esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dispensing Robot System and Laser Spot Location Principle  
### Principe of the Dispensing Robot System  
The location and height measurement system of the dispensing robot is shown in Figure 1. The LTDS was fixed on the end of the manipulator to measure the workpiece in real time. It was designed based on direct-injection laser triangulation imaging [26]. The laser light emitted by a laser diode (LD) was focused by a collimated lens and projected vertically onto the object to form reflected light. The reflected light propagated in space by way of diffuse reflection, and then the reflected light was focused by the receiver lens in the direction of the observation angle \(\varepsilon\) and formed a laser imaging spot on the photosensitive imaging device (CMOS). The position of the imaging spot on the CMOS was obtained by the spot location algorithm. When the object moves s, the position of the imaging spot moves \(\Delta x\) on the CMOS accordingly, and the displacement s can be calculated according to Equation (1), where \(l\) and \(f_{po}\) are the object distance and image distance of the receiver lens group, and \(\beta\) is the imaging angle:  
\[s=\frac{\Delta x\cdot l\sin\beta}{f_{po}\cdot\sin\varepsilon-\Delta x\cdot l \sin(\alpha+\beta)} \tag{1}\]  
During the high-speed movement when the dispensing robot was working, the surface and height of the workpiece could suddenly change, which inevitably led to distortion of the laser imaging spot and reduced the measurement accuracy. The different surfaces and heights of the workpiece led to a change in the shape and position shift of the imaging waveform, respectively. According to the characteristics of an actual imaging waveform, considering the static and dynamic working mode of the robot, we proposed a fast laser intensity iterative adjustment method to obtain an ideal waveform for laser spot location, in order to satisfy measurement accuracy and response speed in both.
## 3 Scan for Heritage BIM Processing for Ad Deir Tomb
### Data Collection and Preprocessing  
Data collection and pre-processing involved the utilization of the Mensi GS100 Laser Scanner to acquire the surface point cloud data for our investigation. This advanced scanner boasts a remarkable maximum capture range of 100 m, a wide 360-degree by 60-degree field of view, and an impressive precision of 6 millimetres. Concurrently with data collection, the scanner's attached camera captured calibrated video snapshots at a resolution of 768 \(\times\) 576 pixels, seamlessly integrating them with the corresponding point measurements [46]. Given the scale and complexity of the structure, a single laser scan proved insufficient to cover the entirety of Al-Deir tomb. Consequently, five distinct viewpoint scans were meticulously conducted to address occlusions and obstructions, resulting in the acquisition of nearly 6 million points with a ground sampling distance (GSD) of 3 cm.  
The 3D terrestrial laser scanning process involved the emission of laser pulses from the scanner, which bounce off surfaces and return to the scanner, allowing for precise measurements of distance and elevation. This methodology ensures not only geometric accuracy but also captures detailed texture and color information, providing a comprehensive representation of the scanned object. In the case of Al-Deir tomb, the scanner's ability to capture texture alongside geometric data enabled the creation of highly detailed models that accurately depict the monument's visual appearance.  
Moreover, the versatility of TLS allows for the scanning of large-scale structures with intricate details. However, due to the limitations of line-of-sight and potential occlusions, multiple scans from different viewpoints are often necessary to comprehensively capture the entire structure. This meticulous approach ensures that even obscured areas are accounted for, resulting in a comprehensive and accurate representation of the architectural features.  
Following data acquisition, post-processing steps are undertaken to refine and enhance the collected point cloud data. This includes noise reduction to eliminate any extraneous data points and alignment of multiple scans to create a seamless, unified model. Through the integration of advanced software tools like Cloud Compare v. 2.11.3, the individual scans are aligned into a single coordinate system, ensuring spatial coherence and accuracy across the dataset. Figure 5 depicts [PERSON]'s completed model as a coloured point cloud. Figure 6 depicts the monument's mesh model.
