esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Suggestions  
Based on contact mechanics and tribology, [PERSON] et al. [26] analyzed the calculation method of the volume loss experienced by the ore pass wall following the collision with the sharp and blunt face of the rock block. Only the damage and failure of the shaft wall under normal load were considered, and the combined action of both normal and tangential load on the rock block was not considered. The boundary between the elastic deformation and the plastic deformation of the wall materials is unclear.  
The research results of this paper show that the quality of the ore-rock block, the velocity and angle of the ore-rock block impact with the ore pass wall, the structure parameters of the ore pass, and the physical and mechanical parameters of the ore pass  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Elastic Modulus/(Gpa)** & **Poisson’s Ratio** & **Cohesion/(Mpa)** & **Density/(kg\(\cdot\)m\({}^{-3}\))** & **Friction Coefficient** & **Material Yield Stress/(Mpa)** \\ \hline Ore pass wall & 23.5 & 0.27 & 2.6 & 2500 & 0.3 & 210 \\ Ore-rock block & 52.5 & 0.31 & — & 3200 & — & — \\ \hline \hline \end{tabular} Note: “—” means ‘it does not exist’.  
\end{table}
Table 2: Mechanical parameters of the ore pass model and of the ore-rock block.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. of Impacting Point**} & \multirow{2}{*}{\(\alpha\)/(\({}^{\circ}\))} & \multirow{2}{*}{\(\beta\)/(\({}^{\circ}\))} & \multicolumn{2}{c}{**Experimental Intrusion Depth \(\delta_{\text{res}}\)/\(\mu\)m**} & **Calculating** & \multirow{2}{*}{**Relative Error\%**} \\  & & & **a Sharp Surface** & & **a Blunt Surface** & & \\ \hline
1 & 50 & 5 & — & 30 & 31 & 2.82 \\
2 & 50 & 8 & — & 29 & 30 & 3.79 \\
3 & 50 & 10 & — & 28 & 29 & 5.04 \\
4 & 50 & 14 & 29 & — & 28 & 4.83 \\
5 & 50 & 17 & 27 & — & 26  
wall are the main factors affecting the impact-induced damage to the shaft wall. This is consistent with the findings of [PERSON] et al. (2013, 2014) and [PERSON] et al. (2017). Therefore, it is essential to take appropriate measures to prevent damage to the ore pass wall caused by the ore-rock impact in the context of actual production whenever feasible.  
1. The ore pass should be constructed using good rock mass quality or its walls should be subjected to effective reinforcement measures to increase the resistance of the shaft wall to the damage and destruction caused by the impact of rock movement.
2. Reasonable ore pass structure parameters should be selected, changing the movement state of the ore after entering the main ore pass and reducing the speed of the collision between the ore-rock block and the ore pass wall.
3. During the design phase of the ore pass, minimizing the height of the main ore pass is advisable to reduce the impact energy of the falling ore-rock block whenever feasible.
4. Installing a screen at the entrance of the ore pass restricts the size of the ore-rock blocks entering the main ore pass. Meanwhile, this is beneficial to reduce speed of ores entering the ore pass.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.17 The 5G Internet of Things (5 GIoT)  
Another technology affecting many parts of the 5G cellular network environment is the Internet of Things (IoT). The development of the 5G mobile network is important to the evolution of the Internet of Things, which will link many things to the Internet, including sensors, gadgets, items, and appliances. These apps will gather vast information from numerous devices and sensors. 5G will enable extremely rapid Internet connectivity for data gathering, transport, control, and processing. Because of its flexibility, unused spectrum availability, and inexpensive implementation costs, 5G is the most efficient technology for IoT, which aims to create a network of linked. These heterogeneous devices capture data in real time over a limited amount of time [111]. 5G will provide extremely fast Internet access for data gathering, transport, control, and processing. Because it is a flexible network with an unused spectrum and allows a very low-cost deployment, 5G is the most effective technology for IoT [112]. The 5G IoT applications include smart homes, smart cities, smart farming, autonomous cars, and industrial IoT.  
