esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We have studied the average intensity, lateral shift of the intensity maxima and the _PIB_ in the receiver plane in the case of NUC beams endowed with a rectangular symmetry. The beams propagate through free space or in the presence of either isotropic- or anisotropic turbulence. The calculations are based on the approximate analytical expression for the propagation of the spectral density derived in this paper. The beam was shown to have either a diamond profile for \(x_{0}=y_{0}=0\) or a sector-like profile for \(x_{0}=y_{0}>0\) in the far field on free-space propagation. Even though on the propagation in the presence of anisotropic turbulence the NUC beams still can keep its diamond or sector-like shape in a weak turbulence regime, they will degenerate to an elliptical shape in moderate or strong turbulence. The evolution of locally sharpened and locally shifted intensity maxima is closely related to the strength of the turbulence and the anisotropic coefficients. The sharpened intensity maxima decrease gradually and disappear finally with increasing strength of the turbulence. In addition, the behavior of the _PIB_ of the NUC beams and the Gaussian beam with the same initial beam width for both isotropic and anisotropic beams has been compared. Our results show that the _PIB_ of the NUC beam can be even higher than that of the Gaussian beam in the presence of a very weak turbulence regime. In free-space optical (FSO) communications, the average receiving energy (ARE) is one of the important factors affecting optical communication. High ARE is preferred in FSO communications; however, a light beam propagating through the atmosphere is subject to phase fluctuations due to atmospheric turbulence, which causes the extra beam spreading beyond the spread due to the diffraction. This effect will reduce the ARE in the receiver plane. The NUC beams exhibit the higher _PIB_ propagation in turbulence under certain conditions. We believe that this propagation feature of the NUC beams may have important applications in FSO communication and remote sensing.  
W.D. (Data curation, Writing--original draft); [PERSON]. (Supervision, Formal analysis, Writing--review and editing); [PERSON]. (Supervision, Writing--review and editing); Y.C. (Supervision, Writing--review and editing); B.J.H. (Writing--review and editing).  
National Natural Science Foundation of China (NSFC) (11474213, 11874046, 91750201); The Priority Academic Program Development of Jiangsu Higher Education Institutions; Qing Lan Project of Jiangsu Province; Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX17_2024). China Scholarship Council (CSC) (201706920085).  
The authors declare no conflict of interest.  
Figure 8: Dependence of the parameter P on (**a**) the width of a receiver aperture, (**b**) the anisotropic factor \(\mu_{x}\) with \(\mu_{y}=1\), \(D=0.02\) m, (**c**) the strength of turbulence with \(D=0.02\) m.
## 4 Empirical Insights from Sectorial and Regional Studies
### AI and Financial Inclusion in African SMEs: A Fintech-Driven Perspective  
In their regional analysis of AI-driven financial inclusion, [PERSON] et al. [117] examine the transformative role of AI and fintech in supporting the growth of SMEs within the African financial services sector. Drawing on the Nigerian and broader context of sub-Saharan Africa, the study highlights how the integration of AI--when combined with digital wallets, alternative credit scoring models, and mobile-based fintech platforms--has reduced access barriers for small companies to finance. From a TOE perspective, the technological enablers included AI-based mobile lending tools and cloud-based financial platforms, which address structural weaknesses in traditional banking infrastructure. Organizationally, SMEs that successfully adopted these tools demonstrated agile leadership and openness to platform-based innovation, although skill shortages and limited digital literacy remained persistent inhibitors. Environmentally, regulatory uncertainty and under-developed data protection frameworks were noted as external barriers to scaling AI-based financial services.  
Quantitatively, the paper reports a significant increase in SME access to microloans and reductions in operational costs, though long-term profitability impacts remain underexplored. The authors recommend targeted policy interventions and investment in AI literacy to support inclusion efforts. This case study underscores how the adoption of AI in emerging economies intersects with broader goals such as financial inclusion and the survival of small businesses, extending the relevance of the TOE-DOI framework to contexts where digital infrastructure and regulatory ecosystems are still evolving.
