were recorded by Adseal, while the significant high Ca peak was recorded by BC-EndoSequence (\(p<0.001\)). The Si peak was significantly high in BC-HiFlow and low in BC-EndoSequence. The P peak was significantly high in Adseal at \(p<0.001\), while low in both BC-sealers with no significant difference between them (\(p=0.751\)). As a radio-opacifier, BC-HiFlow contains significant higher zirconium (Zr) peak than BC-EndoSequence, while Adseal contained bismuth (Bi) peak (\(p<0.001\)).  
The development of porous hierarchical structure (calcium carbonate) was shown on BC-HiFlow surface. The spheroidal and rod-like (aragonite) crystalline structure was detected over the BC-EndoSequence surface with the development of cubic shaped crystals (calcium hydroxide) (Figure 3K), while rectangular shaped-crystals (calcium phosphate) were shown on the Adseal surface after immersion (Figure 3G-I). The voids and pores were predominant on the surfaces of all tested sealers with different distribution at the end of water immersion time (at day 28), (Figure 3-L). The greatest value was observed on the Adseal surface (Figure 3L).  
After immersion in water (day 28), the elemental analysis (EDX) of BC-HiFlow (Figure 3M) showed decrease in C, Si and Zr, while significantly increase in P and Ca peaks (\(p<0.001\)). The spectra of BC-EndoSequence (Figure 3N) showed marked decrease in C, but significant increase in Si, P, Zr and Ca. The spectra of Adseal (Figure 3O) showed significant increase in Si and P peak, while decrease in Bi (\(p<0.001\)), and no significant changes in Ca peak (\(p>0.05\)).  
Figure 2: FTIR spectra at 4000–2000 and 2000–400 cm\({}^{-1}\) of BC-HiFlow (**A,B**), BC-EndoSequence (**C,D**) and Adseal (**E,F**) respectively showed their composition.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 4 Materials and Methods
### Datasets  
To ensure comprehensive and robust evaluation, our experiments were conducted on three benchmark datasets: FLIR, KAIST, and M\({}^{3}\)FD. These datasets represent a wide range of multispectral detection challenges under varying environmental and lighting conditions.
## 4 Performance Evaluation  
Checking the optimality of the applied threshold resulting from different techniques provides objective facts for validating the good segmentation result. There are two types of evaluation in this regard: an unsupervised evaluation method that can be used without prior references for the segmented images and the supervised evaluation, which needs references for comparing pixels and matching them with the desired locations in the reference images. A segmentation benchmark for the evaluation ranges between 0 and 1 which reflect poor and precise results, respectively. The best results occur if the evaluation scores are maximized.
# Predicting Software Cohesion Metrics with Machine Learning Techniques
## 2 Related Work
In this case, a numerical value was unable to be obtained by limiting cohesion with only three scales. The SCCM (scoped class cohesion metric) [17] was proposed in the study by Wanjiku et al., and a tool was developed to calculate this metric. The proposed metric is normalized between 0 and 1. Calculation is provided using numbers of public, private, and protected methods, as well as attributes. With the total value of general and specific cohesion, overall class cohesion was calculated. It is thought that calculating the numbers of private and protected methods and attributes separately and combining them later requires more formulas and effort. Chen et al. [18] recommended the OntRECoh (Online Ontological Cohesion Assessment for SRE Design Quality) tool in their study. This approach is designed to calculate the cohesion values of the system and to offer improvement suggestions with a web-based interface. In this study, with reverse engineering after the coding, the required design was created with the UML diagram. While it is possible to calculate the source-code-based cohesion, it is thought that it will require extra time and effort to create the redesign diagram over the code created by reverse engineering and then calculate the cohesion. Many studies have been carried out in the literature to measure the cohesion value of the software. Considering the studies, it is seen that software cohesion has been attempted to be measured with new formulas or tools. Not every tool measures every metric, and thus the user should look up which tool measures which metric, which is a time-consuming process. The aim of our study was to propose a machine-learning-based methodology for predicting cohesion metrics so that our methodology can be easily applied by the researchers. To the best of our knowledge, there is no study in the literature that predicts cohesion metrics using machine learning techniques, and thus our study is the first in this manner.
