esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.9 Navigation I  
In [77], the authors propose an innovative autonomous navigation method for mobile robots operating in unknown environments using a combination of the RL algorithm and Simultaneous Localization and Mapping (SLAM). The authors used a navigation technique referred to as \"_cell decomposition and potential field_\" [77], where a ready-made or precise map of the environment is required. This work sought to tackle navigation difficulties in complex environments by merging RL for route exploration with SLAM.  
The authors in [77] used the DDPG RL algorithm to steer a robot to a specific location through the sensor data that were received. The authors developed an approach that correlates sensory data with continuous linear and angular velocities for robot locomotion. An ROS served as the middleware to execute and connect the algorithm on the robot and the sensors inside the Gazebo simulator. ROS middleware was used to collect data from the Laser Range Finder (LRF) and odometry sensors. It input these data into SLAM to construct a map, which utilized the DDPG algorithm to identify the robot position. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work shows that the integration of SLAM with DDPG significantly enhances navigation performance. The RL-SLAM combination achieved faster convergence, fewer episodes, and higher success rates with reduced collisions. The achievement is attributed to the precise reward system facilitated by the map data, which helps the robot improve its obstacle avoidance capabilities. The authors mention that integrating the online map data into the reward system enhances the robot's navigation skills. The real-world experiment confirms the effectiveness of the approach in practical scenarios such as search-and-rescue missions or autonomous deliveries.
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 4 Metabolic Pathways Associated with Pathogenicity
### Antibiotic Resistance
#### 4.3.3 Adaptive Resistance  
Unlike other types of resistance, adaptive resistance depends on growth circumstances that trigger regulatory events in the cell, and susceptibility usually reverses when inducing conditions are removed. Adaptive resistance is basically induced by environmental stresses and by the presence of specific antibiotics [151]. In _P. aeruginosa_, the best characterized adaptive resistance mechanism is biofilm formation, associated with the development of resistance to polymyxins, aminoglycos, and cationic antimicrobial peptides [196]. It is already known that biofilm-producing bacteria are more resistant to antimicrobial agents and to the host's immune response compared to other bacteria [197]. For example, eDNA, in addition to being a major component of the _P. aeruginosa_ biofilm matrix, also has the ability to acidify the cellular environment, changing membrane permeability and causing structural changes in the lipid A of LPS, leading to different responses to antimicrobials like colistin or to other polymyxins [198].
# Two-Phase Deep Learning-Based EDoS Detection System
## 4 System Design
### Internal Modules
#### 4.3.3 ANN Model-Period Detector  
The ANN module aims to learn a set of features, as shown in Table 2, to detect whether there is an attack during a time period of 5 s. In this study, we employ classic feed-forward neural networks trained with a back-propagation algorithm. The ANN model parameters are presented in Table 3. Our proposed ANN model has an input layer of 10 nodes, 3 hidden layers, and an output layer of 1 node. Because our ANN model is a binary classifier, the activation function of the output layer is a sigmoid function:  
\[f(z)=\frac{1}{1+e^{-z}} \tag{15}\]  
A 10-dimensional vector \(x\) whose elements correspond to input variables will be fit into the ANN model. The trained model will be calculated based on the learned parameters to output a \(z\) value, and this \(z\) value will then output a new value \(f(z)\) between zero and one by the sigmoid function. Based on this \(f(z)\) value, the period detector classifies whether the observed time period is abnormal or normal.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results  
We present an overview of the benchmark results achieved by various methods across multiple video description datasets, including MSVD [49], TACoS [50], MPII-MD [51], MPII-MD [51], TGIF [52], Youtube2 text [75], MSR-VTT [7], Charades [53], VTW [54], ActivityNet Captions [2], YouCook2 [4], BDD-X [55], VideoStory [56], M-VAD [57], VATEX [58], TVC [59], ViTT [21], VC_NBA_2022 [60], and WTS [61]. The methods are evaluated using a range of automatic metrics: BLEU [8], METEOR [9], CIDEr [10], ROUGE-L [11], SODA [12], SPICE [13], and WMD [14].
## 2 Key Technology Discussion
### Bandwidth Extension with Low Supply Voltage  
#### Parallel-Plate Couplers:  
Parallel-plate couplers represent a novel approach to achieving wideband power combining in Doherty amplifiers [10; 11; 12]. These couplers utilize planar configurations to achieve low phase variation and high bandwidth, addressing the limitations of traditional transmission line structures. The compact nature of parallel-plate designs also facilitates easier integration into RF front-end modules, which is essential for modern handset applications. The wideband characteristics of parallel-plate couplers enable the DPA to handle multiple frequency bands without significant performance degradation, making them particularly suitable for the diverse frequency allocations utilized in 5G technology.  
