esa-sceva/satcom-chunk-collection huggingface dataset:
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 5 Challenges and Limitations of the Currently Available Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Refs.** & **Year** & **Success Rate [\%]** & **AI** & **Main Contribution** & **Methodology** & **Challenges** \\ \hline
[12] & 2012 & - & \(\times\) & Loss coefficient & Loss estimation & Load curve behavior \\ \hline
[13] & 2022 & 98.12 & \(\times\) & Parseval Identity & Spectral losses analysis & Energy flow—coefficient of Variation \\ \hline
[14] & 2017 & 100 & \(\times\) & MV distribution analysis & Energy inflow & Bidirectional power flow \\ \hline
[15] & 2011 & N/A & \(\times\) & Visual Basic program & Technical losses in cables and overhead lines & Flat voltage profile \\ \hline
[16] & 2015 & - & \(\times\) & Open DDS & Load flow analysis & Circuit length \\ \hline
[17] & 2024 & N/A & \(\times\) & Independent factors & Power injections and EDN topology & Physical characteristics \\ \hline
[18] & 2018 & - & \(\times\) & K—value & Forward–Backward Sweep & Automatic parameter updating \\ \hline
[19] & 2024 & N/A & \(\times\) & RMS current value & Overhead power line—waveform and temperature & THD current form \\ \hline
[20] & 2016 & N/A & \(\times\) &
\begin{tabular}{c} Load graphics for \\ 1000 KVA Transformer \\ \end{tabular} & The no-load and load losses & - \\ \hline
[22] & 2012 & N/A & \(\times\) & Joule losses & Predictive functions & Outdoor temperatures \\ \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis on evaluation criteria.
## 4 Results and Discussion  
Residual stress was measured for each specimen using three groups of transducers and an X-ray stress analyzer, with the detection area being divided into 10 equal blocks. The mean value of the residual stress was obtained from 10 different blocks for each sample. Figure 7a shows the LCR wave results for the 1, 2.5, and 5 MHz transducers, respectively, and a comparison with the X-ray diffraction data for different specimens is also given. As shown in Figure 7b, the difference between the X-ray and ultrasonic methods at different frequencies was more evident when normalized values were used to display residual stress measurements.  
The largest deviation of the ultrasonic and X-ray results for the 1 MHz frequency was 123.24 MPa (sample S2). This large deviation resulted from the different thickness of the layers for the ultrasonic and X-ray diffraction methods (ca. 5.98 mm (0.23 in) and 200 (0.0078 in)). The deviation of the ultrasonic and X-ray results for the 2.5 and 5 MHz frequencies were 90.50 and 54.50 MPa, respectively. This suggests that the X-ray diffraction is a surface method, which penetrates no more than 1 mm. The average deviations of all the specimens were 39.90, 56.08, and 79.01 MPa for the tests involving 5, 2.5, and 1 MHz transducers, respectively. These results suggested that the LCR wave provided the mean residual stresses within a certain transducer penetration depth. Therefore, where the residual stress is to be measured at an exact distance from the surface, the ultrasonic method is not the ideal option.  
For the S2 specimen, the residual stresses measured by the 5, 2.5, and 1 MHz transducers are shown in Figure 8. The deviations of the 10 different specimens were \(\pm\)23, \(\pm\)19, and \(\pm\)13 MPa for the tests involving 5, 2.5, and 1 MHz transducers, respectively. The samples with different test frequencies were compared to show that the LCR wave method is more accurate at low frequencies. Therefore, the deviation of the 5 MHz LCR wave is higher than the deviation of the 2.5 MHz LCR wave, and the minimum deviation happens for the 1 MHz LCR wave. The higher errors obtained for higher frequencies can be explained by the low frequency transducers, which create echoes in the receiver that are sharper and more powerful than the high frequency transducers. Therefore, it is desirable to increase the \"gain\" by using higher frequency transducers to avoid low resolutions and higher time-of-flight errors.  
Figure 7: Comparison between X-ray and LCR waves for different specimens. (**a**) The residual stress for different specimens; (**b**) The normalized value of residual stress for different specimens.
