esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
High purity Hf (Alfa Aesar, Haverhill, MA, USA, 99.6%), Zr (Alfa Aesar, Haverhill, MA, USA, 99.8%), Ti (Kojundo Chemical, Saitama, Japan, 99.9%), Ni (Kojundo Chemical, Saitama, Japan, 99.9%), Sn (Alfa Aesar, Haverhill, MA, USA, 99.85%), and Sb (5N Plus, Saint-Laurent, Canada, 99.999%) were used as raw materials. The pelletized mixture of raw materials (\(\sim\)5 g) according to the designed compositions (Hf\({}_{0.35}\)Zr\({}_{0.35}\)Ti\({}_{0.3}\)NiSn\({}_{1-x}\)Sb\({}_{x}\), \(x=\) 0, 0.01, 0.02, 0.03) with 10 wt.% excess Sb was melted by using an arc melting system. The acquired ingot was put into a graphite tube with a 0.4 mm nozzle, and melted at \(\sim\)1550 K using an induction heating system. After that, molten alloy was injected under a pressure of 40 kPa Ar onto a Cu wheel rotating with linear speed of 40 m s\({}^{-1}\). During this TRMS (Vacuum R.S.P. System, Y&I Tech, Paju, South Korea) process, Ar atmosphere of 30 kPa was maintained. The obtained melt-spun ribbons were pulverized into powders and compacted by SPS (SPS 632 LX, SPS Syntax, city, Japan) at 1103 K for 10 min under 60 MPa. Highly dense polycrystalline bulks (10 mm in diameter and 2 mm in thickness) with relative density \(>\)95% were fabricated.  
The microstructural characteristics of the melt spun ribbon and SPSed bulks were investigated with scanning electron microscopy (SEM) (JSM-7600F, JEOL, Tokyo, Japan). Phase analyses for the SPSed bulks were carried out by X-ray diffraction (XRD, Smartlab, Rigaku, Tokyo, Japan) with Cu K\(\alpha\) radiation (\(\lambda=1.5418\) A). Electronic transport properties (\(S\) and \(\sigma\)) were measured from 300 K to 800 K by using a commercial instrument (ZEM-3, ULVAC-RICO, Yokohama, Japan). The \(\kappa\) (\(=\rho_{\mathrm{s}}\)C\({}_{\mathrm{p}}\lambda\), where \(\rho_{\mathrm{s}}\) is the density, \(C_{\mathrm{p}}\) is the specific heat capacity, and \(\lambda\) is the thermal diffusivity) was calculated from separate measurements of \(\rho_{\mathrm{s}}\), C\({}_{\mathrm{p}}\), and \(\lambda\). The \(\rho_{\mathrm{s}}\) was measured based on the Archimedes method. The temperature dependent \(C_{\mathrm{p}}\) and \(\lambda\) were collected by using differential scanning calorimetry (DSC, DSC8270, Rigaku, Tokyo, Japan) and a laser-flash instrument (TC-1200 RH, ULVAC-RICO, Yokohama, Japan), respectively.
# A Simple AMC Antenna for Liquid Monitoring in an Infusion Bag
## 2 Antenna Geometry  
Figure 2a depicts the scenario in which the proposed antenna is applied, consisting of two separate components: the dipole antenna and the backed AMC. In practical applications, the dipole antenna and the AMC at the back are attached to the opposite sides of the infusion bag to facilitate functionality. Figure 2b presents the side view of the application scenario. In the with-liquid state, the dipole antenna and AMC are separated, whereas in the without-liquid state, these two components reassemble into a single unit. The geometry of the proposed antenna and its AMC structure are displayed in Figure 2c. The printed dipole with the dimensions 38.5 mm \(\times\) 3.75 mm is fabricated using silver nanoparticle ink (conductivity \(\sigma=1\times 10^{7}\)) via inkjet printing on a 0.1 mm thick Kapton layer, which has a dielectric constant of \(\varepsilon_{r}=\)3.5 and a loss tangent of tan \(\delta=0.008\). The size of the Kapton layer is 100 mm \(\times\) 20 mm. The AMC surface, positioned at the back of the infusion bag, features square loop unit cells as its elements, arranged in a 1 \(\times\) 4 array. The structure of the AMC mainly consists of three layers, namely a 1 \(\times\) 4 AMC unit cell, a layer of 7 mm foam, and a ground plane. The AMC surface consists of a total of four square loop unit cells. The spacing between each square loop unit cell is 1 mm. A 7 mm foam is positioned between the ground plane and the metal loop AMC surface. In order to maintain flexibility, the four AMC surfaces are also inkjet-printed onto the foam with silver nanoparticle ink. The dimensions of the proposed antenna sensor are detailed in Table 1.  
