esa-sceva/satcom-chunk-collection huggingface dataset:
# Game-Based Resource Allocation Mechanism in B5G HetNets with Incomplete Information
## 4 Simulation Results
### Leader Game  
When obtaining the BNE in the follower game among mobile users, the game process turned to the leader game, where the two networks competed with each other for the users in their own covered area and conclusively maximized their utilities.  
Figure 9 shows the load trajectories of two networks, which is related to the number of connected users and their bandwidth requesting strategies. For WLAN, when changing with the increment of users' bandwidth requests, its load increased to a maximum (i.e., 9.8 MHz) and then decreased gradually to a stable value (i.e., 9.4 MHz). But for WIMAX, its load increases monotonously to a stable value (i.e., 49.2 MHz). This phenomenon can be explained by the effect of the network's load level on users' actions. Specifically, the network capacity of WLAN was smaller than WIMAX, so it was earlier for WLAN to reach its saturation point. Sequentially, as a rise in the cost was caused by the congestion of networks, users transferred their bandwidth requests to another network with a lower load level (i.e., WIMAX).  
Figure 10 represents the best response pricing strategies of the two networks. The Nash equilibrium is the intersection of the strategy adaptation trajectories of two networks. The concave utility of each network proves the existence of the equilibrium. Using the backward induction and distributed iteration algorithm, we got the Nash equilibrium point to satisfy the maximal utility of the two networks. Consequently, the hole Stackelberg game with incomplete information reached its perfect equilibrium point.  
Figure 8: Average utility of all mobile users obtained through Bayesian Nash equilibrium (BNE) and Nash equilibrium (NE).  
Figure 9: Network load with the number of iterations.  
The above simulation results not only prove the existence and uniqueness of both the follower game and leader game, but also show the interactions among both mobile users and HetNets in B5G environments. Meanwhile, the optimal bandwidth requesting strategy and optimal pricing strategy for each mobile user and network are also illustrated in the above results, which further validates the correctness of theoretical deduction in Section 3.
# An Overview of Variants and Advancements of PSO Algorithm
## 2 Concept of Particle Swarm Optimization (PSO) Technique
### Updating of Velocity and Position of Particle in the Swarm  
#### 2.1.1 Updating of Velocity  
In this algorithm, the regulation of velocity is considered as a major feature, as it is the main mechanism used to move the position of a particle to search for an optimal solution in the search space. [PERSON] used the maximum values of velocity, and discussed results for different values of velocity. The velocity of particle k in the swarm in the \(\rm(i+1)^{th}\) iteration, is updated, according to the following Equation (1):  
\[\rm V_{k}(i\ +\ 1)\ =\rm V_{k}(i\ )\ \ +\ c_{1}\ r_{1}\Big{(}p_{best,i}^{k}\ -\ X_{k}(i)\Big{)}\ +\ c_{2}\ r_{2}\Big{(}g_{best,i}\ -\ X_{k}(i)\Big{)} \tag{1}\]  
where \(\rm V_{k}(i\ +\ 1)\) is the velocity of particle k at the \(\rm(i+1)^{th}\) iteration; \(\rm X_{k}(i)\) is the position of particle k in the \(\rm i^{th}\) iteration; \(\rm p_{best,i}^{k}\) is individual best position of the particle k in the \(\rm i^{th}\) iteration; \(\rm g_{best,i}\) is the global best position of the any particle in the \(\rm i^{th}\) iteration;\(\rm x_{1}\) and \(\rm x_{2}\) are the real acceleration coefficients that control how much the global and individual best positions should influence the particle's velocity; \(\rm r_{1}\) and \(\rm r_{2}\) are uniformly distributed random numbers in the range 0 and 1, used to maintain an adequate level of diversity in the population. There are three components of the velocity update Equation (1) of kth particle in the (i + 1)th iteration, namely:  
* Momentum part;
* Cognitive part;
* Social part.  
A brief description of the velocity equation has been illustrated in Table 2.
# Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
## 3 Proposed Model
### Feature Extraction  
#### 3.4.1 Convolutional Neural Network  
We propose a dataset of Urdu handwritten numerals with 10 labels and 13,000 images in all. Initially, all the images are standardized by dividing current pixel value by sum of all pixels. This represents image pixels in range of 0 to 1. Then, each image is resized to new dimensions while ensuring that no information is lost. The CNN model consists of four core substructures which are used repeatedly with different nonlinearities to bring the best results.  
