esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Ablation Experiments
### Effectiveness of GA Module  
We validated the design of the GA module in Figure 1 by comparing three settings: (1) WSA + MLC module; (2) follow a GA after WSA; (3) follow multiple GAs after WSA. We compare the performance with or without the GA module in Table 6. The result clearly shows that with a GA, the performance improves by 1.1% [EMAIL_ADDRESS]. This experiment demonstrated that the GA was beneficial to make the localization precise. However, when we replaced a single GA module with two or three GAs, the performance dropped by 1.2% and 1.1% [EMAIL_ADDRESS], respectively. Using a GA module can help improve performance, indicating that only considering local features is not conducive to the model capturing sufficient features, especially for instances with a long duration. While noise may be  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Projection Method** & **Window Size** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@avg** \\ \hline Conv1D & 9 & 75.9 & 72.6 & 67.2 & 57.2 & 41.8 & 62.9 \\ linear & 9 & 75.1 & 71.9 & 65.5 & 55.8 & 43.1 & 62.3 \\ Conv1D & 4 & 75.6 & 72.3 & 65.7 & 56.6 & 42.3 & 62.5 \\ Conv1D & 6 & 76.0 & 72.8 & 66.4 & 56.1 & 42.2 & 62.7 \\ Conv1D & 12 & 75.2 & 72.2 & 65.5 & 55.7 & 42.0 & 62.2 \\ Conv1D & 18 & 76.1 & 73.1 & 66.9 & 56.6 & 42.1 & 62.9 \\ Conv1D & full & 75.5 & 72.8 & 65.8 & 55.8 & 42.0 & 62.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study on the window size in WSA. We report the mAP at tIoU from 0.3 to 0.7 and the average mAP in [0.3,0.4,0.5,0.6,0.7] on THUMOS14.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **k = 1,3** & **k = 5,7** & **k = 9** & **k = 11** & **k = 13** & **k = 15** & **[EMAIL_ADDRESS]** & **mAP@avg** \\ \hline WSA + GA & & & & & & & 65.9 & 62.0 \\ WSA + GA + MLC & \(\surd\) & & & & & & 66.5 & 62.9 \\ WSA + GA + MLC & \(\surd\) & \(\surd\) & & & & & 65.4 & 62.0 \\ WSA + GA + MLC & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & & 66.2 & 62.3 \\ WSA + GA + MLC & \(\surd\) & \(\surd\) & \(\surd\) & & & & 65.6 & 62.0 \\ WSA + GA + MLC & \(\surd\) & \(\surd\) & & \(\surd\) & & & 67.2 & 62.9 \\ WSA + GA + MLC & \(\surd\) & \(\surd\) & & & \(\surd\) & & 65.6 & 62.1 \\ WSA + GA + MLC & \(\surd\) & \(\surd\) & & & & \(\surd\) & 65.3 & 62.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study on the kernel size in MLC. \(\surd\) indicates whether MLC contains the 1D convolution with corresponding kernel size or not, respectively.  
introduced, the benefits are greater. Stacking more than two GA modules will amplify the noise in a cumulative manner, negating the benefits of capturing global features.
## 5 Experimental Results
### Experimental Setup  
To conduct our experiments, we utilized a collection of Python libraries for data processing, visualization, and model development. Pandas and NumPy facilitated data manipulation, while Matplotlib was used for data visualization [58]. The Keras Functional API, a deep learning framework, provided the foundation for training our predictor and one-class detector [59]. For the predictor, we employed various machine learning models, including LSTM and a transformer, due to their ability to process time-series data by leveraging their memory structures. The architectures of the predictors are detailed in Tables 2 and 3 for the LSTM and transformer, respectively, where # means number of. For the LSTM-based predictor, Table 2 shows that our hyperparameter optimization process determined an optimal architecture consisting of one input layer, three hidden LSTM layers, and one output layer. Each of the three LSTM layers contains 128 units (or cells) and uses a Sigmoid activation function.  
For the autoencoder, we used a CNN architecture because of its capacity to capture intricate patterns in the input data. The autoencoder architecture comprises an encoder with one convolutional layer of 32 filters and a max pooling layer, and a decoder that mirrors this structure. The latent space is represented by a dense layer with 32 units. The hyperparameters for these models were chosen manually by initially testing various configurations and selecting the ones that yielded the best performance. Additionally, the data samples were split, with 2/3 allocated for training and 1/3 for testing. For training, the Adam optimizer was employed for 30 epochs with a learning rate of 0.0001, a batch size  
\begin{table}
\begin{tabular}{c|c c c} \hline \multirow{2}{*}{**Architecture**} & \multicolumn{3}{c}{**Hyperparameters**} \\ \cline{2-4}  & **Architecture Layer** & **\# Units** & **Activation Function** \\ \hline \multirow{4}{*}{LSTM} & Input & 48 & Linear \\ \cline{2-4}  & LSTM & 128 & Sigmoid \\ \cline{1-1} \cline{2-4}  & LSTM & 128 & Sigmoid \\ \cline{1-1} \cline{2-4}  & Output & 1 & Softmax \\ \hline \end{tabular}
\end{table}
Table 2: The configuration settings of the LSTM-based predictor.  
