esa-sceva/satcom-chunk-collection huggingface dataset:
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Localization Accuracy  
This subsection simulates the localization accuracy of the proposed algorithm under the influence of different errors. Set \(\sigma_{\mathbf{\theta}}=0.2\sigma_{1}\), \(\sigma_{\mathbf{\varphi}}=0.3\sigma_{1}\), and \(\sigma_{\mathbf{r}}=0.3\sigma_{1}\), where \(\sigma_{1}\) is the localization parameter error. Let \(\sigma_{\mathbf{h}}=0.5\sigma_{2}\) and \(\sigma_{\mathbf{s}}=0.2\sigma_{2}\), where \(\sigma_{2}\) is the systematic error parameter.  
**Remark 3**: _The effects of the signal-noise ratio (SNR) and propagation channel will ultimately affect the estimation accuracy of the DOA and TDOA. Therefore, the error parameters \(\sigma_{1}\) and \(\sigma_{2}\) can ultimately reflect the individual errors. This paper focuses on the localization algorithm and thus mainly simulates the effect of the DOA error and TDOA error on the positioning accuracy._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & **1** & **2** & **3** & **4** & **5** \\ \hline
**Longitude** & 119.27\({}^{\circ}\) E & 114.03\({}^{\circ}\) E & 114.54\({}^{\circ}\) E & 112.54\({}^{\circ}\) E & 116.00\({}^{\circ}\) E \\
**Latitude** & 26.05\({}^{\circ}\) N & 30.58\({}^{\circ}\) N & 38.04\({}^{\circ}\) N & 33.00\({}^{\circ}\) N & 29.71\({}^{\circ}\) N \\
**Reflection height** & 365.00 & 385.00 & 350.00 & 390.00 & 370.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Longitude and latitude (E: east, N: north) and the corresponding ionospheric reflection height for land-based stations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite** & **1** & **2** & **3** & **4** & **5** \\ \hline
**Longitude** & 124.32\({}^{\circ}\) E & 118.35\({}^{\circ}\) E & 120.87\({}^{\circ}\) E & 116.56\({}^{\circ}\) E & 122.54\({}^{\circ}\) E \\
**Latitude** & 30.57\({}^{\circ}\) N & 29.19\({}^{\circ}\) N & 32.65\({}^{\circ}\) N & 27.32\({}^{\circ}\) N & 29.62\({}^{\circ}\) N \\
**Altitude (km)** & 1000.00 & 1000.00 & 1200.00 & 1200.00 & 1200.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Longitude and latitude (E: east, N: north) and the corresponding altitude for satellites.  
Firstly, set \(\sigma_{h}=3\) km and \(\sigma_{\bullet}=2\) km and simulate the localization accuracy with \(\sigma_{1}\). Figure 5 is the localization result. Then, let \(\sigma_{\emptyset}=1.5^{\circ},\sigma_{\varphi}=2.25^{\circ}\), and \(\sigma_{\mathbf{r}}=0.5\) km, and Figure 6 is the corresponding result.  
From Figures 5 and 6, the positioning accuracy of the proposed ICWLS algorithm for land-based DOA localization and satellite TDOA localization can reach the corresponding CCRLB. In addition, it can be seen that the fusion result can significantly improve the localization accuracy, which further proves the effectiveness of the proposed fusion method.  
Figure 4: Localization results and uncertainty error ellipses.
# RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities
## 3 The Proposed Model
### Collection of Maintenance Data  
Generally, maintenance data includes different attributes such as the location, equipment number, equipment sub-section number, maintenance type maintenance start date, date of maintenance, end of maintenance, the spare parts used, quantities of the spare parts, description of the repair or replacement, etc. This information is stored in a maintenance database that is also linked to an integrated hardware management or enterprise resource planning (ERP) database. The data cleansing process removes the unwanted records or orphan records that have been entered by users incorrectly or are unnecessary because of a system error. After cleanup, a separate table or database view is created to store these clean data for classification, forecasting, or trend analysis of other mining activities.  