1. The input layer contains raw pixel values, and, in this case, each image of the size \(32\times 32\times 3\) pixels is fed to the CNN. Here, 32 represents the width and height of the image while 3 is the color channels--red, green, and blue.
2. The convolution layer connects local receptive field of the input with neurons in the next layer. This is achieved through a simple dot product of kernel and input image. A kernel size of \(3\times 3\) is maintained throughout the model, whereas padding is set to 1. It is followed by batch normalization of convolution layer. Each output of convolution layer uses the ReLU activation function followed by pooling layer. ReLU activations work better than sigmoid function in terms of gradient vanishing problems. ReLU was picked out of other nonlinearities (e.g., tanh, sigmoid) after comparing their results in our CNN model. Batch normalization is applied after each convolution layer to improve generalization [35].
3. The pooling layer down samples input along spatial dimensions. One of the most famous pooling layers is 'Max Pooling' which is used here to extract the highest pixel value in the current space. These extracted features are then fed to the classifiers which are discussed further. Figure 10 depicts the architecture of our proposed model and Table 2 provides an analysis of required computation resources and learning parameters.  
Figure 9: Sample of (**a**) rotated image, (**b**) flipped image, and (**c**) noisy image.
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 3 Current Telecommunications Landscape in Brazil
### Market Players and Competition  
A group of multinational companies have formed an alliance called \"Open RAN do Brasil\" to promote standards in telecom networks for the technology [29]. The group advocates for a set of goals to accelerate architecture adoption. Other objectives include fostering innovation, creating a competitive and secure ecosystem for 5G, and aligning industry, academia, and research and development institutes, focusing on security and open standards. The remaining companies face challenges in a multi-vendor environment due to various solutions. Open architecture and supplier diversification are common in infrastructure sharing for operators looking to reduce deployment costs.  
Developing o-RAN-compatible equipment requires a significant upfront investment in research and development (R&D). Brazilian manufacturers may struggle with these costs, especially given Brazil's economic challenges and relatively high interest rates [30].  
Brazilian manufacturers face stiff competition from established global players that have already invested heavily in o-RAN technology. This includes companies from countries with more developed tech sectors and lower production costs. Although Brazil has a large population, the domestic market for advanced telecommunications equipment is relatively small compared to global markets. This can make it difficult for local manufacturers to achieve economies of scale [31].  
Brazil has recently experienced economic instability, making long-term planning and investment difficult for domestic manufacturers. Although Brazil's technology ecosystem is growing, it is not as developed as other countries. This can make it challenging to create the complex systems required for o-RAN. For Brazilian manufacturers to achieve scale,  
Figure 1: Base stations per MNOs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
2.390 MHz to 2.400 MHz & 34 authorizations, held by 6 companies \\ \hline
3.700 MHz to 3.800 MHz & 30 authorizations, held by 6 companies \\ \hline
27.5 GHz to 27.9 GHz & 2 authorizations, held by 1 company \\ \hline \hline \end{tabular}
\end{table}
Table 3: Brazilian authorizations per frequencies [28].  
they probably need to export. However, they may face challenges in meeting international standards and competing in global markets.
