esa-sceva/satcom-chunk-collection huggingface dataset:
# Surgical Tool Detection in Open Surgery Videos
## 3 Dataset
### Data Splits  
The dataset was divided into a training set (10 videos), a validation set (two videos), and a test set (three videos). There are 12,715 images for a training set (65%), 1807 images for a validation set (9%), and 5038 images for a test set (26%). The distribution of the number of instances per surgical tool category in the three splits is shown in Figure 7. Because not all categories appear in all videos, we ensured all categories were present in the training set. We split the rest of the videos between the validation and test sets. The distribution of the category in each set is different because data are split by the _video-based split_, as shown in Figure 7. Another way to perform a split, the _frame-based split_, can generate a more uniform distribution among the three sets. However, the _frame-based split_ would have the  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & **Real** & **Bbox** & **Number of** & **Number of** & **Avg. Instances** & **Number of Surgical** \\  & **Env.** & **Annotated?** & **Annotated frames** & **Annotated Instances** & **per Frame** & **Tool Categories** \\ \hline _Montically invasive surgery_ (MS): & & & - & - & - & \\ m2 colle16-tool [19] & ✓ & & 23,000 & - & - & 1 \\ Ch  
model be trained and evaluated on almost the same data. In real-world surgery, the data distribution drastically change every surgery, or video due to the various surgery type, operative environments, and lighting conditions. To tackle the tool detection in these challenging settings, we chose _video-based split_.  
Figure 7 indicates that the dataset is highly imbalanced; consequently, accurate instrument classification is more challenging. _Tweezers_ contains the largest number of instances because surgeons use them commonly for all procedures. On the other hand, _Ruler_ contains the smallest number of instances, as surgeons use it only for specific purposes (e.g., measuring tumors). Furthermore, there are other visual challenges because of the high inter-category similarity among tools. For instance, _Forceps_, _Needle Holders_, and _Scissors_ categories have similar appearances, as shown in Figure 4.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 3 Results
The \"checkboard\" data set was used and the running time of each step in the proposed method as follows: The patches fusion step cost 4.609 s. The guided filter step cost 0.016 s. The comparison of each registration step of the three methods is shown in Figure 3. Each iteration time of the Oreifej method fluctuated around 260 s, whereas the iteration time of the proposed method decreases with increasing of iterations, and the time was less than the Oreifej method except for in the first iteration. Furthermore, the average running time of the proposed method was 249.859 s, less than the Oreifej method, which was 263.060 s. Compared with the previous method, obviously, the proposed method had a decreasing running time as iterations went on, whereas uptime of the previous method rebounded evidently with an average of 255.522 s. Overall, the proposed method kept a clear pattern of deceleration in the iteration time, which is very useful with the improvement of processor hardware acceleration. Figure 2: The recovery results of different methods after five iterations. The regions marked with red rectangles are inferior to our method. The green rectangles mark the regions with less distortion than our method.
# Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision
## 3 Results and Discussion  
### Effect of Feature-Point-Matching Algorithm  
In this method, the markers projected by the lasers are used as the feature points, and the center of the markers is extracted by identifying the blob features with DoG. A section in the middle of the pipe with an outer diameter of 299 mm is selected for tests, and the actual feature-point-extraction results are shown in Figure 11, in which (a) and (b) show the feature-point-extraction results of the left camera and the right camera, respectively. It is seen from the figures that the markers projected on the surface of the pipe are almost completely identified and extracted, meaning that the expected effect is achieved.  
Figure 10: (**a**) The experimental system built in this paper; (**b**) coordinate measuring machine for reference.  
Figure 9: Fitting-error distribution for diameter.  
With the proposed feature-point-matching algorithm based on affine distance transformation, after the feature points are accurately extracted, the feature points provided by the left and right cameras are converted into matching elements \(F_{L}\) and \(F_{R}\), respectively, according to Formula (4), as shown in Figure 12. It is not difficult to find that after the affine distance transformation, the markers experience obvious amplitude changes and no longer follow the original single and identical distributions. In particular, amplitude oscillation appears near the 24 th matching element. These obvious features provide the basis for the correspondence matching.  