Only the maintenance date information for a single device is used to retrieve the sequential patterns. The complexity of generating the sets of items or the pattern combination increases as the number of items increases. In the case of sequential pattern mining, the possible number of models using Generalized Sequential Models (GSP) extraction [17] is \(n^{2}+n(n-1)/2\), where \(n\) is the number of elements present in the database. For example, 51 patterns can be generated using 6 items with a length of 1. In this case, an a priori algorithm was used in a GSP to cut all the candidates. In this study, sequential maintenance activities with time stamps are used because the time information can be used more practically in maintenance compared to the sequences without time information. A pattern is called sequential when the sequence support is greater than or equal to the threshold support values. For the time slot sequences, four other threshold values such as the MinInterval, MaxInterval, MinWholeInterval, and MaxWholeInterval are used with the Minsupp values. In this study, these thresholds were identified, the sequential models were generated and the results were compared with different threshold values. However, in reality, these values must be suggested by decision-makers or experts.
## 5 Conclusions  
In this study, the collection, transport and steam sterilization of biomedical waste and the associated GHG emissions from these processes were investigated. The results of the steam sterilization system performed effective treatment for biomedical wastes containing needles, syringes and other non-hazardous and non-pathological infectious wastes. _Bacillus stearothermophilus_ bacteria were investigated for the effectiveness of steam on bacteria during the sterilization process. It was observed during this study that the steam autoclave performed most effectively at a contact time of 45 min, a temperature of 150 \({}^{\circ}\)C, and at a steam pressure of 5 bar, to inactivate _Bacillus stearothermophilus_. Under these operational conditions, daily bioindicator tests showed that the removal rates for _Bacillus stearothermophilus_ were always greater than 6 log\({}_{10}\).  
It was shown in this study that the biomedical waste collection, transport and sterilization processes generated a total of GHG emissions of 5573 tCO\({}_{2}\)-e from 2009 to 2019. A large part of the GHG emissions generated in this study was from the combustion of diesel fuel by biomedical waste collection and transportation vehicles and the natural gas consumed at the sterilization plant. On the other hand, the use of electricity at the sterilization plant produced less GHG emissions than that of diesel and electricity use. Furthermore, the average GWF was calculated as 0.269 tCO\({}_{2}\)-e per ton of biomedical waste collected, transported and sterilized. The biomedical waste treatment by steam sterilization seems to be a safe and cost-effective treatment method compared to incineration, which can release hazardous air pollutants and GHGs. However, precautions should be taken to reduce the amount of GHG emissions by, for example, using electricity-powered biomedical waste collection vehicles, using solar energy panels on the roofs of sterilization plants, and also using biodegradable biomedical waste collection bags.  
Life cycle assessment (LCA) can provide decision-support tools. There are many combined mitigation strategies that can be implemented cost effectively by the public or private sector, using LCA and other decision-support tools. Therefore, as a future work, a complete and comprehensive study of LCA should be conducted to determine the complete GHG emissions from medical waste collection, transport and sterilization process.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author would like to thank Kocaeli Metropolitan Municipality (Turkey), IZMIT Waste and Residue Treatment Incleration and Utilization Corporation (IZAYDAS) for providing the data, and Imam Abdulrahman Bin Faisal University for academic support.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 5 Articulation of Formation Reconfiguration Strategies for Multi-UAVs with Variable Swarm Scale
### Introduction of Formation Control Protocol  
In accordance with Definition 1, a consensus-based formation control protocol is introduced from [39] to accomplish formation control of the leader-follower multi-UAVs (5) and (6):  
\[\begin{split} u_{i}=&-\sum_{j\in\mathcal{N}_{i}}K_{ 1}[(q_{i}-q_{0}+\Delta_{i})-(q_{j}-q_{0}+\Delta_{j})]-\sum_{j\in\mathcal{N}_{i }}K_{2}(p_{i}-p_{j})\\ &-K_{3}(q_{i}-q_{0}+\Delta_{i})-K_{4}(p_{i}-p_{0}),i=1,\ldots,N. \end{split} \tag{30}\]  
Therein, \(K_{1},K_{2}\) are positive definite matrices, while \(K_{3},K_{4}\) are positive semi-definite matrices in the sense that the \(i\)th follower can receive the signals from the leader and are null matrices otherwise. \(\mathcal{N}_{i}\) denotes the neighbor set of the \(i\)th follower. \(\Delta_{i}\) stands for the desired relative position of the \(i\)th follower with respect to the leader, which satisfies \(\Delta_{i}=q_{0}-q_{i}\).  