## 3 Background and Methods
### Interpretable Representation
If we focus in more detail on the operation of the SAHP method, we can say that it explains the prediction of a specific instance by computing the contribution of each feature to the prediction. Features' values can be thought of as parts of a whole. This part can be an individual feature value or a group of feature values. Calculated Shapley values tell us how to fairly distribute the prediction among the features. When interpreting image classification, pixels can be grouped into super-pixels and then the prediction distributed among them. SHAP explanation is obtained by the formula  
\[g(z^{{}^{\prime}})=\phi_{0}+\sum_{n=1}^{M}\phi_{n}z^{{}^{\prime}}_{n} \tag{7}\]  
where \(g\) presents the model we want to explain, \(z^{\prime}\in\{0,1\}^{M}\) is a simplified feature or group of features (coalition), \(M\) is the maximum coalition size, and \(\phi_{n}\) is the Shapley value feature attribution for a feature \(n\)[51].
# Data Driven Approach for Eye Disease Classification with Machine Learning
## 3 Methodology Adopted  
The literature review demonstrated that almost all existing studies are disease based and, thus, the algorithms developed or used were tested for specific eye diseases, for example, glaucoma disease prediction in Reference [13] and refractive error classification in Reference [16]. Accordingly, no general solution is available. This is primarily due to the lack of standardization in recording medical information, a major difficulty that has hindered general automated solutions. A critical challenge in applying machine-learning methods to data obtained by multiple experts is inconsistencies owing to differences in expression style/vocabulary for disease description and diagnosis. Hence, a standardized framework is required to facilitate direct data entry by experts as efficiently as possible, which in turn may facilitate classification, as shown in the results section of this paper (Section 4). Overall, the following are the innovative contributions this paper makes:  
* Automatic conversion of data into a structured format directly collected from medical experts using ICD coding;
* State-of-the-art data modeling for fast and accurate disease classification;
* Support for features selection in classification;
* Dynamic updating of codes to incorporate new symptoms and clinical observations; and
* Improved classification results using conventional machine-learning methods.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
###### Abstract  
Wireless networks employing millimeter-wave (mmWave) and Massive Multiple-Input Multiple-Output (MIMO) technologies are a key approach to boost network capacity, coverage, and quality of service (QoS) for future communications. They deploy symmetric antennas on a large scale in order to enhance the system throughput and data rate. However, increasing the number of antennas and Radio Frequency (RF) chains results in high computational complexity and more energy requirements. Therefore, to solve these problems, this paper proposes a low-complexity hybrid beamforming scheme for mmWave Massive-MIMO 5G wireless networks. The proposed algorithm is on the basis of alternating the minimum mean square error (Alt-MMSE) hybrid beamforming technique in which the orthogonal properties of the digital matrix were designed, and then the MSE of the transmitted and received signal was reduced. The phase of the analog matrix was obtained from the updated digital matrix. Simulation results showed that the proposed hybrid beamforming algorithm had better performance than existing state-of-the-art algorithms, and similar performance with the optimal digital precoding algorithm.  
massive MIMO; precoding scheme; hybrid beamforming; mmWave; RF chain +
Footnote †: journal:  
_Article_  
Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 4 Methods
### Testbed Setup  
The current test environment, on which OMF has been enabled, initially consists of nine test nodes (TNs) arranged in a grid network, as illustrated in Figure 2. Each TN on the OMF-enabled testbed consists of the couple communication device and research device. Communication devices are mainly used to enable access to the provided research devices. Research devices, on the other hand, are only used for running experiments and can, in turn, form their separate research networks according to a specified experiment description. Each communication and research device forming a TN is directly connected over a point-to-point link.  
