esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Radial Point Interpolation (RPI) Technique-Based Error Recovery  
The radial point interpolation (RPI) technique takes into account both the polynomials basis function (PBF) and radial basis functions (RBF) for interpolation [27]. Consider a problem domain \(\Omega\). To approximate a function u(x) in \(\Omega\), the node interpolant uh(x) is defined in domain \(\Omega\) by  
\[\mathrm{u}^{\mathrm{h}}(\mathrm{x})=\sum_{i=1}^{n}B_{i}(r)\;a_{i}+\sum_{j=1}^{m }p_{j}(x)b_{j}(x) \tag{1}\]  
with the constraint condition  
\[\sum_{i=1}^{n}p_{ij}(x)a_{i}=0,\;\mathrm{j}=1-\mathrm{m}, \tag{2}\]  
where \(B_{i}(r)\) is the radial basis functions, \(n\) is the number of nodes in the neighborhood of \(x\), \(p_{j}(x)\) is the monomial in the space coordinates \(x^{T}=[x,y]\), and coefficient \(a_{i}\) and \(b_{j}\) are the interpolant constant m is the number of polynomial basis function.  
In the radial basis function \(B_{i}(r)\), the variable is only the distance, \(r\), between the interpolation node \(\mathbf{x}\) and a node \(\mathbf{x}_{i}\). For a two-dimensional problem, \(r\) is defined as  
\[r=[(x_{j}-x_{i})^{2}+(y_{j}-y_{j})^{0.5}] \tag{3}\]  
There are a number of radial basis functions. The multi-quadrics (MQ) radial function and thin-plate splint radial basis function (TPS) are used in the present study. It has been proved that this term can improve interpolation accuracy. In Equation (1), only limited number \(p_{j}(x)\) is sufficient, i.e., m << n. The following linear polynomial basis is employed.  
\[P^{T}(x)=[1\;x\;y], \tag{4}\]  
The coefficient \(a_{i}\) and \(b_{j}\) in Equation (1) can be determined by enforcing Equation (1) to be satisfied at the n nodes surrounding node \(x\). Equation (1) can be rewritten in matrix form as  
\[\begin{bmatrix}u\\ 0\end{bmatrix}=\begin{bmatrix}B_{0}&P\\ P^{T}&0\end{bmatrix}\begin{bmatrix}a\\ b\end{bmatrix}=\mathrm{G}\;a_{0} \tag{5}\]  
where  
\[P^{T}=\begin{bmatrix}1&1&--&1\\ x_{1}&x_{2}&--&x_{n}\\ y_{1}&y_{2}&--&y_{n}\end{bmatrix} \tag{6}\]  
\[B_{0}=\begin{bmatrix}B_{1}(r_{1})&B_{2}(r_{1})&--&B_{n}(r_{1})\\ B_{1}(r_{2})&B_{2}(r_{2})&--&B_{n}(r_{2})\\ --&--&--&--\\ B_{1}(r_{n})&B_{2}(r_{n})&--&B_{n}(r_{n})\end{bmatrix} \tag{7}\]  
The PIM-based error-recovery technique and error estimators directed mesh updating schemes are implemented in 2D-finite element computer software. The program is also incorporated with standard patch least-square error-recovery technique and incompressible-elasticity formulation. The software is installed on intel core i7 computer with 2.6 GHz processor and 16 GB RAM to analyze the incompressible-elastic problems. Figure 1 depicts the error-recovery approach coupled with the adaptive incompressible-elastic finite-element analysis flow chart.
