esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Datasets
### Himawari-8 AHI Data  
The GEO meteorological satellite, Himawari-8, was successfully launched on 7 October 2014 by the Japan Meteorological Agency and started operating on 7 July 2015. The sensor  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Satellite/Instrument**} & \multirow{2}{*}{**Product**} & \multicolumn{2}{c}{**Resolution**} & \multirow{2}{*}{**Collection/Version**} \\ \cline{3-4}  & & **Spatial** & & **Temporal** \\ \hline Himawari-8 AHI & Level 2 Aerosol Product & 5 km & 10 min & V3.0 \\ Terra, Aqua-MODIS & Level 2 Cloud Product & 5 km & 10 min & V1.0 \\ Terra, Aqua-MODIS & MOD04\_L2/MYD04\_L2 & 10 km & daily & C6.1 \\ CALIPSO-CALIOP & CALI0\_L2\_05 kmAPro & 5 km & daily & V4.20 \\ CALI\_LID\_L2\_VFM & 5 km & daily & V4.20 \\ AERONET & AERONET Level 2.0 & - & 15 min & V3 \\ MAN & Level 2.0 AOD & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of datasets used in this study.  
AHI onboard Himawari-8 carries 16 bands from visible to infrared wavelengths with high spatial resolution ranging from 0.5 to 2 km and produces a full disk over the Asia-Pacific region at 10-min intervals. The target data to be recovered are the AHI Level 2 (L2) Version 3.0 AOD products at a spatial and temporal resolution of 5 km and 10 min, respectively, as listed in Table 1. To obtain AOD retrievals with low uncertainty, we extract AHI AOD at 500 nm with relatively high confidence (quality assurance flag: \"very good\" and \"good\"). In addition, the AHI L2 Version 1.0 cloud property products are adopted in this study to guarantee that most of the AHI AOD retrievals are cloud-free. The aforementioned datasets are obtained from the Japan Aerosol Exploration Agency (JAXA, Tokyo, Japan) Himawari Monitor website at [[http://www.eorc.jaxa.jp/ptree/index.html](http://www.eorc.jaxa.jp/ptree/index.html)]([http://www.eorc.jaxa.jp/ptree/index.html](http://www.eorc.jaxa.jp/ptree/index.html)) (accessed on 5 March 2022).
## 3 Macro-Element Application
### Non-Linear Range
#### 3.2.3 Bi-Directional Cyclic Loading  
The last test covers the bi-directional cyclic loading response of the outlined case study in Figure 7. The goal is to see if the expected hysteretic behaviour of the flexural trusses is well reproduced. Both positive and negative vertical bending capacities are similar, hence both the flexural trusses, ft1 and ft2, need to trace the same behaviour. This was sustained by Figure 4 wherein the axial Cauchy stress \(\sigma_{11}\) faced in both flexural trusses are similar in magnitude and, owing to the binary of the bending force, have an opposite signal.  
Regarding the hysteretic behaviour, after completion of the first cycle (A\(\rightarrow\)F, \(t=2.25\)s), as depicted in Figure 11c, the tensile peak is reached at A and the first tensile unloading branch initiates at B with a stiffness given by \(E_{B}=\left(1-d_{t}^{B}\right)E_{0}\) until C. The first compressive re-loading branch starts at C with a stiffness \(E_{0}\), since a value of 1 has been defined for the recovery parameter \(\omega_{c}\) and so, predictably, the maximum compressive stress at D reaches the same magnitude as A, i.e., the maximum quasi-static envelope. In the same manner, the unloading branch (E\(\rightarrow\)F) has a stiffness given by \(\left(1-d_{c}^{E}\right)E_{0}\), and the first re-loading tensile branch (F\(\rightarrow\)G) has the same stiffness as the last unloading tensile branch (B\(\rightarrow\)C). This is equivalent for the first reloading compressive regime I\(\rightarrow\)J, which follows the same path as the last unloading phase E\(\rightarrow\)F. It is also noticeable that H and L have the same (in magnitude) axial stress \(\sigma_{11}\) as the last point of the quasi-static envelope. The material constitutive behaviour formulation defines minimum stress, or the designated residual strength, to be the last effective stress given as input, see Figure 11. Thus, after reaching point H, the damage parameter remains stationary (Figure 11b).  
