esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Quantitative Analysis of Pore-Throat Network
### Quantitative Analysis of Throat  
#### 5.2.1 Throat Radius  
The throats serve as the connection between the pores and play a vital role in the permeability of rocks. Figure 16 shows the distribution of throat radius in the REVs of the carbonate samples, from which positive skewness was observed for all the samples. The throat radius of carbonate was primarily in the range of 0-200 \(\upmu\)m. The average throat radius was smaller than the average pore radius (see Section 5.1.1). The average radius of throat was larger for samples A and C compared to samples B, D and E, similar to that observed for the average radius of pores in Section 5.1.1.  
A comparison for the distribution of throat radius between the sandstone and the carbonate is provided in Figure 17, where a confidence interval of 95% was imposed. As shown in Figure 17, the average throat radius of carbonate was much larger than that of sandstone. The 95% confidence interval for the throat radius of sandstone was 0-60 \(\upmu\)m (sample S), while for carbonate, it was 0-330 \(\upmu\)m (samples A and C), 0-160 \(\upmu\)m (samples B, D and E), respectively.
## 5 Performance Analysis
### Discussion  
The D-LEACH protocol was found to be superior to LEACH and MH-LEACH in terms of network life, power consumption, and BS data reception. The reasons for this are as follows.  
1. LEACH and MH-LEACH transmit data to the CH, closest to the sensor node, and do not consider the transmission distance or direction of transmission. Even if the BS is located close to the sensor node, the data are sent to the distant CH. D-LEACH determines the transmission direction by comparing the relative distances of the sensor node (to the BS, and the sensor node) to the CH. It transmits data to the CH near the BS and saves energy consumed by direct transmission if there is no CH that meets these conditions. This shows excellence even when the network size increases. Sensor nodes consume a large amount of energy in data transmission, which is related to the lifespan of sensor nodes. Saving transmission energy extends the life of the sensor node, which extends the lifetime of the network.  
Figure 21: The number of packets received by base station (BS) while the network is stable.  
Figure 20: Energy consumption (300 \(\times\) 300 m\({}^{2}\)).  
2. During CH selection, LEACH and MH-LEACH do not check the residual energy of the sensor node. If a sensor node with a small residual energy is selected as the CH, it consumes a large amount of energy in data merging, compression, and transmission, and causing the sensor node to die quickly. D-LEACH ensures that sensor nodes with residual energy below the critical residual energy are not selected as the CH, thus maintaining the energy balance of the sensor nodes.  
The lifetime of WSNs affects the network performance of the IoT system, and the lifetime of WSNs is determined by the lifetime of the sensor nodes. D-LEACH is thus superior to LEACH and MH-LEACH in terms of network lifetime, power consumption, and data throughput.
# Determining the Optimal Configuration of the Multi-Ring Tree for Bluetooth Multi-Hop Networks
## 3 Routing Protocol
### A Self-Routing Algorithm
If \(j\) is less than \(i\) and Equation (3) is satisfied, then the packet will be forwarded in a counterclockwise direction in the ring subnet; otherwise, the packet is forwarded in a clockwise direction. \[\left|r_{j}-r_{i}\right|\leq P/2 \tag{3}\]  
The packet routing of the multi-ring tree network is generally classified into two cases: the intra-root and the inter-root routing. In the intra-root subnet routing case, a source node and a destination node are located at the same tree subnet under a root; the addresses of the Ring-ID and Root-ID in the packet header are the same. The destination of the root then computes the location of the destination according to the Tree-ID and forwards it to the destination via the resulting paths of the tree-addressing criterion. In the inter-root subnet routing case, the packets are first forwarded from the tree subnet to the root of the source node. The source root \(r_{\text{s}}\) then checks the Ring-ID and delivers packets to the ring layer of the destination root by the Ring-ID. After the packets are delivered to the ring layer of the destination, the packets are propagated to the root of the destination \(r_{d}\) according to Equation (3). Finally, the destination root \(r_{d}\) transmits the packets downstream to locate the destination node by the intra-root routing method when the destination is in its spanning tree subnet. As a result, the scatternet self-routing protocol of the multi-ring tree can be achieved by the proposed addressing method. In addition, the pseudo code of the multi-ring tree self-routing algorithm is listed in Algorithm 2.
