esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fiber Efficiency  
The fiber efficiency coefficient is an index that can estimate the reinforcing effect of increasing the GF content. [PERSON] et al. presented a modified rule of mixtures equation for the fiber efficiency factor in discontinuous short fiber-reinforced composites [14; 15]. In addition, [PERSON] estimated the effect of LFT on the fiber content, length, and orientation using a modified rule of mixtures equation [16]. The modified rule of the mixture equation is as follows:  
\[\lambda_{\sigma}=\frac{\sigma_{c}-\sigma_{m}(1-V_{f})}{\sigma_{f}V_{f}} \tag{2}\]  
\[\lambda_{E}=\frac{E_{c}-E_{m}(1-V_{f})}{E_{f}V_{f}} \tag{3}\]  
where \(\lambda_{\sigma}\) is the fiber efficiency factor for the tensile strength of the composite, \(\sigma_{c}\), \(\sigma_{m}\), and \(\sigma_{f}\) are the tensile strengths of the composites, matrices, and fibers, respectively, \(\lambda_{E}\) is the fiber efficiency factor of the tensile modulus of the composite, and \(E_{c}\), \(E_{m}\), and \(E_{f}\) are the tensile modulus of the composites, matrices, and fibers, respectively.  
Tables 7 and 8 show the fiber efficiency results for the PA6/GF LFT composites. PA6/GF-P refers to the specimen prepared via the pultrusion process, and PA6/GF-D refers to the specimen prepared via the direct extrusion process. High \(\lambda_{\sigma}\) and \(\lambda_{E}\) values can lead to a significant increase in the mechanical properties of the composites. In most cases listed in Table 7; Table 8, the fiber efficiency factor decreased as the fiber content increased from 40 wt% to 50 wt%. This indicates that, as the fiber content is further increased, the increase in the mechanical properties can be less predictable, which is more pronounced in \(\lambda_{\sigma}\) than in \(\lambda_{E}\). The pultrusion process shows a higher \(\lambda_{\sigma}\) than the direct extrusion process. Therefore, a higher tensile strength can be expected in the pultrusion process than in the direct extrusion process, and the pultrusion system is a more efficient manufacturing method for the GF/PA6 LFTs.
## 2 Battery Modeling Methods
### Electrochemical Model
The full order model is reduced by considering three aspects: a polynomial approach for the ion concentration in the electrode, a state space model for the concentration in the electrolyte and linearized potential, and electrochemical kinetics [61]. After assuming that the electrolyte concentration is constant, the electrochemical model is simplified through an approximate solution of the diffusion equation in the active material [62]. The diffusion process is represented by a high pass filter and a pure integrator in [63]. The electrical analogy of the charge migration and diffusion equations are utilized for accelerating the simulation time of the electrochemical model. However, even if the electrochemical model is simplified, the calculation process is still complex and the accuracy is hardly guaranteed under diverse operating conditions.
###### Abstract  
The use of deep learning (DL) segmentation in cardiac MRI has the potential to streamline the radiology workflow, particularly for the measurement of myocardial strain. Recent efforts in DL motion tracking models have drastically reduced the time needed to measure the heart's displacement field and the subsequent myocardial strain estimation. However, the selection of initial myocardial reference points is not automated and still requires manual input from domain experts. Segmentation of the myocardium is a key step for initializing reference points. While high-performing myocardial segmentation models exist for _cine_ images, this is not the case for tagged images. In this work, we developed and compared two novel DL models (nnU-net and Segmentation ResNet VAE) for the segmentation of myocardium from tagged CMR images. We implemented two methods to transform cardiac _cine_ images into tagged images, allowing us to leverage large public annotated _cine_ datasets. The cine-to-tagged methods included (i) a novel physics-driven transformation model, and (ii) a generative adversarial network (GAN) style transfer model. We show that pretrained models perform better (+2.8 Dice coefficient percentage points) and converge faster (\(6\times\)) than models trained from scratch. The best-performing method relies on a pretraining with an unpaired, unlabeled, and structure-preserving generative model trained to transform _cine_ images into their tagged-appearing equivalents. Our state-of-the-art myocardium segmentation network reached a Dice coefficient of 0.828 and 95 th percentile Hausdorff distance of 4.745 mm on a held-out test set. This performance is comparable to existing state-of-the-art segmentation networks for _cine_ images.  
