esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Evolutionary Grid Optimization and Computer Vision Techniques for Petri Dish Detection
#### 3.3.1 Petri Dish Preprocessing for Fitness Function Evaluation
The parameters are derived manually from a set of training images (accumulator threshold: 50, minimum length: 400 px (10%), maximum gap: 50 px (1.5%). After the Hough Line extraction, closing is used to connect neighboring lines. Additionally, based on an angle that does not appear in real-world scenarios, wrongly detected lines that are not part of the grid are identified as noise and removed. The maximum and minimum angles are defined as \(10/-10\) degrees to allow the capture of a simple picture of the Petri dish without perfect alignment of all utilities. Our experiments further showed a potential benefit in using two different values for vertical and horizontal lines depending on the direction of the light source, which highlights only vertical or horizontal lines. The final extracted binary image contains valid lines of the grid structure in the original image to be used for the fitness function of the evolution strategy. The extracted lines themselves cannot be used to extract the whole grid. Reflections and shadows in the original image reduce the visibility of all lines, and therefore, not all lines could be extracted sufficiently with this approach. The extracted binary image still contains the overall grid structure of the underlying engravings in the Petri dish that can be used as a template to fit an artificially created grid (solution candidate) into the binary mask. For the line detection, multiple parameters are necessary for a successful detection, similar to the circle detection. Besides filtering algorithms used to remove non-vertical or non-horizontal lines, multiple parameters are used for the line detection itself. For the workflow at hand, the most important parameters are the accumulator threshold, the maximum gap, and the minimum length of lines. These also have been evaluated. The above methods effectively extract sufficient horizontal and vertical lines from the Petri dish. This is of utmost importance as it serves as a basis for accurately evaluating possible solution candidates of the evolution strategy algorithm. Furthermore, this process ensures that the assessment is carried out with high precision and accuracy.
## 7 Conclusions and Future Work  
This research presents SafeStride as an IoT device targeted at fall detection and activity logging of assistive walkers by applying machine learning on the IMU sensor data. A walker dataset is developed and shared with the public. Various machine learning algorithms are evaluated using the dataset, and the Random Forest classifier is among the best, which achieves an overall accuracy of 98.59% with no missed falls. The SafeStride system prototype is tested in the lab environment, covering a wider range of fall scenarios. It has demonstrated accurate and reliable performance in detecting falls, counting steps, and motion classification in real time. Additionally, the IoT device connects with cloud resources through wireless communication, further expanding its capabilities and potential for prompt response if a fall event occurs.  
For future work, one promising avenue is to explore the integration of other sensing modalities into the SafeStride system, such as cameras, radars, pressure sensors, etc., to create a more robust and comprehensive fall detection system. Moreover, user feedback and field testing should be pursued to validate the system in real-world scenarios involving various types of falls and environmental conditions.  
An important contribution of this work is the creation of the walker dataset shared on Kaggle [6]. This dataset serves as a valuable resource for training and evaluating machine learning and deep learning models for fall detection. The availability of this dataset to the public also encourages further research and development in the field, thereby driving progress in fall detection and enhancing care for the elderly and individuals with physical disabilities. Overall, the SafeStride system represents a significant step forward in the field of fall detection, harnessing the power of machine learning to provide a more reliable and responsive solution. The results obtained are very encouraging and demonstrate the potential for new advances in IoT for healthcare.  
Conceptualization, M.H.; methodology, M.H. and A.G.; software, M.H. and A.G.; validation, M.H. and A.G.; formal analysis, M.H.; investigation, A.G.; resources, A.G.; writing, M.H. and A.G.; visualization, A.G.; supervision, A.G.; project administration, A.G. All authors have read and agreed to the published version of the manuscript.  
[PERSON] is supported by the Fulbright-SENACYT Scholarship.  
Not applicable.  
