esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach
## 4 System Architecture
### Sensors  
#### 4.1.1 Ublox NEO-6M GPS Module  
This module is a high-performance, compact GPS receiver designed to provide accurate position data. It operates by receiving signals from multiple GPS satellites orbiting the Earth. The module uses its antenna to capture these signals, which include each satellite's location and time information. It then processes the signals to calculate the distance between the satellites and the receiver. Using this distance information along with the known satellite positions, the NEO-6M triangulates the receiver's position on the Earth's surface. The module outputs the calculated position and time data via serial communication (UART), which can be utilized by connected devices, such as a robot car, for navigation and tracking purposes. Tables 1 and 2 shows the default configuration of this module and the default configuration of the NEO-6M GPS module respectively.  
Furthermore, a computer application, u-center, is available, allowing connection to the GPS module via a TTL to USB connector. This application enables the configuration of various parameters of the module to meet specific requirements. Figure 6 shows the interface of the u-center application connected to the GPS module.  
Figure 5: System architecture diagram.
# Numerical Analysis of Friction Reduction of Grouting in Long-Distance Pipe Jacking
## 3 Case Study
### Overview of the Numerical Model  
In an electric utility corridor project located in a soft soil area, \"F\"-type steel reinforced concrete pipes with an outer diameter of 3600 mm and an inner diameter of 3000 mm are planned for use. \"F\"-type-steel-reinforced concrete pipes are named after the F-type joint that they use. Due to their excellent sealing performance and durability, they are widely used in urban engineering projects [19] as shown in Figure 5. Each pipe section is 2.5 m in length, with a total jacking length of 625 m. The soil layer of the pipe consists of silly clay.  
The finite element software PLAXIS 3D (Edition 2017) is used for the calculation. Considering that the impact range of the tunnel construction is 3 to 5 times the larger value between the height and width of the tunnel section, the model's soil domain is set to a height of 43.6 m (the depth is a boundary between two strata) and a width of 60 m to eliminate boundary effects. The ten-node elements are set. In order to consider the different effect of the grout, the interface reduction coefficients are set to different values.
# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 3 Experiment Results
### Detecting the Breast Cancer Area
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AP (Benign)** & **AP (Malignant)** & **Best PF1** & **Best PF1** & **Best Threshold** & **Best Threshold** \\  & & **(Benign)** & **(Malignant)** & **(Benign)** & **(Malignant)** \\ \hline ROI-SSD [26] & 0.77 & 0.82 & 0.75 & 0.77 & 0.55 & 0.55 \\ ROI-RPN [27] & 0.75 & 0.80 & 0.73 & 0.75 & 0.54 & 0.54 \\ ROI-RPCN [28] & 0.73 & 0.78 & 0.71 & 0.73 & 0.52 & 0.52 \\ Ours & 0.81 & 0.86 & 0.79 & 0.81 & 0.56 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average results across all datasets. threshold to the gradCAM image. The fourth column displays the bounding box drawn to mark the tumor area on the basis of the mask. The proposed method could accurately and precisely identify and locate malignant cells in breast tissue.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Accuracy
#### 3.2.2 Approach  
The accuracy for the reflective measurement technologies was assessed using the Mystery panels and the Permaflect panels. Figures 9 and 11 show the spectral profile of the Permaflect panels and Mystery panels, respectively. Calibration for the Mystery panels was available at 11 specific wavelengths. So, these were used to evaluate ASD and UAS measurements in a limited hyperspectral space. Calibration of the Permaflect panels was  
Figure 10: Traceability for reflective instruments (**top**) and thermal instruments (**bottom**).  
available from 350 to 2500 nm. So, for these targets, banded accuracy measurements were made to replicate the Landsat OLI spectral bands.
## 2 Materials and Methods  
Currently, low strain integrity testing [43; 44] is one of the most common methods used for pile integrity control. This method of non-destructive control is based on the principle of acoustic flaw detection, founded on the physical phenomenon of elastic wave propagation--namely, the analysis of the passage and reflection of an acoustic wave in a pile [45; 46; 47]. The elastic wave is generated with handheld hammer impact on a pile top that is dry and clean. The wave spreads along the pile's body and reflects off the pile defects, as well as from the pile toe. The reflected waves are recorded by a sensor mounted on the pile head and further processed by special computer software.  
Although this method is widely used and has clear advantages, it also has a number of limitations [18; 48]. For example, small defects, inclusions, and minor changes in the pile cross-section cannot be detected using this method. The acoustic properties of the surrounding soil also affect the accuracy of the pile length determination. In addition, in most cases, the test method does not allow for estimation of the pile integrity after the first significant signal anomaly.  
