esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Predictive Modeling Methods
### Self-Attention  
Conventional convolutional layers can only acquire local fault information and cannot simultaneously focus on long-range feature data, ignoring the correlation between overall and local information. The proposal of Transformer changes this situation, which mainly relies on SA for feature acquisition at different distances and makes up for the CNN's shortcomings in the lack of global information acquisition ability. The relationship between the CNN and SA is shown in Figure 6. SA essentially linearly projects the input data to obtain the query matrix \(Q\), the keyword matrix \(K\), and the value matrix \(V\). The dot product between \(Q\) and all \(K\) is then computed, and a Softmax activation function is used to determine the weight coefficients. Finally, multiplying with \(V\) yields an attention-weighted feature map. The specific formula for SA is shown below:  
\[\text{Attention}(Q,K,V)=\text{Soft max}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{9}\]  
Figure 5: Gated recurrent unit.  
where \(Q\), \(K\), and \(V\) correspond to dimensions \(d_{q}\), \(d_{q}\), and \(d_{q}\), respectively, \(\sqrt{d_{k}}\) is a scaling factor to keep the gradient stable, and Softmax is the normalized activation function. The structure of SA is shown in Figure 7.  
SA can dynamically adjust the feature weights by calculating the correlation between different locations in the bridge deflection input sequence, ensuring that the model can pay more attention to the spatiotemporal features that have a greater impact on deflection when predicting. This mechanism allows the model to selectively pay attention to some unnecessary anomalous features.
## 4 Clinical Applications of AI to CHF Management
### Application of AI to Echocardiography, ECG, and Cardiac MRI
Cardio-HART HF prediction reaches significantly higher overall performance compared to the best ECG criteria, with a sensitivity of 83 per cent, specificity of 87 per cent and a positive predictive value of 70 per cent. CHART has the potential to enable effective widespread screening of patients for the early detection of CVD onset and resolve many 'inconclusive ECG' results, thereby reducing time in referral decisions [66]. Along the same line, in a recent retrospective study, the authors developed novel machine learning (ML) models to predict HF-related mortality, incorporating social determinants of health. In this study, the AI-powered tool outperformed traditional logistic regression models for prognostic prediction [67].
###### Abstract  
The Bi-directional Evolutionary Structural Optimization (BESO) method, owing to its algorithmic simplicity and strong scalability, has emerged as one of the most prevalent topology optimization methodologies in current research and industrial applications. To overcome the limitations of existing commercial finite element software (e.g., ABAQUS), particularly regarding the closed architecture of topology optimization modules and low efficiency in 3D complex structure optimization, this study proposes an ABAQUS-MATLAB cooperative framework. This innovative approach implements direct read/write operations via Python scripts on CAE/ODB model databases, coupled with MATLAB-based master control programs for sensitivity analysis, mesh filtering, and design variable updating. Compared with conventional integration methods employing INP/FIL file interactions, the proposed framework reduces computational time through MATLAB's advanced matrix operations while maintaining solution accuracy. Validation cases including 2D cantilever beams and 3D wheel hubs demonstrate the method's precision and computational efficiency. Practical applications in lightweight design of a hydraulic transmission test bench adapter support achieved 31% volume reduction while satisfying strength and stiffness requirements, significantly lowering material costs. The developed cooperative framework provides an extensible solution for high-efficiency topology optimization of complex engineering structures, balancing algorithmic transparency with practical applicability.  
BESO method; ABAQUS-MATLAB co-simulation; 3D topology optimization; lightweight design Article
## 5 Cross-Layer Cluster-Based Energy-Efficient Protocol  
In this section, we explain the procedure of the CCBE that is proposed in this paper. The CCBE employs the self-organization technique for clustering of WSNs. In the proposed scheme, each node has to perform the basic task of sensing the field parameters, form data packets, and to communicate with the CH. Clustering in WSNs means partitioning nodes in a network into different clusters. The network model considered in this paper is a hexagonal structure [29], shown in Figure 1 with sensor nodes and the sink node. The sink node is constant and fixed for each simulation. Sensor nodes are homogeneous in nature, are assigned with a unique identifier and have the same capability. They are able to switch between active and sleeping states. CH nodes can forward the collected messages to their next hop CHs in the direction of the sink nodes. In CCBE, each node shares information about the current energy state, location, cluster-ID and the CH-ID with its one hop neighbors. The nodes of CCBE will be in four different modes. The four modes are described as follows.  