myocardial segmentation; deep learning; MRI tagging; cardiac MRI; domain transformation; dataset augmentation +
Footnote †: journal:bioengineering
# Non-Rotationally Symmetric Field Mapping for Back-Scanned Step/Stare Imaging System
## 1 Introduction
Even though the imaging systems can be designed for high performance imaging and have no distortion when there is no motion, the image wander and image blur for off-axis field points still exist. The amount and significance of the blurring depends on the angular change of the FSM, the angular FOV, and the number of pixels (on the FPA) across the FOV. This problem becomes apparent with the increase of the angle change of FSMs, the increase of the FOV and the decrease of the pixel pitch. Now staring arrays have been fabricated in the mid-wave infrared (MWIR) on the format order of \(8\times 8\) K with 10 \(\upmu\)m pitch [22; 23; 24]. Long-wave infrared (LWIR) detectors are not as advanced as MWIR detectors, but they are also making significant progress in larger format size and smaller detector pitch [18]. In this situation, the sensors with super definitions FPAs and long focal length require tight control on the imaging distortion, and even a single pixel shift may cause too much blur for acceptable system performance. Step/stare and IRST systems are widely applied, but this problem of image blurring due to off-axis field point wander is not well recognized and solved. Therefore, the research objective is to find optimal field mapping between object space and image space, which would make all field points stable on the FPA during back-scanning and the image blurring could be mitigated. At the same time, we want to design a proper optical system that can implement these field mappings. In this manuscript, the mathematical model of non-rotationally symmetric field mapping between object space and image space is established in Section 2. A design result of back-scanned step/stare imaging system based on this model is designed in Section 3. Experimental simulation results are analyzed in Section 4.
## 5 Experiments
### In-Depth Analysis
#### 5.6.3 Evaluation in Extreme Scenarios  
To further investigate the performance of the ICD method under extreme conditions, where each new class has only 1 or 2 samples, we conducted experiments with 2-way 1-shot and 2-way 2-shot settings. The results of these experiments are illustrated in Figure 6.  
Under extreme conditions with very few samples, we observe a significant decline in the performance of all methods, indicating the substantial challenge posed by such scenarios. However, the ICD method still outperforms other methods under these conditions. We attribute this advantage primarily to the contrastive learning objective constructed based on the semantic-boundary-smoothing method. This approach provides richer and more effective gradient information for model training under limited sample conditions, thereby enhancing the model's performance and stability with sparse data resources.
## 2 Materials and Methods
### Numerical Calculation Models
#### 2.1.3 Turbulence Model  
The standard \(k\)-\(\varepsilon\) model [33], a widely used two-equation RANS model known for its strong applicability, characterizes turbulence viscosity \(\mu_{t}\) by turbulent kinetic energy \(k\) and turbulent dissipation rate \(\varepsilon\). The governing equations of the standard \(k\)-\(\varepsilon\) model are as follows:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial\left(\rho ku_{j}\right)}{ \partial x_{j}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\left(\mu+\frac{\mu_{ t}}{\sigma_{k}}\right)\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}-\rho\varepsilon \tag{9}\]\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial\left(\rho\varepsilon u _{j}\right)}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\left(\mu+ \frac{\mu_{t}}{\sigma_{\varepsilon}}\right)\frac{\partial\varepsilon}{ \partial x_{j}}\Bigg{]}+C_{\varepsilon 1}\frac{\varepsilon}{K}G_{k}-C_{\varepsilon 2} \rho\frac{\varepsilon^{2}}{k} \tag{10}\]  
The turbulent viscosity coefficient \(\mu_{t}\) is defined as:  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\varepsilon} \tag{11}\]  
where \(G_{k}\) is the turbulent kinetic energy term, \(\rho\) is the fluid density, \(u_{j}\) is the component of gas velocity, and \(\mu\) is the laminar viscosity. The assumed model constants are \(\sigma_{k}\) = 1.0, \(\sigma_{\varepsilon}\) = 1.3, \(\sigma_{\varepsilon 1}\) = 1.44, \(\sigma_{\varepsilon 2}\) = 1.92, and \(C_{\mu}\) = 0.09 [34].
