esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Hydroological modeling relies on the inputs provided by General Circulation Model (GCM) data, as this allows researchers to investigate the effects of climate change on water resources. But there is high uncertainty in the climate projections with various ensembles and variables. Therefore, it is very important to carry out bias correction in order to analyze the impacts of climate change at a regional level. The performance evaluation of bias correction methods for precipitation, maximum temperature, and minimum temperature in the Upper Bhima sub-basin has been investigated. Four bias correction methods are applied for precipitation viz. linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Three bias correction methods are applied for temperature viz. linear scaling (LS), variance scaling (VS), and distribution mapping (DM). The evaluation of the results from these bias correction methods is performed using the Kolmogorov-Smirnov non-parametric test. The results indicate that bias correction methods are useful in reducing biases in model-simulated data, which improves their reliability. The results of the distribution mapping bias correction method have been proven to be more effective for precipitation, maximum temperature, and minimum temperature data from CMIP5-simulated data.  
global climate models; coupled model intercomparison project phase 5; Australian community climate and earth-system simulator; bias correction; Kolmogorov-Smirnov test; Upper Bhima sub-basin +
Footnote †: journal: Applied sciences
## 2 Materials and Methods  
### Study Site, Sample Collection and Spore Count  
In this study, tested bee colonies were naturally infected with _V. ceranae_ and maintained at the apiary site of the Department of Apiculture, Institute of Animal Science, Hellenic Agricultural Organization DEMETER (Ag. Mamas Chalkidiki, North Greece), where more than 100 honey bee colonies are located only for experimental purposes, as has been reported previously [21]. Adult bee samples were collected from all colonies in the apiary, and the number of Nosema spores per bee for each colony was determined. Six colonies in total, three with low Nosema spore counts and three with high Nosema spore counts, were selected and formed the two groups A and B, respectively.  
In order to count _V. ceranae_ spore levels and extract DNA, 120 bees were collected (17 May 2021) from the outer frames of each hive. Half (60 bees) were stored in ethanol at 4 \({}^{\circ}\)C and used for metataxonomics. The rest (60 bees) were used for spore counting. Detection and spore counting were performed according to a modified method described by [PERSON] and [PERSON] [22; 23]. Initially, abdomens of bees were removed and macerated with 5 mL distilled water, then filtered through a thin cotton mesh and rinsed with 4 mL of distilled water. Afterwards, a sample of 3 mL was taken and centrifuged for 5 min at 800 g. Finally, the supernatant was discarded, 3 mL water was added to the pellet and the sample was homogenized. Spores were counted using a hemocytometer slide. An observed _V. ceranae_ spore in the hemocytometer entire grid (25 \(\times\) 16 = 400 small squares) corresponded to an average of 1500 spores per bee. Reported counts refer to the number of spores per bee.
###### Abstract  
The segment control of active twist rotor is investigated to evaluate the effectiveness in rotor power reduction. A numerical model for predicting the isolated rotor power and loads in steady level flights is deployed and validated. A parametric sweep of the amplitude and phase angle for uniform single-harmonic active twist control is conducted to demonstrate the mechanism of active twist control in rotor power reduction. The optimal control schedules and segment layouts of the segment twist control for power reduction while considering saturation limits are obtained using an optimization framework based on genetic algorithm. Up to 5-seg configuration is considered. The results indicate that the segment twist control reduces the rotor power more than the uniform twist control by applying divergent control schedules to each segment. The load distribution of the rotor disk is harmonized in both circumferential and spanwise directions. The 2-seg and 3-seg control configurations are appropriate, while the configurations with more segments yield limited benefits and they may be penalized with an increase in system complexity.  
helicopter; active twist rotor; segment control; power reduction
## 3 Proposed Method
### Feature Fusion Module and Multi-Scale Skip Connection Based on Residual Network
#### 3.3.2 Multi-Scale Skip Connection Based on Residual Network  
Skip connection usually connects each layer in the down-sampling module to the corresponding network layer in the up-sampling module, but such skip connection does not make use of the feature information at different scales. Many studies have shown that feature maps at different scales yield different information, with low-level feature maps capturing rich spatial information and highlighting the edge details of the target, while high-level feature maps capture positional information and locate the position of the target. However, as they pass through the up-sampling and down-sampling modules in turn, this information is diluted and faded. Inspired by the UNet3+ [54] network, this paper proposes a multi-scale skip connection structure based on a residual network, which uses the residual network to supplement the loss of feature information caused by convolution operation, thus enhancing the feature representation, and uses multi-scale skip connection to make full use of multi-scale features, combining low-level and high-level feature maps at different scales, in order to reduce the semantic differences between encoding and decoding modules; based on this, we can effectively achieve the transfer of feature information and make full use of multi-scale features.  