The dataset is available at [[https://www.kaggle.com/dsv/6493939](https://www.kaggle.com/dsv/6493939)]([https://www.kaggle.com/dsv/6493939](https://www.kaggle.com/dsv/6493939)), accessed on 18 September 2023.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoT & Internet of Things \\ CNN & Convolutional Neural Network \\ BLE & Bluetooth Low Energy \\ IMU & Inertial Measurement Unit \\ \end{tabular}
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 4 Discussion  
In this work, the models of LCM and TCM RTK together with the ISB estimation were first derived in Section 2. Then, with the observations collected from the IGS, we assessed the performance of signal quality in BDS-3/GPS/Galileo in terms of pseudo range multipath and TF phase combination by computing the statistics with an elevation interval of 2\({}^{\circ}\). The results showed the better performance of B2a/L5/E5a in pseudo range multipath than B1C/L1/E1. Specifically, the RMS of pseudo range multipath of B2a/L5/E5a is 0.1-0.2 m lower than that B1C/L1/E1 for all the considered elevations. As for the TF phase combination, with the three different frequencies used, we compared its RMS in different GNSSs. With the IFCB corrected, the largest RMS of TF phase combination appears in Galileo for all the elevations. The RMS of BDS-3 remains at a similar level to GPS when the elevation is higher than 40\({}^{\circ}\). However, the lowest RMS appears in BDS-3 when the elevation is lower than 40\({}^{\circ}\). Then, the short- and long-term stability of ISBs was evaluated with identical types of receivers. For the short-term ISB, both phase and code ISBs had a low STD within the precision of observations and a zero mean. Thus, it is reasonable to regard the ISB between identical types of receivers as a constant with continuous observations. Further research was conducted on the stability of long-term ISB. The PDF and the QQ plot demonstrated the stability of the long-term ISBs of both phase and code observation. The ISBs fit with the zero-mean normal distribution so well that we can regard the ISBs as random noise and ignore them in inter-system DD observations. The mean value and STD within the day and the boxplot within the month were also analyzed to validate the long-term stability of ISBs. The results showed the mean value of phase ISB is close to zero during the period whereas that of code ISB varies within 10 cm. In addition, the STDs of all the phase ISBs is around 0.01 cycles on various days, whereas that of code ISB is within 0.2 m between B1C/L1/E1 and 0.12 m between B2a/L5/E5a. In the box plots, even the outliers remain lower than 0.06 cycles for phase ISB and 1.6 m for code ISB. The statistics of the ISBs demonstrate that the ISBs can be regarded as a fixed bias that can be calibrated in advance and corrected in the inter-system DD observations. In addition, the zero-mean characteristics of ISBs between identical types of receivers mean the ISBs can be corrected by ignoring them directly. Finally, we assessed the performance of RTK positioning with different models. The comparison of SS RTK with TS RTK demonstrated that when more satellites are applied, a better configuration of the mixed constellation is obtained, so the positioning of both the float and fixed solutions and the SR of the ambiguity resolution are much higher in TS RTK. With TS applied, 100% SR can be achieved, but the SR of SS RTK remains under 80%. However, with increasing cutoff elevation angle, the position accuracy and the SR decrease
## 4 Results and Discussion
### Experimental Result Discussion  
The above results have presented the impacts of different precise orbit/clock products, the hybrid weight function, and the mixed-frequency GNSS measurements on the smartphone PPP in both the static and dynamic experiments. As is shown in Tables 4 and 5, the positioning accuracy of the smartphone PPP based on the mixed-frequency GNSS measurements is higher than the smartphone PPP based on single-frequency GNSS observations and double-frequency GNSS observations. Also, the smartphone PPP based on the hybrid weight function is more effective in identifying GNSS data quality than the elevation-dependent weight function and the C/N\({}_{0}\)-dependent weight function. Meanwhile, based on such a smartphone PPP model, the impacts of different precise orbit/clock products on positioning have illustrated that the main influences of the BDS PPP-B2b products on smartphone PPP are in the vertical direction, with a slight impact in the horizontal component. This proves that the BDS PPP-B2b service-based smartphone PPP can be used to provide high-accuracy positioning services for these applications, focusing on the horizontal positioning accuracy.
## 5 Conclusions  
This manuscript has been developed to implement risk and reliability analysis of DTs, using failure data for maintenance planning, scheduling, and optimization by using digitalization of DT failures. The results obtained with each Weibull analysis have been compared using the MLE metrics. Digitalization of the DT data helps to determine the most suitable repair/replace strategy for DTs to improve reliability while decreasing the risk of unavailability, by making more confident data-driven decisions. Digitalization can be carried out using CMMS. From the digitalization of DT-failure data, a maintenance or reliability manager can identify maintenance strategies by leveraging historical data to improve the reliability indices (SAIDI, SAIFI, CAIDI, and ENS) of power-distribution systems without increasing costs. Furthermore, scarce resources could be managed with this approach depending on the electricity supply contracts. Hence, this methodology can be implemented at an early stage for digital transformation of power-distribution systems.  