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 5 Results
### Quantitative Evaluation Results  
#### 5.1.1 Clustering Evaluation  
The clustering evaluation metrics for the TrendFlow framework are presented in Table 1. The best score is made bold from each metric. DBSCAN does not have any results, since it failed to detect the number of clusters correctly.  
In Table 1, we present a comparison of five different clustering algorithms employed in the TrendFlow framework. The evaluation of the clustering results is based on three metrics: the adjusted rand index (ARI), the silhouette score, and the Calinski-Harabasz index (CHI).  
The Gaussian mixture model (GMM) achieved the highest ARI with a score of 0.7202, indicating that it has the best performance in terms of producing similar cluster assignments to a ground truth labeling. Meanwhile, spectral clustering yielded the second-highest ARI score of 0.7087, followed closely by the K-means clustering algorithm at 0.6944. Agglomerative clustering had a lower ARI score of 0.6201. Unfortunately, the DBSCAN algorithm did not yield any results for this evaluation, as it failed to detect the correct number of clusters, which is three in this context.  
In terms of the silhouette score, which measures the intra-cluster similarity and inter-cluster dissimilarity, K-means obtained the highest value at 0.0843, followed by GMM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Adjusted Rand Index** & **Silhouette Score** & **Calinski-Harabasz Index** \\ \hline K-means & 0.6944 & **0.0843** & **17.362** \\ GMM & **0.7202** & 0.0839 & 17.356 \\ DBSCAN & - & - & - \\ Agglomerative & 0.6201 & 0.0782 & 16.261 \\ Spectral & 0.7087 & 0.0836 & 17.349 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clustering evaluation results for the clustering algorithms used in TrendFlow framework.  
with a score of 0.0839 and spectral clustering with 0.0836. Agglomerative clustering had the lowest silhouette score of 0.0782, suggesting that it might produce clusters with lower intra-cluster similarity or higher inter-cluster similarity compared to the other algorithms.  
Lastly, the Calinski-Harabasz index assesses the quality of clusters by considering both intra-cluster variance and inter-cluster variance. Here, K-means performed the best with a score of 17.362, closely followed by GMM at 17.356 and the spectral clustering algorithm at 17.349. Agglomerative clustering had the lowest CHI score of 16.261.  
In conclusion, the Gaussian mixture model performed the best in terms of the adjusted rand index, while K-means had the highest scores for the silhouette score and Calinski-Harabasz index. The choice of the most suitable clustering algorithm for the TrendFlow framework would depend on the desired qualities of the resulting clusters, such as higher intra-cluster similarity, lower inter-cluster similarity, or a closer alignment with a ground truth labeling.
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 1 Introduction  
Cardiovascular diseases are the leading cause of death in the world, and the number of people with heart failure is rising dramatically. Over the years, implantable medical devices have seen tremendous progress as they help with the early diagnosis of symptoms related to cardiovascular diseases. Therefore, there is an urgent need to develop an inductive coupling system that facilitates early diagnoses even before hospitalization. A significant amount of research is devoted to monitoring the hemodynamic parameters of the heart for the early diagnosis of heart failure. Some of the existing works are discussed in the following literature review.
## 3 Prediction Results of Mining Surface Subsidence of Northern Pei County for Reusing Land Resources
### The Influence of Geological and Mining Conditions on Prediction Parameters
#### 3.1.6 Mining Degree  
Mining degree refers to the surface subsidence extent caused by the mining under the fixed geological and mining conditions. The ratio of mining width and mining depth D/H is usually taken as the measurement of mining degree. When the mining degree is small, the subsidence coefficient and the horizontal movement coefficient increase with the growing value of D/H. When the mining degree reaches the full-mining, the subsidence coefficient and the horizontal movement coefficient basically remain stable.  
In addition, the inflection point offset can be influenced by the mining degree. When the mining degree is small, the overlying strata form the voussoir beam structure, and the roof caving is insufficient that causes the inflection point offset to become larger. As the mining degree increase, the inflection point offset will decrease. The inflection point offset keeps a certain value when the mining degree reaches full-mining. Consequently, the mining degree does not have much effect on the mining influence propagation angle and the tangent of the major influence angle.