of 164, and mean squared error as the loss function for both the predictor models and the autoencoder model. Finally, Scikit-learn was used to assess the detector's performance [60].
## 5 Findings  
The code of this experiment was implemented in Python using Keras and TensorFlow frameworks. The result of localization of PCB defects found in PCB is demonstrated in Figure 6. For solders, the generated output was the classification of sample into \"defect\" or \"normal\". The running time for processing of one image is less than 0.5 s at computer with CPU i7, RAM 16 GB, and GPU Nvidia GeForce GTX 1060 6 GB. The performance measures of defect detection for different architectures are provided in Tables 1 and 2 for solders and in Tables 3 and 4 for PCBs. Figures 7 and 8 depict the behavior of MCC for A0, A1, and A2 for solders and PCBs, respectively.  
Among all the types of defects for PCB dataset, the analysis of the results showed that for missing washers and surface texture defects, the correctness of detection was extremely low, around 0. Using the ResNet50 [34] network, instead of VGG16, for the method described did not change the behavior of the results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Samples** & **F1** & **MCC** & **Recall** & **Precision** & **F1** & **MCC** & **Recall** & **Precision** \\ \hline
8 & 0.80 & 0.78 & 0.80 & 0.80 & 0.77 & 0.76 & 0.79 & 0.76 \\ \hline
16 & 0.83 & 0.82 & 0.85 & 0.81 & 0.81 & 0.80 & 0.82 & 0.80 \\ \hline
24 & 0.84 & 0.83 & 0.89 & 0.79 & 0.82 & 0.81 & 0.83 & 0.81 \\ \hline
32 & 0.83 & 0.82 & 0.86 & 0.81 & 0.82 & 0.81 & 0.84 & 0.80 \\ \hline
40 & 0.86 & 0.85 & 0.89 & 0.83 & 0.83 & 0.82 & 0.88 & 0.80 \\ \hline
48 & 0.83 & 0.82 & 0.82 & 0.84 & 0.81 & 0.80 & 0.84 & 0.78 \\ \hline
56 & 0.83 & 0.82 & 0.82 & 0.84 & 0.81 & 0.79 & 0.84 & 0.78 \\ \hline
64 & 0.83 & 0.82 & 0.91 & 0.77 & 0.82 & 0.81 & 0.85 & 0.79 \\ \hline
72 & 0.84 & 0.83 & 0.83 & 0.84 & 0.80 & 0.78 & 0.86 & 0.75 \\ \hline
78 & 0.83 & 0.82 & 0.87 & 0.80 & 0.79 & 0.78 & 0.88 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Defect detection of PCBs for A1 and A2.  
Figure 7: Solder dataset: MCC for A0, A1, and A2.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Comparison with Existing Studies  
The performance of the proposed models is further compared with existing studies that utilized the same dataset for experiments. Table 13 shows the comparison of the accuracy of the proposed CNN with [68; 69; 70; 71]. The study [68] employed an optimized logistic regression for heart disease detection and obtained a 0.85 accuracy score. On the other hand, both [69; 71] made use of the Naive Bayes model and obtained accuracy scores of 0.74 and 0.86, respectively. The authors used a K-NN model in [70] for the same purpose and obtained a better accuracy of 0.92. In comparison, the proposed model obtained an accuracy score of 0.9398 and proves to be better than these studies.
# Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks
## 5 Results  
Our experimental setup uses the previously described mathematical framework to analyze the system performance as a function of several parameters. Moreover, the employed system parameters are described in Tables 1 and 2, which follow typical implementations for underwater acoustic sensors. The proposed transmit strategy was numerically evaluated in terms of the FEC coding rate at L2, for given \(M\) and \(p\), which minimizes the energy consumption for a given target success delivery probability, as a function of either the received signal-to-noise ratio or the transmit link distance. We consider an acoustic link whose system parameters are listed in Table 2, while some of these parameters are taken from the WHOI modem [30; 34]. In this section, all analysis in terms of SNR refers to the average SNR.
