esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Co-Saliency
### Evaluation of Co-Saliency  
Although not specifically designed for co-saliency estimation with image groups, the Average Precision score AP is often applied for evaluation in this specific domain ([67; 68; 69; 89; 94; 95]). It is proportional to the area under the Precision/Recall curve, generated as defined in Section 3.3.  
Other measures commonly used for co-saliency evaluation are the \(F_{\beta}\) ([37; 67; 68; 69; 94; 95; 96]) and the area under the ROC curve AUC ([67; 68; 69; 70]).  
The dataset/method matrix for co-saliency estimation presented in Table 6 suggests using either iCoseg [98] or MSRC [86] as a comparison benchmark. We decided to focus on iCoseg, due to the extreme variability of MSRC versions adopted by different methods. Results are reported in Table 8: the overall best performance is reached by [PERSON] et al. [95], followed by [PERSON] et al. [67] and [PERSON] et al. [94] for \(F_{\beta}\) and Average Precision (AP).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & \(F_{\beta}\uparrow\) & **AP**\(\uparrow\) & **AUC**\(\uparrow\) \\ \hline [PERSON] 2019 [67] & 0.855 & 0.906 & 0.974 \\ [PERSON] 2018 [94] & 0.850 & 0.911 & - \\ [PERSON] 2018 [68] & 0.820 & 0.878 & 0.968 \\ [PERSON] 2018 [69] & 0.823 & 0.896 & **0.979** \\ [PERSON] 2018 [95] & **0.873 \({}^{*}\)** & **0.920 \({}^{*}\)** & - \\ [PERSON] 2017 [89] & 0.810 & 0.868 & - \\ \hline \hline \end{tabular} \({}^{*}\) Values inferred from graphs in the corresponding publication.  
\end{table}
Table 8: Quantitative comparison of selected methods for co-saliency estimation on the iCoseg dataset. Best results are highlighted in boldface.  
All these solutions are VGG-16-based neural networks, adopting a late-fusion approach. This common pattern can be justified as semantic interpretation is particularly relevant in a domain that requires finding common elements across different images. At the same time, the recent inter-saliency/intra-saliency paradigm, although promising in the context of the corresponding publications, is possibly not yet mature enough. In this specific evaluation setup, in fact, the work by [PERSON] et al. [89] presents the lowest performance. It should be noted, however, that the corresponding solution performs the selection of image groups in a completely unsupervised manner, while all other methods rely on existing annotated clusters.
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 3 The Proposed POI Recommendation Scheme
### Recommendation Phase
#### 3.4.1 SVD CF for Highly Active User Groups  
The cluster is subdivided through collaborative filtering based on the singular value decomposition (SVD) model [7]. The FCM algorithm is applied to determine whether a specific user is highly active, and the activity index of that user is calculated in the proposed scheme [13]. Model-based collaborative filtration is implemented for users with high activity levels.  
Figure 6: Extraction of top-category pattern for all users.  
The following is the procedure for recommending POIs using the SVD collaborative filtering algorithm. First, an interaction matrix is created. This matrix expresses the interaction information between users and visited locations and is used to identify the user's preferences and interests. The columns symbolize visited locations, whereas the rows represent users. Second, matrix decomposition is performed. Each of the user-feature matrix (U), feature-feature matrix (\(\sum\)), and visited-place-feature matrix (V\({}^{\text{T}}\)) is represented by one of the three decomposed matrices. A vector that denotes the attributes of each user and a diagonal matrix that indicates the correlation between the attributes are the components of these matrices. The diagonal elements represent the significance of the features, which are listed in the order of relevance. Third, dimension reduction is performed. By carefully selecting only the most significant components of the feature-feature matrix, the proposed scheme reduces dimensionality. The values of the diagonal elements are used to determine the significant elements, and only the top k important elements are selected. This reduces dimensionality and minimizes the loss of information. After this task is completed, POIs that align with the user's attributes are recommended through SVD collaborative filtering [14].