In the long run, all devices that benefit from communication networks are expected to be linked, and the number of connected devices will surpass human devices. Connectivity has become a real concern for M2M communication due to the rising availability of mobile broadband. The tremendous traffic growth expected from machine-type communication as a result of billions of linked gadgets, on the other hand, would lead the network to become overcrowded. As a result, network connection and capacity must be boosted by orders of magnitude, which may be accomplished by the implementation of additional 5G technologies, such as network densification, dense small cell deployment, and massive MIMO [26].  
The Internet of Things may also be thought of as a network of ordinary physical items, such as cars, appliances, electronics, and buildings. Wearable smartwatches, health monitors, washing machines, and microwave ovens are examples of IoT goods that detect data and transfer it to a distant server, often through the Internet. The server can also give commands to the device remotely. The server data is then analyzed to understand more about the underlying activity [35].  
IoT and 5G might easily be combined with more powerful wireless technologies to form the same ecosystem capable of meeting the present demand for IoT devices. 5G will have an impact on both nature and the expansion of IoT devices. As the 5G process proceeds, multinational companies will discover the need for cross-industry collaboration in defining and expanding the 5G system [113].
# Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting
## 7 Simulation Results
By integrating these algorithms, especially within a GA framework, the overall complexity increased, but the approach ensured a simultaneous optimization of robot base placement while addressing collisions, singularities, and precise kinematic configurations. The authors in [17] developed a comprehensive framework for optimizing robot placement in large-scale manufacturing environments, utilizing an intricate blend of energy consumption models and quality metrics like dimensional accuracy. Central to this framework was the application of a simulated annealing algorithm--a heuristic method celebrated for its ability to approximate global optima efficiently. Complementing the simulated annealing was the Recursive Newton-Euler Algorithm (RNEA), which calculated the energy requirements of robotic movements. Moreover, the framework integrated a multi-objective optimization model that simultaneously addressed energy conservation and printing quality, navigating the complex trade-offs between these objectives. Additionally, the system incorporated task decomposition and scheduling algorithms to manage the assignment and coordination of tasks among multiple robots, ensuring collision-free operation and optimizing production timelines. The computational methods employed, like simulated annealing, can be resource-intensive and might not scale well with larger numbers of robots or more complex tasks. Additionally, the effectiveness of these algorithms can depend heavily on initial conditions and parameter settings, which may complicate their implementation and optimization in practical scenarios.
## 3 Auto-Adaptive Multi-Objective Task Selection Approach
### Task Utility Function  
This function will guide the optimal task distribution search regarding well-balanced solutions where both the travelling cost and the team connectivity level are considered to evaluate the current targets. The objectives are implemented using utility functions such as (i) _path_ utility function takes the travelling costs to deliver a notion of how beneficial--concerning distance--the tasks under consideration are; (ii) _connectivity_ utility function gives the robots a connectivity awareness ability.  
The _task_ utility function \(\Phi_{i}:[0,1]\times T\times R^{M}\times S^{m\times n}\rightarrow[0,1]\), is defined as follows:  
\[\Phi_{i}(\alpha,T_{j},R,E_{known})=\alpha\cdot\Psi_{i}(T_{j},E_{known})+ \beta\cdot\Omega_{i}(T_{j},R) \tag{7}\]  
s.t.  
\[1\leq i\leq M=|R|,1\leq j\leq N=|T|\]  
\[\alpha+\beta=1\mid\alpha,\beta\in[0,1]\]  
Given the current state of the fleet \(R\) and the current environment knowledge \(E_{known}\), the function \(\Phi_{i}\) estimates the utility obtained by a robot \(R_{i}\) in case of selecting the task \(T_{j}\). The current fleet state refers to both the location of the assigned tasks in case of assigned robots and the robot positions otherwise. The terms \(\Psi\) and \(\Omega\) represent _path_ utility and _connectivity_ utility functions, respectively. The weights \(\alpha\) and \(\beta\) work as tuning parameters that permit to adjust the kind of solutions the system will search for. If \(\alpha=1\) during the whole exploration, then the system would only intent to spread out the fleet. On the contrary, if \(\alpha=0\) then the system would always search for potentially fully connected solutions. Otherwise, when (\(0<\alpha<1\)) the system will balance both _path_ utility and _connectivity_ utility. As a result, sometimes the robots could choose other tasks than the closest to favour the team connectivity level. This possibility is deeply analysed further below in Section 4.  
