esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
## 3 Results
### Evaluating the RPs for Maize Crop Indicators  
The correlations between observed and RP-derived values of FD are relatively strong for all products, with RFHR and FYRE Climate showing the highest correlations with the reference dataset (Table 3). Good agreement between RPs and the reference dataset is also seen for the study sites, with LCCCs greater than 0.7 for 86% of cases (considering all stations and all RPs) (Figure 4d). IQD values further confirm the previous results. A comparable agreement between the RPs and the reference dataset is seen on both global and local scales (Table 3 and Figure 4c,f). Overall, RPs display minimal biases (Figure 4b). Figure 4e illustrates that, on a local scale, the bias values remain insignificant, with values ranging between \(-2\%\) and \(2\%\), in most cases (considering all stations and RPs).  
Overall, the results show a good level of precision in reproducing the FD values by the RPs, with no significant overestimation or underestimation. None of the RPs stands out as best for all stations; the most notable differences are seen for the Clermont-FD and Tarbes-Ossun stations, where ERA5 and ERA5-Land perform rather poorly at reproducing the FD values compared to the other RPs.  
Figure 3: Same as Figure 2, with the indicator HyI.  
Regarding the DDs indicator, FYRE Climate displays the highest correlation with the reference dataset with a global coefficient of 0.9 and coefficients greater than 0.8 for 90% of stations (Table 3 and Figure 5d). The rest of the RPs exhibit relatively weaker correlations, with RFHR performing slightly better in reproducing DDs values. LCCC values for RFHR remain quite acceptable, with a global value of 0.71 and correlations greater than 0.6 for 70% of the stations--a notable improvement compared to ERA5, ERA5-Land, and SCOPE Climate, where only 10% of stations exhibit similar correlations. The same findings are shown by IQD values (Figure 5c,f). FYRE Climate is the closest to the reference dataset, with a global IQD of 0.16, followed by RFHR and SCOPE Climate. ERA5 and ERA5-Land show poor performances with an IQD greater than 3 in most cases.  
For each RP, the CDF curve is consistently and systematically shifted downward (i.e., towards the lower values) of the reference line, indicating an underestimation of DDs values (Figure 5c). Results in (Table 3, Figure 5b,e) further confirm this assumption, where bias values are always negative. Although RFHR still underestimates the DDs indicator, a slight improvement is seen compared to ERA5-Land at reproducing the DDs values; the KDE curve is shifted toward the right (zero) and the average bias is reduced by approximately half.  
Figure 4: Same as Figure 2, with the indicator FD.
## 3 Structural Design and Analysis of QZS Vibration Isolation Systems
### Potential Energy and Negative Stiffness Derivation
This leads to some errors in the \(U_{a}\) terms calculated here using the superposition method. The advantage of the superposition method is that the derivation and calculation process is more concise and clear, although the accuracy is slightly lower compared to the elliptic integration method used by [PERSON] (2013); however, here, the boundary conditions are not sufficient to establish the differential equations needed for the elliptic integration, and, therefore, there is no better method to solve this problem for the time being, so the \(U_{a}\) term will be corrected in later chapters. The elastic potential energy stored in the positive stiffness spring due to tension and compression is as follows:  
\[U_{b}=\frac{1}{2}k\delta_{1}^{2} \tag{23}\]  
Finally, calculate the elastic potential energy \(U_{d}\) stored due to the length compression of the compliant LS. In Equation (11), the length of the LS after compression is computed, subtracting this from \(l_{0}\) gives the compression length. The elastic potential energy is then given by the following ([PERSON] 1998):  
\[U_{d}=\frac{AE}{2l_{0}}\left[u_{x}-\frac{\delta_{2}{}^{2}\pi^{2}}{4(l_{0}-u_{ x})}\right]^{2} \tag{24}\]  
Differentiating \(U_{a}\), \(U_{b}\), \(U_{c}\), and \(U_{d}\), we obtain the following:  
\[\frac{\partial U_{a}}{\partial\delta_{1}}=\frac{EI\delta_{1}\pi^{4}}{8(l_{0}- u_{x})^{3}} \tag{25}\]  
Figure 11: Comparison of finite element deflection curve and approximate deflection curve, with a \(l_{0}\) of 80 mm and a \(u_{x}\) of 0.3 mm. \[\frac{\partial U_{b}}{\partial\delta_{1}}=k\delta_{1} \tag{26}\]  
\[\frac{\partial U_{c}}{\partial u_{x}}=4E1\pi^{2}\Big{[}(l_{0}-u_{x})^{-2}+2(l_{0 }-u_{x})^{-3}\Big{]} \tag{27}\]  
Figure 10:0  
The numerical value of \(\delta_{2}\) in Equation (28) can be calculated using Equation (12). The equilibrium condition in Equation (3) states that the zero-stiffness system is composed of a positive stiffness spring and a negative stiffness component. Therefore, by substituting material parameters and dimensional parameters into Equation (3), the stiffness coefficient \(k\) required for the spring in the zero-stiffness system can be determined, where the stiffness coefficient \(k_{1}=-k\) for the negative stiffness component. Figure 10:1  
From Equation (29), it can be seen that the negative stiffness \(k_{1}\) is influenced by the displacement \(\delta_{1}\) during vibration, as shown in Figure 10:2 The numerical value of the negative stiffness is relatively insensitive to the displacement, hence, in calculations, a small displacement value can be arbitrarily chosen to determine the numerical value of the negative stiffness.
