esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Evaluation of Rotordynamic Coefficients for Compliant Foil Gas Seal
## 4 Conclusions  
To obtain a high-performance aero-engine, the numerical evaluation of the gas seal was studied to improve the efficiency in many studies. However, many numerical analyses of rotordynamic coefficients for gas seals have been used in face gas seal, smoothing, and spiral cylindrical gas seals for aero-engines, including CFD analysis. This study analyzes the T-shaped groove of the compliant gas seal, and the steady performance of the compliant foil gas seal on the operating parameters is obtained by FDM. Subsequently, the steady analysis can be extended to evaluate rotordynamic coefficients of the compliant foil gas seal. These conclusions are mentioned in this study as follows:  
1. For leakage and gas force evaluation of the compliant foil gas seal, the inlet pressure has the largest influence on the performance of the compliant foil gas seal. For the amplitude angle and friction, the impact of eccentricity ratio is greater than other factors. It is clear that speed is relatively small for the sealing system.
2. Regarding the direct stiffness coefficient and direct damping evaluation, the analysis results show the same tendency as the operation parameters change. The peak values of the direct coefficients increase with the growth of the eccentric ratio. However, the peak values of the direct coefficients decrease with the growth of pressure ratio and speed, which is harmful to the stability of the rotor system, so the initial operating values should be paid more attention when the parameters of the T-shaped groove need to be optimized further.
3. Regarding the cross-coupled stiffness and cross-coupled damping coefficient evaluation, the results show that they are almost symmetrical about the special value. Furthermore, they reach the peak point with the same value of the eccentric ratio, speed, and inlet pressure.
4. For the rotordynamic coefficient evaluation, it is necessary for plotting the prediction curve to obtain more parameters and coefficients in the future.  
Conceptualization, X.W. and M.L.; methodology, X.W.; software, X.W.; validation, X.W.; formal analysis, X.W.; investigation, X.W.; resources, X.W. and M.L.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W., S.K.-W., and X.H.; project administration, M.L.; funding acquisition, S.K.-W. All authors have read and agreed to the published version of the manuscript. This research was funded by the China Scholarship Council (grant no. 201708740009).  
The current research has been supported by the China Scholarship Council (grant no. 201708740009).  
We declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 8: (**a**) Description of stiffness coefficient with speed. (**b**) Description of damping coefficient with speed.
# Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding
## 2 Transformer Model  
A transformer [24; 32] is a popular deep learning model that has often replaced RNN or LSTM for NLP tasks. In the language translation task, the transformer model adopts encoder-decoder architecture. For example, to convert a sentence from Vietnamese to Korean, the encoder learns the representation of the given Vietnamese sentence and feeds it into the decoder. The decoder then generates the corresponding sentence in the Korean language.
## 3 Materials and Methods
### Evaluation Metrics  
In the experiments, various networks with differing configurations will be evaluated and compared using standard metrics, namely accuracy, precision, recall, specificity, and F1-score.  
For a given patch, a model classifies it as either invasive ductal carcinoma (IDC+) or normal tissue (IDC\(-\)), representing the positive (P) and negative (N) classes, respectively. True Positive (TP) and True Negative (TN) refer to cases where the model correctly classified a sample as IDC+ and IDC\(-\), respectively. False Positive (FP) and False Negative (FN) occur when the model misclassified an IDC\(-\) sample as IDC+, and vice versa. The counts of these occurrences in the test set were utilized to construct a confusion matrix and calculate the evaluation metrics, as outlined in Equations (4a)-(4e). Accuracy measures the ratio of correctly classified samples relative to the total cases.  