The final matching effect is shown in Figure 13. The dark area displays the final matching result. It is found that despite the noise in the matching elements, the algorithm proposed in this paper still achieves correct matching of feature points satisfactorily. Many experiments have also proven that the accurate matching rate of this method can reach more than 98%.  
Figure 11: (**a**) Extraction of feature points for left camera; (**b**) extraction of feature points for right camera.  
Figure 12: Feature-point-matching elements.  
Figure 13: Feature-point-matching results.
# Application of Improved Sliding Mode and Artificial Neural Networks in Robot Control
###### Abstract  
Mobile robots are autonomous devices capable of self-motion, and are utilized in applications ranging from surveillance and logistics to healthcare services and planetary exploration. Precise trajectory tracking is a crucial component in robotic applications. This study introduces the use of improved sliding surfaces and artificial neural networks in controlling mobile robots. An enhanced sliding surface, combined with exponential and hyperbolic tangent approach laws, is employed to mitigate chattering phenomena in sliding mode control. Nonlinear components of the sliding control law are estimated using artificial neural networks. The weights of the neural networks are updated online using a gradient descent algorithm. The stability of the system is demonstrated using Lyapunov theory. Simulation results in MATLAB/Simulink R2024a validate the effectiveness of the proposed method, with rise times of 0.071 s, an overshoot of 0.004%, and steady-state errors approaching zero meters. Settling times were 0.0978 s for the \(x\)-axis and 0.0902 s for the \(y\)-axis, and chattering exhibited low amplitude and frequency.  
sliding mode control; mobile robot; improved sliding surface; artificial neural network; MATLAB/Simulink
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
###### Abstract  
Rehabilitation aids help people with temporal or permanent disabilities during the rehabilitation process. However, these solutions are usually expensive and, consequently, inaccessible outside of professional medical institutions. Rapid advances in software development, Internet of Things (IoT), robotics, and additive manufacturing open up a way to affordable rehabilitation solutions, even to the general population. Imagine a rehabilitation aid constructed from accessible software and hardware with local production. Many obstacles exist to using such technology, starting with the development of unified software for custom-made devices. In this paper, we address open issues in designing rehabilitation aids by proposing an extensive rehabilitation platform. To demonstrate our concept, we developed a unique platform, RehabHand. The main idea is to use domain-specific language and code generation techniques to enable loosely coupled software and hardware solutions. The main advantage of such separation is support for modular and a higher abstraction level by enabling therapists to write rehabilitation exercises in natural, domain-specific terminology and share them with patients. The same platform provides a hardware-independent part that facilitates the integration of new rehabilitation devices. Experience in implementing RehabHand with three different rehabilitation devices confirms that such rehabilitation technology can be developed, and shows that implementing a hardware-independent rehabilitation platform might not be as challenging as expected.  
rehabilitation aid; movement observation; rehabilitation; assistive technology; robot-assisted rehabilitation; additive manufacturing; local production; human-computer interaction; domain-specific languages; code generation +
Footnote †: journal: Applied Sciences
## 2 Study Region and Data  
### Study Region  
The study region is composed of Hebei province, Beijing City and Tianjin City (HBT), where the latitude spans from 36.0\({}^{\circ}\) N to 42.5\({}^{\circ}\) N and the longitude spans from 113.5\({}^{\circ}\) E to 119.7\({}^{\circ}\) E (Figure 1). The northwestern region is a mountainous area where the Taihang and Yanshan mountain ridges are located. To the east is the Bohai Sea and to the southeast is part of the flat North China (Huabei) Plain. The study region is the largest industrial and the most densely populated area in northern China. Particularly, this region, including the mega-city Beijing--the capital of China--is an important core area of the current economic development in China.