As shown in Figure 5, an overview of the multi-scale skip connection based on a residual network is given. Each layer of the decoding module contains small-scale and same-scale feature maps from the encoding module and large-scale feature maps from the decoding module, resulting in the multi-scale capture of low-level detail features and high-level semantic features.  
As shown in Figure 6, the feature map of how to construct the fourth layer in the decoding module is given. For a layer in the decoding module, the feature maps from the same scale of the encoding module, the feature maps from the smaller scale of the encoding module after the maximum pooling operation, and the feature maps from the larger scale of the decoding module after the bilinear interpolation operation are stitched together by the channel dimension while reducing the redundant information, and they then are inputted to the feature fusion module simultaneously with the output from the previous layer. The fused feature map is finally obtained.  
Figure 5: The proposed multi-scale skip connection based on the residual network, where \(X_{En}^{1}\), \(X_{En}^{2}\), \(X_{En}^{3}\), and \(X_{En}^{4}\) are features of different scales in the encoding module, and \(X_{De}^{1}\), \(X_{De}^{2}\), \(X_{De}^{3}\), and \(X_{De}^{4}\) are features of different scales in the decoding module. Features with the same number in superscript on the right indicate features of the same scale in the encoding and decoding modules.
## 2 Materials and Methods
### DNA Extraction, Polymerase Chain Reaction (PCR), and TRFLP Analysis  
Genomic soil DNA was extracted with the use of a GeneMATRIX Soil DNA Purification Kit (EURx, Gdansk, Poland) according to the manufacturer's protocol. The amount of DNA obtained was determined using the QuantiFluor(r) dsDNA Dye (Promega, WI, USA) on a GloMax(r)-Multi Microplate Multimode Reader (Promega Corporation, Madison, WI, USA). The quality of the soil DNA was assessed using a Lambda Bio+ spectrophotometer (PerkinElmer, Boston, MA, USA) with an ultra-micro sample volume spectrometer cell TrayCell (Hellma GmbH, Mullheim/Baden, Germany).  
Bacterial (16S rDNA) and fungal (ITS) gene amplification was performed using the primers and conditions reported by [PERSON] et al. [28]. The 25 uL reaction mixture consisted of: 15 uL GoTaq(r) G2 Colorless Master Mix (Promega Corporation, Madison, WI, USA), 0.5 uM of each primer, and 2 uL (5 ng/uL) of soil DNA. The purity of the PCR product was determined by agarose gel electrophoresis (1.2%, \(w/v\)).  
The amplification products were purified using Exo-BAP Mix (EURx, Gdansk, Poland) according to the manufacturer's assay, and then 7 uL of the purified product was digested with 6U of the restriction enzyme MspI (10 U/uL) (Promega Corporation, Madison, WI, USA) in restriction buffer in a final volume of 10 uL for 2 h at 37 \({}^{\circ}\)C. The resulting product was desalted using the PCR/DNA Clean-Up Purification Kit (Promega Corporation, Madison, WI, USA). Further procedures, including the TRFLP protocol, are described in [PERSON] et al. [3].  
The relative number of restriction fragments (TRFs) was determined by the quotient of the area of each peak and the sum of the area of all peaks in one sample. The analysis included peaks in the 50-600 bp range with a relative abundance greater than 1%.