\[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{4a}\]  
Focusing on correct IDC+ classification, precision and recall measure the ratio of correctly classified positive samples to the total positive samples and to the predicted positive samples, respectively. In contrast, specificity measures the ratio of correctly classified IDC\(-\) samples relative to the total number of actual samples in this class.  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4b}\]  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4c}\]  
\[Specificity=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{4d}\]  
The F1-score is calculated as the harmonic mean of precision and recall, offering a balanced metric that evaluates their trade-off.  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{4e}\]  
In addition, Receiver Operating Characteristic (ROC) curve plots the model's performance across various classification thresholds. It shows the trade-off between TP rate (recall) and FP rate (1--precision). The Area Under Curve (AUC) is derived from ROC. AUC values closer to 1 signify that the model can reliably differentiate between IDC+ and IDC\(-\) classes, whereas those near 0.5 reflect performance comparable to random guessing. Values below 0.5 suggest systematically incorrect classification or potential label reversal, indicating the model's failure to learn any meaningful pattern.
# Femtosecond Optical Nonlinearity of Nanodiamond Suspensions
## 5 Conclusions  
The nonlinear absorption of suspensions of HP-HT and detonation nanodiamonds at a wavelength of 795 nm of femtosecond excitation differ from each other significantly. At the same parameters of femtosecond laser pumping and experimental conditions, saturable absorption occurred in an aqueous suspension of detonation nanodiamonds accompanied by short-term nonlinear bleaching, while in an aqueous suspension of HP-HT nanodiamonds, two-photon absorption appeared, leading to optical limiting. The nonlinear absorption coefficient of the aqueous suspension of HP-HT nanodiamonds decreased linearly with the decreasing nanoparticle concentration. The important parameters characterizing the saturable absorption of detonation nanodiamonds in an aqueous suspension were determined. The results obtained show that the energy structures of HP-HT and detonation nanodiamonds differ from each other significantly. Aqueous suspensions of HP-HT nanodiamonds can be used for the optical limiting of high-power femtosecond laser pulses while suspensions of detonation nanodiamonds possess properties of the saturable absorber and can be employed for shortening the duration and to remove the pedestal (\"tail\") of the laser pulses.  
Conceptualization, G.M.M. and V.V.V.; methodology, G.M.M., V.V.V. and K.G.M.; software, V.V.V.; validation, G.M.M. and O.A.S.; formal analysis, V.V.V.; investigation, G.M.M., V.V.V., K.G.M., T.N.M. and A.N.A.; resources, N.A.N. and O.A.S.; data curation, T.N.M.; writing--original draft preparation, G.M.M. and V.V.V.; writing--review and editing, G.M.M., V.V.V., N.A.N. and O.A.S.; visualization, T.N.M.; supervision, G.M.M. and O.A.S.; project administration, G.M.M.; funding acquisition, G.M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education and Science of the Russian Federation, state registration number AAAA-A19-119021890083-0, by the Academy of Finland grants no. 298298 and 323053, Flagship Programme \"Photonics Research and Innovation (PREIN)\" decision no. 320165.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors wish to thank [PERSON] for his help in experiments. This study was performed using equipment of the Shared Use Center \"Center of Physical and Physicochemical Methods of Analysis and Study of the Properties and Surface Characteristics of Nanostructures, Materials, and Products\" UdmFRC UB RAS.  
The authors declare no conflict of interest.
## 2 Experimental
### Validation of the HPLC-PDA Method  
The simultaneous analysis method was validated as described by the International Conference on Harmonisation (ICH) guidelines [37] for specificity, linearity, limit of detection (LODs), limit of quantification (LOQs), precision, analyte recovery, and solution stability.  
Specificities were determined to confirm that L7 GC, CDR, C7 dGlu, and DSM were selectively separated from other compounds in the crude extract and determined using the retention times and absorbance wavelengths of the chromatograms obtained by analyzing samples and standard solutions with HPLC-PDA.  
