esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### CNN Module  
The confusion matrix for the optimized CNN model is shown in Figure 10. Observation of the diagonal elements displays high numbers in the confusion matrix, indicating the good performance of the classifier model. The values of _TP_ = 63,270, _TN_ = 10,452, _FP_ = 7263, and _FN_ = 1495, respectively, based on the 2 \(\times\) 2 confusion matrix. Based on the equations, the accuracy is calculated to be 89.3%. The CNN model implemented on NVIDIA Jetson has a similar degree of accuracy as the simulated model of approximately 89 percent, with a run time of 5.31 s. The precision, recall, and F1-score are calculated for normal and abnormal beats separately. Based on the equations, Table 5 lists the precision, recall, and F1 scores for the binary classification CNN model. The imbalanced dataset has a high F1 score of 0.94 for normal beat detection and 0.7 for abnormal beat detection. AUROC values for other machine learning work with different models and datasets are presented in Table 6. As can be seen from the AUROC value of our work, it is 0.7% higher than the prior proposed CNN models. An ML model's receiver operating characteristic (ROC), the ratio of true positive rate to false positive rate, yields a plot as shown in Figure 11. The model performs well, as indicated by the ROC curve (AUC) area. We also have trained our CNN model with standard cross-validation techniques and tested it on unseen patient data. It gives a 0.755 AUC score, which is 0.131 lower than our proposed model. Thus, our CNN-based deep learning approach showed potential for better prediction of SCA beat annotations using a real-world database with imbalanced classes, avoiding the possibility of overfitting and implementing on the NVIDIA Jetson board.
## 4 Data Description  
Four tests are performed to test the proposed method with simulated and real data, and the information and names of datasets are shown in Table 3. Tests 1, 2, and 3 train and validate the proposed method with simulated kinematic data (SIMTU, SIMTR, and SIMTE), real static data (STU, STR1/2, and STE), and real kinematic data (KTU, KTR1/2, and KTE1), respectively. Test 4 further validates the trained detectors with the training and test data (KTE2) collected in two different multipath environments (near a wall on the rooftop and near a building). Test 5 compares the performance of the proposed method with those of two existing multipath mitigation methods (simple elevation based model and SIGMA-\(\epsilon\) model). More details about data collection of tuning, training, and test datasets are described in the following section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tests** & **Tuning Dataset** & **Training Dataset** & **Test Dataset** & **Remarks** \\ \hline
1 & SIMTU & SIMTR & SIMTE & Simulated data \\
2 & STU & STR1+STR2 & STE & Real static data \\
3 & KTU & KTR1+KTR2 & KTE1 & Real kinematic data \\
4 & KTU & KTR1+KTR2 & KTE2 & Real kinematic data \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of tests with tuning, training, and test datasets.
## 4 Discussion
### Alternative Understanding of Scales  
Among the six selected radii, the high-level relations of Integration, Choice and Combined Centrality with street vending distribution are generally at R2000, R5000 and R10,000 (Figure 3). The results are consistent with earlier studies that have shown the significance of prediction power for small markets a larger scale [18]. However, street vending also closely interacts with pedestrians and other groups of people, suggesting that it carries not only an economic dimension but also a social one with dynamic features. In terms of pedestrians, due to walking distance, the spatial configuration serves as the basis for pedestrian movement at local and micro-scales [1,46], in line with the radii of 800m and 1200 m in our study. The inconsistent findings suggest that although in our hypothesis street vendors closely trace pedestrian movement, the two groups of street users have distinct concerns and socio-spatial demands. For example, unlike street vendors, most pedestrians prefer to walk a shorter distance wherever possible and tend to select the most convenient paths for their journeys whether via major roads or inner neighbourhood streets and alleyways [46,49]. They are also often related to 'adjacent locations' for to-movement at the local scale [1].  