# Bioactive Peptides from _Lupinus_ spp. Seed Proteins-State-of-the-Art and Perspectives
## 3 Lupin Bioactive Peptides and Their Properties
### Osteoprotective and Anti-Inflammatory Peptides  
Inflammation is an immune response to the body that involves different immune system cells, releasing various substances (inflammatory mediators) that help to identify and kill pathogens, aging, and tumour cells. Excessive and uncontrolled inflammatory changes often lead to chronic diseases. It is not a coincidence that nutrition and immunity are strongly related [45]. Plant and animal sources were already reported to possess immunomodulatory peptides released during the digestion process affecting in this way the cellular functions and immunological responses [46].  
Different studies have demonstrated the anti-inflammatory effects of lupin protein hydrolysates, obtained after enzymatic hydrolysis. An octapeptide, GPETAFLR (isolated from _L. angustifolius_ L.), was observed to have osteoprotective and anti-inflammatory activity, evaluated in a human monocytic cell line [47] and THP-1-derived macrophages [48]. LPS have shown to reduce the gene expression of osteoclastogenic cytokines--TNF-\(\alpha\), IL-18, and IL-6, and GPETAFLR--to raise anti-osteoclastogenic cytokine (IL-4, and IL-10) gene expression [30].  
Non-alcoholic fatty liver disease (NAFLD) is a health problem associated with obesity and dyslipidemia, which affects the world's population. It is caused by an accumulation of fat in the liver, and in this regard, the lupin peptide's role was investigated in the prevention of NAFLD in the hepatic tissues of mice [49].  
Possessing the two activities, osteoprotective and anti-inflammatory, GPETAFLR is used also in the prevention of osteoclast-related diseases and chronic inflammation.  
Besides GPETAFLR, several more lupin peptides (from _L. albus_) have shown their anti-inflammatory properties: AKIODKEGIPDQQR, LIFAGKQLEDGR, LDDARRAEK, IQODKEGIPDQQR, RRAIGK, and RDDAASCLVR (isolated via in vitro gastrointestinal digestion).  
A recent study also observed the anti-inflammatory activity of lupin peptides, focusing on the effects of p38 MAPK on TNF-mediated inflammatory responses and the pro-inflammatory cytokine production [49].
# The Seepage and Soil Plug Formation in Suction Caisons in Sand Using Visual Tests
## 3 Test Results and Discussion
### Prediction of Soil Plug Height  
The relationship between the height of the soil plug \(h_{\text{sp}}\) and the hydraulic gradient \(v\)/\(k\) was proposed by using quadratic regression, and can be expressed as follows:  
\[\frac{h_{sp}}{h}\ =\ A+B\frac{S}{\gamma^{\prime}h}+C\left(\frac{S}{\gamma^{ \prime}h}\right)^{2}+D\frac{v}{k}+E\left(\frac{v}{k}\right)^{2}+F\frac{Sv}{ \gamma^{\prime}hk}, \tag{2}\]  
where \(A\)-\(F\) are constant coefficients (given in Table 4). Figure 10 shows the comparison of actual values and fitted values for the dimensionless soil plug \(h_{\text{sp}}\)/\(h\). Fitted values are evenly distributed around the actual values. Compared with the actual values, the fitted values have an average relative error of about 28.74%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(A\) & \(B\) & \(C\) & \(D\) & \(E\) & \(F\) \\ \hline
0.57495 & \(-\)0.49370 & 0.09759 & 0.05075 & \(-\)0.02168 & 0.02252 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal value of constant coefficients for \(h_{\text{sp}}\)/\(h\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(h\) = 5 cm} & \multicolumn{2}{c}{\(h\) = 10 cm} & \multicolumn{2}{c}{\(h\) = 15 cm} \\ \cline{2-7}  & \(S\) = 0.1 kPa & \(S\) = 1.5 kPa & \(S\) = 2.5 kPa & \(S\) = 3.0 kPa & \(S\) = 3.5 kPa & \(S\) = 4.0 kPa \\ \hline \(V_{\text{settlement}}\) (cm\({}^{3}\)) & 0.544 & 0.970 & 0.020 & 0.473 & 0.000 & 0.000 \\ \(V_{\text{sp}}\) (cm\({}^{3}\)) & 0.344 & 1.520 & 0.519 & 1.954 & 0.849 & 2.827 \\ \(V_{\text{settlement}}\)/\(V_{\text{sp}}\) & 1.581 & 0.638 & 0.039 & 0.242 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The volume of soil plug and soil settlement in tests.  