# Application of Ultrasonic Nondestructive Testing for Breeding of Meat Pigeons
###### Abstract  
The aims of this study were to assess the correlation of breast muscle thickness (BMT) with the breast muscle weight (BMW), body weight (BW), breast width, and breast depth (BD) of meat pigeons, and to explore the feasibility of ultrasonic nondestructive testing (UNDT) for non-invasive measurements of BMT. It has been proven that using ultrasonic nondestructive testing specifically for evaluating the pectoral musculature yield in other poultry species such as Peking ducks and Yangzhou geese is feasible. The BMT of Carneau and Silver King pigeons (\(n=103\) each; age, 28 days) was measured at two points by UNDT. The correlation between the BMT at two points and BMW was analyzed. The results demonstrated that the correlation coefficients between BMT at point A and BMW were 0.907 and 0.897 in Carneau and Silver King pigeons, respectively, with significant regression relationships (\(p<0.01\)). Variety and sex had little effect on BMT measurements. Six optimal univariate linear regression equations were established. The regression relationship was very significant, as determined by the F-test (\(p<0.01\)), indicating an actual regression relationship between the variables of each equation and that UNDT can be applied for the breeding of meat pigeons. This represents the first application of ultrasonic nondestructive testing specifically for evaluating pectoral musculature yield in pigeons intended for meat production.  
Keywords: meat pigeon; ultrasound in vivo measurement; breast muscle thickness; correlation analysis 1
Footnote 1: _Applied Sciences_  
[[https://creativecommons.org/](https://creativecommons.org/)]([https://creativecommons.org/](https://creativecommons.org/))
# Study and Investigation on 5G Technology: A Systematic Review
## 1 Introduction
### Key Contributions  
The objective of this survey is to provide a detailed guide of 5G key technologies, methods to researchers, and to help with understanding how the recent works addressed 5G problems and developed solutions to tackle the 5G challenges; i.e., what are new methods that must be applied and how can they solve problems? Highlights of the research article are as follows.  
* This survey focused on the recent trends and development in the era of 5G and novel contributions by the researcher community and discussed technical details on essential aspects of the 5G advancement.
* In this paper, the evolution of the mobile network from 1G to 5G is presented. In addition, the growth of mobile communication under different attributes is also discussed.
* This paper covers the emerging applications and research groups working on 5G & different research areas in 5G wireless communication network with a descriptive taxonomy.
* This survey discusses the current vision of the 5G networks, advantages, applications, key technologies, and key features. Furthermore, machine learning prospects are also explored with the emerging requirements in the 5G era. The article also focused on technical aspects of 5G IoT Based approaches and optimization techniques for 5G.
* we provide an extensive overview and recent advancement of emerging technologies of 5G mobile network, namely, MIMO, Non-Orthogonal Multiple Access (NOMA), mmWave, Internet of Things (IoT), Machine Learning (ML), and optimization. Also, a technical summary is discussed by highlighting the context of current approaches and corresponding challenges.
* Security challenges and considerations while developing 5G technology are discussed.
* Finally, the paper concludes with the future directives.  
The existing survey focused on architecture, key concepts, and implementation challenges and issues. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products.
# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 8 Future Direction  
Deep learning-based software like Enlitic's Curie I ENDEXTM is being used to help radiologists manage and analyze medical imaging data in an efficient way [158]. In addition, Medtronic PillCamTM COLON CAD, Olympus CAD EYET, and EndoBrainRSI(Fujifilm, Tokyo, Japan) are currently being used by physicians to automatically detect GI abnormalities in CE images [159]. Although there are just few examples of computer-aided bleeding detection, it is promising as the technology is continuing to develop. More real-life effective algorithms are being introduced by researchers. This review paper will help guide them towards algorithms that could be used by practicing physicians. Recently, a new technique called a Generative Adversarial Network (GAN) has been introduced to generate synthetic images [160; 161; 162; 163]. So, in the future, the dataset limitation may be overcome by collecting more data using synthetic CE images. Additionally, DL algorithms may be a better alternative for detecting bleeding in CE images. Several articles introduced ML and DL algorithms concurrently, such as DL for feature extraction and ML for bleeding image categorization. The integrated models considerably improved the performance of computer-aided bleeding detection systems. From the literature review, the majority of articles used the same patient or subject data for the training and testing datasets as they randomly selected images for training and testing. This introduces bias in an experimental setup. Same-patient CE images should not be mixed in training and testing datasets for classifying bleeding.
