esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### CH Selection Algorithms Based on Si
#### 3.1.3 Cluster Head Selection Algorithm Based on ACO and ABC
In this case, one possible solution is to use the mobile sink to seek the energy balance. According to [25], CH transformation is based on \"significant differences in energy in different regions\", which can enable preventing relay nodes from dying too quickly. In the SI-based CH selection algorithm, it heavily relies on the performance of selected SI algorithm to locate the optimal solution and the use of SI to find the CH according to the requirements (parameters such as residual energy and distance of nodes). It is important to combine SI algorithms with other approaches to improve the analytic performance further. For instance, the PSO algorithm is limited to giving the local optimum solution; thus, using other algorithms can prevent the PSO from falling into local optimum. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Year** & **Key** & **Uniform (U)** & **Static(S)** & **Number of** & **Size of the** & **Multi-hop(M)/Single-hop(S)** & **CH Selection** \\  & **Technology** & **Random (R)** & **Dynamic (D)** & **Clusters** & **Cluster** & **Cluster** & **Factor** \\ \hline LEACH & 2000 & — & R & S & V & V & S & Cyclic random \\
[19] & 2018 & PSO & R & S & V & V & M & E \\  & Tabu & & & F & & S & E,C \\
[20] & 2016 & PSO & R & S & F & S & E,D2S,C \\  & & & & & & & E,D2S,C \\
[21] & 2016 & PSO & R & D & F & V & S & average intra-cluster \\  & & & & & & & distance \\
[22] & 2016 & PSO & R & S & F & F & S & - \\  & & & & & & & \\
[23] & 2017 & BFO & S & V & V & S & E \\
[24] & 2019 & PSO & R & S & F & V & M & E \\
[25] & 2017 & ACO & U & S & F & F & M & Below threshold: E,C \\
[26] & 2016 & ACO & R & D & F & F & M & E,D2N, LQI \\
[27] & 2017 & EACO & U & D & F & F & M & E,C,delay \\  & & & & & & & \\
[28] & 2017 & ABC & R & D & F & F & S & E,D2S,C \\
[29] & 2017 & ABC & R & D & F & V & S & E,D2B \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CH selection algorithms based on SL
## 4 Experiments Results and Analysis
### Experimental Data, Multiple Features, and Evaluation Metrics
#### 4.1.2 L Evaluation Metrics and Parameter Settings  
1. Evaluation Metrics  
The evaluation metrics of the model in this paper use the accuracy, recall, and F1 value of recognition results, and the calculation formula of each metric is [27]:  
\[P=\frac{\textit{Predict the number of correct entities}}{\textit{Total number of predicted entities}} \tag{17}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metacharacter** & **Description** \\ \hline - & Used to match the first line of an input word \\  & Used to place the next character token, or an octal escape, or a \\  & backward reference \\  & + & Match the previous subexpression one or more times \\  &? & Match the preceding subexpressions zero or once \\  & * & Match the preceding subexpressions any number of times \\  & \$ & Used to match the end of a line of input \\ \hline \hline \end{tabular}
\end{table}
Table 1: Metacharacter and description in regular expression.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **NUM** & **Entity Samples** & **Entity Samples (English)** \\ \hline Company & 3375 & \(  
\[R=\frac{\textit{Predict the number of correct entities}}{\textit{Total number of standard entities}} \tag{18}\]  
\[F1=\frac{2*P*R}{P+R}\]
2. Parameter Setting  
Although studies have been conducted using the BERT-BiGRU-CRF model for NER tasks, there is still a gap in NER research in the area of shipping enterprise credit. With the popularity of deep learning, deep learning can use more and more frameworks, the most popular today are the following three: TensorFlow, Keras, and PyTorch. PyTorch uses imperative programming with a simple design, so it is easy to implement, and the code is easy to understand; through the technique of reverse automatic derivation, it can achieve zero latency to arbitrarily change the behavior of the neural network to achieve dynamics, so it is highly flexible and easy to debug, and in many reviews, PyTorch's speed performance outperforms frameworks such as TensorFlow and Keras. TensorFlow is a combination of a lower-level symbolic computation library and a higher-level network specification library and is pure symbolic programming, so while it is more efficient, it is more complex to implement, and the graph definition is static, so it is not as flexible and convenient as PyTorch. Keras is a high-level application programming interface (API) that runs on top of other deep learning frameworks. It has grown rapidly due to its ease of use and syntactic simplicity but is lacking in performance due to its common usage and is not as flexible as TensorFlow and PyTorch. Therefore, the experiments are based on Windows platform, using python 3.7 as the runtime environment, and the NER model is built by pytorch framework. In the process of model training, the setting of parameters is very important. The optimal parameters of the model in this paper are obtained by training the parameters on the data in the training set. The specific parameter settings are shown in Table 4.
