esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To further address practical engineering environments and the influence of multiple corrosion pits on the damage evolution process, this study innovatively integrated a traffic-bridge-wind simulation analysis model. The research systematically investigated the fatigue-damage evolution patterns of single and double corrosion pits in suspension bridge cables. Based on the principles of continuous damage mechanics theory, a damage evolution model for high-strength steel wires was meticulously developed within the finite element software ANSYS. Using the life and death element method, the intricate process of damage evolution in wires under single pit conditions and various pit distribution forms was simulated. This simulation not only visually elucidated the progression of damage at corrosion pits but also facilitated a comprehensive analysis of the stress state and fatigue life of high-strength steel wires.
###### Abstract  
This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission's six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency's (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission.  
SAR; floodwater mapping; InSAR coherence; urban areas; Sentinel-1 2019 11 107 doi:10.3390/rs11020107
## 5 Discussion
### Effects of Problem Formulations on the Performance  
To address the partial observability, we have taken a simple approach by stacking 3 previous frames along the channel dimension according to [19]. However, the trained agent still shows limited performance with larger initial rotation angle away from the target dolly. One potential explanation is that the historical observation is still not long enough to mitigate partial observability. In the work of [49], they use LSTM with increasing the historical length of 20 steps. The performance is reported to be better than stacking 3 previous frames. This approach be potentially effective, but at the cost of a longer training duration.  
We have also tried a simpler environmental setting, where the agent tries to navigate to the door and the mobile robot is only equipped with frontal grey-scaled camera and three previous frames are stacked. Importantly, the door is designed with distinct grey-scaled color from other objects so that the agent can recognize the target state from the grey-scaled observation. We have merely implemented the SAC algorithm with random starts, but without pre-trained feature encoders. Interestingly, the learned policy is mostly optimal and the agent learns to rotate at the beginning to search for the goal and moves towards the door as soon as it gets detected. An investigation of whether increasing historical length can significantly increase the performance with larger initial rotation angles will be investigated further.  
In some intermediate trials, we have found that the agent relies on the LiDAR readings to infer the goal position instead of relying on the visual observations. Therefore, we have tried one variant forcing our RL agent to perform goal detection via visual observation, whereas LiDAR readings are only intended for collision avoidance. To this end, the maximal detectable range of LiDARs has been set to a maximum of 1.5 m, and the pre-processing of the LiDAR observation is also rescaled into \([0,1]\) correspondingly. Strangely, only with this change, the complete training ends up with failure. The agent fails highly frequently even with the simplest optimistic initial state. The reason for this is still unknown, might be potentially related with network initialization as mostly of beams ends up with the maximal readings of one after rescaling, breaking the assumption that the input features a normal distribution on which most weight initialization is based.  
We have also conducted some simple trials for reward shaping. In one attempt, the reward term \(r_{CD}\) has been set to be of the same as \(r_{C}\), namely, not distinguishing collision with dolly or other obstacles. Our findings show that the small penalty of dolly collision definitely encourages the agent to approach that area and simplifies the training.
## 2 Experimental Test
### Aging Test and Capacity Fade Curve Acquisition  
A LiFePO4/C battery cell was aged with a mission profile from the energy storage system providing the primary frequency regulation service for the grid [25]. As seen in Figure 2, the mission profile had a length of one week, and the SOC varied from 10 to 90%. One full equivalent cycle (FEC) was defined as one fully charging plus one fully discharging. For the tested 2.5 Ah battery, 5 Ah went through the battery after each FEC. There was a total 4560 FEC during the overall aging process. In order to determine the capacity of the LiFePO4/C battery during its lifespan, the capacity test was performed following the one-week aging test. The capacity test consisted of a full charging process using a constant current (1C-rate)-constant voltage (3.6 V) method and a full discharging process with a 1C-rate current. The cut-off voltage for charging and discharging was 3.6 V and 2 V, respectively. The capacity obtained during the discharging test was considered as the capacity value. As shown in Figure 3, the capacity decreased from 2.57 Ah to 2.14 Ah, and the aging test was stopped because the battery reached its end-of-life when the capacity faded by 20% [26].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Nominal capacity & 2.5 Ah \\ Nominal voltage & 3.3 V \\ Charge voltage & 3.6 V \\ Cut-off voltage & 2.0 V \\ Maximum continuous charge current & 10 A (4C) \\ Maximum continuous discharge current & 50 A \\ \hline \hline \end{tabular}
\end{table}
Table 1: The datasheet of the LiFePO4/C battery.  