## 3 Numerical Simulation
### Model Establishment  
The UDEC Trigon model was established to analyze the mechanical behavior of the surrounding rocks of the inby fender and the PRR as the breaks located at different positions in the main roof during the last weighting. The distance between the break line and the center of the PRR was set to be 0 m, 3 m, 6 m and 9 m, respectively, as shown in Figure 6. There was no vertical displacement at the bottom of the model and no horizontal displacement at the two vertical boundaries. The excavation length was 80 m and the fender width during the face stop was determined to be 5 m. According to in-situ stress measurements of this mining area [19], the vertical stress was 5 MPa, the maximum and minimum horizontal principal stresses were 6.3 MPa and 3.7 MPa, respectively, and the minimum horizontal principal stress was parallel to the model plane. The Mohr-Coulomb constitutive model was adopted. To improve the computational efficiency and facilitate the observation of the fracture distribution and the stress evolution, the UDEC Trigon logic was utilized to divide the surrounding rocks of the PRR into triangular blocks with an average size of 0.3 m. The block size was small enough in relation to the whole model and had been proven to accurately reflect the mechanical behavior of a rock mass [20]. Other areas of the model were divided into rectangular blocks.  
\begin{table}
\begin{tabular}{l} In the normal direction \\ \(\Delta\sigma_{n}=-k_{n}\Delta u_{n}\) \\ Where \(\Delta\sigma_{n}\) and \(\Delta u_{n}\) are the effective normal stress increment and normal displacement increment, respectively, and \(k_{n}\) is the normal stiffness of the contact. \\ In the shear direction, if \\ \(\mid\tau_{s}\mid\,\leq c+\sigma_{n}\tan\varphi=\tau_{s}\max\) \\ then \\ \(\Delta\tau_{s}=-k_{s}\Delta u_{s}\)\({}^{e}\) \\ or else, if \\ \(\mid\tau_{s}\mid\,\geq\,\tau_{s}\max\) \\ then \\ \(\tau_{s}=\text{sign}\left(\Delta u_{s}\right)\,\tau_{s}\max\) \\ where \(\varphi\) and \(c\) are the cohesion and friction angle of the contact, \\ respectively. \(\Delta u_{s}\)\({}^{e}\) is the elastic component of the incremental shear displacement, and \(\Delta u_{s}\) is the total incremental shear displacement. \\ a. Coulomb friction law [16; 17] \\ \end{tabular}
\end{table}
Table 1: The mechanical behavior of blocks in UDEC Trigon.
## 3 The Proposed Method
### Loss Function  
In our proposed architecture for light-field occlusion removal, Mean Squared Error (MSE) is employed as the primary loss function to optimize the performance of the network. The MSE loss function is defined as  
\[L_{\text{MSE}}=\frac{1}{N}\sum_{i=1}^{N}(I_{\text{out}}^{i}-I_{\text{gt}}^{i}) ^{2}, \tag{12}\]  
where \(I_{\text{out}}^{i}\) represents the predicted pixel value at the \(i\)-th position and \(I_{\text{gt}}^{i}\) denotes the corresponding ground-truth pixel value, with \(N\) indicating the total number of pixels. The mean squared difference between true and anticipated pixel values is measured by this loss function, which gives a quantitative indicator of how well the occlusion removal occurred.  
The integration of MSE within our architecture facilitates precise supervision during training and plays a crucial role in minimizing pixel-wise errors across various scales and levels of detail, directly contributing to the effectiveness of occlusion handling by penalizing deviations between the reconstructed and true images. This ensures that the model learns to produce accurate and high-quality results and enhances the practical applicability of the method in real-world scenarios.