###### Abstract
For instance, welding allows for the joining of large parts that exceed the build volume of AM machines [19], the combination of different materials manufactured separately, or the repair of damaged or defective sections without remanufacturing the entire part. These capabilities expand the practical applicability of AM and make solid-state welding techniques like Friction Stir Welding (FSW) especially attractive due to their low heat input and ability to produce high-integrity joints with minimal distortion, when the appropriate tool is chosen [20; 21]. Additively manufactured (AM) components typically exhibit a high degree of anisotropy due to the layer-by-layer deposition of the material, particularly in filament-based processes [22], even with 100% infill density [22]. Other AM techniques, such as those using powder beds, also present a certain level of anisotropy [23]. When Friction Stir Welding (FSW) is applied to join AM components, the degree of anisotropy is often significantly reduced as a result of the intense material stirring and plastic deformation that promotes microstructural homogenization across the weld zone [24; 25]. The literature analysis shows that many works have focused on welding components manufactured by AM using different plastics, evaluating the mechanical response of the welded parts. [PERSON] et al. [26] investigated the optimum welding parameters to join PMMA-welded and extruded specimens, producing favorable results in terms of mechanical properties. Similarly, [PERSON] et al. optimized the FSW parameters for 3D-printed polypropylene/graphene (PP/GNP) nanocomposites. Some studies have focused on the weldability of ABS-printed parts [27; 28]. [PERSON] et al. performed an optimization in the joint between ABS and PETG [19] without material addition. For PLA, some studies have been performed without material addition [29]. Therefore, studying FSW on AM components is crucial for advancing hybrid manufacturing techniques and ensuring the structural reliability of critical components [30; 31; 32]. The goal of this study is to analyze the prototype's functionality on AM-manufactured components in PLA (polylactic acid), the quality of the welds, the process parameters,and the device's performance. To address these gaps, the current work aims to apply and characterize the Friction Stir Welding (FSW) process on polymeric materials, specifically PLA, produced through additive manufacturing [13; 33; 34; 35; 36]. Test specimens with varying characteristics were fabricated and joined using FSW. Visual inspections were conducted to assess the feasibility of welds, as the use of different colors in the base parts allows them to be evaluated easily. Additionally, this work optimized the process parameters to achieve high-quality welds in polymeric materials. This research contributes to the broader field of advanced manufacturing by combining two innovative technologies--Friction Stir Welding and additive manufacturing--and exploring their synergistic potential. The outcomes are particularly relevant for industries seeking efficient, high-quality joining solutions for polymer-based components.
## 2 Network Structure and Fault/Event Model
### Event Model  
Fault-free sensor nodes in an event region are expected to measure some unusual values, reporting a 1 to the cluster head. In a fault-prone sensor network, however, incorrect reports due to faults are likely to occur, causing a false alarm. To cope with the false alarms while correctly detecting events, reducing the effect of faults along with a proper threshold is required. More specifically, the threshold needs to be sufficiently high to greatly reduce false alarm rate and low enough to achieve high event detection accuracy. In setting the threshold, the area of an event region plays an important role. For convenience we assume that an event region is a circle with radius \(r\). Then the ratio of an event region area \(A_{ER}\) to a grid area \(A_{G}\) can be given by  
\[\frac{A_{ER}}{A_{G}}=\frac{\pi r^{2}}{l^{2}} \tag{1}\]  
For \(l=mr\), the ratio \(\frac{A_{ER}}{A_{G}}\) for four different values of \(m\), 1, 2, 3, and 4, are 3.14, 0.79, 0.35, and 0.2, respectively. In the case of \(m\) = 4, for example, at most 20% of the sensor nodes (in a grid) on average are in an event region, making it difficult to select a threshold value satisfying performance requirements. In a grid with \(n\) sensor nodes, the number of sensor nodes expected to be in an event region is \(n\cdot\frac{A_{ER}}{A_{G}}=\frac{n\pi}{m^{2}}\) on average. The numbers for various values of \(n\) and \(m\) are given in Table 1. If \(n\) = 15 and \(m\) = 4, for example, 3 nodes on average are expected to be in an event region, difficult to distinguish between events and faults as the fault probability increases.  
For \(n\) ranging between 10 and 20, \(m\) needs to be less than 4 to have a few sensor nodes on average in an event region. In developing an event detection scheme, we will also take relatively small event regions into account to effectively adapt to varying network conditions.
## 5 Results
### Results of t-Test and Structure Model
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Independent Variable** & **Dependent Variable** & **t** & **Sig.** \\ \hline \multirow{4}{*}{Gender} & PE & 0.767 & \(p\) > 0.05 \\  & EE & \(-\)2.452 & \(p\) > 0.05 \\ \cline{1-1}  & PPS & 1.718 & \(p\) > 0.05 \\ \cline{1-1}  & BI & 1.285 & \(p\) > 0.05 \\ \hline \end{tabular} Note. PPS: perceived physical safety; EE: effort expectancy; PE: performance expectancy; BI: behavioral intention; STG: standing group; SIG: sitting group (2-tailed).  