(2) Multi-Section Output Networks:  
As shown in Figure 3, employing multi-section output networks is another effective strategy for extending the bandwidth of Doherty amplifiers. By cascading multiple quarter-wavelength sections or using coupled-line structures, designers can create output networks that maintain effective impedance matching across a wider frequency range [3,20]. By using this multi-section approach, the output impedance of carrier and peaking amplifier is transferred to R instead of 2R in saturation. As a result, the output power will be doubled. As shown in Figure 4, the benefits of enhanced bandwidth and performance make it a worthwhile investment in the context of achieving the operational requirements for 5G applications.  
In conclusion, the key technology discussions surrounding Doherty power amplifiers for 5G applications highlight the innovations and strategies employed to overcome the inherent challenges in linearity, efficiency, and bandwidth. Through these advancements, DPAs can meet the stringent demands of modern communication systems, ensuring reliable and efficient performance in a rapidly evolving technological landscape [20, 21, 22].
###### Abstract  
A comparative study was carried out using magnetic nanoparticles (MNPs) for the fabrication of non-enzymatic sensors for the continuous and rapid detection and monitoring of H\({}_{2}\)O\({}_{2}\). Various MNPs, differing in terms of their synthesis procedure and modification, were synthesized and characterized by different techniques. The electrochemical catalytic activity of the synthesized MNPs toward the reduction in H\({}_{2}\)O\({}_{2}\) was investigated by cyclic voltammetry. The naked MNPs showed the highest catalytic activity among all the synthesized MNPs. The biosensor based on the naked MNPs was then applied in the determination of H\({}_{2}\)O\({}_{2}\) using chronoamperometry. The parameters such as the applied cathodic potential and the amount of MNPs on the developed biosensor were optimized. Moreover, the analytical figures of merit, including reproducibility (RSD = 6.14%), sensitivity (m = 0.0676 \(\upmu\)A \(\upmu\)M\({}^{-1}\)), limit of detection (LOD) = 27.02 \(\upmu\)mol L\({}^{-1}\), and limit of quantification (LOQ) = 89.26 \(\upmu\)mol L\({}^{-1}\) of the developed biosensor indicate satisfactory analysis. Finally, MNPs were successfully utilized for the determination of H\({}_{2}\)O\({}_{2}\) in milk.  
Keywords:magnetic nanoparticle; nanozyme; chronoamperometry; biosensor; peroxidase-like activity +
Footnote †: journal: Applied Soft. 2020, 10, 6756; doi:10.3390/app10196756
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 6 Discussion  
In this study, the main objective was to develop a diversion decision-making framework for selecting alternative truck routes to circumvent congested highway segments. To achieve this objective, data were collected, prepared, and utilized to design and build a dynamic routable network dataset for the state of Florida.  
Additionally, the ArcGIS platform was utilized to generate an alternative route that accommodates truck characteristics and constraints. Predefined alternative route selection criteria were developed, taking into consideration road conditions, truck weight and height restrictions, and neighborhood impact. Truck diversion strategies are fundamental as an approach to congestion mitigation.  
Three case studies were presented to demonstrate the application of the proposed diversion decision framework. For each case study, two scenarios were designed and simulated as a base scenario with no diversion and an optimized scenario with a diversion strategy. The resulting benefits were quantified and the two scenarios were compared to validate the developed framework.  
For example, the first case study was an incident on I-75 Northbound, beyond mile marker 258. By comparing the two scenarios, it can be seen that the optimized route application reduced the average delay time by 42 min. The benefits of implementing the proposed framework were estimated as a total annual saving of USD 52 million by comparing the travel times, fuel consumption, and economic impacts between the two scenarios.  
This approach goes beyond the selection of alternative routes by featuring a cost-benefit analysis to quantify the savings from delay time and fuel consumption for the rerouted trucks, which provides decision makers with measurable benefits that justify diversion strategies.  
Overall, the findings of this study shed considerable light on the impact of truck diversion on the performance of a road network. The systematic approach used in this study included alternative route selection criteria, such as truck characteristics of weight and size, to ensure that the alternative route could accommodate the diverted truck traffic.