The potential reasons why there is better prediction power for street vendors at middle and larger scales has been further uncovered by on-site observation and interviews. Firstly, street vending practices need to balance accessibility and multiple practical restrictions, as highlighted by the interview comments: '(Street vending can have a) negative impact on nearby store businesses', the need for'sufficient space for the showcase' and 'avoiding urban manager patrols' and so on. This finding is in line with existing studies that clearly illustrate that street vending locations tend to be near prosperous areas [21]; vendors usually emerge in hidden spaces but keep close to the major roads or the key entrances of large public spaces [20]. Secondly, although street vendors emerge temporarily, they often stay for a longer duration of time and sometimes appear repeatedly at the same spot, compared with pedestrians who are passing quickly by. As a result, street vendors prefer to select a relatively comfortable spot from a wide range, such as a shaded location by trees or buildings. In short, street vendors must calibrate these complex factors over a large area when selecting a location in which to vend. Vending distribution is more typically related to a syntactic analysis at middle and larger scales than at small scales when compared with pedestrian movement. This also implies that in space syntax studies, the prediction power at different scales is likely related to the complexity levels of a vending location.
## 3 Design Optimization
### Performance Metrics & Design Criteria
#### 3.3.1 Band Width  
The operational robustness of rotationally controlled valves correlates with their standard deviation \(\Delta\Omega\) (7) of their critical spin rate \(\Omega\) (5). This spread varies with the parameters \(\{\gamma_{k}\}\) like \(R\), \(U_{0}\) and \(p_{0}\), the dimensions delineating in the geometry \(\Gamma\), and their tolerances \(\{\Delta\gamma_{k}\}\) mainly owing to precision of manufacturing, assembly, liquid metering and the ambient pressure \(p_{0}\). For a CP-DF siphon valve with default geometry \(\Gamma\) (Figure 1), the dependency of \(\Delta\Omega\) on the compression volume \(V_{\text{C},0}\) with common geometrical manufacturing tolerances \(\Delta\gamma_{k}\) (Appendix A.1), and the precision of the loaded liquid volume \(\Delta U_{0}\) is displayed in Figure 2. Accordingly, if disc space and radial ordering allow, it is favorable to enlarge the (primary) volume \(V_{\text{C},0}\) compression chamber until the curve saturates (Figure 2a), and to move \(\Gamma\) to outer radial positions \(R\) (Figure 2b).  
As already pointed out, the band width \(\Delta\Omega\) (7) assumes a pivotal role in directing the layout of fluidically multiplexed, centrifugal microfluidic LoaD systems. Common optimization tasks of valving structures \(\Gamma\) in multiplexed liquid handling scenarios are thus often intertwined with restricting the spread \(\Delta\Omega\) of each valving step. We introduce the following normalized metric:  
\[\overline{\Delta\Omega}=\frac{\Delta\Omega}{\omega_{\text{max}}-\omega_{\text{ min}}} \tag{9}\]  
whose computational minimization by a wide range of well-established numerical routines is available in widely available software packages.
# Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images
## 4 Superpixel Algorithm Incorporating Image Statistics
### Superpixel Regions
The vertices of the graph will be the superpixel regions, and edges define which regions are neighbours to one another; the vertices and edges have the form  
\[\mathcal{V} =\{0,1,...n_{R}\} \tag{32}\] \[\mathcal{E} =\{k_{v1}^{\star},k_{v2}^{\star}\}, \tag{33}\]  
with \(\mathcal{V}\) being a set of all of the region indexes \((k)\). The edges in the graph \(\mathcal{E}\) are described by a set of double-valued elements, \(k_{v1}\) and \(k_{v2}\), with an individual line edge element connecting two vertices given by \(E_{v}=(k_{v1},k_{v2})\), where \(k_{v1}\) and \(k_{v2}\) are the indexes of two neighbouring regions \(R_{k1}\) and \(R_{k2}\). With the graph being undirected, the ordering of the elements in an edge is ignored, such that \((k_{v1},k_{v2})\equiv(k_{v2},k_{v1})\). Additionally, under this independence from element ordering, the connection between two vertices \(k_{v1}\) and \(k_{v2}\) is described by a single edge without duplication. For example, only one of the two edge elements \(E_{a}=(k_{v1},k_{v2})\) and \(E_{b}=(k_{v2},k_{v1})\) are allowed to be a member of the set \(\mathcal{E}\). The adjacency graph is used to definite a region's neighbours and will be used when optimizing region placement and size. The neighbourhood of a region will also be used as part of the weighting to determine the optimal prostate segmentation graph-cut. The neighbours to a region, \(R_{k}\), are defined as a set of all of the regions, \(R_{m}\), which have a graph edge connected to \(R_{k}\). The set of neighbours, \(\mathcal{N}_{k}\), is derived from the elements \(E_{v}=(k_{v1},k_{v2})\) in the edge set \(\mathcal{E}\), where \(\mathcal{N}_{k}\) is given by  