This work proposes a method of cluster analysis, the so-called ANN classifier, for interpretation of results and identification of pile defects. An important feature of the proposed solution is the data set proposed for use in creating the ANN classifier. The aggregation of this dataset will be based on the characteristic points of the signal spectrum obtained from the sensors. In this paper, we suggest using the peak points of the signal spectrum as such points. The principle of finding characteristic points is used in related fields, such as power engineering [49; 50], medicine [51; 52], and so on.  
The development of an ANN classifier. Data processing was conducted using Matlab software and the Classification Learner application. This software is widely used for various engineering applications [53; 54].  
The proposed method has low computational complexity and can be realized on inexpensive equipment, allowing for quick interpretation of pile integrity test results in the field.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 9 Conclusions  
The best PLC technology must be identified and carefully selected for Smart Metering. Not all PLC technologies are created equal and not all PLC technologies should be judged by the poor performance of some of them.  
The key parameters for evaluation of the communication performance of a Smart Metering network are data rate (throughput), robustness (availability and stability), noise immunity, achievable communication distances, and adaptation of the communication paths to network topology changes.  
The framework for PLC communication simulations in NS-3 was chosen because it was freely available, a new extension in the form of implementation of G.hn standard was provided among the PLC research community, and also libraries for a large number of communication technologies are implemented in NS-3. Thanks to implementation of multiple technologies in NS-3, for example, the simulation of PLC for the access part and LTE for the transport part of Smart Grid networks could be provided.  
This article shows an evaluation of the throughput and influence of particular parameters on the throughput via simulation in NS-3. The achievable communication distance for the real topology scenario was evaluated via simulation and measurements. The on-field measurements of BB-PLC show a maximum distance for successful BPL communication without a repeater of 339 m in on-field very noisy conditions (worst-case noise scenario).  
In the simulation of basic topologies, it was shown that the attenuation of the power line is proportional to the length of the power line, and noise sources will have the largest impact on the capacity. The simulation also shows the significant influence of the overhead power line and the transition between underground and overhead power lines on the throughput.  
The main contribution of the article is the proposal of the number and position of the repeaters for a real topology thanks to the simulation, also supported by the verification of simulation results with real on-file measurements. According to results (the number and position of repeaters), the necessity of repeaters in the transition between underground and overhead power lines is significant.  
The future work will be focused on implementation of the open-source G.hn model in the NS-3 simulator [64]. This G.hn model includes the repeating function, therefore simulations of long line network topologies (e.g., network for electricity metering purpose) will be provided.  
Conceptualization, P.M.; formal analysis, P.S.; investigation, P.M.; methodology, P.M., R.F., and J.S.; validation, P.S.; writing--original draft, P.M.; writing--review and editing, J.M., P.S., and Z.H.  
The research described in this paper was financed by the National Sustainability Program under grant LO1401. For the research, infrastructure of the SIX center was used.  
The authors declare no conflict of interest.
# A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
## 4 Experiments and Results
### Pathology Detection Accuracy  
Apart from the ten-fold cross validation tests carried out in the designing phase of the tool, the goodness of detection was also assessed using voice and query data from 45 unseen subjects (nine normal and 36 pathological). The normal group consisted of four men and five woman while there were 13 men and 23 women in the group of pathological voices. This set of data was collected after the designing process was completed. Figures 3 and 4 summarize results of these additional tests, where DET and ROC curves as well as EER and AUC values are presented. The EER points on the DET curves are given by the intersection points of the curves with the diagonal shown by the dashed line (see Figure 3).  
Figure 2: A fragment of the decision tree created using the six audio parameters and the GFI (Glottal function index) parameter.  
The obtained EER values are very encouraging bearing in mind the simplicity of the techniques: EER of 11.1% using association rules, 10.3% using the decision tree and 9.5% when combining (via weighted averaging) results obtained from both the rules and the tree. The combination weights were set to be proportional to the detection accuracy obtained in the designing process based on the ten-fold cross validation. As expected, a combination of the two data modalities improves pathology  
Figure 4: ROC (Receiver operating characteristic) curves and AUC (Area under the curve) for unseen voice and query data.  
Figure 3: DET (Detection error trade-off) curves and EER (Equal error rate) for unseen voice and query data.  
detection accuracy. As can be seen from the DET and ROC curves, the combined classifier is not only the most accurate classifier around the EER operating point, but also shows the lowest false alarm probability (or highest specificity) near the low miss probability (or high sensitivity) mode of operation, which can be considered as an appealing property when screening for disorders in preventive health-care.  
The ten-fold cross validation performed on the data sets described in Sections 2.1 and 2.2 resulted in EER of 17.3% for the voice data, 12.7% for the query data and 11.8% when combining results obtained from the two modalities via weighted averaging. The higher error rates obtained for these much larger data sets are expected due to larger diversity of the data. A larger fraction of mild laryngeal pathology cases were present in these larger data sets compared to the set of data collected after the designing process was completed. Cases of mild laryngeal pathology are usually more difficult to distinguish from the normal (healthy) ones compared to the cases of more severe pathology.