It has been demonstrated that the cyclic behaviour is reproduced as it was expected. Both positive and negative vertical bending moments are represented (truss ft1 and ft2). It may be highlighted, again, that the latter holds true because total recovery effects have been defined for tensile and compressive regimes (\(\omega_{t}=1\) and \(\omega_{c}=1\)). Furthermore, the model exhibits the capacity of allocating in memory the damage variables, for both the preceding tensile and compressive cycle of each flexural trusses, allowing thus the calculation of the onward un(re)-loading cycle.  
Figure 10: Uni–directional cyclic behaviour (Note: only the positive moments are plotted, i.e., the part of the unloading branch for negative bending moments has been disregarded for the sake of readiness).
## 3 Methodology
### Study Cases: Geothermal Wells 1 and LV5
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(T_{\text{C}}\) (\({}^{\circ}\)C) & **25** & \multicolumn{2}{c}{**30**} & \multicolumn{2}{c}{**35**} \\ \hline \(T_{\text{G}}\) (\({}^{\circ}\)C) & **65** & **70** & **65** & **70** & **65** & **70** & **70** \\ \hline \multicolumn{10}{c}{**Thermal Power (kW)**} \\ \hline Cooling capacity (\(\dot{Q}_{E}\)) & 22.00 & 21.80 & 19.80 & 20.40 & 12.40 \\ \hline Condenser heat duty (\(\dot{Q}_{C}\)) & 24.66 & 24.70 & 22.11 & 23.04 & 13.95 \\ \hline Low-pressure absorber heat output (\(\dot{Q}_{A\_LP}\)) & 35.99 & 35.50 & 39.28 & 37.58 & 50.62 \\ \hline Low-pressure generator heat input (\(\dot{Q}_{G\_LP}\)) & 38.85 & 38.58 & 41.67 & 40.36 & 51.64 \\ \hline High-pressure absorber heat output (\(\dot{Q}_{A\_HP}\)) & 31.48 & 31.61 & 28.62 & 29.90 & 18.38 \\ \hline High-pressure generator heat input (\(\dot{Q}_{G\_HP}\)) & 31.20 & 31.35 & 28.32 & 29.61 & 18.17 \\ \hline \multicolumn{10}{c}{**Mechanical Power (kW)**} \\ \hline Solution pump 1 (\(\dot{W}_{LP}\)) & 0.07 & 0.06 & 0.20 & 0.13 & 0.72 \\ \hline Solution pump 2 (\(\dot{W}_{HP}\)) & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.01 \\ \hline \multicolumn{10}{c}{**Performance Indicator**} \\ \hline Coefficient of performance, COP (-) & 0.31 & 0.31 & 0.28 & 0.29 & 0.18 \\ \hline Exergy efficiency, \(\eta_{Ex}\) (-) & 0.31 & 0.28 & 0.37 & 0.34 & 0.26 \\ \hline Irreversibility, \(\dot{l}\) (kW) & 10.92 & 11.77 & 10.13 & 11.06 & 9.76 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance summary of the modeling of well W-LV5 coupled to a half-effect cooling system. Case study: Refrigeration (\(T_{E}=-6\), \(PF=0.7\)). From Table 5, it is possible to see that the half-effect cooling system was capable of operating at generation temperatures of 65 and 70 \({}^{\circ}\)C at condenser temperatures of 25, 30, and 35 \({}^{\circ}\)C. For this case, the cooling system was able to provide up to 22 kW of refrigeration at \(T_{E}=-6\)\({}^{\circ}\)C with a coefficient of performance of 0.31. From Table 6, it can be observed that the system could provide up to 24.4 kW of cooling for air conditioning (\(T_{E}=10\)\({}^{\circ}\)C), achieving a coefficient of performance of 0.35. Comparing the results obtained from well W1 to well W-LV5, it was clear that the cooling capacity obtained from the absorption cooling system was directly related to the power of each well. Tables 4 and 5 show that even for a geothermal well with low power, it was still possible to produce a cooling effect, and of course if the temperatures and power of the geothermal well were higher, as in the case of well W-LV5, the cooling capacity increased considerably.
## 5 Conclusions  
This study aimed to evaluate the influence of curing time on mixtures containing RAP, stabilized with emulsion and cement in the base layers of flexible pavements, yielding the following conclusions:  
* A minimum curing time of 7 days was required to achieve typical RM values for granular materials used in base courses. With values between 500 and 600 MPa. Indicating good behavior in terms of resilience modulus. The RM increased 80% increase compared to the material tested immediately after compaction.