# Enhanced Efficiency and Reduced Side Lobe Level Convex Conformal Reflectarray
## 6 Conclusions  
In this paper, an innovative convex reflectarray antenna is presented, which is particularly suitable for use in mobile communication systems since it guarantees good performance but also high integrability in the case that it needs to be mounted on a curved surface; according to the proposed strategy, its performance is optimized acting on the degrees of freedom represented by the feed. The procedure significantly reduces the design computational time, decoupling the electromagnetic problem in two steps, the first of which fixes the radiation features of the feed, while in the second one, the synthesis of the relevant horn antenna is done. The experimental characterization of a prototype confirms  
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & \multicolumn{2}{c}{**SLL, E-Plane [dB]**} & \multicolumn{2}{c}{**SLL, H-Plane [dB]**} & \multicolumn{2}{c}{**Efficiency [\%]**} \\
**Freq.** & **Opt.** & **”Max** & **Opt.** & **”Max** & **Opt.** & **”Max** \\
**[GHz]** & **Feed** & **Eff.**\({}^{*}\) & **Feed** & **Eff.**\({}^{*}\) & **Feed** & **Eff.**\({}^{*}\) \\  & & **Feed** & & **Feed** & & **Feed** \\ \hline
29 & \(-11\) & \(-9\) & \(-19\) & \(-16.6\) & 44.87 & 43.15 \\ \hline
30 & \(-17.7\) & \(-15.5\) & \(-23\) & \(-18\) & 51.7 & 49.5 \\ \hline
31 & \(-12.8\) & \(-10\) & \(-25\) & \(-19\) & 48.66 & 46.37 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison between the proposed CRA (opt. feed) and another illuminated using a horn designed to maximize the CRA efficiency.  
Figure 12: Radiation patterns in the E (left) and H (right) planes at 30 GHz for the configuration, using the optimized feed and that adopts a horn designed to maximize the entire antenna system, both evaluated at 30 GHz.  
the effectiveness of the design procedure and shows a significant enhancement in CRA performance, with respect to the different configurations taken for comparison.  
Conceptualization, all authors; methodology, all authors; software, M.B. and G.A.; prototyping., D.M. and F.C.; validation, all authors; formal analysis, M.B., G.A., M.O., O.P. and P.P.; investigation, all authors; writing--original draft preparation, all authors; writing--review and editing, P.P. and M.O.; supervision, P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank [PERSON] from the Politecnico di Torino for his valuable help in the experimental characterization of the antenna prototype.  
The authors declare no conflict of interest.
## 3 Methodology
### Definitions and Problems
#### 3.1.2 Problems  
Origin of the Problems: In the evaluation of ship navigation safety, it is crucial not only to obtain accurate ship trajectories but also to predict the future trajectory direction of ships. This is of significant importance for assessing future ship risks and making proactive decisions. The main problems studied and discussed in this paper are as follows:  
**Problem 1**.: _How to accurately predict the ship's trajectory for a certain period in the future?_  
To accurately predict the ship's trajectory for a certain period in the future, the ship trajectory data are first processed (\(\varphi_{d}\)) to obtain trajectory data suitable for the input structure of the model. Then, this processed ship trajectory data are fed into the trajectory prediction model (\(f_{m}\)) to obtain the ship's predicted trajectory for the future. The formula is shown in (1). The trajectory prediction is expressed as shown in Figure 2.  
\[\{Po_{n+1}^{j},Po_{n+2}^{j},\cdots,Po_{m}^{j}\}=f_{m}(\varphi_{d}(\{Po_{1}^{j}, Po_{2}^{j},\cdots,Po_{n}^{j}\},\pi),\theta) \tag{1}\]  
**Problem 2**.: _How to measure the effectiveness of ship trajectory prediction models?_  
To evaluate the ship trajectory prediction model (\(f_{m}\)), Mean Absolute Error (\(MAE\)) is utilized for optimization, shown in Formula (2). Mean Squared Error (\(MSE\)), Root Mean Square Error (\(RMSE\)), Average Displacement Error (\(ADE\)), and Final Displacement Error (\(FDE\)) are used to assess the accuracy of the trajectory prediction model.  
\[\text{Optimal }f_{m}\leftarrow\min(MAE) \tag{2}\]  
\[\text{Euluate }f_{m}\leftarrow\text{mean}\{MSE,RMSE,ADE,FDE\} \tag{3}\]  
Figure 1: Architecture of ship trajectory prediction method based on TRFM model.