## 5 Simulation Results and Analysis
### Communication Performance Analysis  
Like radar performance, communication performance, too, is evaluated by two figures: the first figure gives a comparison of the desired communication waveform and the far-field synthesized waveform, whereas the second figure gives the SER versus the SNR.  
The waveform synthesized in the communication direction is shown in Figure 11. As with radar waveforms, the synthesized and the desired communication waveforms seem almost exact. The sample errors in this case, Figure 11 (lower), are defined as  
\[\mathbf{e}_{C}=\mathbf{x}_{C}-\mathbf{a}(\theta_{C})\mathbf{S}. \tag{41}\]  
Figure 9: Synthesized radar waveform: (**Upper**) desired waveform vs. far-field synthesized waveform, (**Lower**) difference between the desired waveform vs. far-field synthesized waveform. The asterisk sign (*) represents vector-matrix multiplication.  
Figure 11: Synthesized communication waveform: (**Upper**) desired waveform vs. far-field synthesized waveform, (**lower**) difference between the desired waveform vs. far-field synthesized waveform.  
Figure 10: Comparison of different methods: detection probability vs. SNR.  
Figure 12 shows the SER plotted against the SNR. Again, for comparison, the SER versus SNR graphs of other methods are provided. The '2 bits per symbol' graph represents the theoretical values. Again, the graphs of FFRED-40% [56] and the proposed ADMM method are very close, although FFRED-40% has a relatively better performance. Both methods outperform the directly normalized waveform method [26].
## 3 Experimental Preparation
### Indicators for Experimental Evaluation  
The evaluation metrics used in this study include precision (P), recall (R), mAP50, mAP50-95, the number of parameters, and detection speed. A confusion matrix is employed to calculate the differences between actual and predicted values, serving as a critical tool to assess the performance of target classification. Precision, recall, and the number of parameters are fundamental metrics typically measured using the confusion matrix, which is based on true positives (TPs), true negatives (TNs), false positives (FFs), and false negatives (FNs). The confusion matrix drawn from the true and predicted values of the dataset is shown in Figure 6, illustrating the model's classification performance across different classes.  
Figure 5: Augmented-image datasets.  
Figure 6: _Cont._  
As illustrated in Figure 6, the improved network maintains a 100% classification accuracy for the \"background\" category while successfully increasing the recognition accuracy of the \"cigarette\" category from 0.83 to 0.85 and reducing the misclassification rate from 0.17 to 0.15. This indicates that the optimization strategy has effectively enhanced the model's ability to identify the target category while ensuring stable background detection. Further improvements can be explored to minimize misclassification and enhance recall, ultimately boosting the overall model performance.  
The comprehensive performance of the model was evaluated using mAP50, with an IOU accuracy threshold set at 0.5. Accuracy is defined as the ratio of correctly predicted instances to the total number of positive instances in the dataset. The mathematical expression for accuracy is provided below:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{11}\]  
Recall measures the ability of a model to correctly identify in all samples. The mathematical expression for recall is given below:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{12}\]  
TP represents the number of positive category samples correctly detected by the model, FN represents the number of positive category samples missed by the model, FP denotes the number of negative category samples incorrectly detected as positive, and TN refers to the number of negative category samples correctly identified by the model. Assuming that the number of detection categories is n, the mathematical expression for the model's average precision (mAP) across all categories is given as follows:  
\[\text{mAP}=\frac{\sum\limits_{\text{i=1}}^{\text{n}}\text{AP}_{\text{i}}}{ \text{n}} \tag{13}\]
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 1 Introduction
Meanwhile, the two sets for the server are tasks \(T~{}=~{}\{t_{1},~{}t_{2},~{}t_{3},~{}\ldots~{}t_{n}\}\) and workers W= \(\{w_{1},~{}w_{2},~{}w_{3},~{}\ldots~{}w_{m}\}\). The requesters send a query (SC-Query), which includes the task and its constraints, to the SC server. Then, any available workers would consequently send their locations to the server. Thus, each requested task is assigned to a certain worker by the server. The workers should be assigned tasks in a manner that achieves the maximum number of completed tasks by minimizing the total traveled distance by the worker to complete said tasks. Applying this scenario to the accident reporting application mentioned previously will lead to covering a large number of accidents with minimal travel costs. At the same time, it will allow for distributing tasks between workers fairly in order to avoid the overload of the workers and to reach a balanced workload among them. Indeed, the worker must move within a certain plan and in a perfect way between the different locations to complete their assigned tasks. Therefore, the server must achieve optimal scheduling based on the three objectives mentioned earlier. With the above in mind, we propose the MOTSO model to deal with the conflicting objectives in order to achieve optimal task scheduling; the assumptions underlying our model are that workers are volunteers, that each worker should complete one task only, and that all workers are reliable. We tested our model using real and synthetic datasets, and we conducted extensive experiments to evaluate the performance of the MOTSO model. The results of our experiments show the effectiveness of the proposed model for small- and large-scale environments.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT in the Sea  
#### 3.3.1 Internet of Ships (IoS)  
The IoS refers to the interconnection of a large number of smart, physical devices or infrastructures associated with ships, ports, or maritime transportation systems, aiming at improved shipping industry in terms of efficiency, safety, and environmental sustainability. The IoS has found its applications a broad spectrum of scenarios, including collaborative decision making, route planning and optimization, cargo tracking, safety enhancements, automatic fault detection, preemptive maintenance, energy-efficient operations, automatic berthing and environmental monitoring [41].  
