esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbial Precipitation of Calcium Carbonate for Crack Healing and Stabilization of Sandy Soils
## 2 Materials and Methods  
### Bacterial Cultivation  
The Gram-positive bacterium _Sporosarcina pasteurii_, an endospore-forming facultative anaerobe, can produce urease to decompose urea into carbonate that reacts with calcium to form calcite, which is the key to biomineralization [23]. In this study, _S. pasteurii_ (KCTC 3558) was obtained from the Korean Collection for Type Culture (KCTC, Daejeon, Republic of Korea) for the experiment. The fully assembled genome sequence of _S. pasteurii_, KCTC 3558, is deposited in the NCBI database (NCBI Genome UID = 30565) and the JGI database (JGI Genome ID = Gp0384160). The genome size of _S. pasteurii_ KCTC 3558 was determined to be 3.3 Mb. The growth medium for _S. pasteurii_ cultivation and carbonate precipitation contained 10 g/L of yeast extract, 5 g/L of proteose peptone, 1 g/L of glucose, and 24 g/L of NaCl [10]. The medium was autoclaved at 121 \({}^{\circ}\)C and 1.2 kgf/cm\({}^{2}\) for 20 min. The pH of the medium was about 6.6. _S. pasteurii_ KCTC 3558 was cultivated in the growth medium modified with 20 g/L of urea (sterilized using a 0.2 \(\upmu\)m filter) under aerobic conditions at 30 \({}^{\circ}\)C for 7 d. During the microbial growth period, _S. pasteurii_ KCTC 3558 hydrolyzed urea into ammonium, increasing the pH of the medium. To measure the optical density of cell culture solutions, a disposable cuvette with a path length of 10 mm was used. After 3 d of microbial growth, the OD\({}_{600}\) value reached 1 (approx. 10\({}^{7}\) cells/mL) and the pH increased to 9.
# Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR
## 3 Kinematic and Dynamic Analysis
### Force Analysis of RSTAR  
The RSTAR is designed to climb vertically between the walls of the tail (or in tubes) by applying pressure to the walls of the tail. First, the wheels must have a sufficient torque for climbing vertically. To rotate the wheels, the torque required by each of the side motors \(T_{WR}\) is:  
\[T_{WR}=\frac{m_{R}g}{2}r_{R}\sin(\theta) \tag{7}\]  
which is equivalent to 0.0532 Nm of torque, not including friction on each motor to climb vertically. The RSTAR must apply a normal force \(F_{N}\) to the walls (see Figure 9):  
\[F_{N}>\frac{m_{R}g}{\mu} \tag{8}\]  
The coefficient of friction (COF) between the RSTAR wheels and the BSTAR tail is \(\mu\) = 0.8. Because the weight of RSTAR is 380 g, it must apply a minimum normal force of 4.9 N. The torques acting on the sprawl and FBEM joints, respectively \(M_{sprawl}\) and \(M_{FBEM}\), can be calculated using:  
\[M_{sprawl}=(\mathbf{r}\times\mathbf{F_{N}})\cdot\overset{\kappa}{\mathbf{z}} \tag{9}\]  
and  
\[M_{FBEM}=(\mathbf{r}\times\mathbf{F_{N}})\cdot\left(\begin{array}{c}\cos( \rho)\\ \sin(\rho)\end{array}\right) \tag{10}\]  
where \(\mathbf{r}\) is the vector distance from the sprawl joint to the wall:  
\[\mathbf{r}=\left[\begin{array}{c}(L_{xR}+L_{bar}\cos(\gamma))\cos(\rho)+L_ {4R}\sin(\rho)\\ -(L_{xR}+L_{bar}\cos(\gamma))\sin(\rho)+L_{4R}\cos(\rho)\\ L_{bar}\sin(\gamma)\end{array}\right]. \tag{11}\]  
Figure 10 presents the minimum torques as a function of the sprawl \(\rho_{R}\) and FBEM \(\gamma\) angles that must be applied by the sprawl and FBEM joints to maintain the normal force of 4.9 Newtons to the walls needed to climb vertically. The black lines present the values of \(\rho_{R}\) and \(\gamma\) when the robot is in contact with walls of the tail (the tail width is 21 cm).  
