esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Influence of SRR (with constant SRF and Good SNR)  
One further experiment was made to check the pattern of FVI properly. GF2 PAN (4 m) is fused with Landsat-8 OLI MS (30 m, 40 m, 48 m and 60 m). In GF2 to Landsat-8 OLI (30 m, 40 m, 48 m and 60 m) fusion, there is a uniform relationship between SRR and FVI quality (CC and RMSE) for ARVI (92.56%, 22.59%), (92.11%, 23.05%), (91.88% and 23.43%) and (91.17% and 23.45%) in SRR 1:8, 1:10, 1:12 and 1:15 respectively (Table 7). By increasing SRR from 1:8 to 1:15; all image fusion techniques showed a uniform result (Figure 5). This uniformity could be due to the constant SRF (Landsat-8 OLI and GF2) and better SNR than the previous multi-sensor fusion. However, the HPF still has a lower quality and seems to be sensitive. ARVI and GNDVI have quite lower RMSE, high CC so they are well separated in all image fusion and all SRR (Figure 5). However, for other VI (except ARVI and GNDVI), the CC and RMSE graph is almost same and overlapped (except for HPF). The quantitative evaluation results show that the brightness and information retention of GS fusion image was the best [52]. In the fusion of the near-infrared band, the GS method can be considered as the best [58]. All VI equations contain at least one NIR band and GS fusion has a good result in NIR band which might be the result behindthe good quality (low RMSE and high CC) in all FVI and for all SRR. In general, the quality of FVI is decreasing with decreasing SRR (1:8 to 1:15) (Figure 5).  
However, if we compare all three figures (Figures 3-5) together, we find that the higher effect on FVI quality is due to SNR and SRF rather than SRR. With decreasing SRR (keeping the effect of SRF and SNR minimum), the quality decreases at a very slow rate. The VI having R and NIR bands in their VI equations has a similar pattern. Their CC and RMSE are almost identical (NDVI, OSAVI, SAVI, EVI) with a similar pattern (Figure 5). The remaining VI, including other bands in their VI equation (B and G bands), has also a similar pattern. The CC and RMSE values have a similar pattern for ARVI and  
Figure 4: Trend of FVI quality with Image Fusion and SRR(Different SRF and SNR (Good to Poor)).  
Figure 5: Trend of FVI quality with Image Fusion and SRR (Constant SRF and Good SNR).  
GNDVI but different from NDVI, OSAVI, SAVI and EVI. In short, ARVI and GNDVI have the least impact in all cases with all fusion methods. In general, image fusion seems to be insensitive with VI and SRR. The HPF seems to be sensitive with VI and SRR. However, this indicates that the influence of SRF and SNR is higher than SRR.
## 4 A Multi-View Detecting Framework
### Earth Mover's Distance  
The EMD [37] is a method for comparing two multiple dimensional probability distributions over a region. It was first proposed as a matrix to retrieve images in the computer vision domain. However, it has been applied to many other fields [38; 39]. The EMD calculates the lowest costs of transferring one distribution into another, given that two distributions indicate different ways of accumulating a certain amount of dirt in a region. A distance function defines the cost needed to move dirt between certain piles. It is frequency-aware, as it considers the magnitude of discovered differences, and the difference is determined by the ground distance function that can express different perceptions of similarity [40]. Below we formally introduce the EMD.  
Figure 4: Overview of the proposed framework.  
Let \(P\) be a probability distribution with \(p_{1},\ldots,p_{m}\in P\) as different clusters and \(w_{p1},\ldots,w_{pm}\in\mathbb{R}^{+}\) as the associated weight for these clusters. Another probability distribution Q has the same notations \((q_{1},w_{q1}),\ldots,(q_{n},w_{qn})\). A ground distance \(D=d(p_{i},q_{j})\) between cluster \(p_{i}\) and \(p_{j}\) is defined. We seek to find a flow \(F=(f_{i,j})\in\mathbb{R}^{m\times n}\) that minimises the overall costs to transfer \(P\) to \(Q\). The following constraints should be followed:  
* Non-negativity flow: \(f_{i,j}\geq 0,\forall 1\leq i\leq m,1\leq j\leq n\).
* Sent and receive flow should not exceed weights in \(P\) and \(Q\):
* \(\sum_{j=1}^{n}f_{i,j}\leq w_{pi}\), \(\forall 1\leq j\leq n\);
* \(\sum_{i=1}^{m}f_{i,j}\leq w_{qj}\), \(\forall 1\leq i\leq m\).