To assess linearity, stock solutions of L7 GC, CDR, C7 dGlu, and DSM were diluted with six target concentration ranges (1%, 10%, 50%, 80%, 100%, and 120%) to obtain L7 GC and C7 dGlu at six target concentrations--i.e., 3.00, 30.00, 150.00, 240.00, 300.00, and 360.00 \(\upmu\)g/mL. CDR and DSM were obtained at 5.00, 50.00, 250.00, 400.00, 500.00, and 600.00 \(\upmu\)g/mL. Samples were injected six times (\(n=6\)). Calibration curves were subjected to linear regression analysis according to the equation y = ax + b, where y is the peak area, x is the sample concentration, a is the slope, and b is the y-intercept of the regression line. Linearity was established when the correlation coefficients (\(t^{2}\)) were >0.999.  
The limits of detection (LODs) and limits of quantification (LOQs) were calculated using the standard deviations (SDs) of y-intercepts and slopes of calibration curves and the equations LOD = 3.3 \(\times\)\(\sigma\)/S and LOQ = 10 \(\times\)\(\sigma\)/S (\(\sigma\) = SD of y-intercept, S = slope of the calibration curve).  
Intra- and inter-day tests were performed at three concentrations (low, medium, and high) in linear ranges--i.e., L7 GC at 10.00, 100.00, and 250.00 \(\upmu\)g/mL; C7 dGlu at 5.00, 50.00, and 125.00 \(\upmu\)g/mL; and CDR and DSM at 12.50, 125.00, and 312.50 \(\upmu\)g/mL. Intra-day precision was determined by analyzing three concentrations of each compound in quintuplicate in one day, and inter-day precision was measured by analyzing the same three concentrations five times (\(n=5\)) on days 1, 3, and 5. Precisions were defined as relative standard deviations (%RSD), and %RSD was calculated using the standard deviation (SD)/mean \(\times\) 100.  
Analyte recoveries were calculated to confirm the percentage recoveries of analytes from _L. chitensis_ extracts. Recovery experiments were performed by spiking _L. chitensis_ extract with L7 GC, CDR, C7 dGlu, and DSM standards at three concentrations (low, medium, and high) and testing them in triplicate. Recovery % was calculated using (amount of analyte in spiked sample -- amount of analyte in the sample)/amount of spiked standard \(\times\) 100.  
Solution stabilities of L7 GC, CDR, C7 dGlu, and DSM were checked by storing stock solutions at room temperature or 4 \({}^{\circ}\)C for 0, 6, 24, 48, and 72 h. The analysis was repeated five times (\(n=5\)), and the % difference in the areas of each peak in the obtained chromatograms was calculated.
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 7 Following Stages of Ultrasound Research
### Equipment  
It has been widely documented that ultrasound technology's main challenge is delivering the same treatment intensity when the equipment is scaled up to industry settings. Several factors must be considered, such as the volume of the treatment chamber and the system's operation mode (batch or continuous), among others. However, one of the main drawbacks, as of today, is the sonotrode. The acoustic energy during sonication is delivered in the treatment medium by a sonotrode or horn tip. Most of the research in ultrasound for food processing and preservation has been conducted using devices with an attached sonotrode. These tips are commonly made of titanium and its alloys. However, the intense cavitation also affects the surface of the sonotrodes.  
The implosions and explosions during sonication can remove metallic particles from the sonotrode surface. It has been observed and widely reported that the erosion or pitting of sonotrodes during processing [11; 104; 105]. This is a significant concern in food processing because of the deposition of metal particles in the product. A few studies have been conducted about the toxicity of these metallic particulates in food after processing [105]. The findings reported that these particulates do not represent a toxicological concern for human health.  
Furthermore, a few attempts have been made to reduce this problem, and some solutions include covering the sonotrodes in epoxy or using inert materials such as quartz or pyrex [12; 106]. These efforts have reduced the number of metallic particles eroded from the horn tips. However, this change is also reducing the intensity of the treatment and hence the microbial inactivation. Some promising materials in constructing improved sonotrodes include niobium and its alloys [107]. However, the re-design of sonotrodes to reduce the leaching of metallic particles into the medium is a research need that must be addressed while optimizing the pasteurization process by ultrasound soon. In this stage, the construction of sonotrodes needs to be made from strong materials that can provide the same intensity of treatment as titanium horn tips but also reduces the erosion of the sonotrode surface and are made of materials considered safe to be in contact with food.