## 3 Modifications to the Reference Network
### Four-Head ASFF Detector  
Adaptive spatial feature fusion (ASFF) reweighs feature maps from different pyramid levels and performs fusion using learned control parameters. As usual, this process is differentiable and can be optimized through back-propagation to improve the model's detection performance [22]. The ASFF structure diagram is shown in Figure 3.  
The PAN-FPN structure in YOLOv8 can extract information from multiple scales, but its feature fusion process is fixed and lacks flexibility. Feature maps of different resolutions are fused in a fixed weighted manner, and the best fusion strategy cannot be adaptively selected according to the characteristics of the target. This fixed fusion method may lead to feature loss and feature inconsistency problems when detecting very small targets like blueberries. To optimize feature fusion, improve detection accuracy, and reduce the missed detection rate and false detection rate in complex environments, this paper proposes a four-head adaptive spatial feature fusion detection head (Detect-FASFF) based on ASFF. The original equations were extended to fuse four levels, with the structure shown in Figure 4 and feature calculation shown in Equations (1) and (2).  
\[y^{l}_{ij}=\alpha^{l}_{ij}\cdot x^{1\to l}+\beta^{l}_{ij}\cdot x^{2 \to l}+\gamma^{l}_{ij}\cdot x^{3\to l}+\delta^{l}_{ij}\cdot x^{4\to l} \tag{1}\]  
\[\alpha^{l}_{ij}=e^{\lambda^{l}_{ij}}/(e^{\lambda^{l}_{ij}}+e^{\lambda^{l}_{ij}} +e^{\lambda^{l}_{ij}}+e^{\lambda^{l}_{ij}}+e^{\delta^{l}_{ij}}) \tag{2}\]  
Figure 3: ASFF structure.  
Here, \(x_{ij}^{s\to l}\) represents a feature map that is rescaled from the size of a layer \(s\) to the size of a layer \(l\). Then, \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\) are weight parameters that indicate the importance of the feature map of each layer, the values of which are learned during training through gradient descent to effectively adjust the contribution of each layer's feature map to the final output, and \(\alpha^{l}_{ij}\), \(\beta^{l}_{ij}\), \(\gamma^{l}_{ij}\), and \(\delta^{l}_{ij}\) are the values of \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\) at position \((i,j)\), respectively.
# Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
## 4 Experimental Results
### Bag-of-Visual-Words with MSER Features  
The four aforementioned database scenarios used with the SURF mechanism were carried out again using MSER as a feature point detector. In these scenarios, the MSER detector was used to detect the points of interest, while SURF was employed as a feature descriptor for every detected point. The identification accuracy, processing time and error were calculated in every case and are reported in Table 2. Visual word histograms and confusion matrices were measured during the empirical work but are omitted from the paper to avoid figure redundancy. Table 2 illustrates the degradation in the obtained number of features and identification accuracy. It also shows very short processing times compared to Table 1, which is normal due to the small number of extracted features.  
On the other hand, the obtained results from using both the SURF and MSER detectors in the BoVW paradigm were compared against other state-of-the-art methods in Table 3. The table confirms the similar performance of BoVW and our previous method that uses only the SIFT detector to calculate a small score between all the database images. Driven by the comparison results in Table 3, it is highly feasible in terms of accuracy to utilize the BoVW technique in cattle identification.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 6 Effects of Carbon-Based NPs on Medicinal and Aromatic Plants
### MwCNTs
#### 6.5.2 Elicitor Effect  
In vitro cultivation of _Thymus daenensis_ seeds in MS medium containing MWCNTs, which were found to pass across the plant cell wall and enter the cellular cytoplasm, affected early germination of plants, and stimulated the production of plant biomass up to a dose of 250 \(\mu\)g/mL, while further increasing of MWCNTs concentration reduced the biomass. Moreover, MWCNTs upregulated the activities of enzymes and antioxidants and elicited biosynthesis of some secondary metabolites and antioxidants, the optimum dose of MWCNTs being 250 \(\mu\)g/mL [157]. Treatment of _Satureja knuzistanica_ Jamzad plants grown in vitro with 250 mg/L MWCNTs resulted in RA content of 140.49 mg/g (d.w.), while at in vivo exposure of _S. kluzistanica_ to MWCNTs for 24 h, the RA content of 7.13 mg RA/g (d.w.) was achieved [310]. Stimulation of callus induction, biosynthesis of secondary metabolites, and improved antioxidant capacity in medicinal plant _S. kluzestanica_ grown in vitro by MWCNTs was also reported previously by [PERSON] and [PERSON] [158].  
