esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The ANN model can attain efficient computational offloading, smart division of tasks and cooperation among BSs, and an efficient routing policy. The ANN model can also be used to acquire the computation results without interruption due to movement [16; 17; 18]. Therefore, aiming at the problem of computation and communication in USNet, the computational offloading, smart division of tasks, cooperation between BSs based on coalition game, and efficient transmission mechanism were investigated to reduce the total service time and improve the reliability of the communication. The main contributions of our work are given as follows:  
First, we present the mathematic formula and theoretical model of the coalition game among BSs. Based on the coalition game theory, the cooperation policy among USNs, SNs, and BSs is formulated. The coalition is established only if the tasks are divided and processed in parallel among BSs and better service reliability is provided. Second, we study the strategy of smart task division and parallel processing to reduce the total service time. In addition, the cooperative computing method between BSs and an effective transmission mechanism is studied to reduce the communication time, enhance service reliability, and decrease the failure of service sessions due to the movement of BSs. Third, because the computing task unloading, task division, task processing, and transmission process in this scheme are complex problems, the ANN model is introduced to reduce the complexity of the algorithm and the overall cost of service. Finally, simulation results indicate that our proposed method outperforms the exiting techniques, i.e., Greedy, Always-Migrate, and Random Process, in terms of a total service time, success rate, and failure rate. The remainder of this paper is organized as follows: In Section 2, we review the related works. In Section 3, the system model is analyzed. Section 4 gives a detailed description of the scheme for cooperative computing offloading. Section 5 illustrates the performance evaluation of our proposed methods. Finally, the work is concluded in Section 6.
# Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study
## 5 Discussion
Inspired by the proposed network architecture, it is possible to include multiple decoders, one for each tracer, to separate multi-tracer signals. It is noteworthy that our method does not rely on prior knowledge of the tracers, such as their half-lives, kinetic models or input functions. Theoretically, the proposed DL-based method can be extended to other tracer combinations. The proposed method presupposes that there are enough training data available. However, this is usually not the case in medical imaging. Further developing more advanced network architectures to enable better separation performance based on small training sets is crucial. Lastly, our method will ultimately need to be rigorously evaluated with clinical data. Further study would need to focus on training the model using phantom and Monte Carlo simulation data. The model could then potentially be fine tuned with real patient data.
# Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems +
## 2 Materials and Methods
### Proposed Confidence-Based Voting Approach  
The neural network and the fuzzy classification method presented in previous section return some measure of confidence of the classifier together with the class number. The fuzzy rule bases generated by HEFCA are used to find the winner rule by comparing the membership values of every rule, so that the value \(\mu_{A_{w}}(x_{i})CF_{w}\) changes in range (0,1). If this value equals one, then the classifier fully describes this instance with this rule, and if the membership is zero, than among all the rules in the base, even the winner rule has low confidence about the true class number. Furthermore, if all rules have zero confidence in their decision, then the instance is rejected, i.e., considered as misclassified. Although it is possible, the HEFCA usually generated rule bases, which described even the test sample fully.  
The neural network models with softmax layer [26] also have a set of confidence values returned, which are also considered as probabilities. The values of the softmax layer are estimated as follows:  
\[\sigma(z)_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{k}e^{z_{j}}} \tag{6}\]where \(z\) vector is the softmax layer input, \(k\) is the number of classes, \(i=1,\ldots,k\). If the neural network is completely unaware about the class number, all \(\sigma(z)_{i}\) are equal to \(1/k\), however, this situation could be only observed after initialization in most scenarios. So, the neural network does not have the rejected classification situation like fuzzy logic classifiers.  
To use the fuzzy logic classifier together with other classifier, the following combination algorithm is proposed: if the membership value returned by the fuzzy logic is lower than a threshold, then the second, assisting classifier is used. That is, the main idea is to use the explainable fuzzy classifier in most cases, but apply the second, more accurate classifier, be it neural network, decision tree or random forest in more complicated cases.  
The flowchart of the confidence-based voting is presented in Figure 2.  
The following rule was applied in this study: after the fuzzy classifier returned the membership value and class number for all instances of the test set, 25% of instances with lowest membership values are chosen, and a threshold value is set. These instances are classified by the assisting method. The resulting method was called the Confidence-Based Voting (CBV). The next section contains the experiments and results.