\[ \begin{split} N_{k1}&=\{k_{v1},\forall E_{v}\text{ where }k_{v2}=k\}\\ N_{k2}&=\{k_{v2},\forall E_{v}\text{ where }k_{v1}=k\}\\ &\mathcal{N}_{k}=N_{k1}\cup N_{k2}\end{split}  \tag{34}\]  
such that \(\mathcal{N}_{k}\) contains all of the \(k_{v1}\) and \(k_{v2}\) regions that are paired with (connected to) \(k\) in any edges of the graph \(\mathcal{E}\). To initialize the regions and the graph, a region \(R_{k}\) is created for each cluster \(C_{j}\) such that \(k=j\), resulting in an initial one-to-one mapping between the clusters and regions. As pixel clusters are created in a grid pattern (see Section 4.1 and Figure 6a), the graph \(\mathcal{G}\) can be easily created by finding the (four-connected) grid squares that adjoin each other; see Figure 6b,c. With the pixel clusters, superpixel regions, and graph structure created, the algorithm will then work to reduce the number of superpixel regions while simultaneously enlarging the area (number of pixels) contained within those regions. The relationship between the individual pixels, the pixel clusters, and superpixel regions, along with the graph structure, are shown in Figure 5. Explicitly, in Figure 5, \(R_{1}\) is a superpixel region comprising three pixel clusters, \(C_{1},C_{2}\), and \(C_{3}\), with neighbouring regions \(R_{2}\) and \(R_{3}\) connected via graph edges.
# Ultrawideband Low-Profile and Miniaturized Spoof Plasmonic Vivaldi Antenna for Base Station
###### Abstract  
Stable radiation pattern, high gain, and miniaturization are necessary for the ultra-wideband antennas in the 2G/3G/4G/5G base station applications. Here, an ultrawideband and miniaturized spoof plasmonic antipodal Vivaldi antenna (AVA) is proposed, which is composed of the AVA and the loaded periodic grooves. The designed operating frequency band is from 1.8 GHz to 6 GHz, and the average gain is 7.24 dBi. Furthermore, the measured results show that the radiation patterns of the plasmonic AVA are stable. The measured results are in good agreement with the simulation results.  
Keywords:vivaldi antenna; miniaturized; high gain; surface plasmons; ultrawideband +
Footnote †: journal: Applied Physics Letters
## 3 Methods  
### Spatial and Temporal Matchup  
The 9 variables collected for the retrieval of ET\({}_{0}\) have different spatial and temporal resolutions, as summarized in Table 1. For use as input features for the ML-based ET\({}_{0}\) model, the variables should be spatially and temporally co-located to construct a matchup database. The MODIS LAI and FPAR data use a sinusoidal projection; the GPM precipitation data use a geographic projection; the LDAPS meteorological variables use a Lambert Conformal Conic (LCC) projection. In addition, the datasets have various spatial resolutions. We converted all datasets into the geographic projections based on latitude and longitude with a 500 m (15 arc-second) grid to construct a matchup database for the various data sources. The transformation of the coordinate reference system (CRS) was performed using the MRT and GDAL. The adjustment of spatial resolution was carried out using the bilinear interpolation that is based on the distance between the center points of each pixel. For temporal resolutions, we adjusted all datasets to an interval of 1 day. MODIS NDVI was converted through cubic spline interpolation, assuming the gradual change over a year. MODIS LAI and FPAR data from the 8-day composite were rearranged to daily values using the nearest neighbor method under the assumption that they do not usually change within 8 days. LDAPS meteorological variables such as air temperature, land surface temperature, soil temperature, relative humidity, and wind speed at noon were treated as representative values for the day.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 4 Proposed Low-Complexity Demapping Algorithm  
This section proposes an improved low-complexity NUC demapping algorithm named SCSR, which consists of two processes. The first process is to create the subsets, and the second process is to calculate the LLRs of the received symbol according to the subsets. It should be noted that the process of creating subsets is performed off-line. This process only needs to be executed once and the obtained subsets are stored in the receiver. Therefore, it does not increase the demapping complexity.
