esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the literature, different methods of hybridization have been presented and their pros and cons from different points of view have been studied. The main topologies examined in the scientific literature can be divided into passive and active topologies. The passive topology which is the simplest hybridization method is achieved by the direct parallel connection of two or more energy storage technology. This topology is cheap, light, easy to implement, highly dynamic, yet has some negative points. Due to the direct connection, power sharing cannot be controlled, and the usable capacity is limited by the operating voltage of the battery. To operate each energy storage system in an optimalway, an active topology is introduced. There are many types of active topologies based on the number of decoupled energy storage systems with convertors, such as DC/DC convertors [8]. Hybrid energy storage systems (HESS) also have been studied from the control strategy point of view in many studies. In [9], a novel controller for a HESS has been proposed which aims to decrease the frequency effect induced on the SC in the process of power sharing. In [10] a new combination of SC and multi-speed transmission system and the usage of regenerative breaking energy was used to increase the energy density of the EVs and make them comparable with ICEVs. In [11], a near-optimal power management strategy was proposed. The presented method was verified for different state of charge and state of health of the battery which can reduce the C-rate of the battery by 10%. In [12], authors investigated the effect of driving cycle characteristics on the optimization result of the HESS and concluded that those results can be generalized to practical bus lines. As stated before, none of the available researches considered the application of LiC in the HESS and the effect of this combination on the lifetime performance was not studied before. This paper investigates the effect of hybridization of LiB and LiC on the lifetime performance of a HESS for an urban electric bus. In this regard, a developed lifetime model of a 20 Ah nickel manganese cobalt oxide (NMC) lithium-ion battery in combination with a LiC 2300 F lifetime model developed in our laboratory (mobility, logistics, and automotive technology (MOBI) research center, Vrije Universiteit Brussel (VUB)) was used for simulation purposes. The result approves the effectiveness of hybridization by increasing the lifetime by 16%. This paper is organized as follows: in Section 2 the electro-thermal and lifetime model of a NMC 20 Ah battery is briefly explained. Section 3 presents a detailed description of the LiC electro-thermal and lifetime model. The hybridization and load sharing methodology is illustrated in Section 4. Further analysis of the driving cycle and power and energy requirements for a pure electric bus is presented in Section 5. Simulation results and discussions are given in Section 6 and, finally, the conclusion is presented in Section 7.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 4 Results
### Temperature Prediction  
Figure 5 compares the performance of MLTs in living room temperature prediction based on IAQ monitoring in the kitchen. The following performance metrics were used: R\({}^{2}\) (Figure 5a), MAPE (Figure 5b), and RMSE (Figure 5c). Their median values are shown in Table 3.  
As shown in Figure 5a, linear MLTs offered the best quality in temperature prediction. Coefficients of determination were highest in the case of MLR (R\({}^{2}\) = 0.72) and MLP2 (R\({}^{2}\) = 0.70). The naive approach (R\({}^{2}\) = 0.70) offered comparable quality, while the quality of nonlinear MLTs (DT, RFR, KNN, and MLP1) was very low, as shown by a small R\({}^{2}\) (0.09-0.43). Considering the percentage error of T prediction, the naive approach yielded the lowest score of MAPE (2.68%) (Figure 5b). Still, it was comparable to the error offered by linear methods MLR (MAPE = 2.71%) and MLP2 (MAPE = 2.84%). When applying nonlinear MLTs, the prediction errors were considerably higher (MAPE = 5.30-6.38%). Regarding the RMSE of T prediction (Figure 5c), the errors were smallest in the case of MLR (RMSE = 0.89 \({}^{\circ}\)C) and MLP2 (RMSE = 0.92 \({}^{\circ}\)C), which are linear MLTs. They outperformed the naive approach, with RMSE = 1.24 \({}^{\circ}\)C. When using nonlinear MLTs, temperature prediction was loaded with bigger errors (RMSE = 2.04-2.25 \({}^{\circ}\)C). In summary, it is possible  
Figure 4: Time series of measurement data that were collected for three days during the IAQ monitoring study in (**a**) apartment A3 and (**b**) apartment A4.  
to predict the temperature in the living room of the apartment based on IAQ monitoring in the kitchen with an error smaller than 1 \({}^{\circ}\)C using linear MLTs. It is crucial to note that this result was achieved by prediction models trained on IAQ monitoring data collected in different apartments than where the prediction was realized. The best prediction was achieved using MLR, and the quality of this model was the highest. Such a result could not be attained with the naive approach. Nonlinear MLTs were an even less viable alternative to linear methods in the case of temperature predictions.