# Numerical Phase-Field Model Validation for Dissolution of Minerals
## 2 Dissolution Mechanisms  
### Types of Dissolution  
Chemical dissolution of minerals occurs as a congruent or an incongruent reaction, depending upon the type of a mineral [72]. Congruent dissolution of a solid mineral is a chemical reaction which completely dissolves the mineral and all products of this reaction are dissolved species. An obvious example would be calcite CaCO\({}_{3}\) and NaCl [73; 74]:  
\[\mathrm{A_{a}B_{b}(s)\to aA(aq)+bB(aq)} \tag{1}\]  
when the primary solid phase is altered and at the same time a secondary solid phase is formed, incongruent dissolution occurs, for example the alteration of albite to gibbsite NaAlSi\({}_{3}\)O\({}_{8}\), or Kaolinite [75; 76], which requires a more advanced thermodynamic modelling approaches to be integrated in the PF:  
\[\mathrm{A_{a}B_{b}(s)\to cC(s)+dD(aq)} \tag{2}\]
# Comprehensive Optimization of Air Quality in Kitchen Based on Auxiliary Evaluation Indicators
## 2 Description of Numerical Method  
### Mathematical Model  
The fundamental equations of fluid dynamics include the continuity equation, the momentum conservation equation, and the energy conservation equation. These equations describe the time-dependent variations of physical quantities in a fluid, such as velocity and temperature, and are used to analyze flow processes with transient behavior. Mathematically, they are expressed as follows,  
Mass conservation equation:  
\[\frac{\partial\rho}{\partial t}+\
abla\Big{(}\rho\overset{\rightarrow}{u} \Big{)}=0 \tag{1}\]  
Momentum conservation equation:  
\[\frac{\partial\overset{\rightarrow}{u}}{\partial t}+\Big{(}\overset{ \rightarrow}{u}\cdot\
abla\Big{)}\overset{\rightarrow}{u}=f-\frac{1}{\rho} \
abla p+\frac{\mu}{\rho}\
abla^{2}\overset{\rightarrow}{u} \tag{2}\]  
Energy conservation equation:  
\[\frac{\partial(\rho T)}{\partial t}+\text{div}(\rho\overset{\rightarrow}{u}T )=\text{div}\Big{(}\frac{K}{c_{p}}\text{grad}T\Big{)}+S_{T} \tag{3}\]where \(\rho\) in kg/m\({}^{3}\) is the fluid density, \(t\) in sec is the time, \(u\) in m/s is the velocity vector, \(f\) in N is the external force acting on the fluid per unit volume, \(p\) in N/m\({}^{2}\) is the pressure, \(\mu\) in N\(\cdot\)s/m\({}^{2}\) is the dynamic viscosity constant, \(T\) in \(K\) is the temperature of the kitchen airflow, \(K\) in W/(m\({}^{2}\cdot\)K) is the heat transfer coefficient in the kitchen, \(S_{T}\) in kg\(\cdot\)K/(m\({}^{3}\cdot\)s) is the viscous dissipation term, \(c_{p}\) in J/(kg\(\cdot\)K) is the specific heat. Equations (1)-(3) can be used to calculate the fluid density \(\rho\), velocity vector \(u\), and temperature \(T\).
# Methods of Atmospheric Coherence Length Measurement
## 3 Coherence Length \(r_{0}\) Direct Measurement Method
### Wavefront Phase Structure Function Method  
The DIMM outlined in Section 3.1 is used to measure \(r_{0}\) based on the variance of the arrival angle undulation after turbulence, and its accuracy is highly related to the target elevation angle, thus it requires hundreds of frames of data for analysis and calculation. Unlike the DIMM, an instantaneous method for measuring \(r_{0}\) has been proposed [45; 46; 47], which is known as the wavefront structure function method. According to the Kolmogorov theory, assuming that atmospheric turbulence is statistically homogeneous and isotropic as well as temporally ergodic, the phase structure function can be expressed as the time average of the phase difference between two points on the receiving plane, which is  
\[D_{\Phi}(\overrightarrow{r_{1}},\overrightarrow{r_{2}})=D_{\Phi}(r)=\Big{<} [\Phi(r)-\Phi(0)]^{2}\Big{>}, \tag{17}\]  
where \(r=|\overrightarrow{r_{1}}-\overrightarrow{r_{2}}|\) denotes the distance between \(r_{1}\) and \(r_{2}\), and \(\Phi(r)\) is the phase at the distance \(r\) from the reference point.  
