esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The aims of this study were to compare the performance of ECDSA and RSA TLS cipher suites and evaluate the energy consumption impact of the different ECC curves and RSA key sizes when using a resource-constrained IoT node capable of running at different clock frequencies.  
After analyzing all the tests, it can be concluded that, in the selected scenarios, ECDSA can be presented as a greener alternative than RSA for securing resource-constrained IoT devices. Regarding clock frequency, the selected hardware platform presented better results in terms of energy efficiency and response time when using the highest available frequency. By testing different working frequencies, it was possible to show the impact of software optimizations that can improve individual ECC curve performance.  
The obtained results also emphasize the importance of testing and measuring empirically the performance of the different algorithms supported by hardware platforms. The created testbed allowed accurately comparing the different cipher suites and configurations in terms of security, energy consumption and throughput. For instance, the impact of certain software implementations and optimizations was demonstrated, which can make weaker security alternatives perform worse in terms of energy consumption than more secure approaches. To sum up, real-world scenario testing is a good tool for accurately finding which security algorithm and configuration is the best fitted for an IoT application.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; and [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. All authors approved the final version of the manuscript.  
This work was funded by the Xunta de Galicia (ED431C 2016-045, ED341D R2016/012, and ED431G/01), the Agencia Estatal de Investigacion of Spain (TEC2013-47141-C4-1-R, TEC2015-69648-REDC, and TEC2016-75067-C4-1-R) and ERDF funds of the EU (AEI/FEDER, UE).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AES & Advanced Encryption Standard \\ API & Application Programming Interface \\ DTLS & Datagram Transport Layer Security \\ ECC & Elliptic Curve Cryptography \\ ECDHE & Elliptic curve Diffie-Hellman Ephemeral \\ ECDSA & Elliptic Curve Digital Signature Algorithm \\ FRAM & Ferroelectric RAM \\ GCM & Galois/Counter Mode \\ GPIO & General-Purpose Input/Output \\ HTTP & Hypertext Transport Protocol \\ HTTPS & Hypertext Transport Protocol Secure \\ I2C & Inter-Integrated Circuit \\ IoT & Internet of Things \\ MPI & Multi-precision integer \\ NIST & National Institute of Standards and Technology \\ RSA & Rivest-Shamir-Adleman \\ SBC & Single Board Computer \\ SDK & Software Development Kit \\ \end{tabular}
# Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder
## 3 Designing a Machine-Learning-Based Decoder for the Heavy Hexagonal Code
### Convolutional-Neural-Network-Architecture-Based Decoder
To avoid local optimizers and improve parameter adjustment speed and accuracy during gradient descent after each iteration, we used the currently popular Adam optimizer. When the quantity of data and the number of iterations increased, in order to solve the problem of gradient dispersion in the backpropagation process, we adopted the RestNet [46] structure in the CNN network. The main role of ResNet networks is to solve the problem of gradient vanishing in network training by connecting across layers. Specifically, ResNet networks build deep networks  
Figure 3: The figure shows the various components and processes involved in the algorithm and how they interact with each other. The topological properties of the code are utilized to map the heavy hexagonal-coded image data into the CNN input and a new feature map is generated by performing convolution operations using a set convolution kernel. Finally, the trained classification or prediction results are available at the output layer. by introducing residual blocks. Each residual block consists of two or three convolutional layers that include a cross-layer join. The cross-layer connection adds the input feature map directly to the output of the residual block, thus forming the residuals. In this way, the network learns the changes in the residuals instead of learning the mapping relationship between the inputs and outputs directly. The output of the RestNet network can be represented as:  
\[y=F(x,\{W_{i}\})+x. \tag{11}\]  
where \(F(x,\{W_{i}\})\) represents the residual function, and \(\{W_{i}\}\) represents the weight parameters learned inside the residual function. This defines the error between the predicted value and true value of the neural network at this moment. \[L=-\sum_{x}q(x)\log r(x), \tag{12}\]  
\(q(x)\) represents the true predicted result, and \(r(x)\) represents the output result. The ResNet architecture can build deeper models by using residual functions to obtain the residual blocks at 7 and 14 iterations. Moreover, it reduces the dimension of our convolution operation, increases the iteration speed, and further reduces the convolution kernel dimension to ensure a reduced network depth and achieve better convolutional effects.
## 2 Theory and Implementation
### Dynamic Temperature Compensation  
In our previous study, we discussed the steady state system and obtained a good linear transfer function between the Ratio and the CO\({}_{2}\) concentration, as shown in Figure 5.  
Figure 4: Schematic diagram of Non Dispersive Infrared (NDIR) system.  