Figure 9: The height of soil plug and soil settlement outside the caisson.  
Figure 11 shows the relationship between \(h_{\text{sp}}\)/\(h\) and \(\gamma\)/\(k\) when \(h=15\) cm. It is shown that the dimensionless soil plug \(h_{\text{sp}}\)/\(h\) first increases and then decreases with the increase of \(\
u\)/\(k\). The reason behind the decreased trend is that the soil plug is subjected to downward friction applied by the inner wall of the suction caisson. The combination of Equations (1) and (2) is used to predict roughly the height of soil plugs in suction caissons in sand during suction penetration, guiding project designs, and construction.
## 3 Results
### Analysis of the LipGt6 Catalytic Triad  
An esterase from _G. thermodentifricans_ (PDB: 7E16), a member of the GDSL hydrolase family with 32.06% amino acid sequence homology to LipGt6, was used to construct the LipGt6 model. The structural model of LipGt6 shows five central \(\beta\)-sheet structures surrounded by nine \(\alpha\)-helices. The predicted catalytic triad that contains serine, aspartate, and histidine is clearly present on the surface mode structure (Figure 6a). To confirm that the predicted catalytic triad was the actual active site, site-directed mutagenesis was used to substitute these three amino acid residues of the catalytic triad (Ser47, Asp221, and His224) with alanine. The engineered proteins were overexpressed in _E. coli_ BL21-codonplus (DE3) cells, and the residual enzyme activity of the mutants was assayed after the proteins were purified by Ni-affinity chromatography (Figure 6b). The reported catalytic triad of the GDSL family lipase Lip29 consists of Ser53, His230, and Asp227, which are commonly conserved residues [14]. LipGt6 exhibited less than 20% activity after mutation of Ser47 and His224, and mutation of Asp221 did not affect the activity. _Bacillus_ sp. K91 encodes Est8, an esterase with reduced activity upon mutation of Ser, Asp, and His residues, while still retaining detectable activity [23]. Based on the results obtained with this mutation, the catalytic residues Ser and His of LipGt6 were more critical than Asp. The potential catalytic triad found in known GDSL lipases may not be the critical amino acid that participates in  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chemical Reagents** & **Relative Activity (\%)** \\ \hline CK & 100.00 \(\pm\) 3.93 \\ 1 mM SDS & 40.05 \(\pm\) 1.94 \\ 1\% Triton X-100 & 41.70 \(\pm\) 2.18 \\ 1\% Tween-80 & 28.03 \(\pm\) 1.55 \\ 1\% Tween-20 & 29.26 \(\pm\) 1.68 \\ 1\% sodium deoxycholate & 75.72 \(\pm\) 5.76 \\ 1\% H\({}_{2}\)O\({}_{2}\) & 69.50 \(\pm\) 16.61 \\ 5\% NaClO & 4.09 \(\pm\) 0.17 \\ 1\% NaClO & 10.70 \(\pm\) 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effects of surfactants and other chemical reagents on LipGt6.  
Figure 5: Effects of metal ions on LipGt6 activity. The enzyme was subjected to activity assays after treatment with 5 mM metal ions (Zn\({}^{2+}\), Mg\({}^{2+}\), Ca\({}^{2+}\), Mn\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), Fe\({}^{2+}\), and Na\({}^{+}\)) for 1 h at 50 \({}^{\circ}\)C. The relative activity of the control without metal ions was regarded as 100%. Data points are the average of triplicate experiments, and error bars represent standard deviation.  
the lipolytic reaction of LipGt6. The specific lipolytic activity of LipGt6 may involve an unknown catalytic motif.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 3 Methodology
### Statistics Collection  
The final phase of each experiment involved logging and preserving key statistics to be used in the performance evaluation of the operators. The most significant metrics collected were execution time \(T(O)\) and memory usage \(M(O)\).  
Let the dataset metadata \(\text{Meta}(D)\) be represented as follows:  
\[\text{Meta}(D)=(n,f,\text{Size}_{\text{MB}},\text{Clusters})\]  
where \(\text{Size}_{\text{MB}}\) is the size of the dataset in megabytes, and Clusters is the number of clusters for KMeans datasets.  