# Seismic Performance of Modal Transfer Stations on Soft Clays
###### Abstract  
In densely populated urban zones, seismic performance evaluation of strategic infrastructure during seismic events has become more challenging because the distance between surface and underground structures has been shortened to optimize the urban environment functionality. This is even more important in transit transfer stations, which usually comprise tunnels, bridges, and buildings, in which wave propagation interference is exacerbated. This paper explores the seismic interactions between on-ground and underground structures in soft-soil environments, focusing on a typical urban modal transfer station in Mexico City. The study is conducted through comprehensive parametric analyses using 3D numerical simulations in FLAC3D (v.6.0), considering both intraplate and interplate earthquakes, to assess the effect of differences in their frequency content, duration, and intensity. Multiple scenarios are considered in the numerical study, and the relative distances among the structures are varied to investigate both detrimental and beneficial interaction effects, and to identify the zone of influence where this interaction leads to ground motion variability. The study's findings established the key variables in the interaction between underground and on-ground structures, providing valuable insights into the seismic design and retrofitting of urban infrastructure in densely populated areas.  
Keywords:seismic interaction; soft soils; urban infrastructure; numerical modeling +
Footnote †: journal: Applied sciences
# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 3 Short-Term Traffic Load Prediction Based on DBO-BiGRU  
Based on the dynamic, non-linear and temporal logic of information flow in communication networks, a lightweight deep-learning temporal prediction model, DBO-BiGRU, is proposed to achieve accurate prediction of the cumulative traffic load size of each link.
## 1 Introduction
### Feature Extraction Methods  
In this study, VMD and PE algorithms were employed to distill the fault characteristics inherent in the gearbox transmission. Figure 2 delineates the comprehensive flowchart outlining the intricate process of feature extraction, expounded in the subsequent steps. This systematic approach ensures a meticulous analysis, shedding light on the nuanced anomalies and operational inefficiencies within the gearbox transmission. The approach is outlined as follows:  
1. VMD decomposition of the vibration signal of the gearbox transmission is performed to obtain K components.
2. The number of correlations between the original transmission vibration signal and the modal components decomposed by VMD is calculated. A value less than 0.1 indicates that it is a non-effective state component. A value less than 0.1 indicates that it is a non-valid state component, and the component is removed.
3. The entropy of each modal component is calculated as a feature quantity.  
Figure 1: Overall flowchart of the proposed model.  
Figure 2: Flow chart of fault feature extraction.