# The Impact of Optogenetics on Regenerative Medicine
## 3 Biomedical Applications of Optogenetics
### Applications in Bone Repairing  
The development of optogenetic tools has improved research on the role of Ca\({}^{2+}\) channels in cell regeneration. The opening of Ca\({}^{2+}\) channels is typically allowed by proteins stimulated with blue light; this biological condition has been linked to cell proliferation in specific degenerative diseases [32]. Recent research has demonstrated that the application of electrical stimulation promoted the activation of calcium channels in osteoblasts: Ca\({}^{2+}\) oscillations were monitored in mesenchymal stem cells during osteogenic differentiation. The role of Ca\({}^{2+}\) in osteoblasts was investigated through the optogenetic technique, using the blue light stimulation (488 nm) to activate the Ca\({}^{2+}\) channels (BACCS) BACCS is an optogenetic tool: MC3T3-E1 cells were used as a model for the differentiation towards osteoblasts, suggesting the effective expression of BACCS under luminous impulse [32] highlights how the Ca\({}^{2+}\) channels play a fundamental role in osteoblast differentiation [33].
# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 4 Conclusions  
In long-distance infrared detection, targets are characterized by their small size and sparse feature information within complex scenes, posing significant challenges for the accurate and efficient detection of dim and small targets. To address challenges related to dim and small target feature information, low signal-to-noise ratio, and detection difficulties in infrared images, we propose a dim and small target detection algorithm that mimics vision focus via dual-input information and fuses locally extracted feature information with global features in a multi-scale manner. Our approach utilizes local components to suppress complex background input and amplify target features. Simultaneously, the fusion design incorporates global and local input features at multiple scales, enabling the extraction of both spatial and semantic information during dim and small target detection. Furthermore, incorporating a \(1\times 1\) convolution module and using the concat operation effectively reduces the computational load of the model during the feature extraction and fusion process. Experimental results show that the mAP\({}_{50}\) and mAP\({}_{50.95}\) of the proposed algorithm improve by more than 2% and 3.6%, respectively, on datasets with different scenes and noise compared to the baseline algorithm, achieving an average accuracy of 75% even in complex scenes. In the future, we will continue refining the vision focus mechanism and further enhance the model's generalization ability. Our goal is to achieve accurate detection of dim and small targets in various infrared scenes while better adapting to the analysis and processing requirements of infrared environments.  
Conceptualization, X.L. and Z.Y.; methodology, Q.S. and J.L.; writing--original draft preparation, C.Z.; writing--review and editing, B.W. All authors have read and agreed to the published version of the manuscript.  
This material is based on research/work supported by the National Science and Technology Foundation of China under Grant 2023-JCJCQ-JJ-0976.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the editors and anonymous reviewers for their valuable comments, which helped improve this paper.  
The authors declare no conflicts of interest.
## 3 Tunnel Detection Method
### Layout of the Survey Lines  
We set up 15 survey lines (Figure 2) to ensure the integrity of the detection data, adequate resolution, and adequate signal-to-noise ratio in the measurement area, and to guarantee that the design is convenient, economic, and reasonable for actual flight. In total, 11 parallel survey lines, numbered L01-L11 from north to south, were set along the tunnel direction on both sides of the tunnel central line L06. In actual flight, L06 was set as a repeated flight line. Each measuring line was 9.9 km long, extending 700 m at the entrance and exit of the tunnel. Among the 11 survey lines, the interval of adjacent lines was set to 30 m for L4-L8, and 50 m for L1-L4 and L8-L11. Simultaneously, to identify the abnormal area better, four vertical survey lines were arranged in the location above the tunnel where the terrain fluctuated violently, the surface water system was developed, and tunnel burial depth was large. The corresponding number from the exit to the entrance was T1-T4.  
Figure 2: Flight line map over a satellite image. (**a**) General survey line layout. (**b**) Tunnel exit survey line layout. (**c**) Tunnel entrance survey line layout.
## 2 Theoretical Analysis
### Implementation of CV Mode with ZPA Condition
The LCC-S compensation topology can implement CV charging mode under ZPA conditions, regardless of the battery equivalent resistance, which varies from 7 to 40 \(\Omega\), as shown in Figure 9a. Moreover, the gradient of the voltage transfer gain \(G_{\text{UU}}\) line at approximately 85 kHz is insensitive to the change of the working frequency, indicating the LCC-S compensation topology can provide a stable CV-output over a wide frequency range. As shown in Figure 9b, when the operating frequency is less than the resonant frequency, \(Q_{\text{IN}}\) is positive under high load conditions, indicating that the system presents inductive, but when the operating frequency is greater than the resonant frequency, \(Q_{\text{IN}}\) is negative under high load conditions, indicating that the system shows capacitive. It is worth noting that \(Q_{\text{IN}}\) has three ZPA points when \(R_{\text{L}}=7\)\(\Omega\), indicating that the system first appears as capacitive and then appears as inductive near the resonance frequency point. Therefore, in order to ensure the soft-switching of the AC switch, the parameter design of compensated inductance \(L_{1}\) is designed to make the actual value slightly greater than the theoretical value.