## 2 Materials and Methods
### Method
#### 2.6.4 Secure Connections to the Cloud Data Analysis Platform  
Once the functional tests have been carried out and it has been verified that the devices are communicating correctly with the data analysis platform in the cloud, it is necessary to establish secure connections to guarantee the privacy and security of the data [33]. For this, it is essential to configure user authentication and access control to the data analysis platform in the cloud using tools such as Firebase Authentication or Google Cloud IAM. Establish secure connections by implementing encryption protocols, such as SSL or TLS, to communicate between the devices and the data analysis platform in the cloud. Implement additional security measures, such as activity monitoring and logging, to detect potential unauthorized access attempts [34]. Finally, perform regular security testing to identify potential vulnerabilities and ensure connections remain secure and reliable. Implementing adequate security measures and conducting standard tests to detect possible vulnerabilities and keep links fast and reliable is essential.
## 3 Results and Discussion
### TPC and TAC of Experimental Chips  
The TPC and TAC, determined with FRAP and ABTS**+** assays, of the experimental chips are shown in Figure 2. As already previously found by us, HSR addition determined a significant increase in the TPC and TAC in comparison to control recipes, with comparable values on same fortification percentages (i.e., 5% HSR), although in a different recipe (i.e., shortbread cookies) [23], and as previously demonstrated by other authors in different food matrices [25; 26]. In particular, HSR addition determined a 1.6-times increase in the TPC of HSc5% in comparison to CTRc (1.50 \(\pm\) 0.03 mg GAE/g in HSc5% in comparison to 2.39 \(\pm\) 0.03 mg GAE/g in CTRc, Figure 2A) and more than a double increase in the TAC of HSc5% determined with both the assays in comparison to CTRc (1.02 \(\pm\) 0.01 mmol Fe\({}^{2+}\)/g of HSc5% in comparison to 0.37 \(\pm\) 0.00 mmol TE/g of CTRc in the FRAP assay, Figure 2B; 0.66 \(\pm\) 0.06 mmol TE/g of HSc5% in comparison to 0.32 \(\pm\) 0.03 mmol TE/g of CTRc in the ABTS**+** assay, Figure 2C).  
For TPC and TAC, the comparison with the literature was difficult due different recipes, extraction methods, and applied analytical methods. For TPC, although similar values were found also in the paper of [PERSON] et al. (1.02-1.18 mg GAE/g) [9], it should be considered that the recipe in this study provided for a 50% maximum percentage of lentil flour combined with a \"base flour\" prepared by mixing wheat flour (80%), corn flour (20%), onion powder (2%), garlic powder (2%), and cumin (2%) [9]. Higher values were found for TPC in the papers of [PERSON] et al. (3.83 mg GAE/g), even if on extruded snacks based on 49% whole lentil flour (i.e., with seed coats) and 49% potato starch, but they were  
Figure 2: Total polyphenols content (TPC) and total antioxidant capacity (TAC) of experimental chips. (**A**) Total phenolic content (TPC) (mg GAE/g DW); (**B**) ferric reducing antioxidant power assay (FRAP) (mmol Fe\({}^{2+}\)/g DW); (**C**) ABTS**+** radical scavenging activity (mmol TE/g DW). Data represents mean \(\pm\) standard deviation of n = 3 biological replicates and n = 2 technical replicates. Different letters indicate significant differences (\(p\leq\) 0.05) according to one-way analysis of variance. CTRc: control chip; HSc5%: hazelnut skin chip 5%; GAE: gallic acid equivalent; TE: Trolox equivalent.  
also obtained with different extraction method [27]. For TAC, the comparison is even more complicated considering that in these studies, the DPPH method was used, and the results were compared to different standard curves [9; 27].