\end{table} 
Table 6: Independent sample T-test analysis of gender differences in PE, EE, PPS, and BI. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**STG**} & \multicolumn{3}{c}{**SIG**} \\  & & **(n = 20)** & & **(n = 20)** & \\ \cline{2-5}  & & **Mean** & **SD** & & **Mean** & **SD** \\ \hline Male & 10 & & & 10 & \\ Female & 10 & & & 10 & \\ Mean age (years) & & 71.25 & 7.25 & & 67.90 & 7.25 \\ PPS1 & & 6.05 & 0.51 & & 6.80 & 0.41 \\ PPS2 & & 5.55 & 0.76 & & 6.45 & 0.76 \\ PPS3 & & 5.80 & 0.70 & & 6.65 & 0.49 \\ PPS4 & & 6.05 & 0.60 & & 6.75 & 0.44 \\ PPS5 & & 6.10 & 0.44 & & 6.95 & 0.22 \\ EE1 & & 6.50 & 0.61 & & 6.65 & 0.49 \\ EE2 & & 6.50 & 0.61 & & 6.75 & 0.55 \\ EE3 & & 6.55 & 0.60 & & 6.50 & 0.69 \\ EE4 & & 6.65 & 0.49 & & 6.65 & 0.49 \\ PE1 & & 6.35 & 0.67 & & 5.35 & 0.74 \\ PE2 & & 6.30 & 0.73 & & 5.55 & 0.83 \\ PE3 & & 6.55 & 0.60 & & 5.50 & 0.76 \\ PE4 & & 6.55 & 0.51 & & 5.10 & 1.12 \\ PE5 & & 6.35 & 0.59 & & 5.45 & 0.89 \\ PE6 & & 6.55 & 0.51 & & 5.45 & 0.89 \\ \hline \hline \end{tabular} Note. SP: physical posture; PPS: perceived physical safety; EE: effort expectancy; PE: performance expectancy. All tests are two-tailed \(t\)-tests (*** \(p\) < 0.001).  
\end{table} 
Table 5: Basic descriptive results of measurement items for STG and SIG. The structure model results are shown in Figure 3. Overall, the extended model constructs PP and PPS yield acceptable discriminant validity with PE. This research yields an extended UTAUT model for PPS and PP as two factors predicting PE.
# Overview of Clean Automotive Thermal Propulsion Options for India to 2030
## 2 The Current and Future Technological State-of-the-Art
### Advanced Internal Combustion Engine Solutions for Near Zero Emissions
#### 2.4.3 Split Cycle Engines  
Split cycle engines separate the intake-compression and power-exhaust strokes, physically, by employing two separate chambers which are in communication. The technology claims very low NOx levels and carbon reduction of up to 15-17% [29]. Water can be used by injection into the compression cylinder as well as other versions that use Liquid Nitrogen with the technology claiming near zero-emission levels (for an ultimate zero emissions vehicle - ZEV) [29].  
Another project claims an increase in engine efficiency from 48% to 53% [30]. Peak efficiencies of up to 60% using conventional fuels have also been reported [31] through accurate control of temperature at the start of the combustion being attained through separating the compression and expansion process and air-preheating. High efficiency is attained by reducing compression work with isothermal compression and intracycle waste heat recovery. Through this technology, very low emissions can be achieved by the reaction of the fuel in the dilute mixture at lower temperatures. Immediate cooling of the charge air for the combustion process to attain isothermal compression is the key factor to enable intra-cycle waste heat recovery and overall high efficiency of the cycle. This engine system uses a variant of the recuperated Brayton cycle having two main differences which are: (1) the process of compression is near isothermal and (2) reciprocating compressors and expanders are used instead of turbines which are commonly used in gas turbines. Intrinsically, the combustion process is irregular, allowing higher combustion temperatures to be sustained within material limits which caps the potential efficiency of the gas turbines. The isothermal process can be achieved in different ways such as multi-stage compression with intercooling, adding non-evaporating heat transfer fluid such as water in the compression stroke and addition of Liquid Nitrogen during compression cylinder in the compression stroke cycle [31]. It would be beneficial to introduce liquid nitrogen during this stage, as nitrogen will act as a diluent in the combustion chamber and will help in reducing the NOx emissions from the engine.  