In this study, we analyze and observe the change of Ratio which showed a dynamic drift before the system into steady state (40 min), shown in Figure 6. There is a rising phase which shows the increasing of the Ratio for the curve in Figure 6 and a subsequent falling phase which shows the decreasing of Ratio. To compensate for dynamic drift, we discussed the physical phenomenon of the infrared light source and the sensor during rising and falling phase.
# A Dynamic Parameter Tuning Strategy for Decomposition-Based Multi-Objective Evolutionary Algorithms
## 4 Discussion
### Experimental Results and Analysis
#### 4.3.2 Analysis of IGD Performance Index Results  
The mean, standard deviation, median, and quartiles for the IGDs of the algorithms MOEA/D-SPS, MOEA/D-PBI, MOEA/D-AGG, MOEA/D-TCHE, and MOEA/D-2 PBI, and the algorithm MOEA/D-DPA proposed in this paper, are given in Tables 3 and 4. After running the IGDs independently for 25 runs, respectively, in Tables 3 and 4, the data with better significance in each algorithm are blackened. Combined with Tables 3 and 4, it can be seen that the DPA strategy significantly improves the performance compared with the original PBI strategy on the ZDT2 and ZDT4 problems, and the obtained IGD metrics are the best among the five decomposition strategies. On the ZDT3 test problem, the IGD metrics obtained by the MOEA/D-DPA algorithm are better than those obtained by the MOEA/D-PBI algorithm, which proves that the improved dynamic parameterization algorithm has a certain enhancement on the quality of the solution on the bi-objective problem. Among the seven test functions of the tri-objective problem, the DPA strategy finds the optimal IGD values on the DTLZ2, DTLZ3, DTLZ4, and DTLZ6 test problems, but underperforms on the DTLZ7 test problem. Therefore, it is concluded that the DPA tuning \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **MOEA/D-DPA** & **MOEA/D-SPS** & **MOEA/D-PBI** & **MOEA/D-AGG** & **MOEA/D-TCHE** & **MOEA/D-2 PBI** \\ \hline ZDT1 & 4.27E-3 (2.3E-4) & 4.08E-3 (8.7E-4) & 7.02E-3 (7.2E-3) & **9.09E-4 (6.3E-4)** & 2.57E-3 (1.7E-3) & 1.22E-2 (4.4E-3) \\ ZDT2 & **4.72E-4 (4.2E-4)** & 4.36E-3 (3.3E-3) & 1.07E-2 (9.2E-3) & 1.30E-2 (1.1E-3) & 2.91E-3 (2.2E-3) & 2.21E-2 (8.0E-3) \\ ZDT3 & 1.37E-2 (1.4E-4) & 1.73E-2 (5.5E-3) & 8.71E-3 (2.8E-3) & 3.77E-3 (3.0E-3) & 6.78E-3 (1.0E-3) & 9.88E-3 (1.1E-3) \\ ZDT4 & **6.75E-3 (6.5E-3)** & 3.01E-2 (2.7E-2) & 3.01E-2 (2.7E-2) & 3.64E-2 (2.7E-2) & 3.75E-2 (3.9E-2) & 5.19E-1 (2.7E-1) \\ DTLZ1 & 8.94E-4 (2.5E-5) & 1.05E-3 (2.1E-4) & 5.61E+4 (3.4E-5) & 4.09E-3 (5.2E-9) & 8.00E-3 (3.2E-2) & 3.03E-2 (5.4E-2) \\ DTLZ2 & **3.55E-4 (2.6E-6)** & 6.32E+4 (2.3E-5) & 7.62E-4 (2.6E-5) & 5.35E-3 (2.1E-11) & 7.73E-4 (1.2E-5) & 1.08E-3 (7.8E-5) \\ DTLZ3 & **6.24E-4 (6.2E-5)** & 1.07E-3 (6.7E-3) & 1.08E-2 (5.5E-2) & 1.14E-2 (6.8E-3) & 1.64E-1 (2.9E-1) & 1.28E-1 (5.1E-1) \\ DTLZ4 & **7.82E-4 (6.4e-5)** & 1.17E-3 (2.2E-4) & 1.14E-3 (2.0E-4) & 5.88E-3 (5.6E-12) & 1.31E-3 (5.8E-4) & 1.63E-3 (2.6E-4) \\ DTLZ5 & 1.03E-3 (1.0E-5) & 1.08E-3 (7.7E-5) & 1.45E-3 (2.9E-5) & 3.09E-2 (1.6E-10) & **9.90E-4 (3.9E-5)** & 9.85E-4 (1.4E-4) \\ DTLZ6 & 1.54E-3 (5.2E-5) & 1.79E-3 (1.1E-4) & 2.19E-3 (9.1E-5) & 4.65E-2 (1.9E-2) & 1.38E-3 (1.3E-4) & **8.96E-4 (7.3E-4)** \\ DTLZ7 & 4.12E-2 (1.0E-2) & 4.00E-2 (1.0E-2) & **9.90E-3 (6.5E-4)** & 1.84E-2 (6.8E-3) & 3.02E-2 (4.8E-3) & 2.93E-2 (5.1E-3) \\ \hline \end{tabular}
\end{table} 
Table 4: IGD median and quartile of algorithms.