# Instability Risk Assessment for Deep Excavation of Soil-Rock Combinations Containing Groundwater
## 4 Conclusions  
This study combined a variety of theories to construct a dynamic evaluation model for calculating the instability risk of deep excavation with a soil-rock combination. A stability assessment of the Anshan Road foundation excavation was carried out using the improved RBF neural network, which verified the effectiveness of the proposed model. The main conclusions are as follows:  
1. The combined weights method is effective in screening potential risk factors. In this paper, a factor selection model was first constructed. The UR was used for the preliminary identification of potential risk factors. Then, the AHP-entropy method was applied to calculate the combined weights of the factors, with which the risk factors were selected. This factor selection model was valid, as verified through gray correlation analysis;
2. An excavation risk evaluation index system was established for dynamic assessment. The dynamic evaluation of excavation safety was realized based on the improved RBF network, using real-time monitoring data as an evaluation index. A risk assessment of unit K was conducted and the risk grades at 10 January 2019 and 30 May 2019 were class II and class III, respectively. Additionally, comparison of the prediction results with different models verified the validity of the proposed model. The model in this paper had better accuracy and was less dependent on the original data.
3. The model needs further improvement. The model could dynamically analyze the influence of each factor on the risk level of a deep excavation with soil-rock combination in construction. This has significant value in preventing excavation disasters and reducing unnecessary losses. However, a number of drawbacks remain, limiting further applications. This model needs to be gradually improved through continuous application.  
Conceptualization, L.Z., Z.W. and S.L.; methodology, L.Z. and S.L.; software, W.Z.; validation, K.L.; formal analysis, W.Z. and K.L.; investigation, L.Z. and Z.W.; data curation, W.Z. and K.L.; writing--original draft preparation, L.Z., W.Z. and Z.W.; writing--review and editing, S.L.; supervision, L.Z.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Demonstration Project of Benefiting People with Science and Technology of Qingdao, China (Grant No. 23-2-8-cspz-13-nsh), the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2022 QD102), the National Natural Science Foundation of China, China (Grant No. 42272334), the Open Project Program of Engineering Research Center of Concrete Technology under Marine Environment, Ministry of Education, China (Grant No. TMduracon2022029).  
Not applicable.  
Data Availability Statement: The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 9 Deep Learning for Intention Estimation
However, due to the improbability of proper adaptation and handling to changes in pedestrian movement, these techniques provided poor results in terms of predicting future pedestrian movements [1; 178]. Other than DL approaches, there have been two typical approaches for predicting the future actions of a pedestrian [178]. One approach is based on dynamical motion modelling (DMM) [178; 189; 203]. This approach is able to predict the motion trajectories for different scenarios. However, the model assumes that all trajectories inhibit similar dynamics. This leads the model to have a lower accuracy when predicting long-term motion modelling for intent estimation. Planning-based models (PBMs) [213; 214], has shown better results for long-term predictions. However, the model requires the final destination information of the VRU, which is a difficult task for a moving vehicle to infer. Although the DMM and PBM approaches have proven to be quite powerful, they are dependent on hand-crafted features (e.g., HOG, Haar-like, DPM, etc.). As discussed earlier, hand-crafted features for detection are limited. The same can be said for hand-crafted features for intent estimation. When the autonomous vehicle processes an unseen or complex situation, it may not be able to react properly as the hand-crafted features create a generalisation of certain situations. That is because feature selections and parameters when designed by an expert, rather than using real-time information collected from sensors. This causes these techniques to be rather restricted and under-perform in previously unseen situations. To solve this problem, [202] proposed a data-driven (i.e., DL) approach, where the initial motion trajectories [178] are used for long-term intent estimation, enabling for predicting future positions of a VRU up to 4 seconds ahead. A Recurrent Neural Network (RNN)+Long Short-Term Memory (LSTM) approach was adopted. The RNN+LSTM method is typically used for time series problem.
# Corrosion Behavior and Conductivity of TiNb and TiNb Coated Steel for Metallic Bipolar Plates
## 3 Results and Discussion
### X-ray Photoelectron Spectroscopy Analysis  
The chemical states of Ti and Nb in the surface layers of the TiNb and TiNbN films were examined by XPS. The original Nb 3d and Ti 2p spectra are shown in Figures 4 and 5. After fitting all the spectra with backgrounds subtracted, Nb 3d and Ti 2p peaks were analyzed according to the NIST XPS Database [28], relative literatures and possible reactions in testing environment. In Figure 4a, it's deduced that the highest doublet peaks are related to 3d\({}_{5/2}\) and 3d\({}_{3/2}\) of Nb in Nb\({}_{2}\)O\({}_{5}\) and the lower peaks represent metal Nb [29; 30; 31; 32]. From the Ti 2p fitted spectra in Figure 4b, two peaks are observed corresponding to Ti 2p\({}_{3/2}\) and Ti 2p\({}_{1/2}\) in TiO\({}_{2}\), respectively [33; 34]. The testing results reveal that the surface layer of the TiNb coating is primarily composed of Nb\({}_{2}\)O\({}_{5}\) and TiO\({}_{2}\), which means the TiNb coating can be oxidized spontaneously in air and form a stable metallic oxide film. Figure 5 shows XPS  
Figure 3: Energy dispersive spectrometry (EDS) results of (**a**) TiNbN/316L SS and (**b**) TiNb/316L SS.  