* The samples were more susceptible to permanent deformation when subjected to the repeated load triaxial test. Although the 28-day curing reduced the material's total PD by up to 58%, the material still showed high accumulated deformation at the end of the 150,000 cycles of the tests. None of the tests showed a plastic deformation rate below \(10^{-7}\), but they exhibited a tendency towards accommodation after 150,000 cycles. This indicates that even after 28 days of curing, the emulsion was unable to completely stabilize the mixture.
* The plastic deformation behavior observed in the triaxial tests must be taken into account when designing pavements containing RAP and asphalt emulsion. In addition, the analysis of this material in the repeated load triaxial test explains the premature deformations observed during the technical inspection mentioned in this study.  
Conceptualization, R.P.K. and L.M.C.; methodology, R.P.K., L.M.C. and A.C.R.G.; software, R.P.K.; formal analysis, S.N.M. and A.C.R.G.; investigation, R.P.K. and L.M.C.; data curation, R.P.K. and L.M.C.; writing--original draft preparation, R.P.K., L.M.C. and R.T.; writing--review and editing, L.M.C., R.P.K., R.T., A.C.R.G. and S.N.M.; supervision, A.C.R.G.; project administration, A.C.R.G.; funding acquisition, S.N.M. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordination for the Improvement of Higher Education Personnel-Brazil (CAPES)--Finance Code 001.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
###### Abstract  
Specific Emitter Identification (SEI) is a specialized technique for identifying different emitters by analyzing the unique characteristics embedded in received signals, known as Radio Frequency Fingerprints (RFFs), and SEI plays a crucial role in civilian applications. Recently, various SEI methods based on deep learning have been proposed. However, in real-world scenarios, the scarcity of accurately labeled data poses a significant challenge to these methods, which typically rely on large-scale supervised training. To address this issue, we propose a novel SEI framework based on self-supervised contrastive learning. Our approach comprises two stages: an unsupervised pretraining phase that uses contrastive loss to learn discriminative RFF representations from unlabeled data, and a supervised fine-tuning stage regularized through virtual adversarial training (VAT) to improve generalization under limited labels. This framework enables effective feature learning while mitigating overfitting. To validate the effectiveness of the proposed method, we collected real-world satellite navigation signals using a 40-meter antenna and conducted extensive experiments. The results demonstrate that our approach achieves outstanding SEI performance, significantly outperforming several mainstream SEI methods, thereby highlighting the practical potential of contrastive self-supervised learning in satellite transmitter identification.  
s 2025 17 2659 Article
## 3 Model Definitions
### Propagation Model  
A signal \(x\) is transmitted through a line-of-sight channel, during which it is distorted before the signal \(z\) is received. The traditional propagation model [82] for this channel is  
\[z=ax(t-\tau)e^{j2\pi f_{c}\tau}, \tag{3}\]  
where \(a\) is the complex channel attenuation. We assume a basic line-of-sight channel attenuation [84]  
\[a^{2}=\bigg{(}\frac{\lambda}{4\pi d}\bigg{)}^{2}G_{Tx}G_{Rx}, \tag{4}\]  
where \(G_{Tx}\) and \(G_{Rx}\) are the transmitter and receiver antenna gains, \(\lambda\) is the signal wavelength, and \(d\) is the distance between the two users.  
For a transmission from A to B, the misaligned clocks and relative velocity between the platforms may induce a carrier frequency offset \(\zeta\). We extend the model in [12], which includes this carrier offset, and make the appropriate substitutions [83], using (1):  
\[z(t_{B})=|a|x(t_{B}-t_{A,Tx}^{(n)}-\tau^{(n)}+T^{(n)})e^{jp}+n(t_{B}); \tag{5}\]  
\[p=2\pi(\tilde{\phi}+\zeta_{B}t_{B}-(f_{cr,n,B}+\epsilon_{B}+\zeta_{B})\tau_{B }), \tag{6}\]  
where \(t_{B}\) is the clock B time reference, \(n\) is complex additive white Gaussian noise, \(\tilde{\phi}\) is the phase noise induced by the hardware, channel, and miscalibration, \(f_{cr,n,B}\) is the nominal clock B carrier frequency, \(\epsilon_{B}\) is the error between this nominal frequency and the actual  
Figure 3: Depiction of two interactions between radios A and B. Interactions are indexed by \(n\). The time offset is labelled \(T^{(\cdot)}\) and the propagation delay is labelled \(\tau^{(\cdot)}\). Transmit events are labeled with up arrows, and corresponding receive events are labeled with down arrows. The clocks are misaligned, so the receive timestamps \(t_{(\cdot),Rx}^{(\cdot)}\) follow (1) and (2).  
frequency, and \(\tilde{\tau}_{B}=\tau^{(n)}-T^{(n)}\). The corresponding propagation model from B to A may be obtained by making the relevant substitutions [83].
