esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 3 Related Works
The article [5] discusses the significant problem of biological buildup in ship hulls, which leads to increased drag, fuel consumption, corrosion, and biological pollution. The article indicates that the HISMAR robot employs a new navigation system to achieve autonomous operation. This system utilizes a combination of sensors and software for localization and mapping. Specifically, it includes an optical dead reckoning system (ODRS), which uses optical mouse sensors to measure displacement and reduce the impact of wheel slippage. Additionally, a magnetic landmark recognition system (MLRS) is used to detect the internal structure of the ship's hull, providing data for mapping and navigation. The data from the ODRS and MLRS are used by the software to create a magnetic landmark map of the ship's hull. This map is then used to plan the robot's working trajectory. Article [7] discusses advanced perception, navigation, and planning methods for Autonomous Underwater Vehicles (AUVs), conducting inspections of the hulls of ships. It addresses challenges posed by the underwater environment and complex structures, presenting sonar- and vision-based SLAM algorithms and path planning for comprehensive surface coverage. The Hovering Autonomous Underwater Vehicle (HAUV) utilizes several localization technologies. For basic navigation, it employs a Doppler velocity log (DVL) locked to the hull or seafloor, an inertial measurement unit (IMU) for attitude, and a depth sensor. More advanced drift-free localization is achieved through Simultaneous Localization and Mapping (SLAM). This SLAM system integrates data from an imaging sonar and a monocular camera to build a map and simultaneously localize the vehicle. In addition to the methodological approaches discussed earlier, several datasets with similar characteristics were identified in the literature. In the following, we briefly describe a selection of them, highlighting their relevance and limitations in the context of our research. The CoreNav-GP dataset [10] contains GPS and IMU data collected in various environments, similar in scope to the types considered in this research. However, it turned out to be inappropriate for our purposes. Attempts to unpack the files using Python scripts often resulted in unexpected end-of-file errors, and the dataset appears to lack a reference position (ground truth). For these reasons, CoreNav-GP was excluded from further consideration. The KITTI dataset [11] is a well-known benchmark that provides a variety of sensory data for autonomous driving, including GPS and stereo vision, and was initially considered. However, it lacks inertial measurements (IMU) and wheel encoder data, which are essential for the evaluation of the methods considered in this study. As a result, it was not used in our experiments. The last of the datasets found was the Rosario dataset [12], which turned out to be the most relevant to our research. It included the sensor data required for our project and featured a movement pattern analogous to the expected motion of the robot along the hull of the ship.
## 1 Introduction
This lack of detailed understanding makes it challenging for policymakers and urban planners to develop targeted strategies to improve their cities' smart city status. The main objective of this study was to provide policymakers and planners with precision in identifying the most critical factors contributing to successful smart city initiatives. This study broadened the scope beyond previous works by isolating 45 key factors in 140 cities. The resulting dataset was divided into two groups to identify the key determinants of high-ranking smart cities. By employing a random forest (RF) algorithm, this study identified the most significant factors differentiating top-performing cities from others. Multiple linear regression is then utilized to explore the interrelationships among these identified factors. The findings of this research will provide critical insights for policymakers and urban planners, enabling them to prioritize investments and initiatives that have the greatest potential to improve respective urban areas and elevate their cities' rankings. Moreover, in using robust statistical methodologies, this study advances smart city research and provides a solid foundation for future studies. The research questions for this study are:  
1. What are the current key factors that differentiate top-ranking smart cities from other cities? 2. How do the identified factors interact with each other to influence a city's overall smart city performance? 3. What are the implications of the findings for policymakers and urban planners in developing effective smart city strategies? The remainder of this paper is divided into the following sections. Section 2 thoroughly reviews recent literature on smart cities, highlighting existing research and the theoretical frameworks and methodologies employed in previous studies. Moreover, Section 2 also delves into the factors influencing smart city development, the challenges and opportunities associated with implementing smart city initiatives, and the role of technology in driving urban transformation. Section 3 outlines the research methodology, including data collection, data processing, and the statistical techniques employed in the study. Section 4 presents the analysis results, focusing on identifying the key factors that contribute to high-ranking smart cities and the relationships among these factors. Section 5 discusses the findings, drawing implications for policymakers and urban planners. Section 6 summarizes the key findings, highlighting the research contributions and providing recommendations for future research.
