esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mechanism of Lithium Ion Separated from Brine Solution
When the evaporation ratio is greater than 16%, it is in Stage II, during which the lithium-magnesium mass ratio changes minimally, and the ratio of lithium loss decreases.). Figure 7 shows the changes in the magnesium-lithium ratio and ion concentrations of magnesium and lithium in brine with different magnesium-lithium mass ratios after cooling. It can be seen from the figure that the magnesium-lithium ratio of brine decreases by about one-third during the cooling process; when the mass ratio of magnesium to lithium is 5:1, the lithium loss is 25.06%, and when the mass ratio of magnesium to lithium is greater than 5:1, the lithium loss can be controlled within 5%. In addition, the enrichment trend of lithium in the liquid phase is smaller at 40 \({}^{\circ}\)C compared to 60-50 \({}^{\circ}\)C and 40-25 \({}^{\circ}\)C. This can be attributed to the fact that at 40 \({}^{\circ}\)C, magnesium ions have a larger charge than lithium ions, enabling them to undergo rapid movement under the influence or an electric field, forming a thicker diffusion layer and resulting in alterations in the microstructure of brine [2]. As is shown in Figure 6, LiCl exists in brine in ion form, while Li\({}_{2}\)SO\({}_{4}\) exists in cluster form. Combined with the radial distribution function in Figure 4, the distance between hydration layers around Li\({}_{2}\)SO\({}_{4}\) increases as the temperature decreases, promoting Li\({}_{2}\)SO\({}_{4}\) crystallization and affecting the concentration of lithium in the solution. As a result, during the cooling process, LiCl exists in the liquid phase in ion form, while Li\({}_{2}\)SO\({}_{4}\) clusters exist in an ion pairing structure. Changes in the hydration layers around Li\({}_{2}\)SO\({}_{4}\) clusters due to temperature variation promote the formation of lithium sulfate crystals, causing the loss of Li\({}^{+}\) in the liquid phase in the form of Li\({}_{2}\)SO\({}_{4}\) clusters, thus affecting the separation of magnesium and lithium. Figure 6: Microstructural changes in various particle clusters in brine. Figure 7: Changes in magnesium concentration during cooling process with different lithium mass ratios.
# Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone
## 1 Introduction
The large and growing number of smartphones (about four billion phones in 2021 [29]), the continuous performance improvement, and expansion of their features help to explain the current trend to use smartphones in a wide variety of applications, replacing or substituting other specific and conventional sensors. The use of embedded sensors in mobile phones offers important advantages, among which it is worth highlighting the ubiquity and low cost of the equipment and the ease of access and dissemination of information (data) thanks to its communication capabilities [30]. Smartphones have different sensors, such as photo and video cameras, microphones, GPSs, or accelerometers. The rich endowment of sensors integrated into cell phones presents the opportunity to use these appliances (gadgets) to develop sensors systems [31; 32; 33] suitable to monitor areas as different as the environment [34], traffic [35], or human health [36], among others. Following this trend, this paper proposes and develops a new nonintrusive and infield method to measure the speed of induction motors, based on vibration spectral analysis, using a simple smartphone. To achieve that goal, the vibration signal is first acquired using a common smartphone. A free application [37], previously installed on the phone, is then used to access the information of the accelerometer built into the smartphone, creating a csv file with the information on the temporal evolution of the vibration in each of the three spatial directions. The vibration record (csv file) is sent wirelessly to the computer. Using a tailor-made routine developed in MATLAB, the csv file with the axial vibration records is loaded, and the frequency spectrum of the motor vibration is obtained. A peak detection algorithm leads to estimate the motor speed as corresponding to the frequency at which the maximum amplitude of the vibration spectrum occurs. The proposed method is quite simple and very easy to use since the vibrations are available at any point of the motor frame or even on its bench or adjoining structure. Additionally, the method does not depend on the parameters of the motor or how it is fed. It is a nonintrusive contact method that can be used without altering the service of the motor and without extra measuring devices. After this introduction, the rest of the document is organised as follows. Section 2 presents the theoretical background of the proposed method to measure rotor speed by spectral vibration signature analysis. Section 3 shows the main results of the set of tests carried out to prove the method. In Section 4, a short discussion of the proposed method is presented. To end, Section 5 summarises the most relevant conclusions of this research.