## 2 Materials and Methods  
A high-quality polycrystalline sample of the Gd\(\gamma\)(BO\({}_{3}\))(PO\({}_{4}\))\({}_{2}\)O\({}_{6}\) compound was synthesized using a high-temperature solid-state reaction method. The starting materials used for the synthesis were NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\) (99.99%), Gd\({}_{2}\)O\({}_{3}\) (99.99%), and H\({}_{3}\)BO\({}_{3}\) (99.99%). Prior to the mixing process, all raw materials should be subjected to drying in an oven for 24 h to eliminate any adsorbed moisture. Additionally, weighing should be conducted in an inert nitrogen environment to avoid hydration. These reagents were mixed in stoichiometric proportions and thoroughly ground in an agate mortar. To compensate for the volatilization of NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\) and H\({}_{3}\)BO\({}_{3}\) at high temperatures, an excess of these precursors was added. After sufficient grinding, the mixture was initially heated at 773 K for 5 h. Subsequently, the sample was heated at 1473 K for 10 h, then removed from the furnace and reground. The grinding and heating process at 1473 K was repeated until a pure phase was obtained. All heating processes involve placing the sample in an alumina crucible and are carried out in air. The crystal structure of the synthesized compound was analyzed at room temperature by powder X-ray diffraction (XRD) using a diffractometer with Cu K\(\alpha\) radiation (\(\lambda\) = 1.5406 A). The XRD data were collected over a 20 range of 10\({}^{\circ}\) to 60\({}^{\circ}\) with a step size of 0.02\({}^{\circ}\) and a scanning speed of 2\({}^{\circ}\) per minute. To investigate the magnetic properties of the compound, temperature-dependent magnetization measurements were performed using a Quantum Design Physical Properties Measurement System (PPMS-9) equipped with a vibrating sample magnetometer (VSM) option. The measurements were carried out in the temperature range of 2-300 K under an applied magnetic field of 0.05 T. Field-dependent magnetization measurements were also conducted at various temperatures using the VSM option of the PPMS-9, with applied magnetic fields up to 7 T.
# Motion Planning for Mobile Robot with Modified BIT+
## 2 Modified BIT* Planner
### Sampling Strategy of Informed RRT*  
In order to solve the sampling randomness of RRT* and improve converging rate. Informed RRT* is introduced in [14]. Firstly, it uses original RRT* to generate a initial path. After that, an ellipse with the start point and end point as the focal points and the path cost as the long axis is determined. For mobile robot's path planning in 2-D space, we use \(X\) to represents the set of every point on the map. \(g(x)\) and \(h(x)\) denotes the cost from start state \(x_{start}\) to a state \(x\in X\) and the cost from \(x\) to goal state \(x_{goal}\). For example, in Figure 2, we use Euclidean distance as cost function and \(C_{best}\) denotes the length of current path solution (\(C_{best}\) is infinity when there is no path solution). Then, an ellipse can be drawn. According to the properties of ellipse, we can get the following inequality:  
\[||x-x_{start}||+||x_{goal}-x||<C_{best} \tag{1}\]  
Figure 1: The whole framework: Firstly the modified BIT* (batch informed trees) planner get a path according start state, goal state and the map. Then the MPC (Model Predictive Control) optimizer find the optimal control sequence v and \(\omega\) and send them to the mobile robot as motion command.  
where \(x\) is inside this ellipse. Then we define the estimated cost \(f(x)=g(x)+h(x)\). It is obvious that the estimated cost of \(x\) is less than current path cost \(C_{best}\). So points within the ellipse have the potential to improve the current path solution.  
In next iterations, the algorithm continue sampling to get a better solution. Rather than sampling in the whole map, the sampling point will be constrained in a unit circle and then they will be transformed to the ellipse area. The transformation process is illustrated in Algorithm 1[14].  
