esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of the Proposed Formula in the Field
### Field Results  
The formula proposed in Equation (4) has been applied to hot spots detected in the field. In that way, the correct action to take has been identified. In fact, hot spots where it was necessary to act would have had another recommendation if it had not been applied, which could have caused an untimely failure.  
Figure 1 shows the hot spot detected in a transformer bushing connector of a 66/15 kV transformer. The values of the parameters when the inspection was carried out were the following: hot spot temperature: 26 \({}^{\circ}\)C; ambient air temperature: 10 \({}^{\circ}\)C; current: 200 A; wind speed: 1 m/s. The nominal current of the circuit is 720 A. As the difference between the temperatures of the hot spot and the ambient air is 16 \({}^{\circ}\)C, Table 1 indicates the _probable deficiency_ and the recommended action: _repair as time permits_. However, to know the temperature of the hot spot under the nominal conditions of the facility, Equation (5) must be applied. Thus, the extrapolation is to a current of 720 A and no wind is made. In that case, the hot spot would reach a temperature of 68 \({}^{\circ}\)C and the recommended action is _repair immediately_ (Table 1). Following that recommendation, the hot spot was repaired and the need for immediate repair was confirmed. This confirms the need to extrapolate the hot spot temperature to correctly identify the severity of the problem.  
Figure 2 shows the case of a hot spot on a 66 kV wall bushing connector. The parameters measured during the thermographic inspection were the following: hot spot temperature: 50 \({}^{\circ}\)C; ambient air temperature: 32 \({}^{\circ}\)C; current: 200 A; wind speed: 1 m/s. The nominal current of the circuit is 720 A. The difference between the temperatures of the hot spot and the ambient air is 18 \({}^{\circ}\)C. Therefore, Table 1 indicates _probable deficiency_ and the recommended action: _repair as time permits_. However, Equation (5) indicates that the hot spot would reach 77 \({}^{\circ}\)C when extrapolating the temperature to the nominal conditions of the facility and without wind. In this case, the recommended action from Table 1 is to _repair immediately_. According to that recommendation, the hot spot was repaired immediately, and its severity was confirmed. This reaffirms the need to extrapolate the hot spot measurement obtained to its nominal condition values, and that, by design, can be achieved.
###### Abstract  
Sensor networks, as a special subtype of wireless networks, consist of sets of wirelessly connected sensor nodes often placed in hard-to-reach environments. Therefore, it is expected that sensor nodes will not be powered from the power grid. Instead, sensor nodes have their own power sources, the replacement of which is often impractical and requires additional costs, so it is necessary to ensure minimum energy consumption. For that reason, the energy efficiency of wireless sensor networks used for monitoring environmental parameters is essential, especially in remote networking scenarios. In this paper, an overview of the latest research progress on wireless sensor networks based on LoRa was provided. Furthermore, analyses of energy consumption of sensor nodes used in agriculture to observe environmental parameters were conducted using the results of real measurements in the field, as well as simulations carried out based on collected data about real equipment. Optimization methods of energy consumption, in terms of choosing the appropriate data collection processes from the conducted field measurements, as well as the settings of network radio parameters imitating real conditions used in conducted simulations were highlighted. In the analyses, special emphasis was placed on choosing the optimal packet size. Unlike in other papers analyzing energy efficiency of LoRa communication, in this paper, it was proven that the adjustment of the transmission speed to the actual size of the packet is important for better energy efficiency of communication and that it can reduce energy consumption considerably. Moreover, in the paper, the contents of a packet that can be used in precision agriculture is suggested in order to prove that the 6-bit packet is sufficient for energy-efficient collection of parameters from the environment, in contrast to the 11-bit packets used in standard commercially available equipment.  
lora technology; wireless sensor networks; energy efficiency; environmental monitoring; precision agriculture +
Footnote †: journal: Sensors  
0000-0001-5000-5000-5000][PERSON]  
1 Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; [EMAIL_ADDRESS] (K.G.); [EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] ([PERSON].)