**Remark 5**: _The formation reconfiguration process of a UAV swarm under dynamic swarm scale can be divided into two phases: swarm communication topology reconfiguration and swarm geometry reconfiguration. The article focuses on the former. In fact, protocol (30) is a universal consensus-based control protocol whose effectiveness has been well demonstrated in existing works, such as [39]. Therefore, the proof regarding protocol (30) is omitted from the article._
# Numerical Investigation of Thermal-Flow Characteristics in Heat Exchanger with Various Tube Shapes
## 3 Numerical Treatment
### Definition of Parameters  
The air-side thermal exchange rate of annular fins with flat and oval tubes is evaluated as follows:  
\[\varphi=\dot{m}c_{p}(T_{out}-T_{in}) \tag{8}\]  
where, \(\dot{m}=\rho_{a}A_{in}u_{in}\).  
The convective thermal exchange coefficient over the finned-tube surface may be determined as:  
\[h=\frac{\varphi}{\left(A_{t}+\eta A_{f}\right)\cdot\Delta T_{LMTD}} \tag{9}\]  
with the logarithmic mean temperature difference:  
\[\Delta T_{LMTD}=\frac{T_{out}-T_{in}}{\ln\frac{(T_{b}-T_{in})}{(T_{b}-T_{out})}} \tag{10}\]  
where, \(A_{f}\) is the total annular fin surface area, \(A_{t}\) is the total tube surface area, and \(\eta\) is the annular fin efficiency. The annular fin efficiency is given by the formula used in Refs. [61; 62] as:  
\[\eta=\frac{\int\left(T-T_{a}\right)dA}{\int\left(T_{b}-T_{a}\right)dA} \tag{11}\]  
The air temperature \(T_{a}\) is the average temperature of the ambient air calculated from both the inlet and outlet region of the computational domain, and \(T_{b}\) is the tube base temperature.  
The following dimensionless parameters were utilized to determine the hydrothermal details:  
\[Re=\frac{\rho_{a}u_{\max}D_{h}}{\mu_{a}} \tag{12}\]  
\[j=\frac{Nu}{Re\,Pr^{1/3}} \tag{13}\]  
\[f=\frac{\Delta P}{\frac{1}{2}\rho_{a}u_{\max}^{2}}\frac{D_{h}}{L_{f}} \tag{14}\]  
Here, \(L_{f}\) is the flow length.  
Figure 4: Grid structure used in simulations.  
For the best selection of tube shape with annular fins, it is important to calculate the Colburn J and \(f\) friction factors; therefore, we used the performance evaluation criterion \(P_{cc}\) defined in Refs. [35; 38; 39] as follows:  
\[P_{cc}=\frac{j}{f} \tag{15}\]  
To take into account the influence of tube patterns on the thermo-hydraulic efficiency of the cross-flow thermal device, we used the global performance criterion \(G_{pc}\), which is defined in Refs. [39; 62] as follows:  
\[G_{pc}=\frac{\varphi}{\Delta P\dot{V}} \tag{16}\]  
where, \(\dot{V}\) is the volumetric flow rate and is the total air-side heat transfer rate.
## 4 Experiments and Results
### Architecture Search
#### 4.2.2 Result of Architecture Search
The other parameters, which are the same as those in DARTS [3], are defined as follows:  
* Cutout regularization [20]: 16;
* Drop-path [22] of probability: 0.2;
* Auxiliary towers [23] of weight: 0.4;
* SGD [15] optimizer's weight decay: 0.0003;
* Momentum: 0.9;
* Initial learning rate: 0.025;
* Batch size: 96'
* Number of training data: 50,000;
* Total epochs: 600. An example cell architecture found by DG-DARTS is shown in Figure 7 for the CIFAR10 data set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cluster Number** & **Operation Name** & \(\mathbf{\alpha}\) & **Sum of \(\mathbf{\alpha}\)** \\ \hline \multirow{3}{*}{Cluster 1} & max\_pool\_3 \(\times\) 3 & 0.0617 & \\ \cline{2-4}  & avg\_pool\_3 \(\times\) 3 & 0.0455 & 0.1824 \\ \cline{2-4}  & skip\_connect & 0.0752 & \\ \hline \multirow{3}{*}{Cluster 2} & sep\_conv\_3 \(\times\) 3 & 0.1527 & \\ \cline{2-4}  & sep\_conv\_5 \(\times\) 5 & 0.1450 & 0.3027 \\ \hline \multirow{3}{*}{Cluster 3} & dil\_conv\_3 \(\times\) 3 & 0.1253 & \\ \cline{2-4}  & dil\_conv\_5 \(\times\) 5 & 0.1248 & 0.2501 \\ \hline None & none & 0.2648 & 0.2648 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cluster and weight for first mixed operation after stage 1. Figure 7: _Cont_.
# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 3 Problem Definition and Formulation  
Given the significant influence of social media and news on investor behavior in the stock market [140; 141], we employ machine-learning (ML) techniques for leveraging such information to predict stock prices in the North American context. Predicting stock prices accurately, especially during periods of heightened market uncertainty, presents a unique challenge for existing prediction models. Notably, stock-price volatility experiences a substantial fluctuation during economic downturns [142] which, in turn, adds complexity to accurate forecasting. Trade volume serves as a more effective predictor of panic-induced volatility than the traditional inputs commonly used in the literature (e.g., [143]). Furthermore, some studies (e.g., [144; 32; 145]) revealed that investors' reactions to news differ significantly during times of market panic. Therefore, beyond the development of an ML-based prediction model, our study investigates the public panic's impact on the price prediction. Hence, we seek to compare the predictive efficacy of the developed model in periods of public panic with its performance in more stable market conditions. This dual focus aims to provide an understanding of the model's reliability under varying market conditions, which enhances its practical applicability in real-world scenarios.  
Both the accuracy and applicability of NN models in predicting stock prices based on technical variables have been explored extensively in recent years (e.g., [21; 23; 24; 25; 26; 27; 28; 29; 38; 40; 41; 49; 55; 59; 60; 89; 124; 146]). Building on this foundation, our study leverages NN techniques to incorporate market-sentiment information into the stock-price prediction process. According to the literature, NN techniques fall into five main categories: (i) multiple-layer perceptron (MLP) (e.g., [13; 116; 121; 37; 94; 120; 113]), (ii) long short-term memory (LSTM) (e.g., [52; 147]), (iii) convolutional NN (CNN) (e.g., [37; 119]), (iv) recurrent NN (RNN) (e.g., [17; 19]), and (v) heuristic (e.g., [89; 96; 138]).  
Multiple-layer perceptron (MLP) and long short-term memory (LSTM) have emerged as the most commonly applied techniques in stock-price prediction. Consequently, we adopt these two prevalent network types for formulating an advanced stock-price prediction model. To this end, several crucial design decisions on NN configuration should be made. These decisions encompass determining the optimal number of layers, nodes, and training epochs and selecting the appropriate cost functions and optimizers. It is worth noting that, within the extensive body of research utilizing NN as the primary algorithm, there is a notable absence of standardization concerning their design. Moreover, the predominant focus in NN design research lies in hyper-parameter optimization through algorithms rather than in establishing empirical standards. This lack of proven baselines hinders researchers from effectively comparing and advancing various NN architectures [148].
## 2 Materials and Methods
### Extraction and Analysis of Texture Features  
TA analysis was performed by a different fellowship-trained dentomaxillofacial radiologist with five years of CT and MRI experience who was also unaware of the final diagnosis. The radiologist manually selected the first sagittal slice in the most central region in all cases, where the condyle was more visible. Two additional sequential parallel sections, one more medial and another more distal, were also analyzed. Each DICOM slice was processed and converted into bitmap format by using OnDemand3D software (CyberMed Inc., Seoul, Republic of Korea). The same radiologist chose the region of interest (ROI) by drawing lines that intersected the outer and inner edges of the condyles in the sagittal view as well as the medial and lateral borders in the sagittal perspective. Next, to enhance precision in locating the center of the condyles, a line was drawn in the middle of these aforementioned lines to guide the outline of the ROI with a diameter of 6.5 mm at the center of the condyle (Figure 1). TA analysis of CT and MRI images was based on parameters (i.e., features) extracted by using MaZda software, version 4.6 (Technical University of Lodz, Poland), on a 15-inch MacBook Pro notebook (Apple, Los Altos, CA, USA) with an Intel(r) CoreTMi5 (Intel, Santa Clara, CA, USA), 2.4 GHz, 4 GB RAM, 1067 MHz, DDR3 processor (Samsung, Suwon, Republic of Korea) and Microsoft Windows version 10. The same diameter of the ROI was used in the three sections to create volumetric data, that is, values of the volume of interest (VOI) for texture analysis [29]. The texture parameters from each of the three sections were extracted, and the mean value was calculated for each condyle.  
