esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 5 Pricing: Creating Incentive for Caching  
Pricing wireless services has mattered since commercial cellular systems were first deployed. Conventional pricing mechanisms are designed for on-demand transmissions, because today's RANs serve as bit-pipelines only. In this case, a user is charged for her or his access time or data volume received. With caching, however, not all the received data is needed by a user. As no one is willing to pay for what he or she does not need, revolutionary pricing mechanisms are desired in cache-empowered RANs. One straightforward solution is to charge a user for what he or she requests, rather than what he or she receives. In general, pricing should ensure that a user pays a lower service fee, while the RAN's operator has more profit [36]; otherwise no one is willing to adopt caching.
# Performance of Narrow Band Wide Area Networks with Gateway Diversity
###### Abstract  
This paper quantifies the coverage area of Low-Power Wide-Area Networks (LPWAN) for Packet Success Rates (PSR) above 85%, where acceptable Quality of Service (QoS) can be achieved. The network consists of battery-operated end-nodes (ENs) and multiple stationary gateways (GWs). We consider asynchronous communication that uses ALOHA-based random channel access. Each transmission from the ENs can be received by multiple GWs. Such spatial diversity results in favorable Signal-to-Noise ratios (SNR). The LoRa modulation is assumed and its specific features, such as IQ inversion, further contribute to decreasing the impact of interference. An increase in the GW density improves network performance, which allows support for a larger density of end-nodes as well as increasing the coverage area. Our simulation results show that a suburban area of up to 1.44 km\({}^{2}\) can be covered with five GWs with up to fifty end-nodes with a PSR greater than 86%.  
coverage area; gateway; gateway diversity; IoT; LoRa; LPWAN; PER; PSR; RSSI; SNR +
Footnote †: journal:
## 2 Problem Description  
### Aiming Strategy Problem  
#### 2.1.1 Practical Aiming Strategy Optimization  
The principle of the operation of the heliostat field is shown in Figure 1. This study optimizes aiming strategies for the thermal receiver of the heliostat plant, aiming at vertical shifts of aiming points on the cylindrical receiver, shown in Figure 2. The plant is divided into 32 concentric sections, with adjustable height \(H\) for heliostats targeting their receiver panels. The receiver is composed of 32 panels, each corresponding to the sector heliostat field among the full heliostat field. The aiming point of heliostat \(k\) can be denoted by \(\mu_{k}\), with \(n\) aiming points on the thermal receiver, so that \(\mu_{k}\) must satisfy \(\mu_{k}\in\{1,2,\ldots,n\}\). The aiming strategy of the heliostat field can be denoted as an m-dimensional vector \(\mu=[\mu_{1},\mu_{2},\ldots,\mu_{m}]\), where \(m\) denotes the number of decision variables in real-time optimization and will be shown in the next part.  
Following the aiming strategy \(\mu\), the flux of the square at coordinates \((x,y)\) can be denoted by \(\Psi_{(x,y)}(\mu)\), and corresponding flux density \(f_{x,y}(\mu)\) is evaluated by Equation (1):  
\[f_{(x,y)}(\mu)\approx\Psi_{(x,y)}(\mu)/Q_{(x,y)}, \tag{1}\]  
where \(Q_{(x,y)}\) denotes the square area at the coordinate \((x,y)\).  
The corrosion and thermal stress limitations on the central thermal receiver are transformed into a maximum allowable flux density. Allowed flux density (AFD) depends on solar thermal receiver geometry factors: number of panels \(N_{p}\), tube diameter \(d\), receiver diameter \(D\), and height \(H\). The AFD limitation at coordinates \((x,y)\) is denoted as \(AFD_{(x,y)}\)  
Figure 1: The principle of the operation of the heliostat plant.  
