esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
## 4 Discussion
As it can be seen from the figure, this relationship is very complicated, and it is difficult to propose an analytical formula for the entire range of parameters. This effect is probably due to the random size distribution of the aggregates added to the LWC and their local neighborhood with a varied topological structure. It is also important whether they are closed or open volume aggregates. Therefore, for the values of interest between the measurement points, it is worth using prediction of the results generated by the ANN. Figures 6 and 7 present plots of thermal conductivity as a function of porosity and density of the LWC based on laboratory test results and ANN prediction, respectively. This dependence is valid only to LWC with GEGA and GAA additives. As for the relationship between density and porosity, the analytical relationship between thermal conductivity and porosity cannot be reasonably proposed. However, in the case of the dependence of thermal conductivity on density, a roughly linear relationship between these quantities is visible. This relationship can be approximated by another class of functions (e.g., logistic function, polynomial of degree 3) in order to obtain a better fit for small densities, however, it seems more correct to use the ANN prediction. Based on the analysis test results it was found that ANN is a useful tool for designing of LWC not only with specific mechanical parameters, but also with specific physical properties such as density and thermal insulation.
# A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
## 4 Behavior-Based Sharding Hashgraph Framework
### Network Model  
The network model is built on the mobile ad hoc network, which is also called a wireless multi-hop network without infrastructures. In the network, neighbor shards are two shards that have at least one pair of neighbor nodes. Since nodes within a shard are in geographical proximity, there are more neighbor nodes and multiple communication paths between every two nodes. Hence, the transmission delay in a shard is less. Nevertheless, due to opportunistic communications caused by node mobility, neighbor nodes change over time, leading to changes of neighbor shards. Hence, the connection channels between shards are not always reliable, and the communication among them has a certain delay.
###### Abstract  
This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and routing strategies to effectively tackle the challenges of data management in smart cities. Our protocol employs hierarchical Data Fusion Head (DFH), relay DFHs, and marine predators algorithm, the latter of which is a reliable metaheuristic algorithm which incorporates a fitness function that optimizes parameters such as how closely the Sensor Nodes (SNs) of a data fusion group (DFG) are gathered together, the distance to the sink node, proximity to SNs within the data fusion group, the remaining energy (RE), the Average Scale of Building Occlusions (ASBO), and Primary DFH (PDFH) rotation frequency. A key innovation in our approach is the introduction of data fusion techniques to minimize redundant data transmissions and enhance data quality within DFG. By consolidating data from multiple SNs using fusion algorithms, our protocol reduces the volume of transmitted information, leading to significant energy savings. Our protocol supports both direct routing, where fused data flow straight to the sink node, and multi-hop routing, where a PDF relay is chosen based on an influential relay cost function that considers parameters such as RE, distance to the sink node, and ASBO. Given that the proposed protocol incorporates efficient failure recovery strategies, data redundancy management, and data fusion techniques, it enhances overall system resilience,thereby ensuring high protocol performance even in unforeseen circumstances. Thorough simulations and comparative analysis reveal the protocol's superior performance across key performance metrics, namely, network lifespan, energy consumption, throughput, and average delay. When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. Specifically, our protocol extends network lifespan by 299% over PSO-EEC, 264% over LDIWPSO, 306% over OFCA, and 249% over NPSOP. It also reduces energy consumption by 254% relative to PSO-EEC, 264% compared to LDIWPSO, 247% against OFCA, and 253% over NPSOP. The throughput improvements reach 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By fusing data and optimizing routing strategies, our protocol sets a new benchmark for energy-efficient IoT DFG, offering a robust solution for diverse IoT deployments.  