## 8 Open Issues
### Real-Time Authorization  
BYOD encompasses a wide range of smart devices that contact the organisation's server and depend on providing real-time data access. Therefore, a delay in procuring an access decision within a BYOD environment may lead to unauthorised access to sensitive data, resulting in vulnerabilities to the system. In the scenario mentioned earlier, it can be inferred that access decision-making must remain close to the origin of access requests, which can provide the advantage of real-time data for the access control system, thus enabling access decisions with a minimal end-to-end delay. Moreover, authorised access must be ensured throughout the session, not only at the time of the request. In addition, real-time authorization means continuous access decisions throughout the session, not only when requesting access.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Article**} & \multicolumn{2}{c}{**RQ8: Data Type**} \\ \cline{2-3}  & **Flood Data** & **Traffic Data** \\ \hline [PERSON] et al. [50] & Flood depth, velocity, and duration & \multirow{2}{*}{The road dataset, and Traffic data} \\ \hline [PERSON] et al. [3] & High-resolution citywide surface water inundation depth data, fluvial inundation data, flood hazard data & Ordnance Survey Integrated Transport Network (ITN) data, The Environment Agency National Receptor Database (NRD) including critical infrastructure nodes and vulnerable locations \\ \hline [PERSON] et al. [37] & NPCC20 s SLR projection for NYC, NYC floodplain topography & The most recent GIS dataset of city facilities and a single line street base (i.e., LION), default turn \\ \hline \multirow{5}{*}{[PERSON] et al. [28]} & Rainfall event, Regional hydrologic service report, Digital Terrain Model (DTM) (10 m resolution), Land Use-Land Cover (1:25,000), Pedology (1:25,000), High-resolution DTM (1 m), Road network (1:2000), River network (1:2000), Building footprint (1:2000), Roughness coefficient \\ \hline [PERSON] et al. [43] & The scenario of a 0.5 m sea level rise, the shapefiles containing the land surface and building elevations for the entire San Francisco Bay Area & A population file, A full network file, and A list of daily activity for 500,000 agents \\ \hline [PERSON] et al. [67] & - & A road network, OD commuter data, and 1 m resolution flood maps \\ \hline \multirow{5}{*}{[PERSON] et al. [68]} & Measurements and photos form flooded roads in 2016 & Streets with speed reduction or closure, Road network from OpenStreetMap (OSM) including road types and speed limits, an activity-based traffic demand model was set to predict the attributes of trips (purpose, origin, destination, and timing) \\ \hline [PERSON] et al. [26] & - & Road network, Traffic flow, The data on the administrative division, Commutate data, and Flood hazard maps \\ \hline \multirow{5}{*}{[PERSON] et al. [44]} & The external tidal forcings at the ocean boundaries, water velocities obtained at multiple points along the boundaries & Full-day activity chains of individual agents, a connected network, transit schedules, and an updated transportation networks based on inundation information \\ \hline [PERSON] et al. [39] & Geographic data, 3 h rainfall profiles & Rainfall-Vehicle speed curve, Rainfall-Depth curve, Road network, Traffic flow \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont.