## 4 Simulation Results and Discussion
### Case Studies
#### 4.1.4 Case Study 4 (Without Shunt Resistor)  
This case study investigated the system's behavior without a shunt resistor. The fault current and corresponding voltage profile of the single line-to-ground fault are depicted in Figure 6. The r-SFCL without a shunt resistor could limit the fault current to 1.45 and 1.2 kA for the first and second peak, respectively, representing a reduction of 76.8% for the first peak compared to the case without the r-SFCL. The voltage dropped to about 14.43 kV\({}_{\text{rms}}\). The r-SFCL's temperature is shown in Figure 6c; the maximum temperature reached in this case study was 499 K. The r-SFCL did not recover, because the heat generated by the fault was equal to the coolant energy of 190 K at 14 s.  
Figure 6: Fault response without and with r-SFCL without a shunt resistor: (**a**) fault current; (**b**) voltage drop; (**c**) r-SFCL temperature.
# Multi-Lidar System Localization and Mapping with Online Calibration
## 3 Back-end Optimization and Map Construction
### Z-Axis Error Limitation Based on Ground Constraints  
As the vehicle's travel distance increases, the cumulative error of the \(z\)-axis also progressively increases, as indicated by the following:  
\[\text{error}_{z}=S\times\sin\phi \tag{29}\]  
where \(S\) is the actual travel distance of the vehicle and \(\phi\) is the pitch angle.  
This paper adopts a constraint to extend the planar point cloud feature into a ground to mitigate the drift issue of the \(z\)-axis.  
The fitted global ground equation is first initialized:  
\[A_{w}x+B_{w}y+C_{w}z+D_{w}=0 \tag{30}\]  
RANSAC fitting is concurrently applied to the current frame's ground point cloud to obtain the ground equation:  
\[A_{i}x+B_{i}y+C_{i}z+D_{i}=0 \tag{31}\]  
Since the two plane equations are in different coordinate systems, it becomes essential to project the global plane equation PW from the world coordinate system to the radar coordinate system of the current frame \(i\), as expressed in the following equation:  
\[P^{\prime}_{w}=T^{w}_{i}P_{w} \tag{32}\]  
Upon obtaining the two plane equations that share a unified coordinate system, the transformation relationship between the two normal vectors comes into play for constructing the residual, as illustrated in Figure 8.  
Figure 7: Loop detection based on Scan Context descriptor.  
Considering the two angles in Figure 8, the normal vectors of the two planes can be made entirely consistent. Subsequently, the intersection point of the two planes is measured to formulate the residual in three dimensions as follows:  
\[\alpha=\arctan\left(\frac{y}{x}\right) \tag{33}\]  
\[\beta=\arctan\left(\frac{z}{\sqrt{y^{2}+x^{2}}}\right) \tag{34}\]  
\[\text{error}_{d}=|D_{w}-D_{i}| \tag{35}\]  
These residual items in all three dimensions are integrated into the least squares problem. Through this process, optimization is conducted collectively with other constraints.
# Study of Green Synthesis of Ultrasmall Gold Nanoparticles Using _Citrus Sinnensis_ [PERSON]
## 2 Materials and Methods
### Analysis and Characterization of AuNPs by UV-vis, EDS, TEM, DLS, FT-IR  
UV-visible (UV-vis) absorption spectra of the AuNPs solutions were recorded in the wavelength range of 400-750 nm on a double beam Shimadzu UV-vis spectrophotometer. The elemental analysis of AuNPs was processed by a scanning electron microscope (SEM) (JEOLJSM-6360A, (Columbia, MO, USA) equipped with energy-dispersive spectrometer (EDS). JEOLJEM-1400 120 kV TEM (JEOL, Columbia, MO, USA) was used for taking TEM images of the AuNPs.  
The TEM samples preparation and images acquisition principle are all followed by our previous study [22]. Briefly, 5.0 \(\upmu\)L of the AuNPs solution droplet was deposited onto carbon-coated copper grids, and the excess solution was wiped away with a filter paper (see Section 2.2) after 30 min. Then, the copper grids were dried in air for 10 min. In order to guarantee enough images that could represent a significant statistical amount of the tested AuNPs, 10 TEM images with the same magnification were taken for each sample batch. The hydrodynamic diameters were measured by DLS, using a Zetasizer Nano (Malvern Panalytical, Malvern, UK). FT-IR measurement was carried out using Cary 660, FT-IR spectrophotometer by employing KBr pellet technique. The FT-IR spectra were collected from 32 scans at a resolution of 4 cm\({}^{-1}\) in the transmission mode (4000-400 cm\({}^{-1}\)).