# Towards a Reference Architecture for Cargo Ports
## 5 A Reference Architecture for a Cargo Port  
Now that we have seen the different conceptual units that together make up a cargo port system, we can show its corresponding reference architecture. See Figure 13. This RA combines the patterns shown earlier and includes the process patterns, stakeholders, and use cases described above. In this RA, we included the patterns for: loading and unloading of containers to/from a ship at a cargo port loading facility [16]; a pattern for the delivery of cargo port containers (drayage) [15]; a pattern for ship arrivals and departures; and a pattern for a container terminal physical structure [58]. Patterns may exhibit overlapping classes. As indicated earlier, this is not a complete RA for cargo ports.  
A brief explanation of the architecture follows: a typical cargo port, _Port_, contains many container terminals, _Quay_. Each container terminal should include port security and access control functions. Drivers arriving at the entrance, _GateAccess_, intend to either pick  
Figure 13: Partial reference architecture for a cargo port.  
up or drop off a container, destined for import or export, respectively. The movement of containers between a port terminal and an inland distribution point or rail terminal is referred to as port drayage. Each single port drayage transaction is an instance of _DrayageTransaction_. For each drayage transaction, there is one _TruckDriver_ in charge of a _Truck_ and a _Container_. Ports include loading and unloading facilities such as _Crane_s and _Storage_ units. During unloading, _CraneOperators_ move containers from _Ships_ to storage units; during loading, crane operators move containers to ships. _CraneSchedulers_ are in charge of assigning crane operators to cranes. _StorageManager_ is in charge of assigning storage locations and warehousing facilities. _Employees_ are members of the Port Authority organization. Arrival at a port and departure from a port are two separate but very important aspects of a ship's voyage. Both of these procedures are critical and involve a number of different complexities. _ShipStay_ represents each stay of a _Ship_ in a berth, either an _Arrival_ or a _Departure_. The ship may also require the assistance of a _Tugboat_ for maneuvering inside the port area and completing entrance and mooring operations. Similar operations are carried out when the ship leaves the port area. Multiple tugboats can be associated with multiple ships. The associations between ships and tugboats are temporary. _TransportMean_ represents all the transportation means: trucks moving containers in and out of the terminal, railroad trains carrying the containers in and out of the terminal, or possibly other ships. We do not claim completeness in this RA, but it can be used as a framework to guide the design of the cargo port system.
## 3 Proposed SPMIPv6 Protocol
### Application Scenarios of IP-WSN based on SPMIPv6
#### 3.3.5 Vehicle Telematics  
SPMIPv6 based IP-WSN play an important role in intelligent transportation systems. Incorporated in roads, vehicles, and traffic signals, they contribute to the improvement of safety of transporting systems. Through traffic or air-quality monitoring, they increase the possibilities in terms of traffic flow optimization and help reducing road congestion.