## 5 Results and Discussion
### Performance of R-CNN
However, having more parameters also means that the model is more prone to overfitting and requires more computational resources during both training and inference. In this comparison, Cascade R-CNN has the largest number of parameters, with 107 million, followed by Sparse R-CNN with 77.8 million and Mask R-CNN with 64.1 million parameters. On the other hand, Fast and Faster R-CNN are some of the smallest models, with 42.5 million and 53.2 million parameters, respectively. * Performance: To evaluate the ability of models to cope with all adverse conditions, the mean detection performance over all conditions is considered in this comparison. Sparse R-CNN has the highest overall accuracy, with a mean precision of 55.4%, a mean recall of 46.6%, and the highest mean mAP@.5 value of 51.4%. The differences in performance can be attributed to the architectures of the models and the methods they use to extract features from the input image. * Epoch: The number of epochs indicates the number of times the model has been trained on the dataset. The number of epochs needed for the model to be trained varies due to the application of the early stopping function. Apart from R-CNN, Fast R-CNN and Faster R-CNN had the smallest number of epochs, with 233 and 227, respectively. Meanwhile, the largest model Cascade R-CNN also has the largest number of epochs, with 274. Overall, the results in Table 7 suggest that the different models have their strengths and weaknesses, and the choice of model will depend on the specific requirements of the task at hand. For instance, if accuracy is a priority, Cascade R-CNN and Sparse R-CNN are suitable models, while if inference speed is a concern, Mask R-CNN with the lowest inference time is more suitable. Ultimately, Sparse R-CNN provides both the highest accuracy and a low inference time. These results could be because of the differences in the architectures and optimization techniques used in each of these models. The Cascade R-CNN model has a multi-stage architecture, which enables it to refine its predictions at each stage, resulting in improved performance. On the other hand, the Sparse R-CNN model uses a sparse proposal generation technique, which helps it to be more efficient and faster, leading to better performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Mean\_P** & **Mean\_R** & **Mean\_mAP@.5** & **Number of Parameters** & **Model Size** & **Epoch** & **Inference Time** \\ \hline R-CNN & 48.7\% & 39.9\% & 44.6\% & 34 M & 149.4 MB & 194 & 189.8 ms \\ Fast R-CNN & 50.8\% & 42.8\% & 46.8\% & 42.5 M & 223.7 MB & 233 & 178.5 ms \\ Faster R-CNN & 52.3\% & 44.0\% & 48.4\% & 53.2 M & 271.2 MB & 227 & 175.2 ms \\ Mask R-CNN & 52.1\% & 43.9\% & 48.9\% & 64.1 M & 370.8 MB & 246 & 141.6 ms \\ Cascade R-CNN & 54.2\% & 45.8\% & 50.5\% & 107 M & 537.3 MB & 274 & 214.1 ms \\ Sparse R-CNN & 55.4\% & 46.6\% & 51.4\% & 77.8 M & 415.4 MB & 251 & 146.4 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of R-CNN models under multiple indicators.
## 1 Introduction
### Contribution
A simulated annealing algorithm is then applied to explore the solution space through probabilistic perturbations, enhancing the global optimality of beam allocation. * Frequency-Power Joint Allocation: we propose a joint optimization method that integrates the Hungarian algorithm and water-filling theorem. The Hungarian algorithm is used to efficiently match users with sub-bands, while water-filling is applied to optimize intra-beam power distribution. Finally, we apply convex optimization to regulate inter-beam power allocation, leading to enhanced system throughput for the LEO network. The remainder of this paper is structured as follows. Section 2 introduces the system model and formulates the joint resource allocation problem in the heterogeneous GEO-LEO satellite network, encompassing beam-footprint partitioning, time-slot beam scheduling, and frequency-power allocation with the goal of maximizing the overall system throughput. Section 3 details the proposed beam-hopping-based resource allocation framework, which employs a progressive, dimension-wise optimization strategy. Section 4 presents numerical simulations and analyzes the performance of the proposed algorithms in terms of throughput and computational efficiency. Finally, Section 5 summarizes the key findings and outlines potential directions for future research.
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 3 Results
### SVM-Based Classifier with Mean-Normalized Raw Signals  
The confusion matrix in Table 4 demonstrates that the mean normalization of raw Bluetooth signals yields a dramatic enhancement in classifier performance. The diagnostical accuracy climbs to 84.29%, and the effective accuracy reaches 95.83%, indicating that almost every class's strongest normalized column entry now lies on the diagonal.  
Focusing on the Rusins wearable devices, mean normalization virtually eliminates the mutual confusion that plagued earlier preprocessing strategies. Class 1 attains a diagonal value of 0.91, Class 2 rises to 0.99, and Class 3 reaches a perfect 1.00. Classes 4 and 6 likewise achieve 1.00, while Class 5 improves to 0.85 and Class 7 to 0.90. Finally, Class 22 attains 0.95--up from single-digit or low-tens diagonal values in the unnormalized case--showing that mean normalization effectively highlights each wearable's intrinsic RF signature.  