Figure 9: The Rising STAR climbs onto the tail and secures itself by pushing its wheels against the tail housing.  
Figure 10 shows that the torque which must be applied by the sprawl is almost twice as large as the torque applied by the FBEM. In order to reduce the torques applied by each of the mechanisms, the width of the ramp (zero sprawl and zero FBEM) should ideally be increased. However, given that the width of the ramp \(W_{tail}\) (the black line in the figure) is restricted by the size of the robot, the best strategy consists of using a maximum FBEM angle (\(\gamma\) = 55 degrees) and a minimum sprawl (\(\rho\) = 20 degrees) which results in the smallest torques for both joints (nearly 0.20 Nm).
## 1 Introduction
[PERSON] et al. (2017) laid out a model. They conducted an experimental study to demonstrate the impact of pipe velocity, mud properties, annular clearance, and pipe eccentricity on the surge and swab pressures [12]. [PERSON] et al. (2013) utilized a Kalman filter alongside differential pressure equations to predict and calibrate real-time surge and swab pressures for Herschel-Bulkley fluids [13]. [PERSON] et al. created a prediction model for swabs and surges in concentric annuli using computational fluid dynamics methods in 2016. They attained an accuracy of up to 75% by comparing the results of experiments and simulations [14]. By considering fluid compressibility, formation, and pipe elasticity, [PERSON] et al. (2012) created a steady-state swab and surge prediction model using narrow slot geometry and regression techniques [15]. [PERSON] et al. (2015) crafted a numerical model for estimating annular pressure loss in eccentric annuli [16]. [PERSON] et al. (2016) utilized numerical simulations and regression techniques to forecast swab and surge pressures during drilling operations, achieving a maximum error of \(\pm 3\%\) compared to experimental measurements [17]. [PERSON] et al. (2018) investigated pressure loss using artificial neural network techniques and parametric studies [18]. An analytical model was built by [PERSON] et al. (2018) to compute pressure surges in Herschel-Bulkley fluids resulting from drill string movement [19]. [PERSON] et al. (2020) created a two-layer neural network to forecast swab and surge pressures. They then conducted a parametric study to determine how different parameters affected swabs and surge pressures [20]. In 2021, [PERSON] and co-authors employed numerical and random forest models to explore the dynamics of drilling fluid flow through an eccentric annulus during tripping operations. Additionally, they examined the impact of eccentricity on annular velocity and perceived viscosity profiles [21]. Deep learning techniques were used by [PERSON] et al. (2022) to predict equivalent circulating mud density during tripping and drilling operations [22]. [PERSON] et al. (2023) [23] also simulated the swab/surge phenomenon based on Bingham plastic, power-law, and Robertson and Stiff models. Results showed the inconsistency and the model prediction deviations among the models. Moreover, the work presented six machine learning models and predictions of the field telemetry swab dataset. Table 1 provides the reviewed swab / surge models for yield power law fluid behaviors along with the model controlling parameters.
## 2 Experimental Investigation  
### Description of the Problem and Basic Parameters of the Tested Specimens  
In cooperation with the structural connection manufacturer, the set of experiments was prepared to examine the anchors exposed to the tensile loading. It was one part of the wider research program focused on the special beam-to-column joint. This joint consisted of the modular vertical support (short corbel) for connecting steel and composite steel-concrete beams to the columns (Figure 1). The embedded part of this unique support allows for creating flat surfaces on the column's face, and the bolt holder has a relatively high resistance.  
Thus, the traditional detail of beam-to-column joints (e.g., hook from reinforcement as shown in Figure 2a) can be substituted by a short steel anchor with supplementary reinforcement (e.g., in the form of stirrups (Figure 2b)). This solution's main advantage is the unification and the possibility of repeated use of the same detail in the prefabrication process.  