Future Works  
Future research should be carried out to evaluate the application of the other probability-distribution functions that fit the failure data of DT failures and the failures of other major components in power-distribution systems for data-driven decision-making using CMMS. This would help to adopt the optimum maintenance scheduling for power-distribution systems aiming for digital transformation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Transformer** & **Log Likelihood** & **AIC** & **BIC** & **AD** \\
**Category** & & & & & \\ \hline
11 kV to 400 V & \(-\)13.1355 & 42.2709 & 29.0435 & 2.91429 \\
100 kVA & \(-\)26.59 & 59.5801 & 12.7236 & 2.25092 \\ \hline
11 kV to 400 V & \(-\)65.1467 & 135.093 & 136.074 & 1.48386 \\
250 kVA & \(-\)22.1699 & 51.3398 & 48.1316 & 2.2968 \\ \hline
11 kV to 400 V & \(-\)9.18554 & NA & 20.5683 & 3.76559 \\
630 kVA & \(-\)9.18554 & 46.6682 & 42.2517 & 2.17347 \\ \hline
33 kV to 400 V & \(-\)19.3341 & 46.6682 & 42.2517 & 2.17347 \\ \hline
33 kV to 400 V & \(-\)151.298 & 306.889 & 310.165 & 0.627865 \\
160 kVA & \(-\)141.136 & 286.58 & 289.747 & 0.655464 \\ \hline
33 kV to 400 V & \(-\)67.2275 & 139.205 & 140.344 & 1.05074 \\
33 kV to 400 V & \(-\)28.8044 & 64.0089 & 61.7678 & 1.92899 \\
630 kVA & \(-\)12.2087 & 40.4173 & 27.1899 & 2.99284 \\ \hline
33 kV to 400 V & \(-\)19.3341 & 46.6682 & 42.2517 & 2.17347 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Goodness of fit for the Weibull analysis of DT-failure data.  
**Author Contributions:** Writing--original draft, methodology, software, validation, and formal analysis, A.M.S.R.H.A.; writing--review, editing, and supervision, R.M.C.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Rapid Synthesis of Silver Nanowires in the Polyol Process with Conventional and Microwave Heating
## 2 Materials and Methods
### Characterisation of Materials  
The morphology of fabricated materials was examined by an SEM microscope, TM 3000 (Hitachi, Tokyo, Japan), working at an accelerating voltage of 15 kV. A drop of the suspension was placed on a plate, dried at room temperature and then visualised. The diameter of AgNPs was determined using the image analysis software ImageJ. The average diameter and length of investigated structures were determined based on an analysis of 50 and 30 objects, respectively, with an aspect ratio greater than 10, observed in an SEM image. For HRTEM analysis of the product, a JEOL JEM 3010 field emission microscope working at an accelerating voltage of 300 kV was applied. The UV-Vis spectra of post-reaction mixture samples were recorded with a U-2800A spectrophotometer (Hitachi, Japan) in a quartz cuvette. Investigations into the crystalline phase were performed using X-ray diffractometry (XRD), with a X-Pert Pro device (Phillips, the Netherlands) and Cu K\(\alpha\) radiation (\(\lambda\) = 0.15418 nm). The results were analysed with the software Match! 3.1. Energy dispersive spectroscopy (EDS) analyses were carried out in a Phenom ProX SEM microscope (ThermoFisher Scientific, Waltham, MA, USA) to determine the atomic composition of selected samples of the synthesis product. The concentration of Ag(I) ions in the post-reaction solution was determined with an Eag/S-01 ion-selective electrode (Hydromet, Gliwice, Poland). The total conversion \(\alpha\) was calculated from the equation:  
\[\alpha=\frac{c_{i}-c_{f}}{c_{i}}\cdot 100\% \tag{2}\]  
where \(c_{i}\), \(c_{f}\) stand for, respectively, the initial and final Ag(I) concentrations.
# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 3 Preliminary  
### Convolutional Neural Networks (CNN)  
CNNs are very common in the field of image classification. They achieve high accuracy in many classification tasks by automatically detecting the features in a dataset of images. CNNs are better compared to traditional methods, where the filters that enable feature extraction have to be specified before computation. A typical CNN consists of four layers: a Convolution layer, Pooling layer, ReLU layer and Fully Connected layer. Each layer and its function is described here briefly. The CNN architecture, in general, is shown in Figure 1. A convolution layer extracts features, a pooling layer combines these features and reduces dimension, ReLU layers remove negative values, and the fully connected layers are responsible for classification.  
The object-detection CNN architecture used in this paper comprises of two parts--a feature extractor and a bounding box predictor.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Network Administration
#### 4.4.3 Spectrum Administration  
Spectrum administration involves efficiently allocating the electromagnetic spectrum for users in the wireless network to reduce interference and optimize utilization of the spectrum. In spectrum management, radio frequency spectrum is efficiently allocated and regulated for the better functioning of communication services.  