# Safety Risk Assessment of Highway Bridge Construction Based on Cloud Entropy Power Method
## 4 Project Example Analysis
### Risk Weight Calculation Based on Cloud Entropy Weight Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Risk Indicator** & \(\mathbf{Ex_{j}}\) & \(\mathbf{Ex_{n}}\) & \(\mathbf{He}\) & \(\mathbf{\omega_{j}}\) & \(\mathbf{\beta_{j}}\) \\ \hline C1 Personnel quality & 69.600 & 10.152 & 1.791 & 0.091 & 0.082 \\ C2 Organizational completeness & 55.800 & 23.011 & 6.243 & 0.059 & 0.066 \\ C3 Construction management strength & 71.400 & 15.892 & 1.734 & 0.083 & 0.084 \\ C4 Temporary structure safety & 74.200 & 14.438 & 5.015 & 0.088 & 0.088 \\ C5 Construction material error & 79.000 & 15.629 & 4.246 & 0.092 & 0.093 \\ C6 Material equipment stacking & 68.300 & 13.573 & 3.008 & 0.083 & 0.081 \\ C7 Mechanical failure & 55.600 & 24.878 & 4.212 & 0.058 & 0.066 \\ C8 Geological conditions & 75.300 & 15.428 & 5.133 & 0.088 & 0.089 \\ C9 Climatic environmental conditions & 87.400 & 10.202 & 2.600 & 0.114 & 0.103 \\ C10 Natural disasters & 63.200 & 21.031 & 6.530 & 0.069 & 0.075 \\ C11 Rationality of the design & 77.000 & 14.288 & 5.956 & 0.092 & 0.091 \\ C12 Process maturity & 71.000 & 15.040 & 3.617 & 0.084 & 0.084 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Objective weighting of risk indicators. 3. After the improvement, the difference in the weight distribution becomes larger, and the weight after the improvement is generally between 5% and 11%. The weight range between the top and bottom indicators nearly doubles, which can avoid the problem of averaging weight values. In summary, the traditional weight calculation method using Equation (12) only uses the expected value \(E_{x}\) in the cloud model for calculation, without making full use of the variation in entropy in the cloud model, and cannot guarantee the objectivity of the results. If the expected values \(E_{x}\) of all indicators are not very different, the calculated weight value will tend to be averaged and cannot objectively reflect the actual situation. However, in fact, the entropy \(E_{n}\) of each indicator varies greatly. Based on this finding, this paper proposes an improved entropy weighting method of the cloud model shown in Equation (13) to replace this traditional weighting method, which can effectively avoid the problem of averaging the weights of evaluation indicators.
## 4 Results and Discussion  
### Underground Implementation of a Digital-Twin-Based Spatial Object Extraction Algorithm  
#### 4.1.1 Linear Spatial Object Extraction Process  
To extract 3D objects, point cloud data were gathered from the sensors and subjected to preprocessing, and feature points (descriptions) were computed. These data were then compared with the information stored in the object database to extract details such as location and direction. The algorithm clustered the point cloud data and extracted feature points, then compared them to obtain information such as location and direction. This process is depicted in Figure 3.  
The first phase of the process is sampling, which involves choosing sample points for object extraction within the point cloud. Second, region of interest (RoI) filtering is undertaken to isolate areas of interest, minimizing resource usage and time consumption. Third, outlier (noise) removal filtering is conducted to eliminate points that do not exist in the point cloud. These are points from sensor errors that were initially purged from the internal process of the sensor but still lingered.  
The fourth phase is background filtering, which omits fixed structures such as walls, slabs, and floors when line objects are extracted. Points belonging to fixed regions are removed for being part of the background. In the fifth step, the algorithm executes segmentation to categorize objects, finding correlations among points based on their K-nearest neighbors, minimum cluster, and maximum cluster values.  