Figure 1: Progressive corbel with supplementary reinforcement (Peikko Group) [28].  
Experimental research was focused on the following essential points:  
* the influence of the shape and dimensions of the beams and their reinforcement on the anchorage system resistance,
* the influence of the supplementary reinforcement on the resistance of the anchorage and its global effect,
* load transfer from the anchor head to the concrete.  
Detailed results of all of the experiments (14 different specimens), including the detailed material and geometric characteristics, are described in [29]. Specimens in the shape of short beams, marked 1B and 3C, were selected as input models for the numerical analysis.  
A steel anchor with an embedment depth of 140 mm was cast in both specimens. The anchor's shaft was formed by the reinforcement rebar \(\O\) 25, and the head of the anchor was created by forging at high temperature. The concrete specimens had a square cross-sectional shape (280 \(\times\) 280 mm) and a length of 1500 mm. The concrete compressive strength was determined by the testing of cube-shaped concrete samples with an edge length of 150 mm (specimen 1B--\(\ell_{\text{c,cube}}\) = 40,07 MPa; specimen 3C--\(\ell_{\text{c,cube}}\) = 52,50 MPa).  
They were reinforced with rebars (B500B), and the reinforcement scheme and geometry of the specimens are presented in Figure 3a. The arrangement of supplementary reinforcement of the headed anchor is shown in Figure 3b. The difference between the specimens was only in the presence of supplementary reinforcement around the anchor (Figure 4a,b). The supplementary reinforcement consisted of a pair of stirrups \(\O\) 10 supplemented by four horizontal rebars \(\O\) 10 (Figure 4b).  
Figure 3: Reinforcement scheme of experimentally tested specimens: (**a**) global reinforcement of specimens; (**b**) detail of supplementary reinforcement.  
Figure 2: Different types of anchoring the beam reinforcement in the columns: (**a**) traditional anchorage via hooked rebar; (**b**) headed anchor (headed bar) with supplementary reinforcement.
## 3 Numerical Validation and Results
### Joint Damage  
The present section investigates the impact of joint damage degree and quantity on overall bridge performance. To conduct a sensitivity analysis, six cases were selected, including three types of joint damage degrees (DJ-1, DJ-2, DJ-3) and three ranges of damaged joints (RJ-1, RJ-2, RJ-3). The corresponding joint damage statuses are described in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Analysis Case** & **Description** \\ \hline DJ-1 & Mid-span joint set to “contact” behavior with friction coefficient 1.40 \\ DJ-2 & Mid-span joint set to “contact” behavior with friction coefficient 1.00 \\ DJ-3/RJ-1 & Mid-span joint set to “contact” behavior with friction coefficient 0.70 \\ RJ-2 & All joints from the position of 3/8 span to 5/8 span set to “contact” behavior with friction coefficient 0.70 \\ RJ-3 & All joints from the position of 1/4 span to 3/4 span set to “contact” behavior with friction coefficient 0.70 \\ No damage condition & “Tie Constraint” is employed to simulate each interaction. \\ \end{tabular}
\end{table}
Table 5: Summary of the joint damage case.  
Figure 13: Deflection for different crack patterns.  
The comparison results of deflection curves under different joint damage conditions are presented in Figure 15. It can be observed that, regardless of the extent of joint damage for discretized joint damage types (DJ Series), it had a negligible impact on the overall stiffness distribution of the structure. For the segment with continuous damage joint distribution, the wider the range of joint damage, the wider the range of beam stiffness redistribution, and the greater the corresponding mid-span deflection. Specifically, for RJ-2 and RJ-3 conditions, there was an increase in the maximum mid-span deflection by 2.7% and 5.4%, respectively, compared to undamaged conditions. In fact, for small-span CPCR bridges, the impact of joint damage was generally significant, particularly at the quarter position. This was due to the larger shear ratio of quarter spans under small span conditions, which necessitates higher joint requirements. For long-span CPCR bridges, however, section bending moment dominated and limited the effects of joint damage on structural integrity.