Several other types of split cycles engine exist with offering the potential in their optimised versions of achieving eventually, well above 60%, with power densities challenging or even exceeding in some cases those of electric motor drives in Electric Vehicles (e.g., the concentric rotary engine, SARM). The carbon emissions can be reduced significantly by this technology, wherein the alternative fuels such as biofuel, e-fuel, biogas, hydrogen or hyphae appear to be compatible with its combustion process [32].
## 2 Metal-Organic Frameworks
Recent advancements have focused on synthesizing MOFs using biocompatible metals such as Ca\({}^{2+}\), Mg\({}^{2+}\), Fe\({}^{3+}\), and Zn\({}^{2+}\), combined with endogenous organic linkers such as amino acids, peptides, and cyclodext (CDs). These building blocks are generally recognized for their low toxicity and compatibility with living organisms [110]. Importantly, CD-MOFs, synthesized from \(\gamma\)-CD and alkali metals (Na\({}^{+}\) and K\({}^{+}\)), have demonstrated potential for food-related applications due to their biodegradability and edibility [112]. However, it is also important to note that even MOFs composed of the same metal ions can exhibit different toxicity profiles depending on the nature of their linker (Figure 2). For example, hydrophobic linkers such as nitro- or methyl-functionalized treophthalic acids are proven to increase toxicity in some cell lines, whereas hydrophilic linkers like 1,3,5-benzenetricarboxylic acid (BTC) show reduced cytotoxicity. Furthermore, toxicity does not always correlate directly with the individual toxicity of the organic linker, as the synthesized MOF and its physicochemical properties (e.g., surface charge, and hydrophilicity) can modify cellular interactions [113]. In vivo studies have further demonstrated that different linkers affect the biodistribution and clearance of MOFs in organs such as the liver and spleen, highlighting their potential influence on systemic toxicity. Although some linkers have been implicated in elevated toxicity (e.g., fumaric acid or BDC-NO\({}_{2}\)), others like BTC or functionalized imidazolates have shown better biocompatibility depending on the context and host model [114; 115]. Overall, while organic linkers do not unilaterally determine toxicity, their selection and combination with the appropriate metal ions is a critical design parameter for developing safe MOFs for agrifood applications.
## 2 Materials and Methods
### Module One: Rapid Redundant Feature Generation (RRG)
#### 2.3.1 Efficient Spatial Feature Optimization Module (ESFO)  
The output feature maps from convolutional layers often contain a significant amount of redundancy, with some feature maps being similar to each other. The generation of feature maps usually involves numerous convolution operations, leading to high computational complexity and an increase in memory usage and FLOPs. Therefore, it is unnecessary to generate these redundant feature maps through a multitude of convolution operations individually. In Figure 1, The ESFO strategy considers the output feature maps as \"derived feature maps\" obtained through the linear transformation of intrinsic feature maps, with the formula as follows:  
\[y_{ij}=\Phi_{i,j}(y_{0i}),\quad\forall i=1,\dots,m,\quad j=1,\dots,s. \tag{5}\]  
In this context, \(y_{0i}\) is the \(i\)-th intrinsic feature map within \(Y_{0}\), and \(\Phi_{i,j}\) is the linear operation used to generate the \(j\)-th derived feature map \(y_{ij}\). These derived feature maps are then concatenated with the intrinsic feature maps to form a complete convolution layer, as follows:  
\[Y=concatenate(Y_{0},Y_{ij}). \tag{6}\]  
In the formula, \(Y\) represents the final output feature map, \(Y_{0}\) denotes the original intrinsic feature maps, and \(Y_{ij}\) signifies the derived feature maps, generated by applying the linear transformation \(\Phi_{i,j}\) to \(Y_{0}\). The concatenate operation, by merging the derived feature maps with the intrinsic feature maps, effectively expands the dimension of the feature representation without significantly increasing the computational burden. This concatenation strategy ensures that the network, while reducing the number of parameters and computational costs, can still capture and retain the richness and diversity of the input data. The application of linear transformations allows each derived feature map to be processed independently and in parallel, thereby enhancing the overall computational efficiency.