At the sixth stage, the preprocessing ends, and objects are generated by identifying feature points through the key point extraction process, which isolates actual objects. Based on this, the description process employs descriptors to compare feature points. In the seventh step, the algorithm matches linear objects by comparing the points with existing data, and correspondence grouping is executed to form object clusters.  
Finally, the iterative closest point refinement process makes refinements through repeated points, and an administrator undertakes the hypothesis verification step to validate the hypothesis.  
Figure 3: Linear spatial object extraction process.
# IoT Services for Monitoring Food Supply Chains
## 3 Methodology and Implementation
### Platform Implementation
Sensors are pre-calibrated and configured with unique device and application key identifiers. Testing parameters include validating transmission intervals of 15 and 30 min, ensuring signal strength (RSSI \(>-120\) dBm), and acceptable signal-to-noise ratios (SNR \(>-10\) dB). Data integrity is verified through the packet delivery success rate. LoRaWAN configurations include setting devices to Class A for energy efficiency, enabling Adaptive Data Rate (ADR) to optimise spreading factors (we have used SF7-SF12 based on distance), and selecting frequency bands compliant with European Telecommunications Standards Institute (ETSI) regulations such as the EU863-870 MHz Band. Gateways are installed with line-of-sight to maximise range, and backend servers are configured to decode and store sensor payloads for visualisation and analysis. Regarding the gateway's software environment, it was configured with ChirpStack Gateway OS (version 4.8.0), which integrates the packet forwarder and network bridge components necessary for LoRaWAN communication. This version was selected for its stability, compatibility with multi-channel LoRa configurations, and seamless integration with the ChirpStack Network Server (version 4.13.0). The Software Implementation component serves a crucial role in the overall system architecture by receiving data that has been processed and transmitted from the Field Implementation component. Once the data streams reach the Software Implementation component, they are hosted on a Web Server, ensuring that they are accessible to the end users through a web application. A REST API allows developers to integrate and utilise the information streams in various applications and services.
## 3 Simulation Study
More important, it is also a limited test of the protocols ability to estimate capacity, interference and needed resource reservation of protocols. The density of the receivers covers a larger range, but there are a few protocols that have only been evaluated for a limited range of variability. As a group, the protocols seems to have been evaluated for sufficient range of possible receiver densities. Multicast is usually related to real time applications such as video streaming, audio conference. This is reflected in the universal use of Constant Bit Rate traffic generators (CBR). However, the large packet size more closely reflects packet sizes used in video and not in voice. The evaluations encompass different metrics. The common ones are PDR (Packet Delivery Ratio), success rate, average latency, packet loss, and routing overhead. These are network centric and do not reflect the group aspect of multicast. Examples of such performance metrics are rate of successful joins and the fraction of groups using overloaded distribution trees [29]. There is no common set of usage scenarios, and therefore also no common performance metrics, topology and traffic scenario. The diversity in the simulation scenarios is therefore to be expected. However, the QoS aspect of the protocols necessitates mechanisms for resource reservation and resource usage. We had therefore expected a larger fraction of the simulation studies to include competing traffic from other groups and unicast traffic.
###### Abstract  
Different parameters of the circular-arc, trapezoidal and equal cross-section-shaped flow channels were analyzed, and the core volume goodness factor was used for the comparison of the three different types of flow channels. During the experiment, the Reynolds number (\(Re\)) on the air side ranged from 1200 to 5100. The results showed that the overall heat transfer performance of the three channels in this paper are circular-arc, trapezoidal and equal cross-section in order from good to bad. The overall heat transfer enhancement performance of the circular-arc flow channel is the best, which is 9-26.2% and 3.6-11.8% higher than that of the equal and trapezoidal cross-section flow channels, respectively. This showed that although the divergent flow channel structure reduces the fluid velocity in the flow process, it weakens the convective heat transfer performance in the flow channel. However, this gradually decreasing cross-sectional area improves the downstream heat transfer area and reduces the pressure drop in the flow process, thus promoting the overall heat transfer performance. With the increase in the circular radius (\(R\)), both the \(j\) and \(f\) factors increase, and the highest overall heat transfer performance is obtained at \(R=300\) mm. The convective heat transfer coefficient increases with the decrease in the inlet height.  