Within the Uzundurukan smartphone twins, mean normalization, also yields near-perfect separability for most classes. Classes 8, 15, and 16 each achieve diagonal entries of 1.00, and Class 4 improves to 0.90. Nonetheless, a few residual confusions persist: Class 9 holds at 0.77 but still shows off-diagonal mass in Classes 13 and 17, and Class 10, while reaching 0.83, assigns 0.10 of its samples to Class 9. Twin devices in Classes 11 and 12 also display a slight reciprocal overlap, with Class 11 at 0.60 (and 0.37 in Class 12) and Class 12 at 0.87, with minor dispersion into other classes. These remaining misclassifications suggest that, although mean normalization captures gross amplitude consistency, more advanced feature extraction may be required to fully disentangle the most closely matched RF fingerprints.
## 4 SAR Payload Unit Design
### Digital Control Unit  
As depicted in Figure 14, the implemented DCU had a mass of 0.9 kg and DC power consumption of 35 W. The external and internal configurations of the DCU are presented in Figure 14a,b, respectively. The DCU of the micro-SAR satellite largely controlled the payload components, generated a wideband transmission waveform following the PDDS method with a 12-bit DAC chip (Teledyne EV12 DS130 BMGC), compensated the waveform distortion using a pre-distortion method, and converted and processed digital reception signals using a 12-bit analog-to-digital converter chip (Texas Instruments ADC12 DJ3200). In addition, the DCU determined the SAR payload operation modes according to the command received from the ground object through the platform (Figure 8); controlled the SAU (EFE), RFU, and PSU; and acquired the status information of each SAR payload unit and reported it to the platform. Subsequently, a wideband LFM signal was generated for image acquisition, and the received raw data were formatted as Consultative Committee for Space Data Systems (CCSDS) using high-speed analog-to-digital conversion of the received signal and were transmitted to the platform.  
The DCU hardware architecture primarily contained a high-performance flash-memory -based FPGA and an MCU, as shown in Figure 15, which are the microchip radiation-tolerant PolarFire FPGA RTPF500 TS and Microchip Radiation-Tolerant 32-bit ARM Cortex-M7 MCU SAMV71Q2 IRT, respectively.  
Figure 14: (**a**) External configuration and (**b**) internal configuration of the S-STEP DCU.
# REGNet: Ray-Based Enhancement Grouping for 3D Object Detection Based on Point Cloud
## 5 Conclusions  
In this paper, we propose a novel REGNet to tackle the challenging task of 3D object detection in indoor scenes. Our method is inspired by VoteNet, based on which we propose the foreground-aware module, voting-aware module, and cluster-aware module to improve the detection performance. The foreground-aware module establishes relationships between foreground and background points to obtain more expressive seed point features. The voting-aware module proposes ray-based voting with a transformer strategy to calculate the contributions of each vote centers. The cluster-aware module is used to adaptively aggregate multiple clusters to achieve better performance. Qualitative and quantitative experimental results on the public ScanNet V2 and SUN RGB-D datasets show that our REGNet achieves reasonable performance improvement in 3D object detection.  
Conceptualization, F.Z. and J.R.; methodology, F.Z.; software, Q.Z.; validation, P.S. and Q.Z.; formal analysis, F.Z. and Q.Q.; investigation, F.Z.; resources, F.Z. and Q.Q.; data curation, J.R.; writing--original draft preparation, F.Z.; writing--review and editing, Q.Q. and Y.L.; visualization, Q.Z.; supervision, F.Z.; project administration, F.Z. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the R&D Program of Beijing Municipal Education Commission (KM202310009002), and in part by Beijing Natural Science Foundation (423023), and in part by Beijing University Yunnan Innovation Institute Funding Technology Plan (2021) of Yunnan Provincial Key RD Program (202103 AN080001-003).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **Frames/s** \\ \hline MLCVNet [29] & 64.5 & 41.4 & 5.37 \\ BRNet [39] & 66.1 & 50.9 & 7.37 \\ HSDNet [30] & 67.2 & 48.1 & 3.75 \\ GFNet [31] & 66.3 & 48.5 & 6.64 \\ MCGNet [41] & 67.5 & 50.1 & 3.59 \\ RRNet [34] & 70.2 & 54.2 & 4.75 \\ \hline REGNet & 70.7 & 55.3 & 4.69 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison on realistic inference speed on ScanNet V2 with MLCVNet, BRNet, H3 DNet, GFNet, MCGNet, and RBGNet.  