# A New \"Doctor and Patient\" Optimization Algorithm: An Application to Energy Commitment Problem
## 3 Doctor and Patient Optimization (DPO)
### Implementation of DPO
#### 5.2.4 Comparison DPO and Other Algorithms on EC Problem  
In order to evaluate the performance of the proposed algorithm in solving the EC, the other eight algorithms mentioned in this paper have been implemented on the EC problem. The results of this simulation are presented in Table 9. This table specifies the value of the objective function for each of the optimization algorithms. The proposed DPO algorithm is the best optimizer among the compared algorithms with the value of the objective function equal to \(2.1153\times 10^{7}\) Dollar. WOA with the value of the objective function \(2.1739\times 10^{7}\) Dollar, MPA with the value of the objective function \(2.2365\times 10^{7}\) Dollar, GWO with the value of the objective function \(2.4257\times 10^{7}\) Dollar, GOA with the value of the objective function \(2.7592\times 10^{7}\) Dollar, TLBO with the value of the objective function \(3.2648\times 10^{7}\) Dollar, GSA with the value of the objective function \(6.7624\times 10^{7}\) Dollar, PSO with The value of the target function is \(5.2158\times 10^{8}\) Dollar, and the GA with the value of the target function of \(8.5146\times 10^{8}\) Dollar are ranked second to ninth, respectively. Based on the results, the proposed algorithm has a high ability to solve the EC problem and is much more competitive than the other eight algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hour** & **Import** & **Export** \\ \hline
**Petroleum** & 0 & \(-\)399,217 \\
**Liquid Gas** & 2451.409 & 0 \\
**Fuel Oil** & 0 & \(-\)29,054.3 \\
**Gas Oil** & 0 & \(-\)478.824 \\
**Kerosene** & 0 & \(-\)297.468 \\
**Gasoline** & 20,384.46 & 0 \\
**Plane Fuel** & 29,34.952 & 0 \\
**Natural Gas** & 12,502.36 & 0 \\
**Coke Gas** & 0 & \(-\)92.2559 \\
**Coal** & 906.6509 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Import and export of carriers (BOE).  
Figure 2: Convergence curve of doctor and patient optimization (DPO) on energy commitment (EC) solving.
###### Abstract  
This study aimed to present a simplified and safe method to reposition the bone segment with easy identification and removing bone interference using a robot arm and image-guided navigation and to assess the accuracy for maxillary orthongathic surgery on phantom skulls. A surgical system consists of a robot arm with specialized end-effector, and image-guided navigation including the optical tracking system. The end-effector was designed to reflect the surgical procedures including identification and removal of bone interference and repositioning of the bone segment. To evaluate the handling and accuracy of this system, 10 phantom-based experiments were conducted according to four surgical plans. Mean absolute deviations at the upper central incisor were \(0.10\pm 0.15\) mm medio-laterally, \(0.05\pm 0.07\) mm antero-posteriorly, and \(0.12\pm 0.15\) mm supero-inferiorly. There was no significant difference in deviations between anterior and posterior regions of the maxilla. The mean root mean square deviation was \(0.18\pm 0.16\) mm, and ranged from \(0.05\) mm to \(0.54\) mm. The robot arm and image-guided navigation assisted surgical system would be helpful to manage bone interferences and reposition bone segments with improved accuracy. Though further technological advances are necessary, this study may provide a basis for developing clinically applicable robot assisted system for orthognathic surgery.  
**Keywords:** orthognathic surgery; maxillary repositioning; robot arm assisted surgery; navigation surgery; end-effector +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Comparison between the Proposed Approach and the Handcrafted Features Approach  
To validate the effectiveness of the proposed method, the handcrafted features approach was used for comparison. An eighteen-dimensional feature vector for each signal mentioned in Section 3.2 became an alternative, substituting original signals to train a classifier. The comparison results are shown in Table 2. The recognition rates in time domain, frequency domain, time-frequency domain and the fusion of the former three are listed respectively in the 'Handcrafted approach' column. It can be seen that testing accuracies in each individual domain are lower than the proposed method, but the fusion performance can slightly outperform the accuracy of the proposed method. However, the handcrafted features approach has to face the problem of severe workload burden with collected data increases. Balancing the efficiency and the accuracy, the proposed method is most suitable for industrial applications. It can be seen that accuracies in the manual extracting features method have a large fluctuation range from 0.796 to 0.981 by employing different features from Table 2. Such a problem can be avoided in the proposed method with good robustness.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No. of the Middle Layer Neurons** & **Accuracy** \\ \hline
10 & 0.958 \\
50 & 0.962 \\
100 & 0.960 \\
150 & 0.965 \\
200 & 0.968 \\
250 & 0.965 \\
300 & 0.970 \\
350 & 0.967 \\
400 & 0.968 \\
450 & 0.970 \\
500 & 0.972 \\
550 & 0.970 \\
600 & 0.972 \\
650 & 0.972 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy variation with the number of middle-layer neurons.  