## 1 Introduction
[19] conducted wind tunnel experiments on a non-uniform cross-section box girder and found that the cross-sectional shape of the bridge greatly affects the wind velocities above the track. To improve the wind resistance of trains and bridges, extensive research has been conducted. [PERSON] et al. [20] found that wind barriers with lower porosity at lower positions have better shielding effects by changing the porosity of different parts of the wind barrier. [PERSON] et al. [21] proposed a variable-shape glass-fiber-reinforced plastic (GFRP) wind barrier that can reduce wind loads on bridges. [PERSON] et al. [22] effectively improved the vortex-induced vibration performance of bridges by arranging wind barriers according to certain rules. [PERSON] et al. [23] found that the turning effect of wind barrier blades significantly regulates the flow field on the bridge in their study of the aerodynamic performance of pedestrian bridges. Based on [PERSON]'s work, [PERSON] et al. [24; 25] proposed a new type of louvered wind barrier and found that louvered wind barriers with a certain inclination angle can regulate the surface wind pressure distribution of trains, thereby reducing the train's moment coefficient under the premise of minimal aerodynamic changes to the bridge. This paper studies a new type of WGB that deflects the airflow horizontally for aerodynamic design. This wind barrier has been applied to many new high-speed railway bridges. Through wind tunnel experiments, the study investigates the effects of three WGB parameters on the aerodynamic characteristics of HST and bridges. To reveal the windproof performance of the new WGB, we measured the mean wind pressure and pulsating wind pressure on the surface of the train and bridge. In addition, the wind field characteristics on the bridge deck were also tested, and the wind velocity and turbulence field after the wind barrier were analyzed, including the three-direction wind velocity components and turbulence intensity, to comprehensively evaluate the windproof performance. The rest of this paper is organized as follows: Section 2 introduces the outline of the experimental procedure. Section 3 describes the effect of the WGB on the aerodynamic characteristics of the train-bridge system. Section 4 analyzes the effect of the WGB on the wind field characteristics around the bridge deck. Finally, conclusions are drawn in Section 5.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 3 Adaptive Bat Algorithm Optimizes Observation Matrix  
In the field of signal processing, compressed sensing technology has been widely used as the latest technology. Since the observation matrix and reconstruction algorithm have a certain influence on the effect of signal reconstruction, this paper designs a novel observation matrix and reconstruction algorithm for this phenomenon. In order to avoid the individual falling into local extremum and achieve better reconstruction effect of the optimized observation matrix, the paper designs the comparison of different observation matrices with different algorithms, so as to compare the signal reconstruction effect.  
To enhance the reconstruction accuracy and minimize the reconstruction error, an adaptive bat algorithm is proposed to optimize the observation matrix. In this section, compressive sensing theory and the search method of ABA are introduced in detail; additionally, the strategy of optimizing the observation matrix based on the adaptive bat algorithm is described.
###### Abstract  
Full-waveform velocity inversion has long been a primary focus in seismic exploration. Full-waveform inversion techniques employing physics-informed recurrent neural networks (PIRNNs) have recently gained significant scholarly attention. However, these approaches demand considerable storage to capture spatiotemporal seismic wave propagation fields and their gradient information, often exceeding the memory capabilities of current GPU resources during field data processing. This study proposes a full-waveform inversion method utilizing a dual-branch PIRNN architecture to effectively minimize GPU resource consumption. The primary PIRNN branch performs forward-wave equation modeling at the original scale and computes the discrepancy between synthetic and observed seismic records. Additionally, a downscaled spatiotemporal PIRNN branch is introduced, transforming the original-scale error into a loss function via scale decomposition, which drives the inversion process in the downscaled domain. This dual-branch design necessitates recording only the spatiotemporal field and gradient information of the downscaled branch, significantly reducing GPU memory requirements. The proposed dual-branch PIRNN framework was validated through full-waveform inversions on synthetic horizontal-layer models and the Marmousi model across various scales. The results demonstrate that this approach markedly reduces resource consumption while maintaining high inversion accuracy.  