Matrix \(U\) and \(V\) is obtained by singular value decomposition of matrix \(M\). The expression of \(M\) is shown as below:  
\[\begin{split}& M=ml^{T}\\ & m=(x_{goal}-x_{start})/||x_{goal}-x_{start}||_{2}\end{split} \tag{2}\]  
```
1 if\(C_{best}<\infty\)then
2\(C_{min}\leftarrow||x_{goal}-x_{start}||_{2}\);
3\(x_{center}\leftarrow(x_{start}+x_{goal})/2\);
4\(x_{circle}\leftarrow\) SampleInUnitCircle ;
5\(L\leftarrow\) diag{\(\frac{c_{bat}}{\sqrt{\frac{c_{bat}^{2}-c_{min}^{2}}{2}}}\)};
6\(U,V\leftarrow\) SVD(\(M\));
7\(C\gets U\)diag{\(1,\det(U)\det(V)\}V^{T}\);
8\(x_{srand}=CLx_{circle}+x_{center}\);
9
10 else
11\(x_{srand}\leftarrow\) SampleInMap;
12
13 return\(x_{srand}\);
```  
**Algorithm 1**InformedSample(\(x_{start},x_{goal},C_{best}\)).
# A Comparison of Energy Management System for a DC Microgrid
## 3 DC Microgrid Model  
The general model of the microgrid is posed as a problem of optimization. The optimization model [20] is given by Equations (13)-(16). It should be noted that, in this model, it is considered that the period of time T is composed of a set of time stages tz (\(\forall\)= 1,..., end), such that T= (t1,..., tend).  
\[\text{Minimize }F_{T}=\sum_{t_{i}=1}^{t_{end}}f^{i}\Big{(}x^{t_{i}}\Big{)} \tag{13}\] \[\text{Subject to }n^{t_{i}}\Big{(}x^{t_{i}}\Big{)}=0;\ \forall t_{i}\in T\] (14) \[z^{t_{i}}\Big{(}x^{t_{i}}\Big{)}\leq 0;\ \forall t_{i}\in T\] (15) \[\underline{x}\leq x^{t_{i}}\leq\overline{x_{i}};\forall t_{i}\in T \tag{16}\]  
The terms of the optimization model are as follows: the objective function \(F_{T}\) to be optimized in stage i of the time interval \(T\), \(n^{t_{i}}\Big{(}x^{t_{i}}\Big{)}\) represents the set of equality constrains that are raised as the power balance equations in all the connection nodes of the microgrid, as well as other operating conditions that must be fulfilled unconditionally, \(z^{t_{i}}\Big{(}x^{t_{i}}\Big{)}\) is a set of constraints of inequalities that represent the maximum and minimum values that can be physically taken by the different renewable energy sources that form the microgrid, x is the set of variables to optimize for energy management composed of subsets \(x^{t_{i}}_{R^{i}}\), \(x^{t_{i}}_{pv}\) and \(x^{t_{i}}_{S}\) such x = [\(x^{t_{i}}_{R^{i}}\), \(x^{t_{i}}_{pv}\), \(x^{t_{i}}_{S}\)]. Where, \(x^{t_{i}}_{S^{t}}\), \(x^{t_{i}}_{R}\) and \(x^{t_{i}}_{pv}\) represent batteries variables, microgrid and photovoltaic modules, respectively. These decision variables are specified for each element of the microgrid in the final part of the specific model of the DC microgrid section. The upper and lower limits, \(\overline{x}\) and \(\underline{y}\), of these variables are formulated through the inequality constraints, see in Equation (16). The physical values of the limits of these variables are specified in Appendix A and Table A1.