# Two-Stage Configuration of User-Side Hybrid Energy Storage Based on Fuzzy Optimization
## 3 Two-Stage Optimization Algorithm Based on Fuzzy Optimization  
### Multi-Objective Fuzzy Optimization  
In the above model, there are two competing objective functions, and it is impossible for both of them to achieve the optimum at the same time. For the multi-objective programming problem, it is advisable to use the theory of fuzzy sets to transform the problem into a single-objective problem. On the premise of satisfying all constraints, considering user investment and smoothing photovoltaic output, the total cost of investing in supercapacitors and the total proportion of high-frequency components in the output need to be as small as possible. There is an upper limit and no lower limit in these two objectives. Therefore, the reduced half-line is chosen as their membership function, as shown in Figure 3. In the membership function, the ordinate represents the satisfaction of each subject (i.e., grid and user) and the abscissa represents the index of each subject. It can be seen from Figure 3 that the main body will eventually reach the most satisfactory level.  
Figure 3: Membership function of two optimization objectives.  
Two membership functions can be expressed as  
\[\mu(f_{1}(x))=\begin{cases}1,&\text{if }f_{1}(x)<\text{C01};\\ \frac{\text{C01}+D01-f_{1}(x)}{D01},&\text{if }\text{C01}\leq f_{1}(x)\leq \text{C01}+D01;\\ 0,&\text{if }f_{1}(x)>\text{C01}+D01.\end{cases} \tag{28}\]  
\[\mu(f_{2}(x))=\begin{cases}1,&\text{if }f_{2}(x)<\text{C02};\\ \frac{\text{C02}+D02-f_{2}(x)}{D02},&\text{if }\text{C02}\leq f_{2}(x)\leq \text{C02}+D02;\\ 0,&\text{if }f_{2}(x)>\text{C02}+D02.\end{cases} \tag{29}\]  
where C01 is the result when the minimum investment of users is taken as the optimization objective, C02 is the result when the smoothest photovoltaic output is considered separately, \(C01+D01\) is the maximum acceptable value for user investment, and \(C02+D02\) is the maximum allowable value for the proportion of high-frequency components in the PV output. Taking \(\lambda\) as the smallest subordinate variable in all subordinate functions, satisfaction can be expressed as  
\[\lambda=Min:\{\mu(f_{1}(x)),\mu(f_{2}(x))\} \tag{30}\]  
According to the principle of maximum membership degree, the original multi-objective optimal allocation problem can be transformed into a membership degree that satisfies all constraints, i.e., satisfaction maximum. At this time, the optimization results will comprehensively satisfy of both users and grids and can be simply represented by the following models.  
\[Min:-\lambda \tag{31}\]  
\[s.t.C01\leq f_{1}(x)+D01*\lambda\leq\text{C01}+D01 \tag{32}\]  
\[C02\leq f_{2}(x)+D02*\lambda\leq\text{C02}+D02 \tag{33}\]  
\[0\leq\lambda\leq 1 \tag{34}\]  
Supercapacitor Operation Constraints: (4)-(9).
## 2 Materials and Methods
### FCG Assessment of the Energy Release Rate with VCCT
#### 2.1.1 Geometrical Modelling  
The numerical assessment utilizes the mechanical properties in Table 1 and the Paris coefficient material parameter obtained from the experiment as stated in Table 2, respectively. The 3D finite element of SENT specimen in Figure 1 (left) for crack growth analysis is modelled using 72,000 elements and 24,558 nodes with tetrahedral meshing. One of the criteria to significantly enhance the accuracy of VCCT in FE analysis is to use higher-order elements. The geometry model was created using the characteristic size of 2 mm and subsequently discretized with patran-tetra remeshing type to increase the nodes since the solution of the governing equations is executed at the nodes in FE analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Young Modulus (GPa)** & **Yield Strength (MPa)** & **Poisson Ratio** \\ \hline S355 Steel & 213 & 355 & 0.3 \\ S960 Steel & 220 & 974 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties for high-strength steel S355 and S960.  
Figure 4: Computation of energy release rate using virtual crack closure technique.  
The remeshing-based 3D tetrahedral solid element allows fully general crack paths and change in crack surfaces. The limitation is that the region where the crack is located must support global remeshing. The crack is defined by means of a faceted surface, which is basically a mesh made out of tetrahedral meshing. The actual crack is defined by the intersection of the faceted surface and the mesh illustrated in the clipped model in Figure 5a. To overcome the singularity effect that occurs in a very small area along the element edges [13]. The singularity in VCCT Marc automatically generates a special mapped mesh around the new crack fronts for good accuracy. The node along the crack front with the highest loading based on the specified growth increment while the other nodes will use the scaled down growth increment. A template crack with crack initiation and remeshing is used to estimate the crack growth in mode I, with the shape of the crack front. Figure 5b shows adaptive remeshing with global remesh criteria assigned to the contact body with global patran-tetra properties, timing and location. The SENT model is defined as the contact body to be remeshed separately from the standard remeshing criteria of the faceted surface, as crack growth activates the automatic remeshing. For the elements in the crack surface extension due to growth, where all front nodes are growing the same amount. The part of the crack surface which is inside the surface mesh will eventually form the new updated crack front.