spectra of Nb 3d and Ti 2p of the TiNbN coating. The fitted Nb 3d spectra depicted in Figure 5a indicate that the major peaks representing Nb 3d\({}_{5/2}\) and Nb 3d\({}_{3/2}\) can be ascribed to the formation of Nb\({}_{2}\)O\({}_{5}\), NbN, and NbO [31; 32; 35]. The Ti 2p fitted spectra in Figure 5b reveal the existence of TiN [36]. But the two largest peaks stem from Ti 2p\({}_{3/2}\) and Ti 2p\({}_{1/2}\) in TiO\({}_{2}\)[37; 38]. From the fitted results, it is obvious that under the sputtering conditions mentioned above, TiN and NbN were synthesized. Due to the exposure of the coating to air, stable oxides of Ti and Nb were also formed in the outer layer of TiNbN coating. Since the main surface composition difference between TiNb and TiNbN coatings lies in TiN and NbN, metallic nitrides could play an important role in exhibiting some different properties for TiNb/316L SS and TiNbN/316L SS.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 5 Improved Harmony Search for Node Depth Adjustment
### Initialization of Harmony Memory  
The harmony memory (HM) in Equation (15) is a matrix with the length of HMS and width of \(N_{s}\) that stores solution vectors:  
\[\mathbf{H}\mathbf{M}=\left[\begin{array}{ccccc}d_{1}^{1}&d_{2}^{1}&\cdots&d_ {N_{s}-1}^{1}&d_{N_{s}}^{1}\\ d_{1}^{2}&d_{2}^{2}&\cdots&d_{N_{s}-1}^{2}&d_{N_{s}}^{2}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ d_{1}^{HMS-1}&d_{2}^{HMS-1}&\cdots&d_{N_{s}-1}^{HMS-1}&d_{N_{s}-1}^{HMS-1}\\ d_{1}^{HMS}&d_{2}^{HMS}&\cdots&d_{N_{s}-1}^{HMS}&d_{N_{s}}^{HMS}\end{array} \right]. \tag{15}\]  
For traditional HM algorithms, solution vectors in this step are randomly generated within moving distance range. However, this is not efficient because those nodes far from target are very likely to provide little information about target. In this step, we modify the probability of generating a new harmony. Instead of using a random generation probability, we make generation probability increase with distance to target decreasing. We assume that the probability is linearly correlated to moving distance, and the scale factor is \(p_{0}\). Thus, within the moving range, we will have the cumulative distribution function as follows:  
\[\begin{split}&\int_{d_{min}}^{d_{max}}p(l)\mathbf{d}l=1,\\ & p(l)=p_{0}l,\end{split} \tag{16}\]  
where \(l\) is the moving distance, \(d_{min}\) and \(d_{max}\) are the lower and upper bounds of moving distance range; we always have \(d_{min}=0\), which means that nodes will not move, and \(d_{max}\) is dynamically set to enable nodes moving to the same depth as predicted positions of nodes. Thus, we can solve Equation (16) and get the value of \(p_{0}\) and generation probability \(p(l)\) of moving distance \(l\):  
\[\begin{split}& p_{0}=\frac{2}{d_{max}^{2}-d_{min}^{2}},\\ & p(l)=\frac{2l}{d_{max}^{2}-d_{min}^{2}}.\end{split} \tag{17}\]  
With this generation probability method, we have initial solution vectors to fill the HM matrix. The moving distance leading node close to the target will provide more useful information to track the target. Therefore, this method will improve searching speed and accuracy. It should be noted that this generation probability method is not only used in the initial step, but it is also used in the third step, in which we may need to generate a vector within range.  
After the HM is generated with different moving distance combinations of activated nodes in each solution vector, the quality of each is evaluated with estimation accuracy, and it is calculated with the proposed objective function in Equation (14). As shown in Equation (14), the smaller the value is, the better the vector is.