###### Abstract  
The _Bacillus velexensis_ GJ-7 strain isolated from the rhizosphere soil of _Panax notoginseng_ showed high nematicidal activity and therefore has been considered a biological control agent that could act against the root-knot nematode _Meloidogyne hapla_. However, little was known about whether the GJ-7 strain could produce volatile organic compounds (VOCs) that were effective in biocontrol against _M. hapla_. In this study, we evaluated the nematicidal activity of VOCs produced by the fermentation of GJ-7 in three-compartment Petri dishes. The results revealed that the mortality rates of _M. hapla_ J2s were 85% at 24 h and 97.1% at 48 h after treatment with the VOCs produced during GJ-7 fermentation. Subsequently, the VOCs produced by the GJ-7 strain were identified through solid-phase micro-extraction gas chromatography mass spectrometry (SPME-GC/MS). Six characteristic VOCs from the GJ-7 strain fermentation broth were identified, including 3-methyl-1-butanol, 3-methyl-2-pentanone, 5-methyl-2-hexanone, 2-heptanone, 2,5-dimethylpyrazine, and 6-methyl-2-heptanone. The in vitro experimental results from 24-well culture plates showed that the six volatiles had direct-contact nematicidal activity against _M. hapla_ J2s and inhibition activity against egg hatching. In addition, 3-methyl-1-butanol and 2-heptanone showed significant fumigation effects on _M. hapla_ J2s and eggs. Furthermore, all six of the VOCs repelled _M. hapla_ J2 juveniles in 2% water agar Petri plates. The above data suggested that the VOCs of _B. velexensis_ GJ-7 acted against _M. hapla_ through multiple prevention and control modes (including direct-contact nematicidal activity, fumigat activity, and repellent activity), and therefore could be considered as potential biocontrol agents against root-knot nematodes.  
Bacillus velexensis_ GJ-7; _Meloidogyne hapla_; solid-phase micro-extraction gas chromatography-mass spectrometry; volatile organic compounds (VOCs); biological control
## 2 State of Art
### Hybrid Models for Traffic Prediction  
Before focusing on models specifically designed for CO2 emissions prediction, it is essential to recognize recent studies utilizing hybrid models for traffic flow prediction. Accurate traffic modeling is directly correlated with CO2 emissions, and hybrid models, combining statistical approaches with deep learning techniques, have shown great potential for capturing complex traffic dynamics.  
For instance, [PERSON] et al. (2024) proposed a hybrid model [24], the FD-Markov-LSTM, which integrates a fundamental diagram (FD) to identify traffic states, a Markovian model to capture probabilistic transitions between these states, and an LSTM network to capture residual temporal dependencies not accounted for by traditional approaches. This model was validated on traffic data from Beijing and Los Angeles, demonstrating high accuracy in different congestion scenarios. Similarly, [PERSON] et al. (2022) developed a CNN-LSTM hybrid model that integrates traffic flow models with deep learning to predict real-time traffic on complex urban highways [25]. These models highlight the effectiveness of hybrid approaches for traffic prediction, which can be extended to CO2 emissions prediction due to the strong relationship between traffic conditions and vehicle emissions. Hence, the strong performance demonstrated by these hybrid models for traffic flow prediction can be leveraged to refine CO2 emissions estimation, given the close correlation between traffic dynamics and greenhouse gas production. However, while these models effectively capture complex spatiotemporal variations in traffic, their initial design does not systematically consider environmental parameters linked to CO2 emissions (e.g., fuel consumption, vehicle types, and weather conditions). Therefore, in the following sections, we focus on models specifically developed for CO2 emissions prediction, incorporating additional environmental parameters and precise traffic characteristics to improve the granularity and robustness of the estimates.
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 4 Performance Evaluation
### Simulation Environment  
In the NS3 simulation, we conducted a comparative analysis between the AO-AOMDV and the classical reactive routing protocols AOMDV and AODV. We employed a Markov Gaussian mobility model for the UAV nodes, which were uniformly distributed in a 3D space of 2000 m \(\times\) 2000 m \(\times\) 300 m. The variation in the Z-axis ranged from 100 m to 300 m. The UAV speeds ranged from 10 m/s to 40 m/s. The maximum transmission range for the UAVs was set to 250 m. At the MAC layer, we utilized the IEEE 802.11n protocol. At the application layer, a random UAV node was selected as the server, while other UAV nodes served as clients, taking turns sending data packets to the server. The detailed parameters employed in our simulation are summarized in Table 1.