# SDN-Based Survivability Analysis for V2I Communications
## 6 Conclusions  
This paper proposed a probabilistic model-checking method to quantitatively analyze the survivability for the SDN-based V2I network. We investigated the SDN-based vehicle network communication architecture and established the SDN-based V2I vehicle network communication model, in which the vehicle node fault and link fault type were considered. On the basis of the proposed communication model, we provided the survivability definition of SDN-based V2I vehicle networks. Combined with MPTCP protocol, we proposed a Markov chain-based survivability model of the SDN-based V2I network. Moreover, a probabilistic model-checking method was investigated to validate the V2I network survivability model. In particular, we analyzed the probability and expected reward under various conditions from the point of view of time. The analysis results showed that, under the action of MPTCP, multiple V2I failures occur, the QoS of the network decreases, but the network still has a certain survival ability. Moreover, for a higher fault repair rate, the service capacity of the network will be improved quickly, and the survivability will be further improved. In summary, the results showed that our proposed survivability enhancement method and analysis method of probabilistic model checking are effective for SDN-based V2I vehicle networks. Additionally, the proposed survivability model was limited to a certain application environment and did not realize a general survivability model. In our future research work, we will evaluate and compare the survivability of other vehicle networks' models and summarize a suitable general model for the vehicle network survivability. Specifically, we will explore more effective strategies to improve the survivability of vehicle networks and study the survivability of vehicle networks for 5G and 6G combined with the artificial intelligence approach.  
[PERSON] and [PERSON] conceived and designed the study. [PERSON] and [PERSON] performed the simulations. [PERSON] wrote the paper. [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
This work is supported by the National Natural Science Foundation of China (No. 61971245 and 61801249).  
The authors would like to thank the anonymous reviewers for their useful comments and careful reading of the manuscript.  
The authors declare that they have no conflicts of interest.  
Figure 13: The expected reward of the link failure number in the V2I network with time (corresponding to \(\lambda_{1}\) and \(\lambda_{2}\)).
## 7 Emerging Trends in Reinforcement Learning for Robotics
### Safe Exploration in DRL for Robotics  
Ensuring safe exploration is a critical challenge in deep reinforcement learning (DRL) for robotic applications. While traditional RL methods rely on trial-and-error exploration, real-world deployment necessitates strategies that prevent unsafe actions during training and execution. Recent advancements in safe DRL have introduced several approaches, including constrained optimization, risk-aware policy learning, and uncertainty-aware exploration techniques.  
One approach to safe exploration is through constrained optimization, where policies are trained under explicit safety constraints. [PERSON] et al. [64] introduced Safe Set Guided State-wise Constrained Policy Optimization (S-3 PO), a method that ensures zero training violations by employing a safety monitor with black-box dynamics. Their approach improves sample efficiency while preventing unsafe transitions. Similarly, [PERSON] et al. [65] proposed an Extra Safety Budget (ESB-CPO) framework that balances exploration efficiency with safety compliance, dynamically adjusting constraints throughout training.  
Hierarchical reinforcement learning has also been explored for safe navigation. [PERSON] et al. [66] developed a constrained hierarchical RL framework that integrates a safety layer to modify low-level policies, reducing collision rates while maintaining high performance. Additionally, [PERSON] et al. [67] introduced a Safety-Oriented Search technique that biases policy optimization towards safer actions, leveraging an evolutionary cost optimization framework.  
Uncertainty-aware policies have been proposed to address model inaccuracies in dynamic environments. [PERSON] and [PERSON] [68] developed a Monte Carlo dropout-basedmethod to estimate uncertainty in state-action pairs, enabling safer decision-making in robotic control. Their approach significantly reduces risk while maintaining policy efficiency. Furthermore, [PERSON] and [PERSON] [69] introduced a model-based constrained RL approach that leverages an ensemble of neural networks to estimate uncertainty and ensure robust constraint satisfaction.  