# Cardiovascular Risk in HIV Patients: Ageing Analysis of the Involved Genes
## 4 Discussion  
Antiretroviral therapy (ART) significantly improved life expectancy among PLWHs with great modification in mortality and morbidity patterns [77]. CVD events increase with age, but it is unclear why they occur most frequently in PLWHs compared to general populations of similar age [78]. We focus on linking ageing, cardiovascular diseases and HIV, aiming to identify and study the roles of genes involved in these pathologies. We based our analyses on the genes' dis-regulated pathways in patients with HIV, selecting the genes that are simultaneously the most expressed in aortic and coronary tissue. We compared the gene expression of TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1 in the general population, in patients with HIV, and in patients with cardiovascular disease, obtaining interesting results. The findings presented in the analysis highlight the intricate relationship between genetic pathways involved in HIV infection and cardiovascular diseases. The dysregulation of genes such as TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1 in individuals living with HIV not only impacts viral pathogenesis but also contributes to the development of cardiovascular complications.  
The upregulation of TLR4, a key regulator of proinflammatory cytokines, in HIV patients, particularly in those not on antiretroviral therapy, underscores the role of inflammation in HIV pathogenesis and its potential link to cardiovascular diseases. Similarly, the increased activation of CXCR4 in advanced HIV disease indicates its involvement in viral entry and immune dysregulation, which can also impact cardiovascular health.  
Furthermore, the dysregulation of mTOR, NCF2, NCF4, and IRS1 in HIV-infected individuals sheds light on the metabolic disturbances and insulin resistance observed in these patients, which are known risk factors for cardiovascular diseases. The interplay between these genetic pathways not only affects the progression of HIV-related complications but also influences the development of cardiovascular events in this population.  
The identification of specific polymorphisms and gene expressions associated with cardiovascular diseases in individuals living with HIV provides valuable insights into potential genetic markers for risk stratification and targeted interventions. Understanding these genetic pathways' molecular mechanisms can pave the way for personalized approaches to managing cardiovascular risks in PLWHs.  
Overall, the integration of genetic studies in the context of HIV infection and cardiovascular diseases offers a comprehensive understanding of the intricate interplay between viral pathogenesis, immune responses, metabolic dysregulation, and cardiovascular complications. Finally, bioinformatics analysis tools allowed us to define a pipeline for studying comorbidities in chronic diseases by focusing on genes and ages, but most of all related to cardiovascular conditions concerning infections and other interesting conditions such as long-term COVID-19.
## 3 Integrated Method for SoS Architecture Selection and Reconfiguration
### Mathematical Formulation for Reconfiguration
That is, during the initial architecture design, different potential systems vary in function capacity, communication bandwidth, and reconfiguration time; the SoS architect needs to select appropriate systems so that the reconfiguration strategy might be more effective and lead to better resilience. We currently select only three of the reconfigurability enablers, while others can be considered in the future. **Function capacity. ** The number of activities that system \(i\) can support is constrained by this system's function capacity \(FC_{i}\). Both the initial architecture configuration and reconfiguration are constrained by this reconfigurability enabler. Function capacity here is a normalized abstract value. A concrete example would be the maximum target tracking  
Figure 5: SoS effectiveness change process during disruption and reconfiguration. capacity (e.g., 20 targets). This value can be converted to a normalized value between 0 and 100 (e.g., tracking capacity of 20 is normalized to 90 and \(unit\_fc_{ik}\) is 4.5). That is, \(FC_{i}=90\). If a radar system \(i\) is assigned to support 10 target tracking activities (\(K=10\)), it does not violate the following constraint. \[\sum_{k\in K}\left(unit\_fc_{ik}\cdot x_{ik}^{init,reconf}\right)\leq FC_{i}, \forall i\in I \tag{4}\]  
**Communication bandwidth. ** Whenever a physical system \(j\) is selected to perform a particular function/activity \(l\), it consumes some portion of the communication bandwidth, depending on the support level, for exchanging information with adjacent systems \(i\) that conduct connected activities \(k\). The total amount of consumed bandwidth cannot exceed the limit \(BW_{j}\). \[\sum_{i\in I\&i\
eq j}\sum_{k\in K}\sum_{l\in K}\left(unit\_comm_{kl}\cdot x_{ ik}^{init,reconf}\cdot x_{jl}^{init,reconf}\right)\leq BW_{j},\forall j\in I \tag{5}\]  