## 2 ITS Applications and Communication Technologies, Critical Design Requirements, and Related Works
### Review of Intelligent Transportation Systems (ITS)
From a systemic point of view, all vehicles must be uniquely identifiable, warning messages must be deliverable, and vehicle location and kinematics information (known as PVT) should be available [16]. Authentication procedures should also be evident to support links for individual and group communication [16]. In [17], an exclusive review of V2V, V2I, and V2X communications is introduced, and the role of DSRC and WAVE technologies in road safety services is mentioned. The advanced WAVE protocol based on 802.11p is designed to optimize the coordination and cooperation among vehicles and infrastructures. Similarly, the bidirectional V2I links between RFID sensors, traffic lights, cameras, lane markers, signages, and parking meters utilize DSRC frequencies to transact data. Concretely, the authors present the benefits and the challenges of the commercial employment of connected vehicle implementations, such as the financial costs and the lack of universal standardization [17]. The technical radio requirements for the three major ITS applications are included in Table 2[12; 15]. Specifically, safety-critical vehicular CP is the most demanding in \begin{table}
\begin{tabular}{l c c} \hline \hline
**Communication Technology** & **Class** & **Wireless Standard** \\ \hline Dedicated short-range communications (DSRC) & Legacy & IEEE 802.11p \\ Vehicle Information and Communication System (VICS) & Legacy & Infrared/Microwaves \\ Electronic Fee Collection (EFC) & Legacy & Infrared/Microwaves \\ Transport and Traffic Telematics (TTT) & Legacy & Infrared/Microwaves \\ ITS G5 & Advanced & IEEE 802.11p \\ Wireless Access in Vehicular Environments (WAVE) & Advanced & IEEE 802.11p \\ Communication Access for Land Mobiles (CALM) & Advanced & ISO 212182018 \\ LTE–V2X & Advanced & LTE/4G \\ NR—V2X & Advanced & NR/5G \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Taxonomy of ITS communication technologies. Figure 1: ITS scenarios and connected vehicle services. terms of the sampling rate, latency, and reliability, but the transmission rate is of less importance [12; 15]. The latency requirement incorporates the propagation delay as well as the processing delay. The reliability, which is defined as the percentage of time when packets are delivered in the correct order and without losses, is very critical because the rapidly changing network topology could result in outdated/invalid position solutions.
# The Noise-Reduction Characteristics of Microstructure of Dragonfly Wing Leading Vein
## 2 Bionic Modeling of Dragonfly Wing Microstructure
### Modeling and Design of Bionic Wing and Leading-Edge Microstructure
Therefore, the probe for measuring the noise in the simulation was placed above the center of the leading edge of the wing to collect the pressure fluctuation. In order to explore the influence more accurately of leading-edge microstructure on the aerodynamic noise of the bionic wings, it was necessary to filter the trailing edge noise generated by the bionic wings. Therefore, the overall sound pressure level (OASPL) was used to represent the noise generated by the bionic wings with different leading-edge microstructures. The definition of the overall sound pressure level is as follows:  
\[\text{OASPL}=10*log_{10}(\sum_{i}10^{[SPL_{i}/(10\text{dB})]})\text{dB} \tag{4}\]  
The value of \(SPL_{i}\) is the sound pressure level between the frequency ranges (\(f_{1}\leq f\leq f_{2}\)) of the center of the 1/3 octave frequency band. The frequency range (\(f_{1}\leq f\leq f_{2}\)) chosen for the analysis in the overall sound pressure level was set from \(f_{1}\) (100 Hz) to \(f_{2}\) (10 kHz). The basis for selection is as follows: The decision on \(f_{1}\) was due to the consideration of the initial frequency of the sound level meter. Moreover, the noise-reduction effect of serrated microstructure in low-frequency regions was not obvious. The measurement of the OASPL from the beginning of \(f_{1}\) (100 Hz) had little effect on the comparison of that produced by different leading-edge microstructure. Moreover, \(f_{2}\) was decided upon the exclusion of the possible influences by the airfoil self-noise from the trailing edge [6]. The OASPL calculated by this method could more accurately reflect the influence of the leading-edge microstructure on the aerodynamic noise of the bionic wing.