# Extraction of Novel Bioactive Peptides from Fish Protein Hydrolysates by Enzymatic Reactions
## 3 Application of Enzymatic Hydrolysis for Fish Protein Isolation
### Bioactive Peptides' Production Using Animal-Based Enzymes  
Bioactive peptides from fish protein hydrolyses can also be produced using enzymes derived from animals, such as pepsin [69]. These enzymes of animal origin are known to yield potent bioactive peptides derived from protein sources. There are wide applications of pepsin which have been applied in several fish hydrolysate production. A recent study conducted by [PERSON] et al. [70], which focused on the antioxidant activities of Lionfish protein hydrolyses, revealed that using pepsin and pancreatic enzymes during hydrolysis resulted in large amounts of polypeptides with metal-chelating activities. Other bioactivities, such as the ACE-inhibitory activities of _Kawakawa_ fish protein hydrolyses, were also investigated by [PERSON] and [PERSON] [71], using the enzyme pepsin. The authors indicated that the recovered enzymes from the Skipjack tuna viscera produced good-quality hydrolyses with bioactivities. Moreover, [PERSON] et al. [72] studied the pepsin-derived visceral protein hydrolysate from _Papassius_ and showed prominent antioxidant activities compared to papain-derived hydrolyses. In another study conducted by [PERSON] et al. [73], the peptide fractions derived from sturgeon fish muscles hydrolyzed by pepsin revealed the presence of anti-inflammatory peptides. It was further revealed that the peptides have anti-inflammatory effects against macrophages. It is interesting to note that the use of animal-derived enzymes to produce fish protein hydrolyses has high potential in industrial production, with high overall quality in the products.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
## 3 Results and Discussion
### Quantitative Results
#### 3.4.1 Performance Metrics Comparison
The Transformer model exhibited the most balanced performance across all tested values of \(K\), as demonstrated in Table 6. Its Top-K Accuracy was consistently strong, and its MRR tended to be the highest among the three architectures (Table 7). The self-attention mechanism within the Transformer proves especially valuable for capturing complex semantic relationships in job descriptions and candidate resumes, which can span multiple sentences or paragraphs. As a result, the embedding space separation between positive and negative examples was more pronounced, with average positive similarities often being substantially higher than average negative similarities. The relatively low final loss value further confirms that the Transformer model generalized effectively, ranking correct candidates near the top of the list in most cases. This advantage was particularly evident in scenarios where job requirements were very detailed or when resumes described multiple, interconnected skillsets. Overall, the Transformer emerged as the most robust architecture in terms of consistently ranking performance across a variety of metrics and \(K\) values, while LSTM and GRU still maintained a competitive edge in certain ranking thresholds and exhibited strong general performance. Each model's strengths might be more or less relevant depending on the specific priorities of a recruitment scenario (e.g., precision for top suggestions, coverage in broader candidate lists, or handling particularly lengthy text documents).