# Using Graph Neural Networks for Social Recommendations
## 1 Introduction
By leveraging the expressive power of GNNs, these models hold great promise for enhancing the accuracy and effectiveness of recommender systems. The utilization of graph learning techniques in RSs provides a valuable avenue for leveraging the rich and interconnected nature of user-item interactions. By incorporating graph structures and employing advanced methods like GNNs, RSs can effectively harness the power of heterogeneous and interconnected data sources to generate more accurate and personalized recommendations. As the field continues to advance, graph learning-based methods are poised to play a pivotal role in the evolution of RSs, offering novel approaches to address the challenges posed by diverse and interconnected data. In this paper, we propose RelationalNet, an architecture that leverages GNNs to represent the relationships between users and items using various graph forms, which in turn are fed into the prediction process to yield more accurate recommendations. In particular, the proposed architecture encompasses the collaborative filtering notion of user-item and item-item connections, while also incorporating the users' social network relationships (user-user). Such a rich and robust representation allows for more accurate predictions, even in the case of sparse user-item input data, which is most often the main characteristic of such systems, and can be used to address the cold-start problem, as item-item connections are inherent to the application domain and not dependent on the users' ratings (or absence thereof). Our approach is an extension of the successful DiffNet++ architecture [13] that introduces additional types of embeddings. Via experimental evaluation using the Yelp dataset, we demonstrate that our approach not only outperforms more established graph-based social recommendation models but also improves on the recently proposed DiffNet++ in generating accurate top-\(n\) recommendations for the user. The rest of the paper is organized as follows: in Section 2, we review in detail the state of the art (SOTA) in the areas of social recommenders, GNNs, and their intersection; in Section 3, we discuss in detail the proposed architecture of the RelationalNet model; in Section 4, we present our methodology as well as the results of the experimental evaluation that compares RelationalNet with SOTA architectures, demonstrating the strengths of our approach; finally, we conclude with our plans for future work in Section 5.
# Blockchain-Based Loyalty Management System
## 2 Literature Review
### Background
#### 2.1.1 Traditional Loyalty Programs  
Currently, traditional loyalty programs are based on a variety of mechanisms, and it is possible to classify them based on several categories: stamp cards, points, cashback, tiers, and coalition [4]. However, despite being a dominant choice and offering several tools to attract new customers and retain them, these programs have problems related to customer and company perspectives [5].  
Starting with the customer perspective, the main current difficulties are as follows:  
* The first step in joining a new loyalty program is usually to provide the system with personal information, which nowadays, with unquestionable security concerns, may imply a constraint for some users. Additionally, customers feel that they belong to too many loyalty programs, since each brand has its own program [6]:
* With traditional approaches, the customer does not own assets such as points, tiers, and others. Instead, the program controls and stores the assets inside its database. Additionally, the value of the assets is changeable due to the company having the power to decide when to inflate or deflate it. These programs naturally have limits due to the way they are implemented. An example is that rewards only have value within the scope of the application and cannot be transferred to another system [5];
* The customer experience of traditional loyalty programs is not satisfactory. Despite their popularity, customers are less motivated to continue interacting with the system due to the effort-reward relationship. Most programs set an expiration date for rewards, which indirectly requires the customer to spend them before the deadline, even if he/she does not intend to. Another factor that reduces the customer experience is the lack of customization in the rewards for customers, with their needs and desires not usually considered (each customer is different, but compensation is the same for all) [7]. From the perspective of companies, the main difficulties consist of the following:
* Companies are responsible for protecting all the information of customers. Moreover, to process the transactions, they rely on third party entities, which makes them very costly. Apart from the need for intermediaries, a company is also responsible for infrastructure management, which adds overwhelming complexity and increases costs;
* Building partnerships with the traditional approach is complex due to the nature of the programs. Usually, brands conceive of loyalty programs with the intention of expanding their own business, and therefore, said program becomes restricted to its original scope. Whenever a company decides to build a partnership with another company, the typical solution consists of creating a new loyalty program. Consequently, another issue arises: that of who owns the assets (since with the traditional approach, the customers do not own them).