The aiming strategy aims to maximize thermal receiver output, modeled as \(\sum_{(x,y)}\Psi_{(x,y)}(\mu)\) while adhering to AFD limitations. Thus, real-time aiming strategy optimization can be formulated as an optimization problem without additional constraints.  
\[\begin{array}{l}F(M_{l})=\max\sum\limits_{(x,y)}\Psi_{(x,y)}(\mu)\\ \text{s.t.}\hskip 28.452756 ptf_{(x,y)}(\mu)\leq AFD_{(x,y)}\\ \hskip 28.452756 pt\mu_{k}\in\{1,2,\ldots,n\}\\ \hskip 28.452756 ptM_{l}\in\{1,2,\ldots,L\}\end{array} \tag{2}\]
# On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks
## 1 Introduction
It performs on average of 50% better than the maximum independent set and minimum connected dominating set algorithms in terms of active node ratio while providing two times reduction in energy consumption. However, there are few literatures devoted to the study of wireless sensor network connectivity and networking algorithm from the aspect of probability theory. Based on the predecessors' research with the connectivity of wireless sensor networks, this paper studies this problem and presents the innovation on the combination of the probability theory and a new network algorithm called connectivity and energy efficiency (CEE) algorithm with the high connectivity. By applying the principles of computer network router to the wireless sensor nodes, aiming at achieving the maximum probability of connectivity and minimizing the energy consumption of the entire network, the algorithm wakes up as few neighbor nodes as possible, to improve the service life of the entire network.
## 1 Introduction
To overcome this limitation, we propose a distributed multi-agent DRL-based method for determining task partitioning ratios. Unlike prior techniques that primarily focused on deciding whether to offload tasks or balancing between local execution and a single MECS, our approach considers task partitioning across multiple MECSs simultaneously. This enables the optimization of both throughput and delay, akin to existing HSR-MEC studies. Additionally, to reduce the dimensionality of the DRL action space, the proposed algorithm is designed with a two-part structure, further enhancing its computational efficiency and applicability. This paper is organized as follows: Section 2 presents the proposed system model, and Section 3 introduces the proposed algorithm. Section 4 also evaluates the performance of the proposed technique through experimental results. Finally, Section 5 concludes the paper and outlines future research directions.
# Effect of Metallic Waste Addition on the Physical and Mechanical Properties of Cement-Based Mortars
## 2 Materials and Methods
### Physical Characterisation of the Test Specimens  
To evaluate the effect of the type and content of recycled metallic waste on the physical properties of the cement mortars after 7 and 28 days, bulk density (\(\rho\)) and water accessible porosity (\(n\)) of each test specimen with and without metallic waste were measured and determined according to the ASTM C642 Standard [28]. Then, the representative values of density and porosity of each cement mortar mixture were calculated as the average value of 12 specimens without metallic waste and 24 specimens with metallic waste for each curing age.
# NOVA Mobility Assistive System: Developed and Remotely Controlled with IOPT-Tools
###### Abstract  
In this paper, a Mobility Assistive System (NOVA-MAS) and a model-driven development approach are proposed to support the acquisition and analysis of data, infrastructures control, and dissemination of information along public roads. A literature review showed that the work related to mobility assistance of pedestrians in wheelchairs has a gap in ensuring their safety on road. The problem is that pedestrians in wheelchairs and scooters often do not enjoy adequate and safe lanes for their circulation on public roads, having to travel sometimes side by side with vehicles and cars moving at high speed. With NOVA-MAS, city infrastructures can obtain information regarding the environment and provide it to their users/vehicles, increasing road safety in an inclusive way, contributing to the decrease of the accidents of pedestrians in wheelchairs. NOVA-MAS not only supports information dissemination, but also data acquisition from sensors and infrastructures control, such as traffic light signs. For that, it proposed a development approach that supports the acquisition of data from the environment and its control while using a tool framework, named IOPT-Tools (Input-Output Place-Transition Tools). IOPT-Tools support controllers' specification, validation, and implementation, with remote operation capabilities. The infrastructures' controllers are specified through IOPT Petri net models, which are then simulated using computational tools and verified using state-space-based model-checking tools. In addition, an automatic code generator tool generates the C code, which supports the controllers' implementation, avoiding manual codification errors. A set of prototypes were developed and tested to validate and conclude on the feasibility of the proposals.  
mobility assistive system; wheelchairs; mobility scooters; design automation tools; remote monitoring; model-driven development; petri nets +
Footnote †: journal: Journal of Electronics Electronics and Electronics
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 4 BackCom: Signal Processing and Detection
### Signal Detection in BackCom
#### 4.2.1 Coherent Detection  
This detection technique considers the phase information of the signals while detecting signals. The phase information can be extracted from the nearby source of carrier waves, due to less attenuation and other signal degradation parameters. Hence, coherent detection can effectively be employed in the BiS setup of BackCom. A coherent receiver for BiS radio was proposed to employ a bistatic  
Figure 14: \(\mu\)mo decoding in BackCom.  