# On the Use of Learnheuristics in Vehicle Routing Optimization Problems with Dynamic Inputs
## 3 Literature Review
### Literature on the DVRP  
The first formulation of the dynamic routing problem was proposed by [15] some decades ago. However, it was not until the beginning of this century when the popularity of the DVRP started to increase significantly. Several types of dynamism have been studied through the years. For instance, authors in reference [16] consider dispatch with dynamic velocities. Using a tabu search metaheuristic, these authors improve previous speed models, where the speed of the vehicle is a piecewise continuous function over time and while satisfying the non-passing condition. Similarly, authors in reference [17] work with dynamic velocities based on traffic congestion models. In [18], the authors model future demands with dynamism. Their method predicts a new customer request and plans accordingly in order to satisfy it. The probability of request occurrence is generated from historical data. Later, authors in reference [19] analyze the dynamism coming from the flow of time. Likewise, those in reference [20] study time-varying travel times in vehicle routing. A smoothing and piecewise continuous function is employed to define these travel times, which are known in advance (i.e., before planning the routes). Authors in reference [21] define times relying on a queuing theory model. A hybrid framework is presented in [22], where state space variables are used to predict new demands based on historical data. Another hybrid metaheuristic method can be found in [23]. Their method requires this data to build the model beforehand. For a comprehensive review on the DVRP, the reader is referred to [24]. Although in these versions of the DVRP the inputs are dynamic, in none of them is the dynamism influenced by the decisions of the planner, as it is the case in our paper. Thus, this kind of dynamism remains quite unexplored despite being of high importance in practical routing problems.
# A Multidisciplinary Approach for Improving Resource Efficiency in the Indian Surimi Supply Chain
## 3 Synthesis of Key ReValue Results
### Valorisation Options for Upscaling
Treatment of wash water with acid led to the removal of 87% of protein from the wash water. Several extraction technologies were applied on RRM to extract gelatine indicating the potential of RRM to be used as sources for marine gelatine. Techniques for stabilization, concentration, and fractionation of protein-rich fractions from RRM were developed in ReValue project. Ingredients obtained from marine RRM can exhibit undesirable fish taste and smell, which can be originated from raw material or be a consequence of degradation and oxidation. Nowadays, microencapsulation is highly recommended in different food industries because of the benefits provided, such as thermostability enhancement, bioactive compound protection, controlled release, volatiles maintaining, odour shelter, and texture/sense improvement [39]. Microencapsulation and dehydration were found to be suitable techniques for stabilisation of the protein-rich fractions. Experimental work performed by Indian partners in ReValue with hydrolysate obtained from Pink Perch head and viscera indicated that microencapsulation can be used to make hydrolysate thermostable and mask the fish odour. The ratio between the wall material and core material as well as the microencapsulation process was selected as described by Sarabandi et al. [40]. Different wall materials were used for microencapsulation including maltodextrin, gum Arabic, sodium alginates, and carboxyl methyl cellulose which were able to mask the fishy odour and would improve the acceptability of food applications (described in detail in ReValue report by Kaushik et al., 2021 [41]). Developed and optimised technologies were proven by up-scaling them by producing valuable and stable ingredients for consumer markets. Fish protein hydrolysates were obtained from RRM, stabilised by microencapsulation, and were incorporated into ready to cook soup, veal burgers, and chicken nuggets. Chicken meatballs have been prepared by incorporating fish gelatine, extracted from gelatine-rich fish RRM. Evaluation of functional, nutritional, and sensory properties of produced food products indicate the potential of the use of fish RRM as starting materials for extraction and production of valuable food ingredients. To exploit various national and international markets for ingredients derived from RRM, the Indian seafood industry must adhere to the international regulatory frameworks to ensure quality control, sustainability, and freshness which were summarized in ReValue project and described in the following section.