## 4 Discussion
### Vegetation Trends on LULC Classes and Impacts of LULC Changes on Vegetation  
Information on LULC changes is important to understand the reasons for changes in vegetation biomass [46]. The declining vegetative biomass observed in commercial cultivation, shrub/grassland and subsistence cultivation and residential areas suggests several reasons for the changes in vegetative biomass in the district. The decline in NDVI in commercial cultivation could be due to land tenure conflicts following the land redistribution programme in the GDSM [12], which has resulted in land lying fallow. The vegetation decline in subsistence cultivation is due to the declining rainfall. [PERSON] et al. [17] noted that smallholder farmers in the GSDM district find it difficult to achieve high crop yields due to unreliable rainfall. Subsistence farming areas are most affected by drought, so more fields have been abandoned [12; 17; 57].  
The highest conversion in the district over the past 30 years is shrub/grassland to bare/exposed rock and eroded land (fifth common conversion), which occurred due to recurrent drought and extreme weather events such as flash floods [17]. These factors, together with dispersive soils [35] predispose the district to soil and water erosion. The increase in vegetation production in bare, eroded land, subsistence cultivation and thicket/dense bush may be due to bush encroachment. The overgrazing that occurred in abandoned cropping fields in communal land (subsistence cultivation) has led to bare soil and erosion. Since this has encouraged bush encroachment, an increase in NDVI was recorded [12; 15]. The increase in NDVI in mines/quarries was due to vegetation development when most operational mines declined, as a result of declined commodity demand and a lack of water [36].
# Optimizing Vehicle Body Cross-Sections Using a Parametric Mathematical Model
## 2 Establish the Geometric Simplified Model of Truss Body Structure  
Currently, passenger cars utilize load-bearing bodies, which are typically categorized into four structures: truss body, monocoque body, overall sheet metal space frame, and composite body.  
The truss body structure deviates from traditional design by utilizing thin plate stamping parts to form the body. The body skeleton is composed of aluminum alloy extrusion parts, allowing for complex and variable cross-section shapes that are determined by the load borne by the beam structure. This design significantly reduces the weight of the body. Meanwhile, the truss body structure has a low manufacturing cost, a good lightweight effect, and a short design cycle, making it especially suitable for preliminary development research. Figure 1a displays the initial aluminum body skeleton of a new pure electric vehicle at the conceptual design stage.  
Figure 1: Initial skeleton made of aluminum. (**a**) Body skeleton; and (**b**) Simplified wireframe model of body geometry.  
Due to the need for rapid solution modifications during the conceptual design phase, it is challenging to directly use a structurally detailed body model to analyze multiple technical solutions with significant differences [8]. Therefore, a fully parameterized model that is suitable for quick modifications must be established. To better meet the needs of conceptual design, the body skeleton structure shown in Figure 1 is first simplified as follows:  
1. To closely resemble the real model, we have selected the main load-bearing beams and have not considered the beams that have less impact on the body performance due to the arrangement of the battery and other accessories.
2. We have extracted the main feature points and feature data of the body structure based on its shape and recorded the coordinates of each feature point in the global coordinate system.
3. The curved beam is approximated by multiple straight beams.  
The wireframe model of the body structure is constructed using this method. The connection relationship between the linear beams is then checked for accuracy and smoothness. The simplified geometrical model of the body, obtained after completion, is shown in Figure 1b.
###### Abstract  
Increasing industrial development and digital transformations have given rise to a technology called Digital Twin (DT) that has the potential to break the barrier between physical and cyberspace. DT is a virtual and dynamic model enabled through a bidirectional data flow that creates high-reliability models with interconnection and fusion between the physical and digital systems for full integration. In smart manufacturing, this technology is increasingly used in research and industry. However, the studies conducted do not provide a definition or a single integrally connected model. To develop the Digital Twin shown in this research, the literature was reviewed to learn about the enabling technologies and architectures used at the industrial level. Then, a methodology was used to obtain the physical process information, create the digital environment, communicate the physical environment, apply simulation models in the digital environment, and parameterize the simulation environment with the physical process in real-time to obtain the digital twin supported with augmented reality. The system was implemented in the MPS-500 modular production station that has industrial sensors and actuators. The virtual environment was designed with Blender and Vuforia to create the augmented reality environment. In the proposed methodology, robust devices (field and control level) and low-cost embedded systems were used for the creation and communication of the virtual environment (monitoring and control); for the joint work of these technologies, they were carried out through the use of the following protocols: Open Platform Communications United Architecture (OPC UA), Ethernet, and machine to machine (M2M), with which a communication was achieved between the different levels of the automation pyramid. The results show that the proposed methodology for the implementation of the DT allows bidirectional communication between the physical and virtual environment and can also be visualized with the support of AR, thus providing its characteristics to the proposed DT. Digital Twin is an essential factor in creating virtual environments and improving applications between the real and digital world, establishing a bidirectional communication through the Ethernet protocol, with a communication time of approximately 100 ms. This technology interacts with the virtual environment and performs mappings, thus achieving timely and dynamic adjustment. This improves data management and production and incorporates process simulation and physical control in real-time, allowing to execute and trigger actions in the physical equipment simultaneously.  