* Cluster head (CH): While in CH mode, it broadcasts messages claiming its election to CMs. The CH then allocates time slots to different CMs registered in its database. The CH gathers and aggregate information from its CMs. Thus, they are responsible for conveying the complete information of their CMs. CHs are responsible for gathering, aggregating and forwarding the data to the sink or optimal distance CH in the direction of the sink node. CH sends or receives messages between the adjacent CHs or to the sink node at regular intervals using the assigned time slot.
* Cluster member (CM): A CM is a member that belongs to a particular cluster; it regularly transmits the collected information to its CH.  
Figure 2: Optimal cluster head (CH) distance \(r\) variation with \(\beta\).  
* Dead node: This is a state in which the sensor node cannot operate anymore, because its energy has been depleted completely or it has broken down. The node can neither transmit nor receive the data. In addition, the node is considered to be in this state when its residual energy (\(E_{res}\)) is below \(0.05\) J.
* Isolated node: This means node does not have any one-hop neighbors, either to transmit or receive data, or it does not receive any CH messages.  
The CH re-election happens only when the current CH value falls below the threshold value. Clustering solutions can be combined with TDMA-based schemes to reduce the cost of idle listening. CCBE is divided into the setup, slot allocation and steady transmission phase.
## 1 Introduction  
Despite the fact that new technologies, equipment, and machinery are emerging, as well as the implementation of automation and robotics, labour remains an important resource in the construction industry. Actually, labour, as one of the active resources, drives the duration of construction works in terms of single tasks or the whole construction stage of a project. Construction work planning is of key importance and involves, among other things, the definition of work tasks, the choice of technology, and the estimation of the required resources and durations for individual tasks. Actual durations depend on the efficiency, productivity, or performance achieved on a construction site.  
The terms mentioned--efficiency, productivity, or performance--are used interchangeably (as seen in the state-of-the-art part of the paper) and refer to the results or output of construction works measured against the use of resources engaged in executing these works. The fact is that even in the case of the most careful planning performed by the main contractor and/or subcontractors, schedules and baselines are verified by the actual progress of works. Deviations from planned durations seem to be inevitable.  
The aim of the paper is to present an approach based on both quantitative and qualitative analysis of construction work efficiency. Efficiency is understood herein as a work's output measured in units specific to certain construction work (representing weight, length, surface) or volume per hour. The output of construction work is measured against labour in terms of the number of engaged workers and the number of working hours spent on the execution of construction work. The quantitative approach relies on the analysis of data, including construction work efficiency measures, fitting triangular continuous distribution, and probabilistic analysis of efficiency. On the other hand, the implementation of support vector machines (SVM) as a machine learning tool is proposed for the qualitative approach. As a result, the solution to the classification problem is expected, which allows recognising whether the efficiency of construction work for a given combination of factors is above or below a certain value called critical efficiency.  
Another objective is to present the results of the application of the proposed approach to an example of system formwork assembly works for the three types of reinforced concrete structural members--columns, walls, and slabs. The applied analysis was carried out with the use of real-life data obtained from a construction project as recorded on a construction site. The application of a triangular distribution for quantitative work efficiency analysis precedes the application of machine learning methods for qualitative predictions of efficiency.  
The paper's content includes a brief discussion of the background and a state-of-the-art review, a presentation of the approach for efficiency analysis, the introduction of the data used in the course of the applied analysis, a case study and research results, a discussion of the advantages and drawbacks of the presented approach, and conclusions.