The training time \(T(O)\) was measured using Python's time module and included only the time taken for the operator to fit the dataset:  
\[T(O)=\text{End}\_\text{time}-\text{Start}\_\text{time}\]  
Memory usage \(M(O)\) was collected via the Spark UI REST API by aggregating the memory usage across all executors:  
\[M(O)=\sum_{i=1}^{e}\text{memoryUsed}_{i}\]  
where \(e\) is the number of executors. The collected metrics were stored and later used for performance analysis.  
In conclusion, the two key metrics--execution time and memory usage--were the most representative values for assessing the performance of each operator. These values formed the foundation for predicting operator behavior and optimizing the Spark cluster's configuration.
## 3 Research Results--Analysis and Discussion
### Uniaxial Compressive Strength (UCS)
#### 3.1.2 Effects of Waste-Based Stabilisers on UCS  
Among waste materials, fly ash (by varying the content in the mixture with clayey soil in the amount of 10%, 15%, and 20%) and rock flour (with 5%, 10%, and 15% content) were considered as chemical stabilisers.  
Addition of fly ash/rock flour resulted in a weaker improvement compared to conventional stabilisers (Figure 3). What is also noticeable is that the change in UCS value is more pronounced with the increase in the content of added waste material (percentage share of fly ash/rock flour in the mixture), whereas the time interval that has passed since the chemical treatment did not have a significant effect.  
The maximum UCS values obtained with the addition of **fly ash** in the amount of 10% (307 kPa) and 20% (342 kPa) were lower than those with the addition of 15% fly ash (371 kPa). This result points to the conclusion that the optimal content of fly ash can be considered 15%. The UCS value obtained by the test performed 3 days after the treatment did not change significantly with further passage of time.  
A similar trend of change in UCS value was observed on samples with the addition of **rock flour**. Namely, compared to the natural soil state (205 kPa), the maximum improvement (279 kPa) was achieved with the addition of 10% rock flour 7 days after the treatment. Similar results were obtained after 3 days (268 kPa) and after 28 days since the treatment (275 kPa). The addition of 5% and 15% rock flour did not contribute to a significant increase in the UCS value. This finding points to the conclusion that 10% can be considered the optimal content of rock flour.
## 2 Model Description
### Pressure Losses Model
#### 2.6.1 Line Pressure Drop  
The present model considers different relations for the line pressure drop depending on the fluid flow regime. The line pressure drop is evaluated as  
\[\Delta p_{1}=\frac{dp}{dz}\,L \tag{13}\]  
For the _single-phase regime_, the Colebrook correlation was adopted [34]:  
\[\begin{cases}\left(\frac{dp}{dz}\right)_{\text{sp}}=\frac{2\,G^{2}}{\rho D_{ \text{h}}}\,f\\ \frac{1}{\sqrt{f}}=-2\,\log\!\left(\frac{\epsilon}{3\,\gamma D_{\text{h}}}+ \frac{2\,51}{Re\sqrt{f}}\right)\end{cases} \tag{14}\]  
Like the Blasius and the von Karman-Nikuradse relations, this correlation is commonly used for fluids at high saturation temperatures. However, later studies conducted by [PERSON] et al. [35; 36] and [PERSON] et al. [37] verified that these equations predict pressure losses with single-phase cryogenic fluids with good accuracy. The advantage of using the Colebrook correlation lies in its accuracy, even for small-diameter ducts.  
For the _two-phase regime_, numerous correlations have been formulated in the literature to estimate the pressure drop of straight ducts. Several studies, such as those of [PERSON] and [PERSON] [25], [PERSON] and [PERSON] [38], [PERSON] et al. [39], and [PERSON] et al. [40; 41], compared the many available relationships with data for different cryogenic fluids and duct diameters. Analyses of these reviews showed that the Friedel [42] and the Muller-Steinhagen and Hack [43] correlations have the highest overall accuracy and appear to be the most suitable for use within a computational code, at least for larger pipelines. Therefore, in this context, the Muller-Steinhagen and Hack correlation was adopted:  
\[\left(\frac{dp}{dz}\right)_{\text{tp}}=\left\{\left(\frac{dp}{dz}\right)_{ \text{sp,l}}+2\!\left[\left(\frac{dp}{dz}\right)_{\text{sp,v}}-\left(\frac{ dp}{dz}\right)_{\text{sp,l}}\right]\!\psi\right\}\!\left(1-\psi\right)^{1/3}+\left( \frac{dp}{dz}\right)_{\text{sp,v}}\psi^{3} \tag{15}\]  
where \(\left(\frac{dp}{dz}\right)_{\text{sp,l}}\) and \(\left(\frac{dp}{dz}\right)_{\text{sp,v}}\) are the pressure gradients, determined by the single-phase correlation assuming the saturated liquid and saturated vapour properties.
