esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### LSTM
#### 4.3.2 Sensitivity
HKOA-LSTM  
As illustrated in Figure 17a, akin to the hysteresis prediction outcomes, the HKOA-LSTM model utilised for forecasting the sensitivity of the flexible sensing materials demonstrates commendable performance on the training set. In the test set, the model's R\({}^{2}\) score reaches its peak with three hidden layers and declines at the addition of a fourth layer. Consequently, increasing the number of hidden layers does not enhance the network's generalisation capacity and may result in data overfitting. Figure 17b illustrates the impact of the neurone count per layer on model performance, with the number of hidden layers fixed at three. In the training dataset, it was seen that R\({}^{2}\) continually achieved an exceptional level as the number of neurones rose. For the test dataset, the R\({}^{2}\) value diminishes progressively from 20 to 40, increasing from 40 to 70, reaching a maximum of 70, with R\({}^{2}\) values of 1 and 0.9803 for the training and test sets, respectively. The research indicates that the quest for high-performance sensing materials will inevitably alter their mechanical and electrical properties. The concentration of conduc  
Figure 16: R\({}^{2}\) values of the LSTM model for forecasting sensitivity based on (a) the number of hidden layers and (b) the number of neurones in the hidden layer. Figure 17: R\({}^{2}\) values of the HKOA-LSTM model for forecasting sensitivity based on (a) the number of hidden layers; (b) the number of neurones within the hidden layer. tive particles and the MT of the sensor material influence the sensitivity and hysteresis performance, suggesting that optimising sensitivity throughout a broad pressure range may compromise the hysteresis performance. Consequently, an appropriate filler ratio and MT are necessary to equilibrate the sensitivity and hysteresis characteristics.
# Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
###### Abstract  
Statistical and machine learning modelling techniques have been effectively used in the healthcare domain and the prediction of epidemiological chronic diseases such as diabetes, which is classified as an epidemic due to its high rates of global prevalence. These techniques are useful for the processes of description, prediction, and evaluation of various diseases, including diabetes. This paper models diabetes disease in Saudi Arabia using the most relevant risk factors, namely smoking, obesity, and physical inactivity for adults aged \(\geq\)25 years. The aim of this study is based on developing statistical and machine learning models for the purpose of studying the trends in incidence rates of diabetes over 15 years (1999-2013) and to obtain predictions for future levels of the disease up to 2025, to support health policy planning and resource allocation for controlling diabetes. Different models were developed, namely Multiple Linear Regression (MLR), Support Vector Regression (SVR), Bayesian Linear Regression (BLM), Adaptive Neuro-Fuzzy Inference model (ANFIS), and Artificial Neural Network (ANN). The performance of the developed models is evaluated using four statistical metrices: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination R-squared. Based on the results, it can be observed that the overall performance for all proposed models was reasonably good; however, the best results were achieved by the ANFIS model with RMSE = 0.04 and R\({}^{2}\) = 0.99 for men's training data, and RMSE = 0.02 and R\({}^{2}\) = 0.99 for women's training data.  
machine learning; diabetes; regression; statistical metrices +
Footnote †: journal: _Algorithms_
## 3 Simulation Results
### Case 4: SST as a Power Factor Improvement Device  
In the ICC of SST-VSC, the quadrature component of current was maintained at zero using PI regulation, which forced SST not to draw reactive power from the grid, thus maintaining unity power factor at the grid side. When the \(q\)-component of current in ICC was maintained at any positive value, SST drew leading VAR from the grid, making SST as a PFI device, and at the same time maintaining its own feature of transformer.  
When the current at the grid side was set to 45\({}^{\circ}\) leading, the simulation result shows that SST maintained its function of the PFI device when it was delivering power to a 1 p.u. rated inductive load. As compared to the conventional transformers, this is a remarkable achievement of the proposed SST design. The proposed circuit as a part of the interconnected system had the ability to improve the overall power factor of the system while meeting the lagging reactive power requirement of the load. However, the current at the grid side increased as expected. Figure 23 shows the voltage and current of phase A at the grid side, and filtered output voltage of the SST.  
When SST as a PFI device was investigated by the pre-tuned PI controller parameters, simulation results show that SST-VSC took more time (0.7 s) to settle its steady state value (see Figure 24). The voltage droop control at the outer layer of the SST-VSC could be re-tuned to reduce the settling time of the SST-VSC.  
Figure 23: SST simulation results as the power factor improvement (PFI) device. (**a**) The input current leads the input voltage. (**b**) SST filtered output voltage.  
Figure 24: SST-VSC response working as the PFI device with induction motor loading.