## 4 Ion Irradiation-Induced Amorphisation of Cobalt Nanoparticles
### Introduction  
In this section we discuss the effects of SHII on the crystallographic phase of Co NPs embedded in SiO\({}_{2}\). XAS is used in conjunction with ED to characterise the irradiation-induced evolution of the FCC-to-HCP phase transformation as functions of irradiation energy and fluence. The mechanisms potentially responsible for this change in phase are then discussed.  
Large 13.2 \(\pm\) 3.7 nm NPs (as determined by both TEM and SAXS) were prepared following the methods discussed in Section 2. The NP samples were irradiated at room temperature with Au ions at energies of 9, 27, 54, 89, 110 or 185 MeV, where the ion penetration depth was beyond that of the NP distribution. Au ion fluences ranged from 1 \(\times\) 10\({}^{10}\)-2 \(\times\) 10\({}^{13}\) /cm\({}^{2}\).
# Body-to-Body Cooperation in Internet of Medical Things: Toward Energy Efficiency Improvement
## 1 Introduction
### Related Work
Energy harvesting IoT health-based outage probability reduction, utilising inter-wireless body area network cooperation is introduced in [21]. Finally, a WBSN in an IoT health-based application, with the aim of minimizing delay and energy consumption is studied in [22]. The limitations of the proposed protocols in [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] can be elaborated as follows: (1) the cooperation between hybrid devices (such as sensors, Wi-Fi, smart devices, and so on) was not considered; (2) the cooperation between different WBSNs in IoMT was not studied; (3) finally, neither energy efficiency nor outage probability have been analysed in an IoMT environment. The contributions from this paper are summarised as follows:  
1. A new IoMT system is proposed that describes the journey of the data from the human body to the health cloud over four tiers. 2. We propose a new protocol that allows neighbouring WBSNs to cooperate with each other through different communication technologies and devices within the IoMT system. The proposed protocol is called the 'inter-WBSN cooperation in IoMT system', which is abbreviated as 'IWC-IoMT'. 3. A mathematical model of the proposed protocol is formulated and derived for two important metrics: outage probability and energy efficiency. The mathematical model describes the transmission of the data between WBSNs and beyond their communication range. 4. We reveal that the proposed IWC-IoMT protocol can achieve better performance in terms of the outage probability and energy efficiency of the IoMT system. The rest of the paper is organised as follows: a WBSN network architecture is described in Section 2, which includes two sub-sections: WBSN in IoT-based health networks and new inter-WBSN cooperation, as well as the basic operation of the proposed protocols. In Section 3, the link analysis and outage probability of direct transmission and the proposed protocol are inspected. The energy efficiency of the different transmission scenario and the proposed protocol are investigated in Section 4. The performance and results are presented in Section 5. Finally, conclusions are drawn and directions for future work are proposed in Section 6.
# Design and Optimization of Origami-Inspired Orthopyramid-Like Core Panel for Load Damping
## 7 Conclusions and Prospect  
Investigating the origami-inspired core panels could lead to the production of structures used in loads and energy damping applications. In the present work, 3D printing with PLA filaments was used to fabricate origami-inspired orthopyramid-like core panels. The compression and impact test was carried out on the fabricated panels. Finite element modeling was conducted using static structures for compression tests and explicit dynamics for an impact event. The results showed that the tips are the stress concentration zones. The deformation of the tips explains the load damping ability. The optimal similarity parameter for the 4-edged element was obtained via the GA algorithm. We found that the optimal similarity parameter \(w\) is 0.8104. The corresponding tip angle \(\beta\) was 72.94 \({}^{\circ}\), the bottom surface side length \(L\) was 21.95 mm, and side length \(S\) was 19.56 mm.  
Figure 21: Comparison with the optimized panel.  
Figure 20: Basic flow chart of the algorithm.  
However, there are also some limitations to our work. During the study of the load damping properties, several design hypotheses have been made, such as the panel material, panel thickness, and element thickness. We lack a generic mathematical formulation of the design problem. Further, more optimization objectives of the optimization algorithm should be included, not only the stress index.  
In the future, different materials' influences of the compression behavior should be studied. Multiobject optimization towards the geometry parameter in compression and impact simulation should be conducted, and it is important to investigate the tessellation pattern model toward a variety of edges.  
All authors have equally contributed to this article.  