## 5 Results
### Performance of Machine Learning Classifiers  
To measure the performance of each classifier, we used the widely-accepted performance statistics: Accuracy, precision, recall, and F1-score [58]. For model evaluation, we used 10-fold cross-validation in all experiments. The RF classifier used n = 100 estimators with max depth set to 40. Other parameters were left as default by the scikit-learn library. Both datasets were evaluated with the same model configurations. To allow reproducing the same splits across different experiments, we used the same seed for generating the random state for both datasets.  
Table 2 and 3 describe the comparative performance of the five classifiers against each performance metric in the two experiments. The metrics represent the weighted average of the cross-validation. We learn the following from these tables:  
* The performance of all classifiers was better in the FPG-labeled dataset as compared to the HbA1c-labeled dataset;
* SVM performed best on the HbA1c-labeled dataset while RF performed best on the FPG-labeled dataset;
* There was no change in the performance of SVM after feature elimination in both cases, while all the other classifiers saw an improvement or a decrease in the performance after feature elimination;
* The performance of DT and EVM classifiers improved, but that of RF decreased, after feature elimination in both cases.  
The classification results are comparable to existing approaches for diabetes classification [9; 31; 32; 34].
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 2 Related Work
### Handover Schemes in Ultra-Dense Networks  
Introducing more advanced handover plans for ultra-dense networks where UL preference is not the same as DL preference is necessary. However, related work for smart handover at the time of writing is limited, and the vast majority of the proposed handover plans neglect the UL preference. For example, [PERSON] et al. [8] propose an astute handover process algorithm ensuring load balance in 5G networks and enhanced the QoS during the handover of videos from mobile cameras in environmental monitoring. They utilize base station limits, the movement of mobile nodes, signal strength, and generated traffic as parameters for handover decision-making. However, they do not indicate the criteria utilized for choosing candidate BS.  
To the best of our knowledge, the majority of intelligent handover schemes are based on skipping technique. In this context, [PERSON], [PERSON], and [PERSON] [9] developed two different handover techniques adapted to ultra-dense networks. They managed to reduce the handover rate and handover cost in order to meet the required quality of service. Therefore, based on skipping techniques, the principal objective of the proposed techniques is to decrease the high handover rates. Another different handover issue was studied by [PERSON] et al. [10; 11], where reducing handover delay was the principal objective of their research. Using stochastic geometry, they propose a mathematical framework that incorporates handover delay into the rate analysis. The proposed handover schemes sacrifice prioritization of user connection to reduce the handover delay and enhance the long-term average rate. [PERSON] et al. in [12] redefine their past work for a multi-level system and introduce a compelling arrangement of topology-aware handover skipping. Based on BS intensity, they handle the interaction between a handover cost and a client throughput. In this paper [12], they propose an intelligent handover scheme, while making use of topology awareness and user trajectory estimation.
# Groundwater Prediction Using Machine-Learning Tools
## 4 Groundwater Prediction Methodology
### Feature Selection
#### 4.3.3 Global Feature Generation Using Gaussian Mixture Models  
In this subsection we describe how we used Gaussian mixture models (GMMs) to generate global features. This idea came from observing that regions of high groundwater level seemed to form shapes that could be described as Gaussian distributions, which propagated from image to image. The means and the covariances of these Gaussian-shaped features can be used as global features that describe the motion of high regions from image to image. To apply GMM to an image, we converted the image to a set of pixel locations by randomly selecting pixels with selection probability proportional to the pixel's scaled intensity (see Figure 5). These pixel locations were fed to the GMM algorithm which returned means, covariance matrices, and weights of Gaussian clusters. In this study, we set up the algorithm to have only one cluster. The pixel located at the cluster mean and the two eigenvalues of the covariance matrix were used as global features.  
We applied GMM to image 10 (two months previous), and image 11 (one month previous), yielding a total of 8 global features. As described above, our application of GMM involved a randomization when choosing pixel locations. To account for this randomness, when evaluating models that used GMM features we created 100 different models using different randomization. From those 100 results we took the per-pixel averages to get a single model, and took the RMSE and MAE for this model to obtain accuracy estimates.