## 1 Introduction
Data signals generally have strong time redundancy, but there are more characters in the signal and the distribution probability of characters is relatively uniform. If entropy coding or dictionary compression is directly used to compress data signals, not only is the compression ratio low, but the compression time is also long, which cannot meet the compression requirements. If we can group similar contexts together, the symbols that follow them are likely to be the same, and a very simple and efficient compression strategy can be exploited. Among context-based algorithms, the most famous is the partial matching prediction (PPM) algorithm, which was first proposed by [PERSON] and [PERSON] [20] in 1984. For time-series signals, the ARIMA model and RNN model can accurately predict, but their algorithm complexity is high, so they are mostly used for time series with low real-time requirements such as air temperature and wind speed. Unfortunately, in the process of data compression, massive data prediction is required, and the ARIMA model and the RNN model take a lot of time. Therefore, they do not meet the real-time requirements of data compression [18; 19]. Figure 1: Block diagram of data compression and decompression. To solve the problem of periodic signal compression, we propose an efficient lossless compression method for periodic signals based on the adaptive dictionary. According to the data value of the past, the dictionary model is used to predict the current data value. The prediction coding usually does not directly code the signal, but codes the prediction error. Because the adaptive dictionary predictive coding concentrates most of the data in a small amount of data, it effectively reduces the amount of data to obtain a high compression ratio.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 1 Introduction
### Previous Work on Sub-Connected Architectures  
The previous works mainly focused on fully connected architectures. However, sub-connected architectures, where each RF chain is only connected to a subset of the available antennas, is more suited for practical applications due to its lower complexity. Narrowband fixed sub-connected hybrid architectures for single user systems were addressed in [26; 27]. The authors of [26] proposed a two-layer optimization method jointly exploiting the interference alignment and fractional programming principles. First, the analog precoder and combiner were optimized via the alternating-direction optimization method and then the precoder and combiner were optimized based on an effective MIMO channel coefficient. In [27] two analog precoder schemes for high and low signal-to-noise ratio (SNR) condition were developed. For multi-user sub-connected narrowband architecture, some approaches were also proposed in [28; 29; 30; 31; 32]. In [28], the total achievable rate optimization problem with nonconvex constraints was decomposed into a series of sub-rate optimization problems for each sub-antennaarray, and then a successive interference cancelation (SIC) based hybrid precoder was proposed. A low-complexity hybrid precoding and combining design was discussed in [29], where a virtual path was performed to maximize the channel gain and then, based on the effective channel, a zero-forcing precoding was applied to manage the interference. The scheme proposed in [30] efficiently controlled the multi-user interference by sequentially computing the analog part of the equalizer over the RF chains, using a dictionary obtained from the array response vectors. In [31], the Gram-Schmidt (GS) based antenna selection (AS) algorithm was used to obtain an appropriate antenna subset for the overlapped, interlaced, and dynamic architectures. Solutions for wideband sub-connected hybrid architectures were also considered in [32; 33]. In [32], solutions for fully connected, fixed, and dynamic subconnected OFDM single user hybrid precoding were designed to maximize the sum rate. Precoding techniques for multi-user downlink massive mmWave MIMO-OFDM systems were proposed in [33]. A unified heuristic design for both fully connected and sub-connected hybrid structures was developed by maximizing the overall spectral efficiency.
## 5 Performance Evaluation
### Simulation Setup and the Performance Metrics  
In this work, the time-sensitive periodic data reporting, utilizing the wireless sensor networks with mobile sinks is a major contender for the evaluation criteria, especially for the smart monitoring application. This is due to two aspects. First, because the routing protocol must effectively service the entire network, the periodic data applications necessitate the reporting of data from every node in the system. Second, the need to reduce the energy use and reporting delays for the time-sensitive applications, the time-sensitive application scenario is best for evaluating the effectiveness of the modified ring routing under these two constraints. The simulation parameters are discussed in Table 2.  
A topology of the different network densities as mentioned in the simulation parameters' deployed sensor nodes in an area of 1000 \(\times\) 1000 sqm is considered. A single sink, initially situated at the network center, that can move with a controlled mobility model in a constant speed, is simulated when the criteria is fulfilled. Again, the lifespan achieving tests are conducted and give the network convergence factors, based on node's battery depletion.  
We contrasted LBDD, MSRP, Mob M-LEACH, and the ring routing with our suggested modified ring routing method. To compare the performance of several algorithms equally, we executed the algorithms using the same network model. A packet's throughput and the end-to-end delay [36] metrics demonstrate how quickly it arrives at its destination, in comparison to the time needed for it to go there. It displays the quality of service the network provided. The energy use represents the energy that is still in the network after all operations have been completed. This contributes to the network lifespan that successfully carries out the application. The network convergence factor also calculates the network availability. Measuring the control overhead is a crucial metric for our investigation since it confirms the value of the ring building in the mobile sinks.