# HISEA-1: The First C-Band SAR Miniaturized Satellite for Ocean and Coastal Observation
## 1 Introduction
According to in-orbit tests, the peak-to-sidelobe ratio (PRLR) is less than 27 dB, the integrated sidelobe ratio (ISLR) is less than \(-\)13 dB, the azimuth ambiguity-to-signal ratio (AASR) is less than \(-\)20 dB, and the range-ambiguity-to-signal ratio (RASR) is less than \(-\)20 dB, while the Noise Equal Sigma Zero (\(NE\sigma^{0}\)) is less than \(-\)22 dB and the absolute radiometric accuracy is 2.0 dB (3\(\sigma\)); all above satisfy the design requirements. At this moment, the HISEA-1 satellite is still in the commissioning phase and the image processing plugin is also developing. When in the operation phase, a plugin based on SAR remote-sensing software, such as SNAP and ENVI-SARscape, will be provided. We are also developing our dataset system with official and detailed instructions of data availability; as a commercial satellite, we will also release some free SAR images to the public. Based on the first batch of SAR images, we further analyzed and confirmed the powerful functions of these obtained SAR images. The results showed considerable information and landcover, including cities, mountains, farmlands, forests, rivers, lakes, glaciers, and coasts. Some typical data were initially analyzed in the next section.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 6 Conclusions  
In this paper, we propose a directional-area-forwarding-based energy-efficient opportunistic routing (shorted as DEOR) algorithm for the post-disaster MIoT network. Firstly, in order to restore post-disaster data transmission of MIoT in the planar accident area rescue scenarios, such as for a fully mechanized coal face, we design a multi-hop opportunity routing network architecture composed of one sink node and several survival sensor nodes. Then, according to the forwarding area constraint, energy constraint, and neighbor node degree constraint, DEOR constructs the candidate forwarding sets to restrict the number of duplicate packets and improve the energy utilization of nodes. Moreover, to meet multiple quality-of-service requirements of the post-disaster MIoT, a routing quality function is designed by considering the directional angle, transmission distance, and residual energy attributes of nodes. DEOR selects a relay node to forward packets based on the priority of the nodes in the CFS, which ensures the network load balancing. Finally, we propose a recovery mechanism aimed at bypassing the void area and forwarding the packets continuously, which can reduce retransmissions and improve network connectivity. The simulation results demonstrated the proposed DEOR algorithm achieves better performance compared to the ORR, OBRN, and ECSOR in terms of energy consumption, average hop count, packet delivery ratio, and network lifetime.  
In our future work, we intend to investigate a more reasonable method to obtain the location information of sensor nodes for post-disaster mining scenarios. Moreover, how a realistic post-disaster mine roadway simulation experimental platform can be constructed is also a meaningful research topic.  
Conceptualization, Q.Z. and W.Y.; writing--original draft preparation, Q.Z., W.Y. and L.Z.; writing--review and editing, Q.Z, W.Y. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Nature Science Foundation of China under Grant 6207/1032.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Case Study of Developing Liver Cancer Diagnosis
### Customizing the Platform for HCC Diagnosis
#### 5.3.3 Integrating an Image Feature Classifier and an HCC Type Classifier  
The appearance of HCC on CT changes over time as the tumor grows and develops a blood supply. These changes are particularly evident when contrast agents are used, like CECT. The contrast enhancement pattern over different phases (arterial, venous, and delayed) provides important diagnostic information. HCC typically shows hypereenhancement in the arterial phase (due to its increased arterial blood supply) and washout in the venous or delayed phases (due to its lack of portal venous blood supply).  
We applied the Long Short-Term Memory (LSTM) algorithm, a type of RNN, to capture these temporal changes. The input to the LSTM consists of a sequence of medical feature sets observed on different phases of the same detected tumor, where the medical features are identified by the CNN Image Feature Classifier. Hence, we integrated the CNN Image Feature Classifier (Step 6 in the process) and the LSTM HCC Type Classifier (Step 7 in the process).  