## 4 Noble Metal-Based Electrocatalysts for ORR and HER  
As described in the aforementioned section, there are two possible pathways of the HER: (i) Volmer-Tafel and (ii) Volmer-Heyrovsky. Both of these mechanisms involve the adsorption of the hydrogen atom (i.e., H\({}^{\text{ads}}\)) on the catalyst surface as an intermediate step, and this ultimately becomes the rate-determining step (i.e., RDS) for the HER kinetics. In this context, the binding energy of H\({}^{\text{ads}}\) on the surface of the catalyst should appropriately balance, meaning that the lower binding energy affects the Volmer step, while strong bond formation severely hampers the desorption process of molecular hydrogen. In brief, the ideal electrocatalysts should have the hydrogen adsorption energies nearly at (\(\Delta\text{G}_{\text{H}}=0\)) [22]. As widely discussed in the literature, based on this concept, experimental and theoretical studies provide a clear relationship between the HER performance (i.e., exchange current) and the metal-H\({}^{\text{ads}}\) bond strengths (Figure 3a) [23]. Similar to HER, according to the Sabatier principle, the binding energy of ORR intermediate species on the catalyst surface should be appropriately balanced and the relationship between the experimentally measured ORR performance and the calculated \(E_{\text{ads}}\) of intermediate adsorbates demonstrates a volcano shape, (Figure 3b) [24]. It is evident from Figure 3a,b that Pt stands on top of the volcano plot and thus is considered as the state-of-the-art electrocatalyst to boost ORR and HER kinetics. However, high cost and limited availability are two major obstacles for the commercial viability of Pt-based catalysts. In the past decade, several studieshave highlighted effective strategies to alleviate the economic issues of Pt-based catalysts via reducing the Pt-loading or incorporating Pt with other cheap transition metals in the form of alloys, core-shells, cluster-in-clusters, and/or surface decoration [25]. Meanwhile, significant progress has been made in the Pt-free NCs via incorporating Pd, Au, Ir, and so on with 3d-transition metals in various binary and ternary combinations to improve the kinetics of the reaction. Moreover, the structural and electronic properties of heterogeneous NCs have also been tuned via optimizing the particle size and coupling with different support materials [26; 27; 28]. In contrast, despite unprecedented progress in HER and ORR catalysts benefiting from the aforementioned alternative catalyst design strategies, the catalytic performance is still far from practicability and scalability. These approaches are discussed with more details in the following sections.
# Extracting Weld Bead Shapes from Radiographic Testing Images with U-Net
## 2 Methods
### U-Net  
In recent years, several studies have been conducted on image segmentation techniques owing to the increasing interest in self-driving vehicles and object recognition [14]. Deep learning models are often used for video and image segmentation. U-Net is an end-to-end fully convolutional network-based model [15] designed for image segmentation, and has been used for image segmentation in applications such as X-ray and magnetic resonance imaging (MRI) [16].  
Figure 1: The redistribution will push some pixels over the clip limit again (region shaded green in the figure). It can be left as it is, but if this is undesirable, the redistribution procedure can be repeated recursively until the excess is negligible.  
U-net has a U-shaped network configuration, as shown in Figure 2. As it uses a convolutional neural network (CNN) [17] structure, it contains several layers and extracts features from images. After extracting the features, transpose convolution is used to begin the image segmentation based on these features. The U-shaped structure can be broadly divided into three parts. The first is the contracting path, which examines a broad range of image pixels and extracts context information. The second is the expanding path, which combines context information with pixel location information (localization) to distinguish the object to which each pixel belongs. The third is the bottleneck, which converts the contracting path to the expanding path.  
In the contracting path, a downsampling process is repeated to generate a feature map. The bottleneck is the segment that converts the contracting path to an expanding path. In the expanding path, the feature map is upsampled. Additionally, the expanding path performs the role of combining the contextual information that was generated in the contracting path with the location information using skip connections. If the size of an image is reduced and increased again, detailed pixel information is lost, which is a major challenge in segmentation requiring dense pixel-by-pixel predictions. Clearer image results are obtained from the decoder part through the skip connections, which directly pass important information from the encoder (contracting path) to the decoder (expanding path), allowing for more accurate predictions.
# Using Connected Vehicle Data to Evaluate National Trip Trends
## 2 Objectives and Scope  
With an impending shift in the automobile industry towards alternative fuel vehicles, agile monitoring of trip trends for both ICEV, as well as HV and EV, is important to help guide state and national investments in alternative fuel infrastructure.  
The objective of this paper is to estimate national trip trends by passenger vehicle type, specifically trip length and trip duration, using CV data for the month of December 2022 and to compare the estimations with results from the NHTS 2017 trends. This CV data is also used to visualize inter-state spatial travel patterns among US census places and cities. The final objective is to evaluate daily trip trends and patterns by vehicle type for four states (one in each time zone)--Indiana (Eastern), Texas (Central), Wyoming (Mountain), and California (Pacific).