Spraying of the leaves of two-month-old _Salvia verticillata_ L with MWCNTs (0-1000 mg/L) resulted in their absorption via the epidermal cells layer into the parenchymal cells of the exposed leaves; the treatment reduced levels of assimilation pigments and caused oxidative stress in a dose-dependent manner. However, at application of 50 and 1000 mg/L MWCNTs, an (approximate) 4-fold increase of RA compared to the control was observed, whereby the activity and gene expression patterns of RA synthase correlated with the RA accumulation. While lower ROS levels at application of lower MWCNT concentrations contributed to improved production of secondary metabolites, treatment with higher concentrations was phytotoxic due to increased oxidative stress [155]. The \(\gamma\)-irradiation of _Salvia memorosa_ callus, using a dose 70 Gy, resulted in a high yielding cell line able to produce up to 18.53, 5.21, 1.9, and 7.59 mg/g d.w. of RA, salvianolic acid B, ferulic acid, and cinnamic acid, respectively. The cell suspension culture prepared of irradiated callus and elicited with 100 mg/L MWCNT-COOH (outer diameter of 20-30 nm) achieved fresh and dry biomass of 268.47 g/L and 22.17 g/L, respectively. Both treatment with \(\gamma\)-irradiation and elicitation using MWCNT-COOH pronouncedly improved the antioxidant activity of cultures. These findings showed that improved production of secondary metabolites in _S. memorosa_ achieved by combination of cell line selection via \(\gamma\)-irradiation with elicitation with MWCNT-COOH could be utilized for large-scale production of phenolic compounds [159].  
MWCNTs increased the production of biomass production in callus culture of _Catharanthus roseus_ cultivated in the dark, promoted the biosynthesis of total produced alkaloids, whereby strong improvement in the production of vinblastine and vincristine alkaloids was observed, not only with callus cultivation at dark, but also in light conditions [311].
## 5 Conclusions  
In this article, we have provided evidence that the spectral whitening procedure [6], originally conceived and developed as an unsupervised preprocessing method for SLC data to improve the despeckling of their modulus, may also be useful for coherent data processing. Thus, for SAR interferometry, the trade-off between the ideal situations of focused targets and uncorrelated speckle can be relaxed. Although not specifically addressed here, the unsupervised nature of the solution--namely, blind estimation and inversion of the frequency window--allows any type of correlation impairing the SLC data, including that originating from the resampling of the slave over the master, to be removed.  
Possible developments will concern the investigation of the effects of whitening on multipass DInSAR processing chains, which are of key importance for applications in risk monitoring. This is especially important in mixed scenarios characterized by the presence of both rural and built-up environments and requires ad hoc preprocessing of the stack of InSAR observations [53] before selecting the set of coherent points for interferometric processing [54; 55]. Future research will also test the possible benefits of applying speckle whitening in the field of SAR tomography of natural media [56].  
Conceptualization, L.A.; methodology, L.A. and F.L.; software, L.A. and A.A.; validation, L.A., A.A. and F.L.; resources, L.A.; data curation, A.A.; writing--original draft preparation, L.A.; writing--review and editing, L.A. and F.L. All authors have read and agreed to the published version of the manuscript.  
This work has been supported in part by the University of Pisa in the framework of project PRA 2022 101 \"Decision support systems for territorial networks for managing ecosystem services\".  
The data presented in this study have been provided by former co-authors and are not publicly available for copyright reasons.  
The authors gratefully acknowledge the following people for their invaluable contributions: [PERSON] of the University of Florence, Florence, Italy: [PERSON] and [PERSON] of IREA-CNR, Naples, Italy; [PERSON] of the University of the Bundeswehr Munich, Neubiberg, Germany; [PERSON] of IFAC-CNR, Sesto Fiorentino, Italy; and, last but not least, [PERSON], former student of the University of Florence, now with MOVYON, part of the Autostrade per l'Italia Group. The COSMO-SkyMed dataset and facilities (raw data and SAR processor) were kindly provided by IREA-CNR. The TerraSAR-X/TanDEM-X data, along with the related InSAR processing and display facilities, were courtesy of DLR (Deutsches zentrum fur Luft- und Raumfahrt--German Aerospace Center).  