Figure 6: The convergency curve of loss function during training of the final determined CNNs.
However, there is a scarcity of studies pertaining to this issue, and theoretical research trails behind actual engineering practices. Hence, it is critical to scrutinize the soil settlement patterns of deep foundation pits with pile-raft foundations during the crossing of large-diameter shield tunnels. This will aid in simplifying analysis of the distribution pattern of pile-side frictional resistance under the excavation of large-diameter shield tunnels. To attain the objective, the present study employed a settlement prediction approach that accommodates the engineering traits in constructing a large-diameter shield tunnel that crosses a deep foundation pit with pile-raft foundations. The model's precision was substantiated by means of numerical simulation and on-site monitoring data, and the influence of various construction methods on soil settlement at the foundation pit's bottom was comprehensively scrutinized. Considering the load characteristics of a large-diameter shield tunnel crossing a deep foundation pit, this study explores the soil settlement patterns at the foundation pit's bottom and proposes two correction factors to adjust the traditional settlement curve, factoring in the engineering load characteristics. The amended soil settlement curve at the foundation pit's bottom precisely reflects the results of numerical simulation. This study presents a settlement curve correction technique that accounts for the load characteristics of large-diameter shield tunnels traversing deep foundation pits, providing potential theoretical guidance for analogous engineering projects in the future.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 2 Challenges Posed by the Current Healthcare System
### Problems with Healthcare Information Systems  
The advancements in information communications technology have potential to bring a significant transformation in the healthcare system by connecting medical devices, automating financial transactions and preventing errors to enhance consumer confidence in the health system. However, the healthcare systems are very complex, as they include communication and processing of heterogeneous health information, optimal allocation of available resources and their administrative management simultaneously. Existing wireless technologies (3G, 4G and WiMAX) exploit macro-cells to provide a wider range suitable for lower data rates. These technologies are mainly suitable for smart health monitoring devices, social interactions and wellness monitoring applications [14]. For healthcare systems, we require a heterogeneous wireless technology with multiple frequencies ranges that can provide a guaranteed high data rate and very low latency for medical services like remote surgery.
## 3 Results
### Wound Healing  
Cell migration using the scratch wound assay was measured (Figure 5A) and the percentage of wound migration area at 24, 48 and 72 h is shown in Figure 5B. All blue(r)m oxygen fluid groups (0.6, 1.2 and 2.4%) showed significantly higher wound closure rates at 24 and 48 h than the 0.12% CHX group (\(p\) < 0.01). At every time point, the 0.6% blue(r)m oxygen fluid group showed the highest percentage of wound migration. By 72 h, no significant differences were found among the groups of blue(r)m oxygen fluid and the untreated control. In contrast, CHX exposure significantly impaired cell migration at all time points (24, 48 and 72 h) compared to both the blue(r)m oxygen fluid groups and the control (\(p\) < 0.01). These results implied that while blue(r)m oxygen fluid promoted migration in a concentration-dependent manner, exposure to CHX significantly suppressed the migratory response of HGnF cells.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Experimental Groups**} \\ \cline{2-6}
**In Vitro Assays** & \multirow{2}{*}{**Control**} & \multicolumn{3}{c}{**blue®m Oxygen Fluid**} & \multirow{2}{*}{**0.12\% CHX**} \\ \cline{3-3} \cline{5-6}  & & **0.6\%** & & **1.2\%** & & **2.4\%** \\ \hline Intracellular ROS & \multirow{2}{*}{98.75 \(\pm\) 13.86} & \multirow{2}{*}{104.83 \(\pm\) 7.63} & \multirow{2}{*}{115.09 \(\pm\) 8.79} & \multirow{2}{*}{122.87 \(\pm\) 19.04} & \multirow{2}{*}{1027.01 \(\pm\) 93.36} \\ detection & & & & & \\ \hline Quantification of inflammatory cytokines & & & & & \\ IL-1\(\beta\) & 24.25 \(\pm\) 1.52 & 19.87 \(\pm\) 2.48 & 20.42 \(\pm\) 1.33 & 20.87 \(\pm\) 1.14 & 56.37 \(\pm\) 4.75 \\ IL-6 & 20.42 \(\pm\) 2.15 & 17.98 \(\pm\) 2.52 & 17.77 \(\pm\) 2.83 & 15.17 \(\pm\) 2.35 & 55.48 \(\pm\) 3.15 \\ TNF-\(\alpha\) & 8.56 \(\pm\) 1.46 & 5.55 \(\pm\) 1.36 & 2.81 \(\pm\) 0.82 & 2.67 \(\pm\) 0.69 & 17.45 \(\pm\) 3.34 \\ MMP-8 & 24.52 \(\pm\) 2.26 & 16.01 \(\pm\) 2.49 & 20.19 \(\pm\) 0.68 & 23.78 \(\pm\) 1.26 & 40.03 \(\pm\) 4.54 \\ TIMP-1 & 10.18 \(\pm\) 3.08 & 10.02 \(\pm\) 1.41 & 9.03 \(\pm\) 1.37 & 6.06 \(\pm\) 0.33 & 18.13 \(\pm\) 4.