Figure 15: Signal detection in BackCom.  
coherent uncoded reception scheme [66]. This type of detection works by separating a carrier emitter from the receiver. However, in a bistatic setup, the coherent reception is difficult for three unknown channel links. These links are between carrier-emitter and reader, carrier-emitter and tag, and tag and reader. The reception also depends on many new parameters related to scattering radio. Figure 16 exhibits the design of the coherent correlation receiver with four correlators. This work assumes that channel state information is available with equiprobable transmitted bits [66]. \(h\) denotes the maximum likelihood estimate of the channel, \(y(t)\) shows the received signals at the backscatter receiver, and \(F_{0}\) and \(F_{1}\) represent two orthogonal frequencies exponential of the received signal. The complex vector \(\mathbf{r}\) is base band equivalent of the received signal for duration \(T\) of a single bit. However, obtaining the phase information is very difficult in some cases. The detection of signals without considering phase information is discussed next.
## 4 Point Cloud Segmentation
### Supervoxel Generation  
We generated supervoxels to reduce the number of point clouds and increase computation speed without losing adjacency relations between points. Supervoxel generation is based on the concept of voxel cloud connectivity segmentation (VCCS) [36], which uses color, the distance between points, and fast point feature histograms (FPFH) to divide voxels into groups. To enhance overall calculation efficiency, only the distance between points and the normal direction were used to generate supervoxels.
Moreover, as electronic devices continue to miniaturize, materials that offer tunable electronic and magnetic properties become increasingly valuable. This tunability can lead to more efficient and versatile components in spintronic devices, potentially revolutionizing data storage, processing speeds, and energy efficiency. Moreover, the unique properties of perovskites, such as their high-temperature stability and diverse electrical behaviors, make them suitable candidates for applications that demand a robust performance under extreme conditions.
# Thermal Energy Storage for Building Load Management: Application to Electrically Heated Floor
## Nomenclature  
\begin{tabular}{l l} \(\eta\) & Energy conversion efficiency (\%) \\ \(\rho\) & Density of the storage substance (kg/m\({}^{3}\)) \\ \(T\) & Temperature difference between the storage substance temperature and the indoor air temperature (K) \\ \(C_{p}\) & Specific heat of the storage substance (kWh/kg\(\cdot\)K) \\ \(c_{eff,peak}\) & Energy prices during off-peak (\$/kWh) \\ \(c_{peak}\) & Energy prices during peak (\$/kWh) \\ \(c_{ini\_1}\) & Initial cost of the heating system with storage (\$) \\ \(c_{om\_1}\) & Operational and maintenance costs of the heating system with storage (\$) \\ \(c_{om\_2}\) & Operational and maintenance costs of the heating system without storage (\$) \\ \(CES_{1}\) & Average energy price for \(N_{d}\) days with storage system (\$/kWh) \\ \(CES_{2}\) & Average energy price for \(N_{d}\) days without storage system (\$/kWh) \\ \(E_{off,peak\_2}\) & Consumption during off-peak periods with storage system per day (kWh/day) \\ \(E_{off,peak\_1}\) & Consumption during off-peak periods without storage system per day (kWh/day) \\ \(E_{peak\_2}\) & Consumption during peak periods without the storage system per day (kWh/day) \\ \(E_{peak\_1}\) & Consumption during peak periods with the storage system per day (kWh/day) \\ \(E_{tot\_2}\) & Consumption for space heating for \(N_{d}\) days without the storage system (kWh/day) \\ \(E_{tot\_1}\) & Consumption for space heating for \(N_{d}\) days with the storage system (kWh/day) \\ \(E_{tot\_2\_par}\) & Consumption for space heating for one year without the storage system (kWh/year) \\ \(E_{tot\_1\_var}\) & Consumption for space heating for one year with the storage system (kWh/year) \\ e & Thickness of the concrete layer (m) \\ h & Heat transfer coefficient of upper surface of the EHF, taking convection and radiation into consideration (W/m\({}^{2}\cdot\)K) \\ \(V\) & Volume of the storage system (m\({}^{3}\)) \\ CT & Charging time (h) \\ DT & Discharging time (h) \\ ESD & Energy Storage Density (kWh/m\({}^{3}\)) \\ NMBE & Normalized Mean Bias Error (\%) \\ N\({}_{PB}\) & Payback period (years) \\ \((P_{max})_{Heating\ syst}\) & Maximal power of the heating system (\(kW\)) \\ \end{tabular}