Although in this double-objective function the symbol \(\beta\) could be substituted by \(1-\alpha\), it is preserved for the sake of generality: if the weighted sum had more than two terms, it would not be possible to express all weights as \(\alpha\) functions.
# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 4 Experiment and Analysis
### The Practical Application in Photovoltaic Power Generation Forecasting  
This section provides an in-depth exploration of the application of the GCN-Informer model for forecasting photovoltaic power generation. The conventional method for predicting photovoltaic power generation relies on the backpropagation (BP) neural network, which is plagued by challenges like local minima and sluggish convergence rates. Consequently, these issues lead to suboptimal identification accuracy and diminished reliability in existing prediction methodologies.  
To address these limitations, we introduce the GCN-Informer model, which harnesses extensive power generation data and incorporates information from adjacent time points within the power grid. By capitalizing on the historical power grid data for photovoltaic power generation prediction, the GCN-Informer model brings about a substantial improvement in the dependability and precision of power generation forecasting. This innovation bolsters our ability to deliver more reliable and accurate predictions in the realm of power generation.  
Furthermore, the GCN-Informer model exhibits strong versatility, as it can be interchangeably used with other deep learning models in the photovoltaic industry, highlighting its significant practicality. However, potential technical limitations may arise during industrial implementation, such as compatibility issues with legacy systems.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 3 Proposed WMSCCN for Fault Diagnosis  
In order to meet the challenges of variable working conditions and noise pollution and further improve the generalization and anti-noise performance of the algorithm, a novel capsule network based on wide convolution and multi-scale convolution (WMSCCN) for fault diagnosis is proposed. Figure 1 shows the network structure of the proposed WMSCCN algorithm, which includes wide convolutional layers, a multi-scale convolutional layer, a primary capsule layer, and a digit capsule layer. In addition, a 1D vibration signal with the size of 2048 is the input of the proposed WMSCCN algorithm, without any manual feature extraction process. In the proposed WMSCCN algorithm, the wide convolutional layers use the larger size convolution kernels, and the multi-scale convolutional layer uses several convolution kernels of different sizes to learn the features of different time scales. In the multi-scale convolutional layer, in order to ensure that the feature maps of convolution kernels with different sizes have the same size, the zero-padding technique is used. In addition, Batch Normalization (BN) technology is used to speed up the training process, and dropout technology is used to reduce the risk of overfitting. On this basis, the adaptive batch normalization (AdaBN) algorithm is further introduced to enhance the adaptability of WMSCCN under noise and load changes. The loss function is used to determine the error between the predicted value of the algorithm and the true value. In the proposed WMSCCN algorithm, Margin Loss is selected as the loss function.