# A CNN-Based Method for Heavy-Metal Ion Detection
###### Abstract  
Data processing is an essential component of heavy-metal ion detection. Most of the research now uses a conventional data-processing approach, which is inefficient and time-consuming. The development of an efficient and accurate automatic measurement method for heavy-metal ions has practical implications. This paper proposes a CNN-based heavy-metal ion detection system, which can automatically, accurately, and efficiently detect the type and concentration of heavy-metal ions. First, we used square-wave voltammetry to collect data from heavy-metal ion solutions. For this purpose, a portable electrochemical constant potential instrument was designed for data acquisition. Next, a dataset of 1200 samples was created after data preprocessing and data expansion. Finally, we designed a CNN-based detection network, called HMID-NET. HMID-NET consists of a backbone and two branch networks that simultaneously detect the type and concentration of the ions in the solution. The results of the assay on 12 sets of solutions with different ionic species and concentrations showed that the proposed HMID-NET algorithm ultimately obtained a classification accuracy of 99.99% and a mean relative error of 8.85% in terms of the concentration.  
heavy-metal ion detection; convolutional neural networks; electrochemical potentiostat +
Footnote †: journal: Applied Sciences
# Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications
## 4 Simulation and Performance Analysis
Along with the increase of the number of D2D content requesters, the channel resources are relatively decreased, and the interference between D2D clusters is relatively increased. Hence, the D2D accessed probability is continuously decreased. When the channel resources are sufficient, the impact of D2D content requesters on the accessed probability is much smaller than when the channel resources are scarce. Figure 7 shows the impact of the number of D2D content providers on the D2D throughput. As the number of D2D content providers increases, D2D throughput continues to increase, but the trend slows. The more D2D content providers in the cell, the more users may exist in the D2D cluster. Hence, the D2D throughput can be further increased with NOMA. However, with more users in the D2D cluster, the interference between them also becomes more complicated. Moreover, there are certain standards when D2D users are clustered. As the number of D2D content providers in the cell increases, the number of users in the cluster may increase as well, but it will not increase infinitely. As users in the cluster gradually become saturated, D2D throughput increases with a slower trend. Moreover, the more cellular channels represent the more available channel resources. More D2D clusters can access in the cellular channel with small interference when channel resources are sufficient. Therefore, more the cellular channels, the faster the D2D throughput increases. Figure 8 illustrates the impact of the number of D2D content requesters on the D2D throughput. Along with the increase of the number of D2D content requesters, the D2D throughput continues to increase. It can be seen that D2D throughput increases at a faster rate when the channel resources are sufficient because of the relatively small interference. Moreover, our algorithm is obviously superior to that in Reference [17] because of the one-to-one D2D communication Reference [17] uses. Since [17] did not consider NOMA in D2D groups, the throughput is lower than that in this paper, and our advantage becomes larger with more D2D users. Figure 6: Accessed probability for different numbers of cellular channels with different numbers of D2D content requesters, \(K=200\).