AFFI; multiscale decomposition; dual-physics-informed recurrent neural network (PIRNN); full-waveform inversion (FWI); vertical seismic profiling (VSP)
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 1 Introduction
### ECG Features  
Cardiac fatigue can prolong repolarization and increase electrical instability, reflected in the QT interval. Specifically, QTc prolongation (corrected QT interval) indicates repolarization abnormalities, and the QT Variability Index (QTVI) is sensitive to sympathetic stress and early cardiac dysfunction [3]. T-wave Alternans (TWA) reflects microvolt-level beat-to-beat variability in T-wave morphology, linked to ventricular repolarization instability; elevated TWA suggests metabolic fatigue, myocardial stress, or increased arrhythmic risk [4]. Fatigue and ischemic stress can cause transient ST depression or elevation, especially in endurance athletes or those under hemodynamic stress. Finally, a delayed return to baseline HR after exertion suggests autonomic fatigue and impaired vagal reactivation.
## 4 Prototypes and Validation
The subscripts \"\(sa\)\" and \"\(ca\)\" represent the square and circular structures parameters with addition of the averaged dimensional deviations. It is observed that the shift in resonant frequency of the square structure is around 2.16 GHz, whereas, for the circular structure it is just around 0.48 GHz. It is obvious that the shift in the resonant frequency is much smaller in the case of the circular-loops than the square-loops. Therefore, under the condition of non-negligible fabrication tolerances, the FSS structure with an array of circular loops produces less sensitive, more predictable and accurate results than the square-loop structures, especially when a large-size FSS structure to cover mm-wave frequency range is required. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Parameter** & \
\begin{tabular}{c} **Designed** \\ **Value (mil)** \\ \end{tabular} &
\begin{tabular}{c} **Fabricated** \\ **Value (mil)** \\ \end{tabular} & **Error (\%)** \\ \hline \multirow{2}{*}{Loop length “\(a\)”} & \(d_{s}\) (square) & 24 & 23.693–23.606 & 1.46 \\  & \(d_{c}\) (circular) & 27 & 27.098–26.941 & 0.07 \\ \hline \multirow{2}{*}{Loop width “\(w\)”} & \(w_{s}\) (square) & 04 & 3.783–3.724 & 6.16 \\  & \(w_{c}\) (circular) & 04 & 4.266–4.108 & 4.68 \\ \hline \multirow{2}{*}{Gap between the loops “\(g\)”} & \(g_{s}\) (square) & 36 & 36.429–36.279 & 0.98 \\  & \(g_{c}\) (circular) & 33 & 33.244–33.008 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the designed and fabricated parameter values. Figure 7: Fabricated FSS filters: (**a**) Square-loop structure, (**b**) Circular-loop structure, (**c**) Fabricated size of the FSS filters is 19.7 \(\times\) 6.3 inches (500 \(\times\) 160 mm).
# Recent Development of Aptamer Sensors for the Quantification of Aflatoxin B1
## 3 Conclusions  
A variety of classical strategies have been employed for the detection of AFB1; however, their drawbacks, including time consumption, expensive equipment, and low portability, have limited their further use. On account of the high selectivity of aptamers toward specific targets, aptamers can be used to replace antibodies for the quantification of analytes. In this review, we described various aptamer probes for the detection of AFB1. As listed in Table 1, the probes consist of colorimetric, electrochemical, and fluorescent methods. The probes utilize colorimetric, electrochemical, and fluorescent methods. Although great breakthroughs have been made, the stability of aptamer probes should be improved by the modification of DNA. Moreover, aptamer probes associated with signal amplification strategies can be employed to improve the sensitivity of the probe.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Probe** & **LOD (g\(\cdot\)L\({}^{-1}\))** & **Linear (g\(\cdot\)L\({}^{-1}\))** & **Practical** & **Ref.** \\ \hline Colorimetry & Fe\({}_{3}\)O\({}_{4}\)/GO & / & \(1.0\times 10^{-5}\)–\(2.0\times 10^{-4}\) & peanut & 18 \\ \hline Colorimetry & Split DNAzyme & \(1.0\times 10^{-7}\) & \(1.0\times 10^{-7}\)–\(1.0\times 10^{-2}\) & corn & 19 \\ \hline Colorimetry & Catalytic DNA circuit & \(6.2\times 10^{-10}\) & \(3.1\times 10^{-9}\)–\(3.1\times 10^{-4}\) & rice & 20 \\ \hline Colorimetry & Responsive hydrogel & \(5.5\times 10^{-10}\) & \(0\)–\(1.9\times 10^{-5}\) & Beer & 21 \\ \hline Electrochemistry & GO & \(1.3\times 10^{-7}\) & \(1.0\times 10^{-6}\)–\(2.0\times 10^{-5}\) & / & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different strategies for AFB1 detection.  