Keywords:IoT; data fusion; optimization algorithm; MPA; routing; scheduling; network lifetime; efficient energy; network impairments
###### Abstract  
In this study, multiwalled carbon nanotubes (MWCNTs) were incorporated into vulcanized natural rubber (VNR) matrixes to create nanocomposites with improved mechanical, thermal, and electrical properties. The interfacial interaction of the MWCNTs with the VNR matrix was quantitatively evaluated based on the crosslink density value calculated using the Flory-Rehner methodology. Various rheometric parameters were influenced by the addition of the MWCNTs, including minimum torque (M\({}_{\text{L}}\)), maximum torque (M\({}_{\text{H}}\)), and scorch time (t\({}_{\text{S1}}\)). The MWCNTs significantly enhanced the vulcanization of the composites based on the VNR matrix. This study highlights the impact of MWCNTs on crosslink density, improving mechanical properties and reducing swelling in the VNR matrix. We discovered that the MWCNTs and the VNR matrix interact strongly, which improved the mechanical properties of the matrix. The MWCNTs improved the hardness, tensile strength, and abrasion resistance of the VNR/MWCNT nanocomposites. Based on dynamic mechanical analysis, MWCNT incorporation improved stiffness as indicated by a change in storage modulus and glass transition temperatures. The addition of MWCNTs to the VNR/MWCNT nanocomposites significantly improved their electrical properties, reaching a percolation threshold where conductive pathways were formed, enhancing their overall conductivity. Overall, this study demonstrates the versatility and functionality of VNR/MWCNT nanocomposites for a variety of applications, including sensors, electromagnetic shielding, and antistatic blankets.  
conductive nanocomposites; vulcanized natural rubber; MWCNT; Lorenz-Park methodology +
Footnote †: journal: Applied Sciences
# Learning Spatial-Temporal Background-Aware Based Tracking
###### Abstract
However, the main disadvantage of this method is that the regularization term introduces time-consuming computational resources. The BACF tracker [23] fully exploits the negative training sample surrounding background area, and effectively enhances the discriminative performance of the learned filter to complicated appearance variance. The STRCF [24] tracker introduces spatial-temporal regularization based on the SRDCF tracker, which effectively gains the robustness toward appearance changes, and thus deals well with boundary effects. The ARCF [25] tracker suppresses the occurrence of aberrance by suppressing the rate of change of the response map, which can effectively handle the boundary effect problem. In this paper, in order to solve the above-mentioned problems efficiently, we develop a robust and efficient DCF based tracker, which is mainly dedicated to handing the boundary effect problem. The main contributions of this paper are summarized as follows:  
1. A spatial-temporal regularization background-aware DCF based model is proposed, which can deal robustly with boundary effects and complex appearance changes. 2. Our model can effectively be solved by the alternating direction multiplier method (ADMM), and each sub-problem has a corresponding closed solution. 3. The proposed tracker gains promising tracking performance and significantly outperforms than other state-of-the-art DCF based tracker in accurate and overlap success rate. The remaining of the work is structured as follows: Section 2 introduces related works, Section 3 presents the proposed tracking framework, and Section 4 introduces experimental verification and analysis. Finally, the conclusions are introduced in Section 5.
## 6 Implementation of the SMC and the MPPT Algorithm
### Summary of the Design Process  
The design of both the SMC and MPPT first-order filter is based on the parameters defined for the P&O algorithm (\(\Delta v_{pv}\) and \(T_{a}\)) and the maximum switching frequency (\(F_{sw}\)). The design equations proposed in this paper for both the SMC and filter are summarized as follows:  
1. The derivative limits of the MPPT reference (\(SR_{p}\) and \(SR_{n}\)) are calculated from (24) and (26) using \(k_{1}\);
2. The filter derivative \(SR_{f}\) is calculated from \(SR_{p}\) and \(SR_{n}\) using (31);
3. The value of \(\Omega\) is calculated from \(SR_{f}\) and \(\Delta v_{pv}\) using (35);The value of \(k_{1}\) is calculated from \(\Omega\) and \(t_{s}\) with (38);  
5. The value of \(H\) is calculated from \(F_{sw}\) and \(k_{1}\) with (43).  
The previous summary shows that steps 1, 2, 3, and 4 depend on each other; therefore, the simultaneous equation system formed by (24), (26), (31), (35) and (38) must be solved. Finally, the parameter \(H\) is calculated using (43). The following section illustrates this design process using a practical application example.