The communication devices forming the access network to the research devices are running an Ubuntu 24.04 operating system. To connect the communication devices to each other, the Quagga network routing software suite, which provides an implementation of the Routing Information Protocol (RIP), was used to manage dynamic routing and communication in the network. The research devices, also running Ubuntu 24.04, are equipped with 4 GB of RAM, 256 GB of storage, three wired Gigabit Ethernet interfaces and one wireless dual-band Linksys interface.  
The testbed uses private IPv4 address space in the 192.168.0.0/24 range. The local network range is 192.168.N.0/24, where N is the number of TNs in the test environment. Communication devices of the testbed are reachable as 192.168.N.1 from within the test environment whereas research devices are accessible via 192.168.N.2. Finally, another machine serving as server component, equipped with 4 GB of RAM and Gigabit Ethernet network interfaces, acts as the OMF server. On this host, the EC and AM components are operated which provide experiment controlling services.  
The proposed OMF-enabled test environment provides the benefit that it automatizes the deployments and executions of any measurement experiments, collect their final estimates, and then transport them back to the experiment controller without requiring any physical access to the corresponding experimental nodes used. Furthermore, additional third-party management and measurement tools can easily be integrated into the OMF architecture to provide a more comprehensive test environment. Finally, independently administered OMF-enabled network segments can simply be federated together to form a uniform management domain.
# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
## 4 Experiments
### Experimental Results  
#### 4.3.1 Defogging Experimental Results  
To validate the effectiveness of the defogging network proposed in this paper, a comparative analysis was conducted between the IA network [33] and the multi-scale adaptive (MSA) network introduced in this study. With \(\beta\) values ranging from 0.05 to 0.14, the defogging effects of the IA and the multi-scale adaptive networks are illustrated in Figure 2. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are employed as technical metrics to evaluate the defogging performance of different algorithms. As shown in the figure, the PSNR for the fog image, the defogged image using IA, and the defogged image using MSA are 13.4221, 12.713, and 13.472, respectively; the SSIM values are 0.6594, 0.7398, and 0.7672, respectively. From these PSNR and SSIM values, it is evident that MSA achieves the most satisfactory defogging results for railway images, exhibiting the highest PSNR and SSIM values. The superior performance of MSA can be attributed to the incorporation of a multi-scale network, which enables the algorithm to adaptively remove dense fog while simultaneously enhancing critical target information within the images through multiple filters.  
To further validate the reliability of the proposed algorithm, we conducted tests on PSNR and SSIM using 200 images. The experimental results are presented in Table 1. As shown in the table, the proposed MSA network consistently outperforms the IA algorithm. Specifically, MSA achieves higher PSNR and SSIM values, indicating its significant advantage in preserving image quality and detail. The MSA algorithm is better equipped to handle images with varying fog densities, thereby enhancing the accuracy and robustness of target detection. These results further confirm the potential and reliability of MSA in practical applications, providing an effective solution for defogging railway images.  
Figure 2: _Cont._
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 4 Model Solution
### Variable Neighborhood Structure Design  
Expand the search range by changing the neighborhood structure so as to make it easier for the algorithm to obtain the global optimal solution. In this work, the following two operations are used to change the neighborhood structure.  
(1) Point exchange.  
Select any two customer points \(i\) and \(j\), \(i,j\in C\) in the current truck-drone route and exchange their positions until every single customer point is involved with a point exchange. The objective function value is calculated for the truck-drone route generated by each exchange. Conduct the aspiration criterion and update the tabu list.  
(2) Link exchange.  
Randomly select a customer point \(i\), \(i\in C\) in the current truck-drone route, and separate out a part that contains customer point \(i\) as a sub-route \(W_{i}\) from the truck-drone route. Then select another customer point \(j\). If customer points \(i\) and \(j\) are on the same truck-drone route and are not adjacent, reverse the link between \(i\) and \(j\); If customer points \(i\) and \(j\) are not on the same truck-drone route, separate out a part that contains customer point \(j\) as a sub-route \(W_{j}\). Then, exchange part of the links on \(W_{i}\) and \(W_{j}\) to connect customer points \(i\) and \(j\). Calculate the objective function value for the truck-drone route generated by each exchange. Conduct the aspiration criterion and update the tabu list.
# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 3 Methods  
### Lstm  
As vanilla RNNs, with only input gates and output gates, suffer from vanishing or exploding gradients caused by error back-flow problems, the main challenge for vanilla RNNs is to handle the long-term dependencies.  
To secure the long-term dependencies, LSTM additionally inserted forget gates for the update and control of cell states, regulating the information flow [29]. LSTM can handle the imbalanced data and efficiently captures a sequence of time-lagged observations as inputs for time-series classification to predict specific faults at any given time ahead. The original LSTM model can be precisely stated as follows:  
\[f_{j}=\sigma\left(W_{f}x_{j}+U_{f}h_{j-1}+b_{f}\right) \tag{3}\]  
\[i_{j}=\sigma\left(W_{i}x_{j}+U_{i}h_{j-1}+b_{i}\right) \tag{4}\]  
\[O_{j}=\sigma\left(W_{o}x_{j}+U_{o}h_{j-1}+b_{o}\right) \tag{5}\]  
\[\overline{C}_{j}=\sigma_{c}\left(W_{c}x_{j}+U_{c}h_{j-1}+b_{c}\right) \tag{6}\]  
\[C_{j}=f_{j}\odot C_{j-1}+i_{j}\odot\overline{C}_{j} \tag{7}\]  
\[h_{j}=\sigma_{h}\left(C_{j}\right)\odot O_{j} \tag{8}\]  
Herein, \(x_{j}\) is the neuron input at the timestamp \(j\); \(h_{j-1}\) is the cell state at the previous timestamp; \(j-1\), \(f_{j}\), \(i_{j}\), and \(O_{j}\) stand for the forget, input, and output gates, respectively, all determined across the sigmoid nonlinearity, \(\sigma\), with the given weights \(W_{f}\), \(W_{i}\), \(W_{o}\), \(W_{c}\), \(U_{f}\), \(U_{i}\), \(U_{o}\), \(U_{c}\), and the assigned biases, \(b_{f}\), \(b_{i}\), \(b_{o}\), \(b_{c}\). The memory cell, \(\overline{C}_{j}\), from Equation (6) is estimated through an activation function, \(\sigma_{c}\), which is a hyperbolic tangent layer, _Tanh_, by default. Then, the current cell state \(C_{j}\) in Equation (7) is updated regarding the previous cell state, \(C_{j-1}\), and the estimated cell state, \(\overline{C}_{j}\), with the element-wise product operator, \(\odot\). Finally, the output vector, \(h_{j}\), in Equation (8), also known as a hidden layer, is obtained from the element-wise product of the output gate, \(O_{j}\), and the cell state, \(C_{j}\), across an activation function, \(\sigma_{h}\), which is also _Tanh_ by default.  
Based on the dependencies in LSTM, the forget gate, \(f_{j}\), controls the fraction of \(C_{j-1}\), to store in \(C_{j}\), filtering \(h_{j-1}\) and \(x_{j}\) through the sigmoid gate, \(\sigma\). The input gate, \(i_{j}\), controls the fraction of the estimated memory cell, \(\overline{C}_{j}\), provided to \(C_{j}\) through the sigmoid nonlinearity, \(\sigma\); the output gate, \(O_{j}\), controls the fraction of \(C_{j}\) flowing into the output vector, \(h_{j}\), through \(\sigma_{h}\). Therefore, the LSTM architecture can be drawn in Figure 6.