# Optical Fibres for Condition Monitoring of Railway Infrastructure--Encouraging Data Source or
## 2 Optical Fibres--A Brief Overview
### Fibre Bragg Grating
[25] in South Korea aiming to measure gauge change processes. Two FBG sensor arrays (each array has five sensors) were attached to the rails and measured the strain when a train passed from a standard gauge to broad gauge. Monitoring the rail temperature, displacement and strain is decisive, especially in regions with harsh environments. To address this problem, [PERSON] et al. [26] installed on a 1960 m long high-speed track section in Hong Kong an array of various FBG sensors. This includes 42 FBG temperature sensors, 117 FBG displacement sensors and 126 FBG stress and strain sensors. The system is designed to work as an early warning and prediction system of track structures in challenging areas. [PERSON] et al. [27] monitored the lateral displacements and vibrations of unreinforced and geogrid-reinforced ballast utilising FBG sensors. FBG-based liquid level sensors are capable of monitoring differential settlements of railway tracks which was demonstrated by [PERSON] et al. [28]. Additionally, the results of the method correspond to theoretical considerations and predictions. Utilising FBG-based strain sensors enables the condition monitoring of fishplates, switchblades and stretcher bars. [PERSON] et al. [29] examined the potential of such sensors on a tramway with arrays of sensors. FBG-based sensors are applied to monitor railway bridges too. [PERSON] et al. [30] used strain sensitive FBG sensors and compared the results with electrical resistance strain gauges. In conclusion, the measurements from this measurement campaign match with the results of electrical resistance strain gauges. Another experiment using 15 FBG sensors was set up by [PERSON] et al. [31] in order to monitor the bridge strengthening effect. This was achieved by measuring the vertical deflection, dynamic load bending moment and transverse vibration of the bridge. The developed system is scalable and can thus be used to monitor the condition of large span and arch bridges. [PERSON] et al. [32] monitored the contact force and vertical acceleration of pantograph catenary. The results indicate that FBG sensors can replace electrical-based sensors setups and thus monitor overhead line defects. This is confirmed by the observations made by [PERSON] [33], who outlined the possibility to monitor catenary condition by simply mounting FBG sensors between the pantograph and overhead contact wire. [PERSON] et al. [34] designed an FBG sensor-based intrusion detection system to protect the railway area from unauthorized intrusion. Actually, they used a ribbed mat integrated with FBG strain sensors which was installed near the railway track. Additionally, a couple of FBG accelerometers were attached to the rail tracks. The results showed that the mat is capable of detecting suspicious events such as human walking in the vicinity of railway tracks. Additionally, [PERSON] et al. [35] demonstrated a liquefied petroleum gas monitoring system in railway tunnels based on FBGs and long-period gratings (LPGs).
# Predicting Student Academic Performance by Means of Associative Classification
## 2 Literature Review
Hence, there is a need for tailoring prediction models to the learning context under analysis and to deepen the analysis of the extracted correlations among data. In this way, this paper explores the potential of associative classification models, which combine accuracy and interpretability. **(C) What are the most effective classification techniques? ** Various classification techniques have successfully been applied to early predict student performance. Specifically, a significant effort has been devoted to training traditional and Deep Neural Networks (e.g., [28; 29]). In parallel, established approaches such as Support Vector Machines [30], distance-based classifiers [31], ensembles of classification methods (i.e., Gradient Boosting and Random Forest) [32], and time series forecasting methods [6] have achieved fairly high accuracy values. This paper is, to the best of our knowledge, the first attempt to use associative models to address early student performance prediction. **(D) Can we make Learning Analytics solutions transparent to the end-users? ** Explainable Learning Analytics (XLA) focuses on enhancing the transparency of Machine Learning techniques in support of the Learning Analytics domain [33]. Since most of the ML models act as black-boxes, the outcomes are often hardly interpretable. Hence, the XLA community aims at making the ML outcomes explainable by tailoring them to the particular stakeholders and end-users [8]. For instance, in [9] the authors used the Open University Learning Analytics Dataset to predict students' outcomes. They highlighted the need for using XAI in the educational field. In the context of automated essay scoring, the authors in [34] have studied the impact and trustworthiness of neural networks by means of the SHAP explanation framework [35]). Similar attempts have been made in the domains of computation thinking [36] and knowledge tracing [29]. SHAP produces visual explanations by correlating the input features with the target class. Conversely, the associative classifiers adopted in the present work are not aimed at studying the impact value of a specific feature, but rather focuses on identifying the specific combinations of feature values that are likely to be relevant to predict a given class label. A parallel effort has been devoted to explaining the early predictions of student performance using tree-based models (e.g., [10; 11]). The associative classifiers used in the present work are known to be more accurate than traditional decision trees and rule-based algorithms because they rely on global, co-occurrence-based models [12].
# Soil Moisture Influence on the FTIR Spectrum of Salt-Affected Soils
## 2 Materials and Methods  
### Soil Sampling Sites  
The study area is the coastal area of Hai Phong-Thai Binh located in the Red River Delta, with four estuaries (Figure 1): Van Uc (1), Thai Binh (2), Tra Ly (3) and Red River (4), that are strongly affected by sediment transport and tidal dynamics. According to the WRB reference soil groups [14], there are four main reference soil groups in the study area: Fluvisol (FL), Gleysol (GL), Arenosol (AR), and Cambisol (CM). According to the soil maps, soil salinity accounts for about 27 % of the total area in Hai Phong, Thai Binh and Nam Dinh. The process of soil salinization is affecting not only Fluvisols, but also Gleysols and Arenosols.  