## 3 Results
### Multi-Physical-Physiological Data Approaches  
Figure 7 illustrates the various combinations of physical and physiological signals identified in the 18 studies classified as multi-physical-physiological. As depicted in the graph, the prevalence of this type of study remains limited. Most of these studies were conducted from 2021 to 2023, which are the last 3 years from the 6-year timeframe this study encompasses, except for two studies that were published in 2018; therefore, it can be said that this is a relatively new trend. Based on the analysis of these studies, these trends can be highlighted:  
* Studies tend to combine multiple types of signals, such as EEG, facial expressions, voice signals, GSR, and other physiological data. Combining signals aims to take advantage of the complementarity of different modalities to improve accuracy in emotion detection.
* Machine learning models, in particular CNNs, are widely used in signal fusion for emotion recognition. CNN models can effectively process data from multiple modalities.
* Applications are also being explored in the health and wellness domain, such as emotion detection for emotional health analysis of people in smart environments.
* The use of standardized and widely accepted databases is important for comparing results between different studies; however, these are still limited.
* The trend towards non-intrusive sensors and wireless technology enables data collection in more natural and less intrusive environments, which facilitates the practical application of these systems in everyday environments.
## 5 Simulation Results and Discussions
### Complexity Analysis  
To compare the complexity of the presented detection schemes, the mathematical operations are converted to the number of real floating point operations (flops). For this purpose, one complex multiplication, one complex addition, one complex magnitude, and one complex square operations are counted as six, two, three, and four flops, respectively [33]. For fair comparison, we assume that the quantities \(D_{n}(k)\)'s and \([\bar{P}_{n}(k)]^{2}\)'s are prior known at the D2D UE. To calculate \(v\)-shifted differential correlations \(\tilde{Y}_{l}(k+v)\) and \(\tilde{Y}_{l}(-k+v+1)\) for \(2M+1\) trials, it has to perform \(N_{p}+2M-1\) complex multiplications. To get the quantity \(\Omega_{n}(v)\) in the RCSD scheme, \(9N_{p}-19\) flops are required. Finally, (10) requires \(6(N_{p}+2M-1)+(2M+1)(9N_{p}-15)\) flops. Since some operations are pre-calculated in (9), \(2(5N_{p}-9)\) flops are required to get (11). In the case of the RCJD scheme, (14) requires \(N_{b,n}\) complex multiplications and \(N_{p}-3\) complex additions. Accordingly, the total number of flops is \(6(N_{p}+2M-1)+(2M+1)(6N_{p}-9+6\sum_{n=1}^{2}N_{b,n})\). Concerning the proposed sequential detection method, \(10(N_{p}+2M)\) flops are necessary to compute \(v\)-shifted correlation \([R_{l}(k+v)]^{2}\) for all \(v\)'s. The proposed scheme computes (22) with \(4N_{p}\) flops. Since the quantity in (28) has been already calculated during the IFO-matching process in (22), no extra flop is needed. Considering \((2M+1)\) hypotheses, \(10(N_{p}+2M)+4(2M+1)N_{p}\) flops are eventually needed. Figure 2 plots the number of flops used in the joint detectors versus \(M\). Obviously, we can see that the proposed detection scheme has the lowest complexity in comparison to the benchmark schemes, irrespective of the value of \(M\). It is observed that the complexity of the proposed method is considerably reduced, as the local oscillator's stability is getting worse. For \(M=3\), the number of flops of the proposed approach can be decreased by 49.7% and 40.1%, compared to the RCSD and RCJD methods, respectively.
## 4 Discussion
### Mathematical Modeling  
In the experiment performed by [PERSON] et al. [6], the researchers performed an analysis of kiwiberry drying using convective, microwave, and vacuum methods. They established that, for drying at 70 \({}^{\circ}\)C, [PERSON]'s model offered the best fit for CD and VD methods. In the cited research, R\({}^{2}\) varied between 0.999 and 0.997, RMSE varied between 0.0041 and 0.0147, and \(\chi^{2}\) varied between \(1.756\times 10^{-5}\) and \(1.82\times 10^{-6}\). Statistical analysis of kinetics for drying Kiwi showed the best model fit from simplified [PERSON]'s second law of diffusion. The results obtained in the current research are similar. The coefficient of determination in the current experiment (R\({}^{2}\)) was in the range between 0.998 to 0.999, RMSE deviated between 0.0052 and 0.0255, and \(\chi^{2}\) values ranged from \(9.275\times 10^{-5}\) to \(6.72\times 10^{-4}\).  