**Ease of reconfiguration. ** We consider the ease of reconfiguration as the reconfiguration time \(T_{RECON}\), as shown in Figure 5. The reconfiguration process refers to adding new connections between two nodes while disconnecting existing links between two nodes. The total reconfiguration time cannot exceed a pre-determined threshold \(RT^{upp}\). The \"logic\" function is to convert the integer values to the logicals. The following inequation depends on the assumption that the reconfiguration actions are in series rather than in parallel. When the supportive techniques for reconfiguration become more developed, simultaneous reconfiguration actions all-at-once will be more likely to occur. \[\sum_{i\in I}\sum_{k\in K}\left(time_{ik}^{conn}\cdot\logic
## 5 Performance Evaluation
### Performance Analysis
#### 5.3.5 Impact of Modulation and Coding Scheme (MCS)  
Usually, a higher modulation and coding scheme (MCS) is a better choice for short-distance communication, whereas, for long-distance communication, a lower MCS value is preferable for achieving better network performance. A higher MCS is not good for a larger distance because it results in more inaccurate received packets at the physical layer and hence increases the number of packet losses. On the other hand, although a lower MCS is more robust for receiving correct packets at the physical layer, even for long-distance communication, a lower MCS also fits a reduced amount of data in a sub-channel. This is due to the reduced modulation and effective code rate per channel. Hence, in this simulation campaign, we want to see which MCS value is a better fit for our simulation settings.  
Figure 8 shows the impact of varying modulation and coding schemes under simulation default settings (specifically with communication range = 600 m and \(\mu=0\)). The results show that increasing MCS results in a slowly increasing better performance until MCS = 15 is reached; after that, it starts declining and plummets at MCS = 20, and, after that, its performance again starts improving. Hence, we recommend using an MCS value of 15 for the set communication range to achieve an overall better performance.  
Figure 8: \(Cont.\)  
Figure 7: Impact of packet size.  
A further study has been conducted to find the impact of MCS on different \(\mu\) values as shown in Figure 9. This set of results confirms that \(\mu=2\) can achieve a greater performance than \(\mu=0\) and \(\mu=1\) with a lower MCS value. In other words, the result shows that, while for \(\mu=0\) and \(\mu=1\), we need to increase the MCS value up to 15, in contrast, for \(\mu=2\), we obtain the same or better result just for MCS = 10 (and do not need to increase the MCS value up to 15).  
Figure 8: Impact of modulation and coding scheme (MCS).  
Figure 9: Impact of modulation and coding scheme (MCS) with different \(\mu\) values.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 7 Recent Advances in Deep Learning-Based SLAM
### Loop Closure Detection for Underwater SLAM  
Loop closure detection, which identifies revisits to previously explored locations, plays a critical role in underwater SLAM for real-time navigation and mapping. It ensures the accuracy of SLAM systems by correcting accumulated drift in position estimates and is essential for creating consistent and reliable maps.  
Li et al. [120] present a novel real-time pose-graph SLAM algorithm tailored for underwater ship hull inspections, utilizing a forward-looking sonar to challenges in acoustic underwater SLAM. The algorithm employs a CNN for saliency detection based on the sensitivity of learned global features, followed by both saliency-aware loop closure proposals and robust data association. Furthermore, Bonin-Font et al. [141] introduce a novel global image descriptor named net hash-based loop closure (NetHALOC), trained using a simple CNN.  
More recently, Siamese neural networks have emerged as powerful tools for underwater loop closure applications. As demonstrated by Burguera et al. [119], visual loop detection (VLD) can be performed within an underwater VSLAM framework by utilizing convolutional Siamese networks, as shown in Figure 13. This is carried out by passing two underwater images, each to a separate branch of the Siamese network, where pairs that do not close the loop are rejected. Similarly, Tan et al. [122] employ Siamese networks for loop closure in bathymetric point clouds, which is particularly challenging due to the limited presence of distinguishable landmarks on the seabed and the significant drift inherent in dead-reckoning navigation. Moreover, PLNet [125] also utilizes shared-weight multi-branch 3D convolutions with self-attention for matching localization.  