The structure of the integration and information flow in this integration are shown in Figure 12.
# YOLO-OHFD: A YOLO-Based Oriented Hair Follicle Detection Method for Robotic Hair Transplantation
## 5 Conclusions  
The YOLO-OHFD method marks a significant advancement in the field of robotic hair transplantation. It addresses critical challenges such as mutual occlusion and variable orientations of hair follicles by effectively integrating oriented bounding boxes, enhanced feature extraction, and alignment techniques. The method's high precision and efficiency demonstrate its potential to significantly improve the outcomes of hair transplantation procedures by providing reliable, rapid, and accurate hair follicle detection.  
This research contributes to the broader field of medical imaging and robotic surgery by providing a framework that could be adapted for other complex recognition tasks in medical robotics. Future research will focus on optimizing the computational efficiency of YOLO-OHFD to facilitate wider adoption and integration into clinical settings. Moreover, efforts will be directed towards enhancing the robustness of the model against variations in image quality and exploring its applicability in dynamic surgical environments.  
The possibility of extending this framework to classify hair follicles based on individual hair quality could also be explored. This would enable personalized treatment planning in hair transplantation, potentially extending the applicability of YOLO-OHFD beyond hair loss treatments to other dermatological and surgical practices, thus broadening the impact of this research in medical technology.  
X.L.: Software, Methodology, Writing--original draft. H.W.: Supervision, Validation, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 62173351, and in part by the Project of Innovation-Driven Plan in Central South University under Grant 2021 zzts0197.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Xiangya Hospital, Central South University (protocol code 20161169 and date of approval 2016-11).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.  
**Acknowledgments:** We sincerely thank [PERSON] and [PERSON] for their significant contributions to this study. Their support in providing the hair follicle imaging dataset and assisting with the categorization of hair follicles in dermoscopy images was invaluable. Their involvement in data collection and result analysis played a crucial role in the success of this research. We deeply appreciate their expertise and collaboration, which have greatly enhanced the quality and impact of this work.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# MonoMR: Synthesizing Pseudo-2.5D Mixed Reality Content from Monocular Videos
## 3 MonoMR System
### Extracting Person Texture Using Background Subtraction  
A segmentation procedure is performed to extract the texture of the detected person in the video. Graph cuts [38] and mask R-CNN [33] are the standard algorithms for the segmentation, however these algorithms have high computational complexity. We propose a simple method to extract the person's texture using a background subtraction algorithm for efficient texture extraction.  
Given that most videos constantly change foreground and background, the foreground objects extracted with the vanilla background subtraction algorithm based on image difference do not have sufficient quality. We use a k-nearest neighbor (KNN)-based background subtraction method [39], one of the Gaussian mixture model (GMM)-based methods. GMM-based algorithms are robust to repeated, slow motion, and constantly changing lighting conditions. Thus, these algorithms can be used in most videos that have constantly changing foregrounds and backgrounds. Notably, the KNN-based background subtraction algorithm automatically updates the parameters in real-time and selects only the components required by each pixel. Therefore, the processing time of the KNN-based algorithm is reduced compared with the existing GMM algorithms, even with better quality. In addition, the background image without foreground objects can be acquired with this algorithm, and this image can be utilized in the generated content.  
The foreground image extracted by the KNN method may contain noises and holes. In order to remove them, morphological operations are applied to the foreground image. After noise removal, only the moving objects' textures are extracted by masking with the bounding boxes. Figure 4 shows the results of the background subtraction procedure.  
Figure 4: Results of the texture extraction procedure. (**a**) Input image, (**b**) foreground mask, (**c**) foreground segments, and (**d**) background image.  
Figure 3: Proposed method for estimating the pseudo-3D position of a person in the image. The ankle position detected in the image coordinate system (\(i\), \(j\)) is mapped using a homography matrix to estimate the real-world coordinate system (\(x\), \(z\)).