## 1 Introduction
Based on two iterative learning algorithms, a learning output-feedback fault-tolerant control (LOF\({}^{2}\)TC) scheme was explored so that accurate formation configuration maintenance could be fulfilled for spacecraft formation flying systems. Finally, simulation studies were provided to illustrate the effectiveness and superiority of the proposed robust fault reconstruction and reconfigurable fault tolerant control for spacecraft formation flying systems. In this paper, the main contributions that are worth emphasizing are summarized in the following three points. Motivated by sliding mode methodology, a new ILO was designed for robust thruster bias fault reconstruction for spacecraft formation flying systems, subject to space perturbations. In addition, compared with the existing ILOs, a new ILO stability analysis methodology was provided in detail. Based on two iterative learning algorithms, a new LOF\({}^{2}\)TC approach was explored for accurate spacecraft formation configuration maintenance in presence of space perturbations and possible uncertainties. It is worth noting that iterative learning algorithms were not developed for spacecraft formation control systems. Therefore, the proposed ILO-based robust thruster fault reconstruction and LOF\({}^{2}\)TC approach provide an extension for spacecraft application. The rest of this paper is organized as follows. In Section 2, the considered spacecraft formation control system model is established and the main problems are formulated. In Section 3, a new ILO is constructed to reconstruct actuator faults and estimate system states simultaneously, and the stability analysis for the proposed ILO is also derived in detail. In Section 4, the LOF\({}^{2}\)TC approach is presented for configuration maintenance under thruster faults, space perturbations, and possible uncertainties, and, then, its stability analysis is provided. In Section 5, simulation studies are presented, and Section 6 concludes this paper.
## 3 Results
### Wrinkle Analyses Based on the FE Method  
Facial expressions significantly impact skin deformation, influencing both the distribution and orientation of facial wrinkles. The way the skin undergoes compression plays a crucial role in determining where and how wrinkles develop. Understanding the mechanical environment of the skin is key to investigating wrinkle formation. In this study, the FE method is employed to quantitatively examine the relationship between wrinkle formation and muscle-induced compression. Figure 6 presents the strain distributions of skin wrinkling across different skin models with hypodermis thicknesses of 0.2, 0.5, 1.0, and 2.0 mm. At low levels of muscle compression (approximately 5% compressive strain), the skin remains relatively smooth with minimal buckling. As muscle compression increases, wrinkles begin to form on the skin. These wrinkles create peaks and valleys, resulting in tensile and compressive strains, respectively.  
Figure 4: Distributions of principal strain and principal direction during happy expressions: (**a**) \(\varepsilon_{1}\), (**b**) \(\varepsilon_{2}\), (**c**) \(\varepsilon_{2}\).  
Figure 5: Comparison of maximum compression and facial wrinkles: **(a)** the minimum principal strain distribution during a surprise expression, **(b)** forehead lines, **(c)** the minimum principal strain distribution during a fear expression, **(d)** glabellum lines, **(e)** the minimum principal strain distribution during a happy expression, **(f)** crow’s feet [34]. Arrows superimposed on the strain field denote the direction of the principal compressive, whereas those annotated on the wrinkle morphology highlight sites of surface buckles.  
Figure 6: FE simulation results of skin wrinkling under varying compression for models with different hypodermis thicknesses: **(a)** 0.2 mm, **(b)** 0.5 mm, **(c)** 1.0 mm, and **(d)** 2.0 mm.  
As the hypodermis thickens, the skin becomes increasingly resistant to wrinkling under muscle compression. Figure 7a presents the x-direction strain at Point A in Figure 2 during wrinkle formation, comparing FE models with varying hypodermis thicknesses under identical muscle compression. The results indicate that as the hypodermis thickness increases, the strain transmitted to the skin surface decreases.
## 5 Simulation Experiment and Analysis
### Knowledge Correlation Analysis  
To validate the impact of the proposed knowledge base on search results, we randomly placed multiple static and dynamic targets, obstacles, and threat areas in a single scene. We then conducted repeated learning and training using the algorithm presented in this paper. Subsequently, we analyzed the deviation between target position information predicted by action templates matched with our knowledge base and actual target position information across different iterations through comparison. These experiments confirm both the effectiveness of our knowledge base as well as RDIRV's expansion effect on it. Specific error analysis results are shown in Figure 11.  