circular-arc shaped; curved flow channel; circular radius; core volume goodness factor Article
# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 3 System Structure and Design  
The software structure of the system developed in this study was based on the monitoring database, which is supported by a Brower/Server model with WebGIS and Web3D technologies (Figure 2). Using SQL (Structure Query Language) server, Visual Studio and skyline platform (a 3D GIS application development platform), a multi-structure for application-information and data was constructed, and the service was oriented to different applications, offering convenience for reuse and the expansion of system functions.  
Figure 1: Location of the study area within the northwestern region of Hubei Province, China.  
Figure 2: Software structure of the landslide monitoring and early warning system.  
Data storage and management services were integrated into the system, including those for real-time monitoring data, historical monitoring data, rock and soil mass characteristics data, emergency information, and basic geographical data. The assembly level of the system offers components relevant for the operational sector, including the analysis of landslide monitoring data and an early warning module. After the assembly level integration, the service sector offers simple but practical operational services, primarily including three-dimensional spatial analysis, real-time monitoring services, landslide data consulting and management, early warning analysis, a disaster publishing service, and a system management service. The performance sector is responsible for human-computer interaction services and a thematic map-making output service.  
According to the operational procedures and application requirements for a landslide early warning service, the system has five main functions:  
1. Collection and management of multiple datasets pertaining to landslides, including fundamental geographical data, real-time and historical monitoring data, acquirement, transfer, preprocessing and management of emergency service data.
2. Query research and spatial statistical analysis, including comprehensive querying of each monitoring site (e.g., feature, section, and sphere) and statistical analysis of feature data.
3. Landslide early warning and related information publishing. Based on the features of the potential landslide location and its real-time monitoring data, a model for landslide forecasting and emergency warning that is able to publish potential disaster evaluation information to the public and relevant departments is selected.
4. Expression and analysis of three-dimensional information, including a three-dimensional depiction of the target area and key monitoring locations. Focused on data relating to flow fields and groundwater level, a combination of functions and needs was developed to address landslide prediction and the evaluation of relevant three-dimensional spatial analysis.
5. Support functions, including user management, operational right management, data management and maintenance, and other regular functions.
## 4 Conclusions  
In this paper, we propose an SC-FDE scheme for synthetic aperture communications. The transmitter sent a particular set of data at different locations. Spatial and temporal diversities are obtained using multiple transmission combinations. We exploited two receiver algorithms, MRC-FDE and MRC-HTFDE, and we demonstrated error-free receptions after five transmission combinations using convolution decoding. The addition of a few receiver elements increased the output SNR and decreased the BER significantly and caused a slight increase in the cost of the system.  
This work was supported in part by the National Key R&D Program of China under Grant 2016 YFC1400200, the National Natural Science Foundation of China (Grant Nos. 61471298, 61531015, 61401499) and the NPU Foundation for Fundamental Research(Grant No. 3102015 BJ016). The authors would like to thank all the participants of the lake experiment. The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions on the original manuscript.  
All of the authors contributed extensively to the preparation of this manuscript. [PERSON] and [PERSON] conceived the work and designed the algorithms. [PERSON] prepared the channel estimation methods, [PERSON] and [PERSON] analyzed the experiment data, and [PERSON] designed the lake experiments. [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 11: Scatterplot of the equalized first packet signal using the five transmissions with conventional FDE and HTFDE: (**a**) using the 1 st element with FDE; (**b**) using elements 1 and 2 with FDE; (**c**) using elements 1 to 3 with FDE; (**d**) using the 1 st element with HTFDE; (**e**) using elements 1 and 2 with HTFDE; (**f**) using elements 1 to 3 with HTFDE.