GLCM is a statistical method for the calculation of the properties of spatial relationships between pixels [29]. Texture parameters were computed from the GLCM, corresponding to distances of 1, 2, and 3 pixels in the four directions of the image (i.e., horizontal, diagonal, vertical, and anti-diagonal, corresponding to 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\), respectively).  
To minimize the factors at play and select seven parameters with a stronger connection with bone tissue, we used a recent study as a basis to select seven texture parameters [29]. The Supplementary Table S1 shows the texture features computed by using MaZda software.  
Figure 1: Representative sagittal images showing the central section of the condyle and the manual selection of the ROI to extract texture and image parameters from CT (**A**) and MRI images (**B**).  
To provide a comprehensive overview of our methodological approach, Figure 2 presents a visual representation of the entire pipeline, from image acquisition to texture analysis. This flowchart illustrates the key steps in our process, including image acquisition, analysis and selection, preparation for texture analysis, feature extraction, and data analysis.
## 5 Conclusions  
This research has demonstrated an innovative, markerless 3D digitization method for capturing the geometry of physical objects in challenging or constrained environments. The proposed system effectively overcomes the need for external reference markers or sensor movement around the object by combining stereo vision with a projected laser line. This facilitates in situ scanning where conventional techniques may fail. Experimental testing and comparisons to a commercial 3D scanner have shown that the prototype achieves acceptable accuracy and repeatability, even on complex surfaces. However, the performance is sensitive to a variety of intrinsic and extrinsic parameters, such as camera resolution, laser quality, and environmental conditions, which require further research. To enhance the system's robustness, it is imperative to improve hardware components, automate parameter calibration, and assess the system's performance in more challenging industrial and clinical environments. The insights gathered and the successes achieved to date underscore the system's promise for reverse engineering, non-destructive testing, medical diagnostics, and other specialized applications.  
Conceptualization, A.V. and M.K.; methodology, A.V. and M.K.; software, A.V.; validation, M.K. and M.P.; formal analysis, J.K. and J.T.; investigation, J.K. and J.T.; resources, M.K. and M.P.; data curation, J.K. and J.T.; writing--original draft preparation, A.V., J.K. and J.T.; writing--review and editing, A.V., J.K. and J.T.; visualization, A.V., J.K. and J.T.; supervision, M.K. and M.P.; project administration, M.K. and M.P.; funding acquisition, M.K. and M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated during the current study and analyzed during the current study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
This research was funded by project KEGA 002 TUKE-4/2023 and VEGA 1/0121/23, granted by the Ministry of Education, Research, Development and Youth of the Slovak Republic, and by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I05-03-V02-00042.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3D & Three-dimensional \\
2D & Two-dimensional \\ ICP & Iterative Closest Point \\ SLAM & Simultaneous Localization and Mapping \\ CAD & Computer Aided Design \\ CMM & Coordinate Measuring Machine \\ LiDAR & Light Detection and Ranging \\ \end{tabular}
## 3 SD-GPSR Routing Method
### Design of the Control Layer
#### 3.2.1 Location Discovery and Update  
We use an active approach for location discovery and update, where all nodes periodically use Packet-in messages to proactively report their own location information and neighbor relationships. During the initial operation of the network, all nodes encapsulate their own information (including IP address, MAC address, neighbor relationships, etc.) into Packet-in messages and use them to report to the controller. Upon receiving a Packet-in message, the controller identifies it as a location update packet based on the message type in the packet header. The controller then queries its local node information set. If the controller finds that the information of the node is already stored in the node information set, it updates the information. Otherwise, it adds the information of the node to the node information set.