Figure 2: (**a**) Schematic diagram of the infusion bag and designed antenna structure in the application scenario. (**b**) Side view of the application scenario. (**c**) Geometry of the dipole and AMC proposed.
# Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
## 5 Simulation and Experimental Verification
### Simulation Verification
The waveform of the fifth component is shown in Figure 8. It can be seen that the SNR is still not high enough. The peak frequency of this component frequency band is 2104 Hz, which is taken as the value of f, the damping ratio \(\psi=0.08\), the support interval \(w_{s}=470\), and the time shift \(\
u=1/f_{s}\). The overcomplete dictionary \(D\) is constructed according to the determined parameters and is substituted into the FSS algorithm together with the fifth component signal to solve the sparse coefficient and reconstruct the signal. The SNR is greatly improved by observing the reconstructed waveform, as shown in Figure 9a. At the same time, from Figure 9b,  
Figure 3:0 Time domain waveform of the fifth component. Figure 3:1 Correlation filtering result of original signal: (**a**) The correlation coefficient \(\kappa_{r}\) between the base atom and the original signal; (**b**) Resonance frequency of each point in \(\kappa_{r}\); and (**c**) Damping ratio of each point in \(\kappa_{r}\). the fault characteristic frequency and frequency doubling can be readily found. The fault characteristic frequency of 54 Hz obtained from the observation diagram is consistent with that of the fault characteristic frequency of 54 Hz set by simulation.
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 2 Materials and Methods
### Two Approaches to Utilize Masks
#### 2.3.2 Neural Beamformer
The Generalized Cross-Correlation (GCC) method [22] is adopted to align the data from different processing methods. The masked data are utilized by MVDR beamformer. During the postfiltering, the output of MVDR beamformer is multiplied by IRM obtained by the mask estimation to get the final output. In the proposed system, each neural beamformer is set as an MVDR beamformer, based on a single-channel IRM estimation. With the same network structure, two higher quality signals are used as input for the second beamforming. The enhancement performance is improved with a little additional system complexity. It is also achievable to combine different types of neural beamformers to meet our requirements. Figure 4: The overall diagram of our proposed system.
## 2 Materials and Methods
### Fungal Strains, Culture Conditions, and Spore Production  
Six _Penicillium_ strains (_P. digitatum_ CECT 2954, _P. commune_ CECT 20767, _P. expansum_ CECT 2278, _P. roqueforti_ CECT 2905, _P. camemberti_ CECT 2267, _P. brevicopatum_ CECT 2316) and four _Aspergillus_ strains (_A. parasitic_ CECT 2681, _A. niger_ CECT 2088, _A. steynii_ CECT 20510, _A. lacticoffeatus_ CECT 20581) were sourced from the Spanish Type Culture Collection (Valencia, Spain).  
Six Fusarium strains (_F. proliferatum_ ITEM 12072, F. verticillioides ITEM 12052, _F. sporotrichoides_ ITEM 12168, _F. langsethuie_ ITEM 11031, _F. poae_ ITEM 9151, _F. graminearum_ ITEM 126) and two Aspergillus strains (_A. flavus_ ITEM 8111, _A. carbonarius_ ITEM 5010) were obtained from the Agro-Food Microbial Culture Collection (Bari, Italy).  
_Penicillium verrucosum_ VTT D-01847 was obtained from the VTT Culture Collection (Finland). Two _Neofusicoccum_ species (_N. batangarum_, _N. parvum_), two _Colletotrichum_ species (_C. acutatum_, _C. gloeosporoides_), two species of the oomycete _Pltyophthora_ (_Ph. parvispora_, _Ph. nicotianae_) and two species of _Trichoderma_ (_T. asperellum_ IMI 393899, _T. atroviride_ TS) were obtained from the collection of the Molecular Plant Pathology Laboratory of the Di3A, University of Catania (Catania, Italy).  
The mycotoxicigenic fungi were cryopreserved in sterile 30% glycerol at -80 \({}^{\circ}\)C, but before antifungal studies they were defrosted and cultured in PDB at 25 \({}^{\circ}\)C for 48 h and inoculated on PDA plates to obtain spores.  
All other fungal strains were maintained on potato dextrose agar (PDA) at room temperature and subcultures were made every 20 days.  
The strains of _Neofusicocum_ and _Colletotrichum_ were grown on oatmeal agar (OMA) to increase the production of conidia [9].  