Beyond policy optimization, risk-aware exploration strategies have been studied to enhance safety. [PERSON] et al. [70] proposed SafeTAC, a Tsallis entropy-regularized safe RL method that improves exploration without violating safety constraints. Their method successfully mitigates overly conservative behaviors while optimizing performance in high-dimensional robotic tasks.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Complexity of Algorithm 1: Computation of Non-Dominated QoS Paths  
This algorithm computes non-dominated QoS paths in a graph. The worst-case complexity is determined by analyzing its loops and path-processing steps.
## 2 Related Work
### Key Computing Components  
Hive is a tool of the Apache Hadoop project. When the deployment of Hive system is completed, it will start a HiveServer service to receive and process the client's Hive-QL commands. The client can connect Hive at the Hadoop platform through a command line interface (CLI) or Java database connectivity (JDBC) driver and then it send Hive-QL commands to Hive. After Hive receives a Hive-QL command, the Hive-QL command will be compiled to a Java program, and Hadoop MapReduce executes the job requested from a query. Once a Hive-QL query invokes the table creation, the system will build another Hive's own table (a transfer table) for Hive at the Hadoop platform and creates its metadata at the same time. Metadata will be stored in the relational database MySQL called Metastore [21], and thereafter Hive-QL query is able to get the information in Metastore to look for where data are located and how many data to retrieve, as shown in Figures 4 and 5. Notice that Hive's metastore information is commonly used for Impala and SparkSQL so that Impala/SparkSQL sharing the same Hive's metastore information is individually able to perform the rapid data retrieval from HDFS where the very large amount of data has been stored in HDFS.  
Impala, developed by Cloudera, is used to speed up this kind of SQL-Like query statement; it uses its own MPP (massively parallel processing) query engine. Impala uses LLVM (low level virtual machine, written in C++) [22] to compile these statements. Using an LLVM can significantly  
Figure 4: Creating table and storing the information in the metastore.  
Figure 5: SQL query command to the tools.  
reduce the compiling cost. Impala is capable of handling Hive-like SQL commands, and fortunately is able to access Hive's metaststore information as well. The main function of Spark is the same as Hadoop MapReduce, but it uses in-memory cluster computing. The storage system is also compatible with HDFS. SparkSQL is a Hive-like system based on Spark MapReduce. It is fully compatible with Hive as well as being capable of accessing Hive's metaststore information like metadata stored in the MySQL database.  
Hue is a web-based graphical interface [23] for Hadoop and Impala. Hue in Hadoop or Impala is just like phpMyAdmin in MySQL. This study employed Hue as a user-machine interface because users can easily perform operations and observations in addressing the problem of the Hadoop ecosystem's difficulty of operation.  
Memcached [24] is a key-value type of distributed memory caching system. The key length is limited to 250 characters and a single datum cannot exceed 1 MB. Currently, it is often used in websites and database search result caches. In Figure 6, the system has provided Spymemcached metaststore information to locate the data in the memory cache or in the disk cache so that Spymemcached is able to update data between the memory cache and the disk cache asynchronously. This paper intends to implement rapid data retrieval the in-memory cache and the in-disk cache if there is no need to start Hive, Impala, or SparkSQL for dealing with an SQL query.