* All weights possible have to be sent: \(\sum_{i=1}^{m}\sum_{j=1}^{n}f_{i,j}=min(\sum_{i=1}^{m}w_{pi},\sum_{j=1}^{n}w_ {qj})\) The optimal flow \(F\) is defined as \[EMD(P,Q)=min\frac{\sum_{i=1}^{m}\sum_{j=1}^{n}f_{i,j}d(p_{1},q_{j})}{\sum_{i=1} ^{m}\sum_{j=1}^{n}f_{i,j}}.\] (2)
# Design Techniques for Low-Voltage RF/mm-Wave Circuits in Nanometer CMOS Technologies
###### Abstract  
This paper reviews state-of-the-art design approaches for low-voltage radio frequency (RF) and millimeter-wave (mm-wave) CMOS circuits. Effective design techniques at RF/mm-wave frequencies are described, including body biasing in fully depleted (FD) silicon-on-insulator (SOI) CMOS technologies and circuit topologies based on integrated reactive components (i.e., capacitors, inductors and transformers). The application of low-voltage design techniques is discussed for the main RF/mm-wave circuit blocks, i.e., low-noise amplifiers (LNAs), mixers and power amplifiers (PAs), highlighting the main design tradeoffs.  
b +
Footnote †: journal: Applied
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 4 Experimental Results and Analysis
### Cluster Lifetime  
This is the time elapsed since the formation of a cluster to its destruction. When a clustering algorithm complete execution, the fittest node adopts the role of CH and takes over the responsibility of managing the cluster. The fitness value of CHs decreases with the passage of time. If it falls below some threshold, the clustering algorithm will be recalled. The shorter the cluster lifetime, the more number of times clustering will be recalled. This increases the computational and communicational overhead in the network.  
Figures 9 and 10 shows the comparison of cluster lifetime between EALC, CACONET, and GWOCNET. EALC completely outperforms the GWO, whereas it tightly competes with CACONET. The figures also indicate that the cluster lifetime decreases with the increase in the number of nodes in the network. This reduction is due to the fact that cluster has more number of members which cause more changes in the topological arrangement of the network.
## 4 Existence of Nash Equilibrium and Solving Algorithm
### Nash Equilibrium Solving Algorithm  
According to the game mechanism and the mathematical model constructed above, the user's strategy can be directly optimized when the operator's price strategies are given. Therefore, the model is simplified to a two-layer programming problem, with the government's payoff as the objective function of the upper layer and the operator's payoff as the objective function of the lower layer. The users' utility function is used for travel selection, and their travel conditions are the constraints of the upper and lower layers. In this paper, the improved particle swarm optimization algorithm is used to solve the problem. When the bi-level programming model reaches stable state, the adjustment of any subject's strategy cannot make its own benefit better. Therefore, the fitness function of the improved particle swarm optimization (PSO) algorithm is constructed as follows:  
\[fitness=max\{|\Delta U_{O}|,0\}+max\{|\Delta U_{G}|,0\} \tag{19}\]  
The solution process is shown in Figure 3.  
1. Initialize the government's subsidy rates \(S_{c1},S_{c2}\), and the operator's pricing strategies \(p_{s},p_{t}\) according to the constraints (initialize the population).
2. Calculate the users' utility function and determine their travel mode. Then calculating the government's and operator's utility functions based on the users' travel choice, the initial value of the fitness function can be obtained.
3. Update the particles' position and velocity based on the linearly decreasing inertia weight and the asynchronous change learning factor, and then update the optimal values of the individuals and group.
4. Repeat step 2 and 3 until the bi-level programming reaches equilibrium.
5. Output the optimal strategies of the government, operator and users.
## 3 Results and Discussion  
### Determination of Fatty Acid Content in Seed Oil  
The assessment of fatty acid composition of prickly pear seed oil was carried out by GC-FID after compound trans-esterification. The results are presented in Table 2. The results show that the different cultivars of prickly pear seed oil have a maximum content of linoleic acid (C18:2), followed by oleic acid (C18:1), palmitic acid (C16:0), stearic acid (C18:0), and finally palmitoleic acid (C16:1), with the highest relative concentration being around 1% in the oil from the yellow variety. Notably, the content of this latter in the yellow cultivar was almost ten times higher than the other two, and hence it can be considered as a characteristic factor of its seed oil. Nevertheless, further investigations are needed to elucidate if this variability is related to genetic factors or to other conditions.  