Soil samples were taken of Fluvisols and Arenosols with an electrical conductivity (EC\({}_{1:5}\)) below 1 dS/m. The sampling method followed the Vietnamese protocol of soil sampling in agricultural areas TCVN 4046-85 [47]. Samples were collected from 11 separate points (Figure 1). At each point, soil was taken from 20 adjacent positions and mixed together, amounting to a weight of 1 kg. Samples were taken at a depth of 0-10 cm and stored in a labelled bag. The samples were brought to the laboratory and pre-treated according to ISO 11464:2006 [48]. The soil samples were air-dried in a cool place. Gravel and plant roots were removed. The samples were crushed with a ceramic mortar, sieved through a 2 mm sieve and mixed prior to physical and chemical analysis and spectroscopic research.
## 1 Introduction
[18] proposed an improved version of the adaptive growth method to solve the problem of the dynamic performance making it difficult to effectively grow stiffeners in a 3D box structure due to the effects of structural characteristics on natural frequencies. Based on the variable density topology optimization method, [PERSON] et al. [19] carried out sensitivity analysis, used the optimal criterion method to solve the optimization model, and according to the topology optimization results, the layout of the vibrating table surface reinforcement was optimized to obtain a reasonable shaking table structure. [PERSON] et al. [20] proposed a heuristic reinforcement growth design method, where the reinforcement grows and branches from a given initial point in the direction that makes the structural performance optimal, while removing the reinforcement that contributes little to the structural performance, gradually forming an optimal reinforcement layout. [PERSON] et al. [21] developed an effective B-spline parameterization method for the stiffener layout optimization of shell structures. Compared with the standard density-based method, the proposed method produces checkerboard-free design results with a clear layout and naturally avoids overhanging stiffeners. However, since the topology optimization design takes the thickness [14] or density [22] of the element as the design variable, the design of the reinforcement layout on the thin plate is transformed into the distribution of the substrate material. So, there are problems such as the unclear distribution of reinforcement and uneven or discontinuous cross-section of reinforcement [23], which make application in engineering designs difficult. The objective of this paper is to propose a clear, simple and easy-to-implement approach for reinforcing the rib design of thin-walled structures based on the limit load analysis method. Different forms of reinforcing ribs with the same weight set at the continuous or discontinuous regions of the structure were studied, assessing their reinforcement effects on the limit load of the structure. Then, rational rib forms and arrangements were proposed. As an example, the approach was verified for its feasibility and effectiveness in the reinforcing rib design for an engine test module.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 4 Construction and Operation of SOUNET
### Phase 1: Network Initialization  
After node deployment, the sink begins the network initialization by flooding a control packet named _HELLO_, which contains the sender's _ID_ and _LV_. On receiving the _HELLO_, sensor nodes identify their parent and hierarchical level. The nodes ignore the _HELLO_s received from the same or lower level. The _HELLO_ flooding continues until the _HELLO_ reaches leaf nodes. Naturally, the revision of the _HELLO_ with new senders' _IDs_ and _LV_s occurs at every flooding. Due to packet collisions, however, some sensor nodes may not receive the _HELLO_ successfully. Such sensor nodes join the network later by overhearing a _DATA_ packet in the next phase.
# Study of Exposure Uniformity of UV LED Exposure System for Wafer-Level Camera Lenses
###### Abstract  
Wafer-level camera lenses are a very promising process for camera lens fabrication. However, there exist some problems with this technology, such as uneven exposure due to curing non-uniformities. In this study, an optical simulation was implemented to simulate the UV light-emitting diodes (LEDs) curing process. We design the LED arrangement, and then find the corresponding LED and adjust the LED power to improve exposure uniformity. The simulation results are very close to the experimental data, and the uniformity is also within the standard range.  
of columns of the fly-eye lens. [PERSON]. [16] developed a crystalloid structure with a cone-shape in LED to increase the FWHM angle to 160\({}^{\circ}\) and validated this approach with an optical design and simulation software. [PERSON]. [17] presented an exploratory design of a focus-variable UV curing optical system with an LED ring array and studied the influence of the reflector on the UV curing system. [PERSON]. [18] presented an optical design that uses UV LED as the light source and stipulated the utilization rate of light, uniformity, size and working distance. [PERSON] et al. presented a feedback reversing design method [19] improved uniformity of illumination distribution through the design of optical lenses and the computation of the packaging density of LED arrays. [PERSON] et al. proposed an optical design process [20]. The number of LEDs needed to produce the target luminance is calculated from the combined optical efficiencies of the components. Then an appropriate array configuration is sought based on the illuminance distribution function for meeting the target uniformity. [PERSON] et al. presented a direct LED backlight unit (DLBLU) with extended sources based on an analytical design method [21] of uniform illumination is investigated, and developed an analytical calculation method of the light spread function (LSF) for achieving highly uniform illumination into the diffuser plate of a DLBLU.  