There have been many recent developments in the IoS. Fault diagnosis in IoS is something that has been studied and handled through the use of deep learning to improve performance in shipping. In [191] the authors propose a fault tolerance scheme that uses a privacy-preserving federated learning approach. The scheme is able to, without data leakage, manage shipping agents to develop a model by sharing parameters. Federated learning is used to help with a lack of insufficient data to help in training a deep learning model. To protect fault data, the authors encrypt the parameters using a Paillier-based communication scheme. The fault diagnosis is handled by a control algorithm. This algorithm is adaptive and changes the model for training. Analysis of the method proves this to be an effective fault diagnosis scheme when tested on a fault dataset.  
The Internet of Ships has not been studied from a security perspective in the current literature.
## 3 Methods
### Improved DWA Algorithm
#### 3.2.3 Simulation Experiment
In the Scenario 3 comparison experiment, as shown in Figure 10, where the red rectangle represents the start point, and the yellow circle represents the target point. Firstly, the distance between the vehicle and the obstacle and the target point is relatively large, resulting in a larger \(\alpha\) and \(\gamma\), and a moderate \(\beta\), and the vehicle quickly moves towards the target point. As the obstacle slowly approaches, the output is a larger \(\beta\), a moderate \(\gamma\), and a smaller \(\alpha\), giving priority to ensuring the obstacle avoidance needs of the vehicle. Finally, when the distance between the vehicle and the target point and the obstacle is small (about 370 iterations), the fuzzy controller outputs a moderate \(\alpha\) and \(\beta\), and a smaller \(\gamma\), to ensure the safety of the vehicle while approaching the target point. From Table 4, the average planning time of the DWA algorithm is 225.044 s, and the average planning time of the improved DWA algorithm is 232.528 s. In the three experimental environments, the average path length of the improved DWA is shortened by 5.90%, 2.02%, and 3.45% compared to the DWA, respectively. From the above analysis, it can be seen that, although the improved DWA algorithm sacrifices a certain amount of time, the environmental adaptability of the algorithm is enhanced, and its success rate in the three environments is very high.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.3.6 Random Forest  
Random forest (RF) is a novel machine learning algorithm combining [PERSON]'s \"bootstrap aggregating\" idea and [PERSON]'s \"random subspace method\" [31]. An RF classifier contains many decision trees, and each tree is grown from a bootstrap sample of the response variable. The best split is selected from a random subset of variables at each tree node and then grows the tree to the maximum extent without pruning. Predictions can be made from new data by aggregating the output of all trees. RF is effective and quickly deals with a large amount of data.  
The number of spanning decision trees (ntree) and the number of selective splitting attributes (mtry) in random forest models directly affect the accuracy of the results. Generally, with the increase of the number of ntree, the test error will gradually decrease, but if the ntree is too large, the model will over-fit. Mtry represents the maximum number of features randomly selected by each node of the decision tree. In the traditional decision tree model, mtry reduces the diversity of a single tree. However, RF is based on the idea of ensemble learning. Reducing mtry will not only improve the algorithm speed but may also reduce the test error. This is also an improvement of the RF model based on the Bagging ensemble learning method. Generally, the cross-validation method is used to select ntree and mtry to obtain more suitable values [32].
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 4 Simulation and Discussion  
The simulation of the entire system was carried out in the MATLAB 8.3 Simulink environment. The PI speed controller, fuzzy controller, adaptive hybrid fuzzy-PI controller, and ANFIS controller-based simulation of the five-phase PMBLDC motor drive were designed using its mathematical model. The Simulink view of the ANFIS speed controller was implemented to drive a five-phase PMBLDC motor, as shown in Figure 5. The FIS system was developed using the file fpinew. The tuned values of the proportional gain constant \(\mathrm{Kp}=1/800\) and the Integral gain \(\mathrm{Ki}=1/200\) were found using the Ziegler-Nichols method.  
The simulation was carried out for two different loading conditions, such as step load variation and linear load variation, as discussed in [25].