## 3 Results
### Use Cases  
To validate the architecture, two use cases were developed. Since the main idea is to validate the architecture and its flexibility, some components were shared between both scenarios (such as the ERP), and others were completely different, suitable, and tailored for each one of the cases. For instance, one of the MES used follows the proposal from Section 3.3.3, and the other follows a more conventional approach trying to demonstrate that the architecture and this platform are compatible with both approaches. This section is not meant to describe to the reader how each component works but that each component is compliant with the architecture interfaces.  
The ERP, located in the cloud, sends a production order to MES. In the MES, a manager from the factory will check if the order can be produced and accept or reject the order, accordingly. If the order is accepted, the MES sends the production order to the machine, within the factory, so it can start production. The machine gives the status of production to the MES as well as a confirmation when the production is finished. The transactions of this process are registered in the blockchain, and thus it can be reliably accessed, for example, by the ERP. This process is demonstrated in Figure 24.  
Figure 24: Sequence diagram from interactions for use cases.  
In this scenario, energy consumption was one type of information agreed on by all stakeholders as vital data that should be exchanged in a trustable manner. With this information, the client can map and be informed of the impact that the production of his/her product has on the environment.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 5 Experimental Environment and Results
### Results
Moreover, NTRU key generation enhances security based on the probabilistic hardness assumptions underlying lattice problems and relies on random polynomial selection to ensure unpredictability to prevent attackers from predicting or reversing keys. Moreover, for the effective operation of SSI-PQM, it is necessary to establish a separate certificate issuance system for generating PQC public keys and then providing them to the IoT devices. Since the PQC decryption performed by the IoT device is executed only once for secret key exchange, it has a insignificant impact on the encryption of communication using MACsec. Table 5 lists the comparison of the security function with the previous IoT platforms in terms of IoT network security aspects. It includes authentication, access control, network separation (or secure routing), encryption, detection, and SDN [30; 31; 32]. Our proposed scheme supports all the security aspects except detection. Detection can be easily applied without affecting the response time by mirroring traffic through the CN.
## 1 Introduction
Considering the advantages and disadvantages mentioned in the previous studies, the oxy-fuel combustion concept shows some potential in terms of emission reduction due to a nitrogen-free combustion. This new possibility motivates the exploration of this combustion system in the operating mode of an engine powered only with fuel, O\({}_{2}\), and EGR. To the knowledge of the authors, all previous studies have been performed by using conventional combustion chambers architectures with only external modifications on the engine hardware, but unfortunately no work studying the influence of the combustion chamber design on the system performance has been done up to the moment. For this reason, this paper aims to study the potential of the oxy-fuel combustion process in CI engines searching for a suitable combustion system configuration through the application of an innovative optimization method coupled with CFD simulations, the main contributins of which are indicated as follows. * A computational engine model that reproduces a full load engine operating condition was developed and the model was validated. This model is then evaluated in oxy-fuel combustion operating mode, providing suitable thermodynamic conditions in the exhaust line. * A novel methodology that combines the CFD code with the Particle Swarm Optimization (PSO) algorithm was used to perform the optimization study in a reasonable period of time, thanks to the integration of tools for fast and accurate data post-processing. * The study involves the analysis of several inputs (combustion chamber design and engine settings variables) to obtain the best set of parameters that reduces the soot emissions and maximizes the efficiency, considering the constraints of the CO emissions and the maximum pressure (\(p_{max}\)) of the engine. This article is structured as follows. Section 1 has presented a general overview of the work. Section 2 presents the methodology, the engine configuration, computational models, and the tools used in this work. In Section 3, the obtained results are presented and discussed. Section 4 presents the conclusions of the study.