Based on the reconstructed wavefront by the SHWS, the phase structure function is immediately obtained [48]. According to the Kolmogorov model, the phase structure function can be expressed as  
\[D_{\Phi}(r)=(\frac{\lambda}{2\pi})^{2}6.88(\frac{r}{r_{0}})^{5/3}[1-0.975(r/D)^ {1/3}]+2\sigma^{2}, \tag{18}\]  
where \(D\) is the diameter of the subaperture of the telescope, and \(2\sigma^{2}\) is the scattering coefficient, which is generally negligible. The schematic sketch of the wavefront phase structure function method is shown in Figure 5. The optical beam originating from the laser propagates through the atmospheric channel. After passing through a concave-convex lens, it is scaled up to a plane wave. Subsequently, another concave-convex lens is applied to scale it down to the receiver side. It is divided into two parts by a beamsplitter. One part is transmitted to the CCD for imaging, and the other is collimated by the lens and incident to the SHWS. The measurement of \(r_{0}\) using the wavefront structure function method is performed by using five steps.  
1. Obtain the Zernike 1 coefficient of the wavefront aberration for a single frame using the SHWS;
2. Subtract the initial Zernike 0 coefficient of the FSO system from the Zernike 1 coefficient of wavefront aberration, noted as Zernike 2;
3. Calculate the corresponding wavefront structure function from Zernike 2, and record it as the measured value;
4. Calculate the corresponding theoretical wavefront structure function according to Equation (17);
5. Find the atmospheric coherence length \(r_{0}\) by the least-squares estimation based on the measured value of the wavefront structure function from step (3) and the theoretical value of the wavefront structure function from step (4).
## 1 Introduction
The proposed systems could also use water from fire hydrants and could therefore be extended to fire accidents in buildings or cities. Figure 1 illustrates some of these operating scenarios. Figure 1: Application scenarios of the proposed systems: (**a**) Boat fire; (**b**) Fire on a bridge; (**c**) Fire in medium-rise buildings; (**d**) Fire at the border of a water area. We consider three novel designs of the water-powered aerial firefighting system with different motion characteristics. Each has a water pump to pressurize water and a flexible hinge to convey the water to a nozzle assembly to generate thrust. An additional nozzle is used to spray water for firefighting. The three designs are different in their ways of flight operation. They are, namely, flow rate control, nozzle rotation control, and weight distribution control. Their detailed structures are explained in Section 3. Then, we formulate the systems' dynamical characteristics. In the sections after, linear controllers with the same structure are implemented and simulation studies are conducted for all systems. The objective is to validate the maneuverability of each design and reveal the actuating method with the most desirable characteristics to be further developed. Accordingly, the novelties and contributions of this study can be summarized as follows:  
* Novel water-powered aerial firefighting systems with three different actuation methods are proposed and designed, namely flow rate control, nozzle rotation control, and weight distribution control. * Mathematical models of these novel systems are derived in form of coupled ordinary-partial differential equations. The models reveal the motion characteristics of each designed system. * A linear motion control scheme is designed and implemented for the three systems. * Simulation tests validate and compare the systems' maneuvering characteristics. The simulation scenario provides movements in all directions, but with different timing or reference to validate the responses in each direction and realize the interactions between them. Accordingly, the best solution can be obtained.