# Validation and Application of Two New Core Stability Tests in Professional Football
## 4 Discussion
### Study I: (validation study)
Another study designed by [PERSON] et al. [31] evaluated the intratester reliability of isometric strength tests by using a HHD device in a workplace environment, and found excellent ICCs for back flexion and extension tests (0.97-0.93, respectively). Two distinct methodologies have been traditionally used in sport science for strength evaluation: isokinetic testing or isometric HHD [19]. Isokinetic dynamometers are computerized machines able to provide different variables of muscle strength (i.e., peak force, endurance, power, optimum angle of torque production, etc.). Isokinetic dynamometry is considered the \"gold standard\" for trunk muscle strength evaluation, mainly because it allows a controlled and accurate assessment of a large number of muscle force parameters, but the high technology requirements, as well as the time consumption, of these isokinetic protocols, along with their substantial economic cost, could limit the generalization of its use. Alternatively, hand-held dynamometry (HHD) strength evaluation has been shown to be a reliable, objective way to obtain strength measurements [23; 24; 25; 26]. HHD is a tool that can be placed between the practitioner's hand and the tested body part of the athlete. [PERSON] et al. [22] recently demonstrated, in a systematic review, the moderate-to-good reliability and validity scores between the HHD vs. isokinetic testing procedures. Considering the cost of isokinetic testing devices and their logistical limitations for routine clinical testing purposes, the HHD could be considered a practical standard for muscle strength assessment in the training field or clinical setting. These results support those from our study, and enlarge the scientific verification of the use of HHD-based core strength evaluations in the field training arena, or the clinical setting itself, enabling clinicians to routinely perform quantitative and objective core body region strength evaluations on a science-based foundation. In the authors opinion, the employment of previously analyzed and validated tests, with adequate reliability and reproducibility, guarantees the internal methodological quality standards that this kind of in-the-field performed test have to demonstrate in order to ensure the appropriate extrapolation of these evaluation methodologies. Moreover, a robust validation process can also enable different researchers in the development of these routines and result interpretations.
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 2 Methods
### MEO and LEO Satellite Observing Probability  
For GNSS MEO or LEO satellites in approximately circular orbits, previous GAPM is effective. The velocity of east-west direction \(V_{\lambda}\) and north-south direction \(V_{\varphi}\) for the position (\(\varphi\),\(\lambda\)) can be written as [16]:  
\[\left\{\begin{array}{l}V_{\lambda}=\sqrt{\frac{GM}{R}}\cdot\frac{\cos i_{ \text{orb}}}{\cos\varphi}\\ V_{\varphi}=\sqrt{\frac{GM}{R}}\cdot\sqrt{1-\left(\frac{\cos i_{\text{orb}}}{ \cos\varphi}\right)^{2}}\end{array}\right., \tag{1}\]  
where \(GM\) is the product of the gravitational constant and the earth's mass; \(R\) is the norm of a satellite position vector; \(i_{\text{orb}}\) is the satellite's orbit incline angle to the equator; \(\varphi\) is geocentric latitude; \(\lambda\) is longitude.  
As Equation (1) shows, the velocity of satellites appearing in one grid element is a function related to the latitude. So, for satellites appearing in the grid elements with the same geocentric latitude, the \(V_{\varphi}\) term would be the same. Based on the hypothesis that GNSS satellites are distributed symmetrically in the east-west direction, the in and out satellites of the grid are balanced in this direction [15]. Therefore, the observing probability of a satellite depends on \(V_{\varphi}\) only. The satellite observing probability can be modified as [15]:  
\[P^{M/L}=\left\{\begin{array}{l}\frac{k}{V_{\varphi}}=\sqrt{\frac{R}{CM}} \frac{k\cos\varphi}{\sqrt{\cos^{2}\varphi-\cos^{2}i_{\text{orb}}}}\quad,| \varphi|<i_{\text{orb}},\\ 0,\;\;|\varphi|\geq i_{\text{orb}}\end{array}\right., \tag{2}\]  
where \(k\) is the probability constant for a specific GNSS MEO or LEO satellite constellation. The superscript \(M\)/\(L\) stands for GNSS MEO or LEO satellites.  
As mentioned above, the spherical orbit surface can be divided into \(1^{\circ}\times 1^{\circ}\) grids centered in \((\varphi_{i},\lambda_{j})\):  
\[\left\{\begin{array}{l}\varphi_{i}=i-0.5\;i=-89,-88,\ldots,90\\ \lambda_{j}=j-0.5\;j=-179,-178,\ldots,180\end{array}\right.. \tag{3}\]  
In addition, the probability constant \(k\) can be calculated as [16]:  
\[k=\frac{n}{\sum\limits_{i=-89}^{90}\sum\limits_{j=-179}^{180}\frac{1}{V_{ \varphi}}}, \tag{4}\]  
where \(n\) is the total number of GNSS MEO or LEO satellites.