Figure 15: Schematic representation of the SERS aptasensor for the determination of AFB1 based on gold nano-triangles. Reproduced from [35]. Copyright 2017, Elsevier.  
**Author Contributions:** Conceptualization, Y.J. and G.Z.; methodology, P.L.; writing--original draft preparation, Y.J. and Z.L.; writing--review and editing, Y.J.; visualization, B.Y.  
**Funding:** This research was funded by the National Natural Science Foundation of China (21705071, 21605074, 21805125), the Natural Science Foundation of Guangdong province of China (2016A030310362, 2014A030310274, 2017A030310604, 2018A030313101), the Initiatory Financial Support from Lingnan Normal University (ZL1611) and the Research Group of Rare Earth Resource Exploiting and Luminescent Materials (2017 KCXTD022).  
**Acknowledgments:** The authors gratefully acknowledge the support of [PERSON] during the preparation of the literature.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 The F-Channel Platform
### Coordination Module
#### 3.2.4. Auction Wizard and Bid Submission Wizard
The third and final constraint that is used in the process can be seen as a way of ensuring that the total accepted amount of the product offered by the FSPs is equal to the total accepted amount of that same product required by the SOs. All the constraints shown here must be respected in each of the observed time intervals \(\tau\) from the set of intervals of interest. The optimization problem is given in the linear form, making it suitable for solving by using one of the widely accepted optimization techniques based on the linear programming principles. The only step remaining, once the optimization cycle is completed and the bids that will be accepted are chosen, is forwarding the information regarding the results of the auction to all the interested sides. The results of each of the auctions can be accessed in two ways, with the first of those being the direct message that participants in the auction process will receive via the platform. The second way is by looking into the publicly available areas of the F-channel platform, in which the results of every conducted auction will be published for the sake of the transparency of the energy market.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 2 Model of High Voltage Cable Grounding System
### Leakage Current  
The leakage current is caused by the potential difference between the cable core and the metal sheath when the cable is in operation [13]. HV cables use XLPE as the material of the main insulation layer, whose impedance is much larger than the capacitive resistance between the core and the metal sheath, therefore, the leakage current is mainly capacitive current. Taking minor section A1 as an example, the equivalent two-port network is shown in Figure 3, \(Z_{\rm A1}\) is the equivalent impedance of the main insulation of minor section A1, \(Z_{\rm LA1}\) and \(Z_{\rm RA1}\) are the equivalent impedances of the left and right ends of the metal sheath of A1, and the capacitive current \(I_{\rm CA1}\) flowing through the main insulation of minor section A1 is shown in Equations (1) and (2)  
\[I_{\rm CA1}={\rm j}\omega CU_{\rm A}I_{\rm A1} \tag{1}\]  
\[C=\frac{4\pi\varepsilon_{0}\varepsilon_{\rm r}}{\ln(\frac{D_{\rm C}+2\delta}{ D_{\rm C}})} \tag{2}\]  
where \(C\) is the capacitance of the main insulation per unit length, \(U_{\rm A}\) is the A-phase operating voltage, is the cable length \(l_{\rm A1}\) for minor section A1, \(\varepsilon_{0}\) is the vacuum dielectric  
Figure 1: HV cable grounding system model.  
Figure 2: Schematic diagram of sheath current loops.  
constant, \(\varepsilon_{\rm r}\) is the relative dielectric constant of XLPE, \(D_{\rm c}\) is the outer diameter of the cable core, and \(\delta\) is the thickness of the insulation layer.  