Addressing the complexities of dynamic underwater environments, Wang et al. [121] propose a novel loop closure detection method using a variational autoencoder network with a dual branch on the encoder side. This unsupervised approach avoids extensive data labeling and incorporates a semantic object segmentation module to handle fast-moving objects in the underwater environment.  
Figure 12: Pipeline for DL-based pose estimation, including encoding visual and inertial features through convolutions and processing the temporal data through LSTMs. (Adapted from Sudevan et al. [137]).
## Appendix A Direct Time Integration
### The Newmark-\(\beta\) Method  
The Newmark method was originally introduced to solve second order differential equations arising from dynamic equilibrium in structural dynamics [44]. The Newmark method essentially approximates the velocity and displacement vectors at time \(\tau+\Delta\tau\) using the following expressions:  
\[\begin{split}\prescript{n+1}{}{\mathbf{q}}&=\prescript{ n}{}{\mathbf{q}}+\Delta\tau\cdot\prescript{n}{}{\mathbf{\dot{q}}}+\Delta\tau^{2} \left(\frac{1}{2}-\beta\right)\cdot\prescript{n}{}{\mathbf{\ddot{q}}}+\Delta \tau^{2}\beta\cdot\prescript{n+1}{}{\mathbf{\ddot{q}}}\\ \prescript{n+1}{}{\mathbf{\dot{q}}}&=\prescript{n}{ }{\mathbf{\dot{q}}}+\Delta\tau\left(1-\gamma\right)\cdot\prescript{n}{}{ \mathbf{\ddot{q}}}+\Delta\tau\gamma\cdot\prescript{n+1}{}{\mathbf{\ddot{q}}} \end{split} \tag{10}\]  
where \(\beta=0.25\) and \(\gamma=0.5\) in the Newmark-\(\beta\) method to ensure stability and a lack of numerical damping [44]. It is clear that an assumption on one of the vectors (either displacement, velocity, or acceleration) at time \(\tau_{\mathrm{n+1}}\) must be made since this is an implicit scheme and the system is nonlinear. In detail, if the system was linear, e.g., \(\mathbf{M}\mathbf{\ddot{q}}+\mathbf{C}\mathbf{\dot{q}}+\mathbf{K}\mathbf{q}= \mathbf{f}_{\mathbf{E}}\), one would be able to find the correct solution in one iteration by solving a system composed of the equilibrium equation and Newmark's constitutive relations, i.e.,  
\[\begin{split}\begin{cases}\prescript{n+1}{}{\mathbf{q}}&= \prescript{n}{}{\mathbf{q}}+\Delta\tau\cdot\prescript{n}{}{\mathbf{\dot{q}}}+ \Delta\tau^{2}\left(\frac{1}{2}-\beta\right)\cdot\prescript{n}{}{\mathbf{ \ddot{q}}}+\Delta\tau^{2}\beta\cdot\prescript{n+1}{}{\mathbf{\ddot{q}}}\\ \prescript{n+1}{}{\mathbf{\dot{q}}}&=\prescript{n}{}{\mathbf{\dot{q}}}+ \Delta\tau\left(1-\gamma\right)\cdot\prescript{n}{}{\mathbf{\ddot{q}}}+\Delta \tau\gamma\cdot\prescript{n+1}{}{\mathbf{\ddot{q}}}\\ \prescript{n+1}{}{\mathbf{\ddot{q}}}&=-\mathbf{M}^{-1}\left(\mathbf{C} \cdot\prescript{n+1}{}{\mathbf{\dot{q}}}+\mathbf{K}\cdot\prescript{n+1}{}{ \mathbf{q}}+\mathbf{f}_{\mathbf{ED}}\right).\end{cases}\end{split} \tag{11}\]  
The system above has three equations and three unknowns, so the algorithm does not require any iterations. Unfortunately, friction introduces nonlinearity, so assumptions and a predictor-corrector method are required. The cleverness of the initial assumption and of the corrections operated can increase the algorithm efficiency by a factor of \(\approx\)2. The following subsections present and compare two alternatives: the standard all-purpose algorithm and one which was purposely developed for friction-induced nonlinearities.