Prickly fig seed oil, like most vegetable oils, belongs to the category of polyunsaturated oils. Its chemical composition comprises mainly linoleic acid and oleic acid and is thus  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**R.T. (min)** & **Fatty Acid** & **Rel. \% Yellow** & **Rel. \% Orange** & **Rel. \% Green** \\ \hline
4.66 & C14:0 & 0.079 & 0.061 & 0.069 \\
5.61 & C16:0 & 11.66 & 11.87 & 11.99 \\
5.89 & C16:1 & 0.99 & 0.11 & 0.13 \\
7.31 & C18:0 & 3.18 & 3.15 & 3.19 \\
7.69 & C18:1 & 20.36 & 19.99 & 20.14 \\
8.49 & C18:2 & 62.63 & 63.12 & 61.93 \\
9.52 & C18:3 & 0.24 & 0.19 & 0.21 \\
10.29 & C20:0 & 0.28 & 0.37 & 0.28 \\
10.90 & C20:1 & 0.53 & 0.61 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fatty acids composition of the seed oil from different cultivars (yellow, orange, and green) of prickly pear (g/100 g oil).  
very similar in composition to corn oil. For this reason, it is therefore a good alternative source for essential fatty acids compared to other vegetable and seed oils. Regarding these main components, the amounts determined in the three prickly pear cultivars from the Souk Ahras area reflect those already reported in the same plant species growing in Tunisia (C18:1 = 19.96%; C18:2 = 67.60%) [26] and Algeria (specific region or area not specified; C18:1 = 12.92%; C18:2 = 55.81%) [27], for example. Conversely, variations in respect to other prickly pear seed oils were observed for the less abundant components. One of the particularities of the prickly fig oils studied in this work was the presence of traces of myristic acid. This data does not agree with the results of [PERSON] et al. [5], [PERSON] and [PERSON] [28], or [PERSON] et al. [29], for example, which showed the absence of this fatty acid in Tunisian prickly fig oil. However, small amounts of C14:0 (0.15%) have been reported in seeds from species growing in Algeria (specific region or area not specified) [27]. Hence, this could be a characteristic feature of Algerian prickly fig oil.
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 1 Introduction: Addressing Challenges in Fire Safety Engineering with Surrogate Modelling  
### Surrogate Modelling  
#### 1.1.1 Surrogate Modelling Definition and Motivation  
Fire safety engineering faces several challenges that hinder the development of accurate and efficient predictive models. Experimental testing is often expensive, and results can be highly variable due to differing procedures, making direct comparisons difficult [1]. Additionally, certain fire phenomena, such as spalling in concrete, remain not fully understood, adding uncertainty to fire performance assessments [2]. Where the underlying physics are sufficiently understood, numerical simulations can complement experiments; however,such simulations are often computationally demanding. These limitations highlight the need for efficient modelling approaches that can capture the complexity of fire behaviour while reducing computational costs.  
In surrogate modelling, the results of a computationally expensive model are used as training data for a computationally efficient \"surrogate\". Such surrogate models have become a fundamental tool in engineering thanks to their ability to approximate the complex physical phenomena governing behaviour while significantly reducing computational effort [3; 4; 5]. This substantial decrease in computational time makes surrogate modelling particularly valuable for high-dimensional and nonlinear systems [3; 6; 7]. Additionally, surrogate models facilitate optimisation [8; 9], uncertainty quantification [10; 11; 12], risk assessment [10; 11; 12], and real-time decision-making [13; 14; 15]. As a result, their use has expanded across various engineering disciplines, including aerodynamics [16; 17], thermodynamics [18; 19], and materials science [20; 21; 22]. Considering the above, surrogate modelling has particular promise for fire safety engineering (FSE), as conceptually elaborated in Figure 1. Establishing the state-of-the-art in FSE, with a particular focus on physics-informed surrogate modelling approaches, will be the focus of Section 1.3 further in this paper.
# Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes
## 2 Materials and Methods
### Improved Wild Horse Optimizer
#### 2.7.1 Cubic Chaos Mapping  
The traditional wild horse optimizer performs optimization by randomly generating the initial population, which leads to individuals being easily concentrated in a few regions, and to the algorithm easily falling into a local optimum later. To address such problems, most studies introduced chaotic mapping mechanisms for improvement [17; 18; 19; 20], such as logistic mapping, Fuch mapping, and tent mapping, to enhance the diversity of the initial population and improve the solution efficiency of the algorithm.  