The comparison of errors under different iterations in the same scenario is illustrated in Figure 11. Figure 11 indicates that after iterating 500 times, the maximum error is recorded as 0.2382 and the minimum error as 0.0267, which can be observed that during the initial stage of task execution, by invoking the knowledge base matching action template, the UAV can predict the target's location with a small margin of error. Figure 11b demonstrates that after iterating 1000 times, the maximum error decreases to 0.1496 while the minimum error reduces to 0.0153. As iterations are performed, it becomes evident that the UAV's understanding of its environment becomes increasingly comprehensive. Meanwhile, RDIRV continues to replay optimal decisions resulting in decreased error value. The results depicted in Figure 11c demonstrate that after 1500 iterations, the maximum error is recorded  
Figure 10: Comparison of average rewards of four algorithms in three scenarios.  
as 0.0523 while the minimum error stands at 0.0074. As the training progresses to a certain extent, the UAV's proficiency in the task area reaches its zenith and there is a continuous enhancement in the knowledge base content, resulting in significantly reduced predicted error values. The knowledge base is expanded and shared among multiple UAVs, enabling a collective learning process. As the action template in the knowledge base grows, the available actions are reduced for UAVs to explore unknown environments they have never been to before, thereby enhancing the learning efficiency of KD-SAC. Through comparative analysis, we can observe the effectiveness and efficiency of utilizing the knowledge base for addressing multi-UAV cooperative path planning problems.  
Figure 11: _Cont._
## Materials and Methods
### Effect of LAB on the Growth of Helicobacter pylori Strains  
To evaluate the effect of LAB on _H. pylori_ growth, standard strains ATCC 43504 and ATCC 700392, and three babA2, vacA, and cagA positive clinical strains--named 1L, 1B, and 2--isolated from gastric biopsies of pediatric patients, were used. These strains were selected since these genes are associated with higher risk of peptic ulcer disease and intestinal metaplasia [14]. All the strains were obtained from the collection of the Hospital Infantil de Mexico Federico Gomez. _H. pylori_ strains were grown on Casman agar base (BBL) plates supplemented with 5% defibrinated sheep blood at 37 \({}^{\circ}\)C under microaerophilic conditions (5% CO\({}_{2}\)). The strains were identified using Gram stain, morphology, urease, catalase, and oxidase tests.  
LAB were inoculated onto the surface of the first layer of MRS agar in Petri dishes and were incubated for 24 h at 37 \({}^{\circ}\)C. A second layer of Mueller-Hinton II agar medium (Becton Dickinson, Franklin Lakes, NJ, USA) supplemented with 5% defibrinated sheep blood was used as culture medium for _H. pylori_. Strains were inoculated in a concentration equivalent to the 2.0 McFarland standard. The plates were incubated at 37 \({}^{\circ}\)C under microaerophilic conditions for 72 h. The minimal inhibitory concentration for clarithromycin was used as positive control and was determined by agar dilution method following the Clinical and Laboratory Standards Institute (CLSI M45-A) guidelines. Results were expressed as \"total inhibition\", \"partial inhibition\", or \"no inhibition\". Strains were tested for urease activity using the urease rapid test (Urea/Christensen, BBL, MD) before and after being in contact with LAB.
## 2 Material and Methods
### Tensile Strength Measurement for Controlling Variables
#### 3.2.1 Tensile Strength Measurement with and without Fabric Tape Reinforcement for Variable Control  
In this evaluation, the tensile strength was measured with and without the reinforcement of fabric tape to assess the ability to restrain the elongation of the polymer film. The measurement results are presented in Table 2 and Figure 13.  
According to the results, compared to the unreinforced test specimens, A-1 showed a difference of approximately 120% in tensile strength, A-2 of 242%, B-1 of 214%, B-2 of 122%, C-1 of 98%, and C-2 of 112%.  
Figure 14b shows a comparison of the extracted raw data from the recorded load and displacement during the measurement process for the test specimens with fabric tape applied. The selected range corresponded to the interval of 0-70 mm, which was the range where the load values were measured during the peel-out test. We examined how much deformation occurred from the point where the initial tension was applied to the selected measurement range. The obtained graph is shown in Figure 15, where all six types of specimens showed a maximum load before stabilizing at a constant load value in the range of approximately 20 mm. It can be concluded that the polymer film layer with a minimum elongation of 200% or more and the fabric tape with an elongation rate of approximately 5-10% were combined to restrain the elongation of the polymer film and provide consistent resistance to the applied load, resulting in an elongation rate of approximately 15-20%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specimen Types** & **A-1** & **A-2** & **B-1** & **B-2** & **C-1** & **C-2** \\ \hline Unreinforced & & & & & & \\ tensile strength & 5.7 & 3.3 & 4.3 & 13.6 & 14.6 & 10.4 \\ \hline Fabric tape & & & & & & \\ tensile adhesive strength & 6.9 & 8.