Figure 1: The flowchart of the test schedules.
# Highly Sensitive Liquid M-Z Waveguide Sensor Based on Polymer Suspended Slot Waveguide Structure
## 7 Conclusions  
In this paper, we optimized the device parameters to enhance \(I_{out}\), \(S_{d}\), \(S_{w}\), and \(\eta\). A suspended branch arm with a slot structure that exhibits a high sensitivity was designed. The average sensitivity of the suspended MZI sensor based on the slot waveguide is 972.1 dB/RIU and the average detection resolution is 1.6 \(\times\) 10\({}^{-6}\) RIU, which is about 1.5 times higher than that of the suspended strip waveguide structure, 1.6 times higher than that of the non-suspended slot structure, and 2.1 times higher than that of the non-suspended strip waveguide structure. In this paper, we proposed a liquid sensor based on a polymer suspended slot waveguide compatible with imprinting and redissolving technology, which can tackle the existing problems of fabrication cost and light scattering loss of silicon slot waveguides. The liquid M-Z waveguide sensor based on the polymer suspended slot waveguide structure we proposed is expected to have great application potential in the field of liquid sensing.  
Figure 10: The schematic diagram of the mechanical analysis of the device: (**a**) the entry position of the central coupling region; (**b**) the slot waveguide; and (**c**) the 3D device structure.  
**Author Contributions:** Conceptualization, Y.Y. and J.H.; methodology, Y.Y. and J.H.; software, J.H., Y.Y. and X.W.; validation, Y.Y. and J.H.; formal analysis, Y.Y.; investigation, J.H., Y.Y. and X.W.; resources, Y.Y.; data curation, J.H. and X.G.; writing--original draft preparation, J.H. and X.W.; writing--review and editing, X.W., Y.X. and G.S.; visualization, J.H., X.W. and L.L.; supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Industrialization R & D projects of SZTU (20221063010032) Guangdong Education Department Major Focus Areas for Universities (2022 ZDZX1024) and College Students' Innovation and Entrepreneurship Competition of SZTU (S202214655003).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Optimization of GREI Networks and Management Trade-off  
The design and optimization of the spatial configuration of GREI were founded on combinatorial optimization within graph theory. The Hanging Gardens Algorithm (HGA) [31] was used here to determine an optimal GREI network featuring various degrees of decentralization of layout (\(DDL\)). The HGA algorithm commences with the identification of candidate flow outlets (\(N_{CO}\)), corresponding to a centralized layout. \(DDL\) are then introduced progressively through the selection of other feasible outlets. Subsequent tiers of \(DDL\) are then introduced through the selection of other viable outlets. A total of 2 \(\times\)\(NL\) decision variables, where \(NL\) corresponds to the number of loops in the baseline drainage pipes, were required by the HGA algorithm to generate one feasible layout corresponding to a set of \(DDL\). Further details on HGA can be found in [PERSON] et al. [29].  
Figure 2: Overview of Zhujiang New Town, Guangzhou, China. The map illustrates the catchment’s geographical location, drainage network, and sub–catchment distribution (Map data © 2021 Google, Image © 2021 DigitalGlobe, Image © 2021 CNES/Airbus.).  
The degree of decentralization of a GREI network can be quantified as follows:  
\[DDL=\frac{N_{SO}-1}{N_{CO}-1}\times 100\% \tag{1}\]  
where \(N_{SO}\) represents the number of selected outlets, and \(N_{CO}\) denotes the total number of viable candidate outlets. A fully decentralized layout (\(DDL=100\%\)) occurs when all viable outlets are utilized, whereas a completely centralized layout (\(DDL=0\%\)) consolidates drainage to a single outlet (\(N_{SO}=1\)).  