As a KDN can be operated by numerous mobile network providers, a Spectrum Management Scheme (SMS) for seamless handover among numerous mobile network providers with the minimum experience of disruption and delay has been demonstrated by employing blockchain and deep reinforcement learning for spectrum management [302]. Alternatively, another work presents the spectrum administration concept for next-generation 6G mobile networks to efficiently handover spectrum dynamically among multiple network operators driven by the KDN concept to centralize network control while at the same time achieving efficient handover using blockchain and machine learning (long short-term memory)-enabled workflow [303]. Additionally, spectrum access in Cognitive Radio (CR) IoT networks is accomplished via a combination of detection of malicious users using decision tree-based machine learning, a spectrum assigning procedure, a bilateral verification mechanism, and an agreement method for the federated blockchain, created utilizing global-level controllers [304]. Furthermore, a Digital-Twin Edge Network (DITEN) spectrum assignment framework utilizes blockchain-based federated learning to strengthen security with reduced QoS signaling overhead and eliminated centralized mediators, while reinforcement learning is leveraged to allocate spectrum resources [305]. Moreover, research in [306] studies and discusses the tokenization model, distributed ledger, and consensus algorithms along with Recurrent Neural Networks (RNNs)-based AI for intelligent spectrum sharing while studying the effect of the type of smart contract for spectrum trading in next-generation intelligent networks. Likewise, a Spectrum Sharing Algorithm (SSA) has been proposed by using blockchain to authenticate users and record transactions in a secure manner using cryptography along with an Extreme Learning Machine (ELM)-based spectrum sensing approach, while detecting and blocking malicious users in cognitive radio networks [307].
# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
## 1 Introduction
The proposed framework's effectiveness was assessed using the widely adopted IEC 60870-5-104 traffic dataset, which includes various types of malicious attacks such as DOS, Switch, Injection, ConnLoss, and Rogue Dev. We employed four statistical indices--accuracy, precision, F1-score, and the Area Under the Curve (AUC)--to compare the discrimination accuracy of the considered methods. The experimental results demonstrate that the proposed approach outperforms standalone algorithms, with the AE-GRU-based LOF method achieving the highest detection rate. This underscores the potential for the proposed approach to enhance the cybersecurity in smart grids by accurately detecting and mitigating cyber threats. The remaining sections of this paper are structured as follows: Section 2 discusses the related works on deep learning-based detection approaches for cyberattacks in smart grids. Section 3 provides a detailed description of the proposed framework. The performance evaluation experiments and detection results are presented in Section 4. The paper concludes in Section 5, followed by an outline of future work.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 8 Solution of Challenges  
IoT-based drug delivery presents a number of challenges that must be overcome in order to fully realise its potential. One of the most critical challenges is ensuring the security and privacy of patient data. This requires the development of robust security measures, including encryption, authentication, and access control, to safeguard patient information from unauthorised access or data breaches. Interoperability and standardisation of IoT devices and platforms are also necessary to improve compatibility and facilitate seamless communication between devices, ultimately reducing costs and improving performance.  
Another challenge is compliance with regulatory frameworks to ensure patient safety and build trust in the technology. This includes adhering to various regulations and standards such as HIPAA, GDPR, and ISO 13485, which provide guidelines for data privacy, security, and quality management. Additionally, the accuracy and reliability of IoT devices must be ensured through testing and validation processes to ensure the technology can be relied upon to deliver effective and safe healthcare.  
To manage the deployment and maintenance of IoT devices, healthcare organisations must invest in adequate staffing and training. This can help to ensure that the devices are managed properly and patient data remains secure and confidential. Ethical and social considerations should also be addressed to ensure that IoT-based drug delivery systems align with the values and needs of patients, healthcare providers, and other stakeholders.  
While the benefits of IoT-based drug delivery are clear, the cost of developing and implementing such systems can be a significant barrier for many healthcare organisations. To manage costs, it is crucial to adopt better approaches to cost modelling, outlining the expenses associated with hardware and software architectures, and ensuring proper cost estimation.  
Connectivity and reliability are also crucial for IoT-based drug delivery systems. Designers must prioritise connectivity and testing during the early stages of development to ensure that the devices can function continuously without any failures. The use of fog computing can also help to reduce data processing delays that can occur due to the long distance between IoT sensors and data processing devices. By providing faster data processing and reducing reliance on cumbersome cloud storage, fog computing can improve the quality of service and make the IoT more useful for healthcare by allowing doctors to make better-informed decisions.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 7 Correlation Analysis
### SHAP Feature Importance Analysis  
SHAP feature importance analysis revealed that wall thickness, weld type, and pressure were the most dominant factors affecting fatigue life predictions (Figure 11). These findings align with material science principles, where thinner sections experience higher stress concentrations, which accelerate fatigue crack initiation. Similarly, welds introduce residual stresses and discontinuities that significantly reduce fatigue resistance. The high feature importance assigned to pressure reflects its role in influencing hoop stress, which contributes to fatigue damage over multiple cycles. These results confirm that ML models are not only data-driven but also align with established fatigue life theories.