[PERSON] et al. [41] studied the influence of instant control pressure drop on different aspects of drying strawberries using convective, freeze, and swell drying methods. The highest value was established for swell drying with D\({}_{\text{eff}}\) at \(5.00\times 10^{-10}\) m\({}^{2}\)/s against \(0.11\times 10^{-10}\) m\({}^{2}\)/s for control samples obtained using the convective method. [PERSON] and [PERSON] [42] reported the effective diffusivity values of pepper slices comparing the sun, convective, and microwave drying processes. Depending on the parameters of the process, they established values ranging from \(0.31\times 10^{-9}\) m\({}^{2}\)/s to \(87.39\times 10^{-9}\) m\({}^{2}\)/s for sun and microwave methods, respectively. Obtained D\({}_{\text{eff}}\) values were compared by means of Pearson's correlation with determined chemical properties. Significant correlation was found between D\({}_{\text{eff}}\) and TPC (r = \(-0.635\); \(p<0.05\)) and D\({}_{\text{eff}}\) and TCC (r = \(0.697\); \(p<0.05\)) for VD and CD, respectively. In the case of CD, positive value of Pearson's correlation coefficient indicated that faster removal of water, which is linked to faster drying rate, could be related to better retention of carotenoids. No such correlation has been found for VD and TCC. Such results confirm the aforementioned explanation about the sensitivity of carotenoids for extended processing in the presence of air. However, a negative correlation between TPC and D\({}_{\text{eff}}\) demonstrates that degradation of phenolics occurs faster when water is removed faster from kiwiberry and that it is not linked to the presence of air (no significant correlation found for CD).
## 5 Analysis of Engineering Example  
In order to enhance the validation of the calculation outcomes and guarantee the safety of the tunnel, the DouMo Tunnel of Shanghai to Kunming high-speed railway and the LuZhuBa tunnel of the Yichang-Wanzhou Railway are taken as examples for application analysis [29], which are located in Anshun, Guizhou Province and Enshi, Hubei Province.  
1. DouMo Tunnel During the construction of the DouMo Tunnel, a large solution cavity with abundant water and heavy mud was encountered. The measured water pressure ranged from 0.37 to 0.42 MPa, the main strata are limestone and sandstone, with a density of 25 \(\mathrm{kN/m^{3}}\), exhibiting an elastic modulus of 1 GPa, while the rock beam's length connecting the cave and tunnel measured 13 m and the distribution perspective of the cave \(\beta\) was 90\({}^{\circ}\). The separation between the tunnel and the cavern measured 6 m. Due to significant settlement and signs of instability in the tunnel rock beam vault during excavation, curtain grouting was utilized.
2. LuZhuBa Tunnel In the LuZhuBa Tunnel, geophysical data indicated the development of karst at DK205+170, posing a risk of disastrous mud protrusion and collapse during construction. The water pressure stabilized at 0.2 MPa after water discharge from the cavity, the main strata are limestone and sandstone, with a density of 23 \(\mathrm{kN/m^{3}}\), the elastic modulus was 0.9 GPa, the length of the rock beams between the caves was 8 m, and the distribution perspective of the cave \(\beta\) was 30\({}^{\circ}\). The precise distance from the revealed tunnel to the concealed cave was approximately 3.6 m, and the tunnel was deemed to be in a secure state.  
We integrated these two actual engineering cases into the calculations based on previous research [26], and the novel discoveries disclosed in this manuscript are detailed in Table 1, outlining the summarized outcomes.
# Prediction of Friction Resistance for Slurry Pipe Jacking
###### Abstract  
Friction resistance usually constitutes one of the two main components for the calculation of required jacking force. This paper provides a new approach to predict the friction resistance of slurry pipe jacking. First, the existing prediction equations and their establishment methods and essential hypotheses used were carefully summarized and compared, providing good foundations for the establishment of the new model. It was found that the friction resistance can be uniformly calculated by multiplying an effective friction coefficient and the normal force acting on the external surface of the pipe. This effective friction coefficient is introduced to reflect the effect of contact state of pipe-soil-slurry, highly affected by the effect of lubrication and the interaction of pipe-soil-slurry. The critical quantity of pipe-soil contact angle (or width) involved may be calculated by [PERSON]'s contact model. Then, the equation of normal force was rederived and determined, in which the vertical soil stress should be calculated by [PERSON]'s silo model with parameters proposed by the UK Pipe Jacking Association. Different from the existing prediction models, this new approach has taken into full consideration the effect of lubrication, soil properties (such as internal friction angle, cohesion, and void ratio), and design parameters (such as buried depth, overcut, and pipe diameter). In addition, four field cases and a numerical simulation case with various soils and design parameters were carefully selected to check out the capability of the new model. There was greater satisfaction with the measured data as compared to the existing models and the numerical simulation approach, indicating that the new approach not only has higher accuracy but is also more flexible and has a wider applicability. Finally, the influence of buried depth, overcut, and pipe diameter on the friction resistance and lubrication efficiency were analyzed, and the results can be helpful for the future design.  
slurry pipe jacking; friction resistance; effective friction coefficient; pipe-soil-slurry interaction; lubrication efficiency Article