According to the current divider rule, the components \(I_{\rm CLA1}\) and \(I_{\rm CRA1}\) of the capacitive current flowing into both ends of the metal sheath in small section A1 are shown in Equations (3)-(6).  
\[I_{\rm CLA1}=\frac{Z_{\rm m1}-R_{\rm e}-Z_{\rm LA1}}{Z_{\rm m1}}\,I_{\rm CA1} \tag{3}\]  
\[I_{\rm CRA1}=\frac{R_{\rm e}+Z_{\rm LA1}}{Z_{\rm m1}}\,I_{\rm CA1} \tag{4}\]  
\[Z_{\rm m1}=R_{\rm e}+R_{\rm g}+Z_{\rm mA1}+Z_{\rm mB2}+Z_{\rm mC3} \tag{5}\]  
\[Z_{\rm mA1}=Z_{\rm LA1}+Z_{\rm RA1} \tag{6}\]  
where \(Z_{\rm m1}\) is the total impedance of the sheath loop L1, \(R_{\rm e}\) and \(R_{\rm g}\) are the grounding resistances at the first and last ends of the main section of the cross-bonded, and \(Z_{\rm mA1}\) is the total impedance of the minor section A1.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.3 Theorem 1 -- Unlinkability  
A certificateless blind signature is presumed to offer security from the linkability attack if the blind signer has no access to the plain text.  
**Proof.** In our designed scheme, first of all, the requester selects two blind factors (\(\tau\), \(\varphi\)), then performs calculations to find out the value of hash, using \(r=\
ot{\Lambda}_{1}\)(\(m\), \(\Delta_{1,}\)\(\Delta_{2}\), \(\sigma_{\tau}\)), and \(\mathcal{Z}\), using \(\mathcal{Z}=r+\varphi\). Further, the requester sends \(\mathcal{Z}\) to the blind signer. In case the signer wants to see the plain text, it is mandatory for him/her to recover \(m\) from \(r\), where \(r=\
ot{\Lambda}_{1}\)(\(m\), \(\Delta_{1,}\)\(\Delta_{2}\), \(\sigma_{\tau}\)). This, however, is not feasible because of the one-way nature of the hash function. After this, the signer also needs the blind factor \(\varphi\), which is only known to the requester. Thus, the aforementioned discussion clearly justifies that the scheme, decently, fulfills the security property of unlinkability. \(\Box\)
## 1 Introduction
However, some studies have revealed that a composite of maximum apparent temperature provides a closer representation of land surface conditions [36; 37]. Also, the strongest correlation was observed between SMOS Level 2 (L2) SM and the MODIS LST acquisition closer to the daily maximum (e.g., Aqua day) in a previous study [38]. Consistently, better agreement to in situ was also observed in SMOS/MODIS SM disaggregated products when using LST at day times (closer to daily maximum) than at night times [39]. The above-mentioned studies suggest that daily maximum LST is more closely linked to instantaneous SM than collocated LST. In this regard, gaining understanding of the SM-LST link from observations could help improving the representation of LST in current hydrological and climate prediction models, and also refine SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status. This study presents an evaluation of SM and LST dynamics using in situ and satellite observations over the central part of the river Duero basin in Spain. The study period comprises four years, from 2011 to 2014, and provides an assessment of the role of ET and thermal inertia physical processes into the SM-LST link. The in situ data include SM and LST observations from the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) [40]. The satellite data used are the SMOS Barcelona Expert Centre (BEC) Level 3 (L3) SM [41] and the MODIS Terra/Aqua LST. The correlation of instantaneous SM and a variety of LST-derived parameters is analyzed at the daily scale to better understand the fundamental link of the SM-LST relationship. The seasonal dependence of this relationship is evaluated by means of SM and LST temporal correlation using a modeled ET as supporting information. The Hydrological Model for Operational estimates of Recharge and actual Evapotranspiration (HidroMORE) is used to generate the modeled ET [42; 43]. Additionally, a method to estimate the critical SM from the SM-LST coupling/decoupling transitions has been developed and effectively applied to in situ and spaceborne measurements. Section 2 provides a description of the in situ and satellite data used as well as a brief explanation of the ET model. Section 3 describes the applied methodology. Results and discussion are shown in Section 4. Main conclusions and contributions of this work are outlined in Section 5.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 5 Evaluating LEO Satellite Internet with SatScope: Case Studies  
In this chapter, we present case studies that demonstrate the use of SatScope for evaluating the performance of LEO satellite internet.