## 3 Results and Discussion
### Compressive Strength
This temporary gain is attributed to the formation of expansive substances such as ettrinigite and gypsum, which fill the concrete's pores and contribute to greater densification [78; 79]. At prolonged exposure durations (180 and 360 days), the continued formation of these expansive products induces internal expansion stresses, which in turn promote the development and propagation of microcracks. In addition to this phenomenon, the degradation of the C-S-H phase--through its transformation into M-S-H gel--weakens the binder matrix, ultimately resulting in a loss of mechanical strength [41; 78; 80]. These effects, corroborated by the surface deterioration documented Figure 3, are ultimately responsible for surface spalling, material loss, and compressive strength reduction. Figure 5 compares the compressive strength of concretes exposed to the MgSO\({}_{4}\) solution with that of their counterparts cured in water, evaluated at the same time intervals from their casting. At 28 days of exposure, no negative effect on the compressive strength of concretes exposed to this aggressive environment is observed; on the contrary, a strength gain is even recorded compared to the specimens cured in water over the same period. However, at 180 and 360 days, the mixtures incorporating recycled concrete aggregate (RCA) (M3, M4, and M5) exhibit the greatest losses in mechanical performance (ranging from 24% to 29%). This reduction is attributed to the fact that, compared to natural aggregate, RCAs contain more potentially weak points that are vulnerable to sulfate attack, such as adhered mortar, higher porosity, and a greater number of interfacial transition zones (ITZs) [81]. In concretes with RCA, three ITZs can be identified: (i) between the NA and the matrix; (ii) between the adhered mortar on the RCA and the matrix; and (iii) within the RCA itself, i.e., at the original aggregate-mortar interface. The highest porosity in concrete is concentrated in the ITZ [82], where Mg\({}^{2+}\) and SO\({}_{4}\)\({}^{2-}\) ions react with Ca(OH)\({}_{2}\) and monosulfate, forming ettrinigite, gypsum, and brucite. Crystallizations of these phases raise pore pressure and accelerate crack propagation [83]. Additionally, sulfate attack in the presence of Mg\({}^{2+}\) ions not only leads to the formation of brucite, but also of M-S-H, as a result of the substitution of Ca\({}^{2+}\) ions by Mg\({}^{2+}\). This process weakens the material matrix due to the lower cohesion of this compound, resulting in reduced mechanical performance [84].
## 3 Frequentist Estimation
### Maximum Likelihood Estimators
For each \(t_{i},i=1,2,\ldots,n\), if \(t_{i}<1\), \(\lim\limits_{b\to+\infty}\frac{Int_{i}}{1+t_{i}^{b}}=Int_{i}<0\) and if \(t_{i}\geq 1\), \(\lim\limits_{b\to+\infty}\frac{Int_{i}}{1+t_{i}^{b}}=0\), so \(\lim\limits_{b\to+\infty}z_{2}(b)<0\) when there exists an \(i\) such that \(t_{i}<1\). Moreover, it is simple to derive that \(\lim\limits_{b\to+\infty}z_{1}(b)\leq 0\). Thus, \(\lim\limits_{b\to+\infty}z(b)<0\). Subsequently, we are able to prove that the function \(z(b)\) is strictly monotonically decreasing on the interval \((0,+\infty)\). Moreover, as \(b\) varies from \(0\) to \(+\infty\), the sign of the function shifts from positive to negative. Therefore, the solution of \(z(b)=0\) exists and is unique, which indicates that \(\hat{b}\) is unique, and through (17), the uniqueness of \(\hat{a}^{*}\) can be proved. Consequently, the presence and uniqueness of the MLEs of unknown parameters \(a_{1},a_{2},\ldots,a_{s}\) are demonstrated. When using our model to deal with data, to ensure the existence, we just need to appropriately transform the units of physical quantities in the sample data to guarantee that there exists \(t_{i}<1\). Since the explicit solution of the equations is difficult to obtain, we use certain iterative algorithms to calculate the MLEs of unknown parameters. In this paper, in order to maximize \(\ln L(\mathbf{a},b)\), we use the _optim_ function in R language (version 4.3.2).
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 4 Experimental Results
### Parameters Affecting Liquefaction Behavior of Partially Saturated Sands
#### 4.3.3 Back Pressure  
The \(CSR\)-\(N_{\text{liq}}\) curves recorded at varying back pressures are presented in Figure 10 to show the effect of the back pressure. The nearly/fully saturated sand specimens tested at back pressures of 300 and 400 kPa showed an almost identical trend, which suggested that the back pressure had an insignificant effect on the liquefaction resistance of such soils. The results of the partially saturated sand tests under back pressures of 100, 150, and 200 kPa showed that back pressure affected the liquefaction behavior of the partially saturated sand specimens, which were observed to exhibit a lower liquefaction resistance at larger back pressures. This again correlated well with the previous findings of researchers [17, 41].  
Figure 8: Effect of confining stress on liquefaction resistance of partially saturated sand specimens.  
Figure 9: Effect of degree of saturation on liquefaction resistance of partially saturated specimens.