# Applications of Cement-Based Smart Composites to Civil Structural Health Monitoring: A Review
## 3 Applications to SHM of Structural Prototypes
### Medium and Full-Scale Members
From the results of the dynamic tests the following conclusions were drawn: (i) CNT concentration equal to 0.75% allows for detecting the first three vibration modes with high accuracy as confirmed by the off-the-shelf accelerometer sensors, whereas lower dosages were associated with insufficient conductive path inside the cement matrix; (ii) functionalization has great influence on dynamic strain measurements due to the enhanced bonding between CNTs and matrix, with COOH-MWCNTs showing the best results. Figure 9: Externally mounted CSC sensors on a RC beam (adapted from [54]). In ref. [81] the strain-sensing performance of a CSC sensor doped with CNT and embedded in a beam tested under three-point bending was investigated. The CSC sensor was made of water, cement and sand, with a w/c ratio equal to 1:3. MWCNTs were used as fillers in a concentration of 0.8% by weight of cement. Filler dispersion was performed according to the first admixing method, with SDBS as dispersant and sonication. A \(52\times 52\times 150\) mm\({}^{3}\) sensor was embedded into a reinforced mortar beam (the components of the mix were the same as for CSC, with the only exception of CNTs) of dimensions 160 \(\times\) 130 \(\times\) 650 mm\({}^{3}\). Eight corrugated steel bars were placed within the CSC (Figure 10) to enhance strain transfer between beam and sensor by means of a mechanical action in addition to the chemical (inter-material) bonding. The recorded sensing response revealed a non-linear trend since the elastic phase, probably due to the presence of the corrugated steel dowel affecting the load-transfer mechanism. However, the load-strain diagram showed a clear relationship between the changes of slope of the response curve and damage affecting the beam. Relevant information from the previously described experimental studies related to both CSC sensors and their applications to structural prototypes are summarized in Tables 1 and 2, respectively, in order to provide a synthetic but useful database, representing the current state-of-the-art about investigations of CSCs for civil SHM scopes, and for the identification of the main research trends and open issues. Figure 10: CSC sensor with corrugated steel dowels embedded into beam (adapted from [81]).
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Real Time-PCR  
Conventional PCR amplifies the DNA target that is later detected with an end-point analysis. In real-time PCR, the amplification product is assessed as the reaction evolves, in real time, which gives RT-PCR the possibility to track the amplification signals in real time. As for pitfalls, RT-PCR has the need for consistency with regard to reagents used [64], and careful consideration in the assay design, template preparation, and analytical methods [65].  
Real-time PCR has been widely used for the identification of _Candida_ spp. in blood and tissue samples [66], but the same does not apply for oral samples [67]. RT-PCR (using _ITS_ as target) was used to identify _Candida_ species in patients suffering from oral candidiasis, after piercing the tongue [68], with denture-induced stomatitis [69], and with diabetes mellitus [70].
## 3 Accelerated Corrosion Test of Steel Bars in Different Corrosion Degree
### Corrosion potential analysis for 9 cm protective layer  
Based on the ASTM C876 reference standard for the corrosion potential of steel bars, we obtained the critical levels of the corrosion potential of the 9 cm protective layer for slight, moderate, and serious corrosion during the accelerated corrosion periods of 192 h and 312 h, as shown in Figure 20. The test result of the reflected voltage of the ground penetrating radar in the early stage was the same as that for the 4 cm and 6 cm protective layers. The accelerated corrosion was significant at 72 h, which was 120 h earlier than the corrosion potential. The test result indicated that the reflected voltage of the electromagnetic wave of the ground penetrating radar can precisely detect the corroded steel bars on the 4 cm, 6 cm, and 9 cm protective layers.  
Figure 19: Corrosion Voltage for 7 cm Protective Layer.  
Figure 20: Corrosion Voltage for 9 cm Protective Layer.  
The protective layer is 4, 6, 7, and 9 cm. By means of the reflected voltage of the GPR electromagnetic wave, the corrosion acceleration products on the surface of the steel bar of the reinforced concrete structure are measured. Through analog signal coding and formulas 9, 10, 11, and 12, the reflected voltage of the various steel corrosion conditions is calculated and re-calculated. In different protective layers and different current-passing times, the greater the reflected voltage, the more serious the steel corrosion.  
Establishment of a Reference Standard for Determining the Degree of Corrosion of the Reflected Voltage of Electromagnetic Waves  
The image generated by the reflection signal of the electromagnetic wave was digitalized, and the changes of the reflected voltage were calculated by the relative dielectric constant, the reflection coefficient, and the reflection coefficient of reverse power. Then, the correlation between the degree of corrosion and the corrosion potential of the corroded steel bars was established. We explored the changes of reflected voltage by the ground penetrating radar detected in different protective layers and compared them with the STM C876 reference standard. The details are shown in Table 2.  
In this study, the corrosion degree of the reflected voltage value of the electromagnetic wave of 4-9 cm is defined by the corrosion potential ASTM C876, which is, for example, mild corrosion before 192 h, moderate corrosion between 192 and 312 h, and severe corrosion after 312 h, corresponding to the reflected voltage values measured by the transmitter radar at present, as shown in Table 2.