## 4 Gas Generation Potential
### Controlling Factors of Natural Gas Generation Retardation  
Low geothermal gradient  
The geothermal gradient of the Junggar Basin is relatively low. The current geothermal gradient is about 2.1 \({}^{\circ}\)C/100 m, which is significantly lower than that of Basin located in eastern China, such as Yinggehai Basin (about 3.1-4.3 \({}^{\circ}\)C/100 m, reference [26]) and Bohai Bay Basin (about 3.5 \({}^{\circ}\)C/100 m, reference [27]).  
A low geothermal gradient has a noticeable influence in the hydrocarbon generation of source rocks. To reveal the influence of low geothermal gradient on the evolution of source rocks, PetroMod simulation software is used to simulate the hydrocarbon generation process of source rocks with TOC of 3% and HI of 600 mg/g under the background of geothermal gradients of 2.1 \({}^{\circ}\)C/100 m (representing Junggar Basin) and 3.5 \({}^{\circ}\)C/100 m (representing eastern China Basin). It is found that the beginning depth of abundant gas generation with a geothermal gradient of 2.1 \({}^{\circ}\)C/100 m is 2000 m more than with a geothermal gradient of 3.5 \({}^{\circ}\)C/100 m.  
The hydrocarbon generation modeling results show that the geothermal gradient has influence on natural gas generation, and the low geothermal gradient causes gas generation retardation.  
Overpressure  
In addition to lower paleo-temperature affecting hydrocarbon generation, overpressure is an essential factor affecting natural gas generation. Overpressure is developed in the Junggar Basin. It has been mentioned above that there is overpressure development with a pressure coefficient of 1.2-2.0 in the depth of more than 4000 m. R\({}_{\rm o}\) of source rocks in depth of more than 4000 m is significantly lower (Figure 7), indicating that overpressure is another controlling factor in natural gas generation retardation.  
Figure 7: Diagram of the relationship between pressure coefficient, R\({}_{\rm o}\) and depth of the Junggar Basin. (**a**) relationship between pressure coefficient and depth; (**b**) relationship between R\({}_{\rm o}\) and depth.
## 3 Solution Methodology
### Tuning the Optimization Algorithms  
To ensure optimal performance and fair comparison between the analyzed optimization methodologies, a parameter tuning process was carried out. This was done via PSO, following the meta-optimization approach described in [48]. Note that the PSO was not used to solve the MG optimization problem itself but rather to identify the best-performing internal parameters (e.g., population size, number of iterations, inertia weight, and learning coefficients) for each algorithm.  
The PSO algorithm used for tuning was configured with eight particles, a maximum of 300 iterations, cognitive and social acceleration coefficients of 1.494, and an inertia weight that linearly decreased from 0.7 to 0.001. Each candidate parameter set was evaluated over 100 independent runs on a representative test case in order to minimize the average value of the objective function and the variability of the results, thereby ensuring statistical robustness.  
It is important to note that the tuning process was performed independently for each operating mode (grid-connected and islanded) to account for the specific technical constraints and characteristics of each scenario. The final set of tuned parameters for each algorithm, objective function, and system configuration is summarized in Table 2. While the specific parameters of each algorithm were tuned within the ranges reported in the specialized literature, all methods shared a common search space for the general parameters. Specifically, the number of individuals was allowed to vary between 4 and 1000, and the maximum number of iterations ranged from 2 to 15,000. This consistent configuration created a fair benchmarking scenario, where each methodology was given the opportunity to adjust its internal settings to achieve the best possible performance under the same general constraints.  