[17; 18; 19] further investigated the effect of key parameters, which included the joint gap, joint offset, positive joint rotation, negative joint rotation, the angle of the sidewall, groove width, and gasket hardness, on gasket sealing behavior through analyzing the contact pressure distribution and the water-leakage pressure in different scenarios. Continuously, [PERSON] et al. [18] presented a theoretical model similar to that of [PERSON] et al. [14] to illustrate the gasket leakage mechanism. Moreover, with the aim of better guiding the waterproofing design of segment joints, [PERSON] et al. [20] also proposed a joint waterproofing design framework based on experimental and numerical simulation results. The research mentioned in the above literature review has contributed greatly to understanding the sealing performance and failure mechanism of single gaskets for waterproofing. In practice, especially in some underwater segment tunnels, double waterproofing gaskets have been used to improve the robustness of the joints and reduce the failure risk,and the sealing performance and mechanism have also been studied. [PERSON] et al. [21] investigated the leakage behaviors of double gaskets subjected to static ground loads and earthquake shaking. The results indicated that the waterproofing capacity of the longitudinal joints was enhanced through cycling due to the increased bond between the gasket and the gasket groove. [PERSON] et al. [22] estimated the sealing behavior of the double open gasket with joint opening and offset combination using model test results, and no apparent discrepancy was found between single and double EPDM gaskets with the same joint opening and offset. [PERSON] et al. [23] investigated the sealing behavior of double gaskets under four situations using an improved test apparatus that can monitor the water pressure, and the results show that the joint waterproofing capacity depends mainly on one gasket with a higher waterproofing capacity and that only a slight improvement in the waterproofing capacity with the same double gasket is observed. Although watertight sealing and airtight sealing are somewhat similar from a general point of view, very limited literature was found related to the airtightness sealing of joint gaskets in segmental tunnels. Hence, the airtightness sealing performance and failure mechanisms of the joints for segmental tunnels are worth studying to meet the development need of low-to-vacuum maglev segment tunnels. Aiming at the above purposes, this paper first introduced an apparatus invented to perform experimental studies on the mechanical behavior and airtightness sealing capacity of the gasket-in-groove, followed by the corresponding test methods and test results. Next, a 2D finite element analysis model was validated by the test, and finally, both experimental tests and the numerical models with FEA were performed to further investigate thoroughly the airtightness sealing performance and sealing failure mechanism of the gasket-in-groove.
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 4 Conclusions  
This study proposes a data-driven methodology for marine diesel engine performance assessment, effectively revealing performance degradation trends and enabling proactive fault warning through degradation pattern analysis. The results demonstrate the capability to detect and issue warnings for impending faults 20-30 days prior to measurable parameter anomalies. Key findings are summarized as follows:  
(1) Utilizing 43,186 operational data points collected over one year, 37,026 stable samples were obtained through preprocessing and steady-state detection. The CLIQUE clustering algorithm automatically partitioned these samples into 33 operational conditions, with Condition 1 (containing 23,720 samples) identified as the dominant operational range. These results quantitatively validate the algorithm's efficacy in automatic operational condition classification;  
(2) Analysis of the Mahalanobis distances within the dominant operational range yielded a fitted performance degradation curve. While the initial phase followed empirical degradation patterns, subsequent abrupt changes and fluctuations indicated systemic anomalies. Sequential parameter elimination localized the anomaly near the No. 2 cylinder jacket cooling water outlet, with temporal analysis confirming abnormal parameter deviations.  
(3) Comparative analysis revealed that degradation curve destabilization preceded measurable temperature fluctuations at the No. 2 cylinder outlet by approximately 20 days, establishing a critical early-warning window. The time-domain analysis-based warning system demonstrated operational validity, with precise warning triggering at 18:10 on 25 January 2021.  
The proposed methodology provides novel insights for marine diesel engine health management while offering methodological references for intelligent marine equipment condition classification and abrupt change detection. Future research will incorporate additional conventional thermodynamic parameters to enhance holistic engine performance characterization. Concurrently, algorithmic refinements to the SSD framework will implement adaptive window width configuration, establishing a data-driven foundation for integrated ship-shore performance diagnostics.  
H.W., writing--review and editing, methodology, and funding acquisition; Z.W., writing--review and editing, software, and data curation; B.S., writing--original draft and investigation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Shanghai Engineering Research Center of Ship Intelligent Maintenance and Energy Efficiency Control under Grant 20 DZ2252300.  
Not applicable.  
Data Availability Statement: Data care contained within the article.  
Author [PERSON] was employed by the company Shanghai Ocean Shipping Co., Ltd. The remaining author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.