The GREI network design adheres to flow requirements, hydraulic criteria, and standard design specifications, ensuring compliance with urban drainage best practices. Key parameters include telescopic pipe diameter patterns, minimum and maximum cover depths, slopes, flow velocities, and hydraulic reliability. To meet these requirements, the adaptive pipe flow design algorithm proposed by [PERSON] and [PERSON] [32] was implemented, guaranteeing that no flooding occurred under a specified design storm. Each potential network configuration involved \(2\times NP\) decision variables in the genetic algorithm, where NP represents the total number of pipes. Two variables--diameter and longitudinal slope--were considered for each pipe. The design storm adopted was a continuous 6 h precipitation event with a 5-year recurrence interval, generating a total rainfall of 121 mm. The rainfall profile followed the Chicago Hyetograph, as prescribed in the urban drainage design standards by the Ministry of Housing and Urban-Rural Development [33]. This ensured the hydraulic and structural reliability of all generated GREI layouts, balancing operational efficiency and resilience across varying degrees of decentralization.
## 4 Materials and Methods
### Chemical Analysis
#### 4.2.2 LC-MS/MS Analysis  
Methanol extracts (5 mg/mL) from the aerial parts and roots of _S. nigrun_ were diluted 1000 and 100 times with Chromasolv grade methanol (Sigma Aldrich, Steinheim, Germany). Then 100 \(\mathrm{\SIUnitSymbolMicro g}\) of luteolin (Sigma-Aldrich, Steinheim, Germany) was added as an internal standard. The obtained dilutions were then filtered through Phenex-RC syringe filters (Phenomenex, Torrance, CA, USA) and subjected to chemical profiling according to the procedure developed by [PERSON] et al. (2013) [35], which allowed for a comprehensive study of the chemistry of _Solanum_ species. Compounds belonging to the flavonoid, steroid or glycosides, glycoalkaloids or alkaloids were selected for screening as they have the greatest potential for antimicrobial or antioxidant activity. The screening was performed on an LCMS-8045 instrument (Shimadzu, Kyoto, Japan) equipped with a Kinetex 2.6u C18 100A 100 \(\times\) 3.0 mm column with a 3 mm ULTRA shell (Phenomenex, Torrance, CA, USA).  
The mobile phase used for the chromatographic separation consisted of 0.1% aqueous formic acid (A) and methanol (B). A solvent gradient was applied from 10% to 80% B in 0-3 min, 80% B in 3-15 min and 80 to 10% B in 15-20 min. The flow rate and injection volumes were 0.3 mL/min and 10 \(\mathrm{\SIUnitSymbolMicro L}\), respectively. The column temperature was set to 40 \({}^{\circ}\)C.  
Mass spectrometry was performed by electrospray ionization (ESI) under the following conditions: Q3 SIM (\(m/z\) details are given in Table 1 in positive mode); nitrogen for nebulization (flow 3 L/min) and desolvation (temperature 526 \({}^{\circ}\)C); both heating as well as a drying gas flow of 10 L/min; interface; DL and heating block temperatures of 300, 250 and 400 \({}^{\circ}\)C, respectively.  
Flavonoid quantification was based on normalization of the peak area based on the peak area of the internal standard. The analyses were carried out in triplicate.
## 5 Numerical Illustration
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{_O.No._} & \multicolumn{4}{c}{EDAS} & \multicolumn{4}{c}{CODAS} \\ \cline{2-9}  & _Tol.l._ & _P.No._ & _M.No._ & _TC_ & _MT_ & _Tol.l._ & _P.No._ & _M.No._ & _TC_ & _MT_ \\ \hline
1 & 0.074144 & 1 & 9 & 4.58 & 16.97 & 0.074438 & 1 & 9 & 4.57 & 16.93 \\