_Pluytophthora nicotianae_ and _Ph. parvispora_ were grown on OMA for the production of sporangia. From 10-day-old cultures, a zoospore suspension was obtained by flooding each culture plate with 10 mL sterile distilled water. The plates with the sterile distilled water were then refrigerated (5 \({}^{\circ}\)C) for 25 min and incubated in the dark at 25 \({}^{\circ}\)C for 30 min to induce zoospore release [10].  
The OMA was prepared with the protocol [11] slightly modified. In particular, 60 g of rolled oats were added to 600 mL of water and then heated for 40 min. The suspension was sieved and 12 g of agar was added, subsequently reaching the final volume of 1 L and autoclaved for 20 min at 121 \({}^{\circ}\)C. The OMA medium for _Pluytophthora_ was prepared as previously described, but with 75 g of rolled oats and 20 g of agar [11].
## 3 Results
### Metrics  
To quantify the difference between predicted results and actual values, we need to choose proper loss functions. An excellent loss function not only needs to help researchers clearly understand the performance of the model but also needs to help to improve the model parameters. In machine learning, we want the predicted values to be infinitely close to the true values, so we need to minimize the difference. The choice of loss function is crucial in this process. In specific projects, some loss functions calculate a difference gradient that decreases quickly, while others decrease slowly.  
In this study, we use the following metrics to evaluate the performance of the model: Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R\({}^{2}\)). Their formulas are as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert\hat{y}_{i}-y_{i}\rvert \tag{14}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)^{2} \tag{15}\]  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{16}\]  
\[R^{2}=1-\frac{\sum_{i}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i}(\overline{y}_{i}-y_{i })^{2}} \tag{17}\]  
where \(\hat{y}_{i}\) means the predicted value, \(y_{i}\) means the true value, and \(n\) is the number of samples. The smaller the values of MSE, MSE, and MAPE, the better our model performance. As for theR\({}^{2}\), the closer the value is to 1, the better performance will be. MAE is used to measure absolute error, MSE is more sensitive to large errors, MAPE facilitates understanding the relative error size, and R\({}^{2}\) reflects the model's ability to explain variability.
## 4 User Evaluation Method
### Procedure  
We generated several links containing a unique identifier for each participant through which it was possible to take part in the experiment. Each link was sent to the corresponding participant via email, Skype, WhatsApp, or any other platform considered suitable by the same participant. Once the link was opened, an introductory video ([[https://youtu.be/RLoza5](https://youtu.be/RLoza5) ROPUw]([https://youtu.be/RLoza5](https://youtu.be/RLoza5) ROPUw) (accessed on 21 January 2022)) reminded participants to turn on the computer audio and, when possible, to use headphones. It was also recommended to avoid distractions by informing other people living in the same house to avoid disturbing the participants during the experiment. Whenever possible, participants were advised to set the airplane mode on their smartphone to avoid being interrupted by any calls or notifications. For greater clarity, the same recommendations were also recalled in text form before the experiment directly in the web application described in the previous section. A simple informed consent was then shown to participants. The informed consent stated that, by participating in the experiment, users would agree that the data collected would be used for research purposes and would not be associated with specific individuals.  
Figure 4: Coincidences and contrasts between visual rhythmic pattern grid (eight elements in green) and auditory rhythm (10 elements in gray). First and fifth beats of visual rhythm correspond to first and sixth beats of auditory rhythm, respectively (see black arrows), while other beats do not correspond. This alternation between coincidences and contrasts, in addition to irregular pattern of accents (red triangles), was conceived to induce users into error when synchronizing with visual rhythm.  
Then, the participants filled in two fields, i.e., gender and age, and the experiment could start. Once the last trial of the experiment was completed, each participant could fill in a questionnaire on the ease of synchronization for both conditions using a scale from 1 (very difficult) to 6 (very easy) and a complementary text field where they could write any comments on the experiment.
# ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages
## 4 Experiments and Results
### Comparison with Leading Mainstream Contemporary Models  
To confirm the effectiveness of the proposed algorithm over other popular YOLO algorithms, we used the precision (P), recall (R), mAP50, and mAP50-95 as evaluation indicators. The ASHM-YOLOv9 model was subjected to comparative experiments with YOLOv5s, YOLOv7, YOLOv8n, and YOLOv9c in the same environment. The outcomes are displayed in Tables 4-7.  
Figure 11: (**a**) F1 score of YOLOv9; (**b**) F1 score of ASHM-YOLOv9.  
Figure 10: Recognition results of the YOLOv9c and ASHM-YOLOv9 base models in complex environments.  