## 3 Graph Neural Networks for Next-Generation Networks
### GNN in Access Networks
#### Connection Management
In the context of network slicing where diverse services are provided over the same communication infrastructure, resource allocation becomes challenging, especially in dense cellular network scenarios with multiple slices and Base stations (BSs). The key difficulty lies in designing a real-time inter-slice resource management strategy that can handle frequent BS handovers and accommodate varying service requirements. To address this challenge, [PERSON] et al. [59] propose a Multi-agent reinforcement learning (MARL) [63] solution, where each agent represents a BS. The authors utilize a GAT to enhance the temporal and spatial cooperation between agents and develop a data-driven inter-slice resource allocation strategy in real time. The effectiveness of GAT in improving DRL in multi-agent systems is demonstrated through experiments. GAT is employed in conjunction with both the value-based technique _Deep Q-network_ (DQN) [64] and a hybrid approach combining policy-based and value-based methods _Advantage actor-critic_ (A2C) [65], showcasing its potential in enhancing performance and efficiency. Furthermore, [PERSON] et al. [60] aim to optimize radio resource allocation and user association within the context of the Ultra-Dense Network (UDN), where BSs including both micro- and macro-cells are deployed densely. The optimization process is centered on leveraging Channel state information (CSI) and is structured as a synchronous two-stage procedure: user association and power allocation, with the ultimate goal of maximizing the total data rate. To associate UEs to the set of micro- and macro-cells, they propose constructing an undirected graph with nodes as UEs, and edges are weighted based on the similarity between UEs features, which are composed of downlink channel gains from an UE to all the BSs. The spectral clustering algorithm is then employed on this graph to cluster UEs and associate them with appropriate cells. Simultaneously, a GNN model with learnable parameters is employed to capture the interference information and allocate radio power. A graph of BSs is utilized, with node features comprising channel gains of each BS to all users, and unweighted edges connecting cells with overlapping coverage. The GNN updates node features with captured interference information, feeding them into a fully connected network to generate power allocation strategies for every pair of UE and BS. The simulation shows that the method yields superior results and is less time consuming than the existing algorithms within the context of UDN.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 4 Experimental Analysis and Evaluation
### Effectiveness  
We compared the traditional BP algorithm, QL-BP algorithm, and TB-BP algorithm with our system to see whether the routing scheduling schemes can be affected by malicious nodes. We introduced the BP algorithm combined with the blockchain-based (BC) architecture for comparison. The comparative experiments showed the differences between BP, QL-BP, TB-BP, proposed BC and proposed RLBC algorithm in the malicious routing environment. Compared to our system, the other three routing scheduling schemes do not have the addition of a blockchain structure, but the number of malicious nodes and the cheating methods are the same in all of these schemes.
## 3 Methodology
### Discrete Wavelets Transform (DWT)  
DWT is mathematical tool for decomposing data in a top down fashion. DWT represent a function in terms of a rough overall form, and a wide range of details. Despite of the requirement and type of function i.e., signals images etc. DWT offers sublime methodology and technique for representing the amount of detail present [14]. Wavelets perform and offer scale based analysis for a given data. A wide range of applications and usage has been found for these wavelets including signal processing, mathematics and numerical analysis and, for its better performance in signals and image processing it is considered an alternative to Fast Fourier Transform as DWT provide time-frequency representation [15]. When there is a need for processing and analyzing non stationary tool, DWT can be used [16]. Study shows that discrete wavelets transform have high performance in speech signal processing so far and especially when combined with MFCCs, the performance is boosted further [5].  
DWT can be defined as a short wave which carries its energy and information condensed in time. They are limited in space and possessing an average value approaching to zero. Each signal contains two components i.e., low frequency and high frequency components. Low frequency components mostly contain maximum information part, and high frequency components convey quality. That is why in DWT estimation and details are being rated. The estimation is of the high-scale, low-frequency components of the signal and the Details are the low-scale, high-frequency components. In Figure 3b, it is shown that down sampling a given signal can be divided into its component at level one it has cA1, cD1 approximated and detailed components and at level two it has cA2 and cD2 estimated and detailed components. Down sampling ensures the absence of redundancy in a given data [17]. Mathematically, DWT can be expressed in the following Equation (2) as:  
\[DWT\ (x,\ y)=\int_{-\infty}^{\infty}Z(t)\frac{1}{\sqrt{|2^{x}|}}\varphi(\frac{t- 2^{x}y}{2^{x}})dt \tag{2}\]  
The above equation can be used to extract more useful data from audio signal using DWT [18]. In case of DWT the length of feature vector is 24.