This work is supported by the National Key R&D Program of China (No. 2018 YFB1701702), the National Natural Science Foundation of China (Nos. 51675477, 51775489), Zhejiang Provincial Natural Science Foundation of China (No. LZ18E050001). The authors would like to acknowledge the support of Zhejiang Province's Key Laboratory of 3D Printing Process and Equipment.  
The authors declare no conflict of interest.
###### Abstract  
With the continuous expansion of the high-speed railway network in China, long-span railway bridges carrying multiple tracks demand reliable and fast testing procedures and techniques. Bridge dynamic behavior analysis is a critical process in ensuring safe operation of structures. In this study, we present some experimental results of the vibration monitoring of a four-track high-speed railway bridge with a metro-track on each side: the Nanjing-Dashengguan high-speed railway bridge (NDHRB). The results were obtained using a terrestrial microwave radar interferometer named IBIS-S. The radar measurements were interpreted with the support of lidar point clouds. The results of the bridge dynamic response under different loading conditions, including high-speed trains, metro and wind were compared with the existing bridge structure health monitoring (SHM) system, underlining the high spatial (0.5 m) and temporal resolutions (50 Hz-200 Hz) of this technique for railway bridge dynamic monitoring. The detailed results can help engineers capturing the maximum train-induced bridge displacement. The bridge was also monitored by the radar from a lateral position with respect to the bridge longitudinal direction. This allowed us to have a more exhaustive description of the bridge dynamic behavior. The different effects induced by the passage of trains through different tracks and directions were distinguished. In addition, the space deformation map of the wide bridge deck under the eccentric load of trains, especially along the lateral direction (30 m), can help evaluating the running stability of high-speed trains.  
multitrack railway bridge; dynamics; displacements; microwave radar interferometer +
Footnote †: journal: Remote Sensing, 2020, 12, 2594; doi:10.3390/rs12162594
## 3 Results
### Spatio-Temporal Variations of Vegetation Phenology  
#### 3.2.1 Spatial Variations  
The spatial distribution of average vegetation phenology in the SRYR from 2001 to 2019 is shown in Figure 3. Four phenology factors (SOS, EOS, LOS and POS) exhibited obvious gradients from west to east. The average SOS gradually advanced from west to east (Figure 3a). Over the area, 82.5% of the pixels had a vegetation SOS in the range of 120-160 d from May to early June. The vegetation SOS was mostly concentrated in early and mid-June in the colder and drier western SRYR, while it was mostly concentrated in late April in the southeastern SRYR. By contrast, the average EOS was gradually delayed from west to east (Figure 3b). A total of 84.0% of the pixels had a vegetation EOS in the range of 270-300 d (October). The vegetation EOS was mostly less than 290 d in the western SRYR, while it was concentrated in 290-300 d (mid-to-late October) in the southeastern SRYR. The average LOS gradually extended from west to east (Figure 3c). The vegetation LOS was mostly less than 140 d in the western SRYR, while it was more than 160 d in the southeastern SRYR. The length of the vegetation growing season tended to extend under suitable hydrothermal conditions due to better activity from photosynthetic enzymes and soil microorganisms [58]. We observed that the longest LOS (>200 d) was in the southeastern SRYR. Similar to SOS, the average POS showed a pattern of delay from west to east (Figure 3d). The average POS mainly appeared in summer (June-August), and 86.5% of the pixels had a vegetation POS in the range of 200-220 d (late July to early August). The earliest POS (<190 d) was distributed in the southeastern SRYR where marsh was distributed.  
Figure 2: Spatial distribution of the trends of annual and seasonal precipitation and air temperature and their inter-annual variations in the SRYR from 2001 to 2019. (**a**) Annual precipitation (P), (**b**) annual air temperature (T\({}_{\rm a}\)), (**c**) spring precipitation (P\({}_{\rm spr}\)), (**d**) spring air temperature (T\({}_{\rm a\,spr}\)), (**e**) summer precipitation (P\({}_{\rm sum}\)), (**f**) summer air temperature (T\({}_{\rm a\,sum}\)), (**g**) autumn precipitation (P\({}_{\rm aut}\)) and (**h**) autumn air temperature (T\({}_{\rm a\,aut}\)). The inter-annual variations of annual and seasonal precipitation and air temperature in the SRYR from 2001 to 2019 are shown in the upper right corner.