As presented in Table 4, in the identification experiment of the strawberry flower stage, the P, R, mAP50, and mAP50-95 of the ASHM-YOLOv9 model were 97.9%, 96.0%, 98.9% and 88.1%, respectively. The P, mAP50, and mAP50-95 of all the models were greater than those of the other models, but YOLOv5s outperformed the recall, reaching 97.6%. According to Table 5, in the identification experiment of the strawberry growth stage, the P, R, mAP50, and mAP50-95 of the ASHM-YOLOv9 model were 96.5%, 97.1%, 99.1%, and 91.6%, respectively, which were better than those of the other algorithms. According to Table 6, in the identification experiment of the strawberry mature stage, the P, R, mAP50, and mAP50-95 of the ASHM-YOLOv9 model were 98.8%, 98.4%, 99.2%, and 92.4%, respectively. Its P, R, and mAP50-95 values all surpass those of the other models. The recall of the YOLOv5s model is 0.2% greater than that of our model, reaching 99.4%. Table 7 shows the data comparison for the whole stage. The ASHM-YOLOv9 model is optimal for the remaining evaluation metrics, except for the recall, which is 0.1% lower than that of YOLOv5s. The P, R, mAP50, and mAP50-95 of the ASHM-YOLOv9 model were 97.7%, 97.2%, 99.1%, and 90.7%, respectively.  
On the basis of the above data, the ASHM-YOLOv9 model performed the best in terms of all the indicators, surpassing the other models with respect to P and mAP50. There was a small gap between the performance with respect to the recall and the best YOLOv5s, and its performance with respect to mAP @0.5-95 far exceeds that of the other models. This discovery implies that the enhanced ASHM-YOLOv9 model can yield better results in multistage strawberry identification.
## 2 Materials and Methods
### Atmospheric Filtering  
#### 2.4.1 Linear Phase-Based Tropospheric Correction  
To reduce the error caused by atmospheric interactions, we used the linear phase-based model available in the TRAIN, a free module developed by [53].  
\[\Delta\varnothing_{\text{tropo}}=K\Delta_{\varnothing}h+\Delta\varnothing_{0} \tag{3}\]  
where \(\Delta\varnothing_{\text{tropo}}\), \(K\Delta_{\varnothing}\), \(h\), and \(\Delta\varnothing_{0}\) refer to interferometric tropospheric phase contribution, coefficient relating phase to topography, topography, and a constant phase shift, respectively [54]. After StaMPS processing is complete, linear phase-based correction is applied as the final step. More details on these steps can be found in the references [53; 55; 56].
# Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images
###### Abstract  
Flexible needles have been widely used in minimally invasive surgeries, especially in percutaneous interventions. Among the interventions, tip position of the curved needle is very important, since it directly affects the success of the surgeries. In this paper, we present a method to estimate the tip position of a long-curved needle by using 2D transverse ultrasound images from a robotic ultrasound system. Ultrasound is first used to detect the cross section of long-flexible needle. A new imaging approach is proposed based on the selection of numbers of pixels with a higher gray level, which can directly remove the lower gray level to highlight the needle. After that, the needle shape tracking method is proposed by combining the image processing with the Kalman filter by using 3D needle positions, which develop a robust needle tracking procedure from 1 mm to 8 mm scan intervals. Shape reconstruction is then achieved using the curve fitting method. Finally, the needle tip position is estimated based on the curve fitting result. Experimental results showed that the estimation error of tip position is less than 1 mm within 4 mm scan intervals. The advantage of the proposed method is that the shape and tip position can be estimated through scanning the needle's cross sections at intervals along the direction of needle insertion without detecting the tip.  