In this study, we will be investigating a LED module of an exposure system to do an optical simulation and LED arrangement optimization. We also will add the design of the reflective cover to correct the illumination angle to increase uniformity, then confirming the energy consistency at each point within the effective exposure range to achieve uniformity target.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 3 Spatial Compatibility and Channel Modeling  
A set of users is spatially compatible if their channels, \(\mathbf{h}_{k}\), can be separated in space through precoding. The purpose of this section is to define a metric that can evaluate how spatially compatible  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Precoder** & **Number of FLOPs** \\ \hline RZF & \(4K^{3}+2 KN_{T}(4K-1)+K(4N_{T}-1)(K+1)+\) \\  & \(2N_{T}(4K-1)+8K^{2}+7K\) \\ \hline THP & \(16K^{3}/3+2K(4 KN_{T}-K+2)+\) \\  & \(2T[2K+2K^{2}+N_{T}(4K-1)-4]\) \\ \hline BD-THP & \(40 GK_{g}^{3}/3-4 GK_{g}^{2}+\) \\  & \(2 GK_{g}-2 GK_{g}N_{T}+16 GK_{g}^{2}N_{T}+\) \\  & \(T(4 GK_{g}^{2}+4 GK_{g}+8K_{g}N_{T}-8G-2N_{T})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precoder’s complexity [3; 13].  
Figure 4: Precoders complexity versus the number of antennas and users, for \(N_{T}=K\).  
two users are. Assuming a spatial correlation model at the transmission, it can be shown that the separation between the channels of two users will depend only on their spatial correlation matrices and, in turn, on the angle between them relative to the antenna system.
## 3 Results  
### The Medical Outcomes and Related Factors  
#### 3.1.1 The Description of the Sample Data  
The study included 236 patients (135 men; 101 women). Overall, 139 (58.9%) of the patients were above 75 years old (M = 74.1 \(\pm\) 14.6 years old). A total of 66 (28.0%) of the patients' negative outcomes were due to cardiac disease, and 24 (10.2%) due to cancer. Moreover, 111 (47.2%) of the patients' BMI values fell in a normal range (18.5-23.9), and 32.3% were obese (\(\geq\)27.0). More patients were transferred from home than from a long-term care facility (54.7%/47.3%). Half (50%) of the samples' APACHE II scores fell in the 15-24 range. Overall, 47.9% of the patients were liberated from the ventilator, 26.3% were admitted to an RCW, and 25.8% of the samples were certified as either critical or deceased. The samples' basic sociodemographic and clinical features are shown in Table 1.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \(n\) & \% \\ \hline Gender & & \\ Female & 101 & 42.8 \\ Male & 135 & 57.2 \\ Age & & mean \(\pm\) SD = 74.1 \(\pm\) 14.6 \\ \(\leq\)64 & 50 & 21.2 \\ 65–74 & 47 & 19.9 \\ 75–84 & 78 & 33.1 \\ \(\geq\)85 & 61 & 25.8 \\ BMI & & \\ Under weight (<18.5) & 49 & 20.9 \\ Normal weight (18.5–23.9) & 111 & 47.2 \\ Over weight (24.0–26.9) & 41 & 17.4 \\ Obese (\(\geq\)27.0) & 34 & 14.5 \\ Source of admission & & \\ Home & 129 & 54.7 \\ Long-term care institution & 107 & 45.3 \\ APACHE II scores & & mean \(\pm\) SD = 24.58 \(\pm\) 7.70 \\ \(<\)15 & 64 & 27.8 \\ 15-24 & 115 & 50.0 \\ \(\geq\)25 & 51 & 22.2 \\ Main Diagnosis & & \\ Cancer & 24 & 10.2 \\ Cardiac disease & 66 & 28.0 \\ Pneumonia & 41 & 17.4 \\ Cerebrovascular accident & 61 & 25.8 \\ Diabetes & 98 & 41.5 \\ Lower respiratory illnesses & 25 & 10.6 \\ Hypertension & 152 & 64.4 \\ CKD & 42 & 17.8 \\ Liver disease & 15 & 6.4 \\ Dementia & 26 & 11.0 \\ Parkinson’s disease & 13 & 5.5 \\ Miscellaneous & 24 & 10.2 \\ Medical Outcomes & & \\ Critical or deceased & 61 & 25.8 \\ RCW & 62 & 26.3 \\ Weaning & 113 & 47.9 \\ \hline Total & 236 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characters and medical results of patients in a respiratory care center (RCC).