# Strength and Deformation of Pillars during Mining in the Shaft Pillar
## 2 Materials and Methods
### Existing Methods of Solving the Strength versus Slenderness Ratio  
The problem of the effect of the slenderness ratio on the bearing capacity of pillars has been the subject of many papers. All the works express the mathematical dependence of strength on the slenderness ratio. These dependencies are divided into two groups:  
* Linear, e.g., [6, 7, 8, 9], also in [3], this type of dependence is preferred;
* Exponential, e.g., [10, 11, 12, 13, 14, 15, 16, 17, 18, 19].  
It can be seen from the above references that the exponential expression of this dependence prevails.  
Although a large number of researchers have dealt with the issue of the influence of the slenderness ratio on the bearing capacity of coal pillars, most of the available information is associated with mining in South Africa [10, 12, 14, 15, 17], Australia [10], America [11, 16, 19], or India [13], where mining with the room and pillar method is common. In Europe, the longwall mining method has historically been the most common for coal, and, therefore, not much research has been carried out for European conditions.  
When calculating the slenderness ratio, it is important to take into account the shape of the pillars, when we can distinguish square, rectangular, and diamond shapes of pillars in coal mining. For this reason, some authors [10] introduce the concept of effective pillar width into the calculation of the slenderness ratio.  
Many papers deal with mathematical modelling of the effect of the slenderness ratio on compressive strength [20; 21; 22]. The publication [20] is based on the tensile failure of the peripheral parts of the pillars and the shear failure of the inner parts. When examining crystalline rock in the w/h ratio range of 0.5-3.5, the mathematical modelling suggests that the increase in bearing capacity with increasing w/h is roughly linear.  
Other methods of evaluating the effect of the slenderness ratio on the load capacity of pillars are based on statistical methods. A significant contribution to the solution of the bearing capacity of pillars is the monitoring of the parameters of pillars that have experienced a stability failure compared to pillars that have resisted the load acting on them [23; 24].
# Truss Structure Optimization with Subset Simulation and Augmented Lagrangian Multiplier Method
## 2 Subset Simulation Optimization (SSO)
### Implementation Procedure of SSO  
Based on subset simulation, an optimization algorithm is proposed that generally comprises 6 steps:  
1. Initialization. Define the distributional parameters for the design vector \(\mathbf{x}\) and determine the level probability \(\rho\) and the number of samples at a simulation level (i.e., \(N\)). Let \(N^{S}=INT[N\rho]\), where \(INT[\cdot]\) is a function that rounds the number in the bracket down to the nearest integer. Set iteration counter \(K=1\).
2. Monte Carlo simulation. Generate a set of random samples \(\{\mathbf{x}_{i},i=1,2,\ldots N\}\) according to the truncated normal distribution.
3. Selection. Calculate the objective function \(W(\mathbf{x}_{i})\) for the \(N\) random samples, and sort them in ascending order, i.e., \(W_{(1)}\leq\cdots\leq W_{(N)}\). Obtain the first \(N^{S}\) samples from the ascending sequence. Let the sample \(\rho\)-quantile of the objective function be \(\hat{W}_{(1)}\), and set \(\hat{W}_{(1)}=W_{(N^{S})}\), and then define the first intermediate event \(F_{1}=\left\{W\leq\hat{W}_{(1)}\right\}\).
4. Generation. Generate conditional samples using the MMH algorithm from the sample \(\{\mathbf{x}_{1},\ldots,\mathbf{x}_{N^{S}}\}\), and set \(K=K+1\).
5. Selection. Repeat the same implementation as in Step 3.
6. Convergence. If the convergence criterion is met, the optimization is terminated; otherwise, return to Step 4.  
In this study, the stopping criterion is defined as [44; 45]  
\[|\hat{\sigma}_{k}-\hat{\sigma}_{k-1}|\;or\;\left|\frac{\hat{\sigma}_{k}-\hat{ \sigma}_{k-1}}{x_{U}-x_{L}}\right|\leq\varepsilon \tag{4}\]  
where \(\varepsilon\) is the user-specified tolerance and \(\hat{\sigma}_{k}\) is the standard deviation estimator of the samples at the \(k\)th simulation level. Numerical studies [44; 45; 46] suggest that this stopping criterion is preferable to those defined only using a maximum number of function evaluations or by comparing the objective function value difference with a specified tolerance between two consecutive iterations, although the latter ones are frequently used in other stochastic optimization methods. Thus, Equation (4) is adopted in this study.