needle tracking; tip estimation; transverse ultrasound images; percutaneous interventions 2019
## 1 Introduction
However, the most recent studies in FPSO have talked about the applications, such as the direction of arrival estimation for electromagnetic plane waves [40], fractional order filters design [41], fractional fixed-structure \(H_{\infty}\) controller design [42], image segmentation [43; 44; 45; 46], image border detection [47], design of a complementary metal-oxide-semiconductor (CMOS) power amplifier [39], design for an electric power transmission system [48], optimization for a pressurized water reactor (PWR) core loading pattern [49], and optimization of extreme learning machine assignments [50]. This paper proposes an FPSO algorithm with a non-linear time-varying evolution (NTE) based on [PERSON] et al. [16] and [PERSON] et al. [34]. In the proposed FPSO-NTE algorithm, NTE was used to set the inertia weight, cognitive coefficient, and social coefficient. The proposed FPSO-NTE algorithm used four constant coefficients to influence the order of the velocity derivative, the inertia weight, the cognitive parameter, and the social parameter. Therefore, an important issue is how to determine the best combination of the four constant coefficients. The many proposed methods for determining the best combination include the trial-and-error method, one-factor-at-a-time experimental design, full-factorial experimental design, Taguchi method [51], and uniform experimental design [52; 53]. Although most proposed methods and experimental designs are systematic, they all have limitations. To solve this problem, [PERSON] et al. [54] proposed a data-driven approach to uniform experimental design (DAUED) for optimizing the parameters of an auto-alignment machine. The DAUED method optimizes parameters by integrating a ten-level uniform layout with the best combination stage and a stepwise ratio. The FPSO-NTEalgorithm proposed in this study applied the underlying concept of the DAUED method by using DAUED to select the best combination of four constant coefficients. Because a data-driven approach was not used, DAUED was renamed to an adaptive uniform experimental design (AUED) method for this study. The AUFPSO-NTE algorithm automatically obtained the best combination of four constant coefficients because AUED was integrated in FPSO-NTE. This paper is organized as follows. FPSO and the proposed FPSO-NTE are briefly described in Section 2. Section 3 describes how AUED was used in FPSO-NTE to search for the best combination. Section 4 presents and discusses the experimental and simulation results. Finally, Section 5 concludes the study.
###### Abstract  
Water extraction from Synthetic Aperture Radar (SAR) images is crucial for water resource management and maintaining the sustainability of ecosystems. Though great progress has been achieved, there are still some challenges, such as an insufficient ability to extract water edge details, an inability to detect small water bodies, and a weak ability to suppress background noise. To address these problems, we propose the Global Context Attention Feature Fusion Network (GCAFF-Net) in this article. It includes an encoder module for hierarchical feature extraction and a decoder module for merging multi-scale features. The encoder utilizes ResNet-101 as the backbone network to generate four-level features of different resolutions. In the middle-level feature fusion stage, the Attention Feature Fusion module (AFFM) is presented for multi-scale feature learning to improve the performance of fine water segmentation. In the advanced feature encoding stage, the Global Context Atrous Spatial Pyramid Pooling (GCASPP) is constructed to adaptively integrate the water information in SAR images from a global perspective, thereby enhancing the network's ability to express water boundaries. In the decoder module, an attention modulation module (AMM) is introduced to rearrange the distribution of feature importance from the channel-space sequence perspective, so as to better extract the detailed features of water bodies. In the experiment, SAR images from Sentinel-1 system are utilized, and three different water areas with different features and scales are selected for independent testing. The Pixel Accuracy (PA) and Intersection over Union (IoU) values for water extraction are 95.24% and 91.63%, respectively. The results indicate that the network can extract more integral water edges and better detailed features, enhancing the accuracy and generalization of water body extraction. Compared with the several existing classical semantic segmentation models, GCAFF-Net embodies superior performance, which can also be used for typical target segmentation from SAR images.  
SAR; water segmentation; neural network; attention mechanism +
Footnote †: journal: Applied sciences  
_Article_
## 2 Materials and Methods
### Apparatus  
The morphology and size of as-grown Zn-MOF-8 and its composite were analyzed by TEM Morgagnri 268D (Fei Electron Optics, AIMS, Delhi, India). The X-ray diffraction was performed by Rigaku Miniflex-II diffractometer, Tokyo\({}_{o}\), Japan (Cu-K\(\alpha\) radiation, \(\lambda\) = 1.54 A). The FT-IR spectrum was analyzed by Shimazdu IR Affinity-I FT-IR spectrophotometer,(Nakagyo-Ku, Kyoto, Japan ). Ultrasonication was carried out in an ultrasonic water bath, Synergy Techno Support & Solution LLP, Mumbai, India(frequency of 40 KHz). Thermal analysis was performed using the TGA/DSC 3+ Star\({}^{\mathrm{e}}\) system (Mettler Toledo, Leicester, UK) in the temperature range between 25-1000 \({}^{\circ}\)C with a heat rate flow of 10 \({}^{\circ}\)C min\({}^{-1}\) in a static nitrogen atmosphere. Energy dispersive X-ray (EDX) acquired on Bruker-Quan Tax 200 in the textile technology department at Indian Institute of Technology, New Delhi, India ). The electrochemical experiments were performed on an electrochemical workstation PGSTAT302N (metrohm), \(\mu\)-Autolab, Kanaalweg Utrecht, Netherlands, at room temperature with a three-electrode set up system: modified AuE as working electrode, Ag/AgCl as reference electrode and Pt as auxiliary electrode.