# Blockchain: Current Challenges and Future Prospects/Applications
## 4 Blockchain Applications
### Supply Chain  
Supply chain applications are moving in two broad directions. First, in improving efficiency by reducing bureaucratic and other obstacles, and improving operations in order to be more cost-effective. Second, they aim to guarantee the authenticity, provenance, and freshness of materials/products bought by consumers [24]. Supply chain operations are dominated by paper-based methods based on letters of credit (costing 1%-3%) and factoring (costing 5%-10%). These requirements and methods are increasing costs by an estimated trillion dollars a year, while at the same time slowing down transactions considerably [25]. By utilizing the fundamental characteristics of the technology and more specifically disintermediation, the role of intermediaries along the chain is eliminated thus further reducing associated costs. Furthermore, a new trust model is established directly between buyers and sellers based on transparency in the various processes, from tracking delivery of goods to payment processing, with the use of smart contracts. Skuchain [[https://www.skuchain.com/](https://www.skuchain.com/)]([https://www.skuchain.com/](https://www.skuchain.com/)) is a blockchain startup that aims to empower enterprise supply chains utilizing its blockchain technology at the intersection of payments (letter of credit and wire transfer) and finance (operating and short-term trade loans). On another note, Provenance [[https://www.provenance.org/](https://www.provenance.org/)]([https://www.provenance.org/](https://www.provenance.org/)) is focusing on building trust between consumers and retailers by tracking the provenance of goods from the source all the way to the final consumer. In addition to these startups, existing retailers such as Walmart are looking into exploiting the advantages of blockchain technology to improve efficiency and reduce supply chain costs [26], Nestle [24] is targeting the freshness of its milk products, while other blockchain applications are concerned with verifying the authenticity of a product (e.g., it is not a counterfeit) as well as its origin (e.g., when buying a fish, to be able to trace where and when it was caught). Moreover, IBM has developed a blockchain system to support data integration processes between manufacturers, retailers and suppliers to provide greater reliability, transparency and security [27]. The aforementioned projects are indicative examples of how the characteristics of blockchains are opening up new opportunities for traceability applications [28].
## 2 System Model
### T-F Mask Generation for constructing label set for training neural architecture.  
The beamformer output is utilized to compute the T-F mask corresponding to the target source at each time frame, which can lead to dynamic speech enhancement. Let the output of the beamformer at the T-F point (_f, k_) be denoted by \(s^{MVDR}_{k,l^{est}_{i}}(f)\). As pointed out in [7], while the beamforming gain for the location of the target source might be higher than for the locations of interferers, the power spectrum in each T-F window may be dominated by interfering sources, in addition to noise and reverberation, during periods of inactivity of the target source or loud speech from the interfering sources. A spectral power ratio is computed to determine the T-F locations, which are dominated by the target speech and interference, respectively. This spectral power ratio can be expressed as follows:  
\[G^{MVDR}_{k,l^{est}_{i}}(f)=\frac{|s^{MVDR}_{k,l^{est}_{i}}(f)|^{2}}{|s^{MVDR }_{k,l^{est}_{i}}(f)|^{2}}\,\ i\
eq j \tag{11}\]  
In (11), while the numerator represents the power of the target signal (also referred to as SOI) in the spectral domain, the denominator also represents the power of the interferer source, \(s^{MVDR}_{k,l^{est}_{i}}(f)\), identified by the estimated label \(l^{est}_{j}\), in the spectral domain. The binary mask can be selected as:  
\[\mathcal{M}_{k,l^{est}_{i}}(f)=\begin{cases}1,\ if\ G^{MVDR}_{k,l^{est}_{i}}( f)\geq 1\\ 0,\ if\ G^{MVDR}_{k,l^{est}_{i}}(f)<1\end{cases} \tag{12}\]  
Using the mask in (12), the enhanced speech corresponding to the target source can be obtained as follows:  
\[S^{TF}_{k,l^{est}_{i}}(f)=\mathcal{M}_{k,l^{est}_{i}}(f)\cdot s^{MVDR}_{k,l^{ pert}_{i}}(f) \tag{13}\]  
By masking out those T-F bins, where the SOI is overwhelmed by speech from interfering sources as well as background noise, the intelligibility of the SOI can be considerably improved. The binary masks \(\mathcal{M}_{k,l^{est}_{i}}(f)\) are generated from the label set for training the proposed residual dense Graph-U-Net-based GAN, as pictorially depicted in Figures 2 and 3.