The authors declare no conflicts of interest.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 2 Model Architecture
### HiFi-GAN Vocoder  
To optimize both synthesis speed and quality, this study utilizes the HiFi-GAN [23] vocoder to convert the speech spectrum into waveform audio more efficiently and with enhanced fidelity. The HiFi-GAN vocoder comprises a generator and two discriminators. The core of the generator is a convolutional neural network that processes the Mel spectrum as input and produces outputs analogous to real speech. The discriminators continuously up-sample the Mel spectrum until the output sequence matches the time-domain resolution of the original waveform, assessing the authenticity of the synthesized speech. Upon completion of training, the generator is employed directly during synthesis to produce the speech waveform signal.
## 3 Results and Discussion
### Studies on OGN Desorption
#### 3.3.2 The HI Mode  
Partial adsorption of DNA-20 at the surface of investigated MNP was possible; thus, a study on the possibility of its desorption was performed. The OGN was loaded into adsorbent in conditions in which the adsorption degree was the highest (Table 3). Water has the highest elution strength on HILIC; thus, it was tested for elution of adsorbed OGN from MNP. Moreover, water alkalized with ammonia to pH = 9.5 was also investigated. Unfortunately, the recoveries in all cases were significantly low (Table 3). For MNP-Sul and MNP-Mal were below 20%. The highest desorption efficiency in HI mode was achieved for MNP-Ac and water ammonia solution at pH = 9.5 as an elution solution but is still less than 35%. As satisfactory results were not achieved, further studies on the HI mode extraction were abandoned.
# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
## 2 Materials and Methods  
### Sample Preparation  
Seven standard particle samples, including protein, cap fiber, glass, silicone oil microdroplets, silicone tubing, rubber closure, and polystyrene beads, were prepared and used for CNN classification. Among them, cap fiber particles are considered extrinsic and originate from bouffant caps in the production environment. Glass particles, silicone oil microdroplets, silicone tubing particles, and rubber closure particles are considered intrinsic particles originating from the materials that may encounter the process. Protein particles are inherent. Polystyrene beads were used as the focus beads for the FlowCam.  
Cap fibers, silicone tubing, rubber closure particles, and glass particles were prepared using a stainless-steel shredder (FSJ-A03D1, Bear, Foshan, China) separately from the raw material of the spunbonded polypropylene fabric from bouffant caps (I113-1459) (VWR, Atlanta, GA, USA). We purchased 1.5 mL PP microcentrifuge tubes (509-GRD-Q) from Quality Scientific Plastics (Petaluma, CA, USA), 20 mm rubber closures (V10-F597W RS) from Daikyo Seiko, Ltd. (Tochigi, Japan), and 30 mL Fiolax clear vials (39214A) from SCHOTT Vials (Mullheim, Germany). After each class of particle was collected, subvisible particles below 100 \(\upmu\)m in diameter were filtered through a 100 \(\upmu\)m pore-size nylon cell strainer (93100, SPL Life Sciences, Pocheon-si, Korea) and diluted in purified water to form the corresponding particle solution. The same process was performed with pure water as the control to ensure that no new particles were introduced into the filtration process. The silicone oil particle solution was diluted from silicone oil (PMX-200; Aladdin, Shanghai, China) with purified water (1:1000) and sonicated for 3 min. Polystyrene beads were purchased from Micro Measurement Labs (Wheeling, WV, USA), with 3000 25 \(\upmu\)m-diameter polystyrene beads per milliliter.  
In this study, protein particles were prepared by simulating shaking-induced stress. IgG1 antibody (Mab1) formulation solution (25 mg/mL protein in formulation buffer,pH 6.2) was donated by a local biopharmaceutical company. To generate protein particles, the solution was dispensed into five vials and stirred at 400 rpm for 1 h using a magnetic stirrer (YYCJ-35, Yuyong Biotechnology, Shanghai, China).