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
## 2 Materials and Methods
### Style Transfer  
In Section 2.1, we have been able to predict depth maps from simulated virtual endoscopic monocular images (SI-DM dataset). However, our purpose was to obtain depth maps from real ureteroscopic images. Since video images and virtual images belong to two domains, migration of different modality data is a problem of domain adaptation [26]. In order to achieve this, we introduced a style transfer neural network [27]. The style in the paper refers to the different data distribution domains of these two kinds of images, such as color, texture obtained with the same content. Style transfer refers to retaining the image content and transferring the image to the target style from the source style.  
In this paper, we use the image style transfer method to transfer the real endoscopic images to the virtual endoscopic images because the depth image matching under the same data domain is more suitable than the cross-domain matching from the perspective of data distribution and image alignment, so that the depth prediction model trained in Section 2.1 was effective for the real endoscopic images. There are many methods to realize style transfer [28; 29; 30; 31]. In [32], CycleGAN was adopted and unpaired data was used for training. The structure of the CycleGAN is shown in Figure 3. A represents the real endoscopic image domain (EI), while B represents the simulated images domain (SI). It consists of two discriminators and two generators. The input image Input_A generates Fake_B through Generator A to B, and Fake_B generates Rec_A through Generator B to A. After two transformations, Rec_A belongs to the A domain, and the model is optimized by comparing the similarity between the Input_A and the Rec_A, and Input_B is also processed in the same way.
## 6 Simulation Results
### Simulation Setup  
As we focus our performance evaluation study on network layer mechanisms, for our performance results to be independent of the lower layers, we chose to use our home-made simulator that assumes ideal MAC (no packet collision, no delay) and Physical layers (no interference, BER = 0, isotropic radiation pattern). The network can be described as follows. Nodes are deployed in a 1,000 \(\times\) 1,000 square following a two dimensional Poisson Point Process with different intensities \(\lambda\). In such a Poisson Point Process, the total number of nodes is probabilistic and is obtained from a Poisson Law of intensity \(\lambda\), which is correlated to the mean node degree \(\delta\): \(\lambda=\frac{\delta}{\pi R^{2}}\). Nodes are uniformly distributed over the area. Nodes can adapt their range between \(0\) and \(R=200\). We only consider connected networks.  
We compare HECTOR, LTP [10] and VCost [9] for the same samples of node distribution, same source and destination pairs, both randomly chosen. Landmarks and the tree root are randomly chosen among the nodes. Finally, to show the impact of the use of the two sets of coordinates over the guaranteed delivery, we evaluate the performances of the routing schemes over a homogeneous network and over a topology with a crescent hole (see Figure 3).  
All results are the average of statistics retrieved from more than \(100\) simulation runs and meet a \(98\%\) interval. Note that the bootstrap cost induced by the coordinates setting is not integrated in these results. We keep for future work the evaluation of this cost and the maintenance of the virtual coordinates.  
We evaluate the energy consumption overhead (ECO) of each algorithm based on the energy model described in Section 3. As in [23], we use \(c=10^{7}\) and \(\alpha=4\), which leads to an optimal range of \(r^{*}=100\)[24]. To further evaluate the routing protocols, we computed their energy overhead using as reference the optimal centralized energy weighted shortest path (SP) (Dijkstra algorithm [25]). We let \(e_{i}\) and \(e^{*}\) be the energy consumed using any described protocol and the centralized SP protocol, respectively. We define the energy overhead as the ratio \(\frac{e_{i}-e^{*}}{e^{*}}\times 100\). Note that \(0\%\) overhead means that the algorithm consumes the same energy than the optimal algorithm.  
We also evaluate the mean path length and mean hop length obtained for each protocol and give visual results of routing process.  
Figure 3: Network topologies.
## 2 Data
### Precipitation, Drought, and Fire  
Gridded precipitation data developed by Parameter-elevation Regressions on Independent Slopes Model (PRISM) was obtained from PRISM Climate Group [46]. Monthly precipitation at \(\sim\)4 km pixels was used. To evaluate the accuracy of breakpoints in terms of timing, extent, and direction of change relative to potential disturbances, historical drought and fire events were compared with the breakpoints. Mean monthly and annual self-calibrated Palmar Drought Severity Index (PDSI) was acquired from [47]. Fire data were obtained from New Mexico Resources Geographic Information System (RGIS) [48].