## 3 Results and Discussion
### Characterization of Nanocellulose Gels and Films
#### 3.2.6 Water Vapor Permeability of Films  
Water vapor permeability is a fundamental property that measures a material's ability to allow water vapor molecules to pass through its thickness. This property is especially relevant in applications where moisture barrier is crucial, such as food packaging. The results of this property are shown in Table 2. Regarding the permeability rate, WVTR, the lowest value, stands out for CNF\({}_{2}\) films, corroborating what is expected, since these films have higher density. The compaction of the fibrous matrix makes it difficult for water molecules to pass through, and less water can penetrate through the film, as already mentioned [33].  
WVTR values obtained in previous studies are comparable to those obtained in this research work for the uncoated CNF films [34; 35].  
Regarding the results of the wax-coated films, it can be said that wax coating is highly effective as it significantly reduces the WVTR for both films, allowing less water vapor to penetrate through the film. WC CNF\({}_{1}\) and WC CNF\({}_{2}\) show a decrease of 81.4% and 64.5%, respectively. Interestingly, WC CNF\({}_{1}\) has a lower WVTR, indicating that CNF\({}_{1}\) film benefits more from wax coating, mainly because its initial porosity allows better wax penetration and sealing [33]. Comparable results are obtained by [11].  
Wax coating of CNF films is an effective strategy to reduce water vapor permeability, addressing one of the main limitations of pure nanocellulose materials. This modification increases the films' suitability for moisture-sensitive applications while maintaining its biodegradable and sustainable advantages.  
The observed improved water vapor barrier properties of wax-coated CNF films can be explained by the combination of chemical, physical and structural mechanisms. Chemically, the long-chain hydrocarbons and esters of waxes given a hydrophobic barrier prevent water molecules from reaching the underlying CNF matrix, lowering the WVTR. Physically, the wax fills surface pores, lowering the roughness and eliminating pathways for vapor diffusion (SEM images support this statement).
## 4 Discussion of the Findings
### Integration of Findings  
The findings from both the code validation and the empirical validation give a comprehensive view of RestRho's strengths and areas for improvement. The code validation showed that RestRho is well structured, maintainable, low-complexity, and well documented compared with other frameworks. The empirical validation confirmed these aspects and highlighted the system's effectiveness and ease of use for rapid web development. However, the empirical study also revealed challenges related to the learning curve and the need for better documentation and tutorials. These issues need to be addressed to enhance the user experience and accessibility of RestRho.  
The code validation and empirical validation show that RestRho is a powerful tool for minimal coding web development. The high code quality and positive user feedback indicate that RestRho meets its design objectives. Improving documentation and providing more learning resources can further increase its adoption and success.
## 4 Results
### Ablation Experiment  
To verify the effectiveness of network components, we perform detailed ablation studies, using a dual contrastive translation network and MFF module, as well as \(L_{reg}(R)\) in different configurations. TR-GAN indicates the registration network, which contains the unidirectional translation network TP and utilizes the similarity loss \(L_{reg}(R)\). DR-GAN represents the registration network, which contains bidirectional translation networks TP and TR with similarity loss \(L_{reg}(R)\). DTR-GAN denotes the DR-GAN with the MFF module, and similarity loss \(L_{reg}(R)\) is used. In addition, to verify the effectiveness of \(L_{reg}(R)\), we replace \(L_{reg}(R)\) in DTR-GAN with PatchNCE loss, which is used in DFR, named PDTR-GAN. Figures 8 and 9 show the average DSC and ASD of the MRI-CT registration on the Learn2 Reg dataset and the CHAOS dataset, respectively. DTR-GAN achieves the best performance, which is comparable with other methods. The improvements with statistical significance represent that the bidirectional translated network can generate more refined translated images, improving the subsequent image registration performance.  
Figure 8: Boxplot of the distributions of the DSC and ASD of the MRI-CT registration on the Learn2 Reg dataset.  
Figure 7: Visual comparison of registration accuracy using various methods is conducted on the Learn2 Reg and CHAOS datasets. The first two columns display the original inputs, and all columns show vivid colors to visualize the label.  
Tables 4 and 5 show the quantitative results in the enhancing registration performance on the Learn2 Reg and CHAOS datasets, respectively. Compared with DR-GAN, DTR-GAN can capture more multiscale information and improve the registration performance, which is beneficial for generating consistent anatomical features during the image translation process. In addition, compared with PDTR-GAN, the DSC score of DTR-GAN is further improved, which demonstrates the effectiveness of maintaining appearance consistency in multimodal registration. Figures 10 and 11 show the visualization of the registration results of different methods. According to the results, our proposed method using a bidirectional translation network works better than using only a unidirectional network. The results in Figures 10 and 11 indicate that bidirectional translation networks can preserve a consistent shape to enhance the subsequent registration performance.