# Stimuli-Responsive Photonic Crystals
## 2 Theoretical Background
In Figure 1, we show the simulation of the angular dependent transmission of a 1D photonic crystal, consisting of the alternation of five bilayers of TiO\({}_{2}\)-SiO\({}_{2}\) nanoparticles (filling factor = 0.7). The wavelength-dependent refractive index of TiO\({}_{2}\) is given by \(n_{TiO_{2}}(\lambda)=\left(4.99+\frac{1}{96.6\lambda^{1.7}}+\frac{1}{4.60(1 ^{3.95})}\right)^{1/2}\)[14]. From these simulations, it is also possible to determine the electric field at the \(k\)th interface [15].
###### Abstract  
This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during winter in Johannesburg is considered for the simulation using a genetic algorithm (GA). Results obtained demonstrate the effectiveness of demand response aggregators in maximizing the benefits on both sides of the electricity supply chain.  
demand response; day-ahead market; renewable energy sources; genetic algorithm; aggregator +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions
### Adhesion Work and Spalling Work  
As shown in Figure 14, the adhesion work and spalling work between PU and aggregates were calculated using Equations (7) and (8). The adhesion work for the PU-basalt interface is higher than that for the PU-limestone interface, indicating better adhesion properties at the PU-basalt interface. The probable reason for this result is that the surface of basalt contains a high proportion of silicate components, which react readily with the isocyanate groups (-NCO) in the PU to form stronger chemical bonds. However, limestone has a more chemically stable surface and is relatively less reactive with PU. When water is added, the spalling work of the PU-basalt interface is less than that of the PU-limestone interface. This indicates that the water stability is better when basalt is used as an aggregate for PU mixtures. Under acidic conditions, the spalling work of PU-basalt and PU-limestone increased by about 5%, implying a reduction in the adhesion properties of PU to aggregates in an acidic environment. A possible reason for this result is that the acid reacts with the metal oxides on the aggregate surface, weakening their ability to chemically bond with the PU.
## 2 Materials and Methods
### Classification and FTIR Analysis of Collected Objects  
All detectable plastic litter was collected in plastic bags on each beach, and the bags were labelled. Afterwards, plastic litter was brought into the laboratory to sort all the objects according to their polymer type (after visual sorting or FTIR analysis), size (measured as the length of the object on the longest axis), and the category of objects (e.g., food packaging, toys, bottles, Figure 2). More specifically, each sampled object was categorized in terms of the function of its use, as (i) disposable objects, (ii) everyday objects, and (iii) packaging. Polymeric material such as Styrofoam and polyurethane were classified as \"packaging\" and \"building material\", respectively. Plastic pieces and fragments of unclear sources were classified as \"unknown material\" instead.  
According to the dimensions of the considered plastic objects (>5 mm), the possibility of sample contamination by the bags was ruled out after an evaluation of the bag integrity after sample categorization.  
To characterize the polymer type, some objects were directly recognized using visual inspection, due to the presence of their recycling code. For other samples, attenuated total reflectance infrared spectroscopy (ATR-FTIR) was performed to recognize the polymer matrix, due to the fingerprinting of the absorption of specific infrared wavelengths by the polymeric functional groups [35]. Spectra were obtained after rinsing the objects with ultrapure water, to remove loosely adhered material. Then the samples were left to air-dry for 24 h and were analyzed with a Thermo Scientific(M Waltham, MA, USA) Nicolet(tm) iS(tm) 10 FTIR Spectrometer, performing 32 scans in the 4000-650 cm\({}^{-1}\) spectral range. The achieved spectra were then compared with those collected in the Open Specy database [52] to recognize the sample. To validate the polymer recognition, the robustness of the Pearson correlation coefficient (R) was considered: polymer matching was assumed to be accurate only for R values > 0.75.  
A detailed list of sampled items is reported in Supplementary Materials (Table S1).  
Figure 2: Example of litter sorting and categorization (from sampling point CE).
# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 3 Connection-Aware Policy Mechanism (CAP)
### Justifications for the Dynamic Adjustments of the Parameter Weights
\begin{bmatrix}\beta_{11}&\beta_{12}&\beta_{13}&\beta_{14}&\beta_{15}&\beta_{ 16}\\ \beta_{21}&\beta_{22}&\beta_{23}&\beta_{24}&\beta_{25}&\beta_{26}\\ \beta_{31}&\beta_{32}&\beta_{33}&\beta_{34}&\beta_{35}&\beta_{36}\\ \beta_{41}&\beta_{42}&\beta_{43}&\beta_{44}&\beta_{45}&\beta_{46}\\ \beta_{51}&\beta_{52}&\beta_{53}&\beta_{54}&\beta_{55}&\beta_{56}\\ \beta_{61}&\beta_{62}&\beta_{63}&\beta_{64}&\beta_{65}&\beta_{66}\\ \end{bmatrix} \]  
Example  
* For Audio (k = 1), SINR and delay might be prioritized
* For AR (k = 4), the data transmission rate and delay are crucial. * For DataOff (k = 5), the data transmission rate and delay might have higher factors. Calculation Example: To determine the weight for each parameter \(\mathbf{P}_{i}\) in an urban network (_j_ = 1), the connection context is AR (_k_ = 4). 1. Find the base weights for urban networks: \[\alpha_{11},\alpha_{21},\alpha_{31},\alpha_{41},\alpha_{51},\alpha_{61}\]
2. Find the adjustment factors for the AR context: \[\beta_{14},\beta_{24},\beta_{34},\beta_{44},\beta_{54},\beta_{64}\]
3. Compute the final weights: \[\mathbf{w}_{i14}=\mathbf{\alpha}_{i1}\cdot\beta_{i4}\text{ for }\mathbf{i}=1,2,3,4,5,6\]  
This formula can be used to dynamically assign weights to network connection quality parameters based on the connection context policy and type of network being used. By adapting the weights to certain circumstances, the proposed mechanism can significantly increase the reliability and performance of handovers in 5G V2X networks.
###### Abstract  
Data-driven intelligent fault diagnosis has made considerable strides. However, collecting sufficient fault information in real production data is extremely challenging. Therefore, a novel method of bearing fault diagnosis based on two-dimensional (2D) images and cross-domain few-shot learning is proposed. Initially, the approach uses multiscale morphology to convert the bearing's one-dimensional (1D) vibration signal into a 2D image, which preserves the whole information. Second, to address the issue of limited bearing fault data, we extend a substantial amount of natural image knowledge to the converted 2D image based on the improved cross-domain few-shot learning method. A distance-based classifier is employed to prevent the problem of overfitting owing to insufficient data to improve the approach's classification capacity with few samples. The experimental results demonstrate that, with the limited dataset provided, our method outperforms other prevalent methods and has high feasibility and certain engineering applications.  
cross-domain few-shot learning; multiscale morphology; 2D image; bearing; fault diagnosis 2023
# Information-Aware Secure Routing in Wireless Sensor Networks
## 2 Related Work
According to graph theory, a sensor network can be regarded as a graph, sensor nodes can be represented by a set of vertices, and links between sensor nodes are represented by a set of edges between vertices. Trust values are assigned to nodes on the basis of the trust of the adjacent nodes [32; 34]. Weighting trust model. The trust value for each sensor node is determined by the weighted average of the observation values of the sensor node's past behaviors. With the trust value, we can assess the credibility of the sensor node and improve the capability of intrusion tolerance in WSNs [35; 36; 37]. In addition, there are some other types of secure routing models. The authors of [38] developed a secure routing protocol named the Trust-Distrust Protocol. Trust (or distrust) of the nodes is graded according to the fitness value obtained by tests and calculations in the protocol. The authors of [39] proposed a trust-based scheme that enforces collaborative behavior in wireless mobile networks by taking peers' attributes into consideration for an efficient routing scheme. A personalized similarity algorithm for modeling peers' attributes was introduced as a scaling factor for trust evaluation. The authors of [40] proposed a time-based trust-aware routing protocol for Low-Power and Lossy Networks (SecTrust-RPL). The proposed SecTrust framework is mapped to the Routing Protocol for Low-Power and Lossy Networks (RPL) and provides a new trust-aware RPL protocol. Although most of the existing routing protocols for WSNs can meet security requirements, they cannot effectively balance security and energy consumption. In this paper, we propose a hybrid model that combines the directed graph model with the weighting trust model, and we refer to the serial-parallel system in classical reliability theory to achieve secure routing construction. In the model, the calculation of trust values is simplified so that it can effectively balance security and energy consumption.