## 2 Ionosphere-Free PPP Processing Model  
The dual-frequency GNSS ionosphere-free PPP processing scenario can be expressed mathematically as follows [28]:  
\[P_{IF}^{s}=\rho_{r}^{s}+c(dt_{r}+b_{r,\;IF})-cdt_{corr}^{s}+T_{r}^{s}+\varepsilon _{p}^{s} \tag{1}\]\[\Phi_{IF}^{s}=\rho_{r}^{s}+c(dt_{r}+b_{r,\,IF})-cdt_{corr}^{s}+T_{r}^{s}+\overset{ \sim}{N_{IF}}^{s}+\varepsilon_{\Phi}^{s} \tag{2}\]  
where \(P_{IF}\) and \(\Phi_{IF}\) represent ionosphere-free code and phase combinations, respectively; \(\rho_{r}^{s}\) is the satellite receiver geometric range; \(dt_{r}\) denote to receiver clock errors; \(b_{r,IF}\) is the receiver ionosphere-free differential code bias; \(dt_{corr}^{s}\) is the corrected satellite clock parameter after applying the archived real-time and post-processing products; \(T_{r}^{s}\) refers to the tropospheric delay; \(\varepsilon_{(p_{i},\Phi_{i})}\) denote multipath and noisy measurements for code and phase measurements, accordingly; \(\overset{\sim}{N_{IF}}\) refers to the real-value ambiguity parameter, including both code and carrier phase biases, which can be expressed as follows:  
\[\overset{\sim}{N_{IF}}^{s}=\frac{f_{1}^{2}\lambda_{1}N_{1}-f_{2}^{2}\lambda_{2 }N_{2}}{f_{1}^{2}-f_{2}^{2}}+[(b_{r,\,IF}+\delta_{r,\,IF})-(b_{IF}^{s}+\delta_ {IF}^{s})] \tag{3}\]  
where \(f_{1}\) and \(f_{2}\) are the frequencies of \(L_{1}\) and \(L_{2}\), respectively; \(\lambda_{1}\) and \(\lambda_{2}\) are the wavelength of \(L_{1}\) and \(L_{2}\), respectively; \(N_{1}\) and \(N_{2}\) are the carrier-phase ambiguity parameters on \(L_{1}\) and \(L_{2}\), respectively; \(b_{IF}^{s}\) is the satellite ionosphere-free differential code bias; \(\delta_{r,IF}\) and \(\delta_{IF}^{s}\) are the receiver and satellite ionosphere-free carrier phase biases, respectively. It is well known that the receiver differential code bias is lumped in the receiver clock parameter (i.e., \(cdt_{r}^{\sim}=c(dt_{r}+b_{r,IF})\)); thus, the vector of the estimated parameters (\(\overset{\rightarrow}{X_{PPP}}\)) takes the following mathematical expression:  
\[\overset{\rightarrow}{X_{PPP}}=\left[X,\,\,\,\,Y,\,\,\,\,\,\,\,\,\,cdt_{r}^{ \sim},\,\,\,T_{w},\,\,\overset{\sim}{N_{IF}}^{s}\right] \tag{4}\]  
where \(X,Y\), and \(Z\) denote the receiver position; \(T_{w}\) indicates the wet tropospheric delay.
# Predictability of ClinCheck in Overbite Correction with Aligners: A Systematic Review
## 2 Materials and Methods  
This systematic review was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and registered within the PROSPERO International Prospective Register of Systematic Reviews with the ID number CRD420251078610 [30]. The research question was formulated following the PICO strategy: Participant, Intervention, Comparison, Outcome. The population included adult patients with either deep or open bite malocclusions treated with clear aligners (intervention). The purpose of the comparison was to evaluate the predictability of the ClinCheck software and the actual clinical outcomes. The outcome was the overbite value assessed pre-, during and post-treatment.
###### Abstract  
Many landslides triggered by earthquakes have caused a countless loss of life and property, therefore, it is very important to predict landslide hazards accurately. In this work, regional seismic landslide data were obtained via a field survey, remote sensing interpretation, and data collection, and a multilevel physical and mechanical parameter system for seismic landslide hazard assessment was established; this system included a landslide inventory, loose accumulation layers, and geological units, enabling higher accuracy in the data. The Newmark displacement model with a modified correlation coefficient was used to assess the regional seismic landslide hazard in four scenarios (\(a=0.1\), \(0.2\), \(0.3\), \(0.4\)) to study the influence of the landslide hazard at different peak ground accelerations. Moreover, the information value model was used to modify the calculated results to improve their accuracy in the assessment. By assessing the potential seismic landslide hazard in Shimian County in the upper reaches of the Yangtze River, the regional landslide distribution and pattern at different peak ground accelerations were obtained. The results show that with decreasing parameter accuracy in the system, the importance of the landslide inventory increases. When the peak ground acceleration is \(a=0.3\), which can be defined as a high hazard grade, in which the landslide area demonstrates a large-scale sharp increase, a devastating hazard threshold is reached. As the peak ground acceleration increases, the factor controlling landslides transforms from the landslide inventory to the slope, which reflects the reasonableness of the parameters in the system. The input parameters were regarded as important factors for efficiently increasing the accuracy of the results of the Newmark displacement model in the discussion.  
Newmark displacement model; seismic landslide; risk assessment; mechanical parameters +
Footnote †: journal: Applied sciences  
Article