2 & 0.040709 & 2 & 9 & 7.22 & 18.54 & 0.036949 & 2 & 9 & 7.55 & 20.04 \\
3 & 0.039105 & 2 & 9 & 7.35 & 19.14 & 0.041246 & 2 & 9 & 7.17 & 18.35 \\
4 & 0.081597 & 1 & 9 & 4.39 & 15.85 & 0.069137 & 1 & 9 & 4.73 & 17.86 \\
5 & 0.065967 & 2 & 3 & 5.95 & 12.90 & 0.062934 & 2 & 3 & 6.05 & 13.33 \\
6 & 0.089941 & 5 & 10 & 10.18 & 7.77 & 0.09 & 5 & 10 & 10.18 & 7.77 \\
7 & 0.050332 & 9 & 7 & 3.21 & 28.84 & 0.045295 & 9 & 7 & 3.34 & 31.47 \\
8 & 0.108207 & 5 & 3 & 8.75 & 6.98 & 0.13 & 5 & 3 & 7.57 & 6.33 \\
9 & 0.051985 & 2 & 9 & 6.50 & 15.34 & 0.051806 & 2 & 9 & 6.51 & 15.38 \\
10 & 0.088202 & 5 & 7 & 10.35 & 7.86 & 0.089999 & 5 & 7 & 10.18 & 7.77 \\ \hline \end{tabular}
\end{table} 
Table 12: The optimum allocated tolerance of knuckle joint assembly. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{_R.No._} & \multicolumn{4}{c}{EDAS} & \multicolumn{4}{c}{CODAS} \\ \cline{2-9}  & _TC_ & _MT_ & _APS_ & _TC_ & _MT_ & _AS_ \\ \hline
1 & 68.08 & 153.97 & 0.5009 & 68.01 & 154.60 & \(-\)0.0245 \\
2 & 68.83 & 149.68 & 0.5346 & 67.92 & 155.39 & \(-\)0.0106 \\
3 & 69.75 & 147.60 & 0.5308 & 67.92 & 158.54 & \(-\)0.0275 \\
4 & 69.24 & 149.31 & 0.5185 & 67.93 & 155.59 & \(-\)0.0147 \\
5 & 69.07 & 149.32 & 0.5272 & 67.91 & 155.64 & \(-\)0.0109 \\
6 & 68.83 & 149.18 & 0.5433 & 67.82 & 155.70 & 0.0111 \\
7 & 69.11 & 149.15 & 0.5283 & 67.90 & 156.88 & \(-\)0.018 \\
8 & 82.51 & 129.70 & 0.519 & 67.84 & 155.22 & 0.0113 \\
9 & 68.85 & 149.45 & 0.5375 & 67.81 & 156.30 & 0.0085 \\
10 & 69.45 & 148.01 & 0.5367 & 67.90 & 159.17 & \(-\)0.0222 \\
11 & 68.47 & 150.20 & 0.5452 & 67.83 & 156.08 & 0.0062 \\
12 & 68.57 & 151.45 & 0.5181 & 67.90 & 158.74 & \(-\)0.0233 \\
13 & 68.18 & 151.55 & 0.5379 & 67.87 & 154.75 & 0.0075 \\
14 & 69.23 & 148.28 & 0.5417 & 67.82 & 158.41 & \(-\)0.0036 \\
15 & 68.25 & 152.04 & 0.5256 & 67.92 & 156.45 & \(-\)0.0197 \\
16 & 69
## 3 GNN-Based Semantic Dependency Parser
### Encoder  
We concatenate word embeddings and feature embeddings, and feed them into a BiLSTM to obtain contextualized representations.  
Specifically, BiLSTM is a sequence processing model that consists of two reversed unidirectional LSTM networks: One taking the input in a forward direction to capture forward information, and another in a backward direction to capture backward information. BiLSTM can integrate both forward and backward information by concatenating bidirectional information.  
\[x_{i}=e_{i}^{(word)}\oplus e_{i}^{(feat)} \tag{2}\]  
\[\overrightarrow{o_{i}}^{\prime}=LSTM^{(forward)}(x_{i}) \tag{3}\]  
\[\overleftarrow{o_{i}}=LSTM^{(backward)}(x_{i}) \tag{4}\]  
\[o_{i}=\overrightarrow{o_{i}}\oplus\overleftarrow{o_{i}} \tag{5}\]  
where \(x_{i}\) is the concatenation (\(\oplus\)) of the word embeddings and feature embeddings of word \(w_{i}\), \(X\) represents \([x_{1},x_{2},\dots,x_{n}]\). \(O=[o_{1},o_{2},\dots,o_{n}]\) is the contextualized representations of sequence \(X\).  
Then we employ \(K\)-layer GNNs to capture higher-order information by aggregating the representation of \(K\)-order neighborhoods. The node embedding matrix \(R^{(k)}\) in the \(k\)th-layer is computed as Equation (6):  
\[R^{(k)}=\textit{GNNLayer}^{(k-1)}(R^{(k-1)},\bar{A}) \tag{6}\]  
When the _GNNLayer_ is implemented in the _GCN_, the representation \(r_{i}^{(k)}\) of node \(i\) in the \(k\)th layer is computed as Equation (7)  
\[r_{i}^{(k)}=\sigma\Bigg{(}W\sum_{j\in N(i)}r_{j}^{(k-1)}+Br_{i}^{(k-1)}\Bigg{)} \tag{7}\]  
where \(W\) and \(B\) are parameter matrices; \(N(i)\) are neighbors of node \(i\); \(\sigma\) is the active function (ReLU is used); \(r_{i}^{(0)}=o_{i}\).  