digital twin; IIoT; industry 4.0; augmented/virtual reality; CPS 2023
## 5 Conclusions  
Based on 3D point cloud data, this study utilizes Hough space transform to calculate point cloud normal vectors and employs vector differences as criteria for region growing, effectively achieving the identification and information extraction of rock mass structural planes. In the experimental validation using a hexagonal pyramid, six structural planes were successfully identified. The accuracy of the region growing was evaluated using visualization methods and recognition rates \(\omega\). The number of rock mass structural planes identified by this method matched the theoretical results, and the precision of the extracted structural plane orientations also met engineering specification requirements.  
After completing the identification and information extraction of the mass structural planes, these surfaces were used as clustering objects, with the sine squared value of the normal vector differences serving as the clustering distance \(d_{dist}(n_{i},n_{j})\). A cutoff distance \(d_{c}\) was set to filter out outlier surfaces, and the Fast Search and Find of Density Peaks (CFSFDP) clustering method was employed for clustering analysis of the identified surfaces. This approach not only addresses the issue of high complexity in calculating clustering distances with CFSFDP, which is suitable only for small datasets, but also improves clustering accuracy and noise resistance of the method. When applied to high and steep highway slope, the method successfully identified two dominant structural planes groups, with results closely matching those from RocScience Dips 7.0 clustering, and the orientation error of the dominant structural planes was within \(3^{\circ}\). Overall, the method demonstrated strong practical value for large-scale geological surveys of high and steep rock masses. Future research will focus on further improving algorithm efficiency and handling larger datasets.  
Conceptualization, J.Z., Y.X., B.W., Z.Y. and K.Y.; methodology, J.Z., Y.X., B.W., Z.Y. and K.Y.; software, J.Z.; validation, J.Z., Y.X., B.W., Z.Y. and K.Y.; resources, Y.X., B.W., Z.Y. and K.Y.; data curation, J.Z., Z.Y. and K.Y.; writing--original draft preparation, J.Z.; funding acquisition, J.Z., Y.X., B.W., Z.Y. and K.Y. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China (No. 41861054).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the first author upon request.  
**Acknowledgments:** We thank the Second Survey Institute of Yunnan Geological Engineering for their technical support. We also thank the reviewers and editors of the Journal of Applied Sciences for their hard work.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the Yunnan Geological Engineering the Second Investigation Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 2 Related Work
In [25], the author aims to evaluate the positioning performance in terms of accuracy and availability while considering different deployment strategies. To reduce the power consumption of positioning devices, in [26], the author provides a wireless IoT device alliance based on the framework of common interests, energy and physical awareness, which improve the energy efficiency of the whole system, reduce power consumption, and extend the battery life of the equipment. In [27], the author proposes an LPHAP solution under the framework of the 3 GPP NR standard. The research and development of LPHAP technology provide key support for solving the bottleneck problem of IIoT application development, and currently, related research is quite limited. The purpose of this paper is to provide a joint UL-AoA and UL-TDOA for IIoT sensors positioning with a constriction of resource overhead, power restriction, and traffic condition, according to the real IIoT environment [1; 7]. That is to say, an asymmetric IIoT network is considered in this paper. IIoT sensors are power restricted and can sustain a long lifetime without changing the battery (typically, this could be 1-2 years). Further, uplink traffic is dominant according to identified scenarios [2]. The resource overhead for positioning is analyzed in this paper. The work in this paper assumes a millimeter wave communication supporting a finer delay domain resolution, and presents the corresponding test results after giving an overview of PRS and SRS-Pos design. Our simulation-based evaluation shows that network configuration plays a vital role when it comes to achieving a high-accuracy positioning performance, and the joint positioning method outperforms the AoA only and TDOA only. The rest of this paper is organized as follows. In Section 3, DL PRS and UL SRS-Pos are reviewed. Then, the proposed algorithm for IIoT is described in Section 4. Section 5 presents the test results, and the conclusion is drawn in Section 6. For easy description and reading, some notations are summarized in Table 2.