## 2 Bullet Low-Overlap Point Cloud Registration Based on Line Feature Detection
### Extraction of Linear Features by Discrete Hoff Transform  
Many existing alignment algorithms, such as the ICP algorithm, have high requirements for the initial position of the point cloud alignment, and many incorrect feature point matches can occur with small overlap rates, both of which can result in excessive errors in the results. This paper is implemented in the following ways: (1) avoiding the key point detection method and treating the \"line shape\" in the data as the feature to be matched; (2) using a technique that does not depend on the initial configuration of the search results, which is usually not affected by local spurious feature matching between features, because the algorithm in this paper takes into account the global relative position of the detected features; (3) performing a geometric constraint alignment search using a similar idea as in [26]. We draw on this idea by filtering the entire set of impact points by the most linear feature of the bullet marks, discarding widely varying correspondences and extracting the marks as linear features. The detected bullet marks are shown in Figure 4.
## 3 Methods Overview
### Prefix-Tuning  
The prefix-tuning method converts the tokens of the input, target and prefix sequences (denoted as X, D, and P) into embedding sequences \(E_{X}\), \(E_{D}\), and \(E_{P}\). Furthermore, activation h is the output of multiple layers of transformers from the sequence [X,D] or [P,X,D]. After that, activation h of each layer is used as the input of the next layer. They are used as the final representations of the sequences after being processed through all layers. In contrast, the fine-tuning method does not utilize the prefix sequence. Figure 1 specifically compares the operational processes of the two methods.  
The transformer serves as the foundational structure for the pre-trained model, consisting of L transformer blocks. Each layer comprises a multi-head attention (MHA) and a feedforward neural network (FFN). The function is shown in Equation (4). Q, K, and V represent the query, key, and value vectors, respectively. \(d_{k}\) represents the dimension of the key vector. In the transformer, each head is parameterized through attention using weight matrices \(W_{q}^{i}\), \(W_{k}^{i}\), \(W_{v}^{i}\) for the \(i\)-th attention head as shown in Equation (5).  
\[\text{Attn}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V \tag{4}\]  
Figure 1: Main framework comparison of fine-tuning and prefix-tuning.  
\[h_{i}(x,c)=\text{Attn}(xW_{q^{\prime}}^{i}cW_{k}^{i},cW_{v}^{i}) \tag{5}\]  
In Equation (5), \(C\in R^{m\times d}\) is a sequence composed of m vectors and \(x\in R^{d}\) is a query vector. Then, the MHA combines the outputs of each head at each layer and multiplies them by the given initial weight matrix \(W_{o}\in R^{d\times d}\) to obtain the output, as shown in Equation (6).  
\[\text{MHA}(x,c)=\text{Concat}(h_{1}(x,c),h_{2}(x,c)\cdots,h_{3}(x,c))W_{o} \tag{6}\]  
As shown in Figure 2, r adjustable prefix vectors are combined with the keys (K) and values (V) in prefix-tuning. Thus, Equation (5) can be optimized as Equation (7). In this context, \(P_{k}^{i}\) and \(P_{v}^{i}\) are the prefix vectors of key and value for the \(i\)-th attention head, respectively.  
\[h_{i}(x,c)=\text{Attn}(xW_{q^{\prime}}^{i},\text{concat}(P_{k}^{i},cW_{k}^{i} ),\text{concat}(P_{v}^{i},cW_{v}^{i})) \tag{7}\]
# Decentralized Inner-Product Encryption with Constant-Size Ciphertext
## 2 Preliminaries
### Bilinear Maps and Complexity Assumption  
Let \(\mathbb{G}\) and \(\mathbb{G}_{\mathbb{T}}\) be two multiplicative cyclic groups with prime order \(p\). A map \(e\) is called a bilinear map if the following properties hold:  
**Bilinearity**: For \(u,v\in\mathbb{G}\), and \(a,b\in\mathbb{Z}_{p}\), the equation \(e(u^{a},v^{b})=e(u,v)^{ab}\) holds.  
**Non-Degeneracy**: Assume \(g\) is the generator of \(\mathbb{G}\), then, \(e(g,g)\
eq 1\).  
**Computability**: For \(u,v\in\mathbb{G}\), there exists an efficient algorithm to compute \(e(u,v)\).  
Next, we show the complexity assumption, the \(\ell\)-decisional bilinear Diffie-Hellman exponent (\(\ell\)-DBDHE) assumption [29; 30], which the security of our scheme based on.  