In this paper, cubic chaotic mapping is introduced to improve the population initialization strategy of the WHO. The chaotic sequence had better chaotic ergodicity, and the initial wild horse population with more uniform distribution could be obtained by mappingthis chaotic sequence into the solution space of wild horse individuals; the standard cubic mapping formula is expressed as follows.  
\[X_{n+1}=bX_{n}^{3}-cX_{n} \tag{31}\]  
where \(X_{n+1}\) denotes the solution vector of the n-th + 1 individual, and \(b\) and \(c\) are chaotic constants whose different values affect the range of the mapping.
## 3 Results
### NEE and GPP Simulation during 1992-2014  
There are clear seasonal changes in NEE and GPP (Figure 6). During December-March, vegetation was dormant, and the NEE values showed that the ecosystems in the tundra and needle leaf forest sites were weak carbon sources. From May to August, the GPP at the tundra site was lower than that at the needle leaf forest site. The needle leaf forest was also a stronger carbon sink than the tundra.  
The simulation results from 1992 to 2014 showed that the NEE values of the tundra site ranged from \(-123\) to \(8\) gC/m\({}^{2}\)/yr, with an average value of \(-43.65\) gC/m\({}^{2}\)/yr (Figure 7). The GPP values in the tundra forest ranged from \(56\) to \(255\) gC/m\({}^{2}\)/yr, with an average value of \(129\) gC/m\({}^{2}\)/yr. For the needle leaf forest, the average values of NEE and GPP were \(-65\) gC/m\({}^{2}\)/yr and \(299\) gC/m\({}^{2}\)/yr, respectively.
## 3 Proposed Optimization Algorithm
### Received Constellation Optimization  
In taking the received constellation \(\mathbf{s}^{\prime}\) as the optimization variable, optimization problem (7) is reorganized as  
\[(\mathrm{P1}) \quad\max_{\mathbf{s}^{\prime}}f(\mathbf{s}^{\prime})=\min_{m\
eq 1} \|s^{\prime}_{m}-s^{\prime}_{m-1}\|,\] (12) s.t. \[h_{L}\xi\leq\frac{1}{M}\mathbf{1}_{M}^{T}\mathbf{s}^{\prime}\leq (h_{L}+\gamma\mathbf{1}_{M}^{T}\mathbf{h}_{NL})\xi, \tag{13}\] \[\quad\mathbf{0}_{M}\leq\mathbf{s}^{\prime}\leq(h_{L}+\gamma \mathbf{1}_{M}^{T}\mathbf{h}_{NL})\mathbf{1}_{M},\] (14) \[\quad\mathbf{Cs}^{\prime}\leq\mathbf{0}_{M}, \tag{15}\]  
where \(\mathbf{0}_{M}\) is an \(M\)-dimensional vector of all zeros, and \(\mathbf{1}_{M}\) is all ones. (13) is the constraint on the expectation of the received constellation vector. (14) denotes the boundaries for the received constellation \(\mathbf{s}^{\prime}\). (13) and (14) are deduced by (8), (9), and (11), as shown in Appendix A. (15) is the vector representation of \(s^{\prime}_{1}\leq s^{\prime}_{2}\leq\cdots\leq s^{\prime}_{M}\), where \(\mathbf{C}\) is a circulant matrix,  
\[\mathbf{C}=\begin{bmatrix}1&-1&\cdots&0\\ 0&1&-1&\cdots\\ \vdots&\vdots&\vdots&\vdots\\ 0&\cdots&1&-1\end{bmatrix}_{(M-1)\times M}.\]  
As all the constellation distances are non-negative, the minimum distance equals  
\[f(\mathbf{s}^{\prime})= -\max_{m=2}^{M}-\|s^{\prime}_{m}-s^{\prime}_{m-1}\| \tag{16}\] \[= -\frac{1}{\beta}\log(e^{-\beta\|s^{\prime}_{2}-s^{\prime}_{1}\|}+ \cdots+e^{-\beta\|s^{\prime}_{M}-s^{\prime}_{M-1}\|}), \tag{17}\]  
where the logsumexp function in (17) is a differentiable approximation of the max function, and \(\beta\) is a constant parameter affecting the convergence rate and the quality of approximation [17]. The concavity of the minus norm function in (16) makes (12) a convex optimization problem with affine constraints. The logsumexp function in (17) is also convex with a non-negative gradient. Thus, the optimal received constellation vector, assumed as \(\mathbf{s}^{\prime*}\), can be solved with the gradient descent algorithm or other off-the-shelf optimization toolkits [17].
# Varroa Control by Means of a Hyperthermic Device
## 3 Results
### Mathematical Model Simulations  
The development of the colony during of the year was simulated using the BEEHAVE model with environmental parameters similar to those present in the apiary where the experiment was carried out. The output of a single run is shown in Figure 7.  