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
###### Abstract  
This paper studies wave propagation in a periodic parallel-plate waveguide with equilateral triangular holes. A mode-matching method is implemented to analyze the dispersion diagram of the structure possessing glide and mirror symmetries. Both structures present an unexpected high degree of isotropy, despite the triangle not being symmetric with respect to rotations of 90\({}^{\circ}\). We give some physical insight on the matter by carrying out a modal decomposition of the total field on the hole and identifying the most significant modes. Additionally, we demonstrate that the electrical size of the triangular hole plays a fundamental role in the physical mechanism that causes that isotropic behavior. Finally, we characterize the influence of the different geometrical parameters that conform the unit cell (period, triangle size, hole depth, separation between metallic plates). The glide-symmetric configuration offers higher equivalent refractive indexes and widens the stopband compared to the mirror-symmetric configuration. We show that the stopband is wider as the triangle size is bigger, unlike holey structures composed of circular and elliptical holes where an optimal hole size exists.  
metasurfaces; periodic structures; equilateral triangular holes; mode-matching; dispersion analysis; glide symmetry; mirror symmetry; isotropy +
Footnote †: journal: Applied sciences
## 9 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline
**Battery Type** & **Lead Acid** & **Lithium Iron Phosphate** & **Nickel Iron** \\  & **(PbSOq)** & **(LiFePO\({}_{\textbf{4}}\))** & **(Ni-Fe)** \\ \hline Manufacturer & Trojan [21] & Victron [22] & Iron Edison [23] \\ \hline Model & SSIG 06 490 & LFP-12.8/200-a & TN 1000 \\ \hline Nominal capacity (\(S_{BAT}\)) & 490 Ah & 300 Ah & 1000 Ah \\ \hline Nominal voltage (\(V_{BAT}\)) & 6 V & 12.8 V & 1.2 V \\ \hline Round trip efficiency (\(\eta_{rbal}\)) & 85\% & 92\% & 80\% \\ \hline Lifespan in years & 2.5 years & 15 years & 30 years+ \\ \hline Self-discharge rate (\%/day) (\(\sigma\)) & 0.3\% & 0.2\% & 1\% \\ \hline Capital cost (CC) in USD & 410 & 3317 & 1057 \\ \hline Annual O\&M cost in USD & 2.5\% of CC & No maintenance & 2\% of CC \\ \hline Operating temperature & \(-20\)\({}^{\circ}\)C to +45 \({}^{\circ}\)C & \(-20\)\({}^{\circ}\)C to +50 \({}^{\circ}\)C & \(-30\)\({}^{\circ}\)C to +60 \({}^{\circ}\)C \\ \hline Cycle life of the batteries & 750 cycles & 5000 cycles & 11,000+ cycles \\ \hline \end{tabular}
\end{table} 
Table 3: The study’s technical and financial values of the batteries. The results shown in Table 4 were obtained with an LPSP value of 0% for the above-mentioned three configurations using the algorithms shown above. For each configuration, this table shows the optimal LCC values as well as the optimal number of PV panels and batteries.
## 3 Results  
### Redundancies in Sentinel-2 Bands  
As stated in Section 2.3, we start by analyzing the redundancies in Sentinel-2 spectral bands. Figure 5 displays the values of the normalized mutual information correlation for all pairs of bands of L1C (left) and L2A (right) data.  
Both levels of processing show the same patterns and nearly the same values, although L2A has slightly lower values of dependence. This may indicate that atmospheric corrections are able to remove effects with high correlation across bands.  
We observe high values for the red edge bands between B5 and the red band, and between the two SWIR bands. Interestingly, B5 presents a relatively low dependence with respect to B6 and B7, and there is very small redundancy between B8 and B8A (it is, for instance, lower than between green and B5).  
The highest values of mutual information are obtained between adjacent bands of the B6, B7, B8A triplet; B7 being the most similar to the others. B7 therefore seems a good candidate for reconstruction from other bands.  
One limitation of this analysis is that only pairs of bands are compared, and therefore it is impossible to assess if the redundancies between, for instance, B7 and B6 are complementary to those between B7 and B8A, which would allow better reconstruction of B7 from the other two than if these redundancies were the same.  
It is also interesting to note that B5 has all values higher than 0.4 (except for B8), which may indicate either the possibility of reconstructing it from the other bands, or conversely, of it being some sort of pivotal band to reconstruct the others.  
The relatively low value of the mutual information between B8 and B8A may seem surprising since the latter is a subset of the former. Actually, this value is the same for B7 and B8, which are adjacent (see Figure 6). However, B8A has a width less than 20% of that of B8.  
This means that these measures of mutual information are lower bounds of the amount of information that could be reconstructed from other bands.