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 4: Cell proliferation of HGnF cells assessed by Trypan Blue exclusion assay following exposure to different concentrations of blue®m oxygen fluid and CHX over the observation period. Data are presented as mean percentage values \(\pm\) SD (n = 9). Statistical analysis was performed using one-way ANOVA with [PERSON]’s post hoc test (\(p\) < 0.05). \(\phi\) statistically significant difference from CHX. CHX: chlorhexidine.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Novelty and Contribution
#### 1.3.3 Dynamic Pilot Number Adjustment to Reduce Overhead  
* Traditional LIS estimation methods [12] rely on a fixed number of pilots, leading to inefficiencies. Our method uses adaptive pilot clustering, where pilot density increases only when needed.
* Our simulations show that adaptive power optimization helps lower the BER in changing environments better than static power allocation.
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 1 Introduction
In this paper, we propose a methodology to evaluate the dependability of Wireless Visual Sensor Networks. This methodology is based on an analytical approach, using Fault Tree Analysis (FTA) and Continuous Time Markov Chains (CTMCs) to model the network behavior, considering battery, communication links, hardware and visual coverage failures, and the impact of different routing strategies on WVSNs. Since the analytical modeling can be a time-consuming task, the entire methodology has been automated by a framework integrated with the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool [13], which is used to solve the models numerically. The proposed methodology significantly improves both the methodologies proposed by [PERSON] et al. [6] to evaluate the reliability and availability of WSNs in industrial environments and by [PERSON] et al. [14] to evaluate availability of WVSNs for target coverage. We improve these methodologies to support an integrated evaluation of WVSNs' dependability, adapted to the context of area coverage monitoring, in a way that it is possible to specify network failure conditions as a function of a coverage objective. The dependability metrics proposed are mainly focused on the availability. However, it is important to notice that they can be easily adaptable to evaluate the reliability, as shown later. The remainder of this paper is organized as follows: Section 2 presents related works regarding dependability on WSNs and WVSNs. In Section 3, the problem formulation is stated and the theoretical background is presented. The proposed methodology is described in Section 4, detailing the modeling and evaluation phases. Section 5 presents and discusses the results according to the evaluation of different WVSN scenarios. Finally, conclusions are stated in Section 6.
###### Abstract  
The mineralization of soil organic carbon (SOC) is a critical process in the soil carbon cycle. This study aimed to investigate the vertical distribution characteristics and mineralization dynamics of SOC in soils and their aggregates across different steppe types in the Loess Plateau (LP). Soil profiles from three steppe types under varying precipitation gradients were selected: meadow steppe (MS), typical steppe (TS), and desert steppe (DS). A 60-day controlled laboratory incubation study was conducted for carbon mineralization and the influence of climatic and soil properties on SOC mineralization was analyzed. The results showed that the SOC content and cumulative mineralization (C\({}_{\text{M}}\)) in 1-2 mm aggregates were higher than in other particle sizes; SOC content and C\({}_{\text{M}}\) followed the order MS \(>\) TS \(>\) DS and both decreased significantly with increasing soil depth. Correlation analysis revealed that precipitation significantly affected aggregate mineralization (\(p<0.001\)) and that mineralization in the 1-2 mm aggregates was more closely related to mean annual precipitation (MAP), SOC, and water-soluble organic carbon (SWOC). Precipitation primarily controlled SOC mineralization in the 0-50 cm soil layer, while SOC mineralization in the 50-100 cm layer was influenced by soil-related carbon content. Structural Equation Modeling indicated that precipitation influences the mineralization of organic carbon in topsoil indirectly through its direct impact on SOC. In the context of global warming, the SOC turnover rate in high-precipitation areas (MS) was faster than in low-precipitation areas (TS, DS), necessitating greater attention to soil carbon dynamics in these regions.  
Loess Plateau; precipitation impact; soil aggregate; soil organic carbon content; accumulated mineralization amount 11 April 2024  
1