When _GNNLayer_ is implemented in _GAT_, \(r_{i}^{(k)}\) is computed as Equation (8):  
\[r_{i}^{(k)}=\sigma\Bigg{(}W\sum_{j\in N(i)}a_{ij}^{(m)(k-1)}r_{j}^{(k-1)}+Br_{ i}^{(k-1)}\Bigg{)} \tag{8}\]  
where \(a_{ij}^{(m)(k-1)}\) is the attention coefficient of node \(i\) to its neighbour \(j\) in attention head \(m\) at the \((k-1)\)th layer.  
While higher-order information is important, GNNs would suffer from the over-smoothing problem when the number of layer is excessive. Therefore we stack three layers with the past experience.
# Modal Decomposition of the Precessing Vortex Core in a Hydro Turbine Model
## 4 Results and Discussion
### Mean Velocity Distributions  
Next, we address the change of the time-averaged flow characteristics for different operating conditions. Figure 4 shows the distributions of the axial and tangential components of the mean velocity and turbulent kinetic energy (TKE), \(k=({V_{x}^{\prime}}^{2}+{V_{y}^{\prime}}^{2}+{V_{z}^{\prime}}^{2})/2\) for three different flow rates. The flow rate of \(0.3Q_{c}\) corresponds to the deep part load regime with the highest swirl and with the low level and broad pressure fluctuations of \(m=1\). The flow rate of \(0.5Q_{c}\) corresponds to the part load regime with the highest pressure fluctuations amplitude for \(m=1\). The regime with flow rate close to \(Q_{c}\) is considered the optimal regime of the BEP.  
In the case of the deep part load regime at flow rate \(0.3Q_{c}\), there is a large central recirculation zone (CRZ) behind the swirler centerbody. This flow is also characterized by high turbulent fluctuations, with a TKE level of up to \(50\%\) of the bulk flow velocity. It is noteworthy that the fluctuation level inside the recirculation zone is rather weak. The mean flow at the part load (\(Q=0.5Q_{c}\)) is characterized by a smaller CRZ with intensive velocity fluctuations inside. The CRZ is followed by a wake, where the velocity fluctuations are also strong. For the BEP case (\(Q=Q_{c}\)), there is a small CRZ behind the bluff body. The flow is characterized by a local maximum of the TKE downstream of the CRZ without a large turbulent wake. It is noteworthy that there is a region of flow reversal in this case, with two concentric counter-rotating cylindrical rod-like swirling flows. This observation has already been discussed in [32].  
Figure 3: Pressure fluctuation spectra in terms of normalized PSD for the azimuthal modes \(m=1\) and \(m=2\) and different flow rates \(Q/Q_{c}\).
###### Abstract
Here, we investigate the adaptation of the compact side-coupling waveguide OMT design with twofold rotational symmetry introduced in [23; 24] to a two-probe waveguide OMT with transversal coupling ports. This results in a much simpler OMT design, preserving a compact form factor and the performance of equivalent waveguide OMT solutions. The advantages of the proposed waveguide OMT design are twofold:  
* The probing cavity is compact and robust, avoiding small features present in similar previously reported designs [21; 22];
* The probing waveguides can be folded [20], providing a design compatible with additive manufacturing constraints when printing along the longitudinal axis of the common waveguide. These features make the proposed waveguide OMT design particularly suitable for large arrays with an element spacing below a wavelength, which may find applications in next-generation broadband communication satellites. The paper is organized as follows. Section 2 provides a description of the proposed concept and reports the design, manufacture, and test of an OMT operating in the portion of the K-band allocated to broadband communication satellite's downlink. Section 3 discusses the adaptation of the design that implements the folding approach introduced in [20] and highlights the benefits in terms of size reduction and suitability of the design for additive manufacturing techniques. Finally, Section 4 provides some conclusions and perspective on future works.