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 3 System Model  
### System Architecture  
The system architecture shown in Figure 1 represents a typical on-demand data broadcast system in wireless communication medium [4]. The system consists of one server and multiple clients. When a client needs a data item and is not found in its local cache, the client submits a request via the uplink channel to the server, and senses the downlink channel for the transmission of the requested data item. According to a used underlying scheduling algorithm, the server retrieves the data items from the local database and constructs an encoded packet for broadcasting. While a server broadcasts the encoded packet, it also broadcasts the index information of the data items that are being encoded in the coded packet. Hence, when a client receives an encoded packet, it knows which data items from its local cache need to be used in decoding. A client can decode a requested data item from an encoded packet if it has cached all the other data items in the encoded packet except the requested one. Once decoding is successful, the decoded data item is then added in its local cache. The bitwise XOR (exclusive-OR) operation is commonly adopted for encoding and decoding due to its trivial computation overhead [11; 42; 46]. For example, as shown in Figure 1, C3 (i.e., the Client 3) has cached \(d_{1}\). Suppose C3 requests \(d_{2}\) and the server broadcasts an encoded packet \(d_{1}\oplus d_{2}\). C3 can decode \(d_{1}\oplus d_{2}\) and retrieve \(d_{2}\) by such a schedule. For the cache management in a client, the LRU (Least Recently Used) cache replacement policy is adopted. A client will generate a new request only when the previous submitted request is satisfied or misses its deadline, which resembles the closed system model [11].
# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 3 Methods
### Global Feature Learning Group  
Within the proposed sentiment recognition framework, we use and improve the global feature learning group, as shown in Figure 4. We employ GRU with the Attentional Update Gate (AUGRU) to learn global trails in features and identify long-term contextual dependencies, as shown in Figure 5. GRU acts the same as LSTM in capturing long sequences of semantic association information, which can effectively suppress gradient disappearance or explosion. Both outperform traditional RNNs and have lower computational complexity  
Figure 3: Framework diagram of local feature learning group.  
compared to LSTM, and GRU networks are frequently applied to time sequence data [35]; however, GRU still cannot completely solve the gradient disappearance problem, while its role as a variant of RNN has a major drawback of RNN structure itself, i.e., it is not parallel computable. AUGRU retains the original dimensional information of the GRU update gate, which determines the importance of each dimension. Based on the differentiation information, it uses the attention score to measure all dimensions of the update gate, with the result that the less the degree of emotion-independent features, the less the impact on the hidden state. AUGRU is more effective in avoiding the interference of useless parts in speech images and increasing the accuracy of feature extraction.  
The attention mechanism in the global feature learning module can be expressed as:  
\[a_{t}=\frac{exp(y_{t}W)}{\sum_{i=1}^{t}exp(y_{i}W)} \tag{8}\]  
AUGRU calculates global features can be expressed as:  
\[r_{t}=\sigma(W^{r}y_{t}+U^{r}h_{t-1}+b^{r}) \tag{9}\]  
Figure 4: Global feature learning group framework diagram.  
Figure 5: A schematic diagram of AUGRU structure.  
\[u_{t}=\sigma(W^{u}y_{t}+U^{u}h_{t-1}+b^{u}) \tag{10}\]  
\[\tilde{h_{t}}=tanh(W^{h}y_{t}+r_{t}\circ U^{h}h_{t-1}+b^{h}) \tag{11}\]  
\[h_{t}=(1-u_{t})\circ h_{t-1}+u_{t}\circ\tilde{h_{t}}^{{}^{\prime}} \tag{12}\]  
\[\tilde{u_{t}}^{{}^{\prime}}=a_{t}\times u_{t}^{{}^{\prime}} \tag{13}\]  
\[h_{t}^{{}^{\prime}}=(1-\tilde{u_{t}}^{{}^{\prime}})\circ h_{t-1}^{{}^{\prime}}+ \tilde{u_{t}}^{{}^{\prime}}\circ\tilde{h_{t}}^{{}^{\prime}} \tag{14}\]  
where \(u_{t}^{{}^{\prime}}\) is the original update gate of AUGRU, \(\sigma\) is the sigmoid activation function, \(\tilde{u_{t}}^{{}^{\prime}}\) is the attentional update gate. \(h_{t}^{{}^{\prime}}\), \(\circ\) is element-wise product, \(\tilde{h_{t}}^{{}^{\prime}}\) and \(h_{t-1}^{{}^{\prime}}\) are the hidden states. \(y_{t}\) is the input of the global feature learning module and \(h_{t}\) is the t-th hidden state.