**Definition 1** (The \(\ell\)-Decisional Bilinear Diffie-Hellman Exponent Problem).: _Let \(\mathbb{G}\) be a group. \(g\) is a generator of \(\mathbb{G}\), and \(\gamma,s\in\mathbb{Z}_{p}\) are two integers. Given a tuple:_  
\[(g,g^{\gamma},g^{\gamma^{\ell}},g^{\gamma^{\ell}},g^{\gamma^{\ell+2}},\ldots,g ^{\gamma^{2\ell}},g^{s},T),\]  
_decide if \(T=e(g,g)^{\gamma^{\ell+1}s}\) or \(T\in\mathbb{G}_{T}\) is a random element of \(\mathbb{G}_{T}\)._  
Let \(T_{0}=(g,g^{\gamma},g^{\gamma^{2}},\ldots,g^{\gamma^{\ell}},g^{\gamma^{\ell+2} },\ldots,g^{\gamma^{2\ell}},g^{s})\). For an algorithm \(\mathcal{A}\), the advantage of \(\mathcal{A}\) in solving the \(\ell\)-DBDHE problem is defined as:  
\[\mathsf{Adv}_{\mathcal{A}}^{\ell\mathsf{DBDHE}}=\Big{|}\Pr[\mathcal{A}(T_{0}, T=e(g,g)^{\gamma^{\ell+1}s})=1]-\Pr[\mathcal{A}(T_{0},T\stackrel{{ \mathbb{S}}}{{\leftarrow}}\mathbb{G}_{T})=1]\Big{|}.\]  
**Definition 2** (The \(\ell\)-Decisional Bilinear Diffie-Hellman Exponent Assumption).: _We say that the \(\ell\)-decisional bilinear Diffie-Hellman exponent assumption holds if for all PPT algorithms, \(\mathsf{Adv}_{\mathcal{A}}^{\ell\mathsf{DBDHE}}\) is negligible._
# A Swarm Intelligence Solution for the Multi-Vehicle Profitable Pickup and Delivery Problem
## 4 Proposed Method  
In the current study, we employed the Artificial Bee Colony (ABC) algorithm that was first proposed by [PERSON] [13]. It mimics the collaborative and collective foraging behaviour of a bee colony. In the ABC algorithm, there are three different types of bees:scout bees, employed bees, and onlooker bees. Each type has a different role in the exploration and exploitation of food sources. A food source represents the solution to a problem, and better solutions belong to food sources with more nectar. The ABC algorithm works as follows: Initially, the scout bees are assigned randomly to the search space to find initial food sources. Then, the employed bees are sent out to exploit the places discovered by scout bees, with each employed bee assigned to one food source. After that, the employed bees start a neighbourhood search to find a better food source nearby. After finding a better food source, the employed bees leave all previous food sources and exploit only the better ones. These employed bees then return to the hive and share their information about discovered food sources with onlooker bees through what is called the waggle dance. The decision of whether the onlooker bees will follow the employed bees depends on the richness of the food sources. If the onlooker bees do decide to follow, they then become employed bees and repeat the procedure of the employed bees. After several iterations, the food source might be exhausted; at that point, the employed bees become scout bees and start searching randomly to find a new food source to replace the previous one. The flowchart of the ABC algorithm is illustrated in Figure 1[14].  
The ABC heuristic has been used for solving several VRP variants, such as being used by [PERSON] et al. [15] to solve the capacitated vehicle routing problem, by [PERSON] et al. [16] to solve the VRP with time windows, and by [PERSON] et al. [17] to solve the periodic VRP.  
Figure 1: Flowchart of the Artificial Bee Colony algorithm [14].  
In this paper, we aim to improve the initial solutions proposed in [7]. There are many metaheuristics that have been used to enhance the quality of solutions, such as local search (LS), simulated annealing (SA), and VNS. In our study, we also use a metaheuristic that combines greedy and randomised searches and is based on honeybee behaviour, namely the ABC. The main reason for selecting the ABC rather than other honeybee algorithms is its popularity, with the number of studies that have used the ABC compared to other variants reaching 54%, as reported in [PERSON]'s survey [18]. For tackling the MVPPDP, two versions of the ABC are proposed: ABC without clustering (ABC) and ABC with clustering (ABC(C)), as will be explained next.