## 3 Applying the Methodology to the Example of Dabar Power Tunnel
### Geological Setting and Problem Statement
The initial circular cross-section of the tunnel (5.20 m in diameter) was redesigned to a horseshoe shape, due to a change in the excavation method from tunnel boring machine (TBM) to New Austrian Tunnelling Method (NATM) with the drill-and-blast method used for excavation. The old and new shapes of the tunnel, including the example of the supporting system comprising shotcrete and rock bolts, are shown Figure 4. Depending on the quality of the rock mass, several standard support types were designed, including shotcrete lining, reinforcing mesh as appropriate, and radial anchors. A tunnel section of about 8.1 km in limestone rock mass from the Cretaceous age (from chainage km 3 + 766.00 to km 12 + 216.00, with the exception of Eocene limestone sediments) played a dominant role in this study (Figure 3). Continuous mapping of the excavation face and occasional control point load and uniaxial strength tests confirmed that the rock mass was relatively uniform over the entire considered length of the tunnel. The mechanical characteristics of this rock can be characterised with the following parameters: uniaxial compressive strength \(\sigma_{\mathrm{ci}}\) within the range of 50 to 100 MPa and geological strength index (GSI) [27] between 50 and 75, which roughly correspond to RMR values within the range of 55 to 80. This hard rock is jointed and predominantly features two to three sets of joints and, rarely, one or two. A geodetic survey of the convergence movement caused by the excavation was conducted during the excavation of the tunnel together with a geometric survey of the contours. The geodetic survey showed almost no convergence movements (or they were negligibly small), which clearly indicated that the rock mass was globally stable in response to the excavation. However, the formation of over-profiles, and the consequent increased consumption of sprayed concrete, was excessive and hampered the efficiency and progress of the work. For this reason, early in the project, it was necessary to evaluate the quantity of overbreaks and improve the blasting technique. However, even after the blasting technique was improved and adapted to the characteristics of the rock mass, the high volumes of overbreaks persisted. This required making an objective assessment of the causes of overbreaks and distinguishing which ones could be attributed to geological conditions (geological overbreaks) and which ones to deficiencies in blasting (technological overbreaks). The methodology described in previous sections was developed for this purpose, and its application to the case of Dabar tunnel is presented here.
# Experimental Study on Shear Creep Characteristics of Residual Soil with Different Stone Content
## 8 Conclusions  
In this paper, the focus was on investigating the creep characteristics of residual soil with varying stone content. The Burgers model was utilized to accurately fit the creep curve, enabling the determination of the long-term strength index for landslide accumulation bodies. Based on the conducted research, the following conclusions were drawn:  
1. Residual slope soil with varying rock contents exhibits noticeable creep behavior. At lower stress levels, it tends to transition from attenuation creep to steady-state creep. However, under high stress, it undergoes accelerated creep. As the stone content increases, the deformation of the sample shifts from primarily displacement of fine particles to a combination of displacement of coarse and fine particles, as well as damage deformation of large particle block stones. With a higher stone content, the interlocking effect of the particle skeleton becomes more pronounced, making particle rotation more challenging, resulting in a decrease in the shear creep of the sample.
2. The Burgers model is employed to effectively fit the creep curve, particularly during the decelerated creep and steady-state creep stages. As the stone content increases, the four parameters of the Burgers model significantly increase, with the instantaneous elasticity coefficient (G1) and viscosity coefficient (\(\eta\)1) experiencing more pronounced changes. This indicates that, with an increase in stone content, the specimen's abil  
Figure 13: Instantaneous shear strength and long-term strength of specimens with different stone content.  
ity to resist instantaneous deformation and irrecoverable deformation significantly improves, while its ability to resist slow deformation and recovery also increases.
3. The average long-term strength of specimens with different stone content is found to be approximately 54% of their instantaneous strength. As the stone content increases, the ratio of long-term strength to instantaneous strength also increases.  
[PERSON] is responsible for experimental planning and data analysis. [PERSON] is responsible for conducting experiments and writing papers. [PERSON] is responsible for data analysis and paper revision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Key Research and Development Project of Henan Province (Grant No. 241111322900).  
Not applicable.  
All the data used in this research are easily accessible by downloading the various documents appropriately cited in this paper.  
This research work was sponsored by the Key Research and Development Project of Henan Province (Grant No. 241111322900).  
Authors have no conflicts of interest to declare.