Figure 9: Performance analysis. Per-class [EMAIL_ADDRESS] improvement of our REGNet over VoteNet, ImVoteNet, and GFNet on the SUN RGB-D dataset. Best viewed in color.
## 3 Proposed Sensor Network  
The sensor network consists of a broadband light source, which is split into \(N\) number of Sensor Units. A small fraction of light is coupled out to the optical power reference measurement in order to eliminate power fluctuations. One sensor unit refers simply to the PM-PCF Sagnac loop. The main part of light propagates through the fiber coupler, demultplexer (DEMUX) and multiplexer (MUX). At the end, each DWDM channel is assigned to a given detector, which corresponds to the PM-PCF Sagnac loop. The whole scheme of the sensor network proposed is shown in the Figure 1.  
In Figure 1 the OPM (REF) refers to the optical power meter used for reference measurement. OPM corresponds to the optical power meter used for measurement of optical power at the output of the n-th sensor unit, MUX is a multiplexer and DEMUX is a demultiplexer. The experimental setup was performed with one PM-PCF Sagnac loop (sensor unit) in order to prove the concept of the sensor network. Multiplexing of sensor units could lead to the obtaining of the proposed sensor network by selecting different wavelengths as it operates on a wavelength division multiplexing scheme. The presented sensor network proposal could then be implemented practically. The operating wavelength range of DWDM is consistent with the International Telecommunication Union (ITU) recommendations.
# Formation Control of Dual Auto Guided Vehicles Based on Compensation Method in 5G Networks
## 5 Conclusions  
Focusing on the collaborative transport of dual AGVs, this paper researched the formation control based on compensation method in 5G R15 network. Firstly, the RTT is measured and analyzed in physical private network. The result shows that the distribution of RTT is random and RTT is mainly concentrated in 19 \(\pm\) 3 ms. The jitter of RTT is mainly distributed in 0 \(\pm\) 3 ms. Kalman filter method is applied to estimate the RTT, and the result shows the effectiveness of Kalman filter method. The total delay in control system has been discussed considering the communication delay and executing delay. AGV kinematic model has been established and used to estimate the current position of leader AGV considering total delay. Formation model and control method have been established to regulate the motion status of follower AGV to maintain the formation accuracy. Finally, an experiment was carried out. The experiment results show that the control method and process proposed in this paper can effectively improve the accuracy of AGV formation motion.  
This paper does not consider the influence of wheel slip, load change etc. A more complete dynamic model can ensure the posture estimation accuracy in the above situations. Moreover, the effectiveness of the control system in different network loads has not been further analyzed. The further work will focus on the two estimations proposed in the article. The first will be how to use the data to get the better estimation of the current network delay in different network loads, and the second will be how to better estimate the relative posture of the leader AGV considering dynamic model.  
L.W.: conceptualization, methodology, formal analysis, software, validation original draft, review and editing; Q.L.: conceptualization, supervision; C.Z.: software, validation, review and editing; S.Z.: data curation, review and editing, C.G.: validation, review and editing; Y.L.: conceptualization, methodology, review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge the Kunshan Huaheng Welding Co., Ltd. for the support of AGVs and 5G experimental environment.  
The authors declare no conflict of interest.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 6 Benefits of Using a Mobile Manipulator for Railway Track Maintenance
### Efficiency and productivity  
Mobile manipulators are gaining more flexibility and productivity features. Robots are well known for their accuracy and repeatability in many industries. For decades, industrial manipulators have been performing many challenging and repetitive tasks, such as welding in the automotive industry. The same benefits could be available for railway track maintenance if using the correct designs and control strategy. As the manufacturing cost of robots has been reduced gradually [156], some hardware features, such as water and dust resistance, are becoming easy to achieve. Hence, the design and fabrication of outdoor mobile manipulators are becoming more feasible. Moreover, manipulators are now capable of carrying very high payloads. For example, ABB has multiple models of industrial manipulators which can carry more than 200 kg payload [157]. As many railway maintenance tasks require a high payload, these manipulators can perform railway track repair tasks.