## 2 Radio Access Technologies for Direct V2X
### Nr-V2x  
Sidelink was first introduced to address public safety and other device-to-device (D2D) proximity services by 3 GPP in Release 12, and then specifically tailored for V2X in 2017 with Release 14. In 2020, sidelink was added to the latest 5G NR-V2X.  
NR-V2X is based on orthogonal frequency-division multiple access (OFDMA), which implies multi-carrier modulation at the PHY layer and synchronous time-frequency division at the MAC layer. Different options are possible at the PHY layer, indicatedas numerology, which implies in the sub-6 GHz bands either 15, 30, or 60 kHz SCS. The number of subcarriers depends on both the SCS and the channel bandwidth. The granularity for resource allocation is given by the TTI in the time domain and the subchannel in the frequency domain. The TTI corresponds to 14 OFDM symbols and therefore lasts 1, 0.5, or 0.25 ms depending on the numerology. The subchannel corresponds to a certain number of groups of 12 subcarriers, which is set by the network.  
The use of the channel in NR-V2X is based on the principle of orthogonal resources and resource reservation, and can exploit a mechanism called sensing-based semi-persistent scheduling (SB-SPS). Every time a VUE transmits it also adds information to reserve another resource in the future if needed; the reservation can be made periodically for some time, from which the name semi-persistent is derived. Based on this, the neighboring VUEs with something to transmit consider that some resources are reserved and select different ones. No reservation can be performed before the first frame is transmitted. Details in the procedure can be found, for example, in [15; 26].  
Differently from CSMA/CA, the procedure defined for NR-V2X cannot prevent collisions with signals that belong to a different technology. It can be also observed that, even if the resource allocation procedure is a derivation of the SB-SPS of LTE-V2X, it is substantially different in NR-V2X. In LTE-V2X, in fact, the identification of reserved resources is mainly performed based on the average power measured in the last 1 s. In the case of LTE-V2X, periodic transmissions performed by IEEE 802.11p VUEs could lead to the identification of a reservation by cellular VUEs and, as a consequence, reduce the risk of collision. This aspect is further detailed for example in [5; 12]. In contrast, the NR-V2X sidelink makes decisions based on control information that is not available within IEEE 802.11p signals.
## 3 Materials and Methods
### Traffic Direction
#### 3.4.2 Measuring Similarity Between Ship Trajectories  
Some researchers have utilized distance-based similarities between trajectories for clustering studies on ship navigation. Dynamic Time Warping, a nonparametric trajectory similarity measure, can be computed even with different numbers of data points. Additionally, it does not require pre-parameterization, making it adaptable to various shapes and complex data. Consequently, many researchers have utilized DTW for trajectory clustering and pattern extraction. For example, [PERSON] and [PERSON] [53] used DTW as a trajectory similarity measure between trajectories and kernel k-means clustering. [PERSON] and [PERSON] [54] used clustering results based on DTW and DBSCAN to train a recurrent neural network for real-time marine anomaly detection. [PERSON] and [PERSON] [55] also used DTW as a distance measure for DBSCAN based on the statistical characteristics of the ship trajectory distribution to determine the DBSCAN parameters. [PERSON] and [PERSON] [33] proposed a trajectory distance measure that simultaneously considers the similarity of position, heading, and speed and used DBSCAN to extract the ship's course pattern. [PERSON] et al. [56] used DTW as a distance measure and a k-means cluster for clustering. The expression for determining the trajectory of ships \(A\) and \(B\), consisting of position points \(a\) and \(b\), is given in Equation (2), where represents the navigation data of \(A\), excluding the first position point [57].
# Overview of the Research Status of Intelligent Water Conservancy Technology System
###### Abstract  
A digital twin is a new trend in the development of the current smart water conservancy industry. The main research content of intelligent water conservancy is clarified. This paper first summarizes and combs the relevant system architecture of smart water conservancy, and puts forward a smart water conservancy framework based on digital twins, highlighting the characteristics of virtual and real interaction, and symbiosis of the water conservancy twin platform. Secondly, the status quo of intelligent water conservancy \"sky, air, ground and water\" integrated monitoring technology, big data and artificial intelligence, model platform technology, knowledge graph and security technology is analyzed. From the perspective of application, the research progress of each technology in water security, water resources and hydraulic engineering is reviewed. Although the construction of smart water conservancy has made remarkable progress, it still faces many challenges such as data governance, technology integration and innovation, and standardization. In view of these challenges, this paper puts forward a series of countermeasures, and looks forward to the future development direction of intelligent water conservancy.  