The population dynamics of _V. destructor_ were simulated under different control approaches and various re-infestation hypotheses.  
The simulations for the control colonies not subjected to hyperthermia, under different hypotheses of re-infestation, showed high mite levels in the hives at the end of the year even in the case of summer treatment with oxal acid (Figure 8).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Adults** & **Juveniles** \\ \hline
2018 & 43.5\% & 85.3\% \\
2020 & 25.8\% & 93.0\% \\
2021 & 24.8\% & 79.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mortality of adult and juvenile forms of _Varra destructor_ within brood cells due to heat treatments.  
Figure 6: Average daily _Varra destructor_ mortality in 2021.  
Figure 7: Yearly development of worker population of a colony according to the BEEHAVE software.  
Simulating the use of hyperthermia leaded to the total disappearance of the infestation before the end of the year even if re-infestation occurred. In this case, the simulations showed infestation levels at the end of the year that were much lower than those of the control colonies without the need of summer treatments for the same level of re-infestations and even in more critical situations (Figure 9).  
Figure 8: Yearly development of _Varroa destructor_ infestation in colonies not subjected to hyperthermia under different hypotheses of re-infestation: (**a**) no re-infestation; (**b**) 30 mites per month from June to September (120 mites in total); (**c**) 150 mites per month from June to September (600 mites in total); (**d**) 30 mites per month from June to September (120 mites in total) with a dripped oxalic acid treatment at the end of July with 95% efficacy on phoretic mites and efficacy duration of 1 day. Adult mites alive on 31 December: (**a**) 0; (**b**) 2283; (**c**) 2821; (**d**) 1422.  
By adopting an integrated control approach (hyperthermia and summer treatments with oxalic acid), the simulations predicted very low levels of infestation at the end of the year, even in the presence of heavy re-infestation phenomena (Figure 10).  
Figure 9: Yearly development of _Varroa destructor_ infestation in colonies subjected to nine heat treatments under different hypotheses of re-infestation: (**a**) no re-infestation; (**b**) 30 mites per month from June to September (120 mites in total); (**c**) 150 mites per month from June to September (600 mites in total); (**d**) 150 mites per month in June and July and 250 mites per month in August and September (800 mites in total). Adult mites alive on 31 December: (**a**) 0; (**b**) 120; (**c**) 158; (**d**) 242.
## 2 Materials and Methods
### AI-Driven Decision Support
For instance, a sensor, which unexpectedly transmits large volumes of data during off-peak hours, either triggers an alert or is quarantined. This intelligent, adaptive threat detection operates in real time, allowing the system to react faster than manual monitoring to contain breaches. Notably, detection logic can be distributed across edge nodes, aligning with the secure edge computing paradigm by analysing data closer to its source and even collaborating across devices. In addition, all security events feed into a coordinated incident response workflow. As described in Section 2.3.5 alerts from the IDS or other modules can initiate automated containment actions (such as quarantining a compromised IoT node) and notify administrators, thereby closing the loop from detection to response. By leveraging AI in this manner, the framework's security module continuously learns from new attack patterns and adapts, providing a proactive defence that complements conventional encryption and access controls. Protecting privacy is equally paramount. Campus systems routinely process personal data on students and staff, as well as potentially sensitive usage information. The framework's data governance module enforces anonymisation or pseudonymisation where possible, supplemented by rigorous, role-based access control and logging [23; 33]. Regulatory requirements (e.g., GDPR and FERPA) are incorporated by design, mandating user consent and minimal data retention. For example, occupancy sensors aggregately capture student attendance unless individual identities are expressly required for a justified purpose. When heightened transparency is desirable, blockchain-based solutions may be used to record transactions (e.g., asset transfers and maintenance logs) in an immutable ledger [34]. Although added overhead is introduced, the tamper-resistant nature of blockchain enhances data integrity. Beyond these measures, federated learning (FL) techniques would be employed for AI model training on sensitive datasets, whenever applicable. Instead of consolidating all raw data in a central server, the framework can train machine learning models in a decentralised fashion: model updates are computed locally at each site (e.g., on edge servers at different campuses) and then aggregated centrally, without transmitting the underlying private data. This approach preserves privacy by keeping personal information on local devices while still benefitting from collaborative intelligence across the network [35]. Ultimately, trustworthiness remains central: from device firmware to cloud analytics, every layer undergoes rigorous threat modelling. State-of-the-art safeguards, including anomaly detection, encryption, and backup strategies, are embedded throughout the framework to maintain a reliable, privacy-compliant smart campus [36].