# Method to Characterize Potential UAS Encounters Using Open Source Data
## 1 Introduction
### Scope  
We adopted a similar scope as the ASTM standard [9] and the Federal Aviation Administration (FAA) proposed ([[https://www.regulations.gov/docket?D=FAA-2019-1100](https://www.regulations.gov/docket?D=FAA-2019-1100)]([https://www.regulations.gov/docket?D=FAA-2019-1100](https://www.regulations.gov/docket?D=FAA-2019-1100))) UAS Remote Identification (Remote ID) rule for considering close airborne encounters involving UASs. Out of scope was estimating the frequency of potential encounters and non-DAA concerns, such as airworthiness. This scope was informed by the FAA UAS Integration Office, and the standards activities of International Civil Aviation Organization (ICAO), ASTM F38, RTCA SC-147, and RTCA SC-228. The Airborne Collision Avoidance System (ACAS X) unmanned variants [10; 11] were considered as DAA reference architectures, as they are being standardized by the aforementioned organizations.  
The scope encompassed commercial and recreational UASs weighing greater than 0.55 pounds with no restrictions on UAS size, performance, or operating altitude. We assumed that encounters consist of only two aircrafts, but that the general policy for manned formation flight will apply to UASs. We prioritized research to DAA and collision avoidance systems designed to mitigated the risk of midair collision. These prioritized system are the third and final layer for airspace conflict management and are employed after separation provisions and strategic mitigates have failed [12].
# Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
## 4 Experiments
### Evaluation Protocols  
First, multiple UDA tasks are considered to validate the performance of the proposed framework. In each UDA task, a pair of datasets, one labeled and the other unlabeled, are involved to represent the skeleton data distribution shift, i.e., the \"C-View\" evaluation protocol is used to represent the variation in image acquisition configuration and the \"C-Sub\" evaluation protocol is used to represent subject variations across domains.  
* **NTU RGB+D to PKU-MMD.** We consider a single source domain, NTU RGB+D, and two target domains, PKU-MMD part I and PKU-MMD part II. In the first experimentation \"NTU RGB+D to PKU-MMD part I\" (Table 1), skeleton sequences under 50 actions common to both datasets are used for training and testing, while in \"NTU RGB+D to PKU-MMD part II\" (Table 2), 41 single-person actions are used for experiments.  
The experimental results first demonstrate that either baseline model can yield convincing results in its original training domain (e.g., the source-only model in Table 1 trained on NTU RGB+D achieves 69.07% on the \"C-Sub\" testing set and 72.86% on the \"C-View\" testing set of NTU RGB+D, while the target-only model trained on PKU-MMD Part I achieves 85.06% and 87.34% on the testing sets of PKU-MMD Part I). However, given the domain shift between the source and the target domains, either baseline model fails when performing cross-domain action recognition (e.g., in Table 1 the source-only model (trained on NTU RGB+D) only achieves 57.32% on PKU-MMD Part I and similar results are observed for the target-only model when tested on NTU RGB+D data).  
When the proposed UDA framework is used to learn a joint-domain classifier using the (labeled) training set of the source domain and the (unlabeled) training set of the target domain, the results in the second row of Tables 1-4 demonstrate that the resulting UDA model achieves significant improvement on cross-domain action recognition. It achieves a gain of 12.02% in Acc (from 57.32% to 69.34%) and 9.95% in F1 (from 27.40% to 37.35%) over the source-only model performance in Table 1. The UDA model still achieves comparable performance on the source domain compared to the source-only model in most of testing cases (e.g., 69.07% vs. 70.05% under C-sub in Table 1), demonstrating its effectiveness in learning action knowledge from two domains. However, given that target labels are absent, the UDA model expectedly underperforms the fully supervised (i.e., target-only) model. Similar performance is observed for every one of the four cross-domain test scenarios considered. Furthermore, even though the proposed UDA model obtains effective adaptation outcome under both the \"C-View\" and \"C-Sub\" evaluation protocols, it performs better in the \"C-View\" case, which we attribute to the different degree of skeleton data distribution shift.