This systematic tuning procedure improved the overall performance and consistency of each method, ensuring that all algorithms operated under optimized and reproducible conditions throughout the study.  
It is important to clarify that the results presented in Table 2 correspond to a preliminary parameter tuning process applied to each metaheuristic algorithm rather than to the final solutions obtained from solving the main optimization problem. This auxiliary procedure, based on established methodologies in the specialized literature, was carried out to ensure fair comparison conditions and optimal performance in the subsequent application of each algorithm.
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 4 Applications in Energy Field
### Petroleum Supply Chain
#### 4.1.2 Refinery Scheduling
[PERSON] [261] reported a discrete-time-presented multi-periodic MILP model for oil blending and processing optimization problem. Shown by numerical results, this formulation is computationally effective, as it reduces solving time. [PERSON] et al. [255] proposed an approach involving Lagrangian decomposition to solve refinery planning and crude oil operation scheduling integration, which is a large-scale MINLP. The authors introduced a hybrid dual problem to update Lagrange multipliers and then solve each problem separately. [PERSON] et al. [262] reported a unit-specific event-based time representation based on the state task network (STN), an extension of a previous benchmark given by [PERSON] and [PERSON], to solve crude oil refinery operation involving desalting. The proposed model makes it possible to inform the decision maker of resources consumed, such as wash water and sludge. To conclude this section, the built-in ML algorithm could help simplify the COP model or reduce the solving time with certain architecture constructed. Yet not every part of the production process was considered for refinery scheduling, i.e., the omitted elements, such as dependence on uncertain parameters and uncertainties under non-linear constraints, would cause the results to be different.
# Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
## 3 Methods
### Machine Learning Classifiers
#### 3.3.6 Extreme Gradient Boosting  
Extreme gradient boosting (XGB) is a powerful machine learning algorithm that excels at various classification, regression, and ranking tasks [44]. XGB is an ensemble model that utilizes a collection of weak predictive models, typically decision trees, to make accurate predictions. The algorithm constructs an ensemble of decision trees in a sequential manner, with each tree learning from the mistakes made by the previous one to improve the overall predictive power of the model. XGB effectively extracts valuable information from high-dimensional data to enhance the model's generalization ability and computational efficiency through feature splitting and selection.
## 3 Results and Discussion
### Comparison of Model Outputs for Different Landscape Structures
#### 3.3.3 Note and Interpretation of Model Outputs  
Looking at the results, as expected, the increase in the value of the indicator over the 3 years seems to be related to the evolution of the landscape and the cultivation practices, especially the reduction in grasslands at the expense of croplands. A small part of these results can also be explained by urbanisation over the years.  
We can also see that the indicators did not change much in 2015 for 20 and 40 mm (this is the same case for 1994, but more obvious for 2015). This is an interesting finding revealed by the model, probably due to the fact that 20 mm of rain is enough to put the whole network into operation.  
Figure 11: Evolution of connected areas.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Connected Surfaces** & **5 mm** & **20 mm** & **40 mm** \\ \hline
1947 & 25.2 ha & 49.7 ha & 59.7 ha \\ \hline
1994 & 60.8 ha & 81.4 ha & 87.0 ha \\ \hline
2015 & 77.4 ha & 93.2 ha & 97.5 ha \\ \hline \end{tabular}
\end{table}
Table 3: Evolution of connected areas.  
The results found so far tend to show a strong increase in hydrological connectivity. However, it should be noted here that the simulations do not take into account the developments that have taken place over time. Some of them have been implemented, notably by the basin syndicate (fascines, grassed strips, etc.) in order to minimise the impacts of rapid flooding. The results should therefore be taken with the understanding that the model is likely to overestimate connectivity by not taking these features into account. However, the aim here was to analyse the effect of landscape evolution on connectivity, and in a second phase it will be possible to insert the development features to test their effectiveness in reducing connectivity.