digital twin; intelligent water conservancy; virtual-real interaction; standardization; development direction +
Footnote †: journal: Applied Sciences
## 4 Design of the Wireless MEC Sensor  
The AD5933 chip has an I2C interface, which enables it to connect with an external microcontroller to program the frequency sweep and store the data before transmission. The Atmega328 was used as the microcontroller. A high-pass filter was used to attenuate the frequencies lower than the operational frequency of 100 kHz. A gateway using Node MCU 3.9 and NRF24L01 transceiver was also designed to receive the signals and send them to the data cloud through Wi-Fi. The sensor was programmed to activate deep-sleep mode after sending the signals after each minute.  
Figure 10: (**a**) The FE modelling normalized reactance for 1 mm and 2 mm corrosion in 8 mm thick plate. (**b**) The normalized reactance variation for 1 mm and 2 mm uniform corrosion in 8 mm thick plate obtained from experimental studies.  
To reduce the power consumption of the sensor, after taking the measurement and signal transmission, the sensor was set to deep-sleep mode. The sensor can run for a long time on batteries as it only requires 80 \(\mu\)A during its operation and draws only 13 \(\mu\)A in deep-sleep mode
_Int. J. Mol. Sci._**2014**, _15_, 22188-22202; doi:10.3390/ijms151222188  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
Cloning and Characterization of Farnesyl Diphosphate Synthase Gene Involved in Triterpenoids Biosynthesis from _Poria cocos_  
**[PERSON]** \({}^{\dagger,\ast}\)**, [PERSON]** \({}^{\dagger}\) **and [PERSON]**  
Guangdong VTR Bio-Tech Co., Ltd., Zhuhai 519060, Guangdong, China;  
E-Mails: liyangyuanvtr\(@\)163.com (Y.L.); danni\({}_{\dagger}\)vtr\(@\)163.com (D.L.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: believe1234\(@\)126.com;  
Tel.: +86-756-869-5616; Fax: +86-756-868-0250.  
External Editor: [PERSON]  
_Received: 12 September 2014; in revised form: 29 September 2014 / Accepted: 22 October 2014 / Published: 2 December 2014_  
**Abstract:**_Poria cocos_ (_P. cocos_) has long been used as traditional Chinese medicine and triterpenoids are the most important pharmacologically active constituents of this fungus. Farnesyl pyrophosphate synthase (FPS) is a key enzyme of triterpenoids biosynthesis. The gene encoding FPS was cloned from _P. cocos_ by degenerate PCR, inverse PCR and cassette PCR. The open reading frame of the gene is 1086 bp in length, corresponding to a predicted polypeptide of 361 amino acid residues with a molecular weight of 41.2 kDa. Comparison of the _P. cocos_ FPS deduced amino acid sequence with other species showed the highest identity with _Ganoderma lucidum_ (74%). The predicted _P. cocos_ FPS shares at least four conserved regions involved in the enzymatic activity with the FPSs of varied species. The recombinant protein was expressed in _Pichia pastoris_ and purified. Gas chromatography analysis showed that the recombinant FPS could catalyze the formation of farnesyl diphosphate (FPP) from geranyl diphosphate (GPP) and isopentenyl diphosphate (IPP). Furthermore, the expression profile of the FPS gene and content of total triterpenoids under different stages of development and methyl jasmonate treatments were determined. The results indicated that there is a positive correlation between the activity of FPS and the amount of total triterpenoids produced in _P. cocos_.  
**2014**, _15_ **22189**  
**Keywords:** farnesyl diphosphate synthase; methyl jasmonate; _Poria cocos_; triterpenoids
# Impact of Audio Delay and Quality in Network Music Performance +
## 4 Subjective Evaluation Design
### Questions Common to Both Scenarios  
Evaluate your satisfaction. The Perception of Satisfaction (PoSat) variable corresponds to the widely used MOS metric, assessed on a 5-point Likert scale (1 = very dissatisfied, 5 = very satisfied). Given the complexity of musical satisfaction, we complemented this metric with other perceptual dimensions.  
Did you feel anxiety? and Did you feel irritation? Since some participants may have limited familiarity with NMP technology, we included questions addressing emotional responses. Anxiety might stem from using unfamiliar equipment, while poor audio quality or high delay could lead to irritation. Both variables were measured using a 5-point Likert scale (1 = not at all, 5 = very much).