# Evaluation of the GPS Precise Orbit and Clock Corrections from MADOCA Real-Time Products
## 4 Orbit and Clock Evaluation  
### Unification Coordinate Datum  
Considering the geodetic datum of satellite orbit difference between MADOCA real-time and IGS final products [18], the Helmert transformation is used to ensure the consistency of the reference frame. This 7-parameter transformation model can be expressed as follows:  
\[\left[\begin{array}{c}X_{T}\\ Y_{T}\\ Z_{T}\end{array}\right]=M*\left[\begin{array}{ccc}1&-R_{Z}&+R_{Y}\\ +R_{Z}&1&-R_{X}\\ -R_{Y}&+R_{X}&1\end{array}\right]*\left[\begin{array}{c}X_{S}\\ Y_{S}\\ Z_{S}\end{array}\right]+\left[\begin{array}{c}dX\\ dY\\ dZ\end{array}\right], \tag{1}\]  
where \(\left(\begin{array}{ccc}X_{S}&Y_{S}&Z_{S}\end{array}\right)^{T}\) and \(\left(\begin{array}{ccc}X_{T}&Y_{T}&Z_{T}\end{array}\right)^{T}\) are the coordinates of the point at the same epoch in the source geocentric coordinate reference system (i.e., JGD) and the target geocentric coordinate reference system (i.e., ITRF), respectively. In addition, \(\left(\begin{array}{ccc}dX&dY&dZ\end{array}\right)^{T}\) denotes the translation vector, \(R_{X}\), \(R_{Y}\) and \(R_{Z}\) are the three rotation parameters, and M is the scale correction parameter, which is used to the scale correction in the position vector in the source coordinate reference system.  
To solve these seven parameters, 3D coordinate data of 96 epochs in one day as well as the least-squares algorithm are applied to obtain the most probable value of seven parameters for each satellite. Then, transformation between the two coordinate references can be achieved by Equation (1).
## 6 Video Technology in M-Health Systems
### Video and Physiological Signal Synchronization  
The combination of data and video plays an important support role. Improvements in the health delivery process in the context of tele-health requires the synchronization of video and physiological signals to make possible for the physicians located in a Hospital to simultaneously observe the face and the vital signs of the patient located at the place of the accident. Also, effective procedures for medical treatment, diagnosis, feedback and research require the synchronization of both video and biomedical signals. Performing feedback procedures to enhance patient safety in anesthesia operating rooms requires the creation of a permanent and accurate record of clinical events containing synchronized video, audio and vital signs for future offline analysis [57]. Procedures for diagnosing sleep breathing disorders need the synchronization between video recordings and polysomnographic readings to confirm breathing anomalies in pediatrics [58]. Identification, classification and quantification of silent neonatal seizures needs synchronized recordings of video and EEG signals to determine the correlation between the electrical and clinical artifacts in premature babies. Clinical manifestations recorded in video such as lip smacking, fixing of eyeballs or cyclic movement of legs are correlated with EEG recordings during diagnosis procedures [59].  
The biomedical signals being synchronized with the video are usually more than one to provide sufficient medical and visual information to the physician. This information supports the physician in improving the accuracy and effectiveness of medical treatment, diagnosis and feedback. Observing one or more biomedical signals simultaneously with the video of the patient is necessary for assessment and correlation. Many signal- and image-processing challenges lie in the joint processing and transmission of biomedical signals and images. We see challenges in three areas: (i) multi-modality signal synchronization; (ii) joint signal, image and video compression; and (iii) interactive collaborative environments. There are significant synchronization issues for joint decoding. Clearly, one-dimensional biomedical signals should correspond to video images. As an example, we note the synchronization of ECG, respiratory, two-dimensional, and Doppler signals in ultrasound systems. In addition, we note that two-way voice communications must be synchronized to all clinical signals, as well as to real-time video images of the patient and the doctor. Resynchronization in the presence of wireless-communication errors will require innovative error-resilience methods. For well-synchronized signals and images, methods for joint-signal, image, and video compression can be developed [50].
## 3 Results  
### Mission Analysis  
The mission analysis is carried out by monitoring the satellite's capability to power up all subsystems (Power Budget), the heat balance (Thermal Budget), and the capability to store and download the acquired data (Data Budget). In order to have an overall vision of the satellite's status the MOTS End-to-end Performance Simulator (MEPS) simulator has been developed [26]. In order to obtain consistent results, it is necessary to calculate all budgets simultaneously due to the interdependency of all variables that govern the system.